diff --git a/D47crunch/__init__.py b/D47crunch/__init__.py
index 954aef5..d4f9b35 100755
--- a/D47crunch/__init__.py
+++ b/D47crunch/__init__.py
@@ -40,6 +40,8 @@
from colorsys import hls_to_rgb
from matplotlib import rcParams
+typer.rich_utils.STYLE_HELPTEXT = ''
+
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = 'Helvetica'
rcParams['font.size'] = 10
@@ -3298,7 +3300,50 @@ def _cli(
"""
Process raw D47 data and return standardized results.
- See [](https://mdaeron.github.io/D47crunch/#3-command-line-interface-cli) for more details.
+ See [b]https://mdaeron.github.io/D47crunch/#3-command-line-interface-cli[/b] for more details.
+
+ Reads raw data from an input file, optionally excluding some samples and/or analyses, thean standardizes
+ the data based either on the default [b]d13C_VDPB[/b], [b]d18O_VPDB[/b], [b]D47[/b], and [b]D48[/b] anchors or on different
+ user-specified anchors. A new directory (named `output` by default) is created to store the results and
+ the following sequence is applied:
+
+ * [b]D47data.wg()[/b]
+ * [b]D47data.crunch()[/b]
+ * [b]D47data.standardize()[/b]
+ * [b]D47data.summary()[/b]
+ * [b]D47data.table_of_samples()[/b]
+ * [b]D47data.table_of_sessions()[/b]
+ * [b]D47data.plot_sessions()[/b]
+ * [b]D47data.plot_residuals()[/b]
+ * [b]D47data.table_of_analyses()[/b]
+ * [b]D47data.plot_distribution_of_analyses()[/b]
+ * [b]D47data.plot_bulk_compositions()[/b]
+ * [b]D47data.save_D47_correl()[/b]
+
+ Optionally, also apply similar methods for [b]]D48[/b].
+
+ [b]Example CSV file for --anchors option:[/b]
+ [i]
+ Sample, d13C_VPDB, d18O_VPDB, D47, D48
+ ETH-1, 2.02, -2.19, 0.2052, 0.138
+ ETH-2, -10.17, -18.69, 0.2085, 0.138
+ ETH-3, 1.71, -1.78, 0.6132, 0.270
+ ETH-4, , , 0.4511, 0.223
+ [/i]
+ Except for [i]Sample[/i], none of the columns above are mandatory.
+
+ [b]Example CSV file for --exclude option:[/b]
+ [i]
+ Sample, UID
+ FOO-1,
+ BAR-2,
+ , A04
+ , A17
+ , A88
+ [/i]
+ This will exclude all analyses of samples [i]FOO-1[/i] and [i]BAR-2[/i],
+ and the analyses with UIDs [i]A04[/i], [i]A17[/i], and [i]A88[/i].
+ Neither column is mandatory.
"""
data = D47data()
diff --git a/code_examples/virtual_data/output.txt b/code_examples/virtual_data/output.txt
index 0f226e7..2b1c10f 100644
--- a/code_examples/virtual_data/output.txt
+++ b/code_examples/virtual_data/output.txt
@@ -23,65 +23,65 @@ FOO 12 -5.00 28.89 0.3024 0.0031 ± 0.0062 0.0070 0.497
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
-1 Session_01 ETH-1 -4.000 26.000 6.049381 10.706856 16.135579 21.196941 27.780042 2.057827 36.937067 -0.685751 -0.324384 0.045870 0.212791
-2 Session_01 ETH-1 -4.000 26.000 5.995601 10.755323 16.116087 21.285428 27.780042 1.998631 36.986704 -0.696924 -0.333640 0.008600 0.201787
-3 Session_01 ETH-3 -4.000 26.000 5.727341 11.211663 16.713472 22.364770 28.306614 1.695479 37.453503 -0.278056 -0.180158 -0.082015 0.614365
-4 Session_01 ETH-3 -4.000 26.000 5.734896 11.229855 16.740410 22.402091 28.306614 1.702875 37.472070 -0.276998 -0.179635 -0.125368 0.615396
-5 Session_01 FOO -4.000 26.000 -0.848028 2.874679 1.346196 5.439150 4.665655 -5.017230 28.951964 -0.601502 -0.316664 -0.081898 0.302042
-6 Session_01 BAR -4.000 26.000 -9.915975 10.968470 0.153453 21.749385 10.707292 -14.995822 37.241294 -0.286638 -0.301325 -0.157376 0.612868
-7 Session_01 ETH-2 -4.000 26.000 -5.974124 -5.955517 -12.668784 -12.208184 -18.023381 -10.163274 19.943159 -0.694902 -0.336672 -0.063946 0.215880
-8 Session_01 BAR -4.000 26.000 -9.920507 10.903408 0.065076 21.704075 10.707292 -14.998270 37.174839 -0.307018 -0.216978 -0.026076 0.592818
-9 Session_01 FOO -4.000 26.000 -0.838118 2.819853 1.310384 5.326005 4.665655 -5.004629 28.895933 -0.593755 -0.319861 0.014956 0.309692
-10 Session_01 ETH-2 -4.000 26.000 -5.982229 -6.110437 -12.827036 -12.492272 -18.023381 -10.166188 19.784916 -0.693555 -0.312598 0.251040 0.217274
-11 Session_01 ETH-1 -4.000 26.000 6.010276 10.840276 16.207960 21.475150 27.780042 2.011176 37.073454 -0.704188 -0.315986 -0.172089 0.194589
-12 Session_01 ETH-3 -4.000 26.000 5.755174 11.255104 16.792797 22.451660 28.306614 1.723596 37.497816 -0.270825 -0.181089 -0.195908 0.621458
-13 Session_01 ETH-2 -4.000 26.000 -5.991278 -5.995054 -12.741562 -12.184075 -18.023381 -10.180122 19.902809 -0.711697 -0.232746 0.032602 0.199357
-14 Session_01 BAR -4.000 26.000 -9.959983 10.926995 0.053806 21.724901 10.707292 -15.041279 37.199026 -0.300066 -0.243252 -0.029371 0.599675
-15 Session_01 FOO -4.000 26.000 -0.876454 2.906764 1.341194 5.490264 4.665655 -5.048760 28.984806 -0.608593 -0.329808 -0.114437 0.295055
-16 Session_02 ETH-2 -4.000 26.000 -5.950370 -5.959974 -12.650784 -12.197864 -18.023381 -10.143809 19.897777 -0.696916 -0.317263 -0.080604 0.216441
+1 Session_01 BAR -4.000 26.000 -9.920507 10.903408 0.065076 21.704075 10.707292 -14.998270 37.174839 -0.307018 -0.216978 -0.026076 0.592818
+2 Session_01 ETH-3 -4.000 26.000 5.755174 11.255104 16.792797 22.451660 28.306614 1.723596 37.497816 -0.270825 -0.181089 -0.195908 0.621458
+3 Session_01 ETH-1 -4.000 26.000 6.010276 10.840276 16.207960 21.475150 27.780042 2.011176 37.073454 -0.704188 -0.315986 -0.172089 0.194589
+4 Session_01 ETH-2 -4.000 26.000 -5.991278 -5.995054 -12.741562 -12.184075 -18.023381 -10.180122 19.902809 -0.711697 -0.232746 0.032602 0.199357
+5 Session_01 ETH-2 -4.000 26.000 -5.974124 -5.955517 -12.668784 -12.208184 -18.023381 -10.163274 19.943159 -0.694902 -0.336672 -0.063946 0.215880
+6 Session_01 ETH-3 -4.000 26.000 5.727341 11.211663 16.713472 22.364770 28.306614 1.695479 37.453503 -0.278056 -0.180158 -0.082015 0.614365
+7 Session_01 BAR -4.000 26.000 -9.959983 10.926995 0.053806 21.724901 10.707292 -15.041279 37.199026 -0.300066 -0.243252 -0.029371 0.599675
+8 Session_01 ETH-3 -4.000 26.000 5.734896 11.229855 16.740410 22.402091 28.306614 1.702875 37.472070 -0.276998 -0.179635 -0.125368 0.615396
+9 Session_01 ETH-2 -4.000 26.000 -5.982229 -6.110437 -12.827036 -12.492272 -18.023381 -10.166188 19.784916 -0.693555 -0.312598 0.251040 0.217274
+10 Session_01 FOO -4.000 26.000 -0.848028 2.874679 1.346196 5.439150 4.665655 -5.017230 28.951964 -0.601502 -0.316664 -0.081898 0.302042
+11 Session_01 FOO -4.000 26.000 -0.838118 2.819853 1.310384 5.326005 4.665655 -5.004629 28.895933 -0.593755 -0.319861 0.014956 0.309692
+12 Session_01 BAR -4.000 26.000 -9.915975 10.968470 0.153453 21.749385 10.707292 -14.995822 37.241294 -0.286638 -0.301325 -0.157376 0.612868
+13 Session_01 FOO -4.000 26.000 -0.876454 2.906764 1.341194 5.490264 4.665655 -5.048760 28.984806 -0.608593 -0.329808 -0.114437 0.295055
+14 Session_01 ETH-1 -4.000 26.000 6.049381 10.706856 16.135579 21.196941 27.780042 2.057827 36.937067 -0.685751 -0.324384 0.045870 0.212791
+15 Session_01 ETH-1 -4.000 26.000 5.995601 10.755323 16.116087 21.285428 27.780042 1.998631 36.986704 -0.696924 -0.333640 0.008600 0.201787
+16 Session_02 ETH-1 -4.000 26.000 6.019963 10.773112 16.163825 21.331060 27.780042 2.029040 37.042346 -0.692234 -0.324161 -0.051788 0.207075
17 Session_02 ETH-1 -4.000 26.000 6.030532 10.851030 16.245571 21.457100 27.780042 2.037466 37.122284 -0.698413 -0.354920 -0.214443 0.200795
-18 Session_02 ETH-1 -4.000 26.000 6.019963 10.773112 16.163825 21.331060 27.780042 2.029040 37.042346 -0.692234 -0.324161 -0.051788 0.207075
-19 Session_02 BAR -4.000 26.000 -9.936020 10.862339 0.024660 21.563307 10.707292 -15.023836 37.171034 -0.291333 -0.273498 0.070452 0.619812
-20 Session_02 ETH-2 -4.000 26.000 -5.982371 -6.036210 -12.762399 -12.309944 -18.023381 -10.175178 19.819614 -0.701348 -0.277354 0.104418 0.212021
-21 Session_02 ETH-2 -4.000 26.000 -5.993476 -5.944866 -12.696865 -12.149754 -18.023381 -10.190430 19.913381 -0.713779 -0.298963 -0.064251 0.199436
-22 Session_02 ETH-3 -4.000 26.000 5.757137 11.232751 16.744567 22.398244 28.306614 1.731295 37.514660 -0.298533 -0.189123 -0.154557 0.604363
-23 Session_02 ETH-1 -4.000 26.000 5.993918 10.617469 15.991900 21.070358 27.780042 2.006934 36.882679 -0.683329 -0.271476 0.278458 0.216152
-24 Session_02 BAR -4.000 26.000 -9.963888 10.865863 -0.023549 21.615868 10.707292 -15.053743 37.174715 -0.313906 -0.229031 0.093637 0.597041
-25 Session_02 ETH-3 -4.000 26.000 5.719281 11.207303 16.681693 22.370886 28.306614 1.691780 37.488633 -0.296801 -0.165556 -0.065004 0.606143
-26 Session_02 FOO -4.000 26.000 -0.848415 2.849823 1.308081 5.427767 4.665655 -5.018107 28.927036 -0.614791 -0.278426 -0.032784 0.292547
-27 Session_02 FOO -4.000 26.000 -0.819742 2.826793 1.317044 5.330616 4.665655 -4.986618 28.903335 -0.612871 -0.329113 -0.018244 0.294481
-28 Session_02 ETH-3 -4.000 26.000 5.716356 11.091821 16.582487 22.123857 28.306614 1.692901 37.370126 -0.279100 -0.178789 0.162540 0.624067
-29 Session_02 FOO -4.000 26.000 -0.835046 2.870518 1.355370 5.487896 4.665655 -5.004585 28.948243 -0.601666 -0.259900 -0.087592 0.305777
-30 Session_02 BAR -4.000 26.000 -9.957566 10.903888 0.031785 21.739434 10.707292 -15.048386 37.213724 -0.302139 -0.183327 0.012926 0.608897
-31 Session_03 ETH-2 -4.000 26.000 -5.997147 -5.905858 -12.655382 -12.081612 -18.023381 -10.165400 19.891551 -0.706536 -0.308464 -0.137414 0.197550
-32 Session_03 BAR -4.000 26.000 -9.928709 10.989665 0.148059 21.852677 10.707292 -14.976237 37.324152 -0.299358 -0.242185 -0.184835 0.603855
-33 Session_03 ETH-1 -4.000 26.000 6.040566 10.786620 16.205283 21.374963 27.780042 2.045244 37.077432 -0.685706 -0.307909 -0.099869 0.213609
-34 Session_03 FOO -4.000 26.000 -0.823857 2.761300 1.258060 5.239992 4.665655 -4.973383 28.817444 -0.603327 -0.288652 0.114488 0.298751
+18 Session_02 BAR -4.000 26.000 -9.963888 10.865863 -0.023549 21.615868 10.707292 -15.053743 37.174715 -0.313906 -0.229031 0.093637 0.597041
+19 Session_02 FOO -4.000 26.000 -0.835046 2.870518 1.355370 5.487896 4.665655 -5.004585 28.948243 -0.601666 -0.259900 -0.087592 0.305777
+20 Session_02 BAR -4.000 26.000 -9.936020 10.862339 0.024660 21.563307 10.707292 -15.023836 37.171034 -0.291333 -0.273498 0.070452 0.619812
+21 Session_02 ETH-1 -4.000 26.000 5.993918 10.617469 15.991900 21.070358 27.780042 2.006934 36.882679 -0.683329 -0.271476 0.278458 0.216152
+22 Session_02 ETH-3 -4.000 26.000 5.719281 11.207303 16.681693 22.370886 28.306614 1.691780 37.488633 -0.296801 -0.165556 -0.065004 0.606143
+23 Session_02 FOO -4.000 26.000 -0.848415 2.849823 1.308081 5.427767 4.665655 -5.018107 28.927036 -0.614791 -0.278426 -0.032784 0.292547
+24 Session_02 ETH-2 -4.000 26.000 -5.950370 -5.959974 -12.650784 -12.197864 -18.023381 -10.143809 19.897777 -0.696916 -0.317263 -0.080604 0.216441
+25 Session_02 BAR -4.000 26.000 -9.957566 10.903888 0.031785 21.739434 10.707292 -15.048386 37.213724 -0.302139 -0.183327 0.012926 0.608897
+26 Session_02 ETH-3 -4.000 26.000 5.716356 11.091821 16.582487 22.123857 28.306614 1.692901 37.370126 -0.279100 -0.178789 0.162540 0.624067
+27 Session_02 ETH-3 -4.000 26.000 5.757137 11.232751 16.744567 22.398244 28.306614 1.731295 37.514660 -0.298533 -0.189123 -0.154557 0.604363
+28 Session_02 ETH-2 -4.000 26.000 -5.993476 -5.944866 -12.696865 -12.149754 -18.023381 -10.190430 19.913381 -0.713779 -0.298963 -0.064251 0.199436
+29 Session_02 FOO -4.000 26.000 -0.819742 2.826793 1.317044 5.330616 4.665655 -4.986618 28.903335 -0.612871 -0.329113 -0.018244 0.294481
+30 Session_02 ETH-2 -4.000 26.000 -5.982371 -6.036210 -12.762399 -12.309944 -18.023381 -10.175178 19.819614 -0.701348 -0.277354 0.104418 0.212021
+31 Session_03 FOO -4.000 26.000 -0.873798 2.820799 1.272165 5.370745 4.665655 -5.028782 28.878917 -0.596008 -0.277258 0.051165 0.306090
+32 Session_03 ETH-3 -4.000 26.000 5.748546 11.079879 16.580826 22.120063 28.306614 1.723364 37.380534 -0.302133 -0.158882 0.151641 0.598318
+33 Session_03 BAR -4.000 26.000 -9.957114 10.898997 0.044946 21.602296 10.707292 -15.003175 37.230716 -0.284699 -0.307849 0.021944 0.618578
+34 Session_03 ETH-2 -4.000 26.000 -6.008525 -5.909707 -12.647727 -12.075913 -18.023381 -10.177379 19.887608 -0.683183 -0.294956 -0.117608 0.220975
35 Session_03 ETH-1 -4.000 26.000 5.994622 10.743980 16.116098 21.243734 27.780042 1.997857 37.033567 -0.684883 -0.352014 0.031692 0.214449
-36 Session_03 FOO -4.000 26.000 -0.800284 2.851299 1.376828 5.379547 4.665655 -4.951581 28.910199 -0.597293 -0.329315 -0.087015 0.304784
-37 Session_03 ETH-2 -4.000 26.000 -6.008525 -5.909707 -12.647727 -12.075913 -18.023381 -10.177379 19.887608 -0.683183 -0.294956 -0.117608 0.220975
-38 Session_03 ETH-3 -4.000 26.000 5.753467 11.206589 16.719131 22.373244 28.306614 1.723960 37.511190 -0.294350 -0.161838 -0.099835 0.606103
-39 Session_03 ETH-2 -4.000 26.000 -6.000290 -5.947172 -12.697463 -12.164602 -18.023381 -10.167221 19.848953 -0.705037 -0.309350 -0.052386 0.199061
-40 Session_03 ETH-1 -4.000 26.000 6.004078 10.683951 16.045192 21.214355 27.780042 2.010134 36.971642 -0.705956 -0.262026 0.138399 0.193323
-41 Session_03 FOO -4.000 26.000 -0.873798 2.820799 1.272165 5.370745 4.665655 -5.028782 28.878917 -0.596008 -0.277258 0.051165 0.306090
-42 Session_03 ETH-3 -4.000 26.000 5.748546 11.079879 16.580826 22.120063 28.306614 1.723364 37.380534 -0.302133 -0.158882 0.151641 0.598318
-43 Session_03 BAR -4.000 26.000 -9.957114 10.898997 0.044946 21.602296 10.707292 -15.003175 37.230716 -0.284699 -0.307849 0.021944 0.618578
-44 Session_03 BAR -4.000 26.000 -9.952115 11.034508 0.169809 21.885915 10.707292 -15.002819 37.370451 -0.296804 -0.298351 -0.246731 0.606414
-45 Session_03 ETH-3 -4.000 26.000 5.718991 11.146227 16.640814 22.243185 28.306614 1.689442 37.449023 -0.277332 -0.169668 0.053997 0.623187
-46 Session_04 ETH-3 -4.000 26.000 5.751908 11.207110 16.726741 22.380392 28.306614 1.705481 37.480657 -0.285776 -0.155878 -0.099197 0.609567
-47 Session_04 ETH-2 -4.000 26.000 -5.986501 -5.915157 -12.656583 -12.060382 -18.023381 -10.182247 19.889836 -0.709603 -0.268277 -0.130450 0.199604
-48 Session_04 BAR -4.000 26.000 -9.926078 10.884823 0.060864 21.650722 10.707292 -15.002880 37.185606 -0.287358 -0.232425 0.016044 0.611760
-49 Session_04 ETH-2 -4.000 26.000 -5.966627 -5.893789 -12.597717 -12.120719 -18.023381 -10.161842 19.911776 -0.691757 -0.372308 -0.193986 0.217132
-50 Session_04 ETH-1 -4.000 26.000 6.029937 10.766997 16.151273 21.345479 27.780042 2.018148 37.027152 -0.708855 -0.297953 -0.050465 0.193862
-51 Session_04 BAR -4.000 26.000 -9.951025 10.951923 0.089386 21.738926 10.707292 -15.031949 37.254709 -0.298065 -0.278834 -0.087463 0.601230
-52 Session_04 ETH-3 -4.000 26.000 5.739420 11.128582 16.641344 22.166106 28.306614 1.695046 37.399884 -0.280608 -0.210162 0.066645 0.614665
-53 Session_04 ETH-1 -4.000 26.000 6.023822 10.730714 16.121184 21.235757 27.780042 2.012958 36.989833 -0.696908 -0.333582 0.026555 0.205610
-54 Session_04 ETH-1 -4.000 26.000 6.017312 10.735930 16.123043 21.270597 27.780042 2.005824 36.995214 -0.693479 -0.309795 0.023309 0.208980
-55 Session_04 BAR -4.000 26.000 -9.931741 10.819830 -0.023748 21.529372 10.707292 -15.006533 37.118743 -0.302866 -0.222623 0.148462 0.596536
-56 Session_04 ETH-3 -4.000 26.000 5.798016 11.254135 16.832228 22.432473 28.306614 1.752928 37.528936 -0.275047 -0.197935 -0.239408 0.620088
-57 Session_04 FOO -4.000 26.000 -0.853969 2.805035 1.267571 5.353907 4.665655 -5.030523 28.850660 -0.605611 -0.262571 0.060903 0.298685
-58 Session_04 ETH-2 -4.000 26.000 -5.973623 -5.975018 -12.694278 -12.194472 -18.023381 -10.166297 19.828211 -0.701951 -0.283570 -0.025935 0.207135
-59 Session_04 FOO -4.000 26.000 -0.848192 2.777763 1.251297 5.280272 4.665655 -5.023358 28.822585 -0.601094 -0.281419 0.108186 0.303128
-60 Session_04 FOO -4.000 26.000 -0.791191 2.708220 1.256167 5.145784 4.665655 -4.960004 28.750896 -0.586913 -0.276505 0.183674 0.317065
+36 Session_03 ETH-3 -4.000 26.000 5.718991 11.146227 16.640814 22.243185 28.306614 1.689442 37.449023 -0.277332 -0.169668 0.053997 0.623187
+37 Session_03 ETH-2 -4.000 26.000 -6.000290 -5.947172 -12.697463 -12.164602 -18.023381 -10.167221 19.848953 -0.705037 -0.309350 -0.052386 0.199061
+38 Session_03 BAR -4.000 26.000 -9.928709 10.989665 0.148059 21.852677 10.707292 -14.976237 37.324152 -0.299358 -0.242185 -0.184835 0.603855
+39 Session_03 ETH-3 -4.000 26.000 5.753467 11.206589 16.719131 22.373244 28.306614 1.723960 37.511190 -0.294350 -0.161838 -0.099835 0.606103
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+++ b/docs/index.html
@@ -930,3360 +930,3405 @@ 4. API Documentation
40from colorsys import hls_to_rgb
41from matplotlib import rcParams
42
- 43rcParams['font.family'] = 'sans-serif'
- 44rcParams['font.sans-serif'] = 'Helvetica'
- 45rcParams['font.size'] = 10
- 46rcParams['mathtext.fontset'] = 'custom'
- 47rcParams['mathtext.rm'] = 'sans'
- 48rcParams['mathtext.bf'] = 'sans:bold'
- 49rcParams['mathtext.it'] = 'sans:italic'
- 50rcParams['mathtext.cal'] = 'sans:italic'
- 51rcParams['mathtext.default'] = 'rm'
- 52rcParams['xtick.major.size'] = 4
- 53rcParams['xtick.major.width'] = 1
- 54rcParams['ytick.major.size'] = 4
- 55rcParams['ytick.major.width'] = 1
- 56rcParams['axes.grid'] = False
- 57rcParams['axes.linewidth'] = 1
- 58rcParams['grid.linewidth'] = .75
- 59rcParams['grid.linestyle'] = '-'
- 60rcParams['grid.alpha'] = .15
- 61rcParams['savefig.dpi'] = 150
- 62
- 63Petersen_etal_CO2eqD47 = np.array([[-12, 1.147113572], [-11, 1.139961218], [-10, 1.132872856], [-9, 1.125847677], [-8, 1.118884889], [-7, 1.111983708], [-6, 1.105143366], [-5, 1.098363105], [-4, 1.091642182], [-3, 1.084979862], [-2, 1.078375423], [-1, 1.071828156], [0, 1.065337360], [1, 1.058902349], [2, 1.052522443], [3, 1.046196976], [4, 1.039925291], [5, 1.033706741], [6, 1.027540690], [7, 1.021426510], [8, 1.015363585], [9, 1.009351306], [10, 1.003389075], [11, 0.997476303], [12, 0.991612409], [13, 0.985796821], [14, 0.980028975], [15, 0.974308318], [16, 0.968634304], [17, 0.963006392], [18, 0.957424055], [19, 0.951886769], [20, 0.946394020], [21, 0.940945302], [22, 0.935540114], [23, 0.930177964], [24, 0.924858369], [25, 0.919580851], [26, 0.914344938], [27, 0.909150167], [28, 0.903996080], [29, 0.898882228], [30, 0.893808167], [31, 0.888773459], [32, 0.883777672], [33, 0.878820382], [34, 0.873901170], [35, 0.869019623], [36, 0.864175334], [37, 0.859367901], [38, 0.854596929], [39, 0.849862028], [40, 0.845162813], [41, 0.840498905], [42, 0.835869931], [43, 0.831275522], [44, 0.826715314], [45, 0.822188950], [46, 0.817696075], [47, 0.813236341], [48, 0.808809404], [49, 0.804414926], [50, 0.800052572], [51, 0.795722012], [52, 0.791422922], [53, 0.787154979], [54, 0.782917869], [55, 0.778711277], [56, 0.774534898], [57, 0.770388426], [58, 0.766271562], [59, 0.762184010], [60, 0.758125479], [61, 0.754095680], [62, 0.750094329], [63, 0.746121147], [64, 0.742175856], [65, 0.738258184], [66, 0.734367860], [67, 0.730504620], [68, 0.726668201], [69, 0.722858343], [70, 0.719074792], [71, 0.715317295], [72, 0.711585602], [73, 0.707879469], [74, 0.704198652], [75, 0.700542912], [76, 0.696912012], [77, 0.693305719], [78, 0.689723802], [79, 0.686166034], [80, 0.682632189], [81, 0.679122047], [82, 0.675635387], [83, 0.672171994], [84, 0.668731654], [85, 0.665314156], [86, 0.661919291], [87, 0.658546854], [88, 0.655196641], [89, 0.651868451], [90, 0.648562087], [91, 0.645277352], [92, 0.642014054], [93, 0.638771999], [94, 0.635551001], [95, 0.632350872], [96, 0.629171428], [97, 0.626012487], [98, 0.622873870], [99, 0.619755397], [100, 0.616656895], [102, 0.610519107], [104, 0.604459143], [106, 0.598475670], [108, 0.592567388], [110, 0.586733026], [112, 0.580971342], [114, 0.575281125], [116, 0.569661187], [118, 0.564110371], [120, 0.558627545], [122, 0.553211600], [124, 0.547861454], [126, 0.542576048], [128, 0.537354347], [130, 0.532195337], [132, 0.527098028], [134, 0.522061450], [136, 0.517084654], [138, 0.512166711], [140, 0.507306712], [142, 0.502503768], [144, 0.497757006], [146, 0.493065573], [148, 0.488428634], [150, 0.483845370], [152, 0.479314980], [154, 0.474836677], [156, 0.470409692], [158, 0.466033271], [160, 0.461706674], [162, 0.457429176], [164, 0.453200067], [166, 0.449018650], [168, 0.444884242], [170, 0.440796174], [172, 0.436753787], [174, 0.432756438], [176, 0.428803494], [178, 0.424894334], [180, 0.421028350], [182, 0.417204944], [184, 0.413423530], [186, 0.409683531], [188, 0.405984383], [190, 0.402325531], [192, 0.398706429], [194, 0.395126543], [196, 0.391585347], [198, 0.388082324], [200, 0.384616967], [202, 0.381188778], [204, 0.377797268], [206, 0.374441954], [208, 0.371122364], [210, 0.367838033], [212, 0.364588505], [214, 0.361373329], [216, 0.358192065], [218, 0.355044277], [220, 0.351929540], [222, 0.348847432], [224, 0.345797540], [226, 0.342779460], [228, 0.339792789], [230, 0.336837136], [232, 0.333912113], [234, 0.331017339], [236, 0.328152439], [238, 0.325317046], [240, 0.322510795], [242, 0.319733329], [244, 0.316984297], [246, 0.314263352], [248, 0.311570153], [250, 0.308904364], [252, 0.306265654], [254, 0.303653699], [256, 0.301068176], [258, 0.298508771], [260, 0.295975171], [262, 0.293467070], [264, 0.290984167], [266, 0.288526163], [268, 0.286092765], [270, 0.283683684], [272, 0.281298636], [274, 0.278937339], [276, 0.276599517], [278, 0.274284898], [280, 0.271993211], [282, 0.269724193], [284, 0.267477582], [286, 0.265253121], [288, 0.263050554], [290, 0.260869633], [292, 0.258710110], [294, 0.256571741], [296, 0.254454286], [298, 0.252357508], [300, 0.250281174], [302, 0.248225053], [304, 0.246188917], [306, 0.244172542], [308, 0.242175707], [310, 0.240198194], [312, 0.238239786], [314, 0.236300272], [316, 0.234379441], [318, 0.232477087], [320, 0.230593005], [322, 0.228726993], [324, 0.226878853], [326, 0.225048388], [328, 0.223235405], [330, 0.221439711], [332, 0.219661118], [334, 0.217899439], [336, 0.216154491], [338, 0.214426091], [340, 0.212714060], [342, 0.211018220], [344, 0.209338398], [346, 0.207674420], [348, 0.206026115], [350, 0.204393315], [355, 0.200378063], [360, 0.196456139], [365, 0.192625077], [370, 0.188882487], [375, 0.185226048], [380, 0.181653511], [385, 0.178162694], [390, 0.174751478], [395, 0.171417807], [400, 0.168159686], [405, 0.164975177], [410, 0.161862398], [415, 0.158819521], [420, 0.155844772], [425, 0.152936426], [430, 0.150092806], [435, 0.147312286], [440, 0.144593281], [445, 0.141934254], [450, 0.139333710], [455, 0.136790195], [460, 0.134302294], [465, 0.131868634], [470, 0.129487876], [475, 0.127158722], [480, 0.124879906], [485, 0.122650197], [490, 0.120468398], [495, 0.118333345], [500, 0.116243903], [505, 0.114198970], [510, 0.112197471], [515, 0.110238362], [520, 0.108320625], [525, 0.106443271], [530, 0.104605335], [535, 0.102805877], [540, 0.101043985], [545, 0.099318768], [550, 0.097629359], [555, 0.095974915], [560, 0.094354612], [565, 0.092767650], [570, 0.091213248], [575, 0.089690648], [580, 0.088199108], [585, 0.086737906], [590, 0.085306341], [595, 0.083903726], [600, 0.082529395], [605, 0.081182697], [610, 0.079862998], [615, 0.078569680], [620, 0.077302141], [625, 0.076059794], [630, 0.074842066], [635, 0.073648400], [640, 0.072478251], [645, 0.071331090], [650, 0.070206399], [655, 0.069103674], [660, 0.068022424], [665, 0.066962168], [670, 0.065922439], [675, 0.064902780], [680, 0.063902748], [685, 0.062921909], [690, 0.061959837], [695, 0.061016122], [700, 0.060090360], [705, 0.059182157], [710, 0.058291131], [715, 0.057416907], [720, 0.056559120], [725, 0.055717414], [730, 0.054891440], [735, 0.054080860], [740, 0.053285343], [745, 0.052504565], [750, 0.051738210], [755, 0.050985971], [760, 0.050247546], [765, 0.049522643], [770, 0.048810974], [775, 0.048112260], [780, 0.047426227], [785, 0.046752609], [790, 0.046091145], [795, 0.045441581], [800, 0.044803668], [805, 0.044177164], [810, 0.043561831], [815, 0.042957438], [820, 0.042363759], [825, 0.041780573], [830, 0.041207664], [835, 0.040644822], [840, 0.040091839], [845, 0.039548516], [850, 0.039014654], [855, 0.038490063], [860, 0.037974554], [865, 0.037467944], [870, 0.036970054], [875, 0.036480707], [880, 0.035999734], [885, 0.035526965], [890, 0.035062238], [895, 0.034605393], [900, 0.034156272], [905, 0.033714724], [910, 0.033280598], [915, 0.032853749], [920, 0.032434032], [925, 0.032021309], [930, 0.031615443], [935, 0.031216300], [940, 0.030823749], [945, 0.030437663], [950, 0.030057915], [955, 0.029684385], [960, 0.029316951], [965, 0.028955498], [970, 0.028599910], [975, 0.028250075], [980, 0.027905884], [985, 0.027567229], [990, 0.027234006], [995, 0.026906112], [1000, 0.026583445], [1005, 0.026265908], [1010, 0.025953405], [1015, 0.025645841], [1020, 0.025343124], [1025, 0.025045163], [1030, 0.024751871], [1035, 0.024463160], [1040, 0.024178947], [1045, 0.023899147], [1050, 0.023623680], [1055, 0.023352467], [1060, 0.023085429], [1065, 0.022822491], [1070, 0.022563577], [1075, 0.022308615], [1080, 0.022057533], [1085, 0.021810260], [1090, 0.021566729], [1095, 0.021326872], [1100, 0.021090622]])
- 64_fCO2eqD47_Petersen = interp1d(Petersen_etal_CO2eqD47[:,0], Petersen_etal_CO2eqD47[:,1])
- 65def fCO2eqD47_Petersen(T):
- 66 '''
- 67 CO2 equilibrium Δ47 value as a function of T (in degrees C)
- 68 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127).
- 69
- 70 '''
- 71 return float(_fCO2eqD47_Petersen(T))
- 72
- 73
- 74Wang_etal_CO2eqD47 = np.array([[-83., 1.8954], [-73., 1.7530], [-63., 1.6261], [-53., 1.5126], [-43., 1.4104], [-33., 1.3182], [-23., 1.2345], [-13., 1.1584], [-3., 1.0888], [7., 1.0251], [17., 0.9665], [27., 0.9125], [37., 0.8626], [47., 0.8164], [57., 0.7734], [67., 0.7334], [87., 0.6612], [97., 0.6286], [107., 0.5980], [117., 0.5693], [127., 0.5423], [137., 0.5169], [147., 0.4930], [157., 0.4704], [167., 0.4491], [177., 0.4289], [187., 0.4098], [197., 0.3918], [207., 0.3747], [217., 0.3585], [227., 0.3431], [237., 0.3285], [247., 0.3147], [257., 0.3015], [267., 0.2890], [277., 0.2771], [287., 0.2657], [297., 0.2550], [307., 0.2447], [317., 0.2349], [327., 0.2256], [337., 0.2167], [347., 0.2083], [357., 0.2002], [367., 0.1925], [377., 0.1851], [387., 0.1781], [397., 0.1714], [407., 0.1650], [417., 0.1589], [427., 0.1530], [437., 0.1474], [447., 0.1421], [457., 0.1370], [467., 0.1321], [477., 0.1274], [487., 0.1229], [497., 0.1186], [507., 0.1145], [517., 0.1105], [527., 0.1068], [537., 0.1031], [547., 0.0997], [557., 0.0963], [567., 0.0931], [577., 0.0901], [587., 0.0871], [597., 0.0843], [607., 0.0816], [617., 0.0790], [627., 0.0765], [637., 0.0741], [647., 0.0718], [657., 0.0695], [667., 0.0674], [677., 0.0654], [687., 0.0634], [697., 0.0615], [707., 0.0597], [717., 0.0579], [727., 0.0562], [737., 0.0546], [747., 0.0530], [757., 0.0515], [767., 0.0500], [777., 0.0486], [787., 0.0472], [797., 0.0459], [807., 0.0447], [817., 0.0435], [827., 0.0423], [837., 0.0411], [847., 0.0400], [857., 0.0390], [867., 0.0380], [877., 0.0370], [887., 0.0360], [897., 0.0351], [907., 0.0342], [917., 0.0333], [927., 0.0325], [937., 0.0317], [947., 0.0309], [957., 0.0302], [967., 0.0294], [977., 0.0287], [987., 0.0281], [997., 0.0274], [1007., 0.0268], [1017., 0.0261], [1027., 0.0255], [1037., 0.0249], [1047., 0.0244], [1057., 0.0238], [1067., 0.0233], [1077., 0.0228], [1087., 0.0223], [1097., 0.0218]])
- 75_fCO2eqD47_Wang = interp1d(Wang_etal_CO2eqD47[:,0] - 0.15, Wang_etal_CO2eqD47[:,1])
- 76def fCO2eqD47_Wang(T):
- 77 '''
- 78 CO2 equilibrium Δ47 value as a function of `T` (in degrees C)
- 79 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039)
- 80 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)).
- 81 '''
- 82 return float(_fCO2eqD47_Wang(T))
- 83
- 84
- 85def correlated_sum(X, C, w = None):
- 86 '''
- 87 Compute covariance-aware linear combinations
- 88
- 89 **Parameters**
- 90
- 91 + `X`: list or 1-D array of values to sum
- 92 + `C`: covariance matrix for the elements of `X`
- 93 + `w`: list or 1-D array of weights to apply to the elements of `X`
- 94 (all equal to 1 by default)
- 95
- 96 Return the sum (and its SE) of the elements of `X`, with optional weights equal
- 97 to the elements of `w`, accounting for covariances between the elements of `X`.
- 98 '''
- 99 if w is None:
- 100 w = [1 for x in X]
- 101 return np.dot(w,X), (np.dot(w,np.dot(C,w)))**.5
- 102
- 103
- 104def make_csv(x, hsep = ',', vsep = '\n'):
- 105 '''
- 106 Formats a list of lists of strings as a CSV
- 107
- 108 **Parameters**
+ 43typer.rich_utils.STYLE_HELPTEXT = ''
+ 44
+ 45rcParams['font.family'] = 'sans-serif'
+ 46rcParams['font.sans-serif'] = 'Helvetica'
+ 47rcParams['font.size'] = 10
+ 48rcParams['mathtext.fontset'] = 'custom'
+ 49rcParams['mathtext.rm'] = 'sans'
+ 50rcParams['mathtext.bf'] = 'sans:bold'
+ 51rcParams['mathtext.it'] = 'sans:italic'
+ 52rcParams['mathtext.cal'] = 'sans:italic'
+ 53rcParams['mathtext.default'] = 'rm'
+ 54rcParams['xtick.major.size'] = 4
+ 55rcParams['xtick.major.width'] = 1
+ 56rcParams['ytick.major.size'] = 4
+ 57rcParams['ytick.major.width'] = 1
+ 58rcParams['axes.grid'] = False
+ 59rcParams['axes.linewidth'] = 1
+ 60rcParams['grid.linewidth'] = .75
+ 61rcParams['grid.linestyle'] = '-'
+ 62rcParams['grid.alpha'] = .15
+ 63rcParams['savefig.dpi'] = 150
+ 64
+ 65Petersen_etal_CO2eqD47 = np.array([[-12, 1.147113572], [-11, 1.139961218], [-10, 1.132872856], [-9, 1.125847677], [-8, 1.118884889], [-7, 1.111983708], [-6, 1.105143366], [-5, 1.098363105], [-4, 1.091642182], [-3, 1.084979862], [-2, 1.078375423], [-1, 1.071828156], [0, 1.065337360], [1, 1.058902349], [2, 1.052522443], [3, 1.046196976], [4, 1.039925291], [5, 1.033706741], [6, 1.027540690], [7, 1.021426510], [8, 1.015363585], [9, 1.009351306], [10, 1.003389075], [11, 0.997476303], [12, 0.991612409], [13, 0.985796821], [14, 0.980028975], [15, 0.974308318], [16, 0.968634304], [17, 0.963006392], [18, 0.957424055], [19, 0.951886769], [20, 0.946394020], [21, 0.940945302], [22, 0.935540114], [23, 0.930177964], [24, 0.924858369], [25, 0.919580851], [26, 0.914344938], [27, 0.909150167], [28, 0.903996080], [29, 0.898882228], [30, 0.893808167], [31, 0.888773459], [32, 0.883777672], [33, 0.878820382], [34, 0.873901170], [35, 0.869019623], [36, 0.864175334], [37, 0.859367901], [38, 0.854596929], [39, 0.849862028], [40, 0.845162813], [41, 0.840498905], [42, 0.835869931], [43, 0.831275522], [44, 0.826715314], [45, 0.822188950], [46, 0.817696075], [47, 0.813236341], [48, 0.808809404], [49, 0.804414926], [50, 0.800052572], [51, 0.795722012], [52, 0.791422922], [53, 0.787154979], [54, 0.782917869], [55, 0.778711277], [56, 0.774534898], [57, 0.770388426], [58, 0.766271562], [59, 0.762184010], [60, 0.758125479], [61, 0.754095680], [62, 0.750094329], [63, 0.746121147], [64, 0.742175856], [65, 0.738258184], [66, 0.734367860], [67, 0.730504620], [68, 0.726668201], [69, 0.722858343], [70, 0.719074792], [71, 0.715317295], [72, 0.711585602], [73, 0.707879469], [74, 0.704198652], [75, 0.700542912], [76, 0.696912012], [77, 0.693305719], [78, 0.689723802], [79, 0.686166034], [80, 0.682632189], [81, 0.679122047], [82, 0.675635387], [83, 0.672171994], [84, 0.668731654], [85, 0.665314156], [86, 0.661919291], [87, 0.658546854], [88, 0.655196641], [89, 0.651868451], [90, 0.648562087], [91, 0.645277352], [92, 0.642014054], [93, 0.638771999], [94, 0.635551001], [95, 0.632350872], [96, 0.629171428], [97, 0.626012487], [98, 0.622873870], [99, 0.619755397], [100, 0.616656895], [102, 0.610519107], [104, 0.604459143], [106, 0.598475670], [108, 0.592567388], [110, 0.586733026], [112, 0.580971342], [114, 0.575281125], [116, 0.569661187], [118, 0.564110371], [120, 0.558627545], [122, 0.553211600], [124, 0.547861454], [126, 0.542576048], [128, 0.537354347], [130, 0.532195337], [132, 0.527098028], [134, 0.522061450], [136, 0.517084654], [138, 0.512166711], [140, 0.507306712], [142, 0.502503768], [144, 0.497757006], [146, 0.493065573], [148, 0.488428634], [150, 0.483845370], [152, 0.479314980], [154, 0.474836677], [156, 0.470409692], [158, 0.466033271], [160, 0.461706674], [162, 0.457429176], [164, 0.453200067], [166, 0.449018650], [168, 0.444884242], [170, 0.440796174], [172, 0.436753787], [174, 0.432756438], [176, 0.428803494], [178, 0.424894334], [180, 0.421028350], [182, 0.417204944], [184, 0.413423530], [186, 0.409683531], [188, 0.405984383], [190, 0.402325531], [192, 0.398706429], [194, 0.395126543], [196, 0.391585347], [198, 0.388082324], [200, 0.384616967], [202, 0.381188778], [204, 0.377797268], [206, 0.374441954], [208, 0.371122364], [210, 0.367838033], [212, 0.364588505], [214, 0.361373329], [216, 0.358192065], [218, 0.355044277], [220, 0.351929540], [222, 0.348847432], [224, 0.345797540], [226, 0.342779460], [228, 0.339792789], [230, 0.336837136], [232, 0.333912113], [234, 0.331017339], [236, 0.328152439], [238, 0.325317046], [240, 0.322510795], [242, 0.319733329], [244, 0.316984297], [246, 0.314263352], [248, 0.311570153], [250, 0.308904364], [252, 0.306265654], [254, 0.303653699], [256, 0.301068176], [258, 0.298508771], [260, 0.295975171], [262, 0.293467070], [264, 0.290984167], [266, 0.288526163], [268, 0.286092765], [270, 0.283683684], [272, 0.281298636], [274, 0.278937339], [276, 0.276599517], [278, 0.274284898], [280, 0.271993211], [282, 0.269724193], [284, 0.267477582], [286, 0.265253121], [288, 0.263050554], [290, 0.260869633], [292, 0.258710110], [294, 0.256571741], [296, 0.254454286], [298, 0.252357508], [300, 0.250281174], [302, 0.248225053], [304, 0.246188917], [306, 0.244172542], [308, 0.242175707], [310, 0.240198194], [312, 0.238239786], [314, 0.236300272], [316, 0.234379441], [318, 0.232477087], [320, 0.230593005], [322, 0.228726993], [324, 0.226878853], [326, 0.225048388], [328, 0.223235405], [330, 0.221439711], [332, 0.219661118], [334, 0.217899439], [336, 0.216154491], [338, 0.214426091], [340, 0.212714060], [342, 0.211018220], [344, 0.209338398], [346, 0.207674420], [348, 0.206026115], [350, 0.204393315], [355, 0.200378063], [360, 0.196456139], [365, 0.192625077], [370, 0.188882487], [375, 0.185226048], [380, 0.181653511], [385, 0.178162694], [390, 0.174751478], [395, 0.171417807], [400, 0.168159686], [405, 0.164975177], [410, 0.161862398], [415, 0.158819521], [420, 0.155844772], [425, 0.152936426], [430, 0.150092806], [435, 0.147312286], [440, 0.144593281], [445, 0.141934254], [450, 0.139333710], [455, 0.136790195], [460, 0.134302294], [465, 0.131868634], [470, 0.129487876], [475, 0.127158722], [480, 0.124879906], [485, 0.122650197], [490, 0.120468398], [495, 0.118333345], [500, 0.116243903], [505, 0.114198970], [510, 0.112197471], [515, 0.110238362], [520, 0.108320625], [525, 0.106443271], [530, 0.104605335], [535, 0.102805877], [540, 0.101043985], [545, 0.099318768], [550, 0.097629359], [555, 0.095974915], [560, 0.094354612], [565, 0.092767650], [570, 0.091213248], [575, 0.089690648], [580, 0.088199108], [585, 0.086737906], [590, 0.085306341], [595, 0.083903726], [600, 0.082529395], [605, 0.081182697], [610, 0.079862998], [615, 0.078569680], [620, 0.077302141], [625, 0.076059794], [630, 0.074842066], [635, 0.073648400], [640, 0.072478251], [645, 0.071331090], [650, 0.070206399], [655, 0.069103674], [660, 0.068022424], [665, 0.066962168], [670, 0.065922439], [675, 0.064902780], [680, 0.063902748], [685, 0.062921909], [690, 0.061959837], [695, 0.061016122], [700, 0.060090360], [705, 0.059182157], [710, 0.058291131], [715, 0.057416907], [720, 0.056559120], [725, 0.055717414], [730, 0.054891440], [735, 0.054080860], [740, 0.053285343], [745, 0.052504565], [750, 0.051738210], [755, 0.050985971], [760, 0.050247546], [765, 0.049522643], [770, 0.048810974], [775, 0.048112260], [780, 0.047426227], [785, 0.046752609], [790, 0.046091145], [795, 0.045441581], [800, 0.044803668], [805, 0.044177164], [810, 0.043561831], [815, 0.042957438], [820, 0.042363759], [825, 0.041780573], [830, 0.041207664], [835, 0.040644822], [840, 0.040091839], [845, 0.039548516], [850, 0.039014654], [855, 0.038490063], [860, 0.037974554], [865, 0.037467944], [870, 0.036970054], [875, 0.036480707], [880, 0.035999734], [885, 0.035526965], [890, 0.035062238], [895, 0.034605393], [900, 0.034156272], [905, 0.033714724], [910, 0.033280598], [915, 0.032853749], [920, 0.032434032], [925, 0.032021309], [930, 0.031615443], [935, 0.031216300], [940, 0.030823749], [945, 0.030437663], [950, 0.030057915], [955, 0.029684385], [960, 0.029316951], [965, 0.028955498], [970, 0.028599910], [975, 0.028250075], [980, 0.027905884], [985, 0.027567229], [990, 0.027234006], [995, 0.026906112], [1000, 0.026583445], [1005, 0.026265908], [1010, 0.025953405], [1015, 0.025645841], [1020, 0.025343124], [1025, 0.025045163], [1030, 0.024751871], [1035, 0.024463160], [1040, 0.024178947], [1045, 0.023899147], [1050, 0.023623680], [1055, 0.023352467], [1060, 0.023085429], [1065, 0.022822491], [1070, 0.022563577], [1075, 0.022308615], [1080, 0.022057533], [1085, 0.021810260], [1090, 0.021566729], [1095, 0.021326872], [1100, 0.021090622]])
+ 66_fCO2eqD47_Petersen = interp1d(Petersen_etal_CO2eqD47[:,0], Petersen_etal_CO2eqD47[:,1])
+ 67def fCO2eqD47_Petersen(T):
+ 68 '''
+ 69 CO2 equilibrium Δ47 value as a function of T (in degrees C)
+ 70 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127).
+ 71
+ 72 '''
+ 73 return float(_fCO2eqD47_Petersen(T))
+ 74
+ 75
+ 76Wang_etal_CO2eqD47 = np.array([[-83., 1.8954], [-73., 1.7530], [-63., 1.6261], [-53., 1.5126], [-43., 1.4104], [-33., 1.3182], [-23., 1.2345], [-13., 1.1584], [-3., 1.0888], [7., 1.0251], [17., 0.9665], [27., 0.9125], [37., 0.8626], [47., 0.8164], [57., 0.7734], [67., 0.7334], [87., 0.6612], [97., 0.6286], [107., 0.5980], [117., 0.5693], [127., 0.5423], [137., 0.5169], [147., 0.4930], [157., 0.4704], [167., 0.4491], [177., 0.4289], [187., 0.4098], [197., 0.3918], [207., 0.3747], [217., 0.3585], [227., 0.3431], [237., 0.3285], [247., 0.3147], [257., 0.3015], [267., 0.2890], [277., 0.2771], [287., 0.2657], [297., 0.2550], [307., 0.2447], [317., 0.2349], [327., 0.2256], [337., 0.2167], [347., 0.2083], [357., 0.2002], [367., 0.1925], [377., 0.1851], [387., 0.1781], [397., 0.1714], [407., 0.1650], [417., 0.1589], [427., 0.1530], [437., 0.1474], [447., 0.1421], [457., 0.1370], [467., 0.1321], [477., 0.1274], [487., 0.1229], [497., 0.1186], [507., 0.1145], [517., 0.1105], [527., 0.1068], [537., 0.1031], [547., 0.0997], [557., 0.0963], [567., 0.0931], [577., 0.0901], [587., 0.0871], [597., 0.0843], [607., 0.0816], [617., 0.0790], [627., 0.0765], [637., 0.0741], [647., 0.0718], [657., 0.0695], [667., 0.0674], [677., 0.0654], [687., 0.0634], [697., 0.0615], [707., 0.0597], [717., 0.0579], [727., 0.0562], [737., 0.0546], [747., 0.0530], [757., 0.0515], [767., 0.0500], [777., 0.0486], [787., 0.0472], [797., 0.0459], [807., 0.0447], [817., 0.0435], [827., 0.0423], [837., 0.0411], [847., 0.0400], [857., 0.0390], [867., 0.0380], [877., 0.0370], [887., 0.0360], [897., 0.0351], [907., 0.0342], [917., 0.0333], [927., 0.0325], [937., 0.0317], [947., 0.0309], [957., 0.0302], [967., 0.0294], [977., 0.0287], [987., 0.0281], [997., 0.0274], [1007., 0.0268], [1017., 0.0261], [1027., 0.0255], [1037., 0.0249], [1047., 0.0244], [1057., 0.0238], [1067., 0.0233], [1077., 0.0228], [1087., 0.0223], [1097., 0.0218]])
+ 77_fCO2eqD47_Wang = interp1d(Wang_etal_CO2eqD47[:,0] - 0.15, Wang_etal_CO2eqD47[:,1])
+ 78def fCO2eqD47_Wang(T):
+ 79 '''
+ 80 CO2 equilibrium Δ47 value as a function of `T` (in degrees C)
+ 81 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039)
+ 82 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)).
+ 83 '''
+ 84 return float(_fCO2eqD47_Wang(T))
+ 85
+ 86
+ 87def correlated_sum(X, C, w = None):
+ 88 '''
+ 89 Compute covariance-aware linear combinations
+ 90
+ 91 **Parameters**
+ 92
+ 93 + `X`: list or 1-D array of values to sum
+ 94 + `C`: covariance matrix for the elements of `X`
+ 95 + `w`: list or 1-D array of weights to apply to the elements of `X`
+ 96 (all equal to 1 by default)
+ 97
+ 98 Return the sum (and its SE) of the elements of `X`, with optional weights equal
+ 99 to the elements of `w`, accounting for covariances between the elements of `X`.
+ 100 '''
+ 101 if w is None:
+ 102 w = [1 for x in X]
+ 103 return np.dot(w,X), (np.dot(w,np.dot(C,w)))**.5
+ 104
+ 105
+ 106def make_csv(x, hsep = ',', vsep = '\n'):
+ 107 '''
+ 108 Formats a list of lists of strings as a CSV
109
- 110 + `x`: the list of lists of strings to format
- 111 + `hsep`: the field separator (`,` by default)
- 112 + `vsep`: the line-ending convention to use (`\\n` by default)
- 113
- 114 **Example**
+ 110 **Parameters**
+ 111
+ 112 + `x`: the list of lists of strings to format
+ 113 + `hsep`: the field separator (`,` by default)
+ 114 + `vsep`: the line-ending convention to use (`\\n` by default)
115
- 116 ```py
- 117 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']]))
- 118 ```
- 119
- 120 outputs:
+ 116 **Example**
+ 117
+ 118 ```py
+ 119 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']]))
+ 120 ```
121
- 122 ```py
- 123 a,b,c
- 124 d,e,f
- 125 ```
- 126 '''
- 127 return vsep.join([hsep.join(l) for l in x])
- 128
- 129
- 130def pf(txt):
- 131 '''
- 132 Modify string `txt` to follow `lmfit.Parameter()` naming rules.
- 133 '''
- 134 return txt.replace('-','_').replace('.','_').replace(' ','_')
- 135
- 136
- 137def smart_type(x):
- 138 '''
- 139 Tries to convert string `x` to a float if it includes a decimal point, or
- 140 to an integer if it does not. If both attempts fail, return the original
- 141 string unchanged.
- 142 '''
- 143 try:
- 144 y = float(x)
- 145 except ValueError:
- 146 return x
- 147 if '.' not in x:
- 148 return int(y)
- 149 return y
- 150
- 151
- 152def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'):
- 153 '''
- 154 Reads a list of lists of strings and outputs an ascii table
- 155
- 156 **Parameters**
+ 122 outputs:
+ 123
+ 124 ```py
+ 125 a,b,c
+ 126 d,e,f
+ 127 ```
+ 128 '''
+ 129 return vsep.join([hsep.join(l) for l in x])
+ 130
+ 131
+ 132def pf(txt):
+ 133 '''
+ 134 Modify string `txt` to follow `lmfit.Parameter()` naming rules.
+ 135 '''
+ 136 return txt.replace('-','_').replace('.','_').replace(' ','_')
+ 137
+ 138
+ 139def smart_type(x):
+ 140 '''
+ 141 Tries to convert string `x` to a float if it includes a decimal point, or
+ 142 to an integer if it does not. If both attempts fail, return the original
+ 143 string unchanged.
+ 144 '''
+ 145 try:
+ 146 y = float(x)
+ 147 except ValueError:
+ 148 return x
+ 149 if '.' not in x:
+ 150 return int(y)
+ 151 return y
+ 152
+ 153
+ 154def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'):
+ 155 '''
+ 156 Reads a list of lists of strings and outputs an ascii table
157
- 158 + `x`: a list of lists of strings
- 159 + `header`: the number of lines to treat as header lines
- 160 + `hsep`: the horizontal separator between columns
- 161 + `vsep`: the character to use as vertical separator
- 162 + `align`: string of left (`<`) or right (`>`) alignment characters.
- 163
- 164 **Example**
+ 158 **Parameters**
+ 159
+ 160 + `x`: a list of lists of strings
+ 161 + `header`: the number of lines to treat as header lines
+ 162 + `hsep`: the horizontal separator between columns
+ 163 + `vsep`: the character to use as vertical separator
+ 164 + `align`: string of left (`<`) or right (`>`) alignment characters.
165
- 166 ```py
- 167 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']]
- 168 print(pretty_table(x))
- 169 ```
- 170 yields:
+ 166 **Example**
+ 167
+ 168 ```py
+ 169 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']]
+ 170 print(pretty_table(x))
171 ```
- 172 -- ------ ---
- 173 A B C
+ 172 yields:
+ 173 ```
174 -- ------ ---
- 175 1 1.9999 foo
- 176 10 x bar
- 177 -- ------ ---
- 178 ```
- 179
- 180 '''
- 181 txt = []
- 182 widths = [np.max([len(e) for e in c]) for c in zip(*x)]
- 183
- 184 if len(widths) > len(align):
- 185 align += '>' * (len(widths)-len(align))
- 186 sepline = hsep.join([vsep*w for w in widths])
- 187 txt += [sepline]
- 188 for k,l in enumerate(x):
- 189 if k and k == header:
- 190 txt += [sepline]
- 191 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])]
- 192 txt += [sepline]
- 193 txt += ['']
- 194 return '\n'.join(txt)
- 195
- 196
- 197def transpose_table(x):
- 198 '''
- 199 Transpose a list if lists
- 200
- 201 **Parameters**
+ 175 A B C
+ 176 -- ------ ---
+ 177 1 1.9999 foo
+ 178 10 x bar
+ 179 -- ------ ---
+ 180 ```
+ 181
+ 182 '''
+ 183 txt = []
+ 184 widths = [np.max([len(e) for e in c]) for c in zip(*x)]
+ 185
+ 186 if len(widths) > len(align):
+ 187 align += '>' * (len(widths)-len(align))
+ 188 sepline = hsep.join([vsep*w for w in widths])
+ 189 txt += [sepline]
+ 190 for k,l in enumerate(x):
+ 191 if k and k == header:
+ 192 txt += [sepline]
+ 193 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])]
+ 194 txt += [sepline]
+ 195 txt += ['']
+ 196 return '\n'.join(txt)
+ 197
+ 198
+ 199def transpose_table(x):
+ 200 '''
+ 201 Transpose a list if lists
202
- 203 + `x`: a list of lists
+ 203 **Parameters**
204
- 205 **Example**
+ 205 + `x`: a list of lists
206
- 207 ```py
- 208 x = [[1, 2], [3, 4]]
- 209 print(transpose_table(x)) # yields: [[1, 3], [2, 4]]
- 210 ```
- 211 '''
- 212 return [[e for e in c] for c in zip(*x)]
- 213
- 214
- 215def w_avg(X, sX) :
- 216 '''
- 217 Compute variance-weighted average
- 218
- 219 Returns the value and SE of the weighted average of the elements of `X`,
- 220 with relative weights equal to their inverse variances (`1/sX**2`).
- 221
- 222 **Parameters**
+ 207 **Example**
+ 208
+ 209 ```py
+ 210 x = [[1, 2], [3, 4]]
+ 211 print(transpose_table(x)) # yields: [[1, 3], [2, 4]]
+ 212 ```
+ 213 '''
+ 214 return [[e for e in c] for c in zip(*x)]
+ 215
+ 216
+ 217def w_avg(X, sX) :
+ 218 '''
+ 219 Compute variance-weighted average
+ 220
+ 221 Returns the value and SE of the weighted average of the elements of `X`,
+ 222 with relative weights equal to their inverse variances (`1/sX**2`).
223
- 224 + `X`: array-like of elements to average
- 225 + `sX`: array-like of the corresponding SE values
- 226
- 227 **Tip**
+ 224 **Parameters**
+ 225
+ 226 + `X`: array-like of elements to average
+ 227 + `sX`: array-like of the corresponding SE values
228
- 229 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets,
- 230 they may be rearranged using `zip()`:
- 231
- 232 ```python
- 233 foo = [(0, 1), (1, 0.5), (2, 0.5)]
- 234 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333)
- 235 ```
- 236 '''
- 237 X = [ x for x in X ]
- 238 sX = [ sx for sx in sX ]
- 239 W = [ sx**-2 for sx in sX ]
- 240 W = [ w/sum(W) for w in W ]
- 241 Xavg = sum([ w*x for w,x in zip(W,X) ])
- 242 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5
- 243 return Xavg, sXavg
- 244
- 245
- 246def read_csv(filename, sep = ''):
- 247 '''
- 248 Read contents of `filename` in csv format and return a list of dictionaries.
- 249
- 250 In the csv string, spaces before and after field separators (`','` by default)
- 251 are optional.
- 252
- 253 **Parameters**
+ 229 **Tip**
+ 230
+ 231 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets,
+ 232 they may be rearranged using `zip()`:
+ 233
+ 234 ```python
+ 235 foo = [(0, 1), (1, 0.5), (2, 0.5)]
+ 236 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333)
+ 237 ```
+ 238 '''
+ 239 X = [ x for x in X ]
+ 240 sX = [ sx for sx in sX ]
+ 241 W = [ sx**-2 for sx in sX ]
+ 242 W = [ w/sum(W) for w in W ]
+ 243 Xavg = sum([ w*x for w,x in zip(W,X) ])
+ 244 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5
+ 245 return Xavg, sXavg
+ 246
+ 247
+ 248def read_csv(filename, sep = ''):
+ 249 '''
+ 250 Read contents of `filename` in csv format and return a list of dictionaries.
+ 251
+ 252 In the csv string, spaces before and after field separators (`','` by default)
+ 253 are optional.
254
- 255 + `filename`: the csv file to read
- 256 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
- 257 whichever appers most often in the contents of `filename`.
- 258 '''
- 259 with open(filename) as fid:
- 260 txt = fid.read()
- 261
- 262 if sep == '':
- 263 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
- 264 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
- 265 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]]
- 266
- 267
- 268def simulate_single_analysis(
- 269 sample = 'MYSAMPLE',
- 270 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26.,
- 271 d13C_VPDB = None, d18O_VPDB = None,
- 272 D47 = None, D48 = None, D49 = 0., D17O = 0.,
- 273 a47 = 1., b47 = 0., c47 = -0.9,
- 274 a48 = 1., b48 = 0., c48 = -0.45,
- 275 Nominal_D47 = None,
- 276 Nominal_D48 = None,
- 277 Nominal_d13C_VPDB = None,
- 278 Nominal_d18O_VPDB = None,
- 279 ALPHA_18O_ACID_REACTION = None,
- 280 R13_VPDB = None,
- 281 R17_VSMOW = None,
- 282 R18_VSMOW = None,
- 283 LAMBDA_17 = None,
- 284 R18_VPDB = None,
- 285 ):
- 286 '''
- 287 Compute working-gas delta values for a single analysis, assuming a stochastic working
- 288 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values).
- 289
- 290 **Parameters**
- 291
- 292 + `sample`: sample name
- 293 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
- 294 (respectively –4 and +26 ‰ by default)
- 295 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
- 296 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies
- 297 of the carbonate sample
- 298 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and
- 299 Δ48 values if `D47` or `D48` are not specified
- 300 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
- 301 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
- 302 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
- 303 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
- 304 correction parameters (by default equal to the `D4xdata` default values)
- 305
- 306 Returns a dictionary with fields
- 307 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`.
- 308 '''
- 309
- 310 if Nominal_d13C_VPDB is None:
- 311 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB
- 312
- 313 if Nominal_d18O_VPDB is None:
- 314 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB
- 315
- 316 if ALPHA_18O_ACID_REACTION is None:
- 317 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION
- 318
- 319 if R13_VPDB is None:
- 320 R13_VPDB = D4xdata().R13_VPDB
- 321
- 322 if R17_VSMOW is None:
- 323 R17_VSMOW = D4xdata().R17_VSMOW
- 324
- 325 if R18_VSMOW is None:
- 326 R18_VSMOW = D4xdata().R18_VSMOW
- 327
- 328 if LAMBDA_17 is None:
- 329 LAMBDA_17 = D4xdata().LAMBDA_17
- 330
- 331 if R18_VPDB is None:
- 332 R18_VPDB = D4xdata().R18_VPDB
- 333
- 334 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17
+ 255 **Parameters**
+ 256
+ 257 + `filename`: the csv file to read
+ 258 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
+ 259 whichever appers most often in the contents of `filename`.
+ 260 '''
+ 261 with open(filename) as fid:
+ 262 txt = fid.read()
+ 263
+ 264 if sep == '':
+ 265 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
+ 266 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
+ 267 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]]
+ 268
+ 269
+ 270def simulate_single_analysis(
+ 271 sample = 'MYSAMPLE',
+ 272 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26.,
+ 273 d13C_VPDB = None, d18O_VPDB = None,
+ 274 D47 = None, D48 = None, D49 = 0., D17O = 0.,
+ 275 a47 = 1., b47 = 0., c47 = -0.9,
+ 276 a48 = 1., b48 = 0., c48 = -0.45,
+ 277 Nominal_D47 = None,
+ 278 Nominal_D48 = None,
+ 279 Nominal_d13C_VPDB = None,
+ 280 Nominal_d18O_VPDB = None,
+ 281 ALPHA_18O_ACID_REACTION = None,
+ 282 R13_VPDB = None,
+ 283 R17_VSMOW = None,
+ 284 R18_VSMOW = None,
+ 285 LAMBDA_17 = None,
+ 286 R18_VPDB = None,
+ 287 ):
+ 288 '''
+ 289 Compute working-gas delta values for a single analysis, assuming a stochastic working
+ 290 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values).
+ 291
+ 292 **Parameters**
+ 293
+ 294 + `sample`: sample name
+ 295 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
+ 296 (respectively –4 and +26 ‰ by default)
+ 297 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
+ 298 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies
+ 299 of the carbonate sample
+ 300 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and
+ 301 Δ48 values if `D47` or `D48` are not specified
+ 302 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
+ 303 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
+ 304 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+ 305 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
+ 306 correction parameters (by default equal to the `D4xdata` default values)
+ 307
+ 308 Returns a dictionary with fields
+ 309 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`.
+ 310 '''
+ 311
+ 312 if Nominal_d13C_VPDB is None:
+ 313 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB
+ 314
+ 315 if Nominal_d18O_VPDB is None:
+ 316 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB
+ 317
+ 318 if ALPHA_18O_ACID_REACTION is None:
+ 319 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION
+ 320
+ 321 if R13_VPDB is None:
+ 322 R13_VPDB = D4xdata().R13_VPDB
+ 323
+ 324 if R17_VSMOW is None:
+ 325 R17_VSMOW = D4xdata().R17_VSMOW
+ 326
+ 327 if R18_VSMOW is None:
+ 328 R18_VSMOW = D4xdata().R18_VSMOW
+ 329
+ 330 if LAMBDA_17 is None:
+ 331 LAMBDA_17 = D4xdata().LAMBDA_17
+ 332
+ 333 if R18_VPDB is None:
+ 334 R18_VPDB = D4xdata().R18_VPDB
335
- 336 if Nominal_D47 is None:
- 337 Nominal_D47 = D47data().Nominal_D47
- 338
- 339 if Nominal_D48 is None:
- 340 Nominal_D48 = D48data().Nominal_D48
- 341
- 342 if d13C_VPDB is None:
- 343 if sample in Nominal_d13C_VPDB:
- 344 d13C_VPDB = Nominal_d13C_VPDB[sample]
- 345 else:
- 346 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.")
- 347
- 348 if d18O_VPDB is None:
- 349 if sample in Nominal_d18O_VPDB:
- 350 d18O_VPDB = Nominal_d18O_VPDB[sample]
- 351 else:
- 352 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.")
- 353
- 354 if D47 is None:
- 355 if sample in Nominal_D47:
- 356 D47 = Nominal_D47[sample]
- 357 else:
- 358 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.")
- 359
- 360 if D48 is None:
- 361 if sample in Nominal_D48:
- 362 D48 = Nominal_D48[sample]
- 363 else:
- 364 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.")
- 365
- 366 X = D4xdata()
- 367 X.R13_VPDB = R13_VPDB
- 368 X.R17_VSMOW = R17_VSMOW
- 369 X.R18_VSMOW = R18_VSMOW
- 370 X.LAMBDA_17 = LAMBDA_17
- 371 X.R18_VPDB = R18_VPDB
- 372 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17
- 373
- 374 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios(
- 375 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000),
- 376 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000),
- 377 )
- 378 R45, R46, R47, R48, R49 = X.compute_isobar_ratios(
- 379 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
- 380 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
- 381 D17O=D17O, D47=D47, D48=D48, D49=D49,
- 382 )
- 383 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios(
- 384 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
- 385 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
- 386 D17O=D17O,
- 387 )
- 388
- 389 d45 = 1000 * (R45/R45wg - 1)
- 390 d46 = 1000 * (R46/R46wg - 1)
- 391 d47 = 1000 * (R47/R47wg - 1)
- 392 d48 = 1000 * (R48/R48wg - 1)
- 393 d49 = 1000 * (R49/R49wg - 1)
- 394
- 395 for k in range(3): # dumb iteration to adjust for small changes in d47
- 396 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch
- 397 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch
- 398 d47 = 1000 * (R47raw/R47wg - 1)
- 399 d48 = 1000 * (R48raw/R48wg - 1)
- 400
- 401 return dict(
- 402 Sample = sample,
- 403 D17O = D17O,
- 404 d13Cwg_VPDB = d13Cwg_VPDB,
- 405 d18Owg_VSMOW = d18Owg_VSMOW,
- 406 d45 = d45,
- 407 d46 = d46,
- 408 d47 = d47,
- 409 d48 = d48,
- 410 d49 = d49,
- 411 )
- 412
- 413
- 414def virtual_data(
- 415 samples = [],
- 416 a47 = 1., b47 = 0., c47 = -0.9,
- 417 a48 = 1., b48 = 0., c48 = -0.45,
- 418 rd45 = 0.020, rd46 = 0.060,
- 419 rD47 = 0.015, rD48 = 0.045,
- 420 d13Cwg_VPDB = None, d18Owg_VSMOW = None,
- 421 session = None,
- 422 Nominal_D47 = None, Nominal_D48 = None,
- 423 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None,
- 424 ALPHA_18O_ACID_REACTION = None,
- 425 R13_VPDB = None,
- 426 R17_VSMOW = None,
- 427 R18_VSMOW = None,
- 428 LAMBDA_17 = None,
- 429 R18_VPDB = None,
- 430 seed = 0,
- 431 shuffle = True,
- 432 ):
- 433 '''
- 434 Return list with simulated analyses from a single session.
- 435
- 436 **Parameters**
+ 336 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17
+ 337
+ 338 if Nominal_D47 is None:
+ 339 Nominal_D47 = D47data().Nominal_D47
+ 340
+ 341 if Nominal_D48 is None:
+ 342 Nominal_D48 = D48data().Nominal_D48
+ 343
+ 344 if d13C_VPDB is None:
+ 345 if sample in Nominal_d13C_VPDB:
+ 346 d13C_VPDB = Nominal_d13C_VPDB[sample]
+ 347 else:
+ 348 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.")
+ 349
+ 350 if d18O_VPDB is None:
+ 351 if sample in Nominal_d18O_VPDB:
+ 352 d18O_VPDB = Nominal_d18O_VPDB[sample]
+ 353 else:
+ 354 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.")
+ 355
+ 356 if D47 is None:
+ 357 if sample in Nominal_D47:
+ 358 D47 = Nominal_D47[sample]
+ 359 else:
+ 360 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.")
+ 361
+ 362 if D48 is None:
+ 363 if sample in Nominal_D48:
+ 364 D48 = Nominal_D48[sample]
+ 365 else:
+ 366 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.")
+ 367
+ 368 X = D4xdata()
+ 369 X.R13_VPDB = R13_VPDB
+ 370 X.R17_VSMOW = R17_VSMOW
+ 371 X.R18_VSMOW = R18_VSMOW
+ 372 X.LAMBDA_17 = LAMBDA_17
+ 373 X.R18_VPDB = R18_VPDB
+ 374 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17
+ 375
+ 376 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios(
+ 377 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000),
+ 378 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000),
+ 379 )
+ 380 R45, R46, R47, R48, R49 = X.compute_isobar_ratios(
+ 381 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
+ 382 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
+ 383 D17O=D17O, D47=D47, D48=D48, D49=D49,
+ 384 )
+ 385 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios(
+ 386 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
+ 387 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
+ 388 D17O=D17O,
+ 389 )
+ 390
+ 391 d45 = 1000 * (R45/R45wg - 1)
+ 392 d46 = 1000 * (R46/R46wg - 1)
+ 393 d47 = 1000 * (R47/R47wg - 1)
+ 394 d48 = 1000 * (R48/R48wg - 1)
+ 395 d49 = 1000 * (R49/R49wg - 1)
+ 396
+ 397 for k in range(3): # dumb iteration to adjust for small changes in d47
+ 398 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch
+ 399 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch
+ 400 d47 = 1000 * (R47raw/R47wg - 1)
+ 401 d48 = 1000 * (R48raw/R48wg - 1)
+ 402
+ 403 return dict(
+ 404 Sample = sample,
+ 405 D17O = D17O,
+ 406 d13Cwg_VPDB = d13Cwg_VPDB,
+ 407 d18Owg_VSMOW = d18Owg_VSMOW,
+ 408 d45 = d45,
+ 409 d46 = d46,
+ 410 d47 = d47,
+ 411 d48 = d48,
+ 412 d49 = d49,
+ 413 )
+ 414
+ 415
+ 416def virtual_data(
+ 417 samples = [],
+ 418 a47 = 1., b47 = 0., c47 = -0.9,
+ 419 a48 = 1., b48 = 0., c48 = -0.45,
+ 420 rd45 = 0.020, rd46 = 0.060,
+ 421 rD47 = 0.015, rD48 = 0.045,
+ 422 d13Cwg_VPDB = None, d18Owg_VSMOW = None,
+ 423 session = None,
+ 424 Nominal_D47 = None, Nominal_D48 = None,
+ 425 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None,
+ 426 ALPHA_18O_ACID_REACTION = None,
+ 427 R13_VPDB = None,
+ 428 R17_VSMOW = None,
+ 429 R18_VSMOW = None,
+ 430 LAMBDA_17 = None,
+ 431 R18_VPDB = None,
+ 432 seed = 0,
+ 433 shuffle = True,
+ 434 ):
+ 435 '''
+ 436 Return list with simulated analyses from a single session.
437
- 438 + `samples`: a list of entries; each entry is a dictionary with the following fields:
- 439 * `Sample`: the name of the sample
- 440 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
- 441 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample
- 442 * `N`: how many analyses to generate for this sample
- 443 + `a47`: scrambling factor for Δ47
- 444 + `b47`: compositional nonlinearity for Δ47
- 445 + `c47`: working gas offset for Δ47
- 446 + `a48`: scrambling factor for Δ48
- 447 + `b48`: compositional nonlinearity for Δ48
- 448 + `c48`: working gas offset for Δ48
- 449 + `rd45`: analytical repeatability of δ45
- 450 + `rd46`: analytical repeatability of δ46
- 451 + `rD47`: analytical repeatability of Δ47
- 452 + `rD48`: analytical repeatability of Δ48
- 453 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
- 454 (by default equal to the `simulate_single_analysis` default values)
- 455 + `session`: name of the session (no name by default)
- 456 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values
- 457 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults)
- 458 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
- 459 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
- 460 (by default equal to the `simulate_single_analysis` defaults)
- 461 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+ 438 **Parameters**
+ 439
+ 440 + `samples`: a list of entries; each entry is a dictionary with the following fields:
+ 441 * `Sample`: the name of the sample
+ 442 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
+ 443 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample
+ 444 * `N`: how many analyses to generate for this sample
+ 445 + `a47`: scrambling factor for Δ47
+ 446 + `b47`: compositional nonlinearity for Δ47
+ 447 + `c47`: working gas offset for Δ47
+ 448 + `a48`: scrambling factor for Δ48
+ 449 + `b48`: compositional nonlinearity for Δ48
+ 450 + `c48`: working gas offset for Δ48
+ 451 + `rd45`: analytical repeatability of δ45
+ 452 + `rd46`: analytical repeatability of δ46
+ 453 + `rD47`: analytical repeatability of Δ47
+ 454 + `rD48`: analytical repeatability of Δ48
+ 455 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
+ 456 (by default equal to the `simulate_single_analysis` default values)
+ 457 + `session`: name of the session (no name by default)
+ 458 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values
+ 459 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults)
+ 460 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
+ 461 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
462 (by default equal to the `simulate_single_analysis` defaults)
- 463 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
- 464 correction parameters (by default equal to the `simulate_single_analysis` default)
- 465 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations
- 466 + `shuffle`: randomly reorder the sequence of analyses
- 467
- 468
- 469 Here is an example of using this method to generate an arbitrary combination of
- 470 anchors and unknowns for a bunch of sessions:
- 471
- 472 ```py
- 473 .. include:: ../code_examples/virtual_data/example.py
- 474 ```
- 475
- 476 This should output something like:
+ 463 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+ 464 (by default equal to the `simulate_single_analysis` defaults)
+ 465 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
+ 466 correction parameters (by default equal to the `simulate_single_analysis` default)
+ 467 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations
+ 468 + `shuffle`: randomly reorder the sequence of analyses
+ 469
+ 470
+ 471 Here is an example of using this method to generate an arbitrary combination of
+ 472 anchors and unknowns for a bunch of sessions:
+ 473
+ 474 ```py
+ 475 .. include:: ../code_examples/virtual_data/example.py
+ 476 ```
477
- 478 ```
- 479 .. include:: ../code_examples/virtual_data/output.txt
+ 478 This should output something like:
+ 479
480 ```
- 481 '''
- 482
- 483 kwargs = locals().copy()
- 484
- 485 from numpy import random as nprandom
- 486 if seed:
- 487 rng = nprandom.default_rng(seed)
- 488 else:
- 489 rng = nprandom.default_rng()
- 490
- 491 N = sum([s['N'] for s in samples])
- 492 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
- 493 errors45 *= rd45 / stdev(errors45) # scale errors to rd45
- 494 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
- 495 errors46 *= rd46 / stdev(errors46) # scale errors to rd46
- 496 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
- 497 errors47 *= rD47 / stdev(errors47) # scale errors to rD47
- 498 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
- 499 errors48 *= rD48 / stdev(errors48) # scale errors to rD48
- 500
- 501 k = 0
- 502 out = []
- 503 for s in samples:
- 504 kw = {}
- 505 kw['sample'] = s['Sample']
- 506 kw = {
- 507 **kw,
- 508 **{var: kwargs[var]
- 509 for var in [
- 510 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION',
- 511 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB',
- 512 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB',
- 513 'a47', 'b47', 'c47', 'a48', 'b48', 'c48',
- 514 ]
- 515 if kwargs[var] is not None},
- 516 **{var: s[var]
- 517 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O']
- 518 if var in s},
- 519 }
- 520
- 521 sN = s['N']
- 522 while sN:
- 523 out.append(simulate_single_analysis(**kw))
- 524 out[-1]['d45'] += errors45[k]
- 525 out[-1]['d46'] += errors46[k]
- 526 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47
- 527 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48
- 528 sN -= 1
- 529 k += 1
- 530
- 531 if session is not None:
- 532 for r in out:
- 533 r['Session'] = session
- 534
- 535 if shuffle:
- 536 nprandom.shuffle(out)
- 537
- 538 return out
+ 481 .. include:: ../code_examples/virtual_data/output.txt
+ 482 ```
+ 483 '''
+ 484
+ 485 kwargs = locals().copy()
+ 486
+ 487 from numpy import random as nprandom
+ 488 if seed:
+ 489 rng = nprandom.default_rng(seed)
+ 490 else:
+ 491 rng = nprandom.default_rng()
+ 492
+ 493 N = sum([s['N'] for s in samples])
+ 494 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+ 495 errors45 *= rd45 / stdev(errors45) # scale errors to rd45
+ 496 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+ 497 errors46 *= rd46 / stdev(errors46) # scale errors to rd46
+ 498 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+ 499 errors47 *= rD47 / stdev(errors47) # scale errors to rD47
+ 500 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+ 501 errors48 *= rD48 / stdev(errors48) # scale errors to rD48
+ 502
+ 503 k = 0
+ 504 out = []
+ 505 for s in samples:
+ 506 kw = {}
+ 507 kw['sample'] = s['Sample']
+ 508 kw = {
+ 509 **kw,
+ 510 **{var: kwargs[var]
+ 511 for var in [
+ 512 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION',
+ 513 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB',
+ 514 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB',
+ 515 'a47', 'b47', 'c47', 'a48', 'b48', 'c48',
+ 516 ]
+ 517 if kwargs[var] is not None},
+ 518 **{var: s[var]
+ 519 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O']
+ 520 if var in s},
+ 521 }
+ 522
+ 523 sN = s['N']
+ 524 while sN:
+ 525 out.append(simulate_single_analysis(**kw))
+ 526 out[-1]['d45'] += errors45[k]
+ 527 out[-1]['d46'] += errors46[k]
+ 528 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47
+ 529 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48
+ 530 sN -= 1
+ 531 k += 1
+ 532
+ 533 if session is not None:
+ 534 for r in out:
+ 535 r['Session'] = session
+ 536
+ 537 if shuffle:
+ 538 nprandom.shuffle(out)
539
- 540def table_of_samples(
- 541 data47 = None,
- 542 data48 = None,
- 543 dir = 'output',
- 544 filename = None,
- 545 save_to_file = True,
- 546 print_out = True,
- 547 output = None,
- 548 ):
- 549 '''
- 550 Print out, save to disk and/or return a combined table of samples
- 551 for a pair of `D47data` and `D48data` objects.
- 552
- 553 **Parameters**
+ 540 return out
+ 541
+ 542def table_of_samples(
+ 543 data47 = None,
+ 544 data48 = None,
+ 545 dir = 'output',
+ 546 filename = None,
+ 547 save_to_file = True,
+ 548 print_out = True,
+ 549 output = None,
+ 550 ):
+ 551 '''
+ 552 Print out, save to disk and/or return a combined table of samples
+ 553 for a pair of `D47data` and `D48data` objects.
554
- 555 + `data47`: `D47data` instance
- 556 + `data48`: `D48data` instance
- 557 + `dir`: the directory in which to save the table
- 558 + `filename`: the name to the csv file to write to
- 559 + `save_to_file`: whether to save the table to disk
- 560 + `print_out`: whether to print out the table
- 561 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
- 562 if set to `'raw'`: return a list of list of strings
- 563 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
- 564 '''
- 565 if data47 is None:
- 566 if data48 is None:
- 567 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
- 568 else:
- 569 return data48.table_of_samples(
- 570 dir = dir,
- 571 filename = filename,
- 572 save_to_file = save_to_file,
- 573 print_out = print_out,
- 574 output = output
- 575 )
- 576 else:
- 577 if data48 is None:
- 578 return data47.table_of_samples(
- 579 dir = dir,
- 580 filename = filename,
- 581 save_to_file = save_to_file,
- 582 print_out = print_out,
- 583 output = output
- 584 )
- 585 else:
- 586 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
- 587 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
- 588 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:])
- 589
- 590 if save_to_file:
- 591 if not os.path.exists(dir):
- 592 os.makedirs(dir)
- 593 if filename is None:
- 594 filename = f'D47D48_samples.csv'
- 595 with open(f'{dir}/{filename}', 'w') as fid:
- 596 fid.write(make_csv(out))
- 597 if print_out:
- 598 print('\n'+pretty_table(out))
- 599 if output == 'raw':
- 600 return out
- 601 elif output == 'pretty':
- 602 return pretty_table(out)
- 603
- 604
- 605def table_of_sessions(
- 606 data47 = None,
- 607 data48 = None,
- 608 dir = 'output',
- 609 filename = None,
- 610 save_to_file = True,
- 611 print_out = True,
- 612 output = None,
- 613 ):
- 614 '''
- 615 Print out, save to disk and/or return a combined table of sessions
- 616 for a pair of `D47data` and `D48data` objects.
- 617 ***Only applicable if the sessions in `data47` and those in `data48`
- 618 consist of the exact same sets of analyses.***
- 619
- 620 **Parameters**
+ 555 **Parameters**
+ 556
+ 557 + `data47`: `D47data` instance
+ 558 + `data48`: `D48data` instance
+ 559 + `dir`: the directory in which to save the table
+ 560 + `filename`: the name to the csv file to write to
+ 561 + `save_to_file`: whether to save the table to disk
+ 562 + `print_out`: whether to print out the table
+ 563 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+ 564 if set to `'raw'`: return a list of list of strings
+ 565 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+ 566 '''
+ 567 if data47 is None:
+ 568 if data48 is None:
+ 569 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+ 570 else:
+ 571 return data48.table_of_samples(
+ 572 dir = dir,
+ 573 filename = filename,
+ 574 save_to_file = save_to_file,
+ 575 print_out = print_out,
+ 576 output = output
+ 577 )
+ 578 else:
+ 579 if data48 is None:
+ 580 return data47.table_of_samples(
+ 581 dir = dir,
+ 582 filename = filename,
+ 583 save_to_file = save_to_file,
+ 584 print_out = print_out,
+ 585 output = output
+ 586 )
+ 587 else:
+ 588 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
+ 589 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
+ 590 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:])
+ 591
+ 592 if save_to_file:
+ 593 if not os.path.exists(dir):
+ 594 os.makedirs(dir)
+ 595 if filename is None:
+ 596 filename = f'D47D48_samples.csv'
+ 597 with open(f'{dir}/{filename}', 'w') as fid:
+ 598 fid.write(make_csv(out))
+ 599 if print_out:
+ 600 print('\n'+pretty_table(out))
+ 601 if output == 'raw':
+ 602 return out
+ 603 elif output == 'pretty':
+ 604 return pretty_table(out)
+ 605
+ 606
+ 607def table_of_sessions(
+ 608 data47 = None,
+ 609 data48 = None,
+ 610 dir = 'output',
+ 611 filename = None,
+ 612 save_to_file = True,
+ 613 print_out = True,
+ 614 output = None,
+ 615 ):
+ 616 '''
+ 617 Print out, save to disk and/or return a combined table of sessions
+ 618 for a pair of `D47data` and `D48data` objects.
+ 619 ***Only applicable if the sessions in `data47` and those in `data48`
+ 620 consist of the exact same sets of analyses.***
621
- 622 + `data47`: `D47data` instance
- 623 + `data48`: `D48data` instance
- 624 + `dir`: the directory in which to save the table
- 625 + `filename`: the name to the csv file to write to
- 626 + `save_to_file`: whether to save the table to disk
- 627 + `print_out`: whether to print out the table
- 628 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
- 629 if set to `'raw'`: return a list of list of strings
- 630 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
- 631 '''
- 632 if data47 is None:
- 633 if data48 is None:
- 634 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
- 635 else:
- 636 return data48.table_of_sessions(
- 637 dir = dir,
- 638 filename = filename,
- 639 save_to_file = save_to_file,
- 640 print_out = print_out,
- 641 output = output
- 642 )
- 643 else:
- 644 if data48 is None:
- 645 return data47.table_of_sessions(
- 646 dir = dir,
- 647 filename = filename,
- 648 save_to_file = save_to_file,
- 649 print_out = print_out,
- 650 output = output
- 651 )
- 652 else:
- 653 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
- 654 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
- 655 for k,x in enumerate(out47[0]):
- 656 if k>7:
- 657 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47')
- 658 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48')
- 659 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:])
- 660
- 661 if save_to_file:
- 662 if not os.path.exists(dir):
- 663 os.makedirs(dir)
- 664 if filename is None:
- 665 filename = f'D47D48_sessions.csv'
- 666 with open(f'{dir}/{filename}', 'w') as fid:
- 667 fid.write(make_csv(out))
- 668 if print_out:
- 669 print('\n'+pretty_table(out))
- 670 if output == 'raw':
- 671 return out
- 672 elif output == 'pretty':
- 673 return pretty_table(out)
- 674
- 675
- 676def table_of_analyses(
- 677 data47 = None,
- 678 data48 = None,
- 679 dir = 'output',
- 680 filename = None,
- 681 save_to_file = True,
- 682 print_out = True,
- 683 output = None,
- 684 ):
- 685 '''
- 686 Print out, save to disk and/or return a combined table of analyses
- 687 for a pair of `D47data` and `D48data` objects.
- 688
- 689 If the sessions in `data47` and those in `data48` do not consist of
- 690 the exact same sets of analyses, the table will have two columns
- 691 `Session_47` and `Session_48` instead of a single `Session` column.
- 692
- 693 **Parameters**
+ 622 **Parameters**
+ 623
+ 624 + `data47`: `D47data` instance
+ 625 + `data48`: `D48data` instance
+ 626 + `dir`: the directory in which to save the table
+ 627 + `filename`: the name to the csv file to write to
+ 628 + `save_to_file`: whether to save the table to disk
+ 629 + `print_out`: whether to print out the table
+ 630 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+ 631 if set to `'raw'`: return a list of list of strings
+ 632 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+ 633 '''
+ 634 if data47 is None:
+ 635 if data48 is None:
+ 636 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+ 637 else:
+ 638 return data48.table_of_sessions(
+ 639 dir = dir,
+ 640 filename = filename,
+ 641 save_to_file = save_to_file,
+ 642 print_out = print_out,
+ 643 output = output
+ 644 )
+ 645 else:
+ 646 if data48 is None:
+ 647 return data47.table_of_sessions(
+ 648 dir = dir,
+ 649 filename = filename,
+ 650 save_to_file = save_to_file,
+ 651 print_out = print_out,
+ 652 output = output
+ 653 )
+ 654 else:
+ 655 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
+ 656 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
+ 657 for k,x in enumerate(out47[0]):
+ 658 if k>7:
+ 659 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47')
+ 660 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48')
+ 661 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:])
+ 662
+ 663 if save_to_file:
+ 664 if not os.path.exists(dir):
+ 665 os.makedirs(dir)
+ 666 if filename is None:
+ 667 filename = f'D47D48_sessions.csv'
+ 668 with open(f'{dir}/{filename}', 'w') as fid:
+ 669 fid.write(make_csv(out))
+ 670 if print_out:
+ 671 print('\n'+pretty_table(out))
+ 672 if output == 'raw':
+ 673 return out
+ 674 elif output == 'pretty':
+ 675 return pretty_table(out)
+ 676
+ 677
+ 678def table_of_analyses(
+ 679 data47 = None,
+ 680 data48 = None,
+ 681 dir = 'output',
+ 682 filename = None,
+ 683 save_to_file = True,
+ 684 print_out = True,
+ 685 output = None,
+ 686 ):
+ 687 '''
+ 688 Print out, save to disk and/or return a combined table of analyses
+ 689 for a pair of `D47data` and `D48data` objects.
+ 690
+ 691 If the sessions in `data47` and those in `data48` do not consist of
+ 692 the exact same sets of analyses, the table will have two columns
+ 693 `Session_47` and `Session_48` instead of a single `Session` column.
694
- 695 + `data47`: `D47data` instance
- 696 + `data48`: `D48data` instance
- 697 + `dir`: the directory in which to save the table
- 698 + `filename`: the name to the csv file to write to
- 699 + `save_to_file`: whether to save the table to disk
- 700 + `print_out`: whether to print out the table
- 701 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
- 702 if set to `'raw'`: return a list of list of strings
- 703 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
- 704 '''
- 705 if data47 is None:
- 706 if data48 is None:
- 707 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
- 708 else:
- 709 return data48.table_of_analyses(
- 710 dir = dir,
- 711 filename = filename,
- 712 save_to_file = save_to_file,
- 713 print_out = print_out,
- 714 output = output
- 715 )
- 716 else:
- 717 if data48 is None:
- 718 return data47.table_of_analyses(
- 719 dir = dir,
- 720 filename = filename,
- 721 save_to_file = save_to_file,
- 722 print_out = print_out,
- 723 output = output
- 724 )
- 725 else:
- 726 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
- 727 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
- 728
- 729 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical
- 730 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:])
- 731 else:
- 732 out47[0][1] = 'Session_47'
- 733 out48[0][1] = 'Session_48'
- 734 out47 = transpose_table(out47)
- 735 out48 = transpose_table(out48)
- 736 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:])
- 737
- 738 if save_to_file:
- 739 if not os.path.exists(dir):
- 740 os.makedirs(dir)
- 741 if filename is None:
- 742 filename = f'D47D48_sessions.csv'
- 743 with open(f'{dir}/{filename}', 'w') as fid:
- 744 fid.write(make_csv(out))
- 745 if print_out:
- 746 print('\n'+pretty_table(out))
- 747 if output == 'raw':
- 748 return out
- 749 elif output == 'pretty':
- 750 return pretty_table(out)
- 751
- 752
- 753def _fullcovar(minresult, epsilon = 0.01, named = False):
- 754 '''
- 755 Construct full covariance matrix in the case of constrained parameters
- 756 '''
- 757
- 758 import asteval
+ 695 **Parameters**
+ 696
+ 697 + `data47`: `D47data` instance
+ 698 + `data48`: `D48data` instance
+ 699 + `dir`: the directory in which to save the table
+ 700 + `filename`: the name to the csv file to write to
+ 701 + `save_to_file`: whether to save the table to disk
+ 702 + `print_out`: whether to print out the table
+ 703 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+ 704 if set to `'raw'`: return a list of list of strings
+ 705 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+ 706 '''
+ 707 if data47 is None:
+ 708 if data48 is None:
+ 709 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+ 710 else:
+ 711 return data48.table_of_analyses(
+ 712 dir = dir,
+ 713 filename = filename,
+ 714 save_to_file = save_to_file,
+ 715 print_out = print_out,
+ 716 output = output
+ 717 )
+ 718 else:
+ 719 if data48 is None:
+ 720 return data47.table_of_analyses(
+ 721 dir = dir,
+ 722 filename = filename,
+ 723 save_to_file = save_to_file,
+ 724 print_out = print_out,
+ 725 output = output
+ 726 )
+ 727 else:
+ 728 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
+ 729 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
+ 730
+ 731 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical
+ 732 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:])
+ 733 else:
+ 734 out47[0][1] = 'Session_47'
+ 735 out48[0][1] = 'Session_48'
+ 736 out47 = transpose_table(out47)
+ 737 out48 = transpose_table(out48)
+ 738 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:])
+ 739
+ 740 if save_to_file:
+ 741 if not os.path.exists(dir):
+ 742 os.makedirs(dir)
+ 743 if filename is None:
+ 744 filename = f'D47D48_sessions.csv'
+ 745 with open(f'{dir}/{filename}', 'w') as fid:
+ 746 fid.write(make_csv(out))
+ 747 if print_out:
+ 748 print('\n'+pretty_table(out))
+ 749 if output == 'raw':
+ 750 return out
+ 751 elif output == 'pretty':
+ 752 return pretty_table(out)
+ 753
+ 754
+ 755def _fullcovar(minresult, epsilon = 0.01, named = False):
+ 756 '''
+ 757 Construct full covariance matrix in the case of constrained parameters
+ 758 '''
759
- 760 def f(values):
- 761 interp = asteval.Interpreter()
- 762 for n,v in zip(minresult.var_names, values):
- 763 interp(f'{n} = {v}')
- 764 for q in minresult.params:
- 765 if minresult.params[q].expr:
- 766 interp(f'{q} = {minresult.params[q].expr}')
- 767 return np.array([interp.symtable[q] for q in minresult.params])
- 768
- 769 # construct Jacobian
- 770 J = np.zeros((minresult.nvarys, len(minresult.params)))
- 771 X = np.array([minresult.params[p].value for p in minresult.var_names])
- 772 sX = np.array([minresult.params[p].stderr for p in minresult.var_names])
- 773
- 774 for j in range(minresult.nvarys):
- 775 x1 = [_ for _ in X]
- 776 x1[j] += epsilon * sX[j]
- 777 x2 = [_ for _ in X]
- 778 x2[j] -= epsilon * sX[j]
- 779 J[j,:] = (f(x1) - f(x2)) / (2 * epsilon * sX[j])
- 780
- 781 _names = [q for q in minresult.params]
- 782 _covar = J.T @ minresult.covar @ J
- 783 _se = np.diag(_covar)**.5
- 784 _correl = _covar.copy()
- 785 for k,s in enumerate(_se):
- 786 if s:
- 787 _correl[k,:] /= s
- 788 _correl[:,k] /= s
- 789
- 790 if named:
- 791 _covar = {i: {j:_covar[i,j] for j in minresult.params} for i in minresult.params}
- 792 _se = {i: _se[i] for i in minresult.params}
- 793 _correl = {i: {j:_correl[i,j] for j in minresult.params} for i in minresult.params}
- 794
- 795 return _names, _covar, _se, _correl
+ 760 import asteval
+ 761
+ 762 def f(values):
+ 763 interp = asteval.Interpreter()
+ 764 for n,v in zip(minresult.var_names, values):
+ 765 interp(f'{n} = {v}')
+ 766 for q in minresult.params:
+ 767 if minresult.params[q].expr:
+ 768 interp(f'{q} = {minresult.params[q].expr}')
+ 769 return np.array([interp.symtable[q] for q in minresult.params])
+ 770
+ 771 # construct Jacobian
+ 772 J = np.zeros((minresult.nvarys, len(minresult.params)))
+ 773 X = np.array([minresult.params[p].value for p in minresult.var_names])
+ 774 sX = np.array([minresult.params[p].stderr for p in minresult.var_names])
+ 775
+ 776 for j in range(minresult.nvarys):
+ 777 x1 = [_ for _ in X]
+ 778 x1[j] += epsilon * sX[j]
+ 779 x2 = [_ for _ in X]
+ 780 x2[j] -= epsilon * sX[j]
+ 781 J[j,:] = (f(x1) - f(x2)) / (2 * epsilon * sX[j])
+ 782
+ 783 _names = [q for q in minresult.params]
+ 784 _covar = J.T @ minresult.covar @ J
+ 785 _se = np.diag(_covar)**.5
+ 786 _correl = _covar.copy()
+ 787 for k,s in enumerate(_se):
+ 788 if s:
+ 789 _correl[k,:] /= s
+ 790 _correl[:,k] /= s
+ 791
+ 792 if named:
+ 793 _covar = {i: {j:_covar[i,j] for j in minresult.params} for i in minresult.params}
+ 794 _se = {i: _se[i] for i in minresult.params}
+ 795 _correl = {i: {j:_correl[i,j] for j in minresult.params} for i in minresult.params}
796
- 797
- 798class D4xdata(list):
- 799 '''
- 800 Store and process data for a large set of Δ47 and/or Δ48
- 801 analyses, usually comprising more than one analytical session.
- 802 '''
- 803
- 804 ### 17O CORRECTION PARAMETERS
- 805 R13_VPDB = 0.01118 # (Chang & Li, 1990)
- 806 '''
- 807 Absolute (13C/12C) ratio of VPDB.
- 808 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm))
- 809 '''
- 810
- 811 R18_VSMOW = 0.0020052 # (Baertschi, 1976)
- 812 '''
- 813 Absolute (18O/16C) ratio of VSMOW.
- 814 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1))
- 815 '''
- 816
- 817 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005)
- 818 '''
- 819 Mass-dependent exponent for triple oxygen isotopes.
- 820 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250))
- 821 '''
- 822
- 823 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB)
- 824 '''
- 825 Absolute (17O/16C) ratio of VSMOW.
- 826 By default equal to 0.00038475
- 827 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011),
- 828 rescaled to `R13_VPDB`)
- 829 '''
- 830
- 831 R18_VPDB = R18_VSMOW * 1.03092
- 832 '''
- 833 Absolute (18O/16C) ratio of VPDB.
- 834 By definition equal to `R18_VSMOW * 1.03092`.
- 835 '''
- 836
- 837 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17
- 838 '''
- 839 Absolute (17O/16C) ratio of VPDB.
- 840 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`.
- 841 '''
- 842
- 843 LEVENE_REF_SAMPLE = 'ETH-3'
- 844 '''
- 845 After the Δ4x standardization step, each sample is tested to
- 846 assess whether the Δ4x variance within all analyses for that
- 847 sample differs significantly from that observed for a given reference
- 848 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test),
- 849 which yields a p-value corresponding to the null hypothesis that the
- 850 underlying variances are equal).
- 851
- 852 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which
- 853 sample should be used as a reference for this test.
- 854 '''
- 855
- 856 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite)
- 857 '''
- 858 Specifies the 18O/16O fractionation factor generally applicable
- 859 to acid reactions in the dataset. Currently used by `D4xdata.wg()`,
- 860 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`.
- 861
- 862 By default equal to 1.008129 (calcite reacted at 90 °C,
- 863 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)).
- 864 '''
- 865
- 866 Nominal_d13C_VPDB = {
- 867 'ETH-1': 2.02,
- 868 'ETH-2': -10.17,
- 869 'ETH-3': 1.71,
- 870 } # (Bernasconi et al., 2018)
- 871 '''
- 872 Nominal δ13C_VPDB values assigned to carbonate standards, used by
- 873 `D4xdata.standardize_d13C()`.
- 874
- 875 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after
- 876 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
- 877 '''
- 878
- 879 Nominal_d18O_VPDB = {
- 880 'ETH-1': -2.19,
- 881 'ETH-2': -18.69,
- 882 'ETH-3': -1.78,
- 883 } # (Bernasconi et al., 2018)
- 884 '''
- 885 Nominal δ18O_VPDB values assigned to carbonate standards, used by
- 886 `D4xdata.standardize_d18O()`.
- 887
- 888 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after
- 889 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
- 890 '''
- 891
- 892 d13C_STANDARDIZATION_METHOD = '2pt'
- 893 '''
- 894 Method by which to standardize δ13C values:
- 895
- 896 + `none`: do not apply any δ13C standardization.
- 897 + `'1pt'`: within each session, offset all initial δ13C values so as to
- 898 minimize the difference between final δ13C_VPDB values and
- 899 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined).
- 900 + `'2pt'`: within each session, apply a affine trasformation to all δ13C
- 901 values so as to minimize the difference between final δ13C_VPDB
- 902 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB`
- 903 is defined).
- 904 '''
- 905
- 906 d18O_STANDARDIZATION_METHOD = '2pt'
- 907 '''
- 908 Method by which to standardize δ18O values:
- 909
- 910 + `none`: do not apply any δ18O standardization.
- 911 + `'1pt'`: within each session, offset all initial δ18O values so as to
- 912 minimize the difference between final δ18O_VPDB values and
- 913 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined).
- 914 + `'2pt'`: within each session, apply a affine trasformation to all δ18O
- 915 values so as to minimize the difference between final δ18O_VPDB
- 916 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB`
- 917 is defined).
- 918 '''
- 919
- 920 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
- 921 '''
- 922 **Parameters**
- 923
- 924 + `l`: a list of dictionaries, with each dictionary including at least the keys
- 925 `Sample`, `d45`, `d46`, and `d47` or `d48`.
- 926 + `mass`: `'47'` or `'48'`
- 927 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
- 928 + `session`: define session name for analyses without a `Session` key
- 929 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
- 930
- 931 Returns a `D4xdata` object derived from `list`.
- 932 '''
- 933 self._4x = mass
- 934 self.verbose = verbose
- 935 self.prefix = 'D4xdata'
- 936 self.logfile = logfile
- 937 list.__init__(self, l)
- 938 self.Nf = None
- 939 self.repeatability = {}
- 940 self.refresh(session = session)
- 941
- 942
- 943 def make_verbal(oldfun):
- 944 '''
- 945 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
- 946 '''
- 947 @wraps(oldfun)
- 948 def newfun(*args, verbose = '', **kwargs):
- 949 myself = args[0]
- 950 oldprefix = myself.prefix
- 951 myself.prefix = oldfun.__name__
- 952 if verbose != '':
- 953 oldverbose = myself.verbose
- 954 myself.verbose = verbose
- 955 out = oldfun(*args, **kwargs)
- 956 myself.prefix = oldprefix
- 957 if verbose != '':
- 958 myself.verbose = oldverbose
- 959 return out
- 960 return newfun
- 961
- 962
- 963 def msg(self, txt):
- 964 '''
- 965 Log a message to `self.logfile`, and print it out if `verbose = True`
- 966 '''
- 967 self.log(txt)
- 968 if self.verbose:
- 969 print(f'{f"[{self.prefix}]":<16} {txt}')
- 970
- 971
- 972 def vmsg(self, txt):
- 973 '''
- 974 Log a message to `self.logfile` and print it out
- 975 '''
- 976 self.log(txt)
- 977 print(txt)
- 978
- 979
- 980 def log(self, *txts):
- 981 '''
- 982 Log a message to `self.logfile`
- 983 '''
- 984 if self.logfile:
- 985 with open(self.logfile, 'a') as fid:
- 986 for txt in txts:
- 987 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
- 988
- 989
- 990 def refresh(self, session = 'mySession'):
- 991 '''
- 992 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
- 993 '''
- 994 self.fill_in_missing_info(session = session)
- 995 self.refresh_sessions()
- 996 self.refresh_samples()
- 997
- 998
- 999 def refresh_sessions(self):
-1000 '''
-1001 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
-1002 to `False` for all sessions.
-1003 '''
-1004 self.sessions = {
-1005 s: {'data': [r for r in self if r['Session'] == s]}
-1006 for s in sorted({r['Session'] for r in self})
-1007 }
-1008 for s in self.sessions:
-1009 self.sessions[s]['scrambling_drift'] = False
-1010 self.sessions[s]['slope_drift'] = False
-1011 self.sessions[s]['wg_drift'] = False
-1012 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
-1013 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
-1014
-1015
-1016 def refresh_samples(self):
-1017 '''
-1018 Define `self.samples`, `self.anchors`, and `self.unknowns`.
-1019 '''
-1020 self.samples = {
-1021 s: {'data': [r for r in self if r['Sample'] == s]}
-1022 for s in sorted({r['Sample'] for r in self})
-1023 }
-1024 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
-1025 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
-1026
-1027
-1028 def read(self, filename, sep = '', session = ''):
-1029 '''
-1030 Read file in csv format to load data into a `D47data` object.
-1031
-1032 In the csv file, spaces before and after field separators (`','` by default)
-1033 are optional. Each line corresponds to a single analysis.
-1034
-1035 The required fields are:
+ 797 return _names, _covar, _se, _correl
+ 798
+ 799
+ 800class D4xdata(list):
+ 801 '''
+ 802 Store and process data for a large set of Δ47 and/or Δ48
+ 803 analyses, usually comprising more than one analytical session.
+ 804 '''
+ 805
+ 806 ### 17O CORRECTION PARAMETERS
+ 807 R13_VPDB = 0.01118 # (Chang & Li, 1990)
+ 808 '''
+ 809 Absolute (13C/12C) ratio of VPDB.
+ 810 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm))
+ 811 '''
+ 812
+ 813 R18_VSMOW = 0.0020052 # (Baertschi, 1976)
+ 814 '''
+ 815 Absolute (18O/16C) ratio of VSMOW.
+ 816 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1))
+ 817 '''
+ 818
+ 819 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005)
+ 820 '''
+ 821 Mass-dependent exponent for triple oxygen isotopes.
+ 822 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250))
+ 823 '''
+ 824
+ 825 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB)
+ 826 '''
+ 827 Absolute (17O/16C) ratio of VSMOW.
+ 828 By default equal to 0.00038475
+ 829 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011),
+ 830 rescaled to `R13_VPDB`)
+ 831 '''
+ 832
+ 833 R18_VPDB = R18_VSMOW * 1.03092
+ 834 '''
+ 835 Absolute (18O/16C) ratio of VPDB.
+ 836 By definition equal to `R18_VSMOW * 1.03092`.
+ 837 '''
+ 838
+ 839 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17
+ 840 '''
+ 841 Absolute (17O/16C) ratio of VPDB.
+ 842 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`.
+ 843 '''
+ 844
+ 845 LEVENE_REF_SAMPLE = 'ETH-3'
+ 846 '''
+ 847 After the Δ4x standardization step, each sample is tested to
+ 848 assess whether the Δ4x variance within all analyses for that
+ 849 sample differs significantly from that observed for a given reference
+ 850 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test),
+ 851 which yields a p-value corresponding to the null hypothesis that the
+ 852 underlying variances are equal).
+ 853
+ 854 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which
+ 855 sample should be used as a reference for this test.
+ 856 '''
+ 857
+ 858 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite)
+ 859 '''
+ 860 Specifies the 18O/16O fractionation factor generally applicable
+ 861 to acid reactions in the dataset. Currently used by `D4xdata.wg()`,
+ 862 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`.
+ 863
+ 864 By default equal to 1.008129 (calcite reacted at 90 °C,
+ 865 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)).
+ 866 '''
+ 867
+ 868 Nominal_d13C_VPDB = {
+ 869 'ETH-1': 2.02,
+ 870 'ETH-2': -10.17,
+ 871 'ETH-3': 1.71,
+ 872 } # (Bernasconi et al., 2018)
+ 873 '''
+ 874 Nominal δ13C_VPDB values assigned to carbonate standards, used by
+ 875 `D4xdata.standardize_d13C()`.
+ 876
+ 877 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after
+ 878 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
+ 879 '''
+ 880
+ 881 Nominal_d18O_VPDB = {
+ 882 'ETH-1': -2.19,
+ 883 'ETH-2': -18.69,
+ 884 'ETH-3': -1.78,
+ 885 } # (Bernasconi et al., 2018)
+ 886 '''
+ 887 Nominal δ18O_VPDB values assigned to carbonate standards, used by
+ 888 `D4xdata.standardize_d18O()`.
+ 889
+ 890 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after
+ 891 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
+ 892 '''
+ 893
+ 894 d13C_STANDARDIZATION_METHOD = '2pt'
+ 895 '''
+ 896 Method by which to standardize δ13C values:
+ 897
+ 898 + `none`: do not apply any δ13C standardization.
+ 899 + `'1pt'`: within each session, offset all initial δ13C values so as to
+ 900 minimize the difference between final δ13C_VPDB values and
+ 901 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined).
+ 902 + `'2pt'`: within each session, apply a affine trasformation to all δ13C
+ 903 values so as to minimize the difference between final δ13C_VPDB
+ 904 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB`
+ 905 is defined).
+ 906 '''
+ 907
+ 908 d18O_STANDARDIZATION_METHOD = '2pt'
+ 909 '''
+ 910 Method by which to standardize δ18O values:
+ 911
+ 912 + `none`: do not apply any δ18O standardization.
+ 913 + `'1pt'`: within each session, offset all initial δ18O values so as to
+ 914 minimize the difference between final δ18O_VPDB values and
+ 915 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined).
+ 916 + `'2pt'`: within each session, apply a affine trasformation to all δ18O
+ 917 values so as to minimize the difference between final δ18O_VPDB
+ 918 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB`
+ 919 is defined).
+ 920 '''
+ 921
+ 922 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
+ 923 '''
+ 924 **Parameters**
+ 925
+ 926 + `l`: a list of dictionaries, with each dictionary including at least the keys
+ 927 `Sample`, `d45`, `d46`, and `d47` or `d48`.
+ 928 + `mass`: `'47'` or `'48'`
+ 929 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
+ 930 + `session`: define session name for analyses without a `Session` key
+ 931 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
+ 932
+ 933 Returns a `D4xdata` object derived from `list`.
+ 934 '''
+ 935 self._4x = mass
+ 936 self.verbose = verbose
+ 937 self.prefix = 'D4xdata'
+ 938 self.logfile = logfile
+ 939 list.__init__(self, l)
+ 940 self.Nf = None
+ 941 self.repeatability = {}
+ 942 self.refresh(session = session)
+ 943
+ 944
+ 945 def make_verbal(oldfun):
+ 946 '''
+ 947 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
+ 948 '''
+ 949 @wraps(oldfun)
+ 950 def newfun(*args, verbose = '', **kwargs):
+ 951 myself = args[0]
+ 952 oldprefix = myself.prefix
+ 953 myself.prefix = oldfun.__name__
+ 954 if verbose != '':
+ 955 oldverbose = myself.verbose
+ 956 myself.verbose = verbose
+ 957 out = oldfun(*args, **kwargs)
+ 958 myself.prefix = oldprefix
+ 959 if verbose != '':
+ 960 myself.verbose = oldverbose
+ 961 return out
+ 962 return newfun
+ 963
+ 964
+ 965 def msg(self, txt):
+ 966 '''
+ 967 Log a message to `self.logfile`, and print it out if `verbose = True`
+ 968 '''
+ 969 self.log(txt)
+ 970 if self.verbose:
+ 971 print(f'{f"[{self.prefix}]":<16} {txt}')
+ 972
+ 973
+ 974 def vmsg(self, txt):
+ 975 '''
+ 976 Log a message to `self.logfile` and print it out
+ 977 '''
+ 978 self.log(txt)
+ 979 print(txt)
+ 980
+ 981
+ 982 def log(self, *txts):
+ 983 '''
+ 984 Log a message to `self.logfile`
+ 985 '''
+ 986 if self.logfile:
+ 987 with open(self.logfile, 'a') as fid:
+ 988 for txt in txts:
+ 989 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
+ 990
+ 991
+ 992 def refresh(self, session = 'mySession'):
+ 993 '''
+ 994 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
+ 995 '''
+ 996 self.fill_in_missing_info(session = session)
+ 997 self.refresh_sessions()
+ 998 self.refresh_samples()
+ 999
+1000
+1001 def refresh_sessions(self):
+1002 '''
+1003 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
+1004 to `False` for all sessions.
+1005 '''
+1006 self.sessions = {
+1007 s: {'data': [r for r in self if r['Session'] == s]}
+1008 for s in sorted({r['Session'] for r in self})
+1009 }
+1010 for s in self.sessions:
+1011 self.sessions[s]['scrambling_drift'] = False
+1012 self.sessions[s]['slope_drift'] = False
+1013 self.sessions[s]['wg_drift'] = False
+1014 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
+1015 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
+1016
+1017
+1018 def refresh_samples(self):
+1019 '''
+1020 Define `self.samples`, `self.anchors`, and `self.unknowns`.
+1021 '''
+1022 self.samples = {
+1023 s: {'data': [r for r in self if r['Sample'] == s]}
+1024 for s in sorted({r['Sample'] for r in self})
+1025 }
+1026 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
+1027 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
+1028
+1029
+1030 def read(self, filename, sep = '', session = ''):
+1031 '''
+1032 Read file in csv format to load data into a `D47data` object.
+1033
+1034 In the csv file, spaces before and after field separators (`','` by default)
+1035 are optional. Each line corresponds to a single analysis.
1036
-1037 + `UID`: a unique identifier
-1038 + `Session`: an identifier for the analytical session
-1039 + `Sample`: a sample identifier
-1040 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1041
-1042 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1043 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1044 and `d49` are optional, and set to NaN by default.
-1045
-1046 **Parameters**
+1037 The required fields are:
+1038
+1039 + `UID`: a unique identifier
+1040 + `Session`: an identifier for the analytical session
+1041 + `Sample`: a sample identifier
+1042 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1043
+1044 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1045 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1046 and `d49` are optional, and set to NaN by default.
1047
-1048 + `fileneme`: the path of the file to read
-1049 + `sep`: csv separator delimiting the fields
-1050 + `session`: set `Session` field to this string for all analyses
-1051 '''
-1052 with open(filename) as fid:
-1053 self.input(fid.read(), sep = sep, session = session)
-1054
-1055
-1056 def input(self, txt, sep = '', session = ''):
-1057 '''
-1058 Read `txt` string in csv format to load analysis data into a `D47data` object.
-1059
-1060 In the csv string, spaces before and after field separators (`','` by default)
-1061 are optional. Each line corresponds to a single analysis.
-1062
-1063 The required fields are:
+1048 **Parameters**
+1049
+1050 + `fileneme`: the path of the file to read
+1051 + `sep`: csv separator delimiting the fields
+1052 + `session`: set `Session` field to this string for all analyses
+1053 '''
+1054 with open(filename) as fid:
+1055 self.input(fid.read(), sep = sep, session = session)
+1056
+1057
+1058 def input(self, txt, sep = '', session = ''):
+1059 '''
+1060 Read `txt` string in csv format to load analysis data into a `D47data` object.
+1061
+1062 In the csv string, spaces before and after field separators (`','` by default)
+1063 are optional. Each line corresponds to a single analysis.
1064
-1065 + `UID`: a unique identifier
-1066 + `Session`: an identifier for the analytical session
-1067 + `Sample`: a sample identifier
-1068 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1069
-1070 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1071 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1072 and `d49` are optional, and set to NaN by default.
-1073
-1074 **Parameters**
+1065 The required fields are:
+1066
+1067 + `UID`: a unique identifier
+1068 + `Session`: an identifier for the analytical session
+1069 + `Sample`: a sample identifier
+1070 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1071
+1072 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1073 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1074 and `d49` are optional, and set to NaN by default.
1075
-1076 + `txt`: the csv string to read
-1077 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
-1078 whichever appers most often in `txt`.
-1079 + `session`: set `Session` field to this string for all analyses
-1080 '''
-1081 if sep == '':
-1082 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
-1083 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
-1084 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
-1085
-1086 if session != '':
-1087 for r in data:
-1088 r['Session'] = session
-1089
-1090 self += data
-1091 self.refresh()
-1092
-1093
-1094 @make_verbal
-1095 def wg(self, samples = None, a18_acid = None):
-1096 '''
-1097 Compute bulk composition of the working gas for each session based on
-1098 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
-1099 `self.Nominal_d18O_VPDB`.
-1100 '''
-1101
-1102 self.msg('Computing WG composition:')
+1076 **Parameters**
+1077
+1078 + `txt`: the csv string to read
+1079 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
+1080 whichever appers most often in `txt`.
+1081 + `session`: set `Session` field to this string for all analyses
+1082 '''
+1083 if sep == '':
+1084 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
+1085 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
+1086 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
+1087
+1088 if session != '':
+1089 for r in data:
+1090 r['Session'] = session
+1091
+1092 self += data
+1093 self.refresh()
+1094
+1095
+1096 @make_verbal
+1097 def wg(self, samples = None, a18_acid = None):
+1098 '''
+1099 Compute bulk composition of the working gas for each session based on
+1100 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
+1101 `self.Nominal_d18O_VPDB`.
+1102 '''
1103
-1104 if a18_acid is None:
-1105 a18_acid = self.ALPHA_18O_ACID_REACTION
-1106 if samples is None:
-1107 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
-1108
-1109 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1104 self.msg('Computing WG composition:')
+1105
+1106 if a18_acid is None:
+1107 a18_acid = self.ALPHA_18O_ACID_REACTION
+1108 if samples is None:
+1109 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
1110
-1111 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
-1112 R45R46_standards = {}
-1113 for sample in samples:
-1114 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
-1115 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
-1116 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
-1117 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
-1118 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
-1119
-1120 C12_s = 1 / (1 + R13_s)
-1121 C13_s = R13_s / (1 + R13_s)
-1122 C16_s = 1 / (1 + R17_s + R18_s)
-1123 C17_s = R17_s / (1 + R17_s + R18_s)
-1124 C18_s = R18_s / (1 + R17_s + R18_s)
-1125
-1126 C626_s = C12_s * C16_s ** 2
-1127 C627_s = 2 * C12_s * C16_s * C17_s
-1128 C628_s = 2 * C12_s * C16_s * C18_s
-1129 C636_s = C13_s * C16_s ** 2
-1130 C637_s = 2 * C13_s * C16_s * C17_s
-1131 C727_s = C12_s * C17_s ** 2
-1132
-1133 R45_s = (C627_s + C636_s) / C626_s
-1134 R46_s = (C628_s + C637_s + C727_s) / C626_s
-1135 R45R46_standards[sample] = (R45_s, R46_s)
-1136
-1137 for s in self.sessions:
-1138 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
-1139 assert db, f'No sample from {samples} found in session "{s}".'
-1140# dbsamples = sorted({r['Sample'] for r in db})
-1141
-1142 X = [r['d45'] for r in db]
-1143 Y = [R45R46_standards[r['Sample']][0] for r in db]
-1144 x1, x2 = np.min(X), np.max(X)
-1145
-1146 if x1 < x2:
-1147 wgcoord = x1/(x1-x2)
-1148 else:
-1149 wgcoord = 999
-1150
-1151 if wgcoord < -.5 or wgcoord > 1.5:
-1152 # unreasonable to extrapolate to d45 = 0
-1153 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1154 else :
-1155 # d45 = 0 is reasonably well bracketed
-1156 R45_wg = np.polyfit(X, Y, 1)[1]
-1157
-1158 X = [r['d46'] for r in db]
-1159 Y = [R45R46_standards[r['Sample']][1] for r in db]
-1160 x1, x2 = np.min(X), np.max(X)
-1161
-1162 if x1 < x2:
-1163 wgcoord = x1/(x1-x2)
-1164 else:
-1165 wgcoord = 999
-1166
-1167 if wgcoord < -.5 or wgcoord > 1.5:
-1168 # unreasonable to extrapolate to d46 = 0
-1169 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1170 else :
-1171 # d46 = 0 is reasonably well bracketed
-1172 R46_wg = np.polyfit(X, Y, 1)[1]
-1173
-1174 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
+1111 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1112
+1113 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
+1114 R45R46_standards = {}
+1115 for sample in samples:
+1116 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
+1117 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
+1118 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
+1119 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
+1120 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
+1121
+1122 C12_s = 1 / (1 + R13_s)
+1123 C13_s = R13_s / (1 + R13_s)
+1124 C16_s = 1 / (1 + R17_s + R18_s)
+1125 C17_s = R17_s / (1 + R17_s + R18_s)
+1126 C18_s = R18_s / (1 + R17_s + R18_s)
+1127
+1128 C626_s = C12_s * C16_s ** 2
+1129 C627_s = 2 * C12_s * C16_s * C17_s
+1130 C628_s = 2 * C12_s * C16_s * C18_s
+1131 C636_s = C13_s * C16_s ** 2
+1132 C637_s = 2 * C13_s * C16_s * C17_s
+1133 C727_s = C12_s * C17_s ** 2
+1134
+1135 R45_s = (C627_s + C636_s) / C626_s
+1136 R46_s = (C628_s + C637_s + C727_s) / C626_s
+1137 R45R46_standards[sample] = (R45_s, R46_s)
+1138
+1139 for s in self.sessions:
+1140 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
+1141 assert db, f'No sample from {samples} found in session "{s}".'
+1142# dbsamples = sorted({r['Sample'] for r in db})
+1143
+1144 X = [r['d45'] for r in db]
+1145 Y = [R45R46_standards[r['Sample']][0] for r in db]
+1146 x1, x2 = np.min(X), np.max(X)
+1147
+1148 if x1 < x2:
+1149 wgcoord = x1/(x1-x2)
+1150 else:
+1151 wgcoord = 999
+1152
+1153 if wgcoord < -.5 or wgcoord > 1.5:
+1154 # unreasonable to extrapolate to d45 = 0
+1155 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1156 else :
+1157 # d45 = 0 is reasonably well bracketed
+1158 R45_wg = np.polyfit(X, Y, 1)[1]
+1159
+1160 X = [r['d46'] for r in db]
+1161 Y = [R45R46_standards[r['Sample']][1] for r in db]
+1162 x1, x2 = np.min(X), np.max(X)
+1163
+1164 if x1 < x2:
+1165 wgcoord = x1/(x1-x2)
+1166 else:
+1167 wgcoord = 999
+1168
+1169 if wgcoord < -.5 or wgcoord > 1.5:
+1170 # unreasonable to extrapolate to d46 = 0
+1171 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1172 else :
+1173 # d46 = 0 is reasonably well bracketed
+1174 R46_wg = np.polyfit(X, Y, 1)[1]
1175
-1176 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1176 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
1177
-1178 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
-1179 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
-1180 for r in self.sessions[s]['data']:
-1181 r['d13Cwg_VPDB'] = d13Cwg_VPDB
-1182 r['d18Owg_VSMOW'] = d18Owg_VSMOW
-1183
-1184
-1185 def compute_bulk_delta(self, R45, R46, D17O = 0):
-1186 '''
-1187 Compute δ13C_VPDB and δ18O_VSMOW,
-1188 by solving the generalized form of equation (17) from
-1189 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
-1190 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
-1191 solving the corresponding second-order Taylor polynomial.
-1192 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
-1193 '''
-1194
-1195 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+1178 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1179
+1180 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
+1181 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
+1182 for r in self.sessions[s]['data']:
+1183 r['d13Cwg_VPDB'] = d13Cwg_VPDB
+1184 r['d18Owg_VSMOW'] = d18Owg_VSMOW
+1185
+1186
+1187 def compute_bulk_delta(self, R45, R46, D17O = 0):
+1188 '''
+1189 Compute δ13C_VPDB and δ18O_VSMOW,
+1190 by solving the generalized form of equation (17) from
+1191 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
+1192 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
+1193 solving the corresponding second-order Taylor polynomial.
+1194 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
+1195 '''
1196
-1197 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
-1198 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
-1199 C = 2 * self.R18_VSMOW
-1200 D = -R46
-1201
-1202 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
-1203 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
-1204 cc = A + B + C + D
-1205
-1206 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1197 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+1198
+1199 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
+1200 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
+1201 C = 2 * self.R18_VSMOW
+1202 D = -R46
+1203
+1204 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
+1205 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
+1206 cc = A + B + C + D
1207
-1208 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
-1209 R17 = K * R18 ** self.LAMBDA_17
-1210 R13 = R45 - 2 * R17
-1211
-1212 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
+1208 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1209
+1210 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
+1211 R17 = K * R18 ** self.LAMBDA_17
+1212 R13 = R45 - 2 * R17
1213
-1214 return d13C_VPDB, d18O_VSMOW
+1214 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
1215
-1216
-1217 @make_verbal
-1218 def crunch(self, verbose = ''):
-1219 '''
-1220 Compute bulk composition and raw clumped isotope anomalies for all analyses.
-1221 '''
-1222 for r in self:
-1223 self.compute_bulk_and_clumping_deltas(r)
-1224 self.standardize_d13C()
-1225 self.standardize_d18O()
-1226 self.msg(f"Crunched {len(self)} analyses.")
-1227
-1228
-1229 def fill_in_missing_info(self, session = 'mySession'):
-1230 '''
-1231 Fill in optional fields with default values
-1232 '''
-1233 for i,r in enumerate(self):
-1234 if 'D17O' not in r:
-1235 r['D17O'] = 0.
-1236 if 'UID' not in r:
-1237 r['UID'] = f'{i+1}'
-1238 if 'Session' not in r:
-1239 r['Session'] = session
-1240 for k in ['d47', 'd48', 'd49']:
-1241 if k not in r:
-1242 r[k] = np.nan
-1243
-1244
-1245 def standardize_d13C(self):
-1246 '''
-1247 Perform δ13C standadization within each session `s` according to
-1248 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
-1249 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
-1250 may be redefined abitrarily at a later stage.
-1251 '''
-1252 for s in self.sessions:
-1253 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
-1254 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
-1255 X,Y = zip(*XY)
-1256 if self.sessions[s]['d13C_standardization_method'] == '1pt':
-1257 offset = np.mean(Y) - np.mean(X)
-1258 for r in self.sessions[s]['data']:
-1259 r['d13C_VPDB'] += offset
-1260 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
-1261 a,b = np.polyfit(X,Y,1)
-1262 for r in self.sessions[s]['data']:
-1263 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
-1264
-1265 def standardize_d18O(self):
-1266 '''
-1267 Perform δ18O standadization within each session `s` according to
-1268 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
-1269 which is defined by default by `D47data.refresh_sessions()`as equal to
-1270 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
-1271 '''
-1272 for s in self.sessions:
-1273 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
-1274 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
-1275 X,Y = zip(*XY)
-1276 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
-1277 if self.sessions[s]['d18O_standardization_method'] == '1pt':
-1278 offset = np.mean(Y) - np.mean(X)
-1279 for r in self.sessions[s]['data']:
-1280 r['d18O_VSMOW'] += offset
-1281 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
-1282 a,b = np.polyfit(X,Y,1)
-1283 for r in self.sessions[s]['data']:
-1284 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
-1285
-1286
-1287 def compute_bulk_and_clumping_deltas(self, r):
-1288 '''
-1289 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
-1290 '''
-1291
-1292 # Compute working gas R13, R18, and isobar ratios
-1293 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
-1294 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
-1295 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
-1296
-1297 # Compute analyte isobar ratios
-1298 R45 = (1 + r['d45'] / 1000) * R45_wg
-1299 R46 = (1 + r['d46'] / 1000) * R46_wg
-1300 R47 = (1 + r['d47'] / 1000) * R47_wg
-1301 R48 = (1 + r['d48'] / 1000) * R48_wg
-1302 R49 = (1 + r['d49'] / 1000) * R49_wg
-1303
-1304 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
-1305 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
-1306 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
-1307
-1308 # Compute stochastic isobar ratios of the analyte
-1309 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
-1310 R13, R18, D17O = r['D17O']
-1311 )
-1312
-1313 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
-1314 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
-1315 if (R45 / R45stoch - 1) > 5e-8:
-1316 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
-1317 if (R46 / R46stoch - 1) > 5e-8:
-1318 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
-1319
-1320 # Compute raw clumped isotope anomalies
-1321 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
-1322 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
-1323 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
-1324
-1325
-1326 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
-1327 '''
-1328 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
-1329 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
-1330 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
-1331 '''
-1332
-1333 # Compute R17
-1334 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
-1335
-1336 # Compute isotope concentrations
-1337 C12 = (1 + R13) ** -1
-1338 C13 = C12 * R13
-1339 C16 = (1 + R17 + R18) ** -1
-1340 C17 = C16 * R17
-1341 C18 = C16 * R18
-1342
-1343 # Compute stochastic isotopologue concentrations
-1344 C626 = C16 * C12 * C16
-1345 C627 = C16 * C12 * C17 * 2
-1346 C628 = C16 * C12 * C18 * 2
-1347 C636 = C16 * C13 * C16
-1348 C637 = C16 * C13 * C17 * 2
-1349 C638 = C16 * C13 * C18 * 2
-1350 C727 = C17 * C12 * C17
-1351 C728 = C17 * C12 * C18 * 2
-1352 C737 = C17 * C13 * C17
-1353 C738 = C17 * C13 * C18 * 2
-1354 C828 = C18 * C12 * C18
-1355 C838 = C18 * C13 * C18
-1356
-1357 # Compute stochastic isobar ratios
-1358 R45 = (C636 + C627) / C626
-1359 R46 = (C628 + C637 + C727) / C626
-1360 R47 = (C638 + C728 + C737) / C626
-1361 R48 = (C738 + C828) / C626
-1362 R49 = C838 / C626
-1363
-1364 # Account for stochastic anomalies
-1365 R47 *= 1 + D47 / 1000
-1366 R48 *= 1 + D48 / 1000
-1367 R49 *= 1 + D49 / 1000
-1368
-1369 # Return isobar ratios
-1370 return R45, R46, R47, R48, R49
-1371
-1372
-1373 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
-1374 '''
-1375 Split unknown samples by UID (treat all analyses as different samples)
-1376 or by session (treat analyses of a given sample in different sessions as
-1377 different samples).
-1378
-1379 **Parameters**
+1216 return d13C_VPDB, d18O_VSMOW
+1217
+1218
+1219 @make_verbal
+1220 def crunch(self, verbose = ''):
+1221 '''
+1222 Compute bulk composition and raw clumped isotope anomalies for all analyses.
+1223 '''
+1224 for r in self:
+1225 self.compute_bulk_and_clumping_deltas(r)
+1226 self.standardize_d13C()
+1227 self.standardize_d18O()
+1228 self.msg(f"Crunched {len(self)} analyses.")
+1229
+1230
+1231 def fill_in_missing_info(self, session = 'mySession'):
+1232 '''
+1233 Fill in optional fields with default values
+1234 '''
+1235 for i,r in enumerate(self):
+1236 if 'D17O' not in r:
+1237 r['D17O'] = 0.
+1238 if 'UID' not in r:
+1239 r['UID'] = f'{i+1}'
+1240 if 'Session' not in r:
+1241 r['Session'] = session
+1242 for k in ['d47', 'd48', 'd49']:
+1243 if k not in r:
+1244 r[k] = np.nan
+1245
+1246
+1247 def standardize_d13C(self):
+1248 '''
+1249 Perform δ13C standadization within each session `s` according to
+1250 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
+1251 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
+1252 may be redefined abitrarily at a later stage.
+1253 '''
+1254 for s in self.sessions:
+1255 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
+1256 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
+1257 X,Y = zip(*XY)
+1258 if self.sessions[s]['d13C_standardization_method'] == '1pt':
+1259 offset = np.mean(Y) - np.mean(X)
+1260 for r in self.sessions[s]['data']:
+1261 r['d13C_VPDB'] += offset
+1262 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
+1263 a,b = np.polyfit(X,Y,1)
+1264 for r in self.sessions[s]['data']:
+1265 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
+1266
+1267 def standardize_d18O(self):
+1268 '''
+1269 Perform δ18O standadization within each session `s` according to
+1270 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
+1271 which is defined by default by `D47data.refresh_sessions()`as equal to
+1272 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
+1273 '''
+1274 for s in self.sessions:
+1275 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
+1276 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
+1277 X,Y = zip(*XY)
+1278 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
+1279 if self.sessions[s]['d18O_standardization_method'] == '1pt':
+1280 offset = np.mean(Y) - np.mean(X)
+1281 for r in self.sessions[s]['data']:
+1282 r['d18O_VSMOW'] += offset
+1283 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
+1284 a,b = np.polyfit(X,Y,1)
+1285 for r in self.sessions[s]['data']:
+1286 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
+1287
+1288
+1289 def compute_bulk_and_clumping_deltas(self, r):
+1290 '''
+1291 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
+1292 '''
+1293
+1294 # Compute working gas R13, R18, and isobar ratios
+1295 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
+1296 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
+1297 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
+1298
+1299 # Compute analyte isobar ratios
+1300 R45 = (1 + r['d45'] / 1000) * R45_wg
+1301 R46 = (1 + r['d46'] / 1000) * R46_wg
+1302 R47 = (1 + r['d47'] / 1000) * R47_wg
+1303 R48 = (1 + r['d48'] / 1000) * R48_wg
+1304 R49 = (1 + r['d49'] / 1000) * R49_wg
+1305
+1306 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
+1307 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
+1308 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
+1309
+1310 # Compute stochastic isobar ratios of the analyte
+1311 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
+1312 R13, R18, D17O = r['D17O']
+1313 )
+1314
+1315 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
+1316 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
+1317 if (R45 / R45stoch - 1) > 5e-8:
+1318 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
+1319 if (R46 / R46stoch - 1) > 5e-8:
+1320 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
+1321
+1322 # Compute raw clumped isotope anomalies
+1323 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
+1324 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
+1325 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
+1326
+1327
+1328 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
+1329 '''
+1330 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
+1331 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
+1332 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
+1333 '''
+1334
+1335 # Compute R17
+1336 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
+1337
+1338 # Compute isotope concentrations
+1339 C12 = (1 + R13) ** -1
+1340 C13 = C12 * R13
+1341 C16 = (1 + R17 + R18) ** -1
+1342 C17 = C16 * R17
+1343 C18 = C16 * R18
+1344
+1345 # Compute stochastic isotopologue concentrations
+1346 C626 = C16 * C12 * C16
+1347 C627 = C16 * C12 * C17 * 2
+1348 C628 = C16 * C12 * C18 * 2
+1349 C636 = C16 * C13 * C16
+1350 C637 = C16 * C13 * C17 * 2
+1351 C638 = C16 * C13 * C18 * 2
+1352 C727 = C17 * C12 * C17
+1353 C728 = C17 * C12 * C18 * 2
+1354 C737 = C17 * C13 * C17
+1355 C738 = C17 * C13 * C18 * 2
+1356 C828 = C18 * C12 * C18
+1357 C838 = C18 * C13 * C18
+1358
+1359 # Compute stochastic isobar ratios
+1360 R45 = (C636 + C627) / C626
+1361 R46 = (C628 + C637 + C727) / C626
+1362 R47 = (C638 + C728 + C737) / C626
+1363 R48 = (C738 + C828) / C626
+1364 R49 = C838 / C626
+1365
+1366 # Account for stochastic anomalies
+1367 R47 *= 1 + D47 / 1000
+1368 R48 *= 1 + D48 / 1000
+1369 R49 *= 1 + D49 / 1000
+1370
+1371 # Return isobar ratios
+1372 return R45, R46, R47, R48, R49
+1373
+1374
+1375 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
+1376 '''
+1377 Split unknown samples by UID (treat all analyses as different samples)
+1378 or by session (treat analyses of a given sample in different sessions as
+1379 different samples).
1380
-1381 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
-1382 + `grouping`: `by_uid` | `by_session`
-1383 '''
-1384 if samples_to_split == 'all':
-1385 samples_to_split = [s for s in self.unknowns]
-1386 gkeys = {'by_uid':'UID', 'by_session':'Session'}
-1387 self.grouping = grouping.lower()
-1388 if self.grouping in gkeys:
-1389 gkey = gkeys[self.grouping]
-1390 for r in self:
-1391 if r['Sample'] in samples_to_split:
-1392 r['Sample_original'] = r['Sample']
-1393 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
-1394 elif r['Sample'] in self.unknowns:
-1395 r['Sample_original'] = r['Sample']
-1396 self.refresh_samples()
-1397
-1398
-1399 def unsplit_samples(self, tables = False):
-1400 '''
-1401 Reverse the effects of `D47data.split_samples()`.
-1402
-1403 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+1381 **Parameters**
+1382
+1383 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
+1384 + `grouping`: `by_uid` | `by_session`
+1385 '''
+1386 if samples_to_split == 'all':
+1387 samples_to_split = [s for s in self.unknowns]
+1388 gkeys = {'by_uid':'UID', 'by_session':'Session'}
+1389 self.grouping = grouping.lower()
+1390 if self.grouping in gkeys:
+1391 gkey = gkeys[self.grouping]
+1392 for r in self:
+1393 if r['Sample'] in samples_to_split:
+1394 r['Sample_original'] = r['Sample']
+1395 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
+1396 elif r['Sample'] in self.unknowns:
+1397 r['Sample_original'] = r['Sample']
+1398 self.refresh_samples()
+1399
+1400
+1401 def unsplit_samples(self, tables = False):
+1402 '''
+1403 Reverse the effects of `D47data.split_samples()`.
1404
-1405 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
-1406 probably use `D4xdata.combine_samples()` instead to reverse the effects of
-1407 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
-1408 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
-1409 that case session-averaged Δ4x values are statistically independent).
-1410 '''
-1411 unknowns_old = sorted({s for s in self.unknowns})
-1412 CM_old = self.standardization.covar[:,:]
-1413 VD_old = self.standardization.params.valuesdict().copy()
-1414 vars_old = self.standardization.var_names
-1415
-1416 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1405 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+1406
+1407 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
+1408 probably use `D4xdata.combine_samples()` instead to reverse the effects of
+1409 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
+1410 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
+1411 that case session-averaged Δ4x values are statistically independent).
+1412 '''
+1413 unknowns_old = sorted({s for s in self.unknowns})
+1414 CM_old = self.standardization.covar[:,:]
+1415 VD_old = self.standardization.params.valuesdict().copy()
+1416 vars_old = self.standardization.var_names
1417
-1418 Ns = len(vars_old) - len(unknowns_old)
-1419 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
-1420 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
-1421
-1422 W = np.zeros((len(vars_new), len(vars_old)))
-1423 W[:Ns,:Ns] = np.eye(Ns)
-1424 for u in unknowns_new:
-1425 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
-1426 if self.grouping == 'by_session':
-1427 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
-1428 elif self.grouping == 'by_uid':
-1429 weights = [1 for s in splits]
-1430 sw = sum(weights)
-1431 weights = [w/sw for w in weights]
-1432 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
-1433
-1434 CM_new = W @ CM_old @ W.T
-1435 V = W @ np.array([[VD_old[k]] for k in vars_old])
-1436 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
-1437
-1438 self.standardization.covar = CM_new
-1439 self.standardization.params.valuesdict = lambda : VD_new
-1440 self.standardization.var_names = vars_new
-1441
-1442 for r in self:
-1443 if r['Sample'] in self.unknowns:
-1444 r['Sample_split'] = r['Sample']
-1445 r['Sample'] = r['Sample_original']
-1446
-1447 self.refresh_samples()
-1448 self.consolidate_samples()
-1449 self.repeatabilities()
-1450
-1451 if tables:
-1452 self.table_of_analyses()
-1453 self.table_of_samples()
-1454
-1455 def assign_timestamps(self):
-1456 '''
-1457 Assign a time field `t` of type `float` to each analysis.
-1458
-1459 If `TimeTag` is one of the data fields, `t` is equal within a given session
-1460 to `TimeTag` minus the mean value of `TimeTag` for that session.
-1461 Otherwise, `TimeTag` is by default equal to the index of each analysis
-1462 in the dataset and `t` is defined as above.
-1463 '''
-1464 for session in self.sessions:
-1465 sdata = self.sessions[session]['data']
-1466 try:
-1467 t0 = np.mean([r['TimeTag'] for r in sdata])
-1468 for r in sdata:
-1469 r['t'] = r['TimeTag'] - t0
-1470 except KeyError:
-1471 t0 = (len(sdata)-1)/2
-1472 for t,r in enumerate(sdata):
-1473 r['t'] = t - t0
-1474
-1475
-1476 def report(self):
-1477 '''
-1478 Prints a report on the standardization fit.
-1479 Only applicable after `D4xdata.standardize(method='pooled')`.
-1480 '''
-1481 report_fit(self.standardization)
-1482
-1483
-1484 def combine_samples(self, sample_groups):
-1485 '''
-1486 Combine analyses of different samples to compute weighted average Δ4x
-1487 and new error (co)variances corresponding to the groups defined by the `sample_groups`
-1488 dictionary.
-1489
-1490 Caution: samples are weighted by number of replicate analyses, which is a
-1491 reasonable default behavior but is not always optimal (e.g., in the case of strongly
-1492 correlated analytical errors for one or more samples).
-1493
-1494 Returns a tuplet of:
+1418 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1419
+1420 Ns = len(vars_old) - len(unknowns_old)
+1421 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
+1422 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
+1423
+1424 W = np.zeros((len(vars_new), len(vars_old)))
+1425 W[:Ns,:Ns] = np.eye(Ns)
+1426 for u in unknowns_new:
+1427 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
+1428 if self.grouping == 'by_session':
+1429 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
+1430 elif self.grouping == 'by_uid':
+1431 weights = [1 for s in splits]
+1432 sw = sum(weights)
+1433 weights = [w/sw for w in weights]
+1434 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
+1435
+1436 CM_new = W @ CM_old @ W.T
+1437 V = W @ np.array([[VD_old[k]] for k in vars_old])
+1438 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
+1439
+1440 self.standardization.covar = CM_new
+1441 self.standardization.params.valuesdict = lambda : VD_new
+1442 self.standardization.var_names = vars_new
+1443
+1444 for r in self:
+1445 if r['Sample'] in self.unknowns:
+1446 r['Sample_split'] = r['Sample']
+1447 r['Sample'] = r['Sample_original']
+1448
+1449 self.refresh_samples()
+1450 self.consolidate_samples()
+1451 self.repeatabilities()
+1452
+1453 if tables:
+1454 self.table_of_analyses()
+1455 self.table_of_samples()
+1456
+1457 def assign_timestamps(self):
+1458 '''
+1459 Assign a time field `t` of type `float` to each analysis.
+1460
+1461 If `TimeTag` is one of the data fields, `t` is equal within a given session
+1462 to `TimeTag` minus the mean value of `TimeTag` for that session.
+1463 Otherwise, `TimeTag` is by default equal to the index of each analysis
+1464 in the dataset and `t` is defined as above.
+1465 '''
+1466 for session in self.sessions:
+1467 sdata = self.sessions[session]['data']
+1468 try:
+1469 t0 = np.mean([r['TimeTag'] for r in sdata])
+1470 for r in sdata:
+1471 r['t'] = r['TimeTag'] - t0
+1472 except KeyError:
+1473 t0 = (len(sdata)-1)/2
+1474 for t,r in enumerate(sdata):
+1475 r['t'] = t - t0
+1476
+1477
+1478 def report(self):
+1479 '''
+1480 Prints a report on the standardization fit.
+1481 Only applicable after `D4xdata.standardize(method='pooled')`.
+1482 '''
+1483 report_fit(self.standardization)
+1484
+1485
+1486 def combine_samples(self, sample_groups):
+1487 '''
+1488 Combine analyses of different samples to compute weighted average Δ4x
+1489 and new error (co)variances corresponding to the groups defined by the `sample_groups`
+1490 dictionary.
+1491
+1492 Caution: samples are weighted by number of replicate analyses, which is a
+1493 reasonable default behavior but is not always optimal (e.g., in the case of strongly
+1494 correlated analytical errors for one or more samples).
1495
-1496 + the list of group names
-1497 + an array of the corresponding Δ4x values
-1498 + the corresponding (co)variance matrix
-1499
-1500 **Parameters**
-1501
-1502 + `sample_groups`: a dictionary of the form:
-1503 ```py
-1504 {'group1': ['sample_1', 'sample_2'],
-1505 'group2': ['sample_3', 'sample_4', 'sample_5']}
-1506 ```
-1507 '''
-1508
-1509 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
-1510 groups = sorted(sample_groups.keys())
-1511 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
-1512 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
-1513 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
-1514 W = np.array([
-1515 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
-1516 for j in groups])
-1517 D4x_new = W @ D4x_old
-1518 CM_new = W @ CM_old @ W.T
-1519
-1520 return groups, D4x_new[:,0], CM_new
-1521
-1522
-1523 @make_verbal
-1524 def standardize(self,
-1525 method = 'pooled',
-1526 weighted_sessions = [],
-1527 consolidate = True,
-1528 consolidate_tables = False,
-1529 consolidate_plots = False,
-1530 constraints = {},
-1531 ):
-1532 '''
-1533 Compute absolute Δ4x values for all replicate analyses and for sample averages.
-1534 If `method` argument is set to `'pooled'`, the standardization processes all sessions
-1535 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
-1536 i.e. that their true Δ4x value does not change between sessions,
-1537 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
-1538 `'indep_sessions'`, the standardization processes each session independently, based only
-1539 on anchors analyses.
-1540 '''
-1541
-1542 self.standardization_method = method
-1543 self.assign_timestamps()
-1544
-1545 if method == 'pooled':
-1546 if weighted_sessions:
-1547 for session_group in weighted_sessions:
-1548 if self._4x == '47':
-1549 X = D47data([r for r in self if r['Session'] in session_group])
-1550 elif self._4x == '48':
-1551 X = D48data([r for r in self if r['Session'] in session_group])
-1552 X.Nominal_D4x = self.Nominal_D4x.copy()
-1553 X.refresh()
-1554 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
-1555 w = np.sqrt(result.redchi)
-1556 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
-1557 for r in X:
-1558 r[f'wD{self._4x}raw'] *= w
-1559 else:
-1560 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
-1561 for r in self:
-1562 r[f'wD{self._4x}raw'] = 1.
-1563
-1564 params = Parameters()
-1565 for k,session in enumerate(self.sessions):
-1566 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
-1567 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
-1568 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
-1569 s = pf(session)
-1570 params.add(f'a_{s}', value = 0.9)
-1571 params.add(f'b_{s}', value = 0.)
-1572 params.add(f'c_{s}', value = -0.9)
-1573 params.add(f'a2_{s}', value = 0.,
-1574# vary = self.sessions[session]['scrambling_drift'],
-1575 )
-1576 params.add(f'b2_{s}', value = 0.,
-1577# vary = self.sessions[session]['slope_drift'],
-1578 )
-1579 params.add(f'c2_{s}', value = 0.,
-1580# vary = self.sessions[session]['wg_drift'],
-1581 )
-1582 if not self.sessions[session]['scrambling_drift']:
-1583 params[f'a2_{s}'].expr = '0'
-1584 if not self.sessions[session]['slope_drift']:
-1585 params[f'b2_{s}'].expr = '0'
-1586 if not self.sessions[session]['wg_drift']:
-1587 params[f'c2_{s}'].expr = '0'
-1588
-1589 for sample in self.unknowns:
-1590 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
-1591
-1592 for k in constraints:
-1593 params[k].expr = constraints[k]
-1594
-1595 def residuals(p):
-1596 R = []
-1597 for r in self:
-1598 session = pf(r['Session'])
-1599 sample = pf(r['Sample'])
-1600 if r['Sample'] in self.Nominal_D4x:
-1601 R += [ (
-1602 r[f'D{self._4x}raw'] - (
-1603 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
-1604 + p[f'b_{session}'] * r[f'd{self._4x}']
-1605 + p[f'c_{session}']
-1606 + r['t'] * (
-1607 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
-1608 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1609 + p[f'c2_{session}']
-1610 )
-1611 )
-1612 ) / r[f'wD{self._4x}raw'] ]
-1613 else:
-1614 R += [ (
-1615 r[f'D{self._4x}raw'] - (
-1616 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
-1617 + p[f'b_{session}'] * r[f'd{self._4x}']
-1618 + p[f'c_{session}']
-1619 + r['t'] * (
-1620 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
-1621 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1622 + p[f'c2_{session}']
-1623 )
-1624 )
-1625 ) / r[f'wD{self._4x}raw'] ]
-1626 return R
-1627
-1628 M = Minimizer(residuals, params)
-1629 result = M.least_squares()
-1630 self.Nf = result.nfree
-1631 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
-1632 new_names, new_covar, new_se = _fullcovar(result)[:3]
-1633 result.var_names = new_names
-1634 result.covar = new_covar
-1635
-1636 for r in self:
-1637 s = pf(r["Session"])
-1638 a = result.params.valuesdict()[f'a_{s}']
-1639 b = result.params.valuesdict()[f'b_{s}']
-1640 c = result.params.valuesdict()[f'c_{s}']
-1641 a2 = result.params.valuesdict()[f'a2_{s}']
-1642 b2 = result.params.valuesdict()[f'b2_{s}']
-1643 c2 = result.params.valuesdict()[f'c2_{s}']
-1644 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1645
-1646
-1647 self.standardization = result
+1496 Returns a tuplet of:
+1497
+1498 + the list of group names
+1499 + an array of the corresponding Δ4x values
+1500 + the corresponding (co)variance matrix
+1501
+1502 **Parameters**
+1503
+1504 + `sample_groups`: a dictionary of the form:
+1505 ```py
+1506 {'group1': ['sample_1', 'sample_2'],
+1507 'group2': ['sample_3', 'sample_4', 'sample_5']}
+1508 ```
+1509 '''
+1510
+1511 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
+1512 groups = sorted(sample_groups.keys())
+1513 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
+1514 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
+1515 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
+1516 W = np.array([
+1517 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
+1518 for j in groups])
+1519 D4x_new = W @ D4x_old
+1520 CM_new = W @ CM_old @ W.T
+1521
+1522 return groups, D4x_new[:,0], CM_new
+1523
+1524
+1525 @make_verbal
+1526 def standardize(self,
+1527 method = 'pooled',
+1528 weighted_sessions = [],
+1529 consolidate = True,
+1530 consolidate_tables = False,
+1531 consolidate_plots = False,
+1532 constraints = {},
+1533 ):
+1534 '''
+1535 Compute absolute Δ4x values for all replicate analyses and for sample averages.
+1536 If `method` argument is set to `'pooled'`, the standardization processes all sessions
+1537 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
+1538 i.e. that their true Δ4x value does not change between sessions,
+1539 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
+1540 `'indep_sessions'`, the standardization processes each session independently, based only
+1541 on anchors analyses.
+1542 '''
+1543
+1544 self.standardization_method = method
+1545 self.assign_timestamps()
+1546
+1547 if method == 'pooled':
+1548 if weighted_sessions:
+1549 for session_group in weighted_sessions:
+1550 if self._4x == '47':
+1551 X = D47data([r for r in self if r['Session'] in session_group])
+1552 elif self._4x == '48':
+1553 X = D48data([r for r in self if r['Session'] in session_group])
+1554 X.Nominal_D4x = self.Nominal_D4x.copy()
+1555 X.refresh()
+1556 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
+1557 w = np.sqrt(result.redchi)
+1558 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
+1559 for r in X:
+1560 r[f'wD{self._4x}raw'] *= w
+1561 else:
+1562 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
+1563 for r in self:
+1564 r[f'wD{self._4x}raw'] = 1.
+1565
+1566 params = Parameters()
+1567 for k,session in enumerate(self.sessions):
+1568 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
+1569 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
+1570 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
+1571 s = pf(session)
+1572 params.add(f'a_{s}', value = 0.9)
+1573 params.add(f'b_{s}', value = 0.)
+1574 params.add(f'c_{s}', value = -0.9)
+1575 params.add(f'a2_{s}', value = 0.,
+1576# vary = self.sessions[session]['scrambling_drift'],
+1577 )
+1578 params.add(f'b2_{s}', value = 0.,
+1579# vary = self.sessions[session]['slope_drift'],
+1580 )
+1581 params.add(f'c2_{s}', value = 0.,
+1582# vary = self.sessions[session]['wg_drift'],
+1583 )
+1584 if not self.sessions[session]['scrambling_drift']:
+1585 params[f'a2_{s}'].expr = '0'
+1586 if not self.sessions[session]['slope_drift']:
+1587 params[f'b2_{s}'].expr = '0'
+1588 if not self.sessions[session]['wg_drift']:
+1589 params[f'c2_{s}'].expr = '0'
+1590
+1591 for sample in self.unknowns:
+1592 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
+1593
+1594 for k in constraints:
+1595 params[k].expr = constraints[k]
+1596
+1597 def residuals(p):
+1598 R = []
+1599 for r in self:
+1600 session = pf(r['Session'])
+1601 sample = pf(r['Sample'])
+1602 if r['Sample'] in self.Nominal_D4x:
+1603 R += [ (
+1604 r[f'D{self._4x}raw'] - (
+1605 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
+1606 + p[f'b_{session}'] * r[f'd{self._4x}']
+1607 + p[f'c_{session}']
+1608 + r['t'] * (
+1609 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
+1610 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1611 + p[f'c2_{session}']
+1612 )
+1613 )
+1614 ) / r[f'wD{self._4x}raw'] ]
+1615 else:
+1616 R += [ (
+1617 r[f'D{self._4x}raw'] - (
+1618 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
+1619 + p[f'b_{session}'] * r[f'd{self._4x}']
+1620 + p[f'c_{session}']
+1621 + r['t'] * (
+1622 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
+1623 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1624 + p[f'c2_{session}']
+1625 )
+1626 )
+1627 ) / r[f'wD{self._4x}raw'] ]
+1628 return R
+1629
+1630 M = Minimizer(residuals, params)
+1631 result = M.least_squares()
+1632 self.Nf = result.nfree
+1633 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1634 new_names, new_covar, new_se = _fullcovar(result)[:3]
+1635 result.var_names = new_names
+1636 result.covar = new_covar
+1637
+1638 for r in self:
+1639 s = pf(r["Session"])
+1640 a = result.params.valuesdict()[f'a_{s}']
+1641 b = result.params.valuesdict()[f'b_{s}']
+1642 c = result.params.valuesdict()[f'c_{s}']
+1643 a2 = result.params.valuesdict()[f'a2_{s}']
+1644 b2 = result.params.valuesdict()[f'b2_{s}']
+1645 c2 = result.params.valuesdict()[f'c2_{s}']
+1646 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1647
1648
-1649 for session in self.sessions:
-1650 self.sessions[session]['Np'] = 3
-1651 for k in ['scrambling', 'slope', 'wg']:
-1652 if self.sessions[session][f'{k}_drift']:
-1653 self.sessions[session]['Np'] += 1
-1654
-1655 if consolidate:
-1656 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
-1657 return result
-1658
-1659
-1660 elif method == 'indep_sessions':
+1649 self.standardization = result
+1650
+1651 for session in self.sessions:
+1652 self.sessions[session]['Np'] = 3
+1653 for k in ['scrambling', 'slope', 'wg']:
+1654 if self.sessions[session][f'{k}_drift']:
+1655 self.sessions[session]['Np'] += 1
+1656
+1657 if consolidate:
+1658 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1659 return result
+1660
1661
-1662 if weighted_sessions:
-1663 for session_group in weighted_sessions:
-1664 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
-1665 X.Nominal_D4x = self.Nominal_D4x.copy()
-1666 X.refresh()
-1667 # This is only done to assign r['wD47raw'] for r in X:
-1668 X.standardize(method = method, weighted_sessions = [], consolidate = False)
-1669 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
-1670 else:
-1671 self.msg('All weights set to 1 ‰')
-1672 for r in self:
-1673 r[f'wD{self._4x}raw'] = 1
-1674
-1675 for session in self.sessions:
-1676 s = self.sessions[session]
-1677 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
-1678 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
-1679 s['Np'] = sum(p_active)
-1680 sdata = s['data']
-1681
-1682 A = np.array([
-1683 [
-1684 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
-1685 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
-1686 1 / r[f'wD{self._4x}raw'],
-1687 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
-1688 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
-1689 r['t'] / r[f'wD{self._4x}raw']
-1690 ]
-1691 for r in sdata if r['Sample'] in self.anchors
-1692 ])[:,p_active] # only keep columns for the active parameters
-1693 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
-1694 s['Na'] = Y.size
-1695 CM = linalg.inv(A.T @ A)
-1696 bf = (CM @ A.T @ Y).T[0,:]
-1697 k = 0
-1698 for n,a in zip(p_names, p_active):
-1699 if a:
-1700 s[n] = bf[k]
-1701# self.msg(f'{n} = {bf[k]}')
-1702 k += 1
-1703 else:
-1704 s[n] = 0.
-1705# self.msg(f'{n} = 0.0')
-1706
-1707 for r in sdata :
-1708 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
-1709 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1710 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
-1711
-1712 s['CM'] = np.zeros((6,6))
-1713 i = 0
-1714 k_active = [j for j,a in enumerate(p_active) if a]
-1715 for j,a in enumerate(p_active):
-1716 if a:
-1717 s['CM'][j,k_active] = CM[i,:]
-1718 i += 1
-1719
-1720 if not weighted_sessions:
-1721 w = self.rmswd()['rmswd']
-1722 for r in self:
-1723 r[f'wD{self._4x}'] *= w
-1724 r[f'wD{self._4x}raw'] *= w
-1725 for session in self.sessions:
-1726 self.sessions[session]['CM'] *= w**2
-1727
-1728 for session in self.sessions:
-1729 s = self.sessions[session]
-1730 s['SE_a'] = s['CM'][0,0]**.5
-1731 s['SE_b'] = s['CM'][1,1]**.5
-1732 s['SE_c'] = s['CM'][2,2]**.5
-1733 s['SE_a2'] = s['CM'][3,3]**.5
-1734 s['SE_b2'] = s['CM'][4,4]**.5
-1735 s['SE_c2'] = s['CM'][5,5]**.5
-1736
-1737 if not weighted_sessions:
-1738 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
-1739 else:
-1740 self.Nf = 0
-1741 for sg in weighted_sessions:
-1742 self.Nf += self.rmswd(sessions = sg)['Nf']
-1743
-1744 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1662 elif method == 'indep_sessions':
+1663
+1664 if weighted_sessions:
+1665 for session_group in weighted_sessions:
+1666 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
+1667 X.Nominal_D4x = self.Nominal_D4x.copy()
+1668 X.refresh()
+1669 # This is only done to assign r['wD47raw'] for r in X:
+1670 X.standardize(method = method, weighted_sessions = [], consolidate = False)
+1671 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
+1672 else:
+1673 self.msg('All weights set to 1 ‰')
+1674 for r in self:
+1675 r[f'wD{self._4x}raw'] = 1
+1676
+1677 for session in self.sessions:
+1678 s = self.sessions[session]
+1679 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
+1680 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
+1681 s['Np'] = sum(p_active)
+1682 sdata = s['data']
+1683
+1684 A = np.array([
+1685 [
+1686 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
+1687 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
+1688 1 / r[f'wD{self._4x}raw'],
+1689 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
+1690 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
+1691 r['t'] / r[f'wD{self._4x}raw']
+1692 ]
+1693 for r in sdata if r['Sample'] in self.anchors
+1694 ])[:,p_active] # only keep columns for the active parameters
+1695 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
+1696 s['Na'] = Y.size
+1697 CM = linalg.inv(A.T @ A)
+1698 bf = (CM @ A.T @ Y).T[0,:]
+1699 k = 0
+1700 for n,a in zip(p_names, p_active):
+1701 if a:
+1702 s[n] = bf[k]
+1703# self.msg(f'{n} = {bf[k]}')
+1704 k += 1
+1705 else:
+1706 s[n] = 0.
+1707# self.msg(f'{n} = 0.0')
+1708
+1709 for r in sdata :
+1710 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
+1711 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1712 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
+1713
+1714 s['CM'] = np.zeros((6,6))
+1715 i = 0
+1716 k_active = [j for j,a in enumerate(p_active) if a]
+1717 for j,a in enumerate(p_active):
+1718 if a:
+1719 s['CM'][j,k_active] = CM[i,:]
+1720 i += 1
+1721
+1722 if not weighted_sessions:
+1723 w = self.rmswd()['rmswd']
+1724 for r in self:
+1725 r[f'wD{self._4x}'] *= w
+1726 r[f'wD{self._4x}raw'] *= w
+1727 for session in self.sessions:
+1728 self.sessions[session]['CM'] *= w**2
+1729
+1730 for session in self.sessions:
+1731 s = self.sessions[session]
+1732 s['SE_a'] = s['CM'][0,0]**.5
+1733 s['SE_b'] = s['CM'][1,1]**.5
+1734 s['SE_c'] = s['CM'][2,2]**.5
+1735 s['SE_a2'] = s['CM'][3,3]**.5
+1736 s['SE_b2'] = s['CM'][4,4]**.5
+1737 s['SE_c2'] = s['CM'][5,5]**.5
+1738
+1739 if not weighted_sessions:
+1740 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
+1741 else:
+1742 self.Nf = 0
+1743 for sg in weighted_sessions:
+1744 self.Nf += self.rmswd(sessions = sg)['Nf']
1745
-1746 avgD4x = {
-1747 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
-1748 for sample in self.samples
-1749 }
-1750 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
-1751 rD4x = (chi2/self.Nf)**.5
-1752 self.repeatability[f'sigma_{self._4x}'] = rD4x
-1753
-1754 if consolidate:
-1755 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
-1756
-1757
-1758 def standardization_error(self, session, d4x, D4x, t = 0):
-1759 '''
-1760 Compute standardization error for a given session and
-1761 (δ47, Δ47) composition.
-1762 '''
-1763 a = self.sessions[session]['a']
-1764 b = self.sessions[session]['b']
-1765 c = self.sessions[session]['c']
-1766 a2 = self.sessions[session]['a2']
-1767 b2 = self.sessions[session]['b2']
-1768 c2 = self.sessions[session]['c2']
-1769 CM = self.sessions[session]['CM']
-1770
-1771 x, y = D4x, d4x
-1772 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
-1773# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
-1774 dxdy = -(b+b2*t) / (a+a2*t)
-1775 dxdz = 1. / (a+a2*t)
-1776 dxda = -x / (a+a2*t)
-1777 dxdb = -y / (a+a2*t)
-1778 dxdc = -1. / (a+a2*t)
-1779 dxda2 = -x * a2 / (a+a2*t)
-1780 dxdb2 = -y * t / (a+a2*t)
-1781 dxdc2 = -t / (a+a2*t)
-1782 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
-1783 sx = (V @ CM @ V.T) ** .5
-1784 return sx
-1785
-1786
-1787 @make_verbal
-1788 def summary(self,
-1789 dir = 'output',
-1790 filename = None,
-1791 save_to_file = True,
-1792 print_out = True,
-1793 ):
-1794 '''
-1795 Print out an/or save to disk a summary of the standardization results.
-1796
-1797 **Parameters**
+1746 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1747
+1748 avgD4x = {
+1749 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
+1750 for sample in self.samples
+1751 }
+1752 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
+1753 rD4x = (chi2/self.Nf)**.5
+1754 self.repeatability[f'sigma_{self._4x}'] = rD4x
+1755
+1756 if consolidate:
+1757 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1758
+1759
+1760 def standardization_error(self, session, d4x, D4x, t = 0):
+1761 '''
+1762 Compute standardization error for a given session and
+1763 (δ47, Δ47) composition.
+1764 '''
+1765 a = self.sessions[session]['a']
+1766 b = self.sessions[session]['b']
+1767 c = self.sessions[session]['c']
+1768 a2 = self.sessions[session]['a2']
+1769 b2 = self.sessions[session]['b2']
+1770 c2 = self.sessions[session]['c2']
+1771 CM = self.sessions[session]['CM']
+1772
+1773 x, y = D4x, d4x
+1774 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
+1775# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
+1776 dxdy = -(b+b2*t) / (a+a2*t)
+1777 dxdz = 1. / (a+a2*t)
+1778 dxda = -x / (a+a2*t)
+1779 dxdb = -y / (a+a2*t)
+1780 dxdc = -1. / (a+a2*t)
+1781 dxda2 = -x * a2 / (a+a2*t)
+1782 dxdb2 = -y * t / (a+a2*t)
+1783 dxdc2 = -t / (a+a2*t)
+1784 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
+1785 sx = (V @ CM @ V.T) ** .5
+1786 return sx
+1787
+1788
+1789 @make_verbal
+1790 def summary(self,
+1791 dir = 'output',
+1792 filename = None,
+1793 save_to_file = True,
+1794 print_out = True,
+1795 ):
+1796 '''
+1797 Print out an/or save to disk a summary of the standardization results.
1798
-1799 + `dir`: the directory in which to save the table
-1800 + `filename`: the name to the csv file to write to
-1801 + `save_to_file`: whether to save the table to disk
-1802 + `print_out`: whether to print out the table
-1803 '''
-1804
-1805 out = []
-1806 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
-1807 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
-1808 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
-1809 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
-1810 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
-1811 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
-1812 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
-1813 out += [['Model degrees of freedom', f"{self.Nf}"]]
-1814 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
-1815 out += [['Standardization method', self.standardization_method]]
-1816
-1817 if save_to_file:
-1818 if not os.path.exists(dir):
-1819 os.makedirs(dir)
-1820 if filename is None:
-1821 filename = f'D{self._4x}_summary.csv'
-1822 with open(f'{dir}/{filename}', 'w') as fid:
-1823 fid.write(make_csv(out))
-1824 if print_out:
-1825 self.msg('\n' + pretty_table(out, header = 0))
-1826
-1827
-1828 @make_verbal
-1829 def table_of_sessions(self,
-1830 dir = 'output',
-1831 filename = None,
-1832 save_to_file = True,
-1833 print_out = True,
-1834 output = None,
-1835 ):
-1836 '''
-1837 Print out an/or save to disk a table of sessions.
-1838
-1839 **Parameters**
+1799 **Parameters**
+1800
+1801 + `dir`: the directory in which to save the table
+1802 + `filename`: the name to the csv file to write to
+1803 + `save_to_file`: whether to save the table to disk
+1804 + `print_out`: whether to print out the table
+1805 '''
+1806
+1807 out = []
+1808 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
+1809 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
+1810 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
+1811 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
+1812 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
+1813 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
+1814 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
+1815 out += [['Model degrees of freedom', f"{self.Nf}"]]
+1816 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
+1817 out += [['Standardization method', self.standardization_method]]
+1818
+1819 if save_to_file:
+1820 if not os.path.exists(dir):
+1821 os.makedirs(dir)
+1822 if filename is None:
+1823 filename = f'D{self._4x}_summary.csv'
+1824 with open(f'{dir}/{filename}', 'w') as fid:
+1825 fid.write(make_csv(out))
+1826 if print_out:
+1827 self.msg('\n' + pretty_table(out, header = 0))
+1828
+1829
+1830 @make_verbal
+1831 def table_of_sessions(self,
+1832 dir = 'output',
+1833 filename = None,
+1834 save_to_file = True,
+1835 print_out = True,
+1836 output = None,
+1837 ):
+1838 '''
+1839 Print out an/or save to disk a table of sessions.
1840
-1841 + `dir`: the directory in which to save the table
-1842 + `filename`: the name to the csv file to write to
-1843 + `save_to_file`: whether to save the table to disk
-1844 + `print_out`: whether to print out the table
-1845 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1846 if set to `'raw'`: return a list of list of strings
-1847 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1848 '''
-1849 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
-1850 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
-1851 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
-1852
-1853 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
-1854 if include_a2:
-1855 out[-1] += ['a2 ± SE']
-1856 if include_b2:
-1857 out[-1] += ['b2 ± SE']
-1858 if include_c2:
-1859 out[-1] += ['c2 ± SE']
-1860 for session in self.sessions:
-1861 out += [[
-1862 session,
-1863 f"{self.sessions[session]['Na']}",
-1864 f"{self.sessions[session]['Nu']}",
-1865 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
-1866 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
-1867 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
-1868 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
-1869 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
-1870 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
-1871 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
-1872 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
-1873 ]]
-1874 if include_a2:
-1875 if self.sessions[session]['scrambling_drift']:
-1876 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
-1877 else:
-1878 out[-1] += ['']
-1879 if include_b2:
-1880 if self.sessions[session]['slope_drift']:
-1881 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
-1882 else:
-1883 out[-1] += ['']
-1884 if include_c2:
-1885 if self.sessions[session]['wg_drift']:
-1886 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
-1887 else:
-1888 out[-1] += ['']
-1889
-1890 if save_to_file:
-1891 if not os.path.exists(dir):
-1892 os.makedirs(dir)
-1893 if filename is None:
-1894 filename = f'D{self._4x}_sessions.csv'
-1895 with open(f'{dir}/{filename}', 'w') as fid:
-1896 fid.write(make_csv(out))
-1897 if print_out:
-1898 self.msg('\n' + pretty_table(out))
-1899 if output == 'raw':
-1900 return out
-1901 elif output == 'pretty':
-1902 return pretty_table(out)
-1903
-1904
-1905 @make_verbal
-1906 def table_of_analyses(
-1907 self,
-1908 dir = 'output',
-1909 filename = None,
-1910 save_to_file = True,
-1911 print_out = True,
-1912 output = None,
-1913 ):
-1914 '''
-1915 Print out an/or save to disk a table of analyses.
-1916
-1917 **Parameters**
+1841 **Parameters**
+1842
+1843 + `dir`: the directory in which to save the table
+1844 + `filename`: the name to the csv file to write to
+1845 + `save_to_file`: whether to save the table to disk
+1846 + `print_out`: whether to print out the table
+1847 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1848 if set to `'raw'`: return a list of list of strings
+1849 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1850 '''
+1851 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
+1852 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
+1853 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
+1854
+1855 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
+1856 if include_a2:
+1857 out[-1] += ['a2 ± SE']
+1858 if include_b2:
+1859 out[-1] += ['b2 ± SE']
+1860 if include_c2:
+1861 out[-1] += ['c2 ± SE']
+1862 for session in self.sessions:
+1863 out += [[
+1864 session,
+1865 f"{self.sessions[session]['Na']}",
+1866 f"{self.sessions[session]['Nu']}",
+1867 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
+1868 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
+1869 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
+1870 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
+1871 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
+1872 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
+1873 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
+1874 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
+1875 ]]
+1876 if include_a2:
+1877 if self.sessions[session]['scrambling_drift']:
+1878 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
+1879 else:
+1880 out[-1] += ['']
+1881 if include_b2:
+1882 if self.sessions[session]['slope_drift']:
+1883 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
+1884 else:
+1885 out[-1] += ['']
+1886 if include_c2:
+1887 if self.sessions[session]['wg_drift']:
+1888 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
+1889 else:
+1890 out[-1] += ['']
+1891
+1892 if save_to_file:
+1893 if not os.path.exists(dir):
+1894 os.makedirs(dir)
+1895 if filename is None:
+1896 filename = f'D{self._4x}_sessions.csv'
+1897 with open(f'{dir}/{filename}', 'w') as fid:
+1898 fid.write(make_csv(out))
+1899 if print_out:
+1900 self.msg('\n' + pretty_table(out))
+1901 if output == 'raw':
+1902 return out
+1903 elif output == 'pretty':
+1904 return pretty_table(out)
+1905
+1906
+1907 @make_verbal
+1908 def table_of_analyses(
+1909 self,
+1910 dir = 'output',
+1911 filename = None,
+1912 save_to_file = True,
+1913 print_out = True,
+1914 output = None,
+1915 ):
+1916 '''
+1917 Print out an/or save to disk a table of analyses.
1918
-1919 + `dir`: the directory in which to save the table
-1920 + `filename`: the name to the csv file to write to
-1921 + `save_to_file`: whether to save the table to disk
-1922 + `print_out`: whether to print out the table
-1923 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1924 if set to `'raw'`: return a list of list of strings
-1925 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1926 '''
-1927
-1928 out = [['UID','Session','Sample']]
-1929 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
-1930 for f in extra_fields:
-1931 out[-1] += [f[0]]
-1932 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
-1933 for r in self:
-1934 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
-1935 for f in extra_fields:
-1936 out[-1] += [f"{r[f[0]]:{f[1]}}"]
-1937 out[-1] += [
-1938 f"{r['d13Cwg_VPDB']:.3f}",
-1939 f"{r['d18Owg_VSMOW']:.3f}",
-1940 f"{r['d45']:.6f}",
-1941 f"{r['d46']:.6f}",
-1942 f"{r['d47']:.6f}",
-1943 f"{r['d48']:.6f}",
-1944 f"{r['d49']:.6f}",
-1945 f"{r['d13C_VPDB']:.6f}",
-1946 f"{r['d18O_VSMOW']:.6f}",
-1947 f"{r['D47raw']:.6f}",
-1948 f"{r['D48raw']:.6f}",
-1949 f"{r['D49raw']:.6f}",
-1950 f"{r[f'D{self._4x}']:.6f}"
-1951 ]
-1952 if save_to_file:
-1953 if not os.path.exists(dir):
-1954 os.makedirs(dir)
-1955 if filename is None:
-1956 filename = f'D{self._4x}_analyses.csv'
-1957 with open(f'{dir}/{filename}', 'w') as fid:
-1958 fid.write(make_csv(out))
-1959 if print_out:
-1960 self.msg('\n' + pretty_table(out))
-1961 return out
-1962
-1963 @make_verbal
-1964 def covar_table(
-1965 self,
-1966 correl = False,
-1967 dir = 'output',
-1968 filename = None,
-1969 save_to_file = True,
-1970 print_out = True,
-1971 output = None,
-1972 ):
-1973 '''
-1974 Print out, save to disk and/or return the variance-covariance matrix of D4x
-1975 for all unknown samples.
-1976
-1977 **Parameters**
+1919 **Parameters**
+1920
+1921 + `dir`: the directory in which to save the table
+1922 + `filename`: the name to the csv file to write to
+1923 + `save_to_file`: whether to save the table to disk
+1924 + `print_out`: whether to print out the table
+1925 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1926 if set to `'raw'`: return a list of list of strings
+1927 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1928 '''
+1929
+1930 out = [['UID','Session','Sample']]
+1931 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
+1932 for f in extra_fields:
+1933 out[-1] += [f[0]]
+1934 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
+1935 for r in self:
+1936 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
+1937 for f in extra_fields:
+1938 out[-1] += [f"{r[f[0]]:{f[1]}}"]
+1939 out[-1] += [
+1940 f"{r['d13Cwg_VPDB']:.3f}",
+1941 f"{r['d18Owg_VSMOW']:.3f}",
+1942 f"{r['d45']:.6f}",
+1943 f"{r['d46']:.6f}",
+1944 f"{r['d47']:.6f}",
+1945 f"{r['d48']:.6f}",
+1946 f"{r['d49']:.6f}",
+1947 f"{r['d13C_VPDB']:.6f}",
+1948 f"{r['d18O_VSMOW']:.6f}",
+1949 f"{r['D47raw']:.6f}",
+1950 f"{r['D48raw']:.6f}",
+1951 f"{r['D49raw']:.6f}",
+1952 f"{r[f'D{self._4x}']:.6f}"
+1953 ]
+1954 if save_to_file:
+1955 if not os.path.exists(dir):
+1956 os.makedirs(dir)
+1957 if filename is None:
+1958 filename = f'D{self._4x}_analyses.csv'
+1959 with open(f'{dir}/{filename}', 'w') as fid:
+1960 fid.write(make_csv(out))
+1961 if print_out:
+1962 self.msg('\n' + pretty_table(out))
+1963 return out
+1964
+1965 @make_verbal
+1966 def covar_table(
+1967 self,
+1968 correl = False,
+1969 dir = 'output',
+1970 filename = None,
+1971 save_to_file = True,
+1972 print_out = True,
+1973 output = None,
+1974 ):
+1975 '''
+1976 Print out, save to disk and/or return the variance-covariance matrix of D4x
+1977 for all unknown samples.
1978
-1979 + `dir`: the directory in which to save the csv
-1980 + `filename`: the name of the csv file to write to
-1981 + `save_to_file`: whether to save the csv
-1982 + `print_out`: whether to print out the matrix
-1983 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
-1984 if set to `'raw'`: return a list of list of strings
-1985 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1986 '''
-1987 samples = sorted([u for u in self.unknowns])
-1988 out = [[''] + samples]
-1989 for s1 in samples:
-1990 out.append([s1])
-1991 for s2 in samples:
-1992 if correl:
-1993 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
-1994 else:
-1995 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
-1996
-1997 if save_to_file:
-1998 if not os.path.exists(dir):
-1999 os.makedirs(dir)
-2000 if filename is None:
-2001 if correl:
-2002 filename = f'D{self._4x}_correl.csv'
-2003 else:
-2004 filename = f'D{self._4x}_covar.csv'
-2005 with open(f'{dir}/{filename}', 'w') as fid:
-2006 fid.write(make_csv(out))
-2007 if print_out:
-2008 self.msg('\n'+pretty_table(out))
-2009 if output == 'raw':
-2010 return out
-2011 elif output == 'pretty':
-2012 return pretty_table(out)
-2013
-2014 @make_verbal
-2015 def table_of_samples(
-2016 self,
-2017 dir = 'output',
-2018 filename = None,
-2019 save_to_file = True,
-2020 print_out = True,
-2021 output = None,
-2022 ):
-2023 '''
-2024 Print out, save to disk and/or return a table of samples.
-2025
-2026 **Parameters**
+1979 **Parameters**
+1980
+1981 + `dir`: the directory in which to save the csv
+1982 + `filename`: the name of the csv file to write to
+1983 + `save_to_file`: whether to save the csv
+1984 + `print_out`: whether to print out the matrix
+1985 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
+1986 if set to `'raw'`: return a list of list of strings
+1987 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1988 '''
+1989 samples = sorted([u for u in self.unknowns])
+1990 out = [[''] + samples]
+1991 for s1 in samples:
+1992 out.append([s1])
+1993 for s2 in samples:
+1994 if correl:
+1995 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
+1996 else:
+1997 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
+1998
+1999 if save_to_file:
+2000 if not os.path.exists(dir):
+2001 os.makedirs(dir)
+2002 if filename is None:
+2003 if correl:
+2004 filename = f'D{self._4x}_correl.csv'
+2005 else:
+2006 filename = f'D{self._4x}_covar.csv'
+2007 with open(f'{dir}/{filename}', 'w') as fid:
+2008 fid.write(make_csv(out))
+2009 if print_out:
+2010 self.msg('\n'+pretty_table(out))
+2011 if output == 'raw':
+2012 return out
+2013 elif output == 'pretty':
+2014 return pretty_table(out)
+2015
+2016 @make_verbal
+2017 def table_of_samples(
+2018 self,
+2019 dir = 'output',
+2020 filename = None,
+2021 save_to_file = True,
+2022 print_out = True,
+2023 output = None,
+2024 ):
+2025 '''
+2026 Print out, save to disk and/or return a table of samples.
2027
-2028 + `dir`: the directory in which to save the csv
-2029 + `filename`: the name of the csv file to write to
-2030 + `save_to_file`: whether to save the csv
-2031 + `print_out`: whether to print out the table
-2032 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-2033 if set to `'raw'`: return a list of list of strings
-2034 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-2035 '''
-2036
-2037 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
-2038 for sample in self.anchors:
-2039 out += [[
-2040 f"{sample}",
-2041 f"{self.samples[sample]['N']}",
-2042 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2043 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2044 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
-2045 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
-2046 ]]
-2047 for sample in self.unknowns:
-2048 out += [[
-2049 f"{sample}",
-2050 f"{self.samples[sample]['N']}",
-2051 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2052 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2053 f"{self.samples[sample][f'D{self._4x}']:.4f}",
-2054 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
-2055 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
-2056 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
-2057 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
-2058 ]]
-2059 if save_to_file:
-2060 if not os.path.exists(dir):
-2061 os.makedirs(dir)
-2062 if filename is None:
-2063 filename = f'D{self._4x}_samples.csv'
-2064 with open(f'{dir}/{filename}', 'w') as fid:
-2065 fid.write(make_csv(out))
-2066 if print_out:
-2067 self.msg('\n'+pretty_table(out))
-2068 if output == 'raw':
-2069 return out
-2070 elif output == 'pretty':
-2071 return pretty_table(out)
-2072
-2073
-2074 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
-2075 '''
-2076 Generate session plots and save them to disk.
-2077
-2078 **Parameters**
+2028 **Parameters**
+2029
+2030 + `dir`: the directory in which to save the csv
+2031 + `filename`: the name of the csv file to write to
+2032 + `save_to_file`: whether to save the csv
+2033 + `print_out`: whether to print out the table
+2034 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+2035 if set to `'raw'`: return a list of list of strings
+2036 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+2037 '''
+2038
+2039 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
+2040 for sample in self.anchors:
+2041 out += [[
+2042 f"{sample}",
+2043 f"{self.samples[sample]['N']}",
+2044 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2045 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2046 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
+2047 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
+2048 ]]
+2049 for sample in self.unknowns:
+2050 out += [[
+2051 f"{sample}",
+2052 f"{self.samples[sample]['N']}",
+2053 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2054 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2055 f"{self.samples[sample][f'D{self._4x}']:.4f}",
+2056 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
+2057 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
+2058 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
+2059 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
+2060 ]]
+2061 if save_to_file:
+2062 if not os.path.exists(dir):
+2063 os.makedirs(dir)
+2064 if filename is None:
+2065 filename = f'D{self._4x}_samples.csv'
+2066 with open(f'{dir}/{filename}', 'w') as fid:
+2067 fid.write(make_csv(out))
+2068 if print_out:
+2069 self.msg('\n'+pretty_table(out))
+2070 if output == 'raw':
+2071 return out
+2072 elif output == 'pretty':
+2073 return pretty_table(out)
+2074
+2075
+2076 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
+2077 '''
+2078 Generate session plots and save them to disk.
2079
-2080 + `dir`: the directory in which to save the plots
-2081 + `figsize`: the width and height (in inches) of each plot
-2082 + `filetype`: 'pdf' or 'png'
-2083 + `dpi`: resolution for PNG output
-2084 '''
-2085 if not os.path.exists(dir):
-2086 os.makedirs(dir)
-2087
-2088 for session in self.sessions:
-2089 sp = self.plot_single_session(session, xylimits = 'constant')
-2090 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
-2091 ppl.close(sp.fig)
-2092
-2093
-2094 @make_verbal
-2095 def consolidate_samples(self):
-2096 '''
-2097 Compile various statistics for each sample.
-2098
-2099 For each anchor sample:
+2080 **Parameters**
+2081
+2082 + `dir`: the directory in which to save the plots
+2083 + `figsize`: the width and height (in inches) of each plot
+2084 + `filetype`: 'pdf' or 'png'
+2085 + `dpi`: resolution for PNG output
+2086 '''
+2087 if not os.path.exists(dir):
+2088 os.makedirs(dir)
+2089
+2090 for session in self.sessions:
+2091 sp = self.plot_single_session(session, xylimits = 'constant')
+2092 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
+2093 ppl.close(sp.fig)
+2094
+2095
+2096 @make_verbal
+2097 def consolidate_samples(self):
+2098 '''
+2099 Compile various statistics for each sample.
2100
-2101 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
-2102 + `SE_D47` or `SE_D48`: set to zero by definition
-2103
-2104 For each unknown sample:
+2101 For each anchor sample:
+2102
+2103 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
+2104 + `SE_D47` or `SE_D48`: set to zero by definition
2105
-2106 + `D47` or `D48`: the standardized Δ4x value for this unknown
-2107 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
-2108
-2109 For each anchor and unknown:
+2106 For each unknown sample:
+2107
+2108 + `D47` or `D48`: the standardized Δ4x value for this unknown
+2109 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
2110
-2111 + `N`: the total number of analyses of this sample
-2112 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
-2113 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
-2114 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
-2115 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
-2116 variance, indicating whether the Δ4x repeatability this sample differs significantly from
-2117 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
-2118 '''
-2119 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
-2120 for sample in self.samples:
-2121 self.samples[sample]['N'] = len(self.samples[sample]['data'])
-2122 if self.samples[sample]['N'] > 1:
-2123 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
-2124
-2125 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
-2126 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
-2127
-2128 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
-2129 if len(D4x_pop) > 2:
-2130 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
-2131
-2132 if self.standardization_method == 'pooled':
-2133 for sample in self.anchors:
-2134 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2135 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2136 for sample in self.unknowns:
-2137 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
-2138 try:
-2139 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
-2140 except ValueError:
-2141 # when `sample` is constrained by self.standardize(constraints = {...}),
-2142 # it is no longer listed in self.standardization.var_names.
-2143 # Temporary fix: define SE as zero for now
-2144 self.samples[sample][f'SE_D4{self._4x}'] = 0.
-2145
-2146 elif self.standardization_method == 'indep_sessions':
-2147 for sample in self.anchors:
-2148 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2149 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2150 for sample in self.unknowns:
-2151 self.msg(f'Consolidating sample {sample}')
-2152 self.unknowns[sample][f'session_D{self._4x}'] = {}
-2153 session_avg = []
-2154 for session in self.sessions:
-2155 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
-2156 if sdata:
-2157 self.msg(f'{sample} found in session {session}')
-2158 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
-2159 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
-2160 # !! TODO: sigma_s below does not account for temporal changes in standardization error
-2161 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
-2162 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
-2163 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
-2164 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
-2165 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
-2166 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
-2167 wsum = sum([weights[s] for s in weights])
-2168 for s in weights:
-2169 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
-2170
-2171 for r in self:
-2172 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
-2173
-2174
+2111 For each anchor and unknown:
+2112
+2113 + `N`: the total number of analyses of this sample
+2114 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
+2115 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
+2116 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
+2117 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
+2118 variance, indicating whether the Δ4x repeatability this sample differs significantly from
+2119 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
+2120 '''
+2121 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
+2122 for sample in self.samples:
+2123 self.samples[sample]['N'] = len(self.samples[sample]['data'])
+2124 if self.samples[sample]['N'] > 1:
+2125 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
+2126
+2127 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
+2128 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
+2129
+2130 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
+2131 if len(D4x_pop) > 2:
+2132 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
+2133
+2134 if self.standardization_method == 'pooled':
+2135 for sample in self.anchors:
+2136 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2137 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2138 for sample in self.unknowns:
+2139 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
+2140 try:
+2141 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
+2142 except ValueError:
+2143 # when `sample` is constrained by self.standardize(constraints = {...}),
+2144 # it is no longer listed in self.standardization.var_names.
+2145 # Temporary fix: define SE as zero for now
+2146 self.samples[sample][f'SE_D4{self._4x}'] = 0.
+2147
+2148 elif self.standardization_method == 'indep_sessions':
+2149 for sample in self.anchors:
+2150 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2151 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2152 for sample in self.unknowns:
+2153 self.msg(f'Consolidating sample {sample}')
+2154 self.unknowns[sample][f'session_D{self._4x}'] = {}
+2155 session_avg = []
+2156 for session in self.sessions:
+2157 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
+2158 if sdata:
+2159 self.msg(f'{sample} found in session {session}')
+2160 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
+2161 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
+2162 # !! TODO: sigma_s below does not account for temporal changes in standardization error
+2163 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
+2164 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
+2165 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
+2166 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
+2167 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
+2168 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
+2169 wsum = sum([weights[s] for s in weights])
+2170 for s in weights:
+2171 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
+2172
+2173 for r in self:
+2174 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
2175
-2176 def consolidate_sessions(self):
-2177 '''
-2178 Compute various statistics for each session.
-2179
-2180 + `Na`: Number of anchor analyses in the session
-2181 + `Nu`: Number of unknown analyses in the session
-2182 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
-2183 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
-2184 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
-2185 + `a`: scrambling factor
-2186 + `b`: compositional slope
-2187 + `c`: WG offset
-2188 + `SE_a`: Model stadard erorr of `a`
-2189 + `SE_b`: Model stadard erorr of `b`
-2190 + `SE_c`: Model stadard erorr of `c`
-2191 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
-2192 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
-2193 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
-2194 + `a2`: scrambling factor drift
-2195 + `b2`: compositional slope drift
-2196 + `c2`: WG offset drift
-2197 + `Np`: Number of standardization parameters to fit
-2198 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
-2199 + `d13Cwg_VPDB`: δ13C_VPDB of WG
-2200 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
-2201 '''
-2202 for session in self.sessions:
-2203 if 'd13Cwg_VPDB' not in self.sessions[session]:
-2204 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
-2205 if 'd18Owg_VSMOW' not in self.sessions[session]:
-2206 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
-2207 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
-2208 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
-2209
-2210 self.msg(f'Computing repeatabilities for session {session}')
-2211 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
-2212 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
-2213 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
-2214
-2215 if self.standardization_method == 'pooled':
-2216 for session in self.sessions:
-2217
-2218 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
-2219 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2220 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
-2221
-2222 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
-2223 i = self.standardization.var_names.index(f'b_{pf(session)}')
-2224 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
-2225
-2226 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
-2227 i = self.standardization.var_names.index(f'c_{pf(session)}')
-2228 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
-2229
-2230 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
-2231 if self.sessions[session]['scrambling_drift']:
-2232 i = self.standardization.var_names.index(f'a2_{pf(session)}')
-2233 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
-2234 else:
-2235 self.sessions[session]['SE_a2'] = 0.
-2236
-2237 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
-2238 if self.sessions[session]['slope_drift']:
-2239 i = self.standardization.var_names.index(f'b2_{pf(session)}')
-2240 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
-2241 else:
-2242 self.sessions[session]['SE_b2'] = 0.
-2243
-2244 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
-2245 if self.sessions[session]['wg_drift']:
-2246 i = self.standardization.var_names.index(f'c2_{pf(session)}')
-2247 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
-2248 else:
-2249 self.sessions[session]['SE_c2'] = 0.
-2250
-2251 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2252 j = self.standardization.var_names.index(f'b_{pf(session)}')
-2253 k = self.standardization.var_names.index(f'c_{pf(session)}')
-2254 CM = np.zeros((6,6))
-2255 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
-2256 try:
-2257 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
-2258 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
-2259 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
-2260 try:
-2261 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2262 CM[3,4] = self.standardization.covar[i2,j2]
-2263 CM[4,3] = self.standardization.covar[j2,i2]
-2264 except ValueError:
-2265 pass
-2266 try:
-2267 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2268 CM[3,5] = self.standardization.covar[i2,k2]
-2269 CM[5,3] = self.standardization.covar[k2,i2]
-2270 except ValueError:
-2271 pass
-2272 except ValueError:
-2273 pass
-2274 try:
-2275 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2276 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
-2277 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
-2278 try:
-2279 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2280 CM[4,5] = self.standardization.covar[j2,k2]
-2281 CM[5,4] = self.standardization.covar[k2,j2]
-2282 except ValueError:
-2283 pass
-2284 except ValueError:
-2285 pass
-2286 try:
-2287 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2288 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
-2289 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
-2290 except ValueError:
-2291 pass
-2292
-2293 self.sessions[session]['CM'] = CM
+2176
+2177
+2178 def consolidate_sessions(self):
+2179 '''
+2180 Compute various statistics for each session.
+2181
+2182 + `Na`: Number of anchor analyses in the session
+2183 + `Nu`: Number of unknown analyses in the session
+2184 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
+2185 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
+2186 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
+2187 + `a`: scrambling factor
+2188 + `b`: compositional slope
+2189 + `c`: WG offset
+2190 + `SE_a`: Model stadard erorr of `a`
+2191 + `SE_b`: Model stadard erorr of `b`
+2192 + `SE_c`: Model stadard erorr of `c`
+2193 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
+2194 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
+2195 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
+2196 + `a2`: scrambling factor drift
+2197 + `b2`: compositional slope drift
+2198 + `c2`: WG offset drift
+2199 + `Np`: Number of standardization parameters to fit
+2200 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
+2201 + `d13Cwg_VPDB`: δ13C_VPDB of WG
+2202 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
+2203 '''
+2204 for session in self.sessions:
+2205 if 'd13Cwg_VPDB' not in self.sessions[session]:
+2206 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
+2207 if 'd18Owg_VSMOW' not in self.sessions[session]:
+2208 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
+2209 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
+2210 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
+2211
+2212 self.msg(f'Computing repeatabilities for session {session}')
+2213 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
+2214 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
+2215 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
+2216
+2217 if self.standardization_method == 'pooled':
+2218 for session in self.sessions:
+2219
+2220 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
+2221 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2222 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
+2223
+2224 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
+2225 i = self.standardization.var_names.index(f'b_{pf(session)}')
+2226 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
+2227
+2228 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
+2229 i = self.standardization.var_names.index(f'c_{pf(session)}')
+2230 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
+2231
+2232 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
+2233 if self.sessions[session]['scrambling_drift']:
+2234 i = self.standardization.var_names.index(f'a2_{pf(session)}')
+2235 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
+2236 else:
+2237 self.sessions[session]['SE_a2'] = 0.
+2238
+2239 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
+2240 if self.sessions[session]['slope_drift']:
+2241 i = self.standardization.var_names.index(f'b2_{pf(session)}')
+2242 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
+2243 else:
+2244 self.sessions[session]['SE_b2'] = 0.
+2245
+2246 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
+2247 if self.sessions[session]['wg_drift']:
+2248 i = self.standardization.var_names.index(f'c2_{pf(session)}')
+2249 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
+2250 else:
+2251 self.sessions[session]['SE_c2'] = 0.
+2252
+2253 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2254 j = self.standardization.var_names.index(f'b_{pf(session)}')
+2255 k = self.standardization.var_names.index(f'c_{pf(session)}')
+2256 CM = np.zeros((6,6))
+2257 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
+2258 try:
+2259 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
+2260 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
+2261 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
+2262 try:
+2263 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2264 CM[3,4] = self.standardization.covar[i2,j2]
+2265 CM[4,3] = self.standardization.covar[j2,i2]
+2266 except ValueError:
+2267 pass
+2268 try:
+2269 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2270 CM[3,5] = self.standardization.covar[i2,k2]
+2271 CM[5,3] = self.standardization.covar[k2,i2]
+2272 except ValueError:
+2273 pass
+2274 except ValueError:
+2275 pass
+2276 try:
+2277 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2278 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
+2279 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
+2280 try:
+2281 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2282 CM[4,5] = self.standardization.covar[j2,k2]
+2283 CM[5,4] = self.standardization.covar[k2,j2]
+2284 except ValueError:
+2285 pass
+2286 except ValueError:
+2287 pass
+2288 try:
+2289 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2290 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
+2291 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
+2292 except ValueError:
+2293 pass
2294
-2295 elif self.standardization_method == 'indep_sessions':
-2296 pass # Not implemented yet
-2297
-2298
-2299 @make_verbal
-2300 def repeatabilities(self):
-2301 '''
-2302 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
-2303 (for all samples, for anchors, and for unknowns).
-2304 '''
-2305 self.msg('Computing reproducibilities for all sessions')
-2306
-2307 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
-2308 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
-2309 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
-2310 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
-2311 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
-2312
-2313
-2314 @make_verbal
-2315 def consolidate(self, tables = True, plots = True):
-2316 '''
-2317 Collect information about samples, sessions and repeatabilities.
-2318 '''
-2319 self.consolidate_samples()
-2320 self.consolidate_sessions()
-2321 self.repeatabilities()
-2322
-2323 if tables:
-2324 self.summary()
-2325 self.table_of_sessions()
-2326 self.table_of_analyses()
-2327 self.table_of_samples()
-2328
-2329 if plots:
-2330 self.plot_sessions()
-2331
-2332
-2333 @make_verbal
-2334 def rmswd(self,
-2335 samples = 'all samples',
-2336 sessions = 'all sessions',
-2337 ):
-2338 '''
-2339 Compute the χ2, root mean squared weighted deviation
-2340 (i.e. reduced χ2), and corresponding degrees of freedom of the
-2341 Δ4x values for samples in `samples` and sessions in `sessions`.
-2342
-2343 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
-2344 '''
-2345 if samples == 'all samples':
-2346 mysamples = [k for k in self.samples]
-2347 elif samples == 'anchors':
-2348 mysamples = [k for k in self.anchors]
-2349 elif samples == 'unknowns':
-2350 mysamples = [k for k in self.unknowns]
-2351 else:
-2352 mysamples = samples
-2353
-2354 if sessions == 'all sessions':
-2355 sessions = [k for k in self.sessions]
-2356
-2357 chisq, Nf = 0, 0
-2358 for sample in mysamples :
-2359 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2360 if len(G) > 1 :
-2361 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
-2362 Nf += (len(G) - 1)
-2363 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
-2364 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2365 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
-2366 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
-2367
-2368
-2369 @make_verbal
-2370 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
-2371 '''
-2372 Compute the repeatability of `[r[key] for r in self]`
-2373 '''
-2374
-2375 if samples == 'all samples':
-2376 mysamples = [k for k in self.samples]
-2377 elif samples == 'anchors':
-2378 mysamples = [k for k in self.anchors]
-2379 elif samples == 'unknowns':
-2380 mysamples = [k for k in self.unknowns]
-2381 else:
-2382 mysamples = samples
-2383
-2384 if sessions == 'all sessions':
-2385 sessions = [k for k in self.sessions]
-2386
-2387 if key in ['D47', 'D48']:
-2388 # Full disclosure: the definition of Nf is tricky/debatable
-2389 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
-2390 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
-2391 Nf = len(G)
-2392# print(f'len(G) = {Nf}')
-2393 Nf -= len([s for s in mysamples if s in self.unknowns])
-2394# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
-2395 for session in sessions:
-2396 Np = len([
-2397 _ for _ in self.standardization.params
-2398 if (
-2399 self.standardization.params[_].expr is not None
-2400 and (
-2401 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
-2402 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
-2403 )
-2404 )
-2405 ])
-2406# print(f'session {session}: {Np} parameters to consider')
-2407 Na = len({
-2408 r['Sample'] for r in self.sessions[session]['data']
-2409 if r['Sample'] in self.anchors and r['Sample'] in mysamples
-2410 })
-2411# print(f'session {session}: {Na} different anchors in that session')
-2412 Nf -= min(Np, Na)
-2413# print(f'Nf = {Nf}')
-2414
-2415# for sample in mysamples :
-2416# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2417# if len(X) > 1 :
-2418# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
-2419# if sample in self.unknowns:
-2420# Nf += len(X) - 1
-2421# else:
-2422# Nf += len(X)
-2423# if samples in ['anchors', 'all samples']:
-2424# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
-2425 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2426
-2427 else: # if key not in ['D47', 'D48']
-2428 chisq, Nf = 0, 0
-2429 for sample in mysamples :
-2430 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2431 if len(X) > 1 :
-2432 Nf += len(X) - 1
-2433 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
-2434 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2435
-2436 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
-2437 return r
-2438
-2439 def sample_average(self, samples, weights = 'equal', normalize = True):
-2440 '''
-2441 Weighted average Δ4x value of a group of samples, accounting for covariance.
-2442
-2443 Returns the weighed average Δ4x value and associated SE
-2444 of a group of samples. Weights are equal by default. If `normalize` is
-2445 true, `weights` will be rescaled so that their sum equals 1.
-2446
-2447 **Examples**
+2295 self.sessions[session]['CM'] = CM
+2296
+2297 elif self.standardization_method == 'indep_sessions':
+2298 pass # Not implemented yet
+2299
+2300
+2301 @make_verbal
+2302 def repeatabilities(self):
+2303 '''
+2304 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
+2305 (for all samples, for anchors, and for unknowns).
+2306 '''
+2307 self.msg('Computing reproducibilities for all sessions')
+2308
+2309 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
+2310 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
+2311 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
+2312 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
+2313 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
+2314
+2315
+2316 @make_verbal
+2317 def consolidate(self, tables = True, plots = True):
+2318 '''
+2319 Collect information about samples, sessions and repeatabilities.
+2320 '''
+2321 self.consolidate_samples()
+2322 self.consolidate_sessions()
+2323 self.repeatabilities()
+2324
+2325 if tables:
+2326 self.summary()
+2327 self.table_of_sessions()
+2328 self.table_of_analyses()
+2329 self.table_of_samples()
+2330
+2331 if plots:
+2332 self.plot_sessions()
+2333
+2334
+2335 @make_verbal
+2336 def rmswd(self,
+2337 samples = 'all samples',
+2338 sessions = 'all sessions',
+2339 ):
+2340 '''
+2341 Compute the χ2, root mean squared weighted deviation
+2342 (i.e. reduced χ2), and corresponding degrees of freedom of the
+2343 Δ4x values for samples in `samples` and sessions in `sessions`.
+2344
+2345 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
+2346 '''
+2347 if samples == 'all samples':
+2348 mysamples = [k for k in self.samples]
+2349 elif samples == 'anchors':
+2350 mysamples = [k for k in self.anchors]
+2351 elif samples == 'unknowns':
+2352 mysamples = [k for k in self.unknowns]
+2353 else:
+2354 mysamples = samples
+2355
+2356 if sessions == 'all sessions':
+2357 sessions = [k for k in self.sessions]
+2358
+2359 chisq, Nf = 0, 0
+2360 for sample in mysamples :
+2361 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2362 if len(G) > 1 :
+2363 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
+2364 Nf += (len(G) - 1)
+2365 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
+2366 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2367 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
+2368 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
+2369
+2370
+2371 @make_verbal
+2372 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
+2373 '''
+2374 Compute the repeatability of `[r[key] for r in self]`
+2375 '''
+2376
+2377 if samples == 'all samples':
+2378 mysamples = [k for k in self.samples]
+2379 elif samples == 'anchors':
+2380 mysamples = [k for k in self.anchors]
+2381 elif samples == 'unknowns':
+2382 mysamples = [k for k in self.unknowns]
+2383 else:
+2384 mysamples = samples
+2385
+2386 if sessions == 'all sessions':
+2387 sessions = [k for k in self.sessions]
+2388
+2389 if key in ['D47', 'D48']:
+2390 # Full disclosure: the definition of Nf is tricky/debatable
+2391 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
+2392 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
+2393 Nf = len(G)
+2394# print(f'len(G) = {Nf}')
+2395 Nf -= len([s for s in mysamples if s in self.unknowns])
+2396# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
+2397 for session in sessions:
+2398 Np = len([
+2399 _ for _ in self.standardization.params
+2400 if (
+2401 self.standardization.params[_].expr is not None
+2402 and (
+2403 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
+2404 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
+2405 )
+2406 )
+2407 ])
+2408# print(f'session {session}: {Np} parameters to consider')
+2409 Na = len({
+2410 r['Sample'] for r in self.sessions[session]['data']
+2411 if r['Sample'] in self.anchors and r['Sample'] in mysamples
+2412 })
+2413# print(f'session {session}: {Na} different anchors in that session')
+2414 Nf -= min(Np, Na)
+2415# print(f'Nf = {Nf}')
+2416
+2417# for sample in mysamples :
+2418# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2419# if len(X) > 1 :
+2420# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
+2421# if sample in self.unknowns:
+2422# Nf += len(X) - 1
+2423# else:
+2424# Nf += len(X)
+2425# if samples in ['anchors', 'all samples']:
+2426# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
+2427 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2428
+2429 else: # if key not in ['D47', 'D48']
+2430 chisq, Nf = 0, 0
+2431 for sample in mysamples :
+2432 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2433 if len(X) > 1 :
+2434 Nf += len(X) - 1
+2435 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
+2436 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2437
+2438 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
+2439 return r
+2440
+2441 def sample_average(self, samples, weights = 'equal', normalize = True):
+2442 '''
+2443 Weighted average Δ4x value of a group of samples, accounting for covariance.
+2444
+2445 Returns the weighed average Δ4x value and associated SE
+2446 of a group of samples. Weights are equal by default. If `normalize` is
+2447 true, `weights` will be rescaled so that their sum equals 1.
2448
-2449 ```python
-2450 self.sample_average(['X','Y'], [1, 2])
-2451 ```
-2452
-2453 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
-2454 where Δ4x(X) and Δ4x(Y) are the average Δ4x
-2455 values of samples X and Y, respectively.
-2456
-2457 ```python
-2458 self.sample_average(['X','Y'], [1, -1], normalize = False)
-2459 ```
-2460
-2461 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
-2462 '''
-2463 if weights == 'equal':
-2464 weights = [1/len(samples)] * len(samples)
-2465
-2466 if normalize:
-2467 s = sum(weights)
-2468 if s:
-2469 weights = [w/s for w in weights]
-2470
-2471 try:
-2472# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
-2473# C = self.standardization.covar[indices,:][:,indices]
-2474 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
-2475 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
-2476 return correlated_sum(X, C, weights)
-2477 except ValueError:
-2478 return (0., 0.)
-2479
-2480
-2481 def sample_D4x_covar(self, sample1, sample2 = None):
-2482 '''
-2483 Covariance between Δ4x values of samples
-2484
-2485 Returns the error covariance between the average Δ4x values of two
-2486 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
-2487 returns the Δ4x variance for that sample.
-2488 '''
-2489 if sample2 is None:
-2490 sample2 = sample1
-2491 if self.standardization_method == 'pooled':
-2492 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
-2493 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
-2494 return self.standardization.covar[i, j]
-2495 elif self.standardization_method == 'indep_sessions':
-2496 if sample1 == sample2:
-2497 return self.samples[sample1][f'SE_D{self._4x}']**2
-2498 else:
-2499 c = 0
-2500 for session in self.sessions:
-2501 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
-2502 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
-2503 if sdata1 and sdata2:
-2504 a = self.sessions[session]['a']
-2505 # !! TODO: CM below does not account for temporal changes in standardization parameters
-2506 CM = self.sessions[session]['CM'][:3,:3]
-2507 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
-2508 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
-2509 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
-2510 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
-2511 c += (
-2512 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
-2513 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
-2514 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
-2515 @ CM
-2516 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
-2517 ) / a**2
-2518 return float(c)
-2519
-2520 def sample_D4x_correl(self, sample1, sample2 = None):
-2521 '''
-2522 Correlation between Δ4x errors of samples
-2523
-2524 Returns the error correlation between the average Δ4x values of two samples.
-2525 '''
-2526 if sample2 is None or sample2 == sample1:
-2527 return 1.
-2528 return (
-2529 self.sample_D4x_covar(sample1, sample2)
-2530 / self.unknowns[sample1][f'SE_D{self._4x}']
-2531 / self.unknowns[sample2][f'SE_D{self._4x}']
-2532 )
-2533
-2534 def plot_single_session(self,
-2535 session,
-2536 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
-2537 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
-2538 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
-2539 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
-2540 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
-2541 xylimits = 'free', # | 'constant'
-2542 x_label = None,
-2543 y_label = None,
-2544 error_contour_interval = 'auto',
-2545 fig = 'new',
-2546 ):
-2547 '''
-2548 Generate plot for a single session
-2549 '''
-2550 if x_label is None:
-2551 x_label = f'δ$_{{{self._4x}}}$ (‰)'
-2552 if y_label is None:
-2553 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
-2554
-2555 out = _SessionPlot()
-2556 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
-2557 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
-2558
-2559 if fig == 'new':
-2560 out.fig = ppl.figure(figsize = (6,6))
-2561 ppl.subplots_adjust(.1,.1,.9,.9)
-2562
-2563 out.anchor_analyses, = ppl.plot(
-2564 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2565 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2566 **kw_plot_anchors)
-2567 out.unknown_analyses, = ppl.plot(
-2568 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2569 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2570 **kw_plot_unknowns)
-2571 out.anchor_avg = ppl.plot(
-2572 np.array([ np.array([
-2573 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2574 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2575 ]) for sample in anchors]).T,
-2576 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
-2577 **kw_plot_anchor_avg)
-2578 out.unknown_avg = ppl.plot(
-2579 np.array([ np.array([
-2580 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2581 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2582 ]) for sample in unknowns]).T,
-2583 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
-2584 **kw_plot_unknown_avg)
-2585 if xylimits == 'constant':
-2586 x = [r[f'd{self._4x}'] for r in self]
-2587 y = [r[f'D{self._4x}'] for r in self]
-2588 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
-2589 w, h = x2-x1, y2-y1
-2590 x1 -= w/20
-2591 x2 += w/20
-2592 y1 -= h/20
-2593 y2 += h/20
-2594 ppl.axis([x1, x2, y1, y2])
-2595 elif xylimits == 'free':
-2596 x1, x2, y1, y2 = ppl.axis()
-2597 else:
-2598 x1, x2, y1, y2 = ppl.axis(xylimits)
-2599
-2600 if error_contour_interval != 'none':
-2601 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
-2602 XI,YI = np.meshgrid(xi, yi)
-2603 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
-2604 if error_contour_interval == 'auto':
-2605 rng = np.max(SI) - np.min(SI)
-2606 if rng <= 0.01:
-2607 cinterval = 0.001
-2608 elif rng <= 0.03:
-2609 cinterval = 0.004
-2610 elif rng <= 0.1:
-2611 cinterval = 0.01
-2612 elif rng <= 0.3:
-2613 cinterval = 0.03
-2614 elif rng <= 1.:
-2615 cinterval = 0.1
-2616 else:
-2617 cinterval = 0.5
-2618 else:
-2619 cinterval = error_contour_interval
-2620
-2621 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
-2622 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
-2623 out.clabel = ppl.clabel(out.contour)
-2624
-2625 ppl.xlabel(x_label)
-2626 ppl.ylabel(y_label)
-2627 ppl.title(session, weight = 'bold')
-2628 ppl.grid(alpha = .2)
-2629 out.ax = ppl.gca()
-2630
-2631 return out
+2449 **Examples**
+2450
+2451 ```python
+2452 self.sample_average(['X','Y'], [1, 2])
+2453 ```
+2454
+2455 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
+2456 where Δ4x(X) and Δ4x(Y) are the average Δ4x
+2457 values of samples X and Y, respectively.
+2458
+2459 ```python
+2460 self.sample_average(['X','Y'], [1, -1], normalize = False)
+2461 ```
+2462
+2463 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
+2464 '''
+2465 if weights == 'equal':
+2466 weights = [1/len(samples)] * len(samples)
+2467
+2468 if normalize:
+2469 s = sum(weights)
+2470 if s:
+2471 weights = [w/s for w in weights]
+2472
+2473 try:
+2474# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
+2475# C = self.standardization.covar[indices,:][:,indices]
+2476 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
+2477 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
+2478 return correlated_sum(X, C, weights)
+2479 except ValueError:
+2480 return (0., 0.)
+2481
+2482
+2483 def sample_D4x_covar(self, sample1, sample2 = None):
+2484 '''
+2485 Covariance between Δ4x values of samples
+2486
+2487 Returns the error covariance between the average Δ4x values of two
+2488 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
+2489 returns the Δ4x variance for that sample.
+2490 '''
+2491 if sample2 is None:
+2492 sample2 = sample1
+2493 if self.standardization_method == 'pooled':
+2494 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
+2495 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
+2496 return self.standardization.covar[i, j]
+2497 elif self.standardization_method == 'indep_sessions':
+2498 if sample1 == sample2:
+2499 return self.samples[sample1][f'SE_D{self._4x}']**2
+2500 else:
+2501 c = 0
+2502 for session in self.sessions:
+2503 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
+2504 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
+2505 if sdata1 and sdata2:
+2506 a = self.sessions[session]['a']
+2507 # !! TODO: CM below does not account for temporal changes in standardization parameters
+2508 CM = self.sessions[session]['CM'][:3,:3]
+2509 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
+2510 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
+2511 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
+2512 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
+2513 c += (
+2514 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
+2515 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
+2516 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
+2517 @ CM
+2518 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
+2519 ) / a**2
+2520 return float(c)
+2521
+2522 def sample_D4x_correl(self, sample1, sample2 = None):
+2523 '''
+2524 Correlation between Δ4x errors of samples
+2525
+2526 Returns the error correlation between the average Δ4x values of two samples.
+2527 '''
+2528 if sample2 is None or sample2 == sample1:
+2529 return 1.
+2530 return (
+2531 self.sample_D4x_covar(sample1, sample2)
+2532 / self.unknowns[sample1][f'SE_D{self._4x}']
+2533 / self.unknowns[sample2][f'SE_D{self._4x}']
+2534 )
+2535
+2536 def plot_single_session(self,
+2537 session,
+2538 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
+2539 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
+2540 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
+2541 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
+2542 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
+2543 xylimits = 'free', # | 'constant'
+2544 x_label = None,
+2545 y_label = None,
+2546 error_contour_interval = 'auto',
+2547 fig = 'new',
+2548 ):
+2549 '''
+2550 Generate plot for a single session
+2551 '''
+2552 if x_label is None:
+2553 x_label = f'δ$_{{{self._4x}}}$ (‰)'
+2554 if y_label is None:
+2555 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
+2556
+2557 out = _SessionPlot()
+2558 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
+2559 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
+2560
+2561 if fig == 'new':
+2562 out.fig = ppl.figure(figsize = (6,6))
+2563 ppl.subplots_adjust(.1,.1,.9,.9)
+2564
+2565 out.anchor_analyses, = ppl.plot(
+2566 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2567 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2568 **kw_plot_anchors)
+2569 out.unknown_analyses, = ppl.plot(
+2570 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2571 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2572 **kw_plot_unknowns)
+2573 out.anchor_avg = ppl.plot(
+2574 np.array([ np.array([
+2575 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2576 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2577 ]) for sample in anchors]).T,
+2578 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
+2579 **kw_plot_anchor_avg)
+2580 out.unknown_avg = ppl.plot(
+2581 np.array([ np.array([
+2582 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2583 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2584 ]) for sample in unknowns]).T,
+2585 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
+2586 **kw_plot_unknown_avg)
+2587 if xylimits == 'constant':
+2588 x = [r[f'd{self._4x}'] for r in self]
+2589 y = [r[f'D{self._4x}'] for r in self]
+2590 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
+2591 w, h = x2-x1, y2-y1
+2592 x1 -= w/20
+2593 x2 += w/20
+2594 y1 -= h/20
+2595 y2 += h/20
+2596 ppl.axis([x1, x2, y1, y2])
+2597 elif xylimits == 'free':
+2598 x1, x2, y1, y2 = ppl.axis()
+2599 else:
+2600 x1, x2, y1, y2 = ppl.axis(xylimits)
+2601
+2602 if error_contour_interval != 'none':
+2603 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
+2604 XI,YI = np.meshgrid(xi, yi)
+2605 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
+2606 if error_contour_interval == 'auto':
+2607 rng = np.max(SI) - np.min(SI)
+2608 if rng <= 0.01:
+2609 cinterval = 0.001
+2610 elif rng <= 0.03:
+2611 cinterval = 0.004
+2612 elif rng <= 0.1:
+2613 cinterval = 0.01
+2614 elif rng <= 0.3:
+2615 cinterval = 0.03
+2616 elif rng <= 1.:
+2617 cinterval = 0.1
+2618 else:
+2619 cinterval = 0.5
+2620 else:
+2621 cinterval = error_contour_interval
+2622
+2623 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
+2624 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
+2625 out.clabel = ppl.clabel(out.contour)
+2626
+2627 ppl.xlabel(x_label)
+2628 ppl.ylabel(y_label)
+2629 ppl.title(session, weight = 'bold')
+2630 ppl.grid(alpha = .2)
+2631 out.ax = ppl.gca()
2632
-2633 def plot_residuals(
-2634 self,
-2635 kde = False,
-2636 hist = False,
-2637 binwidth = 2/3,
-2638 dir = 'output',
-2639 filename = None,
-2640 highlight = [],
-2641 colors = None,
-2642 figsize = None,
-2643 dpi = 100,
-2644 yspan = None,
-2645 ):
-2646 '''
-2647 Plot residuals of each analysis as a function of time (actually, as a function of
-2648 the order of analyses in the `D4xdata` object)
-2649
-2650 + `kde`: whether to add a kernel density estimate of residuals
-2651 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
-2652 + `histbins`: specify bin edges for the histogram
-2653 + `dir`: the directory in which to save the plot
-2654 + `highlight`: a list of samples to highlight
-2655 + `colors`: a dict of `{<sample>: <color>}` for all samples
-2656 + `figsize`: (width, height) of figure
-2657 + `dpi`: resolution for PNG output
-2658 + `yspan`: factor controlling the range of y values shown in plot
-2659 (by default: `yspan = 1.5 if kde else 1.0`)
-2660 '''
-2661
-2662 from matplotlib import ticker
-2663
-2664 if yspan is None:
-2665 if kde:
-2666 yspan = 1.5
-2667 else:
-2668 yspan = 1.0
-2669
-2670 # Layout
-2671 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
-2672 if hist or kde:
-2673 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
-2674 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
-2675 else:
-2676 ppl.subplots_adjust(.08,.05,.78,.8)
-2677 ax1 = ppl.subplot(111)
-2678
-2679 # Colors
-2680 N = len(self.anchors)
-2681 if colors is None:
-2682 if len(highlight) > 0:
-2683 Nh = len(highlight)
-2684 if Nh == 1:
-2685 colors = {highlight[0]: (0,0,0)}
-2686 elif Nh == 3:
-2687 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2688 elif Nh == 4:
-2689 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2690 else:
-2691 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
-2692 else:
-2693 if N == 3:
-2694 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2695 elif N == 4:
-2696 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2697 else:
-2698 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
-2699
-2700 ppl.sca(ax1)
-2701
-2702 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
-2703
-2704 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+2633 return out
+2634
+2635 def plot_residuals(
+2636 self,
+2637 kde = False,
+2638 hist = False,
+2639 binwidth = 2/3,
+2640 dir = 'output',
+2641 filename = None,
+2642 highlight = [],
+2643 colors = None,
+2644 figsize = None,
+2645 dpi = 100,
+2646 yspan = None,
+2647 ):
+2648 '''
+2649 Plot residuals of each analysis as a function of time (actually, as a function of
+2650 the order of analyses in the `D4xdata` object)
+2651
+2652 + `kde`: whether to add a kernel density estimate of residuals
+2653 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
+2654 + `histbins`: specify bin edges for the histogram
+2655 + `dir`: the directory in which to save the plot
+2656 + `highlight`: a list of samples to highlight
+2657 + `colors`: a dict of `{<sample>: <color>}` for all samples
+2658 + `figsize`: (width, height) of figure
+2659 + `dpi`: resolution for PNG output
+2660 + `yspan`: factor controlling the range of y values shown in plot
+2661 (by default: `yspan = 1.5 if kde else 1.0`)
+2662 '''
+2663
+2664 from matplotlib import ticker
+2665
+2666 if yspan is None:
+2667 if kde:
+2668 yspan = 1.5
+2669 else:
+2670 yspan = 1.0
+2671
+2672 # Layout
+2673 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
+2674 if hist or kde:
+2675 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
+2676 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
+2677 else:
+2678 ppl.subplots_adjust(.08,.05,.78,.8)
+2679 ax1 = ppl.subplot(111)
+2680
+2681 # Colors
+2682 N = len(self.anchors)
+2683 if colors is None:
+2684 if len(highlight) > 0:
+2685 Nh = len(highlight)
+2686 if Nh == 1:
+2687 colors = {highlight[0]: (0,0,0)}
+2688 elif Nh == 3:
+2689 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2690 elif Nh == 4:
+2691 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2692 else:
+2693 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
+2694 else:
+2695 if N == 3:
+2696 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2697 elif N == 4:
+2698 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2699 else:
+2700 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
+2701
+2702 ppl.sca(ax1)
+2703
+2704 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
2705
-2706 session = self[0]['Session']
-2707 x1 = 0
-2708# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
-2709 x_sessions = {}
-2710 one_or_more_singlets = False
-2711 one_or_more_multiplets = False
-2712 multiplets = set()
-2713 for k,r in enumerate(self):
-2714 if r['Session'] != session:
-2715 x2 = k-1
-2716 x_sessions[session] = (x1+x2)/2
-2717 ppl.axvline(k - 0.5, color = 'k', lw = .5)
-2718 session = r['Session']
-2719 x1 = k
-2720 singlet = len(self.samples[r['Sample']]['data']) == 1
-2721 if not singlet:
-2722 multiplets.add(r['Sample'])
-2723 if r['Sample'] in self.unknowns:
-2724 if singlet:
-2725 one_or_more_singlets = True
-2726 else:
-2727 one_or_more_multiplets = True
-2728 kw = dict(
-2729 marker = 'x' if singlet else '+',
-2730 ms = 4 if singlet else 5,
-2731 ls = 'None',
-2732 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
-2733 mew = 1,
-2734 alpha = 0.2 if singlet else 1,
-2735 )
-2736 if highlight and r['Sample'] not in highlight:
-2737 kw['alpha'] = 0.2
-2738 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
-2739 x2 = k
-2740 x_sessions[session] = (x1+x2)/2
-2741
-2742 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
-2743 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
-2744 if not (hist or kde):
-2745 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2746 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2747
-2748 xmin, xmax, ymin, ymax = ppl.axis()
-2749 if yspan != 1:
-2750 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
-2751 for s in x_sessions:
-2752 ppl.text(
-2753 x_sessions[s],
-2754 ymax +1,
-2755 s,
-2756 va = 'bottom',
-2757 **(
-2758 dict(ha = 'center')
-2759 if len(self.sessions[s]['data']) > (0.15 * len(self))
-2760 else dict(ha = 'left', rotation = 45)
-2761 )
-2762 )
-2763
-2764 if hist or kde:
-2765 ppl.sca(ax2)
-2766
-2767 for s in colors:
-2768 kw['marker'] = '+'
-2769 kw['ms'] = 5
-2770 kw['mec'] = colors[s]
-2771 kw['label'] = s
-2772 kw['alpha'] = 1
-2773 ppl.plot([], [], **kw)
-2774
-2775 kw['mec'] = (0,0,0)
+2706 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+2707
+2708 session = self[0]['Session']
+2709 x1 = 0
+2710# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
+2711 x_sessions = {}
+2712 one_or_more_singlets = False
+2713 one_or_more_multiplets = False
+2714 multiplets = set()
+2715 for k,r in enumerate(self):
+2716 if r['Session'] != session:
+2717 x2 = k-1
+2718 x_sessions[session] = (x1+x2)/2
+2719 ppl.axvline(k - 0.5, color = 'k', lw = .5)
+2720 session = r['Session']
+2721 x1 = k
+2722 singlet = len(self.samples[r['Sample']]['data']) == 1
+2723 if not singlet:
+2724 multiplets.add(r['Sample'])
+2725 if r['Sample'] in self.unknowns:
+2726 if singlet:
+2727 one_or_more_singlets = True
+2728 else:
+2729 one_or_more_multiplets = True
+2730 kw = dict(
+2731 marker = 'x' if singlet else '+',
+2732 ms = 4 if singlet else 5,
+2733 ls = 'None',
+2734 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
+2735 mew = 1,
+2736 alpha = 0.2 if singlet else 1,
+2737 )
+2738 if highlight and r['Sample'] not in highlight:
+2739 kw['alpha'] = 0.2
+2740 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
+2741 x2 = k
+2742 x_sessions[session] = (x1+x2)/2
+2743
+2744 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
+2745 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
+2746 if not (hist or kde):
+2747 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2748 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2749
+2750 xmin, xmax, ymin, ymax = ppl.axis()
+2751 if yspan != 1:
+2752 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
+2753 for s in x_sessions:
+2754 ppl.text(
+2755 x_sessions[s],
+2756 ymax +1,
+2757 s,
+2758 va = 'bottom',
+2759 **(
+2760 dict(ha = 'center')
+2761 if len(self.sessions[s]['data']) > (0.15 * len(self))
+2762 else dict(ha = 'left', rotation = 45)
+2763 )
+2764 )
+2765
+2766 if hist or kde:
+2767 ppl.sca(ax2)
+2768
+2769 for s in colors:
+2770 kw['marker'] = '+'
+2771 kw['ms'] = 5
+2772 kw['mec'] = colors[s]
+2773 kw['label'] = s
+2774 kw['alpha'] = 1
+2775 ppl.plot([], [], **kw)
2776
-2777 if one_or_more_singlets:
-2778 kw['marker'] = 'x'
-2779 kw['ms'] = 4
-2780 kw['alpha'] = .2
-2781 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
-2782 ppl.plot([], [], **kw)
-2783
-2784 if one_or_more_multiplets:
-2785 kw['marker'] = '+'
-2786 kw['ms'] = 4
-2787 kw['alpha'] = 1
-2788 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
-2789 ppl.plot([], [], **kw)
-2790
-2791 if hist or kde:
-2792 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
-2793 else:
-2794 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
-2795 leg.set_zorder(-1000)
-2796
-2797 ppl.sca(ax1)
+2777 kw['mec'] = (0,0,0)
+2778
+2779 if one_or_more_singlets:
+2780 kw['marker'] = 'x'
+2781 kw['ms'] = 4
+2782 kw['alpha'] = .2
+2783 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
+2784 ppl.plot([], [], **kw)
+2785
+2786 if one_or_more_multiplets:
+2787 kw['marker'] = '+'
+2788 kw['ms'] = 4
+2789 kw['alpha'] = 1
+2790 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
+2791 ppl.plot([], [], **kw)
+2792
+2793 if hist or kde:
+2794 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
+2795 else:
+2796 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
+2797 leg.set_zorder(-1000)
2798
-2799 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
-2800 ppl.xticks([])
-2801 ppl.axis([-1, len(self), None, None])
-2802
-2803 if hist or kde:
-2804 ppl.sca(ax2)
-2805 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
-2806
-2807 if kde:
-2808 from scipy.stats import gaussian_kde
-2809 yi = np.linspace(ymin, ymax, 201)
-2810 xi = gaussian_kde(X).evaluate(yi)
-2811 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
-2812# ppl.plot(xi, yi, 'k-', lw = 1)
-2813 elif hist:
-2814 ppl.hist(
-2815 X,
-2816 orientation = 'horizontal',
-2817 histtype = 'stepfilled',
-2818 ec = [.4]*3,
-2819 fc = [.25]*3,
-2820 alpha = .25,
-2821 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
-2822 )
-2823 ppl.text(0, 0,
-2824 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
-2825 size = 7.5,
-2826 alpha = 1,
-2827 va = 'center',
-2828 ha = 'left',
-2829 )
-2830
-2831 ppl.axis([0, None, ymin, ymax])
-2832 ppl.xticks([])
-2833 ppl.yticks([])
-2834# ax2.spines['left'].set_visible(False)
-2835 ax2.spines['right'].set_visible(False)
-2836 ax2.spines['top'].set_visible(False)
-2837 ax2.spines['bottom'].set_visible(False)
-2838
-2839 ax1.axis([None, None, ymin, ymax])
+2799 ppl.sca(ax1)
+2800
+2801 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
+2802 ppl.xticks([])
+2803 ppl.axis([-1, len(self), None, None])
+2804
+2805 if hist or kde:
+2806 ppl.sca(ax2)
+2807 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
+2808
+2809 if kde:
+2810 from scipy.stats import gaussian_kde
+2811 yi = np.linspace(ymin, ymax, 201)
+2812 xi = gaussian_kde(X).evaluate(yi)
+2813 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
+2814# ppl.plot(xi, yi, 'k-', lw = 1)
+2815 elif hist:
+2816 ppl.hist(
+2817 X,
+2818 orientation = 'horizontal',
+2819 histtype = 'stepfilled',
+2820 ec = [.4]*3,
+2821 fc = [.25]*3,
+2822 alpha = .25,
+2823 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
+2824 )
+2825 ppl.text(0, 0,
+2826 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
+2827 size = 7.5,
+2828 alpha = 1,
+2829 va = 'center',
+2830 ha = 'left',
+2831 )
+2832
+2833 ppl.axis([0, None, ymin, ymax])
+2834 ppl.xticks([])
+2835 ppl.yticks([])
+2836# ax2.spines['left'].set_visible(False)
+2837 ax2.spines['right'].set_visible(False)
+2838 ax2.spines['top'].set_visible(False)
+2839 ax2.spines['bottom'].set_visible(False)
2840
-2841 if not os.path.exists(dir):
-2842 os.makedirs(dir)
-2843 if filename is None:
-2844 return fig
-2845 elif filename == '':
-2846 filename = f'D{self._4x}_residuals.pdf'
-2847 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2848 ppl.close(fig)
-2849
-2850
-2851 def simulate(self, *args, **kwargs):
-2852 '''
-2853 Legacy function with warning message pointing to `virtual_data()`
-2854 '''
-2855 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
-2856
-2857 def plot_distribution_of_analyses(
-2858 self,
-2859 dir = 'output',
-2860 filename = None,
-2861 vs_time = False,
-2862 figsize = (6,4),
-2863 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
-2864 output = None,
-2865 dpi = 100,
-2866 ):
-2867 '''
-2868 Plot temporal distribution of all analyses in the data set.
-2869
-2870 **Parameters**
-2871
-2872 + `dir`: the directory in which to save the plot
-2873 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
-2874 + `dpi`: resolution for PNG output
-2875 + `figsize`: (width, height) of figure
+2841 ax1.axis([None, None, ymin, ymax])
+2842
+2843 if not os.path.exists(dir):
+2844 os.makedirs(dir)
+2845 if filename is None:
+2846 return fig
+2847 elif filename == '':
+2848 filename = f'D{self._4x}_residuals.pdf'
+2849 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2850 ppl.close(fig)
+2851
+2852
+2853 def simulate(self, *args, **kwargs):
+2854 '''
+2855 Legacy function with warning message pointing to `virtual_data()`
+2856 '''
+2857 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
+2858
+2859 def plot_distribution_of_analyses(
+2860 self,
+2861 dir = 'output',
+2862 filename = None,
+2863 vs_time = False,
+2864 figsize = (6,4),
+2865 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
+2866 output = None,
+2867 dpi = 100,
+2868 ):
+2869 '''
+2870 Plot temporal distribution of all analyses in the data set.
+2871
+2872 **Parameters**
+2873
+2874 + `dir`: the directory in which to save the plot
+2875 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
2876 + `dpi`: resolution for PNG output
-2877 '''
-2878
-2879 asamples = [s for s in self.anchors]
-2880 usamples = [s for s in self.unknowns]
-2881 if output is None or output == 'fig':
-2882 fig = ppl.figure(figsize = figsize)
-2883 ppl.subplots_adjust(*subplots_adjust)
-2884 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2885 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2886 Xmax += (Xmax-Xmin)/40
-2887 Xmin -= (Xmax-Xmin)/41
-2888 for k, s in enumerate(asamples + usamples):
-2889 if vs_time:
-2890 X = [r['TimeTag'] for r in self if r['Sample'] == s]
-2891 else:
-2892 X = [x for x,r in enumerate(self) if r['Sample'] == s]
-2893 Y = [-k for x in X]
-2894 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
-2895 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
-2896 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
-2897 ppl.axis([Xmin, Xmax, -k-1, 1])
-2898 ppl.xlabel('\ntime')
-2899 ppl.gca().annotate('',
-2900 xy = (0.6, -0.02),
-2901 xycoords = 'axes fraction',
-2902 xytext = (.4, -0.02),
-2903 arrowprops = dict(arrowstyle = "->", color = 'k'),
-2904 )
-2905
-2906
-2907 x2 = -1
-2908 for session in self.sessions:
-2909 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2910 if vs_time:
-2911 ppl.axvline(x1, color = 'k', lw = .75)
-2912 if x2 > -1:
-2913 if not vs_time:
-2914 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
-2915 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2916# from xlrd import xldate_as_datetime
-2917# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
-2918 if vs_time:
-2919 ppl.axvline(x2, color = 'k', lw = .75)
-2920 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
-2921 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
-2922
-2923 ppl.xticks([])
-2924 ppl.yticks([])
-2925
-2926 if output is None:
-2927 if not os.path.exists(dir):
-2928 os.makedirs(dir)
-2929 if filename == None:
-2930 filename = f'D{self._4x}_distribution_of_analyses.pdf'
-2931 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2932 ppl.close(fig)
-2933 elif output == 'ax':
-2934 return ppl.gca()
-2935 elif output == 'fig':
-2936 return fig
-2937
-2938
-2939 def plot_bulk_compositions(
-2940 self,
-2941 samples = None,
-2942 dir = 'output/bulk_compositions',
-2943 figsize = (6,6),
-2944 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
-2945 show = False,
-2946 sample_color = (0,.5,1),
-2947 analysis_color = (.7,.7,.7),
-2948 labeldist = 0.3,
-2949 radius = 0.05,
-2950 ):
-2951 '''
-2952 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
-2953
-2954 By default, creates a directory `./output/bulk_compositions` where plots for
-2955 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
-2956
-2957
-2958 **Parameters**
-2959
-2960 + `samples`: Only these samples are processed (by default: all samples).
-2961 + `dir`: where to save the plots
-2962 + `figsize`: (width, height) of figure
-2963 + `subplots_adjust`: passed to `subplots_adjust()`
-2964 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
-2965 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
-2966 + `sample_color`: color used for replicate markers/labels
-2967 + `analysis_color`: color used for sample markers/labels
-2968 + `labeldist`: distance (in inches) from replicate markers to replicate labels
-2969 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
-2970 '''
-2971
-2972 from matplotlib.patches import Ellipse
+2877 + `figsize`: (width, height) of figure
+2878 + `dpi`: resolution for PNG output
+2879 '''
+2880
+2881 asamples = [s for s in self.anchors]
+2882 usamples = [s for s in self.unknowns]
+2883 if output is None or output == 'fig':
+2884 fig = ppl.figure(figsize = figsize)
+2885 ppl.subplots_adjust(*subplots_adjust)
+2886 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2887 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2888 Xmax += (Xmax-Xmin)/40
+2889 Xmin -= (Xmax-Xmin)/41
+2890 for k, s in enumerate(asamples + usamples):
+2891 if vs_time:
+2892 X = [r['TimeTag'] for r in self if r['Sample'] == s]
+2893 else:
+2894 X = [x for x,r in enumerate(self) if r['Sample'] == s]
+2895 Y = [-k for x in X]
+2896 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
+2897 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
+2898 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
+2899 ppl.axis([Xmin, Xmax, -k-1, 1])
+2900 ppl.xlabel('\ntime')
+2901 ppl.gca().annotate('',
+2902 xy = (0.6, -0.02),
+2903 xycoords = 'axes fraction',
+2904 xytext = (.4, -0.02),
+2905 arrowprops = dict(arrowstyle = "->", color = 'k'),
+2906 )
+2907
+2908
+2909 x2 = -1
+2910 for session in self.sessions:
+2911 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2912 if vs_time:
+2913 ppl.axvline(x1, color = 'k', lw = .75)
+2914 if x2 > -1:
+2915 if not vs_time:
+2916 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
+2917 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2918# from xlrd import xldate_as_datetime
+2919# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
+2920 if vs_time:
+2921 ppl.axvline(x2, color = 'k', lw = .75)
+2922 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
+2923 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
+2924
+2925 ppl.xticks([])
+2926 ppl.yticks([])
+2927
+2928 if output is None:
+2929 if not os.path.exists(dir):
+2930 os.makedirs(dir)
+2931 if filename == None:
+2932 filename = f'D{self._4x}_distribution_of_analyses.pdf'
+2933 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2934 ppl.close(fig)
+2935 elif output == 'ax':
+2936 return ppl.gca()
+2937 elif output == 'fig':
+2938 return fig
+2939
+2940
+2941 def plot_bulk_compositions(
+2942 self,
+2943 samples = None,
+2944 dir = 'output/bulk_compositions',
+2945 figsize = (6,6),
+2946 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
+2947 show = False,
+2948 sample_color = (0,.5,1),
+2949 analysis_color = (.7,.7,.7),
+2950 labeldist = 0.3,
+2951 radius = 0.05,
+2952 ):
+2953 '''
+2954 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
+2955
+2956 By default, creates a directory `./output/bulk_compositions` where plots for
+2957 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
+2958
+2959
+2960 **Parameters**
+2961
+2962 + `samples`: Only these samples are processed (by default: all samples).
+2963 + `dir`: where to save the plots
+2964 + `figsize`: (width, height) of figure
+2965 + `subplots_adjust`: passed to `subplots_adjust()`
+2966 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
+2967 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
+2968 + `sample_color`: color used for replicate markers/labels
+2969 + `analysis_color`: color used for sample markers/labels
+2970 + `labeldist`: distance (in inches) from replicate markers to replicate labels
+2971 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
+2972 '''
2973
-2974 if samples is None:
-2975 samples = [_ for _ in self.samples]
-2976
-2977 saved = {}
+2974 from matplotlib.patches import Ellipse
+2975
+2976 if samples is None:
+2977 samples = [_ for _ in self.samples]
2978
-2979 for s in samples:
+2979 saved = {}
2980
-2981 fig = ppl.figure(figsize = figsize)
-2982 fig.subplots_adjust(*subplots_adjust)
-2983 ax = ppl.subplot(111)
-2984 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-2985 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-2986 ppl.title(s)
-2987
-2988
-2989 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
-2990 UID = [_['UID'] for _ in self.samples[s]['data']]
-2991 XY0 = XY.mean(0)
-2992
-2993 for xy in XY:
-2994 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
-2995
-2996 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
-2997 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
-2998 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-2999 saved[s] = [XY, XY0]
-3000
-3001 x1, x2, y1, y2 = ppl.axis()
-3002 x0, dx = (x1+x2)/2, (x2-x1)/2
-3003 y0, dy = (y1+y2)/2, (y2-y1)/2
-3004 dx, dy = [max(max(dx, dy), radius)]*2
-3005
-3006 ppl.axis([
-3007 x0 - 1.2*dx,
-3008 x0 + 1.2*dx,
-3009 y0 - 1.2*dy,
-3010 y0 + 1.2*dy,
-3011 ])
-3012
-3013 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
+2981 for s in samples:
+2982
+2983 fig = ppl.figure(figsize = figsize)
+2984 fig.subplots_adjust(*subplots_adjust)
+2985 ax = ppl.subplot(111)
+2986 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+2987 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+2988 ppl.title(s)
+2989
+2990
+2991 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
+2992 UID = [_['UID'] for _ in self.samples[s]['data']]
+2993 XY0 = XY.mean(0)
+2994
+2995 for xy in XY:
+2996 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
+2997
+2998 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
+2999 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
+3000 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3001 saved[s] = [XY, XY0]
+3002
+3003 x1, x2, y1, y2 = ppl.axis()
+3004 x0, dx = (x1+x2)/2, (x2-x1)/2
+3005 y0, dy = (y1+y2)/2, (y2-y1)/2
+3006 dx, dy = [max(max(dx, dy), radius)]*2
+3007
+3008 ppl.axis([
+3009 x0 - 1.2*dx,
+3010 x0 + 1.2*dx,
+3011 y0 - 1.2*dy,
+3012 y0 + 1.2*dy,
+3013 ])
3014
-3015 for xy, uid in zip(XY, UID):
+3015 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
3016
-3017 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
-3018 vector_in_display_space = xy_in_display_space - XY0_in_display_space
-3019
-3020 if (vector_in_display_space**2).sum() > 0:
+3017 for xy, uid in zip(XY, UID):
+3018
+3019 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
+3020 vector_in_display_space = xy_in_display_space - XY0_in_display_space
3021
-3022 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
-3023 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
-3024 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
-3025 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
-3026
-3027 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
+3022 if (vector_in_display_space**2).sum() > 0:
+3023
+3024 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
+3025 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
+3026 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
+3027 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
3028
-3029 else:
+3029 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
3030
-3031 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3031 else:
3032
-3033 if radius:
-3034 ax.add_artist(Ellipse(
-3035 xy = XY0,
-3036 width = radius*2,
-3037 height = radius*2,
-3038 ls = (0, (2,2)),
-3039 lw = .7,
-3040 ec = analysis_color,
-3041 fc = 'None',
-3042 ))
-3043 ppl.text(
-3044 XY0[0],
-3045 XY0[1]-radius,
-3046 f'\n± {radius*1e3:.0f} ppm',
-3047 color = analysis_color,
-3048 va = 'top',
-3049 ha = 'center',
-3050 linespacing = 0.4,
-3051 size = 8,
-3052 )
-3053
-3054 if not os.path.exists(dir):
-3055 os.makedirs(dir)
-3056 fig.savefig(f'{dir}/{s}.pdf')
-3057 ppl.close(fig)
-3058
-3059 fig = ppl.figure(figsize = figsize)
-3060 fig.subplots_adjust(*subplots_adjust)
-3061 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-3062 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-3063
-3064 for s in saved:
-3065 for xy in saved[s][0]:
-3066 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
-3067 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
-3068 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
-3069 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-3070
-3071 x1, x2, y1, y2 = ppl.axis()
-3072 ppl.axis([
-3073 x1 - (x2-x1)/10,
-3074 x2 + (x2-x1)/10,
-3075 y1 - (y2-y1)/10,
-3076 y2 + (y2-y1)/10,
-3077 ])
-3078
-3079
-3080 if not os.path.exists(dir):
-3081 os.makedirs(dir)
-3082 fig.savefig(f'{dir}/__all__.pdf')
-3083 if show:
-3084 ppl.show()
-3085 ppl.close(fig)
-3086
-3087
-3088 def _save_D4x_correl(
-3089 self,
-3090 samples = None,
-3091 dir = 'output',
-3092 filename = None,
-3093 D4x_precision = 4,
-3094 correl_precision = 4,
-3095 ):
-3096 '''
-3097 Save D4x values along with their SE and correlation matrix.
-3098
-3099 **Parameters**
+3033 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3034
+3035 if radius:
+3036 ax.add_artist(Ellipse(
+3037 xy = XY0,
+3038 width = radius*2,
+3039 height = radius*2,
+3040 ls = (0, (2,2)),
+3041 lw = .7,
+3042 ec = analysis_color,
+3043 fc = 'None',
+3044 ))
+3045 ppl.text(
+3046 XY0[0],
+3047 XY0[1]-radius,
+3048 f'\n± {radius*1e3:.0f} ppm',
+3049 color = analysis_color,
+3050 va = 'top',
+3051 ha = 'center',
+3052 linespacing = 0.4,
+3053 size = 8,
+3054 )
+3055
+3056 if not os.path.exists(dir):
+3057 os.makedirs(dir)
+3058 fig.savefig(f'{dir}/{s}.pdf')
+3059 ppl.close(fig)
+3060
+3061 fig = ppl.figure(figsize = figsize)
+3062 fig.subplots_adjust(*subplots_adjust)
+3063 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+3064 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+3065
+3066 for s in saved:
+3067 for xy in saved[s][0]:
+3068 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
+3069 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
+3070 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
+3071 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3072
+3073 x1, x2, y1, y2 = ppl.axis()
+3074 ppl.axis([
+3075 x1 - (x2-x1)/10,
+3076 x2 + (x2-x1)/10,
+3077 y1 - (y2-y1)/10,
+3078 y2 + (y2-y1)/10,
+3079 ])
+3080
+3081
+3082 if not os.path.exists(dir):
+3083 os.makedirs(dir)
+3084 fig.savefig(f'{dir}/__all__.pdf')
+3085 if show:
+3086 ppl.show()
+3087 ppl.close(fig)
+3088
+3089
+3090 def _save_D4x_correl(
+3091 self,
+3092 samples = None,
+3093 dir = 'output',
+3094 filename = None,
+3095 D4x_precision = 4,
+3096 correl_precision = 4,
+3097 ):
+3098 '''
+3099 Save D4x values along with their SE and correlation matrix.
3100
-3101 + `samples`: Only these samples are output (by default: all samples).
-3102 + `dir`: the directory in which to save the faile (by defaut: `output`)
-3103 + `filename`: the name to the csv file to write to (by default: `D4x_correl.csv`)
-3104 + `D4x_precision`: the precision to use when writing `D4x` and `D4x_SE` values (by default: 4)
-3105 + `correl_precision`: the precision to use when writing correlation factor values (by default: 4)
-3106 '''
-3107 if samples is None:
-3108 samples = sorted([s for s in self.unknowns])
-3109
-3110 out = [['Sample']] + [[s] for s in samples]
-3111 out[0] += [f'D{self._4x}', f'D{self._4x}_SE', f'D{self._4x}_correl']
-3112 for k,s in enumerate(samples):
-3113 out[k+1] += [f'{self.samples[s][f"D{self._4x}"]:.4f}', f'{self.samples[s][f"SE_D{self._4x}"]:.4f}']
-3114 for s2 in samples:
-3115 out[k+1] += [f'{self.sample_D4x_correl(s,s2):.4f}']
-3116
-3117 if not os.path.exists(dir):
-3118 os.makedirs(dir)
-3119 if filename is None:
-3120 filename = f'D{self._4x}_correl.csv'
-3121 with open(f'{dir}/{filename}', 'w') as fid:
-3122 fid.write(make_csv(out))
-3123
-3124
+3101 **Parameters**
+3102
+3103 + `samples`: Only these samples are output (by default: all samples).
+3104 + `dir`: the directory in which to save the faile (by defaut: `output`)
+3105 + `filename`: the name to the csv file to write to (by default: `D4x_correl.csv`)
+3106 + `D4x_precision`: the precision to use when writing `D4x` and `D4x_SE` values (by default: 4)
+3107 + `correl_precision`: the precision to use when writing correlation factor values (by default: 4)
+3108 '''
+3109 if samples is None:
+3110 samples = sorted([s for s in self.unknowns])
+3111
+3112 out = [['Sample']] + [[s] for s in samples]
+3113 out[0] += [f'D{self._4x}', f'D{self._4x}_SE', f'D{self._4x}_correl']
+3114 for k,s in enumerate(samples):
+3115 out[k+1] += [f'{self.samples[s][f"D{self._4x}"]:.4f}', f'{self.samples[s][f"SE_D{self._4x}"]:.4f}']
+3116 for s2 in samples:
+3117 out[k+1] += [f'{self.sample_D4x_correl(s,s2):.4f}']
+3118
+3119 if not os.path.exists(dir):
+3120 os.makedirs(dir)
+3121 if filename is None:
+3122 filename = f'D{self._4x}_correl.csv'
+3123 with open(f'{dir}/{filename}', 'w') as fid:
+3124 fid.write(make_csv(out))
3125
-3126
-3127class D47data(D4xdata):
-3128 '''
-3129 Store and process data for a large set of Δ47 analyses,
-3130 usually comprising more than one analytical session.
-3131 '''
-3132
-3133 Nominal_D4x = {
-3134 'ETH-1': 0.2052,
-3135 'ETH-2': 0.2085,
-3136 'ETH-3': 0.6132,
-3137 'ETH-4': 0.4511,
-3138 'IAEA-C1': 0.3018,
-3139 'IAEA-C2': 0.6409,
-3140 'MERCK': 0.5135,
-3141 } # I-CDES (Bernasconi et al., 2021)
-3142 '''
-3143 Nominal Δ47 values assigned to the Δ47 anchor samples, used by
-3144 `D47data.standardize()` to normalize unknown samples to an absolute Δ47
-3145 reference frame.
-3146
-3147 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)):
-3148 ```py
-3149 {
-3150 'ETH-1' : 0.2052,
-3151 'ETH-2' : 0.2085,
-3152 'ETH-3' : 0.6132,
-3153 'ETH-4' : 0.4511,
-3154 'IAEA-C1' : 0.3018,
-3155 'IAEA-C2' : 0.6409,
-3156 'MERCK' : 0.5135,
-3157 }
-3158 ```
-3159 '''
-3160
-3161
-3162 @property
-3163 def Nominal_D47(self):
-3164 return self.Nominal_D4x
-3165
-3166
-3167 @Nominal_D47.setter
-3168 def Nominal_D47(self, new):
-3169 self.Nominal_D4x = dict(**new)
-3170 self.refresh()
-3171
-3172
-3173 def __init__(self, l = [], **kwargs):
-3174 '''
-3175 **Parameters:** same as `D4xdata.__init__()`
-3176 '''
-3177 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
-3178
-3179
-3180 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
-3181 '''
-3182 Find all samples for which `Teq` is specified, compute equilibrium Δ47
-3183 value for that temperature, and add treat these samples as additional anchors.
-3184
-3185 **Parameters**
+3126
+3127
+3128
+3129class D47data(D4xdata):
+3130 '''
+3131 Store and process data for a large set of Δ47 analyses,
+3132 usually comprising more than one analytical session.
+3133 '''
+3134
+3135 Nominal_D4x = {
+3136 'ETH-1': 0.2052,
+3137 'ETH-2': 0.2085,
+3138 'ETH-3': 0.6132,
+3139 'ETH-4': 0.4511,
+3140 'IAEA-C1': 0.3018,
+3141 'IAEA-C2': 0.6409,
+3142 'MERCK': 0.5135,
+3143 } # I-CDES (Bernasconi et al., 2021)
+3144 '''
+3145 Nominal Δ47 values assigned to the Δ47 anchor samples, used by
+3146 `D47data.standardize()` to normalize unknown samples to an absolute Δ47
+3147 reference frame.
+3148
+3149 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)):
+3150 ```py
+3151 {
+3152 'ETH-1' : 0.2052,
+3153 'ETH-2' : 0.2085,
+3154 'ETH-3' : 0.6132,
+3155 'ETH-4' : 0.4511,
+3156 'IAEA-C1' : 0.3018,
+3157 'IAEA-C2' : 0.6409,
+3158 'MERCK' : 0.5135,
+3159 }
+3160 ```
+3161 '''
+3162
+3163
+3164 @property
+3165 def Nominal_D47(self):
+3166 return self.Nominal_D4x
+3167
+3168
+3169 @Nominal_D47.setter
+3170 def Nominal_D47(self, new):
+3171 self.Nominal_D4x = dict(**new)
+3172 self.refresh()
+3173
+3174
+3175 def __init__(self, l = [], **kwargs):
+3176 '''
+3177 **Parameters:** same as `D4xdata.__init__()`
+3178 '''
+3179 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
+3180
+3181
+3182 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
+3183 '''
+3184 Find all samples for which `Teq` is specified, compute equilibrium Δ47
+3185 value for that temperature, and add treat these samples as additional anchors.
3186
-3187 + `fCo2eqD47`: Which CO2 equilibrium law to use
-3188 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
-3189 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
-3190 + `priority`: if `replace`: forget old anchors and only use the new ones;
-3191 if `new`: keep pre-existing anchors but update them in case of conflict
-3192 between old and new Δ47 values;
-3193 if `old`: keep pre-existing anchors but preserve their original Δ47
-3194 values in case of conflict.
-3195 '''
-3196 f = {
-3197 'petersen': fCO2eqD47_Petersen,
-3198 'wang': fCO2eqD47_Wang,
-3199 }[fCo2eqD47]
-3200 foo = {}
-3201 for r in self:
-3202 if 'Teq' in r:
-3203 if r['Sample'] in foo:
-3204 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
-3205 else:
-3206 foo[r['Sample']] = f(r['Teq'])
-3207 else:
-3208 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
-3209
-3210 if priority == 'replace':
-3211 self.Nominal_D47 = {}
-3212 for s in foo:
-3213 if priority != 'old' or s not in self.Nominal_D47:
-3214 self.Nominal_D47[s] = foo[s]
-3215
-3216 def save_D47_correl(self, *args, **kwargs):
-3217 return self._save_D4x_correl(*args, **kwargs)
-3218
-3219 save_D47_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D47')
+3187 **Parameters**
+3188
+3189 + `fCo2eqD47`: Which CO2 equilibrium law to use
+3190 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
+3191 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
+3192 + `priority`: if `replace`: forget old anchors and only use the new ones;
+3193 if `new`: keep pre-existing anchors but update them in case of conflict
+3194 between old and new Δ47 values;
+3195 if `old`: keep pre-existing anchors but preserve their original Δ47
+3196 values in case of conflict.
+3197 '''
+3198 f = {
+3199 'petersen': fCO2eqD47_Petersen,
+3200 'wang': fCO2eqD47_Wang,
+3201 }[fCo2eqD47]
+3202 foo = {}
+3203 for r in self:
+3204 if 'Teq' in r:
+3205 if r['Sample'] in foo:
+3206 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
+3207 else:
+3208 foo[r['Sample']] = f(r['Teq'])
+3209 else:
+3210 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
+3211
+3212 if priority == 'replace':
+3213 self.Nominal_D47 = {}
+3214 for s in foo:
+3215 if priority != 'old' or s not in self.Nominal_D47:
+3216 self.Nominal_D47[s] = foo[s]
+3217
+3218 def save_D47_correl(self, *args, **kwargs):
+3219 return self._save_D4x_correl(*args, **kwargs)
3220
-3221
-3222class D48data(D4xdata):
-3223 '''
-3224 Store and process data for a large set of Δ48 analyses,
-3225 usually comprising more than one analytical session.
-3226 '''
-3227
-3228 Nominal_D4x = {
-3229 'ETH-1': 0.138,
-3230 'ETH-2': 0.138,
-3231 'ETH-3': 0.270,
-3232 'ETH-4': 0.223,
-3233 'GU-1': -0.419,
-3234 } # (Fiebig et al., 2019, 2021)
-3235 '''
-3236 Nominal Δ48 values assigned to the Δ48 anchor samples, used by
-3237 `D48data.standardize()` to normalize unknown samples to an absolute Δ48
-3238 reference frame.
-3239
-3240 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019),
-3241 Fiebig et al. (in press)):
-3242
-3243 ```py
-3244 {
-3245 'ETH-1' : 0.138,
-3246 'ETH-2' : 0.138,
-3247 'ETH-3' : 0.270,
-3248 'ETH-4' : 0.223,
-3249 'GU-1' : -0.419,
-3250 }
-3251 ```
-3252 '''
-3253
-3254
-3255 @property
-3256 def Nominal_D48(self):
-3257 return self.Nominal_D4x
-3258
-3259
-3260 @Nominal_D48.setter
-3261 def Nominal_D48(self, new):
-3262 self.Nominal_D4x = dict(**new)
-3263 self.refresh()
-3264
-3265
-3266 def __init__(self, l = [], **kwargs):
-3267 '''
-3268 **Parameters:** same as `D4xdata.__init__()`
-3269 '''
-3270 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
-3271
-3272 def save_D48_correl(self, *args, **kwargs):
-3273 return self._save_D4x_correl(*args, **kwargs)
-3274
-3275 save_D48_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D48')
+3221 save_D47_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D47')
+3222
+3223
+3224class D48data(D4xdata):
+3225 '''
+3226 Store and process data for a large set of Δ48 analyses,
+3227 usually comprising more than one analytical session.
+3228 '''
+3229
+3230 Nominal_D4x = {
+3231 'ETH-1': 0.138,
+3232 'ETH-2': 0.138,
+3233 'ETH-3': 0.270,
+3234 'ETH-4': 0.223,
+3235 'GU-1': -0.419,
+3236 } # (Fiebig et al., 2019, 2021)
+3237 '''
+3238 Nominal Δ48 values assigned to the Δ48 anchor samples, used by
+3239 `D48data.standardize()` to normalize unknown samples to an absolute Δ48
+3240 reference frame.
+3241
+3242 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019),
+3243 Fiebig et al. (in press)):
+3244
+3245 ```py
+3246 {
+3247 'ETH-1' : 0.138,
+3248 'ETH-2' : 0.138,
+3249 'ETH-3' : 0.270,
+3250 'ETH-4' : 0.223,
+3251 'GU-1' : -0.419,
+3252 }
+3253 ```
+3254 '''
+3255
+3256
+3257 @property
+3258 def Nominal_D48(self):
+3259 return self.Nominal_D4x
+3260
+3261
+3262 @Nominal_D48.setter
+3263 def Nominal_D48(self, new):
+3264 self.Nominal_D4x = dict(**new)
+3265 self.refresh()
+3266
+3267
+3268 def __init__(self, l = [], **kwargs):
+3269 '''
+3270 **Parameters:** same as `D4xdata.__init__()`
+3271 '''
+3272 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
+3273
+3274 def save_D48_correl(self, *args, **kwargs):
+3275 return self._save_D4x_correl(*args, **kwargs)
3276
-3277class _SessionPlot():
-3278 '''
-3279 Simple placeholder class
-3280 '''
-3281 def __init__(self):
-3282 pass
-3283
-3284_app = typer.Typer(
-3285 add_completion = False,
-3286 context_settings={'help_option_names': ['-h', '--help']},
-3287 rich_markup_mode = 'rich',
-3288 )
-3289
-3290@_app.command()
-3291def _cli(
-3292 rawdata: Annotated[str, typer.Argument(help = "Specify the path of a rawdata input file")],
-3293 exclude: Annotated[str, typer.Option('--exclude', '-e', help = 'The path of a file specifying UIDs and/or Samples to exclude')] = 'none',
-3294 anchors: Annotated[str, typer.Option('--anchors', '-a', help = 'The path of a file specifying custom anchors')] = 'none',
-3295 output_dir: Annotated[str, typer.Option('--output-dir', '-o', help = 'Specify the output directory')] = 'output',
-3296 run_D48: Annotated[bool, typer.Option('--D48', help = 'Also standardize D48')] = False,
-3297 ):
-3298 """
-3299 Process raw D47 data and return standardized results.
-3300
-3301 See [](https://mdaeron.github.io/D47crunch/#3-command-line-interface-cli) for more details.
-3302 """
-3303
-3304 data = D47data()
-3305 data.read(rawdata)
-3306
-3307 if exclude != 'none':
-3308 exclude = read_csv(exclude)
-3309 exclude_uid = {r['UID'] for r in exclude if 'UID' in r}
-3310 exclude_sample = {r['Sample'] for r in exclude if 'Sample' in r}
-3311 else:
-3312 exclude_uid = []
-3313 exclude_sample = []
-3314
-3315 data = D47data([r for r in data if r['UID'] not in exclude_uid and r['Sample'] not in exclude_sample])
-3316
-3317 if anchors != 'none':
-3318 anchors = read_csv(anchors)
-3319 if len([_ for _ in anchors if 'd13C_VPDB' in _]):
-3320 data.Nominal_d13C_VPDB = {
-3321 _['Sample']: _['d13C_VPDB']
-3322 for _ in anchors
-3323 if 'd13C_VPDB' in _
-3324 }
-3325 if len([_ for _ in anchors if 'd18O_VPDB' in _]):
-3326 data.Nominal_d18O_VPDB = {
-3327 _['Sample']: _['d18O_VPDB']
-3328 for _ in anchors
-3329 if 'd18O_VPDB' in _
-3330 }
-3331 if len([_ for _ in anchors if 'D47' in _]):
-3332 data.Nominal_D4x = {
-3333 _['Sample']: _['D47']
-3334 for _ in anchors
-3335 if 'D47' in _
-3336 }
-3337
-3338 data.refresh()
-3339 data.wg()
-3340 data.crunch()
-3341 data.standardize()
-3342 data.summary(dir = output_dir)
-3343 data.plot_residuals(dir = output_dir, filename = 'D47_residuals.pdf', kde = True)
-3344 data.plot_bulk_compositions(dir = output_dir + '/bulk_compositions')
-3345 data.plot_sessions(dir = output_dir)
-3346 data.save_D47_correl(dir = output_dir)
-3347
-3348 if not run_D48:
-3349 data.table_of_samples(dir = output_dir)
-3350 data.table_of_analyses(dir = output_dir)
-3351 data.table_of_sessions(dir = output_dir)
-3352
-3353
-3354 if run_D48:
-3355 data2 = D48data()
-3356 print(rawdata)
-3357 data2.read(rawdata)
-3358
-3359 data2 = D48data([r for r in data2 if r['UID'] not in exclude_uid and r['Sample'] not in exclude_sample])
-3360
-3361 if anchors != 'none':
-3362 if len([_ for _ in anchors if 'd13C_VPDB' in _]):
-3363 data2.Nominal_d13C_VPDB = {
-3364 _['Sample']: _['d13C_VPDB']
-3365 for _ in anchors
-3366 if 'd13C_VPDB' in _
-3367 }
-3368 if len([_ for _ in anchors if 'd18O_VPDB' in _]):
-3369 data2.Nominal_d18O_VPDB = {
-3370 _['Sample']: _['d18O_VPDB']
-3371 for _ in anchors
-3372 if 'd18O_VPDB' in _
-3373 }
-3374 if len([_ for _ in anchors if 'D48' in _]):
-3375 data2.Nominal_D4x = {
-3376 _['Sample']: _['D48']
-3377 for _ in anchors
-3378 if 'D48' in _
-3379 }
-3380
-3381 data2.refresh()
-3382 data2.wg()
-3383 data2.crunch()
-3384 data2.standardize()
-3385 data2.summary(dir = output_dir)
-3386 data2.plot_sessions(dir = output_dir)
-3387 data2.plot_residuals(dir = output_dir, filename = 'D48_residuals.pdf', kde = True)
-3388 data2.plot_distribution_of_analyses(dir = output_dir)
-3389 data2.save_D48_correl(dir = output_dir)
-3390
-3391 table_of_analyses(data, data2, dir = output_dir)
-3392 table_of_samples(data, data2, dir = output_dir)
-3393 table_of_sessions(data, data2, dir = output_dir)
-3394
-3395def __cli():
-3396 _app()
+3277 save_D48_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D48')
+3278
+3279class _SessionPlot():
+3280 '''
+3281 Simple placeholder class
+3282 '''
+3283 def __init__(self):
+3284 pass
+3285
+3286_app = typer.Typer(
+3287 add_completion = False,
+3288 context_settings={'help_option_names': ['-h', '--help']},
+3289 rich_markup_mode = 'rich',
+3290 )
+3291
+3292@_app.command()
+3293def _cli(
+3294 rawdata: Annotated[str, typer.Argument(help = "Specify the path of a rawdata input file")],
+3295 exclude: Annotated[str, typer.Option('--exclude', '-e', help = 'The path of a file specifying UIDs and/or Samples to exclude')] = 'none',
+3296 anchors: Annotated[str, typer.Option('--anchors', '-a', help = 'The path of a file specifying custom anchors')] = 'none',
+3297 output_dir: Annotated[str, typer.Option('--output-dir', '-o', help = 'Specify the output directory')] = 'output',
+3298 run_D48: Annotated[bool, typer.Option('--D48', help = 'Also standardize D48')] = False,
+3299 ):
+3300 """
+3301 Process raw D47 data and return standardized results.
+3302
+3303 See [b]https://mdaeron.github.io/D47crunch/#3-command-line-interface-cli[/b] for more details.
+3304
+3305 Reads raw data from an input file, optionally excluding some samples and/or analyses, thean standardizes
+3306 the data based either on the default [b]d13C_VDPB[/b], [b]d18O_VPDB[/b], [b]D47[/b], and [b]D48[/b] anchors or on different
+3307 user-specified anchors. A new directory (named `output` by default) is created to store the results and
+3308 the following sequence is applied:
+3309
+3310 * [b]D47data.wg()[/b]
+3311 * [b]D47data.crunch()[/b]
+3312 * [b]D47data.standardize()[/b]
+3313 * [b]D47data.summary()[/b]
+3314 * [b]D47data.table_of_samples()[/b]
+3315 * [b]D47data.table_of_sessions()[/b]
+3316 * [b]D47data.plot_sessions()[/b]
+3317 * [b]D47data.plot_residuals()[/b]
+3318 * [b]D47data.table_of_analyses()[/b]
+3319 * [b]D47data.plot_distribution_of_analyses()[/b]
+3320 * [b]D47data.plot_bulk_compositions()[/b]
+3321 * [b]D47data.save_D47_correl()[/b]
+3322
+3323 Optionally, also apply similar methods for [b]]D48[/b].
+3324
+3325 [b]Example CSV file for --anchors option:[/b]
+3326 [i]
+3327 Sample, d13C_VPDB, d18O_VPDB, D47, D48
+3328 ETH-1, 2.02, -2.19, 0.2052, 0.138
+3329 ETH-2, -10.17, -18.69, 0.2085, 0.138
+3330 ETH-3, 1.71, -1.78, 0.6132, 0.270
+3331 ETH-4, , , 0.4511, 0.223
+3332 [/i]
+3333 Except for [i]Sample[/i], none of the columns above are mandatory.
+3334
+3335 [b]Example CSV file for --exclude option:[/b]
+3336 [i]
+3337 Sample, UID
+3338 FOO-1,
+3339 BAR-2,
+3340 , A04
+3341 , A17
+3342 , A88
+3343 [/i]
+3344 This will exclude all analyses of samples [i]FOO-1[/i] and [i]BAR-2[/i],
+3345 and the analyses with UIDs [i]A04[/i], [i]A17[/i], and [i]A88[/i].
+3346 Neither column is mandatory.
+3347 """
+3348
+3349 data = D47data()
+3350 data.read(rawdata)
+3351
+3352 if exclude != 'none':
+3353 exclude = read_csv(exclude)
+3354 exclude_uid = {r['UID'] for r in exclude if 'UID' in r}
+3355 exclude_sample = {r['Sample'] for r in exclude if 'Sample' in r}
+3356 else:
+3357 exclude_uid = []
+3358 exclude_sample = []
+3359
+3360 data = D47data([r for r in data if r['UID'] not in exclude_uid and r['Sample'] not in exclude_sample])
+3361
+3362 if anchors != 'none':
+3363 anchors = read_csv(anchors)
+3364 if len([_ for _ in anchors if 'd13C_VPDB' in _]):
+3365 data.Nominal_d13C_VPDB = {
+3366 _['Sample']: _['d13C_VPDB']
+3367 for _ in anchors
+3368 if 'd13C_VPDB' in _
+3369 }
+3370 if len([_ for _ in anchors if 'd18O_VPDB' in _]):
+3371 data.Nominal_d18O_VPDB = {
+3372 _['Sample']: _['d18O_VPDB']
+3373 for _ in anchors
+3374 if 'd18O_VPDB' in _
+3375 }
+3376 if len([_ for _ in anchors if 'D47' in _]):
+3377 data.Nominal_D4x = {
+3378 _['Sample']: _['D47']
+3379 for _ in anchors
+3380 if 'D47' in _
+3381 }
+3382
+3383 data.refresh()
+3384 data.wg()
+3385 data.crunch()
+3386 data.standardize()
+3387 data.summary(dir = output_dir)
+3388 data.plot_residuals(dir = output_dir, filename = 'D47_residuals.pdf', kde = True)
+3389 data.plot_bulk_compositions(dir = output_dir + '/bulk_compositions')
+3390 data.plot_sessions(dir = output_dir)
+3391 data.save_D47_correl(dir = output_dir)
+3392
+3393 if not run_D48:
+3394 data.table_of_samples(dir = output_dir)
+3395 data.table_of_analyses(dir = output_dir)
+3396 data.table_of_sessions(dir = output_dir)
+3397
+3398
+3399 if run_D48:
+3400 data2 = D48data()
+3401 print(rawdata)
+3402 data2.read(rawdata)
+3403
+3404 data2 = D48data([r for r in data2 if r['UID'] not in exclude_uid and r['Sample'] not in exclude_sample])
+3405
+3406 if anchors != 'none':
+3407 if len([_ for _ in anchors if 'd13C_VPDB' in _]):
+3408 data2.Nominal_d13C_VPDB = {
+3409 _['Sample']: _['d13C_VPDB']
+3410 for _ in anchors
+3411 if 'd13C_VPDB' in _
+3412 }
+3413 if len([_ for _ in anchors if 'd18O_VPDB' in _]):
+3414 data2.Nominal_d18O_VPDB = {
+3415 _['Sample']: _['d18O_VPDB']
+3416 for _ in anchors
+3417 if 'd18O_VPDB' in _
+3418 }
+3419 if len([_ for _ in anchors if 'D48' in _]):
+3420 data2.Nominal_D4x = {
+3421 _['Sample']: _['D48']
+3422 for _ in anchors
+3423 if 'D48' in _
+3424 }
+3425
+3426 data2.refresh()
+3427 data2.wg()
+3428 data2.crunch()
+3429 data2.standardize()
+3430 data2.summary(dir = output_dir)
+3431 data2.plot_sessions(dir = output_dir)
+3432 data2.plot_residuals(dir = output_dir, filename = 'D48_residuals.pdf', kde = True)
+3433 data2.plot_distribution_of_analyses(dir = output_dir)
+3434 data2.save_D48_correl(dir = output_dir)
+3435
+3436 table_of_analyses(data, data2, dir = output_dir)
+3437 table_of_samples(data, data2, dir = output_dir)
+3438 table_of_sessions(data, data2, dir = output_dir)
+3439
+3440def __cli():
+3441 _app()
@@ -4299,13 +4344,13 @@ 4. API Documentation
- 66def fCO2eqD47_Petersen(T):
-67 '''
-68 CO2 equilibrium Δ47 value as a function of T (in degrees C)
-69 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127).
-70
-71 '''
-72 return float(_fCO2eqD47_Petersen(T))
+ 68def fCO2eqD47_Petersen(T):
+69 '''
+70 CO2 equilibrium Δ47 value as a function of T (in degrees C)
+71 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127).
+72
+73 '''
+74 return float(_fCO2eqD47_Petersen(T))
4. API Documentation
77def fCO2eqD47_Wang(T):
-78 '''
-79 CO2 equilibrium Δ47 value as a function of `T` (in degrees C)
-80 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039)
-81 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)).
-82 '''
-83 return float(_fCO2eqD47_Wang(T))
+ 79def fCO2eqD47_Wang(T):
+80 '''
+81 CO2 equilibrium Δ47 value as a function of `T` (in degrees C)
+82 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039)
+83 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)).
+84 '''
+85 return float(_fCO2eqD47_Wang(T))
4. API Documentation
86def correlated_sum(X, C, w = None):
- 87 '''
- 88 Compute covariance-aware linear combinations
- 89
- 90 **Parameters**
- 91
- 92 + `X`: list or 1-D array of values to sum
- 93 + `C`: covariance matrix for the elements of `X`
- 94 + `w`: list or 1-D array of weights to apply to the elements of `X`
- 95 (all equal to 1 by default)
- 96
- 97 Return the sum (and its SE) of the elements of `X`, with optional weights equal
- 98 to the elements of `w`, accounting for covariances between the elements of `X`.
- 99 '''
-100 if w is None:
-101 w = [1 for x in X]
-102 return np.dot(w,X), (np.dot(w,np.dot(C,w)))**.5
+ 88def correlated_sum(X, C, w = None):
+ 89 '''
+ 90 Compute covariance-aware linear combinations
+ 91
+ 92 **Parameters**
+ 93
+ 94 + `X`: list or 1-D array of values to sum
+ 95 + `C`: covariance matrix for the elements of `X`
+ 96 + `w`: list or 1-D array of weights to apply to the elements of `X`
+ 97 (all equal to 1 by default)
+ 98
+ 99 Return the sum (and its SE) of the elements of `X`, with optional weights equal
+100 to the elements of `w`, accounting for covariances between the elements of `X`.
+101 '''
+102 if w is None:
+103 w = [1 for x in X]
+104 return np.dot(w,X), (np.dot(w,np.dot(C,w)))**.5
4. API Documentation
105def make_csv(x, hsep = ',', vsep = '\n'):
-106 '''
-107 Formats a list of lists of strings as a CSV
-108
-109 **Parameters**
+ 107def make_csv(x, hsep = ',', vsep = '\n'):
+108 '''
+109 Formats a list of lists of strings as a CSV
110
-111 + `x`: the list of lists of strings to format
-112 + `hsep`: the field separator (`,` by default)
-113 + `vsep`: the line-ending convention to use (`\\n` by default)
-114
-115 **Example**
+111 **Parameters**
+112
+113 + `x`: the list of lists of strings to format
+114 + `hsep`: the field separator (`,` by default)
+115 + `vsep`: the line-ending convention to use (`\\n` by default)
116
-117 ```py
-118 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']]))
-119 ```
-120
-121 outputs:
+117 **Example**
+118
+119 ```py
+120 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']]))
+121 ```
122
-123 ```py
-124 a,b,c
-125 d,e,f
-126 ```
-127 '''
-128 return vsep.join([hsep.join(l) for l in x])
+123 outputs:
+124
+125 ```py
+126 a,b,c
+127 d,e,f
+128 ```
+129 '''
+130 return vsep.join([hsep.join(l) for l in x])
4. API Documentation
131def pf(txt):
-132 '''
-133 Modify string `txt` to follow `lmfit.Parameter()` naming rules.
-134 '''
-135 return txt.replace('-','_').replace('.','_').replace(' ','_')
+ 133def pf(txt):
+134 '''
+135 Modify string `txt` to follow `lmfit.Parameter()` naming rules.
+136 '''
+137 return txt.replace('-','_').replace('.','_').replace(' ','_')
4. API Documentation
138def smart_type(x):
-139 '''
-140 Tries to convert string `x` to a float if it includes a decimal point, or
-141 to an integer if it does not. If both attempts fail, return the original
-142 string unchanged.
-143 '''
-144 try:
-145 y = float(x)
-146 except ValueError:
-147 return x
-148 if '.' not in x:
-149 return int(y)
-150 return y
+ 140def smart_type(x):
+141 '''
+142 Tries to convert string `x` to a float if it includes a decimal point, or
+143 to an integer if it does not. If both attempts fail, return the original
+144 string unchanged.
+145 '''
+146 try:
+147 y = float(x)
+148 except ValueError:
+149 return x
+150 if '.' not in x:
+151 return int(y)
+152 return y
4. API Documentation
153def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'):
-154 '''
-155 Reads a list of lists of strings and outputs an ascii table
-156
-157 **Parameters**
+ 155def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'):
+156 '''
+157 Reads a list of lists of strings and outputs an ascii table
158
-159 + `x`: a list of lists of strings
-160 + `header`: the number of lines to treat as header lines
-161 + `hsep`: the horizontal separator between columns
-162 + `vsep`: the character to use as vertical separator
-163 + `align`: string of left (`<`) or right (`>`) alignment characters.
-164
-165 **Example**
+159 **Parameters**
+160
+161 + `x`: a list of lists of strings
+162 + `header`: the number of lines to treat as header lines
+163 + `hsep`: the horizontal separator between columns
+164 + `vsep`: the character to use as vertical separator
+165 + `align`: string of left (`<`) or right (`>`) alignment characters.
166
-167 ```py
-168 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']]
-169 print(pretty_table(x))
-170 ```
-171 yields:
+167 **Example**
+168
+169 ```py
+170 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']]
+171 print(pretty_table(x))
172 ```
-173 -- ------ ---
-174 A B C
+173 yields:
+174 ```
175 -- ------ ---
-176 1 1.9999 foo
-177 10 x bar
-178 -- ------ ---
-179 ```
-180
-181 '''
-182 txt = []
-183 widths = [np.max([len(e) for e in c]) for c in zip(*x)]
-184
-185 if len(widths) > len(align):
-186 align += '>' * (len(widths)-len(align))
-187 sepline = hsep.join([vsep*w for w in widths])
-188 txt += [sepline]
-189 for k,l in enumerate(x):
-190 if k and k == header:
-191 txt += [sepline]
-192 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])]
-193 txt += [sepline]
-194 txt += ['']
-195 return '\n'.join(txt)
+176 A B C
+177 -- ------ ---
+178 1 1.9999 foo
+179 10 x bar
+180 -- ------ ---
+181 ```
+182
+183 '''
+184 txt = []
+185 widths = [np.max([len(e) for e in c]) for c in zip(*x)]
+186
+187 if len(widths) > len(align):
+188 align += '>' * (len(widths)-len(align))
+189 sepline = hsep.join([vsep*w for w in widths])
+190 txt += [sepline]
+191 for k,l in enumerate(x):
+192 if k and k == header:
+193 txt += [sepline]
+194 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])]
+195 txt += [sepline]
+196 txt += ['']
+197 return '\n'.join(txt)
4. API Documentation
198def transpose_table(x):
-199 '''
-200 Transpose a list if lists
-201
-202 **Parameters**
+ 200def transpose_table(x):
+201 '''
+202 Transpose a list if lists
203
-204 + `x`: a list of lists
+204 **Parameters**
205
-206 **Example**
+206 + `x`: a list of lists
207
-208 ```py
-209 x = [[1, 2], [3, 4]]
-210 print(transpose_table(x)) # yields: [[1, 3], [2, 4]]
-211 ```
-212 '''
-213 return [[e for e in c] for c in zip(*x)]
+208 **Example**
+209
+210 ```py
+211 x = [[1, 2], [3, 4]]
+212 print(transpose_table(x)) # yields: [[1, 3], [2, 4]]
+213 ```
+214 '''
+215 return [[e for e in c] for c in zip(*x)]
4. API Documentation
216def w_avg(X, sX) :
-217 '''
-218 Compute variance-weighted average
-219
-220 Returns the value and SE of the weighted average of the elements of `X`,
-221 with relative weights equal to their inverse variances (`1/sX**2`).
-222
-223 **Parameters**
+ 218def w_avg(X, sX) :
+219 '''
+220 Compute variance-weighted average
+221
+222 Returns the value and SE of the weighted average of the elements of `X`,
+223 with relative weights equal to their inverse variances (`1/sX**2`).
224
-225 + `X`: array-like of elements to average
-226 + `sX`: array-like of the corresponding SE values
-227
-228 **Tip**
+225 **Parameters**
+226
+227 + `X`: array-like of elements to average
+228 + `sX`: array-like of the corresponding SE values
229
-230 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets,
-231 they may be rearranged using `zip()`:
-232
-233 ```python
-234 foo = [(0, 1), (1, 0.5), (2, 0.5)]
-235 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333)
-236 ```
-237 '''
-238 X = [ x for x in X ]
-239 sX = [ sx for sx in sX ]
-240 W = [ sx**-2 for sx in sX ]
-241 W = [ w/sum(W) for w in W ]
-242 Xavg = sum([ w*x for w,x in zip(W,X) ])
-243 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5
-244 return Xavg, sXavg
+230 **Tip**
+231
+232 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets,
+233 they may be rearranged using `zip()`:
+234
+235 ```python
+236 foo = [(0, 1), (1, 0.5), (2, 0.5)]
+237 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333)
+238 ```
+239 '''
+240 X = [ x for x in X ]
+241 sX = [ sx for sx in sX ]
+242 W = [ sx**-2 for sx in sX ]
+243 W = [ w/sum(W) for w in W ]
+244 Xavg = sum([ w*x for w,x in zip(W,X) ])
+245 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5
+246 return Xavg, sXavg
4. API Documentation
247def read_csv(filename, sep = ''):
-248 '''
-249 Read contents of `filename` in csv format and return a list of dictionaries.
-250
-251 In the csv string, spaces before and after field separators (`','` by default)
-252 are optional.
-253
-254 **Parameters**
+ 249def read_csv(filename, sep = ''):
+250 '''
+251 Read contents of `filename` in csv format and return a list of dictionaries.
+252
+253 In the csv string, spaces before and after field separators (`','` by default)
+254 are optional.
255
-256 + `filename`: the csv file to read
-257 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
-258 whichever appers most often in the contents of `filename`.
-259 '''
-260 with open(filename) as fid:
-261 txt = fid.read()
-262
-263 if sep == '':
-264 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
-265 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
-266 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]]
+256 **Parameters**
+257
+258 + `filename`: the csv file to read
+259 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
+260 whichever appers most often in the contents of `filename`.
+261 '''
+262 with open(filename) as fid:
+263 txt = fid.read()
+264
+265 if sep == '':
+266 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
+267 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
+268 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]]
4. API Documentation
269def simulate_single_analysis(
-270 sample = 'MYSAMPLE',
-271 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26.,
-272 d13C_VPDB = None, d18O_VPDB = None,
-273 D47 = None, D48 = None, D49 = 0., D17O = 0.,
-274 a47 = 1., b47 = 0., c47 = -0.9,
-275 a48 = 1., b48 = 0., c48 = -0.45,
-276 Nominal_D47 = None,
-277 Nominal_D48 = None,
-278 Nominal_d13C_VPDB = None,
-279 Nominal_d18O_VPDB = None,
-280 ALPHA_18O_ACID_REACTION = None,
-281 R13_VPDB = None,
-282 R17_VSMOW = None,
-283 R18_VSMOW = None,
-284 LAMBDA_17 = None,
-285 R18_VPDB = None,
-286 ):
-287 '''
-288 Compute working-gas delta values for a single analysis, assuming a stochastic working
-289 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values).
-290
-291 **Parameters**
-292
-293 + `sample`: sample name
-294 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
-295 (respectively –4 and +26 ‰ by default)
-296 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
-297 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies
-298 of the carbonate sample
-299 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and
-300 Δ48 values if `D47` or `D48` are not specified
-301 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
-302 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
-303 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
-304 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
-305 correction parameters (by default equal to the `D4xdata` default values)
-306
-307 Returns a dictionary with fields
-308 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`.
-309 '''
-310
-311 if Nominal_d13C_VPDB is None:
-312 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB
-313
-314 if Nominal_d18O_VPDB is None:
-315 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB
-316
-317 if ALPHA_18O_ACID_REACTION is None:
-318 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION
-319
-320 if R13_VPDB is None:
-321 R13_VPDB = D4xdata().R13_VPDB
-322
-323 if R17_VSMOW is None:
-324 R17_VSMOW = D4xdata().R17_VSMOW
-325
-326 if R18_VSMOW is None:
-327 R18_VSMOW = D4xdata().R18_VSMOW
-328
-329 if LAMBDA_17 is None:
-330 LAMBDA_17 = D4xdata().LAMBDA_17
-331
-332 if R18_VPDB is None:
-333 R18_VPDB = D4xdata().R18_VPDB
-334
-335 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17
+ 271def simulate_single_analysis(
+272 sample = 'MYSAMPLE',
+273 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26.,
+274 d13C_VPDB = None, d18O_VPDB = None,
+275 D47 = None, D48 = None, D49 = 0., D17O = 0.,
+276 a47 = 1., b47 = 0., c47 = -0.9,
+277 a48 = 1., b48 = 0., c48 = -0.45,
+278 Nominal_D47 = None,
+279 Nominal_D48 = None,
+280 Nominal_d13C_VPDB = None,
+281 Nominal_d18O_VPDB = None,
+282 ALPHA_18O_ACID_REACTION = None,
+283 R13_VPDB = None,
+284 R17_VSMOW = None,
+285 R18_VSMOW = None,
+286 LAMBDA_17 = None,
+287 R18_VPDB = None,
+288 ):
+289 '''
+290 Compute working-gas delta values for a single analysis, assuming a stochastic working
+291 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values).
+292
+293 **Parameters**
+294
+295 + `sample`: sample name
+296 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
+297 (respectively –4 and +26 ‰ by default)
+298 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
+299 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies
+300 of the carbonate sample
+301 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and
+302 Δ48 values if `D47` or `D48` are not specified
+303 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
+304 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
+305 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+306 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
+307 correction parameters (by default equal to the `D4xdata` default values)
+308
+309 Returns a dictionary with fields
+310 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`.
+311 '''
+312
+313 if Nominal_d13C_VPDB is None:
+314 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB
+315
+316 if Nominal_d18O_VPDB is None:
+317 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB
+318
+319 if ALPHA_18O_ACID_REACTION is None:
+320 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION
+321
+322 if R13_VPDB is None:
+323 R13_VPDB = D4xdata().R13_VPDB
+324
+325 if R17_VSMOW is None:
+326 R17_VSMOW = D4xdata().R17_VSMOW
+327
+328 if R18_VSMOW is None:
+329 R18_VSMOW = D4xdata().R18_VSMOW
+330
+331 if LAMBDA_17 is None:
+332 LAMBDA_17 = D4xdata().LAMBDA_17
+333
+334 if R18_VPDB is None:
+335 R18_VPDB = D4xdata().R18_VPDB
336
-337 if Nominal_D47 is None:
-338 Nominal_D47 = D47data().Nominal_D47
-339
-340 if Nominal_D48 is None:
-341 Nominal_D48 = D48data().Nominal_D48
-342
-343 if d13C_VPDB is None:
-344 if sample in Nominal_d13C_VPDB:
-345 d13C_VPDB = Nominal_d13C_VPDB[sample]
-346 else:
-347 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.")
-348
-349 if d18O_VPDB is None:
-350 if sample in Nominal_d18O_VPDB:
-351 d18O_VPDB = Nominal_d18O_VPDB[sample]
-352 else:
-353 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.")
-354
-355 if D47 is None:
-356 if sample in Nominal_D47:
-357 D47 = Nominal_D47[sample]
-358 else:
-359 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.")
-360
-361 if D48 is None:
-362 if sample in Nominal_D48:
-363 D48 = Nominal_D48[sample]
-364 else:
-365 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.")
-366
-367 X = D4xdata()
-368 X.R13_VPDB = R13_VPDB
-369 X.R17_VSMOW = R17_VSMOW
-370 X.R18_VSMOW = R18_VSMOW
-371 X.LAMBDA_17 = LAMBDA_17
-372 X.R18_VPDB = R18_VPDB
-373 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17
-374
-375 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios(
-376 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000),
-377 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000),
-378 )
-379 R45, R46, R47, R48, R49 = X.compute_isobar_ratios(
-380 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
-381 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
-382 D17O=D17O, D47=D47, D48=D48, D49=D49,
-383 )
-384 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios(
-385 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
-386 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
-387 D17O=D17O,
-388 )
-389
-390 d45 = 1000 * (R45/R45wg - 1)
-391 d46 = 1000 * (R46/R46wg - 1)
-392 d47 = 1000 * (R47/R47wg - 1)
-393 d48 = 1000 * (R48/R48wg - 1)
-394 d49 = 1000 * (R49/R49wg - 1)
-395
-396 for k in range(3): # dumb iteration to adjust for small changes in d47
-397 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch
-398 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch
-399 d47 = 1000 * (R47raw/R47wg - 1)
-400 d48 = 1000 * (R48raw/R48wg - 1)
-401
-402 return dict(
-403 Sample = sample,
-404 D17O = D17O,
-405 d13Cwg_VPDB = d13Cwg_VPDB,
-406 d18Owg_VSMOW = d18Owg_VSMOW,
-407 d45 = d45,
-408 d46 = d46,
-409 d47 = d47,
-410 d48 = d48,
-411 d49 = d49,
-412 )
+337 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17
+338
+339 if Nominal_D47 is None:
+340 Nominal_D47 = D47data().Nominal_D47
+341
+342 if Nominal_D48 is None:
+343 Nominal_D48 = D48data().Nominal_D48
+344
+345 if d13C_VPDB is None:
+346 if sample in Nominal_d13C_VPDB:
+347 d13C_VPDB = Nominal_d13C_VPDB[sample]
+348 else:
+349 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.")
+350
+351 if d18O_VPDB is None:
+352 if sample in Nominal_d18O_VPDB:
+353 d18O_VPDB = Nominal_d18O_VPDB[sample]
+354 else:
+355 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.")
+356
+357 if D47 is None:
+358 if sample in Nominal_D47:
+359 D47 = Nominal_D47[sample]
+360 else:
+361 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.")
+362
+363 if D48 is None:
+364 if sample in Nominal_D48:
+365 D48 = Nominal_D48[sample]
+366 else:
+367 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.")
+368
+369 X = D4xdata()
+370 X.R13_VPDB = R13_VPDB
+371 X.R17_VSMOW = R17_VSMOW
+372 X.R18_VSMOW = R18_VSMOW
+373 X.LAMBDA_17 = LAMBDA_17
+374 X.R18_VPDB = R18_VPDB
+375 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17
+376
+377 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios(
+378 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000),
+379 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000),
+380 )
+381 R45, R46, R47, R48, R49 = X.compute_isobar_ratios(
+382 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
+383 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
+384 D17O=D17O, D47=D47, D48=D48, D49=D49,
+385 )
+386 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios(
+387 R13 = R13_VPDB * (1 + d13C_VPDB/1000),
+388 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION,
+389 D17O=D17O,
+390 )
+391
+392 d45 = 1000 * (R45/R45wg - 1)
+393 d46 = 1000 * (R46/R46wg - 1)
+394 d47 = 1000 * (R47/R47wg - 1)
+395 d48 = 1000 * (R48/R48wg - 1)
+396 d49 = 1000 * (R49/R49wg - 1)
+397
+398 for k in range(3): # dumb iteration to adjust for small changes in d47
+399 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch
+400 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch
+401 d47 = 1000 * (R47raw/R47wg - 1)
+402 d48 = 1000 * (R48raw/R48wg - 1)
+403
+404 return dict(
+405 Sample = sample,
+406 D17O = D17O,
+407 d13Cwg_VPDB = d13Cwg_VPDB,
+408 d18Owg_VSMOW = d18Owg_VSMOW,
+409 d45 = d45,
+410 d46 = d46,
+411 d47 = d47,
+412 d48 = d48,
+413 d49 = d49,
+414 )
4. API Documentation
415def virtual_data(
-416 samples = [],
-417 a47 = 1., b47 = 0., c47 = -0.9,
-418 a48 = 1., b48 = 0., c48 = -0.45,
-419 rd45 = 0.020, rd46 = 0.060,
-420 rD47 = 0.015, rD48 = 0.045,
-421 d13Cwg_VPDB = None, d18Owg_VSMOW = None,
-422 session = None,
-423 Nominal_D47 = None, Nominal_D48 = None,
-424 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None,
-425 ALPHA_18O_ACID_REACTION = None,
-426 R13_VPDB = None,
-427 R17_VSMOW = None,
-428 R18_VSMOW = None,
-429 LAMBDA_17 = None,
-430 R18_VPDB = None,
-431 seed = 0,
-432 shuffle = True,
-433 ):
-434 '''
-435 Return list with simulated analyses from a single session.
-436
-437 **Parameters**
+ 417def virtual_data(
+418 samples = [],
+419 a47 = 1., b47 = 0., c47 = -0.9,
+420 a48 = 1., b48 = 0., c48 = -0.45,
+421 rd45 = 0.020, rd46 = 0.060,
+422 rD47 = 0.015, rD48 = 0.045,
+423 d13Cwg_VPDB = None, d18Owg_VSMOW = None,
+424 session = None,
+425 Nominal_D47 = None, Nominal_D48 = None,
+426 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None,
+427 ALPHA_18O_ACID_REACTION = None,
+428 R13_VPDB = None,
+429 R17_VSMOW = None,
+430 R18_VSMOW = None,
+431 LAMBDA_17 = None,
+432 R18_VPDB = None,
+433 seed = 0,
+434 shuffle = True,
+435 ):
+436 '''
+437 Return list with simulated analyses from a single session.
438
-439 + `samples`: a list of entries; each entry is a dictionary with the following fields:
-440 * `Sample`: the name of the sample
-441 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
-442 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample
-443 * `N`: how many analyses to generate for this sample
-444 + `a47`: scrambling factor for Δ47
-445 + `b47`: compositional nonlinearity for Δ47
-446 + `c47`: working gas offset for Δ47
-447 + `a48`: scrambling factor for Δ48
-448 + `b48`: compositional nonlinearity for Δ48
-449 + `c48`: working gas offset for Δ48
-450 + `rd45`: analytical repeatability of δ45
-451 + `rd46`: analytical repeatability of δ46
-452 + `rD47`: analytical repeatability of Δ47
-453 + `rD48`: analytical repeatability of Δ48
-454 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
-455 (by default equal to the `simulate_single_analysis` default values)
-456 + `session`: name of the session (no name by default)
-457 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values
-458 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults)
-459 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
-460 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
-461 (by default equal to the `simulate_single_analysis` defaults)
-462 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+439 **Parameters**
+440
+441 + `samples`: a list of entries; each entry is a dictionary with the following fields:
+442 * `Sample`: the name of the sample
+443 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample
+444 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample
+445 * `N`: how many analyses to generate for this sample
+446 + `a47`: scrambling factor for Δ47
+447 + `b47`: compositional nonlinearity for Δ47
+448 + `c47`: working gas offset for Δ47
+449 + `a48`: scrambling factor for Δ48
+450 + `b48`: compositional nonlinearity for Δ48
+451 + `c48`: working gas offset for Δ48
+452 + `rd45`: analytical repeatability of δ45
+453 + `rd46`: analytical repeatability of δ46
+454 + `rD47`: analytical repeatability of Δ47
+455 + `rD48`: analytical repeatability of Δ48
+456 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas
+457 (by default equal to the `simulate_single_analysis` default values)
+458 + `session`: name of the session (no name by default)
+459 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values
+460 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults)
+461 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and
+462 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified
463 (by default equal to the `simulate_single_analysis` defaults)
-464 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
-465 correction parameters (by default equal to the `simulate_single_analysis` default)
-466 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations
-467 + `shuffle`: randomly reorder the sequence of analyses
-468
-469
-470 Here is an example of using this method to generate an arbitrary combination of
-471 anchors and unknowns for a bunch of sessions:
-472
-473 ```py
-474 .. include:: ../code_examples/virtual_data/example.py
-475 ```
-476
-477 This should output something like:
+464 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor
+465 (by default equal to the `simulate_single_analysis` defaults)
+466 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17
+467 correction parameters (by default equal to the `simulate_single_analysis` default)
+468 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations
+469 + `shuffle`: randomly reorder the sequence of analyses
+470
+471
+472 Here is an example of using this method to generate an arbitrary combination of
+473 anchors and unknowns for a bunch of sessions:
+474
+475 ```py
+476 .. include:: ../code_examples/virtual_data/example.py
+477 ```
478
-479 ```
-480 .. include:: ../code_examples/virtual_data/output.txt
+479 This should output something like:
+480
481 ```
-482 '''
-483
-484 kwargs = locals().copy()
-485
-486 from numpy import random as nprandom
-487 if seed:
-488 rng = nprandom.default_rng(seed)
-489 else:
-490 rng = nprandom.default_rng()
-491
-492 N = sum([s['N'] for s in samples])
-493 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
-494 errors45 *= rd45 / stdev(errors45) # scale errors to rd45
-495 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
-496 errors46 *= rd46 / stdev(errors46) # scale errors to rd46
-497 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
-498 errors47 *= rD47 / stdev(errors47) # scale errors to rD47
-499 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
-500 errors48 *= rD48 / stdev(errors48) # scale errors to rD48
-501
-502 k = 0
-503 out = []
-504 for s in samples:
-505 kw = {}
-506 kw['sample'] = s['Sample']
-507 kw = {
-508 **kw,
-509 **{var: kwargs[var]
-510 for var in [
-511 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION',
-512 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB',
-513 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB',
-514 'a47', 'b47', 'c47', 'a48', 'b48', 'c48',
-515 ]
-516 if kwargs[var] is not None},
-517 **{var: s[var]
-518 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O']
-519 if var in s},
-520 }
-521
-522 sN = s['N']
-523 while sN:
-524 out.append(simulate_single_analysis(**kw))
-525 out[-1]['d45'] += errors45[k]
-526 out[-1]['d46'] += errors46[k]
-527 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47
-528 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48
-529 sN -= 1
-530 k += 1
-531
-532 if session is not None:
-533 for r in out:
-534 r['Session'] = session
-535
-536 if shuffle:
-537 nprandom.shuffle(out)
-538
-539 return out
+482 .. include:: ../code_examples/virtual_data/output.txt
+483 ```
+484 '''
+485
+486 kwargs = locals().copy()
+487
+488 from numpy import random as nprandom
+489 if seed:
+490 rng = nprandom.default_rng(seed)
+491 else:
+492 rng = nprandom.default_rng()
+493
+494 N = sum([s['N'] for s in samples])
+495 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+496 errors45 *= rd45 / stdev(errors45) # scale errors to rd45
+497 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+498 errors46 *= rd46 / stdev(errors46) # scale errors to rd46
+499 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+500 errors47 *= rD47 / stdev(errors47) # scale errors to rD47
+501 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors
+502 errors48 *= rD48 / stdev(errors48) # scale errors to rD48
+503
+504 k = 0
+505 out = []
+506 for s in samples:
+507 kw = {}
+508 kw['sample'] = s['Sample']
+509 kw = {
+510 **kw,
+511 **{var: kwargs[var]
+512 for var in [
+513 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION',
+514 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB',
+515 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB',
+516 'a47', 'b47', 'c47', 'a48', 'b48', 'c48',
+517 ]
+518 if kwargs[var] is not None},
+519 **{var: s[var]
+520 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O']
+521 if var in s},
+522 }
+523
+524 sN = s['N']
+525 while sN:
+526 out.append(simulate_single_analysis(**kw))
+527 out[-1]['d45'] += errors45[k]
+528 out[-1]['d46'] += errors46[k]
+529 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47
+530 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48
+531 sN -= 1
+532 k += 1
+533
+534 if session is not None:
+535 for r in out:
+536 r['Session'] = session
+537
+538 if shuffle:
+539 nprandom.shuffle(out)
+540
+541 return out
4. API Documentation
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
-1 Session_01 ETH-1 -4.000 26.000 6.049381 10.706856 16.135579 21.196941 27.780042 2.057827 36.937067 -0.685751 -0.324384 0.045870 0.212791
-2 Session_01 ETH-1 -4.000 26.000 5.995601 10.755323 16.116087 21.285428 27.780042 1.998631 36.986704 -0.696924 -0.333640 0.008600 0.201787
-3 Session_01 ETH-3 -4.000 26.000 5.727341 11.211663 16.713472 22.364770 28.306614 1.695479 37.453503 -0.278056 -0.180158 -0.082015 0.614365
-4 Session_01 ETH-3 -4.000 26.000 5.734896 11.229855 16.740410 22.402091 28.306614 1.702875 37.472070 -0.276998 -0.179635 -0.125368 0.615396
-5 Session_01 FOO -4.000 26.000 -0.848028 2.874679 1.346196 5.439150 4.665655 -5.017230 28.951964 -0.601502 -0.316664 -0.081898 0.302042
-6 Session_01 BAR -4.000 26.000 -9.915975 10.968470 0.153453 21.749385 10.707292 -14.995822 37.241294 -0.286638 -0.301325 -0.157376 0.612868
-7 Session_01 ETH-2 -4.000 26.000 -5.974124 -5.955517 -12.668784 -12.208184 -18.023381 -10.163274 19.943159 -0.694902 -0.336672 -0.063946 0.215880
-8 Session_01 BAR -4.000 26.000 -9.920507 10.903408 0.065076 21.704075 10.707292 -14.998270 37.174839 -0.307018 -0.216978 -0.026076 0.592818
-9 Session_01 FOO -4.000 26.000 -0.838118 2.819853 1.310384 5.326005 4.665655 -5.004629 28.895933 -0.593755 -0.319861 0.014956 0.309692
-10 Session_01 ETH-2 -4.000 26.000 -5.982229 -6.110437 -12.827036 -12.492272 -18.023381 -10.166188 19.784916 -0.693555 -0.312598 0.251040 0.217274
-11 Session_01 ETH-1 -4.000 26.000 6.010276 10.840276 16.207960 21.475150 27.780042 2.011176 37.073454 -0.704188 -0.315986 -0.172089 0.194589
-12 Session_01 ETH-3 -4.000 26.000 5.755174 11.255104 16.792797 22.451660 28.306614 1.723596 37.497816 -0.270825 -0.181089 -0.195908 0.621458
-13 Session_01 ETH-2 -4.000 26.000 -5.991278 -5.995054 -12.741562 -12.184075 -18.023381 -10.180122 19.902809 -0.711697 -0.232746 0.032602 0.199357
-14 Session_01 BAR -4.000 26.000 -9.959983 10.926995 0.053806 21.724901 10.707292 -15.041279 37.199026 -0.300066 -0.243252 -0.029371 0.599675
-15 Session_01 FOO -4.000 26.000 -0.876454 2.906764 1.341194 5.490264 4.665655 -5.048760 28.984806 -0.608593 -0.329808 -0.114437 0.295055
-16 Session_02 ETH-2 -4.000 26.000 -5.950370 -5.959974 -12.650784 -12.197864 -18.023381 -10.143809 19.897777 -0.696916 -0.317263 -0.080604 0.216441
+1 Session_01 BAR -4.000 26.000 -9.920507 10.903408 0.065076 21.704075 10.707292 -14.998270 37.174839 -0.307018 -0.216978 -0.026076 0.592818
+2 Session_01 ETH-3 -4.000 26.000 5.755174 11.255104 16.792797 22.451660 28.306614 1.723596 37.497816 -0.270825 -0.181089 -0.195908 0.621458
+3 Session_01 ETH-1 -4.000 26.000 6.010276 10.840276 16.207960 21.475150 27.780042 2.011176 37.073454 -0.704188 -0.315986 -0.172089 0.194589
+4 Session_01 ETH-2 -4.000 26.000 -5.991278 -5.995054 -12.741562 -12.184075 -18.023381 -10.180122 19.902809 -0.711697 -0.232746 0.032602 0.199357
+5 Session_01 ETH-2 -4.000 26.000 -5.974124 -5.955517 -12.668784 -12.208184 -18.023381 -10.163274 19.943159 -0.694902 -0.336672 -0.063946 0.215880
+6 Session_01 ETH-3 -4.000 26.000 5.727341 11.211663 16.713472 22.364770 28.306614 1.695479 37.453503 -0.278056 -0.180158 -0.082015 0.614365
+7 Session_01 BAR -4.000 26.000 -9.959983 10.926995 0.053806 21.724901 10.707292 -15.041279 37.199026 -0.300066 -0.243252 -0.029371 0.599675
+8 Session_01 ETH-3 -4.000 26.000 5.734896 11.229855 16.740410 22.402091 28.306614 1.702875 37.472070 -0.276998 -0.179635 -0.125368 0.615396
+9 Session_01 ETH-2 -4.000 26.000 -5.982229 -6.110437 -12.827036 -12.492272 -18.023381 -10.166188 19.784916 -0.693555 -0.312598 0.251040 0.217274
+10 Session_01 FOO -4.000 26.000 -0.848028 2.874679 1.346196 5.439150 4.665655 -5.017230 28.951964 -0.601502 -0.316664 -0.081898 0.302042
+11 Session_01 FOO -4.000 26.000 -0.838118 2.819853 1.310384 5.326005 4.665655 -5.004629 28.895933 -0.593755 -0.319861 0.014956 0.309692
+12 Session_01 BAR -4.000 26.000 -9.915975 10.968470 0.153453 21.749385 10.707292 -14.995822 37.241294 -0.286638 -0.301325 -0.157376 0.612868
+13 Session_01 FOO -4.000 26.000 -0.876454 2.906764 1.341194 5.490264 4.665655 -5.048760 28.984806 -0.608593 -0.329808 -0.114437 0.295055
+14 Session_01 ETH-1 -4.000 26.000 6.049381 10.706856 16.135579 21.196941 27.780042 2.057827 36.937067 -0.685751 -0.324384 0.045870 0.212791
+15 Session_01 ETH-1 -4.000 26.000 5.995601 10.755323 16.116087 21.285428 27.780042 1.998631 36.986704 -0.696924 -0.333640 0.008600 0.201787
+16 Session_02 ETH-1 -4.000 26.000 6.019963 10.773112 16.163825 21.331060 27.780042 2.029040 37.042346 -0.692234 -0.324161 -0.051788 0.207075
17 Session_02 ETH-1 -4.000 26.000 6.030532 10.851030 16.245571 21.457100 27.780042 2.037466 37.122284 -0.698413 -0.354920 -0.214443 0.200795
-18 Session_02 ETH-1 -4.000 26.000 6.019963 10.773112 16.163825 21.331060 27.780042 2.029040 37.042346 -0.692234 -0.324161 -0.051788 0.207075
-19 Session_02 BAR -4.000 26.000 -9.936020 10.862339 0.024660 21.563307 10.707292 -15.023836 37.171034 -0.291333 -0.273498 0.070452 0.619812
-20 Session_02 ETH-2 -4.000 26.000 -5.982371 -6.036210 -12.762399 -12.309944 -18.023381 -10.175178 19.819614 -0.701348 -0.277354 0.104418 0.212021
-21 Session_02 ETH-2 -4.000 26.000 -5.993476 -5.944866 -12.696865 -12.149754 -18.023381 -10.190430 19.913381 -0.713779 -0.298963 -0.064251 0.199436
-22 Session_02 ETH-3 -4.000 26.000 5.757137 11.232751 16.744567 22.398244 28.306614 1.731295 37.514660 -0.298533 -0.189123 -0.154557 0.604363
-23 Session_02 ETH-1 -4.000 26.000 5.993918 10.617469 15.991900 21.070358 27.780042 2.006934 36.882679 -0.683329 -0.271476 0.278458 0.216152
-24 Session_02 BAR -4.000 26.000 -9.963888 10.865863 -0.023549 21.615868 10.707292 -15.053743 37.174715 -0.313906 -0.229031 0.093637 0.597041
-25 Session_02 ETH-3 -4.000 26.000 5.719281 11.207303 16.681693 22.370886 28.306614 1.691780 37.488633 -0.296801 -0.165556 -0.065004 0.606143
-26 Session_02 FOO -4.000 26.000 -0.848415 2.849823 1.308081 5.427767 4.665655 -5.018107 28.927036 -0.614791 -0.278426 -0.032784 0.292547
-27 Session_02 FOO -4.000 26.000 -0.819742 2.826793 1.317044 5.330616 4.665655 -4.986618 28.903335 -0.612871 -0.329113 -0.018244 0.294481
-28 Session_02 ETH-3 -4.000 26.000 5.716356 11.091821 16.582487 22.123857 28.306614 1.692901 37.370126 -0.279100 -0.178789 0.162540 0.624067
-29 Session_02 FOO -4.000 26.000 -0.835046 2.870518 1.355370 5.487896 4.665655 -5.004585 28.948243 -0.601666 -0.259900 -0.087592 0.305777
-30 Session_02 BAR -4.000 26.000 -9.957566 10.903888 0.031785 21.739434 10.707292 -15.048386 37.213724 -0.302139 -0.183327 0.012926 0.608897
-31 Session_03 ETH-2 -4.000 26.000 -5.997147 -5.905858 -12.655382 -12.081612 -18.023381 -10.165400 19.891551 -0.706536 -0.308464 -0.137414 0.197550
-32 Session_03 BAR -4.000 26.000 -9.928709 10.989665 0.148059 21.852677 10.707292 -14.976237 37.324152 -0.299358 -0.242185 -0.184835 0.603855
-33 Session_03 ETH-1 -4.000 26.000 6.040566 10.786620 16.205283 21.374963 27.780042 2.045244 37.077432 -0.685706 -0.307909 -0.099869 0.213609
-34 Session_03 FOO -4.000 26.000 -0.823857 2.761300 1.258060 5.239992 4.665655 -4.973383 28.817444 -0.603327 -0.288652 0.114488 0.298751
+18 Session_02 BAR -4.000 26.000 -9.963888 10.865863 -0.023549 21.615868 10.707292 -15.053743 37.174715 -0.313906 -0.229031 0.093637 0.597041
+19 Session_02 FOO -4.000 26.000 -0.835046 2.870518 1.355370 5.487896 4.665655 -5.004585 28.948243 -0.601666 -0.259900 -0.087592 0.305777
+20 Session_02 BAR -4.000 26.000 -9.936020 10.862339 0.024660 21.563307 10.707292 -15.023836 37.171034 -0.291333 -0.273498 0.070452 0.619812
+21 Session_02 ETH-1 -4.000 26.000 5.993918 10.617469 15.991900 21.070358 27.780042 2.006934 36.882679 -0.683329 -0.271476 0.278458 0.216152
+22 Session_02 ETH-3 -4.000 26.000 5.719281 11.207303 16.681693 22.370886 28.306614 1.691780 37.488633 -0.296801 -0.165556 -0.065004 0.606143
+23 Session_02 FOO -4.000 26.000 -0.848415 2.849823 1.308081 5.427767 4.665655 -5.018107 28.927036 -0.614791 -0.278426 -0.032784 0.292547
+24 Session_02 ETH-2 -4.000 26.000 -5.950370 -5.959974 -12.650784 -12.197864 -18.023381 -10.143809 19.897777 -0.696916 -0.317263 -0.080604 0.216441
+25 Session_02 BAR -4.000 26.000 -9.957566 10.903888 0.031785 21.739434 10.707292 -15.048386 37.213724 -0.302139 -0.183327 0.012926 0.608897
+26 Session_02 ETH-3 -4.000 26.000 5.716356 11.091821 16.582487 22.123857 28.306614 1.692901 37.370126 -0.279100 -0.178789 0.162540 0.624067
+27 Session_02 ETH-3 -4.000 26.000 5.757137 11.232751 16.744567 22.398244 28.306614 1.731295 37.514660 -0.298533 -0.189123 -0.154557 0.604363
+28 Session_02 ETH-2 -4.000 26.000 -5.993476 -5.944866 -12.696865 -12.149754 -18.023381 -10.190430 19.913381 -0.713779 -0.298963 -0.064251 0.199436
+29 Session_02 FOO -4.000 26.000 -0.819742 2.826793 1.317044 5.330616 4.665655 -4.986618 28.903335 -0.612871 -0.329113 -0.018244 0.294481
+30 Session_02 ETH-2 -4.000 26.000 -5.982371 -6.036210 -12.762399 -12.309944 -18.023381 -10.175178 19.819614 -0.701348 -0.277354 0.104418 0.212021
+31 Session_03 FOO -4.000 26.000 -0.873798 2.820799 1.272165 5.370745 4.665655 -5.028782 28.878917 -0.596008 -0.277258 0.051165 0.306090
+32 Session_03 ETH-3 -4.000 26.000 5.748546 11.079879 16.580826 22.120063 28.306614 1.723364 37.380534 -0.302133 -0.158882 0.151641 0.598318
+33 Session_03 BAR -4.000 26.000 -9.957114 10.898997 0.044946 21.602296 10.707292 -15.003175 37.230716 -0.284699 -0.307849 0.021944 0.618578
+34 Session_03 ETH-2 -4.000 26.000 -6.008525 -5.909707 -12.647727 -12.075913 -18.023381 -10.177379 19.887608 -0.683183 -0.294956 -0.117608 0.220975
35 Session_03 ETH-1 -4.000 26.000 5.994622 10.743980 16.116098 21.243734 27.780042 1.997857 37.033567 -0.684883 -0.352014 0.031692 0.214449
-36 Session_03 FOO -4.000 26.000 -0.800284 2.851299 1.376828 5.379547 4.665655 -4.951581 28.910199 -0.597293 -0.329315 -0.087015 0.304784
-37 Session_03 ETH-2 -4.000 26.000 -6.008525 -5.909707 -12.647727 -12.075913 -18.023381 -10.177379 19.887608 -0.683183 -0.294956 -0.117608 0.220975
-38 Session_03 ETH-3 -4.000 26.000 5.753467 11.206589 16.719131 22.373244 28.306614 1.723960 37.511190 -0.294350 -0.161838 -0.099835 0.606103
-39 Session_03 ETH-2 -4.000 26.000 -6.000290 -5.947172 -12.697463 -12.164602 -18.023381 -10.167221 19.848953 -0.705037 -0.309350 -0.052386 0.199061
-40 Session_03 ETH-1 -4.000 26.000 6.004078 10.683951 16.045192 21.214355 27.780042 2.010134 36.971642 -0.705956 -0.262026 0.138399 0.193323
-41 Session_03 FOO -4.000 26.000 -0.873798 2.820799 1.272165 5.370745 4.665655 -5.028782 28.878917 -0.596008 -0.277258 0.051165 0.306090
-42 Session_03 ETH-3 -4.000 26.000 5.748546 11.079879 16.580826 22.120063 28.306614 1.723364 37.380534 -0.302133 -0.158882 0.151641 0.598318
-43 Session_03 BAR -4.000 26.000 -9.957114 10.898997 0.044946 21.602296 10.707292 -15.003175 37.230716 -0.284699 -0.307849 0.021944 0.618578
-44 Session_03 BAR -4.000 26.000 -9.952115 11.034508 0.169809 21.885915 10.707292 -15.002819 37.370451 -0.296804 -0.298351 -0.246731 0.606414
-45 Session_03 ETH-3 -4.000 26.000 5.718991 11.146227 16.640814 22.243185 28.306614 1.689442 37.449023 -0.277332 -0.169668 0.053997 0.623187
-46 Session_04 ETH-3 -4.000 26.000 5.751908 11.207110 16.726741 22.380392 28.306614 1.705481 37.480657 -0.285776 -0.155878 -0.099197 0.609567
-47 Session_04 ETH-2 -4.000 26.000 -5.986501 -5.915157 -12.656583 -12.060382 -18.023381 -10.182247 19.889836 -0.709603 -0.268277 -0.130450 0.199604
-48 Session_04 BAR -4.000 26.000 -9.926078 10.884823 0.060864 21.650722 10.707292 -15.002880 37.185606 -0.287358 -0.232425 0.016044 0.611760
-49 Session_04 ETH-2 -4.000 26.000 -5.966627 -5.893789 -12.597717 -12.120719 -18.023381 -10.161842 19.911776 -0.691757 -0.372308 -0.193986 0.217132
-50 Session_04 ETH-1 -4.000 26.000 6.029937 10.766997 16.151273 21.345479 27.780042 2.018148 37.027152 -0.708855 -0.297953 -0.050465 0.193862
-51 Session_04 BAR -4.000 26.000 -9.951025 10.951923 0.089386 21.738926 10.707292 -15.031949 37.254709 -0.298065 -0.278834 -0.087463 0.601230
-52 Session_04 ETH-3 -4.000 26.000 5.739420 11.128582 16.641344 22.166106 28.306614 1.695046 37.399884 -0.280608 -0.210162 0.066645 0.614665
-53 Session_04 ETH-1 -4.000 26.000 6.023822 10.730714 16.121184 21.235757 27.780042 2.012958 36.989833 -0.696908 -0.333582 0.026555 0.205610
-54 Session_04 ETH-1 -4.000 26.000 6.017312 10.735930 16.123043 21.270597 27.780042 2.005824 36.995214 -0.693479 -0.309795 0.023309 0.208980
-55 Session_04 BAR -4.000 26.000 -9.931741 10.819830 -0.023748 21.529372 10.707292 -15.006533 37.118743 -0.302866 -0.222623 0.148462 0.596536
-56 Session_04 ETH-3 -4.000 26.000 5.798016 11.254135 16.832228 22.432473 28.306614 1.752928 37.528936 -0.275047 -0.197935 -0.239408 0.620088
-57 Session_04 FOO -4.000 26.000 -0.853969 2.805035 1.267571 5.353907 4.665655 -5.030523 28.850660 -0.605611 -0.262571 0.060903 0.298685
-58 Session_04 ETH-2 -4.000 26.000 -5.973623 -5.975018 -12.694278 -12.194472 -18.023381 -10.166297 19.828211 -0.701951 -0.283570 -0.025935 0.207135
-59 Session_04 FOO -4.000 26.000 -0.848192 2.777763 1.251297 5.280272 4.665655 -5.023358 28.822585 -0.601094 -0.281419 0.108186 0.303128
-60 Session_04 FOO -4.000 26.000 -0.791191 2.708220 1.256167 5.145784 4.665655 -4.960004 28.750896 -0.586913 -0.276505 0.183674 0.317065
+36 Session_03 ETH-3 -4.000 26.000 5.718991 11.146227 16.640814 22.243185 28.306614 1.689442 37.449023 -0.277332 -0.169668 0.053997 0.623187
+37 Session_03 ETH-2 -4.000 26.000 -6.000290 -5.947172 -12.697463 -12.164602 -18.023381 -10.167221 19.848953 -0.705037 -0.309350 -0.052386 0.199061
+38 Session_03 BAR -4.000 26.000 -9.928709 10.989665 0.148059 21.852677 10.707292 -14.976237 37.324152 -0.299358 -0.242185 -0.184835 0.603855
+39 Session_03 ETH-3 -4.000 26.000 5.753467 11.206589 16.719131 22.373244 28.306614 1.723960 37.511190 -0.294350 -0.161838 -0.099835 0.606103
+40 Session_03 FOO -4.000 26.000 -0.823857 2.761300 1.258060 5.239992 4.665655 -4.973383 28.817444 -0.603327 -0.288652 0.114488 0.298751
+41 Session_03 BAR -4.000 26.000 -9.952115 11.034508 0.169809 21.885915 10.707292 -15.002819 37.370451 -0.296804 -0.298351 -0.246731 0.606414
+42 Session_03 ETH-1 -4.000 26.000 6.040566 10.786620 16.205283 21.374963 27.780042 2.045244 37.077432 -0.685706 -0.307909 -0.099869 0.213609
+43 Session_03 FOO -4.000 26.000 -0.800284 2.851299 1.376828 5.379547 4.665655 -4.951581 28.910199 -0.597293 -0.329315 -0.087015 0.304784
+44 Session_03 ETH-1 -4.000 26.000 6.004078 10.683951 16.045192 21.214355 27.780042 2.010134 36.971642 -0.705956 -0.262026 0.138399 0.193323
+45 Session_03 ETH-2 -4.000 26.000 -5.997147 -5.905858 -12.655382 -12.081612 -18.023381 -10.165400 19.891551 -0.706536 -0.308464 -0.137414 0.197550
+46 Session_04 FOO -4.000 26.000 -0.791191 2.708220 1.256167 5.145784 4.665655 -4.960004 28.750896 -0.586913 -0.276505 0.183674 0.317065
+47 Session_04 ETH-2 -4.000 26.000 -5.966627 -5.893789 -12.597717 -12.120719 -18.023381 -10.161842 19.911776 -0.691757 -0.372308 -0.193986 0.217132
+48 Session_04 ETH-2 -4.000 26.000 -5.973623 -5.975018 -12.694278 -12.194472 -18.023381 -10.166297 19.828211 -0.701951 -0.283570 -0.025935 0.207135
+49 Session_04 FOO -4.000 26.000 -0.853969 2.805035 1.267571 5.353907 4.665655 -5.030523 28.850660 -0.605611 -0.262571 0.060903 0.298685
+50 Session_04 BAR -4.000 26.000 -9.951025 10.951923 0.089386 21.738926 10.707292 -15.031949 37.254709 -0.298065 -0.278834 -0.087463 0.601230
+51 Session_04 FOO -4.000 26.000 -0.848192 2.777763 1.251297 5.280272 4.665655 -5.023358 28.822585 -0.601094 -0.281419 0.108186 0.303128
+52 Session_04 BAR -4.000 26.000 -9.926078 10.884823 0.060864 21.650722 10.707292 -15.002880 37.185606 -0.287358 -0.232425 0.016044 0.611760
+53 Session_04 ETH-1 -4.000 26.000 6.029937 10.766997 16.151273 21.345479 27.780042 2.018148 37.027152 -0.708855 -0.297953 -0.050465 0.193862
+54 Session_04 ETH-1 -4.000 26.000 6.023822 10.730714 16.121184 21.235757 27.780042 2.012958 36.989833 -0.696908 -0.333582 0.026555 0.205610
+55 Session_04 ETH-2 -4.000 26.000 -5.986501 -5.915157 -12.656583 -12.060382 -18.023381 -10.182247 19.889836 -0.709603 -0.268277 -0.130450 0.199604
+56 Session_04 ETH-3 -4.000 26.000 5.739420 11.128582 16.641344 22.166106 28.306614 1.695046 37.399884 -0.280608 -0.210162 0.066645 0.614665
+57 Session_04 BAR -4.000 26.000 -9.931741 10.819830 -0.023748 21.529372 10.707292 -15.006533 37.118743 -0.302866 -0.222623 0.148462 0.596536
+58 Session_04 ETH-3 -4.000 26.000 5.751908 11.207110 16.726741 22.380392 28.306614 1.705481 37.480657 -0.285776 -0.155878 -0.099197 0.609567
+59 Session_04 ETH-3 -4.000 26.000 5.798016 11.254135 16.832228 22.432473 28.306614 1.752928 37.528936 -0.275047 -0.197935 -0.239408 0.620088
+60 Session_04 ETH-1 -4.000 26.000 6.017312 10.735930 16.123043 21.270597 27.780042 2.005824 36.995214 -0.693479 -0.309795 0.023309 0.208980
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
@@ -5276,69 +5321,69 @@ 4. API Documentation
541def table_of_samples(
-542 data47 = None,
-543 data48 = None,
-544 dir = 'output',
-545 filename = None,
-546 save_to_file = True,
-547 print_out = True,
-548 output = None,
-549 ):
-550 '''
-551 Print out, save to disk and/or return a combined table of samples
-552 for a pair of `D47data` and `D48data` objects.
-553
-554 **Parameters**
+ 543def table_of_samples(
+544 data47 = None,
+545 data48 = None,
+546 dir = 'output',
+547 filename = None,
+548 save_to_file = True,
+549 print_out = True,
+550 output = None,
+551 ):
+552 '''
+553 Print out, save to disk and/or return a combined table of samples
+554 for a pair of `D47data` and `D48data` objects.
555
-556 + `data47`: `D47data` instance
-557 + `data48`: `D48data` instance
-558 + `dir`: the directory in which to save the table
-559 + `filename`: the name to the csv file to write to
-560 + `save_to_file`: whether to save the table to disk
-561 + `print_out`: whether to print out the table
-562 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-563 if set to `'raw'`: return a list of list of strings
-564 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-565 '''
-566 if data47 is None:
-567 if data48 is None:
-568 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
-569 else:
-570 return data48.table_of_samples(
-571 dir = dir,
-572 filename = filename,
-573 save_to_file = save_to_file,
-574 print_out = print_out,
-575 output = output
-576 )
-577 else:
-578 if data48 is None:
-579 return data47.table_of_samples(
-580 dir = dir,
-581 filename = filename,
-582 save_to_file = save_to_file,
-583 print_out = print_out,
-584 output = output
-585 )
-586 else:
-587 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
-588 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
-589 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:])
-590
-591 if save_to_file:
-592 if not os.path.exists(dir):
-593 os.makedirs(dir)
-594 if filename is None:
-595 filename = f'D47D48_samples.csv'
-596 with open(f'{dir}/{filename}', 'w') as fid:
-597 fid.write(make_csv(out))
-598 if print_out:
-599 print('\n'+pretty_table(out))
-600 if output == 'raw':
-601 return out
-602 elif output == 'pretty':
-603 return pretty_table(out)
+556 **Parameters**
+557
+558 + `data47`: `D47data` instance
+559 + `data48`: `D48data` instance
+560 + `dir`: the directory in which to save the table
+561 + `filename`: the name to the csv file to write to
+562 + `save_to_file`: whether to save the table to disk
+563 + `print_out`: whether to print out the table
+564 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+565 if set to `'raw'`: return a list of list of strings
+566 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+567 '''
+568 if data47 is None:
+569 if data48 is None:
+570 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+571 else:
+572 return data48.table_of_samples(
+573 dir = dir,
+574 filename = filename,
+575 save_to_file = save_to_file,
+576 print_out = print_out,
+577 output = output
+578 )
+579 else:
+580 if data48 is None:
+581 return data47.table_of_samples(
+582 dir = dir,
+583 filename = filename,
+584 save_to_file = save_to_file,
+585 print_out = print_out,
+586 output = output
+587 )
+588 else:
+589 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
+590 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw')
+591 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:])
+592
+593 if save_to_file:
+594 if not os.path.exists(dir):
+595 os.makedirs(dir)
+596 if filename is None:
+597 filename = f'D47D48_samples.csv'
+598 with open(f'{dir}/{filename}', 'w') as fid:
+599 fid.write(make_csv(out))
+600 if print_out:
+601 print('\n'+pretty_table(out))
+602 if output == 'raw':
+603 return out
+604 elif output == 'pretty':
+605 return pretty_table(out)
4. API Documentation
606def table_of_sessions(
-607 data47 = None,
-608 data48 = None,
-609 dir = 'output',
-610 filename = None,
-611 save_to_file = True,
-612 print_out = True,
-613 output = None,
-614 ):
-615 '''
-616 Print out, save to disk and/or return a combined table of sessions
-617 for a pair of `D47data` and `D48data` objects.
-618 ***Only applicable if the sessions in `data47` and those in `data48`
-619 consist of the exact same sets of analyses.***
-620
-621 **Parameters**
+ 608def table_of_sessions(
+609 data47 = None,
+610 data48 = None,
+611 dir = 'output',
+612 filename = None,
+613 save_to_file = True,
+614 print_out = True,
+615 output = None,
+616 ):
+617 '''
+618 Print out, save to disk and/or return a combined table of sessions
+619 for a pair of `D47data` and `D48data` objects.
+620 ***Only applicable if the sessions in `data47` and those in `data48`
+621 consist of the exact same sets of analyses.***
622
-623 + `data47`: `D47data` instance
-624 + `data48`: `D48data` instance
-625 + `dir`: the directory in which to save the table
-626 + `filename`: the name to the csv file to write to
-627 + `save_to_file`: whether to save the table to disk
-628 + `print_out`: whether to print out the table
-629 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-630 if set to `'raw'`: return a list of list of strings
-631 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-632 '''
-633 if data47 is None:
-634 if data48 is None:
-635 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
-636 else:
-637 return data48.table_of_sessions(
-638 dir = dir,
-639 filename = filename,
-640 save_to_file = save_to_file,
-641 print_out = print_out,
-642 output = output
-643 )
-644 else:
-645 if data48 is None:
-646 return data47.table_of_sessions(
-647 dir = dir,
-648 filename = filename,
-649 save_to_file = save_to_file,
-650 print_out = print_out,
-651 output = output
-652 )
-653 else:
-654 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
-655 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
-656 for k,x in enumerate(out47[0]):
-657 if k>7:
-658 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47')
-659 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48')
-660 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:])
-661
-662 if save_to_file:
-663 if not os.path.exists(dir):
-664 os.makedirs(dir)
-665 if filename is None:
-666 filename = f'D47D48_sessions.csv'
-667 with open(f'{dir}/{filename}', 'w') as fid:
-668 fid.write(make_csv(out))
-669 if print_out:
-670 print('\n'+pretty_table(out))
-671 if output == 'raw':
-672 return out
-673 elif output == 'pretty':
-674 return pretty_table(out)
+623 **Parameters**
+624
+625 + `data47`: `D47data` instance
+626 + `data48`: `D48data` instance
+627 + `dir`: the directory in which to save the table
+628 + `filename`: the name to the csv file to write to
+629 + `save_to_file`: whether to save the table to disk
+630 + `print_out`: whether to print out the table
+631 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+632 if set to `'raw'`: return a list of list of strings
+633 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+634 '''
+635 if data47 is None:
+636 if data48 is None:
+637 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+638 else:
+639 return data48.table_of_sessions(
+640 dir = dir,
+641 filename = filename,
+642 save_to_file = save_to_file,
+643 print_out = print_out,
+644 output = output
+645 )
+646 else:
+647 if data48 is None:
+648 return data47.table_of_sessions(
+649 dir = dir,
+650 filename = filename,
+651 save_to_file = save_to_file,
+652 print_out = print_out,
+653 output = output
+654 )
+655 else:
+656 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
+657 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw')
+658 for k,x in enumerate(out47[0]):
+659 if k>7:
+660 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47')
+661 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48')
+662 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:])
+663
+664 if save_to_file:
+665 if not os.path.exists(dir):
+666 os.makedirs(dir)
+667 if filename is None:
+668 filename = f'D47D48_sessions.csv'
+669 with open(f'{dir}/{filename}', 'w') as fid:
+670 fid.write(make_csv(out))
+671 if print_out:
+672 print('\n'+pretty_table(out))
+673 if output == 'raw':
+674 return out
+675 elif output == 'pretty':
+676 return pretty_table(out)
4. API Documentation
677def table_of_analyses(
-678 data47 = None,
-679 data48 = None,
-680 dir = 'output',
-681 filename = None,
-682 save_to_file = True,
-683 print_out = True,
-684 output = None,
-685 ):
-686 '''
-687 Print out, save to disk and/or return a combined table of analyses
-688 for a pair of `D47data` and `D48data` objects.
-689
-690 If the sessions in `data47` and those in `data48` do not consist of
-691 the exact same sets of analyses, the table will have two columns
-692 `Session_47` and `Session_48` instead of a single `Session` column.
-693
-694 **Parameters**
+ 679def table_of_analyses(
+680 data47 = None,
+681 data48 = None,
+682 dir = 'output',
+683 filename = None,
+684 save_to_file = True,
+685 print_out = True,
+686 output = None,
+687 ):
+688 '''
+689 Print out, save to disk and/or return a combined table of analyses
+690 for a pair of `D47data` and `D48data` objects.
+691
+692 If the sessions in `data47` and those in `data48` do not consist of
+693 the exact same sets of analyses, the table will have two columns
+694 `Session_47` and `Session_48` instead of a single `Session` column.
695
-696 + `data47`: `D47data` instance
-697 + `data48`: `D48data` instance
-698 + `dir`: the directory in which to save the table
-699 + `filename`: the name to the csv file to write to
-700 + `save_to_file`: whether to save the table to disk
-701 + `print_out`: whether to print out the table
-702 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-703 if set to `'raw'`: return a list of list of strings
-704 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-705 '''
-706 if data47 is None:
-707 if data48 is None:
-708 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
-709 else:
-710 return data48.table_of_analyses(
-711 dir = dir,
-712 filename = filename,
-713 save_to_file = save_to_file,
-714 print_out = print_out,
-715 output = output
-716 )
-717 else:
-718 if data48 is None:
-719 return data47.table_of_analyses(
-720 dir = dir,
-721 filename = filename,
-722 save_to_file = save_to_file,
-723 print_out = print_out,
-724 output = output
-725 )
-726 else:
-727 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
-728 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
-729
-730 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical
-731 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:])
-732 else:
-733 out47[0][1] = 'Session_47'
-734 out48[0][1] = 'Session_48'
-735 out47 = transpose_table(out47)
-736 out48 = transpose_table(out48)
-737 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:])
-738
-739 if save_to_file:
-740 if not os.path.exists(dir):
-741 os.makedirs(dir)
-742 if filename is None:
-743 filename = f'D47D48_sessions.csv'
-744 with open(f'{dir}/{filename}', 'w') as fid:
-745 fid.write(make_csv(out))
-746 if print_out:
-747 print('\n'+pretty_table(out))
-748 if output == 'raw':
-749 return out
-750 elif output == 'pretty':
-751 return pretty_table(out)
+696 **Parameters**
+697
+698 + `data47`: `D47data` instance
+699 + `data48`: `D48data` instance
+700 + `dir`: the directory in which to save the table
+701 + `filename`: the name to the csv file to write to
+702 + `save_to_file`: whether to save the table to disk
+703 + `print_out`: whether to print out the table
+704 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+705 if set to `'raw'`: return a list of list of strings
+706 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+707 '''
+708 if data47 is None:
+709 if data48 is None:
+710 raise TypeError("Arguments must include at least one D47data() or D48data() instance.")
+711 else:
+712 return data48.table_of_analyses(
+713 dir = dir,
+714 filename = filename,
+715 save_to_file = save_to_file,
+716 print_out = print_out,
+717 output = output
+718 )
+719 else:
+720 if data48 is None:
+721 return data47.table_of_analyses(
+722 dir = dir,
+723 filename = filename,
+724 save_to_file = save_to_file,
+725 print_out = print_out,
+726 output = output
+727 )
+728 else:
+729 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
+730 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw')
+731
+732 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical
+733 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:])
+734 else:
+735 out47[0][1] = 'Session_47'
+736 out48[0][1] = 'Session_48'
+737 out47 = transpose_table(out47)
+738 out48 = transpose_table(out48)
+739 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:])
+740
+741 if save_to_file:
+742 if not os.path.exists(dir):
+743 os.makedirs(dir)
+744 if filename is None:
+745 filename = f'D47D48_sessions.csv'
+746 with open(f'{dir}/{filename}', 'w') as fid:
+747 fid.write(make_csv(out))
+748 if print_out:
+749 print('\n'+pretty_table(out))
+750 if output == 'raw':
+751 return out
+752 elif output == 'pretty':
+753 return pretty_table(out)
4. API Documentation
799class D4xdata(list):
- 800 '''
- 801 Store and process data for a large set of Δ47 and/or Δ48
- 802 analyses, usually comprising more than one analytical session.
- 803 '''
- 804
- 805 ### 17O CORRECTION PARAMETERS
- 806 R13_VPDB = 0.01118 # (Chang & Li, 1990)
- 807 '''
- 808 Absolute (13C/12C) ratio of VPDB.
- 809 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm))
- 810 '''
- 811
- 812 R18_VSMOW = 0.0020052 # (Baertschi, 1976)
- 813 '''
- 814 Absolute (18O/16C) ratio of VSMOW.
- 815 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1))
- 816 '''
- 817
- 818 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005)
- 819 '''
- 820 Mass-dependent exponent for triple oxygen isotopes.
- 821 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250))
- 822 '''
- 823
- 824 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB)
- 825 '''
- 826 Absolute (17O/16C) ratio of VSMOW.
- 827 By default equal to 0.00038475
- 828 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011),
- 829 rescaled to `R13_VPDB`)
- 830 '''
- 831
- 832 R18_VPDB = R18_VSMOW * 1.03092
- 833 '''
- 834 Absolute (18O/16C) ratio of VPDB.
- 835 By definition equal to `R18_VSMOW * 1.03092`.
- 836 '''
- 837
- 838 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17
- 839 '''
- 840 Absolute (17O/16C) ratio of VPDB.
- 841 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`.
- 842 '''
- 843
- 844 LEVENE_REF_SAMPLE = 'ETH-3'
- 845 '''
- 846 After the Δ4x standardization step, each sample is tested to
- 847 assess whether the Δ4x variance within all analyses for that
- 848 sample differs significantly from that observed for a given reference
- 849 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test),
- 850 which yields a p-value corresponding to the null hypothesis that the
- 851 underlying variances are equal).
- 852
- 853 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which
- 854 sample should be used as a reference for this test.
- 855 '''
- 856
- 857 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite)
- 858 '''
- 859 Specifies the 18O/16O fractionation factor generally applicable
- 860 to acid reactions in the dataset. Currently used by `D4xdata.wg()`,
- 861 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`.
- 862
- 863 By default equal to 1.008129 (calcite reacted at 90 °C,
- 864 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)).
- 865 '''
- 866
- 867 Nominal_d13C_VPDB = {
- 868 'ETH-1': 2.02,
- 869 'ETH-2': -10.17,
- 870 'ETH-3': 1.71,
- 871 } # (Bernasconi et al., 2018)
- 872 '''
- 873 Nominal δ13C_VPDB values assigned to carbonate standards, used by
- 874 `D4xdata.standardize_d13C()`.
- 875
- 876 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after
- 877 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
- 878 '''
- 879
- 880 Nominal_d18O_VPDB = {
- 881 'ETH-1': -2.19,
- 882 'ETH-2': -18.69,
- 883 'ETH-3': -1.78,
- 884 } # (Bernasconi et al., 2018)
- 885 '''
- 886 Nominal δ18O_VPDB values assigned to carbonate standards, used by
- 887 `D4xdata.standardize_d18O()`.
- 888
- 889 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after
- 890 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
- 891 '''
- 892
- 893 d13C_STANDARDIZATION_METHOD = '2pt'
- 894 '''
- 895 Method by which to standardize δ13C values:
- 896
- 897 + `none`: do not apply any δ13C standardization.
- 898 + `'1pt'`: within each session, offset all initial δ13C values so as to
- 899 minimize the difference between final δ13C_VPDB values and
- 900 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined).
- 901 + `'2pt'`: within each session, apply a affine trasformation to all δ13C
- 902 values so as to minimize the difference between final δ13C_VPDB
- 903 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB`
- 904 is defined).
- 905 '''
- 906
- 907 d18O_STANDARDIZATION_METHOD = '2pt'
- 908 '''
- 909 Method by which to standardize δ18O values:
- 910
- 911 + `none`: do not apply any δ18O standardization.
- 912 + `'1pt'`: within each session, offset all initial δ18O values so as to
- 913 minimize the difference between final δ18O_VPDB values and
- 914 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined).
- 915 + `'2pt'`: within each session, apply a affine trasformation to all δ18O
- 916 values so as to minimize the difference between final δ18O_VPDB
- 917 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB`
- 918 is defined).
- 919 '''
- 920
- 921 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
- 922 '''
- 923 **Parameters**
- 924
- 925 + `l`: a list of dictionaries, with each dictionary including at least the keys
- 926 `Sample`, `d45`, `d46`, and `d47` or `d48`.
- 927 + `mass`: `'47'` or `'48'`
- 928 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
- 929 + `session`: define session name for analyses without a `Session` key
- 930 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
- 931
- 932 Returns a `D4xdata` object derived from `list`.
- 933 '''
- 934 self._4x = mass
- 935 self.verbose = verbose
- 936 self.prefix = 'D4xdata'
- 937 self.logfile = logfile
- 938 list.__init__(self, l)
- 939 self.Nf = None
- 940 self.repeatability = {}
- 941 self.refresh(session = session)
- 942
- 943
- 944 def make_verbal(oldfun):
- 945 '''
- 946 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
- 947 '''
- 948 @wraps(oldfun)
- 949 def newfun(*args, verbose = '', **kwargs):
- 950 myself = args[0]
- 951 oldprefix = myself.prefix
- 952 myself.prefix = oldfun.__name__
- 953 if verbose != '':
- 954 oldverbose = myself.verbose
- 955 myself.verbose = verbose
- 956 out = oldfun(*args, **kwargs)
- 957 myself.prefix = oldprefix
- 958 if verbose != '':
- 959 myself.verbose = oldverbose
- 960 return out
- 961 return newfun
- 962
- 963
- 964 def msg(self, txt):
- 965 '''
- 966 Log a message to `self.logfile`, and print it out if `verbose = True`
- 967 '''
- 968 self.log(txt)
- 969 if self.verbose:
- 970 print(f'{f"[{self.prefix}]":<16} {txt}')
- 971
- 972
- 973 def vmsg(self, txt):
- 974 '''
- 975 Log a message to `self.logfile` and print it out
- 976 '''
- 977 self.log(txt)
- 978 print(txt)
- 979
- 980
- 981 def log(self, *txts):
- 982 '''
- 983 Log a message to `self.logfile`
- 984 '''
- 985 if self.logfile:
- 986 with open(self.logfile, 'a') as fid:
- 987 for txt in txts:
- 988 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
- 989
- 990
- 991 def refresh(self, session = 'mySession'):
- 992 '''
- 993 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
- 994 '''
- 995 self.fill_in_missing_info(session = session)
- 996 self.refresh_sessions()
- 997 self.refresh_samples()
- 998
- 999
-1000 def refresh_sessions(self):
-1001 '''
-1002 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
-1003 to `False` for all sessions.
-1004 '''
-1005 self.sessions = {
-1006 s: {'data': [r for r in self if r['Session'] == s]}
-1007 for s in sorted({r['Session'] for r in self})
-1008 }
-1009 for s in self.sessions:
-1010 self.sessions[s]['scrambling_drift'] = False
-1011 self.sessions[s]['slope_drift'] = False
-1012 self.sessions[s]['wg_drift'] = False
-1013 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
-1014 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
-1015
-1016
-1017 def refresh_samples(self):
-1018 '''
-1019 Define `self.samples`, `self.anchors`, and `self.unknowns`.
-1020 '''
-1021 self.samples = {
-1022 s: {'data': [r for r in self if r['Sample'] == s]}
-1023 for s in sorted({r['Sample'] for r in self})
-1024 }
-1025 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
-1026 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
-1027
-1028
-1029 def read(self, filename, sep = '', session = ''):
-1030 '''
-1031 Read file in csv format to load data into a `D47data` object.
-1032
-1033 In the csv file, spaces before and after field separators (`','` by default)
-1034 are optional. Each line corresponds to a single analysis.
-1035
-1036 The required fields are:
+ 801class D4xdata(list):
+ 802 '''
+ 803 Store and process data for a large set of Δ47 and/or Δ48
+ 804 analyses, usually comprising more than one analytical session.
+ 805 '''
+ 806
+ 807 ### 17O CORRECTION PARAMETERS
+ 808 R13_VPDB = 0.01118 # (Chang & Li, 1990)
+ 809 '''
+ 810 Absolute (13C/12C) ratio of VPDB.
+ 811 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm))
+ 812 '''
+ 813
+ 814 R18_VSMOW = 0.0020052 # (Baertschi, 1976)
+ 815 '''
+ 816 Absolute (18O/16C) ratio of VSMOW.
+ 817 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1))
+ 818 '''
+ 819
+ 820 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005)
+ 821 '''
+ 822 Mass-dependent exponent for triple oxygen isotopes.
+ 823 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250))
+ 824 '''
+ 825
+ 826 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB)
+ 827 '''
+ 828 Absolute (17O/16C) ratio of VSMOW.
+ 829 By default equal to 0.00038475
+ 830 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011),
+ 831 rescaled to `R13_VPDB`)
+ 832 '''
+ 833
+ 834 R18_VPDB = R18_VSMOW * 1.03092
+ 835 '''
+ 836 Absolute (18O/16C) ratio of VPDB.
+ 837 By definition equal to `R18_VSMOW * 1.03092`.
+ 838 '''
+ 839
+ 840 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17
+ 841 '''
+ 842 Absolute (17O/16C) ratio of VPDB.
+ 843 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`.
+ 844 '''
+ 845
+ 846 LEVENE_REF_SAMPLE = 'ETH-3'
+ 847 '''
+ 848 After the Δ4x standardization step, each sample is tested to
+ 849 assess whether the Δ4x variance within all analyses for that
+ 850 sample differs significantly from that observed for a given reference
+ 851 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test),
+ 852 which yields a p-value corresponding to the null hypothesis that the
+ 853 underlying variances are equal).
+ 854
+ 855 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which
+ 856 sample should be used as a reference for this test.
+ 857 '''
+ 858
+ 859 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite)
+ 860 '''
+ 861 Specifies the 18O/16O fractionation factor generally applicable
+ 862 to acid reactions in the dataset. Currently used by `D4xdata.wg()`,
+ 863 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`.
+ 864
+ 865 By default equal to 1.008129 (calcite reacted at 90 °C,
+ 866 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)).
+ 867 '''
+ 868
+ 869 Nominal_d13C_VPDB = {
+ 870 'ETH-1': 2.02,
+ 871 'ETH-2': -10.17,
+ 872 'ETH-3': 1.71,
+ 873 } # (Bernasconi et al., 2018)
+ 874 '''
+ 875 Nominal δ13C_VPDB values assigned to carbonate standards, used by
+ 876 `D4xdata.standardize_d13C()`.
+ 877
+ 878 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after
+ 879 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
+ 880 '''
+ 881
+ 882 Nominal_d18O_VPDB = {
+ 883 'ETH-1': -2.19,
+ 884 'ETH-2': -18.69,
+ 885 'ETH-3': -1.78,
+ 886 } # (Bernasconi et al., 2018)
+ 887 '''
+ 888 Nominal δ18O_VPDB values assigned to carbonate standards, used by
+ 889 `D4xdata.standardize_d18O()`.
+ 890
+ 891 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after
+ 892 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385).
+ 893 '''
+ 894
+ 895 d13C_STANDARDIZATION_METHOD = '2pt'
+ 896 '''
+ 897 Method by which to standardize δ13C values:
+ 898
+ 899 + `none`: do not apply any δ13C standardization.
+ 900 + `'1pt'`: within each session, offset all initial δ13C values so as to
+ 901 minimize the difference between final δ13C_VPDB values and
+ 902 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined).
+ 903 + `'2pt'`: within each session, apply a affine trasformation to all δ13C
+ 904 values so as to minimize the difference between final δ13C_VPDB
+ 905 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB`
+ 906 is defined).
+ 907 '''
+ 908
+ 909 d18O_STANDARDIZATION_METHOD = '2pt'
+ 910 '''
+ 911 Method by which to standardize δ18O values:
+ 912
+ 913 + `none`: do not apply any δ18O standardization.
+ 914 + `'1pt'`: within each session, offset all initial δ18O values so as to
+ 915 minimize the difference between final δ18O_VPDB values and
+ 916 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined).
+ 917 + `'2pt'`: within each session, apply a affine trasformation to all δ18O
+ 918 values so as to minimize the difference between final δ18O_VPDB
+ 919 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB`
+ 920 is defined).
+ 921 '''
+ 922
+ 923 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
+ 924 '''
+ 925 **Parameters**
+ 926
+ 927 + `l`: a list of dictionaries, with each dictionary including at least the keys
+ 928 `Sample`, `d45`, `d46`, and `d47` or `d48`.
+ 929 + `mass`: `'47'` or `'48'`
+ 930 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
+ 931 + `session`: define session name for analyses without a `Session` key
+ 932 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
+ 933
+ 934 Returns a `D4xdata` object derived from `list`.
+ 935 '''
+ 936 self._4x = mass
+ 937 self.verbose = verbose
+ 938 self.prefix = 'D4xdata'
+ 939 self.logfile = logfile
+ 940 list.__init__(self, l)
+ 941 self.Nf = None
+ 942 self.repeatability = {}
+ 943 self.refresh(session = session)
+ 944
+ 945
+ 946 def make_verbal(oldfun):
+ 947 '''
+ 948 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
+ 949 '''
+ 950 @wraps(oldfun)
+ 951 def newfun(*args, verbose = '', **kwargs):
+ 952 myself = args[0]
+ 953 oldprefix = myself.prefix
+ 954 myself.prefix = oldfun.__name__
+ 955 if verbose != '':
+ 956 oldverbose = myself.verbose
+ 957 myself.verbose = verbose
+ 958 out = oldfun(*args, **kwargs)
+ 959 myself.prefix = oldprefix
+ 960 if verbose != '':
+ 961 myself.verbose = oldverbose
+ 962 return out
+ 963 return newfun
+ 964
+ 965
+ 966 def msg(self, txt):
+ 967 '''
+ 968 Log a message to `self.logfile`, and print it out if `verbose = True`
+ 969 '''
+ 970 self.log(txt)
+ 971 if self.verbose:
+ 972 print(f'{f"[{self.prefix}]":<16} {txt}')
+ 973
+ 974
+ 975 def vmsg(self, txt):
+ 976 '''
+ 977 Log a message to `self.logfile` and print it out
+ 978 '''
+ 979 self.log(txt)
+ 980 print(txt)
+ 981
+ 982
+ 983 def log(self, *txts):
+ 984 '''
+ 985 Log a message to `self.logfile`
+ 986 '''
+ 987 if self.logfile:
+ 988 with open(self.logfile, 'a') as fid:
+ 989 for txt in txts:
+ 990 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
+ 991
+ 992
+ 993 def refresh(self, session = 'mySession'):
+ 994 '''
+ 995 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
+ 996 '''
+ 997 self.fill_in_missing_info(session = session)
+ 998 self.refresh_sessions()
+ 999 self.refresh_samples()
+1000
+1001
+1002 def refresh_sessions(self):
+1003 '''
+1004 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
+1005 to `False` for all sessions.
+1006 '''
+1007 self.sessions = {
+1008 s: {'data': [r for r in self if r['Session'] == s]}
+1009 for s in sorted({r['Session'] for r in self})
+1010 }
+1011 for s in self.sessions:
+1012 self.sessions[s]['scrambling_drift'] = False
+1013 self.sessions[s]['slope_drift'] = False
+1014 self.sessions[s]['wg_drift'] = False
+1015 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
+1016 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
+1017
+1018
+1019 def refresh_samples(self):
+1020 '''
+1021 Define `self.samples`, `self.anchors`, and `self.unknowns`.
+1022 '''
+1023 self.samples = {
+1024 s: {'data': [r for r in self if r['Sample'] == s]}
+1025 for s in sorted({r['Sample'] for r in self})
+1026 }
+1027 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
+1028 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
+1029
+1030
+1031 def read(self, filename, sep = '', session = ''):
+1032 '''
+1033 Read file in csv format to load data into a `D47data` object.
+1034
+1035 In the csv file, spaces before and after field separators (`','` by default)
+1036 are optional. Each line corresponds to a single analysis.
1037
-1038 + `UID`: a unique identifier
-1039 + `Session`: an identifier for the analytical session
-1040 + `Sample`: a sample identifier
-1041 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1042
-1043 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1044 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1045 and `d49` are optional, and set to NaN by default.
-1046
-1047 **Parameters**
+1038 The required fields are:
+1039
+1040 + `UID`: a unique identifier
+1041 + `Session`: an identifier for the analytical session
+1042 + `Sample`: a sample identifier
+1043 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1044
+1045 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1046 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1047 and `d49` are optional, and set to NaN by default.
1048
-1049 + `fileneme`: the path of the file to read
-1050 + `sep`: csv separator delimiting the fields
-1051 + `session`: set `Session` field to this string for all analyses
-1052 '''
-1053 with open(filename) as fid:
-1054 self.input(fid.read(), sep = sep, session = session)
-1055
-1056
-1057 def input(self, txt, sep = '', session = ''):
-1058 '''
-1059 Read `txt` string in csv format to load analysis data into a `D47data` object.
-1060
-1061 In the csv string, spaces before and after field separators (`','` by default)
-1062 are optional. Each line corresponds to a single analysis.
-1063
-1064 The required fields are:
+1049 **Parameters**
+1050
+1051 + `fileneme`: the path of the file to read
+1052 + `sep`: csv separator delimiting the fields
+1053 + `session`: set `Session` field to this string for all analyses
+1054 '''
+1055 with open(filename) as fid:
+1056 self.input(fid.read(), sep = sep, session = session)
+1057
+1058
+1059 def input(self, txt, sep = '', session = ''):
+1060 '''
+1061 Read `txt` string in csv format to load analysis data into a `D47data` object.
+1062
+1063 In the csv string, spaces before and after field separators (`','` by default)
+1064 are optional. Each line corresponds to a single analysis.
1065
-1066 + `UID`: a unique identifier
-1067 + `Session`: an identifier for the analytical session
-1068 + `Sample`: a sample identifier
-1069 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1070
-1071 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1072 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1073 and `d49` are optional, and set to NaN by default.
-1074
-1075 **Parameters**
+1066 The required fields are:
+1067
+1068 + `UID`: a unique identifier
+1069 + `Session`: an identifier for the analytical session
+1070 + `Sample`: a sample identifier
+1071 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1072
+1073 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1074 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1075 and `d49` are optional, and set to NaN by default.
1076
-1077 + `txt`: the csv string to read
-1078 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
-1079 whichever appers most often in `txt`.
-1080 + `session`: set `Session` field to this string for all analyses
-1081 '''
-1082 if sep == '':
-1083 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
-1084 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
-1085 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
-1086
-1087 if session != '':
-1088 for r in data:
-1089 r['Session'] = session
-1090
-1091 self += data
-1092 self.refresh()
-1093
-1094
-1095 @make_verbal
-1096 def wg(self, samples = None, a18_acid = None):
-1097 '''
-1098 Compute bulk composition of the working gas for each session based on
-1099 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
-1100 `self.Nominal_d18O_VPDB`.
-1101 '''
-1102
-1103 self.msg('Computing WG composition:')
+1077 **Parameters**
+1078
+1079 + `txt`: the csv string to read
+1080 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
+1081 whichever appers most often in `txt`.
+1082 + `session`: set `Session` field to this string for all analyses
+1083 '''
+1084 if sep == '':
+1085 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
+1086 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
+1087 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
+1088
+1089 if session != '':
+1090 for r in data:
+1091 r['Session'] = session
+1092
+1093 self += data
+1094 self.refresh()
+1095
+1096
+1097 @make_verbal
+1098 def wg(self, samples = None, a18_acid = None):
+1099 '''
+1100 Compute bulk composition of the working gas for each session based on
+1101 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
+1102 `self.Nominal_d18O_VPDB`.
+1103 '''
1104
-1105 if a18_acid is None:
-1106 a18_acid = self.ALPHA_18O_ACID_REACTION
-1107 if samples is None:
-1108 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
-1109
-1110 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1105 self.msg('Computing WG composition:')
+1106
+1107 if a18_acid is None:
+1108 a18_acid = self.ALPHA_18O_ACID_REACTION
+1109 if samples is None:
+1110 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
1111
-1112 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
-1113 R45R46_standards = {}
-1114 for sample in samples:
-1115 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
-1116 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
-1117 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
-1118 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
-1119 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
-1120
-1121 C12_s = 1 / (1 + R13_s)
-1122 C13_s = R13_s / (1 + R13_s)
-1123 C16_s = 1 / (1 + R17_s + R18_s)
-1124 C17_s = R17_s / (1 + R17_s + R18_s)
-1125 C18_s = R18_s / (1 + R17_s + R18_s)
-1126
-1127 C626_s = C12_s * C16_s ** 2
-1128 C627_s = 2 * C12_s * C16_s * C17_s
-1129 C628_s = 2 * C12_s * C16_s * C18_s
-1130 C636_s = C13_s * C16_s ** 2
-1131 C637_s = 2 * C13_s * C16_s * C17_s
-1132 C727_s = C12_s * C17_s ** 2
-1133
-1134 R45_s = (C627_s + C636_s) / C626_s
-1135 R46_s = (C628_s + C637_s + C727_s) / C626_s
-1136 R45R46_standards[sample] = (R45_s, R46_s)
-1137
-1138 for s in self.sessions:
-1139 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
-1140 assert db, f'No sample from {samples} found in session "{s}".'
-1141# dbsamples = sorted({r['Sample'] for r in db})
-1142
-1143 X = [r['d45'] for r in db]
-1144 Y = [R45R46_standards[r['Sample']][0] for r in db]
-1145 x1, x2 = np.min(X), np.max(X)
-1146
-1147 if x1 < x2:
-1148 wgcoord = x1/(x1-x2)
-1149 else:
-1150 wgcoord = 999
-1151
-1152 if wgcoord < -.5 or wgcoord > 1.5:
-1153 # unreasonable to extrapolate to d45 = 0
-1154 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1155 else :
-1156 # d45 = 0 is reasonably well bracketed
-1157 R45_wg = np.polyfit(X, Y, 1)[1]
-1158
-1159 X = [r['d46'] for r in db]
-1160 Y = [R45R46_standards[r['Sample']][1] for r in db]
-1161 x1, x2 = np.min(X), np.max(X)
-1162
-1163 if x1 < x2:
-1164 wgcoord = x1/(x1-x2)
-1165 else:
-1166 wgcoord = 999
-1167
-1168 if wgcoord < -.5 or wgcoord > 1.5:
-1169 # unreasonable to extrapolate to d46 = 0
-1170 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1171 else :
-1172 # d46 = 0 is reasonably well bracketed
-1173 R46_wg = np.polyfit(X, Y, 1)[1]
-1174
-1175 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
+1112 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1113
+1114 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
+1115 R45R46_standards = {}
+1116 for sample in samples:
+1117 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
+1118 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
+1119 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
+1120 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
+1121 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
+1122
+1123 C12_s = 1 / (1 + R13_s)
+1124 C13_s = R13_s / (1 + R13_s)
+1125 C16_s = 1 / (1 + R17_s + R18_s)
+1126 C17_s = R17_s / (1 + R17_s + R18_s)
+1127 C18_s = R18_s / (1 + R17_s + R18_s)
+1128
+1129 C626_s = C12_s * C16_s ** 2
+1130 C627_s = 2 * C12_s * C16_s * C17_s
+1131 C628_s = 2 * C12_s * C16_s * C18_s
+1132 C636_s = C13_s * C16_s ** 2
+1133 C637_s = 2 * C13_s * C16_s * C17_s
+1134 C727_s = C12_s * C17_s ** 2
+1135
+1136 R45_s = (C627_s + C636_s) / C626_s
+1137 R46_s = (C628_s + C637_s + C727_s) / C626_s
+1138 R45R46_standards[sample] = (R45_s, R46_s)
+1139
+1140 for s in self.sessions:
+1141 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
+1142 assert db, f'No sample from {samples} found in session "{s}".'
+1143# dbsamples = sorted({r['Sample'] for r in db})
+1144
+1145 X = [r['d45'] for r in db]
+1146 Y = [R45R46_standards[r['Sample']][0] for r in db]
+1147 x1, x2 = np.min(X), np.max(X)
+1148
+1149 if x1 < x2:
+1150 wgcoord = x1/(x1-x2)
+1151 else:
+1152 wgcoord = 999
+1153
+1154 if wgcoord < -.5 or wgcoord > 1.5:
+1155 # unreasonable to extrapolate to d45 = 0
+1156 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1157 else :
+1158 # d45 = 0 is reasonably well bracketed
+1159 R45_wg = np.polyfit(X, Y, 1)[1]
+1160
+1161 X = [r['d46'] for r in db]
+1162 Y = [R45R46_standards[r['Sample']][1] for r in db]
+1163 x1, x2 = np.min(X), np.max(X)
+1164
+1165 if x1 < x2:
+1166 wgcoord = x1/(x1-x2)
+1167 else:
+1168 wgcoord = 999
+1169
+1170 if wgcoord < -.5 or wgcoord > 1.5:
+1171 # unreasonable to extrapolate to d46 = 0
+1172 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1173 else :
+1174 # d46 = 0 is reasonably well bracketed
+1175 R46_wg = np.polyfit(X, Y, 1)[1]
1176
-1177 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1177 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
1178
-1179 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
-1180 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
-1181 for r in self.sessions[s]['data']:
-1182 r['d13Cwg_VPDB'] = d13Cwg_VPDB
-1183 r['d18Owg_VSMOW'] = d18Owg_VSMOW
-1184
-1185
-1186 def compute_bulk_delta(self, R45, R46, D17O = 0):
-1187 '''
-1188 Compute δ13C_VPDB and δ18O_VSMOW,
-1189 by solving the generalized form of equation (17) from
-1190 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
-1191 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
-1192 solving the corresponding second-order Taylor polynomial.
-1193 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
-1194 '''
-1195
-1196 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+1179 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1180
+1181 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
+1182 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
+1183 for r in self.sessions[s]['data']:
+1184 r['d13Cwg_VPDB'] = d13Cwg_VPDB
+1185 r['d18Owg_VSMOW'] = d18Owg_VSMOW
+1186
+1187
+1188 def compute_bulk_delta(self, R45, R46, D17O = 0):
+1189 '''
+1190 Compute δ13C_VPDB and δ18O_VSMOW,
+1191 by solving the generalized form of equation (17) from
+1192 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
+1193 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
+1194 solving the corresponding second-order Taylor polynomial.
+1195 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
+1196 '''
1197
-1198 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
-1199 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
-1200 C = 2 * self.R18_VSMOW
-1201 D = -R46
-1202
-1203 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
-1204 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
-1205 cc = A + B + C + D
-1206
-1207 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1198 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+1199
+1200 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
+1201 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
+1202 C = 2 * self.R18_VSMOW
+1203 D = -R46
+1204
+1205 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
+1206 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
+1207 cc = A + B + C + D
1208
-1209 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
-1210 R17 = K * R18 ** self.LAMBDA_17
-1211 R13 = R45 - 2 * R17
-1212
-1213 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
+1209 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1210
+1211 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
+1212 R17 = K * R18 ** self.LAMBDA_17
+1213 R13 = R45 - 2 * R17
1214
-1215 return d13C_VPDB, d18O_VSMOW
+1215 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
1216
-1217
-1218 @make_verbal
-1219 def crunch(self, verbose = ''):
-1220 '''
-1221 Compute bulk composition and raw clumped isotope anomalies for all analyses.
-1222 '''
-1223 for r in self:
-1224 self.compute_bulk_and_clumping_deltas(r)
-1225 self.standardize_d13C()
-1226 self.standardize_d18O()
-1227 self.msg(f"Crunched {len(self)} analyses.")
-1228
-1229
-1230 def fill_in_missing_info(self, session = 'mySession'):
-1231 '''
-1232 Fill in optional fields with default values
-1233 '''
-1234 for i,r in enumerate(self):
-1235 if 'D17O' not in r:
-1236 r['D17O'] = 0.
-1237 if 'UID' not in r:
-1238 r['UID'] = f'{i+1}'
-1239 if 'Session' not in r:
-1240 r['Session'] = session
-1241 for k in ['d47', 'd48', 'd49']:
-1242 if k not in r:
-1243 r[k] = np.nan
-1244
-1245
-1246 def standardize_d13C(self):
-1247 '''
-1248 Perform δ13C standadization within each session `s` according to
-1249 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
-1250 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
-1251 may be redefined abitrarily at a later stage.
-1252 '''
-1253 for s in self.sessions:
-1254 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
-1255 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
-1256 X,Y = zip(*XY)
-1257 if self.sessions[s]['d13C_standardization_method'] == '1pt':
-1258 offset = np.mean(Y) - np.mean(X)
-1259 for r in self.sessions[s]['data']:
-1260 r['d13C_VPDB'] += offset
-1261 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
-1262 a,b = np.polyfit(X,Y,1)
-1263 for r in self.sessions[s]['data']:
-1264 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
-1265
-1266 def standardize_d18O(self):
-1267 '''
-1268 Perform δ18O standadization within each session `s` according to
-1269 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
-1270 which is defined by default by `D47data.refresh_sessions()`as equal to
-1271 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
-1272 '''
-1273 for s in self.sessions:
-1274 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
-1275 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
-1276 X,Y = zip(*XY)
-1277 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
-1278 if self.sessions[s]['d18O_standardization_method'] == '1pt':
-1279 offset = np.mean(Y) - np.mean(X)
-1280 for r in self.sessions[s]['data']:
-1281 r['d18O_VSMOW'] += offset
-1282 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
-1283 a,b = np.polyfit(X,Y,1)
-1284 for r in self.sessions[s]['data']:
-1285 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
-1286
-1287
-1288 def compute_bulk_and_clumping_deltas(self, r):
-1289 '''
-1290 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
-1291 '''
-1292
-1293 # Compute working gas R13, R18, and isobar ratios
-1294 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
-1295 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
-1296 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
-1297
-1298 # Compute analyte isobar ratios
-1299 R45 = (1 + r['d45'] / 1000) * R45_wg
-1300 R46 = (1 + r['d46'] / 1000) * R46_wg
-1301 R47 = (1 + r['d47'] / 1000) * R47_wg
-1302 R48 = (1 + r['d48'] / 1000) * R48_wg
-1303 R49 = (1 + r['d49'] / 1000) * R49_wg
-1304
-1305 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
-1306 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
-1307 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
-1308
-1309 # Compute stochastic isobar ratios of the analyte
-1310 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
-1311 R13, R18, D17O = r['D17O']
-1312 )
-1313
-1314 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
-1315 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
-1316 if (R45 / R45stoch - 1) > 5e-8:
-1317 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
-1318 if (R46 / R46stoch - 1) > 5e-8:
-1319 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
-1320
-1321 # Compute raw clumped isotope anomalies
-1322 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
-1323 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
-1324 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
-1325
-1326
-1327 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
-1328 '''
-1329 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
-1330 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
-1331 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
-1332 '''
-1333
-1334 # Compute R17
-1335 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
-1336
-1337 # Compute isotope concentrations
-1338 C12 = (1 + R13) ** -1
-1339 C13 = C12 * R13
-1340 C16 = (1 + R17 + R18) ** -1
-1341 C17 = C16 * R17
-1342 C18 = C16 * R18
-1343
-1344 # Compute stochastic isotopologue concentrations
-1345 C626 = C16 * C12 * C16
-1346 C627 = C16 * C12 * C17 * 2
-1347 C628 = C16 * C12 * C18 * 2
-1348 C636 = C16 * C13 * C16
-1349 C637 = C16 * C13 * C17 * 2
-1350 C638 = C16 * C13 * C18 * 2
-1351 C727 = C17 * C12 * C17
-1352 C728 = C17 * C12 * C18 * 2
-1353 C737 = C17 * C13 * C17
-1354 C738 = C17 * C13 * C18 * 2
-1355 C828 = C18 * C12 * C18
-1356 C838 = C18 * C13 * C18
-1357
-1358 # Compute stochastic isobar ratios
-1359 R45 = (C636 + C627) / C626
-1360 R46 = (C628 + C637 + C727) / C626
-1361 R47 = (C638 + C728 + C737) / C626
-1362 R48 = (C738 + C828) / C626
-1363 R49 = C838 / C626
-1364
-1365 # Account for stochastic anomalies
-1366 R47 *= 1 + D47 / 1000
-1367 R48 *= 1 + D48 / 1000
-1368 R49 *= 1 + D49 / 1000
-1369
-1370 # Return isobar ratios
-1371 return R45, R46, R47, R48, R49
-1372
-1373
-1374 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
-1375 '''
-1376 Split unknown samples by UID (treat all analyses as different samples)
-1377 or by session (treat analyses of a given sample in different sessions as
-1378 different samples).
-1379
-1380 **Parameters**
+1217 return d13C_VPDB, d18O_VSMOW
+1218
+1219
+1220 @make_verbal
+1221 def crunch(self, verbose = ''):
+1222 '''
+1223 Compute bulk composition and raw clumped isotope anomalies for all analyses.
+1224 '''
+1225 for r in self:
+1226 self.compute_bulk_and_clumping_deltas(r)
+1227 self.standardize_d13C()
+1228 self.standardize_d18O()
+1229 self.msg(f"Crunched {len(self)} analyses.")
+1230
+1231
+1232 def fill_in_missing_info(self, session = 'mySession'):
+1233 '''
+1234 Fill in optional fields with default values
+1235 '''
+1236 for i,r in enumerate(self):
+1237 if 'D17O' not in r:
+1238 r['D17O'] = 0.
+1239 if 'UID' not in r:
+1240 r['UID'] = f'{i+1}'
+1241 if 'Session' not in r:
+1242 r['Session'] = session
+1243 for k in ['d47', 'd48', 'd49']:
+1244 if k not in r:
+1245 r[k] = np.nan
+1246
+1247
+1248 def standardize_d13C(self):
+1249 '''
+1250 Perform δ13C standadization within each session `s` according to
+1251 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
+1252 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
+1253 may be redefined abitrarily at a later stage.
+1254 '''
+1255 for s in self.sessions:
+1256 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
+1257 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
+1258 X,Y = zip(*XY)
+1259 if self.sessions[s]['d13C_standardization_method'] == '1pt':
+1260 offset = np.mean(Y) - np.mean(X)
+1261 for r in self.sessions[s]['data']:
+1262 r['d13C_VPDB'] += offset
+1263 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
+1264 a,b = np.polyfit(X,Y,1)
+1265 for r in self.sessions[s]['data']:
+1266 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
+1267
+1268 def standardize_d18O(self):
+1269 '''
+1270 Perform δ18O standadization within each session `s` according to
+1271 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
+1272 which is defined by default by `D47data.refresh_sessions()`as equal to
+1273 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
+1274 '''
+1275 for s in self.sessions:
+1276 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
+1277 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
+1278 X,Y = zip(*XY)
+1279 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
+1280 if self.sessions[s]['d18O_standardization_method'] == '1pt':
+1281 offset = np.mean(Y) - np.mean(X)
+1282 for r in self.sessions[s]['data']:
+1283 r['d18O_VSMOW'] += offset
+1284 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
+1285 a,b = np.polyfit(X,Y,1)
+1286 for r in self.sessions[s]['data']:
+1287 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
+1288
+1289
+1290 def compute_bulk_and_clumping_deltas(self, r):
+1291 '''
+1292 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
+1293 '''
+1294
+1295 # Compute working gas R13, R18, and isobar ratios
+1296 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
+1297 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
+1298 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
+1299
+1300 # Compute analyte isobar ratios
+1301 R45 = (1 + r['d45'] / 1000) * R45_wg
+1302 R46 = (1 + r['d46'] / 1000) * R46_wg
+1303 R47 = (1 + r['d47'] / 1000) * R47_wg
+1304 R48 = (1 + r['d48'] / 1000) * R48_wg
+1305 R49 = (1 + r['d49'] / 1000) * R49_wg
+1306
+1307 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
+1308 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
+1309 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
+1310
+1311 # Compute stochastic isobar ratios of the analyte
+1312 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
+1313 R13, R18, D17O = r['D17O']
+1314 )
+1315
+1316 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
+1317 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
+1318 if (R45 / R45stoch - 1) > 5e-8:
+1319 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
+1320 if (R46 / R46stoch - 1) > 5e-8:
+1321 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
+1322
+1323 # Compute raw clumped isotope anomalies
+1324 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
+1325 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
+1326 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
+1327
+1328
+1329 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
+1330 '''
+1331 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
+1332 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
+1333 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
+1334 '''
+1335
+1336 # Compute R17
+1337 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
+1338
+1339 # Compute isotope concentrations
+1340 C12 = (1 + R13) ** -1
+1341 C13 = C12 * R13
+1342 C16 = (1 + R17 + R18) ** -1
+1343 C17 = C16 * R17
+1344 C18 = C16 * R18
+1345
+1346 # Compute stochastic isotopologue concentrations
+1347 C626 = C16 * C12 * C16
+1348 C627 = C16 * C12 * C17 * 2
+1349 C628 = C16 * C12 * C18 * 2
+1350 C636 = C16 * C13 * C16
+1351 C637 = C16 * C13 * C17 * 2
+1352 C638 = C16 * C13 * C18 * 2
+1353 C727 = C17 * C12 * C17
+1354 C728 = C17 * C12 * C18 * 2
+1355 C737 = C17 * C13 * C17
+1356 C738 = C17 * C13 * C18 * 2
+1357 C828 = C18 * C12 * C18
+1358 C838 = C18 * C13 * C18
+1359
+1360 # Compute stochastic isobar ratios
+1361 R45 = (C636 + C627) / C626
+1362 R46 = (C628 + C637 + C727) / C626
+1363 R47 = (C638 + C728 + C737) / C626
+1364 R48 = (C738 + C828) / C626
+1365 R49 = C838 / C626
+1366
+1367 # Account for stochastic anomalies
+1368 R47 *= 1 + D47 / 1000
+1369 R48 *= 1 + D48 / 1000
+1370 R49 *= 1 + D49 / 1000
+1371
+1372 # Return isobar ratios
+1373 return R45, R46, R47, R48, R49
+1374
+1375
+1376 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
+1377 '''
+1378 Split unknown samples by UID (treat all analyses as different samples)
+1379 or by session (treat analyses of a given sample in different sessions as
+1380 different samples).
1381
-1382 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
-1383 + `grouping`: `by_uid` | `by_session`
-1384 '''
-1385 if samples_to_split == 'all':
-1386 samples_to_split = [s for s in self.unknowns]
-1387 gkeys = {'by_uid':'UID', 'by_session':'Session'}
-1388 self.grouping = grouping.lower()
-1389 if self.grouping in gkeys:
-1390 gkey = gkeys[self.grouping]
-1391 for r in self:
-1392 if r['Sample'] in samples_to_split:
-1393 r['Sample_original'] = r['Sample']
-1394 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
-1395 elif r['Sample'] in self.unknowns:
-1396 r['Sample_original'] = r['Sample']
-1397 self.refresh_samples()
-1398
-1399
-1400 def unsplit_samples(self, tables = False):
-1401 '''
-1402 Reverse the effects of `D47data.split_samples()`.
-1403
-1404 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+1382 **Parameters**
+1383
+1384 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
+1385 + `grouping`: `by_uid` | `by_session`
+1386 '''
+1387 if samples_to_split == 'all':
+1388 samples_to_split = [s for s in self.unknowns]
+1389 gkeys = {'by_uid':'UID', 'by_session':'Session'}
+1390 self.grouping = grouping.lower()
+1391 if self.grouping in gkeys:
+1392 gkey = gkeys[self.grouping]
+1393 for r in self:
+1394 if r['Sample'] in samples_to_split:
+1395 r['Sample_original'] = r['Sample']
+1396 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
+1397 elif r['Sample'] in self.unknowns:
+1398 r['Sample_original'] = r['Sample']
+1399 self.refresh_samples()
+1400
+1401
+1402 def unsplit_samples(self, tables = False):
+1403 '''
+1404 Reverse the effects of `D47data.split_samples()`.
1405
-1406 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
-1407 probably use `D4xdata.combine_samples()` instead to reverse the effects of
-1408 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
-1409 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
-1410 that case session-averaged Δ4x values are statistically independent).
-1411 '''
-1412 unknowns_old = sorted({s for s in self.unknowns})
-1413 CM_old = self.standardization.covar[:,:]
-1414 VD_old = self.standardization.params.valuesdict().copy()
-1415 vars_old = self.standardization.var_names
-1416
-1417 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1406 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+1407
+1408 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
+1409 probably use `D4xdata.combine_samples()` instead to reverse the effects of
+1410 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
+1411 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
+1412 that case session-averaged Δ4x values are statistically independent).
+1413 '''
+1414 unknowns_old = sorted({s for s in self.unknowns})
+1415 CM_old = self.standardization.covar[:,:]
+1416 VD_old = self.standardization.params.valuesdict().copy()
+1417 vars_old = self.standardization.var_names
1418
-1419 Ns = len(vars_old) - len(unknowns_old)
-1420 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
-1421 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
-1422
-1423 W = np.zeros((len(vars_new), len(vars_old)))
-1424 W[:Ns,:Ns] = np.eye(Ns)
-1425 for u in unknowns_new:
-1426 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
-1427 if self.grouping == 'by_session':
-1428 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
-1429 elif self.grouping == 'by_uid':
-1430 weights = [1 for s in splits]
-1431 sw = sum(weights)
-1432 weights = [w/sw for w in weights]
-1433 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
-1434
-1435 CM_new = W @ CM_old @ W.T
-1436 V = W @ np.array([[VD_old[k]] for k in vars_old])
-1437 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
-1438
-1439 self.standardization.covar = CM_new
-1440 self.standardization.params.valuesdict = lambda : VD_new
-1441 self.standardization.var_names = vars_new
-1442
-1443 for r in self:
-1444 if r['Sample'] in self.unknowns:
-1445 r['Sample_split'] = r['Sample']
-1446 r['Sample'] = r['Sample_original']
-1447
-1448 self.refresh_samples()
-1449 self.consolidate_samples()
-1450 self.repeatabilities()
-1451
-1452 if tables:
-1453 self.table_of_analyses()
-1454 self.table_of_samples()
-1455
-1456 def assign_timestamps(self):
-1457 '''
-1458 Assign a time field `t` of type `float` to each analysis.
-1459
-1460 If `TimeTag` is one of the data fields, `t` is equal within a given session
-1461 to `TimeTag` minus the mean value of `TimeTag` for that session.
-1462 Otherwise, `TimeTag` is by default equal to the index of each analysis
-1463 in the dataset and `t` is defined as above.
-1464 '''
-1465 for session in self.sessions:
-1466 sdata = self.sessions[session]['data']
-1467 try:
-1468 t0 = np.mean([r['TimeTag'] for r in sdata])
-1469 for r in sdata:
-1470 r['t'] = r['TimeTag'] - t0
-1471 except KeyError:
-1472 t0 = (len(sdata)-1)/2
-1473 for t,r in enumerate(sdata):
-1474 r['t'] = t - t0
-1475
-1476
-1477 def report(self):
-1478 '''
-1479 Prints a report on the standardization fit.
-1480 Only applicable after `D4xdata.standardize(method='pooled')`.
-1481 '''
-1482 report_fit(self.standardization)
-1483
-1484
-1485 def combine_samples(self, sample_groups):
-1486 '''
-1487 Combine analyses of different samples to compute weighted average Δ4x
-1488 and new error (co)variances corresponding to the groups defined by the `sample_groups`
-1489 dictionary.
-1490
-1491 Caution: samples are weighted by number of replicate analyses, which is a
-1492 reasonable default behavior but is not always optimal (e.g., in the case of strongly
-1493 correlated analytical errors for one or more samples).
-1494
-1495 Returns a tuplet of:
+1419 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1420
+1421 Ns = len(vars_old) - len(unknowns_old)
+1422 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
+1423 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
+1424
+1425 W = np.zeros((len(vars_new), len(vars_old)))
+1426 W[:Ns,:Ns] = np.eye(Ns)
+1427 for u in unknowns_new:
+1428 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
+1429 if self.grouping == 'by_session':
+1430 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
+1431 elif self.grouping == 'by_uid':
+1432 weights = [1 for s in splits]
+1433 sw = sum(weights)
+1434 weights = [w/sw for w in weights]
+1435 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
+1436
+1437 CM_new = W @ CM_old @ W.T
+1438 V = W @ np.array([[VD_old[k]] for k in vars_old])
+1439 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
+1440
+1441 self.standardization.covar = CM_new
+1442 self.standardization.params.valuesdict = lambda : VD_new
+1443 self.standardization.var_names = vars_new
+1444
+1445 for r in self:
+1446 if r['Sample'] in self.unknowns:
+1447 r['Sample_split'] = r['Sample']
+1448 r['Sample'] = r['Sample_original']
+1449
+1450 self.refresh_samples()
+1451 self.consolidate_samples()
+1452 self.repeatabilities()
+1453
+1454 if tables:
+1455 self.table_of_analyses()
+1456 self.table_of_samples()
+1457
+1458 def assign_timestamps(self):
+1459 '''
+1460 Assign a time field `t` of type `float` to each analysis.
+1461
+1462 If `TimeTag` is one of the data fields, `t` is equal within a given session
+1463 to `TimeTag` minus the mean value of `TimeTag` for that session.
+1464 Otherwise, `TimeTag` is by default equal to the index of each analysis
+1465 in the dataset and `t` is defined as above.
+1466 '''
+1467 for session in self.sessions:
+1468 sdata = self.sessions[session]['data']
+1469 try:
+1470 t0 = np.mean([r['TimeTag'] for r in sdata])
+1471 for r in sdata:
+1472 r['t'] = r['TimeTag'] - t0
+1473 except KeyError:
+1474 t0 = (len(sdata)-1)/2
+1475 for t,r in enumerate(sdata):
+1476 r['t'] = t - t0
+1477
+1478
+1479 def report(self):
+1480 '''
+1481 Prints a report on the standardization fit.
+1482 Only applicable after `D4xdata.standardize(method='pooled')`.
+1483 '''
+1484 report_fit(self.standardization)
+1485
+1486
+1487 def combine_samples(self, sample_groups):
+1488 '''
+1489 Combine analyses of different samples to compute weighted average Δ4x
+1490 and new error (co)variances corresponding to the groups defined by the `sample_groups`
+1491 dictionary.
+1492
+1493 Caution: samples are weighted by number of replicate analyses, which is a
+1494 reasonable default behavior but is not always optimal (e.g., in the case of strongly
+1495 correlated analytical errors for one or more samples).
1496
-1497 + the list of group names
-1498 + an array of the corresponding Δ4x values
-1499 + the corresponding (co)variance matrix
-1500
-1501 **Parameters**
-1502
-1503 + `sample_groups`: a dictionary of the form:
-1504 ```py
-1505 {'group1': ['sample_1', 'sample_2'],
-1506 'group2': ['sample_3', 'sample_4', 'sample_5']}
-1507 ```
-1508 '''
-1509
-1510 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
-1511 groups = sorted(sample_groups.keys())
-1512 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
-1513 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
-1514 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
-1515 W = np.array([
-1516 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
-1517 for j in groups])
-1518 D4x_new = W @ D4x_old
-1519 CM_new = W @ CM_old @ W.T
-1520
-1521 return groups, D4x_new[:,0], CM_new
-1522
-1523
-1524 @make_verbal
-1525 def standardize(self,
-1526 method = 'pooled',
-1527 weighted_sessions = [],
-1528 consolidate = True,
-1529 consolidate_tables = False,
-1530 consolidate_plots = False,
-1531 constraints = {},
-1532 ):
-1533 '''
-1534 Compute absolute Δ4x values for all replicate analyses and for sample averages.
-1535 If `method` argument is set to `'pooled'`, the standardization processes all sessions
-1536 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
-1537 i.e. that their true Δ4x value does not change between sessions,
-1538 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
-1539 `'indep_sessions'`, the standardization processes each session independently, based only
-1540 on anchors analyses.
-1541 '''
-1542
-1543 self.standardization_method = method
-1544 self.assign_timestamps()
-1545
-1546 if method == 'pooled':
-1547 if weighted_sessions:
-1548 for session_group in weighted_sessions:
-1549 if self._4x == '47':
-1550 X = D47data([r for r in self if r['Session'] in session_group])
-1551 elif self._4x == '48':
-1552 X = D48data([r for r in self if r['Session'] in session_group])
-1553 X.Nominal_D4x = self.Nominal_D4x.copy()
-1554 X.refresh()
-1555 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
-1556 w = np.sqrt(result.redchi)
-1557 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
-1558 for r in X:
-1559 r[f'wD{self._4x}raw'] *= w
-1560 else:
-1561 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
-1562 for r in self:
-1563 r[f'wD{self._4x}raw'] = 1.
-1564
-1565 params = Parameters()
-1566 for k,session in enumerate(self.sessions):
-1567 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
-1568 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
-1569 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
-1570 s = pf(session)
-1571 params.add(f'a_{s}', value = 0.9)
-1572 params.add(f'b_{s}', value = 0.)
-1573 params.add(f'c_{s}', value = -0.9)
-1574 params.add(f'a2_{s}', value = 0.,
-1575# vary = self.sessions[session]['scrambling_drift'],
-1576 )
-1577 params.add(f'b2_{s}', value = 0.,
-1578# vary = self.sessions[session]['slope_drift'],
-1579 )
-1580 params.add(f'c2_{s}', value = 0.,
-1581# vary = self.sessions[session]['wg_drift'],
-1582 )
-1583 if not self.sessions[session]['scrambling_drift']:
-1584 params[f'a2_{s}'].expr = '0'
-1585 if not self.sessions[session]['slope_drift']:
-1586 params[f'b2_{s}'].expr = '0'
-1587 if not self.sessions[session]['wg_drift']:
-1588 params[f'c2_{s}'].expr = '0'
-1589
-1590 for sample in self.unknowns:
-1591 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
-1592
-1593 for k in constraints:
-1594 params[k].expr = constraints[k]
-1595
-1596 def residuals(p):
-1597 R = []
-1598 for r in self:
-1599 session = pf(r['Session'])
-1600 sample = pf(r['Sample'])
-1601 if r['Sample'] in self.Nominal_D4x:
-1602 R += [ (
-1603 r[f'D{self._4x}raw'] - (
-1604 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
-1605 + p[f'b_{session}'] * r[f'd{self._4x}']
-1606 + p[f'c_{session}']
-1607 + r['t'] * (
-1608 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
-1609 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1610 + p[f'c2_{session}']
-1611 )
-1612 )
-1613 ) / r[f'wD{self._4x}raw'] ]
-1614 else:
-1615 R += [ (
-1616 r[f'D{self._4x}raw'] - (
-1617 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
-1618 + p[f'b_{session}'] * r[f'd{self._4x}']
-1619 + p[f'c_{session}']
-1620 + r['t'] * (
-1621 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
-1622 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1623 + p[f'c2_{session}']
-1624 )
-1625 )
-1626 ) / r[f'wD{self._4x}raw'] ]
-1627 return R
-1628
-1629 M = Minimizer(residuals, params)
-1630 result = M.least_squares()
-1631 self.Nf = result.nfree
-1632 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
-1633 new_names, new_covar, new_se = _fullcovar(result)[:3]
-1634 result.var_names = new_names
-1635 result.covar = new_covar
-1636
-1637 for r in self:
-1638 s = pf(r["Session"])
-1639 a = result.params.valuesdict()[f'a_{s}']
-1640 b = result.params.valuesdict()[f'b_{s}']
-1641 c = result.params.valuesdict()[f'c_{s}']
-1642 a2 = result.params.valuesdict()[f'a2_{s}']
-1643 b2 = result.params.valuesdict()[f'b2_{s}']
-1644 c2 = result.params.valuesdict()[f'c2_{s}']
-1645 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1646
-1647
-1648 self.standardization = result
+1497 Returns a tuplet of:
+1498
+1499 + the list of group names
+1500 + an array of the corresponding Δ4x values
+1501 + the corresponding (co)variance matrix
+1502
+1503 **Parameters**
+1504
+1505 + `sample_groups`: a dictionary of the form:
+1506 ```py
+1507 {'group1': ['sample_1', 'sample_2'],
+1508 'group2': ['sample_3', 'sample_4', 'sample_5']}
+1509 ```
+1510 '''
+1511
+1512 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
+1513 groups = sorted(sample_groups.keys())
+1514 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
+1515 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
+1516 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
+1517 W = np.array([
+1518 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
+1519 for j in groups])
+1520 D4x_new = W @ D4x_old
+1521 CM_new = W @ CM_old @ W.T
+1522
+1523 return groups, D4x_new[:,0], CM_new
+1524
+1525
+1526 @make_verbal
+1527 def standardize(self,
+1528 method = 'pooled',
+1529 weighted_sessions = [],
+1530 consolidate = True,
+1531 consolidate_tables = False,
+1532 consolidate_plots = False,
+1533 constraints = {},
+1534 ):
+1535 '''
+1536 Compute absolute Δ4x values for all replicate analyses and for sample averages.
+1537 If `method` argument is set to `'pooled'`, the standardization processes all sessions
+1538 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
+1539 i.e. that their true Δ4x value does not change between sessions,
+1540 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
+1541 `'indep_sessions'`, the standardization processes each session independently, based only
+1542 on anchors analyses.
+1543 '''
+1544
+1545 self.standardization_method = method
+1546 self.assign_timestamps()
+1547
+1548 if method == 'pooled':
+1549 if weighted_sessions:
+1550 for session_group in weighted_sessions:
+1551 if self._4x == '47':
+1552 X = D47data([r for r in self if r['Session'] in session_group])
+1553 elif self._4x == '48':
+1554 X = D48data([r for r in self if r['Session'] in session_group])
+1555 X.Nominal_D4x = self.Nominal_D4x.copy()
+1556 X.refresh()
+1557 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
+1558 w = np.sqrt(result.redchi)
+1559 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
+1560 for r in X:
+1561 r[f'wD{self._4x}raw'] *= w
+1562 else:
+1563 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
+1564 for r in self:
+1565 r[f'wD{self._4x}raw'] = 1.
+1566
+1567 params = Parameters()
+1568 for k,session in enumerate(self.sessions):
+1569 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
+1570 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
+1571 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
+1572 s = pf(session)
+1573 params.add(f'a_{s}', value = 0.9)
+1574 params.add(f'b_{s}', value = 0.)
+1575 params.add(f'c_{s}', value = -0.9)
+1576 params.add(f'a2_{s}', value = 0.,
+1577# vary = self.sessions[session]['scrambling_drift'],
+1578 )
+1579 params.add(f'b2_{s}', value = 0.,
+1580# vary = self.sessions[session]['slope_drift'],
+1581 )
+1582 params.add(f'c2_{s}', value = 0.,
+1583# vary = self.sessions[session]['wg_drift'],
+1584 )
+1585 if not self.sessions[session]['scrambling_drift']:
+1586 params[f'a2_{s}'].expr = '0'
+1587 if not self.sessions[session]['slope_drift']:
+1588 params[f'b2_{s}'].expr = '0'
+1589 if not self.sessions[session]['wg_drift']:
+1590 params[f'c2_{s}'].expr = '0'
+1591
+1592 for sample in self.unknowns:
+1593 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
+1594
+1595 for k in constraints:
+1596 params[k].expr = constraints[k]
+1597
+1598 def residuals(p):
+1599 R = []
+1600 for r in self:
+1601 session = pf(r['Session'])
+1602 sample = pf(r['Sample'])
+1603 if r['Sample'] in self.Nominal_D4x:
+1604 R += [ (
+1605 r[f'D{self._4x}raw'] - (
+1606 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
+1607 + p[f'b_{session}'] * r[f'd{self._4x}']
+1608 + p[f'c_{session}']
+1609 + r['t'] * (
+1610 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
+1611 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1612 + p[f'c2_{session}']
+1613 )
+1614 )
+1615 ) / r[f'wD{self._4x}raw'] ]
+1616 else:
+1617 R += [ (
+1618 r[f'D{self._4x}raw'] - (
+1619 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
+1620 + p[f'b_{session}'] * r[f'd{self._4x}']
+1621 + p[f'c_{session}']
+1622 + r['t'] * (
+1623 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
+1624 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1625 + p[f'c2_{session}']
+1626 )
+1627 )
+1628 ) / r[f'wD{self._4x}raw'] ]
+1629 return R
+1630
+1631 M = Minimizer(residuals, params)
+1632 result = M.least_squares()
+1633 self.Nf = result.nfree
+1634 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1635 new_names, new_covar, new_se = _fullcovar(result)[:3]
+1636 result.var_names = new_names
+1637 result.covar = new_covar
+1638
+1639 for r in self:
+1640 s = pf(r["Session"])
+1641 a = result.params.valuesdict()[f'a_{s}']
+1642 b = result.params.valuesdict()[f'b_{s}']
+1643 c = result.params.valuesdict()[f'c_{s}']
+1644 a2 = result.params.valuesdict()[f'a2_{s}']
+1645 b2 = result.params.valuesdict()[f'b2_{s}']
+1646 c2 = result.params.valuesdict()[f'c2_{s}']
+1647 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1648
1649
-1650 for session in self.sessions:
-1651 self.sessions[session]['Np'] = 3
-1652 for k in ['scrambling', 'slope', 'wg']:
-1653 if self.sessions[session][f'{k}_drift']:
-1654 self.sessions[session]['Np'] += 1
-1655
-1656 if consolidate:
-1657 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
-1658 return result
-1659
-1660
-1661 elif method == 'indep_sessions':
+1650 self.standardization = result
+1651
+1652 for session in self.sessions:
+1653 self.sessions[session]['Np'] = 3
+1654 for k in ['scrambling', 'slope', 'wg']:
+1655 if self.sessions[session][f'{k}_drift']:
+1656 self.sessions[session]['Np'] += 1
+1657
+1658 if consolidate:
+1659 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1660 return result
+1661
1662
-1663 if weighted_sessions:
-1664 for session_group in weighted_sessions:
-1665 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
-1666 X.Nominal_D4x = self.Nominal_D4x.copy()
-1667 X.refresh()
-1668 # This is only done to assign r['wD47raw'] for r in X:
-1669 X.standardize(method = method, weighted_sessions = [], consolidate = False)
-1670 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
-1671 else:
-1672 self.msg('All weights set to 1 ‰')
-1673 for r in self:
-1674 r[f'wD{self._4x}raw'] = 1
-1675
-1676 for session in self.sessions:
-1677 s = self.sessions[session]
-1678 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
-1679 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
-1680 s['Np'] = sum(p_active)
-1681 sdata = s['data']
-1682
-1683 A = np.array([
-1684 [
-1685 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
-1686 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
-1687 1 / r[f'wD{self._4x}raw'],
-1688 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
-1689 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
-1690 r['t'] / r[f'wD{self._4x}raw']
-1691 ]
-1692 for r in sdata if r['Sample'] in self.anchors
-1693 ])[:,p_active] # only keep columns for the active parameters
-1694 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
-1695 s['Na'] = Y.size
-1696 CM = linalg.inv(A.T @ A)
-1697 bf = (CM @ A.T @ Y).T[0,:]
-1698 k = 0
-1699 for n,a in zip(p_names, p_active):
-1700 if a:
-1701 s[n] = bf[k]
-1702# self.msg(f'{n} = {bf[k]}')
-1703 k += 1
-1704 else:
-1705 s[n] = 0.
-1706# self.msg(f'{n} = 0.0')
-1707
-1708 for r in sdata :
-1709 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
-1710 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1711 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
-1712
-1713 s['CM'] = np.zeros((6,6))
-1714 i = 0
-1715 k_active = [j for j,a in enumerate(p_active) if a]
-1716 for j,a in enumerate(p_active):
-1717 if a:
-1718 s['CM'][j,k_active] = CM[i,:]
-1719 i += 1
-1720
-1721 if not weighted_sessions:
-1722 w = self.rmswd()['rmswd']
-1723 for r in self:
-1724 r[f'wD{self._4x}'] *= w
-1725 r[f'wD{self._4x}raw'] *= w
-1726 for session in self.sessions:
-1727 self.sessions[session]['CM'] *= w**2
-1728
-1729 for session in self.sessions:
-1730 s = self.sessions[session]
-1731 s['SE_a'] = s['CM'][0,0]**.5
-1732 s['SE_b'] = s['CM'][1,1]**.5
-1733 s['SE_c'] = s['CM'][2,2]**.5
-1734 s['SE_a2'] = s['CM'][3,3]**.5
-1735 s['SE_b2'] = s['CM'][4,4]**.5
-1736 s['SE_c2'] = s['CM'][5,5]**.5
-1737
-1738 if not weighted_sessions:
-1739 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
-1740 else:
-1741 self.Nf = 0
-1742 for sg in weighted_sessions:
-1743 self.Nf += self.rmswd(sessions = sg)['Nf']
-1744
-1745 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1663 elif method == 'indep_sessions':
+1664
+1665 if weighted_sessions:
+1666 for session_group in weighted_sessions:
+1667 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
+1668 X.Nominal_D4x = self.Nominal_D4x.copy()
+1669 X.refresh()
+1670 # This is only done to assign r['wD47raw'] for r in X:
+1671 X.standardize(method = method, weighted_sessions = [], consolidate = False)
+1672 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
+1673 else:
+1674 self.msg('All weights set to 1 ‰')
+1675 for r in self:
+1676 r[f'wD{self._4x}raw'] = 1
+1677
+1678 for session in self.sessions:
+1679 s = self.sessions[session]
+1680 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
+1681 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
+1682 s['Np'] = sum(p_active)
+1683 sdata = s['data']
+1684
+1685 A = np.array([
+1686 [
+1687 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
+1688 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
+1689 1 / r[f'wD{self._4x}raw'],
+1690 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
+1691 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
+1692 r['t'] / r[f'wD{self._4x}raw']
+1693 ]
+1694 for r in sdata if r['Sample'] in self.anchors
+1695 ])[:,p_active] # only keep columns for the active parameters
+1696 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
+1697 s['Na'] = Y.size
+1698 CM = linalg.inv(A.T @ A)
+1699 bf = (CM @ A.T @ Y).T[0,:]
+1700 k = 0
+1701 for n,a in zip(p_names, p_active):
+1702 if a:
+1703 s[n] = bf[k]
+1704# self.msg(f'{n} = {bf[k]}')
+1705 k += 1
+1706 else:
+1707 s[n] = 0.
+1708# self.msg(f'{n} = 0.0')
+1709
+1710 for r in sdata :
+1711 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
+1712 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1713 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
+1714
+1715 s['CM'] = np.zeros((6,6))
+1716 i = 0
+1717 k_active = [j for j,a in enumerate(p_active) if a]
+1718 for j,a in enumerate(p_active):
+1719 if a:
+1720 s['CM'][j,k_active] = CM[i,:]
+1721 i += 1
+1722
+1723 if not weighted_sessions:
+1724 w = self.rmswd()['rmswd']
+1725 for r in self:
+1726 r[f'wD{self._4x}'] *= w
+1727 r[f'wD{self._4x}raw'] *= w
+1728 for session in self.sessions:
+1729 self.sessions[session]['CM'] *= w**2
+1730
+1731 for session in self.sessions:
+1732 s = self.sessions[session]
+1733 s['SE_a'] = s['CM'][0,0]**.5
+1734 s['SE_b'] = s['CM'][1,1]**.5
+1735 s['SE_c'] = s['CM'][2,2]**.5
+1736 s['SE_a2'] = s['CM'][3,3]**.5
+1737 s['SE_b2'] = s['CM'][4,4]**.5
+1738 s['SE_c2'] = s['CM'][5,5]**.5
+1739
+1740 if not weighted_sessions:
+1741 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
+1742 else:
+1743 self.Nf = 0
+1744 for sg in weighted_sessions:
+1745 self.Nf += self.rmswd(sessions = sg)['Nf']
1746
-1747 avgD4x = {
-1748 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
-1749 for sample in self.samples
-1750 }
-1751 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
-1752 rD4x = (chi2/self.Nf)**.5
-1753 self.repeatability[f'sigma_{self._4x}'] = rD4x
-1754
-1755 if consolidate:
-1756 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
-1757
-1758
-1759 def standardization_error(self, session, d4x, D4x, t = 0):
-1760 '''
-1761 Compute standardization error for a given session and
-1762 (δ47, Δ47) composition.
-1763 '''
-1764 a = self.sessions[session]['a']
-1765 b = self.sessions[session]['b']
-1766 c = self.sessions[session]['c']
-1767 a2 = self.sessions[session]['a2']
-1768 b2 = self.sessions[session]['b2']
-1769 c2 = self.sessions[session]['c2']
-1770 CM = self.sessions[session]['CM']
-1771
-1772 x, y = D4x, d4x
-1773 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
-1774# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
-1775 dxdy = -(b+b2*t) / (a+a2*t)
-1776 dxdz = 1. / (a+a2*t)
-1777 dxda = -x / (a+a2*t)
-1778 dxdb = -y / (a+a2*t)
-1779 dxdc = -1. / (a+a2*t)
-1780 dxda2 = -x * a2 / (a+a2*t)
-1781 dxdb2 = -y * t / (a+a2*t)
-1782 dxdc2 = -t / (a+a2*t)
-1783 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
-1784 sx = (V @ CM @ V.T) ** .5
-1785 return sx
-1786
-1787
-1788 @make_verbal
-1789 def summary(self,
-1790 dir = 'output',
-1791 filename = None,
-1792 save_to_file = True,
-1793 print_out = True,
-1794 ):
-1795 '''
-1796 Print out an/or save to disk a summary of the standardization results.
-1797
-1798 **Parameters**
+1747 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1748
+1749 avgD4x = {
+1750 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
+1751 for sample in self.samples
+1752 }
+1753 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
+1754 rD4x = (chi2/self.Nf)**.5
+1755 self.repeatability[f'sigma_{self._4x}'] = rD4x
+1756
+1757 if consolidate:
+1758 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1759
+1760
+1761 def standardization_error(self, session, d4x, D4x, t = 0):
+1762 '''
+1763 Compute standardization error for a given session and
+1764 (δ47, Δ47) composition.
+1765 '''
+1766 a = self.sessions[session]['a']
+1767 b = self.sessions[session]['b']
+1768 c = self.sessions[session]['c']
+1769 a2 = self.sessions[session]['a2']
+1770 b2 = self.sessions[session]['b2']
+1771 c2 = self.sessions[session]['c2']
+1772 CM = self.sessions[session]['CM']
+1773
+1774 x, y = D4x, d4x
+1775 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
+1776# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
+1777 dxdy = -(b+b2*t) / (a+a2*t)
+1778 dxdz = 1. / (a+a2*t)
+1779 dxda = -x / (a+a2*t)
+1780 dxdb = -y / (a+a2*t)
+1781 dxdc = -1. / (a+a2*t)
+1782 dxda2 = -x * a2 / (a+a2*t)
+1783 dxdb2 = -y * t / (a+a2*t)
+1784 dxdc2 = -t / (a+a2*t)
+1785 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
+1786 sx = (V @ CM @ V.T) ** .5
+1787 return sx
+1788
+1789
+1790 @make_verbal
+1791 def summary(self,
+1792 dir = 'output',
+1793 filename = None,
+1794 save_to_file = True,
+1795 print_out = True,
+1796 ):
+1797 '''
+1798 Print out an/or save to disk a summary of the standardization results.
1799
-1800 + `dir`: the directory in which to save the table
-1801 + `filename`: the name to the csv file to write to
-1802 + `save_to_file`: whether to save the table to disk
-1803 + `print_out`: whether to print out the table
-1804 '''
-1805
-1806 out = []
-1807 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
-1808 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
-1809 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
-1810 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
-1811 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
-1812 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
-1813 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
-1814 out += [['Model degrees of freedom', f"{self.Nf}"]]
-1815 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
-1816 out += [['Standardization method', self.standardization_method]]
-1817
-1818 if save_to_file:
-1819 if not os.path.exists(dir):
-1820 os.makedirs(dir)
-1821 if filename is None:
-1822 filename = f'D{self._4x}_summary.csv'
-1823 with open(f'{dir}/{filename}', 'w') as fid:
-1824 fid.write(make_csv(out))
-1825 if print_out:
-1826 self.msg('\n' + pretty_table(out, header = 0))
-1827
-1828
-1829 @make_verbal
-1830 def table_of_sessions(self,
-1831 dir = 'output',
-1832 filename = None,
-1833 save_to_file = True,
-1834 print_out = True,
-1835 output = None,
-1836 ):
-1837 '''
-1838 Print out an/or save to disk a table of sessions.
-1839
-1840 **Parameters**
+1800 **Parameters**
+1801
+1802 + `dir`: the directory in which to save the table
+1803 + `filename`: the name to the csv file to write to
+1804 + `save_to_file`: whether to save the table to disk
+1805 + `print_out`: whether to print out the table
+1806 '''
+1807
+1808 out = []
+1809 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
+1810 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
+1811 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
+1812 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
+1813 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
+1814 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
+1815 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
+1816 out += [['Model degrees of freedom', f"{self.Nf}"]]
+1817 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
+1818 out += [['Standardization method', self.standardization_method]]
+1819
+1820 if save_to_file:
+1821 if not os.path.exists(dir):
+1822 os.makedirs(dir)
+1823 if filename is None:
+1824 filename = f'D{self._4x}_summary.csv'
+1825 with open(f'{dir}/{filename}', 'w') as fid:
+1826 fid.write(make_csv(out))
+1827 if print_out:
+1828 self.msg('\n' + pretty_table(out, header = 0))
+1829
+1830
+1831 @make_verbal
+1832 def table_of_sessions(self,
+1833 dir = 'output',
+1834 filename = None,
+1835 save_to_file = True,
+1836 print_out = True,
+1837 output = None,
+1838 ):
+1839 '''
+1840 Print out an/or save to disk a table of sessions.
1841
-1842 + `dir`: the directory in which to save the table
-1843 + `filename`: the name to the csv file to write to
-1844 + `save_to_file`: whether to save the table to disk
-1845 + `print_out`: whether to print out the table
-1846 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1847 if set to `'raw'`: return a list of list of strings
-1848 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1849 '''
-1850 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
-1851 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
-1852 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
-1853
-1854 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
-1855 if include_a2:
-1856 out[-1] += ['a2 ± SE']
-1857 if include_b2:
-1858 out[-1] += ['b2 ± SE']
-1859 if include_c2:
-1860 out[-1] += ['c2 ± SE']
-1861 for session in self.sessions:
-1862 out += [[
-1863 session,
-1864 f"{self.sessions[session]['Na']}",
-1865 f"{self.sessions[session]['Nu']}",
-1866 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
-1867 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
-1868 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
-1869 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
-1870 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
-1871 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
-1872 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
-1873 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
-1874 ]]
-1875 if include_a2:
-1876 if self.sessions[session]['scrambling_drift']:
-1877 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
-1878 else:
-1879 out[-1] += ['']
-1880 if include_b2:
-1881 if self.sessions[session]['slope_drift']:
-1882 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
-1883 else:
-1884 out[-1] += ['']
-1885 if include_c2:
-1886 if self.sessions[session]['wg_drift']:
-1887 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
-1888 else:
-1889 out[-1] += ['']
-1890
-1891 if save_to_file:
-1892 if not os.path.exists(dir):
-1893 os.makedirs(dir)
-1894 if filename is None:
-1895 filename = f'D{self._4x}_sessions.csv'
-1896 with open(f'{dir}/{filename}', 'w') as fid:
-1897 fid.write(make_csv(out))
-1898 if print_out:
-1899 self.msg('\n' + pretty_table(out))
-1900 if output == 'raw':
-1901 return out
-1902 elif output == 'pretty':
-1903 return pretty_table(out)
-1904
-1905
-1906 @make_verbal
-1907 def table_of_analyses(
-1908 self,
-1909 dir = 'output',
-1910 filename = None,
-1911 save_to_file = True,
-1912 print_out = True,
-1913 output = None,
-1914 ):
-1915 '''
-1916 Print out an/or save to disk a table of analyses.
-1917
-1918 **Parameters**
+1842 **Parameters**
+1843
+1844 + `dir`: the directory in which to save the table
+1845 + `filename`: the name to the csv file to write to
+1846 + `save_to_file`: whether to save the table to disk
+1847 + `print_out`: whether to print out the table
+1848 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1849 if set to `'raw'`: return a list of list of strings
+1850 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1851 '''
+1852 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
+1853 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
+1854 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
+1855
+1856 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
+1857 if include_a2:
+1858 out[-1] += ['a2 ± SE']
+1859 if include_b2:
+1860 out[-1] += ['b2 ± SE']
+1861 if include_c2:
+1862 out[-1] += ['c2 ± SE']
+1863 for session in self.sessions:
+1864 out += [[
+1865 session,
+1866 f"{self.sessions[session]['Na']}",
+1867 f"{self.sessions[session]['Nu']}",
+1868 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
+1869 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
+1870 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
+1871 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
+1872 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
+1873 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
+1874 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
+1875 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
+1876 ]]
+1877 if include_a2:
+1878 if self.sessions[session]['scrambling_drift']:
+1879 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
+1880 else:
+1881 out[-1] += ['']
+1882 if include_b2:
+1883 if self.sessions[session]['slope_drift']:
+1884 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
+1885 else:
+1886 out[-1] += ['']
+1887 if include_c2:
+1888 if self.sessions[session]['wg_drift']:
+1889 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
+1890 else:
+1891 out[-1] += ['']
+1892
+1893 if save_to_file:
+1894 if not os.path.exists(dir):
+1895 os.makedirs(dir)
+1896 if filename is None:
+1897 filename = f'D{self._4x}_sessions.csv'
+1898 with open(f'{dir}/{filename}', 'w') as fid:
+1899 fid.write(make_csv(out))
+1900 if print_out:
+1901 self.msg('\n' + pretty_table(out))
+1902 if output == 'raw':
+1903 return out
+1904 elif output == 'pretty':
+1905 return pretty_table(out)
+1906
+1907
+1908 @make_verbal
+1909 def table_of_analyses(
+1910 self,
+1911 dir = 'output',
+1912 filename = None,
+1913 save_to_file = True,
+1914 print_out = True,
+1915 output = None,
+1916 ):
+1917 '''
+1918 Print out an/or save to disk a table of analyses.
1919
-1920 + `dir`: the directory in which to save the table
-1921 + `filename`: the name to the csv file to write to
-1922 + `save_to_file`: whether to save the table to disk
-1923 + `print_out`: whether to print out the table
-1924 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1925 if set to `'raw'`: return a list of list of strings
-1926 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1927 '''
-1928
-1929 out = [['UID','Session','Sample']]
-1930 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
-1931 for f in extra_fields:
-1932 out[-1] += [f[0]]
-1933 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
-1934 for r in self:
-1935 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
-1936 for f in extra_fields:
-1937 out[-1] += [f"{r[f[0]]:{f[1]}}"]
-1938 out[-1] += [
-1939 f"{r['d13Cwg_VPDB']:.3f}",
-1940 f"{r['d18Owg_VSMOW']:.3f}",
-1941 f"{r['d45']:.6f}",
-1942 f"{r['d46']:.6f}",
-1943 f"{r['d47']:.6f}",
-1944 f"{r['d48']:.6f}",
-1945 f"{r['d49']:.6f}",
-1946 f"{r['d13C_VPDB']:.6f}",
-1947 f"{r['d18O_VSMOW']:.6f}",
-1948 f"{r['D47raw']:.6f}",
-1949 f"{r['D48raw']:.6f}",
-1950 f"{r['D49raw']:.6f}",
-1951 f"{r[f'D{self._4x}']:.6f}"
-1952 ]
-1953 if save_to_file:
-1954 if not os.path.exists(dir):
-1955 os.makedirs(dir)
-1956 if filename is None:
-1957 filename = f'D{self._4x}_analyses.csv'
-1958 with open(f'{dir}/{filename}', 'w') as fid:
-1959 fid.write(make_csv(out))
-1960 if print_out:
-1961 self.msg('\n' + pretty_table(out))
-1962 return out
-1963
-1964 @make_verbal
-1965 def covar_table(
-1966 self,
-1967 correl = False,
-1968 dir = 'output',
-1969 filename = None,
-1970 save_to_file = True,
-1971 print_out = True,
-1972 output = None,
-1973 ):
-1974 '''
-1975 Print out, save to disk and/or return the variance-covariance matrix of D4x
-1976 for all unknown samples.
-1977
-1978 **Parameters**
+1920 **Parameters**
+1921
+1922 + `dir`: the directory in which to save the table
+1923 + `filename`: the name to the csv file to write to
+1924 + `save_to_file`: whether to save the table to disk
+1925 + `print_out`: whether to print out the table
+1926 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1927 if set to `'raw'`: return a list of list of strings
+1928 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1929 '''
+1930
+1931 out = [['UID','Session','Sample']]
+1932 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
+1933 for f in extra_fields:
+1934 out[-1] += [f[0]]
+1935 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
+1936 for r in self:
+1937 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
+1938 for f in extra_fields:
+1939 out[-1] += [f"{r[f[0]]:{f[1]}}"]
+1940 out[-1] += [
+1941 f"{r['d13Cwg_VPDB']:.3f}",
+1942 f"{r['d18Owg_VSMOW']:.3f}",
+1943 f"{r['d45']:.6f}",
+1944 f"{r['d46']:.6f}",
+1945 f"{r['d47']:.6f}",
+1946 f"{r['d48']:.6f}",
+1947 f"{r['d49']:.6f}",
+1948 f"{r['d13C_VPDB']:.6f}",
+1949 f"{r['d18O_VSMOW']:.6f}",
+1950 f"{r['D47raw']:.6f}",
+1951 f"{r['D48raw']:.6f}",
+1952 f"{r['D49raw']:.6f}",
+1953 f"{r[f'D{self._4x}']:.6f}"
+1954 ]
+1955 if save_to_file:
+1956 if not os.path.exists(dir):
+1957 os.makedirs(dir)
+1958 if filename is None:
+1959 filename = f'D{self._4x}_analyses.csv'
+1960 with open(f'{dir}/{filename}', 'w') as fid:
+1961 fid.write(make_csv(out))
+1962 if print_out:
+1963 self.msg('\n' + pretty_table(out))
+1964 return out
+1965
+1966 @make_verbal
+1967 def covar_table(
+1968 self,
+1969 correl = False,
+1970 dir = 'output',
+1971 filename = None,
+1972 save_to_file = True,
+1973 print_out = True,
+1974 output = None,
+1975 ):
+1976 '''
+1977 Print out, save to disk and/or return the variance-covariance matrix of D4x
+1978 for all unknown samples.
1979
-1980 + `dir`: the directory in which to save the csv
-1981 + `filename`: the name of the csv file to write to
-1982 + `save_to_file`: whether to save the csv
-1983 + `print_out`: whether to print out the matrix
-1984 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
-1985 if set to `'raw'`: return a list of list of strings
-1986 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1987 '''
-1988 samples = sorted([u for u in self.unknowns])
-1989 out = [[''] + samples]
-1990 for s1 in samples:
-1991 out.append([s1])
-1992 for s2 in samples:
-1993 if correl:
-1994 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
-1995 else:
-1996 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
-1997
-1998 if save_to_file:
-1999 if not os.path.exists(dir):
-2000 os.makedirs(dir)
-2001 if filename is None:
-2002 if correl:
-2003 filename = f'D{self._4x}_correl.csv'
-2004 else:
-2005 filename = f'D{self._4x}_covar.csv'
-2006 with open(f'{dir}/{filename}', 'w') as fid:
-2007 fid.write(make_csv(out))
-2008 if print_out:
-2009 self.msg('\n'+pretty_table(out))
-2010 if output == 'raw':
-2011 return out
-2012 elif output == 'pretty':
-2013 return pretty_table(out)
-2014
-2015 @make_verbal
-2016 def table_of_samples(
-2017 self,
-2018 dir = 'output',
-2019 filename = None,
-2020 save_to_file = True,
-2021 print_out = True,
-2022 output = None,
-2023 ):
-2024 '''
-2025 Print out, save to disk and/or return a table of samples.
-2026
-2027 **Parameters**
+1980 **Parameters**
+1981
+1982 + `dir`: the directory in which to save the csv
+1983 + `filename`: the name of the csv file to write to
+1984 + `save_to_file`: whether to save the csv
+1985 + `print_out`: whether to print out the matrix
+1986 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
+1987 if set to `'raw'`: return a list of list of strings
+1988 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1989 '''
+1990 samples = sorted([u for u in self.unknowns])
+1991 out = [[''] + samples]
+1992 for s1 in samples:
+1993 out.append([s1])
+1994 for s2 in samples:
+1995 if correl:
+1996 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
+1997 else:
+1998 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
+1999
+2000 if save_to_file:
+2001 if not os.path.exists(dir):
+2002 os.makedirs(dir)
+2003 if filename is None:
+2004 if correl:
+2005 filename = f'D{self._4x}_correl.csv'
+2006 else:
+2007 filename = f'D{self._4x}_covar.csv'
+2008 with open(f'{dir}/{filename}', 'w') as fid:
+2009 fid.write(make_csv(out))
+2010 if print_out:
+2011 self.msg('\n'+pretty_table(out))
+2012 if output == 'raw':
+2013 return out
+2014 elif output == 'pretty':
+2015 return pretty_table(out)
+2016
+2017 @make_verbal
+2018 def table_of_samples(
+2019 self,
+2020 dir = 'output',
+2021 filename = None,
+2022 save_to_file = True,
+2023 print_out = True,
+2024 output = None,
+2025 ):
+2026 '''
+2027 Print out, save to disk and/or return a table of samples.
2028
-2029 + `dir`: the directory in which to save the csv
-2030 + `filename`: the name of the csv file to write to
-2031 + `save_to_file`: whether to save the csv
-2032 + `print_out`: whether to print out the table
-2033 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-2034 if set to `'raw'`: return a list of list of strings
-2035 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-2036 '''
-2037
-2038 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
-2039 for sample in self.anchors:
-2040 out += [[
-2041 f"{sample}",
-2042 f"{self.samples[sample]['N']}",
-2043 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2044 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2045 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
-2046 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
-2047 ]]
-2048 for sample in self.unknowns:
-2049 out += [[
-2050 f"{sample}",
-2051 f"{self.samples[sample]['N']}",
-2052 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2053 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2054 f"{self.samples[sample][f'D{self._4x}']:.4f}",
-2055 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
-2056 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
-2057 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
-2058 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
-2059 ]]
-2060 if save_to_file:
-2061 if not os.path.exists(dir):
-2062 os.makedirs(dir)
-2063 if filename is None:
-2064 filename = f'D{self._4x}_samples.csv'
-2065 with open(f'{dir}/{filename}', 'w') as fid:
-2066 fid.write(make_csv(out))
-2067 if print_out:
-2068 self.msg('\n'+pretty_table(out))
-2069 if output == 'raw':
-2070 return out
-2071 elif output == 'pretty':
-2072 return pretty_table(out)
-2073
-2074
-2075 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
-2076 '''
-2077 Generate session plots and save them to disk.
-2078
-2079 **Parameters**
+2029 **Parameters**
+2030
+2031 + `dir`: the directory in which to save the csv
+2032 + `filename`: the name of the csv file to write to
+2033 + `save_to_file`: whether to save the csv
+2034 + `print_out`: whether to print out the table
+2035 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+2036 if set to `'raw'`: return a list of list of strings
+2037 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+2038 '''
+2039
+2040 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
+2041 for sample in self.anchors:
+2042 out += [[
+2043 f"{sample}",
+2044 f"{self.samples[sample]['N']}",
+2045 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2046 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2047 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
+2048 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
+2049 ]]
+2050 for sample in self.unknowns:
+2051 out += [[
+2052 f"{sample}",
+2053 f"{self.samples[sample]['N']}",
+2054 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2055 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2056 f"{self.samples[sample][f'D{self._4x}']:.4f}",
+2057 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
+2058 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
+2059 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
+2060 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
+2061 ]]
+2062 if save_to_file:
+2063 if not os.path.exists(dir):
+2064 os.makedirs(dir)
+2065 if filename is None:
+2066 filename = f'D{self._4x}_samples.csv'
+2067 with open(f'{dir}/{filename}', 'w') as fid:
+2068 fid.write(make_csv(out))
+2069 if print_out:
+2070 self.msg('\n'+pretty_table(out))
+2071 if output == 'raw':
+2072 return out
+2073 elif output == 'pretty':
+2074 return pretty_table(out)
+2075
+2076
+2077 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
+2078 '''
+2079 Generate session plots and save them to disk.
2080
-2081 + `dir`: the directory in which to save the plots
-2082 + `figsize`: the width and height (in inches) of each plot
-2083 + `filetype`: 'pdf' or 'png'
-2084 + `dpi`: resolution for PNG output
-2085 '''
-2086 if not os.path.exists(dir):
-2087 os.makedirs(dir)
-2088
-2089 for session in self.sessions:
-2090 sp = self.plot_single_session(session, xylimits = 'constant')
-2091 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
-2092 ppl.close(sp.fig)
-2093
-2094
-2095 @make_verbal
-2096 def consolidate_samples(self):
-2097 '''
-2098 Compile various statistics for each sample.
-2099
-2100 For each anchor sample:
+2081 **Parameters**
+2082
+2083 + `dir`: the directory in which to save the plots
+2084 + `figsize`: the width and height (in inches) of each plot
+2085 + `filetype`: 'pdf' or 'png'
+2086 + `dpi`: resolution for PNG output
+2087 '''
+2088 if not os.path.exists(dir):
+2089 os.makedirs(dir)
+2090
+2091 for session in self.sessions:
+2092 sp = self.plot_single_session(session, xylimits = 'constant')
+2093 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
+2094 ppl.close(sp.fig)
+2095
+2096
+2097 @make_verbal
+2098 def consolidate_samples(self):
+2099 '''
+2100 Compile various statistics for each sample.
2101
-2102 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
-2103 + `SE_D47` or `SE_D48`: set to zero by definition
-2104
-2105 For each unknown sample:
+2102 For each anchor sample:
+2103
+2104 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
+2105 + `SE_D47` or `SE_D48`: set to zero by definition
2106
-2107 + `D47` or `D48`: the standardized Δ4x value for this unknown
-2108 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
-2109
-2110 For each anchor and unknown:
+2107 For each unknown sample:
+2108
+2109 + `D47` or `D48`: the standardized Δ4x value for this unknown
+2110 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
2111
-2112 + `N`: the total number of analyses of this sample
-2113 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
-2114 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
-2115 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
-2116 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
-2117 variance, indicating whether the Δ4x repeatability this sample differs significantly from
-2118 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
-2119 '''
-2120 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
-2121 for sample in self.samples:
-2122 self.samples[sample]['N'] = len(self.samples[sample]['data'])
-2123 if self.samples[sample]['N'] > 1:
-2124 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
-2125
-2126 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
-2127 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
-2128
-2129 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
-2130 if len(D4x_pop) > 2:
-2131 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
-2132
-2133 if self.standardization_method == 'pooled':
-2134 for sample in self.anchors:
-2135 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2136 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2137 for sample in self.unknowns:
-2138 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
-2139 try:
-2140 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
-2141 except ValueError:
-2142 # when `sample` is constrained by self.standardize(constraints = {...}),
-2143 # it is no longer listed in self.standardization.var_names.
-2144 # Temporary fix: define SE as zero for now
-2145 self.samples[sample][f'SE_D4{self._4x}'] = 0.
-2146
-2147 elif self.standardization_method == 'indep_sessions':
-2148 for sample in self.anchors:
-2149 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2150 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2151 for sample in self.unknowns:
-2152 self.msg(f'Consolidating sample {sample}')
-2153 self.unknowns[sample][f'session_D{self._4x}'] = {}
-2154 session_avg = []
-2155 for session in self.sessions:
-2156 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
-2157 if sdata:
-2158 self.msg(f'{sample} found in session {session}')
-2159 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
-2160 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
-2161 # !! TODO: sigma_s below does not account for temporal changes in standardization error
-2162 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
-2163 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
-2164 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
-2165 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
-2166 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
-2167 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
-2168 wsum = sum([weights[s] for s in weights])
-2169 for s in weights:
-2170 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
-2171
-2172 for r in self:
-2173 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
-2174
-2175
+2112 For each anchor and unknown:
+2113
+2114 + `N`: the total number of analyses of this sample
+2115 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
+2116 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
+2117 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
+2118 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
+2119 variance, indicating whether the Δ4x repeatability this sample differs significantly from
+2120 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
+2121 '''
+2122 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
+2123 for sample in self.samples:
+2124 self.samples[sample]['N'] = len(self.samples[sample]['data'])
+2125 if self.samples[sample]['N'] > 1:
+2126 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
+2127
+2128 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
+2129 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
+2130
+2131 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
+2132 if len(D4x_pop) > 2:
+2133 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
+2134
+2135 if self.standardization_method == 'pooled':
+2136 for sample in self.anchors:
+2137 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2138 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2139 for sample in self.unknowns:
+2140 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
+2141 try:
+2142 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
+2143 except ValueError:
+2144 # when `sample` is constrained by self.standardize(constraints = {...}),
+2145 # it is no longer listed in self.standardization.var_names.
+2146 # Temporary fix: define SE as zero for now
+2147 self.samples[sample][f'SE_D4{self._4x}'] = 0.
+2148
+2149 elif self.standardization_method == 'indep_sessions':
+2150 for sample in self.anchors:
+2151 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2152 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2153 for sample in self.unknowns:
+2154 self.msg(f'Consolidating sample {sample}')
+2155 self.unknowns[sample][f'session_D{self._4x}'] = {}
+2156 session_avg = []
+2157 for session in self.sessions:
+2158 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
+2159 if sdata:
+2160 self.msg(f'{sample} found in session {session}')
+2161 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
+2162 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
+2163 # !! TODO: sigma_s below does not account for temporal changes in standardization error
+2164 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
+2165 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
+2166 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
+2167 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
+2168 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
+2169 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
+2170 wsum = sum([weights[s] for s in weights])
+2171 for s in weights:
+2172 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
+2173
+2174 for r in self:
+2175 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
2176
-2177 def consolidate_sessions(self):
-2178 '''
-2179 Compute various statistics for each session.
-2180
-2181 + `Na`: Number of anchor analyses in the session
-2182 + `Nu`: Number of unknown analyses in the session
-2183 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
-2184 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
-2185 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
-2186 + `a`: scrambling factor
-2187 + `b`: compositional slope
-2188 + `c`: WG offset
-2189 + `SE_a`: Model stadard erorr of `a`
-2190 + `SE_b`: Model stadard erorr of `b`
-2191 + `SE_c`: Model stadard erorr of `c`
-2192 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
-2193 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
-2194 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
-2195 + `a2`: scrambling factor drift
-2196 + `b2`: compositional slope drift
-2197 + `c2`: WG offset drift
-2198 + `Np`: Number of standardization parameters to fit
-2199 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
-2200 + `d13Cwg_VPDB`: δ13C_VPDB of WG
-2201 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
-2202 '''
-2203 for session in self.sessions:
-2204 if 'd13Cwg_VPDB' not in self.sessions[session]:
-2205 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
-2206 if 'd18Owg_VSMOW' not in self.sessions[session]:
-2207 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
-2208 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
-2209 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
-2210
-2211 self.msg(f'Computing repeatabilities for session {session}')
-2212 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
-2213 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
-2214 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
-2215
-2216 if self.standardization_method == 'pooled':
-2217 for session in self.sessions:
-2218
-2219 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
-2220 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2221 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
-2222
-2223 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
-2224 i = self.standardization.var_names.index(f'b_{pf(session)}')
-2225 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
-2226
-2227 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
-2228 i = self.standardization.var_names.index(f'c_{pf(session)}')
-2229 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
-2230
-2231 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
-2232 if self.sessions[session]['scrambling_drift']:
-2233 i = self.standardization.var_names.index(f'a2_{pf(session)}')
-2234 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
-2235 else:
-2236 self.sessions[session]['SE_a2'] = 0.
-2237
-2238 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
-2239 if self.sessions[session]['slope_drift']:
-2240 i = self.standardization.var_names.index(f'b2_{pf(session)}')
-2241 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
-2242 else:
-2243 self.sessions[session]['SE_b2'] = 0.
-2244
-2245 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
-2246 if self.sessions[session]['wg_drift']:
-2247 i = self.standardization.var_names.index(f'c2_{pf(session)}')
-2248 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
-2249 else:
-2250 self.sessions[session]['SE_c2'] = 0.
-2251
-2252 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2253 j = self.standardization.var_names.index(f'b_{pf(session)}')
-2254 k = self.standardization.var_names.index(f'c_{pf(session)}')
-2255 CM = np.zeros((6,6))
-2256 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
-2257 try:
-2258 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
-2259 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
-2260 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
-2261 try:
-2262 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2263 CM[3,4] = self.standardization.covar[i2,j2]
-2264 CM[4,3] = self.standardization.covar[j2,i2]
-2265 except ValueError:
-2266 pass
-2267 try:
-2268 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2269 CM[3,5] = self.standardization.covar[i2,k2]
-2270 CM[5,3] = self.standardization.covar[k2,i2]
-2271 except ValueError:
-2272 pass
-2273 except ValueError:
-2274 pass
-2275 try:
-2276 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2277 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
-2278 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
-2279 try:
-2280 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2281 CM[4,5] = self.standardization.covar[j2,k2]
-2282 CM[5,4] = self.standardization.covar[k2,j2]
-2283 except ValueError:
-2284 pass
-2285 except ValueError:
-2286 pass
-2287 try:
-2288 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2289 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
-2290 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
-2291 except ValueError:
-2292 pass
-2293
-2294 self.sessions[session]['CM'] = CM
+2177
+2178
+2179 def consolidate_sessions(self):
+2180 '''
+2181 Compute various statistics for each session.
+2182
+2183 + `Na`: Number of anchor analyses in the session
+2184 + `Nu`: Number of unknown analyses in the session
+2185 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
+2186 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
+2187 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
+2188 + `a`: scrambling factor
+2189 + `b`: compositional slope
+2190 + `c`: WG offset
+2191 + `SE_a`: Model stadard erorr of `a`
+2192 + `SE_b`: Model stadard erorr of `b`
+2193 + `SE_c`: Model stadard erorr of `c`
+2194 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
+2195 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
+2196 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
+2197 + `a2`: scrambling factor drift
+2198 + `b2`: compositional slope drift
+2199 + `c2`: WG offset drift
+2200 + `Np`: Number of standardization parameters to fit
+2201 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
+2202 + `d13Cwg_VPDB`: δ13C_VPDB of WG
+2203 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
+2204 '''
+2205 for session in self.sessions:
+2206 if 'd13Cwg_VPDB' not in self.sessions[session]:
+2207 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
+2208 if 'd18Owg_VSMOW' not in self.sessions[session]:
+2209 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
+2210 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
+2211 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
+2212
+2213 self.msg(f'Computing repeatabilities for session {session}')
+2214 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
+2215 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
+2216 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
+2217
+2218 if self.standardization_method == 'pooled':
+2219 for session in self.sessions:
+2220
+2221 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
+2222 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2223 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
+2224
+2225 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
+2226 i = self.standardization.var_names.index(f'b_{pf(session)}')
+2227 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
+2228
+2229 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
+2230 i = self.standardization.var_names.index(f'c_{pf(session)}')
+2231 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
+2232
+2233 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
+2234 if self.sessions[session]['scrambling_drift']:
+2235 i = self.standardization.var_names.index(f'a2_{pf(session)}')
+2236 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
+2237 else:
+2238 self.sessions[session]['SE_a2'] = 0.
+2239
+2240 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
+2241 if self.sessions[session]['slope_drift']:
+2242 i = self.standardization.var_names.index(f'b2_{pf(session)}')
+2243 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
+2244 else:
+2245 self.sessions[session]['SE_b2'] = 0.
+2246
+2247 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
+2248 if self.sessions[session]['wg_drift']:
+2249 i = self.standardization.var_names.index(f'c2_{pf(session)}')
+2250 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
+2251 else:
+2252 self.sessions[session]['SE_c2'] = 0.
+2253
+2254 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2255 j = self.standardization.var_names.index(f'b_{pf(session)}')
+2256 k = self.standardization.var_names.index(f'c_{pf(session)}')
+2257 CM = np.zeros((6,6))
+2258 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
+2259 try:
+2260 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
+2261 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
+2262 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
+2263 try:
+2264 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2265 CM[3,4] = self.standardization.covar[i2,j2]
+2266 CM[4,3] = self.standardization.covar[j2,i2]
+2267 except ValueError:
+2268 pass
+2269 try:
+2270 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2271 CM[3,5] = self.standardization.covar[i2,k2]
+2272 CM[5,3] = self.standardization.covar[k2,i2]
+2273 except ValueError:
+2274 pass
+2275 except ValueError:
+2276 pass
+2277 try:
+2278 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2279 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
+2280 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
+2281 try:
+2282 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2283 CM[4,5] = self.standardization.covar[j2,k2]
+2284 CM[5,4] = self.standardization.covar[k2,j2]
+2285 except ValueError:
+2286 pass
+2287 except ValueError:
+2288 pass
+2289 try:
+2290 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2291 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
+2292 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
+2293 except ValueError:
+2294 pass
2295
-2296 elif self.standardization_method == 'indep_sessions':
-2297 pass # Not implemented yet
-2298
-2299
-2300 @make_verbal
-2301 def repeatabilities(self):
-2302 '''
-2303 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
-2304 (for all samples, for anchors, and for unknowns).
-2305 '''
-2306 self.msg('Computing reproducibilities for all sessions')
-2307
-2308 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
-2309 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
-2310 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
-2311 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
-2312 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
-2313
-2314
-2315 @make_verbal
-2316 def consolidate(self, tables = True, plots = True):
-2317 '''
-2318 Collect information about samples, sessions and repeatabilities.
-2319 '''
-2320 self.consolidate_samples()
-2321 self.consolidate_sessions()
-2322 self.repeatabilities()
-2323
-2324 if tables:
-2325 self.summary()
-2326 self.table_of_sessions()
-2327 self.table_of_analyses()
-2328 self.table_of_samples()
-2329
-2330 if plots:
-2331 self.plot_sessions()
-2332
-2333
-2334 @make_verbal
-2335 def rmswd(self,
-2336 samples = 'all samples',
-2337 sessions = 'all sessions',
-2338 ):
-2339 '''
-2340 Compute the χ2, root mean squared weighted deviation
-2341 (i.e. reduced χ2), and corresponding degrees of freedom of the
-2342 Δ4x values for samples in `samples` and sessions in `sessions`.
-2343
-2344 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
-2345 '''
-2346 if samples == 'all samples':
-2347 mysamples = [k for k in self.samples]
-2348 elif samples == 'anchors':
-2349 mysamples = [k for k in self.anchors]
-2350 elif samples == 'unknowns':
-2351 mysamples = [k for k in self.unknowns]
-2352 else:
-2353 mysamples = samples
-2354
-2355 if sessions == 'all sessions':
-2356 sessions = [k for k in self.sessions]
-2357
-2358 chisq, Nf = 0, 0
-2359 for sample in mysamples :
-2360 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2361 if len(G) > 1 :
-2362 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
-2363 Nf += (len(G) - 1)
-2364 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
-2365 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2366 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
-2367 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
-2368
-2369
-2370 @make_verbal
-2371 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
-2372 '''
-2373 Compute the repeatability of `[r[key] for r in self]`
-2374 '''
-2375
-2376 if samples == 'all samples':
-2377 mysamples = [k for k in self.samples]
-2378 elif samples == 'anchors':
-2379 mysamples = [k for k in self.anchors]
-2380 elif samples == 'unknowns':
-2381 mysamples = [k for k in self.unknowns]
-2382 else:
-2383 mysamples = samples
-2384
-2385 if sessions == 'all sessions':
-2386 sessions = [k for k in self.sessions]
-2387
-2388 if key in ['D47', 'D48']:
-2389 # Full disclosure: the definition of Nf is tricky/debatable
-2390 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
-2391 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
-2392 Nf = len(G)
-2393# print(f'len(G) = {Nf}')
-2394 Nf -= len([s for s in mysamples if s in self.unknowns])
-2395# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
-2396 for session in sessions:
-2397 Np = len([
-2398 _ for _ in self.standardization.params
-2399 if (
-2400 self.standardization.params[_].expr is not None
-2401 and (
-2402 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
-2403 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
-2404 )
-2405 )
-2406 ])
-2407# print(f'session {session}: {Np} parameters to consider')
-2408 Na = len({
-2409 r['Sample'] for r in self.sessions[session]['data']
-2410 if r['Sample'] in self.anchors and r['Sample'] in mysamples
-2411 })
-2412# print(f'session {session}: {Na} different anchors in that session')
-2413 Nf -= min(Np, Na)
-2414# print(f'Nf = {Nf}')
-2415
-2416# for sample in mysamples :
-2417# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2418# if len(X) > 1 :
-2419# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
-2420# if sample in self.unknowns:
-2421# Nf += len(X) - 1
-2422# else:
-2423# Nf += len(X)
-2424# if samples in ['anchors', 'all samples']:
-2425# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
-2426 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2427
-2428 else: # if key not in ['D47', 'D48']
-2429 chisq, Nf = 0, 0
-2430 for sample in mysamples :
-2431 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2432 if len(X) > 1 :
-2433 Nf += len(X) - 1
-2434 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
-2435 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2436
-2437 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
-2438 return r
-2439
-2440 def sample_average(self, samples, weights = 'equal', normalize = True):
-2441 '''
-2442 Weighted average Δ4x value of a group of samples, accounting for covariance.
-2443
-2444 Returns the weighed average Δ4x value and associated SE
-2445 of a group of samples. Weights are equal by default. If `normalize` is
-2446 true, `weights` will be rescaled so that their sum equals 1.
-2447
-2448 **Examples**
+2296 self.sessions[session]['CM'] = CM
+2297
+2298 elif self.standardization_method == 'indep_sessions':
+2299 pass # Not implemented yet
+2300
+2301
+2302 @make_verbal
+2303 def repeatabilities(self):
+2304 '''
+2305 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
+2306 (for all samples, for anchors, and for unknowns).
+2307 '''
+2308 self.msg('Computing reproducibilities for all sessions')
+2309
+2310 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
+2311 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
+2312 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
+2313 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
+2314 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
+2315
+2316
+2317 @make_verbal
+2318 def consolidate(self, tables = True, plots = True):
+2319 '''
+2320 Collect information about samples, sessions and repeatabilities.
+2321 '''
+2322 self.consolidate_samples()
+2323 self.consolidate_sessions()
+2324 self.repeatabilities()
+2325
+2326 if tables:
+2327 self.summary()
+2328 self.table_of_sessions()
+2329 self.table_of_analyses()
+2330 self.table_of_samples()
+2331
+2332 if plots:
+2333 self.plot_sessions()
+2334
+2335
+2336 @make_verbal
+2337 def rmswd(self,
+2338 samples = 'all samples',
+2339 sessions = 'all sessions',
+2340 ):
+2341 '''
+2342 Compute the χ2, root mean squared weighted deviation
+2343 (i.e. reduced χ2), and corresponding degrees of freedom of the
+2344 Δ4x values for samples in `samples` and sessions in `sessions`.
+2345
+2346 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
+2347 '''
+2348 if samples == 'all samples':
+2349 mysamples = [k for k in self.samples]
+2350 elif samples == 'anchors':
+2351 mysamples = [k for k in self.anchors]
+2352 elif samples == 'unknowns':
+2353 mysamples = [k for k in self.unknowns]
+2354 else:
+2355 mysamples = samples
+2356
+2357 if sessions == 'all sessions':
+2358 sessions = [k for k in self.sessions]
+2359
+2360 chisq, Nf = 0, 0
+2361 for sample in mysamples :
+2362 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2363 if len(G) > 1 :
+2364 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
+2365 Nf += (len(G) - 1)
+2366 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
+2367 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2368 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
+2369 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
+2370
+2371
+2372 @make_verbal
+2373 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
+2374 '''
+2375 Compute the repeatability of `[r[key] for r in self]`
+2376 '''
+2377
+2378 if samples == 'all samples':
+2379 mysamples = [k for k in self.samples]
+2380 elif samples == 'anchors':
+2381 mysamples = [k for k in self.anchors]
+2382 elif samples == 'unknowns':
+2383 mysamples = [k for k in self.unknowns]
+2384 else:
+2385 mysamples = samples
+2386
+2387 if sessions == 'all sessions':
+2388 sessions = [k for k in self.sessions]
+2389
+2390 if key in ['D47', 'D48']:
+2391 # Full disclosure: the definition of Nf is tricky/debatable
+2392 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
+2393 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
+2394 Nf = len(G)
+2395# print(f'len(G) = {Nf}')
+2396 Nf -= len([s for s in mysamples if s in self.unknowns])
+2397# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
+2398 for session in sessions:
+2399 Np = len([
+2400 _ for _ in self.standardization.params
+2401 if (
+2402 self.standardization.params[_].expr is not None
+2403 and (
+2404 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
+2405 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
+2406 )
+2407 )
+2408 ])
+2409# print(f'session {session}: {Np} parameters to consider')
+2410 Na = len({
+2411 r['Sample'] for r in self.sessions[session]['data']
+2412 if r['Sample'] in self.anchors and r['Sample'] in mysamples
+2413 })
+2414# print(f'session {session}: {Na} different anchors in that session')
+2415 Nf -= min(Np, Na)
+2416# print(f'Nf = {Nf}')
+2417
+2418# for sample in mysamples :
+2419# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2420# if len(X) > 1 :
+2421# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
+2422# if sample in self.unknowns:
+2423# Nf += len(X) - 1
+2424# else:
+2425# Nf += len(X)
+2426# if samples in ['anchors', 'all samples']:
+2427# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
+2428 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2429
+2430 else: # if key not in ['D47', 'D48']
+2431 chisq, Nf = 0, 0
+2432 for sample in mysamples :
+2433 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2434 if len(X) > 1 :
+2435 Nf += len(X) - 1
+2436 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
+2437 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2438
+2439 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
+2440 return r
+2441
+2442 def sample_average(self, samples, weights = 'equal', normalize = True):
+2443 '''
+2444 Weighted average Δ4x value of a group of samples, accounting for covariance.
+2445
+2446 Returns the weighed average Δ4x value and associated SE
+2447 of a group of samples. Weights are equal by default. If `normalize` is
+2448 true, `weights` will be rescaled so that their sum equals 1.
2449
-2450 ```python
-2451 self.sample_average(['X','Y'], [1, 2])
-2452 ```
-2453
-2454 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
-2455 where Δ4x(X) and Δ4x(Y) are the average Δ4x
-2456 values of samples X and Y, respectively.
-2457
-2458 ```python
-2459 self.sample_average(['X','Y'], [1, -1], normalize = False)
-2460 ```
-2461
-2462 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
-2463 '''
-2464 if weights == 'equal':
-2465 weights = [1/len(samples)] * len(samples)
-2466
-2467 if normalize:
-2468 s = sum(weights)
-2469 if s:
-2470 weights = [w/s for w in weights]
-2471
-2472 try:
-2473# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
-2474# C = self.standardization.covar[indices,:][:,indices]
-2475 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
-2476 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
-2477 return correlated_sum(X, C, weights)
-2478 except ValueError:
-2479 return (0., 0.)
-2480
-2481
-2482 def sample_D4x_covar(self, sample1, sample2 = None):
-2483 '''
-2484 Covariance between Δ4x values of samples
-2485
-2486 Returns the error covariance between the average Δ4x values of two
-2487 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
-2488 returns the Δ4x variance for that sample.
-2489 '''
-2490 if sample2 is None:
-2491 sample2 = sample1
-2492 if self.standardization_method == 'pooled':
-2493 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
-2494 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
-2495 return self.standardization.covar[i, j]
-2496 elif self.standardization_method == 'indep_sessions':
-2497 if sample1 == sample2:
-2498 return self.samples[sample1][f'SE_D{self._4x}']**2
-2499 else:
-2500 c = 0
-2501 for session in self.sessions:
-2502 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
-2503 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
-2504 if sdata1 and sdata2:
-2505 a = self.sessions[session]['a']
-2506 # !! TODO: CM below does not account for temporal changes in standardization parameters
-2507 CM = self.sessions[session]['CM'][:3,:3]
-2508 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
-2509 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
-2510 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
-2511 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
-2512 c += (
-2513 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
-2514 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
-2515 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
-2516 @ CM
-2517 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
-2518 ) / a**2
-2519 return float(c)
-2520
-2521 def sample_D4x_correl(self, sample1, sample2 = None):
-2522 '''
-2523 Correlation between Δ4x errors of samples
-2524
-2525 Returns the error correlation between the average Δ4x values of two samples.
-2526 '''
-2527 if sample2 is None or sample2 == sample1:
-2528 return 1.
-2529 return (
-2530 self.sample_D4x_covar(sample1, sample2)
-2531 / self.unknowns[sample1][f'SE_D{self._4x}']
-2532 / self.unknowns[sample2][f'SE_D{self._4x}']
-2533 )
-2534
-2535 def plot_single_session(self,
-2536 session,
-2537 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
-2538 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
-2539 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
-2540 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
-2541 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
-2542 xylimits = 'free', # | 'constant'
-2543 x_label = None,
-2544 y_label = None,
-2545 error_contour_interval = 'auto',
-2546 fig = 'new',
-2547 ):
-2548 '''
-2549 Generate plot for a single session
-2550 '''
-2551 if x_label is None:
-2552 x_label = f'δ$_{{{self._4x}}}$ (‰)'
-2553 if y_label is None:
-2554 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
-2555
-2556 out = _SessionPlot()
-2557 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
-2558 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
-2559
-2560 if fig == 'new':
-2561 out.fig = ppl.figure(figsize = (6,6))
-2562 ppl.subplots_adjust(.1,.1,.9,.9)
-2563
-2564 out.anchor_analyses, = ppl.plot(
-2565 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2566 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2567 **kw_plot_anchors)
-2568 out.unknown_analyses, = ppl.plot(
-2569 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2570 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2571 **kw_plot_unknowns)
-2572 out.anchor_avg = ppl.plot(
-2573 np.array([ np.array([
-2574 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2575 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2576 ]) for sample in anchors]).T,
-2577 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
-2578 **kw_plot_anchor_avg)
-2579 out.unknown_avg = ppl.plot(
-2580 np.array([ np.array([
-2581 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2582 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2583 ]) for sample in unknowns]).T,
-2584 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
-2585 **kw_plot_unknown_avg)
-2586 if xylimits == 'constant':
-2587 x = [r[f'd{self._4x}'] for r in self]
-2588 y = [r[f'D{self._4x}'] for r in self]
-2589 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
-2590 w, h = x2-x1, y2-y1
-2591 x1 -= w/20
-2592 x2 += w/20
-2593 y1 -= h/20
-2594 y2 += h/20
-2595 ppl.axis([x1, x2, y1, y2])
-2596 elif xylimits == 'free':
-2597 x1, x2, y1, y2 = ppl.axis()
-2598 else:
-2599 x1, x2, y1, y2 = ppl.axis(xylimits)
-2600
-2601 if error_contour_interval != 'none':
-2602 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
-2603 XI,YI = np.meshgrid(xi, yi)
-2604 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
-2605 if error_contour_interval == 'auto':
-2606 rng = np.max(SI) - np.min(SI)
-2607 if rng <= 0.01:
-2608 cinterval = 0.001
-2609 elif rng <= 0.03:
-2610 cinterval = 0.004
-2611 elif rng <= 0.1:
-2612 cinterval = 0.01
-2613 elif rng <= 0.3:
-2614 cinterval = 0.03
-2615 elif rng <= 1.:
-2616 cinterval = 0.1
-2617 else:
-2618 cinterval = 0.5
-2619 else:
-2620 cinterval = error_contour_interval
-2621
-2622 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
-2623 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
-2624 out.clabel = ppl.clabel(out.contour)
-2625
-2626 ppl.xlabel(x_label)
-2627 ppl.ylabel(y_label)
-2628 ppl.title(session, weight = 'bold')
-2629 ppl.grid(alpha = .2)
-2630 out.ax = ppl.gca()
-2631
-2632 return out
+2450 **Examples**
+2451
+2452 ```python
+2453 self.sample_average(['X','Y'], [1, 2])
+2454 ```
+2455
+2456 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
+2457 where Δ4x(X) and Δ4x(Y) are the average Δ4x
+2458 values of samples X and Y, respectively.
+2459
+2460 ```python
+2461 self.sample_average(['X','Y'], [1, -1], normalize = False)
+2462 ```
+2463
+2464 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
+2465 '''
+2466 if weights == 'equal':
+2467 weights = [1/len(samples)] * len(samples)
+2468
+2469 if normalize:
+2470 s = sum(weights)
+2471 if s:
+2472 weights = [w/s for w in weights]
+2473
+2474 try:
+2475# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
+2476# C = self.standardization.covar[indices,:][:,indices]
+2477 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
+2478 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
+2479 return correlated_sum(X, C, weights)
+2480 except ValueError:
+2481 return (0., 0.)
+2482
+2483
+2484 def sample_D4x_covar(self, sample1, sample2 = None):
+2485 '''
+2486 Covariance between Δ4x values of samples
+2487
+2488 Returns the error covariance between the average Δ4x values of two
+2489 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
+2490 returns the Δ4x variance for that sample.
+2491 '''
+2492 if sample2 is None:
+2493 sample2 = sample1
+2494 if self.standardization_method == 'pooled':
+2495 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
+2496 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
+2497 return self.standardization.covar[i, j]
+2498 elif self.standardization_method == 'indep_sessions':
+2499 if sample1 == sample2:
+2500 return self.samples[sample1][f'SE_D{self._4x}']**2
+2501 else:
+2502 c = 0
+2503 for session in self.sessions:
+2504 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
+2505 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
+2506 if sdata1 and sdata2:
+2507 a = self.sessions[session]['a']
+2508 # !! TODO: CM below does not account for temporal changes in standardization parameters
+2509 CM = self.sessions[session]['CM'][:3,:3]
+2510 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
+2511 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
+2512 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
+2513 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
+2514 c += (
+2515 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
+2516 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
+2517 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
+2518 @ CM
+2519 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
+2520 ) / a**2
+2521 return float(c)
+2522
+2523 def sample_D4x_correl(self, sample1, sample2 = None):
+2524 '''
+2525 Correlation between Δ4x errors of samples
+2526
+2527 Returns the error correlation between the average Δ4x values of two samples.
+2528 '''
+2529 if sample2 is None or sample2 == sample1:
+2530 return 1.
+2531 return (
+2532 self.sample_D4x_covar(sample1, sample2)
+2533 / self.unknowns[sample1][f'SE_D{self._4x}']
+2534 / self.unknowns[sample2][f'SE_D{self._4x}']
+2535 )
+2536
+2537 def plot_single_session(self,
+2538 session,
+2539 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
+2540 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
+2541 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
+2542 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
+2543 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
+2544 xylimits = 'free', # | 'constant'
+2545 x_label = None,
+2546 y_label = None,
+2547 error_contour_interval = 'auto',
+2548 fig = 'new',
+2549 ):
+2550 '''
+2551 Generate plot for a single session
+2552 '''
+2553 if x_label is None:
+2554 x_label = f'δ$_{{{self._4x}}}$ (‰)'
+2555 if y_label is None:
+2556 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
+2557
+2558 out = _SessionPlot()
+2559 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
+2560 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
+2561
+2562 if fig == 'new':
+2563 out.fig = ppl.figure(figsize = (6,6))
+2564 ppl.subplots_adjust(.1,.1,.9,.9)
+2565
+2566 out.anchor_analyses, = ppl.plot(
+2567 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2568 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2569 **kw_plot_anchors)
+2570 out.unknown_analyses, = ppl.plot(
+2571 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2572 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2573 **kw_plot_unknowns)
+2574 out.anchor_avg = ppl.plot(
+2575 np.array([ np.array([
+2576 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2577 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2578 ]) for sample in anchors]).T,
+2579 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
+2580 **kw_plot_anchor_avg)
+2581 out.unknown_avg = ppl.plot(
+2582 np.array([ np.array([
+2583 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2584 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2585 ]) for sample in unknowns]).T,
+2586 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
+2587 **kw_plot_unknown_avg)
+2588 if xylimits == 'constant':
+2589 x = [r[f'd{self._4x}'] for r in self]
+2590 y = [r[f'D{self._4x}'] for r in self]
+2591 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
+2592 w, h = x2-x1, y2-y1
+2593 x1 -= w/20
+2594 x2 += w/20
+2595 y1 -= h/20
+2596 y2 += h/20
+2597 ppl.axis([x1, x2, y1, y2])
+2598 elif xylimits == 'free':
+2599 x1, x2, y1, y2 = ppl.axis()
+2600 else:
+2601 x1, x2, y1, y2 = ppl.axis(xylimits)
+2602
+2603 if error_contour_interval != 'none':
+2604 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
+2605 XI,YI = np.meshgrid(xi, yi)
+2606 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
+2607 if error_contour_interval == 'auto':
+2608 rng = np.max(SI) - np.min(SI)
+2609 if rng <= 0.01:
+2610 cinterval = 0.001
+2611 elif rng <= 0.03:
+2612 cinterval = 0.004
+2613 elif rng <= 0.1:
+2614 cinterval = 0.01
+2615 elif rng <= 0.3:
+2616 cinterval = 0.03
+2617 elif rng <= 1.:
+2618 cinterval = 0.1
+2619 else:
+2620 cinterval = 0.5
+2621 else:
+2622 cinterval = error_contour_interval
+2623
+2624 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
+2625 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
+2626 out.clabel = ppl.clabel(out.contour)
+2627
+2628 ppl.xlabel(x_label)
+2629 ppl.ylabel(y_label)
+2630 ppl.title(session, weight = 'bold')
+2631 ppl.grid(alpha = .2)
+2632 out.ax = ppl.gca()
2633
-2634 def plot_residuals(
-2635 self,
-2636 kde = False,
-2637 hist = False,
-2638 binwidth = 2/3,
-2639 dir = 'output',
-2640 filename = None,
-2641 highlight = [],
-2642 colors = None,
-2643 figsize = None,
-2644 dpi = 100,
-2645 yspan = None,
-2646 ):
-2647 '''
-2648 Plot residuals of each analysis as a function of time (actually, as a function of
-2649 the order of analyses in the `D4xdata` object)
-2650
-2651 + `kde`: whether to add a kernel density estimate of residuals
-2652 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
-2653 + `histbins`: specify bin edges for the histogram
-2654 + `dir`: the directory in which to save the plot
-2655 + `highlight`: a list of samples to highlight
-2656 + `colors`: a dict of `{<sample>: <color>}` for all samples
-2657 + `figsize`: (width, height) of figure
-2658 + `dpi`: resolution for PNG output
-2659 + `yspan`: factor controlling the range of y values shown in plot
-2660 (by default: `yspan = 1.5 if kde else 1.0`)
-2661 '''
-2662
-2663 from matplotlib import ticker
-2664
-2665 if yspan is None:
-2666 if kde:
-2667 yspan = 1.5
-2668 else:
-2669 yspan = 1.0
-2670
-2671 # Layout
-2672 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
-2673 if hist or kde:
-2674 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
-2675 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
-2676 else:
-2677 ppl.subplots_adjust(.08,.05,.78,.8)
-2678 ax1 = ppl.subplot(111)
-2679
-2680 # Colors
-2681 N = len(self.anchors)
-2682 if colors is None:
-2683 if len(highlight) > 0:
-2684 Nh = len(highlight)
-2685 if Nh == 1:
-2686 colors = {highlight[0]: (0,0,0)}
-2687 elif Nh == 3:
-2688 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2689 elif Nh == 4:
-2690 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2691 else:
-2692 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
-2693 else:
-2694 if N == 3:
-2695 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2696 elif N == 4:
-2697 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2698 else:
-2699 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
-2700
-2701 ppl.sca(ax1)
-2702
-2703 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
-2704
-2705 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+2634 return out
+2635
+2636 def plot_residuals(
+2637 self,
+2638 kde = False,
+2639 hist = False,
+2640 binwidth = 2/3,
+2641 dir = 'output',
+2642 filename = None,
+2643 highlight = [],
+2644 colors = None,
+2645 figsize = None,
+2646 dpi = 100,
+2647 yspan = None,
+2648 ):
+2649 '''
+2650 Plot residuals of each analysis as a function of time (actually, as a function of
+2651 the order of analyses in the `D4xdata` object)
+2652
+2653 + `kde`: whether to add a kernel density estimate of residuals
+2654 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
+2655 + `histbins`: specify bin edges for the histogram
+2656 + `dir`: the directory in which to save the plot
+2657 + `highlight`: a list of samples to highlight
+2658 + `colors`: a dict of `{<sample>: <color>}` for all samples
+2659 + `figsize`: (width, height) of figure
+2660 + `dpi`: resolution for PNG output
+2661 + `yspan`: factor controlling the range of y values shown in plot
+2662 (by default: `yspan = 1.5 if kde else 1.0`)
+2663 '''
+2664
+2665 from matplotlib import ticker
+2666
+2667 if yspan is None:
+2668 if kde:
+2669 yspan = 1.5
+2670 else:
+2671 yspan = 1.0
+2672
+2673 # Layout
+2674 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
+2675 if hist or kde:
+2676 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
+2677 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
+2678 else:
+2679 ppl.subplots_adjust(.08,.05,.78,.8)
+2680 ax1 = ppl.subplot(111)
+2681
+2682 # Colors
+2683 N = len(self.anchors)
+2684 if colors is None:
+2685 if len(highlight) > 0:
+2686 Nh = len(highlight)
+2687 if Nh == 1:
+2688 colors = {highlight[0]: (0,0,0)}
+2689 elif Nh == 3:
+2690 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2691 elif Nh == 4:
+2692 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2693 else:
+2694 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
+2695 else:
+2696 if N == 3:
+2697 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2698 elif N == 4:
+2699 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2700 else:
+2701 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
+2702
+2703 ppl.sca(ax1)
+2704
+2705 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
2706
-2707 session = self[0]['Session']
-2708 x1 = 0
-2709# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
-2710 x_sessions = {}
-2711 one_or_more_singlets = False
-2712 one_or_more_multiplets = False
-2713 multiplets = set()
-2714 for k,r in enumerate(self):
-2715 if r['Session'] != session:
-2716 x2 = k-1
-2717 x_sessions[session] = (x1+x2)/2
-2718 ppl.axvline(k - 0.5, color = 'k', lw = .5)
-2719 session = r['Session']
-2720 x1 = k
-2721 singlet = len(self.samples[r['Sample']]['data']) == 1
-2722 if not singlet:
-2723 multiplets.add(r['Sample'])
-2724 if r['Sample'] in self.unknowns:
-2725 if singlet:
-2726 one_or_more_singlets = True
-2727 else:
-2728 one_or_more_multiplets = True
-2729 kw = dict(
-2730 marker = 'x' if singlet else '+',
-2731 ms = 4 if singlet else 5,
-2732 ls = 'None',
-2733 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
-2734 mew = 1,
-2735 alpha = 0.2 if singlet else 1,
-2736 )
-2737 if highlight and r['Sample'] not in highlight:
-2738 kw['alpha'] = 0.2
-2739 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
-2740 x2 = k
-2741 x_sessions[session] = (x1+x2)/2
-2742
-2743 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
-2744 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
-2745 if not (hist or kde):
-2746 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2747 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2748
-2749 xmin, xmax, ymin, ymax = ppl.axis()
-2750 if yspan != 1:
-2751 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
-2752 for s in x_sessions:
-2753 ppl.text(
-2754 x_sessions[s],
-2755 ymax +1,
-2756 s,
-2757 va = 'bottom',
-2758 **(
-2759 dict(ha = 'center')
-2760 if len(self.sessions[s]['data']) > (0.15 * len(self))
-2761 else dict(ha = 'left', rotation = 45)
-2762 )
-2763 )
-2764
-2765 if hist or kde:
-2766 ppl.sca(ax2)
-2767
-2768 for s in colors:
-2769 kw['marker'] = '+'
-2770 kw['ms'] = 5
-2771 kw['mec'] = colors[s]
-2772 kw['label'] = s
-2773 kw['alpha'] = 1
-2774 ppl.plot([], [], **kw)
-2775
-2776 kw['mec'] = (0,0,0)
+2707 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+2708
+2709 session = self[0]['Session']
+2710 x1 = 0
+2711# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
+2712 x_sessions = {}
+2713 one_or_more_singlets = False
+2714 one_or_more_multiplets = False
+2715 multiplets = set()
+2716 for k,r in enumerate(self):
+2717 if r['Session'] != session:
+2718 x2 = k-1
+2719 x_sessions[session] = (x1+x2)/2
+2720 ppl.axvline(k - 0.5, color = 'k', lw = .5)
+2721 session = r['Session']
+2722 x1 = k
+2723 singlet = len(self.samples[r['Sample']]['data']) == 1
+2724 if not singlet:
+2725 multiplets.add(r['Sample'])
+2726 if r['Sample'] in self.unknowns:
+2727 if singlet:
+2728 one_or_more_singlets = True
+2729 else:
+2730 one_or_more_multiplets = True
+2731 kw = dict(
+2732 marker = 'x' if singlet else '+',
+2733 ms = 4 if singlet else 5,
+2734 ls = 'None',
+2735 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
+2736 mew = 1,
+2737 alpha = 0.2 if singlet else 1,
+2738 )
+2739 if highlight and r['Sample'] not in highlight:
+2740 kw['alpha'] = 0.2
+2741 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
+2742 x2 = k
+2743 x_sessions[session] = (x1+x2)/2
+2744
+2745 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
+2746 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
+2747 if not (hist or kde):
+2748 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2749 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2750
+2751 xmin, xmax, ymin, ymax = ppl.axis()
+2752 if yspan != 1:
+2753 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
+2754 for s in x_sessions:
+2755 ppl.text(
+2756 x_sessions[s],
+2757 ymax +1,
+2758 s,
+2759 va = 'bottom',
+2760 **(
+2761 dict(ha = 'center')
+2762 if len(self.sessions[s]['data']) > (0.15 * len(self))
+2763 else dict(ha = 'left', rotation = 45)
+2764 )
+2765 )
+2766
+2767 if hist or kde:
+2768 ppl.sca(ax2)
+2769
+2770 for s in colors:
+2771 kw['marker'] = '+'
+2772 kw['ms'] = 5
+2773 kw['mec'] = colors[s]
+2774 kw['label'] = s
+2775 kw['alpha'] = 1
+2776 ppl.plot([], [], **kw)
2777
-2778 if one_or_more_singlets:
-2779 kw['marker'] = 'x'
-2780 kw['ms'] = 4
-2781 kw['alpha'] = .2
-2782 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
-2783 ppl.plot([], [], **kw)
-2784
-2785 if one_or_more_multiplets:
-2786 kw['marker'] = '+'
-2787 kw['ms'] = 4
-2788 kw['alpha'] = 1
-2789 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
-2790 ppl.plot([], [], **kw)
-2791
-2792 if hist or kde:
-2793 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
-2794 else:
-2795 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
-2796 leg.set_zorder(-1000)
-2797
-2798 ppl.sca(ax1)
+2778 kw['mec'] = (0,0,0)
+2779
+2780 if one_or_more_singlets:
+2781 kw['marker'] = 'x'
+2782 kw['ms'] = 4
+2783 kw['alpha'] = .2
+2784 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
+2785 ppl.plot([], [], **kw)
+2786
+2787 if one_or_more_multiplets:
+2788 kw['marker'] = '+'
+2789 kw['ms'] = 4
+2790 kw['alpha'] = 1
+2791 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
+2792 ppl.plot([], [], **kw)
+2793
+2794 if hist or kde:
+2795 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
+2796 else:
+2797 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
+2798 leg.set_zorder(-1000)
2799
-2800 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
-2801 ppl.xticks([])
-2802 ppl.axis([-1, len(self), None, None])
-2803
-2804 if hist or kde:
-2805 ppl.sca(ax2)
-2806 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
-2807
-2808 if kde:
-2809 from scipy.stats import gaussian_kde
-2810 yi = np.linspace(ymin, ymax, 201)
-2811 xi = gaussian_kde(X).evaluate(yi)
-2812 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
-2813# ppl.plot(xi, yi, 'k-', lw = 1)
-2814 elif hist:
-2815 ppl.hist(
-2816 X,
-2817 orientation = 'horizontal',
-2818 histtype = 'stepfilled',
-2819 ec = [.4]*3,
-2820 fc = [.25]*3,
-2821 alpha = .25,
-2822 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
-2823 )
-2824 ppl.text(0, 0,
-2825 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
-2826 size = 7.5,
-2827 alpha = 1,
-2828 va = 'center',
-2829 ha = 'left',
-2830 )
-2831
-2832 ppl.axis([0, None, ymin, ymax])
-2833 ppl.xticks([])
-2834 ppl.yticks([])
-2835# ax2.spines['left'].set_visible(False)
-2836 ax2.spines['right'].set_visible(False)
-2837 ax2.spines['top'].set_visible(False)
-2838 ax2.spines['bottom'].set_visible(False)
-2839
-2840 ax1.axis([None, None, ymin, ymax])
+2800 ppl.sca(ax1)
+2801
+2802 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
+2803 ppl.xticks([])
+2804 ppl.axis([-1, len(self), None, None])
+2805
+2806 if hist or kde:
+2807 ppl.sca(ax2)
+2808 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
+2809
+2810 if kde:
+2811 from scipy.stats import gaussian_kde
+2812 yi = np.linspace(ymin, ymax, 201)
+2813 xi = gaussian_kde(X).evaluate(yi)
+2814 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
+2815# ppl.plot(xi, yi, 'k-', lw = 1)
+2816 elif hist:
+2817 ppl.hist(
+2818 X,
+2819 orientation = 'horizontal',
+2820 histtype = 'stepfilled',
+2821 ec = [.4]*3,
+2822 fc = [.25]*3,
+2823 alpha = .25,
+2824 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
+2825 )
+2826 ppl.text(0, 0,
+2827 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
+2828 size = 7.5,
+2829 alpha = 1,
+2830 va = 'center',
+2831 ha = 'left',
+2832 )
+2833
+2834 ppl.axis([0, None, ymin, ymax])
+2835 ppl.xticks([])
+2836 ppl.yticks([])
+2837# ax2.spines['left'].set_visible(False)
+2838 ax2.spines['right'].set_visible(False)
+2839 ax2.spines['top'].set_visible(False)
+2840 ax2.spines['bottom'].set_visible(False)
2841
-2842 if not os.path.exists(dir):
-2843 os.makedirs(dir)
-2844 if filename is None:
-2845 return fig
-2846 elif filename == '':
-2847 filename = f'D{self._4x}_residuals.pdf'
-2848 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2849 ppl.close(fig)
-2850
-2851
-2852 def simulate(self, *args, **kwargs):
-2853 '''
-2854 Legacy function with warning message pointing to `virtual_data()`
-2855 '''
-2856 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
-2857
-2858 def plot_distribution_of_analyses(
-2859 self,
-2860 dir = 'output',
-2861 filename = None,
-2862 vs_time = False,
-2863 figsize = (6,4),
-2864 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
-2865 output = None,
-2866 dpi = 100,
-2867 ):
-2868 '''
-2869 Plot temporal distribution of all analyses in the data set.
-2870
-2871 **Parameters**
-2872
-2873 + `dir`: the directory in which to save the plot
-2874 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
-2875 + `dpi`: resolution for PNG output
-2876 + `figsize`: (width, height) of figure
+2842 ax1.axis([None, None, ymin, ymax])
+2843
+2844 if not os.path.exists(dir):
+2845 os.makedirs(dir)
+2846 if filename is None:
+2847 return fig
+2848 elif filename == '':
+2849 filename = f'D{self._4x}_residuals.pdf'
+2850 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2851 ppl.close(fig)
+2852
+2853
+2854 def simulate(self, *args, **kwargs):
+2855 '''
+2856 Legacy function with warning message pointing to `virtual_data()`
+2857 '''
+2858 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
+2859
+2860 def plot_distribution_of_analyses(
+2861 self,
+2862 dir = 'output',
+2863 filename = None,
+2864 vs_time = False,
+2865 figsize = (6,4),
+2866 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
+2867 output = None,
+2868 dpi = 100,
+2869 ):
+2870 '''
+2871 Plot temporal distribution of all analyses in the data set.
+2872
+2873 **Parameters**
+2874
+2875 + `dir`: the directory in which to save the plot
+2876 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
2877 + `dpi`: resolution for PNG output
-2878 '''
-2879
-2880 asamples = [s for s in self.anchors]
-2881 usamples = [s for s in self.unknowns]
-2882 if output is None or output == 'fig':
-2883 fig = ppl.figure(figsize = figsize)
-2884 ppl.subplots_adjust(*subplots_adjust)
-2885 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2886 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2887 Xmax += (Xmax-Xmin)/40
-2888 Xmin -= (Xmax-Xmin)/41
-2889 for k, s in enumerate(asamples + usamples):
-2890 if vs_time:
-2891 X = [r['TimeTag'] for r in self if r['Sample'] == s]
-2892 else:
-2893 X = [x for x,r in enumerate(self) if r['Sample'] == s]
-2894 Y = [-k for x in X]
-2895 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
-2896 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
-2897 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
-2898 ppl.axis([Xmin, Xmax, -k-1, 1])
-2899 ppl.xlabel('\ntime')
-2900 ppl.gca().annotate('',
-2901 xy = (0.6, -0.02),
-2902 xycoords = 'axes fraction',
-2903 xytext = (.4, -0.02),
-2904 arrowprops = dict(arrowstyle = "->", color = 'k'),
-2905 )
-2906
-2907
-2908 x2 = -1
-2909 for session in self.sessions:
-2910 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2911 if vs_time:
-2912 ppl.axvline(x1, color = 'k', lw = .75)
-2913 if x2 > -1:
-2914 if not vs_time:
-2915 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
-2916 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2917# from xlrd import xldate_as_datetime
-2918# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
-2919 if vs_time:
-2920 ppl.axvline(x2, color = 'k', lw = .75)
-2921 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
-2922 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
-2923
-2924 ppl.xticks([])
-2925 ppl.yticks([])
-2926
-2927 if output is None:
-2928 if not os.path.exists(dir):
-2929 os.makedirs(dir)
-2930 if filename == None:
-2931 filename = f'D{self._4x}_distribution_of_analyses.pdf'
-2932 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2933 ppl.close(fig)
-2934 elif output == 'ax':
-2935 return ppl.gca()
-2936 elif output == 'fig':
-2937 return fig
-2938
-2939
-2940 def plot_bulk_compositions(
-2941 self,
-2942 samples = None,
-2943 dir = 'output/bulk_compositions',
-2944 figsize = (6,6),
-2945 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
-2946 show = False,
-2947 sample_color = (0,.5,1),
-2948 analysis_color = (.7,.7,.7),
-2949 labeldist = 0.3,
-2950 radius = 0.05,
-2951 ):
-2952 '''
-2953 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
-2954
-2955 By default, creates a directory `./output/bulk_compositions` where plots for
-2956 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
-2957
-2958
-2959 **Parameters**
-2960
-2961 + `samples`: Only these samples are processed (by default: all samples).
-2962 + `dir`: where to save the plots
-2963 + `figsize`: (width, height) of figure
-2964 + `subplots_adjust`: passed to `subplots_adjust()`
-2965 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
-2966 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
-2967 + `sample_color`: color used for replicate markers/labels
-2968 + `analysis_color`: color used for sample markers/labels
-2969 + `labeldist`: distance (in inches) from replicate markers to replicate labels
-2970 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
-2971 '''
-2972
-2973 from matplotlib.patches import Ellipse
+2878 + `figsize`: (width, height) of figure
+2879 + `dpi`: resolution for PNG output
+2880 '''
+2881
+2882 asamples = [s for s in self.anchors]
+2883 usamples = [s for s in self.unknowns]
+2884 if output is None or output == 'fig':
+2885 fig = ppl.figure(figsize = figsize)
+2886 ppl.subplots_adjust(*subplots_adjust)
+2887 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2888 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2889 Xmax += (Xmax-Xmin)/40
+2890 Xmin -= (Xmax-Xmin)/41
+2891 for k, s in enumerate(asamples + usamples):
+2892 if vs_time:
+2893 X = [r['TimeTag'] for r in self if r['Sample'] == s]
+2894 else:
+2895 X = [x for x,r in enumerate(self) if r['Sample'] == s]
+2896 Y = [-k for x in X]
+2897 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
+2898 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
+2899 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
+2900 ppl.axis([Xmin, Xmax, -k-1, 1])
+2901 ppl.xlabel('\ntime')
+2902 ppl.gca().annotate('',
+2903 xy = (0.6, -0.02),
+2904 xycoords = 'axes fraction',
+2905 xytext = (.4, -0.02),
+2906 arrowprops = dict(arrowstyle = "->", color = 'k'),
+2907 )
+2908
+2909
+2910 x2 = -1
+2911 for session in self.sessions:
+2912 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2913 if vs_time:
+2914 ppl.axvline(x1, color = 'k', lw = .75)
+2915 if x2 > -1:
+2916 if not vs_time:
+2917 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
+2918 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2919# from xlrd import xldate_as_datetime
+2920# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
+2921 if vs_time:
+2922 ppl.axvline(x2, color = 'k', lw = .75)
+2923 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
+2924 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
+2925
+2926 ppl.xticks([])
+2927 ppl.yticks([])
+2928
+2929 if output is None:
+2930 if not os.path.exists(dir):
+2931 os.makedirs(dir)
+2932 if filename == None:
+2933 filename = f'D{self._4x}_distribution_of_analyses.pdf'
+2934 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2935 ppl.close(fig)
+2936 elif output == 'ax':
+2937 return ppl.gca()
+2938 elif output == 'fig':
+2939 return fig
+2940
+2941
+2942 def plot_bulk_compositions(
+2943 self,
+2944 samples = None,
+2945 dir = 'output/bulk_compositions',
+2946 figsize = (6,6),
+2947 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
+2948 show = False,
+2949 sample_color = (0,.5,1),
+2950 analysis_color = (.7,.7,.7),
+2951 labeldist = 0.3,
+2952 radius = 0.05,
+2953 ):
+2954 '''
+2955 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
+2956
+2957 By default, creates a directory `./output/bulk_compositions` where plots for
+2958 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
+2959
+2960
+2961 **Parameters**
+2962
+2963 + `samples`: Only these samples are processed (by default: all samples).
+2964 + `dir`: where to save the plots
+2965 + `figsize`: (width, height) of figure
+2966 + `subplots_adjust`: passed to `subplots_adjust()`
+2967 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
+2968 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
+2969 + `sample_color`: color used for replicate markers/labels
+2970 + `analysis_color`: color used for sample markers/labels
+2971 + `labeldist`: distance (in inches) from replicate markers to replicate labels
+2972 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
+2973 '''
2974
-2975 if samples is None:
-2976 samples = [_ for _ in self.samples]
-2977
-2978 saved = {}
+2975 from matplotlib.patches import Ellipse
+2976
+2977 if samples is None:
+2978 samples = [_ for _ in self.samples]
2979
-2980 for s in samples:
+2980 saved = {}
2981
-2982 fig = ppl.figure(figsize = figsize)
-2983 fig.subplots_adjust(*subplots_adjust)
-2984 ax = ppl.subplot(111)
-2985 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-2986 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-2987 ppl.title(s)
-2988
-2989
-2990 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
-2991 UID = [_['UID'] for _ in self.samples[s]['data']]
-2992 XY0 = XY.mean(0)
-2993
-2994 for xy in XY:
-2995 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
-2996
-2997 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
-2998 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
-2999 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-3000 saved[s] = [XY, XY0]
-3001
-3002 x1, x2, y1, y2 = ppl.axis()
-3003 x0, dx = (x1+x2)/2, (x2-x1)/2
-3004 y0, dy = (y1+y2)/2, (y2-y1)/2
-3005 dx, dy = [max(max(dx, dy), radius)]*2
-3006
-3007 ppl.axis([
-3008 x0 - 1.2*dx,
-3009 x0 + 1.2*dx,
-3010 y0 - 1.2*dy,
-3011 y0 + 1.2*dy,
-3012 ])
-3013
-3014 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
+2982 for s in samples:
+2983
+2984 fig = ppl.figure(figsize = figsize)
+2985 fig.subplots_adjust(*subplots_adjust)
+2986 ax = ppl.subplot(111)
+2987 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+2988 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+2989 ppl.title(s)
+2990
+2991
+2992 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
+2993 UID = [_['UID'] for _ in self.samples[s]['data']]
+2994 XY0 = XY.mean(0)
+2995
+2996 for xy in XY:
+2997 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
+2998
+2999 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
+3000 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
+3001 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3002 saved[s] = [XY, XY0]
+3003
+3004 x1, x2, y1, y2 = ppl.axis()
+3005 x0, dx = (x1+x2)/2, (x2-x1)/2
+3006 y0, dy = (y1+y2)/2, (y2-y1)/2
+3007 dx, dy = [max(max(dx, dy), radius)]*2
+3008
+3009 ppl.axis([
+3010 x0 - 1.2*dx,
+3011 x0 + 1.2*dx,
+3012 y0 - 1.2*dy,
+3013 y0 + 1.2*dy,
+3014 ])
3015
-3016 for xy, uid in zip(XY, UID):
+3016 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
3017
-3018 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
-3019 vector_in_display_space = xy_in_display_space - XY0_in_display_space
-3020
-3021 if (vector_in_display_space**2).sum() > 0:
+3018 for xy, uid in zip(XY, UID):
+3019
+3020 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
+3021 vector_in_display_space = xy_in_display_space - XY0_in_display_space
3022
-3023 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
-3024 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
-3025 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
-3026 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
-3027
-3028 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
+3023 if (vector_in_display_space**2).sum() > 0:
+3024
+3025 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
+3026 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
+3027 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
+3028 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
3029
-3030 else:
+3030 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
3031
-3032 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3032 else:
3033
-3034 if radius:
-3035 ax.add_artist(Ellipse(
-3036 xy = XY0,
-3037 width = radius*2,
-3038 height = radius*2,
-3039 ls = (0, (2,2)),
-3040 lw = .7,
-3041 ec = analysis_color,
-3042 fc = 'None',
-3043 ))
-3044 ppl.text(
-3045 XY0[0],
-3046 XY0[1]-radius,
-3047 f'\n± {radius*1e3:.0f} ppm',
-3048 color = analysis_color,
-3049 va = 'top',
-3050 ha = 'center',
-3051 linespacing = 0.4,
-3052 size = 8,
-3053 )
-3054
-3055 if not os.path.exists(dir):
-3056 os.makedirs(dir)
-3057 fig.savefig(f'{dir}/{s}.pdf')
-3058 ppl.close(fig)
-3059
-3060 fig = ppl.figure(figsize = figsize)
-3061 fig.subplots_adjust(*subplots_adjust)
-3062 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-3063 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-3064
-3065 for s in saved:
-3066 for xy in saved[s][0]:
-3067 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
-3068 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
-3069 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
-3070 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-3071
-3072 x1, x2, y1, y2 = ppl.axis()
-3073 ppl.axis([
-3074 x1 - (x2-x1)/10,
-3075 x2 + (x2-x1)/10,
-3076 y1 - (y2-y1)/10,
-3077 y2 + (y2-y1)/10,
-3078 ])
-3079
-3080
-3081 if not os.path.exists(dir):
-3082 os.makedirs(dir)
-3083 fig.savefig(f'{dir}/__all__.pdf')
-3084 if show:
-3085 ppl.show()
-3086 ppl.close(fig)
-3087
-3088
-3089 def _save_D4x_correl(
-3090 self,
-3091 samples = None,
-3092 dir = 'output',
-3093 filename = None,
-3094 D4x_precision = 4,
-3095 correl_precision = 4,
-3096 ):
-3097 '''
-3098 Save D4x values along with their SE and correlation matrix.
-3099
-3100 **Parameters**
+3034 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3035
+3036 if radius:
+3037 ax.add_artist(Ellipse(
+3038 xy = XY0,
+3039 width = radius*2,
+3040 height = radius*2,
+3041 ls = (0, (2,2)),
+3042 lw = .7,
+3043 ec = analysis_color,
+3044 fc = 'None',
+3045 ))
+3046 ppl.text(
+3047 XY0[0],
+3048 XY0[1]-radius,
+3049 f'\n± {radius*1e3:.0f} ppm',
+3050 color = analysis_color,
+3051 va = 'top',
+3052 ha = 'center',
+3053 linespacing = 0.4,
+3054 size = 8,
+3055 )
+3056
+3057 if not os.path.exists(dir):
+3058 os.makedirs(dir)
+3059 fig.savefig(f'{dir}/{s}.pdf')
+3060 ppl.close(fig)
+3061
+3062 fig = ppl.figure(figsize = figsize)
+3063 fig.subplots_adjust(*subplots_adjust)
+3064 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+3065 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+3066
+3067 for s in saved:
+3068 for xy in saved[s][0]:
+3069 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
+3070 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
+3071 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
+3072 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3073
+3074 x1, x2, y1, y2 = ppl.axis()
+3075 ppl.axis([
+3076 x1 - (x2-x1)/10,
+3077 x2 + (x2-x1)/10,
+3078 y1 - (y2-y1)/10,
+3079 y2 + (y2-y1)/10,
+3080 ])
+3081
+3082
+3083 if not os.path.exists(dir):
+3084 os.makedirs(dir)
+3085 fig.savefig(f'{dir}/__all__.pdf')
+3086 if show:
+3087 ppl.show()
+3088 ppl.close(fig)
+3089
+3090
+3091 def _save_D4x_correl(
+3092 self,
+3093 samples = None,
+3094 dir = 'output',
+3095 filename = None,
+3096 D4x_precision = 4,
+3097 correl_precision = 4,
+3098 ):
+3099 '''
+3100 Save D4x values along with their SE and correlation matrix.
3101
-3102 + `samples`: Only these samples are output (by default: all samples).
-3103 + `dir`: the directory in which to save the faile (by defaut: `output`)
-3104 + `filename`: the name to the csv file to write to (by default: `D4x_correl.csv`)
-3105 + `D4x_precision`: the precision to use when writing `D4x` and `D4x_SE` values (by default: 4)
-3106 + `correl_precision`: the precision to use when writing correlation factor values (by default: 4)
-3107 '''
-3108 if samples is None:
-3109 samples = sorted([s for s in self.unknowns])
-3110
-3111 out = [['Sample']] + [[s] for s in samples]
-3112 out[0] += [f'D{self._4x}', f'D{self._4x}_SE', f'D{self._4x}_correl']
-3113 for k,s in enumerate(samples):
-3114 out[k+1] += [f'{self.samples[s][f"D{self._4x}"]:.4f}', f'{self.samples[s][f"SE_D{self._4x}"]:.4f}']
-3115 for s2 in samples:
-3116 out[k+1] += [f'{self.sample_D4x_correl(s,s2):.4f}']
-3117
-3118 if not os.path.exists(dir):
-3119 os.makedirs(dir)
-3120 if filename is None:
-3121 filename = f'D{self._4x}_correl.csv'
-3122 with open(f'{dir}/{filename}', 'w') as fid:
-3123 fid.write(make_csv(out))
+3102 **Parameters**
+3103
+3104 + `samples`: Only these samples are output (by default: all samples).
+3105 + `dir`: the directory in which to save the faile (by defaut: `output`)
+3106 + `filename`: the name to the csv file to write to (by default: `D4x_correl.csv`)
+3107 + `D4x_precision`: the precision to use when writing `D4x` and `D4x_SE` values (by default: 4)
+3108 + `correl_precision`: the precision to use when writing correlation factor values (by default: 4)
+3109 '''
+3110 if samples is None:
+3111 samples = sorted([s for s in self.unknowns])
+3112
+3113 out = [['Sample']] + [[s] for s in samples]
+3114 out[0] += [f'D{self._4x}', f'D{self._4x}_SE', f'D{self._4x}_correl']
+3115 for k,s in enumerate(samples):
+3116 out[k+1] += [f'{self.samples[s][f"D{self._4x}"]:.4f}', f'{self.samples[s][f"SE_D{self._4x}"]:.4f}']
+3117 for s2 in samples:
+3118 out[k+1] += [f'{self.sample_D4x_correl(s,s2):.4f}']
+3119
+3120 if not os.path.exists(dir):
+3121 os.makedirs(dir)
+3122 if filename is None:
+3123 filename = f'D{self._4x}_correl.csv'
+3124 with open(f'{dir}/{filename}', 'w') as fid:
+3125 fid.write(make_csv(out))
4. API Documentation
921 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
-922 '''
-923 **Parameters**
-924
-925 + `l`: a list of dictionaries, with each dictionary including at least the keys
-926 `Sample`, `d45`, `d46`, and `d47` or `d48`.
-927 + `mass`: `'47'` or `'48'`
-928 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
-929 + `session`: define session name for analyses without a `Session` key
-930 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
-931
-932 Returns a `D4xdata` object derived from `list`.
-933 '''
-934 self._4x = mass
-935 self.verbose = verbose
-936 self.prefix = 'D4xdata'
-937 self.logfile = logfile
-938 list.__init__(self, l)
-939 self.Nf = None
-940 self.repeatability = {}
-941 self.refresh(session = session)
+ 923 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False):
+924 '''
+925 **Parameters**
+926
+927 + `l`: a list of dictionaries, with each dictionary including at least the keys
+928 `Sample`, `d45`, `d46`, and `d47` or `d48`.
+929 + `mass`: `'47'` or `'48'`
+930 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods.
+931 + `session`: define session name for analyses without a `Session` key
+932 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods.
+933
+934 Returns a `D4xdata` object derived from `list`.
+935 '''
+936 self._4x = mass
+937 self.verbose = verbose
+938 self.prefix = 'D4xdata'
+939 self.logfile = logfile
+940 list.__init__(self, l)
+941 self.Nf = None
+942 self.repeatability = {}
+943 self.refresh(session = session)
4. API Documentation
944 def make_verbal(oldfun):
-945 '''
-946 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
-947 '''
-948 @wraps(oldfun)
-949 def newfun(*args, verbose = '', **kwargs):
-950 myself = args[0]
-951 oldprefix = myself.prefix
-952 myself.prefix = oldfun.__name__
-953 if verbose != '':
-954 oldverbose = myself.verbose
-955 myself.verbose = verbose
-956 out = oldfun(*args, **kwargs)
-957 myself.prefix = oldprefix
-958 if verbose != '':
-959 myself.verbose = oldverbose
-960 return out
-961 return newfun
+ 946 def make_verbal(oldfun):
+947 '''
+948 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`.
+949 '''
+950 @wraps(oldfun)
+951 def newfun(*args, verbose = '', **kwargs):
+952 myself = args[0]
+953 oldprefix = myself.prefix
+954 myself.prefix = oldfun.__name__
+955 if verbose != '':
+956 oldverbose = myself.verbose
+957 myself.verbose = verbose
+958 out = oldfun(*args, **kwargs)
+959 myself.prefix = oldprefix
+960 if verbose != '':
+961 myself.verbose = oldverbose
+962 return out
+963 return newfun
4. API Documentation
964 def msg(self, txt):
-965 '''
-966 Log a message to `self.logfile`, and print it out if `verbose = True`
-967 '''
-968 self.log(txt)
-969 if self.verbose:
-970 print(f'{f"[{self.prefix}]":<16} {txt}')
+ 966 def msg(self, txt):
+967 '''
+968 Log a message to `self.logfile`, and print it out if `verbose = True`
+969 '''
+970 self.log(txt)
+971 if self.verbose:
+972 print(f'{f"[{self.prefix}]":<16} {txt}')
4. API Documentation
973 def vmsg(self, txt):
-974 '''
-975 Log a message to `self.logfile` and print it out
-976 '''
-977 self.log(txt)
-978 print(txt)
+ 975 def vmsg(self, txt):
+976 '''
+977 Log a message to `self.logfile` and print it out
+978 '''
+979 self.log(txt)
+980 print(txt)
4. API Documentation
981 def log(self, *txts):
-982 '''
-983 Log a message to `self.logfile`
-984 '''
-985 if self.logfile:
-986 with open(self.logfile, 'a') as fid:
-987 for txt in txts:
-988 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
+ 983 def log(self, *txts):
+984 '''
+985 Log a message to `self.logfile`
+986 '''
+987 if self.logfile:
+988 with open(self.logfile, 'a') as fid:
+989 for txt in txts:
+990 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
4. API Documentation
991 def refresh(self, session = 'mySession'):
-992 '''
-993 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
-994 '''
-995 self.fill_in_missing_info(session = session)
-996 self.refresh_sessions()
-997 self.refresh_samples()
+ 993 def refresh(self, session = 'mySession'):
+994 '''
+995 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`.
+996 '''
+997 self.fill_in_missing_info(session = session)
+998 self.refresh_sessions()
+999 self.refresh_samples()
4. API Documentation
1000 def refresh_sessions(self):
-1001 '''
-1002 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
-1003 to `False` for all sessions.
-1004 '''
-1005 self.sessions = {
-1006 s: {'data': [r for r in self if r['Session'] == s]}
-1007 for s in sorted({r['Session'] for r in self})
-1008 }
-1009 for s in self.sessions:
-1010 self.sessions[s]['scrambling_drift'] = False
-1011 self.sessions[s]['slope_drift'] = False
-1012 self.sessions[s]['wg_drift'] = False
-1013 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
-1014 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
+ 1002 def refresh_sessions(self):
+1003 '''
+1004 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift`
+1005 to `False` for all sessions.
+1006 '''
+1007 self.sessions = {
+1008 s: {'data': [r for r in self if r['Session'] == s]}
+1009 for s in sorted({r['Session'] for r in self})
+1010 }
+1011 for s in self.sessions:
+1012 self.sessions[s]['scrambling_drift'] = False
+1013 self.sessions[s]['slope_drift'] = False
+1014 self.sessions[s]['wg_drift'] = False
+1015 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD
+1016 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
4. API Documentation
1017 def refresh_samples(self):
-1018 '''
-1019 Define `self.samples`, `self.anchors`, and `self.unknowns`.
-1020 '''
-1021 self.samples = {
-1022 s: {'data': [r for r in self if r['Sample'] == s]}
-1023 for s in sorted({r['Sample'] for r in self})
-1024 }
-1025 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
-1026 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
+ 1019 def refresh_samples(self):
+1020 '''
+1021 Define `self.samples`, `self.anchors`, and `self.unknowns`.
+1022 '''
+1023 self.samples = {
+1024 s: {'data': [r for r in self if r['Sample'] == s]}
+1025 for s in sorted({r['Sample'] for r in self})
+1026 }
+1027 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x}
+1028 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
4. API Documentation
1029 def read(self, filename, sep = '', session = ''):
-1030 '''
-1031 Read file in csv format to load data into a `D47data` object.
-1032
-1033 In the csv file, spaces before and after field separators (`','` by default)
-1034 are optional. Each line corresponds to a single analysis.
-1035
-1036 The required fields are:
+ 1031 def read(self, filename, sep = '', session = ''):
+1032 '''
+1033 Read file in csv format to load data into a `D47data` object.
+1034
+1035 In the csv file, spaces before and after field separators (`','` by default)
+1036 are optional. Each line corresponds to a single analysis.
1037
-1038 + `UID`: a unique identifier
-1039 + `Session`: an identifier for the analytical session
-1040 + `Sample`: a sample identifier
-1041 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1042
-1043 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1044 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1045 and `d49` are optional, and set to NaN by default.
-1046
-1047 **Parameters**
+1038 The required fields are:
+1039
+1040 + `UID`: a unique identifier
+1041 + `Session`: an identifier for the analytical session
+1042 + `Sample`: a sample identifier
+1043 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1044
+1045 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1046 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1047 and `d49` are optional, and set to NaN by default.
1048
-1049 + `fileneme`: the path of the file to read
-1050 + `sep`: csv separator delimiting the fields
-1051 + `session`: set `Session` field to this string for all analyses
-1052 '''
-1053 with open(filename) as fid:
-1054 self.input(fid.read(), sep = sep, session = session)
+1049 **Parameters**
+1050
+1051 + `fileneme`: the path of the file to read
+1052 + `sep`: csv separator delimiting the fields
+1053 + `session`: set `Session` field to this string for all analyses
+1054 '''
+1055 with open(filename) as fid:
+1056 self.input(fid.read(), sep = sep, session = session)
4. API Documentation
1057 def input(self, txt, sep = '', session = ''):
-1058 '''
-1059 Read `txt` string in csv format to load analysis data into a `D47data` object.
-1060
-1061 In the csv string, spaces before and after field separators (`','` by default)
-1062 are optional. Each line corresponds to a single analysis.
-1063
-1064 The required fields are:
+ 1059 def input(self, txt, sep = '', session = ''):
+1060 '''
+1061 Read `txt` string in csv format to load analysis data into a `D47data` object.
+1062
+1063 In the csv string, spaces before and after field separators (`','` by default)
+1064 are optional. Each line corresponds to a single analysis.
1065
-1066 + `UID`: a unique identifier
-1067 + `Session`: an identifier for the analytical session
-1068 + `Sample`: a sample identifier
-1069 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
-1070
-1071 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
-1072 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
-1073 and `d49` are optional, and set to NaN by default.
-1074
-1075 **Parameters**
+1066 The required fields are:
+1067
+1068 + `UID`: a unique identifier
+1069 + `Session`: an identifier for the analytical session
+1070 + `Sample`: a sample identifier
+1071 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values
+1072
+1073 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to
+1074 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48`
+1075 and `d49` are optional, and set to NaN by default.
1076
-1077 + `txt`: the csv string to read
-1078 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
-1079 whichever appers most often in `txt`.
-1080 + `session`: set `Session` field to this string for all analyses
-1081 '''
-1082 if sep == '':
-1083 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
-1084 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
-1085 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
-1086
-1087 if session != '':
-1088 for r in data:
-1089 r['Session'] = session
-1090
-1091 self += data
-1092 self.refresh()
+1077 **Parameters**
+1078
+1079 + `txt`: the csv string to read
+1080 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`,
+1081 whichever appers most often in `txt`.
+1082 + `session`: set `Session` field to this string for all analyses
+1083 '''
+1084 if sep == '':
+1085 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0]
+1086 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()]
+1087 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]]
+1088
+1089 if session != '':
+1090 for r in data:
+1091 r['Session'] = session
+1092
+1093 self += data
+1094 self.refresh()
4. API Documentation
1095 @make_verbal
-1096 def wg(self, samples = None, a18_acid = None):
-1097 '''
-1098 Compute bulk composition of the working gas for each session based on
-1099 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
-1100 `self.Nominal_d18O_VPDB`.
-1101 '''
-1102
-1103 self.msg('Computing WG composition:')
+ 1097 @make_verbal
+1098 def wg(self, samples = None, a18_acid = None):
+1099 '''
+1100 Compute bulk composition of the working gas for each session based on
+1101 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and
+1102 `self.Nominal_d18O_VPDB`.
+1103 '''
1104
-1105 if a18_acid is None:
-1106 a18_acid = self.ALPHA_18O_ACID_REACTION
-1107 if samples is None:
-1108 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
-1109
-1110 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1105 self.msg('Computing WG composition:')
+1106
+1107 if a18_acid is None:
+1108 a18_acid = self.ALPHA_18O_ACID_REACTION
+1109 if samples is None:
+1110 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB]
1111
-1112 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
-1113 R45R46_standards = {}
-1114 for sample in samples:
-1115 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
-1116 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
-1117 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
-1118 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
-1119 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
-1120
-1121 C12_s = 1 / (1 + R13_s)
-1122 C13_s = R13_s / (1 + R13_s)
-1123 C16_s = 1 / (1 + R17_s + R18_s)
-1124 C17_s = R17_s / (1 + R17_s + R18_s)
-1125 C18_s = R18_s / (1 + R17_s + R18_s)
-1126
-1127 C626_s = C12_s * C16_s ** 2
-1128 C627_s = 2 * C12_s * C16_s * C17_s
-1129 C628_s = 2 * C12_s * C16_s * C18_s
-1130 C636_s = C13_s * C16_s ** 2
-1131 C637_s = 2 * C13_s * C16_s * C17_s
-1132 C727_s = C12_s * C17_s ** 2
-1133
-1134 R45_s = (C627_s + C636_s) / C626_s
-1135 R46_s = (C628_s + C637_s + C727_s) / C626_s
-1136 R45R46_standards[sample] = (R45_s, R46_s)
-1137
-1138 for s in self.sessions:
-1139 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
-1140 assert db, f'No sample from {samples} found in session "{s}".'
-1141# dbsamples = sorted({r['Sample'] for r in db})
-1142
-1143 X = [r['d45'] for r in db]
-1144 Y = [R45R46_standards[r['Sample']][0] for r in db]
-1145 x1, x2 = np.min(X), np.max(X)
-1146
-1147 if x1 < x2:
-1148 wgcoord = x1/(x1-x2)
-1149 else:
-1150 wgcoord = 999
-1151
-1152 if wgcoord < -.5 or wgcoord > 1.5:
-1153 # unreasonable to extrapolate to d45 = 0
-1154 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1155 else :
-1156 # d45 = 0 is reasonably well bracketed
-1157 R45_wg = np.polyfit(X, Y, 1)[1]
-1158
-1159 X = [r['d46'] for r in db]
-1160 Y = [R45R46_standards[r['Sample']][1] for r in db]
-1161 x1, x2 = np.min(X), np.max(X)
-1162
-1163 if x1 < x2:
-1164 wgcoord = x1/(x1-x2)
-1165 else:
-1166 wgcoord = 999
-1167
-1168 if wgcoord < -.5 or wgcoord > 1.5:
-1169 # unreasonable to extrapolate to d46 = 0
-1170 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
-1171 else :
-1172 # d46 = 0 is reasonably well bracketed
-1173 R46_wg = np.polyfit(X, Y, 1)[1]
-1174
-1175 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
+1112 assert a18_acid, f'Acid fractionation factor should not be zero.'
+1113
+1114 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB]
+1115 R45R46_standards = {}
+1116 for sample in samples:
+1117 d13C_vpdb = self.Nominal_d13C_VPDB[sample]
+1118 d18O_vpdb = self.Nominal_d18O_VPDB[sample]
+1119 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000)
+1120 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17
+1121 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid
+1122
+1123 C12_s = 1 / (1 + R13_s)
+1124 C13_s = R13_s / (1 + R13_s)
+1125 C16_s = 1 / (1 + R17_s + R18_s)
+1126 C17_s = R17_s / (1 + R17_s + R18_s)
+1127 C18_s = R18_s / (1 + R17_s + R18_s)
+1128
+1129 C626_s = C12_s * C16_s ** 2
+1130 C627_s = 2 * C12_s * C16_s * C17_s
+1131 C628_s = 2 * C12_s * C16_s * C18_s
+1132 C636_s = C13_s * C16_s ** 2
+1133 C637_s = 2 * C13_s * C16_s * C17_s
+1134 C727_s = C12_s * C17_s ** 2
+1135
+1136 R45_s = (C627_s + C636_s) / C626_s
+1137 R46_s = (C628_s + C637_s + C727_s) / C626_s
+1138 R45R46_standards[sample] = (R45_s, R46_s)
+1139
+1140 for s in self.sessions:
+1141 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples]
+1142 assert db, f'No sample from {samples} found in session "{s}".'
+1143# dbsamples = sorted({r['Sample'] for r in db})
+1144
+1145 X = [r['d45'] for r in db]
+1146 Y = [R45R46_standards[r['Sample']][0] for r in db]
+1147 x1, x2 = np.min(X), np.max(X)
+1148
+1149 if x1 < x2:
+1150 wgcoord = x1/(x1-x2)
+1151 else:
+1152 wgcoord = 999
+1153
+1154 if wgcoord < -.5 or wgcoord > 1.5:
+1155 # unreasonable to extrapolate to d45 = 0
+1156 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1157 else :
+1158 # d45 = 0 is reasonably well bracketed
+1159 R45_wg = np.polyfit(X, Y, 1)[1]
+1160
+1161 X = [r['d46'] for r in db]
+1162 Y = [R45R46_standards[r['Sample']][1] for r in db]
+1163 x1, x2 = np.min(X), np.max(X)
+1164
+1165 if x1 < x2:
+1166 wgcoord = x1/(x1-x2)
+1167 else:
+1168 wgcoord = 999
+1169
+1170 if wgcoord < -.5 or wgcoord > 1.5:
+1171 # unreasonable to extrapolate to d46 = 0
+1172 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)])
+1173 else :
+1174 # d46 = 0 is reasonably well bracketed
+1175 R46_wg = np.polyfit(X, Y, 1)[1]
1176
-1177 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1177 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg)
1178
-1179 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
-1180 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
-1181 for r in self.sessions[s]['data']:
-1182 r['d13Cwg_VPDB'] = d13Cwg_VPDB
-1183 r['d18Owg_VSMOW'] = d18Owg_VSMOW
+1179 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}')
+1180
+1181 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB
+1182 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW
+1183 for r in self.sessions[s]['data']:
+1184 r['d13Cwg_VPDB'] = d13Cwg_VPDB
+1185 r['d18Owg_VSMOW'] = d18Owg_VSMOW
4. API Documentation
1186 def compute_bulk_delta(self, R45, R46, D17O = 0):
-1187 '''
-1188 Compute δ13C_VPDB and δ18O_VSMOW,
-1189 by solving the generalized form of equation (17) from
-1190 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
-1191 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
-1192 solving the corresponding second-order Taylor polynomial.
-1193 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
-1194 '''
-1195
-1196 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+ 1188 def compute_bulk_delta(self, R45, R46, D17O = 0):
+1189 '''
+1190 Compute δ13C_VPDB and δ18O_VSMOW,
+1191 by solving the generalized form of equation (17) from
+1192 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05),
+1193 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and
+1194 solving the corresponding second-order Taylor polynomial.
+1195 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014))
+1196 '''
1197
-1198 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
-1199 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
-1200 C = 2 * self.R18_VSMOW
-1201 D = -R46
-1202
-1203 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
-1204 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
-1205 cc = A + B + C + D
-1206
-1207 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1198 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17
+1199
+1200 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17)
+1201 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17
+1202 C = 2 * self.R18_VSMOW
+1203 D = -R46
+1204
+1205 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2
+1206 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C
+1207 cc = A + B + C + D
1208
-1209 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
-1210 R17 = K * R18 ** self.LAMBDA_17
-1211 R13 = R45 - 2 * R17
-1212
-1213 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
+1209 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa)
+1210
+1211 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW
+1212 R17 = K * R18 ** self.LAMBDA_17
+1213 R13 = R45 - 2 * R17
1214
-1215 return d13C_VPDB, d18O_VSMOW
+1215 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1)
+1216
+1217 return d13C_VPDB, d18O_VSMOW
4. API Documentation
1218 @make_verbal
-1219 def crunch(self, verbose = ''):
-1220 '''
-1221 Compute bulk composition and raw clumped isotope anomalies for all analyses.
-1222 '''
-1223 for r in self:
-1224 self.compute_bulk_and_clumping_deltas(r)
-1225 self.standardize_d13C()
-1226 self.standardize_d18O()
-1227 self.msg(f"Crunched {len(self)} analyses.")
+ 1220 @make_verbal
+1221 def crunch(self, verbose = ''):
+1222 '''
+1223 Compute bulk composition and raw clumped isotope anomalies for all analyses.
+1224 '''
+1225 for r in self:
+1226 self.compute_bulk_and_clumping_deltas(r)
+1227 self.standardize_d13C()
+1228 self.standardize_d18O()
+1229 self.msg(f"Crunched {len(self)} analyses.")
4. API Documentation
1230 def fill_in_missing_info(self, session = 'mySession'):
-1231 '''
-1232 Fill in optional fields with default values
-1233 '''
-1234 for i,r in enumerate(self):
-1235 if 'D17O' not in r:
-1236 r['D17O'] = 0.
-1237 if 'UID' not in r:
-1238 r['UID'] = f'{i+1}'
-1239 if 'Session' not in r:
-1240 r['Session'] = session
-1241 for k in ['d47', 'd48', 'd49']:
-1242 if k not in r:
-1243 r[k] = np.nan
+ 1232 def fill_in_missing_info(self, session = 'mySession'):
+1233 '''
+1234 Fill in optional fields with default values
+1235 '''
+1236 for i,r in enumerate(self):
+1237 if 'D17O' not in r:
+1238 r['D17O'] = 0.
+1239 if 'UID' not in r:
+1240 r['UID'] = f'{i+1}'
+1241 if 'Session' not in r:
+1242 r['Session'] = session
+1243 for k in ['d47', 'd48', 'd49']:
+1244 if k not in r:
+1245 r[k] = np.nan
4. API Documentation
1246 def standardize_d13C(self):
-1247 '''
-1248 Perform δ13C standadization within each session `s` according to
-1249 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
-1250 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
-1251 may be redefined abitrarily at a later stage.
-1252 '''
-1253 for s in self.sessions:
-1254 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
-1255 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
-1256 X,Y = zip(*XY)
-1257 if self.sessions[s]['d13C_standardization_method'] == '1pt':
-1258 offset = np.mean(Y) - np.mean(X)
-1259 for r in self.sessions[s]['data']:
-1260 r['d13C_VPDB'] += offset
-1261 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
-1262 a,b = np.polyfit(X,Y,1)
-1263 for r in self.sessions[s]['data']:
-1264 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
+ 1248 def standardize_d13C(self):
+1249 '''
+1250 Perform δ13C standadization within each session `s` according to
+1251 `self.sessions[s]['d13C_standardization_method']`, which is defined by default
+1252 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but
+1253 may be redefined abitrarily at a later stage.
+1254 '''
+1255 for s in self.sessions:
+1256 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']:
+1257 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB]
+1258 X,Y = zip(*XY)
+1259 if self.sessions[s]['d13C_standardization_method'] == '1pt':
+1260 offset = np.mean(Y) - np.mean(X)
+1261 for r in self.sessions[s]['data']:
+1262 r['d13C_VPDB'] += offset
+1263 elif self.sessions[s]['d13C_standardization_method'] == '2pt':
+1264 a,b = np.polyfit(X,Y,1)
+1265 for r in self.sessions[s]['data']:
+1266 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
4. API Documentation
1266 def standardize_d18O(self):
-1267 '''
-1268 Perform δ18O standadization within each session `s` according to
-1269 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
-1270 which is defined by default by `D47data.refresh_sessions()`as equal to
-1271 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
-1272 '''
-1273 for s in self.sessions:
-1274 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
-1275 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
-1276 X,Y = zip(*XY)
-1277 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
-1278 if self.sessions[s]['d18O_standardization_method'] == '1pt':
-1279 offset = np.mean(Y) - np.mean(X)
-1280 for r in self.sessions[s]['data']:
-1281 r['d18O_VSMOW'] += offset
-1282 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
-1283 a,b = np.polyfit(X,Y,1)
-1284 for r in self.sessions[s]['data']:
-1285 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
+ 1268 def standardize_d18O(self):
+1269 '''
+1270 Perform δ18O standadization within each session `s` according to
+1271 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`,
+1272 which is defined by default by `D47data.refresh_sessions()`as equal to
+1273 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage.
+1274 '''
+1275 for s in self.sessions:
+1276 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']:
+1277 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB]
+1278 X,Y = zip(*XY)
+1279 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y]
+1280 if self.sessions[s]['d18O_standardization_method'] == '1pt':
+1281 offset = np.mean(Y) - np.mean(X)
+1282 for r in self.sessions[s]['data']:
+1283 r['d18O_VSMOW'] += offset
+1284 elif self.sessions[s]['d18O_standardization_method'] == '2pt':
+1285 a,b = np.polyfit(X,Y,1)
+1286 for r in self.sessions[s]['data']:
+1287 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
4. API Documentation
1288 def compute_bulk_and_clumping_deltas(self, r):
-1289 '''
-1290 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
-1291 '''
-1292
-1293 # Compute working gas R13, R18, and isobar ratios
-1294 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
-1295 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
-1296 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
-1297
-1298 # Compute analyte isobar ratios
-1299 R45 = (1 + r['d45'] / 1000) * R45_wg
-1300 R46 = (1 + r['d46'] / 1000) * R46_wg
-1301 R47 = (1 + r['d47'] / 1000) * R47_wg
-1302 R48 = (1 + r['d48'] / 1000) * R48_wg
-1303 R49 = (1 + r['d49'] / 1000) * R49_wg
-1304
-1305 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
-1306 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
-1307 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
-1308
-1309 # Compute stochastic isobar ratios of the analyte
-1310 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
-1311 R13, R18, D17O = r['D17O']
-1312 )
-1313
-1314 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
-1315 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
-1316 if (R45 / R45stoch - 1) > 5e-8:
-1317 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
-1318 if (R46 / R46stoch - 1) > 5e-8:
-1319 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
-1320
-1321 # Compute raw clumped isotope anomalies
-1322 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
-1323 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
-1324 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
+ 1290 def compute_bulk_and_clumping_deltas(self, r):
+1291 '''
+1292 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`.
+1293 '''
+1294
+1295 # Compute working gas R13, R18, and isobar ratios
+1296 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000)
+1297 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000)
+1298 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg)
+1299
+1300 # Compute analyte isobar ratios
+1301 R45 = (1 + r['d45'] / 1000) * R45_wg
+1302 R46 = (1 + r['d46'] / 1000) * R46_wg
+1303 R47 = (1 + r['d47'] / 1000) * R47_wg
+1304 R48 = (1 + r['d48'] / 1000) * R48_wg
+1305 R49 = (1 + r['d49'] / 1000) * R49_wg
+1306
+1307 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O'])
+1308 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB
+1309 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW
+1310
+1311 # Compute stochastic isobar ratios of the analyte
+1312 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios(
+1313 R13, R18, D17O = r['D17O']
+1314 )
+1315
+1316 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1,
+1317 # and raise a warning if the corresponding anomalies exceed 0.02 ppm.
+1318 if (R45 / R45stoch - 1) > 5e-8:
+1319 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm')
+1320 if (R46 / R46stoch - 1) > 5e-8:
+1321 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm')
+1322
+1323 # Compute raw clumped isotope anomalies
+1324 r['D47raw'] = 1000 * (R47 / R47stoch - 1)
+1325 r['D48raw'] = 1000 * (R48 / R48stoch - 1)
+1326 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
4. API Documentation
1327 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
-1328 '''
-1329 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
-1330 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
-1331 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
-1332 '''
-1333
-1334 # Compute R17
-1335 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
-1336
-1337 # Compute isotope concentrations
-1338 C12 = (1 + R13) ** -1
-1339 C13 = C12 * R13
-1340 C16 = (1 + R17 + R18) ** -1
-1341 C17 = C16 * R17
-1342 C18 = C16 * R18
-1343
-1344 # Compute stochastic isotopologue concentrations
-1345 C626 = C16 * C12 * C16
-1346 C627 = C16 * C12 * C17 * 2
-1347 C628 = C16 * C12 * C18 * 2
-1348 C636 = C16 * C13 * C16
-1349 C637 = C16 * C13 * C17 * 2
-1350 C638 = C16 * C13 * C18 * 2
-1351 C727 = C17 * C12 * C17
-1352 C728 = C17 * C12 * C18 * 2
-1353 C737 = C17 * C13 * C17
-1354 C738 = C17 * C13 * C18 * 2
-1355 C828 = C18 * C12 * C18
-1356 C838 = C18 * C13 * C18
-1357
-1358 # Compute stochastic isobar ratios
-1359 R45 = (C636 + C627) / C626
-1360 R46 = (C628 + C637 + C727) / C626
-1361 R47 = (C638 + C728 + C737) / C626
-1362 R48 = (C738 + C828) / C626
-1363 R49 = C838 / C626
-1364
-1365 # Account for stochastic anomalies
-1366 R47 *= 1 + D47 / 1000
-1367 R48 *= 1 + D48 / 1000
-1368 R49 *= 1 + D49 / 1000
-1369
-1370 # Return isobar ratios
-1371 return R45, R46, R47, R48, R49
+ 1329 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
+1330 '''
+1331 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`,
+1332 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope
+1333 anomalies (`D47`, `D48`, `D49`), all expressed in permil.
+1334 '''
+1335
+1336 # Compute R17
+1337 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17
+1338
+1339 # Compute isotope concentrations
+1340 C12 = (1 + R13) ** -1
+1341 C13 = C12 * R13
+1342 C16 = (1 + R17 + R18) ** -1
+1343 C17 = C16 * R17
+1344 C18 = C16 * R18
+1345
+1346 # Compute stochastic isotopologue concentrations
+1347 C626 = C16 * C12 * C16
+1348 C627 = C16 * C12 * C17 * 2
+1349 C628 = C16 * C12 * C18 * 2
+1350 C636 = C16 * C13 * C16
+1351 C637 = C16 * C13 * C17 * 2
+1352 C638 = C16 * C13 * C18 * 2
+1353 C727 = C17 * C12 * C17
+1354 C728 = C17 * C12 * C18 * 2
+1355 C737 = C17 * C13 * C17
+1356 C738 = C17 * C13 * C18 * 2
+1357 C828 = C18 * C12 * C18
+1358 C838 = C18 * C13 * C18
+1359
+1360 # Compute stochastic isobar ratios
+1361 R45 = (C636 + C627) / C626
+1362 R46 = (C628 + C637 + C727) / C626
+1363 R47 = (C638 + C728 + C737) / C626
+1364 R48 = (C738 + C828) / C626
+1365 R49 = C838 / C626
+1366
+1367 # Account for stochastic anomalies
+1368 R47 *= 1 + D47 / 1000
+1369 R48 *= 1 + D48 / 1000
+1370 R49 *= 1 + D49 / 1000
+1371
+1372 # Return isobar ratios
+1373 return R45, R46, R47, R48, R49
4. API Documentation
1374 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
-1375 '''
-1376 Split unknown samples by UID (treat all analyses as different samples)
-1377 or by session (treat analyses of a given sample in different sessions as
-1378 different samples).
-1379
-1380 **Parameters**
+ 1376 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'):
+1377 '''
+1378 Split unknown samples by UID (treat all analyses as different samples)
+1379 or by session (treat analyses of a given sample in different sessions as
+1380 different samples).
1381
-1382 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
-1383 + `grouping`: `by_uid` | `by_session`
-1384 '''
-1385 if samples_to_split == 'all':
-1386 samples_to_split = [s for s in self.unknowns]
-1387 gkeys = {'by_uid':'UID', 'by_session':'Session'}
-1388 self.grouping = grouping.lower()
-1389 if self.grouping in gkeys:
-1390 gkey = gkeys[self.grouping]
-1391 for r in self:
-1392 if r['Sample'] in samples_to_split:
-1393 r['Sample_original'] = r['Sample']
-1394 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
-1395 elif r['Sample'] in self.unknowns:
-1396 r['Sample_original'] = r['Sample']
-1397 self.refresh_samples()
+1382 **Parameters**
+1383
+1384 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']`
+1385 + `grouping`: `by_uid` | `by_session`
+1386 '''
+1387 if samples_to_split == 'all':
+1388 samples_to_split = [s for s in self.unknowns]
+1389 gkeys = {'by_uid':'UID', 'by_session':'Session'}
+1390 self.grouping = grouping.lower()
+1391 if self.grouping in gkeys:
+1392 gkey = gkeys[self.grouping]
+1393 for r in self:
+1394 if r['Sample'] in samples_to_split:
+1395 r['Sample_original'] = r['Sample']
+1396 r['Sample'] = f"{r['Sample']}__{r[gkey]}"
+1397 elif r['Sample'] in self.unknowns:
+1398 r['Sample_original'] = r['Sample']
+1399 self.refresh_samples()
4. API Documentation
1400 def unsplit_samples(self, tables = False):
-1401 '''
-1402 Reverse the effects of `D47data.split_samples()`.
-1403
-1404 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+ 1402 def unsplit_samples(self, tables = False):
+1403 '''
+1404 Reverse the effects of `D47data.split_samples()`.
1405
-1406 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
-1407 probably use `D4xdata.combine_samples()` instead to reverse the effects of
-1408 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
-1409 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
-1410 that case session-averaged Δ4x values are statistically independent).
-1411 '''
-1412 unknowns_old = sorted({s for s in self.unknowns})
-1413 CM_old = self.standardization.covar[:,:]
-1414 VD_old = self.standardization.params.valuesdict().copy()
-1415 vars_old = self.standardization.var_names
-1416
-1417 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1406 This should only be used after `D4xdata.standardize()` with `method='pooled'`.
+1407
+1408 After `D4xdata.standardize()` with `method='indep_sessions'`, one should
+1409 probably use `D4xdata.combine_samples()` instead to reverse the effects of
+1410 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the
+1411 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in
+1412 that case session-averaged Δ4x values are statistically independent).
+1413 '''
+1414 unknowns_old = sorted({s for s in self.unknowns})
+1415 CM_old = self.standardization.covar[:,:]
+1416 VD_old = self.standardization.params.valuesdict().copy()
+1417 vars_old = self.standardization.var_names
1418
-1419 Ns = len(vars_old) - len(unknowns_old)
-1420 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
-1421 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
-1422
-1423 W = np.zeros((len(vars_new), len(vars_old)))
-1424 W[:Ns,:Ns] = np.eye(Ns)
-1425 for u in unknowns_new:
-1426 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
-1427 if self.grouping == 'by_session':
-1428 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
-1429 elif self.grouping == 'by_uid':
-1430 weights = [1 for s in splits]
-1431 sw = sum(weights)
-1432 weights = [w/sw for w in weights]
-1433 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
-1434
-1435 CM_new = W @ CM_old @ W.T
-1436 V = W @ np.array([[VD_old[k]] for k in vars_old])
-1437 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
-1438
-1439 self.standardization.covar = CM_new
-1440 self.standardization.params.valuesdict = lambda : VD_new
-1441 self.standardization.var_names = vars_new
-1442
-1443 for r in self:
-1444 if r['Sample'] in self.unknowns:
-1445 r['Sample_split'] = r['Sample']
-1446 r['Sample'] = r['Sample_original']
-1447
-1448 self.refresh_samples()
-1449 self.consolidate_samples()
-1450 self.repeatabilities()
-1451
-1452 if tables:
-1453 self.table_of_analyses()
-1454 self.table_of_samples()
+1419 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r})
+1420
+1421 Ns = len(vars_old) - len(unknowns_old)
+1422 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new]
+1423 VD_new = {k: VD_old[k] for k in vars_old[:Ns]}
+1424
+1425 W = np.zeros((len(vars_new), len(vars_old)))
+1426 W[:Ns,:Ns] = np.eye(Ns)
+1427 for u in unknowns_new:
+1428 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u})
+1429 if self.grouping == 'by_session':
+1430 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits]
+1431 elif self.grouping == 'by_uid':
+1432 weights = [1 for s in splits]
+1433 sw = sum(weights)
+1434 weights = [w/sw for w in weights]
+1435 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:]
+1436
+1437 CM_new = W @ CM_old @ W.T
+1438 V = W @ np.array([[VD_old[k]] for k in vars_old])
+1439 VD_new = {k:v[0] for k,v in zip(vars_new, V)}
+1440
+1441 self.standardization.covar = CM_new
+1442 self.standardization.params.valuesdict = lambda : VD_new
+1443 self.standardization.var_names = vars_new
+1444
+1445 for r in self:
+1446 if r['Sample'] in self.unknowns:
+1447 r['Sample_split'] = r['Sample']
+1448 r['Sample'] = r['Sample_original']
+1449
+1450 self.refresh_samples()
+1451 self.consolidate_samples()
+1452 self.repeatabilities()
+1453
+1454 if tables:
+1455 self.table_of_analyses()
+1456 self.table_of_samples()
4. API Documentation
1456 def assign_timestamps(self):
-1457 '''
-1458 Assign a time field `t` of type `float` to each analysis.
-1459
-1460 If `TimeTag` is one of the data fields, `t` is equal within a given session
-1461 to `TimeTag` minus the mean value of `TimeTag` for that session.
-1462 Otherwise, `TimeTag` is by default equal to the index of each analysis
-1463 in the dataset and `t` is defined as above.
-1464 '''
-1465 for session in self.sessions:
-1466 sdata = self.sessions[session]['data']
-1467 try:
-1468 t0 = np.mean([r['TimeTag'] for r in sdata])
-1469 for r in sdata:
-1470 r['t'] = r['TimeTag'] - t0
-1471 except KeyError:
-1472 t0 = (len(sdata)-1)/2
-1473 for t,r in enumerate(sdata):
-1474 r['t'] = t - t0
+ 1458 def assign_timestamps(self):
+1459 '''
+1460 Assign a time field `t` of type `float` to each analysis.
+1461
+1462 If `TimeTag` is one of the data fields, `t` is equal within a given session
+1463 to `TimeTag` minus the mean value of `TimeTag` for that session.
+1464 Otherwise, `TimeTag` is by default equal to the index of each analysis
+1465 in the dataset and `t` is defined as above.
+1466 '''
+1467 for session in self.sessions:
+1468 sdata = self.sessions[session]['data']
+1469 try:
+1470 t0 = np.mean([r['TimeTag'] for r in sdata])
+1471 for r in sdata:
+1472 r['t'] = r['TimeTag'] - t0
+1473 except KeyError:
+1474 t0 = (len(sdata)-1)/2
+1475 for t,r in enumerate(sdata):
+1476 r['t'] = t - t0
4. API Documentation
1477 def report(self):
-1478 '''
-1479 Prints a report on the standardization fit.
-1480 Only applicable after `D4xdata.standardize(method='pooled')`.
-1481 '''
-1482 report_fit(self.standardization)
+ 1479 def report(self):
+1480 '''
+1481 Prints a report on the standardization fit.
+1482 Only applicable after `D4xdata.standardize(method='pooled')`.
+1483 '''
+1484 report_fit(self.standardization)
4. API Documentation
1485 def combine_samples(self, sample_groups):
-1486 '''
-1487 Combine analyses of different samples to compute weighted average Δ4x
-1488 and new error (co)variances corresponding to the groups defined by the `sample_groups`
-1489 dictionary.
-1490
-1491 Caution: samples are weighted by number of replicate analyses, which is a
-1492 reasonable default behavior but is not always optimal (e.g., in the case of strongly
-1493 correlated analytical errors for one or more samples).
-1494
-1495 Returns a tuplet of:
+ 1487 def combine_samples(self, sample_groups):
+1488 '''
+1489 Combine analyses of different samples to compute weighted average Δ4x
+1490 and new error (co)variances corresponding to the groups defined by the `sample_groups`
+1491 dictionary.
+1492
+1493 Caution: samples are weighted by number of replicate analyses, which is a
+1494 reasonable default behavior but is not always optimal (e.g., in the case of strongly
+1495 correlated analytical errors for one or more samples).
1496
-1497 + the list of group names
-1498 + an array of the corresponding Δ4x values
-1499 + the corresponding (co)variance matrix
-1500
-1501 **Parameters**
-1502
-1503 + `sample_groups`: a dictionary of the form:
-1504 ```py
-1505 {'group1': ['sample_1', 'sample_2'],
-1506 'group2': ['sample_3', 'sample_4', 'sample_5']}
-1507 ```
-1508 '''
-1509
-1510 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
-1511 groups = sorted(sample_groups.keys())
-1512 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
-1513 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
-1514 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
-1515 W = np.array([
-1516 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
-1517 for j in groups])
-1518 D4x_new = W @ D4x_old
-1519 CM_new = W @ CM_old @ W.T
-1520
-1521 return groups, D4x_new[:,0], CM_new
+1497 Returns a tuplet of:
+1498
+1499 + the list of group names
+1500 + an array of the corresponding Δ4x values
+1501 + the corresponding (co)variance matrix
+1502
+1503 **Parameters**
+1504
+1505 + `sample_groups`: a dictionary of the form:
+1506 ```py
+1507 {'group1': ['sample_1', 'sample_2'],
+1508 'group2': ['sample_3', 'sample_4', 'sample_5']}
+1509 ```
+1510 '''
+1511
+1512 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])]
+1513 groups = sorted(sample_groups.keys())
+1514 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups}
+1515 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples])
+1516 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples])
+1517 W = np.array([
+1518 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples]
+1519 for j in groups])
+1520 D4x_new = W @ D4x_old
+1521 CM_new = W @ CM_old @ W.T
+1522
+1523 return groups, D4x_new[:,0], CM_new
4. API Documentation
1524 @make_verbal
-1525 def standardize(self,
-1526 method = 'pooled',
-1527 weighted_sessions = [],
-1528 consolidate = True,
-1529 consolidate_tables = False,
-1530 consolidate_plots = False,
-1531 constraints = {},
-1532 ):
-1533 '''
-1534 Compute absolute Δ4x values for all replicate analyses and for sample averages.
-1535 If `method` argument is set to `'pooled'`, the standardization processes all sessions
-1536 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
-1537 i.e. that their true Δ4x value does not change between sessions,
-1538 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
-1539 `'indep_sessions'`, the standardization processes each session independently, based only
-1540 on anchors analyses.
-1541 '''
-1542
-1543 self.standardization_method = method
-1544 self.assign_timestamps()
-1545
-1546 if method == 'pooled':
-1547 if weighted_sessions:
-1548 for session_group in weighted_sessions:
-1549 if self._4x == '47':
-1550 X = D47data([r for r in self if r['Session'] in session_group])
-1551 elif self._4x == '48':
-1552 X = D48data([r for r in self if r['Session'] in session_group])
-1553 X.Nominal_D4x = self.Nominal_D4x.copy()
-1554 X.refresh()
-1555 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
-1556 w = np.sqrt(result.redchi)
-1557 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
-1558 for r in X:
-1559 r[f'wD{self._4x}raw'] *= w
-1560 else:
-1561 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
-1562 for r in self:
-1563 r[f'wD{self._4x}raw'] = 1.
-1564
-1565 params = Parameters()
-1566 for k,session in enumerate(self.sessions):
-1567 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
-1568 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
-1569 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
-1570 s = pf(session)
-1571 params.add(f'a_{s}', value = 0.9)
-1572 params.add(f'b_{s}', value = 0.)
-1573 params.add(f'c_{s}', value = -0.9)
-1574 params.add(f'a2_{s}', value = 0.,
-1575# vary = self.sessions[session]['scrambling_drift'],
-1576 )
-1577 params.add(f'b2_{s}', value = 0.,
-1578# vary = self.sessions[session]['slope_drift'],
-1579 )
-1580 params.add(f'c2_{s}', value = 0.,
-1581# vary = self.sessions[session]['wg_drift'],
-1582 )
-1583 if not self.sessions[session]['scrambling_drift']:
-1584 params[f'a2_{s}'].expr = '0'
-1585 if not self.sessions[session]['slope_drift']:
-1586 params[f'b2_{s}'].expr = '0'
-1587 if not self.sessions[session]['wg_drift']:
-1588 params[f'c2_{s}'].expr = '0'
-1589
-1590 for sample in self.unknowns:
-1591 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
-1592
-1593 for k in constraints:
-1594 params[k].expr = constraints[k]
-1595
-1596 def residuals(p):
-1597 R = []
-1598 for r in self:
-1599 session = pf(r['Session'])
-1600 sample = pf(r['Sample'])
-1601 if r['Sample'] in self.Nominal_D4x:
-1602 R += [ (
-1603 r[f'D{self._4x}raw'] - (
-1604 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
-1605 + p[f'b_{session}'] * r[f'd{self._4x}']
-1606 + p[f'c_{session}']
-1607 + r['t'] * (
-1608 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
-1609 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1610 + p[f'c2_{session}']
-1611 )
-1612 )
-1613 ) / r[f'wD{self._4x}raw'] ]
-1614 else:
-1615 R += [ (
-1616 r[f'D{self._4x}raw'] - (
-1617 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
-1618 + p[f'b_{session}'] * r[f'd{self._4x}']
-1619 + p[f'c_{session}']
-1620 + r['t'] * (
-1621 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
-1622 + p[f'b2_{session}'] * r[f'd{self._4x}']
-1623 + p[f'c2_{session}']
-1624 )
-1625 )
-1626 ) / r[f'wD{self._4x}raw'] ]
-1627 return R
-1628
-1629 M = Minimizer(residuals, params)
-1630 result = M.least_squares()
-1631 self.Nf = result.nfree
-1632 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
-1633 new_names, new_covar, new_se = _fullcovar(result)[:3]
-1634 result.var_names = new_names
-1635 result.covar = new_covar
-1636
-1637 for r in self:
-1638 s = pf(r["Session"])
-1639 a = result.params.valuesdict()[f'a_{s}']
-1640 b = result.params.valuesdict()[f'b_{s}']
-1641 c = result.params.valuesdict()[f'c_{s}']
-1642 a2 = result.params.valuesdict()[f'a2_{s}']
-1643 b2 = result.params.valuesdict()[f'b2_{s}']
-1644 c2 = result.params.valuesdict()[f'c2_{s}']
-1645 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1646
-1647
-1648 self.standardization = result
+ 1526 @make_verbal
+1527 def standardize(self,
+1528 method = 'pooled',
+1529 weighted_sessions = [],
+1530 consolidate = True,
+1531 consolidate_tables = False,
+1532 consolidate_plots = False,
+1533 constraints = {},
+1534 ):
+1535 '''
+1536 Compute absolute Δ4x values for all replicate analyses and for sample averages.
+1537 If `method` argument is set to `'pooled'`, the standardization processes all sessions
+1538 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
+1539 i.e. that their true Δ4x value does not change between sessions,
+1540 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to
+1541 `'indep_sessions'`, the standardization processes each session independently, based only
+1542 on anchors analyses.
+1543 '''
+1544
+1545 self.standardization_method = method
+1546 self.assign_timestamps()
+1547
+1548 if method == 'pooled':
+1549 if weighted_sessions:
+1550 for session_group in weighted_sessions:
+1551 if self._4x == '47':
+1552 X = D47data([r for r in self if r['Session'] in session_group])
+1553 elif self._4x == '48':
+1554 X = D48data([r for r in self if r['Session'] in session_group])
+1555 X.Nominal_D4x = self.Nominal_D4x.copy()
+1556 X.refresh()
+1557 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False)
+1558 w = np.sqrt(result.redchi)
+1559 self.msg(f'Session group {session_group} MRSWD = {w:.4f}')
+1560 for r in X:
+1561 r[f'wD{self._4x}raw'] *= w
+1562 else:
+1563 self.msg(f'All D{self._4x}raw weights set to 1 ‰')
+1564 for r in self:
+1565 r[f'wD{self._4x}raw'] = 1.
+1566
+1567 params = Parameters()
+1568 for k,session in enumerate(self.sessions):
+1569 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.")
+1570 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.")
+1571 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.")
+1572 s = pf(session)
+1573 params.add(f'a_{s}', value = 0.9)
+1574 params.add(f'b_{s}', value = 0.)
+1575 params.add(f'c_{s}', value = -0.9)
+1576 params.add(f'a2_{s}', value = 0.,
+1577# vary = self.sessions[session]['scrambling_drift'],
+1578 )
+1579 params.add(f'b2_{s}', value = 0.,
+1580# vary = self.sessions[session]['slope_drift'],
+1581 )
+1582 params.add(f'c2_{s}', value = 0.,
+1583# vary = self.sessions[session]['wg_drift'],
+1584 )
+1585 if not self.sessions[session]['scrambling_drift']:
+1586 params[f'a2_{s}'].expr = '0'
+1587 if not self.sessions[session]['slope_drift']:
+1588 params[f'b2_{s}'].expr = '0'
+1589 if not self.sessions[session]['wg_drift']:
+1590 params[f'c2_{s}'].expr = '0'
+1591
+1592 for sample in self.unknowns:
+1593 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5)
+1594
+1595 for k in constraints:
+1596 params[k].expr = constraints[k]
+1597
+1598 def residuals(p):
+1599 R = []
+1600 for r in self:
+1601 session = pf(r['Session'])
+1602 sample = pf(r['Sample'])
+1603 if r['Sample'] in self.Nominal_D4x:
+1604 R += [ (
+1605 r[f'D{self._4x}raw'] - (
+1606 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']]
+1607 + p[f'b_{session}'] * r[f'd{self._4x}']
+1608 + p[f'c_{session}']
+1609 + r['t'] * (
+1610 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']]
+1611 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1612 + p[f'c2_{session}']
+1613 )
+1614 )
+1615 ) / r[f'wD{self._4x}raw'] ]
+1616 else:
+1617 R += [ (
+1618 r[f'D{self._4x}raw'] - (
+1619 p[f'a_{session}'] * p[f'D{self._4x}_{sample}']
+1620 + p[f'b_{session}'] * r[f'd{self._4x}']
+1621 + p[f'c_{session}']
+1622 + r['t'] * (
+1623 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}']
+1624 + p[f'b2_{session}'] * r[f'd{self._4x}']
+1625 + p[f'c2_{session}']
+1626 )
+1627 )
+1628 ) / r[f'wD{self._4x}raw'] ]
+1629 return R
+1630
+1631 M = Minimizer(residuals, params)
+1632 result = M.least_squares()
+1633 self.Nf = result.nfree
+1634 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1635 new_names, new_covar, new_se = _fullcovar(result)[:3]
+1636 result.var_names = new_names
+1637 result.covar = new_covar
+1638
+1639 for r in self:
+1640 s = pf(r["Session"])
+1641 a = result.params.valuesdict()[f'a_{s}']
+1642 b = result.params.valuesdict()[f'b_{s}']
+1643 c = result.params.valuesdict()[f'c_{s}']
+1644 a2 = result.params.valuesdict()[f'a2_{s}']
+1645 b2 = result.params.valuesdict()[f'b2_{s}']
+1646 c2 = result.params.valuesdict()[f'c2_{s}']
+1647 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1648
1649
-1650 for session in self.sessions:
-1651 self.sessions[session]['Np'] = 3
-1652 for k in ['scrambling', 'slope', 'wg']:
-1653 if self.sessions[session][f'{k}_drift']:
-1654 self.sessions[session]['Np'] += 1
-1655
-1656 if consolidate:
-1657 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
-1658 return result
-1659
-1660
-1661 elif method == 'indep_sessions':
+1650 self.standardization = result
+1651
+1652 for session in self.sessions:
+1653 self.sessions[session]['Np'] = 3
+1654 for k in ['scrambling', 'slope', 'wg']:
+1655 if self.sessions[session][f'{k}_drift']:
+1656 self.sessions[session]['Np'] += 1
+1657
+1658 if consolidate:
+1659 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1660 return result
+1661
1662
-1663 if weighted_sessions:
-1664 for session_group in weighted_sessions:
-1665 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
-1666 X.Nominal_D4x = self.Nominal_D4x.copy()
-1667 X.refresh()
-1668 # This is only done to assign r['wD47raw'] for r in X:
-1669 X.standardize(method = method, weighted_sessions = [], consolidate = False)
-1670 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
-1671 else:
-1672 self.msg('All weights set to 1 ‰')
-1673 for r in self:
-1674 r[f'wD{self._4x}raw'] = 1
-1675
-1676 for session in self.sessions:
-1677 s = self.sessions[session]
-1678 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
-1679 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
-1680 s['Np'] = sum(p_active)
-1681 sdata = s['data']
-1682
-1683 A = np.array([
-1684 [
-1685 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
-1686 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
-1687 1 / r[f'wD{self._4x}raw'],
-1688 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
-1689 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
-1690 r['t'] / r[f'wD{self._4x}raw']
-1691 ]
-1692 for r in sdata if r['Sample'] in self.anchors
-1693 ])[:,p_active] # only keep columns for the active parameters
-1694 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
-1695 s['Na'] = Y.size
-1696 CM = linalg.inv(A.T @ A)
-1697 bf = (CM @ A.T @ Y).T[0,:]
-1698 k = 0
-1699 for n,a in zip(p_names, p_active):
-1700 if a:
-1701 s[n] = bf[k]
-1702# self.msg(f'{n} = {bf[k]}')
-1703 k += 1
-1704 else:
-1705 s[n] = 0.
-1706# self.msg(f'{n} = 0.0')
-1707
-1708 for r in sdata :
-1709 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
-1710 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
-1711 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
-1712
-1713 s['CM'] = np.zeros((6,6))
-1714 i = 0
-1715 k_active = [j for j,a in enumerate(p_active) if a]
-1716 for j,a in enumerate(p_active):
-1717 if a:
-1718 s['CM'][j,k_active] = CM[i,:]
-1719 i += 1
-1720
-1721 if not weighted_sessions:
-1722 w = self.rmswd()['rmswd']
-1723 for r in self:
-1724 r[f'wD{self._4x}'] *= w
-1725 r[f'wD{self._4x}raw'] *= w
-1726 for session in self.sessions:
-1727 self.sessions[session]['CM'] *= w**2
-1728
-1729 for session in self.sessions:
-1730 s = self.sessions[session]
-1731 s['SE_a'] = s['CM'][0,0]**.5
-1732 s['SE_b'] = s['CM'][1,1]**.5
-1733 s['SE_c'] = s['CM'][2,2]**.5
-1734 s['SE_a2'] = s['CM'][3,3]**.5
-1735 s['SE_b2'] = s['CM'][4,4]**.5
-1736 s['SE_c2'] = s['CM'][5,5]**.5
-1737
-1738 if not weighted_sessions:
-1739 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
-1740 else:
-1741 self.Nf = 0
-1742 for sg in weighted_sessions:
-1743 self.Nf += self.rmswd(sessions = sg)['Nf']
-1744
-1745 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1663 elif method == 'indep_sessions':
+1664
+1665 if weighted_sessions:
+1666 for session_group in weighted_sessions:
+1667 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x)
+1668 X.Nominal_D4x = self.Nominal_D4x.copy()
+1669 X.refresh()
+1670 # This is only done to assign r['wD47raw'] for r in X:
+1671 X.standardize(method = method, weighted_sessions = [], consolidate = False)
+1672 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}')
+1673 else:
+1674 self.msg('All weights set to 1 ‰')
+1675 for r in self:
+1676 r[f'wD{self._4x}raw'] = 1
+1677
+1678 for session in self.sessions:
+1679 s = self.sessions[session]
+1680 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2']
+1681 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']]
+1682 s['Np'] = sum(p_active)
+1683 sdata = s['data']
+1684
+1685 A = np.array([
+1686 [
+1687 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'],
+1688 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'],
+1689 1 / r[f'wD{self._4x}raw'],
+1690 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'],
+1691 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'],
+1692 r['t'] / r[f'wD{self._4x}raw']
+1693 ]
+1694 for r in sdata if r['Sample'] in self.anchors
+1695 ])[:,p_active] # only keep columns for the active parameters
+1696 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors])
+1697 s['Na'] = Y.size
+1698 CM = linalg.inv(A.T @ A)
+1699 bf = (CM @ A.T @ Y).T[0,:]
+1700 k = 0
+1701 for n,a in zip(p_names, p_active):
+1702 if a:
+1703 s[n] = bf[k]
+1704# self.msg(f'{n} = {bf[k]}')
+1705 k += 1
+1706 else:
+1707 s[n] = 0.
+1708# self.msg(f'{n} = 0.0')
+1709
+1710 for r in sdata :
+1711 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2']
+1712 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t'])
+1713 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t'])
+1714
+1715 s['CM'] = np.zeros((6,6))
+1716 i = 0
+1717 k_active = [j for j,a in enumerate(p_active) if a]
+1718 for j,a in enumerate(p_active):
+1719 if a:
+1720 s['CM'][j,k_active] = CM[i,:]
+1721 i += 1
+1722
+1723 if not weighted_sessions:
+1724 w = self.rmswd()['rmswd']
+1725 for r in self:
+1726 r[f'wD{self._4x}'] *= w
+1727 r[f'wD{self._4x}raw'] *= w
+1728 for session in self.sessions:
+1729 self.sessions[session]['CM'] *= w**2
+1730
+1731 for session in self.sessions:
+1732 s = self.sessions[session]
+1733 s['SE_a'] = s['CM'][0,0]**.5
+1734 s['SE_b'] = s['CM'][1,1]**.5
+1735 s['SE_c'] = s['CM'][2,2]**.5
+1736 s['SE_a2'] = s['CM'][3,3]**.5
+1737 s['SE_b2'] = s['CM'][4,4]**.5
+1738 s['SE_c2'] = s['CM'][5,5]**.5
+1739
+1740 if not weighted_sessions:
+1741 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions])
+1742 else:
+1743 self.Nf = 0
+1744 for sg in weighted_sessions:
+1745 self.Nf += self.rmswd(sessions = sg)['Nf']
1746
-1747 avgD4x = {
-1748 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
-1749 for sample in self.samples
-1750 }
-1751 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
-1752 rD4x = (chi2/self.Nf)**.5
-1753 self.repeatability[f'sigma_{self._4x}'] = rD4x
-1754
-1755 if consolidate:
-1756 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
+1747 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf)
+1748
+1749 avgD4x = {
+1750 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample])
+1751 for sample in self.samples
+1752 }
+1753 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self])
+1754 rD4x = (chi2/self.Nf)**.5
+1755 self.repeatability[f'sigma_{self._4x}'] = rD4x
+1756
+1757 if consolidate:
+1758 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
4. API Documentation
1759 def standardization_error(self, session, d4x, D4x, t = 0):
-1760 '''
-1761 Compute standardization error for a given session and
-1762 (δ47, Δ47) composition.
-1763 '''
-1764 a = self.sessions[session]['a']
-1765 b = self.sessions[session]['b']
-1766 c = self.sessions[session]['c']
-1767 a2 = self.sessions[session]['a2']
-1768 b2 = self.sessions[session]['b2']
-1769 c2 = self.sessions[session]['c2']
-1770 CM = self.sessions[session]['CM']
-1771
-1772 x, y = D4x, d4x
-1773 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
-1774# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
-1775 dxdy = -(b+b2*t) / (a+a2*t)
-1776 dxdz = 1. / (a+a2*t)
-1777 dxda = -x / (a+a2*t)
-1778 dxdb = -y / (a+a2*t)
-1779 dxdc = -1. / (a+a2*t)
-1780 dxda2 = -x * a2 / (a+a2*t)
-1781 dxdb2 = -y * t / (a+a2*t)
-1782 dxdc2 = -t / (a+a2*t)
-1783 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
-1784 sx = (V @ CM @ V.T) ** .5
-1785 return sx
+ 1761 def standardization_error(self, session, d4x, D4x, t = 0):
+1762 '''
+1763 Compute standardization error for a given session and
+1764 (δ47, Δ47) composition.
+1765 '''
+1766 a = self.sessions[session]['a']
+1767 b = self.sessions[session]['b']
+1768 c = self.sessions[session]['c']
+1769 a2 = self.sessions[session]['a2']
+1770 b2 = self.sessions[session]['b2']
+1771 c2 = self.sessions[session]['c2']
+1772 CM = self.sessions[session]['CM']
+1773
+1774 x, y = D4x, d4x
+1775 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t
+1776# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t)
+1777 dxdy = -(b+b2*t) / (a+a2*t)
+1778 dxdz = 1. / (a+a2*t)
+1779 dxda = -x / (a+a2*t)
+1780 dxdb = -y / (a+a2*t)
+1781 dxdc = -1. / (a+a2*t)
+1782 dxda2 = -x * a2 / (a+a2*t)
+1783 dxdb2 = -y * t / (a+a2*t)
+1784 dxdc2 = -t / (a+a2*t)
+1785 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2])
+1786 sx = (V @ CM @ V.T) ** .5
+1787 return sx
4. API Documentation
1788 @make_verbal
-1789 def summary(self,
-1790 dir = 'output',
-1791 filename = None,
-1792 save_to_file = True,
-1793 print_out = True,
-1794 ):
-1795 '''
-1796 Print out an/or save to disk a summary of the standardization results.
-1797
-1798 **Parameters**
+ 1790 @make_verbal
+1791 def summary(self,
+1792 dir = 'output',
+1793 filename = None,
+1794 save_to_file = True,
+1795 print_out = True,
+1796 ):
+1797 '''
+1798 Print out an/or save to disk a summary of the standardization results.
1799
-1800 + `dir`: the directory in which to save the table
-1801 + `filename`: the name to the csv file to write to
-1802 + `save_to_file`: whether to save the table to disk
-1803 + `print_out`: whether to print out the table
-1804 '''
-1805
-1806 out = []
-1807 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
-1808 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
-1809 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
-1810 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
-1811 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
-1812 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
-1813 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
-1814 out += [['Model degrees of freedom', f"{self.Nf}"]]
-1815 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
-1816 out += [['Standardization method', self.standardization_method]]
-1817
-1818 if save_to_file:
-1819 if not os.path.exists(dir):
-1820 os.makedirs(dir)
-1821 if filename is None:
-1822 filename = f'D{self._4x}_summary.csv'
-1823 with open(f'{dir}/{filename}', 'w') as fid:
-1824 fid.write(make_csv(out))
-1825 if print_out:
-1826 self.msg('\n' + pretty_table(out, header = 0))
+1800 **Parameters**
+1801
+1802 + `dir`: the directory in which to save the table
+1803 + `filename`: the name to the csv file to write to
+1804 + `save_to_file`: whether to save the table to disk
+1805 + `print_out`: whether to print out the table
+1806 '''
+1807
+1808 out = []
+1809 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]]
+1810 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]]
+1811 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]]
+1812 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]]
+1813 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]]
+1814 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]]
+1815 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]]
+1816 out += [['Model degrees of freedom', f"{self.Nf}"]]
+1817 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]]
+1818 out += [['Standardization method', self.standardization_method]]
+1819
+1820 if save_to_file:
+1821 if not os.path.exists(dir):
+1822 os.makedirs(dir)
+1823 if filename is None:
+1824 filename = f'D{self._4x}_summary.csv'
+1825 with open(f'{dir}/{filename}', 'w') as fid:
+1826 fid.write(make_csv(out))
+1827 if print_out:
+1828 self.msg('\n' + pretty_table(out, header = 0))
4. API Documentation
1829 @make_verbal
-1830 def table_of_sessions(self,
-1831 dir = 'output',
-1832 filename = None,
-1833 save_to_file = True,
-1834 print_out = True,
-1835 output = None,
-1836 ):
-1837 '''
-1838 Print out an/or save to disk a table of sessions.
-1839
-1840 **Parameters**
+ 1831 @make_verbal
+1832 def table_of_sessions(self,
+1833 dir = 'output',
+1834 filename = None,
+1835 save_to_file = True,
+1836 print_out = True,
+1837 output = None,
+1838 ):
+1839 '''
+1840 Print out an/or save to disk a table of sessions.
1841
-1842 + `dir`: the directory in which to save the table
-1843 + `filename`: the name to the csv file to write to
-1844 + `save_to_file`: whether to save the table to disk
-1845 + `print_out`: whether to print out the table
-1846 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1847 if set to `'raw'`: return a list of list of strings
-1848 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1849 '''
-1850 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
-1851 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
-1852 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
-1853
-1854 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
-1855 if include_a2:
-1856 out[-1] += ['a2 ± SE']
-1857 if include_b2:
-1858 out[-1] += ['b2 ± SE']
-1859 if include_c2:
-1860 out[-1] += ['c2 ± SE']
-1861 for session in self.sessions:
-1862 out += [[
-1863 session,
-1864 f"{self.sessions[session]['Na']}",
-1865 f"{self.sessions[session]['Nu']}",
-1866 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
-1867 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
-1868 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
-1869 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
-1870 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
-1871 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
-1872 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
-1873 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
-1874 ]]
-1875 if include_a2:
-1876 if self.sessions[session]['scrambling_drift']:
-1877 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
-1878 else:
-1879 out[-1] += ['']
-1880 if include_b2:
-1881 if self.sessions[session]['slope_drift']:
-1882 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
-1883 else:
-1884 out[-1] += ['']
-1885 if include_c2:
-1886 if self.sessions[session]['wg_drift']:
-1887 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
-1888 else:
-1889 out[-1] += ['']
-1890
-1891 if save_to_file:
-1892 if not os.path.exists(dir):
-1893 os.makedirs(dir)
-1894 if filename is None:
-1895 filename = f'D{self._4x}_sessions.csv'
-1896 with open(f'{dir}/{filename}', 'w') as fid:
-1897 fid.write(make_csv(out))
-1898 if print_out:
-1899 self.msg('\n' + pretty_table(out))
-1900 if output == 'raw':
-1901 return out
-1902 elif output == 'pretty':
-1903 return pretty_table(out)
+1842 **Parameters**
+1843
+1844 + `dir`: the directory in which to save the table
+1845 + `filename`: the name to the csv file to write to
+1846 + `save_to_file`: whether to save the table to disk
+1847 + `print_out`: whether to print out the table
+1848 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1849 if set to `'raw'`: return a list of list of strings
+1850 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1851 '''
+1852 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions])
+1853 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions])
+1854 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions])
+1855
+1856 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']]
+1857 if include_a2:
+1858 out[-1] += ['a2 ± SE']
+1859 if include_b2:
+1860 out[-1] += ['b2 ± SE']
+1861 if include_c2:
+1862 out[-1] += ['c2 ± SE']
+1863 for session in self.sessions:
+1864 out += [[
+1865 session,
+1866 f"{self.sessions[session]['Na']}",
+1867 f"{self.sessions[session]['Nu']}",
+1868 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}",
+1869 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}",
+1870 f"{self.sessions[session]['r_d13C_VPDB']:.4f}",
+1871 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}",
+1872 f"{self.sessions[session][f'r_D{self._4x}']:.4f}",
+1873 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}",
+1874 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}",
+1875 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}",
+1876 ]]
+1877 if include_a2:
+1878 if self.sessions[session]['scrambling_drift']:
+1879 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"]
+1880 else:
+1881 out[-1] += ['']
+1882 if include_b2:
+1883 if self.sessions[session]['slope_drift']:
+1884 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"]
+1885 else:
+1886 out[-1] += ['']
+1887 if include_c2:
+1888 if self.sessions[session]['wg_drift']:
+1889 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"]
+1890 else:
+1891 out[-1] += ['']
+1892
+1893 if save_to_file:
+1894 if not os.path.exists(dir):
+1895 os.makedirs(dir)
+1896 if filename is None:
+1897 filename = f'D{self._4x}_sessions.csv'
+1898 with open(f'{dir}/{filename}', 'w') as fid:
+1899 fid.write(make_csv(out))
+1900 if print_out:
+1901 self.msg('\n' + pretty_table(out))
+1902 if output == 'raw':
+1903 return out
+1904 elif output == 'pretty':
+1905 return pretty_table(out)
4. API Documentation
1906 @make_verbal
-1907 def table_of_analyses(
-1908 self,
-1909 dir = 'output',
-1910 filename = None,
-1911 save_to_file = True,
-1912 print_out = True,
-1913 output = None,
-1914 ):
-1915 '''
-1916 Print out an/or save to disk a table of analyses.
-1917
-1918 **Parameters**
+ 1908 @make_verbal
+1909 def table_of_analyses(
+1910 self,
+1911 dir = 'output',
+1912 filename = None,
+1913 save_to_file = True,
+1914 print_out = True,
+1915 output = None,
+1916 ):
+1917 '''
+1918 Print out an/or save to disk a table of analyses.
1919
-1920 + `dir`: the directory in which to save the table
-1921 + `filename`: the name to the csv file to write to
-1922 + `save_to_file`: whether to save the table to disk
-1923 + `print_out`: whether to print out the table
-1924 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-1925 if set to `'raw'`: return a list of list of strings
-1926 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1927 '''
-1928
-1929 out = [['UID','Session','Sample']]
-1930 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
-1931 for f in extra_fields:
-1932 out[-1] += [f[0]]
-1933 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
-1934 for r in self:
-1935 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
-1936 for f in extra_fields:
-1937 out[-1] += [f"{r[f[0]]:{f[1]}}"]
-1938 out[-1] += [
-1939 f"{r['d13Cwg_VPDB']:.3f}",
-1940 f"{r['d18Owg_VSMOW']:.3f}",
-1941 f"{r['d45']:.6f}",
-1942 f"{r['d46']:.6f}",
-1943 f"{r['d47']:.6f}",
-1944 f"{r['d48']:.6f}",
-1945 f"{r['d49']:.6f}",
-1946 f"{r['d13C_VPDB']:.6f}",
-1947 f"{r['d18O_VSMOW']:.6f}",
-1948 f"{r['D47raw']:.6f}",
-1949 f"{r['D48raw']:.6f}",
-1950 f"{r['D49raw']:.6f}",
-1951 f"{r[f'D{self._4x}']:.6f}"
-1952 ]
-1953 if save_to_file:
-1954 if not os.path.exists(dir):
-1955 os.makedirs(dir)
-1956 if filename is None:
-1957 filename = f'D{self._4x}_analyses.csv'
-1958 with open(f'{dir}/{filename}', 'w') as fid:
-1959 fid.write(make_csv(out))
-1960 if print_out:
-1961 self.msg('\n' + pretty_table(out))
-1962 return out
+1920 **Parameters**
+1921
+1922 + `dir`: the directory in which to save the table
+1923 + `filename`: the name to the csv file to write to
+1924 + `save_to_file`: whether to save the table to disk
+1925 + `print_out`: whether to print out the table
+1926 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+1927 if set to `'raw'`: return a list of list of strings
+1928 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1929 '''
+1930
+1931 out = [['UID','Session','Sample']]
+1932 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}]
+1933 for f in extra_fields:
+1934 out[-1] += [f[0]]
+1935 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}']
+1936 for r in self:
+1937 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]]
+1938 for f in extra_fields:
+1939 out[-1] += [f"{r[f[0]]:{f[1]}}"]
+1940 out[-1] += [
+1941 f"{r['d13Cwg_VPDB']:.3f}",
+1942 f"{r['d18Owg_VSMOW']:.3f}",
+1943 f"{r['d45']:.6f}",
+1944 f"{r['d46']:.6f}",
+1945 f"{r['d47']:.6f}",
+1946 f"{r['d48']:.6f}",
+1947 f"{r['d49']:.6f}",
+1948 f"{r['d13C_VPDB']:.6f}",
+1949 f"{r['d18O_VSMOW']:.6f}",
+1950 f"{r['D47raw']:.6f}",
+1951 f"{r['D48raw']:.6f}",
+1952 f"{r['D49raw']:.6f}",
+1953 f"{r[f'D{self._4x}']:.6f}"
+1954 ]
+1955 if save_to_file:
+1956 if not os.path.exists(dir):
+1957 os.makedirs(dir)
+1958 if filename is None:
+1959 filename = f'D{self._4x}_analyses.csv'
+1960 with open(f'{dir}/{filename}', 'w') as fid:
+1961 fid.write(make_csv(out))
+1962 if print_out:
+1963 self.msg('\n' + pretty_table(out))
+1964 return out
4. API Documentation
1964 @make_verbal
-1965 def covar_table(
-1966 self,
-1967 correl = False,
-1968 dir = 'output',
-1969 filename = None,
-1970 save_to_file = True,
-1971 print_out = True,
-1972 output = None,
-1973 ):
-1974 '''
-1975 Print out, save to disk and/or return the variance-covariance matrix of D4x
-1976 for all unknown samples.
-1977
-1978 **Parameters**
+ 1966 @make_verbal
+1967 def covar_table(
+1968 self,
+1969 correl = False,
+1970 dir = 'output',
+1971 filename = None,
+1972 save_to_file = True,
+1973 print_out = True,
+1974 output = None,
+1975 ):
+1976 '''
+1977 Print out, save to disk and/or return the variance-covariance matrix of D4x
+1978 for all unknown samples.
1979
-1980 + `dir`: the directory in which to save the csv
-1981 + `filename`: the name of the csv file to write to
-1982 + `save_to_file`: whether to save the csv
-1983 + `print_out`: whether to print out the matrix
-1984 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
-1985 if set to `'raw'`: return a list of list of strings
-1986 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-1987 '''
-1988 samples = sorted([u for u in self.unknowns])
-1989 out = [[''] + samples]
-1990 for s1 in samples:
-1991 out.append([s1])
-1992 for s2 in samples:
-1993 if correl:
-1994 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
-1995 else:
-1996 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
-1997
-1998 if save_to_file:
-1999 if not os.path.exists(dir):
-2000 os.makedirs(dir)
-2001 if filename is None:
-2002 if correl:
-2003 filename = f'D{self._4x}_correl.csv'
-2004 else:
-2005 filename = f'D{self._4x}_covar.csv'
-2006 with open(f'{dir}/{filename}', 'w') as fid:
-2007 fid.write(make_csv(out))
-2008 if print_out:
-2009 self.msg('\n'+pretty_table(out))
-2010 if output == 'raw':
-2011 return out
-2012 elif output == 'pretty':
-2013 return pretty_table(out)
+1980 **Parameters**
+1981
+1982 + `dir`: the directory in which to save the csv
+1983 + `filename`: the name of the csv file to write to
+1984 + `save_to_file`: whether to save the csv
+1985 + `print_out`: whether to print out the matrix
+1986 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`);
+1987 if set to `'raw'`: return a list of list of strings
+1988 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+1989 '''
+1990 samples = sorted([u for u in self.unknowns])
+1991 out = [[''] + samples]
+1992 for s1 in samples:
+1993 out.append([s1])
+1994 for s2 in samples:
+1995 if correl:
+1996 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}')
+1997 else:
+1998 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}')
+1999
+2000 if save_to_file:
+2001 if not os.path.exists(dir):
+2002 os.makedirs(dir)
+2003 if filename is None:
+2004 if correl:
+2005 filename = f'D{self._4x}_correl.csv'
+2006 else:
+2007 filename = f'D{self._4x}_covar.csv'
+2008 with open(f'{dir}/{filename}', 'w') as fid:
+2009 fid.write(make_csv(out))
+2010 if print_out:
+2011 self.msg('\n'+pretty_table(out))
+2012 if output == 'raw':
+2013 return out
+2014 elif output == 'pretty':
+2015 return pretty_table(out)
4. API Documentation
2015 @make_verbal
-2016 def table_of_samples(
-2017 self,
-2018 dir = 'output',
-2019 filename = None,
-2020 save_to_file = True,
-2021 print_out = True,
-2022 output = None,
-2023 ):
-2024 '''
-2025 Print out, save to disk and/or return a table of samples.
-2026
-2027 **Parameters**
+ 2017 @make_verbal
+2018 def table_of_samples(
+2019 self,
+2020 dir = 'output',
+2021 filename = None,
+2022 save_to_file = True,
+2023 print_out = True,
+2024 output = None,
+2025 ):
+2026 '''
+2027 Print out, save to disk and/or return a table of samples.
2028
-2029 + `dir`: the directory in which to save the csv
-2030 + `filename`: the name of the csv file to write to
-2031 + `save_to_file`: whether to save the csv
-2032 + `print_out`: whether to print out the table
-2033 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
-2034 if set to `'raw'`: return a list of list of strings
-2035 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
-2036 '''
-2037
-2038 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
-2039 for sample in self.anchors:
-2040 out += [[
-2041 f"{sample}",
-2042 f"{self.samples[sample]['N']}",
-2043 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2044 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2045 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
-2046 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
-2047 ]]
-2048 for sample in self.unknowns:
-2049 out += [[
-2050 f"{sample}",
-2051 f"{self.samples[sample]['N']}",
-2052 f"{self.samples[sample]['d13C_VPDB']:.2f}",
-2053 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
-2054 f"{self.samples[sample][f'D{self._4x}']:.4f}",
-2055 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
-2056 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
-2057 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
-2058 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
-2059 ]]
-2060 if save_to_file:
-2061 if not os.path.exists(dir):
-2062 os.makedirs(dir)
-2063 if filename is None:
-2064 filename = f'D{self._4x}_samples.csv'
-2065 with open(f'{dir}/{filename}', 'w') as fid:
-2066 fid.write(make_csv(out))
-2067 if print_out:
-2068 self.msg('\n'+pretty_table(out))
-2069 if output == 'raw':
-2070 return out
-2071 elif output == 'pretty':
-2072 return pretty_table(out)
+2029 **Parameters**
+2030
+2031 + `dir`: the directory in which to save the csv
+2032 + `filename`: the name of the csv file to write to
+2033 + `save_to_file`: whether to save the csv
+2034 + `print_out`: whether to print out the table
+2035 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`);
+2036 if set to `'raw'`: return a list of list of strings
+2037 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`)
+2038 '''
+2039
+2040 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']]
+2041 for sample in self.anchors:
+2042 out += [[
+2043 f"{sample}",
+2044 f"{self.samples[sample]['N']}",
+2045 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2046 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2047 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','',
+2048 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', ''
+2049 ]]
+2050 for sample in self.unknowns:
+2051 out += [[
+2052 f"{sample}",
+2053 f"{self.samples[sample]['N']}",
+2054 f"{self.samples[sample]['d13C_VPDB']:.2f}",
+2055 f"{self.samples[sample]['d18O_VSMOW']:.2f}",
+2056 f"{self.samples[sample][f'D{self._4x}']:.4f}",
+2057 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}",
+2058 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}",
+2059 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '',
+2060 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else ''
+2061 ]]
+2062 if save_to_file:
+2063 if not os.path.exists(dir):
+2064 os.makedirs(dir)
+2065 if filename is None:
+2066 filename = f'D{self._4x}_samples.csv'
+2067 with open(f'{dir}/{filename}', 'w') as fid:
+2068 fid.write(make_csv(out))
+2069 if print_out:
+2070 self.msg('\n'+pretty_table(out))
+2071 if output == 'raw':
+2072 return out
+2073 elif output == 'pretty':
+2074 return pretty_table(out)
4. API Documentation
2075 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
-2076 '''
-2077 Generate session plots and save them to disk.
-2078
-2079 **Parameters**
+ 2077 def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi = 100):
+2078 '''
+2079 Generate session plots and save them to disk.
2080
-2081 + `dir`: the directory in which to save the plots
-2082 + `figsize`: the width and height (in inches) of each plot
-2083 + `filetype`: 'pdf' or 'png'
-2084 + `dpi`: resolution for PNG output
-2085 '''
-2086 if not os.path.exists(dir):
-2087 os.makedirs(dir)
-2088
-2089 for session in self.sessions:
-2090 sp = self.plot_single_session(session, xylimits = 'constant')
-2091 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
-2092 ppl.close(sp.fig)
+2081 **Parameters**
+2082
+2083 + `dir`: the directory in which to save the plots
+2084 + `figsize`: the width and height (in inches) of each plot
+2085 + `filetype`: 'pdf' or 'png'
+2086 + `dpi`: resolution for PNG output
+2087 '''
+2088 if not os.path.exists(dir):
+2089 os.makedirs(dir)
+2090
+2091 for session in self.sessions:
+2092 sp = self.plot_single_session(session, xylimits = 'constant')
+2093 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
+2094 ppl.close(sp.fig)
4. API Documentation
2095 @make_verbal
-2096 def consolidate_samples(self):
-2097 '''
-2098 Compile various statistics for each sample.
-2099
-2100 For each anchor sample:
+ 2097 @make_verbal
+2098 def consolidate_samples(self):
+2099 '''
+2100 Compile various statistics for each sample.
2101
-2102 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
-2103 + `SE_D47` or `SE_D48`: set to zero by definition
-2104
-2105 For each unknown sample:
+2102 For each anchor sample:
+2103
+2104 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x`
+2105 + `SE_D47` or `SE_D48`: set to zero by definition
2106
-2107 + `D47` or `D48`: the standardized Δ4x value for this unknown
-2108 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
-2109
-2110 For each anchor and unknown:
+2107 For each unknown sample:
+2108
+2109 + `D47` or `D48`: the standardized Δ4x value for this unknown
+2110 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown
2111
-2112 + `N`: the total number of analyses of this sample
-2113 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
-2114 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
-2115 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
-2116 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
-2117 variance, indicating whether the Δ4x repeatability this sample differs significantly from
-2118 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
-2119 '''
-2120 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
-2121 for sample in self.samples:
-2122 self.samples[sample]['N'] = len(self.samples[sample]['data'])
-2123 if self.samples[sample]['N'] > 1:
-2124 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
-2125
-2126 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
-2127 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
-2128
-2129 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
-2130 if len(D4x_pop) > 2:
-2131 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
-2132
-2133 if self.standardization_method == 'pooled':
-2134 for sample in self.anchors:
-2135 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2136 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2137 for sample in self.unknowns:
-2138 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
-2139 try:
-2140 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
-2141 except ValueError:
-2142 # when `sample` is constrained by self.standardize(constraints = {...}),
-2143 # it is no longer listed in self.standardization.var_names.
-2144 # Temporary fix: define SE as zero for now
-2145 self.samples[sample][f'SE_D4{self._4x}'] = 0.
-2146
-2147 elif self.standardization_method == 'indep_sessions':
-2148 for sample in self.anchors:
-2149 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
-2150 self.samples[sample][f'SE_D{self._4x}'] = 0.
-2151 for sample in self.unknowns:
-2152 self.msg(f'Consolidating sample {sample}')
-2153 self.unknowns[sample][f'session_D{self._4x}'] = {}
-2154 session_avg = []
-2155 for session in self.sessions:
-2156 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
-2157 if sdata:
-2158 self.msg(f'{sample} found in session {session}')
-2159 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
-2160 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
-2161 # !! TODO: sigma_s below does not account for temporal changes in standardization error
-2162 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
-2163 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
-2164 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
-2165 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
-2166 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
-2167 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
-2168 wsum = sum([weights[s] for s in weights])
-2169 for s in weights:
-2170 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
-2171
-2172 for r in self:
-2173 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
+2112 For each anchor and unknown:
+2113
+2114 + `N`: the total number of analyses of this sample
+2115 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample
+2116 + `d13C_VPDB`: the average δ13C_VPDB value for this sample
+2117 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2)
+2118 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal
+2119 variance, indicating whether the Δ4x repeatability this sample differs significantly from
+2120 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`.
+2121 '''
+2122 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']]
+2123 for sample in self.samples:
+2124 self.samples[sample]['N'] = len(self.samples[sample]['data'])
+2125 if self.samples[sample]['N'] > 1:
+2126 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']])
+2127
+2128 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']])
+2129 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']])
+2130
+2131 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']]
+2132 if len(D4x_pop) > 2:
+2133 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1]
+2134
+2135 if self.standardization_method == 'pooled':
+2136 for sample in self.anchors:
+2137 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2138 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2139 for sample in self.unknowns:
+2140 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}']
+2141 try:
+2142 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5
+2143 except ValueError:
+2144 # when `sample` is constrained by self.standardize(constraints = {...}),
+2145 # it is no longer listed in self.standardization.var_names.
+2146 # Temporary fix: define SE as zero for now
+2147 self.samples[sample][f'SE_D4{self._4x}'] = 0.
+2148
+2149 elif self.standardization_method == 'indep_sessions':
+2150 for sample in self.anchors:
+2151 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample]
+2152 self.samples[sample][f'SE_D{self._4x}'] = 0.
+2153 for sample in self.unknowns:
+2154 self.msg(f'Consolidating sample {sample}')
+2155 self.unknowns[sample][f'session_D{self._4x}'] = {}
+2156 session_avg = []
+2157 for session in self.sessions:
+2158 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample]
+2159 if sdata:
+2160 self.msg(f'{sample} found in session {session}')
+2161 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata])
+2162 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata])
+2163 # !! TODO: sigma_s below does not account for temporal changes in standardization error
+2164 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x)
+2165 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5
+2166 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5])
+2167 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1]
+2168 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg))
+2169 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']}
+2170 wsum = sum([weights[s] for s in weights])
+2171 for s in weights:
+2172 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum]
+2173
+2174 for r in self:
+2175 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
4. API Documentation
2177 def consolidate_sessions(self):
-2178 '''
-2179 Compute various statistics for each session.
-2180
-2181 + `Na`: Number of anchor analyses in the session
-2182 + `Nu`: Number of unknown analyses in the session
-2183 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
-2184 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
-2185 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
-2186 + `a`: scrambling factor
-2187 + `b`: compositional slope
-2188 + `c`: WG offset
-2189 + `SE_a`: Model stadard erorr of `a`
-2190 + `SE_b`: Model stadard erorr of `b`
-2191 + `SE_c`: Model stadard erorr of `c`
-2192 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
-2193 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
-2194 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
-2195 + `a2`: scrambling factor drift
-2196 + `b2`: compositional slope drift
-2197 + `c2`: WG offset drift
-2198 + `Np`: Number of standardization parameters to fit
-2199 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
-2200 + `d13Cwg_VPDB`: δ13C_VPDB of WG
-2201 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
-2202 '''
-2203 for session in self.sessions:
-2204 if 'd13Cwg_VPDB' not in self.sessions[session]:
-2205 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
-2206 if 'd18Owg_VSMOW' not in self.sessions[session]:
-2207 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
-2208 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
-2209 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
-2210
-2211 self.msg(f'Computing repeatabilities for session {session}')
-2212 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
-2213 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
-2214 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
-2215
-2216 if self.standardization_method == 'pooled':
-2217 for session in self.sessions:
-2218
-2219 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
-2220 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2221 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
-2222
-2223 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
-2224 i = self.standardization.var_names.index(f'b_{pf(session)}')
-2225 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
-2226
-2227 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
-2228 i = self.standardization.var_names.index(f'c_{pf(session)}')
-2229 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
-2230
-2231 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
-2232 if self.sessions[session]['scrambling_drift']:
-2233 i = self.standardization.var_names.index(f'a2_{pf(session)}')
-2234 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
-2235 else:
-2236 self.sessions[session]['SE_a2'] = 0.
-2237
-2238 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
-2239 if self.sessions[session]['slope_drift']:
-2240 i = self.standardization.var_names.index(f'b2_{pf(session)}')
-2241 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
-2242 else:
-2243 self.sessions[session]['SE_b2'] = 0.
-2244
-2245 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
-2246 if self.sessions[session]['wg_drift']:
-2247 i = self.standardization.var_names.index(f'c2_{pf(session)}')
-2248 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
-2249 else:
-2250 self.sessions[session]['SE_c2'] = 0.
-2251
-2252 i = self.standardization.var_names.index(f'a_{pf(session)}')
-2253 j = self.standardization.var_names.index(f'b_{pf(session)}')
-2254 k = self.standardization.var_names.index(f'c_{pf(session)}')
-2255 CM = np.zeros((6,6))
-2256 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
-2257 try:
-2258 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
-2259 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
-2260 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
-2261 try:
-2262 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2263 CM[3,4] = self.standardization.covar[i2,j2]
-2264 CM[4,3] = self.standardization.covar[j2,i2]
-2265 except ValueError:
-2266 pass
-2267 try:
-2268 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2269 CM[3,5] = self.standardization.covar[i2,k2]
-2270 CM[5,3] = self.standardization.covar[k2,i2]
-2271 except ValueError:
-2272 pass
-2273 except ValueError:
-2274 pass
-2275 try:
-2276 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
-2277 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
-2278 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
-2279 try:
-2280 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2281 CM[4,5] = self.standardization.covar[j2,k2]
-2282 CM[5,4] = self.standardization.covar[k2,j2]
-2283 except ValueError:
-2284 pass
-2285 except ValueError:
-2286 pass
-2287 try:
-2288 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
-2289 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
-2290 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
-2291 except ValueError:
-2292 pass
-2293
-2294 self.sessions[session]['CM'] = CM
+ 2179 def consolidate_sessions(self):
+2180 '''
+2181 Compute various statistics for each session.
+2182
+2183 + `Na`: Number of anchor analyses in the session
+2184 + `Nu`: Number of unknown analyses in the session
+2185 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session
+2186 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session
+2187 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session
+2188 + `a`: scrambling factor
+2189 + `b`: compositional slope
+2190 + `c`: WG offset
+2191 + `SE_a`: Model stadard erorr of `a`
+2192 + `SE_b`: Model stadard erorr of `b`
+2193 + `SE_c`: Model stadard erorr of `c`
+2194 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`)
+2195 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`)
+2196 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`)
+2197 + `a2`: scrambling factor drift
+2198 + `b2`: compositional slope drift
+2199 + `c2`: WG offset drift
+2200 + `Np`: Number of standardization parameters to fit
+2201 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`)
+2202 + `d13Cwg_VPDB`: δ13C_VPDB of WG
+2203 + `d18Owg_VSMOW`: δ18O_VSMOW of WG
+2204 '''
+2205 for session in self.sessions:
+2206 if 'd13Cwg_VPDB' not in self.sessions[session]:
+2207 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB']
+2208 if 'd18Owg_VSMOW' not in self.sessions[session]:
+2209 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW']
+2210 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors])
+2211 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns])
+2212
+2213 self.msg(f'Computing repeatabilities for session {session}')
+2214 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session])
+2215 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session])
+2216 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session])
+2217
+2218 if self.standardization_method == 'pooled':
+2219 for session in self.sessions:
+2220
+2221 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
+2222 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2223 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
+2224
+2225 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}']
+2226 i = self.standardization.var_names.index(f'b_{pf(session)}')
+2227 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5
+2228
+2229 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}']
+2230 i = self.standardization.var_names.index(f'c_{pf(session)}')
+2231 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5
+2232
+2233 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}']
+2234 if self.sessions[session]['scrambling_drift']:
+2235 i = self.standardization.var_names.index(f'a2_{pf(session)}')
+2236 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5
+2237 else:
+2238 self.sessions[session]['SE_a2'] = 0.
+2239
+2240 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}']
+2241 if self.sessions[session]['slope_drift']:
+2242 i = self.standardization.var_names.index(f'b2_{pf(session)}')
+2243 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5
+2244 else:
+2245 self.sessions[session]['SE_b2'] = 0.
+2246
+2247 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}']
+2248 if self.sessions[session]['wg_drift']:
+2249 i = self.standardization.var_names.index(f'c2_{pf(session)}')
+2250 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5
+2251 else:
+2252 self.sessions[session]['SE_c2'] = 0.
+2253
+2254 i = self.standardization.var_names.index(f'a_{pf(session)}')
+2255 j = self.standardization.var_names.index(f'b_{pf(session)}')
+2256 k = self.standardization.var_names.index(f'c_{pf(session)}')
+2257 CM = np.zeros((6,6))
+2258 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]]
+2259 try:
+2260 i2 = self.standardization.var_names.index(f'a2_{pf(session)}')
+2261 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]]
+2262 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2]
+2263 try:
+2264 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2265 CM[3,4] = self.standardization.covar[i2,j2]
+2266 CM[4,3] = self.standardization.covar[j2,i2]
+2267 except ValueError:
+2268 pass
+2269 try:
+2270 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2271 CM[3,5] = self.standardization.covar[i2,k2]
+2272 CM[5,3] = self.standardization.covar[k2,i2]
+2273 except ValueError:
+2274 pass
+2275 except ValueError:
+2276 pass
+2277 try:
+2278 j2 = self.standardization.var_names.index(f'b2_{pf(session)}')
+2279 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]]
+2280 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2]
+2281 try:
+2282 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2283 CM[4,5] = self.standardization.covar[j2,k2]
+2284 CM[5,4] = self.standardization.covar[k2,j2]
+2285 except ValueError:
+2286 pass
+2287 except ValueError:
+2288 pass
+2289 try:
+2290 k2 = self.standardization.var_names.index(f'c2_{pf(session)}')
+2291 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]]
+2292 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2]
+2293 except ValueError:
+2294 pass
2295
-2296 elif self.standardization_method == 'indep_sessions':
-2297 pass # Not implemented yet
+2296 self.sessions[session]['CM'] = CM
+2297
+2298 elif self.standardization_method == 'indep_sessions':
+2299 pass # Not implemented yet
4. API Documentation
2300 @make_verbal
-2301 def repeatabilities(self):
-2302 '''
-2303 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
-2304 (for all samples, for anchors, and for unknowns).
-2305 '''
-2306 self.msg('Computing reproducibilities for all sessions')
-2307
-2308 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
-2309 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
-2310 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
-2311 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
-2312 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
+ 2302 @make_verbal
+2303 def repeatabilities(self):
+2304 '''
+2305 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x
+2306 (for all samples, for anchors, and for unknowns).
+2307 '''
+2308 self.msg('Computing reproducibilities for all sessions')
+2309
+2310 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors')
+2311 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors')
+2312 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors')
+2313 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns')
+2314 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
4. API Documentation
2315 @make_verbal
-2316 def consolidate(self, tables = True, plots = True):
-2317 '''
-2318 Collect information about samples, sessions and repeatabilities.
-2319 '''
-2320 self.consolidate_samples()
-2321 self.consolidate_sessions()
-2322 self.repeatabilities()
-2323
-2324 if tables:
-2325 self.summary()
-2326 self.table_of_sessions()
-2327 self.table_of_analyses()
-2328 self.table_of_samples()
-2329
-2330 if plots:
-2331 self.plot_sessions()
+ 2317 @make_verbal
+2318 def consolidate(self, tables = True, plots = True):
+2319 '''
+2320 Collect information about samples, sessions and repeatabilities.
+2321 '''
+2322 self.consolidate_samples()
+2323 self.consolidate_sessions()
+2324 self.repeatabilities()
+2325
+2326 if tables:
+2327 self.summary()
+2328 self.table_of_sessions()
+2329 self.table_of_analyses()
+2330 self.table_of_samples()
+2331
+2332 if plots:
+2333 self.plot_sessions()
4. API Documentation
2334 @make_verbal
-2335 def rmswd(self,
-2336 samples = 'all samples',
-2337 sessions = 'all sessions',
-2338 ):
-2339 '''
-2340 Compute the χ2, root mean squared weighted deviation
-2341 (i.e. reduced χ2), and corresponding degrees of freedom of the
-2342 Δ4x values for samples in `samples` and sessions in `sessions`.
-2343
-2344 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
-2345 '''
-2346 if samples == 'all samples':
-2347 mysamples = [k for k in self.samples]
-2348 elif samples == 'anchors':
-2349 mysamples = [k for k in self.anchors]
-2350 elif samples == 'unknowns':
-2351 mysamples = [k for k in self.unknowns]
-2352 else:
-2353 mysamples = samples
-2354
-2355 if sessions == 'all sessions':
-2356 sessions = [k for k in self.sessions]
-2357
-2358 chisq, Nf = 0, 0
-2359 for sample in mysamples :
-2360 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2361 if len(G) > 1 :
-2362 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
-2363 Nf += (len(G) - 1)
-2364 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
-2365 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2366 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
-2367 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
+ 2336 @make_verbal
+2337 def rmswd(self,
+2338 samples = 'all samples',
+2339 sessions = 'all sessions',
+2340 ):
+2341 '''
+2342 Compute the χ2, root mean squared weighted deviation
+2343 (i.e. reduced χ2), and corresponding degrees of freedom of the
+2344 Δ4x values for samples in `samples` and sessions in `sessions`.
+2345
+2346 Only used in `D4xdata.standardize()` with `method='indep_sessions'`.
+2347 '''
+2348 if samples == 'all samples':
+2349 mysamples = [k for k in self.samples]
+2350 elif samples == 'anchors':
+2351 mysamples = [k for k in self.anchors]
+2352 elif samples == 'unknowns':
+2353 mysamples = [k for k in self.unknowns]
+2354 else:
+2355 mysamples = samples
+2356
+2357 if sessions == 'all sessions':
+2358 sessions = [k for k in self.sessions]
+2359
+2360 chisq, Nf = 0, 0
+2361 for sample in mysamples :
+2362 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2363 if len(G) > 1 :
+2364 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G])
+2365 Nf += (len(G) - 1)
+2366 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G])
+2367 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2368 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.')
+2369 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
4. API Documentation
2370 @make_verbal
-2371 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
-2372 '''
-2373 Compute the repeatability of `[r[key] for r in self]`
-2374 '''
-2375
-2376 if samples == 'all samples':
-2377 mysamples = [k for k in self.samples]
-2378 elif samples == 'anchors':
-2379 mysamples = [k for k in self.anchors]
-2380 elif samples == 'unknowns':
-2381 mysamples = [k for k in self.unknowns]
-2382 else:
-2383 mysamples = samples
-2384
-2385 if sessions == 'all sessions':
-2386 sessions = [k for k in self.sessions]
-2387
-2388 if key in ['D47', 'D48']:
-2389 # Full disclosure: the definition of Nf is tricky/debatable
-2390 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
-2391 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
-2392 Nf = len(G)
-2393# print(f'len(G) = {Nf}')
-2394 Nf -= len([s for s in mysamples if s in self.unknowns])
-2395# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
-2396 for session in sessions:
-2397 Np = len([
-2398 _ for _ in self.standardization.params
-2399 if (
-2400 self.standardization.params[_].expr is not None
-2401 and (
-2402 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
-2403 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
-2404 )
-2405 )
-2406 ])
-2407# print(f'session {session}: {Np} parameters to consider')
-2408 Na = len({
-2409 r['Sample'] for r in self.sessions[session]['data']
-2410 if r['Sample'] in self.anchors and r['Sample'] in mysamples
-2411 })
-2412# print(f'session {session}: {Na} different anchors in that session')
-2413 Nf -= min(Np, Na)
-2414# print(f'Nf = {Nf}')
-2415
-2416# for sample in mysamples :
-2417# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2418# if len(X) > 1 :
-2419# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
-2420# if sample in self.unknowns:
-2421# Nf += len(X) - 1
-2422# else:
-2423# Nf += len(X)
-2424# if samples in ['anchors', 'all samples']:
-2425# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
-2426 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2427
-2428 else: # if key not in ['D47', 'D48']
-2429 chisq, Nf = 0, 0
-2430 for sample in mysamples :
-2431 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
-2432 if len(X) > 1 :
-2433 Nf += len(X) - 1
-2434 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
-2435 r = (chisq / Nf)**.5 if Nf > 0 else 0
-2436
-2437 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
-2438 return r
+ 2372 @make_verbal
+2373 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'):
+2374 '''
+2375 Compute the repeatability of `[r[key] for r in self]`
+2376 '''
+2377
+2378 if samples == 'all samples':
+2379 mysamples = [k for k in self.samples]
+2380 elif samples == 'anchors':
+2381 mysamples = [k for k in self.anchors]
+2382 elif samples == 'unknowns':
+2383 mysamples = [k for k in self.unknowns]
+2384 else:
+2385 mysamples = samples
+2386
+2387 if sessions == 'all sessions':
+2388 sessions = [k for k in self.sessions]
+2389
+2390 if key in ['D47', 'D48']:
+2391 # Full disclosure: the definition of Nf is tricky/debatable
+2392 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions]
+2393 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum()
+2394 Nf = len(G)
+2395# print(f'len(G) = {Nf}')
+2396 Nf -= len([s for s in mysamples if s in self.unknowns])
+2397# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider')
+2398 for session in sessions:
+2399 Np = len([
+2400 _ for _ in self.standardization.params
+2401 if (
+2402 self.standardization.params[_].expr is not None
+2403 and (
+2404 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session))
+2405 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session))
+2406 )
+2407 )
+2408 ])
+2409# print(f'session {session}: {Np} parameters to consider')
+2410 Na = len({
+2411 r['Sample'] for r in self.sessions[session]['data']
+2412 if r['Sample'] in self.anchors and r['Sample'] in mysamples
+2413 })
+2414# print(f'session {session}: {Na} different anchors in that session')
+2415 Nf -= min(Np, Na)
+2416# print(f'Nf = {Nf}')
+2417
+2418# for sample in mysamples :
+2419# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2420# if len(X) > 1 :
+2421# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ])
+2422# if sample in self.unknowns:
+2423# Nf += len(X) - 1
+2424# else:
+2425# Nf += len(X)
+2426# if samples in ['anchors', 'all samples']:
+2427# Nf -= sum([self.sessions[s]['Np'] for s in sessions])
+2428 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2429
+2430 else: # if key not in ['D47', 'D48']
+2431 chisq, Nf = 0, 0
+2432 for sample in mysamples :
+2433 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ]
+2434 if len(X) > 1 :
+2435 Nf += len(X) - 1
+2436 chisq += np.sum([ (x-np.mean(X))**2 for x in X ])
+2437 r = (chisq / Nf)**.5 if Nf > 0 else 0
+2438
+2439 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.')
+2440 return r
4. API Documentation
2440 def sample_average(self, samples, weights = 'equal', normalize = True):
-2441 '''
-2442 Weighted average Δ4x value of a group of samples, accounting for covariance.
-2443
-2444 Returns the weighed average Δ4x value and associated SE
-2445 of a group of samples. Weights are equal by default. If `normalize` is
-2446 true, `weights` will be rescaled so that their sum equals 1.
-2447
-2448 **Examples**
+ 2442 def sample_average(self, samples, weights = 'equal', normalize = True):
+2443 '''
+2444 Weighted average Δ4x value of a group of samples, accounting for covariance.
+2445
+2446 Returns the weighed average Δ4x value and associated SE
+2447 of a group of samples. Weights are equal by default. If `normalize` is
+2448 true, `weights` will be rescaled so that their sum equals 1.
2449
-2450 ```python
-2451 self.sample_average(['X','Y'], [1, 2])
-2452 ```
-2453
-2454 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
-2455 where Δ4x(X) and Δ4x(Y) are the average Δ4x
-2456 values of samples X and Y, respectively.
-2457
-2458 ```python
-2459 self.sample_average(['X','Y'], [1, -1], normalize = False)
-2460 ```
-2461
-2462 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
-2463 '''
-2464 if weights == 'equal':
-2465 weights = [1/len(samples)] * len(samples)
-2466
-2467 if normalize:
-2468 s = sum(weights)
-2469 if s:
-2470 weights = [w/s for w in weights]
-2471
-2472 try:
-2473# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
-2474# C = self.standardization.covar[indices,:][:,indices]
-2475 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
-2476 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
-2477 return correlated_sum(X, C, weights)
-2478 except ValueError:
-2479 return (0., 0.)
+2450 **Examples**
+2451
+2452 ```python
+2453 self.sample_average(['X','Y'], [1, 2])
+2454 ```
+2455
+2456 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3,
+2457 where Δ4x(X) and Δ4x(Y) are the average Δ4x
+2458 values of samples X and Y, respectively.
+2459
+2460 ```python
+2461 self.sample_average(['X','Y'], [1, -1], normalize = False)
+2462 ```
+2463
+2464 returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
+2465 '''
+2466 if weights == 'equal':
+2467 weights = [1/len(samples)] * len(samples)
+2468
+2469 if normalize:
+2470 s = sum(weights)
+2471 if s:
+2472 weights = [w/s for w in weights]
+2473
+2474 try:
+2475# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples]
+2476# C = self.standardization.covar[indices,:][:,indices]
+2477 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples])
+2478 X = [self.samples[sample][f'D{self._4x}'] for sample in samples]
+2479 return correlated_sum(X, C, weights)
+2480 except ValueError:
+2481 return (0., 0.)
4. API Documentation
2482 def sample_D4x_covar(self, sample1, sample2 = None):
-2483 '''
-2484 Covariance between Δ4x values of samples
-2485
-2486 Returns the error covariance between the average Δ4x values of two
-2487 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
-2488 returns the Δ4x variance for that sample.
-2489 '''
-2490 if sample2 is None:
-2491 sample2 = sample1
-2492 if self.standardization_method == 'pooled':
-2493 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
-2494 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
-2495 return self.standardization.covar[i, j]
-2496 elif self.standardization_method == 'indep_sessions':
-2497 if sample1 == sample2:
-2498 return self.samples[sample1][f'SE_D{self._4x}']**2
-2499 else:
-2500 c = 0
-2501 for session in self.sessions:
-2502 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
-2503 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
-2504 if sdata1 and sdata2:
-2505 a = self.sessions[session]['a']
-2506 # !! TODO: CM below does not account for temporal changes in standardization parameters
-2507 CM = self.sessions[session]['CM'][:3,:3]
-2508 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
-2509 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
-2510 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
-2511 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
-2512 c += (
-2513 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
-2514 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
-2515 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
-2516 @ CM
-2517 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
-2518 ) / a**2
-2519 return float(c)
+ 2484 def sample_D4x_covar(self, sample1, sample2 = None):
+2485 '''
+2486 Covariance between Δ4x values of samples
+2487
+2488 Returns the error covariance between the average Δ4x values of two
+2489 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`),
+2490 returns the Δ4x variance for that sample.
+2491 '''
+2492 if sample2 is None:
+2493 sample2 = sample1
+2494 if self.standardization_method == 'pooled':
+2495 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}')
+2496 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}')
+2497 return self.standardization.covar[i, j]
+2498 elif self.standardization_method == 'indep_sessions':
+2499 if sample1 == sample2:
+2500 return self.samples[sample1][f'SE_D{self._4x}']**2
+2501 else:
+2502 c = 0
+2503 for session in self.sessions:
+2504 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1]
+2505 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2]
+2506 if sdata1 and sdata2:
+2507 a = self.sessions[session]['a']
+2508 # !! TODO: CM below does not account for temporal changes in standardization parameters
+2509 CM = self.sessions[session]['CM'][:3,:3]
+2510 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1])
+2511 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1])
+2512 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2])
+2513 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2])
+2514 c += (
+2515 self.unknowns[sample1][f'session_D{self._4x}'][session][2]
+2516 * self.unknowns[sample2][f'session_D{self._4x}'][session][2]
+2517 * np.array([[avg_D4x_1, avg_d4x_1, 1]])
+2518 @ CM
+2519 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T
+2520 ) / a**2
+2521 return float(c)
4. API Documentation
2521 def sample_D4x_correl(self, sample1, sample2 = None):
-2522 '''
-2523 Correlation between Δ4x errors of samples
-2524
-2525 Returns the error correlation between the average Δ4x values of two samples.
-2526 '''
-2527 if sample2 is None or sample2 == sample1:
-2528 return 1.
-2529 return (
-2530 self.sample_D4x_covar(sample1, sample2)
-2531 / self.unknowns[sample1][f'SE_D{self._4x}']
-2532 / self.unknowns[sample2][f'SE_D{self._4x}']
-2533 )
+ 2523 def sample_D4x_correl(self, sample1, sample2 = None):
+2524 '''
+2525 Correlation between Δ4x errors of samples
+2526
+2527 Returns the error correlation between the average Δ4x values of two samples.
+2528 '''
+2529 if sample2 is None or sample2 == sample1:
+2530 return 1.
+2531 return (
+2532 self.sample_D4x_covar(sample1, sample2)
+2533 / self.unknowns[sample1][f'SE_D{self._4x}']
+2534 / self.unknowns[sample2][f'SE_D{self._4x}']
+2535 )
4. API Documentation
2535 def plot_single_session(self,
-2536 session,
-2537 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
-2538 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
-2539 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
-2540 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
-2541 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
-2542 xylimits = 'free', # | 'constant'
-2543 x_label = None,
-2544 y_label = None,
-2545 error_contour_interval = 'auto',
-2546 fig = 'new',
-2547 ):
-2548 '''
-2549 Generate plot for a single session
-2550 '''
-2551 if x_label is None:
-2552 x_label = f'δ$_{{{self._4x}}}$ (‰)'
-2553 if y_label is None:
-2554 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
-2555
-2556 out = _SessionPlot()
-2557 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
-2558 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
-2559
-2560 if fig == 'new':
-2561 out.fig = ppl.figure(figsize = (6,6))
-2562 ppl.subplots_adjust(.1,.1,.9,.9)
-2563
-2564 out.anchor_analyses, = ppl.plot(
-2565 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2566 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-2567 **kw_plot_anchors)
-2568 out.unknown_analyses, = ppl.plot(
-2569 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2570 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-2571 **kw_plot_unknowns)
-2572 out.anchor_avg = ppl.plot(
-2573 np.array([ np.array([
-2574 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2575 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2576 ]) for sample in anchors]).T,
-2577 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
-2578 **kw_plot_anchor_avg)
-2579 out.unknown_avg = ppl.plot(
-2580 np.array([ np.array([
-2581 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-2582 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-2583 ]) for sample in unknowns]).T,
-2584 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
-2585 **kw_plot_unknown_avg)
-2586 if xylimits == 'constant':
-2587 x = [r[f'd{self._4x}'] for r in self]
-2588 y = [r[f'D{self._4x}'] for r in self]
-2589 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
-2590 w, h = x2-x1, y2-y1
-2591 x1 -= w/20
-2592 x2 += w/20
-2593 y1 -= h/20
-2594 y2 += h/20
-2595 ppl.axis([x1, x2, y1, y2])
-2596 elif xylimits == 'free':
-2597 x1, x2, y1, y2 = ppl.axis()
-2598 else:
-2599 x1, x2, y1, y2 = ppl.axis(xylimits)
-2600
-2601 if error_contour_interval != 'none':
-2602 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
-2603 XI,YI = np.meshgrid(xi, yi)
-2604 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
-2605 if error_contour_interval == 'auto':
-2606 rng = np.max(SI) - np.min(SI)
-2607 if rng <= 0.01:
-2608 cinterval = 0.001
-2609 elif rng <= 0.03:
-2610 cinterval = 0.004
-2611 elif rng <= 0.1:
-2612 cinterval = 0.01
-2613 elif rng <= 0.3:
-2614 cinterval = 0.03
-2615 elif rng <= 1.:
-2616 cinterval = 0.1
-2617 else:
-2618 cinterval = 0.5
-2619 else:
-2620 cinterval = error_contour_interval
-2621
-2622 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
-2623 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
-2624 out.clabel = ppl.clabel(out.contour)
-2625
-2626 ppl.xlabel(x_label)
-2627 ppl.ylabel(y_label)
-2628 ppl.title(session, weight = 'bold')
-2629 ppl.grid(alpha = .2)
-2630 out.ax = ppl.gca()
-2631
-2632 return out
+ 2537 def plot_single_session(self,
+2538 session,
+2539 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4),
+2540 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4),
+2541 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75),
+2542 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75),
+2543 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75),
+2544 xylimits = 'free', # | 'constant'
+2545 x_label = None,
+2546 y_label = None,
+2547 error_contour_interval = 'auto',
+2548 fig = 'new',
+2549 ):
+2550 '''
+2551 Generate plot for a single session
+2552 '''
+2553 if x_label is None:
+2554 x_label = f'δ$_{{{self._4x}}}$ (‰)'
+2555 if y_label is None:
+2556 y_label = f'Δ$_{{{self._4x}}}$ (‰)'
+2557
+2558 out = _SessionPlot()
+2559 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
+2560 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
+2561
+2562 if fig == 'new':
+2563 out.fig = ppl.figure(figsize = (6,6))
+2564 ppl.subplots_adjust(.1,.1,.9,.9)
+2565
+2566 out.anchor_analyses, = ppl.plot(
+2567 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2568 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+2569 **kw_plot_anchors)
+2570 out.unknown_analyses, = ppl.plot(
+2571 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2572 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+2573 **kw_plot_unknowns)
+2574 out.anchor_avg = ppl.plot(
+2575 np.array([ np.array([
+2576 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2577 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2578 ]) for sample in anchors]).T,
+2579 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
+2580 **kw_plot_anchor_avg)
+2581 out.unknown_avg = ppl.plot(
+2582 np.array([ np.array([
+2583 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+2584 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+2585 ]) for sample in unknowns]).T,
+2586 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
+2587 **kw_plot_unknown_avg)
+2588 if xylimits == 'constant':
+2589 x = [r[f'd{self._4x}'] for r in self]
+2590 y = [r[f'D{self._4x}'] for r in self]
+2591 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
+2592 w, h = x2-x1, y2-y1
+2593 x1 -= w/20
+2594 x2 += w/20
+2595 y1 -= h/20
+2596 y2 += h/20
+2597 ppl.axis([x1, x2, y1, y2])
+2598 elif xylimits == 'free':
+2599 x1, x2, y1, y2 = ppl.axis()
+2600 else:
+2601 x1, x2, y1, y2 = ppl.axis(xylimits)
+2602
+2603 if error_contour_interval != 'none':
+2604 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2)
+2605 XI,YI = np.meshgrid(xi, yi)
+2606 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi])
+2607 if error_contour_interval == 'auto':
+2608 rng = np.max(SI) - np.min(SI)
+2609 if rng <= 0.01:
+2610 cinterval = 0.001
+2611 elif rng <= 0.03:
+2612 cinterval = 0.004
+2613 elif rng <= 0.1:
+2614 cinterval = 0.01
+2615 elif rng <= 0.3:
+2616 cinterval = 0.03
+2617 elif rng <= 1.:
+2618 cinterval = 0.1
+2619 else:
+2620 cinterval = 0.5
+2621 else:
+2622 cinterval = error_contour_interval
+2623
+2624 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
+2625 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
+2626 out.clabel = ppl.clabel(out.contour)
+2627
+2628 ppl.xlabel(x_label)
+2629 ppl.ylabel(y_label)
+2630 ppl.title(session, weight = 'bold')
+2631 ppl.grid(alpha = .2)
+2632 out.ax = ppl.gca()
+2633
+2634 return out
4. API Documentation
2634 def plot_residuals(
-2635 self,
-2636 kde = False,
-2637 hist = False,
-2638 binwidth = 2/3,
-2639 dir = 'output',
-2640 filename = None,
-2641 highlight = [],
-2642 colors = None,
-2643 figsize = None,
-2644 dpi = 100,
-2645 yspan = None,
-2646 ):
-2647 '''
-2648 Plot residuals of each analysis as a function of time (actually, as a function of
-2649 the order of analyses in the `D4xdata` object)
-2650
-2651 + `kde`: whether to add a kernel density estimate of residuals
-2652 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
-2653 + `histbins`: specify bin edges for the histogram
-2654 + `dir`: the directory in which to save the plot
-2655 + `highlight`: a list of samples to highlight
-2656 + `colors`: a dict of `{<sample>: <color>}` for all samples
-2657 + `figsize`: (width, height) of figure
-2658 + `dpi`: resolution for PNG output
-2659 + `yspan`: factor controlling the range of y values shown in plot
-2660 (by default: `yspan = 1.5 if kde else 1.0`)
-2661 '''
-2662
-2663 from matplotlib import ticker
-2664
-2665 if yspan is None:
-2666 if kde:
-2667 yspan = 1.5
-2668 else:
-2669 yspan = 1.0
-2670
-2671 # Layout
-2672 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
-2673 if hist or kde:
-2674 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
-2675 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
-2676 else:
-2677 ppl.subplots_adjust(.08,.05,.78,.8)
-2678 ax1 = ppl.subplot(111)
-2679
-2680 # Colors
-2681 N = len(self.anchors)
-2682 if colors is None:
-2683 if len(highlight) > 0:
-2684 Nh = len(highlight)
-2685 if Nh == 1:
-2686 colors = {highlight[0]: (0,0,0)}
-2687 elif Nh == 3:
-2688 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2689 elif Nh == 4:
-2690 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2691 else:
-2692 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
-2693 else:
-2694 if N == 3:
-2695 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
-2696 elif N == 4:
-2697 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
-2698 else:
-2699 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
-2700
-2701 ppl.sca(ax1)
-2702
-2703 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
-2704
-2705 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+ 2636 def plot_residuals(
+2637 self,
+2638 kde = False,
+2639 hist = False,
+2640 binwidth = 2/3,
+2641 dir = 'output',
+2642 filename = None,
+2643 highlight = [],
+2644 colors = None,
+2645 figsize = None,
+2646 dpi = 100,
+2647 yspan = None,
+2648 ):
+2649 '''
+2650 Plot residuals of each analysis as a function of time (actually, as a function of
+2651 the order of analyses in the `D4xdata` object)
+2652
+2653 + `kde`: whether to add a kernel density estimate of residuals
+2654 + `hist`: whether to add a histogram of residuals (incompatible with `kde`)
+2655 + `histbins`: specify bin edges for the histogram
+2656 + `dir`: the directory in which to save the plot
+2657 + `highlight`: a list of samples to highlight
+2658 + `colors`: a dict of `{<sample>: <color>}` for all samples
+2659 + `figsize`: (width, height) of figure
+2660 + `dpi`: resolution for PNG output
+2661 + `yspan`: factor controlling the range of y values shown in plot
+2662 (by default: `yspan = 1.5 if kde else 1.0`)
+2663 '''
+2664
+2665 from matplotlib import ticker
+2666
+2667 if yspan is None:
+2668 if kde:
+2669 yspan = 1.5
+2670 else:
+2671 yspan = 1.0
+2672
+2673 # Layout
+2674 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize)
+2675 if hist or kde:
+2676 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72)
+2677 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15)
+2678 else:
+2679 ppl.subplots_adjust(.08,.05,.78,.8)
+2680 ax1 = ppl.subplot(111)
+2681
+2682 # Colors
+2683 N = len(self.anchors)
+2684 if colors is None:
+2685 if len(highlight) > 0:
+2686 Nh = len(highlight)
+2687 if Nh == 1:
+2688 colors = {highlight[0]: (0,0,0)}
+2689 elif Nh == 3:
+2690 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2691 elif Nh == 4:
+2692 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2693 else:
+2694 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)}
+2695 else:
+2696 if N == 3:
+2697 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])}
+2698 elif N == 4:
+2699 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])}
+2700 else:
+2701 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)}
+2702
+2703 ppl.sca(ax1)
+2704
+2705 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75)
2706
-2707 session = self[0]['Session']
-2708 x1 = 0
-2709# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
-2710 x_sessions = {}
-2711 one_or_more_singlets = False
-2712 one_or_more_multiplets = False
-2713 multiplets = set()
-2714 for k,r in enumerate(self):
-2715 if r['Session'] != session:
-2716 x2 = k-1
-2717 x_sessions[session] = (x1+x2)/2
-2718 ppl.axvline(k - 0.5, color = 'k', lw = .5)
-2719 session = r['Session']
-2720 x1 = k
-2721 singlet = len(self.samples[r['Sample']]['data']) == 1
-2722 if not singlet:
-2723 multiplets.add(r['Sample'])
-2724 if r['Sample'] in self.unknowns:
-2725 if singlet:
-2726 one_or_more_singlets = True
-2727 else:
-2728 one_or_more_multiplets = True
-2729 kw = dict(
-2730 marker = 'x' if singlet else '+',
-2731 ms = 4 if singlet else 5,
-2732 ls = 'None',
-2733 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
-2734 mew = 1,
-2735 alpha = 0.2 if singlet else 1,
-2736 )
-2737 if highlight and r['Sample'] not in highlight:
-2738 kw['alpha'] = 0.2
-2739 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
-2740 x2 = k
-2741 x_sessions[session] = (x1+x2)/2
-2742
-2743 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
-2744 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
-2745 if not (hist or kde):
-2746 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2747 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
-2748
-2749 xmin, xmax, ymin, ymax = ppl.axis()
-2750 if yspan != 1:
-2751 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
-2752 for s in x_sessions:
-2753 ppl.text(
-2754 x_sessions[s],
-2755 ymax +1,
-2756 s,
-2757 va = 'bottom',
-2758 **(
-2759 dict(ha = 'center')
-2760 if len(self.sessions[s]['data']) > (0.15 * len(self))
-2761 else dict(ha = 'left', rotation = 45)
-2762 )
-2763 )
-2764
-2765 if hist or kde:
-2766 ppl.sca(ax2)
-2767
-2768 for s in colors:
-2769 kw['marker'] = '+'
-2770 kw['ms'] = 5
-2771 kw['mec'] = colors[s]
-2772 kw['label'] = s
-2773 kw['alpha'] = 1
-2774 ppl.plot([], [], **kw)
-2775
-2776 kw['mec'] = (0,0,0)
+2707 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$'))
+2708
+2709 session = self[0]['Session']
+2710 x1 = 0
+2711# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self])
+2712 x_sessions = {}
+2713 one_or_more_singlets = False
+2714 one_or_more_multiplets = False
+2715 multiplets = set()
+2716 for k,r in enumerate(self):
+2717 if r['Session'] != session:
+2718 x2 = k-1
+2719 x_sessions[session] = (x1+x2)/2
+2720 ppl.axvline(k - 0.5, color = 'k', lw = .5)
+2721 session = r['Session']
+2722 x1 = k
+2723 singlet = len(self.samples[r['Sample']]['data']) == 1
+2724 if not singlet:
+2725 multiplets.add(r['Sample'])
+2726 if r['Sample'] in self.unknowns:
+2727 if singlet:
+2728 one_or_more_singlets = True
+2729 else:
+2730 one_or_more_multiplets = True
+2731 kw = dict(
+2732 marker = 'x' if singlet else '+',
+2733 ms = 4 if singlet else 5,
+2734 ls = 'None',
+2735 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0),
+2736 mew = 1,
+2737 alpha = 0.2 if singlet else 1,
+2738 )
+2739 if highlight and r['Sample'] not in highlight:
+2740 kw['alpha'] = 0.2
+2741 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw)
+2742 x2 = k
+2743 x_sessions[session] = (x1+x2)/2
+2744
+2745 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1)
+2746 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1)
+2747 if not (hist or kde):
+2748 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2749 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center')
+2750
+2751 xmin, xmax, ymin, ymax = ppl.axis()
+2752 if yspan != 1:
+2753 ymin, ymax = (ymin + ymax)/2 - yspan * (ymax - ymin)/2, (ymin + ymax)/2 + yspan * (ymax - ymin)/2
+2754 for s in x_sessions:
+2755 ppl.text(
+2756 x_sessions[s],
+2757 ymax +1,
+2758 s,
+2759 va = 'bottom',
+2760 **(
+2761 dict(ha = 'center')
+2762 if len(self.sessions[s]['data']) > (0.15 * len(self))
+2763 else dict(ha = 'left', rotation = 45)
+2764 )
+2765 )
+2766
+2767 if hist or kde:
+2768 ppl.sca(ax2)
+2769
+2770 for s in colors:
+2771 kw['marker'] = '+'
+2772 kw['ms'] = 5
+2773 kw['mec'] = colors[s]
+2774 kw['label'] = s
+2775 kw['alpha'] = 1
+2776 ppl.plot([], [], **kw)
2777
-2778 if one_or_more_singlets:
-2779 kw['marker'] = 'x'
-2780 kw['ms'] = 4
-2781 kw['alpha'] = .2
-2782 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
-2783 ppl.plot([], [], **kw)
-2784
-2785 if one_or_more_multiplets:
-2786 kw['marker'] = '+'
-2787 kw['ms'] = 4
-2788 kw['alpha'] = 1
-2789 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
-2790 ppl.plot([], [], **kw)
-2791
-2792 if hist or kde:
-2793 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
-2794 else:
-2795 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
-2796 leg.set_zorder(-1000)
-2797
-2798 ppl.sca(ax1)
+2778 kw['mec'] = (0,0,0)
+2779
+2780 if one_or_more_singlets:
+2781 kw['marker'] = 'x'
+2782 kw['ms'] = 4
+2783 kw['alpha'] = .2
+2784 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other'
+2785 ppl.plot([], [], **kw)
+2786
+2787 if one_or_more_multiplets:
+2788 kw['marker'] = '+'
+2789 kw['ms'] = 4
+2790 kw['alpha'] = 1
+2791 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other'
+2792 ppl.plot([], [], **kw)
+2793
+2794 if hist or kde:
+2795 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9)
+2796 else:
+2797 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5)
+2798 leg.set_zorder(-1000)
2799
-2800 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
-2801 ppl.xticks([])
-2802 ppl.axis([-1, len(self), None, None])
-2803
-2804 if hist or kde:
-2805 ppl.sca(ax2)
-2806 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
-2807
-2808 if kde:
-2809 from scipy.stats import gaussian_kde
-2810 yi = np.linspace(ymin, ymax, 201)
-2811 xi = gaussian_kde(X).evaluate(yi)
-2812 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
-2813# ppl.plot(xi, yi, 'k-', lw = 1)
-2814 elif hist:
-2815 ppl.hist(
-2816 X,
-2817 orientation = 'horizontal',
-2818 histtype = 'stepfilled',
-2819 ec = [.4]*3,
-2820 fc = [.25]*3,
-2821 alpha = .25,
-2822 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
-2823 )
-2824 ppl.text(0, 0,
-2825 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
-2826 size = 7.5,
-2827 alpha = 1,
-2828 va = 'center',
-2829 ha = 'left',
-2830 )
-2831
-2832 ppl.axis([0, None, ymin, ymax])
-2833 ppl.xticks([])
-2834 ppl.yticks([])
-2835# ax2.spines['left'].set_visible(False)
-2836 ax2.spines['right'].set_visible(False)
-2837 ax2.spines['top'].set_visible(False)
-2838 ax2.spines['bottom'].set_visible(False)
-2839
-2840 ax1.axis([None, None, ymin, ymax])
+2800 ppl.sca(ax1)
+2801
+2802 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)')
+2803 ppl.xticks([])
+2804 ppl.axis([-1, len(self), None, None])
+2805
+2806 if hist or kde:
+2807 ppl.sca(ax2)
+2808 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors])
+2809
+2810 if kde:
+2811 from scipy.stats import gaussian_kde
+2812 yi = np.linspace(ymin, ymax, 201)
+2813 xi = gaussian_kde(X).evaluate(yi)
+2814 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1))
+2815# ppl.plot(xi, yi, 'k-', lw = 1)
+2816 elif hist:
+2817 ppl.hist(
+2818 X,
+2819 orientation = 'horizontal',
+2820 histtype = 'stepfilled',
+2821 ec = [.4]*3,
+2822 fc = [.25]*3,
+2823 alpha = .25,
+2824 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)),
+2825 )
+2826 ppl.text(0, 0,
+2827 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm",
+2828 size = 7.5,
+2829 alpha = 1,
+2830 va = 'center',
+2831 ha = 'left',
+2832 )
+2833
+2834 ppl.axis([0, None, ymin, ymax])
+2835 ppl.xticks([])
+2836 ppl.yticks([])
+2837# ax2.spines['left'].set_visible(False)
+2838 ax2.spines['right'].set_visible(False)
+2839 ax2.spines['top'].set_visible(False)
+2840 ax2.spines['bottom'].set_visible(False)
2841
-2842 if not os.path.exists(dir):
-2843 os.makedirs(dir)
-2844 if filename is None:
-2845 return fig
-2846 elif filename == '':
-2847 filename = f'D{self._4x}_residuals.pdf'
-2848 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2849 ppl.close(fig)
+2842 ax1.axis([None, None, ymin, ymax])
+2843
+2844 if not os.path.exists(dir):
+2845 os.makedirs(dir)
+2846 if filename is None:
+2847 return fig
+2848 elif filename == '':
+2849 filename = f'D{self._4x}_residuals.pdf'
+2850 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2851 ppl.close(fig)
4. API Documentation
2852 def simulate(self, *args, **kwargs):
-2853 '''
-2854 Legacy function with warning message pointing to `virtual_data()`
-2855 '''
-2856 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
+ 2854 def simulate(self, *args, **kwargs):
+2855 '''
+2856 Legacy function with warning message pointing to `virtual_data()`
+2857 '''
+2858 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
4. API Documentation
2858 def plot_distribution_of_analyses(
-2859 self,
-2860 dir = 'output',
-2861 filename = None,
-2862 vs_time = False,
-2863 figsize = (6,4),
-2864 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
-2865 output = None,
-2866 dpi = 100,
-2867 ):
-2868 '''
-2869 Plot temporal distribution of all analyses in the data set.
-2870
-2871 **Parameters**
-2872
-2873 + `dir`: the directory in which to save the plot
-2874 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
-2875 + `dpi`: resolution for PNG output
-2876 + `figsize`: (width, height) of figure
+ 2860 def plot_distribution_of_analyses(
+2861 self,
+2862 dir = 'output',
+2863 filename = None,
+2864 vs_time = False,
+2865 figsize = (6,4),
+2866 subplots_adjust = (0.02, 0.13, 0.85, 0.8),
+2867 output = None,
+2868 dpi = 100,
+2869 ):
+2870 '''
+2871 Plot temporal distribution of all analyses in the data set.
+2872
+2873 **Parameters**
+2874
+2875 + `dir`: the directory in which to save the plot
+2876 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially.
2877 + `dpi`: resolution for PNG output
-2878 '''
-2879
-2880 asamples = [s for s in self.anchors]
-2881 usamples = [s for s in self.unknowns]
-2882 if output is None or output == 'fig':
-2883 fig = ppl.figure(figsize = figsize)
-2884 ppl.subplots_adjust(*subplots_adjust)
-2885 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2886 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
-2887 Xmax += (Xmax-Xmin)/40
-2888 Xmin -= (Xmax-Xmin)/41
-2889 for k, s in enumerate(asamples + usamples):
-2890 if vs_time:
-2891 X = [r['TimeTag'] for r in self if r['Sample'] == s]
-2892 else:
-2893 X = [x for x,r in enumerate(self) if r['Sample'] == s]
-2894 Y = [-k for x in X]
-2895 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
-2896 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
-2897 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
-2898 ppl.axis([Xmin, Xmax, -k-1, 1])
-2899 ppl.xlabel('\ntime')
-2900 ppl.gca().annotate('',
-2901 xy = (0.6, -0.02),
-2902 xycoords = 'axes fraction',
-2903 xytext = (.4, -0.02),
-2904 arrowprops = dict(arrowstyle = "->", color = 'k'),
-2905 )
-2906
-2907
-2908 x2 = -1
-2909 for session in self.sessions:
-2910 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2911 if vs_time:
-2912 ppl.axvline(x1, color = 'k', lw = .75)
-2913 if x2 > -1:
-2914 if not vs_time:
-2915 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
-2916 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
-2917# from xlrd import xldate_as_datetime
-2918# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
-2919 if vs_time:
-2920 ppl.axvline(x2, color = 'k', lw = .75)
-2921 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
-2922 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
-2923
-2924 ppl.xticks([])
-2925 ppl.yticks([])
-2926
-2927 if output is None:
-2928 if not os.path.exists(dir):
-2929 os.makedirs(dir)
-2930 if filename == None:
-2931 filename = f'D{self._4x}_distribution_of_analyses.pdf'
-2932 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
-2933 ppl.close(fig)
-2934 elif output == 'ax':
-2935 return ppl.gca()
-2936 elif output == 'fig':
-2937 return fig
+2878 + `figsize`: (width, height) of figure
+2879 + `dpi`: resolution for PNG output
+2880 '''
+2881
+2882 asamples = [s for s in self.anchors]
+2883 usamples = [s for s in self.unknowns]
+2884 if output is None or output == 'fig':
+2885 fig = ppl.figure(figsize = figsize)
+2886 ppl.subplots_adjust(*subplots_adjust)
+2887 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2888 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)])
+2889 Xmax += (Xmax-Xmin)/40
+2890 Xmin -= (Xmax-Xmin)/41
+2891 for k, s in enumerate(asamples + usamples):
+2892 if vs_time:
+2893 X = [r['TimeTag'] for r in self if r['Sample'] == s]
+2894 else:
+2895 X = [x for x,r in enumerate(self) if r['Sample'] == s]
+2896 Y = [-k for x in X]
+2897 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75)
+2898 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25)
+2899 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r')
+2900 ppl.axis([Xmin, Xmax, -k-1, 1])
+2901 ppl.xlabel('\ntime')
+2902 ppl.gca().annotate('',
+2903 xy = (0.6, -0.02),
+2904 xycoords = 'axes fraction',
+2905 xytext = (.4, -0.02),
+2906 arrowprops = dict(arrowstyle = "->", color = 'k'),
+2907 )
+2908
+2909
+2910 x2 = -1
+2911 for session in self.sessions:
+2912 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2913 if vs_time:
+2914 ppl.axvline(x1, color = 'k', lw = .75)
+2915 if x2 > -1:
+2916 if not vs_time:
+2917 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5)
+2918 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session])
+2919# from xlrd import xldate_as_datetime
+2920# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0))
+2921 if vs_time:
+2922 ppl.axvline(x2, color = 'k', lw = .75)
+2923 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15)
+2924 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8)
+2925
+2926 ppl.xticks([])
+2927 ppl.yticks([])
+2928
+2929 if output is None:
+2930 if not os.path.exists(dir):
+2931 os.makedirs(dir)
+2932 if filename == None:
+2933 filename = f'D{self._4x}_distribution_of_analyses.pdf'
+2934 ppl.savefig(f'{dir}/{filename}', dpi = dpi)
+2935 ppl.close(fig)
+2936 elif output == 'ax':
+2937 return ppl.gca()
+2938 elif output == 'fig':
+2939 return fig
4. API Documentation
2940 def plot_bulk_compositions(
-2941 self,
-2942 samples = None,
-2943 dir = 'output/bulk_compositions',
-2944 figsize = (6,6),
-2945 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
-2946 show = False,
-2947 sample_color = (0,.5,1),
-2948 analysis_color = (.7,.7,.7),
-2949 labeldist = 0.3,
-2950 radius = 0.05,
-2951 ):
-2952 '''
-2953 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
-2954
-2955 By default, creates a directory `./output/bulk_compositions` where plots for
-2956 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
-2957
-2958
-2959 **Parameters**
-2960
-2961 + `samples`: Only these samples are processed (by default: all samples).
-2962 + `dir`: where to save the plots
-2963 + `figsize`: (width, height) of figure
-2964 + `subplots_adjust`: passed to `subplots_adjust()`
-2965 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
-2966 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
-2967 + `sample_color`: color used for replicate markers/labels
-2968 + `analysis_color`: color used for sample markers/labels
-2969 + `labeldist`: distance (in inches) from replicate markers to replicate labels
-2970 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
-2971 '''
-2972
-2973 from matplotlib.patches import Ellipse
+ 2942 def plot_bulk_compositions(
+2943 self,
+2944 samples = None,
+2945 dir = 'output/bulk_compositions',
+2946 figsize = (6,6),
+2947 subplots_adjust = (0.15, 0.12, 0.95, 0.92),
+2948 show = False,
+2949 sample_color = (0,.5,1),
+2950 analysis_color = (.7,.7,.7),
+2951 labeldist = 0.3,
+2952 radius = 0.05,
+2953 ):
+2954 '''
+2955 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses.
+2956
+2957 By default, creates a directory `./output/bulk_compositions` where plots for
+2958 each sample are saved. Another plot named `__all__.pdf` shows all analyses together.
+2959
+2960
+2961 **Parameters**
+2962
+2963 + `samples`: Only these samples are processed (by default: all samples).
+2964 + `dir`: where to save the plots
+2965 + `figsize`: (width, height) of figure
+2966 + `subplots_adjust`: passed to `subplots_adjust()`
+2967 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples,
+2968 allowing for interactive visualization/exploration in (δ13C, δ18O) space.
+2969 + `sample_color`: color used for replicate markers/labels
+2970 + `analysis_color`: color used for sample markers/labels
+2971 + `labeldist`: distance (in inches) from replicate markers to replicate labels
+2972 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`.
+2973 '''
2974
-2975 if samples is None:
-2976 samples = [_ for _ in self.samples]
-2977
-2978 saved = {}
+2975 from matplotlib.patches import Ellipse
+2976
+2977 if samples is None:
+2978 samples = [_ for _ in self.samples]
2979
-2980 for s in samples:
+2980 saved = {}
2981
-2982 fig = ppl.figure(figsize = figsize)
-2983 fig.subplots_adjust(*subplots_adjust)
-2984 ax = ppl.subplot(111)
-2985 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-2986 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-2987 ppl.title(s)
-2988
-2989
-2990 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
-2991 UID = [_['UID'] for _ in self.samples[s]['data']]
-2992 XY0 = XY.mean(0)
-2993
-2994 for xy in XY:
-2995 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
-2996
-2997 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
-2998 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
-2999 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-3000 saved[s] = [XY, XY0]
-3001
-3002 x1, x2, y1, y2 = ppl.axis()
-3003 x0, dx = (x1+x2)/2, (x2-x1)/2
-3004 y0, dy = (y1+y2)/2, (y2-y1)/2
-3005 dx, dy = [max(max(dx, dy), radius)]*2
-3006
-3007 ppl.axis([
-3008 x0 - 1.2*dx,
-3009 x0 + 1.2*dx,
-3010 y0 - 1.2*dy,
-3011 y0 + 1.2*dy,
-3012 ])
-3013
-3014 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
+2982 for s in samples:
+2983
+2984 fig = ppl.figure(figsize = figsize)
+2985 fig.subplots_adjust(*subplots_adjust)
+2986 ax = ppl.subplot(111)
+2987 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+2988 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+2989 ppl.title(s)
+2990
+2991
+2992 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']])
+2993 UID = [_['UID'] for _ in self.samples[s]['data']]
+2994 XY0 = XY.mean(0)
+2995
+2996 for xy in XY:
+2997 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color)
+2998
+2999 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color)
+3000 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color)
+3001 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3002 saved[s] = [XY, XY0]
+3003
+3004 x1, x2, y1, y2 = ppl.axis()
+3005 x0, dx = (x1+x2)/2, (x2-x1)/2
+3006 y0, dy = (y1+y2)/2, (y2-y1)/2
+3007 dx, dy = [max(max(dx, dy), radius)]*2
+3008
+3009 ppl.axis([
+3010 x0 - 1.2*dx,
+3011 x0 + 1.2*dx,
+3012 y0 - 1.2*dy,
+3013 y0 + 1.2*dy,
+3014 ])
3015
-3016 for xy, uid in zip(XY, UID):
+3016 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0))
3017
-3018 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
-3019 vector_in_display_space = xy_in_display_space - XY0_in_display_space
-3020
-3021 if (vector_in_display_space**2).sum() > 0:
+3018 for xy, uid in zip(XY, UID):
+3019
+3020 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy))
+3021 vector_in_display_space = xy_in_display_space - XY0_in_display_space
3022
-3023 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
-3024 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
-3025 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
-3026 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
-3027
-3028 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
+3023 if (vector_in_display_space**2).sum() > 0:
+3024
+3025 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5
+3026 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist
+3027 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space
+3028 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space))
3029
-3030 else:
+3030 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color)
3031
-3032 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3032 else:
3033
-3034 if radius:
-3035 ax.add_artist(Ellipse(
-3036 xy = XY0,
-3037 width = radius*2,
-3038 height = radius*2,
-3039 ls = (0, (2,2)),
-3040 lw = .7,
-3041 ec = analysis_color,
-3042 fc = 'None',
-3043 ))
-3044 ppl.text(
-3045 XY0[0],
-3046 XY0[1]-radius,
-3047 f'\n± {radius*1e3:.0f} ppm',
-3048 color = analysis_color,
-3049 va = 'top',
-3050 ha = 'center',
-3051 linespacing = 0.4,
-3052 size = 8,
-3053 )
-3054
-3055 if not os.path.exists(dir):
-3056 os.makedirs(dir)
-3057 fig.savefig(f'{dir}/{s}.pdf')
-3058 ppl.close(fig)
-3059
-3060 fig = ppl.figure(figsize = figsize)
-3061 fig.subplots_adjust(*subplots_adjust)
-3062 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
-3063 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
-3064
-3065 for s in saved:
-3066 for xy in saved[s][0]:
-3067 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
-3068 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
-3069 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
-3070 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
-3071
-3072 x1, x2, y1, y2 = ppl.axis()
-3073 ppl.axis([
-3074 x1 - (x2-x1)/10,
-3075 x2 + (x2-x1)/10,
-3076 y1 - (y2-y1)/10,
-3077 y2 + (y2-y1)/10,
-3078 ])
-3079
-3080
-3081 if not os.path.exists(dir):
-3082 os.makedirs(dir)
-3083 fig.savefig(f'{dir}/__all__.pdf')
-3084 if show:
-3085 ppl.show()
-3086 ppl.close(fig)
+3034 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color)
+3035
+3036 if radius:
+3037 ax.add_artist(Ellipse(
+3038 xy = XY0,
+3039 width = radius*2,
+3040 height = radius*2,
+3041 ls = (0, (2,2)),
+3042 lw = .7,
+3043 ec = analysis_color,
+3044 fc = 'None',
+3045 ))
+3046 ppl.text(
+3047 XY0[0],
+3048 XY0[1]-radius,
+3049 f'\n± {radius*1e3:.0f} ppm',
+3050 color = analysis_color,
+3051 va = 'top',
+3052 ha = 'center',
+3053 linespacing = 0.4,
+3054 size = 8,
+3055 )
+3056
+3057 if not os.path.exists(dir):
+3058 os.makedirs(dir)
+3059 fig.savefig(f'{dir}/{s}.pdf')
+3060 ppl.close(fig)
+3061
+3062 fig = ppl.figure(figsize = figsize)
+3063 fig.subplots_adjust(*subplots_adjust)
+3064 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)')
+3065 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)')
+3066
+3067 for s in saved:
+3068 for xy in saved[s][0]:
+3069 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color)
+3070 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color)
+3071 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color)
+3072 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold')
+3073
+3074 x1, x2, y1, y2 = ppl.axis()
+3075 ppl.axis([
+3076 x1 - (x2-x1)/10,
+3077 x2 + (x2-x1)/10,
+3078 y1 - (y2-y1)/10,
+3079 y2 + (y2-y1)/10,
+3080 ])
+3081
+3082
+3083 if not os.path.exists(dir):
+3084 os.makedirs(dir)
+3085 fig.savefig(f'{dir}/__all__.pdf')
+3086 if show:
+3087 ppl.show()
+3088 ppl.close(fig)
Inherited Members
3128class D47data(D4xdata):
-3129 '''
-3130 Store and process data for a large set of Δ47 analyses,
-3131 usually comprising more than one analytical session.
-3132 '''
-3133
-3134 Nominal_D4x = {
-3135 'ETH-1': 0.2052,
-3136 'ETH-2': 0.2085,
-3137 'ETH-3': 0.6132,
-3138 'ETH-4': 0.4511,
-3139 'IAEA-C1': 0.3018,
-3140 'IAEA-C2': 0.6409,
-3141 'MERCK': 0.5135,
-3142 } # I-CDES (Bernasconi et al., 2021)
-3143 '''
-3144 Nominal Δ47 values assigned to the Δ47 anchor samples, used by
-3145 `D47data.standardize()` to normalize unknown samples to an absolute Δ47
-3146 reference frame.
-3147
-3148 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)):
-3149 ```py
-3150 {
-3151 'ETH-1' : 0.2052,
-3152 'ETH-2' : 0.2085,
-3153 'ETH-3' : 0.6132,
-3154 'ETH-4' : 0.4511,
-3155 'IAEA-C1' : 0.3018,
-3156 'IAEA-C2' : 0.6409,
-3157 'MERCK' : 0.5135,
-3158 }
-3159 ```
-3160 '''
-3161
-3162
-3163 @property
-3164 def Nominal_D47(self):
-3165 return self.Nominal_D4x
-3166
-3167
-3168 @Nominal_D47.setter
-3169 def Nominal_D47(self, new):
-3170 self.Nominal_D4x = dict(**new)
-3171 self.refresh()
-3172
-3173
-3174 def __init__(self, l = [], **kwargs):
-3175 '''
-3176 **Parameters:** same as `D4xdata.__init__()`
-3177 '''
-3178 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
-3179
-3180
-3181 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
-3182 '''
-3183 Find all samples for which `Teq` is specified, compute equilibrium Δ47
-3184 value for that temperature, and add treat these samples as additional anchors.
-3185
-3186 **Parameters**
+ 3130class D47data(D4xdata):
+3131 '''
+3132 Store and process data for a large set of Δ47 analyses,
+3133 usually comprising more than one analytical session.
+3134 '''
+3135
+3136 Nominal_D4x = {
+3137 'ETH-1': 0.2052,
+3138 'ETH-2': 0.2085,
+3139 'ETH-3': 0.6132,
+3140 'ETH-4': 0.4511,
+3141 'IAEA-C1': 0.3018,
+3142 'IAEA-C2': 0.6409,
+3143 'MERCK': 0.5135,
+3144 } # I-CDES (Bernasconi et al., 2021)
+3145 '''
+3146 Nominal Δ47 values assigned to the Δ47 anchor samples, used by
+3147 `D47data.standardize()` to normalize unknown samples to an absolute Δ47
+3148 reference frame.
+3149
+3150 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)):
+3151 ```py
+3152 {
+3153 'ETH-1' : 0.2052,
+3154 'ETH-2' : 0.2085,
+3155 'ETH-3' : 0.6132,
+3156 'ETH-4' : 0.4511,
+3157 'IAEA-C1' : 0.3018,
+3158 'IAEA-C2' : 0.6409,
+3159 'MERCK' : 0.5135,
+3160 }
+3161 ```
+3162 '''
+3163
+3164
+3165 @property
+3166 def Nominal_D47(self):
+3167 return self.Nominal_D4x
+3168
+3169
+3170 @Nominal_D47.setter
+3171 def Nominal_D47(self, new):
+3172 self.Nominal_D4x = dict(**new)
+3173 self.refresh()
+3174
+3175
+3176 def __init__(self, l = [], **kwargs):
+3177 '''
+3178 **Parameters:** same as `D4xdata.__init__()`
+3179 '''
+3180 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
+3181
+3182
+3183 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
+3184 '''
+3185 Find all samples for which `Teq` is specified, compute equilibrium Δ47
+3186 value for that temperature, and add treat these samples as additional anchors.
3187
-3188 + `fCo2eqD47`: Which CO2 equilibrium law to use
-3189 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
-3190 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
-3191 + `priority`: if `replace`: forget old anchors and only use the new ones;
-3192 if `new`: keep pre-existing anchors but update them in case of conflict
-3193 between old and new Δ47 values;
-3194 if `old`: keep pre-existing anchors but preserve their original Δ47
-3195 values in case of conflict.
-3196 '''
-3197 f = {
-3198 'petersen': fCO2eqD47_Petersen,
-3199 'wang': fCO2eqD47_Wang,
-3200 }[fCo2eqD47]
-3201 foo = {}
-3202 for r in self:
-3203 if 'Teq' in r:
-3204 if r['Sample'] in foo:
-3205 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
-3206 else:
-3207 foo[r['Sample']] = f(r['Teq'])
-3208 else:
-3209 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
-3210
-3211 if priority == 'replace':
-3212 self.Nominal_D47 = {}
-3213 for s in foo:
-3214 if priority != 'old' or s not in self.Nominal_D47:
-3215 self.Nominal_D47[s] = foo[s]
-3216
-3217 def save_D47_correl(self, *args, **kwargs):
-3218 return self._save_D4x_correl(*args, **kwargs)
-3219
-3220 save_D47_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D47')
+3188 **Parameters**
+3189
+3190 + `fCo2eqD47`: Which CO2 equilibrium law to use
+3191 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
+3192 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
+3193 + `priority`: if `replace`: forget old anchors and only use the new ones;
+3194 if `new`: keep pre-existing anchors but update them in case of conflict
+3195 between old and new Δ47 values;
+3196 if `old`: keep pre-existing anchors but preserve their original Δ47
+3197 values in case of conflict.
+3198 '''
+3199 f = {
+3200 'petersen': fCO2eqD47_Petersen,
+3201 'wang': fCO2eqD47_Wang,
+3202 }[fCo2eqD47]
+3203 foo = {}
+3204 for r in self:
+3205 if 'Teq' in r:
+3206 if r['Sample'] in foo:
+3207 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
+3208 else:
+3209 foo[r['Sample']] = f(r['Teq'])
+3210 else:
+3211 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
+3212
+3213 if priority == 'replace':
+3214 self.Nominal_D47 = {}
+3215 for s in foo:
+3216 if priority != 'old' or s not in self.Nominal_D47:
+3217 self.Nominal_D47[s] = foo[s]
+3218
+3219 def save_D47_correl(self, *args, **kwargs):
+3220 return self._save_D4x_correl(*args, **kwargs)
+3221
+3222 save_D47_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D47')
Inherited Members
3174 def __init__(self, l = [], **kwargs):
-3175 '''
-3176 **Parameters:** same as `D4xdata.__init__()`
-3177 '''
-3178 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
+ 3176 def __init__(self, l = [], **kwargs):
+3177 '''
+3178 **Parameters:** same as `D4xdata.__init__()`
+3179 '''
+3180 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
Inherited Members
3181 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
-3182 '''
-3183 Find all samples for which `Teq` is specified, compute equilibrium Δ47
-3184 value for that temperature, and add treat these samples as additional anchors.
-3185
-3186 **Parameters**
+ 3183 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'):
+3184 '''
+3185 Find all samples for which `Teq` is specified, compute equilibrium Δ47
+3186 value for that temperature, and add treat these samples as additional anchors.
3187
-3188 + `fCo2eqD47`: Which CO2 equilibrium law to use
-3189 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
-3190 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
-3191 + `priority`: if `replace`: forget old anchors and only use the new ones;
-3192 if `new`: keep pre-existing anchors but update them in case of conflict
-3193 between old and new Δ47 values;
-3194 if `old`: keep pre-existing anchors but preserve their original Δ47
-3195 values in case of conflict.
-3196 '''
-3197 f = {
-3198 'petersen': fCO2eqD47_Petersen,
-3199 'wang': fCO2eqD47_Wang,
-3200 }[fCo2eqD47]
-3201 foo = {}
-3202 for r in self:
-3203 if 'Teq' in r:
-3204 if r['Sample'] in foo:
-3205 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
-3206 else:
-3207 foo[r['Sample']] = f(r['Teq'])
-3208 else:
-3209 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
-3210
-3211 if priority == 'replace':
-3212 self.Nominal_D47 = {}
-3213 for s in foo:
-3214 if priority != 'old' or s not in self.Nominal_D47:
-3215 self.Nominal_D47[s] = foo[s]
+3188 **Parameters**
+3189
+3190 + `fCo2eqD47`: Which CO2 equilibrium law to use
+3191 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127);
+3192 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)).
+3193 + `priority`: if `replace`: forget old anchors and only use the new ones;
+3194 if `new`: keep pre-existing anchors but update them in case of conflict
+3195 between old and new Δ47 values;
+3196 if `old`: keep pre-existing anchors but preserve their original Δ47
+3197 values in case of conflict.
+3198 '''
+3199 f = {
+3200 'petersen': fCO2eqD47_Petersen,
+3201 'wang': fCO2eqD47_Wang,
+3202 }[fCo2eqD47]
+3203 foo = {}
+3204 for r in self:
+3205 if 'Teq' in r:
+3206 if r['Sample'] in foo:
+3207 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.'
+3208 else:
+3209 foo[r['Sample']] = f(r['Teq'])
+3210 else:
+3211 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.'
+3212
+3213 if priority == 'replace':
+3214 self.Nominal_D47 = {}
+3215 for s in foo:
+3216 if priority != 'old' or s not in self.Nominal_D47:
+3217 self.Nominal_D47[s] = foo[s]
Inherited Members
3217 def save_D47_correl(self, *args, **kwargs):
-3218 return self._save_D4x_correl(*args, **kwargs)
+ 3219 def save_D47_correl(self, *args, **kwargs):
+3220 return self._save_D4x_correl(*args, **kwargs)
Inherited Members
3223class D48data(D4xdata):
-3224 '''
-3225 Store and process data for a large set of Δ48 analyses,
-3226 usually comprising more than one analytical session.
-3227 '''
-3228
-3229 Nominal_D4x = {
-3230 'ETH-1': 0.138,
-3231 'ETH-2': 0.138,
-3232 'ETH-3': 0.270,
-3233 'ETH-4': 0.223,
-3234 'GU-1': -0.419,
-3235 } # (Fiebig et al., 2019, 2021)
-3236 '''
-3237 Nominal Δ48 values assigned to the Δ48 anchor samples, used by
-3238 `D48data.standardize()` to normalize unknown samples to an absolute Δ48
-3239 reference frame.
-3240
-3241 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019),
-3242 Fiebig et al. (in press)):
-3243
-3244 ```py
-3245 {
-3246 'ETH-1' : 0.138,
-3247 'ETH-2' : 0.138,
-3248 'ETH-3' : 0.270,
-3249 'ETH-4' : 0.223,
-3250 'GU-1' : -0.419,
-3251 }
-3252 ```
-3253 '''
-3254
-3255
-3256 @property
-3257 def Nominal_D48(self):
-3258 return self.Nominal_D4x
-3259
-3260
-3261 @Nominal_D48.setter
-3262 def Nominal_D48(self, new):
-3263 self.Nominal_D4x = dict(**new)
-3264 self.refresh()
-3265
-3266
-3267 def __init__(self, l = [], **kwargs):
-3268 '''
-3269 **Parameters:** same as `D4xdata.__init__()`
-3270 '''
-3271 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
-3272
-3273 def save_D48_correl(self, *args, **kwargs):
-3274 return self._save_D4x_correl(*args, **kwargs)
-3275
-3276 save_D48_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D48')
+ 3225class D48data(D4xdata):
+3226 '''
+3227 Store and process data for a large set of Δ48 analyses,
+3228 usually comprising more than one analytical session.
+3229 '''
+3230
+3231 Nominal_D4x = {
+3232 'ETH-1': 0.138,
+3233 'ETH-2': 0.138,
+3234 'ETH-3': 0.270,
+3235 'ETH-4': 0.223,
+3236 'GU-1': -0.419,
+3237 } # (Fiebig et al., 2019, 2021)
+3238 '''
+3239 Nominal Δ48 values assigned to the Δ48 anchor samples, used by
+3240 `D48data.standardize()` to normalize unknown samples to an absolute Δ48
+3241 reference frame.
+3242
+3243 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019),
+3244 Fiebig et al. (in press)):
+3245
+3246 ```py
+3247 {
+3248 'ETH-1' : 0.138,
+3249 'ETH-2' : 0.138,
+3250 'ETH-3' : 0.270,
+3251 'ETH-4' : 0.223,
+3252 'GU-1' : -0.419,
+3253 }
+3254 ```
+3255 '''
+3256
+3257
+3258 @property
+3259 def Nominal_D48(self):
+3260 return self.Nominal_D4x
+3261
+3262
+3263 @Nominal_D48.setter
+3264 def Nominal_D48(self, new):
+3265 self.Nominal_D4x = dict(**new)
+3266 self.refresh()
+3267
+3268
+3269 def __init__(self, l = [], **kwargs):
+3270 '''
+3271 **Parameters:** same as `D4xdata.__init__()`
+3272 '''
+3273 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
+3274
+3275 def save_D48_correl(self, *args, **kwargs):
+3276 return self._save_D4x_correl(*args, **kwargs)
+3277
+3278 save_D48_correl.__doc__ = D4xdata._save_D4x_correl.__doc__.replace('D4x', 'D48')
Inherited Members
3267 def __init__(self, l = [], **kwargs):
-3268 '''
-3269 **Parameters:** same as `D4xdata.__init__()`
-3270 '''
-3271 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
+ 3269 def __init__(self, l = [], **kwargs):
+3270 '''
+3271 **Parameters:** same as `D4xdata.__init__()`
+3272 '''
+3273 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
Inherited Members
3273 def save_D48_correl(self, *args, **kwargs):
-3274 return self._save_D4x_correl(*args, **kwargs)
+ 3275 def save_D48_correl(self, *args, **kwargs):
+3276 return self._save_D4x_correl(*args, **kwargs)