fixing example file + allowing lmax_theory to set ell range

examples/mflike_example.yaml

@@ -3,7 +3,7 @@
 debug: True
 
 likelihood:
-  mflike.MFLike:
+  mflike.TTTEEE:
     input_file: LAT_simu_sacc_00044.fits
     cov_Bbl_file: data_sacc_w_covar_and_Bbl.fits
 

mflike/foreground.py

@@ -7,16 +7,26 @@
 If one wants to use this class as standalone, the ``bands`` dictionary is filled when initializing
 ``BandpowerForeground``.
 
-The values of the systematic parameters are set in ``MFLike.yaml``.  They have to be named as
-``cal/calT/calE/alpha`` + ``_`` + experiment_channel string (e.g. ``LAT_93/dr6_pa4_f150``).
+The default values of the systematic parameters are set in the ``TTTEEE/TEEE/TT/EE/TE/etc.yaml`` files.  They have to be named as ``cal/calT/calE/alpha`` + ``_`` + experiment_channel string (e.g. ``LAT_93/dr6_pa4_f150``).
+The default values of the foreground parameters are set in the ``fg_TT/TE/EE.yaml`` files.
+If you want to set different parameters settings, do that in the ``params`` block of the ``yaml`` file you will use for running (see the `examples/mflike_example.yaml <https://github.com/simonsobs/LAT_MFLike/blob/master/examples/mflike_example.yaml>`_).
+
+.. note::
+    Note that when you set different foregrounds/systematics parameters in the ``params`` block 
+    of your running ``yaml``, Cobaya will use the new parameters settings you indicated. If you
+    don't change a parameter setting in the ``params`` block, Cobaya will use its default stored
+    in the files mentioned above. Note that, in the ``.updated.yaml`` file generated by Cobaya, 
+    the ``mflike.BandpowerForeground`` theory block will have a ``params`` block with all the 
+    foreground parameters default settings which may be different from what you are defining in 
+    the general ``params`` block. 
 
 
 The bandpass shifts are applied within the ``_bandpass_construction`` function. There are two possibilities:
     * reading the passband :math:`\tau(\nu)` stored in a sacc file
       (which is the default now)
     * building the passbands :math:`\tau(\nu)`, either as Dirac delta or as top-hat
 
-For the second option, the ``top_hat_band`` dictionary in ``MFLike.yaml`` has to be filled with two keys:
+For the second option, the ``top_hat_band`` dictionary under the ``mflike.BandpowerForeground`` theory block has to be filled with two keys:
     * ``nsteps``: setting the number of frequencies used in the band integration
       (either 1 for a Dirac delta or > 1)
     * ``bandwidth``: setting the relative width :math:`\delta` of the band with respect to
@@ -32,9 +42,11 @@
 
 .. code-block:: yaml
 
-  top_hat_band:
-    nsteps: 1
-    bandwidth: 0
+  theory:
+    mflike.BandpowerForeground:
+      top_hat_band:
+        nsteps: 1
+        bandwidth: 0
 
 """
 

mflike/mflike.py

@@ -33,7 +33,7 @@
 
 If left ``null``, no systematic template is applied.
 
-The values of the systematic parameters are set in ``MFLike.yaml``.  They have to be named as
+The values of the systematic parameters are set in the ``TTTEEE/TTTE/TT/EE/TE/etc.yaml`` fils corresponding to the classes that inherit the ``_MFLike`` one.  They have to be named as
 ``cal/calT/calE/alpha`` + ``_`` + experiment_channel string (e.g. ``LAT_93/dr6_pa4_f150``).
 """
 
@@ -105,7 +105,7 @@ def initialize(self):
         self.log.info("Initialized!")
 
     def get_fg_requirements(self):
-        return {"ells": self.l_bpws,
+        return {"ells": self.l_bpws[:self.lmax_theory + 1],
                 "requested_cls": self.requested_cls,
                 "experiments": self.experiments,
                 "bands": self.bands}
@@ -119,7 +119,8 @@ def get_requirements(self):
         """
 
         return {"fg_totals": self.get_fg_requirements(),
-                "Cl": {k: max(c, self.lmax_theory + 1) for k, c in self.lcuts.items()}}
+               "Cl": {k: self.lmax_theory + 1 for k, _ in self.lcuts.items()}}
+                #"Cl": {k: max(c, self.lmax_theory + 1) for k, c in self.lcuts.items()}}
 
     def logp(self, **params_values):
         cl = self.provider.get_Cl(ell_factor=True)