refactor: minor syntax improvements

AeroViz/__init__.py

@@ -2,13 +2,12 @@
 from AeroViz import plot
 from AeroViz.dataProcess import DataProcess
 from AeroViz.rawDataReader import RawDataReader
-from AeroViz.tools import DataBase, DataReader, DataClassifier
+from AeroViz.tools import DataBase, DataClassifier
 
 __all__ = [
     'plot',
     'RawDataReader',
     'DataProcess',
     'DataBase',
-    'DataReader',
     'DataClassifier'
 ]

AeroViz/plot/optical/PyMieScatt_update.py → AeroViz/dataProcess/Optical/PyMieScatt_update.py

@@ -6,13 +6,6 @@
 from scipy.special import jv, yv
 
 
-def coerceDType(d):
-    if type(d) is not np.ndarray:
-        return np.array(d)
-    else:
-        return d
-
-
 def MieQ(m, wavelength, diameter, nMedium=1.0, asDict=False, asCrossSection=False):
     #  http://pymiescatt.readthedocs.io/en/latest/forward.html#MieQ
     nMedium = nMedium.real
@@ -271,8 +264,8 @@ def Mie_SD(m, wavelength, dp, ndp, nMedium=1.0, SMPS=True, interpolate=False, as
     nMedium = nMedium.real
     m /= nMedium
     wavelength /= nMedium
-    dp = coerceDType(dp)
-    ndp = coerceDType(ndp)
+    dp = np.array(dp)
+    ndp = np.array(ndp)
     _length = np.size(dp)
     Q_ext = np.zeros(_length)
     Q_sca = np.zeros(_length)
@@ -373,8 +366,8 @@ def SF_SD(m, wavelength, dp, ndp, nMedium=1.0, minAngle=0, maxAngle=180, angular
     wavelength /= nMedium
 
     _steps = int(1 + (maxAngle - minAngle) / angularResolution)
-    ndp = coerceDType(ndp)
-    dp = coerceDType(dp)
+    ndp = np.array(ndp)
+    dp = np.array(dp)
     SL = np.zeros(_steps)
     SR = np.zeros(_steps)
     SU = np.zeros(_steps)

AeroViz/dataProcess/SizeDistr/prop.py

@@ -0,0 +1,62 @@
+import numpy as np
+from numpy import exp, log
+from scipy.signal import find_peaks
+
+
+def geometric(dp: np.ndarray,
+              dist: np.ndarray
+              ) -> tuple[float, float]:
+    """ Calculate the geometric mean and standard deviation. """
+
+    _gmd = (((dist * log(dp)).sum()) / dist.sum())
+
+    logdp_mesh, gmd_mesh = np.meshgrid(log(dp), _gmd)
+    _gsd = ((((logdp_mesh - gmd_mesh) ** 2) * dist).sum() / dist.sum()) ** .5
+
+    return exp(_gmd), exp(_gsd)
+
+
+def contribution(dp: np.ndarray,
+                 dist: np.ndarray
+                 ) -> tuple[float, float, float]:
+    """ Calculate the relative contribution of each mode. """
+
+    ultra = dist[(dp >= 11.8) & (dp < 100)].sum() / dist.sum()
+    accum = dist[(dp >= 100) & (dp < 1000)].sum() / dist.sum()
+    coars = dist[(dp >= 1000) & (dp < 2500)].sum() / dist.sum()
+
+    return ultra, accum, coars
+
+
+def mode(dp: np.ndarray,
+         dist: np.ndarray
+         ) -> np.ndarray:
+    """ Find three peak mode in distribution. """
+
+    min_value = np.array([dist.min()])
+    mode, _ = find_peaks(np.concatenate([min_value, dist, min_value]), distance=len(dist) - 1)
+
+    return dp[mode - 1]
+
+
+def properties(dist,
+               dp: np.ndarray,
+               dlogdp: np.ndarray,
+               weighting: str
+               ) -> dict:
+    """ for apply """
+    dist = np.array(dist)
+
+    gmd, gsd = geometric(dp, dist)
+    ultra, accum, coarse = contribution(dp, dist)
+    peak = mode(dp, dist)
+
+    return {key: round(value, 3) for key, value in
+            {f'total_{weighting}': (dist * dlogdp).sum(),
+             f'GMD_{weighting}': gmd,
+             f'GSD_{weighting}': gsd,
+             f'mode_{weighting}': peak[0],
+             f'ultra_{weighting}': ultra,
+             f'accum_{weighting}': accum,
+             f'coarse_{weighting}': coarse}
+            .items()}

AeroViz/plot/__init__.py

@@ -3,7 +3,6 @@
 from . import optical
 from .bar import bar
 from .box import box
-from .hysplit import hysplit
 from .pie import pie, donuts
 from .radar import radar
 from .regression import linear_regression, multiple_linear_regression