1.3.0 revision

Significantly improved graphical inversion routines to correctly handle matplotlib objects as both inputs and outputs - can now manipulate graphs outside of PyMieScatt. Also added AutoMie functions and rolled some other functions to use it. Docs forthcoming.

PyMieScatt.egg-info/PKG-INFO

@@ -1,12 +1,12 @@
 Metadata-Version: 1.1
 Name: PyMieScatt
-Version: 1.2.6
+Version: 1.3.0
 Summary: A collection of forward and inverse Mie solving routines based on Bohren and Huffman's Mie Theory derivations.
 Home-page: http://air.eece.wustl.edu/people/ben-sumlin/
 Author: Benjamin Sumlin
 Author-email: bsumlin@wustl.edu
 License: GPL
-Description: 1.2.6 - Added error bounds as an option for the graphical inversion method. Added new automatic inversion methods Inversion() and Inversion_SD(). Significantly improved the iterative methods.
+Description: 1.3.0 - Added error bounds as an option for the graphical inversion method. Added new automatic inversion methods Inversion() and Inversion_SD(). Significantly improved the iterative methods.
         Docs are hosted at `ReadTheDocs <http://pymiescatt.readthedocs.io/>`_.
 Keywords: Mie Rayleigh scattering absorption extinction light refraction
 Platform: UNKNOWN

PyMieScatt/Mie.py

@@ -137,11 +137,12 @@ def RayleighMieQ(m, wavelength, diameter, asDict=False):
     else:
       return qext, qsca, qabs, g, qpr, qback, qratio
 
-def AutoMieQ(m, wavelength, diameter, asDict=False):
+def AutoMieQ(m, wavelength, diameter, crossover=0.5, asDict=False):
+#  http://pymiescatt.readthedocs.io/en/latest/forward.html#AutoMieQ
   x = np.pi*diameter/wavelength
   if x==0:
     return 0, 0, 0, 1.5, 0, 0, 0
-  elif x<0.5:
+  elif x<crossover:
     return RayleighMieQ(m, wavelength, diameter, asDict=asDict)
   else:
     return MieQ(m, wavelength, diameter, asDict=asDict)
@@ -332,7 +333,7 @@ def MieQ_withDiameterRange(m, wavelength, diameterRange=(10,1000), nd=1000, logD
     diameters = np.logspace(np.log10(diameterRange[0]),np.log10(diameterRange[1]),nd)
   else:
     diameters = np.linspace(diameterRange[0],diameterRange[1],nd)
-  _qD = [MieQ(m,wavelength,diameter) for diameter in diameters]
+  _qD = [AutoMieQ(m,wavelength,diameter) for diameter in diameters]
   qext = np.array([q[0] for q in _qD])
   qsca = np.array([q[1] for q in _qD])
   qabs = np.array([q[2] for q in _qD])
@@ -349,7 +350,7 @@ def MieQ_withWavelengthRange(m, diameter, wavelengthRange=(100,1600), nw=1000, l
       wavelengths = np.logspace(np.log10(wavelengthRange[0]),np.log10(wavelengthRange[1]),nw)
     else:
       wavelengths = np.linspace(wavelengthRange[0],wavelengthRange[1],nw)
-    _qD = [MieQ(m,wavelength,diameter) for wavelength in wavelengths]
+    _qD = [AutoMieQ(m,wavelength,diameter) for wavelength in wavelengths]
   elif type(m) in [np.ndarray,list,tuple] and len(wavelengthRange)==len(m):
     wavelengths=wavelengthRange
     _qD = [MieQ(emm,wavelength,diameter) for emm,wavelength in zip(m,wavelengths)]
@@ -373,7 +374,7 @@ def MieQ_withSizeParameterRange(m, xRange=(1,10), nx=1000, logX=False):
   else:
     xValues = list(np.linspace(xRange[0],xRange[1], nx))
   dValues = [1000*x/np.pi for x in xValues]
-  _qD = [MieQ(m,1000,d) for d in dValues]
+  _qD = [AutoMieQ(m,1000,d) for d in dValues]
   qext = np.array([q[0] for q in _qD])
   qsca = np.array([q[1] for q in _qD])
   qabs = np.array([q[2] for q in _qD])

PyMieScatt/_version.py

@@ -1 +1 @@
-__version__ = "1.2.6"
+__version__ = "1.3.0"

build/lib/PyMieScatt/Mie.py

@@ -137,11 +137,12 @@ def RayleighMieQ(m, wavelength, diameter, asDict=False):
     else:
       return qext, qsca, qabs, g, qpr, qback, qratio
 
-def AutoMieQ(m, wavelength, diameter, asDict=False):
+def AutoMieQ(m, wavelength, diameter, crossover=0.5, asDict=False):
+#  http://pymiescatt.readthedocs.io/en/latest/forward.html#AutoMieQ
   x = np.pi*diameter/wavelength
   if x==0:
     return 0, 0, 0, 1.5, 0, 0, 0
-  elif x<0.5:
+  elif x<crossover:
     return RayleighMieQ(m, wavelength, diameter, asDict=asDict)
   else:
     return MieQ(m, wavelength, diameter, asDict=asDict)
@@ -332,7 +333,7 @@ def MieQ_withDiameterRange(m, wavelength, diameterRange=(10,1000), nd=1000, logD
     diameters = np.logspace(np.log10(diameterRange[0]),np.log10(diameterRange[1]),nd)
   else:
     diameters = np.linspace(diameterRange[0],diameterRange[1],nd)
-  _qD = [MieQ(m,wavelength,diameter) for diameter in diameters]
+  _qD = [AutoMieQ(m,wavelength,diameter) for diameter in diameters]
   qext = np.array([q[0] for q in _qD])
   qsca = np.array([q[1] for q in _qD])
   qabs = np.array([q[2] for q in _qD])
@@ -349,7 +350,7 @@ def MieQ_withWavelengthRange(m, diameter, wavelengthRange=(100,1600), nw=1000, l
       wavelengths = np.logspace(np.log10(wavelengthRange[0]),np.log10(wavelengthRange[1]),nw)
     else:
       wavelengths = np.linspace(wavelengthRange[0],wavelengthRange[1],nw)
-    _qD = [MieQ(m,wavelength,diameter) for wavelength in wavelengths]
+    _qD = [AutoMieQ(m,wavelength,diameter) for wavelength in wavelengths]
   elif type(m) in [np.ndarray,list,tuple] and len(wavelengthRange)==len(m):
     wavelengths=wavelengthRange
     _qD = [MieQ(emm,wavelength,diameter) for emm,wavelength in zip(m,wavelengths)]
@@ -373,7 +374,7 @@ def MieQ_withSizeParameterRange(m, xRange=(1,10), nx=1000, logX=False):
   else:
     xValues = list(np.linspace(xRange[0],xRange[1], nx))
   dValues = [1000*x/np.pi for x in xValues]
-  _qD = [MieQ(m,1000,d) for d in dValues]
+  _qD = [AutoMieQ(m,1000,d) for d in dValues]
   qext = np.array([q[0] for q in _qD])
   qsca = np.array([q[1] for q in _qD])
   qabs = np.array([q[2] for q in _qD])

build/lib/PyMieScatt/_version.py

@@ -1 +1 @@
-__version__ = "1.2.6"
+__version__ = "1.3.0"

docs/forward.rst

@@ -144,6 +144,33 @@ Functions for single particles
 		 'Qsca': 0.00011805645915412197,
 		 'g': 0}
 
+
+.. py:Function:: AutoMieQ(m, wavelength, diameter[, crossover=0.5, asDict=False])
+
+   Returns Mie efficencies of a spherical particle according to either :py:func`MieQ` or :py:func`RayleighMieQ` depending on the magnitude of the size parameter. Good for studying parameter ranges or size distributions.
+
+   **Parameters**
+
+   m : complex
+	The complex refractive index, with the convention *m = n+ik*.
+   wavelength : float
+	The wavelength of incident light, in nanometers.
+   diameter : float
+	The diameter of the particle, in nanometers.
+   crossover : float, optional
+	The size parameter that dictates where calculations switch from Rayleigh approximation to actual Mie.
+   asDict : bool, optional
+	If specified and set to True, returns the results as a dict.
+
+   **Returns**
+
+
+   qext, qsca, qabs, g, qpr, qback, qratio : float
+	The Mie efficencies described above.
+   q : dict
+	If asDict==True, :py:func:`RayleighMieQ` returns a dict of the above values with appropriate keys.
+
+
 .. py:Function:: LowFrequencyMieQ(m, wavelength, diameter[, asDict=False])
 
    Returns Mie efficencies of a spherical particle in the low-frequency regime (:math:`x=\pi\,d_p/\lambda \ll 1`) given refractive index **m**, **wavelength**, and **diameter**. Optionally returns the parameters as a dict when **asDict** is specified and set to True. Uses :py:func:`LowFrequencyMie_ab` to calculate a\ :sub:`n` and b\ :sub:`n`, and follows the same math as :py:func:`MieQ`.
@@ -211,7 +238,7 @@ Functions for single particles across various ranges
 
 .. py:Function:: MieQ_withDiameterRange(m, wavelength[, diameterRange=(10,1000), nd=1000, logD=False])
 
-   Computes the Mie efficencies of particles across a diameter range using :py:func:`MieQ`.
+   Computes the Mie efficencies of particles across a diameter range using :py:func:`MieQ` or :py:func:`AutoMieQ`, depending on the size parameters.
 
    **Parameters**
 
@@ -237,7 +264,7 @@ Functions for single particles across various ranges
 
 .. py:Function:: MieQ_withWavelengthRange(m, diameter[, wavelengthRange=(100,1600), nw=1000, logW=False])
 
-   Computes the Mie efficencies of particles across a wavelength range using :py:func:`MieQ`. This function can optionally take a list, tuple, or numpy.ndarray for **m**. If your particles have a wavelength-dependent refractive index, you can study it by specifying **m** as list-like. When doing so, **m** must be the same size as **wavelengthRange**, which is also specified as list-like in this situation. Otherwise, the function will construct a range from **wavelengthRange[0]** to **wavelengthRange[1]** with **nw** entries.
+   Computes the Mie efficencies of particles across a wavelength range using :py:func:`MieQ` or :py:func:`AutoMieQ`, depending on the size parameters. This function can optionally take a list, tuple, or numpy.ndarray for **m**. If your particles have a wavelength-dependent refractive index, you can study it by specifying **m** as list-like. When doing so, **m** must be the same size as **wavelengthRange**, which is also specified as list-like in this situation. Otherwise, the function will construct a range from **wavelengthRange[0]** to **wavelengthRange[1]** with **nw** entries.
 
    **Parameters**
 
@@ -263,7 +290,7 @@ Functions for single particles across various ranges
 
 .. py:Function:: MieQ_withSizeParameterRange(m[, xRange=(1,10), nx=1000, logX=False])
 
-   Computes the Mie efficencies of particles across a size parameter range (\ :math:`x=\pi\,d_p/\lambda`\ ) using :py:func:`MieQ`.
+   Computes the Mie efficencies of particles across a size parameter range (\ :math:`x=\pi\,d_p/\lambda`\ ) using :py:func:`MieQ` or :py:func:`AutoMieQ`.
 
    **Parameters**
 

docs/index.rst

@@ -18,7 +18,7 @@ You can install PyMieScatt from `The Python Package Index (PyPI) <https://pypi.p
    $ pip install PyMieScatt
 
 
-or from `GitHub <https://github.com/bsumlin/PyMieScatt>`_. The current version is 1.2.6.
+or from `GitHub <https://github.com/bsumlin/PyMieScatt>`_. The current version is 1.3.0.
 
 
 .. toctree::
@@ -38,6 +38,7 @@ Documentation To-Do List
 
 - Inverse Mie Theory functions
 - More example scripts
+- Revision history and update notes
 
 PyMieScatt To-Do List
 ---------------------