Minor cleaning of examples.

Author: Ainamacar

doc/examples/Ag_multiplepeaks.py

@@ -19,7 +19,10 @@
 to evaluate the model on an arbitrary grid.
 
 The peaks extracted by this script are equivalent to those obtained running
- srmise data/Ag_nyquist_qmax30.gr --range 2. 10. --bsrmise=output/Ag_singlepeak.srmise --save output/Ag_multiplepeaks.srmise --pwa output/Ag_multiplepeaks.pwa --plot
+srmise data/Ag_nyquist_qmax30.gr --range 2. 10. \
+    --bsrmise=output/Ag_singlepeak.srmise \
+    --save output/Ag_multiplepeaks.srmise \
+    --pwa output/Ag_multiplepeaks.pwa --plot
 at the command line.
 """
 
@@ -91,19 +94,23 @@ def run(plot=True):
     position = "%f +/- %f" %cov.get((0,0))
     width = "%f +/- %f" %cov.get((0,1))
     area = "%f +/- %f" %cov.get((0,2))
-    print  "Nearest-neighbor peak: position=%s, width=%s, area=%s" %(position, width, area)
-    print "Covariance of width and area for nearest-neighbor peak: ", cov.getcovariance((0,1),(0,2))
+    print  "Nearest-neighbor peak: position=%s, width=%s, area=%s" \
+           %(position, width, area)
+    print "Covariance of width and area for nearest-neighbor peak: ", \
+           cov.getcovariance((0,1),(0,2))
 
     # It is also possible to iterate over peaks directly without using indices.
     # For example, to calculate the total peak area:
     total_area = 0
     for peak in cov.model[:-1]: # Exclude last element, which is the baseline.
-        total_area += peak["area"]    # The "position" and "width" keywords are also
-                                # available for the GaussianOverR peak function.
+        total_area += peak["area"] # The "position" and "width" keywords are
+                                   # also available for the GaussianOverR peak
+                                   # function.
     print "Total area of extracted peaks: ", total_area
 
     # Baseline parameters.
-    print "The linear baseline B(r)=%f*r + %f" % tuple(par for par in cov.model[-1])
+    print "The linear baseline B(r)=%f*r + %f" \
+          % tuple(par for par in cov.model[-1])
 
     # Highly-correlated parameters can indicate difficulties constraining the
     # fit.  This function lists all pairs of parameters with an absolute value

doc/examples/Ag_singlepeak.py

@@ -19,14 +19,17 @@
 parameters, running peak extraction, and saving the results.
 
 This script is equivalent to running
- srmise data/Ag_nyquist_qmax30.gr --range 2. 3.5 --baseline=Polynomial(degree=1) --save output/Ag_singlepeak.srmise --pwa output/Ag_singlepeak.pwa --plot
+srmise data/Ag_nyquist_qmax30.gr --range 2. 3.5 \
+    --baseline=Polynomial(degree=1) --save output/Ag_singlepeak.srmise \
+    --pwa output/Ag_singlepeak.pwa --plot
 at the command line.
 """
 
 import matplotlib.pyplot as plt
 
 from diffpy.srmise import PDFPeakExtraction
 from diffpy.srmise.baselines import Polynomial
+from diffpy.srmise.applications.plot import makeplot
 
 def run(plot=True):
 
@@ -77,10 +80,9 @@ def run(plot=True):
     # Display plot of extracted peak.  It is also possible to plot an existing
     # .srmise file from the command line using
     #     srmise output/Ag_singlepeak.srmise --no-extract --plot
-    # or, for a somewhat prettier plot,
-    #     srmiseplot output/Ag_singlepeak.srmise --show
+    # For additional plotting options, run "srmiseplot --help".
     if plot:
-        ppe.plot()
+        makeplot(ppe)
         plt.show()
 
 if __name__ == '__main__':

doc/examples/C60_multimodelanalysis.py

@@ -96,15 +96,20 @@ def run(plot=True):
     for m in bm:
         filename = "output/C60_multimodel"+str(m)+".pwa"
         cls = ms.classes_idx[m] # Get index of class to which model belongs.
-        bestdgs = ms.modelbestdgs(m) # Get the uncertainties where this model has greatest Akaike probability.
-        maxprob = np.max([ms.classprobs[dg][cls] for dg in bestdgs]) # Get the model's greatest Akaike probability.
+        bestdgs = ms.modelbestdgs(m) # Get the uncertainties where this model
+                                     # has greatest Akaike probability.
+
+        # Get the model's greatest Akaike probability.
+        maxprob = np.max([ms.classprobs[dg][cls] for dg in bestdgs]) 
 
         msg = ["This is a best model determined by MultiModelSelection",
               "Model: %i (of %i)",
               "Class: %i (of %i, tolerance=%f)",
               "Best model for uncertainties: %f-%f",
               "Max Akaike probability: %g"]
-        msg = "\n".join(msg) %(m, len(ms.classes_idx), cls, len(ms.classes), tolerance, np.min(bestdgs), np.max(bestdgs), maxprob)
+        msg = "\n".join(msg) %(m, len(ms.classes_idx), cls, len(ms.classes),
+                               tolerance, np.min(bestdgs), np.max(bestdgs),
+                               maxprob)
         ms.setcurrent(m) # Make this the active model.
         ms.ppe.writepwa(filename, msg)
 
@@ -140,7 +145,7 @@ def run(plot=True):
             ms.setcurrent(bm)
             figdict = makeplot(ms, dcif)
             # Uncomment to save figure.
-            #plt.savefig("output/C60_multimodel"+str(bm)+".png", format="png")
+            # plt.savefig("output/C60_multimodel"+str(bm)+".png", format="png")
 
 
     if plot:

doc/examples/TiO2_initialpeaks.py

@@ -10,7 +10,7 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""Example of peak extraction demonstrating non-default values for many extraction parameters.
+"""Peak extraction with non-default values for many extraction parameters.
 
 This example shows how to specify initial peaks in order to guide the results of
 peak extraction.
@@ -61,9 +61,11 @@ def run(plot=True):
     # terms of position, width (fwhm), and area, and it is important to specify
     # that format is being used so they are correctly changed into the
     # internal parameterization.
-    explicit_guess = [[6.25, .3, 3], [6.5, .3, 3], [6.85, 0.3, 10], [7.1, 0.3, 10], [7.45, 0.3, 20]]
-    peak_function = ppe.pf[0]
-    explicit_peaks = Peaks([peak_function.actualize(e, removable=False, in_format="pwa") for e in explicit_guess])
+    explicit_guess = [[6.25, .3, 3], [6.5, .3, 3], [6.85, 0.3, 10],
+                      [7.1, 0.3, 10], [7.45, 0.3, 20]]
+    pf = ppe.pf[0]
+    explicit_peaks = Peaks([pf.actualize(e, removable=False, in_format="pwa") \
+                            for e in explicit_guess])
     ppe.addpeaks(explicit_peaks)
     if plot:
         plt.figure(2)

doc/examples/TiO2_parameterdetail.py

@@ -10,14 +10,18 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""Example of peak extraction demonstrating non-default values for many extraction parameters.
+"""Peak extraction with non-default values for many extraction parameters.
 
 This example shows how to extract peaks from a crystalline PDF with unreliable
 uncertainties, and shows how various extraction variables may be set.  In
-particular, choosing a peak function and defining baseline parameters explicitly.
+particular, choosing a peak function and defining baseline parameters
+explicitly.
 
 This script is equivalent to running
- srmise data/TiO2_fine_qmax26.gr --range 1.5 10. --dg 0.35 --qmax 26 --resolution .05 --bpoly1=-0.65 0c --save output/TiO2_parameterdetail.srmise --pwa output/TiO2_parameterdetail.pwa --plot
+srmise data/TiO2_fine_qmax26.gr --range 1.5 10. --dg 0.35 --qmax 26 \
+    --resolution .05 --bpoly1=-0.65 0c \
+    --save output/TiO2_parameterdetail.srmise \
+    --pwa output/TiO2_parameterdetail.pwa --plot
 at the command line.
 """
 
@@ -26,6 +30,7 @@
 from diffpy.srmise import PDFPeakExtraction
 from diffpy.srmise.baselines import Polynomial
 from diffpy.srmise.peaks import GaussianOverR
+from diffpy.srmise.applications.plot import makeplot
 
 def run(plot=True):
 
@@ -79,7 +84,8 @@ def run(plot=True):
     blfunc = Polynomial(degree=1)
     slope = -.65 # Play with this value!
     y_intercept = 0.
-    kwds["baseline"] = blfunc.actualize([slope, y_intercept], free=[True, False])
+    kwds["baseline"] = blfunc.actualize([slope, y_intercept],
+                                        free=[True, False])
 
     # The pf (peakfunction) parameter allows setting the shape of peaks to be
     # extracted.  Termination effects are added automatically to the peak
@@ -102,8 +108,8 @@ def run(plot=True):
     # If the PDF does not report qmax, diffpy.srmise attempts to estimate it
     # directly from the data.  This estimate can also be used by setting qmax
     # to "automatic".  An infinite qmax can be specified by setting qmax to 0,
-    # In that case the Nyquist rate is 0 (infinite resolution), and diffpy.srmise
-    # does not consider Nyquist sampling or termination effects.
+    # In that case the Nyquist rate is 0 (infinite resolution), and
+    # diffpy.srmise does not consider Nyquist sampling or termination effects.
     kwds["qmax"] = 26.0 
 
     # This parameter governs whether diffpy.srmise attempts to find a model
@@ -160,10 +166,8 @@ def run(plot=True):
     # Display plot of extracted peak.  It is also possible to plot an existing
     # .srmise file from the command line using
     #     srmise output/TiO2_parameterdetail.srmise --no-extract --plot
-    # or, for a somewhat prettier plot,
-    #     srmiseplot output/TiO2_parameterdetail.srmise --show
     if plot:
-        ppe.plot()
+        makeplot(ppe)
         plt.show()
 
 if __name__ == '__main__':