fix too many leading "#" in string block (#26)

Author: stevenhua0320

doc/examples/fit_initial.py

@@ -20,7 +20,6 @@
 grid."""
 
 import matplotlib.pyplot as plt
-import numpy as np
 
 from diffpy.srmise import PDFPeakExtraction
 from diffpy.srmise.applications.plot import makeplot
@@ -30,11 +29,11 @@
 
 def run(plot=True):
 
-    ## Initialize peak extraction
+    # Initialize peak extraction
     ppe = PDFPeakExtraction()
     ppe.loadpdf("data/C60_fine_qmax21.gr")
 
-    ## Set up interpolated baseline.
+    # Set up interpolated baseline.
     # The FromSequence baseline creates an interpolated baseline from provided
     # r and G(r) values, either two lists or a file containing (r, G(r)) pairs.
     # The baseline has no parameters. This particular baseline was estimated
@@ -43,7 +42,7 @@ def run(plot=True):
     blf = FromSequence("data/C60baseline.dat")
     bl = blf.actualize([])
 
-    ## Set up fitting parameters
+    # Set up fitting parameters
     # A summary of how parameters impact fitting is given below.
     # "rng" - Same as peak extraction
     # "baseline" - Same as peak extraction
@@ -66,7 +65,7 @@ def run(plot=True):
     kwds["dg"] = 5000  # ad hoc, but gives each point equal weight in fit.
     ppe.setvars(**kwds)
 
-    ## Set up termination ripples
+    # Set up termination ripples
     # Peak fitting never changes the peak function, so termination ripples
     # are not applied automatically as they are in peak extraction.
     # Termination ripples require setting the underlying peak function and qmax.
@@ -95,7 +94,7 @@ def run(plot=True):
     # Perform fit.
     ppe.fit()
 
-    ## Save results
+    # Save results
     ppe.write("output/fit_initial.srmise")
     ppe.writepwa("output/fit_initial.pwa")
 

doc/examples/multimodel_known_dG1.py

@@ -38,46 +38,45 @@
 
 import diffpy.srmise.srmiselog as sml
 from diffpy.srmise import MultimodelSelection, PDFPeakExtraction
-from diffpy.srmise.applications.plot import makeplot
 
 
 def run(plot=True):
 
-    ## Suppress mundane output
+    # Suppress mundane output
     # When running scripts, especially involving multiple trials, it can be
     # useful to suppress many of the diffpy.srmise messages.  Valid levels
     # include "debug", "info" (the default), "warning", "error", and
     # "critical."  See diffpy.srmise.srmiselog for more information.
     sml.setlevel("warning")
 
-    ## Initialize peak extraction from saved trial
+    # Initialize peak extraction from saved trial
     ppe = PDFPeakExtraction()
     ppe.read("output/query_results.srmise")
     ppe.clearcalc()
 
-    ## Set up extraction parameters
+    # Set up extraction parameters
     # All parameters loaded from .srmise file.
     # Setting new values will override the previous values.
     kwds = {}
     kwds["rng"] = [10.9, 15]  # Region of PDF with some overlap.
     ppe.setvars(**kwds)
 
-    ## Create multimodel selection object.
+    # Create multimodel selection object.
     # The MultimodelSelection class keeps track of the results of peak
     # extraction as the assumed uncertainty dg is varied.
     ms = MultimodelSelection()
     ms.setppe(ppe)
 
-    ## Define range of dg values
+    # Define range of dg values
     # For the purpose of illustration use 15 evenly-spaced values of dg where
     # 50% < dg < 120% of mean experimental dG in extraction range.
     dg_mean = np.mean(ppe.dy[ppe.getrangeslice()])
     dgs = np.linspace(0.5 * dg_mean, 1.2 * dg_mean, 15)
 
-    ## Perform peak extraction for each of the assumed uncertainties.
+    # Perform peak extraction for each of the assumed uncertainties.
     ms.run(dgs)
 
-    ## Save results
+    # Save results
     # The file known_dG_models.dat saves the models generated above.  The file
     # known_dG_aics.dat saves the value of the AIC of each model when evaluated
     # on a Nyquist-sampled grid using each of the dg values used to generate

doc/examples/multimodel_known_dG2.py

@@ -62,19 +62,19 @@ def run(plot=True):
     # Suppress mundane output
     sml.setlevel("warning")
 
-    ## Create multimodeling object and load diffpy.srmise results from file.
+    # Create multimodeling object and load diffpy.srmise results from file.
     ms = MultimodelSelection()
     ms.load("output/known_dG_models.dat")
     ms.loadaics("output/known_dG_aics.dat")
 
-    ## Use Nyquist sampling
+    # Use Nyquist sampling
     # Standard AIC analysis assumes the data have independent uncertainties.
     # Nyquist sampling minimizes correlations in the PDF, which is the closest
     # approximation to independence possible for the PDF.
     dr = np.pi / ms.ppe.qmax
     (r, y, dr2, dy) = ms.ppe.resampledata(dr)
 
-    ## Classify models
+    # Classify models
     # All models are placed into classes.  Models in the same class
     # should be essentially identical (same peak parameters, etc.)
     # up to a small tolerance determined by comparing individual peaks. The
@@ -90,7 +90,7 @@ def run(plot=True):
     tolerance = 0.2
     ms.classify(r, tolerance)
 
-    ## Summarize various facts about the analysis.
+    # Summarize various facts about the analysis.
     num_models = len(ms.results)
     num_classes = len(ms.classes)
     print("------- Multimodeling Summary --------")
@@ -99,15 +99,15 @@ def run(plot=True):
     print("Range of dgs: %f-%f" % (ms.dgs[0], ms.dgs[-1]))
     print("Nyquist-sampled data points: %i" % len(r))
 
-    ## Get dG usable as key in analysis.
+    # Get dG usable as key in analysis.
     # The Akaike probabilities were calculated for many assumed values of the
     # experimental uncertainty dG, and each of these assumed dG is used as a
     # key when obtaining the corresponding results.  Numerical precision can
     # make recalculating the exact value difficult, so the dg_key method returns
     # the key closest to its argument.
     dG = ms.dg_key(np.mean(ms.ppe.dy))
 
-    ## Find "best" models.
+    # Find "best" models.
     # In short, models with greatest Akaike probability.  Akaike probabilities
     # can only be validly compared if they were calculated for identical data,
     # namely identical PDF values *and* uncertainties, and are only reliable
@@ -120,7 +120,7 @@ def run(plot=True):
     print("Rank  Model  Class  Free         AIC   Prob  File")
     for i in range(len(ms.classes)):
 
-        ## Generate information about best model in ith best class.
+        # Generate information about best model in ith best class.
         # The get(dG, *args, **kwds) method returns a tuple of values
         # corresponding to string arguments for the best model in best class at
         # given dG. When the corder keyword is given it returns the model from
@@ -167,7 +167,7 @@ def run(plot=True):
             # Uncomment line below to save figures.
             # plt.savefig(filename_base + ".png", format="png")
 
-    ## 3D plot of Akaike probabilities
+    # 3D plot of Akaike probabilities
     # This plot shows the Akaike probabilities of all classes as a function
     # of assumed uncertainty dG.  This gives a rough sense of how the models
     # selected by an AIC-based analysis would vary if the experimental

doc/examples/multimodel_unknown_dG1.py

@@ -42,18 +42,18 @@
 
 def run(plot=True):
 
-    ## Suppress mundane output
+    # Suppress mundane output
     # When running scripts, especially involving multiple trials, it can be
     # useful to suppress many of the diffpy.srmise messages.  Valid levels
     # include "debug", "info" (the default), "warning", "error", and
     # "critical."  See diffpy.srmise.srmiselog for more information.
     sml.setlevel("warning")
 
-    ## Initialize peak extraction
+    # Initialize peak extraction
     ppe = PDFPeakExtraction()
     ppe.loadpdf("data/C60_fine_qmax21.gr")
 
-    ## Set up extraction parameters
+    # Set up extraction parameters
     # The FromSequence baseline interpolates (r, G(r)) values read from a
     # specified file.  It has parameters.  This particular baseline was
     # calculated by approximating the C60 sample as a face-centered cubic
@@ -65,22 +65,22 @@ def run(plot=True):
     kwds["cres"] = 0.05
     ppe.setvars(**kwds)
 
-    ## Create multimodel selection object.
+    # Create multimodel selection object.
     # The MultimodelSelection class keeps track of the results of peak
     # extraction as the assumed uncertainty dg is varied.
     ms = MultimodelSelection()
     ms.setppe(ppe)
 
-    ## Define range of dg values
+    # Define range of dg values
     # For the purpose of illustration use 20 evenly-spaced values of dg where
     # 1% < dg < 10% of max gr value between r=1 and 7.25.
     grmax = np.max(ppe.y[ppe.getrangeslice()])
     dgs = np.linspace(0.01 * grmax, 0.10 * grmax, 20)
 
-    ## Perform peak extraction for each of the assumed uncertainties.
+    # Perform peak extraction for each of the assumed uncertainties.
     ms.run(dgs)
 
-    ## Save results
+    # Save results
     # The file C60_models.dat saves the models generated above.  The file
     # C60_aics.dat saves the value of the AIC of each model when evaluated
     # on a Nyquist-sampled grid using each of the dg values used to generate

doc/examples/multimodel_unknown_dG2.py

@@ -78,19 +78,19 @@ def run(plot=True):
     # Suppress mundane output
     sml.setlevel("warning")
 
-    ## Create multimodeling object and load diffpy.srmise results from file.
+    # Create multimodeling object and load diffpy.srmise results from file.
     ms = MultimodelSelection()
     ms.load("output/unknown_dG_models.dat")
     ms.loadaics("output/unknown_dG_aics.dat")
 
-    ## Use Nyquist sampling
+    # Use Nyquist sampling
     # Standard AIC analysis assumes the data have independent uncertainties.
     # Nyquist sampling minimizes correlations in the PDF, which is the closest
     # approximation to independence possible for the PDF.
     dr = np.pi / ms.ppe.qmax
     (r, y, dr2, dy) = ms.ppe.resampledata(dr)
 
-    ## Classify models
+    # Classify models
     # All models are placed into classes.  Models in the same class
     # should be essentially identical (same peak parameters, etc.)
     # up to a small tolerance determined by comparing individual peaks. The
@@ -106,7 +106,7 @@ def run(plot=True):
     tolerance = 0.2
     ms.classify(r, tolerance)
 
-    ## Summarize various facts about the analysis.
+    # Summarize various facts about the analysis.
     num_models = len(ms.results)
     num_classes = len(ms.classes)
     print("------- Multimodeling Summary --------")
@@ -115,7 +115,7 @@ def run(plot=True):
     print("Range of dgs: %f-%f" % (ms.dgs[0], ms.dgs[-1]))
     print("Nyquist-sampled data points: %i" % len(r))
 
-    ## Find "best" models.
+    # Find "best" models.
     # In short, models with greatest Akaike probability.  Akaike probabilities
     # can only be validly compared if they were calculated for identical data,
     # namely identical PDF values *and* uncertainties, and are only reliable
@@ -144,7 +144,7 @@ def run(plot=True):
     print("   Best dG  Model  Class  Free       AIC     Prob  File")
     for dG in best_dGs:
 
-        ## Generate information about best model.
+        # Generate information about best model.
         # The get(dG, *args, **kwds) method returns a tuple of values
         # corresponding to string arguments for the best model in best class at
         # given dG. When the corder keyword is given it returns the model from
@@ -191,7 +191,7 @@ def run(plot=True):
             # Uncomment line below to save figures.
             # plt.savefig(filename_base + ".png", format="png")
 
-    ## 3D plot of Akaike probabilities
+    # 3D plot of Akaike probabilities
     # This plot shows the Akaike probabilities of all classes as a function
     # of assumed uncertainty dG.  This gives a rough sense of how the models
     # selected by an AIC-based analysis would vary if the experimental