fix broken import in doc, change README to rst file.

devutils/prep.py

@@ -6,7 +6,7 @@
 import re
 import sys
 
-__basedir__ = os.getcwdu()
+__basedir__ = os.getcwd()
 
 from numpy.compat import unicode
 

doc/examples/extract_single_peak.py

@@ -28,7 +28,7 @@
 import matplotlib.pyplot as plt
 
 from diffpy.srmise.applications.plot import makeplot
-from diffpy.srmise.baselines import Polynomial
+from diffpy.srmise.baselines.polynomial import Polynomial
 from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
 
 

doc/examples/fit_initial.py

@@ -21,10 +21,11 @@
 
 import matplotlib.pyplot as plt
 
-from diffpy.srmise import PDFPeakExtraction
 from diffpy.srmise.applications.plot import makeplot
-from diffpy.srmise.baselines import FromSequence
-from diffpy.srmise.peaks import Peaks, TerminationRipples
+from diffpy.srmise.baselines.fromsequence import FromSequence
+from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
+from diffpy.srmise.peaks.base import Peaks
+from diffpy.srmise.peaks.terminationripples import TerminationRipples
 
 
 def run(plot=True):

doc/examples/multimodel_known_dG1.py

@@ -37,7 +37,8 @@
 import numpy as np
 
 import diffpy.srmise.srmiselog as sml
-from diffpy.srmise import MultimodelSelection, PDFPeakExtraction
+from diffpy.srmise.multimodelselection import MultimodelSelection
+from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
 
 
 def run(plot=True):

doc/examples/multimodel_known_dG2.py

@@ -35,8 +35,8 @@
 import numpy as np
 
 import diffpy.srmise.srmiselog as sml
-from diffpy.srmise import MultimodelSelection
 from diffpy.srmise.applications.plot import makeplot
+from diffpy.srmise.multimodelselection import MultimodelSelection
 
 # distances from ideal Ag (refined to PDF)
 dcif = np.array(

doc/examples/multimodel_unknown_dG1.py

@@ -36,8 +36,9 @@
 import numpy as np
 
 import diffpy.srmise.srmiselog as sml
-from diffpy.srmise import MultimodelSelection, PDFPeakExtraction
-from diffpy.srmise.baselines import FromSequence
+from diffpy.srmise.baselines.fromsequence import FromSequence
+from diffpy.srmise.multimodelselection import MultimodelSelection
+from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
 
 
 def run(plot=True):

doc/examples/multimodel_unknown_dG2.py

@@ -42,8 +42,8 @@
 import numpy as np
 
 import diffpy.srmise.srmiselog as sml
-from diffpy.srmise import MultimodelSelection
 from diffpy.srmise.applications.plot import makeplot
+from diffpy.srmise.multimodelselection import MultimodelSelection
 
 # distances from ideal (unrefined) C60
 dcif = np.array(

doc/examples/parameter_summary.py

@@ -29,10 +29,10 @@
 
 import matplotlib.pyplot as plt
 
-from diffpy.srmise import PDFPeakExtraction
 from diffpy.srmise.applications.plot import makeplot
-from diffpy.srmise.baselines import Polynomial
-from diffpy.srmise.peaks import GaussianOverR
+from diffpy.srmise.baselines.polynomial import Polynomial
+from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
+from diffpy.srmise.peaks.gaussianoverr import GaussianOverR
 
 
 def run(plot=True):

doc/examples/query_results.py

@@ -29,7 +29,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from diffpy.srmise import ModelCovariance, PDFPeakExtraction
+from diffpy.srmise.modelcluster import ModelCovariance
+from diffpy.srmise.pdfpeakextraction import PDFPeakExtraction
 
 
 def run(plot=True):

src/diffpy/srmise/multimodelselection.py

@@ -507,8 +507,8 @@ def plot3dclassprobs(self, **kwds):
         probfilter = kwds.pop("probfilter", [0.0, 1.0])
         class_size = kwds.pop("class_size", "number")
         norm = kwds.pop("norm", "auto")
-        cmap = kwds.pop("cmap", cm.jet)
-        highlight_cmap = kwds.pop("highlight_cmap", cm.gray)
+        cmap = kwds.pop("cmap", cm.get_cmap("jet"))
+        highlight_cmap = kwds.pop("highlight_cmap", cm.get_cmap("gray"))
         title = kwds.pop("title", True)
         p_alpha = kwds.pop("p_alpha", 0.7)
         scale = kwds.pop("scale", 1.0)

src/diffpy/srmise/peaks/base.py

@@ -189,7 +189,7 @@ def match_at(self, x, y):
             return False
         return any_scaled
 
-    def sort(self, key="position"):
+    def sort(self, reverse=False, key="position"):
         """Sort peaks in order specified by key."""
         keypars = np.array([p[key] for p in self])
         order = keypars.argsort()