diff --git a/pyglance/glance/data.py b/pyglance/glance/data.py
index af8e40591da7b19375260b2c5ce994d5b6f1aabc..93a5ba8060d370cd67f26dc4aaff463bc3109400 100644
--- a/pyglance/glance/data.py
+++ b/pyglance/glance/data.py
@@ -161,38 +161,53 @@ class DataObject (object) :
return DataObject(self.data.copy(), fillValue=self.fill_value, ignoreMask=self.masks.ignore_mask,
overrideFillValue=self.override_fill_value, defaultFillValue=self.default_fill_value)
- def self_analysis(self) :
+ def self_analysis(self, re_do_analysis=False) :
"""
Gather some basic information about a data set
- """
-
- # hang onto the shape for convenience
- shape = self.data.shape
-
- # if there isn't an ignore mask, make an empty one
- if self.masks.ignore_mask is None :
- self.masks.ignore_mask = np.zeros(shape, dtype=np.bool)
- # find the non-finite values
- non_finite_mask = ~np.isfinite(self.data) & ~self.masks.ignore_mask
-
- # find and mark the missing values
- missing_mask = np.zeros(shape, dtype=np.bool)
- # if the data has a fill value, mark where the missing data is
- tempFillValue = self.select_fill_value()
- if tempFillValue is not None :
- missing_mask[self.data == tempFillValue] = True
- missing_mask[self.masks.ignore_mask] = False
-
- # define the valid mask as places where the data is not missing,
- # nonfinite, or ignored
- valid_mask = ~ (missing_mask | non_finite_mask | self.masks.ignore_mask)
+ Note: If the data has already been analyzed it will not do anything
+ unless you send in re_do_analysis=True
+ """
- # set our masks
- self.masks = BasicMaskSetObject(self.masks.ignore_mask, valid_mask,
- non_finite_mask, missing_mask)
+ # data objects are intended to be immutable, build a new one or override to re-analyze
+ if (not self.have_analyzed) or re_do_analysis :
- self.have_analyzed = True
+ # hang onto the shape for convenience
+ shape = self.data.shape
+
+ # if there isn't an ignore mask, make an empty one
+ if self.masks.ignore_mask is None :
+ self.masks.ignore_mask = np.zeros(shape, dtype=np.bool)
+
+ # find the non-finite values
+ #non_finite_mask = ~np.isfinite(self.data) & ~self.masks.ignore_mask
+ #non_finite_mask = ~ (np.isfinite(self.data) | self.masks.ignore_mask)
+ non_finite_mask = np.zeros(shape, dtype=np.bool)
+ np.isfinite(self.data, non_finite_mask)
+ np.logical_or(non_finite_mask, self.masks.ignore_mask, non_finite_mask)
+ np.logical_not(non_finite_mask, non_finite_mask)
+
+ # find and mark the missing values
+ missing_mask = np.zeros(shape, dtype=np.bool)
+ # if the data has a fill value, mark where the missing data is
+ tempFillValue = self.select_fill_value()
+ if tempFillValue is not None :
+ missing_mask[self.data == tempFillValue] = True
+ missing_mask[self.masks.ignore_mask] = False
+
+ # define the valid mask as places where the data is not missing,
+ # nonfinite, or ignored
+ #valid_mask = ~ (missing_mask | non_finite_mask | self.masks.ignore_mask)
+ valid_mask = np.zeros(shape, dtype=np.bool)
+ np.logical_or(missing_mask, non_finite_mask, valid_mask)
+ np.logical_or(self.masks.ignore_mask, valid_mask, valid_mask)
+ np.logical_not(valid_mask, valid_mask)
+
+ # set our masks
+ self.masks = BasicMaskSetObject(self.masks.ignore_mask, valid_mask,
+ non_finite_mask, missing_mask)
+
+ self.have_analyzed = True
def select_fill_value (self) :
"""
@@ -207,10 +222,7 @@ class DataObject (object) :
get the minimum value in this data set
"""
- # TODO it would be better to put this test in the analysis function
- # TODO but first I'd need to be sure that wouldn't break anything
- if not self.have_analyzed :
- self.self_analysis()
+ self.self_analysis()
return delta.min_with_mask(self.data, self.masks.valid_mask)
def get_max (self) :
@@ -218,10 +230,7 @@ class DataObject (object) :
get the maximum value in this data set
"""
- # TODO it would be better to put this test in the analysis function
- # TODO but first I'd need to be sure that wouldn't break anything
- if not self.have_analyzed :
- self.self_analysis()
+ self.self_analysis()
return delta.max_with_mask(self.data, self.masks.valid_mask)
class DiffInfoObject (object) :
diff --git a/pyglance/glance/delta.py b/pyglance/glance/delta.py
index a100b9b3bd2308911ab032114d19c0fea6642102..34942a359353b9852aac07fd3b19752826574e66 100644
--- a/pyglance/glance/delta.py
+++ b/pyglance/glance/delta.py
@@ -65,9 +65,7 @@ def min_with_mask(data, goodMask=None) :
goodData = data[goodMask]
# if we have any good data, get the minimum
- toReturn = None
- if goodData.size > 0 :
- toReturn = np.min(goodData)
+ toReturn = np.min(goodData) if goodData.size > 0 else None
return toReturn
@@ -92,9 +90,7 @@ def max_with_mask(data, goodMask=None) :
goodData = data[goodMask]
# if we have any good data, get the maximum
- toReturn = None
- if goodData.size > 0 :
- toReturn = np.max(goodData)
+ toReturn = np.max(goodData) if goodData.size > 0 else None
return toReturn
diff --git a/pyglance/glance/figures.py b/pyglance/glance/figures.py
index faa4c1176e4700fe2abb84536acbfc2151958258..a35156319cfcab3dc17880105519d78d632340ca 100644
--- a/pyglance/glance/figures.py
+++ b/pyglance/glance/figures.py
@@ -291,6 +291,9 @@ def create_hexbin_plot(dataX, dataY, title, xLabel, yLabel, epsilon=None, units_
# the hexbin plot of the good data
plt.hexbin(dataX, dataY, bins='log', cmap=cm.jet)
plt.axis([dataX.min(), dataX.max(), dataY.min(), dataY.max()])
+ #heatmap, xedges, yedges = np.histogram2d(dataX, dataY, bins=100) #todo, testing
+ #heatmap = log(heatmap + 1)
+ #plt.imshow(heatmap, extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap=cm.jet)
# create a color bar
cb = plt.colorbar()