diff --git a/pyglance/glance/data.py b/pyglance/glance/data.py
index 233a55c43b6054710bb5402c2d809dcc7702d9c7..7e6bde1e5118ac2e4c64bfe647fd04ba21e99350 100644
--- a/pyglance/glance/data.py
+++ b/pyglance/glance/data.py
@@ -180,13 +180,12 @@ class DataObject (object) :
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)
-
+ 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
@@ -204,7 +203,7 @@ class DataObject (object) :
np.logical_or(self.masks.ignore_mask, valid_mask, valid_mask)
np.logical_not(valid_mask, valid_mask)
else :
- np.array([ ], dtype=np.bool)
+ valid_mask = np.array([ ], dtype=np.bool)
# set our masks
self.masks = BasicMaskSetObject(self.masks.ignore_mask, valid_mask,
diff --git a/pyglance/glance/io.py b/pyglance/glance/io.py
index 40d7c139d616bc7ba3800bb2f5d2e001f22c623b..4ffc2816085ac342fa5336cb3ce4f4f497fb5cdd 100644
--- a/pyglance/glance/io.py
+++ b/pyglance/glance/io.py
@@ -476,7 +476,7 @@ class nc (object):
if needed_dtype is not None :
# now figure out where all the corrupted values are, and shift them up to be positive
- needs_fix_mask = (scaled_data_copy < 0.0) & (scaled_data_copy != missing_val)
+ needs_fix_mask = (scaled_data_copy < add_offset) & (scaled_data_copy != missing_val)
# we are adding the 2's complement, but first we're scaling it appropriately
scaled_data_copy[needs_fix_mask] += ((np.iinfo(np.uint16).max + 1.0) * scale_factor) + add_offset
diff --git a/pyglance/glance/stats.py b/pyglance/glance/stats.py
index d1b5a845e7db2235c7f1988f800495b57369b442..86efa7e25a458cdeef913ec19efc159141b9e2af 100644
--- a/pyglance/glance/stats.py
+++ b/pyglance/glance/stats.py
@@ -136,7 +136,7 @@ class MissingValueStatistics (StatisticalData) :
self.is_one_data_set = True
self.desc_text = dataSetDescription
noData = len(dataObject.data.shape) <= 0
-
+
# figure out some basic statistics
self.missing_count = np.sum(dataObject.masks.missing_mask)
self.missing_fraction = float(self.missing_count) / float(dataObject.data.size) if not noData else np.nan
@@ -277,7 +277,7 @@ class FiniteDataStatistics (StatisticalData) :
# if we have a comparison object analyze the data associated with that comparison
elif diffInfoObject is not None :
- no_data = len(diffInfoObject.a_data_object.data.shape) <= 0
+ noData = len(diffInfoObject.a_data_object.data.shape) <= 0
# analyze each of the original data sets that are being compared
self.a_finite_stats = FiniteDataStatistics(dataObject=diffInfoObject.a_data_object, dataSetDescription="a")
@@ -285,15 +285,15 @@ class FiniteDataStatistics (StatisticalData) :
# calculate some common statistics
self.common_finite_count = np.sum(diffInfoObject.a_data_object.masks.valid_mask & diffInfoObject.b_data_object.masks.valid_mask) \
- if not no_data else 0
+ if not noData else 0
# use an exclusive or to check which points are finite in only one of the two data sets
self.finite_in_only_one_count = np.sum((diffInfoObject.a_data_object.masks.valid_mask ^ diffInfoObject.b_data_object.masks.valid_mask) \
& ~diffInfoObject.diff_data_object.masks.ignore_mask) \
- if not no_data else 0
+ if not noData else 0
self.common_finite_fraction = float(self.common_finite_count) / float(diffInfoObject.a_data_object.data.size) \
- if not no_data else np.nan
+ if not noData else np.nan
self.finite_in_only_one_fraction = float(self.finite_in_only_one_count) / float(diffInfoObject.a_data_object.data.size) \
- if not no_data else np.nan
+ if not noData else np.nan
else:
raise ValueError ("No data set was given when requesting statistical analysis of finite values.")
@@ -406,7 +406,7 @@ class NotANumberStatistics (StatisticalData) :
self.is_one_data_set = True
self.desc_text = dataSetDescription
noData = len(dataObject.data.shape) <= 0
-
+
# get some basic statistics
self.nan_count = np.sum(dataObject.masks.non_finite_mask)
self.nan_fraction = float(self.nan_count) / float(dataObject.data.size) if not noData else np.nan
@@ -750,8 +750,8 @@ class NumericalComparisonStatistics (StatisticalData) :
self.mismatch_points_count = np.sum(diffInfoObject.diff_data_object.masks.mismatch_mask)
# calculate some more complex statistics, be careful not to divide by zero
- self.mismatch_points_fraction = float(self.mismatch_points_count) / float(aData.size) if not noData else np.nan
- self.diff_outside_epsilon_fraction = float(self.diff_outside_epsilon_count) / float(total_num_finite_values) if (total_num_finite_values > 0) else np.nan
+ self.mismatch_points_fraction = float(self.mismatch_points_count) / float(aData.size) if not noData else 0.0
+ self.diff_outside_epsilon_fraction = float(self.diff_outside_epsilon_count) / float(total_num_finite_values) if (total_num_finite_values > 0) else 0.0
self.perfect_match_fraction = float(self.perfect_match_count) / float(total_num_finite_values) if (total_num_finite_values > 0) else np.nan
# if desired, do the basic analysis
@@ -799,7 +799,7 @@ class NumericalComparisonStatistics (StatisticalData) :
# if everything's invalid, stop now
noData = np.sum(valid_mask) <= 0
-
+
# calculate and return statistics
root_mean_square_value = delta.calculate_root_mean_square(diffData, valid_mask) if not noData else np.nan
tempDiffData = diffData[valid_mask] if not noData else None
@@ -916,19 +916,19 @@ class StatisticalAnalysis (StatisticalData) :
also returns information about the fractions of failure
"""
-
+
passValues = [ ]
# test the epsilon value tolerance
# get the tolerance for failures compared to epsilon
epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default
-
+
# did we fail based on the epsilon?
failed_fraction = self.comparison.diff_outside_epsilon_fraction
passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)
passValues.append(passed_epsilon)
-
+
# test the nonfinite tolerance
# get the tolerance for failures in amount of nonfinite data (in spatially valid areas)
@@ -938,7 +938,7 @@ class StatisticalAnalysis (StatisticalData) :
non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction
passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)
passValues.append(passed_nonfinite)
-
+
# test if the total failed percentage is acceptable
# get the total percentage of failed data that is acceptable
@@ -947,17 +947,17 @@ class StatisticalAnalysis (StatisticalData) :
# did we fail based on all data failures?
passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)
passValues.append(passed_all_percentage)
-
+
# test the r-squared correlation coefficent
# get the minimum acceptable r-squared correlation coefficient
min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default
-
+
# did we fail based on the r-squared correlation coefficient?
r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation
passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)
passValues.append(passed_r_squared)
-
+
# figure out the overall pass/fail result
didPass = None
for passValue in passValues :