diff --git a/pyglance/glance/compare.py b/pyglance/glance/compare.py
index 7567dec27b08ace0703fb5d679ceb6e1bc960123..3e0f4a1c104c09ce8bfa3097efafe31e7f318aa7 100644
--- a/pyglance/glance/compare.py
+++ b/pyglance/glance/compare.py
@@ -37,7 +37,7 @@ import glance.collocation as collocation
import glance.config_organizer as config_organizer
from glance.util import clean_path, rsync_or_copy_files, get_glance_version_string, get_run_identification_info, setup_dir_if_needed
-from glance.load import get_UV_info_from_magnitude_direction_info, load_variable_data, open_and_process_files, handle_lon_lat_info, handle_lon_lat_info_for_one_file
+from glance.load import get_UV_info_from_magnitude_direction_info, load_variable_data, open_and_process_files, handle_lon_lat_info, handle_lon_lat_info_for_one_file, ValueErrorStringToFloat
from glance.lonlat_util import VariableComparisonError
from glance.constants import *
from glance.gui_constants import A_CONST, B_CONST
@@ -740,226 +740,228 @@ def reportGen_library_call (a_path, b_path, var_list=[ ],
# go through each of the possible variables in our files
# and make a report section with images for whichever ones we can
for displayName in finalNames:
-
- # pull out the information for this variable analysis run
- varRunInfo = finalNames[displayName].copy()
-
- # get the various names
- technical_name, b_variable_technical_name, \
- explanationName = _get_name_info_for_variable(displayName, varRunInfo)
-
- # make sure that it's possible to load this variable
- if not(aFile.file_object.is_loadable_type(technical_name)) or not(bFile.file_object.is_loadable_type(b_variable_technical_name)) :
- LOG.warn(displayName + " is of a type that cannot be loaded using current file handling libraries included with Glance." +
- " Skipping " + displayName + ".")
- continue
-
- LOG.info('analyzing: ' + explanationName)
-
- # load the variable data
- aData = load_variable_data(aFile.file_object, technical_name,
- dataFilter = varRunInfo[FILTER_FUNCTION_A_KEY] if FILTER_FUNCTION_A_KEY in varRunInfo else None,
- variableToFilterOn = varRunInfo[VAR_FILTER_NAME_A_KEY] if VAR_FILTER_NAME_A_KEY in varRunInfo else None,
- variableBasedFilter = varRunInfo[VAR_FILTER_FUNCTION_A_KEY] if VAR_FILTER_FUNCTION_A_KEY in varRunInfo else None,
- altVariableFileObject = dataobj.FileInfo(varRunInfo[VAR_FILTER_ALT_FILE_A_KEY]).file_object if VAR_FILTER_ALT_FILE_A_KEY in varRunInfo else None,
- fileDescriptionForDisplay = "file A")
- bData = load_variable_data(bFile.file_object, b_variable_technical_name,
- dataFilter = varRunInfo[FILTER_FUNCTION_B_KEY] if FILTER_FUNCTION_B_KEY in varRunInfo else None,
- variableToFilterOn = varRunInfo[VAR_FILTER_NAME_B_KEY] if VAR_FILTER_NAME_B_KEY in varRunInfo else None,
- variableBasedFilter = varRunInfo[VAR_FILTER_FUNCTION_B_KEY] if VAR_FILTER_FUNCTION_B_KEY in varRunInfo else None,
- altVariableFileObject = dataobj.FileInfo(varRunInfo[VAR_FILTER_ALT_FILE_B_KEY]).file_object if VAR_FILTER_ALT_FILE_B_KEY in varRunInfo else None,
- fileDescriptionForDisplay = "file B")
-
- # pre-check if this data should be plotted and if it should be compared to the longitude and latitude
- include_images_for_this_variable = ((not(DO_MAKE_IMAGES_KEY in runInfo)) or (runInfo[DO_MAKE_IMAGES_KEY]))
- if DO_MAKE_IMAGES_KEY in varRunInfo :
- include_images_for_this_variable = varRunInfo[DO_MAKE_IMAGES_KEY]
- do_not_test_with_lon_lat = (not include_images_for_this_variable) or (len(lon_lat_data.keys()) <= 0)
-
- # handle vector data
- isVectorData = ( (MAGNITUDE_VAR_NAME_KEY in varRunInfo) and (DIRECTION_VAR_NAME_KEY in varRunInfo) and
- (MAGNITUDE_B_VAR_NAME_KEY in varRunInfo) and (DIRECTION_B_VAR_NAME_KEY in varRunInfo) )
-
- # check if this data can be displayed but
- # don't compare lon/lat sizes if we won't be plotting
- if ( (aData.shape == bData.shape)
- and
- ( do_not_test_with_lon_lat
- or
- ((aData.shape == good_shape_from_lon_lat) and (bData.shape == good_shape_from_lon_lat)) ) ) :
+ try:
+ # pull out the information for this variable analysis run
+ varRunInfo = finalNames[displayName].copy()
- # check to see if there is a directory to put information about this variable in,
- # if not then create it
- variableDir = os.path.join(pathsTemp[OUT_FILE_KEY], './' + displayName)
- varRunInfo[VARIABLE_DIRECTORY_KEY] = variableDir
- varRunInfo[VAR_REPORT_PATH_KEY] = quote(os.path.join(displayName, 'index.html'))
- LOG.debug ("Directory selected for variable information: " + varRunInfo[VAR_REPORT_PATH_KEY])
- setup_dir_if_needed(variableDir, "variable")
+ # get the various names
+ technical_name, b_variable_technical_name, \
+ explanationName = _get_name_info_for_variable(displayName, varRunInfo)
- # form the doc and config paths relative to where the variable is
- upwardPath = './'
- for number in range(len(displayName.split('/'))) : # TODO this is not general to windows
- upwardPath = os.path.join(upwardPath, '../')
- varRunInfo[DOCUMENTATION_PATH_KEY] = quote(os.path.join(upwardPath, 'doc.html'))
- if CONFIG_FILE_NAME_KEY in runInfo :
- varRunInfo[CONFIG_FILE_PATH_KEY] = quote(os.path.join(upwardPath, runInfo[CONFIG_FILE_NAME_KEY]))
+ # make sure that it's possible to load this variable
+ if not(aFile.file_object.is_loadable_type(technical_name)) or not(bFile.file_object.is_loadable_type(b_variable_technical_name)) :
+ LOG.warn(displayName + " is of a type that cannot be loaded using current file handling libraries included with Glance." +
+ " Skipping " + displayName + ".")
+ continue
- # figure out the masks we want, and then do our statistical analysis
- mask_a_to_use = None if do_not_test_with_lon_lat else lon_lat_data[A_FILE_KEY][INVALID_MASK_KEY]
- mask_b_to_use = None if do_not_test_with_lon_lat else lon_lat_data[B_FILE_KEY][INVALID_MASK_KEY]
- LOG.debug("Analyzing " + displayName + " statistically.")
- variable_stats = statistics.StatisticalAnalysis.withSimpleData(aData, bData,
- varRunInfo[FILL_VALUE_KEY], varRunInfo[FILL_VALUE_ALT_IN_B_KEY],
- mask_a_to_use, mask_b_to_use,
- varRunInfo[EPSILON_KEY], varRunInfo[EPSILON_PERCENT_KEY])
+ LOG.info('analyzing: ' + explanationName)
- # add a little additional info to our variable run info before we squirrel it away
- varRunInfo[TIME_INFO_KEY] = datetime.datetime.ctime(datetime.datetime.now()) # todo is this needed?
- didPass, epsilon_failed_fraction, \
- non_finite_fail_fraction, \
- r_squared_value = variable_stats.check_pass_or_fail(epsilon_failure_tolerance=varRunInfo[EPSILON_FAIL_TOLERANCE_KEY] if EPSILON_FAIL_TOLERANCE_KEY in varRunInfo else numpy.nan,
- epsilon_failure_tolerance_default=defaultValues[EPSILON_FAIL_TOLERANCE_KEY],
- non_finite_data_tolerance=varRunInfo[NONFINITE_TOLERANCE_KEY] if NONFINITE_TOLERANCE_KEY in varRunInfo else numpy.nan,
- non_finite_data_tolerance_default=defaultValues[NONFINITE_TOLERANCE_KEY],
- total_data_failure_tolerance=varRunInfo[TOTAL_FAIL_TOLERANCE_KEY] if TOTAL_FAIL_TOLERANCE_KEY in varRunInfo else numpy.nan,
- total_data_failure_tolerance_default=defaultValues[TOTAL_FAIL_TOLERANCE_KEY],
- min_acceptable_r_squared=varRunInfo[MIN_OK_R_SQUARED_COEFF_KEY] if MIN_OK_R_SQUARED_COEFF_KEY in varRunInfo else numpy.nan,
- min_acceptable_r_squared_default=defaultValues[MIN_OK_R_SQUARED_COEFF_KEY],
- )
+ # load the variable data
+ aData = load_variable_data(aFile.file_object, technical_name,
+ dataFilter = varRunInfo[FILTER_FUNCTION_A_KEY] if FILTER_FUNCTION_A_KEY in varRunInfo else None,
+ variableToFilterOn = varRunInfo[VAR_FILTER_NAME_A_KEY] if VAR_FILTER_NAME_A_KEY in varRunInfo else None,
+ variableBasedFilter = varRunInfo[VAR_FILTER_FUNCTION_A_KEY] if VAR_FILTER_FUNCTION_A_KEY in varRunInfo else None,
+ altVariableFileObject = dataobj.FileInfo(varRunInfo[VAR_FILTER_ALT_FILE_A_KEY]).file_object if VAR_FILTER_ALT_FILE_A_KEY in varRunInfo else None,
+ fileDescriptionForDisplay = "file A")
+ bData = load_variable_data(bFile.file_object, b_variable_technical_name,
+ dataFilter = varRunInfo[FILTER_FUNCTION_B_KEY] if FILTER_FUNCTION_B_KEY in varRunInfo else None,
+ variableToFilterOn = varRunInfo[VAR_FILTER_NAME_B_KEY] if VAR_FILTER_NAME_B_KEY in varRunInfo else None,
+ variableBasedFilter = varRunInfo[VAR_FILTER_FUNCTION_B_KEY] if VAR_FILTER_FUNCTION_B_KEY in varRunInfo else None,
+ altVariableFileObject = dataobj.FileInfo(varRunInfo[VAR_FILTER_ALT_FILE_B_KEY]).file_object if VAR_FILTER_ALT_FILE_B_KEY in varRunInfo else None,
+ fileDescriptionForDisplay = "file B")
- varRunInfo[DID_VARIABLE_PASS_KEY] = didPass
- # update the overall pass status
- if didPass is not None :
- didPassAll = didPassAll & didPass
+ # pre-check if this data should be plotted and if it should be compared to the longitude and latitude
+ include_images_for_this_variable = ((not(DO_MAKE_IMAGES_KEY in runInfo)) or (runInfo[DO_MAKE_IMAGES_KEY]))
+ if DO_MAKE_IMAGES_KEY in varRunInfo :
+ include_images_for_this_variable = varRunInfo[DO_MAKE_IMAGES_KEY]
+ do_not_test_with_lon_lat = (not include_images_for_this_variable) or (len(lon_lat_data.keys()) <= 0)
- # based on the settings and whether the variable passsed or failed,
- # should we include images for this variable?
- if (DO_IMAGES_ONLY_ON_FAIL_KEY in varRunInfo) and varRunInfo[DO_IMAGES_ONLY_ON_FAIL_KEY] :
- include_images_for_this_variable = include_images_for_this_variable and (not didPass)
- varRunInfo[DO_MAKE_IMAGES_KEY] = include_images_for_this_variable
+ # handle vector data
+ isVectorData = ( (MAGNITUDE_VAR_NAME_KEY in varRunInfo) and (DIRECTION_VAR_NAME_KEY in varRunInfo) and
+ (MAGNITUDE_B_VAR_NAME_KEY in varRunInfo) and (DIRECTION_B_VAR_NAME_KEY in varRunInfo) )
- # to hold the names of any images created
- image_names = {
- ORIGINAL_IMAGES_KEY: [ ],
- COMPARED_IMAGES_KEY: [ ]
- }
-
- # create the images for this variable
- if (include_images_for_this_variable) :
+ # check if this data can be displayed but
+ # don't compare lon/lat sizes if we won't be plotting
+ if ( (aData.shape == bData.shape)
+ and
+ ( do_not_test_with_lon_lat
+ or
+ ((aData.shape == good_shape_from_lon_lat) and (bData.shape == good_shape_from_lon_lat)) ) ) :
- plotFunctionGenerationObjects = [ ]
+ # check to see if there is a directory to put information about this variable in,
+ # if not then create it
+ variableDir = os.path.join(pathsTemp[OUT_FILE_KEY], './' + displayName)
+ varRunInfo[VARIABLE_DIRECTORY_KEY] = variableDir
+ varRunInfo[VAR_REPORT_PATH_KEY] = quote(os.path.join(displayName, 'index.html'))
+ LOG.debug ("Directory selected for variable information: " + varRunInfo[VAR_REPORT_PATH_KEY])
+ setup_dir_if_needed(variableDir, "variable")
- # if there's magnitude and direction data, figure out the u and v, otherwise these will be None
- aUData, aVData = get_UV_info_from_magnitude_direction_info (aFile.file_object,
- varRunInfo[MAGNITUDE_VAR_NAME_KEY] if (MAGNITUDE_VAR_NAME_KEY) in varRunInfo else None,
- varRunInfo[DIRECTION_VAR_NAME_KEY] if (DIRECTION_VAR_NAME_KEY) in varRunInfo else None,
- lon_lat_data[A_FILE_KEY][INVALID_MASK_KEY]
- if (A_FILE_KEY in lon_lat_data) and (INVALID_MASK_KEY in lon_lat_data[A_FILE_KEY]) else None)
- bUData, bVData = get_UV_info_from_magnitude_direction_info (bFile.file_object,
- varRunInfo[MAGNITUDE_B_VAR_NAME_KEY] if (MAGNITUDE_B_VAR_NAME_KEY) in varRunInfo else None,
- varRunInfo[DIRECTION_B_VAR_NAME_KEY] if (DIRECTION_B_VAR_NAME_KEY) in varRunInfo else None,
- lon_lat_data[B_FILE_KEY][INVALID_MASK_KEY]
- if (B_FILE_KEY in lon_lat_data) and (INVALID_MASK_KEY in lon_lat_data[B_FILE_KEY]) else None)
+ # form the doc and config paths relative to where the variable is
+ upwardPath = './'
+ for number in range(len(displayName.split('/'))) : # TODO this is not general to windows
+ upwardPath = os.path.join(upwardPath, '../')
+ varRunInfo[DOCUMENTATION_PATH_KEY] = quote(os.path.join(upwardPath, 'doc.html'))
+ if CONFIG_FILE_NAME_KEY in runInfo :
+ varRunInfo[CONFIG_FILE_PATH_KEY] = quote(os.path.join(upwardPath, runInfo[CONFIG_FILE_NAME_KEY]))
+
+ # figure out the masks we want, and then do our statistical analysis
+ mask_a_to_use = None if do_not_test_with_lon_lat else lon_lat_data[A_FILE_KEY][INVALID_MASK_KEY]
+ mask_b_to_use = None if do_not_test_with_lon_lat else lon_lat_data[B_FILE_KEY][INVALID_MASK_KEY]
+ LOG.debug("Analyzing " + displayName + " statistically.")
+ variable_stats = statistics.StatisticalAnalysis.withSimpleData(aData, bData,
+ varRunInfo[FILL_VALUE_KEY], varRunInfo[FILL_VALUE_ALT_IN_B_KEY],
+ mask_a_to_use, mask_b_to_use,
+ varRunInfo[EPSILON_KEY], varRunInfo[EPSILON_PERCENT_KEY])
+
+ # add a little additional info to our variable run info before we squirrel it away
+ varRunInfo[TIME_INFO_KEY] = datetime.datetime.ctime(datetime.datetime.now()) # todo is this needed?
+ didPass, epsilon_failed_fraction, \
+ non_finite_fail_fraction, \
+ r_squared_value = variable_stats.check_pass_or_fail(epsilon_failure_tolerance=varRunInfo[EPSILON_FAIL_TOLERANCE_KEY] if EPSILON_FAIL_TOLERANCE_KEY in varRunInfo else numpy.nan,
+ epsilon_failure_tolerance_default=defaultValues[EPSILON_FAIL_TOLERANCE_KEY],
+ non_finite_data_tolerance=varRunInfo[NONFINITE_TOLERANCE_KEY] if NONFINITE_TOLERANCE_KEY in varRunInfo else numpy.nan,
+ non_finite_data_tolerance_default=defaultValues[NONFINITE_TOLERANCE_KEY],
+ total_data_failure_tolerance=varRunInfo[TOTAL_FAIL_TOLERANCE_KEY] if TOTAL_FAIL_TOLERANCE_KEY in varRunInfo else numpy.nan,
+ total_data_failure_tolerance_default=defaultValues[TOTAL_FAIL_TOLERANCE_KEY],
+ min_acceptable_r_squared=varRunInfo[MIN_OK_R_SQUARED_COEFF_KEY] if MIN_OK_R_SQUARED_COEFF_KEY in varRunInfo else numpy.nan,
+ min_acceptable_r_squared_default=defaultValues[MIN_OK_R_SQUARED_COEFF_KEY],
+ )
+
+ varRunInfo[DID_VARIABLE_PASS_KEY] = didPass
+ # update the overall pass status
+ if didPass is not None :
+ didPassAll = didPassAll & didPass
- # if the data is the same size, we can always make our basic statistical comparison plots
- if (aData.shape == bData.shape) :
- plotFunctionGenerationObjects.append(plotcreate.BasicComparisonPlotsFunctionFactory())
+ # based on the settings and whether the variable passsed or failed,
+ # should we include images for this variable?
+ if (DO_IMAGES_ONLY_ON_FAIL_KEY in varRunInfo) and varRunInfo[DO_IMAGES_ONLY_ON_FAIL_KEY] :
+ include_images_for_this_variable = include_images_for_this_variable and (not didPass)
+ varRunInfo[DO_MAKE_IMAGES_KEY] = include_images_for_this_variable
- # if the bin and tuple are defined, try to analyze the data as complex
- # multidimentional information requiring careful sampling
- if (BIN_INDEX_KEY in varRunInfo) and (TUPLE_INDEX_KEY in varRunInfo) :
- plotFunctionGenerationObjects.append(plotcreate.BinTupleAnalysisFunctionFactory())
+ # to hold the names of any images created
+ image_names = {
+ ORIGINAL_IMAGES_KEY: [ ],
+ COMPARED_IMAGES_KEY: [ ]
+ }
+
+ # create the images for this variable
+ if (include_images_for_this_variable) :
- else : # if it's not bin/tuple, there are lots of other posibilities
+ plotFunctionGenerationObjects = [ ]
- # if it's vector data with longitude and latitude, quiver plot it on the Earth
- if isVectorData and (not do_not_test_with_lon_lat) :
- plotFunctionGenerationObjects.append(plotcreate.MappedQuiverPlotFunctionFactory())
+ # if there's magnitude and direction data, figure out the u and v, otherwise these will be None
+ aUData, aVData = get_UV_info_from_magnitude_direction_info (aFile.file_object,
+ varRunInfo[MAGNITUDE_VAR_NAME_KEY] if (MAGNITUDE_VAR_NAME_KEY) in varRunInfo else None,
+ varRunInfo[DIRECTION_VAR_NAME_KEY] if (DIRECTION_VAR_NAME_KEY) in varRunInfo else None,
+ lon_lat_data[A_FILE_KEY][INVALID_MASK_KEY]
+ if (A_FILE_KEY in lon_lat_data) and (INVALID_MASK_KEY in lon_lat_data[A_FILE_KEY]) else None)
+ bUData, bVData = get_UV_info_from_magnitude_direction_info (bFile.file_object,
+ varRunInfo[MAGNITUDE_B_VAR_NAME_KEY] if (MAGNITUDE_B_VAR_NAME_KEY) in varRunInfo else None,
+ varRunInfo[DIRECTION_B_VAR_NAME_KEY] if (DIRECTION_B_VAR_NAME_KEY) in varRunInfo else None,
+ lon_lat_data[B_FILE_KEY][INVALID_MASK_KEY]
+ if (B_FILE_KEY in lon_lat_data) and (INVALID_MASK_KEY in lon_lat_data[B_FILE_KEY]) else None)
- # if the data is one dimensional we can plot it as lines
- elif (len(aData.shape) is 1) :
- plotFunctionGenerationObjects.append(plotcreate.LinePlotsFunctionFactory())
+ # if the data is the same size, we can always make our basic statistical comparison plots
+ if (aData.shape == bData.shape) :
+ plotFunctionGenerationObjects.append(plotcreate.BasicComparisonPlotsFunctionFactory())
- # if the data is 2D we have some options based on the type of data
- elif (len(aData.shape) is 2) :
+ # if the bin and tuple are defined, try to analyze the data as complex
+ # multidimentional information requiring careful sampling
+ if (BIN_INDEX_KEY in varRunInfo) and (TUPLE_INDEX_KEY in varRunInfo) :
+ plotFunctionGenerationObjects.append(plotcreate.BinTupleAnalysisFunctionFactory())
- # if the data is not mapped to a longitude and latitude, just show it as an image
- if (do_not_test_with_lon_lat) :
- plotFunctionGenerationObjects.append(plotcreate.IMShowPlotFunctionFactory())
+ else : # if it's not bin/tuple, there are lots of other posibilities
- # if it's 2D and mapped to the Earth, contour plot it on the earth
- else :
- plotFunctionGenerationObjects.append(plotcreate.MappedContourPlotFunctionFactory())
-
- # plot our lon/lat related info
- image_names[ORIGINAL_IMAGES_KEY], image_names[COMPARED_IMAGES_KEY] = \
- plot.plot_and_save_comparison_figures \
- (aData, bData,
- plotFunctionGenerationObjects,
- varRunInfo[VARIABLE_DIRECTORY_KEY],
- displayName,
- varRunInfo[EPSILON_KEY],
- varRunInfo[FILL_VALUE_KEY],
- missingValueAltInB = varRunInfo[FILL_VALUE_ALT_IN_B_KEY] if FILL_VALUE_ALT_IN_B_KEY in varRunInfo else None,
- lonLatDataDict=lon_lat_data,
- dataRanges = varRunInfo[DISPLAY_RANGES_KEY] if DISPLAY_RANGES_KEY in varRunInfo else None,
- dataRangeNames = varRunInfo[DISPLAY_RANGE_NAMES_KEY] if DISPLAY_RANGE_NAMES_KEY in varRunInfo else None,
- dataColors = varRunInfo[DISPLAY_RANGE_COLORS_KEY] if DISPLAY_RANGE_COLORS_KEY in varRunInfo else None,
- makeSmall=True,
- doFork=runInfo[DO_MAKE_FORKS_KEY],
- shouldClearMemoryWithThreads=runInfo[DO_CLEAR_MEM_THREADED_KEY],
- shouldUseSharedRangeForOriginal=runInfo[USE_SHARED_ORIG_RANGE_KEY],
- doPlotSettingsDict = varRunInfo,
- aUData=aUData, aVData=aVData,
- bUData=bUData, bVData=bVData,
- binIndex= varRunInfo[BIN_INDEX_KEY] if BIN_INDEX_KEY in varRunInfo else None,
- tupleIndex= varRunInfo[TUPLE_INDEX_KEY] if TUPLE_INDEX_KEY in varRunInfo else None,
- binName= varRunInfo[BIN_NAME_KEY] if BIN_NAME_KEY in varRunInfo else 'bin',
- tupleName= varRunInfo[TUPLE_NAME_KEY] if TUPLE_NAME_KEY in varRunInfo else 'tuple',
- epsilonPercent=varRunInfo[EPSILON_PERCENT_KEY] if EPSILON_PERCENT_KEY in varRunInfo else None,
- fullDPI= runInfo[DETAIL_DPI_KEY],
- thumbDPI= runInfo[THUMBNAIL_DPI_KEY],
- units_a= varRunInfo[VAR_UNITS_A_KEY] if VAR_UNITS_A_KEY in varRunInfo else None,
- units_b= varRunInfo[VAR_UNITS_B_KEY] if VAR_UNITS_B_KEY in varRunInfo else None,
- )#histRange= varRunInfo[HISTOGRAM_RANGE_KEY] if HISTOGRAM_RANGE_KEY in varRunInfo else None)
+ # if it's vector data with longitude and latitude, quiver plot it on the Earth
+ if isVectorData and (not do_not_test_with_lon_lat) :
+ plotFunctionGenerationObjects.append(plotcreate.MappedQuiverPlotFunctionFactory())
+
+ # if the data is one dimensional we can plot it as lines
+ elif (len(aData.shape) is 1) :
+ plotFunctionGenerationObjects.append(plotcreate.LinePlotsFunctionFactory())
+
+ # if the data is 2D we have some options based on the type of data
+ elif (len(aData.shape) is 2) :
+
+ # if the data is not mapped to a longitude and latitude, just show it as an image
+ if (do_not_test_with_lon_lat) :
+ plotFunctionGenerationObjects.append(plotcreate.IMShowPlotFunctionFactory())
+
+ # if it's 2D and mapped to the Earth, contour plot it on the earth
+ else :
+ plotFunctionGenerationObjects.append(plotcreate.MappedContourPlotFunctionFactory())
+
+ # plot our lon/lat related info
+ image_names[ORIGINAL_IMAGES_KEY], image_names[COMPARED_IMAGES_KEY] = \
+ plot.plot_and_save_comparison_figures \
+ (aData, bData,
+ plotFunctionGenerationObjects,
+ varRunInfo[VARIABLE_DIRECTORY_KEY],
+ displayName,
+ varRunInfo[EPSILON_KEY],
+ varRunInfo[FILL_VALUE_KEY],
+ missingValueAltInB = varRunInfo[FILL_VALUE_ALT_IN_B_KEY] if FILL_VALUE_ALT_IN_B_KEY in varRunInfo else None,
+ lonLatDataDict=lon_lat_data,
+ dataRanges = varRunInfo[DISPLAY_RANGES_KEY] if DISPLAY_RANGES_KEY in varRunInfo else None,
+ dataRangeNames = varRunInfo[DISPLAY_RANGE_NAMES_KEY] if DISPLAY_RANGE_NAMES_KEY in varRunInfo else None,
+ dataColors = varRunInfo[DISPLAY_RANGE_COLORS_KEY] if DISPLAY_RANGE_COLORS_KEY in varRunInfo else None,
+ makeSmall=True,
+ doFork=runInfo[DO_MAKE_FORKS_KEY],
+ shouldClearMemoryWithThreads=runInfo[DO_CLEAR_MEM_THREADED_KEY],
+ shouldUseSharedRangeForOriginal=runInfo[USE_SHARED_ORIG_RANGE_KEY],
+ doPlotSettingsDict = varRunInfo,
+ aUData=aUData, aVData=aVData,
+ bUData=bUData, bVData=bVData,
+ binIndex= varRunInfo[BIN_INDEX_KEY] if BIN_INDEX_KEY in varRunInfo else None,
+ tupleIndex= varRunInfo[TUPLE_INDEX_KEY] if TUPLE_INDEX_KEY in varRunInfo else None,
+ binName= varRunInfo[BIN_NAME_KEY] if BIN_NAME_KEY in varRunInfo else 'bin',
+ tupleName= varRunInfo[TUPLE_NAME_KEY] if TUPLE_NAME_KEY in varRunInfo else 'tuple',
+ epsilonPercent=varRunInfo[EPSILON_PERCENT_KEY] if EPSILON_PERCENT_KEY in varRunInfo else None,
+ fullDPI= runInfo[DETAIL_DPI_KEY],
+ thumbDPI= runInfo[THUMBNAIL_DPI_KEY],
+ units_a= varRunInfo[VAR_UNITS_A_KEY] if VAR_UNITS_A_KEY in varRunInfo else None,
+ units_b= varRunInfo[VAR_UNITS_B_KEY] if VAR_UNITS_B_KEY in varRunInfo else None,
+ )#histRange= varRunInfo[HISTOGRAM_RANGE_KEY] if HISTOGRAM_RANGE_KEY in varRunInfo else None)
+
+ LOG.info("\tfinished creating figures for: " + explanationName)
- LOG.info("\tfinished creating figures for: " + explanationName)
+ # create the report page for this variable
+ if (runInfo[DO_MAKE_REPORT_KEY]) :
+
+ # hang on to our good % and other info to describe our comparison
+ epsilonPassedPercent = (1.0 - epsilon_failed_fraction) * 100.0
+ finitePassedPercent = (1.0 - non_finite_fail_fraction) * 100.0
+ variableComparisons[displayName] = {
+ PASSED_EPSILON_PERCENT_KEY: epsilonPassedPercent,
+ FINITE_SIMILAR_PERCENT_KEY: finitePassedPercent,
+ R_SQUARED_COEFF_VALUE_KEY: r_squared_value,
+ VARIABLE_RUN_INFO_KEY: varRunInfo
+ }
+
+ LOG.info ('\tgenerating report for: ' + explanationName)
+ report.generate_and_save_variable_report(files,
+ varRunInfo, runInfo,
+ variable_stats.dictionary_form(),
+ spatialInfo,
+ image_names,
+ varRunInfo[VARIABLE_DIRECTORY_KEY], "index.html")
- # create the report page for this variable
- if (runInfo[DO_MAKE_REPORT_KEY]) :
-
- # hang on to our good % and other info to describe our comparison
- epsilonPassedPercent = (1.0 - epsilon_failed_fraction) * 100.0
- finitePassedPercent = (1.0 - non_finite_fail_fraction) * 100.0
- variableComparisons[displayName] = {
- PASSED_EPSILON_PERCENT_KEY: epsilonPassedPercent,
- FINITE_SIMILAR_PERCENT_KEY: finitePassedPercent,
- R_SQUARED_COEFF_VALUE_KEY: r_squared_value,
- VARIABLE_RUN_INFO_KEY: varRunInfo
- }
-
- LOG.info ('\tgenerating report for: ' + explanationName)
- report.generate_and_save_variable_report(files,
- varRunInfo, runInfo,
- variable_stats.dictionary_form(),
- spatialInfo,
- image_names,
- varRunInfo[VARIABLE_DIRECTORY_KEY], "index.html")
-
- # if we can't compare the variable, we should tell the user
- else :
- message = (explanationName + ' ' +
- 'could not be compared. This may be because the data for this variable does not match in shape ' +
- 'between the two files (file A data shape: ' + str(aData.shape) + '; file B data shape: '
- + str(bData.shape) + ')')
- if do_not_test_with_lon_lat :
- message = message + '.'
+ # if we can't compare the variable, we should tell the user
else :
- message = (message + ' or the data may not match the shape of the selected '
- + 'longitude ' + str(good_shape_from_lon_lat) + ' and '
- + 'latitude ' + str(good_shape_from_lon_lat) + ' variables.')
- LOG.warn(message)
-
+ message = (explanationName + ' ' +
+ 'could not be compared. This may be because the data for this variable does not match in shape ' +
+ 'between the two files (file A data shape: ' + str(aData.shape) + '; file B data shape: '
+ + str(bData.shape) + ')')
+ if do_not_test_with_lon_lat :
+ message = message + '.'
+ else :
+ message = (message + ' or the data may not match the shape of the selected '
+ + 'longitude ' + str(good_shape_from_lon_lat) + ' and '
+ + 'latitude ' + str(good_shape_from_lon_lat) + ' variables.')
+ LOG.warn(message)
+ except ValueErrorStringToFloat as e:
+ LOG.warn("Unable to compare "+displayName+": "+str(e))
+
# the end of the loop to examine all the variables
# generate our general report pages once we've analyzed all the variables
diff --git a/pyglance/glance/load.py b/pyglance/glance/load.py
index 58b69625b71b17bdbc926c3dc4d16521517cb956..5ac4e25dc9710318c7ac21709aaeddc9eef257cc 100644
--- a/pyglance/glance/load.py
+++ b/pyglance/glance/load.py
@@ -224,6 +224,9 @@ def open_and_process_files (fileNames):
return files
+class ValueErrorStringToFloat(Exception):
+ pass
+
def load_variable_data(fileObject, variableNameInFile,
forceDType=None,
dataFilter=None,
@@ -252,6 +255,8 @@ def load_variable_data(fileObject, variableNameInFile,
variableData = numpy.array(fileObject[variableNameInFile]) if forceDType is None else numpy.array(fileObject[variableNameInFile], dtype=forceDType)
variableData = variableData.astype(numpy.uint8) if correctForAWIPS else variableData
except Exception, ex :
+ if type(ex) is ValueError and str(ex) == "could not convert string to float: ":
+ raise ValueErrorStringToFloat(str(ex))
import traceback
exceptionToRaise = ValueError('Unable to retrieve ' + variableNameInFile + ' data. The variable name' +
' may not exist in this file or an error may have occured while attempting to' +