def offset_column(input_file, output_file, columns, offset, method, pressure_range=None):
# Load existing file
matrix_obj = OCO_Matrix(input_file)
# Add ability to specify cols individually or using a * to goto end
cols = index_range_list(columns)
if offset.isdigit():
offset = float(offset)
else:
offset = eval(offset)
if pressure_range != None:
pres_col = matrix_obj.labels_lower.index("pressure")
pres_range_arr = pressure_range.split(',')
pres_val_beg = float(pres_range_arr[0])
pres_val_end = float(pres_range_arr[1])
pres_idx_beg = 0
pres_idx_end = matrix_obj.dims[0]
pres_column = []
[ pres_column.append(float(val[pres_col])) for val in matrix_obj.data ]
pres_idx_curr = 0
beg_found = False
for pres_val in pres_column:
if pres_val >= pres_val_beg and not beg_found:
pres_idx_beg = pres_idx_curr
beg_found = True
if pres_val <= pres_val_end:
pres_idx_end = pres_idx_curr + 1
pres_idx_curr += 1
target_rows = range(pres_idx_beg, pres_idx_end)
else:
target_rows = range(matrix_obj.dims[0])
for rowIdx in target_rows:
for colIdx in cols:
#print 'old_val[%d][%d] = %f' % (rowIdx, colIdx, matrix_obj.data[rowIdx][colIdx])
if method == '/':
matrix_obj.data[rowIdx][colIdx] = matrix_obj.data[rowIdx][colIdx] / offset
elif method == '-':
matrix_obj.data[rowIdx][colIdx] = matrix_obj.data[rowIdx][colIdx] - offset
elif method == '*':
matrix_obj.data[rowIdx][colIdx] = matrix_obj.data[rowIdx][colIdx] * offset
else:
matrix_obj.data[rowIdx][colIdx] = matrix_obj.data[rowIdx][colIdx] + offset
#print 'new_val[%d][%d] = %f' % (rowIdx, colIdx, matrix_obj.data[rowIdx][colIdx])
matrix_obj.write(output_file)
def noisify_spectra_obj(matrix_obj,
row_range_spec=None,
noise_cut_off=None,
save_perturb=False):
logger = logging.getLogger(os.path.basename(__file__))
radiance_col = matrix_obj.labels_lower.index("radiance")
noise_col = matrix_obj.labels_lower.index("noise")
if row_range_spec == None or len(row_range_spec) == 0:
row_range = range(matrix_obj.dims[0])
else:
row_range = index_range_list(row_range_spec)
if matrix_obj.dims[1] <= 2 and save_perturb:
new_data = numpy.zeros((matrix_obj.dims[0], matrix_obj.dims[1] + 1),
dtype=float)
new_data[:, 0:2] = matrix_obj.data[:, :]
matrix_obj.labels.append('True-Perturb')
matrix_obj.units.append('W sr-1 m-2')
else:
new_data = matrix_obj.data
if save_perturb:
perturb_col = new_data.shape[1] - 1
if noise_cut_off != None:
mean_noise = 0
for row_idx in row_range:
mean_noise += new_data[row_idx, noise_col]
mean_noise /= len(row_range)
for row_idx in row_range:
radiance_val = new_data[row_idx, radiance_col]
noise_val = new_data[row_idx, noise_col]
new_radiance = random.gauss(radiance_val, noise_val)
if (noise_cut_off != None
and noise_val > float(noise_cut_off) * mean_noise):
logger.error('Bad pixel detected for pixel row %d' % row_idx)
noised_radiance_val = radiance_val
else:
noised_radiance_val = new_radiance
new_data[row_idx, radiance_col] = noised_radiance_val
if save_perturb:
new_data[row_idx, perturb_col] = radiance_val - noised_radiance_val
matrix_obj.data = new_data
def Process_File(source, destination, fileKeywords, moduleSections, valuesDict, mapDict):
logger = logging.getLogger(os.path.basename(__file__))
for currSection in moduleSections:
if str(source) == str(destination):
raise IOError("source and destination must be different. will not overwrite source file")
rows = Apply_Template(moduleSections[0].Get_Keyword_Value("rows"), valuesDict, mapDict=mapDict)
columns = Apply_Template(moduleSections[0].Get_Keyword_Value("columns"), valuesDict, mapDict=mapDict)
identifier = Apply_Template(moduleSections[0].Get_Keyword_Value("identifier"), valuesDict, mapDict=mapDict)
initial_value = Apply_Template(moduleSections[0].Get_Keyword_Value("initial_value"), valuesDict, mapDict=mapDict)
map_filename = Apply_Template(moduleSections[0].Get_Keyword_Value("map_filename"), valuesDict, mapDict=mapDict)
modify = moduleSections[0].Get_Keyword_Value("modify")
# Load ranges from RANGES section of module
max_range_val = None
range_values = {}
for range_sect in moduleSections[0].Get_Section("->RANGES"):
for range_spec in range_sect.Get_Matrix_Data():
(range_name, range_str) = range_spec
if range_str.find(",") > 0:
curr_range = [float(val) for val in range_str.split(",")]
else:
curr_range = [float(val) for val in range_str.split()]
if max_range_val == None:
max_range_val = max(curr_range)
else:
max_range_val = max(max_range_val, max(curr_range))
range_values[range_name] = curr_range
if len(range_values) == 0:
logger.error("No index range list supplied for operating on source: %s" % source)
return
# Load source for data to map agains
data_obj = OCO_Matrix(source)
# Set columns to all if argument not supplied,
# Otherwise try parsing as an index range list failing that try
# using the specified columns as label names
if columns == None:
columns = range(data_obj.dims[1])
else:
try:
columns = index_range_list(columns)
except ValueError, TypeError:
columns = data_obj.find_labels(columns, match_case=False, indexes=True)
def noisify_spectra_obj(matrix_obj, row_range_spec=None, noise_cut_off=None, save_perturb=False):
logger = logging.getLogger(os.path.basename(__file__))
radiance_col = matrix_obj.labels_lower.index("radiance")
noise_col = matrix_obj.labels_lower.index("noise")
if row_range_spec == None or len(row_range_spec) == 0:
row_range = range(matrix_obj.dims[0])
else:
row_range = index_range_list(row_range_spec)
if matrix_obj.dims[1] <= 2 and save_perturb:
new_data = numpy.zeros((matrix_obj.dims[0], matrix_obj.dims[1]+1), dtype=float)
new_data[:, 0:2] = matrix_obj.data[:,:]
matrix_obj.labels.append('True-Perturb')
matrix_obj.units.append('W sr-1 m-2')
else:
new_data = matrix_obj.data
if save_perturb:
perturb_col = new_data.shape[1]-1
if noise_cut_off != None:
mean_noise = 0
for row_idx in row_range:
mean_noise += new_data[row_idx, noise_col]
mean_noise /= len(row_range)
for row_idx in row_range:
radiance_val = new_data[row_idx, radiance_col]
noise_val = new_data[row_idx, noise_col]
new_radiance = random.gauss(radiance_val, noise_val)
if (noise_cut_off != None and noise_val > float(noise_cut_off)*mean_noise):
logger.error('Bad pixel detected for pixel row %d' % row_idx)
noised_radiance_val = radiance_val
else:
noised_radiance_val = new_radiance
new_data[row_idx, radiance_col] = noised_radiance_val
if save_perturb:
new_data[row_idx, perturb_col] = radiance_val - noised_radiance_val
matrix_obj.data = new_data
def random_column(input_file, output_file, columns, mean, std_dev):
# Load existing file
matrix_obj = OCO_Matrix(input_file)
# Add ability to specify cols individually or using a * to goto end
cols = index_range_list(columns)
mean = float(mean)
std_dev = float(std_dev)
target_rows = range(matrix_obj.dims[0])
for rowIdx in target_rows:
for colIdx in cols:
matrix_obj.data[rowIdx][colIdx] = random.gauss(mean, std_dev)
matrix_obj.write(output_file)
def random_column(input_file, output_file, columns, mean, std_dev):
# Load existing file
matrix_obj = OCO_Matrix(input_file)
# Add ability to specify cols individually or using a * to goto end
cols = index_range_list(columns)
mean = float(mean)
std_dev = float(std_dev)
target_rows = range(matrix_obj.dims[0])
for rowIdx in target_rows:
for colIdx in cols:
matrix_obj.data[rowIdx][colIdx] = random.gauss(mean, std_dev)
matrix_obj.write(output_file)
def create_simple_cov(input_file, output_file, scaling, columns):
logger = logging.getLogger(os.path.basename(__file__))
# Load source file
src_obj = OCO_Matrix(input_file)
scaling = [float(v) for v in scaling.split(',')]
if len(scaling) < src_obj.dims[0]:
last_val = scaling[len(scaling)-1]
[ scaling.append(last_val) for nada in range(src_obj.dims[0] - len(scaling)) ]
try:
columns = index_range_list(columns, max_value=src_obj.dims[1])
except:
if not type(columns) is ListType:
col_name_list = [columns]
else:
col_name_list = columns
columns = []
for curr_name in col_name_list:
if curr_name.lower() not in src_obj.labels_lower:
raise IOError('Column named %s not found in file: %s' % (curr_name, input_file))
columns.append( src_obj.labels_lower.index(curr_name.lower()) )
logger.info('cols = ', columns)
num_diags = len(columns) * src_obj.dims[0]
logger.info('num_diags = ', num_diags)
dst_data = numpy.zeros((num_diags, num_diags), dtype=float)
diag_index = 0
for col_index in columns:
for row_index in range(src_obj.dims[0]):
#print '%d, %d => %d, %d' % (row_index, col_index, diag_index, diag_index)
dst_data[diag_index, diag_index] = (src_obj.data[row_index, col_index] * scaling[row_index])**2
diag_index += 1
logger.info('Writing: %s' % input_file)
src_obj.file_id = 'Simple Covariance Created from "%s", scaling: "%s"' % (input_file, ', '.join([str(sc) for sc in scaling]))
src_obj.labels = []
src_obj.data = dst_data
src_obj.units = []
src_obj.write(output_file, auto_size_cols=False, verbose=True)
def scale_uncertainty(input_radiance_file, output_radiance_file, scale_factor, row_range_spec=None):
# Load existing file
matrix_obj = OCO_Matrix(input_radiance_file)
radiance_col = matrix_obj.labels_lower.index("radiance")
noise_col = matrix_obj.labels_lower.index("noise")
if row_range_spec == None or len(row_range_spec) == 0:
row_range = range(matrix_obj.dims[0])
else:
row_range = index_range_list(row_range_spec)
for row_idx in row_range:
radiance_val = matrix_obj.data[row_idx, radiance_col]
new_uncert = radiance_val * float(scale_factor)
matrix_obj.data[row_idx, noise_col] = new_uncert
matrix_obj.write(output_radiance_file)
def scale_uncertainty(input_radiance_file,
output_radiance_file,
scale_factor,
row_range_spec=None):
# Load existing file
matrix_obj = OCO_Matrix(input_radiance_file)
radiance_col = matrix_obj.labels_lower.index("radiance")
noise_col = matrix_obj.labels_lower.index("noise")
if row_range_spec == None or len(row_range_spec) == 0:
row_range = range(matrix_obj.dims[0])
else:
row_range = index_range_list(row_range_spec)
for row_idx in row_range:
radiance_val = matrix_obj.data[row_idx, radiance_col]
new_uncert = radiance_val * float(scale_factor)
matrix_obj.data[row_idx, noise_col] = new_uncert
matrix_obj.write(output_radiance_file)
def Process_File(source, destination, fileKeywords, moduleSections, valuesDict, mapDict):
logger = logging.getLogger(os.path.basename(__file__))
# Load existing file
matrix_obj = OCO_Matrix(source)
for modifySect in moduleSections:
# Add ability to specify cols individually or using a * to goto end
columns = Apply_Template(modifySect.Get_Keyword_Value('columns'), valuesDict, mapDict=mapDict)
rows = Apply_Template(modifySect.Get_Keyword_Value('rows'), valuesDict, mapDict=mapDict)
modify = modifySect.Get_Keyword_Value('modify')
delete = evaluate_bool_str( modifySect.Get_Keyword_Value('delete') )
add_column = evaluate_bool_str( modifySect.Get_Keyword_Value('add_column') )
if columns != None:
try:
columns = index_range_list(columns, max_value=matrix_obj.dims[1])
except:
if not type(columns) is ListType:
col_name_list = [columns]
else:
col_name_list = columns
columns = []
for curr_name in col_name_list:
if curr_name.lower() not in matrix_obj.labels_lower:
if add_column:
matrix_obj.add_column(curr_name)
columns.append( matrix_obj.dims[1] - 1 )
else:
raise IOError('Column named %s not found in file: %s' % (curr_name, source))
columns.append( matrix_obj.labels_lower.index(curr_name.lower()) )
else:
columns = range(matrix_obj.dims[1])
if rows != None:
rows = index_range_list(rows, max_value=matrix_obj.dims[0])
else:
rows = range(matrix_obj.dims[0])
if delete and modify != None:
raise ValueError('delete and modify keywords can not be specified together')
if delete:
if len(columns) > matrix_obj.dims[1]:
raise IOError('More columns to be deleted %d than exist %d in input file %s' % (len(columns), matrix_obj.dims[1], source))
new_data = numpy.zeros((matrix_obj.dims[0], matrix_obj.dims[1]-len(columns)), dtype=numpy.double)
new_labels = []
new_units = []
new_col_idx = 0
for old_col_idx in range(matrix_obj.dims[1]):
if old_col_idx not in columns:
new_labels.append(matrix_obj.labels[old_col_idx])
new_units.append(matrix_obj.units[old_col_idx])
new_data[:,new_col_idx] = matrix_obj.data[:,old_col_idx]
new_col_idx += 1
matrix_obj.data = new_data
matrix_obj.labels = new_labels
matrix_obj.units = new_units
if modify != None and len(modify) > 0:
modifyDict = copy_module.copy(valuesDict)
Get_Constant_Values(modifySect.Get_Section('->CONSTANTS'), modifyDict)
for row_idx in rows:
for col_idx in columns:
modifyDict['original'] = str(matrix_obj.data[row_idx][col_idx])
modify_str = Apply_Template(modify, modifyDict, mapDict=mapDict)
try:
matrix_obj.data[row_idx][col_idx] = eval(modify_str)
except:
raise RuntimeError('Error evaluating modify string: "%s"' % modify_str)
matrix_obj.write(destination, auto_size_cols=False)
def Process_File(source, destination, fileKeywords, moduleSections, valuesDict,
mapDict):
logger = logging.getLogger(os.path.basename(__file__))
for currSection in moduleSections:
if str(source) == str(destination):
raise IOError(
'source and destination must be different. will not overwrite source file'
)
rows = Apply_Template(moduleSections[0].Get_Keyword_Value('rows'),
valuesDict,
mapDict=mapDict)
columns = Apply_Template(moduleSections[0].Get_Keyword_Value('columns'),
valuesDict,
mapDict=mapDict)
identifier = Apply_Template(
moduleSections[0].Get_Keyword_Value('identifier'),
valuesDict,
mapDict=mapDict)
initial_value = Apply_Template(
moduleSections[0].Get_Keyword_Value('initial_value'),
valuesDict,
mapDict=mapDict)
map_filename = Apply_Template(
moduleSections[0].Get_Keyword_Value('map_filename'),
valuesDict,
mapDict=mapDict)
modify = moduleSections[0].Get_Keyword_Value('modify')
# Load ranges from RANGES section of module
max_range_val = None
range_values = {}
for range_sect in moduleSections[0].Get_Section('->RANGES'):
for range_spec in range_sect.Get_Matrix_Data():
(range_name, range_str) = range_spec
if range_str.find(',') > 0:
curr_range = [float(val) for val in range_str.split(',')]
else:
curr_range = [float(val) for val in range_str.split()]
if max_range_val == None:
max_range_val = max(curr_range)
else:
max_range_val = max(max_range_val, max(curr_range))
range_values[range_name] = curr_range
if len(range_values) == 0:
logger.error(
'No index range list supplied for operating on source: %s' %
source)
return
# Load source for data to map agains
data_obj = OCO_Matrix(source)
# Set columns to all if argument not supplied,
# Otherwise try parsing as an index range list failing that try
# using the specified columns as label names
if columns == None:
columns = range(data_obj.dims[1])
else:
try:
columns = index_range_list(columns)
except ValueError, TypeError:
columns = data_obj.find_labels(columns,
match_case=False,
indexes=True)
if columns == None:
columns = range(data_obj.dims[1])
else:
try:
columns = index_range_list(columns)
except ValueError, TypeError:
columns = data_obj.find_labels(columns,
match_case=False,
indexes=True)
# Set rows to all if no argument supplied, otherwise use the specified
# index range list
if rows == None:
rows = range(data_obj.dims[0])
else:
rows = index_range_list(rows)
# Load the modifer string for specifying how to operate on rows and col values
# By default sum all values encountered
if modify == None:
modify = '<current> + <data_result>'
# Set initial value for before we look at source data file
if initial_value == None:
data_result = 0
else:
# Use eval so string is evaluated to the type expected
data_result = eval(intial_value)
# Load value to check against ranges
modify_dict = copy.copy(valuesDict)
def Process_File(source, destination, fileKeywords, moduleSections, valuesDict,
mapDict):
logger = logging.getLogger(os.path.basename(__file__))
# Load existing file
matrix_obj = OCO_Matrix(source)
for modifySect in moduleSections:
# Add ability to specify cols individually or using a * to goto end
columns = Apply_Template(modifySect.Get_Keyword_Value('columns'),
valuesDict,
mapDict=mapDict)
rows = Apply_Template(modifySect.Get_Keyword_Value('rows'),
valuesDict,
mapDict=mapDict)
modify = modifySect.Get_Keyword_Value('modify')
delete = evaluate_bool_str(modifySect.Get_Keyword_Value('delete'))
add_column = evaluate_bool_str(
modifySect.Get_Keyword_Value('add_column'))
if columns != None:
try:
columns = index_range_list(columns,
max_value=matrix_obj.dims[1])
except:
if not type(columns) is ListType:
col_name_list = [columns]
else:
col_name_list = columns
columns = []
for curr_name in col_name_list:
if curr_name.lower() not in matrix_obj.labels_lower:
if add_column:
matrix_obj.add_column(curr_name)
columns.append(matrix_obj.dims[1] - 1)
else:
raise IOError(
'Column named %s not found in file: %s' %
(curr_name, source))
columns.append(
matrix_obj.labels_lower.index(curr_name.lower()))
else:
columns = range(matrix_obj.dims[1])
if rows != None:
rows = index_range_list(rows, max_value=matrix_obj.dims[0])
else:
rows = range(matrix_obj.dims[0])
if delete and modify != None:
raise ValueError(
'delete and modify keywords can not be specified together')
if delete:
if len(columns) > matrix_obj.dims[1]:
raise IOError(
'More columns to be deleted %d than exist %d in input file %s'
% (len(columns), matrix_obj.dims[1], source))
new_data = numpy.zeros(
(matrix_obj.dims[0], matrix_obj.dims[1] - len(columns)),
dtype=numpy.double)
new_labels = []
new_units = []
new_col_idx = 0
for old_col_idx in range(matrix_obj.dims[1]):
if old_col_idx not in columns:
new_labels.append(matrix_obj.labels[old_col_idx])
new_units.append(matrix_obj.units[old_col_idx])
new_data[:, new_col_idx] = matrix_obj.data[:, old_col_idx]
new_col_idx += 1
matrix_obj.data = new_data
matrix_obj.labels = new_labels
matrix_obj.units = new_units
if modify != None and len(modify) > 0:
modifyDict = copy_module.copy(valuesDict)
Get_Constant_Values(modifySect.Get_Section('->CONSTANTS'),
modifyDict)
for row_idx in rows:
for col_idx in columns:
modifyDict['original'] = str(
matrix_obj.data[row_idx][col_idx])
modify_str = Apply_Template(modify,
modifyDict,
mapDict=mapDict)
try:
matrix_obj.data[row_idx][col_idx] = eval(modify_str)
except:
raise RuntimeError(
'Error evaluating modify string: "%s"' %
modify_str)
matrix_obj.write(destination, auto_size_cols=False)
def Process_File(source, destination, fileKeywords, moduleSections, valuesDict, mapDict):
logger = logging.getLogger(os.path.basename(__file__))
logger.debug('')
logger.debug('Reading source file: %s' % source)
modFileObj = L2_Input.Input_File(source)
for pick in moduleSections:
section = Apply_Template(pick.Get_Keyword_Value('section'), valuesDict, mapDict=mapDict)
keyword = Apply_Template(pick.Get_Keyword_Value('keyword'), valuesDict, mapDict=mapDict)
# Resolve template later when can add to the values dictionary
template = pick.Get_Keyword_Value('template', addWhiteSpace=True)
which_line = Apply_Template(pick.Get_Keyword_Value('which_line'), valuesDict, mapDict=mapDict)
which_section = Apply_Template(pick.Get_Keyword_Value('which_section'), valuesDict, mapDict=mapDict)
which_keyword = Apply_Template(pick.Get_Keyword_Value('which_keyword'), valuesDict, mapDict=mapDict)
ignore_missing = Apply_Template(pick.Get_Keyword_Value('ignore_missing'), valuesDict, mapDict=mapDict)
index_format = Apply_Template(pick.Get_Keyword_Value('index_format'), valuesDict, mapDict=mapDict)
delete = Apply_Template(pick.Get_Keyword_Value('delete'), valuesDict, mapDict=mapDict)
indent = Apply_Template(pick.Get_Keyword_Value('indent'), valuesDict, mapDict=mapDict)
unique = evaluate_bool_str(Apply_Template(pick.Get_Keyword_Value('unique'), valuesDict, mapDict=mapDict), False)
if indent == None:
indent = 0
if keyword != None and type(keyword) is not ListType:
keyword = [keyword]
if which_line == None:
which_line = None
elif type(which_line) is ListType or not which_line.isdigit():
raise ValueError('which_line must be a scalar integer')
else:
which_line = int(which_line)
if (template == None or len(template) == 0) and (delete == None or len(delete)) == 0:
raise ValueError('template must be defined for PICK')
if section != None:
if keyword != None:
if delete != None and len(delete) > 0:
logger.debug('Deleting keyword %s->%s' % (section, keyword))
else:
logger.debug('Modifying keyword %s->%s' % (section, keyword))
elif delete != None and len(delete) > 0:
logger.debug('Deleting section %s' % section)
else:
if keyword != None:
if delete != None and len(delete) > 0:
logger.debug('Deleting keyword %s' % keyword)
else:
logger.debug('Modifying keyword %s' % keyword)
elif delete != None and len(delete) > 0:
logger.debug('Deleting lines from root file section:', delete)
# Find the section to modify
if section == None:
modSect = [ modFileObj.rootNode ]
else:
if type(section) is ListType:
modSect = []
for curr_sect_name in section:
for found_sect in modFileObj.rootNode.Get_Section(curr_sect_name):
modSect.append(found_sect)
else:
modSect = modFileObj.rootNode.Get_Section(section)
if len(modSect) == 0:
modSect = [L2_Input.Section(leaf=section)]
if which_line != None:
modFileObj.children.insert(which_line, modSect[0])
else:
modFileObj.children.append(modSect[0])
# If which is defined then filter sections to modify
if keyword != None and which_section != None:
try:
section_indexes = index_range_list(which_section)
except:
section_indexes = []
curr_index = 0
for testSect in modSect:
sectName = testSect.Get_Keyword_Value('name')
if sectName == which_section:
section_indexes.append(curr_index)
curr_index += 1
if len(section_indexes) == 0 and not ignore_missing:
raise IOError('Could not find section named: %s with name keyword: %s in file %s' % (section, which_section, source))
sectChoices = []
for w_idx in section_indexes:
try:
sectChoices.append( modSect[w_idx] )
except:
raise IOError("Section index: %d not found for keyword: %s, section: %s in file %s" % (w_idx, keyword, section, source))
modSect = sectChoices
# Finally modify all chosen sections and chosen keywords
for pickSect in modSect:
pickValDict = copy.copy(valuesDict)
if keyword != None:
for curr_keyname in keyword:
keyword_val = pickSect.Get_Keyword_Value(curr_keyname)
if type(keyword_val) is ListType:
pickValDict[curr_keyname] = ' '.join(keyword_val)
elif keyword_val != None:
pickValDict[curr_keyname] = keyword_val
newValue = Apply_Template(template, pickValDict, mapDict=mapDict)
if delete != None and len(delete) > 0:
try:
delete = index_range_list(delete)
except:
delete = [0]
if keyword != None and len(keyword) > 0:
for curr_keyname in keyword:
pickSect.Delete_Keyword(curr_keyname, which=delete)
elif section != None and len(section) > 0:
modFileObj.Delete_Section(pickSect) # which=delete
else:
# Remove from a location a certain # of times
for dlist_idx in range(len(delete)-1, -1, -1):
del_idx = delete[dlist_idx]
x = pickSect.children.pop(del_idx)
elif keyword != None:
if which_keyword != None:
which_keyword = index_range_list(which_keyword)
for curr_keyname in keyword:
if index_format != None and hasattr(newValue, '__iter__'):
for curr_index, curr_value in enumerate(newValue):
index_keyname = index_format.format(keyword=curr_keyname, index=curr_index+1)
pickSect.Set_Keyword_Value(index_keyname, curr_value, which=which_keyword, indent=indent)
else:
pickSect.Set_Keyword_Value(curr_keyname, newValue, which=which_keyword, indent=indent)
else:
if not type(newValue) is ListType:
newValue = [ newValue ]
for currValue in newValue:
if unique and currValue in modFileObj.Get_Matrix_Data():
continue
newNode = L2_Input.Node('value', currValue + '\n')
if which_line != None:
pickSect.children.insert(which_line, newNode)
else:
pickSect.children.append(newNode)
# Write newly modified file
logger.debug('Writing destination file: %s' % destination)
modFileObj.Write(destination)
# Otherwise try parsing as an index range list failing that try
# using the specified columns as label names
if columns == None:
columns = range(data_obj.dims[1])
else:
try:
columns = index_range_list(columns)
except ValueError, TypeError:
columns = data_obj.find_labels(columns, match_case=False, indexes=True)
# Set rows to all if no argument supplied, otherwise use the specified
# index range list
if rows == None:
rows = range(data_obj.dims[0])
else:
rows = index_range_list(rows)
# Load the modifer string for specifying how to operate on rows and col values
# By default sum all values encountered
if modify == None:
modify = "<current> + <data_result>"
# Set initial value for before we look at source data file
if initial_value == None:
data_result = 0
else:
# Use eval so string is evaluated to the type expected
data_result = eval(intial_value)
# Load value to check against ranges
modify_dict = copy.copy(valuesDict)
def offset_column(input_file,
output_file,
columns,
offset,
method,
pressure_range=None):
# Load existing file
matrix_obj = OCO_Matrix(input_file)
# Add ability to specify cols individually or using a * to goto end
cols = index_range_list(columns)
if offset.isdigit():
offset = float(offset)
else:
offset = eval(offset)
if pressure_range != None:
pres_col = matrix_obj.labels_lower.index("pressure")
pres_range_arr = pressure_range.split(',')
pres_val_beg = float(pres_range_arr[0])
pres_val_end = float(pres_range_arr[1])
pres_idx_beg = 0
pres_idx_end = matrix_obj.dims[0]
pres_column = []
[pres_column.append(float(val[pres_col])) for val in matrix_obj.data]
pres_idx_curr = 0
beg_found = False
for pres_val in pres_column:
if pres_val >= pres_val_beg and not beg_found:
pres_idx_beg = pres_idx_curr
beg_found = True
if pres_val <= pres_val_end:
pres_idx_end = pres_idx_curr + 1
pres_idx_curr += 1
target_rows = range(pres_idx_beg, pres_idx_end)
else:
target_rows = range(matrix_obj.dims[0])
for rowIdx in target_rows:
for colIdx in cols:
#print 'old_val[%d][%d] = %f' % (rowIdx, colIdx, matrix_obj.data[rowIdx][colIdx])
if method == '/':
matrix_obj.data[rowIdx][
colIdx] = matrix_obj.data[rowIdx][colIdx] / offset
elif method == '-':
matrix_obj.data[rowIdx][
colIdx] = matrix_obj.data[rowIdx][colIdx] - offset
elif method == '*':
matrix_obj.data[rowIdx][
colIdx] = matrix_obj.data[rowIdx][colIdx] * offset
else:
matrix_obj.data[rowIdx][
colIdx] = matrix_obj.data[rowIdx][colIdx] + offset
#print 'new_val[%d][%d] = %f' % (rowIdx, colIdx, matrix_obj.data[rowIdx][colIdx])
matrix_obj.write(output_file)
