def format_mapping_file_to_js(mapping_file_data, mapping_file_headers, columns):
"""Write a javascript representation of the mapping file
Inputs:
mapping_file_data: contents of the mapping file
mapping_file_headers: headers of the mapping file
columns: valid columns to use, usually a subset of mapping_file_headers
Outputs:
string: javascript representation of the mapping file
"""
js_mapping_file_string = ''
mapping_file_dict = mapping_file_to_dict(mapping_file_data,
mapping_file_headers)
map_values = []
for k,v in mapping_file_dict.items():
if 'SampleID' in columns:
vals = ["'%s'" % k] + ["'%s'" % v[col]\
for col in mapping_file_headers[1:]]
else:
vals = ["'%s'" % v[col] for col in mapping_file_headers[1:]]
map_values.append("'%s': [%s]" % (k, ','.join(vals)))
if 'SampleID' not in columns:
mapping_file_headers = mapping_file_headers[1:]
# format the mapping file as javascript objects
js_mapping_file_string += 'var g_mappingFileHeaders = [%s];\n' % ','.join(
["'%s'" % col for col in mapping_file_headers])
js_mapping_file_string += 'var g_mappingFileData = { %s };\n' % ','.join(
map_values)
return js_mapping_file_string
def test_mapping_file_to_dict(self):
"""parse_mapping_file functions as expected"""
s1 = ["#sample\ta\tb", "#comment line to skip", "x \t y \t z ", " ", "#more skip", "i\tj\tk"]
exp = ([["x", "y", "z"], ["i", "j", "k"]], ["sample", "a", "b"], ["comment line to skip", "more skip"])
mapres = parse_mapping_file(s1) # map_data, header, comments
mapdict = mapping_file_to_dict(*mapres[:2])
expdict = {"x": {"a": "y", "b": "z"}, "i": {"a": "j", "b": "k"}}
self.assertEqual(mapdict, expdict)
def test_mapping_file_to_dict(self):
"""parse_mapping_file functions as expected"""
s1 = ['#sample\ta\tb', '#comment line to skip',\
'x \t y \t z ', ' ', '#more skip', 'i\tj\tk']
exp = ([['x','y','z'],['i','j','k']],\
['sample','a','b'],\
['comment line to skip','more skip'])
mapres = parse_mapping_file(s1) # map_data, header, comments
mapdict = mapping_file_to_dict(*mapres[:2])
expdict = {'x': {'a': 'y', 'b': 'z'}, 'i': {'a': 'j', 'b': 'k'}}
self.assertEqual(mapdict, expdict)
def format_mapping_file_to_js(mapping_file_data, mapping_file_headers,
columns):
"""Write a javascript representation of the mapping file
Parameters
----------
mapping_file_data : list of str
contents of the mapping file
mapping_file_headers : list of list of str
headers of the mapping file
columns : list of str
valid columns to use, usually a subset of mapping_file_headers
Returns
-------
str
JavaScript representation of the mapping file
"""
map_str = []
mapping_file_dict = mapping_file_to_dict(mapping_file_data,
mapping_file_headers)
map_values = []
for k, v in mapping_file_dict.items():
if 'SampleID' in columns:
vals = ["'%s'" % k
] + ["'%s'" % v[col] for col in mapping_file_headers[1:]]
else:
vals = ["'%s'" % v[col] for col in mapping_file_headers[1:]]
map_values.append("'%s': [%s]" % (k, ','.join(vals)))
if 'SampleID' not in columns:
mapping_file_headers = mapping_file_headers[1:]
# format the mapping file as javascript objects
map_str.append('var g_mappingFileHeaders = [%s];\n' %
','.join(["'%s'" % col for col in mapping_file_headers]))
map_str.append('var g_mappingFileData = { %s };\n' % ','.join(map_values))
map_object = MetadataMap(mapping_file_dict, [])
# make sure the comparison for SampleID is made first because otherwise
# if the metadata map tries to check 'SampleID' it will raise an exception
animatable_categories = [
category for category in columns
if category != 'SampleID' and map_object.isNumericCategory(category)
]
map_str.append('var g_animatableMappingFileHeaders = [%s];\n' %
','.join(["'%s'" % col for col in animatable_categories]))
return ''.join(map_str)
def format_mapping_file_to_js(mapping_file_data, mapping_file_headers,
columns):
"""Write a javascript representation of the mapping file
Parameters
----------
mapping_file_data : list of str
contents of the mapping file
mapping_file_headers : list of list of str
headers of the mapping file
columns : list of str
valid columns to use, usually a subset of mapping_file_headers
Returns
-------
str
JavaScript representation of the mapping file
"""
map_str = []
mapping_file_dict = mapping_file_to_dict(mapping_file_data,
mapping_file_headers)
map_values = []
for k, v in mapping_file_dict.items():
if 'SampleID' in columns:
vals = ["'%s'" % k] + ["'%s'" % v[col]
for col in mapping_file_headers[1:]]
else:
vals = ["'%s'" % v[col] for col in mapping_file_headers[1:]]
map_values.append("'%s': [%s]" % (k, ','.join(vals)))
if 'SampleID' not in columns:
mapping_file_headers = mapping_file_headers[1:]
# format the mapping file as javascript objects
map_str.append('var g_mappingFileHeaders = [%s];\n' %
','.join(["'%s'" % col for col in mapping_file_headers]))
map_str.append('var g_mappingFileData = { %s };\n' % ','.join(map_values))
map_object = MetadataMap(mapping_file_dict, [])
# make sure the comparison for SampleID is made first because otherwise
# if the metadata map tries to check 'SampleID' it will raise an exception
animatable_categories = [category for category in columns
if category != 'SampleID' and
map_object.isNumericCategory(category)]
map_str.append('var g_animatableMappingFileHeaders = [%s];\n' %
','.join(["'%s'" % col for col in animatable_categories]))
return ''.join(map_str)
def format_mapping_file_to_js(mapping_file_data, mapping_file_headers,
columns):
"""Write a javascript representation of the mapping file
Inputs:
mapping_file_data: contents of the mapping file
mapping_file_headers: headers of the mapping file
columns: valid columns to use, usually a subset of mapping_file_headers
Outputs:
string: javascript representation of the mapping file
"""
js_mapping_file_string = ''
mapping_file_dict = mapping_file_to_dict(mapping_file_data,
mapping_file_headers)
map_values = []
for k, v in mapping_file_dict.items():
if 'SampleID' in columns:
vals = ["'%s'" % k] + ["'%s'" % v[col]\
for col in mapping_file_headers[1:]]
else:
vals = ["'%s'" % v[col] for col in mapping_file_headers[1:]]
map_values.append("'%s': [%s]" % (k, ','.join(vals)))
if 'SampleID' not in columns:
mapping_file_headers = mapping_file_headers[1:]
# format the mapping file as javascript objects
js_mapping_file_string += 'var g_mappingFileHeaders = [%s];\n' % ','.join(
["'%s'" % col for col in mapping_file_headers])
js_mapping_file_string += 'var g_mappingFileData = { %s };\n' % ','.join(
map_values)
return js_mapping_file_string
if '&&' in col:
for _col in col.split('&&'):
if _col not in lookup_header:
offending_fields.append(col)
elif col not in lookup_header:
offending_fields.append(col)
else:
# if the user didn't specify the header names display everything
color_by_column_names = [None]
# extract a list of the custom axes provided and each element is numeric
if custom_axes:
custom_axes = custom_axes.strip().strip("'").strip('"').split(',')
# the MetadataMap object makes some checks easier
map_object = MetadataMap(mapping_file_to_dict(mapping_data, header), [])
for axis in custom_axes:
# append the field to the error queue that it belongs to
if axis not in lookup_header:
offending_fields.append(axis)
break
# make sure this value is in the mapping file
elif axis not in color_by_column_names:
color_by_column_names.append(axis)
# perform only if the for loop does not call break
else:
# make sure all these axes are numeric
for axis in custom_axes:
if map_object.isNumericCategory(axis) == False:
non_numeric_categories.append(axis)
def preprocess_mapping_file(data, headers, columns, unique=False, single=False,
clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Inputs:
data: mapping file data
headers: mapping file headers
columns: list of headers to keep, if one of these headers includes two
ampersands, this function will create a new column by merging the delimited
columns.
unique: keep columns where all values are unique
single: keep columns where all values are the same
clones: number of times to replicate the metadata
Outputs:
data: processed mapping file data
headers: processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if 'SampleID' != columns[0]:
columns = ['SampleID'] + columns
# process concatenated columns if needed
merge = []
for column in columns:
if '&&' in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [headers.index(header_name) for header_name in
new_column.split('&&')]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append(''.join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# find columns that have values that are all unique
if unique == True:
columns_to_remove += [column_name for column_name in headers[1::]
if metadata.hasUniqueCategoryValues(column_name)]
# remove categories where there is only one value
if single == True:
columns_to_remove += [column_name for column_name in headers[1::]
if metadata.hasSingleCategoryValue(column_name)]
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data, headers,
columns_to_remove, negate=True)
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0]+'_%d' % index]+element[1::]
for element in data])
data = out_data
return data, headers
def preprocess_mapping_file(data,
headers,
columns,
unique=False,
single=False,
clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Inputs:
data: mapping file data
headers: mapping file headers
columns: list of headers to keep, if one of these headers includes two
ampersands, this function will create a new column by merging the delimited
columns.
unique: keep columns where all values are unique
single: keep columns where all values are the same
clones: number of times to replicate the metadata
Outputs:
data: processed mapping file data
headers: processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if 'SampleID' != columns[0]:
columns = ['SampleID'] + columns
# process concatenated columns if needed
merge = []
for column in columns:
if '&&' in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [
headers.index(header_name)
for header_name in new_column.split('&&')
]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append(''.join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# find columns that have values that are all unique
if unique == True:
columns_to_remove += [
column_name for column_name in headers[1::]
if metadata.hasUniqueCategoryValues(column_name)
]
# remove categories where there is only one value
if single == True:
columns_to_remove += [
column_name for column_name in headers[1::]
if metadata.hasSingleCategoryValue(column_name)
]
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data,
headers,
columns_to_remove,
negate=True)
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + '_%d' % index] + element[1::]
for element in data])
data = out_data
return data, headers
def preprocess_mapping_file(data,
headers,
columns,
unique=False,
single=False,
clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Parameters
----------
data: list of list of str
mapping file data
headers: list of str
mapping file headers
columns: list of str
headers to keep, if one of these headers includes two ampersands,
this function will create a new column by merging the delimited columns
unique: bool, optional
keep columns where all values are unique. Default: false.
single: bool, optional
keep columns where all values are the same. Default: false
clones: int, optional
number of times to replicate the metadata. Default: 0
Returns
-------
data: list of list of str
processed mapping file data
headers: list of str
processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if 'SampleID' != columns[0]:
columns = ['SampleID'] + columns
# process concatenated columns if needed
merge = []
for column in columns:
# the list can contain None so check "if column" before treating as str
if column and '&&' in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [
headers.index(header_name)
for header_name in new_column.split('&&')
]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append(''.join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns that are not included in
# the list of categories that should be kept i. e. columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# the --coloy_by option in the script interface allows the user to
# specify the categories you want to use in the generated plot, this
# the default behaviour is to color by all categories that are not
# unique. If the user specifies a category with with the --color_by
# option and this category contains a unique values, this category must
# still be added thus the structure of the next few lines that
# form the structure for the two different routes. (1) where no value
# is specified in the CLI (the value of columns will be [None, x1, x2,
# x3] where x{1,2,3} are categories requested in other CLI options) and
# (2) where a value is specified in the CLI.
#
# TL;DR
# see https://github.com/biocore/emperor/issues/271
if None in columns:
columns = headers[:]
f_unique = metadata.hasUniqueCategoryValues
f_single = metadata.hasSingleCategoryValue
else:
def f_unique(x):
return metadata.hasUniqueCategoryValues(x) and x not in columns
def f_single(x):
return metadata.hasSingleCategoryValue(x) and x not in columns
# find columns that have values that are all unique
if unique:
for c in headers[1::]:
if f_unique(c):
columns_to_remove.append(c)
# remove categories where there is only one value
if single:
for c in headers[1::]:
if f_single(c):
columns_to_remove.append(c)
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data,
headers,
columns_to_remove,
negate=True)
else:
# when a None is contained in columns, we imply we want to use all the
# available categories in the mapping file, thus just overwrite the
# value
if None in columns:
columns = headers[:]
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a
# suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + '_%d' % index] + element[1::]
for element in data])
data = out_data
return data, headers
def preprocess_mapping_file(data, headers, columns, unique=False, single=False,
clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Parameters
----------
data: list of list of str
mapping file data
headers: list of str
mapping file headers
columns: list of str
headers to keep, if one of these headers includes two ampersands,
this function will create a new column by merging the delimited columns
unique: bool, optional
keep columns where all values are unique. Default: false.
single: bool, optional
keep columns where all values are the same. Default: false
clones: int, optional
number of times to replicate the metadata. Default: 0
Returns
-------
data: list of list of str
processed mapping file data
headers: list of str
processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if 'SampleID' != columns[0]:
columns = ['SampleID'] + columns
# process concatenated columns if needed
merge = []
for column in columns:
# the list can contain None so check "if column" before treating as str
if column and '&&' in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [headers.index(header_name) for header_name in
new_column.split('&&')]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append(''.join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns that are not included in
# the list of categories that should be kept i. e. columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# the --coloy_by option in the script interface allows the user to
# specify the categories you want to use in the generated plot, this
# the default behaviour is to color by all categories that are not
# unique. If the user specifies a category with with the --color_by
# option and this category contains a unique values, this category must
# still be added thus the structure of the next few lines that
# form the structure for the two different routes. (1) where no value
# is specified in the CLI (the value of columns will be [None, x1, x2,
# x3] where x{1,2,3} are categories requested in other CLI options) and
# (2) where a value is specified in the CLI.
#
# TL;DR
# see https://github.com/biocore/emperor/issues/271
if None in columns:
columns = headers[:]
f_unique = metadata.hasUniqueCategoryValues
f_single = metadata.hasSingleCategoryValue
else:
def f_unique(x):
return metadata.hasUniqueCategoryValues(x) and x not in columns
def f_single(x):
return metadata.hasSingleCategoryValue(x) and x not in columns
# find columns that have values that are all unique
if unique:
for c in headers[1::]:
if f_unique(c):
columns_to_remove.append(c)
# remove categories where there is only one value
if single:
for c in headers[1::]:
if f_single(c):
columns_to_remove.append(c)
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data, headers,
columns_to_remove,
negate=True)
else:
# when a None is contained in columns, we imply we want to use all the
# available categories in the mapping file, thus just overwrite the
# value
if None in columns:
columns = headers[:]
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a
# suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0]+'_%d' % index]+element[1::]
for element in data])
data = out_data
return data, headers
# check for all the mapping fields
for col in color_by_column_names:
# for concatenated columns check each individual field
parts = col.split('&&')
offending_fields.extend(p for p in parts if p not in lookup_header)
else:
# if the user didn't specify the header names display everything
color_by_column_names = [None]
# extract a list of the custom axes provided and each element is numeric
if custom_axes:
custom_axes = custom_axes.strip().strip("'").strip('"').split(',')
# the MetadataMap object makes some checks easier
map_object = MetadataMap(mapping_file_to_dict(mapping_data, header),
[])
for axis in custom_axes:
# append the field to the error queue that it belongs to
if axis not in lookup_header:
offending_fields.append(axis)
break
# make sure this value is in the mapping file
elif axis not in color_by_column_names:
color_by_column_names.append(axis)
# perform only if the for loop does not call break
else:
# make sure all these axes are numeric
for axis in custom_axes:
if not map_object.isNumericCategory(axis):
non_numeric_categories.append(axis)
def main():
#option_parser, opts, args = parse_command_line_parameters(**script_info)
input_coords = args.input_coords
map_fp = args.map_fp
output_dir = args.output_dir
color_by_column_names = None #opts.color_by
add_unique_columns = False #opts.add_unique_columns
custom_axes = None #opts.custom_axes
ignore_missing_samples = False #opts.ignore_missing_samples
missing_custom_axes_values = None #opts.missing_custom_axes_values
jackknifing_method = 'IQR' #opts.ellipsoid_method
master_pcoa = None #opts.master_pcoa
taxa_fp = None #opts.taxa_fp
n_taxa_to_keep = False #opts.n_taxa_to_keep
biplot_fp = None #opts.biplot_fp
add_vectors = [None, None] #opts.add_vectors
verbose_output = False #opts.verbose
number_of_axes = 10 #opts.number_of_axes
compare_plots = False #opts.compare_plots
number_of_segments = 8 #opts.number_of_segments
pct_variation_below_one = True #opts.pct_variation_below_one
# add some metadata to the output
emperor_autograph = format_emperor_autograph(map_fp, input_coords, 'HTML')
# verifying that the number of axes requested is greater than 3
if number_of_axes < 3:
print(('You need to plot at least 3 axes.'))
# verifying that the number of segments is between the desired range
if not (4 <= number_of_segments <= 14):
print(('number_of_segments should be between 4 and 14.'))
# append headernames that the script didn't find in the mapping file
# according to different criteria to the following variables
offending_fields = []
non_numeric_categories = []
serial_comparison = True
# can't do averaged pcoa plots _and_ custom axes in the same plot
if custom_axes is not None and isdir(input_coords):
if custom_axes.count(',') > 0:
print(('Jackknifed plots are limited to one custom '
'axis, currently trying to use: %s. Make '
'sure you use only one.' % custom_axes))
# make sure the flag is not misunderstood from the command line interface
if not isdir(input_coords) and compare_plots:
print("Cannot use the '--compare_plots' flag unless the "
"input path is a directory.")
# before creating any output, check correct parsing of the main input files
#try:
mapping_data, header, comments = parse_mapping_file(open(map_fp, 'U'))
try:
pass
except:
sys.exit(("The metadata mapping file '%s' does not seem "
"to be formatted correctly, verify the "
"formatting is QIIME compliant by using "
"validate_mapping_file.py") % map_fp)
else:
# use this set variable to make presence/absensce checks faster
lookup_header = set(header)
mapping_ids = {row[0] for row in mapping_data}
# dir means jackknifing or coordinate comparison type of processing
if isdir(input_coords):
offending_coords_fp = []
coords_headers = []
coords_data = []
coords_eigenvalues = []
coords_pct = []
coord_fps = guess_coordinates_files(input_coords)
# QIIME generates folders of transformed coordinates for the specific
# purpose of connecting all coordinates to a set of origin coordinates.
# The name of this file is suffixed as _transformed_reference.txt
trans_suf = '_transformed_reference.txt'
transformed = [f for f in coord_fps if f.endswith(trans_suf)]
# this could happen and we rather avoid this problem
if len(coord_fps) == 0:
print('Could not use any of the files in the input '
'directory.')
# the master pcoa must be the first in the list of coordinates; however
# if the visualization is not a jackknifed plot this gets ignored
if master_pcoa and not compare_plots:
if master_pcoa in coord_fps: # remove it if duplicated
coord_fps.remove(master_pcoa)
coord_fps = [master_pcoa] + coord_fps # prepend it to the list
# passing a master file means that the comparison is not serial
elif master_pcoa and compare_plots:
serial_comparison = False
# guarantee that the master is the first and is not repeated
if master_pcoa in coord_fps:
coord_fps.remove(master_pcoa)
sorted_filenames = sort_comparison_filenames(coord_fps)
coord_fps = [master_pcoa] + sorted_filenames
elif master_pcoa is None and len(transformed):
master_pcoa = transformed[0]
serial_comparison = False
# Note: the following steps are to guarantee consistency.
# remove the master from the list and re-add it as a first element
# the rest of the files must be sorted alphabetically so the result
# will be: ['unifrac_transformed_reference.txt',
# 'unifrac_transformed_q1.txt', 'unifrac_transformed_q2.txt'] etc
coord_fps.remove(master_pcoa)
coord_fps = [master_pcoa] + sort_comparison_filenames(coord_fps)
for fp in coord_fps:
try:
parsed = parse_coords(open(fp, 'U'))
except (ValueError, QiimeParseError):
offending_coords_fp.append(fp)
# do not add any of the data and move along
continue
else:
# pack all the data correspondingly only if it was correctly
# parsed
coords_headers.append(parsed[0])
coords_data.append(parsed[1])
coords_eigenvalues.append(parsed[2])
coords_pct.append(parsed[3])
# in case there were files that couldn't be parsed
if offending_coords_fp:
errout = ', '.join(offending_coords_fp)
sys.exit(("The following file(s): '%s' could not be "
"parsed properly. Make sure the input folder "
"only contains coordinates files.") % errout)
# check all files contain the same sample identifiers by flattening the
# list of available sample ids and returning the sample ids that are
# in one of the sets of sample ids but not in the globablly shared ids
_coords_headers = set(flatten(coords_headers))
_per_file_missing = [_coords_headers - set(e) for e in coords_headers]
non_shared_ids = set(flatten(_per_file_missing))
if non_shared_ids:
errout = ', '.join(non_shared_ids)
sys.exit(("The following sample identifier(s): '%s' "
"are not shared between all the files. The "
"files used to make a jackknifed PCoA plot "
"or coordinate comparison plot (procustes "
"plot) must share all the same sample "
"identifiers between each other.") % errout)
# number of samples ids that are shared between coords and mapping
# files
sids_intersection = mapping_ids.intersection(_coords_headers)
# sample ids that are not mapped but are in the coords
sids_difference = _coords_headers.difference(mapping_ids)
# used to perform different validations in the script, very similar for
# the case where the input is not a directory
number_intersected_sids = len(sids_intersection)
required_number_of_sids = len(coords_headers[0])
else:
try:
parsed = parse_coords(open(input_coords, 'U'))
# this exception was noticed when there were letters in the coords file
# other exeptions should be catched here; code will be updated then
except (ValueError, QiimeParseError):
sys.exit(("The PCoA file '%s' does not seem to be a "
"coordinates formatted file, verify by "
"manually inspecting the contents.") %
input_coords)
else:
coords_headers = parsed[0]
coords_data = parsed[1]
coords_eigenvalues = parsed[2]
coords_pct = parsed[3]
# number of samples ids that are shared between coords and mapping
# files
sids_intersection = mapping_ids.intersection(coords_headers)
# sample ids that are not mapped but are in the coords
sids_difference = set(coords_headers).difference(mapping_ids)
number_intersected_sids = len(sids_intersection)
required_number_of_sids = len(coords_headers)
if taxa_fp:
try:
# This should really use BIOM's Table.from_tsv
# for summarized tables the "otu_ids" are really the "lineages"
parsed = parse_otu_table(open(taxa_fp, 'U'), count_map_f=float,
remove_empty_rows=True)
except ValueError(e):
sys.exit(("There was a problem parsing the --taxa_fp: "
"%s" % e.message))
else:
otu_sample_ids = parsed[0]
lineages = parsed[1]
otu_table = parsed[2]
# make sure there are matching sample ids with the otu table
if not sids_intersection.issuperset(otu_sample_ids):
sys.exit("The sample identifiers in the OTU table must "
"have at least one match with the data in the "
"mapping file and with the coordinates file. "
"Verify you are using input files that belong "
"to the same dataset.")
if len(lineages) <= 1:
sys.exit("Contingency tables with one or fewer rows "
"are not supported, please try passing a "
"contingency table with more than one row.")
else:
# empty lists indicate that there was no taxa file passed in
otu_sample_ids, lineages, otu_table = [], [], []
# sample ids must be shared between files
if number_intersected_sids <= 0:
sys.exit('None of your sample identifiers match between the'
' mapping file and the coordinates file. Verify '
'you are using a coordinates file and a mapping '
'file that belong to the same dataset.')
# the intersection of the sample ids in the coords and the sample ids in
# the mapping file must at the very least include all ids in the coords
# file Otherwise it isn't valid; unless --ignore_missing_samples is set
# True
if number_intersected_sids != required_number_of_sids:
if ignore_missing_samples:
# keep only the samples that are mapped in the mapping file
coords_headers, coords_data = keep_samples_from_pcoa_data(
coords_headers, coords_data, sids_intersection)
else:
message = ("The metadata mapping file has fewer sample "
"identifiers than the coordinates file. Verify you are "
"using a mapping file that contains at least all the "
"samples contained in the coordinates file(s). You can "
"force the script to ignore these samples by passing "
"the '--ignore_missing_samples' flag.")
if verbose_output:
missing_ids = ', '.join(sids_difference)
message += ' Offending sample identifier(s): %s.' % missing_ids
sys.exit(message)
# ignore samples that exist in the coords but not in the mapping file,
# note: we're using sids_intersection so if --ignore_missing_samples is
# enabled we account for unmapped coords, else the program will exit before
# this point
header, mapping_data = filter_mapping_file(mapping_data, header,
sids_intersection,
include_repeat_cols=True)
# catch the errors that could occur when filling the mapping file values
if missing_custom_axes_values:
# the fact that this uses parse_metadata_state_descriptions makes the
# following option '-x Category:7;PH:12' to work as well as the
# script-interface-documented '-x Category:7 -x PH:12' option
for val in missing_custom_axes_values:
if ':' not in val:
sys.exit("Not valid missing value for custom "
"axes: %s" % val)
_mcav = ';'.join(missing_custom_axes_values)
try:
mapping_data = fill_mapping_field_from_mapping_file(mapping_data,
header, _mcav)
except AssertionError(e):
print(e.message)
except EmperorInputFilesError(e):
print(e.message)
# check that all the required columns exist in the metadata mapping file
if color_by_column_names:
color_by_column_names = color_by_column_names.split(',')
# check for all the mapping fields
for col in color_by_column_names:
# for concatenated columns check each individual field
parts = col.split('&&')
offending_fields.extend(p for p in parts if p not in lookup_header)
else:
# if the user didn't specify the header names display everything
color_by_column_names = [None]
# extract a list of the custom axes provided and each element is numeric
if custom_axes:
custom_axes = custom_axes.strip().strip("'").strip('"').split(',')
# the MetadataMap object makes some checks easier
map_object = MetadataMap(mapping_file_to_dict(mapping_data, header),
[])
for axis in custom_axes:
# append the field to the error queue that it belongs to
if axis not in lookup_header:
offending_fields.append(axis)
break
# make sure this value is in the mapping file
elif axis not in color_by_column_names:
color_by_column_names.append(axis)
# perform only if the for loop does not call break
else:
# make sure all these axes are numeric
for axis in custom_axes:
if not map_object.isNumericCategory(axis):
non_numeric_categories.append(axis)
# make multiple checks for the add_vectors option
if add_vectors != [None, None]:
add_vectors = add_vectors.split(',')
# check there are at the most two categories specified for this option
if len(add_vectors) > 2:
print("The '--add_vectors' option can accept up to "
"two different fields from the mapping file; "
"currently trying to use %d (%s)." %
(len(add_vectors), ', '.join(add_vectors)))
# make sure the field(s) exist
for col in add_vectors:
# concatenated fields are allowed now so check for each field
if '&&' in col:
for _col in col.split('&&'):
if _col not in lookup_header:
offending_fields.append(col)
break
# only execute this block of code if all checked fields exist
else:
# make sure that if it's going to be used for vector
# creation it gets used for coloring and map postprocessing
if col not in color_by_column_names:
color_by_column_names.append(col)
# if it's a column without concatenations
elif col not in lookup_header:
offending_fields.append(col)
break
else:
# check this vector value is in the color by category
if col not in color_by_column_names:
color_by_column_names.append(col)
# perform only if the for loop does not call break
else:
# check that the second category is all with numeric values
if len(add_vectors) == 2:
map_object = MetadataMap(mapping_file_to_dict(mapping_data,
header),
[])
# if it has non-numeric values add it to the list of offenders
if not map_object.isNumericCategory(add_vectors[1]):
msg = add_vectors[1] + '(used in --add_vectors)'
non_numeric_categories.append(msg)
else:
add_vectors.append(None)
# terminate the program for the cases where a mapping field was not found
# or when a mapping field didn't meet the criteria of being numeric
if offending_fields:
sys.exit("Invalid field(s) '%s'; the valid field(s) are: "
"'%s'" % (', '.join(offending_fields),
', '.join(header)))
if non_numeric_categories:
sys.exit(("The following field(s): '%s' contain values "
"that are not numeric, hence not suitable for "
"'--custom_axes' nor for '--add_vectors'. Try "
"the '--missing_custom_axes_values' option to "
"fix these values." %
', '.join(non_numeric_categories)))
# process the coordinates file first, preventing the case where the custom
# axes is not in the coloring categories i. e. in the --colory_by
# categories
preprocessed_coords = preprocess_coords_file(coords_headers, coords_data,
coords_eigenvalues,
coords_pct, header,
mapping_data, custom_axes,
jackknifing_method,
compare_plots,
pct_variation_below_one)
coords_headers = preprocessed_coords[0]
coords_data = preprocessed_coords[1]
coords_eigenvalues = preprocessed_coords[2]
coords_pct = preprocessed_coords[3]
coords_low = preprocessed_coords[4]
coords_high = preprocessed_coords[5]
clones = preprocessed_coords[6]
# process the otu table after processing the coordinates to get custom axes
# (when available) or any other change that occurred to the coordinates
preprocessed_otu_table = preprocess_otu_table(otu_sample_ids, otu_table,
lineages, coords_data,
coords_headers,
n_taxa_to_keep)
otu_coords = preprocessed_otu_table[0]
otu_table = preprocessed_otu_table[1]
otu_lineages = preprocessed_otu_table[2]
otu_prevalence = preprocessed_otu_table[3]
lines = preprocessed_otu_table[4]
# remove the columns in the mapping file that are not informative taking
# into account the header names that were already authorized to be used
# and take care of concatenating the fields for the && merged columns
mapping_data, header = preprocess_mapping_file(mapping_data, header,
color_by_column_names,
not add_unique_columns,
clones=clones)
# create the output directory before creating any other output
if not isdir(opts.output_dir):
makedirs(opts.output_dir)
fp_out = open(join(output_dir, 'index.html'), 'w')
fp_out.write(emperor_autograph+'\n')
fp_out.write(EMPEROR_HEADER_HTML_STRING)
# write the html file
fp_out.write(format_mapping_file_to_js(mapping_data, header, header))
# certain percents being explained cannot be displayed in the GUI
try:
fp_out.write(format_pcoa_to_js(coords_headers, coords_data,
coords_eigenvalues, coords_pct,
custom_axes, coords_low,
coords_high,
number_of_axes=number_of_axes,
number_of_segments=number_of_segments))
except EmperorLogicError(e):
sys.exit(e.message)
fp_out.write(format_taxa_to_js(otu_coords, otu_lineages, otu_prevalence))
fp_out.write(format_vectors_to_js(mapping_data, header, coords_data,
coords_headers, add_vectors[0],
add_vectors[1]))
fp_out.write(format_comparison_bars_to_js(coords_data, coords_headers,
clones, serial_comparison))
has_taxa = taxa_fp is not None
has_input_coords = isdir(input_coords) and not compare_plots
has_add_vectors = add_vectors != [None, None]
has_clones = clones > 0
fp_out.write(format_emperor_html_footer_string(has_taxa, has_input_coords,
has_add_vectors,
has_clones))
fp_out.close()
copy_support_files(output_dir)
# write the biplot coords in the output file if a path is passed
if biplot_fp and taxa_fp:
if biplot_fp.endswith('/') or isdir(biplot_fp):
print("Do not specify a path to a new (path ending "
"in a slash) or existing directory for "
"biplot_fp. The output file will be a "
"tab-delimited text file.")
# make sure this file can be created
try:
fd = open(biplot_fp, 'w')
except IOError:
sys.exit("There was a problem creating the file with "
"the coordinates for the biplots (%s)."
% biplot_fp)
else:
fd.writelines(lines)
fd.close()
