def test_preprocess_coords_file_comparison(self):
"""Check the cases for comparisons plots and the special usages"""
# shouldn't allow a comparison computation with only one file
self.assertRaises(AssertionError, preprocess_coords_file,
self.coords_header, self.coords_data,
self.coords_eigenvalues, self.coords_pct,
self.mapping_file_headers_gradient,
self.mapping_file_data_gradient, None, None, True)
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header, self.jk_coords_data,
self.jk_coords_eigenvalues, self.jk_coords_pcts,
self.jk_mapping_file_headers, self.jk_mapping_file_data,
is_comparison=True, pct_variation_below_one=True)
self.assertEqual(out_coords_header, [
'1_0', '2_0', '3_0', '1_1', '2_1', '3_1', '1_2', '2_2', '3_2',
'1_3', '2_3', '3_3'
])
assert_almost_equal(
out_coords_data,
array([[1.2, 0.1, -1.2], [-2.5, -4., 4.5], [-1.4, 0.05, 1.3],
[2.6, 4.1, -4.7], [-1.5, 0.05, 1.6], [2.4, 4., -4.8],
[-1.5, 0.05, 1.6], [2.4, 4., -4.8]]))
assert_almost_equal(out_eigenvals, self.jk_coords_eigenvalues[0])
assert_almost_equal(out_pcts, self.jk_coords_pcts[0])
self.assertEqual(out_coords_low, None)
self.assertEqual(out_coords_high, None)
self.assertEqual(o_clones, 4)
def test_preprocess_coords_file_comparison(self):
"""Check the cases for comparisons plots and the special usages"""
# shouldn't allow a comparison computation with only one file
self.assertRaises(
AssertionError, preprocess_coords_file,
self.coords_header, self.coords_data, self.coords_eigenvalues,
self.coords_pct, self.mapping_file_headers_gradient,
self.mapping_file_data_gradient, None, None, True)
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header, self.jk_coords_data,
self.jk_coords_eigenvalues, self.jk_coords_pcts,
self.jk_mapping_file_headers, self.jk_mapping_file_data,
is_comparison=True, pct_variation_below_one=True)
self.assertEquals(out_coords_header,
['1_0', '2_0', '3_0', '1_1', '2_1', '3_1', '1_2',
'2_2', '3_2', '1_3', '2_3', '3_3'])
assert_almost_equal(
out_coords_data,
array([[1.2, 0.1, -1.2],
[-2.5, -4., 4.5],
[-1.4, 0.05, 1.3],
[2.6, 4.1, -4.7],
[-1.5, 0.05, 1.6],
[2.4, 4., -4.8],
[-1.5, 0.05, 1.6],
[2.4, 4., -4.8]]))
assert_almost_equal(out_eigenvals, self.jk_coords_eigenvalues[0])
assert_almost_equal(out_pcts, self.jk_coords_pcts[0])
self.assertEquals(out_coords_low, None)
self.assertEquals(out_coords_high, None)
self.assertEquals(o_clones, 4)
def generate_pcoa_cloud_from_point_in_omega(map_headers, map_data, biom_object, metric,
sequences, iterations, axes, tree_object=None):
"""run the randomisations and get a WebGL PCoA plot string representation
Input:
mapping_file_tuple: data and headers tuple for representing the mapping file
biom_object: otu table biom object
metric: string of the name for the beta diversity metric, i. e. 'unifrac'
sequences: number of sequences per sample
iterations: number of iterations to generate the pcoa plot
axes: number of axes to account for
tree_object: tree to perform the beta diversity calculation
Output:
WebGL string representing the PCoA plot
"""
pcoa_input = {'pcoa_headers':[], 'pcoa_values':[], 'eigenvalues':[], 'coords_pct':[]}
for i in range(iterations):
rare_biom_table = get_rare_data(biom_object, sequences)
beta_dm = single_object_beta(rare_biom_table, metric, tree_object)
pcoa_results = pcoa(beta_dm)
pcoa_file = StringIO()
pcoa_file.write(pcoa_results)
pcoa_file.seek(0)
pcoa_headers, pcoa_values, eigenvalues, coords_pct = parse_coords(pcoa_file)
pcoa_file.close()
pcoa_input['pcoa_headers'].append(pcoa_headers)
pcoa_input['pcoa_values'].append(pcoa_values)
pcoa_input['eigenvalues'].append(eigenvalues)
pcoa_input['coords_pct'].append(coords_pct)
if iterations==1:
coords_headers = pcoa_input['pcoa_headers'][0]
coords_data = pcoa_input['pcoa_values'][0]
coords_eigenvalues = pcoa_input['eigenvalues'][0]
coords_pct = pcoa_input['coords_pct'][0]
coords_low, coords_high = None, None
else:
coords_headers, coords_data, coords_eigenvalues, coords_pct, coords_low,\
coords_high, clones = preprocess_coords_file(pcoa_input['pcoa_headers'],
pcoa_input['pcoa_values'], pcoa_input['eigenvalues'],
pcoa_input['coords_pct'], map_headers, map_data, custom_axes=None,
jackknifing_method='IQR', is_comparison=False)
return make_pcoa_plot(coords_headers, coords_data, coords_eigenvalues, coords_pct, \
map_headers, map_data, coords_low, coords_high, True)
# 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:
option_parser.error("Invalid field(s) '%s'; the valid field(s) are:"
" '%s'" % (', '.join(offending_fields), ', '.join(header)))
if non_numeric_categories:
option_parser.error(('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
coords_headers, coords_data, coords_eigenvalues, coords_pct, coords_low,\
coords_high, clones = preprocess_coords_file(coords_headers,coords_data,
coords_eigenvalues, coords_pct, header, mapping_data, custom_axes,
jackknifing_method=jackknifing_method, is_comparison=compare_plots,
pct_variation_below_one=pct_variation_below_one)
# process the otu table after processing the coordinates to get custom axes
# (when available) or any other change that occurred to the coordinates
otu_coords, otu_table, otu_lineages, otu_prevalence, lines =\
preprocess_otu_table(otu_sample_ids, otu_table, lineages,
coords_data, coords_headers, n_taxa_to_keep)
# 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, unique=not add_unique_columns, clones=clones)
# create the output directory before creating any other output
# 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:
option_parser.error("Invalid field(s) '%s'; the valid field(s) are:"
" '%s'" % (', '.join(offending_fields), ', '.join(header)))
if non_numeric_categories:
option_parser.error(('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
coords_headers, coords_data, coords_eigenvalues, coords_pct, coords_low,\
coords_high, clones = preprocess_coords_file(coords_headers,coords_data,
coords_eigenvalues, coords_pct, header, mapping_data, custom_axes,
jackknifing_method=jackknifing_method, is_comparison=compare_plots)
# process the otu table after processing the coordinates to get custom axes
# (when available) or any other change that occurred to the coordinates
otu_coords, otu_table, otu_lineages, otu_prevalence, lines =\
preprocess_otu_table(otu_sample_ids, otu_table, lineages,
coords_data, coords_headers, n_taxa_to_keep)
# 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, unique=not add_unique_columns, clones=clones)
# create the output directory before creating any other output
def test_preprocess_coords_file(self):
"""Check correct processing is applied to the coords"""
# case with custom axes
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.coords_header, self.coords_data, self.coords_eigenvalues,
self.coords_pct, self.mapping_file_headers_gradient,
self.mapping_file_data_gradient, ['Time'])
expected_coords_data = array(
[[0.03333333, -0.2, -0.1, 0.06, -0.06],
[0.03333333, -0.3, 0.04, -0.1, 0.15],
[0.2, 0.1, -0.1, -0.2, 0.08],
[-0.3, 0.04, -0.01, 0.06, -0.34]])
self.assertEquals(out_coords_header, self.coords_header)
self.assertEquals(out_coords_high, None)
self.assertEquals(out_coords_low, None)
assert_almost_equal(self.coords_eigenvalues, array([1, 2, 3, 4]))
assert_almost_equal(self.coords_pct, array([40, 30, 20, 10]))
self.assertEquals(o_clones, 0)
# check each individual value because currently cogent assertEquals
# fails when comparing the whole matrix at once
for out_el, exp_el in zip(out_coords_data, expected_coords_data):
for out_el_sub, exp_el_sub in zip(out_el, exp_el):
self.assertAlmostEquals(out_el_sub, exp_el_sub)
# case for jackknifing, based on qiime/tests/test_util.summarize_pcoas
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header, self.jk_coords_data,
self.jk_coords_eigenvalues, self.jk_coords_pcts,
self.jk_mapping_file_headers, self.jk_mapping_file_data,
jackknifing_method='sdev', pct_variation_below_one=True)
self.assertEquals(out_coords_header, ['1', '2', '3'])
assert_almost_equal(out_coords_data, array([[1.4, -0.0125, -1.425],
[-2.475, -4.025, 4.7]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05]))
assert_almost_equal(out_pcts, array([0.8, 0.1, 0.1]))
self.assertEquals(o_clones, 0)
# test the coords are working fine
assert_almost_equal(out_coords_low,
array([[-0.07071068, -0.0375, -0.10307764],
[-0.04787136, -0.025, -0.07071068]]))
assert_almost_equal(out_coords_high,
array([[0.07071068, 0.0375, 0.10307764],
[0.04787136, 0.025, 0.07071068]]))
# test custom axes and jackknifed plots
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header_gradient, self.jk_coords_data_gradient,
self.jk_coords_eigenvalues_gradient,
self.jk_coords_pcts_gradient,
self.jk_mapping_file_headers_gradient,
self.jk_mapping_file_data_gradient, custom_axes=['Time'],
jackknifing_method='sdev', pct_variation_below_one=True)
self.assertEquals(out_coords_header,
['PC.354', 'PC.355', 'PC.635', 'PC.636'])
assert_almost_equal(out_coords_data,
array([[-2.4, 1.15, 0.55, -0.95, 0.85],
[0.73333333, -2.4, -3.5, 4.25, 1.025],
[0.73333333, 0.5, 0.45, 3.5, 1.2505],
[2.3, 0.6325, 0.2575, 1.0675, 2.125]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05, 0.]))
assert_almost_equal(out_pcts, array([0.8, 0.1, 0.1, 0.]))
# test the coords are working fine
assert_almost_equal(
out_coords_low,
array([[0., -0.25980762, -0.25, -0.25],
[0., -0.5, -0.25, -0.725],
[0., -0.85, -0., -0.24983344],
[0., -0.02809953, -0.07877976, -0.04787136]]))
assert_almost_equal(
out_coords_high,
array([[1.00000000e-05, 2.59807621e-01, 2.50000000e-01,
2.50000000e-01],
[1.00000000e-05, 5.00000000e-01, 2.50000000e-01,
7.25000000e-01],
[1.00000000e-05, 8.50000000e-01, 0.00000000e+00,
2.49833445e-01],
[1.00000000e-05, 2.80995255e-02, 7.87797563e-02,
4.78713554e-02]]))
self.assertEquals(o_clones, 0)
# test that pct_variation_below_one is working
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header_gradient, self.jk_coords_data_gradient,
self.jk_coords_eigenvalues_gradient,
self.jk_coords_pcts_gradient,
self.jk_mapping_file_headers_gradient,
self.jk_mapping_file_data_gradient, custom_axes=['Time'],
jackknifing_method='sdev', pct_variation_below_one=False)
self.assertEquals(out_coords_header,
['PC.354', 'PC.355', 'PC.635', 'PC.636'])
assert_almost_equal(
out_coords_data,
array([[-2.4, 1.15, 0.55, -0.95, 0.85],
[0.73333333, -2.4, -3.5, 4.25, 1.025],
[0.73333333, 0.5, 0.45, 3.5, 1.2505],
[2.3, 0.6325, 0.2575, 1.0675, 2.125]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05, 0.]))
assert_almost_equal(out_pcts, array([80, 10, 10, 0]))
def test_preprocess_coords_file(self):
"""Check correct processing is applied to the coords"""
# case with custom axes
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.coords_header, self.coords_data, self.coords_eigenvalues,
self.coords_pct, self.mapping_file_headers_gradient,
self.mapping_file_data_gradient, ['Time'])
expected_coords_data = array([[0.03333333, -0.2, -0.1, 0.06, -0.06],
[0.03333333, -0.3, 0.04, -0.1, 0.15],
[0.2, 0.1, -0.1, -0.2, 0.08],
[-0.3, 0.04, -0.01, 0.06, -0.34]])
self.assertEqual(out_coords_header, self.coords_header)
self.assertEqual(out_coords_high, None)
self.assertEqual(out_coords_low, None)
assert_almost_equal(self.coords_eigenvalues, array([1, 2, 3, 4]))
assert_almost_equal(self.coords_pct, array([40, 30, 20, 10]))
self.assertEqual(o_clones, 0)
# check each individual value because currently cogent assertEquals
# fails when comparing the whole matrix at once
for out_el, exp_el in zip(out_coords_data, expected_coords_data):
for out_el_sub, exp_el_sub in zip(out_el, exp_el):
self.assertAlmostEqual(out_el_sub, exp_el_sub)
# case for jackknifing, based on qiime/tests/test_util.summarize_pcoas
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header, self.jk_coords_data,
self.jk_coords_eigenvalues, self.jk_coords_pcts,
self.jk_mapping_file_headers, self.jk_mapping_file_data,
jackknifing_method='sdev', pct_variation_below_one=True)
self.assertEqual(out_coords_header, ['1', '2', '3'])
assert_almost_equal(
out_coords_data,
array([[1.4, -0.0125, -1.425], [-2.475, -4.025, 4.7]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05]))
assert_almost_equal(out_pcts, array([0.8, 0.1, 0.1]))
self.assertEqual(o_clones, 0)
# test the coords are working fine
assert_almost_equal(
out_coords_low,
array([[-0.07071068, -0.0375, -0.10307764],
[-0.04787136, -0.025, -0.07071068]]))
assert_almost_equal(
out_coords_high,
array([[0.07071068, 0.0375, 0.10307764],
[0.04787136, 0.025, 0.07071068]]))
# test custom axes and jackknifed plots
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header_gradient, self.jk_coords_data_gradient,
self.jk_coords_eigenvalues_gradient,
self.jk_coords_pcts_gradient,
self.jk_mapping_file_headers_gradient,
self.jk_mapping_file_data_gradient, custom_axes=['Time'],
jackknifing_method='sdev', pct_variation_below_one=True)
self.assertEqual(out_coords_header,
['PC.354', 'PC.355', 'PC.635', 'PC.636'])
assert_almost_equal(
out_coords_data,
array([[-2.4, 1.15, 0.55, -0.95, 0.85],
[0.73333333, -2.4, -3.5, 4.25, 1.025],
[0.73333333, 0.5, 0.45, 3.5, 1.2505],
[2.3, 0.6325, 0.2575, 1.0675, 2.125]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05, 0.]))
assert_almost_equal(out_pcts, array([0.8, 0.1, 0.1, 0.]))
# test the coords are working fine
assert_almost_equal(
out_coords_low,
array([[0., -0.05, -0.25980762, -0.25, -0.25],
[0., -0.1, -0.5, -0.25, -0.725],
[0., -0., -0.85, -0., -0.24983344],
[0., -0.0283945, -0.02809953, -0.07877976, -0.04787136]]))
assert_almost_equal(
out_coords_high,
array([[
1.00000000e-05, 5.0000000e-02, 2.59807621e-01, 2.50000000e-01,
2.50000000e-01
],
[
1.00000000e-05, 1.0000000e-01, 5.00000000e-01,
2.50000000e-01, 7.25000000e-01
],
[
1.00000000e-05, 0.0, 8.50000000e-01, 0.00000000e+00,
2.49833445e-01
],
[
1.00000000e-05, 2.83945417e-02, 2.80995255e-02,
7.87797563e-02, 4.78713554e-02
]]))
self.assertEqual(o_clones, 0)
# test that pct_variation_below_one is working
out_coords_header, out_coords_data, out_eigenvals, out_pcts,\
out_coords_low, out_coords_high, o_clones = preprocess_coords_file(
self.jk_coords_header_gradient, self.jk_coords_data_gradient,
self.jk_coords_eigenvalues_gradient,
self.jk_coords_pcts_gradient,
self.jk_mapping_file_headers_gradient,
self.jk_mapping_file_data_gradient, custom_axes=['Time'],
jackknifing_method='sdev', pct_variation_below_one=False)
self.assertEqual(out_coords_header,
['PC.354', 'PC.355', 'PC.635', 'PC.636'])
assert_almost_equal(
out_coords_data,
array([[-2.4, 1.15, 0.55, -0.95, 0.85],
[0.73333333, -2.4, -3.5, 4.25, 1.025],
[0.73333333, 0.5, 0.45, 3.5, 1.2505],
[2.3, 0.6325, 0.2575, 1.0675, 2.125]]))
assert_almost_equal(out_eigenvals, array([0.81, 0.14, 0.05, 0.]))
assert_almost_equal(out_pcts, array([80, 10, 10, 0]))
option_parser.error("Invalid field(s) '%s'; the valid field(s) are: "
"'%s'" %
(', '.join(offending_fields), ', '.join(header)))
if non_numeric_categories:
option_parser.error(
("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)
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()