def test_get_taxa_coords(self):
otu_table = np.array([[2, 0, 0, 1], [1, 1, 1, 1], [0, 2, 2, 1]], float)
sample_names = list('WXYZ')
otu_names = list('abc')
res = bp.get_taxa_coords(otu_table, [.4, .2, .1, .9])
otu_coords = range(3)
otu_coords[0] = .4 * 2 / 3 + .9 * 1 / 3
otu_coords[1] = .4 * 1 / 4 + .2 * 1 / 4 + .1 * 1 / 4 + .9 * 1 / 4
otu_coords[2] = .4 * 0 / 5 + .2 * 2 / 5 + .1 * 2 / 5 + .9 * 1 / 5
assert_almost_equal(res, otu_coords)
def preprocess_otu_table(otu_sample_ids, otu_table, lineages,
coords_data, coords_headers, N=0):
"""Preprocess the OTU table to to generate the required data for the biplots
Input:
otu_sample_ids: sample identifiers for the otu_table
otu_table: contingency table
lineages: taxonomic assignments for the OTUs in the otu_table
coords_data: principal coordinates data where the taxa will be mapped
N: number of most prevalent taxa to keep, by default will use all
Output:
otu_coords: coordinates representing the N most prevalent taxa in otu_table
otu_table: N most prevalent OTUs from the input otu_table
otu_lineages: taxonomic assignments corresponding to the N most prevalent
OTUs
otu_prevalence: vector with the prevalence scores of the N highest values
lines: coords where the N most prevalent taxa will be positioned in the
biplot
"""
# return empty values if any of the taxa data is empty
if (otu_sample_ids == []) or (otu_table == array([])) or (lineages == []):
return [], [], [], [], ''
# this means there's only one or fewer rows in the contingency table
if len(otu_table) <= 1 or len(lineages) <= 1:
raise EmperorUnsupportedComputation, "Biplots are not supported for "+\
"contingency tables with one or fewer rows"
# if this element is a list take the first headers and coordinates
# both of these will be the master coordinates, i. e. where data is centered
if type(coords_data) == list and type(coords_headers) == list:
coords_data = coords_data[0]
coords_headers = coords_headers[0]
# re-arrange the otu table so it matches the order of the samples in the
# coordinates data & remove any sample that is not in the coordinates header
otu_sample_ids, otu_table = sort_taxa_table_by_pcoa_coords(coords_headers,
otu_table, otu_sample_ids)
# retrieve the prevalence and the coords prior the filtering
prevalence = get_taxa_prevalence(otu_table)
bi_plot_coords = get_taxa_coords(otu_table, coords_data)
o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence =\
extract_taxa_data(bi_plot_coords, otu_table, lineages, prevalence, N)
lines = '\n'.join(make_biplot_scores_output({'coord': o_otu_coords,
'lineages': o_otu_lineages}))
return o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence, lines
def test_get_taxa_coords(self):
otu_table = np.array([[2, 0, 0, 1],
[1, 1, 1, 1],
[0, 2, 2, 1]], float)
sample_names = list('WXYZ')
otu_names = list('abc')
res = bp.get_taxa_coords(otu_table, [.4, .2, .1, .9])
otu_coords = range(3)
otu_coords[0] = .4 * 2 / 3 + .9 * 1 / 3
otu_coords[1] = .4 * 1 / 4 + .2 * 1 / 4 + .1 * 1 / 4 + .9 * 1 / 4
otu_coords[2] = .4 * 0 / 5 + .2 * 2 / 5 + .1 * 2 / 5 + .9 * 1 / 5
assert_almost_equal(res, otu_coords)
def main():
print "\nWarning: make_3d_plots.py is being deprecated in favor of make_emperor.py, and will no longer be available in QIIME 1.8.0-dev.\n"
option_parser, opts, args = parse_command_line_parameters(**script_info)
prefs, data, background_color, label_color, ball_scale, arrow_colors= \
sample_color_prefs_and_map_data_from_options(opts)
plot_scaled= 'scaled' in opts.scaling_method
plot_unscaled= 'unscaled' in opts.scaling_method
if opts.output_format == 'invue':
# validating the number of points for interpolation
if (opts.interpolation_points<0):
option_parser.error('The --interpolation_points should be ' +\
'greater or equal to 0.')
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
#Open and get coord data
data['coord'] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'])
# if no samples overlapped between mapping file and otu table, exit
if len(data['coord'][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
if opts.output_dir:
create_dir(opts.output_dir,False)
dir_path=opts.output_dir
else:
dir_path='./'
filepath=opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not \
fname.startswith('.')]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
generate_3d_plots_invue(prefs, data, dir_path, filename, \
opts.interpolation_points, opts.polyhedron_points, \
opts.polyhedron_offset)
#finish script
return
# Potential conflicts
if not opts.custom_axes is None and os.path.isdir(opts.coord_fname):
# can't do averaged pcoa plots _and_ custom axes in the same plot
option_parser.error("Please supply either custom axes or multiple coordinate \
files, but not both.")
# check that smoothness is an integer between 0 and 3
try:
ellipsoid_smoothness = int(opts.ellipsoid_smoothness)
except:
option_parser.error("Please supply an integer ellipsoid smoothness \
value.")
if ellipsoid_smoothness < 0 or ellipsoid_smoothness > 3:
option_parser.error("Please supply an ellipsoid smoothness value \
between 0 and 3.")
# check that opacity is a float between 0 and 1
try:
ellipsoid_alpha = float(opts.ellipsoid_opacity)
except:
option_parser.error("Please supply a number for ellipsoid opacity.")
if ellipsoid_alpha < 0 or ellipsoid_alpha > 1:
option_parser.error("Please supply an ellipsoid opacity value \
between 0 and 1.")
# check that ellipsoid method is valid
ellipsoid_methods = ['IQR','sdev']
if not opts.ellipsoid_method in ellipsoid_methods:
option_parser.error("Please supply a valid ellipsoid method. \
Valid methods are: " + ', '.join(ellipsoid_methods) + ".")
# gather ellipsoid drawing preferences
ellipsoid_prefs = {}
ellipsoid_prefs["smoothness"] = ellipsoid_smoothness
ellipsoid_prefs["alpha"] = ellipsoid_alpha
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
#Open and get coord data
data['coord'] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'])
# if no samples overlapped between mapping file and otu table, exit
if len(data['coord'][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
# process custom axes, if present.
custom_axes = None
if opts.custom_axes:
custom_axes = process_custom_axes(opts.custom_axes)
get_custom_coords(custom_axes, data['map'], data['coord'])
remove_nans(data['coord'])
scale_custom_coords(custom_axes,data['coord'])
# process vectors if requested
if opts.add_vectors:
add_vectors={}
add_vectors['vectors'] = opts.add_vectors.split(',')
add_vectors['weight_by_vector'] = opts.weight_by_vector
if len(add_vectors)>3:
raise ValueError, 'You must add maximum 3 columns but %s' % opts.add_vectors
# Validating Vectors values
if opts.vectors_algorithm:
axes_number = len(data['coord'][1][1])
if opts.vectors_axes<0 or opts.vectors_axes>axes_number:
raise ValueError, 'vectors_algorithm should be between 0 and the max number' +\
'of samples/pcoa-axes: %d' % len(data['coord'][1][1])
if opts.vectors_axes == 0:
opts.vectors_axes = axes_number
add_vectors['vectors_axes'] = opts.vectors_axes
valid_chars = '_.abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
for c in opts.vectors_path:
if c not in valid_chars:
raise ValueError, 'vectors_path (%s) has invalid chars' % opts.vectors_path
add_vectors['vectors_output'] = {}
add_vectors['vectors_algorithm']=opts.vectors_algorithm
add_vectors['eigvals'] = data['coord'][3]
add_vectors['window_size'] = None
# checks specific for the modified first difference algorithm
if add_vectors['vectors_algorithm'] == 'wdiff':
try:
add_vectors['window_size'] = int(opts.window_size)
except TypeError:
raise TypeError, 'Specify --window_size as an integer'
# sanity check as the value can only be greater or equal to one
if add_vectors['window_size'] < 1:
raise ValueError, 'The value of window_size is invalid, '+\
'the value must be greater than zero, not %d' % add_vectors['window_size']
else:
add_vectors['vectors_algorithm'] = None
add_vectors['vectors_path'] = opts.vectors_path
else:
add_vectors = None
if opts.taxa_fname != None:
# get taxonomy counts
# get list of sample_ids that haven't been removed
sample_ids = data['coord'][0]
# get taxa summaries for all sample_ids
lineages, taxa_counts = get_taxa(opts.taxa_fname, sample_ids)
data['taxa'] = {}
data['taxa']['lineages'] = lineages
data['taxa']['counts'] = taxa_counts
# get average relative abundance of taxa
data['taxa']['prevalence'] = get_taxa_prevalence(data['taxa']['counts'])
# get coordinates of taxa (weighted mean of sample scores)
data['taxa']['coord'] = get_taxa_coords(data['taxa']['counts'],
data['coord'][1])
# trim results, do NOT change order
# check: https://github.com/qiime/qiime/issues/677
remove_rare_taxa(data['taxa'],nkeep=opts.n_taxa_keep)
# write taxa coords if requested
if not opts.biplot_output_file is None:
output = make_biplot_scores_output(data['taxa'])
fout = open(opts.biplot_output_file,'w')
fout.write('\n'.join(output))
fout.close()
if opts.output_dir:
create_dir(opts.output_dir,False)
dir_path=opts.output_dir
else:
dir_path='./'
qiime_dir=get_qiime_project_dir()
jar_path=os.path.join(qiime_dir,'qiime/support_files/jar/')
data_dir_path = get_random_directory_name(output_dir=dir_path,
return_absolute_path=False)
try:
os.mkdir(data_dir_path)
except OSError:
pass
data_file_path=data_dir_path
jar_dir_path = os.path.join(dir_path,'jar')
try:
os.mkdir(jar_dir_path)
except OSError:
pass
shutil.copyfile(os.path.join(jar_path,'king.jar'), os.path.join(jar_dir_path,'king.jar'))
filepath=opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not \
fname.startswith('.')]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
try:
action = generate_3d_plots
except NameError:
action = None
#Place this outside try/except so we don't mask NameError in action
if action:
action(prefs,data,custom_axes,background_color,label_color,dir_path, \
data_file_path,filename,ellipsoid_prefs=ellipsoid_prefs, \
add_vectors=add_vectors, plot_scaled=plot_scaled, \
plot_unscaled=plot_unscaled)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
prefs, data, background_color, label_color, ball_scale, arrow_colors = sample_color_prefs_and_map_data_from_options(
opts
)
scaling_methods = opts.scaling_method.split(",")
plot_scaled = False
plot_unscaled = False
for i in scaling_methods:
if i.lower() == "scaled":
plot_scaled = True
elif i.lower() == "unscaled":
plot_unscaled = True
if not (plot_scaled or plot_unscaled):
raise ValueError, "You must choose a valid scaling method (scaled or unscaled)"
if opts.output_format == "invue":
# validating the number of points for interpolation
if opts.interpolation_points < 0:
option_parser.error("The --interpolation_points should be " + "greater or equal to 0.")
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error("Every line of every coord file must " + "have the same number of columns.")
# Open and get coord data
data["coord"] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data["map"], data["coord"])
# if no samples overlapped between mapping file and otu table, exit
if len(data["coord"][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
if opts.output_dir:
create_dir(opts.output_dir, False)
dir_path = opts.output_dir
else:
dir_path = "./"
filepath = opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not fname.startswith(".")]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
generate_3d_plots_invue(
prefs, data, dir_path, filename, opts.interpolation_points, opts.polyhedron_points, opts.polyhedron_offset
)
# finish script
return
# Potential conflicts
if not opts.custom_axes is None and os.path.isdir(opts.coord_fname):
# can't do averaged pcoa plots _and_ custom axes in the same plot
option_parser.error(
"Please supply either custom axes or multiple coordinate \
files, but not both."
)
# check that smoothness is an integer between 0 and 3
try:
ellipsoid_smoothness = int(opts.ellipsoid_smoothness)
except:
option_parser.error(
"Please supply an integer ellipsoid smoothness \
value."
)
if ellipsoid_smoothness < 0 or ellipsoid_smoothness > 3:
option_parser.error(
"Please supply an ellipsoid smoothness value \
between 0 and 3."
)
# check that opacity is a float between 0 and 1
try:
ellipsoid_alpha = float(opts.ellipsoid_opacity)
except:
option_parser.error("Please supply a number for ellipsoid opacity.")
if ellipsoid_alpha < 0 or ellipsoid_alpha > 1:
option_parser.error(
"Please supply an ellipsoid opacity value \
between 0 and 1."
)
# check that ellipsoid method is valid
ellipsoid_methods = ["IQR", "sdev"]
if not opts.ellipsoid_method in ellipsoid_methods:
option_parser.error(
"Please supply a valid ellipsoid method. \
Valid methods are: "
+ ", ".join(ellipsoid_methods)
+ "."
)
# gather ellipsoid drawing preferences
ellipsoid_prefs = {}
ellipsoid_prefs["smoothness"] = ellipsoid_smoothness
ellipsoid_prefs["alpha"] = ellipsoid_alpha
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error("Every line of every coord file must " + "have the same number of columns.")
# Open and get coord data
data["coord"] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data["map"], data["coord"])
# if no samples overlapped between mapping file and otu table, exit
if len(data["coord"][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
# process custom axes, if present.
custom_axes = None
if opts.custom_axes:
custom_axes = process_custom_axes(opts.custom_axes)
get_custom_coords(custom_axes, data["map"], data["coord"])
remove_nans(data["coord"])
scale_custom_coords(custom_axes, data["coord"])
# process vectors if requested
if opts.add_vectors:
add_vectors = {}
add_vectors["vectors"] = opts.add_vectors.split(",")
add_vectors["weight_by_vector"] = opts.weight_by_vector
if len(add_vectors) > 3:
raise ValueError, "You must add maximum 3 columns but %s" % opts.add_vectors
# Validating Vectors values
if opts.vectors_algorithm:
axes_number = len(data["coord"][1][1])
if opts.vectors_axes < 0 or opts.vectors_axes > axes_number:
raise ValueError, "vectors_algorithm should be between 0 and the max number" + "of samples/pcoa-axes: %d" % len(
data["coord"][1][1]
)
if opts.vectors_axes == 0:
opts.vectors_axes = axes_number
add_vectors["vectors_axes"] = opts.vectors_axes
valid_chars = "_.abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
for c in opts.vectors_path:
if c not in valid_chars:
raise ValueError, "vectors_path (%s) has invalid chars" % opts.vectors_path
add_vectors["vectors_output"] = {}
add_vectors["vectors_algorithm"] = opts.vectors_algorithm
add_vectors["eigvals"] = data["coord"][3]
add_vectors["window_size"] = None
# checks specific for the modified first difference algorithm
if add_vectors["vectors_algorithm"] == "wdiff":
try:
add_vectors["window_size"] = int(opts.window_size)
except TypeError:
raise TypeError, "Specify --window_size as an integer"
# sanity check as the value can only be greater or equal to one
if add_vectors["window_size"] < 1:
raise ValueError, "The value of window_size is invalid, " + "the value must be greater than zero, not %d" % add_vectors[
"window_size"
]
else:
add_vectors["vectors_algorithm"] = None
add_vectors["vectors_path"] = opts.vectors_path
else:
add_vectors = None
if opts.taxa_fname != None:
# get taxonomy counts
# get list of sample_ids that haven't been removed
sample_ids = data["coord"][0]
# get taxa summaries for all sample_ids
lineages, taxa_counts = get_taxa(opts.taxa_fname, sample_ids)
data["taxa"] = {}
data["taxa"]["lineages"] = lineages
data["taxa"]["counts"] = taxa_counts
# get average relative abundance of taxa
data["taxa"]["prevalence"] = get_taxa_prevalence(data["taxa"]["counts"])
remove_rare_taxa(data["taxa"], nkeep=opts.n_taxa_keep)
# get coordinates of taxa (weighted mean of sample scores)
data["taxa"]["coord"] = get_taxa_coords(data["taxa"]["counts"], data["coord"][1])
data["taxa"]["coord"]
# write taxa coords if requested
if not opts.biplot_output_file is None:
output = make_biplot_scores_output(data["taxa"])
fout = open(opts.biplot_output_file, "w")
fout.write("\n".join(output))
fout.close()
if opts.output_dir:
create_dir(opts.output_dir, False)
dir_path = opts.output_dir
else:
dir_path = "./"
qiime_dir = get_qiime_project_dir()
jar_path = os.path.join(qiime_dir, "qiime/support_files/jar/")
data_dir_path = get_random_directory_name(output_dir=dir_path, return_absolute_path=False)
try:
os.mkdir(data_dir_path)
except OSError:
pass
data_file_path = data_dir_path
jar_dir_path = os.path.join(dir_path, "jar")
try:
os.mkdir(jar_dir_path)
except OSError:
pass
shutil.copyfile(os.path.join(jar_path, "king.jar"), os.path.join(jar_dir_path, "king.jar"))
filepath = opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not fname.startswith(".")]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
try:
action = generate_3d_plots
except NameError:
action = None
# Place this outside try/except so we don't mask NameError in action
if action:
action(
prefs,
data,
custom_axes,
background_color,
label_color,
dir_path,
data_file_path,
filename,
ellipsoid_prefs=ellipsoid_prefs,
add_vectors=add_vectors,
plot_scaled=plot_scaled,
plot_unscaled=plot_unscaled,
)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
prefs, data, background_color, label_color, ball_scale, arrow_colors= \
sample_color_prefs_and_map_data_from_options(opts)
if opts.output_format == 'invue':
# validating the number of points for interpolation
if (opts.interpolation_points<0):
option_parser.error('The --interpolation_points should be ' +\
'greater or equal to 0.')
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
#Open and get coord data
data['coord'] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'])
# if no samples overlapped between mapping file and otu table, exit
if len(data['coord'][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
if opts.output_dir:
create_dir(opts.output_dir,False)
dir_path=opts.output_dir
else:
dir_path='./'
filepath=opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not \
fname.startswith('.')]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
generate_3d_plots_invue(prefs, data, dir_path, filename, \
opts.interpolation_points, opts.polyhedron_points, \
opts.polyhedron_offset)
#finish script
return
# Potential conflicts
if not opts.custom_axes is None and os.path.isdir(opts.coord_fname):
# can't do averaged pcoa plots _and_ custom axes in the same plot
option_parser.error("Please supply either custom axes or multiple coordinate \
files, but not both.")
# check that smoothness is an integer between 0 and 3
try:
ellipsoid_smoothness = int(opts.ellipsoid_smoothness)
except:
option_parser.error("Please supply an integer ellipsoid smoothness \
value.")
if ellipsoid_smoothness < 0 or ellipsoid_smoothness > 3:
option_parser.error("Please supply an ellipsoid smoothness value \
between 0 and 3.")
# check that opacity is a float between 0 and 1
try:
ellipsoid_alpha = float(opts.ellipsoid_opacity)
except:
option_parser.error("Please supply a number for ellipsoid opacity.")
if ellipsoid_alpha < 0 or ellipsoid_alpha > 1:
option_parser.error("Please supply an ellipsoid opacity value \
between 0 and 1.")
# check that ellipsoid method is valid
ellipsoid_methods = ['IQR','sdev']
if not opts.ellipsoid_method in ellipsoid_methods:
option_parser.error("Please supply a valid ellipsoid method. \
Valid methods are: " + ', '.join(ellipsoid_methods) + ".")
# gather ellipsoid drawing preferences
ellipsoid_prefs = {}
ellipsoid_prefs["smoothness"] = ellipsoid_smoothness
ellipsoid_prefs["alpha"] = ellipsoid_alpha
# make sure that coord file has internally consistent # of columns
coord_files_valid = validate_coord_files(opts.coord_fname)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
#Open and get coord data
data['coord'] = get_coord(opts.coord_fname, opts.ellipsoid_method)
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'])
# if no samples overlapped between mapping file and otu table, exit
if len(data['coord'][0]) == 0:
print "\nError: OTU table and mapping file had no samples in common\n"
exit(1)
if opts.taxa_fname != None:
# get taxonomy counts
# get list of sample_ids that haven't been removed
sample_ids = data['coord'][0]
# get taxa summaries for all sample_ids
lineages, taxa_counts = get_taxa(opts.taxa_fname, sample_ids)
data['taxa'] = {}
data['taxa']['lineages'] = lineages
data['taxa']['counts'] = taxa_counts
# get average relative abundance of taxa
data['taxa']['prevalence'] = get_taxa_prevalence(data['taxa']['counts'])
remove_rare_taxa(data['taxa'],nkeep=opts.n_taxa_keep)
# get coordinates of taxa (weighted mean of sample scores)
data['taxa']['coord'] = get_taxa_coords(data['taxa']['counts'],
data['coord'][1])
data['taxa']['coord']
# write taxa coords if requested
if not opts.biplot_output_file is None:
output = make_biplot_scores_output(data['taxa'])
fout = open(opts.biplot_output_file,'w')
fout.write('\n'.join(output))
fout.close()
# process custom axes, if present.
custom_axes = None
if opts.custom_axes:
custom_axes = process_custom_axes(opts.custom_axes)
get_custom_coords(custom_axes, data['map'], data['coord'])
remove_nans(data['coord'])
scale_custom_coords(custom_axes,data['coord'])
if opts.output_dir:
create_dir(opts.output_dir,False)
dir_path=opts.output_dir
else:
dir_path='./'
qiime_dir=get_qiime_project_dir()
jar_path=os.path.join(qiime_dir,'qiime/support_files/jar/')
data_dir_path = get_random_directory_name(output_dir=dir_path,
return_absolute_path=False)
try:
os.mkdir(data_dir_path)
except OSError:
pass
data_file_path=data_dir_path
jar_dir_path = os.path.join(dir_path,'jar')
try:
os.mkdir(jar_dir_path)
except OSError:
pass
shutil.copyfile(os.path.join(jar_path,'king.jar'), os.path.join(jar_dir_path,'king.jar'))
filepath=opts.coord_fname
if os.path.isdir(filepath):
coord_files = [fname for fname in os.listdir(filepath) if not \
fname.startswith('.')]
filename = os.path.split(coord_files[0])[-1]
else:
filename = os.path.split(filepath)[-1]
try:
action = generate_3d_plots
except NameError:
action = None
#Place this outside try/except so we don't mask NameError in action
if action:
action(prefs,data,custom_axes,background_color,label_color,dir_path, \
data_file_path,filename,ellipsoid_prefs=ellipsoid_prefs)
