def test_remove_nans(self):
"""remove_nans: Deletes any samples with NANs in their coordinates"""
coord_data = array([[10,60,-0.219044992,0.079674486,0.09233683],
[20,55,-0.042258081, nan,0.024837603],
[30,50,0.080504323,-0.212014503,-0.088353435]])
coords = [self.coord_header, coord_data]
remove_nans(coords)
exp_header = ["Sample1","Sample3"]
exp_coords = array([[10,60,-0.219044992,0.079674486,0.09233683],
[30,50,0.080504323,-0.212014503,-0.088353435]])
self.assertEqual(coords[0],exp_header)
self.assertEqual(coords[1],exp_coords)
def test_remove_nans(self):
"""remove_nans: Deletes any samples with NANs in their coordinates"""
coord_data = array([[10, 60, -0.219044992, 0.079674486, 0.09233683],
[20, 55, -0.042258081, nan, 0.024837603],
[30, 50, 0.080504323, -0.212014503, -0.088353435]])
coords = [self.coord_header, coord_data]
remove_nans(coords)
exp_header = ["Sample1", "Sample3"]
exp_coords = array([[10, 60, -0.219044992, 0.079674486, 0.09233683],
[30, 50, 0.080504323, -0.212014503, -0.088353435]])
self.assertEqual(coords[0], exp_header)
self.assertEqual(coords[1], exp_coords)
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 len(opts.coord_fnames.split(',')) < 2 and opts.edges_file is None:
option_parser.error('Please provide at least two ' +\
'coordinate files or a custom edges file')
#Open and get coord data (for multiple coords files)
coord_files = process_coord_filenames(opts.coord_fnames)
coord_files_valid = validate_coord_files(coord_files)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
num_coord_files = len(coord_files)
data['edges'], data['coord'] = \
get_multiple_coords(coord_files, opts.edges_file, opts.serial)
# if the edges file wasn't supplied, we appended _i to each file's samples
# therefore we now add duplicated samples with _0, _1,... to mapping file
if opts.edges_file is None:
newmap = [data['map'][0]]
for i in xrange(len(coord_files)):
for sample in data['map'][1:]:
newsample = ['%s_%d' %(sample[0],i)]
newsample.extend(sample[1:])
newmap.append(newsample)
data['map'] = newmap
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'],data['edges'])
if(len(data['coord'][1]) == 0):
raise ValueError, '\n\nError: None of the sample IDs in the coordinates files were present in the mapping file.\n'
# 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'])
# Generate random output file name and create directories
if opts.output_dir:
create_dir(opts.output_dir)
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
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=coord_files[0]
filename=filepath.strip().split('/')[-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:
generate_3d_plots(prefs, data, custom_axes,
background_color, label_color,
dir_path, data_dir_path, filename,
ball_scale=ball_scale, arrow_colors=arrow_colors,
user_supplied_edges=not(opts.edges_file is None))
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)
if len(opts.coord_fnames) < 2 and opts.edges_file is None:
option_parser.error('Please provide at least two ' +\
'coordinate files or a custom edges file')
#Open and get coord data (for multiple coords files)
coord_files = opts.coord_fnames
coord_files_valid = validate_coord_files(coord_files)
if not coord_files_valid:
option_parser.error('Every line of every coord file must ' +\
'have the same number of columns.')
num_coord_files = len(coord_files)
data['edges'], data['coord'] = \
get_multiple_coords(coord_files, opts.edges_file, opts.serial)
# if the edges file wasn't supplied, we appended _i to each file's samples
# therefore we now add duplicated samples with _0, _1,... to mapping file
if opts.edges_file is None:
newmap = [data['map'][0]]
for i in xrange(len(coord_files)):
for sample in data['map'][1:]:
newsample = ['%s_%d' %(sample[0],i)]
newsample.extend(sample[1:])
newmap.append(newsample)
data['map'] = newmap
# remove any samples not present in mapping file
remove_unmapped_samples(data['map'],data['coord'],data['edges'])
if(len(data['coord'][1]) == 0):
raise ValueError, '\n\nError: None of the sample IDs in the coordinates files were present in the mapping file.\n'
# 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'])
# Generate random output file name and create directories
if opts.output_dir:
create_dir(opts.output_dir)
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
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=coord_files[0]
filename=filepath.strip().split('/')[-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:
generate_3d_plots(prefs, data, custom_axes,
background_color, label_color,
dir_path, data_dir_path, filename,
ball_scale=ball_scale, arrow_colors=arrow_colors,
user_supplied_edges=not(opts.edges_file is None))
def main():
print "\nWarning: compare_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
)
if len(opts.coord_fnames) < 2 and opts.edges_file is None:
option_parser.error("Please provide at least two " + "coordinate files or a custom edges file")
# Open and get coord data (for multiple coords files)
coord_files = opts.coord_fnames
coord_files_valid = validate_coord_files(coord_files)
if not coord_files_valid:
option_parser.error("Every line of every coord file must " + "have the same number of columns.")
num_coord_files = len(coord_files)
data["edges"], data["coord"] = get_multiple_coords(coord_files, opts.edges_file, opts.serial)
# if the edges file wasn't supplied, we appended _i to each file's samples
# therefore we now add duplicated samples with _0, _1,... to mapping file
if opts.edges_file is None:
newmap = [data["map"][0]]
for i in xrange(len(coord_files)):
for sample in data["map"][1:]:
newsample = ["%s_%d" % (sample[0], i)]
newsample.extend(sample[1:])
newmap.append(newsample)
data["map"] = newmap
# remove any samples not present in mapping file
remove_unmapped_samples(data["map"], data["coord"], data["edges"])
if len(data["coord"][1]) == 0:
raise ValueError, "\n\nError: None of the sample IDs in the coordinates files were present in the mapping file.\n"
# 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"])
# Generate random output file name and create directories
if opts.output_dir:
create_dir(opts.output_dir)
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
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 = coord_files[0]
filename = filepath.strip().split("/")[-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:
generate_3d_plots(
prefs,
data,
custom_axes,
background_color,
label_color,
dir_path,
data_dir_path,
filename,
ball_scale=ball_scale,
arrow_colors=arrow_colors,
user_supplied_edges=not (opts.edges_file is None),
)
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 preprocess_coords_file(coords_header,
coords_data,
coords_eigenvals,
coords_pct,
mapping_header,
mapping_data,
custom_axes=None,
jackknifing_method=None,
is_comparison=False):
"""Process a PCoA data and handle customizations in the contents
Inputs:
coords_header: list of sample identifiers in the PCoA file _or_ list of
lists with sample identifiers for each coordinate file (if jackknifing or
comparing plots)
coords_data: matrix of coordinates in the PCoA file _or_ list of numpy
arrays with coordinates for each file (if jackknifing or comparing plots)
coords_eigenvals: numpy array with eigenvalues for the coordinates file _or_
list of numpy arrays with the eigenvalues (if jackknifing or comparing plots
)
coords_pct: numpy array with a the percent explained by each principal
coordinates axis _or_ a list of lists with numpy arrays (if jackknifing or
comparing plots)
mapping_header: mapping file headers names
mapping_data: mapping file data
custom_axes: name of the mapping data fields to add to coords_data
jackknifing_method: one of 'sdev' or 'IRQ', defaults to None, for more info
see qiime.util.summarize_pcoas
is_comparison: whether or not the inputs should be considered as the ones
for a comparison plot
Outputs:
coords_header: list of sample identifiers in the PCoA file
coords_data: matrix of coordinates in the PCoA file with custom_axes if
provided
coords_eigenvalues: either the eigenvalues of the input coordinates or the
average eigenvalues of the multiple coords that were passed in
coords_pct: list of percents explained by each axis as given by the master
coordinates i. e. the center around where the values revolve
coords_low: coordinates representing the lower edges of an ellipse; None if
no jackknifing is applied
coords_high: coordinates representing the highere edges of an ellipse; None
if no jackknifing is applied
clones: total number of input files
This controller function handles any customization that has to be done to
the PCoA data prior to the formatting. Note that the first element in each
list (coords, headers, eigenvalues & percents) will be considered the master
set of coordinates.
Raises: AssertionError if a comparison plot is requested but a list of data
is not passed as input
"""
# prevent obscure and obfuscated errors
if is_comparison:
assert type(coords_data) == list, "Cannot process a comparison with "+\
"the data from a single coordinates file"
mapping_file = [mapping_header] + mapping_data
coords_file = [coords_header, coords_data]
# number PCoA files; zero for any case except for comparison plots
clones = 0
if custom_axes and type(coords_data) == ndarray:
# sequence ported from qiime/scripts/make_3d_plots.py @ 9115351
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
elif type(coords_data) == list and is_comparison == False:
# take the first pcoa file as the master set of coordinates
master_pcoa = [
coords_header[0], coords_data[0], coords_eigenvals[0],
coords_pct[0]
]
# support pcoas must be a list of lists where each list contain
# all the elements that compose a coordinates file
support_pcoas = [[h, d, e, p] for h, d, e, p in zip(
coords_header, coords_data, coords_eigenvals, coords_pct)]
# do not apply procrustes, at least not for now
coords_data, coords_low, coords_high, eigenvalues_average,\
identifiers = summarize_pcoas(master_pcoa, support_pcoas,
method=jackknifing_method, apply_procrustes=False)
# custom axes and jackknifing is a tricky thing to do, you only have to
# add the custom values to the master file which is represented as the
# coords_data return value. Since there is really no variation in that
# axis then you have to change the values of coords_high and of
# coords_low to something really small so that WebGL work properly
if custom_axes:
coords_file = [master_pcoa[0], coords_data]
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
# this opens support for as many custom axes as needed
axes = len(custom_axes)
coords_low[:, 0:axes] = zeros([coords_low.shape[0], axes])
coords_high[:,
0:axes] = ones([coords_high.shape[0], axes]) * 0.00001
coords_data = coords_file[1]
# return a value containing coords_low and coords_high
return identifiers, coords_data, eigenvalues_average, master_pcoa[3],\
coords_low, coords_high, clones
# comparison plots are processed almost individually
elif type(coords_data) == list and is_comparison:
# indicates the number of files that were totally processed so other
# functions/APIs are aware of how many times to replicate the metadata
clones = len(coords_data)
out_headers, out_coords = [], []
for index in range(0, clones):
headers_i = coords_header[index]
coords_i = coords_data[index]
# tag each header with the the number in which those coords came in
out_headers.extend(
[element + '_%d' % index for element in headers_i])
if index == 0:
# numpy can only stack things if they have the same shape
out_coords = coords_i
# the eigenvalues and percents explained are really the ones
# belonging to the the first set of coordinates that was passed
coords_eigenvals = coords_eigenvals[index]
coords_pct = coords_pct[index]
else:
out_coords = vstack((out_coords, coords_i))
coords_file = [out_headers, out_coords]
if custom_axes:
# this condition deals with the fact that in order for the custom
# axes to be added into the original coordinates, we have to add the
# suffix for the sample identifiers that the coordinates have
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + '_%d' % index] +
element[1::] for element in mapping_data])
mapping_file = [mapping_header] + out_data
# sequence ported from qiime/scripts/make_3d_plots.py @ 9115351
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
# if no coords summary is applied, return None in the corresponding values
# note that the value of clones will be != 0 for a comparison plot
return coords_file[0], coords_file[1], coords_eigenvals, coords_pct, None,\
None, clones
def preprocess_coords_file(
coords_header,
coords_data,
coords_eigenvals,
coords_pct,
mapping_header,
mapping_data,
custom_axes=None,
jackknifing_method=None,
is_comparison=False,
):
"""Process a PCoA data and handle customizations in the contents
Inputs:
coords_header: list of sample identifiers in the PCoA file _or_ list of
lists with sample identifiers for each coordinate file (if jackknifing or
comparing plots)
coords_data: matrix of coordinates in the PCoA file _or_ list of numpy
arrays with coordinates for each file (if jackknifing or comparing plots)
coords_eigenvals: numpy array with eigenvalues for the coordinates file _or_
list of numpy arrays with the eigenvalues (if jackknifing or comparing plots
)
coords_pct: numpy array with a the percent explained by each principal
coordinates axis _or_ a list of lists with numpy arrays (if jackknifing or
comparing plots)
mapping_header: mapping file headers names
mapping_data: mapping file data
custom_axes: name of the mapping data fields to add to coords_data
jackknifing_method: one of 'sdev' or 'IRQ', defaults to None, for more info
see qiime.util.summarize_pcoas
is_comparison: whether or not the inputs should be considered as the ones
for a comparison plot
Outputs:
coords_header: list of sample identifiers in the PCoA file
coords_data: matrix of coordinates in the PCoA file with custom_axes if
provided
coords_eigenvalues: either the eigenvalues of the input coordinates or the
average eigenvalues of the multiple coords that were passed in
coords_pct: list of percents explained by each axis as given by the master
coordinates i. e. the center around where the values revolve
coords_low: coordinates representing the lower edges of an ellipse; None if
no jackknifing is applied
coords_high: coordinates representing the highere edges of an ellipse; None
if no jackknifing is applied
clones: total number of input files
This controller function handles any customization that has to be done to
the PCoA data prior to the formatting. Note that the first element in each
list (coords, headers, eigenvalues & percents) will be considered the master
set of coordinates.
Raises: AssertionError if a comparison plot is requested but a list of data
is not passed as input
"""
# prevent obscure and obfuscated errors
if is_comparison:
assert type(coords_data) == list, (
"Cannot process a comparison with " + "the data from a single coordinates file"
)
mapping_file = [mapping_header] + mapping_data
coords_file = [coords_header, coords_data]
# number PCoA files; zero for any case except for comparison plots
clones = 0
if custom_axes and type(coords_data) == ndarray:
# sequence ported from qiime/scripts/make_3d_plots.py @ 9115351
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
elif type(coords_data) == list and is_comparison == False:
# take the first pcoa file as the master set of coordinates
master_pcoa = [coords_header[0], coords_data[0], coords_eigenvals[0], coords_pct[0]]
# support pcoas must be a list of lists where each list contain
# all the elements that compose a coordinates file
support_pcoas = [[h, d, e, p] for h, d, e, p in zip(coords_header, coords_data, coords_eigenvals, coords_pct)]
# do not apply procrustes, at least not for now
coords_data, coords_low, coords_high, eigenvalues_average, identifiers = summarize_pcoas(
master_pcoa, support_pcoas, method=jackknifing_method, apply_procrustes=False
)
# custom axes and jackknifing is a tricky thing to do, you only have to
# add the custom values to the master file which is represented as the
# coords_data return value. Since there is really no variation in that
# axis then you have to change the values of coords_high and of
# coords_low to something really small so that WebGL work properly
if custom_axes:
coords_file = [master_pcoa[0], coords_data]
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
# this opens support for as many custom axes as needed
axes = len(custom_axes)
coords_low[:, 0:axes] = zeros([coords_low.shape[0], axes])
coords_high[:, 0:axes] = ones([coords_high.shape[0], axes]) * 0.00001
coords_data = coords_file[1]
# return a value containing coords_low and coords_high
return identifiers, coords_data, eigenvalues_average, master_pcoa[3], coords_low, coords_high, clones
# comparison plots are processed almost individually
elif type(coords_data) == list and is_comparison:
# indicates the number of files that were totally processed so other
# functions/APIs are aware of how many times to replicate the metadata
clones = len(coords_data)
out_headers, out_coords = [], []
for index in range(0, clones):
headers_i = coords_header[index]
coords_i = coords_data[index]
# tag each header with the the number in which those coords came in
out_headers.extend([element + "_%d" % index for element in headers_i])
if index == 0:
# numpy can only stack things if they have the same shape
out_coords = coords_i
# the eigenvalues and percents explained are really the ones
# belonging to the the first set of coordinates that was passed
coords_eigenvals = coords_eigenvals[index]
coords_pct = coords_pct[index]
else:
out_coords = vstack((out_coords, coords_i))
coords_file = [out_headers, out_coords]
if custom_axes:
# this condition deals with the fact that in order for the custom
# axes to be added into the original coordinates, we have to add the
# suffix for the sample identifiers that the coordinates have
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + "_%d" % index] + element[1::] for element in mapping_data])
mapping_file = [mapping_header] + out_data
# sequence ported from qiime/scripts/make_3d_plots.py @ 9115351
get_custom_coords(custom_axes, mapping_file, coords_file)
remove_nans(coords_file)
scale_custom_coords(custom_axes, coords_file)
# if no coords summary is applied, return None in the corresponding values
# note that the value of clones will be != 0 for a comparison plot
return coords_file[0], coords_file[1], coords_eigenvals, coords_pct, None, None, clones
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)