def test_reorder_samples(self):
""" test that regural and irregular order give the same results """
model = "linear"
# Test normal order
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
# Test altered
model = "linear"
# order = [5, 1, 3, 4, 0, 2]
x_lbl = ['s6', 's2', 's4', 's5', 's1', 's3']
x = asarray([[0.0,0.0,0.0,0.0,0.0,0.0],[9.0,0.0,7.0,8.0,0.0,6.0],[14.0,0.0,0.0,13.0,0.0,0.0],[15.0,0.0,0.0,0.0,0.0,0.0],[5.0,1.0,3.0,4.0,0.0,2.0],[12.0,0.0,10.0,11.0,0.0,0.0]])
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y=asarray([[0.0,1.0,2.0,3.0,4.0,5.0],[0.0,0.0,6.0,7.0,8.0,9.0],[0.0,0.0,0.0,10.0,11.0,12.0],[0.0,0.0,0.0,0.0,13.0,14.0],[0.0,0.0,0.0,0.0,0.0,15.0],[0.0,0.0,0.0,0.0,0.0,0.0]])
vals_exp = [1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.,15.]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (y_lbl,y), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
def test_reorder_samples(self):
""" test that regural and irregular order give the same results """
model = "linear"
# Test normal order
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
# Test altered
model = "linear"
# order = [5, 1, 3, 4, 0, 2]
x_lbl = ['s6', 's2', 's4', 's5', 's1', 's3']
x = asarray([[0.0,0.0,0.0,0.0,0.0,0.0],[9.0,0.0,7.0,8.0,0.0,6.0],[14.0,0.0,0.0,13.0,0.0,0.0],[15.0,0.0,0.0,0.0,0.0,0.0],[5.0,1.0,3.0,4.0,0.0,2.0],[12.0,0.0,10.0,11.0,0.0,0.0]])
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y=asarray([[0.0,1.0,2.0,3.0,4.0,5.0],[0.0,0.0,6.0,7.0,8.0,9.0],[0.0,0.0,0.0,10.0,11.0,12.0],[0.0,0.0,0.0,0.0,13.0,14.0],[0.0,0.0,0.0,0.0,0.0,15.0],[0.0,0.0,0.0,0.0,0.0,0.0]])
vals_exp = [1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.,15.]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (y_lbl,y), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
def test_models_semivariograms(self):
""" test the semivariogram fitting models """
# All models should return the same x_vals, y_vals, x_fit
# because we are using the same x
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
model = "nugget"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 5.0, 5.0, 5.0, 5.0, 5.0],[0.0, 0.0, 5.0, 5.0, 5.0, 5.0],[0.0, 0.0, 0.0, 5.0, 5.0, 5.0],[0.0, 0.0, 0.0, 0.0, 5.0, 5.0],[0.0, 0.0, 0.0, 0.0, 0.0, 5.0]])
y_vals_exp = [2.3000000143667378]*(len(x)*2)
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "exponential"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [-9.44475054741e-09, -9.44475054741e-09, -9.44475054741e-09, -9.44475054741e-09, 0.999999998426, 2.0000000063, 3.00000001417, 4.00000002204, 5.99999999885, 6.99999998726]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "gaussian"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [0.17373665, 0.17373665, 0.17373665, 0.17373665, 0.54915494, 1.55978856, 2.91608962, 4.28808694, 6.24510109, 6.74689019]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "periodic"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [0.23248033, 0.23248033, 0.23248033, 0.23248033, 0.5528678, 1.45081215, 2.74913327, 4.19164973, 6.39844476, 6.72728412]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "linear"
y_lbl = x_lbl
y = x
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
def test_models_semivariograms(self):
""" test the semivariogram fitting models """
# All models should return the same x_vals, y_vals, x_fit
# because we are using the same x
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
model = "nugget"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 5.0, 5.0, 5.0, 5.0, 5.0],[0.0, 0.0, 5.0, 5.0, 5.0, 5.0],[0.0, 0.0, 0.0, 5.0, 5.0, 5.0],[0.0, 0.0, 0.0, 0.0, 5.0, 5.0],[0.0, 0.0, 0.0, 0.0, 0.0, 5.0]])
y_vals_exp = [2.3000000143667378]*(len(x)*2)
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "exponential"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [-9.44475054741e-09, -9.44475054741e-09, -9.44475054741e-09, -9.44475054741e-09, 0.999999998426, 2.0000000063, 3.00000001417, 4.00000002204, 5.99999999885, 6.99999998726]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "gaussian"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [0.17373665, 0.17373665, 0.17373665, 0.17373665, 0.54915494, 1.55978856, 2.91608962, 4.28808694, 6.24510109, 6.74689019]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "periodic"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [0.23248033, 0.23248033, 0.23248033, 0.23248033, 0.5528678, 1.45081215, 2.74913327, 4.19164973, 6.39844476, 6.72728412]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, y_vals_exp)
model = "linear"
y_lbl = x_lbl
y = x
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl,x), (x_lbl,x), model, [])
self.assertFloatEqual(x_vals, vals_exp)
self.assertFloatEqual(y_vals, vals_exp)
self.assertFloatEqual(x_fit, vals_exp)
self.assertFloatEqual(y_fit, vals_exp)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.binning is None:
ranges = []
else:
# simple ranges format validation
if opts.binning.count('[')!=opts.binning.count(']') or\
opts.binning.count('[')!=opts.binning.count(','):
raise ValueError, "The binning input has an error: '%s'; " % opts.binning +\
"\nthe format should be [increment1,top_limit1][increment2,top_limit2]"
# spliting in ranges
rgn_txt = opts.binning.split('][')
# removing left [ and right ]
rgn_txt[0] = rgn_txt[0][1:]
rgn_txt[-1] = rgn_txt[-1][:-1]
# converting into int
ranges = []
max = 0
for i,r in enumerate(rgn_txt):
values = map(float,r.split(','))
if len(values)!=2:
raise ValueError, "All ranges must have only 2 values: [%s]" % r
elif i+1!=len(rgn_txt):
if values[0]>values[1]:
raise ValueError, "The bin value can't be greater than the max value: [%s]" % r
elif values<0:
raise ValueError, "This value can not be negative: [%s]" % r
elif max>values[1]:
raise ValueError, "This value can not smaller than the previous one: [%s]" % r
else:
max=values[1]
ranges.append(values)
x_samples, x_distmtx = parse_distmat(open(opts.input_path_x,'U'))
y_samples, y_distmtx = parse_distmat(open(opts.input_path_y,'U'))
(x_val,y_val,x_fit,y_fit) = fit_semivariogram(x_distmtx, y_distmtx, opts.model, ranges)
plot(x_val, y_val, 'o', color="white")
plot(x_fit, y_fit, linewidth=2.0, color="blue")
x_label = 'Distance (m)'
y_label = 'Community Dissimilarity'
fig_title = 'Semivariogram (%s)' % opts.model
xlabel(x_label)
ylabel(y_label)
title(fig_title)
savefig(opts.output_path)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
category = opts.category
mapping_fp = opts.mapping_fp
colors_used = []
if (category and mapping_fp == None) or (category == None and mapping_fp):
option_parser.error('If coloring by a metadata category, both the '
'category and the mapping file must be supplied.')
elif mapping_fp and category:
mapping_data, mapping_headers, _ = parse_mapping_file(
open(mapping_fp, 'U'))
if category not in mapping_headers:
option_parser.error("The category supplied must exist in the "
"metadata mapping file, '%s' does not exist." %
category)
index = mapping_headers.index(category)
categories = list(set([line[index] for line in mapping_data]))
list_of_plots = []
if opts.binning is None:
ranges = []
else:
# simple ranges format validation
if opts.binning.count('[')!=opts.binning.count(']') or\
opts.binning.count('[')!=opts.binning.count(','):
raise ValueError, "The binning input has an error: '%s'; " % +\
"\nthe format should be [increment1,top_limit1][increment2,top_limit2]"
# spliting in ranges
rgn_txt = opts.binning.split('][')
# removing left [ and right ]
rgn_txt[0] = rgn_txt[0][1:]
rgn_txt[-1] = rgn_txt[-1][:-1]
# converting into int
ranges = []
max = 0
for i, r in enumerate(rgn_txt):
try:
values = map(float, r.split(','))
except ValueError:
raise ValueError, "Not a valid format for binning %s" % opts.binning
if len(values) != 2:
raise ValueError, "All ranges must have only 2 values: [%s]" % r
elif i + 1 != len(rgn_txt):
if values[0] > values[1]:
raise ValueError, "The bin value can't be greater than the max value: [%s]" % r
elif values < 0:
raise ValueError, "This value can not be negative: [%s]" % r
elif max > values[1]:
raise ValueError, "This value can not smaller than the previous one: [%s]" % r
else:
max = values[1]
ranges.append(values)
x_samples, x_distmtx = parse_distmat(open(opts.input_path_x, 'U'))
y_samples, y_distmtx = parse_distmat(open(opts.input_path_y, 'U'))
if opts.ignore_missing_samples:
ignoring_from_x = list(set(x_samples) - set(y_samples))
ignoring_from_y = list(set(y_samples) - set(x_samples))
if opts.verbose:
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_x,
ignoring_from_x)
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_y,
ignoring_from_y)
print '\nOnly using: %s\n' % (
list(set(x_samples) & set(y_samples)))
x_file = StringIO(\
filter_samples_from_distance_matrix((x_samples, x_distmtx), ignoring_from_x))
x_samples, x_distmtx = parse_distmat(x_file)
y_file = StringIO(\
filter_samples_from_distance_matrix((y_samples, y_distmtx), ignoring_from_y))
y_samples, y_distmtx = parse_distmat(y_file)
else:
if x_distmtx.shape != y_distmtx.shape:
raise ValueError, 'The distance matrices have different sizes. ' +\
'You can cancel this error by passing --ignore_missing_samples'
figure()
if category == None:
x_val, y_val, x_fit, y_fit, func_text = fit_semivariogram(
(x_samples, x_distmtx), (y_samples, y_distmtx), opts.model, ranges)
plot(x_val,
y_val,
color=opts.dot_color,
marker=opts.dot_marker,
linestyle="None",
alpha=opts.dot_alpha)
plot(x_fit,
y_fit,
linewidth=2.0,
color=opts.line_color,
alpha=opts.line_alpha)
else:
for index, single_category in enumerate(categories):
good_sample_ids = sample_ids_from_metadata_description(
open(mapping_fp), '%s:%s' % (category, single_category))
_y_samples, _y_distmtx = parse_distmat(
StringIO(
filter_samples_from_distance_matrix((y_samples, y_distmtx),
good_sample_ids,
negate=True)))
_x_samples, _x_distmtx = parse_distmat(
StringIO(
filter_samples_from_distance_matrix((x_samples, x_distmtx),
good_sample_ids,
negate=True)))
x_val, y_val, x_fit, y_fit, func_text = fit_semivariogram(
(_x_samples, _x_distmtx), (_y_samples, _y_distmtx), opts.model,
ranges)
# retrieve one of the colors the "QIIME" colors and add it to the
# list of used colors for the creation of the legends in the plot
color_only = get_qiime_hex_string_color(index)
colors_used.append(color_only)
plot(x_val,
y_val,
color=color_only,
marker=opts.dot_marker,
linestyle="None",
alpha=opts.dot_alpha)
plot(x_fit,
y_fit,
linewidth=2.0,
color=color_only,
alpha=opts.line_alpha,
label=single_category)
if opts.x_min != None and opts.x_max != None:
xlim([opts.x_min, opts.x_max])
if opts.y_min != None and opts.y_max != None:
ylim([opts.y_min, opts.y_max])
x_label = opts.x_label
y_label = opts.y_label
fig_title = '%s (%s)' % (opts.fig_title, opts.model)
xlabel(x_label)
ylabel(y_label)
if opts.print_model:
title(fig_title + ' ' + func_text)
else:
title(fig_title)
savefig(opts.output_path)
# print the legends after the figure is exported to avoid conflicts
if category:
# if there's a desired format, use that, else default it to png
_, extension = splitext(opts.output_path)
# remove the dot, else, make_legend will add it to the filename
extension = extension.replace('.', '')
if extension == '':
extension = 'png'
make_legend(categories, colors_used, 0, 0, 'black', 'white',
opts.output_path, extension, 80)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
category = opts.category
mapping_fp = opts.mapping_fp
colors_used = []
if (category and mapping_fp is None) or (category is None and mapping_fp):
option_parser.error('If coloring by a metadata category, both the '
'category and the mapping file must be supplied.')
elif mapping_fp and category:
mapping_data, mapping_headers, _ = parse_mapping_file(open(mapping_fp,
'U'))
if category not in mapping_headers:
option_parser.error("The category supplied must exist in the "
"metadata mapping file, '%s' does not exist." % category)
index = mapping_headers.index(category)
categories = list(set([line[index] for line in mapping_data]))
list_of_plots = []
if opts.binning is None:
ranges = []
else:
# simple ranges format validation
if opts.binning.count('[') != opts.binning.count(']') or\
opts.binning.count('[') != opts.binning.count(','):
raise ValueError("The binning input has an error: '%s'; " % +
"\nthe format should be [increment1,top_limit1][increment2,top_limit2]")
# spliting in ranges
rgn_txt = opts.binning.split('][')
# removing left [ and right ]
rgn_txt[0] = rgn_txt[0][1:]
rgn_txt[-1] = rgn_txt[-1][:-1]
# converting into int
ranges = []
max = 0
for i, r in enumerate(rgn_txt):
try:
values = map(float, r.split(','))
except ValueError:
raise ValueError(
"Not a valid format for binning %s" %
opts.binning)
if len(values) != 2:
raise ValueError(
"All ranges must have only 2 values: [%s]" %
r)
elif i + 1 != len(rgn_txt):
if values[0] > values[1]:
raise ValueError(
"The bin value can't be greater than the max value: [%s]" %
r)
elif values < 0:
raise ValueError(
"This value can not be negative: [%s]" %
r)
elif max > values[1]:
raise ValueError(
"This value can not smaller than the previous one: [%s]" %
r)
else:
max = values[1]
ranges.append(values)
x_samples, x_distmtx = parse_distmat(open(opts.input_path_x, 'U'))
y_samples, y_distmtx = parse_distmat(open(opts.input_path_y, 'U'))
if opts.ignore_missing_samples:
ignoring_from_x = list(set(x_samples) - set(y_samples))
ignoring_from_y = list(set(y_samples) - set(x_samples))
if opts.verbose:
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_x, ignoring_from_x)
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_y, ignoring_from_y)
print '\nOnly using: %s\n' % (list(set(x_samples) & set(y_samples)))
x_file = StringIO(
filter_samples_from_distance_matrix((x_samples, x_distmtx), ignoring_from_x))
x_samples, x_distmtx = parse_distmat(x_file)
y_file = StringIO(
filter_samples_from_distance_matrix((y_samples, y_distmtx), ignoring_from_y))
y_samples, y_distmtx = parse_distmat(y_file)
else:
if x_distmtx.shape != y_distmtx.shape:
raise ValueError('The distance matrices have different sizes. ' +
'You can cancel this error by passing --ignore_missing_samples')
figure()
if category is None:
x_val, y_val, x_fit, y_fit, func_text = fit_semivariogram(
(x_samples, x_distmtx), (y_samples, y_distmtx), opts.model, ranges)
plot(
x_val,
y_val,
color=opts.dot_color,
marker=opts.dot_marker,
linestyle="None",
alpha=opts.dot_alpha)
plot(
x_fit,
y_fit,
linewidth=2.0,
color=opts.line_color,
alpha=opts.line_alpha)
else:
# not all the categories that are going to be enumerated are found in
# the distance matrices i.e. the mapping file is a superset that can
# contain more samples than the distance matrices
used_categories = deepcopy(categories)
for index, single_category in enumerate(categories):
good_sample_ids = sample_ids_from_metadata_description(
open(mapping_fp), '%s:%s' % (category, single_category))
try:
_y_samples, _y_distmtx = parse_distmat(StringIO(
filter_samples_from_distance_matrix((y_samples, y_distmtx),
good_sample_ids, negate=True)))
_x_samples, _x_distmtx = parse_distmat(StringIO(
filter_samples_from_distance_matrix((x_samples, x_distmtx),
good_sample_ids, negate=True)))
except ValueError:
# no samples found for this category
used_categories.remove(single_category)
continue
x_val, y_val, x_fit, y_fit, func_text = fit_semivariogram(
(_x_samples, _x_distmtx), (_y_samples, _y_distmtx),
opts.model, ranges)
# retrieve one of the colors the "QIIME" colors and add it to the
# list of used colors for the creation of the legends in the plot
color_only = get_qiime_hex_string_color(index)
colors_used.append(color_only)
plot(x_val, y_val, color=color_only, marker=opts.dot_marker,
linestyle="None", alpha=opts.dot_alpha)
plot(x_fit, y_fit, linewidth=2.0, color=color_only,
alpha=opts.line_alpha, label=single_category)
# set plot limits if requested
x_lb, x_ub = xlim()
y_lb, y_ub = ylim()
if opts.x_min is not None:
x_lb = opts.x_min
if opts.x_max is not None:
x_ub = opts.x_max
if opts.y_min is not None:
y_lb = opts.y_min
if opts.y_max is not None:
y_ub = opts.y_max
xlim(x_lb, x_ub)
ylim(y_lb, y_ub)
x_label = opts.x_label
y_label = opts.y_label
fig_title = '%s (%s)' % (opts.fig_title, opts.model)
xlabel(x_label)
ylabel(y_label)
if opts.print_model:
title(fig_title + ' ' + func_text)
else:
title(fig_title)
savefig(opts.output_path)
# print the legends after the figure is exported to avoid conflicts
if category:
# if there's a desired format, use that, else default it to png
_, extension = splitext(opts.output_path)
# remove the dot, else, make_legend will add it to the filename
extension = extension.replace('.', '')
if extension == '':
extension = 'png'
make_legend(used_categories, colors_used, 0, 0, 'black', 'white',
opts.output_path, extension, 80)
def test_models_semivariograms(self):
""" test the semivariogram fitting models """
# All models should return the same x_vals, y_vals, x_fit
# because we are using the same x
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray([[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],
[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],
[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
model = "nugget"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 5.0, 5.0, 5.0, 5.0, 5.0],
[0.0, 0.0, 5.0, 5.0, 5.0, 5.0],
[0.0, 0.0, 0.0, 5.0, 5.0, 5.0],
[0.0, 0.0, 0.0, 0.0, 5.0, 5.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 5.0]])
y_vals_exp = [2.3000000143667378] * (len(x) * 2)
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp)
model = "exponential"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],
[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],
[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],
[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
x_vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
y_vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 22.0, 33.0, 44.0, 66.0, 77.0]
x_fit_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
y_fit_exp = [
-1.481486808707005, -1.481486808707005, -1.481486808707005,
-1.481486808707005, 9.72783464904061, 20.937152199747878,
32.14646584698613, 43.355775583612704, 65.7743833464588,
76.983681369107
]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (y_lbl, y), model, [])
assert_almost_equal(x_vals, x_vals_exp)
assert_almost_equal(y_vals, y_vals_exp)
assert_almost_equal(x_fit, x_fit_exp)
assert_almost_equal(y_fit, y_fit_exp, decimal=2)
model = "gaussian"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],
[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],
[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],
[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [
0.17373844, 0.17373844, 0.17373844, 0.17373844, 0.54915099,
1.5597716, 2.91606171, 4.2880578, 6.24509872, 6.74690541
]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp, decimal=2)
model = "periodic"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray([[0.0, 1.0, 22.0, 33.0, 44.0, 55.0],
[0.0, 0.0, 66.0, 77.0, 88.0, 99.0],
[0.0, 0.0, 0.0, 1010.0, 1111.0, 1212.0],
[0.0, 0.0, 0.0, 0.0, 1313.0, 1414.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1515.0]])
y_vals_exp = [
0.2324873886681871, 0.2324873886681871, 0.2324873886681871,
0.2324873886681871, 0.5528698895985695, 1.4508010363573784,
2.7491053124879112, 4.191607473962063, 6.39840364731269,
6.727263101495738
]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp, decimal=2)
model = "linear"
y_lbl = x_lbl
y = x
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, vals_exp, decimal=2)
def test_models_semivariograms(self):
""" test the semivariogram fitting models """
# All models should return the same x_vals, y_vals, x_fit
# because we are using the same x
x_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
x = asarray(
[[0.0,
1.0,
2.0,
3.0,
4.0,
5.0],
[0.0,
0.0,
6.0,
7.0,
8.0,
9.0],
[0.0,
0.0,
0.0,
10.0,
11.0,
12.0],
[0.0,
0.0,
0.0,
0.0,
13.0,
14.0],
[0.0,
0.0,
0.0,
0.0,
0.0,
15.0]])
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
model = "nugget"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray(
[[0.0,
5.0,
5.0,
5.0,
5.0,
5.0],
[0.0,
0.0,
5.0,
5.0,
5.0,
5.0],
[0.0,
0.0,
0.0,
5.0,
5.0,
5.0],
[0.0,
0.0,
0.0,
0.0,
5.0,
5.0],
[0.0,
0.0,
0.0,
0.0,
0.0,
5.0]])
y_vals_exp = [2.3000000143667378] * (len(x) * 2)
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp)
model = "exponential"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray(
[[0.0,
1.0,
22.0,
33.0,
44.0,
55.0],
[0.0,
0.0,
66.0,
77.0,
88.0,
99.0],
[0.0,
0.0,
0.0,
1010.0,
1111.0,
1212.0],
[0.0,
0.0,
0.0,
0.0,
1313.0,
1414.0],
[0.0,
0.0,
0.0,
0.0,
0.0,
1515.0]])
x_vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
y_vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 22.0, 33.0, 44.0, 66.0, 77.0]
x_fit_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
y_fit_exp = [-1.481486808707005, -1.481486808707005, -1.481486808707005,
-1.481486808707005, 9.72783464904061, 20.937152199747878,
32.14646584698613, 43.355775583612704, 65.7743833464588,
76.983681369107]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (y_lbl, y), model, [])
assert_almost_equal(x_vals, x_vals_exp)
assert_almost_equal(y_vals, y_vals_exp)
assert_almost_equal(x_fit, x_fit_exp)
assert_almost_equal(y_fit, y_fit_exp, decimal=2)
model = "gaussian"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray(
[[0.0,
1.0,
22.0,
33.0,
44.0,
55.0],
[0.0,
0.0,
66.0,
77.0,
88.0,
99.0],
[0.0,
0.0,
0.0,
1010.0,
1111.0,
1212.0],
[0.0,
0.0,
0.0,
0.0,
1313.0,
1414.0],
[0.0,
0.0,
0.0,
0.0,
0.0,
1515.0]])
y_vals_exp = [0.17373844, 0.17373844, 0.17373844, 0.17373844,
0.54915099, 1.5597716 , 2.91606171, 4.2880578 ,
6.24509872, 6.74690541]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp, decimal=2)
model = "periodic"
y_lbl = ['s1', 's2', 's3', 's4', 's5', 's6']
y = asarray(
[[0.0,
1.0,
22.0,
33.0,
44.0,
55.0],
[0.0,
0.0,
66.0,
77.0,
88.0,
99.0],
[0.0,
0.0,
0.0,
1010.0,
1111.0,
1212.0],
[0.0,
0.0,
0.0,
0.0,
1313.0,
1414.0],
[0.0,
0.0,
0.0,
0.0,
0.0,
1515.0]])
y_vals_exp = [0.2324873886681871, 0.2324873886681871,
0.2324873886681871, 0.2324873886681871,
0.5528698895985695, 1.4508010363573784,
2.7491053124879112, 4.191607473962063,
6.39840364731269, 6.727263101495738]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, y_vals_exp, decimal=2)
model = "linear"
y_lbl = x_lbl
y = x
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram(
(x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, vals_exp, decimal=2)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.binning is None:
ranges = []
else:
# simple ranges format validation
if opts.binning.count('[')!=opts.binning.count(']') or\
opts.binning.count('[')!=opts.binning.count(','):
raise ValueError, "The binning input has an error: '%s'; " % +\
"\nthe format should be [increment1,top_limit1][increment2,top_limit2]"
# spliting in ranges
rgn_txt = opts.binning.split('][')
# removing left [ and right ]
rgn_txt[0] = rgn_txt[0][1:]
rgn_txt[-1] = rgn_txt[-1][:-1]
# converting into int
ranges = []
max = 0
for i,r in enumerate(rgn_txt):
try:
values = map(float,r.split(','))
except ValueError:
raise ValueError, "Not a valid format for binning %s" % opts.binning
if len(values)!=2:
raise ValueError, "All ranges must have only 2 values: [%s]" % r
elif i+1!=len(rgn_txt):
if values[0]>values[1]:
raise ValueError, "The bin value can't be greater than the max value: [%s]" % r
elif values<0:
raise ValueError, "This value can not be negative: [%s]" % r
elif max>values[1]:
raise ValueError, "This value can not smaller than the previous one: [%s]" % r
else:
max=values[1]
ranges.append(values)
x_samples, x_distmtx = parse_distmat(open(opts.input_path_x,'U'))
y_samples, y_distmtx = parse_distmat(open(opts.input_path_y,'U'))
if opts.ignore_missing_samples:
ignoring_from_x = list(set(x_samples)-set(y_samples))
ignoring_from_y = list(set(y_samples)-set(x_samples))
if opts.verbose:
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_x, ignoring_from_x)
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_y, ignoring_from_y)
print '\nOnly using: %s\n' % (list(set(x_samples) & set(y_samples)))
x_file = StringIO(\
filter_samples_from_distance_matrix((x_samples, x_distmtx), ignoring_from_x))
x_samples, x_distmtx = parse_distmat(x_file)
y_file = StringIO(\
filter_samples_from_distance_matrix((y_samples, y_distmtx), ignoring_from_y))
y_samples, y_distmtx = parse_distmat(y_file)
else:
if x_distmtx.shape!=y_distmtx.shape:
raise ValueError, 'The distance matrices have different sizes. ' +\
'You can cancel this error by passing --ignore_missing_samples'
(x_val,y_val,x_fit,y_fit,func_text) =\
fit_semivariogram((x_samples,x_distmtx), (y_samples,y_distmtx), opts.model, ranges)
plot(x_val, y_val, color=opts.dot_color, marker=opts.dot_marker, linestyle="None", alpha=opts.dot_alpha)
plot(x_fit, y_fit, linewidth=2.0, color=opts.line_color, alpha=opts.line_alpha)
if opts.x_min!=None and opts.x_max!=None:
xlim([opts.x_min,opts.x_max])
if opts.y_min!=None and opts.y_max!=None:
ylim([opts.y_min,opts.y_max])
x_label = opts.x_label
y_label = opts.y_label
fig_title = '%s (%s)' % (opts.fig_title, opts.model)
xlabel(x_label)
ylabel(y_label)
if opts.print_model:
title(fig_title + ' ' + func_text)
else:
title(fig_title)
savefig(opts.output_path)
def test_reorder_samples(self):
""" test that regural and irregular order give the same results """
model = "linear"
# Test normal order
x_lbl = ["s1", "s2", "s3", "s4", "s5", "s6"]
x = asarray(
[
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],
[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],
[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 15.0],
]
)
y_lbl = ["s1", "s2", "s3", "s4", "s5", "s6"]
y = asarray(
[
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],
[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],
[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 15.0],
]
)
vals_exp = [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl, x), (x_lbl, x), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, vals_exp)
# Test altered
model = "linear"
# order = [5, 1, 3, 4, 0, 2]
x_lbl = ["s6", "s2", "s4", "s5", "s1", "s3"]
x = asarray(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[9.0, 0.0, 7.0, 8.0, 0.0, 6.0],
[14.0, 0.0, 0.0, 13.0, 0.0, 0.0],
[15.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[5.0, 1.0, 3.0, 4.0, 0.0, 2.0],
[12.0, 0.0, 10.0, 11.0, 0.0, 0.0],
]
)
y_lbl = ["s1", "s2", "s3", "s4", "s5", "s6"]
y = asarray(
[
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
[0.0, 0.0, 6.0, 7.0, 8.0, 9.0],
[0.0, 0.0, 0.0, 10.0, 11.0, 12.0],
[0.0, 0.0, 0.0, 0.0, 13.0, 14.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 15.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
)
vals_exp = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]
x_vals, y_vals, x_fit, y_fit, func_text = fit_semivariogram((x_lbl, x), (y_lbl, y), model, [])
assert_almost_equal(x_vals, vals_exp)
assert_almost_equal(y_vals, vals_exp)
assert_almost_equal(x_fit, vals_exp)
assert_almost_equal(y_fit, vals_exp)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.binning is None:
ranges = []
else:
# simple ranges format validation
if opts.binning.count('[')!=opts.binning.count(']') or\
opts.binning.count('[')!=opts.binning.count(','):
raise ValueError, "The binning input has an error: '%s'; " % +\
"\nthe format should be [increment1,top_limit1][increment2,top_limit2]"
# spliting in ranges
rgn_txt = opts.binning.split('][')
# removing left [ and right ]
rgn_txt[0] = rgn_txt[0][1:]
rgn_txt[-1] = rgn_txt[-1][:-1]
# converting into int
ranges = []
max = 0
for i,r in enumerate(rgn_txt):
try:
values = map(float,r.split(','))
except ValueError:
raise ValueError, "Not a valid format for binning %s" % opts.binning
if len(values)!=2:
raise ValueError, "All ranges must have only 2 values: [%s]" % r
elif i+1!=len(rgn_txt):
if values[0]>values[1]:
raise ValueError, "The bin value can't be greater than the max value: [%s]" % r
elif values<0:
raise ValueError, "This value can not be negative: [%s]" % r
elif max>values[1]:
raise ValueError, "This value can not smaller than the previous one: [%s]" % r
else:
max=values[1]
ranges.append(values)
x_samples, x_distmtx = parse_distmat(open(opts.input_path_x,'U'))
y_samples, y_distmtx = parse_distmat(open(opts.input_path_y,'U'))
if opts.ignore_missing_samples:
ignoring_from_x = list(set(x_samples)-set(y_samples))
ignoring_from_y = list(set(y_samples)-set(x_samples))
if opts.verbose:
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_x, ignoring_from_x)
print '\nFrom %s we are ignoring: %s\n' % (opts.input_path_y, ignoring_from_y)
print '\nOnly using: %s\n' % (list(set(x_samples) & set(y_samples)))
x_file = StringIO(\
filter_samples_from_distance_matrix((x_samples, x_distmtx), ignoring_from_x))
x_samples, x_distmtx = parse_distmat(x_file)
y_file = StringIO(\
filter_samples_from_distance_matrix((y_samples, y_distmtx), ignoring_from_y))
y_samples, y_distmtx = parse_distmat(y_file)
else:
if x_distmtx.shape!=y_distmtx.shape:
raise ValueError, 'The distance matrices have different sizes. ' +\
'You can cancel this error by passing --ignore_missing_samples'
(x_val,y_val,x_fit,y_fit,func_text) =\
fit_semivariogram((x_samples,x_distmtx), (y_samples,y_distmtx), opts.model, ranges)
plot(x_val, y_val, color=opts.dot_color, marker=opts.dot_marker, linestyle="None", alpha=opts.dot_alpha)
plot(x_fit, y_fit, linewidth=2.0, color=opts.line_color, alpha=opts.line_alpha)
if opts.x_min!=None and opts.x_max!=None:
xlim([opts.x_min,opts.x_max])
if opts.y_min!=None and opts.y_max!=None:
ylim([opts.y_min,opts.y_max])
x_label = opts.x_label
y_label = opts.y_label
fig_title = '%s (%s)' % (opts.fig_title, opts.model)
xlabel(x_label)
ylabel(y_label)
if opts.print_model:
title(fig_title + ' ' + func_text)
else:
title(fig_title)
savefig(opts.output_path)
