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comparison src/breadcrumbs/hclust/hclust.py @ 0:0de566f21448 draft default tip

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v2
author sagun98
date Thu, 03 Jun 2021 18:13:32 +0000
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-1:000000000000 0:0de566f21448
1 #!/usr/bin/env python
2
3 import sys
4 import numpy as np
5 import matplotlib
6 matplotlib.use('Agg')
7 import scipy
8 import pylab
9 import scipy.cluster.hierarchy as sch
10 import scipy.spatial.distance as dis
11 from scipy import stats
12
13 # User defined color maps (in addition to matplotlib ones)
14 bbcyr = {'red': ( (0.0, 0.0, 0.0),
15 (0.25, 0.0, 0.0),
16 (0.50, 0.0, 0.0),
17 (0.75, 1.0, 1.0),
18 (1.0, 1.0, 1.0)),
19 'green': ( (0.0, 0.0, 0.0),
20 (0.25, 0.0, 0.0),
21 (0.50, 1.0, 1.0),
22 (0.75, 1.0, 1.0),
23 (1.0, 0.0, 1.0)),
24 'blue': ( (0.0, 0.0, 0.0),
25 (0.25, 1.0, 1.0),
26 (0.50, 1.0, 1.0),
27 (0.75, 0.0, 0.0),
28 (1.0, 0.0, 1.0))}
29
30 bbcry = {'red': ( (0.0, 0.0, 0.0),
31 (0.25, 0.0, 0.0),
32 (0.50, 0.0, 0.0),
33 (0.75, 1.0, 1.0),
34 (1.0, 1.0, 1.0)),
35 'green': ( (0.0, 0.0, 0.0),
36 (0.25, 0.0, 0.0),
37 (0.50, 1.0, 1.0),
38 (0.75, 0.0, 0.0),
39 (1.0, 1.0, 1.0)),
40 'blue': ( (0.0, 0.0, 0.0),
41 (0.25, 1.0, 1.0),
42 (0.50, 1.0, 1.0),
43 (0.75, 0.0, 0.0),
44 (1.0, 0.0, 1.0))}
45 my_colormaps = [ ('bbcyr',bbcyr),
46 ('bbcry',bbcry)]
47
48
49
50 def read_params(args):
51 import argparse as ap
52 import textwrap
53
54 p = ap.ArgumentParser( description= "TBA" )
55
56 p.add_argument( '--in', '--inp', metavar='INPUT_FILE', type=str,
57 nargs='?', default=sys.stdin,
58 help= "the input archive " )
59
60 p.add_argument( '--out', metavar='OUTPUT_FILE', type=str,
61 nargs = '?', default=None,
62 help= " the output file, image on screen"
63 " if not specified. " )
64
65 p.add_argument( '-m', metavar='method', type=str,
66 choices=[ "single","complete","average",
67 "weighted","centroid","median",
68 "ward" ],
69 default="average" )
70
71 dist_funcs = [ "euclidean","minkowski","cityblock","seuclidean",
72 "sqeuclidean","cosine","correlation","hamming",
73 "jaccard","chebyshev","canberra","braycurtis",
74 "mahalanobis","yule","matching","dice",
75 "kulsinski","rogerstanimoto","russellrao","sokalmichener",
76 "sokalsneath","wminkowski","ward"]
77 p.add_argument( '-d', metavar='distance function', type=str,
78 choices=dist_funcs,
79 default="euclidean" )
80 p.add_argument( '-f', metavar='distance function for features', type=str,
81 choices=dist_funcs,
82 default="d" )
83
84 p.add_argument( '--dmf', metavar='distance matrix for features', type=str,
85 default = None )
86 p.add_argument( '--dms', metavar='distance matrix for samples', type=str,
87 default = None )
88
89
90 p.add_argument( '-l', metavar='sample label', type=str,
91 default = None )
92
93 p.add_argument( '-s', metavar='scale norm', type=str,
94 default = 'lin', choices = ['log','lin'])
95
96 p.add_argument( '-x', metavar='x cell width', type=float,
97 default = 0.1)
98 p.add_argument( '-y', metavar='y cell width', type=float,
99 default = 0.1 )
100
101 p.add_argument( '--minv', metavar='min value', type=float,
102 default = 0.0 )
103 p.add_argument( '--maxv', metavar='max value', type=float,
104 default = None )
105
106 p.add_argument( '--xstart', metavar='x coordinate of the top left cell '
107 'of the values',
108 type=int, default=1 )
109 p.add_argument( '--ystart', metavar='y coordinate of the top left cell '
110 'of the values',
111 type=int, default=1 )
112 p.add_argument( '--xstop', metavar='x coordinate of the bottom right cell '
113 'of the values (default None = last row)',
114 type=int, default=None )
115 p.add_argument( '--ystop', metavar='y coordinate of the bottom right cell '
116 'of the values (default None = last column)',
117 type=int, default=None )
118
119 p.add_argument( '--perc', metavar='percentile for ordering and rows selection', type=int, default=None )
120 p.add_argument( '--top', metavar='selection of the top N rows', type=int, default=None )
121 p.add_argument( '--norm', metavar='whether to normalize columns (default 0)', type=int, default=0 )
122
123 p.add_argument( '--sdend_h', metavar='height of the sample dendrogram', type=float,
124 default = 0.1 )
125 p.add_argument( '--fdend_w', metavar='width of the feature dendrogram', type=float,
126 default = 0.1 )
127 p.add_argument( '--cm_h', metavar='height of the colormap', type=float,
128 default = 0.03 )
129 p.add_argument( '--cm_ticks', metavar='label for ticks of the colormap', type=str,
130 default = None )
131
132 p.add_argument( '--font_size', metavar='label_font_size', type=int,
133 default = 7 )
134 p.add_argument( '--feat_dend_col_th', metavar='Color threshold for feature dendrogram', type=float,
135 default = None )
136 p.add_argument( '--sample_dend_col_th', metavar='Color threshold for sample dendrogram', type=float,
137 default = None )
138 p.add_argument( '--clust_ncols', metavar='Number of colors for clusters', type=int,
139 default = 7 )
140 p.add_argument( '--clust_line_w', metavar='Cluster line width', type=float,
141 default = 1.0 )
142 p.add_argument( '--label_cols', metavar='Label colors', type=str,
143 default = None )
144 p.add_argument( '--label2cols', metavar='Label to colors mapping file', type=str,
145 default = None )
146 p.add_argument( '--sdend_out', metavar='File for storing the samples dendrogram in PhyloXML format', type=str,
147 default = None )
148 p.add_argument( '--fdend_out', metavar='File for storing the features dendrogram in PhyloXML format', type=str,
149 default = None )
150
151
152 p.add_argument( '--pad_inches', metavar='Proportion of figure to be left blank around the plot', type=float,
153 default = 0.1 )
154
155
156 p.add_argument( '--flabel', metavar='Whether to show the labels for the features', type=int,
157 default = 0 )
158 p.add_argument( '--slabel', metavar='Whether to show the labels for the samples', type=int,
159 default = 0 )
160
161 p.add_argument( '--legend', metavar='Whether to show the samples to label legend', type=int,
162 default = 0 )
163 p.add_argument( '--legend_font_size', metavar='Legend font size', type=int,
164 default = 7 )
165 p.add_argument( '--legend_ncol', metavar='Number of columns for the legend', type=int,
166 default = 3 )
167 p.add_argument( '--grid', metavar='Whether to show the grid (only black for now)', type=int,
168 default = 0 )
169
170 col_maps = ['Accent', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'Dark2', 'GnBu',
171 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'Paired',
172 'Pastel1', 'Pastel2', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr',
173 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn',
174 'Reds', 'Set1', 'Set2', 'Set3', 'Spectral', 'YlGn', 'YlGnBu',
175 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone',
176 'brg', 'bwr', 'cool', 'copper', 'flag', 'gist_earth',
177 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow',
178 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray',
179 'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow',
180 'seismic', 'spectral', 'spring', 'summer', 'terrain', 'winter'] + [n for n,c in my_colormaps]
181 p.add_argument( '-c', metavar='colormap', type=str,
182 choices = col_maps, default = 'jet' )
183
184 return vars(p.parse_args())
185
186 # Predefined colors for dendrograms brances and class labels
187 colors = [ "#B22222","#006400","#0000CD","#9400D3","#696969","#8B4513",
188 "#FF1493","#FF8C00","#3CB371","#00Bfff","#CDC9C9","#FFD700",
189 "#2F4F4F","#FF0000","#ADFF2F","#B03060" ]
190
191 def samples2classes_panel(fig, samples, s2l, idx1, idx2, height, xsize, cols, legendon, fontsize, label2cols, legend_ncol ):
192 from matplotlib.patches import Rectangle
193 samples2labels = dict([(l[0],l[1])
194 for l in [ll.strip().split('\t')
195 for ll in open(s2l)] if len(l) > 1])
196
197 if label2cols:
198 labels2colors = dict([(l[0],l[1]) for l in [ll.strip().split('\t') for ll in open(label2cols)]])
199 else:
200 cs = cols if cols else colors
201 labels2colors = dict([(l,cs[i%len(cs)]) for i,l in enumerate(set(samples2labels.values()))])
202 ax1 = fig.add_axes([0.,1.0,1.0,height],frameon=False)
203 ax1.set_xticks([])
204 ax1.set_yticks([])
205 ax1.set_ylim( [0.0, height] )
206 ax1.set_xlim( [0.0, xsize] )
207 step = xsize / float(len(samples))
208 labels = set()
209 added_labels = set()
210 for i,ind in enumerate(idx2):
211 if not samples[ind] in samples2labels or \
212 not samples2labels[samples[ind]] in labels2colors:
213 fc, ll = "k", None
214 else:
215 ll = samples2labels[samples[ind]]
216 ll = None if ll in added_labels else ll
217 added_labels.add( ll )
218 fc = labels2colors[samples2labels[samples[ind]]]
219
220 rect = Rectangle( [float(i)*step, 0.0], step, height,
221 facecolor = fc,
222 label = ll,
223 edgecolor='b', lw = 0.0)
224 labels.add( ll )
225 ax1.add_patch(rect)
226 ax1.autoscale_view()
227
228 if legendon:
229 ax1.legend( loc = 2, ncol = legend_ncol, bbox_to_anchor=(1.01, 3.),
230 borderpad = 0.0, labelspacing = 0.0,
231 handlelength = 0.5, handletextpad = 0.3,
232 borderaxespad = 0.0, columnspacing = 0.3,
233 prop = {'size':fontsize}, frameon = False)
234
235 def samples_dend_panel( fig, Z, Z2, ystart, ylen, lw ):
236 ax2 = fig.add_axes([0.0,1.0+ystart,1.0,ylen], frameon=False)
237 Z2['color_list'] = [c.replace('b','k') for c in Z2['color_list']]
238 mh = max(Z[:,2])
239 sch._plot_dendrogram( Z2['icoord'], Z2['dcoord'], Z2['ivl'],
240 Z.shape[0] + 1, Z.shape[0] + 1,
241 mh, 'top', no_labels=True,
242 color_list=Z2['color_list'] )
243 for coll in ax2.collections:
244 coll._linewidths = (lw,)
245 ax2.set_xticks([])
246 ax2.set_yticks([])
247 ax2.set_xticklabels([])
248
249 def features_dend_panel( fig, Z, Z2, width, lw ):
250 ax1 = fig.add_axes([-width,0.0,width,1.0], frameon=False)
251 Z2['color_list'] = [c.replace('b','k').replace('x','b') for c in Z2['color_list']]
252 mh = max(Z[:,2])
253 sch._plot_dendrogram(Z2['icoord'], Z2['dcoord'], Z2['ivl'], Z.shape[0] + 1, Z.shape[0] + 1, mh, 'right', no_labels=True, color_list=Z2['color_list'])
254 for coll in ax1.collections:
255 coll._linewidths = (lw,)
256 ax1.set_xticks([])
257 ax1.set_yticks([])
258 ax1.set_xticklabels([])
259
260
261 def add_cmap( cmapdict, name ):
262 my_cmap = matplotlib.colors.LinearSegmentedColormap(name,cmapdict,256)
263 pylab.register_cmap(name=name,cmap=my_cmap)
264
265 def init_fig(xsize,ysize,ncol):
266 fig = pylab.figure(figsize=(xsize,ysize))
267 sch._link_line_colors = colors[:ncol]
268 return fig
269
270 def heatmap_panel( fig, D, minv, maxv, idx1, idx2, cm_name, scale, cols, rows, label_font_size, cb_offset, cb_l, flabelson, slabelson, cm_ticks, gridon, bar_offset ):
271 cm = pylab.get_cmap(cm_name)
272 bottom_col = [ cm._segmentdata['red'][0][1],
273 cm._segmentdata['green'][0][1],
274 cm._segmentdata['blue'][0][1] ]
275 axmatrix = fig.add_axes( [0.0,0.0,1.0,1.0],
276 axisbg=bottom_col)
277 if any([c < 0.95 for c in bottom_col]):
278 axmatrix.spines['right'].set_color('none')
279 axmatrix.spines['left'].set_color('none')
280 axmatrix.spines['top'].set_color('none')
281 axmatrix.spines['bottom'].set_color('none')
282 norm_f = matplotlib.colors.LogNorm if scale == 'log' else matplotlib.colors.Normalize
283 im = axmatrix.matshow( D, norm = norm_f( vmin=minv if minv > 0.0 else None,
284 vmax=maxv),
285 aspect='auto', origin='lower', cmap=cm, vmax=maxv)
286
287 axmatrix2 = axmatrix.twinx()
288 axmatrix3 = axmatrix.twiny()
289
290 axmatrix.set_xticks([])
291 axmatrix2.set_xticks([])
292 axmatrix3.set_xticks([])
293 axmatrix.set_yticks([])
294 axmatrix2.set_yticks([])
295 axmatrix3.set_yticks([])
296
297 axmatrix.set_xticklabels([])
298 axmatrix2.set_xticklabels([])
299 axmatrix3.set_xticklabels([])
300 axmatrix.set_yticklabels([])
301 axmatrix2.set_yticklabels([])
302 axmatrix3.set_yticklabels([])
303
304 if any([c < 0.95 for c in bottom_col]):
305 axmatrix2.spines['right'].set_color('none')
306 axmatrix2.spines['left'].set_color('none')
307 axmatrix2.spines['top'].set_color('none')
308 axmatrix2.spines['bottom'].set_color('none')
309 if any([c < 0.95 for c in bottom_col]):
310 axmatrix3.spines['right'].set_color('none')
311 axmatrix3.spines['left'].set_color('none')
312 axmatrix3.spines['top'].set_color('none')
313 axmatrix3.spines['bottom'].set_color('none')
314 if flabelson:
315 axmatrix2.set_yticks(np.arange(len(rows))+0.5)
316 axmatrix2.set_yticklabels([rows[r] for r in idx1],size=label_font_size,va='center')
317 if slabelson:
318 axmatrix.set_xticks(np.arange(len(cols)))
319 axmatrix.set_xticklabels([cols[r] for r in idx2],size=label_font_size,rotation=90,va='top',ha='center')
320 axmatrix.tick_params(length=0)
321 axmatrix2.tick_params(length=0)
322 axmatrix3.tick_params(length=0)
323 axmatrix2.set_ylim(0,len(rows))
324
325 if gridon:
326 axmatrix.set_yticks(np.arange(len(idx1)-1)+0.5)
327 axmatrix.set_xticks(np.arange(len(idx2))+0.5)
328 axmatrix.grid( True )
329 ticklines = axmatrix.get_xticklines()
330 ticklines.extend( axmatrix.get_yticklines() )
331 #gridlines = axmatrix.get_xgridlines()
332 #gridlines.extend( axmatrix.get_ygridlines() )
333
334 for line in ticklines:
335 line.set_linewidth(3)
336
337 if cb_l > 0.0:
338 axcolor = fig.add_axes([0.0,1.0+bar_offset*1.25,1.0,cb_l])
339 cbar = fig.colorbar(im, cax=axcolor, orientation='horizontal')
340 cbar.ax.tick_params(labelsize=label_font_size)
341 if cm_ticks:
342 cbar.ax.set_xticklabels( cm_ticks.split(":") )
343
344
345 def read_table( fin, xstart,xstop,ystart,ystop, percentile = None, top = None, norm = False ):
346 mat = [l.rstrip().split('\t') for l in open( fin )]
347
348 if fin.endswith(".biom"):
349 sample_labels = mat[1][1:-1]
350 m = [(mm[-1]+"; OTU"+mm[0],np.array([float(f) for f in mm[1:-1]])) for mm in mat[2:]]
351 #feat_labels = [m[-1].replace(";","_").replace(" ","")+m[0] for m in mat[2:]]
352 #print len(feat_labels)
353 #print len(sample_labels)
354 else:
355 sample_labels = mat[0][xstart:xstop]
356 m = [(mm[xstart-1],np.array([float(f) for f in mm[xstart:xstop]])) for mm in mat[ystart:ystop]]
357
358 if norm:
359 msums = [0.0 for l in m[0][1]]
360 for mm in m:
361 for i,v in enumerate(mm[1]):
362 msums[i] += v
363
364 if top and not percentile:
365 percentile = 90
366
367 if percentile:
368 m = sorted(m,key=lambda x:-stats.scoreatpercentile(x[1],percentile))
369 if top:
370 if fin.endswith(".biom"):
371 #feat_labels = [mm[-1].replace(";","_").replace(" ","")+mm[0] for mm in m[:top]]
372 feat_labels = [mm[0] for mm in m[:top]]
373 else:
374 feat_labels = [mm[0] for mm in m[:top]]
375 if norm:
376 m = [np.array([n/v for n,v in zip(mm[1],msums)]) for mm in m[:top]]
377 else:
378 m = [mm[1] for mm in m[:top]]
379 else:
380 if fin.endswith(".biom"):
381 feat_labels = [mm[0] for mm in m]
382 else:
383 feat_labels = [mm[0] for mm in m]
384 if norm:
385 m = [np.array([n/v for n,v in zip(mm[1],msums)]) for mm in m]
386 else:
387 m = [mm[1] for mm in m]
388 #m = [mm[1] for mm in m]
389
390 D = np.matrix( np.array( m ) )
391
392 return D, feat_labels, sample_labels
393
394 def read_dm( fin, n ):
395 mat = [[float(f) for f in l.strip().split('\t')] for l in open( fin )]
396 nc = sum([len(r) for r in mat])
397
398 if nc == n*n:
399 dm = []
400 for i in range(n):
401 dm += mat[i][i+1:]
402 return np.array(dm)
403 if nc == (n*n-n)/2:
404 dm = []
405 for i in range(n):
406 dm += mat[i]
407 return np.array(dm)
408 sys.stderr.write( "Error in reading the distance matrix\n" )
409 sys.exit()
410
411
412 def exp_newick( inp, labels, outfile, tree_format = 'phyloxml' ):
413 n_leaves = int(inp[-1][-1])
414 from Bio import Phylo
415 import collections
416 from Bio.Phylo.BaseTree import Tree as BTree
417 from Bio.Phylo.BaseTree import Clade as BClade
418 tree = BTree()
419 tree.root = BClade()
420
421 subclades = {}
422 sb_cbl = {}
423
424 for i,(fr,to,bl,nsub) in enumerate( inp ):
425 if fr < n_leaves:
426 fr_c = BClade(branch_length=-1.0,name=labels[int(fr)])
427 subclades[fr] = fr_c
428 sb_cbl[fr] = bl
429 if to < n_leaves:
430 to_c = BClade(branch_length=-1.0,name=labels[int(to)])
431 subclades[to] = to_c
432 sb_cbl[to] = bl
433 for i,(fr,to,bl,nsub) in enumerate( inp ):
434 fr_c = subclades[fr]
435 to_c = subclades[to]
436 cur_c = BClade(branch_length=bl)
437 cur_c.clades.append( fr_c )
438 cur_c.clades.append( to_c )
439 subclades[i+n_leaves] = cur_c
440
441 def reset_rec( clade, fath_bl ):
442 if clade.branch_length < 0:
443 clade.branch_length = fath_bl
444 return
445 for c in clade.clades:
446 reset_rec( c, clade.branch_length )
447 clade.branch_length = fath_bl-clade.branch_length
448
449 tree.root = cur_c
450 reset_rec( tree.root, 0.0 )
451 tree.root.branch_length = 0.0
452 Phylo.write(tree, outfile, tree_format )
453
454 def hclust( fin, fout,
455 method = "average",
456 dist_func = "euclidean",
457 feat_dist_func = "d",
458 xcw = 0.1,
459 ycw = 0.1,
460 scale = 'lin',
461 minv = 0.0,
462 maxv = None,
463 xstart = 1,
464 ystart = 1,
465 xstop = None,
466 ystop = None,
467 percentile = None,
468 top = None,
469 norm = False,
470 cm_name = 'jet',
471 s2l = None,
472 label_font_size = 7,
473 feat_dend_col_th = None,
474 sample_dend_col_th = None,
475 clust_ncols = 7,
476 clust_line_w = 1.0,
477 label_cols = None,
478 sdend_h = 0.1,
479 fdend_w = 0.1,
480 cm_h = 0.03,
481 dmf = None,
482 dms = None,
483 legendon = False,
484 label2cols = None,
485 flabelon = True,
486 slabelon = True,
487 cm_ticks = None,
488 legend_ncol = 3,
489 pad_inches = None,
490 legend_font_size = 7,
491 gridon = 0,
492 sdend_out = None,
493 fdend_out= None):
494
495 if label_cols and label_cols.count("-"):
496 label_cols = label_cols.split("-")
497
498 for n,c in my_colormaps:
499 add_cmap( c, n )
500
501 if feat_dist_func == 'd':
502 feat_dist_func = dist_func
503
504 D, feat_labels, sample_labels = read_table(fin,xstart,xstop,ystart,ystop,percentile,top,norm)
505
506 ylen,xlen = D[:].shape
507 Dt = D.transpose()
508
509 size_cx, size_cy = xcw, ycw
510
511 xsize, ysize = max(xlen*size_cx,2.0), max(ylen*size_cy,2.0)
512 ydend_offset = 0.025*8.0/ysize if s2l else 0.0
513
514 fig = init_fig(xsize,ysize,clust_ncols)
515
516 nfeats, nsamples = len(D), len(Dt)
517
518 if dmf:
519 p1 = read_dm( dmf, nfeats )
520 Y1 = sch.linkage( p1, method=method )
521 else:
522 p1 = dis.pdist( D, feat_dist_func )
523 Y1 = sch.linkage( p1, method=method ) # , metric=feat_dist_func )
524 #Y1 = sch.linkage( D, method=method, metric=feat_dist_func )
525 Z1 = sch.dendrogram(Y1, no_plot=True, color_threshold=feat_dend_col_th)
526
527 if fdend_out:
528 exp_newick( Y1, feat_labels, fdend_out )
529
530 if dms:
531 p2 = read_dm( dms, nsamples )
532 Y2 = sch.linkage( p2, method=method )
533 else:
534 p2 = dis.pdist( Dt, dist_func )
535 Y2 = sch.linkage( p2, method=method ) # , metric=dist_func )
536 #Y2 = sch.linkage( Dt, method=method, metric=dist_func )
537 Z2 = sch.dendrogram(Y2, no_plot=True, color_threshold=sample_dend_col_th)
538
539 if sdend_out:
540 exp_newick( Y2, sample_labels, sdend_out )
541
542 if fdend_w > 0.0:
543 features_dend_panel(fig, Y1, Z1, fdend_w*8.0/xsize, clust_line_w )
544 if sdend_h > 0.0:
545 samples_dend_panel(fig, Y2, Z2, ydend_offset, sdend_h*8.0/ysize, clust_line_w)
546
547 idx1, idx2 = Z1['leaves'], Z2['leaves']
548 D = D[idx1,:][:,idx2]
549
550 if s2l:
551 samples2classes_panel( fig, sample_labels, s2l, idx1, idx2, 0.025*8.0/ysize, xsize, label_cols, legendon, legend_font_size, label2cols, legend_ncol )
552 heatmap_panel( fig, D, minv, maxv, idx1, idx2, cm_name, scale, sample_labels, feat_labels, label_font_size, -cm_h*8.0/ysize, cm_h*0.8*8.0/ysize, flabelon, slabelon, cm_ticks, gridon, ydend_offset+sdend_h*8.0/ysize )
553
554 fig.savefig( fout, bbox_inches='tight',
555 pad_inches = pad_inches,
556 dpi=300) if fout else pylab.show()
557
558 if __name__ == '__main__':
559 pars = read_params( sys.argv )
560
561 hclust( fin = pars['in'],
562 fout = pars['out'],
563 method = pars['m'],
564 dist_func = pars['d'],
565 feat_dist_func = pars['f'],
566 xcw = pars['x'],
567 ycw = pars['y'],
568 scale = pars['s'],
569 minv = pars['minv'],
570 maxv = pars['maxv'],
571 xstart = pars['xstart'],
572 ystart = pars['ystart'],
573 xstop = pars['xstop'],
574 ystop = pars['ystop'],
575 percentile = pars['perc'],
576 top = pars['top'],
577 norm = pars['norm'],
578 cm_name = pars['c'],
579 s2l = pars['l'],
580 label_font_size = pars['font_size'],
581 feat_dend_col_th = pars['feat_dend_col_th'],
582 sample_dend_col_th = pars['sample_dend_col_th'],
583 clust_ncols = pars['clust_ncols'],
584 clust_line_w = pars['clust_line_w'],
585 label_cols = pars['label_cols'],
586 sdend_h = pars['sdend_h'],
587 fdend_w = pars['fdend_w'],
588 cm_h = pars['cm_h'],
589 dmf = pars['dmf'],
590 dms = pars['dms'],
591 legendon = pars['legend'],
592 label2cols = pars['label2cols'],
593 flabelon = pars['flabel'],
594 slabelon = pars['slabel'],
595 cm_ticks = pars['cm_ticks'],
596 legend_ncol = pars['legend_ncol'],
597 pad_inches = pars['pad_inches'],
598 legend_font_size = pars['legend_font_size'],
599 gridon = pars['grid'],
600 sdend_out = pars['sdend_out'],
601 fdend_out = pars['fdend_out'],
602 )
603