1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
|
#cython: language_level=3
from obitools3.apps.progress cimport ProgressBar # @UnresolvedImport
from obitools3.dms import DMS
from obitools3.uri.decode import open_uri
from obitools3.apps.optiongroups import addMinimalInputOption, addTaxonomyOption
from obitools3.dms.view import RollbackException
from obitools3.apps.config import logger
from obitools3.dms.capi.obiview cimport COUNT_COLUMN
from obitools3.utils cimport tostr
from functools import reduce
import math
import time
import sys
from cpython.exc cimport PyErr_CheckSignals
__title__="Compute basic statistics for attribute values"
'''
`obi stats` computes basic statistics for attribute values of sequence records.
The sequence records can be categorized or not using one or several ``-c`` options.
By default, only the number of sequence records and the total count are computed for each category.
Additional statistics can be computed for attribute values in each category, such as:
- minimum value (``-m`` option)
- maximum value (``-M`` option)
- mean value (``-a`` option)
- variance (``-v`` option)
- standard deviation (``-s`` option)
The result is a contingency table with the different categories in rows, and the
computed statistics in columns.
'''
# TODO: when is the taxonomy possibly used?
def addOptions(parser):
addMinimalInputOption(parser)
addTaxonomyOption(parser)
group=parser.add_argument_group('obi stats specific options')
group.add_argument('-c','--category-attribute',
action="append", dest="stats:categories",
metavar="<Attribute Name>",
default=[],
help="Attribute used to categorize the records.")
group.add_argument('-m','--min',
action="append", dest="stats:minimum",
metavar="<Attribute Name>",
default=[],
help="Compute the minimum value of attribute for each category.")
group.add_argument('-M','--max',
action="append", dest="stats:maximum",
metavar="<Attribute Name>",
default=[],
help="Compute the maximum value of attribute for each category.")
group.add_argument('-a','--mean',
action="append", dest="stats:mean",
metavar="<Attribute Name>",
default=[],
help="Compute the mean value of attribute for each category.")
group.add_argument('-v','--variance',
action="append", dest="stats:var",
metavar="<Attribute Name>",
default=[],
help="Compute the variance of attribute for each category.")
group.add_argument('-s','--std-dev',
action="append", dest="stats:sd",
metavar="<Attribute Name>",
default=[],
help="Compute the standard deviation of attribute for each category.")
def statistics(values, attributes, func):
stat={}
lstat={}
for var in attributes:
if var in values:
stat[var]={}
lstat[var]=0
for c in values[var]:
v = values[var][c]
m = func(v)
stat[var][c]=m
lm=len(str(m))
if lm > lstat[var]:
lstat[var]=lm
return stat, lstat
def minimum(values, options):
return statistics(values, options['minimum'], min)
def maximum(values, options):
return statistics(values, options['maximum'], max)
def mean(values, options):
def average(v):
s = reduce(lambda x,y:x+y,v,0)
return float(s)/len(v)
return statistics(values, options['mean'], average)
def variance(v):
if len(v)==1:
return 0
s = reduce(lambda x,y:(x[0]+y,x[1]+y**2),v,(0.,0.))
var = round(s[1]/(len(v)-1) - s[0]**2/len(v)/(len(v)-1), 5) # round to go around shady python rounding stuff when var is actually 0
if var == -0.0: # then fix -0 to +0 if was rounded to -0
var = 0.0
return var
def varpop(values, options):
return statistics(values, options['var'], variance)
def sd(values, options):
def stddev(v):
return math.sqrt(variance(v))
return statistics(values, options['sd'], stddev)
def run(config):
DMS.obi_atexit()
logger("info", "obi stats")
# Open the input
input = open_uri(config['obi']['inputURI'])
if input is None:
raise Exception("Could not read input view")
i_view = input[1]
if 'taxoURI' in config['obi'] and config['obi']['taxoURI'] is not None:
taxo_uri = open_uri(config['obi']['taxoURI'])
if taxo_uri is None or taxo_uri[2] == bytes:
raise Exception("Couldn't open taxonomy")
taxo = taxo_uri[1]
else :
taxo = None
statistics = set(config['stats']['minimum']) | set(config['stats']['maximum']) | set(config['stats']['mean']) | set(config['stats']['var']) | set(config['stats']['sd'])
total = 0
catcount={}
totcount={}
values={}
lcat=0
# Initialize the progress bar
pb = ProgressBar(len(i_view), config)
for i in range(len(i_view)):
PyErr_CheckSignals()
pb(i)
line = i_view[i]
category = []
for c in config['stats']['categories']:
try:
if taxo is not None:
loc_env = {'sequence': line, 'line': line, 'taxonomy': taxo}
else:
loc_env = {'sequence': line, 'line': line}
v = eval(c, loc_env, line)
lv=len(str(v))
if lv > lcat:
lcat=lv
category.append(v)
except:
category.append(None)
if 4 > lcat:
lcat=4
category=tuple(category)
catcount[category]=catcount.get(category,0)+1
try:
totcount[category]=totcount.get(category,0)+line[COUNT_COLUMN]
except KeyError:
totcount[category]=totcount.get(category,0)+1
for var in statistics:
if var in line and line[var] is not None:
v = line[var]
if var not in values:
values[var]={}
if category not in values[var]:
values[var][category]=[]
values[var][category].append(v)
pb(i, force=True)
print("", file=sys.stderr)
mini, lmini = minimum(values, config['stats'])
maxi, lmaxi = maximum(values, config['stats'])
avg, lavg = mean(values, config['stats'])
varp, lvarp = varpop(values, config['stats'])
sigma, lsigma = sd(values, config['stats'])
pcat = "%%-%ds" % lcat
if config['stats']['minimum']:
minvar= "min_%%-%ds" % max(len(x) for x in config['stats']['minimum'])
else:
minvar= "%s"
if config['stats']['maximum']:
maxvar= "max_%%-%ds" % max(len(x) for x in config['stats']['maximum'])
else:
maxvar= "%s"
if config['stats']['mean']:
meanvar= "mean_%%-%ds" % max(len(x) for x in config['stats']['mean'])
else:
meanvar= "%s"
if config['stats']['var']:
varvar= "var_%%-%ds" % max(len(x) for x in config['stats']['var'])
else:
varvar= "%s"
if config['stats']['sd']:
sdvar= "sd_%%-%ds" % max(len(x) for x in config['stats']['sd'])
else:
sdvar= "%s"
hcat = ""
for x in config['stats']['categories']:
hcat += pcat % x
hcat += "\t"
for x in config['stats']['minimum']:
hcat += minvar % x
hcat += "\t"
for x in config['stats']['maximum']:
hcat += maxvar % x
hcat += "\t"
for x in config['stats']['mean']:
hcat += meanvar % x
hcat += "\t"
for x in config['stats']['var']:
hcat += varvar % x
hcat += "\t"
for x in config['stats']['sd']:
hcat += sdvar % x
hcat += "\t"
hcat += "count\ttotal"
print(hcat)
sorted_stats = sorted(catcount.items(), key = lambda kv:(totcount[kv[0]]), reverse=True)
for i in range(len(sorted_stats)):
c = sorted_stats[i][0]
for v in c:
if type(v) == bytes:
print(pcat % tostr(v)+"\t", end="")
else:
print(pcat % str(v)+"\t", end="")
for m in config['stats']['minimum']:
print((("%%%dd" % lmini[m]) % mini[m][c])+"\t", end="")
for m in config['stats']['maximum']:
print((("%%%dd" % lmaxi[m]) % maxi[m][c])+"\t", end="")
for m in config['stats']['mean']:
print((("%%%df" % lavg[m]) % avg[m][c])+"\t", end="")
for m in config['stats']['var']:
print((("%%%df" % lvarp[m]) % varp[m][c])+"\t", end="")
for m in config['stats']['sd']:
print((("%%%df" % lsigma[m]) % sigma[m][c])+"\t", end="")
print("%d" %catcount[c]+"\t", end="")
print("%d" %totcount[c]+"\t")
input[0].close(force=True)
logger("info", "Done.")
|
