In [1]:
from pyneurovault.api import *
In [2]:
collections = get_collections()
Extracting NeuroVault collections meta data...
http://neurovault.org/api/collections/?limit=100&format=json
Found 787 results.
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=100
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=200
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=300
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=400
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=500
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=600
Retrieving http://neurovault.org/api/collections/?format=json&limit=100&offset=700
In [3]:
collections
Out[3]:
DOI
acquisition_orientation
add_date
authors
autocorrelation_model
b0_unwarping_software
contributors
coordinate_space
description
doi_add_date
...
used_high_pass_filter
used_intersubject_registration
used_motion_correction
used_motion_regressors
used_motion_susceptibiity_correction
used_orthogonalization
used_reaction_time_regressor
used_slice_timing_correction
used_smoothing
used_temporal_derivatives
0
None
None
2015-10-02T23:54:43.816421Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
1
None
2014-06-04T02:15:05.515327Z
None
Some class data used in AFNI bootcamp class
None
...
None
None
None
None
None
None
None
None
None
None
2
None
2014-06-18T15:40:20.041953Z
None
None
...
None
None
None
None
None
None
None
None
None
None
3
None
None
2014-07-30T22:39:46.918965Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
4
None
None
2014-07-31T17:04:05.582696Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
5
None
None
2014-08-12T08:37:34.607843Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
6
None
2014-08-12T11:35:49.636043Z
None
None
...
None
None
None
None
None
None
None
None
None
None
7
None
None
2014-08-12T17:12:55.173918Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
8
None
2015-05-04T17:55:36.378776Z
None
None
...
None
None
None
None
None
None
None
None
None
None
9
None
None
2015-12-24T03:53:13.460759Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
10
None
None
2015-09-15T14:33:15.777390Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
11
None
None
2015-05-06T00:04:33.064787Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
12
None
None
2016-08-22T21:43:42.546141Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
13
None
None
2015-03-26T23:35:59.454511Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
14
None
2014-08-19T21:30:34.087480Z
None
Resting state connectivity of music-induced an...
None
...
None
None
None
None
None
None
None
None
None
None
15
None
None
2015-08-09T00:06:12.276281Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
16
None
None
2015-11-03T05:14:49.427479Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
17
None
None
2015-10-29T12:19:12.421295Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
18
None
axial
2014-08-19T23:45:03.141994Z
mni
Study of music-induced analgesia in fibromyalg...
None
...
None
True
True
None
None
None
None
True
True
None
19
None
None
2015-10-05T08:53:55.176579Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
20
None
None
2015-10-12T01:37:37.145778Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
21
10.1038/nn.3673
2014-03-25T20:52:35.182187Z
Tom Schonberg, Akram Bakkour, Ashleigh M Hover...
None
2014-03-25T20:52:35.182187Z
...
None
None
None
None
None
None
None
None
None
None
22
None
None
2015-03-10T14:03:16.352681Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
23
None
2015-04-01T22:04:47.630216Z
None
None
...
None
None
None
None
None
None
None
None
None
None
24
10.1073/pnas.0905267106
2016-01-02T04:09:40.658881Z
S. M. Smith, P. T. Fox, K. L. Miller, D. C. Gl...
None
2016-01-02T04:29:57.184430Z
...
None
None
None
None
None
None
None
None
None
None
25
None
2013-06-13T15:30:25.762530Z
None
None
...
None
None
None
None
None
None
None
None
None
None
26
None
None
2014-08-11T10:58:26.046529Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
27
10.1038/ncomms8751
2016-05-03T06:38:04.016016Z
Fikret Işik Karahanoğlu and Dimitri Van De Ville
None
2016-05-03T06:38:04.015516Z
...
None
None
None
None
None
None
None
None
None
None
28
None
2014-07-30T17:43:41.267477Z
None
None
...
None
None
None
None
None
None
None
None
None
None
29
None
None
2014-07-23T22:26:03.769776Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
757
10.1371/journal.pone.0147200
2016-07-04T12:10:46.747050Z
Sérgio Gomes da Silva, Alexandre Aparecido de ...
None
Clinical evidence has shown that physical exer...
2016-07-04T12:10:46.746602Z
...
None
None
None
None
None
None
None
None
None
None
758
None
None
2016-07-21T16:55:43.620969Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
759
10.1523/JNEUROSCI.4402-15.2016
2016-06-15T19:34:12.828444Z
A. de la Vega, L. J. Chang, M. T. Banich, T. D...
None
2016-06-15T19:34:12.828013Z
...
None
None
None
None
None
None
None
None
None
None
760
10.1002/hbm.23289
2016-08-11T23:28:31.148032Z
J. Michael Tyszka and Wolfgang M. Pauli
None
2016-08-11T23:28:31.147558Z
...
None
None
None
None
None
None
None
None
None
None
761
None
None
2016-06-02T18:56:57.630585Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
762
None
None
2016-07-21T19:50:03.490807Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
763
None
None
2016-09-01T15:26:55.675288Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
764
None
None
2016-07-04T14:55:06.649223Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
765
None
2016-08-12T03:25:07.877213Z
None
This collection contains the images from the p...
None
...
None
None
None
None
None
None
None
None
None
None
766
10.1371/journal.pone.0140134
2015-09-29T15:22:25.413375Z
Ann S. Choe, Craig K. Jones, Suresh E. Joel, J...
None
We have acquired a longitudinal single-subject...
2016-05-03T11:38:56.980924Z
...
None
None
None
None
None
None
None
None
None
None
767
None
2016-06-02T21:12:28.809243Z
None
These images are the regression parameters fro...
None
...
None
None
None
None
None
None
None
None
None
None
768
10.1523/JNEUROSCI.4561-15.2016
2016-08-12T14:59:47.270794Z
K. L. Campbell, D. Samu, S. W. Davis, L. Geerl...
None
Unthresholded component maps from the ICA anal...
2016-08-12T14:59:47.270307Z
...
None
None
None
None
None
None
None
None
None
None
769
None
None
2016-09-01T15:41:19.550773Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
770
10.1016/j.dcn.2016.06.002
2016-08-12T15:44:27.393998Z
Tae-Ho Lee and Eva H. Telzer
None
To see the influences of the functional networ...
2016-08-12T15:44:27.393508Z
...
None
None
None
None
None
None
None
None
None
None
771
None
None
2016-08-12T22:22:57.724176Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
772
None
2016-07-07T21:34:47.166434Z
None
We compared resting-state functional connectiv...
None
...
None
None
None
None
None
None
None
None
None
None
773
None
2016-08-18T17:00:32.427948Z
None
None
...
None
None
None
None
None
None
None
None
None
None
774
None
None
2016-08-19T11:32:13.422501Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
775
None
None
2016-07-08T08:52:31.794676Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
776
None
None
2016-08-15T06:39:08.105284Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
777
None
None
2016-08-19T18:18:29.852410Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
778
None
None
2016-08-20T21:44:46.989495Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
779
None
None
2016-07-09T21:49:20.966178Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
780
10.1002/hbm.21299
2016-08-22T00:41:07.753155Z
Thomas Nickl-Jockschat, Ute Habel, Tanja Maria...
None
This study’s objective was to perform an Anato...
2016-08-22T00:41:07.752618Z
...
None
None
None
None
None
None
None
None
None
None
781
None
None
2016-07-11T07:15:55.010520Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
782
None
None
2016-07-26T13:42:01.521232Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
783
None
None
2016-07-26T16:28:03.379419Z
None
None
None
None
None
None
...
None
None
None
None
None
None
None
None
None
None
784
10.1016/j.bandl.2015.08.004
2014-10-01T12:42:09.456039Z
Torgeir Moberget, Eva Hilland, Stein Andersson...
None
Functional neuroimaging studies consistently r...
2015-10-30T00:18:50.339407Z
...
None
None
None
None
None
None
None
None
None
None
785
10.1016/j.neuroimage.2014.05.023
2015-03-11T19:00:17.646736Z
Robert P. Spunt and Ralph Adolphs
None
The ability to impute mental states to others,...
2015-03-11T19:00:17.646736Z
...
None
None
None
None
None
None
None
None
None
None
786
10.1016/j.biopsych.2015.03.027
2015-02-22T20:28:12.235047Z
Lars Schulze, Christian Schmahl and Inga Niedt...
None
Disturbances in the processing and regulation ...
2015-02-22T20:28:12.235047Z
...
None
None
None
None
None
None
None
None
None
None
787 rows × 97 columns
In [15]:
data = get_images(limit=100)
Extracting NeuroVault images meta data...
http://neurovault.org/api/images/?limit=100&format=json
Found 14228 results.
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=100
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=200
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=300
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=400
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=500
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=600
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=700
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=800
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=900
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1000
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1100
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1200
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1300
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1400
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1500
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1600
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In [21]:
data
Out[21]:
Action Observation
Acupuncture
Age
Anti-Saccades
Braille Reading
Breath-Holding
CIAS
Chewing/Swallowing
Classical Conditioning
Counting/Calculation
...
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random_property_1
random_property_2
reduced_representation
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thumbnail
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http://neurovault.org/media/images/877/fsl_cou...
NaN
http://neurovault.org/images/12909/
14221
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
100.000000
NaN
NaN
http://neurovault.org/media/images/877/fsl_cou...
NaN
NaN
NaN
http://neurovault.org/media/images/877/fsl_cou...
NaN
http://neurovault.org/images/12910/
14222
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
100.000000
NaN
NaN
http://neurovault.org/media/images/877/fsl_cou...
NaN
NaN
NaN
http://neurovault.org/media/images/877/fsl_cou...
NaN
http://neurovault.org/images/12913/
14223
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
7.065766
NaN
NaN
http://neurovault.org/media/images/877/spm_exa...
NaN
NaN
NaN
http://neurovault.org/media/images/877/spm_exa...
NaN
http://neurovault.org/images/12914/
14224
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
24.460595
NaN
NaN
http://neurovault.org/media/images/890/nidm_00...
NaN
NaN
NaN
http://neurovault.org/media/images/890/nidm_00...
NaN
http://neurovault.org/images/13099/
14225
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
7.065766
NaN
NaN
http://neurovault.org/media/images/1111/spm_ex...
NaN
NaN
NaN
http://neurovault.org/media/images/1111/spm_ex...
NaN
http://neurovault.org/images/14961/
14226
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
1.017598
NaN
NaN
None
NaN
NaN
task001_shapes_vs_baseline
http://neurovault.org/media/images/1804/glass_...
NaN
http://neurovault.org/images/26900/
14227
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
1.017598
NaN
NaN
http://neurovault.org/media/images/1805/transf...
NaN
NaN
task001_number_vs_baseline
http://neurovault.org/media/images/1805/glass_...
NaN
http://neurovault.org/images/27388/
14228 rows × 132 columns
In [4]:
df = get_json_df('images')
http://neurovault.org/api/images/?format=json
Found 14229 results.
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=100
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=200
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=300
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=400
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=500
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=600
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=700
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=800
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=900
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1000
Retrieving http://neurovault.org/api/images/?format=json&limit=100&offset=1100
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-4-07d260ad9bb2> in <module>()
----> 1 df = get_json_df('images')
/Users/llr14/anaconda/lib/python2.7/site-packages/pyneurovault/utils.pyc in get_json_df(data_type, pks, params, extend_url, debug)
152 else:
153 url = "http://neurovault.org/api/%s/%s?%sformat=json" % (data_type, extend_url, params)
--> 154 return pandas.DataFrame(get_json(url))
155
156 else:
/Users/llr14/anaconda/lib/python2.7/site-packages/pyneurovault/utils.pyc in get_json(url)
94 print "Retrieving %s" % json_single["next"]
95 try:
---> 96 json_single = get_url(json_single["next"])
97 json_single = json.loads(json_single.decode("utf-8"))
98 json_all = json_all + json_single['results']
/Users/llr14/anaconda/lib/python2.7/site-packages/pyneurovault/utils.pyc in get_url(url)
54 def get_url(url):
55 request = Request(url)
---> 56 response = urlopen(request)
57 return response.read()
58
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in urlopen(url, data, timeout, cafile, capath, cadefault, context)
152 else:
153 opener = _opener
--> 154 return opener.open(url, data, timeout)
155
156 def install_opener(opener):
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in open(self, fullurl, data, timeout)
427 req = meth(req)
428
--> 429 response = self._open(req, data)
430
431 # post-process response
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in _open(self, req, data)
445 protocol = req.get_type()
446 result = self._call_chain(self.handle_open, protocol, protocol +
--> 447 '_open', req)
448 if result:
449 return result
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in _call_chain(self, chain, kind, meth_name, *args)
405 func = getattr(handler, meth_name)
406
--> 407 result = func(*args)
408 if result is not None:
409 return result
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in http_open(self, req)
1226
1227 def http_open(self, req):
-> 1228 return self.do_open(httplib.HTTPConnection, req)
1229
1230 http_request = AbstractHTTPHandler.do_request_
/Users/llr14/anaconda/lib/python2.7/urllib2.pyc in do_open(self, http_class, req, **http_conn_args)
1199 else:
1200 try:
-> 1201 r = h.getresponse(buffering=True)
1202 except TypeError: # buffering kw not supported
1203 r = h.getresponse()
/Users/llr14/anaconda/lib/python2.7/httplib.pyc in getresponse(self, buffering)
1134
1135 try:
-> 1136 response.begin()
1137 assert response.will_close != _UNKNOWN
1138 self.__state = _CS_IDLE
/Users/llr14/anaconda/lib/python2.7/httplib.pyc in begin(self)
451 # read until we get a non-100 response
452 while True:
--> 453 version, status, reason = self._read_status()
454 if status != CONTINUE:
455 break
/Users/llr14/anaconda/lib/python2.7/httplib.pyc in _read_status(self)
407 def _read_status(self):
408 # Initialize with Simple-Response defaults
--> 409 line = self.fp.readline(_MAXLINE + 1)
410 if len(line) > _MAXLINE:
411 raise LineTooLong("header line")
/Users/llr14/anaconda/lib/python2.7/socket.pyc in readline(self, size)
478 while True:
479 try:
--> 480 data = self._sock.recv(self._rbufsize)
481 except error, e:
482 if e.args[0] == EINTR:
KeyboardInterrupt:
In [6]:
!ls
2016-09-06-neurovault_metadata.csv all_tasks.rdf
2016-09-06-pyneurovault.ipynb all_tasks_parsed.txt
all_concepts.rdf cogatlas_explore.ipynb
In [8]:
df = pd.read_csv('2016-09-06-neurovault_metadata.csv')
/Users/llr14/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (100,130) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
In [28]:
wh = np.where(~np.isnan(df['Acupuncture']))
df.loc[wh].loc[:,'cognitive_contrast_cogatlas']
Out[28]:
3015 NaN
6879 NaN
6914 NaN
6915 NaN
6917 NaN
6918 NaN
6960 NaN
6961 NaN
8366 NaN
8400 NaN
8448 NaN
8502 NaN
8595 NaN
8714 NaN
8734 NaN
8735 NaN
8737 NaN
8738 NaN
8739 NaN
8758 NaN
8759 NaN
8761 NaN
8762 NaN
8763 NaN
8764 NaN
8765 NaN
8766 NaN
8767 NaN
8768 NaN
8777 NaN
8778 NaN
8791 NaN
8832 NaN
8833 NaN
8834 NaN
8842 NaN
8998 NaN
9103 NaN
9206 NaN
9552 NaN
9593 NaN
9711 NaN
9946 NaN
12988 NaN
13323 NaN
13346 NaN
13347 NaN
13348 NaN
13349 NaN
13350 NaN
13368 NaN
13369 NaN
13370 NaN
13371 NaN
13372 NaN
13381 NaN
13382 NaN
13383 NaN
13622 NaN
13742 NaN
Name: cognitive_contrast_cogatlas, dtype: object
In [40]:
cogatlas_col = [c for c in df.columns if 'cogatlas' in c]
In [7]:
import pandas as pd
In [48]:
df[cogatlas_col]
Out[48]:
cognitive_contrast_cogatlas
cognitive_contrast_cogatlas_id
cognitive_paradigm_cogatlas
cognitive_paradigm_cogatlas_id
contrast_definition_cogatlas
0
None
None
None
None
None
1
parametric loss
cnt_4cacee8ad4b14
mixed gambles task
trm_4cacee4a1d875
None
2
difference from indifference
cnt_5175b81f53bf3
mixed gambles task
trm_4cacee4a1d875
None
3
None
None
mixed gambles task
trm_4cacee4a1d875
None
4
parametric gain
cnt_5181f79e38fb2
mixed gambles task
trm_4cacee4a1d875
None
5
task minus baseline
cnt_55ef1305643e9
mixed gambles task
trm_4cacee4a1d875
None
6
None
None
None
None
7
None
None
None
None
8
None
None
None
None
9
None
None
None
None
10
None
None
None
None
11
None
None
None
None
12
None
None
None
None
13
None
None
None
None
14
None
None
None
None
15
None
None
rest eyes open
trm_4c8a834779883
16
None
None
rest eyes open
trm_4c8a834779883
17
None
None
rest eyes open
trm_4c8a834779883
18
None
None
rest eyes open
trm_4c8a834779883
19
None
None
rest eyes open
trm_4c8a834779883
20
None
None
rest eyes open
trm_4c8a834779883
21
None
None
rest eyes open
trm_4c8a834779883
22
None
None
rest eyes open
trm_4c8a834779883
23
None
None
rest eyes open
trm_4c8a834779883
24
None
None
rest eyes open
trm_4c8a834779883
25
None
None
rest eyes open
trm_4c8a834779883
26
None
None
rest eyes open
trm_4c8a834779883
27
None
None
rest eyes open
trm_4c8a834779883
28
None
None
rest eyes open
trm_4c8a834779883
29
None
None
rest eyes open
trm_4c8a834779883
...
...
...
...
...
...
14198
None
None
electric stimulation
trm_4e68e08950157
None
14199
None
None
emotion processing fMRI task paradigm
trm_550b5b066d37b
None
14200
None
None
None
None
None
14201
None
None
None
None
None
14202
NaN
NaN
NaN
NaN
NaN
14203
NaN
NaN
NaN
NaN
NaN
14204
NaN
NaN
NaN
NaN
NaN
14205
NaN
NaN
NaN
NaN
NaN
14206
NaN
NaN
NaN
NaN
NaN
14207
NaN
NaN
NaN
NaN
NaN
14208
NaN
NaN
NaN
NaN
NaN
14209
NaN
NaN
NaN
NaN
NaN
14210
NaN
NaN
NaN
NaN
NaN
14211
NaN
NaN
NaN
NaN
NaN
14212
NaN
NaN
NaN
NaN
NaN
14213
NaN
NaN
NaN
NaN
NaN
14214
NaN
NaN
NaN
NaN
NaN
14215
NaN
NaN
NaN
NaN
NaN
14216
NaN
NaN
NaN
NaN
NaN
14217
NaN
NaN
NaN
NaN
NaN
14218
NaN
NaN
NaN
NaN
NaN
14219
NaN
NaN
NaN
NaN
NaN
14220
NaN
NaN
NaN
NaN
NaN
14221
NaN
NaN
NaN
NaN
NaN
14222
NaN
NaN
NaN
NaN
NaN
14223
NaN
NaN
NaN
NaN
NaN
14224
NaN
NaN
NaN
NaN
NaN
14225
NaN
NaN
NaN
NaN
NaN
14226
None
None
emotion processing fMRI task paradigm
trm_550b5b066d37b
None
14227
None
None
None
None
None
14228 rows × 5 columns
In [77]:
xtab_df = pd.crosstab(df['cognitive_contrast_cogatlas_id'], df['cognitive_paradigm_cogatlas_id'])
In [78]:
xtab_df
Out[78]:
cognitive_paradigm_cogatlas_id
None
trm_4b7497a289534
trm_4c8989e1f3df7
trm_4c898acd1f28e
trm_4c898c0786246
trm_4c898c33ee5f8
trm_4c898c8bf1b4f
trm_4c898f079d05e
trm_4c89903149aeb
trm_4c8990e187dc7
...
tsk_4a57abb949bb1
tsk_4a57abb949bcd
tsk_4a57abb949bf6
tsk_4a57abb949d40
tsk_4a57abb949dbb
tsk_4a57abb949dd6
tsk_4a57abb949df1
tsk_4a57abb949e1a
tsk_4a57abb949e27
tsk_4a57abb949e98
cognitive_contrast_cogatlas_id
None
151
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
Other
2
0
1
0
0
1
3
1
0
0
...
0
0
0
1
0
0
1
0
1
0
cnt_4b44a4d544ee7
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
4
0
0
cnt_4b4a5473705db
0
0
0
0
0
0
0
0
0
0
...
0
1
0
0
0
0
0
0
0
0
cnt_4b82d27f31194
0
9
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4c6b5fd5bfc41
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
4
0
0
cnt_4c6b600d4b80f
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
2
0
0
cnt_4cacee8ad4b14
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4cacf26718047
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4d949c8f3101c
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4d949c9b01604
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4de56f7a17762
0
0
0
0
0
0
0
0
0
0
...
0
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0
0
0
0
0
0
0
0
cnt_4de6b87a44743
0
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0
0
0
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...
0
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0
0
0
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0
0
0
cnt_4de6bfac7a994
0
0
0
0
0
0
0
0
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...
0
0
0
0
0
0
0
0
0
0
cnt_4de6c62e9b5da
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4de6d2747197f
0
0
0
0
0
0
0
0
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0
...
0
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0
0
0
0
0
0
0
0
cnt_4de96cd8c9aad
0
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0
0
0
0
2
0
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...
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0
0
0
0
0
0
0
cnt_4decfedb91973
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4ded2aa1ea79d
0
0
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4dee593ebfcc7
0
0
0
0
0
0
0
0
1
0
...
0
0
0
0
0
0
0
0
0
0
cnt_4deeb0951bd72
0
0
0
0
0
0
0
0
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0
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0
0
0
0
0
0
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cnt_4df28c7e2f855
0
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1
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0
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0
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cnt_4df689e663ffe
0
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0
0
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0
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cnt_4df69c712873f
0
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...
0
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0
0
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0
0
0
cnt_4df92bccc5ae1
0
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0
0
0
0
0
0
0
0
...
0
0
0
7
0
0
0
0
0
0
cnt_4dfa94d49dc90
0
0
0
0
0
0
0
0
0
0
...
0
0
0
0
0
4
0
0
0
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cnt_4dffaa78530b6
0
0
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0
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0
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0
...
0
0
0
0
0
0
0
0
0
0
cnt_4e00fd3de3b0a
0
0
0
0
0
0
0
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0
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0
0
0
0
0
0
cnt_4e025d8d96ecf
0
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0
0
0
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0
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0
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cnt_4e03b8c2457b4
0
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1
...
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
cnt_553e85d4af020
0
0
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0
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0
0
0
0
0
...
0
0
0
0
0
0
0
0
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cnt_553e85f11747b
0
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0
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...
0
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0
0
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0
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cnt_553e85fc78496
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0
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cnt_553e86042df77
0
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...
0
0
0
0
0
0
0
0
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0
cnt_553e860cb6e0a
0
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0
0
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...
0
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0
0
0
0
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cnt_553e8616191c4
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0
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...
0
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0
0
0
0
0
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cnt_553e865074ad4
0
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0
0
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...
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0
0
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cnt_553e865d69738
0
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0
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0
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0
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0
0
0
0
0
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cnt_553e8672a9f1d
0
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0
...
0
0
0
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0
0
0
0
0
0
cnt_553e868c4a13c
0
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0
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cnt_553ebfc3ab40d
0
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0
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0
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cnt_553fc4f4362c1
0
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cnt_553fc50126261
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158 rows × 88 columns
In [52]:
%matplotlib inline
In [53]:
from matplotlib import pyplot as plt
In [79]:
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1, 1, 1)
ax.matshow(xtab_df);
In [80]:
import numpy as np
In [81]:
np.sum(xtab_df, axis=0)
Out[81]:
cognitive_paradigm_cogatlas_id
None 153
trm_4b7497a289534 9
trm_4c8989e1f3df7 7
trm_4c898acd1f28e 2
trm_4c898c0786246 1
trm_4c898c33ee5f8 1
trm_4c898c8bf1b4f 5
trm_4c898f079d05e 2
trm_4c89903149aeb 1
trm_4c8990e187dc7 1
trm_4c8991c5beb0a 3
trm_4c899211a965c 15
trm_4c8a834779883 72
trm_4c8a8467304e2 9
trm_4c8a84f20dde2 5
trm_4c8a85c5c75eb 7
trm_4cacee4a1d875 11
trm_4cacf22a22d80 8
trm_4cacf3fbc503b 14
trm_4d559bcd67c18 25
trm_4d55a2bbcfdff 10
trm_4d949c5b0e380 2
trm_4da6318f7381b 4
trm_4da6327154fd4 33
trm_4da86cb034ff6 2
trm_4da87f383435b 17
trm_4da88a8e13f26 1
trm_4da890594742a 11
trm_4e6114e7b1ff2 2
trm_4e68e08950157 1
...
trm_550b54a8b30f4 26
trm_550b557e5f90e 13
trm_550b5a47aa23e 3
trm_550b5b066d37b 23
trm_551b0ee81fb6b 30
trm_553e73e29cf7d 24
trm_553e77e53497d 5
trm_553e85265f51e 21
trm_553ebfc390256 1
trm_553ec64e6cb1b 1
trm_55c686e0824e8 1
trm_55c691909c580 2
tsk_4a57abb949846 11
tsk_4a57abb949923 2
tsk_4a57abb949934 1
tsk_4a57abb9499a8 1
tsk_4a57abb949a0d 2
tsk_4a57abb949a4f 6
tsk_4a57abb949aca 1
tsk_4a57abb949b1c 1
tsk_4a57abb949bb1 1
tsk_4a57abb949bcd 1
tsk_4a57abb949bf6 5
tsk_4a57abb949d40 8
tsk_4a57abb949dbb 6
tsk_4a57abb949dd6 4
tsk_4a57abb949df1 1
tsk_4a57abb949e1a 14
tsk_4a57abb949e27 1
tsk_4a57abb949e98 4
dtype: int64
In [86]:
xtab_df_sumrow = np.sum(xtab_df > 0, axis=1)
In [92]:
len(xtab_df_sumrow)
Out[92]:
158
In [88]:
xtab_df_sumrow[xtab_df_sumrow > 1]
Out[88]:
cognitive_contrast_cogatlas_id
None 4
Other 28
cnt_523c7d1fc539f 2
dtype: int64
In [90]:
df.ix[df['cognitive_contrast_cogatlas_id'] == 'cnt_523c7d1fc539f', cogatlas_col]
Out[90]:
cognitive_contrast_cogatlas
cognitive_contrast_cogatlas_id
cognitive_paradigm_cogatlas
cognitive_paradigm_cogatlas_id
contrast_definition_cogatlas
983
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
3650
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
3695
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
3738
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
5930
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
12479
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
aberrant behavior checklist - community
trm_523c7a0a73cf5
None
13580
Hyperactivity/Noncompliance
cnt_523c7d1fc539f
social judgment task
trm_4f2453ce33f16
None
In [44]:
df.ix[df['cognitive_contrast_cogatlas']=='task minus baseline',cogatlas_col]
Out[44]:
cognitive_contrast_cogatlas
cognitive_contrast_cogatlas_id
cognitive_paradigm_cogatlas
cognitive_paradigm_cogatlas_id
contrast_definition_cogatlas
5
task minus baseline
cnt_55ef1305643e9
mixed gambles task
trm_4cacee4a1d875
None
10829
task minus baseline
cnt_55ef1305643e9
mixed gambles task
trm_4cacee4a1d875
None
In [97]:
df.to_csv('2016-09-06-neurovault_metadata.csv',
index=False, encoding='utf-8')
In [98]:
!pwd
/Users/contematto/github/notebooks
In [ ]:
Content source: dankessler/cogfusion
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