In [ ]:
import sys
sys.path.append('..')
In [2]:
%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os
In [3]:
from seizure.tasks import load_mat_data
import seizure.tasks
seizure.tasks.task_predict = True
In [9]:
itr = load_mat_data('../seizure-data','Dog_2','preictal')
In [10]:
for f in itr:
for key in f.keys():
if not key.startswith('_'):
break
#print f[key].dtype.fields.keys()
print key,f[key]['data'][0][0].shape,f[key]['channels'][0][0].shape[1],
for k in f[key].dtype.fields:
if k != 'data' and k != 'channels':
print k,f[key][k][0][0][0][0],
print
preictal_segment_1 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_2 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_3 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_4 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_5 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_6 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_7 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_8 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_9 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_10 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_11 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_12 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_13 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_14 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_15 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_16 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_17 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_18 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_19 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_20 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_21 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_22 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_23 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_24 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_25 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_26 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_27 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_28 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_29 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_30 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_31 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_32 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_33 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_34 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_35 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_36 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
preictal_segment_37 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 1
preictal_segment_38 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 2
preictal_segment_39 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 3
preictal_segment_40 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 4
preictal_segment_41 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 5
preictal_segment_42 (16, 239766) 16 data_length_sec 600 sampling_frequency 399.609756098 sequence 6
In [22]:
f[key]['sequence'][0,0][0,0]
Out[22]:
6
In [6]:
for f in load_mat_data('../seizure-data','Patient_2','preictal'):
for key in f.keys():
if not key.startswith('_'):
break
print ' '.join([str(v[0]) for v in f[key]['channels'][0][0][0].ravel()])
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
LTG_01 LTG_02 LTG_03 LTG_04 LTG_05 LTG_06 LTG_07 LTG_08 LTG_09 LTG_10 LTG_11 LTG_12 LTG_13 LTG_14 LTG_15 LTG_16 LTG_17 LTG_18 LTG_19 LTG_20 LTG_21 LTG_22 LTG_23 LTG_24
In [7]:
f[key]
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-7-861f93e132f0> in <module>()
----> 1 f[key]
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/displayhook.pyc in __call__(self, result)
250 self.start_displayhook()
251 self.write_output_prompt()
--> 252 format_dict, md_dict = self.compute_format_data(result)
253 self.write_format_data(format_dict, md_dict)
254 self.update_user_ns(result)
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/displayhook.pyc in compute_format_data(self, result)
152
153 """
--> 154 return self.shell.display_formatter.format(result)
155
156 def write_format_data(self, format_dict, md_dict=None):
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc in format(self, obj, include, exclude)
198 md = None
199 try:
--> 200 data = formatter(obj)
201 except:
202 # FIXME: log the exception
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc in __call__(self, obj)
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc in warn_format_error(method, self, *args, **kwargs)
231 """decorator for warning on failed format call"""
232 try:
--> 233 r = method(self, *args, **kwargs)
234 except NotImplementedError as e:
235 # don't warn on NotImplementedErrors
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc in __call__(self, obj)
685 type_pprinters=self.type_printers,
686 deferred_pprinters=self.deferred_printers)
--> 687 printer.pretty(obj)
688 printer.flush()
689 return stream.getvalue()
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/lib/pretty.pyc in pretty(self, obj)
441 if callable(meth):
442 return meth(obj, self, cycle)
--> 443 return _default_pprint(obj, self, cycle)
444 finally:
445 self.end_group()
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
561 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs:
562 # A user-provided repr. Find newlines and replace them with p.break_()
--> 563 output = _safe_repr(obj)
564 for idx,output_line in enumerate(output.splitlines()):
565 if idx:
/Users/udi/anaconda/lib/python2.7/site-packages/IPython/lib/pretty.pyc in _safe_repr(obj)
166 """Don't assume repr is not broken."""
167 try:
--> 168 return repr(obj)
169 except Exception as e:
170 return _failed_repr(obj, e)
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc in array_repr(arr, max_line_width, precision, suppress_small)
1551 if arr.size > 0 or arr.shape==(0,):
1552 lst = array2string(arr, max_line_width, precision, suppress_small,
-> 1553 ', ', "array(")
1554 else: # show zero-length shape unless it is (0,)
1555 lst = "[], shape=%s" % (repr(arr.shape),)
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in array2string(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter)
452 else:
453 lst = _array2string(a, max_line_width, precision, suppress_small,
--> 454 separator, prefix, formatter=formatter)
455 return lst
456
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in _array2string(a, max_line_width, precision, suppress_small, separator, prefix, formatter)
326 lst = _formatArray(a, format_function, len(a.shape), max_line_width,
327 next_line_prefix, separator,
--> 328 _summaryEdgeItems, summary_insert)[:-1]
329 return lst
330
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in _formatArray(a, format_function, rank, max_line_len, next_line_prefix, separator, edge_items, summary_insert)
527 s += _formatArray(a[-1], format_function, rank-1, max_line_len,
528 " " + next_line_prefix, separator, edge_items,
--> 529 summary_insert).rstrip()+']\n'
530 return s
531
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in _formatArray(a, format_function, rank, max_line_len, next_line_prefix, separator, edge_items, summary_insert)
498 s, line = _extendLine(s, line, word, max_line_len, next_line_prefix)
499
--> 500 word = format_function(a[-1])
501 s, line = _extendLine(s, line, word, max_line_len, next_line_prefix)
502 s += line + "]\n"
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in repr_format(x)
229
230 def repr_format(x):
--> 231 return repr(x)
232
233 def _array2string(a, max_line_width, precision, suppress_small, separator=' ',
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc in array_str(a, max_line_width, precision, suppress_small)
1613
1614 """
-> 1615 return array2string(a, max_line_width, precision, suppress_small, ' ', "", str)
1616
1617 def set_string_function(f, repr=True):
/Users/udi/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in array2string(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter)
446 if isinstance(x, tuple):
447 x = _convert_arrays(x)
--> 448 lst = style(x)
449 elif reduce(product, a.shape) == 0:
450 # treat as a null array if any of shape elements == 0
KeyboardInterrupt:
In [10]:
400*600
Out[10]:
240000
In [14]:
from seizure.transforms import Resample
r = Resample(100)
data = np.sin(np.linspace(0.,2.*np.pi,500))
pl.plot(r.apply(data));
In [15]:
np.linspace(0.,2.,5)
Out[15]:
array([ 0. , 0.5, 1. , 1.5, 2. ])
In [17]:
239766 / 600.
Out[17]:
399.61
In [73]:
a = np.array([[[10,2,3],[4,5,6]],[[7,8,9],[2,11,12]],[[3,12,5],[2,11,12]],[[7,8,9],[2,11,12]]])
In [77]:
np.sort(a,axis=0)[0,:,:]
Out[77]:
array([[3, 2, 3],
[2, 5, 6]])
In [ ]:
Content source: udibr/seizure-prediction
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