Merge pull request #163 from arokem/requirements

Requirements

Author: arokem

doc/conf.py

@@ -59,7 +59,6 @@
               'sphinx.ext.doctest',
               'sphinx.ext.intersphinx',
               'sphinx.ext.todo',
-              'sphinx.ext.pngmath',
               'numpydoc',
               'sphinx.ext.inheritance_diagram',
               'ipython_console_highlighting',

doc/examples/event_related_fmri.py

@@ -27,7 +27,7 @@
 
 import os
 
-from matplotlib.mlab import csv2rec
+import numpy as np
 import matplotlib.pyplot as plt
 
 import nitime
@@ -40,14 +40,15 @@
 
 """
 
-Next, we load the data into a recarray from the csv file, using csv2rec
+Next, we load the data into an array from the csv file, using ``np.loadtxt``
 
 """
 
 data_path = os.path.join(nitime.__path__[0], 'data')
 
-data = csv2rec(os.path.join(data_path, 'event_related_fmri.csv'))
+fname = os.path.join(data_path, 'event_related_fmri.csv')
 
+data = np.genfromtxt(fname, dtype=float, delimiter=',', names=True)
 
 """
 
@@ -56,15 +57,15 @@
 One TimeSeries is initialized for the BOLD data:
 """
 
-t1 = ts.TimeSeries(data.bold, sampling_interval=TR)
+t1 = ts.TimeSeries(data['bold'], sampling_interval=TR)
 
 """
 
 And another one for the events (the different stimuli):
 
 """
 
-t2 = ts.TimeSeries(data.events, sampling_interval=TR)
+t2 = ts.TimeSeries(data['events'], sampling_interval=TR)
 
 """
 

doc/examples/filtering_fmri.py

@@ -28,7 +28,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.mlab import csv2rec
 
 
 """
@@ -60,7 +59,8 @@
 
 data_path = os.path.join(nitime.__path__[0], 'data')
 
-data_rec = csv2rec(os.path.join(data_path, 'fmri_timeseries.csv'))
+data = np.loadtxt(os.path.join(data_path, 'fmri_timeseries.csv'),
+                  skiprows=1, delimiter=',')
 
 # Extract ROI information from the csv file headers:
 roi_names = np.array(data_rec.dtype.names)

doc/examples/granger_fmri.py

@@ -50,7 +50,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.mlab import csv2rec
 
 import nitime
 import nitime.analysis as nta
@@ -77,7 +76,9 @@
 
 data_path = os.path.join(nitime.__path__[0], 'data')
 
-data_rec = csv2rec(os.path.join(data_path, 'fmri_timeseries.csv'))
+fname = os.path.join(data_path, 'fmri_timeseries.csv')
+
+data_rec = np.genfromtxt(fname, dtype=float, delimiter=',', names=True)
 
 roi_names = np.array(data_rec.dtype.names)
 nseq = len(roi_names)

doc/examples/multi_taper_coh.py

@@ -28,7 +28,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.mlab import csv2rec
 import scipy.stats.distributions as dist
 from scipy import fftpack
 
@@ -52,7 +51,9 @@
 
 data_path = os.path.join(nitime.__path__[0], 'data')
 
-data_rec = csv2rec(os.path.join(data_path, 'fmri_timeseries.csv'))
+fname = os.path.join(data_path, 'fmri_timeseries.csv')
+
+data_rec = np.genfromtxt(fname, dtype=float, delimiter=',', names=True)
 
 
 """

doc/examples/resting_state_fmri.py

@@ -57,6 +57,9 @@
 
 data_rec = csv2rec(os.path.join(data_path, 'fmri_timeseries.csv'))
 
+data_rec = np.genfromtxt(os.path.join(data_path, 'fmri_timeseries.csv'),
+                         names=True, delimiter=',')
+
 """
 
 This data structure contains in its dtype a field 'names', which contains the
@@ -133,15 +136,15 @@
 
 xc = C.xcorr_norm
 
-idx_lcau = np.where(roi_names == 'lcau')[0]
-idx_rcau = np.where(roi_names == 'rcau')[0]
-idx_lput = np.where(roi_names == 'lput')[0]
-idx_rput = np.where(roi_names == 'rput')[0]
+idx_lcau = np.where(roi_names == 'LCau')[0]
+idx_rcau = np.where(roi_names == 'RCau')[0]
+idx_lput = np.where(roi_names == 'LPut')[0]
+idx_rput = np.where(roi_names == 'RPut')[0]
 
 fig02 = plot_xcorr(xc,
                    ((idx_lcau, idx_rcau),
                     (idx_lcau, idx_lput)),
-                   line_labels=['rcau', 'lput'])
+                   line_labels=['RCau', 'LPut'])
 
 """
 

requirements-dev.txt

@@ -0,0 +1,5 @@
+sphinx
+pytest
+pytest-cov
+nibabel
+networkx