danlurie

Process Process-25:
Traceback (most recent call last):
  File "/home2/dlurie/Canopy/appdata/canopy-1.0.3.1262.rh5-x86_64/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/home2/dlurie/Canopy/appdata/canopy-1.0.3.1262.rh5-x86_64/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/home2/dlurie/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/CPAC/pipeline/cpac_pipeline.py", line 4339, in prep_workflow
    workflow.run(updatehash=True, plugin='MultiProc', plugin_args={'n_procs': c.numCoresPerSubject})
  File "/home2/dlurie/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/nipype/pipeline/engine.py", line 689, in run
    runner.run(execgraph, updatehash=updatehash, config=self.config)

danlurie

def ttestconv( oldfile ):
	
	newfile = os.path.basename(oldfile)
	#Read in Variables (Case Sensitive)
	
	print gen_code
	#gen_code = raw_input("Please Enter Gender Code (Case Sensitive):")
	#age_code = raw_input("Please Enter Age Code (Case Sensitive):")
	ttest_code = raw_input("Please Enter T Test Code (Case Sensitive):")
	

danlurie

In [1]: age_code = "[CONY_48]"

In [2]: gen_code ="[CONY_49]"

In [3]: from code
code                   codecs                 codetools
codeconvert_dl_130211  codeop

In [3]: from codeconvert_dl_130211 import ttestconv

danlurie

# Define a function that creates a row of histograms for MeanFD, MeanDVARS, Pct. Overthreshold Volumes, and tSNR
def func_quad (site_name, meanfd_column, meandvars_column, pctvols_column, tsnr_column, **kwargs):
	sns.set_context("notebook")
	fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(15, 3))
	suptitle(site_name, size="x-large", position=(0.5,1.05))
	ax1.hist(meanfd_column, 70, histtype="stepfilled", alpha=.7, color='steelblue')
	avg_meanfd = np.mean(meanfd_column)
	ax1.text(0.85, .925,'Mean = '+str(avg_meanfd)[:5], horizontalalignment='center', verticalalignment='center', transform = ax1.transAxes)
	ax1.set_title('Mean FD')
	ax2.hist(meandvars_column, 70, histtype="stepfilled", alpha=.7, color='olivedrab')

danlurie

# Define a function that creates site-specific data frames based on a range of subject numbers (specified as regex)
# One data frame for motion data, one for SNR data
# Prints number of rows in each new DF
def slice_site (site_name, sub_range):
	snr_df = site_name+"_snr"
	site_name = combined[combined['Subject'].str.contains(sub_range)]
	snr_df = snr[snr['subject'].str.contains(sub_range)]
	print "Subjects in motion data = "+str(len(site_name.index))
	print "Subjects in SNR data = "+str(len(snr_df.index))

danlurie

# Define a function that creates site-specific data frames based on a range of subject numbers (specified as regex)
# One data frame for motion data, one for SNR data
# Prints number of rows in each new DF
def slice_site (site_name, sub_range):
	snr_df = site_name+"_snr"
	foo = combined[combined['Subject'].str.contains(sub_range)]
	vars()[snr_df] = snr[snr['subject'].str.contains(sub_range)]
	print "Scans in motion data = "+str(len(foo.index))
	print "Scans in SNR data = "+str(len(vars()[snr_df].index))
	#import code

danlurie

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

combined = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/temp_motion_326.csv', sep=',')

snr = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/tsnr_temp_328.csv', sep=',')

danlurie

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

combined = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/temp_motion_326.csv', sep=',')

snr = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/tsnr_temp_328.csv', sep=',')

danlurie

#NYU
nyu_csc = combined[combined['Subject'].str.contains("25[0-1][0-9][0-9]|252[0-2][0-9]|2523[0-2]")]
nyu_csc_snr = snr[snr['subject'].str.contains("25[0-1][0-9][0-9]|252[0-2][0-9]|2523[0-2]")]
print len(nyu_csc.index)
print len(nyu_csc_snr.index)

# this makes some pretty graphs useing the data frames just created.
func_quad(site_name = "NYU", meanfd_column = nyu_csc.MeanFD, 
          meandvars_column = nyu_csc.MeanDVARS, pctvols_column = nyu_csc[nyu_csc.columns[37]],
          tsnr_column = nyu_csc_snr.tsnr)

danlurie

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
 
combined = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/temp_motion_326.csv', sep=',')
snr = pd.read_csv('/Users/daniel.lurie/Dropbox/CMI/INDI/CoRR/qc/tsnr_temp_328.csv', sep=',')