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from pyneurovault import api
import os
import pandas as pd
import matplotlib.pyplot as plt
import time
import nibabel as nb
import numpy as np
import pickle
from nltools.analysis import Predict, apply_mask, Roc
%matplotlib inline

outfolder = "/Users/lukechang/Downloads/nv_tmp"


    

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tic = time.time() #Start Timer

# Pain Collection
collection = 504

# Will extract all collections and images in one query to work from
nv = api.NeuroVault()

# Download all images to file
standard = os.path.join(os.path.dirname(api.__file__),'data','MNI152_T1_2mm_brain.nii.gz')
nv.download_images(dest_dir = outfolder,target=standard, collection_ids=[collection],resample=False)

# Create Variables
collection_data = nv.get_images_df().ix[nv.get_images_df().collection_id == collection,:].reset_index()
img_index = sorted((e,i) for i,e in enumerate(collection_data.file))
index = [x[1] for x in img_index]
img_file = [x[0] for x in img_index]

dat = nb.funcs.concat_images([os.path.join(outfolder,'original',str(x) + '.nii.gz') for x in collection_data.image_id[index]])
holdout = [int(x.split('_')[-2]) for x in img_file]
heat_level = [x.split('_')[-1].split('.')[0] for x in img_file]
Y_dict = {'High':3,'Medium':2,'Low':1}
Y = np.array([Y_dict[x] for x in heat_level])

# Pickle for later use
# Saving the objects:
with open(os.path.join(outfolder,'Pain_Data.pkl'), 'w') as f:
    pickle.dump([dat,holdout,Y], f)

print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer


    

    




Extracting NeuroVault collections meta data...
Found 238 results.
Extracting NeuroVault images meta data...
Found 6302 results.
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=1000
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=2000
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=3000
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=4000
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=5000
Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=6000
NeuroVault Object (nv) Includes <nv.images><nv.collections>
Elapsed: 172.62 seconds

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tic = time.time() #Start Timer

# Getting back the objects:
with open(os.path.join(outfolder,'Pain_Data.pkl')) as f:
    dat, holdout, Y = pickle.load(f)
print 'Load Pickled File - Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer


    

    




Load Pickled File - Elapsed: 17.75 seconds

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tic = time.time() #Start Timer

## Test Prediction with kfold xVal
# SVR
# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5}, **{'kernel':"linear"})
# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'loso':holdout}, **{'kernel':"linear"})
# negvneu.predict()
# print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer

# Ridge
negvneu = Predict(dat,Y,algorithm='ridge', output_dir=outfolder, cv_dict = {'type':'kfolds','folds':5})
negvneu.predict()

# # Principal Components Regression
# negvneu = Predict(dat,Y,algorithm='pcr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5})
# negvneu.predict()
print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer


    

    




overall Root Mean Squared Error: 0.00
overall Correlation: 1.00
overall CV Root Mean Squared Error: 0.98
overall CV Correlation: 0.55
Total Elapsed: 6.10 seconds






    













    

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tic = time.time() #Start Timer

# Load data using nibabel
pines = nb.load(os.path.join(outfolder, 'ridge_weightmap.nii.gz'))

pexpd = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='dot_product', save_output=True)
pexpc = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='correlation', save_output=True)

plt.subplot(2, 1, 1)
plt.plot(pexpd)
plt.title('Pattern Expression')
plt.ylabel('Dot Product')

plt.subplot(2, 1, 2)
plt.plot(pexpc)
plt.xlabel('Subject')
plt.ylabel('Correlation')

print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer


    

    




Elapsed: 44.62 seconds






    




/usr/local/lib/python2.7/site-packages/nilearn/signal.py:50: UserWarning: Standardization of 3D signal has been requested but would lead to zero values. Skipping.
  warnings.warn('Standardization of 3D signal has been requested but '






    

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tic = time.time() #Start Timer

# Create Variables
negvneu.yfit
include = (negvneu.Y==3) | (negvneu.Y==1)
input_values = negvneu.yfit[include]
binary_outcome = negvneu.Y[include]
binary_outcome = binary_outcome==3

# Single-Interval
roc = Roc(input_values=input_values, binary_outcome=binary_outcome)
roc.plot()
roc.summary()

print roc.tpr
roc.accuracy

# # Forced Choice 
# roc_fc = Roc(input_values=input_values, binary_outcome=binary_outcome, forced_choice=True)
# roc_fc.plot()
# roc_fc.summary()

print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer


    

    




------------------------
.:ROC Analysis Summary:.
------------------------
Accuracy:           0.84
Accuracy SE:        0.11
Accuracy p-value:   0.00
Sensitivity:        0.93
Specificity:        0.75
AUC:                0.88
PPV:                0.79
------------------------
[ 1.          1.          1.         ...,  0.03571429  0.03571429
  0.03571429]
Total Elapsed: 1.30 seconds






    

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from nilearn.input_data import NiftiMasker
mask = nb.load('/Users/lukechang/Github/neurolearn/nltools/resources/MNI152_T1_2mm_brain_mask_dil.nii.gz')
nifti_masker = NiftiMasker(mask_img=mask)
pcr = nb.load(os.path.join(outfolder,'pcr_weightmap.nii.gz'))
ridge = nb.load(os.path.join(outfolder,'ridge_weightmap.nii.gz'))
pcr_mask = nifti_masker.fit_transform(pcr)
ridge_mask = nifti_masker.fit_transform(ridge)
np.corrcoef(pcr_mask,ridge_mask)


    

    
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<nltools.analysis.Predict instance at 0x107dfd9e0>

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## Univariate Regression


    

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