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import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from nltools.stats import regress, zscore
from nltools.data import Brain_Data, Design_Matrix
from nltools.stats import find_spikes 
from nltools.mask import expand_mask

def make_motion_covariates(mc, tr):
    z_mc = zscore(mc)
    all_mc = pd.concat([z_mc, z_mc**2, z_mc.diff(), z_mc.diff()**2], axis=1)
    all_mc.fillna(value=0, inplace=True)
    return Design_Matrix(all_mc, sampling_freq=1/tr)

base_dir = '/srv/lab/fmri/tutorials/Sherlock/fmriprep'

fwhm=6
tr = 1.5
outlier_cutoff = 3

file_list = [x for x in glob.glob(os.path.join(base_dir, '*/func/*preproc*gz')) if 'denoise' not in x]
f = file_list[0]
sub = os.path.basename(f).split('_')[0]

data = Brain_Data(f)
smoothed = data.smooth(fwhm=fwhm)

spikes = smoothed.find_spikes(global_spike_cutoff=outlier_cutoff, diff_spike_cutoff=outlier_cutoff)
covariates = pd.read_csv(glob.glob(os.path.join(base_dir, sub, 'func', '*tsv'))[0], sep='\t')
mc = covariates[['trans_x','trans_y','trans_z','rot_x', 'rot_y', 'rot_z']]
mc_cov = make_motion_covariates(mc, tr)
csf = covariates['csf'] # Use CSF from fmriprep output
dm = Design_Matrix(pd.concat([csf, mc_cov, spikes.drop(labels='TR', axis=1)], axis=1), sampling_freq=1/tr)
dm = dm.add_poly(order=2, include_lower=True) # Add Intercept, Linear and Quadratic Trends

smoothed.X = dm
stats = smoothed.regress()
stats['residual'].data = np.float32(stats['residual'].data) # cast as float32 to reduce storage space
stats['residual'].write(os.path.join(base_dir, sub, 'func', f'{sub}_denoise_smooth{fwhm}mm_task-sherlockPart1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))

---------------------------------------------------------------------------
RecursionError                            Traceback (most recent call last)
/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
    700                 type_pprinters=self.type_printers,
    701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
    703             printer.flush()
    704             return stream.getvalue()

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    375                 if cls in self.type_pprinters:
    376                     # printer registered in self.type_pprinters
--> 377                     return self.type_pprinters[cls](obj, self, cycle)
    378                 else:
    379                     # deferred printer

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/IPython/lib/pretty.py in inner(obj, p, cycle)
    605             p.pretty(key)
    606             p.text(': ')
--> 607             p.pretty(obj[key])
    608         p.end_group(step, end)
    609     return inner

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    392                         if cls is not object \
    393                                 and callable(cls.__dict__.get('__repr__')):
--> 394                             return _repr_pprint(obj, self, cycle)
    395 
    396             return _default_pprint(obj, self, cycle)

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    698     """A pprint that just redirects to the normal repr function."""
    699     # Find newlines and replace them with p.break_()
--> 700     output = repr(obj)
    701     lines = output.splitlines()
    702     with p.group():

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/nltools/data/brain_data.py in __repr__(self)
    233             self.X.shape,
    234             os.path.basename(self.mask.get_filename()),
--> 235             self.file_name,
    236         )
    237 

... last 1 frames repeated, from the frame below ...

/usr/local/miniconda3/envs/naturalistic/lib/python3.7/site-packages/nltools/data/brain_data.py in __repr__(self)
    233             self.X.shape,
    234             os.path.basename(self.mask.get_filename()),
--> 235             self.file_name,
    236         )
    237 

RecursionError: maximum recursion depth exceeded

fdr_thr = fdr(pd.Series(isrsa_nn_r).values)
print(f'FDR Threshold: {fdr_thr}')

view_img(threshold(isrsa_nn_r_brain, isrsa_nn_p_brain, thr=fdr_thr).to_nifti())

nltools.stats.fdr(p, q=0.05)
    Determine FDR threshold given a p value array and desired false discovery rate q. Written by Tal Yarkoni

    Parameters
            p – (np.array) vector of p-values
            q – (float) false discovery rate level

    Returns
        (float) p-value threshold based on independence or positive
            dependence

    Return type
        fdr_p