diff --git a/docs/conf.py b/docs/conf.py
index b71f1d7..d0b457c 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -69,7 +69,7 @@
 # The short X.Y version.
 version = u'0.2'
 # The full version, including alpha/beta/rc tags.
-release = u'0.2.0'
+release = u'0.2.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/examples/debug_predict.py b/examples/debug_predict.py
deleted file mode 100644
index 2d64f20..0000000
--- a/examples/debug_predict.py
+++ /dev/null
@@ -1,450 +0,0 @@
-
-# -*- coding: utf-8 -*-
-"""
-=============================
-debug predict
-=============================
-
-This example shows debugging process for predict.  Delete before pip push.
-
-"""
-
-# Code source: Lucy Owen & Andrew Heusser
-# License: MIT
-
-
-import supereeg as se
-import sys
-import numpy as np
-from supereeg.helpers import _corr_column, _count_overlapping
-try:
-    from itertools import zip_longest
-except:
-    from itertools import izip_longest as zip_longest
-
-from scipy.stats import zscore
-#
-# def round_it(locs, places):
-#     """
-#     Rounding function
-#
-#     Parameters
-#     ----------
-#     locs : float
-#         Number be rounded
-#
-#     places : int
-#         Number of places to round
-#
-#     Returns
-#     ----------
-#     result : float
-#         Rounded number
-#
-#
-#     """
-#     return np.round(locs, decimals=places)
-#
-# def get_rows(all_locations, subj_locations):
-#     """
-#         This function indexes a subject's electrode locations in the full array of electrode locations
-#
-#         Parameters
-#         ----------
-#         all_locations : ndarray
-#             Full array of electrode locations
-#
-#         subj_locations : ndarray
-#             Array of subject's electrode locations
-#
-#         Returns
-#         ----------
-#         results : list
-#             Indexs for subject electrodes in the full array of electrodes
-#
-#         """
-#     if subj_locations.ndim == 1:
-#         subj_locations = subj_locations.reshape(1, 3)
-#     inds = np.full([1, subj_locations.shape[0]], np.nan)
-#     for i in range(subj_locations.shape[0]):
-#         possible_locations = np.ones([all_locations.shape[0], 1])
-#         try:
-#             for c in range(all_locations.shape[1]):
-#                 possible_locations[all_locations[:, c] != subj_locations[i, c], :] = 0
-#             inds[0, i] = np.where(possible_locations == 1)[0][0]
-#         except:
-#             pass
-#     inds = inds[~np.isnan(inds)]
-#     return [int(x) for x in inds]
-#
-# def known_unknown(fullarray, knownarray, subarray=None, electrode=None):
-#     """
-#         This finds the indices for known and unknown electrodes in the full array of electrode locations
-#
-#         Parameters
-#         ----------
-#         fullarray : ndarray
-#             Full array of electrode locations - All electrodes that pass the kurtosis test
-#
-#         knownarray : ndarray
-#             Subset of known electrode locations  - Subject's electrode locations that pass the kurtosis test (in the leave one out case, this is also has the specified location missing)
-#
-#         subarray : ndarray
-#             Subject's electrode locations (all)
-#
-#         electrode : str
-#             Index of electrode in subarray to remove (in the leave one out case)
-#
-#         Returns
-#         ----------
-#         known_inds : list
-#             List of known indices
-#
-#         unknown_inds : list
-#             List of unknown indices
-#
-#         """
-#     ## where known electrodes are located in full matrix
-#     known_inds = get_rows(round_it(fullarray, 3), round_it(knownarray, 3))
-#     ## where the rest of the electrodes are located
-#     unknown_inds = list(set(range(np.shape(fullarray)[0])) - set(known_inds))
-#     if not electrode is None:
-#         ## where the removed electrode is located in full matrix
-#         rm_full_ind = get_rows(round_it(fullarray, 3), round_it(subarray[int(electrode)], 3))
-#         ## where the removed electrode is located in the unknown index subset
-#         rm_unknown_ind = np.where(np.array(unknown_inds) == np.array(rm_full_ind))[0].tolist()
-#         return known_inds, unknown_inds, rm_unknown_ind
-#     else:
-#         return known_inds, unknown_inds
-#
-#
-# def chunker(iterable, n, fillvalue=None):
-#     #"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
-#     args = [iter(iterable)] * n
-#     return zip_longest(fillvalue=fillvalue, *args)
-#
-# def time_by_file_index_bo(bo, ave_data, known_inds, unknown_inds):
-#     """
-#     Session dependent function that calculates that finds either the timeseries or the correlation of the predicted and actual timeseries for a given location chunked by 25000 timepoints
-#
-#     Parameters
-#     ----------
-#     fname : Data matrix (npz file)
-#         The data to be analyzed.
-#         Filename containing fields:
-#             Y - time series
-#             R - electrode locations
-#             fname_labels - session number
-#             sample_rate - sampling rate
-#
-#     ave_data: ndarray
-#         Average correlation matrix
-#
-#     known_inds: list
-#         Indices for known electrodes in average matrix
-#
-#     unknown_inds: list
-#         Indices for unknown electrodes in average matrix
-#
-#     electrode_ind: int
-#         Index for estimated location in average matrix (location in unknown_inds)
-#
-#     k_flat_removed: list
-#         Indices of good channels (pass kurtosis test) in Y
-#
-#     electrode: int
-#         Index of held out location in known_inds
-#
-#     time_series: boolean
-#         True: output is predicted and actual timeseries
-#         False: output is predicted and actual correlation
-#
-#     Returns
-#     ----------
-#     results : pandas dataframe
-#         If timeseries input is:
-#         True: output is predicted and actual timeseries
-#         False: output is predicted and actual correlation
-#
-#
-#     """
-#     file_inds = np.unique(np.atleast_2d(bo.sessions.as_matrix()))
-#     Kaa = np.float32(ave_data[known_inds, :][:, known_inds])
-#     Kaa_inv = np.linalg.pinv(Kaa)
-#     Kba = np.float32(ave_data[unknown_inds, :][:, known_inds])
-#     results = []
-#     for i in file_inds:
-#         if np.shape(np.atleast_2d(bo.sessions.as_matrix()))[1] == 1:
-#             fname_labels = np.atleast_2d(bo.sessions.as_matrix()).T
-#         else:
-#             fname_labels = np.atleast_2d(bo.sessions.as_matrix())
-#         next_inds = np.where(fname_labels == i)[1]
-#         ### this code should incorporate the average voltage of the known (subject) electrodes and the average for the unknown (the other subjects)
-#         block_results = []
-#         next = np.zeros((bo.get_data().shape[0], ave_data.shape[0]))
-#         ### right now, this doesn't use an overlap in time, but this needs to be addressed when I see edge effects
-#         for each in chunker(next_inds, 1000):
-#
-#             next[:, unknown_inds] = np.squeeze(np.dot(np.dot(Kba, Kaa_inv),
-#                                                zscore(np.float32(
-#                                                    bo.get_data().as_matrix()[filter(lambda v: v is not None, each), :])).T).T)
-#             next[:, known_inds] = np.squeeze(zscore(np.float32(bo.get_data().as_matrix()[filter(lambda v: v is not None, each), :])))
-#             if block_results==[]:
-#                 block_results = next
-#             else:
-#                 block_results = np.vstack((block_results, next))
-#         if results==[]:
-#             results = block_results
-#         else:
-#             results = np.vstack((block_results, results))
-#
-#         return results
-
-#
-# # simulate 100 locations
-# locs = se.simulate_locations(n_elecs=100, random_seed=True)
-#
-# # simulate brain object
-# bo = se.simulate_bo(n_samples=1000, sample_rate=100, cov='random', locs=locs, noise=0, random_seed=True)
-#
-# # sample 10 locations, and get indices
-# sub_locs = locs.sample(90, replace=False, random_state=123).sort_values(['x', 'y', 'z']).index.values.tolist()
-#
-# # index brain object to get sample patient
-# bo_sample = bo[: ,sub_locs]
-#
-# # plot sample patient locations
-# bo_sample.plot_locs()
-#
-# # plot sample patient data
-# bo_sample.plot_data()
-#
-# Model = se.Model(data=bo, locs=locs)
-#
-# R = Model.get_locs().as_matrix()
-#
-# R_K_subj = bo_sample.get_locs().as_matrix()
-#
-# known_inds, unknown_inds = known_unknown(R, R_K_subj, R_K_subj)
-#
-#
-#
-# recon_data = time_by_file_index_bo(bo_sample, Model.get_model(z_transform=False), known_inds, unknown_inds)
-#
-# bo_r = se.Brain(data=recon_data, locs = R, sample_rate=bo.sample_rate, sessions=bo.sessions.as_matrix())
-#
-#
-# corrs_1 = _corr_column(bo.get_data().as_matrix(), bo_r.get_data().as_matrix())
-#
-# print('correlations with timeseries recon  = ' + str(corrs_1[unknown_inds].mean()))
-#
-#
-# bo_s = Model.predict(bo_sample, nearest_neighbor=False)
-#
-# recon_labels = np.where(np.array(bo_s.label) != 'observed')
-#
-# corrs = _corr_column(bo.get_data().as_matrix(), bo_s.get_data().as_matrix())
-#
-# print('correlations with predict function = ' + str(corrs[recon_labels].mean()))
-#
-# assert np.allclose(corrs, corrs_1)
-
-
-########## debug case 1 - null set ##################
-
-# set random seed to default and noise to 0
-random_seed = np.random.seed(123)
-noise = 0
-
-# locs
-locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
-
-# create model locs from 75 locations
-mo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create covariance matrix from random seed
-c = se.create_cov(cov='random', n_elecs=100)
-
-# pull out model from covariance matrix
-data = c[:, mo_locs.index][mo_locs.index, :]
-
-# create model from subsetted covariance matrix and locations
-model = se.Model(data=data, locs=mo_locs, n_subs=1)
-
-# create brain object from the remaining locations - first find remaining 25 locations
-sub_locs = locs[~locs.index.isin(mo_locs.index)]
-
-# create a brain object with all gray locations
-bo = se.simulate_bo(n_samples=1000, sample_rate=100, locs=locs, noise=noise, random_seed=random_seed)
-
-# parse brain object to create synthetic patient data
-data = bo.data.iloc[:, sub_locs.index]
-
-# put data and locations together in new sample brain object
-bo_sample = se.Brain(data=data.as_matrix(), locs=sub_locs, sample_rate=100)
-
-# predict activity at all unknown locations
-recon = model.predict(bo_sample, nearest_neighbor=False)
-
-# get reconstructed indices
-recon_labels = np.where(np.array(recon.label) != 'observed')
-
-# actual = bo.data.iloc[:, unknown_ind]
-actual_data = bo.get_zscore_data()[:, recon_labels[0]]
-
-recon_data = recon[:, recon_labels[0]].get_data().as_matrix()
-corr_vals = _corr_column(actual_data, recon_data)
-
-print('case 1 (null set) correlation = ' +str(corr_vals.mean()))
-
-
-
-
-########## debug case 2 - subset ##################
-
-# set random seed to default and noise to 0
-random_seed = np.random.seed(123)
-noise = 0
-
-# locs
-locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
-
-# create model locs from 50 locations
-mo_locs = locs.sample(100, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create covariance matrix from random seed
-c = se.create_cov(cov='random', n_elecs=100)
-
-# pull out model from covariance matrix
-data = c[:, mo_locs.index][mo_locs.index, :]
-
-# create model from subsetted covariance matrix and locations
-model = se.Model(data=data, locs=mo_locs, n_subs=1)
-
-# create brain object from subset of model locations
-sub_locs = mo_locs.sample(25, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create a brain object with all gray locations
-bo = se.simulate_bo(n_samples=1000, sample_rate=100, locs=mo_locs, noise=noise, random_seed=random_seed)
-
-# parse brain object to create synthetic patient data
-data = bo.data.iloc[:, sub_locs.index]
-
-# put data and locations together in new sample brain object
-bo_sample = se.Brain(data=data.as_matrix(), locs=sub_locs, sample_rate=100)
-
-# predict activity at all unknown locations
-recon = model.predict(bo_sample, nearest_neighbor=False)
-
-# get reconstructed indices
-recon_labels = np.where(np.array(recon.label) != 'observed')
-
-# actual = bo.data.iloc[:, unknown_ind]
-actual_data = bo.get_zscore_data()[:, recon_labels[0]]
-
-recon_data = recon[:, recon_labels[0]].get_data().as_matrix()
-corr_vals = _corr_column(actual_data, recon_data)
-
-print('case 2 (subset of model) correlation = ' +str(corr_vals.mean()))
-
-########## debug case 3 - overlapping set ##################
-
-# set random seed to default and noise to 0
-random_seed = np.random.seed(123)
-noise = 0
-
-# locs
-locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
-
-# create model locs from 75 locations
-mo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create covariance matrix from random seed
-c = se.create_cov(cov='random', n_elecs=100)
-
-# pull out model from covariance matrix
-data = c[:, mo_locs.index][mo_locs.index, :]
-
-# create model from subsetted covariance matrix and locations
-model = se.Model(data=data, locs=mo_locs, n_subs=1)
-
-# create brain object from all the locations - first find remaining 25 location
-sub_locs = locs[~locs.index.isin(mo_locs.index)]
-
-# then add 25 locations subsetted from model locations
-sub_locs = sub_locs.append(mo_locs.sample(25, random_state=random_seed).sort_values(['x', 'y', 'z']))
-
-# then subsample 25 from those locations to get some overlapping
-sub_locs.sample(25, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create a brain object with all gray locations
-bo = se.simulate_bo(n_samples=1000, sample_rate=100, locs=locs, noise=noise, random_seed=random_seed)
-
-# parse brain object to create synthetic patient data
-data = bo.data.iloc[:, sub_locs.index]
-
-# put data and locations together in new sample brain object
-bo_sample = se.Brain(data=data.as_matrix(), locs=sub_locs, sample_rate=100)
-
-# predict activity at all unknown locations
-recon = model.predict(bo_sample, nearest_neighbor=False)
-
-# get reconstructed indices
-recon_labels = np.where(np.array(recon.label) != 'observed')
-
-# actual = bo.data.iloc[:, unknown_ind]
-actual_data = bo.get_zscore_data()[:, recon_labels[0]]
-
-recon_data = recon[:, recon_labels[0]].get_data().as_matrix()
-corr_vals = _corr_column(actual_data, recon_data)
-
-print('case 3 (some overlap of model) correlation = ' +str(corr_vals.mean()))
-
-########## debug case 4 - model subset of brain object ##################
-
-# set random seed to default and noise to 0
-random_seed = np.random.seed(123)
-noise = 0
-
-# locs
-locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
-
-# create brain locs from 75 locations
-bo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create model locs from 50 locations
-mo_locs = bo_locs.sample(50, random_state=random_seed).sort_values(['x', 'y', 'z'])
-
-# create covariance matrix from random seed
-c = se.create_cov(cov='random', n_elecs=100)
-
-# pull out model from covariance matrix
-data = c[:, mo_locs.index][mo_locs.index, :]
-
-# create model from subsetted covariance matrix and locations
-model = se.Model(data=data, locs=mo_locs, n_subs=1)
-
-
-# create a brain object with all gray locations
-bo = se.simulate_bo(n_samples=1000, sample_rate=100, locs=locs, noise=noise, random_seed=random_seed)
-
-# parse brain object to create synthetic patient data
-data = bo.data.iloc[:, bo_locs.index]
-
-# put data and locations together in new sample brain object
-bo_sample = se.Brain(data=data.as_matrix(), locs=bo_locs, sample_rate=100)
-
-# predict activity at all unknown locations
-recon = model.predict(bo_sample, nearest_neighbor=False)
-
-# get reconstructed indices - since model is entirely a subset of brain object,
-# there should be no reconstructed locations
-recon_labels = np.where(np.array(recon.label) != 'observed')
-
-# actual = bo.data.iloc[:, unknown_ind]
-actual_data = bo_sample.get_zscore_data()
-
-recon_data = recon.get_data().as_matrix()
-corr_vals = _corr_column(actual_data, recon_data)
-
-print('case 4 (model subset of brain locs) correlation = ' +str(corr_vals.mean()))
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 3220855..ca7ac16 100755
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
 
 setup(
     name='supereeg',
-    version='0.2.0',
+    version='0.2.1',
     description=DESCRIPTION,
     long_description=' ',
     author='Contextual Dynamics Laboratory',