def main():
input_data = ju.read(INPUT_JSON)
manifest_file = input_data.get('manifest_file')
manifest_file = os.path.join(TOP_DIR, manifest_file)
log_level = input_data.get('log_level', logging.DEBUG)
logging.getLogger().setLevel(log_level)
# get cache, metric
logging.debug("loading regional matrix")
cache = VoxelModelCache(manifest_file=manifest_file)
df_metric = cache.get_normalized_connection_density(dataframe=True)
# plot
fig = plot(df_metric,
STRUCTURES,
cache,
GRID_KWS,
CBAR_KWS,
HEATMAP_KWS,
figsize=FIGSIZE)
fig.savefig(OUTPUT_FILE, **SAVEFIG_KWARGS)
plt.close(fig)
def main():
input_data = ju.read(INPUT_JSON)
manifest_file = input_data.get('manifest_file')
manifest_file = os.path.join(TOP_DIR, manifest_file)
log_level = input_data.get('log_level', logging.DEBUG)
logging.getLogger().setLevel(log_level)
# get cache, metric
logging.debug("loading regional matrix")
cache = VoxelModelCache(manifest_file=manifest_file)
df_metric = cache.get_normalized_connection_density(dataframe=True)
logging.debug("getting cortical network")
df_cortex = get_cortical_df(df_metric, cache)
# get projection types
full_ipsi, cortex_ipsi = get_pt((df_metric, df_cortex))
full_contra, cortex_contra = get_pt((df_metric, df_cortex), pt="contra")
logging.debug("Computing gaussian mixture model fits for max: %s" %
MAX_COMPONENTS)
dfs = (full_ipsi, full_contra, cortex_ipsi, cortex_contra)
labels = ("full-ipsi", "full-contra", "cortex-ipsi", "cortex-contra")
frames = []
for d, l in zip(dfs, labels):
# log transform
d = np.log10(d[d > 0]).reshape(-1, 1)
# normality test
_, p_value = stats.shapiro(d)
# gmm
gmm, bic = fit_gmm(d, MAX_COMPONENTS, **GMM_PARAMS)
columns = ('mean', 'var', 'weight')
print("", l, "-" * 40, sep="\n")
print("shapiro-wilk p_value : %.5g" % p_value)
print("optimal n components : %d" % gmm.n_components)
print("bic : %.5g" % bic)
print('\t'.join(columns))
print("----\t---\t------")
attrs = tuple(
map(np.ravel, (gmm.means_, gmm.covariances_, gmm.weights_)))
for x in zip(*attrs):
print("%.2f\t%.2f\t%.3f" % x)
df = pd.DataFrame(dict(zip(columns, attrs)))
df.index.name = 'n_components'
frames.append(df)
df = pd.concat(frames, keys=labels).unstack()
df.to_csv(OUTPUT_FILE)
def main():
input_data = ju.read(INPUT_JSON)
manifest_file = input_data.get('manifest_file')
manifest_file = os.path.join(TOP_DIR, manifest_file)
log_level = input_data.get('log_level', logging.DEBUG)
logging.getLogger().setLevel(log_level)
# get cache, metric
logging.debug("loading regional matrix")
cache = VoxelModelCache(manifest_file=manifest_file)
df_metric = cache.get_normalized_connection_density(dataframe=True)
logging.debug("getting cortical network")
df_cortex = get_cortical_df(df_metric, cache)
# get projection types
full_ipsi, cortex_ipsi = get_pt((df_metric, df_cortex))
full_contra, cortex_contra = get_pt((df_metric, df_cortex), pt="contra")
logging.debug("Computing distribution fits for")
logging.debug("%s" % DISTRIBUTIONS)
fitter = DistFit(DISTRIBUTIONS)
dfs = (full_ipsi, full_contra, cortex_ipsi, cortex_contra)
labels = ("full-ipsi", "full-contra", "cortex-ipsi", "cortex-contra")
frames = []
for d, l in zip(dfs, labels):
fitter.fit(d[d > 0])
logging.debug(l)
logging.debug(str(fitter))
frames.append(results_to_df(fitter))
df = pd.concat(frames, keys=labels).unstack()
df.to_csv(OUTPUT_FILE)
if __name__ == "__main__":
input_data = ju.read(INPUT_JSON)
manifest_file = input_data.get('manifest_file')
manifest_file = os.path.join(TOP_DIR, manifest_file)
log_level = input_data.get('log_level', logging.DEBUG)
logging.getLogger().setLevel(log_level)
# configure
colors = sns.color_palette(n_colors=2)
# get cache, metric
cache = VoxelModelCache(manifest_file=manifest_file)
df_metric = cache.get_normalized_connection_density(dataframe=True)
logging.debug("getting cortical network")
df_cortex = get_cortical_df(df_metric, cache)
# region acs
region_acs = df_metric.index.values
logging.debug("computing distances")
d = get_distances(region_acs, cache)
d = to_dataframe(d, df_metric.index, df_metric.columns)
d_cortex = get_cortical_df(d, cache)
# get projection types
full_ipsi = get_pt((d, df_metric), thresh=0)
full_contra = get_pt((d, df_metric), thresh=0, pt="contra")
