def cluster_analysis(clean_feature_file,
feature_names,
output_dir,
method='ward'):
print datetime.now().strftime('starting:%Y-%m-%d %H:%M:%S')
df_f = pd.read_csv(clean_feature_file)
# all_feature_names=gl_feature_names
print "There are %d neurons in this dataset" % df_f.shape[0]
REMOVE_OUTLIERS = 1 #clipping the dataset
if REMOVE_OUTLIERS > 0:
postfix = "_ol_removed"
else:
postfix = "_ol_clipped_5_glonly"
if method == "ap" or method == "all":
fc.run_affinity_propagation(df_f, feature_names, output_dir, postfix)
num_clusters = 1000
if method == "ward" or method == "all":
fc.run_ward_cluster(df_features=df_f,
feature_names=feature_names,
num_clusters=num_clusters,
output_dir=output_dir,
output_postfix=postfix,
experiment_type='bigneuron',
low=500,
high=1500,
plot_heatmap=0,
RemoveOutliers=REMOVE_OUTLIERS)
print datetime.now().strftime('end:%Y-%m-%d %H:%M:%S')
return
def cluster_analysis(clean_feature_file,feature_names,output_dir,feature_set_type, method='ward',swc_path = None):
print datetime.now().strftime('starting:%Y-%m-%d %H:%M:%S')
if (swc_path == None):
swc_path = "./SWC"
df_f = pd.read_csv(clean_feature_file)
# all_feature_names=gl_feature_names
print "There are %d neurons in this dataset" % df_f.shape[0]
REMOVE_OUTLIERS = 1 #clipping the dataset
if REMOVE_OUTLIERS > 0:
postfix = "_ol_removed_"+feature_set_type
else:
postfix = "_ol_clipped_5_glonly"
if method == "ap" or method == "all":
fc.run_affinity_propagation(df_f, feature_names, output_dir, postfix,swc_path,REMOVE_OUTLIERS)
num_clusters = 12
if method == "ward" or method == "all":
fc.run_ward_cluster(df_features=df_f, feature_names=feature_names, num_clusters=num_clusters,
output_dir= output_dir,
output_postfix=postfix,experiment_type='ivscc', low=8, high = 35, plot_heatmap=0,
RemoveOutliers=REMOVE_OUTLIERS, swc_path=swc_path)
print datetime.now().strftime('end:%Y-%m-%d %H:%M:%S')
return
def cluster_analysis(clean_feature_file,feature_names,output_dir, method='ward'):
print datetime.now().strftime('starting:%Y-%m-%d %H:%M:%S')
df_f = pd.read_csv(clean_feature_file)
# all_feature_names=gl_feature_names
print "There are %d neurons in this dataset" % df_f.shape[0]
REMOVE_OUTLIERS = 1 #clipping the dataset
if REMOVE_OUTLIERS > 0:
postfix = "_ol_removed"
else:
postfix = "_ol_clipped_5_glonly"
if method == "ap" or method == "all":
fc.run_affinity_propagation(df_f, feature_names, output_dir, postfix)
num_clusters = 1000
if method == "ward" or method == "all":
fc.run_ward_cluster(df_features=df_f, feature_names=feature_names, num_clusters=num_clusters,output_dir= output_dir,
output_postfix=postfix,experiment_type='bigneuron', low=500, high = 1500, plot_heatmap=0, RemoveOutliers=REMOVE_OUTLIERS)
print datetime.now().strftime('end:%Y-%m-%d %H:%M:%S')
return
def cluster_analysis(clean_feature_file,
feature_names,
output_dir,
feature_set_type,
method='ward',
swc_path=None):
print datetime.now().strftime('starting:%Y-%m-%d %H:%M:%S')
if (swc_path == None):
swc_path = "./SWC"
df_f = pd.read_csv(clean_feature_file)
# all_feature_names=gl_feature_names
print "There are %d neurons in this dataset" % df_f.shape[0]
REMOVE_OUTLIERS = 1 #clipping the dataset
if REMOVE_OUTLIERS > 0:
postfix = "_ol_removed_" + feature_set_type
else:
postfix = "_ol_clipped_5_glonly"
if method == "ap" or method == "all":
fc.run_affinity_propagation(df_f, feature_names, output_dir, postfix,
swc_path, REMOVE_OUTLIERS)
num_clusters = 12
if method == "ward" or method == "all":
fc.run_ward_cluster(df_features=df_f,
feature_names=feature_names,
num_clusters=num_clusters,
output_dir=output_dir,
output_postfix=postfix,
experiment_type='ivscc',
low=8,
high=35,
plot_heatmap=0,
RemoveOutliers=REMOVE_OUTLIERS,
swc_path=swc_path)
print datetime.now().strftime('end:%Y-%m-%d %H:%M:%S')
return
