# Set up options
usage = """usage: %prog [options] output_file data_file
"""
parser = OptionParser(usage)
## fetch the args
(options, args) = parser.parse_args()
## parameter error
if len(args) < 2:
parser.error("incorrect number of arguments")
output_filename = args[0]
input_filename = args[1]
data = rif.read_intermediate_datafile(input_filename)
## get means
control_means = []
treatment_means = []
control_stds = []
treatment_stds = []
for task_name in data[ 'Mean_Median_Task_Oscillation_Amplitude_By_Task__Control'].keys():
control_mean = float( data[ 'Mean_Median_Task_Oscillation_Amplitude_By_Task__Control'][task_name][0] )
treatment_mean = float( data[ 'Mean_Median_Task_Oscillation_Amplitude_By_Task__Treatment'][task_name][0] )
control_means.append( control_mean )
import read_intermediate_files as rif
data_structure = rif.read_intermediate_datafile('intermediate_file_format.txt')
print str(data_structure)
rif.output_to_intermediate_format(data_structure)
usage = """usage: %prog [options] output_file control_data_file treatment_data_file
"""
parser = OptionParser(usage)
## fetch the args
(options, args) = parser.parse_args()
## parameter error
if len(args) < 3:
parser.error("incorrect number of arguments")
output_filename = args[0]
control_filename = args[1]
treatment_filename = args[2]
control_data = rif.read_intermediate_datafile(control_filename)
treatment_data = rif.read_intermediate_datafile(treatment_filename)
def calculate_means(data):
## calculate means across replicates
#gather the values
tasks = {}
for task_and_replicate in data[
'Task_Oscillation_Amplitudes_By_Sample_And_Task'].keys():
taskname = task_and_replicate.split('_')[0]
if not taskname in tasks:
tasks[taskname] = []
usage = """usage: %prog [options] control_amplitudes.txt treatment_amplitudes.txt
"""
#Permitted types for outfile are png, pdf, ps, eps, and svg"""
parser = OptionParser(usage)
## fetch the args
(options, args) = parser.parse_args()
## parameter error
if len(args) < 2:
parser.error("incorrect number of arguments")
control_filename = args[0]
treatment_filename = args[1]
control_data = rif.read_intermediate_datafile(control_filename)['Median_Task_Oscillation_Amplitudes_By_Task'] ## there's only one thing
treatment_data = rif.read_intermediate_datafile(treatment_filename)['Median_Task_Oscillation_Amplitudes_By_Task'] ## there's only one thing
print 'Mean_Median_Task_Oscillation_Amplitude_By_Task__Control'
print "Task_Name,mean,median,std,ste,variance"
for task_name in control_data.keys():
control_medians = control_data[task_name]
control_medians = [ float(val) for val in control_medians ]
median = np.median ( control_medians )
mean = np.mean ( control_medians )
std = np.std ( control_medians )
ste = std / math.sqrt( len ( control_medians ) )
variance = np.var ( control_medians )
print task_name + "," + str(mean) + "," + str(median) + "," + str(std) + "," + str(ste) + "," + str(variance)
filenamebits = file.split('_')
## interpret the filename, which should be in the following format: 33_Andn_Backbone__mann_whitney_u_stats__control_vs_punish_xor
## 1 11 12
backbone_task = translation[filenamebits[1]]
fluctuating_task = translation[
filenamebits[12]] # should be the same as [-1]
punish_or_nopunish = filenamebits[11]
if not backbone_task in data_structure.keys():
data_structure[backbone_task] = {} ## keyed by the fluctuating task
if not fluctuating_task in data_structure[backbone_task].keys():
data_structure[backbone_task][fluctuating_task] = {
} ## keyed by the punish/nopunish
data = rif.read_intermediate_datafile(file)
data_structure[backbone_task][fluctuating_task][punish_or_nopunish] = data
means = {}
pvalues = {}
for backbone_task in data_structure.keys(): ## loop through the backbone tasks
means[backbone_task] = {}
pvalues[backbone_task] = {}
for fluctuating_task in data_structure[backbone_task].keys():
for punish_or_nopunish in data_structure[backbone_task][
fluctuating_task].keys():
if len(data_structure[backbone_task][fluctuating_task]
[punish_or_nopunish].keys()) > 0:
usage = """usage: %prog [options] output_file control_data_file treatment_data_file
"""
parser = OptionParser(usage)
## fetch the args
(options, args) = parser.parse_args()
## parameter error
if len(args) < 3:
parser.error("incorrect number of arguments")
output_filename = args[0]
control_filename = args[1]
treatment_filename = args[2]
control_data = rif.read_intermediate_datafile(control_filename)
treatment_data = rif.read_intermediate_datafile(treatment_filename)
def calculate_means( data ):
## calculate means across replicates
#gather the values
tasks = {}
for task_and_replicate in data['Task_Oscillation_Amplitudes_By_Sample_And_Task'].keys():
taskname = task_and_replicate.split('_')[0]
if not taskname in tasks:
tasks[taskname] = []
tasks[taskname].append( data['Task_Oscillation_Amplitudes_By_Sample_And_Task'][ task_and_replicate ] )
# Set up options
usage = """usage: %prog [options] output_file data_file
"""
parser = OptionParser(usage)
## fetch the args
(options, args) = parser.parse_args()
## parameter error
if len(args) < 2:
parser.error("incorrect number of arguments")
output_filename = args[0]
input_filename = args[1]
data = rif.read_intermediate_datafile(input_filename)
## get means
control_means = []
treatment_means = []
control_stds = []
treatment_stds = []
for task_name in data[
'Mean_Median_Task_Oscillation_Amplitude_By_Task__Control'].keys():
control_mean = float(
data['Mean_Median_Task_Oscillation_Amplitude_By_Task__Control']
[task_name][0])
treatment_mean = float(
import read_intermediate_files as rif
data_structure = rif.read_intermediate_datafile('intermediate_file_format.txt')
print str(data_structure)
rif.output_to_intermediate_format( data_structure )
continue
filenamebits = file.split('_')
## interpret the filename, which should be in the following format: 33_Andn_Backbone__mann_whitney_u_stats__control_vs_punish_xor
## 1 11 12
backbone_task = translation[ filenamebits[1] ]
fluctuating_task = translation[ filenamebits[12] ] # should be the same as [-1]
punish_or_nopunish = filenamebits[11]
if not backbone_task in data_structure.keys():
data_structure[backbone_task] = {} ## keyed by the fluctuating task
if not fluctuating_task in data_structure[backbone_task].keys():
data_structure[backbone_task][fluctuating_task] = {} ## keyed by the punish/nopunish
data = rif.read_intermediate_datafile(file)
data_structure[backbone_task][fluctuating_task][ punish_or_nopunish ] = data
means = {}
pvalues = {}
for backbone_task in data_structure.keys(): ## loop through the backbone tasks
means[ backbone_task ] = {}
pvalues[ backbone_task ] = {}
for fluctuating_task in data_structure[ backbone_task ].keys():
for punish_or_nopunish in data_structure[ backbone_task ][ fluctuating_task ].keys():
if len(data_structure[ backbone_task ][ fluctuating_task ][ punish_or_nopunish ].keys()) > 0: