Python read_intermediate_datafile Examples

Example #1

File: task_oscillation__plot_differences_in_means.py Project: voidptr/research_scripts

# 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 )

Example #2

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)

Example #3

File: longtail.py Project: voidptr/ce_rapid_adaptation_data

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] = []

Example #4

File: task_oscillation__output_differences_in_mean_and_mann_whitney_u_test.py Project: voidptr/ce_rapid_adaptation_data

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)

Example #5

    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:

Example #6

File: longtail.py Project: voidptr/research_scripts

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 ] )

Example #7

# 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(

Example #8

File: test_read_intermediate_files.py Project: voidptr/research_scripts

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 )

Example #9

File: task_oscillation__collect_all_data_and_plot_3d_chart__bar3d.py Project: voidptr/research_scripts

        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: