data_sigma = np.std(data_matrix_arrays, axis=0)
data_2sigma = 2 * data_sigma
#make matrix of all processed arrays
relevant_arrays = [
time_array, default_array, data_mean, data_median, data_sigma, data_2sigma
]
all_relevant_arrays = np.zeros((len(relevant_arrays), num_timepoints))
for i in range(len(relevant_arrays)):
all_relevant_arrays[i, :] = relevant_arrays[i]
#save numpy matrix in folder with same filename as data-files and new suffix
filename_stddev = input_datafolder + "/" + data_filename + "_stddev.npy"
np.save(filename_stddev, all_relevant_arrays)
if raw_input("Would you like to store results in /hume/? y/n") == "y":
result_folder = folder.hume_folder() + "results/"
filename_stddev = result_folder + data_filename + "_stddev%d.npy" % input_index
np.save(filename_stddev, all_relevant_arrays)
#add comment regarding analysis in README
with open(input_datafolder + '/README.md', 'a') as readmefile:
readmefile.write('\n')
readmefile.write("Analyzed " + input_datafolder + '\n')
readmefile.write(
"file consists of one %s array with the following arrays:" %
(all_relevant_arrays.shape, ) + '\n')
readmefile.write("* Default array (No fudge factor) for " + array_name +
'\n')
readmefile.write("* Mean value for " + array_name + '\n')
readmefile.write("* Median value for " + array_name + '\n')
readmefile.write("* one sigma value for " + array_name + '\n')
"""
This script is for picking an experiment on the stornext folder,
get all the adequate results and write them to the results-folder
in \thesis\
"""
import os
from save_results import save_results
from directory_master import Foldermap
folder = Foldermap()
stornext_folder = folder.stornext_folder()
hume_folder = folder.hume_folder()
results_folder = "latex/thesis/results/" #direction to thesis-results-folder from /Master/
if __name__ == '__main__':
#which experiment?
inventory = dict(enumerate(os.listdir(stornext_folder)))
question = "Choose the index of the appropriate experiment?\n%s" % (
inventory)
response = int(raw_input(question))
experiment = inventory[response]
get_directory = stornext_folder + experiment + "/"
save_directory = hume_folder + results_folder + experiment + "/"
#which arrays?
loa_array_strings = []
#nb_nsm, rate_nsm
loa_array_strings.append("num_nsm")
#elem & iso yield+ism
iso_list = ["Re-185", "Re-187", "Os-187", "Os-188", "Os-186", "W-184"]
elem_list = ["Re", "Os", "W"]
"""
Go through /stornext/-directories and plot various data-sets
with mean + regions and such.
Decide which data is to be stored in /results/
"""
from directory_master import Foldermap
from plot_data_files import plot_all_mean_sigma_extrema, plot_all_time_hist
import matplotlib.pyplot as pl
#Get relevant directory-names for uio-systems
folder_instance = Foldermap()
dir_stornext = folder_instance.stornext_folder()
dir_hume = folder_instance.hume_folder()
#decide on variables to plot!
loa_elem = ["Re", "Os"]
loa_re_isos = ["Re-187", "Re-185"]
loa_os_isos = ["Os-187", "Os-188"]
loa_ism_isos = ["ism_iso_" + iso for iso in loa_re_isos + loa_os_isos]
loa_ism_elem = ["ism_elem_" + elem for elem in loa_elem]
loa_yield_isos = ["yield_" + iso for iso in loa_re_isos + loa_os_isos]
loa_array_strings = ["num_nsm", "m_locked"] + \
loa_ism_elem + \
loa_ism_isos + \
loa_yield_isos
if __name__ == '__main__':
print "Plotting data for the following arrays:"
print loa_array_strings
dir_experiment = dir_stornext + "MCExperiment1/"
