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Update param_study_moduls.py

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Should work with new dict keys
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JakHolzer 2020-10-21 14:00:09 +02:00 committed by GitHub
parent 8361fac5c3
commit 9783e5e7aa
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1 changed files with 76 additions and 15 deletions
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91
pyzebra/param_study_moduls.py
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@ -1,11 +1,12 @@
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from load_1Dnn import load_1D
from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
from load_1D import load_1D
from ccl_dict_operation import add_dict
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pickle
import scipy.io as sio
def load_dats(filepath):
@ -17,6 +18,7 @@ def load_dats(filepath):
file_list = list()
with open(filepath, "r") as infile:
col_names = next(infile).split(",")
col_names = [col_names[i].rstrip() for i in range(len(col_names))]
for line in infile:
if "END" in line:
break
@ -24,8 +26,6 @@ def load_dats(filepath):
elif isinstance(filepath, list):
data_type = "list"
file_list = filepath
print(file_list)
dict1 = {}
for i in range(len(file_list)):
if not dict1:
@ -38,12 +38,12 @@ def load_dats(filepath):
dict1 = add_dict(dict1, load_1D(file_list[i][0]))
else:
dict1 = add_dict(dict1, load_1D(file_list[i]))
dict1["meas"][str("M" + str(i + 1))]["params"] = {}
dict1["meas"][i + 1]["params"] = {}
if data_type == "txt":
for x in range(len(col_names) - 1):
dict1["meas"][str("M" + str(i + 1))]["params"][col_names[x + 1]] = file_list[i][
x + 1
]
dict1["meas"][i + 1]["params"][
col_names[x + 1]
] = file_list[i][x + 1]
return dict1
@ -55,7 +55,7 @@ def create_dataframe(dict1):
# create dictionary to which we pull only wanted items before transforming it to pd.dataframe
pull_dict = {}
pull_dict["filenames"] = list()
for key in dict1["meas"]["M1"]["params"]:
for key in dict1["meas"][1]["params"]:
pull_dict[key] = list()
pull_dict["temperature"] = list()
pull_dict["mag_field"] = list()
@ -67,7 +67,9 @@ def create_dataframe(dict1):
# populate the dict
for keys in dict1["meas"]:
if "file_of_origin" in dict1["meas"][keys]:
pull_dict["filenames"].append(dict1["meas"][keys]["file_of_origin"].split("/")[-1])
pull_dict["filenames"].append(
dict1["meas"][keys]["file_of_origin"].split("/")[-1]
)
else:
pull_dict["filenames"].append(dict1["meta"]["original_filename"].split("/")[-1])
for key in dict1["meas"][keys]["params"]:
@ -143,3 +145,62 @@ def make_graph(data, sorting_parameter, style):
plt.clim(color_matrix.mean(), color_matrix.max())
return fig
def save_dict(obj, name):
""" saves dictionary as pickle file in binary format
:arg obj - object to save
:arg name - name of the file
NOTE: path should be added later"""
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_dict(name):
"""load dictionary from picle file
:arg name - name of the file to load
NOTE: expect the file in the same folder, path should be added later
:return dictionary"""
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
# pickle, mat, h5, txt, csv, json
def save_table(data, filetype, name, path=None):
print("Saving: ", filetype)
path = "" if path is None else path
if filetype == "pickle":
# to work with uncertanities, see uncertanity module
with open(path + name + '.pkl', 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
if filetype == "mat":
# matlab doesent allow some special character to be in var names, also cant start with
# numbers, in need, add some to the romove_character list
data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
data = data.drop(columns=['fit_area', 'int_area'])
remove_characters = [" ", "[", "]", "{", "}", "(",")"]
for character in remove_characters:
data.columns = [data.columns[i].replace(character,"") for i in range(len(data.columns))]
sio.savemat((path + name + '.mat'), {name: col.values for name, col in data.items()})
if filetype == "csv" or "txt":
data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
data = data.drop(columns=['fit_area', 'int_area', 'om', 'Counts'])
if filetype == "csv":
data.to_csv(path + name + '.csv')
if filetype == "txt":
with open((path + name + '.txt'), 'w') as outfile:
data.to_string(outfile)
if filetype == "h5":
hdf = pd.HDFStore((path + name + ".h5"))
hdf.put("data", data)
hdf.close()
if filetype == "json":
data.to_json((path + name + ".json"))