# Copyright 2018 Regents of the University of Colorado. All Rights Reserved.
# Released under the MIT license.
# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.
# Verify current version before use at: https://github.com/MAVENSDC/PyTplot
import os
import pickle
import numpy as np
import pytplot
from pytplot.options import options
from pytplot.store_data import store_data
from pytplot.tplot_options import tplot_options
from scipy.io import readsav
[docs]def tplot_restore(filename):
"""
This function will restore tplot variables that have been saved with the "tplot_save" command.
.. note::
This function is compatible with the IDL tplot_save routine.
If you have a ".tplot" file generated from IDL, this procedure will restore the data contained in the file.
Not all plot options will transfer over at this time.
Parameters:
filename : str
The file name and full path generated by the "tplot_save" command.
Returns:
None
Examples:
>>> # Restore the saved data from the tplot_save example
>>> import pytplot
>>> pytplot.tplot_restore('C:/temp/variable1.pytplot')
"""
#Error check
if not (os.path.isfile(filename)):
print("Not a valid file name")
return
#Check if the restored file was an IDL file
if filename.endswith('.tplot'):
temp_tplot = readsav(filename)
for i in range(len(temp_tplot['dq'])):
if isinstance(temp_tplot['dq'][i][0], str):
print("Error reading variable; this error occurs when the variable wasn't loaded in IDL when the SAV file was created.")
continue
data_name = temp_tplot['dq'][i][0].decode("utf-8")
temp_x_data = temp_tplot['dq'][i][1][0][0].squeeze()
#Pandas reads in data the other way I guess
if len(temp_tplot['dq'][i][1][0][2].shape) == 4:
temp_y_data = np.transpose(temp_tplot['dq'][i][1][0][2], axes=(3, 2, 1, 0))
elif len(temp_tplot['dq'][i][1][0][2].shape) == 3:
temp_y_data = np.transpose(temp_tplot['dq'][i][1][0][2], axes=(2, 1, 0))
elif len(temp_tplot['dq'][i][1][0][2].shape) == 2:
temp_y_data = np.transpose(temp_tplot['dq'][i][1][0][2])
else:
temp_y_data = temp_tplot['dq'][i][1][0][2]
# variable contains V1, V2 and V3 (e.g., DF as a function of energy, theta, phi)
if len(temp_tplot['dq'][i][1][0]) == 10:
temp_v1_data = temp_tplot['dq'][i][1][0][4]
temp_v2_data = temp_tplot['dq'][i][1][0][6]
temp_v3_data = temp_tplot['dq'][i][1][0][8]
#Change from little endian to big endian, since pandas apparently hates little endian
#We might want to move this into the store_data procedure eventually
if (temp_x_data.dtype.byteorder == '>'):
temp_x_data = temp_x_data.byteswap().newbyteorder()
if (temp_y_data.dtype.byteorder == '>'):
temp_y_data = temp_y_data.byteswap().newbyteorder()
if (temp_v1_data.dtype.byteorder == '>'):
temp_v1_data = temp_v1_data.byteswap().newbyteorder()
if (temp_v2_data.dtype.byteorder == '>'):
temp_v2_data = temp_v2_data.byteswap().newbyteorder()
if (temp_v3_data.dtype.byteorder == '>'):
temp_v3_data = temp_v3_data.byteswap().newbyteorder()
# support time-varying depends
if len(temp_v1_data.shape) == 2:
temp_v1_data = np.transpose(temp_v1_data)
if len(temp_v2_data.shape) == 2:
temp_v2_data = np.transpose(temp_v2_data)
if len(temp_v3_data.shape) == 2:
temp_v3_data = np.transpose(temp_v3_data)
store_data(data_name, data={'x': temp_x_data, 'y': temp_y_data, 'v1': temp_v1_data, 'v2': temp_v2_data, 'v3': temp_v3_data})
# variable contains V1, V2 (e.g., DF as a function of energy, angle)
elif len(temp_tplot['dq'][i][1][0]) == 8:
temp_v1_data = temp_tplot['dq'][i][1][0][4]
temp_v2_data = temp_tplot['dq'][i][1][0][6]
#Change from little endian to big endian, since pandas apparently hates little endian
#We might want to move this into the store_data procedure eventually
if (temp_x_data.dtype.byteorder == '>'):
temp_x_data = temp_x_data.byteswap().newbyteorder()
if (temp_y_data.dtype.byteorder == '>'):
temp_y_data = temp_y_data.byteswap().newbyteorder()
if (temp_v1_data.dtype.byteorder == '>'):
temp_v1_data = temp_v1_data.byteswap().newbyteorder()
if (temp_v2_data.dtype.byteorder == '>'):
temp_v2_data = temp_v2_data.byteswap().newbyteorder()
# support time-varying depends
if len(temp_v1_data.shape) == 2:
temp_v1_data = np.transpose(temp_v1_data)
if len(temp_v2_data.shape) == 2:
temp_v2_data = np.transpose(temp_v2_data)
store_data(data_name, data={'x': temp_x_data, 'y': temp_y_data, 'v1': temp_v1_data, 'v2': temp_v2_data})
#If there are 4 fields, that means it is a spectrogram
# 6 fields is a spectrogram with a time varying Y axis
elif len(temp_tplot['dq'][i][1][0]) == 5 or len(temp_tplot['dq'][i][1][0]) == 6:
temp_v_data = temp_tplot['dq'][i][1][0][4]
#Change from little endian to big endian, since pandas apparently hates little endian
#We might want to move this into the store_data procedure eventually
if (temp_x_data.dtype.byteorder == '>'):
temp_x_data = temp_x_data.byteswap().newbyteorder()
if (temp_y_data.dtype.byteorder == '>'):
temp_y_data = temp_y_data.byteswap().newbyteorder()
if (temp_v_data.dtype.byteorder == '>'):
temp_v_data = temp_v_data.byteswap().newbyteorder()
# support time-varying depends
if len(temp_v_data.shape) == 2:
temp_v_data = np.transpose(temp_v_data)
store_data(data_name, data={'x':temp_x_data, 'y':temp_y_data, 'v':temp_v_data})
else:
#Change from little endian to big endian, since pandas apparently hates little endian
#We might want to move this into the store_data procedure eventually
if (temp_x_data.dtype.byteorder == '>'):
temp_x_data = temp_x_data.byteswap().newbyteorder()
if (temp_y_data.dtype.byteorder == '>'):
temp_y_data = temp_y_data.byteswap().newbyteorder()
store_data(data_name, data={'x':temp_x_data, 'y':temp_y_data})
if temp_tplot['dq'][i][3].dtype.names is not None:
for option_name in temp_tplot['dq'][i][3].dtype.names:
options(data_name, option_name, temp_tplot['dq'][i][3][option_name][0])
pytplot.data_quants[data_name].attrs['plot_options']['trange'] = temp_tplot['dq'][i][4].tolist()
pytplot.data_quants[data_name].attrs['plot_options']['create_time'] = temp_tplot['dq'][i][6]
for option_name in temp_tplot['tv'][0][0].dtype.names:
if option_name == 'TRANGE':
# x_range of [0, 0] causes tplot to create an empty figure
if temp_tplot['tv'][0][0][option_name][0][0] != 0 or temp_tplot['tv'][0][0][option_name][0][1] != 0:
tplot_options('x_range', temp_tplot['tv'][0][0][option_name][0])
if option_name == 'WSIZE':
tplot_options('wsize', temp_tplot['tv'][0][0][option_name][0])
if option_name == 'VAR_LABEL':
tplot_options('var_label', temp_tplot['tv'][0][0][option_name][0])
if 'P' in temp_tplot['tv'][0][1].tolist():
for option_name in temp_tplot['tv'][0][1]['P'][0].dtype.names:
if option_name == 'TITLE':
tplot_options('title', temp_tplot['tv'][0][1]['P'][0][option_name][0])
#temp_tplot['tv'][0][1] is all of the "settings" variables
#temp_tplot['tv'][0][1]['D'][0] is "device" options
#temp_tplot['tv'][0][1]['P'][0] is "plot" options
#temp_tplot['tv'][0][1]['X'][0] is x axis options
#temp_tplot['tv'][0][1]['Y'][0] is y axis options
####################################################################
else:
in_file = open(filename,"rb")
temp = pickle.load(in_file)
num_data_quants = temp[0]
for i in range(0, num_data_quants):
if isinstance(temp[i+1], dict):
# NRV variable
pytplot.data_quants[temp[i+1]['name']] = temp[i+1]
else:
pytplot.data_quants[temp[i+1].name] = temp[i+1]
pytplot.tplot_opt_glob = temp[num_data_quants+1]
in_file.close()
return