end_range = n * leaf_size + start_range + 1
pixel_range = np.arange(start_range, end_range, leaf_size)
sample_range_x = np.round(grid_x, 1)
sample_range_y = np.round(grid_y, 1)
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure, cmap='Greys_r')
plt.savefig(filename)
plt.show()
######## Code ########
# Set up the data given to us
train_list, test_list, train_ids, test_ids, train, test, y, y_train, classes = data_setup.data(
)
# fit_transform() calculates the mean and std and also centers and scales data
x_train = StandardScaler().fit_transform(train)
x_test = StandardScaler().fit_transform(test)
# We need to reshape our images so they are all the same dimensions
train_mod_list = data_setup.reshape_img(train_list, global_max_dim)
test_mod_list = data_setup.reshape_img(test_list, global_max_dim)
# Grab the dimensions we are using for our images
image_size = global_max_dim
# Find the flattened size
original_dim = image_size * image_size
# Set up our validation set (10% of data)
import model
from matplotlib import pyplot as plt
import cornerplot
import time
import json
import os
import csv
start = time.time()
n_params = len(parameters)
output_directory = 'out/'
os.makedirs(os.path.dirname(output_directory), exist_ok=True)
x, x_full, opacity_grid, bin_indices, ydata, yerr, wavelength_centre, wavelength_err = data_setup.data(
)
len_x = len(x)
## Run PyMultinest ##
b = ns_setup.Priors(1, n_params)
pymultinest.run(
b.loglike,
b.prior,
n_params,
loglike_args=[len_x, x_full, bin_indices, opacity_grid, ydata, yerr],
outputfiles_basename=output_directory + planet_name + '_',
resume=False,
verbose=True,
n_live_points=live)
json.dump(parameters, open(output_directory + planet_name + '_params.json',
import model
from matplotlib import pyplot as plt
import cornerplot
import time
import json
import os
import csv
start = time.time()
n_params = len(parameters)
output_directory = '/home/aline/Desktop/PhD/HELIOS-T-master/out/'
os.makedirs(os.path.dirname(output_directory), exist_ok=True)
x, x_full, integral_grid, bin_indices, ydata, yerr, wavelength_centre, wavelength_err = data_setup.data(
)
len_x = len(x)
## Run PyMultinest ##
b = ns_setup.Priors(1, n_params)
pymultinest.run(
b.loglike,
b.prior,
n_params,
loglike_args=[len_x, x_full, bin_indices, integral_grid, ydata, yerr],
outputfiles_basename=output_directory + planet_name + '_',
resume=False,
verbose=True,
n_live_points=live)
json.dump(parameters, open(output_directory + planet_name + '_params.json',
