-
Notifications
You must be signed in to change notification settings - Fork 0
/
sample_gauss_generator.py
46 lines (37 loc) · 1.07 KB
/
sample_gauss_generator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 17 18:00:59 2020
@author: marti
"""
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
#import tensorflow_addons as tfa #AdamW
import tensorflow_probability as tfp#normal dist
from copy import deepcopy
import sys
import matplotlib.pyplot as plt
import logging
from datetime import datetime
import numpy as np
import pandas as pd
from normal_dist_calculator import generate_tensor_mixture_model
from Reparameterizer import reparameterizer, normalize_profiles,renormalize_profiles
import pickle
import argparse
import EinastoSim
np.random.seed(42)
tf.random.set_seed(42)
plt.close("all")
num_profile_train = 1
kg = 1
r = np.linspace(0,1000,101)
rs = np.asarray([r for i in range(num_profile_train)])
gaussians, parameters,constituents_train = EinastoSim.generate_n_k_gaussian_parameters(rs,num_profile_train,kg)
plt.figure()
plt.plot(r,gaussians[0])
param = parameters[0]
plt.title("{},{}".format(param[0],param[1]))
for const in constituents_train[0]:
plt.plot(r,const)