ipashchenko

import pandas as pd
import numpy as np

# Reading file with circular elliptic cores
df = pd.read_csv("df_result_cg.csv")
df["dr_ratio"] = df["dr_obs"]/df["dr_true"]
df["phi_app"] = np.arctan(np.tan(df["fi"])/np.sin(df["theta"]))

# These are optional!
# This chooses only sources with models with equal number of components

ipashchenko

import numpy as np
from scipy.integrate import quad
from astropy.cosmology import LambdaCDM

# cm in pc
pc = 3.086 * 1E+18


def beta(Gamma):
    """

ipashchenko

import numpy as np
import george
import emcee
import pickle
from functools import partial
import scipy.optimize as op
import matplotlib.pyplot as plt
from tqdm import tqdm

ipashchenko

import pickle
import numpy as np
from scipy.optimize import minimize
import celerite
from celerite import terms
import emcee
import matplotlib.pyplot as plt

# Ilya: We only need x, y, yerr 1D arrays. Substitute your's
with open("0716+714_umrao_lc.pkl", "rb") as fo:

ipashchenko

import numpy as np
import george
import pickle
from functools import partial
import scipy.optimize as op
import matplotlib.pyplot as plt


def is_embraced(curve, means, widths):
    """

ipashchenko

import pickle
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt


def fit_robustly(t, y, yerr, draws=500, chains=None, njobs=4, tune=500,
                 n_new=None, t_new=None, show_traceplot=False,
                 show_fitted_GP=True, file_fig=None, progressbar=True):
    """

ipashchenko

#!/bin/sh

#
# Build GCC with support for offloading to NVIDIA GPUs.
#

work_dir=$HOME/offload/wrk
install_dir=$HOME/offload/install

# Location of the installed CUDA toolkit

ipashchenko

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

ipashchenko

import matplotlib.pyplot as plt
import numpy as np
import seaborn

from keras.layers import Input, Dense, merge, ELU, Dropout
from keras.models import Model
from keras.regularizers import l2
from keras import backend as K
from keras.optimizers import rmsprop, adam

ipashchenko

import numpy as np
from scipy.stats import gaussian_kde


def get_cdf_of_difference(data_1, data_2, diff_value=0):
    data_1 = np.asarray(data_1)
    data_2 = np.asarray(data_2)
    kde_1 = gaussian_kde(data_1)
    data_1.sort()
    data_2.sort()