philastrophist

from __future__ import division
import pymc3 as pm
import numpy as np
from theano import tensor as T
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import seaborn as sns

philastrophist

from functools import partial
from itertools import cycle

import numpy as np
from astroML.density_estimation import XDGMM
import pytest
from matplotlib.patches import Ellipse


def no_selection(x):

philastrophist

#! encoding=<utf8>
from __future__ import division
import numpy as np
from fastkde import fastKDE
from warnings import warn

__all__ = ['find_mode']


def make_indices(dimensions):

philastrophist

import os
import sys

import kali.carma

from kali.kepler import keplerLC
import math as math
import numpy as np
import matplotlib
matplotlib.rcParams['text.usetex'] = False

philastrophist

Fetch a Tree with Neo4j

Today I came across a really interesting StackOverflow question:

Given a forest of trees in a Neo4j REST server, I`m trying to return a single tree given the root vertex. Being each tree quite large, I need a de-duplicated list of all vertices and edges in order to be able to reconstruct the full tree on the client side.

philastrophist

import emcee
from sklearn.mixture import GaussianMixture
import vegas
from astropy.cosmology import Planck15
import numpy as np
from scipy import stats
from astropy import units as u
import matplotlib.pyplot as plt

jy_factor = (u.W / u.Hz / u.m / u.m).to(u.Jy)

philastrophist

def asymmetric_fractional_error_bars(n, total, mass=0.68, centre='peak'):
    """
    returns the error bar for the fraction $x = n/total$ ($f^{+upper}_{-lower}$)
    Use `mass` to specify the errorbar width
    n: number of successes
    total: total number of trials
    :mass: the desired probability mass contained with the error bar
            default=68%, which is ~equivalent to 1 sigma 
    :centre: return either the 'mean', 'median', or most likely of the probability distribution.
             Probably you want 'peak' which is equivalent to n / total