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import collections
import dataclasses
import itertools
import typing as tp
from functools import partial

import numpy as np
from scipy.optimize import minimize

DEFAULT_CI_SIZE = 0.95

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import numpy as np
from pathlib import Path
import h5py


def resolve_name(f, name_ref):
    return ''.join([chr(i) for i in f[name_ref]])


def flat(x):

N-McA

import numpy as np

import theano.tensor as T
import pymc3 as pm

from utils import add_params_property

import ranking
from ranking import tennis_data, MPTrueSkill1V1NoDrawRanker

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def tf_pca(x):
    '''
        Compute PCA on the bottom two dimensions of x,
        eg assuming dims = [..., observations, features]
    '''
    # Center
    x -= tf.reduce_mean(x, -2, keepdims=True)

    # Currently, the GPU implementation of SVD is awful.
    # It is slower than moving data back to CPU to SVD there

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import keras.backend as kb
from keras.layers import Layer

def _kb_linspace(num):
    num = kb.cast(num, kb.floatx())
    return kb.arange(0, num, dtype=kb.floatx()) / (num - 1)

def _kb_grid_coords(width, height):
    w, h = width, height
        

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#!/usr/bin/env python3

import subprocess
import shlex
import os
from pathlib import Path


def chdir_to_script_location():
    abspath = os.path.abspath(__file__)

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#!/usr/bin/env python3

import subprocess
import shlex
import os
from pathlib import Path


def chdir_to_script_location():
    abspath = os.path.abspath(__file__)

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#!/usr/bin/env python3

import subprocess
import shlex
import os
from pathlib import Path


def chdir_to_script_location():
    abspath = os.path.abspath(__file__)

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function isString(s) {
  return (typeof s === 'string' || s instanceof String)
}

export function toBaseUnit(value, decimals, BN) {
  if (!isString(value)) {
    throw new Error('Pass strings to prevent floating point precision issues.')
  }
  const ten = new BN(10);
  const base = ten.pow(new BN(decimals));

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def get_perceptual_axis_center_in_figure_space(fig, ax):
    
    xs = ax.get_xlim()
    if ax.get_xscale() == 'log':
        xs = np.log10(xs)
        c_x = sum(xs) / 2
        c_x = 10**c_x
    else:
        c_x = sum(xs) / 2