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import numpy as np
import matplotlib
import scipy 
from scipy.optimize import curve_fit
#matplotlib,pgetu,emcee,astropy,warnings
#from scipy import ndimage,optimize; from scipy.optimize import fmin
import matplotlib.pyplot as plt
import sys
import datetime
import matplotlib.dates as mdates

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def match(path, expr):
    names = expr.split('.')[1:]
    curr_name = 0
    curr_path = 0
    wildpath = False
    
    while curr_path < len(path):
        
        el = path[curr_path]
        

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# See below for algorithm details
# https://web.archive.org/web/20160418004149/http://freespace.virgin.net/hugo.elias/graphics/x_water.htm

// GRID_MAX refers to the maximum position of an LED via map_led_to_point
#define LED_GRID_MAX_Y 65
#define LED_GRID_MAX_X 255

// Number to shift right by to get to ripple grid
#define GRID_COARSE 4

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# Import a library of functions called 'pygame'
import pygame
import numpy as np

# Width and height of display
WIDTH = 255>>0
HEIGHT = 65>>0

# Downscale to ripple grid
SHFT = 2

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<json> ::= <primitive> | <container>

<primitive> ::= <number> | <string> | <boolean>
; Where:
; <number> is a valid real number expressed in one of a number of given formats
; <string> is a string of valid characters enclosed in quotes
; <boolean> is one of the literal strings 'true', 'false', or 'null' (unquoted)

<container> ::= <object> | <array>
<array> ::= '[' [ <json> *(', ' <json>) ] ']' ; A sequence of JSON values separated by commas

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========================================
A simple solution
========================================

How To Make Your Own VR Headset

First, let's start with the basics. It's not a bad idea to make your own headset. The best way to make it is to buy an Oculus Rift.


========================================

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import numpy as np
import tensorflow as tf

"""
# Generate some random data
# data   = n x m,  n = num examples, m = number features
# labels = n x c,  n = num examples, c = number classes
"""

KARPATHY_CONST = 0.00025

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;

[ExecuteInEditMode]
public class DynBoneCopy : MonoBehaviour {

    public DynamicBone template;
    public bool Make_Permenant;

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def combine_weights(in_list):
    """
    Returns a 1D tensor of the input list of (nested lists of) tensors, useful
    for doing things like comparing current weights with old weights for EWC.
    
    1.) For all elements in input list, (ln 3)
          if a list combine it recursively 
          else leave it alone
    2.) From resulting list, get all non-none elements and flatten them (ln 2)
    3.) If resulting list is empty return None (ln 1)

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from __future__ import division
import numpy as np
import tensorflow as tf

# Launch the graph
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:

    NUM_EXAMPLES = 8096