Dapid

from __future__ import division

import keras
import keras.backend as K
from keras.models import Model, load_model
from keras.layers import Input, Dropout, BatchNormalization
from keras.layers import Conv2D, Conv1D
from keras.layers.core import Lambda
from keras.regularizers import l2
from keras.layers.advanced_activations import ELU

Dapid

import keras.backend as K
from keras.models import Model, load_model
from keras.layers import Input, BatchNormalization
from keras.layers import Conv1D


def add_1D_conv(model, filters, kernel_size):
    model = Conv1D(filters, kernel_size)(model)
    model = BatchNormalization()(model)
    return model

Dapid

$ pip install pygaussdca
Collecting pygaussdca
  Using cached https://files.pythonhosted.org/packages/f9/e5/5df8d0224318f5811c64e026ae4cd5af37288238a0b259186983e55be19c/pyGaussDCA-0.1.2.tar.gz
Requirement already satisfied: numpy in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pygaussdca) (1.16.0)
Requirement already satisfied: scipy in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pygaussdca) (1.2.0)
Requirement already satisfied: pythran>=0.8.6 in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pygaussdca) (0.9.1)
Requirement already satisfied: ply>=3.4 in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pythran>=0.8.6->pygaussdca) (3.11)
Requirement already satisfied: networkx>=2 in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pythran>=0.8.6->pygaussdca) (2.2)                                                
Requirement already satisfied: gast in /home/david/.virtualenv/py36/lib/python3.6/site-packages (from pythran>=0.8.6->py

Dapid

dimension,indicator_id,indicator_name,iso3,country_name,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,9999
Composite indices,146206,HDI rank,AFG,Afghanistan,,,,,,,,,,,,,,,,,,,,,,,,,,,168,168,
Composite indices,146206,HDI rank,ALB,Albania,,,,,,,,,,,,,,,,,,,,,,,,,,,69,68,
Composite indices,146206,HDI rank,DZA,Algeria,,,,,,,,,,,,,,,,,,,,,,,,,,,83,85,
Composite indices,146206,HDI rank,AND,Andorra,,,,,,,,,,,,,,,,,,,,,,,,,,,35,35,
Composite indices,146206,HDI rank,AGO,Angola,,,,,,,,,,,,,,,,,,,,,,,,,,,145,147,
Composite indices,146206,HDI rank,ATG,Antigua and Barbuda,,,,,,,,,,,,,,,,,,,,,,,,,,,70,70,
Composite indices,146206,HDI rank,ARG,Argentina,,,,,,,,,,,,,,,,,,,,,,,,,,,47,47,
Composite indices,146206,HDI rank,ARM,Armenia,,,,,,,,,,,,,,,,,,,,,,,,,,,84,83,
Composite indices,146206,HDI rank,AUS,Australia,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,

Dapid

import numpy as np
from scipy import stats

import pylab as plt


def compare(x, y, keys, name):
    keys_ = set(keys).intersection(x).intersection(y)
    print(set(keys) - keys_)
    keys = set(keys).intersection(x).intersection(y)

Dapid

import random
import tqdm
import numpy as np
import pylab as plt
def trial():
  coins = []
  for _ in range(10000):
    coin = random.randint(0, 1)
    # Monday
    coins.append(coin)

Dapid

import time

import numpy

import jax.numpy as np
from jax import random, grad, jit
from jax import vmap


def _compute_single_loss(h, J, sigma, N, lambda_h, lambda_j):

Dapid

import os

# os.environ['CUDA_VISIBLE_DEVICES'] = '1'

import time

import numpy

import jax.numpy as np
from jax import random, grad, jit

Dapid

import numpy as np
import netCDF4 as nc
from scipy import stats

import pylab as plt
import seaborn as sns

rootgrp = nc.Dataset('HadCRUT.4.6.0.0.median.nc')  

t = rootgrp['time'][:] / 365 + 1850

Dapid

\version "2.19.83"
\language "espanol"

\header {
  title = "Amazing Grace"
  composer = "Trad. Scottish"
  %tagline = ""
}