asifr

[{"articles": [[2010, 6], [2011, 10], [2012, 11], [2013, 23], [2006, 1]], 
"total": 51, "name": "The Journal of neuroscience"}, 
{"articles": [[2008, 1], [2010, 3], [2011, 4], [2012, 17], [2013, 10]], 
"total": 35, "name": "Nature neuroscience"}]

asifr

$IsAuth = false;
$User = array();

// connect to database
try { $db = new PDO('sqlite:'.BASEPATH.DBNAME); $db->setAttribute(PDO::ATTR_ERRMODE, PDO::ERRMODE_WARNING); } catch (PDOException $e) { die('Unable to open database. SQLite reported: '.$e->getMessage()); }

// create table if it doesn't already exist
if (count($db->query('SELECT 1 FROM sqlite_master WHERE name = \'users\' AND type=\'table\'')->fetchAll()) == 0) { $db->exec("CREATE TABLE users (id INTEGER NOT NULL,name VARCHAR(255) DEFAULT '',email VARCHAR(255) DEFAULT '',token VARCHAR(255) DEFAULT '',vars TEXT,created INTEGER,status INTEGER NOT NULL DEFAULT 0,PRIMARY KEY (id));"); }

// Set token for user and send email

asifr

asifr

import pyspark.sql.functions as F
from pyspark.sql import Window
from pyspark.sql.column import Column


def overlaps(start_first, end_first, start_second, end_second):
    return (end_first >= start_second) & (end_second >= start_first)


def eventXHrFromNow(hours: int, time_col: str, start_col: str, end_col: str) -> Column:

asifr

"""
Creates a new connection to spark and makes available: 
`spark`, `sq` (`SQLContext`), `F`, and `Window` in the global namespace.
"""
from textwrap import dedent
import findspark
import os

def _formulate_pyspark_submit_args(submit_args=None):
    pass

asifr

import numpy as np

def ffill(arr: np.ndarray):
    arr = arr.T
    mask = np.isnan(arr)
    idx = np.where(~mask, np.arange(mask.shape[1]), 0)
    np.maximum.accumulate(idx, axis=1, out=idx)
    out = arr[np.arange(idx.shape[0])[:, None], idx].T
    return out

asifr

from typing import Dict, List, Tuple, Optional
import numpy as np
import pandas as pd


def ffill(arr: np.ndarray):
    arr = arr.T
    mask = np.isnan(arr)
    idx = np.where(~mask, np.arange(mask.shape[1]), 0)
    np.maximum.accumulate(idx, axis=1, out=idx)

asifr

import numpy as np

def outlier_detect(data, threshold=1, method="IQR"):
    assert method in ["IQR", "STD", "MAD"], "Method must be one of IQR|STD|MAD"

    if method == "IQR":
        IQR = np.quantile(data, 0.75) - np.quantile(data, 0.25)
        lower = np.quantile(data, 0.25) - (IQR * threshold)
        upper = np.quantile(data, 0.75) + (IQR * threshold)
    if method == "STD":

asifr

import numpy as np

def pad_sequences(
    sequences, maxlen=None, dtype="int32", padding="pre", truncating="pre", value=0.0
):
    if not hasattr(sequences, "__len__"):
        raise ValueError("`sequences` must be iterable.")
    lengths = []
    for x in sequences:
        if not hasattr(x, "__len__"):

asifr

import numpy as np

def resample(x, t, start, end, step):
    bins = np.arange(start, end+step, step)
    inds = np.digitize(t,bins)
    n = x.shape[0]
    y = np.empty((n, len(bins))) * np.nan
    for i in range(n):
        y[i,inds[i,:]] = x[i,:]
    return y, bins