mthh

# -*- coding: utf-8 -*-
"""
@author: mthh
Convert a csv file (containing WKT geometry) into a shapefile
"""

from osr import SpatialReference
import ogr
from csv import DictReader
import sys

mthh

# -*- coding: utf-8 -*-
# The MIT License (MIT)
#
# « Copyright (c) 2015, mthh
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is

mthh

# -*- coding: utf-8 -*-
"""
@author: mthh
"""
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt

import gpd_lite_toolbox as glt

mthh

# -*- coding: utf-8 -*-
"""
@author: mthh
-------------
Example of loading node coordinates from an osrm or osrm.node file
in order to search the nearest OSRM node of an input points dataset.
"""
import csv
import sys
import numpy as np

mthh

#!/usr/bin/env Rscript
require(base)
require(methods)
require(stats)
require(pbdZMQ)
require(jsonlite)

startServer_test <- function(port){
  ctx = zmq.ctx.new()
  socket = zmq.socket(ctx, .pbd_env$ZMQ.ST$REP)

mthh

require(pbdZMQ)
require(methods)
require(stats)
require(base)

R_Worker <- function(identifiant, worker_url = 'ipc:///tmp/feeds/workers'){
  ctx = zmq.ctx.new()
  socket = zmq.socket(ctx, .pbd_env$ZMQ.ST$REQ)
  zmq.setsockopt(socket, .pbd_env$ZMQ.SO$IDENTITY, identifiant)
  zmq.connect(socket, worker_url)

mthh

# -*- coding: utf-8 -*-
"""
R client (through Rpy2) trying to be used in a background for a WebPage
providing a R interactive console.
@author: mz
"""
import threading
import time
import zmq
import sys

mthh

# -*- coding: utf-8 -*-
"""
@author: mz
"""
import threading
import time
import zmq
import sys
import os
import pickle

mthh

getHexGrid <- function(spdf, cellsize){
  boundingBox <- bbox(spdf)

  boundingBox[1,1] <- boundingBox[1,1] - cellsize
  boundingBox[2,1] <- boundingBox[2,1] - cellsize
  boundingBox[2,2] <- boundingBox[2,2] + cellsize
  cols = ceiling(((boundingBox[1,2] - boundingBox[1,1]) + 3 * cellsize) / cellsize)
  rows = ceiling((boundingBox[2,2] - boundingBox[2,1]) / cellsize)
 

mthh

# -*- coding: utf-8 -*-
"""
Some helpers functions to try some (one dimension) data classification methods.
"""
import numpy as np
from operator import ge, le
from math import floor, log10


def head_tail_break(values, direction="head"):