geogradient

def create_pin_files(data_frame, outdir = '/outdir/path/only/' ):
    """  
    Batch process the creation of BEAM-VISAT (http://www.brockmann-consult.de/cms/web/beam/) pin files. 
    Outputs a "pin_sampling_date_batched.placemark" file.
    Uses pandas data_frame as input. 
    Expected column names in the input data frame are:
        ["IS_DATE"] YYYYMMDD format
        ["SITE"]  Name of the Placemark, ie. station, sampling site or Cast ID
        ["Latitude"]  Latitude of Placemark in decimal degrees using WGS84 datum
        ["Longitude"] Longitude of Placemark in decimal degrees using WGS84 datum

geogradient

#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "[José M. Beltrán](<beltran.data@gmail.com>)"
__credits__ = ["José M. Beltrán"]
__license__ = "GPL-3.0"

"""
The json file has the following scheme:

{"20020614":{"KEY1":{"KEY1.1":"path_to_file_01", ...},

urschrei

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from descartes import PolygonPatch
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape

# We can extract the London Borough boundaries by filtering on the AREA_CODE key
mp = MultiPolygon(
    [shape(pol['geometry']) for pol in fiona.open('data/boroughs/boroughs.shp')
    if pol['properties']['AREA_CODE'] == 'LBO'])

vgoklani

# dropping a database via pymongo
from pymongo import Connection
c = Connection()
c.drop_database('mydatabase')

# drop a collection via pymongo
from pymongo import Connection
c = Connection()
c['mydatabase'].drop_collection('mycollection')