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"""
Marshmallow wrappers to produce GeoJSON instead of a flat dictionary.
"""
from flask_marshmallow import Marshmallow
import geoalchemy2
from geoalchemy2.shape import from_shape, to_shape
from marshmallow import fields, pre_load, post_dump, ValidationError
import marshmallow_sqlalchemy as msqla
from shapely import geometry

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"""
Simple app that generates a podcasst XML feed for Radicalized

To be fair this could be easily adapted to any audiobook / set of NP3s - you'd
just need to change the image, the path to the music and all the descriptions.

Requires:

- feedgen
- flask

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<?xml version="1.0" encoding="UTF-8"?>
<inkscape-extension xmlns="http://www.inkscape.org/namespace/inkscape/extension">
	<_name>Source Code Text</_name>
	<id>org.henry.poster_text</id>
	<dependency type="executable" location="extensions">poster_text.py</dependency>
	<dependency type="executable" location="extensions">inkex.py</dependency>
	<param name="tab" type="notebook">
        <page name="Options" _gui-text="Options">
              	<param name="directory" type="string" _gui-text="Directory to search for code:">~/</param>
                <param name="pattern" type="string" _gui-text="Filename pattern to match:">py</param>

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.properties as $propertiess | {
    "type": "FeatureCollection",
    "features": [

        (.geometries[] |
         {
             "type": "Feature",
             "properties": (.properties + $propertiess),
             "geometry": del(.properties)
         }

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mkdir -p KML

for resolution in $(seq 1 3); do
    h3ToHier ${resolution} | h3ToGeoBoundary 1 res${resolution}cells.kml "Res ${resolution} Cells" > KML/res${resolution}cells.kml
    h3ToHier ${resolution} | h3ToGeo 1 res${resolution}centers.kml "Res ${resolution} Centers" > KML/res${resolution}centers.kml
done

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import arcpy
import pandas as pd


def build_df_from_arcpy(table, index_col=None):
    """
    Build a pandas dataframe from an ArcGIS Feature Class.
    
    Uses the arcpy Search Cursor to loop over a feature class in pandas and
    generate a pandas dataframe. If the dataset is a feature class with a

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# coding: utf-8
from __future__ import print_function

from datetime import date, timedelta, datetime
import json
from time import sleep

from twython import Twython, TwythonRateLimitError

APP_KEY = 'YOUR_APP_KEY'

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