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#!/usr/bin/env python

"""
2020 update:
- More iterators, fewer lists
- Python 3 compatible
- Processes files in parallel
(one thread per CPU, but that's not really how it works)
"""

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PROJ.4 string:
+proj=tmerc +lat_0=49.8333333333333 +lon_0=6.16666666666667 +k=1 +x_0=80000 +y_0=100000 +ellps=intl +towgs84=-189.6806,18.3463,-42.7695,-0.33746,-3.09264,2.53861,0.4598 +units=m +no_defs +type=crs

WKT2:2019 string:
PROJCRS["LUREF / Luxembourg TM",
    BASEGEOGCRS["LUREF",
        DATUM["Luxembourg Reference Frame",
            ELLIPSOID["International 1924",6378388,297,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,

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"""
Extract GPS coordinates and filename, output to CSV file
Run this file from the same directory the images are in
run using ./process_exif.py or python process_exif.py, or
%run process_exif.py from an IPython instance

Ensure you have PIL installed
refer to http://www.exiv2.org/tags.html for a full detailed tag listing

This is what the GPSInfo dict looks like for an iPhone 5 jpg:

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Output Elevation, Lat, Long, and Timestamp series from GPX to CSV
Requires gpxpy
This script expects your input GPX to be located in a subdir named 'data'

"""

import os

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from statsmodels.stats.outliers_influence import summary_table
from statsmodels.sandbox.regression.predstd import wls_prediction_std

# carry out yr fit

# ols cinv
st, data, ss2 = summary_table(ols_fit, alpha=0.05)

fittedvalues = data[:,2]
predict_mean_se  = data[:,3]

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created by Stephan Hügel on 2017-03-02
The MIT License (MIT)
Copyright (c) 2017 Stephan Hügel
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

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import math


def calculate_polygons(startx, starty, endx, endy, radius):
    """ 
    Calculate a grid of hexagon coordinates of the given radius
    given lower-left and upper-right coordinates 
    Returns a list of lists containing 6 tuples of x, y point coordinates
    These can be used to construct valid regular hexagonal polygons
    

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# sample invocation:
# pdftotext -f 5 /tmp/The_Complete_Works_of_H.P._Lovecraft.pdf - | python3 horror.py | uniq > lovecraft_word_pairs_sorted.txt

import re
import sys
import nltk
import wordfreq

re_word = re.compile(r'[^\-\w]')

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Adapted from https://www.quora.com/How-do-you-compute-the-distance-between-two-non-convex-polygons-in-linear-time/answer/Tom-Dreyfus

Calculating the Minimum Distance Between Two Non-Convex Polygons

See Amato, Nancy M (1994) for details of the difference between separation (sigma: σ) and closest visible vertex (CVV).

Refer to P and Q as the two polygons with n and m vertices, respectively.
For the purposes of this discussion, a key insight is that it is enough to find the closest edge to each vertex in order to compute the minimum separation between P and Q.
This means iterating over all vertices, and finding a nearest neighbour. Thus, a time complexity in O((m + n) * log(m * n)) should be expected.

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/// Return evenly-spaced samples from an array using [Bresenham's line algorithm](https://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html)
/// Adapted from https://observablehq.com/@mbostock/evenly-spaced-sampling
///
/// # Explanation
/// This works because the problem is equivalent to drawing a line on a `num_samples` x `arr` grid of
/// discrete pixels:
/// `x` coordinates are in the range `0…arr - 1` and `y` coordinates are in the range `0…num_samples - 1`
/// We then proceed as if we were drawing a line from the origin to `arr - 1, num_samples - 1`:
/// Whenever the `y` coordinate changes we choose a new element from `x`.
///