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import datetime
import pandas as pd
import os
import re
import subprocess
import time


### assumes you have run:
# cd path/mobility-report-data-extractor

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import pandas as pd

def data_weighted_kmeans(df, x_val, y_val, wt_val=None, k=1, centers=None, max_iters=100):
    """Revised version of the code found here, allowing the use of a data frame instead of a list of dicts.
    - https://github.com/leapingllamas/medium_posts/observation_weighted_kmeans
    
    Re-citing original code referenced by leapingllamas github above:
    - http://people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans.html
    - http://people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans_w_03.m
       

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import pandas as pd
import itertools
df = pd.DataFrame({'group': ['a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b'],
                   'date': [0, 1, 1, 1, 1, 2, 3, 4, 4],
                   'old': [1, 8, 2, 2, 5, 5, 4, 10, 7],
                   'new': [2, 5, 5, 8, 2, 4, 7, 1, 10]})
print(df)

### jumbled: the `new` value of a row is not the same as the next row's `old` value
#   group  date  old  new

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import json
import os
import re
import requests
import subprocess
import time
import urllib
import yaml

### uncommment / fill in the below

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// every second, we copy all LED values forward one pixel
EVERY_N_MILLISECONDS(1000)
{
  for(int i=NUM_LEDS-1; i>0; i--)
  {
    leds[i] = leds[i-1]
  }
  leds[0] = CRGB(0, 0, 0)
}

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unredir-if-possible = false;
opacity-rule = ["70:class_g = 'URxvt'"];
fade-in-step = 0.02;
fade-out-step = 0.02;
fade-delta = 2;
inactive-dim = 0.1;
shadow = true;
shadow-radius = 7;
shadow-offset-x = -7;
shadow-offset-y = -7;

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font pango:Hack 10

hide_edge_borders both
for_window [class="^.*"] border pixel 1
for_window [class=".*"] title_format " %title"
gaps inner 15
gaps outer 10

set $mod Mod4
floating_modifier $mod

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