sca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "from scipy.signal import find_peaks\n",
    "from skimage import io \n",
    "import rasterio\n",
    "import subprocess as sp\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allocate date, snow depth, sca, and % basin space\n",
    "dt     = []\n",
    "sd_lst = np.array([])\n",
    "sc_lst = np.array([])\n",
    "pb_lst = np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130403_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130429_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130503_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130525_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130601_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20130608_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140323_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140407_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140413_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140420_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140428_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140502_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140511_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140517_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140527_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140531_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20140605_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150218_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150306_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150325_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150403_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150409_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150415_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150427_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150501_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150528_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20150608_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160326_clipped.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/meganmason/Documents/projects/thesis/analysis/virtual_envs/data_analysis/lib/python3.6/site-packages/ipykernel_launcher.py:31: RuntimeWarning: invalid value encountered in greater\n",
      "/home/meganmason/Documents/projects/thesis/analysis/virtual_envs/data_analysis/lib/python3.6/site-packages/ipykernel_launcher.py:50: RuntimeWarning: invalid value encountered in greater\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160401_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160407_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160416_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160426_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160509_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160527_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160607_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160613_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160620_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160625_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160701_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20160708_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170129_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170303_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170401_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170502_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170604_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170709_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170717_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170727_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170816_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20180423_clipped.tif\n",
      "/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20180528_clipped.tif\n"
     ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meanSD (m)</th>\n",
       "      <th>SCA (km)</th>\n",
       "      <th>% basin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-04-03</th>\n",
       "      <td>1.456748</td>\n",
       "      <td>590.093682</td>\n",
       "      <td>50.007939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-29</th>\n",
       "      <td>1.146422</td>\n",
       "      <td>452.968977</td>\n",
       "      <td>38.387201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-03</th>\n",
       "      <td>1.069508</td>\n",
       "      <td>389.105030</td>\n",
       "      <td>32.975003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-25</th>\n",
       "      <td>0.946180</td>\n",
       "      <td>206.605858</td>\n",
       "      <td>17.508971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>0.945787</td>\n",
       "      <td>155.446059</td>\n",
       "      <td>13.173395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-08</th>\n",
       "      <td>0.845570</td>\n",
       "      <td>94.211339</td>\n",
       "      <td>7.984012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-23</th>\n",
       "      <td>1.065971</td>\n",
       "      <td>585.332593</td>\n",
       "      <td>49.604457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-07</th>\n",
       "      <td>1.221284</td>\n",
       "      <td>704.831855</td>\n",
       "      <td>59.731513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-13</th>\n",
       "      <td>1.106676</td>\n",
       "      <td>623.311542</td>\n",
       "      <td>52.823012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-20</th>\n",
       "      <td>1.152036</td>\n",
       "      <td>518.800971</td>\n",
       "      <td>43.966184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-28</th>\n",
       "      <td>0.994763</td>\n",
       "      <td>639.850003</td>\n",
       "      <td>54.224577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-02</th>\n",
       "      <td>0.967446</td>\n",
       "      <td>476.591725</td>\n",
       "      <td>40.389129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-11</th>\n",
       "      <td>0.925490</td>\n",
       "      <td>389.631115</td>\n",
       "      <td>33.019586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-17</th>\n",
       "      <td>0.881488</td>\n",
       "      <td>275.969483</td>\n",
       "      <td>23.387244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-27</th>\n",
       "      <td>0.761768</td>\n",
       "      <td>216.909518</td>\n",
       "      <td>18.382163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>0.723393</td>\n",
       "      <td>158.054074</td>\n",
       "      <td>13.394413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-05</th>\n",
       "      <td>0.693358</td>\n",
       "      <td>105.551626</td>\n",
       "      <td>8.945053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-18</th>\n",
       "      <td>0.619591</td>\n",
       "      <td>516.220585</td>\n",
       "      <td>43.747507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-06</th>\n",
       "      <td>0.669872</td>\n",
       "      <td>606.101874</td>\n",
       "      <td>51.364566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-25</th>\n",
       "      <td>0.691449</td>\n",
       "      <td>373.482382</td>\n",
       "      <td>31.651049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-03</th>\n",
       "      <td>0.667585</td>\n",
       "      <td>265.298424</td>\n",
       "      <td>22.482917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-09</th>\n",
       "      <td>0.604441</td>\n",
       "      <td>753.736973</td>\n",
       "      <td>63.876015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-15</th>\n",
       "      <td>0.768686</td>\n",
       "      <td>332.946879</td>\n",
       "      <td>28.215837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-27</th>\n",
       "      <td>0.614702</td>\n",
       "      <td>507.650463</td>\n",
       "      <td>43.021226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.693789</td>\n",
       "      <td>201.420113</td>\n",
       "      <td>17.069501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-28</th>\n",
       "      <td>0.652949</td>\n",
       "      <td>172.888436</td>\n",
       "      <td>14.651562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>0.631837</td>\n",
       "      <td>63.015776</td>\n",
       "      <td>5.340320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-26</th>\n",
       "      <td>1.885713</td>\n",
       "      <td>926.173374</td>\n",
       "      <td>78.489269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>1.930678</td>\n",
       "      <td>813.252518</td>\n",
       "      <td>68.919705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-07</th>\n",
       "      <td>1.703818</td>\n",
       "      <td>899.767826</td>\n",
       "      <td>76.251511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-16</th>\n",
       "      <td>1.658462</td>\n",
       "      <td>860.515014</td>\n",
       "      <td>72.925001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-26</th>\n",
       "      <td>1.618644</td>\n",
       "      <td>835.733350</td>\n",
       "      <td>70.824860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>1.561280</td>\n",
       "      <td>779.881877</td>\n",
       "      <td>66.091685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-27</th>\n",
       "      <td>1.261186</td>\n",
       "      <td>550.090582</td>\n",
       "      <td>46.617846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>1.075456</td>\n",
       "      <td>357.636089</td>\n",
       "      <td>30.308143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-13</th>\n",
       "      <td>0.916325</td>\n",
       "      <td>297.232449</td>\n",
       "      <td>25.189191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-20</th>\n",
       "      <td>0.952675</td>\n",
       "      <td>192.935948</td>\n",
       "      <td>16.350504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-25</th>\n",
       "      <td>0.991579</td>\n",
       "      <td>97.654277</td>\n",
       "      <td>8.275786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.958851</td>\n",
       "      <td>62.277962</td>\n",
       "      <td>5.277793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-08</th>\n",
       "      <td>1.070701</td>\n",
       "      <td>31.427056</td>\n",
       "      <td>2.663310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-29</th>\n",
       "      <td>2.796001</td>\n",
       "      <td>999.775342</td>\n",
       "      <td>84.726724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>3.560972</td>\n",
       "      <td>1021.127406</td>\n",
       "      <td>86.536221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>3.271415</td>\n",
       "      <td>996.477679</td>\n",
       "      <td>84.447261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-02</th>\n",
       "      <td>3.066384</td>\n",
       "      <td>934.034652</td>\n",
       "      <td>79.155479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-04</th>\n",
       "      <td>2.396974</td>\n",
       "      <td>748.833300</td>\n",
       "      <td>63.460449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09</th>\n",
       "      <td>1.566113</td>\n",
       "      <td>236.796563</td>\n",
       "      <td>20.067505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>1.537321</td>\n",
       "      <td>147.905454</td>\n",
       "      <td>12.534361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-27</th>\n",
       "      <td>1.633154</td>\n",
       "      <td>76.112218</td>\n",
       "      <td>6.450188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>1.794257</td>\n",
       "      <td>25.155994</td>\n",
       "      <td>2.131864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23</th>\n",
       "      <td>1.297332</td>\n",
       "      <td>873.222852</td>\n",
       "      <td>74.001937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-28</th>\n",
       "      <td>1.092142</td>\n",
       "      <td>290.248313</td>\n",
       "      <td>24.597315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            meanSD (m)     SCA (km)    % basin\n",
       "2013-04-03    1.456748   590.093682  50.007939\n",
       "2013-04-29    1.146422   452.968977  38.387201\n",
       "2013-05-03    1.069508   389.105030  32.975003\n",
       "2013-05-25    0.946180   206.605858  17.508971\n",
       "2013-06-01    0.945787   155.446059  13.173395\n",
       "2013-06-08    0.845570    94.211339   7.984012\n",
       "2014-03-23    1.065971   585.332593  49.604457\n",
       "2014-04-07    1.221284   704.831855  59.731513\n",
       "2014-04-13    1.106676   623.311542  52.823012\n",
       "2014-04-20    1.152036   518.800971  43.966184\n",
       "2014-04-28    0.994763   639.850003  54.224577\n",
       "2014-05-02    0.967446   476.591725  40.389129\n",
       "2014-05-11    0.925490   389.631115  33.019586\n",
       "2014-05-17    0.881488   275.969483  23.387244\n",
       "2014-05-27    0.761768   216.909518  18.382163\n",
       "2014-05-31    0.723393   158.054074  13.394413\n",
       "2014-06-05    0.693358   105.551626   8.945053\n",
       "2015-02-18    0.619591   516.220585  43.747507\n",
       "2015-03-06    0.669872   606.101874  51.364566\n",
       "2015-03-25    0.691449   373.482382  31.651049\n",
       "2015-04-03    0.667585   265.298424  22.482917\n",
       "2015-04-09    0.604441   753.736973  63.876015\n",
       "2015-04-15    0.768686   332.946879  28.215837\n",
       "2015-04-27    0.614702   507.650463  43.021226\n",
       "2015-05-01    0.693789   201.420113  17.069501\n",
       "2015-05-28    0.652949   172.888436  14.651562\n",
       "2015-06-08    0.631837    63.015776   5.340320\n",
       "2016-03-26    1.885713   926.173374  78.489269\n",
       "2016-04-01    1.930678   813.252518  68.919705\n",
       "2016-04-07    1.703818   899.767826  76.251511\n",
       "2016-04-16    1.658462   860.515014  72.925001\n",
       "2016-04-26    1.618644   835.733350  70.824860\n",
       "2016-05-09    1.561280   779.881877  66.091685\n",
       "2016-05-27    1.261186   550.090582  46.617846\n",
       "2016-06-07    1.075456   357.636089  30.308143\n",
       "2016-06-13    0.916325   297.232449  25.189191\n",
       "2016-06-20    0.952675   192.935948  16.350504\n",
       "2016-06-25    0.991579    97.654277   8.275786\n",
       "2016-07-01    0.958851    62.277962   5.277793\n",
       "2016-07-08    1.070701    31.427056   2.663310\n",
       "2017-01-29    2.796001   999.775342  84.726724\n",
       "2017-03-03    3.560972  1021.127406  86.536221\n",
       "2017-04-01    3.271415   996.477679  84.447261\n",
       "2017-05-02    3.066384   934.034652  79.155479\n",
       "2017-06-04    2.396974   748.833300  63.460449\n",
       "2017-07-09    1.566113   236.796563  20.067505\n",
       "2017-07-17    1.537321   147.905454  12.534361\n",
       "2017-07-27    1.633154    76.112218   6.450188\n",
       "2017-08-16    1.794257    25.155994   2.131864\n",
       "2018-04-23    1.297332   873.222852  74.001937\n",
       "2018-05-28    1.092142   290.248313  24.597315"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/'\n",
    "# path = '/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/batch_test'\n",
    "#path = '/home/meganmason/Documents/projects/thesis/data/processing_lidar/depths_3m/clipped/20170401_clipped.tif'\n",
    "\n",
    "\n",
    "for f in sorted(glob.glob(os.path.join(path, '*.tif'))):\n",
    "    print(f)\n",
    "    dt_str = f.split('/')[-1]\n",
    "\n",
    "    for c in dt_str:\n",
    "        dt_str = \"\".join([c for c in dt_str if c.isnumeric()])\n",
    "        \n",
    "                        #         for c in dt_str:\n",
    "                        #             if c.is_numeric():\n",
    "                        #                 dtst.append(c)\n",
    "                        #         d_str = \"\".join(dst)   \n",
    "\n",
    "    raster = io.imread(f) #path\n",
    "\n",
    "#     io.imshow(raster[:,:,0]) #snow depth, band1\n",
    "#     io.show()\n",
    "\n",
    "#     io.imshow(raster[:,:,1]) #alpha mask, band2\n",
    "#     io.show()\n",
    "\n",
    "    # convert to numpy array\n",
    "    array = np.array(raster[:,:,0]) #RAW array of lidar tif\n",
    "\n",
    "    # \"snow mask\", array of data and nans\n",
    "    sno_mask = array.copy()\n",
    "    idx=sno_mask > 0.001\n",
    "    sno_mask[~idx] = np.nan\n",
    "    \n",
    "#     plt.imshow(sno_mask) \n",
    "#     plt.title('mask')\n",
    "#     plt.colorbar()\n",
    "#     plt.show()\n",
    "\n",
    "    # meanSD -- caclulate mean snow depth (meanSD), without zero snow pixels\n",
    "    mu_sd = np.nanmean(sno_mask) #mean snow depth, from sca's\n",
    "#     print(\"mean snow depth\", mu_sd,'meters')\n",
    "\n",
    "\n",
    "    # SCA -- pixs snow cover * pixel size    \n",
    "    ras =  rasterio.open(f) #getting pixel size\n",
    "    gt = ras.transform\n",
    "    pixelSizeX = gt[0]\n",
    "    pixelSizeY =-gt[4] #notice the -gt\n",
    "\n",
    "    sca_km = np.sum(array>0) * pixelSizeX * pixelSizeY /1000**2 #in km\n",
    "#     print('Snow Covered Area (SCA) = ', sca_km, 'km^2')\n",
    "#     sca = np.sum(array>0) * pixelSizeX * pixelSizeY\n",
    "\n",
    "    # percent basin coverage\n",
    "    p_basin = sca_km/1180 *100  #per Hedrick et al. 2018\n",
    "#     print('% Basin snow cover', p_basin, '%')\n",
    "\n",
    "\n",
    "    dt.append(dt_str) #appends date str to list\n",
    "    \n",
    "    sd_lst = np.append(sd_lst,mu_sd)\n",
    "    sc_lst = np.append(sc_lst,sca_km)\n",
    "    pb_lst = np.append(pb_lst,p_basin)\n",
    "    \n",
    "\n",
    "\n",
    "dt = pd.to_datetime(dt) #convert to datetime\n",
    "\n",
    "# create pandas DateFrame\n",
    "df1 = pd.DataFrame(zip(sd_lst,sc_lst,pb_lst), columns = ['meanSD (m)','SCA (km)','% basin'],index = dt)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot DataFrame results (ONE PLOT, SNOW DEPTH)\n",
    "years = sorted(set([dt.year for dt in df1.index])) #set find uniques in list comp (loop that appends for you)\n",
    "\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "for year in years:\n",
    "    # year by year snow depth\n",
    "    startTime = pd.to_datetime(\"10-01-{}\".format(year-1))\n",
    "    endTime = pd.to_datetime(\"09-30-{}\".format(year))\n",
    "    \n",
    "    # annual subset of DataFrame\n",
    "    data = df1[(df1.index >= startTime) & (df1.index <= endTime)] #option, .loc\n",
    "#     print('this is data',data['meanSD (m)'].idxmax())\n",
    "\n",
    "    # find annual peak\n",
    "    peak = data['meanSD (m)'].idxmax()\n",
    "    \n",
    "#     print(type(peak))\n",
    "    date_ = pd.Timestamp.date(peak)\n",
    "    \n",
    "    # plot snow depth and peaks\n",
    "    plt.plot(data.index, data['meanSD (m)'],'r.-', markersize=8)\n",
    "    plt.plot(peak, data['meanSD (m)'].loc[peak])#,'k*', markersize=10) #.loc for 'manually' set indexes, vs .iloc\n",
    "    \n",
    "#     arrow_properties = dict(\n",
    "#     facecolor=\"black\", width=5,\n",
    "#     headwidth=16, shrink=0)\n",
    "\n",
    "    plt.annotate('{}'.format(date_),xy=(peak, data['meanSD (m)'].loc[peak])) #, arrowprops={'arrowstyle': '-|>'})#dict(arrowstyle = '->'))\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "   \n",
    "    ax.grid(which='major')\n",
    "    ax.minorticks_on()\n",
    "    ax.grid(which='minor', ls='--')\n",
    "    ax.grid(which='major', axis='x', ls='-', lw=1.5, c='darkGrey')\n",
    "#     ax.annotate('{}'.format(date_),xy=(peak, data['meanSD (m)'].loc[peak]))#, xytext = ('offset points')) \n",
    "                \n",
    "#                 arrowprops={'arrowstyle': '-|>'})\n",
    "#     ax.set_xticks(years)\n",
    "#     ax.xaxis.set_major_locator(years)\n",
    "#     ax.set_xticklabels(labels=years)    \n",
    "\n",
    "# sns.set_style('darkgrid')\n",
    "# sns.set_style(\"ticks\")\n",
    "# sns.set_context('notebook')\n",
    "\n",
    "#annotate\n",
    "# plt.annotate('here',xy=(iy_min, ix_min))\n",
    "\n",
    "\n",
    "plt.title('Tuolumne Basin mean snow depth')\n",
    "plt.xlabel('Year'); plt.ylabel('Snow Depth [m]')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# for i in data:\n",
    "#     plt.plot(dt.year, df1['meanSD (m)'], '-o', markersize=5)\n",
    "\n",
    "# DataFrame Plot\n",
    "# df1.plot(y='meanSD (m)', use_index = True)\n",
    "# df1.plot(x=dt.years, y='meanSD (m)')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#do elevation stuff here...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/meganmason/Documents/projects/thesis/analysis/virtual_envs/data_analysis/lib/python3.6/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot DataFrame results (THREE PLOTS, DF1)\n",
    "years = sorted(set([dt.year for dt in df1.index])) #set find uniques in list comp (loop that appends for you)\n",
    "\n",
    "titles = ['Mean Snow Depth (m)', 'SCA (km^2)', 'Percent Basin Coverage']\n",
    "colors = ['r.-','g.-','b.-']\n",
    "\n",
    "for idx,i in enumerate(df1):\n",
    "    fig = plt.figure(figsize=(20,5))\n",
    "    for year in years:\n",
    "        # year by year snow depth\n",
    "        startTime = pd.to_datetime(\"10-01-{}\".format(year-1))\n",
    "        endTime = pd.to_datetime(\"09-30-{}\".format(year))\n",
    "\n",
    "        # annual subset of DataFrame\n",
    "        data = df1[(df1.index >= startTime) & (df1.index <= endTime)] #option, .loc\n",
    "\n",
    "        # find annual peak\n",
    "        peak = data[i].idxmax()\n",
    "\n",
    "        date_ = pd.Timestamp.date(peak)\n",
    "\n",
    "        # plot snow depth and peaks\n",
    "        plt.plot(data.index, data[i], colors[idx], markersize=8)\n",
    "        plt.plot(peak, data[i].loc[peak])#,'k*', markersize=10) #.loc for 'manually' set indexes, vs .iloc\n",
    "        plt.annotate('{}'.format(date_),xy=(peak, data[i].loc[peak])) #, arrowprops={'arrowstyle': '-|>'})#dict(arrowstyle = '->'))\n",
    "        \n",
    "    for ax in fig.get_axes():\n",
    "\n",
    "        ax.grid(which='major')\n",
    "        ax.minorticks_on()\n",
    "        ax.grid(which='minor', ls='--')\n",
    "        ax.grid(which='major', axis='x', ls='-', lw=1.5, c='darkGrey')\n",
    "\n",
    "    plt.title('Tuolumne Basin {}'.format(titles[idx]), fontsize=24)\n",
    "    plt.xlabel('Year'); plt.ylabel('{}'.format(titles[idx]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, a=plt.subplots(3,1)\n",
    "fig,(ax1,ax2,ax3) = plt.subplots(3,1, sharex=True,figsize=(18,16))\n",
    "\n",
    "ax1.plot(df1['meanSD (m)'], '-o', markersize=5)\n",
    "ax1.set_title('Mean Snow Depth')\n",
    "\n",
    "ax2.plot(df1['SCA (km)'], '-o', markersize=5)\n",
    "ax2.set_title('SCA (km^2)')\n",
    "\n",
    "ax3.plot(df1['% basin'], '-o', markersize=5)\n",
    "ax3.set_title('Percent basin covered')\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "    ax.grid(True)\n",
    "    \n",
    "# for xy in zip(mu_sd):\n",
    "# #     ax.annotate(dt,xy=(dt,mu_sd))\n",
    "#     ax.annotate('%s,%s)' % xy, xy=xy)\n",
    "\n",
    "# for k, v in df1.iterrows():\n",
    "#     ax.annotate(k, v)\n",
    "    \n",
    "\n",
    "# a[0].plot(df1['meanSD (m)'])\n",
    "# a[1].plot(df1['SCA (km)'])\n",
    "# a[2].plot(df1['% basin'])\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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