visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hood Snow Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook has the useful bits of `explore.ipynb`, and relies on the data in `prepared_data.csv` and `prepared_weather.csv`. I used this notebook to prepare the heatmap and graphs for my poster. I tried to comment enough to explain what's going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "sb.set_theme(style=\"whitegrid\", font=\"NotoSans Nerd Font\")\n",
    "\n",
    "DATA = Path(\"E:/sensing/hood_snow/data\").resolve()\n",
    "\n",
    "MAX_CLOUDS = 0.4  # 40% cloud cover is max acceptable\n",
    "SNOWLINE_CRIT = 0.4  # 40% snow coverage is where the snowline falls\n",
    "\n",
    "FIG_WIDTH_IN = 16\n",
    "FIG_HEIGHT_IN = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>contour</th>\n",
       "      <th>pct_snow</th>\n",
       "      <th>pct_clouds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>1300</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>1400</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  contour  pct_snow  pct_clouds\n",
       "0  2017-01-03     1300     0.626       0.501\n",
       "1  2017-01-03     1400     0.619       0.501\n",
       "2  2017-01-03     1500     0.721       0.501\n",
       "3  2017-01-03     1600     0.745       0.501\n",
       "4  2017-01-03     1700     0.808       0.501"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this data was already cleaned in the following ways:\n",
    "# - dropped all contours < 1300m (they're mostly NULL pct_snow due to AOI)\n",
    "# - dropped 2018-08-16 (imagery for this date is misaligned)\n",
    "data = pd.read_csv(DATA.joinpath(\"prepared_data.csv\"))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place</th>\n",
       "      <th>date</th>\n",
       "      <th>precip</th>\n",
       "      <th>temp_avg</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>snow_water</th>\n",
       "      <th>snow_depth</th>\n",
       "      <th>elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Snotel</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>48.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Snotel</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>48.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>25.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Snotel</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>48.3</td>\n",
       "      <td>20.7</td>\n",
       "      <td>15.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Snotel</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>48.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>24.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Snotel</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>48.3</td>\n",
       "      <td>20.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    place        date  precip  temp_avg  temp_min  temp_max  snow_water  \\\n",
       "0  Snotel  2017-01-03    48.3      12.4       9.9      15.1        26.0   \n",
       "1  Snotel  2017-01-04    48.3      14.0       9.0      17.6        25.6   \n",
       "2  Snotel  2017-01-05    48.3      20.7      15.6      25.3        24.2   \n",
       "3  Snotel  2017-01-06    48.3      23.5      13.1      31.5        24.2   \n",
       "4  Snotel  2017-01-07    48.3      20.8      12.6      33.6        24.2   \n",
       "\n",
       "   snow_depth  elev  \n",
       "0         NaN  1635  \n",
       "1         NaN  1635  \n",
       "2         NaN  1635  \n",
       "3         NaN  1635  \n",
       "4         NaN  1635  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this weather was prepared by making the ski resort weather telemetry\n",
    "# match the format of the snotel telemetry. Ski weather was aggregated per-day\n",
    "# with mean/min/max on temperature, sum on precipitation, and max on snow_depth.\n",
    "# This data also cleaned by dropping temp values in the 0.1 percentile (very low outliers).\n",
    "# NOTE: that snow_depth != snow_water, which is why they're separate columns.\n",
    "# data units are US customary (inches, ⁰F)\n",
    "weather = pd.read_csv(DATA.joinpath(\"prepared_weather.csv\"))\n",
    "# snow_depth, a measurement at Timberline Lodge, is VERY noisy in the summers\n",
    "# with very high values. This cleans most of them, but there are a few other\n",
    "# bad values in the data that are not as easy to remove. However, I don't think\n",
    "# they will affect my intended use of this field.\n",
    "weather = weather.drop(weather[weather.snow_depth >= 250].index)\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_clouds(data: pd.DataFrame, max_clouds_pct: float = 1, pct_clouds_col: str = \"pct_clouds\") -> pd.DataFrame:\n",
    "    \"\"\"Drop data with percent cloud coverage greater than `max_clouds`.\"\"\"\n",
    "    return data.drop(data.loc[data[pct_clouds_col] > max_clouds_pct].index)  # drop cloudy days\n",
    "\n",
    "\n",
    "def date_str_to_dt(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"convert \"date\" column from string to datetime\"\"\"\n",
    "    df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "    return df\n",
    "\n",
    "\n",
    "def format_date_ticks(old_ticks: list[plt.Text], fmt: str = \"%Y-%m\") -> list[str]:\n",
    "    \"\"\" change datetime ticks from default format to the one specified in `fmt`.\"\"\"\n",
    "    text = [l.get_text() for l in old_ticks]\n",
    "    return pd.to_datetime(text).strftime(fmt)\n",
    "\n",
    "\n",
    "def create_heatmap(data: pd.DataFrame,\n",
    "                   max_cloud_pct: float = 1,\n",
    "                   date_col: str = \"date\", contour_col: str = \"contour\", pct_snow_col: str = \"pct_snow\",\n",
    "                   ax: plt.Axes = None, cmap=\"viridis\", cbar: bool = False) -> plt.Axes:\n",
    "    \"\"\"\n",
    "    create a heatmap of data with date on the x axis and contour on the y axis. \n",
    "    rows with more than `max_clouds_pct` will be dropped.\n",
    "    \"\"\"\n",
    "    # massage data\n",
    "    fewer_clouds = drop_clouds(data, max_cloud_pct)\n",
    "    fewer_clouds = date_str_to_dt(fewer_clouds)\n",
    "    data_heatmap = (fewer_clouds\n",
    "                    .pivot(index=contour_col, columns=date_col, values=pct_snow_col)\n",
    "                    .sort_index(axis=0, ascending=False))\n",
    "    # create the actual heatmap\n",
    "    ax = sb.heatmap(data_heatmap, cmap=cmap, ax=ax, cbar=cbar)\n",
    "    # format axes labels\n",
    "    ax.set_xticklabels(format_date_ticks(ax.get_xticklabels()))\n",
    "    ax.set_title(f\"Snow coverage when cloud cover {max_cloud_pct*100:.0f}% or less\")\n",
    "    ax.set_ylabel(\"Elevation (m)\")\n",
    "    ax.set_xlabel(\"Date\")\n",
    "    return ax\n",
    "\n",
    "# ax:plt.Axes= create_heatmap(data,max_cloud_pct=MAX_CLOUDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_snowline_elevation(data: pd.DataFrame, critical_snow_pct: float,\n",
    "                            date_col: str = \"date\", contour_col: str = \"contour\", snow_col: str = \"pct_snow\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    find the contour level at which the snow level first falls below some\n",
    "    \"critical\" percentage. 0.3 or 0.4 seem to be fine.\n",
    "        __this is the \"snowline\"__\n",
    "\n",
    "    return a new dataframe with the snowline elevation contour for each date.\n",
    "    \"\"\"\n",
    "    reshaped = data.pivot(index=date_col, columns=contour_col, values=snow_col)\n",
    "    critical_levels = []\n",
    "    for date in reshaped.index:\n",
    "        # help smooth data\n",
    "        # for each contour level, take the mean of the next lower to next higher\n",
    "        # this is a 2D \"focal statistic\"\n",
    "        # actually don't. doesn't seem to help at all\n",
    "        window_mean = reshaped.loc[date]  # .rolling(window=3, center=True).mean()\n",
    "        # idxmax() is weird here, but since True is max of bool, this finds the\n",
    "        # index of the first True\n",
    "        level = window_mean.gt(critical_snow_pct).idxmax()\n",
    "        critical_levels.append((date, level, critical_snow_pct))\n",
    "\n",
    "    return pd.DataFrame(critical_levels, columns=[\"date\", \"contour\", \"critical\"])\n",
    "\n",
    "\n",
    "def calc_snowline_movement(data_levels: pd.DataFrame, min_contour: int = 1300,\n",
    "                           date_col: str = \"date\", contour_col: str = \"contour\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    use a dataframe of the snowline elevation at each date (output from\n",
    "    `find_snowline_elevation()`) and find in each year the first date at which\n",
    "    the snowline is above `min_countour` and the date of the maximum snowline.\n",
    "    Then use that information to calculate the time period, elevation difference,\n",
    "    and rate of the change (meters per day).\n",
    "\n",
    "    return a new dataframe with these data per year.\n",
    "    \"\"\"\n",
    "    levels = data_levels.copy()\n",
    "    levels[\"year\"] = pd.to_datetime(levels[date_col]).dt.year\n",
    "\n",
    "    def year_snowline_movement(year: int) -> pd.Series:\n",
    "        begin_idx = levels[levels[\"year\"] == year][contour_col].gt(min_contour).idxmax()\n",
    "        begin_idx -= 1  # prev line finds next idx after min_contour, so get previous\n",
    "        end_idx = levels[levels[\"year\"] == year][contour_col].idxmax()\n",
    "        begin = pd.to_datetime(levels.loc[begin_idx, date_col])\n",
    "        end = pd.to_datetime(levels.loc[end_idx, date_col])\n",
    "        span = end - begin\n",
    "        low = levels.loc[begin_idx, contour_col]\n",
    "        high = levels.loc[end_idx, contour_col]\n",
    "        elev_diff = high - low\n",
    "        return pd.Series({\n",
    "            \"year\": year,\n",
    "            \"begin\": begin,\n",
    "            \"end\": end,\n",
    "            \"span\": span,\n",
    "            \"low\": low,\n",
    "            \"high\": high,\n",
    "            \"elev_diff\": elev_diff,\n",
    "            \"rate\": elev_diff/span.days})\n",
    "\n",
    "    return pd.DataFrame([year_snowline_movement(year)\n",
    "                         for year in levels[\"year\"].unique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>contour</th>\n",
       "      <th>critical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-03</td>\n",
       "      <td>1300</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>1300</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>1800</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>1900</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2023-11-08</td>\n",
       "      <td>1300</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2023-11-18</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2023-11-23</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2023-11-28</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>2023-12-23</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  contour  critical\n",
       "0    2017-05-03     1300       0.4\n",
       "1    2017-05-23     1300       0.4\n",
       "2    2017-07-02     1700       0.4\n",
       "3    2017-07-17     1800       0.4\n",
       "4    2017-07-22     1900       0.4\n",
       "..          ...      ...       ...\n",
       "203  2023-11-08     1300       0.4\n",
       "204  2023-11-18     1700       0.4\n",
       "205  2023-11-23     1500       0.4\n",
       "206  2023-11-28     1700       0.4\n",
       "207  2023-12-23     1500       0.4\n",
       "\n",
       "[208 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snowline = find_snowline_elevation(drop_clouds(data, MAX_CLOUDS), SNOWLINE_CRIT)\n",
    "display(snowline)\n",
    "\n",
    "\n",
    "def snowline_lineplot(snowline: pd.DataFrame, ax: plt.Axes = None, color: str = None) -> plt.Axes:\n",
    "    \"\"\"create the main lineplot showing the snowline elevation vs time.\"\"\"\n",
    "    sl = date_str_to_dt(snowline)\n",
    "    pal = sb.color_palette() if color is None else sb.color_palette([color])\n",
    "    # setting hue here is a hack to get palette to be used\n",
    "    ax = sb.lineplot(sl, x=\"date\", y=\"contour\", hue=\"critical\",\n",
    "                     legend=False,\n",
    "                     ax=ax, palette=pal)\n",
    "    ax.set_xlabel(\"Date\")\n",
    "    ax.set_ylabel(\"Elevation (m)\")\n",
    "    ax.set_ylim(bottom=1200, top=3000)  # default sucked\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>span</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>elev_diff</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>100 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2200</td>\n",
       "      <td>900</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-05-13</td>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>115 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2600</td>\n",
       "      <td>1300</td>\n",
       "      <td>11.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>115 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2600</td>\n",
       "      <td>1300</td>\n",
       "      <td>11.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2020-10-09</td>\n",
       "      <td>130 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2800</td>\n",
       "      <td>1500</td>\n",
       "      <td>11.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>2021-09-24</td>\n",
       "      <td>115 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2400</td>\n",
       "      <td>1100</td>\n",
       "      <td>9.565217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-21</td>\n",
       "      <td>2022-09-24</td>\n",
       "      <td>95 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2600</td>\n",
       "      <td>1300</td>\n",
       "      <td>13.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-05-22</td>\n",
       "      <td>2023-09-09</td>\n",
       "      <td>110 days</td>\n",
       "      <td>1300</td>\n",
       "      <td>2700</td>\n",
       "      <td>1400</td>\n",
       "      <td>12.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      begin        end     span   low  high  elev_diff       rate\n",
       "0  2017 2017-05-23 2017-08-31 100 days  1300  2200        900   9.000000\n",
       "1  2018 2018-05-13 2018-09-05 115 days  1300  2600       1300  11.304348\n",
       "2  2019 2019-05-13 2019-09-05 115 days  1300  2600       1300  11.304348\n",
       "3  2020 2020-06-01 2020-10-09 130 days  1300  2800       1500  11.538462\n",
       "4  2021 2021-06-01 2021-09-24 115 days  1300  2400       1100   9.565217\n",
       "5  2022 2022-06-21 2022-09-24  95 days  1300  2600       1300  13.684211\n",
       "6  2023 2023-05-22 2023-09-09 110 days  1300  2700       1400  12.727273"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snowline_movement = calc_snowline_movement(snowline)\n",
    "display(snowline_movement)\n",
    "\n",
    "\n",
    "def snowline_movement_lines(snowline_movement: pd.DataFrame, color: str = None, ax: plt.Axes = None) -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Annotates ax with lines showing the movement in each year. \n",
    "    Reshapes data to create 2 points for each year.\n",
    "    \"\"\"\n",
    "    snlm = snowline_movement.set_index(\"year\")\n",
    "    frames = []\n",
    "    for year in snlm.index:\n",
    "        s = snlm.loc[year]\n",
    "        d = pd.DataFrame([\n",
    "            (year, s.begin, s.low),\n",
    "            (year, s.end, s.high)], columns=[\"year\", \"date\", \"elev\"])\n",
    "        d = date_str_to_dt(d)\n",
    "        frames.append(d)\n",
    "    snlm = pd.concat(frames).reset_index(drop=True)\n",
    "    # display(snlm)\n",
    "\n",
    "    pal = sb.color_palette() if color is None else sb.color_palette([color])\n",
    "    return sb.lineplot(snlm, x=\"date\", y=\"elev\", hue=\"year\",\n",
    "                       legend=False, palette=pal, lw=6,  ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_to_week(row: pd.Series) -> pd.Series:\n",
    "    \"\"\"Change dates in the same ISO week to the same date for aggregation.\"\"\"\n",
    "    d = pd.to_datetime(row[\"date\"]).strftime(\"%Y-%W-0\")\n",
    "    row[\"date_week\"] = pd.to_datetime(d, format=\"%Y-%W-%w\")\n",
    "    return row\n",
    "\n",
    "\n",
    "def create_temp_lineplot(weather: pd.DataFrame, places: list[str], ax: plt.Axes = None) -> plt.Axes:\n",
    "    \"\"\"Create the plot of temperature vs time. `places` determines which stations are plotted.\"\"\"\n",
    "    # calculate weekly mean temp\n",
    "    w = (weather\n",
    "         .apply(lambda r: date_to_week(r), axis=1)\n",
    "         .groupby([\"place\", \"date_week\"])[\"temp_avg\"]\n",
    "         .mean())\n",
    "    w = w.loc[places]\n",
    "    w = w.reset_index()\n",
    "\n",
    "    legend = len(places) > 1\n",
    "    ax: plt.Axes = sb.lineplot(w, x=\"date_week\", y=\"temp_avg\", hue=\"place\", legend=legend, ax=ax)\n",
    "    ax.set_xlabel(\"Date\")\n",
    "    ax.set_ylabel(\"Temperature (⁰F)\")\n",
    "    ax.set_title(\"Weekly mean temperature\")\n",
    "    if legend:\n",
    "        ax.get_legend().set_title(\"Place\")\n",
    "    return ax\n",
    "\n",
    "\n",
    "# create_temp_lineplot(weather, places=[\"Snotel\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>snow_depth</th>\n",
       "      <th>place</th>\n",
       "      <th>elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7915</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>21.1</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>22.4</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>24.1</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9000</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9315</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>23.4</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9632</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-08-30</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9928</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-06-22</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10121</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>26.6</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year       date  snow_depth  place  elev\n",
       "7915   2017 2017-07-16        21.1  Lodge  1770\n",
       "8258   2018 2018-06-24        22.4  Lodge  1770\n",
       "8617   2019 2019-06-18        24.1  Lodge  1770\n",
       "9000   2020 2020-07-05        24.2  Lodge  1770\n",
       "9315   2021 2021-06-27        23.4  Lodge  1770\n",
       "9632   2022 2022-08-30         3.8  Lodge  1770\n",
       "9928   2023 2023-06-22        24.2  Lodge  1770\n",
       "10121  2024 2024-01-01        26.6  Lodge  1770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>year</th>\n",
       "      <th>date</th>\n",
       "      <th>place</th>\n",
       "      <th>elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-07-11</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-06-12</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>Snotel</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7915</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9000</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9315</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9632</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-08-30</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9928</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-06-22</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10121</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>Lodge</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year       date   place  elev\n",
       "178    2017 2017-06-30  Snotel  1635\n",
       "521    2018 2018-06-08  Snotel  1635\n",
       "889    2019 2019-06-11  Snotel  1635\n",
       "1266   2020 2020-06-22  Snotel  1635\n",
       "1631   2021 2021-06-22  Snotel  1635\n",
       "2015   2022 2022-07-11  Snotel  1635\n",
       "2351   2023 2023-06-12  Snotel  1635\n",
       "2554   2024 2024-01-01  Snotel  1635\n",
       "7915   2017 2017-07-16   Lodge  1770\n",
       "8258   2018 2018-06-24   Lodge  1770\n",
       "8617   2019 2019-06-18   Lodge  1770\n",
       "9000   2020 2020-07-05   Lodge  1770\n",
       "9315   2021 2021-06-27   Lodge  1770\n",
       "9632   2022 2022-08-30   Lodge  1770\n",
       "9928   2023 2023-06-22   Lodge  1770\n",
       "10121  2024 2024-01-01   Lodge  1770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_no_snow_date(weather: pd.DataFrame, snow_amount_col: str = \"snow_water\", amount_cutoff: float = 25,  date_col: str = \"date\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Finds the date in each year when the snow is gone at a weather station.\n",
    "    Does this by finding the first date/index where the snow amount falls\n",
    "    below a specified value.\n",
    "    \"\"\"\n",
    "    # amount_cutoff=1 works ok for snow_water (snotel)\n",
    "    # amount_cutoff=25 works ok for snow_depth (lodge)\n",
    "    w = weather.drop(index=weather[weather[snow_amount_col].isna()].index)\n",
    "    w = date_str_to_dt(w)\n",
    "    w[\"year\"] = w[date_col].dt.year\n",
    "    found = []\n",
    "    for y in w[\"year\"].unique():\n",
    "        first = w[w[\"year\"] == y][snow_amount_col].lt(amount_cutoff).idxmax()\n",
    "        found.append(w.loc[first][[\"year\", \"date\", snow_amount_col, \"place\", \"elev\"]])\n",
    "    return pd.DataFrame(found)\n",
    "\n",
    "\n",
    "# do just one location\n",
    "no_snow = find_no_snow_date(weather, snow_amount_col=\"snow_depth\", amount_cutoff=25)\n",
    "display(no_snow)\n",
    "\n",
    "# run both locations with snow amount information, drop disparate columns, and merge\n",
    "all_no_snow = pd.concat([\n",
    "    find_no_snow_date(weather, snow_amount_col=\"snow_water\",\n",
    "                      amount_cutoff=1).drop(columns=[\"snow_water\"]),\n",
    "    find_no_snow_date(weather, snow_amount_col=\"snow_depth\",\n",
    "                      amount_cutoff=25).drop(columns=[\"snow_depth\"]),\n",
    "])\n",
    "display(all_no_snow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create snow coverage heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PLOT_ROWS = 1\n",
    "fig, axes = plt.subplots(nrows=PLOT_ROWS)\n",
    "fig.set_size_inches(w=FIG_WIDTH_IN, h=FIG_HEIGHT_IN*PLOT_ROWS)\n",
    "\n",
    "# create_heatmap(data, max_cloud_pct=1.0, ax=axes[0])\n",
    "create_heatmap(data, max_cloud_pct=MAX_CLOUDS, ax=axes, cbar=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig(\"media/heatmap.svg\", dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create snowline and temperature graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben\\AppData\\Local\\Temp\\ipykernel_12220\\3589615684.py:19: UserWarning: \n",
      "The palette list has fewer values (1) than needed (7) and will cycle, which may produce an uninterpretable plot.\n",
      "  return sb.lineplot(snlm, x=\"date\", y=\"elev\", hue=\"year\",\n",
      "C:\\Users\\Ben\\AppData\\Local\\Temp\\ipykernel_12220\\3288409394.py:9: UserWarning: The palette list has more values (10) than needed (1), which may not be intended.\n",
      "  ax = sb.lineplot(sl, x=\"date\", y=\"contour\", hue=\"critical\",\n",
      "C:\\Users\\Ben\\AppData\\Local\\Temp\\ipykernel_12220\\36776474.py:13: UserWarning: \n",
      "The palette list has fewer values (1) than needed (2) and will cycle, which may produce an uninterpretable plot.\n",
      "  sb.scatterplot(all_no_snow, x=\"date\", y=\"elev\", hue=\"place\", ax=axes[0],\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PLOT_ROWS = 2\n",
    "fig, axes = plt.subplots(nrows=PLOT_ROWS, sharex=True)\n",
    "fig.set_size_inches(w=FIG_WIDTH_IN, h=0.875*FIG_HEIGHT_IN*PLOT_ROWS)\n",
    "# fig.subplots_adjust(hspace=0.7) # doesn't seem to be doing anything, maybe cause tight_layout\n",
    "\n",
    "# create snowline plot\n",
    "snowline_movement_lines(snowline_movement, ax=axes[0], color=(0.5, 1, 1))\n",
    "snowline_lineplot(snowline, ax=axes[0])\n",
    "# add annotations for validation sites\n",
    "# the dots get placed under movement lines, but I can fix them in inkscape\n",
    "# axes[0].vlines(x=all_no_snow[\"date\"], ymin=1300, ymax=1635, colors=\"#0AA\", linestyles=\"dotted\")\n",
    "for elev in all_no_snow[\"elev\"].unique():\n",
    "    axes[0].axhline(y=elev, color=\"#AAA\", linestyle=\":\")\n",
    "sb.scatterplot(all_no_snow, x=\"date\", y=\"elev\", hue=\"place\", ax=axes[0],\n",
    "               palette=sb.color_palette([\"#0BB\"]), legend=False)\n",
    "axes[0].set_title(\"Model snowline and movement rate, with Validation points\")\n",
    "\n",
    "# create temperature plot\n",
    "create_temp_lineplot(weather, places=[\"Snotel\"], ax=axes[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig(\"media/linegraphs.svg\", dpi=300, transparent=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}