{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Camera Derived Snow Depths\n", "\n", "**Goal**: Plot all camera location with pit locations. Then plot a timeseries of one camera in the trees and one in the open.\n", "\n", "**Approach**:\n", "\n", "1. Grab all the Site data for the pits and all the camera locations\n", "2. Plot the pits and sites all together \n", "3. Grab and Plot the Vegetated and Open Camera sites \n", "\n", "\n", "## Process\n", "\n", "### Step 1: Grab all the pit and camera locations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 18 camera locations\n", "Found 226 pit site locations\n" ] } ], "source": [ "from snowexsql.db import get_db\n", "from snowexsql.data import PointData, SiteData\n", "from snowexsql.conversions import query_to_geopandas\n", "\n", "from datetime import date\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Connect to the database \n", "db_name = 'db.snowexdata.org/snowex'\n", "engine, session = get_db(db_name, credentials='./credentials.json')\n", "\n", "# Grab all the point data that was that was measured with a camera\n", "qry = session.query(PointData)\n", "qry = qry.filter(PointData.instrument == 'camera')\n", "qry = qry.filter(PointData.site_name == 'Grand Mesa')\n", "qry = qry.filter(PointData.utm_zone == 12)\n", "\n", "# Convert it to a geopandas df \n", "camera_depths = query_to_geopandas(qry, engine)\n", "\n", "# Grab all the unique pits geometry objects (locations)\n", "qry = session.query(SiteData.geom)\n", "qry = qry.filter(SiteData.site_name == 'Grand Mesa')\n", "qry = qry.filter(SiteData.date > date(2019, 10,1))\n", "qry = qry.filter(SiteData.date < date(2021, 10,1))\n", "\n", "qry = qry.distinct()\n", "pits = query_to_geopandas(qry, engine)\n", "\n", "# Print out how many of each that we found\n", "print(f'Found {len(camera_depths[\"geom\"].unique())} camera locations')\n", "print(f'Found {len(pits.index)} pit site locations')\n", "\n", "# End our database session to avoid hanging transactions\n", "session.close()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Plot our camera and Pit locations" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot our pits as triangles\n", "ax = pits.plot(marker='^', color='gray', edgecolor='k', label='Pits')\n", "\n", "# Plot our cameras as squares\n", "ax = camera_depths.plot(ax=ax, color='magenta', edgecolor='k', marker='s', label='Cameras')\n", "\n", "# Don't use scientific notation on the ticks for utm coords\n", "ax.ticklabel_format(style='plain', useOffset=False)\n", "ax.set_xlabel('Easting [m]')\n", "ax.set_ylabel('Northing [m]')\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Grab/Plot the Vegetated and Open Camera sites " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "nbsphinx-thumbnail", "nbsphinx-gallery" ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Grab the open site data from the db\n", "open_site = 'W1A'\n", "veg_site = 'W9A'\n", "qry = session.query(PointData).filter(PointData.equipment.contains(open_site))\n", "df_open = query_to_geopandas(qry,engine)\n", "\n", "# Grab the vegetated site from the db \n", "qry = session.query(PointData).filter(PointData.equipment.contains(veg_site))\n", "df_veg = query_to_geopandas(qry,engine)\n", "\n", "# Set the date as the index for easy plotting/reading\n", "df_open = df_open.set_index('date')\n", "df_veg = df_veg.set_index('date')\n", "\n", "# Plot the 2 datasets by date!\n", "ax = df_open['value'].plot(label=f'Open ({open_site})')\n", "df_veg['value'].plot(ax=ax, label=f'Vegetated ({veg_site})')\n", "\n", "# Mess with some labeling to make it look nice\n", "ax.legend()\n", "ax.set_ylabel('Snow Depth [cm]')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "session.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }