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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Import Packages:\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "import xarray as xr\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "from matplotlib.ticker import ScalarFormatter\n", | ||
| "import cmaps\n", | ||
| "import metpy.calc as mpcalc\n", | ||
| "from metpy.units import units\n", | ||
| "\n", | ||
| "import scipy\n", | ||
| "\n", | ||
| "#import geocat.datafiles as gdf\n", | ||
| "import geocat.viz as gv" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "raw", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Open a netCDF data file using xarray default engine and load the data into xarrays\n", | ||
| "ds = xr.open_dataset(gdf.get(\"netcdf_files/mxclim.nc\"))\n", | ||
| "\n", | ||
| "# Extract variables\n", | ||
| "U = ds.U[0, :, :]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "image/png": 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UVVVFRERxcXGUl5dHcXFxFBUVRWFhYXz22WexatWqePfdd+Ott96KjRs3RpZlse+++8YNN9wQEydOjC5dujR4BnEMAEDeGDp0aMyePTvee++9uOeee+Lxxx+Pjz76qP4K8tb7IGdZVv+aTp06xfDhw+Piiy+OMWPGRGFhYaPXz2V/+87tQG1tbZSWlkZNTU2UlJS09TgAAGynuXttwYIF8corr8SSJUtixYoVsWHDhigqKor9998/jj766DjuuOMadZV4Z1w5BgAgrw0cODAGDhzYKmv5Qh4AACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAAHlrwIABcf755+/Wuf/0T//U5F8WIo4BAMhbixcvjo8++mi3zl22bFksXry4SeuJYwAA9ggbNmyIgoKCJr2HOAYAoN1bsWJFVFdXR+/evZv0Pk1LawAAaEbTpk2LadOmbXPsrbfeilNOOWWXr1m/fn1UV1fHunXrYsyYMU1aXxwDAJA3Fi9eHFVVVfWPc7lcrF69eptju3LKKafETTfd1KT1xTEAAHnjBz/4QYwYMSIiIrIsi1NOOSWOOOKIuOOOO3Z6fi6Xiy5dukT//v2jZ8+eTV5fHAMAkDf69esX/fr1q3/8ne98J4466qgYPnx4q6wvjgEAyFu783GK5uRuFQAAkLhyDABA3nvhhRfi2WefjYULF8a6desiy7KdnpfL5WL27NmNXkccAwCQtz7//PMYPXp0zJo1KyJil1G8VS6Xa9J64hgAgLz1b//2b/HCCy9E9+7d45JLLomhQ4fG/vvvHx06tMyng8UxAAB56/HHH4+OHTvGCy+8EBUVFS2+ni/kAQCQtz799NM4+OCDWyWMI8QxAAB5rKSkJAoKWu/DDuIYAIC89f3vfz+qq6tjwYIFrbKeOAYAIG9dc8018Xd/93dx9tlnx/z581t8vXbzhbzKysqorKyMjRs3tvUoAAC0gB/96Ec7PV5aWhp//OMfY+jQoXH00UfHwIEDo2vXrjs9N5fLxX333dfoGXLZ190sLs/U1tZGaWlp1NTURElJSVuPAwDAdhrba81xe7ZcLhebN29u9OvbzZVjAAD2bA888EBbjyCOAQDIDxdddFFbj+ALeQAAsJU4BgCAxMcqAADIW7u6g8XOdOzYMbp16xYHHXRQnHjiiVFeXt7g9cQxAAB568EHH4yIv96FIiJiZzda2/65rY/Ly8tj2rRpcdhhh+32euIYAIC89cADD8SCBQvi5ptvjq5du8bZZ58dRx55ZHTr1i3Wrl0bb731Vjz99NNRV1cXV1xxRRxwwAHx7rvvxpNPPhmvvfZanHzyyTF//vwoLi7erfXc5xgAgGbVnL22aNGiGDJkSFRUVMSjjz4a3/rWt3Y4Z82aNXHeeefFq6++GvPmzYsBAwZEXV1djB49On7/+9/HhAkTYurUqbu1ni/kAQCQt6699trYsGHDLsM4IqJ79+7xyCOPxPr16+Paa6+NiIiuXbvG/fffH7lcLmbOnLnb64ljAADy1uzZs2Pw4MG7DOOt9t133xg8eHD87//+b/2xPn36xKGHHho1NTW7vZ44BgAgb61ZsyZWrVq1W+euWrUq1qxZs82xwsLC+i/o7Q5xDABA3jr44INj0aJF8cwzz3zlec8880wsXLgwDjnkkG2OL1y4MHr16rXb64ljAADy1qWXXhpZlsW5554bN910Uyxbtmyb5z/55JO4+eab4/zzz49cLheXXnpp/XNvvvlmrF69Oo499tjdXs+t3AAAyFvjxo2LV199NR544IGYPHlyTJ48Ofbbb7/o1q1brFu3LlasWBERf73H8cUXXxw//vGP619bVVUVw4cPjwsvvHC313MrNwAAmlVL9NoTTzwRt956a8ybN2+bXwTSoUOHOO6442LSpEnxj//4j01ex5VjAADy3pgxY2LMmDGxbt26+OCDD6Kuri66du0a3/72t2OfffZptnXEMQAA7cY+++wTRx99dIu9vy/kAQBA4soxAAB54aGHHoqIiB49esT3vve9bY41REO+gLc9X8gDAKBZNbbXOnToELlcLgYNGhTV1dXbHGuIzZs3N+j8v+XKMQAAeeHCCy+MXC4XxcXFOxxrLeIYAIC88OCDD+7WsZbkC3kAAJCIYwAA2o0tW7bEp59+GkuXLm2R9xfHAADkvZkzZ8Zpp50W3bp1iwMOOCAGDBiwzfP//u//HhdccEF8+umnTVpHHAMAkNeuuOKKOPPMM2P27NmxefPm6NSpU2x/w7Xi4uKYPn16zJgxo0lriWMAAPLWk08+Gf/5n/8ZBx54YDzzzDNRV1cXQ4cO3eG8c845JyIifve73zVpPXerAAAgb1VWVkYul4vHH388jj/++F2et++++0b//v3j/fffb9J6rhwDAJC35s+fH6WlpV8Zxlv16tUrPvzwwyatJ44BAMhbX3zxRXzrW9/arXM///zz6NixY5PWE8cAAOSt0tLS+OCDD+LLL7/8yvNWr14d7733XgwcOLBJ64ljAADy1qhRo2L9+vVx2223feV5U6ZMiU2bNsUZZ5zRpPXEMQAAeevKK6+Mbt26xTXXXBOXX355vPfee/XPbdmyJf785z/Hj370o7jtttuiZ8+eMWHChCat524VAADkrT59+sT//M//xOjRo2Pq1KkxderU+uc6deoUERFZlkVRUVHMmDEj9ttvvyat58oxAAB5bfjw4fH222/HxIkTo1+/fpFlWf1PcXFx/OQnP4k333wzhg0b1uS1ctn2v14kz9XW1kZpaWnU1NRESUlJW48DAMB2WrrX6urqYvXq1bHPPvtE9+7dm/W9fawCAIB2pWvXrtG1a9cWeW8fqwAAgMSVYwAA8saUKVOa/B7XXXddo1/rM8cAADSrpvRahw4dIpfLNWn9zZs3N/q1rhwDAJA3vvOd7+wyjufMmRPdu3ePY445psXWF8cAAOSNqqqqXT7XoUOHOPLII+PFF19ssfV9IQ8AABJxDAAAiTgGAIBEHAMAQCKOAQAgEccAAJC4lRsAAHnjoYce+srnly9f/rXnXHjhhY1e32/IAwCgWbXlb8jL5XKxadOmRr/elWMAAPJG3759m/zro5tCHAMAkDcWL17cpuv7Qh4AACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBA0m7iuLKyMsrKymLEiBFtPQoAAHuodhPH48ePj+rq6qiqqmrrUQAA2EO1mzgGAICWJo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBA0iZx/Mwzz8SgQYPi4IMPjnvvvbctRgAAgB0UtPaCmzZtikmTJsWLL74Y3bt3j2OPPTZGjx4dRUVFrT0KAABso9WvHM+bNy8GDx4cffr0iW7dusV3v/vdeP7551t7DAAA2EGD4/ill16KM888Mw488MDI5XLx9NNP73DOXXfdFf3794/OnTtHeXl5zJ07t/65jz76KPr06VP/uKSkJD788MPGTQ8AAM2owXFcV1cXRx11VPz617/e6fPTp0+PiRMnxuTJk2P+/Plx0kknxemnnx5Lly6NiIgsy3Z4TS6X2+V6X3zxRaxZs6b+Z+3atQ0dGQAAdkuD4/j000+PG264IUaPHr3T56dOnRoXX3xxXHLJJXHYYYfF7bffHqWlpXH33XdHRESfPn22uVJcW1sbxcXFu1zvxhtvjB49etT/lJWVNXRkAADYLc36meONGzfG66+/HiNHjtzm+MiRI+OVV16JiIiKiop4++2348MPP4y1a9fGzJkzY9SoUbt8z6uvvjpWr15d/1NdXd2cIwMAQL1mvVvFihUrYvPmzdG7d+9tjvfu3TuWLVv21wULCuLWW2+Nk08+ObZs2RJXXHFF7Lfffrt8z8LCwigsLKx/vGbNmuYcGQAA6rXIrdy2/wxxlmXbHDvrrLPirLPOaomlAQCg0Zr1YxU9e/aMjh071l8l3mr58uU7XE0GAIB806xxvNdee0V5eXnMmjVrm+OzZs2KYcOGNedSAADQ7Br8sYp169bFBx98UP940aJF8cYbb0RRUVH07ds3Jk2aFGPHjo0hQ4bECSecEL/5zW9i6dKlMW7cuGYdHAAAmluD4/i1116Lk08+uf7xpEmTIiLioosuigcffDDOO++8WLlyZUyZMiU+/vjjOPzww2PmzJnRr1+/5psaAABaQC7b2W/lyGO1tbVRWloaNTU1UVJS0tbjAACwnfbca836mWMAAGjPxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABIxDEAACTiGAAAEnEMAACJOAYAgEQcAwBAIo4BACARxwAAkIhjAABI2k0cV1ZWRllZWYwYMaKtRwEAYA+Vy7Isa+shGmLp0qXRr1+/mDdvXhQXF7f1OAAAbOfjjz+OioqKWLJkSfTt27etx2mQgrYeoKFqamoiIqKioqKNJwEA4KvU1NS0uzhud1eOV61aFfvtt1+8/fbb0aNHj7Yehxa2du3aKCsri+rq6ujWrVtbj0MLs9/fLPb7m8V+f7OsXr06Dj/88Fi5cmUUFRW19TgN0u6uHBcU/HXk0tLS6N69extPQ0tbs2ZNRET06dPHfn8D2O9vFvv9zWK/v1m27vHWbmtP2s0X8gAAoKWJYwAASNpdHBcWFsb1118fhYWFbT0KrcB+f7PY728W+/3NYr+/Wdrzfre7L+QBAEBLaXdXjgEAoKWIYwAASMQxAAAk4hgAABJxDAAASV7G8V133RX9+/ePzp07R3l5ecydO/crz58zZ06Ul5dH586dY8CAAfFf//VfrTQpzaEh+/3UU0/FaaedFr169Yru3bvHCSecEM8//3wrTktTNfTv91Z/+MMfoqCgII4++uiWHZBm1dD9/uKLL2Ly5MnRr1+/KCwsjIEDB8b999/fStPSVA3d74cffjiOOuqo2HvvvaO4uDh++MMfxsqVK1tpWpripZdeijPPPDMOPPDAyOVy8fTTT3/ta9pNr2V55r//+7+zTp06Zb/97W+z6urqbMKECVnXrl2zJUuW7PT8hQsXZnvvvXc2YcKErLq6Ovvtb3+bderUKXviiSdaeXIao6H7PWHChOzmm2/O5s2bl/3lL3/Jrr766qxTp07Zn/70p1aenMZo6H5v9dlnn2UDBgzIRo4cmR111FGtMyxN1pj9Puuss7LjjjsumzVrVrZo0aLs//7v/7I//OEPrTg1jdXQ/Z47d27WoUOH7Fe/+lW2cOHCbO7cudngwYOzs88+u5UnpzFmzpyZTZ48OXvyySeziMhmzJjxlee3p17LuziuqKjIxo0bt82xQw89NLvqqqt2ev4VV1yRHXroodsc+/GPf5wdf/zxLTYjzaeh+70zZWVl2S9/+cvmHo0W0Nj9Pu+887Jrr702u/7668VxO9LQ/X722WezHj16ZCtXrmyN8WhmDd3v//iP/8gGDBiwzbE77rgjKykpabEZaRm7E8ftqdfy6mMVGzdujNdffz1Gjhy5zfGRI0fGK6+8stPX/PGPf9zh/FGjRsVrr70WX375ZYvNStM1Zr+3t2XLlli7dm0UFRW1xIg0o8bu9wMPPBALFiyI66+/vqVHpBk1Zr9/97vfxZAhQ+KWW26JPn36xCGHHBI///nPY/369a0xMk3QmP0eNmxY1NbWxsyZMyPLsvjkk0/iiSeeiH/4h39ojZFpZe2p1wraeoC/tWLFiti8eXP07t17m+O9e/eOZcuW7fQ1y5Yt2+n5mzZtihUrVkRxcXGLzUvTNGa/t3frrbdGXV1dnHvuuS0xIs2oMfv9/vvvx1VXXRVz586NgoK8+scVX6Mx+71w4cJ4+eWXo3PnzjFjxoxYsWJF/Mu//EusWrXK547zXGP2e9iwYfHwww/HeeedFxs2bIhNmzbFWWedFXfeeWdrjEwra0+9lldXjrfK5XLbPM6ybIdjX3f+zo6Tnxq631s9+uij8Ytf/CKmT58e+++/f0uNRzPb3f3evHlzXHDBBfHLX/4yDjnkkNYaj2bWkL/fW7ZsiVwuFw8//HBUVFTEd7/73Zg6dWo8+OCDrh63Ew3Z7+rq6vjpT38a1113Xbz++uvx3HPPxaJFi2LcuHGtMSptoL30Wl5diunZs2d07Nhxh3/LXL58+Q7/trHVAQccsNPzCwoKYr/99muxWWm6xuz3VtOnT4+LL744Hn/88Tj11FNbckyaSUP3e+3atfHaa6/F/Pnz4yc/+UlE/DWesiyLgoKCeOGFF+KUU05pldlpuMb8/S4uLo4+ffpEjx496o8ddthhkWVZ1NbWxsEHH9yiM9N4jdnvG2+8MU488cS4/PLLIyLiyCOPjK5du8ZJJ50UN9xwQ15dSaTp2lOv5dWV47322ivKy8tj1qxZ2xyfNWtWDBs2bKevOeGEE3Y4/4UXXoghQ4ZEp06dWmxWmq4x+x3x1yvGP/jBD+KRRx7x2bR2pKH73b1793jrrbfijTfeqP8ZN25cDBo0KN5444047rjjWmt0GqExf79PPPHE+Oijj2LdunX1x/7yl79Ehw4doqSkpEXnpWkas9+ff/55dOiwbYZ07NgxIv7/FUX2HO2q19roi4C7tPVWMPfdd19WXV2dTZw4MevatWu2ePHiLMuy7KqrrsrGjh1bf/7WW4P867/+a1ZdXZ3dd999eXtrEHbU0P1+5JFHsoKCgqyysjL7+OOP638+++yztvoj0AAN3e/tuVtF+9LQ/V67dm1WUlKSjRkzJnvnnXeyOXPmZAcffHB2ySWXtNUfgQZo6H4/8MADWUFBQXbXXXdlCxYsyF5++eVsyJAhWUVFRVv9EWiAtWvXZvPnz8/mz5+fRUQ2derUbP78+fW37mvPvZZ3cZxlWVZZWZn169cv22uvvbJjjz02mzNnTv1zF110UTZ8+PBtzq+qqsqOOeaYbK+99soOOuig7O67727liWmKhuz38OHDs4jY4eeiiy5q/cFplIb+/f5b4rj9aeh+v/vuu9mpp56adenSJSspKckmTZqUff755608NY3V0P2+4447srKysqxLly5ZcXFx9v3vfz+rra1t5alpjBdffPEr//e4PfdaLsv8fxcAABCRZ585BgCAtiSOAQAgEccAAJCIYwAASMQxAAAk4hgAABJxDAAAiTgGAIBEHAMAQCKOAQAgEccAAJD8P07w49W+NtKxAAAAAElFTkSuQmCC", | ||
| "text/plain": [ | ||
| "<Figure size 800x800 with 2 Axes>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Generate figure (set its size (width, height) in inches) and axes\n", | ||
| "plt.figure(figsize=(8, 8))\n", | ||
| "ax = plt.axes()\n", | ||
| "\n", | ||
| "# Set y-axis to have log-scale\n", | ||
| "plt.yscale('log')\n", | ||
| "\n", | ||
| "# Specify which contours should be drawn\n", | ||
| "levels = np.linspace(-55, 55, 23)\n", | ||
| "\n", | ||
| "# Plot contour lines\n", | ||
| "lines = U.plot.contour(ax=ax,\n", | ||
| " levels=levels,\n", | ||
| " colors='black',\n", | ||
| " linewidths=0.5,\n", | ||
| " linestyles='solid',\n", | ||
| " add_labels=False)\n", | ||
| "\n", | ||
| "# Create second y-axis to show geo-potential height.\n", | ||
| "axRHS = gv.add_height_from_pressure_axis(ax, heights=[4, 8])\n", | ||
| "\n", | ||
| "plt.show();" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "geocat_viz_build", | ||
| "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": "3.10.0" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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