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May 24, 2019 23:34
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pylab inline\n", | |
| "import pysumma as ps\n", | |
| "import xarray as xr\n", | |
| "from matplotlib import cm\n", | |
| "import seaborn as sns\n", | |
| "from matplotlib.collections import LineCollection\n", | |
| "from matplotlib.colors import ListedColormap, BoundaryNorm\n", | |
| "sns.set_context('talk')\n", | |
| "mpl.style.use('ggplot')\n", | |
| "mpl.rcParams['figure.figsize'] = (18, 12)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Open file - this is just the same as doing `s.output` from a ps.Simulation object\n", | |
| "ds = xr.open_dataset('./output/template_output_output_default_timestep.nc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "year = lambda x: slice('{}/10/01'.format(x), '{}/09/30'.format(x+1))\n", | |
| "ds = ds.sel(time=year(2013), hru=1)\n", | |
| "depth = ds['iLayerHeight_mean']\n", | |
| "temp = ds['mLayerTemp_mean']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def justify(a, invalid_val=np.nan, axis=1, side='right'): \n", | |
| " \"\"\"\n", | |
| " Justifies a 2D array\n", | |
| "\n", | |
| " Parameters\n", | |
| " ----------\n", | |
| " A : ndarray\n", | |
| " Input array to be justified\n", | |
| " axis : int\n", | |
| " Axis along which justification is to be made\n", | |
| " side : str\n", | |
| " Direction of justification. It could be 'left', 'right', 'up', 'down'\n", | |
| " It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0.\n", | |
| "\n", | |
| " \"\"\"\n", | |
| " if invalid_val is np.nan:\n", | |
| " mask = ~np.isnan(a)\n", | |
| " else:\n", | |
| " mask = a!=invalid_val\n", | |
| " justified_mask = np.sort(mask,axis=axis)\n", | |
| " if (side=='up') | (side=='left'):\n", | |
| " justified_mask = np.flip(justified_mask,axis=axis)\n", | |
| " out = np.full(a.shape, invalid_val) \n", | |
| " if axis==1:\n", | |
| " out[justified_mask] = a[mask]\n", | |
| " else:\n", | |
| " out.T[justified_mask.T] = a.T[mask.T]\n", | |
| " return out" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_layers(var, depth, ax=None, colormap='viridis'):\n", | |
| " # Preprocess the data\n", | |
| " vmask = var != -9999\n", | |
| " dmask = depth != -9999\n", | |
| " depth.values = justify(depth.where(dmask).values)\n", | |
| " var.values = justify(temp.where(vmask).values)\n", | |
| " lo_depth = depth.where(depth > 0).T\n", | |
| " hi_depth = depth.where(depth < 0).T\n", | |
| " var = var.T\n", | |
| " time = depth.time.values\n", | |
| " \n", | |
| " # Map colors to full range of data\n", | |
| " norm = plt.Normalize(np.nanmin(var), np.nanmax(var))\n", | |
| " cmap = matplotlib.cm.get_cmap(colormap)\n", | |
| " rgba = cmap(norm(var))\n", | |
| " \n", | |
| " # Create axes if needed\n", | |
| " if not ax:\n", | |
| " fig, ax = plt.subplots(figsize=(18,8))\n", | |
| " \n", | |
| " # Plot soil layers - need to reverse because we plot bottom down\n", | |
| " for l in lo_depth.ifcToto.values[:-1][::-1]:\n", | |
| " y = lo_depth[l]\n", | |
| " y[np.isnan(y)] = 0\n", | |
| " ax.vlines(time, ymin=-y, ymax=0, color=rgba[l])\n", | |
| " \n", | |
| " # Plot snow layers - plot top down\n", | |
| " for l in hi_depth.ifcToto.values[:-1]:\n", | |
| " y = hi_depth[l]\n", | |
| " y[np.isnan(y)] = 0\n", | |
| " ax.vlines(time, ymin=0, ymax=-y, color=rgba[l])\n", | |
| " \n", | |
| " # Add the colorbar\n", | |
| " mappable = cm.ScalarMappable(norm=norm, cmap=cmap)\n", | |
| " mappable.set_array(var.values.flatten())\n", | |
| " plt.gcf().colorbar(mappable, label=var.long_name, ax=ax)\n", | |
| " return ax" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/pool0/data/andrbenn/.conda/all/lib/python3.7/site-packages/matplotlib/colors.py:512: RuntimeWarning: invalid value encountered in less\n", | |
| " xa[xa < 0] = -1\n", | |
| "/pool0/data/andrbenn/.conda/all/lib/python3.7/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": { | |
| "text/plain": [ | |
| "[<matplotlib.lines.Line2D at 0x7f7401092fd0>]" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1296x576 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "ax1 = plot_layers(temp, depth)\n", | |
| "ds['scalarSnowDepth_mean'].sel(time=year(2013)).plot(color='red',linewidth=3, ax=ax1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "@deathbeds/jupyterlab-fonts": { | |
| "styles": { | |
| ":root": { | |
| "--jp-code-font-size": "16px", | |
| "--jp-content-font-size1": "16px" | |
| } | |
| } | |
| }, | |
| "kernelspec": { | |
| "display_name": "all", | |
| "language": "python", | |
| "name": "all" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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