From 832d2673cc91e2623efdb146604899884bec0049 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Thu, 12 Sep 2019 12:45:40 -0600 Subject: [PATCH 01/27] Add Notebooks demonstrating use of GIPL model --- docs/demos/Example_01_Basic_Use_GIPL.ipynb | 381 ++++++++++++++++++ docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb | 340 ++++++++++++++++ docs/examples.rst | 4 + 3 files changed, 725 insertions(+) create mode 100644 docs/demos/Example_01_Basic_Use_GIPL.ipynb create mode 100644 docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb diff --git a/docs/demos/Example_01_Basic_Use_GIPL.ipynb b/docs/demos/Example_01_Basic_Use_GIPL.ipynb new file mode 100644 index 00000000..76a08c8e --- /dev/null +++ b/docs/demos/Example_01_Basic_Use_GIPL.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic use of the GIPL model\n", + "\n", + "Before you begin, install:\n", + "\n", + "```conda install -c conda-forge pymt pymt_gipl seaborn```\n", + "\n", + "\n", + "**GIPL** (Geophysical Institute Permafrost Laboratory) is an implicit finite difference one-dimensional heat flow numerical model. \n", + "\n", + "\\begin{equation}\n", + "\\frac{\\partial H(x, t)}{\\partial \\tau}=\\frac{\\partial}{\\partial x}\\left(k(x, t) \\frac{\\partial t(x, \\tau)}{\\partial x}\\right)\n", + "\\end{equation}\n", + "\n", + "The model uses fine vertical resolution grid which preserves the latent-heat effects in the phase transition zone, even under conditions of rapid or abrupt changes in the temperature fields. It includes upper boundary condition (usually air temperature), constant geothermal heat flux at the lower boundary, and initial temperature distribution with depth. The other inputs are snow depth, snow thermal conductivity, etc. The core output is temperature distributions at different depths.\n", + "\n", + "**References**\n", + "\n", + "Marchenko, S., Romanovsky, V., & Tipenko, G. (2008, June). Numerical modeling of spatial permafrost dynamics in Alaska. In Proceedings of the ninth international conference on permafrost (Vol. 29, pp. 1125-1130). Institute of Northern Engineering, University of Alaska Fairbanks.\n", + "\n", + "Jafarov, E. E., Marchenko, S. S., and Romanovsky, V. E.: Numerical modeling of permafrost dynamics in Alaska using a high spatial resolution dataset, The Cryosphere, 6, 613-624, https://doi.org/10.5194/tc-6-613-2012, 2012." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------\n", + "Some libs should be imported.\n", + "\n", + "**pymt.models** is required.\n", + "\n", + "**others** are used for implementing and plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33;01m➡ models: FrostNumber, Ku, Hydrotrend, GIPL, Cem, Waves\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "import pymt.models\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.colors as mcolors\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "sns.set(style='whitegrid', font_scale= 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Import the GIPL model**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The 1D GIPL Model'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gipl = pymt.models.GIPL()\n", + "gipl.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the setup method to provide a default GIPL configuration file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('gipl_config.cfg', '/Users/mpiper/projects/GIPL-BMI-Fortran/Notebooks')\n" + ] + } + ], + "source": [ + "defaults = gipl.setup('.')\n", + "print(defaults)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Initialze the model**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "gipl.initialize('gipl_config.cfg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Get the soil depth in the model**\n", + "\n", + "It is used for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('soil__temperature', 'model_soil_layer__count')\n", + "('land_surface_air__temperature', 'snowpack__depth', 'snow__thermal_conductivity', 'soil_water__volume_fraction', 'soil_unfrozen_water__a', 'soil_unfrozen_water__b')\n" + ] + } + ], + "source": [ + "# List input and output variable names.\n", + "print(gipl.output_var_names)\n", + "print(gipl.input_var_names)\n", + "\n", + "# Get soil depth: [unit: m]\n", + "depth = gipl.get_grid_z(2)\n", + "n_depth = int(len(depth))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Get the length of forcing data**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final soil temperatures will be (176, 365)\n" + ] + } + ], + "source": [ + "# Get the length of forcing data\n", + "ntime = int(gipl.end_time)\n", + "\n", + "# Define a variable to store soil temperature through the time period\n", + "\n", + "tsoil = np.zeros((n_depth, ntime)) * np.nan\n", + "\n", + "print('Final soil temperatures will be ', tsoil.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Run the model and plot the results**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Soil Surface (0.0m)')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[10,4.5])\n", + "ax = fig.add_subplot(1,2,1)\n", + "plt.ylim([15,0])\n", + "plt.xlim([-20,20])\n", + "plt.xlabel('Soil Temperature ($^oC$)')\n", + "plt.ylabel('Depth (m)')\n", + "plt.plot([0,0],[15,0],'k--')\n", + "\n", + "for i in np.arange(int(ntime)):\n", + " gipl.update() # Update Once\n", + " tsoil[:,i] = gipl.get_value('soil__temperature') # save all temperature to a matrix\n", + " plt.plot(tsoil[depth>=0,i], depth[depth>=0],color = [0.7,0.7,0.7], alpha = 0.1) # plot result of each time\n", + " \n", + "plt.plot(tsoil[depth>=0,:].max(axis=1) , depth[depth>=0], 'r', linewidth = 2, label = 'Max' ) # Max\n", + "plt.plot(tsoil[depth>=0,:].min(axis=1) , depth[depth>=0], 'b', linewidth = 2, label = 'Min' ) # Min\n", + "plt.plot(tsoil[depth>=0,:].mean(axis=1), depth[depth>=0], 'k', linewidth = 2, label = 'Mean') # Mean\n", + "plt.legend()\n", + "plt.title('ALT='+str(depth[depth>=0][np.argmin(np.abs(tsoil[depth>=0,:].max(axis=1)))])+'m')\n", + "\n", + "ax2 = fig.add_subplot(1,2,2)\n", + "ax2.plot(tsoil[40,:], color = 'k', alpha = 0.7)\n", + "plt.title('Soil Surface (0.0m)')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[9,4])\n", + "divnorm = mcolors.DivergingNorm(vmin=-25., vcenter=0., vmax=10)\n", + "plt.contourf(np.arange(ntime), depth, tsoil, np.linspace(-25,10,15), \n", + " norm = divnorm,\n", + " cmap=\"RdBu_r\", extend = 'both')\n", + "\n", + "plt.ylim([5,0])\n", + "cb = plt.colorbar()\n", + "plt.xlabel('Day')\n", + "plt.ylabel('Depth (m)')\n", + "cb.ax.set_ylabel('Soil Temperature ($^oC$)')\n", + "\n", + "plt.contour(np.arange(ntime), depth, tsoil, [0]) # ZERO " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------\n", + "### Try to simulate a warming (10%) of air temperature on the permafrost" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gipl.initialize('gipl_config.cfg')\n", + "fig = plt.figure(figsize=[5,8])\n", + "plt.ylim([15,0])\n", + "plt.xlim([-20,20])\n", + "plt.xlabel('Soil Temperature ($^oC$)')\n", + "plt.ylabel('Depth (m)')\n", + "plt.plot([0,0],[15,0],'k--')\n", + "\n", + "for i in np.arange(int(ntime)):\n", + " tair_raw = gipl.get_value('land_surface_air__temperature')\n", + " gipl.set_value('land_surface_air__temperature', tair_raw + abs(tair_raw)*0.1)\n", + " gipl.update()\n", + " tsoil[:,i] = gipl.get_value('soil__temperature')\n", + " plt.plot(tsoil[depth>=0,i], depth[depth>=0],color = [0.7,0.7,0.7], alpha = 0.1)\n", + " \n", + "plt.plot(tsoil[depth>=0,:].max(axis=1), depth[depth>=0], 'r', linewidth = 2, label = 'Max')\n", + "plt.plot(tsoil[depth>=0,:].min(axis=1), depth[depth>=0], 'b', linewidth = 2, label = 'Min')\n", + "plt.plot(tsoil[depth>=0,:].mean(axis=1), depth[depth>=0], 'k', linewidth = 2, label = 'Mean')\n", + "plt.legend()" + ] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb b/docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb new file mode 100644 index 00000000..c5614738 --- /dev/null +++ b/docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Coupling GIPL and ECSimpleSnow models\n", + "\n", + "Before you begin, install:\n", + "\n", + "```conda install -c conda-forge pymt pymt_gipl pymt_ecsimplesnow seaborn```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33;01m➡ models: FrostNumber, Ku, Hydrotrend, GIPL, ECSimpleSnow, Cem, Waves\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "import pymt.models\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.colors as mcolors\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "sns.set(style='whitegrid', font_scale= 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load ECSimpleSnow module from PyMT" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 1D Snow Model\n", + "('snowpack__depth', 'snowpack__mass-per-volume_density')\n", + "('precipitation_mass_flux', 'land_surface_air__temperature', 'precipitation_mass_flux_adjust_factor', 'snow_class', 'open_area_or_not', 'snowpack__initial_depth', 'snowpack__initial_mass-per-volume_density')\n" + ] + } + ], + "source": [ + "ec = pymt.models.ECSimpleSnow()\n", + "print(ec.name)\n", + "\n", + "# List input and output variable names.\n", + "print(ec.output_var_names)\n", + "print(ec.input_var_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load GIPL module from PyMT" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 1D GIPL Model\n", + "('soil__temperature', 'model_soil_layer__count')\n", + "('land_surface_air__temperature', 'snowpack__depth', 'snow__thermal_conductivity', 'soil_water__volume_fraction', 'soil_unfrozen_water__a', 'soil_unfrozen_water__b')\n" + ] + } + ], + "source": [ + "gipl = pymt.models.GIPL()\n", + "print(gipl.name)\n", + "\n", + "# List input and output variable names.\n", + "print(gipl.output_var_names)\n", + "print(gipl.input_var_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the setup method on both ECSimpleSnow and GIPL to get default configuration files and data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('snow_model.cfg', '/Users/mpiper/projects/GIPL-BMI-Fortran/Notebooks')\n", + "('gipl_config.cfg', '/Users/mpiper/projects/GIPL-BMI-Fortran/Notebooks')\n" + ] + } + ], + "source": [ + "ec_defaults = ec.setup('.')\n", + "print(ec_defaults)\n", + "\n", + "gipl_defaults = gipl.setup('.')\n", + "print(gipl_defaults)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ec.initialize('snow_model.cfg')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gipl.initialize('gipl_config.cfg')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Get soil depth: [unit: m]\n", + "depth = gipl.get_grid_z(2)\n", + "n_depth = int(len(depth))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final soil temperatures will be (176, 365)\n" + ] + } + ], + "source": [ + "# Get the length of forcing data:\n", + "ntime = int(gipl.end_time)\n", + "\n", + "# Define a variable to store soil temperature through the time period\n", + "\n", + "tsoil = np.zeros((n_depth, ntime)) * np.nan\n", + "\n", + "print('Final soil temperatures will be ', tsoil.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[12,6])\n", + "\n", + "ax2 = fig.add_subplot(2,3,1)\n", + "ax2.set_title('Air Temperature (Input)')\n", + "\n", + "ax3 = fig.add_subplot(2,3,2)\n", + "ax3.set_title('Precipition (Input)')\n", + "\n", + "ax4 = fig.add_subplot(2,3,4)\n", + "ax4.set_title('Snow Depth (EC Output)')\n", + "\n", + "ax5 = fig.add_subplot(2,3,5)\n", + "ax5.set_title('Snow Density (EC Output)')\n", + "\n", + "ax1 = fig.add_subplot(2,3,(3,6))\n", + "ax1.set_ylim([15,0])\n", + "ax1.set_xlim([-20,20])\n", + "ax1.set_xlabel('Soil Temperature ($^oC$)')\n", + "ax1.set_ylabel('Depth (m)')\n", + "ax1.plot([0,0],[15,0],'k--')\n", + "\n", + "for i in np.arange(365):\n", + " \n", + " ec.update() # Update Snow Model Once\n", + " \n", + " # Get output from snow model\n", + " tair = ec.get_value('land_surface_air__temperature')\n", + " prec = ec.get_value('precipitation_mass_flux')\n", + " snd = ec.get_value('snowpack__depth', units='m')\n", + " rsn = ec.get_value('snowpack__mass-per-volume_density', units = 'g cm-3')\n", + " \n", + " # Pass value to GIPL model\n", + " gipl.set_value('land_surface_air__temperature', tair)\n", + " gipl.set_value('snowpack__depth', snd)\n", + " gipl.set_value('snow__thermal_conductivity', rsn * rsn * 2.846)\n", + " \n", + " gipl.update() # Update GIPL model Once\n", + " \n", + " tsoil[:,i] = gipl.get_value('soil__temperature') # Save results to a matrix\n", + " \n", + " ax1.plot(tsoil[depth>=0,i], depth[depth>=0],color = [0.7,0.7,0.7], alpha = 0.1)\n", + " \n", + " ax2.scatter(i, tair, c = 'k')\n", + " ax3.scatter(i, prec, c = 'k')\n", + " ax4.scatter(i, snd , c = 'k')\n", + " ax5.scatter(i, rsn , c = 'k')\n", + " \n", + "ax1.plot(tsoil[depth>=0,:].max(axis=1), depth[depth>=0], 'r', linewidth = 2, label = 'Max')\n", + "ax1.plot(tsoil[depth>=0,:].min(axis=1), depth[depth>=0], 'b', linewidth = 2, label = 'Min')\n", + "ax1.plot(tsoil[depth>=0,:].mean(axis=1), depth[depth>=0], 'k', linewidth = 2, label = 'Mean')\n", + "ax1.legend()\n", + "ax1.set_title('Ground Temperatures (GIPL output)')\n", + "\n", + "ax2.set_xticks([])\n", + "ax3.set_xticks([])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[9,4])\n", + "divnorm = mcolors.DivergingNorm(vmin=-25., vcenter=0., vmax=10)\n", + "plt.contourf(np.arange(ntime), depth, tsoil, np.linspace(-25,10,15), \n", + " norm = divnorm,\n", + " cmap=\"RdBu_r\", extend = 'both')\n", + "\n", + "plt.ylim([5,0])\n", + "cb = plt.colorbar()\n", + "plt.xlabel('Day')\n", + "plt.ylabel('Depth (m)')\n", + "cb.ax.set_ylabel('Soil Temperature ($^oC$)')\n", + "\n", + "plt.contour(np.arange(ntime), depth, tsoil, [0]) # ZERO " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples.rst b/docs/examples.rst index ceb578c9..5858521e 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -21,6 +21,7 @@ Single Models * :doc:`Frost Number Model ` (macOS, Linux, Windows) * :doc:`Kudryavtsev Model ` (macOS, Linux, Windows) +* :doc:`GIPL Model` (macOS, Linux) * :doc:`Coastline Evolution Model ` (macOS, Linux) * :doc:`Hydrotrend` (macOS, Linux) * :doc:`Sedflux3D ` (macOS, Linux) @@ -31,6 +32,7 @@ Coupled Models -------------- * :doc:`Coastline Evolution Model + Waves ` (macOS, Linux) +* :doc:`GIPL + ECSimpleSnow Models ` (macOS, Linux) .. Sphinx emits a warning if documents aren't in a toctree. @@ -42,10 +44,12 @@ Coupled Models + + From d1ac4801f1eb0743e11b0a1e2b3f6ddcabb27d1b Mon Sep 17 00:00:00 2001 From: gantian127 Date: Wed, 25 Sep 2019 10:14:24 -0600 Subject: [PATCH 02/27] fix issue for import plugins error --- docs/demos/subside.ipynb | 47 ++++++++++------------------------------ 1 file changed, 12 insertions(+), 35 deletions(-) diff --git a/docs/demos/subside.ipynb b/docs/demos/subside.ipynb index 2a29cf39..a3822359 100644 --- a/docs/demos/subside.ipynb +++ b/docs/demos/subside.ipynb @@ -66,22 +66,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m✓ Avulsion\u001b[39;49;00m\n", - "\u001b[32m✓ Plume\u001b[39;49;00m\n", - "\u001b[32m✓ Sedflux3D\u001b[39;49;00m\n", - "\u001b[32m✓ Subside\u001b[39;49;00m\n", - "\u001b[32m✓ Hydrotrend\u001b[39;49;00m\n", - "\u001b[32m✓ FrostNumber\u001b[39;49;00m\n", - "\u001b[32m✓ Ku\u001b[39;49;00m\n", - "\u001b[32m✓ Cem\u001b[39;49;00m\n", - "\u001b[32m✓ Waves\u001b[39;49;00m\n", - "\u001b[32m✓ Child\u001b[39;49;00m\n" + "\u001b[33;01m➡ models: Avulsion, Plume, Sedflux3D, Subside, FrostNumber, Ku, ExponentialWeatherer, Flexure, FlowAccumulator, FlowDirectorD8, FlowDirectorDINF, FlowDirectorSteepest, FlowRouter, LinearDiffuser, OverlandFlow, SoilMoisture, StreamPowerEroder, TransportLengthHillslopeDiffuser, Vegetation, Hydrotrend, Cem, Waves\u001b[39;49;00m\n" ] } ], "source": [ - "from pymt import plugins\n", - "subside = plugins.Subside()" + "import pymt.models\n", + "subside = pymt.models.Subside()" ] }, { @@ -368,7 +359,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -377,7 +368,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -405,22 +396,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -436,27 +427,13 @@ "plt.imshow(dz.reshape((500, 500)))\n", "plt.colorbar()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (pymt)", "language": "python", - "name": "python3" + "name": "pymt-dev" }, "language_info": { "codemirror_mode": { @@ -468,7 +445,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 72d8433af7c457e677a8b04c1d229945efb2eeed Mon Sep 17 00:00:00 2001 From: gantian127 Date: Wed, 25 Sep 2019 10:19:24 -0600 Subject: [PATCH 03/27] fix issue for import plugins error and get_time_step(), get_current_time() error --- docs/demos/sedflux3d.ipynb | 42 +++++++++++++++----------------------- 1 file changed, 17 insertions(+), 25 deletions(-) diff --git a/docs/demos/sedflux3d.ipynb b/docs/demos/sedflux3d.ipynb index 0eadb90d..24f2f0e4 100644 --- a/docs/demos/sedflux3d.ipynb +++ b/docs/demos/sedflux3d.ipynb @@ -4,8 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sedflux3D", - "\n", + "## Sedflux3D\n", "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/sedflux3d.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux`\n", "\n" @@ -41,12 +40,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33;01m➡ plugins: Child, Avulsion, Plume, Sedflux3D, Subside\u001b[39;49;00m\n" + "\u001b[33;01m➡ models: Avulsion, Plume, Sedflux3D, Subside, FrostNumber, Ku, ExponentialWeatherer, Flexure, FlowAccumulator, FlowDirectorD8, FlowDirectorDINF, FlowDirectorSteepest, FlowRouter, LinearDiffuser, OverlandFlow, SoilMoisture, StreamPowerEroder, TransportLengthHillslopeDiffuser, Vegetation, Hydrotrend, Cem, Waves\u001b[39;49;00m\n" ] } ], "source": [ - "from pymt import plugins" + "import pymt.models" ] }, { @@ -62,7 +61,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = plugins.Sedflux3D()" + "model = pymt.models.Sedflux3D()" ] }, { @@ -122,9 +121,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "sedflux_3d_argv.txt sedflux_3d_process.kvf sedflux_3d_sediment.kvf\r\n", - "sedflux_3d_bathy.csv sedflux_3d_river.kvf\r\n", - "sedflux_3d_init.kvf sedflux_3d_sea_level.csv\r\n" + "sedflux_3d_argv.txt sedflux_3d_process.kvf sedflux_3d_sediment.kvf\r\n", + "sedflux_3d_bathy.csv sedflux_3d_river.kvf\r\n", + "sedflux_3d_init.kvf sedflux_3d_sea_level.csv\r\n" ] } ], @@ -203,7 +202,7 @@ "source": [ "for t in range(10):\n", " model.update()\n", - " print(model.get_current_time())" + " print(model.time)" ] }, { @@ -259,7 +258,7 @@ } ], "source": [ - "model.get_time_step()" + "model.time_step" ] }, { @@ -287,7 +286,7 @@ ], "source": [ "model.update_until(200., units='year')\n", - "model.get_current_time()" + "model.time" ] }, { @@ -305,7 +304,7 @@ ], "source": [ "model.time_units = 'year'\n", - "print(model.get_current_time())" + "print(model.time)" ] }, { @@ -459,7 +458,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -476,12 +475,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -498,20 +497,13 @@ " model.update()\n", "model.quick_plot('sea_water__depth', vmin=-200, vmax=200, cmap='BrBG_r')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (pymt)", "language": "python", - "name": "python3" + "name": "pymt-dev" }, "language_info": { "codemirror_mode": { @@ -523,7 +515,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 2fb9664075946288c8c96a23b99dfdc38ba71ae9 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Wed, 25 Sep 2019 10:26:07 -0600 Subject: [PATCH 04/27] fix issue for import plugins error and get_current_time() eror --- docs/demos/child.ipynb | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/docs/demos/child.ipynb b/docs/demos/child.ipynb index dae4cc82..64c8c3fc 100644 --- a/docs/demos/child.ipynb +++ b/docs/demos/child.ipynb @@ -4,8 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CHILD Landscape Evolution Model", - "\n", + "## CHILD Landscape Evolution Model\n", "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/child.ipynb\n", "* Install command: `$ conda install notebook pymt_child`\n", "\n" @@ -71,9 +70,9 @@ } ], "source": [ - "from pymt import plugins\n", + "import pymt.models\n", "\n", - "model = plugins.Child()" + "model = pymt.models.Child()" ] }, { @@ -542,7 +541,7 @@ "source": [ "for t in range(10):\n", " model.update()\n", - " print(model.get_current_time())" + " print(model.time)" ] }, { @@ -594,7 +593,7 @@ ], "source": [ "model.update_until(201.5, units='year')\n", - "print(model.get_current_time())" + "print(model.time)" ] }, { @@ -887,7 +886,7 @@ "outputs": [], "source": [ "dz_dt = .02\n", - "now = model.get_current_time()\n", + "now = model.time\n", "times, dt = np.linspace(now, now + 5000., 50, retstep=True)\n", "for time in times:\n", " model.update_until(time)\n", @@ -951,7 +950,7 @@ } ], "source": [ - "model.update_until(model.get_current_time() + 5000.)\n", + "model.update_until(model.time + 5000.)\n", "model.quick_plot('land_surface__elevation', edgecolors='k', vmin=-200, vmax=200, cmap='BrBG_r')" ] }, @@ -986,9 +985,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (pymt)", "language": "python", - "name": "python3" + "name": "pymt-dev" }, "language_info": { "codemirror_mode": { @@ -1000,7 +999,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From b1c08211e650913f09adde9965e90c4bc105c203 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Wed, 25 Sep 2019 10:28:21 -0600 Subject: [PATCH 05/27] fix import plugins error and get_current_time() error --- docs/demos/sedflux3d_and_child.ipynb | 23 +++++++---------------- 1 file changed, 7 insertions(+), 16 deletions(-) diff --git a/docs/demos/sedflux3d_and_child.ipynb b/docs/demos/sedflux3d_and_child.ipynb index 441bbb2b..b57cf321 100644 --- a/docs/demos/sedflux3d_and_child.ipynb +++ b/docs/demos/sedflux3d_and_child.ipynb @@ -4,8 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sedflux3D + CHILD", - "\n", + "## Sedflux3D + CHILD\n", "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/sedflux3d_and_child.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux pymt_child`\n", "\n" @@ -15,20 +14,12 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33;01m➡ plugins: Child, Avulsion, Plume, Sedflux3D, Subside\u001b[39;49;00m\n" - ] - } - ], + "outputs": [], "source": [ "# Some magic to make plots appear within the notebook\n", "%matplotlib inline\n", "import numpy as np # In case we need to use numpy\n", - "import pymt" + "import pymt.models" ] }, { @@ -46,8 +37,8 @@ } ], "source": [ - "child = pymt.plugins.Child()\n", - "sedflux = pymt.plugins.Sedflux3D()\n", + "child = pymt.models.Child()\n", + "sedflux = pymt.models.Sedflux3D()\n", "\n", "child_in, child_dir = child.setup(\n", " \"_child\",\n", @@ -189,7 +180,7 @@ "metadata": {}, "outputs": [], "source": [ - "now = child.get_current_time()\n", + "now = child.time\n", "times = np.arange(now, now + 1000, 1.0)\n", "sedflux.update()\n", "child.update()\n", @@ -279,7 +270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.5" } }, "nbformat": 4, From 5127e3253c27241ba2e69b87c54ff13c560c6b23 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Tue, 8 Oct 2019 15:24:54 -0600 Subject: [PATCH 06/27] Add Kang and Tian as contributors --- AUTHORS.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/AUTHORS.rst b/AUTHORS.rst index c561aa3c..2032a410 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -12,6 +12,8 @@ Contributors ------------ * Niels Drost +* Tian Gan * Albert Kettner * Irina Overeem * Scott Stewart +* Kang Wang From 0c2f8efb0de70dfc6e8ddd8b300d34acb9d117c8 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Tue, 8 Oct 2019 15:25:46 -0600 Subject: [PATCH 07/27] Add Font Awesome icons for operating systems From the license (https://fontawesome.com/license/free), they're free and CC BY 4.0 licensed, and the files themselves contain the attribution. --- docs/_static/apple.svg | 1 + docs/_static/linux.svg | 1 + docs/_static/windows.svg | 1 + 3 files changed, 3 insertions(+) create mode 100644 docs/_static/apple.svg create mode 100644 docs/_static/linux.svg create mode 100644 docs/_static/windows.svg diff --git a/docs/_static/apple.svg b/docs/_static/apple.svg new file mode 100644 index 00000000..e24ed63e --- /dev/null +++ b/docs/_static/apple.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/_static/linux.svg b/docs/_static/linux.svg new file mode 100644 index 00000000..ca9b9a85 --- /dev/null +++ b/docs/_static/linux.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/_static/windows.svg b/docs/_static/windows.svg new file mode 100644 index 00000000..586ba25d --- /dev/null +++ b/docs/_static/windows.svg @@ -0,0 +1 @@ + \ No newline at end of file From 8e9df52bc6b65f1d450a8d63a087f454739b3d5a Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Tue, 8 Oct 2019 15:32:34 -0600 Subject: [PATCH 08/27] Use OS icons instead of text --- docs/examples.rst | 31 ++++++++++++++++++++++--------- 1 file changed, 22 insertions(+), 9 deletions(-) diff --git a/docs/examples.rst b/docs/examples.rst index 5858521e..c7ea099d 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -19,20 +19,33 @@ have to install Jupyter Notebook: Single Models ------------- -* :doc:`Frost Number Model ` (macOS, Linux, Windows) -* :doc:`Kudryavtsev Model ` (macOS, Linux, Windows) -* :doc:`GIPL Model` (macOS, Linux) -* :doc:`Coastline Evolution Model ` (macOS, Linux) -* :doc:`Hydrotrend` (macOS, Linux) -* :doc:`Sedflux3D ` (macOS, Linux) -* :doc:`Flexural Subsidence ` (macOS, Linux) +* :doc:`Frost Number Model ` |macOS| |Linux| |Windows| +* :doc:`Kudryavtsev Model ` |macOS| |Linux| |Windows| +* :doc:`GIPL Model` |macOS| |Linux| +* :doc:`Coastline Evolution Model ` |macOS| |Linux| +* :doc:`Hydrotrend` |macOS| |Linux| +* :doc:`Sedflux3D ` |macOS| |Linux| +* :doc:`Flexural Subsidence ` |macOS| |Linux| Coupled Models -------------- -* :doc:`Coastline Evolution Model + Waves ` (macOS, Linux) -* :doc:`GIPL + ECSimpleSnow Models ` (macOS, Linux) +* :doc:`Coastline Evolution Model + Waves ` |macOS| |Linux| +* :doc:`GIPL + ECSimpleSnow Models ` |macOS| |Linux| + + +.. |macOS| image:: _static/apple.svg + :height: 15px + :alt: macOS + +.. |Linux| image:: _static/linux.svg + :height: 15px + :alt: Linux + +.. |Windows| image:: _static/windows.svg + :height: 15px + :alt: Windows .. Sphinx emits a warning if documents aren't in a toctree. From 11b7a0c8f095819230727867da1c4e6d88f87047 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Tue, 8 Oct 2019 15:42:32 -0600 Subject: [PATCH 09/27] Add Notebook for ECSimpleSnow Courtesy of @wk1984. --- docs/demos/ECSnow_PyMT.ipynb | 219 +++++++++++++++++++++++++++++++++++ docs/examples.rst | 2 + 2 files changed, 221 insertions(+) create mode 100644 docs/demos/ECSnow_PyMT.ipynb diff --git a/docs/demos/ECSnow_PyMT.ipynb b/docs/demos/ECSnow_PyMT.ipynb new file mode 100644 index 00000000..19513903 --- /dev/null +++ b/docs/demos/ECSnow_PyMT.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ECSimpleSnow component in PyMT\n", + "\n", + "### It‘s an empirical algorithm to melt snow according to the surface temperature and increase snow depth according to the precipitation that has fallen since the last time step.\n", + "\n", + "### See details: \n", + "\n", + "**Brown, R. D., Brasnett, B., & Robinson, D. (2003). Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmosphere-Ocean, 41(1), 1-14.**\n", + "\n", + "**URL:** https://www.tandfonline.com/doi/abs/10.3137/ao.410101\n", + "\n", + "### Source code in Fortran:\n", + "\n", + "**URL:** https://github.com/permamodel/Snow_BMI_Fortran\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### load module" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33;01m➡ models: FrostNumber, Ku, Hydrotrend, ECSimpleSnow, Cem, Waves\u001b[39;49;00m\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "# Load PyMT model(s)\n", + "import pymt.models\n", + "ec = pymt.models.ECSimpleSnow()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### load example configuration and inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Call setup to get default config and data files.\n", + "defaults = ec.setup('.')\n", + "print(defaults)\n", + "\n", + "cfg_filename = defaults[0]\n", + "\n", + "%cat $cfg_filename" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### initialize by using default example data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('snowpack__depth', 'snowpack__mass-per-volume_density')\n", + "('precipitation_mass_flux', 'land_surface_air__temperature', 'precipitation_mass_flux_adjust_factor', 'snow_class', 'open_area_or_not', 'snowpack__initial_depth', 'snowpack__initial_mass-per-volume_density')\n" + ] + } + ], + "source": [ + "# Initialize the model with the defaults.\n", + "ec.initialize('snow_model.cfg')\n", + "ec.set_value('snow_class',2)\n", + "ec.set_value('open_area_or_not', 1)\n", + "\n", + "# List input and output variable names.\n", + "print(ec.get_output_var_names())\n", + "print(ec.get_input_var_names())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implement the simple snow model for the first year as an example" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Air Temperature Unit: C\n", + "Snow Depth Unit: cm\n", + "Snow Density Unit: kg per m3\n" + ] + }, + { + "data": { + "image/png": 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Nw2iCekbPNLuUm2gYRhPUM3qqamrtGiYahtEE9YyhQGrtGiYahtEEjSKspTUgj4lGD+M4DoVCgYGBAQqFQmqbu0mmUYQ1VWVqaqrDJWo/Jho9guM4jI6OIiKL6bLLLmNhYQFVZWFhoa9W8UoSy5Ytq3ts06ZNnHfeeR0sTfsx0egBHMfh0ksvZf/+/Uv2Hzp0aMn3fglcmzSeeuqphse3bNmSKrE30egBpqenOXLkSKi8ae0nJ5kwcUHTJPYmGl3GcZxIjkB9Ebg2YdTzDPWTJmcvE40uUumWRMHWQ+k9isUi69atC8yXFoO21HOD7QarV6/W+fn+WemgUChEfgP10v/LWIrjOExMTATmy2azzM7O9lzAaBHZqqqrA/P10o+w30RjYGAgkgjkcjn27NnTxhIZzRL2RdCL/8uwomHdky6yfPny0HltkaRkELb7uHfv3sR2U0w0uoTjODz99NMN82QyGUSEfD7PDTfc0HPNWeNYisUiAwPhHqukjqiYaLQRx3EYGxtb4rA1NjaG4zhceeWVDYdZs9ksc3NzHDlyhB07dphgJIgrrrgiVL7EDp+HWYatVgLOB7YDDwJX1Tj+M8C3gReBj4e5ZpqWZay1bF/YlMlkEr28n6G6atWqwP9zLpfrdjGXQMhlGWO1NEQkA3wGuABYBVwiIquqsj0F/Bbwp3HukUT8c0XWrl3LwYMHY13nyJEj1rJIONu2baNUKnW7GG0hbvfkTcCDqvoTVX0J+BJwkT+Dqj6pqt8H4j05CcNxHCYnJxfnijQTXNYcuNLBxo0bUdW6jl9B7ue9SlzReCXwsO/7Lm9f33LllVdy4MCBpq8zPDxsDlwpo95LIKkvh7iiUUs6Yzl8iMikiMyLyPzu3btjFqe7OI7D3r17m77OwMAA119/vXVNUsbMzAzZbHbJPhHhwgsv7FKJmiOuaOwCTvN9PxV4NM6FVHVWVVer6uoVK1bELE53adXQ2ec//3kTjBRSLBZZu3btkm6KqjI3N5dIX424ovF94HQReZWIDAMXAze3rljJohVDZyJigpFiNm/efIz3b1JDHQzGOUlVD4nIR4CvAxngelXdJiLrvOPXiMhPA/PAy4AjIvJRYJWqPtuisvcMK1eubHoWY5gJT0ZyqfdiSaKvRmznLlXdrKqvU9XXqOqMt+8aVb3G235cVU9V1Zep6oneduoEAwjVN81kMpTL5WOG4USEUqnExo0b21U8owdIlTE0jDNHp1JSnbtyuVygE485a/U35XJZs9nskt9FNpvtqd8FIZ27YnVPjKM0GjkRkdARuYx0U7FXTU9Ps3PnTlauXMnMzEwi7VgmGhFxHGfJP77RUGsim55G2ygWi4kUiWpswloEqr0+FxYW2LdvX9385qRl+EnLkhQWhCcCUSNt9dLf1ugulReO32u41yJ4WeSuNhAl0lYvRmYyuke9F04+n2fHjh2dL1ANLHJXGwhro7AoW0Y15qfRp4QJVQ/wtre9rWeanEZvkCY/DRONCBSLRd75zncG5rvtttvaXxgjUdSatJbNZhNpLDfRiIDjOHz7298OzNdMLA0jnRSLRWZnZ8nn84txX3vJCBoFM4RGIOzoSSaTOWYdVsPodcwQ2gbCGq0mJyfbXBLD6B4mGhEIMlplMhmbfGakHhONCNQzZpXLZVSVQ4cOmWAYqcdEIwJpMmYZ3SENruRmCDWMDtHrruRmCDWMHmN6evqYiPVJDPlnomEYHSItruQmGhFIQ3/U6B5pcSU30QhJrVgak5OTJhxGaFLjSh4mJmCnUi/HCM3n8zXjf+bz+W4XzUgQ5XJZ8/m8iojm8/lExgi10ZOQ1IulYXFAjbRgoyctJi39UcNoFhONkNRb2ySp63EaRlxMNEKyefPmSPsNI62YaISk3pT4pI2xG90lDcP2tu5JCBzHQURqGkLNpmGEpdqNvDJsD/SEG3lowgyx1ErA+cB24EHgqhrHBfgL7/jdwNlB1wwaci2Xy4FLIHYyiUhPDZkZvU29Yft2/TZLpVKk8hFyyDWuYGSAHwOvBoaBu3BXhPfnuRD4R0883gx8N+i6jUSjXC7r0NBQ14WiOhlGWESk47/PKMIRVjTi2jTeBDyoqj9R1ZeALwEXVeW5CPi8V57vACeKyMkx78f09DQHDx6Me3pbyGQy3S6CkSC60ZWdnZ1t+TXjisYrgYd933d5+6LmQUQmRWReROZ3795d94a9aHC0AMJGFLrhLt6O32hc0ai1+IfGyIOqzqrqalVdvWLFiro37EWDYz6f73YRjARRLBYZGOjsgGU7WsNxa7ALOM33/VTg0Rh5QjMzM8PQ0FDc01vO4OBg8iYaGV3niiuu6Oj92hLkOozhozrhDtX+BHgVRw2hZ1bleQ9LDaHfC7puUkZPRkdHbdTEiE2pVGq7UbSdoyexJ6yJyIXAp3FHUq5X1RkRWecJ0TXirl94Ne7Q7AHgN1W14Wy0Xp6wZhhpJ+yEtdjOXaq6Gdhcte8a37YCH457fcMwehNzIzcMIxImGoZhRMJEwzCMSJhoGIYRCRMNwzAiYaJhGEYkTDQMw4iEiYZhGJHoqSUMRGQ3UDuuXvsYA/Z0+J6dwuqWTLpVt7yq1p816tFTotENRGQ+jOtsErG6JZNer5t1TwzDiISJhmEYkTDRgNbHQ+sdrG7JpKfr1vc2DcMwomEtDcMwImGiYRhGJPpCNEQkIyL/LiJf874vF5FbROQB7/MkX971IvKgiGwXkXd3r9TBiMgOEfmhiNwpIvPevrTU7UQR+bKI3C8i94nIuWmom4ic4f2/KulZEfloouoWJiZg0hPw28BfAV/zvn8Kb1U44Crgk972Ktx4p8fhxj/9MZDpdvkb1GsHMFa1Ly11mwMu97aHgRPTUjdfHTPA40A+SXXr+h+uA/+YU4EtwDt9orEdONnbPhnY7m2vB9b7zv06cG6369CgbrVEI/F1A14GPIRnqE9T3arq8y7gjqTVrR+6J58Gfgc44tv3ClV9DMD7fLm3P9QCTz2EAt8Qka0iUolVn4a6vRrYDdzgdSuvE5ER0lE3PxcDX/S2E1O3VIuGiPwy8KSqbg17So19vTwm/RZVPRu4APiwiLytQd4k1W0QOBvYpKpvBPbjNtnrkaS6ASAiw8B7gb8JylpjX1frlmrRAN4CvFdEduCuN/tOESkDT1TWlfU+n/Tyt3SBp3ajqo96n08CX8FdYzcNddsF7FLV73rfv4wrImmoW4ULgB+o6hPe98TULdWioarrVfVUVS3gNgW/qaoTwM3AWi/bWuCr3vbNwMUicpyIvAo4Hfheh4sdChEZEZETKtu4/eN7SEHdVPVx4GEROcPbNQ7ci1c3EVmJa8+52TuemLr5uISjXRNI0v+t28agDhqd3sFRQ2gO1zj6gPe53JdvGtdCvR24oNvlrqrDW4FvAf8BPIPbbP8RsA2Y7nTdgNuAF4DngGeBrbjdiONacO2zgHngbuDvgZPq1c0rx9/16v+tRt2ywF7gp3z7EvObNDfyhCAiLwN2AiXgJtxhyP8EPK6qd3epTLcBZVWtGCp/AdfwvBc4Tzv04/KXoxP363dS3T1JGa8DUNUvquphVX1eVb9REQwRuUxE/k1E/lREnhaRh0TkgsrJInKKiNwsIk95jkL/xdu/TESeF5Ex7/t/F5FDnkghIn8kIp8OKpyq7lfV23CNe+firuWLiAyIyFUi8mMR2SsiN4nIcu9YQURURNaKyE4R2SMi074yv0lE5j0HqCdE5H9XnTcoIjO44nm1iOwTkatF5DMi8mf+8onIP4jIR+P+8Y2jmGgkhx8Bh0VkTkQu8HsM+vhF3CbsGK6z0OdEpGJ9/yKuUe0U4APAH4vIuKq+AHwfeLuX72240dPe4vt+e9hCqupO3G7Ff/J2/RbwPu/6pwBPA5+pOu2twBm4tovfF5Gf9fZvADao6suA1+C2sKrvNw38K/ARVR1V1Y/gOoZdIiIDAJ4gjrPUhmDExEQjIajqs7gPlwKfBXZ7LYdX+LItqOpnVfUw7oNzMvAKETnNO/d3VfUFVb0TuA5Y4513O/B2ERkEXg/8hfd9GW6X418jFvdRYLm3fQWuvWWXqr4IfAL4gHevCn/otZzuwvV+fIO3/yDwWhEZU9V9qvqdMDdX1e/h2n3GvV0XA7fp0ZEKowlMNBKEqt6nqpep6qnAz+G+uf1dh8d9eQ94m6NevqdU9Tlf3gWOOgndjmsoPhv4IXALbsvgzcCDqho1XuUrgae87TzwFRF5RkSeAe4DDgN+sXvct33AKzPAh3C7ZfeLyPc9v5uwzAET3vYE8IVoVTDqYaKRUFT1fuBGXPEI4lFgeWWI1mMl8Ii3/S3c7sH7gdtV9V7v+HuI0DUB8Fo153C0dfIwrsX/RF9apqqP1L+Ki6o+oKqX4HpHfhL4smdwPSZrjX1l4CIReQPws7gjMEYLMNFICCLyMyLyMRE51ft+Gu5Yf2CTXVUfxhWGP/EMn6/HfYs73vEDuMOlH+aoSHwLt2sRSjREJCsib8f1L/gesNk7dA0wIyJ5L98KEbko5DUnRGSFqh7BHWIGt5VSzRO4rueLqOouXFvNF4C/VdXnw9zTCMZEIzk8h2vo/K6I7McVi3uAj4U8/xKggNvq+ArwB6p6i+/47cAQRx2HbgdOAP4l4LpXi8hzuA/up4G/Bc73HnRwjZk3486Rec4r9y+GLPP5wDYR2edd52LPcFvNBlw7ydMi8he+/XPAz2Ndk5ZifhpGavHm4pSBgk/EjCaxloaRSkRkCLgSuM4Eo7WYaBipw/PzeAZ3yDnQMc2IhnVPDMOIhLU0DMOIxGBwls4xNjamhUKh28UwjL5k69atezTEAtA9JRqFQoH5+fluF8Mw+hIRWQiTz7onhmFEwkTDMIxImGgYhhEJEw3DMCJhomEYRiRMNAyjxUxNTSEix6SBgQGmpqa6XbymMdEwjBbgOA5jY2OICJs2baqZR1XZtGnTEiE54YQTcBynw6Vtjp7y0zCMJOI4DmvXruXw4VqhPhqzb98+LrvsMgCKxWKLS9YerKVhGE1y5ZVXxhKMCocOHWJ6ejo4Y49gomEYTbJ3796mr7Fz584WlKQzmGgYRhO0yh6xcuXKllynE5hoGEYTtKJbMTg4yMzMTAtK0xlMNAyjCRYW6s/xEhFKpRLlcpmRkVpB1GF0dJQbb7wxMUZQsNETw2iKTCZT0wgqIhw5cjTKYJJEIYjILQ0RyYjIv4vI17zvy0XkFhF5wPs8yZd3vbdu6HYReXcrC24YvUC9UZM0R8SL0z25EneVrApXAVtU9XRgi/cdEVmFuxzembih6DeKSKa54hpG7+A4DkeXyl1KPp/vcGk6RyTR8BbqeQ/uOqAVLsJdXwLv832+/V9S1RdV9SHgQeBNzRXXMHqH6enpmi0KEUmUYTMqUVsanwZ+B/CHhH+Fqj4G4H2+3Nv/Stwl+Srs4ujaoYaReOr5VqhqqmwY1YQWDW/x3SdVdWvYU2rsO0aWRWRSROZFZH737t1hi2MYXWf58uU19+dyuQ6XpLNEaWm8BXiviOwAvgS8U0TKwBMicjKA9/mkl38XcJrv/FNxlwRcgqrOqupqVV29YkVgTFPDMLpMaNFQ1fWqeqqqFnANnN9U1QncdTrXetnW4i4AjLf/YhE5TkReBZzO0XVCDSPxPPXUU5H2p4VWOHf9f8AvicgDwC9531HVbcBNwL3APwEfVtX4s3oMo8eo5/qdJJfwOMQSDVW9TVV/2dveq6rjqnq69/mUL9+Mqr5GVc9Q1X9sVaENoxe48MILjxlyzWazqR45AXMjN4xYOI7D3NzckiFXEWHt2rWpHjkBEw3DiMX09DQHDhxYsk9V2bx5c5dK1DlMNAwjBvV8NJIUFyMuJhqGEYN+NYKCiYZhxGJmZoZsNrtkXz8YQcFEwzBiUSwWWbt2LZmMOwczk8n0hREUTDQMIxaV0ZPK1PjDhw8zNzeXuOUI4mCiYRgxqDV6cuDAgURFFY+LiYZhxMBGTwzDiISNnhiGEQkbPTEMIxLFYpHZ2Vny+TwiQj6fZ3Z2ti9GT6SXAqCuXr1a5+fnu10Mw+hLRGSrqq4OymctDcMwImGiYRgxcByHQqHAwMAAhUKhL/wzKkQ9JI4cAAAgAElEQVSJEbpMRL4nIneJyDYR+UNv/ydE5BERudNLF/rOsXVPjNThOA6Tk5MsLCygqiwsLDA5Odk3whHapiFutJERVd0nIkPAv+GugXI+sE9V/7Qq/yrgi7jLFpwC3Aq8rlH0LrNpGEmgUCjUXI4xn8+zY8eOzheoRbTcpqEu+7yvQ15qpDi27omRSvrZsQuiL5aUEZE7cSOO36Kq3/UOfURE7haR633LMtq6J0Yq6WfHLogoGqp6WFXPwl2O4E0i8nPAJuA1wFnAY8Cfedlt3RMjlfSzYxfEDyz8DHAbcL6qPuGJyRHgsxztgti6J0Yq6WfHLohmCF0BHFTVZ0TkeOAbwCeBrZVlGUXk/wV+UVUvFpEzgb/iqCF0C3C6GUINozcJawgdjHDNk4E5b+X3AeAmVf2aiHxBRM7C7XrsAK4Ad90TEamse3IIW/fEMFKBuZEbhgGYG7lhGG3CRMMwItLPLuQQzaZhGH1PxYW8Euqv4kIO9M3oibU0DCMC/RwbtIKJhmFEoN9dyMFEwzAi0e8u5GCiYRiR6HcXcjDRMIxI9LsLOZhzl2EYHubcZRhGWzDRMAwjEiYahmFEwkTDMIxImGgYhhEJEw3DMCLRinVPlovILSLygPd5ku8cW/fEMFJGlJbGi8A7VfUNuEGEzxeRNwNXAVtU9XTckH5XweK6JxcDZ+KujbLRi/plGImm36fGt2Ldk4uAOW//HPA+b9vWPTFSR7+vrgatWffkFZXAwt7ny73stu6JkTpsanxr1j2ph617YqQOmxrfgnVPgCdE5GQA7/NJL5ute2KkDpsaH230ZIWInOhtHw+cB9wP3Ays9bKtBb7qbd8MXCwix4nIq4DTge+1quCG0Q1sanxr1j35NnCTiHwI2An8Kti6J0Y6qUyBn56eZufOnaxcuZKZmRmbGt8tbGq8YXQPmxpvGEZbMNEwDCMSJhqGYUTCRMMwjEiYaBiGEQkTDcMwImGiYRhGJEw0DMOIhImGYRiRMNEwDCMSJhqGYUTCRMMwIjA1NcXg4CAiwuDgIFNTU90uUseJMsvVMPqa8847jy1btix+P3z4MJs2bQJg48aN3SpWx7GWhmGEwHGcJYLhZ3Z2tsOl6S4mGoYRgssvv7zuscOH+ytMTJTIXaeJyD+LyH3euidXevs/ISKPiMidXrrQd46te2IknjPPPJMXXnih7vFMpr9W5ojS0jgEfExVfxZ4M/Bhb20TgD9X1bO8tBls3RMj2TiOw+joKCLCvffe2zDv5ORkh0rVG4Q2hHrLE1SWKnhORO6j8ZIEi+ueAA+JSGXdk283UV7DaDvVBs9GDA8P95URFGLaNESkALwR+K636yMicreIXO9bljHUuie2hIHRCziOw9jYGCISWjAArr/++jaWqjeJLBoiMgr8LfBRVX0W2AS8BnepxseAP6tkrXH6MQFJbQkDo5s4jsNxxx3HxMQEe/fujXTu+Ph4XwUUrhB1hbUhXMFwVPXvAFT1CW8RpSPAZzm69GKodU8Mo1tMTU0xMTHBSy+9FPncVatWceutt7ahVL1PlNETAT4H3Keq/9u3/2RftvcD93jbtu6J0ZNUWhcVx6yojI+Ps23bthaXKjlE8Qh9C7AG+KG3nivA7wGXiMhZuF2PHcAVYOueGL2H4zh88IMfjNWyAFi2bBnXXXddX3ZJ/EQZPfk3atspNjc4Zwbon6WnjJ7FcRzWrFlDnHV+BgcHufHGG/teLCrY3BOjL7j88stjCcb4+Hjf2i7qYW7kRuoJ8uisRT6fp1wum2DUwETDSC2O4zA4OBjo0emnVCqhquzYscO6I3Uw0TASid8Zq5LGxsZwHGeJ70XYyWSjo6OUy+W+8+6Mhar2TDrnnHPUSDalUklFRHFH03R0dFTL5XLL71G5frNpfHy8pWVLMsC8hnhOuy4U/mSikVzK5bJmMpmGD2grBMQEo32EFQ3rnhhNc95554XqCuzbt4+JiYnYIfKmpqZiO2RV088enc1iQ65GbOI+xHFC5EWZeRrEqlWr+tqjs1mspWGEYmpqioGBgSWGx2be+ps2bcJxnFD3jTrztBGlUskEo1nC9GE6lcym0VuUy2UdGRlpmQ2hVqpn52il7QKzX4SCkDYN654YNWlld6AR+/bt47LLLgNY9Is488wzI/lWNMLmi7Qe6570IbV8HESETCbD1NRUxwSjwqFDh5iengZaJxgVv4vnn3/eBKPVhGmOdCpZ96S9tLrJXy8tW7ZMy+WylstlHRoaCnWOiOj4+Hik+4iIlkolzeVyi/tyuVzL/UL6BcxPw/AT9YGMkwYHB495YMvlsg4MDHTkXkZzhBUN656knIpLdTu7GyJCqVTi4MGDx3QFisUin//85xkaGmrZ/erdy+gQYZTFFSFOA/4ZuA/YBlzp7V8O3AI84H2e5DtnPfAgsB14d9A9rKXRWtrVHSmVSpHLUi6Xl3QjoiZrWbQfWt09AU4Gzva2TwB+BKwCPgVc5e2/Cvikt70KuAs4DngV8GMg0+geJhrNUSqVAl2546aKnaJZ/PNSwqZVq1a14K9jBBFWNEJ3T1T1MVX9gbf9HG6L45W465vMednmgPd524vrnqjqQ7gtjjdh4DgOhUKBgYEBCoVCKCenoOsNDg6yadOmtiwRODIy0rJRiJUrV0bKPzw8bM5YvUYYZalOQAHYCbwMeKbq2NPe59XAhG//54AP1LjWJDAPzK9cubKtStoLlMtlHR4eXvImHR4ejv0W78SISCu7BeVyuWv3NhpDu0ZPgFFgK/D/eN/ricZnaojGrzS6dj90T5YtW1bz4cjlcpGu06xYVDwkg64Tx34RxOjoaKgytuPeRn3aIhrAEPB14Ld9+7YDJ+tRu8d2b3s9sN6X7+vAuY2un0TRKJfLms/nVUQ0n883fDMGDXsGnR/2OmEFo0ItW0jYssShVmvLn9oRg8MIpuWigRuJ/PPAp6v2/y+WGkI/5W2fyVJD6E9ImSE0SlcjbLM8m81qqVRaMudjYGBg8a3brGD0ytu7IrbAomC1U6iMYNohGm/1fnh3A3d66UIgB2zBHXLdAiz3nTONO2qyHbgg6B5JE416Q4i1uhphm+SN0imnnBL73FaNfhjpJaxotGLdE4DxOueket2Temt/Vu93HId9+/Y1fb9HH42+qmUul2PDhg3mCGW0DJvl2gHWrVvX0fuVSiULkGu0DXMjb4JcLhe4v1WtjLDlsYjaRrsx0WiCX/u1Xwvc34lWhohQLpfZs2ePdUOMtmOiERPHcZibm6t57LrrruO8885DRDrSyvjCF75gYmF0DHGNpr3B6tWrdX5+vtvFCEWhUGBhYaGpa+RyubrG1CjX2LNnT1PXMAwAEdmqqquD8llLIyY7d+5s6vyRkRE2bNjQcMp4JpOhXC7XtZ0AbNiwoalyGEZUTDRiEnXiVTXXXnstxWKRG264oaYo5HI55ubmuOOOOxq2RqxbYnQaE42YvPa1r419bqlUWnzYi8Uie/bsOcaBZs+ePdxxxx0tWxzIMFqF2TRi4DgOExMTsc4dGRkJbRwdHBxsONXd7BlGKzGbRgtxHIfR0dHFqN1xBWN4eJhrr702dP6g2BhmzzC6gYlGAI7jsGbNGvbv39/UdfL5PNdff31oG0SYwDxmzzC6QpgJKp1KQRPWmo0z2a2Uz+ejzh1anAHaymsavUEnfseV5R2iQNqWMIiyhkYvJRGJNbu0USzNbDZrM1YTSqd/x1GEI3WiEfTm7cUUR+2D6pvJZEwwEkynf8eZTCZ02cKKRmJsGs06U7WbUqlEuVwmn88jIuTzeb7whS/Enjw2MzNDNptdsi+bzTI3N2e2jATT6d9xOwJNh24FANcDTwL3+PZ9AngEX1Ae37FIa55oglsaIhJazaMQJZSgkQzS0NKIIhpvA86uIRofr5E38ponmmCbRq+E0DN6nzTYNKKse/IvwFMhs7d8zZNGLted4rjjjmN4eHjx+8DAgAW8MSLRqd9xZanMdvw2I3mEikgB+Jqq/pz3/RPAZcCzuGuXfExVnxaRq4HvqGrZy/c54B9V9cs1rjmJu/YJK1euPKfZmaOGYcSjUx6hm4DXAGcBjwF/Vrl/jbw11UlVZ1V1taquXrFiRZPFMQyj3TQlGqr6hKoeVtUjwGc52gXZhbtgdIVTgehRcQ3D6DmaEg0ROdn39f3APd72zcDFInKciLwKOB34XjP3MgyjNwgdjVxEvgi8AxgTkV3AHwDvEJGzcLseO4ArAFR1m4jcBNwLHAI+rKptGDA2DKPT9NTUeBHZDXTaEjoGpHV+udUtmXSrbnlVDTQs9pRodAMRmQ9jMU4iVrdk0ut1S4wbuWEYvYGJhmEYkTDRgNluF6CNWN2SSU/Xre9tGoZhRMNaGoZhRMJEwzCMSPSFaIhIRkT+XUS+5n1fLiK3iMgD3udJvrzrReRBEdkuIu/uXqmDEZEdIvJDEblTROa9fWmp24ki8mURuV9E7hORc9NQNxE5w/t/VdKzIvLRRNUtzPz5pCfgt4G/wp2hC/Ap4Cpv+yrgk9pEHJAu1msHMFa1Ly11mwMu97aHgRPTUjdfHTPA40A+SXXr+h+uA/+YU4EtwDt9orEdONnbPhnY7m2vB9b7zv06cG6369CgbrVEI/F1A14GPIRnqE9T3arq8y7gjqTVrR+6J58Gfgc44tv3ClV9DMD7fLm3/5XAw758u7x9vYoC3xCRrV5cEkhH3V4N7AZu8LqV14nICOmom5+LgS9624mpW6pFQ0R+GXhSVbeGPaXGvl4ek36Lqp4NXAB8WETe1iBvkuo2iBtacpOqvhHYj9tkr0eS6gaAiAwD7wX+JihrjX1drVuqRQN4C/BeEdkBfAl4p4iUgScq0/q9zye9/ImKA6Kqj3qfTwJfwY1nkoa67QJ2qep3ve9fxhWRNNStwgXAD1T1Ce97YuqWatFQ1fWqeqqqFnCbgt9U1QnceB9rvWxrga962z0bB0RErhGR/+H7PiIiJ1S2cfvH95DAulWjqo8DD4vIGd6ucdwwC4mvm49LONo1gSTVrdvGoA4and7BUUNoDtc4+oD3udyXbxrXQr0duKAL5bwNeBo4LiDfq3Gt6ncB24BpX90exLXhHAJeAA4D+4AXgZe6VbeIf4ezcOPO3g38PXCSV7f7gQO99n+LWLcssBf4Kd++nv1NVidzI+8hvMDNPwb+A7hCVYP6u5XzBlX1UJ1jl+EOXb61RcVsG43q4cuzDviAqp7XrnsYjUl19ySBXAp8B7iRo01VAETkRhH5I2/7HSKyS0R+V0QeB26IeiMR+TkR+aaIPO05T73Pd+xLIrLBczLaLyK3icjLRWSjiDwjIttE5Od9+R8Xkd/xHLGeEpFZETnOd/z9InK3d+6/isiqqnM/LiLbcKPaIyK/LyIPichzInKPiLzH2/9G3NGwd4jIPq/uiMh3RGTCd811InKrt71MRFRESiLyY7yQlI3qbzTGRKO3uBRwvPRuEXlFg7w/DSzHdQyabJDvGETkZcAtwOdwo0RdClwvIq/1Zft14OPe8UFcMbsdtxm9GdcZyc8luL4wZwBvBP6bd683AxuB3/TO/QLw9yLiDzX568AvecfBbYb/X8BPAZ8EviQiY6r678BHgdtUdVRVfzpCtX8ZOAd4Y8j6G3Uw0egRROStuAJwk7pDxD8GfqPBKUeAP1B3QarnI97u/bgr5TnqRpP/PvAPwK/48vyNqt7lXfurwH+o6l+rG+v1Jlxh8LNBVR9V1d3An+CKCLhxY69W1a3evWZxvRvP8Z375965zwN493lMVY+o6hdwl/7054/DjKo+490jTP2NOoQOLGy0nbXAN1S1Ehvyr7x9f14n/25VfSHmvfLA20TkGd++QVwDbIUnfNvP1/g+WnVNvwPSAnCK716/JiL/zXd8mKUOSv5zEZEPAVcCK71do7gtgmbw3yNM/Y06mGj0ACJyPPBrQKbST8d9G58oIm9Q1btqnNaMBfthXIH6v5u4RjV+X4KVHPUleBj4P6r6Z8eesshiXUTkdcBf4nZ1vqeqR0Tkfo46OdWq937cEYkKtbot/vPaUf++wbonvcH7cIdFV+EONZ4F/Czwr7j97Vbz97h9+18XkSERGRaRN3sPbFx+S0ROFpExXO/Nv/b2zwL/VURWi8uoiLxXRLJ1rjOK2/XaDQx4oyV+W8MTwGkiMuTbdyfwAc/o+TO4S4U2oh317xtMNHqDtcANqrpTVR+vJOBqoFhlNGwaVX0aeDeucfIx3FbBHwFDjc4L4EvAP+P6GfwQz1CqqncAvwVcCzwD/AjXVlNvmc4fANfg+mg8hjuzc96X5Z9wJ+o9Ke76O3j3GsQVmlmg3Kigbap/32B+GkbTeF2qD6jqv3W7LEb7sZaGYRiRMNEwDCMS1j0xDCMS1tIwDCMSPeWnMTY2poVCodvFMIy+ZOvWrXs0xALQPSUahUKB+fn54IyGYbQcEVkIk8+6J4ZhRMJEwzCMSCRSNBzHoVAoMDAwQKFQwHGcbhfJMPqGnrJphMFxHCYnJzlw4AAACwsLTE664SSKxWI3i2YYfUHiWhrT09OLglHhwIEDTE9Pd6lEhtFfJE40du7cGWm/YRitJXGisXz58pr7V65cWXO/YRitJVGi4TgOzz777DH7h4eHmZmZ6UKJDKPzdHsgoKfmnqxevVobOXcVCgUWFo71P8nlcuzZs6fGGYaRLqoHAgCy2Syzs7NNDwSIyFZVXR2UL1EtjXp2i6eeempxu9sqbBjtpBcGAloiGiJyvYg8KSL3+PYt99bNeMD7PKnZ+9SzW1T2T01NsWbNGhYWFlDVxeFYEw4jLfTCQECrWho3AudX7bsK2KKqp+MuM9do1e9QzMzMkM0uDS0pIlx44YU4jsM111xDdXfLhmONNFHvxbl8+fLOtbBbuD5lAXcticr37cDJ3vbJwPaga5xzzjkaRKlUUhFR3BiTCqiI6Ojo6JJ91ccNIw2USqWav/HBwcEl37PZrJbL5UjXBuY1xLPeTpvGK1T1MU+YHgNeXiuTiEyKyLyIzO/evTvwops3bz6mNaGq7Nu3r+45NhxrpIXNmzfX3H/o0NLladvZwu66IVRVZ1V1taquXrGi8VR+x3Fqjp4EEXY4tmJEFREGBwcRETOmGj1FFNtF2+wcYZojYRJt7p6Uy2XNZrN1uyCNUhgaXT9OU88w2kE+nw/9u8/n85GuTQ90T27m6Mrna3HXA41NraGmMOTz+aavb8ZUo1eoNRhQi2w22z6HxzDKEpSAL+IuOnMQ2AV8CHcF8C24i+dsAZYHXadRS6Pa+FmdhoaGdHh4OHYLIej6hGyxGEY9yuWy5vN5FRHN5/OxW6/lcllHRkYa/lbHx8cjX5dOtjRU9RJVPVlVh1T1VFX9nKruVdVxVT3d+3wq+Er1aWTMzOfzXH755ZxwwgmL+3K5HLOzswChhqKCjKWZTCZGqQ3DpeLJ2ciHKIpjYlCr+5vf/Gb7bHFhlKVTKapNo9KSqHesVCrVPaeaekNZWEvDaAH1bBEjIyOq2vj3HfZa1SmXy0UqIyFbGl0XCn8K8tPwN+9yuZzmcjkVEc1kMpEMo34DUeWaUc4xjKg06v6WSqWGv8FKVybsb9WfonSBUikaFZoZSfG3GqJcp1Qqhf7jG0Y1uVyu7m8rk8mEstlVO3C1urURVjS67qcRh7gjKX4KhQLr1q0LfZ25uTnz1zDawuHDhwNtagcPHjzGgSsMe/fujVusuiRqanyFgYEBulHufD7Pjh07On5fI/kE/WZHRkbYv39/W+4d9llJ5dT4Ct1yC4/jjWoYEPybbZdg5HK5ll8zkaIR1sGl1diwqxGXbvxmh4aG2LBhQ8uvm0jRKBaLrF27tuP3PXz4cMfvaaSDYrHI7OxsR188N9xwQ1uW9UikaADcdNNNHb9nWJd0w6hFsVhkbm6OoaGhtt8rn8+3bR2gxIpGO6zCjagE+zGMOFS8PdesWRNrFCQK7Q60nVjRaDerVq1CRBa/q6oNuxqxqHYhb+XIX6lUWmLszOVyXH/99e1dbTCMM0enUljnLtXGzjKtSPW8TM0ztL/xe2VWfiNBk8+ienGGTa3+LZJmj1BV95/X6A9acc2tuJxXz4CNmyx0YP8SN+ZKOwSjHTFeUi8aXiXrpmr881bCzFWxloZRTdg5StUPc9S5UWF+m+0IChVWNPrGplEsFtmxYwdHjhwJtGBns1ne8Y531DxmxtD+JUz4vIWFBSYmJjjhhBMWp7i3cqheRJibm2uvzSKAtouGiJwvIttF5EERaXoZAz/1vN2CvOCKxSI33HDDknwDA+6fIp/PMzs7y4MPPljz3HqBXY30E8UTed++fahqS72IRYR169Z1VTCA9nZPgAzwY+DVwDBwF7CqXv6o3ZNyuaxDQ0NLmm5DQ0Mtabo1mnWYy+UsZmgfUi6XQ0V4a0fqxG+OXrBpAOcCX/d9Xw+sr5c/qmioti6EWjVB/dfh4WETjj4kjWLhq1tPiMYHgOt839cAV9fLH0c02kWYt4r/uLU++oNWDfXncrnAWC5RI281S1jRaLdNQ2rs0yUZIi6W1CmKxWJF6OriP753714++MEPmvNXipmammqJJ/Lw8DAbNmxgdnZ2cWqC35EQXGN8OyabtYQwyhI30YHuSTuJ45RjQ7LppJX2jEpc0Orrt6ObHQVCtjTaGoRHRAaBHwHjwCPA94HfUNVttfKHDcLTKRzHYWJiIvJ57fybGt1hbGyspfOdevE30hNBeFT1EPAR4OvAfcBN9QSjFykWi5GDmNSa+hwlNH0tHMdhbGwMEUFEGBsbs25QB3Ecp+MTJHuaMM2RTqVe656o1h7WDUp+aq1yH8UFuN79bfSmc7R67kinDZxhoRdGT6KmXhQN1eB5Lv5UvTxCvX5wGNtHuVxu6IJs9pPO0Oj/XZnbFPb30So/onZgotFiyuWyDgwMBP4oKj+IoPxBE9/CLK9gk+faT9ALQ0Tq/q9GR0cVws+G7TYmGm2gXC4HvlVyuZyWSqXALk1QK8EWcOoNgv4Plf9BL4x+NIuJRpuIY+Oo93ZqRJjhPVvAqf0E/R+SKA71CCsafTPLtVVMT09z8ODBpq6hqkxMTDQcBQkzOcomz7WfRv+HXC7X/cljXcBEIyKtnLXYyIt0ZmbmGC/BasJM1TaaY2ZmpmYYhYpXZ18SpjnSqdTr3ZN2zXKsZ5uoNVwb5rxm65j0vnmr8If264d5RphNo/W0K9Zjo1GQesbXdoR7qzUK0I77JIF+/FuYaLSBdsVSCOuz0e4WQD1R7MdRmnp/i3aF2usFTDTaQDtaGmFGUjpFPVFstT9IN7pAUe/Z6AWR1haHiUYbqNVkHRoaCuX01Sj1yg+wEy2NbjT749wzrH9GmjDRaBO13lilUqkt3ZNOv5E78UB3owsU555BRu80euOaaHSQVkRzqhaIUqnUFUNcu4WqU12gVtzTWhomGm0hymS2Rql6MadmJrr1MklpaTQ6r5fsUK3ERKNDhDGOtnqxnCT7UHTLplEtymFCC9Qqq4ik1n2/I6IB/CqwDTgCrK46th54ENgOvDvM9ZIoGkHDsNlstmZXoxUpqVb8bthq4i510U/Obp0SjZ8FzgBu84sGsAp3jZPjgFfhrn2SCbpeEkWjUUvD/yPzexdGaVEE5Ulid6XTD2JcQ2i/iEWFjnZPaojGkgDCuOH+zg26ThJFI2pzO0ycjEoaHx8PNLImzYrfje5JVENos9HWkkq3ReNqYML3/XPAB4Kuk0TRUI3+VvLnbyQIYdbGaHVLo91v2F42hAbFS+nVMH2tomWiAdwK3FMjXeTLUy0an6khGr9S5/qTwDwwv3Llys78dbpMnK5KrdTqt18nWgHdGHINU6+wLcA0tza63dLom+5JVKJ0T4JaGFGCE4dpPXSiFdCt+S1Bf4OwIp5EG1JYui0aZ7LUEPoTUmoIjUorWhhRfrhRWg+daAX06uzRsH/7pNmQotAR0QDeD+wCXgSeYOlqatO4oybbgQvCXK8fRKPZmbJRH7Aob/ZOtQJ6YWQiTLxXa2m0saXRqtQPotFsSyPqAxal9dDJVkA3hSNunNe0rzVjotGjNGvTiPqwRRk5qOSNGnI/zuhRN+fVxPm7DwwMpFowVE00epq4s2JHRkYiP2xxRw7CPsStnHYe1PRvpnXSTBT5tM41qcZEo4eJ87YbGhqq2wdv9mFrxpYRp0xxDK7NOlw1OxO5HzDR6GGCjKGVpf5yudySB71doxtxH5ZGLaZGZYoqUo3qHtYw2YxgpNn46cdEo4dp1NIYGBio2yKoLPNXnZr1VKwXeSyTydQ9JyhITdC8jihdmqCWWRjiCkYvDAd3ChONHiZsDA7/NOxG5zQjGkFlqUfQgxzFlT7IPhHUMguaql4uh1uHt5bw9YtgqJpo9Dxhf7gVI1yzD2k9Gl03jl2iFS2fKGX0/41qEXe0qp9aGBVMNHqcKMbQyhs57o+80Vu90XXjjIBEHWkI0+II8+DXE7hm/GLSvFxBLUw0epwob8DKAxVGXMLcxy8w9UYVRkZGYpV/dHS0Lf4a5XK5YQS0eobXTnvgJhkTjQTgf8sGTckOIzK1HpygIdF6xxsZZOuVvzqknv+Bq9WiCOoaVd83zihKsx64ja6dNkw0Eki9IcxKMzlovkQtL88ggQnzJg7jQFavFVB5+Gu1KOLcd3x8vGbe8fHxumVrdlZxmiep+THRSCgjIyN1Wxuq4W0JQc35isCEfRM38qEIeijrCV2YgMvV941jS/G3cuKOovQDJhoJpdGPV7Vxy6BCmAfZ320I8yau97ZttvkfpcsVNDwcxjM2qiu52TRMNHqeINFo9KYtlUqhHuLqIVH/m7hRN6OasP4mjR7yMLaNyr3i2HT89QtTppGRkdD+I2nDRCOh1GvKVx70IHfyZt6c9Wwmtc5p1lbgn2beyPmq4rjVymNz6QIAAAqbSURBVNGjRqmfRKIaE42EUi4HL+wT90Ft5HdQ7+GqjNxU02y3xN/aCdPSCBLEeuuYRCln2gMHB9ER0QD+F3A/cDfwFeBE37G+WCypHcSdlRq3hdHomvXsBM36P/i7EmEWWg6q8/Dw8GL3LMwkv6h/n36gU6LxLmDQ2/4k8Elvu28WS+oGUZvc9VoLfoIe3Grxaral4RejVto0/N+HhoZCjZb0m+dnPToiGksu5MYLdfRoK8OikbeRIJ+Neg9gvYcjzLWqnbXi2jRqBQGqN6rhF7xmIm8FiY3RHdH4B7y1ToiwWBJ9uO5Jq2hlNyWsAPn7/VEe4kwmEzi/pF4Zqu0VrRaOfvHDCKJlokG4xZKmcW0a4n0PvViSP1lLIxqteEjivL2rH/owghM0HFoRlCDntso5zdpUgkS0H2mZaAReANYC3wayvn3WPekAYTwqGz3AcbsY1aMMcReqrhXCr1Hy06xY9KMfRhAdEQ3gfOBeYEXVflssqQPEfWCiupAHPcBB16nn5xG1teCnmZifjSKS9TNhRWOA5rgaOAG4RUTuFJFrcP+724CbcAXln4APq+rhJu9lVJHL5SKfIyLMzMwAsHPnzpaUY2Zmhmw2e8x9APL5PLOzsxSLxSXHp6enKy+YUMSpaz0OH7afYlOEUZZOJWtphKeZkPwV6rUQ6tkVKqmWE1S1baKWz4SfKK2MwcHBps6vTmb4rA3mEZpu4nYtqo2gtaasN2r61/O89FPrupW5MXEMr5VhV78Ixe2emOGzPiYaKSfum7aWbSGKF2VQEF/VxoJW7SIfNw0NDUU2BIdxcutnTDRSTFDQm0bdDv816nUfwnhoNqJVw6FBKWpsDOuWNMZEI6U0GiatNL3r2Tsq8zOCZrKGifjViHZ4bbYimednY0w0Ukq9B7J6/kScPr//Tdwo2lZFmKpbKnFc26H2GrVhUtTuibU0GmOikVLCLs0Yp4tQHSWr3oM8NDR0jG0ijo2hkiq2hqjnl0ql0CNIZgANxkQjpQQ1/UWk7vKNUd/EcR7kuKkyTBu2xeEPShSmdWOCEYyJRkppRXTtKG/iThk1K2UIE7KwXhDhoKhnRmNMNFKM357Qioe10VBkp42aYV3c6/1dgqKeGfUx0egTWvGgNqJWy6bVNo3qFOQr0sigGRT1zKiPiUafEHbtkKB+f6PWRr2RkqDRk5GRkUCX9Fopm802tMuEcTAzomOi0SfUW5XN/wBWHuggW0i7mvKtjH/hr5PRWkw0+ohSqbTY4qiMntRqnocZaQgzGS3OA9sqwQjTRTHiYaJh1CTq8GS9SW1RhaPVBlXz7mw9YUWj2XgaRoKYmppi7969gfmmp6eXbB84cGDJ8QMHDnDllVdSKBQYGBigUCjgOE7NazmOQ6FQYGFhYTHGRhBh8q1cuTLUtYw2EEZZ6iXgf+KueXIn8A3gFN8xW/ekh4hiVwi7Jok/hV2FrXK96utWvlecvBp5eppNoz3QoXB/L/Nt/xZwjbdt6570GFG6B357QdzzGp1bsYvUs5PU8reoPtdoPR0RjSUXclsWm3zbFli4h4jbYojigRp2/kuQPaKR2BjtI6xoNG3TEJEZEXkYKAK/7+1+JfCwL9sub1+t8ydFZF5E5nfv3t1scYw6hLEB1IrnWSwWmZ2dJZ/PIyLk8/m68Tqr71HvnkFlqRe7tFUxTY0mCVIVQqx7okdbF3/obdu6Jz1GvRbD6Oho5OZ+2BGVuCMv1tLoDnShe5IH7lHrnvQszfhb+M/N5XJLPD2jepOGuVcrhnmNaHRENIDTfdv/Ffiyt23rnqSIILtGOx5om0PSecKKRmUZxViIyN8CZwBHgAVgnao+4h2bBj4IHAI+qqr/GHS91atX6/z8fOzyGO2h4mfRiHw+z44dOzpTIKMtiMhWVV0dmK8Z0Wg1Jhq9ycDAAGF+J730WzKiE1Y0zCPUCCTMyEsmk+lASYxewETDCOTCCy8MzGNLHfYPJhpGIJs3bw7Mk8/nO1ASoxcw0TACCXKqymazi4tKG+nHRMMIpJFNo5YXqZFuTDSMQGZmZshms0v2ZbNZyuUyO3bsMMHoM0w0jEBqzT+x1kX/MtjtAhjJoCIQ09PT7Ny5czFQjwlH/2GiYYTCcRwmJycXo3gtLCwwOTkJmHD0G9Y9MUJRL+yfPzSg0R+YaBihsBgXRgUTDSMUcQPqGOnDRMMIRb1hV3Pq6j9MNIxQVIZdR0ZGFve98MIL3HHHHV0sldENTDSM0Nxxxx3s379/8fuRI0fYtGkTU1NTXSyV0WlaIhoi8nERUREZ8+1bLyIPish2EXl3K+5jdJfZ2dlI+4100rSfhoicBvwSsNO3bxVwMW7Yv1OAW0Xkdapq86cTTL3p7zYtvr9oRUvjz4HfwY0XWeEi4Euq+qKqPoS70tqbWnAvo4vUC7RjAXj6i6ZEQ0TeCzyiqndVHbJ1T1JIxQM07H4jnQR2T0TkVuCnaxyaBn4PeFet02rsqxlAUlVngVlwY4QGlcfoHhs3bgRcG8bhw4fJZDJMTk4u7jf6g0DRUNXzau0XkZ/HXZ7gLm+V71OBH4jIm3BbFqf5sp8KPNp0aY2us3HjRhOJPid290RVf6iqL1fVgqoWcIXibFV9HLgZuFhEjhORVwGnA99rSYkNw+gqbZnlqqrbROQm4F7cdU8+bCMnhpEOWiYaXmvD/30GMB9jw0gZ5hFqGEYkTDQMw4hETy3LKCK7cdeE7SRjwJ4O37NTWN2SSbfqllfVFUGZeko0uoGIzIdZvzKJWN2SSa/XzbonhmFEwkTDMIxImGh4LuwpxeqWTHq6bn1v0zAMIxrW0jAMIxJ9IRoikhGRfxeRr3nfl4vILSLygPd5ki9vYiKOicgOEfmhiNwpIvPevrTU7UQR+bKI3C8i94nIuWmom4ic4f2/KulZEfloouqmqqlPwG8DfwV8zfv+KeAqb/sq4JPe9irgLuA43Bm8PwYy3S5/g3rtAMaq9qWlbnPA5d72MHBiWurmq2MGeBzIJ6luXf/DdeAfcyqwBXinTzS2Ayd72ycD273t9cB637lfB87tdh0a1K2WaCS+bsDLgIfwbG5pqltVfd4F3JG0uvVD9+TTuOEIj/j2vUJVHwPwPl/u7Q8dcaxHUOAbIrJVRCrhs9JQt1cDu4EbvG7ldSIyQjrq5udi4IvedmLqlmrREJFfBp5U1a1hT6mxr5eHl96iqmcDFwAfFpG3NcibpLoNAmcDm1T1jcB+3CZ7PZJUNwBEZBh4L/A3QVlr7Otq3VItGsBbgPeKyA7gS8A7RaQMPCEiJwN4n096+RMVcUxVH/U+nwS+ghu8OQ112wXsUtXvet+/jCsiaahbhQuAH6jqE973xNQt1aKhqutV9VR1Y31cDHxTVSdwI4ut9bKtBb7qbScm4piIjIjICZVt3P7xPaSgbupGf3tYRM7wdo3jBnRKfN18XMLRrgkkqW7dNgZ10Oj0Do4aQnO4xtEHvM/lvnzTuBbq7cAF3S53g/q8GteqfhewDZhOS928sp4FzAN3A38PnJSiumWBvcBP+fYlpm7mEWoYRiRS3T0xDKP1mGgYhhEJEw3DMCJhomEYRiRMNAzDiISJhmEYkTDRMAwjEiYahmFE4v8Hk8ngenylLykAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=[4,9])\n", + "h0 = plt.subplot(3,1,1)\n", + "h1 = plt.subplot(3,1,2)\n", + "h2 = plt.subplot(3,1,3)\n", + "\n", + "h0.title.set_text('Snow Depth')\n", + "h1.title.set_text('Snow Density')\n", + "h2.title.set_text('Air Temperature')\n", + "\n", + "print('Air Temperature Unit:', ec.get_var_units('land_surface_air__temperature'))\n", + "print('Snow Depth Unit:' , ec.get_var_units('snowpack__depth'))\n", + "print('Snow Density Unit:' , ec.get_var_units('snowpack__mass-per-volume_density'))\n", + "\n", + "for i in np.arange(365):\n", + " \n", + " ec.update()\n", + " \n", + " tair = ec.get_value('land_surface_air__temperature') \n", + " snd = ec.get_value('snowpack__depth', units='m')\n", + " rsn = ec.get_value('snowpack__mass-per-volume_density')\n", + " \n", + " units = ec.get_var_units('snowpack__depth')\n", + " \n", + " h0.scatter(ec.time, snd, c='k') \n", + " h1.scatter(ec.time, rsn, c='k')\n", + " h2.scatter(ec.time,tair, c='k')\n", + " \n", + " \n", + "# ec.finalize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison with Observations at Barrow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Comparison](https://github.com/permamodel/Snow_BMI_Fortran/blob/master/data/Barrow.png?raw=true)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples.rst b/docs/examples.rst index c7ea099d..0e2dd830 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -22,6 +22,7 @@ Single Models * :doc:`Frost Number Model ` |macOS| |Linux| |Windows| * :doc:`Kudryavtsev Model ` |macOS| |Linux| |Windows| * :doc:`GIPL Model` |macOS| |Linux| +* :doc:`ECSimpleSnow Model ` |macOS| |Linux| |Windows| * :doc:`Coastline Evolution Model ` |macOS| |Linux| * :doc:`Hydrotrend` |macOS| |Linux| * :doc:`Sedflux3D ` |macOS| |Linux| @@ -66,3 +67,4 @@ Coupled Models + From 8631370467c21639d5f720efb93662f85ee8744d Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Tue, 8 Oct 2019 15:58:18 -0600 Subject: [PATCH 10/27] Update header on ECSimpleSnow Notebook --- docs/demos/ECSnow_PyMT.ipynb | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/demos/ECSnow_PyMT.ipynb b/docs/demos/ECSnow_PyMT.ipynb index 19513903..f4280bec 100644 --- a/docs/demos/ECSnow_PyMT.ipynb +++ b/docs/demos/ECSnow_PyMT.ipynb @@ -4,19 +4,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ECSimpleSnow component in PyMT\n", + "# ECSimpleSnow component\n", "\n", - "### It‘s an empirical algorithm to melt snow according to the surface temperature and increase snow depth according to the precipitation that has fallen since the last time step.\n", + "ECSimpleSnow is an empirical algorithm to melt snow according to the surface temperature and increase snow depth according to the precipitation that has fallen since the last time step.\n", "\n", - "### See details: \n", + "## Details: \n", "\n", "**Brown, R. D., Brasnett, B., & Robinson, D. (2003). Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmosphere-Ocean, 41(1), 1-14.**\n", "\n", "**URL:** https://www.tandfonline.com/doi/abs/10.3137/ao.410101\n", "\n", - "### Source code in Fortran:\n", + "## Source code in Fortran:\n", "\n", - "**URL:** https://github.com/permamodel/Snow_BMI_Fortran\n" + "**URL:** https://github.com/permamodel/Snow_BMI_Fortran\n", + "\n", + "## For this Notebook\n", + "\n", + "Before you begin, install:\n", + "```\n", + "conda install -c conda-forge pymt pymt_ecsimplesnow numpy scipy\n", + "```\n" ] }, { From ba6862b5f8e92092ca7d83ce89cde3aa3d3d1e86 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Wed, 16 Oct 2019 13:00:29 -0600 Subject: [PATCH 11/27] docs: enable binder (#97) * add environment configuration file * create notebooks folder and add welcome notebook * add binder link in README file to link welcome notebook * remove demos folder * update docs to add example notebook binder links * update binder links from fork repo to pymt repo * fix typo in example.rst file --- README.rst | 3 +- docs/examples.rst | 57 ++++++++++++------- environment.yml | 13 +++++ {docs/demos => notebooks}/ECSnow_PyMT.ipynb | 0 .../Example_01_Basic_Use_GIPL.ipynb | 0 .../Example_02_GIPL_ECSimpleSnow.ipynb | 0 notebooks/Welcome.ipynb | 47 +++++++++++++++ {docs/demos => notebooks}/cem.ipynb | 0 {docs/demos => notebooks}/cem_and_waves.ipynb | 0 {docs/demos => notebooks}/child.ipynb | 0 {docs/demos => notebooks}/frost_number.ipynb | 0 {docs/demos => notebooks}/hydrotrend.ipynb | 0 {docs/demos => notebooks}/ku.ipynb | 0 {docs/demos => notebooks}/quickstart.py | 0 {docs/demos => notebooks}/sedflux3d.ipynb | 0 .../sedflux3d_and_child.ipynb | 0 {docs/demos => notebooks}/subside.ipynb | 0 17 files changed, 98 insertions(+), 22 deletions(-) create mode 100644 environment.yml rename {docs/demos => notebooks}/ECSnow_PyMT.ipynb (100%) rename {docs/demos => notebooks}/Example_01_Basic_Use_GIPL.ipynb (100%) rename {docs/demos => notebooks}/Example_02_GIPL_ECSimpleSnow.ipynb (100%) create mode 100644 notebooks/Welcome.ipynb rename {docs/demos => notebooks}/cem.ipynb (100%) rename {docs/demos => notebooks}/cem_and_waves.ipynb (100%) rename {docs/demos => notebooks}/child.ipynb (100%) rename {docs/demos => notebooks}/frost_number.ipynb (100%) rename {docs/demos => notebooks}/hydrotrend.ipynb (100%) rename {docs/demos => notebooks}/ku.ipynb (100%) rename {docs/demos => notebooks}/quickstart.py (100%) rename {docs/demos => notebooks}/sedflux3d.ipynb (100%) rename {docs/demos => notebooks}/sedflux3d_and_child.ipynb (100%) rename {docs/demos => notebooks}/subside.ipynb (100%) diff --git a/README.rst b/README.rst index 7c26ed7a..658e020f 100644 --- a/README.rst +++ b/README.rst @@ -37,7 +37,8 @@ Code style: black - + + Launch Binder

PyMT is an Open Source Python package, developed by the diff --git a/docs/examples.rst b/docs/examples.rst index 0e2dd830..ccd7ace1 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -18,35 +18,50 @@ have to install Jupyter Notebook: Single Models ------------- +* Frost Number Model |binder-frost_number| +.. |binder-frost_number| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Ffrost_number.ipynb -* :doc:`Frost Number Model ` |macOS| |Linux| |Windows| -* :doc:`Kudryavtsev Model ` |macOS| |Linux| |Windows| -* :doc:`GIPL Model` |macOS| |Linux| -* :doc:`ECSimpleSnow Model ` |macOS| |Linux| |Windows| -* :doc:`Coastline Evolution Model ` |macOS| |Linux| -* :doc:`Hydrotrend` |macOS| |Linux| -* :doc:`Sedflux3D ` |macOS| |Linux| -* :doc:`Flexural Subsidence ` |macOS| |Linux| +* Kudryavtsev Model |binder-ku| +.. |binder-ku| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fku.ipynb +* GIPL Model |binder-GIPL| +.. |binder-GIPL| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FExample_01_Basic_Use_GIPL.ipynb -Coupled Models --------------- +* ECSimpleSnow Model |binder-ECSnow| +.. |binder-ECSnow| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FECSnow_PyMT.ipynb + +* Coastline Evolution Model |binder-cem| +.. |binder-cem| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem.ipynb + +* Hydrotrend |binder-hydrotrend| +.. |binder-hydrotrend| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fhydrotrend.ipynb -* :doc:`Coastline Evolution Model + Waves ` |macOS| |Linux| -* :doc:`GIPL + ECSimpleSnow Models ` |macOS| |Linux| +* Sedflux3D |binder-sedflux3d| +.. |binder-sedflux3d| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsedflux3d.ipynb +* Flexural Subsidence |binder-subside| +.. |binder-subside| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsubside.ipynb + + +Coupled Models +-------------- -.. |macOS| image:: _static/apple.svg - :height: 15px - :alt: macOS +* Coastline Evolution Model + Waves |binder-cem_and_waves| +.. |binder-cem_and_waves| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem_and_waves.ipynb -.. |Linux| image:: _static/linux.svg - :height: 15px - :alt: Linux +* GIPL + ECSimpleSnow Models |binder-GIPL_and_ECSnow| +.. |binder-GIPL_and_ECSnow| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FExample_02_GIPL_ECSimpleSnow.ipynb -.. |Windows| image:: _static/windows.svg - :height: 15px - :alt: Windows .. Sphinx emits a warning if documents aren't in a toctree. diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..e58e29b6 --- /dev/null +++ b/environment.yml @@ -0,0 +1,13 @@ +name: test-environment +channels: + - conda-forge +dependencies: + - python=3.7 + - pymt=1.0.3 + - pymt_gipl + - pymt_ecsimplesnow + - seaborn + - matplotlib + - pandas + - numpy + diff --git a/docs/demos/ECSnow_PyMT.ipynb b/notebooks/ECSnow_PyMT.ipynb similarity index 100% rename from docs/demos/ECSnow_PyMT.ipynb rename to notebooks/ECSnow_PyMT.ipynb diff --git a/docs/demos/Example_01_Basic_Use_GIPL.ipynb b/notebooks/Example_01_Basic_Use_GIPL.ipynb similarity index 100% rename from docs/demos/Example_01_Basic_Use_GIPL.ipynb rename to notebooks/Example_01_Basic_Use_GIPL.ipynb diff --git a/docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb b/notebooks/Example_02_GIPL_ECSimpleSnow.ipynb similarity index 100% rename from docs/demos/Example_02_GIPL_ECSimpleSnow.ipynb rename to notebooks/Example_02_GIPL_ECSimpleSnow.ipynb diff --git a/notebooks/Welcome.ipynb b/notebooks/Welcome.ipynb new file mode 100644 index 00000000..04945eae --- /dev/null +++ b/notebooks/Welcome.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Welcome to Pymt demo Notebooks\n", + "\n", + "This page provides links to notebooks that provide an introduction to Pymt and its use for different models. \n", + "\n", + " * [Coastline Evolution Model](cem.ipynb)\n", + " * [Coastline Evolution Model + Waves](cem_and_waves.ipynb)\n", + " * [ECSimpleSnow component](ECSnow_PyMT.ipynb)\n", + " * [Geophysical Institute Permafrost Laboratory (GIPL) Model](Example_01_Basic_Use_GIPL.ipynb)\n", + " * [GIPL + ECSimpleSnow](Example_02_GIPL_ECSimpleSnow.ipynb)\n", + " * [Frost Number Model](frost_number.ipynb)\n", + " * [HydroTrend Model](hydrotrend.ipynb)\n", + " * [Kudryavtsev Model](ku.ipynb)\n", + " * [Sedflux3D Model](sedflux3d.ipynb)\n", + " * [Flexural Subsidence Model](subside.ipynb)\n", + "\n", + " \n" + ] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/demos/cem.ipynb b/notebooks/cem.ipynb similarity index 100% rename from docs/demos/cem.ipynb rename to notebooks/cem.ipynb diff --git a/docs/demos/cem_and_waves.ipynb b/notebooks/cem_and_waves.ipynb similarity index 100% rename from docs/demos/cem_and_waves.ipynb rename to notebooks/cem_and_waves.ipynb diff --git a/docs/demos/child.ipynb b/notebooks/child.ipynb similarity index 100% rename from docs/demos/child.ipynb rename to notebooks/child.ipynb diff --git a/docs/demos/frost_number.ipynb b/notebooks/frost_number.ipynb similarity index 100% rename from docs/demos/frost_number.ipynb rename to notebooks/frost_number.ipynb diff --git a/docs/demos/hydrotrend.ipynb b/notebooks/hydrotrend.ipynb similarity index 100% rename from docs/demos/hydrotrend.ipynb rename to notebooks/hydrotrend.ipynb diff --git a/docs/demos/ku.ipynb b/notebooks/ku.ipynb similarity index 100% rename from docs/demos/ku.ipynb rename to notebooks/ku.ipynb diff --git a/docs/demos/quickstart.py b/notebooks/quickstart.py similarity index 100% rename from docs/demos/quickstart.py rename to notebooks/quickstart.py diff --git a/docs/demos/sedflux3d.ipynb b/notebooks/sedflux3d.ipynb similarity index 100% rename from docs/demos/sedflux3d.ipynb rename to notebooks/sedflux3d.ipynb diff --git a/docs/demos/sedflux3d_and_child.ipynb b/notebooks/sedflux3d_and_child.ipynb similarity index 100% rename from docs/demos/sedflux3d_and_child.ipynb rename to notebooks/sedflux3d_and_child.ipynb diff --git a/docs/demos/subside.ipynb b/notebooks/subside.ipynb similarity index 100% rename from docs/demos/subside.ipynb rename to notebooks/subside.ipynb From 8d9371e9f00cf526a5130cc84dd5d7592a48df6d Mon Sep 17 00:00:00 2001 From: Eric Hutton Date: Thu, 17 Oct 2019 22:47:39 -0600 Subject: [PATCH 12/27] docs: add table of models (#99) * add list of useful links * add page with available models table * make table with link to binder examples * move installation to install * add __slots__ to grids for new Dataset * add script for creating model table * add link to the notebooks * rename notebooks * resolve cross-references with more than one target * create symbolic link to each notebook * add grid attributes to __slots__ --- README.rst | 9 +- docs/conf.py | 13 ++ docs/examples.rst | 61 ++++---- docs/index.rst | 3 +- docs/{installation.rst => install.rst} | 0 docs/models.rst | 138 ++++++++++++++++++ docs/quickstart.rst | 4 +- docs/scripts/make_table.py | 69 +++++++++ docs/usage.rst | 2 +- .../{ECSnow_PyMT.ipynb => ecsimplesnow.ipynb} | 0 ...ple_01_Basic_Use_GIPL.ipynb => gipl.ipynb} | 0 ...Snow.ipynb => gipl_and_ecsimplesnow.ipynb} | 0 notebooks/{Welcome.ipynb => welcome.ipynb} | 6 +- pymt/framework/bmi_ugrid.py | 16 ++ 14 files changed, 286 insertions(+), 35 deletions(-) rename docs/{installation.rst => install.rst} (100%) create mode 100644 docs/models.rst create mode 100755 docs/scripts/make_table.py rename notebooks/{ECSnow_PyMT.ipynb => ecsimplesnow.ipynb} (100%) rename notebooks/{Example_01_Basic_Use_GIPL.ipynb => gipl.ipynb} (100%) rename notebooks/{Example_02_GIPL_ECSimpleSnow.ipynb => gipl_and_ecsimplesnow.ipynb} (100%) rename notebooks/{Welcome.ipynb => welcome.ipynb} (87%) diff --git a/README.rst b/README.rst index 658e020f..80f216bb 100644 --- a/README.rst +++ b/README.rst @@ -29,15 +29,13 @@ Coverage Status - - Code Health License: MIT Code style: black - + Launch Binder

@@ -62,6 +60,11 @@ that expose the * A plug-in framework for adding additional BMI-enabled models to the framework +Quick links: + * `Installation instructions `_ + * `User documentation `_ + * `List of available models `_ + This material is based upon work supported by the National Science Foundation under Grant No. `1831623`_, diff --git a/docs/conf.py b/docs/conf.py index f58dc82e..7dba65fb 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,6 +23,19 @@ # sys.path.insert(0, os.path.abspath('..')) from unittest.mock import MagicMock +from sphinx.domains.python import PythonDomain + + +class PatchedPythonDomain(PythonDomain): + def resolve_xref(self, env, fromdocname, builder, typ, target, node, contnode): + if 'refspecific' in node: + del node['refspecific'] + return super(PatchedPythonDomain, self).resolve_xref( + env, fromdocname, builder, typ, target, node, contnode) + + +def setup(sphinx): + sphinx.override_domain(PatchedPythonDomain) class Mock(MagicMock): diff --git a/docs/examples.rst b/docs/examples.rst index ccd7ace1..4077f316 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -18,35 +18,41 @@ have to install Jupyter Notebook: Single Models ------------- -* Frost Number Model |binder-frost_number| + +======================================================= ===================== +Model Notebook +======================================================= ===================== +:doc:`Frost Number Model ` |binder-frost_number| +:doc:`Kudryavtsev Model ` |binder-ku| +:doc:`GIPL Model ` |binder-GIPL| +:doc:`ECSimpleSnow Model ` |binder-ECSnow| +:doc:`Coastline Evolution Model ` |binder-cem| +:doc:`Hydrotrend ` |binder-hydrotrend| +:doc:`Sedflux3D ` |binder-sedflux3d| +:doc:`Flexural Subsidence ` |binder-subside| +======================================================= ===================== + .. |binder-frost_number| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Ffrost_number.ipynb -* Kudryavtsev Model |binder-ku| .. |binder-ku| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fku.ipynb -* GIPL Model |binder-GIPL| .. |binder-GIPL| image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FExample_01_Basic_Use_GIPL.ipynb + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fgipl.ipynb -* ECSimpleSnow Model |binder-ECSnow| .. |binder-ECSnow| image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FECSnow_PyMT.ipynb + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fecsimplesnow.ipynb -* Coastline Evolution Model |binder-cem| .. |binder-cem| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem.ipynb -* Hydrotrend |binder-hydrotrend| .. |binder-hydrotrend| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fhydrotrend.ipynb -* Sedflux3D |binder-sedflux3d| .. |binder-sedflux3d| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsedflux3d.ipynb -* Flexural Subsidence |binder-subside| .. |binder-subside| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsubside.ipynb @@ -54,13 +60,18 @@ Single Models Coupled Models -------------- -* Coastline Evolution Model + Waves |binder-cem_and_waves| +========================================================================== ======================== +Models Notebook +========================================================================== ======================== +:doc:`Coastline Evolution Model + Waves ` |binder-cem_and_waves| +:doc:`GIPL + ECSimpleSnow Models ` |binder-GIPL_and_ECSnow| +========================================================================== ======================== + .. |binder-cem_and_waves| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem_and_waves.ipynb -* GIPL + ECSimpleSnow Models |binder-GIPL_and_ECSnow| .. |binder-GIPL_and_ECSnow| image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2FExample_02_GIPL_ECSimpleSnow.ipynb + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fgipl_and_ecsimplesnow.ipynb .. @@ -71,15 +82,15 @@ Coupled Models :maxdepth: 1 :hidden: - - - - - - - - - - - - + + + + + + + + + + + + diff --git a/docs/index.rst b/docs/index.rst index 40049fea..f5c6358e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -57,8 +57,9 @@ this part of the documentation is for you. :maxdepth: 2 quickstart - installation + install usage + models examples glossary diff --git a/docs/installation.rst b/docs/install.rst similarity index 100% rename from docs/installation.rst rename to docs/install.rst diff --git a/docs/models.rst b/docs/models.rst new file mode 100644 index 00000000..9838e394 --- /dev/null +++ b/docs/models.rst @@ -0,0 +1,138 @@ +.. _available_models: + +Available Models +================ + +The following table lists the models that are currently available through +pymt. + + +================================ ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================= +.. Summary +================================ ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================= +Avulsion Avulsion dictates the movement of rivermouths along a coastline by modeling the changes of river channel angles through the floodplain as a stochastic random walk process. +Cem The Coastline Evolution Model addresses predominately sandy, wave-dominated coastlines on time scales ranging from years to millenia and on spatial scales ranging from kilometers to hundreds of kilometers. CEM simulates planview coastline evolution due to wave-driven alongshore sediment transport. This model can incorporate river influence and transport fluvial sediment from one or more point sources along the coastline. + + |binder-Cem| +Child CHILD computes the time evolution of a topographic surface z(x,y,t) by fluvial and hillslope erosion and sediment transport. + + |binder-Child| +ECSimpleSnow ECSimpleSnow was orginally developed by Ross Brown and Bruce Brasnett in Environment Canada (EC). It is an empirical algorithm to melt snow according to the surface temperature and increase in snow depth according to the precipitation that has fallen since the last analysis time. It is a semi-empirical temperature index model. It provides a quick and acceptable answer when you only have very limited inputs. The one deficiency of the model is that it does not take account of the heat budget of the snowpack which means it will melt snow too quickly in the spring. + + |binder-ECSimpleSnow| +ExponentialWeatherer Exponential weathering of bedrock on hillslopes. Uses exponential soil + production function in the style of Ahnert (1976). +Flexure Simulate lithospheric flexure. +FlowAccumulator Component to accumulate flow and calculate drainage area. This is + accomplished by first finding flow directions by a user-specified + method and then calculating the drainage area and discharge. + Optionally, spatially variable runoff can be set either by the + model grid field 'water__unit_flux_in'. +FlowDirectorD8 Single-path (steepest direction) flow direction finding on raster grids by the D8 method. This method considers flow on all eight links such that flow is possible on orthogonal and on diagonal links. +FlowDirectorDINF Directs flow by the D infinity method (Tarboton, 1997). Each node is + assigned two flow directions, toward the two neighboring nodes that are on + the steepest subtriangle. Partitioning of flow is done based on the aspect + of the subtriangle. +FlowDirectorSteepest Find the steepest single-path steepest descent flow + directions. It is equivalent to D4 method in the special case of a raster grid + in that it does not consider diagonal links between nodes. For that capability, + use FlowDirectorD8. +FlowRouter Single-path (steepest direction) flow routing, and calculates flow directions, drainage area, and (optionally) discharge. +FrostNumber From Nelson and Outcalt (1987), the 'frost number', a dimensionless ratio defined by manipulation of either freezing and thawing degree-day sums or frost and thaw penetration depths, can be used to define an unambiguous latitudinal zonation of permafrost continuity. The index is computed using several variables influencing the depth of frost and thaw penetration, and can be related mathematically to the existence and continuity of permafrost. Although the frost number is a useful device for portraying the distribution of contemporary permafrost at continental scales, it is not capable of detecting relict permafrost and should not be mapped over small areas unless numerous climate stations are located in the region of interest. + + |binder-FrostNumber| +GIPL GIPL (Geophysical Institute Permafrost Laboratory) is an implicit + finite difference one-dimensional heat flow numerical model. The + model uses a fine vertical resolution grid which preserves the + latent-heat effects in the phase transition zone, even under + conditions of rapid or abrupt changes in the temperature fields. It + includes upper boundary condition (usually air temperature), + constant geothermal heat flux at the lower boundary (typically from + 500 to 1000 m) and initial temperature distribution with depth. The + other inputs are precipitation, prescribed water content and thermal + properties of the multilayered soil column. As an output the model + produces temperature distributions at different depths, active layer + thickness and calculates time of freeze up. The results include + temperatures at different depths and active layer thickness, + freeze-up days. + + + |binder-GIPL| +Hydrotrend Climate-driven hydrological water balance and transport model that simulates water discharge and sediment load at a river outlet. HydroTrend simulates water and sediment fluxes at a daily timescale based on drainage basin characteristics and climate. HydroTrend can provide this river flux information to other components like CEM and Sedflux2D or Sedflux3D + + |binder-Hydrotrend| +Ku The Kudryavtsev et al. (1974), or Ku model, presents an approximate solution of the Stefan problem. The model provides a steady-state solution under the assumption of sinusoidal air temperature forcing. It considers snow, vegetation, and soil layers as thermal damping to variation of air temperature. The layer of soil is considered to be a homogeneous column with different thermal properties in the frozen and thawed states. The main outputs are annual maximum frozen/thaw depth and mean annual temperature at the top of permafrost (or at the base of the active layer). It can be applied over a wide variety of climatic conditions. + + |binder-Ku| +LinearDiffuser 2D diffusion using an explicit finite-volume method. +OverlandFlow Simulate overland flow using de Almeida approximations. Landlab component + that simulates overland flow using the de Almeida et al., 2012 + approximations of the 1D shallow water equations to be used for + 2D flood inundation modeling. This component calculates discharge, + depth and shear stress after some precipitation event across any raster grid. +Plume Plume simulates the sediment transport and deposition of single-grain size sediment from a river mouth entering into a marine basin by creating a turbulent jet. The model calculates a steady-state hypopycnal plume as a result of river water and sediment discharge based on simplified advection-diffusion equations. The model allows for plume deflection due to systematic coastal currents or Coriolis force +Rafem The River Avulsion and Floodplain Evolution Model (RAFEM) is a cellular model that simulates river and floodplain morphodynamics over large space and timescales. Cell size is larger than the channel belt width, and natural levees, which maintain a bankfull elevation above the channel bed, exist within a river cell. The river course is determined using a steepest-descent methodology, and erosion and deposition along the river profile are modeled as a linear diffusive process. An avulsion occurs when the riverbed becomes super-elevated relative to the surrounding floodplain, but only if the new steepest-descent path to sea level is shorter than the prior river course. If the new path to sea level is not shorter, then a crevasse splay is deposited in the adjacent river cells. The model has been designed to couple with the Coastline Evolution Model through the CSDMS Basic Model Interface. +Sedflux3D Sedflux3D is a basin filling stratigraphic model. Sedflux3d simulates long-term marine sediment transport and accumulation into a three-dimensional basin over time scales of tens of thousands of years. It simulates the dynamics of strata formation of continental margins based on distribution of river plumes and tectonics. + + |binder-Sedflux3D| +SoilMoisture Landlab component that simulates root-zone average soil moisture at each + cell using inputs of potential evapotranspiration, live leaf area index, + and vegetation cover. +StreamPowerEroder A simple, explicit implementation of a stream power algorithm. +Subside The model is used to simulate the lithospheric load changes as the model evolves. Depending upon how the load distribution develops, this flexure can result in the basin uplifting or subsiding (or both). The pattern of subsidence in time and space largely determines the gross geometry of time-bounded units because it controls the rate at which space is created for sedimentation. + + |binder-Subside| +TransportLengthHillslopeDiffuser Hillslope diffusion component in the style of Carretier et al. + (2016, ESurf), and Davy and Lague (2009) +Vegetation Landlab component that simulates net primary productivity, biomass + and leaf area index at each cell based on inputs of root-zone + average soil moisture. + + Zhou, X., Istanbulluoglu, E., & Vivoni, E. R. (2013). Modeling the + ecohydrological role of aspect controlled radiation on tree grass shrub + coexistence in a semiarid climate. Water Resources Research, + 49(5), 2872-2895. +Waves Generates a shallow-water wave climate for a longshore transport module based on a user-defined distribution. + + |binder-Waves| +================================ ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================= + +.. |binder-ECSimpleSnow| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fecsimplesnow.ipynb + + +.. |binder-Cem| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem.ipynb + + +.. |binder-Waves| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fcem_and_waves.ipynb + + +.. |binder-GIPL| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fgipl.ipynb + + +.. |binder-Child| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fchild.ipynb + + +.. |binder-Sedflux3D| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsedflux3d.ipynb + + +.. |binder-FrostNumber| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Ffrost_number.ipynb + + +.. |binder-Hydrotrend| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fhydrotrend.ipynb + + +.. |binder-Subside| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fsubside.ipynb + + +.. |binder-Ku| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2Fku.ipynb + diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 41fa9d6c..febdf780 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -27,7 +27,7 @@ You can get *pymt* directly from :term:`conda-forge`: $ conda install pymt -c conda-forge Installing into a :term:`conda environment` is strongly recommended. -Check the :doc:`installation guide` for more detailed +Check the :doc:`installation guide` for more detailed information about installing *pymt*. @@ -223,7 +223,7 @@ Now display the plot: :alt: Mean daily water discharge from the Hydrotrend model. A more detailed example of using Hydrotrend -can be found in the :doc:`demos/hydrotrend` +can be found in the :doc:`notebooks/hydrotrend` Jupyter Notebook. An expanded description of the *pymt* methods used in this example can be found in the :doc:`usage` section. diff --git a/docs/scripts/make_table.py b/docs/scripts/make_table.py new file mode 100755 index 00000000..2aa69f30 --- /dev/null +++ b/docs/scripts/make_table.py @@ -0,0 +1,69 @@ +#! /usr/bin/env python +import os +import textwrap +from tabulate import tabulate + +import click +import yaml + + +def construct_rows(notebook_index=None): + import pymt.models + + notebook_index = notebook_index or {} + + rows = [] + for name in pymt.models.__all__: + summary = pymt.models.__dict__[name]().summary + if name in notebook_index: + summary = os.linesep.join( + [ + summary, + "", + "|binder-{0}|".format(name), + ] + ) + rows.append((name, summary)) + + return rows + + +@click.command() +@click.argument("dest", type=click.File("w")) +@click.option( + "--notebook-index", type=click.File("r"), help="index mapping model names to notebooks", +) +def make_table(dest, notebook_index): + if notebook_index: + index = yaml.safe_load(notebook_index) + else: + index = {} + + header = """ + .. _available_models: + + Available Models + ================ + + The following table lists the models that are currently available through + pymt. + + """ + print(textwrap.dedent(header).lstrip(), file=dest) + + rows = construct_rows(notebook_index=index) + print(tabulate(sorted(rows), headers=("Summary",), tablefmt="rst"), file=dest) + + footer = [] + for name, notebooks in index.items(): + footer.append( + """ + .. |binder-{0}| image:: https://mybinder.org/badge_logo.svg + :target: https://mybinder.org/v2/gh/csdms/pymt.git/master?filepath=notebooks%2F{1} + """.format(name, notebooks[0]) + ) + print(textwrap.dedent(os.linesep.join(footer)), file=dest) + + +if __name__ == "__main__": + make_table() diff --git a/docs/usage.rst b/docs/usage.rst index 32b9d3f4..9f863f24 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -9,7 +9,7 @@ configure, run, and couple models. We assume here that you have installed *pymt*, -following the instructions in the :doc:`installation` guide. +following the instructions in the :doc:`install` guide. Below, we'll use the `CEM`_ and `Waves`_ models. Install them with: diff --git a/notebooks/ECSnow_PyMT.ipynb b/notebooks/ecsimplesnow.ipynb similarity index 100% rename from notebooks/ECSnow_PyMT.ipynb rename to notebooks/ecsimplesnow.ipynb diff --git a/notebooks/Example_01_Basic_Use_GIPL.ipynb b/notebooks/gipl.ipynb similarity index 100% rename from notebooks/Example_01_Basic_Use_GIPL.ipynb rename to notebooks/gipl.ipynb diff --git a/notebooks/Example_02_GIPL_ECSimpleSnow.ipynb b/notebooks/gipl_and_ecsimplesnow.ipynb similarity index 100% rename from notebooks/Example_02_GIPL_ECSimpleSnow.ipynb rename to notebooks/gipl_and_ecsimplesnow.ipynb diff --git a/notebooks/Welcome.ipynb b/notebooks/welcome.ipynb similarity index 87% rename from notebooks/Welcome.ipynb rename to notebooks/welcome.ipynb index 04945eae..be7978c2 100644 --- a/notebooks/Welcome.ipynb +++ b/notebooks/welcome.ipynb @@ -10,9 +10,9 @@ "\n", " * [Coastline Evolution Model](cem.ipynb)\n", " * [Coastline Evolution Model + Waves](cem_and_waves.ipynb)\n", - " * [ECSimpleSnow component](ECSnow_PyMT.ipynb)\n", - " * [Geophysical Institute Permafrost Laboratory (GIPL) Model](Example_01_Basic_Use_GIPL.ipynb)\n", - " * [GIPL + ECSimpleSnow](Example_02_GIPL_ECSimpleSnow.ipynb)\n", + " * [ECSimpleSnow component](ecsimplesnow.ipynb)\n", + " * [Geophysical Institute Permafrost Laboratory (GIPL) Model](gipl.ipynb)\n", + " * [GIPL + ECSimpleSnow](gipl_and_ecsimplesnow.ipynb)\n", " * [Frost Number Model](frost_number.ipynb)\n", " * [HydroTrend Model](hydrotrend.ipynb)\n", " * [Kudryavtsev Model](ku.ipynb)\n", diff --git a/pymt/framework/bmi_ugrid.py b/pymt/framework/bmi_ugrid.py index 6f76d814..7a585e1e 100644 --- a/pymt/framework/bmi_ugrid.py +++ b/pymt/framework/bmi_ugrid.py @@ -20,6 +20,8 @@ def coordinate_names(rank): class _Base(xr.Dataset): + __slots__ = "bmi", "grid_id", "grid_type", "ndim", "metadata" + def __init__(self, bmi, grid_id): self.bmi = bmi self.grid_id = grid_id @@ -96,6 +98,8 @@ def set_offset(self, data=None): class Scalar(_Base): + __slots__ = () + def __init__(self, *args): super(Scalar, self).__init__(*args) @@ -114,6 +118,8 @@ def __init__(self, *args): class Vector(_Base): + __slots__ = () + def __init__(self, *args): super(Vector, self).__init__(*args) @@ -132,6 +138,8 @@ def __init__(self, *args): class Points(_Base): + __slots__ = () + def __init__(self, *args): super(Points, self).__init__(*args) @@ -149,6 +157,8 @@ def __init__(self, *args): class Unstructured(_Base): + __slots__ = () + def __init__(self, *args): super(Unstructured, self).__init__(*args) @@ -168,6 +178,8 @@ def __init__(self, *args): class StructuredQuadrilateral(_Base): + __slots__ = () + def __init__(self, *args): super(StructuredQuadrilateral, self).__init__(*args) @@ -203,6 +215,8 @@ def __init__(self, *args): class Rectilinear(_Base): + __slots__ = () + def __init__(self, *args): super(Rectilinear, self).__init__(*args) @@ -238,6 +252,8 @@ def __init__(self, *args): class UniformRectilinear(_Base): + __slots__ = () + def __init__(self, *args): super(UniformRectilinear, self).__init__(*args) From 3be2770aa89eab45951d99c192c773c9f8d92e70 Mon Sep 17 00:00:00 2001 From: Eric Hutton Date: Fri, 18 Oct 2019 15:19:13 -0600 Subject: [PATCH 13/27] fix: broken links to the docs in README (#100) --- README.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 80f216bb..2a6d2e91 100644 --- a/README.rst +++ b/README.rst @@ -39,6 +39,11 @@ Launch Binder

+Quick links: + * `User documentation `_ + * `Installation instructions `_ + * `List of available models `_ + PyMT is an Open Source Python package, developed by the `Community Surface Dynamics Modeling System `_ (CSDMS), that provides the necessary tools used for the coupling of models @@ -60,11 +65,6 @@ that expose the * A plug-in framework for adding additional BMI-enabled models to the framework -Quick links: - * `Installation instructions `_ - * `User documentation `_ - * `List of available models `_ - This material is based upon work supported by the National Science Foundation under Grant No. `1831623`_, From 8b6b460e741aa2d6052e402877b38eac70bc8640 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Thu, 14 Nov 2019 09:55:31 -0700 Subject: [PATCH 14/27] Add links to the Help Desk --- docs/examples.rst | 6 ++++++ docs/install.rst | 6 ++++++ docs/quickstart.rst | 6 ++++++ 3 files changed, 18 insertions(+) diff --git a/docs/examples.rst b/docs/examples.rst index 4077f316..876e23e6 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -15,6 +15,12 @@ have to install Jupyter Notebook: $ conda install notebook +If you encounter any problems when running these Notebooks, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + Single Models ------------- diff --git a/docs/install.rst b/docs/install.rst index 541c3a4b..e181793c 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -17,6 +17,12 @@ possibly modifying it, it's best to install it :ref:`from source`. +If you encounter any problems when installing *pymt*, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + .. _stable-release: diff --git a/docs/quickstart.rst b/docs/quickstart.rst index febdf780..12e94518 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -6,6 +6,12 @@ If you want to dig deeper, links are provided at each step to more detailed information either here in the User Guide or elsewhere. +If you encounter any problems when installing or running *pymt*, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + Install conda ------------- From ef110ed5b01d29e99d66ca38e46d62769d65b9ad Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:18:23 -0700 Subject: [PATCH 15/27] update frost_number.ipynb github link --- notebooks/frost_number.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/frost_number.ipynb b/notebooks/frost_number.ipynb index 62b5752d..868f29f2 100644 --- a/notebooks/frost_number.ipynb +++ b/notebooks/frost_number.ipynb @@ -6,7 +6,7 @@ "source": [ "## Frost Number Model\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/frost_number.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/frost_number.ipynb\n", "* Install command:\n", "```\n", "$ conda install notebook pymt_permamodel\n", @@ -14,7 +14,7 @@ "\n", "* Download a local copy of the notebook:\n", "```\n", - "$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/docs/demos/frost_number.ipynb\n", + "$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/notebooks/frost_number.ipynb\n", "```\n", "\n", "* Start a Jupyter Notebook session in the current directory:\n", From 84fff94cb5a0b64f12512dd60e2cd09cb4115f0d Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:22:01 -0700 Subject: [PATCH 16/27] update cem.ipynb github link --- notebooks/cem.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/cem.ipynb b/notebooks/cem.ipynb index 3687812a..fea85402 100644 --- a/notebooks/cem.ipynb +++ b/notebooks/cem.ipynb @@ -13,11 +13,11 @@ "source": [ "## Coastline Evolution Model\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/cem.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/cem.ipynb\n", "* Install command: `$ conda install notebook pymt_cem`\n", "* Download local copy of notebook:\n", "\n", - " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/docs/demos/cem.ipynb`\n", + " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/notebooks/cem.ipynb`\n", "\n", "This example explores how to use a BMI implementation using the CEM model as an example.\n", "\n", From 0d8c5dc189acd3afc12b7e703a73e801e21ea3b5 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:24:50 -0700 Subject: [PATCH 17/27] update cem_and_waves.ipynb github link --- notebooks/cem_and_waves.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/cem_and_waves.ipynb b/notebooks/cem_and_waves.ipynb index 9d511dda..0e3b951a 100644 --- a/notebooks/cem_and_waves.ipynb +++ b/notebooks/cem_and_waves.ipynb @@ -13,7 +13,7 @@ "source": [ "## Coastline Evolution Model + Waves\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/cem_and_waves.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/cem_and_waves.ipynb\n", "* Install command: `$ conda install notebook pymt_cem`\n", "\n", "This example explores how to use a BMI implementation to couple the Waves component with the Coastline Evolution Model component.\n", From f60da98da57d3494bf88f536ebe21d831d59de03 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:27:30 -0700 Subject: [PATCH 18/27] update child.ipynb github link --- notebooks/child.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/child.ipynb b/notebooks/child.ipynb index 64c8c3fc..3ae5ba12 100644 --- a/notebooks/child.ipynb +++ b/notebooks/child.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "## CHILD Landscape Evolution Model\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/child.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/child.ipynb\n", "* Install command: `$ conda install notebook pymt_child`\n", "\n" ] From bc3e539632ee635618ddcedf3a077ab6e883d40b Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:30:01 -0700 Subject: [PATCH 19/27] update hydrotrend.ipynb github link --- notebooks/hydrotrend.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/hydrotrend.ipynb b/notebooks/hydrotrend.ipynb index 10ff01e6..f8b82e99 100644 --- a/notebooks/hydrotrend.ipynb +++ b/notebooks/hydrotrend.ipynb @@ -6,11 +6,11 @@ "source": [ "## HydroTrend\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/hydrotrend.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/hydrotrend.ipynb\n", "* Package installation command: `$ conda install notebook pymt_hydrotrend`\n", "* Command to download a local copy:\n", "\n", - " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/docs/demos/hydrotrend.ipynb`\n", + " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/notebooks/hydrotrend.ipynb`\n", "\n", "HydroTrend is a 2D hydrological water balance and transport model that simulates water discharge and sediment load at a river outlet. You can read more about the model, find references or download the source code at: https://csdms.colorado.edu/wiki/Model:HydroTrend.\n", "\n", From a2b356f656cbc8d2aa523f40dae8cf55730f1796 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:32:31 -0700 Subject: [PATCH 20/27] update hydrotrend.ipynb github link --- notebooks/hydrotrend.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/hydrotrend.ipynb b/notebooks/hydrotrend.ipynb index f8b82e99..445313ab 100644 --- a/notebooks/hydrotrend.ipynb +++ b/notebooks/hydrotrend.ipynb @@ -511,7 +511,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.4" } }, "nbformat": 4, From 31bb10dcae350de6cc3fe20f3c7f3bf3cc368fef Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:32:52 -0700 Subject: [PATCH 21/27] update ku.ipynb github link --- notebooks/ku.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/ku.ipynb b/notebooks/ku.ipynb index 437a5b92..383f8a98 100644 --- a/notebooks/ku.ipynb +++ b/notebooks/ku.ipynb @@ -6,11 +6,11 @@ "source": [ "## Kudryavtsev Model\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/ku.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/ku.ipynb\n", "* Install command: `$ conda install notebook pymt_permamodel`\n", "* Download local copy of notebook:\n", "\n", - " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/docs/demos/ku.ipynb`\n", + " `$ curl -O https://raw.githubusercontent.com/csdms/pymt/master/notebooks/ku.ipynb`\n", "\n", "### Introduction to Permafrost Processes - Lesson 2 Kudryavtsev Model\n", "\n", @@ -998,7 +998,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.4" } }, "nbformat": 4, From 3abe90e960bf05687899213876b2caa198125a33 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:34:54 -0700 Subject: [PATCH 22/27] update sedflux3d.ipynb github link --- notebooks/sedflux3d.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/sedflux3d.ipynb b/notebooks/sedflux3d.ipynb index 24f2f0e4..868254e4 100644 --- a/notebooks/sedflux3d.ipynb +++ b/notebooks/sedflux3d.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "## Sedflux3D\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/sedflux3d.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/sedflux3d.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux`\n", "\n" ] From 655e0f4abaa25edd80430c164d94023c2acf0c8e Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:36:24 -0700 Subject: [PATCH 23/27] update sedflux3d_and_child.ipynb github link --- notebooks/sedflux3d_and_child.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/sedflux3d_and_child.ipynb b/notebooks/sedflux3d_and_child.ipynb index b57cf321..9a48c4a3 100644 --- a/notebooks/sedflux3d_and_child.ipynb +++ b/notebooks/sedflux3d_and_child.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "## Sedflux3D + CHILD\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/sedflux3d_and_child.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/sedflux3d_and_child.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux pymt_child`\n", "\n" ] @@ -270,7 +270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.4" } }, "nbformat": 4, From 75d59b8ae5d1f5ef8aa38864c5741afb4e9cee76 Mon Sep 17 00:00:00 2001 From: gantian127 Date: Thu, 9 Jan 2020 14:52:12 -0700 Subject: [PATCH 24/27] update subside.ipynb github link --- notebooks/subside.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/subside.ipynb b/notebooks/subside.ipynb index a3822359..80b770bb 100644 --- a/notebooks/subside.ipynb +++ b/notebooks/subside.ipynb @@ -13,7 +13,7 @@ "source": [ "## Flexural Subsidence\n", "\n", - "* Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/subside.ipynb\n", + "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/subside.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux`\n", "\n", "This example explores how to use a BMI implementation using sedflux's subsidence model as an example.\n", @@ -431,9 +431,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (pymt)", + "display_name": "Python 3", "language": "python", - "name": "pymt-dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -445,7 +445,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.4" } }, "nbformat": 4, From 273f99075605d2dbd545dfb53ac0921fc578ba76 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Mon, 13 Jan 2020 12:28:48 -0700 Subject: [PATCH 25/27] Relax required Python 3.6 version This allows Python 3.6.1 or greater to be installed, which fixes an import error from the `typing` module: ImportError: cannot import name 'AsyncGenerator' which in turn fixes the failing docs build in pymt. h/t https://github.com/pgjones/quart/issues/19#issuecomment-385890334 --- docs/environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/environment.yml b/docs/environment.yml index 1e7fffc8..421416cf 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -2,7 +2,7 @@ name: pymt_docs channels: - conda-forge dependencies: -- python==3.6 +- python=3.6 - pandoc - pip - nbformat From d46d2a41f603739e09e80d72cdd7440056dcdcdf Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Mon, 13 Jan 2020 12:30:53 -0700 Subject: [PATCH 26/27] Update output from pymt.cfunits.units.Units.reftime doctest This change appears to be caused by a change to the underlying netCDF4.netcdftime.utime module. This caused the doctest to fail, but no unit tests failed. --- pymt/cfunits/units.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymt/cfunits/units.py b/pymt/cfunits/units.py index 72fccb81..71a502d6 100644 --- a/pymt/cfunits/units.py +++ b/pymt/cfunits/units.py @@ -1849,7 +1849,7 @@ def reftime(self): :Examples: >>> repr(Units('days since 1900-1-1').reftime) -'cftime.datetime(1900, 1, 1, 0, 0, 0, 0, -1, 1)' +'cftime.datetime(1900-01-01 00:00:00)' >>> str(Units('days since 1900-1-1 03:00').reftime) '1900-01-01 03:00:00' From 4c95e938156cb49eb2dcca015c636e2894cc1f96 Mon Sep 17 00:00:00 2001 From: Mark Piper Date: Thu, 14 Nov 2019 09:55:31 -0700 Subject: [PATCH 27/27] Add links to the Help Desk --- docs/examples.rst | 6 ++++++ docs/install.rst | 6 ++++++ docs/quickstart.rst | 6 ++++++ 3 files changed, 18 insertions(+) diff --git a/docs/examples.rst b/docs/examples.rst index 4077f316..876e23e6 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -15,6 +15,12 @@ have to install Jupyter Notebook: $ conda install notebook +If you encounter any problems when running these Notebooks, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + Single Models ------------- diff --git a/docs/install.rst b/docs/install.rst index 541c3a4b..e181793c 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -17,6 +17,12 @@ possibly modifying it, it's best to install it :ref:`from source`. +If you encounter any problems when installing *pymt*, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + .. _stable-release: diff --git a/docs/quickstart.rst b/docs/quickstart.rst index febdf780..12e94518 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -6,6 +6,12 @@ If you want to dig deeper, links are provided at each step to more detailed information either here in the User Guide or elsewhere. +If you encounter any problems when installing or running *pymt*, +please visit us at the `CSDMS Help Desk`_ +and explain what occurred. + +.. _CSDMS Help Desk: https://github.com/csdms/help-desk + Install conda -------------