From c8a3755defc90747a8ac748ec709f2d66a3bfaeb Mon Sep 17 00:00:00 2001 From: Erik Holmgren Date: Thu, 17 Jun 2021 10:40:12 +0200 Subject: [PATCH 01/15] Notebooks moved over from the old edu branch. Added .ipynb_checkpoints to .gitignore. --- .gitignore | 1 + notebooks/distributed_run.ipynb | 244 ++++++++++++++++++ notebooks/first_steps.ipynb | 327 ++++++++++++++++++++++++ notebooks/sensitivity_study.ipynb | 412 ++++++++++++++++++++++++++++++ notebooks/welcome.ipynb | 53 ++++ 5 files changed, 1037 insertions(+) create mode 100644 notebooks/distributed_run.ipynb create mode 100644 notebooks/first_steps.ipynb create mode 100644 notebooks/sensitivity_study.ipynb create mode 100644 notebooks/welcome.ipynb diff --git a/.gitignore b/.gitignore index f80601dd..3f8f01cb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +.ipynb_checkpoints .idea/ venv/ htmlcov/ diff --git a/notebooks/distributed_run.ipynb b/notebooks/distributed_run.ipynb new file mode 100644 index 00000000..4f9df2e3 --- /dev/null +++ b/notebooks/distributed_run.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "configured-banks", + "metadata": {}, + "source": [ + "# A first distributed simulation\n", + "\n", + "Until now, we've only been working with single grid point simulations. This is fine for learning and playing around with different configurations of the model since the runs takes less time. A more typical use case of COSIPY is to run a distributed simulation over the entire surface of the glacier, which gives us a much more detailed view of the glacier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "amazing-april", + "metadata": {}, + "outputs": [], + "source": [ + "# Have to change the cwd for the ipython session, otherwise COSIPY\n", + "# will look for things in the wrong places.\n", + "import os\n", + "import sys\n", + "# This is not really a good method, if cell is re run we end up in the\n", + "# wrong directory.\n", + "os.chdir('./../')\n", + "sys.path.append(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "universal-repair", + "metadata": {}, + "outputs": [], + "source": [ + "from cosipy.utils import edu_utils\n", + "# cfg gives us the NAMELIST\n", + "import cfg\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "certain-millennium", + "metadata": {}, + "outputs": [], + "source": [ + "# Have to tell matplotlib to plot inline\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "identified-findings", + "metadata": {}, + "source": [ + "Setting up a distributed simulation is fairly easy. All we need is 2D input data (3D with time). This data could either be in the form of gcm/reanalysis output or interpolated from station data. For the tutorials we provide a distributed input file `Zhadang_ERA5_2009_dst.nc` for Zhadang. We can take a look at it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "funky-marker", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the namelist for path to data and input netcdf. Have to add\n", + "# input in between.\n", + "input_path = cfg.NAMELIST['data_path'] + 'input/'\n", + "with xr.open_dataset(input_path+'Zhadang/Zhadang_ERA5_2009_dst.nc') as ds:\n", + " ds = ds.isel(time=slice(0, -1)).load()\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "informed-polish", + "metadata": {}, + "source": [ + "As you can see in the output above, the input data contains 91 gridpoints. However, only 17 of these are actually within the glacier boundaries.\n", + "\n", + "
\n", + "
\n", + " Can you figure out a way to confirm the above statement? Click me for a hint\n", + " For the total number of gridpoints it is as simple as taking the product of the number of longitudes and latitudes. For the points within the glacier, take a look at the mask. You can plot it or \"show\" the data by pressing the storage symbol in the output above.\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "nonprofit-million", + "metadata": {}, + "outputs": [], + "source": [ + "# Empty cell for the reader\n" + ] + }, + { + "cell_type": "markdown", + "id": "pleased-oxide", + "metadata": {}, + "source": [ + "## Running a distributed simulation\n", + "\n", + "Now that we've confirmed that the data is of the right dimension, we can initialize the datasets needed to run the simulation. First we have to adapt the `NAMELIST` to point to the distributed input data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "least-robinson", + "metadata": {}, + "outputs": [], + "source": [ + "# Copoy the namelist.\n", + "NAMELIST = cfg.NAMELIST.copy()\n", + "NAMELIST['input_netcdf']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "convertible-damages", + "metadata": {}, + "outputs": [], + "source": [ + "# We change the input_netcdf\n", + "NAMELIST['input_netcdf'] = 'Zhadang/Zhadang_ERA5_2009_dst.nc'\n", + "NAMELIST['input_netcdf']" + ] + }, + { + "cell_type": "markdown", + "id": "eligible-estonia", + "metadata": {}, + "source": [ + "With this done we can initialize the datasets needed for running the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "north-christmas", + "metadata": {}, + "outputs": [], + "source": [ + "IO, DATA, RESULTS = edu_utils.create_IO(NAMELIST)" + ] + }, + { + "cell_type": "markdown", + "id": "catholic-lotus", + "metadata": {}, + "source": [ + "The output above also confirms that we have 17 glacier gridpoints in our input data.\n", + "\n", + "## Running the model\n", + "We are now ready to run the model. This is just as simple as in the one dimensional case. In the background the results of each gridpoint is calculated individually, we're basically stacking multiple point simulations next to each other. `edu_utils.run_model` distributes the work so that gridpoints are executed in parallel. Note however that this still might take some time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "material-shoot", + "metadata": {}, + "outputs": [], + "source": [ + "edu_utils.run_model(DATA, IO, NAMELIST, RESULTS)" + ] + }, + { + "cell_type": "markdown", + "id": "intelligent-sunglasses", + "metadata": {}, + "source": [ + "We can as before take a quick look at the results. First however, we have to reduce the dimensions of the data. A simple way is to select a single timestep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "answering-ensemble", + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS.isel(time=1).MB.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "metropolitan-regression", + "metadata": {}, + "source": [ + "Alternatively we can also reduce one of the dimensions by taking the mean of it, creating a so called Hovmöller diagram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "approved-debate", + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS.MB.mean(dim='lon').plot();" + ] + }, + { + "cell_type": "markdown", + "id": "incorporated-anthony", + "metadata": {}, + "source": [ + "## Next steps\n", + "[Back to overview](welcome.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosipy", + "language": "python", + "name": "cosipy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/first_steps.ipynb b/notebooks/first_steps.ipynb new file mode 100644 index 00000000..1e294313 --- /dev/null +++ b/notebooks/first_steps.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First steps with COSIPY\n", + "\n", + "This is the first tutorial to get you started with using COSIPY. In it we will go through how to\n", + "\n", + "* Setup a simulation for a single gridpoint\n", + "* Run the simulation and save the results\n", + "* Do simple visualisations of the results using matplotlib\n", + "\n", + "First we have to import the python libraries we are going to use" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Have to change the cwd for the ipython session, otherwise COSIPY\n", + "# will look for things in the wrong places.\n", + "import os\n", + "import sys\n", + "# This is not really a good method, if cell is re run we end up in the\n", + "# wrong directory.\n", + "os.chdir('./../')\n", + "sys.path.append(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# edu_utils has some quality of life functions\n", + "from cosipy.utils import edu_utils\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Have to tell matplotlib to plot inline\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create input/output files\n", + "First we have to creat input/ouput files for the simulation. This is handled by the `IOClass` of COSIPY which creates an xarray dataset containing information about the spatial and temporal coordinate, the data varialbes and the attributes used in the simulation. \n", + "\n", + "Before we initialize these files, it is interesting to have a look at the varialbes and constants used by the `io` module and the rest of the model, they are contained in a dictionary called `NAMELIST` which is available from the `cfg` module:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cfg\n", + "NAMELIST = cfg.NAMELIST\n", + "NAMELIST" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are loaded from the files `config.cfg` and `constants.cfg` located in the root directory of cosipy. We can change any of these variables to customise our simulation. This can by done changing the value of a variable in the previously mentioned files, however for this to take affect we have to reload the `cfg` moduel. \n", + "\n", + "A much simpler way is to change it directly in the `NAMELIST` before we begin initializing our run, directly in this session. For instance we can change the variable `time_end` to adjust the temporal extent of our simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First take a look at the default value\n", + "NAMELIST['time_end']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Then we change it\n", + "NAMELIST['time_end'] = '2009-01-08T00:00'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize the `IOClass` and the datasets holding the input and output data for our simulation. For this we have a utility function `create_IO`, which takes the `NAMELIST` and returns an instance of the `IOClass` and the `DATA` and `RESULTS` datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IO, DATA, RESULTS = edu_utils.create_IO(NAMELIST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the `IOClass` ready, we can initialise the data files needed for our run:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above gives us some helpful insight about the data. We can see that the data covers the period 2009-01-01 to 2009-01-31, and that we have specified the integration time to 2009-01-01 to 2009-01-08 by changing `NAMELIST['time_end']`. We also see that we have one gridpoint which we will perform the calculations on.\n", + "\n", + "It also shows some quality control on the input data, more on this later.\n", + "\n", + "Note that we don't have to change `NAMELIST['time_end']` as we did above - if we don't, the simulation will run using the default parameters from `config.cfg` and `constants.cfg`.\n", + "\n", + "Also note that the `IOClass` along with the files DATA, RESULTS and RESTART has to be re-initliazed after a variable has been changed in `NAMELIST`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running COSIPY\n", + "Now we are ready to do a simple point simulation with COSIPY. This example is run on the Zhadang glacier. We can change this by changing the `input_netcdf` in `NAMELIST` to point at the files we want to use. In these tutorials data for Zhadang and Hintereisferner are included.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then run the model by simply calling the `run_model` function from the `edu_utils`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note that this can take some time.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "edu_utils.run_model(DATA, IO, NAMELIST, RESULTS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thats it! Now we are ready to inspect the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing the results\n", + "To get a quick overview of the variables in our dataframe, we can simply call it " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that our data is stored in an xarray dataset - a very handy format for interacting with netcdf files in python. To check which data variables are available, one can simply click on the label \"Data Variables\", which will expand a list of the variables.\n", + "\n", + "We can quckly plot a variable, the surface mass balance (`surfMB`) for instance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS.surfMB.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the use of xarray-datasets for storing the data makes it possible to levarage the built in plotting functionality of xarray and quickly visualize the output.\n", + "\n", + "For a slightly more advanced plot, one can still make use of matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(figsize=(7,8), nrows=2, sharex=True,\n", + " gridspec_kw = {'height_ratios':[2,1]})\n", + "RESULTS.surfMB.plot(ax=ax1)\n", + "RESULTS.TS.plot(ax=ax2, color='C1')\n", + "# Easier to make use of xarray plotting capabilites and then remove unwanted lables.\n", + "ax1.set_xlabel('')\n", + "ax2.set_title('')\n", + "# Get the time span of our dataset to use for title\n", + "time_span = RESULTS.time[0].values, RESULTS.time[-1].values\n", + "time_span = np.datetime_as_string(time_span, unit='m')\n", + "# Set the title of the plot.\n", + "ax1.set_title(f'Surface mass balance and Snowfall at Zhadang \\n between {time_span[0]} and' +\\\n", + " f' {time_span[1]}')\n", + "# Fixes the spacing between parts of the plots.\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice the scale of the surface mass balance.** In the plot above, we see the surface mass blance (blue) and the 2-meter air temperature (orange). Even though the air temperautre stays below 0°C (273.15K) for the entire period, the mass balance is still negative during the day. We will investigate this is the following section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + " Question: Can you think of a variable to plot as a way to explain the positive surface mass balance during the 6th of January? Click me for a hint\n", + " Try plotting the snowfall, i.e. RESULTS.SNOWFALL.plot().\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A look at what is causing the melt\n", + "\n", + "Since the temperature never reaches above zero, melt is not causing the small loss of mass during the days seen in the plot above. Instead it is sublimation, the process when a substance transitions from solid to gas without passing through the liquid state. We can plot the sublimation quickly to confirm this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS.SUBLIMATION.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "[Back to overview](welcome.ipynb)\n", + "\n", + "[Sensitivity studies with COSIPY](sensitivity_study.ipynb)" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/sensitivity_study.ipynb b/notebooks/sensitivity_study.ipynb new file mode 100644 index 00000000..2e04421c --- /dev/null +++ b/notebooks/sensitivity_study.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "judicial-headquarters", + "metadata": {}, + "source": [ + "# Sensitivity studies using COSIPY\n", + "\n", + "In this tutorial we will go through how to do a sensitivity study of the surface mass balance of a glacier using COSIPY. For a sensitivity study we want to check how much changing a constant affects the calculation of a certain variables, i.e. how sensitive it is. We can do this on essentially any variable, but in our case we will focus on the surface mass balance of a glacier.\n", + "\n", + "Just as in [First steps in COSIPY](first_steps.ipynb) we begin with importing some useful libraries and the cosipy functions we need." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "judicial-personal", + "metadata": {}, + "outputs": [], + "source": [ + "# Have to change the cwd for the ipython session, otherwise COSIPY\n", + "# will look for things in the wrong places.\n", + "import os\n", + "import sys\n", + "# This is not really a good method, if cell is re run we end up in the\n", + "# wrong directory.\n", + "os.chdir('./../')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "front-collector", + "metadata": {}, + "outputs": [], + "source": [ + "from cosipy.utils import edu_utils\n", + "# cfg gives us the NAMELIST\n", + "import cfg\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "persistent-refund", + "metadata": {}, + "outputs": [], + "source": [ + "# Have to tell matplotlib to plot inline\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "hundred-boulder", + "metadata": {}, + "source": [ + "## Choosing the constant to investigate\n", + "\n", + "Before we can continue with setting up the simulations, we have to choose the constant we want to change. As you might remember, all the constants that are used by COSIPY are stored in the NAMELIST. Let's take a look at it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "prescription-madrid", + "metadata": {}, + "outputs": [], + "source": [ + "cfg.NAMELIST" + ] + }, + { + "cell_type": "markdown", + "id": "fleet-norman", + "metadata": {}, + "source": [ + "This contains a lot of different things and can be a bit overwhelming at first. It contains everything from the configuration of where to store files to constants used in some of the submodules used for example calculating the densification of snow.\n", + "\n", + "If we plan to look at the surface mass balance in winter, the albedo of fresh snow is a good guess of a variable that might have an impact on the calculations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "automated-google", + "metadata": {}, + "outputs": [], + "source": [ + "cfg.NAMELIST['albedo_fresh_snow']" + ] + }, + { + "cell_type": "markdown", + "id": "recent-refund", + "metadata": {}, + "source": [ + "Let's say we want to change this 10% in either direction. Since the model is fetching the constants from `NAMELIST`, the plan is to change the albedo of fresh snow in `NAMELIST` before we use it to intialize the model." + ] + }, + { + "cell_type": "markdown", + "id": "integrated-structure", + "metadata": {}, + "source": [ + "### Initializing the namelists and data files\n", + "\n", + "We begin by initialising three instances of the namelist. We then keep one as default while we change the albedo of fresh snow in the two others" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yellow-olive", + "metadata": {}, + "outputs": [], + "source": [ + "# We create a copy of the namelist so we don't change the original.\n", + "# Default\n", + "NAMELIST_def = cfg.NAMELIST.copy()\n", + "# Up 10%\n", + "NAMELIST_up = cfg.NAMELIST.copy()\n", + "# We overwrite the default value. We multiply it from cfg since we can then\n", + "# run the cell multiple times without performing the multiplication multiple\n", + "# times.\n", + "NAMELIST_up['albedo_fresh_snow'] = cfg.NAMELIST['albedo_fresh_snow'] * 1.1\n", + "# Down 10%\n", + "NAMELIST_dn =cfg.NAMELIST.copy()\n", + "NAMELIST_dn['albedo_fresh_snow'] = cfg.NAMELIST['albedo_fresh_snow'] * 0.9" + ] + }, + { + "cell_type": "markdown", + "id": "individual-frederick", + "metadata": {}, + "source": [ + "Did it work?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "korean-success", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"NAMELIST_dn: {NAMELIST_dn['albedo_fresh_snow']:.3f}\\nNAMELIST_def: \\\n", + "{NAMELIST_def['albedo_fresh_snow']:.3f}\\nNAMELIST_up: \\\n", + "{NAMELIST_up['albedo_fresh_snow']:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "pretty-artist", + "metadata": {}, + "source": [ + "
\n", + " Question: Can you figure out what to change in the cells above to alter the albedo by 5% instead of 10%?\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "literary-portuguese", + "metadata": {}, + "source": [ + "We can now go on to creating the files used for storing the data and results of the simulation. Just as with the namlist we need one set of instances for each experiment we want to run, in this case three. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "orange-bahamas", + "metadata": {}, + "outputs": [], + "source": [ + "# Default\n", + "IO_def, DATA_def, RESULTS_def = edu_utils.create_IO(NAMELIST_def)\n", + "# Up\n", + "IO_up, DATA_up, RESULTS_up = edu_utils.create_IO(NAMELIST_up)\n", + "# Down\n", + "IO_dn, DATA_dn, RESULTS_dn = edu_utils.create_IO(NAMELIST_dn)" + ] + }, + { + "cell_type": "markdown", + "id": "super-dictionary", + "metadata": {}, + "source": [ + "## Running multiple simulations\n", + "\n", + "We have now prepared the namelists and data/results files for the three different runs we want to do. The runs can initilaized as it was done in the [First steps in COSIPY](first_steps.ipynb) notebook, and changing the namelist and data/results variables manually for each run. But to save us writing the same code multiple times, and to give some inspiration how one might go about running a higher number of simulations, we will now do it in a loop. First we have to put all we need into a list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "assigned-japanese", + "metadata": {}, + "outputs": [], + "source": [ + "# List of lists with our experiments\n", + "exp_list = [[NAMELIST_def, DATA_def, RESULTS_def, IO_def],\n", + " [NAMELIST_dn, DATA_dn, RESULTS_dn, IO_dn],\n", + " [NAMELIST_up, DATA_up, RESULTS_up, IO_up]\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "viral-bhutan", + "metadata": {}, + "source": [ + "We can index this list to get the results of experiment 2 as `exp_list[1][2]`." + ] + }, + { + "cell_type": "markdown", + "id": "stable-straight", + "metadata": {}, + "source": [ + "
\n", + " Remember that python is zero-indexed.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "backed-howard", + "metadata": {}, + "source": [ + "Let's setup the loop and run the simulations. This will take some time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "asian-utilization", + "metadata": {}, + "outputs": [], + "source": [ + "for exp in exp_list:\n", + " # Call run_model once for each experiment\n", + " edu_utils.run_model(exp[1], exp[3], exp[0], exp[2])" + ] + }, + { + "cell_type": "markdown", + "id": "massive-judge", + "metadata": {}, + "source": [ + "## A look at the results\n", + "\n", + "When the simulations has finished running it is time to take a look at the results. Since we're keeping the results in `exp_list` we can again leverage the power of loops to save us writing some code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "synthetic-library", + "metadata": {}, + "outputs": [], + "source": [ + "labels = ['Default', '-10%', '+10%']\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "for exp, label in zip(exp_list, labels):\n", + " # Get the data and plot it RESULTS are kept at the third spot, index 2\n", + " exp[2].surfMB.plot(ax=ax, label=label)\n", + "plt.legend(); " + ] + }, + { + "cell_type": "markdown", + "id": "rental-conditions", + "metadata": {}, + "source": [ + "
\n", + "
\n", + " \n", + " Question: Can you explain why the surface mass balance looks like it does? Click me for an explanation!\n", + "

We are looking at three different surface mass balance series calculated with three different values for the albedo of fresh snow. The regular dips in the surface mass balance is the diurnal cycle, the glacier lose mass during the day while it remains constant during the night. When the albedo is increased the surface of the glacier will reflect more of the incoming solar radiation during the day. This leads to a decrease in the energy available for melt, thus reducing the melt and resulting in a less negative surface mass balance.\n", + "

\n", + "On the other hand, when the albedo is decreased the reflectivity of the glacier is reduced. This increases the energy available for melt which is why the surface mass balance is more negative.

\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "casual-retail", + "metadata": {}, + "source": [ + "*Here you can write your answer (double click to open the cell)* \n" + ] + }, + { + "cell_type": "markdown", + "id": "according-physiology", + "metadata": {}, + "source": [ + "## Another sensitivity study\n", + "\n", + "Try to come up with another variable to use for a sensitivity study. The loop for running the simulation is provided below, but you have to initialise the namelist(s), data and result files and the list containing your experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "assured-burke", + "metadata": {}, + "outputs": [], + "source": [ + "# Pick a variable to nudge\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bridal-america", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialise the namelists and datafiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "organized-masters", + "metadata": {}, + "outputs": [], + "source": [ + "# Pack it all into a list as above\n" + ] + }, + { + "cell_type": "markdown", + "id": "written-prefix", + "metadata": {}, + "source": [ + "
\n", + " Are you ready to run the simulation?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "educated-apollo", + "metadata": {}, + "outputs": [], + "source": [ + "for exp in exp_list:\n", + " # Call run_model once for each experiment\n", + " edu_utils.run_model(exp[1], exp[3], exp[0], exp[2])" + ] + }, + { + "cell_type": "markdown", + "id": "julian-gambling", + "metadata": {}, + "source": [ + "Try plotting the results of your study!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "weird-condition", + "metadata": {}, + "outputs": [], + "source": [ + "# Do some plotting here.\n" + ] + }, + { + "cell_type": "markdown", + "id": "sought-possibility", + "metadata": {}, + "source": [ + "## Next steps\n", + "[Back to overview](welcome.ipynb)\n", + "\n", + "[Temperature bias experiments](temp_sensitivity.ipynb)\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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/welcome.ipynb b/notebooks/welcome.ipynb new file mode 100644 index 00000000..f445312c --- /dev/null +++ b/notebooks/welcome.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](https://cryo-tools.org/wp-content/uploads/2019/11/COSIPY-logo-2500px.png)\n", + "\n", + "# Welcome to the COSIPY educational notebooks!\n", + "\n", + "COSIPY - the **Co**upled **S**nowpack and **I**ce surface energy and mass balance model in **Py**thon - is an open-source model for simulating distributed snow and glacier mass changes.\n", + "\n", + "In the following notebooks we will go through some basic concepts of COSIPY, how it works and what you can do with it. \n", + "\n", + "If jupyter notebooks are a new concept to you, we recommend you to take a look at [this great introduction](https://edu.oggm.org/en/latest/notebooks_howto.html) from the [OGGM-team](https://oggm.org/).\n", + "\n", + "These notebooks can be rendered as a static webpage, or if you want to be able to play around with the model, open them on [MyBinder](https://mybinder.org/) by clicking the following button [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Holmgren825/cosipy/edu?filepath=notebooks%2Fwelcome.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A list of the tutorials\n", + "- [First steps with COSIPY](first_steps.ipynb) Explains how to setup and run COSIPY on one grid point on a glaicer.\n", + "- [Sensitivity studies using COSIPY](sensitivity_study.ipynb) Show you how to do a simple sensitivity study using COSIPY.\n", + "- [Temperature bias experiments](temp_sensitivity.ipynb) Extends on the sensitivity study notebook and explains how to set up experiments to test the temperature sensitivity.\n", + "- [A distributed run](distributed_run.ipynb) Goes through how to set up a distributed run and briefly discusses how this work." + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 387cadec82a1888b731980656bc670c786b40d97 Mon Sep 17 00:00:00 2001 From: Erik Holmgren Date: Thu, 17 Jun 2021 10:47:37 +0200 Subject: [PATCH 02/15] Edu utils. --- cosipy/utils/edu_utils.py | 80 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 cosipy/utils/edu_utils.py diff --git a/cosipy/utils/edu_utils.py b/cosipy/utils/edu_utils.py new file mode 100644 index 00000000..f24ba9a8 --- /dev/null +++ b/cosipy/utils/edu_utils.py @@ -0,0 +1,80 @@ +'''Small utility module to clean up the edu notebooks''' +import os +import sys +import numpy as np +from itertools import product +from dask.distributed import Client, as_completed, progress + +from cosipy.cpkernel.cosipy_core import cosipy_core +from cosipy.cpkernel.io import IOClass + +def create_IO(NAMELIST, restart=False): + '''Create io, data, results for the run''' + # Create the essentials. + IO = IOClass(NAMELIST) + DATA = IO.create_data_file() + RESULT = IO.create_result_file() + + # If we want the restart as well + if restart: + RESTART = IO.create_restart_file() + + return IO, DATA, RESULT, RESTART + + return IO, DATA, RESULT + + +def run_model(DATA, IO, NAMELIST, RESULT, RESTART=None, + stake_names=None, df_stakes_data=None): + # List to store the jobs "futures" + futures = [] + # Start initializing the cluster + with Client() as client: + print(f'Model is running, check progress here: Progress is currently diabled') + ny = DATA.dims[NAMELIST['northing']] + nx = DATA.dims[NAMELIST['easting']] + # We create the jobs that we then submit to the client. + for y, x in product(range(ny), range(nx)): + # We check if the data is within the glacier. + mask = DATA.MASK.isel(lat=y, lon=x) + if mask == 1: + # Check for faulty values. + if np.isnan(DATA.isel(lat=y, lon=x).to_array()).any(): + print('ERROR!!!!!!!!!!! There are NaNs in the dataset') + sys.exit() + # If all is good, append the job to the list. + futures.append(client.submit(cosipy_core, DATA.isel(lat=y, lon=x), y, x, + NAMELIST, stake_names=stake_names, + stake_data=df_stakes_data)) + # Then we can do the jobs. + progress(futures) + # Create numpy arrays which aggregates all local results + IO.create_global_result_arrays() + + + for future in as_completed(futures): + # Here we are unpacking the results from the model run, + # getting ready to save it to our RESULTS dataframe. + indY, indX, local_restart, RAIN, SNOWFALL, LWin, LWout, H, LE, B,\ + QRR, MB, surfMB, Q, SNOWHEIGHT, TOTALHEIGHT, TS, ALBEDO, NLAYERS,\ + ME, intMB, EVAPORATION, SUBLIMATION, CONDENSATION, DEPOSITION,\ + REFREEZE, subM, Z0, surfM, MOL, LAYER_HEIGHT, LAYER_RHO, LAYER_T,\ + LAYER_LWC, LAYER_CC, LAYER_POROSITY, LAYER_ICE_FRACTION,\ + LAYER_IRREDUCIBLE_WATER, LAYER_REFREEZE, stake_names,\ + stat, df_eval = future.result() + + + + IO.copy_local_to_global(indY, indX, RAIN, SNOWFALL, LWin, LWout, H, LE, + B, QRR, MB, surfMB, Q, SNOWHEIGHT, TOTALHEIGHT, + TS, ALBEDO, NLAYERS, ME, intMB, EVAPORATION, + SUBLIMATION, CONDENSATION, DEPOSITION, + REFREEZE, subM, Z0, surfM, MOL, LAYER_HEIGHT, + LAYER_RHO, LAYER_T, LAYER_LWC, LAYER_CC, + LAYER_POROSITY, LAYER_ICE_FRACTION, + LAYER_IRREDUCIBLE_WATER, + LAYER_REFREEZE) + + # Write results to file + IO.write_results_to_file() + print('Finished!') From 71ff30b54b4e3356364b414c563756cb053fa38a Mon Sep 17 00:00:00 2001 From: Erik Holmgren Date: Thu, 17 Jun 2021 12:47:39 +0200 Subject: [PATCH 03/15] New way to configure options of the run. Relies on a small dictionary that only holds what you want to change. Major difference from the old way is that it default to None, hence if one want to use the standard configuration, this object be left empty. In the case it contains a key value pair, it is passed along the modules. These in turn uses a small utility `read_opt` function to overwrite the respective key-value pair in the globals() dictionary of the caller function. --- cosipy/cpkernel/cosipy_core.py | 34 +++++++++++++++----------- cosipy/cpkernel/init.py | 6 ++++- cosipy/cpkernel/io.py | 5 +++- cosipy/modules/albedo.py | 5 +++- cosipy/modules/densification.py | 5 +++- cosipy/modules/evaluation.py | 7 ++++-- cosipy/modules/heatEquation.py | 16 +++++++++++- cosipy/modules/penetratingRadiation.py | 7 +++++- cosipy/modules/percolation.py | 20 ++++++++++++++- cosipy/modules/refreezing.py | 20 +++++++++++++-- cosipy/modules/roughness.py | 6 ++++- cosipy/modules/surfaceTemperature.py | 7 +++++- cosipy/utils/edu_utils.py | 1 - cosipy/utils/options.py | 6 +++++ 14 files changed, 117 insertions(+), 28 deletions(-) create mode 100644 cosipy/utils/options.py diff --git a/cosipy/cpkernel/cosipy_core.py b/cosipy/cpkernel/cosipy_core.py index 1d9cf090..c666c8b2 100644 --- a/cosipy/cpkernel/cosipy_core.py +++ b/cosipy/cpkernel/cosipy_core.py @@ -19,9 +19,10 @@ from cosipy.cpkernel.init import init_snowpack, load_snowpack from cosipy.cpkernel.io import IOClass +from cosipy.utils.options import read_opt -def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_data=None): +def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_data=None, opt_dict=None): """ Cosipy core function, which perform the calculations on one core. Params @@ -49,6 +50,9 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat max_layers = int(DATA.max_layers.values) z = float(DATA.ZLVL.values) + # Replace imported variables with content of the opt_dict. If it's empty + # nothing happens. + read_opt(opt_dict, globals()) # Local variables nt = len(DATA.time.values) #accessing DATA is expensive _RRR = np.full(nt, np.nan) @@ -96,14 +100,14 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat # Initialize snowpack or load restart grid #-------------------------------------------- if GRID_RESTART is None: - GRID = init_snowpack(DATA) + GRID = init_snowpack(DATA, opt_dict) else: GRID = load_snowpack(GRID_RESTART) # Create the local output datasets if not coupled RESTART = None if not WRF_X_CSPY: - IO = IOClass(DATA) + IO = IOClass(DATA, opt_dict) RESTART = IO.create_local_restart_dataset() # hours since the last snowfall (albedo module) @@ -222,12 +226,12 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat #-------------------------------------------- # Calculate albedo and roughness length changes if first layer is snow #-------------------------------------------- - alpha = updateAlbedo(GRID) + alpha = updateAlbedo(GRID, opt_dict) #-------------------------------------------- # Update roughness length #-------------------------------------------- - z0 = updateRoughness(GRID) + z0 = updateRoughness(GRID, opt_dict) #-------------------------------------------- # Surface Energy Balance @@ -237,7 +241,7 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat # Penetrating SW radiation and subsurface melt if SWnet > 0.0: - subsurface_melt, G_penetrating = penetrating_radiation(GRID, SWnet, dt) + subsurface_melt, G_penetrating = penetrating_radiation(GRID, SWnet, dt, opt_dict) else: subsurface_melt = 0.0 G_penetrating = 0.0 @@ -248,13 +252,15 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat if LWin is not None: # Find new surface temperature (LW is used from the input file) fun, surface_temperature, lw_radiation_in, lw_radiation_out, sensible_heat_flux, latent_heat_flux, \ - ground_heat_flux, rain_heat_flux, rho, Lv, MOL, Cs_t, Cs_q, q0, q2 \ - = update_surface_temperature(GRID, dt, z, z0, T2[t], RH2[t], PRES[t], sw_radiation_net, U2[t], RAIN, SLOPE, LWin=LWin[t]) + ground_heat_flux, rain_heat_flux, sw_radiation_net, rho, Lv, MOL, Cs_t, Cs_q, q0, q2 \ + = update_surface_temperature(GRID, dt, alpha, z, z0, T2[t], RH2[t], PRES[t], + G_resid, U2[t], RAIN, SLOPE, LWin=LWin[t], opt_dict=opt_dict) else: # Find new surface temperature (LW is parametrized using cloud fraction) fun, surface_temperature, lw_radiation_in, lw_radiation_out, sensible_heat_flux, latent_heat_flux, \ - ground_heat_flux, rain_heat_flux, rho, Lv, MOL, Cs_t, Cs_q, q0, q2 \ - = update_surface_temperature(GRID, dt, z, z0, T2[t], RH2[t], PRES[t], sw_radiation_net, U2[t], RAIN, SLOPE, N=N[t]) + ground_heat_flux, rain_heat_flux, sw_radiation_net, rho, Lv, MOL, Cs_t, Cs_q, q0, q2 \ + = update_surface_temperature(GRID, dt, alpha, z, z0, T2[t], RH2[t], PRES[t], + G_resid, U2[t], RAIN, SLOPE, N=N[t], opt_dict=opt_dict) #-------------------------------------------- # Surface mass fluxes [m w.e.q.] @@ -286,22 +292,22 @@ def cosipy_core(DATA, indY, indX, GRID_RESTART=None, stake_names=None, stake_dat #-------------------------------------------- # Percolation #-------------------------------------------- - Q = percolation(GRID, melt + condensation + RAIN/1000.0 + lwc_from_melted_layers, dt) + Q = percolation(GRID, melt + condensation + RAIN/1000.0 + lwc_from_melted_layers, dt, opt_dict) #-------------------------------------------- # Refreezing #-------------------------------------------- - water_refreezed = refreezing(GRID) + water_refreezed = refreezing(GRID, opt_dict) #-------------------------------------------- # Solve the heat equation #-------------------------------------------- - solveHeatEquation(GRID, dt) + solveHeatEquation(GRID, dt, opt_dict) #-------------------------------------------- # Calculate new density to densification #-------------------------------------------- - densification(GRID, SLOPE, dt) + densification(GRID, SLOPE, dt, opt_dict) #-------------------------------------------- # Calculate mass balance diff --git a/cosipy/cpkernel/init.py b/cosipy/cpkernel/init.py index e6ef4926..779e5666 100644 --- a/cosipy/cpkernel/init.py +++ b/cosipy/cpkernel/init.py @@ -3,10 +3,14 @@ from constants import * from config import * from cosipy.cpkernel.grid import * +from cosipy.utils.options import read_opt -def init_snowpack(DATA): +def init_snowpack(DATA, opt_dict): ''' INITIALIZATION ''' + # Read and set options + read_opt(opt_dict, globals()) + ##-------------------------------------- ## Check for WRF data ##-------------------------------------- diff --git a/cosipy/cpkernel/io.py b/cosipy/cpkernel/io.py index 6c2a12b8..76dabda5 100644 --- a/cosipy/cpkernel/io.py +++ b/cosipy/cpkernel/io.py @@ -13,12 +13,15 @@ from constants import * from config import * import configparser +from cosipy.utils.options import read_opt class IOClass: - def __init__(self, DATA=None): + def __init__(self, DATA=None, opt_dict=None): """ Init IO Class""" + # Read and set options + read_opt(opt_dict, globals()) # Read variable list from file config = configparser.ConfigParser() config.read('./cosipy/output') diff --git a/cosipy/modules/albedo.py b/cosipy/modules/albedo.py index 5348745b..dc23500e 100644 --- a/cosipy/modules/albedo.py +++ b/cosipy/modules/albedo.py @@ -1,9 +1,12 @@ import numpy as np from constants import albedo_method, albedo_fresh_snow, albedo_firn, albedo_ice, \ albedo_mod_snow_aging, albedo_mod_snow_depth, snow_ice_threshold +from cosipy.utils.options import read_opt -def updateAlbedo(GRID): +def updateAlbedo(GRID, opt_dict): """ This methods updates the albedo """ + # Read and set options + read_opt(opt_dict, globals()) albedo_allowed = ['Oerlemans98'] if albedo_method == 'Oerlemans98': alphaMod = method_Oerlemans(GRID) diff --git a/cosipy/modules/densification.py b/cosipy/modules/densification.py index fa7e16a1..151eae7e 100644 --- a/cosipy/modules/densification.py +++ b/cosipy/modules/densification.py @@ -2,13 +2,16 @@ from constants import densification_method, snow_ice_threshold, minimum_snow_layer_height, \ zero_temperature, ice_density from numba import njit +from cosipy.utils.options import read_opt -def densification(GRID,SLOPE,dt): +def densification(GRID,SLOPE,dt, opt_dict=None): """ Densification of the snowpack Args: GRID :: GRID-Structure dt :: integration time """ + # Read and set options + read_opt(opt_dict, globals()) densification_allowed = ['Boone', 'Vionnet', 'empirical', 'constant'] if densification_method == 'Boone': diff --git a/cosipy/modules/evaluation.py b/cosipy/modules/evaluation.py index b641d388..7c0113ce 100644 --- a/cosipy/modules/evaluation.py +++ b/cosipy/modules/evaluation.py @@ -1,11 +1,14 @@ import numpy as np from config import eval_method, obs_type +from cosipy.utils.options import read_opt -def evaluate(stake_names, stake_data, df_): +def evaluate(stake_names, stake_data, df_, opt_dict=None): """ This methods evaluates the simulation with the stake measurements stake_name :: """ - + + # Read and set options + read_opt(opt_dict, globals()) if eval_method == 'rmse': stat = rmse(stake_names, stake_data, df_) else: diff --git a/cosipy/modules/heatEquation.py b/cosipy/modules/heatEquation.py index 0a2481ff..cdece316 100644 --- a/cosipy/modules/heatEquation.py +++ b/cosipy/modules/heatEquation.py @@ -1,8 +1,22 @@ import numpy as np from numba import njit +from cosipy.utils.options import read_opt + +def solveHeatEquation(GRID, dt, opt_dict=None): + + # Read and set options + read_opt(opt_dict, globals()) + + heatEquation_method = 'default' + heatEquation_allowed = ['default'] + + if heatEquation_method == 'default': + heatEquation_default(GRID,dt) + else: + raise ValueError("Heat equation = \"{:s}\" is not allowed, must be one of {:s}".format(heatEquation_method, ", ".join(heatEquation_allowed))) @njit -def solveHeatEquation(GRID, dt): +def heatEquation_default(GRID, dt): """ Solves the heat equation on a non-uniform grid dt :: integration time diff --git a/cosipy/modules/penetratingRadiation.py b/cosipy/modules/penetratingRadiation.py index 6d989519..0dfb89c2 100644 --- a/cosipy/modules/penetratingRadiation.py +++ b/cosipy/modules/penetratingRadiation.py @@ -2,8 +2,13 @@ from constants import penetrating_method, snow_ice_threshold, spec_heat_ice, \ zero_temperature, ice_density, water_density, lat_heat_melting from numba import njit +from cosipy.utils.options import read_opt + +def penetrating_radiation(GRID, SWnet, dt, opt_dict=None): + + # Read and set options + read_opt(opt_dict, globals()) -def penetrating_radiation(GRID, SWnet, dt): penetrating_allowed = ['Bintanja95'] if penetrating_method == 'Bintanja95': subsurface_melt, Si = method_Bintanja(GRID, SWnet, dt) diff --git a/cosipy/modules/percolation.py b/cosipy/modules/percolation.py index b4fd1306..84f64600 100644 --- a/cosipy/modules/percolation.py +++ b/cosipy/modules/percolation.py @@ -1,8 +1,26 @@ import numpy as np from numba import njit +from cosipy.utils.options import read_opt + + +def percolation(GRID, water, dt, opt_dict=None): + + # Read and set options + read_opt(opt_dict, globals()) + + percolation_method = 'bucket' + percolation_allowed = ['bucket'] + + if percolation_method == 'bucket': + Q = bucket(GRID,water,dt) + else: + raise ValueError("Percolation method = \"{:s}\" is not allowed, must be one of {:s}".format(percolation_method, ", ".join(percolation_allowed))) + + return Q + @njit -def percolation(GRID, water, dt): +def bucket(GRID, water, dt): """ Percolation and refreezing of melt water through the snow- and firn pack Args: diff --git a/cosipy/modules/refreezing.py b/cosipy/modules/refreezing.py index e3dae6b6..f3cc9898 100644 --- a/cosipy/modules/refreezing.py +++ b/cosipy/modules/refreezing.py @@ -2,9 +2,25 @@ from constants import zero_temperature, spec_heat_ice, ice_density, \ water_density, lat_heat_melting from numba import njit +from cosipy.utils.options import read_opt -@njit -def refreezing(GRID): +def refreezing(GRID, opt_dict=None): + + # Read and set options + read_opt(opt_dict, globals()) + + refreezing_method = 'default' + refreezing_allowed = ['default'] + + if refreezing_method == 'default': + water_refreezed = refreezing_default(GRID) + else: + raise ValueError("Refreezing method = \"{:s}\" is not allowed, must be one of {:s}".format(refreezing_method, ", ".join(refreezing_allowed))) + + return water_refreezed + +#@njit +def refreezing_default(GRID): # water refreezed water_refreezed = 0.0 diff --git a/cosipy/modules/roughness.py b/cosipy/modules/roughness.py index 23595a22..f54fb1b0 100644 --- a/cosipy/modules/roughness.py +++ b/cosipy/modules/roughness.py @@ -1,8 +1,12 @@ from constants import roughness_method, roughness_fresh_snow, \ roughness_firn, roughness_ice, snow_ice_threshold, \ aging_factor_roughness +from cosipy.utils.options import read_opt -def updateRoughness(GRID): +def updateRoughness(GRID, opt_dict=None): + + # Read and set options + read_opt(opt_dict, globals()) roughness_allowed = ['Moelg12'] if roughness_method == 'Moelg12': diff --git a/cosipy/modules/surfaceTemperature.py b/cosipy/modules/surfaceTemperature.py index 8f8b4f0f..18de34c2 100644 --- a/cosipy/modules/surfaceTemperature.py +++ b/cosipy/modules/surfaceTemperature.py @@ -5,9 +5,11 @@ from scipy.optimize import minimize, newton from numba import njit from types import SimpleNamespace +from cosipy.utils.options import read_opt -def update_surface_temperature(GRID, dt, z, z0, T2, rH2, p, SWnet, u2, RAIN, SLOPE, LWin=None, N=None): +def update_surface_temperature(GRID, dt, alpha, z, z0, T2, rH2, p, G, u2, RAIN, SLOPE, LWin=None, + N=None, opt_dict=None): """ This methods updates the surface temperature and returns the surface fluxes Given: @@ -47,6 +49,9 @@ def update_surface_temperature(GRID, dt, z, z0, T2, rH2, p, SWnet, u2, RAIN, SLO phi :: Stability correction term [-] """ + # Read and set options + read_opt(opt_dict, globals()) + #Interpolate subsurface temperatures to selected subsurface depths for GHF computation B_Ts = interp_subT(GRID) diff --git a/cosipy/utils/edu_utils.py b/cosipy/utils/edu_utils.py index f24ba9a8..2b24d84e 100644 --- a/cosipy/utils/edu_utils.py +++ b/cosipy/utils/edu_utils.py @@ -4,7 +4,6 @@ import numpy as np from itertools import product from dask.distributed import Client, as_completed, progress - from cosipy.cpkernel.cosipy_core import cosipy_core from cosipy.cpkernel.io import IOClass diff --git a/cosipy/utils/options.py b/cosipy/utils/options.py new file mode 100644 index 00000000..3a6226d2 --- /dev/null +++ b/cosipy/utils/options.py @@ -0,0 +1,6 @@ +def read_opt(opt_dict, glob): + """ Reads the opt_dict and overwrites the key-value pairs in glob - the calling function's + globals() dictionary.""" + if opt_dict is not None: + for key in opt_dict: + glob[key] = opt_dict[key] From e06a815b5dfc9dab7b2a4849f3fa9f6b655a9c03 Mon Sep 17 00:00:00 2001 From: Erik Holmgren Date: Thu, 17 Jun 2021 17:31:51 +0200 Subject: [PATCH 04/15] Updated the notebooks to the new opt_dict configuration. 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