+
+
+ 
+
+
Documentation
+
+
What is PySDM?
+
+
+ PySDM is a package for simulating the
dynamics of population of particles undergoing diffusional and collisional growth (and breakage).
+ The package features a Pythonic high-performance (multi-threaded CPU & CUDA GPU) implementation of the
Super-Droplet Method (SDM) Monte-Carlo algorithm
+ for representing collisional growth (
Shima et al. 2009), hence the name.
+ It is intended to serve as a building block for simulation systems modelling
fluid flows involving a dispersed phase,
+ with PySDM being responsible for representation of the dispersed phase.
+ Currently, the development is focused on atmospheric
cloud physics applications, in particular on modelling the dynamics of
+ particles immersed in moist air using the particle-based (a.k.a. super-droplet) approach to represent aerosol/cloud/rain microphysics.
+ The key goal of PySDM is to enable rapid development and independent reproducibility of simulations in cloud microphysics
+ while being free from the two-language barrier commonly separating prototype and high-performance research code.
+ PySDM ships with a set of examples reproducing results from literature and serving as tutorials.
+ The animation shown here depicts a flow-coupled simulation in which the flow is resolved using PySDM's sibling project:
PyMPDATA.
+ The examples include also single-column setups (with PyMPDATA used for advection) as well as adiabatic cloud parcel model setups
+ (with
PySDM alone sufficient to constitute a microphysics-resolving cloud parcel model in Python).
+
+
+
What is the difference between PySDM and PySDM-examples?
+
+ PySDM is a Python package that provides the implementation of SDM that can be used in your own projects.
+
+
+ PySDM-examples is a Python package that provides examples of how to use PySDM.
+ The package contains common code used in PySDM examples Jupyter notebooks, as well as in PySDM test suite.
+
+
The two projects exist separately on PyPI, but their development and issue tracking is hosted at the same GitHub repository.
+
+
Important links
+
+
+ | PySDM |
+ PySDM-examples |
+
+
+ |
+
+ |
+
+
+ |
+
+
+
+
+  
+ |
+
+
+
+
Installation
+
+ PySDM is available on PyPI and can be installed using pip:
+
+
pip install PySDM
+
Note: the way above will not install test-time dependencies, to install them and run the tests, likely the most convenient way is:
+
git clone https://github.com/open-atmos/PySDM.git
+pip install -e PySDM[tests] -e PySDM/examples[tests]
+pytest PySDM
+
(the above should be a viable way to set up development environment for PySDM, see also our Python dev hints Wiki
+ and PySDM HOWTOs for further information)
+
+ PySDM-examples is also available on PyPI and can be installed using pip:
+
+
pip install PySDM-examples
+
Note: this will also install PySDM if needed, but the examples package wheels do not include the Jupyter notebooks - only common code used from the notebooks.
+ All PySDM example notebooks can be viewed on GitHub and feature header cells with badges enabling single-click execution on either
+ Google Colab or mybinder.org platforms.
+ To try the notebooks out locally, use:
+
+
git clone https://github.com/open-atmos/PySDM.git
+pip install -e PySDM -e PySDM/examples
+jupyter-notebook PySDM/examples
+
+
Contributing, reporting issues, seeking support
+
+ Submitting new code to both packages is done through the same GitHub repository via
+ Pull requests.
+
+
+ Issues regarding any incorrect, unintuitive or undocumented behaviour of PySDM or PySDM-examples
+ are best to be reported on the
+ GitHub issue tracker.
+
+
+ We encourage to use the GitHub Discussions
+ feature (rather than the issue tracker) for seeking support in understanding,
+ using and extending PySDM code.
+
+
+
Bibliography with code cross-references
+
+ The list below summarises all literature references included in PySDM codebase and includes links to both the referenced papers, as well as to the referring PySDM source files.
+
+ {% include "bibliography.html" %}
+
EOF
+
+ 
![\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d97571d624ee4aaebef6ee975d561949", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_2.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "221ba44bdc9d4b98bf3f1529b41caa08", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./plot_grid.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1cf12b98d3a24dc8a9232faaf8b9d3b3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./top_panels.pdf\"), HTML(value=\"…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature = 300 * si.K\n", + "delta_0 = {\n", + " '2H': -38 * PER_MILLE,\n", + " '18O': -6 * PER_MILLE,\n", + "}\n", + "delta_a = {\n", + " '2H': -86 * PER_MILLE,\n", + " '18O': -12 * PER_MILLE,\n", + "}\n", + "plot_x = np.linspace(1, .01, 50)\n", + "humidities = (0, .25, .5, .75, .95)\n", + "\n", + "fig, axs = pyplot.subplot_mosaic([delta_0.keys()], tight_layout=True)\n", + "plot_y = {}\n", + "for isotope, delta_0_i in delta_0.items():\n", + " plot_y[isotope] = {}\n", + " for h in humidities:\n", + " plot_y[isotope][h] = delta(\n", + " delta_liq_0=delta_0_i,\n", + " remaining_water_fraction=plot_x,\n", + " humidity=h,\n", + " delta_atmos=delta_a[isotope],\n", + " alpha=getattr(formulae.isotope_equilibrium_fractionation_factors, f'alpha_l_{isotope}')(temperature),\n", + " D_ratio_vs_light=getattr(formulae.isotope_diffusivity_ratios, f'ratio_{isotope}_heavy_to_light')(temperature)\n", + " )\n", + " axs[isotope].plot(\n", + " plot_x,\n", + " in_unit(plot_y[isotope][h], PER_MILLE),\n", + " label=f'RH={h*100:.2g}%',\n", + " color='k',\n", + " linewidth=1 + h,\n", + " )\n", + " axs[isotope].set_xlim(1, 0)\n", + " axs[isotope].set_xlabel(\"fraction of remaining water\")\n", + " axs[isotope].grid()\n", + "axs[\"2H\"].legend()\n", + "axs[\"2H\"].set_ylabel('$\\\\delta^2H$ [‰]')\n", + "axs[\"18O\"].set_ylabel('$\\\\delta^{18}O$ [‰]')\n", + "axs[\"2H\"].set_ylim(-50, 200)\n", + "axs[\"18O\"].set_ylim(-10, 40)\n", + "show_plot('top_panels.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45565e875bbe0629", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T18:02:37.550956Z", + "start_time": "2025-03-04T18:02:37.386737Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1020ebafd9e64488be20ea5ce251af1c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./bottom_panel.pdf\"), HTML(val…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pyplot.subplots(figsize=(7,3.5))\n", + "x = np.linspace(-12, 0, 20) * PER_MILLE\n", + "ax.plot(\n", + " in_unit(x, PER_MILLE),\n", + " in_unit(const.CRAIG_1961_SLOPE_COEFF * x + const.CRAIG_1961_INTERCEPT_COEFF, PER_MILLE),\n", + " label = f'y = {const.CRAIG_1961_SLOPE_COEFF}x + {in_unit(const.CRAIG_1961_INTERCEPT_COEFF, PER_MILLE):.2g}',\n", + " color= 'orange',\n", + " linewidth=2\n", + ")\n", + "for h in reversed(humidities):\n", + " bottom_plot = ax.plot(\n", + " in_unit(plot_y['18O'][h], PER_MILLE), \n", + " in_unit(plot_y['2H'][h], PER_MILLE),\n", + " label=f'RH={h*100:.2g}%',\n", + " color='k',\n", + " linewidth=1 + h\n", + " )\n", + "ax.set_xlim(-10, 30)\n", + "ax.set_ylim(-50, 150)\n", + "ax.set_xlabel('$\\\\delta ^{18}O$ [‰]')\n", + "ax.set_ylabel('$\\\\delta ^2H$ [‰]')\n", + "ax.grid()\n", + "ax.legend()\n", + "show_plot(\"bottom_panel.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fbe39f7-c935-4ceb-a503-99f6da19450e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/__init__.py b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..05c050fdfb091ca36c46efead9e44d7ef5043d0a --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/__init__.py @@ -0,0 +1,22 @@ +# pylint: disable=invalid-name +""" +ripening-focused parcel-model example based on +[Grabowski & Pawlowska 2023 (GRL)](https://doi.org/10.1029/2022GL101917) + +figure_1.ipynb: +.. include:: ./figure_1.ipynb.badges.md + +figure_2.ipynb: +.. include:: ./figure_2.ipynb.badges.md + +figure_3.ipynb: +.. include:: ./figure_3.ipynb.badges.md + +figure_4.ipynb: +.. include:: ./figure_4.ipynb.badges.md + +figure_ripening_rate.ipynb: +.. include:: ./figure_ripening_rate.ipynb.badges.md +""" +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..52575c6b6145a26261295d9987f038f6151f9a10 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb @@ -0,0 +1,11756 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### based on Fig. 1 from Wojciech Grabowski and Hanna Pawlowska 2023 (Geophysical Research Letters 50(3)) 'Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem'\n", + "\n", + "https://doi.org/10.1029/2022GL101917" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:00.814600Z", + "start_time": "2024-02-01T07:33:00.806057Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:34:06.001360Z", + "start_time": "2024-02-01T07:34:05.994758Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from matplotlib import ticker\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si, in_unit\n", + "from PySDM.products import ParcelDisplacement, AmbientRelativeHumidity\n", + "\n", + "TRIVIA = Formulae().trivia\n", + "\n", + "from PySDM_examples.Grabowski_and_Pawlowska_2023 import Settings, Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:30.631530Z", + "start_time": "2024-02-01T07:33:03.622223Z" + } + }, + "outputs": [], + "source": [ + "products = (\n", + " ParcelDisplacement(name='z'),\n", + " AmbientRelativeHumidity(name='S_max_percent', unit='%', var='RH'),\n", + ")\n", + "\n", + "vertical_velocity = 0.25 * si.m / si.s\n", + "output = {\n", + " case: Simulation(Settings(\n", + " vertical_velocity=vertical_velocity,\n", + " dt=1*si.s if 'CI' not in os.environ else 50 * si.s,\n", + " n_sd=200 if 'CI' not in os.environ else 10,\n", + " aerosol=case\n", + " ), products=products).run()\n", + " for case in (\"pristine\", \"polluted\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:34.961425Z", + "start_time": "2024-02-01T07:33:30.667482Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a505ec117514460dae4b24fa6ba1bf86", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig1.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# pylint: disable=too-many-arguments\n", + "def plot_R(axes, output_vol, output_crit_vol, output_z, k):\n", + " for drop_id, volume in enumerate(output_vol):\n", + " if drop_id % k == 0:\n", + " if TRIVIA.radius(volume=volume)[10] > 0.03 * si.um:\n", + " crit_volume = output_crit_vol[drop_id]\n", + " if np.all(volume0.03*si.um:\n", + " if np.all(volume\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6fc7aa7e9fea41bfa2528da70426c10e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig2.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# pylint: disable=too-many-arguments\n", + "def plot_R(axes, output_vol, output_crit_vol, output_z, k):\n", + " for drop_id, volume in enumerate(output_vol):\n", + " if drop_id%k==0:\n", + " if TRIVIA.radius(volume=volume)[10] > 0.03*si.um:\n", + " crit_volume=output_crit_vol[drop_id]\n", + " if np.all(volume0.03*si.um:\n", + " if np.all(volume\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cf7959fa32048d0aba3deda69994530", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig3.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(3, 2, figsize=(13, 10))\n", + "axN1, axN2, axR1, axR2, axA1, axA2 = axs[0,0], axs[0,1], axs[1,0], axs[1,1], axs[2,0], axs[2,1]\n", + "\n", + "for i, aerosol in enumerate((\"polluted\", \"pristine\")):\n", + " for w in vertical_velocity:\n", + " r = np.array(output[w][aerosol]['products']['r_vol'])\n", + " n = np.array(output[w][aerosol]['products']['n_act'])\n", + " a = np.array(output[w][aerosol]['products']['area_std'])/(4 * np.pi)\n", + " z = np.array(output[w][aerosol]['products']['z'])\n", + " axs[1, i].plot(z, np.where(r>2*si.um, in_unit(r, si.um), np.nan))\n", + " axs[0, i].plot(z, np.where(r>2*si.um, in_unit(n, si.cm**-3), np.nan))\n", + " axs[2, i].plot(z, np.where(r>2*si.um, in_unit(a, si.um**2), np.nan))\n", + " z_last = z\n", + " axs[1,i].plot(\n", + " z_last,\n", + " np.where(r>2*si.um, 2 * z_last**(1/3), np.nan),\n", + " color='grey',\n", + " linestyle='--'\n", + " )\n", + " axs[2,i].plot(\n", + " z_last,\n", + " np.where(r>2*si.um, 20, np.nan),\n", + " color='grey',\n", + " linestyle='--'\n", + " )\n", + "\n", + "for ax in (axN1, axN2, axR1, axR2, axA1, axA2):\n", + " ax.set_xlim(10, 1000)\n", + " ax.set_xscale('log')\n", + " ax.legend(legend)\n", + "for ax in [axR1,axR2]:\n", + " ax.set_ylabel('mean volume radius [μm]')\n", + "for ax in [axN1,axN2]:\n", + " ax.set_ylabel('concentration [cm$^{-3}$ STP]')\n", + "for ax in [axA1,axA2]:\n", + " ax.set_xlabel('height [m]')\n", + " ax.set_ylabel(r'area st. dev. / 4$\\pi$ [μm$^2$]')\n", + " \n", + "axN1_, axN2_, axR1_, axR2_, axA1_, axA2_ = axN1.twinx(), axN2.twinx(), axR1.twinx(), axR2.twinx(), axA1.twinx(), axA2.twinx()\n", + "for ax in (axR1, axR2, axR1_, axR2_, axA1, axA2, axA1_, axA2_):\n", + " ax.set_yscale('log')\n", + " ax.set_ylim(1, 100)\n", + "for ax in (axN1, axN1_):\n", + " ax.set_ylim(300, 600)\n", + "for ax in (axN2, axN2_):\n", + " ax.set_ylim(0, 300)\n", + "axN1.set_title('POL')\n", + "axN2.set_title('PRI')\n", + "for ax in (axN1_, axN2_, axR1_, axR2_, axA1_, axA2_):\n", + " ax.yaxis.set_major_formatter(ticker.NullFormatter())\n", + "show_plot(\"fig3.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..204f6f145833288abd89ba0506cefa5382bc1d84 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb @@ -0,0 +1,2279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_4.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### based on Fig. 4 from Wojciech Grabowski and Hanna Pawlowska 2023 (Geophysical Research Letters 50(3)) 'Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem'\n", + "\n", + "https://doi.org/10.1029/2022GL101917" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:32:54.227488Z", + "start_time": "2024-02-01T07:32:54.224383Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:47.088494Z", + "start_time": "2024-02-01T07:33:47.085534Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot, ticker\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM.physics import si\n", + "from PySDM.products import ParcelDisplacement, ActivatedMeanRadius, RadiusStandardDeviation\n", + "from PySDM_examples.Grabowski_and_Pawlowska_2023 import Settings, Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:35:01.224144Z", + "start_time": "2024-02-01T07:34:32.159387Z" + } + }, + "outputs": [], + "source": [ + "products=(\n", + " ParcelDisplacement(name='z'),\n", + " ActivatedMeanRadius(name='r_act',count_activated=True, count_unactivated=False),\n", + " RadiusStandardDeviation(name=\"radius_std\", count_activated=True, count_unactivated=False),\n", + ")\n", + "vertical_velocity = (\"0.25\", \"1\", \"4\")\n", + "output = { velocity:\n", + " {\n", + " case: Simulation(Settings(\n", + " vertical_velocity=float(velocity),\n", + " dt=1*si.s if 'CI' not in os.environ else 50 * si.s,\n", + " n_sd=200 if 'CI' not in os.environ else 10,\n", + " aerosol=case\n", + " ), products=products).run()\n", + " for case in (\"pristine\", \"polluted\")\n", + " }\n", + " for velocity in vertical_velocity\n", + "}\n", + "legend = [velocity + ' m/s' for velocity in vertical_velocity]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:47.079285Z", + "start_time": "2024-02-01T07:33:45.490499Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ff75786e93ce4fe2b0ec4b6e4b56de15", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig4.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(2, 1, figsize=(6, 8))\n", + "np.seterr(invalid=\"ignore\")\n", + "for axis, aerosol in ((axs[0], \"pristine\"), (axs[1], \"polluted\")):\n", + " for w in vertical_velocity:\n", + " r=np.array(output[w][aerosol]['products']['r_act'])\n", + " d=np.array(output[w][aerosol]['products']['radius_std'])\n", + " axis.plot(\n", + " np.array(output[w][\"polluted\"]['products']['z']),\n", + " np.where(r > 2 * si.um, np.divide(d,r), np.nan)\n", + " )\n", + "\n", + "for ax in axs:\n", + " ax.set_xscale('log')\n", + " ax.set_yscale('log')\n", + " ax.set_ylim(0.01, 1)\n", + " ax.set_xlim(10, 1000)\n", + " ax.set_ylabel('rel. dispersion')\n", + " ax.legend(legend)\n", + "axs[1].set_xlabel('height [m]')\n", + "axs[0].set_title('PRI')\n", + "axs[1].set_title('POL')\n", + "\n", + "ax0, ax1 = axs[0].twinx(), axs[1].twinx()\n", + "for ax in (ax0, ax1):\n", + " ax.set_yscale('log')\n", + " ax.set_ylim(0.01, 1)\n", + " ax.yaxis.set_major_formatter(ticker.NullFormatter())\n", + "show_plot(\"fig4.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..95080394700ad36c1adca1cc691b9ffb950c16ad --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb @@ -0,0 +1,13825 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/figure_ripening_rate.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### based on figures from Wojciech Grabowski and Hanna Pawlowska 2023 (Geophysical Research Letters 50(3)) 'Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem'\n", + "\n", + "https://doi.org/10.1029/2022GL101917" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:32:49.758270Z", + "start_time": "2024-02-01T07:32:49.753751Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:41.208496Z", + "start_time": "2024-02-01T07:33:41.204376Z" + } + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM.physics import si\n", + "from PySDM.products import ParcelDisplacement, RipeningRate\n", + "from PySDM_examples.Grabowski_and_Pawlowska_2023 import Settings, Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:39.121598Z", + "start_time": "2024-02-01T07:32:51.956620Z" + } + }, + "outputs": [], + "source": [ + "products = (\n", + " ParcelDisplacement(name='z'),\n", + " RipeningRate(name='ripening')\n", + ")\n", + "\n", + "vertical_velocity = (\"0.25\",\"1\",\"4\")\n", + "output = {\n", + " velocity: {\n", + " case: Simulation(Settings(\n", + " vertical_velocity=float(velocity),\n", + " dt=1*si.s if 'CI' not in os.environ else 50 * si.s,\n", + " n_sd=200 if 'CI' not in os.environ else 10,\n", + " aerosol=case\n", + " ), products=products).run()\n", + " for case in (\"pristine\", \"polluted\")\n", + " }\n", + " for velocity in vertical_velocity\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:40.919953Z", + "start_time": "2024-02-01T07:33:39.123726Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2738292b897646d4889191e6658f4274", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_ripening.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "for aerosol in (\"polluted\", \"pristine\"):\n", + " for i, velocity in enumerate(vertical_velocity):\n", + " axs[i].scatter(\n", + " output[velocity][aerosol]['products']['z'],\n", + " output[velocity][aerosol]['products']['ripening']\n", + " )\n", + " axs[i].set_title(\"w = \" + velocity+\" m/s\")\n", + "\n", + "for ax in axs:\n", + " ax.set_xlim(10, 1000)\n", + " ax.set_ylim(0, 5e8)\n", + " ax.set_xscale('log')\n", + " ax.set_xlabel('height [m]')\n", + " ax.set_ylabel('ripening rate [s kg]$^{-1}$')\n", + " ax.legend([\"polluted\", \"pristine\"])\n", + "show_plot(\"fig_ripening.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:41.195154Z", + "start_time": "2024-02-01T07:33:40.919286Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0bf3dfd00c7641c7b2e7f726f9151362", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_ripening_zoom.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for aerosol in (\"polluted\", \"pristine\"):\n", + " pyplot.scatter(\n", + " output[\"0.25\"][aerosol]['products']['z'],\n", + " output[\"0.25\"][aerosol]['products']['ripening']\n", + " )\n", + "pyplot.title(\"w = 0.25 m/s\")\n", + "pyplot.xscale('log')\n", + "pyplot.yscale('log')\n", + "pyplot.xlim(10, 1000)\n", + "pyplot.xlabel('height [m]')\n", + "pyplot.ylabel('ripening rate [s kg]$^{-1}$')\n", + "pyplot.legend([\"polluted\", \"pristine\"])\n", + "show_plot('fig_ripening_zoom.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/settings.py b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..c8bf10a2ee2630ddd135ee17b5051a6fb2ac8271 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/settings.py @@ -0,0 +1,98 @@ +import numpy as np +from pystrict import strict + +from PySDM import Formulae +from PySDM.dynamics import condensation +from PySDM.initialisation.spectra import Lognormal, Sum +from PySDM.physics import si + +condensation_tolerance = condensation.DEFAULTS.rtol_thd / 100 + + +@strict +class Settings: + def __init__( + self, + *, + aerosol: str, + vertical_velocity: float, + dt: float, + n_sd: int, + initial_temperature: float = 283 * si.K, + initial_pressure: float = 900 * si.mbar, + initial_relative_humidity: float = 0.97, + displacement: float = 1000 * si.m, + mass_accommodation_coefficient: float = 0.3, + rtol_thd: float = condensation_tolerance, + rtol_x: float = condensation_tolerance, + ): + self.formulae = Formulae(constants={"MAC": mass_accommodation_coefficient}) + self.n_sd = n_sd + self.aerosol_modes_by_kappa = { + "pristine": { + 1.28: Sum( + ( + Lognormal( + norm_factor=125 / si.cm**3, m_mode=11 * si.nm, s_geom=1.2 + ), + Lognormal( + norm_factor=65 / si.cm**3, m_mode=60 * si.nm, s_geom=1.7 + ), + ) + ) + }, + "polluted": { + 1.28: Sum( + ( + Lognormal( + norm_factor=160 / si.cm**3, m_mode=29 * si.nm, s_geom=1.36 + ), + Lognormal( + norm_factor=380 / si.cm**3, m_mode=71 * si.nm, s_geom=1.57 + ), + ) + ) + }, + }[aerosol] + + const = self.formulae.constants + self.vertical_velocity = vertical_velocity + self.initial_pressure = initial_pressure + self.initial_temperature = initial_temperature + pv0 = ( + initial_relative_humidity + * self.formulae.saturation_vapour_pressure.pvs_water(initial_temperature) + ) + self.initial_vapour_mixing_ratio = const.eps * pv0 / (initial_pressure - pv0) + self.t_max = displacement / vertical_velocity + self.timestep = dt + self.output_interval = self.timestep + self.rtol_thd = rtol_thd + self.rtol_x = rtol_x + + @property + def initial_air_density(self): + const = self.formulae.constants + dry_air_density = ( + self.formulae.trivia.p_d( + self.initial_pressure, self.initial_vapour_mixing_ratio + ) + / self.initial_temperature + / const.Rd + ) + return dry_air_density * (1 + self.initial_vapour_mixing_ratio) + + @property + def nt(self) -> int: + nt = self.t_max / self.timestep + nt_int = round(nt) + np.testing.assert_almost_equal(nt, nt_int) + return nt_int + + @property + def steps_per_output_interval(self) -> int: + return int(self.output_interval / self.timestep) + + @property + def output_steps(self) -> np.ndarray: + return np.arange(0, self.nt + 1, self.steps_per_output_interval) diff --git a/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/simulation.py b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..93348f3149cd1afd4591197a9e481222cc66b050 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Grabowski_and_Pawlowska_2023/simulation.py @@ -0,0 +1,116 @@ +import numpy as np +from PySDM_examples.utils import BasicSimulation + +from PySDM import Builder +from PySDM.backends import CPU +from PySDM.backends.impl_numba.test_helpers import scipy_ode_condensation_solver +from PySDM.dynamics import AmbientThermodynamics, Condensation +from PySDM.environments import Parcel +from PySDM.initialisation.hygroscopic_equilibrium import equilibrate_wet_radii +from PySDM.initialisation.sampling.spectral_sampling import ConstantMultiplicity +from PySDM.physics import si + + +class Simulation(BasicSimulation): + def __init__( + self, + settings, + products=None, + scipy_solver=False, + ): + builder = Builder( + n_sd=settings.n_sd, + backend=CPU( + formulae=settings.formulae, override_jit_flags={"parallel": False} + ), + environment=Parcel( + dt=settings.timestep, + p0=settings.initial_pressure, + initial_water_vapour_mixing_ratio=settings.initial_vapour_mixing_ratio, + T0=settings.initial_temperature, + w=settings.vertical_velocity, + mass_of_dry_air=44 * si.kg, + ), + ) + builder.add_dynamic(AmbientThermodynamics()) + builder.add_dynamic( + Condensation(rtol_thd=settings.rtol_thd, rtol_x=settings.rtol_x) + ) + for attribute in ( + "critical saturation", + "equilibrium saturation", + "critical volume", + ): + builder.request_attribute(attribute) + + env = builder.particulator.environment + volume = env.mass_of_dry_air / settings.initial_air_density + attributes = { + k: np.empty(0) + for k in ("dry volume", "kappa times dry volume", "multiplicity") + } + + assert len(settings.aerosol_modes_by_kappa.keys()) == 1 + kappa = tuple(settings.aerosol_modes_by_kappa.keys())[0] + spectrum = settings.aerosol_modes_by_kappa[kappa] + + r_dry, n_per_volume = ConstantMultiplicity(spectrum).sample_deterministic( + settings.n_sd + ) + v_dry = settings.formulae.trivia.volume(radius=r_dry) + attributes["multiplicity"] = np.append( + attributes["multiplicity"], n_per_volume * volume + ) + attributes["dry volume"] = np.append(attributes["dry volume"], v_dry) + attributes["kappa times dry volume"] = np.append( + attributes["kappa times dry volume"], v_dry * kappa + ) + r_wet = equilibrate_wet_radii( + r_dry=settings.formulae.trivia.radius(volume=attributes["dry volume"]), + environment=env, + kappa_times_dry_volume=attributes["kappa times dry volume"], + ) + attributes["volume"] = settings.formulae.trivia.volume(radius=r_wet) + + super().__init__( + particulator=builder.build(attributes=attributes, products=products) + ) + if scipy_solver: + scipy_ode_condensation_solver.patch_particulator(self.particulator) + + self.output_attributes = { + "volume": tuple([] for _ in range(self.particulator.n_sd)), + "dry volume": tuple([] for _ in range(self.particulator.n_sd)), + "critical saturation": tuple([] for _ in range(self.particulator.n_sd)), + "equilibrium saturation": tuple([] for _ in range(self.particulator.n_sd)), + "critical volume": tuple([] for _ in range(self.particulator.n_sd)), + "multiplicity": tuple([] for _ in range(self.particulator.n_sd)), + } + self.settings = settings + + self.__sanity_checks(attributes, volume) + + def __sanity_checks(self, attributes, volume): + for attribute in attributes.values(): + assert attribute.shape[0] == self.particulator.n_sd + np.testing.assert_approx_equal( + sum(attributes["multiplicity"]) / volume, + sum( + mode.norm_factor + for mode in self.settings.aerosol_modes_by_kappa.values() + ), + significant=4, + ) + + def _save(self, output): + for key, attr in self.output_attributes.items(): + attr_data = self.particulator.attributes[key].to_ndarray() + for drop_id in range(self.particulator.n_sd): + attr[drop_id].append(attr_data[drop_id]) + super()._save(output) + + def run(self): + output_products = super()._run( + self.settings.nt, self.settings.steps_per_output_interval + ) + return {"products": output_products, "attributes": self.output_attributes} diff --git a/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..65e8cefdf047927fe958ef231f3f1b5b3c928a26 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e0cb5788-74b3-4da4-8095-880d3505c755", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Graf_et_al_2019/Table_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "2e2202e7d784a1cb", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### reproducing Table 1 from Graf et al. 2019: https://doi.org/10.5194/acp-19-747-2019\n", + "\n", + "Calculated difference in isotopic composition for vapour vs. liquid/solid in equilibrium \n", + "Two different isotopic compositions \n", + "Exemplifies computation of: δ18O, δ2H, excess-d " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "551e99a9150d82e9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:00:58.789303Z", + "start_time": "2024-12-06T13:00:58.763298Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:01.045913Z", + "start_time": "2024-12-06T13:00:58.838016Z" + } + }, + "outputs": [], + "source": [ + "from PySDM import Formulae\n", + "from PySDM.physics import si, in_unit\n", + "from IPython.display import display, HTML" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9e79217e-e30a-4ebe-a14f-532dca3a3d8b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:01.624239Z", + "start_time": "2024-12-06T13:01:01.193934Z" + } + }, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " isotope_equilibrium_fractionation_factors='Majoube1970+Majoube1971+MerlivatAndNief1967',\n", + " isotope_meteoric_water_line='Dansgaard1964'\n", + ")\n", + "const = formulae.constants\n", + "\n", + "alphas = {\n", + " '18O_l': formulae.isotope_equilibrium_fractionation_factors.alpha_l_18O,\n", + " '2H_l': formulae.isotope_equilibrium_fractionation_factors.alpha_l_2H,\n", + " '18O_s': formulae.isotope_equilibrium_fractionation_factors.alpha_i_18O,\n", + " '2H_s': formulae.isotope_equilibrium_fractionation_factors.alpha_i_2H\n", + "}\n", + "excess_d = formulae.isotope_meteoric_water_line.excess_d\n", + "\n", + "CASES = {\n", + " 'A': {'18O': -10 * const.PER_MILLE, '2H': -80 * const.PER_MILLE},\n", + " 'B': {'18O': -25 * const.PER_MILLE, '2H': -200 * const.PER_MILLE}\n", + "}\n", + "for case in CASES.values():\n", + " case['excess'] = excess_d(delta_2H=case['2H'], delta_18O=case['18O'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "65fb351f-2bf7-4118-a063-3ea961403db0", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:02.314607Z", + "start_time": "2024-12-06T13:01:01.649028Z" + } + }, + "outputs": [], + "source": [ + "# see text just below eq. (4) in the paper\n", + "alpha_20C_l = alphas['18O_l'](20 * si.K + const.T0)\n", + "alpha_20C_2H = alphas['2H_l'](20 * si.K + const.T0)\n", + "assert f\"{alpha_20C_l:.4f}\" == \"1.0098\"\n", + "assert f\"{alpha_20C_2H:.4f}\" == \"1.0850\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dbbf7dc7ddffe08", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:03.131925Z", + "start_time": "2024-12-06T13:01:02.329497Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "table_data = {}\n", + "for TC in (20, 0):\n", + " table_data[TC] = {}\n", + " for phase in (('s', 'l') if TC==0 else ('l',)):\n", + " table_data[TC][f'{phase}_v'] = {}\n", + " for case, delta_v in CASES.items():\n", + " case_data = table_data[TC][f'{phase}_v'][case] = {}\n", + " for isotope in ('18O', '2H'):\n", + " reference_ratio = getattr(const, f\"VSMOW_R_{isotope}\")\n", + " Rv = formulae.trivia.isotopic_delta_2_ratio(\n", + " delta=delta_v[isotope],\n", + " reference_ratio=reference_ratio\n", + " )\n", + " R_phase = alphas[f'{isotope}_{phase}'](TC * si.K + const.T0) * Rv\n", + " delta_phase = formulae.trivia.isotopic_ratio_2_delta(\n", + " ratio=R_phase,\n", + " reference_ratio=reference_ratio\n", + " )\n", + " \n", + " case_data[f'delta_{phase}_{isotope}'] = delta_phase\n", + " case_data[f'diff_delta_{isotope}'] = delta_phase - delta_v[isotope]\n", + " excess_vapour = excess_d(delta_2H=delta_v['2H'], delta_18O=delta_v['18O'])\n", + " excess_phase = excess_d(delta_2H=case_data[f'delta_{phase}_2H'], delta_18O=case_data[f'delta_{phase}_18O'])\n", + " case_data[\"diff_d_excess\"] = excess_phase - excess_vapour" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2f24d9cb-a36a-4ebd-87cf-3867f2bf9281", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:03.166287Z", + "start_time": "2024-12-06T13:01:03.158099Z" + } + }, + "outputs": [], + "source": [ + "table_html = \"\"\n", + "table_html += \"\"\"\n", + " \n", + " composition of vapour\n", + " Δl-v@20∘C\n", + " Δl-v@0∘C\n", + " Δs-v@0∘C\n", + " \n", + "\"\"\"\n", + "row_labels = {\n", + " 'diff_delta_2H': 'δ2H',\n", + " 'diff_delta_18O': 'δ18O',\n", + " 'diff_d_excess': 'd',\n", + "}\n", + "for case in ('A', 'B'):\n", + " for row, var in enumerate(('diff_delta_2H', 'diff_delta_18O', 'diff_d_excess')):\n", + " table_html += f\"\"\"\n", + " 0 else ' style=\"border-top:1px black solid\"'}>\n", + " {row_labels[var]}\n", + " {in_unit(CASES[case][var[var.rindex(\"_\")+1:]], const.PER_MILLE)}‰\n", + " {in_unit(table_data[20]['l_v'][case][var], const.PER_MILLE):.1f}‰\n", + " {in_unit(table_data[ 0]['l_v'][case][var], const.PER_MILLE):.1f}‰\n", + " {in_unit(table_data[ 0]['s_v'][case][var], const.PER_MILLE):.1f}‰\n", + " \n", + " \"\"\"\n", + "table_html += \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d80a1710aaff2ef", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:03.238062Z", + "start_time": "2024-12-06T13:01:03.231275Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " composition of vapour\n", + " Δl-v@20∘C\n", + " Δl-v@0∘C\n", + " Δs-v@0∘C\n", + " \n", + "\n", + " \n", + " δ2H\n", + " -80.0‰\n", + " 78.2‰\n", + " 103.3‰\n", + " 121.3‰\n", + " \n", + " \n", + " \n", + " δ18O\n", + " -10.0‰\n", + " 9.7‰\n", + " 11.6‰\n", + " 15.1‰\n", + " \n", + " \n", + " \n", + " d\n", + " 0.0‰\n", + " 0.7‰\n", + " 10.5‰\n", + " 0.6‰\n", + " \n", + " \n", + " \n", + " δ2H\n", + " -200.0‰\n", + " 68.0‰\n", + " 89.9‰\n", + " 105.4‰\n", + " \n", + " \n", + " \n", + " δ18O\n", + " -25.0‰\n", + " 9.5‰\n", + " 11.4‰\n", + " 14.9‰\n", + " \n", + " \n", + " \n", + " d\n", + " 0.0‰\n", + " -8.4‰\n", + " -1.6‰\n", + " -13.4‰\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(HTML(table_html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "902db5d8-c140-47c2-b53a-2a5bd745a8bc", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:03.273435Z", + "start_time": "2024-12-06T13:01:03.270969Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/__init__.py b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3b2ddcc308ee8ab9e95fb3072837021f933eea87 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/__init__.py @@ -0,0 +1,11 @@ +# pylint: disable=invalid-name +""" +Parcel-model Condensation,Isotope example +[Graf.et.al 2019](https://doi.org/10.5194/acp-19-747-2019) + +figure_4.ipynb: +.. include:: ./figure_4.ipynb.badges.md + +Table_1.ipynb: +.. include:: ./Table_1.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6342f5a51a53b40d281ee9d02322765715721d5d --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb @@ -0,0 +1,1509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "327f2a018faf3142", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "aa160d9c48befc58", + "metadata": { + "collapsed": false + }, + "source": [ + "based on [Graf et al. 2019](https://doi.org/10.5194/acp-19-747-2019)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b2c87898f6737935", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:19.373289Z", + "start_time": "2024-12-06T13:01:19.360208Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2432d43d15e97625", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:43:41.429069Z", + "start_time": "2024-01-11T08:43:41.423952Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM.physics import si\n", + "from PySDM.environments import Parcel\n", + "from PySDM.backends import CPU\n", + "from PySDM import Builder, Formulae\n", + "from PySDM.dynamics import AmbientThermodynamics, Condensation\n", + "from PySDM.initialisation.spectra import Lognormal\n", + "from PySDM.initialisation.sampling import spectral_sampling\n", + "from PySDM.initialisation.hygroscopic_equilibrium import equilibrate_wet_radii\n", + "from PySDM.initialisation import discretise_multiplicities\n", + "from PySDM import products" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "81d815991be0cc9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:35.791569Z", + "start_time": "2024-01-11T08:42:35.352112Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "formulae = Formulae()\n", + "const = formulae.constants\n", + "pvs_water = formulae.saturation_vapour_pressure.pvs_water\n", + "\n", + "RH0 = .75\n", + "p0 = 950 * si.hPa\n", + "T0 = const.T0 + 12 * si.K\n", + "alt_initial = 500 * si.m \n", + "alt_final = 3500 * si.m\n", + "\n", + "total_displacement = alt_final - alt_initial\n", + "initial_water_vapour_mixing_ratio = const.eps / (p0 / RH0 / pvs_water(T0) - 1)\n", + "\n", + "n_sd = 1\n", + "dt = 10 * si.s\n", + "vertical_velocity = 2.5 * si.m / si.s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dada2ae5b4083f0e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:35.796706Z", + "start_time": "2024-01-11T08:42:35.793581Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "env = Parcel(\n", + " # given in the paper\n", + " p0=p0,\n", + " T0=T0,\n", + " initial_water_vapour_mixing_ratio=initial_water_vapour_mixing_ratio,\n", + " # arbitrary choices\n", + " w=vertical_velocity,\n", + " dt=dt,\n", + " mass_of_dry_air=1e3 * si.kg,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47f3f883e4e67ed", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:36.874577Z", + "start_time": "2024-01-11T08:42:35.804074Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "builder = Builder(backend=CPU(formulae=formulae), n_sd=n_sd, environment=env)\n", + "builder.add_dynamic(AmbientThermodynamics())\n", + "builder.add_dynamic(Condensation())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f98070a3d550d65", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:36.878312Z", + "start_time": "2024-01-11T08:42:36.876718Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "spectrum = Lognormal(norm_factor=1e4 / si.mg, m_mode=50 * si.nm, s_geom=1.5)\n", + "kappa = .5 * si.dimensionless\n", + "cloud_range = (.5 * si.um, 25 * si.um)\n", + "output_interval = 4\n", + "output_points = 40" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5ec5eabc3c1d3872", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:36.883Z", + "start_time": "2024-01-11T08:42:36.880940Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "r_dry, specific_concentration = spectral_sampling.Logarithmic(spectrum).sample_deterministic(n_sd)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ce639f01f48ff348", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:37.072808Z", + "start_time": "2024-01-11T08:42:36.886365Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "v_dry = formulae.trivia.volume(radius=r_dry)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "28897f499330f699", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:38.909344Z", + "start_time": "2024-01-11T08:42:37.088206Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "r_wet = equilibrate_wet_radii(r_dry=r_dry, environment=builder.particulator.environment, kappa_times_dry_volume=kappa * v_dry)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8cf40dfb18e7e082", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:38.913147Z", + "start_time": "2024-01-11T08:42:38.909929Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "attributes = {\n", + " 'multiplicity': discretise_multiplicities(specific_concentration * builder.particulator.environment.mass_of_dry_air),\n", + " 'dry volume': v_dry,\n", + " 'kappa times dry volume': kappa * v_dry,\n", + " 'volume': formulae.trivia.volume(radius=r_wet)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f36faa7368db791e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:39.067069Z", + "start_time": "2024-01-11T08:42:38.920056Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "particulator = builder.build(\n", + " attributes=attributes,\n", + " products=(\n", + " products.PeakSaturation(name='S_max'),\n", + " products.ParcelDisplacement(name=\"z\"),\n", + " products.AmbientTemperature(name=\"T\")\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "843a224f900fb94d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:45.925004Z", + "start_time": "2024-01-11T08:42:39.077019Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "n_steps = total_displacement / (dt * vertical_velocity)\n", + "\n", + "output = {name:[] for name in particulator.products}\n", + "levels = {\n", + " 'CB': None,\n", + " '0C': None\n", + "}\n", + "\n", + "ONE_AND_ONLY_GRID_ELEMENT = 0\n", + "\n", + "for step in range(math.ceil(n_steps)):\n", + " particulator.run(steps=1)\n", + " for name, product in particulator.products.items():\n", + " product_buffer_reference = product.get()\n", + " output[name].append(product_buffer_reference[ONE_AND_ONLY_GRID_ELEMENT])\n", + " \n", + " if levels['CB'] is None and output[\"S_max\"][-1] > 1:\n", + " levels['CB'] = .5 * (output[\"z\"][-1] + output[\"z\"][-2])\n", + " \n", + " if levels['0C'] is None and output[\"T\"][-1] < const.T0:\n", + " levels['0C'] = .5 * (output['z'][-1] + output[\"z\"][-2])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a363f02a36a714d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:46.314435Z", + "start_time": "2024-01-11T08:42:45.927080Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "30b01b7b859146a7897eba2e25623cf6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_4a.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot\n", + "\n", + "fig, axs = pyplot.subplots(1, 1, squeeze=False)\n", + "xy1 = axs[0,0]\n", + "xy2 = xy1.twiny()\n", + "\n", + "xy1.plot(\n", + " np.asarray(output['S_max']) * 100,\n", + " np.asarray(output['z']) + alt_initial\n", + ")\n", + "xy2.plot(\n", + " np.asarray(output['T']) - const.T0,\n", + " np.asarray(output['z']) + alt_initial\n", + ")\n", + "for level in levels.values():\n", + " xy1.axhline(y=level + alt_initial, linestyle='--', color='black')\n", + "\n", + "xy1.set_xlim(75, 107)\n", + "xy2.set_xlim(-10, 16)\n", + "xy1.set_ylim(alt_initial, alt_final)\n", + "xy1.set_xlabel(\"RH / %\")\n", + "xy2.set_xlabel(\"T / C\")\n", + "xy1.set_ylabel(\"z / m\")\n", + "show_plot('fig_4a.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5acab115cd92fd9b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:46.317574Z", + "start_time": "2024-01-11T08:42:46.313381Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d0173c4dbd123f7b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:46.323642Z", + "start_time": "2024-01-11T08:42:46.319974Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "430e8a3643291372", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-11T08:42:46.326817Z", + "start_time": "2024-01-11T08:42:46.322560Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/__init__.py b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3fdbefa5af9e004c3ea192f9c9a7d242158d34c6 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/__init__.py @@ -0,0 +1,11 @@ +# pylint: disable=invalid-name +""" +aqueous-chemistry parcel-model example based on +[Jaruga & Pawlowska 2018 (GMD)](https://doi.org/10.5194/gmd-11-3623-2018) + +fig_2.ipynb: +.. include:: ./fig_2.ipynb.badges.md + +fig_3.ipynb: +.. include:: ./fig_3.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8e514afad7f152b0ef34608cc5b34c643e27165b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb @@ -0,0 +1,2631 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_2.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Jaruga and Pawlowska (Geosci. Model Dev. 11) \"_libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme_\" \n", + "https://doi.org/10.5194/gmd-11-3623-2018" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T10:37:16.995005Z", + "start_time": "2024-12-06T10:37:16.990766Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:01:43.025041Z", + "start_time": "2024-12-06T13:01:39.759448Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Kreidenweis_et_al_2003 import Settings, Simulation\n", + "from PySDM import products as PySDM_products\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.utils.widgets import display, FloatProgress\n", + "from PySDM.physics import si\n", + "from PySDM.physics.constants import PPT, PPB\n", + "import numpy as np\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:46:50.614980Z", + "start_time": "2023-12-29T11:46:50.611464Z" + } + }, + "outputs": [], + "source": [ + "n_points = 6 if 'CI' not in os.environ else 2\n", + "nsd = np.logspace(0, n_points-1, num=n_points, base=2.0, dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:46:51.202427Z", + "start_time": "2023-12-29T11:46:50.619344Z" + } + }, + "outputs": [], + "source": [ + "default_settings = Settings(1,1,1)\n", + "products = (\n", + " PySDM_products.PeakSaturation(name='S_max'),\n", + " PySDM_products.ParticleConcentration(name='n_c_cm3', unit='cm^-3', radius_range=default_settings.cloud_radius_range),\n", + " PySDM_products.Acidity(name='pH_conc_H_volume_weighted', radius_range=default_settings.cloud_radius_range),\n", + " PySDM_products.AqueousMoleFraction('S_VI', name='aq_S_VI_ppb', unit='ppb')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:47:53.602057Z", + "start_time": "2023-12-29T11:46:51.204609Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b097ae97b641411bb9a4521e3fef2466", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, max=1.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulations = []\n", + "progress = FloatProgress(value=0.0, min=0.0, max=1.0)\n", + "display(progress)\n", + "for it in range(n_points):\n", + " settings = Settings(\n", + " dt=1*si.s, \n", + " n_sd=nsd[it], \n", + " n_substep=5 if 'CI' not in os.environ else 1\n", + " )\n", + " settings.output_interval = 25 * si.s\n", + " simulation = Simulation(settings, products)\n", + " output = simulation.run()\n", + " simulations.append({'settings': settings, 'output': output})\n", + " progress.value += 1/n_points" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:47:53.608022Z", + "start_time": "2023-12-29T11:47:53.604097Z" + } + }, + "outputs": [], + "source": [ + "x = np.log2(nsd)\n", + "smax = []\n", + "droplet_number = []\n", + "pH = []\n", + "sulfate_ppt = []\n", + "for simulation in simulations:\n", + " smax.append((np.nanmax(simulation['output'][\"S_max\"]) - 1) * 100)\n", + " droplet_number.append(np.nanmax(simulation['output'][\"n_c_cm3\"]))\n", + " pH.append(simulation['output'][\"pH_conc_H_volume_weighted\"][-1])\n", + " S_VI = simulation['output'][\"aq_S_VI_ppb\"]\n", + " sulfate_ppt.append((S_VI[-1] - S_VI[0]) * PPB / PPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:47:54.143480Z", + "start_time": "2023-12-29T11:47:53.610737Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "81193b96e70244d4ab894aa58c177edc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_2\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(2, 2, figsize=(10,7))\n", + "\n", + "axs[0][0].plot(x, droplet_number, 'o-')\n", + "axs[0][0].set_ylabel('Droplet conc. [cm-3]')\n", + "axs[0][0].set_ylim(0, 1800)\n", + "\n", + "axs[0][1].plot(x, smax, 'o-')\n", + "axs[0][1].set_ylabel('Max. supersat [%]')\n", + "axs[0][1].set_ylim(0.24, 0.61)\n", + "\n", + "axs[1][0].plot(x, pH, 'o-')\n", + "axs[1][0].set_ylabel('Average pH')\n", + "axs[1][0].set_ylim(4.7, 5.2)\n", + "\n", + "axs[1][1].plot(x, sulfate_ppt, 'o-')\n", + "axs[1][1].set_ylabel('Total sulfate production [ppt]')\n", + "axs[1][1].set_ylim(135, 180)\n", + "\n", + "for axss in axs:\n", + " for ax in axss:\n", + " ax.set_xlabel('ln_2(number of super-droplets)')\n", + " ax.grid()\n", + " ax.set_xlim(0,10)\n", + "\n", + "show_plot(\"fig_2\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ecd503daaa93b1f0358d76ad928fcc23de7858d7 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb @@ -0,0 +1,4540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jaruga_and_Pawlowska_2018/fig_3.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### based on Jaruga and Pawlowska (Geosci. Model Dev. 11) \"_libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme_\" \n", + "https://doi.org/10.5194/gmd-11-3623-2018" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T10:37:21.599199Z", + "start_time": "2024-12-06T10:37:21.589064Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T10:37:32.026002Z", + "start_time": "2024-12-06T10:37:29.060774Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Kreidenweis_et_al_2003 import Settings, Simulation\n", + "from PySDM.physics import si\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM.products import AqueousMassSpectrum\n", + "from PySDM.products.aqueous_chemistry.aqueous_mass_spectrum import SpecificAqueousMassSpectrum\n", + "from matplotlib import pyplot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:45:52.512360Z", + "start_time": "2023-12-29T11:45:02.979951Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "settings = Settings(dt=1*si.s, n_sd=16 if 'CI' in os.environ else 256, n_substep=5)\n", + "products = (\n", + " SpecificAqueousMassSpectrum(\n", + " key=\"S_VI\",\n", + " dry_radius_bins_edges=settings.dry_radius_bins_edges,\n", + " name='dm_S_VI/dlog_10(dry diameter)_spec',\n", + " unit='ug/kg'\n", + " ),\n", + " AqueousMassSpectrum(\n", + " key=\"S_VI\",\n", + " dry_radius_bins_edges=settings.dry_radius_bins_edges,\n", + " name='dm_S_VI/dlog_10(dry diameter)',\n", + " specific=False,\n", + " unit='ug/m^3'\n", + " ),\n", + ")\n", + "simulation = Simulation(settings, products)\n", + "output = simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:45:53.862839Z", + "start_time": "2023-12-29T11:45:52.515932Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5c6b0a6dc62345299f1f469d453b63c7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_3\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "528000b81b5c4357a7d7977805a9eada", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_3\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for product in products:\n", + " fig, axs = pyplot.subplots(1, 2, figsize=(11,5))\n", + " labels = {0:\"first\", -1:\"last\"}\n", + " for ax in axs:\n", + " for step, label in labels.items():\n", + " ax.step(\n", + " 2e6 * settings.dry_radius_bins_edges[:-1],\n", + " output[product.name][step],\n", + " label=f'step: {label}'\n", + " )\n", + " ax.set_ylabel(f'dS(VI)/dlog_10(D) [{product.unit}]')\n", + " ax.set_xlabel('dry diameter [µm]')\n", + " ax.set_xlim([.01, 1])\n", + " ax.grid()\n", + " ax.set_xscale('log')\n", + "\n", + " axs[0].set_yscale('log')\n", + " axs[0].set_ylim([.01, 15])\n", + " axs[1].set_ylim([0, 15])\n", + " axs[1].legend()\n", + "\n", + " show_plot(\"fig_3\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8641514bbccfa3e6f9df39dd7255407c17421570 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb @@ -0,0 +1,2000 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a68bae6de370294c", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "67f1a1164c2629c6", + "metadata": { + "collapsed": false + }, + "source": [ + "#### based on Fig. 1 from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "605d5fe5", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T11:31:45.383883Z", + "start_time": "2024-03-04T11:31:45.379202Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T11:31:50.944088Z", + "start_time": "2024-03-04T11:31:50.361661Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c9f88210450743e28203d8c6db9c215a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_1.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM.physics import in_unit, si\n", + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, table_3\n", + "settings = Settings(aerosol=\"modified polluted\", cloud_type=\"Sc\")\n", + "trivia = settings.formulae.trivia\n", + "\n", + "TABLE3_DR = table_3.RD[1] - table_3.RD[0]\n", + "np.testing.assert_almost_equal(np.diff(table_3.RD), TABLE3_DR)\n", + "\n", + "X_UNIT = si.um\n", + "Y_UNIT = si.cm**-3\n", + "\n", + "rd = np.logspace(\n", + " np.log10(.01 * si.um),\n", + " np.log10(.5 * si.um)\n", + ")\n", + "pdf = settings.dry_radii_spectrum.size_distribution(rd)\n", + "dN_dlogr = trivia.dn_dlogr(rd, pdf)\n", + "\n", + "pyplot.loglog(\n", + " in_unit(rd, X_UNIT),\n", + " in_unit(dN_dlogr, Y_UNIT),\n", + " label='Modified polluted'\n", + ")\n", + "pyplot.loglog(\n", + " in_unit(table_3.RD, X_UNIT),\n", + " in_unit(trivia.dn_dlogr(table_3.RD, table_3.NA / TABLE3_DR), Y_UNIT),\n", + " label='VOCALS GCCN'\n", + ")\n", + "pyplot.legend()\n", + "pyplot.xlabel(\"r$_d$ (µm)\")\n", + "pyplot.xlim(\n", + " in_unit(rd[0], si.um),\n", + " in_unit(100 * si.um, si.um)\n", + ")\n", + "pyplot.ylabel(\"dN/dlogr (cm$^{-3}$)\")\n", + "pyplot.ylim(1e-4, 1e3)\n", + "pyplot.grid()\n", + "show_plot('fig_1.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ecdcaaa-0d41-49cf-ae2c-7748e8e8dca3", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T11:31:48.375619Z", + "start_time": "2024-03-04T11:31:48.372442Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..348af85d87200f6aa091f84da5daaa91180ee0c1 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb @@ -0,0 +1,2377 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70b9d65563d58a62", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_3_and_Tab_4_upper_rows.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "d1dc2b61d9602b70", + "metadata": { + "collapsed": false + }, + "source": [ + "#### based on Fig. 3 and Table 4 (Modified Polluted) from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "879850e1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:36:09.631730Z", + "start_time": "2024-03-24T15:36:09.627733Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:52.064243Z", + "start_time": "2024-03-24T15:51:49.276931Z" + } + }, + "outputs": [], + "source": [ + "from IPython.display import display, HTML\n", + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, Simulation\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Jensen_and_Nugent_2017.plotting import figure, compute_table_values\n", + "\n", + "settings = Settings(aerosol=\"modified polluted\", cloud_type=\"Sc\")\n", + "simulation = Simulation(settings)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b184e2c6ac313a7a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:53.232151Z", + "start_time": "2024-03-24T15:51:52.064598Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "output = simulation.run(steps_per_output_interval= 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7cd7b3fd3774c9b4", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:53.707434Z", + "start_time": "2024-03-24T15:51:53.246458Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "94d7ea1d02ad49589d7de1154ede53f2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./Fig_3new.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "masks = figure(\n", + " output=output,\n", + " settings=settings,\n", + " simulation=simulation,\n", + " plot_drops_with_dry_radii_um=(.02, .031, .152, .337, .5),\n", + " xlim_r_um=(0, 15),\n", + " xlim_S_percent=(-1, 1),\n", + " return_masks=True\n", + ")\n", + "\n", + "show_plot(\"Fig_3new.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "eabaafa09612b16e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:53.713353Z", + "start_time": "2024-03-24T15:51:53.710236Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "table_values = compute_table_values(\n", + " height_above_cb_m = (50, 100, 150, 200, 250, 300),\n", + " height_cb_m = 900,\n", + " products=output[\"products\"],\n", + " ascent_mask = masks[\"ascent\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9336dc4ba0d6b0dc", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:53.726471Z", + "start_time": "2024-03-24T15:51:53.724599Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "table_html = \"\"\n", + "\n", + "table_html += \"\"\"\n", + "\n", + " \n", + " Aerosols \n", + " z (m) \n", + " Mean cloud drop radius (μm)\n", + " Drop spectral width (μm)\n", + " Drop dispersion\n", + " \n", + "\"\"\"\n", + "row_labels = {\n", + " 'ascent': 'Mod.polluted (Updraft)',\n", + " 'descent': 'Mod.polluted (Downdraft)'\n", + "}\n", + "for case, data in table_values.items():\n", + " for var in data:\n", + " table_html += f\"\"\"\n", + " \n", + " {row_labels[case]}\n", + " {var[0]}\n", + " {var[1]}\n", + " {var[2]}\n", + " {var[3]}\n", + " \n", + " \"\"\" \n", + "table_html += \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5b615403-60e5-4769-ba7d-644fa5821af8", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-24T15:51:53.997179Z", + "start_time": "2024-03-24T15:51:53.987186Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " Aerosols \n", + " z (m) \n", + " Mean cloud drop radius (μm)\n", + " Drop spectral width (μm)\n", + " Drop dispersion\n", + " \n", + "\n", + " \n", + " Mod.polluted (Updraft)\n", + " 50\n", + " 5.47\n", + " 0.43\n", + " 0.079\n", + " \n", + " \n", + " \n", + " Mod.polluted (Updraft)\n", + " 100\n", + " 6.78\n", + " 0.39\n", + " 0.058\n", + " \n", + " \n", + " \n", + " Mod.polluted (Updraft)\n", + " 150\n", + " 7.71\n", + " 0.37\n", + " 0.047\n", + " \n", + " \n", + " \n", + " Mod.polluted (Updraft)\n", + " 200\n", + " 8.45\n", + " 0.35\n", + " 0.041\n", + " \n", + " \n", + " \n", + " Mod.polluted (Updraft)\n", + " 250\n", + " 9.08\n", + " 0.33\n", + " 0.037\n", + " \n", + " \n", + " \n", + " Mod.polluted (Updraft)\n", + " 300\n", + " 9.64\n", + " 0.32\n", + " 0.034\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 300\n", + " 9.64\n", + " 0.32\n", + " 0.034\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 250\n", + " 9.11\n", + " 0.34\n", + " 0.038\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 200\n", + " 8.48\n", + " 0.37\n", + " 0.044\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 150\n", + " 7.74\n", + " 0.41\n", + " 0.053\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 100\n", + " 6.83\n", + " 0.46\n", + " 0.068\n", + " \n", + " \n", + " \n", + " Mod.polluted (Downdraft)\n", + " 50\n", + " 5.57\n", + " 0.57\n", + " 0.102\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(HTML(table_html))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..04b55554dd3daebd2ae59c3434623f8240708c35 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb @@ -0,0 +1,4966 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ba7d7fea4c326de4", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_4_and_7_and_Tab_4_bottom_rows.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "83463dbe10afd52", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "#### based on Fig. 4, Fig.7 and Table 4 (Modified Polluted + GCCN) from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f2673625", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:53:02.092999Z", + "start_time": "2024-11-08T21:53:02.087100Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:53:04.307601Z", + "start_time": "2024-11-08T21:53:02.327950Z" + } + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "\n", + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, Simulation\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Jensen_and_Nugent_2017.plotting import figure, CLOUD_BASE, find_drop_ids_by_dry_size, compute_table_values\n", + "from PySDM.physics import in_unit, si\n", + "from IPython.display import display, HTML\n", + "from PySDM.physics.constants import PER_CENT\n", + "\n", + "settings = Settings(aerosol=\"modified polluted\", cloud_type=\"Sc\")\n", + "simulation = Simulation(settings, gccn=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "153fe91c37fbdea7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:52:51.116093Z", + "start_time": "2024-11-08T21:52:39.350484Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "output = simulation.run(steps_per_output_interval= 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a99bff26cf55cd96", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:52:51.994997Z", + "start_time": "2024-11-08T21:52:51.126533Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "04df5485904c4e3fbb9d9a3faa08a489", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./Fig_4.pdf\"), HTML(value=\"./Fig_7.pdf\"), HTML(value=\"\"\n", + "table_html += \"\"\"\n", + " \n", + " Aerosols \n", + " z (m) \n", + " Mean cloud drop radius (μm)\n", + " Drop spectral width (μm)\n", + " Drop dispersion\n", + " \n", + "\"\"\"\n", + "row_labels = {\n", + " 'ascent': 'Mod.polluted + GCCN (Updraft)',\n", + " 'descent': 'Mod.polluted + GCCN (Downdraft)'\n", + "}\n", + "\n", + "for case, data in table_values.items():\n", + " for var in data: \n", + " table_html += f\"\"\"\n", + " \n", + " {row_labels[case]}\n", + " {var[0]}\n", + " {var[1]}\n", + " {var[2]}\n", + " {var[3]}\n", + " \n", + " \"\"\" \n", + "\n", + "table_html += \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e7743277-0296-451f-8d66-e0519ec942f2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:52:52.397504Z", + "start_time": "2024-11-08T21:52:52.394077Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " Aerosols \n", + " z (m) \n", + " Mean cloud drop radius (μm)\n", + " Drop spectral width (μm)\n", + " Drop dispersion\n", + " \n", + "\n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 50\n", + " 5.44\n", + " 0.44\n", + " 0.082\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 100\n", + " 6.74\n", + " 0.40\n", + " 0.059\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 150\n", + " 7.67\n", + " 0.37\n", + " 0.049\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 200\n", + " 8.42\n", + " 0.35\n", + " 0.042\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 250\n", + " 9.05\n", + " 0.34\n", + " 0.038\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Updraft)\n", + " 300\n", + " 9.60\n", + " 0.33\n", + " 0.034\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 300\n", + " 9.60\n", + " 0.33\n", + " 0.034\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 250\n", + " 9.07\n", + " 0.35\n", + " 0.039\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 200\n", + " 8.44\n", + " 0.38\n", + " 0.045\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 150\n", + " 7.69\n", + " 0.42\n", + " 0.054\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 100\n", + " 6.77\n", + " 0.47\n", + " 0.070\n", + " \n", + " \n", + " \n", + " Mod.polluted + GCCN (Downdraft)\n", + " 50\n", + " 5.48\n", + " 0.59\n", + " 0.107\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(HTML(table_html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36fe694861b15027", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-08T21:52:52.407327Z", + "start_time": "2024-11-08T21:52:52.406086Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb90a2a0c971b374", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..465780f2ebf0a80ba79c3f32a11df110659d7b66 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb @@ -0,0 +1,3225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "804357ff91efdde4", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_5.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "434b4c67fd5f2954", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "#### based on Fig. 5 from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f1636433", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:33:00.814600Z", + "start_time": "2024-02-01T07:33:00.806057Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-15T09:43:07.594652Z", + "start_time": "2024-02-15T09:43:05.810743Z" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, Simulation\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Jensen_and_Nugent_2017.plotting import figure\n", + "\n", + "settings = Settings(aerosol=\"pristine\", cloud_type=\"Sc\")\n", + "simulation = Simulation(settings, gccn=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fecc0b0999661682", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-15T09:43:05.058585Z", + "start_time": "2024-02-15T09:42:57.041759Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "output = simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d6502667395327ab", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-15T09:43:05.807516Z", + "start_time": "2024-02-15T09:43:05.060081Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f2d41ad850744f618bd635dcca954212", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./Fig_5.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure(\n", + " output=output,\n", + " settings=settings,\n", + " simulation=simulation,\n", + " plot_drops_with_dry_radii_um=(.1, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n", + " xlim_r_um=(0, 60),\n", + " xlim_S_percent= (-1.5,1.5)\n", + ")\n", + "show_plot(\"Fig_5.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eae39c956cc61196", + "metadata": { + "collapsed": false, + "is_executing": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6b6a3ead6cbf1476f5477a0909a69018263a87fc --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb @@ -0,0 +1,2353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f37f66f49adc99b", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_6.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "af8a61829aba28c", + "metadata": { + "collapsed": false + }, + "source": [ + "#### based on Fig. 6 from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "284358c5", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T14:32:02.816749Z", + "start_time": "2024-03-04T14:32:02.812278Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T16:25:16.587545Z", + "start_time": "2024-03-04T16:25:14.461586Z" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, Simulation\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Jensen_and_Nugent_2017.plotting import figure\n", + "from PySDM.physics import si\n", + "\n", + "settings = Settings(aerosol=\"modified polluted\", cloud_type=\"Cu\", dt=.5 * si.s)\n", + "simulation = Simulation(settings, gccn=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6e80d470aed47e31", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T14:32:17.032250Z", + "start_time": "2024-03-04T14:32:08.997662Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "output = simulation.run(\n", + " n_steps=int(1800 * si.m / settings.vertical_velocity / settings.dt),\n", + " steps_per_output_interval=5\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6341693a2ee4bd21", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-04T14:32:17.774679Z", + "start_time": "2024-03-04T14:32:17.033939Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ce978fbfbd9b4aea9bc15f871c64da80", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./Fig_6.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure(\n", + " output=output,\n", + " settings=settings,\n", + " simulation=simulation,\n", + " plot_drops_with_dry_radii_um=(.1, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n", + " xlim_r_um=(0, 60),\n", + " xlim_S_percent=(-1.5, 1.5)\n", + ")\n", + "show_plot(\"Fig_6.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b900e87feeaef621", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-14T11:19:44.850768Z", + "start_time": "2024-02-14T11:19:44.847362Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6bbfa8b1b927a2c801f2c8256ea22f453e5c38d9 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb @@ -0,0 +1,1956 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b03a1d7a3d3802d6", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jensen_and_Nugent_2017/Fig_8.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "b4ff3463274b433f", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "#### based on Fig. 8 from [Jensen and Nugent (JAS 74) \"_Condensational Growth of Drops Formed on Giant Sea-Salt Aerosol Particles_\"](https://doi.org/10.1175/JAS-D-15-0370.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-22T11:39:46.833587Z", + "start_time": "2024-03-22T11:39:46.829389Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c91b89db396d508c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-23T11:52:01.383132Z", + "start_time": "2024-03-23T11:51:59.196688Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Jensen_and_Nugent_2017 import Settings, Simulation\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Jensen_and_Nugent_2017.plotting import figure\n", + "\n", + "settings = Settings(aerosol=\"modified polluted\", cloud_type=\"Sc\")\n", + "simulation = Simulation(settings, gccn=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "846333e445434f6d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-22T11:40:03.001232Z", + "start_time": "2024-03-22T11:39:53.824734Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "output = simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5503b3380487f50e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-23T11:52:14.808782Z", + "start_time": "2024-03-23T11:52:14.490511Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f8a203c020d4574aaec0e810a9ffbd5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./Fig_8.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "masks = figure(\n", + " output=output,\n", + " settings=settings,\n", + " simulation=simulation,\n", + " plot_drops_with_dry_radii_um=(.1, 4.2),\n", + " xlim_r_um=(0, 60),\n", + " xlim_S_percent=(-1, 1),\n", + " return_masks=True\n", + ")\n", + "show_plot(\"Fig_8.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/__init__.py b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6c5602d73b1eebc09c0fe1f0a537cda8be14e02b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/__init__.py @@ -0,0 +1,22 @@ +""" +Fig_1.ipynb: +.. include:: ./Fig_1.ipynb.badges.md + +Fig_3_and_Tab_4_upper_rows.ipynb: +.. include:: ./Fig_3_and_Tab_4_upper_rows.ipynb.badges.md + +Fig_4_and_7_and_Tab_4_bottom_rows.ipynb: +.. include:: ./Fig_4_and_7_and_Tab_4_bottom_rows.ipynb.badges.md + +Fig_5.ipynb: +.. include:: ./Fig_5.ipynb.badges.md + +Fig_6.ipynb: +.. include:: ./Fig_6.ipynb.badges.md + +Fig_8.ipynb: +.. include:: ./Fig_8.ipynb.badges.md +""" + +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/plotting.py b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/plotting.py new file mode 100644 index 0000000000000000000000000000000000000000..c798e15f8e9e5834ea2bf30b977a937b38f84c34 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/plotting.py @@ -0,0 +1,110 @@ +from matplotlib import pyplot +import numpy as np +from PySDM.physics import si, in_unit +from PySDM.physics.constants import PER_CENT + +CLOUD_BASE = 300 * si.m + + +def find_drop_ids_by_dry_size(plot_drops_with_dry_radii_um, simulation_r_dry): + drop_ids = [] + for drop_size_um in plot_drops_with_dry_radii_um: + drop_id = (np.abs(simulation_r_dry - drop_size_um * si.um)).argmin() + drop_ids.append(drop_id) + return drop_ids + + +def compute_table_values(height_above_cb_m, height_cb_m, products, ascent_mask): + """constructing data for Table 4""" + data = {"ascent": [], "descent": []} + for level_idx, height_m in enumerate(products["z"]): + rounded_height_above_cb_m = np.round(height_m - height_cb_m) + if rounded_height_above_cb_m in height_above_cb_m: + r_mean = products["r_mean_act"][level_idx] + r_standard_deviation = products["r_std_act"][level_idx] + r_relative_dispersion = r_standard_deviation / r_mean + + data["ascent" if ascent_mask[level_idx] else "descent"].append( + ( + f"{rounded_height_above_cb_m:.0f}", + f"{in_unit(r_mean, si.um):.2f}", + f"{in_unit(r_standard_deviation, si.um):.2f}", + f"{r_relative_dispersion:.3f}", + ) + ) + data["ascent"].append(data["descent"][0]) + return data + + +def figure( + *, + output, + settings, + simulation, + plot_drops_with_dry_radii_um, + xlim_r_um: tuple, + xlim_S_percent: tuple, + return_masks: bool = False, +): + y_axis = np.asarray(output["products"]["z"]) - settings.z0 - CLOUD_BASE + + masks = {} + if settings.t_end_of_ascent is None: + masks["ascent"] = np.full_like(output["products"]["t"], True, dtype=bool) + else: + masks["ascent"] = np.asarray(output["products"]["t"]) < settings.t_end_of_ascent + masks["descent"] = np.logical_not(masks["ascent"]) + + colors = {"ascent": "r", "descent": "b"} + + _, axs = pyplot.subplot_mosaic( + mosaic=[["r", "S"]], width_ratios=[3, 1], sharey=True, tight_layout=True + ) + + for label, mask in masks.items(): + axs["S"].plot( + in_unit(np.asarray(output["products"]["S_max"]), PER_CENT)[mask], + y_axis[mask], + label=label, + color=colors[label], + ) + axs["S"].set_xlim(*xlim_S_percent) + axs["S"].set_xlabel("S (%)", fontsize="15") + axs["S"].legend(fontsize="10") + + drop_ids = find_drop_ids_by_dry_size( + plot_drops_with_dry_radii_um=plot_drops_with_dry_radii_um, + simulation_r_dry=simulation.r_dry, + ) + + for ( + drop_id + ) in ( + drop_ids + ): # TODO #1266: bug! why rightmost drop is not 500 nm if size range is set to end at 500 nm??? + for label, mask in masks.items(): + axs["r"].plot( + in_unit(np.asarray(output["attributes"]["radius"][drop_id]), si.um)[ + mask + ], + y_axis[mask], + label=( + f"{in_unit(simulation.r_dry[drop_id], si.um):.2} µm" + if label == "ascent" + else "" + ), + color=colors[label], + ) + axs["r"].legend() + axs["r"].set_xlim(*xlim_r_um) + axs["r"].set_xlabel("r$_c$ (µm)", fontsize="15") + axs["r"].set_ylabel("Height above cloud base (m)", fontsize="15") + # pyplot.xticks(fontsize=12) + # pyplot.yticks(fontsize=12) + + for ax in axs.values(): + ax.grid() + + if return_masks: + return masks + return None diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/settings.py b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..9f7006832d33adc7ff30753d74ba17128367f4c3 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/settings.py @@ -0,0 +1,66 @@ +from typing import Optional + +from pystrict import strict +from PySDM import Formulae +from PySDM.physics import si +from PySDM.initialisation.spectra import Lognormal, Sum + +INITIAL_RELATIVE_HUMIDITY = 0.8561 +INITIAL_TEMPERATURE = 284.3 * si.K +INITIAL_PRESSURE = 938.5 * si.hPa +INITIAL_ALTITUDE = 600 * si.metres + + +@strict +class Settings: + def __init__(self, *, aerosol: str, cloud_type: str, dt: Optional[float] = None): + self.p0 = INITIAL_PRESSURE + self.RH0 = INITIAL_RELATIVE_HUMIDITY + self.T0 = INITIAL_TEMPERATURE + self.z0 = INITIAL_ALTITUDE + self.t_end_of_ascent = 1500 * si.s if cloud_type == "Sc" else None + self.dt = dt or 1 * si.s # TODO #1266: not found in the paper yet + + # Table 1 from [Petters & Kreidenweis 2007](https://doi.org/10.5194/acp-7-1961-2007) + self.kappa = 1.28 + + self.formulae = Formulae( + saturation_vapour_pressure="FlatauWalkoCotton", # TODO #1266: Bolton + diffusion_kinetics="GrabowskiEtAl2011", + diffusion_thermics="GrabowskiEtAl2011", + constants={ + # values from appendix B + "MAC": 0.036, + "HAC": 0.7, + }, + ) + + self.vertical_velocity = { + # Table 2 in the paper + "Sc": lambda t: (1 if t < self.t_end_of_ascent else -1) * 0.4 * si.m / si.s, + "Cu": 2 * si.m / si.s, + }[cloud_type] + + self.dry_radii_spectrum = { + # Table 1 in the paper + "modified polluted": Sum( + ( + Lognormal( + norm_factor=48 / si.cm**3, m_mode=0.029 * si.um, s_geom=1.36 + ), + Lognormal( + norm_factor=114 / si.cm**3, m_mode=0.071 * si.um, s_geom=1.57 + ), + ) + ), + "pristine": Sum( + ( + Lognormal( + norm_factor=125 / si.cm**3, m_mode=0.011 * si.um, s_geom=1.2 + ), + Lognormal( + norm_factor=65 / si.cm**3, m_mode=0.06 * si.um, s_geom=1.7 + ), + ) + ), + }[aerosol] diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/simulation.py b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..827d833fffdbade94570471f2b0fb6187f38c835 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/simulation.py @@ -0,0 +1,128 @@ +import numpy as np +from PySDM_examples.utils import BasicSimulation +from PySDM_examples.Jensen_and_Nugent_2017.settings import Settings +from PySDM_examples.Jensen_and_Nugent_2017 import table_3 +from PySDM import Builder +from PySDM.physics import si +from PySDM.backends import CPU +from PySDM.products import ( + PeakSaturation, + ParcelDisplacement, + Time, + ActivatedMeanRadius, + RadiusStandardDeviation, +) +from PySDM.environments import Parcel +from PySDM.dynamics import Condensation, AmbientThermodynamics, Coalescence +from PySDM.dynamics.collisions.collision_kernels import Geometric +from PySDM.initialisation.sampling.spectral_sampling import Logarithmic + +# note: 100 in caption of Table 1 +N_SD_NON_GCCN = 100 + + +class Simulation(BasicSimulation): + def __init__( + self, + settings: Settings, + gccn: bool = False, + gravitational_coalsecence: bool = False, + ): + const = settings.formulae.constants + pvs_water = settings.formulae.saturation_vapour_pressure.pvs_water + initial_water_vapour_mixing_ratio = const.eps / ( + settings.p0 / settings.RH0 / pvs_water(settings.T0) - 1 + ) + + n_gccn = np.count_nonzero(table_3.NA) if gccn else 0 + + builder = Builder( + n_sd=N_SD_NON_GCCN + n_gccn, + backend=CPU( + formulae=settings.formulae, override_jit_flags={"parallel": False} + ), + environment=Parcel( + dt=settings.dt, + mass_of_dry_air=666 * si.kg, + p0=settings.p0, + initial_water_vapour_mixing_ratio=initial_water_vapour_mixing_ratio, + T0=settings.T0, + w=settings.vertical_velocity, + z0=settings.z0, + ), + ) + + additional_derived_attributes = ("radius", "equilibrium saturation") + for additional_derived_attribute in additional_derived_attributes: + builder.request_attribute(additional_derived_attribute) + + builder.add_dynamic( + AmbientThermodynamics() + ) # TODO #1266: order matters here, but error message is not saying it! + builder.add_dynamic(Condensation()) + if gravitational_coalsecence: + builder.add_dynamic(Coalescence(collision_kernel=Geometric())) + + self.r_dry, n_in_unit_volume = Logarithmic( + spectrum=settings.dry_radii_spectrum, + ).sample_deterministic(builder.particulator.n_sd - n_gccn) + + if gccn: + nonzero_concentration_mask = np.nonzero(table_3.NA) + self.r_dry = np.concatenate( + [self.r_dry, table_3.RD[nonzero_concentration_mask]] + ) + n_in_unit_volume = np.concatenate( + [n_in_unit_volume, table_3.NA[nonzero_concentration_mask]] + ) # TODO #1266: check which temp, pres, RH assumed in the paper for NA??? + + pd0 = settings.formulae.trivia.p_d( + settings.p0, initial_water_vapour_mixing_ratio + ) + rhod0 = settings.formulae.state_variable_triplet.rhod_of_pd_T(pd0, settings.T0) + + attributes = builder.particulator.environment.init_attributes( + n_in_dv=n_in_unit_volume + * builder.particulator.environment.mass_of_dry_air + / rhod0, + kappa=settings.kappa, + r_dry=self.r_dry, + ) + + super().__init__( + builder.build( + attributes=attributes, + products=( + PeakSaturation(name="S_max"), + ParcelDisplacement(name="z"), + Time(name="t"), + ActivatedMeanRadius( + name="r_mean_act", count_activated=True, count_unactivated=False + ), + RadiusStandardDeviation( + name="r_std_act", count_activated=True, count_unactivated=False + ), + ), + ) + ) + + # TODO #1266: copied from G & P 2023 + self.output_attributes = { + attr: tuple([] for _ in range(self.particulator.n_sd)) + for attr in additional_derived_attributes + } + + def run( + self, *, n_steps: int = 2250, steps_per_output_interval: int = 10 + ): # TODO #1266: essentially copied from G & P 2023 + output_products = super()._run( + nt=n_steps, steps_per_output_interval=steps_per_output_interval + ) + return {"products": output_products, "attributes": self.output_attributes} + + def _save(self, output): # TODO #1266: copied from G&P 2023 + for key, attr in self.output_attributes.items(): + attr_data = self.particulator.attributes[key].to_ndarray() + for drop_id in range(self.particulator.n_sd): + attr[drop_id].append(attr_data[drop_id]) + super()._save(output) diff --git a/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/table_3.py b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/table_3.py new file mode 100644 index 0000000000000000000000000000000000000000..9dce83260cb8f7f9d61e5b9c20f7c9e4f8624912 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jensen_and_Nugent_2017/table_3.py @@ -0,0 +1,55 @@ +import numpy as np + +from PySDM.physics import si + +NA = ( + np.asarray( + [ + 111800, + 68490, + 38400, + 21820, + 13300, + 8496, + 5486, + 3805, + 2593, + 1919, + 1278, + 998.4, + 777.9, + 519.5, + 400.5, + 376.9, + 265.3, + 212.4, + 137.8, + 121.4, + 100.9, + 122.2, + 50.64, + 38.3, + 55.47, + 21.45, + 12.95, + 43.23, + 26.26, + 30.5, + 4.385, + 4.372, + 4.465, + 4.395, + 4.427, + 4.411, + 0, + 0, + 0, + 4.522, + 0, + 4.542, + ] + ) + / si.m**3 +) + +RD = np.linspace(0.8, 9, num=len(NA), endpoint=True) * si.um diff --git a/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/__init__.py b/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5a2385c73d9d3244928353e29585745bd0601dc7 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/__init__.py @@ -0,0 +1,11 @@ +# pylint: disable=invalid-name +""" +kinetic fractionation factors used in modelling isotopic effect +in snow formation, replacing saturation fractionation factors with +alpha_{saturation} alpha_{kinetic} +(see [Jouzel and Merlivat 1984](https://doi.org/10.1029/JD089iD07p11749)) + + +fig_8_9.ipynb: +.. include:: ./fig_8_9.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb b/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..590b38c25cfeb762bf543a55adad5c8e9f55259d --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb @@ -0,0 +1,3127 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e251450efd0afa5d", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Jouzel_and_Merlivat_1984/fig_8_9.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "841538e30be219a1", + "metadata": {}, + "source": [ + "# figs 8 and 9 from [Jouzel and Merlivat 1984](https://doi.org/10.1029/JD089iD07p11749) \"_Deuterium and oxygen 18 in precipitation: Modeling of the isotopic effects during snow formation_\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "466d06cabb3174f3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-25T23:25:40.862379Z", + "start_time": "2025-06-25T23:25:40.859115Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-25T23:25:42.736860Z", + "start_time": "2025-06-25T23:25:40.951437Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM_examples.Jouzel_and_Merlivat_1984.thermodynamic_profiles import ice_saturation_curve_4\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "87e416d611926ad5", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-25T23:25:43.030796Z", + "start_time": "2025-06-25T23:25:42.742196Z" + } + }, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " isotope_diffusivity_ratios=\"Stewart1975\",\n", + " isotope_equilibrium_fractionation_factors=\"Majoube1970\",\n", + " isotope_kinetic_fractionation_factors=\"JouzelAndMerlivat1984\"\n", + ")\n", + "const = formulae.constants\n", + "svp = formulae.saturation_vapour_pressure\n", + "C2K = formulae.trivia.C2K\n", + "K2C = formulae.trivia.K2C\n", + "\n", + "n_points = 100\n", + "T_0_50 = C2K(np.linspace(0.0, -50, n_points))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3f97b336a478a65a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-25T23:25:44.982409Z", + "start_time": "2025-06-25T23:25:43.041753Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c15bde98c9c4698979a4cb47ad75d48", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_8.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a478d96b93d34ed2936063de54732783", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_9.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a553869ce5db4af693432bb8e480d2aa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_1.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize = (10, 6)\n", + "fig, axs = pyplot.subplots(1, 3, figsize=figsize, sharey=True)\n", + "\n", + "Y = np.asarray(output['t']) - 196 * si.s\n", + "\n", + "axs[0].plot(output['liquid water mixing ratio'], Y)\n", + "axs[0].set_ylim(0, 2400 * si.s)\n", + "axs[0].set_xlim(0, 2.5)\n", + "axs[0].set_yticks(np.linspace(0, 2400, 13, endpoint=True))\n", + "axs[0].set_ylabel('Time above cloud base (s)')\n", + "axs[0].set_xlabel('Liquid Water Content (g/kg)')\n", + "\n", + "axs[1].plot(np.asarray(output['aq_S_IV_ppb']) + np.asarray(output['gas_S_IV_ppb']), Y)\n", + "axs[1].set_xticks(np.linspace(0, 0.2, 5, endpoint=True))\n", + "axs[1].set_xlabel('SO2 conc (total ppb)')\n", + "\n", + "for product in (\n", + " 'pH_pH_number_weighted', \n", + " 'pH_pH_volume_weighted', \n", + " 'pH_conc_H_number_weighted', \n", + " 'pH_conc_H_volume_weighted'\n", + "):\n", + " axs[2].plot(output[product], Y, label=product)\n", + "axs[2].set_xlim(3.7, 5)\n", + "axs[2].set_xticks(np.linspace(3.8, 5, 7, endpoint=True))\n", + "axs[2].set_xlabel('average pH')\n", + "axs[2].legend()\n", + "\n", + "for ax in axs:\n", + " ax.grid()\n", + "show_plot('fig_1.pdf')" + ] + } + ], + "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": "3.8.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/settings.py b/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..a80eed0fdf1be85c665d85fe559a1c54d73f94b8 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/settings.py @@ -0,0 +1,100 @@ +import numpy as np +from chempy import Substance +from pystrict import strict + +from PySDM import Formulae +from PySDM.dynamics.impl.chemistry_utils import AQUEOUS_COMPOUNDS +from PySDM.initialisation import spectra +from PySDM.initialisation.sampling import spectral_sampling as spec_sampling +from PySDM.physics import si +from PySDM.physics.constants import PPB, PPM + + +@strict +class Settings: + def __init__( + self, + dt: float, + n_sd: int, + n_substep: int, + spectral_sampling_class: spec_sampling.SpectralSampling = spec_sampling.Logarithmic, + spectral_sampling_method: str = "sample_deterministic", + ): + self.formulae = Formulae( + saturation_vapour_pressure="AugustRocheMagnus", + constants={"g_std": 10 * si.m / si.s**2}, + ) + const = self.formulae.constants + self.DRY_RHO = 1800 * si.kg / (si.m**3) + self.dry_molar_mass = Substance.from_formula("NH4HSO4").mass * si.gram / si.mole + + self.system_type = "closed" + + self.t_max = (2400 + 196) * si.s + self.output_interval = 10 * si.s + self.dt = dt + + self.w = 0.5 * si.m / si.s + + self.n_sd = n_sd + self.n_substep = n_substep + + self.p0 = 950 * si.mbar + self.T0 = 285.2 * si.K + pv0 = 0.95 * self.formulae.saturation_vapour_pressure.pvs_water(self.T0) + self.initial_water_vapour_mixing_ratio = const.eps * pv0 / (self.p0 - pv0) + self.kappa = 0.61 + + self.cloud_radius_range = (0.5 * si.micrometre, 25 * si.micrometre) + + self.mass_of_dry_air = 44 + + # note: rho is not specified in the paper + rho0 = 1 + + self.r_dry, self.n_in_dv = getattr( + spectral_sampling_class( + spectrum=spectra.Lognormal( + norm_factor=566 / si.cm**3 / rho0 * self.mass_of_dry_air, + m_mode=0.08 * si.um / 2, + s_geom=2, + ) + ), + spectral_sampling_method, + )(n_sd) + + self.ENVIRONMENT_MOLE_FRACTIONS = { + "SO2": 0.2 * PPB, + "O3": 50 * PPB, + "H2O2": 0.5 * PPB, + "CO2": 360 * PPM, + "HNO3": 0.1 * PPB, + "NH3": 0.1 * PPB, + } + + self.starting_amounts = { + "moles_" + + k: ( + self.formulae.trivia.volume(self.r_dry) + * self.DRY_RHO + / self.dry_molar_mass + if k in ("N_mIII", "S_VI") + else np.zeros(self.n_sd) + ) + for k in AQUEOUS_COMPOUNDS + } + + self.dry_radius_bins_edges = ( + np.logspace(np.log10(0.01 * si.um), np.log10(1 * si.um), 51, endpoint=True) + / 2 + ) + + @property + def nt(self): + nt = self.t_max / self.dt + assert nt == int(nt) + return int(nt) + + @property + def steps_per_output_interval(self) -> int: + return int(self.output_interval / self.dt) diff --git a/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/simulation.py b/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..425893e2178ea1e15591f03626b8c09071ad92c6 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Kreidenweis_et_al_2003/simulation.py @@ -0,0 +1,126 @@ +import numpy as np +from PySDM_examples.utils import BasicSimulation + +import PySDM.products as PySDM_products +from PySDM import Builder +from PySDM.backends import CPU +from PySDM.dynamics import AmbientThermodynamics, AqueousChemistry, Condensation +from PySDM.dynamics.impl.chemistry_utils import AQUEOUS_COMPOUNDS, GASEOUS_COMPOUNDS +from PySDM.environments import Parcel +from PySDM.physics import si + + +class Simulation(BasicSimulation): + def __init__(self, settings, products=None): + builder = Builder( + n_sd=settings.n_sd, + backend=CPU( + formulae=settings.formulae, override_jit_flags={"parallel": False} + ), + environment=Parcel( + dt=settings.dt, + mass_of_dry_air=settings.mass_of_dry_air, + p0=settings.p0, + initial_water_vapour_mixing_ratio=settings.initial_water_vapour_mixing_ratio, + T0=settings.T0, + w=settings.w, + ), + ) + + attributes = builder.particulator.environment.init_attributes( + n_in_dv=settings.n_in_dv, + kappa=settings.kappa, + r_dry=settings.r_dry, + include_dry_volume_in_attribute=False, + ) + attributes = { + **attributes, + **settings.starting_amounts, + } + + builder.add_dynamic(AmbientThermodynamics()) + builder.add_dynamic(Condensation()) + builder.add_dynamic( + AqueousChemistry( + environment_mole_fractions=settings.ENVIRONMENT_MOLE_FRACTIONS, + system_type=settings.system_type, + n_substep=settings.n_substep, + dry_rho=settings.DRY_RHO, + dry_molar_mass=settings.dry_molar_mass, + ) + ) + + products = products or ( + PySDM_products.AmbientRelativeHumidity(name="RH", unit="%"), + PySDM_products.WaterMixingRatio( + name="liquid water mixing ratio", + radius_range=[1 * si.um, np.inf], + unit="g/kg", + ), + PySDM_products.ParcelDisplacement(name="z"), + PySDM_products.AmbientPressure(name="p"), + PySDM_products.AmbientTemperature(name="T"), + PySDM_products.AmbientDryAirDensity(name="rhod"), + PySDM_products.AmbientWaterVapourMixingRatio( + name="water vapour mixing ratio", + var="water_vapour_mixing_ratio", + unit="g/kg", + ), + PySDM_products.Time(name="t"), + *( + PySDM_products.AqueousMoleFraction( + comp, unit="ppb", name=f"aq_{comp}_ppb" + ) + for comp in AQUEOUS_COMPOUNDS + ), + *( + PySDM_products.GaseousMoleFraction( + comp, unit="ppb", name=f"gas_{comp}_ppb" + ) + for comp in GASEOUS_COMPOUNDS + ), + PySDM_products.Acidity( + name="pH_pH_number_weighted", + radius_range=settings.cloud_radius_range, + weighting="number", + attr="pH", + ), + PySDM_products.Acidity( + name="pH_pH_volume_weighted", + radius_range=settings.cloud_radius_range, + weighting="volume", + attr="pH", + ), + PySDM_products.Acidity( + name="pH_conc_H_number_weighted", + radius_range=settings.cloud_radius_range, + weighting="number", + attr="conc_H", + ), + PySDM_products.Acidity( + name="pH_conc_H_volume_weighted", + radius_range=settings.cloud_radius_range, + weighting="volume", + attr="conc_H", + ), + PySDM_products.TotalDryMassMixingRatio( + settings.DRY_RHO, name="q_dry", unit="ug/kg" + ), + PySDM_products.PeakSaturation(unit="%", name="S_max_percent"), + PySDM_products.ParticleSpecificConcentration( + radius_range=settings.cloud_radius_range, name="n_c_mg", unit="mg^-1" + ), + PySDM_products.AqueousMassSpectrum( + key="S_VI", + dry_radius_bins_edges=settings.dry_radius_bins_edges, + name="dm_S_VI/dlog_10(dry diameter)", + unit="ug / m^3", + ), + ) + + particulator = builder.build(attributes=attributes, products=products) + self.settings = settings + super().__init__(particulator=particulator) + + def run(self): + return super()._run(self.settings.nt, self.settings.steps_per_output_interval) diff --git a/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/__init__.py b/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8c592ccbbc5f5059b91c27164f021e2ad961410f --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/__init__.py @@ -0,0 +1,8 @@ +# pylint: disable=invalid-name +""" +Formulae-only example depicting equilibrium isotopic fractionation over ice based on +[Lamb et al. 2017](https://doi.org/10.1073/pnas.1618374114) + +fig_4.ipynb: +.. include:: ./fig_4.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb b/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..962beb02ec12f2c9b8e952eaf921903f97675a46 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb @@ -0,0 +1,1801 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "624a95a895b096b2", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lamb_et_al_2017/fig_4.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "c288f2ecf3a1c2f1", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "#### based on Fig. 4 from Lamb et al. 2017 (PNAS) \"_Laboratory measurements of HDO/H2O isotopic fractionation during ice deposition in simulated cirrus clouds_\"\n", + "https://doi.org/10.1073/pnas.1618374114" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0294286f-145a-4f58-a97b-a0a42486e2cf", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-10T23:26:11.044820926Z", + "start_time": "2024-02-10T23:26:11.037101465Z" + } + }, + "outputs": [], + "source": [ + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM import Formulae\n", + "from PySDM.physics import constants_defaults\n", + "from matplotlib import pyplot\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "61856a8bd8035c7e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-10T23:26:30.381539986Z", + "start_time": "2024-02-10T23:26:30.368003667Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "PAPERS = {\n", + " 'MerlivatAndNief1967': 'red',\n", + " 'EllehojEtAl2013': 'blue',\n", + " 'LambEtAl2017': 'black',\n", + "}\n", + "\n", + "T = np.linspace(180, constants_defaults.T0)\n", + "alphas = {\n", + " paper: Formulae(\n", + " isotope_equilibrium_fractionation_factors=paper\n", + " ).isotope_equilibrium_fractionation_factors.alpha_i_2H(T)\n", + " for paper in PAPERS\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "129c78ed6b12197e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-10T23:26:31.079798656Z", + "start_time": "2024-02-10T23:26:30.696551402Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f92b021944f4409bba97909e7e7e267c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_4.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for label, alpha in alphas.items():\n", + " pyplot.plot(T, alpha, label=label, color=PAPERS[label])\n", + "pyplot.grid()\n", + "pyplot.legend()\n", + "pyplot.xlabel('Temperature (K)')\n", + "pyplot.xlim(T[0], T[-1])\n", + "pyplot.xticks(np.linspace(180, 270, 10))\n", + "pyplot.ylabel('$α_{eq}$')\n", + "pyplot.ylim(1.13, 1.6)\n", + "pyplot.yticks(np.linspace(1.15, 1.6, 10))\n", + "show_plot('fig_4.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b6e881ffac9b428", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/__init__.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ac98484029a4365561facefd429951f14a0bce4b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/__init__.py @@ -0,0 +1,19 @@ +# pylint: disable=invalid-name +""" +parcel-model example focused on surfactants based on +[Lowe et al. 2019 (Nature Comm.)](https://doi.org/10.1038/s41467-019-12982-0) + +fig_1.ipynb: +.. include:: ./fig_1.ipynb.badges.md + +fig_2.ipynb: +.. include:: ./fig_2.ipynb.badges.md + +fig_3.ipynb: +.. include:: ./fig_3.ipynb.badges.md + +fig_s2.ipynb: +.. include:: ./fig_s2.ipynb.badges.md +""" +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol.py new file mode 100644 index 0000000000000000000000000000000000000000..9d47470e47444a726a1b5e24be257fbcf8bdb139 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol.py @@ -0,0 +1,209 @@ +from chempy import Substance +from pystrict import strict + +from PySDM.initialisation import spectra +from PySDM.initialisation.aerosol_composition import DryAerosolMixture +from PySDM.physics import si + + +@strict +class AerosolMarine(DryAerosolMixture): + def __init__( + self, water_molar_volume: float, Forg: float = 0.2, Acc_N2: float = 134 + ): + Aitken = { + "palmitic": Forg, + "(NH4)2SO4": (1 - Forg), + "NaCl": 0, + } + Accumulation = { + "palmitic": Forg, + "(NH4)2SO4": 0, + "NaCl": (1 - Forg), + } + + super().__init__( + ionic_dissociation_phi={ + "palmitic": 1, + "(NH4)2SO4": 3, + "NaCl": 2, + }, + is_soluble={ + "palmitic": False, + "(NH4)2SO4": True, + "NaCl": True, + }, + densities={ + "palmitic": 0.852 * si.g / si.cm**3, + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "NaCl": 2.16 * si.g / si.cm**3, + }, + compounds=("palmitic", "(NH4)2SO4", "NaCl"), + molar_masses={ + "palmitic": 256.4 * si.g / si.mole, + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "NaCl": Substance.from_formula("NaCl").mass * si.gram / si.mole, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Aitken), + "kappa": self.kappa(Aitken, water_molar_volume=water_molar_volume), + "nu_org": self.nu_org(Aitken), + "spectrum": spectra.Lognormal( + norm_factor=226 / si.cm**3, m_mode=19.6 * si.nm, s_geom=1.71 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, m_mode=69.5 * si.nm, s_geom=1.7 + ), + }, + ) + + color = "dodgerblue" + + +@strict +class AerosolBoreal(DryAerosolMixture): + def __init__( + self, water_molar_volume: float, Forg: float = 0.668, Acc_N2: float = 540 + ): + # note: SOA1 or SOA2 unclear from the paper + Aitken = { + "SOA1": Forg, + "SOA2": 0, + "(NH4)2SO4": (1 - Forg) / 2, + "NH4NO3": (1 - Forg) / 2, + } + Accumulation = { + "SOA1": 0, + "SOA2": Forg, + "(NH4)2SO4": (1 - Forg) / 2, + "NH4NO3": (1 - Forg) / 2, + } + + super().__init__( + ionic_dissociation_phi={ + "SOA1": 1, + "SOA2": 1, + "(NH4)2SO4": 3, + "NH4NO3": 2, + }, + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "NH4NO3": Substance.from_formula("NH4NO3").mass * si.gram / si.mole, + "SOA1": 190 * si.g / si.mole, + "SOA2": 368.4 * si.g / si.mole, + }, + densities={ + "SOA1": 1.24 * si.g / si.cm**3, + "SOA2": 1.2 * si.g / si.cm**3, + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "NH4NO3": 1.72 * si.g / si.cm**3, + }, + compounds=("SOA1", "SOA2", "(NH4)2SO4", "NH4NO3"), + is_soluble={ + "SOA1": False, + "SOA2": False, + "(NH4)2SO4": True, + "NH4NO3": True, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Aitken), + "kappa": self.kappa(Aitken, water_molar_volume=water_molar_volume), + "nu_org": self.nu_org(Aitken), + "spectrum": spectra.Lognormal( + norm_factor=1110 / si.cm**3, m_mode=22.7 * si.nm, s_geom=1.75 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, + m_mode=82.2 * si.nm, + s_geom=1.62, + ), + }, + ) + + color = "yellowgreen" + + +@strict +class AerosolNascent(DryAerosolMixture): + def __init__( + self, water_molar_volume: float, Acc_Forg: float = 0.3, Acc_N2: float = 30 + ): + Ultrafine = { + "SOA1": 0.52, + "SOA2": 0, + "(NH4)2SO4": 0.48, + } + Accumulation = { + "SOA1": 0, + "SOA2": Acc_Forg, + "(NH4)2SO4": (1 - Acc_Forg), + } + super().__init__( + ionic_dissociation_phi={ + "SOA1": 1, + "SOA2": 1, + "(NH4)2SO4": 3, + }, + molar_masses={ + "SOA1": 190 * si.g / si.mole, + "SOA2": 368.4 * si.g / si.mole, + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + }, + densities={ + "SOA1": 1.24 * si.g / si.cm**3, + "SOA2": 1.2 * si.g / si.cm**3, + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + }, + compounds=("SOA1", "SOA2", "(NH4)2SO4"), + is_soluble={ + "SOA1": False, + "SOA2": False, + "(NH4)2SO4": True, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Ultrafine), + "kappa": self.kappa(Ultrafine, water_molar_volume=water_molar_volume), + "nu_org": self.nu_org(Ultrafine), + "spectrum": spectra.Lognormal( + norm_factor=2000 / si.cm**3, m_mode=11.5 * si.nm, s_geom=1.71 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, m_mode=100 * si.nm, s_geom=1.70 + ), + }, + ) + + color = "orangered" diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol_code.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol_code.py new file mode 100644 index 0000000000000000000000000000000000000000..7f61a69c753faa0ec354b023dbb2e12a44b84874 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/aerosol_code.py @@ -0,0 +1,261 @@ +from chempy import Substance +from pystrict import strict + +from PySDM.initialisation import spectra +from PySDM.initialisation.aerosol_composition import DryAerosolMixture +from PySDM.physics import si + + +@strict +class AerosolMarine(DryAerosolMixture): + # cd MAV + # MODAL_PARS.CONC = [223 137]; %[number/cm3] + # MODAL_PARS.GSD = [1.68 1.68]; + # MODAL_PARS.GEOMEAN_DIAM = [0.0390 0.139]; %[um] + # DENSITY(4:5) = 0.852; %Set organic density. Palmitic acid 256.4 [gmol-1] + # MASS_FRAC = [0 0.8 0 0.2 0 0 0 0.0;... + # 0 0.0 0 0.2 0 0 0 0.8]; + # NAT = 2; + # NMODE = [1 1]; + # DENSITY = [1.841, 1.78, 1.77, 0.852, 1.5, 2., 2.65, 2.165]; %[gcm-3] + + def __init__( + self, water_molar_volume: float, Forg: float = 0.2, Acc_N2: float = 137 + ): + Aitken = { + "palmitic": Forg, + "(NH4)2SO4": (1 - Forg), + "NaCl": 0, + } + Accumulation = { + "palmitic": Forg, + "(NH4)2SO4": 0, + "NaCl": (1 - Forg), + } + + super().__init__( + ionic_dissociation_phi={ + "palmitic": 1, + "(NH4)2SO4": 3, + "NaCl": 2, + }, + is_soluble={ + "palmitic": False, + "(NH4)2SO4": True, + "NaCl": True, + }, + densities={ + "palmitic": 0.852 * si.g / si.cm**3, + "(NH4)2SO4": 1.78 * si.g / si.cm**3, + "NaCl": 2.165 * si.g / si.cm**3, + }, + compounds=("palmitic", "(NH4)2SO4", "NaCl"), + molar_masses={ + "palmitic": 256.4 * si.g / si.mole, + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "NaCl": Substance.from_formula("NaCl").mass * si.gram / si.mole, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Aitken), + "kappa": self.kappa( + mass_fractions=Aitken, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Aitken), + "spectrum": spectra.Lognormal( + norm_factor=223 / si.cm**3, m_mode=19.6 * si.nm, s_geom=1.68 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + mass_fractions=Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, m_mode=69.5 * si.nm, s_geom=1.68 + ), + }, + ) + + color = "dodgerblue" + + +@strict +class AerosolBoreal(DryAerosolMixture): + # cd HYY + # MODAL_PARS.CONC = [1110 540]; + # MODAL_PARS.GSD = [1.75 1.62]; + # MODAL_PARS.GEOMEAN_DIAM = [0.0453 0.1644]; + # DENSITY(4:5) = [1.2 1.4]; %Set organic density + # DENSITY(1) = 1.72; + # INORG_MASS_RATIO = 0.1515/0.1559; %ammonium sulfate:ammonium nitrate mass + # FORG = 0.60; + # MASS_FRAC = [(1-FORG)/(1+INORG_MASS_RATIO) 0 ... + # INORG_MASS_RATIO*(1-FORG)/(1+INORG_MASS_RATIO) FORG... + # 0 0 0 0;... + # (1-FORG)/(1+INORG_MASS_RATIO) 0 ... + # INORG_MASS_RATIO*(1-FORG)/(1+INORG_MASS_RATIO) 0 ... + # FORG 0 0 0]; + # NAT = 2; + # NMODE = [1 1]; + + # DENSITY = [1.841, 1.78, 1.77, 1.5, 1.5, 2., 2.65, 2.165]; %[gcm-3] + # DENSITY = [1.72, 1.78, 1.77, 1.2, 1.4, 2., 2.65, 2.165]; %[gcm-3] + + def __init__( + self, water_molar_volume: float, Forg: float = 0.668, Acc_N2: float = 540 + ): + # TODO #1247: SOA1 or SOA2 unclear from the paper + # TODO #1247: CAN'T FIND WHERE NH4NO3 PROPERTIES ARE DEFINED IN ICPM + INORG_MASS_RATIO = 0.1515 / 0.1559 + Aitken = { + "SOA1": Forg, + "SOA2": 0, + "(NH4)2SO4": INORG_MASS_RATIO * (1 - Forg) / (1 + INORG_MASS_RATIO), + "NH4NO3": (1 - Forg) / (1 + INORG_MASS_RATIO), + } + Accumulation = { + "SOA1": 0, + "SOA2": Forg, + "(NH4)2SO4": INORG_MASS_RATIO * (1 - Forg) / (1 + INORG_MASS_RATIO), + "NH4NO3": (1 - Forg) / (1 + INORG_MASS_RATIO), + } + + super().__init__( + ionic_dissociation_phi={ + "SOA1": 1, + "SOA2": 1, + "(NH4)2SO4": 3, + "NH4NO3": 2, + }, + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "NH4NO3": Substance.from_formula("NH4NO3").mass * si.gram / si.mole, + "SOA1": 190 * si.g / si.mole, # TODO #1247: 190 OR 200? + "SOA2": 368.4 * si.g / si.mole, # TODO #1247: 368.4 OR 200? + }, + densities={ + "SOA1": 1.2 * si.g / si.cm**3, + "SOA2": 1.4 * si.g / si.cm**3, + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "NH4NO3": 1.72 * si.g / si.cm**3, + }, + compounds=("SOA1", "SOA2", "(NH4)2SO4", "NH4NO3"), + is_soluble={ + "SOA1": False, + "SOA2": False, + "(NH4)2SO4": True, + "NH4NO3": True, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Aitken), + "kappa": self.kappa( + mass_fractions=Aitken, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Aitken), + "spectrum": spectra.Lognormal( + norm_factor=1110 / si.cm**3, m_mode=22.65 * si.nm, s_geom=1.75 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + mass_fractions=Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, + m_mode=82.2 * si.nm, + s_geom=1.62, + ), + }, + ) + + color = "yellowgreen" + + +@strict +class AerosolNascent(DryAerosolMixture): + # cd NUM + # MODAL_PARS.CONC = [2000 30]; + # MODAL_PARS.GSD = [1.71 1.703]; + # MODAL_PARS.GEOMEAN_DIAM = [0.023 0.200]; + # DENSITY(4:5) = [1.2 1.24]; %Set organic density + # MASS_FRAC = [0 0 0.48 0.52 0 0 0 0;... + # 0 0 0.70 0.0 0.3 0 0 0]; + # NAT = 2; + # NMODE = [1 1]; + + # DENSITY = [1.841, 1.78, 1.77, 1.2, 1.24, 2., 2.65, 2.165]; %[gcm-3] + + def __init__( + self, water_molar_volume: float, Acc_Forg: float = 0.3, Acc_N2: float = 30 + ): + # TODO #1247: CAN'T FIND WHEN PHI IS MULTIPLIED FOR KÖHLER B IN ICPM CODE + Ultrafine = { + "SOA1": 0.52, + "SOA2": 0, + "(NH4)2SO4": 0.48, + } + Accumulation = { + "SOA1": 0, + "SOA2": Acc_Forg, + "(NH4)2SO4": (1 - Acc_Forg), + } + super().__init__( + ionic_dissociation_phi={ + "SOA1": 1, + "SOA2": 1, + "(NH4)2SO4": 3, + }, + molar_masses={ + "SOA1": 190 * si.g / si.mole, # TODO #1247: 190 OR 200? + "SOA2": 368.4 * si.g / si.mole, # TODO #1247: 368.4 OR 200? + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + }, + densities={ + "SOA1": 1.2 * si.g / si.cm**3, + "SOA2": 1.24 * si.g / si.cm**3, + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + }, + compounds=("SOA1", "SOA2", "(NH4)2SO4"), + is_soluble={ + "SOA1": False, + "SOA2": False, + "(NH4)2SO4": True, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(Ultrafine), + "kappa": self.kappa( + mass_fractions=Ultrafine, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Ultrafine), + "spectrum": spectra.Lognormal( + norm_factor=2000 / si.cm**3, m_mode=11.5 * si.nm, s_geom=1.71 + ), + }, + { + "f_org": 1 - self.f_soluble_volume(Accumulation), + "kappa": self.kappa( + mass_fractions=Accumulation, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(Accumulation), + "spectrum": spectra.Lognormal( + norm_factor=Acc_N2 / si.cm**3, m_mode=100 * si.nm, s_geom=1.703 + ), + }, + ) + + color = "orangered" diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/constants_def.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/constants_def.py new file mode 100644 index 0000000000000000000000000000000000000000..aad63332fc037d5ed88b538bc61e193b13d1b8c3 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/constants_def.py @@ -0,0 +1,13 @@ +from PySDM.physics import constants_defaults, si + +LOWE_CONSTS = { + "sgm_org": 40 * si.mN / si.m, + "delta_min": 0.1 + * si.nm, # 0.2 in the paper, but 0.1 matches the paper plot fig 1c and 1d + "MAC": 1, + "HAC": 1, + "c_pd": 1006 * si.joule / si.kilogram / si.kelvin, + "g_std": 9.81 * si.metre / si.second**2, + "Md": constants_defaults.R_str / 287.058 * si.joule / si.kelvin / si.kg, + "Mv": constants_defaults.R_str / 461 * si.joule / si.kelvin / si.kg, +} diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5346acf09f05bf3d36dcaaf3949ee655c1997db8 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb @@ -0,0 +1,5523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Fig. 1 from Lowe et al. 2019 (Nature Comm.) \"_Key drivers of cloud response to surface-active organics_\"\n", + "https://doi.org/10.1038/s41467-019-12982-0" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:23.879155Z", + "start_time": "2023-12-29T11:52:23.870444Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "from PySDM import Formulae\n", + "from PySDM.initialisation import spectra\n", + "from PySDM.physics import si\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM_examples.Lowe_et_al_2019.aerosol_code import AerosolMarine, AerosolBoreal, AerosolNascent\n", + "from PySDM_examples.Lowe_et_al_2019.constants_def import LOWE_CONSTS" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:30.261687Z", + "start_time": "2023-12-29T11:52:28.588326Z" + } + }, + "outputs": [], + "source": [ + "FORMULAE = Formulae(constants=LOWE_CONSTS)\n", + "WATER_MOLAR_VOLUME = FORMULAE.constants.water_molar_volume\n", + "cases = {\n", + " 'Marine (MA)': AerosolMarine(water_molar_volume=WATER_MOLAR_VOLUME),\n", + " 'Boreal (HYY)': AerosolBoreal(water_molar_volume=WATER_MOLAR_VOLUME),\n", + " 'NUM event (NE)': AerosolNascent(water_molar_volume=WATER_MOLAR_VOLUME)\n", + "}\n", + "\n", + "formulae_bulk = Formulae(\n", + " surface_tension='Constant',\n", + " constants=LOWE_CONSTS, \n", + ")\n", + "formulae_surf = Formulae(\n", + " surface_tension='CompressedFilmOvadnevaite',\n", + " constants=LOWE_CONSTS,\n", + ")\n", + "\n", + "r_wet = np.logspace(np.log(150 * si.nm), np.log(3000 * si.nm), base=np.e, num=100)\n", + "r_dry = 50 * si.nm\n", + "v_wet = formulae_surf.trivia.volume(r_wet)\n", + "v_dry = formulae_surf.trivia.volume(r_dry)\n", + "T = 300 * si.K\n", + "r_wet_ticks_nm = (300, 500, 700, 1000, 3000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig. 1 a" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:32.024468Z", + "start_time": "2023-12-29T11:52:30.265333Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1bf7183749b7407abc174d1c93f5b383", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_1a.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.logspace(np.log(5 * si.nm), np.log(1000 * si.nm), base=np.e)\n", + "for k, v in cases.items():\n", + " spec = spectra.Sum(\n", + " tuple(v.modes[i]['spectrum'] for i in range(len(v.modes)))\n", + " )\n", + " pyplot.loglog(x / si.nm, spec.size_distribution(x)*x / si.cm**-3, label=k, color=v.color)\n", + "pyplot.ylim(1, 2500)\n", + "pyplot.xlim(5, 1000)\n", + "pyplot.legend()\n", + "pyplot.grid()\n", + "pyplot.xlabel('Dry aerosol radius [nm]')\n", + "pyplot.ylabel('Aerosol number concentration\\n size distribution [cm$^{-3}$]')\n", + "show_plot(\"fig_1a.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig. 1 c" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:34.847271Z", + "start_time": "2023-12-29T11:52:32.038091Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0f04be1a13d4445e92d2f500c5ac9849", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_1c.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rd3 = r_dry**3\n", + " \n", + "for formulae in (formulae_bulk, formulae_surf):\n", + " for k, v in cases.items():\n", + " sigma = formulae.surface_tension.sigma(np.nan, v_wet, v_dry, v.modes[0]['f_org'])\n", + " model = formulae.surface_tension.__name__\n", + " RH_eq = formulae.hygroscopicity.RH_eq(r_wet, T, v.modes[0]['kappa'][model], rd3, sigma)\n", + " pyplot.plot(\n", + " r_wet / si.nm, \n", + " (RH_eq - 1) * 100, \n", + " label=f\"{k}\" if model == 'Constant' else \"\",\n", + " color=v.color, \n", + " linestyle='-' if model == 'Constant' else '--'\n", + " )\n", + " pyplot.plot(r_wet / si.nm, np.ones_like(r_wet), color=\"k\", linestyle='-' if model == 'Constant' else '--', label=f\"{model}\")\n", + "pyplot.grid()\n", + "pyplot.xscale('log')\n", + "pyplot.xticks(r_wet_ticks_nm, r_wet_ticks_nm)\n", + "pyplot.xlabel('Wet radius [nm]')\n", + "pyplot.xlim(r_wet[0] / si.nm, r_wet[-1] / si.nm)\n", + "pyplot.ylabel('Equilibrium supersaturation [%]')\n", + "yticks = (-.1, 0, .1, .2, .3)\n", + "pyplot.yticks(yticks, yticks)\n", + "pyplot.ylim(yticks[0], 0.35)\n", + "pyplot.legend()\n", + "show_plot(\"fig_1c.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig. 1 d" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:35.602881Z", + "start_time": "2023-12-29T11:52:34.847287Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "828ab372642943abbecdea3b846fa682", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_1d.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for k, v in cases.items():\n", + " sigma = formulae_surf.surface_tension.sigma(np.nan, v_wet, v_dry, v.modes[0]['f_org'])\n", + " pyplot.plot(r_wet / si.nm, sigma / (si.mN / si.m), label=k, color=v.color, linestyle='--')\n", + "pyplot.plot(r_wet / si.nm, np.full_like(r_wet, formulae_bulk.constants.sgm_w / (si.mN / si.m)), label='pure water', color='gray')\n", + "pyplot.grid()\n", + "pyplot.xscale('log')\n", + "pyplot.xticks(r_wet_ticks_nm, r_wet_ticks_nm)\n", + "yticks = (20, 40, 60, 80)\n", + "pyplot.yticks(yticks, yticks)\n", + "pyplot.xlim(r_wet[0] / si.nm, r_wet[-1] / si.nm)\n", + "pyplot.ylim(11, 85)\n", + "pyplot.xlabel('Wet radius [nm]')\n", + "pyplot.ylabel('Surface tension [mN m$^{-1}$]')\n", + "pyplot.legend()\n", + "show_plot(\"fig_1d.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T11:52:35.607915Z", + "start_time": "2023-12-29T11:52:35.603936Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.9 ('pysdm')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b14f34a08619f4a218d80d7380beed3f0c712c89ff93e7183219752d640ed427" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b7b8dd498d80a1c3fef45c7293847a475fba7715 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb @@ -0,0 +1,3866 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_2.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Fig. 2 from Lowe et al. 2019 (Nature Comm.) \"_Key drivers of cloud response to surface-active organics_\" \n", + "https://doi.org/10.1038/s41467-019-12982-0" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T07:32:09.342496Z", + "start_time": "2025-06-15T07:32:09.336429Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T07:32:12.834828Z", + "start_time": "2025-06-15T07:32:09.355937Z" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Lowe_et_al_2019 import Settings, Simulation\n", + "from PySDM_examples.Lowe_et_al_2019.aerosol_code import AerosolBoreal, AerosolMarine, AerosolNascent\n", + "from PySDM_examples.Lowe_et_al_2019.constants_def import LOWE_CONSTS\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.initialisation.spectra import Sum\n", + "from PySDM.physics import si\n", + "\n", + "import numpy as np\n", + "import matplotlib\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T07:32:51.723624Z", + "start_time": "2025-06-15T07:32:12.926684Z" + } + }, + "outputs": [], + "source": [ + "output = {}\n", + "\n", + "FORMULAE = Formulae(constants=LOWE_CONSTS)\n", + "WATER_MOLAR_VOLUME = FORMULAE.constants.water_molar_volume\n", + "for aerosol in (\n", + " AerosolMarine(water_molar_volume=WATER_MOLAR_VOLUME), \n", + " AerosolBoreal(water_molar_volume=WATER_MOLAR_VOLUME), \n", + " AerosolNascent(water_molar_volume=WATER_MOLAR_VOLUME)\n", + " ):\n", + " for model in ('Constant', 'CompressedFilmOvadnevaite'):\n", + " key = f\"{aerosol.__class__.__name__}-{model}\"\n", + " settings = Settings(\n", + " dz=1*si.m, n_sd_per_mode=100, \n", + " model=model,\n", + " aerosol=aerosol,\n", + " )\n", + " simulation = Simulation(settings)\n", + " output[key] = simulation.run()\n", + " output[key]['color'] = aerosol.color\n", + " output[key]['Na_tot'] = Sum(\n", + " tuple(settings.aerosol.modes[i]['spectrum']\n", + " for i in range(len(settings.aerosol.modes)))).norm_factor" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T07:32:52.168894Z", + "start_time": "2025-06-15T07:32:51.735042Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b2652dcdf01049caad07ac627062377a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_2ab.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "877680c3932042c1beaf8dbcd3a0f0a4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_2c.pdf\"), 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", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from PySDM_examples.Lowe_et_al_2019.plot_helper import plot_contours\n", + "plot_contours(subplot_list, updraft_list, forg_list, output, actfrac=False, save=False)\n", + "plot_contours(subplot_list, updraft_list, forg_list, output, actfrac=True, save=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.9 ('pysdm')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b14f34a08619f4a218d80d7380beed3f0c712c89ff93e7183219752d640ed427" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3330489f7abdf3218711d25be0116f39eb903b74 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb @@ -0,0 +1,3687 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Lowe_et_al_2019/fig_s2.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Fig. S2 from Lowe et al. 2019 (Nature Comm.) \"_Key drivers of cloud response to surface-active organics_\" \n", + "https://doi.org/10.1038/s41467-019-12982-0" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:31:36.116404Z", + "start_time": "2024-12-13T12:31:36.111703Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:31:39.101800Z", + "start_time": "2024-12-13T12:31:36.119217Z" + } + }, + "outputs": [], + "source": [ + "from contextlib import contextmanager\n", + "\n", + "import numba\n", + "\n", + "from PySDM_examples.Lowe_et_al_2019 import Settings, Simulation\n", + "from PySDM_examples.Lowe_et_al_2019.aerosol_code import AerosolBoreal, AerosolMarine, AerosolNascent\n", + "from PySDM_examples.Lowe_et_al_2019.constants_def import LOWE_CONSTS\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.initialisation.spectra import Sum\n", + "from PySDM.physics import si, in_unit\n", + "\n", + "import numpy as np\n", + "from joblib import Parallel, delayed, parallel_backend\n", + "\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:31:39.227328Z", + "start_time": "2024-12-13T12:31:39.223339Z" + } + }, + "outputs": [], + "source": [ + "@contextmanager\n", + "def numba_threading_disabled():\n", + " numba_original_num_threads = numba.get_num_threads()\n", + " numba.set_num_threads(1)\n", + " try:\n", + " yield\n", + " finally:\n", + " numba.set_num_threads(numba_original_num_threads)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:31:39.282772Z", + "start_time": "2024-12-13T12:31:39.241180Z" + } + }, + "outputs": [], + "source": [ + "CI = 'CI' in os.environ\n", + "nRes = 10\n", + "updraft_list = np.linspace(0.2, 2.4, 2 if CI else nRes)\n", + "models = ('Constant', 'CompressedFilmOvadnevaite')\n", + "\n", + "FORMULAE = Formulae(\n", + " constants=LOWE_CONSTS,\n", + ")\n", + "WATER_MOLAR_VOLUME = FORMULAE.constants.Mv / FORMULAE.constants.rho_w\n", + "aerosols = (\n", + " AerosolMarine(water_molar_volume=WATER_MOLAR_VOLUME), \n", + " AerosolBoreal(water_molar_volume=WATER_MOLAR_VOLUME), \n", + " AerosolNascent(water_molar_volume=WATER_MOLAR_VOLUME)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:36:11.987889Z", + "start_time": "2024-12-13T12:31:39.295893Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tasks scheduled: 60\n", + "updrafts: [0.2 0.44444444 0.68888889 0.93333333 1.17777778 1.42222222\n", + " 1.66666667 1.91111111 2.15555556 2.4 ]\n" + ] + } + ], + "source": [ + "def compute(keyname, settings):\n", + " simulation = Simulation(settings)\n", + " out = simulation.run()\n", + " out['updraft'] = settings.w\n", + " out['org_fraction'] = settings.aerosol.modes[0]['f_org']\n", + " out['color'] = settings.aerosol.color\n", + " out['Na_tot'] = Sum(\n", + " tuple(settings.aerosol.modes[i]['spectrum']\n", + " for i in range(len(settings.aerosol.modes)))).norm_factor\n", + " return keyname, out\n", + "\n", + "print(f'tasks scheduled: {len(models) * len(aerosols) * len(updraft_list)}')\n", + "print('updrafts:', updraft_list)\n", + "\n", + "with numba_threading_disabled():\n", + " with parallel_backend(backend='threading', n_jobs=-2):\n", + " output = dict(Parallel(verbose=0)(\n", + " delayed(compute)(f\"w{w:.2f}_{aerosol.__class__.__name__}_{model}\", Settings(\n", + " dz = 10 * si.m if CI else 1 * si.m,\n", + " n_sd_per_mode = 10 if CI else 100,\n", + " model = model,\n", + " aerosol = aerosol,\n", + " w = w * si.m / si.s,\n", + " ))\n", + " for w in updraft_list\n", + " for model in models\n", + " for aerosol in aerosols\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:36:13.253574Z", + "start_time": "2024-12-13T12:36:12.074515Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f6e827fe0c2c4b1cb2affda4e6ce268b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./tmpf6g1c1hr.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = pyplot.subplots(1, 3, figsize=(10,3))\n", + "\n", + "for key, out_item in output.items():\n", + " ll = \"_\".join(key.split(\"_\", maxsplit=1)[1:]).replace(\"Aerosol\",\"\").replace(\"CompressedFilm\",\"\")\n", + " cc, disp = {\n", + " \"Marine\": (\"b\", -0.1),\n", + " \"Boreal\": (\"g\", -0.05),\n", + " \"Nascent\": (\"r\", 0.0),\n", + " }[ll.split(\"_\", maxsplit=1)[0]]\n", + " w, a, h = {\n", + " \"Constant\": (0.05, 0.4, \"\"),\n", + " \"Ovadnevaite\": (0.025, 0.6, \"//\"),\n", + " }[ll.split(\"_\", maxsplit=1)[1]]\n", + "\n", + " ax = axes[0]\n", + " label = ll if key.split(\"_\", maxsplit=1)[0] == \"w0.20\" else ''\n", + " common_kwargs = {\n", + " 'color': cc,\n", + " 'width': w,\n", + " 'alpha': a,\n", + " 'hatch': h\n", + " }\n", + "\n", + " ax.bar(\n", + " out_item[\"updraft\"] + disp,\n", + " in_unit(out_item[\"lwp\"], si.g / si.m**2),\n", + " **common_kwargs, label=label\n", + " )\n", + " if label != '':\n", + " ax.legend(loc = 0) #bbox_to_anchor=(5, 1))\n", + "\n", + " ax.set_xlabel(\"w [m s$^{-1}$]\")\n", + " ax.set_ylabel(\"LWP [g m$^{-2}$]\")\n", + " ax.set_ylim(27,35) # 27,33\n", + "\n", + " ax = axes[1]\n", + " ax.bar(out_item[\"updraft\"] + disp, out_item[\"tau\"], **common_kwargs)\n", + " ax.set_xlabel(\"w [m s$^{-1}$]\")\n", + " ax.set_ylabel(\"$\\\\tau$, optical depth\")\n", + " ax.set_ylim(0,13) # 0,11\n", + "\n", + " ax = axes[2]\n", + " ax.bar(out_item[\"updraft\"] + disp, out_item[\"albedo\"], **common_kwargs)\n", + " ax.set_xlabel(\"w [m s$^{-1}$]\")\n", + " ax.set_ylabel(\"$\\\\alpha$, cloud albedo\")\n", + " ax.set_ylim(0,0.5) # 0,0.45\n", + "\n", + "pyplot.tight_layout()\n", + "show_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-13T12:36:13.274167Z", + "start_time": "2024-12-13T12:36:13.271871Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b14f34a08619f4a218d80d7380beed3f0c712c89ff93e7183219752d640ed427" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/plot_helper.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/plot_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..bc877270a8ac52e2b473ec5362b86d9a903963e1 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/plot_helper.py @@ -0,0 +1,182 @@ +import numpy as np +from matplotlib import pyplot +from open_atmos_jupyter_utils import show_plot + + +def plot_profiles(subplot_list, updraft_list, forg_list, output, save=False): + _, axes = pyplot.subplots( + len(subplot_list), + len(updraft_list), + sharex=False, + sharey=True, + figsize=(3 * len(updraft_list), 4 * len(subplot_list)), + ) + + for k, subplot in enumerate(subplot_list): + for i, w in enumerate(updraft_list): + for _, Forg in enumerate(forg_list): + key = subplot + f"_w{w:.2f}_f{Forg:.2f}_" + var = "CDNC_cm3" + z = np.array(output[key + "CompressedFilmOvadnevaite"]["z"]) + CDNC_film = np.array(output[key + "CompressedFilmOvadnevaite"][var]) + CDNC_bulk = np.array(output[key + "Constant"][var]) + + cmap = pyplot.get_cmap("Spectral") + if len(subplot_list) > 1: + ax = axes[k, i] + else: + ax = axes[i] + + ax.plot(CDNC_film, z, "--", color=cmap(Forg)) + ax.plot(CDNC_bulk, z, "-", color=cmap(Forg), label=f"{Forg:.2f}") + + if i == 0: + ax.set_ylabel("Parcel displacement [m]") + ax.set_title(subplot, loc="left", weight="bold") + if i == len(updraft_list) - 1 and k == 0: + ax.legend(title="Forg", loc=2) + if k == 0: + ax.set_title(f"w = {w:.2f} m/s") + if k == len(subplot_list) - 1: + ax.set_xlabel("CDNC [cm$^{-3}$]") + if save: + show_plot("fig3_parcel_profiles.pdf") + + +def plot_contours( + subplot_list, updraft_list, forg_list, output, actfrac=False, save=False +): + _, axes = pyplot.subplots(2, 2, sharex=True, sharey=True, figsize=(14, 10)) + + for subplot in subplot_list: + dCDNC = np.zeros((len(updraft_list), len(forg_list))) + for i, w in enumerate(updraft_list): + for j, Forg in enumerate(forg_list): + key = subplot + f"_w{w:.2f}_f{Forg:.2f}_" + if actfrac: + var = "Activated Fraction" + Naer = 1.0 + CDNC_film = output[key + "CompressedFilmOvadnevaite"][var][0] * Naer + CDNC_bulk = output[key + "Constant"][var][0] * Naer + else: + var = "CDNC_cm3" + z = np.array(output[key + "CompressedFilmOvadnevaite"]["z"]) + wz = np.where(z == z[-1])[0][0] + CDNC_film = np.array( + output[key + "CompressedFilmOvadnevaite"][var] + )[wz] + CDNC_bulk = np.array(output[key + "Constant"][var])[wz] + dCDNC[i, j] = (CDNC_film - CDNC_bulk) / CDNC_bulk * 100.0 + + if subplot == "a": + ax = axes[0, 0] + ax.set_title( + "MA Accum. mode conc. N$_2 = 30$ cm$^{-3}$", fontsize=13, loc="right" + ) + ax.contour( + forg_list, + updraft_list, + dCDNC, + levels=[10, 25], + colors=["#1fa8f2", "#4287f5"], + linestyles=[":", "--"], + linewidths=4, + ) + p = ax.contourf( + forg_list, + updraft_list, + dCDNC, + cmap="Blues", + levels=np.linspace(0, 90, 11), + extend="both", + ) + if subplot == "b": + ax = axes[0, 1] + ax.set_title( + "MA Accum. mode conc. N$_2 = 135$ cm$^{-3}$", fontsize=13, loc="right" + ) + ax.contour( + forg_list, + updraft_list, + dCDNC, + levels=[10, 25], + colors=["#1fa8f2", "#4287f5"], + linestyles=[":", "--"], + linewidths=4, + ) + p = ax.contourf( + forg_list, + updraft_list, + dCDNC, + cmap="Blues", + levels=np.linspace(0, 90, 11), + extend="both", + ) + if subplot == "c": + ax = axes[1, 0] + ax.set_title( + "HYY Accum. mode conc. N$_2 = 160$ cm$^{-3}$", fontsize=13, loc="right" + ) + ax.contour( + forg_list, + updraft_list, + dCDNC, + levels=[10, 25], + colors=["#04c753", "#157d3f"], + linestyles=[":", "--"], + linewidths=4, + ) + p = ax.contourf( + forg_list, + updraft_list, + dCDNC, + cmap="Greens", + levels=np.linspace(0, 75, 11), + extend="both", + ) + if subplot == "d": + ax = axes[1, 1] + ax.set_title( + "HYY Accum. mode conc. N$_2 = 540$ cm$^{-3}$", fontsize=13, loc="right" + ) + ax.contour( + forg_list, + updraft_list, + dCDNC, + levels=[10, 25], + colors=["#04c753", "#157d3f"], + linestyles=[":", "--"], + linewidths=4, + ) + p = ax.contourf( + forg_list, + updraft_list, + dCDNC, + cmap="Greens", + levels=np.linspace(0, 75, 11), + extend="both", + ) + + ax.set_title(subplot, weight="bold", loc="left") + if subplot in ("c", "d"): + ax.set_xlabel("Organic mass fraction") + ax.set_yscale("log") + ax.set_yticks([0.1, 1, 10]) + ax.set_yticklabels(["0.1", "1", "10"]) + ax.set_xlim([0, 1]) + if subplot in ("a", "c"): + ax.set_ylabel("Updraft [ms$^{-1}$]") + pyplot.colorbar(p, ax=ax, label=r"$\Delta_{CDNC}$ [%]") + + pyplot.rcParams.update({"font.size": 15}) + if save: + if actfrac: + pyplot.savefig( + "fig_3_Scrit.png", dpi=200, facecolor="w", bbox_inches="tight" + ) + show_plot("fig_3_Scrit.pdf") + else: + pyplot.savefig( + "fig_3_rcrit.png", dpi=200, facecolor="w", bbox_inches="tight" + ) + show_plot("fig_3_rcrit.pdf") diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/settings.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..847788e15eeba01b5915fb6799f833af3c0709e3 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/settings.py @@ -0,0 +1,90 @@ +from functools import lru_cache + +import numpy as np +from PySDM_examples.Lowe_et_al_2019.constants_def import LOWE_CONSTS +from pystrict import strict + +from PySDM import Formulae +from PySDM.backends import CPU +from PySDM.initialisation.aerosol_composition import DryAerosolMixture +from PySDM.physics import si + + +@lru_cache +def backend(model): + return CPU( + formulae=Formulae( + surface_tension=model, + constants=LOWE_CONSTS, + diffusion_kinetics="LoweEtAl2019", + diffusion_thermics="LoweEtAl2019", + latent_heat_vapourisation="Lowe2019", + saturation_vapour_pressure="Lowe1977", + optical_albedo="Bohren1987", + optical_depth="Stephens1978", + ), + override_jit_flags={"parallel": False}, + ) + + +@strict +class Settings: + def __init__( + self, + dz: float, + n_sd_per_mode: int, + aerosol: DryAerosolMixture, + model: str, + w: float = 0.32 * si.m / si.s, + ): + assert model in ("Constant", "CompressedFilmOvadnevaite") + self.model = model + self.n_sd_per_mode = n_sd_per_mode + self.backend = backend(model) + self.formulae = self.backend.formulae + const = self.formulae.constants + self.aerosol = aerosol + + max_altitude = 200 * si.m + self.w = w + self.t_max = max_altitude / self.w + self.dt = dz / self.w + self.output_interval = 1 * self.dt + + self.p0 = 980 * si.mbar + self.T0 = 280 * si.K + pv0 = 0.999 * self.formulae.saturation_vapour_pressure.pvs_water(self.T0) + self.initial_water_vapour_mixing_ratio = const.eps * pv0 / (self.p0 - pv0) + + self.cloud_radius_range = (0.5 * si.micrometre, np.inf) + + self.mass_of_dry_air = 44 + + self.wet_radius_bins_edges = np.logspace( + np.log10(4 * si.um), np.log10(12 * si.um), 128 + 1, endpoint=True + ) + + @property + def rho0(self): + const = self.formulae.constants + rhod0 = ( + self.formulae.trivia.p_d(self.p0, self.initial_water_vapour_mixing_ratio) + / self.T0 + / const.Rd + ) + return rhod0 * (1 + self.initial_water_vapour_mixing_ratio) + + @property + def nt(self) -> int: + nt = self.t_max / self.dt + nt_int = round(nt) + np.testing.assert_almost_equal(nt, nt_int) + return nt_int + + @property + def steps_per_output_interval(self) -> int: + return int(self.output_interval / self.dt) + + @property + def output_steps(self) -> np.ndarray: + return np.arange(0, self.nt + 1, self.steps_per_output_interval) diff --git a/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/simulation.py b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..0fd85de9525a3e1e8a992904a36eb31a0da4b88c --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Lowe_et_al_2019/simulation.py @@ -0,0 +1,141 @@ +import numpy as np +from PySDM_examples.utils import BasicSimulation + +import PySDM.products as PySDM_products +from PySDM import Builder +from PySDM.dynamics import AmbientThermodynamics, Condensation +from PySDM.environments import Parcel +from PySDM.initialisation.hygroscopic_equilibrium import equilibrate_wet_radii +from PySDM.initialisation.spectra import Sum +from PySDM.initialisation.sampling.spectral_sampling import ConstantMultiplicity + + +class Simulation(BasicSimulation): + def __init__(self, settings, products=None): + n_sd = settings.n_sd_per_mode * len(settings.aerosol.modes) + builder = Builder( + n_sd=n_sd, + backend=settings.backend, + environment=Parcel( + dt=settings.dt, + mass_of_dry_air=settings.mass_of_dry_air, + p0=settings.p0, + initial_water_vapour_mixing_ratio=settings.initial_water_vapour_mixing_ratio, + T0=settings.T0, + w=settings.w, + ), + ) + + attributes = { + "dry volume": np.empty(0), + "dry volume organic": np.empty(0), + "kappa times dry volume": np.empty(0), + "multiplicity": np.ndarray(0), + } + initial_volume = settings.mass_of_dry_air / settings.rho0 + for mode in settings.aerosol.modes: + r_dry, n_in_dv = ConstantMultiplicity( + spectrum=mode["spectrum"] + ).sample_deterministic(settings.n_sd_per_mode) + v_dry = settings.formulae.trivia.volume(radius=r_dry) + attributes["multiplicity"] = np.append( + attributes["multiplicity"], n_in_dv * initial_volume + ) + attributes["dry volume"] = np.append(attributes["dry volume"], v_dry) + attributes["dry volume organic"] = np.append( + attributes["dry volume organic"], mode["f_org"] * v_dry + ) + attributes["kappa times dry volume"] = np.append( + attributes["kappa times dry volume"], + v_dry * mode["kappa"][settings.model], + ) + for attribute in attributes.values(): + assert attribute.shape[0] == n_sd + + np.testing.assert_approx_equal( + np.sum(attributes["multiplicity"]) / initial_volume, + Sum( + tuple( + settings.aerosol.modes[i]["spectrum"] + for i in range(len(settings.aerosol.modes)) + ) + ).norm_factor, + significant=5, + ) + r_wet = equilibrate_wet_radii( + r_dry=settings.formulae.trivia.radius(volume=attributes["dry volume"]), + environment=builder.particulator.environment, + kappa_times_dry_volume=attributes["kappa times dry volume"], + f_org=attributes["dry volume organic"] / attributes["dry volume"], + ) + attributes["volume"] = settings.formulae.trivia.volume(radius=r_wet) + + if settings.model == "Constant": + del attributes["dry volume organic"] + + builder.add_dynamic(AmbientThermodynamics()) + builder.add_dynamic(Condensation()) + + products = products or ( + PySDM_products.ParcelDisplacement(name="z"), + PySDM_products.Time(name="t"), + PySDM_products.PeakSaturation(name="S_max"), + PySDM_products.AmbientRelativeHumidity(name="RH"), + PySDM_products.ActivatedParticleConcentration( + name="CDNC_cm3", + unit="cm^-3", + count_activated=True, + count_unactivated=False, + ), + PySDM_products.ParticleSizeSpectrumPerVolume( + radius_bins_edges=settings.wet_radius_bins_edges + ), + PySDM_products.ActivableFraction(name="Activated Fraction"), + PySDM_products.WaterMixingRatio(), + PySDM_products.AmbientDryAirDensity(name="rhod"), + PySDM_products.ActivatedEffectiveRadius( + name="reff", count_activated=True, count_unactivated=False + ), + PySDM_products.ParcelLiquidWaterPath( + name="lwp", count_activated=True, count_unactivated=False + ), + PySDM_products.CloudOpticalDepth(name="tau"), + PySDM_products.CloudAlbedo(name="albedo"), + ) + + particulator = builder.build(attributes=attributes, products=products) + self.settings = settings + super().__init__(particulator=particulator) + + def _save_scalars(self, output): + for k, v in self.particulator.products.items(): + if len(v.shape) > 1 or k in ("lwp", "Activated Fraction", "tau", "albedo"): + continue + value = v.get() + if isinstance(value, np.ndarray) and value.size == 1: + value = value[0] + output[k].append(value) + + def _save_final_timestep_products(self, output): + output["spectrum"] = self.particulator.products[ + "particle size spectrum per volume" + ].get() + + for name, args_call in { + "Activated Fraction": lambda: {"S_max": np.nanmax(output["S_max"])}, + "lwp": lambda: {}, + "tau": lambda: { + "effective_radius": output["reff"][-1], + "liquid_water_path": output["lwp"][0], + }, + "albedo": lambda: {"optical_depth": output["tau"]}, + }.items(): + output[name] = self.particulator.products[name].get(**args_call()) + + def run(self): + output = {k: [] for k in self.particulator.products} + for step in self.settings.output_steps: + self.particulator.run(step - self.particulator.n_steps) + self._save_scalars(output) + self._save_final_timestep_products(output) + return output diff --git a/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/__init__.py b/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dd4ffe2934064c0598833becbf558d9359e15a6b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/__init__.py @@ -0,0 +1,7 @@ +# pylint: disable=invalid-name +""" +Fig. 1 from [Matsushima et al. 2023 (GMD)](https://doi.org/10.5194/gmd-16-6211-2023) + +figure_1.ipynb: +.. include:: ./figure_1.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb b/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..63d62f8a165b217587038d9281184acc025152e2 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb @@ -0,0 +1,16028 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1790321c-2424-4ebd-9e26-14637e1ec29f", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Matsushima_et_al_2023/figure_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "6bc29eca-b273-4418-bedc-4787635bc19d", + "metadata": {}, + "source": [ + "Fig. 1 from [Matsushima 2023 (GMD)](https://doi.org/10.5194/gmd-16-6211-2023) depicting alpha-sampling in PySDM" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3642a633-e611-4001-8bb0-cbe95e12f232", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c87ff404-3a80-4d0c-a955-3bc7d0a4d31e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from PySDM.physics import si, in_unit\n", + "from PySDM.initialisation.sampling.spectral_sampling import AlphaSampling\n", + "from PySDM.initialisation import spectra\n", + "\n", + "from matplotlib import pyplot, colors\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "10da5008-8e20-43a1-8d11-0ade29c9a492", + "metadata": {}, + "outputs": [], + "source": [ + "n_sd = 2**16\n", + "spectrum = spectra.Sum((\n", + " spectra.Lognormal(\n", + " norm_factor=90 / si.cm**3,\n", + " m_mode=0.03 * si.um,\n", + " s_geom=1.28,\n", + " ),\n", + " spectra.Lognormal(\n", + " norm_factor=15 / si.cm**3,\n", + " m_mode=0.14 * si.um,\n", + " s_geom=1.75,\n", + " ),\n", + "))\n", + "\n", + "samplings = {\n", + " alpha: AlphaSampling(\n", + " spectrum,\n", + " alpha=alpha,\n", + " interp_points=100000,\n", + " size_range=(10 * si.nm, 5000 * si.nm),\n", + " dist_1_inv=lambda y, size_range: np.exp((np.log(size_range[1]) - np.log(size_range[0])) * y + np.log(size_range[0]))\n", + " ) for alpha in np.linspace(0, 1, 11) \n", + "}\n", + "\n", + "xas, yas = {}, {}\n", + "for alpha, sampling in samplings.items():\n", + " xas[alpha], yas[alpha] = sampling.sample_deterministic(n_sd)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "858d3450-6107-4d33-b88b-daa5402e4ac7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "daeba983b9ce4c04b04221c099f18313", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_1.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3771059a2ed24463929c89934681bf2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_2.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def trans_x(TC):\n", + " x = 1e6 / (TC + const.T0)**2\n", + " return x\n", + "\n", + "def trans_x_inv(x):\n", + " TK = 1 / np.sqrt(x / 1e6)\n", + " return TK - const.T0\n", + "\n", + "def trans_y(alpha):\n", + " y = 1e3 * np.log10(np.where(alpha > 0, alpha, np.nan))\n", + " return y\n", + " \n", + "def trans_y_inv(y):\n", + " alpha = 10**(y / 1e3)\n", + " return alpha\n", + "\n", + "line_1_TC = np.linspace(10*si.K, -20*si.K, N_POINTS)\n", + "pyplot.plot(trans_x(line_1_TC), trans_y(alphas.alpha_l_2H(line_1_TC + const.T0)))\n", + "\n", + "line_2_TC = np.linspace(0*si.K, -40*si.K, N_POINTS)\n", + "pyplot.plot(trans_x(line_2_TC), trans_y(alphas.alpha_i_2H(line_2_TC + const.T0)))\n", + "\n", + "pyplot.grid()\n", + "pyplot.xlabel(\"$10^6/T^2$\")\n", + "pyplot.ylabel(\"$10^3 logα$\")\n", + "pyplot.xlim(12, 19)\n", + "pyplot.ylim(35, 88)\n", + "\n", + "twinx = pyplot.gca().secondary_yaxis('right', functions=(trans_y_inv, trans_y))\n", + "twinx.set_ylabel(\"α\")\n", + "\n", + "twiny = pyplot.gca().secondary_xaxis('top', functions=(trans_x_inv, trans_x))\n", + "twiny.set_xlabel(\"T [C]\")\n", + "\n", + "show_plot(\"fig_2.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/__init__.py b/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..961f5b25ee3bf99c645f4b816e2b0efb5ff5faa6 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/__init__.py @@ -0,0 +1,8 @@ +# pylint: disable=invalid-name +""" +isotopic adjustment time plot following +[Friedman et al. 1962 (JGR)](https://doi.org/10.1029/JZ067i007p02761) + +fig_19.ipynb: +.. include:: ./fig_19.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb b/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2ee73bba0f391922c77cbf7acab29ce2fabb9447 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb @@ -0,0 +1,2958 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "06b74a1b-fc97-497b-ad08-7e253c6a7378", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Miyake_et_al_1968/fig_19.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "634d2789-0aac-40d3-bf6c-0eb2f32d42cd", + "metadata": {}, + "source": [ + "#### based on Fig. 19 from Miyake et al. 1968 \"_An Isotopic Study on Meteoric Precipitation_\"\n", + "https://doi.org/10.2467/mripapers1950.19.2_243\n", + "\n", + "Notes:\n", + "- extended to cover Oxygen-17\n", + "- different ventilation coefficients depicted (neglect, Froessling, Prupparcher & Rasmussen)\n", + "- different diffusivity ratios used for different isotopes (with D_heavy/D approximated with D_heavy/D_light), visible but insignificant differences" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "xyz", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:05:16.231225Z", + "start_time": "2025-06-27T19:05:16.228134Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2eb77505-705a-4c94-aec3-d371bde8d375", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:05:17.905536Z", + "start_time": "2025-06-27T19:05:16.315608Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si, in_unit\n", + "from PySDM.physics.constants_defaults import T0\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5411525a-0e07-4ee5-89bc-ff2363f65359", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:05:17.920695Z", + "start_time": "2025-06-27T19:05:17.918597Z" + } + }, + "outputs": [], + "source": [ + "VENTILATION_VARIANTS = ('Neglect', 'Froessling1938', 'PruppacherAndRasmussen1979')\n", + "AIR_DENSITY = 1 * si.kg / si.m**3\n", + "ANY_NUMBER = 44." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e7a49798-b6b8-4ab4-8382-f1bc25f13f0c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:05:23.130278Z", + "start_time": "2025-06-27T19:05:17.928495Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "91762d0e9bf74cea9e177739e3c0067f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_19.pdf\"), HTML(value=\" int: + return int(self.simulation_time / self.dt) # TODO #413 + + @property + def steps_per_output_interval(self) -> int: + return int(self.output_interval / self.dt) + + @property + def n_spin_up(self) -> int: + return int(self.spin_up_time / self.dt) + + @property + def output_steps(self) -> np.ndarray: + return np.arange(0, self.n_steps + 1, self.steps_per_output_interval) + + @property + def n_sd(self): + return self.grid[0] * self.grid[1] * self.n_sd_per_gridbox + + @property + def initial_vapour_mixing_ratio_profile(self): + return np.full(self.grid[-1], self.initial_water_vapour_mixing_ratio) + + @property + def initial_dry_potential_temperature_profile(self): + return np.full( + self.grid[-1], + self.formulae.state_variable_triplet.th_dry( + self.th_std0, self.initial_water_vapour_mixing_ratio + ), + ) diff --git a/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..543c2687f3c800572120d78eab21fb0edb55a003 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb @@ -0,0 +1,3563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Morrison_and_Grabowski_2007/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Fig. 1 from Morrison & Grabowski 2007 (J. Atmos. Sci. 64) \"_Comparison of Bulk and Bin Warm-Rain Microphysics Models Using a Kinematic Framework_\"\n", + "https://doi.org/10.1175/JAS3980" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:38:54.741681Z", + "start_time": "2024-02-01T07:38:54.737942Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:39:30.127698Z", + "start_time": "2024-02-01T07:39:30.121597Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from PySDM_examples.Morrison_and_Grabowski_2007.strato_cumulus import StratoCumulus\n", + "from PySDM_examples.utils.kinematic_2d.fields import nondivergent_vector_field_2d, z_vec_coord, x_vec_coord\n", + "from PySDM.physics import si\n", + "from PySDM import Formulae\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:38:57.757865Z", + "start_time": "2024-02-01T07:38:56.824675Z" + } + }, + "outputs": [], + "source": [ + "settings = StratoCumulus(Formulae(), rhod_w_max=1 * si.m/si.s * si.kg/si.m**3)\n", + "settings.grid = (64, 64)\n", + "settings.size = (2*si.km, 1*si.km)\n", + "settings.dt = 1 * si.s\n", + "\n", + "rho_times_courant = nondivergent_vector_field_2d(\n", + " grid=settings.grid,\n", + " size=settings.size,\n", + " dt=settings.dt,\n", + " stream_function=settings.stream_function,\n", + " t=np.nan\n", + ")\n", + "dz = settings.size[-1] / settings.grid[-1]\n", + "dx = settings.size[0] / settings.grid[0]\n", + "\n", + "levels = np.linspace(-1.5, 1.5, 7) * si.m/si.s" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:38:58.020987Z", + "start_time": "2024-02-01T07:38:57.760239Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ca56ec86c3e84df4871afae6679cf9f8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./tmp95yc0a4e.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z_vec_coords = tuple(coord * settings.size[i]/si.km for i, coord in enumerate(z_vec_coord(settings.grid)))\n", + "pyplot.clabel(pyplot.contour(\n", + " *z_vec_coords,\n", + " rho_times_courant[-1] * dz / settings.dt / settings.rhod_of_zZ(z_vec_coords[-1]/settings.size[-1]),\n", + " levels=levels\n", + "))\n", + "pyplot.grid()\n", + "pyplot.title('vertical velocity [m/s]')\n", + "pyplot.xlabel('X [km]')\n", + "pyplot.ylabel('Z [km]')\n", + "show_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:38:58.241556Z", + "start_time": "2024-02-01T07:38:58.020663Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d4d72150fd01461191986743cc2d665d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./tmpswbu1_2g.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_vec_coords = tuple(coord * settings.size[i]/si.km for i, coord in enumerate(x_vec_coord(settings.grid)))\n", + "pyplot.clabel(pyplot.contour(\n", + " *x_vec_coords,\n", + " rho_times_courant[0] * dx / settings.dt / settings.rhod_of_zZ(x_vec_coords[0]/settings.size[0]),\n", + " levels=levels\n", + "))\n", + "pyplot.title('horizontal velocity [m/s]')\n", + "pyplot.grid()\n", + "pyplot.xlabel('X [km]')\n", + "pyplot.ylabel('Z [km]')\n", + "show_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/strato_cumulus.py b/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/strato_cumulus.py new file mode 100644 index 0000000000000000000000000000000000000000..bef5a5c89ba054f9177ec7c3812c3cd28146a969 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Morrison_and_Grabowski_2007/strato_cumulus.py @@ -0,0 +1,34 @@ +import numpy as np +from PySDM_examples.Morrison_and_Grabowski_2007.common import Common + +from PySDM.physics import si + + +class StratoCumulus(Common): + def __init__(self, formulae, rhod_w_max: float): + super().__init__(formulae) + self.th_std0 = 289 * si.kelvins + self.initial_water_vapour_mixing_ratio = 7.5 * si.grams / si.kilogram + self.p0 = 1015 * si.hectopascals + self.rhod_w_max = rhod_w_max + + def stream_function(self, xX, zZ, _): + X = self.size[0] + return ( + -self.rhod_w_max * X / np.pi * np.sin(np.pi * zZ) * np.cos(2 * np.pi * xX) + ) + + def rhod_of_zZ(self, zZ): + p = getattr( + self.formulae.hydrostatics, + "p_of_z_assuming_const_th_and_initial_water_vapour_mixing_ratio", + )( + self.p0, + self.th_std0, + self.initial_water_vapour_mixing_ratio, + z=zZ * self.size[-1], + ) + rhod = self.formulae.state_variable_triplet.rho_d( + p, self.initial_water_vapour_mixing_ratio, self.th_std0 + ) + return rhod diff --git a/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/__init__.py b/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..efcbac81f40f200ab519cd4de23d3596922acc73 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/__init__.py @@ -0,0 +1,9 @@ +""" +based on [Niedermeier et al. 2014](https://doi.org/10.1002/2013GL058684) + +fig_2.ipynb: +.. include:: ./fig_2.ipynb.badges.md +""" + +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb b/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..28b6f3a99237ff32a7ea703960ff20b140e40f3f --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb @@ -0,0 +1,2396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9c727d1b", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Niedermeier_et_al_2014/fig_2.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "2ac71b75", + "metadata": {}, + "source": [ + "#### based on Fig. 2 from Niedermeier et al. 2014 (Geophys. Res. Lett. 41) \"_A computationally efficient description of heterogeneous freezing: A simplified version of the Soccer ball model_\" \n", + "https://doi.org/10.1002/2013GL058684" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "36425cff812f5c82", + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-03T12:14:00.329145Z", + "start_time": "2025-07-03T12:14:00.323216Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7d32494f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-03T12:14:21.853024Z", + "start_time": "2025-07-03T12:14:01.215834Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si\n", + "from PySDM_examples.Niedermeier_et_al_2014 import Settings, Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c38109d8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-03T12:15:57.331889Z", + "start_time": "2025-07-03T12:14:21.887996Z" + } + }, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " heterogeneous_ice_nucleation_rate='ABIFM',\n", + " particle_shape_and_density=\"MixedPhaseSpheres\",\n", + " constants = {\n", + " 'ABIFM_M': 70,\n", + " 'ABIFM_C': -10\n", + " }\n", + ")\n", + "\n", + "output = []\n", + "for T0 in (formulae.trivia.C2K(TC) for TC in (-19, -23)):\n", + " settings = Settings(\n", + " in_sampling_n=1800,\n", + " initial_temperature=T0 * si.K,\n", + " timestep=1 * si.s,\n", + " formulae=formulae\n", + " )\n", + " simulation = Simulation(settings)\n", + " output.append(simulation.run())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3df1ee0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-03T12:17:49.612143Z", + "start_time": "2025-07-03T12:17:49.102262Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b873cf4a16b4cbb9aff062f4ea6f808", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_2.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2bba3ca1a8074667a2eb25fd66c24afb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig_3.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, axs = pyplot.subplots(2, 2, figsize=(9, 7), tight_layout=True)\n", + "\n", + "axs[0, 0].set_title('A')\n", + "axs[0, 1].set_title('B')\n", + "axs[0, 0].sharex(axs[1, 0])\n", + "axs[0, 1].sharex(axs[1, 1])\n", + "\n", + "alpha_axl = axs[0, 0]\n", + "alpha_axr = alpha_axl.twinx()\n", + " \n", + "plots = []\n", + "for color, eps_key, ax in (\n", + " ('blue', '18O', alpha_axl), \n", + " ('red', '2H', alpha_axr), \n", + " ('purple', '17O', alpha_axl)\n", + "):\n", + " plots += ax.plot(\n", + " F,\n", + " in_unit(enrichments[eps_key], const.PER_MILLE),\n", + " label=eps_key,\n", + " color=color\n", + " )\n", + " if ax.get_ylabel() == \"\":\n", + " ax.set_ylabel(\"E [‰]\")\n", + " ax.set_ylabel(ax.get_ylabel() + \" \" + eps_key)\n", + "pyplot.legend(plots, [l.get_label() for l in plots])\n", + "pyplot.xlim(*F[[0, -1]])\n", + "alpha_axl.grid(None)\n", + "alpha_axl.set_ylim(0, 35)\n", + "alpha_axr.set_ylim(0, 100)\n", + "\n", + "excess_axl = axs[1, 0]\n", + "excess_axr = excess_axl.twinx()\n", + "plots = []\n", + "for color, fun, args, mult, label, ax in (\n", + " ('green', 'excess_d', (deltas['2H'], deltas['18O']), const.PER_MILLE, \"d-excess [‰]\", excess_axl),\n", + " ('orange', 'excess_17O', (deltas['17O'], deltas['18O']), const.PER_MEG, \"$^{17}O$-excess [per meg]\", excess_axr),\n", + "):\n", + " plots += ax.plot(\n", + " F,\n", + " in_unit(getattr(excess, fun)(*args), mult),\n", + " color=color,\n", + " label=label\n", + " )\n", + " ax.set_ylabel(label, color=color)\n", + "pyplot.legend(plots, [l.get_label() for l in plots])\n", + "excess_axl.grid(None)\n", + "excess_axl.set_ylim(-68, 15)\n", + "excess_axr.set_ylim(-100, 42)\n", + "\n", + "axs[1, 0].set_xlabel(\"F [1]\")\n", + "axs[1, 0].set_xticks(np.linspace(*F[[0,-1]], 8))\n", + "\n", + "\n", + "axs[0, 1].set_ylabel(\"$δ^2H$ [‰]\")\n", + "axs[0, 1].grid()\n", + "axs[0, 1].set_ylim(-65, 60)\n", + "axs[0, 1].set_xlim(-10, 18)\n", + "axs[0, 1].set_xticks(np.linspace(-10, 15, 6))\n", + "\n", + "x = np.linspace(-10e-3, 10e-3, 100)\n", + "axs[0, 1].plot(\n", + " in_unit(x, const.PER_MILLE),\n", + " in_unit(x * const.CRAIG_1961_SLOPE_COEFF + const.CRAIG_1961_INTERCEPT_COEFF, const.PER_MILLE),\n", + " color='black',\n", + " linestyle='--',\n", + " label=\"GMWL\"\n", + ")\n", + "axs[0, 1].plot(\n", + " in_unit(deltas[\"18O\"], const.PER_MILLE),\n", + " in_unit(deltas[\"2H\"], const.PER_MILLE),\n", + " color='black'\n", + ")\n", + "axs[0, 1].legend()\n", + "\n", + "excess_axl = axs[1, 1]\n", + "excess_axr = excess_axl.twinx()\n", + "plots = []\n", + "for color, fun, args, mult, label, ax in (\n", + " ('green', 'excess_d', (deltas['2H'], deltas['18O']), const.PER_MILLE, \"d-excess [‰]\", excess_axl),\n", + " ('orange', 'excess_17O', (deltas['17O'], deltas['18O']), const.PER_MEG, \"$^{17}O$-excess [per meg]\", excess_axr),\n", + "):\n", + " plots += ax.plot(\n", + " in_unit(deltas[\"18O\"], const.PER_MILLE),\n", + " in_unit(getattr(excess, fun)(*args), mult),\n", + " color=color,\n", + " label=label\n", + " )\n", + " ax.set_ylabel(label, color=color)\n", + "pyplot.legend(plots, [l.get_label() for l in plots])\n", + "excess_axl.grid(None)\n", + "excess_axl.set_ylim(-100, 20)\n", + "excess_axr.set_ylim(-120, 40)\n", + "\n", + "axs[1, 1].set_xlabel(\"$δ^{18}O$ [‰]\")\n", + "\n", + "show_plot(\"fig_3.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbe8dfbe-6369-42ae-b8d4-26e24c37e7f5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb b/PySDM/source/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4af8480ab1e2bd6d34bbcc4787e5bccd362c2853 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb @@ -0,0 +1,2009 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d310c75", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Pierchala_et_al_2022/fig_4.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "014768e7", + "metadata": {}, + "source": [ + "### based on Fig. 4 from Pierchala et al. 2020 (Geochim. Cosmochim. Acta) \"_Quantification the diffusion-induced fractionation of $^{1}H_{2} ^{17}O$ isotopologue in air accompanying the process of water evaporation_\" (https://doi.org/10.1016/j.gca.2022.01.020)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "31dc796b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:42:09.762313Z", + "start_time": "2024-02-01T07:42:09.758552Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "81882cc4", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:42:39.780234Z", + "start_time": "2024-02-01T07:42:39.776716Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from PySDM import Formulae\n", + "from PySDM.physics import in_unit\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b4cfc727", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:42:11.407870Z", + "start_time": "2024-02-01T07:42:11.388196Z" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Pierchala_et_al_2022.commons import deltas_0_SMOW, TABLE_1, TABLE_2\n", + "\n", + "formulae = Formulae(\n", + " isotope_equilibrium_fractionation_factors='BarkanAndLuz2005+HoritaAndWesolowski1994',\n", + " isotope_meteoric_water_line='Dansgaard1964+BarkanAndLuz2007',\n", + " isotope_ratio_evolution='RayleighDistillation',\n", + " isotope_kinetic_fractionation_factors='CraigGordon',\n", + ")\n", + "const = formulae.constants\n", + "\n", + "F = np.linspace(1, .3, 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2c443b9d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:42:12.909082Z", + "start_time": "2024-02-01T07:42:11.412698Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3722f4f5658c43989add6745f7f54692", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_4.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "048cfc9a488644618dd0949c256c127f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./ventilation_…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature = 273.15 *si.K\n", + "radii = np.linspace(0, 2.5, 100) * si.mm\n", + "fig, ax = pyplot.subplots(1)\n", + "sqrt_re_times_cbrt_sc, vent_coeff = f(radii, temperature)\n", + "\n", + "isotopes = (\n", + " \"2H\",\n", + " \"18O\",\n", + " \"17O\"\n", + ")\n", + "ax.plot(sqrt_re_times_cbrt_sc, vent_coeff, label=\"H2O\")\n", + "for iso in isotopes:\n", + " ax.plot(\n", + " sqrt_re_times_cbrt_sc,\n", + " f_heavy(vent_coeff, temperature, isotope_name=iso),\n", + " label=f\"{iso} - substituted water\"\n", + " )\n", + "ax.set_title(\"\")\n", + "ax.set_xlabel(\"Sc$^{1/3}$Re$^{1/2}$ [1]\")\n", + "ax.set_ylabel(\"Ventilation coefficient [1]\")\n", + "\n", + "ax.legend()\n", + "ax2 = ax.secondary_xaxis(\"top\", functions=(\n", + " interp1d(sqrt_re_times_cbrt_sc,in_unit(radii, si.mm), bounds_error=False, fill_value=\"extrapolate\"),\n", + " interp1d(in_unit(radii, si.mm), sqrt_re_times_cbrt_sc, bounds_error=False, fill_value=\"extrapolate\")\n", + "))\n", + "ax2.set_ticks(np.linspace(0, 5, 11))\n", + "ax2.set_xlabel(\"equivalent drop radius [mm]\")\n", + "ax.grid()\n", + "show_plot(\"ventilation_coefficient_for_isotopes.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6e88d3799948f9d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-08T01:13:45.876630Z", + "start_time": "2025-06-08T01:13:45.556739Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "404285692919415aa0521ad313917d47", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./ventila…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 10\n", + "_, ax = pyplot.subplots(1)\n", + "ax.plot(sqrt_re_times_cbrt_sc[-n:], vent_coeff[-n:], label=\"H2O\")\n", + "for isotope in isotopes:\n", + " ax.plot(\n", + " sqrt_re_times_cbrt_sc[-n:],\n", + " f_heavy(vent_coeff[-n:], temperature, isotope_name=isotope),\n", + " label=f\"{isotope} - substituted water\"\n", + " )\n", + "ax.set_title(\"\")\n", + "ax.set_xlabel(\"Sc$^{1/3}$Re$^{1/2}$ [1]\")\n", + "ax.set_ylabel(\"Ventilation coefficient [1]\")\n", + "\n", + "ax.legend()\n", + "ax2 = ax.secondary_xaxis(\"top\", functions=(\n", + " interp1d(sqrt_re_times_cbrt_sc,in_unit(radii, si.mm), bounds_error=False, fill_value=\"extrapolate\"),\n", + " interp1d(in_unit(radii, si.mm), sqrt_re_times_cbrt_sc, bounds_error=False, fill_value=\"extrapolate\")\n", + "))\n", + "ax2.set_ticks(np.linspace(0, 5, 11))\n", + "ax2.set_xlabel(\"equivalent drop radius [mm]\")\n", + "ax.grid()\n", + "show_plot(\"ventilation_coefficient_for_isotopes_zoom.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a5cd5c8032c9c8e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-08T01:13:45.892254Z", + "start_time": "2025-06-08T01:13:45.890733Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3c0f563-0fd1-41c0-9fb1-618a49b1f0eb", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-08T01:13:45.901772Z", + "start_time": "2025-06-08T01:13:45.900092Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Pyrcel/__init__.py b/PySDM/source/examples/PySDM_examples/Pyrcel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b570ce34c008f7b07fdd0eccbfca3779409e8b4a --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pyrcel/__init__.py @@ -0,0 +1,10 @@ +# pylint: disable=invalid-name +""" +parcel-model example based on the test case from +[Pyrcel package docs](https://pyrcel.readthedocs.io/) + +example_basic_run.ipynb: +.. include:: ./example_basic_run.ipynb.badges.md +""" +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb b/PySDM/source/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3a582a1c497efa22d704f7b53c2b8c14b47fd564 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb @@ -0,0 +1,4168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Pyrcel/example_basic_run.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Example Figure from Pyrcel code documentation https://pyrcel.readthedocs.io/en/latest/examples/basic_run.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:09:30.494539Z", + "start_time": "2025-06-15T08:09:30.484507Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:09:34.080888Z", + "start_time": "2025-06-15T08:09:30.501765Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from scipy.ndimage import uniform_filter1d\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si\n", + "from PySDM.initialisation.spectra import Lognormal\n", + "from PySDM.products import (\n", + " ParcelDisplacement, AmbientTemperature, AmbientRelativeHumidity,\n", + " ParticleSizeSpectrumPerVolume, ParticleVolumeVersusRadiusLogarithmSpectrum\n", + ")\n", + "\n", + "from PySDM_examples.Pyrcel import Settings, Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:09:39.109034Z", + "start_time": "2025-06-15T08:09:34.191376Z" + } + }, + "outputs": [], + "source": [ + "settings = Settings(\n", + " dz = 1 * si.m,\n", + " n_sd_per_mode = (5, 5),\n", + " aerosol_modes_by_kappa = {\n", + " .54: Lognormal(\n", + " norm_factor=850 / si.cm ** 3,\n", + " m_mode=15 * si.nm,\n", + " s_geom=1.6\n", + " ),\n", + " 1.2: Lognormal(\n", + " norm_factor=10 / si.cm ** 3,\n", + " m_mode=850 * si.nm,\n", + " s_geom=1.2\n", + " )\n", + " },\n", + " vertical_velocity = 1.0 * si.m / si.s,\n", + " initial_pressure = 775 * si.mbar,\n", + " initial_temperature = 274 * si.K,\n", + " initial_relative_humidity = .98,\n", + " displacement = 250 * si.m,\n", + " formulae = Formulae(constants={'MAC': .3})\n", + ")\n", + "\n", + "dry_radius_bin_edges = np.logspace(\n", + " np.log10(1e-3 * si.um),\n", + " np.log10(5e0 * si.um),\n", + " 33, endpoint=False\n", + ")\n", + "\n", + "simulation = Simulation(settings, products=(\n", + " ParcelDisplacement(\n", + " name='z'),\n", + " AmbientRelativeHumidity(\n", + " name='S_max_percent', unit='%', var='RH'),\n", + " AmbientTemperature(\n", + " name='T'),\n", + " ParticleSizeSpectrumPerVolume(\n", + " name='dry:dN/dR', radius_bins_edges=dry_radius_bin_edges, dry=True),\n", + " ParticleVolumeVersusRadiusLogarithmSpectrum(\n", + " name='dry:dV/dlnR', radius_bins_edges=dry_radius_bin_edges, dry=True),\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:09:50.439522Z", + "start_time": "2025-06-15T08:09:39.121807Z" + } + }, + "outputs": [], + "source": [ + "output = simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:10:33.947302Z", + "start_time": "2025-06-15T08:10:33.222745Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2c65384c54db4e3e9bdb53680f672ded", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./supersaturation.pdf\"), HT…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(1, 2, sharey=True, figsize=(10, 5))\n", + "\n", + "axS = axs[0]\n", + "axS.plot(np.asarray(output['products']['S_max_percent'])-100, output['products']['z'], color='black')\n", + "axS.set_ylabel('Displacement [m]')\n", + "axS.set_xlabel('Supersaturation [%]')\n", + "axS.set_xlim(0, 0.7)\n", + "axS.set_ylim(0, 250)\n", + "axS.text(0.3, 52, f\"max S = {np.nanmax(output['products']['S_max_percent'])-100:.2f}%\")\n", + "axS.grid()\n", + "\n", + "axT = axS.twiny()\n", + "axT.xaxis.label.set_color('red')\n", + "axT.tick_params(axis='x', colors='red')\n", + "axT.plot(output['products']['T'], output['products']['z'], color='red')\n", + "rng = (270, 274)\n", + "axT.set_xlim(*rng)\n", + "axT.set_xticks(np.linspace(*rng, num=5))\n", + "axT.set_xlabel('Temperature [K]')\n", + "\n", + "axR = axs[1]\n", + "axR.set_xscale('log')\n", + "axR.set_xlim(1e-2, 1e1)\n", + "for drop_id, volume in enumerate(output['attributes']['volume']):\n", + " axR.plot(\n", + " settings.formulae.trivia.radius(volume=np.asarray(volume)) / si.um,\n", + " output['products']['z'],\n", + " color='magenta' if drop_id < settings.n_sd_per_mode[0] else 'blue',\n", + " label='sulfate' if drop_id == 0 else 'sea salt' if drop_id == settings.n_sd_per_mode[0] else ''\n", + " )\n", + "axR.legend(loc='upper right')\n", + "axR.set_xlabel('Droplet radius [μm]')\n", + "\n", + "show_plot(\"supersaturation.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-15T08:09:50.808752Z", + "start_time": "2024-02-01T18:50:47.502563Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "86be8c039f3c41409ee42fcc1aaf5210", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./size_dist.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = pyplot.subplots(1, 2, figsize=(10, 5), sharex=True)\n", + "for i, name in enumerate([\"dry:dN/dR\",\"dry:dV/dlnR\",]):\n", + " spec = output['products'][name][0]\n", + " if name.endswith(\"/dR\"):\n", + " spec /= np.diff(np.log(dry_radius_bin_edges))\n", + " name = name.replace('/dR', '/dlnR')\n", + " axs[i].step(\n", + " dry_radius_bin_edges[:-1] / si.um,\n", + " uniform_filter1d(spec, size=3),\n", + " where='pre'\n", + " )\n", + " axs[i].set_ylabel(name)\n", + " axs[i].set_xlabel(\"Aerosol dry radius [μm]\")\n", + " axs[i].grid()\n", + " axs[i].set_xscale('log')\n", + " axs[i].set_yscale('log')\n", + "pyplot.xlim(1e-3, 5e0)\n", + "\n", + "show_plot(\"size_dist.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Pyrcel/profile_plotter.py b/PySDM/source/examples/PySDM_examples/Pyrcel/profile_plotter.py new file mode 100644 index 0000000000000000000000000000000000000000..b8c508aa4aed049d7a542a9c8b4246bdbf55aef3 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pyrcel/profile_plotter.py @@ -0,0 +1,71 @@ +import numpy as np +from matplotlib import pyplot +from open_atmos_jupyter_utils import show_plot + +from PySDM.physics.constants import si + + +class ProfilePlotter: + def __init__(self, settings, legend=True, log_base=10): + self.settings = settings + self.format = "pdf" + self.legend = legend + self.log_base = log_base + self.ax = pyplot + self.fig = pyplot + + def show(self): + pyplot.tight_layout() + show_plot() + + def save(self, file): + # self.finish() + pyplot.savefig(file, format=self.format) + + def plot(self, output): + self.plot_data(self.settings, output) + + def plot_data(self, settings, output): + _, axs = pyplot.subplots(1, 2, sharey=True, figsize=(10, 5)) + axS = axs[0] + if output["products"].get("S_max"): + SS_percent = (np.asarray(output["products"]["S_max"]) - 1) * 100 + else: + SS_percent = np.asarray(output["products"]["S_max_percent"]) - 100 + axS.plot( + SS_percent, + output["products"]["z"], + color="black", + ) + axS.set_ylabel("Displacement [m]") + axS.set_xlabel("Supersaturation [%]") + axS.set_xlim(0, 0.7) + axS.set_ylim(0, 250) + axS.text(0.3, 52, f"max SS = {np.nanmax(SS_percent):.2f}%") + axS.grid() + + axT = axS.twiny() + axT.xaxis.label.set_color("red") + axT.tick_params(axis="x", colors="red") + axT.plot(output["products"]["T"], output["products"]["z"], color="red") + rng = (272, 274) + axT.set_xlim(*rng) + axT.set_xticks(np.linspace(*rng, num=5)) + axT.set_xlabel("Temperature [K]") + + axR = axs[1] + axR.set_xscale("log") + axR.set_xlim(1e-2, 1e2) + for drop_id, volume in enumerate(output["attributes"]["volume"]): + axR.plot( + settings.formulae.trivia.radius(volume=np.asarray(volume)) / si.um, + output["products"]["z"], + color="magenta" if drop_id < settings.n_sd_per_mode[0] else "blue", + label=( + "mode 1" + if drop_id == 0 + else "mode 2" if drop_id == settings.n_sd_per_mode[0] else "" + ), + ) + axR.legend(loc="upper right") + axR.set_xlabel("Droplet radius [μm]") diff --git a/PySDM/source/examples/PySDM_examples/Pyrcel/settings.py b/PySDM/source/examples/PySDM_examples/Pyrcel/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..1a36a2bad6be4892f1c0986324421cabad3df681 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pyrcel/settings.py @@ -0,0 +1,64 @@ +from typing import Dict + +import numpy as np +from pystrict import strict + +from PySDM import Formulae +from PySDM.initialisation.impl.spectrum import Spectrum + + +@strict +class Settings: + def __init__( + self, + dz: float, + n_sd_per_mode: tuple, + aerosol_modes_by_kappa: Dict[float, Spectrum], + vertical_velocity: float, + initial_temperature: float, + initial_pressure: float, + initial_relative_humidity: float, + displacement: float, + formulae: Formulae, + ): + self.formulae = formulae + self.n_sd_per_mode = n_sd_per_mode + self.aerosol_modes_by_kappa = aerosol_modes_by_kappa + + const = self.formulae.constants + self.vertical_velocity = vertical_velocity + self.initial_pressure = initial_pressure + self.initial_temperature = initial_temperature + pv0 = initial_relative_humidity * formulae.saturation_vapour_pressure.pvs_water( + initial_temperature + ) + self.initial_vapour_mixing_ratio = const.eps * pv0 / (initial_pressure - pv0) + self.t_max = displacement / vertical_velocity + self.timestep = dz / vertical_velocity + self.output_interval = self.timestep + + @property + def initial_air_density(self): + return self.formulae.state_variable_triplet.rho_of_rhod_and_water_vapour_mixing_ratio( + rhod=self.formulae.trivia.p_d( + self.initial_pressure, self.initial_vapour_mixing_ratio + ) + / self.initial_temperature + / self.formulae.constants.Rd, + water_vapour_mixing_ratio=self.initial_vapour_mixing_ratio, + ) + + @property + def nt(self) -> int: + nt = self.t_max / self.timestep + nt_int = round(nt) + np.testing.assert_almost_equal(nt, nt_int) + return nt_int + + @property + def steps_per_output_interval(self) -> int: + return int(self.output_interval / self.timestep) + + @property + def output_steps(self) -> np.ndarray: + return np.arange(0, self.nt + 1, self.steps_per_output_interval) diff --git a/PySDM/source/examples/PySDM_examples/Pyrcel/simulation.py b/PySDM/source/examples/PySDM_examples/Pyrcel/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..aafdb85aa56a6a650f18273676f7a8bd3893d138 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Pyrcel/simulation.py @@ -0,0 +1,108 @@ +import numpy as np +from PySDM_examples.utils import BasicSimulation + +from PySDM import Builder +from PySDM.backends import CPU +from PySDM.backends.impl_numba.test_helpers import scipy_ode_condensation_solver +from PySDM.dynamics import AmbientThermodynamics, Condensation +from PySDM.environments import Parcel +from PySDM.initialisation.hygroscopic_equilibrium import equilibrate_wet_radii +from PySDM.initialisation.sampling.spectral_sampling import ConstantMultiplicity +from PySDM.physics import si + + +class Simulation(BasicSimulation): + def __init__( + self, + settings, + products=None, + scipy_solver=False, + rtol_thd=1e-10, + rtol_x=1e-10, + mass_of_dry_air=44 * si.kg, + ): + n_sd = sum(settings.n_sd_per_mode) + builder = Builder( + n_sd=n_sd, + backend=CPU( + formulae=settings.formulae, override_jit_flags={"parallel": False} + ), + environment=Parcel( + dt=settings.timestep, + p0=settings.initial_pressure, + initial_water_vapour_mixing_ratio=settings.initial_vapour_mixing_ratio, + T0=settings.initial_temperature, + w=settings.vertical_velocity, + mass_of_dry_air=mass_of_dry_air, + ), + ) + builder.add_dynamic(AmbientThermodynamics()) + builder.add_dynamic(Condensation(rtol_thd=rtol_thd, rtol_x=rtol_x)) + + volume = ( + builder.particulator.environment.mass_of_dry_air + / settings.initial_air_density + ) + attributes = { + k: np.empty(0) + for k in ("dry volume", "kappa times dry volume", "multiplicity") + } + for i, (kappa, spectrum) in enumerate(settings.aerosol_modes_by_kappa.items()): + sampling = ConstantMultiplicity(spectrum) + r_dry, n_per_volume = sampling.sample_deterministic( + settings.n_sd_per_mode[i] + ) + v_dry = settings.formulae.trivia.volume(radius=r_dry) + attributes["multiplicity"] = np.append( + attributes["multiplicity"], n_per_volume * volume + ) + attributes["dry volume"] = np.append(attributes["dry volume"], v_dry) + attributes["kappa times dry volume"] = np.append( + attributes["kappa times dry volume"], v_dry * kappa + ) + r_wet = equilibrate_wet_radii( + r_dry=settings.formulae.trivia.radius(volume=attributes["dry volume"]), + environment=builder.particulator.environment, + kappa_times_dry_volume=attributes["kappa times dry volume"], + ) + attributes["volume"] = settings.formulae.trivia.volume(radius=r_wet) + + super().__init__( + particulator=builder.build(attributes=attributes, products=products) + ) + if scipy_solver: + scipy_ode_condensation_solver.patch_particulator(self.particulator) + + self.output_attributes = { + "volume": tuple([] for _ in range(self.particulator.n_sd)) + } + self.settings = settings + + self.__sanity_checks(attributes, volume) + + def __sanity_checks(self, attributes, volume): + for attribute in attributes.values(): + assert attribute.shape[0] == self.particulator.n_sd + np.testing.assert_approx_equal( + sum(attributes["multiplicity"]) / volume, + sum( + mode.norm_factor + for mode in self.settings.aerosol_modes_by_kappa.values() + ), + significant=4, + ) + + def _save(self, output): + for key, attr in self.output_attributes.items(): + attr_data = self.particulator.attributes[key].to_ndarray() + for drop_id in range(self.particulator.n_sd): + attr[drop_id].append(attr_data[drop_id]) + super()._save(output) + + def run(self, observers=()): + for observer in observers: + self.particulator.observers.append(observer) + output_products = super()._run( + self.settings.nt, self.settings.steps_per_output_interval + ) + return {"products": output_products, "attributes": self.output_attributes} diff --git a/PySDM/source/examples/PySDM_examples/Rogers_1975/__init__.py b/PySDM/source/examples/PySDM_examples/Rogers_1975/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0bc8eb68a8d18330f41b76303cc3b953edd8e8ea --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Rogers_1975/__init__.py @@ -0,0 +1,9 @@ +""" +based on Rogers 1975 (Atmosphere) +https://doi.org/10.1080/00046973.1975.9648397 + +fig_1.ipynb: +.. include:: ./fig_1.ipynb.badges.md +""" + +# pylint: disable=invalid-name diff --git a/PySDM/source/examples/PySDM_examples/Rogers_1975/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Rogers_1975/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2b7ac946c7d31e7ff212856a5df6e22c645dde38 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Rogers_1975/fig_1.ipynb @@ -0,0 +1,1593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bfd1a1e7-29d9-443d-9eb4-ef75151ffcc7", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Rogers_1975/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Rogers_1975/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Rogers_1975/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "ca764a8c-a315-4dd3-9814-bd59401f6ccd", + "metadata": {}, + "source": [ + "## basic parcel simulation based on [Rogers 1975](https://doi.org/10.1080/00046973.1975.9648397) \"_An Elementary Parcel Model with Explicit Condensation and Supersaturation_\" and implemented using SciPy+Pint but without PySDM - for illustrative purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "99b453fa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.540441Z", + "start_time": "2025-05-31T22:04:02.496744Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7dadd922", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.558544Z", + "start_time": "2025-05-31T22:04:02.545638Z" + } + }, + "outputs": [], + "source": [ + "import collections\n", + "import functools\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from matplotlib import pyplot\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.constants as const\n", + "import chempy" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0ecc07d1-88e4-4a4f-a469-fe4f14b31ac4", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.768131Z", + "start_time": "2025-05-31T22:04:02.621182Z" + } + }, + "outputs": [], + "source": [ + "import pint\n", + "si = pint.UnitRegistry()\n", + "si.setup_matplotlib()\n", + "si.define('fraction = [] = frac')\n", + "si.define('percent = 1e-2 frac = pct')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "31cdfaaa-31be-46c6-9dcf-257024240cd6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.781844Z", + "start_time": "2025-05-31T22:04:02.774125Z" + } + }, + "outputs": [], + "source": [ + "class Storage:\n", + " \"\"\" state vector representation with each element having its own Pint-compatible\n", + " physical dimension, thus allowing to seamlessly couple Pint and scipy.odeint\n", + " (all methods return objects that inherit from `numpy.ndarray` but are additionally\n", + " equipped with .VAR unit-aware setters and getters, allowing both unit-unaware\n", + " whole-array expressions (e.g., `state += dt * deriv`) as well as unit-aware \n", + " operations on state vars (e.g., `state.T = 300 * si.K` or `state.m[:] = ...`) \"\"\"\n", + "\n", + " var_units = {\n", + " 'p': si.Pa,\n", + " 'T': si.K,\n", + " 'S': si.dimensionless,\n", + " 'r': si.metre,\n", + " }\n", + " \n", + " der_unit = si.second\n", + "\n", + " @staticmethod\n", + " def __make_storage(shape, deriv=False):\n", + " def getter(self, idx, unit):\n", + " return self[idx] * unit\n", + "\n", + " def setter(self, value, idx, unit):\n", + " self[idx] = (value.to(unit) / unit).magnitude\n", + "\n", + " properties = {}\n", + " for i, key in enumerate(Storage.var_units.keys()):\n", + " kwargs = {\n", + " 'unit': Storage.var_units[key] / (Storage.der_unit if deriv else 1),\n", + " 'idx': i\n", + " }\n", + " properties[key] = property(\n", + " functools.partial(getter, **kwargs),\n", + " functools.partial(setter, **kwargs),\n", + " ) \n", + " \n", + " return type(\"StorageImpl\", (np.ndarray,), {**properties, \"__module__\": __name__})(shape)\n", + "\n", + " @staticmethod\n", + " def make_state():\n", + " \"\"\" returns a newly allocated unit-aware storage \"\"\"\n", + " return Storage.__make_storage((len(Storage.var_units),))\n", + "\n", + " @staticmethod\n", + " def make_deriv(state):\n", + " \"\"\" returns a newly allocated unit-aware storage with size of `state` and derivative dimensions \"\"\"\n", + " return Storage.__make_storage(state.shape, deriv=True)\n", + "\n", + " @staticmethod\n", + " def view_state(array):\n", + " \"\"\" returns a newly allocated unit-aware storage with size and data from unit-unaware `array` \"\"\"\n", + " storage = Storage.__make_storage(array.shape)\n", + " storage[:] = array[:]\n", + " return storage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "69a2d947-8581-4382-9939-07088584933b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.808299Z", + "start_time": "2025-05-31T22:04:02.788508Z" + } + }, + "outputs": [], + "source": [ + "# constants (coefficients with modified 10 exponents to match SI units)\n", + "C = {\n", + " # gas constant for dry air\n", + " \"R\": const.R * si.joule / si.kelvin / si.mole / (\n", + " 0.76 * chempy.Substance.from_formula(\"N2\").mass * si.gram / si.mole\n", + " + 0.23 * chempy.Substance.from_formula(\"O2\").mass * si.gram / si.mole\n", + " + 0.01 * chempy.Substance.from_formula(\"Ar\").mass * si.gram / si.mole\n", + " ),\n", + " # standard acceleration of gravity\n", + " \"g\": const.g * si.metre / si.second**2,\n", + " # latent heat of condensation\n", + " \"L\": 2.5e3 * si.J / si.g,\n", + " # specific gravity of vapour vs. dry air\n", + " \"eps\": .622,\n", + " # specific heat at constant pressure of dry air\n", + " \"cp\": 1005 * si.joule / si.kilogram / si.kelvin,\n", + " # density of liquid water\n", + " \"rho_L\": 1 * si.kg / si.litre,\n", + " # eq A.1\n", + " \"K\": lambda T: 2.42e-2 * (393/(T.to(si.K).magnitude + 120)) * (T.to(si.K).magnitude/273)**(3/2) * si.J / si.m / si.s / si.K,\n", + " # eq A.2\n", + " \"D_over_K\": lambda p, T: 8.28 / 2.42 * T.to(si.K).magnitude / p.to(si.dyne / si.cm**2).magnitude * si.m**3 / si.J * si.K,\n", + " # eq A.3\n", + " \"e_s\": lambda T: 2.75e12 * np.exp(-5.44e3 / (T/si.K)) * si.ubar,\n", + "}\n", + "C = collections.namedtuple(\"C\", C.keys())(**C)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7d9b98c0-971c-41a0-87c2-f7adcfc958a1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.822694Z", + "start_time": "2025-05-31T22:04:02.814783Z" + } + }, + "outputs": [], + "source": [ + "np.testing.assert_approx_equal(actual=C.e_s(273.15 * si.K).to(si.mbar).magnitude, desired=6.1, significant=2)\n", + "np.testing.assert_approx_equal(actual=C.K(273.15 * si.K).to(si.J / si.m / si.s / si.K).magnitude, desired=2.4e-2, significant=2)\n", + "np.testing.assert_approx_equal(\n", + " actual=(C.K(169.75 * si.K) * C.D_over_K(1000 * si.hPa, 169.75 * si.K)).to(si.m**2 / si.s).magnitude,\n", + " desired=9.34e-6,\n", + " significant=2\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d2011171-3291-4c0e-ad4e-23ba8bf25f52", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.831038Z", + "start_time": "2025-05-31T22:04:02.827859Z" + } + }, + "outputs": [], + "source": [ + "class System: # pylint: disable=too-few-public-methods\n", + " \"\"\" ODE system \"\"\"\n", + " def __init__(self, *, U, nu_0):\n", + " self.U = U\n", + " self.nu_0 = nu_0\n", + "\n", + " def __call__(self, _, state):\n", + " state = Storage.view_state(state)\n", + " deriv = Storage.make_deriv(state)\n", + "\n", + " # eq (8)\n", + " rho = state.p / C.R / state.T\n", + " \n", + " # eq (5) multiplied by Δt\n", + " deriv.p = -rho * C.g * self.U \n", + "\n", + " # eq (2)\n", + " K = C.K(state.T)\n", + " Fk = C.L**2 * C.eps * C.rho_L / K / C.R / state.T**2\n", + " Fd = C.R * state.T * C.rho_L / C.eps / C.D_over_K(state.p, state.T) / K / C.e_s(state.T)\n", + " sigma = (state.S - 1) / (Fk + Fd)\n", + " \n", + " # eq (1)\n", + " deriv.r = sigma / state.r\n", + " \n", + " # time derivative of eq (4)\n", + " dksi_dt = 4 * np.pi * C.rho_L * self.nu_0 * state.r**2 * deriv.r\n", + " \n", + " # eq (6) divided by dt, multiplied by T\n", + " deriv.T = (\n", + " state.T * C.R / C.cp * deriv.p / state.p + \n", + " C.L / C.cp * dksi_dt\n", + " )\n", + "\n", + " # eq (10)\n", + " Q1 = C.L * C.g * C.eps / C.R / C.cp / state.T**2 - C.g / C.R / state.T\n", + " Q2 = C.R * state.T / C.eps / C.e_s(state.T) + C.eps * C.L**2 / C.cp / state.T / state.p\n", + " deriv.S = Q1 * self.U - rho * Q2 * dksi_dt\n", + " \n", + " return deriv" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0dc34252-4ac9-4d23-89f5-793239183eb7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.837783Z", + "start_time": "2025-05-31T22:04:02.835253Z" + } + }, + "outputs": [], + "source": [ + "def solve(system, state, t_max, max_step):\n", + " integ = scipy.integrate.solve_ivp(\n", + " system,\n", + " [0, t_max / Storage.der_unit],\n", + " state,\n", + " max_step=max_step.to(Storage.der_unit).magnitude,\n", + " method='LSODA'\n", + " )\n", + " assert integ.success, integ.message\n", + " return Storage.view_state(integ.y), integ.t * Storage.der_unit" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ceff8ab7-8b6d-46a1-83e8-83b2fc893380", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:02.949536Z", + "start_time": "2025-05-31T22:04:02.845319Z" + } + }, + "outputs": [], + "source": [ + "storage_state = Storage.make_state()\n", + "storage_state.p = 800 * si.mbar\n", + "storage_state.T = (273.15 + 7) * si.K\n", + "storage_state.S = 1 * si.dimensionless\n", + "storage_state.r = 8 * si.um\n", + "\n", + "rho0 = storage_state.p / C.R / storage_state.T\n", + "ode_system = System(U=10 * si.m / si.s, nu_0=200 / si.cm**3 / rho0)\n", + "solution, tsteps = solve(ode_system, storage_state, t_max=20 * si.s, max_step=.5 * si.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f00aa5f8-219a-41dd-98fd-c8a411a502b9", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-31T22:04:03.158032Z", + "start_time": "2025-05-31T22:04:02.953629Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1f7eafe07a9b44cb818bc21953e497d2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_1.pdf\"), HTML(value=\"" + ], + "image/svg+xml": "\n\n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_4.pdf\"), HTML(value=\"" + ], + "image/svg+xml": "\n\n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_5.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "74f75df34825493fb9dff65c3505f8ff", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./tmpgfeualwr.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_SD = widgets.IntSlider(value=14, min=12, max=18, step=1, description='$log_2(n_{SD})$', continuous_update=False)\n", + "n_step = widgets.IntSlider(value=3600, step=100, min=100, max=3600, description='$n_{step}$', continuous_update=False)\n", + "n_plot = widgets.IntSlider(value=3, step=1, min=1, max=8, description='$n_{plot}$', continuous_update=False)\n", + "sliders = widgets.HBox([n_SD, n_step, n_plot])\n", + "\n", + "adaptive = widgets.Checkbox(value=False, description='adaptive dt')\n", + "smooth = widgets.Checkbox(value=True, description='smooth plot')\n", + "gpu = widgets.Checkbox(value=False, description='GPU')\n", + "options = [adaptive, smooth]\n", + "if ThrustRTC.ENABLE:\n", + " options.append(gpu)\n", + "boxes = widgets.HBox(options)\n", + "freezer = widgets.Freezer([n_SD, n_step, n_plot, adaptive, gpu])\n", + "inputs = {\n", + " 'freezer': freezer, 'n_SD': n_SD, 'n_step': n_step, 'n_plot': n_plot,\n", + " 'adaptive': adaptive, 'smooth': smooth, 'gpu': gpu\n", + "}\n", + "\n", + "if 'CI' not in os.environ:\n", + " widgets.display(sliders, boxes, progbar, widgets.interactive_output(demo, inputs))\n", + "else:\n", + " demo(**{k:v.value for k,v in inputs.items()})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T17:14:51.419820Z", + "start_time": "2024-12-16T17:14:51.418628Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/settings.py b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0d524baebc7bfc75d97d36477b32cb5fa4d25c --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/settings.py @@ -0,0 +1,35 @@ +from typing import Optional + +import numpy as np +from pystrict import strict + +from PySDM import Formulae +from PySDM.dynamics.collisions.collision_kernels import Golovin +from PySDM.initialisation import spectra +from PySDM.physics import si + + +@strict +class Settings: + def __init__(self, steps: Optional[list] = None): + steps = steps or [0, 1200, 2400, 3600] + self.formulae = Formulae() + self.n_sd = 2**13 + self.n_part = 2**23 / si.metre**3 + self.X0 = self.formulae.trivia.volume(radius=30.531 * si.micrometres) + self.dv = 1e6 * si.metres**3 + self.norm_factor = self.n_part * self.dv + self.rho = 1000 * si.kilogram / si.metre**3 + self.dt = 1 * si.seconds + self.adaptive = False + self.seed = 44 + self.steps = steps + self.kernel = Golovin(b=1.5e3 / si.second) + self.spectrum = spectra.Exponential(norm_factor=self.norm_factor, scale=self.X0) + self.radius_bins_edges = np.logspace( + np.log10(10 * si.um), np.log10(5e3 * si.um), num=128, endpoint=True + ) + + @property + def output_steps(self): + return [int(step / self.dt) for step in self.steps] diff --git a/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/spectrum_plotter.py b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/spectrum_plotter.py new file mode 100644 index 0000000000000000000000000000000000000000..f989cec287a0ab0d293476317de14ed9a7c0fc99 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/spectrum_plotter.py @@ -0,0 +1,165 @@ +import matplotlib +import numpy as np +from matplotlib import pyplot +from open_atmos_jupyter_utils import show_plot +from packaging import version +from PySDM_examples.Shima_et_al_2009.error_measure import error_measure + +from PySDM.physics.constants import si + +_matplotlib_version_3_3_3 = version.parse("3.3.0") +_matplotlib_version_actual = version.parse(matplotlib.__version__) + + +class SpectrumColors: + def __init__(self, begining="#2cbdfe", end="#b317b1"): + self.b = begining + self.e = end + + def __call__(self, value: float): + bR, bG, bB = int(self.b[1:3], 16), int(self.b[3:5], 16), int(self.b[5:7], 16) + eR, eG, eB = int(self.e[1:3], 16), int(self.e[3:5], 16), int(self.e[5:7], 16) + R = bR + int((eR - bR) * value) + G = bG + int((eG - bG) * value) + B = bB + int((eB - bB) * value) + result = f"#{hex(R)[2:4]}{hex(G)[2:4]}{hex(B)[2:4]}" + return result + + +class SpectrumPlotter: + def __init__(self, settings, title=None, grid=True, legend=True, log_base=10): + self.settings = settings + self.format = "pdf" + self.colors = SpectrumColors() + self.smooth = False + self.smooth_scope = 2 + self.legend = legend + self.grid = grid + self.title = title + self.xlabel = "particle radius [µm]" + self.ylabel = "dm/dlnr [g/m^3/(unit dr/r)]" + self.log_base = log_base + self.ax = pyplot + self.fig = pyplot + self.finished = False + + def finish(self): + if self.finished: + return + self.finished = True + if self.grid: + self.ax.grid() + + base_arg = { + "base" + + ( + "x" if _matplotlib_version_actual < _matplotlib_version_3_3_3 else "" + ): self.log_base + } + if self.title is not None: + try: + self.ax.title(self.title) + except TypeError: + self.ax.set_title(self.title) + try: + self.ax.xscale("log", **base_arg) + self.ax.xlabel(self.xlabel) + self.ax.ylabel(self.ylabel) + except AttributeError: + self.ax.set_xscale("log", **base_arg) + self.ax.set_xlabel(self.xlabel) + self.ax.set_ylabel(self.ylabel) + if self.legend: + self.ax.legend() + + def show(self): + self.finish() + pyplot.tight_layout() + show_plot() + + def save(self, file): + self.finish() + pyplot.savefig(file, format=self.format) + + def plot( + self, spectrum, t, label=None, color=None, title=None, add_error_to_label=False + ): + error = self.plot_analytic_solution(self.settings, t, spectrum, title) + if label is not None and add_error_to_label: + label += f" error={error:.4g}" + self.plot_data(self.settings, t, spectrum, label, color) + return error + + def plot_analytic_solution(self, settings, t, spectrum, title): + if t == 0: + analytic_solution = settings.spectrum.size_distribution + else: + + def analytic_solution(x): + return settings.norm_factor * settings.kernel.analytic_solution( + x=x, t=t, x_0=settings.X0, N_0=settings.n_part + ) + + volume_bins_edges = self.settings.formulae.trivia.volume( + settings.radius_bins_edges + ) + dm = np.diff(volume_bins_edges) + dr = np.diff(settings.radius_bins_edges) + + pdf_m_x = volume_bins_edges[:-1] + dm / 2 + pdf_m_y = analytic_solution(pdf_m_x) + + pdf_r_x = settings.radius_bins_edges[:-1] + dr / 2 + pdf_r_y = pdf_m_y * dm / dr * pdf_r_x + + x = pdf_r_x * si.metres / si.micrometres + y_true = ( + pdf_r_y + * self.settings.formulae.trivia.volume(radius=pdf_r_x) + * settings.rho + / settings.dv + * si.kilograms + / si.grams + ) + + self.ax.plot(x, y_true, color="black") + + if spectrum is not None: + y = spectrum * si.kilograms / si.grams + error = error_measure(y, y_true, x) + self.title = ( + title or f"error measure: {error:.2f}" + ) # TODO #327 relative error + return error + return None + + def plot_data(self, settings, t, spectrum, label, color): + if self.smooth: + scope = self.smooth_scope + if t != 0: + new = np.copy(spectrum) + for _ in range(2): + for i in range(scope, len(spectrum) - scope): + new[i] = np.mean(spectrum[i - scope : i + scope + 1]) + scope = 1 + for i in range(scope, len(spectrum) - scope): + spectrum[i] = np.mean(new[i - scope : i + scope + 1]) + + x = settings.radius_bins_edges[:-scope] + dx = np.diff(x) + self.ax.plot( + (x[:-1] + dx / 2) * si.metres / si.micrometres, + spectrum[:-scope] * si.kilograms / si.grams, + label=label or f"t = {t}s", + color=color + or self.colors(t / (self.settings.output_steps[-1] * self.settings.dt)), + ) + else: + self.ax.step( + settings.radius_bins_edges[:-1] * si.metres / si.micrometres, + spectrum * si.kilograms / si.grams, + where="post", + label=label or f"t = {t}s", + color=color + or self.colors(t / (self.settings.output_steps[-1] * self.settings.dt)), + ) diff --git a/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_example.py b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_example.py new file mode 100644 index 0000000000000000000000000000000000000000..e2c5c6d4879ce5c30b70b192e5a0a3d314d1118b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_example.py @@ -0,0 +1,47 @@ +from PySDM.backends import CPU +from PySDM.builder import Builder +from PySDM.dynamics import Coalescence +from PySDM.environments import Box +from PySDM.initialisation.sampling.spectral_sampling import ConstantMultiplicity +from PySDM.products import ParticleVolumeVersusRadiusLogarithmSpectrum, WallTime + + +def run(settings, observers=()): + builder = Builder( + n_sd=settings.n_sd, + backend=CPU(formulae=settings.formulae), + environment=Box(dv=settings.dv, dt=settings.dt), + ) + attributes = {} + sampling = ConstantMultiplicity(settings.spectrum) + attributes["volume"], attributes["multiplicity"] = sampling.sample_deterministic( + settings.n_sd + ) + coalescence = Coalescence( + collision_kernel=settings.kernel, adaptive=settings.adaptive + ) + builder.add_dynamic(coalescence) + products = ( + ParticleVolumeVersusRadiusLogarithmSpectrum( + settings.radius_bins_edges, name="dv/dlnr" + ), + WallTime(), + ) + particulator = builder.build(attributes, products) + if hasattr(settings, "u_term") and "terminal velocity" in particulator.attributes: + particulator.attributes["terminal velocity"].approximation = settings.u_term( + particulator + ) + + for observer in observers: + particulator.observers.append(observer) + + vals = {} + particulator.products["wall time"].reset() + for step in settings.output_steps: + particulator.run(step - particulator.n_steps) + vals[step] = particulator.products["dv/dlnr"].get()[0] + vals[step][:] *= settings.rho + + exec_time = particulator.products["wall time"].get() + return vals, exec_time diff --git a/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_plotter.py b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_plotter.py new file mode 100644 index 0000000000000000000000000000000000000000..ec4f239ab4cfa79b0114b20b0a37c55b4d001ec7 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_plotter.py @@ -0,0 +1,129 @@ +import numpy as np +from matplotlib import pyplot +from open_atmos_jupyter_utils import show_plot + +from PySDM.physics.constants import si + + +class SpectrumColors: + def __init__(self, begining="#2cbdfe", end="#b317b1"): + self.b = begining + self.e = end + + def __call__(self, value: float): + bR, bG, bB = int(self.b[1:3], 16), int(self.b[3:5], 16), int(self.b[5:7], 16) + eR, eG, eB = int(self.e[1:3], 16), int(self.e[3:5], 16), int(self.e[5:7], 16) + R = bR + int((eR - bR) * value) + G = bG + int((eG - bG) * value) + B = bB + int((eB - bB) * value) + result = f"#{hex(R)[2:4]}{hex(G)[2:4]}{hex(B)[2:4]}" + return result + + +class SpectrumPlotter: + def __init__(self, settings, grid=True, legend=True, log_base=10): + self.settings = settings + self.format = "pdf" + self.colors = SpectrumColors() + self.smooth = False + self.smooth_scope = 2 + self.legend = legend + self.grid = grid + self.xlabel = "particle radius [µm]" + self.ylabel = "dm/dlnr [g/m$^3$]" + self.log_base = log_base + self.ax = pyplot + self.fig = pyplot + self.finished = False + + def finish(self): + if self.finished: + return + self.finished = True + if self.grid: + self.ax.grid() + + self.ax.xscale("log") + self.ax.xlabel(self.xlabel) + self.ax.ylabel(self.ylabel) + if self.legend: + self.ax.legend() + + def show(self): + self.finish() + pyplot.tight_layout() + show_plot() + + def save(self, file): + self.finish() + pyplot.savefig(file, format=self.format) + + def plot(self, spectrum, t): + self.plot_analytic_solution(self.settings, t) + self.plot_data(self.settings, t, spectrum) + + def plot_analytic_solution(self, settings, t): + def analytic_solution(x): + return settings.norm_factor * settings.kernel.analytic_solution( + x=x, t=t, x_0=settings.X0, N_0=settings.n_part + ) + + if t == 0: + analytic_solution = settings.spectrum.size_distribution + + volume_bins_edges = self.settings.formulae.trivia.volume( + settings.radius_bins_edges + ) + dm = np.diff(volume_bins_edges) + dr = np.diff(settings.radius_bins_edges) + + pdf_m_x = volume_bins_edges[:-1] + dm / 2 + pdf_m_y = analytic_solution(pdf_m_x) + + pdf_r_x = settings.radius_bins_edges[:-1] + dr / 2 + pdf_r_y = pdf_m_y * dm / dr * pdf_r_x + + x = pdf_r_x * si.metres / si.micrometres + y_true = ( + pdf_r_y + * self.settings.formulae.trivia.volume(radius=pdf_r_x) + * settings.rho + / settings.dv + * si.kilograms + / si.grams + ) + + self.ax.plot(x, y_true, color="black") + + def plot_data(self, settings, t, spectrum): + if self.smooth: + scope = self.smooth_scope + if t != 0: + new = np.copy(spectrum) + for _ in range(2): + for i in range(scope, len(spectrum) - scope): + new[i] = np.mean(spectrum[i - scope : i + scope + 1]) + scope = 1 + for i in range(scope, len(spectrum) - scope): + spectrum[i] = np.mean(new[i - scope : i + scope + 1]) + + x = settings.radius_bins_edges[:-scope] + dx = np.diff(x) + self.ax.plot( + (x[:-1] + dx / 2) * si.metres / si.micrometres, + spectrum[:-scope] * si.kilograms / si.grams, + label=f"t = {t}s", + color=self.colors( + t / (self.settings.output_steps[-1] * self.settings.dt) + ), + ) + else: + self.ax.step( + settings.radius_bins_edges[:-1] * si.metres / si.micrometres, + spectrum * si.kilograms / si.grams, + where="post", + label=f"t = {t}s", + color=self.colors( + t / (self.settings.output_steps[-1] * self.settings.dt) + ), + ) diff --git a/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_settings.py b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_settings.py new file mode 100644 index 0000000000000000000000000000000000000000..6343e518d65112d12d29e7bc51189b9b46115e91 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shima_et_al_2009/tutorial_settings.py @@ -0,0 +1,35 @@ +from typing import Optional + +import numpy as np +from pystrict import strict + +from PySDM import Formulae +from PySDM.dynamics.collisions.collision_kernels import Golovin +from PySDM.initialisation import spectra +from PySDM.physics import si + + +@strict +class Settings: + def __init__(self, steps: Optional[list] = None): + steps = steps or [0, 1200, 2400, 3600] + self.formulae = Formulae() + self.n_sd = 2**13 + self.n_part = 2**23 / si.metre**3 + self.X0 = self.formulae.trivia.volume(radius=30.531 * si.micrometres) + self.dv = 1e6 * si.metres**3 + self.norm_factor = self.n_part * self.dv + self.rho = 1000 * si.kilogram / si.metre**3 + self.dt = 1 * si.seconds + self.adaptive = False + self.seed = 44 + self.steps = steps + self.kernel = Golovin(b=1.5e3 / si.second) + self.spectrum = spectra.Exponential(norm_factor=self.norm_factor, scale=self.X0) + self.radius_bins_edges = np.logspace( + np.log10(10 * si.um), np.log10(5e4 * si.um), num=256, endpoint=True + ) + + @property + def output_steps(self): + return [int(step / self.dt) for step in self.steps] diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/__init__.py b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..420b694e0fad91481d0a844848da7530649358f4 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/__init__.py @@ -0,0 +1,11 @@ +# pylint: disable=invalid-name +""" +single-column prescribed-flow constant-temperature example from +[Shipway & Hill 2012](https://doi.org/10.1002/qj.1913) + +fig_1.ipynb: +.. include:: ./fig_1.ipynb.badges.md +""" +from .plot import plot +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..864e0d7b3b4b11a0258fb4a09b2c501c1aaa3387 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### based on Fig. 1 from Shipway & Hill 2012 (Q. J. Royal Meteo. Soc. 138) \"_Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework_\" \n", + "https://doi.org/10.1002/qj.1913\n", + "\n", + "**NOTES**: \n", + "- constant momentum profile rather than constant velocity profile is used herein\n", + "- enabling precipitation interpretted as turning on sedimentation and collisions\n", + "- pressure at z=0 not given in the paper, assumed (see settings.py)\n", + "- domain extended below z=0 to mimic particle inflow" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T15:12:04.969183Z", + "start_time": "2023-12-29T15:12:03.369050Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from PySDM_examples.Shipway_and_Hill_2012 import Settings, Simulation, plot\n", + "from PySDM.physics import si\n", + "from PySDM.exporters import NetCDFExporter_1d" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T15:13:57.683736Z", + "start_time": "2023-12-29T15:12:04.972230Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "common_params = {\n", + " \"n_sd_per_gridbox\": 256,\n", + " \"dt\": 5 * si.s,\n", + " \"dz\": 50 * si.m,\n", + " \"p0\": 990 * si.hPa,\n", + " \"kappa\": .9,\n", + " \"particles_per_volume_STP\": 50 / si.cm**3\n", + "}\n", + "if 'CI' in os.environ:\n", + " common_params[\"t_max\"] = 10 * common_params[\"dt\"]\n", + " common_params[\"n_sd_per_gridbox\"] = 16 \n", + "\n", + "output = {}\n", + "settings = {}\n", + "simulation = {}\n", + "for rho_times_w in (\n", + " 2 * si.kg/si.m**3 * si.m/si.s,\n", + " 3 * si.kg/si.m**3 * si.m/si.s\n", + "):\n", + " for precip in (\n", + " False, \n", + " True,\n", + " ):\n", + " key = f\"rhow={rho_times_w}_p={precip}\"\n", + " settings[key] = Settings(\n", + " **common_params,\n", + " rho_times_w_1=rho_times_w,\n", + " precip=precip\n", + " )\n", + " simulation[key] = Simulation(settings[key])\n", + " output[key] = simulation[key].run().products" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T15:14:06.730859Z", + "start_time": "2023-12-29T15:13:57.682941Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac7c8b37d0a449dda5e561ad1b2daf5f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./qc_rhow=3_p=True.pdf\"), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for p in (False, True):\n", + " for rho_times_w in (2, 3):\n", + " plot(var='cloud water mixing ratio', qlabel='cloud water mixing ratio [g/kg]', fname=f'qc_rhow={rho_times_w}_p={p}.pdf',\n", + " output=output[f'rhow={rho_times_w}.0_p={p}'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T15:14:11.163980Z", + "start_time": "2023-12-29T15:14:06.728728Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab77d9059b8e4ac595ebe832ee345c1a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./qr_rhow=3_p=True.pdf\"), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = True\n", + "for rho_times_w in (2, 3):\n", + " plot(var='rain water mixing ratio', qlabel='rain water mixing ratio [g/kg]', fname=f'qr_rhow={rho_times_w}_p={p}.pdf',\n", + " output=output[f'rhow={rho_times_w}.0_p={p}'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-29T15:14:11.348122Z", + "start_time": "2023-12-29T15:14:11.160894Z" + } + }, + "outputs": [], + "source": [ + "for p in (False, True):\n", + " for rho_times_w in (2, 3):\n", + " key = f\"rhow={rho_times_w}.0_p={p}\"\n", + " filename = 'products_' + key + '.nc'\n", + " nc_exporter = NetCDFExporter_1d(output[key], settings[key], simulation[key], filename)\n", + " nc_exporter.run()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "vscode": { + "interpreter": { + "hash": "b43cf254c70d60c2e21a7f71ba113e70c1694742e72407132919c841d907074b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/mpdata_1d.py b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/mpdata_1d.py new file mode 100644 index 0000000000000000000000000000000000000000..d891c583286bffc64d1ab953dbcf7ddebedbf994 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/mpdata_1d.py @@ -0,0 +1,82 @@ +from functools import cached_property + +import numpy as np + +from PyMPDATA import Options, ScalarField, Solver, Stepper, VectorField +from PyMPDATA.boundary_conditions import Extrapolated + +from PySDM.impl import arakawa_c + + +class MPDATA_1D: + def __init__( + self, + nz, + dt, + advector_of_t, + advectee_of_zZ_at_t0, + g_factor_of_zZ, + mpdata_settings, + ): + self.__t = 0 + self.dt = dt + self.advector_of_t = advector_of_t + + self._grid = (nz,) + self._options = Options( + n_iters=mpdata_settings["n_iters"], + infinite_gauge=mpdata_settings["iga"], + nonoscillatory=mpdata_settings["fct"], + third_order_terms=mpdata_settings["tot"], + ) + bcs = (Extrapolated(),) + zZ_scalar = arakawa_c.z_scalar_coord(self._grid) / nz + self._g_factor = ScalarField( + data=g_factor_of_zZ(zZ_scalar), + halo=self._options.n_halo, + boundary_conditions=bcs, + ) + self._advector = VectorField( + data=(np.full(nz + 1, advector_of_t(0)),), + halo=self._options.n_halo, + boundary_conditions=bcs, + ) + self._advectee = ScalarField( + data=advectee_of_zZ_at_t0(zZ_scalar), + halo=self._options.n_halo, + boundary_conditions=bcs, + ) + + @cached_property + def solver(self): + return Solver( + stepper=Stepper( + options=self._options, grid=self._grid, non_unit_g_factor=True + ), + advectee=self._advectee, + advector=self._advector, + g_factor=self._g_factor, + ) + + @property + def solver_cached(self): + return "solver" in self.__dict__ + + @property + def advectee(self): + return (self.solver.advectee if self.solver_cached else self._advectee).get() + + @property + def advector(self): + return ( + self.solver.advector if self.solver_cached else self._advector + ).get_component(0) + + def update_advector_field(self): + self.__t += 0.5 * self.dt + self.advector[:] = self.advector_of_t(self.__t) + np.testing.assert_array_less(np.abs(self.advector), 1) + self.__t += 0.5 * self.dt + + def __call__(self, _): + self.solver.advance(1) diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/plot.py b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..4a768ca7f334d53225cc785329510c35bf7df2c4 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/plot.py @@ -0,0 +1,113 @@ +import numpy as np +from matplotlib import pyplot +from open_atmos_jupyter_utils import show_plot + +from PySDM.physics import convert_to, si + + +def plot( + var, qlabel, fname, output, vmin=None, vmax=None, cmin=None, cmax=None, line=None +): + line = line or {15: ":", 20: "--", 25: "-", 30: "-."} + dt = output["t"][1] - output["t"][0] + dz = output["z"][1] - output["z"][0] + tgrid = np.concatenate(((output["t"][0] - dt / 2,), output["t"] + dt / 2)) + zgrid = np.concatenate(((output["z"][0] - dz / 2,), output["z"] + dz / 2)) + convert_to(zgrid, si.km) + + fig = pyplot.figure(constrained_layout=True) + gs = fig.add_gridspec(25, 5) + ax1 = fig.add_subplot(gs[:-1, 0:4]) + if cmin is not None and cmax is not None: + mesh = ax1.pcolormesh( + tgrid, zgrid, output[var], cmap="BuPu", vmin=cmin, vmax=cmax + ) + else: + mesh = ax1.pcolormesh(tgrid, zgrid, output[var], cmap="BuPu") + + ax1.set_xlabel("time [s]") + ax1.set_ylabel("z [km]") + ax1.set_ylim(0, None) + + cbar = fig.colorbar(mesh, fraction=0.05, location="top") + cbar.set_label(qlabel) + + ax2 = fig.add_subplot(gs[:-1, 4:], sharey=ax1) + ax2.set_xlabel(qlabel) + # ax2.set_yticks([], minor=False) + + last_t = 0 + for i, t in enumerate(output["t"]): + x, z = output[var][:, i], output["z"].copy() + convert_to(z, si.km) + params = {"color": "black"} + for line_t, line_s in line.items(): + if last_t < line_t * si.min <= t: + params["ls"] = line_s + ax2.plot(x, z, **params) + if vmin is not None and vmax is not None: + ax1.axvline(t, ymin=vmin, ymax=vmax, **params) + else: + ax1.axvline(t, **params) + last_t = t + + show_plot(filename=fname, inline_format="png") + + +def plot_plusminus( + plus_vars, + minus_vars, + qlabel, + fname, + output, + vmin=None, + vmax=None, + line=None, + cmin=0.0, + cmax=None, +): + line = line or {15: ":", 20: "--", 25: "-", 30: "-."} + dt = output["t"][1] - output["t"][0] + dz = output["z"][1] - output["z"][0] + tgrid = np.concatenate(((output["t"][0] - dt / 2,), output["t"] + dt / 2)) + zgrid = np.concatenate(((output["z"][0] - dz / 2,), output["z"] + dz / 2)) + convert_to(zgrid, si.km) + + fig = pyplot.figure(constrained_layout=True) + gs = fig.add_gridspec(25, 5) + ax1 = fig.add_subplot(gs[:-1, 0:4]) + plot_var = 0.0 * output[plus_vars[0]] + for plus_var in plus_vars: + plot_var += output[plus_var] + for minus_var in minus_vars: + plot_var -= output[minus_var] + + mesh = ax1.pcolormesh(tgrid, zgrid, plot_var, cmap="BuPu", vmin=cmin, vmax=cmax) + + ax1.set_xlabel("time [s]") + ax1.set_ylabel("z [km]") + ax1.set_ylim(0, None) + + cbar = fig.colorbar(mesh, fraction=0.05, location="top") + cbar.set_label(qlabel) + + ax2 = fig.add_subplot(gs[:-1, 4:], sharey=ax1) + ax2.set_xlabel(qlabel) + # ax2.set_yticks([], minor=False) + + last_t = 0 + for i, t in enumerate(output["t"]): + x, z = plot_var[:, i], output["z"].copy() + convert_to(z, si.km) + params = {"color": "black"} + for line_t, line_s in line.items(): + if last_t < line_t * si.min <= t: + params["ls"] = line_s + ax2.plot(x, z, **params) + if vmin is not None and vmax is not None: + ax1.axvline(t, ymin=vmin, ymax=vmax, **params) + else: + ax1.axvline(t, **params) + last_t = t + + show_plot(filename=fname, inline_format="png") diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/settings.py b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..b74374da0f3072c9fa2847f4e72b5f28702e09d4 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/settings.py @@ -0,0 +1,189 @@ +from typing import Iterable, Optional + +import numpy as np +from numdifftools import Derivative +from scipy.integrate import solve_ivp +from scipy.interpolate import interp1d + +from PySDM import Formulae +from PySDM.dynamics import condensation +from PySDM.initialisation import spectra +from PySDM.physics import si +from PySDM.dynamics.collisions.collision_kernels import Geometric + + +class Settings: + def __dir__(self) -> Iterable[str]: + return ( + "n_sd_per_gridbox", + "p0", + "kappa", + "rho_times_w_1", + "particles_per_volume_STP", + "dt", + "dz", + "precip", + "z_max", + "t_max", + "cloud_water_radius_range", + "rain_water_radius_range", + "r_bins_edges_dry", + "r_bins_edges", + ) + + def __init__( + self, + *, + n_sd_per_gridbox: int, + p0: float = 1007 * si.hPa, # as used in Olesik et al. 2022 (GMD) + kappa: float = 1, + rho_times_w_1: float = 2 * si.m / si.s * si.kg / si.m**3, + particles_per_volume_STP: int = 50 / si.cm**3, + dt: float = 1 * si.s, + dz: float = 25 * si.m, + z_max: float = 3000 * si.m, + z_part: Optional[tuple] = None, + t_max: float = 60 * si.minutes, + precip: bool = True, + enable_condensation: bool = True, + formulae: Formulae = None, + save_spec_and_attr_times=(), + collision_kernel=None, + old_buggy_density_formula=False, + ): + self.formulae = formulae or Formulae() + self.n_sd_per_gridbox = n_sd_per_gridbox + self.p0 = p0 + self.kappa = kappa + self.rho_times_w_1 = rho_times_w_1 + self.particles_per_volume_STP = particles_per_volume_STP + self.dt = dt + self.dz = dz + self.precip = precip + self.enable_condensation = enable_condensation + self.z_part = z_part + self.z_max = z_max + self.t_max = t_max + self.collision_kernel = collision_kernel or Geometric(collection_efficiency=1) + + t_1 = 600 * si.s + self.rho_times_w = lambda t: ( + rho_times_w_1 * np.sin(np.pi * t / t_1) if t < t_1 else 0 + ) + apprx_w1 = rho_times_w_1 / self.formulae.constants.rho_STP + self.particle_reservoir_depth = ( + (2 * apprx_w1 * t_1 / np.pi) // self.dz + 1 + ) * self.dz + + self.wet_radius_spectrum_per_mass_of_dry_air = spectra.Lognormal( + norm_factor=particles_per_volume_STP / self.formulae.constants.rho_STP, + m_mode=0.08 / 2 * si.um, + s_geom=1.4, + ) + + self._th = interp1d( + (0.0 * si.m, 740.0 * si.m, 3260.00 * si.m), + (297.9 * si.K, 297.9 * si.K, 312.66 * si.K), + fill_value="extrapolate", + ) + + self.water_vapour_mixing_ratio = interp1d( + (-max(self.particle_reservoir_depth, 1), 0, 740, 3260), + (0.015, 0.015, 0.0138, 0.0024), + fill_value="extrapolate", + ) + + self.thd = ( + lambda z_above_reservoir: self.formulae.state_variable_triplet.th_dry( + self._th(z_above_reservoir), + self.water_vapour_mixing_ratio(z_above_reservoir), + ) + ) + + self.rhod0 = self.formulae.state_variable_triplet.rho_d( + p=p0, + water_vapour_mixing_ratio=self.water_vapour_mixing_ratio(0 * si.m), + theta_std=self._th(0 * si.m), + ) + + def drhod_dz(z_above_reservoir, rhod): + if z_above_reservoir < 0: + return 0 + water_vapour_mixing_ratio = self.water_vapour_mixing_ratio( + z_above_reservoir + ) + d_water_vapour_mixing_ratio__dz = Derivative( + self.water_vapour_mixing_ratio + )(z_above_reservoir) + T = self.formulae.state_variable_triplet.T( + rhod[0], self.thd(z_above_reservoir) + ) + p = self.formulae.state_variable_triplet.p( + rhod[0], T, water_vapour_mixing_ratio + ) + lv = self.formulae.latent_heat_vapourisation.lv(T) + return self.formulae.hydrostatics.drho_dz( + p, T, water_vapour_mixing_ratio, lv + ) / ( + 1 + water_vapour_mixing_ratio + ) - rhod * d_water_vapour_mixing_ratio__dz / ( + 1 + water_vapour_mixing_ratio + ) ** ( + 2 if not old_buggy_density_formula else 1 + ) + + z_span = (-self.particle_reservoir_depth, self.z_max) + z_points = np.linspace(*z_span, 2 * self.nz + 1) + rhod_solution = solve_ivp( + fun=drhod_dz, + t_span=z_span, + y0=np.asarray((self.rhod0,)), + t_eval=z_points, + max_step=dz / 2, + ) + assert rhod_solution.success + self.rhod = interp1d(z_points, rhod_solution.y[0]) + + self.mpdata_settings = {"n_iters": 3, "iga": True, "fct": True, "tot": True} + self.condensation_rtol_x = condensation.DEFAULTS.rtol_x + self.condensation_rtol_thd = condensation.DEFAULTS.rtol_thd + self.condensation_adaptive = True + self.condensation_update_thd = False + self.coalescence_adaptive = True + + self.number_of_bins = 100 + self.r_bins_edges_dry = np.logspace( + np.log10(0.001 * si.um), + np.log10(1 * si.um), + self.number_of_bins + 1, + endpoint=True, + ) + self.r_bins_edges = np.logspace( + np.log10(0.001 * si.um), + np.log10(10 * si.mm), + self.number_of_bins + 1, + endpoint=True, + ) + self.cloud_water_radius_range = [1 * si.um, 50 * si.um] + self.rain_water_radius_range = [50 * si.um, np.inf * si.um] + self.save_spec_and_attr_times = save_spec_and_attr_times + + @property + def n_sd(self): + return self.nz * self.n_sd_per_gridbox + + @property + def nz(self): + assert ( + self.particle_reservoir_depth / self.dz + == self.particle_reservoir_depth // self.dz + ) + nz = (self.z_max + self.particle_reservoir_depth) / self.dz + assert nz == int(nz) + return int(nz) + + @property + def nt(self): + nt = self.t_max / self.dt + assert nt == int(nt) + return int(nt) diff --git a/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/simulation.py b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..d20c7527a373eb5ba819a639bfc7aae248adf04e --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Shipway_and_Hill_2012/simulation.py @@ -0,0 +1,271 @@ +from collections import namedtuple + +import numpy as np +from PySDM_examples.Shipway_and_Hill_2012.mpdata_1d import MPDATA_1D + +import PySDM.products as PySDM_products +from PySDM import Builder +from PySDM.backends import CPU +from PySDM.dynamics import ( + AmbientThermodynamics, + Coalescence, + Condensation, + Displacement, + EulerianAdvection, +) +from PySDM.environments.kinematic_1d import Kinematic1D +from PySDM.impl.mesh import Mesh +from PySDM.initialisation.sampling import spatial_sampling, spectral_sampling + + +class Simulation: + def __init__(self, settings, backend=CPU): + self.nt = settings.nt + self.z0 = -settings.particle_reservoir_depth + self.save_spec_and_attr_times = settings.save_spec_and_attr_times + self.number_of_bins = settings.number_of_bins + + self.particulator = None + self.output_attributes = None + self.output_products = None + + self.mesh = Mesh( + grid=(settings.nz,), + size=(settings.z_max + settings.particle_reservoir_depth,), + ) + + env = Kinematic1D( + dt=settings.dt, + mesh=self.mesh, + thd_of_z=settings.thd, + rhod_of_z=settings.rhod, + z0=-settings.particle_reservoir_depth, + ) + + def zZ_to_z_above_reservoir(zZ): + z_above_reservoir = zZ * (settings.nz * settings.dz) + self.z0 + return z_above_reservoir + + mpdata = MPDATA_1D( + nz=settings.nz, + dt=settings.dt, + mpdata_settings=settings.mpdata_settings, + advector_of_t=lambda t: settings.rho_times_w(t) * settings.dt / settings.dz, + advectee_of_zZ_at_t0=lambda zZ: settings.water_vapour_mixing_ratio( + zZ_to_z_above_reservoir(zZ) + ), + g_factor_of_zZ=lambda zZ: settings.rhod(zZ_to_z_above_reservoir(zZ)), + ) + + _extra_nz = settings.particle_reservoir_depth // settings.dz + _z_vec = settings.dz * np.linspace( + -_extra_nz, settings.nz - _extra_nz, settings.nz + 1 + ) + self.g_factor_vec = settings.rhod(_z_vec) + + self.builder = Builder( + n_sd=settings.n_sd, + backend=backend(formulae=settings.formulae), + environment=env, + ) + self.builder.add_dynamic(AmbientThermodynamics()) + + if settings.enable_condensation: + self.builder.add_dynamic( + Condensation( + adaptive=settings.condensation_adaptive, + rtol_thd=settings.condensation_rtol_thd, + rtol_x=settings.condensation_rtol_x, + update_thd=settings.condensation_update_thd, + ) + ) + self.builder.add_dynamic(EulerianAdvection(mpdata)) + + self.products = [] + if settings.precip: + self.add_collision_dynamic(self.builder, settings, self.products) + + displacement = Displacement( + enable_sedimentation=settings.precip, + precipitation_counting_level_index=int( + settings.particle_reservoir_depth / settings.dz + ), + ) + self.builder.add_dynamic(displacement) + self.attributes = self.builder.particulator.environment.init_attributes( + spatial_discretisation=spatial_sampling.Pseudorandom(), + spectral_discretisation=spectral_sampling.ConstantMultiplicity( + spectrum=settings.wet_radius_spectrum_per_mass_of_dry_air + ), + kappa=settings.kappa, + collisions_only=not settings.enable_condensation, + z_part=settings.z_part, + ) + self.products += [ + PySDM_products.WaterMixingRatio( + name="cloud water mixing ratio", + unit="g/kg", + radius_range=settings.cloud_water_radius_range, + ), + PySDM_products.WaterMixingRatio( + name="rain water mixing ratio", + unit="g/kg", + radius_range=settings.rain_water_radius_range, + ), + PySDM_products.AmbientDryAirDensity(name="rhod"), + PySDM_products.AmbientDryAirPotentialTemperature(name="thd"), + PySDM_products.ParticleSizeSpectrumPerVolume( + name="wet spectrum", radius_bins_edges=settings.r_bins_edges + ), + PySDM_products.ParticleConcentration( + name="nc", radius_range=settings.cloud_water_radius_range + ), + PySDM_products.ParticleConcentration( + name="nr", radius_range=settings.rain_water_radius_range + ), + PySDM_products.ParticleConcentration( + name="na", radius_range=(0, settings.cloud_water_radius_range[0]) + ), + PySDM_products.MeanRadius(), + PySDM_products.EffectiveRadius( + radius_range=settings.cloud_water_radius_range + ), + PySDM_products.SuperDropletCountPerGridbox(), + PySDM_products.AveragedTerminalVelocity( + name="rain averaged terminal velocity", + radius_range=settings.rain_water_radius_range, + ), + PySDM_products.AmbientRelativeHumidity(name="RH", unit="%"), + PySDM_products.AmbientPressure(name="p"), + PySDM_products.AmbientTemperature(name="T"), + PySDM_products.AmbientWaterVapourMixingRatio( + name="water_vapour_mixing_ratio" + ), + ] + if settings.enable_condensation: + self.products.extend( + [ + PySDM_products.RipeningRate(name="ripening"), + PySDM_products.ActivatingRate(name="activating"), + PySDM_products.DeactivatingRate(name="deactivating"), + PySDM_products.PeakSaturation(), + PySDM_products.ParticleSizeSpectrumPerVolume( + name="dry spectrum", + radius_bins_edges=settings.r_bins_edges_dry, + dry=True, + ), + ] + ) + if settings.precip: + self.products.extend( + [ + PySDM_products.CollisionRatePerGridbox( + name="collision_rate", + ), + PySDM_products.CollisionRateDeficitPerGridbox( + name="collision_deficit", + ), + PySDM_products.CoalescenceRatePerGridbox( + name="coalescence_rate", + ), + PySDM_products.SurfacePrecipitation(), + ] + ) + self.particulator = self.builder.build( + attributes=self.attributes, products=tuple(self.products) + ) + + self.output_attributes = { + "cell origin": [], + "position in cell": [], + "radius": [], + "multiplicity": [], + } + self.output_products = {} + for k, v in self.particulator.products.items(): + if len(v.shape) == 0: + self.output_products[k] = np.zeros(self.nt + 1) + elif len(v.shape) == 1: + self.output_products[k] = np.zeros((self.mesh.grid[-1], self.nt + 1)) + elif len(v.shape) == 2: + number_of_time_sections = len(self.save_spec_and_attr_times) + self.output_products[k] = np.zeros( + (self.mesh.grid[-1], self.number_of_bins, number_of_time_sections) + ) + + @staticmethod + def add_collision_dynamic(builder, settings, _): + builder.add_dynamic( + Coalescence( + collision_kernel=settings.collision_kernel, + adaptive=settings.coalescence_adaptive, + ) + ) + + def save_scalar(self, step): + for k, v in self.particulator.products.items(): + if len(v.shape) > 1: + continue + if len(v.shape) == 1: + self.output_products[k][:, step] = v.get() + else: + self.output_products[k][step] = v.get() + + def save_spectrum(self, index): + for k, v in self.particulator.products.items(): + if len(v.shape) == 2: + self.output_products[k][:, :, index] = v.get() + + def save_attributes(self): + for k, v in self.output_attributes.items(): + v.append(self.particulator.attributes[k].to_ndarray()) + + def save(self, step): + self.save_scalar(step) + time = step * self.particulator.dt + if len(self.save_spec_and_attr_times) > 0 and ( + np.min( + np.abs( + np.ones_like(self.save_spec_and_attr_times) * time + - np.array(self.save_spec_and_attr_times) + ) + ) + < 0.1 + ): + save_index = np.argmin( + np.abs( + np.ones_like(self.save_spec_and_attr_times) * time + - np.array(self.save_spec_and_attr_times) + ) + ) + self.save_spectrum(save_index) + self.save_attributes() + + def run(self): + mesh = self.particulator.mesh + + assert "t" not in self.output_products and "z" not in self.output_products + self.output_products["t"] = np.linspace( + 0, self.nt * self.particulator.dt, self.nt + 1, endpoint=True + ) + self.output_products["z"] = np.linspace( + self.z0 + mesh.dz / 2, + self.z0 + (mesh.grid[-1] - 1 / 2) * mesh.dz, + mesh.grid[-1], + endpoint=True, + ) + + self.save(0) + for step in range(self.nt): + mpdata = self.particulator.dynamics["EulerianAdvection"].solvers + mpdata.update_advector_field() + if "Displacement" in self.particulator.dynamics: + self.particulator.dynamics["Displacement"].upload_courant_field( + (mpdata.advector / self.g_factor_vec,) + ) + self.particulator.run(steps=1) + self.save(step + 1) + + Outputs = namedtuple("Outputs", "products attributes") + output_results = Outputs(self.output_products, self.output_attributes) + return output_results diff --git a/PySDM/source/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb b/PySDM/source/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..05c66011779935c02a5eea29629642274090ce7f --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb @@ -0,0 +1,2076 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Singer_Ward/MWE_joss_paper.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MWE for JOSSv2 paper code snippets" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:09:56.811832Z", + "start_time": "2024-12-10T19:09:56.803208Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:10:04.060283Z", + "start_time": "2024-12-10T19:09:56.816551Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from PySDM import Formulae\n", + "from PySDM.physics import si\n", + "from PySDM_examples.Singer_Ward.aerosol import AerosolBetaCaryophylleneDark\n", + "\n", + "FORMULAE = Formulae()\n", + "WATER_MOLAR_VOLUME = FORMULAE.constants.Mv / FORMULAE.constants.rho_w\n", + "aerosol = AerosolBetaCaryophylleneDark(water_molar_volume=WATER_MOLAR_VOLUME)\n", + "formulae_bulk = Formulae(surface_tension='Constant')\n", + "formulae_ovad = Formulae(\n", + " surface_tension='CompressedFilmOvadnevaite',\n", + " constants={\n", + " 'sgm_org': 35 * si.mN / si.m,\n", + " 'delta_min': 1.75 * si.nm\n", + " }\n", + ")\n", + "formulae_ruehl = Formulae(\n", + " surface_tension='CompressedFilmRuehl',\n", + " constants={\n", + " 'RUEHL_nu_org': aerosol.modes[0]['nu_org'],\n", + " 'RUEHL_A0': 115e-20 * si.m * si.m,\n", + " 'RUEHL_C0': 6e-7,\n", + " 'RUEHL_m_sigma': 0.3e17 * si.J / si.m**2,\n", + " 'RUEHL_sgm_min': 35 * si.mN / si.m\n", + " }\n", + ")\n", + "formulae_sl = Formulae(\n", + " surface_tension='SzyszkowskiLangmuir',\n", + " constants={\n", + " 'RUEHL_nu_org': aerosol.modes[0]['nu_org'],\n", + " 'RUEHL_A0': 115e-20 * si.m * si.m,\n", + " 'RUEHL_C0': 6e-7,\n", + " 'RUEHL_sgm_min': 35 * si.mN / si.m\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:10:07.831134Z", + "start_time": "2024-12-10T19:10:04.397617Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c705f18abf4744a5af86ea30b1067979", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./Singer_fig1_kohler.pdf…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot\n", + "lines = {'Constant': '-', 'CompressedFilmOvadnevaite': '--', 'CompressedFilmRuehl': ':', 'SzyszkowskiLangmuir': '-.'}\n", + "colors = {'Constant': 'k', 'CompressedFilmOvadnevaite': 'C0', 'CompressedFilmRuehl': 'C1', 'SzyszkowskiLangmuir': 'C2'}\n", + "\n", + "for formulae in (formulae_bulk, formulae_ovad, formulae_ruehl, formulae_sl): \n", + " r_wet = np.logspace(np.log(50 * si.nm), np.log(2000 * si.nm), base=np.e, num=100)\n", + " sigma = np.ones(len(r_wet))\n", + " for j,vw in enumerate(formulae_ovad.trivia.volume(r_wet)):\n", + " sigma[j] = formulae.surface_tension.sigma(\n", + " 300 * si.K,\n", + " vw,\n", + " formulae_ovad.trivia.volume(50 * si.nm),\n", + " aerosol.modes[0]['f_org']\n", + " )\n", + " RH_eq = formulae.hygroscopicity.RH_eq(\n", + " r_wet,\n", + " 300 * si.K,\n", + " aerosol.modes[0]['kappa'][formulae.surface_tension.__name__],\n", + " (50 * si.nm)**3,\n", + " sigma\n", + " )\n", + " model = formulae.surface_tension.__name__\n", + " pyplot.plot(\n", + " r_wet / si.nm,\n", + " (RH_eq - 1)*100,\n", + " label=f\"{model}\",\n", + " color=colors[model],\n", + " linestyle=lines[model]\n", + " )\n", + "pyplot.grid()\n", + "pyplot.legend(title=\"Surface tension model\", fontsize=10, loc=4)\n", + "pyplot.xscale('log')\n", + "r_wet_ticks_nm = (100, 200, 300, 500, 1000, 2000)\n", + "pyplot.xticks(r_wet_ticks_nm, r_wet_ticks_nm)\n", + "pyplot.xlim(r_wet[0] / si.nm, r_wet[-1] / si.nm)\n", + "pyplot.ylabel('Equilibrium supersaturation [%]')\n", + "yticks = (-.2, -.1, 0, .1, .2, .3, .4)\n", + "pyplot.yticks(yticks, yticks)\n", + "pyplot.ylim(yticks[0], .45)\n", + "pyplot.xlabel('Wet radius [nm]')\n", + "show_plot(\"Singer_fig1_kohler.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.9 ('pysdm')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b14f34a08619f4a218d80d7380beed3f0c712c89ff93e7183219752d640ed427" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Singer_Ward/__init__.py b/PySDM/source/examples/PySDM_examples/Singer_Ward/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..738c9374e685ccc5fff09c0e97e8caf630929736 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Singer_Ward/__init__.py @@ -0,0 +1,9 @@ +""" +kohler.ipynb: +.. include:: ./kohler.ipynb.badges.md + +MWE_joss_paper.ipynb: +.. include:: ./MWE_joss_paper.ipynb.badges.md +""" + +# pylint: disable=invalid-name diff --git a/PySDM/source/examples/PySDM_examples/Singer_Ward/aerosol.py b/PySDM/source/examples/PySDM_examples/Singer_Ward/aerosol.py new file mode 100644 index 0000000000000000000000000000000000000000..ffe62ec307feb67257edd7ee1b38b6ce93e41fc7 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Singer_Ward/aerosol.py @@ -0,0 +1,185 @@ +from chempy import Substance +from pystrict import strict + +from PySDM.initialisation import spectra +from PySDM.initialisation.aerosol_composition import DryAerosolMixture +from PySDM.physics import si + + +@strict +class AerosolBetaCaryophylleneDark(DryAerosolMixture): + def __init__(self, water_molar_volume: float, Forg: float = 0.8, N: float = 400): + mode = { + "(NH4)2SO4": (1 - Forg), + "bcary_dark": Forg, + } + + super().__init__( + compounds=("(NH4)2SO4", "bcary_dark"), + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "bcary_dark": 299 * si.gram / si.mole, + }, + densities={ + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "bcary_dark": 1.20 * si.g / si.cm**3, + }, + is_soluble={ + "(NH4)2SO4": False, + "bcary_dark": True, + }, + ionic_dissociation_phi={ + "(NH4)2SO4": 3, + "bcary_dark": 1, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(mode), + "kappa": self.kappa( + mass_fractions=mode, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(mode), + "spectrum": spectra.Lognormal( + norm_factor=N / si.cm**3, m_mode=50.0 * si.nm, s_geom=1.75 + ), + }, + ) + + color = "red" + + +class AerosolBetaCaryophylleneLight(DryAerosolMixture): + def __init__(self, water_molar_volume: float, Forg: float = 0.8, N: float = 400): + mode = { + "(NH4)2SO4": (1 - Forg), + "bcary_light": Forg, + } + + super().__init__( + compounds=("(NH4)2SO4", "bcary_light"), + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "bcary_light": 360 * si.gram / si.mole, + }, + densities={ + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "bcary_light": 1.50 * si.g / si.cm**3, + }, + is_soluble={ + "(NH4)2SO4": False, + "bcary_light": True, + }, + ionic_dissociation_phi={ + "(NH4)2SO4": 3, + "bcary_light": 1, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(mode), + "kappa": self.kappa( + mass_fractions=mode, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(mode), + "spectrum": spectra.Lognormal( + norm_factor=N / si.cm**3, m_mode=50.0 * si.nm, s_geom=1.75 + ), + }, + ) + + color = "orange" + + +@strict +class AerosolAlphaPineneDark(DryAerosolMixture): + def __init__(self, water_molar_volume: float, Forg: float = 0.8, N: float = 400): + mode = { + "(NH4)2SO4": (1 - Forg), + "apinene_dark": Forg, + } + + super().__init__( + compounds=("(NH4)2SO4", "apinene_dark"), + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "apinene_dark": 209 * si.gram / si.mole, + }, + densities={ + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "apinene_dark": 1.27 * si.g / si.cm**3, + }, + is_soluble={ + "(NH4)2SO4": False, + "apinene_dark": True, + }, + ionic_dissociation_phi={ + "(NH4)2SO4": 3, + "apinene_dark": 1, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(mode), + "kappa": self.kappa( + mass_fractions=mode, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(mode), + "spectrum": spectra.Lognormal( + norm_factor=N / si.cm**3, m_mode=50.0 * si.nm, s_geom=1.75 + ), + }, + ) + + color = "green" + + +@strict +class AerosolAlphaPineneLight(DryAerosolMixture): + def __init__(self, water_molar_volume: float, Forg: float = 0.8, N: float = 400): + mode = { + "(NH4)2SO4": (1 - Forg), + "apinene_light": Forg, + } + + super().__init__( + compounds=("(NH4)2SO4", "apinene_light"), + molar_masses={ + "(NH4)2SO4": Substance.from_formula("(NH4)2SO4").mass + * si.gram + / si.mole, + "apinene_light": 265 * si.gram / si.mole, + }, + densities={ + "(NH4)2SO4": 1.77 * si.g / si.cm**3, + "apinene_light": 1.51 * si.g / si.cm**3, + }, + is_soluble={ + "(NH4)2SO4": False, + "apinene_light": True, + }, + ionic_dissociation_phi={ + "(NH4)2SO4": 3, + "apinene_light": 1, + }, + ) + self.modes = ( + { + "f_org": 1 - self.f_soluble_volume(mode), + "kappa": self.kappa( + mass_fractions=mode, water_molar_volume=water_molar_volume + ), + "nu_org": self.nu_org(mode), + "spectrum": spectra.Lognormal( + norm_factor=N / si.cm**3, m_mode=50.0 * si.nm, s_geom=1.75 + ), + }, + ) + + color = "lightgreen" diff --git a/PySDM/source/examples/PySDM_examples/Singer_Ward/kohler.ipynb b/PySDM/source/examples/PySDM_examples/Singer_Ward/kohler.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9454fd7bc46e185567f3287eeecd1908f4947a84 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Singer_Ward/kohler.ipynb @@ -0,0 +1,2963 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Singer_Ward/kohler.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Singer_Ward/kohler.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Singer_Ward/kohler.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example of using different surface tension models and the resulting Köhler curves\n", + "Parameters for surface tension models (and aerosol physiochemical description) loosely based on laboratory experiments by RXW, described in Ward et al., in prep. (2022)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:16:37.800284Z", + "start_time": "2024-12-10T19:16:37.794555Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:16:39.922261Z", + "start_time": "2024-12-10T19:16:37.805127Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si\n", + "\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM_examples.Singer_Ward.aerosol import AerosolBetaCaryophylleneDark" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:16:45.169404Z", + "start_time": "2024-12-10T19:16:40.030188Z" + } + }, + "outputs": [], + "source": [ + "# aerosol creation\n", + "FORMULAE = Formulae()\n", + "WATER_MOLAR_VOLUME = FORMULAE.constants.Mv / FORMULAE.constants.rho_w\n", + "aerosol = AerosolBetaCaryophylleneDark(water_molar_volume=WATER_MOLAR_VOLUME)\n", + "\n", + "# formulae creation\n", + "formulae_bulk = Formulae(surface_tension='Constant')\n", + "formulae_ovad = Formulae(\n", + " surface_tension='CompressedFilmOvadnevaite',\n", + " constants={\n", + " 'sgm_org': 35 * si.mN / si.m,\n", + " 'delta_min': 1.75 * si.nm\n", + " }\n", + ")\n", + "formulae_ruehl = Formulae(\n", + " surface_tension='CompressedFilmRuehl',\n", + " constants={\n", + " 'RUEHL_nu_org': aerosol.modes[0]['nu_org'],\n", + " 'RUEHL_A0': 115e-20 * si.m * si.m,\n", + " 'RUEHL_C0': 6e-7,\n", + " 'RUEHL_m_sigma': 0.3e17 * si.J / si.m**2,\n", + " 'RUEHL_sgm_min': 35 * si.mN / si.m\n", + " }\n", + ")\n", + "formulae_sl = Formulae(\n", + " surface_tension='SzyszkowskiLangmuir',\n", + " constants={\n", + " 'RUEHL_nu_org': aerosol.modes[0]['nu_org'],\n", + " 'RUEHL_A0': 115e-20 * si.m * si.m,\n", + " 'RUEHL_C0': 6e-7,\n", + " 'RUEHL_sgm_min': 35 * si.mN / si.m\n", + " }\n", + ")\n", + "\n", + "# aerosol and thermodynamic conditions\n", + "T = 300 * si.K\n", + "r_dry = 50 * si.nm\n", + "v_dry = formulae_ovad.trivia.volume(r_dry)\n", + "\n", + "# plotting info\n", + "r_wet = np.logspace(np.log(50 * si.nm), np.log(2000 * si.nm), base=np.e, num=100)\n", + "v_wet = formulae_ovad.trivia.volume(r_wet)\n", + "r_wet_ticks_nm = (100, 200, 300, 500, 1000, 2000)\n", + "lines = {'Constant': '-', 'CompressedFilmOvadnevaite': '--', 'CompressedFilmRuehl': ':', 'SzyszkowskiLangmuir': '-.'}\n", + "colors = {'Constant': 'k', 'CompressedFilmOvadnevaite': 'C0', 'CompressedFilmRuehl': 'C1', 'SzyszkowskiLangmuir': 'C2'}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-10T19:16:47.426715Z", + "start_time": "2024-12-10T19:16:45.178408Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "71891e2c057b44599b877e63f2180046", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./Singer_fig1.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axes = pyplot.subplots(2, 1, figsize=(6,6), sharex=True, sharey=False)\n", + "\n", + "for formulae in (formulae_bulk, formulae_ovad, formulae_ruehl, formulae_sl):\n", + " model = formulae.surface_tension.__name__\n", + " sigma = np.ones(len(v_wet))\n", + " if model in (\"CompressedFilmRuehl\", \"SzyszkowskiLangmuir\"):\n", + " for j,vw in enumerate(v_wet):\n", + " sigma[j] = formulae.surface_tension.sigma(T, vw, v_dry, aerosol.modes[0]['f_org'])\n", + " else:\n", + " sigma = formulae.surface_tension.sigma(T, v_wet, v_dry, aerosol.modes[0]['f_org'])\n", + " RH_eq = formulae.hygroscopicity.RH_eq(r_wet, T, aerosol.modes[0]['kappa'][model], r_dry**3, sigma)\n", + "\n", + " if not type(sigma) is type(v_wet):\n", + " sigma = np.ones(len(v_wet)) * sigma\n", + " # plot surface tension\n", + " axes[0].plot(\n", + " r_wet / si.nm,\n", + " sigma / (si.mN / si.m),\n", + " label=f\"{model}\", \n", + " color=colors[model],\n", + " linestyle=lines[model]\n", + " )\n", + " \n", + " # plot Köhler curve\n", + " axes[1].plot(\n", + " r_wet / si.nm, \n", + " (RH_eq - 1)*100, \n", + " color=colors[model], \n", + " linestyle=lines[model]\n", + " )\n", + "\n", + "# plot attributes\n", + "for ax in axes:\n", + " ax.grid()\n", + " ax.set_xscale('log')\n", + " ax.set_xticks(r_wet_ticks_nm, r_wet_ticks_nm)\n", + " ax.set_xlim(r_wet[0] / si.nm, r_wet[-1] / si.nm)\n", + "\n", + "ax = axes[0]\n", + "ax.set_ylabel('Surface tension [mN m$^{-1}$]')\n", + "yticks = (30, 40, 50, 60, 70, 80)\n", + "ax.set_yticks(yticks, yticks)\n", + "ax.set_ylim(30, 80)\n", + "ax.legend(title=\"Surface tension model\", fontsize=10, loc=4)\n", + "\n", + "ax = axes[1]\n", + "ax.set_ylabel('Equilibrium supersaturation [%]')\n", + "yticks = (-.2, -.1, 0, .1, .2, .3, .4)\n", + "ax.set_yticks(yticks, yticks)\n", + "ax.set_ylim(yticks[0], .45)\n", + "ax.set_xlabel('Wet radius [nm]')\n", + "\n", + "show_plot(\"Singer_fig1.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.9 ('pysdm')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b14f34a08619f4a218d80d7380beed3f0c712c89ff93e7183219752d640ed427" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/__init__.py b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..baa2774fa3eaf2f6d97a84ba64596f8b792a76df --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/__init__.py @@ -0,0 +1,7 @@ +# pylint: disable=invalid-name +""" +homogeneous nucleation event example based on Fig. B1. in +[Spichtinger et al. 2023](https://doi.org/10.5194/acp-23-2035-2023) +""" +from .simulation import Simulation +from .settings import Settings diff --git a/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/__init__.py b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/reference_bulk.py b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/reference_bulk.py new file mode 100644 index 0000000000000000000000000000000000000000..6aec8eb50785ba53bfa861b51898efc3fd114b10 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/reference_bulk.py @@ -0,0 +1,47 @@ +""" +reference results for bulk scheme in Fig B1. in +[Spichtinger et al. 2023](https://doi.org/10.5194/acp-23-2035-2023) +""" + +import numpy as np + + +def bulk_model_reference_array(): + + initial_temperatures = np.array([196.0, 216.0, 236.0]) + updrafts = np.array([0.05, 0.1, 0.3, 0.5, 1.0, 3.0, 5.0, 10.0]) + + dim_size = (np.shape(initial_temperatures)[0], np.shape(updrafts)[0]) + ni_bulk_ref = np.zeros(dim_size) + + # T = 196 + ni_bulk_ref[0, 0] = 643686.1316903427 + ni_bulk_ref[0, 1] = 2368481.0609527444 + ni_bulk_ref[0, 2] = 20160966.984670535 + ni_bulk_ref[0, 3] = 49475281.81718969 + ni_bulk_ref[0, 4] = 131080662.23620115 + ni_bulk_ref[0, 5] = 401046528.70428866 + ni_bulk_ref[0, 6] = 627442148.3402529 + ni_bulk_ref[0, 7] = 1151707310.2210448 + + # T = 216 + ni_bulk_ref[1, 0] = 60955.84292640147 + ni_bulk_ref[1, 1] = 189002.0792186534 + ni_bulk_ref[1, 2] = 1200751.6897658105 + ni_bulk_ref[1, 3] = 2942110.815055958 + ni_bulk_ref[1, 4] = 10475282.894692907 + ni_bulk_ref[1, 5] = 90871045.40856971 + ni_bulk_ref[1, 6] = 252175505.460412 + ni_bulk_ref[1, 7] = 860335156.4717773 + + # T = 236 + ni_bulk_ref[2, 0] = 13049.108886452004 + ni_bulk_ref[2, 1] = 40422.244759544985 + ni_bulk_ref[2, 2] = 237862.49854786208 + ni_bulk_ref[2, 3] = 545315.7805748513 + ni_bulk_ref[2, 4] = 1707801.469906006 + ni_bulk_ref[2, 5] = 11128055.66932415 + ni_bulk_ref[2, 6] = 27739585.111447476 + ni_bulk_ref[2, 7] = 101799566.47225031 + + return initial_temperatures, updrafts, ni_bulk_ref diff --git a/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/simulation_data.py b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/simulation_data.py new file mode 100644 index 0000000000000000000000000000000000000000..3b7f6cb59708557898140f57ec12d0901487ca00 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/data/simulation_data.py @@ -0,0 +1,22 @@ +import numpy as np + + +def saved_simulation_ensemble_mean(): + + ni_ens_mean = np.array( + [ + [0.00000000e00, 0.00000000e00, 0.00000000e00], + [0.00000000e00, 0.00000000e00, 0.00000000e00], + [1.62069821e03, 0.00000000e00, 0.00000000e00], + [5.25377025e06, 9.75512904e05, 4.51431097e05], + [3.67137290e07, 5.45240337e06, 1.53884530e06], + [7.96514420e07, 1.13878118e07, 3.26386880e06], + [2.19385480e08, 3.62240242e07, 9.00657591e06], + [1.00631095e09, 2.34443408e08, 4.90577208e07], + [1.73457062e09, 5.20774276e08, 1.16040316e08], + ] + ) + T = np.array([196.0, 216.0, 236.0]) + w = np.array([0.01, 0.03, 0.05, 0.1, 0.3, 0.5, 1.0, 3.0, 5.0]) + + return T, w, ni_ens_mean diff --git a/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a5a011c138ca79394033ba4a1c8b742eabbbd213 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb @@ -0,0 +1,2023 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "340efac1581b14ae", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Spichtinger_et_al_2023/fig_B1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "a6b09eaef75333df", + "metadata": {}, + "source": [ + "\n", + "#### based on Fig. B1 from Spichtinger et al. 2023 (ACP) \"_Impact of formulations of the homogeneous nucleation rate on ice nucleation events in cirrus_\"\n", + "\n", + "(work in progress)\n", + "\n", + "https://doi.org/10.5194/acp-23-2035-2023" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T14:20:31.231964Z", + "start_time": "2025-05-15T14:20:31.221457Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "69ce798ec8b87121", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T14:32:48.496132Z", + "start_time": "2025-05-15T14:32:47.161494Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "from PySDM_examples.Spichtinger_et_al_2023 import Simulation, Settings\n", + "from PySDM_examples.Spichtinger_et_al_2023.data import simulation_data, reference_bulk\n", + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fabd7ea8e8a11996", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T14:20:46.119340Z", + "start_time": "2025-05-15T14:20:46.116341Z" + } + }, + "outputs": [], + "source": [ + "calculate_data = False\n", + "save_to_file = False\n", + "read_from_json = False" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1acd9d93e2af385c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-19T10:08:33.165481Z", + "start_time": "2025-05-19T10:08:32.903294Z" + } + }, + "outputs": [], + "source": [ + "if calculate_data:\n", + "\n", + " initial_temperatures = np.array([196.0, 216.0, 236.0])\n", + " updrafts = np.array([0.01, 0.03, 0.05, 0.1, 0.3, 0.5, 1.0, 3.0, 5.0])\n", + " number_of_ensemble_runs = 5\n", + " seeds = [124670285330, 439785398735, 9782539783258, 12874192127481, 12741731272]\n", + "\n", + " dim_updrafts = len(updrafts)\n", + " dim_initial_temperatures = len(initial_temperatures)\n", + "\n", + " number_concentration_ice = np.zeros(\n", + " [dim_updrafts, dim_initial_temperatures, number_of_ensemble_runs]\n", + " )\n", + "\n", + " for i in range(dim_updrafts):\n", + " for j in range(dim_initial_temperatures):\n", + " for k in range(number_of_ensemble_runs):\n", + " setting = Settings(n_sd=50000,\n", + " w_updraft=updrafts[i],\n", + " T0=initial_temperatures[j],\n", + " seed=seeds[k],\n", + " dt=0.1)\n", + " model = Simulation(setting)\n", + " number_concentration_ice[i, j, k] = model.run()\n", + "\n", + " if save_to_file:\n", + " file_name = \"data/ni_w_T_ens_\" + str(number_of_ensemble_runs) + \".json\"\n", + " data_file = {\n", + " \"ni\": number_concentration_ice.tolist(),\n", + " \"T\": initial_temperatures.tolist(),\n", + " \"w\": updrafts.tolist(),\n", + " }\n", + " with open(file_name, \"w\", encoding=\"utf-8\") as file:\n", + " json.dump(data_file, file)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3821d0f892f4af29", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-15T14:32:54.180974Z", + "start_time": "2025-05-15T14:32:51.733640Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c70e79a37b70462485cffc12b0adc5f7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_B1.pdf\"), HTML(value=\" 0.0 and RHi < 130.0: + print("break") + break + RHi_old = RHi + + return output["ni"][-1] diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/__init__.py b/PySDM/source/examples/PySDM_examples/Srivastava_1982/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a851a3dc89093c63faceae85cfee656a3da749aa --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/__init__.py @@ -0,0 +1,19 @@ +""" +box-model examples feat. analytic solution for breakup from +[Srivastava 1982 (JAS)](https://doi.org/10.1175/1520-0469(1982)039%3C1317:ASMOPC%3E2.0.CO;2) + +figures.ipynb: +.. include:: ./figures.ipynb.badges.md +""" + +from .equations import Equations, EquationsHelpers +from .example import ( + add_to_plot_simulation_results, + coalescence_and_breakup_eq13, + compute_log_space, + get_coalescence_analytic_results, + get_processed_results, + get_pysdm_secondary_products, +) +from .settings import Settings, SimProducts +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/equations.py b/PySDM/source/examples/PySDM_examples/Srivastava_1982/equations.py new file mode 100644 index 0000000000000000000000000000000000000000..f7d05697761648a293de7dacd8be31ee4e8730ca --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/equations.py @@ -0,0 +1,96 @@ +import numpy as np + + +class Equations: + """Equations from Srivastava 1982: "A Simple Model of Particle Coalescence and Breakup" + (https://doi.org/10.1175/1520-0469(1982)039%3C1317:ASMOPC%3E2.0.CO;2) + note: all equations assume constant fragment mass""" + + @property + def alpha_star(self): + """see eq. 6""" + return self._alpha_star or self.alpha / self.c / self.M + + @property + def beta_star(self): + """see eq. 6""" + return self._beta_star or self.beta / self.c + + def tau(self, t): + """see eq. 6""" + return self.c * self.M * t + + def __init__( + self, *, M=None, c=None, alpha=None, beta=None, alpha_star=None, beta_star=None + ): + if alpha_star and (alpha or M or c): + raise ValueError("conflicting parameter") + self.M = M + self.c = c + self.alpha = alpha + self.beta = beta + + self._beta_star = beta_star + self._alpha_star = alpha_star + + if alpha_star and beta_star: + amb = alpha_star - beta_star + self._A = amb / 2 / alpha_star + self._B = 1 / np.sqrt( + (0.5 / alpha_star + beta_star / alpha_star) + + amb**2 / (4 * alpha_star**2) + ) + + def eq12(self): + """equilibrium (τ→∞) mean mass under collisions and spontaneous breakup + (no collisional breakup) + expressed as a ratio to fragment mass (i.e., dimensionless)""" + equilibrium_mean_mass_to_frag_mass_ratio = ( + 0.5 + (0.25 + 0.5 / self.alpha_star) ** 0.5 + ) + return equilibrium_mean_mass_to_frag_mass_ratio + + def eq13(self, m0, tau): + """mean mass expressed as a ratio to fragment mass as a function of + dimensionless scaled time (τ) under coalescence and collisional breakup + (no spontaneous breakup)""" + mean_mass_to_frag_mass_ratio = self._eq13(m0, tau) + return mean_mass_to_frag_mass_ratio + + def _eq13(self, m0, tau): + ebt = np.exp(-self.beta_star * tau) + return m0 * ebt + (1 + 0.5 / self.beta_star) * (1 - ebt) + + def eq14(self): + """equilibrium (τ→∞) mean mass expressed as a ratio to fragment mass for + under collisional merging and breakup (no spontaneous breakup)""" + equilibrium_mean_mass_to_frag_mass_ratio = 1 + 0.5 / self.beta_star + return equilibrium_mean_mass_to_frag_mass_ratio + + def eq15(self, m): + return (m - self._A) * self._B + + def eq15_m_of_y(self, y): + return (y / self._B) + self._A + + def eq16(self, tau): + return tau * self.alpha_star / self._B + + def eq10(self, m0, tau): + """ratio of mean mass to fragment size mass as a function of scaled time + for the case of coalescence only""" + mean_mass_to_frag_mass_ratio = m0 + tau / 2 + return mean_mass_to_frag_mass_ratio + + +class EquationsHelpers: + def __init__(self, total_volume, total_number_0, rho, frag_mass): + self.total_volume = total_volume + self.total_number_0 = total_number_0 + self.rho = rho + self.frag_mass = frag_mass + + def m0(self): + mean_volume_0 = self.total_volume / self.total_number_0 + m0 = self.rho * mean_volume_0 / self.frag_mass + return m0 diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/example.py b/PySDM/source/examples/PySDM_examples/Srivastava_1982/example.py new file mode 100644 index 0000000000000000000000000000000000000000..10d86cce1e70a7193bf31d58590b8f8c92a08c42 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/example.py @@ -0,0 +1,266 @@ +from collections import namedtuple + +import numpy as np +from matplotlib import pyplot +from PySDM_examples.Srivastava_1982.equations import Equations, EquationsHelpers +from PySDM_examples.Srivastava_1982.settings import SimProducts +from PySDM_examples.Srivastava_1982.simulation import Simulation + +from PySDM.dynamics import Collision +from PySDM.dynamics.collisions.breakup_efficiencies import ConstEb +from PySDM.dynamics.collisions.breakup_fragmentations import ConstantMass +from PySDM.dynamics.collisions.coalescence_efficiencies import ConstEc +from PySDM.dynamics.collisions.collision_kernels import ConstantK + +NO_BOUNCE = ConstEb(1) + + +def coalescence_and_breakup_eq13( + settings=None, n_steps=256, n_realisations=2, title=None, warn_overflows=True +): + # arrange + seeds = list(range(n_realisations)) + + collision_rate = settings.srivastava_c + settings.srivastava_beta + simulation = Simulation( + n_steps=n_steps, + settings=settings, + collision_dynamic=Collision( + collision_kernel=ConstantK(a=collision_rate), + coalescence_efficiency=ConstEc(settings.srivastava_c / collision_rate), + breakup_efficiency=NO_BOUNCE, + fragmentation_function=ConstantMass(c=settings.frag_mass), + warn_overflows=warn_overflows, + ), + ) + + x = np.arange(n_steps + 1, dtype=float) + sim_products = simulation.run_convergence_analysis(x, seeds=seeds) + + secondary_products = get_pysdm_secondary_products( + products=sim_products, total_volume=settings.total_volume + ) + + pysdm_results = get_processed_results(secondary_products) + + equations = Equations( + M=settings.total_volume * settings.rho / settings.frag_mass, + c=settings.srivastava_c, + beta=settings.srivastava_beta, + ) + equation_helper = EquationsHelpers( + settings.total_volume, + settings.total_number_0, + settings.rho, + frag_mass=settings.frag_mass, + ) + m0 = equation_helper.m0() + + x_log = compute_log_space(x) + analytic_results = get_breakup_coalescence_analytic_results( + equations, settings, m0, x, x_log + ) + + prods = [ + k + for k in list(pysdm_results.values())[0].keys() + if k != SimProducts.PySDM.total_volume.name + ] + + add_to_plot_simulation_results( + prods, + settings.n_sds, + x, + pysdm_results=pysdm_results, + analytic_results=analytic_results, + title=title, + ) + + results = namedtuple("_", "pysdm, analytic")( + pysdm=pysdm_results, analytic=analytic_results + ) + return results + + +def get_processed_results(res): + processed = {} + for n_sd in res.keys(): + processed[n_sd] = {} + for prod in res[n_sd].keys(): + processed[n_sd][prod] = {} + all_runs = np.asarray(list(res[n_sd][prod].values())) + + processed[n_sd][prod]["avg"] = np.mean(all_runs, axis=0) + processed[n_sd][prod]["max"] = np.max(all_runs, axis=0) + processed[n_sd][prod]["min"] = np.min(all_runs, axis=0) + processed[n_sd][prod]["std"] = np.std(all_runs, axis=0) + + return processed + + +# TODO #1045 (not needed) +def get_pysdm_secondary_products(products, total_volume): + pysdm_results = products + for n_sd in products.keys(): + pysdm_results[n_sd][ + SimProducts.Computed.mean_drop_volume_total_volume_ratio.name + ] = {} + + for k in pysdm_results[n_sd][SimProducts.PySDM.total_numer.name].keys(): + pysdm_results[n_sd][ + SimProducts.Computed.mean_drop_volume_total_volume_ratio.name + ][k] = compute_drop_volume_total_volume_ratio( + mean_volume=total_volume + / products[n_sd][SimProducts.PySDM.total_numer.name][k], + total_volume=total_volume, + ) + return pysdm_results + + +def get_coalescence_analytic_results(equations, settings, m0, x, x_log): + mean_mass10 = ( + equations.eq10(m0, equations.tau(x * settings.dt)) * settings.frag_mass + ) + mean_mass_ratio_log = equations.eq10(m0, equations.tau(x_log * settings.dt)) + + return get_analytic_results(equations, settings, mean_mass10, mean_mass_ratio_log) + + +def get_breakup_coalescence_analytic_results(equations, settings, m0, x, x_log): + mean_mass13 = ( + equations.eq13(m0, equations.tau(x * settings.dt)) * settings.frag_mass + ) + mean_mass_ratio_log = equations.eq13(m0, equations.tau(x_log * settings.dt)) + + return get_analytic_results(equations, settings, mean_mass13, mean_mass_ratio_log) + + +def get_analytic_results(equations, settings, mean_mass, mean_mass_ratio): + res = {} + res[SimProducts.Computed.mean_drop_volume_total_volume_ratio.name] = ( + compute_drop_volume_total_volume_ratio( + mean_volume=mean_mass / settings.rho, total_volume=settings.total_volume + ) + ) + res[SimProducts.PySDM.total_numer.name] = equations.M / mean_mass_ratio + return res + + +def compute_log_space(x, shift=0, num_points=1000, eps=1e-1): + assert eps < x[1] + return ( + np.logspace(np.log10(x[0] if x[0] != 0 else eps), np.log10(x[-1]), num_points) + + shift + ) + + +# TODO #1045 (not needed) +def compute_drop_volume_total_volume_ratio(mean_volume, total_volume): + return mean_volume / total_volume * 100 + + +def add_to_plot_simulation_results( + prods, + n_sds, + x, + pysdm_results=None, + analytic_results=None, + title=None, +): + fig = pyplot.figure(layout="constrained", figsize=(10, 4)) + _wide = 14 + _shrt = 8 + _mrgn = 1 + gs = fig.add_gridspec(nrows=20, ncols=2 * _wide + _shrt + 2 * _mrgn) + axs = ( + fig.add_subplot(gs[2:, _shrt + _mrgn : _shrt + _mrgn + _wide]), + fig.add_subplot(gs[2:, 0:_shrt]), + fig.add_subplot(gs[2:, _shrt + 2 * _mrgn + _wide :]), + ) + axs[1].set_ylim([6, 3500]) + + expons = [3, 5, 7, 9, 11] + + axs[1].set_yscale(SimProducts.PySDM.super_particle_count.plot_yscale) + axs[1].set_yticks([2**e for e in expons], [f"$2^{{{e}}}$" for e in expons]) + + if title: + fig.suptitle(title) + + ylims = {} + for prod in prods: + ylims[prod] = (np.inf, -np.inf) + for n_sd in n_sds: + ylims[prod] = ( + min(ylims[prod][0], 0.75 * np.amin(pysdm_results[n_sd][prod]["avg"])), + max(ylims[prod][1], 1.25 * np.amax(pysdm_results[n_sd][prod]["avg"])), + ) + + for i, prod in enumerate(prods): + # plot numeric + if pysdm_results: + for n_sd in n_sds: + y_model = pysdm_results[n_sd][prod] + + axs[i].step( + x, + y_model["avg"], + where="mid", + label=f"initial #SD: {n_sd}", + linewidth=1 + np.log(n_sd) / 3, + ) + axs[i].fill_between( + x, + y_model["avg"] - y_model["std"], + y_model["avg"] + y_model["std"], + alpha=0.2, + ) + + # plot analytic + if analytic_results: + add_analytic_result_to_axs(axs[i], prod, x, analytic_results) + + # cosmetics + axs[i].set_ylabel(SimProducts.get_prod_by_name(prod).plot_title) + + axs[i].grid() + axs[i].set_xlabel("step: t / dt") + + if prod != SimProducts.PySDM.super_particle_count.name: + bottom = ylims[prod][0] + top = ylims[prod][1] + + slope = ( + pysdm_results[n_sds[-1]][prod]["avg"][-1] + - pysdm_results[n_sds[-1]][prod]["avg"][0] + ) + if prod == SimProducts.PySDM.total_numer.name and slope < 0: + axs[i].set_ylim(0.2 * bottom, top) + else: + axs[i].set_ylim(bottom, top) + + axs[0].legend() + return fig, axs + + +def add_analytic_result_to_axs(axs_i, prod, x, res, key=""): + if prod != SimProducts.PySDM.super_particle_count.name: + x_theory = x + y_theory = res[prod] + + if prod == SimProducts.PySDM.total_numer.name: + if y_theory.shape != x_theory.shape: + x_theory = compute_log_space(x) + + axs_i.set_yscale(SimProducts.PySDM.total_numer.plot_yscale) + axs_i.set_xscale(SimProducts.PySDM.total_numer.plot_xscale) + axs_i.set_xlim(x_theory[0], None) + + axs_i.plot( + x_theory, + y_theory, + label=f"analytic {key}", + linestyle=":", + linewidth=2, + color="black", + ) diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/figures.ipynb b/PySDM/source/examples/PySDM_examples/Srivastava_1982/figures.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..36b8f5e84d1fae0672861ca9da6cbc492e7adf2e --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/figures.ipynb @@ -0,0 +1,2632 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Srivastava_1982/figures.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Srivastava_1982/figures.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Srivastava_1982/figures.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO #1417" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:46:34.616957Z", + "start_time": "2024-02-01T07:46:34.611862Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:46:50.777167Z", + "start_time": "2024-02-01T07:46:50.771110Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot\n", + "import numpy as np\n", + "from PySDM_examples.Srivastava_1982.equations import Equations\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Figure 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:46:36.530872Z", + "start_time": "2024-02-01T07:46:36.148902Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "051c06cb71564492bd950cc8dad49862", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./fig1.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_star = 1e-5\n", + "beta_star = 1e-4\n", + "\n", + "pyplot.title(\"fig 1 (note: value from paper: m_E=215)\")\n", + "for m0 in (100, 450):\n", + " eqs = Equations(alpha_star=alpha_star, beta_star=beta_star)\n", + " tau = np.linspace(0, 900)\n", + " y0 = eqs.eq15(m0)\n", + " x = eqs.eq16(tau)\n", + " y = (y0 + np.tanh(x)) / (1 + y0 * np.tanh(x))\n", + " pyplot.plot(tau, eqs.eq15_m_of_y(y), label=f\"$m(τ, m_0={m0})$\")\n", + "\n", + "pyplot.axhline(eqs.eq12(), linestyle=\"--\", label=\"$m_E$\")\n", + "pyplot.xlabel(\"τ\")\n", + "pyplot.ylabel(\"mass\")\n", + "pyplot.grid()\n", + "pyplot.legend()\n", + "show_plot('fig1.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## coalescence (equation 13) and breakup (equation 14) analytic" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-01T07:46:36.648339Z", + "start_time": "2024-02-01T07:46:36.544540Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "004a03e599ba4996b34013df27237e70", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value=\"./eq_13_14.pdf\")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_star = 1e-5\n", + "beta_star = 1e-4\n", + "eqs = Equations(alpha_star=alpha_star, beta_star=beta_star)\n", + "\n", + "tau = np.linspace(0, 90000)\n", + "m0 = 100\n", + "pyplot.title(\"equations (13) and (14)\")\n", + "pyplot.plot(tau, eqs.eq13(m0, tau), label=f\"m(τ, m_0={m0})\")\n", + "pyplot.axhline(eqs.eq14(), linestyle=\"--\", label=\"$m_E$\")\n", + "pyplot.xlabel(\"τ\")\n", + "pyplot.ylabel(\"mass\")\n", + "pyplot.grid()\n", + "pyplot.legend()\n", + "pyplot.show()\n", + "show_plot('eq_13_14.pdf')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/settings.py b/PySDM/source/examples/PySDM_examples/Srivastava_1982/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..8765ce6bec0adae5e841b9af72532674e05e6d1f --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/settings.py @@ -0,0 +1,79 @@ +from PySDM.backends import CPU +from PySDM.physics import constants_defaults + +DUMMY_FRAG_MASS = -1 + + +class SimProd: + def __init__(self, name, plot_title=None, plot_xscale=None, plot_yscale=None): + self.name = name + self.plot_title = plot_title or name + self.plot_yscale = plot_yscale + self.plot_xscale = plot_xscale + + +class SimProducts: + class PySDM: + total_numer = SimProd( + name="total numer", + plot_title="total droplet numer", + plot_xscale="log", + plot_yscale="log", + ) + total_volume = SimProd(name="total volume") + super_particle_count = SimProd( + name="super-particle count", plot_xscale="log", plot_yscale="log" + ) + + class Computed: + mean_drop_volume_total_volume_ratio = SimProd( + name="mean drop volume / total volume %", + plot_title="mean drop mass / total mass %", + ) + + @staticmethod + def get_prod_by_name(name): + for class_obj in (SimProducts.PySDM, SimProducts.Computed): + for attribute_str in dir(class_obj): + if not attribute_str.startswith("__"): + attribute = getattr(class_obj, attribute_str) + if attribute.name == name: + return attribute + return None + + +class Settings: + """interprets parameters from Srivastava 1982 in PySDM context""" + + def __init__( + self, + *, + n_sds, + dt, + dv, + total_number, + drop_mass_0, + srivastava_c, + srivastava_beta=None, + frag_mass=DUMMY_FRAG_MASS, + rho=constants_defaults.rho_w, + backend_class=CPU, + ): + self.backend_class = backend_class + + self.rho = rho + self.total_number_0 = total_number + self.total_volume = self.total_number_0 * drop_mass_0 / self.rho + self.dt = dt + self.dv = dv + self.frag_mass = frag_mass + + self.prods = ( + SimProducts.PySDM.total_volume.name, + SimProducts.PySDM.total_numer.name, + SimProducts.PySDM.super_particle_count.name, + ) + self.n_sds = n_sds + + self.srivastava_c = srivastava_c + self.srivastava_beta = srivastava_beta diff --git a/PySDM/source/examples/PySDM_examples/Srivastava_1982/simulation.py b/PySDM/source/examples/PySDM_examples/Srivastava_1982/simulation.py new file mode 100644 index 0000000000000000000000000000000000000000..3e8746961e65c188afb137b9ce0395b0b6a3bf85 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Srivastava_1982/simulation.py @@ -0,0 +1,85 @@ +import numpy as np +from PySDM_examples.Srivastava_1982.settings import SimProducts + +from PySDM import Builder, Formulae +from PySDM.environments import Box +from PySDM.products import SuperDropletCountPerGridbox, VolumeFirstMoment, ZerothMoment + + +class Simulation: + def __init__( + self, n_steps, settings, collision_dynamic=None, double_precision=True + ): + self.collision_dynamic = collision_dynamic + self.settings = settings + self.n_steps = n_steps + + self.double_precision = double_precision + + self.simulation_res = { + n_sd: {prod: {} for prod in self.settings.prods} + for n_sd in self.settings.n_sds + } + + def build(self, n_sd, seed, products): + env = Box(dt=self.settings.dt, dv=self.settings.dv) + builder = Builder( + backend=self.settings.backend_class( + formulae=Formulae( + constants={"rho_w": self.settings.rho}, + fragmentation_function="ConstantMass", + seed=seed, + ), + double_precision=self.double_precision, + ), + n_sd=n_sd, + environment=env, + ) + builder.add_dynamic(self.collision_dynamic) + particulator = builder.build( + products=products, + attributes={ + "multiplicity": np.full(n_sd, self.settings.total_number_0 / n_sd), + "volume": np.full( + n_sd, + self.settings.total_volume / self.settings.total_number_0, + ), + }, + ) + return particulator + + def run_convergence_analysis(self, x, seeds): + for n_sd in self.settings.n_sds: + for seed in seeds: + products = ( + SuperDropletCountPerGridbox( + name=SimProducts.PySDM.super_particle_count.name + ), + VolumeFirstMoment(name=SimProducts.PySDM.total_volume.name), + ZerothMoment(name=SimProducts.PySDM.total_numer.name), + ) + + particulator = self.build(n_sd, seed, products) + + for prod in self.settings.prods: + self.simulation_res[n_sd][prod][seed] = np.full( + self.n_steps + 1, -np.inf + ) + + for step in range(len(x)): + if step != 0: + particulator.run(steps=1) + for prod in self.settings.prods: + (self.simulation_res[n_sd][prod][seed][step],) = ( + particulator.products[prod].get() + ) + + np.testing.assert_allclose( + actual=self.simulation_res[n_sd][ + SimProducts.PySDM.total_volume.name + ][seed], + desired=self.settings.total_volume, + rtol=1e-3, + ) + + return self.simulation_res diff --git a/PySDM/source/examples/PySDM_examples/Stewart_1975/__init__.py b/PySDM/source/examples/PySDM_examples/Stewart_1975/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..afdb351fb9650c8c9ff7d707d76761a340007728 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Stewart_1975/__init__.py @@ -0,0 +1,7 @@ +""" +based on Stewart 1975 (J. Geophys. Res.) +https://doi.org/10.1029/JC080i009p01133 + +fig_1.ipynb: +.. include:: ./fig_1.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Stewart_1975/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Stewart_1975/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c2e729e59508d58d7e76e3191347f5cc12e9307e --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Stewart_1975/fig_1.ipynb @@ -0,0 +1,3069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c79d78cdb747b88f", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Stewart_1975/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Stewart_1975/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Stewart_1975/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "f79591b3517712d6", + "metadata": {}, + "source": [ + "### based on Fig. 1 from Stewart 1975 (J. Geophys. Res.) \"_Stable Isotope FractionationDue to Evaporation and Isotopic Exchange of Falling Waterdrops: Applications to Atmospheric Processes and Evaporation of Lakes_\" (https://doi.org/10.1029/JC080i009p01133)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b647df9dbc16b65f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.938615Z", + "start_time": "2025-04-15T21:17:25.929461Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "60e08ca8f2b2fa2b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.948710Z", + "start_time": "2025-04-15T21:17:25.945438Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si, in_unit\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM_examples.Kinzer_And_Gunn_1951.table_1_and_2 import table1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c1cf8f167c8737a7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.964308Z", + "start_time": "2025-04-15T21:17:25.958878Z" + } + }, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " ventilation='PruppacherAndRasmussen1979',\n", + " diffusion_thermics='Neglect',\n", + " air_dynamic_viscosity='ZografosEtAl1987',\n", + " terminal_velocity='RogersYau',\n", + " particle_shape_and_density='LiquidSpheres'\n", + ")\n", + "const = formulae.constants\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a88c53d14de54da", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.972569Z", + "start_time": "2025-04-15T21:17:25.970070Z" + } + }, + "outputs": [], + "source": [ + "radii = np.asarray(table1['Diameter [cm]'])[:-2] / 2 * si.cm \n", + "\n", + "temperature = 293.15 * si.K\n", + "eta_air\\\n", + " = formulae.air_dynamic_viscosity.eta_air(temperature)\n", + "air_density = const.p_STP/const.Rd/temperature\n", + "Sc = formulae.trivia.air_schmidt_number(\n", + " dynamic_viscosity=eta_air, \n", + " diffusivity=formulae.diffusion_thermics.D(\n", + " T=temperature, \n", + " p=const.p_STP\n", + " ), \n", + " density=air_density,\n", + ")\n", + "Re = formulae.particle_shape_and_density.reynolds_number(\n", + " radius=radii,\n", + " velocity_wrt_air=formulae.terminal_velocity.v_term(radii),\n", + " dynamic_viscosity=eta_air,\n", + " density=air_density,\n", + ")\n", + "sqrt_re_times_cbrt_sc = formulae.trivia.sqrt_re_times_cbrt_sc(Re=Re,Sc=Sc)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cbe557905243f8f0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.978487Z", + "start_time": "2025-04-15T21:17:25.976966Z" + } + }, + "outputs": [], + "source": [ + "def F_of_f(ventilation_coefficient):\n", + " return (\n", + " (ventilation_coefficient - 1) \n", + " / np.sqrt(Sc * Re / 4 / np.pi)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "24bafd5305aae7e1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:25.985046Z", + "start_time": "2025-04-15T21:17:25.983268Z" + } + }, + "outputs": [], + "source": [ + "ventilation_coefficient_kinzer_gunn\\\n", + " = (np.array(table1[f\"{int(formulae.trivia.K2C(temperature))} [deg C]\"][:-2]) \n", + " / 4 / np.pi / in_unit(radii,si.cm))\n", + "ventilation_factor_kinzer_gunn = F_of_f(ventilation_coefficient_kinzer_gunn)\n", + "\n", + "ventilation_coefficient_beard_pruppacher = formulae.ventilation.ventilation_coefficient(sqrt_re_times_cbrt_sc)\n", + "ventilation_factor_beard_pruppacher = F_of_f(ventilation_coefficient_beard_pruppacher)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "eeddfa81b58f2cc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:26.121401Z", + "start_time": "2025-04-15T21:17:25.990812Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9342197088054cfa9edb878681b73fa9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_1.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac31f80139f14cf3befd3f3d21a6ef11", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./ventilation_coeff.pdf\")…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_K_G_coeff = plt.plot(\n", + " in_unit(radii, si.mm),\n", + " ventilation_coefficient_kinzer_gunn,\n", + " label='Kinzer & Gunn'\n", + ")\n", + "plot_B_P_coeff = plt.plot(\n", + " in_unit(radii, si.mm),\n", + " ventilation_coefficient_beard_pruppacher,\n", + " label='Beard & Pruppacher'\n", + ")\n", + "plt.xlabel(\"Radius [mm]\")\n", + "plt.ylabel(\"Ventilation coefficient\")\n", + "plt.grid()\n", + "plt.legend()\n", + "show_plot(\"ventilation_coeff.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de5551a1734044f0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-15T21:17:26.261628Z", + "start_time": "2025-04-15T21:17:26.259960Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/__init__.py b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..168e50fdb1cf8115a29d32baf65e8c6d76699262 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/__init__.py @@ -0,0 +1,8 @@ +# pylint: disable=invalid-name +""" +ParaView visualisation of a parcel-model example based on the test case from +[Pyrcel package docs](https://pyrcel.readthedocs.io/) + +paraview_parcel_model.ipynb: +.. include:: ./paraview.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d49dee62e73729ad7e79fabd8299aa4ff718058b --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ea16d42c", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Strzabala_2025_BEng/paraview.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "bdde4d45", + "metadata": {}, + "source": [ + "Runs a parcel simulation based on Pyrcel documentation setup, and demonstrates export to VTK and visualisation using Paraview\n", + "\n", + "(requires `pvpython` command in PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1829d6ee", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6dcbcc6d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import subprocess\n", + "import platform\n", + "import pathlib\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si\n", + "from PySDM.initialisation.spectra import Lognormal\n", + "from PySDM.products import (\n", + " ParcelDisplacement,\n", + " AmbientTemperature,\n", + " AmbientDryAirDensity,\n", + " AmbientRelativeHumidity,\n", + " ParticleSizeSpectrumPerVolume,\n", + " ParticleVolumeVersusRadiusLogarithmSpectrum,\n", + ")\n", + "from PySDM.exporters import VTKExporterParcel\n", + "\n", + "from PySDM_examples.Pyrcel import Settings, Simulation\n", + "import PySDM_examples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cd6288b5", + "metadata": {}, + "outputs": [], + "source": [ + "settings = Settings(\n", + " dz=10 * si.m,\n", + " n_sd_per_mode=(25, 25),\n", + " aerosol_modes_by_kappa={\n", + " 0.54: Lognormal(\n", + " norm_factor=850 / si.cm**3,\n", + " m_mode=15 * si.nm,\n", + " s_geom=1.6,\n", + " ),\n", + " 1.2: Lognormal(\n", + " norm_factor=10 / si.cm**3,\n", + " m_mode=850 * si.nm,\n", + " s_geom=1.2,\n", + " ),\n", + " },\n", + " vertical_velocity=1.0 * si.m / si.s,\n", + " initial_pressure=775 * si.mbar,\n", + " initial_temperature=274 * si.K,\n", + " initial_relative_humidity=0.90,\n", + " displacement=1000 * si.m,\n", + " formulae=Formulae(constants={\"MAC\": 0.3}),\n", + ")\n", + "\n", + "dry_radius_bin_edges = np.logspace(\n", + " np.log10(1e-3 * si.um), np.log10(5e0 * si.um), 33, endpoint=False\n", + ")\n", + "\n", + "simulation = Simulation(\n", + " settings,\n", + " products=(\n", + " ParcelDisplacement(name=\"z\"),\n", + " AmbientRelativeHumidity(name=\"S_max_percent\", unit=\"%\", var=\"RH\"),\n", + " AmbientTemperature(name=\"T\"),\n", + " ParticleSizeSpectrumPerVolume(\n", + " name=\"dry:dN/dR\", radius_bins_edges=dry_radius_bin_edges, dry=True\n", + " ),\n", + " ParticleVolumeVersusRadiusLogarithmSpectrum(\n", + " name=\"dry:dV/dlnR\", radius_bins_edges=dry_radius_bin_edges, dry=True\n", + " ),\n", + " AmbientDryAirDensity(),\n", + " ),\n", + " mass_of_dry_air = 66666 * si.kg,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3ff50a25", + "metadata": {}, + "outputs": [], + "source": [ + "output = simulation.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "15d59657", + "metadata": {}, + "outputs": [], + "source": [ + "e = VTKExporterParcel(n_sd=simulation.particulator.n_sd, output=output, mass_of_dry_air=simulation.particulator.environment.mass_of_dry_air)\n", + "for step in settings.output_steps:\n", + " e.export_products(step, simulation)\n", + " e.export_attributes(step, simulation)\n", + "e.write_pvd()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a6ea0e6", + "metadata": {}, + "outputs": [], + "source": [ + "product = pathlib.Path(\"./output/sd_products.pvd\").absolute()\n", + "attributes = pathlib.Path(\"./output/sd_attributes.pvd\").absolute()\n", + "\n", + "paraview_script = pathlib.Path(PySDM_examples.__file__).parent / 'Strzabala_2025_BEng' / \"paraview_parcel_model.py\"\n", + "\n", + "args = [\n", + " \"pvpython\",\n", + " \"--force-offscreen-rendering\",\n", + " str(paraview_script),\n", + " \"--sd-products-pvd\",\n", + " str(product),\n", + " \"--sd-attributes-pvd\",\n", + " str(attributes),\n", + " \"--output-animation-path\",\n", + " str(pathlib.Path(\"./output/parcel_animation.ogv\").absolute()),\n", + " \"--output-screenshot-path\",\n", + " str(pathlib.Path(\"./output/last_frame.pdf\").absolute()),\n", + "]\n", + "result = subprocess.run(\n", + " args,\n", + " check=platform.system() != \"Windows\",\n", + " capture_output=True,\n", + " text=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e65c734-7012-4eff-8099-daa1119cb97d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "baea1aff-648f-41c0-b97b-4bf8f0b6de31", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d64ba1c-5eb9-4141-90a9-5a3da6ad7a71", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview_parcel_model.py b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview_parcel_model.py new file mode 100644 index 0000000000000000000000000000000000000000..85cc5488eb3f4d7577ab2dc8f0d0ca159400b131 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Strzabala_2025_BEng/paraview_parcel_model.py @@ -0,0 +1,244 @@ +#!/usr/bin/env pvpython +""" +ParaView pvpython script for visualizing sd_products and sd_attributes. +""" + +import argparse +from collections import namedtuple +import pathlib +from paraview import simple as pvs # pylint: disable=import-error + +pvs._DisableFirstRenderCameraReset() # pylint: disable=protected-access + + +def cli_using_argparse(argparse_parser): + """ + Command line interface using argparse. + """ + argparse_parser.add_argument( + "--sd-products-pvd", + dest="sd_products_pvd", + help="Path to sd_products.pvd", + ) + argparse_parser.add_argument( + "--sd-attributes-pvd", + dest="sd_attributes_pvd", + help="Path to sd_attributes.pvd", + ) + argparse_parser.add_argument( + "--output-animation-path", + help="Output path for the animation file.", + ) + argparse_parser.add_argument( + "--output-screenshot-path", + help="Output path for the screenshot file.", + ) + + argparse_parser.add_argument( + "--scalarbar-title-size", + type=int, + default=30, + help="Font size for scalar bar titles.", + ) + argparse_parser.add_argument( + "--scalarbar-label-size", + type=int, + default=30, + help="Font size for scalar bar labels.", + ) + argparse_parser.add_argument( + "--axes-title-size", + type=int, + default=30, + help="Font size for axes titles.", + ) + argparse_parser.add_argument( + "--axes-label-size", + type=int, + default=30, + help="Font size for axes labels.", + ) + + +parser = argparse.ArgumentParser( + description="ParaView pvpython script for visualizing sd_products and sd_attributes PVD files." +) +cli_using_argparse(parser) +args = parser.parse_args() + +sd_productspvd = pvs.PVDReader( + registrationName="sd_products.pvd", FileName=args.sd_products_pvd +) +sd_attributespvd = pvs.PVDReader( + registrationName="sd_attributes.pvd", FileName=args.sd_attributes_pvd +) + +setup = { + "renderView1": pvs.GetActiveViewOrCreate("RenderView"), + "sd_productspvdDisplay": pvs.Show( + sd_productspvd, + pvs.GetActiveViewOrCreate("RenderView"), + "UnstructuredGridRepresentation", + ), + "sd_attributespvdDisplay": pvs.Show( + sd_attributespvd, + pvs.GetActiveViewOrCreate("RenderView"), + "UnstructuredGridRepresentation", + ), + "rHlookup_table": pvs.GetColorTransferFunction("RH"), + "volumelookup_table": pvs.GetColorTransferFunction("volume"), +} + +animation_setup = namedtuple("setup", setup.keys())(**setup) + + +def configure_color_bar_and_display( + display, lookup_table, kind, *, anim_setup=animation_setup +): + """ + Configure color bar and display settings. + """ + display.Representation = "Surface" + display.SetScalarBarVisibility(anim_setup.renderView1, True) + color_bar = pvs.GetScalarBar(lookup_table, anim_setup.renderView1) + color_bar.LabelColor = [0.0, 0.0, 0.0] + color_bar.DrawScalarBarOutline = 1 + color_bar.ScalarBarOutlineColor = [0.0, 0.0, 0.0] + color_bar.TitleColor = [0.0, 0.0, 0.0] + + color_bar.TitleFontSize = args.scalarbar_title_size + color_bar.LabelFontSize = args.scalarbar_label_size + + if kind == "prod": + display.Opacity = 0.4 + display.DisableLighting = 1 + display.Diffuse = 0.76 + lookup_table.RescaleTransferFunction(90.0, 101.0) + lookup_table.ApplyPreset("Black, Blue and White", True) + lookup_table.NanColor = [0.67, 1.0, 1.0] + else: + display.PointSize = 13.0 + display.RenderPointsAsSpheres = 1 + display.Interpolation = "PBR" + lookup_table.RescaleTransferFunction(1e-18, 1e-13) + lookup_table.ApplyPreset("Cold and Hot", True) + lookup_table.MapControlPointsToLogSpace() + lookup_table.UseLogScale = 1 + lookup_table.NumberOfTableValues = 16 + lookup_table.InvertTransferFunction() + + +def configure_data_axes_grid(display, kind): + """ + Configure data axes grid settings. + """ + display.DataAxesGrid.GridAxesVisibility = 1 + display.DataAxesGrid.XTitle = "" + display.DataAxesGrid.YTitle = "" + display.DataAxesGrid.XAxisUseCustomLabels = 1 + display.DataAxesGrid.YAxisUseCustomLabels = 1 + + display.DataAxesGrid.XTitleFontSize = args.axes_title_size + display.DataAxesGrid.XLabelFontSize = args.axes_label_size + display.DataAxesGrid.YTitleFontSize = args.axes_title_size + display.DataAxesGrid.YLabelFontSize = args.axes_label_size + + if kind == "prod": + display.DataAxesGrid.ZTitle = "" + display.DataAxesGrid.GridColor = [0.0, 0.0, 0.0] + display.DataAxesGrid.ShowGrid = 1 + display.DataAxesGrid.LabelUniqueEdgesOnly = 0 + display.DataAxesGrid.ZAxisUseCustomLabels = 1 + display.DataAxesGrid.ZTitleFontSize = args.axes_title_size + display.DataAxesGrid.ZLabelFontSize = args.axes_label_size + else: + display.DataAxesGrid.ZTitle = " Z [m] " + display.DataAxesGrid.ZLabelColor = [0.0, 0.0, 0.0] + display.DataAxesGrid.ZTitleColor = [0.0, 0.0, 0.0] + display.DataAxesGrid.ZTitleFontSize = args.axes_title_size + display.DataAxesGrid.ZLabelFontSize = args.axes_label_size + + +def configure_view_appearance(render_view): + """ + Configure view appearance settings.""" + render_view.OrientationAxesLabelColor = [0.0, 0.0, 0.0] + render_view.OrientationAxesOutlineColor = [0.0, 0.0, 0.0] + render_view.OrientationAxesXVisibility = 0 + render_view.OrientationAxesYVisibility = 0 + render_view.OrientationAxesZColor = [0.0, 0.0, 0.0] + render_view.UseColorPaletteForBackground = 0 + render_view.Background = [1.0, 1.0, 1.0] + + +def set_camera_view(render_view): + """ + Set camera view settings. + """ + layout1 = pvs.GetLayout() + layout1.SetSize(1592, 1128) + render_view.CameraPosition = [ + 1548.95, + -1349.49, + 699.27, + ] + render_view.CameraFocalPoint = [ + -1.37e-13, + 3.18e-13, + 505.00, + ] + render_view.CameraViewUp = [ + -0.07, + 0.06, + 0.99, + ] + render_view.CameraParallelScale = 534.08 + + +def save_animation_and_screenshot(render_view, animation_path, screenshot_path): + """ + Save animation and screenshot. + """ + animation_scene = pvs.GetAnimationScene() + animation_scene.UpdateAnimationUsingDataTimeSteps() + + pvs.SaveAnimation(animation_path, render_view, FrameRate=15) + + if not screenshot_path: + return + + time_steps = sd_productspvd.TimestepValues + if not time_steps: + return + + last_time = time_steps[-1] + render_view.ViewTime = last_time + + for reader in (sd_productspvd, sd_attributespvd): + reader.UpdatePipeline(last_time) + + pvs.ExportView( + filename=str(pathlib.Path(screenshot_path)), + view=render_view, + Rasterize3Dgeometry=False, + GL2PSdepthsortmethod="BSP sorting (slow, best)", + ) + + pvs.RenderAllViews() + + +configure_color_bar_and_display( + animation_setup.sd_productspvdDisplay, animation_setup.rHlookup_table, kind="prod" +) +configure_data_axes_grid(animation_setup.sd_productspvdDisplay, kind="prod") +configure_view_appearance(animation_setup.renderView1) +configure_color_bar_and_display( + animation_setup.sd_attributespvdDisplay, + animation_setup.volumelookup_table, + kind="attr", +) +configure_data_axes_grid(animation_setup.sd_attributespvdDisplay, kind="attr") +set_camera_view(animation_setup.renderView1) +save_animation_and_screenshot( + animation_setup.renderView1, args.output_animation_path, args.output_screenshot_path +) diff --git a/PySDM/source/examples/PySDM_examples/Toon_et_al_1980/__init__.py b/PySDM/source/examples/PySDM_examples/Toon_et_al_1980/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PySDM/source/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb b/PySDM/source/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..73872ff675681e49c511bf97200df7a1274bd2e8 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb @@ -0,0 +1,2309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Toon_et_al_1980/fig_1.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "based on Fig. 1 from [Toon, Turco & Pollack (1980): \"A physical model of Titan’s clouds\", Icarus 43(3)](https://doi.org/10.1016/0019-1035(80)90173-6) " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from matplotlib import pyplot\n", + "\n", + "from PySDM.physics import si, in_unit\n", + "from PySDM import Formulae" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " hydrostatics='VariableGIsothermal',\n", + " constants={\n", + " 'g_std': 117 * si.cm / si.s**2,\n", + " 'celestial_body_radius': 2.8e8 * si.cm,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "p0 = {}\n", + "p0['CH$_4$'] = 25 * si.mbar\n", + "p0['CH$_4$+N$_2$'] = 250 * si.mbar\n", + "\n", + "molar_masses = {\n", + " 'CH$_4$': 16 * si.g / si.mol,\n", + " 'CH$_4$+N$_2$': 28 * si.g / si.mol,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c8a6b4b60174bd4b6a2e0212d7f7401", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_1.pdf\"), HTML(value=\"\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3d190775f5340bea63c0008a247a8fd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_1.pdf\"), HTML(value=\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f37ff8092bf145b4805cea5401603446", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./tmp2s5xx3k5.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot(*, particles, params_phys, params_comp, params_bins, rng, time=0):\n", + " \"\"\" plots the particle state as both a histogram as well as population scatter plot\n", + " (with random coordinates shuffled for the purpose of plotting) \"\"\"\n", + " _, axs = pyplot.subplot_mosaic(\n", + " [['hist'], ['part']],\n", + " figsize=(11, 6),\n", + " sharex=True,\n", + " tight_layout=True,\n", + " )\n", + " for k in particles:\n", + " axs['hist'].hist(\n", + " x=x_of_mass(particles[k]['mass']),\n", + " weights=particles[k]['mult'] / params_phys.norm * particles[k]['mass'],\n", + " bins=params_bins.count,\n", + " range=(params_bins.min_x, params_bins.max_x),\n", + " label=f'{k}',\n", + " alpha=.666,\n", + " density=True,\n", + " )\n", + " axs['part'].scatter(\n", + " x_of_mass(particles[k]['mass']),\n", + " rng.uniform(0, 1, params_comp.n_part),\n", + " s=.25 + 2 * particles[k]['mult'] / (params_phys.norm / params_comp.n_part)\n", + " )\n", + " lin_x, d_x = np.linspace(params_bins.min_x, params_bins.max_x, 256, retstep=True)\n", + " x_mean = lin_x[:-1] + d_x / 2\n", + " m_mean = mass_of_x(x_mean)\n", + " dn_dm = analytic_solution(mass_kg=m_mean, time_s=time, params_phys=params_phys)\n", + " axs['hist'].plot(\n", + " x_mean,\n", + " dn_dm * np.diff(mass_of_x(lin_x)) / np.diff(lin_x) * params_phys.norm * m_mean,\n", + " color='black',\n", + " label='Golovin solution'\n", + " )\n", + " axs['hist'].legend()\n", + " axs['hist'].set_ylabel(r'pdf(x) $\\cdot$ mass(x)')\n", + " axs['hist'].set_title(f'time: {time:.1f} s')\n", + " axs['hist'].set_xlim(params_bins.min_x, params_bins.max_x)\n", + " axs['part'].set_xlabel(r'$x = ln(\\sqrt[3]{m})$')\n", + " axs['part'].set_yticks([])\n", + " axs['part'].set_ylim(0,1)\n", + " for vl in axs.values():\n", + " vl.grid()\n", + " show_plot(inline_format='png')\n", + "\n", + "plot(particles=PARTICLES, params_phys=PARAMS_PHYS, params_comp=PARAMS_COMP, params_bins=PARAMS_BINS, rng=RNG, time=1e-10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def coagulate(*, params_phys, params_comp, particles, rng, graphs):\n", + " \"\"\" performs Monte-Carlo coagulation of the particles using a simplified SDM algorithm for an additive kernel \"\"\"\n", + " n_pairs = params_comp.n_part // 2\n", + " p_scale = params_comp.n_part * (params_comp.n_part - 1) / 2 / n_pairs\n", + " for _ in range(params_comp.n_step): \n", + " non_overlapping_pairs = rng.permutation(params_comp.n_part)[: 2 * n_pairs].reshape(-1, 2)\n", + " u01 = rng.uniform(0, 1, n_pairs)\n", + " for samp,part in particles.items():\n", + " for alpha, pair in enumerate(non_overlapping_pairs):\n", + " j, k = pair\n", + " if part['mult'][j] < part['mult'][k]:\n", + " j, k = k, j\n", + " kern = params_phys.b_per_s * (part['mass'][j] + part['mass'][k])\n", + " prob = part['mult'][j] * kern * params_comp.dt_s / params_phys.dv_m3\n", + "\n", + " gamma = np.ceil(prob * p_scale - u01[alpha])\n", + " if gamma != 0:\n", + " gamma_t = min(gamma, part['mult'][j] // part['mult'][k])\n", + " deficit = (gamma - gamma_t) * part['mult'][k]\n", + " graphs[samp].add_edge(\n", + " j, k, mult_transfer = gamma_t*part['mult'][k],\n", + " total_mass_transfer = gamma_t*part['mass'][j] * part['mult'][k],\n", + " mass_transfer = part['mass'][j],\n", + " total_mass_deficit=deficit * part['mass'][j],\n", + " size_deficit=deficit*part['mass'][j] / part['mult'][k],\n", + " mult_deficit=deficit\n", + " )\n", + " if part['mult'][j] > gamma_t * part['mult'][k]: \n", + " part['mult'][j] -= gamma_t * part['mult'][k]\n", + " part['mass'][k] += gamma_t * part['mass'][j] \n", + " else:\n", + " part['mult'][j] = part['mult'][k] // 2\n", + " part['mult'][k] -= part['mult'][k] // 2\n", + " part['mass'][k] += gamma_t * part['mass'][j]\n", + " part['mass'][j] = part['mass'][k]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "B = {}\n", + "for pkey in PARTICLES.keys():\n", + " B[pkey] = nx.DiGraph()\n", + " for i in range(PARAMS_COMP.n_part):\n", + " B[pkey].add_node(i, mass_i=PARTICLES[pkey]['mass'][i], mult_i=PARTICLES[pkey]['mult'][i])\n", + "\n", + "coagulate(particles=PARTICLES, params_comp=PARAMS_COMP, params_phys=PARAMS_PHYS, rng=RNG, graphs = B)\n", + "\n", + "for pkey in PARTICLES.keys():\n", + " for i in range(PARAMS_COMP.n_part):\n", + " B[pkey].nodes[i]['mass_f'] = PARTICLES[pkey]['mass'][i]\n", + " B[pkey].nodes[i]['mult_f'] = PARTICLES[pkey]['mult'][i]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f0e15813ac234520baec62fd7b3ae7b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./sdm_network.pdf\"), HTML(value…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph_types = (\"total_mass\", \"mass\", \"mult\")\n", + "\n", + "fig, axes = pyplot.subplot_mosaic(\n", + " (\n", + " ['sampling: uniform random in x' + t for t in graph_types],\n", + " ['sampling: uniform random in ln(x)' + t for t in graph_types],\n", + " ['sampling: constant multiplicity' + t for t in graph_types],\n", + " ),\n", + " figsize=(9, 9),\n", + " tight_layout=True,\n", + ")\n", + "titles = {\n", + " 'sampling: uniform random in x': 'uniform-in-x',\n", + " 'sampling: uniform random in ln(x)': 'uniform-in-log(x)',\n", + " 'sampling: constant multiplicity': 'constant-multiplicity',\n", + " 'total_mass': 'Total Mass',\n", + " 'mult': 'Multiplicity',\n", + " 'mass': 'Individual Mass',\n", + "}\n", + "for sampling_key, v in B.items():\n", + " pos = nx.forceatlas2_layout(v, scaling_ratio=0.2, strong_gravity=True)\n", + " for graph_type in graph_types:\n", + " ax = axes[sampling_key + graph_type]\n", + " if graph_type == \"total_mass\":\n", + " attr_i = np.array([v.nodes[i]['mass_i'] * v.nodes[i]['mult_i'] for i in v.nodes])\n", + " attr_f = np.array([v.nodes[i]['mass_f'] * v.nodes[i]['mult_f'] for i in v.nodes])\n", + " scale = 1e4\n", + " scale_edge = 1e2\n", + " else:\n", + " attr_i = np.array([v.nodes[i][graph_type + \"_i\"] for i in v.nodes])\n", + " attr_f = np.array([v.nodes[i][graph_type + \"_f\"] for i in v.nodes])\n", + " scale = 1e14 if graph_type == \"mass\" else 1e-9\n", + " scale_edge = 1e4 if graph_type == \"mass\" else 1e-11\n", + " nx.draw(v, pos, ax=ax, \n", + " node_size= scale * attr_i, \n", + " alpha= 0.4,\n", + " width=[0.2 + scale_edge * v.edges[e][graph_type +'_transfer'] for e in v.edges],\n", + " arrowsize=5,\n", + " with_labels=False\n", + " )\n", + " nx.draw_networkx_edges(\n", + " v,\n", + " pos=pos,\n", + " ax=ax,\n", + " edgelist=[e for e in v.edges if v.edges[e]['mult_deficit']>0],\n", + " edge_color='red',\n", + " width=[0.2 + 1e-11 * v.edges[e]['mult_transfer'] for e in v.edges if v.edges[e]['mult_deficit']>0],\n", + " label='deficit',\n", + " arrowsize=5,\n", + " )\n", + " nx.draw_networkx_nodes(\n", + " v,\n", + " pos,\n", + " ax=ax, \n", + " node_size= scale * attr_f,\n", + " node_color='purple',\n", + " alpha=0.3,\n", + " label='final size'\n", + " )\n", + " if sampling_key == 'sampling: uniform random in x':\n", + " ax.set_title(titles[graph_type], fontsize=12)\n", + " if graph_type == \"total_mass\":\n", + " ax.annotate(\n", + " titles[sampling_key],\n", + " xy=(-0.15, 0.5),\n", + " xycoords='axes fraction',\n", + " va='center', \n", + " fontsize=12,\n", + " rotation=90\n", + " )\n", + "show_plot('sdm_network', inline_format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/__init__.py b/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..29e89d250b60217d7c4c6b0d8c63d15c4229ac51 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/__init__.py @@ -0,0 +1,10 @@ +# pylint: disable=invalid-name +""" +parcel-model condensation-evaporation example based on +[Yang et al. 2018 (ACP)](https://doi.org/10.5194/acp-18-7313-2018) + +fig_2.ipynb: +.. include:: ./fig_2.ipynb.badges.md +""" +from .settings import Settings +from .simulation import Simulation diff --git a/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb b/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a2e1ca33369a54ced8ac81cf5751750a0d766e96 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Yang_et_al_2018/fig_2.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### based on Fig. 2 from Yang et al. 2018 (Atmos. Chem. Phys. 18) \"_Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation_\" \n", + "https://doi.org/10.5194/acp-18-7313-2018" + ] + }, + { + "cell_type": "code", + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2025-12-08T21:21:28.343055Z", + "start_time": "2025-12-08T21:21:28.338005Z" + } + }, + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T21:21:33.550909Z", + "start_time": "2025-12-08T21:21:28.346833Z" + } + }, + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from open_atmos_jupyter_utils import show_plot\n", + "from PySDM_examples.Yang_et_al_2018 import Simulation, Settings\n", + "from PySDM.physics.constants import PER_CENT, si, in_unit" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T21:21:33.563483Z", + "start_time": "2025-12-08T21:21:33.561152Z" + } + }, + "source": [ + "ix51 = 40\n", + "ix503 = 99" + ], + "outputs": [], + "execution_count": 3 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T21:21:33.569763Z", + "start_time": "2025-12-08T21:21:33.566532Z" + } + }, + "source": [ + "def runner():\n", + " settings = Settings(dt_output=10 * si.minutes)\n", + "\n", + " assert int(round(in_unit(settings.r_dry[ix51], si.nm))) == 51\n", + " assert int(round(in_unit(settings.r_dry[ix503], si.nm))) == 503\n", + "\n", + " output = Simulation(settings).run()\n", + " mass = settings.mass_of_dry_air\n", + " result = {\n", + " 'r_bins_edges': settings.r_bins_edges,\n", + " 'n': settings.n / mass,\n", + " 'dt_max': settings.dt_max,\n", + " 'rtol_thd': settings.rtol_thd,\n", + " 'rtol_x': settings.rtol_x,\n", + " }\n", + " array_keys = (\n", + " 'r_mean_gt_1_um', 'z', 'S', 't', 'T',\n", + " 'r_act', 'r_bins_values', 'r',\n", + " )\n", + "\n", + " result.update({k: output[k] for k in array_keys})\n", + " result['liquid_water_mixing_ratio'] = (\n", + " output[\"water_vapour_mixing_ratio\"][0] - output[\"water_vapour_mixing_ratio\"]\n", + " )\n", + " result['r_bins_values'] = result['r_bins_values'].T / mass\n", + " result['r'] = result['r'].T\n", + " return result\n" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T21:22:03.540018Z", + "start_time": "2025-12-08T21:21:33.572803Z" + } + }, + "source": "out = runner()", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Thermodynamical and microphysical properties of an adiabatic cloud parcel with upward and downward oscillations. \n", + "Fig. 2, subplots a, b & c \n", + "(a) Liquid water mixing ratio changes with height. \n", + "(b) Cloud parcel saturation ratio changes with height. \n", + "(c) Radii changes of two selected cloud droplets with height." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T21:22:04.327426Z", + "start_time": "2025-12-08T21:22:03.561721Z" + } + }, + "source": [ + "figsize=(9,6)\n", + "fig, ax = plt.subplots(1, 3, sharey=True, figsize=figsize)\n", + " \n", + "xunit_q = si.gram / si.kilogram\n", + "xunit_S = PER_CENT\n", + "xunit_r = si.um\n", + "\n", + "z = out['z']\n", + "tols = f\"rtol_x = {out['rtol_x']}, rtol_thd = {out['rtol_thd']}\"\n", + "\n", + "ax[0].plot(in_unit(out['liquid_water_mixing_ratio'], xunit_q), z)\n", + "ax[0].set(\n", + " xlabel='liquid water mixing ratio [g/kg]',\n", + " ylim=[800, 1300],\n", + " ylabel=\"metres\"\n", + ")\n", + "ax[1].plot(in_unit(out['S']+1, xunit_S), z)\n", + "ax[1].set(\n", + " xlabel=\"saturation ratio [%]\",\n", + " xlim=[99.75, 100.25],\n", + " title=f\"dt_max = {out['dt_max']}, \"+tols\n", + ")\n", + "ax[2].plot(\n", + " in_unit(out['r'][ix51], xunit_r), z,\n", + " label=\"r$_d$ = 51 nm\"\n", + ")\n", + "ax[2].plot(\n", + " in_unit(out['r'][ix503], xunit_r), z,\n", + " label=\"r$_d$ = 503 nm\"\n", + ")\n", + "ax[2].set(xlabel=\"radius [micrometre]\")\n", + "ax[2].legend()\n", + "\n", + "for a in ax:\n", + " a.grid()\n", + "\n", + "fig.tight_layout()\n", + "show_plot(filename='q_S_rd.pdf')" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "image/svg+xml": "\n\n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "HBox(children=(HTML(value=\"./q_S_rd.pdf\"), HTML(value=\" r$_\\text{cr}$)\",\n", + ")\n", + "ax.plot(\n", + " in_unit(out['t'], xunit),\n", + " in_unit(out[\"r_mean_gt_1_um\"], yunit),\n", + " \"--\",\n", + " color=\"gray\",\n", + " label=r\"r$_\\text{mean}$ (r > 1 $\\mu$m)\"\n", + ")\n", + "ax.set(\n", + " ylabel=r'r$_\\text{d}$ [$\\mu$m]',\n", + " xlabel='time [h]',\n", + " ylim=(0,20),\n", + " title=\"Cloud Droplet Size Distribution\\n\" + rf\"dt_max = {out['dt_max']}, {tols}\"\n", + ")\n", + "ax.grid()\n", + "ax.legend(loc=\"upper right\")\n", + "\n", + "fig.tight_layout()\n", + "fig.colorbar(cm, label=r\"cloud droplet size distribution [cm$^{-3}$ $\\mu$m$^{-1}$]\")\n", + "\n", + "show_plot(filename='spectrum.pdf', inline_format='png')" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "HBox(children=(HTML(value=\"./spectrum.pdf\"), HTML(value=\"= settings.dt_max: + self.n_substeps += 1 + self.formulae = Formulae( + diffusion_coordinate=settings.coord, + saturation_vapour_pressure="AugustRocheMagnus", + ) + self.bins_edges = self.formulae.trivia.volume(settings.r_bins_edges) + + env = Parcel( + dt=dt_output / self.n_substeps, + mass_of_dry_air=settings.mass_of_dry_air, + p0=settings.p0, + initial_water_vapour_mixing_ratio=self.formulae.constants.eps + / ( + settings.p0 + / settings.RH0 + / self.formulae.saturation_vapour_pressure.pvs_water(settings.T0) + - 1 + ), + T0=settings.T0, + w=settings.w, + z0=settings.z0, + ) + builder = Builder( + backend=backend( + formulae=self.formulae, override_jit_flags={"parallel": False} + ), + n_sd=settings.n_sd, + environment=env, + ) + + environment = builder.particulator.environment + builder.add_dynamic(AmbientThermodynamics()) + condensation = Condensation( + adaptive=settings.adaptive, + rtol_x=settings.rtol_x, + rtol_thd=settings.rtol_thd, + dt_cond_range=settings.dt_cond_range, + ) + builder.add_dynamic(condensation) + + products = [ + PySDM_products.ParticleSizeSpectrumPerVolume( + name="Particles Wet Size Spectrum", + radius_bins_edges=settings.r_bins_edges, + ), + PySDM_products.CondensationTimestepMin(name="dt_cond_min"), + PySDM_products.CondensationTimestepMax(name="dt_cond_max"), + PySDM_products.RipeningRate(), + PySDM_products.MeanRadius( + name="r_mean_gt_1_um", radius_range=(1 * si.um, np.inf) + ), + PySDM_products.ActivatedMeanRadius( + name="r_act", count_activated=True, count_unactivated=False + ), + PySDM_products.Time(name="t"), + ] + + attributes = environment.init_attributes( + n_in_dv=settings.n, kappa=settings.kappa, r_dry=settings.r_dry + ) + + self.particulator = builder.build(attributes, products) + + self.n_steps = settings.n_steps + + def save(self, output): + _sp = self.particulator + cell_id = 0 + output["r_bins_values"].append( + (_sp.products["Particles Wet Size Spectrum"].get()).T + ) + volume = _sp.attributes["volume"].to_ndarray() + output["r"].append((self.formulae.trivia.radius(volume=volume)).T) + output["S"].append(_sp.environment["RH"][cell_id] - 1) + output["t"].append(_sp.products["t"].get()) + for key in ("water_vapour_mixing_ratio", "T", "z"): + output[key].append(_sp.environment[key][cell_id]) + for key in ( + "dt_cond_max", + "dt_cond_min", + "ripening rate", + "r_mean_gt_1_um", + "r_act", + ): + output[key].append(_sp.products[key].get()[cell_id].copy()) + + def run(self): + output = { + key: [] + for key in ( + "r", + "S", + "z", + "t", + "water_vapour_mixing_ratio", + "T", + "r_bins_values", + "dt_cond_max", + "dt_cond_min", + "ripening rate", + "r_mean_gt_1_um", + "r_act", + ) + } + + self.save(output) + for _ in range(self.n_steps): + self.particulator.run(self.n_substeps) + self.save(output) + for k, v in output.items(): + output[k] = np.asarray(v) + return output diff --git a/PySDM/source/examples/PySDM_examples/Zaba_et_al/__init__.py b/PySDM/source/examples/PySDM_examples/Zaba_et_al/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3773e97f96f3bc1f01f9fd2d08a23904865e9b08 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Zaba_et_al/__init__.py @@ -0,0 +1,7 @@ +""" +global_meteoric_water_line.ipynb: +.. include:: ./global_meteoric_water_line.ipynb.badges.md + +timescales_comparison.ipynb: +.. include:: ./timescales_comparison.ipynb.badges.md +""" diff --git a/PySDM/source/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb b/PySDM/source/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..568c8e8dbf456e03b9005f4e986810462a70e407 --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb @@ -0,0 +1,5548 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "490fce21c0ca4207", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Zaba_et_al/global_meteoric_water_line.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "43c65ccf2810efd1", + "metadata": {}, + "source": [ + "## Global Meteoric Water Line\n", + "GMWL is a linear relation between isotopic deltas for oxygen-18 and deuterium originating from [Craig 1961](https://doi.org/10.1126/science.133.3465.1702). Here, we explore how the linear relationship coefficients can be linked with equilibrium fractionation factors $\\alpha(T)$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6d10078ca20ca3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:00.275675Z", + "start_time": "2025-03-04T17:00:00.270292Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "15b88e60-9744-4c62-984a-e1939b4ed93e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:02.300940Z", + "start_time": "2025-03-04T17:00:00.377959Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from PySDM import Formulae\n", + "from PySDM.physics.constants import si, in_unit, PER_MILLE\n", + "from matplotlib import pyplot\n", + "from open_atmos_jupyter_utils import show_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb6cad0de94412", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:02.341477Z", + "start_time": "2025-03-04T17:00:02.321357Z" + } + }, + "outputs": [], + "source": [ + "f = {}\n", + "alphas = {}\n", + "variants = {\n", + " \"HoritaAndWesolowski1994\",\n", + " \"Majoube1971\",\n", + " \"VanHook1968\"\n", + "}\n", + "for variant in variants:\n", + " f[variant] = Formulae(isotope_equilibrium_fractionation_factors=variant)\n", + " alphas[variant] = {}\n", + " alphas[variant]['18O'] = f[variant].isotope_equilibrium_fractionation_factors.alpha_l_18O\n", + " alphas[variant]['2H'] = f[variant].isotope_equilibrium_fractionation_factors.alpha_l_2H" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "aa1eb33a-5250-40dd-9cb6-2f9955ab573e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:02.348231Z", + "start_time": "2025-03-04T17:00:02.345731Z" + } + }, + "outputs": [], + "source": [ + "def a_coeff(alpha_D, alpha_O, B):\n", + " \"\"\" slope coefficient assuming isotopic equilibrium with SMOW \"\"\"\n", + " return (alpha_D - 1 - B) / (alpha_O - 1)\n", + "\n", + "def b_coeff(alpha_D, alpha_O, A):\n", + " \"\"\" intercept coefficient assuming isotopic equilibrium with SMOW \"\"\"\n", + " return alpha_D - 1 - A * (alpha_O - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8d3f24ff-7692-477c-af53-1aff4ad6eab7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:03.118540Z", + "start_time": "2025-03-04T17:00:02.362296Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1461c6ab79d945c2a8a2971f9f419312", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_Majoube1971.pdf\"), HT…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0a6bc6b2ade449269ecbb7ed1a98b6e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_VanHook1968.pdf\"), HT…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "86a96580ebaf42da98f0abad9ffff763", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_HoritaAndWesolows…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T = np.linspace(280, 290) * si.K\n", + "thick = 2\n", + "thin = 1\n", + "lines = {}\n", + "for variant in variants:\n", + " const = f[variant].constants\n", + " lines[variant] = {}\n", + " fig, axs = pyplot.subplots(1, 2, figsize=(9, 2))\n", + " for db in (5 * PER_MILLE, 0, -5 * PER_MILLE):\n", + " b = const.CRAIG_1961_INTERCEPT_COEFF + db\n", + " label = f\"b={in_unit(b, PER_MILLE):.3g}\"\n", + " lines[variant][label] = a_coeff(alphas[variant]['2H'](T), alphas[variant]['18O'](T), b)\n", + " axs[0].plot(T, lines[variant][label], label=f\"b={in_unit(b, PER_MILLE):.3g}‰\", linewidth=thick if db==0 else thin)\n", + " axs[0].set_ylabel('a coefficient')\n", + " axs[0].hlines(const.CRAIG_1961_SLOPE_COEFF, xmin=T[0], xmax=T[-1], color='k', linewidth=thick, linestyle='--')\n", + " \n", + " for da in (-.5, 0, .5):\n", + " a = const.CRAIG_1961_SLOPE_COEFF + da\n", + " label = f'{a=}'\n", + " lines[variant][label] = in_unit(b_coeff(alphas[variant]['2H'](T), alphas[variant]['18O'](T), a), PER_MILLE)\n", + " plot_b = axs[1].plot(T, lines[variant][label], label=label, linewidth=thick if da==0 else thin)\n", + " axs[1].set_ylabel('b coefficient [‰]')\n", + " axs[1].hlines(in_unit(const.CRAIG_1961_INTERCEPT_COEFF, PER_MILLE), xmin=T[0], xmax=T[-1], color='k', linewidth=thick, linestyle='--')\n", + " \n", + " for ax in axs:\n", + " ax.legend()\n", + " ax.grid()\n", + " ax.set_xlabel('temperature [K]')\n", + " fig.suptitle(variant)\n", + " show_plot(f'fig_{variant}.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcc88407f5d5cb63", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-04T17:00:03.123429Z", + "start_time": "2025-03-04T17:00:03.121568Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PySDM/source/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb b/PySDM/source/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5232a4ccdf9a340d0b1ffb62d5684996696ba52f --- /dev/null +++ b/PySDM/source/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb @@ -0,0 +1,5223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da62f9aa77468520", + "metadata": {}, + "source": [ + "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb)\n", + "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb)\n", + "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Zaba_et_al/timescales_comparison.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "382963a1f97b851e", + "metadata": {}, + "source": [ + "# Isotopic relaxation (e-folding) timescales comparison \n", + "1. Comparison for different environment settings.\n", + "2. Equations for timescales from:\n", + "- [Bolin 1958 (Proc. 2nd UN Intl Conf. Peaceful Uses of Atomic Energy)](https://digitallibrary.un.org/record/3892725),\n", + "- [Gedzelman & Arnold 1994 (J. Geophys. Res. Atmos. 99)](https://doi.org/10.1029/93JD03518), \n", + "- [Miyake et al. 1968 (Pap. Meteorol. Geophys. 19)](https://doi.org/10.2467/mripapers1950.19.2_243),\n", + "- [Jouzel et al. 1975 (J. Geophys. Res. 80)](https://doi.org/10.1029/JC080i036p05015) \n", + "\n", + "\n", + "compared with derived formula from Fick's an Fourier laws.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f508348dd491651d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:31.278096Z", + "start_time": "2025-06-27T19:16:31.275258Z" + } + }, + "outputs": [], + "source": [ + "import os, sys\n", + "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", + "if 'google.colab' in sys.modules:\n", + " !pip --quiet install open-atmos-jupyter-utils\n", + " from open_atmos_jupyter_utils import pip_install_on_colab\n", + " pip_install_on_colab('PySDM-examples', 'PySDM')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "129521104d738ea8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:33.005419Z", + "start_time": "2025-06-27T19:16:31.360076Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from functools import partial\n", + "from open_atmos_jupyter_utils import show_plot\n", + "\n", + "from PySDM import Formulae\n", + "from PySDM.physics import si, in_unit\n", + "from PySDM.physics.constants import PER_MILLE, PER_CENT" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c680144e7ad3b94a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:33.225362Z", + "start_time": "2025-06-27T19:16:33.016542Z" + } + }, + "outputs": [], + "source": [ + "formulae = Formulae(\n", + " terminal_velocity=\"RogersYau\",\n", + " particle_shape_and_density=\"LiquidSpheres\",\n", + " ventilation=\"PruppacherAndRasmussen1979\",\n", + " isotope_equilibrium_fractionation_factors=\"VanHook1968\",\n", + " isotope_diffusivity_ratios=\"HellmannAndHarvey2020+GrahamsLaw\",\n", + " isotope_relaxation_timescale=\"ZabaEtAl\",\n", + ")\n", + "CONST = formulae.constants\n", + "T_10C = formulae.trivia.C2K(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c48e16c325b074bd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:33.233767Z", + "start_time": "2025-06-27T19:16:33.231813Z" + } + }, + "outputs": [], + "source": [ + "isotopes = (\"2H\", \"18O\", \"17O\", \"3H\")\n", + "isotopes_attr = {\n", + " isotope: {\n", + " \"alpha\": getattr(\n", + " formulae.isotope_equilibrium_fractionation_factors, f\"alpha_l_{isotope}\"\n", + " ),\n", + " \"D_ratio_heavy_to_light\": getattr(\n", + " formulae.isotope_diffusivity_ratios, f\"ratio_{isotope}_heavy_to_light\"\n", + " ),\n", + " \"VSMOW\": getattr(CONST, f\"VSMOW_R_{isotope}\"),\n", + " \"latex_label\": f\"$^{{{{{''.join(filter(str.isdigit, isotope))}}}}}{''.join(filter(str.isalpha, isotope))}$\"\n", + " }\n", + " for isotope in isotopes\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c7e3ba154814221f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:34.483980Z", + "start_time": "2025-06-27T19:16:33.240085Z" + } + }, + "outputs": [], + "source": [ + "pressure = CONST.p_STP\n", + "temperature = T_10C\n", + "D_light = formulae.diffusion_thermics.D(temperature, pressure)\n", + "rho_s = (\n", + " formulae.saturation_vapour_pressure.pvs_water(temperature) / CONST.Rv / temperature\n", + ")\n", + "\n", + "\n", + "def vent_coeff(r, T, p):\n", + " eta_air = formulae.air_dynamic_viscosity.eta_air(T)\n", + " air_density = p / CONST.Rd / T\n", + " Sc = formulae.trivia.air_schmidt_number(\n", + " dynamic_viscosity=eta_air,\n", + " diffusivity=D_light,\n", + " density=air_density,\n", + " )\n", + " Re = formulae.particle_shape_and_density.reynolds_number(\n", + " radius=r,\n", + " velocity_wrt_air=formulae.terminal_velocity.v_term(r),\n", + " dynamic_viscosity=eta_air,\n", + " density=air_density,\n", + " )\n", + " return formulae.ventilation.ventilation_coefficient(\n", + " formulae.trivia.sqrt_re_times_cbrt_sc(Re, Sc)\n", + " )\n", + "\n", + "tau_fun = partial(\n", + " formulae.isotope_relaxation_timescale.tau,\n", + " rho_s=rho_s,\n", + " Fk=formulae.drop_growth.Fk(\n", + " temperature,\n", + " K=formulae.diffusion_thermics.K(temperature, pressure),\n", + " lv=formulae.latent_heat_vapourisation.lv(temperature),\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3b24504da41e588c", + "metadata": {}, + "source": [ + "## Comparison for different environment settings" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f40e7ef104f39549", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:38.674206Z", + "start_time": "2025-06-27T19:16:34.501037Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d48535beb394c3cb5fc74b7dcbff799", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"./fig_isotopes.pdf\"), HTML(val…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "saturation = 1\n", + "\n", + "radii = np.linspace(0.05, 0.2) * si.cm\n", + "temperature = T_10C\n", + "\n", + "for isotope in isotopes:\n", + " R_vap = isotopes_attr[isotope][\"VSMOW\"] * 3\n", + " alpha = isotopes_attr[isotope][\"alpha\"](temperature)\n", + " R_liq = 0.4 * R_vap * alpha\n", + " D = D_light * vent_coeff(r=radii, T=temperature, p=pressure)\n", + " pyplot.plot(\n", + " in_unit(radii, si.cm),\n", + " tau_fun(\n", + " alpha=alpha,\n", + " D_iso=np.asarray(isotopes_attr[isotope][\"D_ratio_heavy_to_light\"](\n", + " temperature\n", + " )) * D,\n", + " D=D,\n", + " radius=radii,\n", + " S=saturation,\n", + " R_vap=R_vap,\n", + " R_liq=R_liq,\n", + " ),\n", + " label=isotope,\n", + " )\n", + "pyplot.title(f\"e-folding timescales for S={saturation:.3g}\")\n", + "pyplot.gca().set(\n", + " xlabel=\"Radius [cm]\",\n", + " ylabel=\"$\\\\tau$ [s]\",\n", + ")\n", + "pyplot.legend()\n", + "pyplot.grid()\n", + "show_plot(\"fig_isotopes\")" + ] + }, + { + "cell_type": "markdown", + "id": "614a63477f43a0a4", + "metadata": {}, + "source": [ + "# Timescales comparison\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f9f0586f1f1b5654", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:38.718298Z", + "start_time": "2025-06-27T19:16:38.716462Z" + } + }, + "outputs": [], + "source": [ + "radii = np.asarray((0.01, 0.05, 0.1, 0.2)) * si.cm\n", + "\n", + "isotope = \"18O\"\n", + "alpha = isotopes_attr[isotope][\"alpha\"](temperature)\n", + "VSMOW = isotopes_attr[isotope][\"VSMOW\"]\n", + "R_liq = 100 * VSMOW * alpha\n", + "\n", + "tau_max = 5000 * si.s" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8725f8626044723f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-27T19:16:40.190413Z", + "start_time": "2025-06-27T19:16:38.741370Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "](.…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "d_excess_from_fig2 = np.array((10, 8, 7, 6.8, 6, 5.3, 4, 2.5, 0)) * PER_MILLE\n",
+ "T_from_fig2 = np.linspace(-50, -10, n_points)\n",
+ "\n",
+ "\n",
+ "pyplot.plot(\n",
+ " K2C(temperature[::-1]),\n",
+ " in_unit(d_excess, PER_MILLE)[::-1],\n",
+ " label=\"d-excess, TODO #1601 (WORK IN PROGRESS - NO MATCH WITH THE PAPER YET)\",\n",
+ ")\n",
+ "\n",
+ "pyplot.plot(\n",
+ " T_from_fig2, in_unit(d_excess_from_fig2, PER_MILLE), \".\", label=\"eq. (2) prediction\"\n",
+ ")\n",
+ "pyplot.gca().set(xlabel=\"Temperature (C)\", ylabel=\"d-excess (‰)\")\n",
+ "pyplot.grid()\n",
+ "pyplot.legend()\n",
+ "show_plot(\"fig_2\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34e05ba4f6fb8e89",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-06-25T12:41:25.933336Z",
+ "start_time": "2025-06-25T12:41:25.931149Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed8d36417a5336d0",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-06-25T12:41:25.943651Z",
+ "start_time": "2025-06-25T12:41:25.941809Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/__init__.py b/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b9da7036e95de93a6da4403e77bed87948520a
--- /dev/null
+++ b/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/__init__.py
@@ -0,0 +1,9 @@
+# pylint: disable=invalid-name
+"""
+Formulae-only example depicting changes in drop and ambient vapour isotopic composition
+upon evaporation of rain in subsaturated environment based on
+[Gedzelman and Arnold 1994](https://doi.org/10.1029/93JD03518)
+
+fig_2.ipynb:
+.. include:: ./fig_2.ipynb.badges.md
+"""
diff --git a/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb b/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..453114ec7620d5f7630df4e3ff8e52355ebf605e
--- /dev/null
+++ b/PySDM/source/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb
@@ -0,0 +1,11604 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "624a95a895b096b2",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "source": [
+ "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb)\n",
+ "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb)\n",
+ "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c288f2ecf3a1c2f1",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "source": [
+ "#### based on Fig. 2 from Gedzelman and Arnold 1994 (JGR) \"_Modeling the isotopic composition of precipitation_\"\n",
+ "https://doi.org/10.1029/93JD03518\n",
+ "\n",
+ "**notes**:\n",
+ "- \"b\" defined in eq. (18) lacks multiplication by $\\rho_\\text{s}$ - units do not match, plot matches the one generated with $\\rho_\\text{s}$ included - seems a typo in eq. (18)\n",
+ "\n",
+ "**extentions (see notebook cells below first figure):**\n",
+ "- multiple isotopes (D, $^{18}$O, $^{17}$O)\n",
+ "- modern parameterisations: Horita & Wesolowski ){kind=link}
![" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f37ff8092bf145b4805cea5401603446", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=\"](.…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def trans_x(T):\n",
+ " return 1e3 / T\n",
+ "\n",
+ "def trans_y(alpha):\n",
+ " return 1e2 * np.log(alpha)\n",
+ "\n",
+ "\n",
+ "pyplot.figure(figsize=(8,9))\n",
+ "\n",
+ "for isotopologue, color in {\n",
+ " \"TOT\": \"brown\",\n",
+ " \"DOT\": \"red\",\n",
+ " \"DOD\": \"orange\",\n",
+ " \"3H\": \"navy\",\n",
+ " \"2H\": \"blue\",\n",
+ " \"18O\": \"cyan\",\n",
+ " \"17O\": \"magenta\", \n",
+ "}.items():\n",
+ " line_1_T = np.linspace(1000/2.65 * si.K, 1000/3.8 * si.K, N_POINTS)\n",
+ " pyplot.plot(\n",
+ " trans_x(line_1_T),\n",
+ " trans_y(getattr(alphas, f\"alpha_l_{isotopologue}\")(line_1_T)),\n",
+ " label=isotopologue,\n",
+ " color=color\n",
+ " )\n",
+ "\n",
+ " line_2_T = np.linspace(1000/3.6 * si.K, 1000/4.3 * si.K, N_POINTS)\n",
+ " pyplot.plot(\n",
+ " trans_x(line_2_T),\n",
+ " trans_y(getattr(alphas, f\"alpha_i_{isotopologue}\")(line_2_T)),\n",
+ " color=color\n",
+ " )\n",
+ "\n",
+ "pyplot.legend()\n",
+ "pyplot.grid()\n",
+ "pyplot.xlabel(\"$10^3/T$\")\n",
+ "pyplot.ylabel(\"$10^2 ln(P_{H_2O}/P_X)$\")\n",
+ "pyplot.xlim(2.4, 4.7)\n",
+ "pyplot.ylim(-2, 40)\n",
+ "\n",
+ "show_plot(\"fig_1.pdf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96d5f5bed97e273c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/__init__.py b/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a37a8e6284ba371a298a1b42f6b73201608d83d
--- /dev/null
+++ b/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/__init__.py
@@ -0,0 +1,10 @@
+"""
+SDM adaptivity example based on
+[Ware et al. 2025 (arXiv)](https://doi.org/10.48550/arXiv.2509.05536)
+
+fig_10.ipynb:
+.. include:: ./fig_10.ipynb.badges.md
+
+"""
+
+# pylint: disable=invalid-name
diff --git a/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb b/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..17548cb4874a9d0b0a8085523af1537e50021305
--- /dev/null
+++ b/PySDM/source/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://github.com/open-atmos/PySDM/blob/main/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb)\n",
+ "[](https://mybinder.org/v2/gh/open-atmos/PySDM.git/main?urlpath=lab/tree/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb)\n",
+ "[](https://colab.research.google.com/github/open-atmos/PySDM/blob/main/examples/PySDM_examples/Ware_et_al_2025/fig_10.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fig. 10 from [Ware et al. 2025](https://doi.org/10.48550/arXiv.2509.05536) \n",
+ "(code below is independent of PySDM, it includes a local implementation of the SDM scheme)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, sys\n",
+ "os.environ[){kind=link}