Merge branch 'main' into issue_1449

.github/workflows/projects.yml

@@ -4,7 +4,7 @@ on:
   issues:
     types:
       - opened
-  pull_request:
+  pull_request_target:
     types:
       - opened
 

.github/workflows/tests.yml

@@ -186,7 +186,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - run: |
           echo pip_user_site=$(python -c "import sysconfig; print(sysconfig.get_path('purelib'))") >> $GITHUB_ENV
-          echo toml_ci_md5=$(cat pyproject.toml setup.py examples/pyproject.toml examples/setup.py tests/devops_tests/requirements.txt .github/workflows/tests+artifacts+pypi.yml \
+          echo toml_ci_md5=$(cat pyproject.toml setup.py examples/pyproject.toml examples/setup.py tests/devops_tests/requirements.txt .github/workflows/tests.yml \
           | python -c "import hashlib;print(hashlib.md5(open(0,'rb').read()).hexdigest())") >> $GITHUB_ENV
       - uses: actions/cache@v4
         id: cache

PySDM/products/size_spectral/size_standard_deviation.py

@@ -27,20 +27,21 @@ def __init__(
         ActivationFilteredProduct.__init__(
             self, count_activated=count_activated, count_unactivated=count_unactivated
         )
+        self.tmp = None
 
     def register(self, builder):
         builder.request_attribute(self.attr)
         for base_class in (ActivationFilteredProduct, MomentProduct):
             base_class.register(self, builder)
+        self.tmp = np.empty_like(self.buffer)
 
     def _impl(self, **kwargs):
         ActivationFilteredProduct.impl(self, attr=self.attr, rank=1)
-        tmp = np.empty_like(self.buffer)
-        tmp[:] = -self.buffer**2
+        self.tmp[:] = -self.buffer**2
         ActivationFilteredProduct.impl(self, attr=self.attr, rank=2)
-        tmp[:] += self.buffer
-        tmp[:] = np.sqrt(tmp)
-        return tmp
+        self.tmp[:] += self.buffer
+        self.tmp[:] = np.sqrt(self.tmp)
+        return self.tmp
 
 
 RadiusStandardDeviation = register_product()(_SizeStandardDeviation)

README.md

@@ -1,4 +1,4 @@
-# <img src="https://raw.githubusercontent.com/open-atmos/PySDM/main/.github/pysdm_logo.svg" width=100 height=146 alt="pysdm logo">
+# <img src="https://raw.githubusercontent.com/open-atmos/PySDM/main/docs/logos/pysdm_logo.svg" width=100 height=146 alt="pysdm logo">
 
 [![Python 3](https://img.shields.io/static/v1?label=Python&logo=Python&color=3776AB&message=3)](https://www.python.org/)
 [![LLVM](https://img.shields.io/static/v1?label=LLVM&logo=LLVM&color=gold&message=Numba)](https://numba.pydata.org)
@@ -17,7 +17,7 @@
 
 [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.html)
 
-[![Github Actions Build Status](https://github.com/open-atmos/PySDM/workflows/tests+artifacts+pypi/badge.svg?branch=main)](https://github.com/open-atmos/PySDM/actions)
+[![Github Actions Build Status](https://github.com/open-atmos/PySDM/workflows/tests/badge.svg?branch=main)](https://github.com/open-atmos/PySDM/actions)
 [![Appveyor Build status](http://ci.appveyor.com/api/projects/status/github/open-atmos/PySDM?branch=main&svg=true)](https://ci.appveyor.com/project/slayoo/pysdm/branch/main)
 [![Coverage Status](https://codecov.io/gh/open-atmos/PySDM/branch/main/graph/badge.svg)](https://app.codecov.io/gh/open-atmos/PySDM)    
 [![PyPI version](https://badge.fury.io/py/PySDM.svg)](https://pypi.org/project/PySDM)
@@ -35,6 +35,8 @@ The package features a Pythonic high-performance implementation of the
   Super-Droplet Method (SDM) Monte-Carlo algorithm for representing collisional growth 
   ([Shima et al. 2009](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.441)), hence the name. 
 
+PySDM documentation is maintained at: [https://open-atmos.github.io/PySDM](https://open-atmos.github.io/PySDM) 
+
 There is a growing set of [example Jupyter notebooks](https://open-atmos.github.io/PySDM/PySDM_examples.html) exemplifying how to perform 
   various types of calculations and simulations using PySDM.
 Most of the example notebooks reproduce results and plot from literature, see below for 
@@ -65,14 +67,6 @@ For an overview of PySDM features (and the preferred way to cite PySDM in papers
 PySDM includes an extension of the SDM scheme to represent collisional breakup described in [de Jong, Mackay et al. 2023](10.5194/gmd-16-4193-2023).   
 For a list of talks and other materials on PySDM as well as a list of published papers featuring PySDM simulations, see the [project wiki](https://github.com/open-atmos/PySDM/wiki).
 
-A [pdoc-generated](https://pdoc.dev/) documentation of PySDM public API is maintained at: [https://open-atmos.github.io/PySDM](https://open-atmos.github.io/PySDM) 
-
-## Example Jupyter notebooks (reproducing results from literature):
-
-See [PySDM-examples README](https://github.com/open-atmos/PySDM/blob/main/examples/README.md).
-
-![animation](https://github.com/open-atmos/PySDM/wiki/files/kinematic_2D_example.gif)
-
 ## Dependencies and Installation
 
 PySDM dependencies are: [Numpy](https://numpy.org/), [Numba](http://numba.pydata.org/), [SciPy](https://scipy.org/), 

docs/markdown/pysdm_landing.md

@@ -1,6 +1,6 @@
 # Introduction
 
-<img align="left" src="https://raw.githubusercontent.com/open-atmos/PySDM/main/.github/pysdm_logo.svg" width=150 height=219 alt="pysdm logo" style="padding-right:1em">
+<img align="left" src="https://raw.githubusercontent.com/open-atmos/PySDM/main/docs/logos/pysdm_logo.svg" width=150 height=219 alt="pysdm logo" style="padding-right:1em">
 
 PySDM offers a set of building blocks for development of atmospheric cloud
 simulation systems revolving around the particle-based microphysics modelling concept

docs/templates/index.html.jinja2

@@ -24,25 +24,32 @@
     <main class="pdoc">
         <div style="display: flex; align-items: center;">
             <a href="https://open-atmos.github.io/PySDM/index.html">
-                <img src="https://raw.githubusercontent.com/open-atmos/PySDM/main/.github/pysdm_logo.svg" style="height:10em" alt="PySDM logo">
+                <img src="https://raw.githubusercontent.com/open-atmos/PySDM/main/docs/logos/pysdm_logo.svg" style="height:10em" alt="PySDM logo">
             </a>
             <h1 style="padding-left: 0.5em; color: #13A4DE; font-weight: normal ">Documentation</h1>
     </div>
     <div style="padding-top: 2em">
         <h2>What is PySDM?</h2>
         <p>
-            PySDM is a package for simulating the dynamics of population of particles.
-            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 package features a Pythonic high-performance implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for representing collisional growth 
-            (<a href="https://doi.org/10.1002/qj.441">Shima et al. 2009</a>), hence the name.
+            <video controls style="float:right; width:40%">
+                <source src="https://github.com/open-atmos/PySDM/wiki/files/animation_save.mp4" type="video/mp4">
+                Your browser does not support the video tag.
+            </video>
+            PySDM is a package for simulating the <mark>dynamics of population of particles undergoing diffusional and collisional growth (and breakage)</mark>.
+            The package features a Pythonic high-performance (multi-threaded CPU &amp; CUDA GPU) implementation of the <mark>Super-Droplet Method (SDM) Monte-Carlo algorithm</mark> 
+              for representing collisional growth (<a href="https://doi.org/10.1002/qj.441">Shima et al. 2009</a>), hence the name.
+            It is intended to serve as a building block for simulation systems modelling <mark>fluid flows involving a dispersed phase</mark>,
+               with PySDM being responsible for representation of the dispersed phase.
+            Currently, the development is focused on atmospheric <mark>cloud physics applications</mark>, 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.
         </p>
     </div>
     <div style="padding-top: 0.5em">
         <h2>What is the difference between PySDM and PySDM-examples?</h2>
         <p>
-            PySDM is a Python package that provides the implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm.
-            It is a library that can be used in your own projects.
+            PySDM is a Python package that provides the implementation of SDM that can be used in your own projects.
         </p>
         <p>
             PySDM-examples is a Python package that provides examples of how to use PySDM.
@@ -159,7 +166,7 @@ jupyter-notebook PySDM/examples</code></pre>
         </p>
         <p>
           Development of PySDM was supported by:
-          <ul>
+          <ul style="list-style:square; margin-left:1em;">
             <li><a href="https://www.fnp.org.pl/en/">Foundation for Polish Science</a> (grant no. POIR.04.04.00-00-5E1C/18 co-financed by the European Union under the European Regional Development Fund)</li>
             <li><a href="https://ncn.gov.pl/en">Polish National Science Centre</a> (grant no. 2020/39/D/ST10/01220)</li>
             <li><a href="https://science.energy.gov">US Department of Energy</a> via the <a href="https://www.krellinst.org/csgf/">Computational Science Graduate Fellowship</a> and via the <a href="https://asr.science.energy.gov/">Atmospheric System Research Program</a> (grant no. DE-SC0021034)</li>

examples/PySDM_examples/Berry_1967/example_fig_6.py

@@ -15,7 +15,7 @@ def full_params(params):
 
 def print_collection_efficiency_portrait(params):
     size = 2 * 5.236
-    plt.figure(num=1, figsize=(size / 2, size))
+    plt.figure(figsize=(size / 2, size))
     points = 200
     Y_c = np.zeros(points)
     x_values = np.linspace(0, 1, points)

examples/PySDM_examples/Berry_1967/spectrum_plotter.py

@@ -8,7 +8,7 @@
 class SpectrumPlotter(SuperSpectrumPlotter):
     def __init__(self, settings, title=None, grid=True, legend=False):
         size = 2 * 5.236
-        pyplot.figure(num=1, figsize=(size, size * 0.54))
+        pyplot.figure(figsize=(size, size * 0.54))
         pyplot.xlabel("particle radius [µm]")
         pyplot.ylabel("dm/dlnr [g/m^3/(unit dr/r)]")
         super().__init__(settings, title=title, grid=grid, legend=legend, log_base=2)