diff --git a/.readthedocs.yml b/.readthedocs.yml index 72e65e00d..b591e0096 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -19,9 +19,12 @@ formats: all # Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 + apt_packages: + - libhdf4-dev tools: python: "3.12" + # Optionally set the version of Python and requirements required to build your docs python: install: diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index faa302d06..d73fbfa13 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -1,5 +1,5 @@ -sphinx -sphinx-issues +sphinx==5.3.0 +sphinx-issues==4.1.0 ipython nbsphinx sphinx-rtd-theme @@ -11,3 +11,4 @@ scipy sqlalchemy jinja2==3.0.3 sphinx_gallery +pyhdf \ No newline at end of file diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 6231a2448..eb8bacc27 100755 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -35,12 +35,15 @@ 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', 'sphinx.ext.githubpages', + 'sphinx.ext.autosummary', 'sphinx.ext.todo', 'sphinx.ext.autosectionlabel', 'sphinx_issues', 'nbsphinx', 'IPython.sphinxext.ipython_console_highlighting', 'sphinx.ext.autodoc', + 'sphinx.ext.autodoc.typehints', + 'sphinx.ext.napoleon', "sphinx_gallery.load_style", ] @@ -74,7 +77,7 @@ # General information about the project. project = 'python' -copyright = '2018-2023, Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Mandy Hewitt' +copyright = '2018-2024, Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Mandy Hewitt' author = 'Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Mandy Hewitt' # The version info for the project you're documenting, acts as replacement for @@ -82,9 +85,9 @@ # built documents. # # The short X.Y version. -version = '86' +version = '88' # The full version, including alpha/beta/rc tags. -release = '86g' +release = '88a' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -101,6 +104,9 @@ # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' +# Whether to include type hints in the doc, and where +autodoc_typehints = 'both' + # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for @@ -167,8 +173,11 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'python.tex', 'python Documentation', - 'Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Mandy Hewitt', 'manual'), + ( + master_doc, 'python.tex', 'python Documentation', + author, + 'manual' + ), ] # -- Options for manual page output --------------------------------------- diff --git a/docs/sphinx/source/config.py b/docs/sphinx/source/config.py deleted file mode 100644 index 551831d47..000000000 --- a/docs/sphinx/source/config.py +++ /dev/null @@ -1,6 +0,0 @@ -import os -import sys - -sys.path.insert(0, os.path.abspath('../../../py_progs/')) -extensions = [ -'sphinx.ext.autodoc',] diff --git a/docs/sphinx/source/examples/demo-models/quasar/quasar.rst b/docs/sphinx/source/examples/demo-models/quasar/quasar.rst index 47d74d365..d57a44830 100644 --- a/docs/sphinx/source/examples/demo-models/quasar/quasar.rst +++ b/docs/sphinx/source/examples/demo-models/quasar/quasar.rst @@ -7,7 +7,9 @@ This particular document focuses on Model A from M20. As with most of the demo m The wind is equatorial, and illuminated by an isotropic SED. -.. todo:: more description needed +.. note:: + + more description needed Important Parameters ============================ @@ -42,4 +44,4 @@ Outputs ============================ References -============================ \ No newline at end of file +============================ diff --git a/docs/sphinx/source/meta.rst b/docs/sphinx/source/meta.rst index 266113539..1592fc22f 100644 --- a/docs/sphinx/source/meta.rst +++ b/docs/sphinx/source/meta.rst @@ -290,7 +290,7 @@ The above link contains full documentation of the commands. A module in py_progs .. code :: rst - .. automodule:: py_read_output.py + .. automodule:: py_read_output :members: For this to work properly, docstrings have to be in a reasonable rst format. We might consider using the `napoleon extension `_ if this is not to our taste. diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb index ab4514000..1c31f83be 100644 --- a/docs/sphinx/source/plotting/plot_spectrum.ipynb +++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb @@ -25,28 +25,6 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'cv_test/cv_standard.png'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "%matplotlib inline\n", "import plot_spec \n", @@ -54,7 +32,8 @@ "smooth = 21\n", "fname = \"cv_test/cv_standard\"\n", "plot_spec.do_all_angles(fname, smooth, wmin, wmax)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -67,15 +46,6 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Freq.', 'Lambda', 'Created', 'WCreated', 'Emitted', 'CenSrc', 'Disk', 'Wind', 'HitSurf', 'Scattered', 'A10P0.50', 'A28P0.50', 'A45P0.50', 'A62P0.50', 'A80P0.50']\n" - ] - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import astropy.io.ascii as io \n", @@ -83,7 +53,8 @@ "s = io.read(\"{}.spec\".format(fname))\n", "\n", "print (s.colnames)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -113,33 +84,12 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "angle = 45\n", "field = \"A{:.0f}P0.50\".format(angle)\n", "plt.plot(s[\"Lambda\"], s[field])" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -152,30 +102,11 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Created luminosity was 4.478638412507001e+34\n", - "The emitted luminosity was 3.90951228511005e+34\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import plot_tot\n", "plot_tot.doit(fname, smooth)" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/docs/sphinx/source/plotting/plot_wind.ipynb b/docs/sphinx/source/plotting/plot_wind.ipynb index 626cd2780..9db56cb3a 100644 --- a/docs/sphinx/source/plotting/plot_wind.ipynb +++ b/docs/sphinx/source/plotting/plot_wind.ipynb @@ -32,28 +32,6 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'cv_test/cv_standard_log_t_e.png'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt \n", @@ -61,7 +39,8 @@ "import plot_wind\n", "fname = \"cv_test/cv_standard.master.txt\"\n", "plot_wind.doit(fname, var=\"t_e\")" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -77,15 +56,6 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz']\n" - ] - } - ], "source": [ "import matplotlib.pyplot as plt\n", "import astropy.io.ascii as io \n", @@ -93,7 +63,8 @@ "data = io.read(fname)\n", "\n", "print (data.colnames)" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -107,47 +78,11 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no description for column x\n", - "z -- left-hand lower cell corner z-coordinate, cm\n", - "xcen -- cell centre x-coordinate, cm\n", - "zcen -- cell centre z-coordinate, cm\n", - "i -- cell index (column)\n", - "j -- cell index (row)\n", - "inwind -- is the cell in wind (0), partially in wind (1) or out of wind (<0)\n", - "converge -- how many convergence criteria is the cell failing?\n", - "v_x -- x-velocity, cm/s\n", - "v_y -- y-velocity, cm/s\n", - "v_z -- z-velocity, cm/s\n", - "vol -- volume in cm^3\n", - "rho -- density in g/cm^3\n", - "ne -- electron density in cm^-3\n", - "t_e -- electron temperature in K\n", - "t_r -- radiation temperature in K\n", - "h1 -- H1 ion fraction\n", - "he2 -- He2 ion fraction\n", - "c4 -- C4 ion fraction\n", - "n5 -- N5 ion fraction\n", - "o6 -- O6 ion fraction\n", - "dmo_dt_x -- momentum rate, x-direction\n", - "dmo_dt_y -- momentum rate, y-direction\n", - "dmo_dt_z -- momentum rate, z-direction\n", - "ip -- U ionization parameter\n", - "xi -- xi ionization parameter\n", - "ntot -- total photons passing through cell\n", - "nrad -- total wind photons produced in cell\n", - "nioniz -- total ionizing photons passing through cell\n" - ] - } - ], "source": [ "import py_plot_util as util \n", "descr = util.get_windsave_descriptions(data)" - ] + ], + "outputs": [] }, { "attachments": {}, @@ -161,26 +96,6 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/matthewsj/.mpi_temp/ipykernel_33261/816639112.py:2: RuntimeWarning: divide by zero encountered in log10\n", - " plt.pcolormesh(x,z, np.log10(ne))\n" - ] - }, - { - "data": { - "image/png": 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iHsO4PuW0cRPSkNnlpjtLvvLL1WYNABeJhA8gAABws2TNQLhzbQgAAEnibABh5zB1vjdnnzp1Sl/96ld1zTXXaMKECcrLy9N9992nd955x6gNAggAABwUjwDifG/Ofv/993XgwAF9/etf14EDB/Tiiy/qzTff1JIlS4zaYAgDAIAkc743Z2dnZw/b8PGHP/yhrr/+enV2dqqgoGBMbSR8AMEqDACAm1mytxTz7MYJfX19YeXRfKlkb2+vPB6PLr300jHXSfghDJ/Pp7a2NjU1NcW7KwAADBOtIYz8/HxlZ2eHjpqamqj07+TJk1q9erXuvfdeZWVljblewmcgAAC4GHR1dYX9go9G9uHUqVO65557FAwGtXnzZqO6BBAAADgoWss4s7KyjDIEF3Lq1Cnddddd6ujo0CuvvGJ8bwIIAAAc5MZ9IM4GD//1X/+lPXv2aNKkScb3IIAAACDJnO/N2Xl5ebrzzjt14MAB7dq1S4FAQEeOHJEk5eTkKC0tbUxtJHwAwSoMQCpetdG4junW1CkR/C/mMd2aOoI/tDzB87/c76N+/cLD5o0ANsQjA3G+N2evW7dOv/jFLyRJs2fPDqu3Z88eLVy4cExtJHwAwbswAABuZlkeWTYCiEjqXujN2Rd6q/ZYJPwyTgAAEHsJn4EAAMDNgvLY2kjKTl0nEUAAAOAgN67CiAYCCAAAHBSPORCxwBwIAABgLOEzECzjBAC4WbIOYSR8BoKXaQEA3OzsEIadw40SPoAAAACxl/BDGAAAuJllcwjDrRkIAgjAhYpWm21N7ckwb8NjOG3IMt2WWlKK4c+9SLbL/s1Pv2xeCYghS5KdjR/t7xnpDIYwAACAMTIQAAA4KCiPPOxECQAATLCRFAAAwIcSPgPBRlIAADcLWh552EjKfdhICgDgZpZl/3CjhM9AAADgZsyBAAAA+BAZCAAAHJSsGQgCCAAAHJSskygJIACHzfym2bbUkuQZb3h9BIuQUk6Z1zH1ux/8H+cbARAXBBAAADjI7koKVmEAAHAROhNA2JkDEcXORBGrMAAAgDEyEAAAOIhVGAAAwJj14WGnvhsl/BCG3+9XYWGhSktL490VAAAuGgkfQPAuDACAm50dwrBzuBFDGAAAOClJxzAIIAAAcJLdLAIZCCA5fGrDBrMKhrtKSpInYPYDIxa7Sv6/GnaVBPAXBBAAADiInSgBAICxZN0HIuFXYQAAgNgjAwEAgJMsj72JkC7NQBBAAADgoGSdA8EQBgAAMEYGAgAAJ7GRFAAAMMUqDAAAgA+RgQAAwGkuHYawgwACF7UrfvQ980qGW1ObbkstSZ7TxjWM23jza2xNDcQCQxgu5ff7VVhYqNLS0nh3BQCA4awoHIYaGxu1ePFi5eXlyePxaOfOnWGfv/jii1q0aJEmT54sj8ej1tZW4zYSPoDw+Xxqa2tTU1NTvLsCAIArDAwMqLi4WLW1taN+/pnPfEbf+c53Im6DIQwAABzlUSTDjOH1zXi9Xnm93lE/r6iokCS99dZbkXaKAAIAAEdFaR+Ivr6+sOL09HSlp6fbuLE9CT+EAQDAxSA/P1/Z2dmho6amJq79IQMBAICTopSB6OrqUlZWVqg4ntkHiQACAABnReltnFlZWWEBRLwxhAEAAIyRgQAAwEHxeJ13f3+/2tvbQ+cdHR1qbW1VTk6OCgoK9O6776qzs1PvvPOOJOnQoUOSpClTpmjKlCljaoMMBAAATorDRlLNzc2aM2eO5syZI0mqrq7WnDlz9I1vfEOS9Itf/EJz5szRbbfdJkm65557NGfOHD355JNjboMMBJLGjOcimJE8PoJxyaBZHetUJHG6WZ32quoI2gCQrBYuXCjrPKmL+++/X/fff7+tNgggAABwUpQmUboNAQQAAA7yWGcOO/XdiAACAAAnRWkfCLdhEiUAADBGBgIAACcxBwIAABhjCAMAAOAMMhAAADgpSTMQBBAAADgpSQMIhjAAAIAxMhBwrU/XPWp0fdrHzGcqBwPmMXTgtFkdw52vJUkd/+th80oA3ClJV2HEJQOxbNkyTZw4UXfeeWdY+a5duzRz5kxdddVVeuqpp+LRNQAAoursTpR2DjeKSwCxatUqPfPMM2Flp0+fVnV1tV555RUdOHBAjz/+uN599914dA8AAFxAXAKIsrIyZWZmhpXt379fRUVFmjp1qjIzM3Xrrbeqvr4+Ht0DACB64vA671gwDiAaGxu1ePFi5eXlyePxaOfOncOu2bx5s2bMmKGMjAyVlJRo7969F7zvO++8o6lTp4bOp02bpsOHD5t2DwAAxIBxADEwMKDi4mLV1taO+PmOHTtUVVWltWvX6uDBg1qwYIG8Xq86OzvPe9+R3lvu8bhz4ggAAGPlkc05EPH+AqMwXoXh9Xrl9XpH/XzDhg1avny5HnzwQUnSpk2bVF9fry1btqimpmbUelOnTg3LOPzxj3/UDTfcMOr1g4ODGhwcDJ339fWZfA0AAGBDVOdADA0NqaWlReXl5WHl5eXl2rdv33nrXn/99fr973+vw4cP68SJE9q9e7cWLVo06vU1NTXKzs4OHfn5+VH5DgAARNXZZZx2DheKagBx9OhRBQIB5ebmhpXn5ubqyJEjofNFixbpC1/4gnbv3q1p06apqalJ48aN0/e//32VlZVpzpw5evjhhzVp0qRR21qzZo16e3tDR1dXVzS/CgAA0ZGkkygd2Ujqo3MXLMsKKxttdcWSJUu0ZMmSMbWRnp6u9PT0yDsJAAAiFtUAYvLkyUpNTQ3LNkhST0/PsKwEAAAXhSR9F0ZUA4i0tDSVlJSooaFBy5YtC5U3NDTo9ttvj2ZTIX6/X36/X4FAwJH7IzpurF9jXCdzvNm4XyCCccKhU+b/CwymmNU5FXDn+CWA2LC7m6Rbd6I0/unZ39+v9vb20HlHR4daW1uVk5OjgoICVVdXq6KiQnPnztW8efO0detWdXZ2asWKFVHt+Fk+n08+n099fX3Kzs52pA0AABDOOIBobm5WWVlZ6Ly6ulqSVFlZqW3btunuu+/WsWPHtH79enV3d2vWrFnavXu3pk+fHr1eAwCQKBjCOGPhwoUjbvp0rpUrV2rlypURdwoAgKSRpAFEXN6FAQAAElvCBxB+v1+FhYUqLS2Nd1cAABiG13m7lM/nU1tbm5qamuLdFQAAhkvSnSgd2UgKAAB8iDkQAAAAZ5CBAADAQWwk5VLsRJkYJo5/37jO6aBZguxUINW4jfcj+D8zaDge+eb//LpxGwCSCEMY7sQkSgAAYi/hMxAAALia3aWYLs1AEEAAAOAkhjAAAADOIAMBAICTyEC4E1tZAwDcjK2sXYpVGAAAxF7CBxAAACD2mAMBAICTknQOBAEEAAAOYitr4ByV+5cbXT853fx1tENBs8fz/dOXGLcRif1/++2YtAMAkWpsbNQTTzyhlpYWdXd3q66uTkuXLg19blmWHn30UW3dulXvvfeebrjhBvn9fhUVFY25DeZAAADgNMvGEYGBgQEVFxertrZ2xM+/+93vasOGDaqtrVVTU5OmTJmiv/mbv9GJEyfG3AYZCAAAnBSlORB9fX1hxenp6UpPTx+xitfrldfrHfl2lqVNmzZp7dq1+vznPy9J2r59u3Jzc/X888/roYceGlO3Ej4DwT4QAICLQX5+vrKzs0NHTU1NRPfp6OjQkSNHVF5eHipLT0/XX//1X2vfvn1jvk/CZyB8Pp98Pp/6+vqUnZ0d7+4AABAmWpMou7q6lJWVFSofLftwIUeOHJEk5ebmhpXn5ubq7bffHvN9Ej6AAADA1aI0hJGVlRUWQNjl8YRPbrcsa1jZ+ST8EAYAABi7KVOmSPpLJuKsnp6eYVmJ8yGAAADAQW57F8aMGTM0ZcoUNTQ0hMqGhob06quvav78+WO+D0MYAAA4KQ47Ufb396u9vT103tHRodbWVuXk5KigoEBVVVX69re/rauuukpXXXWVvv3tb+tjH/uY7r333jG3QQABAECSaW5uVllZWei8urpaklRZWalt27bpK1/5ij744AOtXLkytJHUyy+/rMzMzDG3QQABAICT4pCBWLhwoSxr9Ioej0fr1q3TunXrIu4WAQQi8on0fqPrTwXNp9sMGm5lPS4lw7iNf13wQ+M6AGAiWd+FkfCTKNlICgDgana2sbabvXBQwgcQPp9PbW1tampqindXAAC4aDCEAQCAk+IwByIWCCAAAHAQcyAAAAA+RAYCAAAnMYQBAABMMYQBAADwITIQAAA4iSEMAABgjAACyeq7bV7jOpddYnb9yaBhBUnvB9OM6wAAYiPh50CwlTUAwM08UTjcKOEDCLayBgC4WpK+C4MhDAAAHMQyTgAAgA+RgQAAwEmswgAAABFxaRBgB0MYAADAGBkIAAAclKyTKAkgAABwUpLOgWAIAwAAGCMDAeVe0mtc55SVanR9JFtZXxLMMLr+O9f+zLgNAHAaQxgAAMAcQxgAAABnkIEAAMBBDGEAAABzSTqEQQABAICTkjSASPg5EH6/X4WFhSotLY13VwAAuGgkfADh8/nU1tampqameHcFAIBhzs6BsHO4EUMYAAA4iSEMAACAM8hAQDmp/cZ1jHeiTEkzbuOSQMC4DgC4jcey5LEiTyPYqeskAggAAJzEEAYAAMAZZCAAAHAQO1ECAABzDGEAAACcQQYCAAAHMYQBAADMJekQBgEEAAAOStYMBHMgAABIMidOnFBVVZWmT5+u8ePHa/78+VF/ZxQBBAAATrKicBh68MEH1dDQoGeffVZvvPGGysvLdcstt+jw4cP2v8+HGMJIQvve/qTR9ZemmMeRQcPY86Q1aNxGqoLGdQDAjWI5DPHBBx/o5z//uf7lX/5Fn/vc5yRJ69at086dO7VlyxZ961vfiko7BBAAACSAvr6+sPP09HSlp6cPu+706dMKBALKyMgIKx8/frxee+21qPWHIQwAAJxkWfYPSfn5+crOzg4dNTU1IzaXmZmpefPm6Zvf/KbeeecdBQIBPffcc3r99dfV3d0dta9FBgIAAAdFaxVGV1eXsrKyQuUjZR/OevbZZ/WlL31JU6dOVWpqqq677jrde++9OnDgQOQd+QgyEAAAJICsrKyw43wBxKc+9Sm9+uqr6u/vV1dXl/bv369Tp05pxowZUesPAQQAAE6KwyqMsyZMmKDLL79c7733nurr63X77bdHfrOPYAgDAAAHeYJnDjv1TdXX18uyLM2cOVPt7e16+OGHNXPmTD3wwAORd+QjyEAAAJBkent75fP5dPXVV+u+++7TZz/7Wb388su65JJLotaGqwKI733veyoqKtKsWbP03HPPxbs7AADYF4chjLvuukv//d//rcHBQXV3d6u2tlbZ2dn2v8s5XDOE8cYbb+j5559XS0uLJOnmm2/W3/3d3+nSSy+Nb8cAALCBd2E47A9/+IPmz5+vjIwMZWRkaPbs2XrppZfi3S0AAOyJ0j4QbhO1DERjY6OeeOIJtbS0qLu7W3V1dVq6dGnYNZs3b9YTTzyh7u5uFRUVadOmTVqwYIEkadasWXr00Ud1/PhxSdIrr7yiT37SbEtmnHFpitm20QF5jNsIWmZ1TlquSXYBAKIgahmIgYEBFRcXq7a2dsTPd+zYoaqqKq1du1YHDx7UggUL5PV61dnZKUkqLCzUqlWrdNNNN2nZsmUqLS3VuHGj/9IZHBxUX19f2AEAgNucHcKwc7hR1AIIr9erb33rW/r85z8/4ucbNmzQ8uXL9eCDD+rTn/60Nm3apPz8fG3ZsiV0zUMPPaQDBw5oz549SktL05VXXjlqezU1NWFbeubn50frqwAAED1x3AfCSTGZAzE0NKSWlhaVl5eHlZeXl2vfvn2h856eHknSoUOHtH//fi1atGjUe65Zs0a9vb2ho6ury5nOAwCAYWIyMH306FEFAgHl5uaGlefm5urIkSOh86VLl+r48eOaMGGCfvzjH593CGO0t5ABAOAmyboKI6Yz2zye8Il3lmWFlZ2bjQAAICnYXUnh0lUYMRnCmDx5slJTU8OyDdKZIYuPZiUAAID7xSSASEtLU0lJiRoaGsLKGxoaNH/+fFv39vv9KiwsVGlpqa37AADghGRdhRG1IYz+/n61t7eHzjs6OtTa2qqcnBwVFBSourpaFRUVmjt3rubNm6etW7eqs7NTK1assNWuz+eTz+dTX19f1LfpBADANrsrKZI9gGhublZZWVnovLq6WpJUWVmpbdu26e6779axY8e0fv16dXd3a9asWdq9e7emT58erS4AAOA6TKK8gIULF8q6wESPlStXauXKldFqEgAAxEnC7y/s9/vl9/sVCATi3RXXyEwxe3l8IILoNmi4+/UllvkL7W+Y3mFcBwBcJ2idOezUdyHXvEwrUj6fT21tbWpqaop3VwAAGI6dKAEAAM5I+CEMAADczCObkyij1pPoIoAAAMBJ7EQJAABwRsIHEOxECQBws2TdiTLhAwhWYQAAXI1VGAAAAGcwiRIAAAd5LEseGxMh7dR1EgEEAABOCn542KnvQgQQAAA4KFkzEAk/B4JVGAAAxF7CBxCswgAAuFqSrsJgCAMAACexEyUAAMAZZCAAAHCQ3d0k3boTJQEEAABOYggDAADgjIQPIFjGCQBwM0/Q/uFGCR9AsIwTAOBqZ4cw7BwulPABBAAAiD0mUQIA4CS7m0G5MwFBAAEAgJOS9V0YBBAAADiJZZwAAABnkIEAAMBJliQ7SzHdmYAggAAAwEnJOgci4Ycw2EgKAIDYS/gAgo2kAACuZsnmRlJmzZ0+fVpf+9rXNGPGDI0fP16f/OQntX79egWD0d3SkiEMAACcFONVGI8//riefPJJbd++XUVFRWpubtYDDzyg7Oxs/eM//mPk/fgIAggAAJLIb37zG91+++267bbbJElXXHGFfvrTn6q5uTmq7ST8EAYAAK4WjMIhqa+vL+wYHBwcsbnPfvaz+vd//3e9+eabkqTf/e53eu2113TrrbdG9WuRgUhCU1MzHW8jaLgm6ZQVcKgnAOBu0VqFkZ+fH1b+yCOPaN26dcOu/+pXv6re3l5dffXVSk1NVSAQ0GOPPaYvfvGLEfdhJAQQAAAkgK6uLmVlZYXO09PTR7xux44deu655/T888+rqKhIra2tqqqqUl5eniorK6PWHwIIAACcFKVJlFlZWWEBxGgefvhhrV69Wvfcc48k6ZprrtHbb7+tmpoaAggAABJGjFdhvP/++0pJCZ/imJqayjJOAAASSowDiMWLF+uxxx5TQUGBioqKdPDgQW3YsEFf+tKXIu/DCAggAABIIj/84Q/19a9/XStXrlRPT4/y8vL00EMP6Rvf+EZU2yGAAADASUFJHpv1DWRmZmrTpk3atGmTjUYvLOH3geBdGAAANzu7jNPO4UYJH0DwLgwAAGKPIQwAAJwU40mUsUIAAQCAk4KW5LERBATdGUAk/BAGAACIPTIQAAA4iSEMAABgzmYAIXcGEAxhAAAAY2QgAABwEkMYAADAWNCSrWEIl67CIIAAAMBJVvDMYae+CzEHAgAAGCMDAQCAk5gDAQAAjCXpHAiGMAAAgDEyEAAAOClJhzASPgPh9/tVWFio0tLSeHcFAIDhLP0liIjoiPcXGFnCBxA+n09tbW1qamqKd1cAALhoMIQBAICTknQIgwACAAAnBYOSbGwGFWQjKQAAkCTIQAAA4CSGMAAAgDECCAAAYIydKAEAAM4gAwEAgIMsKyjLxiu57dR1EgEEAABOsix7wxAunQPBEAYAADBGBgIAACdZNidRujQDQQABAICTgkHJY2Meg0vnQDCEAQAAjJGBAADASQxhAAAAU1YwKMvGEIZbl3EyhAEAAIyRgQAAwEkMYQAAAGNBS/IQQAAAABOWJcnOMk53BhDMgQAAAMZcFUBs3LhRRUVFKiws1KpVq2S5NOoCAGCsrKBl+3Aj1wQQf/7zn1VbW6uWlha98cYbamlp0W9/+9t4dwsAAHusoP3DhVw1B+L06dM6efKkJOnUqVO67LLL4twjAAAwkqhlIBobG7V48WLl5eXJ4/Fo586dw67ZvHmzZsyYoYyMDJWUlGjv3r2hzz7xiU/on/7pn1RQUKC8vDzdcsst+tSnPhWt7gEAEBcMYVzAwMCAiouLVVtbO+LnO3bsUFVVldauXauDBw9qwYIF8nq96uzslCS999572rVrl9566y0dPnxY+/btU2NjY7S6BwBAfDCEcX5er1der3fUzzds2KDly5frwQcflCRt2rRJ9fX12rJli2pqavRv//ZvuvLKK5WTkyNJuu222/Tb3/5Wn/vc50a83+DgoAYHB0Pnvb29kqS+vr5ofaWEFTwRcL4Nw01RTkXwP8CpCfxbAnDG2d8VsZisf1qnbO0jdVqnoteZKIrJHIihoSG1tLRo9erVYeXl5eXat2+fJCk/P1/79u3TyZMndckll+hXv/qV/v7v/37Ue9bU1OjRRx8dVp6fnx/dziOOsuPdAQBJ7tixY8rOduZnTVpamqZMmaLXjuy2fa8pU6YoLS0tCr2KnpgEEEePHlUgEFBubm5YeW5uro4cOSJJuvHGG3Xrrbdqzpw5SklJ0c0336wlS5aMes81a9aouro6dH78+HFNnz5dnZ2djj0MdpWWlqqpqcm1947kHmOtM5brLnTNaJ+PVN7X16f8/Hx1dXUpKyvrgv2LByefh2jcP1Gfh9E+u9ifCTc/D2O91vTf/Hyf9fb2qqCgIJT1dkJGRoY6Ojo0NDRk+15paWnKyMiIQq+iJ6arMDweT9i5ZVlhZY899pgee+yxMd0rPT1d6enpw8qzs7Nd+8MhNTXVsb5F496R3GOsdcZy3YWuGe3z89XLysq6KJ+HaNw/UZ+HC312sT4Tbn4exnptpP/m5/ssJcXZ3QwyMjJc94s/WmKyD8TkyZOVmpoayjac1dPTMywrkcx8Pp+r7x3JPcZaZyzXXeia0T538r+rk5zut937J+rzYNIPt3Hzzwgnn4exXhvpv3miPg9u57EcmEHi8XhUV1enpUuXhspuuOEGlZSUaPPmzaGywsJC3X777aqpqbHdZl9fn7Kzs9Xb2+vavy4QOzwP+CieCZyL58G+qA1h9Pf3q729PXTe0dGh1tZW5eTkqKCgQNXV1aqoqNDcuXM1b948bd26VZ2dnVqxYkVU2k9PT9cjjzwy4rAGLj48D/gongmci+fBvqhlIH71q1+prKxsWHllZaW2bdsm6cxGUt/97nfV3d2tWbNmaePGjaMu0wQAAO7lyBAGAABIbq55mRYAAEgcBBAAAMAYAQQAADBGAAEAAIxdNAHE9773PRUVFWnWrFl67rnn4t0dxMGyZcs0ceJE3XnnnWHlu3bt0syZM3XVVVfpqaeeilPvEGujPQ+jlSO5jfTv3tXVpYULF6qwsFDXXnutXnjhhTj20H0uilUYb7zxhiorK0Mv7rr55pv1y1/+Updeeml8O4aY2rNnj/r7+7V9+3b97Gc/kySdPn1ahYWF2rNnj7KysnTdddfp9ddfd3R/fLjDSM/D+cqR3Eb6d+/u7taf/vQnzZ49Wz09Pbruuut06NAhTZgwIc69dYeLIgPxhz/8QfPnzw/tST579my99NJL8e4WYqysrEyZmZlhZfv371dRUZGmTp2qzMxM3Xrrraqvr49TDxFLIz0P5ytHchvp3/3yyy/X7NmzJUmXXXaZcnJy9O6778ahd+6UEAFEY2OjFi9erLy8PHk8Hu3cuXPYNZs3b9aMGTOUkZGhkpIS7d27N/TZrFmztGfPHh0/flzHjx/XK6+8osOHD8fwG8Auu8/AaN555x1NnTo1dD5t2jSejQTg1POAxBSL56G5uVnBYFD5+flR6nXiS4gAYmBgQMXFxaqtrR3x8x07dqiqqkpr167VwYMHtWDBAnm9XnV2dko6886NVatW6aabbtKyZctUWlqqceNi+iJS2GT3GRjNSCN4H31rLNzHqecBicnp5+HYsWO67777tHXr1mh2O/FZCUaSVVdXF1Z2/fXXWytWrAgru/rqq63Vq1ePeI/ly5dbu3btcqqLcJidZ2DPnj3WHXfcETr/9a9/bS1dujR0vmrVKusnP/lJ9DsNx0TzebhQOdwv2s/DyZMnrQULFljPPPOMI/1NZAmRgTifoaEhtbS0qLy8PKy8vLw8NGlSOvPqcEk6dOiQ9u/fr0WLFsW0n3DOWJ+BkVx//fX6/e9/r8OHD+vEiRPavXs3z0aCs/M8IPnYeR4sy9L999+vm266SRUVFU52MyElfB7/6NGjCgQCys3NDSvPzc3VkSNHQudLly7V8ePHNWHCBP34xz9mCCOJjPUZWLRokQ4cOKCBgQFNmzZNdXV1Ki0t1fe//32VlZUpGAzqK1/5iiZNmhTrr4Aosvs8jFaOxGTneRgcHNSOHTt07bXXhuZVPPvss7rmmmti+RVcK2l+i3503NqyrLAy/vJIfhd6BkZbXbFkyRItWbLE0b4h9iJ9HliFk5wifR6CwaCj/UpkCT+EMXnyZKWmpoZFktKZIYuPRpxITjwDOBfPA87F8+CchA8g0tLSVFJSooaGhrDyhoYGzZ8/P069QizxDOBcPA84F8+DcxJiCKO/v1/t7e2h846ODrW2tionJ0cFBQWqrq5WRUWF5s6dq3nz5mnr1q3q7OzUihUr4thrRBPPAM7F84Bz8TzESVzXgIzRnj17LEnDjsrKytA1fr/fmj59upWWlmZdd9111quvvhq/DiPqeAZwLp4HnIvnIT4uindhAACA6Er4ORAAACD2CCAAAIAxAggAAGCMAAIAABgjgAAAAMYIIAAAgDECCAAAYIwAAgAAGCOAAAAAxgggAACAMQIIAABgjAACAAAY+/893BZ3NAusfgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "x, z, ne, inwind = util.wind_to_masked(data, value_string=\"ne\", return_inwind=True)\n", "plt.pcolormesh(x,z, np.log10(ne))\n", @@ -188,7 +103,8 @@ "plt.xlim(1e9,1e12)\n", "plt.ylim(1e8,1e12)\n", "cbar = plt.colorbar()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -203,46 +119,11 @@ "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7363719978102.189\n", - "12772.992700729927\n", - "37305.83941605839\n", - "1214.8115635036497\n", - "11854.087591240876\n", - "145555532.84671533\n", - "5.267621167883212e-06\n", - "0.41242660718248175\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import py_plot_output as plot \n", "plot.make_wind_plot(data, \"cv_standard_wind\", var = [\"ne\", \"t_e\", \"t_r\", \"xi\", \"ntot\", \"v_x\", \"h1\", \"c4\"], shape=(4,2) )" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -257,26 +138,6 @@ "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/matthewsj/.mpi_temp/ipykernel_33261/4163463376.py:3: RuntimeWarning: divide by zero encountered in log10\n", - " plt.pcolormesh(x,z, np.log10(c3_frac))\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "carbon_ion = io.read(\"cv_test/cv_standard.C.frac.txt\")\n", "x, z, c3_frac, inwind = util.wind_to_masked(carbon_ion, value_string=\"i03\", return_inwind=True)\n", @@ -285,14 +146,15 @@ "plt.xlim(1e9,1e12)\n", "plt.ylim(1e8,1e12)\n", "cbar = plt.colorbar()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "source": [], + "outputs": [] } ], "metadata": { diff --git a/docs/sphinx/source/py_progs.rst b/docs/sphinx/source/py_progs.rst index 3050e01f7..d5b5c9ca2 100644 --- a/docs/sphinx/source/py_progs.rst +++ b/docs/sphinx/source/py_progs.rst @@ -1,21 +1,21 @@ Python Scripts -############## +################################ -There are several `Python` (the scripting language) scripts written to prepare input for +There are several `Python` scripts written to prepare input for and analyse the output of *python* (the C code). -Some of the more useful scripts/modules are documented below. Alternatively, you can generate documentation -for all the scripts by navigating to :code:`docs/pydocs` and running :code:`write_docs.py`. The resulting output file can be found `here <../../pydocs/doc_index.html>`_. +Some of the more useful scripts/modules are documented below. +Alternatively, you can generate documentation for all the scripts by navigating to :code:`docs/pydocs` and running :code:`write_docs.py`. +The resulting output file can be found `here <../../pydocs/doc_index.html>`_. .. admonition :: Warning to user - The scripts documented here form an incomplete and inhomogenous list, in the sense that they have been developed by different people at different times and do not fit nicely together as a single python package. Some of the scripts should still be useful, particularly if you consult example notebookes, but use with caution! + The scripts documented here form an incomplete and inhomogenous list, in the sense that they have been developed by different people at different times and do not fit nicely together as a single python package. + Some of the scripts should still be useful, particularly if you consult example notebookes, but use with caution! -.. todo:: Finish adding modules below. .. toctree:: + :maxdepth: 2 + :glob: - py_progs/plotting - py_progs/testing - py_progs/utilities - py_progs/py4py \ No newline at end of file + py_progs/* diff --git a/docs/sphinx/source/py_progs/MakeMacro.rst b/docs/sphinx/source/py_progs/MakeMacro.rst index 5a23f6f92..b47a56195 100644 --- a/docs/sphinx/source/py_progs/MakeMacro.rst +++ b/docs/sphinx/source/py_progs/MakeMacro.rst @@ -86,5 +86,12 @@ without the -guess option to produce a final set of files **Note that at present the routine does not handle the collisional x-sections, though this should be straightforward to add** +API Documentation +================= +.. autosummary:: + :toctree: MakeMacro + MakeMacro + MacroCombine + RedoPhot diff --git a/docs/sphinx/source/py_progs/MakeMacro.sphinx_cannot_doc b/docs/sphinx/source/py_progs/MakeMacro.sphinx_cannot_doc new file mode 100644 index 000000000..b61815331 --- /dev/null +++ b/docs/sphinx/source/py_progs/MakeMacro.sphinx_cannot_doc @@ -0,0 +1 @@ +Macro2Simple diff --git a/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst b/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst new file mode 100644 index 000000000..5b3744935 --- /dev/null +++ b/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst @@ -0,0 +1,37 @@ +MacroCombine +============ + +.. automodule:: MacroCombine + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + add_gtot + plot_xsec + read_phot + redo_lines + redo_phot + reweight + steer + write_phot + xguess + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst b/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst new file mode 100644 index 000000000..f1564ef29 --- /dev/null +++ b/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst @@ -0,0 +1,38 @@ +MakeMacro +========= + +.. automodule:: MakeMacro + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + calculate_oscillator_strength + doit + get_collisions + get_f + get_levels + get_lines + get_phot + make_phot + print_elvlc + write_phot + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst b/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst new file mode 100644 index 000000000..47a60f86a --- /dev/null +++ b/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst @@ -0,0 +1,34 @@ +RedoPhot +======== + +.. automodule:: RedoPhot + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + extrap + plot_phot + read_phot + redo_one + steer + write_phot_tab + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development.rst b/docs/sphinx/source/py_progs/development.rst new file mode 100644 index 000000000..4815ad265 --- /dev/null +++ b/docs/sphinx/source/py_progs/development.rst @@ -0,0 +1,33 @@ +Developer Tools +--------------- + +Several scripts have been written to assist in developing and documenting **Python**. + + +Documentation +============= + +.. autosummary:: + :toctree: development + + autogenerate_parameter_docs + autogenerate_rtd_pages + dox + dox_check + + + +Development +=========== + +.. autosummary:: + :toctree: development + + add_param + update_param + run_indent + init_extern + make_rtheta + pf_check + CheckAtomic + retro diff --git a/docs/sphinx/source/py_progs/development/CheckAtomic.rst b/docs/sphinx/source/py_progs/development/CheckAtomic.rst new file mode 100644 index 000000000..14f0dd443 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/CheckAtomic.rst @@ -0,0 +1,35 @@ +CheckAtomic +=========== + +.. automodule:: CheckAtomic + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + analyze_lines + doit + get_element_table + get_ion_tab + get_level_tab + get_line_tab + xread + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/add_param.rst b/docs/sphinx/source/py_progs/development/add_param.rst new file mode 100644 index 000000000..dad8c838f --- /dev/null +++ b/docs/sphinx/source/py_progs/development/add_param.rst @@ -0,0 +1,32 @@ +add\_param +========== + +.. automodule:: add_param + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + do_one + doit + read_file + read_table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst b/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst new file mode 100644 index 000000000..7d00c9706 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst @@ -0,0 +1,38 @@ +autogenerate\_parameter\_docs +============================= + +.. automodule:: autogenerate_parameter_docs + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + autogenerate_parameter_docs + deprecate_documentation + intersect_documentation + list_existing_documentation + list_input_files + my_represent_scalar + parse_param_to_dict + read_parameters + should_use_block + yaml_output + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst b/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst new file mode 100644 index 000000000..c34e55ecf --- /dev/null +++ b/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst @@ -0,0 +1,35 @@ +autogenerate\_rtd\_pages +======================== + +.. automodule:: autogenerate_rtd_pages + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + autogenerate_rtd_pages + output_parameter + read_yaml + steer + write_header_by_level + write_rst + write_str_indent + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/dox.rst b/docs/sphinx/source/py_progs/development/dox.rst new file mode 100644 index 000000000..4c22a375d --- /dev/null +++ b/docs/sphinx/source/py_progs/development/dox.rst @@ -0,0 +1,36 @@ +dox +=== + +.. automodule:: dox + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + do_many + doit + gen_file_header + gen_header + is_installed + read_file + read_table + steer + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/dox_check.rst b/docs/sphinx/source/py_progs/development/dox_check.rst new file mode 100644 index 000000000..15ee07ce8 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/dox_check.rst @@ -0,0 +1,29 @@ +dox\_check +========== + +.. automodule:: dox_check + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/init_extern.rst b/docs/sphinx/source/py_progs/development/init_extern.rst new file mode 100644 index 000000000..8e6f4e35b --- /dev/null +++ b/docs/sphinx/source/py_progs/development/init_extern.rst @@ -0,0 +1,29 @@ +init\_extern +============ + +.. automodule:: init_extern + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/make_rtheta.rst b/docs/sphinx/source/py_progs/development/make_rtheta.rst new file mode 100644 index 000000000..495d5049d --- /dev/null +++ b/docs/sphinx/source/py_progs/development/make_rtheta.rst @@ -0,0 +1,31 @@ +make\_rtheta +============ + +.. automodule:: make_rtheta + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + gen_model + get_inputs + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/pf_check.rst b/docs/sphinx/source/py_progs/development/pf_check.rst new file mode 100644 index 000000000..d95114600 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/pf_check.rst @@ -0,0 +1,31 @@ +pf\_check +========= + +.. automodule:: pf_check + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + steer + travis + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/retro.rst b/docs/sphinx/source/py_progs/development/retro.rst new file mode 100644 index 000000000..e6065bf74 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/retro.rst @@ -0,0 +1,41 @@ +retro +===== + +.. automodule:: retro + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + check4differences + check_completion + check_inputs + compile_many + compile_one + get_python_source_directory + log2table + plot_many + plot_two + read_table + run_many + run_one + xsmooth + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/run_indent.rst b/docs/sphinx/source/py_progs/development/run_indent.rst new file mode 100644 index 000000000..27ace212a --- /dev/null +++ b/docs/sphinx/source/py_progs/development/run_indent.rst @@ -0,0 +1,33 @@ +run\_indent +=========== + +.. automodule:: run_indent + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + do_all + do_changed + doit + get_gnu + steer + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/development/update_param.rst b/docs/sphinx/source/py_progs/development/update_param.rst new file mode 100644 index 000000000..1a17cb127 --- /dev/null +++ b/docs/sphinx/source/py_progs/development/update_param.rst @@ -0,0 +1,32 @@ +update\_param +============= + +.. automodule:: update_param + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + find_parameter_files + get_root_from_filepath + main + update_parameter + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/plotting.rst b/docs/sphinx/source/py_progs/plotting.rst index caed8685a..9b299913c 100644 --- a/docs/sphinx/source/py_progs/plotting.rst +++ b/docs/sphinx/source/py_progs/plotting.rst @@ -1,12 +1,10 @@ Plotting ---------- +Several scripts have been developed to plot **Python** output files. -py_plot_output -================================= -.. automodule:: py_plot_output - :members: -plot_wind -================================= -.. automodule:: plot_wind - :members: \ No newline at end of file +.. toctree:: + :maxdepth: 2 + :glob: + + plotting/* diff --git a/docs/sphinx/source/py_progs/plotting/emissivity_plot.rst b/docs/sphinx/source/py_progs/plotting/emissivity_plot.rst new file mode 100644 index 000000000..42dcd2431 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/emissivity_plot.rst @@ -0,0 +1,5 @@ +`emissivity_plot.py` +================================= + +.. automodule:: emissivity_plot + :members: \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/plotting/plot_spec.rst b/docs/sphinx/source/py_progs/plotting/plot_spec.rst new file mode 100644 index 000000000..9237980c6 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/plot_spec.rst @@ -0,0 +1,5 @@ +`plot_spec.py` +================================= + +.. automodule:: plot_spec + :members: \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/plotting/plot_tot.rst b/docs/sphinx/source/py_progs/plotting/plot_tot.rst new file mode 100644 index 000000000..416017962 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/plot_tot.rst @@ -0,0 +1,5 @@ +`plot_tot.py` +================================= + +.. automodule:: plot_tot + :members: diff --git a/docs/sphinx/source/py_progs/plotting/plot_wind.rst b/docs/sphinx/source/py_progs/plotting/plot_wind.rst new file mode 100644 index 000000000..78b2dd6bc --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/plot_wind.rst @@ -0,0 +1,5 @@ +`plot_wind.py` +================================= + +.. automodule:: plot_wind + :members: \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/plotting/plot_wind_1d.rst b/docs/sphinx/source/py_progs/plotting/plot_wind_1d.rst new file mode 100644 index 000000000..37f0e1dda --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/plot_wind_1d.rst @@ -0,0 +1,5 @@ +`plot_wind_1d.py` +================================= + +.. automodule:: plot_wind_1d + :members: \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/plotting/plotlevels.rst b/docs/sphinx/source/py_progs/plotting/plotlevels.rst new file mode 100644 index 000000000..4ecc8bca6 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/plotlevels.rst @@ -0,0 +1,5 @@ +`PlotLevels.py` +================================= + +.. automodule:: PlotLevels + :members: diff --git a/docs/sphinx/source/py_progs/plotting/py_plot_output.rst b/docs/sphinx/source/py_progs/plotting/py_plot_output.rst new file mode 100644 index 000000000..708882f4c --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/py_plot_output.rst @@ -0,0 +1,5 @@ +`py_plot_output.py` +================================= + +.. automodule:: py_plot_output + :members: diff --git a/docs/sphinx/source/py_progs/plotting/qdisk_plot.rst b/docs/sphinx/source/py_progs/plotting/qdisk_plot.rst new file mode 100644 index 000000000..6e8cdbe79 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/qdisk_plot.rst @@ -0,0 +1,5 @@ +`qdisk_plot.py` +================================= + +.. automodule:: qdisk_plot + :members: diff --git a/docs/sphinx/source/py_progs/plotting/sphinx_cannot_doc/pl_loop_comp.rst.undocable b/docs/sphinx/source/py_progs/plotting/sphinx_cannot_doc/pl_loop_comp.rst.undocable new file mode 100644 index 000000000..b61b4a504 --- /dev/null +++ b/docs/sphinx/source/py_progs/plotting/sphinx_cannot_doc/pl_loop_comp.rst.undocable @@ -0,0 +1,5 @@ +`pl_loop_comp.py` +================================= + +.. automodule:: pl_loop_comp + :members: diff --git a/docs/sphinx/source/py_progs/regression.rst b/docs/sphinx/source/py_progs/regression.rst index 8b11e72f1..56f15a369 100644 --- a/docs/sphinx/source/py_progs/regression.rst +++ b/docs/sphinx/source/py_progs/regression.rst @@ -2,7 +2,7 @@ Regression ---------- Primarily to verify that changes made to Python do not inadvertently cause unexpected changes -if models, several rutines exist to run a fixed set of (relatively fast) models that are +if models, several routines exist to run a fixed set of (relatively fast) models that are **nominally** contained in Examples/regress. Developers are encouraged to use this routines, before they merge anything into one of the @@ -10,81 +10,89 @@ major branches of Python. The routines involved are - * regression.py - * regression_check.py - * regression_plot.py + * `regression.py` + * `regression_check.py` + * `regression_plot.py` - The primary routine is regression.py. All of the routines can be run with a -h switch - to obtain information of the full set of command line options +The primary routine is regression.py. All of the routines can be run with a -h switch +to obtain information of the full set of command line options. - Setup - ##### +Setup +===== - Typically one should set up a directory, e.g Regression to run the routines, and, if for example, - py87f, is the version of Python being used when you set up the directory, being run. +Typically one should set up a directory, e.g Regression to run the routines, and, if for example, +py87f, is the version of Python being used when you set up the directory, being run. - Python should be compiled with mpicc before running the regression program +Python should be compiled with mpicc before running the regression program Basic Procedure -############### +=============== - regression.py py87f +Run:: - This will create a directory py87f_231108 where 231108 is the current date. The pf files from - the regression directory as well as various ancillary files will be copied into this directory, - and all of the models contained therein will run sequentially. - In the absence of command line - switches the routines will be run with a default number of processors (currently 3). - Assuming this is the first time the program is run, no comparison to previous runs will be made + regression.py py87f - The models have been selected - to test a variety of types of models and/or to highlight areas of concern. As a result, the models that are run are likely - to change occassionaly. - - Once changes have been made to python, one reruns the program, e.g. +This will create a directory py87f_231108 where 231108 is the current date. The pf files from +the regression directory as well as various ancillary files will be copied into this directory, +and all of the models contained therein will run sequentially. +In the absence of command line +switches the routines will be run with a default number of processors (currently 3). +Assuming this is the first time the program is run, no comparison to previous runs will be made - regression.py py +The models have been selected +to test a variety of types of models and/or to highlight areas of concern. As a result, the models that are run are likely +to change occassionaly. - This will created a directory py_2311108 (assuming it this is the same day) and repead the previous - precedured. +Once changes have been made to python, one reruns the program, e.g:: - **If the program is run on the same day with the same version of python, the older models - will be overwritten. Typically one can avoid this by using py one time and py with the version number - a second time. But there is also a command line switch to specify the name of the run time directory** + regression.py py - Assuming all of the various models run to complesion, regression.py will call subroutines in regression_check.py - and regression_plot.py to make comparasions between the model just run and the previous one. The plots (one for each model) - will be contained in a directory called Xcompare. +This will create a directory py_2311108 (assuming it this is the same day) and repead the previous +precedured. +**If the program is run on the same day with the same version of python, the older models +will be overwritten. Typically one can avoid this by using py one time and py with the version number +a second time. But there is also a command line switch to specify the name of the run time directory** - Interpretion of the results - ########################### +Assuming all of the various models run to complesion, regression.py will call subroutines in regression_check.py +and regression_plot.py to make comparasions between the model just run and the previous one. The plots (one for each model) +will be contained in a directory called Xcompare. - The models that are run are simple models, and to allow one to proceed quickly, none of the models is run to convergence. - The outputs compare the spectra that were produced in the two runs of the program, both by looking to see how many lines in - the ionization and detailed spectra have changed, and by generating plots that show comparisions of the spectra. - Many times the results will be identical, but if a change between two versions of the program results in a different - sequence of random numbers, then the spectra will change simply as a result of random noise, which is not a concern. - There is no easy way to quantify changes that are due to this effect or something else, and - so one simply through experience has to gauge the results by inspecting the plots that are produced.. +Interpretation of the results +============================== + +The models that are run are simple models, and to allow one to proceed quickly, none of the models is run to convergence. +The outputs compare the spectra that were produced in the two runs of the program, both by looking to see how many lines in +the ionization and detailed spectra have changed, and by generating plots that show comparisions of the spectra. + +Many times the results will be identical, but if a change between two versions of the program results in a different +sequence of random numbers, then the spectra will change simply as a result of random noise, which is not a concern. +There is no easy way to quantify changes that are due to this effect or something else, and +so one simply through experience has to gauge the results by inspecting the plots that are produced.. Comments and additions -###################### +====================== Although regression.py generally produces a comparison betwen the set of models being run and the last set of models that wre run, one can use -regression_check.py to compare any two sets of runs. +regression_check.py to compare any two sets of runs:: -retression_check.py run1 run2 + regression_check.py run1 run2 where one gives the names of the two directories to be compared. - While the regression procedure described here is generally set up to run on the models that are contained in the Examples/regress directory, regression.py has switches that allow one to do tests on models that are in any input directory. This can be useful, if one wishes to test different models in order to solve specific problems, or to run a set of models sequentially. +API Documentation +================= +.. autosummary:: + :toctree: regression - + regression + regression_check + regression_plot + regression_nsh diff --git a/docs/sphinx/source/py_progs/regression/regression.rst b/docs/sphinx/source/py_progs/regression/regression.rst new file mode 100644 index 000000000..b82979b6b --- /dev/null +++ b/docs/sphinx/source/py_progs/regression/regression.rst @@ -0,0 +1,34 @@ +regression +========== + +.. automodule:: regression + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + check_one + doit + py_hydro + run_cmds + steer + sum_errors + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/regression/regression_check.rst b/docs/sphinx/source/py_progs/regression/regression_check.rst new file mode 100644 index 000000000..79765e935 --- /dev/null +++ b/docs/sphinx/source/py_progs/regression/regression_check.rst @@ -0,0 +1,34 @@ +regression\_check +================= + +.. automodule:: regression_check + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + diff_two_files + doit + get_other_directory + read_file + read_table + steer + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/regression/regression_nsh.rst b/docs/sphinx/source/py_progs/regression/regression_nsh.rst new file mode 100644 index 000000000..a81caeed2 --- /dev/null +++ b/docs/sphinx/source/py_progs/regression/regression_nsh.rst @@ -0,0 +1,34 @@ +regression\_nsh +=============== + +.. automodule:: regression_nsh + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + check_one + doit + py_hydro + run_cmds + steer + sum_errors + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/regression/regression_plot.rst b/docs/sphinx/source/py_progs/regression/regression_plot.rst new file mode 100644 index 000000000..2f7ea0883 --- /dev/null +++ b/docs/sphinx/source/py_progs/regression/regression_plot.rst @@ -0,0 +1,36 @@ +regression\_plot +================ + +.. automodule:: regression_plot + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + do_all + doit + doit_two + plot_spec + plot_tot + read_file + read_table + xsmooth + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running.rst b/docs/sphinx/source/py_progs/running.rst new file mode 100644 index 000000000..2177feafe --- /dev/null +++ b/docs/sphinx/source/py_progs/running.rst @@ -0,0 +1,21 @@ +Running Python +-------------- + +.. autosummary:: + :toctree: running + + ModSum + pf_grid + run_many + +Several scripts have been developed to run **Python** and **Cloudy** simulations for comparison. + +.. autosummary:: + :toctree: running + + cloudy_pl_loop + py79_pl_loop + python_pl_loop + + + diff --git a/docs/sphinx/source/py_progs/running/ModSum.rst b/docs/sphinx/source/py_progs/running/ModSum.rst new file mode 100644 index 000000000..47369434a --- /dev/null +++ b/docs/sphinx/source/py_progs/running/ModSum.rst @@ -0,0 +1,33 @@ +ModSum +====== + +.. automodule:: ModSum + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + get_models + get_pf + get_status + make_master + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst b/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst new file mode 100644 index 000000000..467aefb39 --- /dev/null +++ b/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst @@ -0,0 +1,23 @@ +cloudy\_pl\_loop +================ + +.. automodule:: cloudy_pl_loop + + + + + + + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running/pf_grid.rst b/docs/sphinx/source/py_progs/running/pf_grid.rst new file mode 100644 index 000000000..8521da79e --- /dev/null +++ b/docs/sphinx/source/py_progs/running/pf_grid.rst @@ -0,0 +1,36 @@ +pf\_grid +======== + +.. automodule:: pf_grid + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + cleanse + create_parameter_files + create_runfile + doit + expand_array + export_results + get_input + log_ints + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running/py79_pl_loop.rst b/docs/sphinx/source/py_progs/running/py79_pl_loop.rst new file mode 100644 index 000000000..21dd10b8d --- /dev/null +++ b/docs/sphinx/source/py_progs/running/py79_pl_loop.rst @@ -0,0 +1,23 @@ +py79\_pl\_loop +============== + +.. automodule:: py79_pl_loop + + + + + + + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running/python_pl_loop.rst b/docs/sphinx/source/py_progs/running/python_pl_loop.rst new file mode 100644 index 000000000..176a2277b --- /dev/null +++ b/docs/sphinx/source/py_progs/running/python_pl_loop.rst @@ -0,0 +1,23 @@ +python\_pl\_loop +================ + +.. automodule:: python_pl_loop + + + + + + + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/running/run_many.rst b/docs/sphinx/source/py_progs/running/run_many.rst new file mode 100644 index 000000000..fa03f4c8a --- /dev/null +++ b/docs/sphinx/source/py_progs/running/run_many.rst @@ -0,0 +1,33 @@ +run\_many +========= + +.. automodule:: run_many + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + get_no_jobs + read_file + run_one + steer + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing.rst b/docs/sphinx/source/py_progs/testing.rst index 5444cfdf8..d9ff5e2fb 100644 --- a/docs/sphinx/source/py_progs/testing.rst +++ b/docs/sphinx/source/py_progs/testing.rst @@ -1,7 +1,13 @@ Checking Runs and Testing --------------------------- -run_check -================================= -.. automodule:: run_check - :members: \ No newline at end of file +.. autosummary:: + :toctree: testing + + balmer_decrement + compare_ion + CompareAtomic + grid_check + run_check + test_masterfiles + xcompare \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing.sphinx_cannot_doc b/docs/sphinx/source/py_progs/testing.sphinx_cannot_doc new file mode 100644 index 000000000..fabd7f953 --- /dev/null +++ b/docs/sphinx/source/py_progs/testing.sphinx_cannot_doc @@ -0,0 +1,3 @@ +compare_one +pyfits_eval +pyfits_eval2 \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/CompareAtomic.rst b/docs/sphinx/source/py_progs/testing/CompareAtomic.rst new file mode 100644 index 000000000..976e08d58 --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/CompareAtomic.rst @@ -0,0 +1,31 @@ +CompareAtomic +============= + +.. automodule:: CompareAtomic + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + compare_files + doit + xread + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/balmer_decrement.rst b/docs/sphinx/source/py_progs/testing/balmer_decrement.rst new file mode 100644 index 000000000..1055b557c --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/balmer_decrement.rst @@ -0,0 +1,29 @@ +balmer\_decrement +================= + +.. automodule:: balmer_decrement + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + BalmerTest + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/compare_ion.rst b/docs/sphinx/source/py_progs/testing/compare_ion.rst new file mode 100644 index 000000000..568d559f5 --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/compare_ion.rst @@ -0,0 +1,31 @@ +compare\_ion +============ + +.. automodule:: compare_ion + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + compare_ion + edge + xsmooth + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/grid_check.rst b/docs/sphinx/source/py_progs/testing/grid_check.rst new file mode 100644 index 000000000..847ef8d41 --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/grid_check.rst @@ -0,0 +1,30 @@ +grid\_check +=========== + +.. automodule:: grid_check + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + read_file + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/run_check.rst b/docs/sphinx/source/py_progs/testing/run_check.rst new file mode 100644 index 000000000..11190557d --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/run_check.rst @@ -0,0 +1,38 @@ +run\_check +========== + +.. automodule:: run_check + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + check_completion + doit + how_many_dimensions + make_html + plot_converged + py_error + read_diag + steer + windsave2table + xwindsave2table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/test_masterfiles.rst b/docs/sphinx/source/py_progs/testing/test_masterfiles.rst new file mode 100644 index 000000000..362c21668 --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/test_masterfiles.rst @@ -0,0 +1,31 @@ +test\_masterfiles +================= + +.. automodule:: test_masterfiles + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + check_run + run_file + run_test + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/testing/xcompare.rst b/docs/sphinx/source/py_progs/testing/xcompare.rst new file mode 100644 index 000000000..36794de4a --- /dev/null +++ b/docs/sphinx/source/py_progs/testing/xcompare.rst @@ -0,0 +1,37 @@ +xcompare +======== + +.. automodule:: xcompare + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + add_lines + doit + doublet + plot_spec + plot_tot + singlet + windsave2table + xplot + xsmooth + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities.rst b/docs/sphinx/source/py_progs/utilities.rst index 2ac1f04c6..ba254d702 100644 --- a/docs/sphinx/source/py_progs/utilities.rst +++ b/docs/sphinx/source/py_progs/utilities.rst @@ -1,18 +1,18 @@ Utility, I/O and Imports -------------------------- -py_read_output -================================= -.. automodule:: py_read_output - :members: +.. autosummary:: + :toctree: utilities -py_plot_util -================================= -.. automodule:: py_plot_util - :members: + import_1d + import_cyl + import_rtheta + py_classes + py_error + py_plot_util + py_read_output + hydro_2_python + kpar + watchdog + xhtml - -import_cyl -================================= -.. automodule:: import_cyl - :members: \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities.sphinx_cannot_doc b/docs/sphinx/source/py_progs/utilities.sphinx_cannot_doc new file mode 100644 index 000000000..8cf01db47 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities.sphinx_cannot_doc @@ -0,0 +1 @@ +photo_xs diff --git a/docs/sphinx/source/py_progs/utilities/hydro_2_python.rst b/docs/sphinx/source/py_progs/utilities/hydro_2_python.rst new file mode 100644 index 000000000..462c8e923 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/hydro_2_python.rst @@ -0,0 +1,30 @@ +hydro\_2\_python +================ + +.. automodule:: hydro_2_python + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + get_hdf_data + get_ndf_data + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/import_1d.rst b/docs/sphinx/source/py_progs/utilities/import_1d.rst new file mode 100644 index 000000000..829e9ad65 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/import_1d.rst @@ -0,0 +1,31 @@ +import\_1d +========== + +.. automodule:: import_1d + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + read_file + read_table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/import_cyl.rst b/docs/sphinx/source/py_progs/utilities/import_cyl.rst new file mode 100644 index 000000000..f58164d9b --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/import_cyl.rst @@ -0,0 +1,31 @@ +import\_cyl +=========== + +.. automodule:: import_cyl + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + read_file + read_table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/import_rtheta.rst b/docs/sphinx/source/py_progs/utilities/import_rtheta.rst new file mode 100644 index 000000000..4990c8b44 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/import_rtheta.rst @@ -0,0 +1,31 @@ +import\_rtheta +============== + +.. automodule:: import_rtheta + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + read_file + read_table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/kpar.rst b/docs/sphinx/source/py_progs/utilities/kpar.rst new file mode 100644 index 000000000..11f3077d4 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/kpar.rst @@ -0,0 +1,33 @@ +kpar +==== + +.. automodule:: kpar + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + cpar + opar + rdpar + read_file + read_table + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/py_classes.rst b/docs/sphinx/source/py_progs/utilities/py_classes.rst new file mode 100644 index 000000000..35e2bea3e --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/py_classes.rst @@ -0,0 +1,34 @@ +py\_classes +=========== + +.. automodule:: py_classes + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + chianti_level + chianti_rad + level + line + specclass + spectotclass + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/py_error.rst b/docs/sphinx/source/py_progs/utilities/py_error.rst new file mode 100644 index 000000000..18c5092fa --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/py_error.rst @@ -0,0 +1,29 @@ +py\_error +========= + +.. automodule:: py_error + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + doit + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/py_plot_util.rst b/docs/sphinx/source/py_progs/utilities/py_plot_util.rst new file mode 100644 index 000000000..c60fc8102 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/py_plot_util.rst @@ -0,0 +1,36 @@ +py\_plot\_util +============== + +.. automodule:: py_plot_util + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + get_flux_at_wavelength + get_pywind_summary + get_windsave_descriptions + parse_rcparams + read_pywind_smart + run_py_wind + smooth + wind_to_masked + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/py_read_output.rst b/docs/sphinx/source/py_progs/utilities/py_read_output.rst new file mode 100644 index 000000000..a44e3616f --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/py_read_output.rst @@ -0,0 +1,38 @@ +py\_read\_output +================ + +.. automodule:: py_read_output + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + read_convergence + read_emissivity + read_pf + read_pywind + read_pywind_summary + read_spectrum + read_spectrum_to_class + setpars + thinshell_read + write_pf + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/watchdog.rst b/docs/sphinx/source/py_progs/utilities/watchdog.rst new file mode 100644 index 000000000..7de8e6517 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/watchdog.rst @@ -0,0 +1,30 @@ +watchdog +======== + +.. automodule:: watchdog + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + help + strip_error + + + + + + + + + + + + + diff --git a/docs/sphinx/source/py_progs/utilities/xhtml.rst b/docs/sphinx/source/py_progs/utilities/xhtml.rst new file mode 100644 index 000000000..956a267b4 --- /dev/null +++ b/docs/sphinx/source/py_progs/utilities/xhtml.rst @@ -0,0 +1,41 @@ +xhtml +===== + +.. automodule:: xhtml + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + add_list + begin + end + h1 + h2 + h3 + hline + image + link + paragraph + preformat + table + test + + + + + + + + + + + + + diff --git a/py_progs/CheckAtomic.py b/py_progs/CheckAtomic.py index 39acc5be0..058b64ea2 100755 --- a/py_progs/CheckAtomic.py +++ b/py_progs/CheckAtomic.py @@ -2,17 +2,15 @@ # coding: utf-8 ''' - Space Telescope Science Institute +Summarize an atomic data file in order to see how large arrays defined in atomic.h must be -Synopsis: -Summarize an atomic data file in order to see -how large arrays defined in atomic.h must be +Command line usage (if any): + usage:: -Command line usage (if any): + CheckAtomic.py masterfile - usage: CheckAtomic.py masterfile Description: @@ -40,7 +38,7 @@ History: -240511 ksl Coding begun + 240511 ksl Coding begun ''' diff --git a/py_progs/CompareAtomic.py b/py_progs/CompareAtomic.py index e15e52d2c..0f7106935 100755 --- a/py_progs/CompareAtomic.py +++ b/py_progs/CompareAtomic.py @@ -2,13 +2,7 @@ # coding: utf-8 ''' - Space Telescope Science Institute - -Synopsis: - -Simple routine to determine differences between files -read in two -sets of atomic data. +Simple routine to determine differences between files read in two sets of atomic data. Command line usage (if any): diff --git a/py_progs/DoDocs b/py_progs/DoDocs deleted file mode 100644 index e69de29bb..000000000 diff --git a/py_progs/MacroCombine.py b/py_progs/MacroCombine.py index 56f099357..b2d50a62d 100755 --- a/py_progs/MacroCombine.py +++ b/py_progs/MacroCombine.py @@ -1,12 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -Combine Chianti levels to create a more succint -MacroAtom model +Combine Chianti levels to create a more succinct MacroAtom model Command line usage (if any): diff --git a/py_progs/MakeMacro.py b/py_progs/MakeMacro.py index 523a1d97d..89b6b64b9 100755 --- a/py_progs/MakeMacro.py +++ b/py_progs/MakeMacro.py @@ -1,24 +1,28 @@ #!/usr/bin/env python """ - Space Telescope Science Institute +Uses Chianti and Topbase to create the base for macro-atom models. -Synopsis: Use Chianti and Topbase to create a set of data files that +Use Chianti and Topbase to create a set of data files that can be used as the basis for creating macro-atom models of various ions Command line usage (if any): - usage: MakeHMacro.py ion_name nlevels [True] + usage:: + + MakeHMacro.py ion_name nlevels [True] where the ion name is in Chianti notation, e.g c_4 for C IV, fe_25 for Fe XXV and nlevels is the number of energy levels to include in the model and the optional True implies this is the top ion to include. - *Changes as per 27/08/20 + * Changes as per 27/08/20 - usage (in terminal window): MakeMacro.py ion_name nlevels True/False + usage (in terminal window):: + + MakeMacro.py ion_name nlevels True/False Description: @@ -48,9 +52,11 @@ History: -191227 ksl Coding begun -221227 ksl Relooked at routine, verified it seemed to work and cleaned up some of the comments. - The functionality is unchanged. +191227 ksl + Coding begun +221227 ksl + Relooked at routine, verified it seemed to work and cleaned up some of the comments. + The functionality is unchanged. """ @@ -58,7 +64,11 @@ from astropy.io import ascii import numpy import os -import ChiantiPy.core as ch + +# Do not call this when we're on ReadTheDocs +if not os.environ.get('READTHEDOCS'): + import ChiantiPy.core as ch + import numpy as np from astropy.table import Table, join from astropy.io import ascii diff --git a/py_progs/ModSum.py b/py_progs/ModSum.py index f1502bd54..46e009aa7 100755 --- a/py_progs/ModSum.py +++ b/py_progs/ModSum.py @@ -1,12 +1,7 @@ #!/usr/bin/env python # coding: utf-8 ''' - Space Telescope Science Institute - -Synopsis: - -Create a summary of the differences between various .pf files -in a directory +Create a summary of the differences between various .pf files in a directory Command line usage (if any): diff --git a/py_progs/README.md b/py_progs/README.md index 9b8fecded..58735a7b4 100644 --- a/py_progs/README.md +++ b/py_progs/README.md @@ -2,7 +2,6 @@ Description ########### This directory contains python scripts for use with the C code python. -You will need to run `write_docs.py` from that directory to generate this documentation. The basic python modules required can be found in `requirements.txt`. diff --git a/py_progs/RedoPhot.py b/py_progs/RedoPhot.py index 44eb2b82a..3c0384a20 100755 --- a/py_progs/RedoPhot.py +++ b/py_progs/RedoPhot.py @@ -1,13 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -Extend phot tables retrieved from Topbase to higher energies -and produce a plot file which shows the extended x-section -file +Extend phot tables retrieved from Topbase to higher energies and produce a plot file which shows the extended x-section file Command line usage (if any): diff --git a/py_progs/add_param.py b/py_progs/add_param.py index e1ab76aad..2af845f97 100755 --- a/py_progs/add_param.py +++ b/py_progs/add_param.py @@ -1,12 +1,6 @@ #!/usr/bin/env python - ''' - Space Telescope Science Institute - -Synopsis: - -The purpose of this routine is to add a new parameter to an existing parameter -file. +The purpose of this routine is to add a new parameter to an existing parameter file. The routine uses a paremeter already existing in the parameter file to find where to place the new parameter @@ -14,18 +8,23 @@ Command line usage (if any): - usage: add_param.py new_param value old_param + usage:: + + add_param.py new_param value old_param where - new_param is the new variable including what is normally included in + new_param + is the new variable including what is normally included in parentheses. This string will be written to the new .pf file. - value is the default value to assign to the new parameter, which can be a string - old_param is the the variable after which the new parameter is to + value + is the default value to assign to the new parameter, which can be a string + old_param + is the the variable after which the new parameter is to be placed. Note that this is a minimum match, which is to say that one does not need to include any more of the old parameter than what is required to make a unique identification -The various paremeters may need to be enclosed in quotes. + The various paremeters may need to be enclosed in quotes. Description: @@ -36,7 +35,7 @@ value after the old_param. If a new paremeter file is merited it is written to a file with a prefix - of new_ + of `new_` If new parameter files are written a command file MoveEm is created, which can be run by "source MoveEm" to replace the old .pf files with the new ones. @@ -54,7 +53,7 @@ History: -190803 ksl Coding begun + 190803 ksl Coding begun ''' @@ -75,9 +74,7 @@ def read_file(filename,char=''): History: 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup - script so there was flexibility to - read any ascii file + 141209 ksl Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' try: diff --git a/py_progs/autogenerate_parameter_docs.py b/py_progs/autogenerate_parameter_docs.py index d994ebd53..ab303c2c5 100755 --- a/py_progs/autogenerate_parameter_docs.py +++ b/py_progs/autogenerate_parameter_docs.py @@ -1,6 +1,8 @@ #!/usr/bin/env python # -*- coding: -*- """ +Generates `.yml` format descriptions of the parameters in the C files. + This script goes through the (non-blacklisted) input .c files, and identifies the input variables, their types and any notes (e.g. units, choices). @@ -16,11 +18,11 @@ Print the full text of all .yaml files that would be created to the screen. -w / --write - Move any deprecated parameters to $PYTHON/parameters/old/, - then write any new parameters to in $PYTHON/parameters/ + Move any deprecated parameters to `$PYTHON/parameters/old/`, + then write any new parameters to in `$PYTHON/parameters/` Note, this will not over-write any parameters that have changed types - but not names e.g. rdflo('thing') to rdint('thing'). + but not names e.g. `rdflo('thing')` to `rdint('thing')`. -h / --help Prints this documentation. @@ -44,25 +46,22 @@ are not committed, but it is up to the user to sort out what s/he wants to do. - The command to list files in a directory that are not tracked is + The command to list files in a directory that are not tracked is:: - git ls-files --other + git ls-files --other if one is in the directory in question. - The command to remove files in a directory (from within that directory) - is: - git clean -f - - The recommendation is to + The command to remove files in a directory (from within that directory) is:: - * to clean both $PYTHON/parameters/, and $PYTHON/parameters/old/ from your - local directories before using writing files using this routine, and - then + git clean -f - * to add and comit all of the files that are produced before going on to other - stages of activities associated with documentation. + The recommendation is to: + * to clean both $PYTHON/parameters/, and $PYTHON/parameters/old/ from your + local directories before using writing files using this routine, and then + * to add and comit all of the files that are produced before going on to other + stages of activities associated with documentation. """ import os @@ -495,7 +494,7 @@ def autogenerate_parameter_docs(): print("Cannot write documentation as it will not work in case-insensitive OSes (e.g. Mac, Windows)") return - if len(sys.argv) is 1: + if len(sys.argv) == 1: # If we're not running in write mode print("Documentation for parameters that no longer exist:") for param in deprecated_documentation: diff --git a/py_progs/autogenerate_rtd_pages.py b/py_progs/autogenerate_rtd_pages.py index bd82ce6ff..4cd33c13e 100755 --- a/py_progs/autogenerate_rtd_pages.py +++ b/py_progs/autogenerate_rtd_pages.py @@ -1,5 +1,7 @@ #!/usr/bin/env python """ +Converts `.yml` format parameter descriptions to `.rst` files. + This script goes through the created yaml documentation and converts them to .rst files, then writes them to an output directory diff --git a/py_progs/balmer_decrement.py b/py_progs/balmer_decrement.py index 69176688b..7560f649d 100755 --- a/py_progs/balmer_decrement.py +++ b/py_progs/balmer_decrement.py @@ -1,21 +1,21 @@ #!/usr/bin/env python ''' - balmer_decrement.py +Runs tests of the Balmer decrement for a one zone thin shell Python model. -runs tests of the Balmer decrement for a one zone -thin shell Python model. Involves running py_wind on -a wind_save file and reading some output files. Compares -to Osterbrock values. +Involves running py_wind on a wind_save file and reading some output files. +Compares to Osterbrock values. Usage: - python balmer_decrement.py root_filename - python balmer_decrement.py -h for help + + * `python balmer_decrement.py root_filename` + * `python balmer_decrement.py -h` for help Requirements: py_wind numpy matplotlib - py_plot_util, py_read_output from $PYTHON/py_progs in the python path + py_plot_util, py_read_output from $PYTHON/py_progs in the python path + Notes: This routine is a routine to check the results of running a one zone model intended to produce the Balmer decrements. It does not run the @@ -23,8 +23,12 @@ ''' import numpy as np import sys, os -PYTHON = os.environ["PYTHON"] -sys.path.append("$PYTHON/py_progs/") + +# Do not call this when we're on ReadTheDocs +if not os.environ.get('READTHEDOCS'): + PYTHON = os.environ["PYTHON"] + sys.path.append("$PYTHON/py_progs/") + import py_plot_util as util import py_read_output as rd diff --git a/py_progs/cloudy_pl_loop.py b/py_progs/cloudy_pl_loop.py index 73f6b9fe9..e9a836655 100755 --- a/py_progs/cloudy_pl_loop.py +++ b/py_progs/cloudy_pl_loop.py @@ -1,9 +1,7 @@ #!/usr/bin/env python -i ''' - UNLV - -Synopsis: +Carries out a series of Cloudy simulations for comparison with Python. This routine carries out a series of cloudy simulations for comparison with thin shell python simulations made in python_pl_loop, the partner code. diff --git a/py_progs/compare_ion.py b/py_progs/compare_ion.py index d992ed056..8dee81bf1 100755 --- a/py_progs/compare_ion.py +++ b/py_progs/compare_ion.py @@ -1,13 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -This routine is used for diagnosing isses with spectra -produced during ionization cycles - +This routine is used for diagnosing isses with spectra produced during ionization cycles Command line usage (if any): @@ -43,7 +37,7 @@ import matplotlib.pyplot as plt import numpy as np -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve def xsmooth(flux,smooth=21): diff --git a/py_progs/dox.py b/py_progs/dox.py index 74994f033..1e68737c5 100755 --- a/py_progs/dox.py +++ b/py_progs/dox.py @@ -1,19 +1,15 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - - Check whether c routines have proper dox headers, and if not write a new - file with a dummy dox header for the user to update +Check whether c routines have proper dox headers, and if not write a new file with a dummy dox header for the user to update Command line usage: - usage: dox.py whatever.c to process a single file - dox.py *.c or -all to process all c files in a directory - dox.py -h to get this help message + usage: + * `dox.py whatever.c` to process a single file + * `dox.py *.c` or `-all` to process all c files in a directory + * `dox.py -h` to get this help message Description: @@ -21,29 +17,29 @@ reports on the results. If one or more headers are missing, for a file named for exampe foo.c, it will write out - a new file named new_foo.c with a basic header installed. + a new file named `new_foo.c` with a basic header installed. - This basic headers in this routine should be edited and the file, in this case new_foo.c + This basic headers in this routine should be edited and the file, in this case `new_foo.c` copied back to foo.c Primary routines: - doit processes a single file - do_many processes all of the files in a directory, by calling doit multiplee times + `doit` processes a single file + `do_many` processes all of the files in a directory, by calling doit multiple times steer processes the command line Notes: - Files that begin 'new_' will not be checked + Files that begin `new_` will not be checked dox.py can give incorrect results if the routine is not properly indented - This routine does not check for old style headers used prior to the introductin of doxygen + This routine does not check for old style headers used prior to the introduction of doxygen to Python, since that conversion should be complete. History: -180912 ksl Coding begun + 180912 ksl Coding begun ''' @@ -95,9 +91,7 @@ def read_file(filename,char=''): History: 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup - script so there was flexibility to - read any ascii file + 141209 ksl Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' try: diff --git a/py_progs/dox_check.py b/py_progs/dox_check.py index 58d9f9f1d..0832b2668 100755 --- a/py_progs/dox_check.py +++ b/py_progs/dox_check.py @@ -1,28 +1,26 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - Determine which files lack doxygen commments Command line usage (if any): - usage: dox_check.py filename + usage:: + + dox_check.py filename Description: Primary routines: - doit + `doit` Notes: History: -180404 ksl Coding begun + 180404 ksl Coding begun ''' diff --git a/py_progs/grid_check.py b/py_progs/grid_check.py index 49ec94eb2..a0825bc49 100755 --- a/py_progs/grid_check.py +++ b/py_progs/grid_check.py @@ -1,20 +1,20 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: +Checks .sig files to see which runs in a grid have completed. When running a grid of models, it may not be clear whether all of the runs have been completed, especially if time limits are placed on the individual runs. This routine checks the -.sig files to see which of a list of .pf files have been +.sig files to see which of a list of .pf files have been completed. Command line usage (if any): - usage: grid_check.py filename + usage:: + + grid_check.py filename where filename contains a list of the .pf files that need to be checked @@ -61,8 +61,10 @@ def read_file(filename,char=''): History: - 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup + 110729 ksl + Added optional delimiters + 141209 ksl + Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' diff --git a/py_progs/hydro_2_python.py b/py_progs/hydro_2_python.py index f7aa8a9a4..cbf115d75 100755 --- a/py_progs/hydro_2_python.py +++ b/py_progs/hydro_2_python.py @@ -1,10 +1,9 @@ #!/usr/bin/env python ''' - Southampton University +Parses a hydro input file into an astropy table. - -Synopsis: +Synopsis: This is a simple program which will parse a hydro input file into an astropy table that python can read in as a wind diff --git a/py_progs/import_1d.py b/py_progs/import_1d.py index 86a45771e..422406895 100755 --- a/py_progs/import_1d.py +++ b/py_progs/import_1d.py @@ -1,13 +1,10 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +Create a 1-d spherical `.pf` file from a `windsave2table` file. -Synopsis: - -Read the master file produced by windsave2table for a -1d spherical model and produce a file which can be -be imported into Python +Read the master file produced by windsave2table for a 1d spherical +model and produce a file which can be imported into Python Command line usage (if any): @@ -50,8 +47,10 @@ def read_file(filename,char=''): History: - 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup + 110729 ksl + Added optional delimiters + 141209 ksl + Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' diff --git a/py_progs/import_cyl.py b/py_progs/import_cyl.py index 606246370..f7e4f07d2 100755 --- a/py_progs/import_cyl.py +++ b/py_progs/import_cyl.py @@ -2,10 +2,11 @@ ''' -Synopsis: - Read the master file produced by windsave2table for a - model created in cylindrical coordinates and produce - a file which can be imported into Python and run +Create a cylindrical `.pf` file from a `windsave2table` file. + +Read the master file produced by windsave2table for a +model created in cylindrical coordinates and produce +a file which can be imported into Python and run Command line usage (if any): diff --git a/py_progs/import_rtheta.py b/py_progs/import_rtheta.py index dbec24995..0bdf595eb 100755 --- a/py_progs/import_rtheta.py +++ b/py_progs/import_rtheta.py @@ -1,9 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: +Create a polar coordinate `.pf` file from a `windsave2table` file. Read the master file produced by windsave2table for a rtheta (polar-coordinate mode model and produce @@ -56,8 +54,10 @@ def read_file(filename,char=''): History: - 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup + 110729 ksl + Added optional delimiters + 141209 ksl + Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' diff --git a/py_progs/init_extern.py b/py_progs/init_extern.py index 3dfce0fc2..52269e39b 100755 --- a/py_progs/init_extern.py +++ b/py_progs/init_extern.py @@ -1,13 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -Create a .c file which initializes all of the externs -in a .h file - +Create a .c file which initializes all of the externs in a .h file. Command line usage (if any): diff --git a/py_progs/kpar.py b/py_progs/kpar.py index cb66aaa69..2bfa33920 100644 --- a/py_progs/kpar.py +++ b/py_progs/kpar.py @@ -1,10 +1,6 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - A python version of kpar @@ -22,7 +18,8 @@ History: -160704 ksl Coding begun + 160704 ksl + Coding begun ''' @@ -41,8 +38,10 @@ def read_file(filename,char=''): History: - 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup + 110729 ksl + Added optional delimiters + 141209 ksl + Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' diff --git a/py_progs/make_rtheta.py b/py_progs/make_rtheta.py index 4cf29ab2a..9b9b02d26 100755 --- a/py_progs/make_rtheta.py +++ b/py_progs/make_rtheta.py @@ -1,20 +1,22 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +Create a simple rtheta model file. Synopsis: -Create a model which can be imported into -Python of an rtheta grid, with either -either equal angles in theta or angles -set so that the size of cells at a single -radius is the same + Create a model which can be imported into + Python of an rtheta grid, with either + either equal angles in theta or angles + set so that the size of cells at a single + radius is the same Command line usage (if any): - usage: make_rtheta.py file.pf + usage:: + + make_rtheta.py file.pf Description: @@ -65,7 +67,8 @@ History: -230601 ksl Coding begun + 230601 ksl + Coding begun ''' diff --git a/py_progs/pf_check.py b/py_progs/pf_check.py index b5358270b..d137bca98 100755 --- a/py_progs/pf_check.py +++ b/py_progs/pf_check.py @@ -1,18 +1,20 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +Checks if `.pf` files in a directory need modification. Synopsis: -Check all of the pf files in a directory to see if -they require modification, alternatively run pieces of travis -to make sure the parameter files will run. + Check all of the pf files in a directory to see if + they require modification, alternatively run pieces of travis + to make sure the parameter files will run. Command line usage (if any): - usage: pf_check.py directory_name + usage:: + + pf_check.py directory_name Description: @@ -23,13 +25,16 @@ In order that ther original directory not be affected, it first copies all of the .pf files to a directory - with the name + with the name:: + pf_check_directory_date - The full path names to directories in the $PYTHON/examples - do not need to be given, so for example + The full path names to directories in the `$PYTHON/examples` + do not need to be given, so for example:: + pf_check.py beta - will test the parameter files in $PYTHON/examples/beta. + + will test the parameter files in `$PYTHON/examples/beta`. After running Python in -i mode for each of the files, the routine diffs the .out.pf file with the original.pf @@ -51,10 +56,13 @@ Primary routines: - doit - the main routine which runs everything - travis - the special routine for parsing the .travis.yaml file to + doit + the main routine which runs everything + travis + the special routine for parsing the .travis.yaml file to get the commands to run - steer - interpret the command line + steer + interpret the command line Notes: @@ -65,7 +73,8 @@ History: -190304 ksl Coding begun + 190304 ksl + Coding begun ''' diff --git a/py_progs/pf_grid.py b/py_progs/pf_grid.py index 5adc60123..6b13c1988 100755 --- a/py_progs/pf_grid.py +++ b/py_progs/pf_grid.py @@ -1,14 +1,14 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +This is a program that is designed to expand a .pf file into a grid of .pf files. -Synopsis: - This is a program that is designed to expand a .pf file into a grid of .pf files. - Usage: - pf_grid.py base.pf + + Use:: + + pf_grid.py base.pf where base.pf is a correctly formatted parameter file, with variables that one wishes to grid indicated by ? @@ -34,28 +34,29 @@ The two possibities that are supported by the program are: - a list format which means one must enclose everything in [], e.g. - [14,27,32] - which would say that for this variable you want values of 14, 27, and 32. + #. a list format which means one must enclose everything in [], e.g. [14,27,32] + which would say that for this variable you want values of 14, 27, and 32. - a special format to create a logarithminc grid of variables, e.g. - log_ints(xmin,xmax,n) - where xmin and xmax are the minimum and maximum values of the variable in - question and n is the number of variables. Note that you enter the - actual values of xmin and xmax and not the log of this. For example: + #. a special format to create a logarithminc grid of variables, e.g. log_ints(xmin,xmax,n) + where xmin and xmax are the minimum and maximum values of the variable in + question and n is the number of variables. Note that you enter the + actual values of xmin and xmax and not the log of this. For example:: + log_ints(1.e16.,1.e18.,3) - will end up produciing a list with variables [1.e16,1.e17,1.e18] - Note also that this input is treated within the routine as a function, - and so here the inputs have to be enclosed in parentheses. + will end up produciing a list with variables [1.e16,1.e17,1.e18] + + Note also that this input is treated within the routine as a function, + and so here the inputs have to be enclosed in parentheses. Returns: + The program produces a lot of files: a file which lists what was varied and the values for them. The name of this file is the same as basename, with a .ls extensition attached, e.g vwhyi.ls - This is intended for use with a fitting program + This is intended for use with a fitting program:: # Variable Disk.mdot(msol/yr) # Variable Wind.mdot(msol/yr) @@ -65,8 +66,9 @@ ... a file which can run python for the grid, one model after another. This has the prefix - Run_ and the remainder is the basename, e.g something like, Run_vwhyi. It begins - something like + `Run_` and the remainder is the basename, e.g something like, `Run_vwhyi`. It begins + something like:: + #!/usr/bin/env bash py vwhyi_0000 py vwhyi_0001 @@ -78,16 +80,19 @@ A number of .pf files, that are like the original pf files, but now the $ have been replaced with the variables associated with the grid. - Notes: + If the program finds .pf files that seems to have been created earlier, it will ask you if you want to delete them History: -0709 ksl Coded and debugged -1701 ksl Updated for Python3. It should be backward compatible. I have partially - but not completely updated the style to the way I would write this program - today. + + 0709 ksl + Coded and debugged + 1701 ksl + Updated for Python3. It should be backward compatible. I have partially + but not completely updated the style to the way I would write this program + today. ''' diff --git a/py_progs/photo_xs.py b/py_progs/photo_xs.py index 54442c59f..65b1d804c 100755 --- a/py_progs/photo_xs.py +++ b/py_progs/photo_xs.py @@ -1,17 +1,19 @@ #!/usr/bin/env python ''' - University of Southampton -- JM -- March 2015 - - photo_xs.py +University of Southampton -- JM -- March 2015 Synopsis: + various utilities for processing photoionization cross-sections Usage: - to tabulate and save VFKY data: + + to tabulate and save VFKY data:: + photo_xs.py tab photo_fkvy.data output_filename - to plot all vfky xsections: + to plot all vfky xsections:: + photo_xs.py plotv photo_fkvy.data ''' @@ -68,10 +70,8 @@ def sigma_phot(vfky, freq): class Photo(object): - '''This is a general class for photoionization data''' - def __init__(self): self.type = None self.z = None @@ -85,17 +85,19 @@ def __init__(self): self.fname = None def read_topbase_file(self, filename, mode = "Top"): - ''' read in XS info from Topbase XS data in Python format - :INPUT: - filename string - atomic data filename e.g. topbase_h1_phot.py - :OUTPUT: - top topbase class instance - topbase class instance containing information - for this filename + INPUT: + + filename (string): + atomic data filename e.g. topbase_h1_phot.py + + OUTPUT: + + top (topbase class instance): + topbase class instance containing information for this filename + ''' self.fname = filename self.type = "Topbase" @@ -136,14 +138,16 @@ def read_topbase_file(self, filename, mode = "Top"): def read_vfky_file(self, filename): - ''' read in XS info from Verner XS data in Python format - :INPUT: - filename string - atomic data filename e.g. topbase_h1_phot.py - :OUTPUT: + INPUT: + + filename (string): + atomic data filename e.g. `topbase_h1_phot.py` + + OUTPUT: + populates members of class such as z, f0, etc. ''' @@ -163,7 +167,6 @@ def read_vfky_file(self, filename): return 0 def write_file(self, output_filename): - ''' write a class out to a tabulated file ''' @@ -190,16 +193,17 @@ def write_file(self, output_filename): def tabulate_vfky(self): - ''' read in XS info from Topbase XS data in Python format - :INPUT: + INPUT: + self + :OUTPUT: - top topbase class instance - topbase class instance containing information - for this filename + + top (topbase class instance): + topbase class instance containing information for this filename ''' # We need to start our tabulation just a tiny way up from from the threshold, otherwise it is equal to zero. @@ -234,7 +238,6 @@ def tabulate_vfky(self): return 0 def plot_all(self): - '''plot all Xsections - makes a lot of plots!!''' if self.tabulated == False or self.type == None: diff --git a/py_progs/plot_spec.py b/py_progs/plot_spec.py index 9ecce0a78..3ba25bb8e 100755 --- a/py_progs/plot_spec.py +++ b/py_progs/plot_spec.py @@ -1,5 +1,4 @@ #!/usr/bin/env python - ''' Space Telescope Science Institute @@ -47,7 +46,7 @@ import numpy import matplotlib.pyplot as pylab import matplotlib.ticker as mtick -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve from scipy.optimize import leastsq # from ksl import io diff --git a/py_progs/plot_tot.py b/py_progs/plot_tot.py index c09ff73bb..be5534648 100755 --- a/py_progs/plot_tot.py +++ b/py_progs/plot_tot.py @@ -1,23 +1,21 @@ #!/usr/bin/env python - ''' - Space Telescope Science Institute - -Synopsis: - Plot the emergent spectrum from the log_spec_tot file Command line usage (if any): - usage: plot_tot.py [-smooth 11] rootname + usage:: + + plot_tot.py [-smooth 11] rootname - where - rootname is the rootname of the files in the run - -smooth is an opticnal parameter indicating how - much smooth of the orignnal spectrum is to - be done. The default is 11 bins + rootname: + is the rootname of the files in the run + `-smooth`: + is an opticnal parameter indicating how + much smooth of the orignnal spectrum is to + be done. The default is 11 bins Description: @@ -31,8 +29,10 @@ History: -130620 ksl Coding begun -141125 ksl Updated to use astropy.io + 130620 ksl + Coding begun + 141125 ksl + Updated to use astropy.io ''' @@ -40,7 +40,7 @@ import numpy import pylab from astropy.io import ascii -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve @@ -70,9 +70,11 @@ def doit(rootname='sphere',smooth=21,fig_no=2): Plot the spectra contained in the spec_tot file - 141125 ksl Updated for new formats which use astropy - 191210 ksl Modified so that what is ploted is nuL_nu, and - did a better job at setting limits for the plot + 141125 ksl + Updated for new formats which use astropy + 191210 ksl + Modified so that what is ploted is nuL_nu, and + did a better job at setting limits for the plot ''' # Make sure we only have the rootname diff --git a/py_progs/plot_wind.py b/py_progs/plot_wind.py index e790f21e6..528c58b6c 100755 --- a/py_progs/plot_wind.py +++ b/py_progs/plot_wind.py @@ -30,7 +30,7 @@ from astropy.io import ascii import numpy import matplotlib.pyplot as pylab -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve import subprocess from matplotlib import tri diff --git a/py_progs/plot_wind_1d.py b/py_progs/plot_wind_1d.py index 8173c8c70..68c916c89 100755 --- a/py_progs/plot_wind_1d.py +++ b/py_progs/plot_wind_1d.py @@ -1,17 +1,14 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -This is a general purpose routine for plotting variables -written out by windsave2table for 1d models +This is a general purpose routine for plotting variables written out by windsave2table for 1d models Command line usage (if any): - usage: plot_wind_1d.py filename + usage:: + + plot_wind_1d.py filename Description: @@ -23,8 +20,9 @@ History: -191102 ksl Coding begun. This routine is largely parallel to the routine - plot_wind.py (which is intended for 2d models +191102 ksl + Coding begun. This routine is largely parallel to the routine + plot_wind.py (which is intended for 2d models ''' @@ -166,17 +164,25 @@ def doit(filename='7MsolBigGapEXT.0.master.txt',var='t_r',grid='ij',inwind='',sc plot_dir='',root=''): ''' - Plot a single variable from an astropy table (normally created with windsave2table, with various + Plot a single variable from an astropy table (normally created with windsave2table, with various options - where var is the variable to plot - where grid can be ij, log, or anything else. If ij then the plot will be in grid coordinates, if log - the plot will be in on a log scale in physical coordiantes. If anything else, the plot will be - on a linear scale in physical coordiantes - where scale indicates how the variable should be plotted. guess tells the routine to make a sensible choice - linear implies the scale should be linear and log implies a log scale should be used - where zmin and zmax overide the max and mimimum in the array (assuming these limits are with the range of - the variable) + Arguments: + var: + is the variable to plot + grid: + can be ij, log, or anything else. If ij then the plot will be in grid coordinates, if log + the plot will be in on a log scale in physical coordiantes. If anything else, the plot will be + on a linear scale in physical coordiantes + scale: + indicates how the variable should be plotted. guess tells the routine to make a sensible choice + linear implies the scale should be linear and log implies a log scale should be used + zmin: + overide the max and mimimum in the array (assuming these limits are with the range of + the variable) + zmax: + overide the max and mimimum in the array (assuming these limits are with the range of + the variable) Description: diff --git a/py_progs/py79_pl_loop.py b/py_progs/py79_pl_loop.py index 80cd88487..0f4e5b127 100755 --- a/py_progs/py79_pl_loop.py +++ b/py_progs/py79_pl_loop.py @@ -2,11 +2,8 @@ ''' - UNLV - -Synopsis: - This routine carries out a series of thin shell python simulations. + The wind mdot is set to produce a hydrogen density of 1e7 - to change this one had to change the wind_mdot parameter. The loop is carried out over the 2-10kev luminosity of the central source. A luminosity of diff --git a/py_progs/py_classes.py b/py_progs/py_classes.py index ede735009..76079ab17 100755 --- a/py_progs/py_classes.py +++ b/py_progs/py_classes.py @@ -1,13 +1,6 @@ #!/usr/bin/env python ''' - University of Southampton -- JM -- 30 September 2013 - - classes.py - -Synopsis: - classes - - This is a set of classes for use with the radiative transfer code python +This is a set of classes for use with the radiative transfer code python Usage: diff --git a/py_progs/py_error.py b/py_progs/py_error.py index 48e8a5728..ce7a13c04 100755 --- a/py_progs/py_error.py +++ b/py_progs/py_error.py @@ -1,10 +1,7 @@ #!/usr/bin/env python ''' - -University of Southampton, James Matthews, 130722 - -"py_error.py" +Post-run parser for error logs from parallel simulation runs. This is the post-processing code to deal with the error logs in PYTHON's parallel mode. It basically sums the number of errors @@ -22,9 +19,8 @@ History: -1307 JM Coding began -- initial tests conducted successfully -1802 ksl Updated to be Python3 compatable, to write the results - to an astropy table, and to be callable from another routine + 1307 JM Coding began -- initial tests conducted successfully + 1802 ksl Updated to be Python3 compatable, to write the results to an astropy table, and to be callable from another routine ''' diff --git a/py_progs/py_plot_util.py b/py_progs/py_plot_util.py index 2cdc94725..060301d26 100755 --- a/py_progs/py_plot_util.py +++ b/py_progs/py_plot_util.py @@ -1,8 +1,6 @@ #!/usr/bin/env python ''' -Synopsis: - various utilities for processing Python outputs and plotting - spectra and wind properties +various utilities for processing Python outputs and plotting spectra and wind properties Usage: diff --git a/py_progs/py_read_output.py b/py_progs/py_read_output.py index c61d1776f..7e4352ae1 100755 --- a/py_progs/py_read_output.py +++ b/py_progs/py_read_output.py @@ -1,5 +1,7 @@ #!/usr/bin/env python ''' +Reads outputs from simulation runs. + Synopsis: This program enables one to read outputs from the Python radiative transfer code. Where possible, we use the astropy.io module to read outputs. diff --git a/py_progs/python_pl_loop.py b/py_progs/python_pl_loop.py index 7d8f06a09..c7b091caf 100755 --- a/py_progs/python_pl_loop.py +++ b/py_progs/python_pl_loop.py @@ -2,11 +2,8 @@ ''' - UNLV - -Synopsis: - This routine carries out a series of thin shell python simulations. + The wind mdot is set to produce a hydrogen density of 1e7 - to change this one had to change the wind_mdot parameter. The loop is carried out over the 2-10kev luminosity of the central source. A luminosity of diff --git a/py_progs/regression.py b/py_progs/regression.py index 70e78ffbc..29b62fe9c 100755 --- a/py_progs/regression.py +++ b/py_progs/regression.py @@ -1,33 +1,34 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -Execute a series of short python runs to test whether a version -of python is working. +Execute a series of short python runs to test whether a version of python is working. Command line usage (if any): - usage: regression.py [-np 3 -x 'whatever' -pf_dir test -out_dir foo] version - - where - - version the executable of python - -np 3 the number of processors with which to run (default 3) - -pf_dir test the directory containing all of the .pf files which will be run - The defaults is $PYTHON/examples/regress. One does not need - to provide the full path name to the directory. The routine doit - first searches the current workind directory for the directory and then - looks in $PYTHON/examples/ - -x '-v/c' Extra switches to be applied to the run, such as using linear Doppler - shifts. Note that these will be applied to everything except the - hydro calculation so use with caution. This should be a single string - -out_dir foo The directory (below the current working directory) where the - tests will run. The defauld is constructed for the version - and the data + usage:: + + regression.py [-np 3 -x 'whatever' -pf_dir test -out_dir foo] version + + + version + the executable of python + `-np 3` + the number of processors with which to run (default 3) + `-pf_dir test` + the directory containing all of the .pf files which will be run + The defaults is `$PYTHON/examples/regress`. One does not need + to provide the full path name to the directory. The routine doit + first searches the current workind directory for the directory and then + looks in `$PYTHON/examples/` + `-x '-v/c'` + Extra switches to be applied to the run, such as using linear Doppler + shifts. Note that these will be applied to everything except the + hydro calculation so use with caution. This should be a single string + `-out_dir foo` + The directory (below the current working directory) where the + tests will run. The default is constructed for the version + and the data Description: @@ -40,10 +41,13 @@ Primary routines: - doit: Internal routine which runs python on all of the pf files of interest. Use - this if working in a python shell - steer: A routine to parse the command lineh - check_one: A routine which oversees checking of the runs + doit: + Internal routine which runs python on all of the pf files of interest. + Use this if working in a python shell + steer: + A routine to parse the command lineh + check_one: + A routine which oversees checking of the runs Notes: @@ -56,9 +60,9 @@ This file should contain lines for at least the pf files to which one wishes to apply these switches. The line in the file should read - py switches and the pf file name, e.g + py switches and the pf file name, e.g:: - py -gamma agn_gamma.pf + py -gamma agn_gamma.pf The global switches obtained from the command line are applied after the individual swithches @@ -69,12 +73,14 @@ History: -170903 ksl Coding begun -180225 ksl nsh had added a special test for hydro. This is useful in the sense that - it allows one to deal with situations where several runs must be carreid out - sequentially but but the mechanism that he chose makes - it difficult to add new tests, because the standard and speciial tests are - not sufficently isolated from one another. +170903 ksl + Coding begun +180225 ksl + nsh had added a special test for hydro. This is useful in the sense that + it allows one to deal with situations where several runs must be carreid out + sequentially but but the mechanism that he chose makes + it difficult to add new tests, because the standard and speciial tests are + not sufficently isolated from one another. ''' @@ -205,11 +211,13 @@ def doit(version='py',pf_dir='',out_dir='',np=3,switches='',outputfile='Summary. History: - 170903 ksl Bagan work - 170904 ksl Updated how routine looks for input directories - and attempted to get a more readable stderr output - Also, eliminted pf files with the extension .out.pf - because these are mostlikely duplicates + 170903 ksl + Bagan work + 170904 ksl + Updated how routine looks for input directories + and attempted to get a more readable stderr output + Also, eliminted pf files with the extension .out.pf + because these are mostlikely duplicates ''' @@ -414,22 +422,24 @@ def py_hydro(version,pf_dir,outputfile): Notes: - This routine is run from within the current working directory, - which is the directory created earlier be doit - + This routine is run from within the current working directory, + which is the directory created earlier be doit - History + History: - 1801 nsh - 1802 ksl Modified this routine so that it is more standalone - than previously. Special regression tests need to - be isolated from the internal logic of doit so they - can be added/removed easily. - 2908 kdl Eliminated the checks for a difference in heating and - and cooling rates. If this is important, a check of - these should be done in regression_checks, and the - check should be made between this run and a previous - run, not one against a file made in the distant past. + 1801 nsh + Development started + 1802 ksl + Modified this routine so that it is more standalone + than previously. Special regression tests need to + be isolated from the internal logic of doit so they + can be added/removed easily. + 2908 kdl + Eliminated the checks for a difference in heating and + and cooling rates. If this is important, a check of + these should be done in regression_checks, and the + check should be made between this run and a previous + run, not one against a file made in the distant past. ''' out_dir=os.getcwd() diff --git a/py_progs/regression_check.py b/py_progs/regression_check.py index 802d09762..0d11f0f01 100755 --- a/py_progs/regression_check.py +++ b/py_progs/regression_check.py @@ -1,12 +1,7 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - -Compare two regression runs, highligting the differences -between them +Compare two regression runs, highligting the differences between them Command line usage (if any): diff --git a/py_progs/regression_nsh.py b/py_progs/regression_nsh.py index 26d979478..e5234e6bc 100755 --- a/py_progs/regression_nsh.py +++ b/py_progs/regression_nsh.py @@ -1,44 +1,42 @@ #!/usr/bin/env python - ''' - Space Telescope Science Institute +Execute a series of short python runs to test whether a version of python is working. -Synopsis: -Execute a series of short python runs to test whether a version -of python is working. +Command line usage (if any): + usage:: -Command line usage (if any): + regression.py [-np 3 -pf_dir test -out_dir foo] version - usage: regression.py [-np 3 -pf_dir test -out_dir foo] version + where - where + version + the executable of python + -np 3 + the number of processors with which to run (default 3) + -pf_dir test + the directory containing all of the .pf files which will be run + The defaults is $PYTHON/examples/regress. One does not need + to provide the full path name to the directory. The routine doit + first searches the current workind directory for the directory and then + looks in $PYTHON/examples/ + -out_dir foo + The directory (below the current working directory) where the + tests will run. The defauld is constructed for the version and the data - version the executable of python - -np 3 the number of processors with which to run (default 3) - -pf_dir test the directory containing all of the .pf files which will be run - The defaults is $PYTHON/examples/regress. One does not need - to provide the full path name to the directory. The routine doit - first searches the current workind directory for the directory and then - looks in $PYTHON/examples/ - -out_dir foo The directory (below the current working directory) where the - tests will run. The defauld is constructed for the version - and the data -Description: +Description: The basic process is as follows 1. Create a directory in which to work and intialize it 2. Copy all of the relevant .pf files to this directory - 3. Switch to the working directory and run all the models, performing - some basic checks + 3. Switch to the working directory and run all the models, performing some basic checks Primary routines: - doit: Internal routine which runs python on all of the pf files of interest. Use - this if working in a python shell + doit: Internal routine which runs python on all of the pf files of interest. Use this if working in a python shell steer: A routine to parse the command lineh check_one: A routine which oversees checking of the runs @@ -46,10 +44,10 @@ Regression here means to run a series of models. These routines do not compare the models to earlier runs - + History: -170903 ksl Coding begun + 170903 ksl Coding begun ''' @@ -169,10 +167,7 @@ def doit(version='py',pf_dir='',out_dir='',np=3,outputfile='Summary.txt'): History: 170903 ksl Bagan work - 170904 ksl Updated how routine looks for input directories - and attempted to get a more readable stderr output - Also, eliminted pf files with the extension .out.pf - because these are mostlikely duplicates + 170904 ksl Updated how routine looks for input directories and attempted to get a more readable stderr output Also, eliminted pf files with the extension .out.pf because these are mostlikely duplicates ''' diff --git a/py_progs/regression_plot.py b/py_progs/regression_plot.py index 2f026adda..dc3ae8b09 100755 --- a/py_progs/regression_plot.py +++ b/py_progs/regression_plot.py @@ -1,18 +1,16 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +Create plots which compare spectra (and other properties) of two runs of Python. -Synopsis: - -Create plots which compare spectra (and possibly other properties -of two runs of Python. This may become a basis for some kind -of ipython notebook +This may become a basis for some kind of ipython notebook Command line usage (if any): - usage: regression_plot.py run1 run2 [model] + usage:: + + regression_plot.py run1 run2 [model] where run1 and run 2 are in directories containing the runs to compare. If model is given, then only that model is compared. @@ -40,7 +38,7 @@ import pylab from glob import glob -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve import time @@ -66,10 +64,8 @@ def read_file(filename,char=''): History: - 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup - script so there was flexibility to - read any ascii file + 110729 ksl Added optional delimiters + 141209 ksl Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' try: diff --git a/py_progs/retro.py b/py_progs/retro.py index 7215f5b92..90252f18e 100755 --- a/py_progs/retro.py +++ b/py_progs/retro.py @@ -1,14 +1,14 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +Run multiple commits on the same `.pf` file to see where changes arose. -Synopsis: -The functions here are intended to ease running a collection -of commits on the same parameter file so that one can locate -where a change in Python occured. +Synopsis: + The functions here are intended to ease running a collection + of commits on the same parameter file so that one can locate + where a change in Python occured. Command line usage (if any): @@ -19,20 +19,25 @@ Description: Primary routines: - log2table - create an ascii table that summarizes the log and contains + + log2table - + create an ascii table that summarizes the log and contains entries for compiling and running python many times - compile_many - create at lot of Python executables in a local directory - run_many - runs a set of python executables - plot_many - plot consecutive sets of the outputs + compile_many - + create at lot of Python executables in a local directory + run_many - + runs a set of python executables + plot_many - + plot consecutive sets of the outputs Notes: To use this, create a directory where one wants to run - Create a text file by running + Create a text file by running:: - git log > commit.txt (or whatever + git log > commit.txt (or whatever Edit this file so it includes only the commit range in which you are interested. @@ -60,7 +65,7 @@ History: -210220 ksl Coding begun + 210220 ksl Coding begun ''' @@ -77,7 +82,7 @@ -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve diff --git a/py_progs/run_check.py b/py_progs/run_check.py index 021d4c96e..464456c15 100755 --- a/py_progs/run_check.py +++ b/py_progs/run_check.py @@ -1,8 +1,6 @@ #!/usr/bin/env python ''' -Synopsis: - Sumarize a model run with python, ultimately generating - an html file with various plots, etc. +Sumarize a model run with python, ultimately generating an html file with various plots, etc. Command line usage (if any): diff --git a/py_progs/run_indent.py b/py_progs/run_indent.py index 2daa14f5c..a23fc5100 100755 --- a/py_progs/run_indent.py +++ b/py_progs/run_indent.py @@ -1,27 +1,25 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - Indent in a controlled manner the .c files used by Python - Command line usage (if any): - usage: run_indent.py filename to indent a single file - run_indent.py *.c to indent all of the .c files in a directory - run_indent.py *.h to indent all of the .h files - run_indent -all to indent all of the c and .h files + * `run_indent.py filename` to indent a single file + * `run_indent.py *.c` to indent all of the .c files in a directory + * `run_indent.py *.h` to indent all of the .h files + * `run_indent -all` to indent all of the c and .h files Description: Primary routines: - doit processes a single file - steer processes the command calling either doi or do_all - do_all processes all the .c and .h files in a directory + doit + processes a single file + steer + processes the command calling either doi or do_all + do_all + processes all the .c and .h files in a directory Notes: @@ -31,7 +29,7 @@ History: -180913 ksl Coding begun + 180913 ksl Coding begun ''' @@ -94,19 +92,19 @@ def doit(filename='lines.c'): Notes: - If gnuindent is not found, then this function - is a NOP. + If gnuindent is not found, then this function + is a NOP. - The indented version is first written to another - file. Then we check to see if the indented file - is different from the original. + The indented version is first written to another + file. Then we check to see if the indented file + is different from the original. - If the newly indented version is differnt from the - original, then it is copied back to the original. + If the newly indented version is differnt from the + original, then it is copied back to the original. - This is done so that a file that is unchanged is not - "touched', which would cause a recompilation when - mske is uesed. + This is done so that a file that is unchanged is not + "touched', which would cause a recompilation when + mske is uesed. History: @@ -152,9 +150,10 @@ def do_all(ignore_list=None): ''' Indent all of the .c and .h files in a directory in a standard way - ignore_list list of file strings to ignore - if NoneType or blank array then nothing is ignored - gets converted to numpy array inside function + ignore_list + list of file strings to ignore + if NoneType or blank array then nothing is ignored + gets converted to numpy array inside function ''' if get_gnu()=='': return diff --git a/py_progs/run_many.py b/py_progs/run_many.py index 2e2694c4a..59193ea70 100755 --- a/py_progs/run_many.py +++ b/py_progs/run_many.py @@ -1,22 +1,24 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute +This routine runs a set of models. -Synopsis: +Each model is run in single processor mode, so the parallelism here is job parallel. -This routine runs a set of models. Each model is run in -single processor mode, so the parallelism here is job parallel. +Command line usage (if any): + usage:: -Command line usage (if any): + run_many.py -jobs x -np y filename - usage: run_many.py -jobs x -np y filename + `-njobs` + is the number of jobs to run simultaneously + `-np` + is the number of processors per job + `filename` + is a list of the .pf files to run - where -njobs is the number of jobs to run simultaneously - -np is the number of processors per job - filename is a list of the .pf files to run Description: @@ -35,7 +37,7 @@ History: -190319 ksl Coding begun + 190319 ksl Coding begun ''' @@ -60,9 +62,7 @@ def read_file(filename,char=''): History: 110729 ksl Added optional delimiters - 141209 ksl Reinstalled in my standard startup - script so there was flexibility to - read any ascii file + 141209 ksl Reinstalled in my standard startup script so there was flexibility to read any ascii file ''' try: diff --git a/py_progs/test_masterfiles.py b/py_progs/test_masterfiles.py index fe095c4b4..17fd981b3 100755 --- a/py_progs/test_masterfiles.py +++ b/py_progs/test_masterfiles.py @@ -1,12 +1,9 @@ #!/usr/bin/env python ''' - University of Cambridge -- JM -- October 2019 +Test the masterfiles specified in the arrays all point to the right files. - test_masterfiles +needs python to be compiled. -Synopsis: - test the masterfiles specified in the arrays all point to the right files. - needs python to be compiled. Usage: test_masterfiles.py [PYTHON VERSION] ''' @@ -14,7 +11,9 @@ import subprocess, os, sys # set env variable -PYTHON = os.environ["PYTHON"] +# Do not call this when we're on ReadTheDocs +if not os.environ.get('READTHEDOCS'): + PYTHON = os.environ["PYTHON"] # change these if you want to test different files. These are all in data/ as of October 2019 macro_files = ['h20', 'h10_hetop_lohe1_standard80', 'h10_standard80', 'h10_hetop_standard80', 'h20_hetop_standard80'] diff --git a/py_progs/update_param.py b/py_progs/update_param.py index e3787b82c..c24c9c37a 100644 --- a/py_progs/update_param.py +++ b/py_progs/update_param.py @@ -1,11 +1,13 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- +""" +Use this script to (mass) update either the name of a parameter or to update the value of a parameter. -"""Use this script to (mass) update either the name of a parameter or to update -the value of a parameter. The main purpose is to change multiple parameter files -at once. As such, the script recursively searches for all parameter files from +The main purpose is to change multiple parameter files at once. +As such, the script recursively searches for all parameter files from the calling directory and updates them. Can also be used on a single parameter -file or for parameter files with a given root.""" +file or for parameter files with a given root. +""" import argparse as ap diff --git a/py_progs/watchdog.py b/py_progs/watchdog.py index a94e1cce9..425639a86 100755 --- a/py_progs/watchdog.py +++ b/py_progs/watchdog.py @@ -1,10 +1,7 @@ #!/usr/bin/env python ''' - -University of Southampton, James Matthews, 130722 - -"watchdog.py" +During-run parser for error logs from parallel simulation runs. This is the during-processing watchdog code to deal with the error logs in PYTHON's parallel mode. Very similar to py_error.py diff --git a/py_progs/write_docs.py b/py_progs/write_docs.py deleted file mode 100755 index 3225a5ef5..000000000 --- a/py_progs/write_docs.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python -''' - -Synopsis: - -Find all of the python files in a directory and write out html -pydoc documentation of them in the current working directory - - -Command line usage (if any): - - write_docs.py [directory_name] - - if the directory name is missing then assume - the routine is being run from the pydocs directory - and the the directory to be documented is ../../py_progs - -Description: - - The routine finds all of the .py files in the named directory and - generates a shell script which runs pydocs to create html help - files for each .py file. Assuming the directory name is mydir - It then creates a another html file, doc_mydir.html which simply - contain links to all of the individual files - - - -Primary routines: - -Notes: - - If you want to get an html help for a specific package the python - command is simply - pydocs package_name - The help html files have links to the packages such as pyfits - but these are incorrect because pydocs assumes all the help - files are in the current working directory, which they are likely - not. - - The does not delete help from routines that have been deleted from - a package, which is a problem. One could fix this, but one would - then need to keep some kind of database so that you knew what files - should exist. - - Note that you should watch the written output. If it gives - and odd looking error which writes out some of the code it - means that one has not put - - if __name__ == "__main__": - - in front of the main routine. If this happens you will need - to indent all of the lines in main, and then rerun this - script - - - -History: - -100805 ksl Coding begun -111111 ksl Modified so it actually used the entire name for the file. - pydoc will then use that file to make the doucmentation - Otherwise it searches for it in the path and that is less - likely to be what I want. -180126 ksl Modified for use with python -''' -import sys -import os -import pydoc -from MarkupPy import markup - - -def make_toplevel(dirname, names): - ''' - Create an html page to point to all - of the individual help pages that have - been made - ''' - # First get the name of the directory - dirname = dirname.replace('/', ' ') - dirname = dirname.strip() - dirname = dirname.split() - dirname = dirname[len(dirname)-1] - - html_name = 'doc_py_progs.html' - - # Start a page - page = markup.page() - - page.init(title="Documentation for %s" % dirname) - - page.h1("Documentation for python scripts in the directory %s" % dirname) - - page.p('''This page lists the scripts that exist. Some of these scripts will be useful to users, and some - will not. - At present, there is no obvious way to tell, except to look read the documentation associated with - each script by following the link, or to have seen a reference to a particular script in some other - place in the documentation set. - ''') - - page.p('''To use the scripts, one will need to have the py_progs directory in their PYTHONPATH. Occasionally - one will need to be prepared to install modules using conda or pip.''') - - page.h2('The scripts') - - items = [] - for name in names: - item = markup.oneliner.a(name, href="./%s.html" % name) - items.append(item) - - page.ul(class_='mylist') - page.li(items, class_='myitem') - page.ul.close() - - page.p('Warning: This page is rewritten every whenever write_docs.py is run on this directoryand so this page should not be edited') - with open(html_name, 'w') as file: - file.write('%s' % page) - file.close() - - -def write_docs(dirname='../../py_progs'): - ''' - Locate all of the .py files in dirname and - write out help in the current working directory - using pydocs - ''' - # First, we delete all the existing documentation in this directory - for item in os.listdir('.'): - if item.endswith(".html"): - os.remove(item) - - # Now, we write new docs - pydoc.writedocs(dirname) - - # Now make a page that points to all the html pages - # that have already been made - roots = [ - item.replace('.py', '') - for item in os.listdir(dirname) - if item.endswith('.py') - ] - make_toplevel(dirname, roots) - - # Now check that we have the files we expected - got_all = True - for root in roots: - if not os.path.isfile(root+'.html'): - print('Failed to create an html file for %s.py' % root) - got_all = False - - if got_all: - print('html files were created for all of the .py scripts') - return 0 - - print( - 'Failed to generate documentation for some files.\n' - 'Please look at the earlier output to find more details on the error.' - ) - return 1 # Return nonzero to indicate error - - -# Next lines permit one to run the routine from the command line -if __name__ == "__main__": - if len(sys.argv) == 1: - write_docs() - elif len(sys.argv) == 2 and sys.argv[1] == '-h': - print(__doc__) - elif len(sys.argv) == 2: - write_docs(sys.argv[1]) - else: - print('usage: write_docs.py dirname or -h for info') diff --git a/py_progs/xcompare.py b/py_progs/xcompare.py index 78673e198..a40d24c61 100755 --- a/py_progs/xcompare.py +++ b/py_progs/xcompare.py @@ -1,13 +1,8 @@ #!/usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - Perform a standardized comparison between two Python runs - Command line usage (if any): usage: xcompare.py filename @@ -28,7 +23,7 @@ import os import matplotlib.pyplot as plt -from scipy.signal import boxcar +from scipy.signal.windows import boxcar from scipy.signal import convolve import numpy as np from astropy.io import ascii diff --git a/py_progs/xhtml.py b/py_progs/xhtml.py index a4e410619..010447456 100755 --- a/py_progs/xhtml.py +++ b/py_progs/xhtml.py @@ -1,10 +1,6 @@ #! /usr/bin/env python ''' - Space Telescope Science Institute - -Synopsis: - This is a small set of routines that creates html files