Merge pull request #22 from sibirrer/ajshajib-patch-1

Update codecov settings
sibirrer · Dec 4, 2023 · 74fd54f · 74fd54f
2 parents 2e70aeb + 6695d95
commit 74fd54f
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Showing 4 changed files with 73 additions and 46 deletions.
diff --git a/codecov.yml b/codecov.yml
@@ -4,3 +4,6 @@ comment:                  # this is a top-level key
   require_changes: false  # if true: only post the comment if coverage changes
   require_base: false        # [true :: must have a base report to post]
   require_head: true       # [true :: must have a head report to post]
+ignore:
+  - "setup.py"
+  - "test/*"
diff --git a/hierarc/Likelihood/KDELikelihood/chain.py b/hierarc/Likelihood/KDELikelihood/chain.py
@@ -20,15 +20,21 @@ def __init__(
         loglsamples=None,
         rescale=True,
     ):
-        """
+        """For keywords in the `kw` parameter, see
+        https://wiki.cosmos.esa.int/planck-legacy-archive/index.php/Cosmological_Parameters
 
-        :param kw: (str). Planck base cosmology keyword. For example, "base" or "base_omegak". See https://wiki.cosmos.esa.int/planck-legacy-archive/index.php/Cosmological_Parameters.
-        :param probe: (str). Planck probe combination. For example, "plikHM_TTTEEE_lowl_lowE" for default Planck results
+        :param kw: (str). Planck base cosmology keyword. For example, "base" or
+            "base_omegak".
+        :param probe: (str). Planck probe combination. For example,
+            "plikHM_TTTEEE_lowl_lowE" for default Planck results
         :param params: (dictionnary). Dictionnary containing the samples.
         :param default_weights: (numpy array). Default weights associated to the samples.
         :param cosmology: (str). Astropy cosmology
-        :param loglsamples: (numpy array). Corresponding Loglikelihood of the samples (optionnal).
-        :param rescale: (bool). Rescale the chains between 0 and 1 for all parameters. This is absolutely necessary if you want to evaluate a KDE on these chains.
+        :param loglsamples: (numpy array). Corresponding Loglikelihood of the samples
+            (optionnal).
+        :param rescale: (bool). Rescale the chains between 0 and 1 for all
+            parameters. This is absolutely necessary if you want to evaluate a KDE on
+            these chains.
         """
         self.kw = kw
         self.probe = probe
@@ -142,15 +148,12 @@ def rescale_from_unity(self, verbose=False):
 
 
 def import_Planck_chain(datapath, kw, probe, params, cosmology, rescale=True):
-    """Special function to parse Planck files. Return a Chain object.
+    """Special function to parse Planck files. Return a Chain object. For keywords in
+    the `kw` parameter, see https://wiki.cosmos.esa.int/planck-legacy-archive/index.php/Cosmological_Parameters
 
-    :param datapath: (str). Path to the Planck chain :param kw: (str). Planck base
-    cosmology keyword. For example, "base" or "base_omegak". See
-    https://wiki.cosmos.esa.int/planck-legacy-archive/index.php/Cosmological_Parameters.
     :param datapath: (str). Path to the Planck chain
     :param kw: (str). Planck base cosmology keyword. For example, "base" or
-        "base_omegak". See https://wiki.cosmos.esa.int/planck-legacy-
-        archive/index.php/Cosmological_Parameters.
+        "base_omegak".
     :param probe: (str). Planck probe combination. For example,
         "plikHM_TTTEEE_lowl_lowE" for default Planck results
     :param params: (list). List of cosmological parameters. ["h0", "om"] for FLCDM.

diff --git a/hierarc/Sampling/mcmc_sampling.py b/hierarc/Sampling/mcmc_sampling.py
@@ -7,9 +7,11 @@ class MCMCSampler(object):
     """Class which executes the different sampling  methods."""
 
     def __init__(self, *args, **kwargs):
-        """Initialise the classes of the chain and for parameter options :param args:
-        positional arguments for the CosmoLikelihood() instance :param kwargs: keyword
-        arguments for the CosmoLikelihood() instance."""
+        """Initialise the classes of the chain and for parameter options.
+
+        :param args: positional arguments for the CosmoLikelihood() instance
+        :param kwargs: keyword arguments for the CosmoLikelihood() instance.
+        """
         self.chain = CosmoLikelihood(*args, **kwargs)
         self.param = self.chain.param
 

diff --git a/notebooks/double_source_plane.ipynb b/notebooks/double_source_plane.ipynb
@@ -21,6 +21,7 @@
     "import copy\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.patches as mpatches\n",
+    "\n",
     "%matplotlib inline\n",
     "\n",
     "# import lenstronomy and hierArc modules\n",
@@ -30,9 +31,10 @@
     "\n",
     "# matplotlib configs\n",
     "from pylab import rc\n",
-    "rc('axes', linewidth=2)\n",
-    "rc('xtick',labelsize=15)\n",
-    "rc('ytick',labelsize=15)"
+    "\n",
+    "rc(\"axes\", linewidth=2)\n",
+    "rc(\"xtick\", labelsize=15)\n",
+    "rc(\"ytick\", labelsize=15)"
    ]
   },
   {
@@ -52,12 +54,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from hierarc.Likelihood.LensLikelihood.double_source_plane import beta_double_source_plane\n",
+    "from hierarc.Likelihood.LensLikelihood.double_source_plane import (\n",
+    "    beta_double_source_plane,\n",
+    ")\n",
     "\n",
     "# define a cosmology\n",
-    "cosmology = 'FLCDM'  # Flat LCDM cosmology \n",
+    "cosmology = \"FLCDM\"  # Flat LCDM cosmology\n",
     "# other options are: \"FwCDM\", \"w0waCDM\", \"oLCDM\"\n",
-    "kwargs_cosmo_true = {'h0': 70, 'om': 0.3}  # cosmological model of the forecast\n",
+    "kwargs_cosmo_true = {\"h0\": 70, \"om\": 0.3}  # cosmological model of the forecast\n",
     "\n",
     "# create astropy.cosmology instance of input cosmology\n",
     "cosmo_param = CosmoParam(cosmology=cosmology)\n",
@@ -73,26 +77,31 @@
     "\n",
     "sigma_beta = 0.05  # relative precision on Einstein radius ratio\n",
     "\n",
+    "\n",
     "def draw_lens():\n",
     "    \"\"\"\n",
     "    draw the likelihood object of a double source plane lens\n",
     "    \"\"\"\n",
-    "    z_lens = np.random.uniform(low=0.2, high=0.5) \n",
-    "    z_source1 = np.random.uniform(low=z_lens+0.2, high=3)\n",
+    "    z_lens = np.random.uniform(low=0.2, high=0.5)\n",
+    "    z_source1 = np.random.uniform(low=z_lens + 0.2, high=3)\n",
     "    z_source2 = np.random.uniform(low=z_source1, high=5)\n",
     "    beta = beta_double_source_plane(z_lens, z_source1, z_source2, cosmo_true)\n",
     "    beta_measured = beta + np.random.normal(loc=0, scale=sigma_beta * beta)\n",
-    "    kwargs_likelihood = {'z_lens': z_lens, 'z_source_1': z_source1, 'z_source_2': z_source2, \n",
-    "                         'beta_dspl': beta_measured, 'sigma_beta_dspl': sigma_beta * beta,\n",
-    "                         'likelihood_type': 'DSPL'}\n",
+    "    kwargs_likelihood = {\n",
+    "        \"z_lens\": z_lens,\n",
+    "        \"z_source_1\": z_source1,\n",
+    "        \"z_source_2\": z_source2,\n",
+    "        \"beta_dspl\": beta_measured,\n",
+    "        \"sigma_beta_dspl\": sigma_beta * beta,\n",
+    "        \"likelihood_type\": \"DSPL\",\n",
+    "    }\n",
     "    return kwargs_likelihood\n",
     "\n",
     "\n",
     "kwargs_dspl_list = []\n",
     "\n",
     "for i in range(num_dspl):\n",
-    "    kwargs_dspl_list.append(draw_lens())\n",
-    "\n"
+    "    kwargs_dspl_list.append(draw_lens())"
    ]
   },
   {
@@ -115,30 +124,36 @@
     "n_run = 200\n",
     "n_burn = 200\n",
     "\n",
-    "cosmology = 'FwCDM'\n",
-    "\n",
-    "kwargs_mean_start = {'kwargs_cosmo': {'h0': 70, 'om': 0.3, 'w': -1}}\n",
-    "\n",
-    "kwargs_sigma_start = {'kwargs_cosmo': {'h0': 10, 'om': 0.05, 'w': 0.2}}\n",
+    "cosmology = \"FwCDM\"\n",
     "\n",
+    "kwargs_mean_start = {\"kwargs_cosmo\": {\"h0\": 70, \"om\": 0.3, \"w\": -1}}\n",
     "\n",
-    "kwargs_bounds = {'kwargs_lower_cosmo': {'h0': 0, 'om': 0, 'w': -2},\n",
-    "                'kwargs_upper_cosmo': {'h0': 200, 'om': 1, 'w': 0},\n",
-    "                'kwargs_fixed_cosmo': {'h0': kwargs_cosmo_true['h0']}}\n",
+    "kwargs_sigma_start = {\"kwargs_cosmo\": {\"h0\": 10, \"om\": 0.05, \"w\": 0.2}}\n",
     "\n",
     "\n",
+    "kwargs_bounds = {\n",
+    "    \"kwargs_lower_cosmo\": {\"h0\": 0, \"om\": 0, \"w\": -2},\n",
+    "    \"kwargs_upper_cosmo\": {\"h0\": 200, \"om\": 1, \"w\": 0},\n",
+    "    \"kwargs_fixed_cosmo\": {\"h0\": kwargs_cosmo_true[\"h0\"]},\n",
+    "}\n",
     "\n",
     "\n",
     "# joint options for hierArc sampling\n",
-    "kwargs_sampler = {'cosmology': cosmology, 'kwargs_bounds': kwargs_bounds,\n",
-    "                  'lambda_mst_sampling': False,  \n",
-    "                  'anisotropy_sampling': False,\n",
-    "                  'kappa_ext_sampling': False, 'kappa_ext_distribution': 'NONE', \n",
-    "                  'alpha_lambda_sampling': False, 'sigma_v_systematics': False,\n",
-    "                  'log_scatter': False, \n",
-    "                  'custom_prior': None, 'interpolate_cosmo': False,\n",
-    "                  'num_redshift_interp': 100, 'cosmo_fixed': None}\n",
-    "\n"
+    "kwargs_sampler = {\n",
+    "    \"cosmology\": cosmology,\n",
+    "    \"kwargs_bounds\": kwargs_bounds,\n",
+    "    \"lambda_mst_sampling\": False,\n",
+    "    \"anisotropy_sampling\": False,\n",
+    "    \"kappa_ext_sampling\": False,\n",
+    "    \"kappa_ext_distribution\": \"NONE\",\n",
+    "    \"alpha_lambda_sampling\": False,\n",
+    "    \"sigma_v_systematics\": False,\n",
+    "    \"log_scatter\": False,\n",
+    "    \"custom_prior\": None,\n",
+    "    \"interpolate_cosmo\": False,\n",
+    "    \"num_redshift_interp\": 100,\n",
+    "    \"cosmo_fixed\": None,\n",
+    "}"
    ]
   },
   {
@@ -149,9 +164,13 @@
    "outputs": [],
    "source": [
     "mcmc_sampler = MCMCSampler(kwargs_dspl_list, **kwargs_sampler)\n",
-    "mcmc_samples, log_prob = mcmc_sampler.mcmc_emcee(n_walkers, n_burn, n_run, kwargs_mean_start, kwargs_sigma_start)\n",
+    "mcmc_samples, log_prob = mcmc_sampler.mcmc_emcee(\n",
+    "    n_walkers, n_burn, n_run, kwargs_mean_start, kwargs_sigma_start\n",
+    ")\n",
     "\n",
-    "corner.corner(mcmc_samples, show_titles=True, labels=mcmc_sampler.param_names(latex_style=True))\n",
+    "corner.corner(\n",
+    "    mcmc_samples, show_titles=True, labels=mcmc_sampler.param_names(latex_style=True)\n",
+    ")\n",
     "plt.show()"
    ]
   },