Merge branch 'develop'

.gitignore

@@ -1,12 +1,21 @@
 # ignore everything in root
 /*
-# except for this file
-!asteca.py
+# except for these files
 !README.md
-!CHANGELOG.rst
 !LICENSE.txt
-# and this folder
-!/packages
-# But do ignore these files
+!poetry.lock
+!pyproject.toml
+!.readthedocs.yaml
+
+# and these folderers
+!/asteca
+!/docs
+
+# But ignore these files
 *.pyc
-*.pickle
+
+# and ignore these folders
+/docs/build/*
+/docs/autoapi/*
+# Jupyter Notebook
+.ipynb_checkpoints

.readthedocs.yaml

@@ -0,0 +1,35 @@
+# Read the Docs configuration file for Sphinx projects
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+    # You can also specify other tool versions:
+    # nodejs: "20"
+    # rust: "1.70"
+    # golang: "1.20"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+  # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
+  # builder: "dirhtml"
+  # Fail on all warnings to avoid broken references
+  # fail_on_warning: true
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#   - pdf
+#   - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+  install:
+    - requirements: docs/requirements.txt

README.md

@@ -1,15 +1,19 @@
 
 # ASteCA [Automated Stellar Cluster Analysis]
 [![AA](https://img.shields.io/badge/A%26A-576--A6,%202015-yellowgreen.svg)][1]
-[![License](https://img.shields.io/badge/license-GPLv3-red.svg)][2]
+[![License](https://img.shields.io/badge/license-MIT-blue)][2]
 
-The ASteCA package is designed to fully automatize the usual tests
-applied on star clusters in order to estimate their characteristics and
-fundamental parameters. The package is still be *under heavy development*.
-For an up to date description please refer to the
-[documentation][3]. The latest release can be accessed [here][4].
+**ASteCA** is a package designed to automatize the usual analysis applied on star
+clusters, in order to estimate their characteristics and fundamental parameters.
 
-If you use this package please cite its accompanying [article][1] using the
+Install with:
+
+```
+pip install asteca
+```
+
+See the [documentation](https://asteca.github.io) for more details. If you use this
+package in your research, please cite its accompanying [article][1] using the following
 Bibtex:
 
 ````
@@ -27,7 +31,7 @@ Bibtex:
 ````
 
 [1]: http://www.aanda.org/articles/aa/abs/2015/04/aa24946-14/aa24946-14.html
-[2]: http://www.gnu.org/licenses/gpl-3.0.en.html
+[2]: https://opensource.org/license/mit/
 [3]: http://asteca.github.io
 [4]: https://github.com/asteca/asteca/releases/latest
 

asteca/__init__.py

@@ -0,0 +1,32 @@
+from .isochrones import isochrones
+from .synthetic import synthetic
+from .cluster import cluster
+from .likelihood import likelihood
+
+from contextlib import suppress
+import importlib.metadata
+from pathlib import Path
+
+
+def extract_version() -> str:
+    """Returns either the version of the installed package or the one
+    found in nearby pyproject.toml
+
+    https://stackoverflow.com/a/76206192/1391441
+
+    """
+    with suppress(FileNotFoundError, StopIteration):
+        pp_file = Path(__file__).parent.parent / "pyproject.toml"
+        with open(pp_file, encoding="utf-8") as pyproject_toml:
+            version = (
+                next(line for line in pyproject_toml if line.startswith("version"))
+                .split("=")[1]
+                .strip("'\"\n ")
+            )
+            return f"{version}"
+    # return importlib.metadata.version(__package__
+    #                                   or __name__.split(".", maxsplit=1)[0])
+    return importlib.metadata.version(__package__ or __name__)
+
+
+__version__ = extract_version()

asteca/cluster.py

@@ -0,0 +1,168 @@
+from dataclasses import dataclass
+from typing import Optional
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+@dataclass
+class cluster:
+    r"""Define a ``cluster`` object.
+
+    Parameters
+    ----------
+    cluster_df : pd.DataFrame
+        pandas DataFrame with the cluster's loaded data
+    magnitude : str
+        Name of the DataFrame column that contains the magnitude
+    e_mag : str
+        Name of the DataFrame column that contains the magnitude's uncertainty
+    color : str
+        Name of the DataFrame column that contains the color
+    e_color : str
+        Name of the DataFrame column that contains the color's uncertainty
+    ra: str, optional, default=None
+        Name of the DataFrame column that contains the right ascension (RA)
+    dec: str, optional, default=None
+        Name of the DataFrame column that contains the declination (DEC)
+    plx: str, optional, default=None
+        Name of the DataFrame column that contains the parallax
+    pmra: str, optional, default=None
+        Name of the DataFrame column that contains the RA proper motion
+    pmde: str, optional, default=None
+        Name of the DataFrame column that contains the DEC proper motion
+    color2: str, optional, default=None
+        Name of the DataFrame column that contains the second color
+    e_color2: str, optional, default=None
+        Name of the DataFrame column that contains the second color's uncertainty
+
+    """
+    cluster_df: pd.DataFrame
+    magnitude: str
+    e_mag: str
+    color: str
+    e_color: str
+    ra: Optional[str] = None
+    dec: Optional[str] = None
+    plx: Optional[str] = None
+    pmra: Optional[str] = None
+    pmde: Optional[str] = None
+    color2: Optional[str] = None
+    e_color2: Optional[str] = None
+
+    def __post_init__(self):
+        # Load photometry
+        self._load()
+
+    def _load(self):
+        """
+        The photometry is store with a '_p' to differentiate from the self.magnitude,
+        self.color, etc that are defined with the class is called.
+        """
+        print("Reading and processing cluster data")
+
+        self.mag_p = np.array(self.cluster_df[self.magnitude])
+        self.e_mag_p = np.array(self.cluster_df[self.e_mag])
+
+        self.colors_p = [np.array(self.cluster_df[self.color])]
+        if self.color2 is not None:
+            self.colors_p.append(np.array(self.cluster_df[self.color2]))
+        self.e_colors_p = [np.array(self.cluster_df[self.e_color])]
+        if self.e_color2 is not None:
+            self.e_colors_p.append(np.array(self.cluster_df[self.e_color2]))
+
+        if self.ra is not None:
+            self.ra_v = self.cluster_df[self.ra]
+            self.dec_v = self.cluster_df[self.dec]
+
+        print("Cluster object generated\n")
+
+    def radecplot(self):
+        r"""Generate a (RA, DEC) plot.
+
+        Returns
+        -------
+        matplotlib.axis
+            Matplotlib axis object
+
+        """
+        ra = self.ra_v  # cluster_df[self.ra].values
+        dec = self.dec_v  # cluster_df[self.dec].values
+        mag = self.mag_p  # self.cluster_df[self.magnitude].values
+
+        msk = ~np.isnan(mag)
+        ra = ra[msk]
+        dec = dec[msk]
+        mag = mag[msk]
+
+        # Mag to flux
+        sizes = 10 ** (mag / -2.5)
+        # Normalize to 1
+        sizes /= sizes.max()
+        # Set min, max
+        sizes = 1 + 75 * sizes
+
+        f, ax = plt.subplots()
+        plt.scatter(ra, dec, s=sizes, c="k", alpha=0.7)
+        plt.xlabel("RA")
+        plt.ylabel("DEC")
+        plt.gca().invert_xaxis()
+
+        return ax
+
+    def clustplot(self, ax=None, binar_prob=None):
+        r"""Generate a color-magnitude plot.
+
+        Parameters
+        ----------
+        ax : matplotlib.axis, optional, default=None
+            Matplotlib axis where to draw the plot
+        binar_prob : numpy.array, optional, default=None
+            Array with probabilities of being a binary system for each observed star
+
+        Returns
+        -------
+        matplotlib.axis
+            Matplotlib axis object
+
+
+        """
+        if ax is None:
+            f, ax = plt.subplots()
+
+        if binar_prob is not None:
+            msk_binar = binar_prob > 0.5
+
+        mag_col = self.magnitude
+        col_col = self.color
+
+        if binar_prob is None:
+            ax.scatter(
+                self.colors_p[0],
+                self.mag_p,
+                c="green",
+                alpha=0.5,
+                label=f"Observed, N={len(self.mag_p)}",
+            )
+        else:
+            ax.scatter(
+                self.colors_p[0][~msk_binar],
+                self.mag_p[~msk_binar],
+                c="green",
+                alpha=0.5,
+                label=f"Observed (single), N={len(self.mag_p[~msk_binar])}",
+            )
+            ax.scatter(
+                self.colors_p[0][msk_binar],
+                self.mag_p[msk_binar],
+                c="red",
+                alpha=0.5,
+                label=f"Observed (binary), N={len(self.mag_p[msk_binar])}",
+            )
+
+        ax.set_ylim(max(self.mag_p) + .5, min(self.mag_p) - .5)
+        ax.set_xlabel(col_col)
+        ax.set_ylabel(mag_col)
+        ax.legend()
+
+        return ax