Merge branch 'release-040'

CHANGELOG.md

@@ -1,5 +1,38 @@
 # Change Log
 
+
+## [[v0.4.0]][309] - 2021-05-03
+
+### Changed
+
+* Compensate cluster's mass for binaries masses? ([488][308])
+* Estimate individual per-star masses ([484][307])
+* Improve performance of synth cluster generation (3) ([506][306])
+* Simplify isochrones download/handling ([497][305])
+* Add CS 37 COLIBRI track + deprecate old versions 10 & 11 of PARSEC ([495][304])
+* Optimal radius too large for some clusters ([510][303])
+* Project equatorial coordinates before processing ([237][302])
+* Add eccentricity parameter to KP fit? ([480][301])
+* Finish working on enhanced King profile fitting ([456][300])
+* Remove KDE_stds and mp_flag parameters ([500][299])
+* Simplify input of structure parameters ([512][298])
+* Deprecate all likelihoods except Tremmel ([507][297])
+* Interpolate IMF masses into the isochrones, not the other way around ([503][296])
+* Add minimum binary mass ratio to fundamental parameters? ([504][295])
+* Deprecate Anderson-Darling test ([499][294])
+* Deprecate "Read mode" ([498][293])
+* Add IMF and PMF curves obtention ([96][292])
+* Convert pixel coordinates to RA & DEC ([203][291])
+* Add ZAMS to CMD final plot ([160][290])
+* Add semi_input.dat checking to checker ([214][289])
+* Add weighted spatial density map ([167][288])
+* Generate output CMD-CCD plots for the mean+median+mode ([479][287])
+* Exact circle area using geometry instead of Monte Carlo ([446][286])
+* Use the maximum number of members in the optimal radius? ([494][285])
+* Add 1-sigma region to King profile ([478][284])
+* Turn off MP coloring in D2 plots for binned likelihoods ([473][283])
+
+
 ## [[v0.3.1]][282] - 2020-06-19
 
 Only the `ptemcee` method is kept, all others are now deprecated.
@@ -812,10 +845,30 @@ ________________________________________________________________________________
 [280]: https://github.com/asteca/ASteCA/commit/3ab2b30d3d107972734112e7f0bd8ce12709ebdc
 [281]: https://github.com/asteca/ASteCA/issues/468
 [282]: https://github.com/asteca/asteca/releases/tag/v0.3.1
-
-
-
-
-
-
-
+[283]: https://github.com/asteca/ASteCA/issues/473
+[284]: https://github.com/asteca/ASteCA/issues/478
+[285]: https://github.com/asteca/ASteCA/issues/494
+[286]: https://github.com/asteca/ASteCA/issues/446
+[287]: https://github.com/asteca/ASteCA/issues/479
+[288]: https://github.com/asteca/ASteCA/issues/167
+[289]: https://github.com/asteca/ASteCA/issues/214
+[290]: https://github.com/asteca/ASteCA/issues/160
+[291]: https://github.com/asteca/ASteCA/issues/203
+[292]: https://github.com/asteca/ASteCA/issues/96
+[293]: https://github.com/asteca/ASteCA/issues/498
+[294]: https://github.com/asteca/ASteCA/issues/499
+[295]: https://github.com/asteca/ASteCA/issues/504
+[296]: https://github.com/asteca/ASteCA/issues/503
+[297]: https://github.com/asteca/ASteCA/issues/507
+[298]: https://github.com/asteca/ASteCA/issues/512
+[299]: https://github.com/asteca/ASteCA/issues/500
+[300]: https://github.com/asteca/ASteCA/issues/456
+[301]: https://github.com/asteca/ASteCA/issues/480
+[302]: https://github.com/asteca/ASteCA/issues/237
+[303]: https://github.com/asteca/ASteCA/issues/510
+[304]: https://github.com/asteca/ASteCA/issues/495
+[305]: https://github.com/asteca/ASteCA/issues/497
+[306]: https://github.com/asteca/ASteCA/issues/506
+[307]: https://github.com/asteca/ASteCA/issues/484
+[308]: https://github.com/asteca/ASteCA/issues/488
+[309]: https://github.com/asteca/asteca/releases/tag/v0.4.0

packages/_version.py

@@ -1 +1 @@
-__version__ = "v0.3.1"
+__version__ = "v0.4.0"

packages/asteca_run.py

@@ -55,18 +55,25 @@ def main():
     # files are read and stored here.
     cl_files, pd = check_all(mypath, file_end)
 
+    # Prepare tracks and other required data
+    if pd['best_fit_algor'] != 'n':
+        from packages.synth_clust import tracksPrep
+        pd = tracksPrep.main(pd)
+
     # Import here to ensure the check has passed and all the necessary
     # packages are installed.
     from packages import func_caller
 
+    print("Full check done.\n\nNumber of clusters to analyze: {}".format(
+        len(cl_files)))
     # Iterate through all cluster files.
     for cl_file in cl_files:
         try:
             # Call module that calls all sub-modules sequentially.
             func_caller.main(cl_file, pd)
         except Exception:
-            print('\n!!! --> {}/{} '.format(cl_file[-2], cl_file[-1]) +
-                  'could not be successfully processed <-- !!!\n')
+            print('\n!!! --> {}/{} '.format(cl_file[-2], cl_file[-1])
+                  + 'could not be successfully processed <-- !!!\n')
             print(traceback.format_exc())
 
     # End of run.

packages/aux_funcs.py

@@ -5,11 +5,11 @@
 from astropy.stats import sigma_clipped_stats
 
 
-def flatten(l):
+def flatten(lst):
     """
     Source: https://stackoverflow.com/a/2158532/1391441
     """
-    for el in l:
+    for el in lst:
         if isinstance(el, Iterable) and not\
                 isinstance(el, (str, bytes)):
             for sub in flatten(el):
@@ -57,12 +57,14 @@ def kde1D(data, xmin=None, xmax=None, bw=None, xr_fr=.1):
     return kde_x, kde
 
 
-def circFrac(cent, rad, x0, x1, y0, y1, N_tot, rand_01_MC, cos_t, sin_t):
+def circFrac(cent, rad, x0, x1, y0, y1, rand_01_MC, cos_t, sin_t):
     """
     Use Monte Carlo to estimate the fraction of the area of a circle centered
     in (cx, cy) with a radius of 'rad', that is located within the frame given
     by the limits 'x0, x1, y0, y1'.
     """
+    N_tot = len(rand_01_MC)
+
     cx, cy = cent
     # Source: https://stackoverflow.com/a/50746409/1391441
     # r = rad * np.sqrt(np.random.uniform(0., 1., N_tot))
@@ -77,3 +79,51 @@ def circFrac(cent, rad, x0, x1, y0, y1, N_tot, rand_01_MC, cos_t, sin_t):
     # The area is the points within circle and frame over the points within
     # circle.
     return msk_xy.sum() / N_tot
+
+
+def ellipFrac(
+        cent, a, theta, ecc, x0, x1, y0, y1, rand_01_MC, cos_t, sin_t):
+    """
+    Use Monte Carlo to estimate the fraction of the area of an ellipse centered
+    in (cx, cy) and rotated an angle theta, with a semi-major axis 'a' and
+    eccentricity 'ecc', that is located within the frame given by the limits
+    'x0, x1, y0, y1'.
+    """
+    N_tot = len(rand_01_MC)
+
+    cx, cy = cent
+    b = a * np.sqrt(1 - ecc**2)
+    cos_th = np.cos(theta)
+    sin_th = np.sin(theta)
+
+    # r = rad * np.sqrt(np.random.uniform(0., 1., N_tot))
+    # theta = np.random.uniform(0., 1., N_tot) * 2 * np.pi
+    rand_01_MC_a = rand_01_MC * a
+    rand_01_MC_b = rand_01_MC * b
+
+    # https://math.stackexchange.com/questions/2645689/what-is-the-parametric-equation-of-a-rotated-ellipse-given-the-angle-of-rotatio
+
+    xr = cx + rand_01_MC_a * cos_t * cos_th - rand_01_MC_b * sin_t * sin_th
+    yr = cy + rand_01_MC_a * cos_t * sin_th + rand_01_MC_b * sin_t * cos_th
+
+    # Points within the ellipse that are within the frame.
+    msk_xy = (xr > x0) & (xr < x1) & (yr > y0) & (yr < y1)
+
+    return msk_xy.sum() / N_tot
+
+
+def monteCarloPars(N_MC=10000):
+    """
+    Parameters for the Monte Carlo estimation of the fraction of circle/ellipse
+    area within a rectangle. Used by the circFrac(), ellipFrac() functions.
+
+    N_MC: number of Monte Carlo points to use when estimating circular areas.
+    """
+
+    # Obtain these values here so they are not estimated every time the MC
+    # in circFrac() is called.
+    rand_01_MC = np.sqrt(np.random.uniform(0., 1., N_MC))
+    theta = np.random.uniform(0., 1., N_MC) * 2 * np.pi
+    cos_t, sin_t = np.cos(theta), np.sin(theta)
+
+    return rand_01_MC, cos_t, sin_t