Better plots (#73)

* Edits paths of saved files

* Add upper threshold options

* Add upper threshold options

* add latex_float option

* Add weighted stats

* add new plot

csiborgtools/__init__.py

@@ -17,8 +17,7 @@ from csiborgtools import clustering, field, fits, match, read  # noqa
 # Arguments to csiborgtools.read.Paths.
 paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",
                    "postdir": "/mnt/extraspace/rstiskalek/CSiBORG/",
-                   "BORG_dir": "/mnt/extraspace/rstiskalek/BORG/",
-                   "BORG_final_density": "/users/hdesmond/BORG_final/",
+                   "borg_dir": "/users/hdesmond/BORG_final/",
                    "quijote_dir": "/mnt/extraspace/rstiskalek/Quijote",
                    }
 

csiborgtools/read/__init__.py

@@ -19,12 +19,14 @@ from .knn_summary import kNNCDFReader  # noqa
 from .nearest_neighbour_summary import NearestNeighbourReader  # noqa
 from .obs import (SDSS, MCXCClusters, PlanckClusters, TwoMPPGalaxies,  # noqa
                   TwoMPPGroups)
+from .overlap_summary import weighted_stats  # noqa
 from .overlap_summary import (NPairsOverlap, PairOverlap,  # noqa
-                              binned_resample_mean, get_cross_sims)
+                              binned_resample_mean, get_cross_sims)  # noqa
 from .paths import Paths  # noqa
 from .pk_summary import PKReader  # noqa
 from .readsim import (MmainReader, ParticleReader, halfwidth_mask,  # noqa
                       load_clump_particles, load_parent_particles, read_initcm)
 from .tpcf_summary import TPCFReader  # noqa
-from .utils import (cartesian_to_radec, cols_to_structured,  # noqa
-                    radec_to_cartesian, read_h5, real2redshift, M200_to_R200)
+from .utils import (M200_to_R200, cartesian_to_radec,  # noqa
+                    cols_to_structured, radec_to_cartesian, read_h5,
+                    real2redshift)

csiborgtools/read/overlap_summary.py

@@ -19,7 +19,7 @@ from functools import lru_cache
 from os.path import isfile
 
 import numpy
-from tqdm import tqdm
+from tqdm import tqdm, trange
 
 ###############################################################################
 #                         Overlap of two simulations                          #
@@ -293,7 +293,6 @@ class PairOverlap:
 
             if norm_kind is not None:
                 dist[i] /= norm[i]
-
         return numpy.array(dist, dtype=object)
 
     def mass_ratio(self, mass_kind="totpartmass", in_log=True, in_abs=True):
@@ -406,7 +405,7 @@ class PairOverlap:
         vals = self.cat0(par)
         out = [None] * len(self)
         for i, ind in enumerate(self["match_indxs"]):
-            out[i] = numpy.ones(ind.size) * vals[i]
+            out[i] = numpy.ones(len(ind)) * vals[i]
         return numpy.array(out, dtype=object)
 
     def cat0(self, key=None, index=None):
@@ -459,6 +458,43 @@ class PairOverlap:
         return self["match_indxs"].size
 
 
+def weighted_stats(x, weights, min_weight=0, verbose=False):
+    """
+    Calculate the weighted mean and standard deviation of `x` using `weights`
+    for each array of `x`.
+
+    Parameters
+    ----------
+    x : array of arrays
+        Array of arrays of values to calculate the weighted mean and standard
+        deviation for.
+    weights : array of arrays
+        Array of arrays of weights to use for the calculation.
+    min_weight : float, optional
+        Minimum weight required for a value to be included in the calculation.
+    verbose : bool, optional
+        Verbosity flag.
+
+    Returns
+    -------
+    stat : 2-dimensional array of shape `(len(x), 2)`
+        The first column is the weighted mean and the second column is the
+        weighted standard deviation.
+    """
+    out = numpy.full((x.size, 2), numpy.nan, dtype=numpy.float32)
+
+    for i in trange(len(x)) if verbose else range(len(x)):
+        x_, w_ = numpy.asarray(x[i]), numpy.asarray(weights[i])
+        mask = w_ > min_weight
+        x_ = x_[mask]
+        w_ = w_[mask]
+        if len(w_) == 0:
+            continue
+        out[i, 0] = numpy.average(x_, weights=w_)
+        out[i, 1] = numpy.average((x_ - out[i, 0])**2, weights=w_)**0.5
+    return out
+
+
 ###############################################################################
 #                  Overlap of many pairs of simulations.                      #
 ###############################################################################