Fix overlap runs (#125)

* Update nb * Update script * Update script * Rename * Update script * Update script * Remove warning * Ignore minors when extracting MAH * Fix paths bug * Move notebooks * Move files * Rename and delete things * Rename file * Move file * Rename things * Remove old print statement * Add basic MAH plot * Add random MAH path * Output snapshot numbers * Add MAH random extraction * Fix redshift bug * Edit script * Add extracting random MAH * Little updates * Add CB2 redshift * Add some caching * Add diagnostic plots * Add caching * Minor updates * Update nb * Update notebook * Update script * Add Sorce randoms * Add CB2 varysmall * Update nb * Update nb * Update nb * Use catalogue HMF * Move definition of radec2galactic * Update nb * Update import * Update import * Add galatic coords to catalogues * Update nb
2025-05-14 06:31:11 +00:00 · 2024-04-08 11:23:21 +02:00 · 2024-04-08 11:23:21 +02:00 · ee222cd010
commit ee222cd010
parent c71f5a8513
31 changed files with 1813 additions and 798 deletions
--- a/notebooks/utils.py
+++ b/notebooks/utils.py
@ -0,0 +1,132 @@
+# Copyright (C) 2023 Richard Stiskalek
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""
+Various utility functions.
+"""
+
+import numpy
+from scipy.special import erf
+
+dpi = 600
+fout = "../plots/"
+mplstyle = ["science"]
+
+
+def latex_float(*floats, n=2):
+    """
+    Convert a float or a list of floats to a LaTeX string(s). Taken from [1].
+
+    Parameters
+    ----------
+    floats : float or list of floats
+        The float(s) to be converted.
+    n : int, optional
+        The number of significant figures to be used in the LaTeX string.
+
+    Returns
+    -------
+    latex_floats : str or list of str
+        The LaTeX string(s) representing the float(s).
+
+    References
+    ----------
+    [1] https://stackoverflow.com/questions/13490292/format-number-using-latex-notation-in-python  # noqa
+    """
+    latex_floats = [None] * len(floats)
+    for i, f in enumerate(floats):
+        float_str = "{0:.{1}g}".format(f, n)
+        if "e" in float_str:
+            base, exponent = float_str.split("e")
+            latex_floats[i] = r"{0} \times 10^{{{1}}}".format(base,
+                                                              int(exponent))
+        else:
+            latex_floats[i] = float_str
+
+    if len(floats) == 1:
+        return latex_floats[0]
+    return latex_floats
+
+
+def nan_weighted_average(arr, weights=None, axis=None):
+    if weights is None:
+        weights = numpy.ones_like(arr)
+
+    valid_entries = ~numpy.isnan(arr)
+
+    # Set NaN entries in arr to 0 for computation
+    arr = numpy.where(valid_entries, arr, 0)
+
+    # Set weights of NaN entries to 0
+    weights = numpy.where(valid_entries, weights, 0)
+
+    # Compute the weighted sum and the sum of weights along the axis
+    weighted_sum = numpy.sum(arr * weights, axis=axis)
+    sum_weights = numpy.sum(weights, axis=axis)
+
+    return weighted_sum / sum_weights
+
+
+def nan_weighted_std(arr, weights=None, axis=None, ddof=0):
+    if weights is None:
+        weights = numpy.ones_like(arr)
+
+    valid_entries = ~numpy.isnan(arr)
+
+    # Set NaN entries in arr to 0 for computation
+    arr = numpy.where(valid_entries, arr, 0)
+
+    # Set weights of NaN entries to 0
+    weights = numpy.where(valid_entries, weights, 0)
+
+    # Calculate weighted mean
+    weighted_mean = numpy.sum(
+        arr * weights, axis=axis) / numpy.sum(weights, axis=axis)
+
+    # Calculate the weighted variance
+    variance = numpy.sum(
+        weights * (arr - numpy.expand_dims(weighted_mean, axis))**2, axis=axis)
+    variance /= numpy.sum(weights, axis=axis) - ddof
+
+    return numpy.sqrt(variance)
+
+
+def compute_error_bars(x, y, xbins, sigma):
+    bin_indices = numpy.digitize(x, xbins)
+    y_medians = numpy.array([numpy.median(y[bin_indices == i])
+                             for i in range(1, len(xbins))])
+
+    lower_pct = 100 * 0.5 * (1 - erf(sigma / numpy.sqrt(2)))
+    upper_pct = 100 - lower_pct
+
+    y_lower = numpy.full(len(y_medians), numpy.nan)
+    y_upper = numpy.full(len(y_medians), numpy.nan)
+
+    for i in range(len(y_medians)):
+        if numpy.sum(bin_indices == i + 1) == 0:
+            continue
+
+        y_lower[i] = numpy.percentile(y[bin_indices == i + 1], lower_pct)
+        y_upper[i] = numpy.percentile(y[bin_indices == i + 1], upper_pct)
+
+    yerr = (y_medians - numpy.array(y_lower), numpy.array(y_upper) - y_medians)
+
+    return y_medians, yerr
+
+
+def normalize_hexbin(hb):
+    hexagon_counts = hb.get_array()
+    normalized_counts = hexagon_counts / hexagon_counts.sum()
+    hb.set_array(normalized_counts)
+    hb.set_clim(normalized_counts.min(), normalized_counts.max())