1 files changed, 104 insertions, 0 deletions
diff --git a/bin/calc_entropy.py b/bin/calc_entropy.py
new file mode 100755
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+++ b/bin/calc_entropy.py
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+#!/usr/bin/env python3
+#
+# Calculate the entropy within the specified radius.
+#
+# Junhua GU
+# Weitian LI
+# 2016-06-07
+#
+
+import argparse
+import re
+from itertools import groupby
+import numpy as np
+
+
+def isplit(iterable, splitters):
+    """
+    Credit: https://stackoverflow.com/a/4322780/4856091
+    """
+    return [list(g) for k, g in groupby(iterable,
+                                        lambda x:x in splitters) if not k]
+
+
+def get_entropy(data, r):
+    """
+    Get the entropy *at* the specified radius.
+
+    XXX: whether to interpolate first?
+    """
+    radius = data[:, 0]
+    entropy = data[:, 1]
+    s = np.min(entropy[radius > r])
+    return s
+
+
+def read_merged_qdp(infile):
+    """
+    Read merged QDP with multiple group of data separated by "no no no".
+    """
+    lines = map(lambda line: re.sub(r"^\s*no\s+no\s+no.*$", "X",
+                                    line.strip(), flags=re.I),
+                open(infile).readlines())
+    lines = isplit(lines, ("X",))
+    data_groups = []
+    for block in lines:
+        data = [list(map(float, l.split())) for l in block]
+        data.append(np.row_stack(data))
+    return data_groups
+
+
+def calc_error(center_value, mc_values, ci=0.683):
+    """
+    Calculate the uncertainties/errors.
+    """
+    data = np.concatenate([[center_value], mc_values])
+    median, q_lower, q_upper = np.percentile(data, q=(50, 50-50*ci, 50+50*ci))
+    mean = np.mean(data)
+    std = np.std(data)
+    return {
+        "mean":    mean,
+        "std":     std,
+        "median":  median,
+        "q_lower": q_lower,
+        "q_upper": q_upper,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(
+            description="Calculate the entropy within the given radius")
+    parser.add_argument("-C", "--confidence-level", dest="ci",
+                        type=float, default=0.683,
+                        help="confidence level to estimate the errors")
+    parser.add_argument("center_data",
+                        help="calculate central entropy profile " +
+                             "(e.g., entropy_center.qdp)")
+    parser.add_argument("mc_data",
+                        help="Merged QDP file of all the Monte Carlo " +
+                             "simulated entropy profiles " +
+                             "(e.g., summary_entropy.qdp)")
+    parser.add_argument("rout", type=float, help="outer radius (kpc)")
+    args = parser.parse_args()
+
+    center_data = np.loadtxt(args.center_data)
+    center_s = get_entropy(center_data, r=args.rout)
+
+    data_groups = read_merged_qdp(args.mc_data)
+    entropy_list = []
+    for dg in data_groups:
+        s = get_entropy(dg, r=args.rout)
+        entropy_list.append(s)
+    results = calc_error(center_s, entropy_list, ci=args.ci)
+    s_err_lower = results["q_lower"] - center_s
+    s_err_upper = results["q_upper"] - center_s
+
+    print("entropy= %e %+e/%+e keV cm^2 (ci=%.1f%%)" %
+          (center_s, s_err_lower, s_err_upper, args.ci * 100))
+    print("entropy(mean)= %e" % results["mean"])
+    print("entropy(median)= %e" % results["median"])
+    print("entropy(std)= %e" % results["std"])
+
+
+if __name__ == "__main__":
+    main()