From 01e736d0bb09387e497f57b02ac03398402b06a5 Mon Sep 17 00:00:00 2001
From: Peter Teuben <teuben@gmail.com>
Date: Thu, 24 Aug 2023 08:21:00 -0400
Subject: [PATCH] from athenak's distro

---
 athena_read.py | 150 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 150 insertions(+)
 create mode 100644 athena_read.py

diff --git a/athena_read.py b/athena_read.py
new file mode 100644
index 0000000..d8240ba
--- /dev/null
+++ b/athena_read.py
@@ -0,0 +1,150 @@
+# Various functions to read Athena++ output data files
+
+# Python modules
+import re
+import warnings
+from io import open  # Consistent binary I/O from Python 2 and 3
+
+# Other Python modules
+import numpy as np
+
+check_nan_flag = False
+
+
+# Check input NumPy array for the presence of any NaN entries
+def check_nan(data):
+    if np.isnan(data).any():
+        raise FloatingPointError("NaN encountered")
+    return
+
+
+# Wrapper to np.loadtxt() for checks used in regression tests
+def error_dat(filename, **kwargs):
+    data = np.loadtxt(filename,
+                      dtype=np.float64,
+                      ndmin=2,  # prevent NumPy from squeezing singleton dim
+                      **kwargs)
+    if check_nan_flag:
+        check_nan(data)
+    return data
+
+
+# Read .tab files and return dict.
+def tab(filename):
+
+    # Parse header
+    data_dict = {}
+    with open(filename, 'r') as data_file:
+        line = data_file.readline()
+        attributes = re.search(r'time=(\S+)\s+cycle=(\S+)', line)
+        line = data_file.readline()
+        headings = line.split()[1:]
+    data_dict['time'] = float(attributes.group(1))
+    data_dict['cycle'] = int(attributes.group(2))
+    headings = headings[1:]
+
+    # Go through lines
+    data_array = []
+    num_lines = 0
+    with open(filename, 'r') as data_file:
+        first_line = True
+        for line in data_file:
+            # Skip comments
+            if line.split()[0][0] == '#':
+                continue
+
+            # Extract cell indices
+            vals = line.split()
+            if first_line:
+                num_entries = len(vals) - 1
+                first_line = False
+
+            # Extract cell values
+            vals = vals[1:]
+            data_array.append([float(val) for val in vals])
+            num_lines += 1
+
+    # Reshape array
+    array_shape = (num_lines, num_entries)
+    array_transpose = (1, 0)
+    data_array = np.transpose(np.reshape(data_array, array_shape),
+                              array_transpose)
+
+    # Finalize data
+    for n, heading in enumerate(headings):
+        if check_nan_flag:
+            check_nan(data_array[n, ...])
+        data_dict[heading] = data_array[n, ...]
+    return data_dict
+
+
+# Read .hst files and return dict of 1D arrays.
+# Keyword arguments:
+# raw -- if True, do not prune file to remove stale data
+# from prev runs (default False)
+def hst(filename, raw=False):
+    # Read data
+    with open(filename, 'r') as data_file:
+        # Find header
+        header_found = False
+        multiple_headers = False
+        header_location = None
+        line = data_file.readline()
+        while len(line) > 0:
+            if line == '# Athena++ history data\n':
+                if header_found:
+                    multiple_headers = True
+                else:
+                    header_found = True
+                header_location = data_file.tell()
+            line = data_file.readline()
+        if multiple_headers:
+            warnings.warn('Multiple headers found; using most recent data')
+        if header_location is None:
+            raise RuntimeError('athena_read.hst: Could not find header')
+
+        # Parse header
+        data_file.seek(header_location)
+        header = data_file.readline()
+        data_names = re.findall(r'\[\d+\]=(\S+)', header)
+        if len(data_names) == 0:
+            raise RuntimeError('athena_read.hst: Could not parse header')
+
+        # Prepare dictionary of results
+        data = {}
+        for name in data_names:
+            data[name] = []
+
+        # Read data
+        for line in data_file:
+            for name, val in zip(data_names, line.split()):
+                data[name].append(float(val))
+
+    # Finalize data
+    for key, val in data.items():
+        data[key] = np.array(val)
+    if not raw:
+        if data_names[0] != 'time':
+            raise AthenaError('Cannot remove spurious data because time '
+                              'column could not be identified')
+        branches_removed = False
+        while not branches_removed:
+            branches_removed = True
+            for n in range(1, len(data['time'])):
+                if data['time'][n] <= data['time'][n-1]:
+                    branch_index = np.where((data['time'][:n] >=
+                                             data['time'][n]))[0][0]
+                    for key, val in data.items():
+                        data[key] = np.concatenate((val[:branch_index],
+                                                    val[n:]))
+                    branches_removed = False
+                    break
+        if check_nan_flag:
+            for key, val in data.items():
+                check_nan(val)
+    return data
+
+
+# General exception class for these functions
+class AthenaError(RuntimeError):
+    pass