--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/spectrum.py	Fri Apr 25 21:48:27 2025 +0000
@@ -0,0 +1,182 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+#!/usr/bin/env python
+
+# This script is generated with nb2galaxy
+
+# flake8: noqa
+
+import json
+import os
+import shutil
+
+from oda_api.json import CustomJSONEncoder
+
+fn = "data.tsv"  # oda:POSIXPath
+skiprows = 0  # http://odahub.io/ontology#Integer
+sep = "whitespace"  # http://odahub.io/ontology#String ; oda:allowed_value "auto", "comma", "tab", "whitespace", "semicolon"
+column = "c1"  # http://odahub.io/ontology#String
+weight_col = ""  # http://odahub.io/ontology#String
+binning = "logarithmic"  # http://odahub.io/ontology#String ; oda:allowed_value "linear","logarithmic"
+minval = 0  # http://odahub.io/ontology#Float
+maxval = 0  # http://odahub.io/ontology#Float
+nbins = 15  # http://odahub.io/ontology#Integer
+xlabel = "Energy, [eV]"  # http://odahub.io/ontology#String
+ylabel = "Flux E^2, [eV]"  # http://odahub.io/ontology#String
+spec_power = 2.0  # http://odahub.io/ontology#Float
+
+_galaxy_wd = os.getcwd()
+
+with open("inputs.json", "r") as fd:
+    inp_dic = json.load(fd)
+if "C_data_product_" in inp_dic.keys():
+    inp_pdic = inp_dic["C_data_product_"]
+else:
+    inp_pdic = inp_dic
+fn = str(inp_pdic["fn"])
+skiprows = int(inp_pdic["skiprows"])
+sep = str(inp_pdic["sep"])
+column = str(inp_pdic["column"])
+weight_col = str(inp_pdic["weight_col"])
+binning = str(inp_pdic["binning"])
+minval = float(inp_pdic["minval"])
+maxval = float(inp_pdic["maxval"])
+nbins = int(inp_pdic["nbins"])
+xlabel = str(inp_pdic["xlabel"])
+ylabel = str(inp_pdic["ylabel"])
+spec_power = float(inp_pdic["spec_power"])
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+separators = {
+    "tab": "\t",
+    "comma": ",",
+    "semicolon": ";",
+    "whitespace": "\s+",
+    "space": " ",
+}
+
+df = None
+
+if sep == "auto":
+    for name, s in separators.items():
+        try:
+            df = pd.read_csv(fn, sep=s, index_col=False, skiprows=skiprows)
+            if len(df.columns) > 2:
+                sep = s
+                print("Detected separator: ", name)
+                break
+        except Exception as e:
+            print("Separator ", s, " failed", e)
+    assert sep != "auto", "Failed to find valid separator"
+
+if df is None:
+    df = pd.read_csv(fn, sep=separators[sep], index_col=False)
+
+df.columns
+
+def read_data(df, colname, optional=False):
+    for i, c in enumerate(df.columns):
+        if colname == f"c{i+1}":
+            print(colname, c)
+            return df[c].values
+        elif colname == c:
+            print(colname, c)
+            return df[c].values
+
+    assert optional, colname + " column not found"
+    return None
+
+values = read_data(df, column)
+weights = read_data(df, weight_col, optional=True)
+if weights is None:
+    weights = np.ones_like(values)
+
+values, weights
+
+from numpy import log10
+
+if minval == 0:
+    minval = np.min(values)
+
+if maxval == 0:
+    maxval = np.max(values)
+
+if binning == "linear":
+    bins = np.linspace(minval, maxval, nbins + 1)
+else:
+    bins = np.logspace(log10(minval), log10(maxval), nbins + 1)
+bins
+
+bin_val, _ = np.histogram(values, weights=weights, bins=bins)
+len(bin_val), len(bins)
+bin_width = bins[1:] - bins[:-1]
+flux = bin_val / bin_width
+if binning == "linear":
+    spec_point = 0.5 * (bins[1:] + bins[:-1])
+else:
+    spec_point = np.sqrt(bins[1:] * bins[:-1])
+
+plt.figure()
+h = plt.plot(spec_point, flux * spec_point**spec_power)
+
+if binning == "logarithmic":
+    plt.xscale("log")
+    plt.yscale("log")
+
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.savefig("spectrum.png", format="png", dpi=150)
+
+from astropy.table import Table
+from oda_api.data_products import ODAAstropyTable, PictureProduct
+
+names = ("bins_min", "bins_max", "flux")
+
+res = ODAAstropyTable(Table([bins[:-1], bins[1:], flux], names=names))
+
+plot = PictureProduct.from_file("spectrum.png")
+
+histogram_data = res  # http://odahub.io/ontology#ODAAstropyTable
+histogram_picture = plot  # http://odahub.io/ontology#ODAPictureProduct
+
+# output gathering
+_galaxy_meta_data = {}
+_oda_outs = []
+_oda_outs.append(
+    (
+        "out_spectrum_histogram_data",
+        "histogram_data_galaxy.output",
+        histogram_data,
+    )
+)
+_oda_outs.append(
+    (
+        "out_spectrum_histogram_picture",
+        "histogram_picture_galaxy.output",
+        histogram_picture,
+    )
+)
+
+for _outn, _outfn, _outv in _oda_outs:
+    _galaxy_outfile_name = os.path.join(_galaxy_wd, _outfn)
+    if isinstance(_outv, str) and os.path.isfile(_outv):
+        shutil.move(_outv, _galaxy_outfile_name)
+        _galaxy_meta_data[_outn] = {"ext": "_sniff_"}
+    elif getattr(_outv, "write_fits_file", None):
+        _outv.write_fits_file(_galaxy_outfile_name)
+        _galaxy_meta_data[_outn] = {"ext": "fits"}
+    elif getattr(_outv, "write_file", None):
+        _outv.write_file(_galaxy_outfile_name)
+        _galaxy_meta_data[_outn] = {"ext": "_sniff_"}
+    else:
+        with open(_galaxy_outfile_name, "w") as fd:
+            json.dump(_outv, fd, cls=CustomJSONEncoder)
+        _galaxy_meta_data[_outn] = {"ext": "json"}
+
+with open(os.path.join(_galaxy_wd, "galaxy.json"), "w") as fd:
+    json.dump(_galaxy_meta_data, fd)
+print("*** Job finished successfully ***")