#!/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 ***")