Mercurial > repos > astroteam > plot_tools_astro_tool
diff light_curve.py @ 0:2b1759ccaa8b draft default tip
planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit f28a8cb73a7f3053eac92166867a48b3d4af28fd
author | astroteam |
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date | Fri, 25 Apr 2025 21:48:27 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/light_curve.py Fri Apr 25 21:48:27 2025 +0000 @@ -0,0 +1,240 @@ +#!/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 "comma", "tab", "space", "whitespace", "semicolon" +column = "T" # 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 +use_quantile_values = False # http://odahub.io/ontology#Boolean +nbins = 15 # http://odahub.io/ontology#Integer +xlabel = "time, s" # http://odahub.io/ontology#String +ylabel = "Ncounts" # http://odahub.io/ontology#String +plot_mode = "flux" # http://odahub.io/ontology#String ; oda:allowed_value "counts", "flux" + +_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"]) +use_quantile_values = bool(inp_pdic["use_quantile_values"]) +nbins = int(inp_pdic["nbins"]) +xlabel = str(inp_pdic["xlabel"]) +ylabel = str(inp_pdic["ylabel"]) +plot_mode = str(inp_pdic["plot_mode"]) + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +if plot_mode != "counts" and ylabel == "Ncounts": + ylabel = plot_mode # replace default value + +assert minval >= 0 or not use_quantile_values +assert maxval >= 0 or not use_quantile_values +assert minval <= 1 or not use_quantile_values +assert maxval <= 1 or not use_quantile_values +assert minval < maxval or minval == 0 or maxval == 0 + +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 weighted_quantile( + values, quantiles, sample_weight=None, values_sorted=False, old_style=False +): + """Very close to numpy.percentile, but supports weights. + NOTE: quantiles should be in [0, 1]! + :param values: numpy.array with data + :param quantiles: array-like with many quantiles needed + :param sample_weight: array-like of the same length as `array` + :param values_sorted: bool, if True, then will avoid sorting of initial array + :param old_style: if True, will correct output to be consistent with numpy.percentile. + :return: numpy.array with computed quantiles. + """ + values = np.array(values) + quantiles = np.array(quantiles) + if sample_weight is None: + sample_weight = np.ones(len(values)) + sample_weight = np.array(sample_weight) + assert np.all(quantiles >= 0) and np.all( + quantiles <= 1 + ), "quantiles should be in [0, 1]" + + if not values_sorted: + sorter = np.argsort(values) + values = values[sorter] + sample_weight = sample_weight[sorter] + + weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight + if old_style: + # To be convenient with np.percentile + weighted_quantiles -= weighted_quantiles[0] + weighted_quantiles /= weighted_quantiles[-1] + else: + weighted_quantiles /= np.sum(sample_weight) + return np.interp(quantiles, weighted_quantiles, values) + +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 + +delays = read_data(df, column) +weights = read_data(df, weight_col, optional=True) +if weights is None: + weights = np.ones_like(delays) + +if binning != "linear": + min_positive_val = np.min(delays[delays > 0]) + delays[delays <= 0] = ( + min_positive_val # replace zero delays with minimal positive value + ) + +if use_quantile_values: + minval, maxval = weighted_quantile( + delays, [minval, maxval], sample_weight=weights + ) + if minval == maxval: + print("ignoreing minval and maxval (empty range)") + minval = np.min(delays) + maxval = np.max(delays) +else: + if minval == 0: + minval = np.min(delays) + if maxval == 0: + maxval = np.max(delays) + +if minval == maxval: + print("correcting minval and maxval (empty range)") + maxval = minval * 1.1 if minval > 0 else 1e-100 + +from numpy import log10 + +if binning == "linear": + bins = np.linspace(minval, maxval, nbins + 1) +else: + bins = np.logspace(log10(minval), log10(maxval), nbins + 1) +bins + +if plot_mode == "flux" and binning == "logarithmic": + weights = weights / delays + +plt.figure() +h = plt.hist(delays, weights=weights, bins=bins) + +if binning == "logarithmic": + plt.xscale("log") + plt.yscale("log") +plt.xlabel(xlabel) +plt.ylabel(ylabel) +plt.savefig("Histogram.png", format="png", dpi=150) +hist_counts = h[0] +hist_bins = h[1] +hist_mins = hist_bins[:-1] +hist_maxs = hist_bins[1:] + +from astropy.table import Table +from oda_api.data_products import ODAAstropyTable, PictureProduct + +names = ("bins_min", "bins_max", "counts") +res = ODAAstropyTable(Table([hist_mins, hist_maxs, hist_counts], names=names)) + +plot = PictureProduct.from_file("Histogram.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_light_curve_histogram_data", + "histogram_data_galaxy.output", + histogram_data, + ) +) +_oda_outs.append( + ( + "out_light_curve_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 ***")