diff --git a/cosipy/__init__.py b/cosipy/__init__.py index f3fdbb05..29114b6e 100644 --- a/cosipy/__init__.py +++ b/cosipy/__init__.py @@ -12,7 +12,7 @@ from .spacecraftfile import SpacecraftFile -from .ts_map import FastTSMap +from .ts_map import FastTSMap, MOCTSMap from .source_injector import SourceInjector diff --git a/cosipy/ts_map/__init__.py b/cosipy/ts_map/__init__.py index ba8c8c87..d2b8ec43 100644 --- a/cosipy/ts_map/__init__.py +++ b/cosipy/ts_map/__init__.py @@ -1,2 +1,3 @@ from .fast_ts_fit import FastTSMap -from .fast_norm_fit import FastNormFit \ No newline at end of file +from .fast_norm_fit import FastNormFit +from .moc_ts_fit import MOCTSMap diff --git a/cosipy/ts_map/fast_ts_fit.py b/cosipy/ts_map/fast_ts_fit.py index 8e4ca6c9..7ed96dea 100644 --- a/cosipy/ts_map/fast_ts_fit.py +++ b/cosipy/ts_map/fast_ts_fit.py @@ -273,7 +273,7 @@ def get_ei_cds_array(hypothesis_coord, energy_channel, response_path, spectrum, def fast_ts_fit(hypothesis_coord, energy_channel, data_cds_array, bkg_model_cds_array, orientation, response_path, spectrum, cds_frame, - ts_nside, ts_scheme): + ts_nside, ts_scheme, pixel_idx = None): """ Perform a TS fit on a single location at `hypothesis_coord`. @@ -310,7 +310,10 @@ def fast_ts_fit(hypothesis_coord, start_fast_ts_fit = time.time() # get the indices of the pixels to fit - pix = hp.ang2pix(nside = ts_nside, theta = hypothesis_coord.l.deg, phi = hypothesis_coord.b.deg, lonlat = True) + if pixel_idx is None: + pix = hp.ang2pix(nside = ts_nside, theta = hypothesis_coord.l.deg, phi = hypothesis_coord.b.deg, lonlat = True) + else: + pix = pixel_idx # get the expected counts in the flattened cds array start_ei_cds_array = time.time() @@ -331,9 +334,55 @@ def fast_ts_fit(hypothesis_coord, time_fast_ts_fit = end_fast_ts_fit - start_fast_ts_fit return [pix, result[0], result[1], result[2], result[3], result[4], time_ei_cds_array, time_fit, time_fast_ts_fit] + + @staticmethod + def zip_comp(*lists): + + """ + Zip the lists in a way that it expands the lists will one element. + + list1 = [1, 2, 3, 4] + list2 = ["a"] + list3 = [11, 21, 31, 41] + + zip_comp will produce a tuple like this: + ([1, "a", 11], + [2, "a", 21], + [3, "a", 31], + [4, "a", 41]) + + As you can see, it only allows lists with two length: 1 or the max length. + + Parameters + ---------- + lists : list + The input lists + + Returns + ------- + zip : + The zippped array. To expand, please use list(returned_object) + + """ + + all_lengths = np.unique([len(i) for i in lists]) + + if len(all_lengths) > 2: + raise ValueError(f"You have input lists with more than two lengths: {all_lengths}. Can't do zip comprehension!") + + + new_lists = [] + for i in lists: + if len(i) == np.min(all_lengths): + new_lists.append(i*np.max(all_lengths)) + else: + new_lists.append(i) + + return zip(*new_lists) - def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme = "RING", start_method = "fork", cpu_cores = None, ts_nside = None): + def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme = "RING", start_method = "fork", cpu_cores = None, ts_nside = None, + pixel_idx = [None]): """ Perform parallel computation on all the hypothesis coordinates. @@ -353,7 +402,9 @@ def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme cpu_cores : int, optional The number of cpu cores you wish to use for the parallel computation (the default is `None`, which implies using all the available number of cores -1 to perform the parallel computation). ts_nside : int, optional - The nside of the ts map. This must be given if the number of hypothesis_coords isn't equal to the number of pixels of the total ts map, which means that you fit only a portion of the total ts map. (the default is `None`, which means that you fit the full ts map). + The nside of the ts map. This must be given if the number of hypothesis_coords isn't equal to the number of pixels of the total ts map, which means that you fit only a portion of the total ts map. (the default is `None`, which means that you fit the full ts map). + pixel_idx : list, optional + The pixel indices of the corresponding hypothesis_coords. This parameter is used to match the pixels and the ts values in a regional fit case. Returns ------- @@ -389,12 +440,12 @@ def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme cores = cpu_cores logger.info(f"You have total {total_cores} CPU cores, using {cores} CPU cores for parallel computation.") - start = time.time() + start = time.time() multiprocessing.set_start_method(start_method, force = True) pool = multiprocessing.Pool(processes = cores) - results = pool.starmap(FastTSMap.fast_ts_fit, product(hypothesis_coords, [energy_channel], [data_cds_array], [bkg_model_cds_array], - [self._orientation], [self._response_path], [spectrum], [self._cds_frame], - [ts_nside], [ts_scheme])) + results = pool.starmap(FastTSMap.fast_ts_fit, FastTSMap.zip_comp(hypothesis_coords, [energy_channel], [data_cds_array], [bkg_model_cds_array], + [self._orientation], [self._response_path], [spectrum], [self._cds_frame], + [ts_nside], [ts_scheme], pixel_idx)) pool.close() pool.join() diff --git a/cosipy/ts_map/moc_ts_fit.py b/cosipy/ts_map/moc_ts_fit.py new file mode 100644 index 00000000..de47ffcb --- /dev/null +++ b/cosipy/ts_map/moc_ts_fit.py @@ -0,0 +1,329 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Oct 2 15:39:42 2024 + +@author: shengyong +""" + +import numpy as np +from mhealpy import HealpixMap +from mhealpy.pixelfunc.moc import * +from mhealpy.pixelfunc.single import * +from .fast_ts_fit import FastTSMap +import matplotlib.pyplot as plt +from copy import deepcopy +import astropy.units as u +from astropy.coordinates import SkyCoord +from pathlib import Path +import logging +logger = logging.getLogger(__name__) + + +class MOCTSMap(FastTSMap): + + def __init__(self, data, bkg_model, response_path, orientation = None, cds_frame = "local"): + + """ + Initialize the instance of a MOC TS map fit. + + Parameters + ---------- + data : histpy.Histogram + Observed data, which includes counts from both signal and background. + bkg_model : histpy.Histogram + Background model, which includes the background counts to model the background in the observed data. + response_path : str or pathlib.Path + The path to the response file. + orientation : cosipy.SpacecraftFile, optional + The orientation of the spacecraft when data are collected (the default is `None`, which implies the orientation file is not needed). + cds_frame : str, optional + "local" or "galactic", it's the Compton data space (CDS) frame of the data, bkg_model and the response. In other words, they should have the same cds frame (the default is "local", which implied that a local frame that attached to the spacecraft). + + """ + + super().__init__(data, bkg_model, response_path, orientation = orientation, cds_frame = cds_frame) + + + @staticmethod + def upscale_moc_map(m, uniq_mother, new_order): + + """ + Upscale the MOC map on certain mother pixels. All the child pixels will be filled by the value of the mother pixel. + + Parameters + ---------- + m : mhealpy.containers.healpix_map.HealpixMap + The input map to be upscaled. + uniq_mother_pix : int or array + The uniq number the mother pixels to be upscaled. + new_order : int + The order of the child pixels upscaled from the mother pixels. + + Returns + ------- + tuple + The upscaled the map and the unique numbers of the child pixels. + """ + + if not m.is_moc: + raise TypeError("The input map must be a MOC map.") + + # copy the uniq numbers and the data from the original map + new_uniq = deepcopy(m.uniq) + new_data = deepcopy(m.data) + + new_nside = 2**new_order + + uniq_child_all = [] + + for mother_uniq in uniq_mother: + + # get the index of the mother pixel + idx = np.where(new_uniq == mother_uniq)[0][0] # note that idx is the index of the uniq number, not the uniq number + + # get the start and stop of the child pixel number in the NESTED scheme (also the index in the NESTED scheme case) + start_nest = uniq2range(new_nside, mother_uniq)[0] + stop_nest = uniq2range(new_nside, mother_uniq)[1] + + # convert the child pixel number from NESTED scheme to UNIQ scheme + uniq_child = nest2uniq(new_nside, np.arange(start_nest, stop_nest)) + uniq_child_all += list(uniq_child) + + # update the moc map + new_uniq = np.concatenate((new_uniq[:idx], + uniq_child, + new_uniq[idx+1:])) + + new_data = np.concatenate((new_data[:idx], + np.repeat(new_data[idx], stop_nest-start_nest), + new_data[idx + 1:])) + + m_new = HealpixMap(data = new_data, uniq = new_uniq) + + return m_new, np.array(uniq_child_all) + + @staticmethod + def uniq2skycoord(uniq): + + """ + Convert the uniq number to the corresponding central skycoord. + + Parameters + ---------- + uniq : int, list or numpy.ndarray + The uniq number(s) of the pixel(s) + + Returns + ------- + astropy.Coordinates.SkyCoord + The galactic skycoord of the input uniq pixels. + """ + + nside, pix_num_nested = uniq2nest(uniq) + + lon, lat = pix2ang(nside = nside, ipix = pix_num_nested, nest = True, lonlat = True) + + return SkyCoord(l = lon, b = lat, unit = (u.deg, u.deg), frame = "galactic") + + @staticmethod + def uniq2pixidx(m, uniq): + + """ + Convert the uniq to the pixel index in the map. + + Parameters + ---------- + m : mhealpy.containers.healpix_map.HealpixMap + The map that contains the moc pixels + uniq : int, list or numpy.ndarray: + The uniq number(s) of the pixel(s) + + Returns + ------- + list + The list of the pixel index of the corresponding uniq pixels in the map + """ + + return [np.where(m.uniq == i)[0][0] for i in uniq] + + def fill_up_moc_map(pixidx, m, results): + + """ + Fill up the moc map based on the pixidx. + + Parameters + ---------- + pixidx : int or list + The pixel index, not the uniq number of the pixels + m : mhealpy.containers.healpix_map.HealpixMap + The MOC map to be filled + results : numpy.ndarray + The ts fit results. + + Returns + ------- + mhealpy.containers.healpix_map.HealpixMap + The filled map + """ + + if isinstance(pixidx, int): + pixidx = [pixidx] + + for pixidx_ in pixidx: + pixidx_ = int(pixidx_) + + idx = np.where(results[:,0].astype(int) == pixidx_)[0] # idx is the row idx of the result array where the first column equals to pixidx_ + if idx.shape[0] != 1: + raise ValueError(f"Pixel with pixel index {pixidx_} has {idx.shape[0]} fits! ") + else: + m[pixidx_] = results[idx,1] + + return m + + + def moc_ts_fit(self, max_moc_order, top_number, energy_channel, spectrum, start_method = "fork", cpu_cores = None): + + """ + Fit the MOC map. + + Parameters + ---------- + max_moc_order : int + The order of the MOC map to stop the fitting. + top_number : int + The pixels with the top likelihood to will be upscaled. For example, pixels with top eight likelihoods will be considered as mother pixels to be split into the child pixels. + energy_channel : list + The energy channel to be used for the MOC map fitting. + spectrum : + The spectrum model of the source to fit the model. + start_method : str, optional + The starting method of the parallel computation (the default is "fork", which implies using the fork method to start parallel computation). + cpu_cores : int, optional + The number of cpu cores you wish to use for the parallel computation (the default is `None`, which implies using all the available number of cores -1 to perform the parallel computation). + """ + + # initialize the order + order = 0 + + # initialize the 0th order moc map, which is equlivent to a 0th order single resolution map + uniq = nest2uniq(1, np.arange(12)) + moc_map_ts = HealpixMap(data = np.repeat(0, 12), uniq = uniq) + + # make the 0th order fit over all pixels + hypothesis_coords = MOCTSMap.uniq2skycoord(moc_map_ts.uniq) + hypothesis_coords_list = [i for i in hypothesis_coords] # have to split the SkyCoord object into SkyCoord object + pixidx = MOCTSMap.uniq2pixidx(moc_map_ts, moc_map_ts.uniq) + + print(f"fitting order = {order}") + print(f"fitting {len(hypothesis_coords_list)} hypothesis coordinates") + results = self.parallel_ts_fit(hypothesis_coords = hypothesis_coords_list, energy_channel = energy_channel, spectrum = spectrum, pixel_idx = pixidx) + self.ts_array = results[:,1] + + # fill up the 0th order moc map + moc_map_ts = MOCTSMap.fill_up_moc_map(pixidx, moc_map_ts, results) + self.moc_map_ts = moc_map_ts + + # store all ts maps + self.all_maps = [] + self.all_maps += [moc_map_ts] + + + # # if the user requires higher order fit + # threshold = moc_map_ts[:].max() - MOCTSMap.get_chi_critical_value(split_containment) # the threshold value to decide the mother pixels to be split + order += 1 + print("--------------------------------------------------------------------------------") + + # start the while loop + while order <= max_moc_order: + + # decide the mother pixels to divide + # threshold = moc_map_ts[:].max() - MOCTSMap.get_chi_critical_value(split_containment) + top_number_arg_array = np.argpartition(moc_map_ts, -top_number)[-top_number:] + print(f"The top {top_number} ts values are: {top_number_arg_array} in the last iteration, splitting these pixels...") + threshold = min(moc_map_ts[top_number_arg_array]) + + mother_idx = np.where(moc_map_ts[:] >= threshold)[0] + mother_uniq = moc_map_ts.uniq[mother_idx] + + # upscale the resolution of the mother pixels by 1 order, now the moc map is updated + moc_map_ts, child_uniq = MOCTSMap.upscale_moc_map(moc_map_ts, uniq_mother = mother_uniq, new_order = order) + + # get the sky coordinates of the child pixels + hypothesis_coords = MOCTSMap.uniq2skycoord(child_uniq) + hypothesis_coords_list = [i for i in hypothesis_coords] # have to split the SkyCoord object into SkyCoord object list + child_idx = MOCTSMap.uniq2pixidx(moc_map_ts, child_uniq) # child_idx is used to make sure that the ts values are filled into the correct pixels + print(f"fitting order {order} with {len(hypothesis_coords_list)} hypothesis coordinates") + results = self.parallel_ts_fit(hypothesis_coords = hypothesis_coords_list, energy_channel = energy_channel, spectrum = spectrum, ts_nside = 2**order, pixel_idx = child_idx) + self.ts_array = results[:,1] + + # fill up the child pixels + moc_map_ts = MOCTSMap.fill_up_moc_map(child_idx, moc_map_ts, results) + self.moc_map_ts = moc_map_ts + self.all_maps += [moc_map_ts] + + + order +=1 + print("--------------------------------------------------------------------------------") + + + return moc_map_ts + + def plot_ts(self, moc_map = None, skycoord = None, containment = None, save_plot = False, save_dir = "", save_name = "ts_map.png", dpi = 300): + + """ + Plot the containment region of the TS map. + + Parameters + ---------- + ts_array : numpy.ndarray + The array of ts values from parallel ts fit. + skyoord : astropy.coordinates.SkyCoord, optional + The true location of the source (the default is `None`, which implies that there are no coordiantes to be printed on the TS map). + containment : float, optional + The containment level of the source (the default is `None`, which will plot raw TS values). + save_plot : bool, optional + Set `True` to save the plot (the default is `False`, which means it won't save the plot. + save_dir : str or pathlib.Path, optional + The directory to save the plot. + save_name : str, optional + The file name of the plot to be save. + dpi : int, optional + The dpi for plotting and saving. + """ + + + if moc_map is None: + moc_map = self.moc_map_ts + + # decide the critical value + if containment is not None: + critical = MOCTSMap.get_chi_critical_value(containment = 0.9) + max_ts = np.max(moc_map[:]) + + # get plotting canvas + fig = plt.figure(dpi = dpi) + + axMoll = fig.add_subplot(1,1,1, projection = 'mollview') + + # Plot in one of the axes + if containment is None: + plotMoll, projMoll = moc_map.plot(ax = axMoll) + else: + plotMoll, projMoll = moc_map.plot(ax = axMoll, vmin = max_ts-critical, vmax = max_ts) + + moc_map.plot_grid(ax = plt.gca(), color = 'grey', linewidth = 0.1); + + + # plot the sky cooordinates if given + if skycoord is not None: + + axMoll.text(skycoord.l.deg, skycoord.b.deg, "x", size = 4, + horizontalalignment='center', + verticalalignment='center', + transform = axMoll.get_transform('world'), color = "red") + + if save_plot == True: + + fig.savefig(Path(save_dir)/save_name, dpi = dpi) + diff --git a/docs/tutorials/ts_map/Parallel_TS_map_computation.ipynb b/docs/tutorials/ts_map/TS_map_fitting.ipynb similarity index 75% rename from docs/tutorials/ts_map/Parallel_TS_map_computation.ipynb rename to docs/tutorials/ts_map/TS_map_fitting.ipynb index 8608ccc3..d325f4c9 100644 --- a/docs/tutorials/ts_map/Parallel_TS_map_computation.ipynb +++ b/docs/tutorials/ts_map/TS_map_fitting.ipynb @@ -190,12 +190,251 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - 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WARNING Could not import plugin HAWCLike.py. Do you have the relative instrument __init__.py:144\n", + " software installed and configured? \n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=664642;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=176485;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
WARNING Could not import plugin FermiLATLike.py. Do you have the relative instrument __init__.py:144\n", + " software installed and configured? \n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=494683;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=199222;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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WARNING Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal __init__.py:387\n", + " performances in 3ML \n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=929492;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=611332;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
WARNING Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal __init__.py:387\n", + " performances in 3ML \n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=939720;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=398208;file:///Users/shengyong/conda_envs/moc_ts_map/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
WARNING Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal __init__.py:387\n", + " performances in 3ML \n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. 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-208,7 +447,10 @@ "import gc\n", "from cosipy.util import fetch_wasabi_file\n", "import shutil\n", - "import os" + "import os\n", + "\n", + "import logging\n", + "logging.basicConfig(level = logging.INFO)" ] }, { @@ -523,7 +765,16 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 13.890416451295216 minutes\n" + ] + } + ], "source": [ "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [2,3], spectrum = spectrum, ts_scheme = \"RING\", cpu_cores = 56)" ] @@ -575,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -616,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -634,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -663,9 +914,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 13.131452250480653 minutes\n" + ] + }, { "data": { "image/png": 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", @@ -748,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -758,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -814,6 +1073,7 @@ { "cell_type": "markdown", "metadata": {}, + "outputs": [], "source": [ "### Read data and background" ] @@ -827,6 +1087,7 @@ }, { "cell_type": "markdown", + "id": "01980340-c1e7-4ef1-a169-47c7d136852b", "metadata": {}, "source": [ "#### Download the binned data" @@ -843,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -856,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -869,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -891,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -900,7 +1161,7 @@ "Text(0, 0.5, 'Counts')" ] }, - "execution_count": 35, + "execution_count": null, "metadata": {}, "output_type": "execute_result" }, @@ -930,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -949,7 +1210,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -968,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -982,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -992,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1003,9 +1264,18 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 9.980225030581156 minutes\n" + ] + } + ], "source": [ "# Perform the parallel fit\n", "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [1,2], spectrum = spectrum, ts_scheme = \"RING\", \n", @@ -1021,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1031,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1052,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1071,6 +1341,277 @@ "ts.plot_ts(skycoord = coord, containment = 0.9)" ] }, + { + "cell_type": "markdown", + "id": "4557cb37-c7c2-4b43-ad0a-27f3e7cd1780", + "metadata": {}, + "source": [ + "# Example 4 Fit a multi-resolustion map" + ] + }, + { + "cell_type": "markdown", + "id": "53850297-a66a-40c6-9017-dbfe5eb84a0f", + "metadata": {}, + "source": [ + "The TS map we fit above loops over all the pixels of the entire sky, which has already taken a long time. If you want to increase the resolution/order of the TS map, the number of pixels will grow exponentially:\n", + "\n", + "$$\n", + "npix=12\\times4^{order}\n", + "$$\n", + "\n", + "\n", + "For a map of order 3, you will fit the entire sky with 768 pixels. For a map of order 4, you will end up with 3072 pixels to fit! To speed up the fitting, we can fit a multi-resolution map (also called multi-order coverage map, MOC map) instead of the single-resolution map we did before.\n", + "\n", + "The multi-resolution map fitting will reduce the number of pixels to fit by fitting the background region with low resolution while keeping the source region with the details we want. We will use Crab as an example to show you how to fit a multi-resolution map to save your time and computational resources." + ] + }, + { + "cell_type": "markdown", + "id": "0179707d-d5cf-48c2-a214-6540a24f89c8", + "metadata": {}, + "source": [ + "## Data processing" + ] + }, + { + "cell_type": "markdown", + "id": "c9adc073-d40b-459f-8509-69bed195de24", + "metadata": {}, + "source": [ + "The data processing part is the same as the Example 3, so I will put the scripts together to save space.\n", + "\n", + "I assume that the data are download already. If not, please go to Example 3 and run the data downloading cells." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "8e52d977-6729-469b-b02d-6f6418b17e4f", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = Path(\"\") # Current directory by default. Modify if you want a different path\n", + "\n", + "crab_data_path = data_dir/\"Crab_galactic_CDS_binned.hdf5\" # the crab file path\n", + "\n", + "albedo_background_path = data_dir/\"Albedo_galactic_CDS_binned.hdf5\" # the background file path\n", + "\n", + "response_path = data_dir/\"psr_gal_DC2.h5\" # the response path\n", + "\n", + "# Read background model\n", + "bkg_model = Histogram.open(albedo_background_path) # please make sure you adjust the path to the files by yourself.\n", + "bkg_model = bkg_model.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Read the signal and bkg to assemble data = bkg + signal\n", + "signal = Histogram.open(crab_data_path)\n", + "signal = signal.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Here the background is the same as the background model since they are simulations, thus we know the background very well.\n", + "bkg = Histogram.open(albedo_background_path)\n", + "bkg = bkg.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Assemble the signal and background\n", + "data = bkg + signal\n", + "\n", + "# clear redundant data from RAM\n", + "del signal\n", + "del bkg\n", + "_ = gc.collect()\n", + "\n", + "\n", + "# define a powerlaw spectrum\n", + "index = -3\n", + "K = 10**-3 / u.cm / u.cm / u.s / u.keV\n", + "piv = 100 * u.keV\n", + "spectrum = Powerlaw()\n", + "spectrum.index.value = index\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value \n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "f41597cf-55d2-4aa6-879b-e10a3b59bc37", + "metadata": {}, + "outputs": [], + "source": [ + "# Here we will us MOCTSMap instead of FastTSMap, the parameters are same\n", + "moc_fit = MOCTSMap(data = data, \n", + " bkg_model = bkg_model, \n", + " response_path = response_path, \n", + " orientation = None, # we don't need orientation since we are using the precomputed galactic reaponse\n", + " cds_frame = \"galactic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "3ea2930d-bdcd-4662-86be-92cb7d53088b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitting order = 0\n", + "fitting 12 hypothesis coordinates\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 0.04796493848164876 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "The top 8 ts values are: [11 1 6 7 2 9 10 5] in the last iteration, splitting these pixels...\n", + "fitting order 1 with 32 hypothesis coordinates\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 0.12099777857462565 minutes\n", + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "The top 8 ts values are: [ 5 1 30 31 18 17 15 16] in the last iteration, splitting these pixels...\n", + "fitting order 2 with 32 hypothesis coordinates\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 0.12631478706995647 minutes\n", + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "The top 8 ts values are: [28 22 23 33 27 25 30 24] in the last iteration, splitting these pixels...\n", + "fitting order 3 with 32 hypothesis coordinates\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 0.1297250509262085 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "The top 8 ts values are: [49 32 25 30 39 33 31 41] in the last iteration, splitting these pixels...\n", + "fitting order 4 with 32 hypothesis coordinates\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:cosipy.ts_map.fast_ts_fit:You have total 8 CPU cores, using 7 CPU cores for parallel computation.\n", + "INFO:cosipy.ts_map.fast_ts_fit:The time used for the parallel TS map computation is 0.10716596444447836 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "CPU times: user 1.06 s, sys: 273 ms, total: 1.33 s\n", + "Wall time: 33 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# here we need to give the order of map to stop fitting and the top 8 likelihood to find the pixels to upscale the resolution\n", + "moc_map = moc_fit.moc_ts_fit(max_moc_order = 4, # this is the maximum order of the final map\n", + " top_number = 8, # In each iterations, only the pixels with top 8 likelihood values will be split in the next iteration\n", + " energy_channel = [2,3], # The energy channel used to perform the fit.\n", + " spectrum = spectrum)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "b5d18ee0-3800-4436-9d30-8ac0403f1a81", + "metadata": {}, + "outputs": [], + "source": [ + "# This the true location of Crab\n", + "coord = SkyCoord(l=184.5551, b = -05.7877, unit = (u.deg, u.deg), frame = \"galactic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "9f7f8b01-a1a0-40ae-8fd1-900b8b05a276", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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