{ "cells": [ { "cell_type": "markdown", "id": "bbda6046", "metadata": {}, "source": [ "# Understanding the internal structure of `meer21cm`\n", "\n", "The best way to understand the inner workings of a package is always to read the source code. However, in some cases, it is easier to start with some logging to see what part of the source code gets triggered by your calculation. This is of course also very useful for debug purposes. We provide a few examples below." ] }, { "cell_type": "code", "execution_count": 1, "id": "6118760c", "metadata": {}, "outputs": [], "source": [ "from meer21cm import PowerSpectrum, MockSimulation\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from meer21cm.plot import plot_map\n", "\n", "# turn on info level logging\n", "import logging\n", "logging.basicConfig(level=logging.INFO)" ] }, { "cell_type": "code", "execution_count": 2, "id": "545137d4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.dataanalysis:found meerklass_2021_L in predefined settings, using default settings and override the following parameters: nu, nu_min, nu_max, num_pix_x, num_pix_y, wproj\n", "INFO:meer21cm.power:get_enclosing_box: setting self.box_len, self.box_origin, self.box_ndim\n", "INFO:meer21cm.power:get_enclosing_box: setting the model self.kmode and self.mumode to correspond to the field k-modes\n" ] } ], "source": [ "ps = PowerSpectrum(\n", " band='L',\n", " survey='meerklass_2021',\n", ")\n", "ps.get_enclosing_box()" ] }, { "cell_type": "markdown", "id": "d90a89c6", "metadata": {}, "source": [ "You can see that, setting `band='L'` and `survey='meerklass_2021'` trigger the initialisation to automatically set predefined frequency and map dimensions parameters, which determines the survey lightcone.\n", "Then, invoking `get_enclosing_box` triggers the calculation to find the dimensions of the rectangular box that encloses the defined survey lightcone.\n", "\n", "Let's see another example of invoking the model power spectrum for the first tracer (usually assumed to be HI):" ] }, { "cell_type": "code", "execution_count": 3, "id": "21504f62", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.cosmology:get_matter_power_spectrum_camb: setting self._matter_power_spectrum_fnc\n", "INFO:meer21cm.power:get_model_power_noobs_i: setting self._auto_power_tracer_1_model_noobs\n", "INFO:meer21cm.power:get_model_power_i: setting self._auto_power_tracer_1_model\n", "INFO:meer21cm.power:multiplying _auto_power_tracer_1_model with mean_amp_1**2: 1.0**2 to get auto_power_tracer_1_model\n" ] } ], "source": [ "ps.auto_power_tracer_1_model;" ] }, { "cell_type": "markdown", "id": "eb15da9a", "metadata": {}, "source": [ "You can see that, in order to get `ps.auto_power_tracer_1_model`, it internally invokes `get_matter_power_spectrum_camb` to calculate the linear matter power spectrum first. Then `get_model_power_i` is used to set `ps._auto_power_tracer_1_model`, which is then multiplied by `ps.mean_amp_1**2` to get the `ps.auto_power_tracer_1_model`." ] }, { "cell_type": "code", "execution_count": 4, "id": "b3e4cc2b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.power:tracer_bias_2 is None, returning None\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps.auto_power_tracer_2_model;\n", "ps.tracer_bias_2 is None" ] }, { "cell_type": "markdown", "id": "82abb15f", "metadata": {}, "source": [ "You can see that, since `ps.tracer_bias_2` is `None` by default, invoking `ps.auto_power_tracer_2_model` returns `None`. You can set a bias and recalculate:" ] }, { "cell_type": "code", "execution_count": 5, "id": "1b776e6d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.power:get_model_power_noobs_i: setting self._auto_power_tracer_2_model_noobs\n", "INFO:meer21cm.power:get_model_power_i: setting self._auto_power_tracer_2_model\n", "INFO:meer21cm.power:multiplying _auto_power_tracer_2_model with mean_amp_2**2: 1.0**2 to get auto_power_tracer_2_model\n" ] } ], "source": [ "ps.tracer_bias_2 = 1.5\n", "ps.auto_power_tracer_2_model;" ] }, { "cell_type": "markdown", "id": "9ac61d13", "metadata": {}, "source": [ "On the other hand, if you want to add temperature unit to the power spectra, you can do:" ] }, { "cell_type": "code", "execution_count": 6, "id": "9e510acf", "metadata": {}, "outputs": [], "source": [ "ps.omega_hi = 5e-4\n", "ps.mean_amp_1 = \"average_hi_temp\"" ] }, { "cell_type": "code", "execution_count": 7, "id": "4bd9ace9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.power:getting mean_amp_1 from self.average_hi_temp\n", "INFO:meer21cm.power:multiplying _auto_power_tracer_1_model with mean_amp_1**2: 0.00010282763474838113**2 to get auto_power_tracer_1_model\n" ] } ], "source": [ "ps.auto_power_tracer_1_model;" ] }, { "cell_type": "markdown", "id": "286655aa", "metadata": {}, "source": [ "You can see that the code internally invokes the calculation of the mean brightness temperature and multiplies it to the power spectrum.\n", "\n", "Note that, in this case, since the power spectrum calculation has been invoked before and stored within the `_auto_power_tracer_1_model` attribute, the calculation is not repeated, as you can see from the log information.\n", "\n", "Now let us look at a case of generating mock observation:" ] }, { "cell_type": "code", "execution_count": 8, "id": "7ade49d6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.dataanalysis:found meerklass_2021_L in predefined settings, using default settings and override the following parameters: nu, nu_min, nu_max, num_pix_x, num_pix_y, wproj\n" ] } ], "source": [ "mock = MockSimulation(\n", " survey='meerklass_2021',\n", " band='L',\n", " num_discrete_source=1000000,\n", " mean_amp_1='average_hi_temp',\n", " tracer_bias_1=1.5,\n", " tracer_bias_2=1.5,\n", " omega_hi=5e-4,\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "id": "f42fcda6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.power:get_enclosing_box: setting self.box_len, self.box_origin, self.box_ndim\n", "INFO:meer21cm.power:get_enclosing_box: setting the model self.kmode and self.mumode to correspond to the field k-modes\n", "INFO:meer21cm.cosmology:get_matter_power_spectrum_camb: setting self._matter_power_spectrum_fnc\n", "INFO:meer21cm.power:get_model_power_noobs_i: setting self._auto_power_tracer_1_model_noobs\n", "INFO:meer21cm.mock:invoking get_mock_field_from_power assuming lognormal distribution\n", "INFO:meer21cm.mock:get_mock_tracer_field: setting _mock_tracer_field_1\n" ] } ], "source": [ "mock.data = mock.propagate_mock_field_to_data(mock.mock_tracer_field_1);" ] }, { "cell_type": "code", "execution_count": 10, "id": "4c4a2172", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_map(mock.data, mock.wproj, W=mock.W_HI)" ] }, { "cell_type": "code", "execution_count": 11, "id": "1397f5df", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:meer21cm.power:get_model_power_noobs_i: setting self._auto_power_tracer_2_model_noobs\n", "INFO:meer21cm.mock:invoking get_mock_field_from_power assuming lognormal distribution\n", "INFO:meer21cm.mock:get_mock_tracer_field: setting _mock_tracer_field_2\n", "INFO:meer21cm.mock:get_mock_tracer_position_in_box: setting _mock_tracer_position_in_box\n", "INFO:meer21cm.mock:invoking get_mock_tracer_position_in_radecz with flat_sky=False: setting _mock_tracer_position_in_radecz\n", "INFO:meer21cm.mock:propagate_mock_tracer_to_gal_cat: setting ra_gal, dec_gal, z_gal with trim=True\n" ] } ], "source": [ "mock.propagate_mock_tracer_to_gal_cat()" ] }, { "cell_type": "markdown", "id": "db95ba14", "metadata": {}, "source": [ "You can see that, by generating the discrete mock tracers and propagate them into souce catalogues, first the input power spectrum `_auto_power_tracer_2_model_noobs` is calculated. The mock tracer field is then generated and stored in `_mock_tracer_field_2`, which is then used to generate `_mock_tracer_position_in_box`. The source positions in the rectangular box are then used to generate their positions in (RA,Dec,z) and stored in `_mock_tracer_position_in_radecz`. Finally, they are passed to the `ra_gal`, `dec_gal` and `z_gal`, after trimming out sources outside the survey lightcone.\n", "\n", "If you want more detailed logging information, you can turn up the level of logging output to DEBUG by `logging.basicConfig(level=logging.DEBUG)`. Note that however the output will be excessive." ] } ], "metadata": { "kernelspec": { "display_name": "meer21cm", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }