{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyHermes in Jupyter Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**README:**\n",
"\n",
"Choosing to use Jupyter Notebook, as you probably know, has its advantages: \n",
"\n",
" - Intuitive and Convenient. \n",
"\n",
"However, the downside is that it doesn't support parallel computing, which means you can't fully leverage the power of pyhermes. \n",
"\n",
"Therefore, Jupyter Notebook is better suited for small-scale data testing, debugging, and similar purposes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculate convols** \n",
"\n",
" - calculate the `DeltaC` with parameters in file `params_convols.json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" **NOTICE:** \n",
"\n",
" - Calculating `DeltaC` is the initial step for computing subsequent quantities, such as counting, 2PCF, and 3PCF.\n",
"\n",
"The file of `param_convols.json` looks like:\n",
"```json \n",
"{\n",
" \"Convols\": {\n",
" \"J\": 9,\n",
" \"fin\": {\n",
" \"path\": \"https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin\",\n",
" \"format\": \"generic\"\n",
" },\n",
" \"fout_path\": \"./Output_ConvolsData_example.npy\",\n",
" \"SampRate\": 1024,\n",
" \"SimBoxL\": 1000,\n",
" \"window\": {\n",
" \"type\": \"shell\",\n",
" \"R\": 5\n",
" },\n",
" \"wavelet_mode\": \"db2\",\n",
" \"wavelet_level\": 10,\n",
" \"bandwidth\": 1,\n",
" \"threads\": 2\n",
" }\n",
"}\n",
"\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from pyhermes.base.convols import Convols\n",
"from pyhermes.param.parambase import read_param"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_convols.json'\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Convols.fin.path' from './data.bin' to 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin'\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Convols.fout_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:01:52 - \u001b[33mWARNING\u001b[0m - pyhermes.param.parambase:JsonBase - Adding non-default key: 'Convols.window.R'\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Convols.threads' from '1' to '2'\n",
"\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - The task will run on 1 MPI ranks\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:read_particle_data - Selected input particle format: generic\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:dl_rich_pbar - File 'quijote10000.bin' already exists. Skipping download.\n",
"09:01:52 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:read_generic - Reading paricle data from ---> quijote10000.bin <---\n",
"09:01:54 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - Start partition ... \n",
"09:01:54 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - The time for partition data: 0.0161 sec\n",
"09:01:54 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - Start to calculate scaling coefficient... \n",
"09:01:57 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - The time for scaling function: 2.7760 sec\n",
"09:01:57 - \u001b[32mINFO\u001b[0m - pyhermes.utils.math_util:set_window_function - Using window function: shell\n",
"09:01:59 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - Start to calculte FFT\n",
"09:02:02 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - The time for FFT: 2.9848 sec\n",
"09:02:02 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:ConvolsData - Writing data to ---> ./Output_ConvolsData_example.npy <---\n",
"\n",
"09:02:04 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - The time for task: 12.4039 sec\n",
"09:02:04 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Convols - Bye.\n"
]
}
],
"source": [
"param_input = read_param(config_path='param_convols.json')\n",
"\n",
"deltac, pm = Convols(param_task=param_input).run(return_pData=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"406702.9084193025"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deltac.data.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, `deltac` contains all the information from the Convols results, which belong to an instance of `ConvolsData`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'fin_path': 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin', 'fin_format': 'generic', 'orgDsize': 406793, 'J': 9, 'SampRate': 1024, 'window': {'type': 'shell', 'R': 5}, 'SimBoxL': 1000, 'bandwidth': 1, 'wavelet_mode': 'db2', 'wavelet_level': 10}\n"
]
}
],
"source": [
"print(deltac.dict_inht_vonDeltac)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(512, 512, 512)\n",
"\n"
]
}
],
"source": [
"print(deltac.data.shape)\n",
"print(type(deltac))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `pm` variable holds the particle data, which can be directly used for further calculations, such as computing the `3PCF`. This eliminates the need to reload the particle data from disk. You can enable this by setting the `return_pData` argument to `True`; otherwise, only `deltac` will be returned without `pm`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(406793, 3)\n",
"\n"
]
}
],
"source": [
"print(pm.shape)\n",
"print(type(pm))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Counting** \n",
"\n",
" - Method1: Read DeltaC from path in `param_counting.json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file of `param_counting.json` looks like:\n",
"```json \n",
"{\n",
" \"Counting\": {\n",
" \"n_tasks\": 100,\n",
" \"deltac_in_path\": \"./Output_ConvolsData_example.npy\",\n",
" \"fout_path\": \"./Output_CountingData_example.npy\"\n",
" }\n",
"}\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_counting.json'\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.n_tasks' from '1000000' to '100'\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.fout_path' from 'empty' to './Output_CountingData_example.npy'\n",
"\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The task will run on 1 MPI ranks\n",
"09:12:09 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:CountingData - Reading DeltaC data from ---> ./Output_ConvolsData_example.npy <---\n",
"09:12:10 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:CountingData - DeltaC: Shape(512, 512, 512), Min = -0.0303, Max = 0.1416, Mean = 0.0030\n",
"09:12:10 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Start Counting ... \n",
"09:12:11 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Gathering data from all ranks ... \n",
"09:12:11 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The time for counting is: 0.7274 sec\n",
"09:12:11 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:CountingData - Writing data to ---> ./Output_CountingData_example.npy <---\n",
"\n",
"09:12:11 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The time for task: 1.8091 sec\n"
]
}
],
"source": [
"from pyhermes.theory.counting import Counting\n",
"\n",
"param_input = read_param(config_path='param_counting.json')\n",
"\n",
"counting = Counting(param_task=param_input).run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"\n",
"**Counting** \n",
"\n",
" - Method2: Load DeltaC directly from the result of previous step, i.e., `deltac`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTICE:**\n",
"\n",
" - Using the `deltac=` argument in argv will override the `deltac_in_path` setting in `param_couting.json`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_counting.json'\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.n_tasks' from '1000000' to '100'\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Counting.fout_path' from 'empty' to './Output_CountingData_example.npy'\n",
"\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The task will run on 1 MPI ranks\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Loading DeltaC from argument 'deltac'\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Start Counting ... \n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Gathering data from all ranks ... \n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The time for counting is: 0.0014 sec\n",
"09:12:33 - \u001b[33mWARNING\u001b[0m - pyhermes.io.funcs:check_fout - Output file './Output_CountingData_example.npy' already exists! Generating a new file name.\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:CountingData - Writing data to ---> ./Output_CountingData_example_1.npy <---\n",
"\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - The time for task: 0.0081 sec\n",
"09:12:33 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Counting - Bye.\n"
]
}
],
"source": [
"from pyhermes.theory.counting import Counting\n",
"\n",
"param_input = read_param(config_path='param_counting.json')\n",
"\n",
"counting = Counting(param_task=param_input).run(deltac=deltac)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print(type(counting))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `counting` instance will also inherit parameters from `deltac`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'fin_path': 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin', 'fin_format': 'generic', 'orgDsize': 406793, 'J': 9, 'SampRate': 1024, 'window': {'type': 'shell', 'R': 5}, 'SimBoxL': 1000, 'bandwidth': 1, 'wavelet_mode': 'db2', 'wavelet_level': 10}\n"
]
}
],
"source": [
"print(counting.dict_inht_vonDeltac)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2PCF** \n",
"\n",
" - Method1: Read DeltaC from path in `param_2pcf.json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file of `param_2pcf.json` looks like:\n",
"```json \n",
"{\n",
" \"Corr_2PCF\": {\n",
" \"deltac_in_path\": \"./Output_ConvolsData_example.npy\",\n",
" \"fout_path\": \"./Output_Corr2PCFData_example.txt\",\n",
" \"threads\": 2,\n",
" \"R1\": 1.0,\n",
" \"R2\": 150.0,\n",
" \"xi_num\": 30\n",
" }\n",
"}\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_2pcf.json'\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.fout_path' from 'empty' to './Output_Corr2PCFData_example.txt'\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.threads' from '1' to '2'\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.xi_num' from '150' to '20'\n",
"\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The task will run on 1 MPI ranks\n",
"09:18:01 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - Reading DeltaC data from ---> ./Output_ConvolsData_example.npy <---\n",
"09:18:02 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - DeltaC: Shape(512, 512, 512), Min = -0.0303, Max = 0.1416, Mean = 0.0030\n",
"09:18:02 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Start to calculate 2PCF ...\n",
"09:18:06 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 0.00%\n",
"09:18:11 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 10.00%\n",
"09:18:19 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 20.00%\n",
"09:18:27 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 30.00%\n",
"09:18:35 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 40.00%\n",
"09:18:43 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 50.00%\n",
"09:18:51 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 60.00%\n",
"09:18:59 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 70.00%\n",
"09:19:06 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 80.00%\n",
"09:19:15 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 90.00%\n",
"09:19:22 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 100.00%\n",
"09:19:22 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The time for 2PCF: 80.2943 sec\n",
"09:19:22 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - Writing data to ---> ./Output_Corr2PCFData_example.txt <---\n",
"\n",
"09:19:22 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The time for task: 81.4758 sec\n",
"09:19:22 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Bye.\n"
]
}
],
"source": [
"from pyhermes.theory.corr2pcf import Corr_2PCF\n",
"\n",
"param_input = read_param(config_path='param_2pcf.json')\n",
"\n",
"# You can modify your parameters any time, in the style of dict:\n",
"param_input['Corr_2PCF']['xi_num'] = 20\n",
"corr_2pcf = Corr_2PCF(param_task=param_input).run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"\n",
"**2PCF** \n",
"\n",
" - Method2: Load DeltaC directly from the result of previous step, i.e., `deltac`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTICE:** \n",
"\n",
" - Using the `deltac=` argument in argv will override the `deltac_in_path` setting in `param_2pcf.json`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_2pcf.json'\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.fout_path' from 'empty' to './Output_Corr2PCFData_example.txt'\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.threads' from '1' to '2'\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_2PCF.xi_num' from '150' to '20'\n",
"\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The task will run on 1 MPI ranks\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Loading DeltaC from argument 'deltac'\n",
"09:19:31 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Start to calculate 2PCF ...\n",
"09:19:34 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 0.00%\n",
"09:19:46 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 20.00%\n",
"09:19:50 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 20.00%\n",
"09:19:54 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 30.00%\n",
"09:20:01 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 40.00%\n",
"09:20:09 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 50.00%\n",
"09:20:17 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 60.00%\n",
"09:20:24 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 70.00%\n",
"09:20:32 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 80.00%\n",
"09:20:40 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 90.00%\n",
"09:20:47 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Progress: 100.00%\n",
"09:20:47 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The time for 2PCF: 76.7944 sec\n",
"09:20:47 - \u001b[33mWARNING\u001b[0m - pyhermes.io.funcs:check_fout - Output file './Output_Corr2PCFData_example.txt' already exists! Generating a new file name.\n",
"09:20:47 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - Writing data to ---> ./Output_Corr2PCFData_example_1.txt <---\n",
"\n",
"09:20:47 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - The time for task: 76.8570 sec\n",
"09:20:47 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_2PCF - Bye.\n"
]
}
],
"source": [
"from pyhermes.theory.corr2pcf import Corr_2PCF\n",
"\n",
"param_input = read_param(config_path='param_2pcf.json')\n",
"\n",
"# You can modify your parameters any time, in the style of dict:\n",
"param_input['Corr_2PCF']['xi_num'] = 20\n",
"corr_2pcf = Corr_2PCF(param_task=param_input).run(deltac=deltac)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print(type(corr_2pcf))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `corr_2pcf` instance will also inherit parameters from `deltac`:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'fin_path': 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin', 'fin_format': 'generic', 'orgDsize': 406793, 'J': 9, 'SampRate': 1024, 'window': {'type': 'shell', 'R': 5}, 'SimBoxL': 1000, 'bandwidth': 1, 'wavelet_mode': 'db2', 'wavelet_level': 10}\n"
]
}
],
"source": [
"print(corr_2pcf.dict_inht_vonDeltac)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visulization tests:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"xi_num = param_input['Corr_2PCF']['xi_num']\n",
"\n",
"plt.figure(figsize=(7, 5))\n",
"plt.plot(corr_2pcf.r, corr_2pcf.xi * corr_2pcf.r**2, label=rf'$N_\\xi={xi_num}$', lw=7, alpha=0.3)\n",
"plt.xlabel(r'$r\\ [h^{-1}\\ \\mathrm{Mpc}]$', fontsize='18')\n",
"plt.ylabel(r'$r^2 \\xi(r)$', fontsize='18')\n",
"plt.legend(loc='best', fontsize='18')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips:** **Save the 2PCF data anytime**\n",
"\n",
"If you leave `fout_dir` empty in the `param_2pcf.json` file, by default, the 2PCF results will not be saved. However, you can use `.save` to save them at any time."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:21:51 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - Writing data to ---> ./what_ever_any_folder/any_filename <---\n"
]
}
],
"source": [
"corr_2pcf.save('./what_ever_any_folder/any_filename')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips:** **Load the 2PCF data anytime**\n",
"\n",
"You can load the 2PCF data from the results of a previous run's output anytime, then generate the corresponding plot."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from pyhermes.io import Corr2PCFData\n",
"\n",
"corr_2pcf_pre = Corr2PCFData()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:22:04 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr2PCFData - Reading data from ---> ./demo_data/corr2pcf_r5_xinum200.txt <---\n"
]
}
],
"source": [
"corr_2pcf_pre.load(\"./demo_data/corr2pcf_r5_xinum200.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"xi_num_pre = 200\n",
"\n",
"plt.figure(figsize=(7, 5))\n",
"plt.plot(corr_2pcf.r, corr_2pcf.xi * corr_2pcf.r**2, label=rf'$N_\\xi={xi_num}$', lw=7, alpha=0.3)\n",
"plt.plot(corr_2pcf_pre.r, corr_2pcf_pre.xi * corr_2pcf_pre.r**2, label=rf'$N_\\xi={xi_num_pre}$')\n",
"plt.xlabel(r'$r\\ [h^{-1}\\ \\mathrm{Mpc}]$', fontsize='18')\n",
"plt.ylabel(r'$r^2 \\xi(r)$', fontsize='18')\n",
"plt.legend(loc='best', fontsize='18')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3PCF** \n",
"\n",
" - Method1: Read DeltaC, particle and 2pcf info from path in `param_3pcf.json`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file of `param_3pcf.json` looks like:\n",
"```json \n",
"{\n",
" \"Corr_3PCF\": {\n",
" \"fin\": {\n",
" \"path\": \"https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin\",\n",
" \"format\": \"generic\"\n",
" },\n",
" \"deltac_in_path\": \"./Output_ConvolsData_example.npy\",\n",
" \"corr2pcf_in_path\": \"./Output_Corr2PCFData_example.txt\",\n",
" \"fout_path\": \"./Output_Corr3PCFData_example.txt\",\n",
" \"NStheta\": 20,\n",
" \"R1\": 20.0,\n",
" \"R2\": 40.0,\n",
" \"rot_num\": 3\n",
" }\n",
"}\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_3pcf.json'\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.fin.path' from './data.bin' to 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin'\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.corr2pcf_in_path' from 'empty' to './Output_Corr2PCFData_example.txt'\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.fout_path' from 'empty' to './Output_Corr3PCFData_example.txt'\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.rot_num' from '10000' to '3'\n",
"\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The task will run on 1 MPI ranks\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Reading 2PCF data from ---> ./Output_Corr2PCFData_example.txt <---\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Hi tristan, now you already have the corr2pcf info!\n",
"09:23:08 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Reading DeltaC data from ---> ./Output_ConvolsData_example.npy <---\n",
"09:23:10 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - DeltaC: Shape(512, 512, 512), Min = -0.0303, Max = 0.1416, Mean = 0.0030\n",
"09:23:10 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:read_particle_data - Selected input particle format: generic\n",
"09:23:10 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:dl_rich_pbar - File 'quijote10000.bin' already exists. Skipping download.\n",
"09:23:10 - \u001b[32mINFO\u001b[0m - pyhermes.io.funcs:read_generic - Reading paricle data from ---> quijote10000.bin <---\n",
"09:23:10 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Start to calculate 3PCF ...\n",
"09:23:13 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 0.00%\n",
"09:23:18 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 19.05%\n",
"09:23:19 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 19.05%\n",
"09:23:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 28.57%\n",
"09:23:23 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 38.10%\n",
"09:23:26 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 47.62%\n",
"09:23:28 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 57.14%\n",
"09:23:31 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 66.67%\n",
"09:23:34 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 76.19%\n",
"09:23:37 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 85.71%\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 95.24%\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 100.00%\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Finished in 28.8 sec\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The time for 3PCF: 28.8183 sec\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Writing data to ---> ./Output_Corr3PCFData_example.txt <---\n",
"\n",
"09:23:39 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The time for task: 30.9012 sec\n"
]
}
],
"source": [
"from pyhermes.theory.corr3pcf import Corr_3PCF\n",
"\n",
"param_input = read_param(config_path='param_3pcf.json')\n",
"\n",
"# You can modify your parameters any time, in the style of dict:\n",
"param_input['Corr_3PCF']['rot_num'] = 3\n",
"\n",
"corr_3pcf = Corr_3PCF(param_task=param_input).run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3PCF** \n",
"\n",
" - Method2: Load DeltaC, 2pcf and particle data directly from the result of previous step, i.e., `deltac`, `corr_2pcf` and `p_dm`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTICE:**\n",
"\n",
"- Using the `deltac=` and `p_dm=` arguments in argv will override the `deltac_in_path` and `fin-path` settings in `param_3pcf.json`.\n",
"- To load particle data with `p_dm=`, ensure you set `return_pData=True` when calculating `deltac`. If `return_pData=True` is not specified, only `deltac` will be returned. The default is `False`. \n",
"\n",
"For example:\n",
"\n",
"```python\n",
"# Default:\n",
"deltac = Convols(param_task=param_input).run()\n",
"# Enable return:\n",
"deltac, pm = Convols(param_task=param_input).run(return_pData=True)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(512, 512, 512)\n",
"\n",
"\n",
"(20,)\n",
"\n",
"\n",
"(406793, 3)\n",
"\n"
]
}
],
"source": [
"print(deltac.data.shape)\n",
"print(type(deltac))\n",
"print('')\n",
"\n",
"print(corr_2pcf.xi.shape)\n",
"print(type(corr_2pcf))\n",
"print('')\n",
"\n",
"print(pm.shape)\n",
"print(type(pm))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Reading configure file: '/data/luantch/ding_workspace/Hermes/github_zone/PyHermes/tests/param_3pcf.json'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Set default parameters of module ...\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.fin.path' from './data.bin' to 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.deltac_in_path' from 'empty' to './Output_ConvolsData_example.npy'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.corr2pcf_in_path' from 'empty' to './Output_Corr2PCFData_example.txt'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.fout_path' from 'empty' to './Output_Corr3PCFData_example.txt'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.param.parambase:JsonBase - Default 'Corr_3PCF.rot_num' from '10000' to '3'\n",
"\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The task will run on 1 MPI ranks\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Loading Corr2pcf from argument 'corr2pcf'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Hi tristan, now you already have the corr2pcf info!\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Loading DeltaC from argument 'deltac'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Loading Particle data from argument 'p_dm'\n",
"09:27:56 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Start to calculate 3PCF ...\n",
"09:27:58 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 0.00%\n",
"09:28:01 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 19.05%\n",
"09:28:03 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 19.05%\n",
"09:28:04 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 28.57%\n",
"09:28:06 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 38.10%\n",
"09:28:09 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 47.62%\n",
"09:28:11 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 57.14%\n",
"09:28:13 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 66.67%\n",
"09:28:16 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 76.19%\n",
"09:28:18 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 85.71%\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 95.24%\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Progress: 100.00%\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Finished in 23.9 sec\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The time for 3PCF: 23.9216 sec\n",
"09:28:20 - \u001b[33mWARNING\u001b[0m - pyhermes.io.funcs:check_fout - Output file './Output_Corr3PCFData_example.txt' already exists! Generating a new file name.\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Writing data to ---> ./Output_Corr3PCFData_example_1.txt <---\n",
"\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - The time for task: 23.9709 sec\n",
"09:28:20 - \u001b[32mINFO\u001b[0m - pyhermes.pipeline.pipeline:Corr_3PCF - Bye.\n"
]
}
],
"source": [
"from pyhermes.theory.corr3pcf import Corr_3PCF\n",
"\n",
"param_input = read_param(config_path='param_3pcf.json')\n",
"\n",
"# You can modify your parameters any time, in the style of dict:\n",
"param_input['Corr_3PCF']['rot_num'] = 3\n",
"\n",
"corr_3pcf = Corr_3PCF(param_task=param_input).run(deltac=deltac, corr2pcf=corr_2pcf, p_dm=pm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `corr_3pcf` instance will also inherit parameters from `deltac`:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'fin_path': 'https://pyhermes.astroslacker.com/_downloads/906e0695649e3634a5fe8081b9ab2086/quijote10000.bin', 'fin_format': 'generic', 'orgDsize': 406793, 'J': 9, 'SampRate': 1024, 'window': {'type': 'shell', 'R': 5}, 'SimBoxL': 1000, 'bandwidth': 1, 'wavelet_mode': 'db2', 'wavelet_level': 10}\n"
]
}
],
"source": [
"print(corr_3pcf.dict_inht_vonDeltac)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization tests (demo results; further refinement needed for handling Q more effectively):"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"N_rot = param_input['Corr_3PCF']['rot_num']\n",
"\n",
"plt.figure(figsize=(7,6))\n",
"plt.plot(corr_3pcf.theta, corr_3pcf.Q, label=rf'N_rot={N_rot}', lw=7, alpha=0.3)\n",
"plt.xlabel(r'$\\theta\\ [\\mathrm{rad}]$', fontsize='18')\n",
"plt.ylabel(r'$Q(\\theta)$', fontsize='18')\n",
"plt.xlim(0., np.pi/2.)\n",
"plt.legend(loc='best', fontsize='18')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips:** **Save the 3PCF data anytime**\n",
"\n",
"If you leave `fout_dir` empty in the `param_3pcf.json` file, by default, the 3PCF results will not be saved. However, you can use `.save` to save them at any time."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:30:20 - \u001b[33mWARNING\u001b[0m - pyhermes.io.funcs:check_fout - Output file './what_ever_any_folder/any_filename' already exists! Generating a new file name.\n",
"09:30:20 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Writing data to ---> ./what_ever_any_folder/any_filename_1 <---\n"
]
}
],
"source": [
"corr_3pcf.save('./what_ever_any_folder/any_filename')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips:** **Load the 3PCF data anytime**\n",
"\n",
"You can load the 3PCF data from the results of a previous run's output anytime, then generate the corresponding plot."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from pyhermes.io import Corr3PCFData\n",
"\n",
"corr_3pcf_pre = Corr3PCFData()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:30:35 - \u001b[32mINFO\u001b[0m - pyhermes.io.base:Corr3PCFData - Reading data from ---> ./demo_data/corr3pcf_r5_R1.20.0_R2.40.0_rotN10000.txt <---\n"
]
}
],
"source": [
"corr_3pcf_pre.load(\"./demo_data/corr3pcf_r5_R1.20.0_R2.40.0_rotN10000.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"N_rot_pre = 10000\n",
"\n",
"plt.figure(figsize=(7,6))\n",
"plt.plot(corr_3pcf.theta, corr_3pcf.Q, label=rf'N_rot={N_rot}', lw=7, alpha=0.3)\n",
"plt.plot(corr_3pcf_pre.theta, corr_3pcf_pre.Q, label=rf'N_rot={N_rot_pre}')\n",
"plt.xlabel(r'$\\theta\\ [\\mathrm{rad}]$', fontsize='18')\n",
"plt.ylabel(r'$Q(\\theta)$', fontsize='18')\n",
"plt.xlim(0., np.pi/2.)\n",
"plt.legend(loc='best', fontsize='18')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}