Migrate_to_Speasy.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "ac074036-9b0f-4c95-817d-e407f2bebc7b",
"metadata": {},
"source": [
"### We want to change the getdata api calls from amda url, to speasy library.\n",
"\n",
"#### First we take a look at how it was done, and what "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "011e9aa5-2047-431d-810f-28c37652fc77",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from speasy import amda\n",
"import pandas as pd\n",
"\n",
"\n",
"\n",
"#\n",
"# Heliopropa imports\n",
"#\n",
"\n",
"# try to tweak the logging system first\n",
"#\n",
"os.environ['DEBUG'] = '1'\n",
"\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
"from web.run import get_target_config, get_data_for_target, FILE_DATE_FMT, generate_csv_contents"
]
},
{
"cell_type": "markdown",
"id": "f3df7573-9d30-4f08-a9d1-5040466387df",
"metadata": {},
"source": [
"##### Configure dates targets and inputs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b38c421f-b879-4b39-a504-0667a9930aff",
"metadata": {},
"outputs": [],
"source": [
"# \n",
"_target_slug = \"mars\"\n",
"_input_slug = \"om\"\n",
"_start_time = datetime.strptime('20230101', '%Y%m%d')\n",
"_stop_time = datetime.strptime('20230401', '%Y%m%d')"
]
},
{
"cell_type": "markdown",
"id": "56b4d003-fc08-4351-a68d-96abec9787da",
"metadata": {},
"source": [
"##### Get data with old method (amda http requests)\n",
"\n",
"Returns dictionnary of tupples indexed by dates.\n",
"Each tuple contains 10 values:\n",
"\n",
"\n",
"time,vrad,vtan,vtot,btan,temp,pdyn,dens,atse,xhee,yhee"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f580de31-734f-47c2-a09e-c44cbe465a0d",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Exception' object has no attribute 'message'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/01DEV/heliopropa/web/run.py:565\u001b[0m, in \u001b[0;36mretrieve_amda_netcdf\u001b[0;34m(orbiter, what, started_at, stopped_at)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remote_gzip_files \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNODATASET\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 565\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAPI says there\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms no dataset at \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m url)\n\u001b[1;32m 566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remote_gzip_files \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mERROR\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"\u001b[0;31mException\u001b[0m: API says there's no dataset at 'http://amda.irap.omp.eu/DDService/getDataUrl.php?dataSet=tao-mars-sw&StartTime=2023-01-01T00:00:00&StopTime=2023-04-01T00:00:00'.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m target_config \u001b[38;5;241m=\u001b[39m get_target_config( _target_slug)\n\u001b[0;32m----> 2\u001b[0m all_data \u001b[38;5;241m=\u001b[39m \u001b[43mget_data_for_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_config\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_input_slug\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_start_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stop_time\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/01DEV/heliopropa/web/run.py:733\u001b[0m, in \u001b[0;36mget_data_for_target\u001b[0;34m(target_config, input_slug, started_at, stopped_at)\u001b[0m\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m models:\n\u001b[1;32m 732\u001b[0m s0, s1 \u001b[38;5;241m=\u001b[39m _sta_sto(model, started_at, stopped_at)\n\u001b[0;32m--> 733\u001b[0m model_files \u001b[38;5;241m=\u001b[39m \u001b[43mretrieve_amda_netcdf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_config\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mslug\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mslug\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms1\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 736\u001b[0m nc_keys \u001b[38;5;241m=\u001b[39m default_nc_keys\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparameters\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m model:\n",
"File \u001b[0;32m~/01DEV/heliopropa/web/run.py:573\u001b[0m, in \u001b[0;36mretrieve_amda_netcdf\u001b[0;34m(orbiter, what, started_at, stopped_at)\u001b[0m\n\u001b[1;32m 571\u001b[0m success \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 573\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m/3) \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m : \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (retries \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, url, \u001b[43me\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmessage\u001b[49m))\n\u001b[1;32m 574\u001b[0m remote_gzip_files \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 575\u001b[0m errors\u001b[38;5;241m.\u001b[39mappend(e)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Exception' object has no attribute 'message'"
]
}
],
"source": [
"target_config = get_target_config( _target_slug)\n",
"all_data = get_data_for_target(target_config, _input_slug, _start_time, _stop_time )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "885d8f5e-6632-4f7c-9242-548ba475662c",
"metadata": {},
"outputs": [],
"source": [
"# display first elements of the all_data retrieved this way\n",
"small_data_keys = list(all_data.keys())[0:5]\n",
"for k in small_data_keys:\n",
" # dont display first tuple element that is date\n",
" print(k, all_data[k][1:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef11af95-c667-4e43-b1ea-cbb31caf3471",
"metadata": {},
"outputs": [],
"source": [
"# same with the calling method\n",
"all_csv = generate_csv_contents(_target_slug, _input_slug, _start_time, _stop_time)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad8c4f2a-d0d4-4005-9cd0-a75e3a98b469",
"metadata": {},
"outputs": [],
"source": [
"print(all_csv[0:600])"
]
},
{
"cell_type": "markdown",
"id": "9748ab9f-3dc8-4b02-bf4f-a750a35864b5",
"metadata": {},
"source": [
"#### Now rewrite that with speazy\n",
"\n",
"ie, generate a csv file with that new library.p\n",
"That means getting parameters, rename the column as expected by the javascript grappher,\n",
"and concatenate to get a single dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05e8cec5-be4e-4a8a-b73c-469cc946ad05",
"metadata": {},
"outputs": [],
"source": [
"# First, get the dataset ids \n",
"target_config = get_target_config(_target_slug)\n",
"orbit_dataset_id = target_config['orbit']['models'][0]['slug']\n",
"plasma_dataset_id = target_config['models'][_input_slug][0]['slug']\n",
"orbit_dataset_id = orbit_dataset_id.replace('_', '-')\n",
"plasma_dataset_id = plasma_dataset_id.replace('_', '-')\n",
"\n",
"print(f\"Orbit dataset : {orbit_dataset_id}\")\n",
"print(f\"Plsama dataset : {plasma_dataset_id}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af1b616b-d6e0-4ad5-baeb-cce3b9b96fd3",
"metadata": {},
"outputs": [],
"source": [
"# Then the dataset associated\n",
"orbit_dataset = amda.get_dataset(orbit_dataset_id, _start_time, _stop_time)\n",
"plasma_dataset = amda.get_dataset(plasma_dataset_id, _start_time, _stop_time)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e6ea5f1a-aeee-42c6-b031-a69466064333",
"metadata": {},
"outputs": [],
"source": [
"# vrad, vtan, vtot, btan, brad, pdyn, dens, atse, xhee, yhee\n",
"\n",
"# set a dict of amda parameters ids index by the expected column names in the csv\n",
"\n",
"params_dict = {'dens': 'mars_sw_n',\n",
" 'xy_v': 'mars_sw_v',\n",
" 'temp': 'mars_sw_t',\n",
" 'pdyn': 'mars_sw_pdyn',\n",
" 'btan': 'mars_sw_b',\n",
" 'brad': 'mars_sw_bx',\n",
" 'atse': 'mars_sw_da',\n",
" 'xy_hee': 'xyz_mars_hee'}\n",
"\n",
"list_df = []\n",
"for _name, _id in params_dict.items():\n",
" _df = amda.get_data(_id, _start_time, _stop_time).to_dataframe()\n",
" if _name == 'xy_v':\n",
" _df = _df.rename(columns={_df.columns[0]:'vrad', _df.columns[1]:'vtan'})\n",
" elif _name == 'xy_hee':\n",
" _df = _df.drop(_df.columns[2], axis=1)\n",
" _df = _df.rename(columns={_df.columns[0]:'xhee', _df.columns[1]:'yhee'})\n",
" else:\n",
" _df = _df.rename(columns={_df.columns[0]: _name})\n",
"\n",
" # _df = _df[~_df.index.duplicated()]\n",
"\n",
" # resample to frequency, for later concatenation\n",
" _df = _df.resample('1H').mean()\n",
"\n",
" # _df = _df.loc[_df.first_valid_index():_df.last_valid_index()]\n",
"\n",
" list_df.append( _df)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8696bcec-5d54-411a-ad9f-8b161f00556b",
"metadata": {},
"outputs": [],
"source": [
"from math import sqrt\n",
"final_df = pd.concat(list_df, axis=1)\n",
"# Is ther a vtot param ? else build it\n",
"if 'vtot' not in final_df.columns:\n",
" final_df['vtot'] = final_df.apply(lambda x: sqrt(x.vtan*x.vtan+ x.vrad*x.vrad), axis=1)\n",
"\n",
"cols_ordered = ['vrad', 'vtan', 'vtot', 'btan', 'brad', 'temp', 'pdyn', 'dens', 'atse', 'xhee', 'yhee']\n",
"final_df = final_df[cols_ordered]\n",
"\n",
"# and show\n",
"pd.set_option('display.max_columns', None)\n",
"print(final_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "eaec4f41-2524-4562-935a-42b4ce3fdae0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time\n"
]
}
],
"source": [
"final_df.index.name = 'time'\n",
"print(final_df.index.name)"
]
},
{
"cell_type": "markdown",
"id": "bde656ba-98b9-4b03-93a3-c0121be49388",
"metadata": {},
"source": [
"#### Use config to get the parameters dict and send to new method\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f92a9195-c863-48e4-ae62-2b3adcd66143",
"metadata": {},
"outputs": [],
"source": [
"def generate_csv_content_spz(target_slug, input_slug, started_at, stopped_at):\n",
" target_config = get_target_config(target_slug)\n",
" params_dict = target_config['models'][input_slug][0]['parameters']\n",
" \n",
" list_df = []\n",
" for _name, _id in params_dict.items():\n",
" _df = amda.get_data(_id, _start_time, _stop_time).to_dataframe()\n",
" if _name == 'xy_v':\n",
" _df = _df.rename(columns={_df.columns[0]:'vrad', _df.columns[1]:'vtan'})\n",
" elif _name == 'xy_hee':\n",
" _df = _df.drop(_df.columns[2], axis=1)\n",
" _df = _df.rename(columns={_df.columns[0]:'xhee', _df.columns[1]:'yhee'})\n",
" else:\n",
" _df = _df.rename(columns={_df.columns[0]: _name})\n",
" \n",
" # _df = _df[~_df.index.duplicated()]\n",
" \n",
" # resample to frequency, for later concatenation\n",
" _df = _df.resample('1H').mean()\n",
" \n",
" # _df = _df.loc[_df.first_valid_index():_df.last_valid_index()]\n",
" \n",
" list_df.append( _df)\n",
" \n",
" from math import sqrt\n",
" final_df = pd.concat(list_df, axis=1)\n",
" # Is ther a vtot param ? else build it\n",
" if 'vtot' not in final_df.columns:\n",
" final_df['vtot'] = final_df.apply(lambda x: sqrt(x.vtan*x.vtan+ x.vrad*x.vrad), axis=1)\n",
" \n",
" \n",
" cols_ordered = ['vrad', 'vtan', 'vtot', 'btan', 'brad', 'temp', 'pdyn', 'dens', 'atse', 'xhee', 'yhee']\n",
" final_df = final_df[cols_ordered]\n",
" final_df.index.name = 'time'\n",
" return final_df.to_csv(date_format='%Y-%m-%dT%H:%M:%S', float_format=\"%.5f\",\n",
" header=True,\n",
" sep=\",\")\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "e3cb8de7-e959-4a23-8388-5003d1fd5eeb",
"metadata": {},
"outputs": [
{
"data": {
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1T22:00:00,519.32000,5.89804,519.35349,2.43965,-1.80683,5.74984,0.29722,0.65983,63.25870,0.80276,-1.43865\\n2023-03-31T23:00:00,517.32800,5.84618,517.36103,2.57314,-1.89836,5.47295,0.34682,0.77590,63.28480,0.80220,-1.43899\\n'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_csv_content_spz('mars', 'om', '20230101', '20230115')"
]
}
],
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