From a9ad8b6ce319b8ab1485cf86f06cba351e0cd0da Mon Sep 17 00:00:00 2001 From: ChrisC28 Date: Tue, 23 Apr 2024 16:23:56 +1000 Subject: [PATCH] Added new version of WOD_to_CODA --- notebooks/WOD_to_CODAv2.ipynb | 1375 +++++++++++++++++++++++++++++++++ 1 file changed, 1375 insertions(+) create mode 100644 notebooks/WOD_to_CODAv2.ipynb diff --git a/notebooks/WOD_to_CODAv2.ipynb b/notebooks/WOD_to_CODAv2.ipynb new file mode 100644 index 0000000..3416800 --- /dev/null +++ b/notebooks/WOD_to_CODAv2.ipynb @@ -0,0 +1,1375 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "35afc13e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tube1/cha674/Anaconda_Install/miniconda/envs/py3/lib/python3.7/_collections_abc.py:841: MatplotlibDeprecationWarning: \n", + "The examples.directory rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2. In the future, examples will be found relative to the 'datapath' directory.\n", + " self[key] = other[key]\n" + ] + } + ], + "source": [ + "import xarray\n", + "import fsspec\n", + "import numpy as np\n", + "import s3fs\n", + "import proplot\n", + "import os\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "00e5fcca", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/tube1/cha674/CARS_2022/cars-v2/wodpy/wodpy')\n", + "import wodnc" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33e18469", + "metadata": {}, + "outputs": [], + "source": [ + "START_YEAR = 2010\n", + "END_YEAR = 2011" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bfc3c2a", + "metadata": {}, + "outputs": [], + "source": [ + "obs_type = ['ctd','xbt','pfl'] #,'mrb','gld'] #,'drb'] #,'gld']\n", + "#obs_type = ['ctd']\n", + "\n", + "base_s3_url = 's3://noaa-wod-pds'\n", + "\n", + "data_container = {}\n", + "\n", + "output_directory = '/tube1/cha674/CARS_2022_ancillary_data/WOD_2018'\n", + "\n", + "for i_year in range(START_YEAR,END_YEAR+1):\n", + "\n", + " data_container_for_year = {}\n", + " for i_obs_type in obs_type:\n", + " url = base_s3_url + '/' + str(i_year) + '/wod_' + i_obs_type + '_' + str(i_year) + '.nc' #_pfl_2018.nc'\n", + " print(url)\n", + " fs = s3fs.S3FileSystem(anon=True)\n", + " file_obj = fs.open(url)\n", + " ds = xarray.open_dataset(file_obj,engine='h5netcdf')\n", + " data_container_for_year[i_obs_type] = ds\n", + " \n", + " #Write data out\n", + " if not os.path.isdir(os.path.join(output_directory,str(i_year))):\n", + " os.mkdir(os.path.join(output_directory,str(i_year)))\n", + " print(os.path.join(output_directory,str(i_year),'wod_' + i_obs_type + '_' + str(i_year) + '.nc'))\n", + " ds.to_netcdf(os.path.join(output_directory,str(i_year),'wod_' + i_obs_type + '_' + str(i_year) + '.nc'))\n", + " \n", + " data_container[i_year] = data_container_for_year" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7fd7905f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2010\n", + "Profile: 0\n", + "Profile: 1000\n", + "Profile: 2000\n", + "Profile: 3000\n", + "Profile: 4000\n", + "Profile: 5000\n", + "Profile: 6000\n", + "Profile: 7000\n", + "Profile: 8000\n", + "Profile: 9000\n", + "Profile: 10000\n", + "Profile: 11000\n", + "Profile: 12000\n", + "Profile: 13000\n", + "Profile: 14000\n", + "Profile: 15000\n", + "Profile: 16000\n", + "Profile: 17000\n", + "Profile: 18000\n", + "Profile: 19000\n", + "Profile: 20000\n", + "Profile: 21000\n", + "Profile: 22000\n", + "Profile: 23000\n", + "Profile: 24000\n", + "Profile: 25000\n", + "Profile: 26000\n", + "Profile: 27000\n", + "Profile: 28000\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'dsad' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_60493/2143000295.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mdsad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'dsad' is not defined" + ] + } + ], + "source": [ + "WOD_directory = '//oa-decadal-climate/work/observations/CARSv2_ancillary/WOD2018'\n", + "platforms_to_get = ['ctd']\n", + "WOD_file_name_stem = 'wod_'\n", + "\n", + "profile_vars_to_get = ['Oxygen','Temperature','Salinity','z']\n", + "point_vars_to_get = ['lat','lon'] #,'time','wod_unique_cast']\n", + "ancillary_vars_to_get = ['origflagset','country','dataset','Access_no','Recorder',\n", + " 'dbase_orig','Platform','Project','WOD_cruise_identifier',\n", + " 'Institute','needs_z_fix','Ocean_Vehicle','Temperature_Instrument']\n", + "\n", + "\n", + "for i_year in range(START_YEAR,END_YEAR+1):\n", + " print(i_year)\n", + " for i_platform in platforms_to_get:\n", + " \n", + " file_name = WOD_file_name_stem + i_platform + '_' + str(i_year) + '.nc'\n", + " WOD_ragged_object = wodnc.Ragged(os.path.join(WOD_directory,str(i_year),file_name))\n", + " n_profiles = WOD_ragged_object.ncasts()\n", + " \n", + " max_depth_levels = 0\n", + " \n", + " for i_profile in range(0,n_profiles):\n", + " \n", + " WOD_profile_object = wodnc.ncProfile(WOD_ragged_object,i_profile)\n", + " n_depth_levels = WOD_profile_object.n_levels()\n", + " \n", + " if n_depth_levels>max_depth_levels:\n", + " max_depth_levels = n_depth_levels\n", + " \n", + " \n", + " #END for i_profile\n", + " vars_in_dataset = list(WOD_ragged_object.variables().keys())\n", + " \n", + " \n", + " \n", + " profile_data_container = {}\n", + " ancillary_data_container = {}\n", + " attributes_data_container = {}\n", + " \n", + " for i_var in profile_vars_to_get:\n", + " if i_var in vars_in_dataset:\n", + "\n", + " profile_data_container[i_var] = np.nan*np.zeros([n_profiles,max_depth_levels],dtype='float32')\n", + " profile_data_container[i_var + '_WODflag'] = np.nan*np.zeros([n_profiles,max_depth_levels],dtype='int8')\n", + " profile_data_container[i_var + '_origflag'] = np.nan*np.zeros([n_profiles,max_depth_levels],dtype='float32')\n", + " \n", + " attributes_data_container[i_var] = {}\n", + " attributes_data_container[i_var + '_WODflag'] = {}\n", + " attributes_data_container[i_var + '_origflag'] = {}\n", + " #END for i_var\n", + "\n", + " \n", + " \n", + " point_data_container = {}\n", + " for i_var in point_vars_to_get:\n", + " point_data_container[i_var] = np.nan*np.zeros([n_profiles],dtype='float32')\n", + " \n", + " attributes_data_container[i_var] = {}\n", + "\n", + " point_data_container['time'] = np.zeros([n_profiles],dtype='datetime64[ns]')\n", + " point_data_container['wod_unique_cast'] = np.zeros([n_profiles],dtype='int32')\n", + " \n", + " for i_ancillary_var in ancillary_vars_to_get:\n", + " if i_ancillary_var in vars_in_dataset:\n", + " \n", + " ancillary_data_container[i_ancillary_var] = []\n", + " attributes_data_container[i_ancillary_var] = {}\n", + "\n", + " \n", + " \n", + " #Begin the loop through the profiles\n", + " \n", + " \n", + " for i_profile in range(0,n_profiles):\n", + " \n", + " if (i_profile % 1000) ==0:\n", + " print(\"Profile: \", i_profile)\n", + " \n", + " WOD_profile_object = wodnc.ncProfile(WOD_ragged_object,i_profile) \n", + " \n", + " for i_var in profile_data_container:\n", + " current_variable = WOD_profile_object.level_unpack(i_var)\n", + " \n", + " if current_variable.size != 0:\n", + " #Test for missing data on profile\n", + " profile_data_container[i_var][i_profile,0:current_variable.size] = current_variable\n", + " \n", + " \n", + " #END if current_variable.size != 0:\n", + " if i_profile ==0:\n", + " for i_var in profile_data_container:\n", + " variable_attributes = WOD_profile_object.show_variable_attr(i_var)\n", + " for i_attribute in variable_attributes:\n", + " attributes_data_container[i_var][i_attribute] = WOD_profile_object.get_variable_attr(i_var,i_attribute)\n", + " #END for i_attribute\n", + " #for i_var \n", + " #if i_profile ==0 \n", + " \n", + "\n", + " \n", + " #END for i_var\n", + " \n", + " point_data_container['wod_unique_cast'][i_profile] = WOD_profile_object.metadata('wod_unique_cast')\n", + " point_data_container['time'][i_profile] = np.datetime64(WOD_profile_object.datetime())\n", + " for i_var in point_vars_to_get:\n", + " if i_var in vars_in_dataset:\n", + " point_data_container[i_var][i_profile] = WOD_profile_object.metadata(i_var)\n", + " \n", + " if i_profile==0:\n", + " variable_attributes = WOD_profile_object.show_variable_attr(i_var)\n", + " for i_attribute in variable_attributes:\n", + " \n", + " attributes_data_container[i_var][i_attribute] = WOD_profile_object.get_variable_attr(i_var,i_attribute)\n", + " #END if i_var\n", + " #END for i_var\n", + " \n", + " for i_ancillary_var in ancillary_data_container.keys():\n", + " ancillary_data_container[i_ancillary_var].append(WOD_profile_object.metadata(i_ancillary_var))\n", + " if i_profile==0:\n", + " variable_attributes = WOD_profile_object.show_variable_attr(i_ancillary_var)\n", + " for i_attribute in variable_attributes:\n", + "\n", + " attributes_data_container[i_ancillary_var][i_attribute] = WOD_profile_object.get_variable_attr(i_ancillary_var,i_attribute)\n", + " #END for i_attribute\n", + " #END if i_profile\n", + " #END for i_ancillary\n", + " #END for i_profile\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " dsad\n", + " \n", + " \n", + "\n", + " \n", + " cast_index = np.arange(1,n_profiles+1)\n", + " \n", + " \n", + " \n", + " CODA_output_dataset = xarray.DataArray(point_data_container['wod_unique_cast'],dims=['cast'],coords={'cast':cast_index}).to_dataset(name='WOD_id') #attrs = attribute_container_for_year['lat']\n", + " # ).to_dataset(name='latitude')\n", + " \n", + " CODA_output_dataset['time'] = xarray.DataArray(point_data_container['time'],dims=['cast'],coords={'cast':cast_index})\n", + " CODA_output_dataset['time'].encoding['units'] = 'seconds since 1980-01-01'\n", + " CODA_output_dataset['lat'] = xarray.DataArray(point_data_container['lat'],dims=['cast'],coords={'cast':cast_index})\n", + " CODA_output_dataset['lon'] = xarray.DataArray(point_data_container['lon'],dims=['cast'],coords={'cast':cast_index})\n", + "\n", + " \n", + "\n", + " for i_var in profile_data_container.keys():\n", + " CODA_output_dataset[i_var] = xarray.DataArray(profile_data_container[i_var],dims=['cast','z_index'],\n", + " coords= 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