A client API for using dataex services such as:
- Downloading observation data from dataex.
- Insert observation data into dataex.
- Downloading netcdf subset files of HRES, ENS and IMD WRF forecasts.
- Fetching summary information of observation data.
- Downloading HRES/ENS/IMD WRF forecast analysis region data.
dataex-client can be installed using the following commands
$ pip install https://github.com/nzahasan/dataex-client/zipball/masterIt can be used to create the .dataex_auth.json file which is required for authenticating users. The user is prompted for their dataex username and password.
$ dataex_init.py
dataex_netcdf_subset.py script provides a command line tool for getting a netcdf file subset of ecmwf hres or ens forecasts respectively.
For HRES:
$ dataex_netcdf_subset.py --model_type hres --params u10,cp --latbounds 20 40 --lonbounds 80 120 --output filename
Options:
--model_type: str
ens, imd_wrf or hres(default) model
--params -p : str or list of str (e.g. u10,ssr,cp)
Single or comma seperated parameter short names
--latbounds -lat : two float values (e.g. 20.2, 60.5)
South and North latitude float values space seperated
--lonbounds -lon : two float values (e.g. 100.0, 150.24)
West and East latitude float values space seperated
--output : str
output filename
The following parameters are available for subsetting in ECMWF HRES,
u10, ssr, str, sshf, slhf,
d2m, v10, t2m, cp, lsp,
swvl1,swvl2, swvl3, swvl4
The following parameters are available for subsetting in ECMWF ENS,
cp_q5, cp_q25, cp_q50, cp_q75, cp_q95,
t2m_q5, t2m_q25, t2m_q50, t2m_q75, t2m_q95,
lsp_q5, lsp_q25, lsp_q50, lsp_q75, lsp_q95
The following parameters are available for subsetting in IMD WRF,
APCP, T2m, RH2m, U10, V10,
SWNETB, LWNETB, dbz, cldfra
This script is for inserting observation data into dataex. It takes as input a json file and country id.
$ dataex_insert_obs_data.py --country_id 1 --obs_data filename
Options:
country_id : int
id number of country
obs_data : str
input csv or excel file
The column headers in both csv and excel must be start_time, end_time, value, level_id, parameter_id and station_id.
start_time,end_time,value,level_id,parameter_id,station_id
1995-01-01 00:00,1995-01-02 00:00,30.2,2,3,54
1996-01-01 00:00,1996-01-02 00:00,28.2,2,3,54
The time values must be in YYYY-MM-DD HH:MM format for both csv and excel files.
This script is for getting observation data from dataex. The data can be downloaded in either csv or json format.
$ dataex_get_obs_data.py --start_date 1993-01-91 --end_date 1993-02-01 -- station_id 12 --p_id 7 --output_type csv --output filename
Options:
start_date : DateTime
Date in YYYY-MM-DD format
end_date : DateTime
Date in YYYY-MM-DD format
station_id : int
observation station id
parameter_id : int
parameter id
output_type : str
csv, table or json
output : str
output filename
This script is for fetching a summary information of the observation data stored in dataex. This data can be downloaded in either json or csv.
$ dataex_obs_data_summary.py --output filename --output_type csv
Options:
output : str
output file
output_type: str
json or csv
These scripts allow users to download ecmwf hres and ens forecast analysis region data from dataex.
$ dataex_region_data_analysis.py --model_type <str> --reducer <str> --asset_identifier <str> --unique_field <str> --output_format <str> --output <str>
Options:
model_type : str
ens or hres(default)
reducer : str
name of reducer to use
asset_identifier : str
identifier for asset
unique_field : str
unique fields in asset
output_format : str
json or xlsx
output : str
output file
This script allows the user to list the available reducer names in forecast analysis.
Usage:
$ dataex_list_reducers.py --output_format <str> --output <str>
Options:
model_type : str
ens, imd_wrf or hres
output_format : str
json, table or csv
output : str
output filename
This script allows the user to list forecast asset information.
$ dataex_list_user_assets.py