import numpy as np
from pyspedas.tplot_tools import get_data, store_data, options, clip, ylim
from ...satellite.erg.load import load
from ...satellite.erg.get_gatt_ror import get_gatt_ror
from typing import List, Optional, Union
[docs]
def gmag_mm210(
trange: List[str] = ["2020-08-01", "2020-08-02"],
suffix: str = "",
site: Union[str, List[str]] = "all",
datatype: Union[str, List[str]] = "all",
get_support_data: bool = False,
varformat: Optional[str] = None,
varnames: List[str] = [],
downloadonly: bool = False,
notplot: bool = False,
no_update: bool = False,
uname: Optional[str] = None,
passwd: Optional[str] = None,
time_clip: bool = False,
ror: bool = True,
force_download: bool = False,
) -> List[str]:
"""
Load mm210 data from ERG Science Center
Parameters
----------
trange: list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
Default: ['2020-08-01', '2020-08-02']
suffix: str
The tplot variable names will be given this suffix. Default: ''
site: str or list of str
The site or list of sites to load.
Valid values:
'tik', 'zgn', 'yak', 'irt', 'ppi', 'bji',
'lnp', 'mut', 'ptn', 'wtk', 'lmt', 'kat',
'ktn', 'chd', 'zyk', 'mgd', 'ptk', 'msr',
'rik', 'onw', 'kag', 'ymk', 'cbi', 'gua',
'yap', 'kor', 'ktb', 'bik', 'wew', 'daw',
'wep', 'bsv', 'dal', 'can', 'adl', 'kot',
'cst', 'ewa', 'asa', 'mcq', 'all'
Default: ['all']
datatype: str or list of str
The data types to load. Valid values: '1s', '1sec', '1m', '1min', '1h', '1hr', 'all'
Default: 'all'
get_support_data: bool
If true, data with an attribute "VAR_TYPE" with a value of "support_data"
or 'data' will be loaded into tplot. Default: False
varformat: str
The CDF file variable formats to load into tplot. Wildcard character
"*" is accepted. Default: None (all variables will be loaded).
varnames: list of str
List of variable names to load. Default: [] (all variables will be loaded)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables. Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables. Default: False
no_update: bool
If set, only load data from your local cache. Default: False
uname: str
User name. Default: None
passwd: str
Password. Default: None
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword. Default: False
ror: bool
If set, print PI info and rules of the road. Default: True
force_download: bool
Download file even if local version is more recent than server version
Default: False
Returns
-------
Examples
________
>>> import pyspedas
>>> from pyspedas import tplot
>>> mm210_vars=pyspedas.projects.erg.gmag_mm210(trange=["2005-01-01", "2005-01-02"],site='adl',datatype='1min')
>>> tplot('mm210_mag_adl_1min_hdz')
"""
site_code_all = [
"tik",
"zgn",
"yak",
"irt",
"ppi",
"bji",
"lnp",
"mut",
"ptn",
"wtk",
"lmt",
"kat",
"ktn",
"chd",
"zyk",
"mgd",
"ptk",
"msr",
"rik",
"onw",
"kag",
"ymk",
"cbi",
"gua",
"yap",
"kor",
"ktb",
"bik",
"wew",
"daw",
"wep",
"bsv",
"dal",
"can",
"adl",
"kot",
"cst",
"ewa",
"asa",
"mcq",
]
tres_all = ["1sec", "1min", "1h"]
if isinstance(datatype, str):
datatype = datatype.lower()
datatype = datatype.split(" ")
elif isinstance(datatype, list):
for i in range(len(datatype)):
datatype[i] = datatype[i].lower()
if "all" in datatype:
datatype = tres_all
datatype = list(set(datatype).intersection(tres_all))
if len(datatype) < 1:
return
if "1s" in datatype:
index = np.where(np.array(datatype) == "1s")[0][0]
datatype[index] = "1sec"
elif "1m" in datatype:
index = np.where(np.array(datatype) == "1m")[0][0]
datatype[index] = "1min"
elif "1hr" in datatype:
index = np.where(np.array(datatype) == "1hr")[0][0]
datatype[index] = "1h"
if isinstance(site, str):
site_code = site.lower()
site_code = site_code.split(" ")
elif isinstance(site, list):
site_code = []
for i in range(len(site)):
site_code.append(site[i].lower())
if "all" in site_code:
site_code = site_code_all
site_code = list(set(site_code).intersection(site_code_all))
prefix = "mm210_"
if notplot:
loaded_data = {}
else:
loaded_data = []
for site_input in site_code:
for data_type_in in datatype:
fres = data_type_in
if fres == "1h":
fres = "1min"
file_res = 3600.0 * 24
pathformat = (
"ground/geomag/mm210/"
+ fres
+ "/"
+ site_input
+ "/%Y/mm210_"
+ fres
+ "_"
+ site_input
+ "_%Y%m%d_v??.cdf"
)
loaded_data_temp = load(
pathformat=pathformat,
file_res=file_res,
trange=trange,
datatype=datatype,
prefix=prefix,
suffix="_" + site_input + suffix,
get_support_data=get_support_data,
varformat=varformat,
downloadonly=downloadonly,
notplot=notplot,
time_clip=time_clip,
no_update=no_update,
uname=uname,
passwd=passwd,
force_download=force_download,
)
if notplot:
loaded_data.update(loaded_data_temp)
else:
loaded_data += loaded_data_temp
if (len(loaded_data_temp) > 0) and ror:
try:
gatt = get_gatt_ror(downloadonly, loaded_data)
print(
"**************************************************************************"
)
print(gatt["Logical_source_description"])
print("")
print(f'Information about {gatt["Station_code"]}')
print("PI and Host PI(s):")
print(gatt["PI_name"])
print("")
print("Affiliations: ")
print(gatt["PI_affiliation"])
print("")
print("Rules of the Road for 210 MM Data Use:")
for gatt_text in gatt["TEXT"]:
print(gatt_text)
print(f'{gatt["LINK_TEXT"]} {gatt["HTTP_LINK"]}')
print(
"**************************************************************************"
)
except:
print("printing PI info and rules of the road was failed")
if (not downloadonly) and (not notplot):
if fres == "1min":
fres_list = ["1min", "1h"]
else:
fres_list = [fres]
for fres_in in fres_list:
current_tplot_name = (
prefix + "hdz_" + fres_in + "_" + site_input + suffix
)
if current_tplot_name in loaded_data:
get_data_vars = get_data(current_tplot_name)
if get_data_vars is None:
store_data(current_tplot_name, delete=True)
else:
# ;--- Rename
new_tplot_name = (
prefix
+ "mag_"
+ site_input
+ "_"
+ fres_in
+ "_hdz"
+ suffix
)
store_data(current_tplot_name, newname=new_tplot_name)
loaded_data.remove(current_tplot_name)
loaded_data.append(new_tplot_name)
# ;--- Missing data -1.e+31 --> NaN
clip(new_tplot_name, -1e4, 1e4)
get_data_vars = get_data(new_tplot_name)
ylim(
new_tplot_name,
np.nanmin(get_data_vars[1]),
np.nanmax(get_data_vars[1]),
)
# ;--- Labels
options(
new_tplot_name, "legend_names", ["Ch1", "Ch2", "Ch3"]
)
options(new_tplot_name, "Color", ["b", "g", "r"])
options(
new_tplot_name,
"ytitle",
"\n".join(new_tplot_name.split("_")),
)
return loaded_data