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Commit dfee4c9f authored by Qusai Al Shidi's avatar Qusai Al Shidi 💬
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Restructured file structure

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__init__.py deleted 100644 → 0
View file @ 18ebfe2b
from swmfpy import read_wdc_ae
from swmfpy import read_omni_csv
from swmfpy import read_wdc_ae
from swmfpy import write_sw_input
from swmfpy import read_gm_log
paramin/__init__.py deleted 100644 → 0
View file @ 18ebfe2b
from paramin import replace
swmfpy.py 100755 → 100644
View file @ dfee4c9f
#!/usr/bin/env python3
"""swmfpy contains tools for the Space Weather Modeling Framework
"""Executable file to quickly use the tools found in swmfpy.
WIP
"""
__author__ = "Qusai Al Shidi"
__license__ = "MIT"
__version__ = "0.0.1"
__maintainer__ = "Qusai Al Shidi"
__email__ = "qusai@umich.edu"
import datetime as dt
import numpy as np
import pandas as pd
# Executable code
if __name__ == "__main__":
if __name__ = "__main__":
pass
def read_wdc_ae(wdc_filename):
"""Read an AE WDC text file into a dictionary of arrays.
Args:
wdc_filename (str): Filename of wdc data from
http://wdc.kugi.kyoto-u.ac.jp/
Returns:
dict: {"time": array of datetime objects corresponding to time
in UT.
"al","ae"...: Indices.
}
"""
data = {"AL": {"Time": [], "Index": []},
"AE": {"Time": [], "Index": []},
"AO": {"Time": [], "Index": []},
"AU": {"Time": [], "Index": []}}
with open(wdc_filename) as wdc_file:
for line in wdc_file:
ind_data = line.split()
for minute in range(60):
str_min = str(minute)
if minute < 10:
str_min = "0" + str_min
time = dt.datetime.strptime(ind_data[1][:-5]
+ ind_data[1][7:-2]
+ str_min,
"%y%m%d%H%M")
data[ind_data[1][-2:]]["Time"] += [time]
data[ind_data[1][-2:]]["Index"] += [int(ind_data[3+minute])]
return data
def read_omni_csv(filename, filtering=False, **kwargs):
"""Take an OMNI csv file from cdaweb.sci.gsfc.nasa.gov
and turn it into a pandas.DataFrame.
Args:
fnames: dict with filenames from omni .lst files. The keys must be:
density, temperature, magnetic_field, velocity
filtering: default=False Remove points where the value
is >sigma (default: sigma=3) from mean.
Returns: pandas.DataFrame object with solar wind data
Make sure to download the csv files with cdaweb.sci.gsfc.nasa.gov
the header seperated into a json file for safety.
This only tested with OMNI data specifically.
Other Args:
coarseness: default=3, Number of standard deviations above which to
remove if filtering=True.
clean: default=True, Clean the omni data of bad data points
"""
# Read the csv files and set the index to dates
colnames = ['Time', 'Bx [nT]', 'By [nT]', 'Bz [nT]',
'Vx [km/s]', 'Vy [km/s]', 'Vz [km/s]',
'Rho [n/cc]', 'T [K]']
with open(filename, 'r') as datafile:
data = pd.read_csv(datafile, names=colnames, skiprows=1)
data.set_index(pd.to_datetime(data[data.columns[0]]), inplace=True)
data.drop(columns=data.columns[0], inplace=True)
data.index.name = "Time [UT]"
# clean bad data
if kwargs.get('clean', True):
data["By [nT]"] = data["By [nT]"][data["By [nT]"].abs() < 80.]
data["Bx [nT]"] = data["Bx [nT]"][data["Bx [nT]"].abs() < 80.]
data["Bz [nT]"] = data["Bz [nT]"][data["Bz [nT]"].abs() < 80.]
data["Rho [n/cc]"] = data["Rho [n/cc]"][data["Rho [n/cc]"] < 500.]
data["Vx [km/s]"] = data["Vx [km/s]"][data["Vx [km/s]"].abs() < 2000.]
data["Vz [km/s]"] = data["Vz [km/s]"][data["Vz [km/s]"].abs() < 1000.]
data["Vy [km/s]"] = data["Vy [km/s]"][data["Vy [km/s]"].abs() < 1000.]
data["T [K]"] = data["T [K]"][data["T [K]"] < 1.e7]
if filtering:
coarse_filtering(data, kwargs.get('coarseness', 3))
return data.interpolate().bfill().ffill()
def coarse_filtering(data, coarseness=3):
"""Applies coarse filtering to a pandas.DataFrame"""
for column in data.columns:
mean = data[column].abs().mean()
sigma = data[column].std()
data[column] = data[data[column].abs() < mean+coarseness*sigma][column]
def write_sw_input(data, outfilename="IMF.dat", enable_rb=True, **kwargs):
"""Writes the pandas.DataFrame into an input file
that SWMF can read as input IMF (IMF.dat).
Args:
data: pandas.DataFrame object with solar wind data
outfilename: The output file name for ballistic solar wind data. \
(default: "IMF.dat")
enable_rb: Enables solar wind input for the radiation belt model. \
(default: True)
Other paramaters:
gse: (default=False)
Use GSE coordinate system for the file instead of GSM default.
"""
# Generate BATS-R-US solar wind input file
with open(outfilename, 'w') as outfile:
outfile.write("CSV files downloaded from ")
outfile.write("https://cdaweb.gsfc.nasa.gov/\n")
if kwargs.get('gse', False):
outfile.write("#COOR\nGSE\n")
outfile.write("yr mn dy hr min sec msec bx by bz vx vy vz dens temp\n")
outfile.write("#START\n")
for index, rows in data.iterrows():
outfile.write(index.strftime("%Y %m %d %H %M %S") + ' ')
outfile.write(index.strftime("%f")[:3] + ' ')
outfile.write(str(rows['Bx [nT]'])[:7] + ' ')
outfile.write(str(rows['By [nT]'])[:7] + ' ')
outfile.write(str(rows['Bz [nT]'])[:7] + ' ')
outfile.write(str(rows['Vx [km/s]'])[:7] + ' ')
outfile.write(str(rows['Vy [km/s]'])[:7] + ' ')
outfile.write(str(rows['Vz [km/s]'])[:7] + ' ')
outfile.write(str(rows['Rho [n/cc]'])[:7] + ' ')
outfile.write(str(rows['T [K]'])[:7] + ' ')
outfile.write('\n')
# Generate RBE model solar wind input file
if enable_rb:
with open("RB.SWIMF", 'w') as rbfile:
# Choose first element as t=0 header (unsure if this is safe)
rbfile.write(data.index[0].strftime("%Y, %j, %H ")
+ "! iyear, iday, ihour corresponding to t=0\n")
swlag_time = None
if swlag_time in kwargs:
rbfile.write(str(kwargs["swlag_time"]) + " "
+ "! swlag time in seconds "
+ "for sw travel to subsolar\n")
# Unsure what 11902 means but following example file
rbfile.write("11902 data "
+ "P+ NP NONLIN P+ V (MOM)\n")
# Quantity header
rbfile.write("dd mm yyyy hh mm ss.ms "
+ "#/cc km/s\n")
for index, rows in data.iterrows():
rbfile.write(index.strftime("%d %m %Y %H %M %S.%f")
+ " "
+ str(rows['Rho [n/cc]'])[:8]
+ " "
# Speed magnitude
+ str(np.sqrt(rows['Vx [km/s]']**2
+ rows['Vy [km/s]']**2
+ rows['Vz [km/s]']**2))[:8]
+ '\n')
def read_gm_log(filename, colnames=None, index_by_time=True):
"""Make a pandas.DataFrame out of the Dst indeces outputted
from the GM model log.
Args:
filename (str): The filename as a string.
colnames (list(str)): Supply the name of the columns
index_by_time (bool): Change the index to a time index
Returns:
pandas.DataFrame of the log file
Examples:
# To plot AL and Dst get the log files
geo = swmfpy.read_gm_log("run/GM/IO2/geoindex_e20140215-100500.log")
dst = swmfpy.read_gm_log("run/GM/IO2/log_e20140215-100500.log")
# Then plot using pandas features
dst["dst_sm"].plot.line()
geo["AL"].plot.line()
"""
# If column names were not specified
if not colnames:
with open(filename, 'r') as logfile:
# Usually the names are in the second line
logfile.readline()
colnames = logfile.readline().split()
data = pd.read_fwf(filename, header=None, skiprows=2, names=colnames)
if index_by_time:
data.set_index(pd.to_datetime({'year': data['year'],
'month': data['mo'],
'day': data['dy'],
'h': data['hr'],
'm': data['mn'],
's': data['sc']}),
inplace=True)
data.index.names = ['Time [UT]']
return data
swmfpy/__init__.py 0 → 100644
View file @ dfee4c9f
"""A collection of tools to read, write, visualize with the
Space Weather Modeling Framework (SWMF)
"""
__author__ = "Qusai Al Shidi"
__license__ = "MIT"
__version__ = "0.0.1"
__maintainer__ = "Qusai Al Shidi"
__email__ = "qusai@umich.edu"
from . import paramin
from . import io
swmfpy/io.py 0 → 100644
View file @ dfee4c9f
#!/usr/bin/env python3
"""Input/Output tools for SWMF
"""
import datetime as dt
import numpy as np
import pandas as pd
def read_wdc_ae(wdc_filename):
"""Read an AE WDC text file into a dictionary of arrays.
Args:
wdc_filename (str): Filename of wdc data from
http://wdc.kugi.kyoto-u.ac.jp/
Returns:
dict: {"time": array of datetime objects corresponding to time
in UT.
"al","ae"...: Indices.
}
"""
data = {"AL": {"Time": [], "Index": []},
"AE": {"Time": [], "Index": []},
"AO": {"Time": [], "Index": []},
"AU": {"Time": [], "Index": []}}
with open(wdc_filename) as wdc_file:
for line in wdc_file:
ind_data = line.split()
for minute in range(60):
str_min = str(minute)
if minute < 10:
str_min = "0" + str_min
time = dt.datetime.strptime(ind_data[1][:-5]
+ ind_data[1][7:-2]
+ str_min,
"%y%m%d%H%M")
data[ind_data[1][-2:]]["Time"] += [time]
data[ind_data[1][-2:]]["Index"] += [int(ind_data[3+minute])]
return data
def read_omni_csv(filename, filtering=False, **kwargs):
"""Take an OMNI csv file from cdaweb.sci.gsfc.nasa.gov
and turn it into a pandas.DataFrame.
Args:
fnames: dict with filenames from omni .lst files. The keys must be:
density, temperature, magnetic_field, velocity
filtering: default=False Remove points where the value
is >sigma (default: sigma=3) from mean.
Returns: pandas.DataFrame object with solar wind data
Make sure to download the csv files with cdaweb.sci.gsfc.nasa.gov
the header seperated into a json file for safety.
This only tested with OMNI data specifically.
Other Args:
coarseness: default=3, Number of standard deviations above which to
remove if filtering=True.
clean: default=True, Clean the omni data of bad data points
"""
# Read the csv files and set the index to dates
colnames = ['Time', 'Bx [nT]', 'By [nT]', 'Bz [nT]',
'Vx [km/s]', 'Vy [km/s]', 'Vz [km/s]',
'Rho [n/cc]', 'T [K]']
with open(filename, 'r') as datafile:
data = pd.read_csv(datafile, names=colnames, skiprows=1)
data.set_index(pd.to_datetime(data[data.columns[0]]), inplace=True)
data.drop(columns=data.columns[0], inplace=True)
data.index.name = "Time [UT]"
# clean bad data
if kwargs.get('clean', True):
data["By [nT]"] = data["By [nT]"][data["By [nT]"].abs() < 80.]
data["Bx [nT]"] = data["Bx [nT]"][data["Bx [nT]"].abs() < 80.]
data["Bz [nT]"] = data["Bz [nT]"][data["Bz [nT]"].abs() < 80.]
data["Rho [n/cc]"] = data["Rho [n/cc]"][data["Rho [n/cc]"] < 500.]
data["Vx [km/s]"] = data["Vx [km/s]"][data["Vx [km/s]"].abs() < 2000.]
data["Vz [km/s]"] = data["Vz [km/s]"][data["Vz [km/s]"].abs() < 1000.]
data["Vy [km/s]"] = data["Vy [km/s]"][data["Vy [km/s]"].abs() < 1000.]
data["T [K]"] = data["T [K]"][data["T [K]"] < 1.e7]
if filtering:
coarse_filtering(data, kwargs.get('coarseness', 3))
return data.interpolate().bfill().ffill()
def coarse_filtering(data, coarseness=3):
"""Applies coarse filtering to a pandas.DataFrame"""
for column in data.columns:
mean = data[column].abs().mean()
sigma = data[column].std()
data[column] = data[data[column].abs() < mean+coarseness*sigma][column]
def write_sw_input(data, outfilename="IMF.dat", enable_rb=True, **kwargs):
"""Writes the pandas.DataFrame into an input file
that SWMF can read as input IMF (IMF.dat).
Args:
data: pandas.DataFrame object with solar wind data
outfilename: The output file name for ballistic solar wind data. \
(default: "IMF.dat")
enable_rb: Enables solar wind input for the radiation belt model. \
(default: True)
Other paramaters:
gse: (default=False)
Use GSE coordinate system for the file instead of GSM default.
"""
# Generate BATS-R-US solar wind input file
with open(outfilename, 'w') as outfile:
outfile.write("CSV files downloaded from ")
outfile.write("https://cdaweb.gsfc.nasa.gov/\n")
if kwargs.get('gse', False):
outfile.write("#COOR\nGSE\n")
outfile.write("yr mn dy hr min sec msec bx by bz vx vy vz dens temp\n")
outfile.write("#START\n")
for index, rows in data.iterrows():
outfile.write(index.strftime("%Y %m %d %H %M %S") + ' ')
outfile.write(index.strftime("%f")[:3] + ' ')
outfile.write(str(rows['Bx [nT]'])[:7] + ' ')
outfile.write(str(rows['By [nT]'])[:7] + ' ')
outfile.write(str(rows['Bz [nT]'])[:7] + ' ')
outfile.write(str(rows['Vx [km/s]'])[:7] + ' ')
outfile.write(str(rows['Vy [km/s]'])[:7] + ' ')
outfile.write(str(rows['Vz [km/s]'])[:7] + ' ')
outfile.write(str(rows['Rho [n/cc]'])[:7] + ' ')
outfile.write(str(rows['T [K]'])[:7] + ' ')
outfile.write('\n')
# Generate RBE model solar wind input file
if enable_rb:
with open("RB.SWIMF", 'w') as rbfile:
# Choose first element as t=0 header (unsure if this is safe)
rbfile.write(data.index[0].strftime("%Y, %j, %H ")
+ "! iyear, iday, ihour corresponding to t=0\n")
swlag_time = None
if swlag_time in kwargs:
rbfile.write(str(kwargs["swlag_time"]) + " "
+ "! swlag time in seconds "
+ "for sw travel to subsolar\n")
# Unsure what 11902 means but following example file
rbfile.write("11902 data "
+ "P+ NP NONLIN P+ V (MOM)\n")
# Quantity header
rbfile.write("dd mm yyyy hh mm ss.ms "
+ "#/cc km/s\n")
for index, rows in data.iterrows():
rbfile.write(index.strftime("%d %m %Y %H %M %S.%f")
+ " "
+ str(rows['Rho [n/cc]'])[:8]
+ " "
# Speed magnitude
+ str(np.sqrt(rows['Vx [km/s]']**2
+ rows['Vy [km/s]']**2
+ rows['Vz [km/s]']**2))[:8]
+ '\n')
def read_gm_log(filename, colnames=None, index_by_time=True):
"""Make a pandas.DataFrame out of the Dst indeces outputted
from the GM model log.
Args:
filename (str): The filename as a string.
colnames (list(str)): Supply the name of the columns
index_by_time (bool): Change the index to a time index
Returns:
pandas.DataFrame of the log file
Examples:
# To plot AL and Dst get the log files
geo = swmfpy.read_gm_log("run/GM/IO2/geoindex_e20140215-100500.log")
dst = swmfpy.read_gm_log("run/GM/IO2/log_e20140215-100500.log")
# Then plot using pandas features
dst["dst_sm"].plot.line()
geo["AL"].plot.line()
"""
# If column names were not specified
if not colnames:
with open(filename, 'r') as logfile:
# Usually the names are in the second line
logfile.readline()
colnames = logfile.readline().split()
data = pd.read_fwf(filename, header=None, skiprows=2, names=colnames)
if index_by_time:
data.set_index(pd.to_datetime({'year': data['year'],
'month': data['mo'],
'day': data['dy'],
'h': data['hr'],
'm': data['mn'],
's': data['sc']}),
inplace=True)
data.index.names = ['Time [UT]']
return data
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