Skip to content

GitLab

Note: The default ITS GitLab runner is a shared resource and is subject to slowdowns during heavy usage.
You can run your own GitLab runner that is dedicated just to your group if you need to avoid processing delays.

Commit dfee4c9f authored by Qusai Al Shidi's avatar Qusai Al Shidi 💬
Browse files

Restructured file structure

parent 18ebfe2b
Hide whitespace changes
Inline Side-by-side
Showing with 213 additions and 210 deletions
__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
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment