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kaipy/__init__.py
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kaipy/cdaweb_utils.py
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"""Simple utilitiles for fetching data from CDAWeb.
This module provides utility functions which perform simple queries to CDAWeb.
This module currently support all of the spacecraft that are described in
the YAML file used by the satellite comparison script msphSatComp.py.
Author
------
Eric Winter (eric.winter62@gmail.com)
"""
# Import standard modules.
import datetime
import os
import sys
# Import supplemental modules.
from astropy.coordinates import SkyCoord
import astropy.units as u
from cdasws import CdasWs
import numpy as np
from spacepy.coordinates import Coords
import spacepy.datamodel as dm
from spacepy.time import Ticktock
from sunpy.coordinates import frames
# Import project modules.
import kaipy.kaiTools as kaiTools
import kaipy.satcomp.scutils as scutils
# Program constants
# Format string for CDAWeb datetime strings.
CDAWEB_DATETIME_FORMAT = "%Y-%m-%dT%H:%M:%SZ"
def fetch_satellite_geographic_position(spacecraft, when):
"""Fetch the geographic position of a satellite at a specified time.
Fetch the geographic position of a satellite at a specified time.
Data is fetched from CDAWeb. The first returned position is assumed to
correspond to the requested value of "when".
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
when : datetime.datetime
datetime for position fetch.
Returns
-------
sc_rad, sc_lat, sc_lon : float
Geographic longitude and latitude of spacecraft (degrees) and
radius (km).
"""
# Read the CDAWeb spacecraft database.
sc_info = scutils.getScIds()
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = when.strftime(CDAWEB_DATETIME_FORMAT)
# <HACK>
# Use the specified time as the start time, and nudge it by adding 1 minute
# (in seconds) to get the end time.
one_minute = 60
t_end = when + datetime.timedelta(0, one_minute)
# </HACK>
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the satellite position from CDAWeb.
dataset = sc_info[spacecraft]['Ephem']['Id']
variable = sc_info[spacecraft]['Ephem']['Data']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return None, None, None
# The ephemeris is in Cartesian coordinates.
# Assume the first/only position returned is closest position at t0.
# GSM coordinates have r = SQRT(x**2 + y**2 + z**2) as a 4th element,
# and we don't need that, so only copy the first 3 elements of any
# position.
if data[sc_info[spacecraft]['Ephem']['Data']].shape == (3,):
# Only 1 position was returned, so copy its first 3 elements.
xyz = data[sc_info[spacecraft]['Ephem']['Data']][:3]
else:
# More than 1 position was returned, so copy the first 3 elements of
# the first position.
xyz = data[sc_info[spacecraft]['Ephem']['Data']][0, :3] # >1 position returned
# Create a Coords object for the Cartesian position in the coordinate system used by
# the spacecraft, at the start time.
scpos = Coords(
xyz,
sc_info[spacecraft]['Ephem']['CoordSys'],
'car', use_irbem=False
)
scpos.ticks = Ticktock(t0)
# Convert the spacecraft coordinates to geographic spherical coordinates.
smpos = scpos.convert('GEO', 'sph')
sc_rad = smpos.data[0][0]
sc_lat = smpos.data[0][1]
sc_lon = smpos.data[0][2]
# Return the spacecraft radius, longitude and latitude.
return sc_rad, sc_lat, sc_lon
def fetch_satellite_SM_position(spacecraft, when):
"""Fetch the Solar Magnetic position of a satellite at a specified time.
Fetch the Solar Magnetic position of a satellite at a specified time, in
SM cartesian coordinates. Data is fetched from CDAWeb. The first returned
position is assumed to correspond to the requested value of "when".
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
when : datetime.datetime
datetime for position fetch.
Returns
-------
sc_x, sc_y, sc_z : float
Cartesian SM coordinates of spacecraft (units of kilometers).
"""
# Read the CDAWeb spacecraft database.
sc_info = scutils.getScIds()
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = when.strftime(CDAWEB_DATETIME_FORMAT)
# <HACK>
# Use the specified time as the start time, and nudge it by adding 1 minute
# (in seconds) to get the end time.
one_minute = 60
t_end = when + datetime.timedelta(0, one_minute)
# </HACK>
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the satellite position from CDAWeb.
dataset = sc_info[spacecraft]['Ephem']['Id']
variable = sc_info[spacecraft]['Ephem']['Data']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return None, None, None
# The ephemeris is in Cartesian coordinates.
# Assume the first/only position returned is closest position at t0.
# GSM coordinates have r = SQRT(x**2 + y**2 + z**2) as a 4th element,
# and we don't need that, so only copy the first 3 elements of any
# position.
if data[sc_info[spacecraft]['Ephem']['Data']].shape == (3,):
# Only 1 position was returned, so copy its first 3 elements.
xyz = data[sc_info[spacecraft]['Ephem']['Data']][:3]
else:
# More than 1 position was returned, so copy the first 3 elements of
# the first position.
xyz = data[sc_info[spacecraft]['Ephem']['Data']][0, :3] # >1 position returned
# Create a Coords object for the Cartesian position in the coordinate system used by
# the spacecraft, at the start time.
scpos = Coords(
xyz,
sc_info[spacecraft]['Ephem']['CoordSys'],
'car', use_irbem=False
)
scpos.ticks = Ticktock(t0)
# Convert the spacecraft coordinates to SM cartesian coordinates.
smpos = scpos.convert('SM', 'car')
sc_x = smpos.data[0][0]
sc_y = smpos.data[0][1]
sc_z = smpos.data[0][2]
# Return the spacecraft cartesian SM coordinates.
return sc_x, sc_y, sc_z
def fetch_spacecraft_SM_trajectory(spacecraft, t_start, t_end):
"""Fetch the Solar Magnetic trajectory of a spacecraft in a time range.
Fetch the trajectory of a spacecraft in a time range, in Solar Magnetic
Cartesian coordinates. Data is fetched from CDAWeb.
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
t_start, t_end : datetime.datetime
Start and end datetime for trajectory fetch.
Returns
-------
XXX : XXX
XXX
"""
# Read the CDAWeb spacecraft database.
sc_info = scutils.getScIds()
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = t_start.strftime(CDAWEB_DATETIME_FORMAT)
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the satellite position from CDAWeb.
dataset = sc_info[spacecraft]['Ephem']['Id']
variable = sc_info[spacecraft]['Ephem']['Data']
coord_sys = sc_info[spacecraft]['Ephem']['CoordSys']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return None, None, None
# Extract the trajectory times as datetime objects.
# <HACK>
# Add code to handle this in the YAML file.
if 'Epoch_state' in data.keys():
t_trajectory = data['Epoch_state']
elif 'Epoch' in data.keys():
t_trajectory = data['Epoch']
elif 'time_tags__C1_CP_FGM_SPIN' in data.keys():
t_trajectory = data['time_tags__C1_CP_FGM_SPIN']
else:
raise TypeError("Unexpected time variable!")
# </HACK>
# The ephemeris is in Cartesian coordinates in an arbitrary frame.
# GSM coordinates have r = SQRT(x**2 + y**2 + z**2) as a 4th element,
# and we don't need that, so only copy the first 3 elements of any
# position.
if len(data[variable].shape) == 1:
# Only 1 position was returned, so copy its first 3 elements
# into a 2-D array.
xyz_trajectory = np.array([data[variable][:3]])
else:
# More than 1 position was returned, so copy the first 3 elements of
# each position.
xyz_trajectory = data[variable][:, :3] # >1 position
# Create a Coords object for the Cartesian trajectory.
trajectory = Coords(xyz_trajectory, coord_sys, 'car', use_irbem=False)
trajectory.ticks = Ticktock(t_trajectory)
# Convert the spacecraft coordinates to SM cartesian coordinates.
trajectory_sm = trajectory.convert('SM', 'car')
# Return the spacecraft cartesian SM coordinates.
return trajectory_sm.x, trajectory_sm.y, trajectory_sm.z
def fetch_satellite_magnetic_northern_footprint_position(spacecraft, when):
"""Fetch the position of the northern magnetic footprint.
Fetch the positions of the northern magnetic footprint for a spacecraft
at a specified time. Data is fetched from CDAWeb. The first returned
positions are assumed to correspond to the requested value of "when".
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
when : datetime.datetime
datetime for position fetch.
Returns
-------
fp_lon, fp_lat : float
Geographic longitude and latitude (degrees) of northern magnetic
footprint.
"""
# Initialize the footprint position.
fp_lat = None
fp_lon = None
# Read the CDAWeb spacecraft database.
sc_info = scutils.getScIds()
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = when.strftime(CDAWEB_DATETIME_FORMAT)
# <HACK>
# Use the specified time as the start time, and nudge it by adding 1 minute
# (in seconds) to get the end time.
one_minute = 60
t_end = when + datetime.timedelta(0, 3600)
# </HACK>
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the footprint position from CDAWeb.
dataset = sc_info[spacecraft]['MagneticFootprintNorth']['Id']
variable = sc_info[spacecraft]['MagneticFootprintNorth']['Data']
coordsys = sc_info[spacecraft]['MagneticFootprintNorth']['CoordSys']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return fp_lat, fp_lon
# Convert the position if needed.
if coordsys == 'GEO':
if type(variable) is list:
# <HACK>
# Assume index 0 is latitude, index 1 is longitude.
fp_lat = data[variable[0]][0]
fp_lon = data[variable[1]][0]
# </HACK>
else:
raise TypeError("Unexpected variable type!")
elif coordsys == 'GSM':
# The position is in cartesian GSM coordinates (kilometers).
if data[variable].shape == (3,):
# Only 1 position was returned, so copy its first 3 elements.
xyz = data[variable][:3]
else:
# More than 1 position was returned, so copy the first 3 elements of
# the first position.
xyz = data[variable][0, :3] # >1 position returned
# Create a Coords object for the Cartesian position in the coordinate system used by
# the spacecraft, at the start time.
fp_pos_orig = Coords(xyz, coordsys, 'car', use_irbem=False)
fp_pos_orig.ticks = Ticktock(t0)
# Convert the footprint coordinates to geographic spherical coordinates.
fp_pos_sph = fp_pos_orig.convert('GEO', 'sph')
fp_rad = fp_pos_sph.data[0][0]
fp_lat = fp_pos_sph.data[0][1]
fp_lon = fp_pos_sph.data[0][2]
else:
raise TypeError("Unexpected coordinate system: %s" % coordsys)
# Canonicalize longitude to [-180, +180] range.
if fp_lon > 180:
fp_lon = fp_lon - 360
# Return the spacecraft longitude and latitude.
return fp_lat, fp_lon
def fetch_satellite_magnetic_southern_footprint_position(spacecraft, when):
"""Fetch the position of the southern magnetic footprint.
Fetch the positions of the southern magnetic footprint for a spacecraft
at a specified time. Data is fetched from CDAWeb. The first returned
positions are assumed to correspond to the requested value of "when".
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
when : datetime.datetime
datetime for position fetch.
Returns
-------
fp_lon, fp_lat : float
Geographic longitude and latitude (degrees) of southern magnetic
footprint.
"""
# Initialize the footprint position.
fp_lat = None
fp_lon = None
# Read the CDAWeb spacecraft database.
sc_info = scutils.getScIds()
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = when.strftime(CDAWEB_DATETIME_FORMAT)
# <HACK>
# Use the specified time as the start time, and nudge it by adding 1 minute
# (in seconds) to get the end time.
one_minute = 60
t_end = when + datetime.timedelta(0, 3600)
# </HACK>
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the footprint position from CDAWeb.
dataset = sc_info[spacecraft]['MagneticFootprintSouth']['Id']
variable = sc_info[spacecraft]['MagneticFootprintSouth']['Data']
coordsys = sc_info[spacecraft]['MagneticFootprintSouth']['CoordSys']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return fp_lat, fp_lon
# Convert the position if needed.
if coordsys == 'GEO':
if type(variable) is list:
# <HACK>
# Assume index 0 is latitude, index 1 is longitude.
fp_lat = data[variable[0]][0]
fp_lon = data[variable[1]][0]
# </HACK>
else:
raise TypeError("Unexpected variable type!")
elif coordsys == 'GSM':
# The position is in cartesian GSM coordinates (kilometers).
if data[variable].shape == (3,):
# Only 1 position was returned, so copy its first 3 elements.
xyz = data[variable][:3]
else:
# More than 1 position was returned, so copy the first 3 elements of
# the first position.
xyz = data[variable][0, :3] # >1 position returned
# Create a Coords object for the Cartesian position in the coordinate system used by
# the spacecraft, at the start time.
fp_pos_orig = Coords(xyz, coordsys, 'car', use_irbem=False)
fp_pos_orig.ticks = Ticktock(t0)
# Convert the footprint coordinates to geographic spherical coordinates.
fp_pos_sph = fp_pos_orig.convert('GEO', 'sph')
fp_rad = fp_pos_sph.data[0][0]
fp_lat = fp_pos_sph.data[0][1]
fp_lon = fp_pos_sph.data[0][2]
else:
raise TypeError("Unexpected coordinate system: %s" % coordsys)
# Canonicalize longitude to [-180, +180] range.
if fp_lon > 180:
fp_lon = fp_lon - 360
# Return the spacecraft longitude and latitude.
return fp_lat, fp_lon
def fetch_helio_spacecraft_HGS_trajectory(spacecraft, t_start, t_end, mjdc):
"""Fetch the HGS trajectory of a spacecraft in a time range.
Fetch the trajectory of a spacecraft in a time range, in the HGS
frame. Data is fetched from CDAWeb.
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
t_start, t_end : datetime.datetime
Start and end datetime for trajectory fetch.
Returns
-------
XXX : XXX
XXX
"""
# Read the CDAWeb spacecraft database.
spacecraft_data_file = os.path.join(
os.environ["KAIJUHOME"], "kaipy", "satcomp", "sc_helio.json"
)
sc_info = scutils.getScIds(spacecraft_data_file=spacecraft_data_file)
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = t_start.strftime(CDAWEB_DATETIME_FORMAT)
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the satellite position from CDAWeb.
dataset = sc_info[spacecraft]['Ephem']['Id']
variable = sc_info[spacecraft]['Ephem']['Data']
coord_sys = sc_info[spacecraft]['Ephem']['CoordSys']
status, data = cdas.get_data(dataset, variable, t0, t1)
# Return if no data found.
if data is None:
return None, None, None
# Fetch the value of 1 AU in kilometers.
AU_km = u.Quantity(1*u.astrophys.AU, u.km).value
# Fetch the value of 1 Rsun in kilometers.
Rsun_km = u.Quantity(1*u.Rsun, u.km).value
# Compute the conversion factor from AU to Rsun.
Rsun_per_AU = AU_km/Rsun_km
# Create SkyCoord objects for each HGI(t)/HCI(t) position/time.
lon = data["HGI_LON"]
lat = data["HGI_LAT"]
# Convert radius to Rsun.
rad = data["RAD_AU"]*Rsun_per_AU
t = data["Epoch"]
c = SkyCoord(
lon*u.deg, lat*u.deg, rad*u.Rsun,
frame=frames.HeliocentricInertial, obstime=t,
representation_type="spherical"
)
# Create the HGS(t0) coordinate frame.
mjdc_frame = frames.HeliographicStonyhurst(obstime=kaiTools.MJD2UT(mjdc))
# Convert the HGI(t)/HCI(t) spherical (lon, lat, radius) positions to
# HGS(mjdc).
c = c.transform_to(mjdc_frame)
# Extract the Cartesian coordinates in units of Rsun.
x = np.array(c.cartesian.x)
y = np.array(c.cartesian.y)
z = np.array(c.cartesian.z)
# Return the spacecraft Cartesian HGS coordinates.
return x, y, z
def fetch_helio_spacecraft_trajectory(sc_id, t_start, t_end):
"""Fetch the trajectory of a spacecraft in a time range.
Fetch the trajectory of a spacecraft in a time range, in the HGS
frame. Data is fetched from CDAWeb.
Parameters
----------
spacecraft : str
CDAWeb-compliant spacecraft ID.
t_start, t_end : datetime.datetime
Start and end datetime for trajectory fetch.
Returns
-------
XXX : XXX
XXX
"""
# Read the CDAWeb spacecraft database.
sc_metadata_path = os.path.join(
os.environ["KAIJUHOME"], "kaipy", "satcomp", "sc_helio.json"
)
sc_metadata = scutils.getScIds(spacecraft_data_file=sc_metadata_path)
# Create the CDAWeb connection.
cdas = CdasWs()
# Format the start and end time strings.
t0 = t_start.strftime(CDAWEB_DATETIME_FORMAT)
t1 = t_end.strftime(CDAWEB_DATETIME_FORMAT)
# Fetch the satellite position from CDAWeb.
cdaweb_dataset_name = sc_metadata[sc_id]["Ephem"]["Id"]
cdaweb_variable_name = sc_metadata[sc_id]["Ephem"]["Data"]
# coord_sys = sc_metadata[sc_id]["Ephem"]["CoordSys"]
cdaweb_query_status, cdaweb_query_results = cdas.get_data(
cdaweb_dataset_name, cdaweb_variable_name, t0, t1
)
# Return the spacecraft data (could be None).
return cdaweb_query_results
def ingest_helio_spacecraft_trajectory(sc_id, sc_data, MJDc):
"""XXX
XXX
Parameters
----------
XXX : XXX
XXX
Returns
-------
XXX : XXX
XXX
"""
# Read the CDAWeb spacecraft database.
sc_metadata_path = os.path.join(
os.environ["KAIJUHOME"], "kaipy", "satcomp", "sc_helio.json"
)
sc_metadata = scutils.getScIds(spacecraft_data_file=sc_metadata_path)
# Determine the coordinate system used by the CDAWeb ephemeris.
cdaweb_coordinate_system = sc_metadata[sc_id]["Ephem"]["CoordSys"]
# Convert the coordinates to the GH(MJSc) frame.
if cdaweb_coordinate_system == "GSE":
# GSE(t) coordinates from CDAWeb are provided as a single 2-D array
# of Cartesian (x, y, z) values, of shape (n, 3), called "XYZ_GSE".
# These values are in units of kilometers. The ephemeris time is
# in a variable called "Epoch_bin".
# Convert the GSE(t) ephemeris locations to the gamhelio frame
# (GH(MJDc) so that sctrack.x can interpolate gamhelio model data to
# the spacecraft ephemeris locations.
# Radius of Sun in kilometers.
R_SUN_KILOMETERS = u.Quantity(1*u.Rsun, u.km).value
# Convert the GSE(t) Cartesian coordinates from kilometers to R_sun.
x = sc_data["XYZ_GSE"][:, 0]/R_SUN_KILOMETERS
y = sc_data["XYZ_GSE"][:, 1]/R_SUN_KILOMETERS
z = sc_data["XYZ_GSE"][:, 2]/R_SUN_KILOMETERS
t = sc_data["Epoch"] # Always "Epoch" since results are unbinned.
# Create astropy.coordinates.SkyCoord objects for each GSE(t)
# position and time.
c = SkyCoord(
x*u.Rsun, y*u.Rsun, z*u.Rsun,
frame=frames.GeocentricSolarEcliptic, obstime=t,
representation_type="cartesian"
)
# Convert the MJDc from a float MJD to a UTC datetime.
obstime = kaiTools.MJD2UT(MJDc)
# Create the HGS(MJDc) frame for this data.
hgs_frame = frames.HeliographicStonyhurst(obstime=obstime)
# Convert the coordinates to the HGS(MJDc) frame.
# As a SkyCoord object, the converted coordinates are available
# in a variety of coordinate systems.
c = c.transform_to(hgs_frame)
# Convert the coordinates to the GH(MJDc) frame.
x = dm.dmarray(-c.cartesian.x)
y = dm.dmarray(-c.cartesian.y)
z = dm.dmarray(c.cartesian.z)
elif cdaweb_coordinate_system == "HGI":
# Fetch the value of 1 AU in kilometers.
AU_km = u.Quantity(1*u.astrophys.AU, u.km).value
# Fetch the value of 1 Rsun in kilometers.
Rsun_km = u.Quantity(1*u.Rsun, u.km).value
# Compute the conversion factor from AU to Rsun.
Rsun_per_AU = AU_km/Rsun_km
# Fetch time, HGI latitude, longitude, and radius (convert to Rsun).
# Note that these vaariables have different names for different
# spacecraft.
if "HGI_LAT" in sc_metadata[sc_id]["Ephem"]["Data"]:
t = sc_data["Epoch"] # Unbinned
lat = sc_data["HGI_LAT"]
lon = sc_data["HGI_LON"]
# Convert radius to Rsun.
rad = sc_data["RAD_AU"]*Rsun_per_AU
elif "heliographicLatitude" in sc_metadata[sc_id]["Ephem"]["Data"]:
t = sc_data["Epoch"] # Unbinned
lat = sc_data["heliographicLatitude"]
lon = sc_data["heliographicLongitude"]
# Convert radius to Rsun.
rad = sc_data["radialDistance"]*Rsun_per_AU
else:
raise TypeError("Unexpected HGI variable name(s): %s" %
sc_metadata[sc_id]["Ephem"]["Data"])
# Create SkyCoord objects for each HGI(t) position and time.
# Note HGI = Heliographic Inertial = Heliocentric Inertial
c = SkyCoord(
lon*u.deg, lat*u.deg, rad*u.Rsun,
frame=frames.HeliocentricInertial, obstime=t,
representation_type="spherical"
)
# Create the HGS(t0) coordinate frame.
mjdc_frame = frames.HeliographicStonyhurst(obstime=kaiTools.MJD2UT(MJDc))
# Convert the HGI(t) spherical (lon, lat, radius) positions to HGS(MJDc)).
c = c.transform_to(mjdc_frame)
# Convert the coordinates to the GH(MJDc) frame.
x = dm.dmarray(-c.cartesian.x)
y = dm.dmarray(-c.cartesian.y)
z = dm.dmarray(c.cartesian.z)
else:
raise TypeError("Unexpected ephemeris coordinate system: %s!" %
cdaweb_coordinate_system)
# Return the converted coordinates.
return x, y, z

0
kaipy/chimp/__init__.py
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94
kaipy/chimp/chimph5p.py
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@@ -1,94 +0,0 @@
#Various routines to deal with h5p data
import numpy as np
import h5py
#Return time at step n
def tStep(fname,nStp=0):
with h5py.File(fname,'r') as hf:
gID = "Step#%d"%(nStp)
grp = hf.get(gID)
t = grp.attrs.get("time")
return t
#Count number of timesteps in an h5p file
def cntSteps(fname):
with h5py.File(fname,'r') as hf:
grps = hf.values()
grpNames = [str(grp.name) for grp in grps]
#Steps = [stp if "/Step#" in stp for stp in grpNames]
Steps = [stp for stp in grpNames if "/Step#" in stp]
nSteps = len(Steps)
return nSteps
#Count number of particles in an h5p file
def cntTPs(fname):
with h5py.File(fname,'r') as hf:
grp = hf.get("Step#0")
ids = (grp.get("id")[()])
Np = ids.shape[0]
return Np
def bndTPs(fname):
with h5py.File(fname,'r') as hf:
grp = hf.get("Step#0")
ids = (grp.get("id")[()])
Np = ids.shape[0]
nS = ids.min()
nE = ids.max()
return Np,nS,nE
#Find array index for a given particle ID (ie if block doesn't start at 1)
def locPID(fname,pid):
with h5py.File(fname,'r') as hf:
grp = hf.get("Step#0")
ids = grp.get("id")[()]
isP = (ids == pid)
loc = isP.argmax()
if (ids[loc] != pid):
print("Didn't find particle %d ..."%(pid))
loc = None
quit()
return loc
#Given an h5part file, create a time series for a single particle w/ ID = pid
def getH5pid(fname,vId,pid):
p0 = locPID(fname,pid) #Find particle in array
Nt = cntSteps(fname) #Find number of slices
V = np.zeros(Nt)
t = np.zeros(Nt)
with h5py.File(fname,'r') as hf:
for n in range(Nt):
#Create gId
gId = "Step#%d"%(n)
grp = hf.get(gId)
t[n] = grp.attrs.get("time")
V[n] = (grp.get(vId)[()])[p0]
return t,V
#Given an h5part file, create a time series from an input string
def getH5p(fname,vId,Mask=None):
Nt = cntSteps(fname)
Np = cntTPs(fname)
t = np.zeros(Nt)
V = np.zeros((Nt,Np))
with h5py.File(fname,'r') as hf:
for n in range(Nt):
#Create gId
gId = "Step#%d"%(n)
grp = hf.get(gId)
t[n] = grp.attrs.get("time")
V[n,:] = grp.get(vId)[()]
if (Mask is None):
return t,V
else:
return t,V[:,Mask]
#Given an h5p file and step, get one slice of data
def getH5pT(fname,vID="isIn",nStp=0,cutIn=False):
with h5py.File(fname,'r') as hf:
gID = "Step#%d"%(nStp)
V = hf.get(gID).get(vID)[()]
return V

41
kaipy/chimp/chimpviz.py
View File

@@ -1,41 +0,0 @@
#Various routines to make 3D particle trajectories
import numpy as np
import h5py
#Nt = # of time slices
#Np = # of particles
#Nv = # of variables
#xyz = |Nt,Np,3|
#V = |Nt,Np,Nv|
#vIDs = |Nv|
def AddTPs(xyz,V=None,vIDs=None,fOut="tps.h5",nStp=0):
gID = "Step#%d"%(nStp)
oH5 = h5py.File(fOut,'w')
Nt,Np,Nd = xyz.shape
if (vIDs is not None):
Nv = len(vIDs)
else:
Nv = 0
#Create step data
oH5.create_group(gID)
oH5[gID].attrs.create("time",nStp)
for m in range(Np):
lID = "Line#%d"%(m)
#Add connectivity
ijL = np.zeros((Nt-1,2),dtype=int)
ijL[:,0] = np.arange(Nt-1)
ijL[:,1] = np.arange(1,Nt)
#Save line data
oH5[gID].create_group(lID)
oH5[gID][lID].attrs.create("Np",Nt)
oH5[gID][lID].create_dataset("xyz",data=np.single(xyz[:,m,:]))
oH5[gID][lID].create_dataset("LCon",data=ijL)
for i in range(Nv):
oH5[gID][lID].create_dataset(vIDs[i],data=np.single(V[:,m,i]))
oH5.close()

86
kaipy/chimp/kCyl.py
View File

@@ -1,86 +0,0 @@
#Various routines to deal with K-Cylinders from PSDs
import numpy as np
import datetime
import h5py
import kaipy.kaiViz as kv
#Get grid from K-Cyl
def getGrid(fIn,do4D=False):
with h5py.File(fIn,'r') as hf:
X3 = hf["X"][()].T
Y3 = hf["Y"][()].T
Z3 = hf["Z"][()].T
if (do4D):
Ai = hf["A"][()].T
xx = X3[:,:,0]
yy = Y3[:,:,0]
Zi = Z3[0,0,:]
Ki = 10**Zi
Kc = 0.5*(Ki[0:-1] + Ki[1:])
if (do4D):
Ac = 0.5*(Ai[0:-1] + Ai[1:])
return xx,yy,Ki,Kc,Ai,Ac
else:
return xx,yy,Ki,Kc
def getSlc(fIn,nStp=0,vID="jPSD",doWrap=False):
gID = "Step#%d"%(nStp)
with h5py.File(fIn,'r') as hf:
V = hf[gID][vID][()].T
if (doWrap):
return kv.reWrap(V)
else:
return V
#Pressure anisotropy
#doAsym : Px/Pz-1
#!doAsym: Px/(Px+Pz)
def PIso(fIn,nStp=0,pCut=1.0e-3,doAsym=False):
Pxy = getSlc(fIn,nStp,"Pxy")
Pz = getSlc(fIn,nStp,"Pz" )
Pk = 2*Pxy+Pz
Nx,Ny = Pz.shape
pR = np.zeros((Nx,Ny))
for i in range(Nx):
for j in range(Ny):
if (Pk[i,j]>pCut and Pz[i,j]>pCut):
if (doAsym):
pR[i,j] = Pxy[i,j]/Pz[i,j] - 1.0
else:
pR[i,j] = Pxy[i,j]/(Pxy[i,j]+Pz[i,j])
else:
if (doAsym):
pR[i,j] = 0.0
else:
pR[i,j] = np.nan
return pR
#Equatorial grids (option for wrapping for contours)
def getEQGrid(fIn,doCenter=False,doWrap=False):
if (doWrap):
doCenter = True
with h5py.File(fIn,'r') as hf:
xx = hf["X"][()].T
yy = hf["Y"][()].T
if (not doCenter):
return xx,yy
Ngi,Ngj = xx.shape
Ni = Ngi-1; Nj = Ngj-1
xxc = np.zeros((Ni,Nj))
yyc = np.zeros((Ni,Nj))
xxc = 0.25*( xx[0:Ngi-1,0:Ngj-1] + xx[1:Ngi,0:Ngj-1] + xx[0:Ngi-1,1:Ngj] + xx[1:Ngi,1:Ngj])
yyc = 0.25*( yy[0:Ngi-1,0:Ngj-1] + yy[1:Ngi,0:Ngj-1] + yy[0:Ngi-1,1:Ngj] + yy[1:Ngi,1:Ngj])
if (not doWrap):
return xxc,yyc
else:
return kv.reWrap(xxc),kv.reWrap(yyc)

257
kaipy/cmaps/cmDDiv.txt
View File

@@ -1,257 +0,0 @@
#High contrast red-orange-white-cyan-blue colorbar for dBn quick look plots
255 0 1
255 3 2
255 6 3
255 9 3
255 12 4
255 15 5
255 18 6
255 21 7
255 24 8
255 27 9
255 30 10
255 33 11
255 37 12
255 40 13
255 43 14
255 46 14
255 49 15
255 52 16
255 55 17
255 58 18
255 61 19
255 64 20
255 67 21
255 70 22
255 73 23
255 76 23
255 79 24
255 82 25
255 85 26
255 88 27
255 91 28
255 94 29
255 97 30
255 100 31
255 103 32
255 106 33
255 110 34
255 113 34
255 116 35
255 119 36
255 122 37
255 125 38
255 128 39
255 131 40
255 134 41
255 137 42
255 140 43
255 143 44
255 146 44
255 149 45
255 152 46
255 155 47
255 158 48
255 161 49
255 164 50
255 167 51
255 170 52
255 173 53
255 176 54
255 180 54
255 183 55
255 186 56
255 189 57
255 192 58
255 194 59
255 195 63
255 196 66
255 197 69
255 198 72
255 199 75
255 200 78
255 201 81
255 202 84
255 203 87
255 204 90
255 205 93
255 206 96
255 207 100
255 208 103
255 209 106
255 210 109
255 211 112
255 212 115
255 213 118
255 214 121
255 215 124
255 216 127
255 217 130
255 217 133
255 218 136
255 219 140
255 220 143
255 221 146
255 222 149
255 223 152
255 224 155
255 225 158
255 226 161
255 227 164
255 228 167
255 229 170
255 230 173
255 231 176
255 232 180
255 233 183
255 234 186
255 235 189
255 236 192
255 237 195
255 238 198
255 239 201
255 240 204
255 241 207
255 242 210
255 243 213
255 244 217
255 245 220
255 246 223
255 247 226
255 248 229
255 249 232
255 249 235
255 250 238
255 251 241
255 252 244
255 253 247
255 254 250
255 255 253
253 255 255
249 255 255
245 255 255
241 255 255
237 255 255
233 255 255
229 255 255
225 255 255
221 255 255
217 255 255
213 255 255
209 255 255
205 255 255
201 255 255
197 255 255
193 255 255
189 255 255
185 255 255
181 255 255
177 255 255
173 255 255
169 255 255
165 255 255
161 255 255
157 255 255
153 255 255
149 255 255
145 255 255
141 255 255
137 255 255
133 255 255
129 255 255
125 255 255
121 255 255
117 255 255
113 255 255
109 255 255
105 255 255
101 255 255
97 255 255
93 255 255
89 255 255
85 255 255
81 255 255
77 255 255
73 255 255
69 255 255
65 255 255
61 255 255
57 255 255
53 255 255
49 255 255
45 255 255
41 255 255
37 255 255
33 255 255
29 255 255
25 255 255
21 255 255
17 255 255
13 255 255
9 255 255
5 255 255
1 255 255
0 253 255
0 249 255
0 245 255
0 241 255
0 237 255
0 233 255
0 229 255
0 225 255
0 221 255
0 217 255
0 213 255
0 209 255
0 205 255
0 201 255
0 197 255
0 193 255
0 189 255
0 185 255
0 181 255
0 177 255
0 173 255
0 169 255
0 164 255
0 160 255
0 156 255
0 152 255
0 148 255
0 144 255
0 140 255
0 136 255
0 132 255
0 128 255
0 124 255
0 120 255
0 116 255
0 112 255
0 108 255
0 104 255
0 100 255
0 96 255
0 92 255
0 88 255
0 84 255
0 80 255
0 76 255
0 72 255
0 68 255
0 64 255
0 60 255
0 56 255
0 52 255
0 48 255
0 44 255
0 40 255
0 36 255
0 32 255
0 28 255
0 24 255
0 20 255
0 16 255
0 12 255
0 8 255
0 4 255
0 0 255

257
kaipy/cmaps/cmMLT.txt
View File

@@ -1,257 +0,0 @@
#Colorbar for MLT line plots matching color palette of SuperMAG SMRhh
255 194 1
255 191 1
255 188 1
255 185 1
255 182 1
255 179 1
255 176 1
255 173 1
255 170 1
255 167 1
255 164 1
255 160 1
255 157 1
255 154 1
255 151 1
255 148 1
255 145 1
255 142 1
255 139 1
255 136 1
255 133 1
255 130 1
255 127 1
255 124 0
255 121 0
255 118 0
255 115 0
255 112 0
255 109 0
255 106 0
255 103 0
255 100 0
255 97 0
255 94 0
255 91 0
255 87 0
255 84 0
255 81 0
255 78 0
255 75 0
255 72 0
255 69 0
255 66 0
255 63 0
255 60 0
255 57 0
255 54 0
255 51 0
255 48 0
255 45 0
255 42 0
255 39 0
255 36 0
255 33 0
255 30 0
255 27 0
255 24 0
255 21 0
255 17 0
255 14 0
255 11 0
255 8 0
255 5 0
255 2 0
255 0 1
251 0 5
247 0 9
243 0 13
239 0 17
235 0 21
231 0 25
227 0 29
223 0 33
219 0 37
215 0 41
211 0 45
207 0 49
203 0 53
199 0 57
195 0 61
191 0 65
187 0 69
183 0 73
179 0 77
175 0 81
171 0 85
167 0 89
162 0 93
158 0 97
154 0 101
150 0 105
146 0 109
142 0 113
138 0 117
134 0 121
130 0 125
126 0 129
122 0 133
118 0 137
114 0 141
110 0 145
106 0 149
102 0 153
98 0 157
94 0 161
90 0 165
86 0 169
82 0 173
78 0 177
74 0 181
70 0 185
66 0 189
62 0 193
58 0 197
54 0 201
50 0 205
46 0 209
42 0 213
38 0 217
34 0 221
30 0 225
26 0 229
22 0 233
18 0 237
14 0 241
10 0 245
6 0 249
2 0 253
0 2 253
0 6 249
0 10 245
0 14 241
0 18 237
0 22 233
0 26 229
0 30 225
0 34 221
0 38 217
0 42 213
0 46 209
0 50 205
0 54 201
0 58 197
0 62 193
0 66 189
0 70 185
0 74 181
0 78 177
0 82 173
0 86 169
0 90 165
0 94 161
0 98 157
0 102 153
0 106 149
0 110 145
0 114 141
0 118 137
0 122 133
0 126 129
0 130 125
0 134 121
0 138 117
0 142 113
0 146 109
0 150 105
0 154 101
0 158 97
0 162 93
0 167 89
0 171 85
0 175 81
0 179 77
0 183 73
0 187 69
0 191 65
0 195 61
0 199 57
0 203 53
0 207 49
0 211 45
0 215 41
0 219 37
0 223 33
0 227 29
0 231 25
0 235 21
0 239 17
0 243 13
0 247 9
0 251 5
0 255 1
3 255 0
7 254 0
11 253 0
15 252 0
19 251 0
23 250 0
27 249 0
31 248 0
35 247 0
39 246 0
43 245 0
47 245 0
51 244 0
55 243 0
59 242 0
63 241 0
67 240 0
71 239 0
75 238 0
79 237 0
83 236 0
87 235 0
91 234 0
95 233 0
99 232 0
103 231 0
107 230 0
111 229 0
115 228 0
119 227 0
123 226 0
127 225 0
131 224 0
135 223 0
139 222 0
143 222 0
147 221 0
151 220 0
155 219 0
159 218 0
163 217 0
168 216 0
172 215 0
176 214 0
180 213 0
184 212 0
188 211 0
192 210 0
196 209 0
200 208 0
204 207 0
208 206 0
212 205 0
216 204 0
220 203 0
224 202 0
228 201 0
232 200 0
236 200 0
240 199 0
244 198 0
248 197 0
252 196 0
255 195 0

15
kaipy/cmaps/kaimaps.py
View File

@@ -1,15 +0,0 @@
import matplotlib as mpl
import numpy as np
import os
#Pull colormap from text file
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fIn = os.path.join(__location__,"cmDDiv.txt")
Q = np.loadtxt(fIn,skiprows=1)
cmDiv = mpl.colors.ListedColormap(Q/255.0)
fIn = os.path.join(__location__,"cmMLT.txt")
Q = np.loadtxt(fIn,skiprows=1)
cmMLT = mpl.colors.ListedColormap(Q/255.0)

535
kaipy/embiggenUtils.py
View File

@@ -1,535 +0,0 @@
#Various routines to help with restart upscaling
#Updated to work w/ new-form restarts
import h5py
import numpy as np
import scipy
from scipy.spatial import ConvexHull
import kaipy.kaiH5 as kh5
import os
import kaipy.gamera.gamGrids as gg
TINY = 1.0e-8
NumG = 4
IDIR = 0
JDIR = 1
KDIR = 2
#Compare two restart sets to check if they're identical
def CompRestarts(iStr,oStr,nRes,Ri,Rj,Rk):
vIDs = ["X","Y","Z","Gas","oGas","Bxyz","oBxyz","magFlux","omagFlux"]
for i in range(Ri):
for j in range(Rj):
for k in range(Rk):
fIn1 = kh5.genName(iStr,i,j,k,Ri,Rj,Rk,nRes)
fIn2 = kh5.genName(oStr,i,j,k,Ri,Rj,Rk,nRes)
kh5.CheckOrDie(fIn1)
i1H5 = h5py.File(fIn1,'r')
kh5.CheckOrDie(fIn2)
i2H5 = h5py.File(fIn2,'r')
#print("Rijk = %d,%d,%d"%(i,j,k))
for v in vIDs:
Q1 = i1H5[v][:]
Q2 = i2H5[v][:]
#print("\tDelta-%-12s = %12.5e, Tot = %e,%e"%(v,np.abs(Q1-Q2).sum(),np.abs(Q1).sum(),np.abs(Q2).sum()))
# #Lazy interior loop
# if (v == "magFlux"):
# Nd,Nk,Nj,Ni = Q1.shape
# for ii in range(Ni):
# for jj in range(Nj):
# for kk in range(Nk):
# if (np.abs(Q1[:,kk,jj,ii]).min() > TINY):
# dQ = np.abs(Q1[:,kk,jj,ii]-Q2[:,kk,jj,ii]).sum()
# if (dQ>TINY):
# print("ijk, dQ = %d,%d,%d,%e"%(ii,jj,kk,dQ))
# print(Q1[:,kk,jj,ii])
# print(Q2[:,kk,jj,ii])
# #dQ = np.abs(Q1[:,kk,jj,ii]-Q2[:,kk,jj,ii]).sum()
# #dQ = np.abs(Q1[0,kk,jj,ii]-Q2[0,kk,jj,ii]).sum()
#Close up
i1H5.close()
i2H5.close()
#Get full data from a tiled restart file
def PullRestartMPI(bStr,nRes,Ri,Rj,Rk):
doInit = True
for i in range(Ri):
for j in range(Rj):
for k in range(Rk):
fIn = kh5.genName(bStr,i,j,k,Ri,Rj,Rk,nRes)
kh5.CheckOrDie(fIn)
#print("Reading from %s"%(fIn))
#Open input file
iH5 = h5py.File(fIn,'r')
if (doInit):
fIn0 = fIn
#Get size (w/ halos)
Ns,Nv,Nk,Nj,Ni = iH5['Gas'].shape
doGas0 = ('Gas0' in iH5.keys())
Nkp = Nk-2*NumG
Njp = Nj-2*NumG
Nip = Ni-2*NumG
#Get combined size (w/ ghosts)
NkT = Rk*Nkp + 2*NumG
NjT = Rj*Njp + 2*NumG
NiT = Ri*Nip + 2*NumG
#Allocate arrays (global)
nG = np.zeros((Ns,Nv,NkT,NjT,NiT))
oG = np.zeros((Ns,Nv,NkT,NjT,NiT))
if (doGas0):
G0 = np.zeros((Ns,Nv,NkT,NjT,NiT))
else:
G0 = None
nM = np.zeros((3,NkT+1,NjT+1,NiT+1))
oM = np.zeros((3,NkT+1,NjT+1,NiT+1))
nB = np.zeros((3,NkT ,NjT ,NiT ))
oB = np.zeros((3,NkT ,NjT ,NiT ))
X = np.zeros((NkT+1,NjT+1,NiT+1))
Y = np.zeros((NkT+1,NjT+1,NiT+1))
Z = np.zeros((NkT+1,NjT+1,NiT+1))
#Fill w/ nans for testing
nM[:] = np.nan
nG[:] = np.nan
nB[:] = np.nan
oM[:] = np.nan
oG[:] = np.nan
oB[:] = np.nan
X[:] = np.nan
Y[:] = np.nan
Z[:] = np.nan
doInit = False
#Get grids
Corner2Global(X,"X",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Corner2Global(Y,"Y",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Corner2Global(Z,"Z",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
#Get cell-centered quantities
Gas2Global(nG, "Gas",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Gas2Global(oG,"oGas",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
if (doGas0):
Gas2Global(G0,"Gas0",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Bvec2Global(nB, "Bxyz",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Bvec2Global(oB,"oBxyz",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
#Get face-centered quantities
Flux2Global(nM, "magFlux",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
Flux2Global(oM,"omagFlux",iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp)
#print("\tMPI (%d,%d,%d) = [%d,%d]x[%d,%d]x[%d,%d]"%(i,j,k,iS,iE,jS,jE,kS,kE))
#print("\tGrid indices = (%d,%d)x(%d,%d)x(%d,%d)"%(iSg,iEg,jSg,jEg,kSg,kEg))
#Close up
iH5.close()
# print(np.isnan(G).sum(),G.size)
# print(np.isnan(X).sum(),X.size)
# print(np.isnan(M).sum(),M.size)
return X,Y,Z,nG,nM,nB,oG,oM,oB,G0,fIn0
def Gas2Global(G,vID,iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp):
Ns,Nv,NkT,NjT,NiT = G.shape
Nk = Nkp + 2*NumG
Nj = Njp + 2*NumG
Ni = Nip + 2*NumG
locG = np.zeros((Ns,Nv,Nk,Nj,Ni))
locG = iH5[vID][:]
#Set general global/local indices
iS,iE,iSg,iEg = ccL2G(i,Ri,Nip)
jS,jE,jSg,jEg = ccL2G(j,Rj,Njp)
kS,kE,kSg,kEg = ccL2G(k,Rk,Nkp)
G[:,:,kSg:kEg,jSg:jEg,iSg:iEg] = locG[:,:,kS:kE,jS:jE,iS:iE]
def Bvec2Global(B,vID,iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp):
Nd,NkT,NjT,NiT = B.shape
Nk = Nkp + 2*NumG
Nj = Njp + 2*NumG
Ni = Nip + 2*NumG
locB = np.zeros((Nd,Nk,Nj,Ni))
locB = iH5[vID][:]
#Set general global/local indices
iS,iE,iSg,iEg = ccL2G(i,Ri,Nip)
jS,jE,jSg,jEg = ccL2G(j,Rj,Njp)
kS,kE,kSg,kEg = ccL2G(k,Rk,Nkp)
B[:,kSg:kEg,jSg:jEg,iSg:iEg] = locB[:,kS:kE,jS:jE,iS:iE]
def Flux2Global(M,vID,iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp):
Nd,NkT1,NjT1,NiT1 = M.shape
Nk = Nkp + 2*NumG
Nj = Njp + 2*NumG
Ni = Nip + 2*NumG
locM = np.zeros((Nd,Nk+1,Nj+1,Ni+1))
locM = iH5[vID][:]
#Set global/local indices
iS,iE,iSg,iEg = ccL2G(i,Ri,Nip)
jS,jE,jSg,jEg = ccL2G(j,Rj,Njp)
kS,kE,kSg,kEg = ccL2G(k,Rk,Nkp)
M[:,kSg:kEg+1,jSg:jEg+1,iSg:iEg+1] = locM[:,kS:kE+1,jS:jE+1,iS:iE+1]
def ccL2G(i,Ri,Nip):
#Set general case of only active
iS = NumG
iE = iS+Nip+1
iSg = NumG + i*Nip
iEg = iSg+Nip+1
if (i == 0):
#Need low-i ghosts
iS = 0
iSg = iSg-NumG
if (i == (Ri-1)):
iE = iE +NumG
iEg = iEg+NumG
return iS,iE,iSg,iEg
#Pull corner-centered variables w/ halos into global sized grid
def Corner2Global(Q,vID,iH5,i,j,k,Ri,Rj,Rk,Nip,Njp,Nkp):
iS = i*Nip
iE = iS+Nip
jS = j*Njp
jE = jS+Njp
kS = k*Nkp
kE = kS+Nkp
iSg = iS -NumG + NumG
iEg = iS+Nip+NumG+1 + NumG
jSg = jS -NumG + NumG
jEg = jS+Njp+NumG+1 + NumG
kSg = kS -NumG + NumG
kEg = kS+Nkp+NumG+1 + NumG
Q[kSg:kEg,jSg:jEg,iSg:iEg] = iH5[vID][:]
#Push restart data w/ ghosts to an output tiling
def PushRestartMPI(outid,nRes,Ri,Rj,Rk,X,Y,Z,nG,nM,nB,oG,oM,oB,G0,f0,dtScl=1.0):
if (G0 is not None):
doGas0 = True
#print(X.shape,nG.shape,nM.shape,nB.shape)
Ns,Nv,NkT,NjT,NiT = nG.shape
Nkp = (NkT-2*NumG)//Rk
Njp = (NjT-2*NumG)//Rj
Nip = (NiT-2*NumG)//Ri
print("Reading attributes from %s"%(f0))
iH5 = h5py.File(f0,'r')
#print("Splitting (%d,%d,%d) cells into (%d,%d,%d) x (%d,%d,%d) [Cells,MPI]"%(Ni,Nj,Nk,Nip,Njp,Nkp,Ri,Rj,Rk))
#Loop over output slices and create restarts
for i in range(Ri):
for j in range(Rj):
for k in range(Rk):
fOut = kh5.genName(outid,i,j,k,Ri,Rj,Rk,nRes)
#print("Writing to %s"%(fOut))
if (os.path.exists(fOut)):
os.remove(fOut)
#Open output file
oH5 = h5py.File(fOut,'w')
#Transfer attributes to output
for ak in iH5.attrs.keys():
aStr = str(ak)
#print(aStr)
if (aStr == "dt0"):
oH5.attrs.create(ak,dtScl*iH5.attrs[aStr])
print(dtScl*iH5.attrs[aStr])
else:
oH5.attrs.create(ak,iH5.attrs[aStr])
#Base indices
iS = i*Nip
iE = iS+Nip
jS = j*Njp
jE = jS+Njp
kS = k*Nkp
kE = kS+Nkp
#Do subgrids
iSg = iS -NumG + NumG
iEg = iS+Nip+NumG+1 + NumG
jSg = jS -NumG + NumG
jEg = jS+Njp+NumG+1 + NumG
kSg = kS -NumG + NumG
kEg = kS+Nkp+NumG+1 + NumG
ijkX = X[kSg:kEg,jSg:jEg,iSg:iEg]
ijkY = Y[kSg:kEg,jSg:jEg,iSg:iEg]
ijkZ = Z[kSg:kEg,jSg:jEg,iSg:iEg]
#Do face fluxes (same indices as subgrids)
ijknM = nM [:,kSg:kEg,jSg:jEg,iSg:iEg]
ijkoM = oM [:,kSg:kEg,jSg:jEg,iSg:iEg]
#Do cell-centered values
iSg = iS -NumG + NumG
iEg = iS+Nip+NumG + NumG
jSg = jS -NumG + NumG
jEg = jS+Njp+NumG + NumG
kSg = kS -NumG + NumG
kEg = kS+Nkp+NumG + NumG
ijknG = nG[:,:,kSg:kEg,jSg:jEg,iSg:iEg]
ijknB = nB[:,kSg:kEg,jSg:jEg,iSg:iEg]
ijkoG = oG[:,:,kSg:kEg,jSg:jEg,iSg:iEg]
ijkoB = oB[:,kSg:kEg,jSg:jEg,iSg:iEg]
if (doGas0):
ijkG0 = G0[:,:,kSg:kEg,jSg:jEg,iSg:iEg]
#Write vars
oH5.create_dataset( "Gas" ,data=ijknG)
oH5.create_dataset( "magFlux",data=ijknM)
oH5.create_dataset( "Bxyz" ,data=ijknB)
oH5.create_dataset("oGas" ,data=ijkoG)
oH5.create_dataset("omagFlux",data=ijkoM)
oH5.create_dataset("oBxyz" ,data=ijkoB)
oH5.create_dataset("X",data=ijkX)
oH5.create_dataset("Y",data=ijkY)
oH5.create_dataset("Z",data=ijkZ)
if (doGas0):
oH5.create_dataset("Gas0",data=ijkG0)
#Close this output file
oH5.close()
#Close input file
iH5.close()
#Upscale a grid (with ghosts, k-j-i order)
def upGrid(X,Y,Z):
Ngk,Ngj,Ngi = X.shape
Nk = Ngk-2*NumG-1
Nj = Ngj-2*NumG-1
Ni = Ngi-2*NumG-1
#Assuming LFM-style grid, get upper half plane
xx = X[NumG,NumG:-NumG,NumG:-NumG].T
yy = Y[NumG,NumG:-NumG,NumG:-NumG].T
#Now half cells in r-phi polar
rr = np.sqrt(xx**2.0+yy**2.0)
pp = np.arctan2(yy,xx)
NiH = 2*Ni
NjH = 2*Nj
NkH = 2*Nk
rrH = np.zeros((NiH+1,NjH+1))
ppH = np.zeros((NiH+1,NjH+1))
#Embed old points into new grid
for i in range(Ni+1):
for j in range(Nj+1):
rrH[2*i,2*j] = rr[i,j]
ppH[2*i,2*j] = pp[i,j]
#Create I midpoints
for i in range(Ni):
rrH[2*i+1,:] = 0.5*( rrH[2*i,:] + rrH[2*i+2,:] )
ppH[2*i+1,:] = 0.5*( ppH[2*i,:] + ppH[2*i+2,:] )
#Create J midpoints
for j in range(Nj):
rrH[:,2*j+1] = 0.5*( rrH[:,2*j] + rrH[:,2*j+2])
ppH[:,2*j+1] = 0.5*( ppH[:,2*j] + ppH[:,2*j+2])
#Create I-J midpoints
for i in range(Ni):
for j in range(Nj):
rrH[2*i+1,2*j+1] = 0.25*( rrH[2*i,2*j] + rrH[2*i,2*j+2] + rrH[2*i+2,2*j] + rrH[2*i+2,2*j+2] )
ppH[2*i+1,2*j+1] = 0.25*( ppH[2*i,2*j] + ppH[2*i,2*j+2] + ppH[2*i+2,2*j] + ppH[2*i+2,2*j+2] )
#Convert back to 2D Cartesian
xxH = rrH*np.cos(ppH)
yyH = rrH*np.sin(ppH)
#Augment w/ 2D ghosts
xxG,yyG = gg.Aug2D(xxH,yyH,doEps=True,TINY=TINY)
#Augment w/ 3D ghosts
X,Y,Z = gg.Aug3D(xxG,yyG,Nk=NkH,TINY=TINY)
return X.T,Y.T,Z.T
#Return cell centered volume from grid X,Y,Z (w/ ghosts)
def Volume(Xg,Yg,Zg):
Ngk1,Ngj1,Ngi1 = Xg.shape
Nk = Ngk1-1
Nj = Ngj1-1
Ni = Ngi1-1
print("Calculating volume of grid of size (%d,%d,%d)"%(Ni,Nj,Nk))
dV = np.zeros((Nk,Nj,Ni))
ijkPts = np.zeros((8,3))
#Assuming LFM-like symmetry
k = 0
for j in range(Nj):
for i in range(Ni):
ijkPts[:,0] = Xg[k:k+2,j:j+2,i:i+2].flatten()
ijkPts[:,1] = Yg[k:k+2,j:j+2,i:i+2].flatten()
ijkPts[:,2] = Zg[k:k+2,j:j+2,i:i+2].flatten()
dV[:,j,i] = ConvexHull(ijkPts,incremental=False).volume
return dV
#Upscale magnetic fluxes (M) on grid X,Y,Z (w/ ghosts) to doubled grid
def upFlux(M):
#Chop out outer two cells, upscale inside
cM = M[:,2:-2,2:-2,2:-2]
Nd,Nkc,Njc,Nic = cM.shape
Nk = Nkc-1
Nj = Njc-1
Ni = Nic-1
Mu = np.zeros((Nd,2*Nk+1,2*Nj+1,2*Ni+1))
#Loop over coarse grid
print("Upscaling face fluxes ...")
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
ip = 2*i
jp = 2*j
kp = 2*k
#West i face (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip] = 0.25*cM[IDIR,k,j,i]
#East i face (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip+2] = 0.25*cM[IDIR,k,j,i+1]
#South j face (4)
Mu[JDIR,kp:kp+2,jp,ip:ip+2] = 0.25*cM[JDIR,k,j,i]
#North j face (4)
Mu[JDIR,kp:kp+2,jp+2,ip:ip+2] = 0.25*cM[JDIR,k,j+1,i]
#Bottom k face (4)
Mu[KDIR,kp,jp:jp+2,ip:ip+2] = 0.25*cM[KDIR,k,j,i]
#Top k face (4)
Mu[KDIR,kp+2,jp:jp+2,ip:ip+2] = 0.25*cM[KDIR,k+1,j,i]
#Now all exterior faces are done
#12 remaining interior faces
#Interior i faces (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip+1] = 0.5*( Mu[IDIR,kp:kp+2,jp:jp+2,ip] + Mu[IDIR,kp:kp+2,jp:jp+2,ip+2] )
#Interior j faces (4)
Mu[JDIR,kp:kp+2,jp+1,ip:ip+2] = 0.5*( Mu[JDIR,kp:kp+2,jp,ip:ip+2] + Mu[JDIR,kp:kp+2,jp+2,ip:ip+2] )
#Interior k faces (4)
Mu[KDIR,kp+1,jp:jp+2,ip:ip+2] = 0.5*( Mu[KDIR,kp,jp:jp+2,ip:ip+2] + Mu[KDIR,kp+2,jp:jp+2,ip:ip+2] )
#MaxDiv(M)
#MaxDiv(Mu)
return Mu
#Upscale gas variable (G) on grid X,Y,Z (w/ ghosts) to doubled grid
def upGas(G,dV0,dVu,vID="Gas"):
#Chop out outer two cells, upscale inside
cG = G[:,:,2:-2,2:-2,2:-2]
cdV0 = dV0[2:-2,2:-2,2:-2]
Ns,Nv,Nk,Nj,Ni = cG.shape
Gu = np.zeros((Ns,Nv,2*Nk,2*Nj,2*Ni))
print("Upscaling %s variables ..."%(vID))
#Loop over coarse grid
for s in range(Ns):
for v in range(Nv):
print("\tUpscaling Species %d, Variable %d"%(s,v))
Gu[s,v,:,:,:] = upVarCC(cG[s,v,:,:,:],cdV0,dVu)
return Gu
#Upscale Bxyz on grid X,Y,Z (w/ ghosts) to doubled grid
def upCCMag(B,dV0,dVu,vID="Bxyz"):
#Chop out outer two cells, upscale inside
cB = B[:,2:-2,2:-2,2:-2]
cdV0 = dV0[2:-2,2:-2,2:-2]
Nv,Nk,Nj,Ni = cB.shape
Bu = np.zeros((Nv,2*Nk,2*Nj,2*Ni))
print("Upscaling %s variables ..."%(vID))
#Loop over coarse grid
for v in range(Nv):
print("\tUpscaling Variable %d"%(v))
Bu[v,:,:,:] = upVarCC(cB[v,:,:,:],cdV0,dVu)
return Bu
#Upscale single cell-centered variable Q, dV0=dV on coarse, dVu=dV on fine grid
def upVarCC(Q,dV0,dVu):
Nk,Nj,Ni = Q.shape
Qu = np.zeros((2*Nk,2*Nj,2*Ni))
#Loop over coarse grid
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
QdV = Q[k,j,i]*dV0[k,j,i]
dVijk = dVu[2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] #Volumes of the finer subgrid
vScl = dVijk.sum() #Total volume of the finer subchunks
QdVu = QdV*(dVijk/vScl) #Give weighted contribution to each subcell
Qu[2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] = QdVu/dVijk #Scale back to density
#Test conservation
print("\t\tCoarse (Total) = %e"%(Q [:,:,:]*dV0).sum())
print("\t\tFine (Total) = %e"%(Qu[:,:,:]*dVu).sum())
return Qu
#Calculates maximum divergence of mag flux data
def MaxDiv(M):
Nd,Nkc,Njc,Nic = M.shape
Nk = Nkc-1
Nj = Njc-1
Ni = Nic-1
Div = np.zeros((Nk,Nj,Ni))
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
Div[k,j,i] = M[IDIR,k,j,i+1]-M[IDIR,k,j,i]+M[JDIR,k,j+1,i]-M[JDIR,k,j,i]+M[KDIR,k+1,j,i]-M[KDIR,k,j,i]
mDiv = np.abs(Div).max()
bDiv = np.abs(Div).mean()
print("Max/Mean divergence = %e,%e"%(mDiv,bDiv))
print("Sum divergence = %e"%(Div.sum()))
return Div

0
kaipy/gamera/__init__.py
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351
kaipy/gamera/block_gampp.py
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@@ -1,351 +0,0 @@
# Gamera post-processing routines
# Get data from serial/MPI gamera output
import glob
import numpy as np
# Object to use to pull data from HDF5 structure (serial or mpi)
# Initialize,
# gIn = gampp.GameraPipe(fdir,ftag)
# Set grid from data
# gIn.GetGrid()
# V = gIn.GetVar("D",stepnum)
# doFast=True skips various data scraping
idStr = "_0000_0000_0000.gam.h5"
idStr_old = "_0000_0000_0000_000000000000.h5"
class GameraPipe(object):
# Initialize GP object
# fdir = directory to h5 files
# ftag = stub of h5 files
def __init__(self, fdir, ftag, doFast=False, doVerbose=True):
self.fdir = fdir
self.ftag = ftag
# Here just create variables
# -----------
self.isOld = False
# Ranks
self.isMPI = False
self.Nr = 0
self.Ri = 1; self.Rj = 1; self.Rk = 1
# Global/local cells
self.is2D = False
self.Ni = 0; self.Nj = 0; self.Nk = 0
self.dNi = [] ; self.dNj = [] ; self.dNk = []
# Variables/slices
self.Nv = 0; self.Nt = 0; self.Nv0 = 0
self.T = []; self.vIDs = []; self.v0IDs = []
self.s0 = 0; self.sFin = 0
# Grids
self.X = []; self.Y = []; self.Z = []
self.doFast = doFast
self.UnitsID = "NONE"
# Stubs for MJD stuff
self.hasMJD = False
self.MJDs = []
# Scrape data from directory
self.doVerbose = True
self.OpenPipe(doVerbose)
def OpenPipe(self, doVerbose=True):
import kaipy.kaiH5 as kh5
if (doVerbose):
print("Opening pipe: %s : %s" % (self.fdir, self.ftag))
# Test for serial (either old or new naming convention)
fOld = "%s/%s.h5" % (self.fdir, self.ftag)
fNew = "%s/%s.gam.h5" % (self.fdir, self.ftag)
print("fNew ",fNew)
if (len(glob.glob(fOld)) == 1):
# Found old-style serial
self.isMPI = False
if (doVerbose):
print("Found serial database")
f0 = fOld
elif (len(glob.glob(fNew)) == 1):
# Found new-style serial
self.isMPI = False
self.ftag = self.ftag + ".gam" # Add .gam to tag
if (doVerbose):
print("Found serial database")
f0 = fNew
else:
print("%s not found, looking for MPI database" % (fNew))
self.isMPI = True
self.isOld = False
sStr = "%s/%s_*%s" % (self.fdir, self.ftag, idStr)
fIns = glob.glob(sStr)
if (len(fIns) > 1):
print("This shouldn't happen, bailing ...")
if (len(fIns) == 0):
print("No new MPI database found, checking old ...")
self.isMPI = True
self.isOld = True
sStr = "%s/%s_*%s" % (self.fdir, self.ftag, idStr_old)
fIns = glob.glob(sStr)
if (len(fIns) > 1):
print("This shouldn't happen, bailing ...")
if (len(fIns) == 0):
print("No old MPI database found, out of options, bailing ...")
quit()
f0 = fIns[0]
print("fIns ",fIns)
Ns = [int(s) for s in f0.split('_') if s.isdigit()]
self.Ri = Ns[0]
self.Rj = Ns[1]
self.Rk = Ns[2]
self.Nr = self.Ri*self.Rj*self.Rk
if (doVerbose):
print("\tFound %d = (%d,%d,%d) ranks" %
(self.Nr, self.Ri, self.Rj, self.Rk))
self.dNi = np.zeros(self.Ri,dtype=int)
self.dNj = np.zeros(self.Rj,dtype=int)
self.dNk = np.zeros(self.Rk,dtype=int)
# In either case, f0 is defined. use it to get per file stuff
self.Nt, sids = kh5.cntSteps(f0)
if (self.doFast):
self.T = np.zeros(self.Nt)
else:
self.T = kh5.getTs(f0, sids)
self.s0 = sids.min()
self.sFin = sids.max()
if (doVerbose):
print("Found %d timesteps\n\tTime = [%f,%f]" % (
self.Nt, self.T.min(), self.T.max()))
print("\tSteps = [%d,%d]" % (sids.min(), sids.max()))
# Get MJD if present
# MJDs = kh5.getTs(f0, sids, "MJD", -np.inf)
# if (MJDs.max() > 0):
# self.hasMJD = True
# self.MJDs = MJDs
# Get grid stuff
Dims = kh5.getDims(f0)
print("DIMS ",Dims)
Nd=len(Dims)
"""
self.dNi=Dims[0]-1
self.dNj=Dims[1]-1
if (Nd > 2):
self.dNk=Dims[2]-1
else:
self.dNk=0
self.Rk=1
self.Ni=self.dNi*self.Ri
self.Nj=self.dNj*self.Rj
self.Nk=self.dNk*self.Rk
"""
self.Ni = 0
for i in range(self.Ri):
if (self.isOld):
joff = i*self.Rj
fi = "%s/%s_%04d_%04d_%04d_%04d_0000_0000_%012d.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,i,joff)
else:
fi = "%s/%s_%04d_%04d_%04d_%04d_0000_0000.gam.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,i)
print(" fi is ",fi)
dims = kh5.getDims(fi)
self.dNi[i] = dims[0]-1
self.Ni = self.Ni + self.dNi[i]
print(" Ni, dNi ",self.Ni,self.dNi)
self.Nj = 0
for j in range(self.Rj):
if (self.isOld):
fj = "%s/%s_%04d_%04d_%04d_0000_%04d_0000_%012d.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,j,j)
else:
fj = "%s/%s_%04d_%04d_%04d_0000_%04d_0000.gam.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,j)
print(fj)
dims = kh5.getDims(fj)
self.dNj[j] = dims[1]-1
self.Nj = self.Nj + self.dNj[j]
print(" Nj, dNj ",self.Nj,self.dNj)
if (Nd >2 ):
for k in range(self.Rk):
if (self.isOld):
koff = self.Ri*self.Rj*k
fk = "%s/%s_%04d_%04d_%04d_0000_0000_%04d_%012d.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,k,koff)
else:
fk = "%s/%s_%04d_%04d_%04d_0000_0000_%04d.gam.h5" % (self.fdir, self.ftag, self.Ri, self.Rj, self.Rk,k)
print(fk)
dims = kh5.getDims(fk)
self.dNk[k] = dims[2]-1
self.Nk = self.Nk + self.dNk[k]
print(" Nk, dNk ",self.Nk,self.dNk)
# Variables
self.v0IDs=kh5.getRootVars(f0)
self.vIDs=kh5.getVars(f0, sids.min())
self.Nv0=len(self.v0IDs)
self.Nv=len(self.vIDs)
if (Nd > 2):
self.is2D=False
else:
self.is2D=True
if (doVerbose):
if (Nd > 2):
nCells=self.Ni*self.Nj*self.Nk
else:
nCells=self.Ni*self.Nj
print("Grid size = (%d,%d,%d)" % (self.Ni, self.Nj, self.Nk))
print("\tCells = %e" % (nCells))
print("Variables (Root/Step) = (%d,%d)" % (self.Nv0, self.Nv))
print("\tRoot: %s" % (self.v0IDs))
print("\tStep: %s" % (self.vIDs))
self.SetUnits(f0)
if (doVerbose):
print("Units Type = %s" % (self.UnitsID))
print("Pulling grid ...")
self.GetGrid(doVerbose)
def SetUnits(self, f0):
import h5py
with h5py.File(f0, 'r') as hf:
uID=hf.attrs.get("UnitsID", "CODE")
if (not isinstance(uID, str)):
self.UnitsID=uID.decode('utf-8')
# Chunks go from 0,Ri-1 (Python iteration)
def ChunkName(self, i, j, k):
print( "ChunkName %s,%d,%d,%d "%(self.isOld,i,j,k))
if (self.isMPI):
fIn="blah"
n=j + i*self.Rj + k*self.Ri*self.Rj
if (self.isOld):
fRn="%04d_%04d_%04d_%04d_%04d_%04d_%012d" % (self.Ri, self.Rj, self.Rk, i, j, k,n)
fIn=self.fdir+"/"+self.ftag+"_"+fRn+".h5"
else:
fRn="%04d_%04d_%04d_%04d_%04d_%04d" % (self.Ri, self.Rj, self.Rk, i, j, k)
fIn=self.fdir+"/"+self.ftag+"_"+fRn+".gam.h5"
else:
fIn=self.fdir+"/"+self.ftag+".h5"
return fIn
print(fIn)
def GetGrid(self, doVerbose):
import kaipy.kaiH5 as kh5
if (self.is2D):
self.X=np.zeros((self.Ni+1, self.Nj+1))
self.Y=np.zeros((self.Ni+1, self.Nj+1))
else:
self.X=np.zeros((self.Ni+1, self.Nj+1, self.Nk+1))
self.Y=np.zeros((self.Ni+1, self.Nj+1, self.Nk+1))
self.Z=np.zeros((self.Ni+1, self.Nj+1, self.Nk+1))
if (doVerbose):
print("Deli = " , self.dNi)
print("Delj = " , self.dNj)
print("Delk = " , self.dNk)
iS = 0
for i in range(self.Ri):
iE=iS+self.dNi[i]
jS = 0
for j in range(self.Rj):
jE=jS+self.dNj[j]
kS = 0
for k in range(self.Rk):
kE=kS+self.dNk[k]
print("Bounds = (%d,%d,%d,%d,%d,%d)" % (iS, iE, jS, jE, kS, kE))
#print(fIn)
# print("Bounds = (%d,%d,%d,%d,%d,%d)"%fIn.GetDims)
fIn=self.ChunkName(i, j, k)
if (self.is2D):
self.X[iS:iE+1, jS:jE+1]=kh5.PullVar(fIn, "X")
self.Y[iS:iE+1, jS:jE+1]=kh5.PullVar(fIn, "Y")
else:
self.X[iS:iE+1, jS:jE+1, kS:kE+1]=kh5.PullVar(fIn, "X")
self.Y[iS:iE+1, jS:jE+1, kS:kE+1]=kh5.PullVar(fIn, "Y")
self.Z[iS:iE+1, jS:jE+1, kS:kE+1]=kh5.PullVar(fIn, "Z")
kS=k*self.dNk[k]
jS=jS + self.dNj[j]
iS=iS + self.dNi[i]
# Get 3D variable "vID" from Step# sID
def GetVar(self, vID, sID, vScl=None, doVerb=True):
import kaipy.kaiH5 as kh5
if (doVerb):
if (sID is None):
print("Reading %s/%s" % (self.ftag, vID))
else:
print("Reading %s/Step#%d/%s" % (self.ftag, sID, vID))
if (self.is2D):
V=np.zeros((self.Ni, self.Nj))
else:
V=np.zeros((self.Ni, self.Nj, self.Nk))
for i in range(self.Ri):
for j in range(self.Rj):
for k in range(self.Rk):
iS=i*self.dNi
jS=j*self.dNj
kS=k*self.dNk
iE=iS+self.dNi
jE=jS+self.dNj
kE=kS+self.dNk
# print("Bounds = (%d,%d,%d,%d,%d,%d)"%(iS,iE,jS,jE,kS,kE))
fIn=self.ChunkName(i, j, k)
if (self.is2D):
V[iS:iE, jS:jE]=kh5.PullVar(fIn, vID, sID)
else:
V[iS:iE, jS:jE, kS:kE]=kh5.PullVar(fIn, vID, sID)
if (vScl is not None):
V=vScl*V
return V
# Get variable slice of constant i,j,k
# Directions = idir/jdir/kdir strings
# Indexing = (1,Nijk)
# FIXME: Currently pulling whole 3D array and then slicing, lazy
def GetSlice(self, vID, sID, ijkdir='idir', n=1, vScl=None, doVerb=True):
sDirs=['IDIR', 'JDIR', 'KDIR']
ijkdir=ijkdir.upper()
if (not (ijkdir in sDirs)):
print("Invalid slice direction, defaulting to I")
ijkdir='IDIR'
if (sID is None):
cStr="Reading %s/%s" % (self.ftag, vID)
else:
cStr="Reading %s/Step#%d/%s" % (self.ftag, sID, vID)
V=self.GetVar(vID, sID, vScl, doVerb=False)
# Now slice
np=n-1 # Convert from Fortran to Python indexing
if (ijkdir == "IDIR"):
Vs=V[np, :, :]
sStr="(%d,:,:)" % (n)
elif (ijkdir == "JDIR"):
Vs=V[:, np, :]
sStr="(:,%d,:)" % (n)
elif (ijkdir == "KDIR"):
Vs=V[:, :, np]
sStr="(:,:,%d)" % (n)
if (doVerb):
print(cStr+sStr)
return Vs
# Wrappers for root variables (or just set sID to "None")
def GetRootVar(self, vID, vScl=None, doVerb=True):
V=self.GetVar(vID, sID=None, vScl=vScl, doVerb=doVerb)
def GetRootSlice(self, vID, ijkdir='idir', n=1, vScl=None, doVerb=True):
Vs=self.GetSlice(vID, sID=None, ijkdir=ijkdir, n=n, vScl=vScl, doVerb=doVerb)
return Vs

111
kaipy/gamera/deltabViz.py
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@@ -1,111 +0,0 @@
#Various helper routines for making ground DB pics
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.feature.nightshade import Nightshade
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.ticker as mticker
import kaipy.kaiH5 as kh5
import kaipy.kaiTools as ktools
rad2deg = 180.0/np.pi
dbMag = 1000.0
dbLin = 10.0
jMag = 0.5
#Do various decorations on a plot to make it look pretty
def DecorateDBAxis(Ax,crs,utDT):
Ax.add_feature(Nightshade(utDT,alpha=0.10))
Ax.coastlines(resolution='110m', color='black', linewidth=0.25)
gl = Ax.gridlines(crs, draw_labels=True,linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.top_labels = False
gl.left_labels = False
gl.xlines = False
gl.ylines = False
gl.xlocator = mticker.FixedLocator([-120, 0, 120])
gl.ylocator = mticker.FixedLocator([-45, 0, 45])
#Ax.contour(LonC,LatC,smlon,[0.5,90,180,270],linewidths=1.25, linestyles='--',alpha=0.5,colors='grey')
#Get coordinate information
def GetCoords(fname):
cStr = kh5.PullAtt(fname,"CoordinatesID",s0=None)
Re = kh5.PullAtt(fname,"Re",s0=None) #km
try:
CoordID = cStr.decode('utf-8')
except (UnicodeDecodeError, AttributeError):
CoordID = cStr
return CoordID,Re
#Generate uniform lat/lon grid
def GenUniformLL(dbdata,k0=0):
X0 = dbdata.X[:,:,k0]
Y0 = dbdata.Y[:,:,k0]
Z0 = dbdata.Z[:,:,k0]
NLat = Z0.shape[0]-1
NLon = Z0.shape[1]-1
R0 = np.sqrt(X0**2.0 + Y0**2.0 + Z0**2.0)
LonI = np.arctan2(Y0,X0)*rad2deg
LatI = np.arcsin(Z0/R0) *rad2deg
LonI = np.linspace(0,360,NLon+1)
LatI = LatI[:,0]
LonC = 0.5*(LonI[0:-1] + LonI[1:])
LatC = 0.5*(LatI[0:-1] + LatI[1:])
return LatI,LonI,LatC,LonC
def CheckLevel(dbdata,k0,Re):
NLat = dbdata.Z.shape[0]-1
NLon = dbdata.Z.shape[1]-1
Nz = dbdata.Z.shape[2]-1
Rcc = dbdata.GetRootVar("Radcc")
if (k0 >= Nz):
print("Invalid vertical level, only %d levels found!"%(Nz))
sys.exit()
else:
h0 = (Rcc.mean()*Re)-Re
#print("Height = %6.2f [km]"%(h0))
return h0
def GenTStr(Ax,fname,nStp,doVerb=True):
#Get MJD to UT
MJD = kh5.tStep(fname,nStp,aID="MJD")
utS = ktools.MJD2UT([MJD])
utDT= utS[0]
#Get SMRs/SMLs
SMR = kh5.PullAtt(fname,"SMR",nStp)
SMR06 = kh5.PullAtt(fname,"SMR_06",nStp)
SMR12 = kh5.PullAtt(fname,"SMR_12",nStp)
SMR18 = kh5.PullAtt(fname,"SMR_18",nStp)
SMR00 = kh5.PullAtt(fname,"SMR_00",nStp)
SML = kh5.PullAtt(fname,"SML",nStp)
SMU = kh5.PullAtt(fname,"SMU",nStp)
SME = kh5.PullAtt(fname,"SME",nStp)
tStr = utDT.strftime("%m/%d/%Y\n%H:%M:%S")
aStr = "Auroral Indices [nT]\nSME = %8.2f\nSML = %8.2f\nSMU = %8.2f"%(SME,SML,SMU)
#aStr = "SME = %8.2f\nSML = %8.2f\nSMU = %8.2f"%(SME,SML,SMU)
rStr = "Ring Current Indices [nT]\nSMR = %8.2f\nDawn-Dusk = %8.2f\nDay-Night = %8.2f"%(SMR,SMR06-SMR18,SMR12-SMR00)
#rStr = "SMR = %7.2f"%(SMR)
#titStr = tStr + "\n" + aStr + "\n" + rStr
titStr = aStr + "\n" + rStr
#plt.suptitle(tStr,fontsize="x-large")
iSize = "medium"
Ax.set_title(tStr,fontsize="x-large",loc="center")
Ax.set_title(aStr,fontsize=iSize ,loc="right",fontname="monospace")
Ax.set_title(rStr,fontsize=iSize ,loc="left" ,fontname="monospace")
return titStr

810
kaipy/gamera/gamGrids.py
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@@ -1,810 +0,0 @@
#Various routines to generate HDF5 grids to read into Gamera
import numpy as np
from scipy import interpolate
import sys
import os
from scipy.ndimage import gaussian_filter
#Use routine to generate xx,yy = corners of active upper half plane
#Use Aug2D to add ghosts in 2D: xx,yy -> xxG,yyG
#Use Aug3D to rotate in 3D w/ ghosts: xxG,yyG -> X3,Y3,Z3
#Globals
Re = 6.38e+8 #Earth radius [cm]
Rj = 11.209*Re
Rs = 9.5*Re
iRe = 1/Re
Ng = 4
Ni0 = 32
Nj0 = 64
Nk0 = 32
#Gen LFM egg
def genLFM(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=25.0,fIn="lfm.hdf",TINY=1.0e-8):
#Get from LFM data
xx0,yy0 = getLFM(fIn=fIn,Rin=Rin,Rout=Rout)
XX,YY = regrid(xx0,yy0,Ni,Nj,TINY=TINY)
return XX,YY
#Gen elliptical grid
def genEllip(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=30,TINY=1.0e-8):
XX = np.zeros((Ni+1,Nj+1))
YY = np.zeros((Ni+1,Nj+1))
P = np.linspace(0+TINY,np.pi-TINY,Nj+1)
Tau = np.linspace(Rin,Rout,Ni+1)
A = 6
A = 1
#eMax = 0.75
#eI = np.linspace(0,eMax,Ni+1)
e = 0.75
#B = 0.1
#x0 = 0.0
for i in range(Ni+1):
for j in range(Nj+1):
phi = P[j]
e = eI[i]
r = Tau[i]*A*(1-e*e)/(1+e*np.cos(phi))
#XX[i,j] = x0 + A*Tau[i]*np.cos(P[j])
#YY[i,j] = B*Tau[i]*np.sin(P[j])
XX[i,j] = r*np.cos(P[j])
YY[i,j] =r*np.sin(P[j])
return XX,YY
#Gen spherical grid
def genSph(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=30,TINY=1.0e-8):
print("Generating spherical grid w/ radial bounds %3.2f->%3.2f"%(Rin,Rout))
XX = np.zeros((Ni+1,Nj+1))
YY = np.zeros((Ni+1,Nj+1))
R = np.linspace(Rin,Rout,Ni+1)
P = np.linspace(0+TINY,np.pi-TINY,Nj+1)
for i in range(Ni+1):
for j in range(Nj+1):
XX[i,j] = R[i]*np.cos(P[j])
YY[i,j] = R[i]*np.sin(P[j])
return XX,YY
#Gen Gamera egg
#A is theta-warp parameter (<0 to concentrate at poles)
def genEgg(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=30.0,xtail=250,NumSph=5,TINY=1.0e-8,A=0.0):
#Get ellipse parameters
x0,a,b = Egglipses(Ni,Nj,Rin,Rout,xtail,NumSph)
#Eta is index (0,1) for theta mapping
eta = np.linspace(0+TINY,1.0-TINY,Nj+1)
L = np.pi
xC = 0.5*np.pi #Any warping is symmetric
theta = L*eta + A*(xC-L*eta)*(1-eta)*eta
#theta = np.linspace(0+TINY,np.pi-TINY,Nj+1)
xx = np.zeros((Ni+1,Nj+1))
yy = np.zeros((Ni+1,Nj+1))
for j in range(Nj+1):
for i in range(Ni+1):
th = theta[j]
A = (np.cos(th)/a[i])**2.0 + (np.sin(th)/b[i])**2.0
B = -2*np.cos(th)*x0[i]/a[i]**2.0
C = x0[i]**2.0/a[i]**2.0 - 1.0
r = (-B+np.sqrt(B*B-4*A*C))/(2*A)
xx[i,j] = r*np.cos(th)
yy[i,j] = r*np.sin(th)
return xx,yy
#Gen teardrop egg
#A is theta-warp parameter (j stretching)
#Q is egg-warp parameter (i fattening)
def genFatEgg(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=30.0,xtail=250,NumSph=5,TINY=1.0e-8,A=0.0):
#Get ellipse parameters
x0,a,b = Egglipses(Ni,Nj,Rin,Rout,xtail,NumSph)
xSun = np.max(x0+a) #Forward boundary
xBack = np.abs(np.min(x0-a)) #Back boundary
#Eta is index (0,1) for theta mapping
eta = np.linspace(0+TINY,1.0-TINY,Nj+1)
L = np.pi
xC = 0.5*np.pi #Any warping is symmetric
#theta = np.linspace(0+TINY,np.pi-TINY,Nj+1)
xx = np.zeros((Ni+1,Nj+1))
yy = np.zeros((Ni+1,Nj+1))
tau0 = 0.35
di = Ni/8
i1 = Ni-di
AScl = 3*A
for i in range(Ni+1):
#Calculate theta profile for this shell
xTi = np.abs(x0[i]-a[i])
theta = L*eta + A*(xC-L*eta)*(1-eta)*eta #Lines of constant phi
if (xTi>xSun):
#Outer region, keep tailward near-axis region packed tightly
Ai = A - (A-AScl)*RampUp(xTi,xSun,xBack)
thTail = L*eta + Ai*(xC-L*eta)*(1-eta)*eta
#Only use back half (past half-way)
theta[Nj//2:] = thTail[Nj//2:]
for j in range(Nj+1):
th = theta[j]
Ae = (np.cos(th)/a[i])**2.0 + (np.sin(th)/b[i])**2.0
Be = -2*np.cos(th)*x0[i]/a[i]**2.0
Ce = x0[i]**2.0/a[i]**2.0 - 1.0
r = (-Be+np.sqrt(Be*Be-4*Ae*Ce))/(2*Ae)
xx[i,j] = r*np.cos(th)
taui = min(tau0*RampUp(1.0*i,i1,di),1.0)
tauscl = taui*(xx[i,j])/a[i]
tau = min(1.0+tauscl,1)
yy[i,j] = r*np.sin(th)/np.sqrt(tau)
return xx,yy
def RampUp(r,rC,lC):
rScl = (r-rC)/lC
if (rScl <=0):
M = 0
elif (rScl >= 1.0):
M = 1.0
else:
eArg = -1.0/(rScl*rScl)
M = np.exp(1.0)*np.exp(eArg)
return M
#Radial ellipse parameters for egg grid
def Egglipses(Ni=Ni0,Nj=Nj0,Rin=3.0,Rout=30.0,rtail=250.0,NumSph=5):
#Shells
NI = Ni+1
NJ = Nj+1
#Compute Xs, sunward x values
dScl = 1.22*(Rout/27.4675)
#Magic numbers for min/max dx
d1 = dScl*0.74/(NI/32.0)
d2 = dScl*0.985925/(NI/32.0)
dx = np.zeros(Ni+1)
xs = np.zeros(Ni+1)
dx[0:NumSph-1] = d1
dx[NumSph-1:] = np.linspace(d1,d2,Ni-NumSph+2)
xs[0] = Rin
for i in range(Ni):
xs[i+1] = xs[i]+dx[i]
#Now compute tailward x values
#d2 = 40.0/(NI/32.0) #Max dx
d2 = 50.0/(NI/32.0) #Max dx
d2 = (rtail/237.710955)*d2
xp = np.linspace(-4,0,Ni-NumSph+2)
yp = np.tanh(xp)+1
zp = (d2-d1)*yp+d1
xe = np.zeros(Ni+1)
xe[0] = -Rin
dx[0:NumSph-1] = d1
dx[NumSph-1:] = zp
for i in range(Ni):
xe[i+1] = xe[i]-dx[i]
#Now compute Y
d2 = 9.0/(NI/32.0)
xp = np.linspace(-4,0,Ni-NumSph+2)
yp = np.tanh(xp)+1
zp = (d2-d1)*yp+d1
dy = np.zeros(Ni+1)
yc = np.zeros(Ni+1)
dy[0:NumSph-1] = d1
dy[NumSph-1:] = zp
yc[0] = Rin
for i in range(Ni):
yc[i+1] = yc[i]+dy[i]
#Ellipse calculations
a = 0.5*(xs-xe)
xb = (xs+xe)/(xs-xe)
b = yc/np.sqrt(1-xb**2.0)
x0 = 0.5*(xs+xe)
return x0,a,b
#Generate 3D spherical grid with Z-axis north pole
#Spherical :: r,theta,phi = ijk
#theta = [0,1] -> [0,pi]
#phi = [0,1] -> [0,2pi]
def GenKSph(Ni=Ni0,Nj=Nj0,Nk=Nk0,Rin=5,Rout=40,tMin=0.2,tMax=0.8):
#Number of nodes with ghost corners
Ngi = Ni+1+2*Ng
Ngj = Nj+1+2*Ng
Ngk = Nk+1+2*Ng
dx1 = (Rout-Rin)/Ni
dx2 = (tMax-tMin)/Nj
dx3 = (1.0 - 0.0)/Nk
# check that ghosts don't take us across the axis
# need to generalize later to include the axis (do full 4pi)
if ((tMin-Ng*dx2)<=0) or ((tMax+Ng*dx2)>=1.):
sys.exit("Ghost cell region includes the spherical axis. This is not implemented yet.")
r = np.linspace(Rin-Ng*dx1,Rout+Ng*dx1,Ngi)
t = np.linspace(tMin-Ng*dx2,tMax+Ng*dx2,Ngj)*np.pi
p = np.linspace(-Ng*dx3,1.+Ng*dx3,Ngk)*2*np.pi
# note the indexing flag for proper ordering for writeGrid later
R,T,P = np.meshgrid(r,t,p,indexing='ij')
X3 = R*np.sin(T)*np.cos(P)
Y3 = R*np.sin(T)*np.sin(P)
Z3 = R*np.cos(T)
return X3,Y3,Z3
#Generate 3D spherical grid with non-uniform grid in r and with Z-axis north pole
#Spherical :: r,theta,phi = ijk
#theta = [0,1] -> [0,pi]
#phi = [0,1] -> [0,2pi]
def GenKSphNonU(Ni=Ni0,Nj=Nj0,Nk=Nk0,Rin=5,Rout=40,tMin=0.2,tMax=0.8):
#Number of nodes with ghost corners
Ngi = Ni+1+2*Ng
Ngj = Nj+1+2*Ng
Ngk = Nk+1+2*Ng
dx2 = (tMax-tMin)/Nj
dx3 = (1.0 - 0.0)/Nk
# check that ghosts don't take us across the axis
# need to generalize later to include the axis (do full 4pi)
if ((tMin-Ng*dx2)<=0) or ((tMax+Ng*dx2)>=1.):
sys.exit("Ghost cell region includes the spherical axis. This is not implemented yet.")
nu = np.linspace(0,1,Ni+1)
r0 = []
r1 = [((Rout - Rin)*(x*x+x)/2. + Rin) for x in nu]
dxN = Rout - ((Rout-Rin)*(nu[Ni-1]*nu[Ni-1]+nu[Ni-1])/2. + Rin)
dx0 = (Rout-Rin)*(nu[1]*nu[1]+nu[1])/2.
for i in range(Ng):
r1.append(Rout + (i+1)*dxN)
r0.append(Rin - (Ng-i)*dx0)
r = r0 + r1
t = np.linspace(tMin-Ng*dx2,tMax+Ng*dx2,Ngj)*np.pi
p = np.linspace(-Ng*dx3,1.+Ng*dx3,Ngk)*2*np.pi
# note the indexing flag for proper ordering for writeGrid later
R,T,P = np.meshgrid(r,t,p,indexing='ij')
X3 = R*np.sin(T)*np.cos(P)
Y3 = R*np.sin(T)*np.sin(P)
Z3 = R*np.cos(T)
return X3,Y3,Z3
#Generate 3D spherical grid with non-uniform grid in r and with Z-axis north pole
#Spherical :: r,theta,phi = ijk
#theta = [0,1] -> [0,pi]
#phi = [0,1] -> [0,2pi]
def GenKSphNonUGL(Ni=Ni0,Nj=Nj0,Nk=Nk0,Rin=5,Rout=40,tMin=0.2,tMax=0.8):
#Number of nodes with ghost corners
Ngi = Ni+1+2*Ng
Ngj = Nj+1+2*Ng
Ngk = Nk+1+2*Ng
dx2 = (tMax-tMin)/Nj
dx3 = (1.0 - 0.0)/Nk
# check that ghosts don't take us across the axis
# need to generalize later to include the axis (do full 4pi)
if ((tMin-Ng*dx2)<=0) or ((tMax+Ng*dx2)>=1.):
sys.exit("Ghost cell region includes the spherical axis. This is not implemented yet.")
#grid in r
Nwl = 194
Rmid = 64.5
dtau = np.arctan((Rmid-Rin)/Rin)/Nwl #dtau in radians
r1 = [Rin + Rin*(np.tan(i*dtau)) for i in range(Nwl+1)] #194 cells
Nout = Ni - Nwl
coeff = (Rout-Rmid)/Nout*2.-0.9-0.9
dr = [0.9 + coeff*i/(Nout-1) for i in range(Nout)]
r = Rmid
for i in range(Nout):
r = r + dr[i]
r1.append(r)
dx0 = r1[1] - r1[0]
dxN = r1[Ni] - r1[Ni-1]
r0 = [ ]
for i in range(Ng):
r1.append(Rout + (i+1)*dxN)
r0.append(Rin - (Ng-i)*dx0)
r = r0 + r1
t = np.linspace(tMin-Ng*dx2,tMax+Ng*dx2,Ngj)*np.pi
p = np.linspace(-Ng*dx3,1.+Ng*dx3,Ngk)*2*np.pi
# note the indexing flag for proper ordering for writeGrid later
R,T,P = np.meshgrid(r,t,p,indexing='ij')
X3 = R*np.sin(T)*np.cos(P)
Y3 = R*np.sin(T)*np.sin(P)
Z3 = R*np.cos(T)
return X3,Y3,Z3
#Now have 2D grid, augment with ghosts
#KeepOut => Keep inner boundary outside of R=1 (i.e. planet)
def Aug2D(XX,YY,doEps=False,TINY=1.0e-8,KeepOut=True,Rpx=1.15):
Ni = XX.shape[0]-1
Nj = XX.shape[1]-1
if (doEps):
YY[:,0] = TINY
YY[:,-1] = TINY
RR = np.sqrt(XX**2.0 + YY**2.0)
PP = np.arctan2(YY,XX)
R0 = RR.min()
if (R0<=Rpx):
print("Inner boundary below critical (%f), bailing ..."%(Rpx))
quit()
#print("Radial domain = [%3.2f,%3.2f]"%(R0,RR.max()))
xs = RR[:,0]
xe = RR[:,-1]
#print("\tSunward = [%3.2f,%3.2f]"%(xs.min(),xs.max()))
#print("\tTailward = [%3.2f,%3.2f]"%(xe.min(),xe.max()))
xxG = np.zeros((Ni+1+2*Ng,Nj+1+2*Ng))
yyG = np.zeros((Ni+1+2*Ng,Nj+1+2*Ng))
#0:Ng (non-inclusive end), Ng:Ng+nOut
iS = Ng
iE = Ng+Ni+1
jS = Ng
jE = Ng+Nj+1
xxG[iS:iE,jS:jE] = XX
yyG[iS:iE,jS:jE] = YY
#Get spacings, ensure inner boundary is outside of 1
drIn = RR[1,:]-RR[0,:] #Function of j
drAvg = drIn.mean()
RMin = R0-Ng*drAvg
if (RMin<Rpx):
#Adjust spacing to keep inner boundary outside
drAvg = (R0-Rpx-TINY)/Ng
pIn = PP[0,:]
#Do inner I, active J
for i in range(1,Ng+1):
rIn = R0-i*drAvg
xxG[iS-i,jS:jE] = rIn*np.cos(pIn)
yyG[iS-i,jS:jE] = rIn*np.sin(pIn)
#Do outer I, active J
Dx = XX[-1,:]-XX[-2,:]
Dy = YY[-1,:]-YY[-2,:]
Dp = PP[-1,:]-PP[-2,:]
Dr = RR[-1,:]-RR[-2,:]
nD = np.sqrt(Dx**2.0+Dy**2.0)
dBar = nD.mean()
dO = np.minimum(nD,dBar)
#dO = nD
J4 = Nj//4
xOut = XX[-1,:]
yOut = YY[-1,:]
sig = 1.5
jSigS = J4
jSigE = 3*J4
Dx[jSigS:jSigE] = gaussian_filter(Dx[jSigS:jSigE],sigma=sig,mode='nearest')
Dy[jSigS:jSigE] = gaussian_filter(Dy[jSigS:jSigE],sigma=sig,mode='nearest')
xS = xOut[0]+Ng*Dr[0]
xT = xOut[-1]-Ng*Dr[-1]
#Dx[0:J4] = (xS-xOut[0:J4])/Ng
for i in range(0,Ng):
xxG[iE+i,jS:jE] = xOut+(i+1)*dO*Dx/nD
yyG[iE+i,jS:jE] = yOut+(i+1)*dO*Dy/nD
#Now finish by doing all J boundaries
#Just reflect about X-axis
for i in range(0,Ng):
ip = i+1
xxG[:,jS-ip] = xxG[:,jS+ip]
yyG[:,jS-ip] = -yyG[:,jS+ip]
xxG[:,jE-1+ip] = xxG[:,jE-1-ip]
yyG[:,jE-1+ip] = -yyG[:,jE-1-ip]
return xxG,yyG
# vgm: add a version of the above function to extend the grid in the i-direction
# this never worked; keeping for completeness.
# what's worked, though, is the scale option in regrid at the bottom of this file
def Aug2Dext(XX,YY,Nadd): #Nadd -- how many points to add in the i-direction
Ni = XX.shape[0]
Nj = XX.shape[1]
xxG = np.zeros((Ni+Nadd,Nj))
yyG = np.zeros((Ni+Nadd,Nj))
xxG[:-Nadd,:] = XX
yyG[:-Nadd,:] = YY
#Do outer I, active J
Dx = XX[-1,:]-XX[-2,:]
Dy = YY[-1,:]-YY[-2,:]
nD = np.sqrt(Dx**2.0+Dy**2.0)
dBar = nD.mean()
#dO = np.minimum(nD,dBar)
dO = nD
for i in np.arange(Nadd):
# xxG[-Nadd+i:,:] = XX[-1,:] + (i+1)*dO*Dx/nD
# yyG[-Nadd+i:,:] = YY[-1,:] + (i+1)*dO*Dy/nD
xxG[-Nadd+i:,:] = 2*xxG[-Nadd+i-1,:]-xxG[-Nadd+i-2,:]
yyG[-Nadd+i:,:] = 2*yyG[-Nadd+i-1,:]-yyG[-Nadd+i-2,:]
return xxG,yyG
#Do ring recommendations
def genRing(XX,YY,Nk=64,Tol=1.0,doVerb=False):
Ni = XX.shape[0]-1
Nj = XX.shape[1]-1
dTh = 2*np.pi/Nk
#For each ring (j), calculate max ring value
rr = np.sqrt(XX**2.0 + YY**2.0)
pp = np.arctan2(YY,XX)
dI = np.zeros((Ni,Nj))
dJ = np.zeros((Ni,Nj))
dK = np.zeros((Ni,Nj))
for i in range(Ni):
for j in range(Nj):
dR = 0.5*(rr[i+1,j]+rr[i+1,j+1]) - 0.5*(rr[i,j]+rr[i,j+1])
dP = 0.5*(pp[i,j+1]+pp[i+1,j+1]) - 0.5*(pp[i,j]+pp[i+1,j])
Rc = 0.25*( rr[i,j]+rr[i,j+1] + rr[i+1,j]+rr[i+1,j+1] )
Yc = 0.25*( YY[i,j]+YY[i,j+1] + YY[i+1,j]+YY[i+1,j+1] )
dI[i,j] = dR
dJ[i,j] = Rc*dP
dK[i,j] = Yc*dTh
Nrng = Nj//2
NChs = np.zeros(Nrng,dtype=int)
NCha = np.zeros(Nrng,dtype=int)
rS = []
for n in range(Nrng):
kiP = np.max(dK[: ,n]/dI[: ,n])
kjP = np.max(dK[:, n]/dJ[: ,n])
kiM = np.max(dK[:,-n]/dI[:,-n])
kjM = np.max(dK[:,-n]/dJ[:,-n])
dkoMax = np.max([kiP,kjP,kiM,kjM])
dRng = dkoMax/Tol
#Do safe and aggressive
djRs = int(2**np.floor(np.log2(Tol/dkoMax)))
djRa = int(2**np.ceil(np.log2(Tol/dkoMax)))
if (dRng<=1.0):
if (doVerb):
print("Ring %d, MaxScl = %5.3f"%(n+1,1/dRng))
#print("Ring %d, MaxChunk = %5.3f"%(n+1,Nk*dRng))
#print("\tChunking: %d (Safe) / %d (Aggressive)"%(djRs,djRa))
rS.append("%4.2f"%(Nk*dkoMax))
if (djRs>=2):
NChs[n] = Nk/djRs
if (djRa>=2):
NCha[n] = Nk/djRa
#print(rS)
PrintRing(NChs,"Safe")
if (doVerb):
PrintRing(NCha,"Aggressive",doWarn=True)
def PrintRing(NCh,rID="safe",doWarn=False):
print("%s ring configuration ..."%(rID))
if (doWarn):
print("Don't say I didn't warn you")
Nr = (NCh>0).sum()
rStr = '<ring gid="lfm" doRing="T" ' + 'Nr="%d"'%(Nr)
for i in range(Nr):
Nc = NCh[i]
if (Nc<8):
Nc = 8
rStr = rStr + ' Nc%d="%d"'%(i+1,Nc)
rStr = rStr + '/>'
print(rStr)
print("")
#Now do full 3D by rotating about X-axis
def Aug3D(xxG,yyG,Nk=32,TINY=1.0e-8):
Ni = xxG.shape[0]-1-2*Ng
Nj = xxG.shape[1]-1-2*Ng
nFi = Ni+1+2*Ng
nFj = Nj+1+2*Ng
nFk = Nk+1+2*Ng
iS = Ng
iE = Ng+Ni+1
jS = Ng
jE = Ng+Nj+1
kS = Ng
kE = Ng+Nk+1
X3 = np.zeros((nFi,nFj,nFk))
Y3 = np.zeros((nFi,nFj,nFk))
Z3 = np.zeros((nFi,nFj,nFk))
#Angle about axis including ghosts
A = np.linspace(0,2*np.pi,Nk+1)
for n in range(Nk+1):
X3[:,:,kS+n] = xxG
Y3[:,:,kS+n] = yyG*np.cos(A[n])
Z3[:,:,kS+n] = yyG*np.sin(A[n])
# K is periodic so ensure the last entry equals the first
X3[:,:,kE-1] = X3[:,:,kS]
Y3[:,:,kE-1] = Y3[:,:,kS]
Z3[:,:,kE-1] = Z3[:,:,kS]
# correct K periodic ghosts
for i in range(0,Ng):
ip=i+1
X3[:,:,kS-ip] = X3[:,:,kE-1-ip]
Y3[:,:,kS-ip] = Y3[:,:,kE-1-ip]
Z3[:,:,kS-ip] = Z3[:,:,kE-1-ip]
X3[:,:,kE-1+ip] = X3[:,:,kS+ip]
Y3[:,:,kE-1+ip] = Y3[:,:,kS+ip]
Z3[:,:,kE-1+ip] = Z3[:,:,kS+ip]
# correct J reflective ghosts
for k in range(0,nFk):
kp = k+Nk//2
if kp >= kE-1:
kp = k-Nk//2
for i in range(0,Ng):
ip = i+1
X3[:,jS-ip,k] = X3[:,jS+ip,kp]
Y3[:,jS-ip,k] = Y3[:,jS+ip,kp]
Z3[:,jS-ip,k] = Z3[:,jS+ip,kp]
X3[:,jE-1+ip,k] = X3[:,jE-1-ip,kp]
Y3[:,jE-1+ip,k] = Y3[:,jE-1-ip,kp]
Z3[:,jE-1+ip,k] = Z3[:,jE-1-ip,kp]
#Force points to plane
Z3[:,:,Ng] = 0.0
Z3[:,:,-Ng-1] = 0.0
Z3[:,:,Ng+Nk//2] = 0.0
Y3[:,:,Ng+Nk//4] = 0.0
Y3[:,:,-Ng-Nk//4-1] = 0.0
x0 = X3[:,Ng,Ng]
if (x0.min() <= TINY):
print("Spacing error on inner I")
print(x0)
return X3,Y3,Z3
def WriteGrid(X3,Y3,Z3,fOut="gGrid.h5"):
import h5py
#print("Writing out grid to %s"%fOut)
with h5py.File(fOut,'w') as hf:
hf.create_dataset("X",data=X3.T)
hf.create_dataset("Y",data=Y3.T)
hf.create_dataset("Z",data=Z3.T)
#Write grid as CHIMP grid, only active nodes/single precision
def WriteChimp(X3,Y3,Z3,fOut="cGrid.h5"):
Xc = np.single(X3[Ng:-Ng,Ng:-Ng,Ng:-Ng])
Yc = np.single(Y3[Ng:-Ng,Ng:-Ng,Ng:-Ng])
Zc = np.single(Z3[Ng:-Ng,Ng:-Ng,Ng:-Ng])
WriteGrid(Xc,Yc,Zc,fOut=fOut)
#print("Orig shape = %s"%(str(X3.shape)))
#print(" New shape = %s"%(str(Xc.shape)))
def VizGrid(XX,YY,xxG=None,yyG=None,doGhost=False,doShow=True,xyBds=None,fOut="grid.png"):
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import kaipy.kaiViz as kv
fSz = (10,4)
Alph = 0.35
fig = plt.figure(figsize=fSz)
Ax = plt.gca()
C = 'dodgerblue'
Ni = XX.shape[0]-1
Nj = XX.shape[1]-1
G = np.zeros((Ni,Nj))
G[-Ng:,:] = 1.0
Ax.pcolormesh(XX,YY,G,cmap="RdGy")
if (doGhost):
Ax.plot(xxG,yyG,'r-',xxG.T,yyG.T,'r-',linewidth=0.5)
Ax.plot(XX,YY ,color=C,linewidth=1.5,alpha=Alph)
Ax.plot(XX.T,YY.T,color=C,linewidth=1.5,alpha=Alph)
Nr4 = int(Ni/4)
Np4 = int(Nj/4)
C4 = "blue"
LW4 = 2.0
for dx in range(1,4):
j0 = dx*Np4
i0 = dx*Nr4
Ax.plot(XX[:,j0],YY[:,j0],color=C4,linewidth=LW4,alpha=Alph)
Ax.plot(XX[i0,:],YY[i0,:],color=C4,linewidth=LW4,alpha=Alph)
if (xyBds is None):
xyBds = [XX[-1,-1],XX[-1,0],0,np.max(YY)]
kv.SetAx(xyBds,Ax)
kv.addEarth2D(ax=Ax)
kv.SetAxLabs(Ax,'SM-X [Rx]','SM-Y [Rx]')
Ni,Nj = XX.shape
tStr = "Grid Size = %d,%d"%(Ni-1,Nj-1)
Ax.set_title(tStr)
fname,fext = os.path.splitext(fOut)
fPic = fname+".png"
print("Saving image to %s"%(fPic))
kv.savePic(fPic)
if (doShow):
plt.show()
#Read in LFM grid, return upper half plane corners
#Use Rin to cut out inner region, Rout to guarantee at last that much
def getLFM(fIn,Rin=3.0,Rout=25.0):
from pyhdf.SD import SD, SDC
hdffile = SD(fIn)
#Grab x/y/z arrays from HDF file. Scale by Re
#LFM is k,j,i ordering
x3 = iRe*np.double(hdffile.select('X_grid').get())
y3 = iRe*np.double(hdffile.select('Y_grid').get())
z3 = iRe*np.double(hdffile.select('Z_grid').get())
lfmNc = x3.shape #Number of corners (k,j,i)
nk = x3.shape[0]-1
nj = x3.shape[1]-1
ni = x3.shape[2]-1
print("Reading LFM grid from %s, size (%d,%d,%d)"%(fIn,ni,nj,nk))
#Project to plane, transpose and cut
ks = 0 #Upper half x-y plane
xxi = x3[ks,:,:].squeeze().T
yyi = y3[ks,:,:].squeeze().T
#Scale so that inner is Rin
rr = np.sqrt(xxi**2.0+yyi**2.0)
lfmIn = rr.min()
xyScl = Rin/lfmIn
xxi = xxi*xyScl
yyi = yyi*xyScl
rr = np.sqrt(xxi**2.0+yyi**2.0)
#Get min/max radius per I shell
rMin = rr.min(axis=1)
rMax = rr.max(axis=1)
inCut = (rMin>=Rin).argmax()
outCut = (rMin>=Rout).argmax()
#Cut out outer regions to create egg
xxi = xxi[0:outCut+1,:]
yyi = yyi[0:outCut+1,:]
return xxi,yyi
def LoadTabG(fIn="lfmG",Nc=0):
import os
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fInX = os.path.join(__location__,fIn+".X.txt")
fInY = os.path.join(__location__,fIn+".Y.txt")
xxi = np.loadtxt(fInX)
yyi = np.loadtxt(fInY)
if (Nc>0):
xxi = xxi[0:-Nc,:]
yyi = yyi[0:-Nc,:]
return xxi,yyi
#Regrid xx/yy (corners) to new size
def regrid(xxi,yyi,Ni,Nj,Rin=0.0,Rout=0.0,TINY=1.0e-8,scale=False):
iMeth = "cubic"
Ni0 = xxi.shape[0]-1
Nj0 = xxi.shape[1]-1
rr0 = np.sqrt(xxi**2.0 + yyi**2.0)
pp0 = np.arctan2(yyi,xxi)
#index space -> (r,phi)
iLFM = np.linspace(0,1,Ni0+1)
jLFM = np.linspace(0,1,Nj0+1)
if ( (Rin>TINY) or (Rout>TINY) ):
#Rescale radial range
xMin = rr0.min()
xMax = xxi.max()
if (Rin>TINY):
xSclIn = Rin/xMin
else:
xSclIn = 1.0
if (Rout>TINY):
xSclOut = Rout/xMax
else:
xSclOut = 1.0
dxScl = (xSclOut-xSclIn)
#Now rescale each i shell
for i in range(Ni0+1):
rScl = xSclIn + iLFM[i]*dxScl
rr0[i,:] = rScl*rr0[i,:]
#Create interpolants
fR = interpolate.interp2d(iLFM,jLFM,rr0.T,kind=iMeth,fill_value=None)
fP = interpolate.interp2d(iLFM,jLFM,pp0.T,kind=iMeth,fill_value=None)
#Regrid onto new
XXi = np.zeros((Ni+1,Nj+1))
YYi = np.zeros((Ni+1,Nj+1))
si = np.linspace(0,1,Ni+1)
sj = np.linspace(0,1,Nj+1)
for i in range(Ni+1):
for j in range(Nj+1):
r = fR(si[i],sj[j])
phi = fP(si[i],sj[j])
phi = np.maximum(TINY,phi)
phi = np.minimum(np.pi-TINY,phi)
XXi[i,j] = r*np.cos(phi)
YYi[i,j] = r*np.sin(phi)
# vgm: added scaling option to extend the grid for low Mach numbers
# needs playing around with the numbers below
if scale:
dx = (XXi[1:,:]-XXi[:-1,:])
dy = (YYi[1:,:]-YYi[:-1,:])
nscl = dx.shape[0]
scale = np.ones(nscl)
scale[nscl//2:]=1.25 #1.5
scale[3*nscl//4:]=1.5 #2.
scale[-4:]=2. #4.
for i in np.arange(1,Ni+1):
XXi[i,:] = XXi[i-1,:] + scale[i-1]*dx[i-1,:]
YYi[i,:] = YYi[i-1,:] + scale[i-1]*dy[i-1,:]
return XXi,YYi

437
kaipy/gamera/gampp.py
View File

@@ -1,437 +0,0 @@
#Gamera post-processing routines
#Get data from serial/MPI gamera output
import glob
import numpy as np
from kaipy.kaiTools import MJD2UT
import itertools
import kaipy.kdefs as kdefs
#Object to use to pull data from HDF5 structure (serial or mpi)
#Initialize,
#gIn = gampp.GameraPipe(fdir,ftag)
#Set grid from data
#gIn.GetGrid()
#V = gIn.GetVar("D",stepnum)
#doFast=True skips various data scraping
idStr = "_0000_0000_0000.gam.h5"
class GameraPipe(object):
#Initialize GP object
#fdir = directory to h5 files
#ftag = stub of h5 files
def __init__(self,fdir,ftag,doFast=False,doVerbose=True,doParallel=False,nWorkers=4):
self.fdir = fdir
self.ftag = ftag
#Here just create variables
#-----------
#Ranks
self.isMPI = False
self.Nr = 0
self.Ri = 1 ; self.Rj = 1 ; self.Rk = 1
#Global/local cells
self.is2D = False
self.Ni = 0 ; self.Nj = 0 ; self.Nk = 0
self.dNi= 0 ; self.dNj= 0 ; self.dNk= 0
#Variables/slices
self.Nv = 0 ; self.Nt = 0 ; self.Nv0 = 0
self.T = [] ; self.vIDs = [] ; self.v0IDs = []
self.s0 = 0 ; self.sFin = 0
self.sids = np.array([])
#Grids
self.X = [] ; self.Y = [] ; self.Z = []
self.gridLoaded = False
self.doFast = doFast
self.doParallel = doParallel
self.UnitsID = "NONE"
self.nWorkers = nWorkers
#Example file data
self.f0 = []
#Stubs for MJD stuff
self.hasMJD = False
self.MJDs = []
#Stub for UT date time object
self.hasUT = False
self.UT = []
#Scrape data from directory
self.OpenPipe(doVerbose)
def OpenPipe(self,doVerbose=True):
import kaipy.kaiH5 as kh5
if (doVerbose):
print("Opening pipe: %s : %s"%(self.fdir,self.ftag))
#Test for serial (either old or new naming convention)
fOld = "%s/%s.h5"%(self.fdir,self.ftag)
fNew = "%s/%s.gam.h5"%(self.fdir,self.ftag)
if ( len(glob.glob(fOld)) == 1):
#Found old-style serial
self.isMPI = False
if (doVerbose):
print("Found serial database")
f0 = fOld
elif ( len(glob.glob(fNew)) == 1):
#Found new-style serial
self.isMPI = False
self.ftag = self.ftag + ".gam" #Add .gam to tag
if (doVerbose):
print("Found serial database")
f0 = fNew
else:
print("%s not found, looking for MPI database"%(fOld))
self.isMPI = True
sStr = "%s/%s_*%s"%(self.fdir,self.ftag,idStr)
fIns = glob.glob(sStr)
if (len(fIns)>1):
print("This shouldn't happen, bailing ...")
if (len(fIns) == 0):
print("No MPI database found, all out of options, bailing ...")
quit()
f0 = fIns[0]
Ns = [int(s) for s in f0.split('_') if s.isdigit()]
self.Ri = Ns[-5]
self.Rj = Ns[-4]
self.Rk = Ns[-3]
self.Nr = self.Ri*self.Rj*self.Rk
if (doVerbose):
print("\tFound %d = (%d,%d,%d) ranks"%(self.Nr,self.Ri,self.Rj,self.Rk))
#In either case, f0 is defined. use it to get per file stuff
self.Nt,sids = kh5.cntSteps(f0)
if (self.doFast):
self.T = np.zeros(self.Nt)
else:
self.T = kh5.getTs(f0,sids)
self.sids = sids
self.s0 = sids.min()
self.sFin = sids.max()
if (doVerbose):
print("Found %d timesteps\n\tTime = [%f,%f]"%(self.Nt,self.T.min(),self.T.max()))
print("\tSteps = [%d,%d]"%(sids.min(),sids.max()))
#Get MJD if present
MJDs = kh5.getTs(f0,sids,"MJD",-np.inf)
if (MJDs.max()>0):
self.hasMJD = True
self.MJDs = MJDs
self.hasUT = True
self.UT = MJD2UT(MJDs)
#Get grid stuff
Dims = kh5.getDims(f0)
Nd = len(Dims)
self.dNi = Dims[0]-1
self.dNj = Dims[1]-1
if (Nd>2):
self.dNk = Dims[2]-1
else:
self.dNk = 0
self.Rk = 1
self.Ni = self.dNi*self.Ri
self.Nj = self.dNj*self.Rj
self.Nk = self.dNk*self.Rk
#Variables
self.v0IDs = kh5.getRootVars(f0)
self.vIDs = kh5.getVars(f0,sids.min())
self.Nv0 = len(self.v0IDs)
self.Nv = len(self.vIDs)
if (Nd>2):
self.is2D = False
else:
self.is2D = True
if (doVerbose):
if (Nd>2):
nCells = self.Ni*self.Nj*self.Nk
else:
nCells = self.Ni*self.Nj
print("Grid size = (%d,%d,%d)"%(self.Ni,self.Nj,self.Nk))
print("\tCells = %e"%(nCells))
print("Variables (Root/Step) = (%d,%d)"%(self.Nv0,self.Nv))
print("\tRoot: %s"%(self.v0IDs))
print("\tStep: %s"%(self.vIDs))
self.SetUnits(f0)
if (doVerbose):
print("Units Type = %s"%(self.UnitsID))
#print("Pulling grid ...")
self.GetGrid(doVerbose)
self.f0 = f0
def SetUnits(self,f0):
import h5py
with h5py.File(f0,'r') as hf:
uID = hf.attrs.get("UnitsID","CODE")
if (not isinstance(uID,str)):
self.UnitsID = uID.decode('utf-8')
def GetGridParallel(self,doVerbose):
''' Parallel read of grid datasets
'''
import kaipy.kaiH5 as kh5
from concurrent.futures import ProcessPoolExecutor, as_completed
from alive_progress import alive_bar
if (self.is2D):
self.X = np.zeros((self.Ni+1,self.Nj+1))
self.Y = np.zeros((self.Ni+1,self.Nj+1))
coords = [self.X,self.Y]
else:
self.X = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
self.Y = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
self.Z = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
coords = [(self.X,'X'),(self.Y,'Y'),(self.Z,'Z')]
if (doVerbose):
#print("Del = (%d,%d,%d)"%(self.dNi,self.dNj,self.dNk))
titStr = "%s/Grid"%(self.ftag)
else:
titStr = None
files = []
#print("Bounds = (%d,%d,%d,%d,%d,%d)"%(iS,iE,jS,jE,kS,kE))
for (i,j,k) in itertools.product(range(self.Ri),range(self.Rj),range(self.Rk)):
files.append(((self.fdir + "/" + kh5.genName(self.ftag,i,j,k,self.Ri,self.Rj,self.Rk),[i,j,k])))
NrX = max(self.Nr,1)
for data,vID in coords:
datasets = []
with alive_bar(NrX,title=f"{titStr}/{vID}".ljust(kdefs.barLab),length=kdefs.barLen,bar=kdefs.barDef) as bar, \
ProcessPoolExecutor(max_workers=self.nWorkers) as executor:
futures = [executor.submit(kh5.PullVarLoc, fIn, vID, loc=loc) for fIn, loc in files]
for future in as_completed(futures):
datasets.append(future.result())
bar()
for dataset,loc in datasets:
i = loc[0]
j = loc[1]
k = loc[2]
iS = i*self.dNi
jS = j*self.dNj
kS = k*self.dNk
iE = iS+self.dNi
jE = jS+self.dNj
kE = kS+self.dNk
if (self.is2D):
data[iS:iE+1,jS:jE+1] = dataset
else:
data[iS:iE+1,jS:jE+1,kS:kE+1] = dataset
def GetGrid(self,doVerbose):
''' Load Grid from Gamera HDF5 file
'''
import kaipy.kaiH5 as kh5
from alive_progress import alive_bar
if(not self.gridLoaded):
if (self.isMPI and self.doParallel):
self.GetGridParallel(doVerbose)
else:
if (self.is2D):
self.X = np.zeros((self.Ni+1,self.Nj+1))
self.Y = np.zeros((self.Ni+1,self.Nj+1))
else:
self.X = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
self.Y = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
self.Z = np.zeros((self.Ni+1,self.Nj+1,self.Nk+1))
if (doVerbose):
#print("Del = (%d,%d,%d)"%(self.dNi,self.dNj,self.dNk))
titStr = "%s/Grid"%(self.ftag)
else:
titStr = None
NrX = max(self.Nr,1)
with alive_bar(NrX,title=titStr,length=kdefs.barLen) as bar:
for (i,j,k) in itertools.product(range(self.Ri),range(self.Rj),range(self.Rk)):
iS = i *self.dNi
jS = j *self.dNj
kS = k *self.dNk
iE = iS+self.dNi
jE = jS+self.dNj
kE = kS+self.dNk
#print("Bounds = (%d,%d,%d,%d,%d,%d)"%(iS,iE,jS,jE,kS,kE))
if (self.isMPI):
fIn = self.fdir + "/" + kh5.genName(self.ftag,i,j,k,self.Ri,self.Rj,self.Rk)
else:
fIn = self.fdir + "/" + self.ftag + ".h5"
if (self.is2D):
self.X[iS:iE+1,jS:jE+1] = kh5.PullVar(fIn,"X")
self.Y[iS:iE+1,jS:jE+1] = kh5.PullVar(fIn,"Y")
else:
self.X[iS:iE+1,jS:jE+1,kS:kE+1] = kh5.PullVar(fIn,"X")
self.Y[iS:iE+1,jS:jE+1,kS:kE+1] = kh5.PullVar(fIn,"Y")
self.Z[iS:iE+1,jS:jE+1,kS:kE+1] = kh5.PullVar(fIn,"Z")
bar()
else:
print("Grid Previously Loaded")
self.gridLoaded = True
def GetVarParallel(self,vID,sID=None,vScl=None,doVerb=True):
''' Parallel read of Var
'''
import kaipy.kaiH5 as kh5
from concurrent.futures import ProcessPoolExecutor, as_completed
from alive_progress import alive_bar
if (doVerb):
if (sID is None):
titStr = "%s/%s"%(self.ftag,vID)
else:
titStr = "%s/Step#%d/%s"%(self.ftag,sID,vID)
else:
titStr = ''
if (self.is2D):
V = np.zeros((self.Ni,self.Nj))
else:
V = np.zeros((self.Ni,self.Nj,self.Nk))
files = []
#print("Bounds = (%d,%d,%d,%d,%d,%d)"%(iS,iE,jS,jE,kS,kE))
for (i,j,k) in itertools.product(range(self.Ri),range(self.Rj),range(self.Rk)):
files.append(((self.fdir + "/" + kh5.genName(self.ftag,i,j,k,self.Ri,self.Rj,self.Rk),[i,j,k])))
NrX = max(self.Nr,1)
datasets = []
with alive_bar(NrX,title=titStr.ljust(kdefs.barLab),length=kdefs.barLen,bar=kdefs.barDef,disable=not doVerb) as bar, \
ProcessPoolExecutor(max_workers=self.nWorkers) as executor:
futures = [executor.submit(kh5.PullVarLoc, fIn, vID, sID, loc=loc) for fIn, loc in files]
for future in as_completed(futures):
datasets.append(future.result())
bar()
for dataset,loc in datasets:
i = loc[0]
j = loc[1]
k = loc[2]
iS = i*self.dNi
jS = j*self.dNj
kS = k*self.dNk
iE = iS+self.dNi
jE = jS+self.dNj
kE = kS+self.dNk
if (self.is2D):
V[iS:iE,jS:jE] = dataset
else:
V[iS:iE,jS:jE,kS:kE] = dataset
if (vScl is not None):
V = vScl*V
return V
#Get 3D variable "vID" from Step# sID
def GetVar(self,vID,sID=None,vScl=None,doVerb=True):
''' Read Var with name vID
'''
import kaipy.kaiH5 as kh5
from alive_progress import alive_bar
if (self.is2D):
V = np.zeros((self.Ni,self.Nj))
else:
V = np.zeros((self.Ni,self.Nj,self.Nk))
if (self.isMPI and self.doParallel):
V = self.GetVarParallel(vID,sID,vScl,doVerb)
else:
if (doVerb):
if (sID is None):
titStr = "%s/%s"%(self.ftag,vID)
else:
titStr = "%s/Step#%d/%s"%(self.ftag,sID,vID)
else:
titStr = ''
NrX = max(self.Nr,1)
with alive_bar(NrX,title=titStr.ljust(kdefs.barLab),length=kdefs.barLen,disable=not doVerb) as bar:
for (i,j,k) in itertools.product(range(self.Ri),range(self.Rj),range(self.Rk)):
iS = i*self.dNi
jS = j*self.dNj
kS = k*self.dNk
iE = iS+self.dNi
jE = jS+self.dNj
kE = kS+self.dNk
#print("Bounds = (%d,%d,%d,%d,%d,%d)"%(iS,iE,jS,jE,kS,kE))
if (self.isMPI):
fIn = self.fdir + "/" + kh5.genName(self.ftag,i,j,k,self.Ri,self.Rj,self.Rk)
else:
fIn = self.fdir + "/" + self.ftag + ".h5"
if (self.is2D):
V[iS:iE,jS:jE] = kh5.PullVar(fIn,vID,sID)
else:
V[iS:iE,jS:jE,kS:kE] = kh5.PullVar(fIn,vID,sID)
bar()
return V
#FIXME: Currently pulling whole 3D array and then slicing, lazy
def GetSlice(self,vID,sID,ijkdir='idir',n=1,vScl=None,doVerb=True):
''' Get variable slice of constant i,j,k
Directions = idir/jdir/kdir strings
Indexing = (1,Nijk)
'''
sDirs = ['IDIR','JDIR','KDIR']
ijkdir = ijkdir.upper()
if (not (ijkdir in sDirs)):
print("Invalid slice direction, defaulting to I")
ijkdir = 'IDIR'
if (sID is None):
cStr = "Reading %s/%s"%(self.ftag,vID)
else:
cStr = "Reading %s/Step#%d/%s"%(self.ftag,sID,vID)
V = self.GetVar(vID,sID,vScl,doVerb=False)
#Now slice
np = n-1 #Convert from Fortran to Python indexing
if (ijkdir == "IDIR"):
Vs = V[np,:,:]
sStr = "(%d,:,:)"%(n)
elif (ijkdir == "JDIR"):
Vs = V[:,np,:]
sStr = "(:,%d,:)"%(n)
elif (ijkdir == "KDIR"):
Vs = V[:,:,np]
sStr = "(:,:,%d)"%(n)
if (doVerb):
print(cStr+sStr)
return Vs
#Wrappers for root variables (or just set sID to "None")
def GetRootVar(self,vID,vScl=None,doVerb=True):
V = self.GetVar(vID,sID=None,vScl=vScl,doVerb=doVerb)
return V
def GetRootSlice(self,vID,ijkdir='idir',n=1,vScl=None,doVerb=True):
Vs = self.GetSlice(vID,sID=None,ijkdir=ijkdir,n=n,vScl=vScl,doVerb=doVerb)
return Vs

214
kaipy/gamera/lfmG.X.txt
View File

@@ -1,214 +0,0 @@
# X(i,j) coordinates of LFM grid
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kaipy/gamera/lfmG.Y.txt
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@@ -1,214 +0,0 @@
# Y(i,j) coordinates of LFM grid
1.999999876583540587e-09 3.621412585246368276e-02 7.232579861720891956e-02 1.083296776791519839e-01 1.442218109801609027e-01 1.799948418643236292e-01 2.156589329388123588e-01 2.512213699771070896e-01 2.866903286529887129e-01 3.220718807802833150e-01 3.573533151311795719e-01 3.925142837775194571e-01 4.275329176182746660e-01 4.623872292118895722e-01 4.970589888157205816e-01 5.315259869989512609e-01 5.657675842587859272e-01 5.997640233922543196e-01 6.335128575384900707e-01 6.670048519489533145e-01 7.002291978501016256e-01 7.331711546520213174e-01 7.658230245952125648e-01 7.981713177761227129e-01 8.302034056994926914e-01 8.619051976644933610e-01 8.932768079398761829e-01 9.243102878810240952e-01 9.549963432047606782e-01 9.853205363823223095e-01 1.015277464838326926e+00 1.044854114562758562e+00 1.074039332183774231e+00 1.102819049890799041e+00 1.131197199530402431e+00 1.159169454830761614e+00 1.186730952087556412e+00 1.213869766853331589e+00 1.240584622812418436e+00 1.266865954555884466e+00 1.292705400264770210e+00 1.318091985428770263e+00 1.343032200260754871e+00 1.367524565183237728e+00 1.391566372101951776e+00 1.415149522340821697e+00 1.438274836891529462e+00 1.460935763495109008e+00 1.483127157662349260e+00 1.504840698298820501e+00 1.526084199387557572e+00 1.546858267695641498e+00 1.567162876466909216e+00 1.586992798015554040e+00 1.606350395755943739e+00 1.625232164207613961e+00 1.643635303509314083e+00 1.661554645345023395e+00 1.678998120444711750e+00 1.695967695618906479e+00 1.712464823473650366e+00 1.728486629155803866e+00 1.744036424621431713e+00 1.759112770448604479e+00 1.773715020679060839e+00 1.787840457332417365e+00 1.801496137640262285e+00 1.814684490568829300e+00 1.827407656918982326e+00 1.839664647117664309e+00 1.851458690705706012e+00 1.862789793245540171e+00 1.873658547154390286e+00 1.884064334987716860e+00 1.894012389398380458e+00 1.903505038540312277e+00 1.912544240766603565e+00 1.921130300043995431e+00 1.929265666337901974e+00 1.936951067172573149e+00 1.944187544198943129e+00 1.950975659933448236e+00 1.957318617771938829e+00 1.963218084521251017e+00 1.968675411953946019e+00 1.973691294144294783e+00 1.978267143294042185e+00 1.982403738837290152e+00 1.986101892722633266e+00 1.989362417276067996e+00 1.992186639900447531e+00 1.994575279602533335e+00 1.996528874988781288e+00 1.998047806861871667e+00 1.999132479966794218e+00 1.999783143080170911e+00 1.999999999999998002e+00 1.999783140310712026e+00 1.999132473506925578e+00 1.998047794964385782e+00 1.996528857296317794e+00 1.994575255670862246e+00 1.992186605707456426e+00 1.989362374880936146e+00 1.986101845829075119e+00 1.982403686194378123e+00 1.978267092235371338e+00 1.973691250132002972e+00 1.968675377137511395e+00 1.963218051623756955e+00 1.957318587510937125e+00 1.950975638399954804e+00 1.944187544198943352e+00 1.936951079273329102e+00 1.929265691845770903e+00 1.921130340234590506e+00 1.912544240766603565e+00 1.903505038540312277e+00 1.894012389398380458e+00 1.884064319134326482e+00 1.873658547154390508e+00 1.862789793245539949e+00 1.851458690705706012e+00 1.839664629017336406e+00 1.827407656918982326e+00 1.814684490568829300e+00 1.801496137640262063e+00 1.787840457332417365e+00 1.773715020679060839e+00 1.759112770448604701e+00 1.744036424621431935e+00 1.728486672771760713e+00 1.712464823473650366e+00 1.695967740706671334e+00 1.678998090827148015e+00 1.661554614266582464e+00 1.643635303509314305e+00 1.625232116699204754e+00 1.606350431359325093e+00 1.586992846459284845e+00 1.567162925304675269e+00 1.546858316877817829e+00 1.526084298338139078e+00 1.504840648582085771e+00 1.483127157662349482e+00 1.460935763495109008e+00 1.438274788454338537e+00 1.415149372062721733e+00 1.391566116576496093e+00 1.367523944366040300e+00 1.343031880105793219e+00 1.318091552539169431e+00 1.292705135032644170e+00 1.266865397488294409e+00 1.240584291231152214e+00 1.213869431591265924e+00 1.186730565437678964e+00 1.159169407449334388e+00 1.131197059155369722e+00 1.102818933931351886e+00 1.074039332183774453e+00 1.044854114562758562e+00 1.015277390372420374e+00 9.853203084721974259e-01 9.549958718002450331e-01 9.243096534081611804e-01 8.932761656254111227e-01 8.619044661657270234e-01 8.302025069095195775e-01 7.981704119006638454e-01 7.658221696197601291e-01 7.331704099289941823e-01 7.002285603427993443e-01 6.670043876428354279e-01 6.335125867826929547e-01 5.997638864514073243e-01 5.657673996866511290e-01 5.315259403838543673e-01 4.970589900543327389e-01 4.623871342546980734e-01 4.275327261483395369e-01 3.925139461842013788e-01 3.573529995076094590e-01 3.220715143530920588e-01 2.866899602071202313e-01 2.512209750464438063e-01 2.156585363323995641e-01 1.799944327426104829e-01 1.442214722008936523e-01 1.083293825496778745e-01 7.232562328389337269e-02 3.621402556621226149e-02 2.000000410410101762e-09
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kaipy/gamera/magsphere.py
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#Various tools to post-process and analyze Gamera magnetosphere runs
from kaipy.kdefs import *
import kaipy.gamera.gampp
from kaipy.gamera.gampp import GameraPipe
import numpy as np
import glob
import kaipy.kaiH5 as kh5
import timeit
#Object to pull from MPI/Serial magnetosphere runs (H5 data), extends base
ffam = "monospace"
dLabC = "black" #Default label color
dLabFS = "medium" #Default label size
dBoxC = "lightgrey" #Default box color
TINY = 1.0e-8
rmStr = "mixtest"
#Assuming LFM/EGG type grid
class GamsphPipe(GameraPipe):
#Initialize object, rely on base class, take optional unit identifier
def __init__(self,fdir,ftag,doFast=False,uID="Earth"):
print("Initializing %s magnetosphere"%(uID))
#TODO: Add different unit/planet options here
self.MagM = -EarthM0g*1.0e+5
self.bScl = 4.58 #->nT
self.pScl = 1.67e-2 #->nPa
self.vScl = 1.0e+2 #-> km/s
self.tScl = 63.8 #->seconds
#New mix stuff
self.hasRemix = False #Remix data present (new format)
self.mixPipe = None
#Old mix stuff
self.tRMScl = 63.8 #->seconds, for Remix time scaling
self.hasRemixO = False #Remix data present (old format)
self.Nrm = 0 #Number of remix outputs
#2D equatorial grid, stretched polar (Ni,Nj*2+1)
self.xxi = [] ; self.yyi = []
self.xxc = [] ; self.yyc = []
GameraPipe.__init__(self,fdir,ftag,doFast=doFast)
self.Rin = self.xxi[0,0]
def OpenPipe(self,doVerbose=True):
GameraPipe.OpenPipe(self,doVerbose)
self.GetM0()
#Now do magnetosphere specific things
if (self.UnitsID != "CODE"):
self.bScl = 1.0 #->nT
self.pScl = 1.0 #->nPa
self.vScl = 1.0 #-> km/s
self.tScl = 1.0 #->Seconds
self.tRMScl = 63.8 #->Seconds
#Rescale time
self.T = self.tScl*self.T
#Create warped polar slice grid
Nr = self.Ni
Np = 2*(self.Nj)
self.xxi = np.zeros((Nr+1,Np+1))
self.yyi = np.zeros((Nr+1,Np+1))
self.xxc = np.zeros((Nr ,Np ))
self.yyc = np.zeros((Nr ,Np ))
self.BzD = np.zeros((Nr ,Np ))
#Create corners for stretched polar grid
#Use halfway k since it is agnostic to ghosts
eqK = self.Nk//2
#Upper half plane
for j in range(self.Nj):
self.xxi[:,j] = self.X[:,j,eqK]
self.yyi[:,j] = -self.Y[:,j,eqK]
#Lower half plane
for j in range(self.Nj,Np+1):
jp = Np-j
self.xxi[:,j] = self.X[:,jp,eqK]
self.yyi[:,j] = self.Y[:,jp,eqK]
#Get centers for stretched polar grid & BzD
self.xxc = 0.25*(self.xxi[:-1,:-1] + self.xxi[1:,:-1] + self.xxi[:-1,1:] + self.xxi[1:,1:])
self.yyc = 0.25*(self.yyi[:-1,:-1] + self.yyi[1:,:-1] + self.yyi[:-1,1:] + self.yyi[1:,1:])
r = np.sqrt(self.xxc**2.0 + self.yyc**2.0)
rm5 = r**(-5.0)
self.BzD = -r*r*self.MagM*rm5
if (self.hasMJD):
print("Found MJD data")
print("\tTime (Min/Max) = %f/%f"%(self.MJDs.min(),self.MJDs.max()))
#Do remix data things
#Old-style
rmOStr = "%s/%s*.h5"%(self.fdir,rmStr)
rmOuts = glob.glob(rmOStr)
Nrm = len(rmOuts)
if (Nrm>0):
import h5py
print("Found %d ReMIX outputs"%(Nrm))
self.hasRemixO = True
self.Nrm = Nrm
self.tRm = np.zeros(Nrm)
self.nCPCP = np.zeros(Nrm)
self.sCPCP = np.zeros(Nrm)
self.rmOuts = rmOuts
if (not self.doFast):
for i in range(Nrm):
fMix = rmOuts[i]
with h5py.File(fMix,'r') as hf:
Atts = hf.attrs.keys()
if ('t' in Atts):
self.tRm[i] = self.tRMScl*hf.attrs['t']
if ('nCPCP' in Atts):
self.nCPCP[i] = hf.attrs['nCPCP']
self.sCPCP[i] = hf.attrs['sCPCP']
print("\tTime (Min/Max) = %f/%f"%(self.tRm.min(),self.tRm.max()))
#Sort into time ordering
I = self.tRm.argsort()
self.tRm = self.tRm[I]
self.nCPCP = self.nCPCP[I]
self.sCPCP = self.sCPCP[I]
self.rmOuts = [rmOuts[i] for i in I]
#New-style
Stubs = self.ftag.split(".")
rmOStr = "%s/%s.mix.h5"%(self.fdir,Stubs[0])
rmOuts = glob.glob(rmOStr)
Nrm = len(rmOuts)
if (Nrm>0):
mixtag = Stubs[0]+".mix"
self.hasRemix = True
print("Found ReMIX data, reading ...")
self.mixPipe = GameraPipe(self.fdir,mixtag,doVerbose=False)
self.nCPCP = kh5.getTs(rmOStr,sIds=self.sids,aID="nCPCP")
self.sCPCP = kh5.getTs(rmOStr,sIds=self.sids,aID="sCPCP")
#Get magnetic moment from file
def GetM0(self):
import h5py
with h5py.File(self.f0,'r') as hf:
M0 = hf.attrs.get("MagM0",self.MagM)
#Store value
self.MagM = M0
#Get "egg" slice, variable matched to stretched polar grid
#Either equatorial or meridional
def EggSlice(self,vID,sID=None,vScl=None,doEq=True,doVerb=True,numGhost=0):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
#For upper/lower half planes, average above/below
Nk2 = (self.Nk-2*numGhost)//2
Nk4 = (self.Nk-2*numGhost)//4
if (doEq):
ku1 = self.Nk - numGhost - 1
ku2 = numGhost
kl1 = numGhost + Nk2-1
kl2 = numGhost + Nk2
else:
ku1 = numGhost + Nk4 - 1
ku2 = numGhost + Nk4
kl1 = numGhost + 3*Nk4 - 1
kl2 = numGhost + 3*Nk4
Nr = self.Ni
Np = 2*self.Nj
Qk = np.zeros((Nr,Np))
for j in range(Np):
if (j>=self.Nj):
#Lower half plane
jp = Np-j-1
Qk[:,j] = 0.5*( Q[:,jp,kl1] + Q[:,jp,kl2] )
else:
jp = j
Qk[:,j] = 0.5*( Q[:,jp,ku1] + Q[:,jp,ku2] )
return Qk
#Standard equatorial dbz (in nT)
def DelBz(self,s0=0):
Bz = self.EggSlice("Bz",s0) #Unscaled
dbz = Bz*self.bScl - self.BzD
return dbz
#Equatorial magnitude of field (in nT)
def eqMagB(self,s0=0):
Bx = self.EggSlice("Bx",s0) #Unscaled
By = self.EggSlice("By",s0) #Unscaled
Bz = self.EggSlice("Bz",s0) #Unscaled
Beq = self.bScl*np.sqrt(Bx**2.0+By**2.0+Bz**2.0)
return Beq
#Return data for meridional 2D field lines
#Need to use Cartesian grid
def bStream(self,s0=0,xyBds=[-35,25,-25,25],dx=0.05):
#Get field data
U = self.bScl*self.EggSlice("Bx",s0,doEq=False)
V = self.bScl*self.EggSlice("Bz",s0,doEq=False)
x1,y1,gu,gv,gM = self.doStream(U,V,xyBds,dx)
return x1,y1,gu,gv,gM
def vStream(self,s0=0,xyBds=[-35,25,-25,25],dx=0.05):
#Get field data
U = self.vScl*self.EggSlice("Vx",s0,doEq=True)
V = self.vScl*self.EggSlice("Vy",s0,doEq=True)
x1,y1,gu,gv,gM = self.doStream(U,V,xyBds,dx)
return x1,y1,gu,gv,gM
#Map from Gamera step to remix file
def Gam2Remix(self,n):
tGam = self.T[n-self.s0]
if (self.hasRemixO):
#Find nearest time slice
i0 = np.abs(self.tRm-tGam).argmin()
fMix = self.rmOuts[i0]
else:
fMix = None
return fMix
#Get CPCP @ gamera step #n
def GetCPCP(self,n):
n0 = n-self.s0
tGam = self.T[n0]
if (self.hasRemixO): #Old remix style
#Find nearest time slice
i0 = np.abs(self.tRm-tGam).argmin()
cpcp = [self.nCPCP[i0],self.sCPCP[i0]]
elif (self.hasRemix):
cpcp = [self.nCPCP[n0],self.sCPCP[n0]]
else:
cpcp = [0.0,0.0]
return cpcp
#Add time label, xy is position in axis (not data) coords
def AddTime(self,n,Ax,xy=[0.9,0.95],cLab=dLabC,fs=dLabFS,T0=0.0,doBox=True,BoxC=dBoxC):
ffam = "monospace"
HUGE = 1.0e+8
#Decide whether to do UT or elapsed
if (self.hasMJD):
minMJD = self.MJDs[n-self.s0]
else:
minMJD = -HUGE
if (self.hasMJD and minMJD>TINY):
from astropy.time import Time
dtObj = Time(self.MJDs[n-self.s0],format='mjd').datetime
tStr = " " + dtObj.strftime("%H:%M:%S") + "\n" + dtObj.strftime("%m/%d/%Y")
else:
#Get time in seconds
t = self.T[n-self.s0] - T0
Nm = int( (t-T0)/60.0 ) #Minutes, integer
Hr = Nm/60
Min = np.mod(Nm,60)
Sec = np.mod(int(t),60)
tStr = "Elapsed Time\n %02d:%02d:%02d"%(Hr,Min,Sec)
if (doBox):
Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
def AddSW(self,n,Ax,xy=[0.725,0.025],cLab=dLabC,fs=dLabFS,T0=0.0,doBox=True,BoxC=dBoxC,doAll=True):
import kaipy.kaiH5 as kh5
#Start by getting SW data
vIDs = ["D","P","Vx","Bx","By","Bz"]
Nv = len(vIDs)
qSW = np.zeros(Nv)
if (self.isMPI):
fSW = self.fdir + "/" + kh5.genName(self.ftag,self.Ri-1,0,0,self.Ri,self.Rj,self.Rk)
else:
fSW = self.fdir + "/" + self.ftag + ".h5"
for i in range(Nv):
Q = kh5.PullVar(fSW,vIDs[i],n)
qSW[i] = Q[-1,0,0]
D = qSW[0] ; P = qSW[1] ; Vx = qSW[2] ; Bx = qSW[3] ; By = qSW[4] ; Bz = qSW[5]
SWStr = "Solar Wind\n"
MagB = self.bScl*np.sqrt(Bx**2.0+By**2.0+Bz**2.0)
#Clock = atan(by/bz), cone = acos(Bx/B)
r2deg = 180.0/np.pi
if (MagB>TINY):
clk = r2deg*np.arctan2(By,Bz)
cone = r2deg*np.arccos(self.bScl*Bx/MagB)
else:
clk = 0.0
cone = 0.0
if (clk<0):
clk = clk+360.0
Deg = r"$\degree$"
SWStr = "Solar Wind\nIMF: %4.1f [nT], %5.1f"%(MagB,clk) + Deg# + ", %5.2f"%(cone) + Deg
if (doAll):
SWStr = SWStr + "\nDensity: %5.1f [#/cc] \nSpeed: %6.1f [km/s] "%(D,self.vScl*np.abs(Vx))
if (doBox):
Ax.text(xy[0],xy[1],SWStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
else:
Ax.text(xy[0],xy[1],SWStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
def AddCPCP(self,n,Ax,xy=[0.9,0.95],cLab=dLabC,fs=dLabFS,doBox=True,BoxC=dBoxC):
cpcp = self.GetCPCP(n)
tStr = "CPCP (North/South)\n%6.2f / %6.2f [kV]"%(cpcp[0],cpcp[1])
if (doBox):
Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
else:
Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam)
def doStream(self,U,V,xyBds=[-35,25,-25,25],dx=0.05):
from scipy.interpolate import griddata
N1 = int( (xyBds[1]-xyBds[0])/dx )
N2 = int( (xyBds[3]-xyBds[2])/dx )
#Create matching Cartesian grid
x1 = np.linspace(xyBds[0],xyBds[1],N1)
y1 = np.linspace(xyBds[2],xyBds[3],N2)
xx1,yy1 = np.meshgrid(x1,y1)
r1 = np.sqrt(xx1**2.0+yy1**2.0)
#Flatten and interpolate
px = self.xxc.flatten()
py = self.yyc.flatten()
pu = U.flatten()
pv = V.flatten()
pMag = np.sqrt(U**2.0+V**2.0).flatten()
gu = griddata(zip(px,py),pu ,(xx1,yy1),method='linear',fill_value=0.0)
gv = griddata(zip(px,py),pv ,(xx1,yy1),method='linear',fill_value=0.0)
gM = griddata(zip(px,py),pMag,(xx1,yy1),method='linear',fill_value=0.0)
kOut = (r1<=self.Rin*1.01)
gu[kOut] = 0.0
gv[kOut] = 0.0
gM[kOut] = 0.0
return x1,y1,gu,gv,gM
#Replacement for remixpp adding inset remix plots
#Becomes a wrapper to remixpp.CMIViz
def CMIViz(self,AxM=None,nStp=0,doNorth=True,loc="upper left",dxy=[20,20]):
from kaipy.kaiH5 import CheckOrDie
import h5py
import kaipy.gamera.remixpp as remixpp
if (doNorth):
pID = "Potential NORTH"
cID = "Field-aligned current NORTH"
gID = "NORTH"
else:
pID = "Potential SOUTH"
cID = "Field-aligned current SOUTH"
gID = "SOUTH"
if (self.hasRemixO): #Old remix style
fMix = self.Gam2Remix(nStp)
CheckOrDie(fMix)
with h5py.File(fMix,'r') as hf:
P = hf[gID]['Potential'][()]
C = hf[gID]['Field-aligned current'][()]
elif (self.hasRemix): #New style remix
P = self.mixPipe.GetVar(pID,nStp,doVerb=False).T
C = self.mixPipe.GetVar(cID,nStp,doVerb=False).T
RIn = self.xxi[0,0]
llBC = np.arcsin(np.sqrt(1.0/RIn))*180.0/np.pi
nLat,nLon = C.shape
#Now call remixpp.CMIViz
remixpp.CMIPic(nLat,nLon,llBC,P,C,AxM,doNorth,loc,dxy)

582
kaipy/gamera/magsphereRescale.py
View File

@@ -1,582 +0,0 @@
#Various routines to help with restart upscaling
import h5py
import numpy as np
import kaipy.gamera.gamGrids as gg
import scipy
from scipy.spatial import ConvexHull
import kaipy.kaiH5 as kh5
TINY = 1.0e-8
NumG = 4
IDIR = 0
JDIR = 1
KDIR = 2
#Push restart data to an MPI tiling
#fInA is a restart file to pull attributes from
# add oG and oM for one step older for reproducible restart.
# These variables are used by mhd predictor/corrector.
def PushRestartMPI(outid,nRes,Ri,Rj,Rk,X,Y,Z,G,M,oG,oM,fInA,G0=None):
doGas0 = (G0 is not None) # Make sure doGas0 is either True or False
print("Reading attributes from %s"%(fInA))
iH5 = h5py.File(fInA,'r')
Ns,Nv,Nk,Nj,Ni = G.shape
#Create output files
Nkp = Nk//Rk
Njp = Nj//Rj
Nip = Ni//Ri
print("Splitting (%d,%d,%d) cells into (%d,%d,%d) x (%d,%d,%d) [Cells,MPI]"%(Ni,Nj,Nk,Nip,Njp,Nkp,Ri,Rj,Rk))
#Loop over output slices and create restarts
for i in range(Ri):
for j in range(Rj):
for k in range(Rk):
fOut = kh5.genName(outid,i,j,k,Ri,Rj,Rk,nRes)
#Open output file
oH5 = h5py.File(fOut,'w')
iS = i*Nip
iE = iS+Nip
jS = j*Njp
jE = jS+Njp
kS = k*Nkp
kE = kS+Nkp
#Indices for offset ghost grid
iSg = iS -NumG + NumG #Last numG to offset to 0-based index
iEg = iS+Nip+NumG+1 + NumG #Last numG to offset to 0-based index
jSg = jS -NumG + NumG #Last numG to offset to 0-based index
jEg = jS+Njp+NumG+1 + NumG #Last numG to offset to 0-based index
kSg = kS -NumG + NumG #Last numG to offset to 0-based index
kEg = kS+Nkp+NumG+1 + NumG #Last numG to offset to 0-based index
print("Writing %s"%(fOut))
print("\tMPI (%d,%d,%d) = [%d,%d]x[%d,%d]x[%d,%d]"%(i,j,k,iS,iE,jS,jE,kS,kE))
print("\tGrid indices = (%d,%d)x(%d,%d)x(%d,%d)"%(iSg,iEg,jSg,jEg,kSg,kEg))
#Slice subgrids
ijkX = X[kSg:kEg,jSg:jEg,iSg:iEg]
ijkY = Y[kSg:kEg,jSg:jEg,iSg:iEg]
ijkZ = Z[kSg:kEg,jSg:jEg,iSg:iEg]
#Slice pieces out of gas and magflux
ijkG = G [:,:,kS:kE ,jS:jE ,iS:iE ]
ijkM = M [ :,kS:kE+1,jS:jE+1,iS:iE+1]
ijkoG = oG [:,:,kS:kE ,jS:jE ,iS:iE ]
ijkoM = oM [ :,kS:kE+1,jS:jE+1,iS:iE+1]
if (doGas0):
ijkG0 = G0[:,:,kS:kE ,jS:jE ,iS:iE ]
#Write heavy variables
oH5.create_dataset("Gas",data=ijkG)
oH5.create_dataset("magFlux",data=ijkM)
oH5.create_dataset("oGas",data=ijkoG)
oH5.create_dataset("omagFlux",data=ijkoM)
if (doGas0):
oH5.create_dataset("Gas0",data=ijkG0)
#Write subgrid
oH5.create_dataset("X",data=ijkX)
oH5.create_dataset("Y",data=ijkY)
oH5.create_dataset("Z",data=ijkZ)
#Transfer attributes to output
for ak in iH5.attrs.keys():
aStr = str(ak)
oH5.attrs.create(ak,iH5.attrs[aStr])
#Close this output file
oH5.close()
#Close input file
iH5.close()
#Get full data from a tiled restart file
def PullRestartMPI(bStr,nRes,Ri,Rj,Rk,dIn=None,oH5=None):
doInit = True
for i in range(Ri):
for j in range(Rj):
for k in range(Rk):
fID = kh5.genName(bStr,i,j,k,Ri,Rj,Rk,nRes)
print("Reading from %s"%(fID))
#Start with input data
if (dIn is not None):
fIn = dIn + "/" + fID
else:
fIn = fID
iH5 = h5py.File(fIn,'r')
if (doInit):
Ns,Nv,Nkp,Njp,Nip = iH5['Gas'].shape
doGas0 = ('Gas0' in iH5.keys())
Nk = Rk*Nkp
Nj = Rj*Njp
Ni = Ri*Nip
G = np.zeros((Ns,Nv,Nk,Nj,Ni))
oG = np.zeros((Ns,Nv,Nk,Nj,Ni))
if (doGas0):
G0 = np.zeros((Ns,Nv,Nk,Nj,Ni))
else:
G0 = None
M = np.zeros((3,Nk+1,Nj+1,Ni+1))
oM= np.zeros((3,Nk+1,Nj+1,Ni+1))
if (oH5 is not None):
for ka in iH5.attrs.keys():
aStr = str(ka)
#print(aStr)
oH5.attrs.create(ka,iH5.attrs[aStr])
doInit = False
iS = i*Nip
iE = iS+Nip
jS = j*Njp
jE = jS+Njp
kS = k*Nkp
kE = kS+Nkp
#print("MPI (%d,%d,%d) = [%d,%d]x[%d,%d]x[%d,%d]"%(i,j,k,iS,iE,jS,jE,kS,kE))
G[:,:,kS:kE ,jS:jE ,iS:iE ] = iH5['Gas'][:]
oG[:,:,kS:kE ,jS:jE ,iS:iE ] = iH5['oGas'][:]
if (doGas0):
G0[:,:,kS:kE ,jS:jE ,iS:iE ] = iH5['Gas0'][:]
M[ :,kS:kE+1,jS:jE+1,iS:iE+1] = iH5['magFlux'][:]
oM[ :,kS:kE+1,jS:jE+1,iS:iE+1] = iH5['omagFlux'][:]
#Close up
iH5.close()
if (not doGas0):
G0 = None
return G,M,G0,oG,oM
#Downscale a grid (with ghosts, k-j-i order)
def downGrid(X,Y,Z):
Ngk,Ngj,Ngi = X.shape
Nk = Ngk-2*NumG-1
Nj = Ngj-2*NumG-1
Ni = Ngi-2*NumG-1
#Assuming LFM-style grid, get upper half plane
xx = X[NumG,NumG:-NumG,NumG:-NumG].T
yy = Y[NumG,NumG:-NumG,NumG:-NumG].T
#Now half cells in r-phi polar
rr = np.sqrt(xx**2.0+yy**2.0)
pp = np.arctan2(yy,xx)
NiD = Ni//2
NjD = Nj//2
NkD = Nk//2
rrD = np.zeros((NiD+1,NjD+1))
ppD = np.zeros((NiD+1,NjD+1))
for i in range(NiD+1):
for j in range(NjD+1):
rrD[i,j] = rr[2*i,2*j]
ppD[i,j] = pp[2*i,2*j]
#Convert back to 2D Cartesian
xxD = rrD*np.cos(ppD)
yyD = rrD*np.sin(ppD)
#Augment w/ 2D ghosts
xxG,yyG = gg.Aug2D(xxD,yyD,doEps=True,TINY=TINY)
#Augment w/ 3D ghosts
X,Y,Z = gg.Aug3D(xxG,yyG,Nk=NkD,TINY=TINY)
return X,Y,Z
#Upscale a grid (with ghosts, k-j-i order)
def upGrid(X,Y,Z):
Ngk,Ngj,Ngi = X.shape
Nk = Ngk-2*NumG-1
Nj = Ngj-2*NumG-1
Ni = Ngi-2*NumG-1
#Assuming LFM-style grid, get upper half plane
xx = X[NumG,NumG:-NumG,NumG:-NumG].T
yy = Y[NumG,NumG:-NumG,NumG:-NumG].T
#Now half cells in r-phi polar
rr = np.sqrt(xx**2.0+yy**2.0)
pp = np.arctan2(yy,xx)
NiH = 2*Ni
NjH = 2*Nj
NkH = 2*Nk
rrH = np.zeros((NiH+1,NjH+1))
ppH = np.zeros((NiH+1,NjH+1))
#Embed old points into new grid
for i in range(Ni+1):
for j in range(Nj+1):
rrH[2*i,2*j] = rr[i,j]
ppH[2*i,2*j] = pp[i,j]
#Create I midpoints
for i in range(Ni):
rrH[2*i+1,:] = 0.5*( rrH[2*i,:] + rrH[2*i+2,:] )
ppH[2*i+1,:] = 0.5*( ppH[2*i,:] + ppH[2*i+2,:] )
#Create J midpoints
for j in range(Nj):
rrH[:,2*j+1] = 0.5*( rrH[:,2*j] + rrH[:,2*j+2])
ppH[:,2*j+1] = 0.5*( ppH[:,2*j] + ppH[:,2*j+2])
#Create I-J midpoints
for i in range(Ni):
for j in range(Nj):
rrH[2*i+1,2*j+1] = 0.25*( rrH[2*i,2*j] + rrH[2*i,2*j+2] + rrH[2*i+2,2*j] + rrH[2*i+2,2*j+2] )
ppH[2*i+1,2*j+1] = 0.25*( ppH[2*i,2*j] + ppH[2*i,2*j+2] + ppH[2*i+2,2*j] + ppH[2*i+2,2*j+2] )
#Convert back to 2D Cartesian
xxH = rrH*np.cos(ppH)
yyH = rrH*np.sin(ppH)
#Augment w/ 2D ghosts
xxG,yyG = gg.Aug2D(xxH,yyH,doEps=True,TINY=TINY)
#Augment w/ 3D ghosts
X,Y,Z = gg.Aug3D(xxG,yyG,Nk=NkH,TINY=TINY)
return X,Y,Z
#Downscale gBAvg variable (G) on grid X,Y,Z to halved grid.
def downVolt(G):
Nd,Nk,Nj,Ni = G.shape
Gu = np.zeros((Nd,Nk//2,Nj//2,Ni))
print("Downscaling volt variables gBAvg etc...")
#Loop over fine grid
for d in range(Nd):
for i in range(Ni):
for k in range(Nk//2):
for j in range(Nj//2):
j0 = 2*j
k0 = 2*k
Gjk = G[d,k0:k0+2,j0:j0+2,i].mean()
Gu[d,k,j,i] = Gjk
return Gu
#Upscale gBAvg variable (G) on grid X,Y,Z to doubled grid with simple linear interpolation.
#Typical size at quad for example: gBAvg Dataset {3, 128, 96, 1}
def upVolt(G):
try:
Nd,Nk,Nj,Ni = G.shape
Gu = np.zeros((Nd,2*Nk,2*Nj,Ni))
print("Upscaling volt variables gBAvg etc...")
#Loop over coarse grid
for d in range(Nd):
for i in range(Ni):
for k in range(Nk):
for j in range(Nj):
Gjk = G[d,k,j,i]
j0 = 2*j
k0 = 2*k
Gu[d,k0:k0+2,j0:j0+2,i] = Gjk
except:
Nd,Nk,Nj,Ni,Nh = G.shape
Gu = np.zeros((Nd,2*Nk,2*Nj,Ni,Nh))
print("Upscaling volt variables gBHist etc...")
print(G.shape)
#Loop over coarse grid
for h in range(Nh):
for d in range(Nd):
for i in range(Ni):
for k in range(Nk):
for j in range(Nj):
Gjk = G[d,k,j,i,h]
j0 = 2*j
k0 = 2*k
Gu[d,k0:k0+2,j0:j0+2,i,h] = Gjk
return Gu
#Downscale gas variable (G) on grid X,Y,Z (w/ ghosts) to halved grid
def downGas(X,Y,Z,G,Xd,Yd,Zd):
Ns,Nv,Nk,Nj,Ni = G.shape
dV = Volume(X ,Y ,Z )
dVd = Volume(Xd,Yd,Zd)
print("Volume ratio (Coarse/Fine) = %f"%(dVd.sum()/dV.sum()))
Gd = np.zeros((Ns,Nv,Nk//2,Nj//2,Ni//2))
print("Downscaling gas variables ...")
#Loop over coarse grid
for s in range(Ns):
for v in range(Nv):
print("\tDownscaling Species %d, Variable %d"%(s,v))
for k in range(Nk//2):
for j in range(Nj//2):
for i in range(Ni//2):
dVijk = dV[2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] #Volumes of the finer subgrid
dQijk = G[s,v,2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] #stuff density in finer subgrid
#Stuff in the subchunk
QdV = (dVijk*dQijk).sum()
Gd[s,v,k,j,i] = QdV/dVd[k,j,i] #Scale back to density
#Test conservation
print("\t\tCoarse (Total) = %e"%(Gd[s,v,:,:,:]*dVd).sum())
print("\t\tFine (Total) = %e"%(G [s,v,:,:,:]*dV ).sum())
return Gd
#Upscale gas variable (G) on grid X,Y,Z (w/ ghosts) to doubled grid
def upGas(X,Y,Z,G,Xu,Yu,Zu):
Ns,Nv,Nk,Nj,Ni = G.shape
dV = Volume(X ,Y ,Z )
dVu = Volume(Xu,Yu,Zu)
print("Volume ratio (Coarse/Fine) = %f"%(dV.sum()/dVu.sum()))
Gu = np.zeros((Ns,Nv,2*Nk,2*Nj,2*Ni))
print("Upscaling gas variables ...")
#Loop over coarse grid
for s in range(Ns):
for v in range(Nv):
print("\tUpscaling Species %d, Variable %d"%(s,v))
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
QdV = G[s,v,k,j,i]*dV[k,j,i]
dVijk = dVu[2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] #Volumes of the finer subgrid
vScl = dVijk.sum() #Total volume of the finer subchunks
QdVu = QdV*(dVijk/vScl) #Give weighted contribution to each subcell
Gu[s,v,2*k:2*k+2,2*j:2*j+2,2*i:2*i+2] = QdVu/dVijk #Scale back to density
#Test conservation
print("\t\tCoarse (Total) = %e"%(G [s,v,:,:,:]* dV).sum())
print("\t\tFine (Total) = %e"%(Gu[s,v,:,:,:]*dVu).sum())
return Gu
#Upscale RCMCPL variable
def upRCMCpl(Q,N=1):
Nd = len(Q.shape)
if (Nd>=3):
Nk,Ni,Nj = Q.shape
Qr = np.zeros((Nk,Ni*2,Nj))
for n in range(Nk):
Qr[n,:,:] = upRCM1D(Q[n,:,:])
elif (Nd == 1):
#Just leave it alone
if (len(Q) == N):
#Upscale the one dimension
Q2D = np.zeros((N,1))
Q2D[:,0] = Q
Qr2D = upRCM1D(Q2D)
Qr = Qr2D[:,0]
else:
Qr = Q
else:
#2D
Qr = upRCM1D(Q)
return Qr
#Upscale RCM variable
def upRCM(Q,Ni=1,Nj=1,Nk=1):
Nd = len(Q.shape)
if (Nd>=3):
Nk = Q.shape[0]
Qr = np.zeros((Nk,(Nj-2)*2+2,Ni))
for n in range(Nk):
Qr[n,:,:] = upRCM1Dw(Q[n,:,:])
elif (Nd == 1):
if (len(Q) == Nj):
Q2D = np.zeros((Nj,1))
Q2D[:,0] = Q
Qr2D = upRCM1Dw(Q2D)
Qr = Qr2D[:,0]
else:
Qr = Q
else:
Qr = upRCM1Dw(Q)
return Qr
#Upscale first dimension of 2D array w/ strange rcm-wrap
def upRCM1Dw(Q):
Ni,Nj = Q.shape
Nip = Ni-2
Nri = Nip*2+2
Qr = np.zeros((Nri,Nj))
Qr[0:Nri-2,:] = upRCM1D(Q[0:-2,:])
Qr[Nri-2,:] = Qr[0,:]
Qr[Nri-1,:] = Qr[1,:]
return Qr
#Upscale just first dimension of 2D array
def upRCM1D(Q):
Ni,Nj = Q.shape
Qr = np.zeros((Ni*2,Nj))
for j in range(Nj):
for i in range(Ni):
Qij = Q[i,j]
i0 = 2*i
j0 = j
Qr[i0:i0+2,j0] = Qij
return Qr
#Upscale mix variable
def upMIX(Q):
Ni,Nj = Q.shape
Qr = np.zeros((2*Ni,2*Nj))
for j in range(Nj):
for i in range(Ni):
Qij = Q[i,j]
i0 = 2*i
j0 = 2*j
Qr[i0:i0+2,j0:j0+2] = Qij
#print(Q.sum(),Qr.sum()/4)
return Qr
#Downscale mix variable
def downMIX(Q):
Ni,Nj = Q.shape
Qr = np.zeros((Ni//2,Nj//2))
#Loop over coarse grid
for j in range(Nj//2):
for i in range(Ni//2):
i0 = 2*i
j0 = 2*j
Qij = Q[i0:i0+2,j0:j0+2].mean()
Qr[i,j] = Qij
return Qr
#Return cell centered volume (active only) from grid X,Y,Z (w/ ghosts)
def Volume(Xg,Yg,Zg):
Ngk,Ngj,Ngi = Xg.shape
Nk = Ngk-2*NumG-1
Nj = Ngj-2*NumG-1
Ni = Ngi-2*NumG-1
X = Xg[NumG:-NumG,NumG:-NumG,NumG:-NumG]
Y = Yg[NumG:-NumG,NumG:-NumG,NumG:-NumG]
Z = Zg[NumG:-NumG,NumG:-NumG,NumG:-NumG]
print("Calculating volume of grid of size (%d,%d,%d)"%(Ni,Nj,Nk))
dV = np.zeros((Nk,Nj,Ni))
ijkPts = np.zeros((8,3))
#Assuming LFM-like symmetry
k = 0
for j in range(Nj):
for i in range(Ni):
ijkPts[:,0] = X[k:k+2,j:j+2,i:i+2].flatten()
ijkPts[:,1] = Y[k:k+2,j:j+2,i:i+2].flatten()
ijkPts[:,2] = Z[k:k+2,j:j+2,i:i+2].flatten()
dV[:,j,i] = ConvexHull(ijkPts,incremental=False).volume
return dV
#Downscale magnetic fluxes (M) on grid X,Y,Z (w/ ghosts) to halved grid
def downFlux(X,Y,Z,M,Xu,Yu,Zu):
Nd,Nkc,Njc,Nic = M.shape
Nk = Nkc-1
Nj = Njc-1
Ni = Nic-1
Md = np.zeros((Nd,Nk//2+1,Nj//2+1,Ni//2+1))
#Loop over coarse grid cells
print("Downscaling face fluxes ...")
for k in range(Nk//2):
for j in range(Nj//2):
for i in range(Ni//2):
ip = 2*i
jp = 2*j
kp = 2*k
#West/east i faces
Md[IDIR,k ,j ,i ] = M[IDIR,kp:kp+2,jp:jp+2,ip ].sum()
Md[IDIR,k ,j ,i+1] = M[IDIR,kp:kp+2,jp:jp+2,ip+2].sum()
#South/north j faces
Md[JDIR,k ,j ,i ] = M[JDIR,kp:kp+2,jp ,ip:ip+2].sum()
Md[JDIR,k ,j+1,i ] = M[JDIR,kp:kp+2,jp+2,ip:ip+2].sum()
#Bottom/top k faces
Md[KDIR,k ,j ,i ] = M[KDIR,kp ,jp:jp+2,ip:ip+2].sum()
Md[KDIR,k+1,j ,i ] = M[KDIR,kp+2,jp:jp+2,ip:ip+2].sum()
return Md
#Upscale magnetic fluxes (M) on grid X,Y,Z (w/ ghosts) to doubled grid
def upFlux(X,Y,Z,M,Xu,Yu,Zu):
Nd,Nkc,Njc,Nic = M.shape
Nk = Nkc-1
Nj = Njc-1
Ni = Nic-1
Mu = np.zeros((Nd,2*Nk+1,2*Nj+1,2*Ni+1))
#Loop over coarse grid
print("Upscaling face fluxes ...")
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
ip = 2*i
jp = 2*j
kp = 2*k
#West i face (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip] = 0.25*M[IDIR,k,j,i]
#East i face (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip+2] = 0.25*M[IDIR,k,j,i+1]
#South j face (4)
Mu[JDIR,kp:kp+2,jp,ip:ip+2] = 0.25*M[JDIR,k,j,i]
#North j face (4)
Mu[JDIR,kp:kp+2,jp+2,ip:ip+2] = 0.25*M[JDIR,k,j+1,i]
#Bottom k face (4)
Mu[KDIR,kp,jp:jp+2,ip:ip+2] = 0.25*M[KDIR,k,j,i]
#Top k face (4)
Mu[KDIR,kp+2,jp:jp+2,ip:ip+2] = 0.25*M[KDIR,k+1,j,i]
#Now all exterior faces are done
#12 remaining interior faces
#Interior i faces (4)
Mu[IDIR,kp:kp+2,jp:jp+2,ip+1] = 0.5*( Mu[IDIR,kp:kp+2,jp:jp+2,ip] + Mu[IDIR,kp:kp+2,jp:jp+2,ip+2] )
#Interior j faces (4)
Mu[JDIR,kp:kp+2,jp+1,ip:ip+2] = 0.5*( Mu[JDIR,kp:kp+2,jp,ip:ip+2] + Mu[JDIR,kp:kp+2,jp+2,ip:ip+2] )
#Interior k faces (4)
Mu[KDIR,kp+1,jp:jp+2,ip:ip+2] = 0.5*( Mu[KDIR,kp,jp:jp+2,ip:ip+2] + Mu[KDIR,kp+2,jp:jp+2,ip:ip+2] )
#MaxDiv(M)
return Mu
#Calculates maximum divergence of mag flux data
def MaxDiv(M):
Nd,Nkc,Njc,Nic = M.shape
Nk = Nkc-1
Nj = Njc-1
Ni = Nic-1
Div = np.zeros((Nk,Nj,Ni))
for k in range(Nk):
for j in range(Nj):
for i in range(Ni):
Div[k,j,i] = M[IDIR,k,j,i+1]-M[IDIR,k,j,i]+M[JDIR,k,j+1,i]-M[JDIR,k,j,i]+M[KDIR,k+1,j,i]-M[KDIR,k,j,i]
mDiv = np.abs(Div).max()
bDiv = np.abs(Div).mean()
print("Max/Mean divergence = %e,%e"%(mDiv,bDiv))
print("Sum divergence = %e"%(Div.sum()))
return Div

464
kaipy/gamera/msphViz.py
View File

@@ -1,464 +0,0 @@
#Various helper routines for magnetosphere quick look plots
import argparse
from argparse import RawTextHelpFormatter
import matplotlib as mpl
import numpy as np
import kaipy.kaiViz as kv
import kaipy.kaiTools as kt
import kaipy.gamera.magsphere as msph
import kaipy.remix.remix as remix
import os
dbMax = 25.0
dbCM = "RdGy_r"
bzCM = "bwr"
cLW = 0.25
bz0Col = "magenta"
mpiCol = "deepskyblue"
jMax = 10.0 #Max current for contours
eMax = 5.0 #Max current for contours
#Default pressure colorbar
vP = kv.genNorm(vMin=1.0e-2,vMax=10.0,doLog=True)
szStrs = ['small','std','big','bigger','fullD','dm']
szBds = {}
szBds["std"] = [-40.0 ,20.0,2.0]
szBds["big"] = [-100.0,20.0,2.0]
szBds["bigger"] = [-200.0,25.0,2.0]
szBds["fullD"] = [-300.0,30.0,3.0] # full domain for double res
szBds["small"] = [-10.0 , 5.0,2.0]
szBds["dm"] = [-30.0 ,10.0,40.0/15.0]
#Add different size options to argument
def AddSizeArgs(parser):
parser.add_argument('-size',type=str,default="std",choices=szStrs,help="Domain bounds options (default: %(default)s)")
#Return domain size from parsed arguments
def GetSizeBds(args):
szStr = args.size
szBd = szBds[szStr]
xTail = szBd[0]
xSun = szBd[1]
yMax = (xSun-xTail)/szBd[2]
xyBds = [xTail,xSun,-yMax,yMax]
return xyBds
#Plot absolute error in the requested, or given, equatorial field
def PlotEqErrAbs(gsphP,gsphO,nStp,xyBds,Ax,fieldNames,AxCB=None,doClear=True,doDeco=True,vMin=1e-9,vMax=1e-4,doLog=True,doVerb=True):
#specify two gsph objects as gsphPredicted (gsphP) and gsphObserved (gsphO)
#
normAbs = kv.genNorm(vMin,vMax,doLog=doLog)
cmapAbs = "PRGn"
if doLog:
cmapAbs = "magma"
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
cbString = ""
for fn in fieldNames:
cbString = cbString + "'" + fn + "', "
cbString = cbString + " Absolute Error"
kv.genCB(AxCB,normAbs,cbString,cM=cmapAbs)
#Now do main plotting
if (doClear):
Ax.clear()
dataAbs = None
for fn in fieldNames:
dataP = gsphP.EggSlice(fn,nStp,doEq=True,doVerb=doVerb)
dataO = gsphO.EggSlice(fn,nStp,doEq=True,doVerb=doVerb)
if dataAbs is None:
dataAbs = np.square(dataO - dataP)
else:
dataAbs = dataAbs + np.square(dataO - dataP)
dataAbs = np.sqrt(dataAbs)
Ax.pcolormesh(gsphP.xxi,gsphP.yyi,dataAbs,cmap=cmapAbs,norm=normAbs)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Y [Re]')
return dataAbs
#Plot relative error in the requested, or given, equatorial field
def PlotEqErrRel(gsphP,gsphO,nStp,xyBds,Ax,fieldNames,AxCB=None,doClear=True,doDeco=True,vMin=1e-16,vMax=1,doLog=True,doVerb=True):
#specify two gsph objects as gsphPredicted (gsphP) and gsphObserved (gsphO)
#
normRel = kv.genNorm(vMin,vMax,doLog=doLog)
cmapRel = "PRGn"
if doLog:
cmapRel = "magma"
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
cbString = ""
for fn in fieldNames:
cbString = cbString + "'" + fn + "', "
cbString = cbString + " Relative Error"
kv.genCB(AxCB,normRel,cbString,cM=cmapRel)
#Now do main plotting
if (doClear):
Ax.clear()
dataAbs = None
dataBase = None
for fn in fieldNames:
dataP = gsphP.EggSlice(fn,nStp,doEq=True,doVerb=doVerb)
dataO = gsphO.EggSlice(fn,nStp,doEq=True,doVerb=doVerb)
if dataAbs is None:
dataBase = np.square(dataP)
dataAbs = np.square(dataO - dataP)
else:
dataBase = dataBase + np.square(dataP)
dataAbs = dataAbs + np.square(dataO - dataP)
dataBase = np.sqrt(dataBase)
dataAbs = np.sqrt(dataAbs)
# math breaks when Base field is exactly 0
dataBase[dataBase == 0] = np.finfo(np.float32).tiny
dataRel = np.absolute(dataAbs/dataBase)
Ax.pcolormesh(gsphP.xxi,gsphP.yyi,dataRel,cmap=cmapRel,norm=normRel)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Y [Re]')
return dataRel
#Plot absolute error along the requested logical axis
def PlotLogicalErrAbs(gsphP,gsphO,nStp,Ax,fieldNames,meanAxis,AxCB=None,doClear=True,doDeco=True,vMin=1e-16,vMax=1,doLog=True,doVerb=True):
#specify two gsph objects as gsphPredicted (gsphP) and gsphObserved (gsphO)
#
normAbs = kv.genNorm(vMin,vMax,doLog=doLog)
cmapAbs = "PRGn"
if doLog:
cmapAbs = "magma"
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
cbString = ""
for fn in fieldNames:
cbString = cbString + "'" + fn + "', "
cbString = cbString + " Absolute Error along "
if meanAxis == 0:
cbString = cbString + "I axis"
elif meanAxis == 1:
cbString = cbString + "J axis"
elif meanAxis == 2:
cbString = cbString + "K axis"
kv.genCB(AxCB,normAbs,cbString,cM=cmapAbs)
#Now do main plotting
if (doClear):
Ax.clear()
dataAbs = CalcTotalErrAbs(gsphP,gsphO,nStp,fieldNames,doVerb=doVerb,meanAxis=meanAxis)
dataAbs = np.transpose(dataAbs) # transpose to put I/J on the horizontal axis
Ax.pcolormesh(dataAbs,cmap=cmapAbs,norm=normAbs)
if (doDeco):
if meanAxis == 0:
Ax.set_xlabel('J Indices')
Ax.set_ylabel('K Indices')
elif meanAxis == 1:
Ax.set_xlabel('I Indices')
Ax.set_ylabel('K Indices')
elif meanAxis == 2:
Ax.set_xlabel('I Indices')
Ax.set_ylabel('J Indices')
return dataAbs
#Plot relative error along the requested logical axis
def PlotLogicalErrRel(gsphP,gsphO,nStp,Ax,fieldNames,meanAxis,AxCB=None,doClear=True,doDeco=True,vMin=1e-16,vMax=1,doLog=True,doVerb=True):
#specify two gsph objects as gsphPredicted (gsphP) and gsphObserved (gsphO)
#
normRel = kv.genNorm(vMin,vMax,doLog=doLog)
cmapRel = "PRGn"
if doLog:
cmapRel = "magma"
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
cbString = ""
for fn in fieldNames:
cbString = cbString + "'" + fn + "', "
cbString = cbString + " Relative Error along "
if meanAxis == 0:
cbString = cbString + "I axis"
elif meanAxis == 1:
cbString = cbString + "J axis"
elif meanAxis == 2:
cbString = cbString + "K axis"
kv.genCB(AxCB,normAbs,cbString,cM=cmapAbs)
#Now do main plotting
if (doClear):
Ax.clear()
dataRel = CalcTotalErrRel(gsphP,gsphO,nStp,fieldNames,doVerb=doVerb,meanAxis=meanAxis)
dataRel = np.transpose(dataRel) # transpose to put I/J on the horizontal axis
Ax.pcolormesh(dataRel,cmap=cmapRel,norm=normRel)
if (doDeco):
if meanAxis == 0:
Ax.set_xlabel('J Indices')
Ax.set_ylabel('K Indices')
elif meanAxis == 1:
Ax.set_xlabel('I Indices')
Ax.set_ylabel('K Indices')
elif meanAxis == 2:
Ax.set_xlabel('I Indices')
Ax.set_ylabel('J Indices')
return dataRel
#Calculate total cumulative absolute error between two cases
def CalcTotalErrAbs(gsphP,gsphO,nStp,fieldNames,doVerb=True,meanAxis=None):
dataAbs = None
for fn in fieldNames:
dataP = gsphP.GetVar(fn,nStp,doVerb=doVerb)
dataO = gsphO.GetVar(fn,nStp,doVerb=doVerb)
if dataAbs is None:
dataAbs = np.square(dataO - dataP)
else:
dataAbs = dataAbs + np.square(dataO - dataP)
dataAbs = np.sqrt(dataAbs)
return np.mean(dataAbs,axis=meanAxis)
#Calculate total cumulative relative error between two cases
def CalcTotalErrRel(gsphP,gsphO,nStp,fieldNames,doVerb=True,meanAxis=None):
dataAbs = None
dataBase = None
for fn in fieldNames:
dataP = gsphP.GetVar(fn,nStp,doVerb=doVerb)
dataO = gsphO.GetVar(fn,nStp,doVerb=doVerb)
if dataAbs is None:
dataBase = np.square(dataP)
dataAbs = np.square(dataO - dataP)
else:
dataBase = dataBase + np.square(dataP)
dataAbs = dataAbs + np.square(dataO - dataP)
dataBase = np.sqrt(dataBase)
dataAbs = np.sqrt(dataAbs)
# math breaks when Base field is exactly 0
dataBase[dataBase == 0] = np.finfo(np.float32).tiny
dataRel = np.absolute(dataAbs/dataBase)
return np.mean(dataRel,axis=meanAxis)
#Plot equatorial field
def PlotEqB(gsph,nStp,xyBds,Ax,AxCB=None,doClear=True,doDeco=True,doBz=False):
vBZ = kv.genNorm(dbMax)
vDB = kv.genNorm(dbMax)
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
if (doBz):
kv.genCB(AxCB,vBZ,"Vertical Field [nT]",cM=bzCM,Ntk=7)
else:
kv.genCB(AxCB,vDB,"Residual Field [nT]",cM=dbCM,Ntk=7)
#Now do main plotting
if (doClear):
Ax.clear()
Bz = gsph.EggSlice("Bz",nStp,doEq=True)
if (doBz):
Ax.pcolormesh(gsph.xxi,gsph.yyi,Bz,cmap=bzCM,norm=vBZ)
else:
dbz = gsph.DelBz(nStp)
Ax.pcolormesh(gsph.xxi,gsph.yyi,dbz,cmap=dbCM,norm=vDB)
Ax.contour(kv.reWrap(gsph.xxc),kv.reWrap(gsph.yyc),kv.reWrap(Bz),[0.0],colors=bz0Col,linewidths=cLW)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Y [Re]')
return Bz
def PlotMerid(gsph,nStp,xyBds,Ax,doDen=False,doRCM=False,AxCB=None,doClear=True,doDeco=True,doSrc=False):
CMx = "viridis"
if (doDen):
if (doRCM):
vN = kv.genNorm(vMin=1.0,vMax=1.0e+3,doLog=True)
else:
vN = kv.genNorm(0,25)
if (doSrc):
vID = "SrcD"
cbStr = "Source Density [#/cc]"
else:
vID = "D"
cbStr = "Density [#/cc]"
Q = gsph.EggSlice(vID,nStp,doEq=False)
else:
vN = vP
if (doSrc):
vID = "SrcP"
cbStr = "Source Pressure [nPa]"
else:
vID = "P"
cbStr = "Pressure [nPa]"
Q = gsph.EggSlice(vID,nStp,doEq=False)
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
kv.genCB(AxCB,vN,cbStr,cM=CMx)
Ax.pcolormesh(gsph.xxi,gsph.yyi,Q,cmap=CMx,norm=vN)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Z [Re]')
Ax.yaxis.tick_right()
Ax.yaxis.set_label_position('right')
def PlotJyXZ(gsph,nStp,xyBds,Ax,AxCB=None,jScl=None,doDeco=True):
if (jScl is None):
#Just assuming current scaling is nA/m2
jScl = 1.0
vJ = kv.genNorm(jMax)
jCMap = "PRGn"
Nc = 15
cVals = np.linspace(-jMax,jMax,Nc)
if (AxCB is not None):
AxCB.clear()
kv.genCB(AxCB,vJ,"Jy [nA/m2]",cM=jCMap)
Q = jScl*gsph.EggSlice("Jy",nStp,doEq=False)
#Zero out first shell b/c bad derivative
print(Q.shape)
Q[0:2,:] = 0.0
#Ax.contour(kv.reWrap(gsph.xxc),kv.reWrap(gsph.yyc),kv.reWrap(Q),cVals,norm=vJ,cmap=jCMap,linewidths=cLW)
Ax.pcolormesh(gsph.xxi,gsph.yyi,Q,norm=vJ,cmap=jCMap)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Z [Re]')
Ax.yaxis.tick_right()
Ax.yaxis.set_label_position('right')
#Plot equatorial azimuthal electric field
def PlotEqEphi(gsph,nStp,xyBds,Ax,AxCB=None,doClear=True,doDeco=True):
vE = kv.genNorm(eMax)
vEMap = "PRGn"
if (AxCB is not None):
#Add the colorbar to AxCB
AxCB.clear()
kv.genCB(AxCB,vE,r"E$_{phi}$ [mV/m]",cM=vEMap)
#Now do main plotting
if (doClear):
Ax.clear()
Bx = gsph.EggSlice("Bx",nStp,doEq=True)
By = gsph.EggSlice("By",nStp,doEq=True)
Bz = gsph.EggSlice("Bz",nStp,doEq=True)
Vx = gsph.EggSlice("Vx",nStp,doEq=True)
Vy = gsph.EggSlice("Vy",nStp,doEq=True)
Vz = gsph.EggSlice("Vz",nStp,doEq=True)
# calculating some variables to to plot
#E=-VxB
Ex = -(Vy*Bz-Vz*By)*0.001 # [mV/m]
Ey = (Vx*Bz-Vz*Bx)*0.001
Ez = -(Vx*By-Vy*Bx)*0.001
# coordinate transform
ppc = np.arctan2(gsph.yyc,gsph.xxc)
theta = np.pi #eq plane
Er,Et,Ep = kt.xyz2rtp(ppc,theta,Ex,Ey,Ez)
Ax.pcolormesh(gsph.xxi,gsph.yyi,Ep,cmap=vEMap,norm=vE)
kv.SetAx(xyBds,Ax)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM-X [Re]')
Ax.set_ylabel('SM-Y [Re]')
Ax.yaxis.tick_right()
Ax.yaxis.set_label_position('right')
#Add MPI contours
def PlotMPI(gsph,Ax,ashd=0.5):
gCol = mpiCol
for i in range(gsph.Ri):
i0 = i*gsph.dNi
Ax.plot(gsph.xxi[i0,:],gsph.yyi[i0,:],mpiCol,linewidth=cLW,alpha=ashd)
if (gsph.Rj>1):
for j in range(1,gsph.Rj):
j0 = j*gsph.dNj
Ax.plot(gsph.xxi[:,j0], gsph.yyi[:,j0],gCol,linewidth=cLW,alpha=ashd)
Ax.plot(gsph.xxi[:,j0],-gsph.yyi[:,j0],gCol,linewidth=cLW,alpha=ashd)
#X-axis (+)
Ax.plot(gsph.xxi[:,0], gsph.yyi[:,0],gCol,linewidth=cLW,alpha=ashd)
#X-axis (-)
j0 = (gsph.Rj)*gsph.dNj
Ax.plot(gsph.xxi[:,j0], gsph.yyi[:,j0],gCol,linewidth=cLW,alpha=ashd)
def AddIonBoxes(gs,ion):
gsRM = gs.subgridspec(20,20)
wXY = 6
dX = 1
dY = 1
#Northern
ion.init_vars('NORTH')
ax = ion.plot('current' ,gs=gsRM[dY:dY+wXY,dX:dX+wXY],doInset=True)
#ax.set_title("North",fontsize="small")
#Southern
ion.init_vars('SOUTH')
ax = ion.plot('current' ,gs=gsRM[-dY-wXY:-dY,dX:dX+wXY],doInset=True)
def plotPlane(gsph,data,xyBds,Ax,AxCB,var='D',vMin=None,vMax=None,doDeco=True,cmap='viridis',doLog=False,midp=None):
if (AxCB is not None):
AxCB.clear()
if (not midp):
if (vMin is None):
vMin = np.min(data)
if (vMax is None):
vMax = np.max(data)
else:
if ((vMin is None) and (vMax is None)):
vMax = np.max(np.abs([np.min(data),np.max(data)]))
vMin = -1.0*vMax
vNorm = kv.genNorm(vMin,vMax=vMax,doLog=doLog,midP=midp)
kv.genCB(AxCB,vNorm,cbT=var,cM=cmap,Ntk=7)
Ax.pcolormesh(gsph.xxi,gsph.yyi,data,cmap=cmap,norm=vNorm)
kv.SetAx(xyBds,Ax)
return
def plotXY(gsph,nStp,xyBds,Ax,AxCB,var='D',vMin=None,vMax=None,doDeco=True,cmap='viridis',doLog=False,midp=None):
data = gsph.EggSlice(var,nStp,doEq=True)
plotPlane(gsph,data,xyBds,Ax,AxCB,var,vMin=vMin,vMax=vMax,doDeco=doDeco,cmap=cmap,doLog=doLog,midp=midp)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM_X [Re]')
Ax.set_ylabel('SM-Y [Re]')
return data
def plotXZ(gsph,nStp,xzBds,Ax,AxCB,var='D',vMin=None,vMax=None,doDeco=True,cmap='viridis',doLog=False,midp=None):
data = gsph.EggSlice(var,nStp,doEq=False)
plotPlane(gsph,data,xzBds,Ax,AxCB,var,vMin=vMin,vMax=vMax,doDeco=doDeco,cmap=cmap,doLog=doLog,midp=midp)
if (doDeco):
kv.addEarth2D(ax=Ax)
Ax.set_xlabel('SM_X [Re]')
Ax.set_ylabel('SM-Z [Re]')
return data

161
kaipy/gamera/rcmpp.py
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@@ -1,161 +0,0 @@
#Various routines for plotting RCM output data
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
import matplotlib.pyplot as plt
import h5py
import kaipy.kaiViz as kv
import kaipy.gamera.gampp as gampp
import os
import numpy.ma as ma
import matplotlib.patches as patches
import matplotlib.ticker as plticker
rMin = 1.25
rMax = 35.0
rcBds = [-15,10.0,-12.5,12.5]
pCMap = "viridis"
eCol = "slategrey"
rcmCol = "dodgerblue"
gCol = "cyan"
gLW = 0.15
doXYZ = True #How to calculate "equator"
doCut = False
pCut = 1.0e-8
MHDCol = "red"
eLW = 0.05
MHDLW = 0.5
doEll = True
Psi0 = 92.4 #kV
RioRe = 1.018 #Ionosphere height
doCorot = True #Add corotation potential
DenPP = 50.0
ppCol = "orange"
#Get equatorial coordinates, masked if asked to
def RCMEq(rcmdata,nStp,doMask=False,doXYZ=doXYZ, doVerb=True):
bmX = rcmdata.GetVar("xMin",nStp, doVerb=doVerb)
bmY = rcmdata.GetVar("yMin",nStp, doVerb=doVerb)
if (doXYZ):
bmZ = rcmdata.GetVar("zMin",nStp, doVerb=doVerb)
bmP = np.arctan2(bmY,bmX)
bmR = np.sqrt(bmX*bmX + bmY*bmY + bmZ*bmZ)
bmX = bmR*np.cos(bmP)
bmY = bmR*np.sin(bmP)
if (doMask):
I = GetMask(rcmdata,nStp)
bmX = ma.masked_array(bmX,mask=I)
bmY = ma.masked_array(bmY,mask=I)
return bmX,bmY
def GetVarMask(rcmdata,nStp,Qid="P",I=None, doVerb=True):
if (I is None):
I = GetMask(rcmdata,nStp)
Q = rcmdata.GetVar(Qid,nStp, doVerb=doVerb)
Q = ma.masked_array(Q,mask=I)
return Q
def GetPotential(rcmdata,nStp,I=None,NumCP=25, doVerb=True):
if (I is None):
I = GetMask(rcmdata,nStp)
pot = (1.0e-3)*rcmdata.GetVar("pot",nStp, doVerb=doVerb)
if (doCorot):
#Add corotation potential
colat = GetVarMask(rcmdata,nStp,"colat" ,I, doVerb=doVerb)
pcorot = -Psi0*(RioRe)*(np.sin(colat)**2.0)
pot = pot + pcorot
pMag = np.abs(pot).max()
pVals = np.linspace(-pMag,pMag,NumCP)
return pot,pVals
#Calculate mask
#doRCM: Do RCM domain or full closed region
def GetMask(rcmdata,nStp, doVerb=True):
IOpen = rcmdata.GetVar("IOpen",nStp, doVerb=doVerb)
if (doEll):
ioCut = -0.5
else:
ioCut = 0.5
bmX = rcmdata.GetVar("xMin",nStp, doVerb=doVerb)
bmY = rcmdata.GetVar("yMin",nStp, doVerb=doVerb)
bmR = np.sqrt(bmX*bmX + bmY*bmY)
Ir = (bmR<rMin) | (bmR>rMax)
if (doCut):
Prcm = rcmdata.GetVar("P",nStp, doVerb=doVerb)
I = Ir | (IOpen > ioCut) | (Prcm<pCut)
else:
I = Ir | (IOpen > ioCut)
return I
#Take axis and rcmdata object and add pressure plot
def RCMInset(AxRCM,rcmdata,nStp,vP,pCol="k",doPP=True):
if (AxRCM is None):
AxRCM = plt.gca()
bmX,bmY = RCMEq(rcmdata,nStp,doMask=True)
I = GetMask(rcmdata,nStp)
Ni = (~I).sum()
if (Ni == 0):
return
Prcm = GetVarMask(rcmdata,nStp,"P" ,I)
toMHD = GetVarMask(rcmdata,nStp,"toMHD",I)
pot,pVals = GetPotential(rcmdata,nStp,I,NumCP=11)
if (doPP):
Npp = GetVarMask(rcmdata,nStp,"Npsph" ,I)
#Start plotting
AxRCM.pcolor(bmX,bmY,Prcm,norm=vP,cmap=pCMap,shading='auto')
AxRCM.plot(bmX,bmY,color=eCol,linewidth=eLW)
AxRCM.plot(bmX.T,bmY.T,color=eCol,linewidth=eLW)
if (pCol is not None):
AxRCM.contour(bmX,bmY,pot,pVals,colors=pCol,linewidths=MHDLW,alpha=0.5)
if (doPP):
AxRCM.contour(bmX,bmY,Npp,[DenPP],colors=ppCol,linewidths=2*MHDLW,alpha=0.8)
doCon = (nStp>0) and (toMHD.min()<0.5) and (toMHD.max()>0.5)
#Add MHD ingestion contour
if (doCon):
CS1 = AxRCM.contour(bmX,bmY,toMHD,[0.5],colors=MHDCol,linewidths=MHDLW)
manloc = [(0.0,8.0)]
fmt = {}
fmt[0.5] = 'MHD'
AxRCM.clabel(CS1,CS1.levels[::2],inline=True,fmt=fmt,fontsize=5,inline_spacing=25,manual=manloc)
#Add grid
xS = [-10,-5,0,5]
yS = [-10,-5,0,5,10]
for x in xS:
AxRCM.axvline(x,linewidth=gLW,color=gCol)
for y in yS:
AxRCM.axhline(y,linewidth=gLW,color=gCol)
kv.addEarth2D(ax=AxRCM)
kv.SetAx(rcBds,AxRCM)
kv.SetAxLabs(AxRCM,xLab=None,yLab=None)
AxRCM.spines['bottom'].set_color(rcmCol)
AxRCM.spines['top'].set_color(rcmCol)
AxRCM.spines['right'].set_color(rcmCol)
AxRCM.spines['left'].set_color(rcmCol)
AxRCM.set_title("RCM Pressure",fontsize="small",color=rcmCol)
#Add RCM box to other plot
def AddRCMBox(Ax):
if (Ax is None):
Ax = plt.gca()
xy0 = (rcBds[0],rcBds[2])
H = rcBds[3]-rcBds[2]
W = rcBds[1]-rcBds[0]
rcmRec = patches.Rectangle( (xy0),W,H, fill=False,edgecolor=rcmCol)
Ax.add_patch(rcmRec)

139
kaipy/gamera/remixpp.py
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@@ -1,139 +0,0 @@
#Various routines for plotting remix output data
#Generally dependant on basemap
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
import matplotlib.pyplot as plt
import h5py
import kaipy.kaiViz as kv
r2deg = 180.0/np.pi
pType = 'npstere'
#pType = 'npaeqd'
#pType = 'nplaea'
cMax = 2.0 #Max current density
dfs = "medium"
fcMap = "bwr" #Colormap for current
#Potential plots
pMax = 60.0 #Max potential [kV]
doAPC = True #Do solid/dash for potential contours
dpC = 5.0 #Spacing [kV] for potential contours
#pMap = "PRGn"
pMax = 35.0 #Max potential [kV]
apMap = "bone" #Absolute value of potential map
pMap = apMap
NumP = 10
pVals = np.linspace(-pMax,pMax,NumP)
cLW = 0.5
cAl = 0.5 #Contour alpha
lon0 = -90
R0 = 6.5/6.38
dLon = 45.0 #Degree spacing for longitude
dLat = 10.0 #Degree spacing for latitude
AxEC = 'silver'
gC = 'dimgrey'
gDash = [1,0]
gLW = 0.125
#Adds inset figure to axis using data from fmix
#dxy = [width%,height%]
def CMIPic(nLat,nLon,llBC,P,C,AxM=None,doNorth=True,loc="upper left",dxy=[20,20]):
from mpl_toolkits.basemap import Basemap
if (doNorth):
tStr = "North"
else:
tStr = "South"
vC = kv.genNorm(cMax)
vP = kv.genNorm(pMax)
#Now add inset figure to passed axis
wStr = "%f%%"%(dxy[0])
hStr = "%f%%"%(dxy[1])
if (AxM is None):
AxM = plt.gca()
Ax = inset_axes(AxM,width=wStr,height=hStr,loc=loc)
for spine in Ax.spines.values():
spine.set_edgecolor(AxEC)
Ax.patch.set_alpha(0)
if (doNorth):
Ax.set_xlabel(tStr,fontsize=dfs,color=AxEC)
else:
Ax.set_title(tStr,fontsize=dfs,color=AxEC)
bmAx = Basemap(ax=Ax,projection=pType,boundinglat=llBC,lon_0=lon0)
Lat0 = np.linspace(90,llBC,nLat+0)
Lat1 = np.linspace(90,llBC,nLat+1)
if (doNorth):
Lon0 = np.linspace(0,360,nLon+0)
Lon1 = np.linspace(0,360,nLon+1)
else:
Lon0 = np.linspace(360,0,nLon+0)
Lon1 = np.linspace(360,0,nLon+1)
LonC,LatC = np.meshgrid(Lon0,Lat0)
LonI,LatI = np.meshgrid(Lon1,Lat1)
#Now do plotting, start w/ gridding
#Set parallels
gP = np.arange(90-dLat,0.95*llBC,-dLat)
bmAx.drawparallels(gP,latmax=gP.max(),dashes=gDash,linewidth=gLW,color=gC)
#Set meridians
gM = np.arange(0,360,dLon)
for lon in gM:
bmAx.drawgreatcircle(lon,gP.max(),lon,llBC,linewidth=gLW,color=gC)
#Plot data
bmAx.pcolormesh(LonI,LatI,C,latlon=True,norm=vC,cmap=fcMap)
#Do potential contours
if (doAPC):
#Do positive contours
vAPp = kv.genNorm(0,pMax)
pVals = np.arange(dpC,pMax,dpC)
if (P.max()>dpC):
bmAx.contour(LonC,LatC,P,pVals,latlon=True,norm=vAPp,cmap=apMap,alpha=cAl,linewidths=cLW,linestyles='solid')
#Do negative contours
vAPm = kv.genNorm(-pMax,0)
apMapm = apMap+"_r"
pVals = -pVals[::-1]
if (P.min()<-dpC):
bmAx.contour(LonC,LatC,P,pVals,latlon=True,norm=vAPm,cmap=apMapm,alpha=cAl,linewidths=cLW,linestyles='dashed')
else:
bmAx.contour(LonC,LatC,P,pVals,latlon=True,norm=vP,cmap=pMap,alpha=cAl,linewidths=cLW)
def AddPotCB(Ax,Lab="Potential [kV]",Ntk=7):
if (doAPC):
vP = kv.genNorm(0,pMax)
cm = apMap
else:
vP = kv.genNorm(-pMax,pMax)
cm = pMap
kv.genCB(Ax,vP,Lab,cM=cm,Ntk=Ntk)
def AddCBs(Ax1,Ax2,Lab1="Potential [kV]",Lab2="FAC",Ntk1=7,Ntk2=5,doFlip=True):
if (doAPC):
vP = kv.genNorm(0,pMax)
cm = apMap
else:
vP = kv.genNorm(-pMax,pMax)
cm = pMap
kv.genCB(Ax1,vP,Lab1,cM=cm,Ntk=Ntk1)
kv.genCB(Ax2,kv.genNorm(cMax),Lab2,fcMap,Ntk=Ntk2)
if (doFlip):
Ax2.xaxis.tick_top()
Ax2.xaxis.set_label_position('top')

47
kaipy/gamhelio/ConfigScripts/startup.config
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@@ -1,47 +0,0 @@
;Comments and definitions:
;If needed, modify the paths to the grid file, output innerbc file and WSA fits file
;tMin and tMax set the range for theta [tMin, tMax]*pi
;Rin and Rout are inner and outer boundaries in the radial direction
;Ni, Nj, Nk set the number of cells in r, theta, phi directions
;Nghost is the number of ghost cells
;nCS is the number density in the current sheet for pressure balance calculation
;TCS is the temperature in the current sheet for pressure balance calculation
[Gamera]
gameraGridFile = heliogrid.h5
GridDir = ./
gameraIbcFile = innerbc.h5
IbcDir = ./
[Grid]
tMin = 0.1
tMax = 0.9
Rin = 21.5
Rout = 220.
Ni = 128
Nj = 64
Nk = 128
[WSA]
;wsafile is the path to the WSA fits file relative to $KAIJUHOME
;Helio test uses WSA file for Carrington Rotation 2193, by default
wsafile = examples/helio/vel_201708132000R002_ahmi.fits
density_temperature_infile = no
gauss_smooth_width = 0 ; 8
normalized = no
[Constants]
gamma = 1.5
Nghost = 4
Tsolar = 25.38
nCS = 1100. ; in [cm-3]
TCS = 1.e6 ; in [K]
[Normalization]
B0 = 1.e-3 ; in [Gs] equals to 100 [nT]
n0 = 200. ; in [cm-3]

33
kaipy/gamhelio/ConfigScripts/startupOH.config
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@@ -1,33 +0,0 @@
#Modify if needed paths to a grid file, output file and WSA fits file
[Gamera]
gameraGridFile = heliogrid.h5
GridDir = /glade/u/home/elenap/gameraHelio/OHelio/grid
gameraIbcFile = innerbc.h5
IbcDir = /glade/u/home/elenap/gameraHelio/OHelio/ibc
[Grid]
tMin = 0.1
tMax = 0.9
Rin = 1.
Rout = 10.
Ni = 256
Nj = 128
Nk = 256
[WSA]
wsafile = /glade/u/home/elenap/gameraHelio/OHelio/1AUfiles/CR2095/wsa.h5
density_temperature_infile = no
gauss_smooth_width = 0 ; 8
plots = yes
normalized = yes
[Constants]
gamma = 1.5 ;1.05
NO2 = 4
[Normalization] ; should be normalization from inner helio
Xscale = 1.496e8 !in km
B0 = 1.e-3 ; 5 nT = 5.e-5 Gs
n0 = 200. ; cm-3

41
kaipy/gamhelio/ConfigScripts/startupTD.config
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@@ -1,41 +0,0 @@
[Grid]
gameraGridFile = heliogrid.h5
GridDir = /glade/u/home/elenap/gameraHelio/grid
gameraIbcFile = innerbcTD.h5
IbcDir = /glade/u/home/elenap/gameraHelio/ibcTD
tMin = 0.1
tMax = 0.9
Rin = 21.5
Rout = 221.5
Ni = 256
Nj = 128
Nk = 256
[Output]
Prefix = 2012
[Normalization]
Xscale = 6.955e10
[Magnetic polarity]
Monopole : no
[Constants]
gamma = 1.5
NO2 = 4
Tsolar = 25.38
[ADAPT]
adaptdir = /glade/u/home/elenap/gameraHelio/WSAfiles/CR2210
wild_card = vel*2018*R007*.fits
#cadence in days -- make sure to retain units throughout (not Tsolar above)
cadence = 1
density_temperature_infile = no
gauss_smooth_width = 0
plots = yes
normalized = no
[DUMPS]
init = no
BC = yes

0
kaipy/gamhelio/__init__.py
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2978
kaipy/gamhelio/helioViz.py
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File diff suppressed because it is too large Load Diff

699
kaipy/gamhelio/heliosphere.py
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@@ -1,699 +0,0 @@
#Various tools to post-process and analyze Gamera heliosphere runs
from kaipy.kdefs import *
import kaipy.gamera.gampp
from kaipy.gamera.gampp import GameraPipe
import numpy as np
import glob
import kaipy.kaiH5 as kh5
import timeit
#Object to pull from MPI/Serial heliosphere runs (H5 data), extends base
ffam = "monospace"
dLabC = "black" #Default label color
dLabFS = "medium" #Default label size
dBoxC = "lightgrey" #Default box color
TINY = 1.0e-8
MK = 1.e6 #MegaKelvin
#Adapted to helio grid
class GamsphPipe(GameraPipe):
#Initialize object, rely on base class, take optional unit identifier
def __init__(self,fdir,ftag,doFast=False,uID="Inner",doParallel=False,nWorkers=4):
print("Initializing %s heliosphere"%(uID))
#units for inner helio
self.bScl = 100. #->nT
self.vScl = 150. #-> km/s
self.tScl = 4637. #->seconds
self.dScl = 200. #cm-3
self.TScl = 1.e-6/4/np.pi/200./kbltz/MK #in MK
# units for OHelio
#self.bScl = 5. #->nT
#self.vScl = 34.5 #-> km/s
#self.tScl = 1.4e8/34.5
#self.dScl = 10. #cm-3
#self.TScl = 0.144 #in MK
#2D equatorial grid
self.xxi = [] ; self.yyi = [] #corners
self.xxc = [] ; self.yyc = [] #centers
#base class, will use OpenPipe below
GameraPipe.__init__(self,fdir,ftag,doFast=doFast,doParallel=doParallel,nWorkers=nWorkers)
#inner boundary distance
self.R0 = np.sqrt(self.X[0, 0, 0]**2 + self.Y[0, 0, 0]**2 + self.Z[0, 0, 0]**2)
#j and k for radial profile
self.jRad = self.Nj//2
self.kRad = self.Nk//4
def OpenPipe(self,doVerbose=True):
GameraPipe.OpenPipe(self,doVerbose)
if (self.UnitsID != "CODE"):
self.bScl = 1.0 #->nT
self.vScl = 1.0 #-> km/s
self.tScl = 1.0 #-> Seconds
self.dScl = 1.0 #-> cm-3
self.TScl = 1.0/kbltz/MK #-> MKelvin
#Rescale time
self.T = self.tScl*self.T
Neq_a = self.Nj//2 #cell above eq plane
Nr = self.Ni
Np = self.Nk
#corners in eq XY plane
self.xxi = np.zeros((Nr+1,Np+1))
self.yyi = np.zeros((Nr+1,Np+1))
#centers
self.xxc = np.zeros((Nr ,Np ))
self.yyc = np.zeros((Nr ,Np ))
#Grid for equatorial plane. Should probably be done as a separate function
#equatorial plane
#corners i,k in eq plane, j index is Neq_a
self.xxi[:,:] = self.X[:,Neq_a,:]
self.yyi[:,:] = self.Y[:,Neq_a,:]
#centers i,k
self.xxc = 0.25*(self.xxi[:-1,:-1] + self.xxi[1:,:-1] + self.xxi[:-1,1:] + self.xxi[1:,1:])
self.yyc = 0.25*(self.yyi[:-1,:-1] + self.yyi[1:,:-1] + self.yyi[:-1,1:] + self.yyi[1:,1:])
r = np.sqrt(self.xxc**2.0 + self.yyc**2.0)
if (self.hasMJD):
print("Found MJD data")
print("\tTime (Min/Max) = %f/%f"%(self.MJDs.min(),self.MJDs.max()))
#Var eq slice
def EqSlice(self,vID,sID=None,vScl=None,doEq=True,doVerb=True):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
Nj2 = self.Nj//2
#above and below the eq plane
ja = Nj2 - 1
jb = ja + 1
Nr = self.Ni
Np = self.Nk
#equatorial j-slice of var
Qj = np.zeros((Nr,Np))
#taking average above/below eq plane
Qj[:,:] = 0.5*( Q[:,ja,:] + Q[:,jb,:] )
return Qj
#Var theta slice
def jSlice(self,vID,sID=None,vScl=None,doEq=True,doVerb=True,jidx=-1):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
if(jidx == -1):
Nj2 = self.Nj//2
else:
Nj2 = jidx
#above and below the j plane
ja = Nj2 - 1
jb = ja + 1
Nr = self.Ni
Np = self.Nk
#equatorial j-slice of var
Qj = np.zeros((Nr,Np))
#taking average above/below eq plane
Qj[:,:] = 0.5*( Q[:,ja,:] + Q[:,jb,:] )
return Qj
#Radial profile thru cell centers
def RadialProfileGrid(self):
self.GetGrid(doVerbose=True)
#cell corners
x = self.X [:,:,:]
y = self.Y [:,:,:]
z = self.Z [:,:,:]
#cell centers
x_c = 0.125*(x[:-1,:-1,:-1]+x[:-1,:-1,1:]+x[:-1,1:,:-1]+x[:-1,1:,1:]+
x[1:,:-1,:-1]+x[1:,:-1,1:]+x[1:,1:,:-1]+x[1:,1:,1:])
y_c = 0.125*(y[:-1,:-1,:-1]+y[:-1,:-1,1:]+y[:-1,1:,:-1]+y[:-1,1:,1:]+
y[1:,:-1,:-1]+y[1:,:-1,1:]+y[1:,1:,:-1]+y[1:,1:,1:])
z_c = 0.125*(z[:-1,:-1,:-1]+z[:-1,:-1,1:]+z[:-1,1:,:-1]+z[:-1,1:,1:]+
z[1:,:-1,:-1]+z[1:,:-1,1:]+z[1:,1:,:-1]+z[1:,1:,1:])
#radius of cell centers
jR = self.jRad
kR = self.kRad
r = np.sqrt(x_c[:,jR,kR]**2.0 + y_c[:,jR,kR]**2.0 + z_c[:,jR,kR]**2.)
return r
#NOT USED merid plane Y=0
def MeridGrid(self):
#Get Grid
self.GetGrid(doVerbose=True)
Nk2 = self.Nk//2
Nt = self.Nj
#kooking from -Y to XZ plane
xright = self.X[:,:,0] #corners
xleft = self.X [:,:,Nk2]
zright = self.Z[:,:,0] #corners
zleft = self.Z[:,:,Nk2]
#stack right and left together
xmer = np.hstack( (xright, xleft[:,::-1]) ) #reverse j
zmer = np.hstack( (zright, zleft[:,::-1]) ) #reverse j
#cell centers
xmer_c = 0.25*( xmer[:-1,:-1]+xmer[:-1,1:]+xmer[1:,:-1]+xmer[1:,1:] )
xmer_c = np.delete(xmer_c, Nt, axis = 1)
zmer_c = 0.25*( zmer[:-1,:-1]+zmer[:-1,1:]+zmer[1:,:-1]+zmer[1:,1:] )
zmer_c = np.delete(zmer_c, Nt, axis = 1)
return xmer_c, zmer_c
#merid plane from kidx from two halfs
def MeridGridHalfs(self,kidx=None,phi=None):
self.GetGrid(doVerbose=True)
if(kidx):
Nk1 = kidx
Nk2 = self.Nk//2 + Nk1
elif(phi):
Nk1 = int(phi/(2*np.pi)*self.Nk)
Nk2 = self.Nk//2 + Nk1
else:
Nk1 = 0
Nk2 = self.Nk//2
#looking from -Y to XZ plane
xright = self.X[:,:,Nk1] #corners
yright = self.Y[:,:,Nk1] #corners
zright = self.Z[:,:,Nk1] #corners
xleft = self.X[:,:,Nk2]
yleft = self.Y[:,:,Nk2]
zleft = self.Z[:,:,Nk2]
rleft = -np.sqrt(xleft**2. + yleft**2)
xright_c = 0.25*( xright[:-1,:-1]+xright[:-1,1:]+xright[1:,:-1]+xright[1:,1:] )
yright_c = 0.25*( yright[:-1,:-1]+yright[:-1,1:]+yright[1:,:-1]+yright[1:,1:] )
zright_c = 0.25*( zright[:-1,:-1]+zright[:-1,1:]+zright[1:,:-1]+zright[1:,1:] )
r = np.sqrt(xright_c**2 + zright_c**2 + yright_c**2)
#centers: right plane, left plane, radius
return xright, yright, zright, xleft, yleft, zleft, r
#Grid at 1 AU lat lon
def iSliceGrid(self,idx=-1):
#Get Grid
self.GetGrid(doVerbose=True)
rxy = np.sqrt(self.X**2 + self.Y**2)
theta = np.arctan2(rxy,self.Z)
phi = np.arctan2(self.Y,self.X)
#theta [theta < 0] += np.pi/2.
theta = theta - np.pi/2
theta = theta*180./np.pi
phi [phi < 0] += 2*np.pi
phi = phi*180./np.pi
#last i-index == face of the last cell
lat = theta[idx,::-1,:]
lon = phi[idx,:,:]
#these are corners
return lat, lon
#Vars at Y=0
def MeridSlice(self,vID,sID=None,vScl=None,doVerb=True,indx=(None,None)):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
Nk2 = self.Nk//2
kidx, phi = indx
if(kidx):
Nk1 = kidx
Nk2 = self.Nk//2 + Nk1
Np = Nk1 - 1
elif(phi):
Nk1 = int(phi/(2*np.pi)*self.Nk)
Nk2 = self.Nk//2 + Nk1
Np = Nk1 - 1
else:
Nk1 = 0
Nk2 = self.Nk//2
Np = self.Nk
#Nr = self.Ni
#Nt = 2*self.Nj
#XZ meridional slice (k=0) of var
#Qj = np.zeros((Nr,Nt))
Qright = 0.5*( Q[:,:,Nk1] + Q[:,:,Np-1] )
Qleft = 0.5*( Q[:,:,Nk2-1] + Q[:,:,Nk2] )
#print (Qright.shape, Qleft.shape)
#Qj = np.hstack( (Qright, Qleft[:,::-1]) ) #reverse in j
#print (Qj.shape)
return Qright, Qleft
#Var at 1 AU
def iSliceVar(self,vID,sID=None,vScl=None,doVerb=True,idx=-1):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
#cell centered values from the last cell
Qi = Q[idx,:,:]
#cell centered values from the first cell
#Qi = Q[0,:,:]
#jd_c = self.MJDs[sID]
#print ('jd_c = ', jd_c)
return Qi
#Var along 1D radial line
def RadialProfileVar(self,vID,sID=None,vScl=None,doVerb=True):
#Get full 3D variable first
Q = self.GetVar(vID,sID,vScl,doVerb)
#set j and k for a radial profile
jR = self.jRad
kR = self.kRad
Nr = self.Ni
Qi = np.zeros(Nr)
#variable in a cell center
Qi = Q[:,jR,kR]
return Qi
#Radial Profile: Normalized Density
def RadProfDen(self,s0=0):
D = self.RadialProfileVar("D", s0)
r = self.RadialProfileGrid()
Norm = r**2./r[0]/r[0]
D = D*Norm*self.dScl
return D
#Radial Profile: Speed
def RadProfSpeed(self,s0=0):
Vx = self.RadialProfileVar("Vx", s0)
Vy = self.RadialProfileVar("Vy", s0)
Vz = self.RadialProfileVar("Vz", s0)
MagV = self.vScl*np.sqrt(Vx**2.0+Vy**2.0+Vz**2.0)
return MagV
#Radial Profile: Normalized Flux rho*V*r^2
def RadProfFlux(self,s0=0):
D = self.RadialProfileVar("D", s0)
Vx = self.RadialProfileVar("Vx", s0)
Vy = self.RadialProfileVar("Vy", s0)
Vz = self.RadialProfileVar("Vz", s0)
r = self.RadialProfileGrid()
Norm = r[:]**2./r[0]/r[0]
Flux = D*Norm*self.dScl*self.vScl*np.sqrt(Vx**2.0+Vy**2.0+Vz**2.0)
return Flux
#Speed at 1 AU
def iSliceMagV(self,s0=0,idx=-1):
Vx = self.iSliceVar("Vx",s0,idx=idx) #Unscaled
Vy = self.iSliceVar("Vy",s0,idx=idx) #Unscaled
Vz = self.iSliceVar("Vz",s0,idx=idx) #Unscaled
Vi = self.vScl*np.sqrt(Vx**2.0+Vy**2.0+Vz**2.0)
return Vi
#Density at 1 AU
def iSliceD(self,s0=0,idx=-1):
Di = self.iSliceVar("D",s0,idx=idx) #Unscaled
Di = Di*self.dScl
return Di
#Br at 1 AU
def iSliceBr(self,s0=0,idx=-1):
Bx = self.iSliceVar("Bx",s0,idx=idx) #Unscaled
By = self.iSliceVar("By",s0,idx=idx) #Unscaled
Bz = self.iSliceVar("Bz",s0,idx=idx) #Unscaled
self.GetGrid(doVerbose=True)
x = self.X[-1,:,:]
y = self.Y[-1,:,:]
z = self.Z[-1,:,:]
#centers
x_c = 0.25*( x[:-1,:-1]+x[:-1,1:]+x[1:,:-1]+x[1:,1:] )
y_c = 0.25*( y[:-1,:-1]+y[:-1,1:]+y[1:,:-1]+y[1:,1:] )
z_c = 0.25*( z[:-1,:-1]+z[:-1,1:]+z[1:,:-1]+z[1:,1:] )
Br = self.bScl*(Bx*x_c + By*y_c + Bz*z_c)/np.sqrt(x_c**2.+y_c**2.+z_c**2.)
return Br
#Bx at 1AU
def iSliceBx(self,s0=0,idx=-1):
Bx = self.iSliceVar("Bx",s0,idx=idx) #Unscaled
self.GetGrid(doVerbose=True)
x = self.X[-1,:,:]
#centers
x_c = 0.25*( x[:-1,:-1]+x[:-1,1:]+x[1:,:-1]+x[1:,1:] )
BxScl = self.bScl*Bx*x_c
return BxScl
#By at 1AU
def iSliceBy(self,s0=0,idx=-1):
By = self.iSliceVar("By",s0,idx=idx) #Unscaled
self.GetGrid(doVerbose=True)
y = self.Y[-1,:,:]
#centers
y_c = 0.25*( y[:-1,:-1]+y[:-1,1:]+y[1:,:-1]+y[1:,1:] )
ByScl = self.bScl*By*y_c
return ByScl
#Bz at 1AU
def iSliceBz(self,s0=0,idx=-1):
Bz = self.iSliceVar("Bz",s0,idx=idx) #Unscaled
self.GetGrid(doVerbose=True)
z = self.Z[-1,:,:]
#centers
z_c = 0.25*( z[:-1,:-1]+z[:-1,1:]+z[1:,:-1]+z[1:,1:] )
BzScl = self.bScl*Bz*z_c
return BzScl
#Br at first cell
def iSliceBrBound(self,s0=0,idx=-1):
Bx = self.iSliceVar("Bx",s0,idx=idx) #Unscaled
By = self.iSliceVar("By",s0,idx=idx) #Unscaled
Bz = self.iSliceVar("Bz",s0,idx=idx) #Unscaled
self.GetGrid(doVerbose=True)
x = self.X[0,:,:]
y = self.Y[0,:,:]
z = self.Z[0,:,:]
#centers
x_c = 0.25*( x[:-1,:-1]+x[:-1,1:]+x[1:,:-1]+x[1:,1:] )
y_c = 0.25*( y[:-1,:-1]+y[:-1,1:]+y[1:,:-1]+y[1:,1:] )
z_c = 0.25*( z[:-1,:-1]+z[:-1,1:]+z[1:,:-1]+z[1:,1:] )
Br = self.bScl*(Bx*x_c + By*y_c + Bz*z_c)/np.sqrt(x_c**2.+y_c**2.+z_c**2.)
return Br
#temperature at 1 AU
def iSliceT(self,s0=0,idx=-1):
Pi = self.iSliceVar("P",s0,idx=idx) #Unscaled
Di = self.iSliceVar("D",s0,idx=idx) #Unscaled
Temp = Pi/Di*self.TScl
return Temp
#Equatorial speed (in km/s) in eq plane
def eqMagV(self,s0=0):
Vx = self.EqSlice("Vx",s0) #Unscaled
Vy = self.EqSlice("Vy",s0) #Unscaled
Vz = self.EqSlice("Vz",s0) #Unscaled
Veq = self.vScl*np.sqrt(Vx**2.0+Vy**2.0+Vz**2.0)
return Veq
#Equatorial speed (in km/s) in eq plane
def jMagV(self,s0=0,jidx=-1):
Vx = self.jSlice("Vx",s0,jidx=jidx) #Unscaled
Vy = self.jSlice("Vy",s0,jidx=jidx) #Unscaled
Vz = self.jSlice("Vz",s0,jidx=jidx) #Unscaled
Veq = self.vScl*np.sqrt(Vx**2.0+Vy**2.0+Vz**2.0)
return Veq
def eqNormD (self, s0=0):
"""Compute the normalized number density in the equatorial plane.
Compute the normalized number density in the equatorial plane.
The number density is normalized by the factor (r/r0)**2, where r0 is
the radius of the inner edge of the grid (should be 21.5 Rsun).
Parameters
----------
self : GamsphPipe
This object
s0 : int
Simulation step number to fetch
Returns
-------
NormDeq : np.ndarray, shape same as self.xxc
Normalized number density in equatorial plane
Raises
------
None
"""
# Fetch the unscaled data.
D = self.EqSlice("D", s0)
# Compute the normalization factor for each data point.
Norm = (self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
# Convert the number density from code units (dimensionless) to
# physical units (cm**-3), then normalize the data using the scale
# factor.
NormDeq = self.dScl*D*Norm
# Return the data.
return NormDeq
#Normalized density (D*r*r/21.5/21.5 in cm-3) in eq plane
def jNormD (self,s0=0,jidx=-1):
D = self.jSlice("D",s0,jidx=jidx) #Unscaled
Norm = (self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
NormDeq = self.dScl*D*Norm
return NormDeq
#Normalized Br (Br*r*r/21.5/21.5) in eq plane
def eqNormBr (self,s0=0):
Bx = self.EqSlice("Bx",s0) #Unscaled
By = self.EqSlice("By",s0) #Unscaled
Bz = self.EqSlice("Bz",s0) #Unscaled
Br = (Bx*self.xxc + By*self.yyc)*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
NormBreq = self.bScl*Br
return NormBreq
#Normalized Br (Br*r*r/21.5/21.5) in eq plane
def jNormBr (self,s0=0,jidx=-1):
Bx = self.jSlice("Bx",s0,jidx=jidx) #Unscaled
By = self.jSlice("By",s0,jidx=jidx) #Unscaled
Bz = self.jSlice("Bz",s0,jidx=jidx) #Unscaled
Br = (Bx*self.xxc + By*self.yyc)*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
NormBreq = self.bScl*Br
return NormBreq
#Normalized Br (Br*r*r/21.5/21.5) in eq plane
def eqBx (self,s0=0):
Bx = self.EqSlice("Bx",s0) #Unscaled
BxScl = (Bx*self.xxc + Bx*self.yyc)*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
BxScl = self.bScl*BxScl
return BxScl
#Normalized Br (Br*r*r/21.5/21.5) in eq plane
def eqBy (self,s0=0):
By = self.EqSlice("By",s0) #Unscaled
ByScl = (By*self.xxc + By*self.yyc)*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0/self.R0
ByScl= self.bScl*ByScl
return ByScl
#Normalized Br (Br*r*r/21.5/21.5) in eq plane
def eqBz (self,s0=0):
Bz = self.EqSlice("Bz",s0) #Unscaled
BzScl = Bz
BzScl = self.bScl*BzScl
return BzScl
#Temperature T(r/r0) in eq plane
def eqTemp (self,s0=0):
Pres = self.EqSlice("P",s0)
D = self.EqSlice("D",s0)
#T(r/r0)
Temp = Pres/D*self.TScl*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0
return Temp
#Temperature T(r/r0) in eq plane
def jTemp (self,s0=0,jidx=-1):
Pres = self.jSlice("P",s0,jidx=jidx)
D = self.jSlice("D",s0,jidx=jidx)
#T(r/r0)
Temp = Pres/D*self.TScl*np.sqrt(self.xxc**2.0 + self.yyc**2.0)/self.R0
return Temp
#Meridional speed (in km/s) in Y=indx plane
def MerMagV(self,s0=0,indx=(None,None)):
Vxr, Vxl = self.MeridSlice("Vx",s0,indx=indx) #Unscaled
Vyr, Vyl = self.MeridSlice("Vy",s0,indx=indx) #Unscaled
Vzr, Vzl = self.MeridSlice("Vz",s0,indx=indx) #Unscaled
MagVr = self.vScl*np.sqrt(Vxr**2.0+Vyr**2.0+Vzr**2.0)
MagVl = self.vScl*np.sqrt(Vxl**2.0+Vyl**2.0+Vzl**2.0)
return MagVr, MagVl
#Normalized D in Y=indx plane
def MerDNrm(self,s0=0,indx=(None,None)):
xr, yr, zr, xl, yl, zl, r = self.MeridGridHalfs(*indx)
Dr, Dl = self.MeridSlice("D",s0,indx=indx) #Unscaled
Drn = Dr*self.dScl*r*r/self.R0/self.R0
Dln = Dl*self.dScl*r*r/self.R0/self.R0
return Drn, Dln
#Mormalized Br in Y=indx plane
def MerBrNrm(self,s0=0,indx=(None,None)):
xr, yr, zr, xl, yl, zl, r = self.MeridGridHalfs(*indx)
#rxyr = np.sqrt(xr**2. + yr**2)
#rxyl = -np.sqrt(xl**2. + yl**2)
Bxr, Bxl = self.MeridSlice("Bx",s0,indx=indx) #Unscaled
Byr, Byl = self.MeridSlice("By",s0,indx=indx) #Unscaled
Bzr, Bzl = self.MeridSlice("Bz",s0,indx=indx) #Unscaled
#cell centers to calculate Br
xr_c = 0.25*( xr[:-1,:-1]+xr[:-1,1:]+xr[1:,:-1]+xr[1:,1:] )
yr_c = 0.25*( yr[:-1,:-1]+yr[:-1,1:]+yr[1:,:-1]+yr[1:,1:] )
zr_c = 0.25*( zr[:-1,:-1]+zr[:-1,1:]+zr[1:,:-1]+zr[1:,1:] )
xl_c = 0.25*( xl[:-1,:-1]+xl[:-1,1:]+xl[1:,:-1]+xl[1:,1:] )
yl_c = 0.25*( yl[:-1,:-1]+yl[:-1,1:]+yl[1:,:-1]+yl[1:,1:] )
zl_c = 0.25*( zl[:-1,:-1]+zl[:-1,1:]+zl[1:,:-1]+zl[1:,1:] )
#calculating Br
Br_r = (Bxr*xr_c + Byr*yr_c + Bzr*zr_c)*r*self.bScl/self.R0/self.R0
Br_l = (Bxl*xl_c + Byl*yl_c + Bzl*zl_c)*r*self.bScl/self.R0/self.R0
return Br_r, Br_l
#Normalized Temp in Y=indx plane
def MerTemp(self,s0=0,indx=(None,None)):
xr, yr, zr, xl, yl, zl, r = self.MeridGridHalfs(*indx)
Pr, Pl = self.MeridSlice("P",s0,indx=indx) #Unscaled
Dr, Dl = self.MeridSlice("D",s0,indx=indx) #Unscaled
Tempr = Pr/Dr*self.TScl*r/self.R0
Templ = Pl/Dl*self.TScl*r/self.R0
return Tempr, Templ
#Not used for helio as of now
#Return data for meridional 2D field lines
#Need to use Cartesian grid
def bStream(self,s0=0,xyBds=[-35,25,-25,25],dx=0.05):
#Get field data
U = self.bScl*self.EggSlice("Bx",s0,doEq=False)
V = self.bScl*self.EggSlice("Bz",s0,doEq=False)
x1,y1,gu,gv,gM = self.doStream(U,V,xyBds,dx)
return x1,y1,gu,gv,gM
def vStream(self,s0=0,xyBds=[-35,25,-25,25],dx=0.05):
#Get field data
U = self.vScl*self.EggSlice("Vx",s0,doEq=True)
V = self.vScl*self.EggSlice("Vy",s0,doEq=True)
x1,y1,gu,gv,gM = self.doStream(U,V,xyBds,dx)
return x1,y1,gu,gv,gM
#Add time label, xy is position in axis (not data) coords
def AddTime(self,n,Ax,xy=[0.9,0.95],cLab=dLabC,fs=dLabFS,T0=0.0,doBox=True,BoxC=dBoxC):
ffam = "monospace"
HUGE = 1.0e+8
#Decide whether to do UT or elapsed
if (self.hasMJD):
minMJD = self.MJDs[n-self.s0]
else:
minMJD = -HUGE
if (self.hasMJD and minMJD>TINY):
from astropy.time import Time
dtObj = Time(self.MJDs[n-self.s0],format='mjd').datetime
tStr = " " + dtObj.strftime("%H:%M:%S") + "\n" + dtObj.strftime("%m/%d/%Y")
else:
#Get time in seconds
t = self.T[n-self.s0] - T0
Nm = int( (t-T0)/60.0 ) #Minutes, integer
Hr = Nm/60
Min = np.mod(Nm,60)
Sec = np.mod(int(t),60)
tStr = "Elapsed Time\n %02d:%02d:%02d"%(Hr,Min,Sec)
if (doBox):
Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
#def AddSW(self,n,Ax,xy=[0.725,0.025],cLab=dLabC,fs=dLabFS,T0=0.0,doBox=True,BoxC=dBoxC,doAll=True):
# import kaipy.kaiH5 as kh5
# #Start by getting SW data
# vIDs = ["D","P","Vx","Bx","By","Bz"]
# Nv = len(vIDs)
# qSW = np.zeros(Nv)
# if (self.isMPI):
# fSW = self.fdir + "/" + kh5.genName(self.ftag,self.Ri-1,0,0,self.Ri,self.Rj,self.Rk)
# else:
# fSW = self.fdir + "/" + self.ftag + ".h5"
#
# for i in range(Nv):
# Q = kh5.PullVar(fSW,vIDs[i],n)
# qSW[i] = Q[-1,0,0]
# D = qSW[0] ; P = qSW[1] ; Vx = qSW[2] ; Bx = qSW[3] ; By = qSW[4] ; Bz = qSW[5]
# SWStr = "Solar Wind\n"
# MagB = self.bScl*np.sqrt(Bx**2.0+By**2.0+Bz**2.0)
# #Clock = atan(by/bz), cone = acos(Bx/B)
# r2deg = 180.0/np.pi
# if (MagB>TINY):
# clk = r2deg*np.arctan2(By,Bz)
# cone = r2deg*np.arccos(self.bScl*Bx/MagB)
# else:
# clk = 0.0
# cone = 0.0
# if (clk<0):
# clk = clk+360.0
# Deg = r"$\degree$"
# SWStr = "Solar Wind\nIMF: %4.1f [nT], %5.1f"%(MagB,clk) + Deg# + ", %5.2f"%(cone) + Deg
# if (doAll):
# SWStr = SWStr + "\nDensity: %5.1f [#/cc] \nSpeed: %6.1f [km/s] "%(D,self.vScl*np.abs(Vx))
# if (doBox):
# Ax.text(xy[0],xy[1],SWStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
# else:
# Ax.text(xy[0],xy[1],SWStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
#def AddCPCP(self,n,Ax,xy=[0.9,0.95],cLab=dLabC,fs=dLabFS,doBox=True,BoxC=dBoxC):
# cpcp = self.GetCPCP(n)
# tStr = "CPCP (North/South)\n%6.2f / %6.2f [kV]"%(cpcp[0],cpcp[1])
# if (doBox):
# Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam,bbox=dict(boxstyle="round",fc=dBoxC))
# else:
# Ax.text(xy[0],xy[1],tStr,color=cLab,fontsize=fs,transform=Ax.transAxes,family=ffam)

0
kaipy/gamhelio/lib/__init__.py
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83
kaipy/gamhelio/lib/ace.py
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@@ -1,83 +0,0 @@
import numpy as np
import datetime
def cdaweb_mfi(ACE_mf_file):
# Mag. field
ACE_mf_time=[]
ACE_bx=[]
ACE_by=[]
ACE_bz=[]
i=1
for line in open(ACE_mf_file,'r'):
i+=1
if 'dd-mm-yyyy' in line: break
start = i
i = 0
for line in open(ACE_mf_file,'r'):
i+=1
if i<start: continue
if line[0]=='#': break
fields = line.strip().split()
ACE_mf_day = int(fields[0].strip().split('-')[0])
ACE_mf_mon = int(fields[0].strip().split('-')[1])
ACE_mf_year = int(fields[0].strip().split('-')[2])
ACE_mf_hr = int(fields[1].strip().split(':')[0])
ACE_mf_min = int(fields[1].strip().split(':')[1])
ACE_mf_sec = int(float(fields[1].strip().split(':')[2]))
ACE_mf_time.append(datetime.datetime(ACE_mf_year, ACE_mf_mon, ACE_mf_day, ACE_mf_hr, ACE_mf_min, ACE_mf_sec))
ACE_bx.append(float(fields[2]))
ACE_by.append(float(fields[3]))
ACE_bz.append(float(fields[4]))
# invalidate bad data
ACE_bx=np.array(ACE_bx)
ACE_by=np.array(ACE_by)
ACE_bz=np.array(ACE_bz)
ACE_bx[np.abs(np.array(ACE_bx)) > 1.e20] = np.nan
ACE_by[np.abs(np.array(ACE_by)) > 1.e20] = np.nan
ACE_bz[np.abs(np.array(ACE_bz)) > 1.e20] = np.nan
return (ACE_mf_time,ACE_bx,ACE_by,ACE_bz)
def cdaweb_swe(ACE_plasma_file):
# Plasma
ACE_plasma_time=[]
ACE_v=[]
ACE_n=[]
i=1
for line in open(ACE_plasma_file,'r'):
i+=1
if 'dd-mm-yyyy' in line: break
start = i
i = 0
for line in open(ACE_plasma_file,'r'):
i+=1
if i<start: continue
if line[0]=='#': break
fields = line.strip().split()
ACE_plasma_day = int(fields[0].strip().split('-')[0])
ACE_plasma_mon = int(fields[0].strip().split('-')[1])
ACE_plasma_year = int(fields[0].strip().split('-')[2])
ACE_plasma_hr = int(fields[1].strip().split(':')[0])
ACE_plasma_min = int(fields[1].strip().split(':')[1])
ACE_plasma_sec = int(float(fields[1].strip().split(':')[2]))
ACE_plasma_time.append(datetime.datetime(ACE_plasma_year, ACE_plasma_mon, ACE_plasma_day, ACE_plasma_hr, ACE_plasma_min, ACE_plasma_sec))
ACE_n.append(float(fields[2]))
ACE_v.append(float(fields[3]))
# invalidate bad data
ACE_v=np.array(ACE_v)
ACE_n=np.array(ACE_n)
ACE_v[np.abs(np.array(ACE_v)) > 1.e20] = np.nan
ACE_n[np.abs(np.array(ACE_n)) > 1.e20] = np.nan
return (ACE_plasma_time,ACE_v,ACE_n)

29
kaipy/gamhelio/lib/cspice.py
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@@ -1,29 +0,0 @@
import numpy as np
import datetime
def cspice(fname):
dati=[]
x=[]
y=[]
z=[]
r=[]
phi=[]
theta=[]
for line in open(fname,'r'):
fields = line.strip().split()
year = int(fields[0].strip().split('-')[0])
doy = int(fields[0].strip().split('-')[1])
hr = int(fields[1].strip().split(':')[0])
mn = int(fields[1].strip().split(':')[1])
dati.append(datetime.datetime(year,1,1,hr,mn)+datetime.timedelta(days=doy-1))
x.append(float(fields[2]))
y.append(float(fields[3]))
z.append(float(fields[4]))
r.append(float(fields[5]))
theta.append(float(fields[6]))
phi.append(float(fields[7]))
return(dati,x,y,z,r,theta,phi)

293
kaipy/gamhelio/lib/lfmhlib.py
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@@ -1,293 +0,0 @@
from pyhdf.SD import SD, SDC
from numpy import sqrt,arccos,arctan2,cos,sin,pi,roll
import time
def read(filename):
"""
Returns R,theta,phi,Rc,thetac,phic,br,btheta,bphi,vr,vtheta,vphi,rho,cs
"""
hdffile = SD(filename,SDC.READ)
x=hdffile.select('X_grid').get()
y=hdffile.select('Y_grid').get()
z=hdffile.select('Z_grid').get()
bx=hdffile.select('bx_').get()[:-1,:-1,:-1]
by=hdffile.select('by_').get()[:-1,:-1,:-1]
bz=hdffile.select('bz_').get()[:-1,:-1,:-1]
vx=hdffile.select('vx_').get()[:-1,:-1,:-1]
vy=hdffile.select('vy_').get()[:-1,:-1,:-1]
vz=hdffile.select('vz_').get()[:-1,:-1,:-1]
rho=hdffile.select('rho_').get()[:-1,:-1,:-1]
cs=hdffile.select('c_').get()[:-1,:-1,:-1]
t=hdffile.time
hdffile.end()
# =========== Cell centers ==============
xc=0.125*( x[:-1,:-1,:-1]+x[1:,:-1,:-1]+x[:-1,1:,:-1]+x[:-1,:-1,1:]+
x[1:,1:,:-1]+x[1:,:-1,1:]+x[:-1,1:,1:]+x[1:,1:,1:] )
yc=0.125*( y[:-1,:-1,:-1]+y[1:,:-1,:-1]+y[:-1,1:,:-1]+y[:-1,:-1,1:]+
y[1:,1:,:-1]+y[1:,:-1,1:]+y[:-1,1:,1:]+y[1:,1:,1:] )
zc=0.125*( z[:-1,:-1,:-1]+z[1:,:-1,:-1]+z[:-1,1:,:-1]+z[:-1,:-1,1:]+
z[1:,1:,:-1]+z[1:,:-1,1:]+z[:-1,1:,1:]+z[1:,1:,1:] )
# =======================================
R=sqrt(x**2+y**2+z**2)
theta=arccos(z/R)
phi=arctan2(y,x)
phi[phi<0]+=2*pi
Rc=sqrt(xc**2+yc**2+zc**2)
thetac=arccos(zc/Rc)
phic=arctan2(yc,xc)
phic[phic<0]+=2*pi
br = bx*cos(phic)*sin(thetac) + by*sin(phic)*sin(thetac) + bz*cos(thetac)
btheta = bx*cos(phic)*cos(thetac) + by*sin(phic)*cos(thetac) - bz*sin(thetac)
bphi =-bx*sin(phic) + by*cos(phic)
vr = vx*cos(phic)*sin(thetac) + vy*sin(phic)*sin(thetac) + vz*cos(thetac)
vtheta = vx*cos(phic)*cos(thetac) + vy*sin(phic)*cos(thetac) - vz*sin(thetac)
vphi =-vx*sin(phic) + vy*cos(phic)
return(t,R,theta,phi,Rc,thetac,phic,br,btheta,bphi,vr,vtheta,vphi,rho,cs)
def r_theta_phi_uniform(filename):
"""
Return R, theta, phi 1-d arrays, assuming uniform grid
"""
hdffile = SD(filename,SDC.READ)
# note, minimal data are read from file
ni=hdffile.select('X_grid').ni-1
nj=hdffile.select('X_grid').nj-1
nk=hdffile.select('X_grid').nk-1
# first get R; in principle, could just take z along the axis
# but are allowing for the possibility that the axis is cut out
x=hdffile.select('X_grid').get(start=(0,0,0),count=(1,1,ni+1)).squeeze()
y=hdffile.select('Y_grid').get(start=(0,0,0),count=(1,1,ni+1)).squeeze()
z=hdffile.select('Z_grid').get(start=(0,0,0),count=(1,1,ni+1)).squeeze()
R = sqrt(x**2+y**2+z**2)
z=hdffile.select('Z_grid').get(start=(0,0,0),count=(1,nj+1,1)).squeeze()
theta = arccos(z/R[0])
x=hdffile.select('X_grid').get(start=(0,nj/2,0),count=(nk+1,1,1)).squeeze()
y=hdffile.select('Y_grid').get(start=(0,nj/2,0),count=(nk+1,1,1)).squeeze()
phi=arctan2(y,x)
phi[phi<0]+=2*pi
phi[-1]=phi[0]+2*pi
hdffile.end()
R/=6.96e10 # FIX ME HARD CODED UNITS
Rc = 0.5*(R[1:]+R[:-1])
thetac = 0.5*(theta[1:]+theta[:-1])
phic = 0.5*(phi[1:]+phi[:-1])
return R,theta,phi,Rc,thetac,phic
def read_var(filename,var_name):
hdffile = SD(filename,SDC.READ)
if var_name not in ['br','btheta','bphi','vr','vtheta','vphi']:
var=hdffile.select(var_name+'_').get()[:-1,:-1,:-1]
else:
R,theta,phi=r_theta_phi_uniform(filename)
thetac = 0.5*(theta[1:]+theta[:-1])
phic = 0.5*(phi[1:]+phi[:-1])
phic = phic[:,None,None]
thetac = thetac[None,:,None]
if var_name in ['br','btheta','bphi']:
bx=hdffile.select('bx_').get()[:-1,:-1,:-1]
by=hdffile.select('by_').get()[:-1,:-1,:-1]
bz=hdffile.select('bz_').get()[:-1,:-1,:-1]
if var_name=='br':
var = bx*cos(phic)*sin(thetac) + by*sin(phic)*sin(thetac) + bz*cos(thetac)
elif var_name=='btheta':
var = bx*cos(phic)*cos(thetac) + by*sin(phic)*cos(thetac) - bz*sin(thetac)
else:
var =-bx*sin(phic) + by*cos(phic)
else:
vx=hdffile.select('vx_').get()[:-1,:-1,:-1]
vy=hdffile.select('vy_').get()[:-1,:-1,:-1]
vz=hdffile.select('vz_').get()[:-1,:-1,:-1]
if var_name=='vr':
var = vx*cos(phic)*sin(thetac) + vy*sin(phic)*sin(thetac) + vz*cos(thetac)
elif var_name=='vtheta':
var = vx*cos(phic)*cos(thetac) + vy*sin(phic)*cos(thetac) - vz*sin(thetac)
else:
var =-vx*sin(phic) + vy*cos(phic)
hdffile.end()
return(var)
def read_var_point(filename,var_name,i,j,k,thetac,phic):
thetac = thetac[j]
phic = phic[k]
hdffile = SD(filename,SDC.READ)
if var_name not in ['br','btheta','bphi','vr','vtheta','vphi']:
var=hdffile.select(var_name+'_').get(start=(k,j,i),count=(1,1,1)).squeeze()
else:
# R,theta,phi=r_theta_phi_uniform(filename)
if var_name in ['br','btheta','bphi']:
bx=hdffile.select('bx_').get(start=(k,j,i),count=(1,1,1)).squeeze()
by=hdffile.select('by_').get(start=(k,j,i),count=(1,1,1)).squeeze()
bz=hdffile.select('bz_').get(start=(k,j,i),count=(1,1,1)).squeeze()
if var_name=='br':
var = bx*cos(phic)*sin(thetac) + by*sin(phic)*sin(thetac) + bz*cos(thetac)
elif var_name=='btheta':
var = bx*cos(phic)*cos(thetac) + by*sin(phic)*cos(thetac) - bz*sin(thetac)
else:
var =-bx*sin(phic) + by*cos(phic)
else:
vx=hdffile.select('vx_').get(start=(k,j,i),count=(1,1,1)).squeeze()
vy=hdffile.select('vy_').get(start=(k,j,i),count=(1,1,1)).squeeze()
vz=hdffile.select('vz_').get(start=(k,j,i),count=(1,1,1)).squeeze()
if var_name=='vr':
var = vx*cos(phic)*sin(thetac) + vy*sin(phic)*sin(thetac) + vz*cos(thetac)
elif var_name=='vtheta':
var = vx*cos(phic)*cos(thetac) + vy*sin(phic)*cos(thetac) - vz*sin(thetac)
else:
var =-vx*sin(phic) + vy*cos(phic)
hdffile.end()
return(var)
def read_var_islice(filename,var_name,i,thetac,phic):
nk = phic.size
nj = thetac.size
phic = phic[:,None]
thetac = thetac[None,:]
hdffile = SD(filename,SDC.READ)
if var_name not in ['br','btheta','bphi','vr','vtheta','vphi']:
var=hdffile.select(var_name+'_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
else:
if var_name in ['br','btheta','bphi']:
bx=hdffile.select('bx_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
by=hdffile.select('by_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
bz=hdffile.select('bz_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
if var_name=='br':
var = bx*cos(phic)*sin(thetac) + by*sin(phic)*sin(thetac) + bz*cos(thetac)
elif var_name=='btheta':
var = bx*cos(phic)*cos(thetac) + by*sin(phic)*cos(thetac) - bz*sin(thetac)
else:
var =-bx*sin(phic) + by*cos(phic)
else:
vx=hdffile.select('vx_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
vy=hdffile.select('vy_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
vz=hdffile.select('vz_').get(start=(0,0,i),count=(nk,nj,1)).squeeze()
if var_name=='vr':
var = vx*cos(phic)*sin(thetac) + vy*sin(phic)*sin(thetac) + vz*cos(thetac)
elif var_name=='vtheta':
var = vx*cos(phic)*cos(thetac) + vy*sin(phic)*cos(thetac) - vz*sin(thetac)
else:
var =-vx*sin(phic) + vy*cos(phic)
hdffile.end()
return(var)
def read_var_jslice(filename,var_name,j,thetac,phic):
hdffile = SD(filename,SDC.READ)
ni = hdffile.select('X_grid').ni-1
nk = hdffile.select('X_grid').nk-1
phic = phic[:,None]
thetac = thetac[j]
if var_name not in ['br','btheta','bphi','vr','vtheta','vphi']:
if var_name in ['X_grid','Y_grid','Z_grid']:
var=hdffile.select(var_name).get(start=(0,j,0),count=(nk,1,ni)).squeeze()
else:
var=hdffile.select(var_name+'_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
else:
if var_name in ['br','btheta','bphi']:
bx=hdffile.select('bx_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
by=hdffile.select('by_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
bz=hdffile.select('bz_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
if var_name=='br':
var = bx*cos(phic)*sin(thetac) + by*sin(phic)*sin(thetac) + bz*cos(thetac)
elif var_name=='btheta':
var = bx*cos(phic)*cos(thetac) + by*sin(phic)*cos(thetac) - bz*sin(thetac)
else:
var =-bx*sin(phic) + by*cos(phic)
else:
vx=hdffile.select('vx_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
vy=hdffile.select('vy_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
vz=hdffile.select('vz_').get(start=(0,j,0),count=(nk,1,ni)).squeeze()
if var_name=='vr':
var = vx*cos(phic)*sin(thetac) + vy*sin(phic)*sin(thetac) + vz*cos(thetac)
elif var_name=='vtheta':
var = vx*cos(phic)*cos(thetac) + vy*sin(phic)*cos(thetac) - vz*sin(thetac)
else:
var =-vx*sin(phic) + vy*cos(phic)
hdffile.end()
return(var)
def read_var_ikslice(filename,var_name,i,k,tc,pc):
hdffile = SD(filename,SDC.READ)
ni = hdffile.select('X_grid').ni-1
nj = hdffile.select('X_grid').nj-1
if var_name not in ['br','btheta','bphi','vr','vtheta','vphi','bt','bp','vt','vp']:
var=hdffile.select(var_name+'_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
else:
if var_name in ['br','btheta','bphi','bt','bp']:
bx=hdffile.select('bx_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
by=hdffile.select('by_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
bz=hdffile.select('bz_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
if var_name=='br':
var = bx*cos(pc[k])*sin(tc) + by*sin(pc[k])*sin(tc) + bz*cos(tc)
elif (var_name=='btheta' or var_name=='bt'):
var = bx*cos(pc[k])*cos(tc) + by*sin(pc[k])*cos(tc) - bz*sin(tc)
else:
var =-bx*sin(pc[k]) + by*cos(pc[k])
else:
vx=hdffile.select('vx_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
vy=hdffile.select('vy_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
vz=hdffile.select('vz_').get(start=(k,0,i),count=(1,nj,1)).squeeze()
if var_name=='vr':
var = vx*cos(pc[k])*sin(tc) + vy*sin(pc[k])*sin(tc) + vz*cos(tc)
elif (var_name=='vtheta' or var_name=='vt'):
var = vx*cos(pc[k])*cos(tc) + vy*sin(pc[k])*cos(tc) - vz*sin(tc)
else:
var =-vx*sin(pc[k]) + vy*cos(pc[k])
hdffile.end()
return(var)
def get_time(filename):
hdffile = SD(filename, SDC.READ)
t=hdffile.time
hdffile.end()
return(t)
def time_shift(t,var,Tsolar=25.38*24*3600.):
"""
Shift variables to the beginning of the CR.
Parameters:
t: current time
var: variable -- assumed to be a 2D i-slice (nk,nj) of the LFM data
Tsolar: solar rotation period in units of time used in the code. Default to 25.38 days and seconds as time units.
"""
time_shift= (t % Tsolar)/Tsolar
nshift = int(time_shift*(var.shape[0]+1)) # note, using nk+1 here, which gives us full rotation
return roll(var,-nshift,axis=0)

138
kaipy/gamhelio/lib/mas.py
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@@ -1,138 +0,0 @@
from pyhdf.SD import SD,SDC
import os,sys
import ConfigParser
mas_units = {'length':6.96e10,
'time':1445.87,
'vr':481.3711*1.e5,
'vt':481.3711*1.e5,
'vp':481.3711*1.e5,
'n':1.e8,
'rho':1.6726e-16,
'p':0.3875717,
't':2.807067e7,
'br':2.2068908,
'bt':2.2068908,
'bp':2.2068908}
mas_var_names = ['t','rho','vt','vp','vr','bt','bp','br']
def read_var(fname,varname,normalized=False):
f = SD(fname,SDC.READ)
phi = f.select('fakeDim0')[:]
theta = f.select('fakeDim1')[:]
r = f.select('fakeDim2')[:]
var = f.select('Data-Set-2')[:]
f.end()
if normalized:
return(phi,theta,r,var)
else:
return(phi,theta,r*mas_units['length'],var*mas_units[varname])
def units():
return(mas_units)
def read_all_vars(dir,timeLabel,normalized=False):
vars = {}
for var_name in mas_var_names:
vars[var_name]={}
(vars[var_name]['phi'],
vars[var_name]['theta'],
vars[var_name]['r'],
vars[var_name]['data']) = read_var(os.path.join(dir,var_name+'%03d.hdf'%timeLabel),var_name,normalized)
return(vars)
def set_plot_limits(v,plotConfigFile = 'plot.config'):
plotConfig = ConfigParser.ConfigParser()
plotConfig.read(plotConfigFile)
# assume here v has the necessary keys
# bascially, this needs to be called after mas.read_all_varaibles
try:
v['bt']['lims'] = plotConfig.getfloat('MAS','bt_lim_lo'),plotConfig.getfloat('MAS','bt_lim_hi')
v['bt']['fmt'] = plotConfig.get('MAS','bt_fmt')
v['bp']['lims'] = plotConfig.getfloat('MAS','bp_lim_lo'),plotConfig.getfloat('MAS','bp_lim_hi')
v['bp']['fmt'] = plotConfig.get('MAS','bp_fmt')
v['br']['lims'] = plotConfig.getfloat('MAS','br_lim_lo'),plotConfig.getfloat('MAS','br_lim_hi')
v['br']['fmt'] = plotConfig.get('MAS','br_fmt')
v['vr']['lims'] = plotConfig.getfloat('MAS','vr_lim_lo'),plotConfig.getfloat('MAS','vr_lim_hi')
v['vr']['fmt'] = plotConfig.get('MAS','vr_fmt')
v['vt']['lims'] = plotConfig.getfloat('MAS','vt_lim_lo'),plotConfig.getfloat('MAS','vt_lim_hi')
v['vt']['fmt'] = plotConfig.get('MAS','vt_fmt')
v['vp']['lims'] = plotConfig.getfloat('MAS','vp_lim_lo'),plotConfig.getfloat('MAS','vp_lim_hi')
v['vp']['fmt'] = plotConfig.get('MAS','vp_fmt')
v['rho']['lims'] = plotConfig.getfloat('MAS','rho_lim_lo'),plotConfig.getfloat('MAS','rho_lim_hi')
v['rho']['fmt'] = plotConfig.get('MAS','rho_fmt')
v['t']['lims'] = plotConfig.getfloat('MAS','t_lim_lo'),plotConfig.getfloat('MAS','t_lim_hi')
v['t']['fmt'] = plotConfig.get('MAS','t_fmt')
except KeyError:
print('Some keys are missing from the variable dictionary.')
def mas2h5(fname,vname):
from numpy import meshgrid,sin,cos
if vname not in mas_var_names:
sys.exit('Wrong variable name')
fout = os.path.splitext(fname)[0]+'.h5'
import mas
phi,theta,r,var=mas.read_var(fname,vname)
import h5py
f=h5py.File(fout,'w')
P,T,R = meshgrid(phi,theta,r,indexing='ij')
x = R*sin(T)*cos(P)/mas_units['length']
y = R*sin(T)*sin(P)/mas_units['length']
z = R*cos(T)/mas_units['length']
f.create_dataset('x',data= x.astype(dtype='float32'))
f.create_dataset('y',data= y.astype(dtype='float32'))
f.create_dataset('z',data= z.astype(dtype='float32'))
f.create_dataset(vname,data= var.astype(dtype='float32'))
f.close()
# write xmf file
mas2xdmf(fout,vname,phi.size,theta.size,r.size)
def mas2xdmf(h5name,varname,n1,n2,n3):
fname=os.path.splitext(h5name)[0]+'.xmf'
dsname = os.path.basename(h5name)
f = open(fname,'w')
f.write('<?xml version="1.0" ?>' + '\n'+
'<!DOCTYPE Xdmf SYSTEM "Xdmf.dtd" []>' + '\n'+
'<Xdmf Version="2.0">' + '\n'+
' <Domain>' + '\n'+
' <Grid Name="mesh1" GridType="Uniform">' + '\n'+
' <Topology TopologyType="3DSMesh" NumberOfElements="%d %d %d"/>'%(n1,n2,n3) + '\n'+
' <Geometry GeometryType="X_Y_Z">' + '\n'+
' <DataItem Name="X" Dimensions="%d %d %d" NumberType="Float" Precision="4" Format="HDF">' %(n1,n2,n3)+ '\n'+
' %s:/x'%dsname + '\n'+
' </DataItem>' + '\n'+
' <DataItem Name="Y" Dimensions="%d %d %d" NumberType="Float" Precision="4" Format="HDF">' %(n1,n2,n3)+ '\n'+
' %s:/y'%dsname + '\n'+
' </DataItem>' + '\n'+
' <DataItem Name="Z" Dimensions="%d %d %d" NumberType="Float" Precision="4" Format="HDF">'%(n1,n2,n3) + '\n'+
' %s:/z'%dsname + '\n'+
' </DataItem>' + '\n'+
' </Geometry>' + '\n'+
' <Attribute Name="%s" AttributeType="Scalar" Center="Node">'%varname + '\n'+
' <DataItem Dimensions="%d %d %d" NumberType="Float" Precision="4" Format="HDF">'%(n1,n2,n3) + '\n'+
' %s:/%s'%(dsname,varname) + '\n'+
' </DataItem>' + '\n'+
' </Attribute>' + '\n'+
' </Grid>' + '\n'+
' </Domain>' + '\n'+
'</Xdmf>' )
f.close()

51
kaipy/gamhelio/lib/msgr.py
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@@ -1,51 +0,0 @@
import numpy as np
import datetime
def msgr(msgr_file,ave_1h=True):
r=[]
lat=[]
lon=[]
br=[]
bt=[]
bn=[]
dati=[]
for line in open(msgr_file,'r'):
fields = line.strip().split()
year = int(fields[0])
doy = int(fields[1])
hr = int(fields[2])
mn = int(fields[3])
r.append(float(fields[7]))
lat.append(float(fields[8]))
lon.append(float(fields[9]))
br.append(float(fields[10]))
bt.append(float(fields[11]))
bn.append(float(fields[12]))
dati.append(datetime.datetime(year,1,1,hr,mn)+datetime.timedelta(days=doy-1))
if ave_1h:
# assuming 1-min data. Average to 1-h
n=len(lat)-len(lat)%60
br_1h=np.mean(np.array(br)[:n].reshape(-1,60),1)
bt_1h=np.mean(np.array(bt)[:n].reshape(-1,60),1)
bn_1h=np.mean(np.array(bn)[:n].reshape(-1,60),1)
return (dati[30:n+30:60],br_1h,bt_1h,bn_1h)
else:
return (dati,np.array(br),np.array(bt),np.array(bn))
def msgr_v1(msgr_file,year):
data = np.loadtxt(msgr_file)
start_dati = datetime.datetime(year,1,1,0)
dati = [start_dati+datetime.timedelta(days=d-1) for d in data[:,0]]
br=data[:,4]
bt=data[:,5]
bn=data[:,6]
return (dati,br,bt,bn)

128
kaipy/gamhelio/lib/poisson.py
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@@ -1,128 +0,0 @@
from scipy.optimize import newton_krylov,anderson
from numpy import ediff1d,zeros_like,cos,sin,zeros_like,arange
class poisson():
def __init__(self,theta,phi):
self.theta = theta
self.phi = phi
self.nj = theta.shape[0]
self.nk = phi.shape[0]
self.dtheta = theta[1]-theta[0] # generalize to the case of arbitrary grid spacing later: ediff1d(theta)
self.dphi = phi[1]-phi[0] # ediff1d(phi)
def setRHS(self,RHS):
self.RHS = RHS
def residual_xsin2(self,P,lhs=False):
dphi = self.dphi
dtheta = self.dtheta
theta = self.theta
nk = self.nk
d2x = zeros_like(P)
d2y = zeros_like(P)
dy = zeros_like(P)
d2x[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2]) /dphi**2
d2y[1:-1,:] = (P[2:,:] - 2*P[1:-1,:] + P[:-2,:]) /dtheta**2*sin(theta[1:-1,None])**2
dy[1:-1,:] = 0.5*(P[2:,:] - P[:-2,:]) /dtheta*sin(theta[1:-1,None])*cos(theta[1:-1,None])
# periodic boundary
d2x[:,0] = (P[:,1]-2*P[:,0]+P[:,-1]) /dphi**2
d2x[:,-1] = (P[:,0]-2*P[:,-1]+P[:,-2]) /dphi**2
result = self.RHS*sin(theta[:,None])**2
# Pole boundary condition. Note, the below does not mean that d/dtheta=0 literally.
# We set sin(theta)*dPsi/dtheta=0 at the pole (see notes for details),
# and d2y here represents 1/sin(theta)*d/dtheta (sin(theta)*dPsi/dtheta) in that approxiamtion.
# NOTE, BELOW ASSUMES CONSTANT SPACING IN THETA!!! (0.5 FACTOR IS BASED ON THAT).
dy[0,:]=0.
dy[-1,:]=0.
d2y[0,:] = 0.5*(P[1,:]-P[0,:])
d2y[-1,:] = -0.5*(P[-1,:]-P[-2,:])
###################################################################################################
if not lhs:
return d2x + d2y + dy - result
else:
return d2x + d2y + dy
def residual(self,P,lhs=False):
dphi = self.dphi
dtheta = self.dtheta
theta = self.theta
nk = self.nk
d2x = zeros_like(P)
d2y = zeros_like(P)
dy = zeros_like(P)
d2x[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2]) /dphi**2/sin(theta[:,None])**2
d2y[1:-1,:] = (P[2:,:] - 2*P[1:-1,:] + P[:-2,:]) /dtheta**2
dy[1:-1,:] = 0.5*(P[2:,:] - P[:-2,:]) /dtheta/sin(theta[1:-1,None])*cos(theta[1:-1,None])
# periodic boundary
d2x[:,0] = (P[:,1]-2*P[:,0]+P[:,-1]) /dphi**2/sin(theta)**2
d2x[:,-1] = (P[:,0]-2*P[:,-1]+P[:,-2]) /dphi**2/sin(theta)**2
result = self.RHS
# Pole boundary condition. Note, the below does not mean that d/dtheta=0 literally.
# We set sin(theta)*dPsi/dtheta=0 at the pole (see notes for details),
# and d2y here represents 1/sin(theta)*d/dtheta (sin(theta)*dPsi/dtheta) in that approxiamtion.
# NOTE, BELOW ASSUMES CONSTANT SPACING IN THETA!!! (0.5 FACTOR IS BASED ON THAT).
dy[0,:]=0.
dy[-1,:]=0.
d2y[0,:] = 0.5*(P[1,:]-P[0,:])/sin(theta[0])**2 # Psin=0; 0.5*( 0.5*(P[1,:]+P[0,:]) - Psin)
d2y[-1,:] = -0.5*(P[-1,:]-P[-2,:])/sin(theta[-1])**2 #-0.5*( 0.5*(P[-2,:]+P[-1,:]) - P[-1,:].mean())
###################################################################################################
if not lhs:
return d2x + d2y + dy - result
else:
return d2x + d2y + dy
if __name__=='__main__':
from scipy.optimize import newton_krylov,anderson
from pylab import *
import wsa
(phi_wsa_v,theta_wsa_v,phi,theta,br0,v_wsa,n_wsa,T_wsa)=wsa.read('/Users/merkivg1/work/LFM-helio_2.0/WSA/fits/2010_2008_WSA_ADAPT/vel_201001022300R001_ans.fits',False)
(phi_wsa_v,theta_wsa_v,phi,theta,br1,v_wsa,n_wsa,T_wsa)=wsa.read('/Users/merkivg1/work/LFM-helio_2.0/WSA/fits/2010_2008_WSA_ADAPT/vel_201001032300R001_ans.fits',False)
omega=2*pi/27.27
phi_prime=phi-omega*1.
ind0=where(phi_prime>0)[0][0]
phi_prime=roll(phi_prime,-ind0)
phi_prime[phi_prime<0]+=2*pi
import scipy
from scipy.interpolate import interpolate
br1_rolled=roll(br1,-ind0,axis=1)
fbi = scipy.interpolate.RectBivariateSpline(theta,phi,br1_rolled,kx=1,ky=1)
br1_new=fbi(theta,phi_prime)
import poisson
pois = poisson.poisson(theta,phi)
pois.setRHS(br1_new - br0)
guess=zeros_like(br1_new)
sol = newton_krylov(pois.residual,guess, method='lgmres', verbose=1,f_rtol=1.e-6) #iter=100
print('Residual: %g' % abs(pois.residual(sol)).max())
figure();pcolormesh(sol);colorbar();title('Solution')
# check curl
Etheta = -1/sin(theta[:,None])*diff(sol,axis=1)
Ephi = diff(sol,axis=0)
figure();pcolormesh(Etheta);colorbar();title('Etheta')
figure();pcolormesh(Ephi);colorbar();title('Ephi')

24
kaipy/gamhelio/lib/util.py
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@@ -1,24 +0,0 @@
import numpy as np
def sph2cart(a,theta,phi):
ar,at,ap = a
ax = ar*cos(phi)*sin(theta)+at*cos(phi)*cos(theta)-ap*sin(phi)
ay = ar*sin(phi)*sin(theta)+at*sin(phi)*cos(theta)+ap*cos(phi)
az = ar*cos(theta)-at*sin(theta)
return(ax,ay,az)
def radial_interp(vars_from,var_name,radii_to,coef_label='r_interp_coefs',ind_label='r_interp_above_ind'):
q=[]
ind=[]
for r_to in radii_to: # interpolating to LFM cell centers
r_from = vars_from[var_name]['r']
ind_r_a = np.flatnonzero(r_from>=r_to)[0] # MAS radius above
r_a = r_from[ind_r_a]
r_b = r_from[ind_r_a-1]
q.append( (r_a-r_to)/(r_a-r_b) )
ind.append(ind_r_a)
vars_from[var_name][coef_label]=np.asarray(q,dtype='float')
vars_from[var_name][ind_label]=np.asarray(ind,dtype='int')

89
kaipy/gamhelio/lib/wsa.py
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@@ -1,89 +0,0 @@
#!/usr/bin/env python
from astropy.io import fits
from numpy import linspace,pi,meshgrid,sin,cos,zeros,ones,dstack,diff,sqrt,array,savetxt,flatnonzero,insert,asarray,zeros_like,argmin,unravel_index
def read(wsa_file,densTempInfile,normalized=False,verbose=True):
if verbose: info(wsa_file)
hdul = fits.open(wsa_file)
jd_c = hdul[0].header['JULDATE'] #Julian date at the center of the map
n_phi_wsa_v = hdul[0].header['NAXIS1']+1 # number of cell vertices
n_phi_wsa_c = hdul[0].header['NAXIS1'] # number of cell centers
phi_wsa_v = linspace(0,360,n_phi_wsa_v)/180.*pi
phi_wsa_c = 0.5*(phi_wsa_v[:-1]+phi_wsa_v[1:])
n_theta_wsa_v = hdul[0].header['NAXIS2']+1 # number of cell vertices
n_theta_wsa_c = hdul[0].header['NAXIS2'] # number of cell centers
theta_wsa_v = linspace(0,180,n_theta_wsa_v)/180.*pi
theta_wsa_c = 0.5*(theta_wsa_v[:-1]+theta_wsa_v[1:])
bi_wsa = hdul[0].data[0,::-1,:] # note the theta reversal to convert from wsa theta to gamera theta definition
v_wsa = hdul[0].data[1,::-1,:] # note the theta reversal to convert from wsa theta to gamera theta definition
if densTempInfile:
n_wsa = hdul[0].data[2,::-1,:]
T_wsa = hdul[0].data[3,::-1,:]
else:
n_wsa = 112.64+9.49e7/v_wsa**2
T0 = 1.44e6 #8e5
n0 = 300.
B0 = bi_wsa[unravel_index(argmin(abs(n_wsa-n0)),n_wsa.shape)] # this is in nT
n0 = n_wsa[unravel_index(argmin(abs(n_wsa-n0)),n_wsa.shape)] # this is in cm^-3
# T_wsa = n0*T0/n_wsa
# The code below allows total pressure conservation in the non-radial
# direction: nT+B**2/8pi = n0*T0+B0**2/8pi. The unit conversion assumes
# B and B0 in nT (which they should be by now) and botzmann constant in the
# denominator.
T_wsa = n0*T0/n_wsa + (B0**2-bi_wsa**2)/1.38/8./pi*1.e6/n_wsa
# units: B in nT = * 10^-5 G; Kb in Gauss = 1.38*10^-16; n in cm^-3
P_tot_wsa = n_wsa*1.38e-16 * T_wsa + (bi_wsa**2 * 1.e-10)/8./pi
hdul.close()
if normalized:
return(jd_c,phi_wsa_v,theta_wsa_v,phi_wsa_c,theta_wsa_c,bi_wsa,v_wsa,n_wsa*1.67e-24,T_wsa)
else:
return(jd_c,phi_wsa_v,theta_wsa_v,phi_wsa_c,theta_wsa_c,bi_wsa*1.e-5,v_wsa*1.e5,n_wsa*1.67e-24,T_wsa)
def info(wsa_file):
hdul = fits.open(wsa_file)
print(repr(hdul[0].header))
hdul.close()
def plot(wsa_file,savefig):
hdul = fits.open(wsa_file)
bi_wsa = hdul[0].data[0,:,:]
v_wsa = hdul[0].data[1,:,:]
hdul.close()
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(16,12))
ax1 = plt.subplot(311)
p1=ax1.pcolormesh(bi_wsa,cmap='RdBu_r',vmin=bi_wsa.min(),vmax=-bi_wsa.min())
plt.colorbar(p1,ax=ax1).set_label('Br')
ax1.set_xlim((0,bi_wsa.shape[1]))
ax1.set_ylim((0,bi_wsa.shape[0]))
ax2 = plt.subplot(312,sharex=ax1)
p2=ax2.pcolormesh(v_wsa)
plt.colorbar(p2,ax=ax2).set_label('V')
ax2.set_xlim((0,bi_wsa.shape[1]))
ax2.set_ylim((0,bi_wsa.shape[0]))
fig.suptitle(wsa_file)
if not savefig:
plt.show()
else:
plt.savefig(wsa_file[:-4]+'png')
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('wsaFile',help='WSA file to use')
parser.add_argument('--savefig',help='Drop figure?',default=False,action='store_true')
args = parser.parse_args()
info(args.wsaFile)
plot(args.wsaFile,args.savefig)

31
kaipy/gamhelio/lib/wsa2h5.py
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#!/usr/bin/env python
# vgm, Sep 2019: Script to convert WSA fits files into simple h5 files
from astropy.io import fits
import h5py
import glob
wsaFiles = glob.glob('./*.fits')
for wsaFile in wsaFiles:
h5file = wsaFile[:-4]+'h5'
with fits.open(wsaFile) as hdul:
with h5py.File(h5file,'w') as hf:
# take care of the comments
# removing the last ones since we're incorporating them into dataset attributes directly
for key in hdul[0].header[:-4]:
# stores header data as attributes of the file
hf.attrs[key] = hdul[0].header[key]
hf.attrs[key+" COMMENT"] = str(hdul[0].header.comments[key])
# take care of the datasets
br = hf.create_dataset("br",data=hdul[0].data[0])
br.attrs['Units'] = 'nT'
br.attrs['Comment'] = 'Coronal field at RADOUT'
vr = hf.create_dataset("vr",data=hdul[0].data[1])
vr.attrs['Units'] = 'km/s'
vr.attrs['Comment'] = 'Radial velocity at RADOUT'

0
kaipy/gamhelio/wsa2TDgamera/__init__.py
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55
kaipy/gamhelio/wsa2TDgamera/params.py
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@@ -1,55 +0,0 @@
import configparser
class params():
def __init__(self,ConfigFileName):
config = configparser.ConfigParser()
config.read(ConfigFileName)
#self.initLFMfile = config.get('Output','Prefix')+'_%dx%dx%d_mhd_0000000.hdf'%(self.ni,self.nj,self.nk)
self.scale = config.getfloat('Normalization','Xscale')
self.gameraGridFile = config.get('Grid','gameraGridFile')
self.GridDir = config.get('Grid','GridDir')
self.gameraIbcFile = config.get('Grid','gameraIbcFile')
self.IbcDir = config.get('Grid','IbcDir')
self.tMin = config.getfloat('Grid','tMin')
self.tMax = config.getfloat('Grid','tMax')
self.Rin = config.getfloat('Grid','Rin')
self.Rout = config.getfloat('Grid','Rout')
self.Ni = config.getint('Grid','Ni')
self.Nj = config.getint('Grid','Nj')
self.Nk = config.getint('Grid','Nk')
self.gamma = config.getfloat('Constants','gamma')
self.NO2 = config.getint('Constants','NO2')
self.Tsolar = config.getfloat('Constants','Tsolar')
if config.has_section('MAS'):
self.masdir = config.get('MAS','masdir')
self.masTimeLabel = config.get('MAS','masTimeLabel')
self.masFrame = config.get('MAS','masFrame')
self.masFakeRotation = config.getboolean('MAS','masFakeRotation')
if config.has_section('WSA'):
self.wsaFile = config.get('WSA','wsafile')
self.gaussSmoothWidth = config.getint('WSA','gauss_smooth_width')
self.plots = config.getboolean('WSA','plots')
self.densTempInfile = config.getboolean('WSA','density_temperature_infile')
self.normalized = config.getboolean('WSA','normalized')
if config.has_section('ADAPT'):
self.adaptdir = config.get('ADAPT','adaptdir')
self.adaptWildCard = config.get('ADAPT','wild_card')
self.adaptCadence = config.getfloat('ADAPT','cadence')
self.gaussSmoothWidth = config.getint('ADAPT','gauss_smooth_width')
self.plots = config.getboolean('ADAPT','plots')
self.densTempInfile = config.getboolean('ADAPT','density_temperature_infile')
self.normalized = config.getboolean('ADAPT','normalized')
self.dumpInit = config.getboolean('DUMPS','init')
self.dumpBC = config.getboolean('DUMPS','BC')

1
kaipy/gamhelio/wsa2gamera/__init__.py
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@@ -1 +0,0 @@
from . import params

38
kaipy/gamhelio/wsa2gamera/params.py
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@@ -1,38 +0,0 @@
import configparser
class params():
def __init__(self,ConfigFileName):
config = configparser.ConfigParser(inline_comment_prefixes=(';','#'))
config.read(ConfigFileName)
self.gameraGridFile = config['Gamera']['gameraGridFile']
self.GridDir = config['Gamera']['GridDir']
self.gameraIbcFile = config['Gamera']['gameraIbcFile']
self.IbcDir = config['Gamera']['IbcDir']
self.wsaFile = config['WSA']['wsafile']
self.gaussSmoothWidth = config.getint('WSA','gauss_smooth_width')
#self.plots = config.getboolean('WSA','plots')
self.densTempInfile = config.getboolean('WSA','density_temperature_infile')
self.normalized = config.getboolean('WSA','normalized')
self.gamma = config.getfloat('Constants','gamma')
self.Nghost = config.getint('Constants','Nghost')
self.Tsolar = config.getfloat('Constants','Tsolar')
self.TCS = config.getfloat('Constants','TCS')
self.nCS = config.getfloat('Constants','nCS')
self.B0 = config.getfloat('Normalization','B0')
self.n0 = config.getfloat('Normalization','n0')
# self.T0 = config.getfloat('Normalization','T0')
self.tMin = config.getfloat('Grid','tMin')
self.tMax = config.getfloat('Grid','tMax')
self.Rin = config.getfloat('Grid','Rin')
self.Rout = config.getfloat('Grid','Rout')
self.Ni = config.getint('Grid','Ni')
self.Nj = config.getint('Grid','Nj')
self.Nk = config.getint('Grid','Nk')

111
kaipy/kJobs.py
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#Various routines to automate generation of run scripts
#XML input decks, PBS scripts
import xml.etree.ElementTree as et
import xml.dom.minidom
XV0 = 23 #Magic number for XML header length
#Various machine defaults, for now just assuming Cheyenne
Nth = 72 #Thread number
Nc = 72 #CPU number
stkSz = 128
qStr = "regular"
wStr = "12:00:00"
aStr = "UJHB0010"
#----------------
#XML routines
#Create XML block
#Root name (ie Gamera)
#List of block names (sim,prob,xxx)
#List of dictionaries for each block
def genXBlk(rStr,bStrs,bDicts,doHeader=False):
rXML = et.Element(rStr)
Nd = len(bStrs)
for i in range(Nd):
et.SubElement(rXML,bStrs[i],bDicts[i])
oStr = xml2str(rXML,doHeader=doHeader)
oStr = oStr[:-1] #Remove trailing newline
return oStr
#Adds label/dictionary to list
def addXBlk(bStr,bDic,bStrs,bDics):
bStrs.append(bStr)
bDics.append(bDic)
#----------------
#PBS routines
#Create OMP job script
#ComX = command string, ie "pusher.x"
#inStr = input deck name (no trailing .xml)
#mStr = module to load, ie module restore XXX
#Will run,
#ComX inStr.xml > inStr.out
#For job array runs
#Submit w/ qsub -J 1-2 XXX.pbs
def OMPJob(fOut,ComX,inStr,Nthz=None,mod="kaiju",doArray=True):
pbsT = """
#!/bin/bash
#PBS -A %s
#PBS -N %s
#PBS -j oe
#PBS -q %s
#PBS -l walltime=%s
#PBS -l select=%s
source ~/.bashrc
"""
#Generate core/thread string
if (Nthz is None):
Nthz = Nth
cStr = "1:ncpus=%d:ompthreads=%d"%(Nc,Nthz)
pbsHD = pbsT % (aStr,inStr,qStr,wStr,cStr)
if (mod is not None):
mStr = "module restore %s"%(mod)
pbsHD = pbsHD + mStr + "\n"
pbsB = """
module list
hostname
date
export OMP_NUM_THREADS=%d
export OMP_STACKSIZE=%dM
export JNUM=${PBS_ARRAY_INDEX:-0}
%s
date
"""
if (doArray):
xStr = "omplace -nt $OMP_NUM_THREADS %s %s.xml ${JNUM} > %s.${JNUM}.out"%(ComX,inStr,inStr)
else:
xStr = "omplace -nt $OMP_NUM_THREADS %s %s.xml > %s.out"%(ComX,inStr,inStr)
pbsX = pbsB % (Nthz,stkSz,xStr)
oStr = pbsHD+pbsX
with open(fOut,"w") as fID:
fID.write(oStr)
#print(oStr)
#----------------
#Utilities
#Turn XML data to string, optional strip XML version header
def xml2str(xIn,doHeader=False):
xmlStr = xml.dom.minidom.parseString(et.tostring(xIn)).toprettyxml(indent=" ")
xOut = xmlStr
if (not doHeader):
xOut = xmlStr[23:]
return xOut

246
kaipy/kaiH5.py
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@@ -1,246 +0,0 @@
import h5py
#import hdf5plugin
import numpy as np
import os, sys, subprocess
import kaipy.kdefs as kdefs
#Generate MPI-style name
def genName(bStr,i,j,k,Ri,Rj,Rk,nRes=None):
n = k + j*Rk + i*Rj*Rk
if (nRes is None):
fID = bStr + "_%04d_%04d_%04d_%04d_%04d_%04d.gam.h5"%(Ri,Rj,Rk,i,j,k)
else:
fID = bStr + "_%04d_%04d_%04d_%04d_%04d_%04d"%(Ri,Rj,Rk,i,j,k)+".gam.Res.%05d.h5"%(nRes)
return fID
#Generate old-style MPI name
def genNameOld(bStr,i,j,k,Ri,Rj,Rk,nRes=None):
n = k + j*Rk + i*Rj*Rk
if (nRes is None):
fID = bStr + "_%04d_%04d_%04d_%04d_%04d_%04d_%012d.h5"%(Ri,Rj,Rk,i,j,k,n)
else:
fID = bStr + "_%04d_%04d_%04d_%04d_%04d_%04d_%012d"%(Ri,Rj,Rk,i,j,k,n)+".Res.%05d.h5"%(nRes)
return fID
#Quick check and exit routine
def CheckOrDie(fname):
import os.path
isExist = os.path.exists(fname)
if (not isExist):
if (len(fname) == 0):
fname = "<EMPTY>"
sys.exit("Unable to find file: %s"%(fname))
#Check directory exists and make it if not
def CheckDirOrMake(fdir):
import os
isDir = os.path.isdir(fdir)
if (not isDir):
print("Creating %s"%(fdir))
os.makedirs(fdir)
# Stamp file with git hash (using this script's location)
def StampHash(fname):
with h5py.File(fname, 'a') as f5:
cwd = os.path.dirname(os.path.realpath(__file__))
try:
gh = subprocess.check_output(['git', '-C', cwd, 'rev-parse', 'HEAD']).decode('ascii').strip()
except:
print("ERROR: Couldn't grab git hash")
gh = "XXXXX"
f5.attrs['GITHASH'] = gh
# Stamp file with git branch (using this script's location)
def StampBranch(fname):
with h5py.File(fname, 'a') as f5:
cwd = os.path.dirname(os.path.realpath(__file__))
try:
gb = subprocess.check_output(['git', '-C', cwd, 'rev-parse', '--abbrev-ref', 'HEAD']).decode('ascii').strip()
except:
print("ERROR: Couldn't grab git branch")
gb = "XXXXX"
f5.attrs['GITBRANCH'] = gb
#Get git hash from file if it exists
def GetHash(fname):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
rStr = hf.attrs.get("GITHASH","NONE")
try:
hStr = rStr.decode('utf-8')
except (UnicodeDecodeError, AttributeError):
hStr = rStr
return hStr
#Get git branch from file if it exists
def GetBranch(fname):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
rStr = hf.attrs.get("GITBRANCH","NONE")
try:
hStr = rStr.decode('utf-8')
except (UnicodeDecodeError, AttributeError):
hStr = rStr
return hStr
#Return time at step n
def tStep(fname,nStp=0,aID="time",aDef=0.0):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
gID = "Step#%d"%(nStp)
#t = hf[gID].attrs.get(aID,aDef)
if aID in hf[gID].attrs:
t = hf[gID].attrs[aID]
else:
t = aDef
return t
def cntSteps(fname,doTryRecover=True,s0=0):
from alive_progress import alive_it
try:
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
if(kdefs.grpTimeCache in hf.keys() and 'step' in hf[kdefs.grpTimeCache].keys()):
sIds = np.asarray(hf[kdefs.grpTimeCache]['step'])
nSteps = sIds.size
else:
Steps = [grp for grp in alive_it(hf.keys(),title="#-Steps".ljust(kdefs.barLab),length=kdefs.barLen,bar=kdefs.barDef) if "Step#" in grp]
sIds = np.array([str.split(s,"#")[-1] for s in Steps],dtype=int)
sIds.sort()
nSteps = len(Steps)
return nSteps,sIds
except (ValueError, IndexError) as e:
print("!!Warning: h5 file contains unreadable steps")
if not doTryRecover:
print(" Can try again with 'cntSteps(fname,doTryRecover=True)'")
raise ValueError("Unreadable steps")
else:
print("Trying to read again while skipping bad values")
with h5py.File(fname,'r') as hf:
badCounts = 0
s = s0
sIds = []
while badCounts < 2000:
try:
sName = 'Step#' + str(s)
tryReadGrp = hf[sName]
#If still here, step is readable
sIds.append(s)
print("Read step " + str(s))
except ValueError:
badCounts += 1
print("Bad count on step " + str(s))
s+=1
nSteps = len(sIds)
sIds = np.array(sIds)
sIds.sort()
return nSteps,sIds
#More general version of cntSteps, useful for Step#X/Line#Y
def cntX(fname,gID=None,StrX="/Step#"):
with h5py.File(fname,'r') as hf:
if (gID is not None):
grps = hf[gID].values()
else:
grps = hf.values()
grpNames = [str(grp.name) for grp in grps]
#Steps = [stp if "/Step#" in stp for stp in grpNames]
Steps = [stp for stp in grpNames if StrX in stp]
nSteps = len(Steps)
sIds = np.array([str.split(s,"#")[-1] for s in Steps],dtype=int)
sIds.sort()
return nSteps,sIds
def getTs(fname,sIds=None,aID="time",aDef=0.0):
from alive_progress import alive_bar
if (sIds is None):
nSteps,sIds = cntSteps(fname)
Nt = len(sIds)
T = np.zeros(Nt)
CheckOrDie(fname)
titStr = "Time series: %s"%(aID)
with h5py.File(fname,'r') as hf:
if(kdefs.grpTimeCache in hf.keys()):
if(aID in hf[kdefs.grpTimeCache]):
T = np.asarray(hf[kdefs.grpTimeCache][aID])
else:
T[:] = aDef
else:
with alive_bar(Nt,title=titStr.ljust(kdefs.barLab),length=kdefs.barLen,bar=kdefs.barDef) as bar:
for idx, n in enumerate(sIds):
gId = "/Step#%d"%(n)
T[idx] = hf[gId].attrs.get(aID,aDef)
bar()
return T
#Get shape/dimension of grid
def getDims(fname,vID="X",doFlip=True):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
Dims = hf["/"][vID].shape
Ds = np.array(Dims,dtype=int)
if (doFlip):
Ds = np.flip(Ds,axis=0)
return Ds
#Get root variables
def getRootVars(fname):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
vIds = [str(k) for k in hf.keys() if "Step" not in str(k)]
#Remove coordinates from list of root variables
xyzS = ["X","Y","Z"]
vIds = [v for v in vIds if v not in xyzS]
return vIds
#Get variables in initial Step
def getVars(fname,smin):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
gId = "/Step#%d"%(smin)
stp0 = hf[gId]
vIds = [str(k) for k in stp0.keys()]
return vIds
#Get variable data
def PullVarLoc(fname,vID,s0=None,slice=(),loc=None):
''' Pull variable data from HDF5 file
and pass through (i,j,k) MPI rank loc
'''
V = PullVar(fname,vID,s0,slice)
return V,loc
#Get variable data
def PullVar(fname,vID,s0=None,slice=()):
''' Pull variable data from HDF5 file
'''
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
if (s0 is None):
V = hf[vID][slice].T
else:
gId = "/Step#%d"%(s0)
V = hf[gId][vID][slice].T
return V
#Get attribute data from Step#s0 or root (s0=None)
def PullAtt(fname,vID,s0=None):
CheckOrDie(fname)
with h5py.File(fname,'r') as hf:
if (s0 is None):
Q = hf.attrs[vID]
else:
gID = "/Step#%d"%(s0)
Q = hf[gID].attrs[vID]
return Q

188
kaipy/kaiTools.py
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@@ -1,188 +0,0 @@
import numpy as np
import datetime
import os
import glob
import sys
import subprocess
from xml.dom import minidom
from astropy.time import Time
import h5py
import kaipy.kdefs as kd
isotfmt = '%Y-%m-%dT%H:%M:%S.%f'
to_center1D = lambda A: 0.5*(A[:-1]+A[1:])
to_center2D = lambda A: 0.25*(A[:-1,:-1]+A[1:,:-1]+A[:-1,1:]+A[1:,1:])
to_center3D = lambda A: 0.125*(A[:-1,:-1,:-1]+A[-1:,-1:,1:]+A[-1:,1:,-1:]+A[-1:,1:,1:]
+A[1:,:-1,:-1]+A[1:,-1:,1:]+A[1:,1:,-1:]+A[1:,1:,1:])
def L_to_bVol(L, bsurf_nT=kd.EarthM0g*kd.G2nT): # L shell [Rp] to V [Rp/nT]
"""Calculates the flux tube volume [Rp/nT] from the given L shell [Rp]
L : L shell [Rp]
bsurf_nT : Surface field [nT] (Default: Earth)
"""
colat = np.arcsin(np.sqrt(1.0/L))
cSum = 35*np.cos(colat) - 7*np.cos(3*colat) +(7./5.)*np.cos(5*colat) - (1./7.)*np.cos(7*colat)
cSum /= 64.
s8 = np.sin(colat)**8
V = 2*cSum/s8/bsurf_nT
return V # [Rp/nT]
def MJD2UT(mjd):
""" If given single value, will return single datetime.datetime
If given list, will return list of datetime.datetimes
"""
UT = Time(mjd,format='mjd').isot
if type(UT) == str:
return datetime.datetime.strptime(UT,isotfmt)
else:
return [datetime.datetime.strptime(UT[n],isotfmt) for n in range(len(UT))]
def getRunInfo(fdir,ftag):
idStr = "_0000_0000_0000.gam.h5"
isMPI = False
Ri = 0
Rj = 0
Rk = 0
fOld = os.path.join(fdir,ftag+'.h5')
fNew = os.path.join(fdir,ftag+'.gam.h5')
try:
if (os.path.exists(fOld)):
return fOld,isMPI,Ri,Rj,Rk
if (os.path.exists(fNew)):
return fNew,isMPI,Ri,Rj,Rk
fIns = glob.glob(os.path.join(fdir,ftag)+'_*'+idStr)
if (len(fIns) > 1):
raise ValueError('Should not find more that one parallel file')
if (len(fIns) == 0):
raise ValueError('No MPI database found')
else:
isMPI = True
fName = fIns[0]
Ns = [int(s) for s in fName.split('_') if s.isdigit()]
Ri = Ns[-5]
Rj = Ns[-4]
Rk = Ns[-3]
return fName,isMPI,Ri,Rj,Rk
except ValueError as ve:
print(ve)
sys.exit()
#transform from cartesian to spherical
def xyz2rtp(phi,theta,Ax,Ay,Az):
Ar = Ax*np.cos(phi)*np.sin(theta)+Ay*np.sin(phi)*np.sin(theta)+Az*np.cos(theta)
Ap = -Ax*np.sin(phi)+Ay*np.cos(phi)
At = Ax*np.cos(phi)*np.cos(theta)+Ay*np.sin(phi)*np.cos(theta)-Az*np.sin(theta)
return Ar,At,Ap
# Use the Burton 1975 Formula to compute Dst from solar wind parameters
def burtonDst(secs,n,vx,vy,vz,bx,by,bz):
"""
Given time in seconds, density in cm^-3, velocity in km/s and magnetic field in nT
this function will return Dst computed from the Burton 1975 (doi: 10.1029/ja080i031p04204)
"""
v = np.sqrt(vx**2+vy**2+vz**2)
b = np.sqrt(bx**2+by**2+bz**2)
ey = 1.0e-3*vx*bz
a = 3.6e-5
b = 0.2
c = 20.0
d = -1.5e-3
dp = -1.2e-3
fe = d*(np.maximum(0,ey-0.5))
pdyn = 1.6726e-27*1e6*6.248e18*n*v**2
sqrtpdyn = np.sqrt(pdyn)
dst = 0*pdyn
for i in np.arange(len(dst)-1)+1:
dst[i] = dst[i-1] + (secs[i]-secs[i-1])*(fe[i]-a*(dst[i-1]-b*sqrtpdyn[i]+c))
return dst
def newellkp(secs,n,vx,vy,vz,bx,by,bz):
"""
Given time in seconds, density in cm^-3, velocity in km/s and magnetic field in nT
this function will return Kp computed from the Newell 2008 (doi: 10.1029/2007ja012825)
"""
v = np.sqrt(vx**2+vy**2+vz**2)
newellCoup = newellcoupling(vx,vy,vz,bx,by,bz)
kp = np.clip(0.05+2.244e-4*newellCoup+2.844e-6*np.sqrt(n)*v**2,0,9.0)
# Kp is three hour index so compute a three hour rolling average using convolve with first and last value extended
window = int(3*60*60/(secs[1]-secs[0]))
halfwindow = int(window/2)
begin = np.ones(halfwindow)
end = np.ones(halfwindow)
begin[:] = kp[0]
end[:] = kp[-1]
extendkp = np.concatenate((begin,kp,end))
conextendkp = np.convolve(extendkp,np.ones(window)/window,mode='same')
return conextendkp[halfwindow:-halfwindow]
def newellcoupling(vx,vy,vz,bx,by,bz):
"""
Given density in cm^-3, velocity in km/s and magnetic field in nT
this function will return Universial Coupling Function computed from the Newell 2007 (doi: 10.1029/2006ja012015)
"""
v = np.sqrt(vx**2+vy**2+vz**2)
b = np.sqrt(bx**2+by**2+bz**2)
thetac = np.abs(np.arctan2(by,bz))
newcoup = np.power(v,4.0/3.0)*np.power(b,2.0/3.0)*np.power(np.sin(thetac/2.0),8.0/3.0)
return newcoup
#Read SymH from bcwind file
def GetSymH(fBC):
with h5py.File(fBC,'r') as hf:
mjdData = hf['MJD'][()]
tData = hf['T'][()]
dstData = hf['symh'][()]
utData = MJD2UT(mjdData)
return utData,tData,dstData
def interpTSC(gridX, gridY, x, y, var):
""" gridX/gridY: 3-element x & y grid vals (center value is closest grid point to desired interpolation point)
x: dim1 of point of interest
y: dim2 of point of interest
var: 3x3 values of desired variable
"""
weights = interpTSCWeights(gridX, gridY, x, y)
result = 0
for i in range(3):
for j in range(3):
result += weights[i,j]*var[i,j]
return result
def interpTSCWeights(gridX, gridY, x, y):
""" gridX/gridY: 3-element x & y grid vals (center value is closest grid point to desired interpolation point)
x: dim1 of point of interest
y: dim2 of point of interest
"""
dx = np.abs(gridX[0]-gridX[1])
dy = np.abs(gridY[0]-gridY[1])
eta = (x - gridX[1])/dx
zeta = (y - gridY[1])/dy
#print('eta : ' + str(eta))
#print('zeta: ' + str(zeta))
def weight1D(eta):
return np.array([
0.5*(0.5-eta)**2,
0.75 - eta**2,
0.5*(0.5+eta)**2
])
wX = weight1D(eta)
wY = weight1D(zeta)
w2D = np.zeros((3,3))
for i in range(3):
for j in range(3):
w2D[i,j] = wX[i]*wY[j]
return w2D

765
kaipy/kaiViz.py
View File

@@ -1,765 +0,0 @@
#Various scripts to support visualization of Kaiju data
from kaipy.kdefs import *
import h5py
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.colors import Normalize
#Create 2D equatorial grid (Ni,Nj*2+1) from lfm/egg-style
# def genEQGrid(fIn):
# with h5py.File(fIn,'r') as hf:
# Nk,Nj,Ni = hf.get("X")[()].shape
# X3 = hf.get("X")[()].T
# Y3 = hf.get("Y")[()].T
# Z3 = hf.get("Z")[()].T
# print("Ni,Nj,Nk = %d,%d,%d"%(Ni,Nj,Nk))
# #Create grid
# Nr = Ni-1
# Np = 2*(Nj-1)
# xx = np.zeros((Nr+1,Np+1))
# yy = np.zeros((Nr+1,Np+1))
# for i in range(Nr+1):
# for j in range(Nj):
# xx[i,j] = X3[i,j,0]
# yy[i,j] = Y3[i,j,0]
# for j in range(Nj,Np+1):
# jp = Np-j
# xx[i,j] = X3[i,jp,0]
# yy[i,j] = -Y3[i,jp,0]
# return xx,yy
# def getEQVar(fIn,StpN=0,vID="D"):
# gID = "Step#%d"%(StpN)
# with h5py.File(fIn,'r') as hf:
# V = hf.get(gID).get(vID)[()].T
# Ni,Nj,Nk = V.shape #Cell-centered (not node)
# Nr = Ni
# Np = 2*(Nj)
# kp = (Nk)/2
# vv = np.zeros((Nr,Np))
# for i in range(Nr):
# for j in range(Np):
# if (j>=Nj):
# jp = Np-j-1
# vv[i,j] = V[i,jp,kp]
# else:
# jp = j
# vv[i,j] = V[i,jp,0]
# return vv
#Calculate equatorial Bz-D
# def getEQBzD(xx,yy,MagM=-EarthM0g):
# Nip,Njp = xx.shape
# Ni = Nip-1
# Nj = Njp-1
# BzD = np.zeros((Ni,Nj))
# xxc = np.zeros((Ni,Nj))
# yyc = np.zeros((Ni,Nj))
# for i in range(Ni):
# for j in range(Nj):
# xc = 0.25*(xx[i,j]+xx[i+1,j]+xx[i,j+1]+xx[i+1,j+1])
# yc = 0.25*(yy[i,j]+yy[i+1,j]+yy[i,j+1]+yy[i+1,j+1])
# xxc[i,j] = xc
# yyc[i,j] = yc
# r = np.sqrt(xc**2.0+yc**2.0)
# rm5 = r**(-5.0)
# BzD[i,j] = -r*r*MagM*rm5
# return xxc,yyc,BzD
#---------------------------------
#Matplotlib helpers
def SetAx(xyBds=[-1, 1, -1, 1], ax=None, Adj='box'):
"""Set axis plot bounds and aspect ratio.
Set axis plot bounds and aspect ratio.
Parameters
----------
xyBds : list of 4 float, optional, default [-1, 1, -1, 1]
Axis limits for x and y as (xmin, xmax, ymin, ymax).
ax: matplotlib.Axes, optional, default None
Axes object to modify.
Adj: str, optional, default 'box
Specify what aspect of the plot to adjust to achieve equal aspect
ratio.
Returns
-------
None
"""
# If no Axes object is specified, fetch the current Axes object.
if ax is None:
ax = plt.gca()
# Set the axis limits for x and y.
ax.set_xlim(xyBds[0], xyBds[1])
ax.set_ylim(xyBds[2], xyBds[3])
# Make the plot have an equal aspect ratio, adjusting positions as
# allowed by Adj.
ax.set_aspect('equal', adjustable=Adj)
#Set axis labels and locations
def SetAxLabs(Ax,xLab,yLab,doBot=True,doLeft=True,fs="medium"):
Ax.set_xlabel(xLab,fontsize=fs)
Ax.set_ylabel(yLab,fontsize=fs)
if (not doBot):
Ax.xaxis.tick_top()
Ax.xaxis.set_label_position('top')
if (not doLeft):
Ax.yaxis.tick_right()
Ax.yaxis.set_label_position('right')
#Kill labels if not string is None
if (xLab is None):
Ax.xaxis.label.set_visible(False)
Ax.xaxis.set_visible(False)
plt.setp(Ax.get_xticklabels(),visible=False)
if (yLab is None):
Ax.yaxis.label.set_visible(False)
Ax.yaxis.set_visible(False)
plt.setp(Ax.get_yticklabels(),visible=False)
#Set X axis to labels to well formatted date time
def SetAxDate(Ax,fmt='%H:%M \n%Y-%m-%d'):
Ax.xaxis_date()
Ax.xaxis.set_major_formatter(mpl.dates.DateFormatter(fmt))
#Adds 2D earth w/ dawn/dusk
def addEarth2D(Re=1, angle=-90, ax=None):
from matplotlib.patches import Wedge
if ax is None:
ax = plt.gca()
colors=('w','k')
center = 0
theta1, theta2 = angle, angle + 180
w1 = Wedge(center, Re,-90,90, fc=colors[0],ec='k')
w2 = Wedge(center, Re, 90, -90, fc=colors[1],ec='k')
for wedge in [w1, w2]:
ax.add_artist(wedge)
return [w1, w2]
#Add inner cutout
def DrawCut(Rin=2.5,ax=None):
from matplotlib.patches import Wedge
if ax is None:
ax = plt.gca()
w1 = Wedge(0,Rin,0,360,fc=None,ec='k')
ax.add_artist(w1)
return w1
#Take cell-centered polar values and add extra phi layer
#Useful for contour plots through +X
def reWrap(V):
Ni,Nj = V.shape
Vp = np.zeros((Ni,Nj+1))
Vp[:,0:Nj] = V
Vp[:,-1] = V[:,0]
return Vp
#Image files
#Wrapper to save (and trim) figure
def savePic(fOut,dpiQ=300,doTrim=True,bLenX=20,bLenY=None,doClose=False,doEps=False,saveFigure=None):
#Start by saving
import matplotlib.pyplot as plt
if (doEps):
if saveFigure is None:
plt.savefig(fOut,dpi=dpiQ,format='eps')
else:
saveFigure.savefig(fOut,dpi=dpiQ,format='eps')
else:
if saveFigure is None:
plt.savefig(fOut,dpi=dpiQ)
else:
saveFigure.savefig(fOut,dpi=dpiQ)
if (doTrim):
trimFig(fOut,bLenX,bLenY)
if (doClose):
if saveFigure is None:
plt.close('all')
else:
plt.close(saveFigure)
#Use imagemagick to trim whitespace off figure
#doEven: Guarantee even number of pixels in X/Y
def trimFig(fName,bLenX=20,bLenY=None,doEven=True):
import os
if (bLenY is None):
bLenY = bLenX
ComS = 'convert -trim -border %dx%d -bordercolor "#FFFFFF" '%(bLenX,bLenY) + fName + ' ' + fName
os.system(ComS)
if (doEven):
Nx,Ny = picSz(fName)
#print(Nx,Ny)
while ( (Nx % 2) != 0 ):
#print("Shaving X")
ShaveX(fName)
Nx,Ny = picSz(fName)
#print('\t%d,%d'%(Nx,Ny))
while ( (Ny % 2) != 0 ):
#print("Shaving Y")
ShaveY(fName)
Nx,Ny = picSz(fName)
#print('\t%d,%d'%(Nx,Ny))
Nx,Ny = picSz(fName)
if ( ((Nx % 2) != 0) or ((Ny % 2) != 0) ):
print("Parity failure on pic sizing")
print(Nx,Ny)
def picSz(fName):
from PIL import Image
with Image.open(fName) as img:
Nx,Ny = img.size
return Nx,Ny
def ShaveX(fName):
import os
ComS = 'convert -crop -1+0 +repage ' + fName + ' ' + fName
os.system(ComS)
def ShaveY(fName):
import os
ComS = 'convert -crop +0-1 +repage ' + fName + ' ' + fName
os.system(ComS)
#---------------------------------
#Create colorbar with specified midpoint (grabbed from stack overflow)
class MidpointNormalize(Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
#Create norm object for MPL
def genNorm(vMin,vMax=None,doLog=False,doSymLog=False,midP=None,linP=1.0):
from matplotlib.colors import LogNorm
from matplotlib.colors import Normalize
from matplotlib.colors import SymLogNorm
if (vMax is None):
vMin = -np.abs(vMin)
vMax = np.abs(vMin)
if (midP is None):
doMid = False
else:
doMid = True
if (doMid):
vN = MidpointNormalize(vmin=vMin,vmax=vMax,midpoint=midP)
elif (doLog):
vN = LogNorm(vmin=vMin,vmax=vMax)
elif (doSymLog):
vN = SymLogNorm(linthresh=linP,vmin=vMin,vmax=vMax,base=10)
else:
vN = Normalize(vmin=vMin,vmax=vMax)
return vN
#Create colorbar object into specified axis
def genCB(AxCB,vN,cbT="Title",cM="viridis",doVert=False,cbSz="medium",Ntk=None):
from matplotlib import ticker
if (doVert):
cbOr = "vertical"
else:
cbOr = "horizontal"
cmData = plt.get_cmap(cM)
cb = mpl.colorbar.ColorbarBase(AxCB,cmap=cmData,norm=vN,orientation=cbOr)
if (Ntk is not None):
cb.locator = ticker.MaxNLocator(nbins=Ntk)
cb.update_ticks()
cb.set_label(cbT,fontsize=cbSz)
cb.ax.tick_params(labelsize=cbSz)
return cb
def labelStr(data, key, vecComp):
vecLabel = ['x', 'y', 'z']
if key == "Velocity":
key = "Speed"
if vecComp < 3 and vecComp > -1:
label = (
data['GAMERA_' + key].attrs['AXISLABEL'] +
vecLabel[vecComp] + ' [' +
data['GAMERA_' + key].attrs['UNITS'].decode() + ']'
)
else:
key_name = "GAMERA_" + key
label = data[key_name].attrs['AXISLABEL'] + ' [' + data[key_name].attrs['UNITS'].decode() + ']'
return label
def itemPlot(Ax,data,key,plotNum,numPlots,vecComp=-1):
#print(key,vecComp)
if -1 == vecComp:
# if key == "Br":
# # Plot a horizontal line at Br=0. This indicates passage of the
# # heliospheric current sheet.
# Ax.axhline([0], linestyle="--", color="black")
# <HACK>
# if key == "Density":
# # Reduce the density vertical scale.
# Ax.set_ylim(0, 50) # cm**-3
# </HACK>
if key == "Velocity":
maskedData = np.ma.masked_where(
data["GAMERA_inDom"][:]==0.0,
data[key].flatten()[0]["VR"][:]
)
maskedGamera = np.ma.masked_where(
data["GAMERA_inDom"][:]==0.0, data['GAMERA_Speed'][:]
)
else:
maskedData = np.ma.masked_where(
data["GAMERA_inDom"][:]==0.0, data[key][:]
)
maskedGamera = np.ma.masked_where(
data["GAMERA_inDom"][:]==0.0, data['GAMERA_' + key][:]
)
Ax.plot(data['Epoch_bin'],maskedData)
Ax.plot(data['Epoch_bin'],maskedGamera)
else:
maskedData = np.ma.masked_where(data["GAMERA_inDom"][:]==0.0,
data[key][:,vecComp])
Ax.plot(data['Epoch_bin'],maskedData)
maskedGamera = np.ma.masked_where(data["GAMERA_inDom"][:]==0.0,
data['GAMERA_'+key][:,vecComp])
Ax.plot(data['Epoch_bin'],maskedGamera)
if (plotNum % 2) == 0:
left = True
else:
left = False
label = labelStr(data, key,vecComp)
if plotNum == (numPlots-1):
SetAxLabs(Ax,'UT',label,doLeft=left)
SetAxDate(Ax)
else:
SetAxLabs(Ax,None,label,doLeft=left)
return
def compPlot(plotname,scId,data):
numPlots = 0
variables_to_plot = []
keys = data.keys()
#print(keys)
if 'Density' in keys:
numPlots = numPlots + 1
variables_to_plot.append('Density')
if 'Pressue' in keys:
numPlots = numPlots + 1
variables_to_plot.append('Pressue')
if 'Temperature' in keys:
numPlots = numPlots + 1
variables_to_plot.append('Temperature')
if 'MagneticField' in keys:
numPlots = numPlots + 3
variables_to_plot.append('MagneticField')
if 'Velocity' in keys:
numPlots = numPlots + 3
variables_to_plot.append('Velocity')
figsize = (10,10)
fig = plt.figure(figsize=figsize)
gs = fig.add_gridspec(numPlots,1)
plotNum = 0
for key in variables_to_plot:
#print('Plotting',key)
if 'MagneticField' == key or 'Velocity' == key:
doVecPlot = True
else:
doVecPlot = False
if 0 == plotNum:
Ax1 = fig.add_subplot(gs[plotNum,0])
if doVecPlot:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax1,data,key,plotNum,numPlots,vecComp=0)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=1)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=2)
plotNum = plotNum + 1
else:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax1,data,key,plotNum,numPlots)
plotNum = plotNum + 1
else:
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
if doVecPlot:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=0)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=1)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=2)
plotNum = plotNum + 1
else:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax,data,key,plotNum,numPlots)
plotNum = plotNum + 1
Ax1.legend([scId,'GAMERA'],loc='best')
Ax1.set_title(plotname)
plt.subplots_adjust(hspace=0)
savePic(plotname, doClose=True)
def compPlot(plotname,scId,data):
numPlots = 0
keysToPlot = []
keys = data.keys()
#print(keys)
if 'Density' in keys:
numPlots = numPlots + 1
keysToPlot.append('Density')
if 'Pressue' in keys:
numPlots = numPlots + 1
keysToPlot.append('Pressue')
if 'Temperature' in keys:
numPlots = numPlots + 1
keysToPlot.append('Temperature')
if 'MagneticField' in keys:
numPlots = numPlots + 3
keysToPlot.append('MagneticField')
if 'Velocity' in keys:
numPlots = numPlots + 3
keysToPlot.append('Velocity')
figsize = (10,10)
fig = plt.figure(figsize=figsize)
gs = fig.add_gridspec(numPlots,1)
plotNum = 0
for key in keysToPlot:
#print('Plotting',key)
if 'MagneticField' == key or 'Velocity' == key:
doVecPlot = True
else:
doVecPlot = False
if 0 == plotNum:
Ax1 = fig.add_subplot(gs[plotNum,0])
if doVecPlot:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax1,data,key,plotNum,numPlots,vecComp=0)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=1)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=2)
plotNum = plotNum + 1
else:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax1,data,key,plotNum,numPlots)
plotNum = plotNum + 1
else:
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
if doVecPlot:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=0)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=1)
plotNum = plotNum + 1
Ax = fig.add_subplot(gs[plotNum,0],sharex=Ax1)
itemPlot(Ax,data,key,plotNum,numPlots,vecComp=2)
plotNum = plotNum + 1
else:
#print('key',key,'plotNum',plotNum)
itemPlot(Ax,data,key,plotNum,numPlots)
plotNum = plotNum + 1
Ax1.legend([scId,'GAMERA'],loc='best')
Ax1.set_title(plotname)
plt.subplots_adjust(hspace=0)
savePic(plotname)
def trajPlot(plotname,scId,data,toRe):
figsize = (15,5)
# Create the figure in-memory.
mpl.use("AGG")
fig = plt.figure(figsize=figsize)
gs = fig.add_gridspec(1,3)
Ax1 = fig.add_subplot(gs[0,0])
Ax2 = fig.add_subplot(gs[0,1])
Ax3 = fig.add_subplot(gs[0,2])
Ax1.plot(data['Ephemeris'][:,0]*toRe,data['Ephemeris'][:,1]*toRe)
Ax2.plot(data['Ephemeris'][:,0]*toRe,data['Ephemeris'][:,2]*toRe)
Ax3.plot(data['Ephemeris'][:,1]*toRe,data['Ephemeris'][:,2]*toRe)
Ax1.set_title('XY SM')
Ax2.set_title('XZ SM')
Ax3.set_title('YZ SM')
titlestr = (scId + ' - ' + data['Epoch_bin'][0].strftime('%m/%d/%Y - %H:%M:%S') + ' to ' +
data['Epoch_bin'][-1].strftime('%m/%d/%Y - %H:%M:%S'))
fig.suptitle(titlestr)
savePic(plotname, doClose=True)
def get_aspect(ax):
"""Get current aspect ratio of ax
"""
from operator import sub
# Total figure size
figW, figH = ax.get_figure().get_size_inches()
# Axis size on figure
_, _, w, h = ax.get_position().bounds
# Ratio of display units
disp_ratio = (figH * h) / (figW * w)
# Ratio of data units
# Negative over negative because of the order of subtraction
data_ratio = sub(*ax.get_ylim()) / sub(*ax.get_xlim())
return disp_ratio #/ data_ratio
# Methods specific for comparing gamhelio output to data from heliospheric
# spacecraft.
def helio_labelStr(data, key, vecComp):
label = (
data['GAMHELIO_' + key].attrs['AXISLABEL'] +
' [' + data['GAMHELIO_' + key].attrs['UNITS'] + ']'
)
return label
def helioItemPlot_new(Ax, sc_id, data, key, plotNum, numPlots, show_zero=False):
"""Plot a single variable for the comparison plot.
Plot a single variable for the comparison plot.
Parameters
----------
Ax: matplotlib.axes._subplots.AxesSubplot
Matplotlib axis for plot.
data: spacepy.datamodel.SpaceData
Object containing the measured spacecraft data.
key: str
Name of variable to plot.
plotNum: int
Position of plot in sequence of plots.
numPlots: int
Number of plots in sequence.
show_zero ": bool, default False
Set to True to show a black dashed line at the 0 level for the variable.
Returns
-------
None
"""
# Extract the times and values which fall within the gamhelio simulation
# domain.
t = np.ma.masked_where(
data["GAMHELIO_inDom"][:] == 0.0, data["Ephemeris_time"][:]
)
observed = None
if key in data:
observed = np.ma.masked_where(
data["GAMHELIO_inDom"][:] == 0.0, data[key][:]
)
# gamhelio results should always be available.
predicted = np.ma.masked_where(
data["GAMHELIO_inDom"][:] == 0.0, data["GAMHELIO_" + key][:]
)
# Show a black dotted line at y = 0 if requested.
if show_zero:
Ax.axhline(y=0, linestyle="--", color="black")
# Plot the observed and predicted data for the current variable.
# if key in data:
if observed is not None:
Ax.plot(t, observed)
else:
fontsize = 18
Ax.plot(t, [None]*len(t))
Ax.text(
0.5, 0.5, "No spacecraft data available.",
transform=Ax.transAxes,
ha="center", va="center", fontsize=fontsize, color="darkgrey"
)
Ax.plot(t, predicted)
# Even-numbered plots (1-based) show the y-axis label on the left.
left = False
if plotNum % 2 == 1:
left = True
# Compute the y-axis label string.
label = helio_labelStr(data, key, vecComp=-1)
# Show the x-axis on the last plot.
if plotNum == numPlots:
SetAxLabs(Ax, "UT", label, doLeft=left)
SetAxDate(Ax)
else:
SetAxLabs(Ax, None, label, doLeft=left)
# Add the shared legend to the first plot.
if plotNum == 1:
Ax.legend([sc_id, "GAMHELIO"], loc="best")
def helioCompPlot_new(plot_file_path, sc_id, sc_data):
"""Create comparison plots for heliospheric results.
Create comparison plots for heliospheric results and save the plots to a
file. The plots will compare simulation results to measured spacecraft
data. The compared variables are radial velocity, radial magnetic field,
number density, and temperature.
Parameters
----------
plotname : str
Path to file to hold plot image.
scId : str
ID string for spacecraft.
data : spacepy.datamodel.SpaceData
The current spacecraft and model data
Returns
-------
None
"""
# Determine which data are available to plot.
variables_to_plot = ["Speed", "Br", "Density", "Temperature"]
# Compute the number of variables to plot.
n_plots = len(variables_to_plot)
# Create the figure in memory.
mpl.use("AGG")
# Create the figure.
HELIO_COMP_PLOT_FIGSIZE = (10, 10)
fig = plt.figure(figsize=HELIO_COMP_PLOT_FIGSIZE)
# Plot each variable in its own subplot.
for i in range(n_plots):
plotNum = i + 1
variable_name = variables_to_plot[i]
show_zero = False
if variable_name == "Br":
show_zero = True
ax = plt.subplot(n_plots, 1, plotNum)
helioItemPlot_new(
ax, sc_id, sc_data, variable_name,
plotNum, n_plots, show_zero=show_zero
)
# Use the plot file path as the figure title.
fig.suptitle(plot_file_path)
# Stack the plots directly on top of each other.
plt.subplots_adjust(hspace=0)
# Save the figure.
savePic(plot_file_path, doClose=True)
def helioTrajPlot(plot_file_path, sc_id, sc_data):
"""Create a set of trajectory plots for a heliospheric spacecraft.
Create a set of trajectory plots for a heliospheric spacecraft.
These plots are created in the original spacecraft frame.
Parameters
----------
plot_name: str
Path to plot file.
scId: str
Name of spacecraft.
data: spacepy.datamodel.SpaceData
Object containing spacecraft data.
Returns
-------
None
"""
# Set the figure size (inches)
figsize = (15, 5)
# Create the figure in-memory.
mpl.use("AGG")
fig = plt.figure(figsize=figsize)
# Create a grid for the sin-plots.
gs = fig.add_gridspec(1, 3)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[0, 2])
# Plot the components against each other.
if sc_id == "ACE":
X = sc_data["Ephemeris"][:, 0]
Y = sc_data["Ephemeris"][:, 1]
Z = sc_data["Ephemeris"][:, 2]
ax1.plot(X, Y)
ax1.set_xlabel("GSE X (km)")
ax1.set_ylabel("GSE Y (km)")
ax2.plot(X, Z)
ax2.set_xlabel("GSE X (km)")
ax2.set_ylabel("GSE Z (km)")
ax3.plot(Y, Z)
ax3.set_xlabel("GSE Y (km)")
ax3.set_ylabel("GSE Z (km)")
fig.subplots_adjust(wspace=0.25)
# elif sc_id == "Parker_Solar_Probe":
# ax1.plot(sc_data["heliographicLongitude"][:], sc_data["radialDistance"][:])
# ax1.set_xlabel("HGI longitude (deg)")
# ax1.set_ylabel("HGI radius ($R_{sun})$")
# ax2.plot(sc_data["heliographicLatitude"][:], sc_data["radialDistance"][:])
# ax2.set_xlabel("HGI latitude (deg)")
# ax2.set_ylabel("HGI radius ($R_{sun})$")
# ax3.plot(sc_data["heliographicLongitude"][:], sc_data["heliographicLatitude"][:])
# ax3.set_xlabel("HGI longitude (deg)")
# ax3.set_ylabel("HGI latitude (deg)$")
# elif sc_id == "STEREO_A":
# ax1.plot(sc_data["HGI_LON"][:], sc_data["RAD_AU"][:])
# ax1.set_xlabel("HGI longitude (deg)")
# ax1.set_ylabel("HGI radius ($R_{sun})$")
# ax2.plot(sc_data["HGI_LAT"][:], sc_data["RAD_AU"][:])
# ax2.set_xlabel("HGI latitude (deg)")
# ax2.set_ylabel("HGI radius ($R_{sun})$")
# ax3.plot(sc_data["HGI_LON"][:], sc_data["HGI_LAT"][:])
# ax3.set_xlabel("HGI longitude (deg)")
# ax3.set_ylabel("HGI latitude (deg)$")
else:
raise TypeError
# Apply the overall title.
title = (
sc_id + " - " +
sc_data["Ephemeris_Epoch"][0].strftime("%m/%d/%Y - %H:%M:%S") +
" to " +
sc_data["Ephemeris_Epoch"][-1].strftime("%m/%d/%Y - %H:%M:%S")
)
fig.suptitle(title)
# Save the figure to a file.
savePic(plot_file_path, doClose=True)

93
kaipy/kaijson.py
View File

@@ -1,93 +0,0 @@
import os
import json
#Packages needed to handle variable types
import numpy as np
import datetime
dtformat = "%Y-%m-%dT%H:%M:%SZ"
#======
#Custom handlers for non-standard types
#======
#TODO: Handle saving/loading attributes for hdf5 data
class CustomEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, (datetime.time, datetime.datetime)):
return obj.strftime(dtformat)
if isinstance(obj, np.ndarray):
return {'shape': obj.shape, 'data': obj.tolist()}
#Handling annoying numpy types
if isinstance(obj, np.float32):
return "_f32_{:.16f}".format(obj)
if isinstance(obj, np.int64):
return "_i64_{}".format(obj)
return json.JSONEncoder.default(self, obj)
def customhook(dct):
for key in dct.keys():
#Handle datetime
try:
datetime.datetime.strptime(dct[key][0], dtformat)
#If we're still here, it worked
#So go ahead and replace this whole list with the proper datetime objects
newlist = [datetime.datetime.strptime(dtStr, dtformat) for dtStr in dct[key]]
dct[key] = newlist
except:
pass
#Handle numpy arrays
try:
if 'shape' in dct[key].keys():
shape = tuple(dct[key]['shape'])
newdata = np.array(dct[key]['data']).reshape(shape)
dct[key] = newdata
except:
pass
#Handle numpy base variable types
try:
if type(dct[key]) == str and '_f32_' in dct[key]:
dct[key] = np.float32(dct[key].split('_f32_')[1])
if type(dct[key]) == str and '_i64_' in dct[key]:
dct[key] = np.int64(dct[key].split('_i64_')[1])
except:
pass
return dct
#======
#Main functions
#======
def dump(fname, data, action='w'):
"""Store data [dict] in file fname
action: (over)'w'rite or 'a'ppend
"""
with open(fname, action) as jfile:
json.dump(data, jfile, indent=4, cls=CustomEncoder)
def load(fname):
#Pull data[dict] from file
if not os.path.exists(fname):
print("File " + fname + " doesn't exist, can't load json")
return
with open(fname, 'r') as jfile:
data = json.load(jfile, object_hook=customhook)
return data
def dumps(data,noIndent=False):
#Returns a string with the given dictionary in json format
if noIndent:
return json.dumps(data,cls=CustomEncoder)
else:
return json.dumps(data,indent=4,cls=CustomEncoder)
def loads(dataString):
#Returns a dictionary from the provided string if its contents are proper json format
return json.loads(dataString, object_hook=customhook)

207
kaipy/kaixdmf.py
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@@ -1,207 +0,0 @@
#Various routines to help scripts that create XMF files from H5 data
import numpy as np
import h5py
import kaipy.kaiH5 as kh5
import xml.etree.ElementTree as et
import kaipy.kdefs as kdefs
#Add grid info to step
#Geom is topology subelement, iDims is grid size string
def AddGrid(fname,Geom,iDims,coordStrs):
for coordStr in coordStrs:
xC = et.SubElement(Geom,"DataItem")
xC.set("Dimensions",iDims)
xC.set("NumberType","Float")
xC.set("Precision","4")
xC.set("Format","HDF")
text = fname+":/"+coordStr
xC.text = text
#Add data to slice
def AddData(Grid,fname,vID,vLoc,xDims,s0=None):
if (vLoc != 'Other'):
#Add attribute
vAtt = et.SubElement(Grid,"Attribute")
vAtt.set("Name",vID)
vAtt.set("AttributeType","Scalar")
vAtt.set("Center",vLoc)
#Add data item
aDI = et.SubElement(vAtt,"DataItem")
aDI.set("Dimensions",xDims)
aDI.set("NumberType","Float")
aDI.set("Precision","4")
aDI.set("Format","HDF")
if (s0 is None):
aDI.text ="%s:/%s"%(fname,vID)
else:
aDI.text = "%s:/Step#%d/%s"%(fname,s0,vID)
#Add data item to passed element
def AddDI(elt,h5F,nStp,cDims,vId):
aDI = et.SubElement(elt,"DataItem")
aDI.set("Dimensions",cDims)
aDI.set("NumberType","Float")
aDI.set("Precision","4")
aDI.set("Format","HDF")
if (nStp>=0):
aDI.text = "%s:/Step#%d/%s"%(h5F,nStp,vId)
else:
aDI.text ="%s:/%s"%(h5F,vId)
#Get root variables
def getRootVars(fname,gDims):
#Dims = kh5.getDims(fname,doFlip=False) #Do kji ordering
with h5py.File(fname,'r') as hf:
vIds = []
vLocs = []
for k in hf.keys():
#Don't include stuff that starts with step or X,Y,Z
vID = str(k)
doV = True
if ("Step" in vID or kdefs.grpTimeCache in vID ):
doV = False
if ((vID == "X") or (vID=="Y") or (vID=="Z")):
doV = False
if (doV):
Nv = hf[k].shape
vLoc = getLoc(gDims,Nv)
if (vLoc != "Other"):
vIds.append(vID)
vLocs.append(vLoc)
else:
print("Excluding %s"%(vID))
return vIds,vLocs
#Get variables in initial Step
def getVars(fname,gId,gDims,recursive=False):
def getVarName(gId, k, recursive):
if recursive:
# Want to keep the group name, since all vars need to be at the same level in xdmf
spl = gId.split('/')
return '/'.join(spl[1:]) + '/' + str(k)
else:
return str(k)
with h5py.File(fname,'r') as hf:
stp0 = hf[gId]
vIds = []
vLocs = []
for k in stp0.keys():
vID = getVarName(gId,k,recursive)
# If its a group, recursively call this function to get to the vars and add them to the list
if type(stp0[k]) == h5py._hl.group.Group:
vIdR, vLR = getVars(fname, gId+'/'+k, gDims, recursive=True)
for vi, vl in zip(vIdR, vLR):
vIds.append(vi)
vLocs.append(vl)
else:
Nv = stp0[k].shape
vLoc = getLoc(gDims,Nv)
if (vLoc != "Other"):
vIds.append(vID)
vLocs.append(vLoc)
else:
print("Excluding %s"%(vID))
return vIds,vLocs
def AddVectors(Grid,fname,vIds,cDims,vDims,Nd,nStp):
#Velocity (2D)
if ( (Nd == 2) and ("Vx" in vIds) and ("Vy" in vIds) ):
vAtt = et.SubElement(Grid,"Attribute")
vAtt.set("Name","VecV")
vAtt.set("AttributeType","Vector")
vAtt.set("Center","Cell")
fDI = et.SubElement(vAtt,"DataItem")
fDI.set("ItemType","Function")
fDI.set("Dimensions",vDims)
fDI.set("Function","JOIN($0 , $1)")
AddDI(fDI,fname,nStp,cDims,"Vx")
AddDI(fDI,fname,nStp,cDims,"Vy")
#Velocity (3D)
if ( (Nd == 3) and ("Vx" in vIds) and ("Vy" in vIds) and ("Vz" in vIds)):
vAtt = et.SubElement(Grid,"Attribute")
vAtt.set("Name","VecV")
vAtt.set("AttributeType","Vector")
vAtt.set("Center","Cell")
fDI = et.SubElement(vAtt,"DataItem")
fDI.set("ItemType","Function")
fDI.set("Dimensions",vDims)
fDI.set("Function","JOIN($0 , $1 , $2)")
AddDI(fDI,fname,nStp,cDims,"Vx")
AddDI(fDI,fname,nStp,cDims,"Vy")
AddDI(fDI,fname,nStp,cDims,"Vz")
#Magnetic field (2D)
if ( (Nd == 2) and ("Bx" in vIds) and ("By" in vIds) ):
vAtt = et.SubElement(Grid,"Attribute")
vAtt.set("Name","VecB")
vAtt.set("AttributeType","Vector")
vAtt.set("Center","Cell")
fDI = et.SubElement(vAtt,"DataItem")
fDI.set("ItemType","Function")
fDI.set("Dimensions",vDims)
fDI.set("Function","JOIN($0 , $1)")
AddDI(fDI,fname,nStp,cDims,"Bx")
AddDI(fDI,fname,nStp,cDims,"By")
if ( (Nd == 3) and ("Bx" in vIds) and ("By" in vIds) and ("Bz" in vIds)):
vAtt = et.SubElement(Grid,"Attribute")
vAtt.set("Name","VecB")
vAtt.set("AttributeType","Vector")
vAtt.set("Center","Cell")
fDI = et.SubElement(vAtt,"DataItem")
fDI.set("ItemType","Function")
fDI.set("Dimensions",vDims)
fDI.set("Function","JOIN($0 , $1 , $2)")
AddDI(fDI,fname,nStp,cDims,"Bx")
AddDI(fDI,fname,nStp,cDims,"By")
AddDI(fDI,fname,nStp,cDims,"Bz")
#Decide on centering
def getLoc(gDims,vDims):
vDims = np.array(vDims,dtype=int)
dimLocs = []
if len(gDims) != len(vDims):
return "Other"
for d in range(len(gDims)):
Ngd = gDims[d]-1
Nvd = vDims[d]
if Ngd == Nvd:
dimLocs.append("Cell")
elif Ngd == Nvd-1:
dimLocs.append("Node")
else:
dimLocs.append("Other")
if "Other" in dimLocs:
return "Other"
#If all the same, we have consensus
if all(x == dimLocs[0] for x in dimLocs):
return dimLocs[0]
else:
return "Other"
def addHyperslab(Grid,vName,dSetDimStr,vdimStr,startStr,strideStr,numStr,origDSetDimStr,fileText):
vAtt = et.SubElement(Grid, "Attribute")
vAtt.set("Name",vName)
vAtt.set("AttributeType","Scalar")
vAtt.set("Center","Node")
slabDI = et.SubElement(vAtt, "DataItem")
slabDI.set("ItemType","HyperSlab")
slabDI.set("Dimensions",dSetDimStr)
slabDI.set("Type","HyperSlab") # Not sure if redundant, but it works
cutDI = et.SubElement(slabDI,"DataItem")
cutDI.set("Dimensions",vdimStr)
cutDI.set("Format","XML")
cutDI.text = "\n{}\n{}\n{}\n".format(startStr,strideStr,numStr)
datDI = et.SubElement(slabDI,"DataItem")
datDI.set("Dimensions",origDSetDimStr)
datDI.set("DataType","Float")
datDI.set("Precision","4")
datDI.set("Format","HDF")
datDI.text = fileText

75
kaipy/kdefs.py
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@@ -1,75 +0,0 @@
#Main constants
import numpy as np
import alive_progress.animations.bars
#------
#Helpful conversions
#------
G2nT = 1E5 # Gauss->nanoTesla
kev2J = 1.602176634E-16 # keV -> J
ev2J = 1.602176634E-19 # eV -> J
erg2J = 1e-7 # erg -> J
#------
#Physical Constants
#------
Mu0 = 4E-7*np.pi # [Tm/A]
Me_cgs = 9.1093837015E-28 # [g] Electron mass
Mp_cgs = 1.67262192369E-24 # [g] Proton mass
eCharge = 1.602E-19 # [J] Charge of electron
dalton = 1.66053906660*1.0E-27 # [kg] Mass unit
#------
#Planetary Constants
#------
Re_cgs = 6.3781E8 # [cm] Earth's radius
EarthM0g = 0.2961737 # Gauss, Olsen++ 2000
REarth = Re_cgs*1.0e-2 # m
RionE = 6.5 # Earth Ionosphere radius in 1000 km
EarthPsi0 = 92.4 # Corotation potential [kV]
#Saturn
SaturnM0g = 0.21 # Gauss
RSaturnXE = 9.5 # Rx = X*Re
#Jupiter
JupiterM0g = 4.8 # Gauss
RJupiterXE = 11.0 # !Rx = X*Re
#Mercury
MercuryM0g = 0.00345 # Gauss
RMercuryXE = 0.31397 # Rx = X*Re
#Neptune
NeptuneM0g = 0.142 # Gauss
RNeptuneXE = 3.860 # Rx = X*Re
#------
#Helio
#------
Rsolar = 6.957e10 # [cm] Solar radius
kbltz = 1.38e-16 # [erg/K] Boltzmann constant
mp = 1.67e-24 # [g] Proton mass
Tsolar = 25.38 # [days] Siderial solar rotation period
Tsolar_synodic = 27.28 # [days] Synodic solar rotation period
JD2MJD = 2400000.5 # Conversion from JD to MJD: MJD = JD - 2400000.5
Day2s = 86400. # [s] Conversion days => seconds
vc_cgs = 2.99792458e10 # [cm/s] speed of light
#------
#Output defaults
#------
barLen = 30
barLab = 30
#barDef = 'fish'
barDef = alive_progress.animations.bars.bar_factory(tip="><('>", chars='∙',background='')
#------
# I/O
#------
grpTimeCache = "timeAttributeCache"

113
kaipy/lfm2kaiju.py
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@@ -1,113 +0,0 @@
#Various routines to deal with LFM-style data
import numpy as np
import kaipy.gamera.gamGrids as gg
clight = 2.9979e+10 #Speed of light [cm/s]
Mp = 1.6726219e-24 #g
gamma = (5.0/3)
def lfm2gg(fIn,fOut,doEarth=True,doJupiter=False):
from pyhdf.SD import SD, SDC
import h5py
#Choose scaling
if (doEarth):
xScl = gg.Re
print("Using Earth scaling ...")
elif (doJupiter):
xScl = gg.Rj
print("Using Jovian scaling ...")
iScl = 1/xScl
hdffile = SD(fIn)
#Grab x/y/z arrays from HDF file. Scale by Re/Rj/Rs
#LFM is k,j,i ordering
x3 = iScl*np.double(hdffile.select('X_grid').get())
y3 = iScl*np.double(hdffile.select('Y_grid').get())
z3 = iScl*np.double(hdffile.select('Z_grid').get())
lfmNc = x3.shape #Number of corners (k,j,i)
nk = x3.shape[0]-1
nj = x3.shape[1]-1
ni = x3.shape[2]-1
print("Reading LFM grid from %s, size (%d,%d,%d)"%(fIn,ni,nj,nk))
with h5py.File(fOut,'w') as hf:
hf.create_dataset("X",data=x3)
hf.create_dataset("Y",data=y3)
hf.create_dataset("Z",data=z3)
#Get LFM times
def lfmTimes(hdfs):
from pyhdf.SD import SD, SDC
Ts = [ SD(fIn).attributes().get('time') for fIn in hdfs]
return np.array(Ts)
#Get LFM fields
def lfmFields(fIn):
from pyhdf.SD import SD, SDC
hdffile = SD(fIn)
#Get cell-centered fields
Bx3cc,By3cc,Bz3cc = getHDFVec(hdffile,'b')
Vx3cc,Vy3cc,Vz3cc = getHDFVec(hdffile,'v')
# #Get E field, E= - (VxB)/c
# #Do this *BEFORE* subtracting off Earth dipole
# scl = -1.0/clight
# Ex3cc = scl*( Vy3cc*Bz3cc - Vz3cc*By3cc)
# Ey3cc = scl*( Vz3cc*Bx3cc - Vx3cc*Bz3cc)
# Ez3cc = scl*( Vx3cc*By3cc - Vy3cc*Bx3cc)
return Vx3cc,Vy3cc,Vz3cc,Bx3cc,By3cc,Bz3cc
#Get LFM MHD variables
#Convert (D/Cs) -> (n,P)
#Returns units (#/cm3) and (nPa)
def lfmFlow(fIn):
from pyhdf.SD import SD, SDC
hdffile = SD(fIn)
#Get soundspeed [km/s]
C3 = getHDFScl(hdffile,"c",Scl=1.0e-5)
#Get rho [g/cm3]
D3 = getHDFScl(hdffile,"rho")
#Conversion to MKS for P in Pascals
D_mks = (D3*1.0e-3)*( (1.0e+2)**3.0 ) #kg/m3
C_mks = C3*1.0e+3 #m/s
P3 = 1.0e+9*D_mks*C_mks*C_mks/gamma #nPa
n3 = D3/Mp #Number density, #/cm3
return n3,P3
#Get data from HDF-4 file
def getHDFVec(hdffile,qi,Scl=1.0):
qxi = qi+'x_'
qyi = qi+'y_'
qzi = qi+'z_'
Qx3 = hdffile.select(qxi).get()
Qy3 = hdffile.select(qyi).get()
Qz3 = hdffile.select(qzi).get()
#These are too big, corner-sized but corners are poison
#Chop out corners
Qx3cc = Scl*Qx3[:-1,:-1,:-1]
Qy3cc = Scl*Qy3[:-1,:-1,:-1]
Qz3cc = Scl*Qz3[:-1,:-1,:-1]
return Qx3cc,Qy3cc,Qz3cc
def getHDFScl(hdffile,q,Scl=1.0):
qi = q+"_"
Q3 = np.double(hdffile.select(qi).get())
#These are too big, corner-sized but corners are poison
#Chop out corners
Q3cc = Scl*Q3[:-1,:-1,:-1]
return Q3cc

53
kaipy/meta.yaml
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@@ -1,53 +0,0 @@
{% set name = "kaipy" %}
{% set version = "0.0.2" %}
package:
name: "{{ name|lower }}"
version: "{{ version }}"
source:
url: "https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz"
sha256: 86e5f8ab19792f31be6d31d95b8afad7fafc4d76395aa1a4b4f2d2f048fe9527
build:
number: 0
script: "{{ PYTHON }} -m pip install . -vv"
requirements:
host:
- pip
- python
run:
- python
test:
imports:
- kaipy
- kaipy.chimp
- kaipy.gamera
- kaipy.gamhelio
- kaipy.gamhelio.lib
- kaipy.gamhelio.wsa2TDgamera
- kaipy.gamhelio.wsa2gamera
- kaipy.paraview
- kaipy.rcm
- kaipy.rcm.lambdautils
- kaipy.rcm.lambdautils_old
- kaipy.rcm.wmutils
- kaipy.remix
- kaipy.solarWind
requires:
- nose
about:
home: "https://bitbucket.org/aplkaiju/kaiju/src/master/"
license: MIT
license_family: MIT
license_file:
summary: "Postprocessing code for models in the kaiju software system"
doc_url:
dev_url:
extra:
recipe-maintainers:
- your-github-id-here

5
kaipy/notes.txt
View File

@@ -1,5 +0,0 @@
Various installation/information notes can go here.
Generate requirements.txt file w/ this
pip install pipreqs
pipreqs /path/to/project

0
kaipy/paraview/__init__.py
View File

38
kaipy/paraview/pvGam.py
View File

@@ -1,38 +0,0 @@
import paraview.simple as pvs
def pf_calcSpeeds(base):
scriptStr = """
import kaipy.kdefs as kdefs
in0 = inputs[0]
bmag = sqrt(in0.CellData['Bx']**2+in0.CellData['By']**2+in0.CellData['Bz']**2)
vmag = sqrt(in0.CellData['Vx']**2+in0.CellData['Vy']**2+in0.CellData['Vz']**2)
temp_J = in0.CellData['P']*1E-9/(in0.CellData['D']*1E6)
temp_kT = temp_J/(kdefs.kbltz*kdefs.erg2J)
temp_eV = temp_J/kdefs.ev2J
cs = sqrt(temp_J/(kdefs.Mp_cgs*1e-3))
va = bmag*1E-9 / sqrt(kdefs.Mu0 * in0.CellData['D']*1E6 * kdefs.Mp_cgs*1e-3)
msmach = vmag*1E3 / sqrt(cs**2 + va**2)
output.CellData.append(bmag,'Bmag [nT]')
output.CellData.append(vmag,'Vmag [km/s]')
output.CellData.append(temp_kT,'Temp [kT]')
output.CellData.append(temp_eV,'Temp [eV]')
output.CellData.append(cs,'Cs')
output.CellData.append(va,'Va')
output.CellData.append(msmach,'Magnetosonic Mach')
"""
pf = pvs.ProgrammableFilter(Input=base)
pf.Script = scriptStr
pf.RequestInformationScript = ''
pf.RequestUpdateExtentScript = ''
pf.PythonPath = ''
pf.CopyArrays=1
pvs.RenameSource('pf_calcSpeeds', pf)
# Show
renderView1 = pvs.GetActiveViewOrCreate('RenderView')
programmableFilter2Display = pvs.Show(pf, renderView1, 'GeometryRepresentation')

84
kaipy/paraview/pvutils.py
View File

@@ -1,84 +0,0 @@
import paraview.simple as pvs
"""
Notes:
It looks like everything in the pipeline is ran for each individual timestep.
Meaning anything that uses the time value (like addTime below) is getting a single value at a time,
not the entire array of times
"""
defaultUT = '2000-01-01T00:00:00'
def addTime(base, fmt="h", ut0Str=defaultUT):
"""Adds a label showing the simulation time
fmt options: s,m,h,UT
ut0Str: If fmt=UT, associates this argument with simulation t=0 and uses it to calculate subsequent UT values
TODO: Use the run's MJD0 to get ut0Str. Need to manually read the h5 file, meaning more arguments to provide.
"""
# "Therefore unlike other programming languages, Python gives meaning to the indentation rule, which is very simple and also helps to make the code readable."
# https://www.educba.com/indentation-in-python/
scriptStr = r"""
timeVal = vtk.vtkStringArray()
timeVal.SetName('{:s}')
t = self.GetInput().GetInformation().Get(vtk.vtkDataObject.DATA_TIME_STEP())
{:s}
timeVal.InsertNextValue(timeAsString)
self.GetOutput().GetFieldData().AddArray(timeVal)
"""
varName = "time_"+fmt
#Here we choose how to take the variable <t> and turn it into our desired <timeAsString> to be displayed
if fmt=="UT":
if ut0Str==defaultUT:
print("No ut0Str specified, defaulting to " + defaultUT)
prefStr = ''
tScr = r"""
import datetime
isotfmt = '%Y-%m-%dT%H:%M:%S'
ut0Str = '{}'
ut0 = datetime.datetime.strptime(ut0Str,isotfmt)
t = int(t) # Remove decimals
timeAsString = str(ut0+datetime.timedelta(seconds=t))
""".format(ut0Str)
elif fmt=="s":
prefStr = 'Second '
tScr = r"""timeAsString = "{:4.2f}".format(int(t)) """
elif fmt=="m":
prefStr = 'Minute '
tScr = r"""timeAsString = "{:4.2f}".format(t/60) """
elif fmt=="h":
prefStr = 'Hour '
tScr = r"""timeAsString = "{:4.2f}".format(t/3600) """
#Create the programmable filter
pf = pvs.ProgrammableFilter(Input=base)
#Now insert our specific code into the script block
scriptStr = scriptStr.format(varName, tScr)
pf.Script = scriptStr
#Set other parameters
pf.RequestInformationScript = ''
pf.RequestUpdateExtentScript = ''
pf.PythonPath = ''
pf.CopyArrays=1
pvs.RenameSource('pf_Time', pf)
renderView1 = pvs.GetActiveViewOrCreate('RenderView')
pvs.Show(pf, renderView1, 'GeometryRepresentation')
aGD = pvs.AnnotateGlobalData(Input=pf)
aGD.SelectArrays = varName
aGD.Prefix = prefStr
aGD.Format = '%s'
pvs.RenameSource('annotateGD_Time', aGD)
pvs.Show(aGD, renderView1, 'TextSourceRepresentation')
pvs.Hide(pf, renderView1) # Turn off programmable filter view, no need to view after we have the annotation

0
kaipy/rcm/__init__.py
View File

117
kaipy/rcm/dktable
View File

@@ -1,117 +0,0 @@
8.404E+02 4.373E+03 1.187E+04 2.377E+04 4.024E+04 6.127E+04 8.690E+04 1.172E+05
1.523E+05 1.921E+05 2.366E+05 2.860E+05 3.403E+05 3.992E+05 4.634E+05 5.331E+05
6.080E+05 6.884E+05 6.779E+02 3.557E+03 9.690E+03 1.941E+04 3.282E+04 4.991E+04
7.067E+04 9.509E+04 1.234E+05 1.554E+05 1.912E+05 2.309E+05 2.746E+05 3.222E+05
3.737E+05 4.298E+05 4.898E+05 5.545E+05 5.729E+02 3.026E+03 8.265E+03 1.656E+04
2.798E+04 4.251E+04 6.013E+04 8.086E+04 1.047E+05 1.317E+05 1.620E+05 1.955E+05
2.323E+05 2.724E+05 3.161E+05 3.632E+05 4.139E+05 4.684E+05 5.268E+02 2.790E+03
7.633E+03 1.530E+04 2.584E+04 3.925E+04 5.550E+04 7.457E+04 9.651E+04 1.213E+05
1.492E+05 1.799E+05 2.138E+05 2.506E+05 2.908E+05 3.340E+05 3.807E+05 4.308E+05
4.487E+02 2.390E+03 6.556E+03 1.315E+04 2.221E+04 3.370E+04 4.760E+04 6.391E+04
8.264E+04 1.038E+05 1.275E+05 1.537E+05 1.824E+05 2.138E+05 2.480E+05 2.849E+05
3.245E+05 3.673E+05 3.877E+02 2.075E+03 5.707E+03 1.146E+04 1.934E+04 2.934E+04
4.142E+04 5.556E+04 7.177E+04 9.009E+04 1.106E+05 1.332E+05 1.580E+05 1.852E+05
2.146E+05 2.465E+05 2.808E+05 3.176E+05 3.716E+02 1.992E+03 5.482E+03 1.101E+04
1.858E+04 2.818E+04 3.978E+04 5.335E+04 6.891E+04 8.646E+04 1.061E+05 1.278E+05
1.516E+05 1.776E+05 2.058E+05 2.363E+05 2.692E+05 3.046E+05 4.285E+02 2.286E+03
6.276E+03 1.259E+04 2.126E+04 3.226E+04 4.556E+04 6.115E+04 7.904E+04 9.926E+04
1.219E+05 1.469E+05 1.744E+05 2.043E+05 2.369E+05 2.721E+05 3.100E+05 3.507E+05
6.722E+02 3.528E+03 9.611E+03 1.925E+04 3.255E+04 4.949E+04 7.007E+04 9.432E+04
1.223E+05 1.540E+05 1.896E+05 2.289E+05 2.722E+05 3.193E+05 3.705E+05 4.258E+05
4.854E+05 5.496E+05 1.774E+03 8.938E+03 2.404E+04 4.823E+04 8.203E+04 1.259E+05
1.801E+05 2.446E+05 3.195E+05 4.044E+05 4.995E+05 6.048E+05 7.208E+05 8.478E+05
9.864E+05 1.137E+06 1.301E+06 1.477E+06 6.965E+03 3.303E+04 8.826E+04 1.798E+05
3.120E+05 4.866E+05 7.046E+05 9.674E+05 1.277E+06 1.636E+06 2.046E+06 2.510E+06
3.027E+06 3.600E+06 4.230E+06 4.917E+06 5.663E+06 6.471E+06 2.793E+04 1.246E+05
3.327E+05 6.880E+05 1.221E+06 1.960E+06 2.929E+06 4.146E+06 5.627E+06 7.381E+06
9.416E+06 1.174E+07 1.435E+07 1.725E+07 2.045E+07 2.396E+07 2.778E+07 3.191E+07
8.122E+04 3.350E+05 8.800E+05 1.837E+06 3.327E+06 5.467E+06 8.364E+06 1.211E+07
1.678E+07 2.245E+07 2.917E+07 3.699E+07 4.594E+07 5.605E+07 6.738E+07 7.996E+07
9.381E+07 1.090E+08 1.996E+03 5.796E+03 1.157E+04 1.916E+04 2.850E+04 3.948E+04
5.209E+04 6.628E+04 8.216E+04 9.958E+04 1.187E+05 1.394E+05 1.620E+05 1.862E+05
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1.303E+05 1.497E+05 1.706E+05 1.931E+05 2.170E+05 2.427E+05 1.368E+03 3.990E+03
7.965E+03 1.318E+04 1.956E+04 2.704E+04 3.562E+04 4.527E+04 5.598E+04 6.779E+04
8.071E+04 9.477E+04 1.100E+05 1.263E+05 1.440E+05 1.629E+05 1.830E+05 2.046E+05
1.260E+03 3.676E+03 7.341E+03 1.215E+04 1.801E+04 2.491E+04 3.279E+04 4.166E+04
5.152E+04 6.236E+04 7.424E+04 8.711E+04 1.011E+05 1.161E+05 1.323E+05 1.496E+05
1.682E+05 1.880E+05 1.075E+03 3.145E+03 6.282E+03 1.039E+04 1.541E+04 2.129E+04
2.801E+04 3.557E+04 4.396E+04 5.319E+04 6.329E+04 7.425E+04 8.612E+04 9.890E+04
1.126E+05 1.274E+05 1.431E+05 1.600E+05 9.309E+02 2.727E+03 5.451E+03 9.017E+03
1.336E+04 1.846E+04 2.428E+04 3.081E+04 3.807E+04 4.605E+04 5.477E+04 6.424E+04
7.448E+04 8.552E+04 9.738E+04 1.101E+05 1.237E+05 1.382E+05 8.927E+02 2.617E+03
5.231E+03 8.653E+03 1.282E+04 1.771E+04 2.329E+04 2.956E+04 3.652E+04 4.416E+04
5.252E+04 6.159E+04 7.141E+04 8.198E+04 9.336E+04 1.055E+05 1.186E+05 1.325E+05
1.028E+03 3.007E+03 6.007E+03 9.935E+03 1.473E+04 2.035E+04 2.677E+04 3.399E+04
4.201E+04 5.082E+04 6.046E+04 7.092E+04 8.226E+04 9.446E+04 1.076E+05 1.216E+05
1.367E+05 1.527E+05 1.602E+03 4.661E+03 9.303E+03 1.540E+04 2.287E+04 3.164E+04
4.170E+04 5.303E+04 6.563E+04 7.951E+04 9.469E+04 1.112E+05 1.291E+05 1.484E+05
1.691E+05 1.913E+05 2.151E+05 2.405E+05 4.176E+03 1.205E+04 2.413E+04 4.021E+04
6.010E+04 8.369E+04 1.109E+05 1.417E+05 1.760E+05 2.139E+05 2.555E+05 3.008E+05
3.499E+05 4.030E+05 4.602E+05 5.215E+05 5.873E+05 6.575E+05 1.638E+04 4.762E+04
9.728E+04 1.653E+05 2.512E+05 3.544E+05 4.747E+05 6.119E+05 7.659E+05 9.368E+05
1.125E+06 1.330E+06 1.553E+06 1.795E+06 2.055E+06 2.334E+06 2.634E+06 2.954E+06
6.216E+04 1.908E+05 4.100E+05 7.266E+05 1.142E+06 1.656E+06 2.267E+06 2.973E+06
3.774E+06 4.670E+06 5.661E+06 6.747E+06 7.931E+06 9.215E+06 1.060E+07 1.209E+07
1.369E+07 1.541E+07 1.311E+05 4.416E+05 1.030E+06 1.965E+06 3.295E+06 5.051E+06
7.251E+06 9.906E+06 1.302E+07 1.660E+07 2.064E+07 2.516E+07 3.015E+07 3.562E+07
4.158E+07 4.805E+07 5.503E+07 6.254E+07 6.720E+03 3.196E+04 8.530E+04 1.739E+05
3.016E+05 4.710E+05 6.810E+05 9.330E+05 1.232E+06 1.580E+06 1.974E+06 2.418E+06
2.915E+06 3.469E+06 4.077E+06 4.745E+06 5.462E+06 6.244E+06 5.523E+03 2.650E+04
7.075E+04 1.436E+05 2.486E+05 3.872E+05 5.585E+05 7.659E+05 1.007E+06 1.288E+06
1.602E+06 1.962E+06 2.357E+06 2.807E+06 3.289E+06 3.815E+06 4.389E+06 5.007E+06
4.539E+03 2.194E+04 5.871E+04 1.188E+05 2.050E+05 3.185E+05 4.600E+05 6.285E+05
8.251E+05 1.051E+06 1.307E+06 1.593E+06 1.912E+06 2.266E+06 2.656E+06 3.082E+06
3.541E+06 4.038E+06 3.735E+03 1.821E+04 4.868E+04 9.827E+04 1.691E+05 2.624E+05
3.781E+05 5.161E+05 6.757E+05 8.592E+05 1.065E+06 1.296E+06 1.555E+06 1.838E+06
2.148E+06 2.491E+06 2.862E+06 3.260E+06 3.069E+03 1.509E+04 4.044E+04 8.141E+04
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1.266E+06 1.495E+06 1.746E+06 2.020E+06 2.318E+06 2.638E+06 2.524E+03 1.252E+04
3.358E+04 6.749E+04 1.154E+05 1.779E+05 2.556E+05 3.481E+05 4.555E+05 5.772E+05
7.140E+05 8.663E+05 1.034E+06 1.219E+06 1.422E+06 1.643E+06 1.883E+06 2.142E+06
2.079E+03 1.040E+04 2.792E+04 5.608E+04 9.556E+04 1.470E+05 2.106E+05 2.866E+05
3.746E+05 4.744E+05 5.861E+05 7.102E+05 8.469E+05 9.972E+05 1.162E+06 1.340E+06
1.535E+06 1.743E+06 1.713E+03 8.642E+03 2.326E+04 4.664E+04 7.931E+04 1.217E+05
1.740E+05 2.362E+05 3.085E+05 3.904E+05 4.822E+05 5.840E+05 6.955E+05 8.181E+05
9.519E+05 1.097E+06 1.255E+06 1.425E+06 1.414E+03 7.195E+03 1.939E+04 3.888E+04
6.599E+04 1.010E+05 1.440E+05 1.953E+05 2.546E+05 3.221E+05 3.976E+05 4.811E+05
5.729E+05 6.733E+05 7.824E+05 9.011E+05 1.030E+06 1.168E+06 1.169E+03 5.998E+03
1.620E+04 3.247E+04 5.505E+04 8.405E+04 1.196E+05 1.619E+05 2.108E+05 2.664E+05
3.287E+05 3.975E+05 4.731E+05 5.558E+05 6.455E+05 7.429E+05 8.479E+05 9.614E+05
9.691E+02 5.011E+03 1.358E+04 2.719E+04 4.605E+04 7.019E+04 9.969E+04 1.346E+05
1.751E+05 2.210E+05 2.725E+05 3.294E+05 3.920E+05 4.603E+05 5.344E+05 6.145E+05
7.013E+05 7.944E+05 8.056E+02 4.198E+03 1.140E+04 2.284E+04 3.864E+04 5.882E+04
8.340E+04 1.124E+05 1.460E+05 1.841E+05 2.268E+05 2.740E+05 3.259E+05 3.825E+05
4.440E+05 5.104E+05 5.821E+05 6.592E+05 6.721E+02 3.527E+03 9.609E+03 1.925E+04
3.254E+04 4.948E+04 7.006E+04 9.430E+04 1.223E+05 1.540E+05 1.895E+05 2.289E+05
2.721E+05 3.192E+05 3.705E+05 4.258E+05 4.854E+05 5.495E+05 1.579E+04 4.598E+04
9.373E+04 1.593E+05 2.420E+05 3.418E+05 4.573E+05 5.883E+05 7.366E+05 9.026E+05
1.082E+06 1.279E+06 1.494E+06 1.727E+06 1.979E+06 2.249E+06 2.537E+06 2.847E+06
1.297E+04 3.764E+04 7.648E+04 1.294E+05 1.960E+05 2.760E+05 3.683E+05 4.747E+05
5.928E+05 7.257E+05 8.686E+05 1.028E+06 1.198E+06 1.388E+06 1.587E+06 1.800E+06
2.030E+06 2.275E+06 1.065E+04 3.081E+04 6.248E+04 1.054E+05 1.591E+05 2.234E+05
2.984E+05 3.834E+05 4.787E+05 5.848E+05 7.006E+05 8.271E+05 9.640E+05 1.113E+06
1.274E+06 1.447E+06 1.631E+06 1.828E+06 8.758E+03 2.530E+04 5.106E+04 8.592E+04
1.295E+05 1.815E+05 2.418E+05 3.103E+05 3.867E+05 4.719E+05 5.646E+05 6.665E+05
7.776E+05 8.967E+05 1.025E+06 1.164E+06 1.313E+06 1.471E+06 7.199E+03 2.078E+04
4.186E+04 7.021E+04 1.055E+05 1.477E+05 1.963E+05 2.517E+05 3.133E+05 3.818E+05
4.571E+05 5.389E+05 6.278E+05 7.240E+05 8.279E+05 9.394E+05 1.059E+06 1.186E+06
5.926E+03 1.710E+04 3.435E+04 5.746E+04 8.620E+04 1.203E+05 1.598E+05 2.046E+05
2.548E+05 3.101E+05 3.709E+05 4.372E+05 5.090E+05 5.864E+05 6.703E+05 7.602E+05
8.567E+05 9.597E+05 4.886E+03 1.410E+04 2.826E+04 4.718E+04 7.062E+04 9.845E+04
1.306E+05 1.670E+05 2.077E+05 2.525E+05 3.017E+05 3.554E+05 4.136E+05 4.765E+05
5.443E+05 6.171E+05 6.953E+05 7.783E+05 4.034E+03 1.164E+04 2.331E+04 3.882E+04
5.801E+04 8.075E+04 1.070E+05 1.366E+05 1.697E+05 2.062E+05 2.463E+05 2.901E+05
3.372E+05 3.885E+05 4.436E+05 5.026E+05 5.661E+05 6.337E+05 3.336E+03 9.639E+03
1.927E+04 3.204E+04 4.780E+04 6.645E+04 8.791E+04 1.122E+05 1.392E+05 1.691E+05
2.018E+05 2.375E+05 2.761E+05 3.178E+05 3.626E+05 4.109E+05 4.626E+05 5.178E+05
2.765E+03 8.000E+03 1.598E+04 2.653E+04 3.953E+04 5.487E+04 7.252E+04 9.244E+04
1.146E+05 1.392E+05 1.660E+05 1.952E+05 2.268E+05 2.610E+05 2.977E+05 3.372E+05
3.794E+05 4.246E+05 2.298E+03 6.659E+03 1.329E+04 2.205E+04 3.280E+04 4.547E+04
6.004E+04 7.647E+04 9.477E+04 1.150E+05 1.370E+05 1.611E+05 1.871E+05 2.152E+05
2.455E+05 2.779E+05 3.126E+05 3.497E+05 1.915E+03 5.561E+03 1.110E+04 1.839E+04
2.733E+04 3.785E+04 4.992E+04 6.353E+04 7.867E+04 9.537E+04 1.136E+05 1.335E+05
1.550E+05 1.782E+05 2.032E+05 2.300E+05 2.587E+05 2.893E+05 1.602E+03 4.660E+03
9.301E+03 1.540E+04 2.286E+04 3.164E+04 4.170E+04 5.302E+04 6.561E+04 7.949E+04
9.468E+04 1.112E+05 1.290E+05 1.483E+05 1.691E+05 1.913E+05 2.151E+05 2.405E+05

91
kaipy/rcm/enchan.dat
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@@ -1,91 +0,0 @@
90
1 0.0
1 -1.0
1 -2.0
1 -3.0
1 -5.0
1 -7.5
1 -10.0
1 -12.5
1 -15.
1 -20.
1 -35.
1 -50.
1 -70.
1 -100.
1 -125.
1 -200.
1 -350.
1 -600.
1 -800.
1 -1000.
1 -1500.
1 -2500.
1 -3000.
1 -3500.
1 -5000.
1 -6000.
1 -7500.
1 -8700.
2 5.
2 10.
2 15.
2 20.
2 25.
2 30.
2 40.
2 50.
2 60.
2 70.
2 80.
2 90.
2 100.
2 110.
2 120.
2 130.
2 140.
2 150.
2 160.
2 170.
2 180.
2 190.
2 200.
2 210.
2 220.
2 235.
2 250.
2 275.
2 300.
2 350.
2 400.
2 450.
2 500.
2 600.
2 700.
2 800.
2 1000.
2 1250.
2 1500.
2 1750.
2 2000.
2 2500.
2 3000.
2 3500.
2 4000.
2 4500.
2 5000.
2 5500.
2 6000.
2 6500.
2 7000.
2 8000.
2 9000.
2 10000.
2 11000.
2 12000.
2 18000.
2 24000.
2 30000.
2 40000.
2 50000.
2 60000.

45
kaipy/rcm/lambdautils/AlamData.py
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@@ -1,45 +0,0 @@
from dataclasses import dataclass
#from dataclasses_json import dataclass_json
from dataclasses import asdict as dc_asdict
from typing import Optional, List
import kaipy.rcm.lambdautils.AlamParams as aP
# dataclasses_json isn't a default package. Since its only used for reading, don't want to make it a requirement for everyone
try:
from dataclasses_json import dataclass_json
dataclasses_json_module_imported = True
except ModuleNotFoundError:
dataclass_json = None
dataclasses_json_module_imported = False
def conditional_decorator(dec, dataclasses_json_module_imported):
def decorator(func):
if not dataclasses_json_module_imported:
# Return the function unchanged, not decorated.
return func
return dec(func)
return decorator
@dataclass
class Species:
n: int # Number of channels
alams: List[float] # Lambda channel values
amins: List[float] # Lower lambda bounds for species
amaxs: List[float] # Upper lambda bound for species
flav: int # "Flavor", used to distinguish species types in RCM
# 1 = electrons, 2 = protons
fudge: Optional[float] = 0 # "Fudge factor" loss ratio
params: Optional[aP.SpecParams] = None # Parameters used to generate this instance of Species
name: Optional[str] = None
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class AlamData:
""" Main class that most things will interact with
"""
doUsePsphere: bool # Whether or not this dataset includes a zero-energy plasmasphere channel
specs: List[Species] # List of Species objects
params: Optional[aP.AlamParams] = None # Parameters used to generate this instance of AlamData

61
kaipy/rcm/lambdautils/AlamParams.py
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@@ -1,61 +0,0 @@
from dataclasses import dataclass
#from dataclasses_json import dataclass_json
from dataclasses import asdict as dc_asdict
from typing import Optional, List
#Import other things from this package space
import kaipy.rcm.lambdautils.DistTypes as dT
# dataclasses_json isn't a default package. Since its only used for reading, don't want to make it a requirement for everyone
try:
from dataclasses_json import dataclass_json
dataclasses_json_module_imported = True
except ModuleNotFoundError:
dataclass_json = None
dataclasses_json_module_imported = False
def conditional_decorator(dec, dataclasses_json_module_imported):
def decorator(func):
if not dataclasses_json_module_imported:
# Return the function unchanged, not decorated.
return func
return dec(func)
return decorator
# Defines a single species
@dataclass
class SpecParams:
""" Defines a single species
A full species parameter set is defined by both the params listed here AND the ones in whatever DistType is chosen
"""
n: int # Number of channels
amin: float # Lower lambda bound for species
amax: float # Upper lambda bound for species
distType: dT.DistType # DistType params used to generate final lambad distribution
flav: int # "Flavor", used to distinguish species types in RCM
# 1 = electrons, 2 = protons
fudge: Optional[float] = 0 # "Fudge factor" loss ratio
name: Optional[str] = None
def genAlams(self): # This will call the given DistType's 'required' function to generate alams based on its rules
specData = self.distType.genAlamsFromSpecies(self)
return specData
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class AlamParams:
doUsePsphere: bool # Whether or not the resulting dataset will include a zero-energy plasmasphere channel
specParams: List[SpecParams] # List of all specParams to be included in dataset
# These can help to determine some things for higher-level lambda generators
#tiote: Optional[float] = None # Ratio of ion temperature to electron temperature
#ktMax: Optional[float] = None # Energy in eV
emine: Optional[float] = None # Electron min energy in eV
eminp: Optional[float] = None # Proton min energy in eV
emaxe: Optional[float] = None # Electron max energy in eV
emaxp: Optional[float] = None # Proton max energy in eV
L_kt : Optional[float] = None # L value that e/p min/maxes correspond to

179
kaipy/rcm/lambdautils/DistTypes.py
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@@ -1,179 +0,0 @@
from dataclasses import dataclass
#from dataclasses_json import dataclass_json
from dataclasses import asdict as dc_asdict
from typing import Optional, List
import numpy as np
# dataclasses_json isn't a default package. Since its only used for reading, don't want to make it a requirement for everyone
try:
from dataclasses_json import dataclass_json
dataclasses_json_module_imported = True
except ModuleNotFoundError:
dataclass_json = None
dataclasses_json_module_imported = False
def conditional_decorator(dec, dataclasses_json_module_imported):
def decorator(func):
if not dataclasses_json_module_imported:
# Return the function unchanged, not decorated.
return func
return dec(func)
return decorator
def getDistTypeFromKwargs(**kwargs):
""" This takes a set of kwargs and, using a present 'name' key, decides which DistType implementation they belong to
And then returns an object of that DistType
"""
if 'name' in kwargs.keys():
if kwargs['name'] == 'Wolf':
return DT_Wolf.from_dict(kwargs)
elif kwargs['name'] == 'ValueSpec':
return DT_ValueSpec.from_dict(kwargs)
else:
return DistType.from_dict(kwargs)
#------
# Parameters needed to determine lambda distribution
#------
@dataclass
class DistType: # Empty class just so we can force the type in dataclasses below
name: str = "Empty"
#------
# Specific implementations of DistType
#------
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class DT_Manual(DistType): # Also pretty much empty, but can be used to allow user to add anything they want to save
def __post_init__(self):
if self.name == "Empty": self.name = "Manual"
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class DT_Wolf(DistType):
""" Lambda channel spacing based on Wolf's notes
(ask Anthony Sciola or Frank Toffoletto for a copy)
With the addition that there can be 2 p values for the start and end, and pStar transitions between them
"""
p1: float = None
p2: float = None
def __post_init__(self):
self.name = "Wolf"
def genAlamsFromSpecies(self,sP):
return self.genAlams(sP.n,sP.amin,sP.amax)
def genAlams(self, n, amin, amax, kmin=0, kmax=-1):
if kmax == -1: kmax = n
alams = []
for k in range(n):
kfrac = (k-kmin)/(kmax-kmin) # How far through the channel range are we
pstar = (1-kfrac)*self.p1 + kfrac*self.p2
lammax = amax-amin
lam = lammax*((k - kmin + 0.5)/(kmax-kmin + 0.5))**pstar + amin
alams.append(lam)
return alams
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class ValueSpec:
start: float
end: float
scaleType: str # See 'goodScaleTypes' below for valid strings
n: Optional[int] = None # Number of channels
c: Optional[float] = None
""" c has different meanings depending on scaleType
lin: If given, will use set spacing c
log: If given, will use log of base c
spacing_lin: If given, will use as Sum_{k=1}^{N} c*k, where N is calculated based on start, end, and c
"""
def __post_init__(self):
goodScaleTypes = ['lin', 'log', 'spacing_lin']
if self.scaleType not in goodScaleTypes:
print("Error in ValueSpec, scaleType must be in {}, not {}. Defaulting to {}".format(goodScaleTypes, self.scaleType, goodScaleTypes[0]))
self.scaleType = goodScaleTypes[0]
if self.n is None and self.c is None:
if self.scaleType == 'lin': self.c = 1
print("Error in ValueSpec, must provide either (n) or (c). See source code to see what (c) does for each scaleType. Defaulting to " + str(self.c))
def eval(self, doEnd):
# Performs the appropriate operation given self's attributes and returns a list of values
if self.scaleType == 'lin':
line = np.linspace(sL.start, sL.end, sL.n, endpoint=doEnd)
elif self.scaleType == 'log':
lbase = self.c
sign = 1 if self.start > 0 else -1
start = np.log(np.abs(self.start))/np.log(lbase)
end = np.log(np.abs(self.end))/np.log(lbase)
line = np.logspace(start, end, self.n, base=lbase, endpoint=doEnd)
elif self.scaleType == 'spacing_lin':
diff = self.end-self.start
if self.c is not None:
#Set n based on c if needed
#But also force n to be an integer
self.n = int(0.5*(np.sqrt(8*diff/self.c + 1) + 1))
#(Re)calculate c based on integer n
self.c = 2*diff/(self.n**2 + self.n)
print("Spacing_lin: n={}, c={}".format(self.n, self.c))
spacings = np.array([self.c*k for k in range(self.n)])
line = np.array([self.start + np.sum(spacings[:k]) for k in range(self.n)])
return line
#@dataclass_json
@conditional_decorator(dataclass_json, dataclasses_json_module_imported)
@dataclass
class DT_ValueSpec(DistType):
""" Lambda channel spacing based on a series of slope specifications
"""
specList: List[ValueSpec] = None
def __post_init__(self):
self.name = "ValueSpec"
#Check to see if all slopes are contiguous
if len(self.specList) > 1:
tol = 1E-4
for i in range(len(self.specList)-1):
if np.abs(self.specList[i].end - self.specList[i+1].start) > tol:
print("Error creating a DistType_SlopeSpec: SlopeSpec[{}].end ({}) != SlopeSpec[{}].start ({}). Undefined behavior"\
.format(i, self.specList[i].end, i+1, self.specList[i+1].start))
def genAlamsFromSpecies(self, sP):
#See if end points match up
tol = 1E-4
if np.abs(self.specList[0].start - sP.amin) > tol:
print("SpecList[0].start={}, SpecParams.amin={}. Overwriting SpecParams.amin to SpecList[0].start".format(self.specList[0].start, sP.amin))
sP.amin = self.specList[0].start
if np.abs(self.specList[-1].end - sP.amax) > tol:
print("SpecList[-1].end={}, SpecParams.amax={}. Overwriting SpecParams.amax to SpecList[-1].end".format(self.specList[0].start, sP.amin))
sP.amax = self.specList[-1].end
return self.genAlams(sP.n, sP.amin,sP.amax)
def genAlams(self, n, amin, amax):
nSL = len(self.specList)
alams = np.array([])
for i in range(nSL):
doEnd = False if i < nSL-1 else True
alams = np.append(alams, self.specList[i].eval(doEnd))
return alams.tolist()

0
kaipy/rcm/lambdautils/__init__.py
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70
kaipy/rcm/lambdautils/fileIO.py
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import os
import numpy as np
import json
import h5py as h5
from dataclasses import asdict as dc_asdict
import kaipy.kaijson as kj
import kaipy.rcm.lambdautils.AlamParams as aP
import kaipy.rcm.lambdautils.DistTypes as dT
try:
import kaipy.rcm.rcminit as rcminit
hasKaiRCM = True
except:
print("Couldn't load kaipy.rcm.init, can't add dktable to rcmconfig file.")
hasKaiRCM = False
def saveRCMConfig(alamData, params=None, fname = 'rcmconfig.h5'):
f5 = h5.File(fname, 'w')
saveData(f5, alamData)
if params is not None:
saveParams(f5, params)
f5.close()
#These save and load AlamParams so that we can always tell how a lambda distribution was generated
def saveParams(f5, alamParams):
f5.attrs['AlamParams'] = kj.dumps(dc_asdict(alamParams),noIndent=True)
def loadParams(f5):
aPDict = kj.loads(f5.attrs['AlamParams'])
try:
aPObj = aP.AlamParams.from_dict(aPDict)
except AttributeError:
print("ERROR (loadParams): Can't turn dictionary into object. Please install the dataclasses_json module")
print(" Returning as dictionary instead")
return aPDict
#DistTypes won't load correctly using from_dict because we're using inherited classes, need to instantiate ourselves
for sPDict, sPObj in zip(aPDict['specParams'], aPObj.specParams):
sPObj.distType = dT.getDistTypeFromKwargs(**sPDict['distType'])
return aPObj
def saveData(f5, alamData,doPrint=False):
""" Takes an AlamData object, formats it to rcmconfig.h5 style, and saves it
"""
lambdas = np.array([])
flavs = np.array([],dtype=int)
fudges = np.array([])
for spec in alamData.specs:
lambdas = np.append(lambdas, spec.alams)
flavs = np.append(flavs, [spec.flav for f in range(spec.n)])
fudges = np.append(fudges, [spec.fudge for f in range(spec.n)])
if doPrint:
print(lambdas)
print(flavs)
print(fudges)
f5.create_dataset('alamc', data=lambdas)
f5.create_dataset('ikflavc', data=flavs)
f5.create_dataset('fudgec', data=fudges)
if hasKaiRCM:
dktab = rcminit.LoadDKTab()
f5.create_dataset('dktable', data=dktab)
else:
print("Couldn't load kaipy.rcm.init, can't add dktable to rcmconfig file.")

54
kaipy/rcm/lambdautils/genAlam.py
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@@ -1,54 +0,0 @@
""" This script is responsible for taking AlamParam objects and turning it into lambdas and associated attributes for AlamData objects
"""
import numpy as np
import kaipy.rcm.lambdautils.AlamParams as aP
import kaipy.rcm.lambdautils.AlamData as aD
import kaipy.rcm.lambdautils.DistTypes as dT
#------
# Main functions
#------
def getAlamMinMax(alams):
amin = np.array([])
amax = np.array([])
for i in range(len(alams)):
if i == 0:
amax = np.append(amax, 0.5*(np.abs(alams[i])+np.abs(alams[i+1])))
amin = np.append(amin, 0)
elif i == len(alams)-1:
amax = np.append(amax, 1.5*np.abs(alams[i]) - 0.5*np.abs(alams[i-1]))
amin = np.append(amin, 0.5*(np.abs(alams[i])+np.abs(alams[i-1])))
else:
amax = np.append(amax, 0.5*(np.abs(alams[i])+np.abs(alams[i+1])))
amin = np.append(amin, 0.5*(np.abs(alams[i])+np.abs(alams[i-1])))
return amin.tolist(), amax.tolist()
def genSpeciesFromParams(specParams):
""" Takes a SpecParams object and generates a new Species object
"""
alams = specParams.genAlams()
n = len(alams) # !!!Bad workaround. Since genAlams might change n, it should set it in its specParams container itself
amins,amaxs = getAlamMinMax(alams)
flav = specParams.flav
fudge = specParams.fudge
name = specParams.name
return aD.Species(n, alams, amins, amaxs, flav, fudge, params=specParams, name=name)
def genAlamDataFromParams(alamParams):
doUsePsphere = alamParams.doUsePsphere
specList = [genSpeciesFromParams(sP) for sP in alamParams.specParams]
if doUsePsphere:
specList.insert(0,genPsphereSpecies())
return aD.AlamData(doUsePsphere, specList, params=alamParams)
#------
# Helpers
#------
def genPsphereSpecies():
params = dT.DT_Manual(name='Plasmasphere')
return aD.Species(1, [0], [0], [0], 1, 0, params=params, name='Plasmasphere')

7
kaipy/rcm/lambdautils/notes.txt
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Data classes:
DistTypes: Contains different ways of generating lambad distributions
AlamParams: Contains all information needed to produce a unique rcmconfig.h5
AlamData: Contains the final lambda distributions, their mins/maxes, and a copy of the AlamParams used to generate it
Worker scripts:
genAlam: Takes an AlamParams object and returns a complete AlamData object

139
kaipy/rcm/lambdautils/plotter.py
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import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
import kaipy.kaiTools as kT
def plotLambdas_Val_Spac(specDataList, yscale='log',L=None):
doEnergy = True if L is not None else False
if doEnergy:
bVol = kT.L_to_bVol(L)
vm = bVol**(-2/3)
if not isinstance(specDataList, list):
specDataList = [specDataList]
fig = plt.figure(figsize=(10,5))
gs = gridspec.GridSpec(1,2)
AxAlams = fig.add_subplot(gs[:,0])
AxDiffs = fig.add_subplot(gs[:,1])
for specData in specDataList:
chNum = np.array([i for i in range(specData.n)])
if doEnergy:
energies = np.abs(specData.alams)*vm*1E-3 # [ev -> keV]
energyDiff = np.diff(energies)
AxAlams.step(chNum, energies, label=specData.name)
AxDiffs.step(chNum[:-1], energyDiff, label=specData.name)
else:
alams = np.abs(specData.alams)
alamDiff = np.diff(alams)
AxAlams.step(chNum, alams, label=specData.name)
AxDiffs.step(chNum[:-1], alamDiff, label=specData.name)
AxAlams.set_yscale(yscale)
AxAlams.legend()
AxAlams.set_xlabel('Channel Number')
AxAlams.title.set_text("Values")
AxDiffs.set_yscale(yscale)
AxDiffs.legend()
AxDiffs.set_xlabel('Channel Number')
AxDiffs.title.set_text("Spacing")
if doEnergy:
AxAlams.set_ylabel("E [keV]")
AxDiffs.set_ylabel(r"$\Delta$E [keV]")
plt.suptitle("L={} vm={:2.2f}".format(L, vm))
else:
AxAlams.set_ylabel(r"$\lambda$")
AxDiffs.set_ylabel(r"$\Delta\lambda$")
plt.show()
def plotLambdasBySpec(specDataList, yscale='log',L=None):
doEnergy = True if L is not None else False
if doEnergy:
bVol = kT.L_to_bVol(L)
vm = bVol**(-2/3)
if not isinstance(specDataList, list):
specDataList = [specDataList]
specPlotList = []
for i in range(len(specDataList)):
if specDataList[i].name != "Plasmasphere":
specPlotList.append(specDataList[i])
nSpecs = len(specPlotList)
fig = plt.figure(figsize=(10,5))
gs = gridspec.GridSpec(1,nSpecs)
#AxAlams = fig.add_subplot(gs[:,0])
#AxDiffs = fig.add_subplot(gs[:,1])
for i in range(nSpecs):
specData = specPlotList[i]
chNum = np.array([i for i in range(specData.n)])
Ax = fig.add_subplot(gs[:,i])
if doEnergy:
energies = np.abs(specData.alams)*vm*1E-3 # [ev -> keV]
energyDiff = np.diff(energies)
Ax.step(chNum, energies, label='Values')
Ax.step(chNum[:-1], energyDiff, label="Spacing")
Ax.set_ylabel("E [keV]")
else:
alams = np.abs(specData.alams)
alamDiff = np.diff(alams)
Ax.step(chNum, alams, label="Values")
Ax.step(chNum[:-1], alamDiff, label="Spacing")
Ax.set_ylabel(r"$\lambda$")
Ax.set_yscale(yscale)
Ax.legend()
Ax.grid(True)
Ax.set_xlabel('Channel Number')
Ax.title.set_text(specData.name)
if doEnergy:
plt.suptitle("Energy grid values and spacing\n@ L={}, bVol={:2.2f}, vm={:2.2f}".format(L, bVol, vm))
plt.show()
def plotEnergyRange(specDataList, rInner=1.5, rOuter=15, rRes=100):
"""Plot energy range of each species as a function of L shell
"""
rVals = np.linspace(rInner, rOuter, rRes)
bVols = np.array([kT.L_to_bVol(r) for r in rVals])
vms = bVols**(-2/3)
allMin = 1e8
allMax = -1
specPlotList = []
for i in range(len(specDataList)):
specData = specDataList[i]
if specData.name != "Plasmasphere":
specPlotList.append(specData)
allMin = np.min((allMin, np.abs(specData.alams[0])*vms[-1]*1E-3))
allMax = np.max((allMax, np.abs(specData.alams[-1])*vms[0]*1E-3))
nSpecs = len(specPlotList)
allMax
fig = plt.figure(figsize=(10,5))
gs = gridspec.GridSpec(1,nSpecs)
for i in range(nSpecs):
specData = specPlotList[i]
lamMin = np.abs(specData.alams[0])
lamMax = np.abs(specData.alams[-1])
eMins = lamMin*vms*1E-3 # [eV -> keV]
eMaxs = lamMax*vms*1E-3 # [eV -> keV]
Ax = fig.add_subplot(gs[:,i])
Ax.fill_between(rVals, eMins, eMaxs)
Ax.set_yscale('log')
Ax.set_xlabel('L Shell [R$_p$]')
Ax.set_ylabel('Energy [keV]')
Ax.set_ylim([0.8*allMin, 1.2*allMax])
Ax.grid(True)
Ax.set_title(specData.name)
plt.suptitle("Covered enery range w.r.t. dipole L-shell")
plt.show()

128
kaipy/rcm/lambdautils_old/AlamData.py
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import h5py as h5
import numpy as np
import hashlib
class AlamParams:
#All energies in eV
def __init__(self, distType = 'wolf',
num_e = 39 , num_p = 120,
alamMin_e = -1.0 , alamMin_p = 10,
ktMax = 15000, L_kt = 10, tiote=4.0,
p1 = 3.0 , p2 = 1.0,
addPsphere = True):
self.distType = distType
self.num_e = num_e
self.num_p = num_p
self.aMin_e = alamMin_e
self.aMin_p = alamMin_p
self.ktMax = ktMax
self.L_kt = L_kt
self.tiote = tiote
self.p1 = p1
self.p2 = p2
self.doAddPsphere = addPsphere
def getAttrs(self):
return {
'hash': self.getHash(),
'distType': self.distType,
'num_e': self.num_e,
'num_p': self.num_p,
'aMin_e': self.aMin_e,
'aMin_p': self.aMin_p,
'ktMax': self.ktMax,
'L_kt': self.L_kt,
'tiote': self.tiote,
'p1': self.p1,
'p2': self.p2,
'doAddPsphere': self.doAddPsphere
}
# Generate an identifier for the current settings
def getHash(self):
l = [self.num_e, self.num_p, self.aMin_e, self.aMin_p, self.ktMax,
self.L_kt, self.tiote, self.p1, self.p2, self.doAddPsphere]
s = '-'.join(["{:1.2f}".format(i) for i in l]).encode('utf-8')
m = hashlib.md5()
m.update(s)
return m.hexdigest()
class AlamData:
def __init__(self, filename=None, doUsePsphere=False, doReadNow=True, alamdict=None, alamParams=None):
self.filename = filename
self.doUsePsphere = doUsePsphere
if doReadNow and filename is not None:
self.alams, self.amins, self.amaxs = self.readFile(filename)
if alamdict is not None:
self.buildFromData(alamdict)
if alamParams is not None:
self.params = alamParams
else:
self.params = AlamParams()
def readFile(self, fname):
if 'h5' in fname:
return self.readrcmconfig(fname)
def readrcmconfig(self, fname):
f5 = h5.File(fname, 'r')
alamdict = {}
#Initialize
numSpec = max(f5['ikflavc'])
for i in range(1,numSpec+1):
alamdict['spec'+str(i)] = np.array([])
#Populate
for i in range(len(f5['alamc'])):
specStr = 'spec' + str(f5['ikflavc'][i])
alamdict[specStr] = np.append(alamdict[specStr], f5['alamc'][i])
#If doUsePsphere == False, remove 0-channel from electron species
if not self.doUsePsphere:
for k in alamdict.keys():
if alamdict[k][1] < 0: # Find channel with negative alam, i.e. electron channel
alamdict[k] = alamdict[k][1:]
break
#print(alamdict)
#alamdict['spec2'][0] = 5.645
amindict, amaxdict = self.getAlamMinMax(alamdict)
return alamdict, amindict, amaxdict
def buildFromData(self, alamdict):
if self.doUsePsphere == False and alamdict['spec1'][0] == 0:
alamdict['spec1'] = alamdict['spec1'][1:]
self.alams = alamdict
self.amins, self.amaxs = self.getAlamMinMax(self.alams)
#Helper functions, broken out so others can use them too
def getAlamMinMax(self, alamdict):
amindict = {}
amaxdict = {}
for k in alamdict.keys():
amin = np.array([])
amax = np.array([])
alams = alamdict[k]
for i in range(len(alams)):
if i == 0:
amax = np.append(amax, 0.5*(np.abs(alams[i])+np.abs(alams[i+1])))
amin = np.append(amin, 0)
elif i == len(alams)-1:
amax = np.append(amax, 1.5*np.abs(alams[i]) - 0.5*np.abs(alams[i-1]))
amin = np.append(amin, 0.5*(np.abs(alams[i])+np.abs(alams[i-1])))
else:
amax = np.append(amax, 0.5*(np.abs(alams[i])+np.abs(alams[i+1])))
amin = np.append(amin, 0.5*(np.abs(alams[i])+np.abs(alams[i-1])))
amindict[k] = amin
amaxdict[k] = amax
return amindict, amaxdict

18
kaipy/rcm/lambdautils_old/README
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Supporting scripts:
AlamData: class to store alam data, with functions to read from file
plotter: basic plots of eta vs. alam, etc.
Main scripts:
dpetadp: Includes functions for dp->eta and eta->dp
- based on etautils.F90, toRCM.F90, and toMHD.F90
- as main, see the eta distribution for a single value of density, pressure, and vm
dpRangeEval: evaluate how well a certain alam distribution does for a range of densities and pressures
- as main, just enter which rcmconfig file you want to use the alam channels from
- density and pressure ranges set in main
genAlam: generate alam distribution and write it into an rcmconfig.h5
alamTester: Given an alam distribution, runs tests to see how good the distribution is
- smearTest: See if lambda+ and lambda- drift farther than 1 grid cell spacing per timestep (bad)

0
kaipy/rcm/lambdautils_old/__init__.py
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178
kaipy/rcm/lambdautils_old/alamTester.py
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import numpy as np
import h5py as h5
from kaipy.rcm.lambdautils.AlamData import AlamData
from kaipy.rcm.lambdautils.helperdefs import L_to_bVol
import kaipy.rcm.lambdautils.dpRangeEval as dpRangeEval
#Constants
radius_earth_m = 6.371E6
ev = 1.6e-19 # electron volt
# Enter testing through one of these
# They all just use the input data to make an AlamData object, then send to testAlams
def testAlam_arr(alam_arr):
alamdict = {}
iproton = len(alam_arr)-1
while alam_arr[iproton-1] > 0:
iproton -= 1
alamdict['spec1'] = alam_arr[:iproton] # Electrons
alamdict['spec2'] = alam_arr[iproton:] # Assuming sincle ion species
testAlam_dict(alamdict)
def testAlam_dict(alamdict):
alamdata = AlamData(alamdict=alamdict)
testAlams(alamdata)
def testAlam_config(fname):
alamdata = AlamData(filename=fname)
testAlams(alamdata)
def testAlam_AD(alamdata):
testAlams(alamdata)
# Actual alam testing start
def testAlams(alamdata, LSmearTest=2.5, LRangeTest=10, ratioTolerance=0.02):
kap = 5
smearResult = smearTest(alamdata, L=LSmearTest)
maxwellRangeMin, maxwellRangeMax, maxwell_vsq_d, maxwell_vsq_p = ktRangeTest(alamdata, ratioTolerance, L=LRangeTest)
print(ktRangeTest(alamdata, ratioTolerance, L=LRangeTest, kap=kap))
kapRangeMin, kapRangeMax, kap_vsq_d, kap_vsq_p = ktRangeTest(alamdata, ratioTolerance, L=LRangeTest, kap=kap)
if smearResult < 1:
resultstr = "Passed"
else:
resultstr = "Failed"
print("Smear test at L = {}: {}".format(LSmearTest, resultstr))
print(" Worst smear/cellWidth: {:2.2e}".format(smearResult))
print("kT min/max range within {:3.1f}% tolerance:".format(ratioTolerance*100))
print(" Maxwellian : {:1.2e}/{:1.2e} [keV]".format(maxwellRangeMin, maxwellRangeMax))
print(" Variance: D = {:1.2e} P = {:1.2e}".format(maxwell_vsq_d, maxwell_vsq_p))
print(" kappa = {:3.0f}: {:1.2e}/{:1.2e} [keV]".format(kap, kapRangeMin, kapRangeMax))
print(" Variance: D = {:1.2e} P = {:1.2e}".format(kap_vsq_d, kap_vsq_p))
"""
Quantify "smearing" of energy channel by calculating
the difference in drift velocities between lamba+ and
lambda- values over some time dt.
If smear > individual cell width, lambda channel width
is too large.
Inputs:
alamdata: AlamData object
L : L shell in R_e
dt : time-delta in seconds
deg : longitudinal RCM resolution in
Outputs:
Pass/Fail value
0 = Pass, non-zero = maxSmear/cellWidth > 1
"""
def smearTest(alamdata, L=2.5, dt=10, deg=1, doVerbose=False):
# Get width of a grid cell in meters
cellWidth = (L*radius_earth_m)*(np.pi/180*deg)
if doVerbose: print("Cell width: {:1.2e}".format(cellWidth))
bVol = L_to_bVol(L)
vm = bVol**(-2./3.)
if doVerbose: print("bVol = {}, vm = {}".format(bVol, vm))
smeardict = {}
maxSmear = 0
for s in alamdata.alams.keys():
alams = alamdata.alams[s]
amins = alamdata.amins[s]
amaxs = alamdata.amaxs[s]
smear_arr = np.array([])
# Drift velocity ~ W_k*delB/q/B^2
bMag = 3.03E-5*(L)**(-3) # [T]
delB = 9.09E-5*(L)**(-4)/radius_earth_m # [T/m]
const = delB/ev/bMag**2
driftMin = const*alams[1]*vm*ev
driftMax = const*alams[-1]*vm*ev
if doVerbose: print("Drift min/max ({}): {:1.2e}/{:1.2e}".format(s, driftMin, driftMax))
for i in range(len(alams)):
wMinus = amins[i]*vm*ev # [J]
wPlus = amaxs[i]*vm*ev # [J]
smear = const*(wPlus-wMinus) * dt # [m]
smear_arr = np.append(smear_arr, smear)
if smear > maxSmear:
maxSmear = smear
energy = alams[i]*vm*1E-3
drift = const*alams[i]*vm*ev
if(doVerbose):
print("Energy = {:1.2E} [keV] Drift: {:1.2e} [m/s] Smear: {:1.2e} [m] Smear/CellWidth: {:1.2e}".format(energy, drift, smear, smear/cellWidth))
#if maxSmear/cellWidth < 1:
# return 0 # Pass
#else:
return maxSmear/cellWidth # Return ratio of worst offender
# Similar setup in main of dpRangeEval.py
# Calculate X'/X for range ot kT's, then determine min/max kT within tolerance range
def ktRangeTest(alamdata, tol, L=10, kap=-1):
# Defaults for now
nSamples = 100
ktmin = 1E-3 # [keV]
ktmax = 1000 # [keV]
dconst = 10 # [1/cc]
kt_arr = np.logspace(np.log10(ktmin), np.log10(ktmax), nSamples)
dens_arr = np.ones((nSamples))*dconst*1E6 # [1/m^3]
press_arr = np.array([kt*dconst/6.25 * 1E-9 for kt in kt_arr]) # [Pa]
bVol = L_to_bVol(L)
vm = bVol**(-2./3.)
dRatios, pRatios = dpRangeEval.getRatios1D(alamdata, dens_arr, press_arr, vm, kap)
# This next part is not efficient
ktTolMin = kt_arr[0]
ktTolMax = kt_arr[-1]
# Find point closest to 1keV
iStart = np.abs(kt_arr - 1).argmin()
if (np.abs(dRatios[iStart] - 1) > tol) or (np.abs(pRatios[iStart] - 1) > tol):
return -1, -1, -1, -1
# Scan to find kT values where X'/X falls out of tolerance range,
iTolMin = 0
iTolMax = 0
iScan = iStart
while iScan < len(kt_arr):
if (np.abs(dRatios[iScan] - 1) > tol) or (np.abs(pRatios[iScan] - 1) > tol):
iTolMax = iScan - 1
ktTolMax = kt_arr[iTolMax]
iScan = 2*len(kt_arr)
else:
iScan += 1
iScan = iStart
while iScan > -1:
if (np.abs(dRatios[iScan] - 1) > tol) or (np.abs(pRatios[iScan] - 1) > tol):
iTolMin = iScan + 1
ktTolMin = kt_arr[iTolMin]
iScan = -1
else:
iScan -= 1
# Make a metric for how much the ratios deviate from 1 within the tolerance range
var_sq_d = 0
var_sq_p = 0
for k in range(iTolMin, iTolMax):
var_sq_d += np.abs(dRatios[k]-1)**2
var_sq_p += np.abs(pRatios[k]-1)**2
var_sq_d /= (iTolMax-iTolMin)
var_sq_p /= (iTolMax-iTolMin)
return ktTolMin, ktTolMax, var_sq_d, var_sq_p

162
kaipy/rcm/lambdautils_old/dpRangeEval.py
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import numpy as np
import argparse
from argparse import RawTextHelpFormatter
from kaipy.rcm.lambdautils.AlamData import AlamData
import kaipy.rcm.lambdautils.dpetadp as dpetadp
import kaipy.rcm.lambdautils.plotter as plotter
#Constants
boltz = 1.38e-23 # boltzmann constant
ev = 1.6e-19 # electron volt
nt = 1.0E-9
m_e = 9.109E-31
m_p = 1.673E-27
radius_earth_m = 6.371E6
pressure_factor = 2./3.*ev/radius_earth_m*nt
density_factor = nt/radius_earth_m
#Input defaults
f_in = "rcmconfig.h5"
tag = ""
tiote = 4.0
L_in = 10
kap_in = -1
doShowPlot = False
def getEtas(alamData, dens, press, vm, kap):
if kap == kap_in:
etas = dpetadp.dp2eta(dens, press, vm, tiote, alamData)
else:
etas = dpetadp.dp2eta_kap_runov(dens, press, vm, tiote, kap, alamData)
return etas
"""
Calculate how many channels contribute (1-epsilon)*P pressure out of total pressure P
Highest channels contributing epsilon pressure can be ignored by RCM advector
**This implementation keeps all electron channels and only removes ion channels
Need to check how okay this is to do
"""
def getFracChannelUse(alamData, dens_arr, press_arr, vm, kap, epsilon=1E-6):
fracChannelUse = np.zeros((len(press_arr))) # Fraction of total proton channel use
alams_p = alamData.alams['spec2']
for i in range(len(press_arr)):
etas = getEtas(alamData, dens_arr[i], press_arr[i], vm, kap)
dTotal, pTotal = dpetadp.eta2dp(etas, alamData, vm)
numK = len(etas['spec2'])
k_cutoff = numK-1
eps_pressure = 0 # Epsilon pressure
doContinue = True
while k_cutoff > 0:
press_new = pressure_factor*np.abs(alams_p[k_cutoff])*etas['spec2'][k_cutoff]*vm**2.5
if press_new + eps_pressure < epsilon*pTotal: # Safe to include this channel to those that will be cut off
k_cutoff -= 1
eps_pressure += press_new
else:
break
fracChannelUse[i] = (k_cutoff+1)/numK
return fracChannelUse
def getRatios1D(alamData, dens_arr, press_arr, vm, kap):
dRatios = np.zeros((len(press_arr)))
pRatios = np.zeros((len(press_arr)))
for i in range(len(press_arr)):
etas = getEtas(alamData, dens_arr[i], press_arr[i], vm, kap)
dRatios[i], pRatios[i] = dpetadp.eta2dp(etas, alamData, vm)
dRatios[i] /= dens_arr[i]
pRatios[i] /= press_arr[i]
return dRatios, pRatios
def getRatios2D(alamData, dens_arr, press_arr, vm):
dRatios = np.zeros((len(press_arr), len(dens_arr)))
pRatios = np.zeros((len(press_arr), len(dens_arr)))
for i in range(len(press_arr)):
for j in range(len(dens_arr)):
etas = dpetadp.dp2eta(dens_arr[j], press_arr[i], vm, tiote, alamData)
dRatios[i,j], pRatios[i,j] = dpetadp.eta2dp(etas, alamData, vm)
dRatios[i,j] /= dens_arr[j]
pRatios[i,j] /= press_arr[i]
return dRatios, pRatios
if __name__=="__main__":
MainS = """Evaluate DP->eta->DP accuracy for a given alam distribution"""
parser = argparse.ArgumentParser(description=MainS, formatter_class=RawTextHelpFormatter)
parser.add_argument('--file', type=str, metavar="<eta file>",default=f_in,
help="Name of file containing alam channels (default: %(default)s)")
parser.add_argument('--tiote', type=float,default=tiote,
help="T_ion/T_electron (default: %(default)s)")
parser.add_argument('--L', type=float, metavar="[Re]",default=L_in,
help="L shell in [nPa] (default: %(default)s)")
parser.add_argument('--tag', type=str,default=tag,
help="Tag for plot title (default: %(default)s)")
parser.add_argument('--kap', type=float, metavar="K", default=kap_in,
help="Kappa value. If unspecified, Maxwellian is used.")
parser.add_argument("--show", default=False, action='store_true',
help="Show superplot after saving (default: %(default)s)")
args = parser.parse_args()
fname = args.file
tiote = args.tiote
tag = args.tag
Lshell = args.L
kap = args.kap
doShowPlot = args.show
bVol = 0.34 #* 6.371E15 # [Re/nT] * 1E9 * 6.371E6
vm = bVol**(-2./3.)
bVol = dpetadp.L_to_bVol(Lshell)
vm = bVol**(-2./3.)
alamData = AlamData(fname)
nSamples = 100
dMin = 0.01 * 1E6
dMax = 500 * 1E6
pMin = 0.01 * 1E-9
pMax = 1000 * 1E-9
doLog = True
if(doLog):
dens_arr = np.logspace(np.log10(dMin), np.log10(dMax), nSamples)
press_arr = np.logspace(np.log10(pMin), np.log10(pMax), nSamples)
else:
dens_arr = np.linspace(dMin, dMax, nSamples)
press_arr = np.linspace(pMin, pMax, nSamples)
#dRatios, pRatios = getRatios2D(alamData, dens_arr, press_arr, vm)
#plotter.plotRatios_dpRange2D(dens_arr, press_arr, dRatios, pRatios, tag)
# Do range of temperatures instead
ktmin = 1E-3 # [keV]
ktmax = 1000 # [keV]
dconst = 10 # [1/cc]
kt_arr = np.logspace(np.log10(ktmin), np.log10(ktmax), nSamples)
dens_arr = np.ones((nSamples))*dconst*1E6 # [1/m^3]
press_arr = np.array([kt*dconst/6.25 * 1E-9 for kt in kt_arr]) # [Pa]
#print(press_arr)
dRatios, pRatios = getRatios1D(alamData, dens_arr, press_arr, vm, kap)
fracChannelUse = getFracChannelUse(alamData, dens_arr, press_arr, vm, kap)
title = tag
if kap == kap_in:
title += " Maxwellian"
else:
title += " Kappa = " + str(kap)
title += "\nL = {} bVol = {:4.2e}".format(Lshell, bVol)
title += "\nd = {} [1/cc], p = ({:1.2e}, {:1.2e}) [nPa]".format(dconst, press_arr[0]*1E9, press_arr[-1]*1E9)
plotter.plotRatios_tempRange1D(kt_arr, dRatios, pRatios, title, fracChannelUse, doShow=doShowPlot)

321
kaipy/rcm/lambdautils_old/dpetadp.py
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import numpy as np
import argparse
from argparse import RawTextHelpFormatter
from math import gamma
from kaipy.rcm.lambdautils.AlamData import AlamData
from kaipy.rcm.lambdautils.helperdefs import L_to_bVol
import kaipy.rcm.lambdautils.plotter as plotter
#Constants
boltz = 1.38e-23 # boltzmann constant
ev = 1.6e-19 # electron volt
nt = 1.0E-9
m_e = 9.109E-31
m_p = 1.673E-27
radius_earth_m = 6.371E6
pressure_factor = 2./3.*ev/radius_earth_m*nt
density_factor = nt/radius_earth_m
isMain = False
def erfexpdiff(A, xplus, xminus):
#Difference of erf's using Abramowitz & Stegun, 7.1.26
p = 0.3275911
a1 = 0.254829592
a2 = -0.284496736
a3 = 1.421413741
a4 = -1.453152027
a5 = 1.061405429
tp = 1.0/(1+p*xplus)
tm = 1.0/(1+p*xminus)
ep = np.exp(-(xplus**2))
em = np.exp(-(xminus**2))
erfdiff = - (a1*tp + a2*(tp**2.0) + a3*(tp**3.0) + a4*(tp**4.0) + a5*(tp**5.0))*ep \
+ (a1*tm + a2*(tm**2.0) + a3*(tm**3.0) + a4*(tm**4.0) + a5*(tm**5.0))*em
expdiff = 2.0*(xplus*ep - xminus*em)/np.sqrt(np.pi)
#print("efrfdiff:{:1.2e}\texpdiff:{:1.2e}".format(erfdiff, expdiff))
eta = A*(erfdiff-expdiff)
return eta
def dp2eta(dens, press, vm, tiote, alamData): # dens = [1/m^3], press = [Pa]
pion = press*tiote/(1.0+tiote)
pele = press* 1.0/(1.0+tiote)
ti = pion/dens/boltz
te = pele/dens/boltz
if isMain:
print("Ti={:2.2e} [eV]\tTe={:2.2e} [eV]".format(ti*boltz/1.6E-19,te*boltz/1.6E-19))
A0 = (dens/density_factor)/vm**1.5
etadict = {}
for s in alamData.alams.keys(): # s = species
alams = alamData.alams[s]
amins = alamData.amins[s]
amaxs = alamData.amaxs[s]
tk = ti if alams[1] > 0 else te
eta_arr = np.array([])
for k in range(len(alams)):
xplus = np.sqrt(ev*np.abs(amaxs[k])*vm/boltz/tk)
xminus = np.sqrt(ev*np.abs(amins[k])*vm/boltz/tk)
eta = erfexpdiff(A0, xplus, xminus)
eta_arr = np.append(eta_arr, eta)
etadict[s] = eta_arr
return etadict
# Python implementation of Kareem's code (which is an implementation of original kappa notes)
def dp2eta_kap_kfort(dens, press, vm, tiote, kap, alamData):
pion = press*tiote/(1.0+tiote)
pele = press* 1.0/(1.0+tiote)
tk_i = pion/dens
tk_e = pele/dens
if isMain:
print("Ti={:2.2e} [eV]\tTe={:2.2e} [eV]".format(tk_i/1.6E-19,tk_e/1.6E-19))
kap15 = kap-1.5
kapgam = gamma(kap+1.0)/gamma(kap-0.5)
kapbar = kap15
etadict = {}
for s in alamData.alams.keys():
alams = alamData.alams[s]
amins = alamData.amins[s]
amaxs = alamData.amaxs[s]
tk = tk_i if alams[1] > 0 else tk_e
Tev = tk/1.6E-19
E0_ev = Tev*kap15/kap
A0 = (2.0/np.sqrt(np.pi))*(dens/density_factor)/vm**1.5
eta_arr = np.array([])
for k in range(len(alams)):
E_ev = abs(alams[k])*vm # [eV]
kArg = 1 + (E_ev/E0_ev)/kapbar
delscl = (amaxs[k]-amins[k])/E0_ev
etak = A0*kapgam/kapbar**1.5 * np.sqrt(E_ev/E0_ev)*delscl*kArg**(-kap-1)
eta_arr = np.append(eta_arr, etak)
etadict[s] = eta_arr
return etadict
# Runov et al. (2015) pg. 4377 10.1002/2015JA021166
def dp2eta_kap_runov(dens, press, vm, tiote, kap, alamData):
pion = press*tiote/(1.0+tiote)
pele = press* 1.0/(1.0+tiote)
tk_i = pion/dens
tk_e = pele/dens
if isMain:
print("Ti={:2.2e} [eV]\tTe={:2.2e} [eV]".format(tk_i/1.6E-19,tk_e/1.6E-19))
kap15 = kap-1.5
kapgam = gamma(kap+1.0)/gamma(kap-0.5)
etadict = {}
for s in alamData.alams.keys():
alams = alamData.alams[s]
amins = alamData.amins[s]
amaxs = alamData.amaxs[s]
tk = tk_i if alams[1] > 0 else tk_e
m_s = m_p if alams[1] > 0 else m_e
Tev = tk/1.6E-19
E0_ev = Tev*kap15/kap
#E0_ev = Tev
A0 = (2.0/np.sqrt(np.pi))*(dens/density_factor)/vm**1.5
#A0 = (dens/density_factor)/vm**1.5
eta_arr = np.array([])
for k in range(len(alams)):
E_ev = abs(alams[k])*vm # [eV]
kArg = 1 + E_ev/(kap*E0_ev)
delscl = (amaxs[k]-amins[k])*vm/E0_ev
etak = A0*np.sqrt(E_ev/E0_ev) * delscl / (kap)**1.5 * kapgam * kArg**(-kap-1)
#etak = A0*kapgam/kapbar**1.5 * np.sqrt(E_ev/E0_ev)*delscl*kArg**(-kap-1)
eta_arr = np.append(eta_arr, etak)
"""
for k in range(len(alams)):
E_ev_min = abs(amins[k])*vm
E_ev_max = abs(amaxs[k])*vm
kArg_min = (1+E_ev_min/(kap*E0_ev))
kArg_max = (1+E_ev_max/(kap*E0_ev))
consts = A0 /(kap*E0_ev)**1.5 * kapgam
etak = (amaxs[k]-amins[k])*vm/2 * consts*((np.sqrt(E_ev_max)*kArg_max**(-kap-1)) + np.sqrt(E_ev_min)*kArg_min**(-kap-1))
eta_arr = np.append(eta_arr, etak)
"""
etadict[s] = eta_arr
return etadict
def etaLinearRescaler(alamData, etaData, targetN, targetP, vBol):
vm = vBol**(-2/3)
tN = targetN/density_factor#/vm**1.5
tP = targetP/pressure_factor#/vm**2.5
newEtaData = etaData
for s in alamData.alams.keys():
alams = alamData.alams[s]
etas = etaData[s]
#Do the summations
kMax = len(alams)-1
doIterate = True
while doIterate:
S1 = 0 # Sum eta_k
S2 = 0 # Sum eta_k*abs(alam_k)
S3 = 0 # Sum eta_k*abs(alam_k)^2
for k in range(kMax+1):
S1 += etas[k]
S2 += etas[k]*np.abs(alams[k])
S3 += etas[k]*alams[k]**2
denom = 1/(S1*S3 - S2**2)
a = denom*(S3*tN*bVol - S2*1.5*tP*bVol**(5/3))
b = denom*(-S2*tN*bVol + S1*1.5*tP*bVol**(5/3))
print("a, b = {},{}".format(a, b))
#TODO: set new kMax and iterate if necessary
if (a+b*np.abs(alams[kMax]))*etas[kMax] < 0:
etas[kMax] = 0
kMax -= 1
else:
doIterate = False
newEtaData[s] = (a+b*np.abs(alams))*etas
return newEtaData
def eta2dp(etaData, alamData, vm):
dTot = 0
pTot = 0
for s in alamData.alams.keys(): # s = species
alams = alamData.alams[s]
etas = etaData[s]
dSpec, pSpec = eta2dp_s(etas, alams, vm)
if isMain:
print("{}: d = {:1.2e} [1/cc] p = {:1.2e} [nPa]".format(s, dSpec*1E-6, pSpec*1E9))
pTot += pSpec
if "1" not in s:
dTot += dSpec
if isMain:
print("Total: d = {:1.2e} [1/cc] p = {:1.2e} [nPa]".format(dSpec*1E-6, pSpec*1E9))
return dTot, pTot
def eta2dp_s(etas_s, alams_s, vm, k1=-1, k2=-1): # etas_s, alams_s are of a specific species
if k1 == -1:
k1 = 0
if k2 == -1:
k2 = len(etas_s)-1
dens = 0 # [1/m^2]
press = 0 # [Pa]
for k in range(k1,k2+1):
dens += density_factor*etas_s[k]*vm**1.5
press += pressure_factor*np.abs(alams_s[k])*etas_s[k]*vm**2.5
return dens, press
if __name__=="__main__":
isMain = True
MainS = """Calculate DP => RCM etas => DP for a given alam distribution"""
f_in = "rcmconfig.h5"
dens_in = 0.5
press_in = 0.85
tiote_in = 4.0
L_in = 10
kap_in = -1
tag = ""
parser = argparse.ArgumentParser(description=MainS, formatter_class=RawTextHelpFormatter)
parser.add_argument('--file', type=str, metavar="<eta file>",default=f_in,
help="Name of file containing alam channels (default: %(default)s)")
parser.add_argument('--d', type=float, metavar="[1/cc]",default=dens_in,
help="Density in [1/cc] (default: %(default)s)")
parser.add_argument('--p', type=float, metavar="[nPa]",default=dens_in,
help="Pressure in [nPa] (default: %(default)s)")
parser.add_argument('--L', type=float, metavar="[Re]",default=L_in,
help="L shell in [nPa] (default: %(default)s)")
parser.add_argument('--tiote', type=float,default=tiote_in,
help="T_ion/T_electron (default: %(default)s)")
parser.add_argument('--tag', type=str,default=tag,
help="Tag for plot title (default: %(default)s)")
parser.add_argument('--kap', type=float, metavar="K", default=kap_in,
help="Kappa value. If unspecified, Maxwellian is used.")
parser.add_argument("--show", default=False, action='store_true',
help="Show superplot after saving (default: %(default)s)")
args = parser.parse_args()
fname = args.file
dens = args.d*1E6 # 1/cc to 1/m^3
press = args.p*1E-9 # nPa to Pa
Lshell = args.L
tiote = args.tiote
tag = args.tag
kap = args.kap
doShowPlot = args.show
bVol = 0.34 #* 6.371E15 # [Re/nT] * 1E9 * 6.371E6
bVol = L_to_bVol(Lshell)
vm = bVol**(-2./3.)
print("bVol = {}, vm = {}".format(bVol, vm))
alamData = AlamData(filename=fname)
if kap == -1:
etaData = dp2eta(dens, press, vm, tiote, alamData)
else:
#etaData = dp2eta_kap_runov(dens, press, vm, tiote, kap, alamData)
etaData = dp2eta_kap_runov(dens, press, vm, tiote, kap, alamData)
#etaLinearRescaler(alamData, etaData, dens, press, bVol)
dTot, pTot = eta2dp(etaData, alamData, vm) # [1/m^3] and [Pa]
dNew_o_dOrig = dTot/dens
pNew_o_pOrig = pTot/press
dens_cc = dens*1E-6
press_npa = press*1E9
kT = 6.25*press_npa/dens_cc # [keV]
title = tag
if kap == kap_in:
title += " Maxwellian"
else:
title += " Kappa = " + str(kap)
title += "\nd = {:4.2f} [1/cc] p = {:4.2f} [nPa] kT = {:1.2e} [keV]".format(dens_cc, press_npa, kT)
title += "\nL = {} bVol = {:4.2f}".format(Lshell, bVol)
title += "\nD'/D = {:1.2e} P'/P = {:1.2e}".format(dNew_o_dOrig, pNew_o_pOrig)
plotter.plotEtas(alamData, etaData, vm, title, doShow=doShowPlot)
print("p_new/p_0: {:0.2e}".format(pNew_o_pOrig))
print("d_new/d_0: {:0.2e}".format(dNew_o_dOrig))

194
kaipy/rcm/lambdautils_old/genAlam.py
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@@ -1,194 +0,0 @@
import numpy as np
import h5py as h5
import argparse
from argparse import RawTextHelpFormatter
from kaipy.rcm.lambdautils.dpetadp import L_to_bVol
import kaipy.rcm.lambdautils.alamTester as alamTester
import kaipy.rcm.lambdautils.plotter as plotter
from kaipy.rcm.lambdautils.AlamData import AlamParams
try:
import kaipy.rcm.rcminit as rcminit
hasKaiRCM = True
except:
print("Couldn't load kaipy.rcm.init, can't add dktable to rcmconfig file.")
hasKaiRCM = False
EFLAV = 1
PFLAV = 2
EFUDGE = 1./3.
PFUDGE = 0.0
distTypes = {'lin', 'log'}
# Takes full alams, flavs, fudges, and dktable arrays
# Writes arrays to file in rcmconfig.h5 format
def genh5(fname, inputParams, doTests=True, doShowPlot=False):
alams, flavs, fudges = genAlams(inputParams, doTests=doTests, doShowPlot=doShowPlot)
attrs = inputParams.getAttrs()
f5 = h5.File(fname, 'w')
f5.create_dataset('alamc', data=alams)
f5.create_dataset('ikflavc', data=flavs)
f5.create_dataset('fudgec', data=fudges)
if hasKaiRCM:
dktab = rcminit.LoadDKTab()
f5.create_dataset('dktable', data=dktab)
else:
print("Couldn't load kaipy.rcm.init, can't add dktable to rcmconfig file.")
for key in attrs.keys():
f5.attrs[key] = attrs[key]
f5.close()
# Adds completely new channel to alamc, ikflavc, and fudges
def addPsphere(alams, flavs, fudges):
alam_wp = np.concatenate(([0], alams))
flav_wp = np.concatenate(([1], flavs))
fudge_wp = np.concatenate(([0], fudges))
return alam_wp, flav_wp, fudge_wp
# Takes alams as an array, and single values for ikflav and fudge
# Returns arrays ready to write in rcmconfig.h5 format
def genspecData(alams, flav, fudge):
#Very dumb
flavs = np.array([flav for i in range(len(alams))])
fudges = np.array([fudge for i in range(len(alams))])
return alams, flavs, fudges
def wolfAlam(k, kmin, kmax, lammin, lammax, p1, p2):
kfrac = (k-kmin)/(kmax-kmin)
pstar = (1-kfrac)*p1 + kfrac*p2
lammax2 = lammax - lammin
#return lammax*((k - kmin + 0.5)/(kmax-kmin + 0.5))**p
return lammax2*((k - kmin + 0.5)/(kmax-kmin + 0.5))**pstar + lammin
# Main function to get things going
def genAlams(alamParams, doTests=True, doShowPlot=False):
#Unpack the basics
bVol = L_to_bVol(alamParams.L_kt)
vm = bVol**(-2/3)
alamMin_p = alamParams.aMin_p
alamMax_p = 10*(alamParams.ktMax/vm)
numAlam_p = alamParams.num_p
alamMin_e = alamParams.aMin_e
alamMax_e = -1*alamMax_p/alamParams.tiote
numAlam_e = alamParams.num_e
# Gen desired alam distribution
lbase = 10
if alamParams.distType == 'lin':
alams_e = np.linspace(alamMin_e, alamMax_e, numAlam_e, endpoint=True)
alams_p = np.linspace(alamMin_p, alamMax_p, numAlam_p, endpoint=True)
elif alamParams.distType == 'log':
aminlog_e = np.log(np.abs(alamMin_e))/np.log(lbase)
amaxlog_e = np.log(np.abs(alamMax_e))/np.log(lbase)
aminlog_p = np.log(np.abs(alamMin_p))/np.log(lbase)
amaxlog_p = np.log(np.abs(alamMax_p))/np.log(lbase)
alams_e = -1*np.logspace(aminlog_e, amaxlog_e, numAlam_e, base=lbase, endpoint=True)
alams_p = np.logspace(aminlog_p, amaxlog_p, numAlam_p, base=lbase, endpoint=True)
elif alamParams.distType == 'wolf':
alams_e = np.array([])
alams_p = np.array([])
p1 = alamParams.p1
p2 = alamParams.p2
for k in range(numAlam_e):
a = wolfAlam(k, 0, numAlam_e, alamMin_e, alamMax_e, p1, p2)
alams_e = np.append(alams_e, a)
for k in range(numAlam_p):
a = wolfAlam(k, 0, numAlam_p, alamMin_p, alamMax_p, p1, p2)
alams_p = np.append(alams_p, a)
# Questionable implementation
alams_e, flavs_e, fudges_e = genspecData(alams_e, EFLAV, EFUDGE)
alams_p, flavs_p, fudges_p = genspecData(alams_p, PFLAV, PFUDGE)
# Combine all species
alams = np.concatenate((alams_e , alams_p ))
flavs = np.concatenate((flavs_e , flavs_p ))
fudges = np.concatenate((fudges_e, fudges_p))
# Add on plasmasphere channel if desired
if alamParams.doAddPsphere:
alams, flavs, fudges = addPsphere(alams, flavs, fudges)
# Run lambda channel tests
if doTests:
alamTester.testAlam_arr(alams)
# TODO: Write superplot, where fig is returned, and we will save it here
plotter.plotChannels(alams, doShow=doShowPlot)
return alams, flavs, fudges
"""
TODO: Maybe need to make a config file in the future to reduce command line arguments
Especially if we want multi-ion species
"""
if __name__ == "__main__":
MainS = """Generate rcmconfig.h5 file with custom alam distribution"""
ofname_in = "tmprcmconfig.h5"
noPSphere = False
doShowPlot = False
dP = AlamParams() # Default params
parser = argparse.ArgumentParser(description=MainS, formatter_class=RawTextHelpFormatter)
parser.add_argument('--outfile', type=str, metavar="<filename>",default=ofname_in,
help="Name of output file (default: %(default)s)")
parser.add_argument('--L', type=float, default=dP.L_kt,
help="L shell [R_e] at which tmax should be resolved (default: %(default)s R_E)")
parser.add_argument('--tmax', type=float,default=dP.ktMax/1E3,
help="Max desired kT to resolve at L [keV] (default: %(default)s keV)")
parser.add_argument('--tiote', type=float,default=dP.tiote,
help="T_ion/T_electron (default: %(default)s)")
parser.add_argument('--nrgmin_e', type=float,default=dP.aMin_e,
help="Mimumim electron energy [eV] (excluding plasmasphere) (default: %(default)s eV)")
parser.add_argument('--nrgmin_p', type=float,default=dP.aMin_p,
help="Mimumim proton energy [eV] (default: %(default)s eV)")
parser.add_argument('--num_e', type=float,default=dP.num_e,
help="Number of electron channels (excluding plasmasphere) (default: %(default)s)")
parser.add_argument('--num_p', type=float,default=dP.num_p,
help="Number of proton channels (default: %(default)s)")
parser.add_argument('--p1', type=float,default=dP.p1,
help="Value for p1 parameter in Wolf distribution (default: %(default)s)")
parser.add_argument('--p2', type=float,default=dP.p2,
help="Value for p2 parameter in Wolf distribution (default: %(default)s)")
parser.add_argument("--noPsphere", default=False, action='store_true',
help="Don't add plasmasphere channel (default: %(default)s)")
parser.add_argument("--noTests", default=False, action='store_true',
help="Don't run any tests (default: %(default)s)")
parser.add_argument("--show", default=False, action='store_true',
help="Show superplot after saving (default: %(default)s)")
args = parser.parse_args()
ofname = args.outfile
doTests = not args.noTests
doShowPlot = args.show
inputParams = AlamParams(
distType='wolf',
num_e = args.num_e,
num_p = args.num_p,
alamMin_e = args.nrgmin_e,
alamMin_p = args.nrgmin_p,
ktMax = args.tmax*1E3,
L_kt = args.L,
tiote = args.tiote,
p1 = args.p1,
p2 = args.p2,
addPsphere = not args.noPsphere
)
alams, flavs, fudges = genAlams(inputParams, doTests=doTests, doShowPlot=doShowPlot)
genh5(ofname, alams, flavs, fudges)

11
kaipy/rcm/lambdautils_old/helperdefs.py
View File

@@ -1,11 +0,0 @@
import numpy as np
def L_to_bVol(L): # L shell [Re] to V [Re/nT]
bsurf_nT = 3.11E4
colat = np.arcsin(np.sqrt(1.0/L))
cSum = 35*np.cos(colat) - 7*np.cos(3*colat) +(7./5.)*np.cos(5*colat) - (1./7.)*np.cos(7*colat)
cSum /= 64.
s8 = np.sin(colat)**8
V = 2*cSum/s8/bsurf_nT
return V

264
kaipy/rcm/lambdautils_old/plotter.py
View File

@@ -1,264 +0,0 @@
import matplotlib
defaultBackend = matplotlib.get_backend() # Save whatever the default loaded backend is for future GUI use
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import ticker, colors
import numpy as np
from kaipy.rcm.lambdautils.helperdefs import L_to_bVol
titlesize = 16
ylabelsize = 22
xlabelsize = 20
xlabelpad = 8
ylabelpad = 8
ticklabelsize = 16
xticklabelsize = 15
legendsize = 20
def makeShowable():
matplotlib.use(defaultBackend)
import matplotlib.pyplot as plt
from matplotlib import ticker, colors
def plotChannels(alams, doShow=False):
titlesize = 22
ylabelsize = 22
xlabelsize = 20
xlabelpad = 8
ylabelpad = 8
ticklabelsize = 16
xticklabelsize = 15
legendsize = 20
if(doShow): makeShowable()
fig, ax = plt.subplots(1)
# Divide alams by e and i species
iproton = len(alams)-1
while alams[iproton-1] > 0:
iproton -= 1
alams_e = alams[:iproton]
alams_p = alams[iproton:]
k_arr_e = np.array([k for k in range(len(alams_e))])
k_arr_p = np.array([k for k in range(len(alams_p))])
aSpace_e = alams_e[1:]-alams_e[:-1]
aSpace_p = alams_p[1:]-alams_p[:-1]
# Plot alam values first
ax.plot(k_arr_p, alams_p, label="ions")
ax.plot(k_arr_e, np.abs(alams_e), label="electrons")
#ax.plot(k_arr_p[:-1], aSpace_p, '--')
#ax.plot(k_arr_e[:-1], np.abs(aSpace_e), '--')
ax.set_xlabel("Channel #", fontsize=xlabelsize)
ax.set_ylabel("$\\lambda$", fontsize = ylabelsize)
#ax.set_yscale('log')
ax.legend(prop={'size': legendsize})
ax.grid(True)
ax.tick_params(axis="x", labelsize=ticklabelsize)
ax.tick_params(axis="y", labelsize=ticklabelsize)
plt.suptitle("Energy Channel Distribution", fontsize=titlesize)
if(doShow): plt.show()
def plotEtas(alamData, etaData, vm, title, doShow=False):
if(doShow): makeShowable()
fig, ax = plt.subplots()
s = "spec2" # species label for protons
legendText = "Protons"
widths = (alamData.amaxs[s]-alamData.amins[s])
mids = (alamData.amaxs[s]+alamData.amins[s])/2.0
ax.bar(mids, etaData[s]/widths, widths*0.9, label=legendText)
#for i in range(len(alamData.alams[s])):
# ax.plot([alamData.alams[s][i], alamData.alams[s][i]], [0, etaData[s][i]/widths[i]], 'r-')
ax.set_xlabel("Energy Invariant $\lambda$", fontsize=xlabelsize)
ax.set_ylabel("$\\frac{\\eta}{\\Delta \\lambda}$", fontsize=28)
ax.set_ylim((1E15, 5E17))
#ax.set_yscale('log')
ax.set_title(title, fontsize=titlesize)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(ticklabelsize)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(ticklabelsize)
if(doShow): plt.show()
return ax
def plotLambdaVsLShell(alamData, Llow=1.5, Lhigh=10, doShow=False):
nSamples = 100
lshell_arr = np.linspace(Llow, Lhigh, nSamples, endpoint=True)
vm_arr = np.array([L_to_bVol(L) for L in lshell_arr])**(-2/3)
iLams_e = np.array([0, len(alamData.alams['spec1'])-1])
if np.abs(alamData.alams['spec1'][0]) < 1E-5:
iLams_e[0] += 1
iLams_p = np.array([0, len(alamData.alams['spec2'])-1])
lams_e = np.array([np.abs(alamData.alams['spec1'][i]) for i in iLams_e])
lams_p = np.array([alamData.alams['spec2'][i] for i in iLams_p])
print(lams_e)
energies_e = np.zeros((len(iLams_e), nSamples))
energies_p = np.zeros((len(iLams_p), nSamples))
print(energies_e[0])
for i in range(nSamples):
#print(energies_e[:][:])
energies_e[:,i] = lams_e*vm_arr[i]
energies_p[:,i] = lams_p*vm_arr[i]
print(energies_e)
if(doShow): makeShowable()
fig, ax = plt.subplots()
for e_e in energies_e:
line_e, = ax.plot(lshell_arr, e_e, color="red")
for e_p in energies_p:
line_p, = ax.plot(lshell_arr, e_p, color="blue")
ax.set_yscale('log')
ax.set_xlabel("L shell [$R_E$]", fontsize=xlabelsize)
ax.set_ylabel("Channel Energy [eV]", fontsize=ylabelsize)
ax.tick_params(axis="x", labelsize=ticklabelsize)
ax.tick_params(axis="y", labelsize=ticklabelsize)
ax.grid(True)
ax.legend([line_e, line_p], ["Electron min/max", "Ion min/max"])
plt.suptitle("RCM Channel energy range vs. L shell")
if(doShow): plt.show()
return ax
def plotRatios_tempRange1D(kt_arr, dRatios, pRatios, tag, fracChannelUse=None, doShow=False):
#Calc plot bounds based on tolerance of pressure
itolMin = 0
itolMax = len(dRatios)-1
if(doShow): makeShowable()
fig, ax = plt.subplots()
ax.plot(kt_arr[itolMin:itolMax], dRatios[itolMin:itolMax], label="D'/D")
ax.plot(kt_arr[itolMin:itolMax], pRatios[itolMin:itolMax], label="P'/P")
ax.plot([kt_arr[itolMin], kt_arr[itolMax]], [1,1], 'k--')
ax.set_xlabel("kT [keV]", fontsize=xlabelsize)
ax.set_ylabel("X'/X", fontsize=ylabelsize)
ax.set_xscale("log")
ax.set_xlim((kt_arr[itolMin], kt_arr[itolMax]))
ax.set_ylim((0.90, 1.1))
ax.legend(loc='upper left', prop={'size': legendsize})
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(ticklabelsize)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(ticklabelsize)
if fracChannelUse is not None:
ax2=ax.twinx()
ax2.plot(kt_arr, fracChannelUse, '-.', color="red", label="frac. channel use")
ax2.set_ylabel("Fraction of channel use", color="red", fontsize=ylabelsize)
#ax2.set_ylim((0,1))
ax2.tick_params(axis="y", colors="red", labelsize=ticklabelsize)
#for tick in ax2.yaxis.get_major_ticks():
# tick.label.set_fontsize(ticklabelsize)
plt.suptitle(tag, fontsize=titlesize)
if(doShow): plt.show()
return ax, ax2
def plotRatios_dpRange2D(dens_arr, press_arr, dRatios, pRatios, tag, doShow=False):
#dd, pp = np.meshgrid(dens_arr, press_arr, indexing='ij')
dd, pp = np.meshgrid(dens_arr, press_arr)
if(doShow): makeShowable()
fig, (ax1, ax2) = plt.subplots(1,2)
lvl_min = .85
lvl_max = 1.15
levels = np.linspace(lvl_min, lvl_max, 10)
level_ticks = np.linspace(lvl_min, lvl_max, 5, endpoint=True)
level_tick_str = ["{:1.2f}".format(x) for x in level_ticks]
CS_d = ax1.contourf(dd*1E-6, pp*1E9, dRatios, levels, cmap=plt.cm.plasma)
cbar_d = fig.colorbar(CS_d)
cbar_d.set_ticks(level_ticks)
cbar_d.set_ticklabels(level_tick_str)
ax1.set_xlabel("Density [1/cc]")
ax1.set_ylabel("Pressure [nPa]")
ax1.set_xscale("log")
ax1.set_yscale("log")
ax1.set_title("D'/D")
CS_p = ax2.contourf(dd*1E-6, pp*1E9, pRatios, levels, cmap=plt.cm.plasma)
#cbar_p = fig.colorbar(CS_p)
#cbar_p.set_ticks(level_ticks)
#cbar_p.set_ticklabels(level_tick_str)
ax2.set_xlabel("Density [1/cc]")
ax2.set_ylabel("Pressure [nPa]")
ax2.set_xscale("log")
ax2.set_yscale("log")
ax2.set_title("P'/P")
plt.suptitle(tag)
if(doShow): plt.show()
return ax1, ax2
#def superPlot(filename=None):
# fig, axs = plt.subplots()
#Graveyard
""" log levels
#lev_exp = np.linspace(np.floor(np.log10(z.min())-1), np.ceil(np.log10(z.max())+1), 100)
lev_exp = np.linspace(np.floor(np.log10(lvl_min)), np.ceil(np.log10(lvl_max)), 100)
levels = np.power(10, lev_exp)
#lev_tick_exp = np.linspace(np.floor(np.log10(z.min())-1), np.ceil(np.log10(z.max())+1), 5)
lev_tick_exp = np.linspace(np.floor(np.log10(lvl_min)), np.ceil(np.log10(lvl_max)), 5)
level_ticks = np.power(10, lev_tick_exp)
level_tick_str = ["{:1.2e}".format(x) for x in level_ticks]
CS_d = ax.contourf(dd*1E-6, pp*1E9, z, levels, norm=colors.LogNorm(), cmap=plt.cm.plasma)
"""
#Adjust x range for ratio_1D plot
"""
tol_min = 0.75
tol_max = 1.25
for i in range(len(kt_arr)-1):
#Try to capture transitions from in to out of tolerance, and visa-versa
if (pRatios[i] > tol_min and pRatios[i+1] < tol_min) \
or (pRatios[i] < tol_min and pRatios[i+1] > tol_min) \
or (pRatios[i] > tol_max and pRatios[i+1] < tol_max) \
or (pRatios[i] < tol_max and pRatios[i+1] > tol_max):
if itolMin == 0:
itolMin = i
print("set itolMin to" + str(itolMin))
break
for i in range(len(kt_arr)-2, 0, -1):
if (pRatios[i] > tol_min and pRatios[i+1] < tol_min) \
or (pRatios[i] < tol_min and pRatios[i+1] > tol_min) \
or (pRatios[i] > tol_max and pRatios[i+1] < tol_max) \
or (pRatios[i] < tol_max and pRatios[i+1] > tol_max):
itolMax = i
print("set itolMax to" + str(itolMax))
break
"""
#itolMax = len(dRatios)-1
#print("{} (dR = {}, pR = {})".format(itolMin, dRatios[itolMin], pRatios[itolMin]))
#print("{} (dR = {}, pR = {})".format(itolMax, dRatios[itolMax], pRatios[itolMax]))

47
kaipy/rcm/rcminit.py
View File

@@ -1,47 +0,0 @@
#Various routines to generate RCM data
import numpy as np
def LoadLAS1(fIn="rcmlas1"):
import os
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fIn = os.path.join(__location__,fIn)
print("Reading %s"%(fIn))
Q = np.loadtxt(fIn,skiprows=2)
alamc = Q[:,0]
etac = Q[:,1]
ikflavc = Q[:,2]
fudgec = Q[:,3]
return alamc,etac,ikflavc,fudgec
def LoadDKTab(fIn="dktable"):
import os
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fIn = os.path.join(__location__,fIn)
print("Reading %s"%(fIn))
Q = np.loadtxt(fIn)
return Q.flatten()
def LoadEnchan(fIn="enchan.dat"):
import os
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fIn = os.path.join(__location__,fIn)
print("Reading %s"%(fIn))
Q = np.loadtxt(fIn,skiprows=1)
iflavin = Q[:,0]
alamin = Q[:,1]
return iflavin,alamin

92
kaipy/rcm/rcmlas1
View File

@@ -1,92 +0,0 @@
0000000090
(2(TR2,ES23.15), (TR2,I10.10), (TR2,ES23.15))
0.000000000000000E-00 0.000000000000000E+00 0000000001 0.000000000000000E-00
-1.000000000000000E+00 0.000000000000000E+00 0000000001 3.332999944686890E-01
-2.000000000000000E+00 0.000000000000000E+00 0000000001 3.332999944686890E-01
-3.000000000000000E+00 0.000000000000000E+00 0000000001 3.332999944686890E-01
-5.000000000000000E+00 0.000000000000000E+00 0000000001 3.332999944686890E-01
-7.500000000000000E+00 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.000000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.250000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.500000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-2.000000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-3.500000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-5.000000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-7.000000000000000E+01 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.000000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.250000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-2.000000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-3.500000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-6.000000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-8.000000000000000E+02 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.000000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-1.500000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-2.500000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-3.000000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-3.500000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-5.000000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-6.000000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-7.500000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
-8.700000000000000E+03 0.000000000000000E+00 0000000001 3.332999944686890E-01
5.000000000000000E+00 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.500000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.500000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
5.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
6.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
7.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
8.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
9.000000000000000E+01 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.100000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.200000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.300000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.400000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.500000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.600000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.700000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.800000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.900000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.100000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.200000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.350000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.500000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.750000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.500000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.500000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
5.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
6.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
7.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
8.000000000000000E+02 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.250000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.750000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
5.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
5.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
6.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
6.500000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
7.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
8.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
9.000000000000000E+03 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.000000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.100000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.200000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
1.800000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
2.400000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
3.000000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
4.000000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
5.000000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00
6.000000000000000E+04 0.000000000000000E+00 0000000002 0.000000000000000E+00

133
kaipy/rcm/rcmutils.py
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@@ -1,133 +0,0 @@
import numpy as np
import h5py as h5
import kaipy.kdefs as kd
#------
# Factors
#------
massi = kd.Mp_cgs*1e-3 # mass of ions in kg
masse = kd.Me_cgs*1e-3 # mass of lectrons in kg
nt = 1.0e-9 # nt
ev = kd.eCharge # 1ev in J
rx = kd.Re_cgs*1e-2 # radius of earth in m
pressure_factor = 2./3.*ev/rx*nt
specFlux_factor_i = 1/np.pi/np.sqrt(8)*np.sqrt(ev/massi)*nt/rx/1.0e1 # [units/cm^2/keV/str]
specFlux_factor_e = 1/np.pi/np.sqrt(8)*np.sqrt(ev/masse)*nt/rx/1.0e1 # [units/cm^2/keV/str]
TINY = 1.0e-8
def updateFactors(rxNew):
global rx
global pressure_factor
global specFlux_factor_i
global specFlux_factor_e
rx = rxNew # [m]
pressure_factor = 2./3.*ev/rx*nt
specFlux_factor_i = 1/np.pi/np.sqrt(8)*np.sqrt(ev/massi)*nt/rx/1.0e1 # [units/cm^2/keV/str]
specFlux_factor_e = 1/np.pi/np.sqrt(8)*np.sqrt(ev/masse)*nt/rx/1.0e1 # [units/cm^2/keV/str]
#------
# Lambda Channels
#------
#electrons: kStart = kStart, kEnd = kIon
#ions: kStart = kIon, kEnd = len(rcmS0['alamc'])
def getSpecieslambdata(rcmS0, species='ions'):
#Determine what our bounds are
kStart = 1 if rcmS0['alamc'][0] == 0 else 0 # Check channel 0 for plasmasphere
kIon = (rcmS0['alamc'][kStart:]>0).argmax()+kStart
if species == 'electrons':
kEnd = kIon
elif species == 'ions':
kStart = kIon
kEnd = len(rcmS0['alamc'])
ilamc = rcmS0['alamc'][kStart:kEnd] # Cell centers
Nk = len(ilamc)
ilami = np.zeros(Nk+1) # Cell interfaces
for n in range(0, Nk-1):
ilami[n+1] = 0.5*(ilamc[n]+ilamc[n+1])
ilami[Nk] = ilamc[-1] + 0.5*(ilamc[-1]-ilamc[-2])
ilamc = np.abs(ilamc)
ilami = np.abs(ilami)
lamscl = np.diff(ilami)*np.sqrt(ilamc)
result = { 'kStart': kStart,
'kEnd' : kEnd,
'ilamc' : ilamc,
'ilami' : ilami,
'lamscl' : lamscl}
return result
#------
# Domain
#------
def getClosedRegionMask(s5):
rmin = np.sqrt(s5['rcmxmin'][:]**2 + s5['rcmymin'][:]**2)
mask = np.full(s5['rcmxmin'].shape, False)
mask = (s5['rcmvm'][:] <= 0) | (rmin < 1.0)
return mask
#------
# Cumulative pressure
#------
def getCumulPress(ilamc, vm, eetas, doFraction=False):
""" Returns 3D array of cumulative presures
ilamc: [Nk] lambda values (Nk NOT the full number of energy channels, can be subset)
vm : [Nj,Ni] vm value
eetas: [Nk,Nj,Ni] eetas corresponding to ilamc values
doFraction: return cumulative pressure fraction instead of cumulative pressure
"""
Nk = len(ilamc)
Nj,Ni = vm.shape
pCumul = np.zeros((Nk,Nj,Ni))
ilam_kji = ilamc[:,np.newaxis,np.newaxis]
vm_kji = vm[np.newaxis, :, :]
pPar = pressure_factor*ilam_kji*eetas*vm_kji**2.5 * 1E9 # [Pa -> nPa], partial pressures for each channel
pCumul[0] = pPar[0] # First bin's partial press = cumulative pressure
for k in range(1,Nk):
pCumul[k] = pCumul[k-1] + pPar[k] # Get current cumulative pressure by adding this bin's partial onto last bin's cumulative
if not doFraction:
return pCumul
else:
return pCumul/pCumul[-1]
def getValAtLoc_linterp(val, xData, yData, getAxis='y'):
""" Use linear interpolation to find desired x/y values in data
val: x/y value to find y/x location of
xData/yData: 1D arrays of equal length
getAxis: desired axis. If 'y', assumes targets are x values, and visa versa
"""
idx = 0
if getAxis == 'y': # target is x-axis value, find its location in xData
while xData[idx+1] < val: idx += 1
elif getAxis == 'x': # target is y-axis value, find its location in yData
while yData[idx+1] < val: idx += 1
m = (yData[idx+1] - yData[idx])/(xData[idx+1] - xData[idx])
b = yData[idx] - m*xData[idx]
if getAxis == 'y':
loc = m*val + b
elif getAxis == 'x':
loc = (val - b)/m
return loc

112
kaipy/rcm/rcmxdmf.py
View File

@@ -1,112 +0,0 @@
"""
RCM-specific tools to include h5 data in xmf files
"""
import h5py as h5
import kaipy.kaixdmf as kxmf
"""
def AddGrid(fname,Geom,iDims,coordStrs):
for coordStr in coordStrs:
xC = et.SubElement(Geom,"DataItem")
xC.set("Dimensions",iDims)
xC.set("NumberType","Float")
xC.set("Precision","4")
xC.set("Format","HDF")
text = fname+":/"+coordStr
xC.text = text
"""
def addRCMGeom(Geom, dimInfo, rcmInfo, sID):
#Unpack
rcmh5fname = rcmInfo['rcmh5fname']
sIDstr = "Step#" + str(sID)
gridVars = dimInfo['gridVars'] # Expect something like rcmxmin,rcmymin,alamc
gDims = dimInfo['gDims'] # Someone before us should set kji dims
#gDims[1] -= 2 # Remove 2 j vals (jWrap)
#all
dSetDimStr = ' '.join([str(v) for v in gDims])
hsDimStr = "3 2" # row, col
startStr = "2 0"
strideStr = "1 1"
numStr = "359 180"
origDSetDimStr = str(gDims[0])
fileText = "{}:/{}/{}".format(rcmh5fname,sIDstr,'rxmxmin')
kxmf.addHyperslab_DI(Geom, dSetDimStr, hsDimStr, startStr, strideStr, numStr, origDSetDimStr, fileText)
fileText = "{}:/{}/{}".format(rcmh5fname,sIDstr,'rcmymin')
kxmf.addHyperslab_DI(Geom, dSetDimStr, hsDimStr, startStr, strideStr, numStr, origDSetDimStr, fileText)
hsDimStr = "3 1"
startStr = "0"
strideStr = "1"
numStr = "140"
fileText = "{}:/{}/{}".format(rcmh5fname,sIDstr,'alamc')
kxmf.addHyperslab_DI(Geom, dSetDimStr, hsDimStr, startStr, strideStr, numStr, origDSetDimStr, fileText)
def addRCMVars(Grid, dimInfo, rcmInfo, sID): # Used with the mhdrcm presets to add links to given RCM variables
sIDstr = "Step#" + str(sID)
trg_vDims = dimInfo['vDims'] # target file var dims (probably mhdrcm or rcm)
trg_vDimStr = ' '.join([str(v) for v in trg_vDims])
trg_nDims = len(trg_vDims)
rcmh5fname = rcmInfo['rcmh5fname']
rcmVars = rcmInfo['rcmVars'] # List of rcm.h5 variables we want in mhdrcm.xmf
rcmKs = rcmInfo['rcmKs'] # List if rcm.h5 k values for 3d rcm.h5 vars
rcm5 = h5.File(rcmh5fname,'r')
if 'Nk' not in rcmInfo.keys():
rcmInfo['Nj'], rcmInfo['Ni'] = rcm5[sIDstr]['aloct'].shape
rcmInfo['Nk'] = rcm5[sIDstr]['alamc'].shape[0]
#print("Adding Nk to rcmInfo")
Ni = rcmInfo['Ni']
Nj = rcmInfo['Nj']
Nk = rcmInfo['Nk']
for vName in rcmVars:
doHyperslab = False
r_var = rcm5[sIDstr][vName]
r_vShape = r_var.shape
r_vDimStr = " ".join([str(d) for d in r_vShape])
r_nDims = len(r_vShape)
dimTrim = 0
if (r_nDims == 2 and trg_vDims[0] < Nj) or (r_nDims == 3 and trg_vDims[1] < Nj):
doHyperslab = True
dimTrim = (Nj - trg_vDims[0]) if trg_nDims == 2 else (Nj - trg_vDims[1]) # Shortening j-dir
if r_nDims == 2 and doHyperslab == False: # Easy add
#print("Adding " + vName)
kxmf.AddData(Grid,rcmh5fname, vName,"Cell",trg_vDimStr,sID)
continue
#Add data as a hyperslab
if doHyperslab:
#Do 2D stuff. If 3D needed, will be added in a sec
dimStr = "3 2"
startStr = "{} 0".format(dimTrim)
strideStr = "1 1"
numStr = "{} {}".format(Nj-dimTrim, Ni)
text = "{}:/{}/{}".format(rcmh5fname,sIDstr,vName)
if r_nDims == 2:
kxmf.addHyperslab_Attr(Grid,vName,trg_vDimStr,dimStr,startStr,strideStr,numStr,r_vDimStr,text)
continue
elif r_nDims == 3:
dimStr = "3 3"
strideStr = str(Nk+1) + " 1 1"
numStr = "1 {} {}".format(Nj-dimTrim,Ni)
for k in rcmKs:
startStr = "{} {} 0".format(k,dimTrim)
vName_k = vName + "_k{}".format(k)
kxmf.addHyperslab_Attr(Grid,vName_k,trg_vDimStr,dimStr,startStr,strideStr,numStr,r_vDimStr,text)

0
kaipy/rcm/wmutils/__init__.py
View File

169
kaipy/rcm/wmutils/chorus_polynomial.txt
View File

@@ -1,169 +0,0 @@
MLT Kp Intercept L log10(E) L^2 log10(E)^2 L^3 log10(E)^3 log10(E)*L log10(E)*L^2 log10(E)^2*L Adjusted R2
0 1 -9.717181e+00 7.204274e+00 -3.773921e+00 -1.496923e+00 -8.897443e-01 1.122206e-01 1.720684e-01 7.319817e-01 7.886410e-02 3.525678e-01 0.82448
0 2 -2.945411e+00 3.066352e+00 -3.141152e+00 -6.402161e-01 -1.039579e+00 5.404518e-02 1.168904e-01 9.266642e-01 5.994503e-02 3.966304e-01 0.93159
0 3 9.890714e-01 6.886358e-01 -1.466153e+00 -1.451928e-01 -4.153113e-01 2.069758e-02 1.924237e-01 9.012235e-01 5.583555e-02 3.945901e-01 0.95829
0 4 3.852039e+00 -1.176268e+00 -4.278696e-01 2.504448e-01 9.717711e-02 -5.547521e-03 2.740005e-01 9.408564e-01 5.187616e-02 4.049745e-01 0.96932
0 5 6.006599e+00 -2.177471e+00 1.988820e+00 4.797396e-01 1.590386e+00 -2.211532e-02 5.205646e-01 1.016683e+00 3.518574e-02 3.863031e-01 0.97468
0 6 9.362199e+00 -4.631816e+00 2.171715e+00 9.973425e-01 1.628007e+00 -5.584455e-02 5.182470e-01 9.777994e-01 3.918595e-02 3.857173e-01 0.97183
0 7 1.407725e+01 -7.921180e+00 2.665594e+00 1.685897e+00 1.719137e+00 -1.007732e-01 5.024012e-01 8.108719e-01 4.952734e-02 3.557380e-01 0.96121
1 1 -7.359401e+00 5.884180e+00 -2.919064e+00 -1.280964e+00 -7.194898e-01 1.004658e-01 1.848896e-01 4.476771e-01 9.795562e-02 3.246588e-01 0.8113
1 2 -9.211592e-01 1.879919e+00 -2.331452e+00 -4.555934e-01 -9.799721e-01 4.440510e-02 9.607246e-02 5.884254e-01 8.034954e-02 3.569996e-01 0.93061
1 3 2.214357e+00 -1.302451e-01 -1.253408e+00 -2.825422e-02 -6.760874e-01 1.478623e-02 1.114119e-01 6.211463e-01 6.977352e-02 3.569444e-01 0.95885
1 4 4.602497e+00 -1.757413e+00 -5.268525e-01 3.225730e-01 -3.095982e-01 -8.982241e-03 1.642781e-01 7.068561e-01 5.973179e-02 3.663537e-01 0.96771
1 5 5.647597e+00 -2.090594e+00 2.072220e+00 4.521858e-01 1.630797e+00 -2.093337e-02 5.073641e-01 9.731945e-01 2.705271e-02 3.561372e-01 0.9769
1 6 9.041642e+00 -4.539197e+00 2.387552e+00 9.639003e-01 1.697711e+00 -5.406964e-02 5.076273e-01 8.951338e-01 3.382134e-02 3.521105e-01 0.97444
1 7 1.358006e+01 -7.724426e+00 2.883817e+00 1.632235e+00 1.787948e+00 -9.760212e-02 4.934266e-01 7.232415e-01 4.596935e-02 3.247448e-01 0.96432
2 1 -3.874943e+00 4.102231e+00 -1.373509e+00 -1.041292e+00 -4.183570e-01 9.227025e-02 2.361563e-01 -1.138304e-01 1.485082e-01 3.037375e-01 0.7916
2 2 1.135135e+00 8.053878e-01 -1.219720e+00 -3.253353e-01 -8.572429e-01 4.055670e-02 9.456559e-02 1.233136e-01 1.146499e-01 3.188955e-01 0.92407
2 3 3.470548e+00 -8.068562e-01 -5.982210e-01 3.832536e-02 -7.467759e-01 1.380794e-02 7.074457e-02 2.502543e-01 9.399586e-02 3.198763e-01 0.95775
2 4 5.298927e+00 -2.116907e+00 -7.789224e-02 3.310106e-01 -4.724148e-01 -6.974028e-03 9.885310e-02 3.806822e-01 7.604378e-02 3.223885e-01 0.96672
2 5 5.390708e+00 -2.080829e+00 1.977300e+00 4.387184e-01 1.568525e+00 -2.012174e-02 4.844172e-01 9.273081e-01 2.346362e-02 3.360744e-01 0.97834
2 6 8.740577e+00 -4.493841e+00 2.354275e+00 9.429094e-01 1.665505e+00 -5.276517e-02 4.895662e-01 8.388821e-01 3.088775e-02 3.306408e-01 0.97598
2 7 1.312859e+01 -7.579869e+00 2.884980e+00 1.591603e+00 1.778583e+00 -9.498086e-02 4.806610e-01 6.638055e-01 4.400758e-02 3.043388e-01 0.96631
3 1 -3.007360e+00 3.878241e+00 -2.503594e-01 -1.104900e+00 -2.463186e-01 1.027349e-01 2.620712e-01 -5.812605e-01 1.875912e-01 2.802112e-01 0.76887
3 2 1.428129e+00 7.946179e-01 -4.736181e-01 -4.029787e-01 -8.095079e-01 5.002179e-02 9.131136e-02 -2.468938e-01 1.429859e-01 2.920961e-01 0.91249
3 3 3.492771e+00 -7.037845e-01 -6.385505e-02 -4.812099e-02 -7.847110e-01 2.270458e-02 4.455866e-02 -6.340309e-02 1.147853e-01 2.888142e-01 0.95362
3 4 5.076508e+00 -1.881944e+00 3.548632e-01 2.252584e-01 -5.466503e-01 2.435504e-03 5.898316e-02 1.014577e-01 9.081530e-02 2.853255e-01 0.96397
3 5 5.003611e+00 -1.994916e+00 1.763108e+00 4.165897e-01 1.480360e+00 -1.889926e-02 4.619276e-01 9.120572e-01 1.959584e-02 3.219342e-01 0.97861
3 6 8.287509e+00 -4.367077e+00 2.157762e+00 9.136333e-01 1.595472e+00 -5.121285e-02 4.693425e-01 8.258174e-01 2.614249e-02 3.146205e-01 0.97598
3 7 1.256192e+01 -7.383831e+00 2.689740e+00 1.549352e+00 1.722078e+00 -9.259352e-02 4.643921e-01 6.559621e-01 3.934115e-02 2.896038e-01 0.96627
4 1 1.050305e+00 7.848777e-01 9.479916e-01 -3.083059e-01 1.306091e+00 3.153739e-02 4.688779e-01 -2.334505e-01 6.162030e-02 7.291783e-02 0.68054
4 2 4.040210e+00 -1.134349e+00 -1.503822e-01 7.706201e-02 -9.964354e-02 6.942414e-03 1.651045e-01 -2.702495e-02 5.874122e-02 1.475941e-01 0.89639
4 3 4.404270e+00 -1.469072e+00 1.979190e-03 1.664247e-01 -4.765612e-01 -5.731605e-05 3.152533e-02 7.061707e-02 5.191952e-02 1.669078e-01 0.95584
4 4 4.323673e+00 -1.548876e+00 2.847488e-01 2.024510e-01 -4.088867e-01 -3.419682e-03 -1.370144e-03 2.202261e-01 3.701387e-02 1.739477e-01 0.96832
4 5 2.515772e+00 -4.929388e-01 1.519877e+00 1.045296e-01 1.563366e+00 1.304891e-03 4.611017e-01 9.923663e-01 8.081022e-03 3.041743e-01 0.97983
4 6 3.614171e+00 -1.434655e+00 2.191388e+00 3.093934e-01 2.156221e+00 -1.268924e-02 5.007234e-01 1.031511e+00 -8.471922e-03 2.473691e-01 0.97928
4 7 1.181002e+00 8.305165e-02 4.211134e+00 5.334156e-04 3.472403e+00 6.208793e-03 4.135005e-01 8.435557e-01 -4.340292e-02 -2.193345e-02 0.90882
5 1 -3.635509e-01 1.537124e+00 6.839448e-01 -4.374485e-01 1.477097e+00 3.619611e-02 5.420787e-01 -3.200998e-02 4.117443e-02 1.085585e-01 0.66573
5 2 2.730877e+00 -4.037838e-01 -3.262462e-01 -5.815076e-02 8.927091e-02 1.267726e-02 2.225624e-01 1.105053e-01 3.799409e-02 1.519576e-01 0.85975
5 3 3.130800e+00 -7.408152e-01 -8.019317e-03 2.688265e-02 -2.800136e-01 6.361500e-03 6.581481e-02 1.283701e-01 3.327723e-02 1.422884e-01 0.94739
5 4 2.955802e+00 -7.650633e-01 2.303200e-01 5.182572e-02 -2.653012e-01 3.848474e-03 1.075704e-02 2.684518e-01 1.739558e-02 1.381535e-01 0.96545
5 5 2.124358e+00 -4.028353e-01 1.101238e+00 9.212299e-02 1.452351e+00 2.396013e-03 4.613711e-01 1.037621e+00 1.078170e-02 3.217369e-01 0.97517
5 6 5.591571e+00 -2.882277e+00 1.412788e+00 6.001848e-01 1.516588e+00 -3.005855e-02 4.286400e-01 9.915549e-01 1.723768e-02 3.235609e-01 0.95989
5 7 4.501496e-01 3.031204e-01 3.365214e+00 -1.985564e-02 3.409760e+00 6.876650e-03 4.318068e-01 1.082152e+00 -5.730041e-02 1.509701e-03 0.90629
6 1 -1.082603e+00 1.875584e+00 9.310370e-01 -4.950986e-01 1.787906e+00 3.858447e-02 6.521055e-01 1.961963e-02 4.202852e-02 1.523830e-01 0.71376
6 2 2.226915e+00 -1.704689e-01 -3.963971e-01 -1.029844e-01 3.216038e-01 1.479182e-02 3.368555e-01 1.955210e-01 3.696059e-02 1.988433e-01 0.8417
6 3 2.709992e+00 -5.353846e-01 -5.663882e-02 -1.585543e-02 -1.096716e-01 8.494261e-03 1.598446e-01 1.609246e-01 3.461982e-02 1.766625e-01 0.9334
6 4 2.586263e+00 -5.847657e-01 1.943038e-01 1.230286e-02 -1.479929e-01 5.925418e-03 8.955435e-02 2.580448e-01 2.255908e-02 1.664718e-01 0.95531
6 5 1.504292e+00 -1.921066e-01 4.736238e-01 4.748769e-02 1.344912e+00 6.977116e-03 5.404359e-01 1.107407e+00 2.810924e-02 4.008609e-01 0.96374
6 6 4.466400e+00 -2.382881e+00 6.159318e-01 5.076358e-01 1.471883e+00 -2.276222e-02 5.221047e-01 1.166062e+00 2.602738e-02 4.047381e-01 0.94311
6 7 2.351494e-01 1.666321e-01 2.214849e+00 2.901656e-02 3.314877e+00 4.447027e-03 5.253594e-01 1.391421e+00 -5.814033e-02 9.645109e-02 0.91061
7 1 -1.609647e+00 2.138282e+00 9.978059e-01 -5.454796e-01 2.010335e+00 4.129492e-02 7.025682e-01 7.441586e-02 3.843694e-02 1.605779e-01 0.74681
7 2 1.205435e+00 4.161637e-01 -6.240078e-01 -2.239074e-01 5.056336e-01 2.247393e-02 4.051097e-01 2.968045e-01 3.199971e-02 2.171849e-01 0.82391
7 3 1.735397e+00 2.397207e-02 -2.982400e-01 -1.309153e-01 3.509832e-02 1.574091e-02 2.127733e-01 2.436606e-01 2.895244e-02 1.849069e-01 0.91497
7 4 1.747765e+00 -1.078381e-01 -6.498805e-02 -8.557804e-02 -6.364141e-02 1.200978e-02 1.234992e-01 3.194689e-01 1.761723e-02 1.681927e-01 0.94378
7 5 5.800442e-01 2.740269e-01 -1.060418e-01 -3.751396e-02 1.318171e+00 1.332550e-02 5.758035e-01 1.244108e+00 3.008228e-02 4.363843e-01 0.95472
7 6 3.240336e+00 -1.725721e+00 5.217625e-02 3.879902e-01 1.535061e+00 -1.431980e-02 5.734181e-01 1.338752e+00 2.441768e-02 4.362915e-01 0.9364
7 7 -3.419454e-01 3.364636e-01 1.162533e+00 2.115812e-02 3.261958e+00 5.064395e-03 5.677576e-01 1.717444e+00 -7.004800e-02 1.465979e-01 0.91279
8 1 -1.308247e+00 1.888381e+00 9.812949e-01 -4.930137e-01 2.128228e+00 3.763054e-02 7.285720e-01 1.276625e-01 3.701688e-02 1.744106e-01 0.78102
8 2 6.496435e-01 7.090310e-01 -9.210358e-01 -2.847685e-01 6.275309e-01 2.650405e-02 4.616605e-01 4.027105e-01 2.954339e-02 2.436590e-01 0.81642
8 3 1.224925e+00 2.956671e-01 -5.856631e-01 -1.880907e-01 1.435859e-01 1.951742e-02 2.621966e-01 3.290408e-01 2.645165e-02 2.028016e-01 0.89064
8 4 1.362127e+00 9.646588e-02 -2.953666e-01 -1.299670e-01 2.750683e-02 1.496309e-02 1.621236e-01 3.674189e-01 1.694558e-02 1.768604e-01 0.92377
8 5 4.360637e-02 5.273507e-01 -5.878643e-01 -8.277919e-02 1.336013e+00 1.756848e-02 6.253131e-01 1.358198e+00 3.779334e-02 4.803578e-01 0.94084
8 6 2.251631e+00 -1.218175e+00 -5.475816e-01 3.014285e-01 1.634329e+00 -7.652272e-03 6.444620e-01 1.540731e+00 2.635283e-02 4.850513e-01 0.92387
8 7 -6.076540e-01 3.305252e-01 1.344023e-01 4.882027e-02 3.284135e+00 3.630749e-03 6.318609e-01 2.061262e+00 -8.015682e-02 2.045887e-01 0.91829
9 1 -5.066641e-01 1.329285e+00 9.461658e-01 -3.763959e-01 2.240101e+00 2.978094e-02 7.368882e-01 1.972342e-01 3.197202e-02 1.768036e-01 0.81157
9 2 9.160371e-02 1.003259e+00 -1.122999e+00 -3.433058e-01 8.014770e-01 3.033769e-02 5.132520e-01 4.999701e-01 2.610510e-02 2.586349e-01 0.82344
9 3 6.441270e-01 6.074673e-01 -8.280261e-01 -2.502328e-01 3.001515e-01 2.354826e-02 3.102515e-01 4.242577e-01 2.217524e-02 2.137150e-01 0.86982
9 4 9.621762e-01 3.067436e-01 -4.664940e-01 -1.727513e-01 1.712550e-01 1.775645e-02 2.018135e-01 4.221124e-01 1.508525e-02 1.801912e-01 0.90089
9 5 -5.807546e-01 8.358583e-01 -9.227595e-01 -1.324629e-01 1.486863e+00 2.185117e-02 6.931474e-01 1.487158e+00 4.400470e-02 5.199000e-01 0.92804
9 6 9.998263e-01 -5.206567e-01 -9.205089e-01 1.792725e-01 1.868123e+00 1.036909e-03 7.270077e-01 1.728786e+00 2.663989e-02 5.212735e-01 0.91573
9 7 -1.395993e+00 6.936340e-01 -5.686723e-01 6.332513e-04 3.424236e+00 7.041325e-03 7.011817e-01 2.342476e+00 -8.797079e-02 2.473096e-01 0.92407
10 1 6.079346e-01 5.764501e-01 8.115558e-01 -2.184695e-01 2.348472e+00 1.915197e-02 7.311287e-01 3.106179e-01 2.082615e-02 1.686596e-01 0.838
10 2 -7.268649e-01 1.466423e+00 -1.092733e+00 -4.325192e-01 1.038170e+00 3.593924e-02 5.489411e-01 5.537068e-01 2.192478e-02 2.517090e-01 0.83772
10 3 2.556828e-01 7.956948e-01 -1.003445e+00 -2.837742e-01 5.126386e-01 2.553391e-02 3.518927e-01 5.288852e-01 1.452997e-02 2.116595e-01 0.86064
10 4 7.065774e-01 4.202723e-01 -6.093885e-01 -1.918654e-01 3.621433e-01 1.879915e-02 2.357079e-01 5.007935e-01 8.970089e-03 1.736803e-01 0.88263
10 5 -9.592237e-01 1.000408e+00 -1.082641e+00 -1.462954e-01 1.769092e+00 2.324966e-02 7.676456e-01 1.630683e+00 4.522151e-02 5.448054e-01 0.92157
10 6 -5.821246e-02 8.253724e-02 -1.046605e+00 8.037739e-02 2.225443e+00 7.622167e-03 8.093918e-01 1.903019e+00 2.274182e-02 5.378206e-01 0.91615
10 7 -2.338184e+00 1.223269e+00 -7.855223e-01 -8.592414e-02 3.651338e+00 1.259107e-02 7.501100e-01 2.502162e+00 -9.368997e-02 2.579936e-01 0.93182
11 1 1.584285e+00 -9.233146e-02 4.913702e-01 -7.566464e-02 2.481888e+00 9.490070e-03 7.329837e-01 4.912006e-01 3.914822e-03 1.591060e-01 0.85812
11 2 -1.233141e+00 1.724787e+00 -9.581509e-01 -4.738511e-01 1.320496e+00 3.796105e-02 5.644950e-01 6.102217e-01 1.219256e-02 2.222982e-01 0.85947
11 3 1.827068e-01 7.765828e-01 -1.183786e+00 -2.699423e-01 7.669048e-01 2.408920e-02 3.812356e-01 6.690887e-01 2.258100e-04 1.942093e-01 0.86762
11 4 6.871972e-01 3.742267e-01 -7.970033e-01 -1.730136e-01 5.907723e-01 1.701475e-02 2.586978e-01 6.313598e-01 -4.919297e-03 1.544219e-01 0.87667
11 5 -9.381048e-01 9.140218e-01 -1.186769e+00 -1.013428e-01 2.127841e+00 1.981853e-02 8.292878e-01 1.814266e+00 3.555110e-02 5.461664e-01 0.92408
11 6 -4.972398e-01 3.141483e-01 -1.051545e+00 5.992790e-02 2.625577e+00 8.281900e-03 8.692018e-01 2.075338e+00 1.109089e-02 5.285473e-01 0.92303
11 7 -2.912724e+00 1.566532e+00 -7.009429e-01 -1.358704e-01 3.905999e+00 1.504579e-02 7.656498e-01 2.588276e+00 -1.026651e-01 2.357367e-01 0.94093
12 1 3.795952e+00 -1.609479e+00 -3.957772e-01 2.542400e-01 2.626393e+00 -1.324950e-02 7.607905e-01 8.914563e-01 -3.213962e-02 1.607167e-01 0.81496
12 2 9.414399e-01 7.276113e-03 -2.638211e+00 -4.937976e-02 2.464290e+00 5.299087e-03 5.154030e-01 1.694110e+00 -1.217717e-01 1.060381e-02 0.92512
12 3 -1.826336e+00 2.001220e+00 -1.062499e+00 -5.044658e-01 1.421460e+00 3.846524e-02 3.475318e-01 8.563474e-01 -3.515948e-02 7.200227e-02 0.93612
12 4 1.091590e-01 6.744938e-01 -1.006791e+00 -2.180554e-01 1.035742e+00 1.896149e-02 2.513562e-01 8.626383e-01 -3.680987e-02 8.137101e-02 0.90779
12 5 -2.650530e-01 3.934711e-01 -1.363908e+00 4.468812e-02 2.491388e+00 8.265016e-03 8.734229e-01 2.064115e+00 1.198487e-02 5.295803e-01 0.92943
12 6 1.403355e+00 -9.923685e-01 -1.340616e+00 3.576970e-01 2.578394e+00 -1.248317e-02 9.303741e-01 2.201778e+00 1.047900e-02 5.851626e-01 0.89932
12 7 2.509748e+00 -1.973148e+00 -1.179463e+00 5.883023e-01 2.987827e+00 -2.935602e-02 1.016046e+00 2.393637e+00 -1.646417e-02 5.747707e-01 0.92602
13 1 4.746942e+00 -2.558156e+00 -1.186727e+00 4.880320e-01 2.900345e+00 -2.996777e-02 6.265849e-01 1.273604e+00 -7.816415e-02 5.587602e-02 0.88353
13 2 4.831451e+00 -2.725641e+00 -2.085771e+00 5.398800e-01 2.502417e+00 -3.465279e-02 3.921621e-01 1.614453e+00 -1.319834e-01 -6.435277e-02 0.95219
13 3 3.992977e+00 -2.157449e+00 -1.657250e+00 4.149461e-01 1.869796e+00 -2.561423e-02 2.871417e-01 1.343384e+00 -1.034915e-01 -3.756972e-02 0.95133
13 4 2.400486e+00 -1.090502e+00 -1.266064e+00 1.786835e-01 1.361613e+00 -8.450192e-03 2.460354e-01 1.080190e+00 -6.583955e-02 2.563368e-02 0.92219
13 5 -9.144487e-01 8.800053e-01 -1.485896e+00 -2.665061e-02 2.909180e+00 1.033890e-02 9.077201e-01 2.325317e+00 -2.244458e-02 4.854397e-01 0.94403
13 6 -3.069663e+00 2.301743e+00 -2.144761e+00 -3.388892e-01 2.369229e+00 3.262324e-02 9.135671e-01 2.383983e+00 -4.136037e-03 5.984553e-01 0.9073
13 7 -1.111552e+01 7.990864e+00 -3.070002e+00 -1.549400e+00 1.867375e+00 1.118725e-01 9.435024e-01 2.560170e+00 -1.446240e-02 6.814598e-01 0.88757
14 1 1.242540e+00 -2.369845e-01 -1.419802e+00 8.260015e-03 2.972485e+00 1.156809e-03 5.212952e-01 1.408200e+00 -1.014738e-01 -2.296493e-02 0.89436
14 2 1.368811e+00 -4.145601e-01 -2.280856e+00 5.740560e-02 2.450007e+00 -2.985853e-03 3.109832e-01 1.682819e+00 -1.424004e-01 -1.039245e-01 0.96163
14 3 1.228625e+00 -3.074402e-01 -1.908024e+00 3.084710e-02 1.861609e+00 -7.324499e-04 2.322341e-01 1.454456e+00 -1.170960e-01 -6.583994e-02 0.95624
14 4 9.318505e-01 -8.583122e-02 -1.478347e+00 -2.767564e-02 1.499320e+00 4.344851e-03 2.301372e-01 1.230955e+00 -8.575766e-02 -3.931169e-03 0.94057
14 5 1.432796e+00 -7.039165e-01 -1.539426e+00 3.447957e-01 3.325479e+00 -1.743970e-02 9.270213e-01 2.570657e+00 -6.012586e-02 4.282312e-01 0.94925
14 6 8.747952e+00 -5.660286e+00 -1.996150e+00 1.383810e+00 2.770045e+00 -8.538469e-02 9.037252e-01 2.494688e+00 -4.085627e-02 5.001229e-01 0.86508
14 7 2.967365e+00 -1.483125e+00 -2.468505e+00 4.985097e-01 2.507725e+00 -2.839179e-02 9.571531e-01 2.559916e+00 -4.860509e-02 5.434484e-01 0.85267
15 1 1.994942e+00 -6.555317e-01 -9.786397e-01 9.222377e-02 2.990598e+00 -4.051119e-03 5.103009e-01 1.267481e+00 -9.162911e-02 -3.369420e-02 0.89008
15 2 1.963584e+00 -7.859760e-01 -2.306874e+00 1.416005e-01 2.554944e+00 -8.779408e-03 3.022099e-01 1.737735e+00 -1.513754e-01 -1.268836e-01 0.9563
15 3 1.458040e+00 -4.180364e-01 -1.778768e+00 5.630375e-02 1.907537e+00 -2.330213e-03 2.224492e-01 1.426373e+00 -1.165456e-01 -7.909000e-02 0.95286
15 4 2.745900e+00 -1.327290e+00 -1.972995e+00 2.563148e-01 1.682334e+00 -1.611474e-02 2.078639e-01 1.520069e+00 -1.218943e-01 -4.948951e-02 0.93794
15 5 1.826867e+00 -9.040947e-01 -1.520564e+00 4.006556e-01 3.544015e+00 -2.263840e-02 9.401753e-01 2.685085e+00 -7.891802e-02 4.005182e-01 0.94983
15 6 9.225378e+00 -5.938465e+00 -2.074143e+00 1.459699e+00 3.004200e+00 -9.190359e-02 9.271197e-01 2.649451e+00 -6.093322e-02 4.793848e-01 0.86549
15 7 3.091311e+00 -1.498489e+00 -2.306911e+00 5.168763e-01 2.754097e+00 -3.127343e-02 9.674050e-01 2.639684e+00 -6.627824e-02 5.084716e-01 0.85807
16 1 3.455794e+00 -1.486545e+00 -4.241646e-01 2.553252e-01 2.897685e+00 -1.393926e-02 4.953749e-01 1.043180e+00 -7.239104e-02 -3.229877e-02 0.88572
16 2 2.605198e+00 -1.151861e+00 -2.244961e+00 2.195486e-01 2.583749e+00 -1.359540e-02 2.940331e-01 1.720754e+00 -1.512312e-01 -1.393042e-01 0.94717
16 3 1.819715e+00 -5.552250e-01 -1.492429e+00 7.627047e-02 1.815396e+00 -2.575816e-03 2.228693e-01 1.269107e+00 -9.778961e-02 -6.373164e-02 0.94071
16 4 1.261123e+00 -1.728350e-01 -1.180981e+00 -1.378674e-02 1.472255e+00 4.495729e-03 2.288470e-01 1.093459e+00 -6.965027e-02 1.820913e-03 0.92354
16 5 1.662075e+00 -5.909674e-01 -1.032808e+00 3.036510e-01 3.420686e+00 -1.432329e-02 9.422668e-01 2.419683e+00 -5.111377e-02 4.185730e-01 0.93755
16 6 9.062144e+00 -5.641461e+00 -1.647969e+00 1.368319e+00 2.893126e+00 -8.392754e-02 9.367054e-01 2.409603e+00 -3.379991e-02 5.021036e-01 0.83891
16 7 2.972117e+00 -1.238725e+00 -1.911060e+00 4.349748e-01 2.623632e+00 -2.402190e-02 9.727758e-01 2.407711e+00 -3.981335e-02 5.321525e-01 0.8466
17 1 6.065370e+00 -3.036624e+00 -1.204084e-02 5.641127e-01 2.756971e+00 -3.286474e-02 4.653054e-01 8.509755e-01 -5.683971e-02 -3.862627e-02 0.87655
17 2 3.923084e+00 -1.907470e+00 -2.128049e+00 3.679667e-01 2.450187e+00 -2.189545e-02 2.936385e-01 1.601658e+00 -1.351094e-01 -1.236702e-01 0.92472
17 3 1.621678e+00 -2.015437e-01 -6.733390e-01 -3.357035e-02 1.440304e+00 8.215388e-03 2.342461e-01 7.653045e-01 -3.439944e-02 5.107264e-03 0.91385
17 4 1.111911e+00 1.243552e-01 -5.019693e-01 -1.052095e-01 1.134048e+00 1.355940e-02 2.308674e-01 6.698652e-01 -1.594878e-02 5.972407e-02 0.88394
17 5 1.171040e+00 2.179109e-02 -3.418401e-01 1.133921e-01 2.971660e+00 2.502135e-03 9.291163e-01 1.896026e+00 9.636920e-03 4.748118e-01 0.90471
17 6 8.870233e+00 -5.244293e+00 -1.112737e+00 1.223429e+00 2.451975e+00 -6.975656e-02 9.381103e-01 1.935719e+00 2.655594e-02 5.699057e-01 0.78699
17 7 3.603072e+00 -1.434442e+00 -1.722606e+00 4.197760e-01 2.101419e+00 -1.864621e-02 9.686834e-01 2.030916e+00 1.464855e-02 6.122669e-01 0.83498
18 1 1.082397e+01 -5.971812e+00 -4.770886e-01 1.146059e+00 2.577005e+00 -6.751868e-02 4.057286e-01 8.848197e-01 -6.116616e-02 -7.655496e-02 0.83153
18 2 6.135939e+00 -3.064357e+00 -1.681550e+00 5.476164e-01 1.634596e+00 -2.726348e-02 2.771841e-01 1.006313e+00 -5.079006e-02 -1.432417e-02 0.83838
18 3 1.724760e+00 -8.414072e-03 -3.017019e-01 -1.301847e-01 7.400475e-01 2.104870e-02 2.109209e-01 2.444144e-01 4.343080e-02 1.012454e-01 0.85289
18 4 2.442864e+00 -5.483793e-01 -5.906341e-01 -6.476063e-03 5.333321e-01 1.231103e-02 2.145601e-01 3.817825e-01 3.833219e-02 1.472210e-01 0.83846
18 5 1.344505e+00 9.699000e-02 -6.337698e-01 5.032382e-02 2.277124e+00 1.129115e-02 9.031503e-01 1.626972e+00 5.876497e-02 5.640278e-01 0.85781
18 6 9.111154e+00 -5.207996e+00 -1.319588e+00 1.167587e+00 1.705285e+00 -6.148092e-02 8.926992e-01 1.622487e+00 7.799110e-02 6.537055e-01 0.76933
18 7 3.367097e+00 -1.055645e+00 -1.444817e+00 2.936776e-01 1.358158e+00 -6.841480e-03 8.764732e-01 1.570444e+00 6.559029e-02 6.528755e-01 0.85086
19 1 1.045173e+01 -4.941345e+00 2.088544e+00 7.367673e-01 1.576796e+00 -2.060349e-02 3.211369e-01 -6.732945e-01 1.313307e-01 3.047173e-02 0.62546
19 2 3.703805e+00 -5.375542e-01 8.605968e-01 -2.386394e-01 2.000571e-01 5.240058e-02 2.518747e-01 -8.312531e-01 2.203020e-01 2.530480e-01 0.76811
19 3 3.272628e+00 -4.558256e-01 -2.863843e-01 -2.107743e-01 -2.212628e-01 4.758636e-02 2.495467e-01 -3.954864e-01 1.979005e-01 3.445951e-01 0.83723
19 4 4.151799e+00 -1.163107e+00 -7.815812e-01 -3.602281e-02 -3.369410e-01 3.421682e-02 2.939125e-01 -1.301308e-01 1.840421e-01 4.095479e-01 0.84295
19 5 2.430916e+00 -5.853717e-01 -1.651001e+00 1.615683e-01 1.360993e+00 7.354574e-03 8.027559e-01 1.550617e+00 8.437972e-02 6.310538e-01 0.8633
19 6 9.589982e+00 -5.442344e+00 -1.902050e+00 1.178300e+00 7.118181e-01 -5.898198e-02 7.425192e-01 1.372019e+00 1.104244e-01 6.923491e-01 0.81967
19 7 3.876753e+00 -1.278652e+00 -1.571642e+00 3.014910e-01 3.879600e-01 -5.220906e-03 6.825008e-01 1.184419e+00 9.635829e-02 6.463116e-01 0.89373
20 1 8.818423e+00 -3.885616e+00 1.977768e+00 5.171642e-01 3.360926e-01 -6.969479e-05 3.518455e-01 -1.178708e+00 2.501245e-01 2.894164e-01 0.40912
20 2 4.558966e+00 -1.402672e+00 -9.888905e-01 3.233388e-02 -4.743974e-01 3.207056e-02 1.957514e-01 -3.101404e-01 2.026337e-01 3.518889e-01 0.78776
20 3 5.278691e+00 -1.976263e+00 -2.136653e+00 1.696426e-01 -8.774690e-01 2.264980e-02 2.230293e-01 1.211223e-01 1.908473e-01 4.719043e-01 0.87646
20 4 7.510426e+00 -3.536111e+00 -2.728126e+00 5.091674e-01 -9.617025e-01 -2.305694e-05 2.979294e-01 4.199695e-01 1.829638e-01 5.617737e-01 0.8876
20 5 4.348244e+00 -1.800377e+00 -2.704540e+00 3.893456e-01 3.900327e-01 -2.537683e-03 6.766795e-01 1.545482e+00 1.164300e-01 7.199897e-01 0.91058
20 6 1.124045e+01 -6.484624e+00 -2.881991e+00 1.373009e+00 -2.577640e-01 -6.712893e-02 6.072741e-01 1.352019e+00 1.407729e-01 7.735385e-01 0.8799
20 7 4.717043e+00 -1.737761e+00 -1.978561e+00 3.722787e-01 -4.753988e-01 -6.761770e-03 5.151032e-01 1.002508e+00 1.231074e-01 6.697817e-01 0.92448
21 1 4.640154e+00 -2.058466e+00 -2.997508e+00 3.229845e-01 -3.267520e-01 7.138257e-03 5.035649e-01 4.081604e-01 1.794630e-01 5.391301e-01 0.67759
21 2 2.121799e+00 -2.683458e-01 -3.333377e+00 -5.632149e-02 -8.802042e-01 2.931274e-02 1.548270e-01 6.114660e-01 1.282823e-01 4.094238e-01 0.86667
21 3 6.281104e+00 -3.060090e+00 -3.968978e+00 5.414357e-01 -1.146769e+00 -1.021923e-02 1.901006e-01 9.614629e-01 1.213459e-01 5.239261e-01 0.92484
21 4 8.247573e+00 -4.368089e+00 -3.711893e+00 8.203851e-01 -1.261375e+00 -2.889684e-02 2.156578e-01 9.815061e-01 1.295587e-01 5.859233e-01 0.94245
21 5 6.556615e+00 -3.025109e+00 -2.490098e+00 6.514720e-01 -2.943759e-02 -2.238848e-02 5.300817e-01 1.512397e+00 1.036178e-01 6.936618e-01 0.94549
21 6 6.664013e+00 -3.151148e+00 -2.756030e+00 6.730895e-01 -5.035543e-01 -2.392248e-02 5.015257e-01 1.426664e+00 1.177137e-01 7.459815e-01 0.93986
21 7 2.741656e+00 -2.924058e-01 -2.782497e+00 4.167455e-02 -1.626785e+00 1.572766e-02 2.898142e-01 9.446596e-01 1.393182e-01 7.278788e-01 0.93601
22 1 -4.590363e+00 3.849984e+00 -3.307862e+00 -8.018508e-01 -5.969935e-01 6.933581e-02 2.171315e-01 6.565482e-01 9.535360e-02 3.544051e-01 0.77153
22 2 -2.609181e+00 2.507249e+00 -5.141995e+00 -5.146901e-01 -1.571547e+00 5.116362e-02 1.732032e-01 1.201708e+00 8.957430e-02 5.433813e-01 0.9206
22 3 1.080075e+00 3.236284e-01 -3.718565e+00 -5.764888e-02 -1.178401e+00 1.959957e-02 1.928291e-01 1.188282e+00 8.092215e-02 5.341144e-01 0.95301
22 4 4.479736e+00 -1.791747e+00 -2.497527e+00 3.837625e-01 -7.002120e-01 -9.574992e-03 2.565598e-01 1.158360e+00 8.196728e-02 5.367337e-01 0.96423
22 5 7.115847e+00 -2.980620e+00 8.689305e-02 6.478047e-01 7.610591e-01 -2.795117e-02 4.940494e-01 1.193656e+00 7.286485e-02 5.257920e-01 0.96623
22 6 1.034411e+01 -5.385961e+00 1.065337e-01 1.161385e+00 7.716018e-01 -6.171584e-02 4.895997e-01 1.208054e+00 7.274434e-02 5.283104e-01 0.96174
22 7 1.527996e+01 -8.823876e+00 5.233917e-01 1.885894e+00 9.122497e-01 -1.095329e-01 4.840952e-01 1.107045e+00 7.496147e-02 4.965770e-01 0.94807
23 1 -9.118623e+00 6.724055e+00 -3.965887e+00 -1.375352e+00 -8.335226e-01 1.046972e-01 2.009562e-01 8.231081e-01 8.124472e-02 3.770911e-01 0.80809
23 2 -3.740883e+00 3.467194e+00 -3.997971e+00 -7.001114e-01 -1.237937e+00 5.863242e-02 1.334926e-01 1.117128e+00 6.114471e-02 4.509570e-01 0.93032
23 3 5.376906e-01 9.692099e-01 -2.061007e+00 -1.823208e-01 -5.029704e-01 2.387347e-02 2.247389e-01 1.071661e+00 5.850010e-02 4.461086e-01 0.95698
23 4 3.849287e+00 -1.117338e+00 -7.892561e-01 2.559385e-01 8.074780e-02 -5.009557e-03 3.151658e-01 1.066045e+00 5.871764e-02 4.531396e-01 0.96842
23 5 6.541330e+00 -2.454719e+00 1.481125e+00 5.404290e-01 1.340032e+00 -2.428085e-02 5.163963e-01 1.067777e+00 5.079943e-02 4.374805e-01 0.9716
23 6 9.778494e+00 -4.861683e+00 1.537440e+00 1.053167e+00 1.355536e+00 -5.796366e-02 5.118758e-01 1.069339e+00 5.137129e-02 4.387966e-01 0.96811
23 7 1.467645e+01 -8.275620e+00 1.985977e+00 1.769856e+00 1.458738e+00 -1.049781e-01 4.988052e-01 9.306013e-01 5.826336e-02 4.082278e-01 0.95666

173
kaipy/rcm/wmutils/genWM.py
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@@ -1,173 +0,0 @@
import numpy as np
import h5py as h5
from scipy.interpolate import RectBivariateSpline
from kaipy.rcm.wmutils.wmData import wmParams
def genWM(params):
import os
fInChorus = 'chorus_polynomial.txt'
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
fInChorus = os.path.join(__location__,fInChorus)
print("Reading %s"%fInChorus)
return genChorus(params,fInChorus)
# Add wpi-induced electron lifetime model to input file and create an output file
# Writes arrays to file in rcmconfig.h5 format
def genh5(fIn, fOut, inputParams):
if fIn != fOut:
oH5 = h5.File(fOut, 'w')
iH5 = h5.File(fIn, 'r')
for Q in iH5.keys():
sQ = str(Q)
oH5.create_dataset(sQ, data=iH5[sQ])
oH5.attrs.update(iH5.attrs)
else:
oH5 = h5.File(fOut, 'r+')
if not ('Taui' in oH5.keys()):
kpi, mlti, li, eki, taui = genWM(inputParams)
attrs = inputParams.getAttrs()
oH5.create_dataset('Kpi', data=kpi)
oH5.create_dataset('MLTi', data=mlti)
oH5.create_dataset('Li', data=li)
oH5.create_dataset('Eki', data=eki)
oH5.create_dataset('Taui', data=taui)
for key in attrs.keys():
oH5.attrs[key] = attrs[key]
oH5.close()
#read parameters of the polynomial fit, Wang+,2023
def readPoly(fIn):
table = []
with open(fIn, 'r') as file:
# Skip the first row
next(file)
for line in file:
row = line.strip().split('\t')[2:-1] # Discard the first two elements of each row
row = [float(x) for x in row] # Convert the strings to float
rowLen = len(row)
table.append(np.array(row))
return (rowLen,np.array(table))
#Chorus polynomial fit for the electron lifetime
def ChorusPoly(Li,Eki,polyArray):
# The 3-rd Order Polynomial Fit Coefficients of Electron Lifetime Caused by Interaction with Chorus Waves
#(https://doi.org/will be provided)
# Dedong Wang et al., in preparation
# For each Kp (0,1,2...,maxKp) and each MLT (0,1,2,...,23), tau has a polynomial fit of Ek and L.
lenKp,lenMLT,lenParam = polyArray.shape
#Extend polyArray
polyArrayX = polyArray[:,:,:,np.newaxis,np.newaxis]
#Extend Li and Ki
lenL = len(Li)
lenEki = len(Eki)
Lx = np.tile(Li, (lenEki, 1)).T
Lx = Lx[np.newaxis,np.newaxis,:,:]
Ex = np.tile(Eki, (lenL, 1))
Ex = Ex[np.newaxis,np.newaxis,:,:]
tau = np.ones((lenKp,lenMLT,lenL,lenEki))
c0 = polyArrayX[:,:,0,:,:]#Intercept
c1 = polyArrayX[:,:,1,:,:] #L
c2 = polyArrayX[:,:,2,:,:]#log10(E)
c3 = polyArrayX[:,:,3,:,:]# L^2
c4 = polyArrayX[:,:,4,:,:]#log10(E)^2
c5 = polyArrayX[:,:,5,:,:]#L^3
c6 = polyArrayX[:,:,6,:,:]#log10(E)^3
c7 = polyArrayX[:,:,7,:,:]#log10(E)*L
c8 = polyArrayX[:,:,8,:,:]#log10(E)*L^2
c9 = polyArrayX[:,:,9,:,:]#log10(E)^2*L
tau = c0*tau+\
c1*Lx+c2*Ex+\
c3*np.power(Lx,2)+\
c4*np.power(Ex,2)+\
c5*np.power(Lx,3)+\
c6*np.power(Ex,3)+\
c7*Lx*Ex+\
c8*np.power(Lx,2)*Ex+\
c9*Lx*np.power(Ex,2) #in log10(days)
tau = 10.0**tau*(60.*60.*24.) #in seconds
return tau
def ReSample(L,MLT,Qp,xMLT):
Nr,Np = Qp.shape
#Add ghosts in MLT to handle periodic boundary
Ng = 2
Npg = Np+Ng*2
gMLT = np.arange(0-Ng,24+Ng+1)
Qpg = np.zeros((Nr,Npg))
#Set center and then left/right strips
Qpg[:,2:-2] = Qp
Qpg[:,1] = Qp[:,-1]
Qpg[:,0] = Qp[:,-2]
Qpg[:,-1] = Qp[:,0]
Qpg[:,-2] = Qp[:,1]
Q = np.log10(Qpg)
upQ = RectBivariateSpline(L,gMLT,Q,s=10)
Qu = upQ(L,xMLT)
xQp = 10.0**(Qu)
#Enforce equality at overlap point
tauP = 0.5*(xQp[:,0]+xQp[:,-1])
xQp[:, 0] = tauP
xQp[:,-1] = tauP
return xQp
def genChorus(params,fInChorus):
print("Dimension of parameters in Chorus wave model, Kp:",params.nKp,"MLT:",params.nMLT,"L:",params.nL,"Ek:",params.nEk)
dimKp = params.nKp #maximum Kp allowed
rowLen,paramArray = readPoly(fInChorus)
polyArray = paramArray.reshape(24,7,rowLen) #dim MLT: 24, Dim Kp: 7
polyArray = polyArray[:,:dimKp,:] #use Kp = 1,2,...,maxKp
polyArray = polyArray.transpose(1, 0, 2) #shape (,24,rowLen)
lenMLT = 24
#Kpi
startValue = 1.0 #Kp index in real number
endValue = float(dimKp)
lenKp = dimKp
Kpi = np.linspace(startValue, endValue, num=lenKp)
#Eki
startValue = 1.0e-3 #in MeV
endValue = 2.0
lenEk = 155
Eki = np.linspace(np.log10(startValue), np.log10(endValue), lenEk) #in log10(MeV)
#Li
startValue = 3.0 #in Re
endValue = 7.0
lenL = 41
Li = np.linspace(startValue, endValue, num=lenL)
#Tau from polynomial fit
tauP = ChorusPoly(Li,Eki,polyArray)
#expand MLT from 0-23 to 0-24
extraMLT0 = tauP[:, 0, :, :][:,np.newaxis,:,:]
tauE = np.concatenate((tauP, extraMLT0), axis=1)
tauE = tauE.T
#Interpolation in the MLT dimesion
xFac = 4
lenMLTx = lenMLT*xFac+1 # 97
MLTi = np.linspace(0,24,lenMLT+1)
xMLTi = np.linspace(0,24,lenMLTx)
tauX = np.zeros((lenEk,lenL,lenMLTx,lenKp))
# Smoothing in MLT
for i, j in np.ndindex(tauX.shape[0], tauX.shape[3]):
Q = tauE[i, :, :, j]
tauX[i, :, :, j] = ReSample(Li, MLTi, Q, xMLTi)
Eki = 10.0**Eki #in MeV
return Kpi,xMLTi,Li,Eki,tauX

109
kaipy/rcm/wmutils/tauTDS.txt
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@@ -1,109 +0,0 @@
10 5.7366e-02 1.4341e-02 3.5854e-03
20 9.2629e-02 2.3157e-02 5.7893e-03
30 1.1716e-01 2.9291e-02 7.3228e-03
40 1.3527e-01 3.3817e-02 8.4543e-03
50 1.4918e-01 3.7294e-02 9.3235e-03
60 1.6018e-01 4.0044e-02 1.0011e-02
70 1.6907e-01 4.2269e-02 1.0567e-02
80 1.7641e-01 4.4103e-02 1.1026e-02
90 1.8255e-01 4.5638e-02 1.1409e-02
100 1.8774e-01 4.6936e-02 1.1734e-02
150 2.0453e-01 5.1132e-02 1.2783e-02
200 2.1290e-01 5.3226e-02 1.3306e-02
250 2.1743e-01 5.4356e-02 1.3589e-02
300 2.1995e-01 5.4987e-02 1.3747e-02
350 2.2134e-01 5.5336e-02 1.3834e-02
400 2.2208e-01 5.5519e-02 1.3880e-02
450 2.2241e-01 5.5602e-02 1.3900e-02
500 2.2250e-01 5.5624e-02 1.3906e-02
550 2.2246e-01 5.5614e-02 1.3903e-02
600 2.2234e-01 5.5586e-02 1.3896e-02
650 2.2220e-01 5.5549e-02 1.3887e-02
700 2.2204e-01 5.5510e-02 1.3878e-02
750 2.2190e-01 5.5474e-02 1.3869e-02
800 2.2178e-01 5.5444e-02 1.3861e-02
850 2.2169e-01 5.5422e-02 1.3855e-02
900 2.2163e-01 5.5408e-02 1.3852e-02
950 2.2161e-01 5.5403e-02 1.3851e-02
1000 2.2163e-01 5.5407e-02 1.3852e-02
1200 2.2204e-01 5.5510e-02 1.3878e-02
1400 2.2293e-01 5.5732e-02 1.3933e-02
1600 2.2418e-01 5.6045e-02 1.4011e-02
1800 2.2570e-01 5.6425e-02 1.4106e-02
2000 2.2741e-01 5.6853e-02 1.4213e-02
2200 2.2927e-01 5.7317e-02 1.4329e-02
2400 2.3122e-01 5.7806e-02 1.4451e-02
2600 2.3326e-01 5.8314e-02 1.4579e-02
2800 2.3535e-01 5.8837e-02 1.4709e-02
3000 2.3749e-01 5.9371e-02 1.4843e-02
3200 2.3966e-01 5.9916e-02 1.4979e-02
3400 2.4188e-01 6.0469e-02 1.5117e-02
3600 2.4412e-01 6.1031e-02 1.5258e-02
3800 2.4640e-01 6.1601e-02 1.5400e-02
4000 2.4872e-01 6.2179e-02 1.5545e-02
4200 2.5106e-01 6.2766e-02 1.5691e-02
4400 2.5344e-01 6.3360e-02 1.5840e-02
4600 2.5586e-01 6.3964e-02 1.5991e-02
4800 2.5831e-01 6.4576e-02 1.6144e-02
5000 2.6079e-01 6.5198e-02 1.6299e-02
5200 2.6331e-01 6.5828e-02 1.6457e-02
5400 2.6587e-01 6.6468e-02 1.6617e-02
5600 2.6847e-01 6.7117e-02 1.6779e-02
5800 2.7110e-01 6.7776e-02 1.6944e-02
6000 2.7378e-01 6.8444e-02 1.7111e-02
6200 2.7649e-01 6.9122e-02 1.7280e-02
6400 2.7923e-01 6.9808e-02 1.7452e-02
6600 2.8202e-01 7.0505e-02 1.7626e-02
6800 2.8484e-01 7.1210e-02 1.7802e-02
7000 2.8770e-01 7.1924e-02 1.7981e-02
7200 2.9059e-01 7.2648e-02 1.8162e-02
7400 2.9352e-01 7.3380e-02 1.8345e-02
7600 2.9648e-01 7.4121e-02 1.8530e-02
7800 2.9948e-01 7.4870e-02 1.8718e-02
8000 3.0251e-01 7.5628e-02 1.8907e-02
8200 3.0558e-01 7.6395e-02 1.9099e-02
8400 3.0868e-01 7.7169e-02 1.9292e-02
8600 3.1181e-01 7.7952e-02 1.9488e-02
8800 3.1497e-01 7.8742e-02 1.9686e-02
9000 3.1816e-01 7.9540e-02 1.9885e-02
9200 3.2138e-01 8.0346e-02 2.0087e-02
9400 3.2464e-01 8.1159e-02 2.0290e-02
9600 3.2792e-01 8.1980e-02 2.0495e-02
9800 3.3123e-01 8.2808e-02 2.0702e-02
10000 3.3457e-01 8.3643e-02 2.0911e-02
12500 3.7853e-01 9.4631e-02 2.3658e-02
15000 4.2609e-01 1.0652e-01 2.6630e-02
17500 4.7675e-01 1.1919e-01 2.9797e-02
20000 5.3017e-01 1.3254e-01 3.3136e-02
22500 5.8614e-01 1.4653e-01 3.6634e-02
25000 6.4447e-01 1.6112e-01 4.0279e-02
27500 7.0505e-01 1.7626e-01 4.4065e-02
30000 7.6776e-01 1.9194e-01 4.7985e-02
32500 8.3254e-01 2.0813e-01 5.2034e-02
35000 8.9929e-01 2.2482e-01 5.6206e-02
37500 9.6797e-01 2.4199e-01 6.0498e-02
40000 1.0385e+00 2.5963e-01 6.4907e-02
42500 1.1109e+00 2.7772e-01 6.9430e-02
45000 1.1850e+00 2.9625e-01 7.4064e-02
47500 1.2609e+00 3.1522e-01 7.8805e-02
50000 1.3384e+00 3.3461e-01 8.3653e-02
52500 1.4177e+00 3.5441e-01 8.8604e-02
55000 1.4985e+00 3.7462e-01 9.3656e-02
57500 1.5809e+00 3.9523e-01 9.8808e-02
60000 1.6649e+00 4.1623e-01 1.0406e-01
62500 1.7505e+00 4.3761e-01 1.0940e-01
65000 1.8375e+00 4.5937e-01 1.1484e-01
67500 1.9260e+00 4.8150e-01 1.2038e-01
70000 2.0160e+00 5.0400e-01 1.2600e-01
72500 2.1074e+00 5.2685e-01 1.3171e-01
75000 2.2003e+00 5.5006e-01 1.3752e-01
77500 2.2945e+00 5.7362e-01 1.4341e-01
80000 2.3901e+00 5.9752e-01 1.4938e-01
82500 2.4871e+00 6.2176e-01 1.5544e-01
85000 2.5853e+00 6.4634e-01 1.6158e-01
87500 2.6850e+00 6.7124e-01 1.6781e-01
90000 2.7859e+00 6.9647e-01 1.7412e-01
92500 2.8881e+00 7.2202e-01 1.8051e-01
95000 2.9916e+00 7.4789e-01 1.8697e-01
97500 3.0963e+00 7.7408e-01 1.9352e-01
100000 3.2023e+00 8.0057e-01 2.0014e-01

21
kaipy/rcm/wmutils/wmData.py
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@@ -1,21 +0,0 @@
import h5py as h5
import numpy as np
class wmParams:
#All energies in eV
def __init__(self, dim = 4, nKp = 6, nMLT = 97, nL = 41, nEk = 155):
self.dim = dim
self.nKp = nKp
self.nMLT = nMLT
self.nL = nL
self.nEk = nEk
def getAttrs(self):
return {
'tauDim': self.dim,
'nKp': self.nKp,
'nMLT': self.nMLT,
'nL': self.nL,
'nEk': self.nEk,
}

0
kaipy/remix/__init__.py
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851
kaipy/remix/remix.py
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@@ -1,851 +0,0 @@
import h5py
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import sys
Ri = 6.5 # radius of ionosphere in 1000km
Re = 6.38 # radius of Earth in 1000km
mu0o4pi = 1.e-7 # mu0=4pi*10^-7 => mu0/4pi=10^-7
#Setting globals here to grab them from other modules
facMax = 1.5
facCM = cm.RdBu_r
flxCM = cm.inferno
class remix:
def __init__(self,h5file,step):
# create the ion object to store data and coordinates
self.ion = self.get_data(h5file,step)
self.Initialized=False
# DEFINE DATA LIMITS
self.variables = { 'potential' : {'min':-100,
'max': 100},
'current' : {'min':-facMax,
'max':facMax},
'sigmap' : {'min':1,
'max':20},
'sigmah' : {'min':2,
'max':40},
'energy' : {'min':0,
'max':20},
'flux' : {'min':0,
'max':1.e9},
'eflux' : {'min':0,
'max':10.},
'efield' : {'min':-1,
'max':1},
'joule' : {'min':0,
'max':10},
'jhall' : {'min':-2,
'max':2},
'gtype' : {'min':0,
'max':1},
'npsp' : {'min':0,
'max':1.e3},
'Menergy' : {'min':0,
'max':20},
'Mflux' : {'min':0,
'max':1.e10},
'Meflux' : {'min':0,
'max':10.},
'Denergy' : {'min':0,
'max':20},
'Dflux' : {'min':0,
'max':1.e10},
'Deflux' : {'min':0,
'max':10.},
'Penergy' : {'min':0,
'max':240},
'Pflux' : {'min':0,
'max':1.e7},
'Peflux' : {'min':0,
'max':1.}
}
def get_data(self,h5file,step):
ion = {}
with h5py.File(h5file,'r') as f:
ion['X'] = f['X'][:]
ion['Y'] = f['Y'][:]
for h in f['Step#%d'%step].keys():
ion[h] = f['Step#%d'%step][h][:]
# Get spherical coords
ion['R'],ion['THETA'] = self.get_spherical(ion['X'],ion['Y'])
return ion
# TODO: check for variable names passed to plot
def init_vars(self,hemisphere):
h = hemisphere # for shortness
if (h.lower()=='north'):
self.variables['potential']['data'] = self.ion['Potential '+h]
self.variables['current']['data'] =-self.ion['Field-aligned current '+h] # note, converting to common convention (upward=positive)
self.variables['sigmap']['data'] = self.ion['Pedersen conductance '+h]
self.variables['sigmah']['data'] = self.ion['Hall conductance '+h]
self.variables['energy']['data'] = self.ion['Average energy '+h]
self.variables['flux']['data'] = self.ion['Number flux '+h]
if 'RCM grid type '+h in self.ion.keys():
self.variables['gtype']['data'] = self.ion['RCM grid type '+h]
if 'RCM plasmasphere density '+h in self.ion.keys():
self.variables['npsp']['data'] = self.ion['RCM plasmasphere density '+h]*1.0e-6 # /m^3 -> /cc.
# variables['efield']['data'] = efield_n*1.e6
# variables['joule']['data'] = sigmap_n*efield_n**2*1.e-3
if 'Zhang average energy '+h in self.ion.keys():
self.variables['Menergy']['data'] = self.ion['Zhang average energy '+h]
self.variables['Mflux']['data'] = self.ion['Zhang number flux '+h]
self.variables['Meflux']['data'] = self.variables['Menergy']['data']*self.variables['Mflux']['data']*1.6e-9
if 'IM average energy '+h in self.ion.keys():
self.variables['Denergy']['data'] = self.ion['IM average energy '+h]
self.variables['Deflux']['data'] = self.ion['IM Energy flux '+h]
self.variables['Denergy']['data'][self.variables['Denergy']['data']==0] = 1.e-20
self.variables['Denergy']['data'][np.isnan(self.variables['Denergy']['data'])] = 1.e-20
self.variables['Dflux']['data'] = self.variables['Deflux']['data']/self.variables['Denergy']['data']/(1.6e-9)
self.variables['Dflux']['data'][self.variables['Denergy']['data']==1.e-20] = 0.
if 'IM average energy proton '+h in self.ion.keys():
self.variables['Penergy']['data'] = self.ion['IM average energy proton '+h]
self.variables['Peflux']['data'] = self.ion['IM Energy flux proton '+h]
self.variables['Penergy']['data'][self.variables['Penergy']['data']==0] = 1.e-20
self.variables['Penergy']['data'][np.isnan(self.variables['Penergy']['data'])] = 1.e-20
self.variables['Pflux']['data'] = self.variables['Peflux']['data']/self.variables['Penergy']['data']/(1.6e-9)
self.variables['Pflux']['data'][self.variables['Penergy']['data']==1.e-20] = 0.
else: # note flipping the y(phi)-axis
self.variables['potential']['data'] = self.ion['Potential '+h][:,::-1]
self.variables['current']['data'] = self.ion['Field-aligned current '+h][:,::-1]
self.variables['sigmap']['data'] = self.ion['Pedersen conductance '+h][:,::-1]
self.variables['sigmah']['data'] = self.ion['Hall conductance '+h][:,::-1]
self.variables['energy']['data'] = self.ion['Average energy '+h][:,::-1]
self.variables['flux']['data'] = self.ion['Number flux '+h][:,::-1]
if 'RCM grid type '+h in self.ion.keys():
self.variables['gtype']['data'] = self.ion['RCM grid type '+h][:,::-1]
if 'RCM plasmasphere density '+h in self.ion.keys():
self.variables['npsp']['data'] = self.ion['RCM plasmasphere density '+h][:,::-1]*1.0e-6 # /m^3 -> /cc.
if 'Zhang average energy '+h in self.ion.keys():
self.variables['Menergy']['data'] = self.ion['Zhang average energy '+h][:,::-1]
self.variables['Mflux']['data'] = self.ion['Zhang number flux '+h][:,::-1]
self.variables['Meflux']['data'] = self.variables['Menergy']['data']*self.variables['Mflux']['data']*1.6e-9
if 'IM average energy '+h in self.ion.keys():
self.variables['Denergy']['data'] = self.ion['IM average energy '+h][:,::-1]
self.variables['Deflux']['data'] = self.ion['IM Energy flux '+h][:,::-1]
self.variables['Denergy']['data'][self.variables['Denergy']['data']==0] = 1.e-20
self.variables['Denergy']['data'][np.isnan(self.variables['Denergy']['data'])] = 1.e-20
self.variables['Dflux']['data'] = self.variables['Deflux']['data']/self.variables['Denergy']['data']/(1.6e-9)
self.variables['Dflux']['data'][self.variables['Denergy']['data']==1.e-20] = 0.
if 'IM average energy proton '+h in self.ion.keys():
self.variables['Penergy']['data'] = self.ion['IM average energy proton '+h][:,::-1]
self.variables['Peflux']['data'] = self.ion['IM Energy flux proton '+h][:,::-1]
self.variables['Penergy']['data'][self.variables['Penergy']['data']==0] = 1.e-20
self.variables['Penergy']['data'][np.isnan(self.variables['Penergy']['data'])] = 1.e-20
self.variables['Pflux']['data'] = self.variables['Peflux']['data']/self.variables['Penergy']['data']/(1.6e-9)
self.variables['Pflux']['data'][self.variables['Penergy']['data']==1.e-20] = 0.
# convert energy flux to erg/cm2/s to conform to Newell++, doi:10.1029/2009JA014326, 2009
self.variables['eflux']['data'] = self.variables['energy']['data']*self.variables['flux']['data']*1.6e-9
# Mask out Eavg where EnFlux<0.1
# self.variables['energy']['data'][self.variables['sigmap']['data']<=2.5]=0.0
self.Initialized=True
def get_spherical(self,x,y):
# note, because of how the grid is set up in the h5 file,
# the code below produces the first theta that's just shy of 2pi.
# this is because the original grid is staggered at half-cells from data.
# empirically, this is OK for pcolormesh plots under remix.plot.
# but I still fix it manually.
theta=np.arctan2(y,x)
theta[theta<0]=theta[theta<0]+2*np.pi
theta[:,0] -= 2*np.pi # fixing the first theta point to just below 0
r=np.sqrt(x**2+y**2)
return(r,theta)
def distance(self,p0, p1):
""" Calculate the distance between p0 & p1--two points in R**3 """
return( np.sqrt( (p0[0]-p1[0])**2 +
(p0[1]-p1[1])**2 +
(p0[2]-p1[2])**2 ) )
def calcFaceAreas(self,x,y):
"""
Calculate the area of each face in a quad mesh.
>>> calcFaceAreas(numpy.array([[ 0., 1.],[ 1., 0.]]), numpy.array([[ 0.,1.],[ 1.,0.]]), numpy.array([[ 0., 0.],[ 0.,0.]]))
array([[ 2.]])
"""
(nLonP1, nLatP1) = x.shape
(nLon, nLat) = (nLonP1-1, nLatP1-1)
z=np.sqrt(1.0-x**2-y**2)
area = np.zeros((nLon, nLat))
for i in range(nLon):
for j in range(nLat):
left = self.distance( (x[i,j], y[i,j], z[i,j]), (x[i,j+1], y[i,j+1], z[i,j+1]) )
right = self.distance( (x[i+1,j], y[i+1,j], z[i+1,j]), (x[i+1,j+1], y[i+1,j+1], z[i+1,j+1]) )
top = self.distance( (x[i,j+1], y[i,j+1], z[i,j+1]), (x[i+1,j+1], y[i+1,j+1], z[i+1,j+1]) )
bot = self.distance( (x[i,j], y[i,j], z[i,j]), (x[i+1,j], y[i+1,j], z[i+1,j]) )
area[i,j] = 0.5*(left+right) * 0.5*(top+bot)
return area
# TODO: define and consolidate allowed variable names
def plot(self,varname,
ncontours=16, # default number of potential contours
addlabels={},
gs=None,doInset=False,doCB=True,doCBVert=True,doGTYPE=False,doPP=False):
# define function for potential contour overplotting
# to keep code below clean and compact
def potential_overplot(doInset=False):
tc = 0.25*(theta[:-1,:-1]+theta[1:,:-1]+theta[:-1,1:]+theta[1:,1:])
rc = 0.25*(r[:-1,:-1]+r[1:,:-1]+r[:-1,1:]+r[1:,1:])
# trick to plot contours smoothly across the periodic boundary:
# wrap around: note, careful with theta -- need to add 2*pi to keep it ascending
# otherwise, contours mess up
tc = np.hstack([tc,2.*np.pi+tc[:,[0]]])
rc = np.hstack([rc,rc[:,[0]]])
tmp=self.variables['potential']['data']
lower = self.variables['potential']['min']
upper = self.variables['potential']['max']
tmp = np.hstack([tmp,tmp[:,[0]]])
# similar trick to make contours go through the pole
# add pole
tc = np.vstack([tc[[0],:],tc])
rc = np.vstack([0.*rc[[0],:],rc])
tmp = np.vstack([tmp[0,:].mean()*np.ones_like(tmp[[0],:]),tmp])
# finally, plot
if (doInset):
LW = 0.25
alpha = 1
tOff = 0.0
else:
LW = 0.5
alpha = 1
tOff = np.pi/2.
contours = np.linspace(lower,upper,ncontours)
ax.contour(tc+tOff,rc,tmp,contours,colors='black',linewidths=LW,alpha=alpha)
if (not doInset):
# also, print min/max values of the potential
ax.text(73.*np.pi/180.,1.03*r.max(),('min: '+format_str+'\nmax: ' +format_str) %
(tmp.min() ,tmp.max()))
# define function for grid type contour overplotting
# to keep code below clean and compact
def boundary_overplot(con_name,con_level,con_color,doInset=False):
tc = 0.25*(theta[:-1,:-1]+theta[1:,:-1]+theta[:-1,1:]+theta[1:,1:])
rc = 0.25*(r[:-1,:-1]+r[1:,:-1]+r[:-1,1:]+r[1:,1:])
# trick to plot contours smoothly across the periodic boundary:
# wrap around: note, careful with theta -- need to add 2*pi to keep it ascending
# otherwise, contours mess up
tc = np.hstack([tc,2.*np.pi+tc[:,[0]]])
rc = np.hstack([rc,rc[:,[0]]])
tmp=self.variables[con_name]['data']
tmp = np.hstack([tmp,tmp[:,[0]]])
# similar trick to make contours go through the pole
# add pole
tc = np.vstack([tc[[0],:],tc])
rc = np.vstack([0.*rc[[0],:],rc])
tmp = np.vstack([tmp[0,:].mean()*np.ones_like(tmp[[0],:]),tmp])
# finally, plot
if (doInset):
LW = 0.5
alpha = 1
tOff = 0.0
else:
LW = 0.75
alpha = 1
tOff = np.pi/2.
ax.contour(tc+tOff,rc,tmp,levels=con_level,colors=con_color,linewidths=LW,alpha=alpha)
if not self.Initialized:
sys.exit("Variables should be initialized for the specific hemisphere (call init_var) prior to plotting.")
# Aliases to keep things short
x = self.ion['X']
y = self.ion['Y']
r = self.ion['R']
theta = self.ion['THETA']
# List all possible variable names here, add more from the input parameter if needed
cblabels = {'potential' : r'Potential [kV]',
'current' : r'Current density [$\mu$A/m$^2$]',
'sigmap' : r'Pedersen conductance [S]',
'sigmah' : r'Hall conductance [S]',
'energy' : r'Energy [keV]',
'flux' : r'Flux [1/cm$^2$s]',
'eflux' : r'Energy flux [erg/cm$^2$s]',
'ephi' : r'$E_\phi$ [mV/m]',
'etheta' : r'$E_\theta$ [mV/m]',
'efield' : r'|E| [mV/m]',
'joule' : r'Joule heating [mW/m$^2$]',
'jped' : r'Pedersen current [$\mu$A/m]',
'magpert' : r'Magnetic perturbation [nT]',
'Menergy' : r'Mono Energy [keV]',
'Mflux' : r'Mono Flux [1/cm$^2$s]',
'Meflux' : r'Mono Energy flux [erg/cm$^2$s]',
'Denergy' : r'Diffuse Energy [keV]',
'Dflux' : r'Diffuse Flux [1/cm$^2$s]',
'Deflux' : r'Diffuse Energy flux [erg/cm$^2$s]',
'Penergy' : r'Diffuse Proton Energy [keV]',
'Pflux' : r'Diffuse Proton Flux [1/cm$^2$s]',
'Peflux' : r'Diffuse Proton Energy flux [erg/cm$^2$s]'
}
cblabels.update(addlabels) # a way to add cb labels directly through function arguments (e.g., for new variables)
# if limits are given use them, if not use the variables min/max values
if ('min' in self.variables[varname]):
lower = self.variables[varname]['min']
else:
lower = self.variables[varname]['data'].min()
if ('max' in self.variables[varname]):
upper = self.variables[varname]['max']
else:
upper = self.variables[varname]['data'].max()
# define number format string for min/max labels
if varname !='flux' and varname !='Pflux':
if varname == 'jped':
format_str = '%.2f'
else:
format_str = '%.1f'
else:
format_str = '%.1e'
if (varname == 'potential') or (varname == 'current'):
cmap=facCM
elif varname in ['flux','energy','eflux','joule','Mflux','Menergy','Meflux','Dflux','Denergy','Deflux','Pflux','Penergy','Peflux']:
cmap=flxCM
latlblclr = 'white'
elif (varname == 'velocity'):
cmap=cm.YlOrRd
elif (varname == 'efield'):
cmap=None #cm.RdBu_r#None # cm.hsv
elif (varname == 'magpert'):
cmap = cm.YlOrRd
else:
cmap=None # default is used
if varname in ['eflux','Meflux','Deflux','Peflux']:
ri=6500.e3
areaMixGrid = self.calcFaceAreas(x,y)*ri*ri
hp = areaMixGrid*self.variables[varname]['data'][:,:]/(1.6e-9)
power = hp.sum()*1.6e-21
if (varname == 'current'):
ri=6500.e3
areaMixGrid = self.calcFaceAreas(x,y)*ri*ri
fac = self.variables[varname]['data'][:,:]
dfac = areaMixGrid[fac>0.]*fac[fac>0.]/1.0e12
pfac = dfac.sum()
# DEFINE GRID LINES AND LABELS
if (doInset):
circle_list = [15,30,45]
lbls = ["","",str(45)+u'\xb0']
hour_labels = ["","","",""]
LW = 0.25
latlblclr = 'silver'
tOff = 0.0
else:
circle_list = [10,20,30,40]
# convert to string and add degree symbol
lbls = [str(elem)+u'\xb0' for elem in circle_list]
hour_labels = ['06','12','18','00']
LW = 1.0
latlblclr = 'black'
tOff = np.pi/2.
circles = np.sin(np.array(circle_list)*np.pi/180.)
if varname == 'joule':
self.variables[varname]['data'] = self.joule()*1.e3 # convert to mW/m^2
variable = self.variables[varname]['data']
fig = plt.gcf()
# Now plotting
if gs != None:
ax=fig.add_subplot(gs,polar=True)
else:
ax=fig.add_subplot(polar=True)
p=ax.pcolormesh(theta+tOff,r,variable,cmap=cmap,vmin=lower,vmax=upper)
if (not doInset):
if (doCB):
if (doCBVert):
cb=plt.colorbar(p,ax=ax,pad=0.1,shrink=0.85,orientation="vertical")
else:
cb=plt.colorbar(p,ax=ax,pad=0.1,shrink=0.85,orientation="horizontal")
cb.set_label(cblabels[varname])
ax.text(-75.*np.pi/180.,1.2*r.max(),('min: '+format_str+'\nmax: ' +format_str) %
(variable.min() ,variable.max()))
lines, labels = plt.thetagrids((0.,90.,180.,270.),hour_labels)
lines, labels = plt.rgrids(circles,lbls,fontsize=8,color=latlblclr)
# if (doInset):
# lines, labels = plt.rgrids(circles,lbls,fontsize=8,color=latlblclr)
ax.grid(True,linewidth=LW)
# ax.axis([0,2*np.pi,0,r.max()],'tight')
ax.axis([0,2*np.pi,0,r.max()])
if (not doInset):
ax.text(-75.*np.pi/180.,1.2*r.max(),('min: '+format_str+'\nmax: ' +format_str) %
(variable.min() ,variable.max()))
if varname in ['eflux','Meflux','Deflux','Peflux']:
ax.text(75.*np.pi/180.,1.3*r.max(),('GW: '+format_str) % power)
if (varname == 'current'):
ax.text(-(73.+45.)*np.pi/180.,1.3*r.max(),('MA: '+format_str) % pfac)
ax.grid(True)
if varname=='current':
potential_overplot(doInset)
if doGTYPE and varname=='eflux':
boundary_overplot('gtype',[0.01,0.99],'green',doInset)
if doPP and varname=='eflux':
boundary_overplot('npsp',[10],'cyan',doInset)
return ax
# mpl.rcParams['contour.negative_linestyle'] = 'solid'
# if (varname == 'efield' or varname == 'velocity' or varname =='joule'):
# contour(theta+pi/2.,r,variables['potential']['data'][:,2:-1],21,colors='black')
# arange(variables['potential']['min'],variables['potential']['max'],21.),colors='purple')
# FIXME: MAKE WORK FOR SOUTH (I THINK IT DOES BUT MAKE SURE)
def efield(self,returnDeltas=False,ri=Ri*1e3):
if not self.Initialized:
sys.exit("Variables should be initialized for the specific hemisphere (call init_var) prior to efield calculation.")
Psi = self.variables['potential']['data'] # note, these are numbers of cells. self.ion['X'].shape = Nr+1,Nt+1
Nt,Np = Psi.shape
# Aliases to keep things short
x = self.ion['X']
y = self.ion['Y']
# note the change in naming convention from above
# i.e., theta is now the polar angle
# and phi is the azimuthal (what was theta)
# TODO: make consistent throughout
theta = np.arcsin(self.ion['R'])
phi = self.ion['THETA']
# interpolate Psi to corners
Psi_c = np.zeros(x.shape)
Psi_c[1:-1,1:-1] = 0.25*(Psi[1:,1:]+Psi[:-1,1:]+Psi[1:,:-1]+Psi[:-1,:-1])
# fix up periodic
Psi_c[1:-1,0] = 0.25*(Psi[1:,0]+Psi[:-1,0]+Psi[1:,-1]+Psi[:-1,-1])
Psi_c[1:-1,-1] = Psi_c[1:-1,0]
# fix up pole
Psi_pole = Psi[0,:].mean()
Psi_c[0,1:-1] = 0.25*(2.*Psi_pole + Psi[0,:-1]+Psi[0,1:])
Psi_c[0,0] = 0.25*(2.*Psi_pole + Psi[0,-1]+Psi[0,0])
Psi_c[0,-1] = 0.25*(2.*Psi_pole + Psi[0,-1]+Psi[0,0])
# fix up low lat boundary
# extrapolate linearly just like we did for the coordinates
# (see genOutGrid in src/remix/mixio.F90)
# note, neglecting the possibly non-uniform spacing (don't care)
Psi_c[-1,:] = 2*Psi_c[-2,:]-Psi_c[-3,:]
# now, do the differencing
# for each cell corner on the original grid, I have the coordinates and Psi_c
# need to find the gradient at cell center
# the result is the same size as Psi
# first etheta
tmp = 0.5*(Psi_c[:,1:]+Psi_c[:,:-1]) # move to edge center
dPsi = tmp[1:,:]-tmp[:-1,:]
tmp = 0.5*(theta[:,1:]+theta[:,:-1])
dtheta = tmp[1:,:]-tmp[:-1,:]
etheta = dPsi/dtheta/ri # this is in V/m
# now ephi
tmp = 0.5*(Psi_c[1:,:]+Psi_c[:-1,:]) # move to edge center
dPsi = tmp[:,1:]-tmp[:,:-1]
tmp = 0.5*(phi[1:,:]+phi[:-1,:])
dphi = tmp[:,1:]-tmp[:,:-1]
tc = 0.25*(theta[:-1,:-1]+theta[1:,:-1]+theta[:-1,1:]+theta[1:,1:]) # need this additionally
ephi = dPsi/dphi/np.sin(tc)/ri # this is in V/m
if returnDeltas:
return (-etheta,-ephi,dtheta,dphi) # E = -grad Psi
else:
return (-etheta,-ephi) # E = -grad Psi
def joule(self):
etheta,ephi = self.efield()
SigmaP = self.variables['sigmap']['data']
J = SigmaP*(etheta**2+ephi**2) # this is in W/m^2
return(J)
# note, here we're taking the Cartesian average for cell centers
# this is less accurate than the angular averages that are used in efield above
# furthermore, I'm lazily mixing this with dtheta,dphi that came from efield in hCurrents
# TODO: this should be fixed by pulling out the cell-centered tc,pc, and dtheta,dphi
# from the efield calculation and using throughout.
# note, however, that the staggered grid for storage is currently create in the remix Fortran code
# by Cartesian averaging, so if we go to angular in this python postprocessing code,
# we should probably start with going angular in the Fortran code.
def cartesianCellCenters(self):
# Aliases to keep things short
x = self.ion['X']
y = self.ion['Y']
xc = 0.25*(x[:-1,:-1]+x[1:,:-1]+x[:-1,1:]+x[1:,1:])
yc = 0.25*(y[:-1,:-1]+y[1:,:-1]+y[:-1,1:]+y[1:,1:])
r,phi = self.get_spherical(xc,yc)
theta = np.arcsin(r)
return(xc,yc,theta,phi)
# FIXME: MAKE WORK FOR SOUTH
# TODO: write a separate geom function to define cell-centered coords, deltas, and even cosDipAngle (see comment above cartesianCellCenters)
# right now, it's just ugly below where I take dtheta,phi from efield
# and the coordinates separately from cartesianCellCenters.
# it was a lazy solution
def hCurrents(self): # horizontal currents
etheta,ephi,dtheta,dphi = self.efield(returnDeltas=True)
SigmaH = self.variables['sigmah']['data']
SigmaP = self.variables['sigmap']['data']
xc,yc,theta,phi = self.cartesianCellCenters()
cosDipAngle = -2.*np.cos(theta)/np.sqrt(1.+3.*np.cos(theta)**2)
Jh_theta = -SigmaH*ephi/cosDipAngle
Jh_phi = SigmaH*etheta/cosDipAngle
Jp_theta = SigmaP*etheta/cosDipAngle**2
Jp_phi = SigmaP*ephi
# current above is in SI units [A/m]
# i.e., height-integrated current density
return(xc,yc,theta,phi,dtheta,dphi,Jh_theta,Jh_phi,Jp_theta,Jp_phi,cosDipAngle)
# Main function to compute magnetic perturbations using the Biot-Savart integration
# This includes Hall, Pedersen and FAC with the option to do Hall only
# Rin = Inner boundary of MHD grid [Re]
def dB(self,xyz,hallOnly=True,Rin=2.0,rsegments=10):
# xyz = array of points where to compute dB
# xyz.shape should be (N,3), where N is the number of points
# xyz = (x,y,z) in units of Ri
if len(xyz.shape)!=2:
sys.exit("dB input assumes the array of points of (N,3) size.")
if xyz.shape[1]!=3:
sys.exit("dB input assumes the array of points of (N,3) size.")
self.init_vars('NORTH')
x,y,theta,phi,dtheta,dphi,jht,jhp,jpt,jpp,cosDipAngle = self.hCurrents()
z = np.sqrt(1.-x**2-y**2) # ASSUME NORTH
# z = -np.sqrt(1.-x**2-y**2) # ASSUME SOUTH
# fake dimensions for numpy broadcasting
xSource = x[:,:,np.newaxis]
ySource = y[:,:,np.newaxis]
zSource = z[:,:,np.newaxis]
tSource = theta[:,:,np.newaxis]
pSource = phi[:,:,np.newaxis]
dtSource = dtheta[:,:,np.newaxis]
dpSource = dphi[:,:,np.newaxis]
# Hall
jhTheta = jht[:,:,np.newaxis]
jhPhi = jhp[:,:,np.newaxis]
# Pedersen
jpTheta = jpt[:,:,np.newaxis]
jpPhi = jpp[:,:,np.newaxis]
if not hallOnly:
jTheta = jpTheta + jhTheta
jPhi = jpPhi + jhPhi
else:
jTheta = jhTheta
jPhi = jhPhi
# convert to Cartesian for the Biot-Savart summation
# otherwise, spherical coordinates get mixed up betwen the source and destination grids
# theta_unit and phi_unit vectors rotate from point to point and are different on the two grids
jx = jTheta*np.cos(tSource)*np.cos(pSource) - jPhi*np.sin(pSource)
jy = jTheta*np.cos(tSource)*np.sin(pSource) + jPhi*np.cos(pSource)
jz =-jTheta*np.sin(tSource)
# x,y,z are size (ntheta,nphi)
# make array of destination points on the mix grid
# add fake dimension for numpy broadcasting
xDest = xyz[np.newaxis,np.newaxis,:,0]
yDest = xyz[np.newaxis,np.newaxis,:,1]
zDest = xyz[np.newaxis,np.newaxis,:,2]
# up to here things are fast (checked for both source and destination 90x720 grids)
# the operations below are slow and kill the memory becase (90x720)^2 (the size of each array below) is ~30GB
# solution: break the destination grid up into pieces before passing here
# vector between destination and source
Rx = xDest - xSource
Ry = yDest - ySource
Rz = zDest - zSource
R = np.sqrt(Rx**2+Ry**2+Rz**2)
# vector product with the current
# note the multiplication by sin(tSource)*dtSource*dpSource -- area of the surface element
#
# note on normalization
# Efield is computed in V/m, sigma is also in SI units
# Current coming out of hCurrents() should be in SI units [A/m] (height integrated).
# The Biot-Savart law is mu0/4pi*Int( j x r' dV/|r'|^3 ) =
# mu0/4pi Int( j x r'hat r^2 dr sin(theta) dtheta dphi /|r'|^2) =
# mu0/4pi Int( (j dr) x r'hat (r/Ri)^2 sin(theta) dtheta dphi /(|r'|/Ri)^2)
# where we have combined j and dr (= j dr) which is what is coming out of hCurrents in SI units
# and normalized everything else to Ri, which it already is in the code below
# in other words the fields below should be in [T]
dA = np.sin(tSource)*dtSource*dpSource
dBx = mu0o4pi*np.sum( (jy*Rz - jz*Ry)*dA/R**3,axis=(0,1))
dBy = mu0o4pi*np.sum( (jz*Rx - jx*Rz)*dA/R**3,axis=(0,1))
dBz = mu0o4pi*np.sum( (jx*Ry - jy*Rx)*dA/R**3,axis=(0,1))
if not hallOnly:
intx,inty,intz = self.BSFluxTubeInt(xyz,Rinner=Rin*Re/Ri,rsegments=rsegments)
# FIXME: don't fix sign for south
jpara = -self.variables['current']['data'] # note, the sign was inverted by the reader for north, put it back to recover true FAC
jpara = jpara[:,:,np.newaxis]
cosd = abs(cosDipAngle[:,:,np.newaxis]) # note, only need abs value of cosd regardless of hemisphere
# note on normalization
# jpara is in microA/m^2 -- convert to A (1.e-6)
# further, after all is said and done and all distance-like variables are accounted for
# the answer below should be multiplied by Ri in m (6.5e6)
# factors of 1.e6 cancel out and we only have Ri
dBx += Ri*mu0o4pi*np.sum(jpara*dA*cosd*intx,axis=(0,1))
dBy += Ri*mu0o4pi*np.sum(jpara*dA*cosd*inty,axis=(0,1))
dBz += Ri*mu0o4pi*np.sum(jpara*dA*cosd*intz,axis=(0,1))
# finally, convert to spherical *at the destination*
# note, this is ugly because we specified the spherical grid before passing to this function (in calcdB.py)
# FIXME: think about how to make it less ugly
rDest = np.sqrt(xDest**2+yDest**2+zDest**2)
tDest = np.arccos(zDest/rDest)
pDest = np.arctan2(yDest,xDest)
dBr = dBx*np.sin(tDest)*np.cos(pDest) + dBy*np.sin(tDest)*np.sin(pDest) + dBz*np.cos(tDest)
dBtheta = dBx*np.cos(tDest)*np.cos(pDest) + dBy*np.cos(tDest)*np.sin(pDest) - dBz*np.sin(tDest)
dBphi =-dBx*np.sin(pDest) + dBy*np.cos(pDest)
return(dBr,dBtheta,dBphi)
# FIXME: Make work for SOUTH
# Flux-tube Biot-Savart integral \int dl bhat x r'/|r'|^3
def BSFluxTubeInt(self,xyz,Rinner,rsegments = 10):
# xyz = array of points where to compute dB (same as above in dB)
# xyz.shape should be (N,3), where N is the number of points
# xyz = (x,y,z) in units of Ri
#
# Rinner is the radius of the inner boundary of the MHD domain
# expressed in Ri
#
# rsegments: how many segments to break the fluxtube into between ionosphere and Rinner
if len(xyz.shape)!=2:
sys.exit("dB input assumes the array of points of (N,3) size.")
if xyz.shape[1]!=3:
sys.exit("dB input assumes the array of points of (N,3) size.")
xc,yc,theta,phi = self.cartesianCellCenters()
# radii of centers of segments of the flux tube
Rs = np.linspace(1.,Rinner,rsegments+1)
Rcenters = 0.5*(Rs[:-1]+Rs[1:])
dR = Rs[1:]-Rs[:-1]
# add fake dims to conform to theta & phi size
Rcenters = Rcenters[:,np.newaxis,np.newaxis]
dR = dR[:,np.newaxis,np.newaxis]
# theta on the field line (m for magnetosphere)
# note, since theta is in [0,45] deg range or so,
# Rcenters is in the range [1,2] or so, and
# arsin is in the range [-pi/2,pi/2]
# the result below is >0, i.e., we only take the part of the field line
# that lies in the same hemisphere as the ionospheric footpoint
thetam = np.arcsin(np.sqrt(Rcenters)*np.sin(theta))
# note order: R, theta, phi
x = Rcenters*np.sin(thetam)*np.cos(phi)
y = Rcenters*np.sin(thetam)*np.sin(phi)
z = np.sqrt(Rcenters**2-x**2-y**2)
# distance along flux tube for given dR
# dl = dR*np.sqrt(1.+Rcenters*np.sin(theta)**2/4./(1.-Rcenters*np.sin(theta)**2))
dl = dR*np.sqrt(1.+3.*np.cos(thetam)**2)/2./np.cos(thetam)
# unit vector in B-direction (e.g., Baumjohann Eq. 3.1 -- note their use of latitude vs colatitude)
# br = -2.*np.sqrt(1-Rcenters*np.sin(theta)**2)/np.sqrt(4.-3.*Rcenters*np.sin(theta)**2)
# bt = -np.sqrt(Rcenters)*np.sin(theta)/np.sqrt(4.-3.*Rcenters*np.sin(theta)**2)
br = -2.*np.cos(thetam)/np.sqrt(np.sin(thetam)**2+4.*np.cos(thetam)**2)
bt = -np.sin(thetam)/np.sqrt(np.sin(thetam)**2+4.*np.cos(thetam)**2)
# now the same in cartesian
bx = br*np.sin(thetam)*np.cos(phi) + bt*np.cos(thetam)*np.cos(phi)
by = br*np.sin(thetam)*np.sin(phi) + bt*np.cos(thetam)*np.sin(phi)
bz = br*np.cos(thetam) - bt*np.sin(thetam)
# fake dimensions for numpy broadcasting
# remember dimenstions: R,t,p along field line + adding the destination point number
xSource = x[:,:,:,np.newaxis]
ySource = y[:,:,:,np.newaxis]
zSource = z[:,:,:,np.newaxis]
bx = bx[:,:,:,np.newaxis]
by = by[:,:,:,np.newaxis]
bz = bz[:,:,:,np.newaxis]
dl = dl[:,:,:,np.newaxis]
xDest = xyz[np.newaxis,np.newaxis,np.newaxis,:,0]
yDest = xyz[np.newaxis,np.newaxis,np.newaxis,:,1]
zDest = xyz[np.newaxis,np.newaxis,np.newaxis,:,2]
# vector between destination and source
Rx = xDest - xSource
Ry = yDest - ySource
Rz = zDest - zSource
R = np.sqrt(Rx**2+Ry**2+Rz**2)
# vector product with the current
intx = np.sum( dl*(by*Rz - bz*Ry)/R**3,axis=0)
inty = np.sum( dl*(bz*Rx - bx*Rz)/R**3,axis=0)
intz = np.sum( dl*(bx*Ry - by*Rx)/R**3,axis=0)
return(intx,inty,intz)
# Code below is from old mix scripts.
# Keeping for further development but commenting out for now.
# The function is deprecated as of 10-25-10 and is left here only for
# backward compatibility. Use get_time_series for extracting cpcp and
# other quantities as funcitons of time.
# def get_cpcp_time_series(directory):
# from pylab import sort
# import glob,datetime
# files = glob.glob(directory+'*_mix_*.hdf')
# time = []
# cpcp_n = []
# cpcp_s = []
# for file in sort(files):
# print(file)
# (simtime,
# x,y,
# psi_n,psi_s,
# fac_n,fac_s,
# sigmap_n,sigmap_s,
# sigmah_n,sigmah_s,
# energy_n,energy_s,
# flux_n,flux_s) = get_data(file)
# cpcp_n.append(psi_n.max()-psi_n.min())
# cpcp_s.append(psi_s.max()-psi_s.min())
# t = datetime.datetime(simtime[0],simtime[1],simtime[2],
# simtime[3],simtime[4],simtime[5])
# time.append(t)
# return (time,cpcp_n,cpcp_s)
# def get_time_series(directory):
# from pylab import sort,sqrt,nonzero
# import glob,datetime
# import integrate
# files = glob.glob(directory+'*_mix_*.hdf')
# ri=6500.e3
# # Pre-compute some constants:
# (simtime,x,y,psi_n,psi_s,fac_n,fac_s,sigmap_n,sigmap_s,sigmah_n,sigmah_s,
# energy_n,energy_s,flux_n,flux_s) = get_data(files[0])
# x[:,0]=0.0
# y[:,0]=0.0
# z=sqrt(1.0-x**2-y**2)
# areaSphere = integrate.calcFaceAreas(x,y,z)*ri*ri
# time = []
# cpcp_n = []
# cpcp_s = []
# fac_pos_n = []
# fac_neg_n = []
# fac_pos_s = []
# fac_neg_s = []
# for file in sort(files):
# (simtime,
# x,y,
# psi_n,psi_s,
# fac_n,fac_s,
# sigmap_n,sigmap_s,
# sigmah_n,sigmah_s,
# energy_n,energy_s,
# flux_n,flux_s) = get_data(file)
# cpcp_n.append(psi_n.max()-psi_n.min())
# cpcp_s.append(psi_s.max()-psi_s.min())
# pfac = fac_n.copy()
# locs = nonzero(fac_n < 0)
# pfac[locs] = 0.0
# val = areaSphere*pfac[0:180,0:26]
# fac_pos_n.append(val.sum()*1.e-12)
# pfac = fac_n.copy()
# locs = nonzero(fac_n > 0)
# pfac[locs] = 0.0
# val = areaSphere*pfac[0:180,0:26]
# fac_neg_n.append(-val.sum()*1.e-12)
# pfac = fac_s.copy()
# locs = nonzero(fac_s < 0)
# pfac[locs] = 0.0
# val = areaSphere*pfac[0:180,0:26]
# fac_pos_s.append(val.sum()*1.e-12)
# pfac = fac_s.copy()
# locs = nonzero(fac_s > 0)
# pfac[locs] = 0.0
# val = areaSphere*pfac[0:180,0:26]
# fac_neg_s.append(-val.sum()*1.e-12)
# t = datetime.datetime(simtime[0],simtime[1],simtime[2],
# simtime[3],simtime[4],simtime[5])
# time.append(t)
# return (time,cpcp_n,cpcp_s,fac_pos_n,fac_neg_n,fac_pos_s,fac_neg_s)

16
kaipy/requirements.txt
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@@ -1,16 +0,0 @@
alive_progress
astropy
cartopy
cdasws
configparser
h5py
jupyterlab
matplotlib
pandas
progressbar
pyhdf
pyspedas
pytest
slack_sdk
spacepy
sunpy

106
kaipy/satcomp/README.txt
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@@ -1,106 +0,0 @@
Main functions and dictionary structures
scutils.py
getCdasData:
returns dictionary of satellite data, either:
exactly as delivered by Cdaweb
or, full days of all days needed, as delivered by Cdawev
scRCM.py
getSpecieslambdata:
returns:
result{
'ilamc' : lambda values (centers)
'ilami' : lambda interfacts (1 longer than ilamc)
'lamscl' : scaling factor needed to calculate differential flux
}
getSCOmniDiffFlux:
Get differential flux of given spacecraft dataset
returns:
ephdata : EMFISIS trajectory data
dataset{
'name' : spacecraft name
'species' : species name, 'ions' or 'electrons'
'epoch' : list of epoch times
'OmniDiffFlux' : omni-directional differential flux of species
'energies' : energy channels. May be 1D (len(epoch)) or 2D (# channel bins, len(epoch))
}
getRCMTimes:
Note: Needs both rcm.h5 and mhdrcm.h5 to get MJD times
returns:
rcmTimes{
'Nt' : number of time steps
'sIDs' : ordered step ids (int)
'sIDstrs' : step ids to access rcm.h5 keys (str)
'T' : time (from start of simulation)
'MJD' : MJD's
}
getRC_scTrack:
Pull RCM data along a given spacecraft track
sc = data from spacecraft track file (e.g. RBSPB.sc.h5)
returns:
result{
'T' : sc['T']
'MJD' : sc['MJDs']
'MLAT' : sc['MLAT']
'MLON' : sc['MLON']
'vm' : rcm vm (= bVol**(-2/3))
'xmin' : rcm xmin
'ymin' : rcm ymin
'zmin' : rcm zmin
'eqmin' : sqrt(xmin**2 + ymin**2)
'electrons' : speciesdata['electrons'] : {
'energies' : energies in keV
'eetas' : species etas
'diffFlux' : omni-directional differential flux (bMirror adjusted)
}
'ions' : speciesdata['ions']
}
consolidateODFs:
Consolidate RCM track and sc data to same energy grid
returns:
result{
'energyGrid' : shared energy grid
'sc' : {
'name' : sc name
'time' : sc epoch
'diffFlux' : omni diff flux
}
'rcm' : {
'time' : rcm MJDs
'origEGrid' : original energy information
'orig ODF' : original omni diff flux corresponding to 'origEGrid'
'diffFlux' : new omni diff flux mapped to 'energyGrid'
}
}
getIntentitiesVsL:
Calculate rcm pressure on tkl (time, energy, L shell) grid
returns:
result{
'T' : rcm times
'MJD' : rcm MJDs
'lambda' : lambda values
'L_bins' : list of L shell bin values
'energyGrid' : fixed energyGrid values
'press_tkl' : rcm pressure on tkl grid
'press_tl' : rcm pressure on tl grid (summed along energies)
}
getRCM_eqlatlon:
Get (MHD)RCM variables in a nice package
returns:
result{
'T' : rcm times
'MJD' : rcm MJDs
'MLAT' : rcm mlat values (1D)
'MLON' : rcm mlon values (1D)
'xmin' : rcm xmins
'ymin' : rcm ymins
'press' : rcm pressure
}

1366
kaipy/satcomp/scRCM.py
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549
kaipy/satcomp/sc_cdasws_strs.json
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@@ -1,549 +0,0 @@
{
"GOES11" : {
"Ephem" : {
"Id" : "GOES11_K0_MAG",
"Data" : "SC_pos_sm",
"CoordSys" : "SM"
},
"MagneticField" : {
"Id" : "GOES11_K0_MAG",
"Data" : "B_GSM_c",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "GOES11_EPHEMERIS_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "GOES11_EPHEMERIS_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"_testing" : {
"t0" : "2010-12-30T00:00:00Z",
"t1" : "2010-12-31T00:00:00Z"
}
},
"GOES12" : {
"Ephem" : {
"Id" : "GOES12_K0_MAG",
"Data" : "SC_pos_sm",
"CoordSys" : "SM"
},
"MagneticField" : {
"Id" : "GOES12_K0_MAG",
"Data" : "B_GSM_c",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "GOES12_EPHEMERIS_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "GOES12_EPHEMERIS_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"_testing" : {
"t0" : "2010-09-23T00:00:00Z",
"t1" : "2010-09-24T00:00:00Z"
}
},
"GEOTAIL" : {
"Ephem" : {
"Id" : "GE_K0_MGF",
"Data" : "POS",
"CoordSys" : "GSE"
},
"MagneticField" : {
"Id" : "GE_K0_MGF",
"Data" : "IB_vector",
"CoordSys" : "GSE"
},
"Density" : {
"Id" : "GE_H0_CPI",
"Data" : "SW_P_Den"
},
"Velocity" : {
"Id" : "GE_H0_CPI",
"Data" : "SW_Vc",
"CoordSys" : "GSE"
}
},
"RBSPA" : {
"Ephem" : {
"Id" : "RBSP-A_MAGNETOMETER_1SEC-GSM_EMFISIS-L3",
"Data" : "coordinates",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "RBSP-A_MAGNETOMETER_1SEC-GSM_EMFISIS-L3",
"Data" : "Mag",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "RBSPA_REL04_ECT-HOPE-MOM-L3",
"Data" : "Dens_p_30"
},
"Electron_omniflux_RBSPICE" : {
"Id": "RBSP-A-RBSPICE_LEV-3-PAP_ESRHELT",
"Data": ["FEDU", "FEDU_OmniFlux", "MLT", "L_Eq", "L_Star"],
"OmnifluxStr" : "FEDU_OmniFlux",
"EnergyStr" : "FEDU_Energy"
},
"Hydrogen_omniflux_RBSPICE" : {
"Id" : "RBSP-A-RBSPICE_LEV-3-PAP_TOFXEH",
"Data" : ["FPDU_OmniFlux"],
"OmnifluxStr" : "FPDU_OmniFlux",
"EnergyStr" : "FPDU_Energy"
}
},
"RBSPB" : {
"Ephem" : {
"Id" : "RBSP-B_MAGNETOMETER_1SEC-GSM_EMFISIS-L3",
"Data" : "coordinates",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "RBSP-B_MAGNETOMETER_1SEC-GSM_EMFISIS-L3",
"Data" : "Mag",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "RBSPB_REL04_ECT-HOPE-MOM-L3",
"Data" : "Dens_p_30"
},
"Electron_omniflux_RBSPICE" : {
"Id": "RBSP-B-RBSPICE_LEV-3-PAP_ESRHELT",
"Data": ["FEDU", "FEDU_OmniFlux", "MLT", "L_Eq", "L_Star"],
"OmnifluxStr" : "FEDU_OmniFlux",
"EnergyStr" : "FEDU_Energy"
},
"Hydrogen_omniflux_RBSPICE" : {
"Id" : "RBSP-B-RBSPICE_LEV-3-PAP_TOFXEH",
"Data" : ["FPDU_OmniFlux"],
"OmnifluxStr" : "FPDU_OmniFlux",
"EnergyStr" : "FPDU_Energy"
},
"Hydrogen_PAFlux_HOPE" : {
"Id" : "RBSPB_REL04_ECT-HOPE-PA-L3",
"Data" : ["FPDU"],
"PAFluxStr" : "FPDU",
"EnergyStr" : "HOPE_ENERGY_Ion",
"EpochStr" : "Epoch_Ion"
}
},
"CLUSTER1" : {
"Ephem" : {
"Id" : "C1_CP_FGM_SPIN",
"Data" : "sc_pos_xyz_gse__C1_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"MagneticField" : {
"Id" : "C1_CP_FGM_SPIN",
"Data" : "B_vec_xyz_gse__C1_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"Density" : {
"Id" : "C1_PP_CIS",
"Data" : "N_p__C1_PP_CIS",
"EpochStr" : "Epoch__C1_PP_CIS"
},
"Velocity" : {
"Id" : "C1_PP_CIS",
"Data" : "V_p_xyz_gse__C1_PP_CIS",
"CoordSys" : "GSE",
"EpochStr" : "Epoch__C1_PP_CIS"
},
"_testing" : {
"t0" : "2018-12-30T00:00:00Z",
"t1" : "2018-12-31T00:00:00Z"
}
},
"CLUSTER2" : {
"Ephem" : {
"Id" : "C2_CP_FGM_SPIN",
"Data" : "sc_pos_xyz_gse__C2_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"MagneticField" : {
"Id" : "C2_CP_FGM_SPIN",
"Data" : "B_vec_xyz_gse__C2_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"_testing" : {
"t0" : "2018-12-30T00:00:00Z",
"t1" : "2018-12-31T00:00:00Z"
}
},
"CLUSTER3" : {
"Ephem" : {
"Id" : "C3_CP_FGM_SPIN",
"Data" : "sc_pos_xyz_gse__C3_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"MagneticField" : {
"Id" : "C3_CP_FGM_SPIN",
"Data" : "B_vec_xyz_gse__C3_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"Density" : {
"Id" : "C3_PP_CIS",
"Data" : "N_p__C3_PP_CIS"
},
"Velocity" : {
"Id" : "C3_PP_CIS",
"Data" : "V_p_xyz_gse__C3_PP_CIS",
"CoordSys" : "GSE"
},
"_testing" : {
"t0" : "2018-12-30T00:00:00Z",
"t1" : "2018-12-31T00:00:00Z"
}
},
"CLUSTER4" : {
"Ephem" : {
"Id" : "C4_CP_FGM_SPIN",
"Data" : "sc_pos_xyz_gse__C4_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"MagneticField" : {
"Id" : "C4_CP_FGM_SPIN",
"Data" : "B_vec_xyz_gse__C4_CP_FGM_SPIN",
"CoordSys" : "GSE"
},
"Density" : {
"Id" : "C4_PP_CIS",
"Data" : "N_p__C4_PP_CIS"
},
"Velocity" : {
"Id" : "C4_PP_CIS",
"Data" : "V_p_xyz_gse__C4_PP_CIS",
"CoordSys" : "GSE"
},
"_testing" : {
"t0" : "2018-12-30T00:00:00Z",
"t1" : "2018-12-31T00:00:00Z"
}
},
"THEMISA" : {
"Ephem" : {
"Id" : "THA_OR_SSC",
"Data" : "XYZ_GSM",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "THA_L2_FGM",
"Data" : "tha_fgs_gsm",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "THA_L2_MOM",
"Data" : "tha_peim_density",
"EpochStr" : "tha_peim_epoch"
},
"Pressure" : {
"Id" : "THA_L2_MOM",
"Data" : "tha_peim_ptot",
"EpochStr" : "tha_peim_epoch"
},
"Velocity" : {
"Id" : "THA_L2_MOM",
"Data" : "tha_peim_velocity_gsm",
"EpochStr" : "tha_peim_epoch",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "THA_OR_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "THA_OR_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
}
},
"THEMISB" : {
"Ephem" : {
"Id" : "THB_OR_SSC",
"Data" : "XYZ_GSM",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "THB_L2_FGM",
"Data" : "thb_fgs_gsm",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "THB_L2_MOM",
"Data" : "thb_peim_density"
},
"Pressure" : {
"Id" : "THB_L2_MOM",
"Data" : "thb_peim_ptot"
},
"Velocity" : {
"Id" : "THB_L2_MOM",
"Data" : "thb_peim_velocity_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "THB_OR_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "THB_OR_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
}
},
"THEMISC" : {
"Ephem" : {
"Id" : "THC_OR_SSC",
"Data" : "XYZ_GSM",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "THC_L2_FGM",
"Data" : "thc_fgs_gsm",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "THC_L2_MOM",
"Data" : "thc_peim_density"
},
"Pressure" : {
"Id" : "THC_L2_MOM",
"Data" : "thc_peim_ptot"
},
"Velocity" : {
"Id" : "THC_L2_MOM",
"Data" : "thc_peim_velocity_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "THC_OR_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "THC_OR_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
}
},
"THEMISD" : {
"Ephem" : {
"Id" : "THD_OR_SSC",
"Data" : "XYZ_GSM",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "THD_L2_FGM",
"Data" : "thd_fgs_gsm",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "THD_L2_MOM",
"Data" : "thd_peim_density"
},
"Pressure" : {
"Id" : "THD_L2_MOM",
"Data" : "thd_peim_ptot"
},
"Velocity" : {
"Id" : "THD_L2_MOM",
"Data" : "thd_peim_velocity_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "THD_OR_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "THD_OR_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
}
},
"THEMISE" : {
"Ephem" : {
"Id" : "THE_OR_SSC",
"Data" : "XYZ_GSM",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "THE_L2_FGM",
"Data" : "the_fgs_gsm",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "THE_L2_MOM",
"Data" : "the_peim_density"
},
"Pressure" : {
"Id" : "THE_L2_MOM",
"Data" : "the_peim_ptot"
},
"Velocity" : {
"Id" : "THE_L2_MOM",
"Data" : "the_peim_velocity_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintNorth" : {
"Id" : "THE_OR_SSC",
"Data" : ["NorthBtrace_GEO_LAT", "NorthBtrace_GEO_LON"],
"CoordSys" : "GEO"
},
"MagneticFootprintSouth" : {
"Id" : "THE_OR_SSC",
"Data" : ["SouthBtrace_GEO_LAT", "SouthBtrace_GEO_LON"],
"CoordSys" : "GEO"
}
},
"MMS1" : {
"Ephem" : {
"Id" : "MMS1_FGM_SRVY_L2",
"Data" : "mms1_fgm_r_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "MMS1_FGM_SRVY_L2",
"Data" : "mms1_fgm_b_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "MMS1_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms1_dis_numberdensity_fast"
},
"Pressure" : {
"Id" : "MMS1_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms1_dis_pres_bg_fast"
},
"Velocity" : {
"Id" : "MMS1_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms1_dis_bulkv_gse_fast",
"CoordSys" : "GSE"
},
"MagneticFootprintNorth" : {
"Id" : "MMS1_MEC_SRVY_L2_EPHT89D",
"Data" : "mms1_mec_pfn_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintSouth" : {
"Id" : "MMS1_MEC_SRVY_L2_EPHT89D",
"Data" : "mms1_mec_pfs_gsm",
"CoordSys" : "GSM"
}
},
"MMS2" : {
"Ephem" : {
"Id" : "MMS2_FGM_SRVY_L2",
"Data" : "mms2_fgm_r_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "MMS2_FGM_SRVY_L2",
"Data" : "mms2_fgm_b_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "MMS2_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms2_dis_numberdensity_fast"
},
"Pressure" : {
"Id" : "MMS2_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms2_dis_pres_bg_fast"
},
"Velocity" : {
"Id" : "MMS2_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms2_dis_bulkv_gse_fast",
"CoordSys" : "GSE"
},
"MagneticFootprintNorth" : {
"Id" : "MMS2_MEC_SRVY_L2_EPHT89D",
"Data" : "mms2_mec_pfn_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintSouth" : {
"Id" : "MMS2_MEC_SRVY_L2_EPHT89D",
"Data" : "mms2_mec_pfs_gsm",
"CoordSys" : "GSM"
}
},
"MMS3" : {
"Ephem" : {
"Id" : "MMS3_FGM_SRVY_L2",
"Data" : "mms3_fgm_r_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "MMS3_FGM_SRVY_L2",
"Data" : "mms3_fgm_b_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "MMS3_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms3_dis_numberdensity_fast"
},
"Pressure" : {
"Id" : "MMS3_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms3_dis_pres_bg_fast"
},
"Velocity" : {
"Id" : "MMS3_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms3_dis_bulkv_gse_fast",
"CoordSys" : "GSE"
},
"MagneticFootprintNorth" : {
"Id" : "MMS3_MEC_SRVY_L2_EPHT89D",
"Data" : "mms3_mec_pfn_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintSouth" : {
"Id" : "MMS3_MEC_SRVY_L2_EPHT89D",
"Data" : "mms3_mec_pfs_gsm",
"CoordSys" : "GSM"
}
},
"MMS4" : {
"Ephem" : {
"Id" : "MMS4_FGM_SRVY_L2",
"Data" : "mms4_fgm_r_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"MagneticField" : {
"Id" : "MMS4_FGM_SRVY_L2",
"Data" : "mms4_fgm_b_gsm_srvy_l2",
"CoordSys" : "GSM"
},
"Density" : {
"Id" : "MMS4_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms4_dis_numberdensity_fast"
},
"Pressure" : {
"Id" : "MMS4_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms4_dis_pres_bg_fast"
},
"Velocity" : {
"Id" : "MMS4_FPI_FAST_L2_DIS-MOMS",
"Data" : "mms4_dis_bulkv_gse_fast",
"CoordSys" : "GSE"
},
"MagneticFootprintNorth" : {
"Id" : "MMS4_MEC_SRVY_L2_EPHT89D",
"Data" : "mms4_mec_pfn_gsm",
"CoordSys" : "GSM"
},
"MagneticFootprintSouth" : {
"Id" : "MMS4_MEC_SRVY_L2_EPHT89D",
"Data" : "mms4_mec_pfs_gsm",
"CoordSys" : "GSM"
}
}
}

134
kaipy/satcomp/sc_helio.json
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@@ -1,134 +0,0 @@
{
"ACE" : {
"label": "ACE",
"Ephem" : {
"Id" : "AC_OR_SSC",
"Data" : "XYZ_GSE",
"CoordSys" : "GSE",
"Epoch_Name" : "Epoch_bin"
},
"Speed" : {
"Id" : "AC_H2_SWE",
"Data" : "Vp"
},
"MagneticField" : {
"Id" : "AC_H2_MFI",
"Data" : "BGSEc",
"CoordSys" : "GSE"
},
"Density" : {
"Id" : "AC_H2_SWE",
"Data" : "Np"
},
"Temperature" : {
"Id" : "AC_H2_SWE",
"Data" : "Tpr"
}
},
"STEREO_A" : {
"label": "STEREO-A",
"Ephem" : {
"Id" : "STA_HELIO1DAY_POSITION",
"Data" : ["RAD_AU", "HGI_LAT", "HGI_LON"],
"CoordSys" : "HGI",
"Epoch_Name" : "Epoch_bin"
},
"Speed" : {
"Id" : "STA_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaSpeed"
},
"MagneticField" : {
"Id" : "STA_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "BR",
"CoordSys" : "RTN"
},
"Density" : {
"Id" : "STA_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaDensity"
},
"Temperature" : {
"Id" : "STA_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaTemp"
}
},
"STEREO_B" : {
"label": "STEREO-B",
"Ephem" : {
"Id" : "STB_HELIO1DAY_POSITION",
"Data" : ["RAD_AU", "HGI_LAT", "HGI_LON"],
"CoordSys" : "HGI",
"Epoch_Name" : "Epoch_bin"
},
"Speed" : {
"Id" : "STB_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaSpeed"
},
"MagneticField" : {
"Id" : "STB_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "BR",
"CoordSys" : "RTN"
},
"Density" : {
"Id" : "STB_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaDensity"
},
"Temperature" : {
"Id" : "STBA_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "plasmaTemp"
}
},
"Parker_Solar_Probe" : {
"label": "Parker",
"Ephem" : {
"Id" : "PSP_COHO1HR_MERGED_MAG_PLASMA",
"Data" : ["radialDistance", "heliographicLatitude", "heliographicLongitude"],
"CoordSys" : "HGI",
"Epoch_Name" : "Epoch_bin"
},
"Speed" : {
"Id" : "PSP_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "VR",
"CoordSys" : "RTN"
},
"MagneticField" : {
"Id" : "PSP_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "BR",
"CoordSys" : "RTN"
},
"Density" : {
"Id" : "PSP_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "protonDensity"
},
"Temperature" : {
"Id" : "PSP_COHO1HR_MERGED_MAG_PLASMA",
"Data" : "protonTemp"
}
},
"Solar_Orbiter" : {
"label": "SO",
"Ephem" : {
"Id" : "SOLO_HELIO1DAY_POSITION",
"Data" : ["RAD_AU", "HGI_LAT", "HGI_LON"],
"CoordSys" : "HGI",
"Epoch_Name" : "Epoch_bin"
},
"Speed" : {
"Id" : "SOLO_L2_SWA-PAS-GRND-MOM",
"Data" : "V_RTN",
"CoordSys" : "RTN"
},
"MagneticField" : {
"Id" : "SOLO_L2_MAG-RTN-NORMAL-1-MINUTE",
"Data" : "B_RTN",
"CoordSys" : "RTN"
},
"Density" : {
"Id" : "SOLO_L2_SWA-PAS-GRND-MOM",
"Data" : "N"
},
"Temperature" : {
"Id" : "SOLO_L2_SWA-PAS-GRND-MOM",
"Data" : "T"
}
}
}

2194
kaipy/satcomp/scutils.py
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54
kaipy/satcomp/test_cdas.py
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@@ -1,54 +0,0 @@
import datetime
import kaipy.satcomp.scutils as scutils
#TODO: Need to add "epoch" str for each dataset
def test_getscIds():
print("Testing ability to grab spacecraft Ids from json file")
scIdDict = scutils.getScIds()
assert type(scIdDict) == dict, "Returned type is {}, but should be type dict".format(type(scIdDict))
assert len(scIdDict.keys()) != 0, "Dictionary has zero entries"
#Check if every spacecraft entry has any data at all
#Check if every spacecraft entry has an "Ephem" data product
#Check if every spacefraft data product has at least an "Id" and "data" k-v pair
def test_getCdasData():
print("Testing if all data in scId dict is retrievable from cdasws")
scIdDict = scutils.getScIds()
for scName in scIdDict.keys():
print(" " + scName)
scStrs = scIdDict[scName]
for dpStr in scStrs.keys():
if dpStr == '_testing': continue
print(" " + dpStr, end=" : ")
#Get valid time interval for dataset
tStart, tEnd = scutils.getCdasDsetInterval(scStrs[dpStr]['Id'])
#assert tStart != None, "getCdasDsetInterval returned null for dataset " + dpStr
if tStart is None:
print("Bad dset")
continue
t0 = tStart
t0dt = datetime.datetime.strptime(t0, "%Y-%m-%dT%H:%M:%S.%fZ")
t1 = (t0dt + datetime.timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%SZ")
dset_id = scStrs[dpStr]['Id']
dset_vname = scStrs[dpStr]['Data']
if "EpochStr" in scStrs[dpStr].keys():
cdasResult = scutils.getCdasData(dset_id, dset_vname, t0,t1, epochStr=scStrs[dpStr]["EpochStr"])
else:
cdasResult = scutils.getCdasData(dset_id, dset_vname, t0,t1)
assert cdasResult != {}, "getCdasData returned with no information"
print("Good")
if __name__ == "__main__":
test_getscIds()
test_getCdasData()

622
kaipy/solarWind/OMNI.py
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@@ -1,622 +0,0 @@
# Custom
import kaipy.transform
from kaipy.solarWind.SolarWind import SolarWind
from kaipy.kdefs import *
# 3rd party
import numpy
from cdasws import CdasWs
from pyspedas import kyoto
from pytplot import get_data
# Standard
import datetime
import re
class OMNI(SolarWind):
"""
OMNI Solar Wind file from CDAweb [http://cdaweb.gsfc.nasa.gov/].
Data stored in GSE coordinates.
"""
def __init__(self, filename = None, doFilter = False, sigmaVal = 3.0):
SolarWind.__init__(self)
self.filter = doFilter
self.sigma = sigmaVal
self.bad_data = [-999.900,
99999.9, # V
9999.99, # B
999.990, # density
1.00000E+07, # Temperature
9999999.0, # Temperature
99999 # Activity indices
]
self.__read(filename)
def __read(self, filename):
"""
Read the solar wind file & store results in self.data TimeSeries object.
"""
(startDate, dates, data, datanames) = self.__readData(filename)
(dataArray, hasBeenInterpolated) = self._removeBadData(data,datanames)
if self.filter:
(dataArray, hasBeenInterpolated) = self._coarseFilter(dataArray, hasBeenInterpolated)
self._storeDataDict(dates, dataArray, hasBeenInterpolated)
self.__appendMetaData(startDate, filename)
self._appendDerivedQuantities()
def __readData(self, fh):
"""
return 2d array (of strings) containing data from file
"""
#pulling variables from file
time=fh['Epoch']
bx=fh['BX_GSE']
by=fh['BY_GSE']
bz=fh['BZ_GSE']
vx=fh['Vx']
vy=fh['Vy']
vz=fh['Vz']
n=fh['proton_density']
T=fh['T']
ae=fh['AE_INDEX']
al=fh['AL_INDEX']
au=fh['AU_INDEX']
symh=fh['SYM_H']
xBow = fh['BSN_x'] #RE
yBow = fh['BSN_y'] #RE
zBow = fh['BSN_z'] #RE
dates = []
rows = []
for i in range(len(time)):
startTime = time[0]
#calculating minutes from the start time
nMin = self._deltaMinutes(time[i],startTime)
data = [nMin,bx[i],by[i],bz[i],vx[i],vy[i],vz[i],n[i],T[i],ae[i],al[i],au[i],symh[i],xBow[i],yBow[i],zBow[i]]
dates.append( time[i] )
rows.append( data )
datanames = ['Time','Bx','By','Bz','Vx','Vy','Vz','n','Temperature','AE','AL','AU','SYMH','BowShockX','BowShockY','BowShockZ']
return (startTime, dates, rows, datanames)
def __appendMetaData(self, date, filename):
"""
Add standard metadata to the data dictionary.
"""
metadata = {'Model': 'OMNI',
'Source': filename,
'Date processed': datetime.datetime.now(),
'Start date': date
}
self.data.append(key='meta',
name='Metadata for OMNI Solar Wind file',
units='n/a',
data=metadata)
def _removeBadData(self, data, datanames=['Time','Bx','By','Bz','Vx','Vy','Vz','n','Temp','AE','AL','AU','SYMH','BowShockX','BowShockY','BowShockZ'], hasBeenInterpolated = None):
"""
Linearly interpolate over bad data (defined by self.bad_data
list) for each variable in dataStrs.
data: 2d list. Each row is a list containing:
[nMinutes, Bx, By, Bz, Vx, Vy, Vz, rho, temp, ae, al, au, symh]
Returns:
data: interpolated floating-point numpy array
hasBeenInterpolated: 2d array that identifies if bad values were removed/interpolated.
NOTE: This is remarkably similar to __coarseFilter!
Refactoring to keep it DRY wouldn't be a bad idea. . .
"""
#assert( len(data[0]) == 13 )
nvar = len(data[0])
if (hasBeenInterpolated is None):
hasBeenInterpolated = numpy.empty((len(data), nvar-1))
hasBeenInterpolated.fill(False)
for varIdx in range(1,nvar):
lastValidIndex = -1
for curIndex,row in enumerate(data):
if row[varIdx] in numpy.float32(self.bad_data) or numpy.isnan(row[varIdx]) or numpy.ma.is_masked(row[varIdx]):
# This item has bad data.
hasBeenInterpolated[curIndex, varIdx-1] = True
if (lastValidIndex == -1) & (curIndex == len(data)-1):
# Data does not have at least one valid element!
# Setting all values to 0 so that file can still be made
print("No good elements, setting all values to 0 for variable: ", datanames[varIdx])
data[curIndex][varIdx] = 0.
#raise Exception("First & Last datapoint(s) in OMNI "+
# "solar wind file are invalid. Not sure "+
# "how to interpolate across bad data.")
elif (curIndex == len(data)-1):
# Clamp last bad data to previous known good data.
data[curIndex][varIdx] = data[lastValidIndex][varIdx]
else:
# Note the bad data & skip this element for now.
# We will linearly interpolate between valid data
continue
# At this point, curIndex has good data.
if (lastValidIndex+1) == curIndex:
# Set current element containing good data.
data[curIndex][varIdx] = float( row[varIdx] )
else:
# If first index is invalid, clamp to first good value.
if lastValidIndex == -1:
lastValidIndex = 0
data[lastValidIndex][varIdx] = data[curIndex][varIdx]
# Linearly interpolate over bad data.
interpolated = numpy.interp(range(lastValidIndex, curIndex), # x-coords of interpolated values
[lastValidIndex, curIndex], # x-coords of data.
[float(data[lastValidIndex][varIdx]), float(data[curIndex][varIdx])]) # y-coords of data.
# Store the results.
for j,val in enumerate(interpolated):
data[lastValidIndex+j][varIdx] = val
lastValidIndex = curIndex
return (numpy.array(data, float), hasBeenInterpolated)
def _coarseFilter(self, dataArray, hasBeenInterpolated):
"""
Use coarse noise filtering to remove values outside 3
deviations from mean of all values in the plotted time
interval.
Parameters:
dataArray: 2d numpy array. Each row is a list
containing [nMinutes, Bx, By, Bz, Vx, Vy, Vz, rho, temp, ae, al, au, symh]
hasBeenInterpolated: 2d boolean list. Each row is a list
of boolean values denoting whether dataArray[:,1:9] was
derived/interpolated from the raw data (ie. bad points
removed).
Output:
dataArray: same structure as input array with bad elements removed
hasBeenInterpolated: same as input array with interpolated values stored.
NOTE: This is remarkably similar to _removeBadData!
Refactoring to keep it DRY wouldn't be a bad idea. . .
"""
stds = []
means = []
nvar = len(dataArray[0])
for varIdx in range(1,nvar):
stds.append( dataArray[:,varIdx].std() )
means.append( dataArray[:,varIdx].mean() )
# Linearly interpolate over data that exceeds # of standard
# deviations from the mean set by self.sigma (default = 3)
lastValidIndex = -1
for curIndex,row in enumerate(dataArray):
# Are we outside 3 sigma from mean?
if abs(means[varIdx-1] - row[varIdx]) > self.sigma*stds[varIdx-1]:
hasBeenInterpolated[curIndex, varIdx-1] = True
if (curIndex == len(dataArray)-1):
# Clamp last bad data to previous known good data.
dataArray[curIndex][varIdx] = dataArray[lastValidIndex][varIdx]
else:
# Note the bad data & skip this element for now.
# We will linearly interpolate between valid data
continue
if (lastValidIndex+1) != curIndex:
# If first index is invalid, clamp to first good value.
if lastValidIndex == -1:
lastValidIndex = 0
dataArray[lastValidIndex][varIdx] = dataArray[curIndex][varIdx]
# Linearly interpolate over bad data.
interpolated = numpy.interp(range(lastValidIndex, curIndex), # x-coords of interpolated values
[lastValidIndex, curIndex], # x-coords of data.
[float(dataArray[lastValidIndex][varIdx]), float(dataArray[curIndex][varIdx])]) # y-coords of data.
# Store the results.
for j,val in enumerate(interpolated):
dataArray[lastValidIndex+j][varIdx] = val
lastValidIndex = curIndex
return (dataArray, hasBeenInterpolated)
def _storeDataDict(self, dates, dataArray, hasBeenInterpolated):
"""
Populate self.data TimeSeries object via the 2d dataArray read from file.
"""
self._gse2gsm(dates, dataArray)
self.data.append('time_min', 'Time (Minutes since start)', 'min', dataArray[:,0])
# Magnetic field
self.data.append('bx', 'Bx (gsm)', r'$\mathrm{nT}$', dataArray[:,1])
self.data.append('isBxInterped', 'Is index i of By interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,0])
self.data.append('by', 'By (gsm)', r'$\mathrm{nT}$', dataArray[:,2])
self.data.append('isByInterped', 'Is index i of By interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,1])
self.data.append('bz', 'Bz (gsm)', r'$\mathrm{nT}$', dataArray[:,3])
self.data.append('isBzInterped', 'Is index i of Bz interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,2])
# Velocity
self.data.append('vx', 'Vx (gsm)', r'$\mathrm{km/s}$', dataArray[:,4])
self.data.append('isVxInterped', 'Is index i of Vx interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,3])
self.data.append('vy', 'Vy (gsm)', r'$\mathrm{km/s}$', dataArray[:,5])
self.data.append('isVyInterped', 'Is index i of Vy interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,4])
self.data.append('vz', 'Vz (gsm)', r'$\mathrm{km/s}$', dataArray[:,6])
self.data.append('isVzInterped', 'Is index i of Vz interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,5])
# Density
self.data.append('n', 'Density', r'$\mathrm{1/cm^3}$', dataArray[:,7])
self.data.append('isNInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,6])
# Temperature
self.data.append('t', 'Temperature', r'$\mathrm{kK}$', dataArray[:,8]*1e-3)
self.data.append('isTInterped', 'Is index i of T interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,7])
# Activity Indices
self.data.append('ae', 'AE-Index', r'$\mathrm{nT}$', dataArray[:,9])
self.data.append('isAeInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,8])
self.data.append('al', 'AL-Index', r'$\mathrm{nT}$', dataArray[:,10])
self.data.append('isAlInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,9])
self.data.append('au', 'AU-Index', r'$\mathrm{nT}$', dataArray[:,11])
self.data.append('isAuInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,10])
self.data.append('symh', 'SYM/H', r'$\mathrm{nT}$', dataArray[:,12])
self.data.append('isSymHInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,11])
# Bowshock Location
self.data.append('xBS', 'BowShockX (gsm)', r'$\mathrm{RE}$', dataArray[:,13])
self.data.append('isxBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,12])
self.data.append('yBS', 'BowShockY (gsm)', r'$\mathrm{RE}$', dataArray[:,14])
self.data.append('isyBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,13])
self.data.append('zBS', 'BowShockZ (gsm)', r'$\mathrm{RE}$', dataArray[:,15])
self.data.append('iszBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,14])
def _deltaMinutes(self, t1, startDate):
"""
Returns: Number of minutes elapsed between t1 and startDate.
"""
diff = t1 - startDate
return (diff.days*24.0*60.0 + diff.seconds/60.0)
def _gse2gsm(self, dates, dataArray):
"""
Transform magnetic field B and velocity V from GSE to GSM
coordinates. Store results by overwriting dataArray contents.
"""
for i,data in enumerate(dataArray):
d = dates[i]
# Update magnetic field
b_gsm = kaipy.transform.GSEtoGSM(data[1], data[2], data[3], d)
data[1] = b_gsm[0]
data[2] = b_gsm[1]
data[3] = b_gsm[2]
# Update Velocity
v_gsm = kaipy.transform.GSEtoGSM(data[4], data[5], data[6], d)
data[4] = v_gsm[0]
data[5] = v_gsm[1]
data[6] = v_gsm[2]
# Update Bowshock Location
bs_gsm = kaipy.transform.GSEtoGSM(data[13], data[14], data[15], d)
data[13] = bs_gsm[0]
data[14] = bs_gsm[1]
data[15] = bs_gsm[2]
def _readDst(self,startTime,endTime):
dstfile = open("dst.dat",'r')
text = dstfile.readlines()
for i,j in enumerate(text):
if j[0] == '2':
iskip = i
break
dstfile.close()
dat = numpy.genfromtxt("dst.dat",skip_header=iskip, autostrip=True,dtype=None,encoding='utf-8')
dsttime = []
dst = []
fmt='%Y-%m-%dT%H:%M:%S.000'
for i in dat:
timestr = i[0]+"T"+i[1]
currenttime = datetime.datetime.strptime(timestr,fmt)
if currenttime >= startTime and currenttime <= endTime:
dsttime.append(currenttime)
dst.append(i[3])
return (dsttime, dst)
def _getDst(self,startTime,endTime):
# round start String down
startStr = startTime.strftime('%Y-%m-%d')
aware_start = startTime.replace(tzinfo=datetime.timezone.utc)
# round end string up
endStr = (endTime + datetime.timedelta(days=1)).strftime('%Y-%m-%d')
aware_end = endTime.replace(tzinfo=datetime.timezone.utc)
dst_vars = kyoto.dst(trange=[startStr,endStr])
dat = get_data('kyoto_dst')
dsttime = []
dst = []
for i in range(len(dat[0])):
currenttime = datetime.datetime.fromtimestamp(dat[0][i],datetime.timezone.utc)
currenttime = currenttime.astimezone(datetime.timezone.utc)
if currenttime >= aware_start and currenttime <= aware_end:
dt = datetime.datetime(currenttime.year,currenttime.month,currenttime.day,currenttime.hour,currenttime.minute,currenttime.second)
dsttime.append(dt)
dst.append(dat[1][i])
print(dt,dat[1][i])
return (dsttime, dst)
class OMNIW(OMNI):
"""
OMNI Solar Wind file from CDAweb [http://cdaweb.gsfc.nasa.gov/].
Data stored in GSE coordinates.
"""
def __init__(self, fWIND = None, doFilter = False, sigmaVal = 3.0, windowsize = 5):
SolarWind.__init__(self)
self.filter = doFilter
self.sigma = sigmaVal
self.windowsize = windowsize # should be odd. centered on index
self.bad_data = [-999.900,
99999.9, # V
9999.99, # B
999.990, # density
1.00000E+07, # Temperature
9999999.0, # Temperature
99999, # Activity indices
9999000, # SWE del_time
-1e31 # SWE & MFI
]
self.good_quality = [4098,14338]
print('Retrieving solar wind data from CDAWeb')
self.__read(fWIND)
def __read(self, fWIND):
"""
Read the solar wind file & store results in self.data TimeSeries object.
"""
(startDate, endDate, Wdates, Wdata) = self.__readWData(fWIND)
(Odates, Odata) = self.__readOData(startDate,endDate)
(WdataArray, WhasBeenInterpolated) = self._removeBadData(Wdata)
(OdataArray, OhasBeenInterpolated) = self._removeBadData(Odata)
(dates,dataArray, hasBeenInterpolated, dataOrigin) = self.__combineData(Wdates,WdataArray,WhasBeenInterpolated,Odates,OdataArray,OhasBeenInterpolated)
(dataArray, hasBeenInterpolated) = self._removeBadData(dataArray,hasBeenInterpolated)
if self.filter:
(dataArray, hasBeenInterpolated) = self._coarseFilter(dataArray, hasBeenInterpolated)
self._storeDataDict(dates, dataArray, hasBeenInterpolated)
self.__appendMetaData(startDate, fWIND)
self._appendDerivedQuantities()
def __readWData(self, fWIND):
"""
return 2d array (of strings) containing data from file
**TODO: read the fmt and figure out which column is which. This would make things easier
and more user friendly. However, for now, we'll just assume the file is exactly
these quantities in this order
"""
cdas = CdasWs()
print('Retrieving solar wind data from WIND file')
filedata = numpy.genfromtxt(fWIND)
ntimes = numpy.shape(filedata)[0]
yrs = filedata[:,0]
doy = filedata[:,1]
hrs = filedata[:,2]
mns = filedata[:,3]
bx = filedata[:,4] #nT
by = filedata[:,5] #nT
bz = filedata[:,6] #nT
vx = filedata[:,7] #km/s
vy = filedata[:,8] #km/s
vz = filedata[:,9] #km/s
n = filedata[:,10] #n/cc
T = filedata[:,11] #K
xBow = filedata[:,12] #RE
yBow = filedata[:,13] #RE
zBow = filedata[:,14] #RE
t0 = datetime.datetime(int(yrs[0]),1,1,hour=int(hrs[0]),minute=int(mns[0])) + datetime.timedelta(int(doy[0])-1)
t1 = datetime.datetime(int(yrs[-1]),1,1,hour=int(hrs[-1]),minute=int(mns[-1])) + datetime.timedelta(int(doy[-1])-1)
t0r = t0.strftime("%Y-%m-%dT%H:%M:%SZ")
t1r = t1.strftime("%Y-%m-%dT%H:%M:%SZ")
status,fOMNI = cdas.get_data(
'OMNI_HRO_1MIN',
['BX_GSE','BY_GSE','BZ_GSE',
'Vx','Vy','Vz',
'proton_density','T',
'AE_INDEX','AL_INDEX','AU_INDEX','SYM_H',
'BSN_x','BSN_y','BSN_z'],
t0r,t1r)
tOMNI= fOMNI['Epoch'] #datetime
ae = fOMNI['AE_INDEX'] #nT
al = fOMNI['AL_INDEX'] #nT
au = fOMNI['AU_INDEX'] #nT
symh = fOMNI['SYM_H'] #nT
ovx = fOMNI['Vx'] #kms
ovy = fOMNI['Vy'] #kms
ovz = fOMNI['Vz'] #kms
oxBow = fOMNI['BSN_x'] #RE
oyBow = fOMNI['BSN_y'] #RE
ozBow = fOMNI['BSN_z'] #RE
#oxBow = oxBow*1e5/Re_cgs #km -> cm -> Re
#oyBow = oyBow*1e5/Re_cgs #km -> cm -> Re
#ozBow = ozBow*1e5/Re_cgs #km -> cm -> Re
dates = []
rows = []
for i in range(ntimes):
currentTime = datetime.datetime(int(yrs[i]),1,1,hour=int(hrs[i]),minute=int(mns[i])) + datetime.timedelta(int(doy[i])-1)
#calculating minutes from the start time
nMin = self._deltaMinutes(currentTime,t0)
if (xBow[i] in self.bad_data) or (yBow[i] in self.bad_data) or (zBow[i] in self.bad_data):
data = [nMin,bx[i],by[i],bz[i],vx[i],vy[i],vz[i],n[i],T[i],ae[i],al[i],au[i],symh[i],oxBow[i],oyBow[i],ozBow[i]]
else:
data = [nMin,bx[i],by[i],bz[i],vx[i],vy[i],vz[i],n[i],T[i],ae[i],al[i],au[i],symh[i],xBow[i],yBow[i],zBow[i]]
dates.append( currentTime )
rows.append( data )
return (t0r, t1r, dates, rows)
def __readOData(self, t0r,t1r):
"""
return 2d array (of strings) containing data from file
"""
cdas = CdasWs()
#obtain 1 minute resolution observations from OMNI dataset
print('Retrieving solar wind data from CDAWeb')
status,fh = cdas.get_data(
'OMNI_HRO_1MIN',
['BX_GSE','BY_GSE','BZ_GSE',
'Vx','Vy','Vz',
'proton_density','T',
'AE_INDEX','AL_INDEX','AU_INDEX','SYM_H',
'BSN_x','BSN_y','BSN_z'],
t0r,t1r)
#pulling variables from cdaweb
time=fh['Epoch']
bx=fh['BX_GSE']
by=fh['BY_GSE']
bz=fh['BZ_GSE']
vx=fh['Vx']
vy=fh['Vy']
vz=fh['Vz']
n=fh['proton_density']
T=fh['T']
ae=fh['AE_INDEX']
al=fh['AL_INDEX']
au=fh['AU_INDEX']
symh=fh['SYM_H']
xBow = fh['BSN_x'] #RE
yBow = fh['BSN_y'] #RE
zBow = fh['BSN_z'] #RE
#xBow = xBow*1e5/Re_cgs #km -> cm -> Re
#yBow = yBow*1e5/Re_cgs #km -> cm -> Re
#zBow = zBow*1e5/Re_cgs #km -> cm -> Re
dates = []
rows = []
startTime = time[0]
self.startTime = startTime
for i in range(len(time)):
#calculating minutes from the start time
nMin = self._deltaMinutes(time[i],startTime)
data = [nMin,bx[i],by[i],bz[i],vx[i],vy[i],vz[i],n[i],T[i],ae[i],al[i],au[i],symh[i],xBow[i],yBow[i],zBow[i]]
dates.append( time[i] )
rows.append( data )
return (dates, rows)
def __appendMetaData(self, date, filename):
"""
Add standard metadata to the data dictionary.
"""
metadata = {'Model': 'OMNIW',
'Source': filename,
'Date processed': datetime.datetime.now(),
'Start date': self.startTime,
}
self.data.append(key='meta',
name='Metadata for WIND Solar Wind file',
units='n/a',
data=metadata)
def __combineData(self,Wdates,WdataArray,WhasBeenInterpolated,Odates,OdataArray,OhasBeenInterpolated):
"""
This combines the W data with the O data.
Starting with the OMNI data, if there is windowsize consecutive missing data
then use the W data to fill (if W data is good).
"""
nvarW = len(WdataArray[0])
nvarO = len(OdataArray[0])
ntimesW = len(WdataArray[:,0])
ntimesO = len(OdataArray[:,0])
windowsize = self.windowsize
if (nvarW != nvarO): raise Exception("Error: W and O have different vars")
if (ntimesW != ntimesO): raise Exception("Error: W and O have different times")
dates = Wdates
hasBeenInterpolated = OhasBeenInterpolated
dataArray = OdataArray
dataOrigin = numpy.empty(numpy.shape(hasBeenInterpolated)) #0 is interpolated. 1 is OMNI, 2 is WIND
halfwindow = int((windowsize-1)/2)
for varIdx in range(1,nvarO):
for curIndex,row in enumerate(WdataArray):
if OhasBeenInterpolated[curIndex,varIdx-1]:
# Replace only if the whole window is missing data
if (OhasBeenInterpolated[curIndex-halfwindow:curIndex+halfwindow,varIdx-1].all() or OhasBeenInterpolated[curIndex:min(curIndex+windowsize,ntimesO),varIdx-1].all() or OhasBeenInterpolated[max(0,curIndex-windowsize+1):curIndex+1,varIdx-1].all()):
#Check if W is interpolated
if not WhasBeenInterpolated[curIndex,varIdx-1]:
dataArray[curIndex,varIdx] = row[varIdx]
dataArray[curIndex,-3:] = row[-3:]
# Use W if W was not interpolated
dataOrigin[curIndex,varIdx-1] = 2
dataOrigin[curIndex,-3:] = 2
hasBeenInterpolated[curIndex,varIdx-1] = False
hasBeenInterpolated[curIndex,-3:] = False
else:
# use the already interpolated value if W was bad
dataOrigin[curIndex,varIdx-1] = 0
# let's set it to a bad value so it reinterpolates
dataArray[curIndex,varIdx] = -1e31
else:
# use original OMNI if no interp
dataOrigin[curIndex,varIdx-1] = 0
# let's set it to a bad value so it reinterpolates
dataArray[curIndex,varIdx] = -1e31
else:
# use original OMNI if no interp
dataOrigin[curIndex,varIdx-1] = 1
return (dates,dataArray, hasBeenInterpolated, dataOrigin)
if __name__ == '__main__':
import doctest
doctest.testmod()

477
kaipy/solarWind/SWPC.py
View File

@@ -1,477 +0,0 @@
# Custom
import kaipy.transform
from kaipy.solarWind.SolarWind import SolarWind
from kaipy.solarWind.OMNI import OMNI
from kaipy.kdefs import *
# 3rd party
import numpy
import netCDF4 as nc
from cdasws import CdasWs
# Standard
import datetime
import re
import os
class DSCOVRNC(OMNI):
"""
OMNI Solar Wind file from CDAweb [http://cdaweb.gsfc.nasa.gov/].
Data stored in GSE coordinates.
"""
def __init__(self,t0,t1,doFilter = False, sigmaVal = 3.0,dodcx=False,dcxfile=None):
SolarWind.__init__(self)
self.filter = doFilter
self.sigma = sigmaVal
self.bad_data = [-999.900,
99999.9, # V
9999.99, # B
999.990, # density
1.00000E+07, # Temperature
9999999.0, # Temperature
99999, # Activity indices
-99999,
1e+20
]
self.__read(t0,t1,dodcx=dodcx,dcxfile=dcxfile)
def __read(self, t0,t1,dodcx=False,dcxfile=None):
"""
Read the solar wind file & store results in self.data TimeSeries object.
"""
(startDate, dates, data) = self.__readData(t0,t1,dodcx=dodcx,dcxfile=dcxfile)
(dataArray, hasBeenInterpolated) = self._removeBadData(data)
if self.filter:
(dataArray, hasBeenInterpolated) = self._coarseFilter(dataArray, hasBeenInterpolated)
self._storeDataDict(dates, dataArray, hasBeenInterpolated)
self.__appendMetaData(startDate)
self._appendDerivedQuantities()
def __readData(self, t0,t1,dodcx=False,dcxfile=None):
"""
return 2d array (of strings) containing data from file
**TODO: read the fmt and figure out which column is which. This would make things easier
and more user friendly. However, for now, we'll just assume the file is exactly
these quantities in this order
"""
filelist = os.listdir()
pop = []
f1m = []
m1m = []
fmt1 = '%Y%m%d%H%M%S'
fmt2 = '%Y%m%d%H%M%S'
jud0 = datetime.datetime(1970,1,1,0,0,0,0)
for f in filelist:
if f[0:2] == 'oe':
ctime = datetime.datetime.strptime(f[15:29],fmt1)
etime = datetime.datetime.strptime(f[31:45],fmt2)
if (ctime >= t0 and ctime <=t1) or (t0 <= ctime and ctime <= t1) or (t0 <= etime and etime <= t1):
if 'pop' in f:
pop.append(f)
if 'f1m' in f:
f1m.append(f)
if 'm1m' in f:
m1m.append(f)
pop = np.sort(pop)
f1m = np.sort(f1m)
m1m = np.sort(m1m)
if len(pop) != len(f1m) or len(f1m) != len(m1m) or len(pop) != len(m1m):
raise Exception('file list not the same')
if len(pop) == 0 or len(f1m) == 0 or len(m1m) == 0:
raise Exception('missing files for this daterange')
mtime = []
ftime = []
ptime = []
n = []
vx = []
vy = []
vz = []
temp = []
bx = []
by = []
bz = []
satx = []
for i in range(len(pop)):
pfn = pop[i]
ffn = f1m[i]
mfn = m1m[i]
pds = nc.Dataset(pfn) #time, sat_x_gse
fds = nc.Dataset(ffn) #time,proton_density, proton_vx_gse, proton_vy_gse, proton_vz_gse, proton_temperature
mds = nc.Dataset(mfn) #time, bx_gse, by_gse, bz_gse
for k in range(len(mds['time'])):
mtime.append(jud0 + datetime.timedelta(milliseconds=mds['time'][:][k]))
bx.append(mds['bx_gse'][:][k])
by.append(mds['by_gse'][:][k])
bz.append(mds['bz_gse'][:][k])
for k in range(len(fds['time'])):
ftime.append(jud0 + datetime.timedelta(milliseconds=fds['time'][:][k]))
'''
if fds['overall_quality'][:][k] == 0:
n.append(fds['proton_density'][:][k])
vx.append(fds['proton_vx_gse'][:][k])
vy.append(fds['proton_vy_gse'][:][k])
vz.append(fds['proton_vz_gse'][:][k])
temp.append(fds['proton_temperature'][:][k])
else:
n.append(numpy.nan)
vx.append(numpy.nan)
vy.append(numpy.nan)
vz.append(numpy.nan)
temp.append(numpy.nan)
'''
n.append(fds['proton_density'][:][k])
vx.append(fds['proton_vx_gse'][:][k])
vy.append(fds['proton_vy_gse'][:][k])
vz.append(fds['proton_vz_gse'][:][k])
temp.append(fds['proton_temperature'][:][k])
for k in range(len(pds['time'])):
ptime.append(jud0 + datetime.timedelta(milliseconds=pds['time'][:][k]))
satx.append(pds['sat_x_gse'][:][k])
dates = []
rows = []
#timeshift = 46 #minutes
# simple projectile motion
# t = (x - x0)/v
timeshift = int(np.round((np.mean(satx)*-1)/(np.nanmean(vx))/60.0))
startTime = t0 + datetime.timedelta(minutes=timeshift)
#endTime = t0 + datetime.timedelta(minutes=timeshift+ntimes)
endTime = t1
if dodcx or dcxfile != None:
dsttime,dst = self._readDcx(t0,t1,dcxfile)
else:
#dsttime,dst = self._readDst(t0,t1)
dsttime,dst = self._getDst(t0,t1)
ntimes = t1 - t0
ntimes = int(ntimes.total_seconds()/60.0)
print("Starting Time: ",startTime.isoformat())
print("We are using a constant timeshift of: ", timeshift ," minutes")
#itp = 0 #ptime
itf = 0 #ftime
itm = 0 #mtime
itd = 0 #dsttime
for i in range(ntimes):
#currentTime = datetime.datetime(int(yrs[i]),1,1,hour=int(hrs[i]),minute=int(mns[i])) + datetime.timedelta(int(doy[i])-1)
currentTime = t0 + datetime.timedelta(minutes=i)
#calculating minutes from the start time
#nMin = self.__deltaMinutes(currentTime,startTime)
while(mtime[itm] + datetime.timedelta(minutes=timeshift) < currentTime):
itm = itm+1
while(ftime[itf] + datetime.timedelta(minutes=timeshift) < currentTime):
itf = itf+1
while(dsttime[itd] < currentTime):
itd = itd+1
nMin = i
data = [nMin,bx[itm],by[itm],bz[itm],vx[itf],vy[itf],vz[itf],n[itf],temp[itf],0,0,0,dst[itd],0,0,0]
#if currentTime < startTime:
# data = [nMin,bx[0],by[0],bz[0],vx[0],vy[0],vz[0],n[0],temp[0],0.,0.,0.,dst[int(i/60.)]]
#else:
# ic = int(i-timeshift)
# data = [nMin,bx[ic],by[ic],bz[ic],vx[ic],vy[ic],vz[ic],n[ic],temp[ic],0.,0.,0.,dst[int(i/60.)]]
dates.append( currentTime )
rows.append( data )
return (t0, dates, rows)
def _readDcx(self,startTime,endTime,dcxstr = None):
if dcxstr == None:
dcxstr = "dcx.txt"
dat = np.genfromtxt(dcxstr,autostrip=True,dtype=None)
dsttime = []
dst = []
for i in range(len(dat)):
currenttime = datetime.datetime(dat[i][1],dat[i][2],dat[i][3],dat[i][4])
print(i,currenttime,startTime,endTime)
if currenttime >= startTime and currenttime <= endTime:
dsttime.append(currenttime)
dst.append(dat[i][5])
print(currenttime,dat[i][5])
return (dsttime, dst)
def __appendMetaData(self, date):
"""
Add standard metadata to the data dictionary.
"""
metadata = {'Model': 'CUSTOM',
'Source': 'NOAA DSCOVR NC',
'Date processed': datetime.datetime.now(),
'Start date': date
}
self.data.append(key='meta',
name='Metadata for OMNI Solar Wind file',
units='n/a',
data=metadata)
class ACESWPC(DSCOVRNC):
"""
OMNI Solar Wind file from CDAweb [http://cdaweb.gsfc.nasa.gov/].
Data stored in GSE coordinates.
"""
def __init__(self,t0,t1,doFilter = False, sigmaVal = 3.0,dodcx=False,dcxfile=None):
SolarWind.__init__(self)
self.filter = doFilter
self.sigma = sigmaVal
self.bad_data = [-999.900,
99999.9, # V
9999.99, # B
999.990, # density
1.00000E+07, # Temperature
9999999.0, # Temperature
99999, # Activity indices
-99999,
1e+20
]
self.__read(t0,t1,dodcx=dodcx,dcxfile=dcxfile)
def __read(self, t0,t1,dodcx=False,dcxfile=None):
"""
Read the solar wind file & store results in self.data TimeSeries object.
"""
(startDate, dates, data) = self.__readData(t0,t1,dodcx=dodcx,dcxfile=dcxfile)
(dataArray, hasBeenInterpolated) = self._removeBadData(data)
if self.filter:
(dataArray, hasBeenInterpolated) = self._coarseFilter(dataArray, hasBeenInterpolated)
self._storeDataDict(dates, dataArray, hasBeenInterpolated)
self.__appendMetaData(startDate)
self._appendDerivedQuantities()
def __readData(self, t0,t1,dodcx=False,dcxfile=None):
"""
return 2d array (of strings) containing data from file
**TODO: read the fmt and figure out which column is which. This would make things easier
and more user friendly. However, for now, we'll just assume the file is exactly
these quantities in this order
"""
self.__downloadACE(t0,t1)
filelist = os.listdir()
pop = []
f1m = []
m1m = []
fmt1 = '%Y%m%d%H%M%S'
fmt2 = '%Y%m%d%H%M%S'
jud0 = datetime.datetime(1970,1,1,0,0,0,0)
for f in filelist:
if f[0:2] == 'oe':
ctime = datetime.datetime.strptime(f[15:29],fmt1)
etime = datetime.datetime.strptime(f[31:45],fmt2)
if (ctime >= t0 and ctime <=t1) or (t0 <= ctime and ctime <= t1) or (t0 <= etime and etime <= t1):
if 'pop' in f:
pop.append(f)
if 'f1m' in f:
f1m.append(f)
if 'm1m' in f:
m1m.append(f)
pop = np.sort(pop)
f1m = np.sort(f1m)
m1m = np.sort(m1m)
if len(pop) != len(f1m) or len(f1m) != len(m1m) or len(pop) != len(m1m):
raise Exception('file list not the same')
if len(pop) == 0 or len(f1m) == 0 or len(m1m) == 0:
raise Exception('missing files for this daterange')
mtime = []
ftime = []
ptime = []
n = []
vx = []
vy = []
vz = []
temp = []
bx = []
by = []
bz = []
satx = []
for i in range(len(pop)):
pfn = pop[i]
ffn = f1m[i]
mfn = m1m[i]
pds = nc.Dataset(pfn) #time, sat_x_gse
fds = nc.Dataset(ffn) #time,proton_density, proton_vx_gse, proton_vy_gse, proton_vz_gse, proton_temperature
mds = nc.Dataset(mfn) #time, bx_gse, by_gse, bz_gse
for k in range(len(mds['time'])):
mtime.append(jud0 + datetime.timedelta(milliseconds=mds['time'][:][k]))
bx.append(mds['bx_gse'][:][k])
by.append(mds['by_gse'][:][k])
bz.append(mds['bz_gse'][:][k])
for k in range(len(fds['time'])):
ftime.append(jud0 + datetime.timedelta(milliseconds=fds['time'][:][k]))
'''
if fds['overall_quality'][:][k] == 0:
n.append(fds['proton_density'][:][k])
vx.append(fds['proton_vx_gse'][:][k])
vy.append(fds['proton_vy_gse'][:][k])
vz.append(fds['proton_vz_gse'][:][k])
temp.append(fds['proton_temperature'][:][k])
else:
n.append(numpy.nan)
vx.append(numpy.nan)
vy.append(numpy.nan)
vz.append(numpy.nan)
temp.append(numpy.nan)
'''
n.append(fds['proton_density'][:][k])
vx.append(fds['proton_vx_gse'][:][k])
vy.append(fds['proton_vy_gse'][:][k])
vz.append(fds['proton_vz_gse'][:][k])
temp.append(fds['proton_temperature'][:][k])
for k in range(len(pds['time'])):
ptime.append(jud0 + datetime.timedelta(milliseconds=pds['time'][:][k]))
satx.append(pds['sat_x_gse'][:][k])
dates = []
rows = []
#timeshift = 46 #minutes
# simple projectile motion
# t = (x - x0)/v
timeshift = int(np.round((np.mean(satx)*-1)/(np.nanmean(vx))/60.0))
startTime = t0 + datetime.timedelta(minutes=timeshift)
#endTime = t0 + datetime.timedelta(minutes=timeshift+ntimes)
endTime = t1
if dodcx or dcxfile != None:
dsttime,dst = self._readDcx(t0,t1,dcxfile)
else:
dsttime,dst = self._readDst(t0,t1)
ntimes = t1 - t0
ntimes = int(ntimes.total_seconds()/60.0)
print("Starting Time: ",startTime.isoformat())
print("We are using a constant timeshift of: ", timeshift ," minutes")
#itp = 0 #ptime
itf = 0 #ftime
itm = 0 #mtime
itd = 0 #dsttime
for i in range(ntimes):
#currentTime = datetime.datetime(int(yrs[i]),1,1,hour=int(hrs[i]),minute=int(mns[i])) + datetime.timedelta(int(doy[i])-1)
currentTime = t0 + datetime.timedelta(minutes=i)
#calculating minutes from the start time
#nMin = self.__deltaMinutes(currentTime,startTime)
while(mtime[itm] + datetime.timedelta(minutes=timeshift) < currentTime):
itm = itm+1
while(ftime[itf] + datetime.timedelta(minutes=timeshift) < currentTime):
itf = itf+1
while(dsttime[itd] < currentTime):
itd = itd+1
nMin = i
data = [nMin,bx[itm],by[itm],bz[itm],vx[itf],vy[itf],vz[itf],n[itf],temp[itf],0,0,0,dst[itd],0,0,0]
#if currentTime < startTime:
# data = [nMin,bx[0],by[0],bz[0],vx[0],vy[0],vz[0],n[0],temp[0],0.,0.,0.,dst[int(i/60.)]]
#else:
# ic = int(i-timeshift)
# data = [nMin,bx[ic],by[ic],bz[ic],vx[ic],vy[ic],vz[ic],n[ic],temp[ic],0.,0.,0.,dst[int(i/60.)]]
dates.append( currentTime )
rows.append( data )
return (t0, dates, rows)
def __downloadACE(self, t0, t1):
swpcdir = "https://sohoftp.nascom.nasa.gov/sdb/goes/ace/daily/"
magpost = "_ace_mag_1m.txt"
swepost = "_ace_swepam_1m.txt"
dday = (t1-t0).days
current_time = t0
for i in range(dday):
current_time = t0 + datetime.timedelta(days=i)
datestr = current_time.strftime('%Y%m%d')
magstr = swpcdir + datestr + magpost
swestr = swpcdir + datestr + swepost
print("Downloading ACE data: ",datestr + magpost)
print("Downloading ACE data: ",datestr + swepost)
urllib.request.urlretrieve(magstr,datestr + magpost)
urllib.request.urlretrieve(swestr,datestr + swepost)
self.__readACE(datestr,magpost,swepost)
def __readACE(self,datestr,magpost,swepost):
rdfile = open(datestr+magpost,'r')
text = rdfile.readlines()
for i,j in enumerate(text):
if j[0] == '2' or j[0] == '1':
magskip = i
break
rdfile.close()
rdfile = open(datestr+swepost,'r')
text = rdfile.readlines()
for i,j in enumerate(text):
if j[0] == '2' or j[0] == '1':
sweskip = i
break
rdfile.close()
dat = np.genfromtxt(datestr + magpost,skip_header=magskip, autostrip=True,dtype=None)
magtime = []
bx = []
by = []
bz = []
for i in dat:
currenttime = datetime.datetime(i[0],i[1],i[2],i[3],i[5])
print(currenttime)
if currenttime >= t0 and currenttime <= t1:
magtime.append(currenttime)
bx.append(i[7])
bx.append(i[8])
bx.append(i[9])
dat = np.genfromtxt(datestr + swepost,skip_header=sweskip, autostrip=True,dtype=None)
swetime = []
n = []
v = []
t = []
for i in dat:
currenttime = datetime.datetime(i[0],i[1],i[2],i[3],i[5])
print(currenttime)
if currenttime >= t0 and currenttime <= t1:
magtime.append(currenttime)
bx.append(i[7])
bx.append(i[8])
bx.append(i[9])
return (dsttime, dst)
def __appendMetaData(self, date):
"""
Add standard metadata to the data dictionary.
"""
metadata = {'Model': 'CUSTOM',
'Source': 'NOAA DSCOVR NC',
'Date processed': datetime.datetime.now(),
'Start date': date
}
self.data.append(key='meta',
name='Metadata for OMNI Solar Wind file',
units='n/a',
data=metadata)
if __name__ == '__main__':
import doctest
doctest.testmod()

190
kaipy/solarWind/SolarWind.py
View File

@@ -1,190 +0,0 @@
import datetime
import numpy
import kaipy.transform
from kaipy.solarWind.TimeSeries import TimeSeries
from kaipy.solarWind.ols import ols
class SolarWind(object):
"""
Abstract base class for Solar Wind processing
"""
def __init__(self):
"""
Derived classes must read in Solar Wind data and store the
results in a standard way via the a kaipy.TimeSeries object.
Variables that must be set are:
key | name | units
----------------------------------------------
n | Density | #/cc
vx | Velocity vector (gsm) | km/s
vy | Velocity vector (gsm) | km/s
vz | Velocity vector (gsm) | km/s
t | Temperature | k Kelvin
cs | Sound speed | km/s
bx | Magnetic field vector (gsm) | nT
by | Magnetic field vector (gsm) | nT
bz | Magnetic field vector (gsm) | nT
b | Magnetic field vector | nT
time_min | Elapsed time since start | minutes
-----------------------------------------------
meta | Metadata about the run. Must | n/a
| include ['data']['Start Date'] |
| for coordinate transforms. |
Derived classes should make a call to
SolarWind._appendDerivedQuantities() to compute some
additional solar wind variables.
"""
# The TimeSeries object stores all the Solar Wind data.
self.data = TimeSeries()
def _getTiltAngle(self, dateTime):
"""
Get the tilt angle for the current Date & Time
"""
(x,y,z) = kaipy.transform.SMtoGSM(0,0,1, dateTime)
return numpy.arctan2(x,z)
def _gsm2sm(self, dateTime, x,y,z):
"""
Convert from GSM to SM coordinates for the current Date & Time
"""
return kaipy.transform.GSMtoSM(x,y,z, dateTime)
def bxFit(self):
"""
Compute & return coefficients for a multiple linear regression fit of Bx to By & Bz.
Get the fit by applying the linear regression fit:
"""
# Before doing anything, convert to SM coordinates.
bx_sm = []
by_sm = []
bz_sm = []
for i,time in enumerate(self.data.getData('time_min')):
b_sm = self._gsm2sm(self.data.getData('meta')['Start date']+datetime.timedelta(minutes=time),
self.data.getData('bx')[i],
self.data.getData('by')[i],
self.data.getData('bz')[i])
bx_sm.append(b_sm[0])
by_sm.append(b_sm[1])
bz_sm.append(b_sm[2])
bx_sm = numpy.array(bx_sm)
by_sm = numpy.array(by_sm)
bz_sm = numpy.array(bz_sm)
# Now that we're in SM, do the fit!
y = bx_sm
x = numpy.array([by_sm, bz_sm])
linearFit = kaipy.solarWind.ols.ols(y,x.T, y_varnm='bx_sm', x_varnm=['by_sm','bz_sm'])
##Obtain information about the fit via the summary:
#linearFit.summary()
coef = linearFit.b
## Compute the variance... See p
#xbar=numpy.average(y)
#xi = coef[0]+coef[1]*by_sm+coef[2]*bz_sm
#n=0
#variance = 0.0
#for i in range(len(xi)):
# n += 1
# variance += (xi[i]-xbar)**2
#variance /= (n)
#print 'variance is', variance
#print 'std dev is', numpy.sqrt(variance)
#
## Compute chi^2... See p.667-669 of Numerical Recipes in C++
#chisq=0
#xi = self.data.getData('time_min')
#for i in range(len(y)):
# chisq += (y[i]-coef[0]-coef[1]*by_sm[i]-coef[2]*bz_sm[i])**2
#print 'chisquared is', chisq
return coef
def _appendDerivedQuantities(self):
"""
Calculate & append standard derived quantities to the data dictionary.
Note: single '_' underscore so this function can be called by derived classes
"""
# --- Magnetic Field magnitude
if 'b' not in self.data:
b = numpy.sqrt(self.data.getData('bx')**2 +
self.data.getData('by')**2 +
self.data.getData('bz')**2)
self.data.append('b', 'Magnitude of Magnetic Field', r'$\mathrm{nT}$', b)
else:
b = self.data.getData('b')
# --- Velocity Field magnitude
if 'v' not in self.data:
v = numpy.sqrt(self.data.getData('vx')**2 +
self.data.getData('vy')**2 +
self.data.getData('vz')**2)
self.data.append('v', 'Magnitude of Velocity', r'$\mathrm{km/s}$', v)
else:
v = self.data.getData('v')
# -- Sound Speed
if 'cs' not in self.data:
try:
cs = numpy.sqrt(5.0*1e3*self.data.getData('t')*(1.38e-23)/(3.0*1.67e-27)/(1.0e6))
self.data.append('cs', 'Sound Speed', r'$\mathrm{km/s}$', cs)
except KeyError:
raise KeyError('Could not find temperature \'t\'. Cannot compute sound speed (cs) without Temperature (t)!')
# --- Alfven speed
if 'va' not in self.data:
va = (self.data.getData('b') * 1.0e-10 /
numpy.sqrt(1.97e-24*4*numpy.pi*
self.data.getData('n')) )
self.data.append('va', 'Alfven Speed', r'$\mathrm{km/s}$', va)
# --- Magnetosonic mach number (dimensionless)
if 'ms' not in self.data:
ms = v / self.data.getData('cs')
self.data.append('ms', 'Magnetosonic Mach Number', '', ms)
# --- Alfvenic Mach Number (dimensionless)
if 'ma' not in self.data:
ma = v/va
self.data.append('ma', 'Alfvenic Mach Number', '', ma)
# --- Temperature (Kelvin)
if 't' not in self.data:
t = 1e-3*(self.data.getData('cs')**2)*1.0e6*1.67e-27/1.38e-23
self.data.append('t', 'Temperature', r'$\mathrm{kK}$', t)
# --- Hours since start
if 'time_hr' not in self.data:
hr = self.data.getData('time_min')/60.0
self.data.append('time_hr', 'Time (hours since start)', r'$\mathrm{hour}$', hr)
# --- datetime
if 'time' not in self.data:
time = []
for minute in self.data.getData('time_min'):
time.append( self.data.getData('meta')['Start date'] + datetime.timedelta(minutes=minute) )
self.data.append('time', 'Date and Time', r'$\mathrm{Date/Time}$', time)
# --- Compute & store day of year
if 'day' not in self.data:
doy = []
for dt in self.data.getData('time'):
tt = dt.timetuple()
dayFraction = (tt.tm_hour+tt.tm_min/60.+tt.tm_sec/(60.*60.))/24.
doy.append( float(tt.tm_yday) + dayFraction )
self.data.append('time_doy', 'Day of Year', r'$\mathrm{Day}$', doy)

65
kaipy/solarWind/TimeSeries.py
View File

@@ -1,65 +0,0 @@
class TimeSeries(dict):
"""
TimeSeries is a standard Python Dictionary with some helper
functions useful for data processing collections of time series.
"""
def __init__(self, indict={}):
dict.__init__(self)
# not "self.__keydict" because I want it to be easily accessible by subclasses!
self._keydict = {}
for entry in indict:
self[entry] = indict[entry]
def append(self, key, name, units, data):
"""
key: unique identifier of variable
name: more descriptive name of variable
data: time series data array
units: units (grams, for example)
"""
#if self.has_key(key):
if key in self:
raise KeyError('Error: Dictionary already has key \"%s\".' % key)
self[key] = { 'name': name, 'data': data, 'units': units }
def getUnits(self, key):
"""
>>> ts = TimeSeries({'v': {'name': 'Velocity', 'data': [1,2,3], 'units': 'm/s'}})
>>> ts.getUnits('v')
'm/s'
"""
return self[key]['units']
def setUnits(self, key, units):
self[key]['units'] = units
def getName(self, key):
"""
>>> ts = TimeSeries({'v': {'name': 'Velocity', 'data': [1,2,3], 'units': 'm/s'}})
>>> ts.getName('v')
'Velocity'
"""
return self[key]['name']
def setName(self, key, name):
self[key]['name'] = name
def getData(self, key):
"""
>>> ts = TimeSeries({'v': {'name': 'Velocity', 'data': [1,2,3], 'units': 'm/s'}})
>>> ts.getData('v')
[1, 2, 3]
"""
return self[key]['data']
def setData(self, key, data, index=None):
try:
self[key]['data'][index] = data
except TypeError:
self[key]['data'] = data
if __name__ == '__main__':
import doctest
doctest.testmod()

322
kaipy/solarWind/WIND.py
View File

@@ -1,322 +0,0 @@
# Custom
import kaipy.transform
from kaipy.solarWind.SolarWind import SolarWind
from kaipy.solarWind.OMNI import OMNI
# 3rd party
import numpy
import cdasws as cdas
# Standard
import datetime
import re
#'WI_K0_SWE'
#'Np','V_GSM','QF_V','QF_Np','THERMAL_SPD'
#'WI_H0_MFI'
#'BGSM'
class WIND(OMNI):
"""
OMNI Solar Wind file from CDAweb [http://cdaweb.gsfc.nasa.gov/].
Data stored in GSE coordinates.
"""
def __init__(self, fSWE,fMFI,t0,t1,doFilter = False, sigmaVal = 3.0):
SolarWind.__init__(self)
self.filter = doFilter
self.bad_data = [-999.900,
99999.9, # V
9999.99, # B
999.990, # density
1.00000E+07, # Temperature
99999, # Activity indices
9999000, # SWE del_time
-1e31 # SWE & MFI
]
self.good_quality = [4098,14338]#
#self.bad_times =[ '17-03-2015 17:27:07.488',
# '18-03-2015 21:53:49.140'
# ]
#self.bad_fmt = '%d-%m-%Y %H:%M:%S.f'
self.bad_datetime = [ datetime.datetime(2015,3,17,hour=17,minute=27,second=7,microsecond=488*1000),
datetime.datetime(2015,3,18,hour=21,minute=53,second=49,microsecond=140*1000)
]
#obtain 1 minute resolution observations from OMNI dataset
print('Retrieving solar wind data from CDAWeb')
self.__read(fSWE,fMFI,t0,t1)
def __read(self, fSWE,fMFI,t0,t1):
"""
Read the solar wind file & store results in self.data TimeSeries object.
"""
(SWEstartDate, MFIstartDate, DSTstartDate, SWEdata, MFIdata, DSTdata, SWEqf) = self.__readData(fSWE,fMFI,t0,t1)
(SWEdata) = self.__checkGoodData(SWEdata,SWEqf)
(SWEdataArray, SWEhasBeenInterpolated) = self._removeBadData(SWEdata )
(MFIdataArray, MFIhasBeenInterpolated) = self._removeBadData(MFIdata )
(DSTdataArray, DSThasBeenInterpolated) = self._removeBadData(DSTdata)
if self.filter:
(SWEdataArray, SWEhasBeenInterpolated) = self._coarseFilter(SWEdataArray , SWEhasBeenInterpolated )
(MFIdataArray, MFIhasBeenInterpolated) = self._coarseFilter(MFIdataArray , MFIhasBeenInterpolated )
(DSTdataArray, DSThasBeenInterpolated) = self._coarseFilter(DSTdataArray , DSThasBeenInterpolated)
#(SWEdataArray, SWEhasBeenInterpolated) = self.__windowedFilter(SWEdataArray , SWEhasBeenInterpolated )
#(MFIdataArray, MFIhasBeenInterpolated) = self.__windowedFilter(MFIdataArray , MFIhasBeenInterpolated )
#(OMNIdataArray, OMNIhasBeenInterpolated) = self.__windowedFilter(OMNIdataArray, OMNIhasBeenInterpolated)
(dates, dataArray, hasBeenInterpolated) = self.__joinData(SWEdataArray, SWEhasBeenInterpolated,
MFIdataArray, MFIhasBeenInterpolated,
DSTdataArray, DSThasBeenInterpolated,
t0,t1)
self.__storeDataDict(dates, dataArray, hasBeenInterpolated)
self.__appendMetaData(t0, SWEstartDate, fSWE)
self._appendDerivedQuantities()
def __readData(self, fhSWE,fhMFI,t0,t1):
"""
return 2d array (of strings) containing data from file
"""
#print('__readData')
#pulling variables from file
tSWE = fhSWE['Epoch'] # datetime
v = fhSWE['V_GSE'] # km/s
qfv = fhSWE['QF_V'] #
qfn = fhSWE['QF_Np'] #
n = fhSWE['Np'] # #/cc
cs = fhSWE['THERMAL_SPD'] # km/s
pos = fhSWE['SC_pos_gse'] # km
xloc = numpy.mean(pos[:,0]) # km
vx = v[:,0] #km/s
vy = v[:,1] #km/s
vz = v[:,2] #km/s
tMFI = fhMFI['Epoch'] #datetime
b = fhMFI['BGSEc'] #nT
bx = b[:,0] #nT
by = b[:,1] #nT
bz = b[:,2] #nT
tshift = ((0 - xloc) / numpy.mean(vx[abs(vx) < 3000]))/60. # t = (x - x_0)/Vx where X = 0, x_0 = xloc, and Vx is Vx in first data block in km/s.
print('tshift:',tshift,"min from ",xloc,"km with mean vx of",numpy.mean(vx[abs(vx) < 3000]),"km/s")
SWEdates = []
SWErows = []
SWEqf = []
SWEstartTime = tSWE[0]
#badtimes = []
#for i in range(len(self.bad_times)):
# badtimes.append(datetime.datetime.strptime(self.bad_times[i],self.bad_fmt))
#dsttime,dst = self._readDst(t0,t1)
dsttime,dst = self._getDst(t0,t1)
for i in range(len(tSWE)):
for itime in range(len(self.bad_datetime)):
if ( abs(self.__deltaMinutes(tSWE[i],self.bad_datetime[itime])) <= 3./60. ):
qfv[i] = 0
qfn[i] = 0
#calculating minutes from the start time
nMin = self.__deltaMinutes(tSWE[i],t0)+tshift
data = [nMin,n[i],vx[i],vy[i],vz[i],cs[i]]
qf = [qfv[i],qfn[i]]
SWEdates.append( tSWE[i] )
SWErows.append ( data )
SWEqf.append ( qf )
MFIdates = []
MFIrows = []
MFIstartTime = tMFI[0]
for i in range(len(tMFI)):
#calculating minutes from the start time
nMin = self.__deltaMinutes(tMFI[i],t0)+tshift
data = [nMin,bx[i],by[i],bz[i]]
MFIdates.append( tMFI[i] )
MFIrows.append( data )
DSTdates = []
DSTrows = []
DSTstartTime = dsttime[0]
for i in range(len(dsttime)):
nMin = self.__deltaMinutes(dsttime[i],t0)
data = [nMin,0,0,0,dst[i],0,0,0]
DSTdates.append( dsttime[i] )
DSTrows.append( data )
return ( SWEstartTime, MFIstartTime, DSTstartTime, SWErows, MFIrows, DSTrows, SWEqf )
def __checkGoodData(self, data, qf):
"""
Check the quality flag and set to bad data if bad data
"""
nvar = len(data[0])
nqf = len(qf[0])
ntime = len(data)
#print(numpy.shape(data),nvar,nqf,ntime)
for itime in range(ntime):
for iq in range(nqf):
if qf[itime][iq] not in self.good_quality:
for ivar in range(1,nvar):
data[itime][ivar] = -1e31 # SWE & MFI
return ( data )
def __joinData(self, SWEdataArray, SWEhasBeenInterpolated, MFIdataArray, MFIhasBeenInterpolated, DSTdataArray, DSThasBeenInterpolated,t0,t1):
#print('joinData')
ntime = self.__deltaMinutes(t1,t0)
nMin = range(int(ntime))
n = numpy.interp(nMin,SWEdataArray[:,0],SWEdataArray[:,1])
vx = numpy.interp(nMin,SWEdataArray[:,0],SWEdataArray[:,2])
vy = numpy.interp(nMin,SWEdataArray[:,0],SWEdataArray[:,3])
vz = numpy.interp(nMin,SWEdataArray[:,0],SWEdataArray[:,4])
cs = numpy.interp(nMin,SWEdataArray[:,0],SWEdataArray[:,5])
bx = numpy.interp(nMin,MFIdataArray[:,0],MFIdataArray[:,1])
by = numpy.interp(nMin,MFIdataArray[:,0],MFIdataArray[:,2])
bz = numpy.interp(nMin,MFIdataArray[:,0],MFIdataArray[:,3])
ae = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,1])
al = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,2])
au = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,3])
symh = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,4])
xBow = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,5])
yBow = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,6])
zBow = numpy.interp(nMin,DSTdataArray[:,0],DSTdataArray[:,7])
nI = numpy.interp(nMin,SWEdataArray[:,0],SWEhasBeenInterpolated[:,0])
vxI = numpy.interp(nMin,SWEdataArray[:,0],SWEhasBeenInterpolated[:,1])
vyI = numpy.interp(nMin,SWEdataArray[:,0],SWEhasBeenInterpolated[:,2])
vzI = numpy.interp(nMin,SWEdataArray[:,0],SWEhasBeenInterpolated[:,3])
csI = numpy.interp(nMin,SWEdataArray[:,0],SWEhasBeenInterpolated[:,4])
bxI = numpy.interp(nMin,MFIdataArray[:,0],MFIhasBeenInterpolated[:,0])
byI = numpy.interp(nMin,MFIdataArray[:,0],MFIhasBeenInterpolated[:,1])
bzI = numpy.interp(nMin,MFIdataArray[:,0],MFIhasBeenInterpolated[:,2])
aeI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,0])
alI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,1])
auI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,2])
symhI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,3])
xBowI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,4])
yBowI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,5])
zBowI = numpy.interp(nMin,DSTdataArray[:,0],DSThasBeenInterpolated[:,6])
dates = []
dataArray = []
interped = []
hasBeenInterpolated = []
for i in nMin:
dates.append(t0+datetime.timedelta(minutes=i))
arr = [nMin[i],bx[i],by[i],bz[i],vx[i],vy[i],vz[i],n[i],cs[i],ae[i],al[i],au[i],symh[i],xBow[i],yBow[i],zBow[i]]
dataArray.append(arr)
arr = [bxI[i],byI[i],bzI[i],vxI[i],vyI[i],vzI[i],nI[i],csI[i],aeI[i],alI[i],auI[i],symhI[i],xBowI[i],yBowI[i],zBowI[i]]
hasBeenInterpolated.append(arr)
return (dates, numpy.array(dataArray,float), numpy.array(hasBeenInterpolated))
def __storeDataDict(self, dates, dataArray, hasBeenInterpolated):
"""
Populate self.data TimeSeries object via the 2d dataArray read from file.
"""
#print('__storeDataDict')
self._gse2gsm(dates, dataArray)
#print(numpy.shape(dataArray))
#print(numpy.shape(dataArray[:,0]))
self.data.append('time_min', 'Time (Minutes since start)', 'min', dataArray[:,0])
# Magnetic field
self.data.append('bx', 'Bx (gsm)', r'$\mathrm{nT}$', dataArray[:,1])
self.data.append('isBxInterped', 'Is index i of By interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,0])
self.data.append('by', 'By (gsm)', r'$\mathrm{nT}$', dataArray[:,2])
self.data.append('isByInterped', 'Is index i of By interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,1])
self.data.append('bz', 'Bz (gsm)', r'$\mathrm{nT}$', dataArray[:,3])
self.data.append('isBzInterped', 'Is index i of Bz interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,2])
# Velocity
self.data.append('vx', 'Vx (gsm)', r'$\mathrm{km/s}$', dataArray[:,4])
self.data.append('isVxInterped', 'Is index i of Vx interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,3])
self.data.append('vy', 'Vy (gsm)', r'$\mathrm{km/s}$', dataArray[:,5])
self.data.append('isVyInterped', 'Is index i of Vy interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,4])
self.data.append('vz', 'Vz (gsm)', r'$\mathrm{km/s}$', dataArray[:,6])
self.data.append('isVzInterped', 'Is index i of Vz interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,5])
# Density
self.data.append('n', 'Density', r'$\mathrm{1/cm^3}$', dataArray[:,7])
self.data.append('isNInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,6])
## Temperature
#self.data.append('t', 'Temperature', r'$\mathrm{kK}$', dataArray[:,8]*1e-3)
#self.data.append('isTInterped', 'Is index i of T interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,7])
# Sound Speed (Thermal Speed)
self.data.append('cs', 'Sound speed', r'$\mathrm{km/s}$', dataArray[:,8])
self.data.append('isCsInterped', 'Is index i of Cs interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,7])
# Activity Indices
self.data.append('ae', 'AE-Index', r'$\mathrm{nT}$', dataArray[:,9])
self.data.append('isAeInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,8])
self.data.append('al', 'AL-Index', r'$\mathrm{nT}$', dataArray[:,10])
self.data.append('isAlInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,9])
self.data.append('au', 'AU-Index', r'$\mathrm{nT}$', dataArray[:,11])
self.data.append('isAuInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,10])
self.data.append('symh', 'SYM/H', r'$\mathrm{nT}$', dataArray[:,12])
self.data.append('isSymHInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,11])
# Bowshock Location
self.data.append('xBS', 'BowShockX (gsm)', r'$\mathrm{RE}$', dataArray[:,13])
self.data.append('isxBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,12])
self.data.append('yBS', 'BowShockY (gsm)', r'$\mathrm{RE}$', dataArray[:,14])
self.data.append('isyBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,13])
self.data.append('zBS', 'BowShockZ (gsm)', r'$\mathrm{RE}$', dataArray[:,15])
self.data.append('iszBSInterped', 'Is index i of N interpolated from bad data?', r'$\mathrm{boolean}$', hasBeenInterpolated[:,14])
def __appendMetaData(self, date, dateshift, filename):
"""
Add standard metadata to the data dictionary.
"""
metadata = {'Model': 'WIND',
'Source': filename,
'Date processed': datetime.datetime.now(),
'Start date': date,
}
self.data.append(key='meta',
name='Metadata for WIND CDAWeb',
units='n/a',
data=metadata)
def __deltaMinutes(self, t1, startDate):
"""
Returns: Number of minutes elapsed between t1 and startDate.
"""
diff = t1 - startDate
return (diff.days*24.0*60.0 + diff.seconds/60.0)
if __name__ == '__main__':
import doctest
doctest.testmod()

0
kaipy/solarWind/__init__.py
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295
kaipy/solarWind/swBCplots.py
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@@ -1,295 +0,0 @@
"""
Generate plots of 1d time series data stored as pyLTR.TimeSeries
objects. See examples/TimeSeriesPlots.py for usage.
"""
from kaipy.solarWind.TimeSeries import TimeSeries
import datetime
from matplotlib import dates
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import kaipy.kaiViz as kv
import datetime
def BasicPlot(VarDict,Xname,Yname,Xlabel=True,color='b'):
"""
Mostly a wrapper for plt.plot(...)
"""
x=VarDict[Xname]
y=VarDict[Yname]
# y may be a time series of tuples/lists of variables to plot
# simultaneously (e.g., vector components) using different colors
if (np.array(y['data'][0]).size > 1 and
np.array(y['data'][0]).size == len(color) and
all([ylen == np.array(y['data'][0]).size for ylen in map(len,y['data'])]) ):
for i in range(np.array(y['data'][0]).size):
plt.plot(x['data'], [yts[i] for yts in y['data']], color=color[i])
else:
plt.plot(x['data'],y['data'],color=color)
# Xname may point to a list of datetime.datetime objects, in which case
# pyplot must be told to plot these as datetimes
if all([type(ts) == datetime.datetime for ts in x['data']]):
dfmt = dates.DateFormatter('%m/%d/%y-%H:%M')
plt.gca().xaxis.set_major_formatter(dfmt)
elif any([type(ts) == datetime.datetime for ts in x['data']]):
raise Exception('Cannot mix datetime time-axis elements with other types')
if Xlabel:
#locs,labels=plt.xticks()
xStr = x['name']
if len(x['units']) > 0:
xStr += ' ['+x['units']+']'
plt.xlabel(xStr)
else:
plt.xlabel(' ')
#locs,labels=plt.xticks()
#plt.xticks(locs,(' '))
# y['name'] may not be a scalar
if (np.array(y['name']).size) > 1:
plt.ylabel('['+y['units']+']',fontsize='small')
else:
plt.ylabel(y['name']+' ['+y['units']+']',fontsize='small')
def SummaryPlot(VarDict,Xname):
"""
Plot every variable in VarDict.
This is a simple wrapper around the more generic MultiPlotN. This
code may have problems with varDicts that store non-time series
data. You've beeen warned.
"""
#Loop over elements in Dict to make sure they all have data
plotVariables=[]
for var in VarDict.keys():
if isinstance(VarDict[var], dict):
if 'data' in VarDict[var]:
plotVariables.append(var)
MultiPlotN([VarDict], Xname, plotVariables)
def MultiPlot(VarDict,Xname,Items,color='b'):
"""
Plot variables stored in TimeSeries object 'varDict'.
This is a simple wrapper around the more generic MultiPlotN.
"""
MultiPlotN([VarDict], Xname, Items, [color],[])
def MultiPlot2(VarDict,VarDict2,Xname,Items,color1='b',color2='r'):
"""
Plot items (stored in TimeSeries objects VarDict and VarDict2)
against one another. One sub plot for each item.
This is a simple wrapper around the more generic MultiPlotN.
"""
MultiPlotN([VarDict, VarDict2],
Xname,
Items,
[color1, color2],
[])
def MultiPlotN(varDicts, Xname, variables, colors = [], legendLabels=[]):
"""
Creates one subplot for each variable. Each subplot renders a plot
of that variable for each varDict passed.
For example:
- one varDict and 5 variables will give 5 subplots with one line each
- 5 varDicts and one variable will give 1 subplot with 5 lines
Parameters:
varDicts: List of TimeSeries data dictionaries
Xname: key of horizontal axes label (must be defined in all varDicts)
variables: List of keys to plot for each varDict
colors: color of each line to be drawn
legendLabels: Display a legend on the plot
"""
nSubplots = len(variables)
# Set default colors (all blue)
if not colors:
for d in varDicts:
colors.append('b')
# Need to declare these here for proper scope:
axes=None
ax=None
for plotIndex, variable in enumerate(variables):
# Sharing the x-axes applies the same zoom to all subplots when
# user interacts with a single subplot.
if plotIndex == 0:
axes = plt.subplot(nSubplots, 1, plotIndex+1)
ax=axes
else:
ax = plt.subplot(nSubplots, 1, plotIndex+1, sharex=axes)
# Turn off x-axis to prevent pointillism problem with upper subplots.
#ax.xaxis.set_visible(False)
# Alternate vertical axes
if plotIndex%2 == 0:
#ax.yaxis.tick_left()
ax.yaxis.set_ticks_position('left')
ax.yaxis.set_label_position('left')
else:
#ax.yaxis.tick_right()
ax.yaxis.set_ticks_position('right')
ax.yaxis.set_label_position('right')
# Fill in subplot data
for (idx, data) in enumerate(varDicts):
if plotIndex < nSubplots-1:
BasicPlot(data, Xname, variable, Xlabel=False, color=colors[idx])
# remove xticklabels from all but bottom subplot...it seems like
# someone was attempting something similar with ax.xaxis.set_visible(False)
# in the past, then commented this out; might be worth asking why -EJR 2/2014
plt.setp(ax.get_xticklabels(), visible=False)
else:
BasicPlot(data, Xname, variable, Xlabel=True, color=colors[idx])
plt.subplots_adjust(hspace=0)
#ax.xaxis.set_visible(True)
plt.subplot(nSubplots, 1, 1)
if legendLabels:
plt.legend(legendLabels, loc='best')
def swQuickPlot(UT,D,Temp,Vx,Vy,Vz,Bx,By,Bz,SYMH,interped,fname,
xBS=None,yBS=None,zBS=None,
t0fmt='%Y-%m-%d %H:%M:%S',
utfmt='%H:%M\n%Y-%m%d',
title="Solar Wind",
doTrim=True,doEps=False,returnAxs=False):
"""
Plot solar wind n,T, dyn p, V, B and sym/h over time period specified by the user.
"""
utall = []
for n in range(len(UT)):
utall.append(datetime.datetime.strptime(UT[n].decode('utf-8'),t0fmt))
# constants
gamma = 5/3.0
mp = 1.67e-27 #Proton mass [kg]w
# calculating the solar wind dynamic pressure
Vmag = np.sqrt(Vx**2+Vy**2+Vz**2)
Pram = mp*D*Vmag**2*1.0e15 # nPa
#Setup figure
fSz = (10,14)
if xBS is None:
Nr = 6
else:
Nr = 7
Nc = 1
clrs = ['#7570b3','#1b9e77','#d95f02','black'] # from colorbrewer2.org for colorblind safe
fig = plt.figure(figsize=fSz)
gs = gridspec.GridSpec(Nr,Nc,hspace=0.05,wspace=0.05)
ax11 = fig.add_subplot(gs[0,0])
ax12 = fig.add_subplot(gs[3,0])
ax21 = fig.add_subplot(gs[1,0])
ax22 = fig.add_subplot(gs[4,0])
ax31 = fig.add_subplot(gs[2,0])
if xBS is None:
ax32 = fig.add_subplot(gs[5,0])
else:
ax32 = fig.add_subplot(gs[6,0])
ax41 = fig.add_subplot(gs[5,0])
smlabel = ['SM-X','SM-Y','SM-Z']
xvec = np.zeros((len(D),3))+1e9
fig.suptitle(title,y=0.92,fontsize=14)
Dlim=np.max(D)-np.min(D)
ax11.plot(utall,D,color=clrs[3])
for i in range(3):
ax11.plot(utall,xvec[:,i],linewidth=4,label=smlabel[i],color=clrs[i])
kv.SetAxLabs(ax11,"","n [cm^-3]",doBot=True,doLeft=True)
ax11.set_ylim(np.min(D)-0.05*Dlim,np.max(D)+0.05*Dlim)
ax11.tick_params(axis="x",direction="in")
ax11.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax11.get_xaxis_transform())
plt.setp(ax11.get_xticklabels(),visible=False)
ax11.legend(ncol=len(smlabel), bbox_to_anchor=(0.5,1),loc='lower center', fontsize='small')
TScl = 1.0e-6
ax21.plot(utall,Temp*TScl,color=clrs[3])
kv.SetAxLabs(ax21,"","T [MK]",doBot=True,doLeft=False)
ax21.tick_params(axis="x",direction="in")
ax21.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax21.get_xaxis_transform())
plt.setp(ax21.get_xticklabels(),visible=False)
ax31.plot(utall,Pram,color=clrs[3])
ax31.xaxis_date()
kv.SetAxLabs(ax31,"","Dynamic P [nPa]",doBot=True,doLeft=True)
ax31.tick_params(axis="x",direction="in")
ax31.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax31.get_xaxis_transform())
plt.setp(ax31.get_xticklabels(),visible=False)
vScl = 1.0e-3
secax12 = ax12.twinx()
ax12.plot(utall,Vx*vScl,color=clrs[0],linewidth=0.95)
secax12.plot(utall,Vy*vScl,color=clrs[1],linewidth=0.95)
secax12.plot(utall,Vz*vScl,color=clrs[2],linewidth=0.95)
secax12.set_ylabel('Vy,z [km/s]')
kv.SetAxLabs(ax12,"","Vx [km/s]",doBot=True,doLeft=True)
ax12.tick_params(axis="x",direction="in")
ax12.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax12.get_xaxis_transform())
plt.setp(ax12.get_xticklabels(),visible=False)
ax22.plot(utall,Bx,color=clrs[0],linewidth=0.95)
ax22.plot(utall,By,color=clrs[1],linewidth=0.95)
ax22.plot(utall,Bz,color=clrs[2],linewidth=0.95)
ax22.axhline(y=0.0, color='black', linestyle='--',alpha=0.6,linewidth=0.9)
kv.SetAxLabs(ax22,"","B [nT]",doBot=True,doLeft=False)
ax22.tick_params(axis="x",direction="in")
ax22.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax22.get_xaxis_transform())
plt.setp(ax22.get_xticklabels(),visible=False)
if xBS is not None:
ax41.plot(utall,xBS,color=clrs[0],linewidth=0.95)
ax41.plot(utall,yBS,color=clrs[1],linewidth=0.95)
ax41.plot(utall,zBS,color=clrs[2],linewidth=0.95)
ax41.axhline(y=0.0, color='black', linestyle='--',alpha=0.6,linewidth=0.9)
kv.SetAxLabs(ax41,"","BowS [RE]",doBot=True,doLeft=False)
ax41.tick_params(axis="x",direction="in")
ax41.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax41.get_xaxis_transform())
plt.setp(ax41.get_xticklabels(),visible=False)
ax32.plot(utall,SYMH,color=clrs[3])
ax32.axhline(y=0.0, color='black', linestyle='--',alpha=0.6,linewidth=0.9)
ax32.fill_between(utall, 0, 1, where=interped, alpha=0.4, transform=ax32.get_xaxis_transform())
ax32.xaxis_date()
xfmt = dates.DateFormatter(utfmt)
ax32.xaxis.set_major_formatter(xfmt)
kv.SetAxLabs(ax32,"UT","SYM/H [nT]",doBot=True,doLeft=True)
# Give axes back to user if they want them
if returnAxs:
axDict = {}
axDict['D'] = ax11
axDict['Temp'] = ax21
axDict['dynP'] = ax31
axDict['V'] = ax12
axDict['B'] = ax22
axDict['symh'] = ax32
if xBS is not None:
axDict['BowS'] = ax41
return axDict
# Otherwise, save the figure and leave
kv.savePic(fname,doEps=doEps,doTrim=doTrim)
plt.close('all')

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