mirror of
https://github.com/JHUAPL/kaipy.git
synced 2026-01-08 23:38:11 -05:00
Remove HDF4 capabilities related to LFM
This commit is contained in:
wiltbemj
@@ -8,7 +8,6 @@ jupyterlab
|
||||
matplotlib
|
||||
pandas
|
||||
progressbar
|
||||
pyhdf
|
||||
pyspedas
|
||||
pytest
|
||||
slack_sdk
|
||||
|
||||
@@ -13,7 +13,6 @@ kaipy requires:
|
||||
- matplotlib
|
||||
- pandas
|
||||
- progressbar
|
||||
- pyhdf
|
||||
- pyspedas
|
||||
- pytest
|
||||
- slack_sdk
|
||||
|
||||
@@ -4,8 +4,6 @@ from scipy import interpolate
|
||||
import sys
|
||||
import os
|
||||
from scipy.ndimage import gaussian_filter
|
||||
import pyhdf
|
||||
from pyhdf.SD import SD, SDC
|
||||
import matplotlib as mpl
|
||||
import matplotlib.cm as cm
|
||||
import matplotlib.pyplot as plt
|
||||
@@ -22,29 +20,6 @@ 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):
|
||||
"""
|
||||
Generate a regridded LFM data.
|
||||
|
||||
Args:
|
||||
Ni (int): Number of grid points in the x-direction.
|
||||
Nj (int): Number of grid points in the y-direction.
|
||||
Rin (float): Inner radius of the region of interest.
|
||||
Rout (float): Outer radius of the region of interest.
|
||||
fIn (str): Input file name for LFM data.
|
||||
TINY (float): A small value used for numerical stability.
|
||||
|
||||
Returns:
|
||||
XX (numpy.ndarray): Regridded x-coordinates.
|
||||
YY (numpy.ndarray): Regridded y-coordinates.
|
||||
"""
|
||||
# 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):
|
||||
"""
|
||||
@@ -936,62 +911,6 @@ def VizGrid(XX,YY,xxG=None,yyG=None,doGhost=False,doShow=True,xyBds=None,fOut="g
|
||||
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):
|
||||
"""
|
||||
Reads LFM grid data from an HDF file and returns the projected and scaled x and y arrays.
|
||||
|
||||
Args:
|
||||
fIn (str): The path to the HDF file.
|
||||
Rin (float): The inner radius for scaling the x and y arrays. Default is 3.0.
|
||||
Rout (float): The outer radius for cutting out the outer regions of the x and y arrays. Default is 25.0.
|
||||
|
||||
Returns:
|
||||
xxi (ndarray): The projected and scaled x array.
|
||||
yyi (ndarray): The projected and scaled y array.
|
||||
"""
|
||||
hdffile = pyhdf.SD.SD(fIn)
|
||||
# Grab x/y/z arrays from HDF file. Scale by Re
|
||||
# LFM is k,j,i ordering
|
||||
iRe = 1.0/kdefs.Re_cgs
|
||||
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):
|
||||
"""
|
||||
LoadTabG function loads tabular data from files LFM X and Y grid files.
|
||||
|
||||
@@ -1,168 +0,0 @@
|
||||
import numpy as np
|
||||
import kaipy.gamera.gamGrids as gg
|
||||
from pyhdf.SD import SD, SDC
|
||||
import h5py
|
||||
|
||||
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):
|
||||
"""
|
||||
Convert LFM grid data to GG (Geospace General) format.
|
||||
|
||||
Args:
|
||||
fIn (str): Input file path of the LFM grid data in HDF format.
|
||||
fOut (str): Output file path for the converted GG format.
|
||||
doEarth (bool, optional): Flag to indicate whether to use Earth scaling. Defaults to True.
|
||||
doJupiter (bool, optional): Flag to indicate whether to use Jovian scaling. Defaults to False.
|
||||
|
||||
Returns:
|
||||
Output file in GG format.
|
||||
"""
|
||||
# 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):
|
||||
"""
|
||||
Get the time attribute from a list of files.
|
||||
|
||||
Parameters:
|
||||
hdfs (list): A list of file paths.
|
||||
|
||||
Returns:
|
||||
Ts (numpy.ndarray): An array containing the time attributes from the input files.
|
||||
"""
|
||||
Ts = [SD(fIn).attributes().get('time') for fIn in hdfs]
|
||||
return np.array(Ts)
|
||||
#Get LFM fields
|
||||
def lfmFields(fIn):
|
||||
"""
|
||||
Retrieves cell-centered fields from an HDF file.
|
||||
|
||||
Args:
|
||||
fIn (str): The path to the HDF file.
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing the following fields:
|
||||
- Vx3cc (numpy.ndarray): X-component of the cell-centered velocity field.
|
||||
- Vy3cc (numpy.ndarray): Y-component of the cell-centered velocity field.
|
||||
- Vz3cc (numpy.ndarray): Z-component of the cell-centered velocity field.
|
||||
- Bx3cc (numpy.ndarray): X-component of the cell-centered magnetic field.
|
||||
- By3cc (numpy.ndarray): Y-component of the cell-centered magnetic field.
|
||||
- Bz3cc (numpy.ndarray): Z-component of the cell-centered magnetic field.
|
||||
"""
|
||||
hdffile = SD(fIn)
|
||||
#Get cell-centered fields
|
||||
Bx3cc, By3cc, Bz3cc = getHDFVec(hdffile, 'b')
|
||||
Vx3cc, Vy3cc, Vz3cc = getHDFVec(hdffile, 'v')
|
||||
|
||||
return Vx3cc, Vy3cc, Vz3cc, Bx3cc, By3cc, Bz3cc
|
||||
|
||||
#Get LFM MHD variables
|
||||
#Convert (D/Cs) -> (n,P)
|
||||
#Returns units (#/cm3) and (nPa)
|
||||
|
||||
def lfmFlow(fIn):
|
||||
"""
|
||||
Calculate the number density and pressure of a fluid flow.
|
||||
|
||||
Parameters:
|
||||
fIn (str): The input file path.
|
||||
|
||||
Returns:
|
||||
tuple (numpy.ndarray): A tuple containing the number density (n3) and pressure (P3) of the fluid flow.
|
||||
|
||||
"""
|
||||
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):
|
||||
"""
|
||||
Retrieves vector components from an HDF file.
|
||||
|
||||
Args:
|
||||
hdffile (HDF file object): The HDF file object from which to retrieve the vector components.
|
||||
qi (str): The base name of the vector components.
|
||||
Scl (float, optional): Scaling factor for the vector components. Default is 1.0.
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing the following fields:
|
||||
Qx3cc (ndarray): The x-component of the vector, with corners removed.
|
||||
Qy3cc (ndarray): The y-component of the vector, with corners removed.
|
||||
Qz3cc (ndarray): The z-component of the vector, with corners removed.
|
||||
"""
|
||||
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):
|
||||
"""
|
||||
Get the HDFScl (HDF Scale) for a given hdffile and variable q.
|
||||
|
||||
Args:
|
||||
hdffile (HDF file object): The HDF file containing the data.
|
||||
q (str): The variable to select from the HDF file.
|
||||
Scl (float, optional): The scaling factor to apply to the selected variable. Default is 1.0.
|
||||
|
||||
Returns:
|
||||
ndarray: The scaled selected variable with corners chopped out.
|
||||
"""
|
||||
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
|
||||
@@ -28,7 +28,6 @@ dependencies = [
|
||||
"matplotlib",
|
||||
"pandas",
|
||||
"progressbar",
|
||||
"pyhdf",
|
||||
"pyspedas",
|
||||
"pytest",
|
||||
"spacepy",
|
||||
|
||||
@@ -8,7 +8,6 @@ jupyterlab
|
||||
matplotlib
|
||||
pandas
|
||||
progressbar
|
||||
pyhdf
|
||||
pyspedas
|
||||
pytest
|
||||
slack_sdk
|
||||
|
||||
Reference in New Issue
Block a user