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dp_plot_tools.py
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dp_plot_tools.py
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def usage():
print ("")
print ("Damien P.'s plotting tools")
print ("This code has no support, or guarantees it won't ruin your data")
print ("computer or life.")
print ("I am bad at python. But in the interests of the death of IDL I am switching.")
print (" Please let me know anything I can improve.")
def flines3d(bx,by,bz,xx,yy,zz,xx0,yy0,nlines,res_incr=1):
dx = xx[2]-xx[1]
dy = yy[2]-yy[1]
dz = zz[2]-zz[1]
nx = xx.size
ny = yy.size
nz = zz.size
zz0 = np.zeros(nlines,dtype='float64')
ds = dz/res_incr
ns = int(nz*res_incr+1)
xline = np.zeros([nlines,ns],dtype='float64')
yline = np.zeros([nlines,ns],dtype='float64')
zline = np.zeros([nlines,ns],dtype='float64')
xline[:,0] = xx0
yline[:,0] = yy0
zline[:,0] = zz0
for j in range(nlines):
for i in range(1,ns):
bx1 = trilinear_interpolate(bx,(xline[j,i-1]-xx[0])/dx,(yline[j,i-1]-yy[0])/dy,(zline[j,i-1]-zz[0])/dz)
by1 = trilinear_interpolate(by,(xline[j,i-1]-xx[0])/dx,(yline[j,i-1]-yy[0])/dy,(zline[j,i-1]-zz[0])/dz)
bz1 = trilinear_interpolate(bz,(xline[j,i-1]-xx[0])/dx,(yline[j,i-1]-yy[0])/dy,(zline[j,i-1]-zz[0])/dz)
b0=np.clip(np.sqrt(bx1*bx1 + by1*by1+bz1*bz1),1.0e-10,None)
xline[j,i]=xline[j,i-1]+ds*bx1/b0
yline[j,i]=yline[j,i-1]+ds*by1/b0
zline[j,i]=zline[j,i-1]+ds*bz1/b0
if xline[j,i] < xx[0]:
xline[j,i] = xx[0]
yline[j,i] = yline[j,i-1]
zline[j,i] = zline[j,i-1]
if xline[j,i] > xx[nx-1]:
xline[j,i] = xx[nx-1]
yline[j,i] = yline[j,i-1]
zline[j,i] = zline[j,i-1]
if zline[j,i] < zz[0]:
zline[j,i] = zz[0]
yline[j,i] = yline[j,i-1]
xline[j,i] = xline[j,i-1]
if zline[j,i] > zz[nz-1]:
zline[j,i] = zz[nz-1]
yline[j,i] = yline[j,i-1]
xline[j,i] = xline[j,i-1]
if yline[j,i] < yy[0]:
yline[j,i] = yy[0]
zline[j,i] = zline[j,i-1]
xline[j,i] = xline[j,i-1]
if yline[j,i] > yy[ny-1]:
zline[j,i] = zline[j,i-1]
yline[j,i] = yy[ny-1]
xline[j,i] = xline[j,i-1]
return xline, yline, zline
def flines2d(bx,bz,xx,zz,xx0,res_incr=1):
dx = xx[2]-xx[1]
dz = zz[2]-zz[1]
nx = xx.size
nz = zz.size
nlines = xx0.size
zz0 = np.zeros(nlines,dtype='float64')
ds = dz/res_incr
ns = int(nz*res_incr+1)*4
xline = np.zeros([nlines,ns],dtype='float64')
zline = np.zeros([nlines,ns],dtype='float64')
xline[:,0] = xx0
zline[:,0] = zz0
for j in range(nlines):
for i in range(1,ns):
bx1 = bilinear_interpolate(bx,(xline[j,i-1]-xx[0])/dx,(zline[j,i-1]-zz[0])/dz)
bz1 = bilinear_interpolate(bz,(xline[j,i-1]-xx[0])/dx,(zline[j,i-1]-zz[0])/dz)
b0 = np.clip(np.sqrt(bx1*bx1 + bz1*bz1),1.0e-10,None)
xline[j,i]=xline[j,i-1]+ds*bx1/b0
zline[j,i]=zline[j,i-1]+ds*bz1/b0
if xline[j,i] < xx[0]:
xline[j,i] = xx[0]
zline[j,i] = zline[j,i-1]
if xline[j,i] > xx[nx-1]:
xline[j,i] = xx[nx-1]
zline[j,i] = zline[j,i-1]
if zline[j,i] < zz[0]:
zline[j,i] = zz[0]
xline[j,i] = xline[j,i-1]
if zline[j,i] > zz[nz-1]:
zline[j,i] = zz[nz-1]
xline[j,i] = xline[j,i-1]
return xline, zline
def deriv_nd_O2(arr,dir,delta=1.0):
## Do a derivative in 3 dim array
## Dir = array indices
## delta = grid spacing
dim = arr.ndim
if dim > 3:
print("Currently coded for only a 3-dim array")
print(arr.ndim)
return None
derr = np.zeros(arr.shape,dtype = np.float64)
derr = -0.5*np.roll(arr,1,dir) + 0.5*np.roll(arr,-1,dir)
if dir == 0:
derr[0,:,:] = -1.5*arr[0,:,:] + 2.0*arr[1,:,:] - 0.5*arr[2,:,:]
derr[-1,:,:] = 1.5*arr[-1,:,:] - 2.0*arr[-2,:,:] + 0.5*arr[-3,:,:]
if dir == 1:
derr[:,0,:] = -1.5*arr[:,0,:] + 2.0*arr[:,1,:] - 0.5*arr[:,2,:]
derr[:,-1,:] = 1.5*arr[:,-1,:] - 2.0*arr[:,-2,:] + 0.5*arr[:,-3,:]
if dir == 2:
derr[:,:,0] = -1.5*arr[:,:,0] + 2.0*arr[:,:,1] - 0.5*arr[:,:,2]
derr[:,:,-1] = 1.5*arr[:,:,-1] - 2.0*arr[:,:,-2] + 0.5*arr[:,:,-3]
derr = derr/delta
return derr
def plotvhslice(a,arr,xyslice,xzslice,xpos,ypos,xax,yax,zax,xrange,yrange,zrange, br = None,title=None,btitle='',beta=None,flines=None,cmap='jet',sym=False,animated=False,cbar=True,xtitle=True,ytitle=True,fontsize=9,asp=[1,1]):
[nx,ny,nz] = arr.shape
r_x = 0
r_y = 0
r_z = xyslice
d_x = nx
d_y = ny
d_z = r_z + 1
x = xax
y = yax
xr = xrange
yr = yrange
xttl = None
if ytitle:
yttl = 'Y (Mm)'
else:
yttl = None
ttl = title
if beta is not None:
bslice = beta[r_x:d_x,r_y:d_y,r_z:d_z]
Y,X = np.meshgrid(np.linspace(y[0],y[-1],bslice.squeeze().shape[1]),
np.linspace(x[0],x[-1],bslice.squeeze().shape[0]))
if flines is not None:
nlines = flines[0].shape[0]
if br is None:
arr_min = arr[r_x:d_x,r_y:d_y,r_z:d_z].min()
arr_max = arr[r_x:d_x,r_y:d_y,r_z:d_z].max()
else:
arr_min = br[0]
arr_max = br[1]
if cbar:
if cbar is 'Top':
cbar1 = 'Top'
cbar2 = False
elif cbar is 'Bottom':
cbar1 = False
cbar2 = True
elif cbar is 'Right':
cbar1 = 'Right'
cbar2 = 'Right'
elif cbar is 'Left':
cbar1 = 'Left'
cbar2 = 'Left'
else:
cbar1 = True
cbar2 = True
else:
cbar1 = False
cbar2 = False
[im1,ax,cbt1,cb,cax] = plotsnapshot(a,arr[r_x:d_x,r_y:d_y,r_z:d_z].squeeze(),title=title,
xax=[x[r_x],x[d_x-1]],yax=[y[r_y],y[d_y-1]],xr=xr,yr=yr,xtitle = xttl, ytitle = yttl,arr_min = arr_min, arr_max = arr_max,btitle=btitle, sym=sym,pos=xpos,asp = asp[0],cmap=cmap,animated=animated,cbar=cbar1)
if beta is not None:
CF = ax.contour(bslice.squeeze().T,(1.0,),colors = "black",extent=[x[0],x[-1],y[0],y[-1]]
,linestyles='dashed',linewidth=0.3)
ax.plot(xr,[y[xzslice],y[xzslice]])
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%02.1f'))
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%02.1f'))
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fontsize)
cax.tick_params(labelsize=fontsize)
r_x = 0
r_y = xzslice
r_z = 0
d_x = nx
d_y = r_y + 1
d_z = nz
x = xax
y = zax
xr = xrange
yr = zrange
if xtitle:
xttl = 'X (Mm)'
else:
xttl = None
if ytitle:
yttl = 'Z (Mm)'
else:
yttl = None
ttl = 'y ' + "{0:.3f}".format(y[r_y]*1.0e-8)
if beta is not None:
bslice = beta[r_x:d_x,r_y:d_y,r_z:d_z]
Y,X = np.meshgrid(np.linspace(y[0],y[-1],bslice.squeeze().shape[1]),
np.linspace(x[0],x[-1],bslice.squeeze().shape[0]))
if br is None:
arr_min = arr[r_x:d_x,r_y:d_y,r_z:d_z].min()
arr_max = arr[r_x:d_x,r_y:d_y,r_z:d_z].max()
else:
if (np.size(br) == 2):
arr_min = br[0]
arr_max = br[1]
elif (np.size(br) == 4):
arr_min = br[2]
arr_max = br[3]
else:
arr_min = arr[r_x:d_x,r_y:d_y,r_z:d_z].min()
arr_max = arr[r_x:d_x,r_y:d_y,r_z:d_z].max()
[im2,ax,cbt2, cb,cax] = plotsnapshot(a,arr[r_x:d_x,r_y:d_y,r_z:d_z].squeeze(),
xax=[x[r_x],x[d_x-1]],yax=[y[r_z],y[d_z-1]],xr=xr,yr=yr,title = None, xtitle = xttl, ytitle = yttl,btitle=btitle, arr_min = arr_min, arr_max = arr_max,sym=sym,pos=ypos,asp = asp[1],cmap=cmap,animated=animated,cbar=cbar2)
if beta is not None:
CF = ax.contour(bslice.squeeze().T,(1.0,),colors = "black",extent=[x[0],x[-1],y[0],y[-1]]
,linestyles='dashed',linewidth=0.3)
if flines is not None:
for i in range(nlines):
ax.plot(flines[0][i,:]*1.0e-8,flines[1][i,:]*1.0e-8,'w')
ax.plot(xr,[y[xyslice],y[xyslice]])
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fontsize)
cax.tick_params(labelsize=fontsize)
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%02.1f'))
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%02.1f'))
return im1,im2,cbt1,cbt2
def plotsnapshot(fig,arr,xax=None,yax=None,pos=0,xr=None,yr=None, title = None , xtitle = None ,ytitle = None,btitle = '', arr_min = None, arr_max = None,nticks=5,sym = False,asp = 'auto',cmap='jet',animated=False,cbar=True,bsize=None):
## Add some kind of command to do the aspect
if arr.ndim != 2:
print ("Array not 2-dimensional! arr.ndim = ", arr.ndim)
return None
nx = arr.shape[0]
ny = arr.shape[1]
if xax is None:
xax = np.arange(nx)
else:
xax = np.arange(nx)/(nx-1)*(xax[1]-xax[0])+xax[0]
if yax is None:
yax = np.arange(ny)
else:
yax = np.arange(ny)/(ny-1)*(yax[1]-yax[0])+yax[0]
if xr is None:
ind_x = np.arange(nx)
else:
ind_x = np.where((xax[:] > xr[0]) & (xax[:] < xr[1]))[0]
if yr is None:
ind_y = np.arange(ny)
else:
ind_y = np.where((yax[:] > yr[0]) & (yax[:] < yr[1]))[0]
if arr_min == None:
arr_min = np.amin(arr[ind_x[0]:ind_x[-1],ind_y[0]:ind_y[-1]])
if arr_min == 0:
arr_min = 0.0
if arr_min != 0:
a_mino = np.floor(np.log10(np.abs(arr_min)))
arr_min = np.floor(arr_min/10.0**(a_mino-1))*10.0**(a_mino-1)
if arr_max == None:
arr_max = np.amax(arr[ind_x[0]:ind_x[-1],ind_y[0]:ind_y[-1]])
if arr_max == 0:
arr_max = 0.0
if arr_max != 0:
a_maxo = np.floor(np.log10(np.abs(arr_max)))
arr_max = np.ceil(arr_max/10.0**(a_maxo-1))*10.0**(a_maxo-1)
if sym:
arr_max = max(abs(arr_max),abs(arr_min))
arr_min = -arr_max
if arr_max == arr_min:
cbarticks = [arr_min,arr_max]
if arr_max != arr_min:
cbarticks = np.linspace(arr_min, arr_max, nticks, endpoint=True)
ax = fig.add_subplot(pos)
if title is not None:
ax.set_title(title)
if xtitle is not None:
ax.set_xlabel(xtitle)
if ytitle is not None:
ax.set_ylabel(ytitle)
img = ax.imshow(arr.T,interpolation='none', vmin=arr_min, vmax=arr_max,origin='lower',
extent=[xax[0],xax[-1],yax[0],yax[-1]], aspect=asp,cmap=plt.get_cmap(cmap),animated=animated)
if xr:
ax.set_xlim(xr)
if yr:
ax.set_ylim(yr)
divider = make_axes_locatable(ax)
if cbar:
if (cbar is True) or (cbar is 'Right'):
cax = divider.append_axes("right", size="5%", pad=0.1)
cb = fig.colorbar(img,cax=cax,ticks = cbarticks, format=ticker.FuncFormatter(fmt))
if cbar is 'Left':
cax = divider.append_axes("left", size="5%", pad=0.1)
cb = fig.colorbar(img,cax=cax,ticks = cbarticks, format=ticker.FuncFormatter(fmt))
if cbar is 'Top':
cax = divider.append_axes("top", size="5%", pad=0.1)
cb = fig.colorbar(img,cax=cax,ticks = cbarticks,orientation='horizontal', format=ticker.FuncFormatter(fmt))
if cbar is 'Bottom':
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cb = fig.colorbar(img,cax=cax,ticks = cbarticks,orientation='horizontal', format=ticker.FuncFormatter(fmt))
cb.set_label(btitle)
else:
cax = []
cb = []
return img,ax,cbarticks,cb,cax
def trilinear_interpolate(im,x,y,z):
x = np.asarray(x, dtype = 'float64')
y = np.asarray(y, dtype = 'float64')
z = np.asarray(z, dtype = 'float64')
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
z0 = np.floor(z).astype(int)
z1 = z0+1
x0 = np.clip(x0, 0, im.shape[0]-1);
x1 = np.clip(x1, 0, im.shape[0]-1);
y0 = np.clip(y0, 0, im.shape[1]-1);
y1 = np.clip(y1, 0, im.shape[1]-1);
z0 = np.clip(z0, 0, im.shape[2]-1);
z1 = np.clip(z1, 0, im.shape[2]-1);
Iaaa = im[x0,y0,z0] # y0, x0 ]
Iaab = im[x0,y0,z1] #y1, x0 ]
Iaba = im[x0,y1,z0] #y0, x1 ]
Iabb = im[x0,y1,z1] #y1, x1 ]
Ibaa = im[x1,y0,z0] # y0, x0 ]
Ibab = im[x1,y0,z1] #y1, x0 ]
Ibba = im[x1,y1,z0] #y0, x1 ]
Ibbb = im[x1,y1,z1] #y1, x1 ]
waaa = (x1-x) * (y1-y) * (z1-z)
waab = (x1-x) * (y1-y) * (z-z0)
waba = (x1-x) * (y-y0) * (z1-z)
wabb = (x1-x) * (y-y0) * (z-z0)
wbaa = (x-x0) * (y1-y) * (z1-z)
wbab = (x-x0) * (y1-y) * (z-z0)
wbba = (x-x0) * (y-y0) * (z1-z)
wbbb = (x-x0) * (y-y0) * (z-z0)
return waaa*Iaaa + waab*Iaab + waba*Iaba + wabb*Iabb + wbaa*Ibaa + wbab*Ibab + wbba*Ibba + wbbb*Ibbb
def bilinear_interpolate(im, x, y):
x = np.asarray(x, dtype = 'float64')
y = np.asarray(y, dtype = 'float64')
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
x0 = np.clip(x0, 0, im.shape[0]-1);
x1 = np.clip(x1, 0, im.shape[0]-1);
y0 = np.clip(y0, 0, im.shape[1]-1);
y1 = np.clip(y1, 0, im.shape[1]-1);
Ia = im[x0,y0] # y0, x0 ]
Ib = im[x0,y1] #y1, x0 ]
Ic = im[x1,y0] #y0, x1 ]
Id = im[x1,y1] #y1, x1 ]
wa = (x1-x) * (y1-y)
wb = (x1-x) * (y-y0)
wc = (x-x0) * (y1-y)
wd = (x-x0) * (y-y0)
return wa*Ia + wb*Ib + wc*Ic + wd*Id
def fmt(x, pos):
a, b = '{:.2e}'.format(x).split('e')
b = int(b)
if (abs(x) == 0.0):
outfmt = '{:03.2f}'.format(x)
elif((abs(x) >= 1000.0) or (abs(x) <= 1.0e-3)):
outfmt = r'${}\times10^{{{}}}$'.format(a, b)
elif (abs(x) >= 100.0):
outfmt = '{:04.1f}'.format(x)
elif (abs(x) >= 10.0):
outfmt = '{:03.1f}'.format(x)
elif (abs(x) < 1.0e-2):
outfmt = '{:04.3f}'.format(x)
else:
outfmt = '{:03.2f}'.format(x)
return outfmt
def fig_open(figsize=None,numx=1,numy=1,hr = None, wr = None):
fig = plt.figure(figsize=figsize)
if not hr:
hr = np.tile(1,numy)
if not wr:
wr = np.tile(1,numx)
gs = gridspec.GridSpec(numy,numx,height_ratios=hr,width_ratios=wr)
return fig,gs
def fig_saveandcls(fig,name,dir,dpi=20):
fig.savefig(dir+name,dpi=dpi)
fig.clf()
plt.close()
return
def test_MHS(bkg,ptb = None):
if ptb:
bx = bkg.bx + ptb.bx
by = bkg.by + ptb.by
bz = bkg.bz + ptb.bz
rho = bkg.rho + ptb.rho
pe = bkg.pe + ptb.pe
else:
bx = bkg.bx
by = bkg.by
bz = bkg.bz
rho = bkg.rho
pe = bkg.pe
dbxdy = deriv_nd_O2(bx,1,delta=bkg.y[1]-bkg.y[0])
dbzdy = deriv_nd_O2(bz,1,delta=bkg.y[1]-bkg.y[0])
dbydx = deriv_nd_O2(by,0,delta=bkg.xax[1]-bkg.xax[0])
dbzdx = deriv_nd_O2(bz,0,delta=bkg.xax[1]-bkg.xax[0])
dbxdz = deriv_nd_O2(bx,2,delta=bkg.zax[1]-bkg.zax[0])
dbydz = deriv_nd_O2(by,2,delta=bkg.zax[1]-bkg.zax[0])
dpdx = deriv_nd_O2(pe,0,delta=bkg.xax[1]-bkg.xax[0])
dpdy = deriv_nd_O2(pe,1,delta=bkg.yax[1]-bkg.yax[0])
dpdz = deriv_nd_O2(pe,2,delta=bkg.zax[1]-bkg.zax[0])
jx = (dbzdy-dbydz)
jy = (-dbzdx+dbxdz)
jz = (dbydx-dbxdy)
eq_x = (dpdx - (jy*bz - jz*by))/abs(dpdx+(jy*bz - jz*by))
eq_y = (dpdy - (-jx*bz + jz*bx))/abs(dpdy+(-jx*bz - jz*bx))
eq_z = (dpdz - (jx*by - jy*bx) + rho*2.74e4)/abs(dpdz+(jx*by - jy*bx)-rho*2.74e4)
def inttostring(ii,ts_size=4):
## For an integer ii, and a timestamp size ts_size, create the string needed
## for loading the file, i.e. Alfven_0000
str_num = str(ii)
for bb in range(len(str_num),4,1):
str_num = '0' + str_num
return str_num
import math
import numpy as np
import sys
from subprocess import call
import time
import getopt
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.animation as animation
from matplotlib import gridspec
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.interpolate import griddata
print ("Importing dp_plot_tools.py")