yi = np.linspace(0, 2048, 256 + 1)
# yi = np.linspace(orders_band[0]-1, orders_band[-1]+1,
# len(orders_band)*10)
gi = GridInterpolator(xi, yi)
from mpl_toolkits.axes_grid1 import ImageGrid
fig2 = plt.figure(figsize=(14, 7))
grid = ImageGrid(fig2, 111, (1, 2), share_all=True)
x_det, y_det = x, y
plot_detected(grid[0], im, x_det, y_det)
ax2 = grid[1]
im = check_dx1(ax2, x, y, dx, gi, mystd=2 * mystd)
from mpl_toolkits.decorator import colorbar
cb = colorbar(im, ax=ax2, loc=1)
cb.set_label(r"$\Delta\lambda$ [pixel]")
check_dx2(ax2, x, y, dx)
ax1 = grid[0]
ax1.set_xlim(0, 2048)
ax1.set_ylim(0, 2048)
ax1.set_xlabel("x-pixel")
ax1.set_ylabel("y-pixel")
fig2.tight_layout()
fig3 = plt.figure()
ax = fig3.add_subplot(111)
#ax.hist(dx, bins=np.linspace(-10, 10, 50))
yi = np.linspace(0, 2048, 256+1)
# yi = np.linspace(orders_band[0]-1, orders_band[-1]+1,
# len(orders_band)*10)
gi = GridInterpolator(xi, yi)
from mpl_toolkits.axes_grid1 import ImageGrid
fig2 = plt.figure(figsize=(14, 7))
grid = ImageGrid(fig2, 111, (1, 2), share_all=True)
x_det, y_det = x, y
plot_detected(grid[0], im, x_det, y_det)
ax2 = grid[1]
im = check_dx1(ax2, x, y, dx, gi, mystd=2*mystd)
from mpl_toolkits.decorator import colorbar
cb = colorbar(im, ax=ax2, loc=1)
cb.set_label(r"$\Delta\lambda$ [pixel]")
check_dx2(ax2, x, y, dx)
ax1 = grid[0]
ax1.set_xlim(0, 2048)
ax1.set_ylim(0, 2048)
ax1.set_xlabel("x-pixel")
ax1.set_ylabel("y-pixel")
fig2.tight_layout()
fig3 = plt.figure()
ax=fig3.add_subplot(111)
#ax.hist(dx, bins=np.linspace(-10, 10, 50))
def check_thar_transorm(thar_products, thar_echell_products):
# to check the fit results.
from storage_descriptions import (COMBINED_IMAGE_DESC,
THAR_ALIGNED_JSON_DESC)
combined_im = thar_products[COMBINED_IMAGE_DESC].data
thar_echell_products = thar_echell_products[THAR_ALIGNED_JSON_DESC]
affine_tr = thar_echell_products["affine_tr"]
affine_tr_mask = thar_echell_products["affine_tr_mask"]
xy1f, xy2f = thar_echell_products["xy1f"], thar_echell_products["xy2f"]
xy1f_tr = affine_tr.transform(xy1f) #[:,0], xy1f[:,1])
dx_ = xy1f_tr[:,0] - xy2f[:,0]
#dy_ = xy1f_tr[:,1] - xy2f[:,1]
mystd = dx_[affine_tr_mask].std()
mm = [np.abs(dx_) < 3. * mystd]
dx = dx_[mm]
x = xy1f_tr[:,0][mm]
y = xy1f_tr[:,1][mm]
from matplotlib.figure import Figure
if 0: #plot the coverage of previous echellogram
import matplotlib.pyplot as plt
im = igrins_log.get_cal_hdus(band, "thar")[0].data
zemax_xy_list = get_wvl_range(zdata_band, affine_tr)
fig1 = plt.figure(figsize=(8, 8))
ax = fig1.add_subplot(111)
plot_zemax_coverage(ax, im, zemax_xy_list, band)
ax.set_xlim(0, 2048)
ax.set_ylim(0, 2048)
ax.set_xlabel("x-pixel")
ax.set_ylabel("y-pixel")
fig1.tight_layout()
#check_dx(im, x, y, dx, 3*mystd)
if 1:
#orders_band = igrins_orders[band]
xi = np.linspace(0, 2048, 256+1)
yi = np.linspace(0, 2048, 256+1)
# yi = np.linspace(orders_band[0]-1, orders_band[-1]+1,
# len(orders_band)*10)
from grid_interpolator import GridInterpolator
gi = GridInterpolator(xi, yi)
from mpl_toolkits.axes_grid1 import ImageGrid
#fig2 = plt.figure(figsize=(14, 7))
fig2 = Figure(figsize=(14, 7))
grid = ImageGrid(fig2, 111, (1, 2), share_all=True)
x_det, y_det = x, y
plot_detected(grid[0], combined_im, x_det, y_det)
ax2 = grid[1]
im = check_dx1(ax2, x, y, dx, gi, mystd=2*mystd)
try:
from mpl_toolkits.decorator import colorbar
except ImportError:
pass
else:
cb = colorbar(im, ax=ax2, loc=1)
cb.set_label(r"$\Delta\lambda$ [pixel]")
check_dx2(ax2, x, y, dx)
ax1 = grid[0]
ax1.set_xlim(0, 2048)
ax1.set_ylim(0, 2048)
ax1.set_xlabel("x-pixel")
ax1.set_ylabel("y-pixel")
fig2.tight_layout()
fig3 = Figure() #plt.figure()
ax=fig3.add_subplot(111)
#ax.hist(dx, bins=np.linspace(-10, 10, 50))
ax.hist(dx, bins=np.linspace(-5*mystd, 5*mystd, 50))
ax.set_xlabel(r"$\Delta\lambda$ [pixel]")
ax.set_ylabel("counts")
fig3.tight_layout()
return [fig2, fig3]
