def check_order_flat(order_flat_products):
from trace_flat import (prepare_order_trace_plot,
check_order_trace1, check_order_trace2)
from storage_descriptions import ORDER_FLAT_JSON_DESC
mean_order_specs = order_flat_products[ORDER_FLAT_JSON_DESC]["mean_order_specs"]
from trace_flat import (get_smoothed_order_spec,
get_order_boundary_indices,
get_order_flat1d)
# these are duplicated from make_order_flat
s_list = [get_smoothed_order_spec(s) for s in mean_order_specs]
i1i2_list = [get_order_boundary_indices(s, s0) \
for s, s0 in zip(mean_order_specs, s_list)]
p_list = [get_order_flat1d(s, i1, i2) for s, (i1, i2) \
in zip(s_list, i1i2_list)]
fig_list, ax_list = prepare_order_trace_plot(s_list)
x = np.arange(2048)
for s, i1i2, ax in zip(mean_order_specs, i1i2_list, ax_list):
check_order_trace1(ax, x, s, i1i2)
for s, p, ax in zip(mean_order_specs, p_list, ax_list):
check_order_trace2(ax, x, p)
return fig_list
def check_order_flat(order_flat_products):
from trace_flat import (prepare_order_trace_plot, check_order_trace1,
check_order_trace2)
from storage_descriptions import ORDER_FLAT_JSON_DESC
mean_order_specs = order_flat_products[ORDER_FLAT_JSON_DESC][
"mean_order_specs"]
from trace_flat import (get_smoothed_order_spec,
get_order_boundary_indices, get_order_flat1d)
# these are duplicated from make_order_flat
s_list = [get_smoothed_order_spec(s) for s in mean_order_specs]
i1i2_list = [get_order_boundary_indices(s, s0) \
for s, s0 in zip(mean_order_specs, s_list)]
p_list = [get_order_flat1d(s, i1, i2) for s, (i1, i2) \
in zip(s_list, i1i2_list)]
fig_list, ax_list = prepare_order_trace_plot(s_list)
x = np.arange(2048)
for s, i1i2, ax in zip(mean_order_specs, i1i2_list, ax_list):
check_order_trace1(ax, x, s, i1i2)
for s, p, ax in zip(mean_order_specs, p_list, ax_list):
check_order_trace2(ax, x, p)
return fig_list
def make_order_flat(flaton_products, orders, order_map):
from storage_descriptions import (FLAT_NORMED_DESC,
FLAT_MASK_DESC)
flat_normed = flaton_products[FLAT_NORMED_DESC].data
flat_mask = flaton_products[FLAT_MASK_DESC].data
import scipy.ndimage as ni
slices = ni.find_objects(order_map)
mean_order_specs = []
mask_list = []
for o in orders:
sl = (slices[o-1][0], slice(0, 2048))
d_sl = flat_normed[sl].copy()
d_sl[order_map[sl] != o] = np.nan
f_sl = flat_mask[sl].copy()
f_sl[order_map[sl] != o] = np.nan
ff = np.nanmean(f_sl, axis=0)
mask_list.append(ff)
mmm = order_map[sl] == o
ss = [np.nanmean(d_sl[2:-2][:,i][mmm[:,i][2:-2]]) \
for i in range(2048)]
mean_order_specs.append(ss)
from trace_flat import (get_smoothed_order_spec,
get_order_boundary_indices,
get_order_flat1d)
s_list = [get_smoothed_order_spec(s) for s in mean_order_specs]
i1i2_list = [get_order_boundary_indices(s, s0) \
for s, s0 in zip(mean_order_specs, s_list)]
p_list = [get_order_flat1d(s, i1, i2) for s, (i1, i2) \
in zip(s_list, i1i2_list)]
# make flat
x = np.arange(len(s))
flat_im = np.empty(flat_normed.shape, "d")
flat_im.fill(np.nan)
fitted_responses = []
for o, p in zip(orders, p_list):
sl = (slices[o-1][0], slice(0, 2048))
d_sl = flat_normed[sl].copy()
msk = (order_map[sl] == o)
d_sl[~msk] = np.nan
px = p(x)
flat_im[sl][msk] = (d_sl / px)[msk]
fitted_responses.append(px)
flat_im[flat_im < 0.5] = np.nan
from storage_descriptions import (ORDER_FLAT_IM_DESC,
ORDER_FLAT_JSON_DESC)
r = PipelineProducts("order flat")
r.add(ORDER_FLAT_IM_DESC, PipelineImage([], flat_im))
r.add(ORDER_FLAT_JSON_DESC,
PipelineDict(orders=orders,
fitted_responses=fitted_responses,
i1i2_list=i1i2_list,
mean_order_specs=mean_order_specs))
return r
def make_order_flat(flaton_products, orders, order_map):
from storage_descriptions import (FLAT_NORMED_DESC, FLAT_MASK_DESC)
flat_normed = flaton_products[FLAT_NORMED_DESC].data
flat_mask = flaton_products[FLAT_MASK_DESC].data
import scipy.ndimage as ni
slices = ni.find_objects(order_map)
mean_order_specs = []
mask_list = []
for o in orders:
sl = (slices[o - 1][0], slice(0, 2048))
d_sl = flat_normed[sl].copy()
d_sl[order_map[sl] != o] = np.nan
f_sl = flat_mask[sl].copy()
f_sl[order_map[sl] != o] = np.nan
ff = np.nanmean(f_sl, axis=0)
mask_list.append(ff)
mmm = order_map[sl] == o
ss = [np.nanmean(d_sl[2:-2][:,i][mmm[:,i][2:-2]]) \
for i in range(2048)]
mean_order_specs.append(ss)
from trace_flat import (get_smoothed_order_spec,
get_order_boundary_indices, get_order_flat1d)
s_list = [get_smoothed_order_spec(s) for s in mean_order_specs]
i1i2_list = [get_order_boundary_indices(s, s0) \
for s, s0 in zip(mean_order_specs, s_list)]
p_list = [get_order_flat1d(s, i1, i2) for s, (i1, i2) \
in zip(s_list, i1i2_list)]
# make flat
x = np.arange(len(s))
flat_im = np.empty(flat_normed.shape, "d")
flat_im.fill(np.nan)
fitted_responses = []
for o, p in zip(orders, p_list):
sl = (slices[o - 1][0], slice(0, 2048))
d_sl = flat_normed[sl].copy()
msk = (order_map[sl] == o)
d_sl[~msk] = np.nan
px = p(x)
flat_im[sl][msk] = (d_sl / px)[msk]
fitted_responses.append(px)
flat_im[flat_im < 0.5] = np.nan
from storage_descriptions import (ORDER_FLAT_IM_DESC, ORDER_FLAT_JSON_DESC)
r = PipelineProducts("order flat")
r.add(ORDER_FLAT_IM_DESC, PipelineImage([], flat_im))
r.add(
ORDER_FLAT_JSON_DESC,
PipelineDict(orders=orders,
fitted_responses=fitted_responses,
i1i2_list=i1i2_list,
mean_order_specs=mean_order_specs))
return r