# calculate the parameter moving average centers
moving_avg_centers, moving_width = lbcc.moving_averages(calc_query_time_min, calc_query_time_max, weekday,
number_of_days)
# calculate image cadence centers
range_min_date = moving_avg_centers[0] - moving_width/2
range_max_date = moving_avg_centers[-1] + moving_width/2
image_centers = synch_utils.get_dates(
time_min=range_min_date.astype(datetime.datetime),
time_max=range_max_date.astype(datetime.datetime), map_freq=image_freq)
# query histograms
ref_hist_pd = db_funcs.query_hist(db_session=db_session, meth_id=method_id[1],
n_intensity_bins=n_intensity_bins, lat_band=lat_band,
time_min=calc_query_time_min - datetime.timedelta(days=number_of_days),
time_max=calc_query_time_max + datetime.timedelta(days=number_of_days),
instrument=ref_instrument, wavelength=wavelengths)
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(ref_hist_pd.date_obs, image_centers, image_del)
ref_hist_pd = ref_hist_pd.iloc[keep_ind]
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, ref_full_hist = psi_d_types.binary_to_hist(hist_binary=ref_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
# determine date of first AIA image
min_ref_time = db_session.query(func.min(db_class.EUV_Images.date_obs)).filter(
db_class.EUV_Images.instrument == ref_inst
).all()
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# method information
meth_name = "IIT"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
# query for IIT histograms
pd_lbc_hist = db_funcs.query_hist(db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=hist_query_time_min,
time_max=hist_query_time_max)
pd_lbc_hist_srt = pd_lbc_hist.sort_values(by=['image_id'])
# convert the binary types back to arrays
mu_bin_edges, intensity_bin_edges, full_lbc_hist = psi_d_types.binary_to_hist(
pd_lbc_hist_srt, n_mu_bins=None, n_intensity_bins=n_intensity_bins)
# create corrected/original histograms
original_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
corrected_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
for inst_index, instrument in enumerate(inst_list):
print("Applying corrections for", instrument)
#### QUERY IMAGES ####
query_instrument = [
instrument,
]
# get method id
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = get_method_id(db_session, meth_name, meth_desc, var_names=None, var_descs=None, create=False)
for inst_index, instrument in enumerate(inst_list):
print("\nStarting calculations for " + instrument + "\n")
# download all histograms needed for this instrument
range_min_date = np.datetime64(query_time_min) - moving_width/2
range_max_date = np.datetime64(query_time_max) + moving_width/2
# query the histograms
query_instrument = [instrument, ]
pd_hist_all = query_hist(db_session=db_session, meth_id=method_id[1], n_mu_bins=n_mu_bins,
n_intensity_bins=n_intensity_bins, lat_band=lat_band,
time_min=np.datetime64(range_min_date).astype(datetime.datetime),
time_max=np.datetime64(range_max_date).astype(datetime.datetime),
instrument=query_instrument, wavelength=wavelengths)
# check if instrument has any histograms for this timerange
if pd_hist_all.shape[0] == 0:
print("No histograms for", instrument, "in this time range. Skipping")
continue
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(pd_hist.date_obs, image_centers, image_del)
pd_hist = pd_hist.iloc[keep_ind]
# convert the binary types back to arrays
mu_bin_array, intensity_bin_array, full_hist = psi_d_types.binary_to_hist(pd_hist, n_mu_bins,
n_intensity_bins)
# create LBC method
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = get_method_id(db_session, meth_name, meth_desc, var_names=None, var_descs=None, create=True)
# loop over instrument
for instrument in inst_list:
# query EUV images
query_instrument = [instrument, ]
query_pd_all = query_euv_images(db_session=db_session, time_min=hist_query_time_min,
time_max=hist_query_time_max, instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = query_hist(db_session, meth_id=method_id[1], n_mu_bins=n_mu_bins, n_intensity_bins=n_intensity_bins,
lat_band=lat_band, time_min=hist_query_time_min, time_max=hist_query_time_max,
instrument=query_instrument, wavelength=wavelengths)
# compare image results to hist results based on image_id
in_index = query_pd_all.data_id.isin(hist_pd.image_id)
# return only images that do not have corresponding histograms
query_pd = query_pd_all[~in_index]
# check that images remain that need histograms
if query_pd.shape[0] == 0:
print("All" + instrument + " images in timeframe already have associated histograms.")
continue
for index, row in query_pd.iterrows():
print("Processing image number", row.data_id, ".")
var_names=None,
var_descs=None,
create=False)
# HISTOGRAM PARAMETERS TO UPDATE
n_mu_bins = 18 # number of mu bins
n_intensity_bins = 200 # number of intensity bins
lat_band = [-np.pi / 64., np.pi / 64.]
query_instrument = [
"AIA",
]
sqlite_hists = db_funcs.query_hist(sqlite_session,
meth_id=method_id[1],
n_mu_bins=n_mu_bins,
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=query_time_min,
time_max=query_time_max,
instrument=query_instrument)
mu_bin_array, intensity_bin_array, sq_full_hist = psi_dtypes.binary_to_hist(
sqlite_hists, n_mu_bins, n_intensity_bins)
mysql_hists = db_funcs.query_hist(mysql_session,
meth_id=method_id[1],
n_mu_bins=n_mu_bins,
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=query_time_min,
time_max=query_time_max,
instrument=query_instrument)
mu_bin_array, intensity_bin_array, my_full_hist = psi_dtypes.binary_to_hist(
image_row=row,
n_intensity_bins=n_intensity_bins,
R0=R0)
# calculate IIT histogram from LBC
hist = psi_d_types.LBCCImage.iit_hist(lbcc_image, lat_band, log10)
# create IIT histogram datatype
iit_hist = psi_d_types.create_iit_hist(lbcc_image, method_id[1], lat_band,
hist)
# Now query database for histogram to compare
db_hist = db_funcs.query_hist(
db_session,
iit_hist.meth_id,
n_mu_bins=iit_hist.n_mu_bins,
n_intensity_bins=iit_hist.n_intensity_bins,
lat_band=lat_band,
time_min=row.date_obs.to_pydatetime(),
time_max=row.date_obs.to_pydatetime(),
)
# convert binary to histogram data
mu_bin_edges_db, intensity_bin_edges_db, db_full_hist = psi_d_types.binary_to_hist(
hist_binary=db_hist, n_mu_bins=None, n_intensity_bins=n_intensity_bins)
# plot newly calc'd IIT-hist vs DB-IIT-hist
plt.plot(intensity_bin_edges_db[1:], db_full_hist, 'r', label="From DB")
plt.plot(intensity_bin_edges_db[1:], hist, 'b', label="New Calc")
ax = plt.gca()
ax.legend(loc='upper right', title="")
'image_id', 'date_obs', 'hist_mean',
'hist_sum', 'hist_median', 'c70_width',
'z_score'
])
# loop over years
for year in range(min_year, max_year + 1):
print("Starting on year: ", year)
year_min_time = datetime.datetime(year, 1, 1, 0, 0, 0)
year_max_time = datetime.datetime(year + 1, 1, 1, 0, 0, 0)
pd_hist = db_funs.query_hist(db_session=db_session,
meth_id=method_id[1],
n_mu_bins=n_mu_bins,
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=year_min_time,
time_max=year_max_time,
instrument=[
instrument,
],
wavelength=wavelengths)
# check for no data
if pd_hist.shape[0] == 0:
# skip this year
continue
# convert the binary types back to arrays
mu_bin_array, intensity_bin_array, full_hist = psi_d_types.binary_to_hist(
pd_hist, n_mu_bins, n_intensity_bins)
# end_time = time.time()
# array_size = full_hist.size * full_hist.itemsize
mu_bin_edges = np.linspace(0.1, 1.0, n_mu_bins + 1, dtype='float')
intensity_bin_edges = np.linspace(0,
5,
num=n_intensity_bins + 1,
dtype='float')
### PLOT HISTOGRAMS ###
# query histograms
for instrument in inst_list:
query_instrument = [
instrument,
]
pd_hist = query_hist(db_session=db_session,
meth_id=method_id[1],
n_mu_bins=n_mu_bins,
n_intensity_bins=n_intensity_bins,
lat_band=np.array(lat_band).tobytes(),
time_min=hist_plot_query_time_min,
time_max=hist_plot_query_time_max,
instrument=query_instrument)
# convert from binary to usable histogram type
mu_bin_array, intensity_bin_array, full_hist = psi_d_types.binary_to_hist(
pd_hist, n_mu_bins, n_intensity_bins)
# query correct image combos
combo_query = query_inst_combo(db_session, hist_plot_query_time_min,
hist_plot_query_time_max, meth_name,
instrument)
#### PLOT ORIGINAL HISTOGRAMS ####
for plot_index in range(n_hist_plots):
# definitions
plot_hist = full_hist[:, :, plot_index]
date_obs = pd_hist.date_obs[plot_index]
plot_alpha = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_x = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_mean = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_std = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_act_mean = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_mean_act = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
for inst_index, instrument in enumerate(inst_list):
print("Interpolating IIT parameters for " + instrument + ".")
# get all instrument histograms
inst_hist_pd = db_funs.query_hist(
db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=query_time_min - datetime.timedelta(days=number_of_days),
time_max=query_time_max + datetime.timedelta(days=number_of_days),
instrument=[
instrument,
])
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, inst_full_hist = psi_d_types.binary_to_hist(
hist_binary=inst_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
intensity_bin_centers = (intensity_bin_edges[1:] +
intensity_bin_edges[0:-1]) / 2
for date_index, center_date in enumerate(moving_avg_centers):
date_str = str(center_date)
datetime_date = datetime.datetime.strptime(date_str,
# ------------ NO NEED TO UPDATE ANYTHING BELOW ------------- #
# query images
query_pd = db_funs.query_euv_images(db_session, time_min=query_time_min,
time_max=query_time_max, instrument=inst_list,
wavelength=wavelengths)
# get method id
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = db_funs.get_method_id(db_session, meth_name, meth_desc,
var_names=None, var_descs=None, create=False)
# query LBC histograms
hist_pd = db_funs.query_hist(db_session, meth_id=method_id[1],
n_mu_bins=n_mu_bins, n_intensity_bins=n_intensity_bins,
time_min=query_time_min,
time_max=query_time_max, instrument=inst_list,
wavelength=wavelengths)
# convert the binary types back to arrays
mu_bin_array, intensity_bin_array, full_hist = psi_d_types.binary_to_hist(
hist_pd, n_mu_bins, n_intensity_bins)
n_images = query_pd.shape[0]
int_bin_centers = (intensity_bin_array[0:-1] + intensity_bin_array[1:])/2
for im_num, row in query_pd.iterrows():
full_path = os.path.join(hdf_data_dir, row.fname_hdf)
print("Plotting", row.instrument, im_num+1, "of", n_images, "-",
row.date_obs)
bad_im = psi_d_types.read_los_image(full_path)
EasyPlot.PlotImage(bad_im, nfig=0)
plt.waitforbuttonpress()
def plot_iit_histograms(db_session,
hdf_data_dir,
hist_query_time_min,
hist_query_time_max,
inst_list,
ref_inst,
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
R0=1.01,
log10=True):
# start time
start_time = time.time()
#### GET REFERENCE INFO FOR LATER USE ####
# get index number of reference instrument
ref_index = inst_list.index(ref_inst)
# query euv images to get carrington rotation range
ref_instrument = [
ref_inst,
]
euv_images = db_funcs.query_euv_images(db_session,
time_min=hist_query_time_min,
time_max=hist_query_time_max,
instrument=ref_instrument)
# get min and max carrington rotation
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# method information
meth_name = "IIT"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
# query for IIT histograms
pd_lbc_hist = db_funcs.query_hist(db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=hist_query_time_min,
time_max=hist_query_time_max)
pd_lbc_hist_srt = pd_lbc_hist.sort_values(by=['image_id'])
# convert the binary types back to arrays
mu_bin_edges, intensity_bin_edges, full_lbc_hist = psi_d_types.binary_to_hist(
pd_lbc_hist_srt, n_mu_bins=None, n_intensity_bins=n_intensity_bins)
# create corrected/original histograms
original_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
corrected_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
for inst_index, instrument in enumerate(inst_list):
print("Applying corrections for", instrument)
#### QUERY IMAGES ####
query_instrument = [
instrument,
]
rot_images = db_funcs.query_euv_images_rot(db_session,
rot_min=rot_min,
rot_max=rot_max,
instrument=query_instrument)
image_pd = rot_images.sort_values(by=['cr_rot'])
# get time minimum and maximum for instrument
inst_time_min = rot_images.date_obs.min()
inst_time_max = rot_images.date_obs.max()
# query correct image combos
lbc_meth_name = "LBCC"
combo_query_lbc = db_funcs.query_inst_combo(db_session, inst_time_min,
inst_time_max,
lbc_meth_name, instrument)
iit_meth_name = "IIT"
combo_query_iit = db_funcs.query_inst_combo(db_session, inst_time_min,
inst_time_max,
iit_meth_name, instrument)
# query correct image combos
combo_query_lbc = db_funcs.query_inst_combo(db_session,
hist_query_time_min,
hist_query_time_max,
meth_name="LBCC",
instrument=instrument)
# query correct image combos
combo_query_iit = db_funcs.query_inst_combo(db_session,
hist_query_time_min,
hist_query_time_max,
meth_name="IIT",
instrument=instrument)
for index, row in image_pd.iterrows():
# apply LBC
original_los, lbcc_image, mu_indices, use_indices, theoretic_query = lbcc_funcs.apply_lbc(
db_session,
hdf_data_dir,
combo_query_lbc,
image_row=row,
n_intensity_bins=n_intensity_bins,
R0=R0)
#### ORIGINAL LOS DATA ####
# calculate IIT histogram from original data
original_los_hist = psi_d_types.LosImage.iit_hist(
original_los, intensity_bin_edges, lat_band, log10)
# add 1D histogram to array
original_hist_list[:, index] = original_los_hist
#### CORRECTED DATA ####
# apply IIT correction
lbcc_image, iit_image, use_indices, alpha, x = iit_funcs.apply_iit(
db_session,
combo_query_iit,
lbcc_image,
use_indices,
original_los,
R0=R0)
#### CREATE CORRECTED IIT HISTOGRAM #####
# calculate IIT histogram from LBC
hist_iit = psi_d_types.IITImage.iit_hist(iit_image, lat_band,
log10)
# create IIT histogram datatype
corrected_hist = psi_d_types.create_iit_hist(
iit_image, method_id[1], lat_band, hist_iit)
corrected_hist_list[:, index] = corrected_hist.hist
# plotting definitions
color_list = ['red', 'blue', 'black']
linestyle_list = ['solid', 'dashed', 'dashdot']
#### CREATE NEW HISTOGRAM ####
for inst_index, instrument in enumerate(inst_list):
print("Plotting Histograms for", instrument)
#### GET INDICES TO USE ####
# get index of instrument in histogram dataframe
hist_inst = pd_lbc_hist_srt['instrument']
pd_inst_index = hist_inst[hist_inst == instrument].index
#### ORIGINAL HISTOGRAM #####
# define histogram
original_hist = original_hist_list[:, pd_inst_index].sum(axis=1)
# normalize histogram
row_sums = original_hist.sum(axis=0, keepdims=True)
norm_original_hist = original_hist / row_sums
# plot original
Plotting.Plot1d_Hist(norm_original_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=100,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Original LOS Data")
#### LBCC HISTOGRAM #####
# define histogram
lbc_hist = full_lbc_hist[:, pd_inst_index].sum(axis=1)
# normalize histogram
lbc_sums = lbc_hist.sum(axis=0, keepdims=True)
norm_lbc_hist = lbc_hist / lbc_sums
# plot lbcc
Plotting.Plot1d_Hist(norm_lbc_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=200,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Post LBCC")
#### CORRECTED HISTOGRAM ####
# define histogram
corrected_hist = corrected_hist_list[:, pd_inst_index].sum(axis=1)
# normalize histogram
iit_sums = corrected_hist.sum(axis=0, keepdims=True)
norm_corrected_hist = corrected_hist / iit_sums
# plot corrected
Plotting.Plot1d_Hist(norm_corrected_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=300,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Post IIT")
# end time
end_time = time.time()
print("ITT has been applied and original/resulting histograms plotted.")
print("Total elapsed time to apply correction and plot histograms: " +
str(round(end_time - start_time, 3)) + " seconds.")
return None
def create_histograms(db_session,
inst_list,
lbc_query_time_min,
lbc_query_time_max,
hdf_data_dir,
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
log10=True,
R0=1.01,
wavelengths=None):
"""
create and save (to database) IIT-Histograms from LBC Data
@param db_session: connected db session for querying EUV images and saving histograms
@param inst_list: list of instruments
@param lbc_query_time_min: minimum query time for applying lbc fit
@param lbc_query_time_max: maximum query time for applying lbc fit
@param hdf_data_dir: directory of processed images to plot original images
@param n_intensity_bins: number of intensity bins
@param lat_band: latitude band
@param log10: boolean value
@param R0: radius
@return: None, saves histograms to database
"""
# start time
start_time = time.time()
# create IIT method
meth_name = "IIT"
meth_desc = "IIT Fit Method"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc,
var_names=None,
var_descs=None,
create=True)
for instrument in inst_list:
print("Beginning loop for instrument:", instrument)
# query EUV images
query_instrument = [
instrument,
]
image_pd_all = db_funcs.query_euv_images(db_session=db_session,
time_min=lbc_query_time_min,
time_max=lbc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = db_funcs.query_hist(db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=lbc_query_time_min,
time_max=lbc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
if hist_pd.shape[0] == 0:
# use all images in range
in_index = pd.Series([False] * image_pd_all.shape[0])
else:
# compare image results to hist results based on image_id
in_index = image_pd_all.image_id.isin(hist_pd.image_id)
# return only images that do not have corresponding histograms
image_pd = image_pd_all[~in_index]
# check that images remain that need histograms
if image_pd.shape[0] == 0:
print("All " + instrument +
" images in timeframe already have associated histograms.")
continue
# apply LBC
for index, row in image_pd.iterrows():
print("Calculating IIT histogram at time:", row.date_obs)
original_los, lbcc_image, mu_indices, use_indices, theoretic_query = lbcc_funcs.apply_lbc_2(
db_session,
hdf_data_dir,
image_row=row,
n_intensity_bins=n_intensity_bins,
R0=R0)
# check that image load and LBCC application finished successfully
if original_los is None:
continue
# calculate IIT histogram from LBC
hist = psi_d_types.LBCCImage.iit_hist(lbcc_image, lat_band, log10)
# create IIT histogram datatype
iit_hist = psi_d_types.create_iit_hist(lbcc_image, method_id[1],
lat_band, hist)
# add IIT histogram and meta data to database
db_funcs.add_hist(db_session, iit_hist)
end_time = time.time()
print(
"Inter-instrument transformation histograms have been created and saved to the database."
)
print("Total elapsed time for histogram creation: " +
str(round(end_time - start_time, 3)) + " seconds.")
return None
def calc_iit_coefficients(db_session,
inst_list,
ref_inst,
calc_query_time_min,
calc_query_time_max,
weekday=0,
number_of_days=180,
image_freq=2,
image_del=np.timedelta64(30, 'm'),
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
create=False,
wavelengths=None):
# start time
start_time = time.time()
# create IIT method
meth_name = "IIT"
meth_desc = "IIT Fit Method"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc,
var_names=None,
var_descs=None,
create=False)
#### GET REFERENCE INFO FOR LATER USE ####
# get index number of reference instrument
ref_index = inst_list.index(ref_inst)
# query euv images to get carrington rotation range
ref_instrument = [
ref_inst,
]
euv_images = db_funcs.query_euv_images(db_session,
time_min=calc_query_time_min,
time_max=calc_query_time_max,
instrument=ref_instrument,
wavelength=wavelengths)
# get min and max carrington rotation
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# calculate the moving average centers
ref_moving_avg_centers, moving_width = lbcc.moving_averages(
calc_query_time_min, calc_query_time_max, weekday, number_of_days)
# calculate image cadence centers
range_min_date = ref_moving_avg_centers[0] - moving_width / 2
range_max_date = ref_moving_avg_centers[-1] + moving_width / 2
image_centers = synch_utils.get_dates(
time_min=range_min_date.astype(datetime.datetime),
time_max=range_max_date.astype(datetime.datetime),
map_freq=image_freq)
# query histograms
ref_hist_pd = db_funcs.query_hist(
db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=calc_query_time_min - datetime.timedelta(days=number_of_days),
time_max=calc_query_time_max + datetime.timedelta(days=number_of_days),
instrument=ref_instrument,
wavelength=wavelengths)
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(ref_hist_pd.date_obs, image_centers,
image_del)
ref_hist_pd = ref_hist_pd.iloc[keep_ind]
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, ref_full_hist = psi_d_types.binary_to_hist(
hist_binary=ref_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
# determine date of first AIA image
min_ref_time = db_session.query(func.min(
db_class.EUV_Images.date_obs)).filter(
db_class.EUV_Images.instrument == ref_inst).all()
base_ref_min = min_ref_time[0][0]
base_ref_center = base_ref_min + datetime.timedelta(
days=number_of_days) / 2
base_ref_max = base_ref_center + datetime.timedelta(
days=number_of_days) / 2
if (calc_query_time_min - datetime.timedelta(days=7)) < base_ref_center:
# generate histogram for first year of reference instrument
ref_base_hist = ref_full_hist[:, (
ref_hist_pd['date_obs'] >= str(base_ref_min)) & (
ref_hist_pd['date_obs'] <= str(base_ref_max))]
else:
ref_base_hist = None
for inst_index, instrument in enumerate(inst_list):
# check if this is the reference instrument
if inst_index == ref_index:
# loop through moving average centers
for date_index, center_date in enumerate(ref_moving_avg_centers):
print("Starting calculations for", instrument, ":",
center_date)
if center_date > ref_hist_pd.date_obs.max(
) or center_date < ref_hist_pd.date_obs.min():
print("Date is out of instrument range, skipping.")
continue
# determine time range based off moving average centers
min_date = center_date - moving_width / 2
max_date = center_date + moving_width / 2
# get the correct date range to use for image combos
ref_pd_use = ref_hist_pd[
(ref_hist_pd['date_obs'] >= str(min_date))
& (ref_hist_pd['date_obs'] <= str(max_date))]
# save alpha/x as [1, 0] for reference instrument
alpha = 1
x = 0
db_funcs.store_iit_values(db_session, ref_pd_use, meth_name,
meth_desc, [alpha, x], create)
else:
# query euv_images for correct carrington rotation
query_instrument = [
instrument,
]
rot_images = db_funcs.query_euv_images_rot(
db_session,
rot_min=rot_min,
rot_max=rot_max,
instrument=query_instrument,
wavelength=wavelengths)
if rot_images.shape[0] == 0:
print("No images in timeframe for ", instrument, ". Skipping")
continue
# get time minimum and maximum for instrument
inst_time_min = rot_images.date_obs.min()
inst_time_max = rot_images.date_obs.max()
# if Stereo A or B has images before AIA, calc IIT for those weeks
if inst_time_min > calc_query_time_min:
all_images = db_funcs.query_euv_images(
db_session,
time_min=calc_query_time_min,
time_max=calc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
if all_images.date_obs.min() < inst_time_min:
inst_time_min = all_images.date_obs.min()
moving_avg_centers, moving_width = lbcc.moving_averages(
inst_time_min, inst_time_max, weekday, number_of_days)
# calculate image cadence centers
range_min_date = moving_avg_centers[0] - moving_width / 2
range_max_date = moving_avg_centers[-1] + moving_width / 2
image_centers = synch_utils.get_dates(
time_min=range_min_date.astype(datetime.datetime),
time_max=range_max_date.astype(datetime.datetime),
map_freq=image_freq)
inst_hist_pd = db_funcs.query_hist(
db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=inst_time_min -
datetime.timedelta(days=number_of_days),
time_max=inst_time_max +
datetime.timedelta(days=number_of_days),
instrument=query_instrument,
wavelength=wavelengths)
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(inst_hist_pd.date_obs,
image_centers, image_del)
inst_hist_pd = inst_hist_pd.iloc[keep_ind]
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, inst_full_hist = psi_d_types.binary_to_hist(
hist_binary=inst_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
# loops through moving average centers
for date_index, center_date in enumerate(moving_avg_centers):
print("Starting calculations for", instrument, ":",
center_date)
if center_date > inst_hist_pd.date_obs.max(
) or center_date < inst_hist_pd.date_obs.min():
print("Date is out of instrument range, skipping.")
continue
# determine time range based off moving average centers
min_date = center_date - moving_width / 2
max_date = center_date + moving_width / 2
# get proper time-range of reference histograms
if center_date <= base_ref_center:
# if date is earlier than reference (AIA) first year, use reference (AIA) first year
ref_hist_use = ref_base_hist
else:
# get indices for calculation of reference histogram
ref_hist_ind = (ref_hist_pd['date_obs'] >= str(
min_date)) & (ref_hist_pd['date_obs'] <= str(max_date))
ref_hist_use = ref_full_hist[:, ref_hist_ind]
# get the correct date range to use for the instrument histogram
inst_hist_ind = (inst_hist_pd['date_obs'] >= str(min_date)) & (
inst_hist_pd['date_obs'] <= str(max_date))
inst_pd_use = inst_hist_pd[inst_hist_ind]
# get indices and histogram for calculation
inst_hist_use = inst_full_hist[:, inst_hist_ind]
# sum histograms
hist_fit = inst_hist_use.sum(axis=1)
hist_ref = ref_hist_use.sum(axis=1)
# normalize fit histogram
fit_sum = hist_fit.sum()
norm_hist_fit = hist_fit / fit_sum
# normalize reference histogram
ref_sum = hist_ref.sum()
norm_hist_ref = hist_ref / ref_sum
# get reference/fit peaks
ref_peak_index = np.argmax(
norm_hist_ref) # index of max value of hist_ref
ref_peak_val = norm_hist_ref[
ref_peak_index] # max value of hist_ref
fit_peak_index = np.argmax(
norm_hist_fit) # index of max value of hist_fit
fit_peak_val = norm_hist_fit[
fit_peak_index] # max value of hist_fit
# estimate correction coefficients that match fit_peak to ref_peak
alpha_est = fit_peak_val / ref_peak_val
x_est = intensity_bin_edges[
ref_peak_index] - alpha_est * intensity_bin_edges[
fit_peak_index]
init_pars = np.asarray([alpha_est, x_est], dtype=np.float64)
# calculate alpha and x
alpha_x_parameters = iit.optim_iit_linear(norm_hist_ref,
norm_hist_fit,
intensity_bin_edges,
init_pars=init_pars)
# save alpha and x to database
db_funcs.store_iit_values(db_session, inst_pd_use, meth_name,
meth_desc, alpha_x_parameters.x,
create)
end_time = time.time()
tot_time = end_time - start_time
time_tot = str(datetime.timedelta(minutes=tot_time))
print(
"Inter-instrument transformation fit parameters have been calculated and saved to the database."
)
print("Total elapsed time for IIT fit parameter calculation: " + time_tot)
return None