def test_filter_table(build_table, operator):
"""test table filter function with fake table"""
# Test wavelength sort
TEST_DATA = filter_table(build_table, wavelength=operator)
assert all(TEST_DATA['wavelength'] == eval(
"build_table[np.where(build_table['wavelength'] {})]['wavelength']".
format(operator)))
def limb_darkening():
"""The limb darkening form page. """
# Load default form
form = fv.LimbDarkeningForm()
# Reload page with stellar data from ExoMAST
if form.resolve_submit.data:
if form.targname.data.strip() != '':
try:
# Resolve the target in exoMAST
form.targname.data = get_canonical_name(form.targname.data)
data, target_url = get_target_data(form.targname.data)
# Update the form data
form.feh.data = data.get('Fe/H')
form.teff.data = data.get('Teff')
form.logg.data = data.get('stellar_gravity')
form.target_url.data = str(target_url)
except:
form.target_url.data = ''
form.targname.errors = [
"Sorry, could not resolve '{}' in exoMAST.".format(
form.targname.data)
]
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Reload page with appropriate filter data
if form.filter_submit.data:
kwargs = {}
if form.bandpass.data == 'tophat':
kwargs['n_bins'] = 1
kwargs['pixels_per_bin'] = 100
kwargs['wave_min'] = 1 * u.um
kwargs['wave_max'] = 2 * u.um
# Get the filter
bandpass = svo.Filter(form.bandpass.data, **kwargs)
# Update the form data
form.wave_min.data = bandpass.wave_min.value
form.wave_max.data = bandpass.wave_max.value
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Update validation values after a model grid is selected
if form.modelgrid_submit.data:
# Load the modelgrid
mg = ModelGrid(form.modeldir.data, resolution=500)
teff_rng = mg.Teff_vals.min(), mg.Teff_vals.max()
logg_rng = mg.logg_vals.min(), mg.logg_vals.max()
feh_rng = mg.FeH_vals.min(), mg.FeH_vals.max()
# Update the validation parameters by setting validator attributes
setattr(form.teff.validators[1], 'min', teff_rng[0])
setattr(form.teff.validators[1], 'max', teff_rng[1])
setattr(
form.teff.validators[1], 'message',
'Effective temperature must be between {} and {}'.format(
*teff_rng))
setattr(form.logg.validators[1], 'min', logg_rng[0])
setattr(form.logg.validators[1], 'max', logg_rng[1])
setattr(form.logg.validators[1], 'message',
'Surface gravity must be between {} and {}'.format(*logg_rng))
setattr(form.feh.validators[1], 'min', feh_rng[0])
setattr(form.feh.validators[1], 'max', feh_rng[1])
setattr(form.feh.validators[1], 'message',
'Metallicity must be between {} and {}'.format(*feh_rng))
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Validate form and submit for results
if form.validate_on_submit() and form.calculate_submit.data:
# Get the stellar parameters
star_params = [
float(form.teff.data),
float(form.logg.data),
float(form.feh.data)
]
# Log the form inputs
try:
log_exoctk.log_form_input(request.form, 'limb_darkening', DB)
except:
pass
# Load the model grid
model_grid = ModelGrid(form.modeldir.data, resolution=500)
form.modeldir.data = [
j for i, j in form.modeldir.choices if i == form.modeldir.data
][0]
# Grism details
if '.G' in form.bandpass.data.upper(
) and 'GAIA' not in form.bandpass.data.upper():
kwargs = {
'n_bins': form.n_bins.data,
'pixels_per_bin': form.n_pix.data,
'wl_min': form.wave_min.data * u.um,
'wl_max': form.wave_max.data * u.um
}
else:
kwargs = {}
# Make filter object and plot
bandpass = svo.Filter(form.bandpass.data, **kwargs)
bp_name = bandpass.name
bk_plot = bandpass.plot(draw=False)
bk_plot.plot_width = 580
bk_plot.plot_height = 280
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
filt_script, filt_plot = components(bk_plot)
# Trim the grid to nearby grid points to speed up calculation
full_rng = [
model_grid.Teff_vals, model_grid.logg_vals, model_grid.FeH_vals
]
trim_rng = find_closest(full_rng, star_params, n=1, values=True)
# Calculate the coefficients for each profile
ld = lf.LDC(model_grid)
for prof in form.profiles.data:
ld.calculate(*star_params,
prof,
mu_min=float(form.mu_min.data),
bandpass=bandpass)
# Draw a figure for each wavelength bin
tabs = []
for wav in np.unique(ld.results['wave_eff']):
# Plot it
TOOLS = 'box_zoom, box_select, crosshair, reset, hover'
fig = figure(tools=TOOLS,
x_range=Range1d(0, 1),
y_range=Range1d(0, 1),
plot_width=800,
plot_height=400)
ld.plot(wave_eff=wav, fig=fig)
# Plot formatting
fig.legend.location = 'bottom_right'
fig.xaxis.axis_label = 'mu'
fig.yaxis.axis_label = 'Intensity'
tabs.append(Panel(child=fig, title=str(wav)))
final = Tabs(tabs=tabs)
# Get HTML
script, div = components(final)
# Store the tables as a string
file_as_string = str(ld.results[[
c for c in ld.results.dtype.names if ld.results.dtype[c] != object
]])
# Make a table for each profile with a row for each wavelength bin
profile_tables = []
for profile in form.profiles.data:
# Make LaTeX for polynomials
latex = lf.ld_profile(profile, latex=True)
poly = '\({}\)'.format(latex).replace('*',
'\cdot').replace('\e', 'e')
# Make the table into LaTeX
table = filter_table(ld.results, profile=profile)
co_cols = [
c for c in ld.results.colnames
if (c.startswith('c') or c.startswith('e')) and len(c) == 2
and not np.all([np.isnan(i) for i in table[c]])
]
table = table[['wave_min', 'wave_max'] + co_cols]
table.rename_column('wave_min',
'\(\lambda_\mbox{min}\hspace{5px}(\mu m)\)')
table.rename_column('wave_max',
'\(\lambda_\mbox{max}\hspace{5px}(\mu m)\)')
# Add the results to the lists
html_table = '\n'.join(table.pformat(max_width=500, html=True))\
.replace('<table', '<table id="myTable" class="table table-striped table-hover"')
# Add the table title
header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(
profile, poly)
html_table = header + html_table
profile_tables.append(html_table)
return render_template('limb_darkening_results.html',
form=form,
table=profile_tables,
script=script,
plot=div,
file_as_string=repr(file_as_string),
filt_plot=filt_plot,
filt_script=filt_script,
js=js_resources,
css=css_resources)
return render_template('limb_darkening.html', form=form)
def groups_integrations():
"""The groups and integrations calculator form page"""
# Print out pandeia sat values
with open(
resource_filename(
'exoctk',
'data/groups_integrations/groups_integrations_input_data.json')
) as f:
sat_data = json.load(f)['fullwell']
# Load default form
form = fv.GroupsIntsForm()
if request.method == 'GET':
# http://0.0.0.0:5000/groups_integrations?k_mag=8.131&transit_duration=0.09089&target=WASP-18+b
target_name = request.args.get('target')
form.targname.data = target_name
k_mag = request.args.get('k_mag')
form.kmag.data = k_mag
# According to Kevin the obs_dur = 3*trans_dur+1 hours
# transit_dur is in days from exomast, convert first.
try:
trans_dur = float(request.args.get('transit_duration'))
trans_dur *= u.day.to(u.hour)
obs_dur = 3 * trans_dur + 1
form.obs_duration.data = obs_dur
except TypeError:
trans_dur = request.args.get('transit_duration')
if trans_dur == None:
pass
else:
err = 'The Transit Duration from ExoMAST experienced some issues. Try a different spelling or source.'
return render_template('groups_integrations_error.html',
err=err)
return render_template('groups_integrations.html',
form=form,
sat_data=sat_data)
# Reload page with stellar data from ExoMAST
if form.resolve_submit.data:
if form.targname.data.strip() != '':
# Resolve the target in exoMAST
try:
form.targname.data = get_canonical_name(form.targname.data)
data, url = get_target_data(form.targname.data)
# Update the Kmag
kmag = data.get('Kmag')
# Transit duration in exomast is in days, need it in hours
if form.time_unit.data == 'day':
trans_dur = data.get('transit_duration')
obs_dur = 3 * trans_dur + (1 / 24.)
else:
trans_dur = data.get('transit_duration')
trans_dur *= u.Unit('day').to('hour')
obs_dur = 3 * trans_dur + 1
# Model guess
logg_targ = data.get('stellar_gravity') or 4.5
teff_targ = data.get('Teff') or 5500
arr = np.array([tuple(i[1].split()) for i in form.mod.choices],
dtype=[('spt', 'O'), ('teff', '>f4'),
('logg', '>f4')])
mod_table = at.Table(arr)
# If high logg, remove giants from guess list
if logg_targ < 4:
mod_table = filter_table(mod_table, logg=">=4")
teff = min(arr['teff'], key=lambda x: abs(x - teff_targ))
mod_table.add_column(
at.Column(np.array([i[0] for i in form.mod.choices]),
name='value'))
mod_table = filter_table(mod_table, teff="<={}".format(teff))
mod_table.sort(['teff', 'logg'])
# Set the form values
form.mod.data = mod_table[-1]['value']
form.kmag.data = kmag
form.obs_duration.data = obs_dur
form.target_url.data = url
except:
form.target_url.data = ''
form.targname.errors = [
"Sorry, could not resolve '{}' in exoMAST.".format(
form.targname.data)
]
# Send it back to the main page
return render_template('groups_integrations.html',
form=form,
sat_data=sat_data)
if form.validate_on_submit() and form.calculate_submit.data:
# Get the form data
ins = form.ins.data
params = {
'ins':
ins,
'mag':
form.kmag.data,
'obs_time':
form.obs_duration.data,
'sat_max':
form.sat_max.data,
'sat_mode':
form.sat_mode.data,
'time_unit':
form.time_unit.data,
'band':
'K',
'mod':
form.mod.data,
'filt'.format(ins):
getattr(form, '{}_filt'.format(ins)).data,
'subarray'.format(ins):
getattr(form, '{}_subarray'.format(ins)).data,
'filt_ta'.format(ins):
getattr(form, '{}_filt_ta'.format(ins)).data,
'subarray_ta'.format(ins):
getattr(form, '{}_subarray_ta'.format(ins)).data
}
# Get ngroups
params['n_group'] = 'optimize' if form.n_group.data == 0 else int(
form.n_group.data)
# Also get the data path in there
params['infile'] = resource_filename(
'exoctk',
'data/groups_integrations/groups_integrations_input_data.json')
# Convert the obs_time to hours
if params['time_unit'] == 'day':
params['obs_time'] = params['obs_time'] * 24
params['time_unit'] = 'hours'
# Run the calculation
results = perform_calculation(params)
if type(results) == dict:
results_dict = results
one_group_error = ""
zero_group_error = ""
if results_dict['n_group'] == 1:
one_group_error = 'Be careful! This only predicts one group, and you may be in danger of oversaturating!'
if results_dict['max_ta_groups'] == 0:
zero_group_error = 'Be careful! This oversaturated the TA in the minimum groups. Consider a different TA setup.'
if results_dict['max_ta_groups'] == -1:
zero_group_error = 'This object is too faint to reach the required TA SNR in this filter. Consider a different TA setup.'
results_dict['min_sat_ta'] = 0
results_dict['t_duration_ta_max'] = 0
results_dict['max_sat_ta'] = 0
results_dict['t_duration_ta_max'] = 0
if results_dict['max_sat_prediction'] > results_dict['sat_max']:
one_group_error = 'This many groups will oversaturate the detector! Proceed with caution!'
# Do some formatting for a prettier end product
results_dict['filt'] = results_dict['filt'].upper()
results_dict['filt_ta'] = results_dict['filt_ta'].upper()
results_dict['band'] = results_dict['band'].upper()
results_dict['mod'] = results_dict['mod'].upper()
if results_dict['ins'] == 'niriss':
if results_dict['subarray_ta'] == 'nrm':
results_dict['subarray_ta'] = 'SUBTASOSS -- BRIGHT'
else:
results_dict['subarray_ta'] = 'SUBTASOSS -- FAINT'
results_dict['subarray'] = results_dict['subarray'].upper()
results_dict['subarray_ta'] = results_dict['subarray_ta'].upper()
form_dict = {
'miri': 'MIRI',
'nircam': 'NIRCam',
'nirspec': 'NIRSpec',
'niriss': 'NIRISS'
}
results_dict['ins'] = form_dict[results_dict['ins']]
return render_template('groups_integrations_results.html',
results_dict=results_dict,
one_group_error=one_group_error,
zero_group_error=zero_group_error)
else:
err = results
return render_template('groups_integrations_error.html', err=err)
return render_template('groups_integrations.html',
form=form,
sat_data=sat_data)
def limb_darkening():
"""The limb darkening form page"""
# Get all the available filters
filters = svo.filters()['Band']
# Make HTML for filters
filt_list = '\n'.join(['<option value="{0}"{1}> {0}</option>'.format(b, ' selected' if b == 'Kepler.K' else '') for b in filters])
if request.method == 'POST':
if request.form['submit'] == "Retrieve Parameters":
target_name = request.form['targetname']
data = get_target_data(target_name)
feh = data['Fe/H']
teff = data['Teff']
logg = data['stellar_gravity']
limbVars = {'targname':target_name, 'feh': feh, 'teff':teff, 'logg':logg}
return render_template('limb_darkening.html', limbVars=limbVars, filters=filt_list)
elif request.form['submit'] == "Calculate Coefficients":
# Log the form inputs
try:
log_exoctk.log_form_input(request.form, 'limb_darkening', DB)
except:
pass
# Get the input from the form
modeldir = request.form['modeldir']
profiles = list(filter(None, [request.form.get(pf) for pf in PROFILES]))
bandpass = request.form['bandpass']
# protect against injection attempts
bandpass = bandpass.replace('<', '<')
profiles = [str(p).replace('<', '<') for p in profiles]
# Get models from local directory if necessary
if modeldir == 'default':
modeldir = MODELGRID_DIR
# Throw error if input params are invalid
try:
teff = float(request.form['teff'])
logg = float(request.form['logg'])
feh = float(request.form['feh'])
mu_min = float(request.form['mu_min'])
except IOError:
teff = str(request.form['teff']).replace('<', '<')
logg = str(request.form['logg']).replace('<', '<')
feh = str(request.form['feh']).replace('<', '<')
message = 'Could not calculate limb darkening for those parameters.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bandpass or 'None',
profile=', '.join(profiles), models=modeldir,
message=message)
n_bins = request.form.get('n_bins')
pixels_per_bin = request.form.get('pixels_per_bin')
wl_min = request.form.get('wave_min')
wl_max = request.form.get('wave_max')
model_grid = ModelGrid(modeldir, resolution=500)
# No data, redirect to the error page
if not hasattr(model_grid, 'data'):
message = 'Could not find a model grid to load. Please check the path.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bandpass or 'None',
profile=', '.join(profiles),
models=model_grid.path, message=message)
else:
if len(model_grid.data) == 0:
message = 'Could not calculate limb darkening with those parameters.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh,
band=bandpass or 'None',
profile=', '.join(profiles),
models=model_grid.path, message=message)
# Trim the grid to the correct wavelength
# to speed up calculations, if a bandpass is given
min_max = model_grid.wave_rng
try:
kwargs = {'n_bins': int(n_bins)} if n_bins else \
{'pixels_per_bin': int(pixels_per_bin)} if pixels_per_bin else\
{}
if wl_min and wl_max:
kwargs['wl_min'] = float(wl_min) * u.um
kwargs['wl_max'] = float(wl_max) * u.um
# Make filter object
bandpass = svo.Filter(bandpass, **kwargs)
bp_name = bandpass.name
bk_plot = bandpass.plot(draw=False)
bk_plot.plot_width = 580
bk_plot.plot_height = 280
min_max = (bandpass.wave_min.value, bandpass.wave_max.value)
n_bins = bandpass.n_bins
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
filt_script, filt_plot = components(bk_plot)
except:
message = 'Insufficient filter information. Please complete the form and try again!'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bandpass or 'None',
profile=', '.join(profiles),
models=model_grid.path, message=message)
# Trim the grid to nearby grid points to speed up calculation
full_rng = [model_grid.Teff_vals, model_grid.logg_vals, model_grid.FeH_vals]
trim_rng = find_closest(full_rng, [teff, logg, feh], n=1, values=True)
if not trim_rng:
message = 'Insufficient models grid points to calculate coefficients.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bp_name,
profile=', '.join(profiles),
models=model_grid.path, message=message)
elif not profiles:
message = 'No limb darkening profiles have been selected. Please select at least one.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bp_name,
profile=', '.join(profiles),
models=model_grid.path, message=message)
else:
try:
model_grid.customize(Teff_rng=trim_rng[0], logg_rng=trim_rng[1],
FeH_rng=trim_rng[2], wave_rng=min_max)
except:
message = 'Insufficient wavelength coverage to calculate coefficients.'
return render_template('limb_darkening_error.html', teff=teff,
logg=logg, feh=feh, band=bp_name,
profile=', '.join(profiles),
models=model_grid.path, message=message)
# Calculate the coefficients for each profile
ld = lf.LDC(model_grid)
for prof in profiles:
ld.calculate(teff, logg, feh, prof, mu_min=mu_min, bandpass=bandpass)
# Draw a figure for each wavelength bin
tabs = []
for wav in np.unique(ld.results['wave_eff']):
# Plot it
TOOLS = 'box_zoom, box_select, crosshair, reset, hover'
fig = figure(tools=TOOLS, x_range=Range1d(0, 1), y_range=Range1d(0, 1),
plot_width=800, plot_height=400)
ld.plot(wave_eff=wav, fig=fig)
# Plot formatting
fig.legend.location = 'bottom_right'
fig.xaxis.axis_label = 'mu'
fig.yaxis.axis_label = 'Intensity'
tabs.append(Panel(child=fig, title=str(wav)))
final = Tabs(tabs=tabs)
# Get HTML
script, div = components(final)
# Store the tables as a string
file_as_string = str(ld.results[[c for c in ld.results.dtype.names if
ld.results.dtype[c] != object]])
r_eff = mu_eff = ''
# Make a table for each profile with a row for each wavelength bin
profile_tables = []
for profile in profiles:
# Make LaTeX for polynomials
latex = lf.ld_profile(profile, latex=True)
poly = '\({}\)'.format(latex).replace('*', '\cdot').replace('\e', 'e')
# Make the table into LaTeX
table = filter_table(ld.results, profile=profile)
co_cols = [c for c in ld.results.colnames if (c.startswith('c') or
c.startswith('e')) and len(c) == 2 and not
np.all([np.isnan(i) for i in table[c]])]
table = table[['wave_min', 'wave_max'] + co_cols]
table.rename_column('wave_min', '\(\lambda_\mbox{min}\hspace{5px}(\mu m)\)')
table.rename_column('wave_max', '\(\lambda_\mbox{max}\hspace{5px}(\mu m)\)')
# Add the results to the lists
html_table = '\n'.join(table.pformat(max_width=500, html=True))\
.replace('<table', '<table id="myTable" class="table table-striped table-hover"')
# Add the table title
header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(profile, poly)
html_table = header + html_table
profile_tables.append(html_table)
return render_template('limb_darkening_results.html', teff=teff,
logg=logg, feh=feh, band=bp_name, mu=mu_eff,
profile=', '.join(profiles), r=r_eff,
models=model_grid.path, table=profile_tables,
script=script, plot=div,
file_as_string=repr(file_as_string),
filt_plot=filt_plot, filt_script=filt_script,
js=js_resources, css=css_resources)
return render_template('limb_darkening.html', limbVars={}, filters=filt_list)
def limb_darkening():
"""Returns the rendered limb darkening form page.
Returns
-------
``flask.render_template`` obj
The rendered template for the limb-darkening page.
"""
# Load default form
form = fv.LimbDarkeningForm()
# Planet properties
planet_properties = [
'transit_duration', 'orbital_period', 'rp_rs', 'a_rs', 'inclination',
'eccentricity', 'omega'
]
def empty_fields(form):
form.transit_duration.data = form.transit_duration.data or ''
form.orbital_period.data = form.orbital_period.data or ''
form.rp_rs.data = form.rp_rs.data or ''
form.a_rs.data = form.a_rs.data or ''
form.inclination.data = form.inclination.data or ''
form.eccentricity.data = form.eccentricity.data or ''
form.omega.data = form.omega.data or ''
return form
# Reload page with stellar data from ExoMAST
if form.resolve_submit.data:
if form.targname.data.strip() != '':
try:
# Resolve the target in exoMAST
form.targname.data = get_canonical_name(form.targname.data)
data, target_url = get_target_data(form.targname.data)
# Update the star data
form.feh.data = data.get('Fe/H')
form.teff.data = data.get('Teff')
form.logg.data = data.get('stellar_gravity')
form.target_url.data = str(target_url)
# Update the planet data
form.transit_duration.data = data.get('transit_duration')
form.orbital_period.data = data.get('orbital_period')
form.rp_rs.data = data.get('Rp/Rs')
form.a_rs.data = data.get('a/Rs')
form.inclination.data = data.get('inclination')
form.eccentricity.data = data.get('eccentricity')
form.omega.data = data.get('omega')
except Exception:
form.target_url.data = ''
form.targname.errors = [
"Sorry, could not resolve '{}' in exoMAST.".format(
form.targname.data)
]
# Ensure planet fields are not None
form = empty_fields(form)
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Reload page with appropriate filter data
if form.filter_submit.data:
kwargs = {}
if form.bandpass.data == 'tophat':
kwargs['n_bins'] = 1
kwargs['pixels_per_bin'] = 100
kwargs['wave_min'] = 1 * u.um
kwargs['wave_max'] = 2 * u.um
# Get the filter
bandpass = Throughput(form.bandpass.data, **kwargs)
# Update the form data
form.wave_min.data = bandpass.wave_min.value
form.wave_max.data = bandpass.wave_max.value
# Ensure planet fields are not None
form = empty_fields(form)
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Update validation values after a model grid is selected
if form.modelgrid_submit.data:
# Load the modelgrid
mg = ModelGrid(form.modeldir.data, resolution=500)
teff_rng = mg.Teff_vals.min(), mg.Teff_vals.max()
logg_rng = mg.logg_vals.min(), mg.logg_vals.max()
feh_rng = mg.FeH_vals.min(), mg.FeH_vals.max()
# Update the validation parameters by setting validator attributes
setattr(form.teff.validators[1], 'min', float(teff_rng[0]))
setattr(form.teff.validators[1], 'max', float(teff_rng[1]))
setattr(
form.teff.validators[1], 'message',
'Effective temperature must be between {} and {}'.format(
*teff_rng))
setattr(form.logg.validators[1], 'min', float(logg_rng[0]))
setattr(form.logg.validators[1], 'max', float(logg_rng[1]))
setattr(form.logg.validators[1], 'message',
'Surface gravity must be between {} and {}'.format(*logg_rng))
setattr(form.feh.validators[1], 'min', float(feh_rng[0]))
setattr(form.feh.validators[1], 'max', float(feh_rng[1]))
setattr(form.feh.validators[1], 'message',
'Metallicity must be between {} and {}'.format(*feh_rng))
# Ensure planet fields are not None
form = empty_fields(form)
# Send it back to the main page
return render_template('limb_darkening.html', form=form)
# Validate form and submit for results
if form.validate_on_submit() and form.calculate_submit.data:
# Form inputs for logging
form_input = dict(request.form)
# Get the stellar parameters
star_params = [
float(form.teff.data),
float(form.logg.data),
float(form.feh.data)
]
# Load the model grid
model_grid = ModelGrid(form.modeldir.data, resolution=500)
form.modeldir.data = [
j for i, j in form.modeldir.choices if i == form.modeldir.data
][0]
# Grism details
kwargs = {
'n_bins': form.n_bins.data,
'wave_min': form.wave_min.data * u.um,
'wave_max': form.wave_max.data * u.um
}
# Make filter object and plot
bandpass = Throughput(form.bandpass.data, **kwargs)
bk_plot = bandpass.plot(draw=False)
bk_plot.plot_width = 580
bk_plot.plot_height = 280
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
filt_script, filt_plot = components(bk_plot)
# Trim the grid to nearby grid points to speed up calculation
# full_rng = [model_grid.Teff_vals, model_grid.logg_vals, model_grid.FeH_vals]
# trim_rng = find_closest(full_rng, star_params, n=1, values=True)
# Calculate the coefficients for each profile
ld = lf.LDC(model_grid)
for prof in form.profiles.data:
ld.calculate(*star_params,
prof,
mu_min=float(form.mu_min.data),
bandpass=bandpass)
# Check if spam coefficients can be calculated
planet_data = {
param: getattr(form, param).data
for param in planet_properties
}
planet_data['Rp/Rs'] = planet_data['rp_rs']
planet_data['a/Rs'] = planet_data['a_rs']
spam_calc = all([
val is not None and val != '' for key, val in planet_data.items()
])
if spam_calc:
# Make sure non-linear profile is included for spam calculation if all planet parameters are provided
if '4-parameter' not in form.profiles.data:
ld.calculate(*star_params,
'4-parameter',
mu_min=float(form.mu_min.data),
bandpass=bandpass)
# Calculate spam coeffs
planet_data = {key: float(val) for key, val in planet_data.items()}
ld.spam(planet_data=planet_data)
# Draw tabbed figure
final = ld.plot_tabs()
# Get HTML
script, div = components(final)
# Store the tables as a string
keep_cols = [
'Teff', 'logg', 'FeH', 'profile', 'filter', 'wave_min', 'wave_eff',
'wave_max', 'c1', 'e1', 'c2', 'e2', 'c3', 'e3', 'c4', 'e4'
]
print_table = ld.results[[
col for col in keep_cols if col in ld.results.colnames
]]
file_as_string = '\n'.join(
print_table.pformat(max_lines=-1, max_width=-1))
# Make a table for each profile with a row for each wavelength bin
profile_tables = []
for profile in form.profiles.data:
# Make LaTeX for polynomials
latex = lf.ld_profile(profile, latex=True)
poly = '\({}\)'.format(latex).replace('*',
'\cdot').replace('\e', 'e')
# Make the table into LaTeX
table = filter_table(ld.results, profile=profile)
co_cols = [
c for c in ld.results.colnames
if (c.startswith('c') or c.startswith('e')) and len(c) == 2
and not np.all([np.isnan(i) for i in table[c]])
]
table = table[['wave_eff', 'wave_min', 'wave_max'] + co_cols]
table.rename_column('wave_eff',
'\(\lambda_\mbox{eff}\hspace{5px}(\mu m)\)')
table.rename_column('wave_min',
'\(\lambda_\mbox{min}\hspace{5px}(\mu m)\)')
table.rename_column('wave_max',
'\(\lambda_\mbox{max}\hspace{5px}(\mu m)\)')
# Add the results to the lists
html_table = '\n'.join(
table.pformat(max_width=-1, max_lines=-1, html=True)
).replace(
'<table',
'<table id="myTable" class="table table-striped table-hover"')
# Add the table title
header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(
profile, poly)
html_table = header + html_table
profile_tables.append(html_table)
# Add the profile to the form inputs
form_input[profile] = 'true'
# Log the successful form inputs
log_exoctk.log_form_input(form_input, 'limb_darkening', DB)
# Make a table for each profile with a row for each wavelength bin
profile_spam_tables = ''
spam_file_as_string = ''
if ld.spam_results is not None:
# Store SPAM tables as string
keep_cols = [
'Teff', 'logg', 'FeH', 'profile', 'filter', 'wave_min',
'wave_eff', 'wave_max', 'c1', 'c2'
]
print_spam_table = ld.spam_results[[
col for col in keep_cols if col in ld.spam_results.colnames
]]
spam_file_as_string = '\n'.join(
print_spam_table.pformat(max_lines=-1, max_width=-1))
profile_spam_tables = []
for profile in list(np.unique(ld.spam_results['profile'])):
# Make LaTeX for polynomials
latex = lf.ld_profile(profile, latex=True)
poly = '\({}\)'.format(latex).replace('*', '\cdot').replace(
'\e', 'e')
# Make the table into LaTeX
table = filter_table(ld.spam_results, profile=profile)
co_cols = [
c for c in ld.spam_results.colnames
if c.startswith('c') and c not in ['coeffs', 'color']
]
table = table[['wave_eff', 'wave_min', 'wave_max'] + co_cols]
table.rename_column(
'wave_eff', '\(\lambda_\mbox{eff}\hspace{5px}(\mu m)\)')
table.rename_column(
'wave_min', '\(\lambda_\mbox{min}\hspace{5px}(\mu m)\)')
table.rename_column(
'wave_max', '\(\lambda_\mbox{max}\hspace{5px}(\mu m)\)')
# Add the results to the lists
html_table = '\n'.join(
table.pformat(max_width=-1, max_lines=-1, html=True)
).replace(
'<table',
'<table id="myTable" class="table table-striped table-hover"'
)
# Add the table title
header = '<br></br><strong>{}</strong><br><p>\(I(\mu)/I(\mu=1)\) = {}</p>'.format(
profile, poly)
html_table = header + html_table
profile_spam_tables.append(html_table)
return render_template('limb_darkening_results.html',
form=form,
table=profile_tables,
spam_table=profile_spam_tables,
script=script,
plot=div,
spam_file_as_string=repr(spam_file_as_string),
file_as_string=repr(file_as_string),
filt_plot=filt_plot,
filt_script=filt_script,
js=js_resources,
css=css_resources)
return render_template('limb_darkening.html', form=form)
