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regenerate docs for new timeseries functionalities
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Robert Morgan committed Feb 2, 2021
1 parent c5cb9e7 commit f21e56e
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37 changes: 32 additions & 5 deletions docs/check.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -503,7 +503,7 @@ <h1 class="title">Module <code>deeplenstronomy.check</code></h1>
errs = []

# check that transmission curves exist for the bands
if model_name not in [&#39;flat&#39;, &#39;flatnoise&#39;, &#39;variable&#39;, &#39;variablenoise&#39;]:
if model_name not in [&#39;flat&#39;, &#39;flatnoise&#39;, &#39;variable&#39;, &#39;variablenoise&#39;, &#39;static&#39;]:
if not self.checked_ts_bands:
for band in self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;BANDS&#34;].split(&#39;,&#39;):
try:
Expand Down Expand Up @@ -923,11 +923,20 @@ <h1 class="title">Module <code>deeplenstronomy.check</code></h1>
except TypeError:
errs.append(&#34;Listed NITES in GEOMETRY.&#34; + k + &#34;.TIMESERIES.NITES must be numeric&#34;)

# Check validity of PEAK argument, if passed
if &#34;PEAK&#34; in self.config[&#39;GEOMETRY&#39;][k][config_k].keys():
if not isinstance(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;], dict):
try:
peak = int(float(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;]))
del peak
except TypeError:
errs.append(&#34;PEAK argument in GEOMETRY.&#34; + k + &#34;.TIMESERIES.PEAK must be numeric&#34;)

# Impose restriction on num_exposures
if isinstance(self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;], dict):
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)
else:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] != 1:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &lt; 0.99 or self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &gt; 1.01:
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)

elif config_k == &#39;NAME&#39; or config_k == &#39;FRACTION&#39;:
Expand Down Expand Up @@ -1456,7 +1465,7 @@ <h2 class="section-title" id="header-classes">Classes</h2>
errs = []

# check that transmission curves exist for the bands
if model_name not in [&#39;flat&#39;, &#39;flatnoise&#39;, &#39;variable&#39;, &#39;variablenoise&#39;]:
if model_name not in [&#39;flat&#39;, &#39;flatnoise&#39;, &#39;variable&#39;, &#39;variablenoise&#39;, &#39;static&#39;]:
if not self.checked_ts_bands:
for band in self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;BANDS&#34;].split(&#39;,&#39;):
try:
Expand Down Expand Up @@ -1876,11 +1885,20 @@ <h2 class="section-title" id="header-classes">Classes</h2>
except TypeError:
errs.append(&#34;Listed NITES in GEOMETRY.&#34; + k + &#34;.TIMESERIES.NITES must be numeric&#34;)

# Check validity of PEAK argument, if passed
if &#34;PEAK&#34; in self.config[&#39;GEOMETRY&#39;][k][config_k].keys():
if not isinstance(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;], dict):
try:
peak = int(float(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;]))
del peak
except TypeError:
errs.append(&#34;PEAK argument in GEOMETRY.&#34; + k + &#34;.TIMESERIES.PEAK must be numeric&#34;)

# Impose restriction on num_exposures
if isinstance(self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;], dict):
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)
else:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] != 1:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &lt; 0.99 or self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &gt; 1.01:
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)

elif config_k == &#39;NAME&#39; or config_k == &#39;FRACTION&#39;:
Expand Down Expand Up @@ -2439,11 +2457,20 @@ <h3>Methods</h3>
except TypeError:
errs.append(&#34;Listed NITES in GEOMETRY.&#34; + k + &#34;.TIMESERIES.NITES must be numeric&#34;)

# Check validity of PEAK argument, if passed
if &#34;PEAK&#34; in self.config[&#39;GEOMETRY&#39;][k][config_k].keys():
if not isinstance(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;], dict):
try:
peak = int(float(self.config[&#39;GEOMETRY&#39;][k][config_k][&#34;PEAK&#34;]))
del peak
except TypeError:
errs.append(&#34;PEAK argument in GEOMETRY.&#34; + k + &#34;.TIMESERIES.PEAK must be numeric&#34;)

# Impose restriction on num_exposures
if isinstance(self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;], dict):
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)
else:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] != 1:
if self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &lt; 0.99 or self.config[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;num_exposures&#34;] &gt; 1.01:
errs.append(&#34;You must set SURVEY.PARAMETERS.num_exposures to 1 if you use TIMESERIES&#34;)

elif config_k == &#39;NAME&#39; or config_k == &#39;FRACTION&#39;:
Expand Down
74 changes: 53 additions & 21 deletions docs/input_reader.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -534,15 +534,17 @@ <h1 class="title">Module <code>deeplenstronomy.input_reader</code></h1>
return output_dict


def _flatten_and_fill_time_series(self, config_dict, cosmo, configuration, obj_strings, objid):
def _flatten_and_fill_time_series(self, config_dict, cosmo, configuration, obj_strings, objid, peakshift):
&#34;&#34;&#34;
Generate an image info dictionary for each step in the time series

:param config_dict: dictionary built up by self.breakup()
:param configuration: CONFIGURATION_1, CONFIGURATION_2, etc.
:param obj_string: list of the strings targetting the object in the flattened dictionary (e.g. [&#39;PLANE_2-OBJECT_2&#39;])
:param peakshifts: int or float in units of NITES to shift the peak
:return: flattened_and_filled dictionary: dict ready for individual image sim
&#34;&#34;&#34;

output_dicts = []
bands = self.main_dict[&#39;SURVEY&#39;][&#39;PARAMETERS&#39;][&#39;BANDS&#39;].split(&#39;,&#39;)
# Get flattened and filled dictionary
Expand All @@ -555,10 +557,9 @@ <h1 class="title">Module <code>deeplenstronomy.input_reader</code></h1>
lcs = eval(&#39;self.{0}_{1}_lightcurves&#39;.format(configuration, obj_name))
closest_redshift_lcs.append(lcs[&#39;library&#39;][np.argmin(np.abs(redshift - lcs[&#39;redshifts&#39;]))])

# overwrite the image sim dictionary
fake_noise = np.random.normal(scale=0.15, loc=0, size=len(obj_strings) * len(bands) * len(self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]))
noise_idx = 0
for nite in self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]:
# overwrite the image sim dictionary
for orig_nite in self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]:
nite = orig_nite - peakshift
output_dict = base_output_dict.copy()
for band in bands:
for obj_sting, closest_redshift_lc in zip(obj_strings, closest_redshift_lcs):
Expand All @@ -570,14 +571,14 @@ <h1 class="title">Module <code>deeplenstronomy.input_reader</code></h1>
#linearly interpolate between the closest two nights
band_df = closest_redshift_lc[&#39;lc&#39;][closest_redshift_lc[&#39;lc&#39;][&#39;BAND&#39;].values == band].copy().reset_index(drop=True)
closest_nite_indices = np.abs(nite - band_df[&#39;NITE&#39;].values).argsort()[:2]
output_dict[band][obj_string + &#39;-magnitude&#39;] = (band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] - band_df[&#39;MAG&#39;].values[closest_nite_indices[0]]) * (nite - band_df[&#39;NITE&#39;].values[closest_nite_indices[1]]) / (band_df[&#39;NITE&#39;].values[closest_nite_indices[1]] - band_df[&#39;NITE&#39;].values[closest_nite_indices[0]]) + band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] + fake_noise[noise_idx]
output_dict[band][obj_string + &#39;-magnitude&#39;] = (band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] - band_df[&#39;MAG&#39;].values[closest_nite_indices[0]]) * (nite - band_df[&#39;NITE&#39;].values[closest_nite_indices[1]]) / (band_df[&#39;NITE&#39;].values[closest_nite_indices[1]] - band_df[&#39;NITE&#39;].values[closest_nite_indices[0]]) + band_df[&#39;MAG&#39;].values[closest_nite_indices[1]]
output_dict[band][obj_string + &#39;-magnitude_measured&#39;] = np.random.normal(loc=output_dict[band][obj_string + &#39;-magnitude&#39;], scale=0.03)

output_dict[band][obj_string + &#39;-nite&#39;] = nite
output_dict[band][obj_string + &#39;-nite&#39;] = orig_nite
output_dict[band][obj_string + &#39;-peaknite&#39;] = peakshift
output_dict[band][obj_string + &#39;-id&#39;] = closest_redshift_lc[&#39;sed&#39;]
output_dict[band][obj_string + &#39;-type&#39;] = closest_redshift_lc[&#39;obj_type&#39;]

noise_idx += 1

# Use independent observing conditions for each nite if conditions are drawn from distributions
# seeing
if isinstance(self.main_dict[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;seeing&#34;], dict):
Expand Down Expand Up @@ -755,10 +756,20 @@ <h1 class="title">Module <code>deeplenstronomy.input_reader</code></h1>
# Get string referencing the varaible object
obj_strings = [self._find_obj_string(x, k) for x in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;OBJECTS&#39;]]

# Get the PEAK for the configuration
if &#39;PEAK&#39; in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;].keys():
if isinstance(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;], dict):
peakshifts = [self._draw(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;][&#39;DISTRIBUTION&#39;], bands=&#39;b&#39;)[0] for _ in range(v[&#39;SIZE&#39;])]
else:
peakshifts = [float(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;])] * v[&#39;SIZE&#39;]
else:
peakshifts = [0.0] * v[&#39;SIZE&#39;]


for objid in range(v[&#39;SIZE&#39;]):

if time_series:
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid)
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid, peakshifts[objid])
for flattened_image_info in flattened_image_infos:
configuration_sim_dicts[k].append(flattened_image_info)
else:
Expand Down Expand Up @@ -1125,15 +1136,17 @@ <h2 id="args">Args</h2>
return output_dict


def _flatten_and_fill_time_series(self, config_dict, cosmo, configuration, obj_strings, objid):
def _flatten_and_fill_time_series(self, config_dict, cosmo, configuration, obj_strings, objid, peakshift):
&#34;&#34;&#34;
Generate an image info dictionary for each step in the time series

:param config_dict: dictionary built up by self.breakup()
:param configuration: CONFIGURATION_1, CONFIGURATION_2, etc.
:param obj_string: list of the strings targetting the object in the flattened dictionary (e.g. [&#39;PLANE_2-OBJECT_2&#39;])
:param peakshifts: int or float in units of NITES to shift the peak
:return: flattened_and_filled dictionary: dict ready for individual image sim
&#34;&#34;&#34;

output_dicts = []
bands = self.main_dict[&#39;SURVEY&#39;][&#39;PARAMETERS&#39;][&#39;BANDS&#39;].split(&#39;,&#39;)
# Get flattened and filled dictionary
Expand All @@ -1146,10 +1159,9 @@ <h2 id="args">Args</h2>
lcs = eval(&#39;self.{0}_{1}_lightcurves&#39;.format(configuration, obj_name))
closest_redshift_lcs.append(lcs[&#39;library&#39;][np.argmin(np.abs(redshift - lcs[&#39;redshifts&#39;]))])

# overwrite the image sim dictionary
fake_noise = np.random.normal(scale=0.15, loc=0, size=len(obj_strings) * len(bands) * len(self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]))
noise_idx = 0
for nite in self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]:
# overwrite the image sim dictionary
for orig_nite in self.main_dict[&#39;GEOMETRY&#39;][configuration][&#39;TIMESERIES&#39;][&#39;NITES&#39;]:
nite = orig_nite - peakshift
output_dict = base_output_dict.copy()
for band in bands:
for obj_sting, closest_redshift_lc in zip(obj_strings, closest_redshift_lcs):
Expand All @@ -1161,14 +1173,14 @@ <h2 id="args">Args</h2>
#linearly interpolate between the closest two nights
band_df = closest_redshift_lc[&#39;lc&#39;][closest_redshift_lc[&#39;lc&#39;][&#39;BAND&#39;].values == band].copy().reset_index(drop=True)
closest_nite_indices = np.abs(nite - band_df[&#39;NITE&#39;].values).argsort()[:2]
output_dict[band][obj_string + &#39;-magnitude&#39;] = (band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] - band_df[&#39;MAG&#39;].values[closest_nite_indices[0]]) * (nite - band_df[&#39;NITE&#39;].values[closest_nite_indices[1]]) / (band_df[&#39;NITE&#39;].values[closest_nite_indices[1]] - band_df[&#39;NITE&#39;].values[closest_nite_indices[0]]) + band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] + fake_noise[noise_idx]
output_dict[band][obj_string + &#39;-magnitude&#39;] = (band_df[&#39;MAG&#39;].values[closest_nite_indices[1]] - band_df[&#39;MAG&#39;].values[closest_nite_indices[0]]) * (nite - band_df[&#39;NITE&#39;].values[closest_nite_indices[1]]) / (band_df[&#39;NITE&#39;].values[closest_nite_indices[1]] - band_df[&#39;NITE&#39;].values[closest_nite_indices[0]]) + band_df[&#39;MAG&#39;].values[closest_nite_indices[1]]
output_dict[band][obj_string + &#39;-magnitude_measured&#39;] = np.random.normal(loc=output_dict[band][obj_string + &#39;-magnitude&#39;], scale=0.03)

output_dict[band][obj_string + &#39;-nite&#39;] = nite
output_dict[band][obj_string + &#39;-nite&#39;] = orig_nite
output_dict[band][obj_string + &#39;-peaknite&#39;] = peakshift
output_dict[band][obj_string + &#39;-id&#39;] = closest_redshift_lc[&#39;sed&#39;]
output_dict[band][obj_string + &#39;-type&#39;] = closest_redshift_lc[&#39;obj_type&#39;]

noise_idx += 1

# Use independent observing conditions for each nite if conditions are drawn from distributions
# seeing
if isinstance(self.main_dict[&#34;SURVEY&#34;][&#34;PARAMETERS&#34;][&#34;seeing&#34;], dict):
Expand Down Expand Up @@ -1346,10 +1358,20 @@ <h2 id="args">Args</h2>
# Get string referencing the varaible object
obj_strings = [self._find_obj_string(x, k) for x in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;OBJECTS&#39;]]

# Get the PEAK for the configuration
if &#39;PEAK&#39; in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;].keys():
if isinstance(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;], dict):
peakshifts = [self._draw(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;][&#39;DISTRIBUTION&#39;], bands=&#39;b&#39;)[0] for _ in range(v[&#39;SIZE&#39;])]
else:
peakshifts = [float(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;])] * v[&#39;SIZE&#39;]
else:
peakshifts = [0.0] * v[&#39;SIZE&#39;]


for objid in range(v[&#39;SIZE&#39;]):

if time_series:
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid)
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid, peakshifts[objid])
for flattened_image_info in flattened_image_infos:
configuration_sim_dicts[k].append(flattened_image_info)
else:
Expand Down Expand Up @@ -1495,10 +1517,20 @@ <h2 id="args">Args</h2>
# Get string referencing the varaible object
obj_strings = [self._find_obj_string(x, k) for x in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;OBJECTS&#39;]]

# Get the PEAK for the configuration
if &#39;PEAK&#39; in self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;].keys():
if isinstance(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;], dict):
peakshifts = [self._draw(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;][&#39;DISTRIBUTION&#39;], bands=&#39;b&#39;)[0] for _ in range(v[&#39;SIZE&#39;])]
else:
peakshifts = [float(self.main_dict[&#39;GEOMETRY&#39;][k][&#39;TIMESERIES&#39;][&#39;PEAK&#39;])] * v[&#39;SIZE&#39;]
else:
peakshifts = [0.0] * v[&#39;SIZE&#39;]


for objid in range(v[&#39;SIZE&#39;]):

if time_series:
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid)
flattened_image_infos = self._flatten_and_fill_time_series(v.copy(), cosmo, k, obj_strings, objid, peakshifts[objid])
for flattened_image_info in flattened_image_infos:
configuration_sim_dicts[k].append(flattened_image_info)
else:
Expand Down
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