add sph compare experiments

Author: hyunjimoon

main.py

@@ -0,0 +1,16 @@
+# This is a sample Python script.
+
+# Press ⌃R to execute it or replace it with your code.
+# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press ⌘F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    print_hi('PyCharm')
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/

stanify/builders/stan_block_builder.py

@@ -255,7 +255,6 @@ def build_block(self):
         code += "real <lower=0> time_saveper;\n"
         code += "array[N] real integration_times;\n"
         # TODO @Dashadower
-        #  Q1. where code intentions could be logged (answer to theses qs) e.g. reason for filtering out stan_param is to leave _obs
         #  Q2. using `stan_type`, could hierarchy arrays of vector coding be improved?
         #  Q3. which code is better btw R ==1 (excluding to leave _obs) vs else part (including _obs)?
 

stanify/calibrator/draws_data_mapper.py

@@ -7,7 +7,9 @@
 import xarray as xr
 xr.set_options(display_expand_attrs = False)
 import arviz as az
-
+import typing
+if typing.TYPE_CHECKING:
+    import stanify.stan_model
 def trunc4StanNegBinom(real_series):
     """
     DataArray type real series
@@ -16,7 +18,7 @@ def trunc4StanNegBinom(real_series):
     return int_series
 
 
-def draws2data(model, idata_kwargs, data_dict):
+def draws2data(model, idata_kwargs, data_dict) -> az.InferenceData:
     """
     Parameters
     ----------
@@ -34,7 +36,7 @@ def draws2data(model, idata_kwargs, data_dict):
 
     return draws2data_idata
 
-def data2draws(model, idata_kwargs, data_dict):
+def data2draws(model, idata_kwargs, data_dict) -> az.InferenceData:
     """
     Parameters
     ----------
@@ -49,10 +51,9 @@ def data2draws(model, idata_kwargs, data_dict):
     # add observed_data to idata_kwargs
     observed_data = {k: v for k, v in data_dict.items() if k in model.get_obs_vector_names()}
     data2draws_idata = az.from_cmdstanpy(posterior=data2draws_data, observed_data = observed_data, **idata_kwargs)
-
     return data2draws_idata
 
-def draws2data2draws(vensim, setting, precision, numeric, prior, idata_kwargs):
+def draws2data2draws(vensim, setting, precision, numeric, prior, idata_kwargs) -> typing.Tuple[az.InferenceData, "stanify.stan_model.vensim2stan"]:
     """
     vensim: vensim filepath which provides structral assumption
     setting: modeler's selection of which parameter to estimate

stanify/calibrator/visualizer.py

@@ -46,16 +46,16 @@ def plot_qoi(sbc_precision, setting, precision, idata_kwargs, model_name):
             fig, axes = plt.subplots(precision['R'], 1, figsize=(30, 20))
             for r, ax in zip(range(precision['R']), axes):
                 sbc_aux = sbc.sel(region=r)
-                sbc_aux.observed_data[obs_name].plot(hue='prior_draw', x='time', ax=ax, alpha=.3)
+                sbc_aux.observed_data[obs_name].plot(hue='prior_draw', x='time', ax=ax, alpha=.6, figsize = figsize)
                 sbc_aux.posterior_predictive[f'{obs_name}_post'].mean(['draw', 'chain']).plot(hue='prior_draw', x='time', ax=ax,
-                                                                                       alpha=.6, linestyle='dotted')
+                                                                                       alpha=.8, linestyle='dotted', figsize = figsize)
             save_fig(model_name, False, f"{obs_name}_ppc")
             plt.clf()
     else:
         for obs_name in idata_kwargs['prior_predictive']:
-            sbc.observed_data[obs_name].plot(hue='prior_draw', x='time',  alpha=.3, figsize = figsize)
+            sbc.observed_data[obs_name].plot(hue='prior_draw', x='time',  alpha=.6, figsize = figsize)
             sbc.posterior_predictive[f'{obs_name}_post'].mean(['draw', 'chain']).plot(hue='prior_draw', x='time',
-                                                                                       alpha=.6, linestyle='dotted', figsize = figsize)
+                                                                                       alpha=1, linestyle='dotted', figsize = figsize)
             save_fig(model_name, False, f"{obs_name}_ppc")
             plt.clf()