fix(plots): remove additional unused comments from posterior predicti…

…ve extrapolation Signed-off-by: Cameron Smith <cameron.ray.smith@gmail.com>
pinellolab · Sep 16, 2024 · 8174ff9 · 8174ff9
1 parent 44cd705
commit 8174ff9
Showing 1 changed file with 0 additions and 36 deletions.
diff --git a/src/pyrovelocity/plots/_predictive.py b/src/pyrovelocity/plots/_predictive.py
@@ -60,7 +60,6 @@ def posterior_curve(
     output_fig_objects = []
     for figi, gene in enumerate(gene_set):
         (index,) = np.where(adata.var_names == gene)
-        # print(adata.shape, index, posterior_samples["st_mean"].shape)
 
         fig, ax = plt.subplots(3, 4)
         fig.set_size_inches(15, 10)
@@ -90,7 +89,6 @@ def posterior_curve(
                 grid_cell_time.mean(0).flatten() < t0_sample
             ] = 0
             grid_cell_colors = colors[grid_mask_t0_sample]
-            # print(grid_time_samples_st.shape)
 
             im = ax[sample].scatter(
                 posterior_samples["st_mean"][:, index[0]],
@@ -163,13 +161,6 @@ def posterior_curve(
                 :, index[0]
             ].flatten()
 
-            ##u0 = posterior_samples['u_offset'][sample][:, index[0]].flatten()
-            ##s0 = posterior_samples['s_offset'][sample][:, index[0]].flatten()
-            ##u_inf = posterior_samples['u_inf'][sample][:, index[0]].flatten()
-            ##s_inf = posterior_samples['s_inf'][sample][:, index[0]].flatten()
-            ##switching = posterior_samples['switching'][sample][:, index[0]].flatten()
-            ##dt_switching = posterior_samples['dt_switching'][sample][:, index[0]].flatten()
-
             ax[sample + 4].scatter(
                 t0_sample,
                 u0 * uscale,
@@ -219,20 +210,6 @@ def posterior_curve(
                 linewidth=0.5,
                 c="black",
             )
-            # ax[sample].plot(grid_time_samples_st[sample][:, index[0]],
-            #                grid_time_samples_ut[sample][:, index[0]],
-            #                linestyle="--", linewidth=3, color='g')
-            # if sample == 0:
-            #     print(gene, u0 * uscale, s0)
-            #     print(gene, u_inf * uscale, s_inf)
-            #     print(
-            #         t0_sample,
-            #         dt_switching_sample,
-            #         cell_time_sample_min,
-            #         cell_time_sample_max,
-            #         (cell_time_sample <= t0_sample).sum(),
-            #     )
-            #     print(cell_time_sample.shape)
 
             switching = t0_sample + dt_switching_sample
             state0 = (cell_gene_state_grid == 0) & (
@@ -311,7 +288,6 @@ def extrapolate_prediction_sample_predictive(
 
     posterior_samples_list = []
     for tensor_dict in scdl:
-        # print("--------------------")
         u_obs = tensor_dict["U"]
         s_obs = tensor_dict["X"]
         u_log_library = tensor_dict["u_lib_size"]
@@ -396,7 +372,6 @@ def extrapolate_prediction_sample_predictive(
                     axis=-3,
                 )
             )
-            # ).to("cuda:0")
 
         posterior_samples_new_tmp = Predictive(
             pyro.poutine.uncondition(
@@ -408,7 +383,6 @@ def extrapolate_prediction_sample_predictive(
             posterior_samples_new_tmp[key] = posterior_samples[key]
         posterior_samples_list.append(posterior_samples_new_tmp)
 
-    # print(len(posterior_samples_list))
     posterior_samples_new = {}
     for key in posterior_samples_list[0].keys():
         if posterior_samples_list[0][key].shape[-2] == 1:
@@ -417,12 +391,6 @@ def extrapolate_prediction_sample_predictive(
             posterior_samples_new[key] = torch.concat(
                 [element[key] for element in posterior_samples_list], axis=-2
             )
-    # posterior_samples_new = model.generate_posterior_samples(
-    #    adata=adata, batch_size=512, num_samples=8
-    # )
-
-    # for key in posterior_samples_new.keys():
-    #     print(posterior_samples_new[key].shape)
 
     grid_time_samples_ut = posterior_samples_new["ut"]
     grid_time_samples_st = posterior_samples_new["st"]
@@ -443,10 +411,6 @@ def extrapolate_prediction_sample_predictive(
         grid_time_samples_uscale = np.ones(grid_time_samples_uinf.shape)
 
     grid_time_samples_state = posterior_samples_new["cell_gene_state"]
-    # print(grid_time_samples_state.shape)
-    # print(grid_time_samples_uscale.shape)
-    # print(grid_time_samples_ut.shape)
-    # print(grid_time_samples_st.shape)
     if isinstance(grid_time_samples_state, np.ndarray):
         return (
             grid_time_samples_ut,