--- a
+++ b/experiments/simulations/two_dimensional_experiments.py
@@ -0,0 +1,131 @@
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+import seaborn as sns
+import sys
+from two_dimensional import two_d_gpsa
+
+sys.path.append("../..")
+from models.gpsa_vi_lmc import VariationalWarpGP
+
+sys.path.append("../../data")
+from simulated.generate_twod_data import generate_twod_data
+from plotting.callbacks import callback_twod
+from util import ConvergenceChecker
+
+## For PASTE
+import scanpy as sc
+
+sys.path.append("../../../paste")
+from src.paste import PASTE, visualization
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+LATEX_FONTSIZE = 50
+
+n_spatial_dims = 2
+n_views = 2
+# n_outputs = 10
+m_G = 25
+m_X_per_view = 25
+
+MAX_EPOCHS = 2000
+PRINT_EVERY = 25
+N_LATENT_GPS = {"expression": 3}
+
+
+if __name__ == "__main__":
+    n_outputs_list = [10, 25, 50]
+    n_repeats = 3
+
+    error_mat = np.zeros((n_repeats, len(n_outputs_list)))
+    error_mat_paste = np.zeros((n_repeats, len(n_outputs_list)))
+
+    for ii in range(n_repeats):
+        for jj, n_outputs in enumerate(n_outputs_list):
+            X, Y, G_means, model, err_paste = two_d_gpsa(
+                n_outputs=n_outputs,
+                n_epochs=MAX_EPOCHS,
+                plot_intermediate=False,
+                warp_kernel_variance=0.5,
+                n_latent_gps=N_LATENT_GPS,
+            )
+
+            error_mat_paste[ii, jj] = err_paste
+
+            aligned_coords = G_means["expression"].detach().numpy().squeeze()
+            n_samples_per_view = n_samples_per_view = X.shape[0] // n_views
+            view1_aligned_coords = aligned_coords[:n_samples_per_view]
+            view2_aligned_coords = aligned_coords[n_samples_per_view:]
+            err = np.mean(
+                np.sum((view1_aligned_coords - view2_aligned_coords) ** 2, axis=1)
+            )
+
+            error_mat[ii, jj] = err
+
+            if ii == 0:
+
+                import matplotlib
+
+                font = {"size": LATEX_FONTSIZE}
+                matplotlib.rc("font", **font)
+                matplotlib.rcParams["text.usetex"] = True
+
+                fig = plt.figure(figsize=(10, 10))
+                data_expression_ax = fig.add_subplot(211, frameon=False)
+                latent_expression_ax = fig.add_subplot(212, frameon=False)
+                callback_twod(
+                    model,
+                    X,
+                    Y,
+                    data_expression_ax=data_expression_ax,
+                    latent_expression_ax=latent_expression_ax,
+                    X_aligned=G_means,
+                )
+
+                plt.tight_layout()
+                plt.savefig(
+                    "../../plots/two_d_experiments/two_d_simulation_noutputs={}.png".format(
+                        n_outputs
+                    )
+                )
+                # plt.show()
+                plt.close()
+                # import ipdb; ipdb.set_trace()
+
+        import matplotlib
+
+        font = {"size": 30}
+        matplotlib.rc("font", **font)
+        matplotlib.rcParams["text.usetex"] = True
+        plt.figure(figsize=(7, 5))
+
+        error_df_gpsa = pd.melt(
+            pd.DataFrame(error_mat[: ii + 1, :], columns=n_outputs_list)
+        )
+        error_df_gpsa["method"] = ["GPSA"] * error_df_gpsa.shape[0]
+        error_df_paste = pd.melt(
+            pd.DataFrame(error_mat_paste[: ii + 1, :], columns=n_outputs_list)
+        )
+        error_df_paste["method"] = ["PASTE"] * error_df_paste.shape[0]
+
+        error_df = pd.concat([error_df_gpsa, error_df_paste], axis=0)
+        error_df.to_csv("./out/error_vary_n_outputs.csv")
+
+        sns.lineplot(
+            data=error_df, x="variable", y="value", hue="method", err_style="bars"
+        )
+        plt.xlabel("Number of outputs")
+        plt.ylabel("Alignent error")
+        plt.tight_layout()
+        plt.savefig(
+            "../../plots/two_d_experiments/error_plot_n_outputs.png".format(n_outputs)
+        )
+        plt.close()
+
+    import ipdb
+
+    ipdb.set_trace()