Add func to simulate and plot dynamics for motifs

protosignet/plot_results.py

@@ -1,11 +1,14 @@
+import ast
 from pathlib import Path
 
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import seaborn as sns
 
-from protosignet.util import eval_pareto, tag_objectives
+from protosignet.model import sim_signet
+from protosignet.util import calc_n_nodes, eval_pareto, tag_objectives
 
 CUSTOM_PALETTE = ["#648FFF", "#2ECC71", "#8069EC", "#EA822C", "#D143A4", "#F1C40F", "#34495E"]
 
@@ -27,7 +30,7 @@
     "axes.labelsize": 72,
     "xtick.labelsize": 56,
     "ytick.labelsize": 56,
-    "legend.fontsize": 56,
+    "legend.fontsize": 48,
 }
 
 
@@ -45,7 +48,7 @@ def plot_figure_1d(data_dp, fig_fp):
     df_top = df.iloc[df.groupby("obj1")["obj2"].idxmax().values].copy()
     df_top["is_pareto"] = eval_pareto(df_top[["obj1", "obj2"]].to_numpy())
     df_pareto = df_top.loc[df_top["is_pareto"] == 1]
-    # print(df_pareto)
+    print(df_pareto)
     with plt.style.context(("seaborn-v0_8-whitegrid", CUSTOM_STYLE)):
         fig, ax = plt.subplots(figsize=(24, 20))
         sns.scatterplot(data=df_gen_001, x="obj1", y="obj2", edgecolor="#212121", facecolor="#2ECC71", alpha=0.8, linewidth=2, s=600)
@@ -83,12 +86,80 @@ def plot_figure_1d(data_dp, fig_fp):
     plt.close("all")
 
 
+def plot_figure_1e(address, data_dp, fig_fp):
+    """Simulate dynamics for a specified motif.
+
+    Args:
+        address (list): [rep_i, gen_j, pop_k]
+        data_dp (str): absolute path to data directory
+        fig_fp (str): absolute path for saving generated figure
+    """
+    rep_i, gen_j, pop_k = address
+    df_rep = pd.read_csv(data_dp / f"{int(rep_i)}.csv")
+    pop_rep = df_rep["population"].values
+    pop_gen = np.array(ast.literal_eval(pop_rep[int(gen_j)]))
+    n_params = pop_gen.shape[1]
+    n_nodes = calc_n_nodes(n_params)
+    indiv = pop_gen[int(pop_k)].reshape(int(n_nodes), -1)
+    print(indiv)
+    kr = indiv[:, 0]
+    ku = indiv[:, 1]
+    kX = indiv[:, 2:]
+    tu = np.arange(0, 121, 1.0)
+    uu = np.zeros_like(tu)
+    uu[40:80:10] = 1.0  # sparse input
+    uu[80:121:1] = 1.0  # dense input
+    tm, Xm = sim_signet(tu, uu, kr, ku, kX)
+    X1_df = pd.DataFrame({"t": tm, "y": Xm[0], "h": np.ones_like(tm) * 0})
+    X2_df = pd.DataFrame({"t": tm, "y": Xm[1], "h": np.ones_like(tm) * 1})
+    Xm_df = pd.concat([X1_df, X2_df], ignore_index=True)
+    with plt.style.context(("seaborn-v0_8-whitegrid", CUSTOM_STYLE)):
+        fig, ax = plt.subplots(figsize=(24, 20))
+        sns.lineplot(data=Xm_df, x="t", y="y", hue="h", ax=ax, palette=["#8069EC", "#EA822C"], zorder=2.2)
+        ymin, ymax = ax.get_ylim()
+        for t in tu[uu > 0]:
+            ax.axvspan(t, t + 1, color="#648FFF", alpha=0.5, linewidth=0, zorder=2.1)
+        ax.set_ylim(ymin, ymax)
+        handles = [
+            mpl.lines.Line2D([], [], color="#648FFF", linewidth=16, alpha=0.5),
+            mpl.lines.Line2D([], [], color="#8069EC", linewidth=16),
+            mpl.lines.Line2D([], [], color="#EA822C", linewidth=16),
+        ]
+        group_labels = ["Input", "Dense Decoder", "Sparse Decoder"]
+        ax.legend(
+            handles,
+            group_labels,
+            loc="best",
+            markerscale=4,
+            frameon=True,
+            shadow=False,
+            framealpha=1.0,
+            handletextpad=0.4,
+            borderpad=0.2,
+            labelspacing=0.2,
+            handlelength=1,
+        )
+        ax.set_xlabel("Time")
+        ax.set_ylabel("AU")
+        ax.locator_params(axis="x", nbins=10)
+        ax.locator_params(axis="y", nbins=10)
+        fig.tight_layout()
+        fig.canvas.draw()
+        fig.savefig(fig_fp, pad_inches=0.3, dpi=200, bbox_inches="tight", transparent=False)
+    plt.close("all")
+
+
 def main():
     data_dp = Path("/home/phuong/data/protosignet/dual_fm/data/")
     save_dp = Path("/home/phuong/data/protosignet/dual_fm/figs/")
     save_dp.mkdir(parents=True, exist_ok=True)
-    fig_fp = save_dp / "fig_1d.png"
-    plot_figure_1d(data_dp, fig_fp)
+
+    # fig_fp = save_dp / "fig_1d.png"
+    # plot_figure_1d(data_dp, fig_fp)
+
+    for a, address in enumerate([[0, 241, 93], [1, 235, 83], [3, 246, 45]]):
+        fig_fp = save_dp / f"fig_1e_{a}.png"
+        plot_figure_1e(address, data_dp, fig_fp)
 
 
 if __name__ == "__main__":

protosignet/util.py

@@ -79,9 +79,10 @@ def eval_pareto(objectives):
     return np.array(is_pareto)
 
 
-def fetch_indiv(csv_fp, gen_j, pop_k):
-    df = pd.read_csv(Path(csv_fp))
-    pop_rep = df["population"].values
-    pop_gen = np.array(ast.literal_eval(pop_rep[int(gen_j)]))
-    indiv = pop_gen[int(pop_k)]
-    return indiv
+def calc_n_nodes(n_params):
+    x = 0
+    y = 1
+    while y != 0:
+        x += 1
+        y = x**2 + 2 * x - n_params
+    return x