add sliders and graph

.gitignore

@@ -6,4 +6,5 @@ __pycache__/
 *.png
 logs/
 .ipynb_checkpoints/
-*.pdf
+*.pdf
+*.npy

graph.dot

@@ -0,0 +1,34 @@
+# https://gist.github.com/thigm85/5760134
+digraph G {
+    rankdir=LR
+	splines=line
+    nodesep=.05;
+    bgcolor="transparent";
+    penwidth = 0  
+          
+    node [label=""];
+        
+    subgraph cluster_0 {
+		color=white;
+        node [style=solid,color=blue4, shape=circle];
+		x1 x2 x3 x4 x5 x6;
+		label = "input layer";
+	}
+
+	subgraph cluster_1 {
+		color=white;
+		node [style=solid,color=red2, shape=circle];
+		a12 a22 a32 a42;
+		label = "hidden layer";
+	}
+
+	subgraph cluster_2 {
+		color=white;
+		node [style=solid,color=seagreen2, shape=circle];
+		O1;
+		label="output layer";
+	}
+
+    {x1; x2; x3; x4; x5; x6} -> {a12;a22;a32;a42};
+    {a12; a22; a32; a42} -> O1
+}

sliders_nn.py

@@ -0,0 +1,85 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.engine.saving import load_model
+from matplotlib.collections import QuadMesh
+from matplotlib.widgets import Slider
+from sklearn.preprocessing import StandardScaler
+
+from simulation_list import SimulationList
+
+simlist = SimulationList.jsonlines_load()
+train_data = simlist.simlist
+
+X = np.array([[s.mcode, s.wpcode, s.wtcode, s.gammacode, s.alphacode, s.vcode] for s in train_data])
+scaler = StandardScaler()
+scaler.fit(X)
+
+
+
+
+fig, ax = plt.subplots()
+plt.subplots_adjust(bottom=0.35)
+t = np.arange(0.0, 1.0, 0.001)
+mcode_default, gamma_default, wt_default, wp_default = [24.0, 1, 10.0, 10.0]
+
+
+xrange = np.linspace(-0.5, 60.5, 100)
+yrange = np.linspace(0.5, 5.5, 100)
+xgrid, ygrid = np.meshgrid(xrange, yrange)
+mcode = 24.
+wpcode = 10
+wtcode = 10
+gammacode = 1
+
+testinput = np.array([[mcode, wpcode, wtcode, gammacode, np.nan, np.nan]] * 100 * 100)
+testinput[::, 4] = xgrid.flatten()
+testinput[::, 5] = ygrid.flatten()
+testinput = scaler.transform(testinput)
+
+model = load_model("model.hd5")
+
+testoutput = model.predict(testinput)
+outgrid = np.reshape(testoutput, (100, 100))
+mesh = plt.pcolormesh(xgrid, ygrid, outgrid, cmap="Blues", vmin=0, vmax=1)  # type:QuadMesh
+plt.colorbar()
+
+
+axcolor = 'lightgoldenrodyellow'
+ax_mcode = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
+ax_gamma = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
+ax_wt = plt.axes([0.25, 0.20, 0.65, 0.03], facecolor=axcolor)
+ax_wp = plt.axes([0.25, 0.25, 0.65, 0.03], facecolor=axcolor)
+
+s_mcode = Slider(ax_mcode, 'mcode', 21, 25, valinit=mcode_default)
+s_gamma = Slider(ax_gamma, 'gamma', 0.1, 1, valinit=gamma_default)
+s_wt = Slider(ax_wt, 'wt', 10, 20, valinit=wt_default)
+s_wp = Slider(ax_wp, 'wp', 10, 20, valinit=wp_default)
+
+
+def update(val):
+    mcode = s_mcode.val
+    gamma = s_gamma.val
+    wt = s_wt.val
+    wp = s_wp.val
+    testinput = np.array([[mcode, wp, wt, gamma, np.nan, np.nan]] * 100 * 100)
+    testinput[::, 4] = xgrid.flatten()
+    testinput[::, 5] = ygrid.flatten()
+    testinput = scaler.transform(testinput)
+
+    testoutput = model.predict(testinput)
+    outgrid = np.reshape(testoutput, (100, 100))
+    # if not isinstance(datagrid, np.ndarray):
+    #     return False
+    formatedgrid = outgrid[:-1, :-1]
+
+    mesh.set_array(formatedgrid.ravel())
+
+    fig.canvas.draw_idle()
+
+
+s_gamma.on_changed(update)
+s_mcode.on_changed(update)
+s_wp.on_changed(update)
+s_wt.on_changed(update)
+
+plt.show()