format

papers/tutorial/calibration_bsd/data/check_real.py

@@ -1,27 +1,29 @@
 import numpy as np
-from prettyPlot.plotting import *
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import Normalize
+from prettyPlot.plotting import *
 
-target=np.load("target.npz")
-A=np.load("dataset.npz")
-nsim = A['y'].shape[0]
+target = np.load("target.npz")
+A = np.load("dataset.npz")
+nsim = A["y"].shape[0]
 print(f"nsim = {nsim}")
 ceff = []
 beff_fact = []
 ceff_fail = []
 beff_fact_fail = []
 for i in range(nsim):
-    if abs(np.mean(A['y'][i,:])-1) > 1e-6:
-        print(f"beff_fact : {A['beff_fact'][i]:.2f}, ceff : {A['ceff'][i]:.2f}")
-        ceff.append(A['ceff'][i])
-        beff_fact.append(A['beff_fact'][i])
+    if abs(np.mean(A["y"][i, :]) - 1) > 1e-6:
+        print(
+            f"beff_fact : {A['beff_fact'][i]:.2f}, ceff : {A['ceff'][i]:.2f}"
+        )
+        ceff.append(A["ceff"][i])
+        beff_fact.append(A["beff_fact"][i])
     else:
-        ceff_fail.append(A['ceff'][i])
-        beff_fact_fail.append(A['beff_fact'][i])
-fig=plt.figure()
-plt.plot(beff_fact, ceff, 'o', color='k')
-plt.plot(beff_fact_fail, ceff_fail, 'o', color='r')
+        ceff_fail.append(A["ceff"][i])
+        beff_fact_fail.append(A["beff_fact"][i])
+fig = plt.figure()
+plt.plot(beff_fact, ceff, "o", color="k")
+plt.plot(beff_fact_fail, ceff_fail, "o", color="r")
 pretty_labels("beff_fact", "ceff")
 
 
@@ -32,13 +34,24 @@
 cmap = plt.cm.viridis  # Choose a colormap
 color_map = ScalarMappable(norm=norm, cmap=cmap)
 
-fig=plt.figure()
+fig = plt.figure()
 icount = 0
 for i in range(nsim):
-    if abs(np.mean(A['y'][i,:])-1) > 1e-6:
-        plt.plot(A["x"],A["y"][i,:], color=color_map.to_rgba(beff_fact_arr[icount]))
+    if abs(np.mean(A["y"][i, :]) - 1) > 1e-6:
+        plt.plot(
+            A["x"],
+            A["y"][i, :],
+            color=color_map.to_rgba(beff_fact_arr[icount]),
+        )
         icount += 1
-plt.plot(target["x"],target["y"], '^', markersize=10, markeredgecolor='k', markerfacecolor='r')
+plt.plot(
+    target["x"],
+    target["y"],
+    "^",
+    markersize=10,
+    markeredgecolor="k",
+    markerfacecolor="r",
+)
 pretty_labels("x", "y", 14, title="beff_fact")
 
 
@@ -47,17 +60,23 @@
 cmap = plt.cm.viridis  # Choose a colormap
 color_map = ScalarMappable(norm=norm, cmap=cmap)
 
-fig=plt.figure()
+fig = plt.figure()
 icount = 0
 for i in range(nsim):
-    if abs(np.mean(A['y'][i,:])-1) > 1e-6:
-        plt.plot(A["x"],A["y"][i,:], color=color_map.to_rgba(ceff_arr[icount]))
-        icount +=1
-plt.plot(target["x"],target["y"], '^', markersize=10, markeredgecolor='k', markerfacecolor='r')
+    if abs(np.mean(A["y"][i, :]) - 1) > 1e-6:
+        plt.plot(
+            A["x"], A["y"][i, :], color=color_map.to_rgba(ceff_arr[icount])
+        )
+        icount += 1
+plt.plot(
+    target["x"],
+    target["y"],
+    "^",
+    markersize=10,
+    markeredgecolor="k",
+    markerfacecolor="r",
+)
 pretty_labels("x", "y", 14, title="ceff")
 
 
-
 plt.show()
-
-

papers/tutorial/calibration_bsd/make_dataset/flow.py

@@ -2,6 +2,7 @@
 from prettyPlot.plotting import *
 from utils import check_vol_cons, diam2vol, vol2diam
 
+
 class Bubbles:
     def __init__(
         self,
@@ -45,4 +46,3 @@ def breakup(self, id_break: int, new_diam_list: list[float]) -> None:
         # Reset the breakup rate factor
         # This factor is useful for very small timesteps (this is not the efficiency factor)
         self.breakup_rate_factor = 1.0
-

papers/tutorial/calibration_bsd/make_dataset/generate_dataset.py

@@ -1,3 +1,6 @@
+import argparse
+import os
+
 import numpy as np
 from flow import Bubbles
 from models import (
@@ -7,10 +10,10 @@
 )
 from prettyPlot.plotting import *
 from simulation import Simulation
-import os
-import argparse
 
-parser = argparse.ArgumentParser(description="Generate data for training the surrogate")
+parser = argparse.ArgumentParser(
+    description="Generate data for training the surrogate"
+)
 parser.add_argument(
     "-filesave",
     "--filesave",
@@ -21,32 +24,32 @@
     default="dataset.npz",
 )
 args, unknown = parser.parse_known_args()
-data_folder = os.path.join("..","data")
-target = np.load(os.path.join(data_folder,"target.npz"))
+data_folder = os.path.join("..", "data")
+target = np.load(os.path.join(data_folder, "target.npz"))
 x_pdf_target = target["x"]
 beff_fact_data = []
 ceff_data = []
-x_pdf_data = [] 
-y_pdf_data = [] 
+x_pdf_data = []
+y_pdf_data = []
 
 try:
-    os.remove(args.filesave) 
+    os.remove(args.filesave)
 except FileNotFoundError:
     pass
 
 dt = 0.01
 nt = 15000
-isim=0
-nsim=100
-while isim<nsim:
+isim = 0
+nsim = 100
+while isim < nsim:
     isim += 1
     ceff = np.random.uniform(0.02, 2)
     beff_fact = np.random.uniform(0.8, 1.1)
-    
+
     # Initialize
     bubbles = Bubbles(nbubbles=2000, diam=1e-3)
     breakup_kwargs = {
-        "breakup_rate": ceff*beff_fact,
+        "breakup_rate": ceff * beff_fact,
         "dt": dt,
         "n_break": 2,  # if n_break=2, this is binary breakup
         "min_break_diam": 1e-6,  # forbid breakup that lead to diameter < min_break_diam
@@ -66,42 +69,63 @@
         breakup_kwargs=breakup_kwargs,
         coalescence_kwargs=coalescence_kwargs,
     )
-    
-    result = sim.run(window_size=200,kill_failed_sim=True, x_pdf=x_pdf_target)
+
+    result = sim.run(window_size=200, kill_failed_sim=True, x_pdf=x_pdf_target)
     if "x_pdf" in result:
         mean_diameter_history = result["mean_diameter_history"]
         y_pdf = result["y_pdf"]
         x_pdf = result["x_pdf"]
-        start_ave_time = result["start_ave_time"]           
-    
+        start_ave_time = result["start_ave_time"]
+
         # Plot results
         time = np.arange(0, sim.nt * sim.dt, sim.dt)
         plt.figure(figsize=(10, 6))
         plt.plot(time, mean_diameter_history)
-        plt.plot(np.ones(10)*start_ave_time, np.linspace(np.amin(mean_diameter_history), np.amax(mean_diameter_history), 10), '--', color='k')
-        pretty_labels("Time [s]", "Mean bubble diameter [m]", 14, fontname="Times")
+        plt.plot(
+            np.ones(10) * start_ave_time,
+            np.linspace(
+                np.amin(mean_diameter_history),
+                np.amax(mean_diameter_history),
+                10,
+            ),
+            "--",
+            color="k",
+        )
+        pretty_labels(
+            "Time [s]", "Mean bubble diameter [m]", 14, fontname="Times"
+        )
         plt.close()
-        
+
         # Plot equilibrium BSD
         plt.figure(figsize=(10, 6))
         mean_bsd = np.mean(y_pdf, axis=0)
         std_bsd = np.std(y_pdf, axis=0)
         plt.plot(x_pdf, mean_bsd, color="k", linewidth=3)
-        plt.plot(x_pdf, mean_bsd+std_bsd, "--", color="k", linewidth=3)
-        plt.plot(x_pdf, mean_bsd-std_bsd, "--", color="k", linewidth=3)
+        plt.plot(x_pdf, mean_bsd + std_bsd, "--", color="k", linewidth=3)
+        plt.plot(x_pdf, mean_bsd - std_bsd, "--", color="k", linewidth=3)
         pretty_labels("diameter [m]", "Bin count", 14, fontname="Times")
         plt.close()
-        #plt.show()
+        # plt.show()
         print(f"\nSUCCESS beff_fact = {beff_fact} ceff = {ceff}\n")
         beff_fact_data.append(beff_fact)
         ceff_data.append(ceff)
         y_pdf_data.append(mean_bsd)
-        np.savez(os.path.join(data_folder,args.filesave), x=x_pdf_target, y=np.array(y_pdf_data), beff_fact=np.array(beff_fact_data), ceff=np.array(ceff_data))
+        np.savez(
+            os.path.join(data_folder, args.filesave),
+            x=x_pdf_target,
+            y=np.array(y_pdf_data),
+            beff_fact=np.array(beff_fact_data),
+            ceff=np.array(ceff_data),
+        )
     else:
         print(f"\nFAILED beff_fact = {beff_fact} ceff = {ceff}\n")
         beff_fact_data.append(beff_fact)
         ceff_data.append(ceff)
         y_pdf_data.append(np.ones(len(x_pdf_target)))
-        np.savez(os.path.join(data_folder,args.filesave), x=x_pdf_target, y=np.array(y_pdf_data), beff_fact=np.array(beff_fact_data), ceff=np.array(ceff_data))
-
-
+        np.savez(
+            os.path.join(data_folder, args.filesave),
+            x=x_pdf_target,
+            y=np.array(y_pdf_data),
+            beff_fact=np.array(beff_fact_data),
+            ceff=np.array(ceff_data),
+        )

papers/tutorial/calibration_bsd/make_dataset/generate_target.py

@@ -1,3 +1,5 @@
+import os
+
 import numpy as np
 from flow import Bubbles
 from models import (
@@ -7,7 +9,6 @@
 )
 from prettyPlot.plotting import *
 from simulation import Simulation
-import os
 
 # Initialize
 bubbles = Bubbles(nbubbles=2000, diam=1e-3)
@@ -53,13 +54,15 @@
 mean_bsd = np.mean(y_pdf, axis=0)
 std_bsd = np.std(y_pdf, axis=0)
 plt.plot(x_pdf, mean_bsd, color="k", linewidth=3)
-plt.plot(x_pdf, mean_bsd+std_bsd, "--", color="k", linewidth=3)
-plt.plot(x_pdf, mean_bsd-std_bsd, "--", color="k", linewidth=3)
+plt.plot(x_pdf, mean_bsd + std_bsd, "--", color="k", linewidth=3)
+plt.plot(x_pdf, mean_bsd - std_bsd, "--", color="k", linewidth=3)
 pretty_labels("diameter [m]", "Bin count", 14, fontname="Times")
 
-dataFolder = os.path.join("..","data")
+dataFolder = os.path.join("..", "data")
 os.makedirs(dataFolder, exist_ok=True)
 
-np.savez(os.path.join(dataFolder,"target.npz"), x=x_pdf, y=mean_bsd, sigma=std_bsd)
+np.savez(
+    os.path.join(dataFolder, "target.npz"), x=x_pdf, y=mean_bsd, sigma=std_bsd
+)
 
 plt.show()

papers/tutorial/calibration_bsd/make_dataset/main_target.py

@@ -1,3 +1,5 @@
+import os
+
 import numpy as np
 from flow import Bubbles
 from models import (
@@ -7,7 +9,6 @@
 )
 from prettyPlot.plotting import *
 from simulation import Simulation
-import os
 
 # Initialize
 bubbles = Bubbles(nbubbles=2000, diam=1e-3)
@@ -53,18 +54,20 @@
 mean_bsd = np.mean(y_pdf, axis=0)
 std_bsd = np.std(y_pdf, axis=0)
 plt.plot(x_pdf, mean_bsd, color="k", linewidth=3)
-plt.plot(x_pdf, mean_bsd+std_bsd, "--", color="k", linewidth=3)
-plt.plot(x_pdf, mean_bsd-std_bsd, "--", color="k", linewidth=3)
+plt.plot(x_pdf, mean_bsd + std_bsd, "--", color="k", linewidth=3)
+plt.plot(x_pdf, mean_bsd - std_bsd, "--", color="k", linewidth=3)
 pretty_labels("diameter [m]", "Bin count", 14, fontname="Times")
 
-dataFolder = os.path.join("..","data")
+dataFolder = os.path.join("..", "data")
 os.makedirs(dataFolder, exist_ok=True)
 
-np.savez(os.path.join(dataFolder,"target.npz"), x=x_pdf, y=mean_bsd, sigma=std_bsd)
+np.savez(
+    os.path.join(dataFolder, "target.npz"), x=x_pdf, y=mean_bsd, sigma=std_bsd
+)
 
 ## Plot equilibrium BSD
-#plt.figure(figsize=(10, 6))
-#plt.hist(sim.bubbles.diameters, bins=25)
-#pretty_labels("diameter [m]", "Bin count", 14, fontname="Times")
+# plt.figure(figsize=(10, 6))
+# plt.hist(sim.bubbles.diameters, bins=25)
+# pretty_labels("diameter [m]", "Bin count", 14, fontname="Times")
 
 plt.show()

papers/tutorial/calibration_bsd/make_dataset/models.py

@@ -74,5 +74,3 @@ def simple_nary_breakup(bubbles: Bubbles, kwargs: dict) -> None:
         # Check that we are not creating too small of bubbles
         if np.amin(new_diameters) > min_break_diam:
             bubbles.breakup(id_break=id1, new_diam_list=list(new_diameters))
-
-