Merge pull request #15 from NREL/use_prettyPlot

remove plots util and use pretty plot instead

.github/workflows/ci.yml

@@ -39,10 +39,10 @@ jobs:
           format utils true
       - name: Normalizing flow test
         run: |
-          python main_iterative.py tests/input_test
+          python main_iterative.py -i tests/input_test
       - name: Bins test
         run: |
-          python main_iterative.py tests/input_test_bins
+          python main_iterative.py -i tests/input_test_bins
       - name: Parallel normalizing flow test
         run: |
-          mpiexec -np 2 python main_iterative.py tests/input_test
+          mpiexec -np 2 python main_iterative.py -i tests/input_test

data-efficientML/artificialCase/trainGP.py

@@ -7,11 +7,10 @@
 import os
 import warnings
 
-from myProgressBar import printProgressBar
-from plotsUtil import *
 from sklearn.gaussian_process.kernels import RBF
 from sklearn.gaussian_process.kernels import ConstantKernel as C
 from sklearn.gaussian_process.kernels import WhiteKernel
+from prettyPlot.progressBar import print_progress_bar
 
 
 def partitionData(nData, nBatch):

data-efficientML/artificialCase/trainNN.py

@@ -7,12 +7,11 @@
 # NN Stuff
 import tensorflow as tf
 from myNN_better import *
-from myProgressBar import printProgressBar
-from plotsUtil import *
 from sklearn.model_selection import train_test_split
 from tensorflow import keras
 from tensorflow.keras import layers, regularizers
-
+from prettyPlot.progressBar import print_progress_bar
+from parallel import irank, iroot
 
 def partitionData(nData, nBatch):
     # ~~~~ Partition the data across batches
@@ -38,23 +37,25 @@ def getPrediction(model, data):
     ApproxBatchSize = 10000
     nBatch = max(int(round(nPoints / ApproxBatchSize)), 1)
     nData_b, startData_b = partitionData(nPoints, nBatch)
-    printProgressBar(
+    print_progress_bar(
         0,
         nBatch,
         prefix="Eval " + str(0) + " / " + str(nBatch),
         suffix="Complete",
         length=50,
+        extraCond=(irank==iroot),
     )
     for ibatch in range(nBatch):
         start_ = startData_b[ibatch]
         end_ = startData_b[ibatch] + nData_b[ibatch]
         result[start_:end_] = np.squeeze(model(data[start_:end_]))
-        printProgressBar(
+        print_progress_bar(
             ibatch + 1,
             nBatch,
             prefix="Eval " + str(ibatch + 1) + " / " + str(nBatch),
             suffix="Complete",
             length=50,
+            extraCond=(irank==iroot),
         )
 
     return result

data-efficientML/artificialCase/util/myNN_better.py

@@ -2,13 +2,13 @@
 
 import numpy as np
 import tensorflow as tf
-from myProgressBar import printProgressBar
 from tensorflow.keras import backend as K
 from tensorflow.keras import layers, losses, optimizers, regularizers
 from tensorflow.keras.callbacks import CSVLogger
 from tensorflow.keras.constraints import max_norm, unit_norm
 from tensorflow.keras.layers import *
 from tensorflow.keras.models import Model
+from prettyPlot.progressBar import print_progress_bar
 
 
 @tf.function
@@ -72,7 +72,7 @@ def train(
         dsOptimizer = optimizers.Adam(learning_rate=lr)
 
         # Train
-        printProgressBar(
+        print_progress_bar(
             0,
             epochs,
             prefix="Loss=%s  Epoch= %d / %d " % ("?", 0, epochs),
@@ -99,7 +99,7 @@ def train(
                 train_mse_loss += tf.reduce_sum(mse)
                 nSample += mse.shape[0]
 
-            printProgressBar(
+            print_progress_bar(
                 epoch + 1,
                 epochs,
                 prefix="Loss=%.2f  Epoch= %d / %d "