checks if matplotlib backend is inline

livelossplot/core.py

@@ -1,14 +1,16 @@
 from __future__ import division
+import warnings
 
 import matplotlib
 import matplotlib.pyplot as plt
 from IPython.display import clear_output
 
-def check_inline():
-    return "backend_inline" in matplotlib.get_backend()
+def not_inline_warning():
+    backend = matplotlib.get_backend()
+    if "backend_inline" not in backend:
+        warnings.warn("livelossplot requires inline plots.\nYour current backend is: {}\nRun in a Jupyter environment and execute '%matplotlib inline'.".format(backend))
 
 # TODO
-# * check backend
 # * object-oriented API
 # * only integer ticks
 

livelossplot/generic_plot.py

@@ -1,6 +1,6 @@
 from __future__ import division
 
-from .core import draw_plot
+from .core import draw_plot, not_inline_warning
 
 class PlotLosses():
     def __init__(self, figsize=None, cell_size=(6, 4), dynamic_x_axis=False, max_cols=2, max_epoch=None, metric2title={},
@@ -14,6 +14,8 @@ def __init__(self, figsize=None, cell_size=(6, 4), dynamic_x_axis=False, max_col
         self.validation_fmt = validation_fmt
         self.logs = None
 
+        not_inline_warning()
+
     def set_metrics(self, metrics):
         self.base_metrics = metrics
         if self.figsize is None:

livelossplot/keras_plot.py

@@ -1,7 +1,7 @@
 from __future__ import division
 
 from keras.callbacks import Callback
-from .core import draw_plot
+from .core import draw_plot, not_inline_warning
 
 metric2printable = {
     "acc": "Accuracy",
@@ -31,6 +31,8 @@ def __init__(self, figsize=None, cell_size=(6, 4), dynamic_x_axis=False, max_col
         self.max_cols = max_cols
         self.metric2printable = metric2printable.copy()
 
+        not_inline_warning()
+
     def on_train_begin(self, logs={}):
         self.base_metrics = [metric for metric in self.params['metrics'] if not metric.startswith('val_')]
         if self.figsize is None: