DHEIVER commited on
Commit
c339213
·
1 Parent(s): b77a9ba

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +4 -33
app.py CHANGED
@@ -58,34 +58,7 @@ def plot_anomalies(df_test_value, data, anomalies):
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  ax.set_ylabel("Value")
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  ax.set_title("Anomalous Data Points")
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  return fig
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-
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- def format_output(plot, indices):
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- if plot is None:
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- return None, None
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-
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- # Create a new figure and axis for the combined output
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- fig_combined = plt.figure(figsize=(12, 8))
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- gs = fig_combined.add_gridspec(2, 1, height_ratios=[6, 1])
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-
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- # Add the plot to the top axis
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- ax_plot = fig_combined.add_subplot(gs[0])
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- if isinstance(plot, tuple):
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- fig, ax = plot
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- ax.plot()
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- ax_plot.imshow(fig.canvas.renderer.buffer_rgba())
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- else:
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- ax_plot.imshow(plot)
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- ax_plot.axis('off')
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-
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- # Add the text to the bottom axis
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- ax_text = fig_combined.add_subplot(gs[1])
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- ax_text.text(0.5, 0, f"Anomalous Data Indices: {', '.join(indices)}", fontsize=12, ha='center')
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- ax_text.axis('off')
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-
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- return fig_combined, None
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-
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-
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-
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  def master(file):
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  # read file
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  data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
@@ -94,11 +67,9 @@ def master(file):
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  plot1 = plot_test_data(df_test_value)
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  # predict
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  anomalies = get_anomalies(df_test_value)
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- # plot anomalous data points
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  plot2 = plot_anomalies(df_test_value, data, anomalies)
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- # format output
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- anomalous_data_indices_str = ", ".join(map(str, np.where(anomalies)[0]))
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- return format_output(plot2, anomalous_data_indices_str)
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  outputs = gr.outputs.Image()
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@@ -111,4 +82,4 @@ iface = gr.Interface(
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  description="Anomaly detection of timeseries data."
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  )
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- iface.launch()
 
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  ax.set_ylabel("Value")
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  ax.set_title("Anomalous Data Points")
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  return fig
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  def master(file):
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  # read file
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  data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
 
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  plot1 = plot_test_data(df_test_value)
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  # predict
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  anomalies = get_anomalies(df_test_value)
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+ #plot anomalous data points
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  plot2 = plot_anomalies(df_test_value, data, anomalies)
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+ return plot2
 
 
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  outputs = gr.outputs.Image()
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  description="Anomaly detection of timeseries data."
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  )
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+ iface.launch()