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import pandas as pd

df = pd.DataFrame([ibmseti.compamp.SimCompamp(open(file_record,'rb').read()).header()
                       for file_record in raw_signal_files])
df.signal_classification.value_counts()                       

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# Get sample signal
import ibmseti

sample_data = ibmseti.compamp.SimCompamp(open(raw_signal_files[0],'rb').read())
print('File Name:', raw_signal_files[0])
print('Header:', sample_data.header())
# Output
Out [4]: File Name: ./primary_small_v3/a33c85e3-9316-4871-bcdc-10882a7fe6bd.dat
         Header: {'signal_classification': 'narrowband', 
                  'uuid': 'a33c85e3-9316-4871-bcdc-10882a7fe6bd'}

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import os
import glob

base_dir = os.path.join('./primary_small_v3/')
raw_signal_files = glob.glob(base_dir+'*.dat')
len(raw_signal_files)

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from tf_explain.core.smoothgrad import SmoothGrad

explainer = SmoothGrad()

grid1 = explainer.explain(([img], None), model, 281, 80, .2)
grid2 = explainer.explain(([img], None), model, 285, 80, .2)

fig = plt.figure(figsize = (18, 8))
ax1 = fig.add_subplot(1, 3, 1)
ax1.imshow(img_inp / 255.)

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# visualize GradCAM output from Block 14
grid1 = explainer.explain(([img], None), model, 'block14_sepconv1', 281)
grid2 = explainer.explain(([img], None), model, 'block14_sepconv1', 285)

fig = plt.figure(figsize = (18, 8))
ax1 = fig.add_subplot(1, 3, 1)
ax1.imshow(img_inp / 255.)
ax1.imshow(grid1, alpha=0.6)
ax2 = fig.add_subplot(1, 3, 2)
ax2.imshow(img_inp / 255.)

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# visualize GradCAM output from Block 6
grid1 = explainer.explain(([img], None), model, 'block6_sepconv1', 281)
grid2 = explainer.explain(([img], None), model, 'block6_sepconv1', 285)

fig = plt.figure(figsize = (18, 8))
ax1 = fig.add_subplot(1, 3, 1)
ax1.imshow(img_inp / 255.)
ax1.imshow(grid1, alpha=0.6)
ax2 = fig.add_subplot(1, 3, 2)
ax2.imshow(img_inp / 255.)

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from tf_explain.core.grad_cam import GradCAM

explainer = GradCAM()

# get imagenet IDs for cat breeds
imgnet_map['tabby'], imgnet_map['Egyptian_cat']
Out [24]: ('281', '285')

# visualize GradCAM outputs in Block 1
grid1 = explainer.explain(([img], None), model, 'block1_conv2', 281)

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# get label imagenet ID
imgnet_map['Egyptian_cat']

from tf_explain.core.occlusion_sensitivity import OcclusionSensitivity

explainer = OcclusionSensitivity()
img_inp = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(299, 299))
img_inp = tf.keras.preprocessing.image.img_to_array(img_inp)
grid = explainer.explain(([img_inp], None), model, 285, 7)
fig, ax = plt.subplots(figsize=(8, 8))

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explainer = ExtractActivations()
grid = explainer.explain((np.array([img]), None), model, ['block2_sepconv2_act'])
fig, ax = plt.subplots(figsize=(18, 18))
ax.imshow(grid, cmap='binary_r')

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# load imagenet id to class label mappings
import requests

response = requests.get('https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json')
imgnet_map = response.json()
imgnet_map = {v[1]: k for k, v in imgnet_map.items()}

# make model predictions
img = tf.keras.applications.xception.preprocess_input(img)
predictions = model.predict(np.array([img]))