<jupyter_start><jupyter_text>Horizontal Flips<jupyter_code>fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
ev = np.zeros((40,40,19))
for i in range(19):
    ev[:,:,i]=i+1
# ev[:,:,5]=3+1
# ev[:,:,7]=3+1
a=get_plot_array(ev)
ax.imshow(a,
           origin="upper",
           cmap=cmap)
plt.show()
horizontal_map_array_idxs=[0,11,10,9,8,7,6,5,4,3,2,1,12,17,16,15,14,13,18]
fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
ev = np.zeros((40,40,19))
for i in range(19):
    ev[:,:,i]=4*i+1
ev[:,:20,:] = ev[:,:20,horizontal_map_array_idxs] 
a=get_plot_array(ev)
ax.imshow(a,
           origin="upper",
           cmap=cmap)
plt.show()
fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
a=get_plot_array(original_eventdata[index,:,:,0:19])
ax.imshow(a,
           origin="upper",
           cmap=cmap,
           norm=colors.LogNorm(vmax=np.amax(event),
                                         clip=True))
plt.show()
def horizontal_flip(data):
    return np.flip(data[:,:,horizontal_map_array_idxs],axis=1)
fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
event=horizontal_flip(original_eventdata[index,:,:,:19])
a=get_plot_array(event)
ax.imshow(a,
           origin="upper",
           cmap=cmap,
           norm=colors.LogNorm(vmax=np.amax(event),
                                         clip=True))
plt.show()<jupyter_output><empty_output><jupyter_text>Vertical Flipping<jupyter_code>vertical_map_array_idxs=[6,5,4,3,2,1,0,11,10,9,8,7,15,14,13,12,17,16,18]
def vertical_flip(data):
    return np.flip(data[:,:,vertical_map_array_idxs],axis=0)
fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
ev = np.zeros((40,40,19))
for i in range(19):
    ev[:,:,i]=4*i+1
ev[:20,:,:] = ev[:20,:,vertical_map_array_idxs] 
a=get_plot_array(ev)
ax.imshow(a,
           origin="upper",
           cmap=cmap)
plt.show()
fig, ax = plt.subplots(1,1,figsize=(25,25),facecolor='w')
cmap = plt.cm.viridis
cmap.set_bad(color='black')
event=vertical_flip(original_eventdata[index,:,:,:19])
a=get_plot_array(event)
ax.imshow(a,
           origin="upper",
           cmap=cmap,
           norm=colors.LogNorm(vmax=np.amax(event),
                                         clip=True))
plt.show()
geo_file = np.load('/fast_scratch/WatChMaL/data/mPMT_full_geo.npz', allow_pickle=True)
geo_file.files
geo_file['position'].shape<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19427480916030535, 'fraction_numerical': 0.0549618320610687, 'mean_word_length': 4.221115537848606, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '367627', 'license': 'CNRI-Python', 'n_tokens_mistral': 1172, 'n_tokens_neox': 1117, 'n_words': 117, 'path': 'Data Transforms/Developing Transforms.ipynb', 'repo_name': 'WatChMaL/CNN', 'size': '2620'}