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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Create a nwb file from Neo
Create 3 Neo blocks and populate each block with 4 Neo segments, and each segment with 3 Neo analogsignals objects
Step1:  Write a nwb file
Using Neo NWBIO
Step2:  Read the NWB file
Using pynwb
Step3:  Using Neo NWBIO
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Python Code:
blocks = []
# Define Neo blocks
bl0 = neo.Block(name='First block')
bl1 = neo.Block(name='Second block')
bl2 = neo.Block(name='Third block')
print("bl0.segments = ", bl0.segments)      
print("bl1.segments = ", bl1.segments)
print("bl2.segments = ", bl2.segments)
blocks = [bl0, bl1, bl2]
print("blocks = ", blocks)
num_seg = 4 # number of segments
for blk in blocks: 
    for ind in range(num_seg): # number of Segment
        seg = neo.Segment(name='segment %s %d' % (blk.name, ind), index=ind)
        blk.segments.append(seg)
    for seg in blk.segments: # AnalogSignal objects
        # 3 AnalogSignals
        a = AnalogSignal(np.random.randn(num_seg, 44)*pq.nA, sampling_rate=10*kHz)
        b = AnalogSignal(np.random.randn(num_seg, 64)*pq.nA, sampling_rate=10*kHz)
        c = AnalogSignal(np.random.randn(num_seg, 33)*pq.nA, sampling_rate=10*kHz)
        seg.analogsignals.append(a)
        seg.analogsignals.append(b)
        seg.analogsignals.append(c)
blocks
Explanation: Create a nwb file from Neo
Create 3 Neo blocks and populate each block with 4 Neo segments, and each segment with 3 Neo analogsignals objects
End of explanation
filename = 'My_first_dataset_neo9.nwb'
writer = NWBIO(filename, mode='w')
writer.write(blocks)
Explanation: Write a nwb file
Using Neo NWBIO
End of explanation
io = pynwb.NWBHDF5IO(filename, mode='r') # Open a file with NWBHDF5IO
_file = io.read()
print(_file)
_file.acquisition
Explanation: Read the NWB file
Using pynwb
End of explanation
reader = NWBIO(filename, mode='r')
all_blocks = reader.read()
all_blocks
first_block = reader.read_block() # Read the first block
first_block
# Plotting settings
%matplotlib inline
show_bar_plot = False    # Change setting to plot distribution of object sizes in the HDF5 file
plot_single_file = True # Plot all files or a single example file
output_filenames = filename
print("output_filenames = ", output_filenames)
    
# Select the files to plot
filenames = output_filenames
print("filenames = ", filenames)
# Changed
from nwb_docutils.doctools.render import HierarchyDescription, NXGraphHierarchyDescription
import matplotlib.pyplot as plt
        
# Create the plots for all files
file_hierarchy = HierarchyDescription.from_hdf5(filenames)
file_graph = NXGraphHierarchyDescription(file_hierarchy)          
fig = file_graph.draw(show_plot=False,
                      figsize=(12,16),
                      label_offset=(0.0, 0.0065),
                      label_font_size=10)
plot_title = filenames + " \n " + "#Datasets=%i, #Attributes=%i, #Groups=%i, #Links=%i" % (len(file_hierarchy['datasets']), len(file_hierarchy['attributes']), len(file_hierarchy['groups']), len(file_hierarchy['links']))
plt.title(plot_title)
plt.show()
        
# Show a sorted bar plot with the sizes of all datasets in the file
if show_bar_plot:
    d = {i['name']: np.prod(i['size']) for i in file_hierarchy['datasets']}
    l = [w for w in sorted(d, key=d.get, reverse=True)]
    s = [d[w] for w in l]   
    p = np.arange(len(l))   
    fig,ax = plt.subplots(figsize=(16,7))
    ax.set_title(filename)
    ax.bar(p, s, width=1, color='r')
    ax.set_xticks(p+1)  
    ax.set_xticklabels(l)  
    ax.set_yscale("log", nonposy='clip')
    fig.autofmt_xdate(bottom=0.2, rotation=90, ha='right')
    plt.show()
plt.show()
Explanation: Using Neo NWBIO
End of explanation
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