Has anyone an idea how to read this table and parse out to CSV file or something. I need to convert it into graph(if possible).
I really confused how to read this table, not like 'show inventory' or 'show version' there's have a TextFSM Template to parse out the data, how about 'show proc cpu history' there's have TextFSM Template or anything?
show proc cpu history(from show tech.txt)
You need to read the text file in and extract the 3 lines above each graph containing the numerical values. These need to be "rotated" and converted into integers.
The following code would read the text file in and extract the necessary lines. It first skips over all of the initial lines until it finds the header line for the three graphs.
It rotates the values (using *zip(*data)). Next it writes the data out to three separate CSV files, based on the source filenames.
Lastly, it uses the data to display three graphs using the Python matplotlib library (which would need to be installed).
from itertools import dropwhile, takewhile, izip_longest
import matplotlib.pyplot as plt
import csv
import re
import os
from collections import deque
def process_block(block):
# Determine number of leading spaces (varies between files)
start_offset = min(len(block[0]) - len(block[0].lstrip()), len(block[1]) - len(block[1].lstrip()))
# Extract just data. Skip over leading spaces, remove trailing newline and replace any spaces with 0
block = [line[start_offset:].rstrip('\n').replace(' ', '0') for line in block]
# Convert to values
return [int(''.join(entry)) for entry in zip(*block)]
def process_switch_file(figure, switch_filename):
with open(switch_filename) as f_input:
# Extract just the graph lines
lines = list(takewhile(lambda x: "- show logging -" not in x, dropwhile(lambda x: "- show process cpu history -" not in x, f_input)))
headings = ["CPU% per second (last 60 seconds)", "CPU% per minute (last 60 minutes)", "CPU% per hour (last 72 hours)"]
# Keep reading each until a line starting 100 is found, keep the previous 2 lines
data = []
entry = iter(lines)
while True:
block = deque(takewhile(lambda x: not re.match(' *?100 ', x), entry), 2)
if len(block[0]) <= 1:
break
data.append(block)
data = [process_block(block) for block in data]
x_values = [range(0, len(block)) for block in data]
# Get the base filename (without path or extension)
csv_filename = os.path.splitext(os.path.basename(switch_filename))[0]
# Write data to a csv
with open('{}_cpu_seconds.csv'.format(csv_filename), 'wb') as f_output:
csv_output = csv.writer(f_output)
csv_output.writerow(['Second', 'Value'])
csv_output.writerows(list(zip(x_values[0], data[0])))
with open('{}_cpu_minutes.csv'.format(csv_filename), 'wb') as f_output:
csv_output = csv.writer(f_output)
csv_output.writerow(['Minute', 'Value'])
csv_output.writerows(list(zip(x_values[1], data[1])))
with open('{}_cpu_hours.csv'.format(csv_filename), 'wb') as f_output:
csv_output = csv.writer(f_output)
csv_output.writerow(['Hour', 'Value'])
csv_output.writerows(list(zip(x_values[2], data[2])))
fig = plt.figure(figure)
fig.canvas.set_window_title(csv_filename)
for subplot, x, block, heading in zip(range(311, 314), x_values, data, headings):
# Display as graphs
ax = fig.add_subplot(subplot)
ax.set_xlabel(heading)
ax.set_ylim(0, 100)
plt.plot(x, block)
plt.tight_layout()
for figure, filename in enumerate(['switch-2.txt', "switch-3.txt", "Switch.txt"], start=1):
process_switch_file(figure, filename)
plt.show()
The CSV files would start something like:
Minute,Value
0,0
1,0
2,0
3,0
4,3
5,11
And the display would show:
matplotlib can usually be installed using:
pip install matplotlib
