mirror of
https://github.com/Telecominfraproject/wlan-lanforge-scripts.git
synced 2025-11-01 03:07:56 +00:00
1d7578251c
- Import importlib, os, and sys to each python script.
- Append "lanforge-scripts" root directory to the system path, allowing each script to be called from an antecedent directory. e.g.
if 'lanforge-scripts' not in sys.path:
sys.path.append(os.path.join(os.path.abspath(__file__ + "../../../../../")))
- All statements of the form 'from <module> import <class>' replace with:
<module> = importlib.import_module("lanforge-scripts.<directory>.<module>")
<class> = <module>.<class>
155 lines
3.9 KiB
Python
Executable File
155 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import os
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import argparse
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import pandas as pd
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#https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html
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#https://queirozf.com/entries/pandas-dataframe-plot-examples-with-matplotlib-pyplot
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if sys.version_info[0] != 3:
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print("This script requires Python 3")
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exit(1)
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if 'lanforge-scripts' not in sys.path:
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sys.path.append(os.path.join(os.path.abspath(__file__ + "../../../../")))
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class L3CSVParcer():
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def __init__(self,csv_file):
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# left this in for testing
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'''csv_obj = open(csv_file, 'r')
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csv_reader = csv.reader(csv_obj)
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print(csv_reader)
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for row in csv_reader:
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if row[1] == 'rx':
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print(row)'''
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include_summary = ['Time epoch','Time','Monitor','least','most','average']
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self.csv_file = csv_file
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df_s = pd.read_csv(self.csv_file,header = 0, usecols = lambda column : any(substr in column for substr in include_summary))
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print('{}'.format(csv_file))
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csv_file_summary = self.csv_file.replace('results_','results_summary_')
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df_s.to_csv(csv_file_summary, index = False, header=True)
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include_raw = ['Time epoch','Time','Monitor','LT','MT']
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self.csv_file = csv_file
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df_r = pd.read_csv(self.csv_file,header = 0, usecols = lambda column : any(substr in column for substr in include_raw))
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csv_file_raw = self.csv_file.replace('results_','results_raw_')
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df_r.to_csv(csv_file_raw, index = False, header=True)
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'''df_rx_delta = df_r.loc[df['Monitor'] == 'rx_delta']
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df_rx_delta.plot(x='Time epoch', y='average_rx_data')
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plt.show()
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total_cols = len(df.axes[0])
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print(df.columns)
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print(df.loc[df['Monitor'] == 'rx_delta'])
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print(df.loc[df['Monitor'] == 'rx'])
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print(df.loc[df['Monitor'] == 'rx_delta', df.columns != 'Time'])
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df_rx_delta = df.loc[df['Monitor'] == 'rx_delta']
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print(df_rx_delta.describe())
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df_rx_delta.plot(x='Time epoch', y='average_rx_data')
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plt.show()
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df_rx_delta.plot(x='Time', y='average_rx_data')
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plt.show()
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df_rx_drop_pct = df.loc[df['Monitor'] == 'rx_drop_percent']
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print(df_rx_drop_pct)
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df_rx_delta.plot(x='Time epoch', y='rx_drop_percent')
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plt.show()
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df2 = df.filter(regex='LT-s')
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print(df2)
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#plt.plot(df2[0], df2[1]
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#plt.show()
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df2_mean = df2.mean().sort_values(ascending=False)
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print(df2_mean)
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df2_mean_no_outliers = df2_mean[df2_mean(df2_mean.quantile(.10), df2_mean.quantile(.90))]
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print("no outliers")
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print(df2_mean_no_outliers)
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print("Top 10")
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print(df2_mean.head(10))
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print("Bottom 10")
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print(df2_mean.tail(10))
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print("mean others")
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# set display format otherwise get scientific notation
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pd.set_option('display.float_format', lambda x: '%.3f' % x)
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df_mean = df_rx_delta.mean().sort_values()
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#print(df_mean)
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print(df_mean[0])
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#df_uni_cast = [col for col in df_rx_delta if 'LT' in col]
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#df_LT_rx_delta_mean = df_uni_cast.mean().sort_values()
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#print(df_LT_rx_delta_mean)
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x = np.linspace(0, 20, 100)
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plt.plot(x, np.sin(x))
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plt.show()'''
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def main():
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#debug_on = False
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parser = argparse.ArgumentParser(
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prog='quick_test.py',
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formatter_class=argparse.RawTextHelpFormatter,
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epilog='''\
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Useful Information:
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''',
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description='''quick_test.py:
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''')
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parser.add_argument('-i','--infile', help="file of csv data", default='longevity_results_08_14_2020_14_37.csv')
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parser.add_argument('--debug', help='--debug: Enable debugging',default=True)
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args = parser.parse_args()
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#debug_on = args.debug
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if args.infile:
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csv_file_name = args.infile
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L3CSVParcer(csv_file_name)
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if __name__ == "__main__":
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main()
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