ARRL CW Contest

ARRL CW Contest 2022

DV3A log file analysis

Prior to this stage the Cabrillo file had been read in and a dataframe produced and saved as a pickle file. No columns were added to the cabrillo file before this phase.

import pandas as pd
import numpy as np
from ham.band import HamBand
band=HamBand()
df= pd.read_pickle("./cw.pkl") 
#Remove a couple of bad data records
df = df[~df.dxexch.isin(['1','100'])]

# Create some new Columns
df['hour']=pd.to_datetime(df.when).apply(lambda x: x.hour)
# Convert Z time to Philippine time.
df['ph_hour']=(df.hour+8)%24
# Make freq a float
df['freq'] = df.freq.apply(lambda x: float('0'+x))
#Create the Band column
df['band']=df.freq.apply(lambda x: band.khz_to_m(x))
# Rename Exchange to state
df.rename(columns={'dxexch':'state'},inplace=True)

#Check what the DataFrame now looks like
df.head()

# Activity per hour

when_active=df.ph_hour.unique()
when_active.sort()
print("Active Hours")
for h in when_active:
    print(f"\t{h}")

Active Hours
    6
    7
    8
    9
    10
    11
    16
    17
    18

# Activity per hour to the US 
df.groupby(['ph_hour']).agg({'band':'count'}).reset_index().rename(columns={"band":"Count"}).set_index('ph_hour')

# Activity per hour to the US per Band 
df.groupby(['ph_hour','band']).agg({'state':'count'}).reset_index().rename(columns={"band":"Band"}).set_index('ph_hour')

# Create a Pivot table showing when Each State was active
pd.pivot_table(df,index='ph_hour',columns=['state'], aggfunc='count').fillna('-')

# Activity per hour to the US per Band 
df.groupby(['ph_hour','band']).agg({'state':'count'}).reset_index().rename(columns={"band":"Band"}).set_index('ph_hour')