2016 WPX SSB

A45WG log file analysis

import pandas as pd
import numpy as np
import hamcabrillo
from ham.band import HamBand
from ham.dxcc import DxccAll
band=HamBand()
cabdata=hamcabrillo.LoadCab()
df=cabdata.convert_to_df('2016-cqww-ssb.cbr')

#create my dx object from my DxccAll
dx=DxccAll()

def get_country(call):
    fnd = dx.find(call)
    if fnd and fnd.Country_Name:
        return fnd.Country_Name
    else:
        return ""

def get_freq(x):
    try:  
        x=float('0'+x)
    except Exception as err:
        x=0
    return x

df['country']=df.dxcall.apply(lambda x: get_country(x))


# Create some new Columns
df['hour']=pd.to_datetime(df.when).apply(lambda x: x.hour)
# Convert Z time to Oman time.
df['om_hour']=(df.hour+4)%24
# Make freq a float
df['freq'] = df.freq.apply(lambda x:get_freq(x))
#Create the Band column
df['band']=df.freq.apply(lambda x: band.khz_to_m(x))
df

want to open /Users/tim/pe39/lib/python3.9/site-packages/ham/dxcc/data/cty.dat

# Activity per hour

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

Active Hours
    15
    16
    17

df.groupby(['om_hour']).agg({'band':'count'}).reset_index().rename(columns={"band":"Count"}).set_index('om_hour')

# Activity per hour Per Country
df.groupby(['om_hour','band']).agg({'country':'count'}).reset_index().rename(columns={"band":"Band"}).set_index('om_hour')

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

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