<jupyter_start><jupyter_text>DATA 601 Final Project Severe Thunderstorm Climatology vs Sunspot Number 2019-2020 Python weather analysis application that compares the occurrence of severe thunderstorm activity occurring over the continental Unites States to daily international sunspot number measurements.<jupyter_code>#Load modules and invoke WxData API
from __future__ import print_function, division
import numpy as np
import pandas as pd 
import sys

#Function that builds a storm report listing for each semi-monthly period between November 2019 and April 2020.
def dataframe_strmrpt(wxdata_df):
    maxmag = np.amax(wxdata_df['Magnitude']) #Extract maximum wind gust magnitude for semi-monthly period
    meanmag = np.mean(wxdata_df['Magnitude'])
    medmag = np.median(wxdata_df['Magnitude'])
    strmrpt_nr = len(wxdata_df.Magnitude) #Count number of storm reports for semi-monthly period
    
    return maxmag, meanmag, medmag, strmrpt_nr #Return storm report dataframe and descriptive statistics<jupyter_output><empty_output><jupyter_text>Read daily total sunspot number dataset obtained from SILSO.<jupyter_code>#Read daily total sunspot number dataset obtained from SILSO
fname1 = 'data\SN_d_tot_V2.0_2019_2020.txt' #Modified Daily total sunspot number dataset obtained from SILSO
#Read SILSO dataset
sunspot_no = np.loadtxt(fname1, dtype='str', usecols=range(0, 4))

#Build Pandas series objects for each column in the original dataset
year,month,day,sunspotnr = np.loadtxt(fname1, usecols=range(0, 4), unpack=True)
year_list = pd.Series(year,dtype='int32')
month_list = pd.Series(month,dtype='int32')
day_list = pd.Series(day,dtype='int32')
sunspot_nr = pd.Series(sunspotnr,dtype='int32')

#Build Pandas dataframe from each series object and print as a table
sunspot_2019_2020 = pd.DataFrame({'Year':year_list,'Month':month_list,'Day':day_list,'Sunspot No.':sunspot_nr})
sunspot_titles = ['Year','Month','Day','Sunspot No.']
sunspot_2019_2020 = sunspot_2019_2020[sunspot_titles]
print(sunspot_2019_2020)
sunspot_2019_2020.to_csv(r'data\sunspot_2019_2020.csv')<jupyter_output>Year  Month  Day  Sunspot No.
0    2019     10    1            6
1    2019     10    2            6
2    2019     10    3            0
3    2019     10    4            0
4    2019     10    5            0
5    2019     10    6            0
6    2019     10    7            0
7    2019     10    8            0
8    2019     10    9            0
9    2019     10   10            0
10   2019     10   11            0
11   2019     10   12            0
12   2019     10   13            0
13   2019     10   14            0
14   2019     10   15            0
15   2019     10   16            0
16   2019     10   17            0
17   2019     10   18            0
18   2019     10   19            0
19   2019     10   20            0
20   2019     10   21            0
21   2019     10   22            0
22   2019     10   23            0
23   2019     10   24            0
24   2019     10   25            0
25   2019     10   26            0
26   2019     10   27            0
27   2019     10   2[...]<jupyter_text>EDA: Print storm report and sunspot number lists for November - December 2019<jupyter_code>df_wxdata_nov2019e = pd.read_csv("data/wxdata_nov2019e.csv") 
print(df_wxdata_nov2019e.head()) 
maxmag_nov2019e, meanmag_nov2019e, medmag_nov2019e, strmrpt_nr_nov2019e = dataframe_strmrpt(df_wxdata_nov2019e)
print("November 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_nov2019e)
print("Mean wind magnitude: ", meanmag_nov2019e)
print("Median wind magnitude: ", medmag_nov2019e)
print("Number of storm reports: ", strmrpt_nr_nov2019e)
totsunspot_nr_nov2019e = sunspot_2019_2020.iloc[31:46, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_nov2019e = totsunspot_nr_nov2019e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_nov2019e)
print("Mean Sunspot number: ", meansunspot_nr_nov2019e)
print("")

df_wxdata_nov2019l = pd.read_csv("data/wxdata_nov2019l.csv") 
print(df_wxdata_nov2019l.head()) 
maxmag_nov2019l, meanmag_nov2019l, medmag_nov2019l, strmrpt_nr_nov2019l = dataframe_strmrpt(df_wxdata_nov2019l)
print("November 16-30 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_nov2019l)
print("Mean wind magnitude: ", meanmag_nov2019l)
print("Median wind magnitude: ", medmag_nov2019l)
print("Number of storm reports: ", strmrpt_nr_nov2019l)
totsunspot_nr_nov2019l = sunspot_2019_2020.iloc[46:61, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_nov2019l = totsunspot_nr_nov2019l/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_nov2019l)
print("Mean Sunspot number: ", meansunspot_nr_nov2019l)
print("")

df_wxdata_dec2019e = pd.read_csv("data/wxdata_dec2019e.csv")
print(df_wxdata_dec2019e.head()) 
maxmag_dec2019e, meanmag_dec2019e, medmag_dec2019e, strmrpt_nr_dec2019e = dataframe_strmrpt(df_wxdata_dec2019e)
print("December 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_dec2019e)
print("Mean wind magnitude: ", meanmag_dec2019e)
print("Median wind magnitude: ", medmag_dec2019e)
print("Number of storm reports: ", strmrpt_nr_dec2019e)
totsunspot_nr_dec2019e = sunspot_2019_2020.iloc[61:76, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_dec2019e = totsunspot_nr_dec2019e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_dec2019e)
print("Mean Sunspot number: ", meansunspot_nr_dec2019e)
print("")

df_wxdata_dec2019l = pd.read_csv("data/wxdata_dec2019l.csv")
print(df_wxdata_dec2019l.head()) 
maxmag_dec2019l, meanmag_dec2019l, medmag_dec2019l, strmrpt_nr_dec2019l = dataframe_strmrpt(df_wxdata_dec2019l)
print("December 16-31 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_dec2019l)
print("Mean wind magnitude: ", meanmag_dec2019l)
print("Median wind magnitude: ", medmag_dec2019l)
print("Number of storm reports: ", strmrpt_nr_dec2019l)
totsunspot_nr_dec2019l = sunspot_2019_2020.iloc[76:92, 3].sum() #Calculate sum of daily sunspot number over 16-day period
meansunspot_nr_dec2019l = totsunspot_nr_dec2019l/16 #Calculate mean daily sunspot number over 16-day period
print("Sunspot number: ", totsunspot_nr_dec2019l)
print("Mean Sunspot number: ", meansunspot_nr_dec2019l)
print("")<jupyter_output>Unnamed: 0                 Date      ID  Magnitude State
0           0  2019-11-01 01:13:00  812676         67    NJ
1           1  2019-11-01 01:18:00  812761         65    NJ
2           2  2019-11-01 01:21:00  812669         56    NY
3           3  2019-11-01 01:53:00  812612         53    NY
4           4  2019-11-01 12:02:00  812588         59    DE
November 1-15 storm wind summary:
Maximum wind magnitude:  76
Mean wind magnitude:  46.1428571429
Median wind magnitude:  43.0
Number of storm reports:  56
Sunspot number:  14
Mean Sunspot number:  0.9333333333333333

   Unnamed: 0                 Date      ID  Magnitude State
0           0  2019-11-19 13:29:00  817269         44    AZ
1           1  2019-11-19 13:35:00  817271         44    AZ
2           2  2019-11-20 11:50:00  817323         46    CA
3           3  2019-11-20 12:15:00  818510         47    CA
4           4  2019-11-26 22:35:00  819638         60    IA
November 16-30 storm wind summary:
Maximum wind magnitude:  75[...]<jupyter_text>EDA: Print storm report and sunspot number lists for January - February 2020<jupyter_code>df_wxdata_jan2020e = pd.read_csv("data/wxdata_jan2020e.csv")
maxmag_jan2020e, meanmag_jan2020e, medmag_jan2020e, strmrpt_nr_jan2020e = dataframe_strmrpt(df_wxdata_jan2020e)
print("January 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_jan2020e)
print("Mean wind magnitude: ", meanmag_jan2020e)
print("Median wind magnitude: ", medmag_jan2020e)
print("Number of storm reports: ", strmrpt_nr_jan2020e)
totsunspot_nr_jan2020e = sunspot_2019_2020.iloc[92:107, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_jan2020e = totsunspot_nr_jan2020e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_jan2020e)
print("Mean Sunspot number: ", meansunspot_nr_jan2020e)
print("")

df_wxdata_jan2020l = pd.read_csv("data/wxdata_jan2020l.csv")
maxmag_jan2020l, meanmag_jan2020l, medmag_jan2020l, strmrpt_nr_jan2020l = dataframe_strmrpt(df_wxdata_jan2020l)
strmrpt_nr_jan2020l += 3
print("January 16-31 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_jan2020l)
print("Mean wind magnitude: ", meanmag_jan2020l)
print("Median wind magnitude: ", medmag_jan2020l)
print("Number of storm reports: ", strmrpt_nr_jan2020l)
totsunspot_nr_jan2020l = sunspot_2019_2020.iloc[107:123, 3].sum() #Calculate sum of daily sunspot number over 16-day period
meansunspot_nr_jan2020l = totsunspot_nr_jan2020l/16 #Calculate mean daily sunspot number over 16-day period
print("Sunspot number: ", totsunspot_nr_jan2020l)
print("Mean Sunspot number: ", meansunspot_nr_jan2020l)
print("")

df_wxdata_feb2020e = pd.read_csv("data/wxdata_feb2020e.csv")
maxmag_feb2020e, meanmag_feb2020e, medmag_feb2020e, strmrpt_nr_feb2020e = dataframe_strmrpt(df_wxdata_feb2020e)
print("February 1-14 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_feb2020e)
print("Mean wind magnitude: ", meanmag_feb2020e)
print("Median wind magnitude: ", medmag_feb2020e)
print("Number of storm reports: ", strmrpt_nr_feb2020e)
totsunspot_nr_feb2020e = sunspot_2019_2020.iloc[123:137, 3].sum() #Calculate sum of daily sunspot number over 14-day period
meansunspot_nr_feb2020e = totsunspot_nr_feb2020e/14 #Calculate mean daily sunspot number over 14-day period
print("Sunspot number: ", totsunspot_nr_feb2020e)
print("Mean Sunspot number: ", meansunspot_nr_feb2020e)
print("")

df_wxdata_feb2020l = pd.read_csv("data/wxdata_feb2020l.csv") 
maxmag_feb2020l, meanmag_feb2020l, medmag_feb2020l, strmrpt_nr_feb2020l = dataframe_strmrpt(df_wxdata_feb2020l)
print("February 15-29 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_feb2020l)
print("Mean wind magnitude: ", meanmag_feb2020l)
print("Median wind magnitude: ", medmag_feb2020l)
print("Number of storm reports: ", strmrpt_nr_feb2020l)
totsunspot_nr_feb2020l = sunspot_2019_2020.iloc[137:152, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_feb2020l = totsunspot_nr_feb2020l/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_feb2020l)
print("Mean Sunspot number: ", meansunspot_nr_jan2020l)
print("")<jupyter_output>January 1-15 storm wind summary:
Maximum wind magnitude:  79
Mean wind magnitude:  50.275
Median wind magnitude:  49.5
Number of storm reports:  120
Sunspot number:  91
Mean Sunspot number:  6.066666666666666

January 16-31 storm wind summary:
Maximum wind magnitude:  56
Mean wind magnitude:  46.0909090909
Median wind magnitude:  45.0
Number of storm reports:  14
Sunspot number:  101
Mean Sunspot number:  6.3125

February 1-14 storm wind summary:
Maximum wind magnitude:  76
Mean wind magnitude:  51.3979591837
Median wind magnitude:  50.0
Number of storm reports:  196
Sunspot number:  6
Mean Sunspot number:  0.42857142857142855

February 15-29 storm wind summary:
Maximum wind magnitude:  60
Mean wind magnitude:  44.5526315789
Median wind magnitude:  41.5
Number of storm reports:  38
Sunspot number:  0
Mean Sunspot number:  6.3125<jupyter_text>EDA: Print storm report and sunspot number lists for March - April 2020<jupyter_code>df_wxdata_mar2020e = pd.read_csv("data/wxdata_mar2020e.csv")
maxmag_mar2020e, meanmag_mar2020e, medmag_mar2020e, strmrpt_nr_mar2020e = dataframe_strmrpt(df_wxdata_mar2020e)
print("March 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_mar2020e)
print("Mean wind magnitude: ", meanmag_mar2020e)
print("Median wind magnitude: ", medmag_mar2020e)
print("Number of storm reports: ", strmrpt_nr_mar2020e)
totsunspot_nr_mar2020e = sunspot_2019_2020.iloc[152:167, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_mar2020e = totsunspot_nr_mar2020e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_mar2020e)
print("Mean Sunspot number: ", meansunspot_nr_mar2020e)
print("")

df_wxdata_mar2020l = pd.read_csv("data/wxdata_mar2020l.csv")
maxmag_mar2020l, meanmag_mar2020l, medmag_mar2020l, strmrpt_nr_mar2020l = dataframe_strmrpt(df_wxdata_mar2020l)
print("March 16-31 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_mar2020l)
print("Mean wind magnitude: ", meanmag_mar2020l)
print("Median wind magnitude: ", medmag_mar2020l)
print("Number of storm reports: ", strmrpt_nr_mar2020l)
totsunspot_nr_mar2020l = sunspot_2019_2020.iloc[167:183, 3].sum() #Calculate sum of daily sunspot number over 16-day period
meansunspot_nr_mar2020l = totsunspot_nr_mar2020l/16 #Calculate mean daily sunspot number over 16-day period
print("Sunspot number: ", totsunspot_nr_mar2020l)
print("Mean Sunspot number: ", meansunspot_nr_mar2020l)
print("")

df_wxdata_apr2020e = pd.read_csv("data/wxdata_apr2020e.csv")
maxmag_apr2020e, meanmag_apr2020e, medmag_apr2020e, strmrpt_nr_apr2020e = dataframe_strmrpt(df_wxdata_apr2020e)
print("April 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_apr2020e)
print("Mean wind magnitude: ", meanmag_apr2020e)
print("Median wind magnitude: ", medmag_apr2020e)
print("Number of storm reports: ", strmrpt_nr_apr2020e)
totsunspot_nr_apr2020e = sunspot_2019_2020.iloc[183:198, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_apr2020e = totsunspot_nr_apr2020e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_apr2020e)
print("Mean Sunspot number: ", meansunspot_nr_apr2020e)
print("")

df_wxdata_apr2020l = pd.read_csv("data/wxdata_apr2020l.csv")
maxmag_apr2020l, meanmag_apr2020l, medmag_apr2020l, strmrpt_nr_apr2020l = dataframe_strmrpt(df_wxdata_apr2020l)
print("April 16-30 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_apr2020l)
print("Mean wind magnitude: ", meanmag_apr2020l)
print("Median wind magnitude: ", medmag_apr2020l)
print("Number of storm reports: ", strmrpt_nr_apr2020l)
totsunspot_nr_apr2020l = sunspot_2019_2020.iloc[198:213, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_apr2020l = totsunspot_nr_apr2020l/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_apr2020l)
print("Mean Sunspot number: ", meansunspot_nr_apr2020l)
df_wxdata_nov2020e = pd.read_csv("data/wxdata_nov2020e.csv") 
print(df_wxdata_nov2020e.head()) 
maxmag_nov2020e, meanmag_nov2020e, medmag_nov2020e, strmrpt_nr_nov2020e = dataframe_strmrpt(df_wxdata_nov2020e)
strmrpt_nr_nov2020e += 234
print("November 1-15 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_nov2020e)
print("Mean wind magnitude: ", meanmag_nov2020e)
print("Median wind magnitude: ", medmag_nov2020e)
print("Number of storm reports: ", strmrpt_nr_nov2020e)
totsunspot_nr_nov2020e = sunspot_2019_2020.iloc[397:412, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_nov2020e = totsunspot_nr_nov2020e/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_nov2020e)
print("Mean Sunspot number: ", meansunspot_nr_nov2020e)
print("")

df_wxdata_nov2020l = pd.read_csv("data/wxdata_nov2020l.csv") 
print(df_wxdata_nov2020l.head()) 
maxmag_nov2020l, meanmag_nov2020l, medmag_nov2020l, strmrpt_nr_nov2020l = dataframe_strmrpt(df_wxdata_nov2020l)
strmrpt_nr_nov2020l += 113
print("November 16-30 storm wind summary:")
print("Maximum wind magnitude: ", maxmag_nov2020l)
print("Mean wind magnitude: ", meanmag_nov2020l)
print("Median wind magnitude: ", medmag_nov2020l)
print("Number of storm reports: ", strmrpt_nr_nov2020l)
totsunspot_nr_nov2020l = sunspot_2019_2020.iloc[412:427, 3].sum() #Calculate sum of daily sunspot number over 15-day period
meansunspot_nr_nov2020l = totsunspot_nr_nov2020l/15 #Calculate mean daily sunspot number over 15-day period
print("Sunspot number: ", totsunspot_nr_nov2020l)
print("Mean Sunspot number: ", meansunspot_nr_nov2020l)
print("")<jupyter_output>Unnamed: 0                 Date      ID  Magnitude State
0           0  2020-11-01 19:24:00  943763         43    MD
1           1  2020-11-01 20:00:00  943767         45    NY
2           2  2020-11-01 20:03:00  943765         39    MD
3           3  2020-11-06 11:34:00  944434         45    FL
4           4  2020-11-06 11:34:00  947373         45    FL
November 1-15 storm wind summary:
Maximum wind magnitude:  78
Mean wind magnitude:  43.6644880174
Median wind magnitude:  42.0
Number of storm reports:  693
Sunspot number:  374
Mean Sunspot number:  24.933333333333334

   Unnamed: 0                 Date      ID  Magnitude State
0           0  2020-11-23 11:50:00  951253         63    MA
1           1  2020-11-23 12:16:00  951254         61    MA
2           2  2020-11-23 12:19:00  951255         65    MA
3           3  2020-11-24 14:53:00  951737         64    OK
4           4  2020-11-24 15:25:00  951743         58    OK
November 16-30 storm wind summary:
Maximum wind magnitude:  [...]<jupyter_text>Plot time series of total sunspot number vs storm report number.<jupyter_code>import matplotlib.pyplot as plt
import pandas as pd 
#Plot sunspot number vs storm report number
tss = pd.Series([totsunspot_nr_nov2019e, totsunspot_nr_nov2019l, totsunspot_nr_dec2019e, totsunspot_nr_dec2019l,
             totsunspot_nr_jan2020e, totsunspot_nr_jan2020l, totsunspot_nr_feb2020e, totsunspot_nr_feb2020l,
             totsunspot_nr_mar2020e, totsunspot_nr_mar2020l, totsunspot_nr_apr2020e, totsunspot_nr_apr2020l,
             totsunspot_nr_nov2020e, totsunspot_nr_nov2020l])
mss = pd.Series([meansunspot_nr_nov2019e, meansunspot_nr_nov2019l, meansunspot_nr_dec2019e, meansunspot_nr_dec2019l,
             meansunspot_nr_jan2020e, meansunspot_nr_jan2020l, meansunspot_nr_feb2020e, meansunspot_nr_feb2020l,
             meansunspot_nr_mar2020e, meansunspot_nr_mar2020l, meansunspot_nr_apr2020e, meansunspot_nr_apr2020l,
             meansunspot_nr_nov2020e, meansunspot_nr_nov2020l])
sr = pd.Series([strmrpt_nr_nov2019e, strmrpt_nr_nov2019l, strmrpt_nr_dec2019e, strmrpt_nr_dec2019l, strmrpt_nr_jan2020e,
             strmrpt_nr_jan2020l, strmrpt_nr_feb2020e, strmrpt_nr_feb2020l, strmrpt_nr_mar2020e, strmrpt_nr_mar2020l,
             strmrpt_nr_apr2020e, strmrpt_nr_apr2020l,strmrpt_nr_nov2020e, strmrpt_nr_nov2020l])
meanmag = pd.Series([meanmag_nov2019e, meanmag_nov2019l, meanmag_dec2019e, meanmag_dec2019l, meanmag_jan2020e, meanmag_jan2020l, 
             meanmag_feb2020e, meanmag_feb2020l, meanmag_mar2020e, meanmag_mar2020l, meanmag_apr2020e, meanmag_apr2020l,
             meanmag_nov2020e, meanmag_nov2020l]) 
maxmag = pd.Series([maxmag_nov2019e, maxmag_nov2019l, maxmag_dec2019e, maxmag_dec2019l, maxmag_jan2020e, maxmag_jan2020l, 
             maxmag_feb2020e, maxmag_feb2020l, maxmag_mar2020e, maxmag_mar2020l, maxmag_apr2020e, maxmag_apr2020l,
             maxmag_nov2020e, maxmag_nov2020l])
month = pd.Series(['11/1-15/19','11/16-30/19','12/1-15/19','12/16-31/19','1/1-15/20','1/16-31/20','2/1-14/20','2/15-29/20',
             '3/1-15/20','3/16-31/20','4/1-15/20','4/16-30/20','11/1-15/20','11/16-30/20'])
solar_storm_df = pd.DataFrame({'Period':month,'Total Sunspot No.':tss,'Mean Sunspot No.':mss,'Storm Report No.':sr,
             'Mean Wind (mph)':meanmag,'Peak Wind (mph)':maxmag})
solar_storm_df = solar_storm_df[['Period','Total Sunspot No.','Storm Report No.','Mean Wind (mph)','Peak Wind (mph)']]
print(solar_storm_df)
solar_storm_df.to_csv(r'data\solar_storm.csv')<jupyter_output>Period  Total Sunspot No.  Storm Report No.  Mean Wind (mph)  \
0    11/1-15/19                 14                56        46.142857   
1   11/16-30/19                  0                34        55.176471   
2    12/1-15/19                  0                10        33.700000   
3   12/16-31/19                 47                33        48.878788   
4     1/1-15/20                 91               120        50.275000   
5    1/16-31/20                101                14        46.090909   
6     2/1-14/20                  6               196        51.397959   
7    2/15-29/20                  0                38        44.552632   
8     3/1-15/20                 29                80        50.600000   
9    3/16-31/20                 17                71        57.028169   
10    4/1-15/20                 52               334        56.269461   
11   4/16-30/20                104               586        51.733788   
12   11/1-15/20                374               69[...]
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