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040-group_tseries.py
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040-group_tseries.py
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import pandas as pd
import numpy as np
import datetime as datetime
import matplotlib.pyplot as plt
# Creating a panel of timeseries for each group of stations.
# Panel will have a timeseries of 00,06,12,18 ws if that hour has at least 14
# obs per month.
# An average over the group will be an extra plot in the panel.
NAl=['60525Biskra','60549Mecheria','60550Elbayadh',
'60555Touggourt','60559ElOued','60566Ghardaia',
'60580Ouargla','60581HassiMessaoud']
CSar=['60607Timimoun','60611InAmenas','60620Adrar','60630InSalah',
'62103Ghadames','62124Sebha']
WSa=['61223Tombouctou','61226Gao','61230NioroDuSahel','61498Kiffa',
'61499AiounElAtrouss','61492Kaedi','61497Nema','61450Tidjika']
CSal=['61024Agadez','61045Goure','61052Niamey','64753Faya',
'61017Bilma']
Egy=['62387Minya','62393Asyut','62405Luxor','62414Asswan',
'62420Baharia','62423Farafra','62435Kharga']
Sud=['62600WadiHalfa','62640AbuHamed','62650Dongola','62660Karima',
'62680Atbara']
stations=[NAl,CSar,WSa,CSal,Egy,Sud]
#stations = [CSal]
group_names={'NAlgeria':NAl,'CSahara':CSar,'WSahel':WSa,'CSahel':CSal,
'Egypt':Egy,'Sudan':Sud}
group_strings=['NAlgeria','CSahara','WSahel','CSahel', 'Egypt','Sudan']
#group_strings=['CSahara','WSahel']
# Could these two functions be turned into lambda functions?
# Would that be preferable or are these fine?
def meanf(x):
if x.count() > 10:
return x.mean()
def sdf(x):
if x.count() > 10:
return x.std()
def read_file(fname):
'''put the station name into read_file and read_file will return a
dataFrame called wind which has the following columns a dataframe with a
datetime index'''
column_names=["year","month","day","hour","ws"]
dtype={"year":int,"month":int,"day":int,"hour":int,"ws":float}
datafile='/home/sophie/projects/windspeed/data/%s_allwinds.txt' %fname
# specify the columns you want to group together. Can't include hour at
# this point as it is not in the right format.
date_spec = {'date_time': [0,1,2]}
# when you use keep_dat_col it keeps them as objects, not as the dtype you
# read them in as.
wind = pd.read_csv(datafile, sep=" ", names=column_names,
parse_dates=date_spec, keep_date_col=True, index_col=False )
# Dealing with hour - going from 600, 1200 etc to 6,12, 18
wind["hour"]=(wind["hour"]/100).astype(int)
# combining year, month, day that were parsed together into date_time with
# hour, which is now in the correct format.
wind['date_time'] = pd.to_datetime(wind.date_time) + \
wind.hour.astype('timedelta64[h]')
# make datetime the index before making subsections.
wind.index = wind['date_time']
# drop date_time index. For some reason it caused a problem at Niamey if I
# didn't.
#wind.drop('date_time', axis=1, inplace=True)
#Also a good idea to drop duplicate columns.
# For this case, where the datetime object is the same it needs to be
# dropped, otherwise it doesn't let you add more columns, as in
# wind['ws_0'] etc. below
wind.drop_duplicates(['date_time'],inplace=True)
# Adds extra rows where value is kept if it meets isin() criteria. Nan if
# it doesn't.
wind['ws_0']= wind['ws'][wind['hour'].isin([0])]
wind['ws_06']= wind['ws'][wind['hour'].isin([6])]
wind['ws_12']= wind['ws'][wind['hour'].isin([12])]
wind['ws_18']= wind['ws'][wind['hour'].isin([18])]
group = wind.groupby(['year', 'month'])
wind_group = group['ws','ws_0','ws_06','ws_12','ws_18'].agg([meanf,sdf])
return wind_group
def plot_tseries(group):
'''set up n+1 subplots where n is number of stations in the group. Fill in
each plot with timeseries from each station and then a mean of all the
stations. Output to file eps.'''
fig = plt.figure(figsize=(10,10))
for i in range(len(group)):
# just for testing, see what group we are on
print(group_strings[j])
print(type(group))
print(group[i])
# read in one station from the group, read_file will create a group by
# object ready for plotting
wind_group = read_file(group[i])
# check that there is data for the time period of interest
#assert len(wind_group['1990':'1994']) != 0, ('No data for %s in this '
# 'time period so no plot!'% group[i])
if len(wind_group['1990':'1994']) != 0:
# Dump the month part of the index to make the xaxis less crowded
wind_group.index = wind_group.index.droplevel(['month'])
# fig.add_subplot(nrows, ncols, num)
ax = fig.add_subplot(int((len(group)+1)/2), 2, i+1)
plt.title(s=group[i], fontsize=15)
# May not need the if statements if I can solve the x problem below.
# No, I do, so if there are no data in that time period it will be
# caught - as in Ouargla!
#print(len(wind_group.ws_0['meanf']))
wind_group.ws_0['meanf']['1990':'1994'].plot(figsize=(8,8),c='m')
wind_group.ws_06['meanf']['1990':'1994'].plot(figsize=(8,8), c='r')
wind_group.ws_12['meanf']['1990':'1994'].plot(figsize=(8,8),c='b')
wind_group.ws_18['meanf']['1990':'1994'].plot(figsize=(8,8), c='c')
ax.legend(loc=4,bbox_to_anchor=(0.95, 1.05),labels
= ['00','06','12','18'],prop={'size':6})
plt.tight_layout() # very nice! stops the titles overlapping
fig.suptitle(group_strings[j])
fig.savefig('/home/sophie/projects/windspeed/'
'output/%s.png'%(group_strings[j]),dpi=125)
if __name__ == '__main__':
# x is coming as a list and we need it as just an object name.
for j,x in enumerate(stations): plot_tseries(x)
#plot_tseries(NAl)