def process_tweet_paraphrase(input_path, output_path):
raw_tweet_dataset = read_csv(input_path, load_header=True, delimiter="\t")
tweet_dataset = [[
idx,
clean_tweet_text(row[2]),
clean_tweet_text(row[3]),
raw_label_map(row[4])
] for idx, row in enumerate(raw_tweet_dataset)]
header = ["index", "sent1", "sent2", "label"]
write_csv(tweet_dataset, header, output_path, delimiter="\t")
sheet.write(19, 0, 'Species')
sheet.write(19, 1, 'Diameter (cm)')
sheet.write(19, 2, 'Trees/ha')
sheet.write(19, 3, 'Height (m)')
sheet.write(19, 4, 'Total Age')
sheet.write(19, 5, 'BHAge')
###############################################################################
book = Workbook()
for plotfile in glob('*.csv'):
plot, _ = plotfile.split('.')
meas = read_csv(plotfile)
sheet = book.add_sheet(plot)
mgm_sheet(sheet, plot, 0, 0)
for i, r in enumerate(meas):
sheet.write(20+i, 0, r.species) # species
sheet.write(20+i, 1, float(r.dbh)/10.0) # diameter
sheet.write(20+i, 2, 10.0) # trees/ha
sheet.write(20+i, 3, 10.0) # height
sheet.write(20+i, 4, 10.0) # total age
sheet.write(20+i, 5, 10.0) # bhage
book.save('MGM Stands.xls')
def __init__(self):
Kernel.__init__(self)
self.time_dependent = True
# read climate data and measurements (cm^2)
climate = read_csv('kernels/artr/climate.csv')
measurements = read_csv('kernels/artr/survivalDataARTR.csv')
years = sorted(list(set([ int(x.year) for x in measurements ])))
fecundity_measurements = read_csv('kernels/artr/fecdat.csv')
log_areas = {}
for year in years:
log_areas[year] = [ float(x.logArea) for x in measurements
if int(x.year) == year ]
# set attributes
self.n0 = log_areas[years[0]]
self.log_areas = log_areas
self.years = years
self.climate = climate
self.fec_meas = fecundity_measurements
self.mortality_type = 'noexp'
self.fecundity_type = 'uniform_exp'
self._tweak_covariat = None
self._tweak_factor = 1.0
# set parameters
self.params = {
'growth': {
'mean': np.array(
[ -6.134753578, 0.765515413, 0.006574618, 0.362328659 ]),
'covariance': np.array(
[[ 2.3003264050, -3.551420e-04, -9.540608e-04, -1.202835e-01 ],
[ -0.0003551420, 3.699543e-04, 1.274024e-06, -6.374569e-05 ],
[ -0.0009540608, 1.274024e-06, 3.545569e-06, 3.898729e-05 ],
[ -0.1202834507, -6.374569e-05, 3.898729e-05, 6.350332e-03 ]])
},
'survival': {
'mean': np.array(
[ -1.482555395, 0.753372235, 0.003454076, -0.001100492 ]),
'covariance':
np.array(
[[ 1.149244e-01, -1.422819e-02, -4.075111e-04, 5.042504e-05 ],
[ -1.422819e-02, 1.058307e-02, 5.006554e-05, -3.693585e-05 ],
[ -4.075111e-04, 5.006554e-05, 1.520893e-06, -1.896803e-07 ],
[ 5.042504e-05, -3.693585e-05, -1.896803e-07, 1.351324e-07 ]])
},
'fecundity': {
'mean': np.array(
[ 3.16420466, 0.06773083, 0.02146190 ]),
'covariance':
np.array(
[[ 2.900329e-02, 3.790527e-04, 2.113102e-05 ],
[ 3.790527e-04, 1.101385e-04, 8.486999e-06 ],
[ 2.113102e-05, 8.486999e-06, 1.079253e-05 ]])
}
}
# default parameters
self.growth_params = self.params['growth']['mean']
self.survival_params = self.params['survival']['mean']
self.fecundity_params = self.params['fecundity']['mean']
