def predict_freq_prcp_log(self, begin: int, end: int, guess: tuple) \
-> None:
"""Use logarithm module to predict wildfire frequency of the state till
from the year of begin to the year of end,based on the prediction of precipitation."""
fa, fb = self.prcp_freq_logarithm()
date = generate_date_list(begin, end)
# obtain the precipitation data with the given parameters
prcp_predict = self.predict_prcp(begin, end, guess)
# obtain the wildfire frequency data with the given parameters
freq_predict = [models.logarithm(x, fa, fb) for x in prcp_predict]
# show the prediction in figures
figure.figure_line(
'Prediction: wildfire frequency based on the precipitation',
'date', date, 'frequency', freq_predict)
def predict_prcp(self, begin: int, end: int, guess: tuple) -> list:
"""Prediction the precipitation of the state from the year of begin
to the year of the end."""
g = guess
date = generate_date_list(begin, end)
time_lag = calculate_time_lag_list(self.begin, begin, end)
# obtain the precipitation data with the given parameters
prcp_predict = [
models.periodic(x, g[0], g[1], g[2], g[3], g[4], g[5], g[6], g[7])
for x in time_lag
]
# show the prediction in figures
figure.figure_line('Prediction: precipitation', 'date', date,
'precipitation(mm)', prcp_predict)
return prcp_predict
def predict_freq_temp_qua(self, time_range: Tuple[int,
int], max_guess: tuple,
min_guess: tuple, mean_guess: tuple) -> None:
"""Use quadratic module to predict wildfire frequency of the state till
from the year of begin to the year of end,based on the prediction of temperature."""
begin, end = time_range
temp_predict = self.predict_temp((begin, end), max_guess, min_guess,
mean_guess)
f_max, f_min, f_mean = self.temp_freq_quadratic()
date = generate_date_list(begin, end)
# obtain the wildfire frequency data with the given parameters
freq_predict1 = [
models.quadratic(x, f_max[0], f_max[1], f_max[2])
for x in temp_predict[0]
]
freq_predict2 = [
models.quadratic(x, f_min[0], f_min[1], f_min[2])
for x in temp_predict[1]
]
freq_predict3 = [
models.quadratic(x, f_mean[0], f_mean[1], f_mean[2])
for x in temp_predict[2]
]
# show the prediction in figures
figure.figure_line(
'Prediction: wildfire frequency based on the max temperature',
'date', date, 'frequency', freq_predict1)
figure.figure_line(
'Prediction: wildfire frequency based on the min temperature',
'date', date, 'frequency', freq_predict2)
figure.figure_line(
'Prediction: wildfire frequency based on the mean temperature',
'date', date, 'frequency', freq_predict3)
def predict_temp(self, time_range: Tuple[int, int], max_guess: tuple,
min_guess: tuple, mean_guess: tuple) -> tuple:
"""Prediction the temperature of the state from the year of begin to the year of the end."""
g1 = max_guess
g2 = min_guess
g3 = mean_guess
date = generate_date_list(time_range[0], time_range[1])
time_lag = calculate_time_lag_list(self.begin, time_range[0],
time_range[1])
# obtain the temperature data with the given parameters
temp_predict1 = [
models.periodic(x, g1[0], g1[1], g1[2], g1[3], g1[4], g1[5], g1[6],
g1[7]) for x in time_lag
]
temp_predict2 = [
models.periodic(x, g2[0], g2[1], g2[2], g2[3], g2[4], g2[5], g2[6],
g2[7]) for x in time_lag
]
temp_predict3 = [
models.periodic(x, g3[0], g3[1], g3[2], g3[3], g3[4], g3[5], g3[6],
g3[7]) for x in time_lag
]
# show the prediction in figures
figure.figure_line('Prediction: temperature (max mean min)', 'date',
date, 'temp(Fahrenheit)', temp_predict1)
figure.figure_line('Prediction: temperature (max mean min)', 'date',
date, 'temp(Fahrenheit)', temp_predict2)
figure.figure_line('Prediction: temperature (max mean min)', 'date',
date, 'temp(Fahrenheit)', temp_predict3)
return (temp_predict1, temp_predict2, temp_predict3)