import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from datetime import datetime
import pandas as pd
import sys
from core import FeedbackAnalysis as FA
from importlib import reload
reload(FA)
df = FA.FeedbackAnalysis(uptake=('TRENDY', 'LPJ-GUESS_S1_nbp'),
temp='CRUTEM',
time='year',
sink="Earth_Land",
time_range=slice("2008", "2017"))
df.data
df.U
len(df.uptake), len(df.temp), len(df.CO2)
df.data
df.model.summary()
df.data
df.model.params
df.confidence_intervals('const', alpha)
df.model.summary()
time_ranges.pop(-1)
time_ranges.append((str(time_stop - 10), str(time_stop)))
return time_ranges
""" EXECUTION """
for input in tqdm(inputs):
uptake, tempsink, time = input
temp, sink = tempsink
time_start, time_stop = timerange_inputs[uptake[0]][uptake[1]]
time_start = int(time_start)
time_stop = int(time_stop)
time_ranges = build_time_ranges(time_start, time_stop)
# [str(max(1959, time_start)), '2017']
for time_range in time_ranges:
if time == "month":
# Grab last month of previous year as end point.
month_tr = str(int(time_range[1]) - 1)
time_range = (f'{time_range[0]}-01', f'{month_tr}-12')
slice_time_range = slice(*time_range)
df = FA.FeedbackAnalysis(uptake=uptake,
temp=temp,
time=time,
sink=sink,
time_range=slice_time_range)
df.feedback_output()