文章目录
- 介绍
- 注意
- 导入数据
- 函数模块
- 画图
介绍
python语言的科研绘图合集
注意
This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"):
- "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series.
- "Script.py":
BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files.
NOTE: This script requires the Python module: 'pymannkendall'
Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below).
- "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum.
- "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach.
- "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/
- Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.
导入数据
数据可从以下链接下载（画图所需要的所有数据）：
-
百度云盘链接: https://pan.baidu.com/s/1D9qBDOIxDRGtuvUMVKtxBA
-
提取码: fhp3
函数模块
讲下面代码存成tol_colors.py
import numpy as np
from matplotlib.colors import LinearSegmentedColormap, to_rgba_array
def discretemap(colormap, hexclrs):
"""
Produce a colormap from a list of discrete colors without interpolation.
"""
clrs = to_rgba_array(hexclrs)
clrs = np.vstack([clrs[0], clrs, clrs[-1]])
cdict = {}
for ki, key in enumerate(('red','green','blue')):
cdict[key] = [ (i/(len(clrs)-2.), clrs[i, ki], clrs[i+1, ki]) for i in range(len(clrs)-1) ]
return LinearSegmentedColormap(colormap, cdict)
class TOLcmaps(object):
"""
Class TOLcmaps definition.
"""
def __init__(self):
"""
"""
self.cmap = None
self.cname = None
self.namelist = (
'sunset_discrete', 'sunset', 'BuRd_discrete', 'BuRd',
'PRGn_discrete', 'PRGn', 'YlOrBr_discrete', 'YlOrBr', 'WhOrBr',
'iridescent', 'rainbow_PuRd', 'rainbow_PuBr', 'rainbow_WhRd',
'rainbow_WhBr', 'rainbow_discrete')
self.funcdict = dict(
zip(self.namelist,
(self.__sunset_discrete, self.__sunset, self.__BuRd_discrete,
self.__BuRd, self.__PRGn_discrete, self.__PRGn,
self.__YlOrBr_discrete, self.__YlOrBr, self.__WhOrBr,
self.__iridescent, self.__rainbow_PuRd, self.__rainbow_PuBr,
self.__rainbow_WhRd, self.__rainbow_WhBr,
self.__rainbow_discrete)))
def __sunset_discrete(self):
"""
Define colormap 'sunset_discrete'.
"""
clrs = ['#364B9A', '#4A7BB7', '#6EA6CD', '#98CAE1', '#C2E4EF',
'#EAECCC', '#FEDA8B', '#FDB366', '#F67E4B', '#DD3D2D',
'#A50026']
self.cmap = discretemap(self.cname, clrs)
self.cmap.set_bad('#FFFFFF')
def __sunset(self):
"""
Define colormap 'sunset'.
"""
clrs = ['#364B9A', '#4A7BB7', '#6EA6CD', '#98CAE1', '#C2E4EF',
'#EAECCC', '#FEDA8B', '#FDB366', '#F67E4B', '#DD3D2D',
'#A50026']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#FFFFFF')
def __BuRd_discrete(self):
"""
Define colormap 'BuRd_discrete'.
"""
clrs = ['#2166AC', '#4393C3', '#92C5DE', '#D1E5F0', '#F7F7F7',
'#FDDBC7', '#F4A582', '#D6604D', '#B2182B']
self.cmap = discretemap(self.cname, clrs)
self.cmap.set_bad('#FFEE99')
def __BuRd(self):
"""
Define colormap 'BuRd'.
"""
clrs = ['#2166AC', '#4393C3', '#92C5DE', '#D1E5F0', '#F7F7F7',
'#FDDBC7', '#F4A582', '#D6604D', '#B2182B']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#FFEE99')
def __PRGn_discrete(self):
"""
Define colormap 'PRGn_discrete'.
"""
clrs = ['#762A83', '#9970AB', '#C2A5CF', '#E7D4E8', '#F7F7F7',
'#D9F0D3', '#ACD39E', '#5AAE61', '#1B7837']
self.cmap = discretemap(self.cname, clrs)
self.cmap.set_bad('#FFEE99')
def __PRGn(self):
"""
Define colormap 'PRGn'.
"""
clrs = ['#762A83', '#9970AB', '#C2A5CF', '#E7D4E8', '#F7F7F7',
'#D9F0D3', '#ACD39E', '#5AAE61', '#1B7837']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#FFEE99')
def __YlOrBr_discrete(self):
"""
Define colormap 'YlOrBr_discrete'.
"""
clrs = ['#FFFFE5', '#FFF7BC', '#FEE391', '#FEC44F', '#FB9A29',
'#EC7014', '#CC4C02', '#993404', '#662506']
self.cmap = discretemap(self.cname, clrs)
self.cmap.set_bad('#888888')
def __YlOrBr(self):
"""
Define colormap 'YlOrBr'.
"""
clrs = ['#FFFFE5', '#FFF7BC', '#FEE391', '#FEC44F', '#FB9A29',
'#EC7014', '#CC4C02', '#993404', '#662506']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#888888')
def __WhOrBr(self):
"""
Define colormap 'WhOrBr'.
"""
clrs = ['#FFFFFF', '#FFF7BC', '#FEE391', '#FEC44F', '#FB9A29',
'#EC7014', '#CC4C02', '#993404', '#662506']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#888888')
def __iridescent(self):
"""
Define colormap 'iridescent'.
"""
clrs = ['#FEFBE9', '#FCF7D5', '#F5F3C1', '#EAF0B5', '#DDECBF',
'#D0E7CA', '#C2E3D2', '#B5DDD8', '#A8D8DC', '#9BD2E1',
'#8DCBE4', '#81C4E7', '#7BBCE7', '#7EB2E4', '#88A5DD',
'#9398D2', '#9B8AC4', '#9D7DB2', '#9A709E', '#906388',
'#805770', '#684957', '#46353A']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#999999')
def __rainbow_PuRd(self):
"""
Define colormap 'rainbow_PuRd'.
"""
clrs = ['#6F4C9B', '#6059A9', '#5568B8', '#4E79C5', '#4D8AC6',
'#4E96BC', '#549EB3', '#59A5A9', '#60AB9E', '#69B190',
'#77B77D', '#8CBC68', '#A6BE54', '#BEBC48', '#D1B541',
'#DDAA3C', '#E49C39', '#E78C35', '#E67932', '#E4632D',
'#DF4828', '#DA2222']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#FFFFFF')
def __rainbow_PuBr(self):
"""
Define colormap 'rainbow_PuBr'.
"""
clrs = ['#6F4C9B', '#6059A9', '#5568B8', '#4E79C5', '#4D8AC6',
'#4E96BC', '#549EB3', '#59A5A9', '#60AB9E', '#69B190',
'#77B77D', '#8CBC68', '#A6BE54', '#BEBC48', '#D1B541',
'#DDAA3C', '#E49C39', '#E78C35', '#E67932', '#E4632D',
'#DF4828', '#DA2222', '#B8221E', '#95211B', '#721E17',
'#521A13']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#FFFFFF')
def __rainbow_WhRd(self):
"""
Define colormap 'rainbow_WhRd'.
"""
clrs = ['#E8ECFB', '#DDD8EF', '#D1C1E1', '#C3A8D1', '#B58FC2',
'#A778B4', '#9B62A7', '#8C4E99', '#6F4C9B', '#6059A9',
'#5568B8', '#4E79C5', '#4D8AC6', '#4E96BC', '#549EB3',
'#59A5A9', '#60AB9E', '#69B190', '#77B77D', '#8CBC68',
'#A6BE54', '#BEBC48', '#D1B541', '#DDAA3C', '#E49C39',
'#E78C35', '#E67932', '#E4632D', '#DF4828', '#DA2222']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#666666')
def __rainbow_WhBr(self):
"""
Define colormap 'rainbow_WhBr'.
"""
clrs = ['#E8ECFB', '#DDD8EF', '#D1C1E1', '#C3A8D1', '#B58FC2',
'#A778B4', '#9B62A7', '#8C4E99', '#6F4C9B', '#6059A9',
'#5568B8', '#4E79C5', '#4D8AC6', '#4E96BC', '#549EB3',
'#59A5A9', '#60AB9E', '#69B190', '#77B77D', '#8CBC68',
'#A6BE54', '#BEBC48', '#D1B541', '#DDAA3C', '#E49C39',
'#E78C35', '#E67932', '#E4632D', '#DF4828', '#DA2222',
'#B8221E', '#95211B', '#721E17', '#521A13']
self.cmap = LinearSegmentedColormap.from_list(self.cname, clrs)
self.cmap.set_bad('#666666')
def __rainbow_discrete(self, lut=None):
"""
Define colormap 'rainbow_discrete'.
"""
clrs = ['#E8ECFB', '#D9CCE3', '#D1BBD7', '#CAACCB', '#BA8DB4',
'#AE76A3', '#AA6F9E', '#994F88', '#882E72', '#1965B0',
'#437DBF', '#5289C7', '#6195CF', '#7BAFDE', '#4EB265',
'#90C987', '#CAE0AB', '#F7F056', '#F7CB45', '#F6C141',
'#F4A736', '#F1932D', '#EE8026', '#E8601C', '#E65518',
'#DC050C', '#A5170E', '#72190E', '#42150A']
indexes = [[9], [9, 25], [9, 17, 25], [9, 14, 17, 25], [9, 13, 14, 17,
25], [9, 13, 14, 16, 17, 25], [8, 9, 13, 14, 16, 17, 25], [8,
9, 13, 14, 16, 17, 22, 25], [8, 9, 13, 14, 16, 17, 22, 25, 27],
[8, 9, 13, 14, 16, 17, 20, 23, 25, 27], [8, 9, 11, 13, 14, 16,
17, 20, 23, 25, 27], [2, 5, 8, 9, 11, 13, 14, 16, 17, 20, 23,
25], [2, 5, 8, 9, 11, 13, 14, 15, 16, 17, 20, 23, 25], [2, 5,
8, 9, 11, 13, 14, 15, 16, 17, 19, 21, 23, 25], [2, 5, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 7, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 7, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 26, 27], [1, 3, 4, 6, 7, 8,
9, 11, 13, 14, 15, 16, 17, 19, 21, 23, 25, 26, 27], [1, 3, 4,
6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 23, 25, 26,
27], [1, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 20,
22, 24, 25, 26, 27], [1, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15,
16, 17, 18, 20, 22, 24, 25, 26, 27, 28], [0, 1, 3, 4, 6, 7, 8,
9, 10, 12, 13, 14, 15, 16, 17, 18, 20, 22, 24, 25, 26, 27, 28]]
if lut == None or lut < 1 or lut > 23:
lut = 22
self.cmap = discretemap(self.cname, [ clrs[i] for i in indexes[lut-1] ])
if lut == 23:
self.cmap.set_bad('#777777')
else:
self.cmap.set_bad('#FFFFFF')
def show(self):
"""
List names of defined colormaps.
"""
print(' '.join(repr(n) for n in self.namelist))
def get(self, cname='rainbow_PuRd', lut=None):
"""
Return requested colormap, default is 'rainbow_PuRd'.
"""
self.cname = cname
if cname == 'rainbow_discrete':
self.__rainbow_discrete(lut)
else:
self.funcdict[cname]()
return self.cmap
def tol_cmap(colormap=None, lut=None):
"""
Continuous and discrete color sets for ordered data.
Return a matplotlib colormap.
Parameter lut is ignored for all colormaps except 'rainbow_discrete'.
"""
obj = TOLcmaps()
if colormap == None:
return obj.namelist
if colormap not in obj.namelist:
colormap = 'rainbow_PuRd'
print('*** Warning: requested colormap not defined,',
'known colormaps are {}.'.format(obj.namelist),
'Using {}.'.format(colormap))
return obj.get(colormap, lut)
def tol_cset(colorset=None):
"""
Discrete color sets for qualitative data.
Define a namedtuple instance with the colors.
Examples for: cset = tol_cset(<scheme>)
- cset.red and cset[1] give the same color (in default 'bright' colorset)
- cset._fields gives a tuple with all color names
- list(cset) gives a list with all colors
"""
from collections import namedtuple
namelist = ('bright', 'high-contrast', 'vibrant', 'muted', 'medium-contrast', 'light')
if colorset == None:
return namelist
if colorset not in namelist:
colorset = 'bright'
print('*** Warning: requested colorset not defined,',
'known colorsets are {}.'.format(namelist),
'Using {}.'.format(colorset))
if colorset == 'bright':
cset = namedtuple('Bcset',
'blue red green yellow cyan purple grey black')
return cset('#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE',
'#AA3377', '#BBBBBB', '#000000')
if colorset == 'high-contrast':
cset = namedtuple('Hcset',
'blue yellow red black')
return cset('#004488', '#DDAA33', '#BB5566', '#000000')
if colorset == 'vibrant':
cset = namedtuple('Vcset',
'orange blue cyan magenta red teal grey black')
return cset('#EE7733', '#0077BB', '#33BBEE', '#EE3377', '#CC3311',
'#009988', '#BBBBBB', '#000000')
if colorset == 'muted':
cset = namedtuple('Mcset',
'rose indigo sand green cyan wine teal olive purple pale_grey black')
return cset('#CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE',
'#882255', '#44AA99', '#999933', '#AA4499', '#DDDDDD',
'#000000')
if colorset == 'medium-contrast':
cset = namedtuple('Mcset',
'light_blue dark_blue light_yellow dark_red dark_yellow light_red black')
return cset('#6699CC', '#004488', '#EECC66', '#994455', '#997700',
'#EE99AA', '#000000')
if colorset == 'light':
cset = namedtuple('Lcset',
'light_blue orange light_yellow pink light_cyan mint pear olive pale_grey black')
return cset('#77AADD', '#EE8866', '#EEDD88', '#FFAABB', '#99DDFF',
'#44BB99', '#BBCC33', '#AAAA00', '#DDDDDD', '#000000')
def main():
from matplotlib import pyplot as plt
# Change default colorset (for lines) and colormap (for maps).
# plt.rc('axes', prop_cycle=plt.cycler('color', list(tol_cset('bright'))))
# plt.cm.register_cmap('rainbow_PuRd', tol_cmap('rainbow_PuRd'))
# plt.rc('image', cmap='rainbow_PuRd')
# Show colorsets tol_cset(<scheme>).
schemes = tol_cset()
fig, axes = plt.subplots(ncols=len(schemes), figsize=(9, 3))
fig.subplots_adjust(top=0.9, bottom=0.02, left=0.02, right=0.92)
for ax, scheme in zip(axes, schemes):
cset = tol_cset(scheme)
names = cset._fields
colors = list(cset)
for name, color in zip(names, colors):
ax.scatter([], [], c=color, s=80, label=name)
ax.set_axis_off()
ax.legend(loc=2)
ax.set_title(scheme)
plt.show()
# Show colormaps tol_cmap(<scheme>).
schemes = tol_cmap()
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
fig, axes = plt.subplots(nrows=len(schemes))
fig.subplots_adjust(top=0.98, bottom=0.02, left=0.2, right=0.99)
for ax, scheme in zip(axes, schemes):
pos = list(ax.get_position().bounds)
ax.set_axis_off()
ax.imshow(gradient, aspect=4, cmap=tol_cmap(scheme))
fig.text(pos[0] - 0.01, pos[1] + pos[3]/2., scheme, va='center', ha='right', fontsize=10)
plt.show()
# Show colormaps tol_cmap('rainbow_discrete', <lut>).
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
fig, axes = plt.subplots(nrows=23)
fig.subplots_adjust(top=0.98, bottom=0.02, left=0.25, right=0.99)
for lut, ax in enumerate(axes, start=1):
pos = list(ax.get_position().bounds)
ax.set_axis_off()
ax.imshow(gradient, aspect=4, cmap=tol_cmap('rainbow_discrete', lut))
fig.text(pos[0] - 0.01, pos[1] + pos[3]/2., 'rainbow_discrete, ' + str(lut), va='center', ha='right', fontsize=10)
plt.show()
if __name__ == '__main__':
main()
画图
# Python standard library
import os
import io
import sys
import time as timer
from zipfile import ZipFile
from collections import OrderedDict
# Scientific modules
import numpy as np
import pandas as pd
import scipy.optimize
import scipy.stats
import scipy.ndimage
import pymannkendall as mk
import matplotlib.pyplot as plt
import matplotlib.colors as pltc
import matplotlib as mpl
from matplotlib.ticker import MultipleLocator
import matplotlib
# Change standard values
plt.rc('image', interpolation='none') # Change default image interpolation
mm = 1/25.4 # Conversion factor inches to mm for figure sizes.
# Set working directory
wdir = '/Path/to/directory/' # Change to directory containing this script and the data.
sys.path.append(wdir)
# Additional modules provided with this script
import tol_colors
# Define functions
def anom(x, order=2):
''' Split time series into anomaly and trend components
calculation of anomaly by removal of 2-order polynomial trend and climatology
:param x: time series [1D array]
:param order: order of polynomial fit [int] (default=2)
:return: x_A (anomaly component), x_T (trend component)
'''
t_m = range(len(x))
fitx = np.polyfit(t_m, x, order)
fit_fn = np.poly1d(fitx) # fit function
x_T = fit_fn(t_m) # x_T is trend component
x_F = x - x_T # removal of trend
x_C = np.mean(np.reshape(x_F, (int(len(t_m) / 12.), 12)), 0) # x_C is climatology
x_A = x_F - np.tile(x_C, int(len(t_m) / 12.)) # x_A is anomaly, removal of climatology
return x_A, x_T
def norm(x):
''' Normalize time series
:param x: time series [1D array]
:return: normalized time series
'''
return (x - np.mean(x)) / np.std(x)
def tomonthly(x):
''' Interpolate annual flux to monthly flux
:param x: annual time series [1D array]
:return: interpolated monthly time series
'''
return np.interp(np.linspace(0, len(x)-1/12., len(x)*12), np.linspace(0, len(x)-1, len(x)), x) / 12.
def amean(x):
''' Calculate annual average
:param x: monthly time series [1D array]
:return: annual mean
'''
if len(x) % 12 == 0:
return np.nanmean(np.reshape(x, (int(len(x) / 12.), 12)), 1) # annual average
else:
return x
def lambdafn(x_A, ENSOI_m, VOLC_m, inverse=True):
''' Function for determination of lambda
:param x_A: time series anomaly [1D array]
:param ENSOI_m: monthly ENSO index [1D array]
:param VOLC_m: monthly volcanic index [1D array]
:param inverse: minimize based on the inverse correlation coefficient [bool] (default=True)
:return: lambda and corresponding maximum correlation
'''
if inverse == True:
def fuevi(lam): return 1-np.corrcoef((ENSOI_m + lam * VOLC_m, x_A))[0,1]
elif inverse == False:
def fuevi(lam): return np.corrcoef((ENSOI_m + lam * VOLC_m, x_A))[0,1]
lam_maxcor = scipy.optimize.minimize(fuevi, x0=0.5)['x'][0] # maximize correlation between EVI and x_A
maxcor = fuevi(lam_maxcor)
return lam_maxcor, maxcor
def filterfn(x_A, x_T, ref):
'''
Filtering of timeseries by ENSO or EVI index.
:param x_A: time series anomaly component [1D array]
:param x_T: time series trend component [1D array]
:param ref: reference index = ENSOI or EVI [1D array]
:return: filtered time series and corresponding mu of minimum variance
'''
def fu2(mu): return np.var(x_A - mu*ref)
mu_minvar = scipy.optimize.minimize(fu2, x0=1)['x'][0] # determine mu by minimization of variance.
x_U = x_A - mu_minvar*ref
x_n = x_T + x_U # x_n is the filtered anomaly
return x_n, mu_minvar
def rgba2rgb(rgba, background=None):
''' Convert rgba values to rgb. Source: https://stackoverflow.com/questions/50331463/convert-rgba-to-rgb-in-python
:param rgba: rgba values [array]
:param background: background rgb value [tuple] (default = (255,255,255))
:return: rgb values
'''
if background is None:
background = (255, 255, 255)
R, G, B = background
if rgba.ndim == 3: row, col, ch = rgba.shape # 2d array of colors.
elif rgba.ndim == 2: row, ch = rgba.shape # 1d array of colors.
elif rgba.ndim == 1: ch, = rgba.shape
if ch == 3:
return rgba
assert ch == 4, 'RGBA image has 4 channels.'
if rgba.ndim == 3:
rgb = np.zeros((row, col, 3), dtype='float32')
r, g, b, a = rgba[:, :, 0], rgba[:, :, 1], rgba[:, :, 2], rgba[:, :, 3]
a = np.asarray(a, dtype='float32') / 255.
rgb[:, :, 0] = r * a + (1.0 - a) * R
rgb[:, :, 1] = g * a + (1.0 - a) * G
rgb[:, :, 2] = b * a + (1.0 - a) * B
elif rgba.ndim == 2:
rgb = np.zeros((row, 3), dtype='float32')
r, g, b, a = rgba[:, 0], rgba[:, 1], rgba[:, 2], rgba[:, 3]
a = np.asarray(a, dtype='float32') / 255.
rgb[:, 0] = r * a + (1.0 - a) * R
rgb[:, 1] = g * a + (1.0 - a) * G
rgb[:, 2] = b * a + (1.0 - a) * B
elif rgba.ndim == 1:
rgb = np.zeros((3), dtype='float32')
r, g, b, a = rgba[0], rgba[1], rgba[2], rgba[3]
a = np.asarray(a, dtype='float32') / 255.
rgb[0] = r * a + (1.0 - a) * R
rgb[1] = g * a + (1.0 - a) * G
rgb[2] = b * a + (1.0 - a) * B
return np.asarray(rgb, dtype='uint8')
def mktest(t, y):
''' Perform Mann-Kendall test for trend estimation.
:param t: time axis [1D array]
:param y: data time series [1D array]
:return: Mann-Kendall test results
'''
mk_test = mk.original_test(y)
# Intercept has to extrapolated to x=0, in order to get similar output format as scipy.stats.linregress.
return (mk_test.slope, mk_test.intercept - mk_test.slope * t[0], 0, mk_test.p, 0) # placed in tuple to get same output format as scipy.linregress.
def slopest(t, y, method):
''' Wrapper for choosing trend estimation based on linear regression or Mann-Kendall test
:param t: time axis [1D array]
:param y: data time series [1D array]
:param method: linear regression ('linregres') or Mann-Kendall test ('mannkendall') [str]
:return: Regression results
'''
if method == 'linregres':
return scipy.stats.linregress(t, y)
elif method == 'mannkendall':
return mktest(t, y)
def mvavg(x, mavg, edge=False):
''' Calculate moving average
:param x: monthly or annual time series [1D array]
:param mavg: window size [int]
:param edge: choose how to deal with time series edges [bool] (default = False)
:return: y, averaged time series
'''
if edge is True:
if mavg % 2 == 0: # if number is even.
x = np.hstack((np.tile(np.mean(x[:int(mavg/2.)]),int(mavg/2.)), x, np.tile(np.mean(x[int(np.floor(-mavg/2.)):]),int(mavg/2.)-1)))
else: # if number is odd.
x = np.hstack((np.tile(np.mean(x[:int(mavg/2.)]),int(mavg/2.)), x, np.tile(np.mean(x[int(np.floor(-mavg/2.)):]),int(mavg/2.))))
y = np.convolve(x, np.ones(mavg) / float(mavg), 'valid')
else:
y = np.convolve(x, np.ones(mavg) / float(mavg), 'same')
return y
def load_colors_for_plots():
''' Loads line colors, categorial colors and colormap, for use in plotting. Source: https://personal.sron.nl/~pault/
:return: colors, ccat_fig1, ccat_fig2, cmap
'''
# Load line colors for all Figures
colors = tol_colors.tol_cset('high-contrast')
colors = [colors.red, colors.yellow, colors.blue]
# Load category colors for Figure 1.
ccat_fig1 = tol_colors.tol_cset('bright')
ccat_fig1b = tol_colors.tol_cset('high-contrast')
ccat_fig1 = [ccat_fig1.yellow, ccat_fig1b.red, ccat_fig1.green]
# Load category colors for Figure 2.
ccat_fig2 = tol_colors.tol_cset('bright')
ccat_fig2 = [ccat_fig2.yellow, ccat_fig2.grey, 'black', ccat_fig2.red, ccat_fig2.purple, ccat_fig2.cyan, ccat_fig2.green]
# Load colormap for Figure 4.
cmap = plt.get_cmap('RdBu_r', 100)
return colors, ccat_fig1, ccat_fig2, cmap
def data_selector(lustr, FF_China=None, RESPscheme=None):
''' Choose to load one of the three land use emissions dataset time series
:param lustr: 'gcp' (Global Carbon Project), 'han' (Houghton & Nassikas), or 'new' (This study)
:param FF_China: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param RESPscheme: choose respiration scheme, normal ('stable') or increasing role of fire ('incfire')
:return: tuples with LULCC and FF emission time series.
'''
if FF_China is None: FF_China = 'GCP'
if RESPscheme is None: RESPscheme = 'stable'
if lustr == 'gcp':
lu_tuple = (LUgcp, LUgcp_m, LUgcp_error, LUgcp_error_m)
elif lustr == 'han':
lu_tuple = (LUhan, LUhan_m, LUhan_error, LUhan_error_m)
elif lustr == 'new':
if RESPscheme == 'stable':
lu_tuple = (LUnew, LUnew_m, LUnew_error, LUnew_error_m)
elif RESPscheme == 'incfire':
lu_tuple = (datadict['Total_incfire'], datadict['Total_incfire_m'], LUnew_error, LUnew_error_m)
# Select used fossil fuel time series
if FF_China == 'GCP':
ff_tuple = (FFgcp, FFgcp_m, FFgcp_error, FFgcp_error_m)
elif FF_China == 'LIU':
ff_tuple = (FFadj, FFadj_m, FFadj_error, FFadj_error_m)
elif FF_China == 'BP':
ff_tuple = (FFadjBP, FFadjBP_m, FFadjBP_error, FFadjBP_error_m)
return lu_tuple, ff_tuple
# Load data from Excel file
emissions = pd.read_excel(wdir + 'Marle_et_al_Nature_AirborneFraction_Datasheet.xlsx', 'Emissions')
atmosphere = pd.read_excel(wdir + 'Marle_et_al_Nature_AirborneFraction_Datasheet.xlsx', 'Atmosphere')
atmosphere_m = pd.read_excel(wdir + 'Marle_et_al_Nature_AirborneFraction_Datasheet.xlsx', 'Atmosphere_monthly')
faostat = pd.read_excel(wdir + 'Marle_et_al_Nature_AirborneFraction_Datasheet.xlsx', 'FAOStat', index_col=0, header=6) # Load soy, palmoil data
# Settings
C2CO2 = 3.66 # C to C02 conversion factor.
fPG = 2.134 # factor ppm CO2 to Pg C.
year0 = 1959 # default = 1959
year1 = 2019 # defualt = 2019
years = np.arange(year0, year1+1) # annual time vector
t_y = np.arange(len(years)) + int(years[0]) # annual time vector for plots
t_m = np.arange(0.0, len(years), 1 / 12.0) + int(years[0]) # monthly time vector for plots
mavg = 15 # Moving average window (default = 15)
edge = int(np.floor(mavg/2.0)) # extra edge values required for moving average
iset = 3 # Which AF time series to use for plotting (default=3). 0 = annual raw ('AF_a'), 1 = monthly smoothed ('AF_ms'), 2 = monthly smoothed and filtered ('AF_msnv'), 3 = annual smoothed and filtered ('AF_asnv')
trendcalc = 'abs' # absolute trend per decade ('abs', default), or relative growth rate ('rgr'; not fully tested, could give errors)
trendmethod = 'mannkendall' # Linear regression ('linregres') or Mann-Kendall test ('mannkendall', default)
# unit conversion factors
if trendcalc == 'abs': uconv = 10 # per year to per decade.
elif trendcalc == 'rgr': uconv = 100 # fraction to percentage.
cconv = 0.001 # conversion from Tg C to Pg C.
# Load colorsets for plots
colors, ccat_fig1, ccat_fig2, cmap = load_colors_for_plots()
Cgcp, Chan, Cnew = colors
# find index of start year and end year in data sheets
index0 = np.where(emissions['A'] == year0)[0][0] # annual indices
index1 = np.where(emissions['A'] == year1)[0][0]
index0_m = np.where(atmosphere_m['A'] == year0)[0][0] # monthly indices
index1_m = np.where(atmosphere_m['A'] == year1)[0][0]+12-1
# Load fossil fuel emissions
FFgcp = np.array(emissions['N'][index0:index1+1]).astype(float) * cconv
FFchinaGCP = np.array(emissions['O'][index0:index1+1]).astype(float) * cconv
FFchinaLIU = np.array(emissions['P'][index0:index1+1]).astype(float) * cconv
FFchinaBP = np.array(emissions['Q'][index0:index1+1]).astype(float) * cconv
FFadj = FFgcp - FFchinaGCP + FFchinaLIU # Fossil fuels with China emissions replaced by Liu et al. (2015)
FFadjBP = FFgcp - FFchinaGCP + FFchinaBP # Fossil fuels with China emissions replaced by BP
# Load land-use change emissions
LUgcp = np.array(emissions['M'][index0:index1+1]).astype(float) * cconv # LULCC emissions from Global Carbon Project (2020)
LUhan = np.array(emissions['G'][index0:index1+1]).astype(float) * cconv # LULCC emissions from Houghton & Nassikas (2017)
LUnew = np.array(emissions['L'][index0:index1+1]).astype(float) * cconv # LULCC emissions from This Study.
# Load atmospheric CO2
CO2mlo = atmosphere['B'][index0:index1+2] # Atmospheric CO2 Mauna Loa. index +2 because one extra value needed for np.diff.
CO2spo = atmosphere['C'][index0:index1+2] # Atmospheric CO2 South Pole.
CO2 = np.mean([CO2mlo, CO2spo], axis=0).astype(float) # Average of MLO and SPO
CO2 = (CO2 - 280) * fPG # Subtract pre-industrial 280 ppm, convert to Pg C.
CO2_m = np.mean([atmosphere_m['C'][index0_m:index1_m+2], atmosphere_m['D'][index0_m:index1_m+2]], axis=0).astype(float) # Monthly average of MLO and SPO. index +2 because one extra value needed for np.diff.
CO2_m = (CO2_m - 280) * fPG # Subtract pre-industrial 280 ppm, convert to Pg C.
dCO2 = np.diff(CO2)
dCO2_m = np.diff(CO2_m) # -11 instead of -12 because one extra month is needed for np.diff.
# Load ENSO and volcanic indices
ENSO_m = np.array(atmosphere_m['E'][index0_m-12:index1_m+1]).astype(float) # load one year extra (1958), required for 4 month lag.
VOLC_m = np.array(atmosphere_m['F'][index0_m:index1_m+1]).astype(float)
# Define annual errors
LUgcp_error = LUgcp * 0.5 # 50%. Corresponds to about 0.7 Pg C yr-1 in GCP (2020)
LUhan_error = LUhan * 0.5 # 50%
LUnew_error = LUnew * 0.5 # 50%
FFgcp_error = FFgcp * 0.05 # 5%
FFadj_error = (FFgcp - FFchinaGCP) * 0.05 + FFchinaLIU * 0.10 # GCP error=5%, Liu error=10%
FFadjBP_error = (FFgcp - FFchinaGCP) * 0.05 + FFchinaBP * 0.10 # GCP error=5%, BP error=10%
dCO2_error = np.array(atmosphere['E'][index0:index1+1]).astype(float) * fPG
# Interpolate annual fluxes to monthly fluxes
LUgcp_m = tomonthly(LUgcp)
LUhan_m = tomonthly(LUhan)
LUnew_m = tomonthly(LUnew)
FFgcp_m = tomonthly(FFgcp)
FFadj_m = tomonthly(FFadj)
FFadjBP_m = tomonthly(FFadjBP)
# Calculate monthly error
LUgcp_error_m = LUgcp_m * 0.5
LUhan_error_m = LUhan_m * 0.5
LUnew_error_m = LUnew_m * 0.5
FFgcp_error_m = FFgcp_m * 0.05
FFadj_error_m = ((FFgcp_m - tomonthly(FFchinaGCP)) * 0.05 + tomonthly(FFchinaLIU) * 0.10) # GCP error=5%, Liu error=10%
FFadjBP_error_m = ((FFgcp_m - tomonthly(FFchinaGCP)) * 0.05 + tomonthly(FFchinaBP) * 0.10) # GCP error=5%, BP error=10%
dCO2_error_m = np.repeat(dCO2_error, 12) / 12.
# Calculate EVI
ENSOI_m = norm(ENSO_m[12-4:-4]) # effect from enso lagged by 4 months and normalized to get enso index
dCO2_A, dCO2_T = anom(dCO2_m) # Seperate anomaly and trend
dCO2_As = mvavg(dCO2_A, mavg) # Apply moving average to anomaly
dCO2_s = mvavg(dCO2_m, mavg)
lam_maxcor_dCO2_As, maxcor_dCO2_As = lambdafn(dCO2_As, ENSOI_m, VOLC_m, inverse=True) # Calculate maximum correlation between enso and volcanic indices.
lam_maxcor = -16 # lambda ~= -16 (Raupach, 2008), i.e. volcanic contribution to ensoi
EVI_m = norm(ENSOI_m + lam_maxcor * VOLC_m) # Calculate index of combination of enso and volcanos, called EVI.
ENSOI_A, ENSOI_T = anom(ENSOI_m) # ensoi anomaly and trend
EVI_A, EVI_T = anom(EVI_m) # EVI anomaly and trend
ENSOI_s = norm(mvavg(ENSOI_m, mavg)) # moving average with time window mavg, and normalize again
ENSOI_As = norm(mvavg(ENSOI_A, mavg))
EVI_s = norm(mvavg(EVI_m, mavg))
EVI_As = norm(mvavg(EVI_A, mavg))
# Filter atmospheric CO2 growth rate using EVI index
dCO2_sv_m, mu_minvar = filterfn(dCO2_As, dCO2_T, EVI_As/12)
dCO2_sv = np.sum(np.reshape(dCO2_sv_m, [len(years), 12]), 1) # annual sum
# Set up respiration schemes
fraction_stable = np.ones(index1-index0+1) # stable fraction of fire emissions that is thought to respire (main case)
fraction_incfire = np.linspace(3, 1, year1-year0+1) # increasing fraction of fire emissions that is thought to respire (increasing role of fire)
# Load individual LUnew emission components [labels, Excel column, plot color]
metadict = OrderedDict([('Other', ['Other regions', 'J', ccat_fig2[0]]),
('EQAS_peatdrain', ['EQAS peat drainage', 'K', ccat_fig2[1]]),
('EQAS_deco', ['EQAS decomposition', 'F', ccat_fig2[2]]),
('EQAS_peatfire', ['EQAS peat fire', 'E', ccat_fig2[3]]),
('EQAS_fire', ['EQAS fire', 'D', ccat_fig2[4]]),
('ARCD_deco', ['ARCD decomposition', 'C', ccat_fig2[5]]),
('ARCD_fire', ['ARCD fire', 'B', ccat_fig2[6]]),
])
datadict = OrderedDict()
for keyn in metadict.keys():
data = np.array(emissions[metadict[keyn][1]][index0:index1 + 1]).astype(float)
if keyn in ['ARCD_deco', 'EQAS_deco']: # For decomposition emissions calculate also calculate the 'increasing role of fire' scenario.
datadict[keyn] = data * fraction_stable * cconv
datadict[keyn + '_incfire'] = data * fraction_incfire * cconv
else:
datadict[keyn] = data * cconv
del data
datadict['Total'] = np.sum([datadict[keyn] for keyn in metadict.keys()], axis=0) # World LULCC emissions, identical to LUnew.
datadict['Total_incfire'] = datadict['Total'] - datadict['EQAS_deco'] - datadict['ARCD_deco'] + datadict['EQAS_deco_incfire'] + datadict['ARCD_deco_incfire'] # 'increasing role of fire' scenario.
datadict['Total_incfire_m'] = tomonthly(datadict['Total_incfire']) # interpolate to monthly time series.
def plot_Figure1(filename=None):
''' Plot paper Figure 1. Emissions distribution.
Creates plot with stacked values of ARCD: Emissions + Decomposition + line with soy bean and cattle export,
EQAS: Emissions + Peat Drainage + Decomposition + line with palm oil export
:param fign: figure number
:param filename: filename of saved figure (.pdf format). If filename == None, figure is only displayed and not saved.
:return: Figure 1
'''
fontsize = 7
fontsize_legend = 6.5
fontsize_panellabel = 8
align = 'center'
plt.rcParams['hatch.linewidth'] = 0.3
fig, (ax1,ax2) = plt.subplots(2, 1, figsize=(120*mm,80*mm))
p1 = ax1.bar(t_y, datadict['ARCD_deco'], align=align, color=ccat_fig1[0], edgecolor='white', linewidth=0.6, label='Decomposition') # , hatch='')
p2 = ax1.bar(t_y, datadict['ARCD_fire'], bottom=datadict['ARCD_deco'], align=align, color=ccat_fig1[1], edgecolor='white', linewidth=0.6, label='Fire emissions') # , hatch='\\\\\\\\')
# Add soybean and cattle export
ax11 = ax1.twinx()
l1 = ax11.plot(t_y[2:], (faostat['ARCD_SoyBean_Tonnes'] + faostat['ARCD_Cattle_Beef_Tonnes'])/1e6, '--', color='black', label='Soy bean and cattle', linewidth=1)
p3 = ax2.bar(t_y, datadict['EQAS_peatdrain'], align=align, color=ccat_fig1[2], edgecolor='white', linewidth=0.6, label='Peat drainage') # , hatch='.....')
p4 = ax2.bar(t_y, datadict['EQAS_deco'], bottom=datadict['EQAS_peatdrain'], align=align, color=ccat_fig1[0], edgecolor='white', linewidth=0.6, label='Decomposition') # , hatch='')
p5 = ax2.bar(t_y, (datadict['EQAS_fire'] + datadict['EQAS_peatfire']), bottom=(datadict['EQAS_peatdrain'] + datadict['EQAS_deco']), align=align, color=ccat_fig1[1], edgecolor='white', linewidth=0.6, label='Fire emissions') # , hatch='\\\\\\\\')
# Add palmoil export
ax21 = ax2.twinx()
l3 = ax21.plot(t_y[2:], faostat['Indo_PalmOil_Tonnes']/1e6, '--', color='black', label='Palm oil', linewidth=1)
ax1.set_xlim(1958, 2020)
ax1.set_ylim(0, 0.9)
ax1.set_yticks([0, 0.15, 0.3, 0.45, 0.6, 0.75, 0.9])
ax1.grid()
ax1.set_axisbelow(True)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax1.get_yticklabels(), fontsize=fontsize)
ax11.set_ylim(0, 90)
ax11.set_yticks([0,30,60,90])
plt.setp(ax11.get_yticklabels(), fontsize=fontsize)
handles = [p2, p1] + l1
labels = [l.get_label() for l in handles]
ax1.legend(handles, labels, loc='upper left', bbox_to_anchor=(0.0, 0.85), fontsize=fontsize_legend, framealpha=1)
ax1.text(0.06, 0.9, 'a', horizontalalignment='right', verticalalignment='center', transform=ax1.transAxes, fontsize=fontsize_panellabel, weight='bold')
ax1.set_ylabel('Emissions (Pg C year $^{-1}$)', size=fontsize)
ax11.set_ylabel('Export commodities (Mt year $^{-1}$)', size=fontsize, rotation=270, va='bottom')
ax1.yaxis.set_label_coords(-0.1,-0.1)
ax11.yaxis.set_label_coords(1.075,-0.1)
ax2.set_xlim(1958, 2020)
ax2.set_xlabel('Year', fontsize=fontsize)
ax2.set_ylim(0, 1.5)
ax2.set_yticks([0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5])
ax2.grid()
ax2.set_axisbelow(True)
plt.setp(ax2.get_xticklabels(), size=fontsize, visible=True)
plt.setp(ax2.get_yticklabels(),fontsize=fontsize)
ax21.set_ylim(0, 60)
plt.setp(ax21.get_yticklabels(),fontsize=fontsize)
handles = [p5, p4, p3]+l3
labels = [l.get_label() for l in handles]
ax2.legend(handles, labels, loc='upper left', bbox_to_anchor=(0.0, 0.85), fontsize=fontsize_legend, framealpha=1)
ax2.text(0.06, 0.9, 'b', horizontalalignment='right', verticalalignment='center', transform=ax2.transAxes, fontsize=fontsize_panellabel, weight='bold')
if filename:
plt.savefig(filename + '.pdf', bbox_inches='tight')
else:
plt.show()
plot_Figure1(filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure1')
# Determine annual trend for in Abstract text of manuscript:
LUnew_defos = datadict['Total'] - datadict['Other'] # Total deforestation emissions for EQAS and ARCD.
LUnew_defos_reg = slopest(t_y, LUnew_defos, method='linregres')
LUnew_defos_man = slopest(t_y, LUnew_defos, method='mannkendall')
def plot_Figure2(fign, FF_China=None, filename=None):
''' Plot paper Figure 2
:param fign: figure number
:param FF_China: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param filename: filename of saved figure (.pdf format). If filename == None, figure is only displayed and not saved.
:return: Figure 2
'''
fontsize = 7
fontsize_legend = 6.5
fontsize_panellabel = 8
if FF_China is None:
FF_China = 'GCP'
if FF_China == 'GCP': FF = np.array(FFgcp)
elif FF_China == 'LIU': FF = np.array(FFadj)
fig = plt.figure(fign, figsize=(183*mm, 89*mm))
# Plot annual LUC emissions
ax = fig.add_subplot(121)
bottom = np.zeros(index1-index0+1)
for keyn in metadict.keys():
ax.bar(years, datadict[keyn], bottom=bottom, color=metadict[keyn][2], label=metadict[keyn][0], edgecolor='w', width=0.95)
bottom += datadict[keyn]
ax.text(0.03, 0.95, 'a', transform=ax.transAxes, fontsize=fontsize_panellabel, weight='bold')
ax.set_xlim((year0, year1+1))
ax.set_ylim((0, 2.5))
ax.set_yticks((0,0.25,0.5,0.75,1.0,1.25,1.5,1.75,2.0,2.25,2.5))
ax.set_xlabel('Year', fontsize=fontsize)
ax.set_ylabel('Net LULCC emissions (Pg C year$^{-1}$)', fontsize=fontsize)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
h,l = ax.get_legend_handles_labels()
ax.legend(h[::-1], l[::-1], bbox_to_anchor=(0.01, 0.95), loc='upper left', fontsize=fontsize_legend, framealpha=1)
ax.step(years+0.5, bottom, '-', color='lightgrey', linewidth=1.0)
LULCC_this_study = bottom # == LUnew == datadict['Total']
# Plot full carbon budget
ax = fig.add_subplot(122)
colors = tol_colors.tol_cset('vibrant')
Cff = colors.grey
Caccent = colors.teal
ax.step(years, FF, color=Cff, linewidth=1.5, label='Fossil fuel emissions') # 'grey'
ax.step(years, dCO2, color=Caccent ,linewidth=1.5, label='Atmospheric CO$_2$ growth') # 'deepskyblue'
ax.step(years, LUgcp, color=Cgcp, linewidth=1.5, label='LULCC GCP')
ax.step(years, LUhan, color=Chan, linewidth=1.5, label='LULCC H&N')
ax.step(years, LULCC_this_study, color=Cnew, linewidth=1.5, label='LULCC this study')
# add ppm CO2 values as secondary axis.
for i in np.arange(0.5, 4, 0.5):
ax.text(year1+2, i * fPG, str(i), color=Caccent, va='center', fontsize=fontsize)
ax.hlines(i * fPG, year1, year1+1, color=Caccent, linewidth=1.0)
ax.text(year1+7, 2 * fPG, 'ppm CO$_2$ year$^{-1}$', rotation=270, color=Caccent, va='center', fontsize=fontsize)
ax.text(0.03, 0.95, 'b', transform=ax.transAxes, fontsize=fontsize_panellabel, weight='bold')
ax.set_xlim((year0, year1+1))
ax.set_ylim((0, 11))
ax.set_xlabel('Year', fontsize=fontsize)
ax.set_ylabel('Pg C year$^{-1}$', fontsize=fontsize)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
ax.legend(bbox_to_anchor=(0.01, 0.95), loc='upper left', fontsize=fontsize_legend, framealpha=1)
plt.tight_layout()
plt.subplots_adjust(left=0.075, right=0.94)
if filename:
plt.savefig(filename + '.pdf', bbox_inches='tight')
else:
plt.show()
plot_Figure2(2, filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure2')
def calc_AF(lustr, FF_China=None, RESPscheme=None): #, trendcalc=None):
''' Calculation of the Airborne fraction (without Monte-Carlo simulation).
AF_a = annual raw, AF_ms = monthly smoothed, AF_msnv = monthly smoothed and filtered, AF_asnv = annual smoothed and filtered.
:param lustr: 'gcp' (Global Carbon Project), 'han' (Houghton & Nassikas), or 'new' (This study)
:param FF_China: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param RESPscheme: choose respiration scheme, normal ('stable') or increasing role of fire ('incfire')
:return: AF time series, AF error time series, and AF regression results.
=== All results are returned for four data treatments ===:
0 = annual raw ('AF_a') [Reported in paper Figure 3]
1 = monthly smoothed ('AF_ms') [Not used in paper]
2 = monthly smoothed and filtered ('AF_msnv') [Not used in paper]
3 = annual smoothed and filtered ('AF_asnv') [Used for final trend estimates, reported in Table 1, Figure 3 and 4]
'''
if FF_China is None: FF_China = 'GCP'
if RESPscheme is None: RESPscheme = 'stable'
# Select LULCC and FF emissions time series:
lu_tuple, ff_tuple = data_selector(lustr, FF_China, RESPscheme)
lu, lu_m, lu_error, lu_error_m = lu_tuple
FF, FF_m, FF_error, FF_error_m = ff_tuple
# Calculate Airborne fraction
AF_a = dCO2 / (FF + lu) # annual raw
AF_ms = dCO2_s / (FF_m + lu_m) # monthly smoothed
AF_msnv = dCO2_sv_m / (FF_m + lu_m) # monthly smoothed and filtered
AF_asnv = dCO2_sv / (FF + lu) # annual smoothed and filtered
AF_a_error = np.sqrt(dCO2_error**2 * (1/(FF + lu))**2 + (FF_error**2 + lu_error**2) * (-dCO2/(FF + lu)**2)**2) # Calculate error propagation.
AF_ms_error = np.sqrt(dCO2_error_m**2 * (1/(FF_m + lu_m))**2 + (FF_error_m**2 + lu_error_m**2) * (-dCO2_m/(FF_m + lu_m)**2)**2)
AF_msnv_error = np.sqrt(dCO2_error_m**2 * (1/(FF_m + lu_m))**2 + (FF_error_m**2 + lu_error_m**2) * (-dCO2_m/(FF_m + lu_m)**2)**2)
AF_asnv_error = np.sqrt(dCO2_error**2 * (1/(FF + lu))**2 + (FF_error**2 + lu_error**2) * (-dCO2/(FF + lu)**2)**2)
# Calculate trend in Airborne fraction time series.
if trendcalc == 'abs': # Calculate absolute trend.
AF_a_reg = slopest(t_y, AF_a, method=trendmethod)
AF_ms_reg = slopest(t_m[edge:-edge], AF_ms[edge:-edge], method=trendmethod)
AF_msnv_reg = slopest(t_m[edge:-edge], AF_msnv[edge:-edge], method=trendmethod)
AF_asnv_reg = slopest(t_y, AF_asnv, method=trendmethod)
elif trendcalc == 'rgr': # Calculate relative growth rate.
AF_a_reg = slopest(t_y, AF_a / float(np.mean(AF_a)), method=trendmethod)
AF_ms_reg = slopest(t_m[edge:-edge], AF_ms[edge:-edge] / np.mean(AF_ms[edge:-edge]), method=trendmethod)
AF_msnv_reg = slopest(t_m[edge:-edge], AF_msnv[edge:-edge] / np.mean(AF_msnv[edge:-edge]), method=trendmethod)
AF_asnv_reg = slopest(t_y, AF_asnv / float(np.mean(AF_asnv)), method=trendmethod)
# Store regression results. Format: [slope, p-value, standard error]
AF_a_reg = [AF_a_reg[0] * uconv, AF_a_reg[3] * 1, AF_a_reg[4] * uconv]
AF_ms_reg = [AF_ms_reg[0] * uconv, AF_ms_reg[3] * 1, AF_ms_reg[4] * uconv]
AF_msnv_reg = [AF_msnv_reg[0] * uconv, AF_msnv_reg[3] * 1, AF_msnv_reg[4] * uconv]
AF_asnv_reg = [AF_asnv_reg[0] * uconv, AF_asnv_reg[3] * 1, AF_asnv_reg[4] * uconv]
return (AF_a, AF_ms, AF_msnv, AF_asnv), (AF_a_error, AF_ms_error, AF_msnv_error, AF_asnv_error), (AF_a_reg, AF_ms_reg, AF_msnv_reg, AF_asnv_reg)
AFgcp, AFERRgcp, REGgcp = calc_AF('gcp') # AF: time series, AFERR: error time series, and REG: regression results.
AFhan, AFERRhan, REGhan = calc_AF('han')
AFnew, AFERRnew, REGnew = calc_AF('new')
AFnew_b, AFERRnew_b, REGnew_b = calc_AF('new', FF_China='LIU', RESPscheme='stable') # Different scenarios for sensitivity analysis.
AFnew_c, AFERRnew_c, REGnew_c = calc_AF('new', FF_China='GCP', RESPscheme='incfire')
AFnew_d, AFERRnew_d, REGnew_d = calc_AF('new', FF_China='LIU', RESPscheme='incfire')
# # Save AF time series into Excel file
# sheet_AF = np.array([AFnew[0], AFERRnew[0], AFnew[3], AFERRnew[3], AFhan[0], AFERRhan[0], AFhan[3], AFERRhan[3], AFgcp[0], AFERRgcp[0], AFgcp[3], AFERRgcp[3]])
# pd.DataFrame(sheet_AF.T).to_excel(wdir+'Excel_AF_annual_2020.xlsx')
# sheet_AFmonthly = np.array([AFnew[1], AFERRnew[1], AFnew[2], AFERRnew[2], AFhan[1], AFERRhan[1], AFhan[2], AFERRhan[2], AFgcp[1], AFERRgcp[1], AFgcp[2], AFERRgcp[2]])
# pd.DataFrame(sheet_AFmonthly.T).to_excel(wdir+'Excel_AF_monthly_2020.xlsx')
def calc_AF_MonteCarlo(MC, lustr, FF_China=None, RESPscheme=None, bootstrap=False, detrend=False):
''' Calculation of the Airborne fraction using Monte Carlo simulation.
AF_a = annual raw, AF_ms = monthly smoothed, AF_msnv = monthly smoothed and filtered, AF_asnv = annual smoothed and filtered.
:param MC: number of Monte Carlo iterations.
:param lustr: 'gcp' (Global Carbon Project), 'han' (Houghton & Nassikas), or 'new' (This study)
:param FF_China: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param RESPscheme: choose respiration scheme, normal ('stable', default) or increasing role of fire ('incfire')
:param bootstrap: Apply bootstrapping for calculation of trend uncertainty interval (default=False).
:param detrend: Remove trend before calculation, to isolate the error margin around 0 (default=False).
:return:
- MC_slope_median: median of found slopes over MonteCarlo iterations.
- MC_slope_std: standard deviation of found slopes over MonteCarlo iterations.
- MC_std_mean: mean of found standard deviations over MonteCarlo iterations.
- MC_p_mean: mean of found p-values over MonteCarlo iterations.
- MC_slope: Found slopes for all MonteCarlo iterations.
- MC_std: Found standard deviations for all MonteCarlo iterations.
- MC_p: Found p-values for all MonteCarlo iterations.
- MC_AF: Total AF time series for all MonteCarlo iterations.
- MC_interc: Found intercept for all MonteCarlo iterations.
=== All results are returned for four data treatments ===:
0 = annual raw ('AF_a') [Reported in paper Figure 3]
1 = monthly smoothed ('AF_ms') [Not used in paper]
2 = monthly smoothed and filtered ('AF_msnv') [Not used in paper]
3 = annual smoothed and filtered ('AF_asnv') [Used for final trend estimates, reported in Table 1, Figure 3 and 4]
'''
if FF_China is None: FF_China = 'GCP'
if RESPscheme is None: RESPscheme = 'stable'
if bootstrap is True:
fBts = 0.8 # Bootstrap window of at least 80% of time series.
min_length_y = int(np.ceil(len(years) * fBts)) # at least fBts.
min_length_m = int(np.ceil((len(t_m) - mavg) * fBts)) # at least fBts.
start = np.zeros(MC).astype(int)
length = np.zeros(MC).astype(int)
end = np.zeros(MC).astype(int)
# Select used land use and fossil fuel time series
lu_tuple, ff_tuple = data_selector(lustr, FF_China, RESPscheme)
lu, lu_m, lu_error, lu_error_m = lu_tuple
FF, FF_m, FF_error, FF_error_m = ff_tuple
AF_pre = calc_AF(lustr, FF_China=FF_China, RESPscheme=RESPscheme)[0]
AF_a_trend = np.poly1d(np.polyfit(t_y, AF_pre[0], 1))(t_y) # Determine Airborne fraction trend component.
AF_ms_trend = np.poly1d(np.polyfit(t_m, AF_pre[1], 1))(t_m)
AF_msnv_trend = np.poly1d(np.polyfit(t_m, AF_pre[2], 1))(t_m)
AF_asnv_trend = np.poly1d(np.polyfit(t_y, AF_pre[3], 1))(t_y)
print('Monte Carlo starting ... ')
time0 = timer.time()
AF_a = np.zeros((MC, len(years)))
AF_ms = np.zeros((MC, len(t_m)))
AF_msnv = np.zeros((MC, len(t_m)))
AF_asnv = np.zeros((MC, len(years)))
MC_a = np.zeros((4, MC)) # 4 dimensions are: [slope, intercept, p-value, stderr]
MC_ms = np.zeros((4, MC))
MC_msnv = np.zeros((4, MC))
MC_asnv = np.zeros((4, MC))
for mc in range(MC):
FF_mc = FF + FF_error * np.random.randn(len(years)) # Apply random normally-distributed error.
lu_mc = lu + lu_error * np.random.randn(len(years))
dCO2_mc = dCO2 + dCO2_error * np.random.randn(len(years))
FF_m_mc = FF_m + FF_error_m * np.random.randn(len(t_m))
lu_m_mc = lu_m + lu_error_m * np.random.randn(len(t_m))
dCO2_m_mc = dCO2_m + dCO2_error_m * np.random.randn(len(t_m))
dCO2_s_mc = dCO2_s + dCO2_error_m * np.random.randn(len(t_m))
dCO2_sv_mc = dCO2_sv + dCO2_error * np.random.randn(len(years))
dCO2_sv_m_mc = dCO2_sv_m + dCO2_error_m * np.random.randn(len(t_m))
# Calculate Airborne fraction
AF_a[mc] = dCO2_mc / (FF_mc + lu_mc) # annual raw
AF_ms[mc] = dCO2_s_mc / (FF_m_mc + lu_m_mc) # monthly smoothed
AF_msnv[mc] = dCO2_sv_m_mc / (FF_m_mc + lu_m_mc) # monthly smoothed and filtered
AF_asnv[mc] = dCO2_sv_mc / (FF_mc + lu_mc) # annual smoothed and filtered
if detrend is True:
AF_a[mc] = AF_a[mc] - AF_a_trend # Remove time series trend.
AF_ms[mc] = AF_ms[mc] - AF_ms_trend
AF_msnv[mc] = AF_msnv[mc] - AF_msnv_trend
AF_asnv[mc] = AF_asnv[mc] - AF_asnv_trend
if bootstrap is True:
length_y = np.random.randint(min_length_y, len(years)+1) # bootstrap window length
start_y = np.random.randint(len(years) - length_y+1) # bootstrap window start point
end_y = start_y + length_y # bootstrap window end point
length_m = np.random.randint(min_length_m, len(t_m) - mavg +1) # Same, but monthly instead of annual
start_m = np.random.randint(edge, len(t_m) - edge - length_m +1)
end_m = start_m + length_m
mask_y = np.zeros(len(years)).astype(bool)
mask_m = np.zeros(len(t_m)).astype(bool)
mask_y[start_y:end_y] = True # Bootstrap window mask, annual.
mask_m[start_m:end_m] = True # Bootstrap window mask, monthly.
AF_a[mc][~mask_y] = np.nan # Apply bootstrap mask to AF time series.
AF_ms[mc][~mask_m] = np.nan
AF_msnv[mc][~mask_m] = np.nan
AF_asnv[mc][~mask_y] = np.nan
length[mc] = length_m # store iterated bootstrap windows.
start[mc] = start_m
end[mc] = end_m
elif bootstrap is False: # Make empty mask to skip bootstrapping.
mask_y = np.zeros(len(years)).astype(bool)
mask_m = np.zeros(len(t_m)).astype(bool)
mask_y[:] = True
mask_m[edge:-edge] = True
# Calculate trend in Airborne fraction time series.
if trendcalc == 'abs': # Calculate absolute trend.
AF_a_reg = slopest(t_y[mask_y], AF_a[mc][mask_y], method=trendmethod)
AF_ms_reg = slopest(t_m[mask_m], AF_ms[mc][mask_m], method=trendmethod)
AF_msnv_reg = slopest(t_m[mask_m], AF_msnv[mc][mask_m], method=trendmethod)
AF_asnv_reg = slopest(t_y[mask_y], AF_asnv[mc][mask_y], method=trendmethod)
elif trendcalc == 'rgr': # Calculate relative growth rate.
AF_a_reg = slopest(t_y[mask_y], AF_a[mc][mask_y] / float(np.mean(AF_a[mc][mask_y])), method=trendmethod)
AF_ms_reg = slopest(t_m[mask_m], AF_ms[mc][mask_m] / np.mean(AF_ms[mc][mask_m]), method=trendmethod)
AF_msnv_reg = slopest(t_m[mask_m], AF_msnv[mc][mask_m] / np.mean(AF_msnv[mc][mask_m]), method=trendmethod)
AF_asnv_reg = slopest(t_y[mask_y], AF_asnv[mc][mask_y] / float(np.mean(AF_asnv[mc][mask_y])), method=trendmethod)
# Store regression results. Format: [slope, intercept, p-value, standard error]
MC_a[:,mc] = [AF_a_reg[0]*uconv, AF_a_reg[1]*uconv, AF_a_reg[3]*1, AF_a_reg[4]*uconv]
MC_ms[:,mc] = [AF_ms_reg[0]*uconv, AF_ms_reg[1]*uconv, AF_ms_reg[3]*1, AF_ms_reg[4]*uconv]
MC_msnv[:,mc] = [AF_msnv_reg[0]*uconv, AF_msnv_reg[1]*uconv, AF_msnv_reg[3]*1, AF_msnv_reg[4]*uconv]
MC_asnv[:,mc] = [AF_asnv_reg[0]*uconv, AF_asnv_reg[1]*uconv, AF_asnv_reg[3]*1, AF_asnv_reg[4]*uconv]
# Calculate summary statistics over Monte Carlo iterations.
MC_AF = (AF_a, AF_ms, AF_msnv, AF_asnv)
MC_slope = np.array([MC_a[0], MC_ms[0], MC_msnv[0], MC_asnv[0]])
MC_interc = np.array([MC_a[1], MC_ms[1], MC_msnv[1], MC_asnv[1]])
MC_std = np.array([MC_a[3], MC_ms[3], MC_msnv[3], MC_asnv[3]])
MC_p = np.array([MC_a[2], MC_ms[2], MC_msnv[2], MC_asnv[2]])
MC_slope_median = np.mean(MC_slope, axis=1) # median of slope
MC_slope_std = np.std(MC_slope, axis=1) # std of slope
MC_std_mean = np.mean(MC_std, axis=1) # mean of stderr
MC_p_mean = np.mean(MC_p, axis=1) # mean of p-value
time1 = timer.time() - time0
print('Done, duration = ' + str(time1))
return MC_slope_median, MC_slope_std, MC_std_mean, MC_p_mean, MC_slope, MC_std, MC_p, MC_interc, MC_AF[0], MC_AF[1], MC_AF[2], MC_AF[3]
def calc_AF_MonteCarlo_filemanager(filename):
''' Saves or loads Monte-Carlo results for all different data scenarios.
If zip file with filename already exists, previously saved results are loaded.
If no zip file exists yet, the Monte-Carlo simulations are calculated for all different scenarios and saved into a zip file.
:param filename: filename without path and without extension.
:return: MCdict, dictionary collection with all run results.
'''
if (filename is not None) and os.path.isfile(filename + '.zip'):
# Load pre-saved restuls.
MCdict = OrderedDict()
with ZipFile(filename + '.zip', mode='r') as archive:
for keyn in ['gcp', 'han', 'new', 'gcp_s', 'han_s', 'new_s', 'new_b', 'new_c', 'new_d', 'new_e', 'new_f', 'gcp_dt', 'han_dt', 'new_dt']:
ds_npz = np.load(io.BytesIO(archive.read(keyn+'.npz')))
MCdict[keyn] = [ds_npz[filen] for filen in ['MC_slope_median', 'MC_slope_std', 'MC_std_mean', 'MC_p_mean', 'MC_slope', 'MC_std', 'MC_p', 'MC_interc', 'AF_a', 'AF_ms', 'AF_msnv', 'AF_asnv']]
else:
raise Warning('No pre-saved .zip file found with the name: %s, recalculating Monte-Carlo, Please note: this can take several minutes!' % filename)
# Calculate and save results.
MCdict = OrderedDict([('gcp', calc_AF_MonteCarlo(MC, 'gcp')),
('han', calc_AF_MonteCarlo(MC, 'han')),
('new', calc_AF_MonteCarlo(MC, 'new')),
('gcp_s', calc_AF_MonteCarlo(MC, 'gcp', bootstrap=True)),
('han_s', calc_AF_MonteCarlo(MC, 'han', bootstrap=True)),
('new_s', calc_AF_MonteCarlo(MC, 'new', bootstrap=True)),
('new_b', calc_AF_MonteCarlo(MC, 'new', FF_China='LIU', RESPscheme='stable', bootstrap=True)),
('new_c', calc_AF_MonteCarlo(MC, 'new', FF_China='GCP', RESPscheme='incfire', bootstrap=True)),
('new_d', calc_AF_MonteCarlo(MC, 'new', FF_China='LIU', RESPscheme='incfire', bootstrap=True)),
('new_e', calc_AF_MonteCarlo(MC, 'new', FF_China='BP', RESPscheme='stable', bootstrap=True)),
('new_f', calc_AF_MonteCarlo(MC, 'new', FF_China='BP', RESPscheme='incfire', bootstrap=True)),
('gcp_dt', calc_AF_MonteCarlo(MC, 'gcp', detrend=True)),
('han_dt', calc_AF_MonteCarlo(MC, 'han', detrend=True)),
('new_dt', calc_AF_MonteCarlo(MC, 'new', detrend=True)),
])
# Convert dtype from Float64 to Float32 for reduced file size.
for keyn in MCdict.keys():
MCdict[keyn] = [dicti.astype(np.float32) for dicti in MCdict[keyn]]
# Save results into .npz files and then pack them all into one .zip file.
for keyn in MCdict.keys():
ds = MCdict[keyn]
np.savez(filename + '_%s' % keyn, MC_slope_median=ds[0], MC_slope_std=ds[1], MC_std_mean=ds[2], MC_p_mean=ds[3], MC_slope=ds[4], MC_std=ds[5], MC_p=ds[6], MC_interc=ds[7], AF_a=ds[8], AF_ms=ds[9], AF_msnv=ds[10], AF_asnv=ds[11])
zipObj = ZipFile(filename + '.zip', 'w')
for keyn in MCdict.keys():
zipObj.write(filename + '_%s.npz' % keyn, keyn+'.npz') # Pack individual results into single .zip file.
os.remove(filename + '_%s.npz' % keyn) # Remove individual files.
zipObj.close()
return MCdict
MC = 10000 # number of Monte Carlo iterations.
MCdict = calc_AF_MonteCarlo_filemanager(wdir + 'Marle_et_al_Nature_AirborneFraction_MC%s_MK_ts_TREND%s' % (MC, trendcalc))
def plot_Figure3(fign, lustr, FFchinaplot='LIU', filename=None):
''' Plot paper Figure 3
:param fign: figure number
:param lustr: 'gcp' (Global Carbon Project), 'han' (Houghton & Nassikas), or 'new' (This study)
:param FFchinaplot: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param filename: filename of saved figure (.pdf format). If no filename, figure is only displayed and not saved.
:return: Figure 3
'''
fontsize = 8
fontsize_legend = 6.5
fontsize_boxtext = 7
fontsize_panellabel = 8
fig = plt.figure(fign, (183*mm,70*mm))
ax = fig.add_subplot(121)
if lustr == 'gcp':
AFdat = AFgcp
MCdat = MCdict['gcp']
MCdats = MCdict['gcp_s']
AFERRdat = AFERRgcp
AFcol = Cgcp
AFstr = 'GCP'
pltlabels = ['a','b']
elif lustr == 'han':
AFdat = AFhan
MCdat = MCdict['han']
MCdats = MCdict['han_s']
AFERRdat = AFERRhan
AFcol = Chan
AFstr = 'H&N'
pltlabels = ['c', 'd']
elif lustr == 'new':
AFdat = AFnew
MCdat = MCdict['new']
MCdats = MCdict['new_s']
AFERRdat = AFERRnew
AFcol = Cnew
AFstr = 'This study'
pltlabels = ['e', 'f']
t_plot = t_y
t_len = len(years)
pl1, = ax.plot(t_plot, amean(AFdat[0]), c='grey') # Plot AF time series
pl2, = ax.plot(t_plot, amean(AFdat[iset]), c=AFcol)
if lustr in ['gcp', 'new']: colorfillalpha = 0.2
elif lustr == 'han': colorfillalpha = 0.3 # The yellow colors requires a slightly higher alpha for the same visual effect.
ax.fill_between(t_plot, amean(AFdat[0] - AFERRdat[0]), amean(AFdat[0] + AFERRdat[0]), facecolor='grey', alpha=0.2) # Plot AF uncertainty range
ax.fill_between(t_plot, amean(AFdat[iset] - AFERRdat[iset]), amean(AFdat[iset] + AFERRdat[iset]), facecolor=AFcol, alpha=colorfillalpha)
ax.plot(t_plot, np.poly1d(np.polyfit(t_plot, amean(AFdat[iset]),1))(t_plot), c=AFcol) # Plot AF trend line
MC_mask = np.where(~np.isnan(MCdats[8+iset]), 1, MCdats[8+iset]) # Remove bootstrap no-data edges.
MC_line = (MCdats[4][iset] * (t_plot * MC_mask).T + MCdats[7][iset]).T / uconv
MC_linemean = np.nanmedian(MC_line, axis=0)
MC_linestd = np.nanstd(MC_line, axis=0)
MC_linemean_fit = np.poly1d(np.polyfit(t_plot[2:-2], MC_linemean[2:-2], 3))(t_plot)
MC_linestd_fit = np.poly1d(np.polyfit(t_plot[2:-2], MC_linestd[2:-2], 3))(t_plot)
if lustr in ['gcp', 'han']: dashlinealpha = 0.7
elif lustr == 'new': dashlinealpha = 0.9
pl3, = ax.plot(t_plot, MC_linemean_fit - MC_linestd_fit, color=AFcol, linestyle='--', alpha=dashlinealpha) # Plot AF trend line uncertainty
ax.plot(t_plot, MC_linemean_fit + MC_linestd_fit, color=AFcol, linestyle='--', alpha=dashlinealpha)
p_dat = MCdat[3][iset]
if lustr in ['gcp', 'han']:
Pdat = (np.sum([MCdat[4][iset] > 0.0])/float(MC)) # Probability that trend is positive
Pdat2 = (np.sum([MCdat[6][iset] < 0.05])/float(MC)) # Probability that trend is significant
Pdats = (np.sum([MCdats[4][iset] > 0.0])/float(MC)) # Probability that trend is positive
Pdats2 = (np.sum([MCdats[6][iset] < 0.05])/float(MC)) # Probability that trend is significant
elif lustr == 'new':
Pdat = (np.sum([MCdat[4][iset] < 0.0])/float(MC)) # Probability that trend is negative
Pdat2 = (np.sum([MCdat[6][iset] < 0.05])/float(MC)) # Probability that trend is significant
Pdats = (np.sum([MCdats[4][iset] < 0.0])/float(MC)) # Probability that trend is negative
Pdats2 = (np.sum([MCdats[6][iset] < 0.05])/float(MC)) # Probability that trend is significant
data_list = [MCdict['gcp'][4][iset], MCdict['han'][4][iset], MCdict['new'][4][iset], MCdict['gcp_dt'][4][iset], MCdict['han_dt'][4][iset], MCdict['new_dt'][4][iset]]
pval = np.zeros((6,6))
# Example: pval = scipy.stats.t.sf(np.abs(3.0914), 8)*2 (Source: https://www.youtube.com/watch?v=myL_qzuLHTQ)
for i, Di in enumerate(data_list):
for j, Dj in enumerate(data_list):
diff_slope = np.mean(Di) - np.mean(Dj)
diff_std = np.sqrt(np.std(Di) ** 2 + np.std(Dj) ** 2)
pval[i,j] = scipy.stats.t.sf(np.abs(diff_slope / diff_std), t_len - 4) * 2
if trendcalc == 'rgr':
spacer = 0.02
legtext1 = '%s, raw. Mean AF = %.2f' % (AFstr, np.mean(AFdat[0]))
legtext2 = '%s: %.2f $\pm$ %.2f %% yr$^{-1}$' % ('Filter', MCdat[0][iset], MCdat[1][iset])
legtext3 = '%s: %.2f $\pm$ %.2f %% yr$^{-1}$' % ('Filter+Bootstrap', MCdats[0][iset], MCdats[1][iset])
elif trendcalc == 'abs':
spacer = 0.001
legtext1 = '%s, raw, Mean AF = %.2f' % (AFstr, np.mean(AFdat[0]))
legtext2 = '%s: %.3f $\pm$ %.3f decade$^{-1}$' % ('Filter', MCdat[0][iset], MCdat[1][iset])
legtext3 = '%s: %.3f $\pm$ %.3f decade$^{-1}$' % ('Filter+Bootstrap', MCdats[0][iset], MCdats[1][iset])
ax.legend([pl1,pl2,pl3], [legtext1, legtext2, legtext3], fontsize=fontsize_legend, loc='upper right', bbox_to_anchor=(1.01,1.02), labelspacing=0.05, borderpad=0.25, handlelength=1.5, framealpha=1)
ax.text(0.04, 0.93, pltlabels[0], transform=ax.transAxes, fontsize=fontsize_panellabel, weight='bold')
ax.set_xlabel('Year', fontsize=fontsize)
ax.set_ylabel('Airborne fraction (unitless)', fontsize=fontsize)
ax.set_xlim([year0-1, year1+1])
ax.set_ylim([0.1,0.9])
ax.grid(True)
ax.set_axisbelow(True)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
ax = fig.add_subplot(122)
width_bp = 0.8
if lustr in ['gcp', 'han']:
bp1 = ax.boxplot(MCdat[4][0], positions=[4], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp2 = ax.boxplot(MCdat[4][iset], positions=[3], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp3 = ax.boxplot(MCdats[4][0], positions=[2], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp4 = ax.boxplot(MCdats[4][iset], positions=[1], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
for b, bplot in enumerate((bp1, bp2, bp3, bp4)):
bplot['boxes'][0].set(facecolor='white')
if b in [0,2]:
bplot['boxes'][0].set(edgecolor=rgba2rgb(np.array(pltc.to_rgba(AFcol, 0.3))*255)/255.)
ylim = [0.0, 5]
bploc_wisk = np.reshape([item.get_xdata()[1] for bp in [bp1, bp2, bp3, bp4] for item in bp['whiskers']], (4, 2)) # [:,0] is left, [:,1] is right.
bploc_box = np.reshape([item.get_xdata()[0] for bp in [bp1, bp2, bp3, bp4] for item in bp['whiskers']], (4, 2)) # [:,0] is left, [:,1] is right.
if lustr == 'gcp':
ax.text(bploc_wisk[0, 0] - spacer, 4, 'Raw', color='grey', va='center', ha='right', bbox=dict(facecolor='white', pad=0, edgecolor='none'), zorder=100, fontsize=fontsize_boxtext)
ax.text(bploc_wisk[1, 0] - spacer, 3, 'Filter', color='grey', va='center', ha='right', bbox=dict(facecolor='white', pad=2, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[2, 0] - spacer, 2, 'Bootstrap', color='grey', va='center', ha='right', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[3, 0] - spacer, 1, 'Filter+Bootstrap', color='grey', va='center', ha='right', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
elif lustr == 'han':
ax.text(bploc_wisk[0, 1] + spacer, 4, 'Raw', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), zorder=100, fontsize=fontsize_boxtext)
ax.text(bploc_wisk[1, 1] + spacer, 3, 'Filter', color='grey', va='center', bbox=dict(facecolor='white', pad=2, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[2, 1] + spacer, 2, 'Bootstrap', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[3, 1] + spacer, 1, 'Filter+Bootstrap', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
elif lustr == 'new':
bp1 = ax.boxplot(MCdat[4][0], positions=[8], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5, 95])
bp2 = ax.boxplot(MCdat[4][iset], positions=[7], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5, 95])
bp3 = ax.boxplot(MCdats[4][0], positions=[6], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5, 95])
bp4 = ax.boxplot(MCdats[4][iset], positions=[5], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5, 95])
if FFchinaplot == 'LIU':
bp5 = ax.boxplot(MCdict['new_b'][4][iset], positions=[3], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp6 = ax.boxplot(MCdict['new_c'][4][iset], positions=[2], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp7 = ax.boxplot(MCdict['new_d'][4][iset], positions=[1], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
elif FFchinaplot == 'BP':
bp5 = ax.boxplot(MCdict['new_e'][4][iset], positions=[3], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp6 = ax.boxplot(MCdict['new_c'][4][iset], positions=[2], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
bp7 = ax.boxplot(MCdict['new_f'][4][iset], positions=[1], vert=False, sym='', widths=width_bp, patch_artist=True, boxprops=dict(color=AFcol), whis=[5,95])
for b, bplot in enumerate((bp1, bp2, bp3, bp4, bp5, bp6, bp7)):
bplot['boxes'][0].set(facecolor='white')
if b in [0,2]:
bplot['boxes'][0].set(edgecolor=rgba2rgb(np.array(pltc.to_rgba(AFcol, 0.3))*255)/255.)
ylim = [-.05, 9]
bploc_wisk = np.reshape([item.get_xdata()[1] for bp in [bp1, bp2, bp3, bp4, bp5, bp6, bp7] for item in bp['whiskers']], (7, 2)) # [:,0] is left, [:,1] is right.
bploc_box = np.reshape([item.get_xdata()[0] for bp in [bp1, bp2, bp3, bp4, bp5, bp6, bp7] for item in bp['whiskers']], (7, 2)) # [:,0] is left, [:,1] is right.
ax.text(bploc_wisk[0, 1] + spacer, 8, 'Raw', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), zorder=100, fontsize=fontsize_boxtext)
ax.text(bploc_wisk[1, 1] + spacer, 7, 'Filter', color='grey', va='center', bbox=dict(facecolor='white', pad=2, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[2, 1] + spacer, 6, 'Bootstrap', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[3, 1] + spacer, 5, 'Filter+Bootstrap', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[4, 1] + spacer, 3, 'Lower FF emissions China', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), zorder=100, fontsize=fontsize_boxtext)
ax.text(bploc_wisk[5, 1] + spacer, 2, 'Increasing role of fire', color='grey', va='center', bbox=dict(facecolor='white', pad=2, edgecolor='none'), fontsize=fontsize_boxtext)
ax.text(bploc_wisk[6, 1] + spacer, 1, 'Combined adjustments', color='grey', va='center', bbox=dict(facecolor='white', pad=0, edgecolor='none'), fontsize=fontsize_boxtext)
ax.axvline(x=0, c='black', alpha=0.6, zorder=0)
ax.set_xlim([-0.035,0.035])
ax.get_yaxis().set_ticks([])
ax.set_ylim(ylim)
ax.grid(True)
ax.set_axisbelow(True)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
if lustr == 'gcp':
textstr = 'GCP vs. H&N: p = %.2f\nGCP vs. This study: p = %.2f\nGCP vs. no trend: p = %.2f' % (pval[0,1], pval[0,2], pval[0,3])
elif lustr == 'han':
textstr = 'H&N vs. GCP: p = %.2f\nH&N vs. This study: p = %.2f\nH&N vs. no trend: p = %.2f' % (pval[0,1], pval[1,2], pval[1,4])
elif lustr == 'new':
textstr = 'This study vs. GCP: p = %.2f\nThis study vs. H&N: p = %.2f\nThis study vs. no trend: p = %.2f' % (pval[0,2], pval[1,2], pval[2,5])
ax.text(0.015, 0.93, pltlabels[1], transform=ax.transAxes, fontsize=fontsize_panellabel, weight='bold')
if trendcalc == 'rgr':
ax.set_xlabel('Relative growth rate, RGR (% yr$^{-1}$)', fontsize=fontsize)
elif trendcalc == 'abs':
ax.set_xlabel('Trend (decade$^{-1}$)', fontsize=fontsize)
plt.tight_layout()
if filename:
plt.savefig(filename + '.pdf', bbox_inches='tight')
else:
plt.show()
plot_Figure3(3, lustr='gcp', filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure3_MC%s_ab' % MC)
plot_Figure3(4, lustr='han', filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure3_MC%s_cd' % MC)
plot_Figure3(5, lustr='new', filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure3_MC%s_ef' % MC)
def plot_Table1():
''' Calculate Table 1 from paper with trend significance values.
:return: Table 1, dataframe containing significance values.
'''
datalist = [MCdict['gcp'], MCdict['han'], MCdict['new'], MCdict['gcp_s'], MCdict['han_s'], MCdict['new_s']]
Ppos = np.zeros(len(datalist))
Pneg = np.zeros(len(datalist))
P2 = np.zeros(len(datalist))
Ppos2 = np.zeros(len(datalist))
Pneg2 = np.zeros(len(datalist))
for i, MCdat in enumerate(datalist):
Ppos[i] = np.sum([MCdat[4][iset] > 0.0]) / float(MC) # Probability P of a positive trend
Pneg[i] = np.sum([MCdat[4][iset] < 0.0]) / float(MC) # Probability P of a negative trend
P2[i] = np.sum([MCdat[6][iset] < 0.05])/float(MC) # Probability P of a significant trend (both positive and negative)
Ppos2[i] = np.sum([(MCdat[4][iset] > 0.0) & (MCdat[6][iset] < 0.05)]) / float(MC) # Probability P of a significant positive trend
Pneg2[i] = np.sum([(MCdat[4][iset] < 0.0) & (MCdat[6][iset] < 0.05)]) / float(MC) # Probability P of a significant negative trend
dframe = np.array([[Ppos[3],Pneg[3],Ppos2[3],Pneg2[3],Ppos2[3]/(Ppos2[3]+Pneg2[3]),Pneg2[3]/(Ppos2[3]+Pneg2[3])], # Store results in dataframe.
[Ppos[4],Pneg[4],Ppos2[4],Pneg2[4],Ppos2[4]/(Ppos2[4]+Pneg2[4]),Pneg2[4]/(Ppos2[4]+Pneg2[4])],
[Ppos[5],Pneg[5],Ppos2[5],Pneg2[5],Ppos2[5]/(Ppos2[5]+Pneg2[5]),Pneg2[5]/(Ppos2[5]+Pneg2[5])]])
dframe = pd.DataFrame(dframe, index=['GCP','H&N','This study'], columns=['Positive','Negative','Positive p<0.05','Negative p<0.05','p<0.05 fraction positive','p<0.05 fraction negative'])
return dframe
ds_table1 = plot_Table1()
print(ds_table1.to_string())
def calc_AF_MonteCarlo_ARR(MC, size, filename=None, FF_China=None, RESPscheme=None, bootstrap=True):
''' Calculation of the Airborne fraction using a Monte Carlo simulation, based on the linear approximations of the LU time series.
For a (size+1 x size+1) array, the AF is calculated for a range of intercepts and slopes.
Three additional intercepts and slopes are added; those are the linear fits of the gcp, han, and new LU time series.
Those three end up in the extended diagonal of the results arrays. They can be extracted using indices [size+1,size+1], [size+2,size+2], and [size+3,size+3]
AF_a = annual raw, AF_ms = monthly smoothed, AF_msnv = monthly smoothed and filtered, AF_asnv = annual smoothed and filtered.
:param MC: number of Monte Carlo iterations.
:param size: size of data array.
:param filename: filename of saved result (saved in .npz format) [str]
:param FF_China: choose which FF scheme to use, China GCP ('GCP'), China Liu et al. (2015) ('LIU'), or China BP (2021)
:param RESPscheme: choose respiration scheme, normal ('stable', default) or increasing role of fire ('incfire')
:param bootstrap: Apply bootstrapping for calculation of trend uncertainty interval (default=True).
:return:
- MC_slope_median: median of found slopes over MonteCarlo iterations.
- MC_slope_std: standard deviation of found slopes over MonteCarlo iterations.
- MC_std_mean: mean of found standard deviations over MonteCarlo iterations.
- MC_p_mean: mean of found p-values over MonteCarlo iterations.
=== All results are returned for four data treatments ===:
0 = annual raw ('AF_a') [Reported in paper Figure 3]
1 = monthly smoothed ('AF_ms') [Not used in paper]
2 = monthly smoothed and filtered ('AF_msnv') [Not used in paper]
3 = annual smoothed and filtered ('AF_asnv') [Used for final trend estimates, reported in Table 1, Figure 3 and 4]
'''
if FF_China is None: FF_China = 'GCP'
if RESPscheme is None: RESPscheme = 'stable'
if bootstrap is True:
fBts = 0.8 # Bootstrap window of at least 80% of time series.
min_length_y = int(np.ceil(len(years) * fBts)) # at least fBts.
min_length_m = int(np.ceil((len(t_m) - mavg) * fBts)) # at least fBts.
start = np.zeros(MC).astype(int)
length = np.zeros(MC).astype(int)
end = np.zeros(MC).astype(int)
if (filename is not None) and os.path.isfile(filename + '.npz'):
ds = np.load(filename + '.npz')
MC_slope_median = ds['MC_slope_median'][:]
MC_slope_std = ds['MC_slope_std'][:]
MC_std_mean = ds['MC_std_mean'][:]
MC_p_mean = ds['MC_p_mean'][:]
else:
raise Warning('No pre-saved .npz file found with the name: %s, recalculating Monte-Carlo, Please note: this can take up to 30 hours!' % filename)
# Select used land use and fossil fuel time series
_, ff_tuple = data_selector('gcp', FF_China=FF_China, RESPscheme=RESPscheme) # 'gcp' is a placeholder here, not used. Only FF is loaded.
FF, FF_m, FF_error, FF_error_m = ff_tuple
LUgcp_slope, _ = np.polyfit(t_y, LUgcp, 1) # determine slope and b for lu datasets
LUhan_slope, _ = np.polyfit(t_y, LUhan, 1)
LUnew_slope, _ = np.polyfit(t_y, LUnew, 1)
LUgcp_mean = np.mean(LUgcp) # determine mean for lu datasets
LUhan_mean = np.mean(LUhan)
LUnew_mean = np.mean(LUnew)
LUgcp_std = np.std(LUgcp)
LUhan_std = np.std(LUhan)
LUnew_std = np.std(LUnew)
a = np.linspace(-0.005, 0.025, size+1)
a = np.hstack((a, LUgcp_slope, LUhan_slope, LUnew_slope)) # add 3 linear lines with slope of lu datasets
m = np.linspace(0.8, 1.5+(1/100.), size+1)
m = np.hstack((m, LUgcp_mean, LUhan_mean, LUnew_mean))
fsize = size+1+3 # plus 1 for symmetry, plus 3 for 3 LU datasets.
mesha = np.zeros((fsize,fsize))
meshm = np.zeros((fsize,fsize))
MC_slope_median = np.zeros((4,fsize,fsize))
MC_slope_std = np.zeros((4,fsize,fsize))
MC_std_mean = np.zeros((4,fsize,fsize))
MC_p_mean = np.zeros((4,fsize,fsize))
for i in [0,51,52,53]: #range(fsize):
for j in [0,51,52,53]: #range(fsize):
print('i = '+str(i)+' / j = '+str(j) + ' ... ')
time0 = timer.time()
mesha[i,j] = a[i]
meshm[i,j] = m[j]
lu = a[i] * (np.arange(len(years)) - len(years)/2.0) + m[j]
lu_m = tomonthly(lu) # create monthly series by interpolation of yearly series
lu_error = lu * 0.5 # LU errors are set at 50% for all three datasets.
lu_error_m = lu_m * 0.5
MC_a = np.zeros((4, MC)) # 4 dimensions are: [slope, intercept, p-value, stadard error]
MC_ms = np.zeros((4, MC))
MC_msnv = np.zeros((4, MC))
MC_asnv = np.zeros((4, MC))
for mc in range(MC):
FF_mc = FF + FF_error * np.random.randn(len(years)) # Apply random normally-distributed error.
lu_mc = lu + lu_error * np.random.randn(len(years))
dCO2_mc = dCO2 + dCO2_error * np.random.randn(len(years))
FF_m_mc = FF_m + FF_error_m * np.random.randn(len(t_m))
lu_m_mc = lu_m + lu_error_m * np.random.randn(len(t_m))
dCO2_m_mc = dCO2_m + dCO2_error_m * np.random.randn(len(t_m))
dCO2_s_mc = dCO2_s + dCO2_error_m * np.random.randn(len(t_m))
dCO2_sv_mc = dCO2_sv + dCO2_error * np.random.randn(len(years))
dCO2_sv_m_mc = dCO2_sv_m + dCO2_error_m * np.random.randn(len(t_m))
# Calculate Airborne fraction
AF_a = dCO2_mc / (FF_mc + lu_mc) # annual raw
AF_ms = dCO2_s_mc / (FF_m_mc + lu_m_mc) # monthly smoothed
AF_msnv = dCO2_sv_m_mc / (FF_m_mc + lu_m_mc) # monthly smoothed and filtered
AF_asnv = dCO2_sv_mc / (FF_mc + lu_mc) # annual smoothed and filtered
if bootstrap is True:
length_y = np.random.randint(min_length_y, len(years)+1) # bootstrap window length
start_y = np.random.randint(len(years) - length_y+1) # bootstrap window start point
end_y = start_y + length_y # bootstrap window end point
length_m = np.random.randint(min_length_m, len(t_m) - mavg +1) # Same, but monthly instead of annual
start_m = np.random.randint(edge, len(t_m) - edge - length_m +1)
end_m = start_m + length_m
mask_y = np.zeros(len(years)).astype(bool)
mask_m = np.zeros(len(t_m)).astype(bool)
mask_y[start_y:end_y] = True # Bootstrap window mask, annual.
mask_m[start_m:end_m] = True # Bootstrap window mask, monthly.
AF_a[~mask_y] = np.nan # Apply bootstrap mask to AF time series.
AF_ms[~mask_m] = np.nan
AF_msnv[~mask_m] = np.nan
AF_asnv[~mask_y] = np.nan
elif bootstrap is False: # Make empty mask to skip bootstrapping.
mask_y = np.zeros(len(years)).astype(bool)
mask_m = np.zeros(len(t_m)).astype(bool)
mask_y[:] = True
mask_m[edge:-edge] = True
# Calculate trend in Airborne fraction time series.
if trendcalc == 'abs': # Calculate absolute trend.
AF_a_reg = slopest(t_y[mask_y], AF_a[mask_y], method=trendmethod)
AF_ms_reg = slopest(t_m[mask_m], AF_ms[mask_m], method=trendmethod)
AF_msnv_reg = slopest(t_m[mask_m], AF_msnv[mask_m], method=trendmethod)
AF_asnv_reg = slopest(t_y[mask_y], AF_asnv[mask_y], method=trendmethod)
elif trendcalc == 'rgr': # Calculate relative growth rate.
AF_a_reg = slopest(t_y[mask_y], AF_a[mask_y] / float(np.mean(AF_a[mask_y])), method=trendmethod)
AF_ms_reg = slopest(t_m[mask_m], AF_ms[mask_m] / np.mean(AF_ms[mask_m]), method=trendmethod)
AF_msnv_reg = slopest(t_m[mask_m], AF_msnv[mask_m] / np.mean(AF_msnv[mask_m]), method=trendmethod)
AF_asnv_reg = slopest(t_y[mask_y], AF_asnv[mask_y] / float(np.mean(AF_asnv[mask_y])), method=trendmethod)
# Store regression results. Format: [slope, intercept, p-value, standard error]
MC_a[:,mc] = [AF_a_reg[0]*uconv, AF_a_reg[1]*uconv, AF_a_reg[3]*1, AF_a_reg[4]*uconv]
MC_ms[:,mc] = [AF_ms_reg[0]*uconv, AF_ms_reg[1]*uconv, AF_ms_reg[3]*1, AF_ms_reg[4]*uconv]
MC_msnv[:,mc] = [AF_msnv_reg[0]*uconv, AF_msnv_reg[1]*uconv, AF_msnv_reg[3]*1, AF_msnv_reg[4]*uconv]
MC_asnv[:,mc] = [AF_asnv_reg[0]*uconv, AF_asnv_reg[1]*uconv, AF_asnv_reg[3]*1, AF_asnv_reg[4]*uconv]
MC_slope = np.array([MC_a[0], MC_ms[0], MC_msnv[0], MC_asnv[0]])
MC_std = np.array([MC_a[3], MC_ms[3], MC_msnv[3], MC_asnv[3]])
MC_p = np.array([MC_a[2], MC_ms[2], MC_msnv[2], MC_asnv[2]])
# Calculate summary statistics over Monte Carlo iterations.
MC_slope_median[:,i,j] = np.median(MC_slope, axis=1) # median of slope
MC_slope_std[:,i,j] = np.std(MC_slope, axis=1) # std of slope
MC_std_mean[:,i,j] = np.mean(MC_std, axis=1) # mean of stderr
MC_p_mean[:,i,j] = np.mean(MC_p, axis=1) # mean of p-value
time1 = timer.time() - time0
print('Done, duration = ' + str(time1))
print('Monte carlo Finished')
if filename is not None:
np.savez(filename, MC_slope_median=MC_slope_median, MC_slope_std=MC_slope_std, MC_std_mean=MC_std_mean, MC_p_mean=MC_p_mean)
return MC_slope_median, MC_slope_std, MC_std_mean, MC_p_mean
MC = 1000
size = 50
MCarr = calc_AF_MonteCarlo_ARR(MC, size, filename=wdir + 'Marle_et_al_Nature_AirborneFraction_MC%s_MK_run%sx%s_TREND%s' % (MC, size, size, trendcalc))
LINgcp = np.array(MCarr)[:,:,size+1,size+1] # result for linear fit of GCP time series.
LINhan = np.array(MCarr)[:,:,size+2,size+2] # result for linear fit of Hougthon time series.
LINnew = np.array(MCarr)[:,:,size+3,size+3] # result for linear fit of This study time series.
ARR = np.array(MCarr)[:,:,:size+1,:size+1] # result array for range of intercepts and slopes.
def plot_Figure4(fign, filename=None):
''' Plot paper Figure 4
:param fign: figure number
:param filename: filename of saved figure (.pdf format). If no filename, figure is only displayed and not saved.
:return: Figure 4
'''
fontsize = 7
fontsize_legend = 5.5
if trendcalc == 'rgr':
clevels = np.linspace(-0.5,0.5,11)
elif trendcalc == 'abs':
clevels = np.linspace(-0.02,0.02,11)
MC_slope_median = ARR[0]
MC_slope_std = ARR[1]
MC_std_mean = ARR[2]
MC_p_mean = ARR[3]
a = np.linspace(-0.005, 0.025, size + 1)
m = np.linspace(0.8, 1.5 + (1 / 100.), size + 1)
mesha = np.zeros((size+1, size+1))
meshm = np.zeros((size+1, size+1))
for i in range(size+1):
for j in range(size+1):
mesha[i,j] = a[i]
meshm[i,j] = m[j]
LUgcp_slope, _ = np.polyfit(t_y, LUgcp, 1) # determine slope and b for lu datasets
LUhan_slope, _ = np.polyfit(t_y, LUhan, 1)
LUnew_slope, _ = np.polyfit(t_y, LUnew, 1)
LUgcp_mean = np.mean(LUgcp) # determine mean for lu datasets
LUhan_mean = np.mean(LUhan)
LUnew_mean = np.mean(LUnew)
# Create figure
fig = plt.figure(fign, figsize=(89*mm,89*0.75*mm))
ax = fig.add_subplot(111)
d = size/2.5 # For visualization; reducing the amount of corner points smoothens the lines.
d = size/1.5
mesha_plot = scipy.ndimage.zoom(mesha, 1/d)
meshm_plot = scipy.ndimage.zoom(meshm, 1/d)
data_plot = scipy.ndimage.zoom(MC_slope_median[iset][:size+1,:size+1], 1/d)
p = ax.contourf(mesha_plot, meshm_plot, data_plot, levels=clevels, vmin=clevels[0], vmax=clevels[-1], cmap=cmap)
c = ax.contour(mesha_plot, meshm_plot, data_plot, colors='black', linestyles='--', alpha=0.0) # Dummy plot to retrieve line
zero_line = c.collections[4].get_paths()[0].vertices
fitline_zero = np.poly1d(np.polyfit(zero_line[:,0], zero_line[:,1] ,1))
ax.plot(a, fitline_zero(a), c='black', linewidth=1.2)
slope_std_0 = np.mean(MC_slope_std[iset][(MC_slope_median[iset] > -0.001) & (MC_slope_median[iset] < 0.001)])
l_ERR_upper = (MC_slope_median[iset] > slope_std_0).astype(int)
l_ERR_lower = (MC_slope_median[iset] < -slope_std_0).astype(int)
l_ERR_upper_arr = scipy.ndimage.interpolation.zoom(l_ERR_upper.astype(float), 2, order=1)
l_ERR_lower_arr = scipy.ndimage.interpolation.zoom(l_ERR_lower.astype(float), 2, order=1)
l_ERR_upper_arr = np.reshape(l_ERR_upper_arr, (51,2,51,2)).mean(1).mean(2)
l_ERR_lower_arr = np.reshape(l_ERR_lower_arr, (51,2,51,2)).mean(1).mean(2)
l_ERR_upper_arr = np.isclose(l_ERR_upper_arr, np.full((51, 51), 0.5), atol=0.45)
l_ERR_lower_arr = np.isclose(l_ERR_lower_arr, np.full((51, 51), 0.5), atol=0.45)
ERRupper_fit = np.poly1d(np.polyfit(mesha[l_ERR_upper_arr], meshm[l_ERR_upper_arr], 1))
ERRlower_fit = np.poly1d(np.polyfit(mesha[l_ERR_lower_arr], meshm[l_ERR_lower_arr], 1))
ax.plot(a, ERRupper_fit(a), color='black', linestyle='--', alpha=0.8, linewidth=1.2)
ax.plot(a, ERRlower_fit(a), color='black', linestyle='--', alpha=0.8, linewidth=1.2)
#ax.scatter([LUgcp_slope, LUhan_slope, LUnew_slope], [LUgcp_mean, LUhan_mean, LUnew_mean], color='black', s=50)
ax.scatter([LUgcp_slope], [LUgcp_mean], color=Cgcp, s=25, edgecolors='black', linewidth=1.0, zorder=100)
ax.scatter([LUhan_slope], [LUhan_mean], color=Chan, s=25, edgecolors='black', linewidth=1.0, zorder=100)
ax.scatter([LUnew_slope], [LUnew_mean], color=Cnew, s=25, edgecolors='black', linewidth=1.0, zorder=100)
# Set axes
#ax.xaxis.set_tick_params(pad=10)
ax.set_xlim(np.min(mesha), np.max(mesha)) #-0.001)
ax.set_ylim(np.min(meshm), 1.5) #np.max(meshm))
ax.set_adjustable('box')
ax.grid(True)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
ax.set_xlabel('Slope of LULCC emissions (Pg C yr$^{-2}$)', fontsize=fontsize)
ax.set_ylabel('Average of LULCC emissions (Pg C yr$^{-1}$)', fontsize=fontsize)
if trendcalc == 'rgr':
cb = fig.colorbar(p, ticks=clevels)
cb.set_label('Relative growth rate, RGR (% yr$^{-1}$)', size=fontsize)
elif trendcalc == 'abs':
cb = fig.colorbar(p, ticks=clevels)
cb.set_label('Trend (decade$^{-1}$)', size=fontsize)
ax.set_xticks([-0.005,0.005,0.015,0.025])
cb.ax.tick_params(labelsize=fontsize)
ax.xaxis.set_minor_locator(MultipleLocator(0.005))
# Add scatter labels
t1 = ax.text(LUgcp_slope+0.001, LUgcp_mean, u'GCP, Friedlingstein et al. (2020)', fontsize=fontsize_legend)
t2 = ax.text(LUhan_slope+0.009, LUhan_mean+0.025, 'Houghton & Nassikas (2017)', verticalalignment='bottom', horizontalalignment='right', fontsize=fontsize_legend)
t3 = ax.text(LUnew_slope-0.001, LUnew_mean, 'This study', verticalalignment='top', horizontalalignment='right', fontsize=fontsize_legend)
t1.set_bbox(dict(facecolor='white', alpha=1.0, edgecolor='grey', linewidth=0.9, pad=2.0))
t2.set_bbox(dict(facecolor='white', alpha=1.0, edgecolor='grey', linewidth=0.9, pad=2.0))
t3.set_bbox(dict(facecolor='white', alpha=1.0, edgecolor='grey', linewidth=0.9, pad=2.0))
plt.tight_layout()
fig.subplots_adjust(left=0.14, right=0.88)
if filename:
plt.savefig(filename + '.pdf', bbox_inches='tight')
else:
plt.show()
plot_Figure4(6, filename=wdir + 'Marle_et_al_Nature_AirborneFraction_Figure4_MC%s' % MC)
print('End of script.')
