Breakdown modelled concentrations
- Breakdown of modelled concentration
- Breakdown into influence from different anthropogenic emission categories and the total anthropogenic emissions contribution vs the biogenic component
- Breakdown into influence from different anthropogenic emission categories for each footprint
- Average breakdown for defined date range in bar graph containing all stations defined
- Aggregated breakdown in bar graph containing all stations defined, two date ranges compared
- Average breakdown anthropogenic contribution, the biogenic component, and the different fuel- and source categories for the individual stations, two date ranges compared
The option throughout the Notebook, to update the dictionaries, means to update the custom dictionaries which in turn can be used in the notebook "Station fingerprints".
Note that only one custom dictionary can be saved for each of the categories: Updating the dictionaries means to remove the old values.
In [16]:
%run ~/STILT_modules_v2.5.ipynb
%run ~/ICOS_atmObs_modules_v2.5.ipynb
%run ~/for_thesis.ipynb
stations = create_STILT_dictionary()
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import math
import numpy as np
from matplotlib.legend import Legend
Select which stations to include in the analysis
In [2]:
list_stations_for_analysis=select_stations()
Once done with station selection, run this cell to finalize selection
It is also possible to write the name of one, or several, more stations to base the analysis on
In [3]:
only_selection_stations=list_stations_for_analysis.value
all_stations=[]
for value in only_selection_stations:
all_stations.append(value)
additional_station=input('Write additional station(s) here, otherwise leave blank. If several, seperate by whitespace: ')
list_additional_station = additional_station.split()
for item in list_additional_station:
all_stations.append(str(item))
print('Your selection is: ', all_stations)
dictionary_whole_notebook={}
for station in all_stations:
dictionary_whole_notebook[station]={}
icos_stations = [ist[0:3] for ist in all_stations]
ICOS_files = get_ICOS_filename(icos_stations,download=True)
In [4]:
date_range, timeselect, start_date, end_date = date_range_date_hour()
date_index_number = (len(date_range) - 1)
Breakdown into influence from different anthropogenic emission categories and the total anthropogenic emissions contribution vs the biogenic component
The different source- and fuel categories are the same as the breakdown displayed at ICOS-CP's webpage
In [5]:
#only one dictionary needs info about start date, end date etc...
anthro_bio_sensitivity={}
anthro_bio_sensitivity['info']={}
anthro_bio_sensitivity['info']['date_min']=(str(date_range[0].year) + '-' + str(date_range[0].month) + '-' + str(date_range[0].day))
anthro_bio_sensitivity['info']['date_max']=(str(date_range[date_index_number].year) + '-' + str(date_range[date_index_number].month) + '-' + str(date_range[date_index_number].day))
anthro_bio_sensitivity['info']['hours']=(str(timeselect))
anthro_bio_sensitivity['info']['stations']=all_stations
fuel_category_sensitivity={}
source_category_sensitivity={}
station_iterator=iter(all_stations)
for station in station_iterator:
anthro_bio_sensitivity[station]={}
source_category_sensitivity[station]={}
fuel_category_sensitivity[station]={}
df = read_stilt_timeseries(station, date_range)
#averages --> default skip nan
df_mean=df.mean()
anthro_bio_sensitivity[station]['bio']=df_mean['co2.bio']
anthro_bio_sensitivity[station]['anthro']=(df_mean['co2.industry']+df_mean['co2.energy']+ df_mean['co2.transport']+ df_mean['co2.others'])
source_category_sensitivity[station]['energy']=df_mean['co2.industry']
source_category_sensitivity[station]['industry']=df_mean['co2.energy']
source_category_sensitivity[station]['transport']=df_mean['co2.transport']
source_category_sensitivity[station]['other']=df_mean['co2.others']
fuel_category_sensitivity[station]['oil']=df_mean['co2.fuel.oil']
fuel_category_sensitivity[station]['coal']=df_mean['co2.fuel.coal']
fuel_category_sensitivity[station]['gas']=df_mean['co2.fuel.gas']
fuel_category_sensitivity[station]['bio']=df_mean['co2.fuel.bio']
#standard deviation --> default skip nan
#not used currently
df_std=df.std()
matplotlib.rcParams.update({'font.size': 17})
y_pos = np.arange(1)
figure(num=None, figsize=(12, 8), dpi=80, facecolor='w', edgecolor='k')
ax = plt.subplot(111)
ax.yaxis.grid(True)
#stacked on top of each other for bar graph.
for_transport=df_mean['co2.industry']+df_mean['co2.energy']
for_others = for_transport + df_mean['co2.transport']
for_gas= df_mean['co2.fuel.oil'] + df_mean['co2.fuel.coal']
for_bio = for_gas + df_mean['co2.fuel.gas']
for_cement_production= for_bio + df_mean['co2.fuel.bio']
for_waste_burning=for_cement_production + df_mean['co2.cement']
#the remaining contribution comes from emissions from waste burning
waste_burning = (df_mean['co2.energy']+df_mean['co2.industry']+df_mean['co2.transport'] +df_mean['co2.others']) - for_waste_burning
p1 = ax.bar(y_pos-0.75, df_mean['co2.bio'],width=0.2,color='g',align='center')
#total anthropgenic = sum of the source categories
p2 = ax.bar(y_pos-0.25, (df_mean['co2.energy']+df_mean['co2.industry']+df_mean['co2.transport'] +df_mean['co2.others']),width=0.2,color='r',align='center')
#source categories
p3 = ax.bar(y_pos+0.25, df_mean['co2.industry'],width=0.2,color='dimgrey',align='center')
p4 = ax.bar(y_pos+0.25, df_mean['co2.energy'], bottom=df_mean['co2.industry'], width=0.2,color='orange', hatch="/")
p5 = ax.bar(y_pos+0.25, df_mean['co2.transport'],bottom=for_transport,width=0.2,color='maroon',align='center')
p6 = ax.bar(y_pos+0.25, df_mean['co2.others'],bottom=for_others,width=0.2,color='peru',align='center')
#fuel + cement + waste burning
p7 = ax.bar(y_pos+0.75, df_mean['co2.fuel.oil'] ,width=0.2,color='lightcoral',align='center')
p8 = ax.bar(y_pos+0.75, df_mean['co2.fuel.coal'] , bottom=df_mean['co2.fuel.oil'], width=0.2,color='lightskyblue',align='center')
p9 = ax.bar(y_pos+0.75, df_mean['co2.fuel.gas'] ,bottom=for_gas, width=0.2,color='brown',align='center')
p10 = ax.bar(y_pos+0.75, df_mean['co2.fuel.bio'] ,bottom=for_bio,width=0.2,color='green',align='center')
p11 = ax.bar(y_pos+0.75, df_mean['co2.cement'], bottom=for_cement_production,width=0.2,color='grey',align='center', hatch="/")
p12 = ax.bar(y_pos+0.75, waste_burning, bottom=for_waste_burning,width=0.2,color='blue',align='center')
#ax.legend((p1[0], p2[0],p3[0],p4[0],p5[0],p6[0],p7[0],p8[0],p9[0],p10[0], p11[0]), ('Biogenic contribution','Anthropogenic contribution', 'Industry', 'Energy', 'Transport', 'Other', 'Oil', 'Coal', 'Gas', 'Bio', 'Cement production'))
ax.legend((p1[0], p2[0]), ('Biogenic contribution','Anthropogenic contribution'), loc='upper left')
leg = Legend(ax, (p6[0],p5[0],p4[0],p3[0]), ('Others', 'Transport', 'Industry', 'Energy'), loc='lower center')
ax.add_artist(leg)
leg2 = Legend(ax, (p12[0],p11[0],p10[0],p9[0],p8[0],p7[0]), ('Waste burning','Cement production', 'Bio fuel', 'Gas', 'Coal', 'Oil'), loc='lower right')
ax.add_artist(leg2)
plt.ylabel('ppm')
plt.axhline(0, color='black', lw=2)
#list_labels=['Anthropogenic', 'Biogenic', 'Source categories', 'Fuel categories']
plt.xticks(y_pos)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False)
date_index_number = (len(date_range) - 1)
title= ('Biogenic vs. anthropogenic contribution at ' + str(station) + '\n' + str(date_range[0].year) + '-' + str(date_range[0].month) + '-' + str(date_range[0].day) + ' to ' + str(date_range[date_index_number].year) +\
'-' + str(date_range[date_index_number].month) + '-' + str(date_range[date_index_number].day)+ '\n' + 'Hour(s): ' + str(timeselect))
plt.title(title)
plt.show()
Breakdown into influence from different anthropogenic emission categories for each footprint
The data can be displayed in bar- or pie charts for each footprint in the date range (beware of a possibly a lot of output figures!)
In [ ]:
station_iterator=iter(all_stations)
emis_type=input("Select emission category to display: source_categories, fuel_categories or anthro_bio: ")
if emis_type=='source_categories' or emis_type=='fuel_categories':
display_type=input("Choose how to display the data: bar or pie: ")
for station in station_iterator:
df = read_stilt_timeseries(station, date_range)
for date in date_range:
if df.empty:
print ('no STILT data for defined date range for '), station
else:
for_title = (station + ' at ' + str(date)+ '\n'+ 'Modelled CO2 concentration: ' + str("%.2f" % df['co2.stilt'][date]))
if emis_type=='fuel_categories':
colors = ['lightcoral', 'lightskyblue', 'brown', 'green']
if display_type=='pie':
figure(num=None, figsize=(7, 4), dpi=80, facecolor='w', edgecolor='k')
oil_label = ('Oil: ' + str("%.2f" % df['co2.fuel.oil'][date])+ ' ppm')
coal_label = ('Coal: ' + str("%.2f" % df['co2.fuel.coal'][date])+ ' ppm')
gas_label = ('Gas: ' + str("%.2f" % df['co2.fuel.gas'][date])+ ' ppm')
bio_label = ('Fuel bio: ' + str("%.2f" %df['co2.fuel.bio'][date])+ ' ppm')
labels = oil_label, coal_label, gas_label,bio_label
sizes = [(df['co2.fuel.oil'][date]*1000) , (df['co2.fuel.coal'][date]*1000), (df['co2.fuel.gas'][date]*1000), (df['co2.fuel.bio'][date]*1000)]
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.2f%%', radius = 4)
plt.axis('equal')
plt.title(for_title, loc='center')
ax = plt.subplot(111)
ax.yaxis.grid(True)
plt.show()
if display_type == 'bar':
figure(num=None, figsize=(7, 4), dpi=80, facecolor='w', edgecolor='k')
objects = ('Oil', 'Coal', 'Gas', 'Bio')
y_pos = np.arange(len(objects))
sizes = [df['co2.fuel.oil'][date], df['co2.fuel.coal'][date], df['co2.fuel.gas'][date], df['co2.fuel.bio'][date]]
plt.bar(y_pos,
sizes,
color=colors,
align='center')
plt.xticks(y_pos, objects)
plt.ylabel('ppm')
plt.title(for_title, loc='center')
ax = plt.subplot(111)
ax.yaxis.grid(True)
plt.show()
if emis_type=='source_categories':
colors = ['dimgrey', 'orange', 'maroon', 'peru']
if display_type=='pie':
figure(num=None, figsize=(7, 4), dpi=80, facecolor='w', edgecolor='k')
industry_label = ('Industry: ' + str("%.2f" % df['co2.industry'][date])+ ' ppm')
energy_label = ('Energy: ' + str("%.2f" % df['co2.energy'][date])+ ' ppm')
transport_label = ('Transport: ' + str("%.2f" % df['co2.transport'][date])+ ' ppm')
others_label = ('Others: ' + str("%.2f" %df['co2.others'][date])+ ' ppm')
labels = industry_label, energy_label, transport_label,others_label
sizes = [(df['co2.industry'][date]*1000) , (df['co2.energy'][date]*1000), (df['co2.transport'][date]*1000), (df['co2.others'][date]*1000)]
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.2f%%', radius = 8)
plt.axis('equal')
plt.title(for_title, loc='center')
ax = plt.subplot(111)
ax.yaxis.grid(True)
plt.show()
if display_type == 'bar':
figure(num=None, figsize=(7, 4), dpi=80, facecolor='w', edgecolor='k')
objects = ('Industry', 'Energy', 'Transport', 'Others')
y_pos = np.arange(len(objects))
sizes = [df['co2.industry'][date], df['co2.energy'][date], df['co2.transport'][date], df['co2.others'][date]]
plt.bar(y_pos,
sizes,
color=colors,
align='center')
plt.xticks(y_pos, objects)
plt.ylabel('ppm')
plt.title(for_title, loc='center')
ax = plt.subplot(111)
ax.yaxis.grid(True)
plt.show()
if emis_type=='anthro_bio':
colors=['red','green']
figure(num=None, figsize=(7, 4), dpi=80, facecolor='w', edgecolor='k')
objects = ('Anthro', 'Bio')
y_pos = np.arange(len(objects))
sizes = [(df['co2.industry'][date]+df['co2.energy'][date]+ df['co2.transport'][date]+ df['co2.others'][date]), df['co2.bio'][date]]
plt.bar(y_pos,
sizes,
color=colors,
align='center')
plt.xticks(y_pos, objects)
plt.ylabel('ppm')
plt.title(for_title, loc='center')
ax = plt.subplot(111)
ax.yaxis.grid(True)
plt.show()
Average breakdown for defined date range in bar graph containing all stations defined
A bar graph displaying average anthropogenic and biogenic contribution will always be an output when running this cell
The second bar graph contains a breakdown of the anthropogenic emission where the user can choose between source and fuel categories
In [35]:
source_category_input=input("Select emission category to display: source_categories or fuel_categories ")
#one for each station --> just update the names of the lists.
bio_list= []
anthro_list =[]
station_list = []
anthro_bio_list =[]
if source_category_input == 'source_categories':
industry_list =[]
energy_list=[]
transport_list= []
others_list = []
if source_category_input == 'fuel_categories':
oil_list =[]
coal_list = []
gas_list = []
biofuel_list = []
station_iterator=iter(all_stations)
for station in station_iterator:
df = read_stilt_timeseries(station, date_range)
df_mean=df.mean()
if df.empty:
print('no STILT data for defined date range for ', station)
else:
station_list.append(station)
bio_list.append(df_mean['co2.bio'])
anthro_list.append(df_mean['co2.industry']+df_mean['co2.energy']+ df_mean['co2.transport']+ df_mean['co2.others'])
anthro_bio_list.append(df_mean['co2.bio']+ df_mean['co2.industry']+df_mean['co2.energy']+ df_mean['co2.transport']+ df_mean['co2.others'])
#this function runs once for each station and returns the averages that are calculated within the function.
if source_category_input == 'source_categories':
industry_list.append(df_mean['co2.industry'])
energy_list.append(df_mean['co2.energy'])
transport_list.append(df_mean['co2.transport'])
others_list.append(df_mean['co2.others'])
if source_category_input == 'fuel_categories':
oil_list.append(df_mean['co2.fuel.oil'])
coal_list.append(df_mean['co2.fuel.coal'])
gas_list.append(df_mean['co2.fuel.gas'])
biofuel_list.append(df_mean['co2.fuel.bio'])
matplotlib.rcParams.update({'font.size': 19})
sorted_bio_list = [x for _,x in sorted(zip(anthro_list,bio_list))]
sorted_station_list = [x for _,x in sorted(zip(anthro_list,station_list))]
sorted_anthro_list= [x for _,x in sorted(zip(anthro_list,anthro_list))]
sorted_anthro_bio_list =[x for _,x in sorted(zip(anthro_list,anthro_bio_list))]
y_pos = np.arange(len(sorted_station_list))
figure(num=None, figsize=(22, 10), dpi=80, facecolor='w', edgecolor='k')
ax = plt.subplot(111)
p1 = ax.bar(y_pos-0.2, sorted_anthro_bio_list ,width=0.19,color='b',align='center')
p2 = ax.bar(y_pos+0, sorted_anthro_list ,width=0.19,color='r',align='center')
p3 = ax.bar(y_pos+0.2, sorted_bio_list,width=0.19,color='g',align='center', hatch="/")
ax.legend((p1[0], p2[0],p3[0]), ('Total (Anthro+bio)','Anthropogenic', 'Bio') )
plt.ylabel('ppm')
plt.xticks(y_pos, sorted_station_list, rotation='vertical')
plt.axhline(0, color='black', lw=2)
ax.yaxis.grid(True)
#get the index of the last date (start at 0) to access it in the titles
date_index_number = (len(date_range) - 1)
plt.title('Average contribution/uptake of CO2: anthropogenic vs bio by station (ppm)' + '\n' + 'Filtered by smallest to largest anthropogenic contribution' + '\n' + str(date_range[0].year) + '-' + str(date_range[0].month) + '-' + str(date_range[0].day)\
+ ' to ' + str(date_range[date_index_number].year) + '-' + str(date_range[date_index_number].month) + '-' + str(date_range[date_index_number].day)+ '\n' +\
'Hour(s): ' + timeselect)
plt.show()
if source_category_input == 'source_categories':
sorted_industry_list = [x for _,x in sorted(zip(anthro_list,industry_list))]
sorted_energy_list = [x for _,x in sorted(zip(anthro_list,energy_list))]
sorted_transport_list = [x for _,x in sorted(zip(anthro_list,transport_list))]
sorted_others_list = [x for _,x in sorted(zip(anthro_list,others_list))]
y_pos = np.arange(len(sorted_station_list))
figure(num=None, figsize=(20, 10), dpi=80, facecolor='w', edgecolor='k')
ax = plt.subplot(111)
ax.yaxis.grid(True)
for_transport=[x + y for x, y in zip(sorted_industry_list,sorted_energy_list)]
for_others =[x + y + z for x, y, z in zip(sorted_industry_list,sorted_energy_list,sorted_transport_list)]
p1 = ax.bar(y_pos, sorted_industry_list ,width=0.2,color='dimgrey',align='center')
p2 = ax.bar(y_pos, sorted_energy_list, bottom=sorted_industry_list, width=0.2,color='orange',align='center', hatch="/")
p3 = ax.bar(y_pos, sorted_transport_list,bottom=for_transport, width=0.2,color='maroon',align='center')
p4 = ax.bar(y_pos, sorted_others_list, bottom=for_others, width=0.2,color='peru',align='center')
ax.legend((p4[0], p3[0], p2[0], p1[0]), ('Others', 'Transport','Energy','Industry'))
plt.xticks(y_pos, sorted_station_list, rotation='vertical')
plt.ylabel('ppm')
ax.yaxis.grid(True)
plt.title('Average contribution (ppm) by source categories for selected stations' + '\n' + 'Filtered smallest to largest anthropogenic contribution' + '\n' + str(date_range[0].year) + '-' + str(date_range[0].month) + '-' + str(date_range[0].day)\
+ ' to ' + str(date_range[date_index_number].year) + '-' + str(date_range[date_index_number].month) + '-' + str(date_range[date_index_number].day)+ '\n' +\
'Hour(s): ' + timeselect)
plt.show()
if source_category_input == 'fuel_categories':
sorted_oil_list = [x for _,x in sorted(zip(anthro_list,oil_list))]
sorted_coal_list = [x for _,x in sorted(zip(anthro_list,coal_list))]
sorted_gas_list = [x for _,x in sorted(zip(anthro_list,gas_list))]
sorted_biofuel_list = [x for _,x in sorted(zip(anthro_list,biofuel_list))]
y_pos = np.arange(len(sorted_station_list))
figure(num=None, figsize=(25, 10), dpi=80, facecolor='w', edgecolor='k')
ax = plt.subplot(111)
ax.yaxis.grid(True)
for_gas=[x + y for x, y in zip(sorted_oil_list,sorted_coal_list)]
for_biofuel =[x + y + z for x, y, z in zip(sorted_oil_list,sorted_coal_list,sorted_gas_list)]
p1 = ax.bar(y_pos, sorted_oil_list,width=0.2,color='lightcoral',align='center')
p2 = ax.bar(y_pos, sorted_coal_list,bottom=sorted_oil_list,width=0.2,color='lightskyblue',align='center')
p3 = ax.bar(y_pos, sorted_gas_list,bottom=for_gas,width=0.2,color='brown',align='center')
p4 = ax.bar(y_pos, sorted_biofuel_list,bottom=for_biofuel,width=0.2,color='green',align='center')
ax.legend((p4[0], p3[0], p2[0], p1[0]), ('Biofuel', 'Gas', 'Coal', 'Oil'))
ax.yaxis.grid(True)
plt.xticks(y_pos, sorted_station_list)
plt.ylabel('ppm')
#get the index of the last date (start at 0) to access it in the titles
date_index_number = (len(date_range) - 1)
plt.title('Average contribution (ppm) by fuel categories for selected stations' + '\n' + 'Filtered smallest to largest anthropogenic contribution' + '\n' + str(date_range[0].year) + '-' + str(date_range[0].month) + '-' + str(date_range[0].day)\
+ ' to ' + str(date_range[date_index_number].year) + '-' + str(date_range[date_index_number].month) + '-' + str(date_range[date_index_number].day)+ '\n' +\
'Hour(s): ' + timeselect)
plt.show()
Aggregated breakdown in bar graph containing all stations defined, two date ranges compared
Two new date ranges will be defined in this cell
The date ranges anthropogenic and biogenic contributions are output into the same bar graph
In a second bar graph, the defined date ranges are compared in terms of th anthropogenic contribution either
split fuel or source categories
In [36]:
date_range1, timeselect1, start_date1, end_date1=date_range_date_hour()
date_range2, timeselect2, start_date2, end_date2=date_range_date_hour()
source_category_input=input("Select emission category to display: source_categories or fuel_categories ")
list_date_range = [date_range1, date_range2]
matplotlib.rcParams.update({'font.size': 17})
bio_list_both = []
station_list_both = []
anthro_list_both =[]
industry_both = []
energy_both= []
transport_both=[]
others_both = []
oil_both = []
coal_both = []
gas_both = []
biofuel_both = []
stations_missing_data_both=[]
index_first=0
for each_date_range in list_date_range:
bio_list= []
anthro_list =[]
station_list = []
anthro_bio_list =[]
stations_missing_data=[]
if source_category_input == 'source_categories':
industry_list =[]
energy_list=[]
transport_list= []
others_list = []
if source_category_input == 'fuel_categories':
oil_list =[]
coal_list = []
gas_list = []
biofuel_list = []
station_iterator=iter(all_stations)
for station in station_iterator:
if index_first==0:
df = read_stilt_timeseries(station, date_range1)
else:
df = read_stilt_timeseries(station, date_range2)
df_mean=df.mean()
if df.empty:
print ('no STILT data for defined date range ', date_range, 'for station ', station)
stations_missing_data.append(station)
else:
station_list.append(station)
bio_list.append(df_mean['co2.bio'])
anthro_list.append(df_mean['co2.industry']+df_mean['co2.energy']+ df_mean['co2.transport']+ df_mean['co2.others'])
#this function runs once for each station and returns the averages that are calculated within the function.
if source_category_input == 'source_categories':
industry_list.append(df_mean['co2.industry'])
energy_list.append(df_mean['co2.energy'])
transport_list.append(df_mean['co2.transport'])
others_list.append(df_mean['co2.others'])
if source_category_input == 'fuel_categories':
oil_list.append(df_mean['co2.fuel.oil'])
coal_list.append(df_mean['co2.fuel.coal'])
gas_list.append(df_mean['co2.fuel.gas'])
biofuel_list.append(df_mean['co2.fuel.bio'])
#looping over the date_ranges, adding one list from each for each category
station_list_both.append(station_list)
stations_missing_data_both.append(stations_missing_data)
bio_list_both.append(bio_list)
anthro_list_both.append(anthro_list)
if source_category_input == 'source_categories':
industry_both.append(industry_list)
energy_both.append(energy_list)
transport_both.append(transport_list)
others_both.append(others_list)
if source_category_input == 'fuel_categories':
oil_both.append(oil_list)
coal_both.append(coal_list)
gas_both.append(gas_list)
biofuel_both.append(biofuel_list)
index_first=index_first+1
if len(stations_missing_data_both[0])>0 or len(stations_missing_data_both[1])>0:
for each_station in stations_missing_data_both[0]:
print (each_station, ' misses data in the first specified date range. Exclude station from this analysis or run the STILT footprint tool')
for each_station in stations_missing_data_both[1]:
print (each_station, ' misses data in the first specified date range. Exclude station from this analysis or run the STILT footprint tool')
#plot if have all data:
else:
anthro_list_one =anthro_list_both[0]
bio_list_one = bio_list_both[0]
anthro_list_two =anthro_list_both[1]
bio_list_two = bio_list_both[1]
#will be the same for both --> no missing stations
station_list = station_list_both[0]
#sorting by lowest anthropogenic contribution in date range one.
sorted_anthro_list_one = [x for _,x in sorted(zip(anthro_list_one,anthro_list_one))]
sorted_bio_list_one = [x for _,x in sorted(zip(anthro_list_one,bio_list_one))]
sorted_anthro_list_two = [x for _,x in sorted(zip(anthro_list_one,anthro_list_two))]
sorted_bio_list_two = [x for _,x in sorted(zip(anthro_list_one,bio_list_two))]
sorted_station_list = [x for _,x in sorted(zip(anthro_list_one,station_list))]
figure(num=None, figsize=(22, 11), dpi=80, facecolor='w', edgecolor='k')
y_pos = np.arange(len(sorted_station_list))
ax = plt.subplot(111)
p1 = ax.bar(y_pos-0.3, sorted_bio_list_one,width=0.19,color='g',align='center', hatch="/")
p2 = ax.bar(y_pos-0.1, sorted_anthro_list_one, width=0.19,color='r',align='center')
p3 = ax.bar(y_pos+0.1, sorted_bio_list_two ,width=0.19,color='yellowgreen',align='center',hatch="/")
p4 = ax.bar(y_pos+0.3, sorted_anthro_list_two,width=0.19,color='darkorange',align='center')
ax.legend((p1[0], p2[0], p3[0], p4[0]), ('Biogenic component date range one', 'Anthropogenic contribution date range one','Biogenic component date range two', 'Anthropogenic component date range two'))
ax.yaxis.grid(True)
plt.xticks(y_pos, sorted_station_list, rotation='vertical')
plt.axhline(0, color='black', lw=2)
#get the index of the last date (start at 0) to access it in the titles
date_index_number1 = (len(date_range1) - 1)
date_index_number2 = (len(date_range2) - 1)
plt.title('Comparing andthropogenic contribution and the biogenic component' + '\n' + 'Date range one: ' + str(date_range1[0].year) + '-' + str(date_range1[0].month) + '-' + str(date_range1[0].day)\
+ ' to ' + str(date_range1[date_index_number1].year) + '-' + str(date_range1[date_index_number1].month) + '-' + str(date_range1[date_index_number1].day)+ ' Hour(s): ' + timeselect1\
+ '\n' + 'Date range two: ' + str(date_range2[0].year) + '-' + str(date_range2[0].month) + '-' + str(date_range2[0].day)\
+ ' to ' + str(date_range2[date_index_number2].year) + '-' + str(date_range2[date_index_number2].month) + '-' + str(date_range2[date_index_number2].day)+ ' Hour(s): ' + timeselect2)
plt.ylabel('ppm')
plt.show()
if source_category_input == 'source_categories':
industry_list_one =industry_both[0]
energy_list_one = energy_both[0]
transport_list_one =transport_both[0]
others_list_one =others_both[0]
industry_list_two =industry_both[1]
energy_list_two = energy_both[1]
transport_list_two =transport_both[1]
others_list_two =others_both[1]
#sorting lowest anthropogenic contribution in date range one.
sorted_industry_list_one = [x for _,x in sorted(zip(anthro_list_one,industry_list_one))]
sorted_energy_list_one = [x for _,x in sorted(zip(anthro_list_one,energy_list_one))]
sorted_transport_list_one = [x for _,x in sorted(zip(anthro_list_one,transport_list_one))]
sorted_others_list_one = [x for _,x in sorted(zip(anthro_list_one,others_list_one))]
sorted_industry_list_two = [x for _,x in sorted(zip(anthro_list_one,industry_list_two))]
sorted_energy_list_two = [x for _,x in sorted(zip(anthro_list_one,energy_list_two))]
sorted_transport_list_two = [x for _,x in sorted(zip(anthro_list_one,transport_list_two))]
sorted_others_list_two = [x for _,x in sorted(zip(anthro_list_one,others_list_two))]
figure(num=None, figsize=(22, 10), dpi=80, facecolor='w', edgecolor='k')
y_pos = np.arange(len(sorted_station_list))
ax = plt.subplot(111)
p1 = ax.bar(y_pos-0.4, sorted_industry_list_one,width=0.09,color='dimgrey',align='center')
p1 = ax.bar(y_pos-0.3, sorted_industry_list_two ,width=0.09,color='dimgrey',align='center')
p3 = ax.bar(y_pos-0.2, sorted_energy_list_one,width=0.09,color='orange',align='center',hatch="/")
p4 = ax.bar(y_pos-0.1, sorted_energy_list_two,width=0.09,color='orange',align='center',hatch="/")
p5 = ax.bar(y_pos+0.0, sorted_transport_list_one,width=0.09,color='maroon',align='center')
p6 = ax.bar(y_pos+0.1, sorted_transport_list_two,width=0.09,color='maroon',align='center')
p7 = ax.bar(y_pos+0.2, sorted_others_list_one,width=0.09,color='peru',align='center')
p8 = ax.bar(y_pos+0.3, sorted_others_list_two,width=0.09,color='peru',align='center')
ax.legend((p1[0], p3[0],p5[0], p7[0]), ('Industry contribution', 'Energy contribution', 'Transport contribution', 'Other contribution') )
ax.yaxis.grid(True)
plt.xticks(y_pos, sorted_station_list, rotation='vertical')
#add graph for source categories/fuel categories. maybe need to make figure even bigger.
plt.title('Comparing contribution of the different source categories, over two date ranges' + '\n' + 'Date range one (left): ' + str(date_range1[0].year) + '-' + str(date_range1[0].month) + '-' + str(date_range1[0].day)\
+ ' to ' + str(date_range1[date_index_number1].year) + '-' + str(date_range1[date_index_number1].month) + '-' + str(date_range1[date_index_number1].day)+ ' Hour(s): ' + timeselect1\
+ '\n' + 'Date range two (right): ' + str(date_range2[0].year) + '-' + str(date_range2[0].month) + '-' + str(date_range2[0].day)\
+ ' to ' + str(date_range2[date_index_number2].year) + '-' + str(date_range2[date_index_number2].month) + '-' + str(date_range2[date_index_number2].day)+ ' Hour(s): ' + timeselect2)
plt.ylabel('ppm')
plt.show()
if source_category_input == 'fuel_categories':
oil_list_one=oil_both[0]
coal_list_one=coal_both[0]
gas_list_one=gas_both[0]
biofuel_list_one=biofuel_both[0]
oil_list_two =oil_both[1]
coal_list_two = coal_both[1]
gas_list_two =gas_both[1]
biofuel_list_two =biofuel_both[1]
#sorting lowest anthropogenic contribution in date range one.
sorted_oil_list_one = [x for _,x in sorted(zip(anthro_list_one,oil_list_one))]
sorted_coal_list_one = [x for _,x in sorted(zip(anthro_list_one,coal_list_one))]
sorted_gas_list_one = [x for _,x in sorted(zip(anthro_list_one,gas_list_one))]
sorted_biofuel_list_one = [x for _,x in sorted(zip(anthro_list_one,biofuel_list_one))]
sorted_oil_list_two = [x for _,x in sorted(zip(anthro_list_one,oil_list_two))]
sorted_coal_list_two = [x for _,x in sorted(zip(anthro_list_one,coal_list_two))]
sorted_gas_list_two = [x for _,x in sorted(zip(anthro_list_one,gas_list_two))]
sorted_biofuel_list_two = [x for _,x in sorted(zip(anthro_list_one,biofuel_list_two))]
figure(num=None, figsize=(22, 10), dpi=80, facecolor='w', edgecolor='k')
y_pos = np.arange(len(sorted_station_list))
ax = plt.subplot(111)
p1 = ax.bar(y_pos-0.4, sorted_oil_list_one,width=0.09,color='lightcoral',align='center')
p1 = ax.bar(y_pos-0.3, sorted_oil_list_two ,width=0.09,color='lightcoral',align='center')
p3 = ax.bar(y_pos-0.2, sorted_coal_list_one,width=0.09,color='lightskyblue',align='center',hatch="/")
p4 = ax.bar(y_pos-0.1, sorted_coal_list_two,width=0.09,color='lightskyblue',align='center',hatch="/")
p5 = ax.bar(y_pos+0.0, sorted_gas_list_one,width=0.09,color='brown',align='center')
p6 = ax.bar(y_pos+0.1, sorted_gas_list_two,width=0.09,color='brown',align='center')
p7 = ax.bar(y_pos+0.2, sorted_biofuel_list_one,width=0.09,color='green',align='center')
p8 = ax.bar(y_pos+0.3, sorted_biofuel_list_two,width=0.09,color='green',align='center')
ax.legend((p1[0], p3[0],p5[0], p7[0]), ('Oil contribution', 'Coal contribution', 'Gas contribution', 'Biofuel contribution') )
ax.yaxis.grid(True)
plt.xticks(y_pos, sorted_station_list, rotation='vertical')
#add graph for source categories/fuel categories. maybe need to make figure even bigger.
plt.title('Comparing contribution of the different fuel categories, over two date ranges' + '\n' + 'Date range one (left): ' + str(date_range1[0].year) + '-' + str(date_range1[0].month) + '-' + str(date_range1[0].day)\
+ ' to ' + str(date_range1[date_index_number1].year) + '-' + str(date_range1[date_index_number1].month) + '-' + str(date_range1[date_index_number1].day)+ ' Hour(s): ' + timeselect1\
+ '\n' + 'Date range two (right): ' + str(date_range2[0].year) + '-' + str(date_range2[0].month) + '-' + str(date_range2[0].day)\
+ ' to ' + str(date_range2[date_index_number2].year) + '-' + str(date_range2[date_index_number2].month) + '-' + str(date_range2[date_index_number2].day)+ ' Hour(s): ' + timeselect2)
plt.ylabel('ppm')
plt.show()
Average breakdown anthropogenic contribution, the biogenic component, and the different fuel- and source categories for the individual stations, two date ranges compared
Two new date ranges will be defined in this cell
The date ranges anthropogenic and biogenic contributions are output into the same bar graph
In a second bar graph, the defined date ranges are compared in terms of the anthropogenic contribution either
split fuel- or source categories
In [37]:
date_range1, timeselect, start_date, end_date = date_range_date_hour()
date_range2, timeselect2, start_date, end_date = date_range_date_hour()
station_iterator=iter(all_stations)
for station in station_iterator:
df_1 = read_stilt_timeseries(station, date_range1)
df_1_mean=df_1.mean()
df_2 = read_stilt_timeseries(station, date_range2)
df_2_mean=df_2.mean()
matplotlib.rcParams.update({'font.size': 17})
y_pos = np.arange(1)
figure(num=None, figsize=(20, 10), dpi=80, facecolor='w', edgecolor='k')
ax = plt.subplot(111)
ax.yaxis.grid(True)
for_transport1=df_1_mean['co2.industry']+df_1_mean['co2.energy']
for_others1 = for_transport1 + df_1_mean['co2.transport']
for_gas1= df_1_mean['co2.fuel.oil'] + df_1_mean['co2.fuel.coal']
for_bio1 = for_gas1 + df_1_mean['co2.fuel.gas']
for_cement_production1= for_bio1 + df_1_mean['co2.fuel.bio']
for_waste_burning1=for_cement_production1 + df_1_mean['co2.cement']
#the remaining contribution comes from emissions from waste burning
waste_burning1 = (df_1_mean['co2.energy']+df_1_mean['co2.industry']+df_1_mean['co2.transport'] +df_1_mean['co2.others']) - for_waste_burning1
for_transport2=df_2_mean['co2.industry']+df_2_mean['co2.energy']
for_others2 = for_transport2 + df_2_mean['co2.transport']
for_gas2= df_2_mean['co2.fuel.oil'] + df_2_mean['co2.fuel.coal']
for_bio2 = for_gas2 + df_2_mean['co2.fuel.gas']
for_cement_production2= for_bio2 + df_2_mean['co2.fuel.bio']
for_waste_burning2=for_cement_production2 + df_2_mean['co2.cement']
#the remaining contribution comes from emissions from waste burning
waste_burning2 = (df_2_mean['co2.energy']+df_2_mean['co2.industry']+df_2_mean['co2.transport'] +df_2_mean['co2.others']) - for_waste_burning2
p1 = ax.bar(y_pos-0.75, df_1_mean['co2.bio'],width=0.15,color='g',align='center')
p1 = ax.bar(y_pos-0.55, df_2_mean['co2.bio'],width=0.15,color='g',align='center')
p2 = ax.bar(y_pos-0.25, (df_1_mean['co2.energy']+df_1_mean['co2.industry']+df_1_mean['co2.transport'] +df_1_mean['co2.others']),width=0.15,color='r',align='center')
p2 = ax.bar(y_pos-0.05, (df_2_mean['co2.energy']+df_2_mean['co2.industry']+df_2_mean['co2.transport'] +df_2_mean['co2.others']), width=0.15,color='r',align='center')
p3 = ax.bar(y_pos+0.25, df_1_mean['co2.industry'],width=0.15,color='dimgrey',align='center')
p3 = ax.bar(y_pos+0.45, df_2_mean['co2.industry'],width=0.15,color='dimgrey',align='center')
p4 = ax.bar(y_pos+0.25, df_1_mean['co2.energy'], bottom=df_1_mean['co2.industry'], width=0.15,color='orange', hatch="/")
p4 = ax.bar(y_pos+0.45, df_2_mean['co2.energy'], bottom=df_2_mean['co2.industry'], width=0.15,color='orange', hatch="/")
p5 = ax.bar(y_pos+0.25, df_1_mean['co2.transport'],bottom=for_transport1,width=0.15,color='maroon',align='center')
p5 = ax.bar(y_pos+0.45, df_2_mean['co2.transport'],bottom=for_transport2,width=0.15,color='maroon',align='center')
p6 = ax.bar(y_pos+0.25, df_1_mean['co2.others'],bottom=for_others1,width=0.15,color='peru',align='center')
p6 = ax.bar(y_pos+0.45, df_2_mean['co2.others'],bottom=for_others2,width=0.15,color='peru',align='center')
p7 = ax.bar(y_pos+0.75, df_1_mean['co2.fuel.oil'],width=0.15,color='lightcoral',align='center')
p7 = ax.bar(y_pos+0.95, df_2_mean['co2.fuel.oil'],width=0.15,color='lightcoral',align='center')
p8 = ax.bar(y_pos+0.75, df_1_mean['co2.fuel.coal'], bottom=df_1_mean['co2.fuel.oil'], width=0.15,color='lightskyblue',align='center')
p8 = ax.bar(y_pos+0.95, df_2_mean['co2.fuel.coal'], bottom=df_2_mean['co2.fuel.oil'], width=0.15,color='lightskyblue',align='center')
p9 = ax.bar(y_pos+0.75, df_1_mean['co2.fuel.gas'],bottom=for_gas1, width=0.15,color='brown',align='center')
p9 = ax.bar(y_pos+0.95, df_2_mean['co2.fuel.gas'],bottom=for_gas2, width=0.15,color='brown',align='center')
p10 = ax.bar(y_pos+0.75, df_1_mean['co2.fuel.bio'],bottom=for_bio1,width=0.15,color='green',align='center')
p10 = ax.bar(y_pos+0.95, df_2_mean['co2.fuel.bio'],bottom=for_bio2,width=0.15,color='green',align='center')
p11 = ax.bar(y_pos+0.75, df_1_mean['co2.cement'], bottom=for_cement_production1,width=0.15,color='grey',align='center', hatch="/")
p11 = ax.bar(y_pos+0.95, df_2_mean['co2.cement'], bottom=for_cement_production2,width=0.15,color='grey',align='center', hatch="/")
p11 = ax.bar(y_pos+0.75, waste_burning1, bottom=for_waste_burning1,width=0.15,color='grey',align='center', hatch="/")
p11 = ax.bar(y_pos+0.95, waste_burning2, bottom=for_waste_burning2,width=0.15,color='grey',align='center', hatch="/")
ax.legend((p1[0], p2[0]), ('Biogenic contribution','Anthropogenic contribution'), loc='upper left')
leg = Legend(ax, (p6[0],p5[0],p4[0],p3[0]), ('Others', 'Transport', 'Industry', 'Energy'), loc='lower center')
ax.add_artist(leg)
leg2 = Legend(ax, (p12[0],p11[0],p10[0],p9[0],p8[0],p7[0]), ('Waste burning','Cement production', 'Biofuel', 'Gas', 'Coal', 'Oil'), loc='lower right')
ax.add_artist(leg2)
plt.ylabel('ppm')
plt.axhline(0, color='black', lw=2)
plt.xticks(y_pos)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False)
date_index_number1 = (len(date_range1) - 1)
date_index_number2 = (len(date_range2) - 1)
title= ('Biogenic vs. anthropogenic contribution at ' + str(station) + '\n' + 'Date range one (left): ' + str(date_range1[0].year) + '-' + str(date_range1[0].month) + '-' + str(date_range1[0].day) + ' to ' + str(date_range1[date_index_number1].year) +\
'-' + str(date_range1[date_index_number1].month) + '-' + str(date_range1[date_index_number1].day) + ', Hour(s): ' + str(timeselect) + '\n' + 'Date range two (right): ' + str(date_range2[0].year) + '-' + str(date_range2[0].month) + '-' + str(date_range2[0].day) + ' to ' + str(date_range2[date_index_number2].year) +\
'-' + str(date_range2[date_index_number2].month) + '-' + str(date_range2[date_index_number2].day)+ ', Hour(s): ' + str(timeselect2))
plt.title(title)
plt.show()
