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accim_seville_graphs_and_tables.py
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accim_seville_graphs_and_tables.py
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import pandas as pd
df = pd.read_csv('accim_seville_runperiod[srcfreq-hourly[freq-runperiod[frequency_agg_func-sum[standard_outputs-True[CSVconcatenated.csv')
dict_replace = {
'Seville_Present': 'Spain_Seville_Present',
'Seville_ssp126_2050': 'Spain_Seville_SSP126-2050',
'Seville_ssp126_2080': 'Spain_Seville_SSP126-2080',
'Seville_ssp245_2050': 'Spain_Seville_SSP245-2050',
'Seville_ssp245_2080': 'Spain_Seville_SSP245-2080',
'Seville_ssp370_2050': 'Spain_Seville_SSP370-2050',
'Seville_ssp370_2080': 'Spain_Seville_SSP370-2080',
'Seville_ssp585_2050': 'Spain_Seville_SSP585-2050',
'Seville_ssp585_2080': 'Spain_Seville_SSP585-2080',
}
for k, v in dict_replace.items():
df['Source'] = [i.replace(k, v) for i in df['Source']]
df = df.drop(columns=['Unnamed: 0.1'])
df.to_csv('accim_seville_amended_runperiod[srcfreq-hourly[freq-runperiod[frequency_agg_func-sum[standard_outputs-True[CSVconcatenated.csv')
## Section 1
## Energy demand table (Table R1)
from accim.data.postprocessing.main import Table
z = Table(
source_concatenated_csv_filepath='accim_seville_amended_runperiod[srcfreq-hourly[freq-runperiod[frequency_agg_func-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
# level_excluded_zones=['BLOCK1:BATHROOM'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
idf_path='ALJARAFE CENTER_onlyGeometry.idf'
)
# print(*z.df.columns, sep='\n')
z.format_table(
type_of_table='custom',
custom_cols=[i for i in z.df.columns if
'Building_Total' in i
and '(summed)' in i
and 'Energy Demand' in i
],
# split_epw_names=True
)
dict_replace = {
'NS_rite': 'RITE',
'NS_seville': 'Adap Sevilla',
'NS_X': 'Adap ASHRAE55',
}
for k, v in dict_replace.items():
z.df['NameSuffix'] = [i.replace(k, v) for i in z.df['NameSuffix']]
df_backup = z.df.copy()
df_present = z.df.copy()
df_present = df_present[
# (~(df_present['HVACmode'].isin(['HM_2'])))
# &
(df_present['EPW_Scenario'].str.contains('Present'))]
z.df = df_present
z.wrangled_table(
reshaping='pivot',
vars_to_gather=['NameSuffix'],
baseline='Adap Sevilla',
comparison_mode=['baseline compared to others'],
comparison_cols=['relative', 'absolute']
)
# z.wrangled_df_pivoted.to_excel('comparison_present_v00.xlsx')
df_present_wrangled = z.wrangled_df_pivoted.copy()
# old_names = [
# 'CS_BRA Rupp NV[CM_3',
# 'CS_BRA Rupp NV[CM_0',
# 'CS_INT ASHRAE55[CM_3'
# ]
# new_names = [
# 'BRA_Adap_AC',
# 'BRA_Stat_AC',
# 'ASH_Adap_AC',
# ]
# for i in range(len(old_names)):
# # df_present_wrangled.columns = [j[1].replace(old_names[i], new_names[i]) for j in df_present_wrangled]
# df_present_wrangled.rename(columns=lambda s: s.replace(old_names[i], new_names[i]), inplace=True)
df_present_wrangled.rename(index=lambda s: s.replace(' (summed)', ''), inplace=True)
df_present_wrangled.rename(index=lambda s: s.replace('Building_Total_', ''), inplace=True)
# df_present_wrangled.to_excel('section1_tableR1_present_v00.xlsx')
##
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots(
nrows=1, ncols=3,
figsize=(15, 7.5)
)
sns.heatmap(
data=df_present_wrangled.iloc[:, 0:3],
square=True,
annot=True,
cbar_kws={'label': f'Demanda energética (kWh/m²)'},
fmt='g',
ax=ax[0],
xticklabels=[i for i in df_present_wrangled.iloc[:, 0:3].columns]
)
ax[0].set(xlabel='Scenario')
ax[0].set(ylabel='Adaptation profile')
ax[0].set_title('(a) Demanda energética', y=1.05, pad=1, fontdict={'fontsize': 11})
sns.heatmap(
data=df_present_wrangled.loc[:, [i for i in df_present_wrangled.columns if '/' in i]],
square=True,
annot=True,
cbar_kws={'label': f'Variación en la demanda energética (%)'},
fmt='g',
ax=ax[1],
xticklabels=[i for i in df_present_wrangled.loc[:, [i for i in df_present_wrangled.columns if '/' in i]]]
)
ax[1].set(xlabel='Scenario')
ax[1].set(ylabel='Adaptation profile')
ax[1].set_title('(b) Variación en kWh/m²', y=1.05, pad=1, fontdict={'fontsize': 11})
sns.heatmap(
data=df_present_wrangled.loc[:, [i for i in df_present_wrangled.columns if ' - ' in i]],
square=True,
annot=True,
cbar_kws={'label': f'Variación en la demanda energética (%)'},
fmt='g',
ax=ax[2],
xticklabels=[i for i in df_present_wrangled.loc[:, [i for i in df_present_wrangled.columns if ' - ' in i]]]
)
ax[2].set(xlabel='Scenario')
ax[2].set(ylabel='Adaptation profile')
ax[2].set_title('(b) Variación en %', y=1.05, pad=1, fontdict={'fontsize': 11})
plt.tight_layout()
plt.savefig(f'section1_tableR1_present_v00.png')
# z.df_backup.to_excel('to_be_deleted.xlsx')
##
from accim.data.postprocessing.main import Table
z_cc = Table(
source_concatenated_csv_filepath='accim_seville_amended_runperiod[srcfreq-hourly[freq-runperiod[frequency_agg_func-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
idf_path='ALJARAFE CENTER_onlyGeometry.idf'
)
dict_replace = {
'NS_rite': 'RITE',
'NS_seville': 'Adap Sevilla',
'NS_X': 'Adap ASHRAE55',
}
for k, v in dict_replace.items():
z_cc.df['NameSuffix'] = [i.replace(k, v) for i in z_cc.df['NameSuffix']]
# print(*z.df.columns, sep='\n')
z_cc.format_table(
type_of_table='custom',
custom_cols=[
'Building_Total_Total Energy Demand (kWh/m2) (summed)'
],
# split_epw_names=True
)
z_cc.wrangled_table(
reshaping='unstack',
vars_to_gather=[
# 'ComfStand',
# 'ComfMod',
# 'HVACmode',
'EPW_Scenario',
'EPW_Year'
# 'EPW_Scenario-Year'
],
baseline='Present[Present',
# baseline='Present',
comparison_mode=['baseline compared to others'],
comparison_cols=[
'relative',
'absolute'
],
# check_index_and_cols=True,
vars_to_keep=[
'NameSuffix',
# 'EPW_Scenario-Year'
# 'ComfStand',
# 'ComfMod',
# 'HVACmode',
# 'EPW_City_or_subcountry',
]
)
# z_cc.wrangled_df_unstacked.to_excel('section2_climate_change_increase_v01.xlsx')
df_cc = z_cc.wrangled_df_unstacked.copy()
df_cc.rename(columns=lambda s: s.replace(' (summed)', ''), inplace=True)
df_cc.rename(columns=lambda s: s.replace('Building_Total_', ''), inplace=True)
df_cc.rename(columns=lambda s: s.replace('Present[Present', 'Presente'), inplace=True)
df_cc.rename(columns=lambda s: s.replace('[', '_'), inplace=True)
df_cc.columns.names
df_cc = df_cc.droplevel(0, axis=1)
# df_cc = df_cc.round(0)
##
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots(
nrows=1, ncols=3,
figsize=(11.5, 5)
)
sns.heatmap(
data=df_cc.iloc[:, 0:9].transpose().round(0),
square=True,
annot=True,
cbar_kws={'label': f'Demanda energética (kWh/m²)'},
fmt='g',
ax=ax[0],
# xticklabels=[i for i in df_cc.iloc[:, 0:9].columns]
)
ax[0].set(xlabel='Modelo de confort')
ax[0].set(ylabel='Escenario')
ax[0].set_title('(a) Demanda energética', y=1.05, pad=1, fontdict={'fontsize': 11})
sns.heatmap(
data=df_cc.loc[:, [i for i in df_cc.columns if '/' in i]].transpose()*100,
square=True,
annot=True,
cbar_kws={'label': f'Variación en la demanda energética (%)'},
fmt='g',
ax=ax[1],
# xticklabels=[i for i in df_cc.loc[:, [i for i in df_cc.columns if '/' in i]]]
)
ax[1].set(xlabel='Modelo de confort')
ax[1].set(ylabel='Escenario')
ax[1].set_title('(b) Variación en %', y=1.05, pad=1, fontdict={'fontsize': 11})
sns.heatmap(
data=df_cc.loc[:, [i for i in df_cc.columns if ' - ' in i]].transpose().round(0),
square=True,
annot=True,
cbar_kws={'label': f'Variación en la demanda energética (kWh/m²)'},
fmt='g',
ax=ax[2],
# xticklabels=[i for i in df_cc.loc[:, [i for i in df_cc.columns if ' - ' in i]]]
)
ax[2].set(xlabel='Modelo de confort')
ax[2].set(ylabel='Escenario')
ax[2].set_title('(c) Variación en kWh/m²', y=1.05, pad=1, fontdict={'fontsize': 11})
plt.tight_layout()
plt.savefig(f'section2_climate_change_increase_v00.png')
##
from accim.data.postprocessing.main import Table
z = Table(
source_concatenated_csv_filepath='accim_seville_amended_hourly[srcfreq-hourly[freq-hourly[frequency_agg_func-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
# level_excluded_zones=['BLOCK1:BATHROOM'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
idf_path='ALJARAFE CENTER_onlyGeometry.idf'
)
# print(*z.df.columns, sep='\n')
pmot = 'PLANTAX08:OFFICE_ASHRAE 55 Running mean outdoor temperature (°C)'
z.format_table(
type_of_table='custom',
custom_cols=[ # if type_of_table is 'custom', custom_cols is used to filter the desired columns to keep
'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
'Building_Total_Zone Operative Temperature (°C) (mean)',
pmot,
'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
# 'Building_Total_AFN Zone Infiltration Air Change Rate (ach) (summed)'
]
)
print(*z.df_backup.columns, sep='\n')
# df_filtered = z.df.copy()
# df_filtered = df_filtered[
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] < df_filtered['Adaptive Cooling Setpoint Temperature_No Tolerance (°C)'])
# &
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] > df_filtered['Adaptive Heating Setpoint Temperature_No Tolerance (°C)'])
# ]
# z.df = df_filtered
z.df.columns = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.df.columns]
z.val_cols = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.val_cols]
z.df.columns = [i
.replace('_No Tolerance', '')
for i in z.df.columns]
z.val_cols = [i
.replace('_No Tolerance', '')
for i in z.val_cols]
z.scatter_plot(
vars_to_gather_cols=['NameSuffix'],
vars_to_gather_rows=['EPW_Scenario-Year'],
# detailed_rows=['GC01-Florianopolis', 'GC07-Chapeco', 'GC20-Palmas'],
# detailed_cols=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
# custom_rows_order=data_daily.ordered_list,
# custom_cols_order=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
data_on_x_axis=pmot,
data_on_y_sec_axis=[ #list which includes the name of the axis on the first place, and then in the second place, a list which includes the column names you want to plot
[
'Indoor Operative Temperature (°C)',
[
'Adaptive Cooling Setpoint Temperature (°C)',
'Adaptive Heating Setpoint Temperature (°C)',
'Zone Operative Temperature (°C) (mean)',
]
],
],
data_on_y_main_axis=[ # similarly to above, a list including the name of the secondary y-axis and the column names you want to plot in it
[
'Energy (kWh/m2)',
[
'Cooling Energy Demand (kWh/m2)',
'Heating Energy Demand (kWh/m2)',
]
],
],
colorlist_y_sec_axis=[
[
'Indoor Operative Temperature (°C)',
[
'b',
'r',
'g',
]
],
],
colorlist_y_main_axis=[
[
'Energy (kWh/m2)',
[
'cyan',
'orange',
]
]
],
# best_fit_deg_y_main_axis=[1, 1],
supxlabel='Prevailing Mean Outdoor Temperature (°C)', # data label on x axis
figname=f'scatterplot_PMOT_v00_300dpi',
figsize=4,
ratio_height_to_width=0.5,
# confirm_graph=True,
dpi=300
)
##
## Section 1. Graph 1
from accim.data.data_postprocessing import Table
z = Table(
source_concatenated_csv_filepath='Brazil_ACCIM_Present_AC[srcfreq-hourly[freq-hourly[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
z.format_table(
type_of_table='custom',
custom_cols=[i for i in z.df.columns if
'Building_Total' in i
and '(summed)' in i
and 'Energy Demand' in i
and 'Building_Total_Total Energy Demand (kWh/m2) (summed)' not in i
],
)
print(*z.df.columns, sep='\n')
z.df.columns = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.df.columns]
z.val_cols = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.val_cols]
df_h = z.df.copy()
df_h = df_h[
(df_h['EPW_City_or_subcountry'].isin(['GC01-Florianopolis']))
|
(df_h['EPW_City_or_subcountry'].isin(['GC07-Chapeco']))
|
(df_h['EPW_City_or_subcountry'].isin(['GC20-Palmas']))
]
df_h = df_h.reset_index().drop('index', axis=1)
z.df = df_h
z.scatter_plot_with_baseline(
vars_to_gather_cols=['ComfStand', 'ComfMod', 'HVACmode'],
vars_to_gather_rows=['EPW_City_or_subcountry'],
# detailed_rows=['Asahikawa', 'Maebashi', 'Naha'],
# custom_rows_order=data_daily.ordered_list,
data_on_y_axis_baseline_plot=z.val_cols,
baseline='CS_BRA Rupp NV[CM_3[HM_0',
colorlist_baseline_plot_data=['b', 'r'],
supxlabel='BRA_Adap_AC Hourly Energy Demand (kWh/m2)',
supylabel='Reference Hourly Energy Demand (kWh/m2)',
figname='temp_section1_fig1_v02_dpi300',
figsize=3,
confirm_graph=True,
dpi=300,
)
## Section 2
## Energy demand table (Table R2)
from accim.data.data_postprocessing import Table
z = Table(
source_concatenated_csv_filepath='Brazil_ACCIM[freq-runperiod[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
# print(*z.df.columns, sep='\n')
z.format_table(
type_of_table='custom',
custom_cols=[i for i in z.df.columns if
'Building_Total' in i
and '(summed)' in i
and 'Energy Demand' in i
# and 'Energy Consumption' in i
],
# split_epw_names=True
)
df_backup = z.df.copy()
df_MM = z.df.copy()
df_MM = df_MM[
(
(~(df_MM['HVACmode'].isin(['HM_2'])))
&
(df_MM['EPW_Scenario'].isin(['Present']))
)
|
(
(df_MM['HVACmode'].isin(['HM_2']))
&
(df_MM['EPW_Scenario'].isin(['Present']))
&
(df_MM['ComfMod'].isin(['CM_3']))
)
]
z.df = df_MM
z.wrangled_table(
reshaping='unstack',
vars_to_gather=['ComfStand', 'ComfMod', 'HVACmode'],
baseline='CS_BRA Rupp NV[CM_3[HM_2',
comparison_mode=['baseline compared to others'],
comparison_cols=['relative', 'absolute'],
check_index_and_cols=True
)
df_MM_wrangled = z.wrangled_df_unstacked.copy()
old_names = [
'CS_BRA Rupp NV[CM_3[HM_0',
'CS_BRA Rupp NV[CM_0[HM_0',
'CS_INT ASHRAE55[CM_3[HM_0',
'CS_BRA Rupp NV[CM_3[HM_2',
]
new_names = [
'BRA_Adap_AC',
'BRA_Stat_AC',
'ASH_Adap_AC',
'BRA_Adap_MM',
]
for i in range(len(old_names)):
# df_MM_wrangled.columns = [j[1].replace(old_names[i], new_names[i]) for j in df_MM_wrangled]
df_MM_wrangled.rename(columns=lambda s: s.replace(old_names[i], new_names[i]), inplace=True)
df_MM_wrangled.rename(columns=lambda s: s.replace(' (summed)', ''), inplace=True)
df_MM_wrangled.rename(columns=lambda s: s.replace('Building_Total_', ''), inplace=True)
# df_MM_wrangled = df_MM_wrangled.droplevel(0, axis=0)
# df_MM_wrangled = df_MM_wrangled.sort_index()
# df_MM_wrangled = df_MM_wrangled.transpose()
df_MM_wrangled.to_excel('section2_tableR1_unstacked_v00.xlsx')
# print(*df_MM_wrangled.columns, sep='\n')
## Section 2. Graph 1
from accim.data.data_postprocessing import Table
z = Table(
source_concatenated_csv_filepath='Brazil_ACCIM_Present_MM[srcfreq-hourly[freq-hourly[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
z.format_table(
type_of_table='custom',
custom_cols=[i for i in z.df.columns if
'Building_Total' in i
and '(summed)' in i
and 'Energy Demand' in i
and 'Building_Total_Total Energy Demand (kWh/m2) (summed)' not in i
],
)
print(*z.df.columns, sep='\n')
z.df.columns = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.df.columns]
z.val_cols = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.val_cols]
z.scatter_plot_with_baseline(
vars_to_gather_cols=['ComfStand', 'ComfMod', 'HVACmode'],
vars_to_gather_rows=['EPW_City_or_subcountry'],
# detailed_rows=['Asahikawa', 'Maebashi', 'Naha'],
# custom_rows_order=data_daily.ordered_list,
data_on_y_axis_baseline_plot=z.val_cols,
baseline='CS_BRA Rupp NV[CM_3[HM_2',
colorlist_baseline_plot_data=['b', 'r'],
supxlabel='BRA_Adap_MM Hourly Energy Demand (kWh/m2)',
supylabel='Reference Hourly Energy Demand (kWh/m2)',
figname='Fig_07_section2_fig1_v03_300dpi',
figsize=3,
confirm_graph=True,
dpi=300,
best_fit_deg=[1, 1],
)
## Section 2 Graph 2
from accim.data.data_postprocessing import Table
z = Table(
source_concatenated_csv_filepath='Brazil_ACCIM_Present_MM[srcfreq-hourly[freq-hourly[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
level_excluded_zones=['BLOCK1:BATHROOM'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
z.format_table(
type_of_table='custom',
custom_cols=[ # if type_of_table is 'custom', custom_cols is used to filter the desired columns to keep
'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
'Building_Total_Zone Operative Temperature (°C) (mean)',
'BLOCK1:LIVINGROOM_ASHRAE 55 Running mean outdoor temperature (°C)',
'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
# 'Building_Total_AFN Zone Infiltration Air Change Rate (ach) (summed)'
]
)
print(*z.df_backup.columns, sep='\n')
# df_filtered = z.df.copy()
# df_filtered = df_filtered[
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] < df_filtered['Adaptive Cooling Setpoint Temperature_No Tolerance (°C)'])
# &
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] > df_filtered['Adaptive Heating Setpoint Temperature_No Tolerance (°C)'])
# ]
# z.df = df_filtered
z.df.columns = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.df.columns]
z.val_cols = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.val_cols]
z.df.columns = [i
.replace('_No Tolerance', '')
for i in z.df.columns]
z.val_cols = [i
.replace('_No Tolerance', '')
for i in z.val_cols]
z.scatter_plot(
vars_to_gather_cols=['ComfStand', 'ComfMod', 'HVACmode'],
vars_to_gather_rows=['EPW_City_or_subcountry'],
detailed_rows=['GC01-Florianopolis', 'GC07-Chapeco', 'GC20-Palmas'],
detailed_cols=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
# custom_rows_order=data_daily.ordered_list,
custom_cols_order=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
data_on_x_axis='BLOCK1:LIVINGROOM_ASHRAE 55 Running mean outdoor temperature (°C)',
data_on_y_sec_axis=[ #list which includes the name of the axis on the first place, and then in the second place, a list which includes the column names you want to plot
[
'Indoor Operative Temperature (°C)',
[
'Adaptive Cooling Setpoint Temperature (°C)',
'Adaptive Heating Setpoint Temperature (°C)',
'Zone Operative Temperature (°C) (mean)',
]
],
],
data_on_y_main_axis=[ # similarly to above, a list including the name of the secondary y-axis and the column names you want to plot in it
[
'Energy (kWh/m2)',
[
'Cooling Energy Demand (kWh/m2)',
'Heating Energy Demand (kWh/m2)',
]
],
],
colorlist_y_sec_axis=[
[
'Indoor Operative Temperature (°C)',
[
'b',
'r',
'g',
]
],
],
colorlist_y_main_axis=[
[
'Energy (kWh/m2)',
[
'cyan',
'orange',
]
]
],
best_fit_deg_y_main_axis=[1, 1],
supxlabel='Prevailing Mean Outdoor Temperature (°C)', # data label on x axis
figname=f'Fig_08_scatterplot_PMOT_v03_300dpi',
figsize=4,
ratio_height_to_width=0.5,
confirm_graph=True,
dpi=300
)
##
# """
# Traceback (most recent call last):
# File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\site-packages\IPython\core\interactiveshell.py", line 3444, in run_code
# exec(code_obj, self.user_global_ns, self.user_ns)
# File "<ipython-input-2-71a5fffd7d9d>", line 86, in <module>
# z.scatter_plot(
# File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\site-packages\accim\data\datawrangling.py", line 2529, in scatter_plot
# main_y_axis[i][j][k].scatter(
# File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\__init__.py", line 1412, in inner
# return func(ax, *map(sanitize_sequence, args), **kwargs)
# File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axes\_axes.py", line 4369, in scatter
# raise ValueError("x and y must be the same size")
# ValueError: x and y must be the same size
# """
## Section 3
from accim.data.datawrangling import Table
z_cc = Table(
source_concatenated_csv_filepath='Brazil_ACCIM[freq-runperiod[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum'],
manage_epw_names=False,
match_cities=False,
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
# print(*z.df.columns, sep='\n')
z_cc.format_table(
type_of_table='custom',
custom_cols=[i for i in z_cc.df.columns if
'Building_Total' in i
and '(summed)' in i
and 'Energy Demand' in i
],
# split_epw_names=True
)
z_cc.wrangled_table(
reshaping='unstack',
vars_to_gather=[
'EPW_Scenario',
'EPW_Year'
],
baseline='Present[Present',
comparison_mode='baseline compared to others',
comparison_cols=[
'relative',
'absolute'
],
# check_index_and_cols=True,
vars_to_keep=[
'EPW_City_or_subcountry',
'ComfStand',
'ComfMod',
'HVACmode'
]
)
z_cc.wrangled_df_unstacked.to_excel('section3_Table_unstacked_v01.xlsx')
## Section 3 Graph 1 Ventilation hours
from accim.data.data_postprocessing import Table
z = Table(
source_concatenated_csv_filepath='Brazil_ACCIM_CC_MM[freq-hourly[sum_or_mean-sum[standard_outputs-True[CSVconcatenated.csv',
level=['building'],
level_agg_func=['sum', 'mean'],
split_epw_names=True,
normalised_energy_units=True,
rename_cols=True,
energy_units_in_kwh=True,
)
print(*z.df_backup.columns, sep='\n')
##
z.format_table(
type_of_table='custom',
custom_cols=[ # if type_of_table is 'custom', custom_cols is used to filter the desired columns to keep
'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
'Building_Total_Zone Operative Temperature (°C) (mean)',
'BLOCK1:LIVINGROOM_ASHRAE 55 Running mean outdoor temperature (°C)',
'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
'Building_Total_AFN Zone Ventilation Air Change Rate (ach) (summed)',
'Building_Total_AFN Zone Ventilation Air Change Rate (ach) (mean)',
]
)
print(*z.df_backup.columns, sep='\n')
# df_filtered = z.df.copy()
# df_filtered = df_filtered[
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] < df_filtered['Adaptive Cooling Setpoint Temperature_No Tolerance (°C)'])
# &
# (df_filtered['Building_Total_Zone Operative Temperature (°C) (mean)'] > df_filtered['Adaptive Heating Setpoint Temperature_No Tolerance (°C)'])
# ]
# z.df = df_filtered
z.df.columns = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.df.columns]
z.val_cols = [i
.replace('Building_Total_', '')
.replace(' (summed)', '')
for i in z.val_cols]
z.df.columns = [i
.replace('_No Tolerance', '')
for i in z.df.columns]
z.val_cols = [i
.replace('_No Tolerance', '')
for i in z.val_cols]
##
z.time_plot(
vars_to_gather_rows=['EPW_Scenario-Year'],
vars_to_gather_cols=['EPW_City_or_subcountry'],
# detailed_rows=['GC01-Florianopolis', 'GC07-Chapeco', 'GC20-Palmas'],
# detailed_cols=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
# custom_rows_order=data_daily.ordered_list,
# custom_cols_order=['CS_BRA Rupp NV[CM_0[HM_0', 'CS_INT ASHRAE55[CM_3[HM_0', 'CS_BRA Rupp NV[CM_3[HM_2'],
data_on_y_main_axis=[ #list which includes the name of the axis on the first place, and then in the second place, a list which includes the column names you want to plot
[
'Zone Ventilation Air Change Rate (ach)',
[
# 'Adaptive Cooling Setpoint Temperature (°C)',
# 'Adaptive Heating Setpoint Temperature (°C)',
# 'Zone Operative Temperature (°C) (mean)',
'AFN Zone Ventilation Air Change Rate (ach) (mean)',
]
],
],
data_on_y_sec_axis=[ # similarly to above, a list including the name of the secondary y-axis and the column names you want to plot in it
[
'Energy (kWh/m2)',
[
'Cooling Energy Demand (kWh/m2)',
'Heating Energy Demand (kWh/m2)',
]
],
],
colorlist_y_main_axis=[
[
'Zone Ventilation Air Change Rate (ach)',
[
'b',
# 'r',
# 'g',
]
],
],
colorlist_y_sec_axis=[
[
'Energy (kWh/m2)',
[
'cyan',
'orange',
]
]
],
supxlabel='Time',
figname='testing.png',
figsize=4,
ratio_height_to_width=0.33,
)
##
# z.scatter_plot(
# supxlabel='Running Mean Outdoor Temperature (°C)', # data label on x axis
# figname=f'WIP_scatterplot_RMOT_02',
# figsize=4,
# ratio_height_to_width=0.5,
# confirm_graph=True
# )
##
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df_MM_hourly = z.df.copy()
df_MM_hourly = df_MM_hourly[
(df_MM_hourly.Month.isin(['12']))
|
(df_MM_hourly.Month.isin(['01']))
|
(df_MM_hourly.Month.isin(['02']))
|
(df_MM_hourly.Month.isin(['03']))
]
df_MM_hourly = df_MM_hourly.drop(columns=list(
set(df_MM_hourly.columns.to_list()) - set(['Month', 'Hour', 'EPW_City_or_subcountry', 'EPW_Scenario-Year', 'EPW_Scenario', 'EPW_Year']) - set(z.val_cols)
))
df_MM_hourly = df_MM_hourly.set_index([pd.RangeIndex(len(df_MM_hourly))])
df_MM_hourly.Month = [int(float(i)) for i in df_MM_hourly.Month]
df_MM_hourly.Hour = [int(float(i)) for i in df_MM_hourly.Hour]
sns.set_theme(context='paper')
# sns.set_style('darkgrid', {"grid.color": "0.5", "grid.linestyle": ":"})
sns.set_style('whitegrid', {
'axes.facecolor': '0.9',
"grid.color": "0.5",
"grid.linestyle": ":",
'axes.spines.left': True,
'axes.spines.bottom': True,
'axes.spines.right': True,
'axes.spines.top': True
}
)
# sns.axes_style(
# {'axes.facecolor': 'grey'}
# )
g = sns.FacetGrid(
data=df_MM_hourly,
col='EPW_City_or_subcountry',
row='Month',
hue='EPW_Scenario-Year',
# row_order=data_hourly.ordered_list,
col_order=['GC01-Florianopolis', 'GC07-Chapeco', 'GC20-Palmas'],
row_order=[12, 1, 2, 3],
legend_out=True,
sharex=True,
# sharey=False,
sharey=True,
margin_titles=True,
despine=False
)
g.map_dataframe(
sns.lineplot,
x='Hour',