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completely reworked comp.py for new result folder structure
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@ojdo
ojdo committed Dec 5, 2014
1 parent 5c10ac8 commit 3d02902
Showing 1 changed file with 138 additions and 116 deletions.
254 changes: 138 additions & 116 deletions comp.py
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import glob
import matplotlib.gridspec as gs
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import os
Expand All @@ -8,118 +8,140 @@

# INIT

# create list of report files to compare
# derive list of scenario names for column labels/figure captions
result_files = sorted(glob.glob(os.path.join('results', 'scenario_*.xlsx')))
scenario_names = [os.path.basename(rf) # drop folder names, keep filename
.replace('_', ' ') # replace _ with spaces
.replace('.xlsx', '') # drop file extension
.replace('scenario ', '') # drop 'scenario ' prefix
for rf in result_files]
scenario_names = [s[0:s.find('-')] for s in scenario_names] # drop everything after first '-'

# output files basename (without file extension)
output_filename = os.path.join('results', 'comp')

# find base scenario and put at first position
try:
base_scenario = scenario_names.index('base')
result_files.insert(0, result_files.pop(base_scenario))
scenario_names.insert(0, scenario_names.pop(base_scenario))
except ValueError:
pass # do nothing if no base scenario is found

costs = [] # total costs by type and scenario
esums = [] # sum of energy produced by scenario

# READ

for rf in result_files:
with pd.ExcelFile(rf) as xls:
cost = xls.parse('Costs', has_index_names=True)
esum = xls.parse('Energy sums')

# repair broken MultiIndex in the first column
esum.reset_index(inplace=True)
esum.fillna(method='ffill', inplace=True)
esum.set_index(['level_0', 'level_1'], inplace=True)

costs.append(cost)
esums.append(esum)

# merge everything into one DataFrame each
costs = pd.concat(costs, axis=1, keys=scenario_names)
esums = pd.concat(esums, axis=1, keys=scenario_names)

# ANALYSE

# drop redundant 'costs' column label
# make index name nicer for plot
# sort/transpose frame
# convert EUR/a to 1e9 EUR/a
costs.columns = costs.columns.droplevel(1)
costs.index.name = 'Cost type'
costs = costs.sort().transpose()
costs = costs / 1e9

# sum up created energy over all locations, but keeping scenarios (level=0)
# make index name 'Commodity' nicer for plot
# drop all unused commodities and sort/transpose
# convert MWh to GWh
esums = esums.loc['Created'].sum(axis=1, level=0)
esums.index.name = 'Commodity'
used_commodities = (esums.sum(axis=1) > 0)
esums = esums[used_commodities].sort().transpose()
esums = esums / 1e3


# PLOT

fig = plt.figure(figsize=(20, 8))
gs = gs.GridSpec(1, 2, width_ratios=[2, 3])

ax0 = plt.subplot(gs[0])
bp0 = costs.plot(ax=ax0, kind='barh', stacked=True)

ax1 = plt.subplot(gs[1])
esums_colors = [urbs.to_color(commodity) for commodity in esums.columns]
bp1 = esums.plot(ax=ax1, kind='barh', stacked=True, color=esums_colors)

# remove scenario names from second plot
ax1.set_yticklabels('')

# make bar plot edges lighter
for bp in [bp0, bp1]:
for patch in bp.patches:
patch.set_edgecolor(urbs.to_color('Decoration'))

# set limits and ticks for both axes
for ax in [ax0, ax1]:
plt.setp(ax.spines.values(), color=urbs.to_color('Grid'))
ax.yaxis.grid(False)
ax.xaxis.grid(True, 'major', color=urbs.to_color('Grid'), linestyle='-')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

# group 1,000,000 with commas
group_thousands = tkr.FuncFormatter(lambda x, pos: '{:0,d}'.format(int(x)))
ax.xaxis.set_major_formatter(group_thousands)

# legend
lg = ax.legend(frameon=False, loc='upper center',
ncol=len(ax.legend().get_texts()),
bbox_to_anchor=(0.5, 1.08))
plt.setp(lg.get_patches(), edgecolor=urbs.to_color('Decoration'),
linewidth=0.15)

ax0.set_xlabel('Total costs (1e9 EUR/a)')
ax1.set_xlabel('Total energy produced (GWh)')

for ext in ['png', 'pdf']:
fig.savefig('{}.{}'.format(output_filename, ext),
bbox_inches='tight')

# REPORT
with pd.ExcelWriter('{}.{}'.format(output_filename, 'xlsx')) as writer:
costs.to_excel(writer, 'Costs')
esums.to_excel(writer, 'Energy sums')
def get_most_recent_entry(search_dir):
""" Return most recently modified entry from given directory. """
entries = glob.glob(os.path.join(search_dir, "*"))
entries.sort(key=lambda x: os.path.getmtime(x))
return entries[-1]

def glob_result_files(folder_name):
""" Glob result spreadsheets from specified folder. """
glob_pattern = os.path.join(folder_name, 'scenario_*.xlsx')
result_files = sorted(glob.glob(glob_pattern))
return result_files

def compare_scenarios(result_files, output_filename):
""" Create report sheet and plots for given report spreadsheets. """

# derive list of scenario names for column labels/figure captions
scenario_names = [os.path.basename(rf) # drop folder names, keep filename
.replace('_', ' ') # replace _ with spaces
.replace('.xlsx', '') # drop file extension
.replace('scenario ', '') # drop 'scenario ' prefix
for rf in result_files]
scenario_names = [s[0:s.find('-')] for s in scenario_names] # drop everything after first '-'


# find base scenario and put at first position
try:
base_scenario = scenario_names.index('base')
result_files.insert(0, result_files.pop(base_scenario))
scenario_names.insert(0, scenario_names.pop(base_scenario))
except ValueError:
pass # do nothing if no base scenario is found

costs = [] # total costs by type and scenario
esums = [] # sum of energy produced by scenario

# READ

for rf in result_files:
with pd.ExcelFile(rf) as xls:
cost = xls.parse('Costs', has_index_names=True)
esum = xls.parse('Energy sums')

# repair broken MultiIndex in the first column
esum.reset_index(inplace=True)
esum.fillna(method='ffill', inplace=True)
esum.set_index(['level_0', 'level_1'], inplace=True)

costs.append(cost)
esums.append(esum)

# merge everything into one DataFrame each
costs = pd.concat(costs, axis=1, keys=scenario_names)
esums = pd.concat(esums, axis=1, keys=scenario_names)

# ANALYSE

# drop redundant 'costs' column label
# make index name nicer for plot
# sort/transpose frame
# convert EUR/a to 1e9 EUR/a
costs.columns = costs.columns.droplevel(1)
costs.index.name = 'Cost type'
costs = costs.sort().transpose()
costs = costs / 1e9

# sum up created energy over all locations, but keeping scenarios (level=0)
# make index name 'Commodity' nicer for plot
# drop all unused commodities and sort/transpose
# convert MWh to GWh
esums = esums.loc['Created'].sum(axis=1, level=0)
esums.index.name = 'Commodity'
used_commodities = (esums.sum(axis=1) > 0)
esums = esums[used_commodities].sort().transpose()
esums = esums / 1e3

# PLOT

fig = plt.figure(figsize=(20, 8))
gs = gridspec.GridSpec(1, 2, width_ratios=[2, 3])

ax0 = plt.subplot(gs[0])
bp0 = costs.plot(ax=ax0, kind='barh', stacked=True)

ax1 = plt.subplot(gs[1])
esums_colors = [urbs.to_color(commodity) for commodity in esums.columns]
bp1 = esums.plot(ax=ax1, kind='barh', stacked=True, color=esums_colors)

# remove scenario names from second plot
ax1.set_yticklabels('')

# make bar plot edges lighter
for bp in [bp0, bp1]:
for patch in bp.patches:
patch.set_edgecolor(urbs.to_color('Decoration'))

# set limits and ticks for both axes
for ax in [ax0, ax1]:
plt.setp(ax.spines.values(), color=urbs.to_color('Grid'))
ax.yaxis.grid(False)
ax.xaxis.grid(True, 'major', color=urbs.to_color('Grid'), linestyle='-')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

# group 1,000,000 with commas
group_thousands = tkr.FuncFormatter(lambda x, pos: '{:0,d}'.format(int(x)))
ax.xaxis.set_major_formatter(group_thousands)

# legend
lg = ax.legend(frameon=False, loc='upper center',
ncol=len(ax.legend().get_texts()),
bbox_to_anchor=(0.5, 1.08))
plt.setp(lg.get_patches(), edgecolor=urbs.to_color('Decoration'),
linewidth=0.15)

ax0.set_xlabel('Total costs (1e9 EUR/a)')
ax1.set_xlabel('Total energy produced (GWh)')

for ext in ['png', 'pdf']:
fig.savefig('{}.{}'.format(output_filename, ext),
bbox_inches='tight')

# REPORT
with pd.ExcelWriter('{}.{}'.format(output_filename, 'xlsx')) as writer:
costs.to_excel(writer, 'Costs')
esums.to_excel(writer, 'Energy sums')

if __name__ == '__main__':
# get the directory of the supposedly last run
# and retrieve (glob) a list of all result spreadsheets from there
last_run_directory = get_most_recent_entry('result')
result_files = glob_result_files(last_run_directory)

# specify comparison result filename
comp_filename = os.path.join(last_run_directory, 'comp')

# and run the comparison function
compare_scenarios(result_files, comp_filename)

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