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factored that pie charts size reflect change in area
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Sven Eggimann committed Apr 24, 2019
1 parent 3f7ece1 commit cf2da51
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68 changes: 15 additions & 53 deletions energy_demand/plotting/_test.py
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Original file line number Diff line number Diff line change
@@ -1,65 +1,27 @@
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt

# Dataset
dataright = np.random.rand(10, 4)
dataleft = dataright * -1
df_right = pd.DataFrame(dataright, columns=['a', 'b', 'c', 'd'])
df_left = pd.DataFrame(dataleft, columns=['a', 'b', 'c', 'd'])

# Some data
fracs = [15, 30, 45, 10]

# plot WORKDS
#labels_minus = list(df_left.values)
#labels_plus = list(df_right.values)
#labels = labels_minus + labels_plus
# Make figure and axes
fig, axs = plt.subplots(2,2)

'''
df_right.plot(kind='barh', stacked=True, width=1.0)
df_left.plot(kind='barh',stacked=True, width=1.0)'''


fig, ax = plt.subplots(ncols=1, sharey=True)
standard_area_to_define_radius = 4

##ax.invert_xaxis()
##ax.yaxis.tick_right()
#ax.set_yticklabels(labels)
# Add vertical line
ax.axvline(linewidth=1, color='black')
old_area = 1
new_area = 2

df_right.plot(kind='barh', legend=True, ax=ax, width=1.0, stacked=True)
df_left.plot(kind='barh', legend=True, ax=ax, width=1.0, stacked=True)
plt.show()
area_change = new_area / old_area

#raise Exception
old_radius = (1 / standard_area_to_define_radius) * old_area

#https://stackoverflow.com/questions/44049132/python-pandas-plotting-two-barh-side-by-side
#df = pd.DataFrame()
#new_radius = math.sqrt(area_change * new_area / math.pi)
new_radius = math.sqrt(area_change) * old_radius

#df_1[target_cols].plot(kind='barh', x='LABEL', stacked=True, legend=False)
#df_2[target_cols].plot(kind='barh', x='LABEL', stacked=True).invert_xaxis()
axs[1,0].pie(fracs, radius=old_radius)
axs[1,1].pie(fracs, radius=new_radius)

#x_axis = [list(df_right.index)]
#print(x_axis)
#print("...")
#print(np.array([5., 30., 45., 22.]))
##plt.barh(np.arange(4), np.array([5., 30., 45., 22.]), color = 'r')
#plt.barh(x_axis, df_right, color = 'r', stacked='True')
#plt.barh(x_axis, df_right, color='r', stacked=True)
#plt.barh(x_axis, df_left, color='b', stacked=True)

#plt.show()#
#print("..")

'''
import numpy as np
import matplotlib.pyplot as plt
women_pop = np.array([5., 30., 45., 22.])
men_pop = np.array( [5., 25., 50., 20.])
X = np.arange(4)
plt.barh(X, women_pop, color = 'r')
plt.barh(X, -men_pop, color = 'b')
plt.show()
'''
plt.show()
2 changes: 1 addition & 1 deletion energy_demand/plotting/chaudry_et_al.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@
scenarios=scenarios,
weather_scearnio=weather_scenario,
unit=unit,
fueltypes=['heat'])#electricity', 'heat'])
fueltypes=['electricity', 'heat'])

#chaudry_et_al_functions.fig_4(data_container, fueltype=fueltype)
#chaudry_et_al_functions.fig_5(data_container, fueltype=fueltype)
Expand Down
177 changes: 154 additions & 23 deletions energy_demand/plotting/chaudry_et_al_functions.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
"""Functions to generate figures
"""
import os
import math
import pandas as pd
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
from matplotlib import rcParams
from tabulate import tabulate

# Set default font
rcParams['font.family'] = 'sans-serif'
Expand All @@ -20,6 +22,21 @@ def cm2inch(*tupl):
else:
return tuple(i/inch for i in tupl)

def write_list_to_txt(path_result, list_out):
"""Write scenario population for a year to txt file
"""
file = open(path_result, "w")
for entry in list_out:
file.write(entry + "\n")
file.close()

def write_to_txt(path_result, entry):
"""Write scenario population for a year to txt file
"""
file = open(path_result, "w")
file.write(entry + "\n")
file.close()

def export_legend(legend, filename="legend.png"):
"""Export legend as seperate file
"""
Expand Down Expand Up @@ -151,6 +168,8 @@ def fig_3_hourly_comparison(
}
}

annote_crit = False #Add labels

# Select hours to plots
seasonal_week_day = 2
hours_selected = range(24 * (seasonal_week_day) + 1, 24 * (seasonal_week_day + 1) + 1) #TODO
Expand Down Expand Up @@ -215,35 +234,110 @@ def fig_3_hourly_comparison(
# Aggregate annual demand for pie-charts
fig_dict_piecharts[year][mode][scenario] = df_to_plot.sum()

print("--ploting --") #MOVE DOWN ELEMENTS

# ----------
# PLot pie-charts
# ----------
fig_dict_piecharts
for scenario in scenarios:
table_out = []
for mode in [right, left]:

data_pie_chart = fig_dict_piecharts[year][mode][scenario]

# Calculate radius 15 mio corresponds to radius 1 (100%)
abs_size = 15 #100%
radius_terawatt = 34 # 100% (radius 1) corresponds to 15 Terawatt
initial_radius = 1

total_sum = data_pie_chart.sum() / 1000
print("p_size: " + str(total_sum))
p_size = (initial_radius / abs_size) * total_sum
area_change_p = total_sum / radius_terawatt

# Convert that radius reflects the change in area (and not size)
new_radius = math.sqrt(area_change_p) * initial_radius

# write results to txt
total_sum = data_pie_chart.sum()
for index in data_pie_chart.index:
absolute = data_pie_chart.loc[index]
relative = (absolute / total_sum)
table_out.append([mode, index, absolute, relative])

# Explode distance
explode_factor = p_size * 0.1
explode_factor = new_radius * 0.1
explode_distance = [explode_factor for i in range(len(data_pie_chart.index))]

# Plot
fig, ax = plt.subplots(figsize=cm2inch(4.5, 5))

# ---------------------
# Plotting pie chart
plt.pie(
data_pie_chart.values,
explode=explode_distance,
radius=p_size)
# ---------------------
fig, ax = plt.subplots(figsize=cm2inch(4.5, 5))

if not annote_crit:
plt.pie(
data_pie_chart.values,
explode=explode_distance,
radius=new_radius,
wedgeprops=dict(width=new_radius * 0.4))
else:
# ---------------------
# Plot annotations of pie chart
# ---------------------
min_label_crit = 1 #[%] Minimum label criterium
# Round
labels_p = data_pie_chart.values / total_sum
labels = labels_p.round(3) * 100 #to percent

wedges, texts = plt.pie(
data_pie_chart.values,
explode=explode_distance,
radius=new_radius,
wedgeprops=dict(width=0.4),
textprops=dict(color="b"))

for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]

if labels[i] > min_label_crit: #larger than 1 percent
ax.annotate(
labels[i],
xy=(x, y),
xytext=((new_radius + explode_distance[i] + 0.2)*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment,
color='grey',
size=8,
weight="bold")

'''value_crit = 5.0 # [%] Only labes larger than crit are plotted
wedges, texts = plt.pie(
data_pie_chart.values,
explode=explode_distance,
radius=new_radius,
wedgeprops=dict(width=0.5),
startangle=-40)
bbox_props = dict(boxstyle="square, pad=0.1", fc="w", ec="k", lw=0)
kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle="-"),
bbox=bbox_props, zorder=0, va="center")
# Round
labels_p = data_pie_chart.values / total_sum
labels = labels_p.round(3)
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
value = labels[i] * 100 #to percent
if value > value_crit: #larger than 1 percent
ax.annotate(value, xy=(x, y), xytext=((new_radius + 0.4)*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment, **kw)
'''

# Labels
plt.xlabel('')
Expand All @@ -270,27 +364,61 @@ def fig_3_hourly_comparison(
os.path.join("{}__legend.pdf".format(path_out_file[:-4])))
legend.remove()

# Limits
# ------------
#plt.autoscale(enable=True, axis='x', tight=True)
#plt.autoscale(enable=True, axis='y', tight=True)
#plt.tight_layout()

# Remove frame
# ------------
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

#plt.show()
plt.savefig(path_out_file)

# Write out results to txt
table_tabulate = tabulate(
table_out, headers=['mode', 'type', 'absolute', 'relative'],
numalign="right")
write_to_txt(path_out_file[:-4] + ".txt", table_tabulate)

# ----------
# Plot x-y graph
# ----------
for scenario in scenarios:
table_out = []

# Data and plot
# ------------
df_right = fig_dict[year][right][scenario]
df_left = fig_dict[year][left][scenario] * -1 # Convert to minus values
df_left = fig_dict[year][left][scenario]

headers = list(df_right.columns)
headers.insert(0, "hour")
headers.insert(0, "type")
for index_hour in df_right.index:
row = list(df_right.loc[index_hour])
row.insert(0, index_hour)
row.insert(0, 'right')
table_out.append(row)

for index_hour in df_left.index:
row = list(df_left.loc[index_hour])
row.insert(0, index_hour)
row.insert(0, 'left')
table_out.append(row)

# Switch axis
df_left = df_left * -1 # Convert to minus values
#df_to_plot.plot.area()
#df_to_plot.plot.bar(stacked=True)#, orientation='vertical')

table_out.append([])

fig, ax = plt.subplots(figsize=cm2inch(9, 5), ncols=1, sharey=True)

df_right.plot(kind='barh', ax=ax, width=1.0, stacked=True, color=colors)
Expand All @@ -307,11 +435,11 @@ def fig_3_hourly_comparison(

# Customize x-axis
nr_of_bins = 4
bin_value = int(np.max(df_right.values) / 4)
right_ticks = np.array([bin_value * i for i in range(nr_of_bins + 1)])
bin_value = int(np.max(df_right.values) / nr_of_bins)
right_ticks = np.array([bin_value * i for i in range(nr_of_bins + 2)])
left_ticks = right_ticks * -1
left_ticks = left_ticks[::-1]
right_labels = [str(bin_value * i) for i in range(nr_of_bins + 1)]
right_labels = [str(bin_value * i) for i in range(nr_of_bins + 2)]
left_labels = right_labels[::-1]
ticks = list(left_ticks) + list(right_ticks)
labels = list(left_labels) + list(right_labels)
Expand All @@ -329,9 +457,6 @@ def fig_3_hourly_comparison(
ticks = np.array([(bin_width * i) - 0.5 for i in range(nr_of_bins + 1)])
labels = np.array([str(min_bin_value + bin_width * i) for i in range(nr_of_bins + 1)])

#ticks = np.array([(bin_value * i) - 0.5 for i in range(nr_of_bins + 1)])
#labels = np.array([str(bin_value * i) for i in range(nr_of_bins + 1)])

plt.yticks(
ticks=ticks,
labels=labels,
Expand All @@ -358,8 +483,6 @@ def fig_3_hourly_comparison(
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)



# Limits
# ------------
plt.autoscale(enable=True, axis='x', tight=True)
Expand Down Expand Up @@ -388,6 +511,14 @@ def fig_3_hourly_comparison(

#plt.show()
plt.savefig(path_out_file)

# Write out results to txt
table_tabulate = tabulate(
table_out,
headers=headers,
numalign="right")
write_to_txt(path_out_file[:-4] + ".txt", table_tabulate)



def fig_4(data_container):
Expand Down

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