Sorsogon. Step 3.b. Visualize transition scenarios¶

In [1]:

import datetime; print(datetime.datetime.now())

2018-04-09 11:47:05.326151

Notebook Abstract:

The following notebook visualize the the simple transition scenarios by plotting the total consumption over all simulation years and the per-capita consumption rate. Depending on the define scenarios the per-capita consumption rate can be maintained constant. The per-capita consumption value is computed as total consumption divided by population size.

Import libraries¶

In [2]:

from smum.microsim.util_plot import plot_data_projection

The visualization is performed with help of the module function plot_data_projection().

Global variables¶

In [3]:

iterations = 1000
typ = 'resampled'
model_name = 'Sorsogon_Electricity_Water_wbias_projected_dynamic_{}'.format(typ)
reweighted_survey = 'data/survey_{}_{}'.format(model_name, iterations)

Base scenario¶

In [4]:

var = ['Income', 'Water', 'Electricity']
data = plot_data_projection(
    reweighted_survey, var, "{}, {}".format(iterations, typ),
    benchmark_year=2016,
)

../../_images/example_ph_Cb_VisualizeTransitions_9_0.png

Base scenario grouped by Urban-Rural households¶

In [5]:

var = ['Income', 'Water', 'Electricity']
groupby = 'i_Urbanity'
data = plot_data_projection(
    reweighted_survey, var, "{}, {} by {}".format(iterations, typ, groupby),
    benchmark_year = 2016,
    groupby = groupby
)

../../_images/example_ph_Cb_VisualizeTransitions_11_0.png

Base scenario grouped by family size¶

In [6]:

var = ['Income', 'Water', 'Electricity']
groupby = 'i_FamilySize'
data = plot_data_projection(
    reweighted_survey, var, "{}, {} by {}".format(iterations, typ, groupby),
    benchmark_year = 2016,
    groupby = groupby
)

../../_images/example_ph_Cb_VisualizeTransitions_13_0.png

Base scenario grouped by AC ownership¶

In [7]:

var = ['Income', 'Water', 'Electricity']
groupby = 'e_AC'
data = plot_data_projection(
    reweighted_survey, var, "{}, {} by {}".format(iterations, typ, groupby),
    benchmark_year = 2016,
    verbose = False,
    groupby = groupby
)

../../_images/example_ph_Cb_VisualizeTransitions_15_0.png

Scenario 1 compared to base scenario¶

In [8]:

import numpy as np
pr = [i for i in np.linspace(0, 0.3, num=15)]
pr = [0]*6 + pr
scenario_name = 'scenario 1'

In [9]:

reweighted_survey

Out[9]:

'data/survey_Sorsogon_Electricity_Water_wbias_projected_dynamic_resampled_1000'

In [10]:

variables = ['Income', 'Water', 'Electricity']
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {}, alt. scenario 1".format(iterations, typ),
        benchmark_year=2016, pr = pr, scenario_name = scenario_name,
        aspect_ratio = 2,
    )

../../_images/example_ph_Cb_VisualizeTransitions_19_0.png

../../_images/example_ph_Cb_VisualizeTransitions_19_1.png

../../_images/example_ph_Cb_VisualizeTransitions_19_2.png

Scenario 1 grouped by education¶

In [11]:

variables = ['Income', 'Water', 'Electricity']
groupby = 'i_Education'
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {} by {}, alt. scenario 1".format(iterations, typ, groupby),
        #benchmark_year=2016,
        pr = pr, scenario_name = scenario_name,
        groupby = groupby,
        aspect_ratio = 2,
    )

../../_images/example_ph_Cb_VisualizeTransitions_21_0.png

../../_images/example_ph_Cb_VisualizeTransitions_21_1.png

../../_images/example_ph_Cb_VisualizeTransitions_21_2.png

In [12]:

import numpy as np
pr = [i for i in np.linspace(0, 0.6, num=15)]
pr = [0]*6 + pr
scenario_name = 'scenario 2'

Scenario 2 compared to base scenario¶

In [13]:

variables = ['Income', 'Water', 'Electricity']
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {}, alt. scenario 2".format(iterations, typ),
        benchmark_year=2016, pr = pr, scenario_name = scenario_name,
        aspect_ratio = 2,
    )

../../_images/example_ph_Cb_VisualizeTransitions_24_0.png

../../_images/example_ph_Cb_VisualizeTransitions_24_1.png

../../_images/example_ph_Cb_VisualizeTransitions_24_2.png

Scenario 2 grouped by education¶

In [14]:

variables = ['Income', 'Water', 'Electricity']
groupby = 'i_Education'
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {} by {}, alt. scenario 2".format(iterations, typ, groupby),
        #benchmark_year=2016,
        pr = pr, scenario_name = scenario_name,
        groupby = groupby,
        aspect_ratio = 2,
    )

../../_images/example_ph_Cb_VisualizeTransitions_26_0.png

../../_images/example_ph_Cb_VisualizeTransitions_26_1.png

../../_images/example_ph_Cb_VisualizeTransitions_26_2.png

Scenario 2 grouped by education, cross tabulation¶

In [15]:

from smum.microsim.util_plot import cross_tab

In [16]:

a = 'Water'
b = 'i_Education'
ct = cross_tab(a, b, 2030, reweighted_survey + "_{}_scenario 2_0.60.csv", split_a = True)

data saved as: data/Water_i_Education_2030.xlsx

../../_images/example_ph_Cb_VisualizeTransitions_29_1.png

In [17]:

ct

Out[17]:

i_Education	Education_College	Education_Elementary_School	Education_High_School	Education_Post_Graduate	Education_Post_Secondary
Water
Low	NaN	5764.04	2933.70	NaN	70.00
mid-Low	NaN	560.96	2584.57	NaN	9002.94
Middle	1212.00	NaN	94.72	NaN	5369.71
mid-High	9647.67	NaN	NaN	1807.0	282.68
High	4434.33	NaN	NaN	1142.0	NaN

In [18]:

iterations = 1000
typ = 'resampled'
model_name = 'Sorsogon_Electricity_Water_wbias_projected_dynamic_{}'.format(typ)
reweighted_survey = 'data/survey_{}_{}'.format(model_name, iterations)

In [19]:

a = 'Electricity'
b = 'i_Education'
ct = cross_tab(a, b, 2030, reweighted_survey + "_{}_scenario 2_0.60.csv", split_a = True)

data saved as: data/Electricity_i_Education_2030.xlsx

../../_images/example_ph_Cb_VisualizeTransitions_32_1.png

In [20]:

ct

Out[20]:

i_Education	Education_College	Education_Elementary_School	Education_High_School	Education_Post_Graduate	Education_Post_Secondary
Electricity
Low	NaN	2033.00	1886.00	NaN	4222.00
mid-Low	5259.0	1009.25	96.89	283.0	5919.00
Middle	5559.0	2934.69	2283.14	1797.0	56.18
mid-High	NaN	348.06	1346.96	NaN	4509.38
High	4476.0	NaN	NaN	869.0	18.77