"""
Dual Stack Pyramid Plots
========================
"""

# %%
# `Population pyramid <https://en.wikipedia.org/wiki/Population_pyramid>`_ plots can be generated using the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function, which returns a :py:class:`Plotly Go Figure <plotly.graph_objs._figure.Figure>` object.
#
#
# The plot below was generated using a synthetic dataset of patients with subgroups indicating outcome (Death, Discharged, Censored). **Click** the image to view the full interactive and fully annotated Plotly Go figure.
#
# The synthetic dataset is given below as a table (which can easily be converted to a CSV).
#
# .. list-table:: Synthetic dataset of a patient population distribution by age group, sex and outcome
#    :header-rows: 1
#    :widths: auto
#
#    * - Age Group
#      - Sex
#      - Outcome
#      - Number
#    * - 0-5
#      - Male
#      - death
#      - 1
#    * - 0-5
#      - Male
#      - censored
#      - 19
#    * - 0-5
#      - Female
#      - discharged
#      - 29
#    * - 0-5
#      - Female
#      - death
#      - 3
#    * - 0-5
#      - Female
#      - censored
#      - 21
#    * - 5-10
#      - Male
#      - discharged
#      - 27
#    * - 5-10
#      - Male
#      - death
#      - 4
#    * - 5-10
#      - Male
#      - censored
#      - 19
#    * - 5-10
#      - Female
#      - discharged
#      - 29
#    * - 5-10
#      - Female
#      - death
#      - 1
#    * - 5-10
#      - Female
#      - censored
#      - 19
#    * - 10-15
#      - Male
#      - discharged
#      - 33
#    * - 10-15
#      - Male
#      - death
#      - 1
#    * - 10-15
#      - Male
#      - censored
#      - 18
#    * - 10-15
#      - Female
#      - discharged
#      - 30
#    * - 10-15
#      - Female
#      - death
#      - 1
#    * - 10-15
#      - Female
#      - censored
#      - 20
#    * - 15-20
#      - Male
#      - discharged
#      - 32
#    * - 15-20
#      - Male
#      - death
#      - 1
#    * - 15-20
#      - Male
#      - censored
#      - 17
#    * - 15-20
#      - Female
#      - discharged
#      - 27
#    * - 15-20
#      - Female
#      - death
#      - 9
#    * - 15-20
#      - Female
#      - censored
#      - 18
#    * - 20-25
#      - Male
#      - discharged
#      - 36
#    * - 20-25
#      - Male
#      - death
#      - 1
#    * - 20-25
#      - Male
#      - censored
#      - 23
#    * - 20-25
#      - Female
#      - discharged
#      - 30
#    * - 20-25
#      - Female
#      - death
#      - 7
#    * - 20-25
#      - Female
#      - censored
#      - 17
#    * - 25-30
#      - Male
#      - discharged
#      - 37
#    * - 25-30
#      - Male
#      - death
#      - 4
#    * - 25-30
#      - Male
#      - censored
#      - 19
#    * - 25-30
#      - Female
#      - discharged
#      - 31
#    * - 25-30
#      - Female
#      - death
#      - 8
#    * - 25-30
#      - Female
#      - censored
#      - 19
#    * - 30-35
#      - Male
#      - discharged
#      - 39
#    * - 30-35
#      - Male
#      - death
#      - 9
#    * - 30-35
#      - Male
#      - censored
#      - 17
#    * - 30-35
#      - Female
#      - discharged
#      - 34
#    * - 30-35
#      - Female
#      - death
#      - 11
#    * - 30-35
#      - Female
#      - censored
#      - 17
#    * - 35-40
#      - Male
#      - discharged
#      - 41
#    * - 35-40
#      - Male
#      - death
#      - 12
#    * - 35-40
#      - Male
#      - censored
#      - 25
#    * - 35-40
#      - Female
#      - discharged
#      - 36
#    * - 35-40
#      - Female
#      - death
#      - 5
#    * - 35-40
#      - Female
#      - censored
#      - 19
#    * - 40-45
#      - Male
#      - discharged
#      - 39
#    * - 40-45
#      - Male
#      - death
#      - 13
#    * - 40-45
#      - Male
#      - censored
#      - 19
#    * - 40-45
#      - Female
#      - discharged
#      - 33
#    * - 40-45
#      - Female
#      - death
#      - 9
#    * - 40-45
#      - Female
#      - censored
#      - 18
#    * - 45-50
#      - Male
#      - discharged
#      - 42
#    * - 45-50
#      - Male
#      - death
#      - 10
#    * - 45-50
#      - Male
#      - censored
#      - 19
#    * - 45-50
#      - Female
#      - discharged
#      - 40
#    * - 45-50
#      - Female
#      - death
#      - 12
#    * - 45-50
#      - Female
#      - censored
#      - 27
#    * - 50-55
#      - Male
#      - discharged
#      - 39
#    * - 50-55
#      - Male
#      - death
#      - 13
#    * - 50-55
#      - Male
#      - censored
#      - 20
#    * - 50-55
#      - Female
#      - discharged
#      - 45
#    * - 50-55
#      - Female
#      - death
#      - 9
#    * - 50-55
#      - Female
#      - censored
#      - 28
#    * - 55-60
#      - Male
#      - discharged
#      - 41
#    * - 55-60
#      - Male
#      - death
#      - 17
#    * - 55-60
#      - Male
#      - censored
#      - 26
#    * - 55-60
#      - Female
#      - discharged
#      - 46
#    * - 55-60
#      - Female
#      - death
#      - 17
#    * - 55-60
#      - Female
#      - censored
#      - 24
#    * - 60-65
#      - Male
#      - discharged
#      - 41
#    * - 60-65
#      - Male
#      - death
#      - 16
#    * - 60-65
#      - Male
#      - censored
#      - 28
#    * - 60-65
#      - Female
#      - discharged
#      - 49
#    * - 60-65
#      - Female
#      - death
#      - 20
#    * - 60-65
#      - Female
#      - censored
#      - 29
#    * - 65-70
#      - Male
#      - discharged
#      - 41
#    * - 65-70
#      - Male
#      - death
#      - 22
#    * - 65-70
#      - Male
#      - censored
#      - 28
#    * - 65-70
#      - Female
#      - discharged
#      - 49
#    * - 65-70
#      - Female
#      - death
#      - 21
#    * - 65-70
#      - Female
#      - censored
#      - 22
#    * - 70-75
#      - Male
#      - discharged
#      - 50
#    * - 70-75
#      - Male
#      - death
#      - 23
#    * - 70-75
#      - Male
#      - censored
#      - 25
#    * - 70-75
#      - Female
#      - discharged
#      - 43
#    * - 70-75
#      - Female
#      - death
#      - 23
#    * - 70-75
#      - Female
#      - censored
#      - 25
#    * - 75-80
#      - Male
#      - discharged
#      - 47
#    * - 75-80
#      - Male
#      - death
#      - 18
#    * - 75-80
#      - Male
#      - censored
#      - 28
#    * - 75-80
#      - Female
#      - discharged
#      - 54
#    * - 75-80
#      - Female
#      - death
#      - 24
#    * - 75-80
#      - Female
#      - censored
#      - 33
#    * - 80-85
#      - Male
#      - discharged
#      - 55
#    * - 80-85
#      - Male
#      - death
#      - 26
#    * - 80-85
#      - Male
#      - censored
#      - 33
#    * - 80-85
#      - Female
#      - discharged
#      - 54
#    * - 80-85
#      - Female
#      - death
#      - 21
#    * - 80-85
#      - Female
#      - censored
#      - 25
#    * - 85-90
#      - Male
#      - discharged
#      - 54
#    * - 85-90
#      - Male
#      - death
#      - 28
#    * - 85-90
#      - Male
#      - censored
#      - 33
#    * - 85-90
#      - Female
#      - discharged
#      - 52
#    * - 85-90
#      - Female
#      - death
#      - 24
#    * - 85-90
#      - Female
#      - censored
#      - 33
#    * - 90-95
#      - Male
#      - discharged
#      - 55
#    * - 90-95
#      - Male
#      - death
#      - 26
#    * - 90-95
#      - Male
#      - censored
#      - 27
#    * - 90-95
#      - Female
#      - discharged
#      - 52
#    * - 90-95
#      - Female
#      - death
#      - 28
#    * - 90-95
#      - Female
#      - censored
#      - 29
#    * - 96-100
#      - Male
#      - discharged
#      - 52
#    * - 96-100
#      - Male
#      - death
#      - 31
#    * - 96-100
#      - Male
#      - censored
#      - 37
#    * - 96-100
#      - Female
#      - discharged
#      - 58
#    * - 96-100
#      - Female
#      - death
#      - 33
#    * - 96-100
#      - Female
#      - censored
#      - 36
#
# The :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function expects a dataframe with the following columns (in no particular order):
#
# * ``"y_axis"`` - the age group label
# * ``"side"`` - the sex
# * ``"stack_group"`` - the patient outcome
# * ``"value"`` - the number of patients in the category (combination of age group, sex, outcome)
# * ``"left_side"`` - a boolean to indicate where the value should appear, with ``1`` indicating left and ``0`` indicating right
#
# The data source can be in any appropriate form, such as, typically, a CSV. Here are the Python steps required to generate the plot, where males are on the left and females are on the right, using the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid` function:
import io, pandas as pd
from isaricanalytics.visualisation import fig_dual_stack_pyramid
# Load the CSV data from a string buffer
data = pd.read_csv(io.StringIO(
   """
   y_axis,side,stack_group,value,left_side
   0-5,Male,discharged,31,1
   0-5,Male,death,1,1
   0-5,Male,censored,19,1
   0-5,Female,discharged,29,0
   0-5,Female,death,3,0
   0-5,Female,censored,21,0
   5-10,Male,discharged,27,1
   5-10,Male,death,4,1
   5-10,Male,censored,19,1
   5-10,Female,discharged,29,0
   5-10,Female,death,1,0
   5-10,Female,censored,19,0
   10-15,Male,discharged,33,1
   10-15,Male,death,1,1
   10-15,Male,censored,18,1
   10-15,Female,discharged,30,0
   10-15,Female,death,1,0
   10-15,Female,censored,20,0
   15-20,Male,discharged,32,1
   15-20,Male,death,1,1
   15-20,Male,censored,17,1
   15-20,Female,discharged,27,0
   15-20,Female,death,9,0
   15-20,Female,censored,18,0
   20-25,Male,discharged,36,1
   20-25,Male,death,1,1
   20-25,Male,censored,23,1
   20-25,Female,discharged,30,0
   20-25,Female,death,7,0
   20-25,Female,censored,17,0
   25-30,Male,discharged,37,1
   25-30,Male,death,4,1
   25-30,Male,censored,19,1
   25-30,Female,discharged,31,0
   25-30,Female,death,8,0
   25-30,Female,censored,19,0
   30-35,Male,discharged,39,1
   30-35,Male,death,9,1
   30-35,Male,censored,17,1
   30-35,Female,discharged,34,0
   30-35,Female,death,11,0
   30-35,Female,censored,17,0
   35-40,Male,discharged,41,1
   35-40,Male,death,12,1
   35-40,Male,censored,25,1
   35-40,Female,discharged,36,0
   35-40,Female,death,5,0
   35-40,Female,censored,19,0
   40-45,Male,discharged,39,1
   40-45,Male,death,13,1
   40-45,Male,censored,19,1
   40-45,Female,discharged,33,0
   40-45,Female,death,9,0
   40-45,Female,censored,18,0
   45-50,Male,discharged,42,1
   45-50,Male,death,10,1
   45-50,Male,censored,19,1
   45-50,Female,discharged,40,0
   45-50,Female,death,12,0
   45-50,Female,censored,27,0
   50-55,Male,discharged,39,1
   50-55,Male,death,13,1
   50-55,Male,censored,20,1
   50-55,Female,discharged,45,0
   50-55,Female,death,9,0
   50-55,Female,censored,28,0
   55-60,Male,discharged,41,1
   55-60,Male,death,17,1
   55-60,Male,censored,26,1
   55-60,Female,discharged,46,0
   55-60,Female,death,17,0
   55-60,Female,censored,24,0
   60-65,Male,discharged,41,1
   60-65,Male,death,16,1
   60-65,Male,censored,28,1
   60-65,Female,discharged,49,0
   60-65,Female,death,20,0
   60-65,Female,censored,29,0
   65-70,Male,discharged,41,1
   65-70,Male,death,22,1
   65-70,Male,censored,28,1
   65-70,Female,discharged,49,0
   65-70,Female,death,21,0
   65-70,Female,censored,22,0
   70-75,Male,discharged,50,1
   70-75,Male,death,23,1
   70-75,Male,censored,25,1
   70-75,Female,discharged,43,0
   70-75,Female,death,23,0
   70-75,Female,censored,25,0
   75-80,Male,discharged,47,1
   75-80,Male,death,18,1
   75-80,Male,censored,28,1
   75-80,Female,discharged,54,0
   75-80,Female,death,24,0
   75-80,Female,censored,33,0
   80-85,Male,discharged,55,1
   80-85,Male,death,26,1
   80-85,Male,censored,33,1
   80-85,Female,discharged,54,0
   80-85,Female,death,21,0
   80-85,Female,censored,25,0
   85-90,Male,discharged,54,1
   85-90,Male,death,28,1
   85-90,Male,censored,33,1
   85-90,Female,discharged,52,0
   85-90,Female,death,24,0
   85-90,Female,censored,33,0
   90-95,Male,discharged,55,1
   90-95,Male,death,26,1
   90-95,Male,censored,27,1
   90-95,Female,discharged,52,0
   90-95,Female,death,28,0
   90-95,Female,censored,29,0
   96-100,Male,discharged,52,1
   96-100,Male,death,31,1
   96-100,Male,censored,37,1
   96-100,Female,discharged,58,0
   96-100,Female,death,33,0
   96-100,Female,censored,36,0
   """
), skipinitialspace=True)
fig = fig_dual_stack_pyramid(
  data=data,
  title="Population Pyramid Plot of Synthetic Patient Dataset",
  xlabel="Count",
  ylabel="Age Group",
  base_color_map={
      "discharged": "#00c26f",
      "death": "#df0069",
      "censored": "#fff500"
  },
)
fig.update_layout(autosize=True)
fig

# %%
# You should see the plot appearing as given above.
#
# .. note::
#
#    Any dataframe or CSV column names, or dictionary field labels, in the
#    example above that are not specific to the dataset must be as given,
#    otherwise the function may throw an exception or return an incorrect figure.
#
#    The figure height and width parameters can be set using the ``height``
#    and ``width`` parameters, but it may be more convenient to let Plotly handle
#    this using the figure layout `autosize <https://plotly.com/python/reference/layout/#layout-autosize>`_
#    parameter. Refer to the :py:func:`~isaricanalytics.visualisation.fig_dual_stack_pyramid`
#    function docstring for more information.