T3 - Multi-Individual Challenge Time Series

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr

from emodlib.malaria import IntrahostComponent, create_config

duration = 350
n_people = 25

asexuals = np.zeros((n_people, duration))
gametocytes = np.zeros((n_people, duration))

config = create_config()
pp = [IntrahostComponent.create(config) for _ in range(n_people)]
_ = [p.challenge() for p in pp]

for t in range(duration):
    for i, p in enumerate(pp):
        p.update(dt=1)
        asexuals[i, t] = p.parasite_density
        gametocytes[i, t] = p.gametocyte_density

da = xr.DataArray(dims=('individual', 'time', 'channel'),
                  coords=(range(n_people), range(duration), ['parasite_density', 'gametocyte_density']))

da.loc[dict(channel='parasite_density')] = asexuals
da.loc[dict(channel='gametocyte_density')] = gametocytes

da

<xarray.DataArray (individual: 25, time: 350, channel: 2)>
array([[[0., 0.],
        [0., 0.],
        [0., 0.],
        ...,
        [0., 0.],
        [0., 0.],
        [0., 0.]],

       [[0., 0.],
        [0., 0.],
        [0., 0.],
        ...,
        [0., 0.],
        [0., 0.],
        [0., 0.]],

       [[0., 0.],
        [0., 0.],
        [0., 0.],
        ...,
...
        ...,
        [0., 0.],
        [0., 0.],
        [0., 0.]],

       [[0., 0.],
        [0., 0.],
        [0., 0.],
        ...,
        [0., 0.],
        [0., 0.],
        [0., 0.]],

       [[0., 0.],
        [0., 0.],
        [0., 0.],
        ...,
        [0., 0.],
        [0., 0.],
        [0., 0.]]])
Coordinates:
  * individual  (individual) int64 0 1 2 3 4 5 6 7 8 ... 17 18 19 20 21 22 23 24
  * time        (time) int64 0 1 2 3 4 5 6 7 ... 342 343 344 345 346 347 348 349
  * channel     (channel) <U18 'parasite_density' 'gametocyte_density'

xarray.DataArray

• individual: 25
• time: 350
• channel: 2

• 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

  array([[[0., 0.],
          [0., 0.],
          [0., 0.],
          ...,
          [0., 0.],
          [0., 0.],
          [0., 0.]],
  
         [[0., 0.],
          [0., 0.],
          [0., 0.],
          ...,
          [0., 0.],
          [0., 0.],
          [0., 0.]],
  
         [[0., 0.],
          [0., 0.],
          [0., 0.],
          ...,
  ...
          ...,
          [0., 0.],
          [0., 0.],
          [0., 0.]],
  
         [[0., 0.],
          [0., 0.],
          [0., 0.],
          ...,
          [0., 0.],
          [0., 0.],
          [0., 0.]],
  
         [[0., 0.],
          [0., 0.],
          [0., 0.],
          ...,
          [0., 0.],
          [0., 0.],
          [0., 0.]]])

• Coordinates: (3)
  • individual
    (individual)
    int64
    0 1 2 3 4 5 6 ... 19 20 21 22 23 24

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24])

  • time
    (time)
    int64
    0 1 2 3 4 5 ... 345 346 347 348 349

    array([  0,   1,   2, ..., 347, 348, 349])

  • channel
    (channel)
    <U18
    'parasite_density' 'gametocyte_d...

    array(['parasite_density', 'gametocyte_density'], dtype='<U18')

• Indexes: (3)
  • individual
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24],
          dtype='int64', name='individual'))

  • time
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           340, 341, 342, 343, 344, 345, 346, 347, 348, 349],
          dtype='int64', name='time', length=350))

  • channel
    PandasIndex

    PandasIndex(Index(['parasite_density', 'gametocyte_density'], dtype='object', name='channel'))

• Attributes: (0)

import matplotlib.colors as colors

da.sel(channel='parasite_density').plot(aspect=5, size=3, norm=colors.LogNorm(vmin=0.001, vmax=100000))

<matplotlib.collections.QuadMesh at 0x126b96090>

da.plot.line(x='time', row='channel', aspect=5, size=3, add_legend=False, yscale='log')

<xarray.plot.facetgrid.FacetGrid at 0x135bea310>