T2 - Plotting, printing, and saving#
This tutorial provides a brief overview of options for plotting results, printing objects, and saving results.
Click here to open an interactive version of this notebook.
Global plotting configuration#
HPVsim allows the user to set various options that apply to all plots. You can change the font size, default DPI, whether plots should be shown by default, etc. (for the full list, see hpv.options.help()). For example, we might want higher resolution, to turn off automatic figure display, close figures after they’re rendered, and to turn off the messages that print when a simulation is running. We can do this using built-in defaults for Jupyter notebooks (and then run a sim) with:
[1]:
import hpvsim as hpv
hpv.options(jupyter=True, verbose=0) # Standard options for Jupyter notebook
sim = hpv.Sim()
sim.run()
HPVsim 2.0.2 (2024-03-05) — © 2022-2024 by IDM
Loading location-specific demographic data for "nigeria"
[1]:
Sim(<no label>; 1995.0 to 2030.0; pop: 20000 default; epi: 8.74112e+08⚙, 510599♋︎)
Printing objects#
There are three levels of detail available for most objects (sims, multisims, scenarios, and people). The shortest is brief():
[2]:
sim.brief()
Sim(<no label>; 1995.0 to 2030.0; pop: 20000 default; epi: 8.74112e+08⚙, 510599♋︎)
You can get more detail with summarize():
[3]:
sim.summarize()
Simulation summary:
874,111,895 total HPV infections
510,599 total cancers
286,811 total cancer deaths
5.14 mean HPV prevalence (%)
15.21 mean cancer incidence (per 100k)
32.94 mean age of infection (years)
47.79 mean age of cancer (years)
Finally, to show the full object, including all methods and attributes, use disp():
[4]:
sim.disp()
<hpvsim.sim.Sim at 0x7fb0e85b7550>
[<class 'hpvsim.sim.Sim'>, <class 'hpvsim.base.BaseSim'>, <class 'hpvsim.base.ParsObj'>, <class 'hpvsim.base.FlexPretty'>, <class 'sciris.sc_printing.prettyobj'>]
————————————————————————————————————————————————————————————
Methods:
_brief() get_intervention() reset_layer_pars()
_disp() get_interventio... result_keys()
_get_ia() get_t() result_types()
brief() init_analyzers() run()
compute_age_mean() init_genotypes() save()
compute_fit() init_hiv() set_metadata()
compute_results() init_immunity() shrink()
compute_states() init_interventi... step()
compute_summary() init_people() summarize()
copy() init_results() to_df()
disp() init_states() to_excel()
export_pars() init_time_vecs() to_json()
export_results() initialize() update_pars()
finalize() layer_keys() validate_dt()
finalize_analyz... load() validate_init_c...
get_analyzer() load_data() validate_layer_...
get_analyzers() plot() validate_pars()
————————————————————————————————————————————————————————————
Properties:
n
————————————————————————————————————————————————————————————
_default_ver: None
_orig_pars: {'n_agents': 20000, 'total_pop': None, 'pop_scale':
5340.99025, 'ms [...]
analyzers: []
art_datafile: None
complete: True
created: None
data: None
datafile: None
hiv_datafile: None
hivsim: <hpvsim.hiv.HIVsim at 0x7fb0b4db0130>
initialized: True
interventions: []
label: None
npts: 144
pars: {'n_agents': 20000, 'total_pop': None, 'pop_scale':
5340.99025, 'ms [...]
people: People(n=70242; layers: m, c)
popdict: None
popfile: None
res_npts: 36
res_tvec: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, [...]
res_yearvec: array([1995., 1996., 1997., 1998., 1999., 2000., 2001.,
2002., 2003 [...]
resfreq: 4
results: #0. 'infections':
<hpvsim.base.Result at 0x7fb0b4db01c0>
[<class 'h [...]
results_ready: True
short_summary: #0: 'total HPV infections': 874111895.0625
#1: 'total c [...]
summary: #0. 'infections': 31485140.96875
#1. 'dysplasias': [...]
t: 143
tvec: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, [...]
years: array([1995., 1996., 1997., 1998., 1999., 2000., 2001.,
2002., 2003 [...]
yearvec: array([1995. , 1995.25, 1995.5 , 1995.75, 1996. ,
1996.25, 1996.5 [...]
————————————————————————————————————————————————————————————
Plotting options#
While a sim can be plotted using default settings simply by sim.plot(), this is just a small fraction of what’s available. First, note that results can be plotted directly using e.g. Matplotlib. You can see what quantities are available for plotting with sim.results.keys() (remember, it’s just a dict). A simple example of plotting using Matplotlib is:
[5]:
import pylab as plt # Shortcut for import matplotlib.pyplot as plt
plt.plot(sim.results['year'], sim.results['infections']);
However, as you can see, this isn’t ideal since the default formatting is…not great. (Also, note that each result is a Result object, not a simple Numpy array; like a pandas dataframe, you can get the array of values directly via e.g. sim.results.infections.values.)
An alternative, you can also select one or more quantities to plot with the first (to_plot) argument, e.g.
[6]:
sim.plot(to_plot=['infections', 'hpv_incidence']);
While we can save this figure using Matplotlib’s built-in savefig(), if we use HPVsim’s hpv.savefig() we get a couple of advantages:
[7]:
hpv.savefig('my-fig.png')
[7]:
'my-fig.png'
<Figure size 640x480 with 0 Axes>
First, it saves the figure at higher resolution by default (which you can adjust with the dpi argument). But second, it stores information about the code that was used to generate the figure as metadata, which can be loaded later. Made an awesome plot but can’t remember even what script you ran to generate it, much less what version of the code? You’ll never have to worry about that again.
[8]:
hpv.get_png_metadata('my-fig.png')
HPVsim version: 2.0.2
HPVsim branch: Branch N/A
HPVsim hash: Hash N/A
HPVsim date: Date N/A
HPVsim caller branch: Branch N/A
HPVsim caller hash: Hash N/A
HPVsim caller date: Date N/A
HPVsim caller filename: /home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/envs/latest/lib/python3.9/site-packages/hpvsim/misc.py
HPVsim current time: 2024-Mar-15 03:55:58
HPVsim calling file: /tmp/ipykernel_1327/1797766398.py
Customizing plots#
We saw above how to set default plot configuration options for Jupyter. HPVsim provides a lot of flexibility in customizing the appearance of plots as well. There are three different levels at which you can set plotting options: global, just for HPVsim, or just for the current plot. To give an example with changing the figure DPI: - Change the setting globally (for both HPVsim and Matplotlib): sc.options(dpi=150) or pl.rc('figure', dpi=150) (where sc is import sciris as sc) -
Change for HPVsim plots, but not for Matplotlib plots: hpv.options(dpi=150) - Change for the current HPVsim plot, but not other HPVsim plots: sim.plot(dpi=150)
The easiest way to change the style of HPVsim plots is with the style argument. For example, to plot using a built-in Matplotlib style would simply be:
[9]:
sim.plot(style='ggplot');
In addition to the default style ('hpvsim'), there is also a “simple” style. You can combine built-in styles with additional overrides, including any valid Matplotlib commands:
[10]:
sim.plot(style='simple', legend_args={'frameon':True}, style_args={'ytick.direction':'in'});
Although most style handling is done automatically, you can also use it yourself in a with block, e.g.:
[11]:
import numpy as np
with hpv.options.with_style(fontsize=6):
sim.plot() # This will have 6 point font
plt.figure(); plt.plot(np.random.rand(20), 'o') # So will this
Saving options#
Saving sims is also pretty simple. The simplest way to save is simply
[12]:
sim.save('my-sim.sim')
[12]:
'/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/checkouts/latest/docs/tutorials/my-sim.sim'
Technically, this saves as a gzipped pickle file (via sc.saveobj() using the Sciris library). By default this does not save the people in the sim since they are very large (and since, if the random seed is saved, they can usually be regenerated). If you want to save the people as well, you can use the keep_people argument. For example, here’s what it would look like to create a sim, run it halfway, save it, load it, change the overall transmissibility (beta), and finish running it:
[13]:
sim_orig = hpv.Sim(start=2000, end=2030, label='Load & save example')
sim_orig.run(until='2015')
sim_orig.save('my-half-finished-sim.sim') # Note: HPVsim always saves the people if the sim isn't finished running yet
sim = hpv.load('my-half-finished-sim.sim')
sim['beta'] *= 0.3
sim.run()
sim.plot(['infections', 'hpv_incidence', 'cancer_incidence']);
Loading location-specific demographic data for "nigeria"
Aside from saving the entire simulation, there are other export options available. You can export the results and parameters to a JSON file (using sim.to_json()), but probably the most useful is to export the results to an Excel workbook, where they can easily be stored and processed with e.g. Pandas:
[14]:
import pandas as pd
sim.to_excel('my-sim.xlsx')
df = pd.read_excel('my-sim.xlsx')
print(df)
Object saved to /home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-hpvsim/checkouts/latest/docs/tutorials/my-sim.xlsx.
year t infections dysplasias precins cins cancers \
0 2000 0 3.028098e+07 0 0 3.491582e+05 0.000000
1 2001 1 2.619777e+07 0 0 6.807372e+05 0.000000
2 2002 2 2.694640e+07 0 0 8.850191e+05 0.000000
3 2003 3 2.712886e+07 0 0 9.729148e+05 606.177429
4 2004 4 2.650147e+07 0 0 9.523047e+05 3637.064575
5 2005 5 2.603532e+07 0 0 1.008679e+06 1818.532288
6 2006 6 2.569950e+07 0 0 1.034745e+06 2424.709717
7 2007 7 2.461626e+07 0 0 1.024440e+06 3637.064575
8 2008 8 2.425195e+07 0 0 1.060204e+06 4849.419373
9 2009 9 2.350271e+07 0 0 9.341195e+05 8486.484009
10 2010 10 2.365547e+07 0 0 9.577604e+05 7880.306519
11 2011 11 2.214790e+07 0 0 9.032044e+05 8486.484131
12 2012 12 2.190968e+07 0 0 9.104785e+05 10305.016235
13 2013 13 2.127440e+07 0 0 7.268067e+05 9092.661377
14 2014 14 2.047485e+07 0 0 8.037913e+05 10305.016357
15 2015 15 1.674020e+07 0 0 9.044167e+05 13942.080566
16 2016 16 1.535750e+07 0 0 8.516793e+05 15760.613220
17 2017 17 1.521202e+07 0 0 7.934863e+05 14548.258240
18 2018 18 1.426032e+07 0 0 6.486099e+05 15760.613037
19 2019 19 1.413788e+07 0 0 6.667952e+05 15154.435791
20 2020 20 1.308313e+07 0 0 6.801311e+05 16366.790894
21 2021 21 1.290976e+07 0 0 4.667566e+05 24853.274353
22 2022 22 1.181985e+07 0 0 6.455790e+05 29702.693909
23 2023 23 1.124641e+07 0 0 5.352547e+05 23640.919983
24 2024 24 1.062750e+07 0 0 4.734245e+05 23640.919922
25 2025 25 1.015893e+07 0 0 4.528145e+05 26671.807556
26 2026 26 1.006073e+07 0 0 4.910037e+05 23034.742615
27 2027 27 1.019530e+07 0 0 4.509960e+05 19397.677795
28 2028 28 1.011892e+07 0 0 3.958338e+05 18185.323120
29 2029 29 1.049354e+07 0 0 4.206871e+05 21822.387695
30 2030 30 1.006739e+07 0 0 4.309921e+05 21216.210022
detected_cancers cancer_deaths detected_cancer_deaths ... \
0 0 0.000000 0 ...
1 0 0.000000 0 ...
2 0 0.000000 0 ...
3 0 0.000000 0 ...
4 0 0.000000 0 ...
5 0 0.000000 0 ...
6 0 0.000000 0 ...
7 0 0.000000 0 ...
8 0 0.000000 0 ...
9 0 1212.354858 0 ...
10 0 0.000000 0 ...
11 0 1212.354858 0 ...
12 0 2424.709717 0 ...
13 0 3030.887085 0 ...
14 0 3637.064514 0 ...
15 0 4849.419312 0 ...
16 0 5455.596741 0 ...
17 0 8486.484009 0 ...
18 0 6667.951599 0 ...
19 0 7274.129150 0 ...
20 0 6667.951599 0 ...
21 0 12729.725586 0 ...
22 0 8486.484070 0 ...
23 0 9092.661499 0 ...
24 0 9092.661377 0 ...
25 0 18185.323242 0 ...
26 0 18791.500000 0 ...
27 0 13942.081055 0 ...
28 0 18185.322754 0 ...
29 0 23034.741211 0 ...
30 0 17579.145264 0 ...
cum_cancer_treated detected_cancer_incidence cancer_mortality \
0 0 0 0.000000
1 0 0 0.000000
2 0 0 0.000000
3 0 0 0.000000
4 0 0 0.000000
5 0 0 0.000000
6 0 0 0.000000
7 0 0 0.000000
8 0 0 0.000000
9 0 0 1.611747
10 0 0 0.000000
11 0 0 1.530292
12 0 0 2.976789
13 0 0 3.621719
14 0 0 4.229850
15 0 0 5.486969
16 0 0 6.016083
17 0 0 9.106817
18 0 0 6.971203
19 0 0 7.421334
20 0 0 6.637062
21 0 0 12.384194
22 0 0 8.062473
23 0 0 8.424222
24 0 0 8.230769
25 0 0 16.044755
26 0 0 16.144826
27 0 0 11.709186
28 0 0 14.923221
29 0 0 18.456727
30 0 0 13.809130
n_alive cdr cbr hpv_prevalence precin_prevalence \
0 120716592 0.016270 0.042984 0.062541 0.059004
1 123808088 0.013171 0.042058 0.062840 0.059316
2 127039624 0.011996 0.042181 0.061959 0.057311
3 130340872 0.012245 0.042042 0.062149 0.056264
4 133787600 0.012813 0.041775 0.062572 0.055574
5 137421632 0.012373 0.041729 0.061582 0.054169
6 141088384 0.013023 0.041590 0.060060 0.051769
7 144849728 0.011948 0.041472 0.058248 0.049805
8 148751696 0.011451 0.041484 0.056759 0.047369
9 152804592 0.011576 0.041257 0.055723 0.045760
10 156990256 0.010676 0.040968 0.053764 0.043611
11 161281984 0.011903 0.040629 0.051466 0.041167
12 165779216 0.011061 0.040112 0.049408 0.039207
13 170442528 0.012131 0.039441 0.046782 0.037176
14 175020992 0.010719 0.039033 0.045586 0.035397
15 179598240 0.007287 0.038646 0.040054 0.032341
16 184113648 0.011171 0.038159 0.037210 0.029231
17 188909728 0.009890 0.037768 0.034825 0.027119
18 193777936 0.010639 0.037320 0.033122 0.025857
19 198635856 0.009952 0.036956 0.030950 0.024027
20 203576192 0.010377 0.036923 0.028982 0.021790
21 208628688 0.010867 0.036900 0.027145 0.021045
22 213783008 0.010574 0.036890 0.025650 0.019640
23 219034928 0.009708 0.036946 0.024668 0.018881
24 224305632 0.009559 0.036943 0.022390 0.017041
25 229660624 0.009734 0.036952 0.021159 0.016198
26 235216224 0.008257 0.036981 0.020177 0.015476
27 240693040 0.010139 0.036921 0.019676 0.014845
28 246307456 0.009829 0.036916 0.019076 0.014950
29 251974016 0.008728 0.036976 0.018818 0.014701
30 257683584 0.009212 0.036956 0.018433 0.014366
cin_prevalence lsil_prevalence
0 0.001706 0
1 0.003723 0
2 0.005839 0
3 0.007317 0
4 0.008413 0
5 0.009179 0
6 0.009632 0
7 0.009897 0
8 0.010123 0
9 0.010334 0
10 0.010339 0
11 0.009990 0
12 0.009962 0
13 0.009081 0
14 0.009411 0
15 0.009108 0
16 0.009211 0
17 0.008519 0
18 0.008135 0
19 0.007368 0
20 0.007468 0
21 0.006528 0
22 0.006187 0
23 0.005845 0
24 0.005617 0
25 0.005252 0
26 0.004845 0
27 0.004561 0
28 0.004125 0
29 0.004044 0
30 0.003911 0
[31 rows x 65 columns]