hpvsim.misc module

Miscellaneous functions that do not belong anywhere else

date(obj=None, *args, start_date=None, readformat=None, to='date', as_date=None, outformat=None, **kwargs)[source]

Convert any reasonable object – a string, integer, or datetime object, or list/array of any of those – to a date object (or string, pandas, or numpy date).

If the object is an integer, this is interpreted as follows:

  • With readformat=’posix’: treat as a POSIX timestamp, in seconds from 1970

  • With readformat=’ordinal’/’matplotlib’: treat as an ordinal number of days from 1970 (Matplotlib default)

  • With start_date provided: treat as a number of days from this date

Note: in this and other date functions, arguments work either with or without underscores (e.g. start_date or startdate)

  • obj (str/int/date/datetime/list/array) – the object to convert; if None, return current date

  • args (str/int/date/datetime) – additional objects to convert

  • start_date (str/date/datetime) – the starting date, if an integer is supplied

  • readformat (str/list) – the format to read the date in; passed to sc.readdate() (NB: can also use “format” instead of “readformat”)

  • to (str) – the output format: ‘date’ (default), ‘datetime’, ‘str’ (or ‘string’), ‘pandas’, or ‘numpy’

  • as_date (bool) – alternate method of choosing between output format of ‘date’ (True) or ‘str’ (False); if None, use “to” instead

  • outformat (str) – the format to output the date in, if returning a string

  • kwargs (dict) – only used for deprecated argument aliases


either a single date object, or a list of them (matching input data type where possible)

Return type:

dates (date or list)


sc.date('2020-04-05') # Returns datetime.date(2020, 4, 5)
sc.date([35,36,37], start_date='2020-01-01', to='str') # Returns ['2020-02-05', '2020-02-06', '2020-02-07']
sc.date(1923288822, readformat='posix') # Interpret as a POSIX timestamp
New in version 1.0.0.
New in version 1.2.2: “readformat” argument; renamed “dateformat” to “outformat”
New in version 2.0.0: support for np.datetime64 objects
New in version 3.0.0: added “to” argument, and support for pd.Timestamp and np.datetime64 output; allow None
New in version 3.1.0: allow “datetime” output
day(obj, *args, start_date=None, **kwargs)[source]

Convert a string, date/datetime object, or int to a day (int), the number of days since the start day. See also sc.date() and sc.daydiff() <daydiff>`(). If a start day is not supplied, it returns the number of days into the current year.

  • obj (str, date, int, list, array) – convert any of these objects to a day relative to the start day

  • args (list) – additional days

  • start_date (str or date) – the start day; if none is supplied, return days since (supplied year)-01-01.


the day(s) in simulation time (matching input data type where possible)

Return type:

days (int or list)


sc.day(sc.now()) # Returns how many days into the year we are
sc.day(['2021-01-21', '2024-04-04'], start_date='2022-02-22') # Days can be positive or negative
New in version 1.0.0.
New in version 1.2.2: renamed “start_day” to “start_date”

Convenience function to find the difference between two or more days. With only one argument, calculate days since Jan. 1st.


diff  = sc.daydiff('2020-03-20', '2020-04-05') # Returns 16
diffs = sc.daydiff('2020-03-20', '2020-04-05', '2020-05-01') # Returns [16, 26]

doy = sc.daydiff('2022-03-20') # Returns 79, the number of days since 2022-01-01
New in version 1.0.0.
New in version 3.0.0: Calculated relative days with one argument
date_range(start_date=None, end_date=None, interval=None, inclusive=True, as_date=None, readformat=None, outformat=None, **kwargs)

Return a list of dates from the start date to the end date. To convert a list of days (as integers) to dates, use sc.date() instead.

Note: instead of an end date, can also pass one or more of days, months, weeks, or years, which will be added on to the start date via sc.datedelta().

  • start_date (int/str/date) – the starting date, in any format

  • end_date (int/str/date) – the end date, in any format (see also kwargs below)

  • interval (int/str/dict) – if an int, the number of days; if ‘week’, ‘month’, or ‘year’, one of those; if a dict, passed to dt.relativedelta()

  • inclusive (bool) – if True (default), return to end_date inclusive; otherwise, stop the day before

  • as_date (bool) – if True, return a list of datetime.date objects; else, as input type (e.g. strings; note: you can also use “asdate” instead of “as_date”)

  • readformat (str) – passed to sc.date()

  • outformat (str) – passed to sc.date()

  • kwargs (dict) – optionally, use any valid argument to sc.datedelta() to create the end_date


dates1 = sc.daterange('2020-03-01', '2020-04-04')
dates2 = sc.daterange('2020-03-01', '2022-05-01', interval=dict(months=2), asdate=True)
dates3 = sc.daterange('2020-03-01', weeks=5)
New in version 1.0.0.
New in version 1.3.0: “interval” argument
New in version 2.0.0: sc.datedelta() arguments
New in version 3.0.0: preserve input type
load_data(datafile, check_date=False, header='infer', calculate=True, **kwargs)[source]

Load data for comparing to the model output, either from file or from a dataframe. Data is expected to be in wide format, with each row representing a year and columns for each variable by genotype/age/sex.

  • datafile (str/df) – if a string, the name of the file to load (either Excel or CSV); if a dataframe, use directly

  • start_year (int) – first year with data available

  • kwargs (dict) – passed to pd.read_excel()


pandas dataframe of the loaded data

Return type:

data (dataframe)

load(*args, update=True, verbose=True, **kwargs)[source]

Convenience method for sc.loadobj() and equivalent to hpv.Sim.load() or hpv.Scenarios.load().

  • filename (str) – file to load

  • do_migrate (bool) – whether to migrate if loading an old object

  • update (bool) – whether to modify the object to reflect the new version

  • verbose (bool) – whether to print migration information

  • args (list) – passed to sc.loadobj()

  • kwargs (dict) – passed to sc.loadobj()


Loaded object


sim = hpv.load('calib.sim') # Equivalent to hpv.Sim.load('calib.sim')
scens = hpv.load(filename='school-closures.scens', folder='schools')
save(*args, **kwargs)[source]

Convenience method for sc.saveobj() and equivalent to hpv.Sim.save() or hpv.Scenarios.save().

  • filename (str) – file to save to

  • obj (object) – object to save

  • args (list) – passed to sc.saveobj()

  • kwargs (dict) – passed to sc.saveobj()


Filename the object is saved to


hpv.save('calib.sim', sim) # Equivalent to sim.save('calib.sim')
hpv.save(filename='school-closures.scens', folder='schools', obj=scens)
savefig(filename=None, comments=None, fig=None, **kwargs)[source]

Wrapper for Matplotlib’s pl.savefig() function which automatically stores HPVsim metadata in the figure.

By default, saves (git) information from both the HPVsim version and the calling function. Additional comments can be added to the saved file as well. These can be retrieved via hpv.get_png_metadata() (or sciris.sc_plotting.loadmetadata()). Metadata can also be stored for PDF, but cannot be automatically retrieved.

  • filename (str/list) – name of the file to save to (default, timestamp); can also be a list of names

  • comments (str/dict) – additional metadata to save to the figure

  • fig (fig/list) – figure to save (by default, current one); can also be a list of figures

  • kwargs (dict) – passed to fig.savefig()


git_info(filename=None, check=False, comments=None, old_info=None, die=False, indent=2, verbose=True, frame=2, **kwargs)[source]

Get current git information and optionally write it to disk. Simplest usage is hpv.git_info(__file__)

  • filename (str) – name of the file to write to or read from

  • check (bool) – whether or not to compare two git versions

  • comments (dict) – additional comments to include in the file

  • old_info (dict) – dictionary of information to check against

  • die (bool) – whether or not to raise an exception if the check fails

  • indent (int) – how many indents to use when writing the file to disk

  • verbose (bool) – detail to print

  • frame (int) – how many frames back to look for caller info

  • kwargs (dict) – passed to sc.loadjson() (if check=True) or sc.savejson() (if check=False)


hpv.git_info() # Return information
hpv.git_info(__file__) # Writes to disk
hpv.git_info('hpvsim_version.gitinfo') # Writes to disk
hpv.git_info('hpvsim_version.gitinfo', check=True) # Checks that current version matches saved file
check_version(expected, die=False, verbose=True)[source]

Get current git information and optionally write it to disk. The expected version string may optionally start with ‘>=’ or ‘<=’ (== is implied otherwise), but other operators (e.g. ~=) are not supported. Note that e.g. ‘>’ is interpreted to mean ‘>=’.

  • expected (str) – expected version information

  • die (bool) – whether or not to raise an exception if the check fails


hpv.check_version('>=1.7.0', die=True) # Will raise an exception if an older version is used
check_save_version(expected=None, filename=None, die=False, verbose=True, **kwargs)[source]

A convenience function that bundles check_version with git_info and saves automatically to disk from the calling file. The idea is to put this at the top of an analysis script, and commit the resulting file, to keep track of which version of HPVsim was used.

  • expected (str) – expected version information

  • filename (str) – file to save to; if None, guess based on current file name

  • kwargs (dict) – passed to git_info(), and thence to sc.savejson()


hpv.check_save_version('1.3.2', filename='script.gitinfo', comments='This is the main analysis script')
hpv.check_save_version('1.7.2', folder='gitinfo', comments={'SynthPops':sc.gitinfo(sp.__file__)})
get_version_pars(version, verbose=True)[source]

Function for loading parameters from the specified version.

Parameters will be loaded for HPVsim ‘as at’ the requested version i.e. the most recent set of parameters that is <= the requested version. Available parameter values are stored in the regression folder. If parameters are available for versions 1.3, and 1.4, then this function will return the following

  • If parameters for version ‘1.3’ are requested, parameters will be returned from ‘1.3’

  • If parameters for version ‘1.3.5’ are requested, parameters will be returned from ‘1.3’, since HPVsim at version 1.3.5 would have been using the parameters defined at version 1.3.

  • If parameters for version ‘1.4’ are requested, parameters will be returned from ‘1.4’


version (str) – the version to load parameters from


Dictionary of parameters from that version

get_png_metadata(filename, output=False)[source]

Read metadata from a PNG file. For use with images saved with hpv.savefig(). Requires pillow, an optional dependency. Metadata retrieval for PDF and SVG is not currently supported.


filename (str) – the name of the file to load the data from


get_doubling_time(sim, series=None, interval=None, start_day=None, end_day=None, moving_window=None, exp_approx=False, max_doubling_time=100, eps=0.001, verbose=None)[source]

Alternate method to calculate doubling time (one is already implemented in the sim object).


hpv.get_doubling_time(sim, interval=[3,30]) # returns the doubling time over the given interval (single float)
hpv.get_doubling_time(sim, interval=[3,30], moving_window=3) # returns doubling times calculated over moving windows (array)
compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=False, as_scalar='none', eps=1e-09, skestimator=None, estimator=None, **kwargs)[source]

Calculate the goodness of fit. By default use normalized absolute error, but highly customizable. For example, mean squared error is equivalent to setting normalize=False, use_squared=True, as_scalar=’mean’.

  • actual (arr) – array of actual (data) points

  • predicted (arr) – corresponding array of predicted (model) points

  • normalize (bool) – whether to divide the values by the largest value in either series

  • use_frac (bool) – convert to fractional mismatches rather than absolute

  • use_squared (bool) – square the mismatches

  • as_scalar (str) – return as a scalar instead of a time series: choices are sum, mean, median

  • eps (float) – to avoid divide-by-zero

  • skestimator (str) – if provided, use this scikit-learn estimator instead

  • estimator (func) – if provided, use this custom estimator instead

  • kwargs (dict) – passed to the scikit-learn or custom estimator


array of goodness-of-fit values, or a single value if as_scalar is True

Return type:

gofs (arr)


x1 = np.cumsum(np.random.random(100))
x2 = np.cumsum(np.random.random(100))

e1 = compute_gof(x1, x2) # Default, normalized absolute error
e2 = compute_gof(x1, x2, normalize=False, use_frac=False) # Fractional error
e3 = compute_gof(x1, x2, normalize=False, use_squared=True, as_scalar='mean') # Mean squared error
e4 = compute_gof(x1, x2, skestimator='mean_squared_error') # Scikit-learn's MSE method
e5 = compute_gof(x1, x2, as_scalar='median') # Normalized median absolute error -- highly robust
help(pattern=None, source=False, ignorecase=True, flags=None, context=False, output=False)[source]

Get help on HPVsim in general, or search for a word/expression.

  • pattern (str) – the word, phrase, or regex to search for

  • source (bool) – whether to search source code instead of docstrings for matches

  • ignorecase (bool) – whether to ignore case (equivalent to flags=re.I)

  • flags (list) – additional flags to pass to re.findall()

  • context (bool) – whether to show the line(s) of matches

  • output (bool) – whether to return the dictionary of matches


hpv.help('contact', ignorecase=False, context=True)
hpv.help('lognormal', source=True, context=True)
New in version 3.1.2.