'''
Base classes for HPVsim. These classes handle a lot of the boilerplate of the
People and Sim classes (e.g. loading, saving, key lookups, etc.), so those classes
can be focused on the disease-specific functionality.
'''
import numpy as np
import pandas as pd
import sciris as sc
from . import utils as hpu
from . import misc as hpm
from . import defaults as hpd
from . import parameters as hppar
from .version import __version__
# Specify all externally visible classes this file defines
__all__ = ['ParsObj', 'Result', 'BaseSim', 'BasePeople', 'Person', 'FlexDict', 'Contacts', 'Layer']
# Default object getter/setter
obj_set = object.__setattr__
base_key = 'uid' # Define the key used by default for getting length, etc.
#%% Define simulation classes
class FlexPretty(sc.prettyobj):
'''
A class that supports multiple different display options: namely obj.brief()
for a one-line description and obj.disp() for a full description.
'''
def __repr__(self):
''' Use brief repr by default '''
try:
string = self._brief()
except Exception as E:
string = sc.objectid(self)
string += f'Warning, something went wrong printing object:\n{str(E)}'
return string
def _disp(self):
''' Verbose output -- use Sciris' pretty repr by default '''
return sc.prepr(self)
def disp(self, output=False):
''' Print or output verbose representation of the object '''
string = self._disp()
if not output:
print(string)
else:
return string
def _brief(self):
''' Brief output -- use a one-line output, a la Python's default '''
return sc.objectid(self)
def brief(self, output=False):
''' Print or output a brief representation of the object '''
string = self._brief()
if not output:
print(string)
else:
return string
[docs]
class ParsObj(FlexPretty):
'''
A class based around performing operations on a self.pars dict.
'''
def __init__(self, pars):
self.update_pars(pars, create=True)
return
def __getitem__(self, key):
''' Allow sim['par_name'] instead of sim.pars['par_name'] '''
try:
return self.pars[key]
except:
all_keys = '\n'.join(list(self.pars.keys()))
errormsg = f'Key "{key}" not found; available keys:\n{all_keys}'
raise sc.KeyNotFoundError(errormsg)
def __setitem__(self, key, value):
''' Ditto '''
if key in self.pars:
self.pars[key] = value
else:
all_keys = '\n'.join(list(self.pars.keys()))
errormsg = f'Key "{key}" not found; available keys:\n{all_keys}'
raise sc.KeyNotFoundError(errormsg)
return
[docs]
def update_pars(self, pars=None, create=False):
'''
Update internal dict with new pars.
Args:
pars (dict): the parameters to update (if None, do nothing)
create (bool): if create is False, then raise a KeyNotFoundError if the key does not already exist
'''
if pars is not None:
if not isinstance(pars, dict):
raise TypeError(f'The pars object must be a dict; you supplied a {type(pars)}')
if not hasattr(self, 'pars'):
self.pars = pars
if not create:
available_keys = list(self.pars.keys())
mismatches = [key for key in pars.keys() if key not in available_keys]
if len(mismatches):
errormsg = f'Key(s) {mismatches} not found; available keys are {available_keys}'
raise sc.KeyNotFoundError(errormsg)
self.pars.update(pars)
return
[docs]
class Result(object):
'''
Stores a single result -- by default, acts like an array.
Args:
name (str): name of this result, e.g. new_infections
npts (int): if values is None, precreate it to be of this length
scale (bool): whether or not the value scales by population scale factor
color (str/arr): default color for plotting (hex or RGB notation)
**Example**::
import hpvsim as hpv
r1 = hpv.Result(name='test1', npts=10)
r1[:5] = 20
print(r1.values)
'''
def __init__(self, name=None, npts=None, scale=True, color=None, n_rows=0, n_copies=0):
self.name = name # Name of this result
self.scale = scale # Whether or not to scale the result by the scale factor
if color is None:
color = '#000000'
self.color = color # Default color
if npts is None:
npts = 0
npts = int(npts)
if n_rows > 0:
self.values = np.zeros((n_rows, npts), dtype=hpd.result_float)
if n_copies > 0:
self.values = np.zeros((n_copies, n_rows, npts), dtype=hpd.result_float)
else:
self.values = np.zeros(npts, dtype=hpd.result_float)
self.low = None
self.high = None
return
def __eq__(self, other):
return self.npts == other.npts and np.all(self.values == other.values) and self.scale == other.scale
def __repr__(self):
''' Use pretty repr, like sc.prettyobj, but displaying full values '''
output = sc.prepr(self, skip=['values', 'low', 'high'], use_repr=False)
output += 'values:\n' + repr(self.values)
if self.low is not None:
output += '\nlow:\n' + repr(self.low)
if self.high is not None:
output += '\nhigh:\n' + repr(self.high)
return output
def __getitem__(self, key):
''' To allow e.g. result['high'] instead of result.high, and result[5] instead of result.values[5] '''
if isinstance(key, str):
output = getattr(self, key)
else:
output = self.values.__getitem__(key)
return output
def __setitem__(self, key, value):
''' To allow e.g. result[:] = 1 instead of result.values[:] = 1 '''
if isinstance(key, str):
setattr(self, key, value)
else:
self.values.__setitem__(key, value)
return
def __len__(self):
''' To allow len(result) instead of len(result.values) '''
return len(self.values)
def __sum__(self):
''' To allow sum(result) instead of result.values.sum() '''
return self.values.sum()
# Numpy methods
[docs]
def sum(self):
''' To allow result.sum() instead of result.values.sum() '''
return self.values.sum()
[docs]
def mean(self):
''' To allow result.mean() instead of result.values.mean() '''
return self.values.mean()
@property
def npts(self):
return len(self.values)
@property
def shape(self):
return self.values.shape
def set_metadata(obj, **kwargs):
''' Set standard metadata for an object '''
obj.created = kwargs.get('created', sc.now())
obj.version = kwargs.get('version', __version__)
obj.git_info = kwargs.get('git_info', hpm.git_info())
return
[docs]
class BaseSim(ParsObj):
'''
The BaseSim class stores various methods useful for the Sim that are not directly
related to simulating the epidemic. It is not used outside of the Sim object,
so the separation of methods into the BaseSim and Sim classes is purely to keep
each one of manageable size.
'''
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs) # Initialize and set the parameters as attributes
return
def _disp(self):
'''
Print a verbose display of the sim object. Used by repr(). See sim.disp()
for the user version. Equivalent to sc.prettyobj().
'''
return sc.prepr(self)
def _brief(self):
'''
Return a one-line description of a sim -- used internally and by repr();
see sim.brief() for the user version.
'''
# Try to get a detailed description of the sim...
try:
if self.results_ready:
infections = self.results['infections'].sum()
cancers = self.results['cancers'].sum()
results = f'{infections:n}⚙, {cancers:n}♋︎'
else:
results = 'not run'
# Set label string
labelstr = f'"{self.label}"' if self.label else '<no label>'
start = self['start']
if self['end']:
end = self['end']
else:
end = self['start'] + self['n_years']
n_agents = self['n_agents']
network = self['network']
string = f'Sim({labelstr}; {start} to {end}; pop: {n_agents:n} {network}; epi: {results})'
# ...but if anything goes wrong, return the default with a warning
except Exception as E: # pragma: no cover
string = sc.objectid(self)
string += f'Warning, sim appears to be malformed; use sim.disp() for details:\n{str(E)}'
return string
[docs]
def update_pars(self, pars=None, create=False, **kwargs):
''' Ensure that metaparameters get used properly before being updated '''
# Merge everything together
pars = sc.mergedicts(pars, kwargs)
if pars:
# Handle other special parameters
if pars.get('network'):
hppar.reset_layer_pars(pars, force=False)
if pars.get('location'):
location = pars['location']
pars['birth_rates'], pars['death_rates'] = hppar.get_births_deaths(location=location) # Set birth and death rates
if pars.get('n_clusters'):
hppar.add_mixing(pars)
# Call update_pars() for ParsObj
super().update_pars(pars=pars, create=create)
return
@property
def n(self):
''' Count the number of people -- if it fails, assume none '''
try: # By default, the length of the people dict
return len(self.people)
except: # pragma: no cover # If it's None or missing
return 0
[docs]
def get_t(self, dates, exact_match=False, return_date_format=None):
'''
Convert a string, date/datetime object, or int to a timepoint (int).
Args:
date (str, date, int, or list): convert any of these objects to a timepoint relative to the simulation's start day
exact_match (bool): whether or not to demand an exact match to the requested date
return_date_format (None, str): if None, do not return dates; otherwise return them as strings or floats as requested
Returns:
t (int or str): the time point in the simulation cloesst to the requested date
**Examples**::
sim.get_t('2015-03-01') # Get the closest timepoint to the specified date
sim.get_t(3) # Will return 3
sim.get_t('2015') # Can use strings
sim.get_t(['2015.5', '2016.5']) # List of strings, will match as close as possible
sim.get_t(['2015.5', '2016.5'], exact_match=True) # Raises an error since these dates aren't directly simulated
'''
if sc.isstring(dates) or not sc.isiterable(dates):
dates = sc.promotetolist(dates)
tps = []
for date in dates:
if date in ['end', -1]:
date = self['end']
# If it's an integer, make sure it's in the sim tvec
if sc.checktype(date, int):
if date in self.tvec:
tp = date
else:
errormsg = f'The requested timepoint {date} must be within the sim tvec: {self.tvec[0], self.tvec[-1]}.'
raise ValueError(errormsg)
# If it's not an integer, try to convert from date-time format, and if this doesn't work,
# try to interpret it as a float, otherwise raise an error
else:
try:
tp_raw = sc.datetoyear(date) # Get the 'raw' timepoint, not rounded to the nearest timestep
except:
try:
tp_raw = float(date) # This must be float, not int, otherwise some attempts to get t will fail
except:
errormsg = f'Could not understand the provided date {date}; try specifying it as a float or in a format understood by sc.readdate().'
raise ValueError(errormsg)
# If the requested date is within the range of years covered by the sim,
# return the closest date
if (tp_raw >= self['start']) and (tp_raw <= self['end']):
if exact_match:
tp_ind = sc.findinds(self.yearvec, tp_raw)
if len(tp_ind)>0:
tp = tp_ind[0]
else:
errormsg = f'The requested date {date} was not simulated; try exact_match=False to obtain the nearest date.'
raise ValueError(errormsg)
else:
tp = sc.findnearest(self.yearvec, tp_raw) # Get the nearest timestep to the requested one
else:
errormsg = f'The requested date {date} must be within the simulation dates: {self["start"], self["end"]}.'
raise ValueError(errormsg)
tps.append(tp)
tps = np.sort(sc.promotetoarray(tps)) # Ensure they're an array and in order
if return_date_format is not None:
if return_date_format == 'str':
return tps, np.array([str(self.yearvec[tp]) for tp in tps])
elif return_date_format == 'float':
return tps, self.yearvec[tps]
else:
errormsg = f'Could not understand what format to return the dates: requested {return_date_format}, options are str or float.'
raise ValueError(errormsg)
else:
return tps
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def result_keys(self, which='all'):
'''
Get the actual results objects, not other things stored in sim.results.
If which is 'total', return only the main results keys. If 'genotype', return
only genotype keys. If 'all', return all keys.
'''
keys = []
subchoices = ['total', 'genotype', 'sex', 'age', 'type_dist']
if which in ['total']:
keys = [k for k,res in self.results.items() if (res[:].ndim==1) and isinstance(res, Result)]
elif which in ['sex']:
keys = [k for k, res in self.results.items() if 'by_sex' in k and isinstance(res, Result)]
elif which in ['age']:
keys = [k for k, res in self.results.items() if 'by_age' in k and isinstance(res, Result)]
elif which in ['genotype']:
keys = [k for k,res in self.results.items() if 'by_genotype' in k and isinstance(res, Result)]
elif which in ['type_dist']:
keys = [k for k, res in self.results.items() if 'genotype_dist' in k and isinstance(res, Result)]
elif which =='all':
keys = []
for subchoice in subchoices: # Recurse over options
keys += self.result_keys(subchoice)
else:
errormsg = f'Choice "{which}" not available; choices are: {sc.strjoin(subchoices+["all"])}'
raise ValueError(errormsg)
return keys
[docs]
def result_types(self, reskeys):
'''
Figure out what kind of result it is, which determines what plotting style to use
'''
# If it's a single item, make it a list but remember to return a single item
return_list = True
if isinstance(reskeys, str):
return_list = False
reskeys = sc.tolist(reskeys)
# Construct list of result types
result_types = sc.autolist()
for rkey in reskeys:
for type_option in ['total', 'genotype', 'sex', 'age', 'type_dist']:
if rkey in self.result_keys(type_option):
result_types += type_option
# Check that each result is of exactly one type
if len(result_types) != len(reskeys):
errormsg = f"Can't determine unique result types for result_keys {reskeys}."
raise ValueError(errormsg)
if return_list:
return result_types
else:
return result_types[0]
[docs]
def copy(self):
''' Returns a deep copy of the sim '''
return sc.dcp(self)
[docs]
def export_results(self, for_json=True, filename=None, indent=2, *args, **kwargs):
'''
Convert results to dict -- see also to_json().
The results written to Excel must have a regular table shape, whereas
for the JSON output, arbitrary data shapes are supported.
Args:
for_json (bool): if False, only data associated with Result objects will be included in the converted output
filename (str): filename to save to; if None, do not save
indent (int): indent (int): if writing to file, how many indents to use per nested level
args (list): passed to savejson()
kwargs (dict): passed to savejson()
Returns:
resdict (dict): dictionary representation of the results
'''
if not self.results_ready: # pragma: no cover
errormsg = 'Please run the sim before exporting the results'
raise RuntimeError(errormsg)
resdict = {}
resdict['t'] = self.results['t'] # Assume that there is a key for time
if for_json:
resdict['timeseries_keys'] = self.result_keys()
for key,res in self.results.items():
if isinstance(res, Result):
resdict[key] = res.values
if res.low is not None:
resdict[key+'_low'] = res.low
if res.high is not None:
resdict[key+'_high'] = res.high
elif for_json:
if key == 'date':
resdict[key] = [str(d) for d in res] # Convert dates to strings
else:
if isinstance(res, np.ndarray) and (res.ndim == 1):
resdict[key] = res
else:
print(f'WARNING: skipping {key} from export since not 1D array')
if filename is not None:
sc.savejson(filename=filename, obj=resdict, indent=indent, *args, **kwargs)
return resdict
[docs]
def export_pars(self, filename=None, indent=2, *args, **kwargs):
'''
Return parameters for JSON export -- see also to_json().
This method is required so that interventions can specify
their JSON-friendly representation.
Args:
filename (str): filename to save to; if None, do not save
indent (int): indent (int): if writing to file, how many indents to use per nested level
args (list): passed to savejson()
kwargs (dict): passed to savejson()
Returns:
pardict (dict): a dictionary containing all the parameter values
'''
pardict = {}
for key in self.pars.keys():
if key == 'interventions':
pardict[key] = [intervention.to_json() for intervention in self.pars[key]]
elif key == 'start_day':
pardict[key] = str(self.pars[key])
else:
pardict[key] = self.pars[key]
if filename is not None:
sc.savejson(filename=filename, obj=pardict, indent=indent, *args, **kwargs)
return pardict
[docs]
def to_json(self, filename=None, keys=None, tostring=False, indent=2, verbose=False, *args, **kwargs):
'''
Export results and parameters as JSON.
Args:
filename (str): if None, return string; else, write to file
keys (str or list): attributes to write to json (default: results, parameters, and summary)
tostring (bool): if not writing to file, whether to write to string (alternative is sanitized dictionary)
indent (int): if writing to file, how many indents to use per nested level
verbose (bool): detail to print
args (list): passed to savejson()
kwargs (dict): passed to savejson()
Returns:
A unicode string containing a JSON representation of the results,
or writes the JSON file to disk
**Examples**::
json = sim.to_json()
sim.to_json('results.json')
sim.to_json('summary.json', keys='summary')
'''
# Handle keys
if keys is None:
keys = ['results', 'pars', 'summary', 'short_summary']
keys = sc.promotetolist(keys)
# Convert to JSON-compatible format
d = {}
for key in keys:
if key == 'results':
if self.results_ready:
resdict = self.export_results(for_json=True)
d['results'] = resdict
else:
d['results'] = 'Results not available (Sim has not yet been run)'
elif key in ['pars', 'parameters']:
pardict = self.export_pars()
d['parameters'] = pardict
elif key == 'summary':
if self.results_ready:
d['summary'] = dict(sc.dcp(self.summary))
else:
d['summary'] = 'Summary not available (Sim has not yet been run)'
elif key == 'short_summary':
if self.results_ready:
d['short_summary'] = dict(sc.dcp(self.short_summary))
else:
d['short_summary'] = 'Full summary not available (Sim has not yet been run)'
else: # pragma: no cover
try:
d[key] = sc.sanitizejson(getattr(self, key))
except Exception as E:
errormsg = f'Could not convert "{key}" to JSON: {str(E)}; continuing...'
print(errormsg)
if filename is None:
output = sc.jsonify(d, tostring=tostring, indent=indent, verbose=verbose, *args, **kwargs)
else:
output = sc.savejson(filename=filename, obj=d, indent=indent, *args, **kwargs)
return output
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def to_df(self, date_index=False):
'''
Export results to a pandas dataframe
Args:
date_index (bool): if True, use the date as the index
'''
resdict = self.export_results(for_json=False)
resdict = {k:v for k,v in resdict.items() if v.ndim == 1}
df = sc.dataframe.from_dict(resdict)
df['year'] = self.res_yearvec
new_columns = ['t','year'] + df.columns[1:-1].tolist() # Get column order
df = df.reindex(columns=new_columns) # Reorder so 't' and 'date' are first
if date_index:
df = df.set_index('year')
return df
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def to_excel(self, filename=None, skip_pars=None):
'''
Export parameters and results as Excel format
Args:
filename (str): if None, return string; else, write to file
skip_pars (list): if provided, a custom list parameters to exclude
Returns:
An sc.Spreadsheet with an Excel file, or writes the file to disk
'''
if skip_pars is None:
skip_pars = ['genotype_map', 'vaccine_map'] # These include non-string keys so fail at sc.flattendict()
# Export results
result_df = self.to_df(date_index=True)
# Export parameters
pars = {str(k):sc.dcp(v) for k,v in self.pars.items() if k not in skip_pars}
pars['immunity_map'] = {str(k):v for k,v in pars['immunity_map'].items()}
par_df = sc.dataframe.from_dict(sc.flattendict(pars, sep='_'), orient='index', columns=['Value'])
par_df.index.name = 'Parameter'
# Convert to spreadsheet
spreadsheet = sc.Spreadsheet()
spreadsheet.freshbytes()
with pd.ExcelWriter(spreadsheet.bytes, engine='xlsxwriter') as writer:
result_df.to_excel(writer, sheet_name='Results')
par_df.to_excel(writer, sheet_name='Parameters')
spreadsheet.load()
if filename is None:
output = spreadsheet
else:
output = spreadsheet.save(filename)
return output
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def shrink(self, skip_attrs=None, in_place=True):
'''
"Shrinks" the simulation by removing the people and other memory-intensive
attributes (e.g., some interventions and analyzers), and returns a copy of
the "shrunken" simulation. Used to reduce the memory required for RAM or
for saved files.
Args:
skip_attrs (list): a list of attributes to skip (remove) in order to perform the shrinking; default "people"
in_palce (bool): whether to perform the shrinking in place (default), or return a shrunken copy instead
Returns:
shrunken (Sim): a Sim object with the listed attributes removed
'''
from . import interventions as hpvi # To avoid circular imports
from . import analysis as hpva
# By default, skip people (~90% of memory), the popdict (which is usually empty anyway), and _orig_pars (which is just a backup)
if skip_attrs is None:
skip_attrs = ['popdict', 'people', '_orig_pars']
# Create the new object, and copy original dict, skipping the skipped attributes
if in_place:
shrunken = self
for attr in skip_attrs:
setattr(self, attr, None)
else:
shrunken = object.__new__(self.__class__)
shrunken.__dict__ = {k:(v if k not in skip_attrs else None) for k,v in self.__dict__.items()}
# Shrink interventions and analyzers, with a lot of checking along the way
for key in ['interventions', 'analyzers']:
ias = self.pars[key] # List of interventions or analyzers
shrunken_ias = [ia.shrink(in_place=in_place) for ia in ias if isinstance(ia, (hpvi.Intervention, hpva.Analyzer))]
self.pars[key] = shrunken_ias # Actually shrink, and re-store
# Don't return if in place
if in_place:
return
else:
return shrunken
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def save(self, filename=None, keep_people=None, skip_attrs=None, **kwargs):
'''
Save to disk as a gzipped pickle.
Args:
filename (str or None): the name or path of the file to save to; if None, uses stored
kwargs: passed to sc.makefilepath()
Returns:
filename (str): the validated absolute path to the saved file
**Example**::
sim.save() # Saves to a .sim file
'''
# Set keep_people based on whether or not we're in the middle of a run
if keep_people is None:
if self.initialized and not self.results_ready:
keep_people = True
else:
keep_people = False
# Handle the filename
if filename is None:
filename = self.simfile
filename = sc.makefilepath(filename=filename, **kwargs)
self.filename = filename # Store the actual saved filename
# Handle the shrinkage and save
if skip_attrs or not keep_people:
obj = self.shrink(skip_attrs=skip_attrs, in_place=False)
else:
obj = self
hpm.save(filename=filename, obj=obj)
return filename
[docs]
@staticmethod
def load(filename, *args, **kwargs):
'''
Load from disk from a gzipped pickle.
Args:
filename (str): the name or path of the file to load from
kwargs: passed to hpv.load()
Returns:
sim (Sim): the loaded simulation object
**Example**::
sim = hpv.Sim.load('my-simulation.sim')
'''
sim = hpm.load(filename, *args, **kwargs)
if not isinstance(sim, BaseSim): # pragma: no cover
errormsg = f'Cannot load object of {type(sim)} as a Sim object'
raise TypeError(errormsg)
return sim
def _get_ia(self, which, label=None, partial=False, as_list=False, as_inds=False, die=True, first=False):
''' Helper method for get_interventions() and get_analyzers(); see get_interventions() docstring '''
# Handle inputs
if which not in ['interventions', 'analyzers']: # pragma: no cover
errormsg = f'This method is only defined for interventions and analyzers, not "{which}"'
raise ValueError(errormsg)
ia_list = sc.tolist(self.analyzers if which=='analyzers' else self.interventions) # List of interventions or analyzers
n_ia = len(ia_list) # Number of interventions/analyzers
if label == 'summary': # Print a summary of the interventions
df = sc.dataframe(columns=['ind', 'label', 'type'])
for ind,ia_obj in enumerate(ia_list):
df = df.append(dict(ind=ind, label=str(ia_obj.label), type=type(ia_obj)), ignore_index=True)
print(f'Summary of {which}:')
print(df)
return
else: # Standard usage case
position = 0 if first else -1 # Choose either the first or last element
if label is None: # Get all interventions if no label is supplied, e.g. sim.get_interventions()
label = np.arange(n_ia)
if isinstance(label, np.ndarray): # Allow arrays to be provided
label = label.tolist()
labels = sc.promotetolist(label)
# Calculate the matches
matches = []
match_inds = []
for label in labels:
if sc.isnumber(label):
matches.append(ia_list[label]) # This will raise an exception if an invalid index is given
label = n_ia + label if label<0 else label # Convert to a positive number
match_inds.append(label)
elif sc.isstring(label) or isinstance(label, type):
for ind,ia_obj in enumerate(ia_list):
if sc.isstring(label) and ia_obj.label == label or (partial and (label in str(ia_obj.label))):
matches.append(ia_obj)
match_inds.append(ind)
elif isinstance(label, type) and isinstance(ia_obj, label):
matches.append(ia_obj)
match_inds.append(ind)
else: # pragma: no cover
errormsg = f'Could not interpret label type "{type(label)}": should be str, int, list, or {which} class'
raise TypeError(errormsg)
# Parse the output options
if as_inds:
output = match_inds
elif as_list: # Used by get_interventions()
output = matches
else:
if len(matches) == 0: # pragma: no cover
if die:
errormsg = f'No {which} matching "{label}" were found'
raise ValueError(errormsg)
else:
output = None
else:
output = matches[position] # Return either the first or last match (usually), used by get_intervention()
return output
[docs]
def get_interventions(self, label=None, partial=False, as_inds=False):
'''
Find the matching intervention(s) by label, index, or type. If None, return
all interventions. If the label provided is "summary", then print a summary
of the interventions (index, label, type).
Args:
label (str, int, Intervention, list): the label, index, or type of intervention to get; if a list, iterate over one of those types
partial (bool): if true, return partial matches (e.g. 'beta' will match all beta interventions)
as_inds (bool): if true, return matching indices instead of the actual interventions
**Examples**::
tp = hpv.test_prob(symp_prob=0.1)
cb1 = hpv.change_beta(days=5, changes=0.3, label='NPI')
cb2 = hpv.change_beta(days=10, changes=0.3, label='Masks')
sim = hpv.Sim(interventions=[tp, cb1, cb2])
cb1, cb2 = sim.get_interventions(hpv.change_beta)
tp, cb2 = sim.get_interventions([0,2])
ind = sim.get_interventions(hpv.change_beta, as_inds=True) # Returns [1,2]
sim.get_interventions('summary') # Prints a summary
'''
return self._get_ia('interventions', label=label, partial=partial, as_inds=as_inds, as_list=True)
[docs]
def get_intervention(self, label=None, partial=False, first=False, die=True):
'''
Like get_interventions(), find the matching intervention(s) by label,
index, or type. If more than one intervention matches, return the last
by default. If no label is provided, return the last intervention in the list.
Args:
label (str, int, Intervention, list): the label, index, or type of intervention to get; if a list, iterate over one of those types
partial (bool): if true, return partial matches (e.g. 'beta' will match all beta interventions)
first (bool): if true, return first matching intervention (otherwise, return last)
die (bool): whether to raise an exception if no intervention is found
**Examples**::
tp = hpv.test_prob(symp_prob=0.1)
cb = hpv.change_beta(days=5, changes=0.3, label='NPI')
sim = hpv.Sim(interventions=[tp, cb])
cb = sim.get_intervention('NPI')
cb = sim.get_intervention('NP', partial=True)
cb = sim.get_intervention(hpv.change_beta)
cb = sim.get_intervention(1)
cb = sim.get_intervention()
tp = sim.get_intervention(first=True)
'''
return self._get_ia('interventions', label=label, partial=partial, first=first, die=die, as_inds=False, as_list=False)
[docs]
def get_analyzers(self, label=None, partial=False, as_inds=False):
'''
Same as get_interventions(), but for analyzers.
'''
return self._get_ia('analyzers', label=label, partial=partial, as_list=True, as_inds=as_inds)
[docs]
def get_analyzer(self, label=None, partial=False, first=False, die=True):
'''
Same as get_intervention(), but for analyzers.
'''
return self._get_ia('analyzers', label=label, partial=partial, first=first, die=die, as_inds=False, as_list=False)
#%% Define people classes
[docs]
class BasePeople(FlexPretty):
'''
A class to handle all the boilerplate for people -- note that as with the
BaseSim vs Sim classes, everything interesting happens in the People class,
whereas this class exists to handle the less interesting implementation details.
'''
def __init__(self, pars):
''' Initialize essential attributes used for filtering '''
# Set meta attribute here, because BasePeople methods expect it to exist
self.meta = hpd.PeopleMeta() # Store list of keys and dtypes
self.meta.validate()
# Define lock attribute here, since BasePeople.lock()/unlock() requires it
self._lock = False # Prevent further modification of keys
# Load other attributes
self.set_pars(pars)
self.version = __version__ # Store version info
self.contacts = None
self.t = 0 # Keep current simulation time
# Private variables relaying to dynamic allocation
self._data = dict()
self._n = self.pars['n_agents'] # Number of agents (initial)
self._s = self._n # Underlying array sizes
self._inds = None # No filtering indices
return
[docs]
def initialize(self):
''' Initialize underlying storage and map arrays '''
for state in self.meta.states_to_set:
self._data[state.name] = state.new(self.pars, self._n)
self._map_arrays()
self['uid'][:] = np.arange(self.pars['n_agents'])
return
def __len__(self):
''' Length of people '''
try:
arr = getattr(self, base_key)
return len(arr)
except Exception as E:
print(f'Warning: could not get length of People (could not get self.{base_key}: {E})')
return 0
def _len_arrays(self):
''' Length of underlying arrays '''
return len(self._data[base_key])
[docs]
def set_pars(self, pars=None):
'''
Re-link the parameters stored in the people object to the sim containing it,
and perform some basic validation.
'''
orig_pars = self.__dict__.get('pars') # Get the current parameters using dict's get method
if pars is None:
if orig_pars is not None: # If it has existing parameters, use them
pars = orig_pars
else:
pars = {}
elif sc.isnumber(pars): # Interpret as a population size
pars = {'n_agents':pars} # Ensure it's a dictionary
# Copy from old parameters to new parameters
if isinstance(orig_pars, dict):
for k,v in orig_pars.items():
if k not in pars:
pars[k] = v
# Do minimal validation -- needed here since n_agents should be converted to an int when first set
if 'n_agents' not in pars:
errormsg = f'The parameter "n_agents" must be included in a population; keys supplied were:\n{sc.newlinejoin(pars.keys())}'
raise sc.KeyNotFoundError(errormsg)
pars['n_agents'] = int(pars['n_agents'])
pars.setdefault('location', None)
self.pars = pars # Actually store the pars
return
[docs]
def validate(self, sim_pars=None, verbose=False):
'''
Perform validation on the People object.
Args:
sim_pars (dict): dictionary of parameters from the sim to ensure they match the current People object
verbose (bool): detail to print
'''
# Check that parameters match
if sim_pars is not None:
mismatches = {}
keys = ['n_agents', 'network', 'location'] # These are the keys used in generating the population
for key in keys:
sim_v = sim_pars.get(key)
ppl_v = self.pars.get(key)
if sim_v is not None and ppl_v is not None:
if sim_v != ppl_v:
mismatches[key] = sc.objdict(sim=sim_v, people=ppl_v)
if len(mismatches):
errormsg = 'Validation failed due to the following mismatches between the sim and the people parameters:\n'
for k,v in mismatches.items():
errormsg += f' {k}: sim={v.sim}, people={v.people}'
raise ValueError(errormsg)
# Check that the keys match
contact_layer_keys = set(self.contacts.keys())
layer_keys = set(self.layer_keys())
if contact_layer_keys != layer_keys:
errormsg = f'Parameters layers {layer_keys} are not consistent with contact layers {contact_layer_keys}'
raise ValueError(errormsg)
# Check that the length of each array is consistent
expected_len = len(self)
for key in self.keys():
if self[key].ndim == 1:
actual_len = len(self[key])
if actual_len != expected_len: # pragma: no cover
errormsg = f'Length of key "{key}" did not match population size ({actual_len} vs. {expected_len})'
raise IndexError(errormsg)
# Check that the layers are valid
for layer in self.contacts.values():
layer.validate()
return
[docs]
def lock(self):
''' Lock the people object to prevent keys from being added '''
self._lock = True
return
[docs]
def unlock(self):
''' Unlock the people object to allow keys to be added '''
self._lock = False
return
def _grow(self, n):
"""
Increase the number of agents stored
Automatically reallocate underlying arrays if required
Args:
n (int): Number of new agents to add
"""
orig_n = self._n
new_total = orig_n + n
if new_total > self._s:
n_new = max(n, int(self._s / 2)) # Minimum 50% growth
for state in self.meta.states_to_set:
self._data[state.name] = np.concatenate([self._data[state.name], state.new(self.pars, n_new)], axis=self._data[state.name].ndim-1)
self._s += n_new
self._n += n
self._map_arrays()
new_inds = np.arange(orig_n, self._n)
return new_inds
def _map_arrays(self):
"""
Set main simulation attributes to be views of the underlying data
This method should be called whenever the number of agents required changes
(regardless of whether or not the underlying arrays have been resized)
"""
for k in self.keys():
arr = self._data[k]
if self._inds is not None:
row_inds = self._inds
else:
row_inds = slice(None, self._n)
if arr.ndim == 1:
obj_set(self, k, arr[row_inds])
elif arr.ndim == 2:
obj_set(self, k, arr[:, row_inds])
else:
errormsg = 'Can only operate on 1D or 2D arrays'
raise TypeError(errormsg)
return
[docs]
def filter_inds(self, inds):
"""
Store indices to allow for easy filtering of the People object.
Args:
inds (array): filter by these indices
Returns:
A filtered People object, which works just like a normal People object
except only operates on a subset of indices.
"""
# Create a new People object with the same properties as the original
filtered = object.__new__(self.__class__) # Create a new People instance
filtered.__dict__ = {k:v for k,v in self.__dict__.items()} # Copy pointers to the arrays in People
if inds is None: # Reset filtering
filtered._inds = None
elif filtered._inds is None: # Not yet filtered: use the indices directly
filtered._inds = inds
else: # Already filtered: map them back onto the original People indices
filtered._inds = filtered._inds[inds]
# Apply new indices
filtered._map_arrays()
return filtered
[docs]
def filter(self, criteria):
'''
Store indices to allow for easy filtering of the People object.
Args:
criteria (array): a boolean array for the filtering critria
Returns:
A filtered People object, which works just like a normal People object
except only operates on a subset of indices.
'''
if criteria is None:
new_inds = None
elif len(criteria) == len(self): # Main use case: a new filter applied on an already filtered object, e.g. filtered.filter(filtered.age > 5)
new_inds = criteria.nonzero()[0] # Criteria is already filtered, just get the indices
elif len(criteria) == self._len_arrays: # Alternative: a filter on the underlying People object is applied to the filtered object, e.g. filtered.filter(people.age > 5)
new_inds = criteria[self._inds].nonzero()[0] # Apply filtering before getting the new indices
else:
errormsg = f'"criteria" must be boolean array matching either current filter length ({len(self)}) or else the total number of agents ({self._len_arrays()}), not {len(criteria)}'
raise ValueError(errormsg)
return self.filter_inds(new_inds)
[docs]
def unfilter(self):
"""
Set main simulation attributes to be views of the underlying data
This method should be called whenever the number of agents required changes
(regardless of whether or not the underlying arrays have been resized)
"""
return self.filter_inds(inds=None)
def __getitem__(self, key):
''' Allow people['attr'] instead of getattr(people, 'attr')
If the key is an integer, alias `people.person()` to return a `Person` instance
'''
if isinstance(key, int):
return self.person(key)
else:
return self.__getattribute__(key)
def __setitem__(self, key, value):
''' Ditto '''
if self._lock and key not in self.__dict__: # pragma: no cover
errormsg = f'Key "{key}" is not a current attribute of people, and the people object is locked; see people.unlock()'
raise AttributeError(errormsg)
return self.__setattr__(key, value)
def __setattr__(self, attr, value):
''' Ditto '''
if hasattr(self, '_data') and attr in self._data:
# Prevent accidentally overwriting a view with an actual array - if this happens, the updated values will
# be lost the next time the arrays are resized
raise Exception('Cannot assign directly to a dynamic array view - must index into the view instead e.g. `people.uid[:]=`')
else: # If not initialized, rely on the default behavior
obj_set(self, attr, value)
return
def __iter__(self):
''' Iterate over people '''
for i in range(len(self)):
yield self[i]
[docs]
def __add__(self, people2):
''' Combine two people arrays '''
newpeople = sc.dcp(self)
keys = list(self.keys())
for key in keys:
npval = newpeople[key]
p2val = people2[key]
if npval.ndim == 1:
newpeople.set(key, np.concatenate([npval, p2val], axis=0), die=False) # Allow size mismatch
elif npval.ndim == 2:
newpeople.set(key, np.concatenate([npval, p2val], axis=1), die=False)
else:
errormsg = f'Not sure how to combine arrays of {npval.ndim} dimensions for {key}'
raise NotImplementedError(errormsg)
# Validate
newpeople.pars['n_agents'] += people2.pars['n_agents']
newpeople.validate()
# Reassign UIDs so they're unique
newpeople.set('uid', np.arange(len(newpeople)))
return newpeople
[docs]
def addtoself(self, people2):
''' Combine two people arrays, avoiding dcp '''
keys = list(self.keys())
for key in keys:
npval = self[key]
p2val = people2[key]
if npval.ndim == 1:
self.set(key, np.concatenate([npval, p2val], axis=0), die=False) # Allow size mismatch
elif npval.ndim == 2:
self.set(key, np.concatenate([npval, p2val], axis=1), die=False)
else:
errormsg = f'Not sure how to combine arrays of {npval.ndim} dimensions for {key}'
raise NotImplementedError(errormsg)
# Reassign UIDs so they're unique
self.set('uid', np.arange(len(self)))
return
def __radd__(self, people2):
''' Allows sum() to work correctly '''
if not people2: return self
else: return self.__add__(people2)
def _brief(self):
'''
Return a one-line description of the people -- used internally and by repr();
see people.brief() for the user version.
'''
try:
layerstr = ', '.join([str(k) for k in self.layer_keys()])
string = f'People(n={len(self):0n}; layers: {layerstr})'
except Exception as E: # pragma: no cover
string = sc.objectid(self)
string += f'Warning, multisim appears to be malformed:\n{str(E)}'
return string
def set(self, key, value, die=True):
self[key][:] = value[:] # nb. this will raise an exception the shapes don't match, and will automatically cast the value to the existing type
[docs]
def get(self, key):
''' Convenience method -- key can be string or list of strings '''
if isinstance(key, str):
return self[key]
elif isinstance(key, list):
arr = np.zeros((len(self), len(key)))
for k,ky in enumerate(key):
arr[:,k] = self[ky]
return arr
@property
def is_female(self):
''' Boolean array of everyone female '''
return self.sex == 0
@property
def is_female_alive(self):
''' Boolean array of everyone female and alive'''
return ((1-self.sex) * self.alive).astype(bool)
@property
def is_male(self):
''' Boolean array of everyone male '''
return self.sex == 1
@property
def is_male_alive(self):
''' Boolean array of everyone male and alive'''
return (self.sex * self.alive).astype(bool)
@property
def f_inds(self):
''' Indices of everyone female '''
return self.true('is_female')
@property
def m_inds(self):
''' Indices of everyone male '''
return self.true('is_male')
@property
def int_age(self):
''' Return ages as an integer '''
return np.array(self.age, dtype=np.int64)
@property
def round_age(self):
''' Rounds age up to the next highest integer'''
return np.array(np.ceil(self.age))
@property
def dt_age(self):
''' Return ages rounded to the nearest whole timestep '''
dt = self['pars']['dt']
return np.round(self.age*1/dt) / (1/dt)
@property
def is_active(self):
''' Boolean array of everyone sexually active i.e. past debut '''
return ((self.age>self.debut) * (self.alive) * (self.level0)).astype(bool)
@property
def is_female_adult(self):
''' Boolean array of everyone eligible for screening '''
return ((self.age>self.debut) * (self.is_female) * (self.alive)).astype(bool)
@property
def is_virgin(self):
''' Boolean array of everyone not yet sexually active i.e. pre debut '''
return ((self.age<self.debut) * self.alive).astype(bool)
@property
def alive_inds(self):
''' Indices of everyone alive '''
return self.true('alive')
@property
def alive_level0(self):
''' Indices of everyone alive who is a level 0 agent '''
return (self.alive * self.level0).astype(bool)
@property
def alive_level0_inds(self):
''' Indices of everyone alive who is a level 0 agent '''
return self.alive_level0.nonzero()[0]
@property
def n_alive(self):
''' Number of people alive '''
return len(self.alive_inds)
@property
def n_alive_level0(self):
''' Number of people alive '''
return len(self.alive_level0_inds)
# Derived states
@property
def infected(self):
'''
Boolean array of everyone infected. Union of infectious and inactive.
Includes people with cancer, people with latent infections, and people with active infections
'''
return (self.infectious + self.inactive).astype(bool)
@property
def abnormal(self):
'''
Boolean array of everyone with abnormal cells. Union of episomal, transformed, and cancerous
'''
return (self.cin + self.cancerous).astype(bool)
@property
def latent(self):
'''
Boolean array of everyone with latent infection. By definition, these
people have inactive infection and no cancer.
'''
return (self.inactive * ~self.cancerous.any(axis=0)).astype(bool)
@property
def precin(self):
'''
Boolean array of females with HPV whose disease severity level does not meet the threshold for detectable cell changes
'''
return ((self.sex == 0) & self.infectious & ~self.cin).astype(bool)
[docs]
def true(self, key):
''' Return indices matching the condition '''
return self[key].nonzero()[-1]
[docs]
def true_by_genotype(self, key, genotype):
''' Return indices matching genotype-condition'''
return self[key][genotype,:].nonzero()[-1]
[docs]
def false_by_genotype(self, key, genotype):
''' Return indices not matching genotype-condition'''
return (~self[key][genotype,:]).nonzero()[-1]
[docs]
def false(self, key):
''' Return indices not matching the condition '''
return (~self[key]).nonzero()[-1]
[docs]
def defined(self, key):
''' Return indices of people who are not-nan '''
return (~np.isnan(self[key])).nonzero()[0]
[docs]
def undefined(self, key):
''' Return indices of people who are nan '''
return np.isnan(self[key]).nonzero()[0]
[docs]
def count(self, key, weighted=True):
''' Count the number of people for a given key '''
inds = self[key].nonzero()[0]
if weighted:
out = self.scale[inds].sum()
else:
out = len(inds)
return out
[docs]
def count_any(self, key, weighted=True):
''' Count the number of people for a given key for a 2D array if any value matches '''
inds = self[key].sum(axis=0).nonzero()[0]
if weighted:
out = self.scale[inds].sum()
else:
out = len(inds)
return out
[docs]
def count_by_genotype(self, key, genotype, weighted=True):
''' Count the number of people for a given key '''
inds = np.nonzero(self[key][genotype,:])[0]
if weighted:
out = self.scale[inds].sum()
else:
out = len(inds)
return out
[docs]
def keys(self):
''' Returns keys for all non-derived properties of the people object '''
return [state.name for state in self.meta.states_to_set]
[docs]
def person_keys(self):
''' Returns keys specific to a person (e.g., their age) '''
return [state.name for state in self.meta.person]
[docs]
def state_keys(self):
''' Returns keys for different states of a person (e.g., symptomatic) '''
return [state.name for state in self.meta.states]
[docs]
def imm_keys(self):
''' Returns keys for different states of a person (e.g., symptomatic) '''
return [state.name for state in self.meta.imm_states]
def intv_keys(self):
return [state.name for state in self.meta.intv_states]
[docs]
def date_keys(self):
''' Returns keys for different event dates (e.g., date a person became symptomatic) '''
return [state.name for state in self.meta.dates]
[docs]
def dur_keys(self):
''' Returns keys for different durations (e.g., the duration from exposed to infectious) '''
return [state.name for state in self.meta.durs]
[docs]
def layer_keys(self):
''' Get the available contact keys -- try contacts first, then acts '''
try:
keys = list(self.contacts.keys())
except: # If not fully initialized
try:
keys = list(self.pars['acts'].keys())
except: # pragma: no cover # If not even partially initialized
keys = []
return keys
[docs]
def indices(self):
''' The indices of each people array '''
return np.arange(len(self))
[docs]
def to_df(self):
''' Convert to a Pandas dataframe '''
df = sc.dataframe.from_dict({key:self[key] for key in self.keys()})
return df
[docs]
def to_arr(self):
''' Return as numpy array '''
arr = np.empty((len(self), len(self.keys())), dtype=hpd.default_float)
for k,key in enumerate(self.keys()):
if key == 'uid':
arr[:,k] = np.arange(len(self))
else:
arr[:,k] = self[key]
return arr
[docs]
def person(self, ind):
''' Method to create person from the people '''
p = Person()
for key in self.keys():
data = self[key]
if data.ndim == 1:
val = data[ind]
elif data.ndim == 2:
val = data[:,ind]
else:
errormsg = f'Cannot extract data from {key}: unexpected dimensionality ({data.ndim})'
raise ValueError(errormsg)
setattr(p, key, val)
contacts = {}
for lkey, layer in self.contacts.items():
contacts[lkey] = layer.find_contacts(ind)
p.contacts = contacts
return p
[docs]
def to_list(self):
''' Return all people as a list '''
return list(self)
[docs]
def from_list(self, people):
''' Convert a list of people back into a People object '''
# Iterate over people -- slow!
for p,person in enumerate(people):
for key in self.keys():
self[key][p] = getattr(person, key)
return
[docs]
def to_graph(self): # pragma: no cover
'''
Convert all people to a networkx MultiDiGraph, including all properties of
the people (nodes) and contacts (edges).
**Example**::
import hpvsim as hpv
import networkx as nx
sim = hpv.Sim(n_agents=50, pop_type='hybrid', contacts=dict(h=3, s=10, w=10, c=5)).run()
G = sim.people.to_graph()
nodes = G.nodes(data=True)
edges = G.edges(keys=True)
node_colors = [n['age'] for i,n in nodes]
layer_map = dict(h='#37b', s='#e11', w='#4a4', c='#a49')
edge_colors = [layer_map[G[i][j][k]['layer']] for i,j,k in edges]
edge_weights = [G[i][j][k]['beta']*5 for i,j,k in edges]
nx.draw(G, node_color=node_colors, edge_color=edge_colors, width=edge_weights, alpha=0.5)
'''
import networkx as nx
# Copy data from people into graph
G = self.contacts.to_graph()
for key in self.keys():
data = {k:v for k,v in enumerate(self[key])}
nx.set_node_attributes(G, data, name=key)
# Include global layer weights
for u,v,k in G.edges(keys=True):
edge = G[u][v][k]
edge['beta'] *= self.pars['beta_layer'][edge['layer']]
return G
[docs]
def save(self, filename=None, force=False, **kwargs):
'''
Save to disk as a gzipped pickle.
Note: by default this function raises an exception if trying to save a
run or partially run People object, since the changes that happen during
a run are usually irreversible.
Args:
filename (str or None): the name or path of the file to save to; if None, uses stored
force (bool): whether to allow saving even of a run or partially-run People object
kwargs: passed to ``sc.makefilepath()``
Returns:
filename (str): the validated absolute path to the saved file
**Example**::
sim = hpv.Sim()
sim.initialize()
sim.people.save() # Saves to a .ppl file
'''
# Check if we're trying to save an already run People object
if self.t > 0 and not force:
errormsg = f'''
The People object has already been run (t = {self.t}), which is usually not the
correct state to save it in since it cannot be re-initialized. If this is intentional,
use sim.people.save(force=True). Otherwise, the correct approach is:
sim = hpv.Sim(...)
sim.initialize() # Create the people object but do not run
sim.people.save() # Save people immediately after initialization
sim.run() # The People object is
'''
raise RuntimeError(errormsg)
# Handle the filename
if filename is None:
filename = 'hpvsim.ppl'
filename = sc.makefilepath(filename=filename, **kwargs)
self.filename = filename # Store the actual saved filename
hpm.save(filename=filename, obj=self)
return filename
[docs]
@staticmethod
def load(filename, *args, **kwargs):
'''
Load from disk from a gzipped pickle.
Args:
filename (str): the name or path of the file to load from
args (list): passed to ``hpv.load()``
kwargs (dict): passed to ``hpv.load()``
Returns:
people (People): the loaded people object
**Example**::
people = hpv.people.load('my-people.ppl')
'''
people = hpm.load(filename, *args, **kwargs)
if not isinstance(people, BasePeople): # pragma: no cover
errormsg = f'Cannot load object of {type(people)} as a People object'
raise TypeError(errormsg)
return people
[docs]
def make_edgelist(self, contacts):
'''
Parse a list of people with a list of contacts per person and turn it
into an edge list.
'''
# Handle layer keys
lkeys = self.layer_keys()
if len(contacts):
contact_keys = contacts[0].keys() # Pull out the keys of this contact list
lkeys += [key for key in contact_keys if key not in lkeys] # Extend the layer keys
# Initialize the new contacts
new_contacts = Contacts(layer_keys=lkeys)
for lkey in lkeys:
new_contacts[lkey]['f'] = [] # Female in the pair
new_contacts[lkey]['m'] = [] # Male in the pair
# Populate the new contacts
for p,cdict in enumerate(contacts):
for lkey,p_contacts in cdict.items():
n = len(p_contacts) # Number of contacts
new_contacts[lkey]['f'].extend([p]*n) # e.g. [4, 4, 4, 4]
new_contacts[lkey]['m'].extend(p_contacts) # e.g. [243, 4538, 7,19]
# Turn into a dataframe
for lkey in lkeys:
new_layer = Layer(label=lkey)
for ckey,value in new_contacts[lkey].items():
new_layer[ckey] = np.array(value, dtype=new_layer.meta[ckey])
new_contacts[lkey] = new_layer
return new_contacts
[docs]
@staticmethod
def remove_duplicates(df):
''' Sort the dataframe and remove duplicates -- note, not extensively tested '''
f = df[['f', 'm']].values.min(1) # Reassign p1 to be the lower-valued of the two contacts
m = df[['f', 'm']].values.max(1) # Reassign p2 to be the higher-valued of the two contacts
df['f'] = f
df['m'] = m
df.sort_values(['f', 'm'], inplace=True) # Sort by p1, then by p2
df.drop_duplicates(['f', 'm'], inplace=True) # Remove duplicates
df = df[df['f'] != df['m']] # Remove self connections
df.reset_index(inplace=True, drop=True)
return df
[docs]
class Person(sc.prettyobj):
'''
Class for a single person. Note: this is largely deprecated since sim.people
is now based on arrays rather than being a list of people.
'''
def __init__(self, pars=None, uid=None, age=-1, sex=-1, debut=-1, rel_sev=-1, partners=None, current_partners=None,
rship_start_dates=None, rship_end_dates=None, n_rships=None):
self.uid = uid # This person's unique identifier
self.age = hpd.default_float(age) # Age of the person (in years)
self.sex = hpd.default_int(sex) # Female (0) or male (1)
self.partners = partners # Preferred number of partners
self.current_partners = current_partners # Number of current partners
self.rship_start_dates = rship_start_dates # Timepoint at which most recent relationship began
self.rship_end_dates = rship_end_dates # Timepoint of most recent breakup/relationship dissolution
self.n_rships = n_rships # Total number of relationships during the simulation
self.debut = hpd.default_float(debut) # Age of sexual debut
self.rel_sev = hpd.default_float(rel_sev) # Relative severity
return
[docs]
class FlexDict(dict):
'''
A dict that allows more flexible element access: in addition to obj['a'],
also allow obj[0]. Lightweight implementation of the Sciris odict class.
'''
def __getitem__(self, key):
''' Lightweight odict -- allow indexing by number, with low performance '''
try:
return super().__getitem__(key)
except KeyError as KE:
try: # Assume it's an integer
dictkey = self.keys()[key]
return self[dictkey]
except:
raise sc.KeyNotFoundError(KE) # Raise the original error
def keys(self):
return list(super().keys())
def values(self):
return list(super().values())
def items(self):
return list(super().items())
[docs]
class Layer(FlexDict):
'''
A small class holding a single layer of contact edges (connections) between people.
The input is typically arrays including: person 1 of the connection, person 2 of
the connection, the weight of the connection, the duration and start/end times of
the connection. Connections are undirected; each person is both a source and sink.
This class is usually not invoked directly by the user, but instead is called
as part of the population creation.
Args:
f (array): an array of N connections, representing people on one side of the connection
m (array): an array of people on the other side of the connection
acts (array): an array of number of acts per timestep for each connection
dur (array): duration of the connection
start (array): start time of the connection
end (array): end time of the connection
label (str): the name of the layer (optional)
kwargs (dict): other keys copied directly into the layer
Note that all arguments (except for label) must be arrays of the same length,
although not all have to be supplied at the time of creation (they must all
be the same at the time of initialization, though, or else validation will fail).
**Examples**::
# Generate an average of 10 contacts for 1000 people
n = 10_000
n_people = 1000
p1 = np.random.randint(n_people, size=n)
p2 = np.random.randint(n_people, size=n)
beta = np.ones(n)
layer = hpv.Layer(p1=p1, p2=p2, beta=beta, label='rand')
layer = hpv.Layer(dict(p1=p1, p2=p2, beta=beta), label='rand') # Alternate method
# Convert one layer to another with extra columns
index = np.arange(n)
self_conn = p1 == p2
layer2 = hpv.Layer(**layer, index=index, self_conn=self_conn, label=layer.label)
'''
def __init__(self, *args, label=None, **kwargs):
self.meta = {
'f': hpd.default_int, # Female
'm': hpd.default_int, # Male
'acts': hpd.default_float, # Default transmissibility for this contact type
'dur': hpd.default_float, # Duration of partnership
'start': hpd.default_float, # Date of partnership start
'end': hpd.default_float, # Date of partnership end
'age_f': hpd.default_float, # Age of female partner
'age_m': hpd.default_float, # Age of male partner
'cluster_f': hpd.default_int, # Cluster id of female partner
'cluster_m': hpd.default_int # Cluster id of male partner
}
self.basekey = 'f' # Assign a base key for calculating lengths and performing other operations
self.label = label
# Handle args
kwargs = sc.mergedicts(*args, kwargs)
# Initialize the keys of the layers
for key,dtype in self.meta.items():
self[key] = np.empty((0,), dtype=dtype)
# Set data, if provided
for key,value in kwargs.items():
self[key] = np.array(value, dtype=self.meta.get(key))
# Set acts if not provided
key = 'acts'
if key not in kwargs.keys():
self[key] = np.ones(len(self), dtype=self.meta[key])
return
def __len__(self):
try:
return len(self[self.basekey])
except: # pragma: no cover
return 0
def __repr__(self):
''' Convert to a dataframe for printing '''
namestr = self.__class__.__name__
labelstr = f'"{self.label}"' if self.label else '<no label>'
keys_str = ', '.join(self.keys())
output = f'{namestr}({labelstr}, {keys_str})\n' # e.g. Layer("r", f, m, beta)
output += self.to_df().__repr__()
return output
def __contains__(self, item):
"""
Check if a person is present in a layer
Args:
item: Person index
Returns: True if person index appears in any interactions
"""
return (item in self['f']) or (item in self['m'])
@property
def members(self):
"""
Return sorted array of all members
"""
return np.unique([self['f'], self['m']])
[docs]
def validate(self, force=True):
'''
Check the integrity of the layer: right types, right lengths.
If dtype is incorrect, try to convert automatically; if length is incorrect,
do not.
'''
n = len(self[self.basekey])
for key,dtype in self.meta.items():
if dtype:
actual = self[key].dtype
expected = dtype
if actual != expected:
self[key] = np.array(self[key], dtype=expected) # Probably harmless, so try to convert to correct type
actual_n = len(self[key])
if n != actual_n:
errormsg = f'Expecting length {n} for layer key "{key}"; got {actual_n}' # We can't fix length mismatches
raise TypeError(errormsg)
return
[docs]
def get_inds(self, inds, remove=False):
'''
Get the specified indices from the edgelist and return them as a dict.
Args:
inds (int, array, slice): the indices to be removed
'''
output = {}
for key in self.meta_keys():
output[key] = self[key][inds] # Copy to the output object
if remove:
self[key] = np.delete(self[key], inds) # Remove from the original
return output
[docs]
def pop_inds(self, inds):
'''
"Pop" the specified indices from the edgelist and return them as a dict.
Returns in the right format to be used with layer.append().
Args:
inds (int, array, slice): the indices to be removed
'''
return self.get_inds(inds, remove=True)
[docs]
def append(self, contacts):
'''
Append contacts to the current layer.
Args:
contacts (dict): a dictionary of arrays with keys f,m,beta, as returned from layer.pop_inds()
'''
for key in self.keys():
new_arr = contacts[key]
n_curr = len(self[key]) # Current number of contacts
n_new = len(new_arr) # New contacts to add
n_total = n_curr + n_new # New size
self[key] = np.resize(self[key], n_total) # Resize to make room, preserving dtype
self[key][n_curr:] = new_arr # Copy contacts into the layer
return
[docs]
def to_df(self):
''' Convert to dataframe '''
df = pd.DataFrame.from_dict(self)
return df
[docs]
def from_df(self, df, keys=None):
''' Convert from a dataframe '''
if keys is None:
keys = self.meta_keys()
for key in keys:
self[key] = df[key].to_numpy()
return self
[docs]
def to_graph(self): # pragma: no cover
'''
Convert to a networkx DiGraph
**Example**::
import networkx as nx
sim = hpv.Sim(n_agents=20, pop_type='hybrid').run()
G = sim.people.contacts['h'].to_graph()
nx.draw(G)
'''
import networkx as nx
data = [np.array(self[k], dtype=dtype).tolist() for k,dtype in [('f', int), ('m', int), ('beta', float)]]
G = nx.DiGraph()
G.add_weighted_edges_from(zip(*data), weight='beta')
nx.set_edge_attributes(G, self.label, name='layer')
return G
[docs]
def update(self, people, frac=1.0):
'''
Regenerate contacts on each timestep.
This method gets called if the layer appears in ``sim.pars['dynam_layer']``.
The Layer implements the update procedure so that derived classes can customize
the update e.g. implementing over-dispersion/other distributions, random
clusters, etc.
Typically, this method also takes in the ``people`` object so that the
update can depend on person attributes that may change over time (e.g.
changing contacts for people that are severe/critical).
Args:
people (People): the HPVsim People object, which is usually used to make new contacts
frac (float): the fraction of contacts to update on each timestep
'''
# Choose how many contacts to make
n_agents = len(people) # Total number of agents
n_contacts = len(self) # Total number of contacts
n_new = int(np.round(n_contacts*frac)) # Since these get looped over in both directions later
inds = hpu.choose(n_contacts, n_new)
# Create the contacts, not skipping self-connections
self['f'][inds] = np.array(hpu.choose_r(max_n=n_agents, n=n_new), dtype=hpd.default_int) # Choose with replacement
self['m'][inds] = np.array(hpu.choose_r(max_n=n_agents, n=n_new), dtype=hpd.default_int)
self['beta'][inds] = np.ones(n_new, dtype=hpd.default_float)
return
