New distributions code moved down into emod-api from emodpy (Fix #50, was issue 43 in emodpy previously)

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 2.0.36
+current_version = 2.0.38
 commit = False
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)((?P<release>[\+a-z]+)\.(?P<build>\d+))?

docs/bib.md

@@ -1,5 +1,6 @@
-[emodpy]: https://docs.idmod.org/projects/emodpy/en/latest/index.html
-[idmtools]: https://docs.idmod.org/projects/idmtools/en/latest/index.html
+[emodpy]: https://docs.idmod.org/projects/emodpy/
+[emod-api]: https://docs.idmod.org/projects/emod-api/
+[idmtools]: https://docs.idmod.org/projects/idmtools/
 [emod-generic]: https://docs.idmod.org/projects/emod-generic/en/latest/parameter-overview.html
 [idmod]: https://www.idmod.org/
 [idm_pypi]: https://packages.idmod.org/

docs/customization/overrides/partials/integrations/analytics/custom.html

@@ -0,0 +1,21 @@
+{# page partial for GTM analytics code, extends "!page.html"
+#}
+
+{% block extrahead %}
+
+<!-- Google Tag Manager -->
+<script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
+new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],
+j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
+'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);
+})(window,document,'script','dataLayer','GTM-NK4K647');</script>
+<!-- End Google Tag Manager -->
+
+<!-- Google Tag Manager (noscript) -->
+<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-NK4K647"
+height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
+<!-- End Google Tag Manager (noscript) -->
+
+<meta name="google-site-verification" content="L6w2tbHp4xFTm04T0CdrrzqX_T9PH-bpQnGGYuwuAE4" />
+
+{% endblock %}

docs/index.md

@@ -13,7 +13,7 @@ See the [idmtools documentation][idmtools] for a diagram of how each of the rela
 The emod-api package is currently available on a [Python package index server][idm_pypi] managed by the [Institute for Disease Modeling][idmod].
 
 ```
-pip install --extra-index-url=https://packages.idmod.org/api/pypi/pypi-production/simple
+pip install emod-api --extra-index-url=https://packages.idmod.org/api/pypi/pypi-production/simple
 ```
 
 {%

emod_api/__init__.py

@@ -10,4 +10,4 @@
 
 For rest of emod-api documentation, please go to https://github.com/InstituteforDiseaseModeling/emod-api
 """
-__version__ = "2.0.36"
+__version__ = "2.0.38"

emod_api/demographics/base_input_file.py

@@ -1,15 +1,16 @@
 from abc import ABCMeta, abstractmethod
 from datetime import datetime
 
-# from simtools.Utilities.LocalOS import LocalOS
 import getpass
 
 
 class BaseInputFile:
     __metaclass__ = ABCMeta
 
-    def __init__(self, idref):
-        self.idref = idref
+    DEFAULT_ID_REFERENCE = "default_id_reference"
+
+    def __init__(self, idref: str = None):
+        self.idref = self.DEFAULT_ID_REFERENCE if idref is None else idref
 
     @abstractmethod
     def generate_file(self, name):

emod_api/demographics/calculators.py

@@ -0,0 +1,159 @@
+import math
+import numpy as np
+import pandas as pd
+import os
+
+from scipy import sparse as sp
+from scipy.sparse import linalg as la
+from typing import Union
+
+from emod_api.demographics.age_distribution import AgeDistribution
+from emod_api.demographics.mortality_distribution import MortalityDistribution
+
+
+def generate_equilibrium_age_distribution(birth_rate: float = 40.0, mortality_rate: float = 20.0) -> AgeDistribution:
+    """
+    Create an AgeDistribution object representing an equilibrium for birth and mortality rates.
+
+    Args:
+        birth_rate: (float) The birth rate in units of births/year/1000-women
+        mortality_rate: (float) The mortality rate in units of deaths/year/1000 people
+
+    Returns:
+        an AgeDistribution object
+    """
+    from emod_api.demographics.age_distribution import AgeDistribution
+
+    # convert to daily rate per person, EMOD units
+    birth_rate = (birth_rate / 1000) / 365  # what is actually used below
+    mortality_rate = (mortality_rate / 1000) / 365  # what is actually used below
+
+    birth_rate = math.log(1 + birth_rate)
+    mortality_rate = -1 * math.log(1 - mortality_rate)
+
+    # It is important for the age distribution computation that the age-spacing be very fine; I've used 30 days here.
+    # With coarse spacing, the computation in practice doesn't work as well.
+    age_dist_tuple = _computeAgeDist(birth_rate, [i * 30 for i in range(1200)], 1200 * [mortality_rate], 12 * [1.0])
+
+    # The final demographics file, though, can use coarser binning interpolated from the finely-spaced computed distribution.
+    age_bins = list(range(16)) + [20 + 5 * i for i in range(14)]
+    cum_pop_fraction = np.interp(age_bins, [i / 365 for i in age_dist_tuple[2]], age_dist_tuple[1]).tolist()
+    age_bins.extend([90])
+    cum_pop_fraction.extend([1.0])
+    distribution = AgeDistribution(ages_years=age_bins, cumulative_population_fraction=cum_pop_fraction)
+    return distribution
+
+
+def _computeAgeDist(bval, mvecX, mvecY, fVec, max_yr=90):
+    """
+    Compute equilibrium age distribution given age-specific mortality and crude birth rates
+
+    Args:
+        bval: crude birth rate in births per day per person
+        mvecX: list of age bins in days
+        mvecY: List of per day mortality rate for the age bins
+        fVec: Seasonal forcing per month
+        max_yr : maximum agent age in years
+
+    returns EquilibPopulationGrowthRate, MonthlyAgeDist, MonthlyAgeBins
+    author: Kurt Frey
+    """
+
+    bin_size = 30
+    day_to_year = 365
+
+    # Age brackets
+    avecY = np.arange(0, max_yr * day_to_year, bin_size) - 1
+
+    # Mortality sampling
+    mvecX = [-1] + mvecX + [max_yr * day_to_year + 1]
+    mvecY = [mvecY[0]] + mvecY + [mvecY[-1]]
+    mX = np.arange(0, max_yr * day_to_year, bin_size)
+    mX[0] = 1
+    mval = 1.0 - np.interp(mX, xp=mvecX, fp=mvecY)
+    r_n = mval.size
+
+    # Matrix construction
+    BmatRC = (np.zeros(r_n), np.arange(r_n))
+    Bmat = sp.csr_matrix(([bval * bin_size] * r_n, BmatRC), shape=(r_n, r_n))
+    Mmat = sp.spdiags(mval[:-1] ** bin_size, -1, r_n, r_n)
+    Dmat = Bmat + Mmat
+
+    # Math
+    (gR, popVec) = la.eigs(Dmat, k=1, sigma=1.0)
+    gR = np.abs(gR ** (float(day_to_year) / float(bin_size)))
+    popVec = np.abs(popVec) / np.sum(np.abs(popVec))
+
+    # Apply seasonal forcing
+    mVecR = [-2.0, 30.5, 30.6, 60.5, 60.6, 91.5, 91.6, 121.5,
+             121.6, 152.5, 152.6, 183.5, 183.6, 213.5, 213.6, 244.5,
+             245.6, 274.5, 274.6, 305.5, 305.6, 333.5, 335.6, 364.5]
+    fVec = np.flipud([val for val in fVec for _ in (0, 1)])
+    wfVec = np.array([np.mean(np.interp(np.mod(range(val + 1, val + 31), 365),
+                                        xp=mVecR, fp=fVec)) for val in avecY]).reshape(-1, 1)
+    popVec = popVec * wfVec / np.sum(popVec * wfVec)
+
+    # Age sampling
+    avecY[0] = 0
+    avecX = np.clip(np.around(np.cumsum(popVec), decimals=7), 0.0, 1.0)
+    avecX = np.insert(avecX, 0, np.zeros(1))
+
+    return gR.tolist()[0], avecX[:-1].tolist(), avecY.tolist()
+
+
+def generate_mortality_over_time_from_data(data_csv: Union[str, os.PathLike],
+                                           base_year: int) -> MortalityDistribution:
+    """
+    Generate a MortalityDistribution object from a data csv file.
+
+    Args:
+        data_csv: Path to csv file with the mortality rates by calendar year and age bucket.
+        base_year: The calendar year the sim is treating as the base.
+
+    Returns:
+        a MortalityDistribution object.
+    """
+    if base_year < 0:
+        raise ValueError(f"User passed negative value of base_year: {base_year}.")
+    if base_year > 2050:
+        raise ValueError(f"User passed too large value of base_year: {base_year}.")
+
+    # Load csv. Convert rate arrays into DTK-compatiable JSON structures.
+    rates = []  # array of arrays, but leave that for a minute
+    df = pd.read_csv(data_csv)
+    header = df.columns
+    year_start = int(header[1]) # someone's going to come along with 1990.5, etc. Sigh.
+    year_end = int(header[-1])
+    if year_end <= year_start:
+        raise ValueError(f"Failed check that {year_end} is greater than {year_start} in csv dataset.")
+    num_years = year_end - year_start + 1
+    rel_years = list()
+    for year in range(year_start, year_start + num_years):
+        mort_data = list(df[str(year)])
+        rel_years.append(year - base_year)
+
+    age_key = None
+    for trykey in df.keys():
+        if trykey.lower().startswith("age"):
+            age_key = trykey
+            raw_age_bins = list(df[age_key])
+
+    if age_key is None:
+        raise ValueError("Failed to find 'Age_Bin' (or similar) column in the csv dataset. Cannot process.")
+
+    age_bins = list()
+    try:
+        for age_bin in raw_age_bins:
+            left_age = float(age_bin.split("-")[0])
+            age_bins.append(left_age)
+
+    except Exception as ex:
+        raise ValueError(f"Ran into error processing the values in the Age-Bin column. {ex}")
+
+    for idx in range(len(age_bins)):  # 18 of these
+        # mort_data is the array of mortality rates (by year bin) for age_bin
+        mort_data = list(df.transpose()[idx][1:])
+        rates.append(mort_data)  # 28 of these, 1 for each year, eg
+
+    distribution = MortalityDistribution(ages_years=age_bins, mortality_rate_matrix=rates, calendar_years=rel_years)
+    return distribution

emod_api/demographics/demographic_exceptions.py

@@ -48,3 +48,7 @@ class NonMonotonicDistributionException(ValueError):
 
 class InvalidNodeIdException(ValueError):
     pass
+
+
+class ConflictingDistributionsException(ValueError):
+    pass