Added LogCustom class and changed max function on the distribution

cara/apps/calculator/__init__.py

@@ -33,7 +33,7 @@ from .user import AuthenticatedUser, AnonymousUser
 # calculator version. If the calculator needs to make breaking changes (e.g. change
 # form attributes) then it can also increase its MAJOR version without needing to
 # increase the overall CARA version (found at ``cara.__version__``).
-__version__ = "4.1.2"
+__version__ = "4.2"
 
 
 class BaseRequestHandler(RequestHandler):

cara/monte_carlo/data.py

@@ -6,7 +6,7 @@ from scipy import special as sp
 from scipy.stats import weibull_min
 
 import cara.monte_carlo as mc
-from cara.monte_carlo.sampleable import LogNormal,LogCustomKernel,CustomKernel,Uniform, Custom
+from cara.monte_carlo.sampleable import LogCustom, LogNormal,LogCustomKernel,CustomKernel,Uniform, Custom
 
 
 sqrt2pi = np.sqrt(2.*np.pi)
@@ -108,8 +108,10 @@ symptomatic_vl_frequencies = LogCustomKernel(
 # https://iiif.elifesciences.org/lax:65774%2Felife-65774-fig4-figsupp3-v2.tif/full/1500,/0/default.jpg
 viral_load = np.linspace(weibull_min.ppf(0.01, c=3.47, scale=7.01),
                 weibull_min.ppf(0.99, c=3.47, scale=7.01), 30)
-frequencies = weibull_min.pdf(viral_load, c=3.47, scale=7.01)
-covid_overal_vl_data = Custom(bounds=(2, 10), function=lambda d: np.interp(d, viral_load, frequencies, right=0., left=0.), max_function=0.16)
+frequencies_pdf = weibull_min.pdf(viral_load, c=3.47, scale=7.01)
+covid_overal_vl_data = LogCustom(bounds=(2, 10), 
+                        function=lambda d: np.interp(d, viral_load, frequencies_pdf, right=0., left=0.), 
+                        max_function=0.2)
 
 
 # Derived from data in doi.org/10.1016/j.ijid.2020.09.025 and

cara/monte_carlo/sampleable.py

@@ -82,6 +82,30 @@ class Custom(SampleableDistribution):
         return x
 
 
+class LogCustom(SampleableDistribution):
+    """
+    Defines a distribution which follows a custom curve vs. the the log (in base 10)
+    of the random variable. Uses a simple algorithm. This is appropriate for a smooth
+    distribution function (one should know its maximum).
+    """
+    def __init__(self, bounds: typing.Tuple[float, float],
+                 function: typing.Callable, max_function: float):
+        self.bounds = bounds
+        self.function = function
+        self.max_function = max_function
+
+    def generate_samples(self, size: int) -> float_array_size_n:
+        fvalue = np.random.uniform(0,self.max_function,size)
+        x = np.random.uniform(*self.bounds,size)
+        invalid = np.where(fvalue>self.function(x))[0]
+        while len(invalid)>0:
+            fvalue[invalid] = np.random.uniform(0,self.max_function,len(invalid))
+            x[invalid] = np.random.uniform(*self.bounds,len(invalid))
+            invalid = np.where(fvalue>self.function(x))[0]
+
+        return 10 ** x
+
+
 class CustomKernel(SampleableDistribution):
     """
     Defines a distribution which follows a custom curve vs. the