Merge pull request #25 from TomMonks/dev

v0.5.0

CHANGES.md

@@ -1,5 +1,16 @@
 # Change log
 
+## v0.5.0
+
+### Added
+
+* EXPERIMENTAL: added `trace` module with `Traceable` class for colour coding output from different processes and tracking individual patients.
+
+### Fixed
+
+* DIST: fix to `NSPPThinning` sampling to pre-calcualte mean IAT to ensure that correct exponential mean is used.
+* DIST: normal distribution allows minimum value and truncates automaticalled instead of resampling.
+
 ## v0.4.0
 
 * BUILD: Dropped legacy `setuptools` and migrated package build to `hatch`

docs/03_CHANGES.md

@@ -1,5 +1,12 @@
 # Change log
 
+## v0.4.0
+
+### Changes
+
+* BUILD: Dropped legacy `setuptools` and migrated package build to `hatch`
+* BUILD: Removed `setup.py`, `requirements.txt` and `MANIFEST` in favour of `pyproject.toml`
+
 ## v0.3.0
 
 * Distributions classes now have python type hints.

docs/03_trace/data/ed_arrivals.csv

@@ -0,0 +1,19 @@
+period,arrival_rate
+6AM-7AM,2.36666666666667
+7AM-8AM,2.8
+8AM-9AM,8.83333333333333
+9AM-10AM,10.4333333333333
+10AM-11AM,14.8
+11AM-12PM,26.2666666666667
+12PM-1PM,31.4
+1PM-2PM,18.0666666666667
+2PM-3PM,16.4666666666667
+3PM-4PM,12.0333333333333
+4PM-5PM,11.6
+5PM-6PM,28.8666666666667
+6PM-7PM,18.0333333333333
+7PM-8PM,11.5
+8PM-9PM,5.3
+9PM-10PM,4.06666666666667
+10PM-11PM,2.2
+11PM-12AM,2.1

docs/03_trace/data/tbl_row_headers.csv

@@ -0,0 +1,7 @@
+Mean waiting time (mins)
+Triage
+Registation
+Examination
+Non-trauma treatment
+Trauma stabilisation
+Trauma treatment

docs/03_trace/output/table_3.txt

@@ -0,0 +1,16 @@
+\begin{table}
+\tbl{Simulation results that can be verified by our example reproducible pipeline.}
+\label{tab:table3}
+\begin{tabular}{lrrrrr}
+\toprule
+Mean waiting time (mins) & base & triage+1 & exam+1 & treat+1 & triage+exam \\
+\midrule
+Triage & 32.560000 & 1.260000 & 32.560000 & 32.560000 & 1.260000 \\
+Registation & 104.690000 & 131.820000 & 104.690000 & 104.690000 & 131.820000 \\
+Examination & 23.360000 & 24.440000 & 0.140000 & 23.360000 & 0.140000 \\
+Non-trauma treatment & 130.730000 & 133.090000 & 144.500000 & 2.150000 & 147.810000 \\
+Trauma stabilisation & 166.980000 & 189.670000 & 166.980000 & 166.980000 & 189.670000 \\
+Trauma treatment & 14.390000 & 14.770000 & 14.390000 & 14.390000 & 14.770000 \\
+\bottomrule
+\end{tabular}
+\end{table}

docs/_toc.yml

@@ -5,6 +5,9 @@ parts:
   chapters:
   - file: 01_sampling/01_distributions_examples
   - file: 01_sampling/02_time_dependent_examples
+- caption: Debugging
+  chapters:
+  - file: 03_trace/01_model
 - caption: Optimisation
   chapters:
   - file: 02_ovs/03_sw21_tutorial

feature_list.md

@@ -25,6 +25,10 @@ module: warm-up
 * MSER- 5
 
 
+module: trace
+* enhanced trace functions and classes.
+
+
 module: results visualisation
 ** Standard ways to compare scenarios?
 
@@ -51,4 +55,8 @@ module: distributions
 module: distributions
 
 * Empirical
-* NSPP via thinning
+* NSPP via thinning
+
+## v0.5.0
+
+Enhanced trace functionality

sim_tools/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.4.0'
+__version__ = '0.5.0'
 __author__ = 'Thomas Monks'
 
 from . import datasets, distributions, time_dependent, ovs

sim_tools/distributions.py

@@ -169,70 +169,66 @@ def sample(self, size: Optional[int] = None) -> float | np.ndarray:
 
 
 class Normal(Distribution):
-    """
+    '''
     Convenience class for the normal distribution.
     packages up distribution parameters, seed and random generator.
 
-    Option to prevent negative samples by resampling
-
-    """
-
+    Use the minimum parameter to truncate the distribution
+    '''
     def __init__(
         self,
         mean: float,
         sigma: float,
-        allow_neg: Optional[bool] = True,
+        minimum: Optional[float] = None,
         random_seed: Optional[int] = None,
     ):
-        """
+        '''
         Constructor
-
+        
         Params:
         ------
         mean: float
             The mean of the normal distribution
-
+            
         sigma: float
             The stdev of the normal distribution
 
-        allow_neg: bool, optional (default=True)
-            False = resample on negative values
-            True = negative samples allowed.
-
+        minimum: float
+            Truncate the normal distribution to a minimum 
+            value.
+        
         random_seed: int, optional (default=None)
             A random seed to reproduce samples.  If set to none then a unique
             sample is created.
-        """
-        super().__init__(random_seed)
+        '''
+        self.rng = np.random.default_rng(seed=random_seed)
         self.mean = mean
         self.sigma = sigma
-        self.allow_neg = allow_neg
-
+        self.minimum = minimum
+        
     def sample(self, size: Optional[int] = None) -> float | np.ndarray:
-        """
+        '''
         Generate a sample from the normal distribution
-
+        
         Params:
         -------
         size: int, optional (default=None)
             the number of samples to return.  If size=None then a single
             sample is returned.
-        """
-        # initial sample
+        '''
         samples = self.rng.normal(self.mean, self.sigma, size=size)
 
-        # no need to check if neg allowed.
-        if self.allow_neg:
+        if self.minimum is None:
+            return samples
+        elif size is None:
+            return max(self.minimum, samples)
+        else:
+            # index of samples with negative value
+            neg_idx = np.where(samples < 0)[0]
+            samples[neg_idx] = self.minimum
             return samples
 
-        # repeatedly resample negative values
-        negs = np.where(samples < 0)[0]
-        while len(negs) > 0:
-            resample = self.rng.normal(self.mean, self.sigma, size=len(negs))
-            samples[negs] = resample
-            negs = np.where(samples < 0)[0]
 
-        return samples
 
 
 class Uniform(Distribution):

sim_tools/time_dependent.py

@@ -58,6 +58,7 @@ def __init__(
         self.arr_rng = np.random.default_rng(random_seed1)
         self.thinning_rng = np.random.default_rng(random_seed2)
         self.lambda_max = data["arrival_rate"].max()
+        self.min_iat = data["mean_iat"].min()
         # assumes all other intervals are equal in length.
         self.interval = int(data.iloc[1]["t"] - data.iloc[0]["t"])
         self.rejects_last_sample = None
@@ -94,7 +95,7 @@ def sample(self, simulation_time: float) -> float:
             # reject samples if u >= lambda_t / lambda_max
             while u >= (lambda_t / self.lambda_max):
                 self.rejects_last_sample += 1
-                interarrival_time += self.arr_rng.exponential(1 / self.lambda_max)
+                interarrival_time += self.arr_rng.exponential(self.min_iat)
                 u = self.thinning_rng.uniform(0.0, 1.0)
 
             return interarrival_time

sim_tools/trace.py

@@ -0,0 +1,95 @@
+"""
+Simple functionality aiming to enhanced a users a
+ability to trace and debug simulation models.
+"""
+
+from abc import ABC
+from rich.console import Console
+
+DEFAULT_DEBUG = False
+
+CONFIG_ERROR = ("Your trace has not been initialised. " 
+                "Call super__init__(debug=True) in class initialiser" 
+                "or omit debug for default of no trace.")
+
+
+## single rich console - module level.
+_console = Console()
+
+class Traceable(ABC):
+    '''Provides basic trace functionality for a process to subclass
+    
+    Abstract base class Traceable
+    
+    Subclasses must call 
+    
+    super().__init__(debug=True) in their __init__() method to 
+    initialise trace.
+    
+    Subclasses inherit the following methods:
+
+    trace() - use this function print out a traceable event
+
+    _trace_config(): use this function to return a dict containing
+    the trace configuration for the class.
+    '''
+    def __init__(self, debug=DEFAULT_DEBUG):
+        self.debug = debug
+        self._config = self._default_config()
+
+    def _default_config(self):
+        """Returns a default trace configuration"""
+        config = {
+            "name":None, 
+            "name_colour":"bold blue", 
+            "time_colour":'bold blue', 
+            "time_dp":2,
+            "message_colour":'black',
+            "tracked":None
+        }
+        return config
+
+
+    def _trace_config(self):
+        config = {
+            "name":None, 
+            "name_colour":"bold blue", 
+            "time_colour":'bold blue', 
+            "time_dp":2,
+            "message_colour":'black',
+            "tracked":None
+        }
+        return config
+
+
+    def trace(self, time, msg=None, process_id=None):
+        '''
+        Display a trace of an event
+        '''
+
+        if not hasattr(self, '_config'):
+            raise AttributeError(CONFIG_ERROR)
+
+        # if in debug mode
+        if self.debug:
+
+            # check for override to default configs
+            process_config = self._trace_config()
+            self._config.update(process_config)
+
+            # conditional logic to limit tracking to specific processes/entities
+            if self._config['tracked'] is None or process_id in self._config['tracked']:
+
+                # display and format time stamp
+                out = f"[{self._config['time_colour']}][{time:.{self._config['time_dp']}f}]:[/{self._config['time_colour']}]"
+
+                # if provided display and format a process ID 
+                if self._config['name'] is not None and process_id is not None:
+                    out += f"[{self._config['name_colour']}]<{self._config['name']} {process_id}>: [/{self._config['name_colour']}]"
+
+                # format traced event message
+                out += f"[{self._config['message_colour']}]{msg}[/{self._config['message_colour']}]"
+
+                # print to rich console
+                _console.print(out)
+