v5.4.0

CHANGELOG.md

@@ -529,3 +529,11 @@ CHANGELOG
 
 5.3.5 (23/08/2022)
 - various changes set to test, functionality to be confirmed.
+
+5.4.0 (22/09/2022) - put to test, functionality to be confirmed.
+- autohandling the wrong type introduced.
+- introduction of complex non-primitive data types in inputs (list, numlist, strlist, path, pd.df, pd.columns, np.1darray, np.2darray)
+- coloring applied across the whole DualityApp object.
+- datatypes method added.
+- code readability and stability improved.
+- all changes put to the global test.

README.md

@@ -72,7 +72,7 @@ The module itself, it's maintenance, updates and stability, logo, videos, promot
 <img src='https://raw.githubusercontent.com/dkundih/dkundih/main/.logistics/BLUERED_GHiLI.jpg'/>
 
 <p align='center'>
-My name is <b>David Kundih</b> and I am a Master's student of <b>Sustainable mobility and logistics</b> at the <b>University North</b>, currently specializing in <b>Digital transformation of traffic and logistics using Python.</b>
+My name is <b>David Kundih</b> and I am a <b>Master of Engineering in Sustainable Mobility and Logistics</b>, currently specializing in <b>Digital Transformation of Traffic and Logistics using Python.</b>
 </p>
 
 <h4 align='center'>My latest projects:</h4>

_testassets/T_MC.py

@@ -0,0 +1,129 @@
+### TEST ENV ###
+
+# global duality import.
+import duality
+
+# makes multiple instances of the object available.
+from logistics.plugins.metaclass import Meta
+
+# set type of numpy array and pandas particles.
+import numpy as np
+import pandas as pd
+
+# for CLI functionality.
+import os
+from colorama import (
+    Fore,
+    Back,
+    Style,
+    init,
+)
+
+init()
+
+# imports all data types.
+from logistics.plugins.types import *
+
+# imports coloring.
+from logistics.plugins.coloring import *
+
+
+app = duality.DualityApp()
+
+# package.
+class MonteCarlo:
+
+    @app.entry('init')
+    def __init__(
+        self, 
+        list_of_values : NumberVectorAlike = app.store('list_of_values', 'numlist', 'VALUES'), 
+        time_seq : IntegerType = app.store('time_seq', 'int'), 
+        num_sims : IntegerType = app.store('num_sims', 'int'),
+        ref_col : IntegerType = 0, 
+        ref_row : IntegerType = 0,
+        ) -> ReturnType:
+
+        '''
+        * initial launch.
+        '''
+
+        self.list_of_values = list_of_values
+        self.time_seq = time_seq
+        self.num_sims = num_sims
+        self.ref_col = ref_col
+        self.ref_row = ref_row
+
+        return 
+
+    @app.entry('execute', print_val = True)
+    def execute(
+        self,
+        filtered : BooleanType = True,
+        ) -> NumberArrayAlike:
+
+        '''
+        * executes a Monte Carlo simulation on a defined data set.
+        - filtered (True/False) - filters the data setup print and warnings.
+        # DEFAULT: MonteCarlo.execute(filtered : BooleanType = True.)
+        '''
+
+        if filtered == False:
+            print(Fore.GREEN + f'Monte Carlo has been set up for {self.num_sims} simulations in a period of {self.time_seq} time measurement units and executed.' + Fore.RESET)
+            print(Fore.RED + 'NOTE: Use data with reasonable standard deviation in order to prevent exponential growth of the function that cannot be plotted properly, recognize such abnormal values by a + sign anywhere in the data executed below.' + Fore.RESET)
+
+        import numpy as np
+        import pandas as pd
+
+        # this removes pandas warning of highly fragmented DataFrame for newer pandas versions.
+        from warnings import simplefilter
+        simplefilter(action = 'ignore', category = pd.errors.PerformanceWarning)
+        # end of pandas warning removal block.
+
+        try:
+            self.list_of_values = pd.DataFrame(self.list_of_values)
+            self.list_of_values = self.list_of_values.iloc[:, self.ref_col]
+        except:
+            raise KeyError('Impossible to reach a defined key, value pair. Data types supported: dictionary, list, numpy array, pandas DataFrame. Make sure to set index row_col on an existing field. Must be of type: int.')
+
+        today_value = self.list_of_values.iloc[self.ref_col]
+        data = pd.DataFrame()
+        loading = 0
+
+        for num_sim in range(self.num_sims):
+            rand_change = self.list_of_values.pct_change().std()
+            count = 0
+            index_array = []
+            index_array += [today_value + (today_value * np.random.normal(0, rand_change))]
+
+            for num_day in range(self.time_seq):
+                rand_change = self.list_of_values.pct_change().std()
+                if count == self.time_seq:
+                    break
+                index_array += [index_array[count] + (today_value * np.random.normal(0, rand_change))]
+                count += 1
+
+            loading += 1
+            print(end = '\r')
+            print(loading, 'iterations out of', self.num_sims, 'executed so far', end = '')
+
+            data[num_sim] = index_array
+        print(end = '\r')
+        print(Fore.GREEN + 'Monte Carlo simulation set up and ready to plot.' + Fore.RESET)
+        self.results = data
+
+        return data
+
+    @app.entry('change', print_val = True)
+    def get_change(
+        self,
+        ) -> NumberArrayAlike:
+
+        '''
+        * shows the percentage of Monte Carlo simulation value change for every iteration.
+        '''
+
+        return self.results.pct_change()
+
+
+if __name__ == '__main__':
+    app.wheel()

_testenv/FUTURE

@@ -1,3 +1 @@
-Upcoming features regarding file data types handling in inputs as vectors, numpy arrays, pandas DataFrame and file paths. #ID_FUTURE1
-
 Autohandling of missing files in autoinit will be resolved.