Merge pull request #193 from InsightLab/examples

Examples on math module

Author: mxaviersmp

pymove/utils/math.py

@@ -28,6 +28,19 @@ def is_number(value: Union[int, float, str]):
     -------
     boolean
         True if numerical, otherwise False
+
+    Examples
+    --------
+    >>> from pymove.utils.math import is_number
+    >>> a, b, c, d = 50, 22.5, '11.25', 'house'
+    >>> print(is_number(a), type(is_number(a)))
+    True <class 'bool'>
+    >>> print(is_number(b), type(is_number(b)))
+    True <class 'bool'>
+    >>> print(is_number(c), type(is_number(c)))
+    True <class 'bool'>
+    >>> print(is_number(d), type(is_number(d)))
+    False <class 'bool'>
     """
     try:
         float(value)
@@ -55,6 +68,12 @@ def std(values_array: List[float]) -> float:
     squaring with * is over 3 times as fast as with **2
     http://stackoverflow.com/questions/29046346/comparison-of-power-to-multiplication-in-python
 
+    Example
+    -------
+    >>> from pymove.utils.math import std
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> print(std(list), type(std(list)))
+    1.7435595774162693 <class 'float'>
     """
     size = len(values_array)
     mean = sum(values_array) / size
@@ -79,6 +98,12 @@ def avg_std(values_array: List[float]) -> Tuple[float, float]:
     float
         Represents the value of standard deviation.
 
+    Example
+    -------
+    >>> from pymove.utils.math import avg_std
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> print(avg_std(list), type(avg_std(list)))
+    1.9493588689617927 <class 'float'>
     """
     avg = sum(values_array) / len(values_array)
     return avg, std(values_array)
@@ -98,6 +123,12 @@ def std_sample(values_array: List[float]) -> float:
     float
         Represents the value of standard deviation of sample.
 
+    Example
+    -------
+    >>> from pymove.utils.math import std_sample
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> print(std_sample(list), type(std_sample(list)))
+    1.9493588689617927 <class 'float'>
     """
     size = len(values_array)
     return std(values_array) * math.sqrt(size / (size - 1))
@@ -119,6 +150,12 @@ def avg_std_sample(values_array: List[float]) -> Tuple[float, float]:
     float
         Represents the standard deviation of sample.
 
+    Example
+    -------
+    >>> from pymove.utils.math import avg_std_sample
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> print(avg_std_sample(list), type(avg_std_sample(list)))
+    (7.9, 1.9493588689617927) <class 'tuple'>
     """
     avg = sum(values_array) / len(values_array)
     return avg, std_sample(values_array)
@@ -143,6 +180,19 @@ def arrays_avg(
     float
         The mean of the array elements.
 
+    Examples
+    --------
+    >>> from pymove.utils.math import arrays_avg
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> weights = [0.1, 0.3, 0.15, 0.15, 0.3]
+    >>> print('standard average', arrays_avg(list), type(arrays_avg(list)))
+    'standard average 7.9 <class 'float'>'
+    >>> print(
+    >>>    'weighted average: ',
+    >>>     arrays_avg(list, weights),
+    >>>     type(arrays_avg(list, weights))
+    >>> )
+    'weighted average:  1.6979999999999997 <class 'float'>'
     """
     n = len(values_array)
 
@@ -181,7 +231,12 @@ def array_stats(values_array: List[float]) -> Tuple[float, float, int]:
         The sum of the square value of each element in the array.
     int.
         The number of elements in the array.
-
+    Example
+    -------
+    >>> from pymove.utils.math import array_stats
+    >>> list = [7.8, 9.7, 6.4, 5.6, 10]
+    >>> print(array_stats(list), type(array_stats(list)))
+    (39.5, 327.25, 5) <class 'tuple'>
     """
     sum_ = 0
     sum_sq = 0
@@ -215,10 +270,11 @@ def interpolation(x0: float, y0: float, x1: float, y1: float, x: float) -> float
     float.
         Is the interpolated  or extrapolated value.
 
-    Examples
-    --------
-    - interpolation 1: (30, 3, 40, 5, 37) -> 4.4
-    - interpolation 2: (30, 3, 40, 5, 35) -> 4.0
-
+    Example
+    -------
+    >>> from pymove.utils.math import interpolation
+    >>> x0, y0, x1, y1, x = 2, 4, 3, 6, 3.5
+    >>> print(interpolation(x0,y0,x1,y1,x), type(interpolation(x0,y0,x1,y1,x)))
+    7.0 <class 'float'>
     """
     return y0 + (y1 - y0) * ((x - x0) / (x1 - x0))