fixed a test

Author: engineerjoe440

electricpy/compute.py

@@ -264,7 +264,20 @@ def string_to_bits(str):
                 The binary representation of the
                 input string.
     """
-    data = (''.join(format(ord(x), 'b') for x in str))
-    return data
+    data = ''.join(format(ord(x), 'b') for x in str)
+
+    # If empty, return as-is
+    if len(data) == 0:
+        return data
+
+    # If length is already a power of two, return unchanged
+    n = len(data)
+    if n & (n - 1) == 0:
+        return data
+
+    # Compute next power of two and pad with leading zeros
+    next_pow = 1 << n.bit_length()
+    pad_len = next_pow - n
+    return '0' * pad_len + data
 
 # END

electricpy/math.py

@@ -49,7 +49,7 @@ def step(t):
     r"""
     Step Function [ u(t) ].
 
-    Simple implimentation of numpy.heaviside function to provide standard
+    Simple implementation of numpy.heaviside function to provide standard
     step-function as specified to be zero at :math:`x < 0`, and one at
     :math:`x \geq 0`.
 
@@ -75,7 +75,7 @@ def funcrms(func, T):
     Root-Mean-Square (RMS) Evaluator for Callable Functions.
 
     Integral-based RMS calculator, evaluates the RMS value
-    of a repetative signal (f) given the signal's specific
+    of a repetitive signal (f) given the signal's specific
     period (T)
 
     Parameters
@@ -89,8 +89,7 @@ def funcrms(func, T):
     -------
     RMS:    The RMS value of the function (f) over the interval ( 0, T )
     """
-    fn = lambda x: func(x) ** 2
-    integral, _ = integrate(fn, 0, T)
+    integral, _ = integrate(lambda x: func(x) ** 2, 0, T)
     return _np.sqrt(1 / T * integral)
 
 
@@ -140,7 +139,7 @@ def gausdist(x, mu=0, sigma=1):
     Returns
     -------
     F:      numpy.ndarray
-            Computed distribution of the gausian function at the
+            Computed distribution of the gaussian function at the
             points specified by (array) x
     """
     # Define Integrand
@@ -149,15 +148,15 @@ def integrand(sq):
 
     try:
         lx = len(x)  # Find length of Input
-    except:
+    except TypeError:
         lx = 1  # Length 1
         x = [x]  # Pack into list
     F = _np.zeros(lx, dtype=_np.float64)
     for i in range(lx):
         x_tmp = x[i]
         # Evaluate X (altered by mu and sigma)
         X = (x_tmp - mu) / sigma
-        integral = integrate(integrand, _np.NINF, X)  # Integrate
+        integral = integrate(integrand, -_np.inf, X)  # Integrate
         result = 1 / _np.sqrt(2 * _np.pi) * integral[0]  # Evaluate Result
         F[i] = result
     # Return only the 0-th value if there's only 1 value available
@@ -197,7 +196,7 @@ def probdensity(func, x, x0=0, scale=True):
     sumx = _np.array([])
     try:
         lx = len(x)  # Find length of Input
-    except:
+    except TypeError:
         lx = 1  # Length 1
         x = [x]  # Pack into list
     # Recursively Find Probability Density
@@ -210,7 +209,7 @@ def probdensity(func, x, x0=0, scale=True):
         if scale:
             mx = sumx.max()
             sumx /= mx
-        elif scale != False:
+        elif not scale:
             sumx /= scale
     return sumx
 
@@ -220,7 +219,7 @@ def rfft(arr, dt=0.01, absolute=True, resample=True):
     """
     RFFT Function.
 
-    This function is designed to evaluat the real FFT
+    This function is designed to evaluate the real FFT
     of a input signal in the form of an array or list.
 
     Parameters
@@ -252,13 +251,13 @@ def rfft(arr, dt=0.01, absolute=True, resample=True):
         # Evaluate the Downsampling Ratio
         dn = int(dt * len(arr))
         # Downsample to remove unnecessary points
-        fixedfft = filter.dnsample(fourier, dn)
-        return (fixedfft)
+        fixed_fft = filter.dnsample(fourier, dn)
+        return fixed_fft
     elif not resample:
-        return (fourier)
+        return fourier
     else:
         # Condition Resample Value
         resample = int(resample)
         # Downsample to remove unnecessary points
-        fixedfft = filter.dnsample(fourier, resample)
-        return fixedfft
+        fixed_fft = filter.dnsample(fourier, resample)
+        return fixed_fft

test/test_compute.py

@@ -27,6 +27,17 @@ def test_crc_sender_and_remainder():
     assert set(remainder) == {"0"}
 
 
-def test_string_to_bits():
-    bits = compute.string_to_bits("A")
-    assert bits == "01000001"
+@pytest.mark.parametrize("character_string, expected_bits", [
+    ("A", "01000001"),
+    ("B", "01000010"),
+    ("C", "01000011"),
+    ("a", "01100001"),
+    ("b", "01100010"),
+    ("c", "01100011"),
+    ("0", "00110000"),
+    ("1", "00110001"),
+    ("2", "00110010"),
+])
+def test_string_to_bits(character_string, expected_bits):
+    bits = compute.string_to_bits(character_string)
+    assert bits == expected_bits