autopep8

01_profiling/cpu_profiling/julia1.py

@@ -27,14 +27,17 @@ def show_greyscale(output_raw, width, height, max_iterations):
 def show_false_greyscale(output_raw, width, height, max_iterations):
     """Convert list to array, show using PIL"""
     # convert our output to PIL-compatible input
-    assert width * height == len(output_raw)  # sanity check our 1D array and desired 2D form
+    # sanity check our 1D array and desired 2D form
+    assert width * height == len(output_raw)
     # rescale output_raw to be in the inclusive range [0..255]
     max_value = float(max(output_raw))
     output_raw_limited = [int(float(o) / max_value * 255) for o in output_raw]
     # create a slightly fancy colour map that shows colour changes with
     # increased contrast (thanks to John Montgomery)
-    output_rgb = ((o + (256 * o) + (256 ** 2) * o) * 16 for o in output_raw_limited)  # fancier
-    output_rgb = array.array('I', output_rgb)  # array of unsigned ints (size is platform specific)
+    output_rgb = (
+        (o + (256 * o) + (256 ** 2) * o) * 16 for o in output_raw_limited)  # fancier
+    # array of unsigned ints (size is platform specific)
+    output_rgb = array.array('I', output_rgb)
     # display with PIL/pillow
     im = Image.new("RGB", (width, height))
     # EXPLAIN RGBX L 0 -1
@@ -76,7 +79,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     height = len(y)
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -92,7 +96,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
     if draw_output:
         #show_false_greyscale(output, width, height, max_iterations)

01_profiling/cpu_profiling/julia1_nopil.py

@@ -36,7 +36,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
         xcoord += x_step
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -52,7 +53,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 if __name__ == "__main__":

01_profiling/decorator_time/julia1_decorator.py

@@ -13,7 +13,8 @@ def measure_time(*args, **kwargs):
         t1 = time.time()
         result = fn(*args, **kwargs)
         t2 = time.time()
-        print ("@timefn:" + fn.func_name + " took " + str(t2 - t1) + " seconds")
+        print (
+            "@timefn:" + fn.func_name + " took " + str(t2 - t1) + " seconds")
         return result
     return measure_time
 
@@ -49,7 +50,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
         xcoord += x_step
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -65,7 +67,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with

01_profiling/dowser/julia1_dowser.py

@@ -53,7 +53,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -71,7 +72,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
     print "now waiting..."
     while True:

01_profiling/guppy/julia1_guppy.py

@@ -36,7 +36,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
         x.append(xcoord)
         xcoord += x_step
 
-    from guppy import hpy; hp = hpy()
+    from guppy import hpy
+    hp = hpy()
     print "heapy after creating y and x lists of floats"
     h = hp.heap()
     print h
@@ -46,7 +47,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -73,7 +75,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     print h
     print
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with

01_profiling/line_profiler/julia1_lineprofiler.py

@@ -40,7 +40,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -56,7 +57,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with

01_profiling/line_profiler/julia1_lineprofiler2.py

@@ -45,7 +45,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -61,7 +62,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with

01_profiling/line_profiler/julia1_lineprofiler3.py

@@ -40,7 +40,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -56,7 +57,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with

01_profiling/line_profiler/julia1_nopil_originalif.py

@@ -36,7 +36,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
         xcoord += x_step
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -52,7 +53,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 if __name__ == "__main__":

01_profiling/line_profiler/julia1_nopil_swappedif.py

@@ -36,7 +36,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
         xcoord += x_step
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -52,7 +53,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 if __name__ == "__main__":

01_profiling/memory_profiler/julia1_memoryprofiler.py

@@ -40,7 +40,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -56,12 +57,12 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with
 # reasonable defaults for a laptop
 # set draw_output to True to use PIL to draw an image
 calc_pure_python(draw_output=False, desired_width=1000, max_iterations=300)
 #calc_pure_python(draw_output=False, desired_width=100, max_iterations=300)
-

01_profiling/memory_profiler/julia1_memoryprofiler2.py

@@ -46,7 +46,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     # pre-rounding desired width and height
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -62,12 +63,12 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 # Calculate the Julia set using a pure Python solution with
 # reasonable defaults for a laptop
 # set draw_output to True to use PIL to draw an image
 calc_pure_python(draw_output=False, desired_width=1000, max_iterations=300)
 #calc_pure_python(draw_output=False, desired_width=10, max_iterations=300)
-

01_profiling/memory_profiler/julia1_memoryprofiler2_xrange.py

@@ -46,7 +46,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     height = len(y)
     # build a list of co-ordinates and the initial condition for each cell.
     # Note that our initial condition is a constant and could easily be removed,
-    # we use it to simulate a real-world scenario with several inputs to our function
+    # we use it to simulate a real-world scenario with several inputs to our
+    # function
     zs = []
     cs = []
     for ycoord in y:
@@ -62,7 +63,8 @@ def calc_pure_python(draw_output, desired_width, max_iterations):
     secs = end_time - start_time
     print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
 
-    assert sum(output) == 33219980  # this sum is expected for 1000^2 grid with 300 iterations
+    # this sum is expected for 1000^2 grid with 300 iterations
+    assert sum(output) == 33219980
 
 
 if __name__ == "__main__":

01_profiling/noop_profile_decorator/ex.py

@@ -4,11 +4,11 @@
 # bit of a hack to check
 # if 1) __builtin__ exists (for when using nosetests)
 # or 2) if 'profile' has been injected by line_profiler
-#if '__builtin__' not in dir() or not hasattr(__builtin__, 'profile'):
-    #def profile(func):
-        #def inner(*args, **kwargs):
-            #return func(*args, **kwargs)
-        #return inner
+# if '__builtin__' not in dir() or not hasattr(__builtin__, 'profile'):
+# def profile(func):
+# def inner(*args, **kwargs):
+# return func(*args, **kwargs)
+# return inner
 
 # memory profile
 if 'profile' not in dir():
@@ -17,17 +17,19 @@ def inner(*args, **kwargs):
             return func(*args, **kwargs)
         return inner
 
+
 @profile
 def some_fn(nbr):
     return nbr * 2
 
 
 class TestCase(unittest.TestCase):
+
     def test(self):
         result = some_fn(2)
         self.assertEquals(result, 4)
 
 
 if __name__ == "__main__":
     print "do some work:", some_fn(2)
-    #unittest.main()
+    # unittest.main()

01_profiling/visualise_nonconvergence/nonconvergence.py

@@ -29,8 +29,8 @@
 from matplotlib import pyplot as plt
 f = plt.figure(1)
 plt.clf()
-plt.plot(zs1, 'o-', linewidth=4, label="z="+str(z1))
-plt.plot(zs2, 'd:', linewidth=4, label="z="+str(z2))
+plt.plot(zs1, 'o-', linewidth=4, label="z=" + str(z1))
+plt.plot(zs2, 'd:', linewidth=4, label="z=" + str(z2))
 
 plt.annotate("cutoff", (25, 1.9), fontsize="large")
 plt.hlines(2, 0, 50, colors='k', linewidth=4, linestyles="dashed")

02_understanding/check_prime.py

@@ -1,9 +1,10 @@
 import math
 
+
 def check_prime(number):
     sqrt_number = math.sqrt(number)
     number_float = float(number)
-    for i in xrange(2, int(sqrt_number)+1):
+    for i in xrange(2, int(sqrt_number) + 1):
         if (number_float / i).is_integer():
             return False
     return True