def calc_pure_python(show_output):
# make a list of x and y values which will represent q
# xx and yy are the co-ordinates, for the default configuration they'll look like:
# if we have a 1000x1000 plot
# xx = [-2.13, -2.1242, -2.1184000000000003, ..., 0.7526000000000064, 0.7584000000000064, 0.7642000000000064]
# yy = [1.3, 1.2948, 1.2895999999999999, ..., -1.2844000000000058, -1.2896000000000059, -1.294800000000006]
x_step = (float(x2 - x1) / float(w)) * 2
y_step = (float(y1 - y2) / float(h)) * 2
x=[]
y=[]
ycoord = y2
while ycoord > y1:
y.append(ycoord)
ycoord += y_step
xcoord = x1
while xcoord < x2:
x.append(xcoord)
xcoord += x_step
q = []
for ycoord in y:
for xcoord in x:
q.append(complex(xcoord,ycoord))
z = [0+0j] * len(q)
print "Total elements:", len(z)
start_time = datetime.datetime.now()
output = calculate_z.calculate_z(q, maxiter, z)
end_time = datetime.datetime.now()
secs = end_time - start_time
print "Main took", secs
validation_sum = sum(output)
print "Total sum of elements (for validation):", validation_sum
if show_output:
import Image
import numpy as nm
output = nm.array(output)
output = (output + (256*output) + (256**2)*output) * 8
im = Image.new("RGB", (w/2, h/2))
# you can experiment with these x and y ranges
im.fromstring(output.tostring(), "raw", "RGBX", 0, -1)
#im.save('mandelbrot.png')
im.show()
def calc(show_output):
# make a list of x and y values
# make a list of x and y values which will represent q
# xx and yy are the co-ordinates, for the default configuration they'll look like:
# if we have a 1000x1000 plot
# xx = [-2.13, -2.1242, -2.1184000000000003, ..., 0.7526000000000064, 0.7584000000000064, 0.7642000000000064]
# yy = [1.3, 1.2948, 1.2895999999999999, ..., -1.2844000000000058, -1.2896000000000059, -1.294800000000006]
x_step = (float(x2 - x1) / float(w)) * 2
y_step = (float(y1 - y2) / float(h)) * 2
x = []
y = []
ycoord = y2
while ycoord > y1:
y.append(ycoord)
ycoord += y_step
xcoord = x1
while xcoord < x2:
x.append(xcoord)
xcoord += x_step
q = []
for ycoord in y:
for xcoord in x:
q.append(complex(xcoord, ycoord))
z = [0 + 0j] * len(q)
print "Total elements:", len(y) * len(x)
# convert Python lists into numpy arrays
q_np = np.array(q)
z_np = np.array(z)
start_time = datetime.datetime.now()
output = calculate_z.calculate_z(q_np, maxiter, z_np)
end_time = datetime.datetime.now()
secs = end_time - start_time
print "Main took", secs
validation_sum = np.sum(output)
print "Total sum of elements (for validation):", validation_sum
if show_output:
show(output)
return validation_sum
def calc(show_output):
# make a list of x and y values
# make a list of x and y values which will represent q
# xx and yy are the co-ordinates, for the default configuration they'll look like:
# if we have a 1000x1000 plot
# xx = [-2.13, -2.1242, -2.1184000000000003, ..., 0.7526000000000064, 0.7584000000000064, 0.7642000000000064]
# yy = [1.3, 1.2948, 1.2895999999999999, ..., -1.2844000000000058, -1.2896000000000059, -1.294800000000006]
x_step = (float(x2 - x1) / float(w)) * 2
y_step = (float(y1 - y2) / float(h)) * 2
x = []
y = []
ycoord = y2
while ycoord > y1:
y.append(ycoord)
ycoord += y_step
xcoord = x1
while xcoord < x2:
x.append(xcoord)
xcoord += x_step
q = []
for ycoord in y:
for xcoord in x:
q.append(complex(xcoord, ycoord))
z = [0+0j] * len(q)
print "Total elements:", len(y)*len(x)
# convert Python lists into numpy arrays
q_np = np.array(q)
z_np = np.array(z)
start_time = datetime.datetime.now()
output = calculate_z.calculate_z(q_np, maxiter, z_np)
end_time = datetime.datetime.now()
secs = end_time - start_time
print "Main took", secs
validation_sum = np.sum(output)
print "Total sum of elements (for validation):", validation_sum
if show_output:
show(output)
return validation_sum
def calc(chunk):
"""use the calculate_z module's calculate_z to process
the tuple of inps"""
q, maxiter, z = chunk
return calculate_z.calculate_z(q, maxiter, z)
def calc(chunk):
"""use the calculate_z module's calculate_z to process
the tuple of inps"""
q, maxiter, z = chunk
return calculate_z.calculate_z(q, maxiter, z)
