def stats(filename, dtype='f', block=64*1024*1024):
if filename.endswith('.npy'):
fh = open(filename, 'rb')
version = npy.read_magic(fh)
shape, fcont, dtype = npy._read_array_header(fh, version)
n = np.prod(shape)
elif filename.endswith('.bin'):
nb = np.dtype(dtype).itemsize
n = os.path.getsize(filename)
if n % nb != 0:
raise Exception()
n //= nb
shape = [int(n)]
fh = open(filename, 'rb')
else:
raise Exception()
if n == 0:
rmin = float('nan')
rmax = float('nan')
rmean = float('nan')
else:
rmin = float('inf')
rmax = -float('inf')
rsum = 0.0
i = 0
m = 0
while i < n:
b = min(n - i, block)
r = np.fromfile(fh, dtype=dtype, count=b)
j = ~np.isnan(r)
rmin = min(rmin, r[j].min().copy())
rmax = max(rmax, r[j].max().copy())
rsum += r[j].astype('d').sum()
i += b
m += j.sum()
rmean = rsum / m
return rmin, rmax, rmean, list(shape)
def main(
files, dtype='f', shape=[], step=0, power=0, clim=[], transpose=False
):
shape0 = shape
dtype0 = dtype.replace('l', '<').replace('b', '>')
fig = plt.figure(figsize=(12, 7.2))
print(' Min Max Mean Shape')
for filename in files:
dtype = dtype0
shape = shape0
title = os.path.split(filename)[-1]
title = os.path.splitext(title)[0]
if filename.lower().endswith('.txt'):
data = np.loadtxt(filename)
shape = data.shape
elif filename.lower().endswith('.json'):
data = np.asarray(json.load(filename))
shape = data.shape
elif filename.lower().endswith('.sac'):
import obspy.core
data = obspy.core.read(filename)[0].data
shape = data.shape
elif filename.lower().endswith('.npy'):
fh = open(filename, 'rb')
version = npy.read_magic(fh)
shape, fcont, dtype = npy._read_array_header(fh, version)
if not fcont:
shape = shape[::-1]
if len(shape) < 3:
fh.close()
data = np.load(filename)
shape = data.shape
elif filename.lower().endswith('.bin'):
if len(shape) < 3:
data = np.fromfile(filename, dtype)
if shape:
data = data.reshape(shape[::-1]).T
else:
shape = data.shape
else:
fh = open(filename, 'rb')
else:
raise Exception('unknown file type: ' + filename)
fig.clf()
ax = fig.add_subplot(111)
ax.set_title(title)
if len(shape) == 1:
if step:
data = data[::step]
stats(data, title)
if power:
data = data ** power
ax.plot(data)
elif len(shape) == 2:
if transpose or shape[1] == 2:
data = data.T
shape = data.shape
if shape[0] == 2:
if step:
data = data[:, ::step]
stats(data[0], title)
stats(data[1], title)
if power:
data = data[0], data[1] ** power
ax.plot(data[0], data[1])
else:
if step:
data = data[::step, ::step]
stats(data, title)
if power:
data = data ** power
im = ax.imshow(data.T, origin='lower', interpolation='nearest')
plt.colorbar(im, orientation='horizontal')
if clim:
im.set_clim(*clim)
else:
n = shape[0] * shape[1]
m = shape[2]
for i in shape[3:]:
m *= i
for it in range(m):
data = np.fromfile(fh, dtype, n)
data = data.reshape(shape[1::-1]).T
if step:
data = data[::step, ::step]
if transpose:
data = data.T
stats(data, '%s %s' % (title, it))
ax.set_title('%s %s' % (title, it))
if power:
data = data ** power
if it == 0:
im = ax.imshow(
data.T, origin='lower', interpolation='nearest')
fig.colorbar(im, orientation='horizontal')
else:
im.set_array(data.T)
if clim:
im.set_clim(*clim)
else:
im.autoscale()
fig.show()
fig.canvas.draw()
fig.ginput(1, 0, False)
fig.canvas.draw()
fig.ginput(1, 0, False)