def test_do_convolve( self ):
# Make a spike dataset [samp, trace, theta]
data = np.zeros( (1000,100,10) )
data[500,50,:] = 1.0
dt = 0.001
duration = .2
f = (20,40,60)
wavelets = ricker( duration, dt, f )
con = do_convolve( wavelets, data )
truth = np.zeros( (1000,100,10,3) )
for i in range(10):
truth[:wavelets.shape[0], 50, i, :] += wavelets
truth = np.roll( truth, 401, axis=0 )
self.assertTrue( np.allclose( truth, con ) )
def run_script(args):
# ## Make a new string buffer
# in_buf = StringIO.StringIO()
# ## Write the decoded string into the buffer
# buf.write(base64.b64decode(args.wireframe))
# ## Move to the start of the buffer
# in_buf.seek(0)
## Open the image in PIL
#image = Image.open(buf)
processed_data = image_segment(args.wireframe)
#plt.imshow(processed_data)
#image = get_figure_data()
dt = .01
t = 1.0
f= 1.
wave = ricker(t,dt,f)
seismic = [np.convolve(wave, i, mode='same') for i in np.transpose(processed_data)]
plt.imshow(seismic)
image = get_figure_data()
return (image,)
def run_script(args):
# ## Make a new string buffer
# in_buf = StringIO.StringIO()
# ## Write the decoded string into the buffer
# buf.write(base64.b64decode(args.wireframe))
# ## Move to the start of the buffer
# in_buf.seek(0)
## Open the image in PIL
#image = Image.open(buf)
processed_data = image_segment(args.wireframe)
#plt.imshow(processed_data)
#image = get_figure_data()
dt = .01
t = 1.0
f = 1.
wave = ricker(t, dt, f)
seismic = [
np.convolve(wave, i, mode='same') for i in np.transpose(processed_data)
]
plt.imshow(seismic)
image = get_figure_data()
return (image, )
def test_wavelet(self):
# Make a dipping reflectivity field
data = np.zeros( ( 150,100 ) )
data[0,:] = np.arange( 100 ) / 10.0
dips = np.arange( 0,100,2)
dips = np.concatenate( [dips, dips[::-1] ] )
window_size = 10
freq = 20.0
duration = .2
wavelet = ricker( window_size, 1, freq )
for i in range( dips.size ):
data[:,i]= np.convolve( wavelet, np.roll( data[:,i],
dips[i] ),
mode = 'same' )
stepout=1
maxlag=2
dip = dipsteer( data, window_size, stepout=stepout,
maxlag=maxlag, overlap=0.5 )
undipped = np.zeros( dips.shape )
for i in range ( dips.size ):
undipped[i] = ( dip[dips[i],i] )
check = undipped[ maxlag*stepout:-maxlag*stepout ]
test = np.zeros( check.shape )
test[0 : test.size/2 -1] = -2.0
test[ (test.size/2) - 1 ] = -1
test[(test.size/2) ] = 1
test[ (test.size/2)+1 : ] = 2
"""fig = pl.figure()
pl.imshow( dip )
fig.show()
fig2 = pl.figure()
pl.imshow( data)
fig2.show()"""
self.assertTrue( np.allclose( test, check ) )
tick.label.set_fontsize(fs)
for tick in axarr[1,p].yaxis.get_major_ticks():
tick.label.set_fontsize(fs)
#plot horizontal green line on model image, and steady state
axarr[0,p].axhline(y=t, alpha=0.5, lw=2, color = 'g')
fig.tight_layout()
return get_figure_data()
if __name__ == '__main__':
dt =0.001
gain = 1
temp = ricker(0.256,dt,25)
ntraces=50
data =np.zeros((temp.shape[0],ntraces))
for i in range(ntraces):
temp = ricker(0.256,dt,25+10*i)
data[:,i] = temp
wiggle(data,dt,skipt=1,gain=gain)
from agilegeo.wavelet import ricker
import numpy as np
import json
outfile = 'www/seismic.seis'
dt = .001
f1 = 15.0
f2 = 150.0
wavelet = ricker(0.2,dt, 25)
reflect = np.random.randn(1000)
seis = np.convolve(wavelet, reflect, mode='same')
z = np.linspace(1000, 6000, seis.size)
j_dict = {'z':z.tolist(), 'seis':seis.tolist()}
j_out = json.dumps(j_dict)
with open(outfile, 'w') as output:
output.write('var seisData =' + j_out +';')
