示例#1
0
def initialise(file, memmap=False, scan=False):
    #intialise empty parameter dictionary
    #kwargs stands for keyword arguments
    kwargs = {}
    #load file
    if memmap == True:
        ns = np.fromfile(file, dtype=toolbox.su_header_dtype, count=1)['ns']
        sutype = toolbox.typeSU(ns)
        dataset = np.memmap(file, dtype=sutype)
    else:
        dataset = toolbox.read(file)

    #allocate stuff
    #~
    ns = kwargs['ns'] = dataset['ns'][0]
    dt = kwargs['dt'] = dataset['dt'][0] / 1e6

    #also add the time vector - it's useful later
    kwargs['times'] = np.arange(0, dt * ns, dt)

    dataset['trace'] /= np.amax(dataset['trace'])
    dataset['tracr'] = np.arange(dataset.size)

    kwargs['primary'] = 'cdp'
    kwargs['secondary'] = 'offset'
    kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
    kwargs['step'] = 1

    if scan:
        toolbox.scan(dataset)
    return dataset, kwargs
示例#2
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def initialise(file):
    #intialise empty parameter dictionary
    #kwargs stands for keyword arguments
    kwargs = {}
    #load file
    dataset = toolbox.read(file)

    #allocate stuff
    #~
    ns = kwargs['ns'] = dataset['ns'][0]
    dt = kwargs['dt'] = dataset['dt'][0] / 1e6

    #also add the time vector - it's useful later
    kwargs['times'] = np.arange(0, dt * ns, dt)

    dataset['trace'] /= np.amax(dataset['trace'])
    dataset['tracr'] = np.arange(dataset.size)

    kwargs['primary'] = 'cdp'
    kwargs['secondary'] = 'offset'
    kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
    kwargs['step'] = 1

    toolbox.scan(dataset)
    return dataset, kwargs
示例#3
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def initialise(file, memmap=False, scan=False):
        #intialise empty parameter dictionary
        #kwargs stands for keyword arguments
        kwargs = {}
        #load file
        if memmap == True:
                ns = np.fromfile(file, dtype=toolbox.su_header_dtype, count=1)['ns']
                sutype = toolbox.typeSU(ns)
                dataset = np.memmap(file, dtype=sutype)
        else:
                dataset = toolbox.read(file)
        
        
        
        #allocate stuff
        #~ 
        ns = kwargs['ns'] = dataset['ns'][0]
        dt = kwargs['dt'] = dataset['dt'][0]/1e6
        
                       
        #also add the time vector - it's useful later
        kwargs['times'] = np.arange(0, dt*ns, dt)
        
        dataset['trace'] /= np.amax(dataset['trace'])
        dataset['tracr'] = np.arange(dataset.size)
        
        kwargs['primary'] = 'cdp'
        kwargs['secondary'] = 'offset'
        kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
        kwargs['step'] = 1
        
        if scan:
                toolbox.scan(dataset)
        return dataset, kwargs
def initialise(file):
        #intialise empty parameter dictionary
        #kwargs stands for keyword arguments
        kwargs = {}
        #load file
        dataset = toolbox.read(file)
        
        
        
        #allocate stuff
        #~ 
        ns = kwargs['ns'] = dataset['ns'][0]
        dt = kwargs['dt'] = dataset['dt'][0]/1e6
        
                       
        #also add the time vector - it's useful later
        kwargs['times'] = np.arange(0, dt*ns, dt)
        
        dataset['trace'] /= np.amax(dataset['trace'])
        dataset['tracr'] = np.arange(dataset.size)
        
        kwargs['primary'] = 'cdp'
        kwargs['secondary'] = 'offset'
        kwargs['cdp'] = np.sort(np.unique(dataset['cdp']))
        kwargs['step'] = 1
        
        toolbox.scan(dataset)
        return dataset, kwargs
示例#5
0
def initialise(file):
        #intialise empty parameter dictionary
        #kwargs stands for keyword arguments
        kwargs = {}
        #load file
        dataset = toolbox.read(file)
        
        #allocate stuff
        ns = kwargs['ns'] = dataset['ns'][0]
        dt = kwargs['dt'] = dataset['dt'][0]/1e6
                       
        #also add the time vector - it's useful later
        kwargs['times'] = np.linspace(dt, ns*dt, ns*dt*1000)
        
        dataset['trace'] /= np.amax(dataset['trace'])
        kwargs['primary'] = 'sx'
        kwargs['secondary'] = 'gx'
        kwargs['step'] = 1
        
        toolbox.scan(dataset)
        return dataset, kwargs
示例#6
0

@_2d
def scale(dataset):
    dataset['trace'] /= np.amax(np.abs(dataset['trace']))


if __name__ == "__main__":

    file = "/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su"

    data = toolbox.read(file)

    data = data[data['tracf'] > 0]

    toolbox.scan(data)

    ffids = np.unique(data['fldr'])

    parms = {}

    parms['dt'] = data['dt'][0]

    for shot in ffids:
        panel = data[data['fldr'] == shot]
        #print shot

        parms["primary"] = 'fldr'
        parms["step"] = 1
        parms["window"] = 1000
示例#7
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        dataset, params = initialise('cleaned.su')
        
        # we are going to pull out 1 cdp for testing with.
        #firstly, use the scroll tool to view the cdps, and 
        #then pick one near the middle of the volume
        
        print dataset['cdp']
        params['primary'] = 'cdp'
        params['secondary'] = 'offset'
        params['step'] = 20	
        #~ toolbox.display(dataset, None, **params)
        #we then want to extract that single cdp
        #for testing with later. we can do that 
        #the following way
        cdp500 = dataset[dataset['cdp'] == 500]
        toolbox.scan(cdp500)
        #view it
        #~ toolbox.display(cdp500, None, **params)
        #we have the right cdp = but the traces are in the wrong 
        #order. lets sort by offset
        
        cdp500 = np.sort(cdp500, order=['cdp', 'offset'])
                
        #output it for later
        toolbox.cp(cdp500, 'cdp500.su', None)       
        params['clip'] = 6e-4

        toolbox.display(cdp500, None, **params)
        pylab.show()
        
        
示例#8
0
    #intialise dataset and parameter dictionary
    dataset, params = initialise('cleaned.su')

    # we are going to pull out 1 cdp for testing with.
    #firstly, use the scroll tool to view the cdps, and
    #then pick one near the middle of the volume

    print dataset['cdp']
    params['primary'] = 'cdp'
    params['secondary'] = 'offset'
    params['step'] = 20
    #~ toolbox.display(dataset, None, **params)
    #we then want to extract that single cdp
    #for testing with later. we can do that
    #the following way
    cdp500 = dataset[dataset['cdp'] == 500]
    toolbox.scan(cdp500)
    #view it
    #~ toolbox.display(cdp500, None, **params)
    #we have the right cdp = but the traces are in the wrong
    #order. lets sort by offset

    cdp500 = np.sort(cdp500, order=['cdp', 'offset'])

    #output it for later
    toolbox.cp(cdp500, 'cdp500.su', None)
    params['clip'] = 6e-4

    toolbox.display(cdp500, None, **params)
    pylab.show()