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energy-sdk's Introduction

Welcome to NVIDIA's Energy Samples

The Energy Samples is a collection of GPU accelerated algorithms and techniques for the Energy industry.

Open source samples via github

The Energy SDK samples released through github. Please review the license under each repository.

Samples available:

  1. Seismic Facies Analysis using Machine Learning : energy-sdk-rapids-seismic-facies
  2. Learn To Run a Power Network : energy-sdk-l2rpn

NDA samples via NVOnline

Please contact your NVIDIA account manager or representative to start the process of accesses NDA samples via NVOnline. The Energy SDK samples released through NVOnline are under an Energy End User License Agreement (EULA) granting derivative use of the samples.

Samples available:

  1. Forward Propagators for Reverse Time Migration (1, 2, 3 pass ISO, VTI, TTI)
  2. Kirchhoff Depth Migration
  3. Kirchhoff Time Migration
  4. Reverse Time Migration with Compression
  5. Compression with DCT and OpenVDS Samples
  6. Instructions for downloading and configuring public datasets for ingestion into the seismic imaging samples

energy-sdk's People

Contributors

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energy-sdk's Issues

In Unsupervised_Seismic_Facies_DGXA100 getting error while attribute computation.

CODE:

def run_attributes(input_cube, attributes: List[Tuple[str, Callable, Dict[str, str]]]):
datas = [attr_func(input_cube, **attr_kwargs).flatten() for _, attr_func, attr_kwargs in attributes]
datas = da.stack(datas, axis=1)
return ddf.from_dask_array(datas, columns=[attr_name for attr_name, _, _ in attributes])

sys.path.append('./d2geo/attributes')
from d2geo.attributes.CompleTrace import ComplexAttributes
from d2geo.attributes.SignalProcess import SignalProcess
complex_att = ComplexAttributes()
signal_process = SignalProcess()
def amplitude_arr(input_cube):
return da.from_array(input_cube)
funcs = [
('Amplitude', amplitude_arr, {}),
('Envelope', complex_att.envelope, {}),
('Instantaneous Phase', complex_att.instantaneous_phase, {}),
('Instantaneous Frequency', complex_att.instantaneous_frequency, {}),
('Instantaneous Bandwidth', complex_att.instantaneous_bandwidth, {}),
('Dominant Frequency', complex_att.dominant_frequency, {}),
('Cosine Instantaneous Phase', complex_att.cosine_instantaneous_phase, {}),
('Second Derivative', signal_process.second_derivative, {}),
('Reflection Intensity', signal_process.reflection_intensity, {})
]
dataframe = run_attributes(cube, funcs).compute()
dataframe.tail()

ERROR:

ValueError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_2132\1033475186.py in
24 ]
25
---> 26 dataframe = run_attributes(cube, funcs).compute()
27 dataframe.tail()

~\AppData\Local\Temp\ipykernel_2132\4269748235.py in run_attributes(input_cube, attributes)
1 def run_attributes(input_cube, attributes: List[Tuple[str, Callable, Dict[str, str]]]):
2 datas = [attr_func(input_cube, **attr_kwargs).flatten() for _, attr_func, attr_kwargs in attributes]
----> 3 datas = da.stack(datas, axis=1)
4 return ddf.from_dask_array(datas, columns=[attr_name for attr_name, _, _ in attributes])

C:\anaconda\lib\site-packages\dask\array\core.py in stack(seq, axis, allow_unknown_chunksizes)
5058 if not allow_unknown_chunksizes and not all(x.shape == seq[0].shape for x in seq):
5059 idx = first(i for i in enumerate(seq) if i[1].shape != seq[0].shape)
-> 5060 raise ValueError(
5061 "Stacked arrays must have the same shape. The first array had shape "
5062 f"{seq[0].shape}, while array {idx[0] + 1} has shape {idx[1].shape}."

ValueError: Stacked arrays must have the same shape. The first array had shape (207572625,), while array 2 has shape (219576825,).

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