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def axescheck(data, dims=None, shape=None, min_dims=None, max_dims=None, name="Input"): """ Validates the axes and shape of an array-like object. Usage: axescheck(img, dims=3, shape=(None, None, 3)) # Ensures RGB image """ # 1. Type Check if not hasattr(data, 'shape') or not hasattr(data, 'ndim'): raise TypeError(f"'{name}' must be an array-like object with 'shape' and 'ndim' attributes. Got {type(data)}.") data_dims = data.ndim # 2. Exact Dimension Check if dims is not None: if data_dims != dims: raise ValueError(f"'{name}' must have {dims} dimensions, but got {data_dims}.") # 3. Dimension Bounds Check if min_dims is not None and data_dims < min_dims: raise ValueError(f"'{name}' must have at least {min_dims} dimensions, but got {data_dims}.") if max_dims is not None and data_dims > max_dims: raise ValueError(f"'{name}' must have at most {max_dims} dimensions, but got {data_dims}.") # 4. Shape Check (with Wildcards) if shape is not None: if len(shape) != data_dims: raise ValueError(f"'{name}' shape length mismatch: expected {len(shape)} axes, got {data_dims}.") for i, (expected, actual) in enumerate(zip(shape, data.shape)): if expected is not None and actual != expected: raise ValueError(f"'{name}' axis {i} size mismatch: expected {expected}, got {actual}.") return True Scenario A: Machine Learning Data Loader Ensuring a batch of data has the correct shape before feeding it to a model. Pdf Gratis Trata - El Poder De Quererte
Based on standard programming conventions and the typical naming patterns of utility libraries (like Python's matplotlib or validation libraries), is not a widely recognized standard function in major mainstream libraries. It is likely a custom utility function or a typo for argcheck / assert logic.
However, based on the name, it clearly implies
def calculate_returns(matrix): # Ensure we have rows (time) and columns (assets) axescheck(matrix, min_dims=2, name="ReturnsMatrix") # ... proceed with calculation ... Without axescheck , a shape mismatch in a complex pipeline might result in a IndexError or a broadcasting error 50 lines of code later, making debugging difficult. axescheck acts as a bouncer at the door , ensuring data hygiene at the entry point of functions.
def process_batch(images): # Verify we have a batch of 4D tensors (Batch, Height, Width, Channels) # Last axis must be 3 (RGB) axescheck(images, dims=4, shape=(None, None, None, 3), name="ImageBatch") # ... proceed with processing ... Ensuring a time-series matrix is at least 2-dimensional.