function myCustomPlot(varargin) % 1. Extract the axes if provided [ax, args, nargs] = axescheck(varargin:); % 2. If no axes was provided, use the current one (gca) if isempty(ax) ax = gca; end % 3. Extract your data from 'args' x = args1; y = args2; % 4. Perform the plot on the specific axes line(x, y, 'Parent', ax); end Use code with caution. Modern Context: Beyond the Command Line
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def axescheck_numpy(array): if array.ndim != 2: raise ValueError("Axescheck: Expected 2D array") assert np.all(np.isfinite(array)), "Axescheck: Infinite or NaN detected" # Check axis symmetry if required return True axescheck
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Axescheck is a comprehensive process used to verify the accuracy and precision of machine tools, particularly those with multiple axes of movement. It involves checking the alignment of the machine's axes to ensure they are properly calibrated and functioning within specified tolerances. The process typically involves a series of measurements and tests to identify any deviations or errors in the machine's movement, which can then be corrected to prevent inaccuracies. function myCustomPlot(varargin) % 1
fig, ax = plt.subplots(figsize=(8,4)) ax.plot(x, y) ax.set_xlabel("X Label (units)") ax.set_ylabel("Y Label (units)") ax.set_xlim(min(x), max(x)) ax.set_ylim(min(y), max(y)) plt.xticks(rotation=45) plt.tight_layout() plt.show()
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 , ensuring data hygiene at the entry point of functions. Extract your data from 'args' x = args1; y = args2; % 4
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To build robust, production-grade graphics functions that gracefully parse these optional target containers, MATLAB relies on a highly efficient helper function: .