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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def data_as_matrix(self, keys=None, return_basis=False, basis=None, alias=None, start=None, stop=None, step=None, window_length=None, window_step=1, ):
 """ Provide a feature matrix, given a list of data items. I think this will probably fail if there are striplogs in the data dictionary for this well. TODO: Deal with striplogs and other data, if present. Args: keys (list):
 List of the logs to export from the data dictionary. return_basis (bool):
 Whether or not to return the basis that was used. basis (ndarray):
 The basis to use. Default is to survey all curves to find a common basis. alias (dict):
 A mapping of alias names to lists of mnemonics. start (float):
 Optionally override the start of whatever basis you find or (more likely) is surveyed. stop (float):
 Optionally override the stop of whatever basis you find or (more likely) is surveyed. step (float):
 Override the step in the basis from survey_basis. window_length (int):
 The number of samples to return around each sample. This will provide one or more shifted versions of the features. window_step (int):
 How much to step the offset versions. Returns: ndarray. or ndarray, ndarray if return_basis=True """

        if keys is None:
            keys = [k for k, v in self.data.items() if isinstance(v, Curve)]
        else:
            # Only look at the alias list if keys were passed.
            if alias is not None:
                _keys = []
                for k in keys:
                    if k in alias:
                        added = False
                        for a in alias[k]:
                            if a in self.data:
                                _keys.append(a)
                                added = True
                                break
                        if not added:
                            _keys.append(k)
                    else:
                        _keys.append(k)
                keys = _keys

        if basis is None:
            basis = self.survey_basis(keys=keys, step=step)

        # Get the data, or None is curve is missing.
        data = [self.data.get(k) for k in keys]

        # Now cast to the correct basis, and replace any missing curves with
        # an empty Curve. The sklearn imputer will deal with it. We will change
        # the elements in place.
        for i, d in enumerate(data):
            if d is not None:
                data[i] = d.to_basis(basis=basis)
                # Allow user to override the start and stop from the survey.
                if (start is not None) or (stop is not None):
                    data[i] = data[i].to_basis(start=start, stop=stop, step=step)
                    basis = data[i].basis
            else:
                # Empty_like gives unpredictable results
                data[i] = Curve(np.full(basis.shape, np.nan), basis=basis)

        if window_length is not None:
            d_new = []
            for d in data:
                r = d._rolling_window(window_length,
                                      func1d=utils.null,
                                      step=window_step,
                                      return_rolled=False,
                                      )
                d_new.append(r.T)
            data = d_new

        if return_basis:
            return np.vstack(data).T, basis
        else:
            return np.vstack(data).T