# Buggy code
def _parallel_pairwise(X1, X2, metric, metric_params, homology_dimensions, n_jobs):
    metric_func = implemented_metric_recipes[metric]
    effective_metric_params = metric_params.copy()
    none_dict = {dim: None for dim in homology_dimensions}
    samplings = effective_metric_params.pop("samplings", none_dict)
    step_sizes = effective_metric_params.pop("step_sizes", none_dict)

    if X2 is None:
        X2 = X1

    distance_matrices = Parallel(n_jobs=n_jobs)(
        delayed(metric_func)(
            _subdiagrams(X1, [dim], remove_dim=True),
            _subdiagrams(X2[s], [dim], remove_dim=True),
            sampling=samplings[dim],
            step_size=step_sizes[dim],
            **effective_metric_params,
        )
        for dim in homology_dimensions
        for s in gen_even_slices(X2.shape[0], effective_n_jobs(n_jobs))
    )

    distance_matrices = np.concatenate(distance_matrices, axis=1)
    distance_matrices = np.stack(
        [
            distance_matrices[:, i * X2.shape[0] : (i + 1) * X2.shape[0]]
            for i in range(len(homology_dimensions))
        ],
        axis=2,
    )
    return distance_matrices


# Traceback (ValueError)
---------------------------------------------------------------------------
_RemoteTraceback                          Traceback (most recent call last)
_RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\nicho\Anaconda3\lib\site-packages\joblib\externals\loky\process_executor.py", line 418, in _process_worker
    r = call_item()
  File "C:\Users\nicho\Anaconda3\lib\site-packages\joblib\externals\loky\process_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "C:\Users\nicho\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 567, in __call__
    return self.func(*args, **kwargs)
  File "C:\Users\nicho\Anaconda3\lib\site-packages\joblib\parallel.py", line 225, in __call__
    for func, args, kwargs in self.items]
  File "C:\Users\nicho\Anaconda3\lib\site-packages\joblib\parallel.py", line 225, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:\Users\nicho\Anaconda3\lib\site-packages\gtda\diagrams\_metrics.py", line 86, in heats
    diagrams[diagrams < sampling[0, 0]] = sampling[0, 0]
ValueError: assignment destination is read-only
"""

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-132-73d0b2c011fe> in <module>
      7 hk = HeatKernel(sigma=1, n_bins=n_bins, n_jobs=2)
      8 num_dimensions = 1
----> 9 x_t = hk.fit_transform(diagrams)

~\Anaconda3\lib\site-packages\gtda\utils\_docs.py in fit_transform_wrapper(*args, **kwargs)
    104         @wraps(original_fit_transform)
    105         def fit_transform_wrapper(*args, **kwargs):
--> 106             return original_fit_transform(*args, **kwargs)
    107         fit_transform_wrapper.__doc__ = \
    108             make_fit_transform_docs(fit_docs, transform_docs)

~\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    569         if y is None:
    570             # fit method of arity 1 (unsupervised transformation)
--> 571             return self.fit(X, **fit_params).transform(X)
    572         else:
    573             # fit method of arity 2 (supervised transformation)

~\Anaconda3\lib\site-packages\gtda\diagrams\representations.py in transform(self, X, y)
    591             heats)(_subdiagrams(X, [dim], remove_dim=True)[s],
    592                    self._samplings[dim], self._step_size[dim], self.sigma)
--> 593             for dim in self.homology_dimensions_
    594             for s in gen_even_slices(X.shape[0],
    595                                      effective_n_jobs(self.n_jobs)))

~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
    932 
    933             with self._backend.retrieval_context():
--> 934                 self.retrieve()
    935             # Make sure that we get a last message telling us we are done
    936             elapsed_time = time.time() - self._start_time

~\Anaconda3\lib\site-packages\joblib\parallel.py in retrieve(self)
    831             try:
    832                 if getattr(self._backend, 'supports_timeout', False):
--> 833                     self._output.extend(job.get(timeout=self.timeout))
    834                 else:
    835                     self._output.extend(job.get())

~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in wrap_future_result(future, timeout)
    519         AsyncResults.get from multiprocessing."""
    520         try:
--> 521             return future.result(timeout=timeout)
    522         except LokyTimeoutError:
    523             raise TimeoutError()

~\Anaconda3\lib\concurrent\futures\_base.py in result(self, timeout)
    430                 raise CancelledError()
    431             elif self._state == FINISHED:
--> 432                 return self.__get_result()
    433             else:
    434                 raise TimeoutError()

~\Anaconda3\lib\concurrent\futures\_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

ValueError: assignment destination is read-only

# Fixed code
def _parallel_pairwise(X1, X2, metric, metric_params, homology_dimensions, n_jobs):
    metric_func = implemented_metric_recipes[metric]
    effective_metric_params = metric_params.copy()
    none_dict = {dim: None for dim in homology_dimensions}
    samplings = effective_metric_params.pop("samplings", none_dict)
    step_sizes = effective_metric_params.pop("step_sizes", none_dict)

    if X2 is None:
        X2 = X1

    n_columns = len(X2)
    distance_matrices = Parallel(n_jobs=n_jobs)(
        delayed(metric_func)(
            _subdiagrams(X1, [dim], remove_dim=True),
            _subdiagrams(X2[s], [dim], remove_dim=True),
            sampling=samplings[dim],
            step_size=step_sizes[dim],
            **effective_metric_params,
        )
        for dim in homology_dimensions
        for s in gen_even_slices(n_columns, effective_n_jobs(n_jobs))
    )

    distance_matrices = np.concatenate(distance_matrices, axis=1)
    distance_matrices = np.stack(
        [
            distance_matrices[:, i * n_columns : (i + 1) * n_columns]
            for i in range(len(homology_dimensions))
        ],
        axis=2,
    )
    return distance_matrices