[Main] remove deprecated features (#488)

* removed deprecated aliases from unitary_event_analysis.py * updated unitary_event_analysis.ipynb accordingly * removed deprecated aliases from cell_assembly_detection.py * removed deprecated aliases from change_point_detection.py * removed deprecated aliases from conversion.py * stationary poisson process from parallel * fixed statistics.py, changed binsize to bin_size * removed deprecated aliases from cubic.py * removed deprecated aliases from current_source_density.py, refactored test_csd to test_current_source_density * refactor dictionary creation * removed deprecated aliases from gpfa.py, refactored test_gpfa.py to use StationaryPoissonProcess * remove homogeneous_poisson_process from spike_train_generation.py * removed deprecated aliases from gpfa_util.py * removed deprecated aliases from kernels.py * removed deprecated aliases from neo_tools.py * removed deprecated aliases from signal_processing.py * removed deprecated aliases from spade.py * removed deprecated aliases from spectral.py * removed deprecated aliases from spike_train_correlation.py * removed deprecated aliases from spike_train_dissimilarity.py * removed deprecated aliases from spike_train_generation.py * update keyword argument in spade tutorial * fix example code for cubic * fix example code spike_train_generation * fix example code gpfa * removed deprecated aliases from unitary_event_analysis.py * updated unitary_event_analysis.ipynb accordingly * removed deprecated aliases from change_point_detection.py * removed deprecated aliases from conversion.py * stationary poisson process from parallel * fixed statistics.py, changed binsize to bin_size * removed deprecated aliases from cubic.py * removed deprecated aliases from current_source_density.py, refactored test_csd to test_current_source_density * refactor dictionary creation * removed deprecated aliases from gpfa.py, refactored test_gpfa.py to use StationaryPoissonProcess * remove homogeneous_poisson_process from spike_train_generation.py * removed deprecated aliases from gpfa_util.py * removed deprecated aliases from kernels.py * removed deprecated aliases from neo_tools.py * removed deprecated aliases from signal_processing.py * removed deprecated aliases from spectral.py * removed deprecated aliases from spike_train_dissimilarity.py * removed deprecated aliases from spike_train_generation.py * update keyword argument in spade tutorial * fix example code for cubic * fix example code spike_train_generation * add missing import statement for check_neo_consistency * fix imports * fix pep8 * remove "TODO" from statistics tutorial notebook
NeuralEnsemble · Nov 10, 2023 · 2fa63bc · 2fa63bc
1 parent e867b94
commit 2fa63bc
Show file tree

Hide file tree

Showing 28 changed files with 260 additions and 413 deletions.
diff --git a/doc/tutorials/spade.ipynb b/doc/tutorials/spade.ipynb
@@ -52,7 +52,7 @@
    "outputs": [],
    "source": [
     "spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
-    "   rate=5*pq.Hz, A=[0]+[0.98]+[0]*8+[0.02], t_stop=10*pq.s)\n",
+    "   rate=5*pq.Hz, amplitude_distribution=[0]+[0.98]+[0]*8+[0.02], t_stop=10*pq.s)\n",
     "len(spiketrains)"
    ]
   },
@@ -152,7 +152,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "New_Filter",
    "language": "python",
    "name": "python3"
   },
@@ -166,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

diff --git a/doc/tutorials/statistics.ipynb b/doc/tutorials/statistics.ipynb
@@ -520,27 +520,6 @@
    "source": [
     "As predicted by theory, the CV values are clustered around 1."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# TODO"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Spike interval statistics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Statistics across spike trains"
-   ]
   }
  ],
  "metadata": {

diff --git a/doc/tutorials/unitary_event_analysis.ipynb b/doc/tutorials/unitary_event_analysis.ipynb
@@ -495,14 +495,17 @@
    "outputs": [],
    "source": [
     "UE = ue.jointJ_window_analysis(\n",
-    "    spiketrains, bin_size=5*pq.ms, winsize=100*pq.ms, winstep=10*pq.ms, pattern_hash=[3])\n",
+    "    spiketrains, bin_size=5*pq.ms, win_size=100*pq.ms, win_step=10*pq.ms, pattern_hash=[3])\n",
     "\n",
     "plot_ue(spiketrains, UE, significance_level=0.05)\n",
     "plt.show()"
    ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "623e048a0474aa032839f97d38ba0837cc9041adc49a14b480c72f2df8ea99e3"
+  },
   "kernelspec": {
    "display_name": "inm-elephant",
    "language": "python",

diff --git a/elephant/cell_assembly_detection.py b/elephant/cell_assembly_detection.py
@@ -71,28 +71,23 @@
 import copy
 import math
 import time
-import warnings
 
 import numpy as np
 from scipy.stats import f
 
 import elephant.conversion as conv
-from elephant.utils import deprecated_alias
 
 __all__ = [
     "cell_assembly_detection"
 ]
 
 
-@deprecated_alias(data='binned_spiketrain', maxlag='max_lag',
-                  min_occ='min_occurrences',
-                  same_config_cut='same_configuration_pruning')
 def cell_assembly_detection(binned_spiketrain, max_lag, reference_lag=2,
                             alpha=0.05, min_occurrences=1, size_chunks=100,
                             max_spikes=np.inf, significance_pruning=True,
                             subgroup_pruning=True,
                             same_configuration_pruning=False,
-                            bool_times_format=None, verbose=False):
+                            verbose=False):
     """
     Perform the CAD analysis :cite:`cad-Russo2017_e19428` for the binned
     (discretized) spike trains given in the input. The method looks for
@@ -156,14 +151,6 @@ def cell_assembly_detection(binned_spiketrain, max_lag, reference_lag=2,
         efficient), not testing assemblies already formed
         if they appear in the very same configuration.
         Default: False
-    bool_times_format : bool, optional
-
-    .. deprecated:: 0.10.0
-
-       Has no effect, the returning 'times' are always a quantity array
-       specifying the pattern spike times.
-       Default: None
-
     verbose : bool, optional
         Regulates the number of prints given by the method. If true all prints
         are given, otherwise the method does give any prints.
@@ -216,12 +203,6 @@ def cell_assembly_detection(binned_spiketrain, max_lag, reference_lag=2,
                   size_chunks=size_chunks,
                   max_spikes=max_spikes)
 
-    if bool_times_format is not None:
-        warnings.warn("'bool_times_format' is deprecated and has no effect; "
-                      "the returning 'times' are always a quantity array "
-                      "specifying the pattern spike times. Set this parameter "
-                      "to None.", DeprecationWarning)
-
     bin_size = binned_spiketrain.bin_size
     t_start = binned_spiketrain.t_start
 

diff --git a/elephant/change_point_detection.py b/elephant/change_point_detection.py
@@ -45,15 +45,12 @@
 import numpy as np
 import quantities as pq
 
-from elephant.utils import deprecated_alias
-
 __all__ = [
     "multiple_filter_test",
     "empirical_parameters"
 ]
 
 
-@deprecated_alias(dt='time_step')
 def multiple_filter_test(window_sizes, spiketrain, t_final, alpha,
                          n_surrogates=1000, test_quantile=None,
                          test_param=None, time_step=None):
@@ -253,7 +250,6 @@ def _limit_processes(window_sizes, t_final, time_step):
     return limit_processes
 
 
-@deprecated_alias(dt='time_step')
 def empirical_parameters(window_sizes, t_final, alpha, n_surrogates=1000,
                          time_step=None):
     r"""

diff --git a/elephant/conversion.py b/elephant/conversion.py
@@ -84,7 +84,7 @@
 import quantities as pq
 import scipy.sparse as sps
 
-from elephant.utils import is_binary, deprecated_alias, is_time_quantity, \
+from elephant.utils import is_binary, is_time_quantity, \
     check_neo_consistency, round_binning_errors
 
 __all__ = [
@@ -334,7 +334,6 @@ class BinnedSpikeTrain(object):
 
     """
 
-    @deprecated_alias(binsize='bin_size', num_bins='n_bins')
     def __init__(self, spiketrains, bin_size=None, n_bins=None, t_start=None,
                  t_stop=None, tolerance=1e-8, sparse_format="csr"):
         if sparse_format not in ("csr", "csc"):
@@ -387,24 +386,6 @@ def t_stop(self):
         """
         return pq.Quantity(self._t_stop, units=self.units, copy=False)
 
-    @property
-    def binsize(self):
-        """
-        Deprecated in favor of :attr:`bin_size`.
-        """
-        warnings.warn("'.binsize' is deprecated; use '.bin_size'",
-                      DeprecationWarning)
-        return self._bin_size
-
-    @property
-    def num_bins(self):
-        """
-        Deprecated in favor of :attr:`n_bins`.
-        """
-        warnings.warn("'.num_bins' is deprecated; use '.n_bins'",
-                      DeprecationWarning)
-        return self.n_bins
-
     def __repr__(self):
         return f"{type(self).__name__}(t_start={str(self.t_start)}, " \
                f"t_stop={str(self.t_stop)}, bin_size={str(self.bin_size)}; " \
@@ -619,27 +600,6 @@ def bin_centers(self):
         bin_centers = pq.Quantity(bin_centers, units=self.units, copy=False)
         return bin_centers
 
-    def to_sparse_array(self):
-        """
-        Getter for sparse matrix with time points. Deprecated in favor of
-        :attr:`sparse_matrix`.
-
-        Returns
-        -------
-        scipy.sparse.csr_matrix or scipy.sparse.csc_matrix
-            Sparse matrix, version with spike counts.
-
-        See also
-        --------
-        scipy.sparse.csr_matrix
-        to_array
-
-        """
-        warnings.warn("'.to_sparse_array()' function is deprecated; "
-                      "use '.sparse_matrix' attribute directly",
-                      DeprecationWarning)
-        return self.sparse_matrix
-
     def to_sparse_bool_array(self):
         """
         Getter for boolean version of the sparse matrix, calculated from

diff --git a/elephant/cubic.py b/elephant/cubic.py
@@ -22,11 +22,11 @@
 >>> import quantities as pq
 >>> from elephant import statistics
 >>> from elephant.cubic import cubic
->>> from elephant.spike_train_generation import homogeneous_poisson_process
+>>> from elephant.spike_train_generation import StationaryPoissonProcess
 
 >>> np.random.seed(10)
->>> spiketrains = [homogeneous_poisson_process(rate=10*pq.Hz,
-...                t_stop=10 * pq.s) for _ in range(20)]
+>>> spiketrains = [StationaryPoissonProcess(rate=10*pq.Hz,
+...                t_stop=10 * pq.s).generate_spiketrain() for _ in range(20)]
 >>> pop_count = statistics.time_histogram(spiketrains, bin_size=0.1 * pq.s)
 
 2) apply CuBIC to the population count
@@ -52,8 +52,6 @@
 import scipy.special
 import scipy.stats
 
-from elephant.utils import deprecated_alias
-
 __all__ = [
     "cubic"
 ]
@@ -63,7 +61,6 @@
 # Adaptation to python by Pietro Quaglio and Emiliano Torre
 
 
-@deprecated_alias(data='histogram', ximax='max_iterations')
 def cubic(histogram, max_iterations=100, alpha=0.05):
     r"""
     Performs the CuBIC analysis :cite:`cubic-Staude2010_327` on a population

diff --git a/elephant/current_source_density.py b/elephant/current_source_density.py
@@ -42,7 +42,6 @@
 
 import elephant.current_source_density_src.utility_functions as utils
 from elephant.current_source_density_src import KCSD, icsd
-from elephant.utils import deprecated_alias
 
 __all__ = [
     "estimate_csd",
@@ -61,7 +60,6 @@
 py_iCSD_toolbox = ['StandardCSD'] + icsd_methods
 
 
-@deprecated_alias(coords='coordinates')
 def estimate_csd(lfp, coordinates='coordinates', method=None,
                  process_estimate=True, **kwargs):
     """
@@ -209,9 +207,6 @@ def estimate_csd(lfp, coordinates='coordinates', method=None,
     return output
 
 
-@deprecated_alias(ele_xx='x_positions', ele_yy='y_positions',
-                  ele_zz='z_positions', xlims='x_limits', ylims='y_limits',
-                  zlims='z_limits', res='resolution')
 def generate_lfp(csd_profile, x_positions, y_positions=None, z_positions=None,
                  x_limits=[0., 1.], y_limits=[0., 1.], z_limits=[0., 1.],
                  resolution=50):

diff --git a/elephant/gpfa/gpfa.py b/elephant/gpfa/gpfa.py
@@ -75,10 +75,8 @@
 import numpy as np
 import quantities as pq
 import sklearn
-import warnings
 
 from elephant.gpfa import gpfa_core, gpfa_util
-from elephant.utils import deprecated_alias
 
 
 __all__ = [
@@ -212,12 +210,10 @@ class GPFA(sklearn.base.BaseEstimator):
     ...     n_channels = 20
     ...     firing_rates = np.random.randint(low=1, high=100,
     ...                                      size=n_channels) * pq.Hz
-    ...     spike_times = []
-    ...     for rate in firing_rates:
-    ...         spike_times.append(
-    ...              StationaryPoissonProcess(rate=rate).generate_spiketrain())
-    ...     data.append((trial, spike_times))
-
+    >>> spike_times = [StationaryPoissonProcess(rate
+    ...                ).generate_spiketrain() for rate in firing_rates]
+    >>> data.append((trial, spike_times))
+    ...
     >>> gpfa = GPFA(bin_size=20*pq.ms, x_dim=8)
     >>> gpfa.fit(data)  # doctest: +SKIP
     >>> results = gpfa.transform(data, returned_data=['latent_variable_orth',
@@ -232,7 +228,6 @@ class GPFA(sklearn.base.BaseEstimator):
     ...                               'latent_variable'])
     """
 
-    @deprecated_alias(binsize='bin_size')
     def __init__(self, bin_size=20 * pq.ms, x_dim=3, min_var_frac=0.01,
                  tau_init=100.0 * pq.ms, eps_init=1.0E-3, em_tol=1.0E-8,
                  em_max_iters=500, freq_ll=5, verbose=False):
@@ -263,11 +258,6 @@ def __init__(self, bin_size=20 * pq.ms, x_dim=3, min_var_frac=0.01,
         self.fit_info = dict()
         self.transform_info = dict()
 
-    @property
-    def binsize(self):
-        warnings.warn("'binsize' is deprecated; use 'bin_size'")
-        return self.bin_size
-
     def fit(self, spiketrains):
         """
         Fit the model with the given training data.

diff --git a/elephant/gpfa/gpfa_util.py b/elephant/gpfa/gpfa_util.py
@@ -15,10 +15,8 @@
 import scipy as sp
 
 from elephant.conversion import BinnedSpikeTrain
-from elephant.utils import deprecated_alias
 
 
-@deprecated_alias(binsize='bin_size')
 def get_seqs(data, bin_size, use_sqrt=True):
     """
     Converts the data into a rec array using internally BinnedSpikeTrain.