Merge branch 'enh/neo09-2' of INT/multielectrode_grasp into master

I think we can do this all in a single PR. I just tested the code using the new requirements.txt, and all worked fine.

.gitignore

@@ -70,3 +70,19 @@ Thumbs.db
 
 # Things specific to this project #
 ###################################
+code/example_plot.eps
+code/example_plot.pdf
+code/example_plot.png
+code/data_overview_1_Nikos2.eps
+code/data_overview_1_Nikos2.pdf
+code/data_overview_1_Nikos2.png
+code/data_overview_2_Nikos2.eps
+code/data_overview_2_Nikos2.pdf
+code/data_overview_2_Nikos2.png
+code/data_overview_1_Lilou.eps
+code/data_overview_1_Lilou.pdf
+code/data_overview_1_Lilou.png
+code/data_overview_2_Lilou.eps
+code/data_overview_2_Lilou.pdf
+code/data_overview_2_Lilou.png
+

README.md

@@ -88,8 +88,8 @@ The script produces a figure saved in three different graphics file formats.
 ### Directory code/reachgraspio
 Contains the file `reachgraspio.py`, which contains the loading routine specific to the Reach-to-Grasp experiments in this repository. This loading routine merges the recorded data with metadata information from the odML files into a common Neo object. It is recommended that this loading routine is used in combination with the odML and Neo libraries (see below) to work on the data.
 
-### Further subdirectories of code
-The subdirectories `python-neo`, `python-odml`, and `elephant` contain snapshots of the Neo[1], odML[2], and Elephant[3] libraries, respectively, that are required by the example scripts and the reachgraspio loading routine. In short, Neo provides the data model, generic Blackrock loading routines, and APIs used to load the data; odML provides an API to handle the metadata files; and Elephant is a library for the analysis of neuronal data based on the Neo data model that is used by the example script for filtering raw signals to obtain offline filtered LFPs. By modifying the file `load_local_neo_odml_elephant.py` in the code directory it is possible to instruct the example scripts to use system-wide installed versions of these libraries instead of the static snapshots. Note however, that future versions of these libraries may requires adapted versions of the `reachgraspio.py` loading routine (see Updates below).
+### Python dependencies
+The provided code is based on the Python libraries Neo[1], odML[2], and Elephant[3], that are required by the example scripts and the reachgraspio loading routine. In short, Neo provides the data model, generic Blackrock loading routines, and APIs used to load the data; odML provides an API to handle the metadata files; and Elephant is a library for the analysis of neuronal data based on the Neo data model that is used by the example script for filtering raw signals to obtain offline filtered LFPs. To run the provided code install the Python dependencies listed in `code/requirements.txt`. Note however, that the versions specified in the `code/requirements.txt` are fixed to match the provided version of the`reachgraspio.py` loading routine (see Updates below) and more recent versions might exist.
 * [1] https://github.com/NeuralEnsemble/python-neo
 * [2] https://github.com/G-Node/python-odml
 * [3] https://github.com/NeuralEnsemble/elephant
@@ -110,5 +110,5 @@ This includes, in particular, the loading routine reachgraspio.py, which may nee
 ## Licensing
 <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task</span> in the directories `datasets` and `datasets_matlab` by <span xmlns:cc="http://creativecommons.org/ns#" property="cc:attributionName">Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France and Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich, Jülich, Germany</span> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
 
-All code in the directories `code`, `code/python-odml`, `code/python-neo`, `code/elephant`, and `code/reachgraspio` are each published under the BSD 3 clause licenses. See the `LICENSE.txt` or `LICENSE` files in the corresponding directories for the full license.
+All code in the directories `code` and `code/reachgraspio` are each published under the BSD 3 clause licenses. See the `LICENSE.txt` or `LICENSE` files in the corresponding directories for the full license.
 

code/data_overview_1.py

@@ -33,10 +33,6 @@ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 
-# This loads the Neo and odML libraries shipped with this code. For production
-# use, please use the newest releases of odML and Neo.
-import load_local_neo_odml_elephant
-
 import os
 
 import numpy as np
@@ -114,7 +110,7 @@ def get_arraygrid(signals, chosen_el):
         connector_aligned_id = signals.array_annotations['connector_aligned_ids'][sig_idx]
         x, y = int((connector_aligned_id -1)// 10), int((connector_aligned_id - 1) % 10)
 
-        if signals.array_annotations['channel_ids'][sig_idx] == chosen_el:
+        if np.asarray(signals.array_annotations['channel_ids'][sig_idx], dtype=int) == chosen_el:
             array_grid[x, y] = -0.7
         elif rejections[sig_idx]:
             array_grid[x, y] = -0.35
@@ -533,7 +529,8 @@ assert len(el_raw_sig) == 1
 el_raw_sig = el_raw_sig[0]
 
 # plotting raw signal trace of chosen electrode
-chosen_el_idx = np.where(el_raw_sig.array_annotations['channel_ids'] == chosen_el[monkey])[0][0]
+chids = np.asarray(el_raw_sig.array_annotations['channel_ids'], dtype=int)
+chosen_el_idx = np.where(chids == chosen_el[monkey])[0][0]
 ax4.plot(el_raw_sig.times.rescale(plotting_time_unit),
          el_raw_sig[:, chosen_el_idx].squeeze().rescale(raw_signal_unit),
          color='k')
@@ -595,8 +592,8 @@ ax4.text(timebar_xmin + 0.25 * pq.s, timebar_ypos + timebar_labeloffset,
 # =============================================================================
 # get behavioral signals
 ainp_signals = [nsig for nsig in trial_segment.analogsignals if not nsig.annotations['neural_signal']][0]
-
-force_channel_idx = np.where(ainp_signals.array_annotations['channel_ids'] == 141)[0][0]
+chids = np.asarray(ainp_signals.array_annotations['channel_ids'], dtype=int)
+force_channel_idx = np.where(chids == 141)[0][0]
 ainp_trialz_signals = [a for a in trialz_seg.analogsignals if not a.annotations['neural_signal']]
 assert len(ainp_trialz_signals)
 ainp_trialz = ainp_trialz_signals[0][:, force_channel_idx]
@@ -635,14 +632,14 @@ fploff_trz = eventz[stopidx].rescale(plotting_time_unit)
 # plotting grip force and object displacement
 ai_legend = []
 ai_legend_txt = []
-for chidx, chid in enumerate(ainp_signals.array_annotations['channel_ids']):
+for chidx, chid in enumerate(np.asarray(ainp_signals.array_annotations['channel_ids'], dtype=int)):
     ainp = ainp_signals[:, chidx]
-    if ainp.array_annotations['channel_ids'][0] in trialx_chids:
+    if int(ainp.array_annotations['channel_ids'][0]) in trialx_chids:
         ainp_times = ainp.times.rescale(plotting_time_unit)
         mask = (ainp_times > sr) & (ainp_times < stop)
         ainp_ampli = stats.zscore(ainp.magnitude[mask])
 
-        if ainp.array_annotations['channel_ids'][0] != 143:
+        if int(ainp.array_annotations['channel_ids'][0]) != 143:
             color = 'gray'
             ai_legend_txt.append('grip force')
         else:
@@ -652,7 +649,7 @@ for chidx, chid in enumerate(ainp_signals.array_annotations['channel_ids']):
             ax5a.plot(ainp_times[mask], ainp_ampli, color=color)[0])
 
     # get force load of this trial for next plot
-    elif ainp.array_annotations['channel_ids'][0] == 141:
+    elif int(ainp.array_annotations['channel_ids'][0]) == 141:
         ainp_times = ainp.times.rescale(plotting_time_unit)
         mask = (ainp_times > fplon) & (ainp_times < fploff)
         force_av_01 = np.mean(ainp.rescale(behav_signal_unit).magnitude[mask])

code/data_overview_2.py

@@ -33,19 +33,15 @@ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 
-# This loads the Neo and odML libraries shipped with this code. For production
-# use, please use the newest releases of odML and Neo.
-import load_local_neo_odml_elephant
-
 import os
 
 import matplotlib.pyplot as plt
 from matplotlib import gridspec, transforms
 
-import quantities as pq
 import numpy as np
+import quantities as pq
 
-from neo import (AnalogSignal, SpikeTrain)
+from neo import SpikeTrain
 from neo.utils import *
 from reachgraspio import reachgraspio
 
@@ -83,8 +79,8 @@ chosen_events = ['TS-ON', 'WS-ON', 'CUE-ON', 'CUE-OFF', 'GO-ON', 'SR-ON',
 # Load data and metadata for a monkey
 # =============================================================================
 # CHANGE this parameter to load data of the different monkeys
-# monkey = 'Nikos2'
-monkey = 'Lilou'
+monkey = 'Nikos2'
+# monkey = 'Lilou'
 
 datafile = get_monkey_datafile(monkey)
 
@@ -94,11 +90,6 @@ session = reachgraspio.ReachGraspIO(
     verbose=False)
 
 bl = session.read_block(lazy=True)
-    # channels=chosen_els[monkey],
-    # units=[1],  # loading only unit_id 1
-    # load_waveforms=False,
-    # load_events=True,
-    # scaling='voltage')
 
 seg = bl.segments[0]
 
@@ -138,10 +129,10 @@ seg.spiketrains = seg.filter(targdict={'unit_id': chosen_unit},
 seg.analogsignals = seg.filter(targdict={'neural_signal': True},
                                objects='AnalogSignalProxy')
 
-# use most raw data if multiple versions are present
+# use prefiltered data if multiple versions are present
 raw_signal = seg.analogsignals[0]
 for sig in seg.analogsignals:
-    if sig.sampling_rate > raw_signal.sampling_rate:
+    if sig.sampling_rate < raw_signal.sampling_rate:
         raw_signal = sig
 seg.analogsignals = [raw_signal]
 
@@ -150,7 +141,8 @@ seg.analogsignals = [raw_signal]
 seg = load_segment(seg, load_wavefroms=True)
 
 # only keep the chosen electrode signal in the AnalogSignal object
-mask = np.isin(seg.analogsignals[0].array_annotations['channel_ids'], chosen_els[monkey])
+mask = np.isin(np.asarray(seg.analogsignals[0].array_annotations['channel_ids'], dtype=int),
+               chosen_els[monkey])
 
 seg.analogsignals[0] = seg.analogsignals[0][:, mask]
 
@@ -256,7 +248,8 @@ selected_trial = cut_segments[trial_index]
 for el_idx, electrode_id in enumerate(chosen_els[monkey]):
 
     # PLOT ANALOGSIGNALS in upper plot
-    chosen_el_idx = np.where(cut_segments[0].analogsignals[0].array_annotations['channel_ids'] == electrode_id)[0][0]
+    chids = np.asarray(cut_segments[0].analogsignals[0].array_annotations['channel_ids'], dtype=int)
+    chosen_el_idx = np.where(chids == electrode_id)[0][0]
     anasig = selected_trial.analogsignals[0][:, chosen_el_idx]
     ax1.plot(anasig.times.rescale(time_unit),
              np.asarray(anasig.rescale(lfp_unit))
@@ -309,7 +302,8 @@ ax3.set_xlabel('time [%s]' % time_unit, fontdict=fontdict_axis)
 
 
 # plot data for each chosen trial
-chosen_el_idx = np.where(cut_segments[0].analogsignals[0].array_annotations['channel_ids'] == chosen_el[monkey])[0][0]
+chids = np.asarray(cut_segments[0].analogsignals[0].array_annotations['channel_ids'], dtype=int)
+chosen_el_idx = np.where(chids == chosen_el[monkey])[0][0]
 for trial_idx, trial_id in enumerate(trial_indexes):
     trial_spikes = cut_segments[trial_id].filter(channel_id=chosen_el[monkey], objects='SpikeTrain')
     trial_type = cut_segments[trial_id].annotations['trialtype']

code/elephant/CITATION.txt

@@ -1 +0,0 @@
-../../.git/annex/objects/pM/QK/MD5-s150--406acd3d4c454eafea04e056a70e14b8/MD5-s150--406acd3d4c454eafea04e056a70e14b8

code/elephant/LICENSE.txt

@@ -1 +0,0 @@
-../../.git/annex/objects/4K/GQ/MD5-s1506--7a9d8755791957b707aa0a9e26faf593/MD5-s1506--7a9d8755791957b707aa0a9e26faf593

code/elephant/MANIFEST.in

@@ -1 +0,0 @@
-../../.git/annex/objects/K6/Xf/MD5-s583--52fb473cd423b20363501db96b01d042/MD5-s583--52fb473cd423b20363501db96b01d042

code/elephant/PKG-INFO

@@ -1 +0,0 @@
-../../.git/annex/objects/Q1/WX/MD5-s2925--ab354f6fa0ea8caa0a76b6107c79f03e/MD5-s2925--ab354f6fa0ea8caa0a76b6107c79f03e

code/elephant/README.md

@@ -1 +0,0 @@
-../../.git/annex/objects/mM/WG/MD5-s1770--357e859d4bb2713c06ddf80b40373d35/MD5-s1770--357e859d4bb2713c06ddf80b40373d35

code/elephant/doc/Makefile

@@ -1 +0,0 @@
-../../../.git/annex/objects/8k/Ww/MD5-s5572--c4a12f8b6ba74cceea47e8a9fd972340/MD5-s5572--c4a12f8b6ba74cceea47e8a9fd972340

code/elephant/doc/_templates/autosummary/class.rst

@@ -1 +0,0 @@
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code/elephant/doc/acknowledgments.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/mj/MM/MD5-s351--ebfb71a1bf24b00d2557d0159673b6a4/MD5-s351--ebfb71a1bf24b00d2557d0159673b6a4

code/elephant/doc/authors.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/G1/p6/MD5-s2541--2d51cc6845fad7510f6ed8b7d8146b21/MD5-s2541--2d51cc6845fad7510f6ed8b7d8146b21

code/elephant/doc/bib/elephant.bib

@@ -1 +0,0 @@
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code/elephant/doc/citation.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/7J/x2/MD5-s1197--32bed887c47c0d826c404f45f7572698/MD5-s1197--32bed887c47c0d826c404f45f7572698

code/elephant/doc/conf.py

@@ -1 +0,0 @@
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code/elephant/doc/developers_guide.rst

@@ -1 +0,0 @@
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code/elephant/doc/documentation_guide.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/WW/00/MD5-s1248--8f148a9e475a7a821d013cbb10970501/MD5-s1248--8f148a9e475a7a821d013cbb10970501

code/elephant/doc/get_in_touch.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/j5/kJ/MD5-s1075--5462185ac556605e4e053b5d0165db73/MD5-s1075--5462185ac556605e4e053b5d0165db73

code/elephant/doc/images/elephant_favicon.ico

@@ -1 +0,0 @@
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code/elephant/doc/images/elephant_logo.png

@@ -1 +0,0 @@
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code/elephant/doc/images/elephant_logo_sidebar.png

@@ -1 +0,0 @@
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code/elephant/doc/index.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/fg/fG/MD5-s2195--f3ef8b961d5a06b3c54e8612816918ed/MD5-s2195--f3ef8b961d5a06b3c54e8612816918ed

code/elephant/doc/install.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/WW/P3/MD5-s3780--24de5e0d1dc3d2d73488afab86f3418f/MD5-s3780--24de5e0d1dc3d2d73488afab86f3418f

code/elephant/doc/maintainers_guide.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/Z4/GG/MD5-s4780--ba87736b2da033c21f9461c2c58a7dc3/MD5-s4780--ba87736b2da033c21f9461c2c58a7dc3

code/elephant/doc/make.bat

@@ -1 +0,0 @@
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code/elephant/doc/modules.rst

@@ -1 +0,0 @@
-../../../.git/annex/objects/36/3k/MD5-s772--1580ace450f6439c202125e85cd7da07/MD5-s772--1580ace450f6439c202125e85cd7da07

code/elephant/doc/reference/_spike_train_processing.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/MZ/3z/MD5-s182--82fa15302738cb8482ee11d3fcaae07c/MD5-s182--82fa15302738cb8482ee11d3fcaae07c

code/elephant/doc/reference/asset.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/causality.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/38/0W/MD5-s101--f0d20b4a0b246574e3636b87c855b57b/MD5-s101--f0d20b4a0b246574e3636b87c855b57b

code/elephant/doc/reference/cell_assembly_detection.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/change_point_detection.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/conversion.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/cubic.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/current_source_density.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/gpfa.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/kernels.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/neo_tools.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/63/3m/MD5-s115--838c933f8336e8466b20d0e085564c66/MD5-s115--838c933f8336e8466b20d0e085564c66

code/elephant/doc/reference/pandas_bridge.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/parallel.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/phase_analysis.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/signal_processing.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/MM/Jf/MD5-s249--c65b01e17907309b6aae8ff4866afa94/MD5-s249--c65b01e17907309b6aae8ff4866afa94

code/elephant/doc/reference/spade.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/spectral.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/spike_train_correlation.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/spike_train_dissimilarity.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/gg/Fx/MD5-s131--dbd61fc54190a2661643c6658747ee3e/MD5-s131--dbd61fc54190a2661643c6658747ee3e

code/elephant/doc/reference/spike_train_generation.rst

@@ -1 +0,0 @@
-../../../../.git/annex/objects/GF/4J/MD5-s284--70fbd440defb172755b4b2f26a1768b6/MD5-s284--70fbd440defb172755b4b2f26a1768b6

code/elephant/doc/reference/spike_train_surrogates.rst

@@ -1 +0,0 @@
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code/elephant/doc/reference/spike_train_synchrony.rst

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