Initial commit

add interactive notebook

inital commit

add link for polychonry data, brunel_network -> balanced_network

add documentation

update documentation, adding git tracked results

add spikes plot

added

update stochasitc activity results

update stochasitc activity results

separate network images into image folder/

git-annex in rgutzen@PC0W9KFR:~/Projects/eigenangles

git-annex in rgutzen@PC0W9KFR:~/Projects/eigenangles

git-annex in rgutzen@PC0W9KFR:~/Projects/eigenangles

update README

update README

fix markdown links

minor updates to figures and notebooks

update bib

improve figure resolution, use pdf

.gitignore

@@ -0,0 +1,126 @@
+
+.snakemake
+temp*
+sandbox*
+get_*
+set_*
+*.gdf
+*.snakemake_timestamp
+*.npy
+*.pkl
+*.dat
+*.swp
+*.json
+Slurm/
+nest-simulator*/
+
+# git annex folders
+!/stochastic_activity/correlation_matrices/**
+!/polychrony_network/simulation_output/**
+!/balanced_network/simulation_output/**
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*/__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2022, Institute for Neuroscience and Medicine INM-6, Forschungzentrum Juelich.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+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.

README.md

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+# Eigenangles: evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles
+Code and data repository accompanying the manuscript "Eigenangles: evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles" by [Robin Gutzen](https://orcid.org/0000-0001-7373-5962), [Sonja Grün](https://orcid.org/0000-0003-2829-2220), [Michael Denker](https://orcid.org/0000-0003-1255-7300) (2022) https://doi.org/...
+
+[![DOI](https://zenodo.org/badge/DOI/XXXXX/zenodo.XXXXX.svg)](https://doi.org/XXXX/zenodo.XXXXX)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgin.g-node.org%2FINM-6%2Feigenangles/HEAD?labpath=eigenangle_basics.ipynb)
+
+## Keywords
+Statistical Testing | Random Matrix Theory | Neural Network Models | Connectivity-Activity Relation
+
+## Content
+The different applications and testing scenarios of the eigenangle test are separated into the folders [`balanced_network`](./balanced_network), [`stochastic_activity`](./stochastic_activity), and [`polychony_network`](./polychony_network) containing their corresponding workflows (see the respective `README.md` for details). The top-level folder [`scripts`](./scripts) contains a general code basis used by each of the workflows.
+The folder [`paper_figures`](./paper_figures) contains the figures from the publications as generated by either notebooks or scripts in the respective application folders. Figure 1, 2, and 3 are produced by the respective notebook with this folder.
+The interactive jupyter notebook [`eigenangle_basics.ipynb`](https://mybinder.org/v2/git/https%3A%2F%2Fgin.g-node.org%2FINM-6%2Feigenangles/HEAD?labpath=eigenangle_basics.ipynb) presents a step-wise construction and explanation of the eigenangle test and can be executed via the above mybinder badge.
+
+## Data
+The various comparison results of the applications are stored as pandas dataframes in `.csv` files.
+
+The folders `balanced_network/simulation_output`, `polychony_network/simulation_output`, and `stochastic_activity/correlation_matrices` contain larger files and are indexed with [git annex](https://git-annex.branchable.com/). `git clone` only downloads links of these files. To download their content run `git annex sync --content`, or use the gin client.
+
+## Abstract
+Neural systems are often represented by networks, and the strategic comparisons between multiple, similar networks is a prevalent task in many research scenarios.
+In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular the spiking activity correlation and the connectivity.
+The "eigenangle test" is based on quantifying the similarity of two matrices by the angles between their ranked eigenvectors.
+We calibrate the test's behavior with stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in that it is able to evaluate also structural aspects of pairwise measures.
+The principle of the eigenangle test can be applied to compare both the activity correlations as well as the adjacency matrices of certain types of networks and quantify their similarity with the same metric. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity.
+By applying the eigenangle test to the comparison of weight matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features onto the correlated activity.
+Potential applications of the eigenangle test include theoretical explorations, model validation, and data analysis.

balanced_network/README.md

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+
+# Evaluating network rewiring in balanced random network models
+This folder contains the workflow to create and simulate balanced random neural networks, rewire their connectivity by given protocols, compare networks with the eigenangle test, and visualize the results.
+The network configurations are specified via the `config.py` file. The following wildcards in the file paths can update the parameters of the config file as in `N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sex}_sin{sin}_{syndist}` (in the following referred to as {network_specs}). The main functionalities of the workflow are:
+
+* `snakemake rewire_and_redraw` creates and simulates all (rewired) networks with the parameter ranges specified in the config file.
+* `snakemake rewire_comparisons` compares the original network (weight and correlation matrix) with the rewired networks for each initialization, i.e. random seed.
+* `snakemake redraw_comparisons` compares the different network initializations, i.e. across seeds.
+* `snakemake results/{network_specs}/rewiring_results.csv` processes and aggregates all results in that folder into a dataframe.
+* `snakemake simulation_output/{network_specs}/seed_{seed}/{protocol}/<weights/correlations/spikes_{t_start}-{t_stop}ms>.png` plots the corresponding weight-, correlation matrix, or spike rasterplot.
+* `snakemake images/eigenspectrum/{network_specs}_{protocol}.png` plots the corresponding eigenvalue and eigenangle distributions based on 8 random initializations.
+* `snakemake images/pvalue_overview/{network_specs}-{protocol}.png` plots the p-value swarm plots for weights- and correlations comparisons, their ratio, and the firing rate correlations.
+* `snakemake images/pvalue_trend/{network_specs}-{protocol}.png` plots the p-value trends, their ratio, and the rate correlations for the with respect to the corresponding protocol parameters.

balanced_network/Snakefile

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+import config
+import numpy as np
+from pathlib import Path
+
+wildcard_constraints:
+    source_pop = 'E|I',
+    target_pop = 'E|I',
+    fraction = '[\d\.]+',
+    f = '[\d\.]+',
+    N = '\d+',
+    epsilon = '[\d\.]+',
+    eta = '[\d\.]+',
+    mu = '[\d\.]+',
+    seed = '\d+',
+    seed_a = '\d+',
+    seed_b = '\d+',
+    sigma_ex = '[\d\.]+',
+    sigma_in = '[\d\.]+',
+    plot = '[\w\d\-]+',
+    matrix = 'weights|correlations',
+    measures = 'weights|correlations|ratecorr',
+    protocol = '[\w\d\-\.]+',
+    syndist = '[a-z\-]+',
+    network_specs = '[\w\d\.]+',
+    size = '[\d\.]+',
+    length = '\d',
+    connectors = '\d+',
+    connectors_a = '\d+',
+    connectors_b = '\d+',
+
+rule rewire_and_redraw:
+    input:
+        expand('simulation_output/'\
+             + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+             + 'sin{sigma_in}_{syndist}/seed_{seed}/{protocol}/{outfile}', #'{plot}.pdf',
+            N = config.N,
+            f = config.f,
+            mu = config.mu,
+            epsilon = config.epsilon,
+            eta = config.eta,
+            sigma_ex = config.sigma_ex,
+            sigma_in = config.sigma_in,
+            syndist = config.syndist,
+            seed = config.seed,
+            outfile = 'correlations.npy',
+            # plot = ['correlations', 'weights',
+            #         'spikes_50000-60000ms', 'spikes_59000-60000ms'],
+            protocol = ['original']
+                      + expand('shuffle_{frac}_{source}-{target}',
+                               frac=config.shuffle_frac,
+                               source=config.shuffle_source,
+                               target=config.shuffle_target)
+                      + expand('add_{source_frac}{source}-{target_frac}{target}',
+                               source_frac=config.add_source_frac,
+                               target_frac=config.add_target_frac,
+                               source=config.add_source,
+                               target=config.add_target)
+                      + expand('cluster_{num}x{size}{target}_p{epsilon}',
+                               num=config.cluster_number,
+                               size=config.cluster_size,
+                               target=config.cluster_pop,
+                               epsilon=config.cluster_epsilon))
+
+rule redraw_comparisons:
+    input:
+        expand('results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+             + 'sin{sigma_in}_{syndist}/redraw_{protocol}/{measure}_{seed_pair}.csv',
+            N = config.N,
+            f = config.f,
+            mu = config.mu,
+            epsilon = config.epsilon,
+            eta = config.eta,
+            sigma_ex = config.sigma_ex,
+            sigma_in = config.sigma_in,
+            syndist = config.syndist,
+            seed_pair = config.seed_pairs[::int(len(config.seed)/2)],
+            measure = ['correlations', 'weights', 'ratecorr'],
+            protocol =
+                       ['original']
+                     # + expand('cluster_{num}x{size}{target}_p{epsilon}',
+                     #           num=config.cluster_number,
+                     #           size=config.cluster_size,
+                     #           target=config.cluster_pop,
+                     #           epsilon=config.cluster_epsilon)
+                     # + expand('chain_{num}x{size}{target}_j{strength}_p{epsilon}',
+                     #           num=config.chain_length,
+                     #           size=config.chain_size,
+                     #           target=config.chain_pop,
+                     #           strength=config.chain_strength,
+                     #           epsilon=config.chain_epsilon)
+                     )
+
+rule rewire_comparisons:
+    input:
+        expand('results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+             + 'sin{sigma_in}_{syndist}/{protocol}/{measure}_{seed}.csv',
+            N = config.N,
+            f = config.f,
+            mu = config.mu,
+            epsilon = config.epsilon,
+            eta = config.eta,
+            sigma_ex = config.sigma_ex,
+            sigma_in = config.sigma_in,
+            syndist = config.syndist,
+            seed = config.seed,
+            measure = ['correlations', 'weights', 'ratecorr'],
+            protocol =
+                        # expand('sheffle_{frac}_{source}-{target}',
+                        #        frac=3500, #config.shuffle_frac,
+                        #        source=config.shuffle_source,
+                        #        target=config.shuffle_target)
+                     # +
+                      expand('add_{source_frac}{source}-{target_frac}{target}',
+                               source_frac=config.add_source_frac,
+                               target_frac=config.add_target_frac,
+                               source=config.add_source,
+                               target=config.add_target)
+                     # + expand('cluster_{num}x{size}{target}_p{epsilon}',
+                     #           num=config.cluster_number,
+                     #           size=config.cluster_size,
+                     #           target=config.cluster_pop,
+                     #           epsilon=config.cluster_epsilon)
+                       # expand('chain_{length}_{size}{cpop}_j{strength}_p{epsilon}_c{c_a}-{c_b}',
+                       #          length=config.chain_length,
+                       #          c_a=config.connectors[0],
+                       #          c_b=config.connectors[1],
+                       #          size=config.chain_size,
+                       #          cpop=config.chain_pop,
+                       #          strength=config.chain_strength,
+                       #          epsilon=config.chain_epsilon)
+            )
+
+# rule chain_comparisons:
+#     input:
+#         expand('results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+#              + 'sin{sigma_in}_{syndist}/{protocol}/{measure}_{seed}.csv',
+#             N = config.N,
+#             f = config.f,
+#             mu = config.mu,
+#             epsilon = config.epsilon,
+#             eta = config.eta,
+#             sigma_ex = config.sigma_ex,
+#             sigma_in = config.sigma_in,
+#             syndist = config.syndist,
+#             seed = config.seed,
+#             measure = ['correlations', 'weights', 'ratecorr'],
+#             protocol = expand('shuffle_{frac}_{source}-{target}',
+#                                frac=config.shuffle_frac,
+#                                source=config.shuffle_source,
+#                                target=config.shuffle_target)
+#         )
+
+
+# REWIRE EXPERIMENTS
+
+def id_span(N, f, pop):
+    N, f = int(N), float(f)
+    N_exc = int(N*f)
+    if pop == 'E':
+        return (0, N_exc)
+    elif pop =='I':
+        return (N_exc, N)
+    else:
+        raise ValueError
+
+rule shuffle_weights:
+    input:
+        script = 'scripts/shuffle_weights.py',
+        weights = '{root}/N{N}_f{f}_{specs}/original/weights.npy'
+    output:
+        weights = '{root}/N{N}_f{f}_{specs}/'\
+                + 'shuffle_{fraction}_{source_pop}-{target_pop}/weights.npy'
+    params:
+        source_span = lambda w: id_span(w.N, w.f, w.source_pop),
+        target_span = lambda w: id_span(w.N, w.f, w.target_pop),
+    shell:
+        """
+        python {input.script} --input "{input.weights}" \
+                              --output "{output.weights}" \
+                              --source_span {params.source_span} \
+                              --target_span {params.target_span} \
+                              --fraction {wildcards.fraction}
+        """
+
+use rule shuffle_weights as sheffle_weights with:
+    output:
+        weights = '{root}/N{N}_f{f}_{specs}/'\
+                + 'sheffle_{fraction}_{source_pop}-{target_pop}/weights.npy'
+
+
+rule add_weights:
+    input:
+        script = 'scripts/add_weights.py',
+        weights = '{root}/N{N}_f{f}_{specs}/original/weights.npy'
+    output:
+        weights = '{root}/N{N}_f{f}_{specs}/'\
+                + 'add_{source_frac}{source_pop}-{target_frac}{target_pop}'\
+                + '/weights.npy'
+    params:
+        source_span = lambda w: id_span(w.N, w.f, w.source_pop),
+        target_span = lambda w: id_span(w.N, w.f, w.target_pop),
+        weight_mean = lambda w: config.J_ex if w.source_pop == 'E' else config.J_in,
+        weight_std =  lambda w: config.sigma_ex if w.source_pop == 'E' else config.sigma_in,
+        syndist = config.syndist
+    shell:
+        """
+        python {input.script} --input "{input.weights}" \
+                              --output "{output}" \
+                              --source_span {params.source_span} \
+                              --target_span {params.target_span} \
+                              --source_fraction {wildcards.source_frac} \
+                              --target_fraction {wildcards.target_frac} \
+                              --weight_mean {params.weight_mean} \
+                              --weight_std {params.weight_std} \
+                              --syndist {params.syndist}
+        """
+
+rule cluster_weights:
+    input:
+        script = 'scripts/cluster_weights.py',
+        weights = '{root}/N{N}_f{f}_{specs}/original/weights.npy'
+    output:
+        weights = '{root}/N{N}_f{f}_{specs}/'\
+                + 'cluster_{num}x{size}{cpop}_p{epsilon}/weights.npy'
+    params:
+        pop_span = lambda w: id_span(w.N, w.f, w.cpop),
+        weight_mean = lambda w: config.J_ex if w.cpop == 'E' else config.J_in,
+        weight_std =  lambda w: config.sigma_ex if w.cpop == 'E' else config.sigma_in,
+        syndist = config.syndist
+    shell:
+        """
+        python {input.script} --input "{input.weights}" \
+                              --output "{output}" \
+                              --cluster_number {wildcards.num} \
+                              --cluster_fraction {wildcards.size} \
+                              --cluster_prob {wildcards.epsilon} \
+                              --pop_span {params.pop_span} \
+                              --weight_mean {params.weight_mean} \
+                              --weight_std {params.weight_std} \
+                              --syndist {params.syndist}
+        """
+
+rule hub_weights:
+    input:
+        script = 'scripts/hub_weights.py',
+        weights = '{root}/N{N}_f{f}_{specs}/original/weights.npy'
+    output:
+        weights = '{root}/N{N}_f{f}_{specs}/' \
+                + 'hub_{size}{hpop}_j{strength}_p{epsilon}/weights.npy'
+    params:
+        pop_span = lambda w: id_span(w.N, w.f, w.hpop),
+    shell:
+        """
+        python {input.script} --input "{input.weights}" \
+                              --output "{output.weights}" \
+                              --hub_size {wildcards.size} \
+                              --hub_prob {wildcards.epsilon} \
+                              --hub_strength {wildcards.strength} \
+                              --pop_span {params.pop_span}
+        """
+
+def chain_sections(w):
+    min_size = int(w.length)
+    sections = list(range(min_size, int(w.length)+min_size))[::-1]
+    norm = sum(sections)
+    return [i/norm for i in sections]
+
+rule chain_weights:
+    input:
+        script = 'scripts/chain_weights.py',
+        weights = '{root}/N{N}_f{f}_{network_specs}/seed_{seed}/original/weights.npy'
+    output:
+        weights = '{root}/N{N}_f{f}_{network_specs}/seed_{seed}/' \
+                + 'chain_{length}_{size}{cpop}' \
+                + '_j{strength}_p{epsilon}_c{connectors}/weights.npy'
+    params:
+        pop_span = lambda w: id_span(w.N, w.f, w.cpop),
+        chain_sections = chain_sections
+    shell:
+        """
+        python {input.script} --input "{input.weights}" \
+                              --output "{output.weights}" \
+                              --chain_size {wildcards.size} \
+                              --chain_prob {wildcards.epsilon} \
+                              --chain_strength {wildcards.strength} \
+                              --pop_span {params.pop_span} \
+                              --chain_sections "{params.chain_sections}" \
+                              --connectors {wildcards.connectors} \
+                              --seed {wildcards.seed}
+        """
+
+
+# COMPARE NETWORK ACTIVITY AND CONNECTIVITY
+
+rule compare_redrawn_networks:
+    input:
+        script = "../scripts/eigenangle_test.py",
+        matrix_a = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed_a}/{protocol}/{matrix}.npy',
+        matrix_b = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed_b}/{protocol}/{matrix}.npy'
+    output:
+        temp('results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+           + '_sin{sigma_in}_{syndist}/redraw_{protocol}/'\
+           + '{matrix}_{seed_a}-{seed_b}.json')
+    params:
+        bin_num = config.bin_num,
+        is_connectivity = lambda w: True if ('weights' in w.matrix) else False,
+        shuffle_neuron_ids = lambda w, output: 'redraw' in str(output)
+    shell:
+        """
+        python {input.script} --matrix_a {input.matrix_a} \
+                              --matrix_b {input.matrix_b} \
+                              --output {output} \
+                              --N {wildcards.N} \
+                              --bin_num {params.bin_num} \
+                              --mu {wildcards.mu} \
+                              --f {wildcards.f} \
+                              --epsilon {wildcards.epsilon} \
+                              --sigma_ex {wildcards.sigma_ex} \
+                              --sigma_in {wildcards.sigma_in} \
+                              --is_connectivity {params.is_connectivity} \
+                              --shuffle_neuron_ids {params.shuffle_neuron_ids}
+        """
+
+use rule compare_redrawn_networks as compare_rewired_network with:
+    input:
+        script = "../scripts/eigenangle_test.py",
+        matrix_a = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed}/original/{matrix}.npy',
+        matrix_b = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed}/{protocol}/{matrix}.npy',
+    output:
+        temp('results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+           + '_sin{sigma_in}_{syndist}/{protocol}/{matrix}_{seed}.json')
+
+use rule compare_redrawn_networks as compare_chain_networks with:
+    input:
+        script = "../scripts/eigenangle_test.py",
+        matrix_a = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed}/chain_{specs}_'\
+                 + 'c{connectors_a}/{matrix}.npy',
+        matrix_b = 'simulation_output/'\
+                 + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+                 + '_sin{sigma_in}_{syndist}/seed_{seed}/chain_{specs}_'\
+                 + 'c{connectors_b}/{matrix}.npy',
+    output:
+        'results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}'\
+           + '_sin{sigma_in}_{syndist}/chain_{specs}_'\
+           + 'c{connectors_a}-{connectors_b}/{matrix}_{seed}.json'
+#
+# ruleorder:  calc_firing_rate_correlation > score_to_dataframe #> compare_chain_networks > compare_rewired_network
+
+def get_spikes(wildcards):
+    network_specs = wildcards.network_specs
+    protocol = wildcards.protocol
+    seeds = wildcards.seeds
+    path = lambda prcl, s: f'simulation_output/{network_specs}/' \
+                         + f'seed_{s}/{prcl}/spikes.pkl'
+    if 'redraw' in protocol:
+        protocol = protocol.strip('redraw_')
+        return [path(protocol, seed) for seed in seeds.split('-')]
+    elif 'chain' in protocol:
+        protocol, connectors = protocol.split('_c')
+        connectors = connectors.split('-')
+        return [path(f'{protocol}_c{c}', seeds) for c in connectors]
+    else:
+        return [path(p, seeds) for p in ['original', protocol]]
+
+rule calc_firing_rate_correlation:
+    input:
+        script = 'scripts/firing_rate_correlation.py',
+        spikes = get_spikes
+    params:
+        sim_folder = 'simulation_output'
+    output:
+        'results/{network_specs}/{protocol}/ratecorr_{seeds}.csv'
+    shell:
+        """
+        python {input.script} --output "{output}" \
+                              --input "{input.spikes}" \
+                              --protocol {wildcards.protocol} \
+                              --seeds {wildcards.seeds}
+        """
+
+# BUILD AND SIMULATE NETWORK
+
+rule build_network:
+    input:
+        script = 'scripts/build_network.py',
+        config = 'config.py'
+    params:
+        out_config = lambda w, output: Path(output.weights).parents[1] / 'config.yml'
+    output:
+        weights = 'simulation_output/'\
+                + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+                + 'sin{sigma_in}_{syndist}/seed_{seed}/original/weights.npy'
+    shell:
+        """
+        python {input.script} --weights_path {output.weights} \
+                              --out_config {params.out_config} \
+                              --network_config {input.config} \
+                              --N {wildcards.N} \
+                              --f {wildcards.f} \
+                              --mu {wildcards.mu} \
+                              --sigma_ex {wildcards.sigma_ex} \
+                              --sigma_in {wildcards.sigma_in} \
+                              --epsilon {wildcards.epsilon} \
+                              --seed {wildcards.seed} \
+                              --syndist {wildcards.syndist}
+        """
+
+rule simulate_network:
+    input:
+        script = 'scripts/simulate_network.py',
+        weights = 'simulation_output/'\
+                + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+                + 'sin{sigma_in}_{syndist}/seed_{seed}/{protocol}/weights.npy',
+    output:
+        spikes_ex = temp('simulation_output/'\
+                  + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+                  + 'sin{sigma_in}_{syndist}/seed_{seed}/{protocol}/spikes_ex.gdf'),
+        spikes_in = temp('simulation_output/'\
+                  + 'N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}_'\
+                  + 'sin{sigma_in}_{syndist}/seed_{seed}/{protocol}/spikes_in.gdf'),
+    params:
+        simtime = config.simtime,
+        config_path = 'config.py'
+    shell:
+        """
+        python {input.script} --spikes_ex_path {output.spikes_ex} \
+                              --spikes_in_path {output.spikes_in} \
+                              --weights_path {input.weights} \
+                              --network_config {params.config_path} \
+                              --N {wildcards.N} \
+                              --f {wildcards.f} \
+                              --mu {wildcards.mu} \
+                              --sigma_ex {wildcards.sigma_ex} \
+                              --sigma_in {wildcards.sigma_in} \
+                              --epsilon {wildcards.epsilon} \
+                              --simtime {params.simtime} \
+                              --seed {wildcards.seed} \
+                              --eta {wildcards.eta}
+        """
+
+# TRANSFORM RULES
+
+rule nest_to_neo:
+    input:
+        script = 'scripts/nest_to_neo.py',
+        spikes_ex = 'simulation_output/N{N}_f{f}_{specs}/spikes_ex.gdf',
+        spikes_in = 'simulation_output/N{N}_f{f}_{specs}/spikes_in.gdf',
+    output:
+        'simulation_output/N{N}_f{f}_{specs}/spikes.pkl'
+    params:
+        t_start = 0,
+        t_stop = config.simtime
+    shell:
+        """
+        python {input.script} --spikes_ex "{input.spikes_ex}" \
+                              --spikes_in "{input.spikes_in}" \
+                              --output {output} \
+                              --t_stop {params.t_stop} \
+                              --t_start {params.t_start} \
+                              --N {wildcards.N} \
+                              --f {wildcards.f}
+        """
+
+rule create_correlation_matrix_from_spikes:
+    input:
+        script = 'scripts/correlation_matrix_from_spikes.py',
+        spikes = '{spikes_dir}/spikes.pkl',
+    output:
+        correlation = '{spikes_dir}/correlations.npy'
+    params:
+        t_start = config.cut_inital_time,
+        t_stop = config.simtime,
+        bin_size = config.bin_size
+    shell:
+        """
+        python {input.script} --spikes "{input.spikes}" \
+                              --output {output} \
+                              --t_stop {params.t_stop} \
+                              --t_start {params.t_start} \
+                              --bin_size {params.bin_size}
+        """
+
+rule score_to_dataframe:
+    input:
+        script = '../scripts/score_to_dataframe.py',
+        data = 'results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}' \
+             + '_sin{sigma_in}_{syndist}/{protocol}/{matrix}_{seeds}.json'
+    output:
+        'results/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}_sex{sigma_ex}' \
+        + '_sin{sigma_in}_{syndist}/{protocol}/{matrix}_{seeds}.csv'
+    params:
+        bin_num = config.bin_num,
+        simtime = config.simtime
+    shell:
+        """
+        python {input.script} --output "{output}" \
+                              --input "{input.data}" \
+                              --params {wildcards} \
+                              --bin_num {params.bin_num} \
+                              --simtime {params.simtime}
+        """
+
+rule merge_comparison_results:
+    input:
+        script = '../scripts/merge_dataframes.py',
+    params:
+        exclude = ['merged_ratecorr_results', 'merged_comparison_results',
+                   'rewiring_results', 'ratecorr']
+    output:
+        temp('{dir}/merged_comparison_results.csv')
+    shell:
+        """
+        python {input.script} --output "{output}" \
+                              --exclude "{params.exclude}"
+        """
+
+use rule merge_comparison_results as merge_ratecorr_results with:
+    params:
+        exclude = ['merged_ratecorr_results', 'merged_comparison_results',
+                   'rewiring_results', 'weights', 'correlations']
+    output:
+        temp('{dir}/merged_ratecorr_results.csv')
+
+rule process_result_dataframe:
+    input:
+        script = 'scripts/process_result_dataframe.py',
+        comparison_df = '{dir}/merged_comparison_results.csv',
+        ratecorr_df = '{dir}/merged_ratecorr_results.csv'
+    output:
+        '{dir}/rewiring_results.csv'
+    shell:
+        """
+        python {input.script}  --output "{output}" \
+                               --comparison_df "{input.comparison_df}" \
+                               --ratecorr_df "{input.ratecorr_df}"
+        """
+
+# PLOT SIMULATION OUTPUT
+
+rule plot_spiketrains:
+    input:
+        script = '../scripts/plot_spiketrains.py',
+        spikes = 'simulation_output/{network_specs}/seed_{seed}/{protocol}/spikes.pkl'
+    output:
+        'images/rasterplot/{network_specs}_seed{seed}_{protocol}_spikes{t_start}-{t_stop}ms.pdf'
+    shell:
+        """
+        python {input.script} --spikes "{input.spikes}" \
+                              --output "{output}" \
+                              --t_start {wildcards.t_start} \
+                              --t_stop {wildcards.t_stop}
+        """
+
+rule plot_matrix:
+    input:
+        script = '../scripts/plot_matrix.py',
+        matrix = 'simulation_output/{network_specs}/seed_{seed}/{protocol}/{matrix}.npy'
+    output:
+        'images/{matrix}/{network_specs}_seed{seed}_{protocol}.pdf'
+    shell:
+        """
+        python {input.script} --input "{input.matrix}" \
+                              --output "{output}"
+        """
+
+rule plot_eigenspectrum:
+    input:
+        script = 'scripts/plot_eigenspectrum.py',
+        files = expand('simulation_output/N{{N}}_f{{f}}_mu{{mu}}_p{{epsilon}}' \
+                     + '_eta{{eta}}_sex{{sigma_ex}}_sin{{sigma_in}}_{{syndist}}' \
+                     + '/seed_{seed}/{{protocol}}/weights.npy',
+                       seed = range(1,9))
+    output:
+        'images/eigenspectrum/N{N}_f{f}_mu{mu}_p{epsilon}_eta{eta}' \
+      + '_sex{sigma_ex}_sin{sigma_in}_{syndist}_{protocol}.pdf'
+    shell:
+        """
+        python {input.script} --input "{input.files}" \
+                              --output "{output}" \
+                              --N {wildcards.N} \
+                              --f {wildcards.f} \
+                              --mu {wildcards.mu} \
+                              --sigma_ex {wildcards.sigma_ex} \
+                              --sigma_in {wildcards.sigma_in} \
+                              --epsilon {wildcards.epsilon} \
+        """
+
+rule plot_pvalues:
+    input:
+        script = 'scripts/plot_pvalue_{plot_name}.py',
+        dataframe = 'results/{network_specs}/rewiring_results.csv'
+    output:
+        'images/pvalue_{plot_name}/{network_specs}-{protocol}.pdf'
+    shell:
+        """
+        python {input.script} --input "{input.dataframe}" \
+                              --output "{output}" \
+                              --protocol {wildcards.protocol}
+        """

balanced_network/config.py

@@ -0,0 +1,99 @@
+import itertools
+
+# number of neurons
+N = 1000
+# fraction of excitatory cells
+f = 0.8
+# exc. strength (postsynaptic amplitude in mV)
+mu = 0.1
+# connection probability
+epsilon = 0.1
+# external rate relative to threshold rate
+eta = 0.9
+# type of distribution for synaptic weights
+syndist = 'lognormal' # 'truncated-normal' 'lognormal' 'normal'
+# variance of weight sample distributions
+sigma_ex = 0.12
+sigma_in = 0.1
+# simulation time
+simtime = 61000 #ms
+
+###### rewiring parameters ######
+shuffle_source = ['E', 'I']
+shuffle_target = ['E', 'I']
+shuffle_frac = 1.0
+
+add_source = 'E'
+add_target = 'E'
+add_source_frac = [0.2, 10000]
+# add_target_frac > p/(1-p) * add_source_frac
+add_target_frac = [0.02, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0]
+
+cluster_pop = 'E'
+cluster_number = 3
+cluster_size = [0.02, 0.04, 0.06, 0.08]
+cluster_epsilon = [0.2, 0.3, 0.4, 0.5, 0.6]
+
+hub_pop = 'E'
+hub_size = [0.04, 0.06, 0.08, 0.10]
+hub_strength = [0.2, 0.3, 0.4, 0.5]
+hub_epsilon = 0.5
+
+chain_pop = 'E'
+chain_size = [0.05, 0.10, 0.15, 0.20]
+chain_strength = [0.3, 0.4, 0.5, 0.6]
+chain_epsilon = 0.6
+chain_length = 3
+connectors = [0, 30]
+
+###### derived & fixed network parameters ######
+J_ex = mu
+# enforce global balance state
+J_in = -f * J_ex / (1-f)
+
+dt = 0.1  # the resolution in ms
+delay = [.5, 3]  #1.5 # min/max synaptic delays in ms (uniform)
+
+NE = int(f * N)  # number of excitatory neurons
+NI = N - NE # number of inhibitory neurons
+
+in_connection_rule = 'pairwise_bernoulli'
+ex_connection_rule = 'pairwise_bernoulli'
+
+synapse_model = 'static_synapse'
+neuron_model = 'iaf_psc_delta'
+tauMem = 20.0  # time constant of membrane potential in ms
+theta = 20.0  # membrane threshold potential in mV
+neuron_params = {"C_m": 1.0,
+                 "tau_m": tauMem,
+                 "t_ref": 2.0,
+                 "E_L": 0.0,
+                 "V_reset": 0.0,
+                 "V_m": 0.0,
+                 "V_th": theta}
+
+# Driving stiumulus parameters
+stimulus = "poisson_generator"
+parrot_input = False
+CE = epsilon * NE  # estimated number of excitatory synapses per neuron
+CI = epsilon * NI  # estimated number of inhibitory synapses per neuron
+
+if J_ex:
+    nu_th = theta / (J_ex * CE * tauMem)
+else:
+    nu_th = theta / (CE * tauMem)
+
+nu_ex = eta * nu_th
+p_rate = 1000.0 * nu_ex * CE
+p_rate_func = lambda eta: 1000 * eta*nu_th * CE
+
+###### non-network parameters ######
+# cut swinging-in time
+cut_inital_time = 1000 #ms
+# bin size for correlation calculation
+bin_size = 2 #ms
+# derived number of bins
+bin_num = int((simtime - cut_inital_time) / bin_size)
+# seeds for multiple runs with same specification
+seed = list(range(1,101))
+seed_pairs = [f'{i[0]}-{i[1]}' for i in itertools.combinations(seed,2)]

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_1.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,1,0.12,0.1,lognormal,30000,61000,0.002855298535401754,5.169644182290156e-06

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_10.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,10,0.12,0.1,lognormal,30000,61000,0.0024880595116948657,6.082926265738031e-05

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_100.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,100,0.12,0.1,lognormal,30000,61000,0.0008893684263528333,0.08478103302124018

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_11.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,11,0.12,0.1,lognormal,30000,61000,0.0013320546354427085,0.019827120777187623

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_12.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,12,0.12,0.1,lognormal,30000,61000,0.0020292799744888023,0.0008616647478827399

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_13.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,13,0.12,0.1,lognormal,30000,61000,0.0013434850668273188,0.018993883662461408

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_14.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,14,0.12,0.1,lognormal,30000,61000,0.0004914780561003111,0.22390284408157124

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_15.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,15,0.12,0.1,lognormal,30000,61000,0.0012797361300728803,0.02404752075552702

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_16.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,16,0.12,0.1,lognormal,30000,61000,0.0013420432470100572,0.019097317902285527

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_17.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,17,0.12,0.1,lognormal,30000,61000,0.0008458835333864416,0.09569965731315211

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_18.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,18,0.12,0.1,lognormal,30000,61000,0.002026717895572849,0.0008733575648418035

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_19.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,19,0.12,0.1,lognormal,30000,61000,0.002058881128515799,0.0007366241250686412

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_2.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,2,0.12,0.1,lognormal,30000,61000,0.0017410931685037112,0.0035824116002349804

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_20.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,20,0.12,0.1,lognormal,30000,61000,0.0022811428858797666,0.00021319997256041834

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_21.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,21,0.12,0.1,lognormal,30000,61000,-0.00024959871500287566,0.6500666403909479

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_22.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,22,0.12,0.1,lognormal,30000,61000,0.001380071864317805,0.0165242206738464

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_23.csv

@@ -0,0 +1,2 @@
+,N,epsilon,eta,f,matrix,mu,protocol,seeds,sigma_ex,sigma_in,syndist,bin_num,simtime,score,pvalue
+0,1000,0.1,0.9,0.8,correlations,0.1,add_0.2E-0.1E,23,0.12,0.1,lognormal,30000,61000,0.001899694802065152,0.0016728953044518047

balanced_network/results/N1000_f0.8_mu0.1_p0.1_eta0.9_sex0.12_sin0.1_lognormal/add_0.2E-0.1E/correlations_24.csv

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