Welcome to mycelyso’s documentation!

See mycelyso Readme for information.

Indices and tables

• Index
• Module Index
• Search Page

Contents:

mycelyso Readme

Frontmatter

Screenshot

To quickly get a grasp what results can be generated with mycelyso, please take a look at the static demo page of mycelyso Inspector generated with the example dataset.

Installation and Analysis Tutorial Videos

 

These videos shows how to download and unpack mycelyso as well as to run a test analysis using the pre-packages Windows version of mycelyso.

Publication

When using mycelyso for scientific applications, please cite our publication:

Sachs CC, Koepff J, Wiechert W, Grünberger A, Nöh K (2019) mycelyso – high-throughput analysis of Streptomyces mycelium live cell imaging data BMC Bioinformatics, volume 20, 452, doi: 10.1186/s12859-019-3004-1

It is available on the BMC Bioinformatics homepage at DOI: 10.1186/s12859-019-3004-1

Documentation

Documentation can be built using sphinx, but is available online as well at Read the Docs.

License

mycelyso is free/libre open source software under the 2-clause BSD License. See License

Getting mycelyso and Datasets

Example Datasets

You can find an example dataset deposited at zenodo DOI: 10.5281/zenodo.376281.

Ways to install mycelyso

Pre-Bundled Windows Application

If you don’t have a Python 3 installation ready, and want to just run mycelyso, we you can download a pre-packaged version for 64-bit versions of Windows (mycelyso-win64.zip) from AppVeyor.

Please note, that, instead of python -m mycelyso or python -m mycelyso_inspector, the calls would then be mycelyso.exe or mycelyso_inspector.exe.

Docker

Please see the Docker section near the end.

Packages for the conda Package manager

While mycelyso is a pure Python package, it has some dependencies which are a bit more complex to build and might not be present in the PyPI (Python Package Index). Thankfully the conda Package manager / Anaconda environment provides all packages necessary in an easy to use manner. To use it, please download Anaconda (Miniconda could be downloaded as well, but as most packages included in Anaconda are needed anyways, it does hardly provide a size benefit).

You have to enable the necessary channels (we aim to add mycelyso to bioconda lateron):

> conda config --add channels conda-forge
> conda config --add channels bioconda
> conda config --add channels csachs

> conda install -y mycelyso mycelyso-inspector

Please note that this readme assumes you are starting with a fresh install of Anaconda/Miniconda. If you start with an existing installation, individual dependency packages might need to be updated.

Packages from PyPI (for advanced users)

If you have a working Python 3 installation and can eventually fix missing dependencies, you can as well use the PyPI version:

> pip install --user mycelyso mycelyso-inspector

Directly from github (for advanced users)

> pip install --user https://github.com/modsim/mycelyso/archive/master.zip mycelyso-inspector

mycelyso Quickstart

mycelyso is packaged as a Python module, to run it, use the following syntax:

> python -m mycelyso

Which will produce the help screen:

mycelyso INFO
  MYCElium   anaLYsis __ SOftware
  ___   __ _________ / /_ _____ ___         Developed  2015 - 2019 by
 /  ' \/ // / __/ -_) / // (_-</ _ \ __
/_/_/_/\_, /\__/\__/_/\_, /___/\___/'  \.   Christian   C.  Sachs  at
      /___/          /___/              |
            \    `           __     ,''''   Modeling&Simulation Group
             \    `----._ _,'  `'  _/
              ---'       ''      `-'        Research  Centre  Juelich

               For more information visit: github.com/modsim/mycelyso

If you use this software in a publication, please cite our paper:

Sachs CC, Koepff J, Wiechert W, Grünberger A, Nöh K (2019)
mycelyso – high-throughput analysis of Streptomyces mycelium live cell imaging data
BMC Bioinformatics, volume 20, 452, doi: 10.1186/s12859-019-3004-1

usage: __main__.py [-h] [-m MODULES] [-n PROCESSES] [--prompt]
                   [-tp TIMEPOINTS] [-mp POSITIONS] [-t TUNABLE]
                   [--tunables-show] [--tunables-load TUNABLES_LOAD]
                   [--tunables-save TUNABLES_SAVE] [--meta META]
                   [--interactive] [--output OUTPUT]
                   input

positional arguments:
  input                 input file

optional arguments:
  -h, --help            show this help message and exit
  -m MODULES, --module MODULES
  -n PROCESSES, --processes PROCESSES
  --prompt, --prompt
  -tp TIMEPOINTS, --timepoints TIMEPOINTS
  -mp POSITIONS, --positions POSITIONS
  -t TUNABLE, --tunable TUNABLE
  --tunables-show
  --tunables-load TUNABLES_LOAD
  --tunables-save TUNABLES_SAVE
  --meta META, --meta META
  --interactive, --interactive
  --output OUTPUT, --output OUTPUT

To run an analysis, just pass the appropriate filename as a parameter. The desired timepoints can be selected via the --timepoints switch, and if the file contains multiple positions, they can be selected with --positions. Supported file formats are TIFF, OME-TIFF, Nikon ND2 and Zeiss CZI.

The analysis will use all cores present by default. While this is generally desirable, it might lead to consuming too much memory (as each parallel acting process needs a certain additionally amount of memory). If you notice that mycelyso takes up too much memory, try limiting the number of processes via -n. If you choose -n 0, the code will additionally not use the parallel subsystem (multiprocessing).

Running an analysis

To analyze the example dataset, run: (-t BoxDetection=1 is used, as the spores were grown in rectangular growth chambers, which are to be detected. Otherwise, the software will use the whole image, or cropping values as set via -t CropWidth=.../-t CropHeight=.... If the data is pre-segmented (i.e. input is a binary image stack), choose -t SkipBinarization=1.

> python -m mycelyso S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff -t BoxDetection=1

Optionally, you can inspect the segmentation and produced graph on a per-frame basis before running a complete analysis, by adding the --interactive flag, in which case mycelyso will start an interactive viewer.

mycelyso stores all data compressed in HDF5 files, by default it will write a file called output.h5 (can be changed with --output).

> ls -lh --time-style=+
total 1.3G
-rw-rw-r-- 1 sachs sachs 5.4M  output.h5
-rw-rw-r-- 1 sachs sachs 1.5G  S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff

Multiple datasets/positions can be stored in the same file, it will only make problems if the same position is about to be analyzed twice. Binary masks/skeletons are stored in the HDF5 file, as well as GraphML representations of the tracking graphs. The HDF5 file can be investigated with standard HDF5 tools, tabular data is to be opened with pandas, as it is stored with its format.

Results visualization using mycelyso Inspector

However, since the raw data is only interesting if you want to perform custom analyses, it is much more straightforward to use the integrated visualization tool mycelyso Inspector as a helper to take a look at the results:

> python -m mycelyso_inspector

mycelyso Inspector will output the URL it is serving content at, and by default automatically open a browser window with it.

In mycelyso Inspector, you have various information displays: On the top, the HDF5 file / analyzed dataset / position can be selected. On the left, there is a list of graphs available. In the middle, there is the currently selected graph displayed. On the right, there is general information about the whole position (colony level statistics), below the main part is a table with information about individual tracks, and scrolled further down is the possibility to show individual graph tracking in 2D or a colony growth oversight in 3D. Sticky at the bottom is binarized or skeletonized timeline of the position.

The data to all graphs can be downloaded as tab separated text by pressing the right mouse button on a certain graph link (in the left menu) and choosing ‘Save As’.

Information: Occasional warnings in the console about invalid values are due to missing/invalid data points, and are of no particular concern.

WARNING: mycelyso Inspector will serve results from all HDF5 (.h5) files found in the current directory via an embedded webserver. Furthermore as a research tool, no special focus was laid on security, as such, you are assumed to prevent unauthorized access to the tool if you choose to use an address accessible by third parties.

Setting calibration data for TIFF files

TIFF files provide no standard way to set temporal information per frame. To set these parameters manually, e.g. a pixel size of 0.09 µm/pixel and an acquisition interval of 600 s (10 min) use:

> python -m mycelyso "the_file.tif?calibration=0.09;interval=600"

Tunable Parameters

The analysis’ internal workings are dependent upon some tunable parameters. All tunables are listed in the tunables documentation subpage. To check their current value, you can view them all using the --tunables-show command line option, which will as well print documentation. To set individual ones to a different values one can use -t SomeTunable=NewValue. Individual tunables are documented within the API documentation as well.

> python -m mycelyso --tunables-show
> python -m mycelyso -t SomeTunable=42

Docker

Docker a tool allowing for software to be run in pre-defined, encapsulated environments called containers. To run mycelyso via Docker, an image is used which is a self-contained Linux system with mycelyso installed, which can either be preloaded or will be downloaded on the fly.

Use the following commands to run mycelyso via Docker:

To analyze:

> docker run --tty --interactive --rm --volume `pwd`:/data --user `id -u` modsim/mycelyso <parameters ...>

To run mycelyso Inspector:

> docker run --tty --interactive --rm --volume `pwd`:/data --user `id -u` --publish 8888:8888 --entrypoint python modsim/mycelyso -m mycelyso_inspector <parameters ...>

To run interactive mode (display on local X11, under Linux):

> docker run --tty --interactive --rm --volume `pwd`:/data --user `id -u` --env DISPLAY=$DISPLAY --volume /tmp/.X11-unix:/tmp/.X11-unix modsim/mycelyso --interactive <parameters ...>

General remarks: --tty is used to allocate a tty, necessary for interactive usage, like --interactive which connects to stdin/stdout. The --rm switch tells docker to remove the container (not image) again after use. As aforementioned, docker is containerized, i.e. unless explicitly stated, no communication with the outside is possible. Therefore via --volume the current working directory is mapped into the container.

Third Party Licenses

Note that this software contains the following portions from other authors, under the following licenses (all BSD-flavoured):

mycelyso/pilyso/imagestack/readers/external/czifile.py:

czifile.py by Christoph Gohlke, licensed BSD (see file head).

Copyright (c) 2013-2015, Christoph Gohlke, 2013-2015, The Regents of the University of California

License

The 2-clause BSD License

Copyright (c) 2015-2019 Christian C. Sachs, Forschungszentrum Jülich GmbH. 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.

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 OWNER 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.

Example HDF5 Insights

This Jupyter Notebook should give a brief overview how to programmatically analyze the HDF5 files produced by mycelyso. Please note that you can always inspect these files with mycelyso Inspector as well, this tutorial should just give you a hint how to open these files if you might want to write your own analyses.

First, it is assumed that an output.h5 is present in the current directory, with an analysis of the example dataset contained.

You can fetch the example dataset by running get-dataseth.sh or download it manually at https://zenodo.org/record/376281.

Afterwards, analyze it with:

> python -m mycelyso S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff -t BoxDetection=1

Afterwards, you will have an output.h5 in the residing in the directory.

We will be using Pandas to read our data, while the non-tabular data could easily be read with any other HDF5 compatible tool, the tabular data is layed out in a chunked format particular to Pandas, and as such it is easiest to open it with Pandas.

First, some general setup …

%matplotlib inline
%config InlineBackend.figure_formats=['svg']
import pandas
pandas.options.display.max_columns = None
import numpy as np
import networkx as nx
from networkx.readwrite import GraphMLReader

from matplotlib import pyplot, ticker
pyplot.rcParams.update({
    'figure.figsize': (10, 6), 'svg.fonttype': 'none',
    'font.sans-serif': 'Arial', 'font.family': 'sans-serif',
    'image.cmap': 'gray_r', 'image.interpolation': 'none'
})

Opening the HDF5 file

We will load the output.h5 using pandas.HDFStore …

store = pandas.HDFStore('output.h5', 'r')
store

<class 'pandas.io.pytables.HDFStore'>
File path: output.h5
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/result_table                                                                          frame        (shape->[1,208])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/result_table_collected                                                                frame        (shape->[136,27])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000001            frame        (shape->[22,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000002            frame        (shape->[29,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000003            frame        (shape->[11,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000004            frame        (shape->[23,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000005            frame        (shape->[16,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000006            frame        (shape->[14,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000007            frame        (shape->[12,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000008            frame        (shape->[9,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000009            frame        (shape->[17,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000010            frame        (shape->[11,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000011            frame        (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000012            frame        (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000013            frame        (shape->[10,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000014            frame        (shape->[5,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000015            frame        (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000016            frame        (shape->[5,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000017            frame        (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000018            frame        (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000019            frame        (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000020            frame        (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_mapping_track_table_aux_tables/track_table_aux_tables_000000000               frame        (shape->[20,2])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/track_table/track_table_000000000                                              frame        (shape->[20,66])

Now let’s dive a bit into the HDF5 file.

Remember that HDF5 stands for Hierarchical Data Format 5 …

root = store.get_node('/')

print("Root:")
print(repr(root))
print()
print("/results:")
print(repr(root.results))

Root:
/ (RootGroup) ''
  children := ['results' (Group)]

/results:
/results (Group) ''
  children := ['mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff' (Group)]

The key names are dependent on the on-disk path of the analyzed file. Assuming there is only one file analyzed with one position in the file, we pick the first …

for image_file in root.results:
    print(image_file)
    for position in image_file:
        print(position)
        break

/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected (Group) ''

We can now investigate what data is available for that particular position

There is e.g., (binary) data, there are images, and there are various tabular datasets

print("data")
print(position.data)
for node in position.data:
    print(node)

print()

print("nodes")
print(position.images)
for node in position.images:
    print(node)

print()

data
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/banner (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/graphml (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/overall_graphml (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/tunables (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/version (Group) ''

nodes
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images/binary (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images/skeleton (Group) ''

Accessing Graph and Image Data

Let’s for example start with pulling out an image from the file, and displaying it …

binary_images = list(position.images.binary)
skeleton_images = list(position.images.skeleton)

n = 120

total = len(binary_images)
assert 0 <= n < total

print("Total count of images: %d" % (total,))

fig, (ax_l, ax_r) = pyplot.subplots(1, 2, sharey=True)

fig.suptitle('Images of Timepoint #%d:' % (n,))

ax_l.imshow(binary_images[n])
ax_l.set_title('Binary Image')

ax_r.imshow(skeleton_images[n])
ax_r.set_title('Skeleton')

Total count of images: 136

Text(0.5,1,'Skeleton')

Let’s now take a look at the graph data present for the position, display it and overlay it onto the image data …

# The graph structure is saved in GraphML
draw_parameters = dict(node_size=25, node_color='darkgray', linewidths=0, edge_color='darkgray', with_labels=False)

#graphml_data = list([np.array(graphml).tobytes() for graphml in list(position.data.graphml)])
graphml_data = list(position.data.graphml)

graph, = GraphMLReader()(string=np.array(graphml_data[n]).tobytes())

# the following draw function needs separate positions...
# each node has its position saved as attributes:

example_node_id = list(sorted(graph.node.keys()))[1]

print("Example node: %s: %r" % (example_node_id, graph.node[example_node_id],))

other_node_id = list(sorted(graph.adj[example_node_id].keys(), reverse=True))[0]

print("Some other node: %s" % (other_node_id,))


print("The distance between the two nodes is: %.2f px" % (graph.adj[example_node_id][other_node_id]['weight']))

pyplot.title('Graph Representation of Timepoint #%d:' % (n,))

# first draw the graph,
pos = {n_id: (n['x'], n['y']) for n_id, n in graph.node.items()}
nx.draw_networkx(graph, pos=pos, **draw_parameters)

example_nodes = [graph.node[node_id] for node_id in [example_node_id, other_node_id]]

# mark on top the two choosen sample nodes
pyplot.scatter([p['x'] for p in example_nodes], [p['y'] for p in example_nodes], zorder=2)

# then show the corresponding binarized image
pyplot.imshow(binary_images[n])

Example node: 1: {'x': 543.0, 'y': 91.0}
Some other node: 4
The distance between the two nodes is: 192.05 px

<matplotlib.image.AxesImage at 0x7f89d9770128>

Accessing Tabular Data

In the next few cells we’ll take a look at the tabular data stored in the HDF5 file.

There is for example the result_table, which contains compounded information about the whole position:

result_table = store[position.result_table._v_pathname]
result_table

	_mapping_track_table_aux_tables	banner	covered_area_linear_regression_intercept	covered_area_linear_regression_pvalue	covered_area_linear_regression_rvalue	covered_area_linear_regression_slope	covered_area_linear_regression_stderr	covered_area_logarithmic_regression_intercept	covered_area_logarithmic_regression_pvalue	covered_area_logarithmic_regression_rvalue	covered_area_logarithmic_regression_slope	covered_area_logarithmic_regression_stderr	covered_area_optimized_linear_regression_begin	covered_area_optimized_linear_regression_begin_index	covered_area_optimized_linear_regression_end	covered_area_optimized_linear_regression_end_index	covered_area_optimized_linear_regression_intercept	covered_area_optimized_linear_regression_pvalue	covered_area_optimized_linear_regression_rvalue	covered_area_optimized_linear_regression_slope	covered_area_optimized_linear_regression_stderr	covered_area_optimized_logarithmic_regression_begin	covered_area_optimized_logarithmic_regression_begin_index	covered_area_optimized_logarithmic_regression_end	covered_area_optimized_logarithmic_regression_end_index	covered_area_optimized_logarithmic_regression_intercept	covered_area_optimized_logarithmic_regression_pvalue	covered_area_optimized_logarithmic_regression_rvalue	covered_area_optimized_logarithmic_regression_slope	covered_area_optimized_logarithmic_regression_stderr	covered_ratio_linear_regression_intercept	covered_ratio_linear_regression_pvalue	covered_ratio_linear_regression_rvalue	covered_ratio_linear_regression_slope	covered_ratio_linear_regression_stderr	covered_ratio_logarithmic_regression_intercept	covered_ratio_logarithmic_regression_pvalue	covered_ratio_logarithmic_regression_rvalue	covered_ratio_logarithmic_regression_slope	covered_ratio_logarithmic_regression_stderr	covered_ratio_optimized_linear_regression_begin	covered_ratio_optimized_linear_regression_begin_index	covered_ratio_optimized_linear_regression_end	covered_ratio_optimized_linear_regression_end_index	covered_ratio_optimized_linear_regression_intercept	covered_ratio_optimized_linear_regression_pvalue	covered_ratio_optimized_linear_regression_rvalue	covered_ratio_optimized_linear_regression_slope	covered_ratio_optimized_linear_regression_stderr	covered_ratio_optimized_logarithmic_regression_begin	covered_ratio_optimized_logarithmic_regression_begin_index	covered_ratio_optimized_logarithmic_regression_end	covered_ratio_optimized_logarithmic_regression_end_index	covered_ratio_optimized_logarithmic_regression_intercept	covered_ratio_optimized_logarithmic_regression_pvalue	covered_ratio_optimized_logarithmic_regression_rvalue	covered_ratio_optimized_logarithmic_regression_slope	covered_ratio_optimized_logarithmic_regression_stderr	filename	filename_complete	graph_edge_count_linear_regression_intercept	graph_edge_count_linear_regression_pvalue	graph_edge_count_linear_regression_rvalue	graph_edge_count_linear_regression_slope	graph_edge_count_linear_regression_stderr	graph_edge_count_logarithmic_regression_intercept	graph_edge_count_logarithmic_regression_pvalue	graph_edge_count_logarithmic_regression_rvalue	graph_edge_count_logarithmic_regression_slope	graph_edge_count_logarithmic_regression_stderr	graph_edge_count_optimized_linear_regression_begin	graph_edge_count_optimized_linear_regression_begin_index	graph_edge_count_optimized_linear_regression_end	graph_edge_count_optimized_linear_regression_end_index	graph_edge_count_optimized_linear_regression_intercept	graph_edge_count_optimized_linear_regression_pvalue	graph_edge_count_optimized_linear_regression_rvalue	graph_edge_count_optimized_linear_regression_slope	graph_edge_count_optimized_linear_regression_stderr	graph_edge_count_optimized_logarithmic_regression_begin	graph_edge_count_optimized_logarithmic_regression_begin_index	graph_edge_count_optimized_logarithmic_regression_end	graph_edge_count_optimized_logarithmic_regression_end_index	graph_edge_count_optimized_logarithmic_regression_intercept	graph_edge_count_optimized_logarithmic_regression_pvalue	graph_edge_count_optimized_logarithmic_regression_rvalue	graph_edge_count_optimized_logarithmic_regression_slope	graph_edge_count_optimized_logarithmic_regression_stderr	graph_edge_length_linear_regression_intercept	graph_edge_length_linear_regression_pvalue	graph_edge_length_linear_regression_rvalue	graph_edge_length_linear_regression_slope	graph_edge_length_linear_regression_stderr	graph_edge_length_logarithmic_regression_intercept	graph_edge_length_logarithmic_regression_pvalue	graph_edge_length_logarithmic_regression_rvalue	graph_edge_length_logarithmic_regression_slope	graph_edge_length_logarithmic_regression_stderr	graph_edge_length_optimized_linear_regression_begin	graph_edge_length_optimized_linear_regression_begin_index	graph_edge_length_optimized_linear_regression_end	graph_edge_length_optimized_linear_regression_end_index	graph_edge_length_optimized_linear_regression_intercept	graph_edge_length_optimized_linear_regression_pvalue	graph_edge_length_optimized_linear_regression_rvalue	graph_edge_length_optimized_linear_regression_slope	graph_edge_length_optimized_linear_regression_stderr	graph_edge_length_optimized_logarithmic_regression_begin	graph_edge_length_optimized_logarithmic_regression_begin_index	graph_edge_length_optimized_logarithmic_regression_end	graph_edge_length_optimized_logarithmic_regression_end_index	graph_edge_length_optimized_logarithmic_regression_intercept	graph_edge_length_optimized_logarithmic_regression_pvalue	graph_edge_length_optimized_logarithmic_regression_rvalue	graph_edge_length_optimized_logarithmic_regression_slope	graph_edge_length_optimized_logarithmic_regression_stderr	graph_endpoint_count_linear_regression_intercept	graph_endpoint_count_linear_regression_pvalue	graph_endpoint_count_linear_regression_rvalue	graph_endpoint_count_linear_regression_slope	graph_endpoint_count_linear_regression_stderr	graph_endpoint_count_logarithmic_regression_intercept	graph_endpoint_count_logarithmic_regression_pvalue	graph_endpoint_count_logarithmic_regression_rvalue	graph_endpoint_count_logarithmic_regression_slope	graph_endpoint_count_logarithmic_regression_stderr	graph_endpoint_count_optimized_linear_regression_begin	graph_endpoint_count_optimized_linear_regression_begin_index	graph_endpoint_count_optimized_linear_regression_end	graph_endpoint_count_optimized_linear_regression_end_index	graph_endpoint_count_optimized_linear_regression_intercept	graph_endpoint_count_optimized_linear_regression_pvalue	graph_endpoint_count_optimized_linear_regression_rvalue	graph_endpoint_count_optimized_linear_regression_slope	graph_endpoint_count_optimized_linear_regression_stderr	graph_endpoint_count_optimized_logarithmic_regression_begin	graph_endpoint_count_optimized_logarithmic_regression_begin_index	graph_endpoint_count_optimized_logarithmic_regression_end	graph_endpoint_count_optimized_logarithmic_regression_end_index	graph_endpoint_count_optimized_logarithmic_regression_intercept	graph_endpoint_count_optimized_logarithmic_regression_pvalue	graph_endpoint_count_optimized_logarithmic_regression_rvalue	graph_endpoint_count_optimized_logarithmic_regression_slope	graph_endpoint_count_optimized_logarithmic_regression_stderr	graph_junction_count_linear_regression_intercept	graph_junction_count_linear_regression_pvalue	graph_junction_count_linear_regression_rvalue	graph_junction_count_linear_regression_slope	graph_junction_count_linear_regression_stderr	graph_junction_count_logarithmic_regression_intercept	graph_junction_count_logarithmic_regression_pvalue	graph_junction_count_logarithmic_regression_rvalue	graph_junction_count_logarithmic_regression_slope	graph_junction_count_logarithmic_regression_stderr	graph_junction_count_optimized_linear_regression_begin	graph_junction_count_optimized_linear_regression_begin_index	graph_junction_count_optimized_linear_regression_end	graph_junction_count_optimized_linear_regression_end_index	graph_junction_count_optimized_linear_regression_intercept	graph_junction_count_optimized_linear_regression_pvalue	graph_junction_count_optimized_linear_regression_rvalue	graph_junction_count_optimized_linear_regression_slope	graph_junction_count_optimized_linear_regression_stderr	graph_junction_count_optimized_logarithmic_regression_begin	graph_junction_count_optimized_logarithmic_regression_begin_index	graph_junction_count_optimized_logarithmic_regression_end	graph_junction_count_optimized_logarithmic_regression_end_index	graph_junction_count_optimized_logarithmic_regression_intercept	graph_junction_count_optimized_logarithmic_regression_pvalue	graph_junction_count_optimized_logarithmic_regression_rvalue	graph_junction_count_optimized_logarithmic_regression_slope	graph_junction_count_optimized_logarithmic_regression_stderr	graph_node_count_linear_regression_intercept	graph_node_count_linear_regression_pvalue	graph_node_count_linear_regression_rvalue	graph_node_count_linear_regression_slope	graph_node_count_linear_regression_stderr	graph_node_count_logarithmic_regression_intercept	graph_node_count_logarithmic_regression_pvalue	graph_node_count_logarithmic_regression_rvalue	graph_node_count_logarithmic_regression_slope	graph_node_count_logarithmic_regression_stderr	graph_node_count_optimized_linear_regression_begin	graph_node_count_optimized_linear_regression_begin_index	graph_node_count_optimized_linear_regression_end	graph_node_count_optimized_linear_regression_end_index	graph_node_count_optimized_linear_regression_intercept	graph_node_count_optimized_linear_regression_pvalue	graph_node_count_optimized_linear_regression_rvalue	graph_node_count_optimized_linear_regression_slope	graph_node_count_optimized_linear_regression_stderr	graph_node_count_optimized_logarithmic_regression_begin	graph_node_count_optimized_logarithmic_regression_begin_index	graph_node_count_optimized_logarithmic_regression_end	graph_node_count_optimized_logarithmic_regression_end_index	graph_node_count_optimized_logarithmic_regression_intercept	graph_node_count_optimized_logarithmic_regression_pvalue	graph_node_count_optimized_logarithmic_regression_rvalue	graph_node_count_optimized_logarithmic_regression_slope	graph_node_count_optimized_logarithmic_regression_stderr	meta_pos	meta_t	metadata	overall_graphml	track_table	track_table_aux_tables	tunables	version
0	0	0	-209.368383	2.532537e-24	0.734525	0.008969	0.000716	NaN	NaN	NaN	NaN	NaN	39345.176144	65	78338.287784	130	-994.607791	1.677850e-22	0.884206	0.020906	0.001391	47147.290182	78	78338.287784	130	-2.303539	1.205272e-63	0.998338	0.000119	9.727372e-07	-0.028316	2.532537e-24	0.734525	0.000001	9.681107e-08	NaN	NaN	NaN	NaN	NaN	39345.176144	65	78338.287784	130	-0.134516	1.677850e-22	0.884206	0.000003	1.881839e-07	47147.290182	78	78338.287784	130	-11.211959	1.205272e-63	0.998338	0.000119	9.727372e-07	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	/mycelyso/S_lividans_TK24_Complex_Medium_nd046...	-28.385481	6.207684e-15	0.604935	0.001209	0.000138	NaN	NaN	NaN	NaN	NaN	54942.33151	91	81340.338617	136	-445.363712	4.994880e-15	0.873456	0.007417	0.000631	54942.33151	91	81340.338617	136	-8.772886	3.728079e-27	0.966964	0.000178	0.000007	-189.301864	6.799061e-22	0.706908	0.008101	0.0007	NaN	NaN	NaN	NaN	NaN	39345.176144	65	81340.338617	136	-1139.396801	1.110753e-23	0.877234	0.023302	0.001535	47147.290182	78	81340.338617	136	-2.78033	3.708275e-66	0.997503	0.000123	0.000001	-10.07769	1.265490e-16	0.633514	0.000465	0.000049	NaN	NaN	NaN	NaN	NaN	54942.33151	91	81340.338617	136	-157.23131	1.693324e-16	0.892893	0.002662	0.000205	54942.33151	91	81340.338617	136	-6.582629	2.789480e-35	0.986286	0.000136	0.000003	-11.862853	3.182848e-15	0.61005	0.00048	0.000054	NaN	NaN	NaN	NaN	NaN	54942.33151	91	78338.287784	130	-110.650737	1.217144e-17	0.929788	0.001887	0.000123	62741.237858	104	78338.287784	130	-6.592383	2.291108e-19	0.983605	0.000134	0.000005	-21.940543	5.114994e-16	0.623593	0.000945	0.000102	NaN	NaN	NaN	NaN	NaN	54942.33151	91	81340.338617	136	-333.239213	8.587192e-16	0.883997	0.005585	0.00045	54942.33151	91	81340.338617	136	-7.695156	7.400355e-30	0.975361	0.00016	0.000006	0	-1		0	0	21	0	0

Then there is the result_table_collected, which contains collected information about every single frame of the time series of one position:

result_table_collected = store[position.result_table_collected._v_pathname]
result_table_collected

	area	binary	calibration	covered_area	covered_ratio	crop_b	crop_l	crop_r	crop_t	filename	graph_edge_count	graph_edge_length	graph_endpoint_count	graph_junction_count	graph_node_count	graphml	image_sha256_hash	input_height	input_width	meta_pos	meta_t	metadata	shift_x	shift_y	skeleton	timepoint	tunables_hash
0	7393.965475	0	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	0	FLHyF8lkwKef9Q9yEWsgOFzYc4qFCpKyirTRsfsR7/g=	128.245	57.655	0	0		3.0	3.0	0	356.745246	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
1	7393.965475	1	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	1	494VC0oqeVoCO/0IYeZnowKoultCZe+iYTW5/xRIfXQ=	128.245	57.655	0	1		0.0	0.0	1	954.331815	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
2	7393.965475	2	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	2	kg3NjTylgz8a9Z7wnSSmEgxZHxP0tAaj1dxCWuGaMec=	128.245	57.655	0	2		-3.0	-2.0	2	1548.970068	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
3	7393.965475	3	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	3	S6KmMEQmUxMdLbpBnAyTs01xKaGIBjtgP1g/Raq9zqg=	128.245	57.655	0	3		-6.0	-4.0	3	2152.429459	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
4	7393.965475	4	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	4	EM4yxCU5tahPntThJVNQtAus2R69jCszYck1ZHFDhX4=	128.245	57.655	0	4		-4.0	-5.0	4	2754.315663	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
5	7393.965475	5	0.065	11.766625	0.001591	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	5.5	22.899434	5	0	5	5	c+9vT5uE1ozpUvzrkp1EQcG03GORVwOTjxjrZqRPQn4=	128.245	57.655	0	5		-9.0	-5.0	5	3349.845006	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
6	7393.965475	6	0.065	21.931975	0.002966	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	15.5	41.708488	11	1	12	6	xvSVz5s+PLa4Sj8oHuz83v2KXW8W//20bogdtZYFYps=	128.245	57.655	0	6		-8.0	-4.0	6	3954.256373	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
7	7393.965475	7	0.065	18.877300	0.002553	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	11.5	38.285793	9	0	9	7	LDTibVqcoMtulQHwHHQUgtHV1xUFeIk+AnZxudajBL0=	128.245	57.655	0	7		-7.0	-6.0	7	4548.847011	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
8	7393.965475	8	0.065	11.306100	0.001529	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	9.0	21.241934	7	0	7	8	a3O6yoCLPmRkTBo/O7VFHi62Yc2lxx3w7b4BXKCskPk=	128.245	57.655	0	8		-8.0	-5.0	8	5149.800172	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
9	7393.965475	9	0.065	19.612450	0.002652	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	19.0	37.788097	12	3	15	9	R8zOCET5fdw+UveaB1/weWXLjxRewlTgsh6JAe1cl2A=	128.245	57.655	0	9		-9.0	-3.0	9	5747.743609	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
10	7393.965475	10	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	10	bwg71JuWU476X8llCcc7HIpK2W+telAz9PmUgbbG3GI=	128.245	57.655	0	10		-5.0	-4.0	10	6346.900296	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
11	7393.965475	11	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	11	58LrEPmBMhek4StJU2otfhjiYm3Im5//cRvAgkj05mo=	128.245	57.655	0	11		-4.0	-6.0	11	6946.751259	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
12	7393.965475	12	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	12	gpa2zMzRM8K2KE6Lr2AxIaLb+F/gdhuX8XrpRDvxlv8=	128.245	57.655	0	12		-4.0	-5.0	12	7543.367799	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
13	7393.965475	13	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	13	/KsfU2o48XgIY2W1oXsqn6nHxUHs/J/Wv1Z7nj0ZZOk=	128.245	57.655	0	13		-7.0	-4.0	13	8144.258055	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
14	7393.965475	14	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	14	DxApSHRIomGrqNpBitjQEo7QhFrEynEJ8ZmKJrvplnY=	128.245	57.655	0	14		-2.0	-4.0	14	8747.270315	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
15	7393.965475	15	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	15	Co1f04WWFLOobP5pOvdHqNsqTWIINGAZDb73YRPrEMo=	128.245	57.655	0	15		-2.0	-5.0	15	9342.921723	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
16	7393.965475	16	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	16	c4qXuABN6T/+Kqhl1Mu+dDc4DeaFoA6/+/P0O1oXurs=	128.245	57.655	0	16		-4.0	-5.0	16	9944.746882	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
17	7393.965475	17	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	17	rW1XbA7JoDeobq+O88KRJPV2sIinal/XU9yWVK5duzs=	128.245	57.655	0	17		-4.0	-6.0	17	10546.833173	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
18	7393.965475	18	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	18	4VRvPwGvoi38OdaAH11CJhGkpwIjLmbVoXU9VPxOjpw=	128.245	57.655	0	18		-2.0	-6.0	18	11142.278725	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
19	7393.965475	19	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	19	lGBlKy1m69uZFS4+z2qOu01U4TAepF98z5Qy0rgpKq4=	128.245	57.655	0	19		-4.0	-5.0	19	11748.821861	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
20	7393.965475	20	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	20	suQeImrAqjZDCOeXIo7jXiAo1EbKWi7RHyjg/K92eeo=	128.245	57.655	0	20		-5.0	-5.0	20	12354.980074	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
21	7393.965475	21	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	21	g/nSp2+luy9+GumMUPJZjNTIq/fEsVAZDftXGWzWeT8=	128.245	57.655	0	21		-3.0	-5.0	21	12944.765587	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
22	7393.965475	22	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	22	BovPeepsLCC72gmUDKXJRPCAlQ62ZbcCw6khY2exoVQ=	128.245	57.655	0	22		-2.0	-7.0	22	13545.854889	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
23	7393.965475	23	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	23	6ddbC20/XQcL62LLIthfgKK1+hZ471gas/x47xAErgU=	128.245	57.655	0	23		-5.0	-6.0	23	14146.223223	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
24	7393.965475	24	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	24	sKWUFcK2/AkvT7VsD479I5RyUSh42fg419mJ+7NGElc=	128.245	57.655	0	24		-2.0	-4.0	24	14748.335994	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
25	7393.965475	25	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	25	5j1pPeyhTmt8DTk2PXJJY+qXzQLof67lF3iSqHQ7fYs=	128.245	57.655	0	25		3.0	-6.0	25	15343.735260	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
26	7393.965475	26	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	26	uhGgSzijhmEGPdb+vseY5QkDZXRZDiSaAgKqGYgLNY4=	128.245	57.655	0	26		1.0	-7.0	26	15953.863397	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
27	7393.965475	27	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	27	VXsOEGRfM7I4HccxdR/32rUj3tZrSypiQk5SFztQ8BQ=	128.245	57.655	0	27		0.0	-4.0	27	16542.758080	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
28	7393.965475	28	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	28	vjbM5PQTup+sY2oxC7pA0TkBf5sE8TQnR+EkW02XyPU=	128.245	57.655	0	28		0.0	-4.0	28	17142.263416	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
29	7393.965475	29	0.065	0.000000	0.000000	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	0.0	0.000000	0	0	0	29	SO9ouW//cxEuF6b5JioGV6TFtg5CsMLKAoTdx8TPIis=	128.245	57.655	0	29		0.0	-7.0	29	17740.279887	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
106	7393.965475	106	0.065	210.666950	0.028492	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	19.5	170.239411	9	8	17	106	uAHtyApJzNnPOYnpdVOIKWkvOYSmlCkO8ZC9u2gta5o=	128.245	57.655	0	106		0.0	-1.0	106	63947.249755	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
107	7393.965475	107	0.065	207.519325	0.028066	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	20.0	180.808115	10	7	17	107	25jns/xT4PLo4Jxf505fLowf+A2qcVQmWq4ke+5VCMI=	128.245	57.655	0	107		4.0	-2.0	107	64543.707035	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
108	7393.965475	108	0.065	219.763375	0.029722	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	21.5	190.435276	11	7	18	108	OWqQprg2kii5dkmOoNCNbmM2z3lehAazAPO9IRYf9Xo=	128.245	57.655	0	108		2.0	-1.0	108	65139.869557	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
109	7393.965475	109	0.065	247.859625	0.033522	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	25.5	195.382602	12	10	22	109	1qt2o2cQ3+57QE0ZsjDBJPnuBVSWuafV54gucUCPje8=	128.245	57.655	0	109		-1.0	0.0	109	65741.778848	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
110	7393.965475	110	0.065	264.658225	0.035794	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	25.5	210.104377	13	10	23	110	fq3wG1zJ0pYaf1oRLGPElzHf1YE1Qx/TNhCJecgfw48=	128.245	57.655	0	110		0.0	-1.0	110	66340.189219	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
111	7393.965475	111	0.065	280.556900	0.037944	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	39.5	235.773869	18	15	33	111	vSbq+a0wytKuNcRRbUhf8pTJSyWM4kGIuD4SO1R5lh8=	128.245	57.655	0	111		-1.0	-1.0	111	66943.783533	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
112	7393.965475	112	0.065	294.051550	0.039769	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	35.5	248.187748	16	14	30	112	EyE6YpZWqRtaLGY6P7Ls5SbX4NOCZSIt+79qYEa7CfQ=	128.245	57.655	0	112		-2.0	-1.0	112	67544.224723	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
113	7393.965475	113	0.065	316.444050	0.042798	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	36.5	260.646633	17	14	31	113	xMiJu6s5Aibr9FDuX53pjMfDo/NdaTfDU1JBizujn+M=	128.245	57.655	0	113		-3.0	-1.0	113	68144.223215	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
114	7393.965475	114	0.065	342.820725	0.046365	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	40.0	281.374211	19	15	34	114	K+xpRjw5CaAnpr5Wn+S3JBznhdGApuFuWaRgIzjrD98=	128.245	57.655	0	114		-2.0	-2.0	114	68741.153508	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
115	7393.965475	115	0.065	370.257875	0.050076	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	40.5	312.852562	18	16	34	115	Mb4MgU9eSza1UKpwZMoYe9vFydo+CgkQIXXlqImQsT0=	128.245	57.655	0	115		-3.0	-2.0	115	69343.336711	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
116	7393.965475	116	0.065	400.344100	0.054145	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	46.5	336.659457	22	17	39	116	YhrWJOJHelwSMWZ5cireuPWOQerJ3ncgmYWSDmrdeq0=	128.245	57.655	0	116		-6.0	0.0	116	69940.686151	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
117	7393.965475	117	0.065	433.286425	0.058600	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	47.5	368.660910	20	18	38	117	sE4DE63xAmb5NKVMReP7pab2izYSVM6UJAm5DkN0VXg=	128.245	57.655	0	117		-5.0	-1.0	117	70540.386399	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
118	7393.965475	118	0.065	481.265525	0.065089	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	46.5	411.026463	20	18	38	118	81kFT/ZS0drUpl6kKYXzQw/XjlQzxIzPAmd3nL11+jg=	128.245	57.655	0	118		-4.0	-3.0	118	71141.753863	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
119	7393.965475	119	0.065	528.095425	0.071422	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	46.5	442.766625	21	19	40	119	M82K/jsBao445C6NKVnTNij+l6tWyNlSw353uGNiDLY=	128.245	57.655	0	119		-2.0	-3.0	119	71748.778771	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
120	7393.965475	120	0.065	588.665025	0.079614	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	52.0	501.050286	24	19	43	120	uzYdD+ar88aFulsNSqkm0WcNqly45OVPfXdiC2PoGn4=	128.245	57.655	0	120		1.0	-1.0	120	72342.288541	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
121	7393.965475	121	0.065	637.928525	0.086277	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	57.5	542.781477	25	22	47	121	Xnoke73h5X3pqffz22tt/XS5zFL58NQRj3FRRLRVdh0=	128.245	57.655	0	121		1.0	-1.0	121	72942.162923	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
122	7393.965475	122	0.065	616.833100	0.083424	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	60.0	561.784642	25	23	48	122	/xYS1ZdSkDs7iGCM7EUOEplVF6IvONSJfQS/gUjYfbo=	128.245	57.655	0	122		3.0	-3.0	122	73543.257127	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
123	7393.965475	123	0.065	735.441525	0.099465	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	73.5	630.276997	31	29	60	123	u74v7IA9x4990zS2p78PeME6W+CjG3X2WQCoCt4zpzM=	128.245	57.655	0	123		-1.0	-1.0	123	74140.149509	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
124	7393.965475	124	0.065	780.298350	0.105532	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	79.5	682.208179	32	31	63	124	OoMxDjS6CVZqgFUIt9i3uE3edYm+cQgUGHmVAfoMCpk=	128.245	57.655	0	124		-2.0	-2.0	124	74739.753889	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
125	7393.965475	125	0.065	821.783625	0.111142	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	90.5	720.402085	34	37	71	125	QunbYZfVig1yXaR7CahU9lp7tbutNgRNCV2trlfH2ag=	128.245	57.655	0	125		-2.0	-3.0	125	75342.294086	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
126	7393.965475	126	0.065	840.644025	0.113693	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	94.5	750.562416	37	37	74	126	WxArF1YP7mcIyfJ5BwCADhyzu3HjH/EArvQ/ughWwag=	128.245	57.655	0	126		-2.0	-1.0	126	75940.191470	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
127	7393.965475	127	0.065	853.923200	0.115489	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	109.5	773.762895	42	42	84	127	JNQ60hSinRysv9iHUDlvWbajC3pxmetHJCy4umA78k8=	128.245	57.655	0	127		-4.0	-1.0	127	76540.684802	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
128	7393.965475	128	0.065	908.451050	0.122864	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	120.5	826.868598	45	48	93	128	DM/8MVnM0IlU4i6dsYVg6pvjKOEQ0G4+ie+lacKNzto=	128.245	57.655	0	128		-4.0	-1.0	128	77143.279996	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
129	7393.965475	129	0.065	928.084625	0.125519	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	137.0	854.965852	56	51	107	129	vJ6ddwOacUWSEuKgYavl9YYYDhHkZ22SGGd6i5nCv5s=	128.245	57.655	0	129		-3.0	-3.0	129	77739.277364	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
130	7393.965475	130	0.065	995.219875	0.134599	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	164.5	931.113953	59	65	124	130	EMfLUv7Hu2b7NJOcjA4BGr748D3i+uQYqBR3D1+Olyk=	128.245	57.655	0	130		-5.0	-2.0	130	78338.287784	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
131	7393.965475	131	0.065	1042.451150	0.140987	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	177.5	982.963010	63	70	133	131	PZNqH1IrTEzf49uuqlNYGyjdzrx4buvzZJonsP68Etg=	128.245	57.655	0	131		-5.0	-2.0	131	78943.246053	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
132	7393.965475	132	0.065	1043.646825	0.141148	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	186.0	982.474030	61	75	136	132	1fAdMd5ruK5y/zwSWuqWqcCelW2sBWElCNhU6zhaovY=	128.245	57.655	0	132		1.0	-2.0	132	79540.788485	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
133	7393.965475	133	0.065	1023.569625	0.138433	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	191.5	1029.805448	73	73	146	133	bUVUBoCP3NhJCFHhGjHu3czbHLuJQxTkg2iCE6jqeJs=	128.245	57.655	0	133		7.0	-3.0	133	80140.704110	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
134	7393.965475	134	0.065	1035.670025	0.140070	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	218.5	1074.944651	85	80	165	134	mh5CCpK+8DkzZ0Jb95x+XF1OLShiK/B/12l78G/UVgY=	128.245	57.655	0	134		5.0	-1.0	134	80741.868186	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...
135	7393.965475	135	0.065	1135.342000	0.153550	1978	754	1642	4	S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff	279.0	1201.021333	96	113	209	135	ZdYdZ9ud5oLdOJP5XVD1633MzPXv4GR6EjzZLtCgpNo=	128.245	57.655	0	135		4.0	0.0	135	81340.338617	VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y...

136 rows × 27 columns

The per-frame informations contain e.g. the graph length (i.e. the mycelium length), which can be plotted over time:

timepoint = result_table_collected.timepoint / (60*60)
length = result_table_collected.graph_edge_length

pyplot.title('Length over Time')

pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')

pyplot.plot(timepoint, length)

[<matplotlib.lines.Line2D at 0x7f89d964edd8>]

Last but not least, we will look at mycelium level tracking data in the track_table. The track_table is a level deeper in the HDF5 structure, next to tables with individual tracks.

track_table = store[list(position.tables.track_table)[0]._v_pathname]
track_table

	aux_table	count	duration	logarithmic_normalized_regression_intercept	logarithmic_normalized_regression_pvalue	logarithmic_normalized_regression_rvalue	logarithmic_normalized_regression_slope	logarithmic_normalized_regression_stderr	logarithmic_plain_regression_intercept	logarithmic_plain_regression_pvalue	logarithmic_plain_regression_rvalue	logarithmic_plain_regression_slope	logarithmic_plain_regression_stderr	maximum_distance	maximum_distance_num	minimum_distance	minimum_distance_num	normalized_regression_intercept	normalized_regression_pvalue	normalized_regression_rvalue	normalized_regression_slope	normalized_regression_stderr	optimized_logarithmic_normalized_regression_begin	optimized_logarithmic_normalized_regression_begin_index	optimized_logarithmic_normalized_regression_end	optimized_logarithmic_normalized_regression_end_index	optimized_logarithmic_normalized_regression_intercept	optimized_logarithmic_normalized_regression_pvalue	optimized_logarithmic_normalized_regression_rvalue	optimized_logarithmic_normalized_regression_slope	optimized_logarithmic_normalized_regression_stderr	optimized_logarithmic_regression_begin	optimized_logarithmic_regression_begin_index	optimized_logarithmic_regression_end	optimized_logarithmic_regression_end_index	optimized_logarithmic_regression_intercept	optimized_logarithmic_regression_pvalue	optimized_logarithmic_regression_rvalue	optimized_logarithmic_regression_slope	optimized_logarithmic_regression_stderr	optimized_normalized_regression_begin	optimized_normalized_regression_begin_index	optimized_normalized_regression_end	optimized_normalized_regression_end_index	optimized_normalized_regression_intercept	optimized_normalized_regression_pvalue	optimized_normalized_regression_rvalue	optimized_normalized_regression_slope	optimized_normalized_regression_stderr	optimized_regression_begin	optimized_regression_begin_index	optimized_regression_end	optimized_regression_end_index	optimized_regression_intercept	optimized_regression_pvalue	optimized_regression_rvalue	optimized_regression_slope	optimized_regression_stderr	plain_regression_intercept	plain_regression_pvalue	plain_regression_rvalue	plain_regression_slope	plain_regression_stderr	timepoint_begin	timepoint_center	timepoint_end
0	0	22	12596.588071	-7.600037	1.711806e-24	0.997499	0.000182	0.000003	-5.825405	1.711806e-24	0.997499	0.000182	0.000003	57.906361	1.0	5.898107	1.0	-29.091215	1.307881e-14	0.975429	0.000686	0.000035	42345.743439	0	54942.331510	21	-7.700575	1.019580e-23	0.997723	0.000184	0.000003	42345.743439	0	54942.331510	21	-5.925944	1.019580e-23	0.997723	0.000184	0.000003	42345.743439	0	54942.331510	21	-27.740900	4.146086e-14	0.976412	0.000657	0.000033	42345.743439	0	54942.331510	21	-163.618808	4.146086e-14	0.976412	0.003874	0.000197	-171.583111	1.307881e-14	0.975429	0.004046	0.000204	42345.743439	48644.037475	54942.331510
1	1	29	16795.074294	-4.477290	1.479542e-29	0.995785	0.000078	0.000001	-0.891770	1.479542e-29	0.995785	0.000078	0.000001	141.762974	10.0	36.072102	1.0	-9.033740	1.851875e-28	0.994916	0.000169	0.000003	58547.219791	0	75342.294086	29	-4.477290	1.479542e-29	0.995785	0.000078	0.000001	58547.219791	0	75342.294086	29	-0.891770	1.479542e-29	0.995785	0.000078	0.000001	58547.219791	0	75342.294086	29	-9.033740	1.851875e-28	0.994916	0.000169	0.000003	58547.219791	0	75342.294086	29	-325.865976	1.851875e-28	0.994916	0.006103	0.000119	-325.865976	1.851875e-28	0.994916	0.006103	0.000119	58547.219791	66944.756938	75342.294086
2	2	11	5999.376544	-15.868380	6.611100e-08	0.982767	0.000263	0.000016	-15.056252	6.611100e-08	0.982767	0.000263	0.000016	11.042346	3.0	2.252696	1.0	-40.384477	4.331466e-12	0.997984	0.000677	0.000014	60944.406990	0	66943.783533	11	-15.868380	6.611100e-08	0.982767	0.000263	0.000016	60944.406990	0	66943.783533	11	-15.056252	6.611100e-08	0.982767	0.000263	0.000016	60944.406990	0	66943.783533	11	-40.384477	4.331466e-12	0.997984	0.000677	0.000014	60944.406990	0	66943.783533	11	-90.973931	4.331466e-12	0.997984	0.001525	0.000032	-90.973931	4.331466e-12	0.997984	0.001525	0.000032	60944.406990	63944.095262	66943.783533
3	3	23	13195.742519	-5.125910	7.206502e-28	0.998462	0.000085	0.000001	-1.566050	7.206502e-28	0.998462	0.000085	0.000001	111.155492	8.0	35.158275	1.0	-8.832037	1.632991e-21	0.993789	0.000160	0.000004	60944.406990	0	74140.149509	23	-5.125910	7.206502e-28	0.998462	0.000085	0.000001	60944.406990	0	74140.149509	23	-1.566050	7.206502e-28	0.998462	0.000085	0.000001	60944.406990	0	74140.149509	23	-8.832037	1.632991e-21	0.993789	0.000160	0.000004	60944.406990	0	74140.149509	23	-310.519177	1.632991e-21	0.993789	0.005611	0.000137	-310.519177	1.632991e-21	0.993789	0.005611	0.000137	60944.406990	67542.278249	74140.149509
4	4	16	8999.505265	-21.524270	1.726340e-09	0.964524	0.000350	0.000026	-21.201628	1.726340e-09	0.964524	0.000350	0.000026	44.678233	2.0	1.380772	1.0	-223.912438	1.341946e-12	0.987353	0.003504	0.000150	63342.783276	0	72342.288541	15	-22.604725	5.227294e-09	0.965929	0.000367	0.000027	63342.783276	0	72342.288541	15	-22.282082	5.227294e-09	0.965929	0.000367	0.000027	63342.783276	0	72342.288541	15	-215.096004	7.149179e-12	0.987747	0.003370	0.000148	63342.783276	0	72342.288541	15	-296.998464	7.149179e-12	0.987747	0.004654	0.000204	-309.171945	1.341946e-12	0.987353	0.004838	0.000208	63342.783276	67842.535909	72342.288541
5	5	14	7801.478279	-23.838927	1.409835e-09	0.978374	0.000370	0.000023	-23.438081	1.409835e-09	0.978374	0.000370	0.000023	41.950146	6.0	1.493087	1.0	-185.970032	6.770607e-07	0.938601	0.002806	0.000298	65741.778848	0	73543.257127	14	-23.838927	1.409835e-09	0.978374	0.000370	0.000023	65741.778848	0	73543.257127	14	-23.438081	1.409835e-09	0.978374	0.000370	0.000023	65741.778848	0	73543.257127	14	-185.970032	6.770607e-07	0.938601	0.002806	0.000298	65741.778848	0	73543.257127	14	-277.669359	6.770607e-07	0.938601	0.004190	0.000445	-277.669359	6.770607e-07	0.938601	0.004190	0.000445	65741.778848	69642.517987	73543.257127
6	6	12	6600.509694	-43.722131	3.190588e-05	0.914065	0.000690	0.000097	-46.108926	3.190588e-05	0.914065	0.000690	0.000097	23.474375	1.0	0.091924	1.0	-2616.983966	1.009138e-08	0.983252	0.039382	0.002308	65741.778848	0	72342.288541	12	-43.722131	3.190588e-05	0.914065	0.000690	0.000097	65741.778848	0	72342.288541	12	-46.108926	3.190588e-05	0.914065	0.000690	0.000097	65741.778848	0	72342.288541	12	-2616.983966	1.009138e-08	0.983252	0.039382	0.002308	65741.778848	0	72342.288541	12	-240.563324	1.009138e-08	0.983252	0.003620	0.000212	-240.563324	1.009138e-08	0.983252	0.003620	0.000212	65741.778848	69042.033694	72342.288541
7	7	9	4801.564644	-30.119407	1.333225e-05	0.970975	0.000459	0.000043	-30.124026	1.333225e-05	0.970975	0.000459	0.000043	12.486205	2.0	0.995391	1.0	-138.693554	1.941910e-04	0.937004	0.002094	0.000295	66340.189219	0	71141.753863	9	-30.119407	1.333225e-05	0.970975	0.000459	0.000043	66340.189219	0	71141.753863	9	-30.124026	1.333225e-05	0.970975	0.000459	0.000043	66340.189219	0	71141.753863	9	-138.693554	1.941910e-04	0.937004	0.002094	0.000295	66340.189219	0	71141.753863	9	-138.054322	1.941910e-04	0.937004	0.002084	0.000294	-138.054322	1.941910e-04	0.937004	0.002084	0.000294	66340.189219	68740.971541	71141.753863
8	8	17	9596.901268	-22.271064	1.733538e-09	0.957300	0.000343	0.000027	-21.766331	1.733538e-09	0.957300	0.000343	0.000027	57.441002	1.0	1.656543	1.0	-242.634692	9.760773e-15	0.991591	0.003592	0.000121	66943.783533	0	76540.684802	17	-22.271064	1.733538e-09	0.957300	0.000343	0.000027	66943.783533	0	76540.684802	17	-21.766331	1.733538e-09	0.957300	0.000343	0.000027	66943.783533	0	76540.684802	17	-242.634692	9.760773e-15	0.991591	0.003592	0.000121	66943.783533	0	76540.684802	17	-401.934870	9.760773e-15	0.991591	0.005950	0.000201	-401.934870	9.760773e-15	0.991591	0.005950	0.000201	66943.783533	71742.234168	76540.684802
9	9	11	5998.379390	-29.357547	1.979017e-06	0.963090	0.000446	0.000042	-29.552716	1.979017e-06	0.963090	0.000446	0.000042	15.516987	1.0	0.822696	1.0	-194.768001	4.833247e-09	0.990387	0.002907	0.000135	66943.783533	0	72942.162923	11	-29.357547	1.979017e-06	0.963090	0.000446	0.000042	66943.783533	0	72942.162923	11	-29.552716	1.979017e-06	0.963090	0.000446	0.000042	66943.783533	0	72942.162923	11	-194.768001	4.833247e-09	0.990387	0.002907	0.000135	66943.783533	0	72942.162923	11	-160.234763	4.833247e-09	0.990387	0.002392	0.000111	-160.234763	4.833247e-09	0.990387	0.002392	0.000111	66943.783533	69942.973228	72942.162923
10	10	8	4198.065326	-48.245996	6.569861e-06	0.986152	0.000710	0.000049	-48.636193	6.569861e-06	0.986152	0.000710	0.000049	11.633879	1.0	0.676924	1.0	-275.022799	1.801673e-05	0.980590	0.004022	0.000328	68144.223215	0	72342.288541	8	-48.245996	6.569861e-06	0.986152	0.000710	0.000049	68144.223215	0	72342.288541	8	-48.636193	6.569861e-06	0.986152	0.000710	0.000049	68144.223215	0	72342.288541	8	-275.022799	1.801673e-05	0.980590	0.004022	0.000328	68144.223215	0	72342.288541	8	-186.169501	1.801673e-05	0.980590	0.002722	0.000222	-186.169501	1.801673e-05	0.980590	0.002722	0.000222	68144.223215	70243.255878	72342.288541
11	11	7	3600.022869	-47.066691	5.996060e-04	0.960051	0.000645	0.000084	-46.870044	5.996060e-04	0.960051	0.000645	0.000084	14.274661	1.0	1.217315	1.0	-220.575522	1.034271e-07	0.998762	0.003009	0.000067	73543.257127	0	77143.279996	6	-53.306342	1.709971e-03	0.966044	0.000729	0.000097	73543.257127	0	77143.279996	6	-53.109695	1.709971e-03	0.966044	0.000729	0.000097	73543.257127	0	77143.279996	6	-215.315060	2.582288e-06	0.998688	0.002938	0.000075	73543.257127	0	77143.279996	6	-262.106238	2.582288e-06	0.998688	0.003576	0.000092	-268.509877	1.034271e-07	0.998762	0.003662	0.000082	73543.257127	75343.268562	77143.279996
12	12	10	5400.638976	-53.862847	4.870276e-09	0.994214	0.000728	0.000028	-54.398991	4.870276e-09	0.994214	0.000728	0.000028	24.758931	2.0	0.585000	1.0	-604.374842	1.170390e-04	0.926455	0.008061	0.001158	74140.149509	0	79540.788485	9	-56.645897	4.971286e-09	0.996980	0.000764	0.000023	74140.149509	0	79540.788485	9	-57.182041	4.971286e-09	0.996980	0.000764	0.000023	74140.149509	0	79540.788485	9	-546.454943	9.685289e-04	0.899213	0.007296	0.001342	74140.149509	0	79540.788485	9	-319.676142	9.685289e-04	0.899213	0.004268	0.000785	-353.559282	1.170390e-04	0.926455	0.004716	0.000677	74140.149509	76840.468997	79540.788485
13	13	5	2400.535293	-16.201586	1.183782e-04	0.997865	0.000219	0.000008	-12.961744	1.183782e-04	0.997865	0.000219	0.000008	43.186267	2.0	25.529693	1.0	-20.386272	1.192592e-06	0.999900	0.000288	0.000002	74140.149509	0	76540.684802	5	-16.201586	1.183782e-04	0.997865	0.000219	0.000008	74140.149509	0	76540.684802	5	-12.961744	1.183782e-04	0.997865	0.000219	0.000008	74140.149509	0	76540.684802	5	-20.386272	1.192592e-06	0.999900	0.000288	0.000002	74140.149509	0	76540.684802	5	-520.455259	1.192592e-06	0.999900	0.007363	0.000060	-520.455259	1.192592e-06	0.999900	0.007363	0.000060	74140.149509	75340.417155	76540.684802
14	14	7	3598.533895	-16.153025	4.230040e-06	0.994534	0.000217	0.000010	-12.834299	4.230040e-06	0.994534	0.000217	0.000010	60.150146	6.0	27.625136	1.0	-23.722413	8.119266e-10	0.999822	0.000331	0.000003	74739.753889	0	78338.287784	6	-17.117733	2.071218e-05	0.996282	0.000229	0.000010	74739.753889	0	78338.287784	6	-13.799007	2.071218e-05	0.996282	0.000229	0.000010	74739.753889	0	78338.287784	6	-23.752887	1.191299e-07	0.999718	0.000331	0.000004	74739.753889	0	78338.287784	6	-656.176742	1.191299e-07	0.999718	0.009145	0.000109	-655.334883	8.119266e-10	0.999822	0.009134	0.000077	74739.753889	76539.020837	78338.287784
15	15	5	2398.096314	-68.488528	2.257037e-03	0.984743	0.000904	0.000092	-68.048039	2.257037e-03	0.984743	0.000904	0.000092	13.458499	2.0	1.553467	1.0	-248.736869	7.521336e-04	0.992672	0.003283	0.000231	75940.191470	0	78338.287784	5	-68.488528	2.257037e-03	0.984743	0.000904	0.000092	75940.191470	0	78338.287784	5	-68.048039	2.257037e-03	0.984743	0.000904	0.000092	75940.191470	0	78338.287784	5	-248.736869	7.521336e-04	0.992672	0.003283	0.000231	75940.191470	0	78338.287784	5	-386.404560	7.521336e-04	0.992672	0.005099	0.000358	-386.404560	7.521336e-04	0.992672	0.005099	0.000358	75940.191470	77139.239627	78338.287784
16	16	7	3600.019308	-49.432006	1.254883e-03	0.946159	0.000651	0.000100	-49.208246	1.254883e-03	0.946159	0.000651	0.000100	13.193879	3.0	1.250772	1.0	-226.581932	4.779667e-05	0.985555	0.002972	0.000228	76540.684802	0	80140.704110	6	-59.321733	1.200094e-03	0.971580	0.000779	0.000095	76540.684802	0	80140.704110	6	-59.097972	1.200094e-03	0.971580	0.000779	0.000095	76540.684802	0	80140.704110	6	-253.568080	2.703497e-05	0.995752	0.003320	0.000154	76540.684802	0	80140.704110	6	-317.155764	2.703497e-05	0.995752	0.004153	0.000192	-283.402256	4.779667e-05	0.985555	0.003717	0.000286	76540.684802	78340.694456	80140.704110
17	17	8	4201.183384	-56.353086	8.712980e-04	0.928338	0.000745	0.000122	-56.645388	8.712980e-04	0.928338	0.000745	0.000122	20.648160	2.0	0.746543	1.0	-515.121693	2.645955e-06	0.989783	0.006731	0.000396	76540.684802	0	80741.868186	8	-56.353086	8.712980e-04	0.928338	0.000745	0.000122	76540.684802	0	80741.868186	8	-56.645388	8.712980e-04	0.928338	0.000745	0.000122	76540.684802	0	80741.868186	8	-515.121693	2.645955e-06	0.989783	0.006731	0.000396	76540.684802	0	80741.868186	8	-384.560643	2.645955e-06	0.989783	0.005025	0.000296	-384.560643	2.645955e-06	0.989783	0.005025	0.000296	76540.684802	78641.276494	80741.868186
18	18	8	4201.183384	-47.728816	5.333120e-04	0.939326	0.000630	0.000094	-46.987604	5.333120e-04	0.939326	0.000630	0.000094	29.877832	4.0	2.098478	1.0	-284.175628	2.057823e-04	0.956015	0.003721	0.000466	76540.684802	0	80741.868186	8	-47.728816	5.333120e-04	0.939326	0.000630	0.000094	76540.684802	0	80741.868186	8	-46.987604	5.333120e-04	0.939326	0.000630	0.000094	76540.684802	0	80741.868186	8	-284.175628	2.057823e-04	0.956015	0.003721	0.000466	76540.684802	0	80741.868186	8	-596.336198	2.057823e-04	0.956015	0.007807	0.000978	-596.336198	2.057823e-04	0.956015	0.007807	0.000978	76540.684802	78641.276494	80741.868186
19	19	7	3598.588190	-39.294228	4.651180e-04	0.963940	0.000514	0.000063	-38.611318	4.651180e-04	0.963940	0.000514	0.000063	15.595845	1.0	1.979630	1.0	-136.822009	1.485273e-05	0.990960	0.001786	0.000108	77143.279996	0	80741.868186	6	-42.144383	2.888146e-03	0.955793	0.000550	0.000085	77143.279996	0	80741.868186	6	-41.461473	2.888146e-03	0.955793	0.000550	0.000085	77143.279996	0	80741.868186	6	-123.330258	1.094731e-05	0.997297	0.001613	0.000059	77143.279996	0	80741.868186	6	-244.148263	1.094731e-05	0.997297	0.003193	0.000118	-270.856935	1.485273e-05	0.990960	0.003535	0.000214	77143.279996	78942.574091	80741.868186

Let’s find the longest track and try to visualize it:

track_table.sort_values(by=['count'], ascending=False, inplace=True)
particular_tracking_table = track_table.aux_table[0]  # the first

_mapping_track_table_aux_tables = store[list(position.tables._mapping_track_table_aux_tables)[0]._v_pathname]

index = _mapping_track_table_aux_tables.query('_index == @particular_tracking_table').individual_table

the_longest_track = store[getattr(position.tables._individual_track_table_aux_tables, 'track_table_aux_tables_%09d' % (index,))._v_pathname]

the_longest_track

	distance	distance_num	node_id_a	node_id_b	node_next_id_a	node_next_id_b	timepoint	track_table_number
0	5.898107	1.0	0	1	0	1	42345.743439	0
1	7.083879	1.0	0	1	0	2	42943.263915	0
2	7.251955	1.0	0	2	0	1	43545.771926	0
3	8.919651	1.0	0	1	0	1	44144.751331	0
4	9.688499	1.0	0	1	0	1	44744.694663	0
5	11.311585	1.0	0	1	0	1	45344.289949	0
6	12.540052	1.0	0	1	0	1	45939.743908	0
7	14.456596	1.0	0	1	0	1	46545.171155	0
8	16.146596	1.0	0	1	0	1	47147.290182	0
9	18.101215	1.0	0	1	0	1	47744.704740	0
10	20.143139	1.0	0	1	0	1	48338.214147	0
11	22.355845	1.0	0	1	0	1	48945.238245	0
12	25.077399	1.0	0	1	0	1	49539.787734	0
13	27.538952	1.0	0	1	0	1	50142.246928	0
14	31.024734	1.0	0	1	0	1	50745.344198	0
15	33.735136	1.0	0	1	0	1	51344.796590	0
16	37.211679	1.0	0	1	0	1	51944.723954	0
17	40.819015	1.0	0	1	0	1	52542.958177	0
18	45.219417	1.0	0	1	0	1	53141.803414	0
19	49.032112	1.0	0	1	0	1	53741.353184	0
20	53.111341	1.0	0	1	0	1	54341.241176	0
21	57.906361	1.0	0	1	1	2	54942.331510	0

timepoint = the_longest_track.timepoint / (60*60)
length = the_longest_track.distance

pyplot.title('Length over Time')

pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')

pyplot.plot(timepoint, length)

[<matplotlib.lines.Line2D at 0x7f89d9621470>]

Now all tracked hyphae:

pyplot.title('Length over Time')

pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')

for idx, row in track_table.iterrows():
    particular_tracking_table = int(row.aux_table)
    index = _mapping_track_table_aux_tables.query('_index == @particular_tracking_table').individual_table
    track = store[getattr(position.tables._individual_track_table_aux_tables, 'track_table_aux_tables_%09d' % (index,))._v_pathname]

    timepoint = track.timepoint / (60*60)
    length = track.distance - track.distance.min()
    pyplot.plot(timepoint, length)

pyplot.xlim(0, None)

(0, 22.961576228743152)

Example Alternative Growth Fit

Please first see the other Jupyter Notebook, explaining the basics of accessing mycelyso’s HDF5 files. Furthermore, this file assumes the output.h5 described in the other notebook to be present in the current directory.

Within this notebook, we will fit the mycelium length data using a third-party library, croissance (DOI: 10.5281/zenodo.229905 by Lars Schöning (2017)). Please install the current version off github first:

pip install https://github.com/biosustain/croissance/archive/master.zip

First, some general setup …

%matplotlib inline
%config InlineBackend.figure_formats=['svg']
import pandas
pandas.options.display.max_columns = None
import numpy as np
import warnings
import croissance
from croissance.figures import PDFWriter as CroissancePDFWriter
from matplotlib import pyplot

class OutputInstead:
    @classmethod
    def savefig(cls, fig):
        pyplot.gcf().set_size_inches(10, 12)
        pyplot.show()

# croissance's PDFWriter is supposed to write to a PDF
# but we want an inline figure, so we mock some bits
CroissancePDFWriter.doc = OutputInstead
CroissancePDFWriter._include_shifted_exponentials = False
def display_result(result, name="Mycelium Length"):
    return CroissancePDFWriter.write(CroissancePDFWriter, name, result)

warnings.simplefilter(action='ignore', category=FutureWarning)

pyplot.rcParams.update({
    'figure.figsize': (10, 6), 'svg.fonttype': 'none',
    'font.sans-serif': 'Arial', 'font.family': 'sans-serif',
    'image.cmap': 'gray_r', 'image.interpolation': 'none'
})

Opening the HDF5 file

We will load the output.h5 using pandas.HDFStore …

store = pandas.HDFStore('output.h5', 'r')
root = store.get_node('/')
for image_file in root.results:
    print(image_file)
    for position in image_file:
        print(position)
        break

/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected (Group) ''

and load the first growth curve

result_table_collected = store[position.result_table_collected._v_pathname]
timepoint = result_table_collected.timepoint / (60*60)
length = result_table_collected.graph_edge_length

pyplot.title('Length over Time')

pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')

pyplot.plot(timepoint, length)

[<matplotlib.lines.Line2D at 0x7fb7dd6e5160>]

Here, we will use the third party tool croissance to fit the data to an exponential growth model:

curve = pandas.Series(data=np.array(length), index=np.array(timepoint))

estimator = croissance.Estimator()
result = estimator.growth(curve)
# print(result)
print(result.growth_phases)

[GrowthPhase(start=9.928127173487246, end=22.594538504723342, slope=0.36693539043981077, intercept=3.1588539520729086, n0=-25.525547240977755, attributes={'SNR': 172.5009988033075, 'rank': 100.0})]

And furthermore use its plotting functionality to show the results:

print("Growth rate as determined by croissance µ=%.2f" % (result.growth_phases[0].slope,))
display_result(result)

Growth rate as determined by croissance µ=0.37

mycelyso Developer Documentation

Subpackages

mycelyso.tunables

Module contents

This file contains all the tunables available in mycelyso.

You can set them via -t Name=value on the command line.

class mycelyso.tunables.BorderArtifactRemovalBorderSize

Bases: tunable.tunable.Tunable

Remove structures, whose centroid lies within that distance [µm] of a border

default = 10.0

value = 10.0

class mycelyso.tunables.BoxDetection

Bases: tunable.tunable.Tunable

Whether to run the rectangular microfluidic growth structure detection as ROI detection

default = False

value = False

class mycelyso.tunables.CleanUpGaussianSigma

Bases: tunable.tunable.Tunable

Clean up step: Sigma [µm] used for Gaussian filter

default = 0.075

value = 0.075

class mycelyso.tunables.CleanUpGaussianThreshold

Bases: tunable.tunable.Tunable

Clean up step: Threshold used after Gaussian filter (values range from 0 to 1)

default = 0.5

value = 0.5

class mycelyso.tunables.CleanUpHoleFillSize

Bases: tunable.tunable.Tunable

Clean up step: Maximum size of holes [µm²] which will be filled

default = 1.0

value = 1.0

class mycelyso.tunables.CropHeight

Bases: tunable.tunable.Tunable

Crop value (vertical) of the image [pixels]

default = 0

value = 0

class mycelyso.tunables.CropWidth

Bases: tunable.tunable.Tunable

Crop value (horizontal) of the image [pixels]

default = 0

value = 0

class mycelyso.tunables.NodeEndpointMergeRadius

Bases: tunable.tunable.Tunable

Radius in which endpoints are going to be merged [µm]

default = 0.5

value = 0.5

class mycelyso.tunables.NodeJunctionMergeRadius

Bases: tunable.tunable.Tunable

Radius in which junctions are going to be merged [µm]

default = 0.5

value = 0.5

class mycelyso.tunables.NodeLookupCutoffRadius

Bases: tunable.tunable.Tunable

Radius at which nodes will be ignored if they are further away [µm]

default = 2.5

value = 2.5

class mycelyso.tunables.NodeLookupRadius

Bases: tunable.tunable.Tunable

Radius in which nodes will be searched for found pixel structures [µm]

default = 0.5

value = 0.5

class mycelyso.tunables.NodeTrackingEndpointShiftRadius

Bases: tunable.tunable.Tunable

Maximum search radius for endpoints [µm·h⁻¹]

default = 100.0

value = 100.0

class mycelyso.tunables.NodeTrackingJunctionShiftRadius

Bases: tunable.tunable.Tunable

Maximum search radius for junctions [µm·h⁻¹]

default = 5.0

value = 5.0

class mycelyso.tunables.RemoveSmallStructuresSize

Bases: tunable.tunable.Tunable

Remove structures up to this size [µm²]

default = 10.0

value = 10.0

class mycelyso.tunables.SkipBinarization

Bases: tunable.tunable.Tunable

Whether to directly use the input image as binary mask. Use in case external binarization is desired.

default = False

value = False

class mycelyso.tunables.StoreImage

Bases: tunable.tunable.Tunable

Whether to store images in the resulting HDF5. This leads to a potentially much larger output file.

default = False

value = False

class mycelyso.tunables.ThresholdingParameters

Bases: tunable.tunable.Tunable

Parameters for the used binarization method, passed as key1:value1,key2:value2,… string

default = ''

value = ''

class mycelyso.tunables.ThresholdingTechnique

Bases: tunable.tunable.Tunable

Binarization method to use, for available methods see documentation (mycelyso.processing.binarization)

default = 'experimental_thresholding'

classmethod test(value)

value = 'experimental_thresholding'

class mycelyso.tunables.TrackingMaximumCoverage

Bases: tunable.tunable.Tunable

Tracking, maximum covered area ratio at which tracking is still performed

default = 0.2

value = 0.2

class mycelyso.tunables.TrackingMaximumRelativeShrinkage

Bases: tunable.tunable.Tunable

Tracking, maximal relative shrinkage

default = 0.2

value = 0.2

class mycelyso.tunables.TrackingMaximumTipElongationRate

Bases: tunable.tunable.Tunable

Tracking, maximum tip elongation rate [µm·h⁻¹]

default = 100.0

value = 100.0

class mycelyso.tunables.TrackingMinimalGrownLength

Bases: tunable.tunable.Tunable

Tracking, minimal hyphae gained length in track filter [µm]

default = 5.0

value = 5.0

class mycelyso.tunables.TrackingMinimalMaximumLength

Bases: tunable.tunable.Tunable

Tracking, minimal hyphae end length in track filter [µm]

default = 10.0

value = 10.0

class mycelyso.tunables.TrackingMinimumTipElongationRate

Bases: tunable.tunable.Tunable

Tracking, minimum tip elongation rate [µm·h⁻¹]

default = -0.0

value = -0.0

class mycelyso.tunables.TrackingMinimumTrackedPointCount

Bases: tunable.tunable.Tunable

Tracking, minimal time steps in track filter [#]

default = 5

value = 5

mycelyso.highlevel package

Submodules

mycelyso.highlevel.nodeframe module

The nodeframe module contains the NodeFrame class, a representation of the graph of one time lapse frame.

class mycelyso.highlevel.nodeframe.NodeFrame(pf)

Bases: object

Node frame is a representation of an image stack frame on the graph/node level, it populates its values from a PixelFrame passed.

cleanup_adjacency()

Cleans up the adjacency matrix after alterations on the node level have been performed.

Returns:

cycles

Detects whether a cycle exists in the graph.

Returns:

generate_derived_data()

Generates derived data from the current adjacency matrix.

Derived data are shortest paths, as well as connected components.

Returns:

get_connected_nodes(some_node)

Get all nodes which are (somehow) connected to node some_node.

Parameters:some_node – Returns:

get_networkx_graph(with_z=0, return_positions=False)

Convert the adjacency matrix based internal graph representation to a networkx graph representation.

Positions are additionally set based upon the pixel coordinate based positions of the nodes.

Parameters:

• with_z – Whether z values should be set based upon the timepoint the nodes appear on
• return_positions – Whether positions should be returned jointly with the graph

Returns:

get_path(start_node, end_node)

Walks from start_node to end_node in the graph and returns the list of nodes (including both).

Parameters:

• start_node –
• end_node –

Returns:

is_endpoint(i)

Returns whether node i is an endpoint.

Parameters:i – Returns:

is_junction(i)

Returns whether node i is a junction.

Parameters:i – Returns:

prepare_graph(pf)

Prepares the graph from the data stored in the PixelFrame pf. :param pf: PixelFrame :return:

track(successor)

Tracks nodes on this frame to nodes on a successor frame.

Parameters:successor – Returns:

mycelyso.highlevel.pipeline module

The pipeline module contains the mycelyso-Pipeline, assembled from various functions.

class mycelyso.highlevel.pipeline.Mycelyso

Bases: mycelyso.pilyso.application.application.App

The Mycelyso App, implementing a pilyso App.

arguments(argparser)

handle_args()

interactive_run()

options()

class mycelyso.highlevel.pipeline.MycelysoPipeline(args)

Bases: mycelyso.pilyso.pipeline.pipeline.PipelineExecutionContext

The MycelysoPipeline, defining the pipeline (with slight alterations based upon arguments passed via command line).

mycelyso.highlevel.pixelframe module

The pixelframe module contains the PixelFrame class, a representation of one time lapse frame at the binary mask/pixel level.

class mycelyso.highlevel.pixelframe.PixelFrame(image, timepoint=0.0, calibration=1.0)

Bases: object

A PixelFrame represents the pixel graph of one (skeletonized) image stack frame.

create_graph()

Creates the graph from the pixel skeleton.

Returns:

mycelyso.highlevel.steps module

The steps module contains most of the individual, albeit mycelyso-specific processing steps.

mycelyso.highlevel.steps.binarize(image, binary=None)

Binarizes the input image using the experimental thresholding technique.

Parameters:

• image –
• binary –

Returns:

mycelyso.highlevel.steps.clean_up(calibration, binary)

Cleans up the image by removing holes smaller than the configured size.

Parameters:

• calibration –
• binary –

Returns:

>>> clean_up(0.1, np.array([[ True,  True,  True],
...                         [ True, False,  True],
...                         [ True,  True,  True]]))
array([[ True,  True,  True],
       [ True,  True,  True],
       [ True,  True,  True]])

mycelyso.highlevel.steps.convert_to_nodes(skeleton, timepoint, calibration, pixel_frame=None, node_frame=None)

Passes the input skeleton into a PixelFrame and instantiates a NodeFrame based upon that.

Parameters:

• skeleton –
• timepoint –
• calibration –
• pixel_frame –
• node_frame –

Returns:

mycelyso.highlevel.steps.generate_graphml(node_frame, result)

Generates a GraphML representation of a particular frame.

Parameters:

• node_frame –
• result –

Returns:

mycelyso.highlevel.steps.generate_overall_graphml(collected, result)

Generates a GraphML representation of the whole graph of one image stack.

Parameters:

• collected –
• result –

Returns:

mycelyso.highlevel.steps.graph_statistics(node_frame, result=None)

Adds some information about the graph to the results.

Parameters:

• node_frame –
• result –

Returns:

>>> pf = PixelFrame(np.array([[0, 0, 0],
...                           [1, 1, 1],
...                           [0, 0, 0]]), calibration=15.0)
>>> sorted(graph_statistics(NodeFrame(pf)).items())
[('graph_edge_count', 1.0), ('graph_edge_length', 30.0), ('graph_endpoint_count', 2), ('graph_junction_count', 0), ('graph_node_count', 2)]

mycelyso.highlevel.steps.image_statistics(image, calibration, result=None)

Adds some numeric image parameters (i.e. size) to the results.

Parameters:

• image –
• calibration –
• result –

Returns:

>>> sorted(image_statistics(np.array([[0, 0, 0],
...                                   [0, 0, 0],
...                                   [0, 0, 0]]), calibration=15.0).items())
[('area', 2025.0), ('area_pixel', 9), ('input_height', 45.0), ('input_height_pixel', 3), ('input_width', 45.0), ('input_width_pixel', 3)]

mycelyso.highlevel.steps.individual_tracking(collected, tracked_fragments=None, tracked_fragments_fates=None)

After correspondence has been established by NodeFrame#track, reconstructs growing paths over time.

Parameters:

• collected –
• tracked_fragments –
• tracked_fragments_fates –

Returns:

mycelyso.highlevel.steps.prepare_position_regressions(collected, result)

Prepares some regressions over parameters collected per position over time.

Parameters:

• collected –
• result –

Returns:

mycelyso.highlevel.steps.prepare_tracked_fragments(collected, tracked_fragments, tracked_fragments_fates, track_table=None, track_table_aux_tables=None)

Filters and converts tracked growing segments to result datasets.

Parameters:

• collected –
• tracked_fragments –
• tracked_fragments_fates –
• track_table –
• track_table_aux_tables –

Returns:

mycelyso.highlevel.steps.qimshow(image, cmap='gray')

Debug function, quickly shows the passed image via matplotlibs imshow-facilities.

Parameters:

• image –
• cmap –

Returns:

mycelyso.highlevel.steps.quantify_binary(binary, calibration, result=None)

Adds some information about the binary image (i.e. covered ratio, area …) to the results.

Parameters:

• binary –
• calibration –
• result –

Returns:

>>> sorted(quantify_binary(np.array([[0, 0, 0],
...                                  [1, 1, 1],
...                                  [0, 0, 0]]), calibration=15.0).items())
[('covered_area', 675.0), ('covered_area_pixel', 3), ('covered_ratio', 0.3333333333333333)]

mycelyso.highlevel.steps.remove_border_artifacts(calibration, binary)

Removes structures, which are most likely artifacts because their centroid lies near the border.

Parameters:

• calibration –
• binary –

Returns:

>>> remove_border_artifacts(0.1, np.array([[ False, False, False],
...                                        [ False, False,  True],
...                                        [ False, False,  True]]))
array([[False, False, False],
       [False, False, False],
       [False, False, False]])

mycelyso.highlevel.steps.remove_small_structures(calibration, binary)

Cleans up the image by removing structures smaller than the configured size.

Parameters:

• calibration –
• binary –

Returns:

>>> remove_small_structures(0.1, np.array([[ False, False, False],
...                                        [ False, False,  True],
...                                        [ False, False,  True]]))
array([[False, False, False],
       [False, False, False],
       [False, False, False]])

mycelyso.highlevel.steps.set_empty_crops(image, crop_t=None, crop_b=None, crop_l=None, crop_r=None)

Defines crop parameters based upon image size, effectively not cropping at all.

Parameters:

• image –
• crop_t –
• crop_b –
• crop_l –
• crop_r –

Returns:

mycelyso.highlevel.steps.skeletonize(binary, skeleton=None)

Skeletonizes the image using scikit-image’s skeletonize function.

Parameters:

• binary –
• skeleton –

Returns:

>>> skeletonize(np.array([[0, 0, 1, 1],
...                       [0, 0, 1, 1],
...                       [0, 0, 1, 1],
...                       [0, 0, 1, 1]]))
array([[False, False, False, False],
       [False, False,  True, False],
       [False, False,  True, False],
       [False, False, False, False]])

mycelyso.highlevel.steps.skip_if_image_is_below_size(min_height=4, min_width=4)

Raises a Skip exception if the image size falls below the set image size.

Parameters:

• min_height –
• min_width –

Returns:

>>> skip_if_image_is_below_size(32, 32)(np.zeros((16,16)), Meta(0, 0))
Traceback (most recent call last):
 ...
mycelyso.pilyso.pipeline.executor.Skip: Meta(pos=0, t=<class 'mycelyso.pilyso.pipeline.executor.Collected'>)

mycelyso.highlevel.steps.track_multipoint(collected)

Initiates tracking between consecutive NodeFrames.

Parameters:collected – Returns:

Module contents

The highlevel package contains highlevel functionality of mycelyso.

mycelyso.misc package

Submodules

mycelyso.misc.graphml module

The graphml module contains output routines to output GraphML structured data from internal graph representations.

mycelyso.misc.graphml.to_graphml_string(g)

Converts a networkx graph to a GraphML representation.

Parameters:g – graph Returns:graphml string

mycelyso.misc.graphml.to_graphml_writer(g)

Takes a networkx graph and returns a GraphMLWriter containing the graph.

Parameters:g – graph Returns:GraphMLWriter instance

mycelyso.misc.graphml.write_graphml(g, name)

Writes a networkx graph in GraphML format to a file.

Parameters:

• g – graph
• name – filename

Returns:

mycelyso.misc.regression module

The regression modules contains some helpers to perform linear fits on data with non-linear begins or ends.

mycelyso.misc.regression.find_linear_window(x, y, begin=nan, end=nan, window=0.1, condition=('rvalue', 'gt', 0.95), return_begin_end=False, return_nan_if_impossible=True)

Tries to find a continuous window in x/y which (mostly) follows a linear relation subject to condition.

If window is a float, it is seen as relative length of the input lists. Linear regressions will be performed on each window, then the windows will be filtered by the condition (eg that they have a rvalue better than 0.95). Then the range between the first and the last window to follow these conditions will be used to perform the overall regression.

See also scipy.stats.linregress()

Parameters:

• x – Input data, independent value
• y – Input data, dependent value
• begin –
• end –
• window – Window, either
• condition – Condition to check, a tuple of three. The first must be a key of a linear regression result object, the second either ‘gt’ or ‘lt’, and the third the value to compare.
• return_begin_end – If true, return the found range as well
• return_nan_if_impossible – If True, return NaN if no suitable region was found, otherwise throws RuntimeError

Returns:

mycelyso.misc.regression.prepare_optimized_regression(x, y)

First finds an optimal window using find_linear_window(), than performs a linear regression.

Parameters:

• x – independent variable
• y – dependent variable

Returns:

>>> x = np.linspace(1, 100, 100)
>>> y = x * 5 + 10
>>> y[0:10] = 0  # break our nice linear curve
>>> prepare_optimized_regression(x, y)
OrderedDict([('slope', 5.0), ('intercept', 10.0), ('rvalue', 0.9999999999999999), ('pvalue', 0.0), ('stderr', 7.942345602646859e-09), ('begin_index', 10), ('end_index', 100), ('begin', 11.0), ('end', 100.0)])

mycelyso.misc.util module

mycelyso.misc.util.calculate_length(points, times=1, w=5)

Calculates the length of a path.

Paths sampled from pixel grids may contain notable measuring error, if euclidean distances are calculated naively. This method uses an adapted approach from [Cornelisse1984], by repeatedly smoothing the coordinates with a moving average filter before calculating the euclidean distance.

[Cornelisse1984]

Cornelisse and van den Berg (1984) Journal of Microscopy 10.1111/j.1365-2818.1984.tb00544.x

Parameters:

• points – Input points, a numpy array (X, 2)
• times – Times smoothing should be applied
• w – window width of the moving average filter

Returns:

Length of the input path

>>> calculate_length(np.array([[1.0, 1.0],
...                            [5.0, 5.0]]))
5.656854249492381

mycelyso.misc.util.clean_by_radius(points, radius=15.0)

Bins points by radius and returns only one per radius, removing duplicates.

Parameters:

• points – Input points
• radius – Radius

Returns:

Filtered points

>>> clean_by_radius(np.array([[1.0, 1.0],
...                           [1.1, 1.1],
...                           [9.0, 9.0]]), radius=1.5)
array([[1., 1.],
       [9., 9.]])

mycelyso.misc.util.pairwise(iterable)

s -> (s0,s1), (s1,s2), (s2, s3), …

Module contents

The misc package contains various helper functions.

mycelyso.processing package

Submodules

mycelyso.processing.binarization module

The binarization module contains the binarization routine used to segment phase contrast images of mycelium networks into foreground and background.

mycelyso.processing.binarization.bataineh(image, mask=None, window_size=15, return_threshold=False, **kwargs)

Thresholding method as developed by [Bataineh2011a].

[Bataineh2011a]

Bataineh et al. (2011) Pattern Recognit. Lett. DOI: 10.1016/j.patrec.2011.08.001

Parameters:

• image – Input image
• mask – Possible mask denoting a ROI
• window_size – Window size
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.experimental_thresholding(image, mask=None, window_size=15, gaussian_sigma=3.0, shift=0.2, target=-0.5, quotient=1.2, return_threshold=False, **kwargs)

A novel thresholding method basing upon the shape index as defined by [Koenderink1992], and [Bataineh2011] automatic adaptive thresholding. The method is due to be explained in detail in the future.

[Koenderink1992]

Koenderink and van Doorn (1992) Image Vision Comput. DOI: 10.1016/0262-8856(92)90076-F

[Bataineh2011]

Bataineh et al. (2011) Pattern Recognit. Lett. DOI: 10.1016/j.patrec.2011.08.001

Parameters:

• image – Input image
• mask – Possible mask denoting a ROI
• window_size – Window size
• gaussian_sigma – Sigma of the Gaussian used for smoothing
• shift – Shift parameter
• target – Target shape index parameter
• quotient – Quotient parameter
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.feng(image, mask=None, window_size=15, window_size2=30, a1=0.12, gamma=2, k1=0.25, k2=0.04, return_threshold=False, **kwargs)

Thresholding method as developed by [Feng2004].

[Feng2004]

Fend & Tan (2004) IEICE Electronics Express DOI: 10.1587/elex.1.501

Parameters:

• image – Input image
• mask – Possible mask denoting a ROI
• window_size – Window size
• window_size2 – Second window size
• a1 – a1 value
• gamma – gamma value
• k1 – k1 value
• k2 – k2 value
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.mean_and_std(image, window_size=15)

Helper function returning mean and average images sped up using integral images / summed area tables.

Parameters:

• image – Input image
• window_size – Window size

Returns:

tuple (mean, std)

mycelyso.processing.binarization.nick(image, window_size=15, k=-0.1, return_threshold=False, **kwargs)

Thresholding method as developed by [Khurshid2009].

[Khurshid2009]

Khurshid et al. (2009) Proc. SPIE DOI: 10.1117/12.805827

Parameters:

• image – Input image
• window_size – Window size
• k – k value
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.normalize(image)

Normalizes an image to the range 0-1

Parameters:image – Returns:

mycelyso.processing.binarization.phansalkar(image, window_size=15, k=0.25, r=0.5, p=2.0, q=10.0, return_threshold=False, **kwargs)

Thresholding method as developed by [Phansalkar2011].

[Phansalkar2011]

Phansalkar et al. (2011) Proc. ICCSP DOI: 10.1109/ICCSP.2011.5739305

Parameters:

• image – Input image
• window_size – Window size
• k – k value
• r – r value
• p – p value
• q – q value
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.sauvola(image, window_size=15, k=0.5, r=128, return_threshold=False, **kwargs)

Thresholding method as developed by [Sauvola1997].

[Sauvola1997]

Sauvola et al. (1997) Proc. Doc. Anal. Recog. DOI: 10.1109/ICDAR.1997.619831

Parameters:

• image – Input image
• window_size – Window size
• k – k value
• r – r value
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.binarization.wolf(image, mask=None, window_size=15, a=0.5, return_threshold=False, **kwargs)

Thresholding method as developed by [Wolf2004].

[Wolf2004]

Wolf & Jolion (2004) Form. Pattern Anal. & App. DOI: 10.1007/s10044-003-0197-7

Parameters:

• image – Input image
• mask – Possible mask denoting a ROI
• window_size – Window size
• a – a value
• return_threshold – Whether to return a binarization, or the actual threshold values
• kwargs – For compatibility

Returns:

mycelyso.processing.pixelgraphs module

The pixelgraphs module contains various functions to work with skeleton images, and treating the paths of the skeleton as graphs, which can be walked along.

mycelyso.processing.pixelgraphs.get_all_neighbor_nums(num)

Return all set neighbor bits in num.

Parameters:num – Neighborhood representation. Returns:Array of values

mycelyso.processing.pixelgraphs.get_all_neighbors(num)

Return positions for all set neighbor bits in num

Parameters:num – Neighborhood representation Returns:Array of shifts

mycelyso.processing.pixelgraphs.get_connectivity_map(binary)

Returns a ‘connectivity map’, where each value represents the count of neighbors at a position.

Parameters:binary – Binary input image Returns:

mycelyso.processing.pixelgraphs.get_inverse_neighbor_shift(num)

Get the shift corresponding to the inverse direction represented by num.

Parameters:num – Neighborhood bit Returns:Shift (r, c)

mycelyso.processing.pixelgraphs.get_neighborhood_map(binary)

Returns a ‘neighborhood map’, where each value binary encodes the connections at a point.

Parameters:binary – Binary input image Returns:

mycelyso.processing.pixelgraphs.get_next_neighbor(num)

Returns the coordinates represented by a numeric neighbor bit.

Parameters:num – Neighbor bit Returns:Shift (r, c)

mycelyso.processing.pixelgraphs.is_edge(connectivity)

Returns True if connectivity corresponds an edge (is two).

Parameters:connectivity – Scalar or matrix Returns:Boolean or matrix of boolean

mycelyso.processing.pixelgraphs.is_end(connectivity)

Returns True if connectivity corresponds to an endpoint (is one).

Parameters:connectivity – Scalar or matrix Returns:Boolean or matrix of boolean

mycelyso.processing.pixelgraphs.is_junction(connectivity)

Returns True if connectivity corresponds a junction (is greater than two).

Parameters:connectivity – Scalar or matrix Returns:Boolean or matrix of boolean

mycelyso.processing.pixelgraphs.where2d(image)

numpy.where for 2D matrices.

Parameters:image – Input images Returns:Coordinate list where image is non-zero

>>> where2d(np.array([[ 0, 0, 0],
...                   [ 0, 1, 1],
...                   [ 0, 0, 0]]))
array([[1, 1],
       [1, 2]])

Module contents

The processing submodule contains various functions and management classes concerned with image processing of hyphae network images.

Module contents

mycelyso, the MYCElium anaLYsis SOftware