Digitizing stratigraphic diagrams¶

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STRADITIZE (Stratigraphic Diagram Digitizer) is an open-source program that allows stratigraphic figures to be digitized in a single semi-automated operation. It is designed to detect multiple plots of variables analyzed along the same vertical axis, whether this is a sediment core or any similar depth/time series.

Usually, in an age of digital data analysis, gaining access to data from the pre-digital era – or any data that is only available as a figure on a page – remains a problem and an under-utilized scientific resource.

This program tackles this problem by providing a python package to digitize especially pollen diagrams, but also any other type of stratigraphic diagram.

The package is very new and there are many features that will be included in the future. Therefore we would be very pleased to get feedback! To do so, you can contact us or raise an issue on GitHub.

Documentation¶

About straditize¶

Why straditize?¶

In an age of digital data analysis, gaining access to data from the pre-digital era – or any data that is only available as a figure on a page – remains a problem and an under-utilized scientific resource. Whilst there are numerous programs available that allow the digitization of scientific data in a simple x-y graph format, we know of no semi-automated program that can deal with data plotted with multiple horizontal axes that share the same vertical axis, such as pollen diagrams and other stratigraphic figures that are common in the Earth sciences. STRADITIZE (Stratigraphic Diagram Digitizer) is a new open-source program that allows stratigraphic figures to be digitized in a single semi- automated operation. It is designed to detect multiple plots of variables analyzed along the same vertical axis, whether this is a sediment core or any similar depth/time series.

About the author¶

The code and GUI of straditize was developed by Philipp S. Sommer at the Institute of Earth System Dynamics (IDYST) at the University of Lausanne as part of the SNF funded HORNET Project (200021_169598).

The other contributors are Basil A. S. Davis, Manuel Chevalier and Dilan Rech who made significant contributions to the layout, workflow, beta tests and and reviewing of the software.

License¶

straditize is published under the GNU General Public License v3.0 under the copyright of Philipp S. Sommer, 2018-2019

Installation¶

How to install¶

You can either install straditize through a package manager such as conda or pip or install it from source.

Installation using conda¶

We highly recommend to use conda for installing straditize. Here you can install it via manually via the chilipp channel

After having downloaded and installed anaconda, open a terminal (or the Anaconda Prompt on windows) and install straditize from the conda-forge channel. You can choose: We recommend to install straditize into its own environment via:

$ conda create -n straditize -c conda-forge straditize

and then activate this environment via:

$ conda activate straditize

In that way you do not mess up your base environment. Nevertheless you can also install it into an existing environment via:

$ conda install -c conda-forge straditize

In the same terminal, now type straditize to start the software.

Note

The latest master branch on github is always available under the master label on the chilipp channel. Just type:

$ conda install -c chilipp/label/master straditize

to install the latest version from the master branch. Note that you then have to add the conda-forge channel to your default channels via:

$ conda config --add channels conda-forge

Installation using pip¶

If you do not want to use conda for managing your python packages and already have python3 installed on your computer, you can also use the python package manager pip. To be on the safe side, make sure you have the Dependencies installed. If so, open a terminal and install it via:

$ pip install straditize

To open the software, type straditize in the same terminal.

Installation from source¶

To install it from source, make sure you have the Dependencies installed. Download (or clone) the github repository, e.g. via:

git clone https://github.com/Chilipp/straditize.git

and install it via:

pip install .  # or python setup.py install, but pip is recommended

from your terminal. To open the software, type straditize in the same terminal.

Dependencies¶

Required dependencies¶

straditize has been tested for python>=3.6. Furthermore the package is built upon multiple other packages, mainly

psyplot-gui>1.2.0: The graphical user interface for psyplot
PyQt5: Pythons Qt bindings that are required by psyplot-gui (note that PyQt4 is not supported!)
numpy, scipy and pandas: for the data management and compuations
matplotlib>=2.0: The python visualiation package
pillow: for reading and writing images
scikit-image: For image recognition features
openpyxl: For exports to Excel files
netCDF4: A library for saving and storing netCDF files.

Optional dependencies¶

We furthermore recommend to use

tesserocr: for column names recognition. It depends on the tesseract OCR and you can install both (on Linux and MacOS) via:
```
$ conda install -c chilipp tesserocr
```
(see Automatic optical character recognition (OCR) for more information)

Running the tests¶

We use pytest to run our tests. So you can either run clone out the github repository and run:

$ python setup.py test

or install pytest by yourself and run

$ py.test

Alternatively you can build the recipe in the conda-recipe directory via

$ conda build conda-recipe

which will also run the test suite.

Warning

Running the entire test suite in one single process (such as python setup.py test) might be quite memory consumptive because it involves the creation and closing of many PyQt widgets and unfortunately some memory is leaked from one test to another. Therefore we recommend to split the tests into multiple processes, e.g.:

# run the test suite but ignore some modules
python setup.py test -a '--ignore=tests/widgets/test_selection_toolbar.py --ignore=tests/widgets/test_samples_table.py --ignore=tests/widgets/test_beginner.py --ignore=tests/widgets/test_hoya_del_castillo.py'
# run the tests for the previously ignored modules
python setup.py test -a 'tests/widgets/test_selection_toolbar.py
tests/widgets/test_samples_table.py'
python setup.py test -a 'tests/widgets/test_beginner.py'
python setup.py test -a 'tests/widgets/test_hoya_del_castillo.py'

or equivalently with py.test instead of python setup.py test -a. Note that conda build conda-recipe already splits the session into multiple processes.

Nevertheless, you should expect about ~180 tests to be ran and a total memory usage of about 3 to 4GB RAM.

Building the docs¶

The online documentation is accessible as PDF, HTML and Epub under https://straditize.readthedocs.io or https://straditize.rtfd.io. Thanks to the free services by readthedocs.org, the online documentation is build automatically after each commit to the github repository.

To build the docs locally on your machine, check out the github repository and install the requirements in 'docs/environment.yml' and the sphinx_rtd_theme package. The easiest way to do this is via anaconda by typing:

$ conda env create -f docs/environment.yml
$ conda activate straditize_docs
$ conda install sphinx_rtd_theme

Then build the docs via:

$ cd docs
$ make html  # or `make pdf` for a PDF version compiled with Latex

Updating straditize¶

Updating the software depends on how you installed it on your system.

Updating via conda¶

If you installed straditize via conda (see Installation using conda), you can update it via:

$ conda update -c chilipp straditize

Updating via pip¶

If you installed it via pip (see Installation using pip), you can update it via:

$ pip install -U straditize

Updating from source files¶

If you installed it via python setup.py install from the source repository (see Installation from source), just run that command again after having checked out the latest version from github.

Uninstallation¶

The uninstallation depends on the system you used to install straditize. Either you did it via conda (see Uninstallation via conda), via pip or from the source files (see Uninstallation via pip).

Anyway, if you may want to remove the psyplot configuration files. If you did not specify anything else (see psyplot.config.rcsetup.psyplot_fname()), the configuration files for psyplot are located in the user home directory. Under linux and OSX, this is $HOME/.config/psyplot. On other platforms it is in the .psyplot directory in the user home.

Uninstallation via conda¶

If you installed straditize via conda, simply run:

conda uninstall straditize

Uninstallation via pip¶

Uninstalling via pip simply goes via:

pip uninstall straditize

Note, however, that you should use conda if you also installed it via conda.

Straditize Tutorial¶

To introduce you into straditize, we implemented a tutorial into the graphical user interface.

To start it in the GUI, click the Tutorial button at the bottom of the straditizer control.

Tutorials¶

Beginners tutorial to introduce straditize¶

In this tutorial, we digitize a simple small pollen diagram a subset of the Hoya del Castillo dataset from [Basil2007a]. This tutorial will introduce you into the main parts of straditize and the basic workflow.

At the bottom of the straditizer control, you find a navigation panel which helps you to navigate through the tutorial. Click the Next button in this control to start the tutorial. You can skip steps by clicking the Skip button and you can check what you have done using the Check button and then proceed with the next step.

The tutorial will take between 5 and 10 minutes. You can save your current state to a file using File ‣ Save ‣ Save straditizer (see Saving and loading your project) and reload it later via File ‣ Open project ‣ Open straditizer ‣ Project or image

If you have questions or troubles with this tutorial, please open an issue on

https://github.com/Chilipp/straditize/issues

and we will do our best to assist you.

The straditize workflow¶

The straditize workflow, i.e. the program for the next few minutes consists of 6 main steps:

Load the diagram
Select the diagram part (withouth x-axes labels, y-axes labels, etc.)
Clean the diagram part. Only the data parts should be left over, i.e. no y-axes, lines, letters, etc.
Digitize the diagram
Find and edit the samples
Export the data to Excel or CSV

Most of these tasks can be done in an automatic way but you should always review and edit the outcome to make sure that what you do is scientifically reliable.

The interface to these semi-automatic steps is the straditizer control

Click the image to enlarge it¶

You can expand the submenus using the buttons in the control and you can access the user manual using the buttons on the right side.

Before we start with the diagram, we will highlight some of the sections in the GUI:

The ToDo list

The list at the top of the control guides you through the straditization process. It shows the open tasks you should consider.

Most of them will be marked as done automatically, but if you think you are done with one of the tasks, just right-click the task and mark it as done.

The Plot control

This section at the bottom of the control gives you some visual explorations of your diagram and the digitization process. You should, whenever possible, use it’s functionalities, most of the errors can be spotted visually

The Help explorer

This separate widgets can be used to access the user manual and provides guidance for the different functionalities. This should always be your first point of reference if you have any problems.

Load the image of the diagram¶

The first step is to load the image of the diagram into straditize.

Click the button at the top of the straditizer control.
Select the image file beginner-tutorial.png from your hard-disk and click Open

Navigation inside the plot¶

Now you see the stratigraphic diagram (by default) centered at the top of the graphical user interface. Additionally you find a separate window to it’s right with a magnified version of the diagram. It follows your mouse movements inside the diagram and shows you a magnified version of it.

To navigate inside the plot, you can use the default navigation toolbar that is provided by matplotlib

_images/matplotlib-navigation-toolbar.png

Especially the Pan/Zoom button and the zoom-to-rectangle button are of interest for you. You can enable and disable them by clicking on the corresponding button in the toolbar.

The matplotlib docs provide further guidance:

The Pan/Zoom button

This button has two modes: pan and zoom. Click the toolbar button to activate panning and zooming, then put your mouse somewhere over an axes. Press the left mouse button and hold it to pan the figure, dragging it to a new position. When you release it, the data under the point where you pressed will be moved to the point where you released. If you press ‘x’ or ‘y’ while panning the motion will be constrained to the x or y axis, respectively. Press the right mouse button to zoom, dragging it to a new position. The x axis will be zoomed in proportionately to the rightward movement and zoomed out proportionately to the leftward movement. The same is true for the y axis and up/down motions. The point under your mouse when you begin the zoom remains stationary, allowing you to zoom in or out around that point as much as you wish. You can use the modifier keys ‘x’, ‘y’ or ‘CONTROL’ to constrain the zoom to the x axis, the y axis, or aspect ratio preserve, respectively.

With polar plots, the pan and zoom functionality behaves differently. The radius axis labels can be dragged using the left mouse button. The radius scale can be zoomed in and out using the right mouse button.

The Zoom-to-rectangle button: Click this toolbar button to activate this mode. Put your mouse somewhere over an axes and press a mouse button. Define a rectangular region by dragging the mouse while holding the button to a new location. When using the left mouse button, the axes view limits will be zoomed to the defined region. When using the right mouse button, the axes view limits will be zoomed out, placing the original axes in the defined region.

More information can be found in the matplotlib documentation: https://matplotlib.org/users/navigation_toolbar.html

Select the diagram part¶

The second step involves selecting the diagram part. This is the part where your data is displayed, but without the vertical and horizontal axes descriptions.

Being exact in this step significantly reduces the later work and simplifies the automatic digitization.

Click the Select data part button in the digitization control.
Straditize automatically recognizes the data part. But you can change this by moving the crosses around with your mouse (see the note below).
Click the Apply button at the bottom
Done! You now see a red rectangle in your diagram the marks the data part. You can hide it using the Plot control section in the straditizer control. For the sake of this tutorial, expand the Plot control section and remove the rectangle by clicking the icon for the Diagram part

Note

These marks are very common in straditize. In general, you can left-click on a mark to move it around, right-click to delete it, and Shift+left-click on the plot to create a new one.

Note that you need to disable the and tools in the navigation to interact with the marks, by clicking on the corresponding button!

If you want to change the appearance of the marks, see the Marker control section in the straditizer control panel.

Create the diagram reader¶

Now you have to select, which type of diagram you are digitizing. In the example, it is an area diagram which is already selected in the dropdown menu next to the Select data part button.

Click on Convert image to start the digitization.

See also

Selecting the reader

Selecting the columns¶

The next step is, to separate the columns, i.e. to tell straditize where each of the sub diagrams start.

This is simple in our case, since straditize can separate them automatically.

Click the Column starts button. You will see several vertical blue lines appearing on the plot that you can drag and drop such as you did it when selecting the diagram part. You can change their colors using the marker control
straditize recognizes these columns automatically and in our example, you do not have to edit them. Therefore hit the Apply button at the bottom of the control and you are done.

See also

Select the starts for each column

Cleaning the image¶

The next step is to clean the data image to make sure that everything that is not representing real data is removed. In our case, these are y-axes, x-axes and the outer frame. You can also use an external image editing software such as Adobe Photoshop, but we will do this now inside straditize and use some of the automatic recognition functionalities.

Expand the Remove features tab
Click the y-axes button, the y-axes in the plot will be highlighted
Click Remove to remove them
1. Click the x-axes button, the x-axes in the plot will be highlighted
Click Remove to remove them
Finally there is the right part of the diagram frame left

For this, we use the selection toolbar:
1. from the menu, select the column selection tool (click and hold the button to the right of the button in the selection toolbar to open the menu)
2. Draw a rectangle around the right line
  
  and make sure you have the mode activated
3. Click the Remove button when you are done

Digitizing the data¶

The next step, after we cleaned up the image, is the digitization of the diagram. Click the Digitize button.

You now see the lines that result from the digitization. You can remove them in the Plot control section by clicking the button of the Full digitized data row.

Finding and editing samples¶

So far, we have one data point per pixel in the image. However, we have to identify the locations of the samples in order to reproduce the original data.

Straditize assists you with this through automatic sample finding algorithms, or you can load the sample locations from an external files or you just add and edit the samples manually.

Expand the Samples item in the digitization control
Find the samples by clicking the Find samples button. straditize now identified the sample locations based on the extrema in the columns (see Automatic samples identification for an explanation of the algorithms).
To visualize the samples, you can again use the items in the Plot control tab. However, you can (and should!) also edit them by clicking the Edit samples button.
Now you see one horizontal line per sample that you can drag around (left-click), delete (right-click) or you can also add new samples (Shift + left-click). See the Editing samples section for more details.
Finally, click the Apply button or the Cancel button to stop the editing of the samples.

You’re almost done! Your diagram is now digitized and the data could already be exported.

Translating the y-axis¶

To correctly reproduce the diagram data, there are now only to more things to know, that is the scaling of the y- and x-axes in the diagram.

Expand the Axes translations tab in the digitization control
Click the Insert Y-axis values button in the Axes translations section of the straditizer control
Shift-leftclick on the plot to enter the corresponding y-value.
A small dialog will appear where you should enter the y-value to use (in this case, 300)
After hitting the Ok button, you will see a mark on the plot (blue line). You can select the mark via leftclick and drag it to a different location or you can delete it via rightclick.
now repeat steps 2-4 on a second point on the y-axis
- Select another point
- Enter the corresponding value (here 350)
- A new mark is created that you can modify
Click the Apply button at the bottom of the straditizer control when you are done.

Note

If you drag a mark and hold the Shift button while releasing the mouse button, the dialog in point 3 from above will not pop up.

Translating the x-axis¶

As we did for the y-axes, we do it for the x-axes.

Click the Insert X-axis values button in the Axes translations section of the straditizer control
A small dialog will appear where you should enter the value at the column start (in our case, 0)
Shift-leftclick on the plot to enter another x-value.
A small dialog will appear where you should enter the x-value to use
Click the Apply button at the bottom of the straditizer control when you are done.

Column names¶

Finally, we select the column titles and save them in the project.

Each of the sub diagrams has a title, in our case, Pinus, Juniperus, Quercus ilex-type and Chenopodiaceae. These names should be used in the final digitization result and therefore you can include them in straditize:

Expand the Column names item in the straditizer control and click the Edit column names button
In the appearing widget, the colnames editor

you find a table where you can edit the column names. The plot on it’s left also shows a rotated version of the diagram, to help you identifying the column names. You can navigate in this plot using leftclick and zoom in and out using right-click (see matplotlibs docs on interactive navigation with the Pan/Zoom-tool).

Hint

If you have tesserocr installed (see the user manual), you can also just click the Find column names button and you are done.
When you entered the correct names in the table (see image below), click the Edit column names button again to hide the dialog.

Done!¶

That’s it! You can export the samples (see Export your results) to an Excel or CSV file via

File ‣ Export figures ‣ Straditizer data ‣ Samples

Now start with your own diagram!

If you have questions or troubles, please open an issue on

https://github.com/Chilipp/straditize/issues

Thanks for using straditize and happy digitizing!

References¶

Basil2007a: Davis, Basil A. S., and A. C. Stevenson. “The 8.2ka Event and Early-Mid Holocene Forests, Fires and Flooding in the Central Ebro Desert, NE Spain.” Quat. Sci. Rev. , vol. 26, no. 13-14, 2007, pp. 1695-712, doi:10.1016/j.quascirev.2007.04.007.

Advanced tutorial: The Hoya del Castillo pollen diagram¶

In this tutorial, we digitize a stratigraphic diagram based on the data in [Basil2007] and introduce to you how to extract the samples out of an area diagram.

At the bottom of the straditizer control, you find a navigation panel which helps you to navigate through the tutorial. Click the Next button in this control to start the tutorial. You can skip steps by clicking the Skip button and you can check what you have done using the Check button and then proceed with the next step.

The tutorial will take between 15 and 30 minutes. You can save your current state to a file using File ‣ Save ‣ Save straditizer (see Saving and loading your project) and reload it later via File ‣ Open project ‣ Open straditizer ‣ Project or image

If you have questions or troubles with this tutorial, please open an issue on

https://github.com/Chilipp/straditize/issues

and we will do our best to assist you.

Load the image of the diagram¶

The first step is to load the image of the diagram into straditize.

Click the button at the top of the straditizer control.
Select the image file hoya-del-castillo.png from your hard-disk and click Open

Select the diagram part¶

The second step involves selecting the diagram part. This is the part where your data is displayed, but without the vertical and horizontal axes or axes descriptions.

Being exact in this step significantly reduces the later work and simplifies the automatic digitization.

Click the Select data part button in the digitization control.
Click on the upper left cross to select it
Hold the left mouse button and drag the cross to the upper left corner of the diagram. Make sure you don’t include the y-axis and x-axis ticks.

You can also use the zoom and navigation tools in the figure toolbar for navigation.

Now select the cross at the lower right corner of the diagram and move it such that you don’t include the x-axis ticks (i.e. the numbers on the x-axis)
Click the Apply button at the bottom

If you want to change the appearance of the marks, see the Marker control section in the straditizer control panel.

Create the diagram reader¶

Now you have to select, which type of diagram you are digitizing. In the example, it is an area diagram which is already selected in the dropdown menu next to the Select data part button.

Click on Convert image to start the digitization.

See also

Selecting the reader

Identifying the columns¶

The next step is, to automatically separate the columns from each other. This is simple in our case, since straditize can separate them automatically.

Click the Column starts button. You will see several vertical blue lines appearing on the plot that you can drag and drop such as you did it when selecting the diagram part. You can change their colors using the marker control
straditize recognizes these columns automatically and in our example, you do not have to edit them. Therefore hit the Apply button at the bottom of the control and you are done.

See also

Select the starts for each column

Specifying the column names¶

Straditize can handle column names which will then be included in the final export. It interfaces with the tesserocr package and we will use this to minimize our typing amount. However, we have a complex diagram so manual corrections are unavoidable.

Expand the Column names item in the straditizer control and click the Edit column names button.

In the appearing widget, the colnames editor

you find a table where you can edit the column names. The plot on it’s left also shows a rotated version of the diagram, to help you identifying the column names. You can navigate in this plot using leftclick and zoom in and out using right-click (see matplotlibs docs on interactive navigation with the Pan/Zoom-tool)
To improve the text recognition, it is highly recommended to have a clean image with only the column names on it and a sufficient resolution. We have something prepared for you:

Click the Load HR image button and select the hoya-del-castillo-colnames.png image file.
We will now use the automatic finding of column names. For this, click the Find column names button. This will search for the column names in the plot to the left of the table
There will probably be some errors in the column names. Therefore, go through each row in the table and check the name. You can use the Hint button to help you and/or zoom to the column name to see the name in the original
When you entered the correct names in the table, click the Edit column names button again to hide the button.

Remove artifacts¶

Stratigraphic diagrams, and especially pollen diagrams, often have a lot of artifacts in it, that are informative for the reader.

In our case, the diagram is splitted into three temporal zones (HDC-2, HDC-3 and HDC-4, see the Zone column on the right part of the diagram) which are visually separated with horizontal lines. Additionally, the diagram has vertical, dashed lines at each column start (the y-axes for each column).

Before we digitize our diagram, those informative features have to be removed. You can do this in an external image editing software (e.g. Photoshop) but we also implemented several automated algorithms to detect common features and remove them easily.

For our tutorial, we use the Remove lines feature to detect and remove the vertical and horizontal lines.

Horizontal lines¶

Expand the Remove features tab in the Digitization control
Expand the item with the vertical lines and horizontal lines button by clicking on the small arrow on their left
Set the minimum line width to 1 pixel
Click the horizontal lines button. In the plot you will see, that the horizontal lines are red now (if necessary, go with the mouse over the plot and you will see it in the zoom window). You could edit the selection now using the selection toolbar (see The selection toolbar), but for our tutorial, this is not necessary.
Click the Remove button to remove the lines

Vertical lines (y-axes)¶

Enable the maximum line width and set it to 2 pixel
Set the minimum fraction to 30%
Click the vertical lines button and the vertical lines turn red and are marked to be removed.
Click the Remove button to remove the y-axes

Additional automated removal tools are available and fully described in the documentation (see Removing features). But here, we can continue with the digitization.

Digitizing the data¶

The next step, after we cleaned up the image, is the digitization of the diagram. Click the Digitize button.

You now see the lines that result from the digitization. You can remove them in the Plot control section by clicking the button of the Full digitized data row.

Finding and editing samples¶

So far, we have one data point per pixel in the image. However, we have to identify the locations of the samples in order to reproduce the original data.

Straditize assists you with this through automatic sample finding algorithms, or you can load the sample locations from an external files or you just add and edit the samples manually.

Expand the Samples item in the digitization control
Find the samples by clicking the Find samples button. straditize now identified the sample locations based on the extrema in the columns (see Automatic samples identification for an explanation of the algorithms).
To visualize the samples, you can again use the items in the Plot control tab. However, you can (and should!) also edit them by clicking the Edit samples button.
Now you see one horizontal line per sample that you can drag around (left-click), delete (right-click) or you can also add new samples (Shift + left-click). See the Editing samples section for more details.
Finally, click the Apply button or the Cancel button to stop the editing of the samples.

You’re almost done! Your diagram is now digitized and the data could already be exported.

Translating the y-axis¶

To correctly reproduce the diagram data, there are now only to more things to know, that is the scaling of the y- and x-axes in the diagram.

Expand the Axes translations tab in the digitization control
Click the Insert Y-axis values button in the Axes translations section of the straditizer control
Shift-leftclick on the plot to enter the corresponding y-value.
A small dialog will appear where you should enter the y-value to use (in this case, 300)
After hitting the Ok button, you will see a mark on the plot (blue line). You can select the mark via leftclick and drag it to a different location or you can delete it via rightclick.
now repeat steps 2-4 on a second point on the y-axis
- Select another point
- Enter the corresponding value (here 350)
- A new mark is created that you can modify
Click the Apply button at the bottom of the straditizer control when you are done.

Note

If you drag a mark and hold the Shift button while releasing the mouse button, the dialog in point 3 from above will not pop up.

Translating the x-axes¶

Additionally to the vertical axis, we have to tell straditize how to interprete the x-axes of the stratigraphic diagram.

Here we have to deal different units of the x-axes in the diagrams. The first (Charcoal) and last (Pollen Concentration) columns are counts, the pollen taxa in between are in percent.

Therefore we will create column specific readers, one for the first and one for the last column. Then we will insert the x-axis data.

The steps down below are repetitive, but we will describe them for each step in detail.

The Charcoal column¶

Expand the Current reader tab in the digitization control
Click the + button to start the selection for a new reader
Click in the plot on the first column. It will turn red
Click the Apply button and select the area reader in the appearing dialog
In the dropdown menu in the Current reader tab, select the reader for Column 0 to make it the current reader.

Now, we select the values for interpreting the x-axis of this column. The procedure is more or less the same, as before with the y-axis:

Click the Insert X-axis values button in the Axes translations section of the straditizer control
A small dialog will appear where you should enter the x-value of the start of the column (here 0).

Note

The dialog only appears, if the start of one of the selected columns is visible. If this is not the case, Shift-Leftclick on the plot to create a mark.
After hitting the Ok button, you will see a mark on the plot (blue line). You can select the mark via leftclick and drag it to a different location or you can delete it via rightclick.
now repeat steps 2-4 on a second point in the same column. Your diagram should now look something like this:
Click the Apply button at the bottom of the straditizer control and we can continue with the last column

The Pollen Concentration column¶

In the Current reader dropdown menu, select the reader for the columns 1-27
Click the + button to start the selection for a new reader
Click in the plot on the last column. It will turn red
Click the Apply button and select the line reader in the appearing dialog.
In the dropdown menu in the Current reader tab, select the reader for Column 27 to make it the current reader.

Now, we select the values for interpreting the x-axis of this column. The procedure is the same as above:

Click the Insert X-axis values button in the Axes translations section of the straditizer control
In the appearing dialog, enter the x-value of the column start (here again 0) or Shift-Leftclick on the plot and then enter it (see note above).
After hitting the Ok button, you will see a mark on the plot (blue line). Again, you can select the mark, drag it or delete it as before.
now repeat steps 2-4 on a second point in the same column. Your diagram should now look something like this:
Click the Apply button at the bottom of the straditizer control and we can continue with the pollen taxa.

The pollen taxa columns¶

Last but not least, we translate the x-axes informations for the pollen taxa.

Luckily, as it is common for pollen diagrams, they all have the same scaling. Therefore it is enough to perform the above steps just for one of the columns.

In the dropdown menu in the Current reader tab, select the reader for Columns 1-26 to make it the current reader.
Click the Insert X-axis values button in the Axes translations section of the straditizer control
In the appearing dialog, enter the x-value of the column start (here again 0) or Shift-Leftclick on the plot and then enter it (see note above).
After hitting the Ok button, you will see a mark on the plot (blue line). Again, you can select the mark, drag it or delete it as before.
now repeat steps 2-4 on a second point in the same column. Your diagram should now look something like this:
Click the Apply button at the bottom of the straditizer control.

Editing the meta attributes¶

To keep the overview on your project, you can save some meta informations to it, for example, where you have the diagram from and what it represents.

Here for example, we know that the digitized data represents a pollen core from Hoya del Castillo and has been described in detail in [Basil1980].

Click the Attributes button at the bottom of the straditizer control. You know see a table with attributes, that you can rename, delete and fill according to your needs.

Fill the data table:

Digitized by	Your name
sitename	Hoya del Castillo
Lon	-0.5
Lat	41.25
Archive	Pollen
Country	Spain
Restricted	No
Reference	Davis, Basil A. S., and A. C. Stevenson. “The 8.2ka Event and Early-Mid Holocene Forests, Fires and Flooding in the Central Ebro Desert, NE Spain.” Quat. Sci. Rev. , vol. 26, no. 13-14, 2007, pp. 1695-712
DOI	10.1016/j.quascirev.2007.04.007

Note

You can also add new attributes by clicking Rightclick ‣ Insert 1 row inside the table

Done!¶

That’s it! You can export the samples (see Export your results) to an Excel or CSV file via

File ‣ Export figures ‣ Straditizer data ‣ Samples

Now start with your own diagram!

If you have questions or troubles, please open an issue on

https://github.com/Chilipp/straditize/issues

Thanks for using straditize and happy digitizing!

References¶

Basil2007: Davis, Basil A. S., and A. C. Stevenson. “The 8.2ka Event and Early-Mid Holocene Forests, Fires and Flooding in the Central Ebro Desert, NE Spain.” Quat. Sci. Rev. , vol. 26, no. 13-14, 2007, pp. 1695-712, doi:10.1016/j.quascirev.2007.04.007.

Beginners tutorial to introduce straditize¶

Advanced tutorial: The Hoya del Castillo pollen diagram¶

Contributing, asking for assistance and reporting bugs ¶

First off, thanks for taking the time to contribute!

The following set of guidelines for contributing to straditize are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.

Table of Contents

Contributing, asking for assistance and reporting bugs

Code of Conduct ¶

This project and everyone participating in it is governed by the straditize Code of Conduct. By participating, you are expected to uphold this code.

How Can I Contribute?¶

Reporting Bugs ¶

This section guides you through submitting a bug report for straditize. Following these guidelines helps maintainers and the community understand your report, reproduce the behavior, and find related reports.

Before creating bug reports, please check existing issues and pull requests as you might find out that you don’t need to create one. When you are creating a bug report, please include as many details as possible. Fill out the required template, the information it asks for helps us resolve issues faster.

Note: If you find a Closed issue that seems like it is the same thing that you’re experiencing, open a new issue and include a link to the original issue in the body of your new one.

How Do I Submit A (Good) Bug Report?¶

Bugs are tracked as GitHub issues. Create an issue on straditize repository and provide the following information by filling in the template.

Explain the problem and include additional details to help maintainers reproduce the problem:

Use a clear and descriptive title for the issue to identify the problem.
Describe the exact steps which reproduce the problem in as many details as possible.
Provide specific examples to demonstrate the steps. Include links to files or images, or copy/pasteable snippets, which you use in those examples. If you’re providing snippets in the issue, use Markdown code blocks.
Upload the project file. The best is, you you only take the part of the stratigraphic diagram that causes the problems.
Describe the behavior you observed after following the steps and point out what exactly is the problem with that behavior.
Explain which behavior you expected to see instead and why.
Include screenshots and animated GIFs which show you following the described steps and clearly demonstrate the problem. You can use this tool to record GIFs on macOS and Windows, and this tool or this tool on Linux.
If the problem is related to your data structure, include a small example how a similar data structure can be generated

Include details about your configuration and environment:

Which version of straditize and psyplot are you using? You can get the exact version by running straditize -V and psyplot -aV in your terminal , or by starting the psyplot-gui and open Help->Dependencies.
What’s the name and version of the OS you’re using?
Use conda’s diagnostics! If you installed straditize through anaconda, include the output of conda info -a and conda list in your description

Suggesting Enhancements ¶

If you want to change an existing feature, use the change feature template, otherwise fill in the new feature template.

How Do I Submit A (Good) Enhancement Suggestion?¶

Enhancement suggestions are tracked as GitHub issues. Create an issue in the straditize repository and follow these steps:

Use a clear and descriptive title for the issue to identify the suggestion.
Provide a step-by-step description of the suggested enhancement in as many details as possible.
Provide specific examples to demonstrate the steps. Include copy/pasteable snippets which you use in those examples, as Markdown code blocks.
Describe the current behavior and explain which behavior you expected to see instead and why.
Include screenshots and animated GIFs which help you demonstrate the steps or point out the part of psyplot which the suggestion is related to. You can use this tool to record GIFs on macOS and Windows, and this tool or this tool on Linux.
Explain why this enhancement would be useful to most straditize users.
List some other analysis software or applications where this enhancement exists.
Which version of straditize and psyplot are you using? You can get the exact version by running straditize -V and psyplot -aV in your terminal , or by starting the psyplot-gui and open Help->Dependencies.
Specify the name and version of the OS you’re using.

Pull Requests ¶

Fill in the required template
Do not include issue numbers in the PR title
Include screenshots and animated GIFs in your pull request whenever possible.
Document new code based on the Documentation Styleguide
End all files with a newline and follow the PEP8, e.g. by using flake8

Adding new examples ¶

You have new examples? Great! straditize can only be improved through new tutorials. You can either implement it in the graphical user interface (see the straditize.widgets.tutorial module) or you just add them to the sphinx documentation. We are also looking forward to assist you in the implementation and the sharing of your experiences.

Styleguides ¶

Git Commit Messages ¶

Use the present tense (“Add feature” not “Added feature”)
Use the imperative mood (“Move cursor to…” not “Moves cursor to…”)
Limit the first line (summary) to 72 characters or less
Reference issues and pull requests liberally after the first line
When only changing documentation, include [ci skip] in the commit title

Documentation Styleguide ¶

Follow the numpy documentation guidelines.
Use reStructuredText.
Try to not repeat yourself and make use of the straditize.common.docstrings

Example ¶

@docstrings.get_sectionsf('new_function')
def new_function(a=1):
    """Make some cool new feature

    This function implements a cool new feature

    Parameters
    ----------
    a: int
        First parameter

    Returns
    -------
    something awesome
        The result"""
    ...

@docstrings.dedent
def another_new_function(a=1, b=2):
    """Make another cool new feature

    Parameters
    ----------
    %(new_function.parameters)s
    b: int
        Another parameter

    Returns
    -------
    Something even more awesome"""
    ...

**Note:** This document has been inspired by `the contribution
guidelines of
Atom <https://github.com/atom/atom/blob/master/CONTRIBUTING.md#git-commit-messages>`__

Command line usage¶

The straditize.__main__ module defines a simple parser to parse commands from the command line to load a diagram or a straditize project.

It can be run from the command line via:

python -m straditize [options] [arguments]

or simply:

straditize [options] [arguments]

Load a dataset, make the plot and save the result to a file

usage: straditize [-h] [-b [backend]] [-ni] [-rc-gui RC_GUI_FILE]
                  [-inc str [str ...]] [-exc str [str ...]] [--offline]
                  [-pwd str] [-s str] [-c str]
                  [-opengl {'software', 'desktop', 'gles', 'automatic'}]
                  [-o str] [-xlim val val] [-ylim val val] [-f]
                  [-rt { 'area' | 'bars' | 'rounded bars' | 'stacked area' | 'line' }]
                  [-V]
                  [str]

Positional Arguments¶

str: Either the path to a picture to digitize or a previously saved straditizer project (ending with '.pkl')

Output options¶

Options that only have an effect if the -o option is set.

-o, --output: The path to the csv file where to save the digitized diagram

Gui options¶

Options specific to the graphical user interface

-b, --backend

The backend to use. By default, the 'gui.backend' key in the rcParams dictionary is used. If used without options, the default matplotlib backend is used.

Default: False

-ni, --new-instance

If True/set and the output parameter is not set, a new application is created

Default: False

-rc-gui, --rc-gui-file

The path to a yaml configuration file that can be used to update the rcParams

-inc, --include-plugins

The plugin widget to include. Can be either None to load all that are not explicitly excluded by exclude_plugins or a list of plugins to include. List items can be either module names, plugin names or the module name and widget via '<module_name>:<widget>'. Default: None

-exc, --exclude-plugins

The plugin widgets to exclude. Can be either 'all' to exclude all plugins or a list like in include_plugins.. Default: []

Default: []

--offline

If True/set, psyplot will be started in offline mode without intersphinx and remote access for the help explorer

Default: False

-pwd

The path to the working directory to use. Note if you do not provide any fnames or project, but set the pwd, it will switch the pwd of the current GUI.

-s, --script

The path to a python script that shall be run in the GUI. If the GUI is already running, the commands will be executed in this GUI.

-c, --command

Python commands that shall be run in the GUI. If the GUI is already running, the commands will be executed in this GUI

-opengl, --opengl-implementation

Possible choices: software, desktop, gles, automatic

OpenGL implementation to pass to Qt. Possible options are ‘software’, ‘desktop’, ‘gles’ and ‘automatic’ (which let’s PyQt decide).

Straditizer options¶

Options specific pollen diagram digitization

-xlim

The x-limits of the data part of the diagram

-ylim

The y-limits of the data part of the diagram

-f, --full

If True, the image is digitzed and x- and ylim are set to the entire share of the array

Default: False

-rt, --reader-type

Possible choices: area, bars, rounded bars, stacked area, line

Specify the reader type. Default: “area”

Default: “area”

-V, --version

show program’s version number and exit

This program comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under the conditions of the GNU GENERAL PUBLIC LICENSE, Version 3.

Straditize GUI¶

Welcome to the help explorer of the Stratigraphic Diagram Digitizer straditize. Here we guide you through the main functionality of the graphical user interface (GUI) and the necessary steps to digitze your stratigraphic diagram.

Basics and Terminology¶

A stratigraphic diagram consists of multiple diagrams that are vertically or horizontally aligned, i.e. that have either the same x- or y-axis.

This software assumes, that all the subdiagrams share the y-axis, i.e. the vertical axis. If this is not the case for your specific diagram, just rotate your image by 90°.

column

One column in the diagram means one of the subdiagrams (e.g. Betula in the example diagram above). The total number of columns is the number of subdiagrams in your plot.

pixel column

One pixel column is the horizontal pixel index (x-axis), ranging from 0 to the width of your image. Pixel column 0 represents all pixels on the very left side.

row or pixel row

One pixel row (or sometimes only row) is the vertical pixel index (y-axis), ranging from 0 to the height of your image. Row 0 represents all pixels at the top.

diagram part

This is the part where the data is shown (red rectangle in the figure above).

binary image

The binary image is the diagram part converted to black (1) and white (0). Every black pixel in this binary image represents data that should be digitized.

digitize

Digitization means transforming each pixel into a value (see the blue line in the image above). Therefore, in contrast to the samples, you get one data value for each pixel row in the binary image. We also refer to this as the full data.

sample

The samples (red lines in the image above) are a subset of the digitization result. In a very basic sense, the samples represent the original data that was used to create the diagram. In a sediment core for a pollen diagram, it would be one slice of the core that was analyzed under the microscope.

straditizer

Everything that refers to the straditizer refers to the full image of your stratigraphic diagram. Speaking in a programmatically sense, it is the top-level interface to digitize the diagram, an instance of the straditize.straditizer.Straditizer class. You can always access the current straditizer through the stradi variable in the ipython console of the GUI

reader or data reader

This is the object that digitizes the diagram part (red area above). For different diagram types (area plot, bar plot, etc.) you have different readers with different functionalities.

The reader of your diagram can be accessed via typing stradi.data_reader in the ipython console of the GUI.

Straditization steps¶

Straditize provides you lot’s of possibilities for customizing the semi-automatic digitization of your diagram. However, there are certain steps that you should follow. You may not need all of them and you may exchange them to a certain degree as well.

Load a diagram¶

The first step is to load the picture of your diagram.

If you are starting straditize from the psyplot GUI (i.e. not via typing straditize in the terminal or through the app), you should first switch to the straditizer layout via

Windows ‣ Window layouts ‣ Straditizer layout.

This will show you the straditizer control panel.
To load the picture of your diagram, select

File ‣ Open project ‣ Open straditizer ‣ Project or image

and select the image file you want to load.

Alternatively, you can use the use the Control-O (⌘-O on MacOS) keyboard shortcut or the button at the top of the straditizer control.

Select the diagram part¶

The second step involves selecting the diagram part (see the Basics and Terminology) by clicking the Select data part button in the digitization control.

The goal is to select the two outer corners of the diagram and try to avoid vertical and horizontal axes or axes description.

Shift - leftclick on the data diagram. This will create a mark at your current mouse location.
Click on the cross to select it
Hold the left mouse button and drag the cross to the upper left corner of the diagram. Make sure you don’t include the y-axis ticks (i.e. the numbers on the y-axis)
Create another mark by another Shift - leftclick
Move the cross to the lower right corner of the diagram. Make sure you don’t include the x-axis ticks (i.e. the numbers on the x-axis)
Click the Apply button at the bottom

If you want to change the appearance of the marks, see the Marker control section in the straditizer control panel.

Selecting the reader¶

Stratigraphic diagrams and especially pollen diagrams come in different styles and formats.

Straditize supports the following:

area	bars	rounded bars

line	stacked area

For each type there exists a specific reader that you can select from the drop down menu in the Reader initialization section of the straditizer control. If you’re diagram contains a mixture of types, just choose one type for now and you can then later create column specific readers.

After you chose the diagram part and selected the reader type (see the table above), you can now click the Convert image button. This initializes the reader and converts the data image to a binary image, i.e. black and white.

Everything that is black in this image will be considered as data by the reader. So the next steps will include, cleaning up the image and removing unimportant parts.

Select the starts for each column¶

A stratigraphic diagram is defined through different columns, each column represents one variable (or for pollen diagrams, one taxa, species, PFT, etc.). To digitize the data, we have to know, where each of these column starts exactly.

After having clicked the Column starts button, the software automatically estimates the starts of the columns. However, you should check whether they are all recognized correctly.

There are several possibilities to edit the automatically estimated columns:

Hold the Shift button and left-click on the plot to create a new vertical mark.

Then Left-click the mark and drag it to the start of one column.
Delete a column start by right-clicking the mark.
Modify the threshold. It defines the percentage of a pixel column that has to be covered to assume a valid column start. If you have columns that only contain a very small amount of data, you should lower it

See also

Select the ends for each column¶

A pollen diagram is defined through different columns, each column represents one taxa, species, PFT, etc.. To digitize the data, we have to know, where each column starts and ends exactly.

After having clicked the Column starts button, the software automatically estimates the starts of the columns and, based on that, the column ends as well. Clicking the ends button however lets you modify the automatically estimated column ends.

As for the column starts, there are two ways to modify the column ends:

Hold the Shift button and left-click on the plot to create a new vertical mark.

Then Left-click the mark and drag it to the start of one column.
Delete a column start by right-clicking the mark.

See also

Align the columns vertically¶

Sometimes the columns are not aligned properly which makes it hard to identify where the exact sample is located and might lead to misinterpretations of samples. To correct this, we implemented a methodology to align the columns.

After having clicked the Align columns button, multiple marks are created. To correct the vertical displacement, move those marks to the vertical position in each column, that should be on the same vertical level.

You can select the horizontal lines for moving multiple marks and the vertical line for selecting only one mark.

Select one mark on one column by clicking on it’s vertical line
Move the mark vertically (the horizontal line does not matter)
Select another mark of another column
Move this mark to a point that should have the same y-value as the first mark
Click the Apply button

Handling column names¶

Within straditize, you can assign a name to each of the columns in your diagram, which will then turn up as the header in your final export. Usually these names are the titles of your columns, e.g. such as here

To enter the names of the columns, hit the Edit column names button in the Column names section of the straditizer control. This will open a dialog to handle the column names:

On the left side, you have a rotated version of the image that you can control with the options above it. To navigate in this window, place your mouse over it and then you can move (pan) the image with left-click.

Zooming in and out is done via right-click. To zoom in, hold your right mouse button and move your cursor to the upper right corner of your diagram. To zoom out, move it to the lower left corner.

On the right side you have a table with the column names. Initially, these are just increasing numbers starting from 0 for your first column. If you select one column, it will be highlighted in the left image:

You can now enter the name of the column, in this case Artemisia

Automatic optical character recognition (OCR)¶

You don’t have to type the column name, you can also use the builtin text recognition. For this, we rely on the tesseract software that can be installed (on linux and MacOS) via:

conda install -c conda-forge tesseract

see the tesseract feedstock on conda-forge. If you want to automatically find the column names, you also need tesserocr to be installed which can be done via:

pip install tesserocr

or:

conda install -c chilipp tesserocr  # on linux/MacOS

(see https://pypi.org/project/tesserocr/ for more installation options, in particular for Windows).

1. Use a high resolution version of the image¶

To improve the result of the text recognition, we recommend to use a sufficient resolution of about 600 dpi. If the image that you are digitizing does not have this resolution, you can optionally load a higher resolution version of it using the Load HR image button at the upper left of the dialog.

We also recommend to remove everything in this file but the column names to improve the text recognition.

2. Automatically find all column names¶

Check the all columns checkbox and click the Find column names button. This then will look for column names in the image that is displayed on the left of the table and insert them into the table.

3. Separate treatment of column names¶

Now, select the first column in the table, zoom in such that you can see the name of the first column and click the Select column name button.

You won’t be able to zoom or pan in your plot anymore, but you can now draw a rectangle in the plot around the name of the first column or use click the Find column names button (without having the all columns checkbox checked) and it will show up in the plot on the lower right of the diagram.

Here you see the result of the low resolution. If we instead specify a higher resolution image (see above), this can look much better

picture of the first column name in highres

Now click the Recognize button at the bottom and it will use tesseract to read the text in the image and fill in the table

You can now select the next column or click the Apply button on the lower left. The latter will reenable the navigation (pan and zoom) in the plot.

Removing features¶

The diagram part is converted to a binary image where everything black (i.e. 1) represents data (see Selecting the reader). So before the data is digitized, features that do not represent data have to be removed.

Here, you can either use the features of the selection toolbar, or you use one of the automatic removal tools we provide here. Note that you can edit every selection using the mouse selection tools or the automatic tools from the selection toolbar.

After you selected the features to remove, click the Remove button at the bottom of the straditizer control panel.

x-axes	y-axes

horizontal lines	vertical lines

features at column ends	disconnected features

cross column features	small artifacts

Remove features at column ends¶

This function automatically finds the features that touch the end selects them for removement.

_images/basic_diagram_features_at_col_ends.png

Remove cross-column features¶

This function removes features that have a certain amount of pixels in multiple columns. Only those features are selected for removement, that have at least the given Number of pixels in more than one column of the stratigraphic diagram.

Note

This feature should be called before intializing column specific readers.

Remove disconnected features¶

This function automatically recognizes features that are disconnected either from the column start or from the previous feature in the column.

That can be useful, for example, to remove letters or symbols in the diagram.

What features are recognized can be defined in three ways:

Features that have a certain distance from the column start (see Disconnected from the column start
Features that have a certain distance from the previous feature in the column (see Disconnected from the previous feature)
A combination of 1. and 2. (see Disconnected from start and previous)

Disconnected from the column start¶

When only the checkbox from column start is checked, all features that have the given distance from the column start are selected.

_images/basic_diagram_disconnected_01.png

Disconnected from the previous feature¶

When only the checkbox from previous feature is selected, all features that have the given distance from the previous feature in the column (and everything to the right of it) is selected.

_images/basic_diagram_disconnected_03.png

Disconnected from start and previous¶

If both are selected, the selected feature must have the given distance from the column start and from the previous feature in the column to be selected.

_images/basic_diagram_disconnected_02.png

Remove horizontal or vertical lines¶

The features described here automatically remove the y- and x-axes, or in general vertical or horizontal lines that span a certain fraction of the diagram part.

The methodology is simple: If a certain pixel column or row in the binary data image is covered, it is considered as a vertical or horizontal line, respectively.

x-axes	y-axes

horizontal lines	vertical lines

You can modify the recognition using three options

Minimum fraction:: This is the minimum fraction of a pixel column (for or row that must be covered
Minimum line width:: Lines are only selected, if their line width is greater than or equal to the given minimum axis width.
Maximum line width:: Lines are only selected up to the given maximum axis width

Remove small artifacts¶

This method recognizes small artifacts using the skimage.morphology.remove_small_objects() method.

_images/basic_diagram_small_features.png

The pixel size of the artifacts can be determined through the Smaller than field and you can always modify the selection through the tools in the selection toolbar.

If you want a better visualization of the small artifacts that will be removed, use the Highlight selection smaller than button.

Column specific readers¶

Note

If the entire diagram is just based on one diagram type (see Selecting the reader), you can skip this step.

As noted in the reader selection section, you can choose different readers for different columns in order to account for multiple subdiagram styles within the stratigraphic diagram. The current reader, i.e. the one that is selected in the Current reader tab in the Digitization control is the one that can be accessed via the stradi.data_reader attribute in the console. It is also sometimes referred to as the parent reader.

Clicking the + button in the Current reader tab let’s you select columns just by clicking on the data image.

After you selected the necessary columns, hit the Apply button. In the appearing window, select the reader type you want (see Selecting the reader). You now initialized a new child reader for the selected columns that can be set as the parent reader using the dropdown menu in the Current reader tab.

Interpreting exaggerations¶

Visualizing an exaggerated version of the data is a common methodology in pollen diagrams, since even small pollen counts can say a lot about the environment.

Therefore it is common to not only plot the data, but also to exaggerate it by a certain factor, e.g. 10 or two like the red areas in the image below.

You have two choices: Either you remove these exaggerated areas (see Removing features), or you merge them into your results to improve the digitization result.

To include and interprete the exaggerations, straditize can create a new reader dedicated for the exaggerations which you then have to select and specify when they should be used. This is all done in the Exaggerations tab of your digitization control.

Specify the exaggeration factor. This number is usually described in the caption of your diagram and must be greater than 1.
Select the data reader type for the exaggerations (see Selecting the reader). Most common this is one of area or line.
Initialize the reader for the exaggerations by clicking +. You will now find a new reader dedicated to the exaggerations in the dropdown menu of the Current reader tab. But for now, we stick with the original one.
Now, you then have to select the exaggerations. Click the Select exaggerations button and use the tools in the selection toolbar for selecting the features in the diagram that represent the exaggerations. When you’re done with this, click the Select button. You can also repeat this step to select more and more features.
Specify when the exaggerations should be used. This can be either where the unexaggerated data is below a certain percentage of the image width and/or below a certain number of pixels.
When you now click the Digitize exaggerations button (but after you clicked the Digitize button for the original reader), the exaggerations will be digitized and merged into the digitization result of the non-exaggerated reader.

Select occurences¶

Pollen diagrams often mark low taxon percentages to highlight the occurence of a taxon, such as the + in the image below

This is very useful information which can be considered by straditize.

Expand the Occurences tab in the Digitization control section
Click the Select occurences button and select the occurences using the tools from the selection toolbar, especially the tool is useful
Click the Apply button and the selected features in the diagram will be registered as an occurence. If you also checked the Remove on apply checkbox next the to Select occurences button (which you normally should), the selected features in the diagram will be removed, too and you will end up with a plot like this
You can see and edit the occurences using the Edit occurences button. This will create horizontal markers at the positions of the original occurences

In our demonstration, straditize could not distinguish two of the occurence markers because they were too close to each other. But you can just move the mark with Left-click and Shift+Leftclick on the plot to create a new marker

Click the Apply button when you are done.
The marked occurences will appear later in the final data frame with the specified Occurence value.

Digitizing the diagram ¶

The digitization transforms your picture into a table. The outcome is a data frame with one value for each row and column in the data part of your image. After the digitization, you can access this raw data from the command line via:

stradi.data_reader.full_df

and you can plot it using the Full digitized data item in the plot control.

The digitization procedure depends on the reader type that you are using (see Selecting the reader).

Contents

Digitizing the diagram

Digitizing area and line readers ¶

_images/sample_diagram_digitize-explanation.png

This class of stratigraphic diagrams is the most popular within the pollen diagrams and it’s digitization is fully automatic.

The algorithm simply uses the distance of the most right data (black) pixel to the column start.

Digitizing bars ¶

For digitizing bar diagrams, it is crucial that the software finds and distinguishes adjacent bars. This is mainly done through the tolerance. The algorithm is: if the pixel row, compared to the previous row, is higher or smaller by the given tolerance , the software assumes that those two rows belong to two distinct bars.

The image above illustrates this. The reader distinguishes the bars that are clearly separated or were one bar is significantly lower or higher than the other, but it cannot distinguish bars were there is only a small difference in height.

Splitting bars ¶

splitting1

→

splitting2

To overcome this problem, straditize automatically finds the bars that are wider than the others and marks them for splitting. If the straditizer finds bars that are too long, they are shown in the Split too long bars tab of the digitization control. To split one bar:

Double-click an item in the table (the plot will zoom to the bar)
Left-click on the bar to indicate the vertical location for a split (right-click again on the same line to revert this). A red line will appear (left-click, see image above) or disappear (right-click)
Click the symbol to continue with the next bar that is too long or double-click an item in the table. Again split the bar as described in 2.
When you are done, hit the Apply button and the bars will be splitted right were you marked it

Digitizing stacked areas ¶

	initial		column 1
		→
	column 2		column 3
→		→
	column 4		column 5
→		→

Stacked diagrams are more difficult to digitize because of the variety of combinations. The columns might be distinguished by colors or patterns (hatches). Therefore, the user has to select the features corresponding to one column, one after the other.

The digitization here works as follows:

click the Digitize button, you will see a control for navigating within the columns
click the + button to setup a new column. Everything in the column will be selected and now you have to select only those features, that are part of the first column using the selection tools (see the column 1 above).
When you did this, click the Apply button.
click the Edit button if you want to edit the column that you just selected
Click the > button to go to the right column
click the + to add a new column and select the second column (see above)
repeat the steps 3-6 until you digitized all columns

If you want to display your digitization result, use the Full digitized data in the plot control.

Hint

you can change the transparency of the selection with the transparency slider in the selection toolbar. That helps to still see the selected features

Samples¶

samples are the source data that has been used to create the diagram (see Basics and Terminology). The crucial part after the digitization of a diagram is to find out, where these samples exactly are.

Straditize assists you with this through automatic sample finding algorithms, or you can load the sample locations from an external files or you just add and edit the samples manually.

Automatic samples identification¶

straditize has reader specific sample identification algorithms implemented. The overall algorithm is based on two steps:

For each column: Identify the intervals that contain exactly one samples (the rough locations)
Align the overlapping intervals between the columns to estimate the exact location

Where step 2 is the same for all diagram types, step one is diagram specific. For bar diagrams, we use the bars that have been identified during the digitization (see Digitizing bars).

For area and line diagrams on the other hand, we identify the local extrema (minima and maxima) for each column. This strategy works well for pollen diagrams since they are supposed to sum up to 100%. However, this does not generally work for all stratigraphic diagrams.

Load samples locations from external file¶

samples can be loaded from an external CSV (comma-separated) file. The first column in this CSV file is expected to represent the y-locations of the samples. If the y-axis scale has already been specified (see Translating the shared vertical axis), this data should be on the same scale. Otherwise, we assume that the data is in pixel coordinates.

Warning

Make sure, that you first entered the correct y-axis scale. Otherwise the samples might not be interpreted correctly.

Editing samples¶

To edit samples, click the Edit samples button and a new figure will be created with one mark for each sample and a table is shown to you with the samples data. To edit the samples, you can edit the table or the marks in the plots

The sample table¶ _images/samples-editor.png

The sample table shows you the vertical coordinates of the samples and the values for each column obtained through the digitization.

Each row in this table represents one sample in the diagram.

The first column is the vertical location (y-axis) of the sample, the remaining columns are the x-values of the samples. The numbers shown in the table are in pixel coordinates of the diagram part. To interact with the samples, you can

Right click the table to add new or delete old samples/rows below or above the selected row
Edit the numbers in the cells to change the values for the samples.
Fit the x-values of the samples to the data.
1. Either by right-click and Fit selected cells to the data, which will adjust the column values of the mark such, that it fits with the digitized value
  
  original
  
  fitted to data
  
  →
2. Or by using the Fit selected cells to selected data checkbox. If this is checked:
  1. select cells in the table
  2. click on the plot
  3. the cells will be updated with the x-values from the digitization at the click-position

You can also zoom the selection or hide everything else but the selection.

Editing the marks¶ _images/edit-samples-marks.png

Additional to the table, you have a visual representation of the samples in the figure. Here, you have one plot per column each mark represents the vertical location of a sample. The marker x in the line shows you the value of the sample (i.e. the location on the x-axes).

move a sample by
1. left-click a mark and hold the mouse button (the mark will change it’s color)
2. while still holing the mouse button, drag the mark to a different location
delete a sample by right-clicking the mark
add a new sample by holding down the Shift button and left-click on one of the plots.

Translating from pixel to data coordinates¶

All the previous steps in the digitization where in pixel coordinates. However, for the final output, we need to translate this into the real units of the diagram.

Translating the shared vertical axis¶

For your final product, straditize needs to know about how the data in the diagram is scaled, such that we can transform the final sample information from the pixel coordinates into the units of the diagram (e.g. years or meters in case of time or depth).

To translate the y-axis information,

Expand the Axes translations tab in the digitization control
Click the Insert Y-axis values button in the Axes translations section of the straditizer control (if not already done)
Shift-leftclick on the plot to enter the corresponding y-value.
A small dialog will appear where you should enter the y-value to use (in this case, 300)
After hitting the Ok button, you will see a mark on the plot (blue line). You can select the mark via leftclick and drag it to a different location or you can delete it via rightclick.
now repeat steps 2-4 on a second point on the y-axis
- Select another point
- Enter the corresponding value (here 350)
- A new mark is created that you can modify
Click the Apply button at the bottom of the straditizer control when you are done.

Note

If you drag a mark and hold the Shift button while releasing the mouse button, the dialog in point 3 from above will not pop up.

Translating the horizontal axes¶

For your final product, straditize needs to know about how the data in the diagram is scaled, such that we can transform the final sample information from the pixel coordinates into the units of the diagram (e.g. percent for pollen data or Kelvin for temperature, etc.).

To translate the x-axis information,

Expand the Axes translations tab in the digitization control
Click the Insert X-axis values button in the Axes translations section of the straditizer control (if not already done)
Shift-leftclick on the plot in one of the columns to enter the corresponding x-value.
A small dialog will appear where you should enter the x-value to use (in this case, 10)
After hitting the Ok button, you will see a mark on the plot (blue line). You can select the mark via leftclick and drag it to a different location or you can delete it via rightclick.
now repeat steps 3-5 on a second point in the same column
- Select another point
- Enter the corresponding value (here 30)
- A new mark is created that you can modify

7. Click the Apply button at the bottom of the straditizer control when you are done.

Note

If you have different units or different scalings in the diagram, create column specific readers and translate the x-axis separately for each reader/column.

Note

If you drag a mark and hold the Shift button while releasing the mouse button, the dialog in point 3 from above will not pop up.

Export your results¶

Once you found the samples and added the y-axis and x-axis transformations, you can export them to a comma-separated (CSV) or an Excel file.

Just use the export menu via

File ‣ Export figures ‣ Straditizer data ‣ Samples

or hit Control-E (⌘-E on MacOS)

If you want to export the full digitized data, i.e. not only the samples, use

File ‣ Export figures ‣ Straditizer data ‣ Full data

or hit Shift-Control-E (⇧-⌘-E on MacOS)

Straditize helpers¶

Straditize has several generic tools to help you with various aspects.

The selection toolbar¶

The tools in this toolbar provides you the possibility to select features in the diagram. By default, the selected features will then be removed when you click the Apply or : guilabel:Remove button at the bottom of the straditizer window. The processing of the selection however varies depending on what task you are doing at the moment (for exaggerations, the selected features are for example marked as exaggerations).

The selection toolbar can select from different sources, provides differenti mouse tools, can be used in different modes provides additional automatic tools for the selection

Choosing the selection source¶

Using the combo box, you can also choose, where the features should be selected.

Straditizer: If you choose the Straditizer, features will only be selected in the full image and the diagram part is untouched.
Reader: If you choose the Reader, features will only be selected in the diagram image and the original image will be kept untouched
Reader - Greyscale: The same as Reader, but whether too parts are connected or not is based on their color, not only on the binary image (see the tool below).

Selection tools¶

Too facilitate the selection for you, we implemented several tools to select features in the image:

select a rectangle or point

With this tool you can select everything within a rectangle or a single point

select a polygon

This tool can be accessed through the context menu of the tool (click and hold this button to open the menu). If activated and you click on the image, hold the mouse button and drag it around the features you want to select. Everything that is in the shape you draw while pressing the mouse button will be selected.

select based on connectivity or functionality

This tool selects entire features in the image. The exact behaviour depends on what you are doing at the moment. If you hold and drag the mouse, you can select all features within a rectangle

In most cases, the tool just selects a feature based on the connectivity. If the selection source is the Straditizer or Reader - Greyscale, this connectivity is based on the greyscale image. For the Reader selection source, this is based on the binary image.

select based on color

This tool can be accessed through the context menu of the tool (click and hold this button to open the menu). If activated and you click on the image, all connected cells that have the same color are selected. You can also relax this a bit, such that all colors that are close to the selected color will be selected. Furthermore, you can choose the select the colors in the whole plot, i.e. all pixels that have the same (or similar) color as the selected cell will be selected.

select the entire pixel row

This tool also can be accessed through the context menu of the tool (click and hold this button to open the menu). If you click on the image, all selectable features on this horizontal level (i.e. on this pixel row in the image) are selected.

select the entire pixel column

The same as the tool but for the vertical columns.

Selection mode¶

What exatly happens when you use the selection tools desribed above depends on the selection mode that you are in. This can be one of

new selection: When you select something, it will create a new selection
add to selection: Any new selection will be added the current selection
remove from selection: Any new selection will be removed from the current selection

Automatic tools¶

The selection toolbar also provides you some automatic tools to facilitate the selection for you

select all: Everything that can be selected will be selected
expand selection: Every selected pixel will be expanded based on it’s connectivity (see the tool above)
invert selection: Everything that is selected will be unselected and everything that was not previously selected will be selected
clear selection: Everything will be unselected
select everything to the right: For each selected pixel in a column of the diagram part, we also select everything that is to the right of this pixel
select based on a template: This method uses the skimage.feature.match_template() function to find a template within the current selection. This will then be selected or removed from the current selection based on the current mode.

Visualize your progress with the plot control¶

To explore and validate your digitization, straditize implements multiple ways to visualize the results. You can show and remove these static visualizations using the items in the plot control.

Here you can hide the several plot objects using the Visible checkbox or you can plot the objects by clicking the button or remove it by clicking the button.

Note

Please note, that these are static plots. So if you change your data, e.g. through modifying the column starts, you have to remove () and plot () the Column starts item to visualize the changes.

Some of the plotting features only visualize the currently selected columns. So if you have column-specific readers or exaggerations, these options hide and show the plots for the currently selected columns only.

Full image:: The full image is the diagram, that you are digitizing
Reader color image:: This is the diagram part of the currently selected columns in color.
Data background:: This is just a white layer that lies over the Full image in the diagram part. If you want to see the diagram part in the Full image, you have to hide this layer
Binary image:: This is the image that is used to digitize the diagram part. As with the Reader color image, this option shows/hides the binary image for the currently selected columns.
Diagram part:: This draws a red rectangle around the diagram part (see the Basics and Terminology)
Column starts:: Red lines showing you, where each column starts of out of the currently selected columns starts
Full digitized data:: After having hit the Digitize button, you can visualize the full digitized data for the currently selected columns.
Potential samples:: For each column, we identify rough locations where samples may lie (see Automatic samples identification). This plot option lets you visualize these.
Samples:: This option draws horizontal lines to show, the sample locations
Reconstruction:: This plot option draws one line for each column, as the Full digitized data does, but it uses only the x- and y-values at the sample locations.

Configuring the appearance of markers¶

In the digitization, you will work with markers to select parts of your image. These can be the marks to select the diagram part, to separate the columns or to edit your samples.

You can modify the appearance of the marks using the Marker control section in the straditization control. This helps you to better visualize and select different parts in the diagram.

The functionalities are

Auto-hide: Hide the lines of the marks, when they are not selected
Show additionals: When editing the samples, for example, you will also see the rough locations of the samples for each column (little grey crosses). With the Show additionals setting, you can hide or show them
Show vertical lines and Show horizontal lines: If your marks are crosses, e.g. when you are selecting the diagram part, you can hide and show the vertical or horizontal lines with these settings
Drag in y-direction and Drag in x-direction: If unchecked, a mark cannot be moved along the corresponding axes. You can use this option, if you want to change the y-value of a mark without changing the x-value (or the other way around)
Horizontal lines selectable and Vertical lines selectable: If you uncheck one of these, you can prevent selecting a mark from the horizontal or vertical position. By default, you can select and move/drag a mark, by clicking on either of the lines (horizontal or vertical). But, for example, when editing the samples, many marks may lie on the same vertical position. Therefore it can be hard to move the mark of interest if you’re selecting the vertical line of the mark.
Line color: This changes the color of all marks.
Line width: This changes the line width of the marks. Sometimes it is better, if the linewidth increases when you are selecting a line
Line style: You can change the style of the lines, to a dotted line, dashed line, etc.
Marker size and Marker style: The location of the marks, i.e. where vertical and horizontal lines intersect, e.g. on corner of the diagram when selecting the diagram part, has a marker associated with it. It shows you, where the corner of your diagram is, where the sample value is, etc.. To make this more visible, you can change the size and the appearance of the mark.

Saving and loading your project¶

Saving a project¶

You can always save your current state to your hard-disc and continue working at it at a later point. To save a project, click File ‣ Save ‣ Save straditizer

or hit Control-S (⌘-S on MacOS)

This will save your project, or, if you never saved it before or used the Save As option, it will open a file dialog to let you select the target location.

The default and recommended way is to save it is a netCDF file (ending with .nc or .nc4). This is a common self-describing, machine-independent data format that supports the creation, access, and sharing of array-oriented scientific data. We recommend this format, because it works independent of the python libraries that you are using and let’s you easily share the project with others or copy it to another machine.

The other possibility is to save it as a pickle (.pkl) file which uses the built-in pickle module to save the straditizer to a file. The reason, why we generally do not recomment this, is because the saved file might not work anymore if you upgrade your libraries (e.g. numpy, pandas or pillow).

Loading a project¶

Reloading the project works the same way as you open a new image file (see Load a diagram). Just go to File ‣ Open project ‣ Open straditizer ‣ Project or image and choose the project file to load (or use the button at the top of the straditizer control). If the chosen file ends with .nc, .nc4 or .pkl, straditize will recreate the given project.

API Reference¶

Digitizing stratigraphic diagrams with straditize

The core of straditize is the Straditizer class and the reader for the diagram part, the DataReader. Both classes work completely independent of the graphical user interace.

The graphical user interface is implemented as a plugin into the Graphical User Interface for the psyplot package package and is implemented in the straditize.widgets subpackage.

Authors

The code and GUI of straditize was developed by Philipp S. Sommer at the Institute of Earth System Dynamics (IDYST) at the University of Lausanne as part of the SNF funded HORNET Project (200021_169598).

The other contributors are Basil A. S. Davis, Manuel Chevalier and Dilan Rech who made significant contributions to the layout, workflow, beta tests and and reviewing of the software.

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Subpackages¶

straditize.widgets package¶

The straditizer widgets

This module contains widgets to digitize the straditizer diagrams through a GUI

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`EnableButton`	A QPushButton that emits a signal when enabled
`InfoButton`(parent[, fname, rst, name])	A button to display help informations in the help explorer
`StraditizerControlBase`	A base class for the straditizer widget
`StraditizerWidgets`(args, *kwargs)	A widget that contains widgets to control the straditization in a GUI

Functions

`get_doc_file`(fname)	Return the path to a documentation file
`get_icon`(fname)	Return the path of an icon
`get_straditizer_widgets`([mainwindow])	Get the `StraditizerWidgets` from the psyplot GUI mainwindow
`read_doc_file`(fname)	Return the content of a rst documentation file

class straditize.widgets.EnableButton[source]¶

Bases: PyQt5.QtWidgets.QPushButton

A QPushButton that emits a signal when enabled

Attributes

enabled(*args, **kwargs)

A signal that is emitted with a boolean whether if the button is

Methods

setEnabled(b)

Reimplemented to emit the enabled signal

enabled(*args, **kwargs)¶: A signal that is emitted with a boolean whether if the button is enabled or disabled

setEnabled(b)[source]¶: Reimplemented to emit the enabled signal

class straditize.widgets.InfoButton(parent, fname=None, rst=None, name=None)[source]¶

Bases: PyQt5.QtWidgets.QToolButton

A button to display help informations in the help explorer

Parameters

parent (QWidget) – The parent widget
fname (str) – The name of the rst file. If None, specify the rst directly
rst (str) – The restructured text to render when this button is clicked. If None, the fname has to be provided
name (str) – The name to use for the document in the help_explorer

Methods

show_docs()

Show the docs

show_docs()[source]¶

Show the docs

Shows the docs in the in the help_explorer

class straditize.widgets.StraditizerControlBase[source]¶

Bases: object

A base class for the straditizer widget

Methods

`add_info_button`(child[, fname, rst, name, …])	Add an info button to the given QTreeWidgetItem
`connect2apply`(*funcs)	Connect the functions to the `apply_button`
`connect2cancel`(*funcs)	Connect the functions to the `cancel_button`
`enable_or_disable_widgets`(b)	Enable or disable the widgets in this control
`init_straditizercontrol`(straditizer_widgets)	Initialize the straditizer control widget
`refresh`()	Refresh from the straditizer
`setup_children`(item)	Setup the children for this control
`should_be_enabled`(w)	Check if a widget should be enabled

Attributes

`apply_button`	The apply button of the `straditizer_widgets`
`cancel_button`	The cancel button of the `straditizer_widgets`
`help_explorer`	The `psyplot_gui.help_explorer.HelpExplorer` of the
`straditizer`	The current straditizer from the `straditizer_widgets`
`straditizer_widgets`	The `StraditizierWidgets` control
`widgets2disable`	A list of widgets to disable or enable if the apply_button is enabled

add_info_button(child, fname=None, rst=None, name=None, connections=[])[source]¶

Add an info button to the given QTreeWidgetItem

Parameters: child (QTreeWidgetItem) – The item to which to add the infobutton

property apply_button¶: The apply button of the straditizer_widgets

property cancel_button¶: The cancel button of the straditizer_widgets

connect2apply(*funcs)[source]¶

Connect the functions to the apply_button

Parameters: *funcs – The callables that should be connected to the apply_button

connect2cancel(*funcs)[source]¶

Connect the functions to the cancel_button

Parameters: *funcs – The callables that should be connected to the cancel_button

enable_or_disable_widgets(b)[source]¶

Enable or disable the widgets in this control

This method enables or disables the widgets2disable if the should_be_enabled() method evaluates to True

Parameters: b (bool) – If True, enable the widgets, if False, disable them

property help_explorer¶: The psyplot_gui.help_explorer.HelpExplorer of the psyplot_gui.main.mainwindow

init_straditizercontrol(straditizer_widgets, item=None)[source]¶

Initialize the straditizer control widget

This method should be called by every subclass when initializing. It sets the straditizer_widgets, connects the enable_or_disable_widgets() method and adds a new item to the StraditizerWidgets.tree

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

refresh()[source]¶: Refresh from the straditizer

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

property straditizer¶: The current straditizer from the straditizer_widgets

straditizer_widgets = None¶: The StraditizierWidgets control

widgets2disable = []¶: A list of widgets to disable or enable if the apply_button is enabled

class straditize.widgets.StraditizerWidgets(*args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QWidget, psyplot_gui.common.DockMixin

A widget that contains widgets to control the straditization in a GUI

This widget is the basis of the straditize GUI and implemented as a plugin into the psyplot gui. The open straditizers are handled in the _straditizer attribute.

The central parts of this widget are

The combobox to manage the open straditizers
The QTreeWidget in the tree attribute that contains all the controls to interface the straditizer
the tutorial area
the Apply and Cancel button

Methods

`add_info_button`(child[, fname, rst, name, …])	Add an infobutton to the `tree` widget
`add_straditizer`(stradi)	Add a straditizer to the list of open straditizers
`autosave`()	Autosave the current straditizer
`close_all_straditizers`()	Close all straditizers
`close_straditizer`()	Close the current straditizer
`create_straditizer_from_args`(fnames[, …])	Create a straditizer from the given file name
`disable_apply_button`()	Method that is called when the `cancel_button` is clicked
`edit_attrs`()	Edit the attributes of the current straditizer
`get_attr`(stradi, attr)
`raise_figures`()	Raise the figures of the current straditizer in the GUI
`refresh`()	Refresh from the straditizer
`reload_autosaved`()	Reload the autosaved straditizer and close the old one
`reset_control`()	Reset the GUI of straditize
`set_current_stradi`(i)	Set the i-th straditizer to the current one
`show_or_hide_toolbar`()	Show or hide the toolbar depending on the visibility of this widget
`start_tutorial`(state[, tutorial_cls])	Start or stop the tutorial
`switch_to_straditizer_layout`()	Switch to the straditizer layout
`to_dock`(main, args, *kwargs)

Attributes

`always_yes`	Boolean that is True if all dialogs should be answered with Yes
`apply_button`	The apply button
`attrs_button`	The button to edit the straditizer attributes
`autosaved`	Auto-saved straditizers
`axes_translations`	The `straditize.widgets.axes_translations.AxesTranslations` to
`btn_close_stradi`	A button to close the current straditizer
`btn_open_stradi`	A button to open a new straditizer
`btn_reload_autosaved`	A button to reload the last autosaved state
`cancel_button`	The cancel button
`colnames_manager`	The `straditize.widgets.colnames.ColumnNamesManager` to interface
`digitizer`	The `straditize.widgets.data.DigitizingControl` to interface
`hidden`	bool(x) -> bool
`image_rescaler`	The `straditize.widgets.image_correction.ImageRescaler` class to
`image_rotator`	The `straditize.widgets.image_correction.ImageRotator` class to
`info_button`	An `InfoButton` to display the docs
`marker_control`	The `straditize.widgets.marker_control.MarkerControl` to modify
`open_external`(args, *kwargs)
`plot_control`	The `straditize.widgets.plots.PlotControl` to display additional
`progress_widget`	The `straditize.widgets.progress_widget.ProgressWidget` to
`selection_toolbar`	The `straditize.widgets.selection_toolbar.SelectionToolbar` to
`stradi_combo`	A QComboBox to select the current straditizer
`straditizer`	The `straditize.straditizer.Straditizer` instance
`title`	str(object=’’) -> str
`tree`	The QTreeWidget that contains the different widgets for the digitization
`tutorial`	The `straditize.widgets.tutorial.Tutorial` class
`tutorial_button`	The button to start a tutorial

add_info_button(child, fname=None, rst=None, name=None, connections=[])[source]¶

Add an infobutton to the tree widget

Parameters

child (QTreeWidgetItem) – The item to which to add the infobutton
connections (list of QPushButtons) – Buttons that should be clicked when the info button is clicked

add_straditizer(stradi)[source]¶: Add a straditizer to the list of open straditizers

always_yes = False¶: Boolean that is True if all dialogs should be answered with Yes

apply_button = None¶: The apply button

attrs_button = None¶: The button to edit the straditizer attributes

autosave()[source]¶: Autosave the current straditizer

autosaved = []¶: Auto-saved straditizers

axes_translations = None¶: The straditize.widgets.axes_translations.AxesTranslations to handle the y- and x-axis conversions

btn_close_stradi = None¶: A button to close the current straditizer

btn_open_stradi = None¶: A button to open a new straditizer

btn_reload_autosaved = None¶: A button to reload the last autosaved state

cancel_button = None¶: The cancel button

close_all_straditizers()[source]¶: Close all straditizers

close_straditizer()[source]¶: Close the current straditizer

colnames_manager = None¶: The straditize.widgets.colnames.ColumnNamesManager to interface the :straditize.straditizer.Straditizer.colnames_reader`

create_straditizer_from_args(fnames, project=None, xlim=None, ylim=None, full=False, reader_type='area')[source]¶

Create a straditizer from the given file name

This method is called when the psyplot_gui.main.mainwindow receives a ‘straditize’ callback

digitizer = None¶: The straditize.widgets.data.DigitizingControl to interface the :straditize.straditizer.Straditizer.data_reader`

disable_apply_button()[source]¶: Method that is called when the cancel_button is clicked

dock_position = 1¶

edit_attrs()[source]¶

Edit the attributes of the current straditizer

This creates a new dataframe editor to edit the straditize.straditizer.Straditizer.attrs meta informations

get_attr(stradi, attr)[source]¶

hidden = True¶

image_rescaler = None¶: The straditize.widgets.image_correction.ImageRescaler class to rescale the image

image_rotator = None¶: The straditize.widgets.image_correction.ImageRotator class to rotate the image

info_button = None¶: An InfoButton to display the docs

marker_control = None¶: The straditize.widgets.marker_control.MarkerControl to modify the appearance of the marks of the current straditizer

open_external(*args, **kwargs)¶

plot_control = None¶: The straditize.widgets.plots.PlotControl to display additional information on the diagram

progress_widget = None¶: The straditize.widgets.progress_widget.ProgressWidget to display the progress of the straditization

raise_figures()[source]¶: Raise the figures of the current straditizer in the GUI

refresh()[source]¶: Refresh from the straditizer

reload_autosaved()[source]¶: Reload the autosaved straditizer and close the old one

reset_control()[source]¶: Reset the GUI of straditize

selection_toolbar = None¶: The straditize.widgets.selection_toolbar.SelectionToolbar to select features in the stratigraphic diagram

set_current_stradi(i)[source]¶: Set the i-th straditizer to the current one

show_or_hide_toolbar()[source]¶: Show or hide the toolbar depending on the visibility of this widget

start_tutorial(state, tutorial_cls=None)[source]¶

Start or stop the tutorial

Parameters

state (bool) – If False, the tutorial is stopped. Otherwise it is started
tutorial_cls (straditize.widgets.tutorial.beginner.Tutorial) – The tutorial class to use. If None, it will be asked in a QInputDialog

stradi_combo = None¶: A QComboBox to select the current straditizer

straditizer = None¶: The straditize.straditizer.Straditizer instance

switch_to_straditizer_layout()[source]¶

Switch to the straditizer layout

This method makes this widget visible and stacks it with the psyplot content widget

title = 'Stratigraphic diagram digitization'¶

to_dock(main, *args, **kwargs)[source]¶

tree = None¶: The QTreeWidget that contains the different widgets for the digitization

tutorial = None¶: The straditize.widgets.tutorial.Tutorial class

tutorial_button = None¶: The button to start a tutorial

window_layout_action = None¶

straditize.widgets.get_doc_file(fname)[source]¶: Return the path to a documentation file

straditize.widgets.get_icon(fname)[source]¶: Return the path of an icon

straditize.widgets.get_straditizer_widgets(mainwindow=None)[source]¶

Get the StraditizerWidgets from the psyplot GUI mainwindow

Parameters: psyplot_gui.main.MainWindow – The mainwindow to use. If None, the psyplot_gui.main.mainwindow is used.
Returns: The straditizer widgets of the given mainwindow
Return type: StraditizerWidgets

straditize.widgets.read_doc_file(fname)[source]¶: Return the content of a rst documentation file

Subpackages¶

straditize.widgets.tutorial package¶

Tutorials for straditize

The beginner tutorial serves as a first look into the software. The hoya_del_castillo tutorial on the other hand is a true pollen diagram with more than 20 taxa. Both tutorials can be started from the GUI through the Tutorial button.

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Submodules¶

straditize.widgets.tutorial.beginner module¶

The tutorial of straditize

This module contains a guided tour to get started with straditize

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`CleanImagePage`(filename, tutorial)	Tutorial page to clean the diagram part
`ColumnNames`(filename, tutorial)	The page for recognizing column names
`ControlIntro`(filename, tutorial)	Tutorial page for the control
`CreateReader`(filename, tutorial)	The page for creating the reader
`DigitizePage`(filename, tutorial)	The page for digitizing the diagram
`FinishPage`(filename, tutorial)	The last page of the tutorial
`LoadImage`(filename, tutorial)	TutorialPage for loading the straditizer image
`SamplesPage`(filename, tutorial)	The page for finding and editing the samples
`SelectDataPart`(filename, tutorial)	TutorialPage for selecting the data part
`SeparateColumns`(filename, tutorial)	The page for separating the columns
`TranslateXAxis`(filename, tutorial)	The tutorial page for translating the y-axis
`TranslateYAxis`(filename, tutorial)	The tutorial page for translating the y-axis
`Tutorial`(straditizer_widgets)	A tutorial for digitizing an area diagram
`TutorialDocs`(args, *kwargs)	A documentation viewer for the tutorial docs
`TutorialNavigation`(nsteps, validate, *args, …)	A widget for navigating through the tutorial.
`TutorialPage`(filename, tutorial)	A base class for the tutorial pages

class straditize.widgets.tutorial.beginner.CleanImagePage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

Tutorial page to clean the diagram part

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_btn_remove_xaxes`()
`clicked_btn_remove_yaxes`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`icon_to_bytes`(icon)
`skip`()	Skip the steps in this page

Attributes

`btn_remove_xaxes_clicked`	bool(x) -> bool
`btn_remove_yaxes_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

btn_remove_xaxes_clicked = False¶

btn_remove_yaxes_clicked = False¶

clicked_btn_remove_xaxes()[source]¶

clicked_btn_remove_yaxes()[source]¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

icon_to_bytes(icon)[source]¶

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.ColumnNames(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The page for recognizing column names

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_select_names_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`hint_for_start_editing`()
`hint_for_wrong_name`(col, curr, ref)	Display a hint if a name is not correctly set
`skip`()	Skip the steps in this page

Attributes

`column_names`	The column names in the diagram
`is_finished`	Boolean that is True, if the steps are all finished
`select_names_button_clicked`	bool(x) -> bool

activate()[source]¶: Method that is called, when the page is activated

clicked_select_names_button()[source]¶

column_names = ['Pinus', 'Juniperus', 'Quercus ilex-type', 'Chenopodiaceae']¶: The column names in the diagram

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

hint_for_start_editing()[source]¶

hint_for_wrong_name(col, curr, ref)[source]¶: Display a hint if a name is not correctly set

property is_finished¶: Boolean that is True, if the steps are all finished

select_names_button_clicked = False¶

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.ControlIntro(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

Tutorial page for the control

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

activate()

Method that is called, when the page is activated

activate()[source]¶: Method that is called, when the page is activated

class straditize.widgets.tutorial.beginner.CreateReader(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The page for creating the reader

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

is_finished

Boolean that is True, if the steps are all finished

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.DigitizePage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The page for digitizing the diagram

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

is_finished

Boolean that is True, if the steps are all finished

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.FinishPage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The last page of the tutorial

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

show()

Reimplemented to release the help explorer lock

show()[source]¶: Reimplemented to release the help explorer lock

class straditize.widgets.tutorial.beginner.LoadImage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

TutorialPage for loading the straditizer image

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

is_finished

Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.SamplesPage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The page for finding and editing the samples

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.SelectDataPart(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

TutorialPage for selecting the data part

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`check_mark`(mark)
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`display_reference_marks`()
`hint`()	A method that should display a hint to the user
`is_valid_x`(x)
`is_valid_y`(y)
`skip`()	Skip the steps in this page
`validate_corners`()

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished
`marks`	Built-in mutable sequence.
`ref_lims`	The reference x- and y- limits
`remove_data_box`	true if the data box should be removed at the end
`valid_xlims`	Valid ranges for xmin and xmax
`valid_ylims`	Valid ranges for ymin and ymax

activate()[source]¶: Method that is called, when the page is activated

check_mark(mark)[source]¶

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

display_reference_marks()[source]¶

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

is_valid_x(x)[source]¶

is_valid_y(y)[source]¶

marks = []¶

ref_lims = array([[ 258, 1803], [ 375, 1666]])¶: The reference x- and y- limits

remove_data_box = True¶: true if the data box should be removed at the end

skip()[source]¶: Skip the steps in this page

valid_xlims = array([[ 221, 263], [1730, 1922]])¶: Valid ranges for xmin and xmax

valid_ylims = array([[ 346, 403], [1648, 1701]])¶: Valid ranges for ymin and ymax

validate_corners()[source]¶

class straditize.widgets.tutorial.beginner.SeparateColumns(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The page for separating the columns

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished
`ncols`	int([x]) -> integer

activate()[source]¶: Method that is called, when the page is activated

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

ncols = 4¶

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.TranslateXAxis(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The tutorial page for translating the y-axis

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.TranslateYAxis(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

The tutorial page for translating the y-axis

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.beginner.Tutorial(straditizer_widgets)[source]¶

Bases: straditize.widgets.StraditizerControlBase, straditize.widgets.tutorial.beginner.TutorialPage

A tutorial for digitizing an area diagram

Methods

`close`()	Close the tutorial and remove the widgets
`display_hint`(i)	Display the hint for a tutorial page
`get_doc_files`()	Get the rst files for the tutorial
`goto_page`(old, new)	Go to another page
`refresh`()	Refresh from the straditizer
`setup_tutorial_pages`()	Setup the `pages` attribute and initialize the tutorial pages
`show`()	Show the documentation of the tutorial
`skip_page`(i)	Skip a tutorial page
`validate_page`(i[, silent])	Validate a tutorial page

Attributes

`current_page`	The current page of the tutorial (corresponding to the
`load_image_step`	The number of the page that loads the diagram image (i.e.
`navigation`	A `TutorialNavigation` to navigate through the tutorial
`pages`	A list of the `TutorialPages` for this tutorial
`tutorial_docs`	A `TutorialDocs` to display the RST-files of the tutorial

close()[source]¶: Close the tutorial and remove the widgets

property current_page¶: The current page of the tutorial (corresponding to the TutorialNavigation.current_step)

display_hint(i)[source]¶

Display the hint for a tutorial page

Parameters: i (int) – The index of the page in the pages attribute

See also

TutorialPage.hint()

get_doc_files()[source]¶

Get the rst files for the tutorial

Returns

str – The path to the tutorial introduction file
list of str – The paths of the remaining tutorial files

goto_page(old, new)[source]¶

Go to another page

Parameters

old (int) – The index of the old page in the pages attribute that is subject to be deactivated (see TutorialPage.deactivate())
new (int) – The index of the new page in the pages attribute that is subject to be activated (see TutorialPage.activate())

property load_image_step¶: The number of the page that loads the diagram image (i.e. the index of the LoadImage instance in the pages attribute

navigation = None¶: A TutorialNavigation to navigate through the tutorial

pages = []¶: A list of the TutorialPages for this tutorial

refresh()[source]¶: Refresh from the straditizer

setup_tutorial_pages()[source]¶: Setup the pages attribute and initialize the tutorial pages

show()[source]¶: Show the documentation of the tutorial

skip_page(i)[source]¶

Skip a tutorial page

Parameters: i (int) – The index of the page in the pages attribute

See also

TutorialPage.skip()

tutorial_docs = None¶: A TutorialDocs to display the RST-files of the tutorial

validate_page(i, silent=False)[source]¶

Validate a tutorial page

Parameters

i (int) – The index of the page in the pages attribute
silent (bool) – If True, and the page is not yet finished (see TutorialPage.is_finished), the hint is displayed

Returns

True, if the page is_finished

Return type

bool

class straditize.widgets.tutorial.beginner.TutorialDocs(*args, **kwargs)[source]¶

Bases: psyplot_gui.help_explorer.UrlHelp, psyplot_gui.common.DockMixin

A documentation viewer for the tutorial docs

This viewer is accessible through the Tutorial.tutorial_docs attribute and shows the src_file for the tutorial pages

Attributes

title

str(object=’’) -> str

dock_position = 2¶

title = 'Straditize tutorial'¶

class straditize.widgets.tutorial.beginner.TutorialNavigation(nsteps, validate, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QWidget

A widget for navigating through the tutorial. It has a button to go to the previous step and to go to the next step. Furthermore it has a progressbar implemented a hint button

Parameters

nsteps (int) – The total number of steps in the Tutorial
validate (callable) – A callable that takes the current_step as an argument and returns a bool whether the current step is valid and finished, or not

Attributes

`current_step`	The current step in the tutorial
`hint_requested`(args, *kwargs)	Signal that is emitted if the hint of the current step is requested
`skipped`(args, *kwargs)	Signal that is emitted, if the step is skipped
`step_changed`(args, *kwargs)	Signal that is emitted, when the step changes.

Methods

`display_hint`()	Trigger the `hint_requested` signal with the current step
`goto_next_step`()	Increase the `current_step` by one
`goto_prev_step`()	Decrease the `current_step` by one
`maybe_enable_widgets`()	Enable the buttons based on the `current_step`
`setEnabled`(enable)	Enable or disable the navigation buttons
`set_current_step`(i)	Change the `current_step`
`show_info`()	Trigger the `step_changed` signal with the current step
`skip`()	Skip the `current_step` and emit the `skipped` signal

current_step = 0¶: The current step in the tutorial

display_hint()[source]¶: Trigger the hint_requested signal with the current step

goto_next_step()[source]¶: Increase the current_step by one

goto_prev_step()[source]¶: Decrease the current_step by one

hint_requested(*args, **kwargs)¶: Signal that is emitted if the hint of the current step is requested

maybe_enable_widgets()[source]¶: Enable the buttons based on the current_step

setEnabled(enable)[source]¶

Enable or disable the navigation buttons

Parameters: enable (bool) – Whether to enable or disable the buttons

set_current_step(i)[source]¶

Change the current_step

Parameters: i (int) – The current_step to switch to

show_info()[source]¶: Trigger the step_changed signal with the current step

skip()[source]¶: Skip the current_step and emit the skipped signal

skipped(*args, **kwargs)¶: Signal that is emitted, if the step is skipped

step_changed(*args, **kwargs)¶: Signal that is emitted, when the step changes. The first integer is the old step, the second one the current step

class straditize.widgets.tutorial.beginner.TutorialPage(filename, tutorial)[source]¶

Bases: object

A base class for the tutorial pages

Subclasses show implement the show_hint() method and the is_finished() property

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`lock_viewer`(lock)	Set or unset the url lock of the HTML viewer
`refresh`()
`show`()	Show the page and browse the `filename` in the tutorial docs
`show_hint`()	Show a hint to the user
`show_tooltip_at_widget`(tooltip, widget[, …])	Show a tooltip close to a widget
`show_tooltip_in_plot`(tooltip, x, y[, …])	Show a tooltip in the matplotlib figure at the given coordinates
`skip`()	Skip the steps in this page

Attributes

`is_finished`	Boolean that is True, if the steps are all finished
`is_selecting`	True if the user clicked the btn_select_data button
`src_base`	The basename of the stratigraphic diagram image for this tutorial
`src_dir`	The source directory for the docs
`src_file`	The complete path to the of the stratigraphic diagram image for this

activate()[source]¶: Method that is called, when the page is activated

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

property is_selecting¶: True if the user clicked the btn_select_data button

lock_viewer(lock)[source]¶: Set or unset the url lock of the HTML viewer

refresh()[source]¶

show()[source]¶: Show the page and browse the filename in the tutorial docs

show_hint()[source]¶: Show a hint to the user

show_tooltip_at_widget(tooltip, widget, timeout=20000)[source]¶

Show a tooltip close to a widget

Parameters

tooltip (str) – The tooltip to display
widget (QWidget) – The widget that should be close to the tooltip
timeout (int) – The time that the tool tip shall be displayed, in milliseconds

show_tooltip_in_plot(tooltip, x, y, timeout=20000, transform=None)[source]¶

Show a tooltip in the matplotlib figure at the given coordinates

Parameters

tooltip (str) – The tooltip to display
x (float) – The x-coordinate of where to display the tooltip
y (float) – The y-coordinate of where to display the tooltip
timeout (int) – The time that the tool tip shall be displayed, in milliseconds
transform (matplotlib transformation) – The matplotlib transformation to use. If None, the self.stradi.ax.transData transformation is used and x and y are expected to be in data coordinates

skip()[source]¶: Skip the steps in this page

src_base = 'beginner-tutorial.png'¶: The basename of the stratigraphic diagram image for this tutorial

src_dir = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/beginner'¶: The source directory for the docs

src_file = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/beginner/beginner-tutorial.png'¶: The complete path to the of the stratigraphic diagram image for this tutorial

straditize.widgets.tutorial.hoya_del_castillo module¶

The tutorial of straditize

This module contains an advanced guided tour through straditize

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ColumnNames`(filename, tutorial)	The page for recognizing column names
`ColumnNamesOCR`(filename, tutorial)	Tutorial page for column names with tesserocr support
`EditMeta`(filename, tutorial)	Tutorial page for editing the meta attributes
`HoyaDelCastilloTutorial`(straditizer_widgets)	A tutorial for digitizing an area diagram
`LoadImage`(filename, tutorial)	param filename The basename (without ending) of the RST file corresponding to this
`RemoveLines`(filename, tutorial)	Tutorial page for removing horizontal and vertical lines
`SelectDataPart`(filename, tutorial)	TutorialPage for selecting the data part
`SeparateColumns`(filename, tutorial)	The page for separating the columns
`TranslateXAxis`(filename, tutorial)	The tutorial page for translating the x-axes
`TranslateYAxis`(filename, tutorial)	The tutorial page for translating the y-axis
`TutorialPage`(filename, tutorial)	param filename The basename (without ending) of the RST file corresponding to this

class straditize.widgets.tutorial.hoya_del_castillo.ColumnNames(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.ColumnNames

The page for recognizing column names

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Attributes

`column_names`	Built-in mutable sequence.
`select_names_button_clicked`	bool(x) -> bool

column_names = ['Charcoal', 'Pinus', 'Juniperus', 'Quercus ilex-type', 'Quercus suber-type', 'Olea', 'Betula', 'Corylus', 'Carpinus-type', 'Ericaceae', 'Ephedra distachya-type', 'Ephedra fragilis', 'Mentha-type', 'Anthemis-type', 'Artemisia', 'Caryophyllaceae', 'Chenopodiaceae', 'Cruciferae', 'Filipendula', 'Gramineae <40um', 'Gramineae >40<50um', 'Gramineae >50<60um', 'Gramineae >60um', 'Liguliﬂorae', 'Plantago coronopus', 'Pteridium', 'Filicales', 'Pollen Concentration']¶

select_names_button_clicked = False¶

class straditize.widgets.tutorial.hoya_del_castillo.ColumnNamesOCR(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.hoya_del_castillo.ColumnNames

Tutorial page for column names with tesserocr support

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Attributes

`colpic_extents`	Built-in mutable sequence.
`colpic_sizes`	Built-in mutable sequence.

Methods

`hint_for_start_editing`()
`hint_for_wrong_name`(col, curr, ref)	Display a hint if a name is not correctly set

colpic_extents = [(1051, 1127, 573, 588), (1176, 1225, 704, 719), (1338, 1422, 866, 884), (1437, 1584, 964, 982), (1490, 1655, 1016, 1034), (1511, 1550, 1038, 1052), (1533, 1587, 1061, 1075), (1557, 1621, 1083, 1102), (1580, 1699, 1105, 1123), (1600, 1686, 1123, 1138), (1623, 1828, 1151, 1169), (1646, 1783, 1173, 1192), (1665, 1779, 1195, 1215), (1689, 1811, 1218, 1237), (1711, 1793, 1240, 1254), (1766, 1909, 1288, 1306), (1788, 1934, 1310, 1328), (1841, 1929, 1363, 1378), (1862, 1957, 1390, 1407), (1885, 2045, 1407, 1422), (1922, 2116, 1444, 1459), (1945, 2138, 1467, 1482), (1968, 2128, 1490, 1505), (1990, 2083, 1512, 1531), (2012, 2186, 1540, 1559), (2035, 2117, 1563, 1577), (2056, 2126, 1584, 1599), (2080, 2259, 1601, 1616)]¶

colpic_sizes = [(270, 88), (189, 88), (307, 109), (494, 109), (550, 109), (159, 85), (204, 85), (249, 112), (363, 60), (264, 48), (655, 109), (411, 57), (336, 69), (424, 116), (286, 85), (487, 112), (493, 109), (309, 88), (297, 51), (523, 88), (624, 89), (624, 89), (525, 89), (333, 112), (574, 109), (289, 88), (254, 88), (581, 89)]¶

hint_for_start_editing()[source]¶

hint_for_wrong_name(col, curr, ref)[source]¶: Display a hint if a name is not correctly set

class straditize.widgets.tutorial.hoya_del_castillo.EditMeta(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.hoya_del_castillo.TutorialPage

Tutorial page for editing the meta attributes

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

is_finished

Boolean that is True, if the steps are all finished

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.hoya_del_castillo.HoyaDelCastilloTutorial(straditizer_widgets)[source]¶

Bases: straditize.widgets.tutorial.beginner.Tutorial

A tutorial for digitizing an area diagram

Methods

`setup_tutorial_pages`()	Setup the `pages` attribute and initialize the tutorial pages
`show`()	Show the documentation of the tutorial

Attributes

`src_base`	str(object=’’) -> str
`src_dir`	str(object=’’) -> str
`src_file`	str(object=’’) -> str

setup_tutorial_pages()[source]¶: Setup the pages attribute and initialize the tutorial pages

show()[source]¶: Show the documentation of the tutorial

src_base = 'hoya-del-castillo.png'¶

src_dir = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/hoya-del-castillo'¶

src_file = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/hoya-del-castillo/hoya-del-castillo.png'¶

class straditize.widgets.tutorial.hoya_del_castillo.LoadImage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.LoadImage, straditize.widgets.tutorial.hoya_del_castillo.TutorialPage

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

class straditize.widgets.tutorial.hoya_del_castillo.RemoveLines(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.hoya_del_castillo.TutorialPage

Tutorial page for removing horizontal and vertical lines

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_hlines_button`()
`clicked_vlines_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`hlines_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished
`vlines_button_clicked`	bool(x) -> bool

activate()[source]¶: Method that is called, when the page is activated

clicked_hlines_button()[source]¶

clicked_vlines_button()[source]¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

hlines_button_clicked = False¶

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

vlines_button_clicked = False¶

class straditize.widgets.tutorial.hoya_del_castillo.SelectDataPart(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.SelectDataPart

TutorialPage for selecting the data part

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Attributes

`ref_lims`	The reference x- and y- limits
`remove_data_box`	bool(x) -> bool
`valid_xlims`	Valid ranges for xmin and xmax
`valid_ylims`	Valid ranges for ymin and ymax

ref_lims = array([[ 315, 1946], [ 511, 1311]])¶: The reference x- and y- limits

remove_data_box = False¶

valid_xlims = array([[ 310, 319], [1928, 1960]])¶: Valid ranges for xmin and xmax

valid_ylims = array([[ 508, 513], [1307, 1312]])¶: Valid ranges for ymin and ymax

class straditize.widgets.tutorial.hoya_del_castillo.SeparateColumns(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.SeparateColumns

The page for separating the columns

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Attributes

ncols

int([x]) -> integer

ncols = 28¶

class straditize.widgets.tutorial.hoya_del_castillo.TranslateXAxis(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.hoya_del_castillo.TutorialPage

The tutorial page for translating the x-axes

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_add_reader_button`()
`clicked_translations_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`hint_for_col`(col)
`refresh`()
`skip`()	Skip the steps in this page

Attributes

`add_reader_button_clicked`	Built-in mutable sequence.
`is_finished`	Boolean that is True, if the steps are all finished
`xaxis_translations_button_clicked`	bool(x) -> bool

activate()[source]¶: Method that is called, when the page is activated

add_reader_button_clicked = []¶

clicked_add_reader_button()[source]¶

clicked_translations_button()[source]¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

hint_for_col(col)[source]¶

property is_finished¶: Boolean that is True, if the steps are all finished

refresh()[source]¶

skip()[source]¶: Skip the steps in this page

xaxis_translations_button_clicked = False¶

class straditize.widgets.tutorial.hoya_del_castillo.TranslateYAxis(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.hoya_del_castillo.TutorialPage

The tutorial page for translating the y-axis

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Methods

`activate`()	Method that is called, when the page is activated
`clicked_correct_button`()
`deactivate`()	Method that is called, when the page is deactivated
`hint`()	A method that should display a hint to the user
`skip`()	Skip the steps in this page

Attributes

`correct_button_clicked`	bool(x) -> bool
`is_finished`	Boolean that is True, if the steps are all finished

activate()[source]¶: Method that is called, when the page is activated

clicked_correct_button()[source]¶

correct_button_clicked = False¶

deactivate()[source]¶: Method that is called, when the page is deactivated

hint()[source]¶: A method that should display a hint to the user

property is_finished¶: Boolean that is True, if the steps are all finished

skip()[source]¶: Skip the steps in this page

class straditize.widgets.tutorial.hoya_del_castillo.TutorialPage(filename, tutorial)[source]¶

Bases: straditize.widgets.tutorial.beginner.TutorialPage

Parameters

filename (str) – The basename (without ending) of the RST file corresponding to this tutorial page
tutorial (Tutorial) – The tutorial instance

Attributes

`src_base`	str(object=’’) -> str
`src_dir`	str(object=’’) -> str
`src_file`	str(object=’’) -> str

src_base = 'hoya-del-castillo.png'¶

src_dir = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/hoya-del-castillo'¶

src_file = '/home/docs/checkouts/readthedocs.org/user_builds/straditize/checkouts/stable/straditize/widgets/tutorial/hoya-del-castillo/hoya-del-castillo.png'¶

Submodules¶

straditize.widgets.axes_translations module¶

Module for translating the x- and y-axes from pixel into data coordinates

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

AxesTranslations(straditizer_widgets, item)

The control for translating x- and y-axes

class straditize.widgets.axes_translations.AxesTranslations(straditizer_widgets, item)[source]¶

Bases: straditize.widgets.StraditizerControlBase

The control for translating x- and y-axes

This object creates two buttons for translating x- and y-axes from pixel to data coordinates

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

Methods

`marks_for_x`([at_col_start])	Create (or enable) the marks for the x-axis translation
`marks_for_y`()	Create (or enable) the marks for the y-axis translation
`setup_children`(item)	Setup the children for this control
`should_be_enabled`(w)	Check if a widget should be enabled

Attributes

tree

marks_for_x(at_col_start=True)[source]¶: Create (or enable) the marks for the x-axis translation

See also

straditize.straditizer.Straditizer.marks_for_x_values(), straditize.straditizer.Straditizer.update_xvalues()

marks_for_y()[source]¶: Create (or enable) the marks for the y-axis translation

See also

straditize.straditizer.Straditizer.marks_for_y_values(), straditize.straditizer.Straditizer.update_yvalues()

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

property tree¶

straditize.widgets.colnames module¶

Widget for handling column names

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ColumnNamesManager`(straditizer_widgets[, item])	Manage the column names of the reader
`DummyNavigationToolbar2`(canvas)	Reimplemented NavigationToolbar2 just to add an _init_toolbar method

class straditize.widgets.colnames.ColumnNamesManager(straditizer_widgets, item=None, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, psyplot_gui.common.DockMixin, PyQt5.QtWidgets.QSplitter

Manage the column names of the reader

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

Attributes

`NAVIGATION_LABEL`	str(object=’’) -> str
`SELECT_LABEL`	str(object=’’) -> str
`btn_find`	A QPushButton to find column names in the visible part of the
`btn_load_image`	A QPushButton to load the highres image
`btn_recognize`	A QPushButton to recognize text in the `colpic`
`btn_select_colpic`	A checkable QPushButton to initialize a `selector` to select the
`btn_select_names`	The QPushButton in the `straditize.widgets.StraditizerWidgets`
`cb_find_all_cols`	A QCheckBox to find the column names (see `btn_find`) for all
`cb_fliph`	A QCheckBox to set the `straditize.colnames.ColNamesReader.mirror`
`cb_flipv`	A QCheckBox to set the `straditize.colnames.ColNamesReader.flip`
`cb_ignore_data_part`	A QCheckBox to ignore the part within the
`colnames_reader`	The `straditize.straditizer.Straditizer.colnames_reader`
`colnames_table`	A QTableWidget to display the column names
`colpic`	The `PIL.Image.Image` of the column name (see also
`colpic_ax`	The `matplotlib.axes.Axes` to display the `colpic_im`
`colpic_canvas`	The canvas to display the `colpic_im`
`colpic_extents`	The extents of the `colpic` in the `im_rotated`
`colpic_im`	The matplotlib image of the `colpic`
`current_col`	The currently selected column
`fig_h`	The original height of the `main_canvas`
`fig_w`	The original width of the `main_canvas`
`im_rotated`	The matplotlib image of the
`main_ax`	The `matplotlib.axes.Axes` to display the `im_rotated`
`main_canvas`	The canvas to display the `im_rotated`
`rect`	The rectangle to highlight a column (see `highlight_selected_col()`)
`refreshing`	True if the widget is refreshing
`selector`	The `matplotlib.widgets.RectangleSelector` to select the
`txt_rotate`	A QLineEdit to set the `straditize.colnames.ColNamesReader.rotate`

Methods

`adjust_lims`()	Adjust the limits of the `main_ax` to fill the entire figure
`adjust_lims_after_resize`(event)	Adjust the limits of the `main_ax` after resize of the figure
`cancel_colpic_selection`()	Stop the colpic selection in the `im_rotated`
`change_ignore_data_part`(checked)	Change `straditize.colnames.ColNamesReader.ignore_data_part`
`colname_changed`(row, column)	Update the column name in the `colnames_reader`
`create_selector`()	Create the `selector` to enable `colpic` selection
`enable_or_disable_btn_find`(args, *kwargs)
`find_colnames`([warn, full_image, all_cols])	Find the column names automatically
`flip`(checked)	TFlip the image
`highlight_selected_col`()	Highlight the column selected in the `colnames_tables`
`load_image`()	Load a high resolution image
`maybe_tabify`()
`mirror`(checked)	Mirror the image
`plot_colpic`()	Plot the `colpic` in the `colpic_ax`
`read_colpic`()	Recognize the text in the `colpic`
`refresh`()	Refresh from the straditizer
`remove_images`()	Remove the `im_rotated` and the `colpic_im`
`remove_selector`()	Remove and disconnect the `selector`
`replot_figure`()	Remove and replot the `im_rotated`
`reset_control`()	Reset the dialog
`rotate`(val)	Rotate the image
`set_xc_yc`()	Set the x- and y-center before rotating or flipping
`setup_children`(item)	Setup the children for this control
`should_be_enabled`(w)	Check if a widget should be enabled
`to_dock`(main[, title, position])
`toggle_colpic_selection`()	Enable or disable the colpic selection
`toggle_dialog`()	Close the dialog when the `btn_select_names` button is clicked
`update_image`(args, *kwargs)	Update the `colpic` with the extents of the `selector`

NAVIGATION_LABEL = 'Use left-click of your mouse to move the image below and right-click to zoom in and out.'¶

SELECT_LABEL = 'Left-click and hold on the image to select the column name'¶

adjust_lims()[source]¶: Adjust the limits of the main_ax to fill the entire figure

adjust_lims_after_resize(event)[source]¶: Adjust the limits of the main_ax after resize of the figure

btn_find = None¶: A QPushButton to find column names in the visible part of the im_rotated

btn_load_image = None¶: A QPushButton to load the highres image

btn_recognize = None¶: A QPushButton to recognize text in the colpic

btn_select_colpic = None¶: A checkable QPushButton to initialize a selector to select the colpic

btn_select_names = None¶: The QPushButton in the straditize.widgets.StraditizerWidgets to toggle the column names dialog

cancel_colpic_selection()[source]¶: Stop the colpic selection in the im_rotated

cb_find_all_cols = None¶: A QCheckBox to find the column names (see btn_find) for all columns and not just the one selected in the colnames_table

cb_fliph = None¶: A QCheckBox to set the straditize.colnames.ColNamesReader.mirror

cb_flipv = None¶: A QCheckBox to set the straditize.colnames.ColNamesReader.flip

cb_ignore_data_part = None¶: A QCheckBox to ignore the part within the straditize.colnames.ColNamesReader.data_ylim

change_ignore_data_part(checked)[source]¶: Change straditize.colnames.ColNamesReader.ignore_data_part

colname_changed(row, column)[source]¶

Update the column name in the colnames_reader

This method is called when a cell in the colnames_table has been changed and updates the corresponding name in the colnames_reader

Parameters

row (int) – The row of the cell in the colnames_table that changed
column (int) – The column of the cell in the colnames_table that changed

property colnames_reader¶: The straditize.straditizer.Straditizer.colnames_reader of the current straditizer

colnames_table = None¶: A QTableWidget to display the column names

colpic = None¶: The PIL.Image.Image of the column name (see also straditize.colnames.ColNamesReader.colpics)

colpic_ax = None¶: The matplotlib.axes.Axes to display the colpic_im

colpic_canvas = None¶: The canvas to display the colpic_im

colpic_extents = None¶: The extents of the colpic in the im_rotated

colpic_im = None¶: The matplotlib image of the colpic

create_selector()[source]¶: Create the selector to enable colpic selection

property current_col¶: The currently selected column

enable_or_disable_btn_find(*args, **kwargs)[source]¶

fig_h = None¶: The original height of the main_canvas

fig_w = None¶: The original width of the main_canvas

find_colnames(warn=True, full_image=False, all_cols=None)[source]¶: Find the column names automatically

See also

straditize.colnames.ColNamesReader.find_colnames()

flip(checked)[source]¶: TFlip the image

highlight_selected_col()[source]¶: Highlight the column selected in the colnames_tables

See also

straditize.colnames.ColNamesReader.highlight_column()

im_rotated = None¶: The matplotlib image of the straditize.colnames.ColNamesReader.rotated_image

load_image()[source]¶: Load a high resolution image

main_ax = None¶: The matplotlib.axes.Axes to display the im_rotated

main_canvas = None¶: The canvas to display the im_rotated

maybe_tabify()[source]¶

mirror(checked)[source]¶: Mirror the image

plot_colpic()[source]¶: Plot the colpic in the colpic_ax

read_colpic()[source]¶: Recognize the text in the colpic

See also

straditize.colnames.ColNamesReader.recognize_text()

rect = None¶: The rectangle to highlight a column (see highlight_selected_col())

refresh()[source]¶: Refresh from the straditizer

property refreshing¶: True if the widget is refreshing

remove_images()[source]¶: Remove the im_rotated and the colpic_im

remove_selector()[source]¶: Remove and disconnect the selector

replot_figure()[source]¶: Remove and replot the im_rotated

reset_control()[source]¶: Reset the dialog

rotate(val)[source]¶

Rotate the image

Parameters: float – The angle for the rotation

selector = None¶: The matplotlib.widgets.RectangleSelector to select the colpic

set_xc_yc()[source]¶: Set the x- and y-center before rotating or flipping

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

to_dock(main, title=None, position=None, *args, **kwargs)[source]¶

toggle_colpic_selection()[source]¶: Enable or disable the colpic selection

toggle_dialog()[source]¶: Close the dialog when the btn_select_names button is clicked

txt_rotate = None¶: A QLineEdit to set the straditize.colnames.ColNamesReader.rotate

update_image(*args, **kwargs)[source]¶

Update the colpic with the extents of the selector

*args and **kwargs are ignored

class straditize.widgets.colnames.DummyNavigationToolbar2(canvas)[source]¶

Bases: matplotlib.backend_bases.NavigationToolbar2

Reimplemented NavigationToolbar2 just to add an _init_toolbar method

Methods

set_cursor(cursor)

Set the current cursor to one of the Cursors enums values.

set_cursor(cursor)[source]¶

Set the current cursor to one of the Cursors enums values.

If required by the backend, this method should trigger an update in the backend event loop after the cursor is set, as this method may be called e.g. before a long-running task during which the GUI is not updated.

straditize.widgets.data module¶

The main control widget for handling the data

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`BarSplitter`(straditizer_widgets, args, *kwargs)	A widget for splitting bars
`DigitizingControl`(straditizer_widgets, item)	An interface to `straditize.straditizer.Straditizer.data_reader`

Functions

`get_reader_name`(reader)	Get the reader key in the `straditize.binary.readers` dictionary
`int_list2str`(numbers)	Create a short string representation of an integer list

class straditize.widgets.data.BarSplitter(straditizer_widgets, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QTreeWidget, straditize.widgets.StraditizerControlBase

A widget for splitting bars

Methods

`add_toolbar_widgets`()	Add an action to switch between the bars to the matplotlib toolbar
`disconnect`()	Disconnect the events to split an item
`enable_or_disable_widgets`(b)	Enable or disable the widgets in this control
`fill_table`([source])	Fill the table with the bars that should be splitted
`get_overlapping_bars`()	Get the bars the overlap with multiple bars in another column
`go_to_next_bar`()	Go to the `next_item`
`go_to_prev_bar`()	Go to the `previous_item`
`new_split`(y, y0[, draw_figure])	Mark the current item to be splitted at y
`prepare_for_split`(event)	Select or deselect a split location
`refresh`()	Reimplemented to use the `fill_table()` method
`remove_actions`()	Remove the actions added by `add_toolbar_widgets()`
`remove_lines`()	Remove the plotted lines
`remove_split_children`()	Remove all the child items that mark a split
`revert_split`(y, y0)	Revert the split
`set_suggestions_fig_titles`(ax)	Set the title in the suggestion figure with the displayed column
`split_bars`()	Split the bars after they have been separated manually
`start_splitting`(item, args, *kwargs)	Enable the splitting for the selected item
`suggest_splits`()	Find overlaps for the current selected bar in other columns

Attributes

`next_item`	The QTreeWidgetItem for the next bar to split
`prev_action`	The action in the matplotlib toolbar to go to the previous bar to split
`previous_item`	The QTreeWidgetItem for the previous bar to split
`selected_child`	The QTreeWidgetItem that is currently shown in the plot
`suggestions_fig`	A figure to show the other columns

add_toolbar_widgets()[source]¶: Add an action to switch between the bars to the matplotlib toolbar

disconnect()[source]¶: Disconnect the events to split an item

enable_or_disable_widgets(b)[source]¶

Enable or disable the widgets in this control

This method enables or disables the widgets2disable if the should_be_enabled() method evaluates to True

Parameters: b (bool) – If True, enable the widgets, if False, disable them

fill_table(source='too-long')[source]¶

Fill the table with the bars that should be splitted

Parameters

source ({ 'too-long' | 'overlaps' | 'all' }) –

The source with what to fill the table.

too-long: Only display the bars that are considered as too long
overlap: Only display the bars that overlap with multiple bars in another column (see get_overlapping_bars())
all: Display all bars

get_overlapping_bars()[source]¶: Get the bars the overlap with multiple bars in another column

go_to_next_bar()[source]¶: Go to the next_item

go_to_prev_bar()[source]¶: Go to the previous_item

new_split(y, y0, draw_figure=True)[source]¶

Mark the current item to be splitted at y

This method draws a horizontal line at y and adds a new child QTreeWidgetItem to the selected_child to mark the split

Parameters

y (int) – The vertical position of the split in the data image coordinate system
y0 (int) – The vertical start of the data image (see straditize.binary.DataReader.extent)
draw_figure (bool) – If True, draw the figure

property next_item¶: The QTreeWidgetItem for the next bar to split

prepare_for_split(event)[source]¶

Select or deselect a split location

LeftButton selects the given location for a new split (see new_split()), RightButton deselects it (see revert_split())

prev_action = None¶: The action in the matplotlib toolbar to go to the previous bar to split

property previous_item¶: The QTreeWidgetItem for the previous bar to split

refresh()[source]¶: Reimplemented to use the fill_table() method

remove_actions()[source]¶: Remove the actions added by add_toolbar_widgets()

remove_lines()[source]¶: Remove the plotted lines

remove_split_children()[source]¶: Remove all the child items that mark a split

revert_split(y, y0)[source]¶

Revert the split

Parameters

y (int) – The vertical position of the split in the data image coordinate system
y0 (int) – The vertical start of the data image (see straditize.binary.DataReader.extent)

selected_child = None¶: The QTreeWidgetItem that is currently shown in the plot

set_suggestions_fig_titles(ax)[source]¶: Set the title in the suggestion figure with the displayed column

split_bars()[source]¶: Split the bars after they have been separated manually

start_splitting(item, *args, **kwargs)[source]¶: Enable the splitting for the selected item

suggest_splits()[source]¶: Find overlaps for the current selected bar in other columns

suggestions_fig = None¶: A figure to show the other columns

class straditize.widgets.data.DigitizingControl(straditizer_widgets, item)[source]¶

Bases: straditize.widgets.StraditizerControlBase

An interface to straditize.straditizer.Straditizer.data_reader

This widgets contains the functionalities to interface with the data readers for the stratigraphic diagram

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

Methods

`align_vertical`()	Create marks for vertical alignment of the columns
`change_reader`(txt)	Change the current parent reader
`digitize`()	Digitize the data
`digitize_exaggerations`()	Digitize the data
`edit_occurences`()	Enable the editing of occurences
`edit_samples`()	Enable the sample editing
`enable_col_selection_for_new_reader`()	Start the selection process to get a new reader for specific cols
`enable_occurences_selection`()	Enable the selection of occurences
`enable_or_disable_btn_highlight_small_selection`()	Enable the `btn_highlight_small_selection` during a selection
`enable_or_disable_widgets`(args, *kwargs)	Enable or disable the widgets in this control
`fill_cb_readers`()	Fill the `cb_readers` combo based on the current reader
`find_samples`()
`finish_exaggerated_features`()	Save the exaggerations in the exaggerations reader
`init_exaggerated_reader`()	Initialize the reader for exaggeration features
`init_reader`()	Initialize the reader
`load_samples`([fname])	Load the samples of a text file
`maybe_show_btn_reset_columns`()	Show the `btn_reset_columns` if the column starts are set
`maybe_show_btn_reset_samples`()	Show the `btn_reset_samples` if the samples are set
`modify_column_ends`()	Modify the column ends
`new_reader_for_selection`([cls])	Create a new child reader for the selected columns
`refresh`()	Refresh from the straditizer
`remove_hlines`()	Remove horizontal lines
`remove_vlines`()	Remove vertical lines
`remove_xaxes`()	Remove x-axes in the plot
`remove_yaxes`()	Remove y-axes in the plot
`reset_column_starts`()	Reset the column starts
`reset_samples`()	Reset the samples
`select_column_starts`()	Estimate the column starts and draw marks
`select_data_part`([guess_lims])	Enable the selection of the diagram part
`select_exaggerated_features`()	Enable the selection of exaggerated features
`select_occurences`()	Save (and potentially remove) the selected occurences
`set_occurences_value`(value)	Set the `occurences_value`
`setup_children`(item)	Set up the child items for a topLevelItem in the control tree
`should_be_enabled`(w)	Check if a widget should be enabled
`show_cross_column_features`()	Remove cross column features
`show_disconnected_parts`()	Remove disconnected parts
`show_parts_at_column_ends`()	Remove parts that touch the column ends
`show_small_parts`()	Remove parts that touch the column ends
`toggle_bar_split_source`(i)	Fill the `tree_bar_split` based on the `cb_split_source`
`toggle_btn_highlight_small_selection`()	Enable or disable the `btn_highlight_small_selection`
`toggle_sp_max_lw`(state)	Toggle `sp_max_lw` based on `cb_max_lw`
`toggle_txt_edit_rows`(state)	Toggle `txt_edit_rows` based on `cb_edit_separate`
`toggle_txt_from0`(state)	Toggle `txt_from0` based on `cb_from0`
`toggle_txt_fromlast`(state)	Toggle `txt_fromlast` based on `cb_fromlast`
`toggle_txt_tolerance`(s)	Set the visibility of the `txt_tolerance` based on the reader
`update_tolerance`(s)	Set the readers `tolerance`

Attributes

`btn_column_ends`	Button for selecting and modifying column ends
`btn_column_starts`	Button for selecting and modifying column starts, see the `straditize.straditizer.Straditizer.marks_for_column_starts()` method.
`btn_digitize`	Button for digitizing the diagram
`btn_digitize_exag`	Button to digitize the exaggerations
`btn_edit_occurences`	A button to edit the occurences with the
`btn_edit_samples`	A button to edit the samples (see the
`btn_find_samples`	A button to find the samples with the
`btn_highlight_small_selection`	A button to highlight small selections using the
`btn_init_reader`	Button for initializing the reader
`btn_load_samples`	A button to load samples from a file
`btn_new_child_reader`	Button to add a new column-specific child reader
`btn_new_exaggeration`	Button to add an exaggerations reader
`btn_remove_hlines`	button for removing horizontal lines
`btn_remove_vlines`	Button for removing vertical lines
`btn_remove_xaxes`	button for removing x-axes
`btn_remove_yaxes`	Button for removing y-axes
`btn_reset_columns`	Button to reset the column starts and ends
`btn_reset_samples`	A button to reset the samples
`btn_select_data`	Button for selecting the data box, see the `straditize.straditizer.Straditizer.marks_for_data_selection()` method.
`btn_select_exaggerations`	Button to select the exaggerations
`btn_select_occurences`	A button to select occurences in the data part (see the
`btn_show_cross_column`	A button to
`btn_show_disconnected_parts`	Button for removing disconnected parts in the plot.
`btn_show_parts_at_column_ends`	A button to show the parts that touch the column end
`btn_show_small_parts`	A button to `show_small_parts()`
`cb_edit_separate`	A QCheckBox to edit the samples in a separate figure and not inside the
`cb_exag_reader_type`	A QComboBox to select the reader type for exaggerations
`cb_from0`	A QCheckBox to enable and disable the from0 keyword in the
`cb_fromlast`	A QCheckBox to enable and disable the fromlast keyword in the
`cb_max_lw`	QCheckBox to enable and disable the maximum linewidth as a criterion
`cb_reader_type`	Combobox for selecting the reader type
`cb_remove_occurences`	A QCheckBox to remove the occurences in the plot after selection
`cb_split_source`	A QComboBox to select whether to fill the `tree_bar_split` with
`reader`	The `straditize.straditizer.Straditizer.data_reader`
`selection_toolbar`
`sp_max_lw`	A QSpinBox to select the maximum linewidth
`sp_min_lw`	A QSpinBox to select the minimum linewidth
`sp_pixel_tol`	A QSpinBox to set the minimum distance between to samples in the
`tree`	The `straditize.widgets.StraditizerWidgets.tree`
`tree_bar_split`	A `BarSplitter` to split too long bars
`txt_column_thresh`	A QLineEdit to set the threshold for the column starts detection
`txt_cross_column_px`	A QLineEdit to select the minimum pixels (min_px) for a cross column
`txt_edit_rows`	A QLineEdit to specify the number or rows in a plot for editing the
`txt_exag_factor`	A QLineEdit for the exageration factor
`txt_from0`	A QLineEdit to set the from0 keyword for the
`txt_fromlast`	A QLineEdit to set the fromlast keyword for the
`txt_line_fraction`	LineEditor for specifying the fraction of vertical and horizontal lines
`txt_max_len`	A QLinEdit to specify the maximum length of a potential sample to be
`txt_max_small_size`	A QLineEdit to set the size for small parts
`txt_min_highlight`	A QLineEdit to set the maximal size for
`txt_min_len`	A QLinEdit to specify the minimum length of a potential sample to be
`txt_occurences_value`	A QLineEdit to set the value for occurences in the final data
`txt_tolerance`	Line edit for setting the tolerance for bars

align_vertical()[source]¶: Create marks for vertical alignment of the columns

See also

straditize.straditizer.Straditizer.marks_for_vertical_alignment(), straditize.straditizer.Straditizer.align_columns()

btn_column_ends = None¶: Button for selecting and modifying column ends

btn_column_starts = None¶: Button for selecting and modifying column starts, see the straditize.straditizer.Straditizer.marks_for_column_starts() method.

btn_digitize = None¶: Button for digitizing the diagram

btn_digitize_exag = None¶: Button to digitize the exaggerations

btn_edit_occurences = None¶: A button to edit the occurences with the straditize.straditizer.Straditizer.marks_for_occurences() method

btn_edit_samples = None¶: A button to edit the samples (see the straditize.straditizer.Straditizer.marks_for_samples() and straditize.straditizer.Straditizer.marks_for_samples_sep())

btn_find_samples = None¶: A button to find the samples with the straditize.binary.DataReader.find_samples() method

btn_highlight_small_selection = None¶: A button to highlight small selections using the straditize.label_selection.LabelSelection.highlight_small_selections() method

btn_init_reader = None¶: Button for initializing the reader

btn_load_samples = None¶: A button to load samples from a file

btn_new_child_reader = None¶: Button to add a new column-specific child reader

btn_new_exaggeration = None¶: Button to add an exaggerations reader

btn_remove_hlines = None¶: button for removing horizontal lines

btn_remove_vlines = None¶: Button for removing vertical lines

btn_remove_xaxes = None¶: button for removing x-axes

btn_remove_yaxes = None¶: Button for removing y-axes

btn_reset_columns = None¶: Button to reset the column starts and ends

btn_reset_samples = None¶: A button to reset the samples

btn_select_data = None¶: Button for selecting the data box, see the straditize.straditizer.Straditizer.marks_for_data_selection() method.

btn_select_exaggerations = None¶: Button to select the exaggerations

btn_select_occurences = None¶: A button to select occurences in the data part (see the enable_occurences_selection() method)

btn_show_cross_column = None¶: A button to show_cross_column_features()

btn_show_disconnected_parts = None¶: Button for removing disconnected parts in the plot. See the straditize.binary.DataReader.show_disconnected_parts() method

btn_show_parts_at_column_ends = None¶: A button to show the parts that touch the column end

btn_show_small_parts = None¶: A button to show_small_parts()

cb_edit_separate = None¶: A QCheckBox to edit the samples in a separate figure and not inside the original diagram

cb_exag_reader_type = None¶: A QComboBox to select the reader type for exaggerations

cb_from0 = None¶: A QCheckBox to enable and disable the from0 keyword in the straditize.binary.DataReader.show_disconnected_parts() method

cb_fromlast = None¶: A QCheckBox to enable and disable the fromlast keyword in the straditize.binary.DataReader.show_disconnected_parts() method

cb_max_lw = None¶: QCheckBox to enable and disable the maximum linewidth as a criterion

cb_reader_type = None¶: Combobox for selecting the reader type

cb_remove_occurences = None¶: A QCheckBox to remove the occurences in the plot after selection

cb_split_source = None¶: A QComboBox to select whether to fill the tree_bar_split with too long, overlapping, or all bars

change_reader(txt)[source]¶

Change the current parent reader

This changes the straditize.straditizer.Straditizer.data_reader using the straditize.binary.DataReader.set_as_parent() method

Parameters: s (str) – A string matching 'Columns (\d.*)', where the numbers are the columns of the reader to use

digitize()[source]¶

Digitize the data

This method uses the straditize.binary.DataReader.digitize() method to digitize the data of the current reader

digitize_exaggerations()[source]¶

Digitize the data

This method uses the straditize.binary.DataReader.digitize_exaggerated() method to digitize the exaggerated data of the current reader and merge it into the data obtained by the digitize() method.

digitize_item = None¶

edit_occurences()[source]¶

Enable the editing of occurences

This enables the editing of occurences using the straditize.straditizer.Straditizer.marks_for_occurences() method for the occurences selected by the select_occurences() method

edit_samples()[source]¶

Enable the sample editing

This method opens a straditize.widgets.samples_table.MultiCrossMarksEditor or a straditize.widgets.samples_table.SingleCrossMarksEditor to edit the samples in the GUI. Depending on whether the cb_edit_separate is checked or not, we use the straditize.straditizer.Straditizer.marks_for_samples_sep() or straditize.straditizer.Straditizer.marks_for_samples() method.

enable_col_selection_for_new_reader()[source]¶: Start the selection process to get a new reader for specific cols

enable_occurences_selection()[source]¶

Enable the selection of occurences

This method starts the selection of features in the data image and connects the select_occurences() to the apply_button.

enable_or_disable_btn_highlight_small_selection()[source]¶: Enable the btn_highlight_small_selection during a selection

enable_or_disable_widgets(*args, **kwargs)[source]¶

Enable or disable the widgets in this control

This method enables or disables the widgets2disable if the should_be_enabled() method evaluates to True

Parameters: b (bool) – If True, enable the widgets, if False, disable them

fill_cb_readers()[source]¶: Fill the cb_readers combo based on the current reader

find_samples()[source]¶

finish_exaggerated_features()[source]¶

Save the exaggerations in the exaggerations reader

This method finalizes the operation initialized by the select_exaggerated_features() by calling the straditize.binary.DataReader.mark_as_exaggerations() method

init_exaggerated_reader()[source]¶: Initialize the reader for exaggeration features

init_reader()[source]¶

Initialize the reader

Initialize the data reader with the straditize.straditizer.Straditizer.init_reader() method

load_samples(fname=None)[source]¶

Load the samples of a text file

This method askes for a filename to update the samples. The first column in this file is taken as the sample locations. If the y-axis translation is already done, the new data is assumed to be in this transformed unit.

Parameters: fname (str) – The path to the file to use. If None, a QFileDialog is opened and we ask for a name

maybe_show_btn_reset_columns()[source]¶: Show the btn_reset_columns if the column starts are set

maybe_show_btn_reset_samples()[source]¶: Show the btn_reset_samples if the samples are set

modify_column_ends()[source]¶

Modify the column ends

After having selected the column starts, this method enables the modification of the column ends

new_reader_for_selection(cls=None)[source]¶

Create a new child reader for the selected columns

This method finishes the process started by enable_col_selection_for_new_reader()

Parameters: cls (type) – The subclass of the straditize.binary.DataReader class to use for the new reader. If None, a QInputDialog is opened and we ask for a reader

property reader¶: The straditize.straditizer.Straditizer.data_reader

refresh()[source]¶: Refresh from the straditizer

remove_hlines()[source]¶

Remove horizontal lines

This method uses the straditize.binary.DataReader.recognize_hlines() method to identify horizontal lines in the plot

remove_vlines()[source]¶

Remove vertical lines

This method uses the straditize.binary.DataReader.recognize_vlines() method to identify vertical lines in the plot

remove_xaxes()[source]¶

Remove x-axes in the plot

This method uses the straditize.binary.DataReader.recognize_xaxes() method to identify x-axes in the plot

remove_yaxes()[source]¶

Remove y-axes in the plot

This method uses the straditize.binary.DataReader.recognize_yaxes() method to identify y-axes in the plot

reset_column_starts()[source]¶

Reset the column starts

Reset the column starts by calling the straditize.binary.DataReader.reset_column_starts() method

reset_samples()[source]¶

Reset the samples

Reset the samples by calling the straditize.binary.DataReader.reset_samples() method

select_column_starts()[source]¶

Estimate the column starts and draw marks

This methods estimates the column starts (if they are not yet set) based on the threshold in the txt_column_thresh and draws straditize.cross_marks.DraggableVLine marks on the plot.

select_data_part(guess_lims=True)[source]¶

Enable the selection of the diagram part

This method uses the straditize.straditizer.Straditizer.marks_for_data_selection() method to draw cross marks on the image for the diagram part

select_exaggerated_features()[source]¶: Enable the selection of exaggerated features

select_occurences()[source]¶

Save (and potentially remove) the selected occurences

Save the occurences with the straditize.binary.DataReader.get_occurences() method and remove them if the cb_remove_occurences is checked

property selection_toolbar¶

set_occurences_value(value)[source]¶

Set the occurences_value

Set the straditize.binary.DataReader.occurence_value of the data_reader with the given value

Parameters: value (float) – The value to use for occurences

setup_children(item)[source]¶: Set up the child items for a topLevelItem in the control tree

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

show_cross_column_features()[source]¶

Remove cross column features

This method highlights features that span multiple columns using the straditize.binary.DataReader.show_cross_column_features() method. The algorithm can be modified with the txt_cross_column_px line editor

show_disconnected_parts()[source]¶

Remove disconnected parts

This method uses the straditize.binary.DataReader.show_disconnected_parts() to highlight and remove disconnected features in the diagram part. The algorithm can be modified by the txt_fromlast and txt_from0 text editors

show_parts_at_column_ends()[source]¶

Remove parts that touch the column ends

This method highlights features that touch the column ends using the straditize.binary.DataReader.show_parts_at_column_ends() method.

show_small_parts()[source]¶

Remove parts that touch the column ends

This method highlights small features in the data image using the straditize.binary.DataReader.show_small_parts() method. The maximal size of the small features can is taken from the txt_max_small_size line editor

sp_max_lw = None¶: A QSpinBox to select the maximum linewidth

sp_min_lw = None¶: A QSpinBox to select the minimum linewidth

sp_pixel_tol = None¶: A QSpinBox to set the minimum distance between to samples in the sample finding algorithm

toggle_bar_split_source(i)[source]¶

Fill the tree_bar_split based on the cb_split_source

Parameters: i (int) – The BarSplitter.fill_table() is called with either 'too-long' (if i is 0), 'overlaps' (if i is 1) or 'all'

toggle_btn_highlight_small_selection()[source]¶

Enable or disable the btn_highlight_small_selection

This method enables the btn_highlight_small_selection button if we are selecting something at the moment

toggle_sp_max_lw(state)[source]¶: Toggle sp_max_lw based on cb_max_lw

toggle_txt_edit_rows(state)[source]¶: Toggle txt_edit_rows based on cb_edit_separate

toggle_txt_from0(state)[source]¶: Toggle txt_from0 based on cb_from0

toggle_txt_fromlast(state)[source]¶: Toggle txt_fromlast based on cb_fromlast

toggle_txt_tolerance(s)[source]¶

Set the visibility of the txt_tolerance based on the reader

Parameters: s (str) – The reader name. If there is bars in s, then the txt_tolerance is displayed

property tree¶: The straditize.widgets.StraditizerWidgets.tree

tree_bar_split = None¶: A BarSplitter to split too long bars

txt_column_thresh = None¶: A QLineEdit to set the threshold for the column starts detection

txt_cross_column_px = None¶: A QLineEdit to select the minimum pixels (min_px) for a cross column feature

txt_edit_rows = None¶: A QLineEdit to specify the number or rows in a plot for editing the samples in a separate figure (see btn_edit_samples and cb_edit_separate)

txt_exag_factor = None¶: A QLineEdit for the exageration factor

txt_from0 = None¶: A QLineEdit to set the from0 keyword for the straditize.binary.DataReader.show_disconnected_parts() method

txt_fromlast = None¶: A QLineEdit to set the fromlast keyword for the straditize.binary.DataReader.show_disconnected_parts() method

txt_line_fraction = None¶: LineEditor for specifying the fraction of vertical and horizontal lines

txt_max_len = None¶: A QLinEdit to specify the maximum length of a potential sample to be included in the sample finding algorithm (see btn_find_samples)

txt_max_small_size = None¶: A QLineEdit to set the size for small parts

txt_min_highlight = None¶: A QLineEdit to set the maximal size for highlighting small features

txt_min_len = None¶: A QLinEdit to specify the minimum length of a potential sample to be included in the sample finding algorithm (see btn_find_samples)

txt_occurences_value = None¶: A QLineEdit to set the value for occurences in the final data

txt_tolerance = None¶: Line edit for setting the tolerance for bars

update_tolerance(s)[source]¶

Set the readers tolerance

Parameters: s (str or int) – The tolerance for the straditizer.binary.BarDataReader.tolerance attribute

straditize.widgets.data.get_reader_name(reader)[source]¶

Get the reader key in the straditize.binary.readers dictionary

Parameters: reader (straditize.binary.DataReader) – The reader for which to get the key in the straditize.binary.readers dictionary
Returns: The key in the straditize.binary.readers dictionary whose value corresponds to the class of the given reader
Return type: str

straditize.widgets.data.int_list2str(numbers)[source]¶

Create a short string representation of an integer list

Parameters: numbers (list of ints) – Integer list
Returns: The string representation
Return type: str

Examples

[1, 2, 3] becomes '1-3', [1, 2, 3, 5, 7, 8, 9] becomes '1-3, 5, 7-9'

straditize.widgets.image_correction module¶

Image correction methods

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ImageRescaler`(straditizer_widgets, item, …)	A button to rescale the straditize image
`ImageRotator`(straditizer_widgets[, item])	Widget to rotate the image

class straditize.widgets.image_correction.ImageRescaler(straditizer_widgets, item, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, PyQt5.QtWidgets.QPushButton

A button to rescale the straditize image

Methods

`adjust_orig_limits`(args, *kwargs)	Readjust `ax_orig` after changes in `ax_rescale`
`adjust_rescaled_limits`(args, *kwargs)	Readjust `ax_rescale` after changes in `ax_orig`
`close_figs`()	Close the `fig`
`draw_figure`()
`equalize_axes`([event])	Set both axes to the same size
`raise_figure`()	Raise the figure for rescaling
`rescale`([ask])	Rescale and start a new straditizer
`rescale_plot`(percentage)	Replot `im_rescale` after adjustments of the `slider`
`resize_stradi_image`(percentage)	Resize the straditizer image
`should_be_enabled`(w)	Check if a widget should be enabled
`start_rescaling`()	Create the rescaling figure

Attributes

`ax_orig`	The matplotlib axes for the `im_orig`
`ax_rescale`	The matplotlib axes for the `im_rescale`
`fig`	The matplotlib figure for the rescaling
`im_orig`	The matplotlib image for the original diagram
`im_rescale`	The matplotlib image for the rescaled diagram
`rescaling`	Boolean that is true if one of the axes is rescaling
`slider`	A `matplotlib.widgets.Slider` for specifying the size of the

adjust_orig_limits(*args, **kwargs)[source]¶: Readjust ax_orig after changes in ax_rescale

adjust_rescaled_limits(*args, **kwargs)[source]¶: Readjust ax_rescale after changes in ax_orig

ax_orig = None¶: The matplotlib axes for the im_orig

ax_rescale = None¶: The matplotlib axes for the im_rescale

close_figs()[source]¶: Close the fig

draw_figure()[source]¶

equalize_axes(event=None)[source]¶: Set both axes to the same size

fig = None¶: The matplotlib figure for the rescaling

im_orig = None¶: The matplotlib image for the original diagram

im_rescale = None¶: The matplotlib image for the rescaled diagram

raise_figure()[source]¶: Raise the figure for rescaling

rescale(ask=None)[source]¶

Rescale and start a new straditizer

Parameters: ask (bool) – Whether to ask with a QMessageBox. If None, it defaults to the straditize.widgets.StraditizerWidgers.always_yes

rescale_plot(percentage)[source]¶: Replot im_rescale after adjustments of the slider

property rescaling¶: Boolean that is true if one of the axes is rescaling

resize_stradi_image(percentage)[source]¶

Resize the straditizer image

Parameters: percentage (float) – A float between 0 and 100 specifying the target size of the straditize.straditizer.Straditizer.image
Returns: The resized image of the current straditizer
Return type: PIL.Image.Image

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

slider = None¶: A matplotlib.widgets.Slider for specifying the size of the rescaled image

start_rescaling()[source]¶: Create the rescaling figure

class straditize.widgets.image_correction.ImageRotator(straditizer_widgets, item=None, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, PyQt5.QtWidgets.QWidget

Widget to rotate the image

This control mainly adds a QLineEdit txt_rotate to the straditize.widgets.StraditizerWidgets to rotate the image. It also enables the user to specify the rotation angle using two connected CrossMarks. Here the user can decide between a horizontal alignment (btn_rotate_horizontal) or a vertical alignment (btn_rotate_vertical)

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

Attributes

`angle`	The rotation angle from `txt_rotate` as a float
`btn_rotate_horizontal`	A QPushButton for horizontal alignment
`btn_rotate_vertical`	A QPushButton for vertical alignment
`txt_rotate`	A QLineEdit to display the rotation angle

Methods

`draw_figure`()
`enable_or_disable_widgets`(b)	Enable or disable the widgets in this control
`remove_marks`()	Remove the cross marks used for the rotation angle
`rotate_image`()	Rotate the image based on the specified `angle`
`should_be_enabled`(w)	Check if a widget should be enabled
`start_horizontal_alignment`()	Start the horizontal alignment
`start_rotation`()	Start the rotation (if not already started)
`start_vertical_alignment`()	Start the vertical alignment
`update_txt_rotate`(*args[, marks])	Update the `txt_rotate` from the displayed cross marks

property angle¶: The rotation angle from txt_rotate as a float

btn_rotate_horizontal = None¶: A QPushButton for horizontal alignment

btn_rotate_vertical = None¶: A QPushButton for vertical alignment

draw_figure()[source]¶

enable_or_disable_widgets(b)[source]¶

Enable or disable the widgets in this control

This method enables or disables the widgets2disable if the should_be_enabled() method evaluates to True

Parameters: b (bool) – If True, enable the widgets, if False, disable them

remove_marks()[source]¶: Remove the cross marks used for the rotation angle

rotate_image()[source]¶: Rotate the image based on the specified angle

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

start_horizontal_alignment()[source]¶: Start the horizontal alignment

start_rotation()[source]¶: Start the rotation (if not already started)

start_vertical_alignment()[source]¶: Start the vertical alignment

txt_rotate = None¶: A QLineEdit to display the rotation angle

update_txt_rotate(*args, marks=[], **kwargs)[source]¶: Update the txt_rotate from the displayed cross marks

straditize.widgets.marker_control module¶

A widget for controlling the appearance of markers

This module defines the MarkerControl to control the appearance and behaviour of CrossMarks instances in the straditize.straditizer.Straditizer.marks attribute

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ColorLabel`([color])	A QTableWidget with one cell and no headers to just display a color
`MarkerControl`(straditizer_widgets, item, …)	Widget to control the appearance of the marks

class straditize.widgets.marker_control.ColorLabel(color='w', *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QTableWidget

A QTableWidget with one cell and no headers to just display a color

The color to display

Parameters: color (object) – Either a QtGui.QColor object or a color that can be converted to RGBA using the matplotlib.colors.to_rgba() function

Methods

`adjust_height`()	Adjust the height to match the row height
`select_color`(*args)	Select a color using `PyQt5.QtWidgets.QColorDialog.getColor()`
`set_color`(color)	Set the color of the label
`sizeHint`()	Reimplemented to use the rowHeight as height

Attributes

`color`	QtCore.QColor.
`color_changed`(args, *kwargs)	a signal that is emitted with an rgba color if the chosen color changes

adjust_height()[source]¶: Adjust the height to match the row height

color = None¶: QtCore.QColor. The current color that is displayed

color_changed(*args, **kwargs)¶: a signal that is emitted with an rgba color if the chosen color changes

select_color(*args)[source]¶: Select a color using PyQt5.QtWidgets.QColorDialog.getColor()

set_color(color)[source]¶

Set the color of the label

This method sets the given color as background color for the cell and emits the color_changed signal

Parameters: color (object) – Either a QtGui.QColor object or a color that can be converted to RGBA using the matplotlib.colors.to_rgba() function

sizeHint()[source]¶: Reimplemented to use the rowHeight as height

class straditize.widgets.marker_control.MarkerControl(straditizer_widgets, item, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, PyQt5.QtWidgets.QWidget

Widget to control the appearance of the marks

This widget controls the appearance of the straditize.cross_mark.CrossMarks instances in the marks attribute of the straditizer

Parameters

straditizer_widgets (StraditizerWidgets) – The main widget for the straditizer GUI
item (QTreeWidgetItem) – The parent item in the StraditizerWidgets.tree. If given, the setup_children() is called with this item

Methods

`add_toolbar_widgets`(mark)	Add the navigation actions to the toolbar
`change_auto_hide`(auto_hide)	Toggle the `auto_hide`
`change_hline_draggable`(state)	Enable or disable the dragging of horizontal lines
`change_hline_selectable`(state)	Enable or disable the selection of horizontal lines
`change_line_style`(i)	Change the line style of the marks
`change_line_widths`(lw)	Change the linewidth of the marks
`change_marker_size`(markersize)	Change the size of the markers
`change_marker_style`(i)	Change the marker style of the marks
`change_select_colors`(color)	Change the selection color of the marks
`change_selection_line_widths`(lw)	Change the linewidth for selected marks
`change_show_connected_artists`(show)	Change the visibility of connected artists
`change_show_hlines`(state)	Enable of disable the visibility of horizontal lines
`change_show_vlines`(state)	Enable of disable the visibility of vertical lines
`change_unselect_colors`(color)	Change the `straditize.cross_mark.CrossMark.cunselect` color
`change_vline_draggable`(state)	Enable or disable the dragging of vertical lines
`change_vline_selectable`(state)	Enable or disable the selection of vertical lines
`draw_figs`()	Draw the figures of the `marks`
`enable_or_disable_widgets`(b)	Renabled to use the `refresh()` method
`fill_after_adding`(mark)
`fill_from_mark`(mark)	Set the widgets of this `MarkerControl` from a mark
`fill_linestyles`()	Fill the `combo_line_style` combobox
`fill_markerstyles`()	Fill the `combo_marker_style` combobox
`go_to_greater_x_mark`(x)	Move the plot to the next mark with a x-position greater than x
`go_to_greater_y_mark`(y)	Move the plot to the next mark with a y-position greater than y
`go_to_left_mark`()	Move the plot to the previous left cross mark
`go_to_lower_mark`()	Go to the next mark below the current y-limits
`go_to_right_mark`()	Move the plot to the next right cross mark
`go_to_smaller_x_mark`(x)	Move the plot to the next mark with a x-position smaller than x
`go_to_smaller_y_mark`(y)	Move the plot to the next mark with a y-position smaller than x
`go_to_upper_mark`()	Go to the next mark above the current y-limits
`refresh`()	Reimplemented to also set the properties of this widget
`remove_actions`()	Remove the navigation actions from the toolbar
`set_line_style_item`(ls)	Switch the `combo_line_style` to the given linestyle
`set_marker_item`(marker)	Switch the `combo_marker_style` to the given marker
`should_be_enabled`(w)	Check if a widget w should be enabled or disabled
`update_mark`(mark)	Update the properties of a mark to match the settings

Attributes

`line_props`	The properties of the lines as a `dict`
`marks`	The `CrossMarks` of the straditizer
`select_props`	The properties of selected marks as a `dict`

add_toolbar_widgets(mark)[source]¶: Add the navigation actions to the toolbar

change_auto_hide(auto_hide)[source]¶

Toggle the auto_hide

This method disables or enables the auto_hide of the marks

Parameters: auto_hide (bool or PyQt5.QtGui.Qt.Checked or PyQt5.QtGui.Qt.Unchecked) – The value to use for the auto_hide. PyQt5.QtGui.Qt.Checked is equivalent to True

change_hline_draggable(state)[source]¶

Enable or disable the dragging of horizontal lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, the horizontal lines can be dragged and dropped

change_hline_selectable(state)[source]¶

Enable or disable the selection of horizontal lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, the horizontal lines can be selected

change_line_style(i)[source]¶

Change the line style of the marks

Parameters: i (int) – The index of the line style in the line_styles attribute to use

change_line_widths(lw)[source]¶

Change the linewidth of the marks

Parameters: lw (float) – The line width to use

change_marker_size(markersize)[source]¶

Change the size of the markers

Parameters: markersize (float) – The size of the marker to use

change_marker_style(i)[source]¶

Change the marker style of the marks

Parameters: i (int) – The index of the marker style in the marker_styles attribute to use

change_select_colors(color)[source]¶

Change the selection color of the marks

Change the selection color of the marks to the given color.

Parameters: color (PyQt5.QtGui.QColor or a matplotlib color) – The color to use

change_selection_line_widths(lw)[source]¶

Change the linewidth for selected marks

Parameters: lw (float) – The linewidth for selected marks

change_show_connected_artists(show)[source]¶

Change the visibility of connected artists

Parameters: show (bool) – The visibility for the straditize.cross_mark.CrossMarks.set_connected_artists_visible() method

change_show_hlines(state)[source]¶

Enable of disable the visibility of horizontal lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, all horizontal lines are hidden

change_show_vlines(state)[source]¶

Enable of disable the visibility of vertical lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, all vertical lines are hidden

change_unselect_colors(color)[source]¶

Change the straditize.cross_mark.CrossMark.cunselect color

Change the unselection color of the marks to the given color.

Parameters: color (PyQt5.QtGui.QColor or a matplotlib color) – The color to use

change_vline_draggable(state)[source]¶

Enable or disable the dragging of vertical lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, the vertical lines can be dragged and dropped

change_vline_selectable(state)[source]¶

Enable or disable the selection of vertical lines

Parameters: state (Qt.Checked or Qt.Unchecked) – If Qt.Checked, the vertical lines can be selected

draw_figs()[source]¶: Draw the figures of the marks

enable_or_disable_widgets(b)[source]¶: Renabled to use the refresh() method

fill_after_adding(mark)[source]¶

fill_from_mark(mark)[source]¶

Set the widgets of this MarkerControl from a mark

This method sets the color labels, combo boxes, check boxes and text edits to match the properties of the given mark

Parameters: mark (straditize.cross_mark.CrossMark) – The mark to use the attributes from

fill_linestyles()[source]¶: Fill the combo_line_style combobox

fill_markerstyles()[source]¶: Fill the combo_marker_style combobox

go_to_greater_x_mark(x)[source]¶

Move the plot to the next mark with a x-position greater than x

Parameters: x (float) – The reference x-position that shall be smaller than the new centered mark

go_to_greater_y_mark(y)[source]¶

Move the plot to the next mark with a y-position greater than y

Parameters: y (float) – The reference y-position that shall be smaller than the new centered mark

go_to_left_mark()[source]¶: Move the plot to the previous left cross mark

go_to_lower_mark()[source]¶: Go to the next mark below the current y-limits

go_to_right_mark()[source]¶: Move the plot to the next right cross mark

go_to_smaller_x_mark(x)[source]¶

Move the plot to the next mark with a x-position smaller than x

Parameters: x (float) – The reference x-position that shall be greater than the new centered mark

go_to_smaller_y_mark(y)[source]¶

Move the plot to the next mark with a y-position smaller than x

Parameters: y (float) – The reference y-position that shall be smaller than the new centered mark

go_to_upper_mark()[source]¶: Go to the next mark above the current y-limits

property line_props¶: The properties of the lines as a dict

property marks¶: The CrossMarks of the straditizer

refresh()[source]¶: Reimplemented to also set the properties of this widget

remove_actions()[source]¶: Remove the navigation actions from the toolbar

property select_props¶: The properties of selected marks as a dict

set_line_style_item(ls)[source]¶

Switch the combo_line_style to the given linestyle

Parameters: ls (str) – The matplotlib linestyle string

set_marker_item(marker)[source]¶

Switch the combo_marker_style to the given marker

Parameters: marker (str) – A matplotlib marker string

should_be_enabled(w)[source]¶

Check if a widget w should be enabled or disabled

Parameters: w (PyQt5.QtWidgets.QWidget) – The widget to (potentially) enable
Returns: True if the straditizer of this instance has marks. Otherwise False
Return type: bool

update_mark(mark)[source]¶

Update the properties of a mark to match the settings

Parameters: mark (straditize.cross_mark.CrossMarks) – The mark to update

straditize.widgets.menu_actions module¶

The main control widget for a straditizer

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ExportDfDialog`(df, straditizer[, fname])	A QDialog to export a `pandas.DataFrame` to Excel or CSV
`StraditizerMenuActions`(straditizer_widgets)	An object to control the main functionality of a Straditizer

class straditize.widgets.menu_actions.ExportDfDialog(df, straditizer, fname=None, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QDialog

A QDialog to export a pandas.DataFrame to Excel or CSV

Parameters

df (pandas.DataFrame) – The DataFrame to be exported
straditizer (straditize.straditizer.Straditizer) – The source straditizer
fname (str) – The file name to export to

Methods

`accept`(self)
`cancel`()
`export_df`(parent, df, straditizer[, fname, …])	Open a dialog for exporting a DataFrame
`get_open_file_name`()	Ask the user for a filename for saving the data frame

accept(self)[source]¶

cancel()[source]¶

classmethod export_df(parent, df, straditizer, fname=None, exec_=True)[source]¶

Open a dialog for exporting a DataFrame

Parameters: parent (QWidget) – The parent widget

get_open_file_name()[source]¶: Ask the user for a filename for saving the data frame

class straditize.widgets.menu_actions.StraditizerMenuActions(straditizer_widgets)[source]¶

Bases: straditize.widgets.StraditizerControlBase

An object to control the main functionality of a Straditizer

This object is creates menu actions to load the straditizer

Attributes

`all_actions`	`save_actions`, `data_actions` and `text_actions`
`data_actions`	The QActions to save the exported DataFrames and data images
`save_actions`	The QActions to save the straditizer, straditizer image, etc.
`text_actions`	The QActions to save the column names images
`window_layout_action`	The action to

Methods

`create_sliders`(stradi)	Create sliders to navigate in the given axes
`export_final`([fname])	Export the final results
`export_full`([fname])	Export the full digitized data
`finish_loading`(stradi)	Finish the opening of a straditizer
`from_clipboard`()	Open a straditizer from an Image in the clipboard
`import_binary_image`([fname])	Import the binary data reader image from an external file
`import_data_image`([fname])	Import the data reader image from an external file
`import_full_image`([fname])	Import the straditizer image from an external file
`import_text_image`(fname)	Import the column names reader image from an external file
`open_straditizer`([fname])	Open a straditizer from an image or project file
`refresh`()	Refresh from the straditizer
`save_data_image`([fname])	Save the binary image of the data reader
`save_full_image`([fname])	Save the image of the straditizer
`save_straditizer`()	Save the straditizer to a file
`save_straditizer_as`([fname])	Save the straditizer to a file
`save_text_image`([fname])	Save the image of the colnames reader
`set_ax_xlim`(ax_ref)	Define a function to update xlim from a given centered value
`set_ax_ylim`(ax_ref)	Define a function to update ylim from a given centered value
`set_stradi_in_console`()	Set the straditizer in the console of the GUI mainwindow
`setup_children`(item)	Setup the children for this control
`setup_menu_actions`(main)	Create the actions for the file menu
`setup_shortcuts`(main)	Setup the shortcuts when switched to the straditizer layout
`stack_zoom_window`()	Stack the magnifier image above the help explorer
`update_x_navigation_sliders`(ax)	Update the horizontal navigation slider for the given ax`
`update_y_navigation_sliders`(ax)	Update the vertical navigation slider for the given ax`

property all_actions¶: save_actions, data_actions and text_actions

create_sliders(stradi)[source]¶

Create sliders to navigate in the given axes

Parameters: stradi (straditize.straditizer.Straditizer) – The straditizer that just has been opened

data_actions = []¶: The QActions to save the exported DataFrames and data images

export_final(fname=None)[source]¶

Export the final results

This method exports the straditize.straditizer.Straditizer.final_df of the current straditizer

Parameters: fname (str or None) – The path of the target filename where to save the df (see the ExportDialog.export_df())

export_full(fname=None)[source]¶

Export the full digitized data

This method exports the straditize.straditizer.Straditizer.full_df of the current straditizer

Parameters: fname (str or None) – The path of the target filename where to save the df (see the ExportDialog.export_df())

finish_loading(stradi)[source]¶

Finish the opening of a straditizer

This method sets the straditizer.widgets.StraditizerWidgets.straditizer, shows the straditizer image, creates the navigation sliders and sets the straditizer in the console

Parameters: stradi (straditize.straditizer.Straditizer) – The straditizer that just has been opened

from_clipboard()[source]¶

Open a straditizer from an Image in the clipboard

This method uses the PIL.ImageGrab.grabclipboard() function to open a new straditizer from the clipboard.

import_binary_image(fname=None)[source]¶

Import the binary data reader image from an external file

This method imports the straditize.binary.DataReader.binary from an external file and sets it to the current data_reader.

Parameters: fname (str, PIL.Image.Image or None) – The path of the image file or the PIL.Image.Image. If None, a QFileDialog is opened to request the file from the user.

import_data_image(fname=None)[source]¶

Import the data reader image from an external file

This method imports the straditize.binary.DataReader.image from an external file and sets it to the current data_reader.

Parameters: fname (str, PIL.Image.Image or None) – The path of the image file or the PIL.Image.Image. If None, a QFileDialog is opened to request the file from the user.

See also

import_binary_image(): To import the binary file

import_full_image(fname=None)[source]¶

Import the straditizer image from an external file

This method imports the straditize.straditizer.Straditizer.image from an external file and sets it to the current straditizer.

Parameters: fname (str, PIL.Image.Image or None) – The path of the image file or the PIL.Image.Image. If None, a QFileDialog is opened to request the file from the user.

import_text_image(fname)[source]¶

Import the column names reader image from an external file

This method imports the straditize.colnames.ColNamesReader.highres_image from an external file and sets it to the current colnames_reader.

Parameters: fname (str, PIL.Image.Image or None) – The path of the image file or the PIL.Image.Image. If None, a QFileDialog is opened to request the file from the user.

open_straditizer(fname=None, *args, **kwargs)[source]¶

Open a straditizer from an image or project file

Parameters

fname (str, PIL.Image.Image or None) –

The path to the file to import. If None, a QFileDialog is opened and the user is asked for a file name. The action then depends on the ending of fname:

'.nc' or '.nc4': we expect a netCDF file and open it with xarray.open_dataset() and load the straditizer with the straditize.straditizer.Straditizer.from_dataset() constructor
'.pkl': We expect a pickle file and load the straditizer with pickle.load()
any other ending: We expect an image file and use the PIL.Image.open() function

At the end, the loading is finished with the finish_loading() method

refresh()[source]¶: Refresh from the straditizer

save_actions = []¶: The QActions to save the straditizer, straditizer image, etc.

save_data_image(fname=None)[source]¶

Save the binary image of the data reader

Save the straditize.binary.DataReader.binary of the current data_reader

Parameters: %(StraditizerMenuActions._save_image.parameters.fname)s –

save_full_image(fname=None)[source]¶

Save the image of the straditizer

Save the straditize.straditizer.Straditizer.image of the current straditizer

Parameters: %(StraditizerMenuActions._save_image.parameters.fname)s –

save_straditizer()[source]¶: Save the straditizer to a file

save_straditizer_as(fname=None)[source]¶

Save the straditizer to a file

Parameters

fname (str or None) –

If None, a QFileDialog is opened and the user has to provide a filename. The final action then depends on the ending of the chose file:

'.pkl': We save the straditizer with pickle.dump()
else: We use the straditize.straditizer.Straditizer.to_dataset() method and save the resulting dataset using the xarray.Dataset.to_netcdf() method

save_text_image(fname=None)[source]¶

Save the image of the colnames reader

Save the straditize.colnames.ColNamesReader.image of the current colnames_reader

Parameters: fname (str or None) – The path of the target filename where to save the image. If None, A QFileDialog is opened and we ask the user for a filename

static set_ax_xlim(ax_ref)[source]¶

Define a function to update xlim from a given centered value

Parameters: ax_ref (matplotlib.axes.Axes) – The axes whose x-limits to update when the returned function is called
Returns: The function that can be called with a val to set the x-center of the ax_ref
Return type: callable

static set_ax_ylim(ax_ref)[source]¶

Define a function to update ylim from a given centered value

Parameters: ax_ref (matplotlib.axes.Axes) – The axes whose y-limits to update when the returned function is called
Returns: The function that can be called with a val to set the y-center of the ax_ref
Return type: callable

set_stradi_in_console()[source]¶

Set the straditizer in the console of the GUI mainwindow

This sets the current straditizer in psyplots console as the stradi variable

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

setup_menu_actions(main)[source]¶

Create the actions for the file menu

Parameters: main (psyplot_gui.main.MainWindow) – The mainwindow whose menubar shall be adapted

setup_shortcuts(main)[source]¶

Setup the shortcuts when switched to the straditizer layout

Parameters: main (psyplot_gui.main.MainWindow) – The psyplot mainwindow

stack_zoom_window()[source]¶: Stack the magnifier image above the help explorer

text_actions = []¶: The QActions to save the column names images

static update_x_navigation_sliders(ax)[source]¶

Update the horizontal navigation slider for the given ax`

Set the horizontal slider of the straditizer figure depending on the x-limits of it’s corresponding axes

Parameters: ax (matplotlib.axes.Axes) – The straditize.straditizer.Straditizer.ax attribute of a straditizer

static update_y_navigation_sliders(ax)[source]¶

Update the vertical navigation slider for the given ax`

Set the vertical slider of the straditizer figure depending on the y-limits of it’s corresponding axes

Parameters: ax (matplotlib.axes.Axes) – The straditize.straditizer.Straditizer.ax attribute of a straditizer

window_layout_action = None¶: The action to switch_to_straditizer_layout()

straditize.widgets.pattern_selection module¶

A wdiget to select patterns in the image

The PatternSelectionWidget is used by the straditize.widget.selection_toolbar.SelectionToolbar to select patterns in the straditizer image

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`EmbededMplCanvas`([parent])	Ultimately, this is a QWidget (as well as a FigureCanvasAgg, etc.).
`PatternSelectionWidget`(arr, data_obj[, …])	A wdiget to select patterns in the image

class straditize.widgets.pattern_selection.EmbededMplCanvas(parent=None, *args, **kwargs)[source]¶

Bases: matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg

Ultimately, this is a QWidget (as well as a FigureCanvasAgg, etc.).

class straditize.widgets.pattern_selection.PatternSelectionWidget(arr, data_obj, remove_selection=False, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QWidget, psyplot_gui.common.DockMixin

A wdiget to select patterns in the image

This widget consist of an EmbededMplCanvas to display the template for the pattern and uses the skimage.feature.match_template() function to identify it in the arr

See also

straditize.widget.selection_toolbar.SelectionToolbar.start_pattern_selection

Attributes

`selector`	The selector to select the template in the original image
`sl_thresh`	A QSlider to set the threshold for the template correlation
`template`	The template to look for in the `arr`
`template_extents`	The extents of the `template` in the original image
`template_fig`	The `EmbededMplCanvas` to display the `template`
`template_im`	The matplotlib artist of the `template` in the

Methods

`cancel`()
`correlate_template`(arr, template[, …])	Correlate a template with the arr
`maybe_tabify`()
`modify_selection`(i)	Modify the selection based on the correlation threshold
`remove_plugin`()	Remove this plugin and close it
`start_correlation`()	Look for the correlations of template and source
`to_dock`(main[, title, position, docktype])
`toggle_correlation_plot`()	Toggle the correlation plot between `template` and `arr`
`toggle_selection`()	Modifiy the selection (or not) based on the template correlation
`toggle_template_selection`()	Enable or disable the template selection
`update_image`(args, *kwargs)	Update the template image based on the `selector` extents

Parameters

arr (np.ndarray of shape (Ny, Nx)) – The labeled selection array
data_obj (straditize.label_selection.LabelSelection) – The data object whose image shall be selected
remove_selection (bool) – If True, remove the selection on apply

axes = None¶

cancel()[source]¶

correlate_template(arr, template, fraction=False, increment=1, report=True)[source]¶

Correlate a template with the arr

This method uses the skimage.feature.match_template() function to find the given template in the source array arr.

Parameters

arr (np.ndarray of shape (Ny,Nx)) – The labeled selection array (see arr), the source of the given template
template (np.ndarray of shape (nx, ny)) – The template from arr that shall be searched
fraction (float) – If not null, we will look through the given fraction of the template to look for partial matches as well
increment (int) – The increment of the loop with the fraction.
report (bool) – If True and fraction is not null, a QProgressDialog is opened to inform the user about the progress

key_press_cid = None¶

maybe_tabify()[source]¶

modify_selection(i)[source]¶

Modify the selection based on the correlation threshold

Parameters: i (int) – An integer between 0 and 100, the value of the sl_thresh slider

remove_plugin()[source]¶: Remove this plugin and close it

selector = None¶: The selector to select the template in the original image

sl_thresh = None¶: A QSlider to set the threshold for the template correlation

start_correlation()[source]¶: Look for the correlations of template and source

template = None¶: The template to look for in the arr

template_extents = None¶: The extents of the template in the original image

template_fig = None¶: The EmbededMplCanvas to display the template

template_im = None¶: The matplotlib artist of the template in the template_fig

to_dock(main, title=None, position=None, docktype='df', *args, **kwargs)[source]¶

toggle_correlation_plot()[source]¶: Toggle the correlation plot between template and arr

toggle_selection()[source]¶: Modifiy the selection (or not) based on the template correlation

toggle_template_selection()[source]¶: Enable or disable the template selection

update_image(*args, **kwargs)[source]¶: Update the template image based on the selector extents

straditize.widgets.plots module¶

Plot control widgets for straditize

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`PlotControl`(straditizer_widgets, item, …)	A widget for controlling the plot
`PlotControlTable`(straditizer_widgets, *args, …)	A widget to control the plots
`ResultsPlot`(straditizer_widgets)	A widget for plotting the final results

class straditize.widgets.plots.PlotControl(straditizer_widgets, item, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, PyQt5.QtWidgets.QWidget

A widget for controlling the plot

This widgets holds a PlotControlTable to display visual diagnostics in the plot. Additionally it contains zoom buttons (btn_view_global and btn_view_data) and a widget to plot the results (results_plot)

Attributes

`btn_view_data`	A button to zoom to the data
`btn_view_global`	A button to zoom out to the entire stratigraphic diagram
`results_plot`	A `ResultsPlot` to plot the digitized data in a new diagram
`table`	A `PlotControlTable` to display visual diagnostics

Methods

`refresh`()	Refresh from the straditizer
`setup_children`(item)	Setup the children for this control
`zoom_data`()	Zoom to the data part
`zoom_global`()	Zoom out to the full straditgraphic diagram

btn_view_data = None¶: A button to zoom to the data (see zoom_data())

btn_view_global = None¶: A button to zoom out to the entire stratigraphic diagram (see zoom_global())

refresh()[source]¶: Refresh from the straditizer

results_plot = None¶: A ResultsPlot to plot the digitized data in a new diagram

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

table = None¶: A PlotControlTable to display visual diagnostics

zoom_data()[source]¶: Zoom to the data part

See also

straditize.straditizer.Straditizer.show_data_diagram()

zoom_global()[source]¶: Zoom out to the full straditgraphic diagram

See also

straditize.straditizer.Straditizer.show_full_image()

class straditize.widgets.plots.PlotControlTable(straditizer_widgets, *args, **kwargs)[source]¶

Bases: straditize.widgets.StraditizerControlBase, PyQt5.QtWidgets.QTableWidget

A widget to control the plots

This control widget is a table to plot, remove and toggle the visiblity of visual diagnostics for the straditizer. It has two columns: the first to toggle the visibility of the plot, the second to plot and remove the matplotlib artists. The vertical header are the items in the corresponding plot_funcs, can_be_plotted_funcs and/or hide_funcs.

Rows are added to this table using the add_item() method which stores the plotting functions in the plot_funcs and the functions to hide the plot in the hide_funcs. Whether an item can be plotted or not depends on the results of the corresponding callable in the can_be_plotted_funcs.

Methods

`add_item`(what, get_artists[, plot_func, …])	Add a plot object to the table
`adjust_height`()
`can_plot_column_starts`()	Test whether the column starts can be visualized
`can_plot_data_box`()	Test whether the box around the diagram part can be plotted
`can_plot_data_reader_color_image`()	Test if the reader color image can be plotted
`can_plot_full_df`()	Test whether the full_df can be plotted
`can_plot_potential_samples`()	Test whether potential sample regions can be plotted
`can_plot_sample_hlines`()	Test whether the sample lines can be plotted
`can_plot_samples`()	Test whether the samples can be plotted
`draw_figs`(artists)	Draw the figures of the given artists
`enable_or_disable_widgets`(b)	b is ignored and is always set to True
`get_column_start_lines`()	Get the artists of the column starts
`get_data_box`()	Get the plotted `straditize.straditizer.Straditizer.data_box`
`get_data_reader_background`()	Get the `straditize.binary.DataReader.background`
`get_data_reader_color_image`()	Get the `straditize.binary.DataReader.color_plot_im`
`get_data_reader_image`()	Get the `straditize.binary.DataReader.plot_im`
`get_full_df_lines`()	Get the artists of the full_df plot
`get_potential_samples_lines`()	Get the artists of the plot of potential samples
`get_sample_hlines`()	Get the plotted the sample lines
`get_samples_lines`()	Get the artists of the plotted samples
`get_straditizer_image`()	Get the `straditize.straditizer.Straditizer.plot_im`
`plot_column_starts`()	Plot horizontal lines for the column starts
`plot_data_box`()	Plot the data box around the diagram part
`plot_data_reader_color_image`()	Plot the data reader color image
`plot_full_df`()	Plot the `full_df` of the reader
`plot_potential_samples`()	Highlight the regions with potential samples in the plot
`plot_sample_hlines`()	Plot the horizontal sample lines of the reader
`plot_samples`()	Plot the samples of the reader
`refresh`()	Refresh from the straditizer
`remove_column_starts`()	Remove the plotted lines of the column starts
`remove_data_box`()	Remove the box around the diagram part
`remove_data_reader_color_image`()	Remove the `straditize.binary.DataReader.color_plot_im`
`remove_full_df_plot`()	Remove the plot of the full_df
`remove_potential_samples_plot`()	Remove the plot of potential samples
`remove_sample_hlines_plot`()	Remove the sample lines
`remove_samples_plot`()	Remove the plotted samples
`should_be_enabled`(w)	Check if a widget should be enabled
`sizeHint`(self)

Attributes

`can_be_plotted_funcs`	A mapping from plot identifier to a callable that returns `True` if
`col_lines`	Built-in mutable sequence.
`hide_funcs`	A mapping from plot identifier to a callable to hide the corresponding
`plot_funcs`	A mapping from plot identifier to a callable to plot the corresponding
`widgets2disable`	Built-in mutable sequence.

add_item(what, get_artists, plot_func=None, remove_func=None, can_be_plotted=None)[source]¶

Add a plot object to the table

Parameters

what (str) – The description of the plot object
get_artists (function) – A function that takes no arguments and returns the artists
plot_func (function, optional) – A function that takes no arguments and makes the plot.
remove_func (function, optional) – A function that takes no arguments and removes the plot.
can_be_plotted (function, optional) – A function that takes no argument and returns True if the plot can be made.

adjust_height()[source]¶

can_be_plotted_funcs = {}¶: A mapping from plot identifier to a callable that returns True if the corresponding function in the plot_funcs mapping can be called

can_plot_column_starts()[source]¶: Test whether the column starts can be visualized

See also

plot_column_starts()

can_plot_data_box()[source]¶: Test whether the box around the diagram part can be plotted

See also

plot_data_box()

can_plot_data_reader_color_image()[source]¶: Test if the reader color image can be plotted

See also

plot_data_reader_color_image()

can_plot_full_df()[source]¶: Test whether the full_df can be plotted

See also

plot_full_df()

can_plot_potential_samples()[source]¶: Test whether potential sample regions can be plotted

See also

plot_potential_samples()

can_plot_sample_hlines()[source]¶: Test whether the sample lines can be plotted

See also

plot_sample_hlines()

can_plot_samples()[source]¶: Test whether the samples can be plotted

See also

plot_samples()

col_lines = []¶

draw_figs(artists)[source]¶

Draw the figures of the given artists

Parameters: artists (list of matplotlib.artist.Artist) – The artists to draw the canvas from

enable_or_disable_widgets(b)[source]¶: b is ignored and is always set to True

get_column_start_lines()[source]¶: Get the artists of the column starts

See also

plot_column_starts()

get_data_box()[source]¶: Get the plotted straditize.straditizer.Straditizer.data_box

See also

plot_data_box()

get_data_reader_background()[source]¶: Get the straditize.binary.DataReader.background

get_data_reader_color_image()[source]¶: Get the straditize.binary.DataReader.color_plot_im

See also

plot_data_reader_color_image()

get_data_reader_image()[source]¶: Get the straditize.binary.DataReader.plot_im

get_full_df_lines()[source]¶: Get the artists of the full_df plot

See also

plot_full_df()

get_potential_samples_lines()[source]¶: Get the artists of the plot of potential samples

See also

plot_potential_samples()

get_sample_hlines()[source]¶: Get the plotted the sample lines

See also

plot_sample_hlines()

get_samples_lines()[source]¶: Get the artists of the plotted samples

See also

plot_samples()

get_straditizer_image()[source]¶: Get the straditize.straditizer.Straditizer.plot_im

hide_funcs = {}¶: A mapping from plot identifier to a callable to hide the corresponding artists

plot_column_starts()[source]¶: Plot horizontal lines for the column starts

See also

remove_column_starts(), get_column_start_lines(), can_plot_column_starts()

plot_data_box()[source]¶: Plot the data box around the diagram part

See also

straditize.straditizer.Straditizer.draw_data_box(), remove_data_box(), get_data_box(), can_plot_data_box()

plot_data_reader_color_image()[source]¶: Plot the data reader color image

See also

straditize.binary.DataReader.plot_color_image(), remove_data_reader_color_image(), can_plot_data_reader_color_image()

plot_full_df()[source]¶: Plot the full_df of the reader

See also

straditize.binary.DataReader.plot_full_df(), remove_full_df_plot(), get_full_df_lines(), can_plot_full_df()

plot_funcs = {}¶: A mapping from plot identifier to a callable to plot the corresponding artists

plot_potential_samples()[source]¶: Highlight the regions with potential samples in the plot

See also

straditize.binary.DataReader.plot_potential_samples(), remove_potential_samples_plot(), get_potential_samples_lines(), can_plot_potential_samples()

plot_sample_hlines()[source]¶: Plot the horizontal sample lines of the reader

See also

straditize.binary.DataReader.plot_sample_hlines(), remove_sample_hlines_plot(), get_sample_hlines(), can_plot_sample_hlines()

plot_samples()[source]¶: Plot the samples of the reader

See also

straditize.binary.DataReader.plot_samples(), remove_samples_plot(), get_samples_lines(), can_plot_samples()

refresh()[source]¶: Refresh from the straditizer

remove_column_starts()[source]¶: Remove the plotted lines of the column starts

See also

plot_column_starts()

remove_data_box()[source]¶: Remove the box around the diagram part

See also

plot_data_box()

remove_data_reader_color_image()[source]¶: Remove the straditize.binary.DataReader.color_plot_im

See also

plot_data_reader_color_image()

remove_full_df_plot()[source]¶: Remove the plot of the full_df

See also

plot_full_df()

remove_potential_samples_plot()[source]¶: Remove the plot of potential samples

See also

plot_potential_samples()

remove_sample_hlines_plot()[source]¶: Remove the sample lines

See also

plot_sample_hlines()

remove_samples_plot()[source]¶: Remove the plotted samples

See also

plot_samples()

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

sizeHint(self) → QSize[source]¶

property widgets2disable¶

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

class straditize.widgets.plots.ResultsPlot(straditizer_widgets)[source]¶

Bases: straditize.widgets.StraditizerControlBase

A widget for plotting the final results

This widgets contains a QPushButton btn_plot to plot the results using the straditize.binary.DataReader.plot_results() method

Attributes

`btn_plot`	The QPushButton to call the `plot_results()` method
`cb_final`	A QCheckBox whether the samples or the full digitized data shall be
`cb_transformed`	A QCheckBox whether x- and y-axis should be translated from pixel to

Methods

`plot_results`()	Plot the results
`refresh`()	Refresh from the straditizer
`setup_children`(item)	Setup the children for this control

btn_plot = None¶: The QPushButton to call the plot_results() method

cb_final = None¶: A QCheckBox whether the samples or the full digitized data shall be plotted

cb_transformed = None¶: A QCheckBox whether x- and y-axis should be translated from pixel to data units

plot_results()[source]¶

Plot the results

What is plotted depends on the cb_transformed and the cb_final

cb_transformed and cb_final are checked: Plot the straditize.straditizer.Straditizer.final_df
cb_transformed is checked but not cb_final: Plot the straditize.straditizer.Straditizer.full_df
cb_transformed is not checked but cb_final: Plot the straditize.binary.DataReader.sample_locs
cb_transformed and cb_final are both not checked: Plot the straditize.binary.DataReader.full_df

refresh()[source]¶: Refresh from the straditizer

setup_children(item)[source]¶

Setup the children for this control

This method is called to setup the children in the StraditizerWidgets.tree. By default, it just creates a child QTreeWidgetItem and sets this control as it’s widget

Parameters: item (QTreeWidgetItem) – The top level item in the StraditizerWidgets.tree

straditize.widgets.progress_widget module¶

Progress widget for the straditization

The ProgressWidget defined here is a ListWidget to show the current state of the stradititization.

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`ColumnNamesTask`(parent)	Task to specify the column names
`ColumnsTask`(parent)	A task to separate columns
`DataLimitsTask`(parent)	The task to check the data limits
`DataReaderTask`(parent)	A task for initializing the reader
`DigitizeTask`(parent)	Task to digitize the data
`ExportTask`(parent)	Task to export the final results
`InitStraditizerTask`(parent)	The task to initialize a straditizer
`OccurencesTask`(parent)	Task to handle occurences
`ProgressTask`(parent)	The base class for an item that should be shown in the ProgressWidget
`ProgressWidget`(straditizer_widgets, item)	A widget to show the progress of the straditization
`RemoveArtifactsTask`(parent)	Task to clean the binary image
`RemoveLinesTask`(parent)	Task to remove y-axes, x-axes, horizontal and vertical lines
`SamplesTask`(parent)	Task to edit and find samples
`SaveProjectTask`(args, *kwargs)	Task to remember saving the project
`SelectExaggerationsTask`(parent)	Task to handle exaggerations
`XTranslationTask`(parent)	Task to specify the x-axes conversion from pixel to data units
`YTranslationTask`(parent)	Task to specify the y-axis conversion from pixel to data units

class straditize.widgets.progress_widget.ColumnNamesTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to specify the column names

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'column_names'¶

rst_file = 'column_names'¶

summary = 'Specify the column names'¶

task_tooltip = 'Click the Edit column names button to insert the names for each column/variable in the diagram'¶

class straditize.widgets.progress_widget.ColumnsTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

A task to separate columns

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['init_reader']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'columns'¶

rst_file = 'select_column_starts'¶

summary = 'Separate the columns'¶

task_tooltip = 'Separate the columns (subdiagrams) of the stratigraphic diagram'¶

class straditize.widgets.progress_widget.DataLimitsTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

The task to check the data limits

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['init_stradi']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'datalim'¶

rst_file = 'select_data_part'¶

summary = 'Limits of the diagram'¶

task_tooltip = 'Specify the corners for the data part of the diagram by clicking the Select data part button'¶

class straditize.widgets.progress_widget.DataReaderTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

A task for initializing the reader

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['datalim']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'init_reader'¶

rst_file = 'select_reader'¶

summary = 'Initialize the diagram reader'¶

task_tooltip = 'Choose the appropriate reader type and click the Convert image button'¶

class straditize.widgets.progress_widget.DigitizeTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to digitize the data

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['remove_artifacts', 'remove_lines']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'digitize'¶

rst_file = 'digitize'¶

summary = 'Digitize the diagram'¶

task_tooltip = 'Click the `Digitize` button to digitize the diagram'¶

class straditize.widgets.progress_widget.ExportTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to export the final results

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['samples']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'export'¶

rst_file = 'export'¶

summary = 'Export the final data'¶

task_tooltip = 'Export the final data'¶

class straditize.widgets.progress_widget.InitStraditizerTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

The task to initialize a straditizer

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'init_stradi'¶

rst_file = 'load_image'¶

summary = 'Load an image or project'¶

task_tooltip = 'Load a straditizer image or project to get started'¶

class straditize.widgets.progress_widget.OccurencesTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to handle occurences

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'occurences'¶

rst_file = 'occurences'¶

summary = 'Select occurence markers'¶

task_tooltip = 'Pollen diagrams often have markers for low taxon percentages to show their occurence. Mark this task as done if your diagram does not have them.'¶

class straditize.widgets.progress_widget.ProgressTask(parent)[source]¶

Bases: PyQt5.QtWidgets.QListWidgetItem

The base class for an item that should be shown in the ProgressWidget

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`colnames_reader`	The column names reader of the straditizer
`data_reader`	The data reader of the straditizer
`dependencies`	List of `name` attributes that this task is depending on
`dependencies_tasks`
`done`	True if `is_finished` or `done_by_user`
`done_by_user`	boolean that is True, when the task is marked as done by the user
`done_tooltip`	The tooltip when the straditizer is done.
`is_finished`	A property that is True, when the task is finished
`is_ready`	Boolean that is True if the task is ready to be solved
`name`	The name of the task
`progress_widget`	The progress widget that shows this task
`rst_file`	rst file that should be displayed on double click. The filename shuould
`state`	The state of the task
`straditizer`	The straditizer of the GUI
`summary`	The summary of the task that is shown in the progress widget
`task_tooltip`	The tooltip of the item
`try_finished`	Same as `is_finished` but catches every exception

Methods

refresh()

property colnames_reader¶: The column names reader of the straditizer

property data_reader¶: The data reader of the straditizer

dependencies = []¶: List of name attributes that this task is depending on

property dependencies_tasks¶

property done¶: True if is_finished or done_by_user

property done_by_user¶: boolean that is True, when the task is marked as done by the user

done_tooltip = None¶: The tooltip when the straditizer is done. If None, the task_tooltip is used

property is_finished¶: A property that is True, when the task is finished

is_ready = True¶: Boolean that is True if the task is ready to be solved

name = ''¶: The name of the task

property progress_widget¶: The progress widget that shows this task

refresh()[source]¶

rst_file = None¶: rst file that should be displayed on double click. The filename shuould be without .rst ending.

property state¶: The state of the task

property straditizer¶: The straditizer of the GUI

summary = ''¶: The summary of the task that is shown in the progress widget

task_tooltip = ''¶: The tooltip of the item

property try_finished¶: Same as is_finished but catches every exception

class straditize.widgets.progress_widget.ProgressWidget(straditizer_widgets, item)[source]¶

Bases: PyQt5.QtWidgets.QWidget, straditize.widgets.StraditizerControlBase

A widget to show the progress of the straditization

Attributes

`combo_display`	A QComboBox to select which tasks to display (todo, done, not yet ready
`info_label`	A QLabel to display the tooltip of the selected task
`progress_list`	A QListWidget to display the `ProgressTask` instances

Methods

`contextMenuEvent`(event)	Reimplement Qt method
`populate_list`()	Populate the `progress_list`
`refresh`()	Refresh from the straditizer
`setup_menu`()	Set up the context menu
`show_rst`(item[, old])	Show the documentation corresponding to a :class:`ProgressTask
`toggle_done_by_user`()
`update_info_label`(item[, old])	Update the `info_label` from a `ProgressTask`

combo_display = None¶: A QComboBox to select which tasks to display (todo, done, not yet ready or all tasks)

contextMenuEvent(event)[source]¶: Reimplement Qt method

info_label = None¶: A QLabel to display the tooltip of the selected task

populate_list()[source]¶

Populate the progress_list

This method adds instances of the ProgressTask class (or it’s subclasses) to the the progress_list

progress_list = None¶: A QListWidget to display the ProgressTask instances

refresh()[source]¶: Refresh from the straditizer

setup_menu()[source]¶: Set up the context menu

show_rst(item, old=None)[source]¶

Show the documentation corresponding to a :class:`ProgressTask

Parameters

item (ProgressTask) – The task to display it’s rst_file in the psyplot_gui.main.MainWindow.help_explorer
old (ProgressTask) – The old task that has been selected previously (this parameter is ignored)

toggle_done_by_user()[source]¶

update_info_label(item, old=None)[source]¶

Update the info_label from a ProgressTask

Parameters

item (ProgressTask) – The selected task whose tooltip the info_label shall display
old (ProgressTask) – The old task that has been selected previously (this parameter is ignored)

class straditize.widgets.progress_widget.RemoveArtifactsTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to clean the binary image

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'remove_artifacts'¶

rst_file = 'removing_features'¶

summary = 'Clean the diagram image'¶

task_tooltip = 'Remove all artifacts in the diagram part that do not represent data. Mark this task as done when you are finished.'¶

class straditize.widgets.progress_widget.RemoveLinesTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.RemoveArtifactsTask

Task to remove y-axes, x-axes, horizontal and vertical lines

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'remove_lines'¶

rst_file = 'remove_lines'¶

summary = 'Remove y-, x-axes and other lines'¶

task_tooltip = 'In order to clean the diagram part, remove all vertical and horizontal lines in the reader image. Mark this task as done when you are finished.'¶

class straditize.widgets.progress_widget.SamplesTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to edit and find samples

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['digitize']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'samples'¶

rst_file = 'samples'¶

summary = 'Find and edit the samples'¶

task_tooltip = 'Find and edit the samples using the `Edit samples` menu'¶

class straditize.widgets.progress_widget.SaveProjectTask(*args, **kwargs)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to remember saving the project

Attributes

`dependencies`	Built-in mutable sequence.
`is_finished`	True if the project was saved less than 5 minutes ago
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

Methods

`refresh_tooltip`()
`tooltip`()

dependencies = ['init_stradi']¶

property is_finished¶: True if the project was saved less than 5 minutes ago

name = 'save_project'¶

refresh_tooltip()[source]¶

rst_file = 'save_and_load'¶

summary = 'Save the straditizer'¶

property task_tooltip¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to ‘strict’.

tooltip()[source]¶

class straditize.widgets.progress_widget.SelectExaggerationsTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to handle exaggerations

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'exag'¶

rst_file = 'exaggerations'¶

summary = 'Select exagerations'¶

task_tooltip = 'Pollen diagrams often display an exaggerated value of of the taxon percentage. You can select these exaggerations using the Exaggerations menu. Mark this task as done if your diagram does not have them.'¶

class straditize.widgets.progress_widget.XTranslationTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to specify the x-axes conversion from pixel to data units

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['columns']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'xaxes_trans'¶

rst_file = 'xaxis_translation'¶

summary = 'Transform the x-axes'¶

property task_tooltip¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to ‘strict’.

class straditize.widgets.progress_widget.YTranslationTask(parent)[source]¶

Bases: straditize.widgets.progress_widget.ProgressTask

Task to specify the y-axis conversion from pixel to data units

Parameters: parent (QListWidget) – The list that holds this item

Attributes

`dependencies`	Built-in mutable sequence.
`done_tooltip`
`is_finished`	A property that is True, when the task is finished
`name`	str(object=’’) -> str
`rst_file`	str(object=’’) -> str
`summary`	str(object=’’) -> str
`task_tooltip`	str(object=’’) -> str

dependencies = ['init_reader']¶

property done_tooltip¶

property is_finished¶: A property that is True, when the task is finished

name = 'yaxis_trans'¶

rst_file = 'yaxis_translation'¶

summary = 'Transform the y-axis'¶

task_tooltip = 'Transform the y-axis from pixel to data units'¶

straditize.widgets.samples_table module¶

A table for manipulating samples

This module defines the sample editors, either for editing the samples in the straditizer image (SingleCrossMarksEditor) or in a separate Figure (MultiCrossMarksEditor)

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`MultiCrossMarksEditor`(straditizer[, axes])	An editor for cross marks in multiple axes
`MultiCrossMarksModel`(marks, columns, straditizer)	A table model to handle multiple connected cross marks in different axes
`MultiCrossMarksView`(marks, full_df, *args, …)	A table view set up by cross marks from multiple axes
`SingleCrossMarksEditor`(straditizer[, axes])	The editor for cross marks on a single axes
`SingleCrossMarksModel`(args, *kwargs)	A table model to handle cross marks within one single axis
`SingleCrossMarksView`(marks, full_df, *args, …)	A table for visualizing marks from a single axes

class straditize.widgets.samples_table.MultiCrossMarksEditor(straditizer, axes=None, *args, **kwargs)[source]¶

Bases: psyplot_gui.common.DockMixin, PyQt5.QtWidgets.QWidget

An editor for cross marks in multiple axes

Parameters

straditizer (weakref.ref) – The reference to the straditizer
axes (matplotlib.axes.Axes) – The matplotlib axes corresponding to the marks

Attributes

`cb_plot_lines`	Plot the reconstructed data
`straditizer`	A `weakref` to the

Methods

`create_view`([axes])	Create the `MultiCrossMarksView` of the editor
`maybe_show_selection_only`()
`maybe_tabify`()
`maybe_zoom_to_selection`()
`save_samples`()	Save the samples to the `straditizer` without removing them
`to_dock`(main[, title, position, docktype])
`toggle_cb_selection_only`()
`toggle_cb_zoom_to_selection`()
`toggle_fit2selection`()	Enable the fitting so selected digitized data
`toggle_fmt_button`(text)
`toggle_plot_lines`()
`update_format`()	Update the format of the table

Miscallaneous

dock_cls

The QDockWidget for the DataFrameEditor

cb_plot_lines = None¶: Plot the reconstructed data

create_view(axes=None)[source]¶

Create the MultiCrossMarksView of the editor

Parameters: axes (list of matplotlib.axes.Axes) – The matplotlib axes for the marks

dock_cls¶: alias of psyplot_gui.dataframeeditor.DataFrameDock

maybe_show_selection_only()[source]¶

maybe_tabify()[source]¶

maybe_zoom_to_selection()[source]¶

save_samples()[source]¶: Save the samples to the straditizer without removing them

straditizer = None¶: A weakref to the straditizer

to_dock(main, title=None, position=None, docktype='df', *args, **kwargs)[source]¶

toggle_cb_selection_only()[source]¶

toggle_cb_zoom_to_selection()[source]¶

toggle_fit2selection()[source]¶: Enable the fitting so selected digitized data

toggle_fmt_button(text)[source]¶

toggle_plot_lines()[source]¶

update_format()[source]¶: Update the format of the table

class straditize.widgets.samples_table.MultiCrossMarksModel(marks, columns, straditizer, axes=None, occurences_value=-9999)[source]¶

Bases: PyQt5.QtCore.QAbstractTableModel

A table model to handle multiple connected cross marks in different axes

Parameters

marks (list of straditize.cross_mark.CrossMarks) – the initial marks
columns (list of str) – the column names to use
straditizer (straditize.straditizer.Straditizer) – The straditizer that manages the marks
axes (list of matplotlib.axes.Axes) – The matplotlib axes that contain the marks
occurences_value (float) – The value that marks an occurence

Methods

`columnCount`([index])	The number of rows in the table
`data`(self, QModelIndex, role)
`delRow`(irow)
`flags`(index)	Set flags
`get_cell_mark`(row, column)	Get the mark for a given cell in the table
`get_format`()	Return current format
`headerData`(section, orientation[, role])	Set header data
`insertRow`(irow[, xa, ya])	Insert a row into the table
`load_new_marks`(mark)	Add a new mark into the table after they have been added by the user
`plot_lines`()	Connect the samples through visual `lines`
`remove_lines`()	Remove the `lines`
`remove_mark`(mark)	Remove a mark from the table after it has been removed by the user
`reset`()	Reset the model
`rowCount`([index])	The number of rows in the table
`setData`(index, value[, role, change_type])	Cell content change
`set_format`(fmt)	Change display format
`set_marks`(marks, columns)	Set the `marks` attribute from the given columns
`sort_marks`()	Sort the marks based on there y-position
`update_after_move`(old_pos, mark)
`update_lines`()	Update the `lines` or plot them

Attributes

`df`	Get the samples_locs
`fig`	The figure of the cross marks
`iter_marks`	Iter over all marks in the `marks` attribute
`lines`	A list of `matplotlib.lines.Line2D` that connects the cross marks
`marks`	A list `[float, list]` where the first `float` is the vertical

columnCount(index=<PyQt5.QtCore.QModelIndex object>)[source]¶: The number of rows in the table

data(self, QModelIndex, role: int = Qt.DisplayRole) → Any[source]¶

delRow(irow)[source]¶

property df¶: Get the samples_locs

property fig¶: The figure of the cross marks

flags(index)[source]¶: Set flags

get_cell_mark(row, column)[source]¶

Get the mark for a given cell in the table

Parameters

row (int) – The row of the cell
column (int) – The column of the cell

Returns

The corresponding mark from the marks attribute

Return type

straditize.cross_mark.CrossMarks

get_format()[source]¶: Return current format

headerData(section, orientation, role=0)[source]¶: Set header data

insertRow(irow, xa=None, ya=None)[source]¶

Insert a row into the table

Parameters: irow (int) – The row index. If irow is equal to the length of the marks, the rows will be appended

property iter_marks¶: Iter over all marks in the marks attribute

lines = []¶: A list of matplotlib.lines.Line2D that connects the cross marks and plots a reconstruction based on them

load_new_marks(mark)[source]¶

Add a new mark into the table after they have been added by the user

Parameters: mark (straditize.cross_mark.CrossMarks) – The added mark

marks = []¶: A list [float, list] where the first float is the vertical position and the second list is a list of the corresponding CrossMarks instances

plot_lines()[source]¶: Connect the samples through visual lines

remove_lines()[source]¶: Remove the lines

remove_mark(mark)[source]¶

Remove a mark from the table after it has been removed by the user

Parameters: mark (straditize.cross_mark.CrossMarks) – The removed mark

reset()[source]¶: Reset the model

rowCount(index=<PyQt5.QtCore.QModelIndex object>)[source]¶: The number of rows in the table

setData(index, value, role=2, change_type=None)[source]¶: Cell content change

set_format(fmt)[source]¶: Change display format

set_marks(marks, columns)[source]¶

Set the marks attribute from the given columns

Parameters

marks (list of straditize.cross_mark.CrossMarks) – the initial marks
columns (list of str) – the column names to use

sort_marks()[source]¶: Sort the marks based on there y-position

update_after_move(old_pos, mark)[source]¶

update_lines()[source]¶: Update the lines or plot them

See also

plot_lines()

class straditize.widgets.samples_table.MultiCrossMarksView(marks, full_df, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QTableView

A table view set up by cross marks from multiple axes

The model for this table is the MultiCrossMarksModel

Parameters

marks (list of straditize.cross_mark.CrossMarks) – the initial marks
full_df (pandas.DataFrame) – The data fame of the full digitized data

Methods

`contextMenuEvent`(event)	Reimplement Qt method
`delete_selected_rows`()
`fit2data`()	Fit the selected cells to the `full_df`
`init_model`(marks, args, *kwargs)	Initialize the table `MultiCrossMarksModel`
`insert_row_above_selection`()	Insert a row above the selection
`insert_row_below_selection`()	Insert a row below the selection
`moveCursor`(cursor_action, modifiers)	Update the table position.
`resizeEvent`(event)	Update the frozen column dimensions.
`scrollTo`(index, hint)	Scroll the table.
`setup_menu`()	Setup context menu
`show_all_marks`()	Show all marks
`show_selected_marks_only`()	Show only the marks selected in the table
`update_section_height`(logical_index, …)	Update the vertical width of the frozen column when a
`update_section_width`(logical_index, …)	Update the horizontal width of the frozen column when a
`zoom_to_cells`(rows, cols)	Zoom to specific cells in the plot
`zoom_to_selection`()	Zoom to the selected cells in the plot

Attributes

full_df

The pandas.DataFrame representing the full digitized data

contextMenuEvent(event)[source]¶: Reimplement Qt method

delete_selected_rows()[source]¶

fit2data()[source]¶: Fit the selected cells to the full_df

full_df = None¶: The pandas.DataFrame representing the full digitized data from the straditize.binary.DataReader.full_df data frame

init_model(marks, *args, **kwargs)[source]¶: Initialize the table MultiCrossMarksModel

insert_row_above_selection()[source]¶: Insert a row above the selection

insert_row_below_selection()[source]¶: Insert a row below the selection

moveCursor(cursor_action, modifiers)[source]¶

Update the table position.

Updates the position along with the frozen column when the cursor (selector) changes its position

resizeEvent(event)[source]¶

Update the frozen column dimensions.

Updates takes place when the enclosing window of this table reports a dimension change

scrollTo(index, hint)[source]¶

Scroll the table.

It is necessary to ensure that the item at index is visible. The view will try to position the item according to the given hint. This method does not takes effect only if the frozen column is scrolled.

setup_menu()[source]¶: Setup context menu

show_all_marks()[source]¶: Show all marks

See also

show_selected_marks_only()

show_selected_marks_only()[source]¶: Show only the marks selected in the table

See also

show_all_marks()

update_section_height(logical_index, old_size, new_size)[source]¶: Update the vertical width of the frozen column when a change takes place on any of the rows

update_section_width(logical_index, old_size, new_size)[source]¶: Update the horizontal width of the frozen column when a change takes place in the first column of the table

zoom_to_cells(rows, cols)[source]¶

Zoom to specific cells in the plot

Parameters

rows (list of int) – The row indices of the cells
cols (list of int) – The column indicies of the cells

zoom_to_selection()[source]¶: Zoom to the selected cells in the plot

class straditize.widgets.samples_table.SingleCrossMarksEditor(straditizer, axes=None, *args, **kwargs)[source]¶

Bases: straditize.widgets.samples_table.MultiCrossMarksEditor

The editor for cross marks on a single axes

Parameters

straditizer (weakref.ref) – The reference to the straditizer
axes (matplotlib.axes.Axes) – The matplotlib axes corresponding to the marks

Methods

`create_view`([axes])	Create the `SingleCrossMarksView` of the editor
`save_samples`()	Save the samples to the `straditizer` without removing them

create_view(axes=None)[source]¶

Create the SingleCrossMarksView of the editor

Parameters: axes (list of matplotlib.axes.Axes) – The matplotlib axes for the marks

save_samples()[source]¶: Save the samples to the straditizer without removing them

class straditize.widgets.samples_table.SingleCrossMarksModel(*args, **kwargs)[source]¶

Bases: straditize.widgets.samples_table.MultiCrossMarksModel

A table model to handle cross marks within one single axis

Parameters

column_bounds (np.ndarray of shape (N, 2)) – The column boundaries
y0 (float) – The upper extent of the data image

Methods

`columnCount`([index])	The number of rows in the table
`delRow`(irow)
`get_cell_mark`(row, column)	Get the mark for a given cell in the table
`insertRow`(irow[, xa, ya])	Insert a row into the table
`load_new_marks`(mark)	Add a new mark into the table after they have been added by the user
`plot_lines`()	Connect the samples through visual `lines`
`remove_mark`(mark)	Remove a mark from the table after it has been removed by the user
`set_marks`(marks, columns)	Set the `marks` attribute from the given columns
`update_after_move`(old_pos, mark)
`update_lines`()	Update the `lines` or plot them

Attributes

`df`	Get the samples_locs
`iter_marks`	Iter over all marks in the `marks` attribute
`marks`	A list of tuples like `(float, mark)` where `float` is the y-pixel

columnCount(index=<PyQt5.QtCore.QModelIndex object>)[source]¶: The number of rows in the table

delRow(irow)[source]¶

property df¶: Get the samples_locs

get_cell_mark(row, column)[source]¶

Get the mark for a given cell in the table

Parameters

row (int) – The row of the cell
column (int) – The column of the cell

Returns

The corresponding mark from the marks attribute

Return type

straditize.cross_mark.CrossMarks

insertRow(irow, xa=None, ya=None)[source]¶

Insert a row into the table

Parameters: irow (int) – The row index. If irow is equal to the length of the marks, the rows will be appended

property iter_marks¶: Iter over all marks in the marks attribute

load_new_marks(mark)[source]¶

Add a new mark into the table after they have been added by the user

Parameters: mark (straditize.cross_mark.CrossMarks) – The added mark

marks = []¶: A list of tuples like (float, mark) where float is the y-pixel and mark is the corresponding straditize.cross_mark.CrossMarks instance

plot_lines()[source]¶: Connect the samples through visual lines

remove_mark(mark)[source]¶

Remove a mark from the table after it has been removed by the user

Parameters: mark (straditize.cross_mark.CrossMarks) – The removed mark

set_marks(marks, columns)[source]¶

Set the marks attribute from the given columns

Parameters

marks (list of straditize.cross_mark.CrossMarks) – the initial marks
columns (list of str) – the column names to use

update_after_move(old_pos, mark)[source]¶

update_lines()[source]¶: Update the lines or plot them

See also

plot_lines()

class straditize.widgets.samples_table.SingleCrossMarksView(marks, full_df, *args, **kwargs)[source]¶

Bases: straditize.widgets.samples_table.MultiCrossMarksView

A table for visualizing marks from a single axes

Parameters

marks (list of straditize.cross_mark.CrossMarks) – the initial marks
full_df (pandas.DataFrame) – The data fame of the full digitized data

Methods

`fit2data`()	Fit the selected cells to the `full_df`
`init_model`(marks, args, *kwargs)	Initialize the table `SingleCrossMarksModel`
`zoom_to_cells`(rows, cols)	Zoom to specific cells in the plot

fit2data()[source]¶: Fit the selected cells to the full_df

init_model(marks, *args, **kwargs)[source]¶

Initialize the table SingleCrossMarksModel

Parameters: %(SingleCrossMarksModel.parameters)s –

zoom_to_cells(rows, cols)[source]¶

Zoom to specific cells in the plot

Parameters

rows (list of int) – The row indices of the cells
cols (list of int) – The column indicies of the cells

straditize.widgets.selection_toolbar module¶

Module for the selection toolbar

This module defines the selection toolbar that is added to the psyplot_gui.main.MainWindow for selecting features in the stratigraphic diagram and the data reader image.

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`PointOrRectangleSelector`(ax, onselect[, …])	RectangleSelector that allows to select points
`SelectionToolbar`(straditizer_widgets, *args, …)	A toolbar for selecting features in the straditizer and data image

class straditize.widgets.selection_toolbar.PointOrRectangleSelector(ax, onselect, drawtype='box', minspanx=None, minspany=None, useblit=False, lineprops=None, rectprops=None, spancoords='data', button=None, maxdist=10, marker_props=None, interactive=False, state_modifier_keys=None)[source]¶

Bases: matplotlib.widgets.RectangleSelector

RectangleSelector that allows to select points

This class reimplements the matplotlib.widgets.RectangleSelector to select points

Create a selector in ax. When a selection is made, clear the span and call onselect with:

onselect(pos_1, pos_2)

Methods

press(*args, **kwargs)

Button press handler and validator

and clear the drawn box/line. The pos_1 and pos_2 are arrays of length 2 containing the x- and y-coordinate.

If minspanx is not None then events smaller than minspanx in x direction are ignored (it’s the same for y).

The rectangle is drawn with rectprops; default:

rectprops = dict(facecolor='red', edgecolor = 'black',
                 alpha=0.2, fill=True)

The line is drawn with lineprops; default:

lineprops = dict(color='black', linestyle='-',
                 linewidth = 2, alpha=0.5)

Use drawtype if you want the mouse to draw a line, a box or nothing between click and actual position by setting

drawtype = 'line', drawtype='box' or drawtype = 'none'. Drawing a line would result in a line from vertex A to vertex C in a rectangle ABCD.

spancoords is one of ‘data’ or ‘pixels’. If ‘data’, minspanx and minspanx will be interpreted in the same coordinates as the x and y axis. If ‘pixels’, they are in pixels.

button is a list of integers indicating which mouse buttons should be used for rectangle selection. You can also specify a single integer if only a single button is desired. Default is None, which does not limit which button can be used.

Note, typically:: 1 = left mouse button 2 = center mouse button (scroll wheel) 3 = right mouse button

interactive will draw a set of handles and allow you interact with the widget after it is drawn.

state_modifier_keys are keyboard modifiers that affect the behavior of the widget.

The defaults are: dict(move=’ ‘, clear=’escape’, square=’shift’, center=’ctrl’)

Keyboard modifiers, which: ‘move’: Move the existing shape. ‘clear’: Clear the current shape. ‘square’: Makes the shape square. ‘center’: Make the initial point the center of the shape. ‘square’ and ‘center’ can be combined.

press(*args, **kwargs)[source]¶: Button press handler and validator

class straditize.widgets.selection_toolbar.SelectionToolbar(straditizer_widgets, *args, **kwargs)[source]¶

Bases: PyQt5.QtWidgets.QToolBar, straditize.widgets.StraditizerControlBase

A toolbar for selecting features in the straditizer and data image

The current data object is set in the combo and can be accessed through the data_obj attribute. It’s either the straditizer or the data_reader that is accessed

Methods

`add_or_remove_pattern`()	Enable the removing or adding of the pattern selection
`clear_selection`()	Clear the current selection
`create_actions`()	Define the actions for the toolbar and set everything up
`disable_actions`()
`disconnect`(self)
`enable_or_disable_widgets`(b)	Enable or disable the widgets in this control
`end_selection`()	Finish the selection and disconnect everything
`expand_selection`()	Expand the selected areas to select the full labels
`get_xy_slice`(lastx, lasty, x, y)	Transform x- and y-coordinates to `slice` objects
`invert_selection`()	Invert the current selection
`on_poly_select`(points)	Call the `poly_callbacks` after a polygon selection
`on_rect_select`(e0, e1)	Call the `rect_callbacks` after a rectangle selection
`refresh`()	Refresh from the straditizer
`select_all`()	Select all features in the image
`select_everything_to_the_right`()	Selects everything to the right of the current selection
`select_poly`(points)	Select the data defined by a polygon
`select_rect`(slx, sly)	Select the data defined by a rectangle
`set_binary_pattern_mode`()	Set the current pattern mode to the binary pattern
`set_col_wand_mode`()	Set the current wand tool to the color wand
`set_color_wand_mode`()	Set the current wand tool to the color wand
`set_grey_pattern_mode`()	Set the current pattern mode to the binary pattern
`set_label_wand_mode`()	Set the current wand tool to the color wand
`set_poly_select_mode`()	Set the current wand tool to the color wand
`set_rect_select_mode`()	Set the current wand tool to the color wand
`set_row_wand_mode`()	Set the current wand tool to the color wand
`should_be_enabled`(w)	Check if a widget should be enabled
`start_pattern_selection`()	Open the pattern selection dialog
`start_selection`([arr, rgba, rect_callbacks, …])	Start the selection in the current `data_obj`
`toggle_selection`()	Activate selection mode
`uncheck_pattern_selection`()	Disable the pattern selection
`update_alpha`(i)	Set the transparency of the selection image

Attributes

`add_select_action`	The action to add to the current selection with the selection tools
`ax`	The `matplotlib.axes.Axes` of the `data_obj`
`canvas`	The canvas of the `data_obj`
`clear_select_action`	An action to clear the current selection
`combo`	The QCombobox that defines the data object to be used
`data`	The np.ndarray of the `data_obj` image
`data_obj`	The data object as set in the `combo`.
`expand_select_action`	An action to expand the current selection to the full feature
`fig`	The `Figure` of the `data_obj`
`invert_select_action`	An action to invert the current selection
`labels`	The labeled data that is displayed
`new_select_action`	The action to make new selection with one of the selection tools
`poly_callbacks`	The functions to call after the polygon selection
`rect_callbacks`	The functions to call after the rectangle selection.
`remove_select_action`	An action to remove from the current selection with the selection tools
`select_action`	The rectangle selection tool
`select_all_action`	An action to select all features in the `data`
`select_pattern_action`	An action to start a pattern selection
`select_right_action`	An action to select everything in the data column to the right
`selected`(args, *kwargs)	A signal that is emitted when something is selected
`selector`	A `PointOrRectangleSelector` to select features in the image
`toolbar`	The toolbar of the `canvas`
`wand_action`	The wand selection tool
`widgets2disable`	Built-in mutable sequence.

add_or_remove_pattern()[source]¶: Enable the removing or adding of the pattern selection

property add_select_action¶: The action to add to the current selection with the selection tools

property ax¶: The matplotlib.axes.Axes of the data_obj

property canvas¶: The canvas of the data_obj

property clear_select_action¶: An action to clear the current selection

clear_selection()[source]¶: Clear the current selection

combo = None¶: The QCombobox that defines the data object to be used

create_actions()[source]¶: Define the actions for the toolbar and set everything up

property data¶: The np.ndarray of the data_obj image

property data_obj¶

The data object as set in the combo.

Either a Straditizer or a straditize.binary.DataReader instance.

disable_actions()[source]¶

disconnect(self)[source]¶

enable_or_disable_widgets(b)[source]¶

Enable or disable the widgets in this control

This method enables or disables the widgets2disable if the should_be_enabled() method evaluates to True

Parameters: b (bool) – If True, enable the widgets, if False, disable them

end_selection()[source]¶: Finish the selection and disconnect everything

property expand_select_action¶: An action to expand the current selection to the full feature

expand_selection()[source]¶: Expand the selected areas to select the full labels

property fig¶: The Figure of the data_obj

get_xy_slice(lastx, lasty, x, y)[source]¶

Transform x- and y-coordinates to slice objects

Parameters

lastx (int) – The initial x-coordinate
lasty (int) – The initial y-coordinate
x (int) – The final x-coordinate
y (int) – The final y-coordinate

Returns

slice – The slice(lastx, x)
slice – The slice(lasty, y)

property invert_select_action¶: An action to invert the current selection

invert_selection()[source]¶: Invert the current selection

property labels¶: The labeled data that is displayed

property new_select_action¶: The action to make new selection with one of the selection tools

on_poly_select(points)[source]¶

Call the poly_callbacks after a polygon selection

Parameters

e0 (matplotlib.backend_bases.Event) – The initial event
e1 (matplotlib.backend_bases.Event) – The final event

on_rect_select(e0, e1)[source]¶

Call the rect_callbacks after a rectangle selection

Parameters

e0 (matplotlib.backend_bases.Event) – The initial event
e1 (matplotlib.backend_bases.Event) – The final event

property poly_callbacks¶

The functions to call after the polygon selection

If not set manually, it is the select_poly() method. Note that this is cleared at every call of the end_selection().

Callables in this list must accept one argument, a np.ndarray of shape (N, 2). This array defines the N x- and y-coordinates of the points of the polygon

property rect_callbacks¶

The functions to call after the rectangle selection.

If not set manually, it is the select_rect() method. Note that this is cleared at every call of the end_selection().

Callables in this list must accept two arguments (slx, sly): the first one is the x-slice, and the second one the y-slice. They both correspond to the data attribute.

refresh()[source]¶: Refresh from the straditizer

property remove_select_action¶: An action to remove from the current selection with the selection tools

reset_cursor_id = None¶

property select_action¶: The rectangle selection tool

select_all()[source]¶: Select all features in the image

See also

straditize.label_selection.LabelSelection.select_all_labels()

property select_all_action¶: An action to select all features in the data

select_everything_to_the_right()[source]¶: Selects everything to the right of the current selection

property select_pattern_action¶: An action to start a pattern selection

select_poly(points)[source]¶

Select the data defined by a polygon

Parameters: points (np.ndarray of shape (N, 2)) – The x- and y-coordinates of the vertices of the polygon

See also

poly_callbacks()

select_rect(slx, sly)[source]¶

Select the data defined by a rectangle

Parameters

slx (slice) – The x-slice of the rectangle
sly (slice) – The y-slice of the rectangle

See also

rect_callbacks()

property select_right_action¶: An action to select everything in the data column to the right

selected(*args, **kwargs)¶: A signal that is emitted when something is selected

selector = None¶: A PointOrRectangleSelector to select features in the image

set_binary_pattern_mode()[source]¶: Set the current pattern mode to the binary pattern

set_col_wand_mode()[source]¶: Set the current wand tool to the color wand

set_color_wand_mode()[source]¶: Set the current wand tool to the color wand

set_cursor_id = None¶

set_grey_pattern_mode()[source]¶: Set the current pattern mode to the binary pattern

set_label_wand_mode()[source]¶: Set the current wand tool to the color wand

set_poly_select_mode()[source]¶: Set the current wand tool to the color wand

set_rect_select_mode()[source]¶: Set the current wand tool to the color wand

set_row_wand_mode()[source]¶: Set the current wand tool to the color wand

should_be_enabled(w)[source]¶

Check if a widget should be enabled

This function checks if a given widget w from the widgets2disable attribute should be enabled or not

Parameters: w (QWidget) – The widget to check
Returns: True, if the widget should be enabled
Return type: bool

start_pattern_selection()[source]¶

Open the pattern selection dialog

This method will enable the pattern selection by starting a straditize.widgets.pattern_selection.PatternSelectionWidget

start_selection(arr=None, rgba=None, rect_callbacks=None, poly_callbacks=None, apply_funcs=(), cancel_funcs=(), remove_on_apply=True)[source]¶

Start the selection in the current data_obj

Parameters

arr (np.ndarray) – The labeled selection array that is used. If specified, the enable_label_selection() method is called of the data_obj with the given arr. If this parameter is None, then we expect that this method has already been called
rgba (np.ndarray) – The RGBA image that shall be used for the color selection (see the set_color_wand_mode())
rect_callbacks (list) – A list of callbacks that shall be called after a rectangle selection has been made by the user (see rect_callbacks)
poly_callbacks (list) – A list of callbacks that shall be called after a polygon selection has been made by the user (see poly_callbacks)
apply_funcs (list) – A list of callables that shall be connected to the apply_button
cancel_funcs (list) – A list of callables that shall be connected to the cancel_button
remove_on_apply (bool) – If True and the apply_button is clicked, the selected labels will be removed.

toggle_selection()[source]¶: Activate selection mode

property toolbar¶: The toolbar of the canvas

uncheck_pattern_selection()[source]¶: Disable the pattern selection

update_alpha(i)[source]¶

Set the transparency of the selection image

Parameters: i (int) – The transparency between 0 and 100

property wand_action¶: The wand selection tool

property widgets2disable¶

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

straditize.widgets.stacked_area_reader module¶

DataReader for stacked area plots

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

StackedReader(image[, ax, extent, plot, …])

A DataReader for stacked area plots

class straditize.widgets.stacked_area_reader.StackedReader(image, ax=None, extent=None, plot=True, children=[], parent=None, magni=None, plot_background=False, binary=None)[source]¶

Bases: straditize.binary.DataReader, straditize.widgets.StraditizerControlBase

A DataReader for stacked area plots

This reader only works within the straditizer GUI because the digitization (see digitize()) is interactive. The user has to manually distinguish the stacked variables.

Parameters

image (PIL.Image.Image) – The image of the diagram
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
extent (list) – List of four number specifying the extent of the image in it’s source. This extent will be used for the call of matplotlib.pyplot.imshow()
children (list of DataReader) – Child readers for other columns in case the newly created instance is the parent reader
parent (DataReader) – The parent reader.
magni (straditize.magnifier.Magnifier) – The magnifier for the given ax
plot_background (bool) – If True (and plot is True), a white, opaque are is plotted below the plot_im
binary (None) – The binary version of the given image. If not provided, the to_binary_pil() method is used with the given image

Methods

`add_col`()	Create a column out of the current selection
`decrease_current_col`()	Take the previous column as the current column
`digitize`()	Digitize the data interactively
`enable_or_disable_navigation_buttons`()	Enable or disable `btn_prev` and `btn_next`
`get_binary_for_col`(col)	Get the binary array for a specific column
`increase_current_col`()	Take the next column as the current column
`plot_full_df`([ax])	Plot the lines for the digitized diagram
`plot_potential_samples`([excluded, ax, plot_kws])	Plot the ranges for potential samples
`reset_lbl_col`()	Reset the `lbl_col` to display the current column
`resize_axes`(grouper, bounds)	Reimplemented to do nothing
`select_and_add_current_column`()	Select the features for a column and create it as a new one
`select_current_column`()	Select the features of the current column
`update_col`()	Update the current column based on the selection.
`update_plotted_full_df`()	Update the plotted full_df if it is shown

Attributes

`btn_add`	A QPushButton to add a new variable to the current ones
`btn_edit`	A QPushButton to select the features in the image for the current
`btn_next`	A QPushButton to select the next variable during the digitization
`btn_prev`	A QPushButton to select the previous variable during the digitization
`digitize_child`	The QTreeWidgetItem that holds the digitization widgets
`lbl_col`	A QLabel to display the current column
`start_of_current_col`	The first x-pixel of the current column
`strat_plot_identifier`	str(object=’’) -> str

add_col()[source]¶: Create a column out of the current selection

btn_add = None¶: A QPushButton to add a new variable to the current ones (see select_and_add_current_column())

btn_edit = None¶: A QPushButton to select the features in the image for the current variable (see select_current_column())

btn_next = None¶: A QPushButton to select the next variable during the digitization (see increase_current_col())

btn_prev = None¶: A QPushButton to select the previous variable during the digitization (see decrease_current_col())

decrease_current_col()[source]¶: Take the previous column as the current column

digitize()[source]¶

Digitize the data interactively

This method creates a new child item for the digitize button in the straditizer control to manually distinguish the variables in the stacked diagram.

digitize_child = None¶: The QTreeWidgetItem that holds the digitization widgets

enable_or_disable_navigation_buttons()[source]¶

Enable or disable btn_prev and btn_next

Depending on the current column, we disable the navigation buttons btn_prev and btn_next

get_binary_for_col(col)[source]¶: Get the binary array for a specific column

increase_current_col()[source]¶: Take the next column as the current column

lbl_col = None¶: A QLabel to display the current column

plot_full_df(ax=None)[source]¶: Plot the lines for the digitized diagram

plot_potential_samples(excluded=False, ax=None, plot_kws={}, *args, **kwargs)[source]¶: Plot the ranges for potential samples

reset_lbl_col()[source]¶: Reset the lbl_col to display the current column

resize_axes(grouper, bounds)[source]¶: Reimplemented to do nothing

select_and_add_current_column()[source]¶: Select the features for a column and create it as a new one

select_current_column()[source]¶: Select the features of the current column

property start_of_current_col¶: The first x-pixel of the current column

strat_plot_identifier = 'stacked'¶

update_col()[source]¶

Update the current column based on the selection.

This method updates the end of the current column and adds or removes the changes from the columns to the right.

update_plotted_full_df()[source]¶: Update the plotted full_df if it is shown

See also

plot_full_df()

Submodules¶

straditize.main module¶

main module of straditize

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Functions

`get_parser`([create])	Create an argument parser for the command line handling
`main`([exec_])
`start_app`([fname, output, xlim, ylim, full, …])	Start the psyplot GUI with the straditizer setup

straditize.__main__.get_parser(create=True)[source]¶

Create an argument parser for the command line handling

This function creates a funcargparse.FuncArgParser for the usage from the command line

Parameters: create (bool) – If True, the funcargparse.FuncArgParser.create_arguments() method is called

straditize.__main__.main(exec_=True)[source]¶

straditize.__main__.start_app(fname=None, output=None, xlim=None, ylim=None, full=False, reader_type='area', **kwargs)[source]¶

Start the psyplot GUI with the straditizer setup

Parameters

fname (str) – Either the path to a picture to digitize or a previously saved straditizer project (ending with '.pkl')
output (str) – The path to the csv file where to save the digitized diagram
xlim (list of int of length 2) – The x-limits of the data part of the diagram
ylim (list of int of length 2) – The y-limits of the data part of the diagram
full (bool) – If True, the image is digitzed and x- and ylim are set to the entire share of the array
reader_type ({ 'area' | 'bars' | 'rounded bars' | 'stacked area' | 'line' }) – Specify the reader type
name (list of str) – The variable names to plot if the output parameter is set
dims (dict) – A mapping from coordinate names to integers if the project is not given
plot_method (str) – The name of the plot_method to use
project (str) – If set, the project located at the given file name is loaded
engine (str) – The engine to use for opening the dataset (see psyplot.data.open_dataset())
formatoptions (dict) – A dictionary of formatoption that is applied to the data visualized by the chosen plot_method
tight (bool) – If True/set, it is tried to figure out the tight bbox of the figure and adjust the paper size of the output to it
rc_file (str) – The path to a yaml configuration file that can be used to update the rcParams
encoding (str) – The encoding to use for loading the project. If None, it is automatically determined by pickle. Note: Set this to 'latin1' if using a project created with python2 on python3.
enable_post (bool) – Enable the post processing formatoption. If True/set, post processing scripts are enabled in the given project. Only set this if you are sure that you can trust the given project file because it may be a security vulnerability.
seaborn_style (str) – The name of the style of the seaborn package that can be used for the seaborn.set_style() function
output_project (str) – The name of a project file to save the project to
concat_dim (str) – The concatenation dimension if multiple files in fnames are provided
chname (dict) – A mapping from variable names in the project to variable names in the datasets that should be used instead
backend (None or str) – The backend to use. By default, the 'gui.backend' key in the rcParams dictionary is used. Otherwise it can be None to use the standard matplotlib backend or a string identifying the backend
new_instance (bool) – If True/set and the output parameter is not set, a new application is created
rc_gui_file (str) – The path to a yaml configuration file that can be used to update the rcParams
include_plugins (list of str) – The plugin widget to include. Can be either None to load all that are not explicitly excluded by exclude_plugins or a list of plugins to include. List items can be either module names, plugin names or the module name and widget via '<module_name>:<widget>'
exclude_plugins (list of str) – The plugin widgets to exclude. Can be either 'all' to exclude all plugins or a list like in include_plugins.
offline (bool) – If True/set, psyplot will be started in offline mode without intersphinx and remote access for the help explorer
pwd (str) – The path to the working directory to use. Note if you do not provide any fnames or project, but set the pwd, it will switch the pwd of the current GUI.
script (str) – The path to a python script that shall be run in the GUI. If the GUI is already running, the commands will be executed in this GUI.
command (str) – Python commands that shall be run in the GUI. If the GUI is already running, the commands will be executed in this GUI
use_all (bool) – If True, use all variables. Note that this is the default if the output is specified and not name
exec_ (bool) – If True, the main loop is entered.
callback (str) – A unique identifier for the method that should be used if psyplot is already running. Set this parameter to None to avoid sending
opengl_implementation ({'software', 'desktop', 'gles', 'automatic'}) – OpenGL implementation to pass to Qt. Possible options are ‘software’, ‘desktop’, ‘gles’ and ‘automatic’ (which let’s PyQt decide).

straditize.binary module¶

A module to read in and digitize the pollen diagram

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`BarDataReader`(args, *kwargs)	A DataReader for digitizing bar pollen diagrams
`DataReader`(image[, ax, extent, plot, …])	A class to read in and digitize the data files of the pollen diagram
`LineDataReader`(image[, ax, extent, plot, …])	A data reader for digitizing line diagrams
`RoundedBarDataReader`(args, *kwargs)	A bar data reader that can be used for rounded bars

Functions

`groupby_arr`(arr)	Groupby a boolean array
`only_parent`(func)	Call the given func only from the parent reader

class straditize.binary.BarDataReader(*args, **kwargs)[source]¶

Bases: straditize.binary.DataReader

A DataReader for digitizing bar pollen diagrams

Compared to the base DataReader class, this reader implements a different strategy in digitizing and finding the samples. When digitizing the full diagram, we try to find the distinct bars using the get_bars() method. These bars might have to be splitted manually if they are not easy to distinguish. One key element to distinguish to adjacent bars is the specified tolerance.

The base class works for rectangular bars. If you require rounded bars, use the RoundedBarDataReader

Parameters: tolerance (int) – If x0 is the value in a pixel row y and x1 the value in the next pixel row y+1, then the two pixel rows are considered as belonging to different bars if abs(x1 - x0) > tolerance (see the get_bars() method and the tolerance attribute)

Methods

`create_grouper`(ds, columns, args, *kwargs)	Create the grouper that plots the results
`digitize`([do_split, inplace])	Reimplemented to ignore the rows between the bars
`find_potential_samples`(col[, min_len, …])	Find the bars in the column
`from_dataset`(ds, args, *kwargs)	Create a new `DataReader` from a `xarray.Dataset`
`get_bars`(arr[, do_split])	Find the distinct bars in an array
`shift_vertical`(pixels)	Shift the columns vertically.
`to_dataset`([ds])	All the necessary data as a `xarray.Dataset`

Attributes

`min_fract`	The minimum fraction of overlap for two bars to be considered as the
`nc_meta`	dict() -> new empty dictionary
`samples_at_boundaries`	There should not be samples at the boundaries because the first
`tolerance`	Tolerance to distinguish bars.

create_grouper(ds, columns, *args, **kwargs)[source]¶

Create the grouper that plots the results

Parameters

ds (xarray.Dataset) – The dataset with the data
columns (list of int) – The numbers of the columns for which the grouper should be created
fig (matplotlib.figure.Figure) – The matplotlib figure to plot on
x0 (float) – The left boundary of the larger Bbox of the stratigraphic diagram
y0 (int) – The upper boundary of the larger Bbox of the stratigraphic diagram
width (float) – The width of the final axes between 0 and 1
height (float) – The height of the final axis between 0 and 1
ax0 (matplotlib.axes.Axes) – The larger matplotlib axes whose bounding box shall be used.
transformed (bool) – If True, y-axes and x-axes have been translated (see the px2data_x() and px2data_y() methods)
colnames (list of str) – The column names to use in the plot
**kwargs – any other keyword argument that is passed to the psy_strat.stratplot.StratGroup.from_dataset() method

Returns

The grouper that visualizes the given columns in the fig

Return type

psy_strat.stratplot.StratGroup

digitize(do_split=False, inplace=True)[source]¶

Reimplemented to ignore the rows between the bars

Parameters

do_split (bool) – If True and a bar is 1.7 times longer than the mean, it is splitted into two.
inplace (bool) – If True (default), the full_df attribute is updated. Otherwise a DataFrame is returned

find_potential_samples(col, min_len=None, max_len=None, filter_func=None)[source]¶

Find the bars in the column

This method gets the bars in the given col and returns the distinct indices

Parameters

col (int) – The column for which to find the extrema
min_len (int) – The minimum length of one extremum. If the width of the interval where we found an extrumum is smaller than that, the extremum is ignored. If None, this parameter does not have an effect (i.e. min_len=1).
max_len (int) – The maximum length of one extremum. If the width of the interval where we found an extrumum is greater than that, the extremum is ignored. If None, this parameter does not have an effect.
filter_func (function) – A function to filter the extreme. It must accept one argument which is a list of integers representing the indices of the extremum in a

Returns

list of list of int of shape (N, 2) – The list of N extremum locations. Each tuple in this list represents an interval a where one extremum might be located
list of list of int – The excluded extremum locations that are ignored because we could not find a change of sign in the slope.

See also

find_samples()

classmethod from_dataset(ds, *args, **kwargs)[source]¶

Create a new DataReader from a xarray.Dataset

Parameters

ds (xarray.Dataset) – The dataset that has been stored with the to_dataset() method
*args,**kwargs – Any other arguments passed to the DataReader constructor

Returns

The reader recreated from ds

Return type

DataReader

get_bars(arr, do_split=False)[source]¶

Find the distinct bars in an array

Parameters

arr (np.ndarray) – The array to find the bars in
do_split (bool) – If True and a bar is 1.7 times longer than the mean, it is splitted into two.

Returns

list of list of ints – The list of the distinct positions of the bars
list of floats – The heights for each of the bars
list of list of ints – The indices of bars that are longer than 1.7 times the mean of the other bars and should be splitted. If do_split is True, they have been splitted already

max_len = None¶

min_fract = 0.9¶: The minimum fraction of overlap for two bars to be considered as the same sample (see unique_bars())

min_len = None¶

nc_meta = {'bars{reader}_bars': {'dims': ('bars{reader}_bar', 'limit'), 'long_name': 'Boundaries of bars', 'units': 'px'}, 'bars{reader}_full_data_orig': {'dims': ('ydata', 'bars{reader}_column'), 'long_name': 'Full digitized data ignoring bars', 'units': 'px'}, 'bars{reader}_max_len': {'dims': (), 'long_name': 'Maximum length of a bar'}, 'bars{reader}_min_fract': {'dims': (), 'long_name': 'Minimum fraction for overlap estimation'}, 'bars{reader}_min_len': {'dims': (), 'long_name': 'Minimum length of a bar'}, 'bars{reader}_nbars': {'dims': 'bars{reader}_column', 'long_name': 'number of bars per column'}, 'bars{reader}_nsplit': {'dims': 'bars{reader}_column', 'long_name': 'number of the splitted bars'}, 'bars{reader}_splitted': {'dims': ('bar_split', 'limit'), 'long_name': 'Boundaries of bars to split', 'units': 'px'}, 'bars{reader}_tolerance': {'dims': (), 'long_name': 'bar distinguishing tolerance'}, 'binary': {'dims': ('reader', 'ydata', 'xdata'), 'long_name': 'Binary images for data readers'}, 'col_map': {'dims': 'column', 'long_name': 'Mapping from column to reader', 'units': 'reader_index'}, 'column_ends': {'dims': 'column', 'long_name': 'Ends of the columns', 'units': 'px'}, 'column_starts': {'dims': 'column', 'long_name': 'Start of the columns', 'units': 'px'}, 'exag_col_map': {'dims': 'column', 'long_name': 'Mapping from column to exaggerated reader', 'units': 'reader_index'}, 'full_data': {'dims': ('ydata', 'column'), 'long_name': 'Full digitized data', 'units': 'px'}, 'hline': {'long_name': 'Horizontal line location', 'units': 'px'}, 'is_exaggerated': {'dims': 'reader', 'long_name': 'Exaggeration factor'}, 'occurences': {'comments': 'The locations where the only an occurence of a taxa is highlighted without value', 'dims': ('occurence', 'xy'), 'long_name': 'taxa occurences'}, 'reader': {'dims': 'reader', 'long_name': 'index of the reader'}, 'reader_cls': {'dims': 'reader', 'long_name': 'The name of the class constructor'}, 'reader_image': {'dims': ('reader', 'ydata', 'xdata', 'rgba'), 'long_name': 'RGBA images for data readers', 'units': 'color'}, 'reader_mod': {'dims': 'reader', 'long_name': 'The module of the reader class'}, 'rough_locs': {'dims': ('sample', 'column', 'limit'), 'long_name': 'Rough locations for samples'}, 'sample': {'long_name': 'Sample location', 'units': 'px'}, 'samples': {'dims': ('sample', 'column'), 'long_name': 'Sample data', 'units': 'px'}, 'shifted': {'dims': 'column', 'long_name': 'Vertical shift per column', 'units': 'px'}, 'vline': {'long_name': 'Vertical line location', 'units': 'px'}, 'xaxis_translation': {'dims': ('reader', 'px_data', 'limit'), 'long_name': 'Pixel to data mapping for x-axis'}}¶

samples_at_boundaries = False¶: There should not be samples at the boundaries because the first sample is in the middle of the first bar

shift_vertical(pixels)[source]¶

Shift the columns vertically.

Parameters: pixels (list of floats) – The y-value for each column for which to shift the values. Note that theses values have to be greater than or equal to 0

to_dataset(ds=None)[source]¶

All the necessary data as a xarray.Dataset

Parameters: ds (xarray.Dataset) – The dataset in which to insert the data. If None, a new one will be created
Returns: Either the given ds or a new xarray.Dataset instance
Return type: xarray.Dataset

tolerance = 2¶: Tolerance to distinguish bars. If x0 is the value in a pixel row y and x1 the value in the next pixel row y+1, then the two pixel rows are considered as belonging to different bars if abs(x1 - x0) > tolerance

class straditize.binary.DataReader(image, ax=None, extent=None, plot=True, children=[], parent=None, magni=None, plot_background=False, binary=None)[source]¶

Bases: straditize.label_selection.LabelSelection

A class to read in and digitize the data files of the pollen diagram

The source image is stored in the image attribute, the binary array of it is stored in the binary attribute. A labeled version created by the skimage.morphology.label() function, is stored in the labels attribute and can regenerated using the reset_labels() method.

Subclasses of this class should reimplement the digitize() method that digitizes the diagram, and the find_potential_samples() method.

There is always one parent reader stored in the parent attribute. This is then the reader that is accessible through the straditize.straditizer.Straditizer.data_reader attribute and holds the references to other readers in it’s children attribute

Parameters

image (PIL.Image.Image) – The image of the diagram
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
extent (list) – List of four number specifying the extent of the image in it’s source. This extent will be used for the call of matplotlib.pyplot.imshow()
children (list of DataReader) – Child readers for other columns in case the newly created instance is the parent reader
parent (DataReader) – The parent reader.
magni (straditize.magnifier.Magnifier) – The magnifier for the given ax
plot_background (bool) – If True (and plot is True), a white, opaque are is plotted below the plot_im
binary (None) – The binary version of the given image. If not provided, the to_binary_pil() method is used with the given image

Methods

`add_samples`(samples[, rough_locs])	Add samples to the found ones
`close`()
`color_labels`([categorize])	The labels of the colored array
`create_exaggerations_reader`(factor[, cls])	Create a new exaggerations reader for this reader
`create_grouper`(ds, columns, fig, x0, y0, …)	Create the grouper that plots the results
`create_variable`(ds, vname, data, **kwargs)	Insert the data into a variable in an `xr.Dataset`
`digitize`([use_sum, inplace])	Digitize the binary image to create the full dataframe
`digitize_exaggerated`([fraction, absolute, …])	Merge the exaggerated values into the original digitized result
`disable_label_selection`(args, *kwargs)	Disable the label selection
`draw_figure`()	Draw the matplotlib `fig` and the `magni` figure
`end_column_selection`()	End the column selection and rmove the artists
`estimated_column_starts`([threshold])	The estimated column starts as `numpy.ndarray`.
`find_potential_samples`(col[, min_len, …])	Find potential samples in an array
`find_samples`([min_fract, pixel_tol])	Find the samples in the diagram
`found_extrema_per_row`()	Calculate how many columns have a potential sample in each pixel row
`from_dataset`(ds, args, *kwargs)	Create a new `DataReader` from a `xarray.Dataset`
`get_bbox_for_cols`(columns, x0, y0, width, height)	Get the boundary boxes for the columns of this reader in the results
`get_binary_for_col`(col)	Get the binary array for a specific column
`get_cross_column_features`([min_px])	Get features that are contained in two or more columns
`get_disconnected_parts`([fromlast, from0, …])	Identify parts in the `binary` data that are not connected
`get_labeled_array`()	Create a connectivity-based labeled array of the `binary` data
`get_occurences`()	Extract the positions of the occurences from the selection
`get_parts_at_column_ends`([npixels])	Identify parts in the `binary` data that touch the next column
`get_reader_for_col`(col)	Get the reader for a specific column
`get_surrounding_slopes`(indices, arr)
`image_array`()	The RGBA values of the colored image
`is_obstacle`(indices, arr)	Check whether the found extrema is only an obstacle of the picture
`mark_as_exaggerations`(mask)	Mask the given array as exaggerated
`merge_close_samples`(locs[, rough_locs, …])
`merged_binaries`()	Get the binary data from all children and merge them into one array
`merged_labels`()	Get the labeled binary data from all children merged into one array
`new_child_for_cols`(columns, cls[, plot])	Create a new child reader for specific columns
`plot_background`([ax])	Plot a white layer below the `plot_im`
`plot_color_image`([ax])	Plot the colored `image` on a matplotlib axes
`plot_full_df`([ax])	Plot the lines for the digitized diagram
`plot_image`([ax])	Plot the `binary` data image on a matplotlib axes
`plot_other_potential_samples`([tol, …])	Plot potential samples that are not yet in the `samples`
`plot_potential_samples`([excluded, ax, plot_kws])	Plot the ranges for potential samples
`plot_results`(df[, ax, fig, transformed])	Plot the reconstructed diagram
`plot_sample_hlines`([ax])	Plot one horizontal line per sample in the `sample_locs`
`plot_samples`([ax])	Plot the diagram as lines reconstructed from the samples
`px2data_x`(coord)	Transform the pixel coordinates into data coordinates
`recognize_hlines`([fraction, min_lw, max_lw, …])	Recognize horizontal lines in the plot and subtract them
`recognize_vlines`([fraction, min_lw, max_lw, …])	Recognize horizontal lines in the plot and subtract them
`recognize_xaxes`([fraction, min_lw, max_lw, …])	Recognize (and potentially remove) x-axes at bottom and top
`recognize_yaxes`([fraction, min_lw, max_lw, …])	Find (and potentially remove) y-axes in the image
`remove_in_children`(arr, amask)	Update the child reader images after having removed binary data
`remove_plots`()	Remove all plotted artists by this reader
`reset_column_starts`()	Reset the column starts, `full_df`, `shifted`
`reset_image`(image[, binary])	Reset the image for this straditizer
`reset_labels`()	Reset the `labels` array
`reset_samples`()	Reset the samples
`resize_axes`(grouper, bounds)	Resize the axes based on column boundaries
`set_as_parent`()	Set this instance as the parent reader
`set_hline_locs_from_selection`([selection])	Save the locations of horizontal lines
`set_vline_locs_from_selection`([selection])	Save the locations of vertical lines
`shift_vertical`(pixels[, draw])	Shift the columns vertically.
`show_cross_column_features`([min_px, remove])	Highlight and maybe remove cross column features
`show_disconnected_parts`([fromlast, from0, …])	Highlight or remove disconnected parts
`show_parts_at_column_ends`([npixels, remove])	Highlight or remove features that touch the column ends
`show_small_parts`([n, remove])	Highlight and potentially remove small features in the image
`start_column_selection`([use_all])	Enable the user to select columns
`to_binary_pil`(image[, threshold])	Convert an image to a binary
`to_dataset`([ds])	All the necessary data as a `xarray.Dataset`
`to_grey_pil`(image[, threshold])	Convert an image to a greyscale image
`unique_bars`([min_fract, asdict])	Estimate the unique bars
`update_image`(arr, amask)	Update the image after having removed binary data
`update_rgba_image`(arr, mask)	Update the RGBA image from the given 3D-array

Attributes

`all_column_bounds`	The boundaries for the data columns
`all_column_ends`	1D numpy array with the ends for all column (including child reader)
`all_column_starts`	1D numpy array with the ends for all column (including child reader)
`ax`	The matplotlib axes where the `plot_im` is plotted on
`background`	White rectangle that represents the background of the binary image.
`binary`	A 2D numpy array representing the binary version of the `image`
`children`	Child readers for specific columns.
`column_bounds`	The boundaries for the data columns
`column_ends`	1D numpy array with the ends for each column of this reader
`column_starts`	1D numpy array with the starts for each column of this reader
`columns`	The indices of the columns that are handled by this reader
`exaggerated_reader`	The reader that represents the exaggerations
`extent`	The extent of the `plot_im`
`fig`	The matplotlib figure of the `ax`
`full_df`	The full `pandas.DataFrame` of the digitized image
`hline_locs`	`list` or floats. The indexes of horizontal lines
`image`	PIL.Image.Image of the diagram part with mode RGBA
`is_exaggerated`	Exaggeration factor that is not 0 if this reader represents exaggeration
`iter_all_readers`	Iter through the `parent` reader and it’s `children`
`label_arrs`	Built-in mutable sequence.
`labels`	A connectivity-based labeled version of the `binary` data
`magni`	the `straditize.magnifier.Magnifier` for the `ax`
`magni_background`	White rectangle that represents the background of the binary image in the magnifier.
`magni_plot_im`	magnified `plot_im`
`min_fract`	The minimum fraction of overlap for two bars to be considered as the
`nc_meta`	A mapping from variable name to meta information
`non_exaggerated_reader`	The reader that represents the exaggerations
`num_labels`	The maximum label in the `labels` array
`occurences`	A set of tuples marking the position of an occurence
`occurences_dict`	A mapping from column number to an numpy array with the indices of
`occurences_value`	The value that is given to the occurences in the measurements
`parent`	Parent reader for this instance.
`plot_im`	the matplotlib image artist
`rough_locs`	The `pandas.DataFrame` with rough locations for the samples.
`sample_locs`	The `pandas.DataFrame` with locations and values of the
`samples_at_boundaries`	a boolean flag that shall indicate if we assume that the first and last
`shifted`	The number of pixels the columns have been shifted
`strat_plot_identifier`	str(object=’’) -> str
`vline_locs`	`list` or floats. The indexes of vertical lines
`xaxis_px`	The x indices in column pixel coordinates that are used for x-axes

add_samples(samples, rough_locs=None)[source]¶

Add samples to the found ones

Parameters

samples (series, 1d-array or DataFrame) –
The samples. If it is series, we assume that the index represents the y-value of the sample and the value the x-position (see xcolumns). In case of a 1d-array, we assume that the data represents the y-values of the samples. In case of a DataFrame, we assume that the columns correspond to columns in the full_df attribute and are True where we have a sample.

Note that the y-values must be in image coordinates (see extent attribute).
rough_locs (DataFrame) – The rough locations of the new samples (see the rough_locs attribute)

property all_column_bounds¶: The boundaries for the data columns

property all_column_ends¶

1D numpy array with the ends for all column (including child reader)

See also

all_column_starts: The starts for all column
all_column_bounds: The (start, end)-tuple for all of the columns
column_ends: The ends for this specific reader

reader

property all_column_starts¶

1D numpy array with the ends for all column (including child reader)

See also

all_column_ends: The ends for all column
all_column_bounds: The (start, end)-tuple for all of the columns
column_starts: The starts for this specific reader

reader

ax = None¶: The matplotlib axes where the plot_im is plotted on

background = None¶: White rectangle that represents the background of the binary image. This is only plotted by the parent reader

binary = None¶: A 2D numpy array representing the binary version of the image

children = []¶: Child readers for specific columns. Is not empty if and only if the parent attribute is this instance

close()[source]¶

color_labels(categorize=1)[source]¶: The labels of the colored array

property column_bounds¶: The boundaries for the data columns

property column_ends¶

1D numpy array with the ends for each column of this reader

See also

column_starts: The starts for each column
column_bounds: The (start, end)-tuple for each of the columns
all_column_ends: The ends for all columns, including child

reader

property column_starts¶

1D numpy array with the starts for each column of this reader

See also

column_ends: The ends for each column
column_bounds: The (start, end)-tuple for each of the columns
all_column_starts: The starts for all columns, including child

reader

property columns¶: The indices of the columns that are handled by this reader

create_exaggerations_reader(factor, cls=None)[source]¶

Create a new exaggerations reader for this reader

Parameters

factor (float) – The exaggeration factor
cls (type) – The DataReader subclass

Returns

The new exaggerated reader

Return type

instance of cls

create_grouper(ds, columns, fig, x0, y0, width, height, ax0=None, transformed=True, colnames=None, **kwargs)[source]¶

Create the grouper that plots the results

Parameters

ds (xarray.Dataset) – The dataset with the data
columns (list of int) – The numbers of the columns for which the grouper should be created
fig (matplotlib.figure.Figure) – The matplotlib figure to plot on
x0 (float) – The left boundary of the larger Bbox of the stratigraphic diagram
y0 (int) – The upper boundary of the larger Bbox of the stratigraphic diagram
width (float) – The width of the final axes between 0 and 1
height (float) – The height of the final axis between 0 and 1
ax0 (matplotlib.axes.Axes) – The larger matplotlib axes whose bounding box shall be used.
transformed (bool) – If True, y-axes and x-axes have been translated (see the px2data_x() and px2data_y() methods)
colnames (list of str) – The column names to use in the plot
**kwargs – any other keyword argument that is passed to the psy_strat.stratplot.StratGroup.from_dataset() method

Returns

The grouper that visualizes the given columns in the fig

Return type

psy_strat.stratplot.StratGroup

create_variable(ds, vname, data, **kwargs)[source]¶

Insert the data into a variable in an xr.Dataset

Parameters

ds (xarray.Dataset) – The destination dataset
vname (str) – The name of the variable in the nc_meta mapping. This name might include {reader} which will then be replaced by the number of the reader in the iter_all_readers attribute
data (np.ndarray) – The numpy array to store in the variable specified by vname
**kwargs – A mapping from dimension to slicer that should be used to slice the dataset

Returns

The resolved vname that has been used in the dataset

Return type

str

digitize(use_sum=False, inplace=True)[source]¶

Digitize the binary image to create the full dataframe

Parameters

use_sum (bool) – If True, the sum of cells that are not background are used for each column, otherwise the value of the cell is used that has the maximal distance to the column start for each row
inplace (bool) – If True (default), the full_df attribute is updated. Otherwise a DataFrame is returned

Returns

The digitization result if inplace is True, otherwise None

Return type

None or pandas.DataFrame

digitize_exaggerated(fraction=0.05, absolute=8, inplace=True, return_mask=False)[source]¶

Merge the exaggerated values into the original digitized result

Parameters

fraction (float between 0 and 1) – The fraction under which the exaggerated data should be used. Set this to 0 to ignore it.
absolute (int) – The absolute value under which the exaggerated data should be used. Set this to 0 to ignore it.
inplace (bool) – If True (default), the full_df attribute is updated. Otherwise a DataFrame is returned
return_mask (bool) – If True, a boolean 2D array is returned indicating where the exaggerations have been used

Returns

pandas.DataFrame or None – If inplace is False, the digitized result. Otherwise, if return_mask is True, the mask where the exaggerated results have been used. Otherwise None
pandas.DataFrame, optionally – If inplace is False and return_mask is True, a pandas.DataFrame containing the boolean mask where the exaggerated results have been used. Otherwise, this is skipped

disable_label_selection(*args, **kwargs)[source]¶

Disable the label selection

This will disconnect the pick_event and remove the selection images

Parameters: remove (bool) – Whether to remove the selection image from the plot. If None, the _remove attribute is used

See also

enable_label_selection(), remove_selected_labels()

draw_figure()[source]¶: Draw the matplotlib fig and the magni figure

end_column_selection()[source]¶: End the column selection and rmove the artists

estimated_column_starts(threshold=None)[source]¶

The estimated column starts as numpy.ndarray.

We assume a new column a pixel column $i$ if

the previous pixel column $i-1$ did not contain any data ($D(i-1) = 0$)
THE amount of data points doubled compared to $i-1$ ($D(i) geq 2cdot D(i-1)$)
the amount of data points steadily increases within the next few columns to a value twice as large as the previous column ($D(i+n) geq 2cdot D(i-1)$ with $n>0$ and $D(i+j) geq D(i)$ for all $0 < j geq n$)

Each potential column starts must also be covered by a given threshold.

Parameters: threshold (float between 0 and 1) – The fraction that has to be covered to assume a valid column start. By default, 0.1 (i.e. 10 percent)
Returns: The starts for each column
Return type: np.ndarray

property exaggerated_reader¶: The reader that represents the exaggerations

property extent¶: The extent of the plot_im

property fig¶: The matplotlib figure of the ax

find_potential_samples(col, min_len=None, max_len=None, filter_func=None)[source]¶

Find potential samples in an array

This method finds extrema in an array and returns the indices where the extremum might be. The algorithm thereby filters out obstacles by first going over the array, making sure, that there is a change of sign in the slope in the found extremum, and if not, ignores it and flattens it out.

Parameters

col (int) – The column for which to find the extrema
min_len (int) – The minimum length of one extremum. If the width of the interval where we found an extrumum is smaller than that, the extremum is ignored. If None, this parameter does not have an effect (i.e. min_len=1).
max_len (int) – The maximum length of one extremum. If the width of the interval where we found an extrumum is greater than that, the extremum is ignored. If None, this parameter does not have an effect.
filter_func (function) – A function to filter the extreme. It must accept one argument which is a list of integers representing the indices of the extremum in a

Returns

list of list of int of shape (N, 2) – The list of N extremum locations. Each tuple in this list represents an interval a where one extremum might be located
list of list of int – The excluded extremum locations that are ignored because we could not find a change of sign in the slope.

See also

find_samples()

find_samples(min_fract=None, pixel_tol=5, *args, **kwargs)[source]¶

Find the samples in the diagram

This function finds the samples using the find_potential_samples() function. It combines the found extrema from all columns and estimates the exact location using an interpolation of the slope

Parameters

min_fract (float) – The minimum fraction between 0 and 1 that two bars have to overlap such that they are considered as representing the same sample. If None, the min_fract attribute is used
min_len (int) – The minimum length of one extremum. If the width of the interval where we found an extrumum is smaller than that, the extremum is ignored. If None, this parameter does not have an effect (i.e. min_len=1).
max_len (int) – The maximum length of one extremum. If the width of the interval where we found an extrumum is greater than that, the extremum is ignored. If None, this parameter does not have an effect.
filter_func (function) – A function to filter the extreme. It must accept one argument which is a list of integers representing the indices of the extremum in a

Returns

pandas.DataFrame – The x- and y-locations of the samples. The index is the y-location, the columns are the columns in the full_df.
pandas.DataFrame – The rough locations of the samples. The index is the y-location of the columns, the values are lists of the potential sample locations.

found_extrema_per_row()[source]¶

Calculate how many columns have a potential sample in each pixel row

Returns: A series with one entry per pixel row. The values are the number of columns in the diagram that have a potential sample noted in the rough_locs
Return type: pandas.Series

classmethod from_dataset(ds, *args, **kwargs)[source]¶

Create a new DataReader from a xarray.Dataset

Parameters

ds (xarray.Dataset) – The dataset that has been stored with the to_dataset() method
*args,**kwargs – Any other arguments passed to the DataReader constructor

Returns

The reader recreated from ds

Return type

DataReader

property full_df¶: The full pandas.DataFrame of the digitized image

get_bbox_for_cols(columns, x0, y0, width, height)[source]¶

Get the boundary boxes for the columns of this reader in the results plot

This method is used by the plot_results() method to get the Bbox for a psy_strat.stratplot.StratGroup grouper

Parameters

columns (list of int) – The column numbers to use
x0 (float) – The left boundary of the larger Bbox of the stratigraphic diagram
y0 (int) – The upper boundary of the larger Bbox of the stratigraphic diagram
width (float) – The width of the final axes between 0 and 1
height (float) – The height of the final axis between 0 and 1

Returns

The boundary box for the given columns in the matplotlib figure

Return type

matplotlib.transforms.Bbox

See also

plot_results()

get_binary_for_col(col)[source]¶: Get the binary array for a specific column

get_cross_column_features(min_px=50)[source]¶

Get features that are contained in two or more columns

Parameters: min_px (int) – The number of pixels that have to be contained in each column
Returns: The 2D boolean mask with the same shape as the binary array that is True if a data pixel is considered as to belong to a cross column feature
Return type: np.ndarray of dtype bool

get_disconnected_parts(fromlast=5, from0=10, cross_column=False)[source]¶

Identify parts in the binary data that are not connected

Parameters

fromlast (int) – A pixel x1 > x0 is considered as disconnected, if it is at least x1 - x0 >= fromlast. If this is 0, it is ignored and only from0 is considered.
from0 (int) – A pixel is considered as disconnected if it is more than from0 pixels away from the column start. If this is 0, it is ignored and only fromlast is considered
cross_column (bool) – If False, disconnected features are only marked in the column where the disconnection has been detected. Otherwise the entire feature is marked

Returns

The 2D boolean mask with the same shape as the binary array that is True if a data pixel is considered as to be disconnected

Return type

np.ndarray of dtype bool

get_labeled_array()[source]¶: Create a connectivity-based labeled array of the binary data

get_occurences()[source]¶: Extract the positions of the occurences from the selection

get_parts_at_column_ends(npixels=2)[source]¶

Identify parts in the binary data that touch the next column

Parameters: npixels (int) – If a data pixel is less than npixels away from the column end, it is considered to be at the column end and marked
Returns: A boolean mask with the same shape as the binary data that is True where a pixel is considered to be at the column end
Return type: np.ndarray of dtype bool

get_reader_for_col(col)[source]¶

Get the reader for a specific column

Parameters: col (int) – The column of interest
Returns: Either the reader or None if no reader could be found
Return type: DataReader or None

get_surrounding_slopes(indices, arr)[source]¶

hline_locs = None¶: list or floats. The indexes of horizontal lines

image = None¶: PIL.Image.Image of the diagram part with mode RGBA

image_array()[source]¶: The RGBA values of the colored image

is_exaggerated = 0¶: Exaggeration factor that is not 0 if this reader represents exaggeration plots

is_obstacle(indices, arr)[source]¶: Check whether the found extrema is only an obstacle of the picture

property iter_all_readers¶: Iter through the parent reader and it’s children

label_arrs = ['binary', 'labels', 'image_array']¶

labels = None¶: A connectivity-based labeled version of the binary data

magni = None¶: the straditize.magnifier.Magnifier for the ax

magni_background = None¶: White rectangle that represents the background of the binary image in the magnifier. This is only plotted by the parent reader

magni_color_plot_im = None¶

magni_plot_im = None¶: magnified plot_im

mark_as_exaggerations(mask)[source]¶

Mask the given array as exaggerated

Parameters: mask (2D np.ndarray of dtype bool) – A mask with the same shape as the binary array that is True if a cell should be interpreted as the visualization of an exaggeration

merge_close_samples(locs, rough_locs=None, pixel_tol=5)[source]¶

merged_binaries()[source]¶

Get the binary data from all children and merge them into one array

Returns: The binary image with the same shape as the binary data
Return type: np.ndarray of dtype int

merged_labels()[source]¶

Get the labeled binary data from all children merged into one array

Returns: The labeled binary image with the same shape as the label data
Return type: np.ndarray of dtype int

min_fract = 0.9¶: The minimum fraction of overlap for two bars to be considered as the same sample (see unique_bars())

nc_meta = {'binary': {'dims': ('reader', 'ydata', 'xdata'), 'long_name': 'Binary images for data readers'}, 'col_map': {'dims': 'column', 'long_name': 'Mapping from column to reader', 'units': 'reader_index'}, 'column_ends': {'dims': 'column', 'long_name': 'Ends of the columns', 'units': 'px'}, 'column_starts': {'dims': 'column', 'long_name': 'Start of the columns', 'units': 'px'}, 'exag_col_map': {'dims': 'column', 'long_name': 'Mapping from column to exaggerated reader', 'units': 'reader_index'}, 'full_data': {'dims': ('ydata', 'column'), 'long_name': 'Full digitized data', 'units': 'px'}, 'hline': {'long_name': 'Horizontal line location', 'units': 'px'}, 'is_exaggerated': {'dims': 'reader', 'long_name': 'Exaggeration factor'}, 'occurences': {'comments': 'The locations where the only an occurence of a taxa is highlighted without value', 'dims': ('occurence', 'xy'), 'long_name': 'taxa occurences'}, 'reader': {'dims': 'reader', 'long_name': 'index of the reader'}, 'reader_cls': {'dims': 'reader', 'long_name': 'The name of the class constructor'}, 'reader_image': {'dims': ('reader', 'ydata', 'xdata', 'rgba'), 'long_name': 'RGBA images for data readers', 'units': 'color'}, 'reader_mod': {'dims': 'reader', 'long_name': 'The module of the reader class'}, 'rough_locs': {'dims': ('sample', 'column', 'limit'), 'long_name': 'Rough locations for samples'}, 'sample': {'long_name': 'Sample location', 'units': 'px'}, 'samples': {'dims': ('sample', 'column'), 'long_name': 'Sample data', 'units': 'px'}, 'shifted': {'dims': 'column', 'long_name': 'Vertical shift per column', 'units': 'px'}, 'vline': {'long_name': 'Vertical line location', 'units': 'px'}, 'xaxis_translation': {'dims': ('reader', 'px_data', 'limit'), 'long_name': 'Pixel to data mapping for x-axis'}}¶: A mapping from variable name to meta information

new_child_for_cols(columns, cls, plot=True)[source]¶

Create a new child reader for specific columns

Parameters

columns (list of int) – The columns for the new reader
cls (type) – The DataReader subclass
plot (bool) – Plot the binary image

Returns

The new reader for the specified columns

Return type

instance of cls

property non_exaggerated_reader¶: The reader that represents the exaggerations

property num_labels¶: The maximum label in the labels array

property occurences¶

A set of tuples marking the position of an occurence

An occurence, motivated by pollen diagrams, just highlights the existence at a certain point without giving the exact value. In pollen diagrams, these are usually taxa that were found but have a percentage of less than 0.5 %.

This set of tuples (x, y) contains the coordinates of the occurences. The first value in each tuple is the y-value, the second the x-value.

See also

occurences_dict: A mapping from column number to occurences

property occurences_dict¶: A mapping from column number to an numpy array with the indices of an occurence

occurences_value = -9999¶: The value that is given to the occurences in the measurements

parent = None¶: Parent reader for this instance. Might be the instance itself

plot_background(ax=None, **kwargs)[source]¶

Plot a white layer below the plot_im

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on. If not given, the ax attribute is used
**kwargs – Any other keyword that is given to the matplotlib.pyplot.imshow() function

plot_color_image(ax=None, **kwargs)[source]¶

Plot the colored image on a matplotlib axes

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on. If not given, the ax attribute is used
**kwargs – Any other keyword that is given to the matplotlib.pyplot.imshow() function

plot_full_df(ax=None, *args, **kwargs)[source]¶

Plot the lines for the digitized diagram

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
*args,**kwargs – Any other argument and keyword argument that is passed to the matplotlib.pyplot.plot() function

plot_im = None¶: the matplotlib image artist

plot_image(ax=None, **kwargs)[source]¶

Plot the binary data image on a matplotlib axes

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on. If not given, the ax attribute is used and (if this is None, too) a new figure is created
**kwargs – Any other keyword that is given to the matplotlib.pyplot.imshow() function

plot_other_potential_samples(tol=1, already_found=None, *args, **kwargs)[source]¶

Plot potential samples that are not yet in the samples attribute

Parameters

tol (int) – The pixel tolerance for a sample. If the distance between a potential sample and all already existing sample is greater than tolerance, the potential sample will be plotted
already_found (np.ndarray) – The pixel rows of samples that have already been found. If not specified, the index of the sample_locs is used
excluded (bool) – If True, plot the excluded samples instead of the included samples (see the return values in find_potential_samples())
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
plot_kws (dict) – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function. By default, this is equal to {'marker': '+'}
min_len (int) – The minimum length of one extremum. If the width of the interval where we found an extrumum is smaller than that, the extremum is ignored. If None, this parameter does not have an effect (i.e. min_len=1).
max_len (int) – The maximum length of one extremum. If the width of the interval where we found an extrumum is greater than that, the extremum is ignored. If None, this parameter does not have an effect.
filter_func (function) – A function to filter the extreme. It must accept one argument which is a list of integers representing the indices of the extremum in a

plot_potential_samples(excluded=False, ax=None, plot_kws={}, *args, **kwargs)[source]¶

Plot the ranges for potential samples

This method plots the rough locations of potential samples (see find_potential_samples()

Parameters

excluded (bool) – If True, plot the excluded samples instead of the included samples (see the return values in find_potential_samples())
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
plot_kws (dict) – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function. By default, this is equal to {'marker': '+'}
min_len (int) – The minimum length of one extremum. If the width of the interval where we found an extrumum is smaller than that, the extremum is ignored. If None, this parameter does not have an effect (i.e. min_len=1).
max_len (int) – The maximum length of one extremum. If the width of the interval where we found an extrumum is greater than that, the extremum is ignored. If None, this parameter does not have an effect.
filter_func (function) – A function to filter the extreme. It must accept one argument which is a list of integers representing the indices of the extremum in a

plot_results(df, ax=None, fig=None, transformed=True)[source]¶

Plot the reconstructed diagram

This method plots the reconstructed diagram using the psy-strat module.

Parameters

df (pandas.DataFrame) – The data to plot. E.g. the sample_locs or the straditize.straditizer.Straditizer.final_df data
ax (matplotlib.axes.Axes) – The axes to plot on. If None, a new one is created inside the given fig
fig (matplotlib.figure.Figure) – The matplotlib figure to plot on. If not given, the current figure (see matplotlib.pyplot.gcf()) is used
transformed (bool) – If True, y-axes and x-axes have been translated (see the px2data_x() and px2data_y() methods)

Returns

psyplot.project.Project – The newly created psyplot project with the plotters
list of psy_strat.stratplot.StratGroup instances – The groupers for the different columns

plot_sample_hlines(ax=None, **kwargs)[source]¶

Plot one horizontal line per sample in the sample_locs

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
*args,**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.hlines() function

plot_samples(ax=None, *args, **kwargs)[source]¶

Plot the diagram as lines reconstructed from the samples

Parameters

ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
*args,**kwargs – Any other argument and keyword argument that is passed to the matplotlib.pyplot.plot() function

px2data_x(coord)[source]¶

Transform the pixel coordinates into data coordinates

Parameters: coord (1D np.ndarray) – The coordinate values in pixels
Returns: The numpy array starting from 0 with transformed coordinates
Return type: np.ndarray

Notes

Since the x-axes for stratographic plots are usually interrupted, the return values here are relative and therefore always start from 0

recognize_hlines(fraction=0.3, min_lw=1, max_lw=None, remove=False, **kwargs)[source]¶

Recognize horizontal lines in the plot and subtract them

This method removes horizontal lines in the data diagram, i.e. rows whose non-background cells cover at least the specified fraction of the row.

Parameters

fraction (float) – The fraction (between 0 and 1) that has to be covered to recognize a horizontal line
min_lw (int) – The minimum line width for a line
max_lw (int) – The maximum line width for a line or None if it should be ignored
remove (bool) – If True, they will be removed immediately, otherwise they are displayed using the enable_label_selection() method and can be removed through the remove_selected_labels() method

Other Parameters

``**kwargs`` – Additional keywords are parsed to the enable_label_selection() method in case remove is False

Notes

This method has to be called before the digitize() method!

recognize_vlines(fraction=0.3, min_lw=1, max_lw=None, remove=False, **kwargs)[source]¶

Recognize horizontal lines in the plot and subtract them

This method removes horizontal lines in the data diagram, i.e. rows whose non-background cells cover at least the specified fraction of the row.

Parameters

fraction (float) – The fraction (between 0 and 1) that has to be covered to recognize a horizontal line
min_lw (int) – The minimum line width for a line
max_lw (int) – The maximum line width for a line or None if it should be ignored
remove (bool) – If True, they will be removed immediately, otherwise they are displayed using the enable_label_selection() method and can be removed through the remove_selected_labels() method

Other Parameters

``**kwargs`` – Additional keywords are parsed to the enable_label_selection() method in case remove is False

Notes

This method should be called before the column starts are set

recognize_xaxes(fraction=0.3, min_lw=1, max_lw=None, remove=False, **kwargs)[source]¶

Recognize (and potentially remove) x-axes at bottom and top

Parameters

fraction (float) – The fraction (between 0 and 1) that has to be covered to recognize an x-axis
min_lw (int) – The minimum line width of an axis
max_lw (int) – Tha maximum line width of an axis. If not specified, it will be ignored
remove (bool) – If True, they will be removed immediately, otherwise they are displayed using the enable_label_selection() method and can be removed through the remove_selected_labels() method

recognize_yaxes(fraction=0.3, min_lw=0, max_lw=None, remove=False)[source]¶

Find (and potentially remove) y-axes in the image

Parameters

fraction (float) – The fraction (between 0 and 1) that has to be covered to recognize a y-axis
min_lw (int) – The minimum line width of an axis
max_lw (int) – Tha maximum line width of an axis. If not specified, the median if the axes widths is taken
remove (bool) – If True, they will be removed immediately, otherwise they are displayed using the enable_label_selection() method and can be removed through the remove_selected_labels() method

remove_in_children(arr, amask)[source]¶

Update the child reader images after having removed binary data

Calls the update_image() and update_rgba_image() methods for all children

remove_plots()[source]¶: Remove all plotted artists by this reader

reset_column_starts()[source]¶: Reset the column starts, full_df, shifted and occurences

reset_image(image, binary=False)[source]¶

Reset the image for this straditizer

Parameters

image (PIL.Image.Image) – The new image
binary (bool) – If True, then the image is considered as the binary image and the image attribute is not touched

reset_labels()[source]¶: Reset the labels array

reset_samples()[source]¶: Reset the samples

resize_axes(grouper, bounds)[source]¶

Resize the axes based on column boundaries

This method sets the x-limits for the different columns to the given bounds and resizes the axes

Parameters

grouper (psy_strat.stratplot.StratGroup) – The grouper that manages the plot
bounds (np.ndarray of shape (N, 2)) – The boundaries for the columns handled by the grouper

property rough_locs¶

The pandas.DataFrame with rough locations for the samples. It has one row per sample in the sample_locs dataframe and ncols * 2 columns, where ncols is the number of columns in the sample_locs.

If the potential sample sample_locs.iloc[i, col] ranges j to k (see the find_potential_samples() method), the cell at rough_locs.iloc[i, col * 2] specifies the first y-pixel (j) and rough_locs.iloc[i, col * 2 + 1] the last y-pixel (+1), i.e. k where this sample might be located

property sample_locs¶: The pandas.DataFrame with locations and values of the samples

samples_at_boundaries = True¶: a boolean flag that shall indicate if we assume that the first and last rows shall be a sample if they contain non-zero values

set_as_parent()[source]¶: Set this instance as the parent reader

set_hline_locs_from_selection(selection=None)[source]¶

Save the locations of horizontal lines

This methods takes every pixel row in the hline_locs attribute where at least 30% is selected. The digitize method will interpolate at these indices.

set_vline_locs_from_selection(selection=None)[source]¶

Save the locations of vertical lines

This methods takes every pixel column in the vline_locs attribute where at least 30% is selected.

shift_vertical(pixels, draw=True)[source]¶

Shift the columns vertically.

Parameters

pixels (list of floats) – The y-value for each column for which to shift the values. Note that theses values have to be greater than or equal to 0
draw (bool) – If True, the ax is drawn at the end

shifted = None¶: The number of pixels the columns have been shifted

show_cross_column_features(min_px=50, remove=False, **kwargs)[source]¶

Highlight and maybe remove cross column features

Parameters

min_px (int) – The number of pixels that have to be contained in each column
remove (bool) – If True, remove the data in the binary array, etc. If False, the enable_label_selection() method is envoked and the user can select the features to remove
select_all (bool) – If True and remove is False, all labels in arr will be selected and the given selection is ignored
selection (np.ndarray of dtype bool) – A boolean mask with the same shape as arr that is True where a pixel should be selected. If remove is True, only this mask will be used.
img (matplotlib image) – The image for the selection. If not provided, a new image is created
set_picker (bool) – If True, connect the matplotlib pick_event to the pick_label() method

show_disconnected_parts(fromlast=5, from0=10, remove=False, **kwargs)[source]¶

Highlight or remove disconnected parts

Parameters

%(DataReader.get_disconnected_parts.parameters.fromlast|from0)s –
%(DataReader._show_parts2remove.parameters.no_arr)s –

show_parts_at_column_ends(npixels=2, remove=False, **kwargs)[source]¶

Highlight or remove features that touch the column ends

Parameters

%(DataReader.get_parts_at_column_ends.parameters)s –
%(DataReader._show_parts2remove.parameters.no_arr)s –

show_small_parts(n=10, remove=False, **kwargs)[source]¶

Highlight and potentially remove small features in the image

Parameters

n (int) – The maximal size of a feature to be considered as small
remove (bool) – If True, remove the data in the binary array, etc. If False, the enable_label_selection() method is envoked and the user can select the features to remove
select_all (bool) – If True and remove is False, all labels in arr will be selected and the given selection is ignored
selection (np.ndarray of dtype bool) – A boolean mask with the same shape as arr that is True where a pixel should be selected. If remove is True, only this mask will be used.
img (matplotlib image) – The image for the selection. If not provided, a new image is created
set_picker (bool) – If True, connect the matplotlib pick_event to the pick_label() method

See also

skimage.morphology.remove_small_objects()

start_column_selection(use_all=False)[source]¶

Enable the user to select columns

Parameters: use_all (bool) – If True, all columns can be selected. Otherwise only the columns in the columns attribute can be selected

strat_plot_identifier = 'percentages'¶

static to_binary_pil(image, threshold=690)[source]¶

Convert an image to a binary

Parameters

image (PIL.Image.Image) – The RGBA image file
threshold (float) – If the multiplied RGB values in a cell are above the threshold, the cell is regarded as background and will be set to 0

Returns

The binary image of integer type

Return type

np.ndarray of ndim 2

to_dataset(ds=None)[source]¶

All the necessary data as a xarray.Dataset

Parameters: ds (xarray.Dataset) – The dataset in which to insert the data. If None, a new one will be created
Returns: Either the given ds or a new xarray.Dataset instance
Return type: xarray.Dataset

static to_grey_pil(image, threshold=690)[source]¶

Convert an image to a greyscale image

Parameters

image (PIL.Image.Image) – The RGBA image file
threshold (float) – If the multiplied RGB values in a cell are above the threshold, the cell is regarded as background and will be set to 0

Returns

The greyscale image of integer type

Return type

np.ndarray of ndim 2

unique_bars(min_fract=None, asdict=True, *args, **kwargs)[source]¶

Estimate the unique bars

This method puts the overlapping bars of the different columns together

Parameters

min_fract (float) – The minimum fraction between 0 and 1 that two bars have to overlap such that they are considered as representing the same sample. If None, the min_fract attribute is used
asdict (bool) – If True, dictionaries are returned

Returns

A list of the bar locations. If asdict is True (default), each item in the returned list is a dictionary whose keys are the column indices and whose values are the indices for the corresponding column. Otherwise, a list of _Bar objects is returned

Return type

list

update_image(arr, amask)[source]¶

Update the image after having removed binary data

This method is in the remove_callbacks mapping and is called after a pixel has been removed from the binary data. It mainly just calls the reset_labels() method and updates the plot

update_rgba_image(arr, mask)[source]¶

Update the RGBA image from the given 3D-array

This method is in the remove_callbacks mapping and is called after a pixel has been removed from the binary data. It updates the image attribute

Parameters

arr (3D np.ndarray of dtype float) – The image array
mask (boolean mask of the same shape as arr) – The mask of features that shall be set to 0 in arr

vline_locs = None¶: list or floats. The indexes of vertical lines

xaxis_data = None¶

property xaxis_px¶: The x indices in column pixel coordinates that are used for x-axes translations

class straditize.binary.LineDataReader(image, ax=None, extent=None, plot=True, children=[], parent=None, magni=None, plot_background=False, binary=None)[source]¶

Bases: straditize.binary.DataReader

A data reader for digitizing line diagrams

This class does not have a significantly different behaviour than the base DataReader class, but might be improved with more specific features in the future

Parameters

image (PIL.Image.Image) – The image of the diagram
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on
extent (list) – List of four number specifying the extent of the image in it’s source. This extent will be used for the call of matplotlib.pyplot.imshow()
children (list of DataReader) – Child readers for other columns in case the newly created instance is the parent reader
parent (DataReader) – The parent reader.
magni (straditize.magnifier.Magnifier) – The magnifier for the given ax
plot_background (bool) – If True (and plot is True), a white, opaque are is plotted below the plot_im
binary (None) – The binary version of the given image. If not provided, the to_binary_pil() method is used with the given image

Attributes

strat_plot_identifier

str(object=’’) -> str

strat_plot_identifier = 'default'¶

class straditize.binary.RoundedBarDataReader(*args, **kwargs)[source]¶

Bases: straditize.binary.BarDataReader

A bar data reader that can be used for rounded bars

Parameters: tolerance (int) – If x0 is the value in a pixel row y and x1 the value in the next pixel row y+1, then the two pixel rows are considered as belonging to different bars if abs(x1 - x0) > tolerance (see the get_bars() method and the tolerance attribute)

Attributes

tolerance

int([x]) -> integer

tolerance = 10¶

straditize.binary.groupby_arr(arr)[source]¶

Groupby a boolean array

Parameters

arr (np.ndarray of ndim 1 of dtype bool) – An array that can be converted to a numeric array

Returns

keys (np.ndarrayrdi) – The keys in the array
starts (np.ndarray) – The index of the first element that correspond to the key in keys

straditize.binary.only_parent(func)[source]¶: Call the given func only from the parent reader

straditize.colnames module¶

Module for text recognition

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`Bbox`	A bounding box for a column name
`ColNamesReader`(image, bounds[, rotate, …])	A class to recognize the text in an image

class straditize.colnames.Bbox[source]¶

Bases: straditize.colnames._Bbox

A bounding box for a column name

Create new instance of _Bbox(x, y, w, h)

Attributes

`bottom`	The bottom of the box
`bounds`	A list `[x, y, width, height]`
`corners`	A np.ndarray of shape (4, 2) with the corners of the box
`crop_extents`	The extents necessary for PIL.Image.crop
`extents`	A list `[x0, x1, y0, y1]` with `x0 <= x1` and `y0 <= y1`
`height`	The (positive) height
`left`	The left edge of the box
`right`	The right edge of the box
`top`	The top of the box
`width`	The (positive) width
`x0`	The left edge
`x1`	The right edge
`y0`	The lower (bottom) edge
`y1`	The upper (top) edge

Methods

from_dict(d)

Construct a box from the dictionary

property bottom¶: The bottom of the box

property bounds¶: A list [x, y, width, height]

property corners¶: A np.ndarray of shape (4, 2) with the corners of the box

property crop_extents¶: The extents necessary for PIL.Image.crop

property extents¶: A list [x0, x1, y0, y1] with x0 <= x1 and y0 <= y1

classmethod from_dict(d)[source]¶: Construct a box from the dictionary

property height¶: The (positive) height

property left¶: The left edge of the box

property right¶: The right edge of the box

property top¶: The top of the box

property width¶: The (positive) width

property x0¶: The left edge

property x1¶: The right edge

property y0¶: The lower (bottom) edge

property y1¶: The upper (top) edge

class straditize.colnames.ColNamesReader(image, bounds, rotate=45, mirror=False, flip=False, highres_image=None, data_ylim=None)[source]¶

Bases: object

A class to recognize the text in an image

This object handles the column names in the column_names attribute. It also implements several algorithms to automatically read in the column names using the tesserocr package. In particular these are the recognize_text() method to read in one small image and the find_colnames() method to find the column names automatically.

Parameters

image (PIL.Image.Image) – The RGBA image that has the same shape as the original stratigraphic diagram
bounds (np.ndarray of shape (N, 2)) – The boundaries for each column. These are essential for the find_colnames() and the highlight_column() methods
rotate (float) – An angle between 0 and 90 that corresponds to the rotation of the column names
mirror (bool) – If True, the image is mirrored (horizontally)
flip (bool) – If True, the image is flipped (vertically)
highres_image (PIL.Image.Image) – A high resolution version of the image with the same width-to-height ratio
data_ylim (tuple (y0, y1)) – The vertical data limits of the data part that should be ignored in the find_colnames() method if the ignore_data_part is True

Methods

`close`()	Close the column names reader
`create_variable`(ds, vname, data, **kwargs)	Insert the data into a variable in an `xr.Dataset`
`find_colnames`([extents])	Find the names for the columns using tesserocr
`from_dataset`(ds)	Create a `ColNamesReader` for a xarray.Dataset
`get_colpic`(x0, y0, x1, y1)	Extract the picture of the column name
`highlight_column`(col, ax)	Highlight the column in the given axes displaying the
`navigate_to_col`(col, ax)	Navigate to the specified column
`recognize_text`(image)	Recognize the text in an image using tesserocr
`rotate_image`(image)	Modify an image with `rotate`, `flip`, `mirror`
`to_dataset`([ds])	All the necessary data as a `xarray.Dataset`
`transform_point`(x, y[, invert, image])	Transform a point between un-rotated and rotated coordinate system

Attributes

`colpics`	The pictures of the column names
`column_names`	The names of the columns
`data_ylim`	The vertical data limits of the data part that shall be exluded in the
`highres_image`	The `image` attribute with higher resolution and with masked out data part if the `ignore_data_part` attribute is True and the `data_ylim` attribute is not None.
`ignore_data_part`	Boolean flag.
`image`	The RGBA `PIL.Image.Image` that stores the column names
`nc_meta`	dict() -> new empty dictionary
`rotated_image`	The rotated `image` based on the `rotate_image()` method

close()[source]¶: Close the column names reader

property colpics¶: The pictures of the column names

property column_names¶: The names of the columns

create_variable(ds, vname, data, **kwargs)[source]¶: Insert the data into a variable in an xr.Dataset

data_ylim = None¶: The vertical data limits of the data part that shall be exluded in the highres_image if the ignore_data_part is True

find_colnames(extents=None)[source]¶

Find the names for the columns using tesserocr

Parameters

extents (list of floats (x0, y0, x1, y1)) – The extents to crop the rotated_image. We only look for column names in this image

Returns

dict – A mapping from column number to a string (the column name)
dict – A mapping from column number to a PIL.Image.Image (the image of the column name)
dict – A mapping from column number to a Bbox (the bounding box of the corresponding column name)

classmethod from_dataset(ds)[source]¶

Create a ColNamesReader for a xarray.Dataset

Parameters: ds (xarray.Dataset) – The dataset as obtained from the to_dataset() method

get_colpic(x0, y0, x1, y1)[source]¶

Extract the picture of the column name

Parameters

x0 (int) – The left edge
y0 (int) – The upper edge
x1 (int) – The right edge
y1 (int) – The lower edge

Returns

The part of the rotated highres_image cropped out from the given parameters

Return type

PIL.Image.Image

highlight_column(col, ax)[source]¶

Highlight the column in the given axes displaying the rotated_image

This method draws a rotated rectangle highlighting the given column col in the given ax.

Parameters

col (int) – The column number
ax (matplotlib.axes.Axes) – The matplotlib axes on which to plot the rectangle. This ax is expected to show the rotated_image

property highres_image¶: The image attribute with higher resolution and with masked out data part if the ignore_data_part attribute is True and the data_ylim attribute is not None. The data part is then set to white with 0 alpha

ignore_data_part = True¶: Boolean flag. If True, the data part is masked out in the highres_image

image = None¶: The RGBA PIL.Image.Image that stores the column names

navigate_to_col(col, ax)[source]¶

Navigate to the specified column

Change the x- and y-limits of the ax to display the given col based on the column_bounds

Parameters

col (int) – The column number
ax (matplotlib.axes.Axes) – The matplotlib axes for which to update the limits. This ax is expected to show the rotated_image

nc_meta = {'colname': {'dims': 'column', 'long_name': 'Name of the columns'}, 'colnames_bounds': {'dims': ('column', 'limit'), 'long_name': 'The boundaries of the columns for the column names reader', 'units': 'px'}, 'colnames_hr_image': {'dims': ('ycolname_hr', 'xcolname_hr', 'rgba'), 'long_name': 'Highres image for column names reader', 'units': 'color'}, 'colnames_image': {'dims': ('ycolname', 'xcolname', 'rgba'), 'long_name': 'RGBA images for column names reader', 'units': 'color'}, 'colpic': {'dims': ('column', 'colpic_y', 'colpic_x', 'rgba'), 'long_name': 'The pictures of the column names', 'units': 'color'}, 'colpic_extents': {'dims': ('column', 'limit'), 'long_name': 'The limits of the column names pictures', 'units': 'px'}, 'flip_colnames': {'dims': (), 'long_name': 'Flip the column names picture (vertically)'}, 'mirror_colnames': {'dims': (), 'long_name': 'Mirror the column names picture (horizontally)'}, 'rotate_colnames': {'dims': (), 'long_name': 'The rotation angle for column names'}}¶

recognize_text(image)[source]¶

Recognize the text in an image using tesserocr

This method uses the tesserocr.image_to_text() to read in the text in a given image

Parameters: image (PIL.Image.Image) – The image to read in
Returns: The text found in it without newline characters
Return type: str

rotate_image(image)[source]¶

Modify an image with rotate, flip, mirror

This method rotated, mirrors and/or flips the given image based on the rotate, mirror and flip attributes

Parameters: image (PIL.Image.Image) – The source image
Returns: The target image
Return type: PIL.Image.Image

property rotated_image¶: The rotated image based on the rotate_image() method

to_dataset(ds=None)[source]¶

All the necessary data as a xarray.Dataset

Parameters: ds (xarray.Dataset) – The dataset in which to insert the data. If None, a new one will be created
Returns: Either the given ds or a new xarray.Dataset instance
Return type: xarray.Dataset

transform_point(x, y, invert=False, image=None)[source]¶

Transform a point between un-rotated and rotated coordinate system

Parameters

x (float) – The x-coordinate of the point in the source coordinate system
y (float) – The y-coordinate of the point in the source coordinate system
invert (bool) – If True, the source coordinate system is the rotated one (i.e. this method transform from the rotated_image to the coordinate system of the image), other wise from the image to the rotated_image
image (PIL.Image.Image) – The unrotated source image. If None, the image is used. This image defines the source coordinate system (or the target coordinate system if invert is True)

Returns

float – The transformed x-coordinate
float – The transformed y-coordinate

straditize.common module¶

Module of commonly use python objects

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Functions

rgba2rgb(image[, color])

Alpha composite an RGBA Image with a specified color.

straditize.common.rgba2rgb(image, color=(255, 255, 255))[source]¶

Alpha composite an RGBA Image with a specified color.

Source: http://stackoverflow.com/a/9459208/284318

Parameters

image (PIL.Image) – The PIL RGBA Image object
color (tuple) – The rgb color for the background

Returns

The rgb image

Return type

PIL.Image

straditize.cross_mark module¶

Module for a cross mark to select one point in a matplotlib axes

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

`CrossMarkText`(args, *kwargs)	A CrossMarks that opens a QInputDialog after changing the position
`CrossMarks`([pos, ax, selectable, draggable, …])	A set of draggable marks in a matplotlib axes
`DraggableHLine`(y[, ax])	A draggable horizontal line
`DraggableHLineText`(args, *kwargs)	A CrossMarks that opens a QInputDialog after changing the position
`DraggableVLine`(x[, ax])	A draggable vertical line
`DraggableVLineText`(args, *kwargs)	A CrossMarks that opens a QInputDialog after changing the position

class straditize.cross_mark.CrossMarkText(*args, **kwargs)[source]¶

Bases: straditize.cross_mark.CrossMarks

A CrossMarks that opens a QInputDialog after changing the position

Parameters

pos (tuple of 2 arrays) – The initial positions of the crosses. The first item marks the x-coordinates of the points, the second the y-coordinates
ax (matplotlib.axes.Axes) – The axes object to draw to. If not specified and draw_lines is True, the current axes object is used
selectable (list of {'x', 'y'}) – Determine whether only the x-, y-, or both lines should be selectable
draggable (list of {'x', 'y'}) – Determine whether only the x-, y-, or both lines should be draggable
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function
dtype (object) – The data type for the data conversion
message (str) – The message to display in the dialog
label (str) – The label to how this value should be named
value (float) – The initial value to use

Methods

`ask_for_value`([val, label])	Ask for a value for the cross mark
`on_release`(event, args, *kwargs)	Release the mark and unselect it

Attributes

value

The value of this cross mark

ask_for_value(val=None, label=None)[source]¶

Ask for a value for the cross mark

This method opens a QInputDialog to ask for a new value

Parameters

val (float) – The initial value
label (str) – the name of what to ask for

on_release(event, *args, **kwargs)[source]¶

Release the mark and unselect it

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that releases the mark
force (bool) – If True, the mark is released although it does not contain the event
connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are released, too
draw (bool) – If True, the figure is drawn
**kwargs (*args,) – Any other parameter that is passed to the connected lines

value = None¶: The value of this cross mark

class straditize.cross_mark.CrossMarks(pos=(0, 0), ax=None, selectable=['h', 'v'], draggable=['h', 'v'], idx_h=None, idx_v=None, xlim=None, ylim=None, select_props={'c': 'r'}, auto_hide=False, connected_artists=[], lock=True, draw_lines=True, hide_vertical=None, hide_horizontal=None, **kwargs)[source]¶

Bases: object

A set of draggable marks in a matplotlib axes

Parameters

pos (tuple of 2 arrays) – The initial positions of the crosses. The first item marks the x-coordinates of the points, the second the y-coordinates
ax (matplotlib.axes.Axes) – The axes object to draw to. If not specified and draw_lines is True, the current axes object is used
selectable (list of {'x', 'y'}) – Determine whether only the x-, y-, or both lines should be selectable
draggable (list of {'x', 'y'}) – Determine whether only the x-, y-, or both lines should be draggable
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function

Attributes

`ax`	The matplotlib axes to plot on
`block_signals`	Block the emitting of signals of this instance
`connected_artists`	a list of `matplotlib.artist.Artist` whose colors are changed
`fig`	The `matplotlib.figure.Figure` that this mark plots on
`hide_horizontal`	Boolean to control whether the horizontal lines should be hidden
`hide_vertical`	Boolean to control whether the vertical lines should be hidden
`hline`	The current horizontal line
`hlines`	the list of horizontal lines
`idx_h`	The index for vertical lines
`idx_v`	The index for horizontal lines
`line_connections`	The line connections to the current position
`lock`	Class attribute that is set to a `CrossMark` instance to lock the
`moved`	A signal that is emitted when the mark is moved.
`other_connections`	All other connections to the current position
`points`	The x-y-coordinates of the points as a (N, 2)-shaped array
`pos`	The position of the current line
`show_connected_artists`	A boolean to control whether the connected artists should be shown
`vline`	The current vertical line
`vlines`	the list of vertical lines
`x`	The x-position of the mark
`xlim`	The x-limits of the `hlines`
`y`	The y-position of the mark
`ylim`	The x-limits of the `vlines`

Methods

`connect`()	Connect the marks matplotlib events
`connect_marks`(marks[, visible])	Connect multiple marks to each other
`connect_to_marks`(marks[, visible, append])	Append other marks that should be considered for aligning the lines
`contains`(event)	Test if the mark is selected by the given event
`disconnect`()	Disconnect all the stored connection ids
`draw_lines`(**kwargs)	Draw the vertical and horizontal lines
`is_selected_by`(event[, buttons])	Test if the given event selects the mark
`maintain_x`(marks)	Connect marks and maintain a constant horizontal distance
`maintain_y`(marks)	Connect marks and maintain a constant vertical distance between them
`on_motion`(event[, force, move_connected, …])	Move the lines of this mark
`on_press`(event[, force, connected])	Select the mark
`on_release`(event[, force, connected, draw])	Release the mark and unselect it
`remove`([artists])	Remove all lines and disconnect the mark
`set_connected_artists`(artists)	Set the connected artists
`set_connected_artists_visible`(visible)	Set the visibility of the connected artists
`set_current_point`(x, y[, nearest])	Set the current point that is selected
`set_pos`(pos)	Move the point(s) to another position
`set_visible`(b)	Set the visibility of the mark

ax = None¶: The matplotlib axes to plot on

property block_signals¶: Block the emitting of signals of this instance

connect()[source]¶: Connect the marks matplotlib events

static connect_marks(marks, visible=False)[source]¶

Connect multiple marks to each other

Parameters

marks (list of CrossMarks) – A list of marks
visible (bool) – If True, the marks are connected through visible lines

Notes

Different from the connect_to_marks() method, this static function connects each of the marks to the others.

connect_to_marks(marks, visible=False, append=True)[source]¶

Append other marks that should be considered for aligning the lines

Parameters

marks (list of CrossMarks) – A list of other marks
visible (bool) – If True, the marks are connected through visible lines
append (bool) – If True, the marks are appended. This is important if the mark will be moved by the set_pos method

Notes

This method can only be used to connect other marks with this mark. If you want to connect multiple marks within each other, use the connect_marks() static method

connected_artists = []¶: a list of matplotlib.artist.Artist whose colors are changed when this mark is selected

contains(event)[source]¶

Test if the mark is selected by the given event

Parameters: event (ButtonPressEvent) – The ButtonPressEvent that has been triggered

disconnect()[source]¶: Disconnect all the stored connection ids

draw_lines(**kwargs)[source]¶

Draw the vertical and horizontal lines

Parameters: **kwargs – An keyword that is passed to the matplotlib.pyplot.plot() function

property fig¶: The matplotlib.figure.Figure that this mark plots on

hide_horizontal = False¶: Boolean to control whether the horizontal lines should be hidden

hide_vertical = False¶: Boolean to control whether the vertical lines should be hidden

property hline¶: The current horizontal line

hlines = []¶: the list of horizontal lines

property idx_h¶: The index for vertical lines

property idx_v¶: The index for horizontal lines

is_selected_by(event, buttons=[1])[source]¶

Test if the given event selects the mark

Parameters

event (matplotlib.backend_bases.MouseEvent) – The matplotlib event
button (list of int) – Possible buttons to select this mark

Returns

True, if it is selected

Return type

bool

property line_connections¶: The line connections to the current position

lock = None¶: Class attribute that is set to a CrossMark instance to lock the selection of marks

static maintain_x(marks)[source]¶

Connect marks and maintain a constant horizontal distance

Parameters: marks (list of CrossMarks) – A list of marks. If one of the marks is moved horizontally, the others are, too

static maintain_y(marks)[source]¶

Connect marks and maintain a constant vertical distance between them

Parameters: marks (list of CrossMarks) – A list of marks. If one of the marks is moved vertically, the others are, too

moved¶: A signal that is emitted when the mark is moved. Connected function are expected to accept two arguments. One tuple with the old position and the CrossMarks instance itself

on_motion(event, force=False, move_connected=True, restore=True)[source]¶

Move the lines of this mark

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that moves the mark
force (bool) – If True, the mark is moved although it does not contain the event
move_connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are moved, too
restore (bool) – If True, the axes background is restored

on_press(event, force=False, connected=True)[source]¶

Select the mark

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that selects the mark
force (bool) – If True, the mark is selected although it does not contain the event
connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are selected, too

on_release(event, force=False, connected=True, draw=True, *args, **kwargs)[source]¶

Release the mark and unselect it

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that releases the mark
force (bool) – If True, the mark is released although it does not contain the event
connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are released, too
draw (bool) – If True, the figure is drawn
**kwargs (*args,) – Any other parameter that is passed to the connected lines

property other_connections¶: All other connections to the current position

property points¶: The x-y-coordinates of the points as a (N, 2)-shaped array

property pos¶: The position of the current line

remove(artists=True)[source]¶

Remove all lines and disconnect the mark

Parameters: artists (bool) – If True, the connected_artists list is cleared and the corresponding artists are removed as well

set_connected_artists(artists)[source]¶

Set the connected artists

Parameters: artists (matplotlib.artist.Artist) – The artists (e.g. other lines) that should be connected and highlighted if this mark is selected

set_connected_artists_visible(visible)[source]¶

Set the visibility of the connected artists

Parameters: visible (bool) – True, show the connected artists, else don’t

set_current_point(x, y, nearest=False)[source]¶

Set the current point that is selected

Parameters

x (int) – The index of the x-value in the xa attribute
y (int) – The index of the y-value in the ya attribute
nearest (bool) – If not None, x and y are interpreted as x- and y-values and we select the closest one

set_pos(pos)[source]¶

Move the point(s) to another position

Parameters: pos (tuple of 2 arrays) – The positions of the crosses. The first item marks the x-coordinates of the points, the second the y-coordinates

set_visible(b)[source]¶

Set the visibility of the mark

Parameters: b (bool) – If False, hide all horizontal and vertical lines, and the connected_artists

show_connected_artists = True¶: A boolean to control whether the connected artists should be shown at all

property vline¶: The current vertical line

vlines = []¶: the list of vertical lines

property x¶: The x-position of the mark

xlim = None¶: The x-limits of the hlines

property y¶: The y-position of the mark

ylim = None¶: The x-limits of the vlines

class straditize.cross_mark.DraggableHLine(y, ax=None, *args, **kwargs)[source]¶

Bases: straditize.cross_mark.CrossMarks

A draggable horizontal line

Parameters

y (float) – The y-position for the horizontal line
ax (matplotlib.axes.Axes) – The matplotlib axes
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function

Attributes

`hide_vertical`	bool(x) -> bool
`x`	The x-position of the mark

Methods

set_visible(b)

Set the visibility of the mark

hide_vertical = True¶

set_visible(b)[source]¶

Set the visibility of the mark

Parameters: b (bool) – If False, hide all horizontal and vertical lines, and the connected_artists

property x¶: The x-position of the mark

class straditize.cross_mark.DraggableHLineText(*args, **kwargs)[source]¶

Bases: straditize.cross_mark.DraggableHLine

A CrossMarks that opens a QInputDialog after changing the position

Parameters

y (float) – The y-position for the horizontal line
ax (matplotlib.axes.Axes) – The matplotlib axes
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function
dtype (object) – The data type for the data conversion
message (str) – The message to display in the dialog
label (str) – The label to how this value should be named
value (float) – The initial value to use

Methods

`ask_for_value`([val, label])	Ask for a value for the cross mark
`on_release`(event, args, *kwargs)	Release the mark and unselect it

ask_for_value(val=None, label=None)[source]¶

Ask for a value for the cross mark

This method opens a QInputDialog to ask for a new value

Parameters

val (float) – The initial value
label (str) – the name of what to ask for

on_release(event, *args, **kwargs)[source]¶

Release the mark and unselect it

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that releases the mark
force (bool) – If True, the mark is released although it does not contain the event
connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are released, too
draw (bool) – If True, the figure is drawn
**kwargs (*args,) – Any other parameter that is passed to the connected lines

class straditize.cross_mark.DraggableVLine(x, ax=None, *args, **kwargs)[source]¶

Bases: straditize.cross_mark.CrossMarks

A draggable vertical line

Parameters

x (float) – The x-position for the vertical line
ax (matplotlib.axes.Axes) – The matplotlib axes
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function

Attributes

`hide_horizontal`	bool(x) -> bool
`y`	The y-position of the mark

Methods

set_visible(b)

Set the visibility of the mark

hide_horizontal = True¶

set_visible(b)[source]¶

Set the visibility of the mark

Parameters: b (bool) – If False, hide all horizontal and vertical lines, and the connected_artists

property y¶: The y-position of the mark

class straditize.cross_mark.DraggableVLineText(*args, **kwargs)[source]¶

Bases: straditize.cross_mark.DraggableVLine

A CrossMarks that opens a QInputDialog after changing the position

Parameters

x (float) – The x-position for the vertical line
ax (matplotlib.axes.Axes) – The matplotlib axes
idx_h (pandas.Index) – The index for the horizontal coordinates. If not provided, we use a continuous movement along x.
idx_v (pandas.Index) – The index for the vertical coordinates. If not provided, we use a continuous movement along y.
xlim (tuple of floats (xmin, xmax)) – The minimum and maximum x value for the lines
ylim (tuple for floats (ymin, ymax)) – The minimum and maximum y value for the lines
select_props (color) – The line properties for selected marks
auto_hide (bool) – If True, the lines are hidden if they are not selected.
connected_artists (list of artists) – List of artists whose properties should be changed to select_props when this marks is selected
lock (bool) – If True, at most one mark can be selected at a time
draw_lines (bool) – If True, the cross mark lines are drawn. Otherwise, you must call the draw_lines method explicitly
hide_vertical (bool) – Boolean to control whether the vertical lines should be hidden. If None, the default class attribute is used
hide_horizontal (bool) – Boolean to control whether the horizontal lines should be hidden. If None, the default class attribute is used
**kwargs – Any other keyword argument that is passed to the matplotlib.pyplot.plot() function
dtype (object) – The data type for the data conversion
message (str) – The message to display in the dialog
label (str) – The label to how this value should be named
value (float) – The initial value to use

Methods

`ask_for_value`([val, label])	Ask for a value for the cross mark
`on_release`(event, args, *kwargs)	Release the mark and unselect it

ask_for_value(val=None, label=None)[source]¶

Ask for a value for the cross mark

This method opens a QInputDialog to ask for a new value

Parameters

val (float) – The initial value
label (str) – the name of what to ask for

on_release(event, *args, **kwargs)[source]¶

Release the mark and unselect it

Parameters

event (matplotlib.backend_bases.MouseEvent) – The mouseevent that releases the mark
force (bool) – If True, the mark is released although it does not contain the event
connected (bool) – If True, connected marks that should maintain a constant x- and y-distance are released, too
draw (bool) – If True, the figure is drawn
**kwargs (*args,) – Any other parameter that is passed to the connected lines

straditize.evaluator module¶

Evaluator class for the straditize algorithms

Classes

`BaselineScenario`([output_dir])	The baseline evaluation scenario for straditize with data from POLNET
`BlackWhiteScenario`([output_dir])	An evaluation scenario with a binary (black and white) image
`DPI150Scenario`([output_dir])	Another evaluation scenario but with a resolution of 150 dpi
`DPI600Scenario`([output_dir])	Another evaluation scenario but with a resolution of 600 dpi
`ExaggerationsEvaluator`(args, *kwargs)	An evaluator with exaggerations
`ExaggerationsScenario`([output_dir])	An evaluation scenario with an exaggerated plot of low percentages
`NoVerticalsEvaluator`(data, *args[, name, …])	An evaluator for an image without y-axis
`NoVerticalsScenario`([output_dir])	An evaluation scenario without y-axes in the plot
`StraditizeEvaluator`(data, *args[, name, …])	An evaluator for the straditize components

Functions

`print_progressbar`(iteration, total[, …])	Print iterations progress
`rmse`(sim, ref)	Calculate the root mean squared error between simulation and reference

class straditize.evaluator.BaselineScenario(output_dir='.')[source]¶

Bases: object

The baseline evaluation scenario for straditize with data from POLNET

This class uses the default settings of the StraditizeEvaluator and runs the analysis for a given dataset from POLNET.

Methods

`export_evaluator`(evaluator, args, *kwargs)
`init_evaluator`(name, data, args, *kwargs)	Initialize an evaluator for a given data set
`run`(data[, processes])

Attributes

index_names

Built-in mutable sequence.

export_evaluator(evaluator, *args, **kwargs)[source]¶

index_names = ['e_', 'ntaxa', 'nsamples']¶

init_evaluator(name, data, *args, **kwargs)[source]¶: Initialize an evaluator for a given data set

run(data, processes=None)[source]¶

class straditize.evaluator.BlackWhiteScenario(output_dir='.')[source]¶

Bases: straditize.evaluator.BaselineScenario

An evaluation scenario with a binary (black and white) image

Methods

init_evaluator(*args, **kwargs)

Initialize an evaluator for a given data set

init_evaluator(*args, **kwargs)[source]¶: Initialize an evaluator for a given data set

class straditize.evaluator.DPI150Scenario(output_dir='.')[source]¶

Bases: straditize.evaluator.BaselineScenario

Another evaluation scenario but with a resolution of 150 dpi

Methods

export_evaluator(*args, **kwargs)

export_evaluator(*args, **kwargs)[source]¶

class straditize.evaluator.DPI600Scenario(output_dir='.')[source]¶

Bases: straditize.evaluator.BaselineScenario

Another evaluation scenario but with a resolution of 600 dpi

Methods

export_evaluator(*args, **kwargs)

export_evaluator(*args, **kwargs)[source]¶

class straditize.evaluator.ExaggerationsEvaluator(*args, **kwargs)[source]¶

Bases: straditize.evaluator.StraditizeEvaluator

An evaluator with exaggerations

Methods

init_stradi(*args, **kwargs)

init_stradi(*args, **kwargs)[source]¶

class straditize.evaluator.ExaggerationsScenario(output_dir='.')[source]¶

Bases: straditize.evaluator.BaselineScenario

An evaluation scenario with an exaggerated plot of low percentages

Methods

init_evaluator(name, data, *args, **kwargs)

Initialize an evaluator for a given data set

init_evaluator(name, data, *args, **kwargs)[source]¶: Initialize an evaluator for a given data set

class straditize.evaluator.NoVerticalsEvaluator(data, *args, name='data', axislinestyle={'bottom': '-', 'left': '-', 'right': '-', 'top': '-'}, **kwargs)[source]¶

Bases: straditize.evaluator.StraditizeEvaluator

An evaluator for an image without y-axis

Parameters

df (pandas.DataFrame) – The dataframe containing the data to plot.
group_func (function) –
A function that groups the columns in the input df together. It must accept the name of a column and return the corresponding group name:
```
def group_func(col_name: str):
    return "name of it's group"
```
If this parameter is not specified, each column will be assigned to the ‘nogroup’ group that can then be used in the other parameters, such as formatoptions and percentages. Each group may also be divided into subgroups (see below), in this case, the group_func should return the corresponding subgroup.
formatoptions (dict) – The formatoption for each group. Depending on the chosen plot method, this contains the formatoptions for the psyplot plotter.
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on. New axes will be created that cover all the space of the given axes. If this parameter is not specified and fig is None, a new matplotlib figure is created with a new matplotlib axes.
thresh (float) – A minimum number between 0 and 100 (by default 1%) that a percentages column has to fullfil in order to be included in the plot. If a variable is always below this threshold, it will not be included in the figure
percentages (list of str or bool) – The group names (see group_func) that represent percentage values. This variables will be visualized using an area plot and can be rescaled to sum up to 100% using the calculate_percentages parameter. This parameter can also be set to True if all groups shall be considered as percentage data
exclude (list of str) – Either group names of column names in df that should be excluded in the plot
widths (dict) –
A mapping from group name to it’s relative width in the plot. The values of this mapping should some up to 1, e.g.:
```
widths = {'group1': 0.3, 'group2': 0.5, 'group3': 0.2}
```
calculate_percentages (bool or list of str) – If True, rescale the groups mentioned in the percentages parameter to sum up to 100%. In case of a list of str, this parameter represents the group (or variable) names that shall be used for the normalization
min_percentage (float) – The minimum percentage (between 0 and 100) that should be covered by variables displaying percentages data. Each plot in one of the percentages groups will have at least have a xlim from 0 to min_percentage
trunc_height (float) – A float between 0 and 1. The fraction of the ax that should be reserved for the group titles.
fig (matplotlib.Figure) – The matplotlib figure to draw the plot on. If neither ax nor fig is specified, a new figure will be created.
all_in_one (list of str) – The groups mentioned in this parameter will all be plotted in one single axes whereas the default is to plot each variable in a separate plot
stacked (list of str) – The groups mentioned in this parameter will all be plotted in one single axes, stacked onto each other
summed (list of str) – The groups (or subgroups) mentioned in this parameter will be summed and an extra plot will be appended to the right of the stratigraphic diagram
use_bars (list of str or bool) – The variables specified in this parameter (or all variables if use_bars is True) will be visualized by a bar diagram, instead of a line or area plot.
subgroups (dict) –
A mapping from group name to a list of subgroups, e.g.:
```
subgroups = {'Pollen': ['Trees', 'Shrubs']}
```
to divide an overarching group into subgroups.

Methods

`evaluate_column_starts`([close, base])
`evaluate_yaxes_removal`([close])
`export`(args, *kwargs)

evaluate_column_starts(close=True, base='starts_')[source]¶

evaluate_yaxes_removal(close=True)[source]¶

export(*args, **kwargs)[source]¶

class straditize.evaluator.NoVerticalsScenario(output_dir='.')[source]¶

Bases: straditize.evaluator.BaselineScenario

An evaluation scenario without y-axes in the plot

Methods

init_evaluator(name, data, *args, **kwargs)

Initialize an evaluator for a given data set

init_evaluator(name, data, *args, **kwargs)[source]¶: Initialize an evaluator for a given data set

class straditize.evaluator.StraditizeEvaluator(data, *args, name='data', axislinestyle={'bottom': '-', 'left': '-', 'right': '-', 'top': '-'}, **kwargs)[source]¶

Bases: object

An evaluator for the straditize components

Parameters

df (pandas.DataFrame) – The dataframe containing the data to plot.
group_func (function) –
A function that groups the columns in the input df together. It must accept the name of a column and return the corresponding group name:
```
def group_func(col_name: str):
    return "name of it's group"
```
If this parameter is not specified, each column will be assigned to the ‘nogroup’ group that can then be used in the other parameters, such as formatoptions and percentages. Each group may also be divided into subgroups (see below), in this case, the group_func should return the corresponding subgroup.
formatoptions (dict) – The formatoption for each group. Depending on the chosen plot method, this contains the formatoptions for the psyplot plotter.
ax (matplotlib.axes.Axes) – The matplotlib axes to plot on. New axes will be created that cover all the space of the given axes. If this parameter is not specified and fig is None, a new matplotlib figure is created with a new matplotlib axes.
thresh (float) – A minimum number between 0 and 100 (by default 1%) that a percentages column has to fullfil in order to be included in the plot. If a variable is always below this threshold, it will not be included in the figure
percentages (list of str or bool) – The group names (see group_func) that represent percentage values. This variables will be visualized using an area plot and can be rescaled to sum up to 100% using the calculate_percentages parameter. This parameter can also be set to True if all groups shall be considered as percentage data
exclude (list of str) – Either group names of column names in df that should be excluded in the plot
widths (dict) –
A mapping from group name to it’s relative width in the plot. The values of this mapping should some up to 1, e.g.:
```
widths = {'group1': 0.3, 'group2': 0.5, 'group3': 0.2}
```
calculate_percentages (bool or list of str) – If True, rescale the groups mentioned in the percentages parameter to sum up to 100%. In case of a list of str, this parameter represents the group (or variable) names that shall be used for the normalization
min_percentage (float) – The minimum percentage (between 0 and 100) that should be covered by variables displaying percentages data. Each plot in one of the percentages groups will have at least have a xlim from 0 to min_percentage
trunc_height (float) – A float between 0 and 1. The fraction of the ax that should be reserved for the group titles.
fig (matplotlib.Figure) – The matplotlib figure to draw the plot on. If neither ax nor fig is specified, a new figure will be created.
all_in_one (list of str) – The groups mentioned in this parameter will all be plotted in one single axes whereas the default is to plot each variable in a separate plot
stacked (list of str) – The groups mentioned in this parameter will all be plotted in one single axes, stacked onto each other
summed (list of str) – The groups (or subgroups) mentioned in this parameter will be summed and an extra plot will be appended to the right of the stratigraphic diagram
use_bars (list of str or bool) – The variables specified in this parameter (or all variables if use_bars is True) will be visualized by a bar diagram, instead of a line or area plot.
subgroups (dict) –
A mapping from group name to a list of subgroups, e.g.:
```
subgroups = {'Pollen': ['Trees', 'Shrubs']}
```
to divide an overarching group into subgroups.

Attributes

`all_results`
`column_bounds`
`column_ends`
`column_starts`
`data`
`data_xlim`
`data_ylim`
`dpi`
`full_df`
`height`
`results`
`results_column`	The column name in `all_results`
`summed_perc`
`transformed_data`	The `data` in pixel coordinates
`width`

Methods

`close`()
`evaluate_column_starts`([close, base])
`evaluate_full`([close])
`evaluate_sample_accuracy`([close, stradi, base])
`evaluate_sample_position`([close, stradi, base])
`evaluate_yaxes_removal`([close])
`export`(filepath[, dpi, labels])
`from_polnet`(data, args, *kwargs)
`init_stradi`([datalim, columns, names, …])
`run`()	Run all evaluations
`set_xtranslation`(stradi)

property all_results¶

close()[source]¶

property column_bounds¶

property column_ends¶

property column_starts¶

property data¶

property data_xlim¶

property data_ylim¶

property dpi¶

evaluate_column_starts(close=True, base='starts_')[source]¶

evaluate_full(close=True)[source]¶

evaluate_sample_accuracy(close=True, stradi=None, base='samples_')[source]¶

evaluate_sample_position(close=True, stradi=None, base='samples_')[source]¶

evaluate_yaxes_removal(close=True)[source]¶

export(filepath, dpi=300, labels={})[source]¶

classmethod from_polnet(data, *args, **kwargs)[source]¶

property full_df¶

property height¶

init_stradi(datalim=True, columns=True, names=True, digitize=True, samples=True, axes=False)[source]¶

property results¶

property results_column¶: The column name in all_results

run()[source]¶: Run all evaluations

set_xtranslation(stradi)[source]¶

property summed_perc¶

property transformed_data¶: The data in pixel coordinates

property width¶

straditize.evaluator.print_progressbar(iteration, total, prefix='', suffix='', length=100, fill='█')[source]¶

Print iterations progress

Taken from https://stackoverflow.com/a/34325723

Parameters

iteration (int) – current iteration
total (int) – total iterations
prefix (str) – prefix string
suffix (str) – suffix string
decimals (int) – positive number of decimals in percent complete
length (int) – character length of bar
fill (str) – bar fill character

straditize.evaluator.rmse(sim, ref)[source]¶

Calculate the root mean squared error between simulation and reference

Parameters

sim (np.ndarray) – The simluated data
ref (np.ndarray) – The reference data

straditize.label_selection module¶

Module for the LabelSelection class

This module defines the LabelSelection class, a base class for the straditize.straditizer.Straditizer straditize.binary.DataReader classes. This class implements the features to select parts of an image and deletes them. The straditize.widgets.selection_toolbar.SelectionToolbar interfaces with instances of this class.

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

LabelSelection

Class to provide selection functionalities for an image

class straditize.label_selection.LabelSelection[source]¶

Bases: object

Class to provide selection functionalities for an image

This class provides functionalities to select features in an image. A new selection can be started through enable_label_selection() method and selected parts can be removed through the remove_selected_labels() method.

A 2D boolean mask of the selected pixels can be accessed through the selected_part attribute.

This class generally assumes that the array for the selection is a 2D integer array, e.g. obtained from the skimage.morphology.label() function.

The selection of labels is handled through the colormap. The selection is displayed as a matplotlib image on the ax attribute of this instance. If the color for one label is equal to the cselect color, it is considered as selected. Additionally every cell that has a value greater than the original number of labels is considered to be selected.

Cells with a value of -1 are not selected and cells with a value of 0 cannot be selected.

Attributes

`cselect`	The RGBA color for selected polygons
`cunselect`	The RGBA color for unselected polygons
`label_arrs`	List of attribute names of arrays that should be modified if the labels are about to be removed.
`remove_callbacks`	Functions that shall be called before the labels are removed.
`selected_labeled_part`	The selected part as a 2D boolean mask
`selected_labels`	A list of selected labels in the selection array
`selected_part`	The selected part as a 2D boolean mask

Methods

`copy_cmap`(cmap_src, colors)	Copy a colormap with replaced colors
`disable_label_selection`([remove])	Disable the label selection
`enable_label_selection`(arr, ncolors[, img, …])	Start the selection of labels
`get_default_cmap`(ncolors)	The default colormap for binary images
`highlight_small_selections`([n])	Highlight labels that cover only a small portion of cells
`pick_label`(event)	Pick the label selected by the given mouseevent
`remove_selected_labels`([disable])	Remove the selected parts of the diagram
`remove_small_selection_ellipses`()	Remove the ellipes for small features
`select_all_labels`()	Select the entire array
`select_all_other_labels`()	Invert the selection
`select_labels`(selected)	Select a list of labels
`unselect_all_labels`()	Clear the selection

cid_select = None¶

static copy_cmap(cmap_src, colors)[source]¶

Copy a colormap with replaced colors

This function creates a method that has the same name and the same under, over and bad values as the given cmap_src but with replaced colors

Parameters

cmap_src (matplotlib.colors.Colormap) – The source colormap
colors (np.ndarray) – The colors for the colormap

Returns

The new colormap

Return type

matplotlib.colors.Colormap

cselect = [1.0, 0.0, 0.0, 1.0]¶: The RGBA color for selected polygons

cunselect = [0.0, 0.0, 0.0, 0.0]¶: The RGBA color for unselected polygons

disable_label_selection(remove=None)[source]¶

Disable the label selection

This will disconnect the pick_event and remove the selection images

Parameters: remove (bool) – Whether to remove the selection image from the plot. If None, the _remove attribute is used

enable_label_selection(arr, ncolors, img=None, set_picker=False, **kwargs)[source]¶

Start the selection of labels

Parameters

arr (2D np.ndarray of dtype int) – The labeled array that contains the features to select.
ncolors (int) – The maximum of the labels in arr
img (matplotlib image) – The image for the selection. If not provided, a new image is created
set_picker (bool) – If True, connect the matplotlib pick_event to the pick_label() method

get_default_cmap(ncolors)[source]¶: The default colormap for binary images

highlight_small_selections(n=20)[source]¶

Highlight labels that cover only a small portion of cells

This method uses the skimage.morphology.remove_small_objects() to detect and highlight small features in the diagram. Each feature will be highlighted through an ellipsis around it.

See also

remove_small_selection_ellipses()

label_arrs = []¶: List of attribute names of arrays that should be modified if the labels are about to be removed. The attributes might be callable and should then provide the array

pick_label(event)[source]¶: Pick the label selected by the given mouseevent

remove_callbacks = None¶: Functions that shall be called before the labels are removed. The keys must be the attributes in the label_attrs list, values must be list of function that accept too arguments, the array and the boolean mask highlighting the cells that will be set to 0

remove_selected_labels(disable=False)[source]¶

Remove the selected parts of the diagram

This method will call the callbackes in the remove_callbacks attribute for all the attributes in the label_arrs list.

Parameters: disable (bool) – If True, call the disable_label_selection() method at the end

remove_small_selection_ellipses()[source]¶

Remove the ellipes for small features

Removes the ellipses plotted by the highlight_small_selections() method

select_all_labels()[source]¶: Select the entire array

select_all_other_labels()[source]¶: Invert the selection

select_labels(selected)[source]¶

Select a list of labels

Parameters: selected (np.ndarray) – The numpy array of labels that should be selected

property selected_labeled_part¶: The selected part as a 2D boolean mask

property selected_labels¶: A list of selected labels in the selection array

property selected_part¶: The selected part as a 2D boolean mask

unselect_all_labels()[source]¶: Clear the selection

straditize.magnifier module¶

Magnifier class for an image

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

Magnifier(ax_src[, ax])

A magnification of a matplotlib axes

class straditize.magnifier.Magnifier(ax_src, ax=None, *args, **kwargs)[source]¶

Bases: object

A magnification of a matplotlib axes

It zooms into the region where the mouse pointer is, when it enters the source axes. The appearance of the plot is defined by the make_plot() method.

Methods

`adjust_limits`(zoom_val)
`close`()	Close the magnifier and the associated plots
`disconnect`()
`enable_zoom`()
`make_plot`(image, args, *kwargs)
`onenter`(event)
`onleave`(event)
`onmotion`(event)

Attributes

`dx`
`dy`

adjust_limits(zoom_val)[source]¶

ax = None¶

cid_enter = None¶

cid_leave = None¶

cid_motion = None¶

close()[source]¶: Close the magnifier and the associated plots

disconnect()[source]¶

property dx¶

property dy¶

enable_zoom()[source]¶

make_plot(image, *args, **kwargs)[source]¶

onenter(event)[source]¶

onleave(event)[source]¶

onmotion(event)[source]¶

straditize.straditizer module¶

Core module of the Straditizer class

This module defines the Straditizer class, the main object to digitize a stratigraphic diagram

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Classes

Straditizer(image[, ax, plot, attrs])

An object to digitize a stratigraphic diagram

Functions

format_coord_func(ax, ref)

Create a function that can replace the

class straditize.straditizer.Straditizer(image, ax=None, plot=True, attrs=None)[source]¶

Bases: straditize.label_selection.LabelSelection

An object to digitize a stratigraphic diagram

One instance of a straditizer manages the digitization of a PIL.Image.Image hold in the image attribute.

To create a new Straditizer instance, you can either provide a PIL.Image.Image to the class constructor (i.e. stradi = Straditizer(image)) or you use the from_dataset() method.

The reader for the diagram part can be accessed through the data_reader attribute, the reader for the column names through the colnames_reader attribute.

Parameters

image (PIL.Image.Image or np.ndarray) – The image file to process. A numpy array should be 3D with shape (Y, X, 4), where the last channel […, -1] should represent the alpha channel. A PIL.Image.Image will be converted to a RGBA image (if not already)
ax (matplotlib.axes.Axes) – The matplotlib axes. If None, a new one will be created
attrs (dict or pandas.DataFrame) – The attributes for this straditizer

Methods

`adjust_lims`()
`adjust_lims_after_resize`([event])
`adjust_lims_after_zoom`(ax)
`align_columns`()	Shift the columns after the marks have been moved
`close`()
`create_magni_marks`(marks)	Copy the created marks to the magnifiers axes
`create_variable`(ds, vname, data, **kwargs)	Insert the data into a variable in an `xr.Dataset`
`data2px_y`(coord)	Transform the data coordinates into pixel coordinates
`digitize_diagram`()
`draw_data_box`()
`draw_figure`()
`from_dataset`(ds[, ax, plot])	Create a new `Straditizer` from a dataset
`get_attr`(key)
`get_labels`([categorize])
`get_reader_for_column`(col)
`guess_data_lims`([fraction])	Guess the limits of the diagram part
`image_array`()
`init_reader`([reader_type, ax])
`load`(fname[, ax, plot])
`marks_for_column_ends`([threshold])
`marks_for_column_starts`([threshold])
`marks_for_data_selection`([nums, fraction, …])
`marks_for_occurences`()	Create marks for editing the occurences
`marks_for_samples`()
`marks_for_samples_sep`([nrows])
`marks_for_vertical_alignment`()	Create marks for vertical alignment of the columns
`marks_for_x_values`([at_col_start])	Create two marks for selecting the x-values
`marks_for_y_values`()	Create two marks for selecting the x-values
`plot_image`([ax])
`px2data_y`(coord)	Transform the pixel coordinates into data coordinates
`remove_data_box`()	Remove the data_box
`remove_marks`()	Remove any drawn marks
`reset_image`(image[, reader])	Reset the straditizer image
`save`(fname)	Dump the `Straditizer` instance to a file
`set_attr`(key, value)	Update an attribute in the `attrs`
`show_data_diagram`()
`show_full_image`()
`to_dataset`([ds])	All the necessary data as a `xarray.Dataset`
`update_column_ends`()
`update_column_starts`()
`update_data_part`()
`update_image`(arr, mask)	Update the image from the given 3D-array
`update_occurences`([remove])	Set the occurences from the given marks
`update_samples`([remove])
`update_samples_sep`([remove])
`update_xvalues`()
`update_yvalues`()

Attributes

`adjusting`	True if xlim and ylim are adjusted
`attrs`	`pandas.DataFrame`. The attributes of this straditizer
`attrs_dict`
`ax`	The matplotlib axes
`block_signals`	Block the emitting of signals of this instance
`colnames_reader`	The `straditize.colnames.ColNamesReader` for reading the column
`column_indexes`	The horizontal indexes for each column
`data_reader`	The `straditize.binary.DataReader` instance to digitize the
`data_xlim`
`data_ylim`
`fig`
`final_df`
`full_df`
`indexes`
`label_arrs`	Built-in mutable sequence.
`magni`	The `straditize.magnifier.Magnifier` for the diagram image
`mark_added`	A signal that is emitted if a mark has been added.
`mark_cids`	set() -> new empty set object
`mark_removed`	A signal that is emitted, if a mark has been removed.
`nc_meta`	dict() -> new empty dictionary
`valid_attrs`
`yaxis_px`

adjust_lims()[source]¶

adjust_lims_after_resize(event=None)[source]¶

adjust_lims_after_zoom(ax)[source]¶

property adjusting¶: True if xlim and ylim are adjusted

align_columns()[source]¶

Shift the columns after the marks have been moved

This method should be called after the marks_for_vertical_alignment() method to align the columns

attrs = None¶: pandas.DataFrame. The attributes of this straditizer

property attrs_dict¶

ax = None¶: The matplotlib axes

property block_signals¶: Block the emitting of signals of this instance

close()[source]¶

property colnames_reader¶: The straditize.colnames.ColNamesReader for reading the column names

property column_indexes¶: The horizontal indexes for each column

create_magni_marks(marks)[source]¶: Copy the created marks to the magnifiers axes

create_variable(ds, vname, data, **kwargs)[source]¶: Insert the data into a variable in an xr.Dataset

data2px_y(coord)[source]¶

Transform the data coordinates into pixel coordinates

Parameters: coord (1D np.ndarray) – The coordinate values
Returns: The numpy array with transformed coordinates
Return type: np.ndarray

data_reader = None¶: The straditize.binary.DataReader instance to digitize the data

property data_xlim¶

property data_ylim¶

digitize_diagram()[source]¶

draw_data_box()[source]¶

draw_figure()[source]¶

property fig¶

fig_h = None¶

fig_w = None¶

property final_df¶

classmethod from_dataset(ds, ax=None, plot=True)[source]¶

Create a new Straditizer from a dataset

This method uses a dataset that has been exported with the to_dataset() method to intialize a new reader

property full_df¶

get_attr(key)[source]¶

get_labels(categorize=1)[source]¶

get_reader_for_column(col)[source]¶

guess_data_lims(fraction=0.7)[source]¶

Guess the limits of the diagram part

Parameters

fraction (float) – The smallest fraction that has to be covered for a cell to be considered as a corner

Returns

np.array – xmin and xmax of the diagram part (see data_xlim)
np.array – ymin and ymax of the diagram part (see data_ylim)

image_array()[source]¶

property indexes¶

init_reader(reader_type='area', ax=None, **kwargs)[source]¶

label_arrs = ['image_array']¶

classmethod load(fname, ax=None, plot=True)[source]¶

magni = None¶: The straditize.magnifier.Magnifier for the diagram image

mark_added¶: A signal that is emitted if a mark has been added. Functions are expected to accept one argument, the newly created straditize.cross_mark.CrossMarks instance

mark_cids = {}¶

mark_removed¶: A signal that is emitted, if a mark has been removed. Functions are expected to accept one argument, the removed straditize.cross_mark.CrossMarks instance

marks_for_column_ends(threshold=None)[source]¶

marks_for_column_starts(threshold=None)[source]¶

marks_for_data_selection(nums=2, fraction=0.7, guess_lims=True)[source]¶

marks_for_occurences()[source]¶: Create marks for editing the occurences

marks_for_samples()[source]¶

marks_for_samples_sep(nrows=3)[source]¶

marks_for_vertical_alignment()[source]¶

Create marks for vertical alignment of the columns

This method creates one mark for each column. These marks should then be moved to positions that should be on the same vertical level. After that, the align_columns() method has to be called

marks_for_x_values(at_col_start=True)[source]¶

Create two marks for selecting the x-values

Parameters: at_col_start (bool) – If True, and no translation has yet been performed, create a mark at the column start and ask for the corresponding value

marks_for_y_values()[source]¶: Create two marks for selecting the x-values

nc_meta = {'axis': {'long_name': 'Axis coordinate'}, 'data_lims': {'dims': ('axis', 'limit'), 'long_name': 'Limits of the data diagram', 'units': 'px'}, 'done_tasks': {'long_name': 'Tasks that are marked as done by the user'}, 'image': {'dims': ('y', 'x', 'rgba'), 'long_name': 'Full stratigraphic diagram', 'units': 'color'}, 'limit': {'long_name': 'Minimum and maximum limit'}, 'px_data': {'long_name': 'Coordinate for pixel-to-data translations'}, 'yaxis_translation': {'dims': ('px_data', 'limit'), 'long_name': 'Pixel to data mapping for y-axis'}}¶

plot_image(ax=None, **kwargs)[source]¶

px2data_y(coord)[source]¶

Transform the pixel coordinates into data coordinates

Parameters: coord (1D np.ndarray) – The coordinate values in pixels
Returns: The numpy array with transformed coordinates
Return type: np.ndarray

remove_data_box()[source]¶: Remove the data_box

remove_marks()[source]¶: Remove any drawn marks

reset_image(image, reader=False)[source]¶

Reset the straditizer image

Parameters

image (PIL.Image.Image) – The new image to use
reader (bool) – If True, the image of the data reader will be replaced, too

save(fname)[source]¶

Dump the Straditizer instance to a file

Parameters: fname (str) – The file name where to save the instance

set_attr(key, value)[source]¶: Update an attribute in the attrs

show_data_diagram()[source]¶

show_full_image()[source]¶

to_dataset(ds=None)[source]¶

All the necessary data as a xarray.Dataset

Parameters: ds (xarray.Dataset) – The dataset in which to insert the data. If None, a new one will be created
Returns: Either the given ds or a new xarray.Dataset instance
Return type: xarray.Dataset

update_column_ends()[source]¶

update_column_starts()[source]¶

update_data_part()[source]¶

update_image(arr, mask)[source]¶

Update the image from the given 3D-array

Parameters

arr (3D np.ndarray of dtype float) – The image array
mask (boolean mask of the same shape as arr) – The mask of features that shall be set to 0 in arr

update_occurences(remove=True)[source]¶: Set the occurences from the given marks

update_samples(remove=True)[source]¶

update_samples_sep(remove=True)[source]¶

update_xvalues()[source]¶

update_yvalues()[source]¶

property valid_attrs¶

yaxis_data = None¶

property yaxis_px¶

straditize.straditizer.format_coord_func(ax, ref)[source]¶

Create a function that can replace the matplotlib.axes.Axes.format_coord()

Parameters

ax (matplotlib.axes.Axes) – The axes instance
ref (weakref.weakref) – The reference to the Straditizer instance

Returns

The function that can be used to replace ax.format_coord

Return type

function

straditize.version module¶

Version string of straditize

Disclaimer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Changelog¶

v0.1.3¶

Patch that forces the diagram limits to be integers.

v0.1.2¶

This release contains several small bug fixes, mainly for the bar data reader and for duplicated samples and occurences.

v0.1.1¶

This release has been approved by the Journal of Open Source Software (JOSS) in https://github.com/openjournals/joss-reviews/issues/1216

Added¶

Changelog

Changed¶

Thanks to the feedbacks from @ixjlyons and @sgrieve the installation instructions and some other documentation instructions have been improved (see the issues https://github.com/Chilipp/straditize/issues/1, https://github.com/Chilipp/straditize/issues/2, https://github.com/Chilipp/straditize/issues/3, https://github.com/Chilipp/straditize/issues/4 and https://github.com/Chilipp/straditize/issues/8)

How to cite straditize¶

When using straditize, you should at least cite the publication in the Journal of Open Source Software:

Sommer, Philipp, Dilan Rech, Manuel Chevalier, and Basil A. S. Davis. Straditize: Digitizing Stratigraphic Diagrams. Journal of Open Source Software , vol. 4, no. 34, 34, The Open Journal, Feb. 2019, p. 1216, doi:10.21105/joss.01216, https://doi.org/10.21105/joss.00363.

BibTex - EndNote

Furthermore, each release of straditize is associated with a DOI using zenodo.org. If you want to cite a specific version or plugin, please refer to the releases page of straditize.