remove consensus function in docs

Author: Narek Mkhitaryan

docs/source/api_reference/helpers.rst

@@ -24,8 +24,3 @@ ________________________
 .. automethod:: superannotate.SAClient.aggregate_annotations_as_df
 
 ----------
-
-Utility functions
---------------------------------
-
-.. autofunction:: superannotate.SAClient.consensus

docs/source/userguide/quickstart.rst

@@ -25,13 +25,6 @@ It can be installed on Ubuntu with:
 
    sudo apt-get install ffmpeg
 
-To use the :py:obj:`consensus` function on Windows and Mac platforms, you might also need to install the shapely package
-beforehand. The package works well only under the Anaconda distribution with:
-
-.. code-block:: bash
-
-   conda install shapely
-
 ----------
 
 

docs/source/userguide/utilities.rst

@@ -126,64 +126,3 @@ Example of created DataFrame:
 
 Each row represents annotation information. One full annotation with multiple
 attribute groups can be grouped under :code:`instanceId` field.
-
-
-Working with DICOM files
-------------------------
-
-JPEG images with names :file:`<dicom_file_name>_<frame_num>.jpg` will be created
-in :file:`<path_to_output_dir>`. Those JPEG images can be uploaded to
-SuperAnnotate platform using the regular:
-
-.. code-block:: python
-
-   sa.upload_images_from_folder_to_project(project, "<path_to_output_dir>")
-
-Some DICOM files can have image frames that are compressed. To load them, `GDCM :
-Grassroots DICOM library <http://gdcm.sourceforge.net/wiki/index.php/Main_Page>`_ needs to be installed:
-
-.. code-block:: bash
-
-   # using conda
-   conda install -c conda-forge gdcm
-
-   # or on Ubuntu with versions above 19.04
-   sudo apt install python3-gdcm
-
-Computing consensus scores for instances between several projects
------------------------------------------------------------------
-
-
-Consensus is a tool to compare the quallity of the annotations of the same image that is present in several projects.
-To compute the consensus scores:
-
-.. code-block:: python
-
-   res_df = sa.consensus([project_names], "<path_to_export_folder>", [image_list], "<annotation_type>")
-
-Here pandas DataFrame with following columns is returned: creatorEmail, imageName, instanceId, className, area, attribute, projectName, score
-
-.. image:: images/consensus_dataframe.png
-
-Besides the pandas DataFrame there is an option to get the following plots by setting the show_plots flag to True:
-
-* Box plot of consensus scores for each annotators
-* Box plot of consensus scores for each project
-* Scatter plots of consensus score vs instance area for each project
-
-.. code-block:: python
-
-   sa.consensus([project_names], "<path_to_export_folder>", [image_list], "<annotation_type>", show_plots=True)
-
-To the left of each box plot the original score points of that annotator is depicted, the box plots are colored by annotator.
-
-.. image:: images/consensus_annotators_box.png
-
-Analogically the box plots of consensus scores for each project are colored according to project name.
-
-.. image:: images/consensus_projects_box.png
-
-Scatter plot of consensus score vs instance area is separated by projects. Hovering on a point reveals its annotator and image name.
-The points are colored according to class name. Each annotator is represented with separate symbol.
-
-.. image:: images/consensus_scatter.png

src/superannotate/lib/app/interface/sdk_interface.py

@@ -1514,10 +1514,6 @@ def download_export(
         to_s3_bucket=None,
     ):
         """Download prepared export.
-
-        WARNING: Starting from version 1.9.0 :ref:`download_export <ref_download_export>` additionally
-        requires :py:obj:`project` as first argument.
-
         :param project: project name
         :type project: str
         :param export: export name