diff --git a/README.rst b/README.rst
index 53f3dae4..c8dbf9cb 100644
--- a/README.rst
+++ b/README.rst
@@ -2,51 +2,82 @@
 EIS Toolkit
 ====
 
-Related to EIS Horizon EU project. This repository is in early development stage.
+Related to EIS Horizon EU project. This repository is in early
+development stage.
 
 Current contents
 
 - a bunch of different configuration files
-- one preprocessing tool
+- one preprocessing tool (clip raster with polygon)
 - dummy files and functions for testing purposes.
 
+.. important::
+This repository only contains source code related to eis_toolkit python package.
+The user interface will be implemented into separate repository.
+
+
 Testing
 ====
 
-If you wish to just test the installation of eis_toolkit and use it, follow the **For users** section.
-If you want to set up a local development environment for contributing, read also the
+If you wish to just test the installation of eis_toolkit and use it,
+follow the **For users** section. If you want to set up a local
+development environment for contributing, read also the
 **For developers** section.
 
+
 For users
 ----
 
-1. Navigate to the Releases section and download tar.gz or .zip file
-2. Find out the path where python packages (which QGIS uses) are installed e.g. by opening QGIS, navigating to its
-Python console and executing
+1. Navigate to the Releases section and download latest tar.gz or
+.zip file
 
-.. code-block:: python
+2. Create a new virtual environment (VE) by navigating to the folder
+you wish to create the VE in, and by executing
 
-   import imp
+.. code-block:: shell
 
-   str.replace(imp.find_module('numpy')[1], '/numpy', '')
+    python3 -m venv <name_of_your_virtualenv>
 
-3. Open command line and execute
+3. Activate the VE:
+
+- UNIX / MacOS
 
 .. code-block:: shell
 
-   pip install --target=<path_found_in_step_2> -U <path_to_eis_toolkit-dummy_test.tar.gz>
+    source <name_of_your_virtualenv>/bin/activate
+
+- Windows
+
+.. code-block:: shell
+
+    <name_of_your_virtualenv>\Scripts\activate.bat
+
+.. hint::
+You should see (*<name_of_your_virtualenv>*) appearing in front of the command prompt.
+
+4. Install eis_toolkit by running
+
+.. code-block:: shell
+
+   pip install <path_to_eis_toolkit-X.Y.Z.tar.gz>
 
 or
 
 .. code-block:: shell
 
-   pip install --target=<path_found_in_step_2> -U <path_to_eis_toolkit-dummy_test.zip>
+   pip install <path_to_eis_toolkit-X.Y.Z.zip>
 
-4. Go back to QGIS's Python console and run e.g.
+5. Open Python console with
+
+.. code-block:: shell
+
+    python
+
+and run e.g.
 
 .. code-block:: python
 
-   from eis_toolkit.dependency_test.dummy import test_function
+   from eis_toolkit.dummy_tests.dummy import test_function
 
    test_function(12,2)
 
@@ -54,11 +85,42 @@ or
 
 .. code-block:: python
 
-   from eis_toolkit.dependency_test.dummy_gdal import driver_cnt
+   from eis_toolkit.dummy_tests.dummy_gdal import driver_cnt
 
    driver_cnt(1)
 
-In both cases, a result should appear into the QGIS's Python console's output window.
+
+Performing more complex tests
+^^^^
+
+In case you do not want to insert your test commands one by one into the
+command line's python console, you can create a local test file and
+execute it with
+
+.. code-block:: shell
+
+    python <name_of_your_test_file>.py
+
+.. hint::
+Your .py test file can, for example, look like
+
+.. code-block:: python
+
+    import rasterio as rio
+    import numpy as np
+    from matplotlib import pyplot
+    from pathlib import Path
+
+    output_path = Path('/home/pauliina/Downloads/eis_outputs/clip_result.tif')
+    src = rio.open(output_path)
+    arr = src.read(1)
+    # Let's replace No data values with numpy NaN values in order to plot clipped raster
+    # so that the colour changes are visible for human eye
+    arr = np.where(arr<-100, np.nan, arr)
+
+    pyplot.imshow(arr, cmap='gray')
+    pyplot.show()
+
 
 For developers
 ----
@@ -66,7 +128,8 @@ For developers
 Prerequisites
 ^^^^
 
-1. Install `poetry <https://python-poetry.org/>`_ according to your platform's `instructions <https://python-poetry.org/docs/#installation>`_
+1. Install `poetry <https://python-poetry.org/>`_ according to your platform's
+`instructions <https://python-poetry.org/docs/#installation>`_
 
 2. Get your local copy of the repository
 
@@ -74,6 +137,7 @@ Prerequisites
 
    git clone https://github.com/GispoCoding/eis_toolkit.git
 
+
 Set up a local environment
 ^^^^
 
@@ -97,105 +161,57 @@ or prefix your normal shell commands with
 
    poetry run
 
+
 Test the effect of your changes
 ^^^^
 
-Without QGIS
+From command line
 """"
 
-**From the command line**: You can run your code from the command line with the virtual environment by
+You can run your code from the command line within the virtual environment created by poetry.
 
-1. Running (inside of the VE)
+1. Run
 
 .. code-block:: shell
 
    pip install eis_toolkit
 
 
-2. Opening VE's python console with
+2. Open python console with
 
 .. code-block:: shell
 
    python
 
-and running e.g.
+and run e.g.
 
 .. code-block:: python
 
-   from eis_toolkit.dependency_test.dummy import test_function
+   from eis_toolkit.dummy_tests.dummy import test_function
 
    test_function(12,2)
 
-**With JupyterLab**: You can also use `JupyterLab <https://jupyterlab.readthedocs.io/en/stable/>`_ for testing of more complicated functionalities
-(for example if you need to store intermediate results in active memory). Launch JupyterLab with
-
-.. code-block:: shell
-
-   poetry run jupyter lab
 
-The notebooks are found in the `notebooks/` directory. You can import and use
-eis_toolkit's functions in these notebooks in the same way as you normally would.
-
-With QGIS
+With JupyterLab
 """"
 
-1. Find out the path where python packages (which QGIS uses) are installed e.g. by opening QGIS
-   and navigating to Python console and executing
-
-.. code-block:: python
-
-   import imp
+You can also use `JupyterLab <https://jupyterlab.readthedocs.io/en/stable/>`_ for testing purposes
+for example in cases when you want to store intermediate results in active memory.
 
-   str.replace(imp.find_module('numpy')[1], '/numpy', '')
-
-2. Go to command line, navigate inside of the cloned repository and build eis_toolkit with
-
-.. code-block:: shell
-
-   poetry build
-
-3. Install eis_toolkit to the location found in step 1
+Launch JupyterLab with
 
 .. code-block:: shell
 
-   pip install --target=<path_found_in_step_1> -U <path_to_cloned_eis_toolkit_folder>
-
-4. Now eis_toolkit is available in QGIS's python. You can, for example, go back to
-   QGIS's Python console and run
-
-.. code-block:: python
-
-   from eis_toolkit.dependency_test.dummy import test_function
-
-   test_function(12,2)
-
-or
-
-.. code-block:: python
-
- from eis_toolkit.dependency_test.dummy_sklearn import sk_mean
- import numpy as np
-
- x = np.array([[1.0, 2.0, 3.0], [2.0, 2.0, 2.0]])
-
- sk_mean(x)
-
-A result should appear into the QGIS's Python console's output window.
-
-Disclaimer
-====
-
-Any of the functionalities utilizing GDAL or rasterio (python library that depends on GDAL) functions will not
-work when testing eis_toolkit outside of QGIS's Python console unless you have separately taken care of
-installing GDAL library. In order to install GDAL it is necessary to have libgdal and
-its development headers installed. For Ubuntu 20.04 you can achieve this via
+   poetry run jupyter lab
 
-.. code-block:: shell
+The notebooks are found in this repository, under the `notebooks/` directory. You can import and use
+eis_toolkit's functions in these notebooks in the same way as you normally would use any other python package.
 
-    sudo apt-get install libgdal-dev
+.. hint::
+There exists three example notebook files. The first one contains general usage instructions for running and
+modifying JupyterLab notebooks. The second one has been created for testing that dependencies to other
+python packages work and the third one has been created for testing the functionality of the clip tool.
 
-**Note** that GDAL's installation procedure varies a lot between different
-operating systems!
 
 Documentation
 ====
diff --git a/eis_toolkit/dependency_test/__init__.py b/eis_toolkit/dummy_tests/__init__.py
similarity index 100%
rename from eis_toolkit/dependency_test/__init__.py
rename to eis_toolkit/dummy_tests/__init__.py
diff --git a/eis_toolkit/dependency_test/dummy.py b/eis_toolkit/dummy_tests/dummy.py
similarity index 100%
rename from eis_toolkit/dependency_test/dummy.py
rename to eis_toolkit/dummy_tests/dummy.py
diff --git a/eis_toolkit/dependency_test/dummy_gdal.py b/eis_toolkit/dummy_tests/dummy_gdal.py
similarity index 100%
rename from eis_toolkit/dependency_test/dummy_gdal.py
rename to eis_toolkit/dummy_tests/dummy_gdal.py
diff --git a/eis_toolkit/dependency_test/dummy_sklearn.py b/eis_toolkit/dummy_tests/dummy_sklearn.py
similarity index 100%
rename from eis_toolkit/dependency_test/dummy_sklearn.py
rename to eis_toolkit/dummy_tests/dummy_sklearn.py
diff --git a/eis_toolkit/preprocessing/clip.py b/eis_toolkit/preprocessing/clip.py
index 4c070390..a3978101 100644
--- a/eis_toolkit/preprocessing/clip.py
+++ b/eis_toolkit/preprocessing/clip.py
@@ -21,7 +21,7 @@ def clip_ras(rasin: Path, polin: Path, rasout: Path) -> None:
             raise (NotApplicableGeometryTypeException)
         if src.crs != pol_df.crs:
             raise (NonMatchingCrsException)
-        out_image, out_transform = mask(src, [pol_geom], crop=True)
+        out_image, out_transform = mask(src, [pol_geom], crop=True, all_touched=True)
         out_meta = src.meta
 
     out_meta.update({'driver': 'GTiff',
diff --git a/notebooks/testing1.ipynb b/notebooks/testing1.ipynb
index 1c99cb2b..68456fba 100644
--- a/notebooks/testing1.ipynb
+++ b/notebooks/testing1.ipynb
@@ -14,19 +14,11 @@
     "\n",
     "It also acts as a simple introduction to how you could use notebooks when developing eis_toolkit.\n",
     "\n",
-    "*DISCLAIMER*. Any of the functionalities utilizing GDAL functions will not work when testing eis_toolkit **outside of QGIS's Python console** unless you have separately taken care of installing GDAL library.\n",
-    "\n",
     "## General instructions\n",
     "\n",
     "### Packages\n",
     "\n",
-    "Do not install any packages by yourself! We want to keep all packages excplicitly tracked so that the end-user only needs to run `pip install eis_toolkit` once and everything should work!\n",
-    "\n",
-    "### Where's GDAL?\n",
-    "\n",
-    "The GDAL installation is very platform specific, and would be a pain for testers at this point. We currently circumvent this by using the GDAL that comes with QGIS. This means GDAL-related issues will be resolved automatically when we call eis_toolkit functions from QGIS's python. \n",
-    "\n",
-    "This limits eis_toolkit as a standalone python library at this point, as we depend on QGIS for interacting with raster datasets.\n",
+    "Do not install any packages by yourself!\n",
     "\n",
     "### Where can I find data for testing?\n",
     "\n",
@@ -59,8 +51,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from eis_toolkit.dependency_test.dummy import test_function\n",
-    "from eis_toolkit.dependency_test.dummy import test_function2"
+    "from eis_toolkit.dummy_tests.dummy import test_function\n",
+    "from eis_toolkit.dummy_tests.dummy import test_function2"
    ]
   },
   {
@@ -120,7 +112,7 @@
    "source": [
     "Note that above we created own import command for both functions. The other option would have been to\n",
     "```\n",
-    "from eis_toolkit.dependency_test import dummy\n",
+    "from eis_toolkit.dummy_tests import dummy\n",
     "\n",
     "dummy.test_function(1,2)\n",
     "dummy.test_function2(25)\n",
@@ -142,7 +134,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from eis_toolkit.dependency_test import dummy_sklearn"
+    "from eis_toolkit.dummy_tests import dummy_sklearn"
    ]
   },
   {
@@ -184,34 +176,34 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "072134c0-54d4-4b9f-b851-6f06e83030b5",
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c6f3125b-e4ff-4222-b271-50d675a9e487",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "This worked fine!"
+    "from eis_toolkit.dummy_tests.dummy_gdal import driver_cnt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "c6f3125b-e4ff-4222-b271-50d675a9e487",
+   "execution_count": 9,
+   "id": "d7d4ebbc-06f2-44a2-9303-c6d33cb3592e",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'osgeo'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01meis_toolkit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependency_test\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dummy_gdal\n",
-      "File \u001b[0;32m~/PycharmProjects/eis_toolkit/eis_toolkit/dependency_test/dummy_gdal.py:1\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mosgeo\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gdal\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdriver_cnt\u001b[39m(a: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124;03m\"\"\"Tests whether it works to call one of gdal's functions.\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \n\u001b[1;32m      7\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;124;03m        a (int): input determining the functionality of the function\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'osgeo'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GeoTiffien kasittelyyn soveltuvia drivereita:  2\n",
+      "Muuten vaan!\n"
      ]
     }
    ],
    "source": [
-    "from eis_toolkit.dependency_test import dummy_gdal"
+    "driver_cnt(1)\n",
+    "\n",
+    "driver_cnt(3)"
    ]
   },
   {
@@ -219,7 +211,7 @@
    "id": "85676b96-ad38-4703-8982-c80771fcd98e",
    "metadata": {},
    "source": [
-    "This did not work!"
+    "These worked fine!"
    ]
   },
   {
@@ -236,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "c1339bfe-d9b5-4bde-8788-598c8c8bfa0c",
    "metadata": {},
    "outputs": [],
@@ -255,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "acbaa675-8b96-49f1-bf26-a58432e4f6e8",
    "metadata": {},
    "outputs": [
@@ -266,7 +258,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# This fails\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygame\u001b[39;00m\n",
+      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# This fails\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygame\u001b[39;00m\n",
       "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pygame'"
      ]
     }
@@ -286,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "8d59d9f0-2a07-4cb6-844f-1b3c2b5b1e16",
    "metadata": {},
    "outputs": [],
@@ -300,13 +292,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "fc1a04a6-ac06-475a-aba4-7abca3a39ab3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -331,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "c0d3aee8-eeae-46e7-8559-3854ae6f4653",
    "metadata": {},
    "outputs": [],
@@ -351,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "16fa406b-218a-4ea0-90f4-0e654348c5e8",
    "metadata": {},
    "outputs": [],
@@ -369,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "425290ad-ceec-4c55-9ae6-307147ed76f4",
    "metadata": {},
    "outputs": [],
@@ -384,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "5c350ccd-ffda-4965-8bfe-e98af5da2fc5",
    "metadata": {},
    "outputs": [
diff --git a/notebooks/testing_clip.ipynb b/notebooks/testing_clip.ipynb
new file mode 100644
index 00000000..bd545c88
--- /dev/null
+++ b/notebooks/testing_clip.ipynb
@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de6b5fb9-2a80-48b7-8d02-4437d7f6d960",
+   "metadata": {},
+   "source": [
+    "# Test file for clip function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "73d0a0f6-5d1a-49d8-9657-a7c820ba3ca5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio as rio\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "efcd1286-c4d4-46f1-9f59-dca8c091514f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_path = Path('/home/pauliina/Downloads/eis_outputs/clip_result.tif')\n",
+    "# Default open mode is r\n",
+    "src = rio.open(output_path)\n",
+    "arr = src.read(1)\n",
+    "# Let's replace No data values with numpy NaN values in order to plot clipped raster so that the colour changes are visible for human eye\n",
+    "arr = np.where(arr<-100, np.nan, arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "74cba53e-5749-49f5-b355-921a99af90a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pyplot.imshow(arr, cmap='gray')\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de4004eb-d9c2-4e6f-8cbd-6540efbe06c1",
+   "metadata": {},
+   "source": [
+    "This is just to show one example on how you can visually check that your tool works as expected. Compare the image above with the result QGIS gives if you clip small_raster.tif with small_area.shp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3691c2e-58ef-43ff-bda5-16fe4a19e1c1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/test_eis_toolkit.py b/tests/eis_toolkit_test.py
similarity index 65%
rename from tests/test_eis_toolkit.py
rename to tests/eis_toolkit_test.py
index 1ff912dd..d52b95d0 100644
--- a/tests/test_eis_toolkit.py
+++ b/tests/eis_toolkit_test.py
@@ -2,4 +2,5 @@
 
 
 def test_version():
+    """Tests that version number is correct."""
     assert __version__ == '0.1.0'