diff --git a/README.md b/README.md
index ed9e67c..38eb26f 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,21 @@ Standaard setup voor python-projects bij HHNK. In de repo staan een aantal voorb
 Onderstaand staat uitgewerkt welke software wordt gebruikt, hoe testen opgezet kunnen worden, projectopzet en meer.
 
 # Installatie
+## Lokaal
 Om deze repo goed neer te zetten moeten een aantal stappen worden genomen:
-- `git clone` -> Clone de repo
+- `git clone https://github.com/HHNK/python-workflow-demo` -> Clone de repo
 - `pixi install` -> installeer de python environment (draai dit in de repo;`cd python-workflow-demo`)
 - `pixi run postinstall`
 
+## Codespaces
+Github Codespace geven een platform om zonder installaties in de cloud ook te werken met een github branch en vs-code. Via de hoofdpagina van de repo https://github.com/HHNK/python-workflow-demo klikken op de groene `Code` rechtsboven en vanuit daar een Codespace aanmaken.
+
+Vervolgens moet de environment geinstalleerd worden en enkele vs-code extensions (zie (# IDE )). Verdere stappen
+- `curl -fsSL https://pixi.sh/install.sh | sh` in terminal vs-code -> install pixi
+- ctrl+shift+` -> restart shell
+- `pixi install`
+- `pixi run postinstall`
+
 # IDE
 Voor de developer environment zijn er veel opties, we gebruiken [Visual Studio Code](https://code.visualstudio.com/download). Dit biedt goede integratie van notebooks en tal van andere extensions. Uiteindelijk zal persoonlijke voorkeur ook meespelen in de keuze. Om samenwerking binnen het team te bevorderen maken we alleen gebruik van vs-code.
 
@@ -198,4 +208,6 @@ De resultaten zijn op de repo te zien onder [Actions](https://github.com/HHNK/py
 
 
 # AI helpers
-Zelf thuis een [Ollama](https://ollama.com/) installatie draaien waarmee je lokaal AI kan draaien. Met de [Continue](https://docs.continue.dev/getting-started/install) extension zijn code-hints mogelijk zonder de limieten van de free-tier van [Copilot](https://code.visualstudio.com/docs/copilot/overview). Hiervoor is wel een redelijke GPU nodig. Ervaring tot nu is net iets minder dan de online tools.
\ No newline at end of file
+Zelf thuis een [Ollama](https://ollama.com/) installatie draaien waarmee je lokaal AI kan draaien. Met de [Continue](https://docs.continue.dev/getting-started/install) extension zijn code-hints mogelijk zonder de limieten van de free-tier van [Copilot](https://code.visualstudio.com/docs/copilot/overview). Hiervoor is wel een redelijke GPU nodig. Ervaring tot nu is net iets minder dan de online tools.
+
+
diff --git a/bin/install_hrt.sh b/bin/install_hrt.sh
new file mode 100644
index 0000000..a2043a8
--- /dev/null
+++ b/bin/install_hrt.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+set -e  # Exit on error
+
+# Optional: echo commands for debugging
+# set -x
+
+# Check if the directory ../hhnk-research-tools exists
+if [ ! -d "../hhnk-research-tools" ]; then
+    git clone https://github.com/HHNK/hhnk-research-tools/ ../hhnk-research-tools
+fi
+
+# Install the package in editable mode without dependencies
+pip install --no-deps -e ../hhnk-research-tools
\ No newline at end of file
diff --git a/bin/install_htt.sh b/bin/install_htt.sh
new file mode 100644
index 0000000..66d82e5
--- /dev/null
+++ b/bin/install_htt.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+set -e  # Exit on any error
+
+# Clone the repo if it doesn't exist
+if [ ! -d "../hhnk-threedi-tools" ]; then
+    git clone https://github.com/threedi/hhnk-threedi-tools/ ../hhnk-threedi-tools
+fi
+
+# Install with the same pip flags as in the Windows script
+pip install --no-build-isolation --no-deps --disable-pip-version-check -e ../hhnk-threedi-tools
diff --git a/pixi.lock b/pixi.lock
index 74444e8..7d7961e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -242,6 +242,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py312hf9745cd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda
@@ -282,6 +283,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.3-py312h021bea1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/rasterstats-0.20.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
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@@ -299,10 +301,13 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py312ha707e6e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.3.3-py312h7900ff3_3.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.8.2-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.7-py312h21f5128_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.20.1-py312h66e93f0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
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@@ -310,6 +315,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py312hc0a28a1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.1-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh0d859eb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda
@@ -587,6 +593,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.44-h3d7b363_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-11.1.0-py312h078707f_0.conda
@@ -627,6 +634,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/rasterio-1.4.3-py312ha172ac9_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/rasterstats-0.20.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/rav1e-0.6.6-h975169c_2.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/readme_renderer-44.0-pyhd8ed1ab_1.conda
@@ -642,9 +650,12 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py312h451d5c4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-1.8.3-pyh5737063_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/win-64/shapely-2.0.7-py312h3f81574_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/simplejson-3.20.1-py312h4389bb4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
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@@ -652,6 +663,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.4-py312h1a27103_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/svt-av1-3.0.1-he0c23c2_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py312hf9745cd_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py312h451d5c4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda
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       - conda: https://conda.anaconda.org/conda-forge/win-64/shapely-2.0.7-py312h3f81574_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.4-py312h1a27103_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda
@@ -6411,6 +6435,18 @@ packages:
   - pkg:pypi/parso?source=hash-mapping
   size: 75295
   timestamp: 1733271352153
+- conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda
+  sha256: ab52916f056b435757d46d4ce0a93fd73af47df9c11fd72b74cc4b7e1caca563
+  md5: ee23fabfd0a8c6b8d6f3729b47b2859d
+  depends:
+  - numpy >=1.4.0
+  - python >=3.9
+  license: BSD-2-Clause AND PSF-2.0
+  license_family: BSD
+  purls:
+  - pkg:pypi/patsy?source=hash-mapping
+  size: 186594
+  timestamp: 1733792482894
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
   sha256: 1087716b399dab91cc9511d6499036ccdc53eb29a288bebcb19cf465c51d7c0d
   md5: df359c09c41cd186fffb93a2d87aa6f5
@@ -7331,6 +7367,25 @@ packages:
   - pkg:pypi/rasterio?source=hash-mapping
   size: 7869724
   timestamp: 1742429400145
+- conda: https://conda.anaconda.org/conda-forge/noarch/rasterstats-0.20.0-pyhd8ed1ab_1.conda
+  sha256: 2d549a6cbb14d076e123e9e97c79c347cc0c5b82d55771be1fde86001a14ef4b
+  md5: d0bf36963569fa8b1843cb4c3e5cd74b
+  depends:
+  - affine
+  - click >7.1
+  - cligj >=0.4
+  - fiona
+  - numpy >=1.9
+  - python >=3.9
+  - rasterio >=1.0
+  - shapely
+  - simplejson
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/rasterstats?source=hash-mapping
+  size: 20855
+  timestamp: 1734603044778
 - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
   sha256: 91b3c1ced90d04ee2eded1f72cf3cbc19ff05a25e41876ef0758266a5bab009f
   md5: 77d9955b4abddb811cb8ab1aa7d743e4
@@ -7680,6 +7735,35 @@ packages:
   - pkg:pypi/scipy?source=hash-mapping
   size: 15350553
   timestamp: 1739793319263
+- conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda
+  noarch: python
+  sha256: ea29a69b14dd6be5cdeeaa551bf50d78cafeaf0351e271e358f9b820fcab4cb0
+  md5: 62afb877ca2c2b4b6f9ecb37320085b6
+  depends:
+  - seaborn-base 0.13.2 pyhd8ed1ab_3
+  - statsmodels >=0.12
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 6876
+  timestamp: 1733730113224
+- conda: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda
+  sha256: f209c9c18187570b85ec06283c72d64b8738f825b1b82178f194f4866877f8aa
+  md5: fd96da444e81f9e6fcaac38590f3dd42
+  depends:
+  - matplotlib-base >=3.4,!=3.6.1
+  - numpy >=1.20,!=1.24.0
+  - pandas >=1.2
+  - python >=3.9
+  - scipy >=1.7
+  constrains:
+  - seaborn =0.13.2=*_3
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/seaborn?source=hash-mapping
+  size: 227843
+  timestamp: 1733730112409
 - conda: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.3.3-py312h7900ff3_3.conda
   sha256: c6d5d0bc7fb6cbfa3b8be8f2399a3c1308b3392a4e20bd1a0f29a828fda5ab20
   md5: 4840da9db2808db946a0d979603c6de4
@@ -7764,6 +7848,35 @@ packages:
   - pkg:pypi/shapely?source=hash-mapping
   size: 537509
   timestamp: 1741167468116
+- conda: https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.20.1-py312h66e93f0_0.conda
+  sha256: 22041442f0659ef55073ca8536708115cdf6ac19d461a4db6840dc9b4d534ced
+  md5: d1c95aa4908e88bd4216f04b6bb7e297
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/simplejson?source=hash-mapping
+  size: 130844
+  timestamp: 1739781086613
+- conda: https://conda.anaconda.org/conda-forge/win-64/simplejson-3.20.1-py312h4389bb4_0.conda
+  sha256: c99159e7837a7bcf1ef1681d9a0dd048837934f55e4b5bbf98769e775a879b00
+  md5: a19810af5f435a998f79cc9be7285955
+  depends:
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/simplejson?source=hash-mapping
+  size: 129076
+  timestamp: 1739781332359
 - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda
   sha256: 41db0180680cc67c3fa76544ffd48d6a5679d96f4b71d7498a759e94edc9a2db
   md5: a451d576819089b0d672f18768be0f65
@@ -8084,6 +8197,47 @@ packages:
   - pkg:pypi/stack-data?source=hash-mapping
   size: 26988
   timestamp: 1733569565672
+- conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py312hc0a28a1_0.conda
+  sha256: 6cc65ba902b32207e8a697b0e0408a28d6cc166be04f1882c40739a86a253d22
+  md5: 97dc960f3d9911964d73c2cf240baea5
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - numpy <3,>=1.22.3
+  - numpy >=1.19,<3
+  - packaging >=21.3
+  - pandas !=2.1.0,>=1.4
+  - patsy >=0.5.6
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - scipy !=1.9.2,>=1.8
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/statsmodels?source=hash-mapping
+  size: 12103203
+  timestamp: 1727987129263
+- conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.4-py312h1a27103_0.conda
+  sha256: f4324d64753363e31b673379de0f6a6223f6b2d52d30b5b5993392c1b96ac8ee
+  md5: 328f5cc12ebb18fb7739478c33285b03
+  depends:
+  - numpy <3,>=1.22.3
+  - numpy >=1.19,<3
+  - packaging >=21.3
+  - pandas !=2.1.0,>=1.4
+  - patsy >=0.5.6
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - scipy !=1.9.2,>=1.8
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/statsmodels?source=hash-mapping
+  size: 11693380
+  timestamp: 1727987685496
 - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.1-h5888daf_0.conda
   sha256: 43a914e4b8f413d0327dd0eb98425b7c84d9dff6642a90bdae00e60dcc11a26d
   md5: 83ae590ee23da54c162d1f0fbf05bef0
diff --git a/pixi.toml b/pixi.toml
index 0be5092..71a9ce4 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -5,6 +5,15 @@ platforms = ["win-64", "linux-64"]
 version = "2025.1"
 # repository = "https://github.com/HHNK/spoc_hhnk"
 
+
+[target.win-64.tasks]
+hrt-install = "bin/install_hrt.cmd"
+htt-install = "bin/install_htt.cmd"
+
+[target.linux-64.tasks]
+hrt-install = "bash bin/install_hrt.sh"
+htt-install = "bash bin/install_htt.sh"
+
 [tasks]
 #Styling
 sort_imports = "python -m ruff check  ./**/*.py --select I --fix --force-exclude"
@@ -13,10 +22,8 @@ style = {depends-on = ["sort_imports", "fmt"]} # `pixi run style`
 
 lab = "jupyter lab"
 
-htt-install = "bin/install_htt.cmd"
-hrt-install = "bin/install_hrt.cmd"
 pre-commit-install = "pre-commit install --install-hooks -t=pre-push" #-t=pre-commit
-postinstall = { depends-on = ["htt-install", "hrt-install", "pre-commit-install"] }
+postinstall = { depends-on = ["hrt-install", "htt-install", "pre-commit-install"] }
 
 pre-commit-run = "pre-commit run --all-files"
 
@@ -24,6 +31,7 @@ pre-commit-run = "pre-commit run --all-files"
 code = "code"
 code_insiders = "code-insiders"
 
+
 [feature.test.tasks]
 tests = "python -m pytest tests_demo/"
 
@@ -51,6 +59,8 @@ urllib3 = "<2.0" #threedi-api-client
 pytest = "*"
 pytest-cov = "*"
 pre-commit = "*"
+seaborn = "*"
+rasterstats = "*"
 
 [pypi-dependencies]
 python-dotenv = "*"
diff --git a/python_workflow_demo/assignment_geochecks.py b/python_workflow_demo/assignment_geochecks.py
index 9ff12c9..3c9e51c 100644
--- a/python_workflow_demo/assignment_geochecks.py
+++ b/python_workflow_demo/assignment_geochecks.py
@@ -1,9 +1,11 @@
 # %%
 
-import random
-import requests
 import json
+import random
+
 import pandas as pd
+import requests
+
 
 def fetch_temperature_data():
     """
@@ -20,15 +22,15 @@ def fetch_temperature_data():
             "AquoMetadata": {
                 "Compartiment": {"Code": "OW"},
                 "Grootheid": {"Code": "CONCTTE"},
-                "Parameter": {"Code": "Cd"}
+                "Parameter": {"Code": "Cd"},
             }
         },
         "Periode": {
             "Begindatumtijd": "2000-01-01T00:00:00.000+01:00",
-            "Einddatumtijd": "2030-01-01T00:00:00.000+01:00"
-        }
+            "Einddatumtijd": "2030-01-01T00:00:00.000+01:00",
+        },
     }
-    headers = {'Content-Type': 'application/json'}
+    headers = {"Content-Type": "application/json"}
 
     try:
         response = requests.post(url, data=json.dumps(api_payload), headers=headers)
@@ -38,6 +40,7 @@ def fetch_temperature_data():
         print(f"Error while fetching data: {e}")
         return None
 
+
 def extract_measurements(data):
     """
     Extract measurements from the fetched JSON data.
@@ -48,15 +51,16 @@ def extract_measurements(data):
     Returns:
         list: A list of measurement dictionaries.
     """
-    if not data or 'WaarnemingenLijst' not in data:
+    if not data or "WaarnemingenLijst" not in data:
         print("Invalid or empty data received.")
         return []
 
     measurements = []
-    for row in data['WaarnemingenLijst']:
-        measurements.extend(row.get('MetingenLijst', []))
+    for row in data["WaarnemingenLijst"]:
+        measurements.extend(row.get("MetingenLijst", []))
     return measurements
 
+
 def process_measurements(measurements):
     """
     Process the extracted measurements into a pandas DataFrame.
@@ -72,11 +76,12 @@ def process_measurements(measurements):
         return pd.DataFrame()
 
     df = pd.DataFrame(measurements)
-    if 'Meetwaarde' in df.columns:
-        df['waardes'] = df['Meetwaarde'].apply(lambda x: x.get('Waarde_Numeriek') if isinstance(x, dict) else None)
-        df.drop(columns=['Meetwaarde', 'WaarnemingMetadata'], inplace=True, errors='ignore')
+    if "Meetwaarde" in df.columns:
+        df["waardes"] = df["Meetwaarde"].apply(lambda x: x.get("Waarde_Numeriek") if isinstance(x, dict) else None)
+        df.drop(columns=["Meetwaarde", "WaarnemingMetadata"], inplace=True, errors="ignore")
     return df
 
+
 def add_validation_errors(df):
     """
     Adds random validation errors to the Tijdstip and waardes columns in the DataFrame.
@@ -87,12 +92,13 @@ def add_validation_errors(df):
     Returns:
         pd.DataFrame: DataFrame with introduced validation errors.
     """
+
     def introduce_error_tijdstip(tijdstip):
         # Randomly return invalid date formats
         errors = [
-            "INVALID_DATE",                 # Completely invalid
-            "2080-10-100 28:28:28",         # Invalid format and impossible date
-            "2025-05-12T25:61:00.000+01:00" # Invalid time
+            "INVALID_DATE",  # Completely invalid
+            "2080-10-100 28:28:28",  # Invalid format and impossible date
+            "2025-05-12T25:61:00.000+01:00",  # Invalid time
         ]
         return random.choice(errors) if random.random() < 0.5 else tijdstip
 
@@ -100,17 +106,18 @@ def introduce_error_waardes(waarde):
         # Randomly return invalid values
         errors = [
             "STRING_INSTEAD_OF_NUMBER",  # Invalid type
-            random.uniform(1001, 5000), # Weird large numbers
-            None                        # Missing value
+            random.uniform(1001, 5000),  # Weird large numbers
+            None,  # Missing value
         ]
         return random.choice(errors) if random.random() < 0.5 else waarde
 
     # Apply errors to Tijdstip and waardes columns
-    df['Tijdstip'] = df['Tijdstip'].apply(lambda x: introduce_error_tijdstip(x) if random.random() < 0.2 else x)
-    df['waardes'] = df['waardes'].apply(lambda x: introduce_error_waardes(x) if random.random() < 0.2 else x)
-    
+    df["Tijdstip"] = df["Tijdstip"].apply(lambda x: introduce_error_tijdstip(x) if random.random() < 0.2 else x)
+    df["waardes"] = df["waardes"].apply(lambda x: introduce_error_waardes(x) if random.random() < 0.2 else x)
+
     return df
 
+
 def main():
     """
     Main function to fetch, process, and display the temperature data.
@@ -125,7 +132,7 @@ def main():
     # Write test to check your function
     # df = dateformat_validation(df)
 
-    # Assignment 2 
+    # Assignment 2
     # Create validation of dataformat, add as function and push to branch
     # Write test to check your function
     # df = value_validation(df)
@@ -133,6 +140,6 @@ def main():
     print(df.columns)
     print(df)
 
+
 if __name__ == "__main__":
     main()
-