diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 00000000..19759639
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,19 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[Bug] Bug_short_description"
+labels: bug
+assignees: ''
+
+---
+
+Describe_the_bug
+
+Checklist to make sure that the bug report ist complete:
+- [ ] OS: *your_operating_system*, *your_distribution*
+- [ ] RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- [ ] If applicable: Attach full error message
+- [ ] If applicable: Share screenshots/images of your problem
+- [ ] If applicable: Share used input data
+
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/custom.md
new file mode 100644
index 00000000..c23c2cd0
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/custom.md
@@ -0,0 +1,16 @@
+---
+name: Custom issue template
+about: If your issue is not a bug
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
+Providing the following information will help RAMP developers assisting you:
+- [ ] OS: *your_operating_system*, *your_distribution*
+- [ ] RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- [ ] If applicable: Share used input data
+- 
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>
\ No newline at end of file
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 00000000..99e2dd37
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,6 @@
+Fix #Issue
+
+**Changes proposed in this pull request**:
+- Your_changes
+
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 7e2e0c3f..b3a0c2f1 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -5,9 +5,9 @@ name: RAMP tests
 
 on:
   push:
-    branches: [ "main", "development" ]
+    branches: [ "main", "development", "joss-paper"]
   pull_request:
-    branches: [ "main", "development" ]
+    branches: [ "main", "development", "joss-paper"]
 
 permissions:
   contents: read
@@ -26,7 +26,7 @@ jobs:
     - name: Install black
       run: |
         python -m pip install --upgrade pip
-        pip install black
+        pip install black[jupyter]==24.4.2
     - name: Lint with black
       run: |
         black . --check
@@ -37,4 +37,15 @@ jobs:
         pip install -r tests/requirements.txt
     - name: Test with pytest
       run: |
-        pytest tests/
+        coverage run -m pytest tests/
+
+    - name: Check test coverage
+      run: |
+        coverage report -m
+
+    - name: Report to coveralls
+      run: |
+        coveralls
+      env:
+        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALL_TOKEN }}
+        COVERALLS_SERVICE_NAME: github
diff --git a/.github/workflows/python-publish-pypi.yml b/.github/workflows/python-publish-pypi.yml
index c29d1e7a..85145c9a 100644
--- a/.github/workflows/python-publish-pypi.yml
+++ b/.github/workflows/python-publish-pypi.yml
@@ -8,6 +8,9 @@ jobs:
   build_and_deploy:
     name: Build the release and deploy to test pypi
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
     steps:
       - name: Checkout code
         uses: actions/checkout@master
@@ -32,4 +35,4 @@ jobs:
         with:
           verbose: true
           password: ${{ secrets.RAMP_PYPI_PW }}
-          repository_url: https://upload.pypi.org/legacy/
\ No newline at end of file
+          repository_url: https://upload.pypi.org/legacy/
diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml
index 65df7566..066e006e 100644
--- a/.github/workflows/python-pypi-test.yml
+++ b/.github/workflows/python-pypi-test.yml
@@ -14,6 +14,9 @@ jobs:
   build_and_deploy:
     name: Build the release and deploy to test pypi
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
     steps:
       - name: Checkout code
         uses: actions/checkout@master
@@ -39,4 +42,4 @@ jobs:
           skip-existing: true
           verbose: true
           password: ${{ secrets.RAMP_TEST}}
-          repository-url: https://test.pypi.org/legacy/
\ No newline at end of file
+          repository-url: https://test.pypi.org/legacy/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..9dc163c4
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,15 @@
+exclude: '.git'
+default_stages: [commit]
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-added-large-files
+
+  - repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+      - id: black
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 415a6dca..45b817cf 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.7"
+    python: "3.10"
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
@@ -23,4 +23,4 @@ sphinx:
 # Optionally set the version of Python and requirements required to build your docs
 python:
    install:
-      - requirements: docs/docs-requirements.txt
\ No newline at end of file
+      - requirements: docs/docs-requirements.txt
diff --git a/AUTHORS b/AUTHORS
index 5cab0e24..68650fcf 100644
--- a/AUTHORS
+++ b/AUTHORS
@@ -23,3 +23,4 @@ Maria C.G. Hart, Leibniz Universität Hannover <hart@iwi.uni-hannover.de>
 Francesco Sanvito, TU Delft <F.Sanvito@tudelft.nl>
 Gregory Ireland, Reiner Lemoine Institut <Gregory.Ireland@rl-institut.de>
 Sergio Balderrama, Universidad Mayor de San Simon <slbalderrama@fcyt.umss.edu.bo>
+Johann Kraft, Reiner Lemoine Institut <johann.kraft@rl-institut.de>
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 61e8906e..cb968823 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,8 +4,45 @@ Release History
 0.5.2 (dev)
 -----------
 
+**|new|**       Addition of the 'coveralls' badge to the README
+
+**|new|**       Addition of a random-seed functionality to ensure reproducible results if needed
+
+**|new|**       If a `User` instance doesn't have any appliance, its `repr` method prints the user name, and number of users with a message to mention no appliances are assigned to the instance yet. Before it raised a `ValueError`.
+
+**|new|**       Documentation template was changed to sphinx wagtail theme to improve the navigation through the documentation sections
+
+**|new|**       Add issue templates for issue creation on github
+
+**|new|**       Automatic testing of the jupyter notebooks of the documentation, to make sure the examples are always running through
+
+**|new|**       Adding depreciation warning to back-compatibility `Appliance` method in `User` class to let users know they should use the `add_appliance` method instead.
+
+**|new|**       Introduction of an Appliance parameter to model productive use duty cycles: `continuous_use_duty_cycle`
+
+**|changed|**   Expanded and revised documentation, with a particular focus on more and clearer usage examples
+
+**|changed|**   Updated requirements for contributing, including pip dependencies specific to developers
+
+**|changed|**   Expanded test coverage
+
+**|changed|**   Updated .py example input files to match the latest formalism of the RAMP code
+
+**|changed|**   Python version was bumped from 3.8 to 3.10
+
+**|changed|**   Improved the way to run the quantitative tests and the instructions to do so
+
+**|fixed|**     Windows compatibility of path to convert .py to .xlsx
+
+**|fixed|**     `rand_peak_enlarge` is rounded to be at least 1 so that the `peak_time_range` is never empty
+
+**|fixed|**     Running .xlsx files form the command line
+
+**|fixed|**     Ignore profile of appliances if any of their functional time or randomly allocated time of use are 0
+
 0.5.1 (2024-02-08)
------------
+------------------
+
 **|fixed|**     Plotting a cloud of profiles from the command line is fixed
 
 0.5.0 (2023-12-06)
@@ -38,7 +75,7 @@ Release History
 0.4.1 (2023-10-XX)
 ------------------
 
-**|hotfix|**    added option `-o` to the terminal command line interface to enable the user to provide output path to save ramp results. This option is also accessible to python users using `ofname` argument of the `ramp/ramp_run.py::run_usecase` or the `ramp/post_process/post_process.py::export_series` functions. 
+**|hotfix|**    added option `-o` to the terminal command line interface to enable the user to provide output path to save ramp results. This option is also accessible to python users using `ofname` argument of the `ramp/ramp_run.py::run_usecase` or the `ramp/post_process/post_process.py::export_series` functions.
 
 0.4.0 (2023-02-17)
 ------------------
@@ -57,7 +94,7 @@ Release History
 
 **|fixed|**     the way in which the random switch-on time is computed in the `stochastic_process` has been changed so that it is sampled with uniform probability from a concatenated set of functioning windows, rather than for each window separately (which led to short windows having higher concentration of switch-on events and demand peaks)
 
-**|fixed|**     the default value for the `peak_enlarg` parameter has been changed from the mistyped value of 0 to the intended value of 0.15 
+**|fixed|**     the default value for the `peak_enlarg` parameter has been changed from the mistyped value of 0 to the intended value of 0.15
 
 **|new|**       added a paragraph describing the algorithm of RAMP
 
@@ -93,4 +130,3 @@ Release History
 **|changed|**   changed the way in which the probability of coincident switch-on of several identical appliances owned by a single user is computed. Now, it penalisse less the probability of maximum coincidence for off-peak events
 
 **|fixed|**     `s_peak` value is now set by default to 0.5, rather than 1. This fixes an unwanted behaviour in how the `random.gauss` function worked
-
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4bf3c62b..c2dc9f49 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -13,16 +13,40 @@ Some of the resources to look at if you're interested in contributing:
 
 By contributing to RAMP, e.g. through opening a pull request or submitting a patch, you represent that your contributions are your own original work and that you have the right to license them, and you agree that your contributions are licensed under the EUROPEAN UNION PUBLIC LICENCE v. 1.2.
 
+## Open an issue
+
+[Open an issue](https://github.com/RAMP-project/RAMP/issues) to report a bug or for a feature request, please try to provide the information summarized below
+
+- OS: *your_operating_system*, *your_distribution*
+- RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- If applicable: Attach full error message
+- If applicable: Share screenshots/images of your problem
+- If applicable: Share used input data
+
 ## Submitting changes
 
 To contribute changes:
 - Fork the project on GitHub
+- Follow the "Setup" steps below
 - Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
 - Add your name to the [AUTHORS](AUTHORS) file
 - Commit your changes to the feature branch
 - Push the branch to GitHub
 - On GitHub, create a new pull request from the feature branch
 
+### Setup
+
+1. Create a virtual environment and install the dev dependencies with
+
+        pip install -r dev_requirements.txt
+
+2. Install the pre-commit hooks with
+
+        pre-commit install
+
+   This will mainly make sure you can't commit if your code is not linted with black.
+   The pre-commit hook will check if your code is linted and if it is not it will simply lint it for you, you then only need to stage the changes made by the linter and commit again, as simple as that :)
+
 ### Pull requests
 
 Before submitting a pull request, check whether you have:
@@ -44,18 +68,32 @@ Please try to write clear commit messages. One-line messages are fine for small
 
 ## Testing
 
-We have a qualitative testing functionality that allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
+Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions.
+
+Before running the tests locally, you need to install the testing dependencies
+
+```
+pip install -r tests/requirements.txt
+```
+
+### Qualitative testing
+
+The qualitative testing functionality allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
+
+To run the qualitative test, you'll have to run
+ ```
+ python ramp/test/test_run.py
+ ```
+from the root level of this repository.
+
+If you already ran this script, you will be asked if you want to overwrite the results files (if you decide not to, the results are not going to be regenerated from your latest code version). You should compare the results of your code and those saved from the latest stable version thanks to the image which is displayed after the script ran.
 
-This functionality is accessible via `test/test_run.py`. To run the qualitative test, you'll have to go through the following steps:
-   1. run your modified code for the 3 reference input files for 30 days each. This will create 3 corresponding output files in the `results` folder
-   2. run `test/test_run.py` and visualise the comparison between the results of your code and those obtainable with the latest stable version
-   
 Ideally, the difference between reference and new results should be minimal and just due to the stochastic nature of the code. If more pronounced, it should be fully explainable based on the changes made to the code and aligned to the expectations of the developers (i.e. it should reflect a change in the output *wanted* and precisely *sought* with the commit in question).
 
 ### Unit tests
 
-Install `pytest` (`pip install pytest`) and run `pytest tests/` form the root of the repository to run the unit tests
+Run `pytest tests/` form the root of the repository to run the unit tests.
 
 ## Attribution
 
-The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.
\ No newline at end of file
+The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.
diff --git a/README.rst b/README.rst
index 60f88eae..414a7966 100644
--- a/README.rst
+++ b/README.rst
@@ -11,6 +11,9 @@
     :target: https://rampdemand.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status
 
+.. image:: https://coveralls.io/repos/github/RAMP-project/RAMP/badge.svg?branch=main
+   :target: https://coveralls.io/github/RAMP-project/RAMP?branch=main
+
 .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/source/_static/RAMP_logo_basic.png?raw=true
    :width: 300
 
@@ -44,7 +47,7 @@ Installing through pip
 
 .. code-block:: python
 
-   conda create -n ramp python=3.8
+   conda create -n ramp python=3.10
 
 
 2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing
@@ -106,7 +109,7 @@ To have a look to the python files, you can download them using the `download_ex
 
    download_example("the specfic folder directory to save the files")
 
--  ``input_file_1.py``: represents the most basic electric appliances; it is 
+-  ``input_file_1.py``: represents the most basic electric appliances; it is
    an example of how to model lightbulbs, radios, TVs, fridges, and
    other electric appliances. This input file is based on the ones used
    for `the first RAMP publication <https://doi.org/10.1016/j.energy.2019.04.097>`__.
@@ -189,12 +192,15 @@ Other options are documented in the help of `ramp`, which you access with the ``
    ramp -h
 
 
-If you have existing python input files, you can convert them to
-spreadsheet. To do so, go to the ``\ramp`` folder and run
+If you have existing python input files from RAMP version prior to 0.5, you can convert them to
+spreadsheets input files. Simply run
 
 .. code-block:: bash
 
-   python ramp_convert_old_input_files.py -i <path to the input file you wish to convert>
+   ramp_convert -i <path to the .py input file you wish to convert>
+
+If you want to save a RAMP model you created with a .py file into a spreadsheet refer to
+this `example <https://rampdemand.readthedocs.io/en/latest/examples/using_excel/using_excel.html#exporting-the-database>`_
 
 For other examples of command lines options, such as setting date ranges, please visit `the dedicated section  <https://rampdemand.readthedocs.io/en/latest/examples/year_simulation/year_simulation.html#setting-date-range>`_ of the documentation.
 
@@ -260,7 +266,7 @@ we use a more compact formulation:
     random_var_w=0.35 # 35% randomness assigned to the size of the functioning windows
     )
 
-At this point, we can group our different users into a "use case" and run the simulation, 
+At this point, we can group our different users into a "use case" and run the simulation,
 for instance for a whole year.
 
 .. code-block:: python
@@ -269,26 +275,17 @@ for instance for a whole year.
    whole_year_profile = use_case.generate_daily_load_profiles()
 
 Here is your first load for a community including two types of housholds,
-for a total of 23 individual users. Of course, more variations and many more 
-features are possible! For instance, you can simulate loads even for 
-an individual appliance or user. In addition, you can use in-built plotting 
-functionalities to explore your results. Check out the documentation 
+for a total of 23 individual users. Of course, more variations and many more
+features are possible! For instance, you can simulate loads even for
+an individual appliance or user. In addition, you can use in-built plotting
+functionalities to explore your results. Check out the documentation
 for all the possibilities.
 
 Contributing
 ============
 This project is open-source. Interested users are therefore invited to test, comment or contribute to the tool. Submitting issues is the best way to get in touch with the development team, which will address your comment, question, or development request in the best possible way. We are also looking for contributors to the main code, willing to contribute to its capabilities, computational-efficiency, formulation, etc.
 
-To contribute changes:
-
-#. Fork the project on GitHub
-#. Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
-#. Add your name to the `AUTHORS <https://github.com/RAMP-project/RAMP/blob/development/AUTHORS>`_ file
-#. Commit your changes to the feature branch
-#. Push the branch to GitHub
-#. On GitHub, create a new pull request from the feature branch
-
-When committing new changes, please also take care of checking code stability by means of the `qualitative testing <https://github.com/RAMP-project/RAMP/blob/development/CONTRIBUTING.md>`_ functionality.
+To contribute changes please consult our `Contribution guidelines <https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md>`_
 
 
 How to cite
@@ -312,5 +309,4 @@ Unless required by applicable law or agreed to in writing, software distributed
 
 
 .. note::
-
-   This project is under active development!
+   This project is actively maintained and developed. This means that while we provide stable and reliable software releases, we keep developing new features and improvements for upcoming, upgraded versions    of the software.
diff --git a/dev_requirements.txt b/dev_requirements.txt
new file mode 100644
index 00000000..d39a2e2b
--- /dev/null
+++ b/dev_requirements.txt
@@ -0,0 +1,3 @@
+pre-commit
+black==24.4.2
+-r tests/requirements.txt
diff --git a/docs/docs-requirements.txt b/docs/docs-requirements.txt
index 4259dc0b..8c2350b0 100644
--- a/docs/docs-requirements.txt
+++ b/docs/docs-requirements.txt
@@ -11,9 +11,8 @@ sphinx >= 1.6.4
 nbsphinx
 ipykernel
 sphinx-autobuild
-sphinx-pdj-theme # TODO to comment
 sphinx-copybutton
-sphinxjp.themes.sphinxjp
-sphinxjp.themes.basicstrap
+sphinx_wagtail_theme
+myst_parser
 nbformat
 nbconvert
diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
index 9214a21e..6eb6aa3d 100644
--- a/docs/notebooks/cooking_app.ipynb
+++ b/docs/notebooks/cooking_app.ipynb
@@ -7,8 +7,9 @@
    "source": [
     "# Cooking Appliances\n",
     "\n",
-    "In this example, appliances with multiple preferences index and\n",
-    "attributes are modelled.\n",
+    "Some appliances load profiles, highly depend on the user choices and preferences. For example, electric stoves power usage, highly depends on the type of food and that a user wants to cook. This kind of appliances in RAMP are flagged by the user category consumption preferences.\n",
+    "\n",
+    "In this example, we will see how the electric cookstoves with multiple user preferences can be modelled in RAMP.\n",
     "\n",
     "To have a better understanding of RAMP features for modelling this category of appliances, two households are considered:\n",
     "\n",
@@ -26,8 +27,9 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -40,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "77e6919a",
    "metadata": {},
    "outputs": [],
@@ -48,13 +50,13 @@
     "user_1 = User(\n",
     "    user_name=\"Household with single lunch habit\",\n",
     "    num_users=1,\n",
-    "    user_preference=1,  # user_1 has only one lunch preference\n",
+    "    user_preference=1,  # user_1 has only one consumption preference\n",
     ")\n",
     "\n",
     "user_2 = User(\n",
     "    user_name=\"Household with different lunch habits\",\n",
     "    num_users=1,\n",
-    "    user_preference=2,  # user_2 has two lunch preferences\n",
+    "    user_preference=2,  # user_2 has two consumption preferences\n",
     ")"
    ]
   },
@@ -65,6 +67,8 @@
    "source": [
     "### Defining the cycles for cooking soup and rice \n",
     "\n",
+    "For cookstoves, it will be more realistic to have different operation cycles as cooking a food usually requires different levels of heat for the different parts of food processing:\n",
+    "\n",
     "For cooking soup it is assumed that the user needs 25 minutes divided into two parts:\n",
     "\n",
     "| cycle         | power | time |\n",
@@ -89,19 +93,20 @@
    "outputs": [],
    "source": [
     "# soup for lunch\n",
+    "lunch_window = [12 * 60, 12 * 60 + 26]\n",
+    "\n",
     "soup_1 = user_1.add_appliance(\n",
     "    name=\"soup for lunch\",\n",
-    "    power=1200,\n",
-    "    func_time=25,\n",
-    "    func_cycle=25,\n",
-    "    thermal_p_var=0.2,\n",
-    "    fixed_cycle=1,\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    power=1200,  # nominal power of appliance\n",
+    "    func_time=25,  # the cooking time\n",
+    "    func_cycle=25,  # we always need  25 minute for cooking\n",
+    "    fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier\n",
+    "    window_1=lunch_window,  # lunch is always prepared from 12\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window,  # the time window of the working cycle\n",
     ")"
    ]
   },
@@ -135,15 +140,14 @@
     "    power=1200,\n",
     "    func_time=25,\n",
     "    func_cycle=25,\n",
-    "    thermal_p_var=0.2,\n",
     "    fixed_cycle=1,\n",
-    "    pref_index=1,  # the first preference\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    window_1=lunch_window,\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window,\n",
+    "    pref_index=1,  # the first preference\n",
     ")"
    ]
   },
@@ -160,612 +164,52 @@
     "    power=1200,\n",
     "    func_time=15,\n",
     "    func_cycle=15,\n",
-    "    thermal_p_var=0.2,\n",
-    "    pref_index=2,  # the second preference\n",
     "    fixed_cycle=1,\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    window_1=lunch_window,\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=600,  # power of the second cycle\n",
     "    t_12=10,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window,\n",
+    "    pref_index=2,  # the second preference\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5b789866",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
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-       "      <td>Household with single lunch habit</td>\n",
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-       "      <td>1200</td>\n",
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-       "      <td>900</td>\n",
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-       "      <td>750</td>\n",
-       "      <td>750</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                  0  \\\n",
-       "user_name                         Household with single lunch habit   \n",
-       "num_users                                                         1   \n",
-       "user_preference                                                   1   \n",
-       "name                                                 soup for lunch   \n",
-       "number                                                            1   \n",
-       "power                                                        1200.0   \n",
-       "num_windows                                                       1   \n",
-       "func_time                                                        25   \n",
-       "time_fraction_random_variability                                  0   \n",
-       "func_cycle                                                       25   \n",
-       "fixed                                                            no   \n",
-       "fixed_cycle                                                       1   \n",
-       "occasional_use                                                    1   \n",
-       "flat                                                             no   \n",
-       "thermal_p_var                                                   0.2   \n",
-       "pref_index                                                        0   \n",
-       "wd_we_type                                                        2   \n",
-       "p_11                                                           1200   \n",
-       "t_11                                                              5   \n",
-       "cw11_start                                                      720   \n",
-       "cw11_end                                                        900   \n",
-       "p_12                                                            750   \n",
-       "t_12                                                             20   \n",
-       "cw12_start                                                        0   \n",
-       "cw12_end                                                          0   \n",
-       "r_c1                                                              0   \n",
-       "p_21                                                              0   \n",
-       "t_21                                                              0   \n",
-       "cw21_start                                                        0   \n",
-       "cw21_end                                                          0   \n",
-       "p_22                                                              0   \n",
-       "t_22                                                              0   \n",
-       "cw22_start                                                        0   \n",
-       "cw22_end                                                          0   \n",
-       "r_c2                                                              0   \n",
-       "p_31                                                              0   \n",
-       "t_31                                                              0   \n",
-       "cw31_start                                                        0   \n",
-       "cw31_end                                                          0   \n",
-       "p_32                                                              0   \n",
-       "t_32                                                              0   \n",
-       "cw32_start                                                        0   \n",
-       "cw32_end                                                          0   \n",
-       "r_c3                                                              0   \n",
-       "window_1_start                                                  720   \n",
-       "window_1_end                                                    900   \n",
-       "window_2_start                                                    0   \n",
-       "window_2_end                                                      0   \n",
-       "window_3_start                                                    0   \n",
-       "window_3_end                                                      0   \n",
-       "random_var_w                                                      0   \n",
-       "\n",
-       "                                                                     1  \\\n",
-       "user_name                         Household with different lunch habit   \n",
-       "num_users                                                            1   \n",
-       "user_preference                                                      2   \n",
-       "name                                                    soup for lunch   \n",
-       "number                                                               1   \n",
-       "power                                                           1200.0   \n",
-       "num_windows                                                          1   \n",
-       "func_time                                                           25   \n",
-       "time_fraction_random_variability                                     0   \n",
-       "func_cycle                                                          25   \n",
-       "fixed                                                               no   \n",
-       "fixed_cycle                                                          1   \n",
-       "occasional_use                                                       1   \n",
-       "flat                                                                no   \n",
-       "thermal_p_var                                                      0.2   \n",
-       "pref_index                                                           1   \n",
-       "wd_we_type                                                           2   \n",
-       "p_11                                                              1200   \n",
-       "t_11                                                                 5   \n",
-       "cw11_start                                                         720   \n",
-       "cw11_end                                                           900   \n",
-       "p_12                                                               750   \n",
-       "t_12                                                                20   \n",
-       "cw12_start                                                           0   \n",
-       "cw12_end                                                             0   \n",
-       "r_c1                                                                 0   \n",
-       "p_21                                                                 0   \n",
-       "t_21                                                                 0   \n",
-       "cw21_start                                                           0   \n",
-       "cw21_end                                                             0   \n",
-       "p_22                                                                 0   \n",
-       "t_22                                                                 0   \n",
-       "cw22_start                                                           0   \n",
-       "cw22_end                                                             0   \n",
-       "r_c2                                                                 0   \n",
-       "p_31                                                                 0   \n",
-       "t_31                                                                 0   \n",
-       "cw31_start                                                           0   \n",
-       "cw31_end                                                             0   \n",
-       "p_32                                                                 0   \n",
-       "t_32                                                                 0   \n",
-       "cw32_start                                                           0   \n",
-       "cw32_end                                                             0   \n",
-       "r_c3                                                                 0   \n",
-       "window_1_start                                                     720   \n",
-       "window_1_end                                                       900   \n",
-       "window_2_start                                                       0   \n",
-       "window_2_end                                                         0   \n",
-       "window_3_start                                                       0   \n",
-       "window_3_end                                                         0   \n",
-       "random_var_w                                                         0   \n",
-       "\n",
-       "                                                                     2  \n",
-       "user_name                         Household with different lunch habit  \n",
-       "num_users                                                            1  \n",
-       "user_preference                                                      2  \n",
-       "name                                                    rice for lunch  \n",
-       "number                                                               1  \n",
-       "power                                                           1200.0  \n",
-       "num_windows                                                          1  \n",
-       "func_time                                                           15  \n",
-       "time_fraction_random_variability                                     0  \n",
-       "func_cycle                                                          15  \n",
-       "fixed                                                               no  \n",
-       "fixed_cycle                                                          1  \n",
-       "occasional_use                                                       1  \n",
-       "flat                                                                no  \n",
-       "thermal_p_var                                                      0.2  \n",
-       "pref_index                                                           2  \n",
-       "wd_we_type                                                           2  \n",
-       "p_11                                                              1200  \n",
-       "t_11                                                                 5  \n",
-       "cw11_start                                                         720  \n",
-       "cw11_end                                                           900  \n",
-       "p_12                                                               600  \n",
-       "t_12                                                                10  \n",
-       "cw12_start                                                           0  \n",
-       "cw12_end                                                             0  \n",
-       "r_c1                                                                 0  \n",
-       "p_21                                                                 0  \n",
-       "t_21                                                                 0  \n",
-       "cw21_start                                                           0  \n",
-       "cw21_end                                                             0  \n",
-       "p_22                                                                 0  \n",
-       "t_22                                                                 0  \n",
-       "cw22_start                                                           0  \n",
-       "cw22_end                                                             0  \n",
-       "r_c2                                                                 0  \n",
-       "p_31                                                                 0  \n",
-       "t_31                                                                 0  \n",
-       "cw31_start                                                           0  \n",
-       "cw31_end                                                             0  \n",
-       "p_32                                                                 0  \n",
-       "t_32                                                                 0  \n",
-       "cw32_start                                                           0  \n",
-       "cw32_end                                                             0  \n",
-       "r_c3                                                                 0  \n",
-       "window_1_start                                                     720  \n",
-       "window_1_end                                                       900  \n",
-       "window_2_start                                                       0  \n",
-       "window_2_end                                                         0  \n",
-       "window_3_start                                                       0  \n",
-       "window_3_end                                                         0  \n",
-       "random_var_w                                                         0  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can have an overview of data inputs by usering User.export_to_dataframe method\n",
-    "user_lunch = UseCase(users=[user_1, user_2], date_start=\"2020-01-01\")\n",
-    "user_lunch.export_to_dataframe().T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c5c1d61",
-   "metadata": {},
-   "source": [
-    "### Generating a profile for some months"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e4ea14ed",
+   "id": "25b5eea1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 90 day(s) from 2020-01-01 00:00:00 until 2020-03-31 00:00:00\n"
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n",
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
      ]
     }
    ],
    "source": [
-    "# number of days\n",
-    "n_days = 90\n",
-    "\n",
-    "user_lunch.initialize(num_days=n_days)\n",
-    "# storing all the profiles for all the users\n",
-    "profiles = pd.DataFrame(\n",
-    "    index=pd.date_range(start=\"2020-01-01\", periods=1440 * n_days, freq=\"T\")\n",
-    ")\n",
-    "\n",
-    "# here we need to use the\n",
-    "\n",
-    "for user in user_lunch.users:\n",
-    "    # storing daily profiles for a user\n",
-    "    user_profiles = []\n",
-    "    for day_idx, day in enumerate(user_lunch.days):\n",
-    "        single_profile = user.generate_single_load_profile(\n",
-    "            prof_i=day_idx,  # the day to generate the profile\n",
-    "            day_type=get_day_type(day),\n",
-    "        )\n",
-    "\n",
-    "        user_profiles.extend(single_profile)\n",
-    "\n",
-    "    profiles[user.user_name] = user_profiles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa34afee",
-   "metadata": {},
-   "source": [
-    "Considering that the second user has the possibility of cooking rice for lunch, which has a less energy-intensive cooking cycle, we expect to see a higher energy consumption for the user that only eats soup, in most of the cases."
+    "number_of_days = 5\n",
+    "user_1_profiles = {}\n",
+    "user_2_profiles = {}\n",
+    "for day in range(1, number_of_days + 1):\n",
+    "    user_1_profiles[f\"day {day}\"] = pd.Series(user_1.generate_single_load_profile())\n",
+    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "04545584",
+   "execution_count": 7,
+   "id": "ee119e57",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<Axes: title={'center': 'daily energy consumption'}>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -773,17 +217,33 @@
     }
    ],
    "source": [
-    "# daily energy consumption\n",
-    "profiles.resample(\"1d\").sum().plot(title=\"daily energy consumption\")"
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
+    "\n",
+    "\n",
+    "i = 0\n",
+    "for name, df in dict(\n",
+    "    user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    "    user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=True)\n",
+    "    axes[i].set_title(name)\n",
+    "    i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe622733-3577-43f2-85f3-f53efbe32b7f",
+   "cell_type": "markdown",
+   "id": "a5af9eb2",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "As it can be observed, user_1 always have the same demand profile for lunch prepration while user_2 can have two different profiles (for example on day 3 and 4, the user cooks rice for lunch)!"
+   ]
   }
  ],
  "metadata": {
@@ -802,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
new file mode 100644
index 00000000..2d9bc0bc
--- /dev/null
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5e40c9fc-0a96-4198-8607-adb68a0a7a8e",
+   "metadata": {},
+   "source": [
+    "# Example output featured in RAMP's publication in JOSS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e08c5a-fd06-4d11-a3b3-8f598c40964d",
+   "metadata": {},
+   "source": [
+    "The code in this notebook showcases how to produce a typical RAMP visual output. The same output is used as an illustrative figure in RAMP's publication in JOSS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "280523b3-4580-4616-a260-fed9366e701c",
+   "metadata": {},
+   "source": [
+    "### Step 1. Importing some of the user types available from the Example Input File 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0465f589-cc6c-40de-9df7-5731e699b4d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ramp import UseCase\n",
+    "from ramp.example.input_file_1 import LMI, LI\n",
+    "\n",
+    "LMI.num_users = 2\n",
+    "LI.num_users = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5219a716-7b85-489b-9905-224c9533bc81",
+   "metadata": {},
+   "source": [
+    "### Step 2. Creating a UseCase and generating a list of load profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d506451-7951-4d54-b9b6-df76f1fe6762",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 1 day(s) from 2024-05-15 00:00:00 until 2024-05-16 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI], random_seed=20240110)\n",
+    "uc.initialize(num_days=1)\n",
+    "\n",
+    "profiles = uc.generate_daily_load_profiles(cases=[1, 2, 3, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0befd74-7c37-4ce5-9831-1fb68d2df72b",
+   "metadata": {},
+   "source": [
+    "### Step 3. Post-processing and plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "13d1fe35-2c1c-4641-9448-ea9a54e4e095",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Time (hours:minutes)')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a custom plot function based on the in-built 'plot.shadow()' functionality\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 22})\n",
+    "\n",
+    "fig, ax1 = profiles.shadow(figsize=(16, 8))\n",
+    "for ln in range(0, len(profiles.columns) + 1):\n",
+    "    ax1.get_lines()[ln].set_color(\n",
+    "        \"royalblue\"\n",
+    "    )  # edits the defualt colour of the shadow-plot lines\n",
+    "ax1.legend([\"5-day average\", \"Single days(s)\"])\n",
+    "ax1.margins(0)\n",
+    "\n",
+    "ax1.set_xticks(ax1.get_xticks()[1:])\n",
+    "\n",
+    "myFmt = mdates.DateFormatter(\"%H:%M\")\n",
+    "ax1.xaxis.set_major_formatter(myFmt)\n",
+    "\n",
+    "ax1.set_ylabel(\"Normalised power (kW/kW-Peak)\", fontsize=\"large\", labelpad=20)\n",
+    "ax1.set_xlabel(\"Time (hours:minutes)\", fontsize=\"large\", labelpad=20)"
+   ]
+  }
+ ],
+ "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index d7d0ce38..c85348d5 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -5,7 +5,13 @@
    "id": "0b4c1189",
    "metadata": {},
    "source": [
-    "# Fixed-Flat Appliance"
+    "# Fixed-Flat Appliance\n",
+    "\n",
+    "Some appliances exhibit deterministic consumption patterns, such as security lights used for safety purposes, which often adhere to specific schedules. In RAMP, such appliances are categorized as **flat appliances** indicating they lack random variability in total usage time.\n",
+    "\n",
+    "When multiple appliances of the same type are consistently switched on together, they can be attributed with the 'fixed' flag in their definition. This synchronizes the switch-on and switch-off events for appliances of the same type.\n",
+    "\n",
+    "For instance, let's model the security lights of a school to illustrate these characteristics:"
    ]
   },
   {
@@ -16,8 +22,9 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -48,26 +55,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "63d2c6f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "indoor_bulb = school.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\",\n",
+    "security_lights = school.add_appliance(\n",
+    "    name=\"Security Light Bulb\",\n",
     "    number=10,\n",
     "    power=25,\n",
     "    num_windows=1,\n",
     "    func_time=210,\n",
-    "    time_fraction_random_variability=0.2,\n",
     "    func_cycle=60,\n",
     "    fixed=\"yes\",  # This means all the 'n' appliances of this kind are always switched-on together\n",
     "    flat=\"yes\",  # This means the appliance is not subject to random variability in terms of total usage time\n",
-    ")\n",
-    "indoor_bulb.windows(\n",
     "    window_1=[1200, 1440],  # from 20:00 to 24:00\n",
-    "    window_2=[0, 0],\n",
-    "    random_var_w=0.35,\n",
     ")"
    ]
   },
@@ -76,12 +78,14 @@
    "id": "2928b830",
    "metadata": {},
    "source": [
-    "### Initialize the usecase (it defines the peak time range and simulation time)"
+    "### Generating the profiles using UseCase class\n",
+    "\n",
+    "Similar to previous example, you can generate the load profiles using the UseCase class. In this example, we use another functionality of the UseCase class by identifying the starting date of the simulation and the number of days for generating the profiles:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "0ce9c747",
    "metadata": {},
    "outputs": [
@@ -94,78 +98,110 @@
     }
    ],
    "source": [
-    "school_case = UseCase(users=[school], date_start=\"2023-01-01\")\n",
+    "school_case = UseCase(\n",
+    "    users=[school],  # users of the usecase\n",
+    "    date_start=\"2023-01-01\",  # start date\n",
+    ")\n",
+    "\n",
+    "# when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days\n",
     "school_case.initialize(num_days=7)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "e6f59618",
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "18a0760d-4864-47e3-b2c1-30952bc2410a",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "### Generating a profile for the 1st week of the year\n",
-    "\n",
-    "From the usecase directly"
+    "fixed_flat = school_case.generate_daily_load_profiles()\n",
+    "fixed_flat = pd.DataFrame(fixed_flat, columns=[\"fixed_flat\"])\n",
+    "fixed_flat.plot()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "18a0760d-4864-47e3-b2c1-30952bc2410a",
+   "cell_type": "markdown",
+   "id": "03124f17",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "first_week = school_case.generate_daily_load_profiles(flat=True)"
+    "As it can be seen, in each day, all the 10 lights with 25 Watt power (aggregated power of 250) are always switched on together on the specified schedule."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "779b95d7-2fc4-442e-858a-02dd228fcfad",
+   "id": "9a5316d0",
    "metadata": {},
    "source": [
-    "or from the user"
+    "To clarify the impact of parameters, let's examine how the profiles would vary under different combinations:\n",
+    "\n",
+    "1.  no_fixed_no_flat consumption: when appliances are not synchronized and they can randomly switch on during the window time\n",
+    "\n",
+    "2. fixed_no_flat consumption: when appliances are synchronized and they can randomly switch on during the window time\n",
+    "\n",
+    "3. fixed_flat consumption (original example): when appliances are synchronized and they are always switched on during the window time\n",
+    "\n",
+    "4. no_fixed_flat consumption: when appliances are not synchronized but they are always switched on during the window time\n",
+    "\n",
+    "By observing the load profiles generated under these different combinations, we can gain insights into how each parameter influences overall energy consumption and demand patterns within simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bb051491",
+   "execution_count": 6,
+   "id": "773ca6cd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "first_week = []\n",
-    "\n",
-    "for day_idx, day in enumerate(school_case.days):\n",
-    "    first_week.extend(\n",
-    "        school.generate_single_load_profile(\n",
-    "            prof_i=day_idx,  # the day to generate the profile\n",
-    "            peak_time_range=school_case.peak_time_range,\n",
-    "            day_type=get_day_type(day),\n",
-    "        )\n",
-    "    )"
+    "# no fixed and no flat property\n",
+    "security_lights.fixed = \"no\"\n",
+    "security_lights.flat = \"no\"\n",
+    "no_fix_no_flat = school_case.generate_daily_load_profiles()\n",
+    "no_fix_no_flat = pd.DataFrame(no_fix_no_flat, columns=[\"no_fix_no_flat\"])\n",
+    "\n",
+    "# not flat but fixed\n",
+    "security_lights.fixed = \"yes\"\n",
+    "fix_no_flat = school_case.generate_daily_load_profiles()\n",
+    "fix_no_flat = pd.DataFrame(fix_no_flat, columns=[\"fix_no_flat\"])\n",
+    "\n",
+    "# not fixed but flat\n",
+    "security_lights.fixed = \"no\"\n",
+    "security_lights.flat = \"yes\"\n",
+    "no_fixed_flat = school_case.generate_daily_load_profiles()\n",
+    "no_fixed_flat = pd.DataFrame(no_fixed_flat, columns=[\"no_fixed_flat\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "b28eb307-d5db-47fd-9e45-e3e746f628d6",
+   "execution_count": 7,
+   "id": "498c54f7",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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       "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1200x400 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -173,17 +209,25 @@
     }
    ],
    "source": [
-    "first_week = pd.DataFrame(first_week, columns=[\"household\"])\n",
-    "first_week.plot()"
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))\n",
+    "\n",
+    "\n",
+    "for i, df in enumerate([no_fix_no_flat, fix_no_flat, fixed_flat, no_fixed_flat]):\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
+    "    axes[i].set_title(df.columns[0])\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f811d970-650d-4342-9dee-8d80ca20ddae",
+   "cell_type": "markdown",
+   "id": "784764bb",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "As it can be seen, in case of no flat and no fix, both scheduling of appliances and the switched on events of appliances are randomized. While when appliances are fixed, not flat, their scheduling is randomized keeping their switch on events synced as it can be observed that power usage is always kept at 250 Watt with more intermittent swithced on/off events. "
+   ]
   }
  ],
  "metadata": {
@@ -202,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 330ff16b..316e2a58 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -5,7 +5,7 @@
    "id": "74d8e1ab",
    "metadata": {},
    "source": [
-    "# Appliances with multiple cycles"
+    "# Appliances with duty cycles"
    ]
   },
   {
@@ -14,21 +14,30 @@
    "metadata": {},
    "source": [
     "\n",
-    "An example of an appliance with multiple cycle is a fridge. Fridges\n",
-    "usually have different duty cycles, which can be estimated based on seasonal\n",
-    "temperature trends and/or frequency of user interaction (e.g., how often the \n",
-    "door gets opened).\n",
+    "Some appliances may operate based on fixed duty cycle. For instance, you may think of a\n",
+    "dishwasher that follows a pre-determined program. This can be modelled by means of the \n",
+    "`fixed_cycle` parameter, which allows to define a duty cycle consisting of up to two temporal\n",
+    "operation segments with distinct power consumption levels. \n",
     "\n",
-    "In this example a fridge with 3 different duty cycles is modelled. The\n",
-    "time windows are defined for 3 different cycles across 3 different season\n",
-    "types:\n",
+    "Furthermore, we acknowledge that some of the appliances that can operate based on duty cucles\n",
+    "may still follow multiple, different duty cycles throughout a day or across seasons. An example\n",
+    "of an appliance characterised by variable duty cycles is a fridge. Fridges tend to follow \n",
+    "different duty cycles throughout the day, depending on the ambient temperature (which also varies\n",
+    "across seasons) and/or frequency of user interaction (e.g., how often the door gets opened).\n",
+    "Accordingly, RAMP allows modelling up to 3 different duty cycles per appliance, giving room for\n",
+    "modulating the appliance's behaviour depending on any given factor of interest.\n",
+    "\n",
+    "In this example, we model a fridge with 3 different duty cycles, which are representative of its\n",
+    "operation during a 'hot' season. The time windows are defined for 3 different cycles throughout\n",
+    "the day, corresponding to a different temperature and frequency of user interaction. For instance,\n",
+    "between 08:00 and 20:00, we assume that the fridge operates more intensely due to the combined effects\n",
+    "of temperature and frequent use. In the early morning, it operates at an intermediate intensity, \n",
+    "and during the night, it operates based on its standard cycle.\n",
     "\n",
     "\n",
     "| season type |               Standard cycle              |  Intermediate cycle |   Intensive cycle   |\n",
     "|-------------|:-----------------------------------------:|:-------------------:|:-------------------:|\n",
-    "| Hot         | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 07:59:00 | 08:00:00 - 20:00:00 |\n",
-    "| Warm        | 00:00:00 - 04:59:00 & 18:01:00 - 23:59:00 | 05:00:00 - 09:39:00 | 09:40:00 - 18:00:00 |\n",
-    "| Cold        | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 20:00:00 |          -          |"
+    "| Hot         | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 07:59:00 | 08:00:00 - 20:00:00 |"
    ]
   },
   {
@@ -70,7 +79,7 @@
    "outputs": [],
    "source": [
     "# creating the appliance\n",
-    "fridge = household.Appliance(\n",
+    "fridge = household.add_appliance(\n",
     "    name=\"Fridge\",\n",
     "    number=1,\n",
     "    power=200,\n",
@@ -91,7 +100,7 @@
    "outputs": [],
    "source": [
     "# setting the functioning windows\n",
-    "fridge.windows([0, 1440])  # always on during the whole year"
+    "fridge.windows([0, 1440])  # always on during the whole day, for all days of the year"
    ]
   },
   {
@@ -99,7 +108,9 @@
    "id": "9ca3e204",
    "metadata": {},
    "source": [
-    "### Assigining the specific cycles"
+    "### Characterising the specific cycles\n",
+    "\n",
+    "As anticipated above, each cycle is characterised by two operation segments at different power levels. They can be defined as per the code below. In addition, it is possible to apply a random variability (`r_c1`, `r_c2`, `r_c3`, one for each specific cycle) to the exact duration of the two segments (see the API documentation). In this example, we do not apply such variability."
    ]
   },
   {
@@ -112,27 +123,18 @@
     "# assiging the specific cycles\n",
     "# first cycle: standard cycle\n",
     "fridge.specific_cycle_1(\n",
-    "    p_11=200,\n",
-    "    t_11=20,\n",
-    "    p_12=5,\n",
-    "    t_12=10,\n",
+    "    p_11=200,  # power level for the first operation segment\n",
+    "    t_11=10,  # duration of the first operation segment\n",
+    "    p_12=5,  # power level for the second operation segment\n",
+    "    t_12=20,  # duration of the second operation segment\n",
+    "    r_c1=0,  # random variability assigned to the duration of each segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
-    "fridge.specific_cycle_2(\n",
-    "    p_21=200,\n",
-    "    t_21=15,\n",
-    "    p_22=5,\n",
-    "    t_22=15,\n",
-    ")\n",
+    "fridge.specific_cycle_2(p_21=200, t_21=15, p_22=5, t_22=15, r_c2=0)\n",
     "\n",
     "# third cycle: intensive cycle\n",
-    "fridge.specific_cycle_3(\n",
-    "    p_31=200,\n",
-    "    t_31=10,\n",
-    "    p_32=5,\n",
-    "    t_32=20,\n",
-    ")"
+    "fridge.specific_cycle_3(p_31=200, t_31=20, p_32=5, t_32=10, r_c3=0)"
    ]
   },
   {
@@ -140,7 +142,7 @@
    "id": "d503eb1f",
    "metadata": {},
    "source": [
-    "After defining the cycle power and duration parameters, the time windows of year at which the cycles happens should be specifid by:"
+    "After defining the cycle power and duration parameters, the time windows within a day at which the cycles occur should be specified by means of the 'cycle window' (`cw`). In fact, up to two time windows within a day can be specified for each of the 3 cycles. For instance, according to the table discussed earlier, for the standard `specific_cycle_1` we may define an occurrence in the early morning (`cw11` in the code below) as well as one in the late evening (`cw12`).The windows defined across all cycles should not overlap."
    ]
   },
   {
@@ -152,7 +154,7 @@
    "source": [
     "# defining cycle behaviour\n",
     "fridge.cycle_behaviour(\n",
-    "    cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]\n",
+    "    cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
     ")"
    ]
   },
@@ -161,18 +163,18 @@
    "id": "bdeb861f",
    "metadata": {},
    "source": [
-    "### Buidling the profiles"
+    "### Building the profiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "9b9c1541",
    "metadata": {},
    "outputs": [],
    "source": [
     "use_case = UseCase(users=[household])\n",
-    "peak_time_range = use_case.calc_peak_time_range()"
+    "use_case.peak_time_range = use_case.calc_peak_time_range()"
    ]
   },
   {
@@ -180,13 +182,32 @@
    "execution_count": 8,
    "id": "49589857",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      2020-12-16\n",
+      "0          0.001\n",
+      "1          0.001\n",
+      "2          0.001\n",
+      "3          0.001\n",
+      "4          0.001\n",
+      "...          ...\n",
+      "1435       0.001\n",
+      "1436       0.001\n",
+      "1437       0.001\n",
+      "1438       0.001\n",
+      "1439       0.001\n",
+      "\n",
+      "[1440 rows x 1 columns]\n"
+     ]
+    }
+   ],
    "source": [
     "# days to build the profiles\n",
     "days = [\n",
-    "    \"2020-05-16\",\n",
-    "    \"2020-08-16\",\n",
-    "    \"2020-12-16\",\n",
+    "    \"2020-12-16\",  # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
     "]\n",
     "\n",
     "profiles = pd.DataFrame(index=range(0, 1440), columns=days)\n",
@@ -194,48 +215,140 @@
     "for day_idx, day in enumerate(days):\n",
     "    profile = household.generate_single_load_profile(\n",
     "        prof_i=day_idx,  # the day to generate the profile\n",
-    "        peak_time_range=peak_time_range,\n",
     "        day_type=get_day_type(day),\n",
     "    )\n",
     "\n",
-    "    profiles[day] = profile"
+    "    profiles[day] = profile\n",
+    "\n",
+    "print(profiles)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "fa5688ff",
+   "id": "2143c46d-b324-4844-ae06-f55d81d48bce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "# plotting a part of the days\n",
+    "profiles.iloc[400:500].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dacea141-4969-4d31-8991-45f8ad252b53",
+   "metadata": {},
+   "source": [
+    "## Appliances with continuous duty cycle or productive use\n",
+    "\n",
+    "By default, RAMP models duty cycle in contunuous mode. Given time of use window some switch-on events of random duration are generated and filled with duty cycles. If the duration of the switch-on events is longer than one duty cycle, the load profile is filled with repetitions of the duty cycle.\n",
+    "\n",
+    "In case of productive uses such as welding, milling or the use of machinery in a carpentry, where appliances are switched on many times within the working hours, switch-on events duration should be limited to the duration of the duty cycle, resulting in one duty cycle per switch-on event.\n",
+    "\n",
+    "In order to model an appliance having a productive use duty cycle, set the attribute `continuous_duty_cycle` to `0` as in this example below\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5f2985fb-dec1-456a-b198-4c5a3a2acd1c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "      2020-05-16  2020-08-16  2020-12-16\n",
-      "0          0.001       5.000       0.001\n",
-      "1          0.001       5.000       0.001\n",
-      "2          0.001       5.000       0.001\n",
-      "3          5.000       5.000       0.001\n",
-      "4          5.000       5.000       0.001\n",
-      "...          ...         ...         ...\n",
-      "1435       0.001       0.001       0.001\n",
-      "1436       0.001       0.001       0.001\n",
-      "1437       0.001       0.001       0.001\n",
-      "1438       0.001       0.001       0.001\n",
-      "1439       0.001       0.001       0.001\n",
-      "\n",
-      "[1440 rows x 3 columns]\n"
+      "You will simulate 1 day(s) from 2024-03-19 00:00:00 until 2024-03-20 00:00:00\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
-    "print(profiles)"
+    "test_user = User(user_name=\"test_user\", num_users=1)\n",
+    "\n",
+    "# Create test appliance\n",
+    "test_appliance = test_user.add_appliance(\n",
+    "    name=\"test_appliance_with_duty_cycles\",\n",
+    "    func_time=4 * 60,  # runs for 2 hours per day\n",
+    "    num_windows=1,\n",
+    "    window_1=[6 * 60, 20 * 60],  # usage timeframe from 06:00 to 20:00\n",
+    "    fixed_cycle=1,  # appliance uses duty cycles\n",
+    "    # Duty cycle 1\n",
+    "    p_11=8000,  # power of the first cycle\n",
+    "    t_11=2,  # time needed for the first cycle\n",
+    "    p_12=2000,  # power of the second cycle\n",
+    "    t_12=18,  # time needed for the second cycle\n",
+    "    continuous_duty_cycle=0,  # appliance run the duty cycle once per switch on event\n",
+    ")\n",
+    "# Create and initialize UseCase\n",
+    "uc = UseCase(name=\"duty_cycle_test\", users=[test_user])\n",
+    "uc.initialize(num_days=1)\n",
+    "\n",
+    "daily_load_profile = pd.DataFrame(\n",
+    "    uc.generate_daily_load_profiles(),\n",
+    "    index=uc.datetimeindex,\n",
+    ")\n",
+    "\n",
+    "daily_load_profile.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "751c06be-deb2-4029-a31f-3ba3b56d5d8b",
+   "metadata": {},
+   "source": [
+    "In order to illustrate how a continuous cycle woud look like, let's set the `continuous_duty_cycle` back to `1`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "2143c46d-b324-4844-ae06-f55d81d48bce",
+   "execution_count": 11,
+   "id": "04aec0b2-8a81-4456-b9d4-3cc16387927a",
    "metadata": {},
    "outputs": [
     {
@@ -244,13 +357,13 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -262,8 +375,22 @@
     }
    ],
    "source": [
-    "# plotting a part of the days\n",
-    "profiles.iloc[400:500].plot()"
+    "test_appliance.continuous_duty_cycle = 1\n",
+    "\n",
+    "daily_load_profile = pd.DataFrame(\n",
+    "    uc.generate_daily_load_profiles(),\n",
+    "    index=uc.datetimeindex,\n",
+    ")\n",
+    "\n",
+    "daily_load_profile.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67bbb185-fd3b-4a40-b4e4-5968a70990ea",
+   "metadata": {},
+   "source": [
+    "One can see that the switch on events are longer and more than one duty cycle run within the switch on event"
    ]
   }
  ],
@@ -283,7 +410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb
index 6cfef8c9..c2f03638 100644
--- a/docs/notebooks/occasional_use.ipynb
+++ b/docs/notebooks/occasional_use.ipynb
@@ -7,17 +7,11 @@
    "source": [
     "# Appliances with occasional use\n",
     "\n",
-    "There are some appliances that are occasionally included in the mix of\n",
-    "appliances that the user switches on during the day. For example, an iron,\n",
-    "a stereo, printers, etc.\n",
+    "There are some appliances that are occasionally included in the mix of appliances that the user switches on during the day. For example, an iron, a stereo, printers, etc.\n",
     "\n",
-    "Within RAMP, the user may specify the probability of using an appliance\n",
-    "on the daily mix with a parameter called **occasional_use**.\n",
+    "Within RAMP, the user may specify the probability of using an appliance on the daily mix with a parameter called **occasional_use**.\n",
     "\n",
-    "When `occasional_use = 0`, the appliance is always present in the mix, and\n",
-    "when `occasional_use = 1`,  the appliance is never present. Any in-between\n",
-    "values will lead to a probabilistic calculation to decide whether the appliance\n",
-    "is used or not on a given day.\n",
+    "When `occasional_use = 1`, the appliance is always present in the mix, and when `occasional_use = 0`,  the appliance is never present. Any in-between values will lead to a probabilistic calculation to decide whether the appliance is used or not on a given day.\n",
     "\n",
     "The following example investigates the effect of this parameter by modelling two user categories:\n",
     "* A household that uses a computer occasionally\n",
@@ -32,7 +26,8 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
+    "from ramp import User, UseCase\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd"
    ]
   },
@@ -67,9 +62,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210,\n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=0.5,  # 50% chance of occasional use,\n",
-    "    window_1=[510, 750],\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
@@ -85,72 +81,25 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210,\n",
-    "    time_fraction_random_variability=0.2,\n",
-    "    func_cycle=10,\n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=1,  # always present in the mix of appliances,\n",
-    "    window_1=[510, 750],\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c241c086",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Checking the maximum profile of the two appliances\n",
-    "\n",
-    "max_profile_c1 = pd.DataFrame(computer_0.maximum_profile, columns=[computer_0.name])\n",
-    "max_profile_c2 = pd.DataFrame(computer_1.maximum_profile, columns=[computer_1.name])\n",
-    "\n",
-    "max_profile_c1.plot()\n",
-    "max_profile_c2.plot()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f60846e8",
    "metadata": {},
    "source": [
-    "### Generating profiles"
+    "### Generating profiles\n",
+    "As the profiles of each specific User category is important, we will use the User object profile genertor methods for 5 consecutive days:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "320b7296",
    "metadata": {},
    "outputs": [
@@ -158,66 +107,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 5 day(s) from 2023-12-01 00:00:00 until 2023-12-06 00:00:00\n"
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n",
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
      ]
     }
    ],
    "source": [
-    "use_case = UseCase(users=[household, school])\n",
-    "use_case.initialize(5)"
+    "number_of_days = 5\n",
+    "household_profiles = []\n",
+    "school_profiles = []\n",
+    "\n",
+    "for day in range(1, number_of_days + 1):\n",
+    "    household_profiles.extend(household.generate_single_load_profile(prof_i=day))\n",
+    "\n",
+    "    school_profiles.extend(school.generate_single_load_profile(prof_i=day))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f9a65f08",
+   "execution_count": 6,
+   "id": "fadd6c63",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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YKLy9vcXdd98t5syZU+O5MseS51qI211CZ86cKSIiIoRGoxE9evQQe/bsqTHOVPiwdE5CCJGdnS0effRRERQUJAICAsRDDz0kzp07V2OcqfBh6bVaM3+iupCEEMJ+b+IQqcNHH32EkSNH4quvvsJ9993n6MshIlIVhg8iIx566CGcOXMGP/30EyRJcvTlEBGpCj/tQmRg27Zt+P777/HZZ59h1apVDB5ERPWAdz6IDEiShICAAIwePRrr1q2r0YuCiIjqjv+yEhlgFiciqn/scEpERESKYvggIiIiRTnd2y46nQ45OTkIDAxksR8REZGLEEKgpKQEUVFR8PAwf2/D6cJHTk4OoqOjHX0ZREREZIPs7Gw0adLE7BinCx9ViyRlZ2fLFkEiIiIi51VcXIzo6OhaFzsEnDB8VL3VEhQUxPBBRETkYiwpmWDBKRERESmK4YOIiIgUxfBBREREinK6mg8iInJNQgjcunULlZWVjr4Uqife3t7w9PSs83EYPoiIqM7Ky8uRm5uL69evO/pSqB5JkoQmTZogICCgTsdh+CAiojrR6XS4cOECPD09ERUVBR8fHzaJVCEhBC5duoTffvsNrVq1qtMdEIYPIiKqk/Lycuh0OkRHR8PPz8/Rl0P1qFGjRvjll19QUVFRp/DBglMiIrKL2lpqk+uz1x0t/kkhIiIiRTF8EBERkaKsCh8LFiyAJEmyr7Zt2+r337x5E0lJSQgLC0NAQAASExORn59v94smIiJyJwsWLECXLl3qdIwvv/wSkiShsLDQ5JhNmzYhJCSkTuexhNV3Pu655x7k5ubqv77++mv9vhdeeAGffPIJtm/fjv379yMnJwePPPKIXS+YiIjIXiZOnIiRI0fW2G7JCzXZzupPu3h5eSEiIqLG9qKiImzYsAFbt25F//79AQAbN25Eu3btcOjQIfTu3dvo8crKylBWVqb/ubi42NpLInIJaT/kYUvGRbS5dRadCz+Hv8YDGk8PlFfqoA1tgsAWvVBy4iNU6nTIKbwBjZcnvDwlaL094efjieIbFZAgIdBX/te26EYFPCUJHhLgp/FCQXEZym7dbvIUHqiFJAHeXrd/zyi8XgFvTwkSbo+tLrD8Ekp8Gsm2XdFEw1t3A4EVf5iZnYSskL6ILM1CUEVBjb2hN7MBScIVjfFlti9fK8f18ltoHKiFR2BjZEY8jkfjWqFJA35ygkiNrA4f586dQ1RUFLRaLeLi4pCSkoKYmBhkZmaioqIC8fHx+rFt27ZFTEwMDh48aDJ8pKSk4JVXXrF9BkQuoKJSh/99OxMA8KLPa+js8TNg2IvpKoDz1R50y8TBSqw48VUrxtZR74J3636Qq7e/3vnJC/MKhuPNiT3qfkxyCCEEblQo3+nU19uzXnqMfPDBB5g3bx5++uknREZGYurUqXjxxRf1+yVJwo4dO2R3UUJCQrBy5UpMnDgR5eXlmDFjBj744ANcvXoV4eHhePbZZ5GcnAwAKCwsxMyZM/HRRx+hrKwM3bt3x4oVK9C5c2fZdbz99tuYO3curl69iiFDhmD9+vX6JezLysowa9YsbNu2DcXFxfpj9Ohh+u/Rpk2bMG/ePPzxxx9ISEhA37597fismWZV+OjVqxc2bdqENm3aIDc3F6+88gruu+8+nDp1Cnl5efDx8anxXlF4eDjy8vJMHjM5ORkzZszQ/1xcXIzo6GjrZkHk5Cp1Qv+9P24CAHZU9sHvoiEe8TyAKOmKfv97t/qhACGyxzcO1KCg5PYdwuYN/RHq7wMAuFRShotXau8oGRmshZeHB7KvysfeExUErfftz+p3z30H3rrb58iMGIVyTz/E5m6Dj+729Rb5ROB0o8E1ju1ffhmdLn2i/7nEpxFONRqq/7nh9fNodfUAAOBcg/vwh19L2eN/v3oDecW3z/GQxyE088iHv3QT+3++XOu8yHndqKhE+3lpip/39KsJ8POxbwurzMxMjBo1CgsWLMDo0aPx7bff4s9//jPCwsIwceJEi46xevVqfPzxx3jvvfcQExOD7OxsZGdn6/c/9thj8PX1xe7duxEcHIw33ngDAwYMwNmzZxEaGgoAOH/+PHbu3IlPP/0UV69exahRo7B48WIsWrQIADB79mx88MEHeOutt9C0aVMsXboUCQkJ+Omnn/THMJSRkYHJkycjJSUFI0eOxJ49ezB//vy6P2EWsOq/0JAhQ/Tfd+rUCb169ULTpk3x3nvvwdfX16YL0Gg00Gg0Nj2WyJVtu9UfGaIdenhkycLHfyoH4pRoIRs7sV0zbPr2FwDA6ge7Iq5zFABg86FfsWznqVrP9XyXVgj29cayT0/Ltu8b9QCaN/S//cPST4Drt8NH7NiFQGAE8I9dQOntYBB8V2vETVhV8+C5J4A37oSPwPCWiJtsMO7794APb4ePVv0nolXHR2UPX7rnR6z58vZtn3beF9EMLFInZX366ac12oUbrk+zfPlyDBgwAHPnzgUAtG7dGqdPn8Y//vEPi8PHxYsX0apVK/Tt2xeSJKFp06b6fV9//TUOHz6MgoIC/evhsmXLsHPnTrz//vt45plnANzuJLtp0yb9nY5x48YhPT0dixYtQmlpKdauXYtNmzbpX6vXr1+PvXv3YsOGDZg1a1aNa1q1ahUGDx6M2bNn6+f17bffYs+ePRbNqS7qFA9DQkLQunVr/PTTTxg4cCDKy8tRWFgou/uRn59vtEaEiIjUy9fbE6dfTXDIea314IMPYu3atbJtGRkZeOKJJwAAZ86cwYgRI2T7+/Tpg5UrV6KystKiTp8TJ07EwIED0aZNGwwePBgPPfQQBg0aBAA4ceIErl27hrCwMNljbty4gfPn77wf26xZM33wAIDIyEgUFNyusTp//jwqKirQp08f/X5vb2/07NkTZ86cMXpNZ86cwcMPPyzbFhcX5/zh49q1azh//jzGjRuH2NhYeHt7Iz09HYmJiQCArKwsXLx4EXFxcXa5WCI1kCBqH0Tk4iRJsvvbH/XF398fd999t2zbb7/9ZtUxJEmCEPK/2xUVFfrvu3XrhgsXLmD37t34/PPPMWrUKMTHx+P999/HtWvXEBkZiS+//LLGcQ1/mff29q5xTp1OZ9V1Ogur/mTMnDkTw4YNQ9OmTZGTk4P58+fD09MTY8aMQXBwMCZPnowZM2YgNDQUQUFBmDp1KuLi4kwWmxIRETm7du3a4ZtvvpFt++abb9C6dWv9XY9GjRohNzdXv//cuXM1VvgNCgrC6NGjMXr0aDz66KMYPHgwrly5gm7duiEvLw9eXl5o1qyZTdfYsmVL+Pj44JtvvtG/pVNRUYEjR45g+vTpJueVkZEh23bo0CGbzm8tq8LHb7/9hjFjxuDy5cto1KgR+vbti0OHDqFRo9sfzVuxYgU8PDyQmJiIsrIyJCQkYM2aNfVy4USuztT9D4GalfqGxfuSie3mSNUeZ+xY1Y5s5ASmTlZte42LMn+RhsN5T4ic0YsvvogePXpg4cKFGD16NA4ePIjXX39d9vrWv39/vP7664iLi0NlZSXmzJkju1OxfPlyREZGomvXrvDw8MD27dsRERGBkJAQxMfHIy4uDiNHjsTSpUvRunVr5OTk4LPPPsPDDz+M7t2713qN/v7+eO655zBr1iyEhoYiJiYGS5cuxfXr1zF58mSjj3n++efRp08fLFu2DCNGjEBaWpoib7kAVoaPbdu2md2v1WqRmpqK1NTUOl0UERGRs+jWrRvee+89zJs3DwsXLkRkZCReffVVWbHpa6+9hkmTJuG+++5DVFQUVq1ahczMTP3+wMBALF26FOfOnYOnpyd69OiBXbt26Rfj27VrF15++WVMmjQJly5dQkREBO6//36Eh4dbfJ2LFy+GTqfDuHHjUFJSgu7duyMtLQ0NGjQwOr53795Yv3495s+fj3nz5iE+Ph5//etfsXDhQtueKCu4xhtyRC5O8Fd6q7AuhpSyadMmo9sfeOABWQ1HYmKivp7RmKioKKSlyT9abNgd9emnn8bTTz9t8vGBgYFYvXo1Vq9ebXT/ggULsGDBAtm26dOny95S0Wq1Zo9RfU4A8OSTT+LJJ5+UbTPsX1JfuLAckQuQannrgojIlTB8EDlIVW1HzRqPWmo+ZPUfloUSSTJeHyKZKiDRf2/ixCYPUu0xJo9rOPrONmP1LkSkPgwfREREpCiGDyIiIlIUwweRAoRBASWLKWvHN1+I1I3hg8gF8MWYiNSE4YPIQarufwghGd1uyFSRqeVNxoyXpkrGGosZfm+XJmNmxtYYzphF5A4YPoiIiEhRDB9ECjDs68Oaj9rxOSJSN4YPIhcgWfr+ChEpZtOmTbJVZ+vLxIkTMXLkyHo/j5IYPogc5E6TMePbDZnsBWbhuW43Gaul3sJhTcbu4P0OUtqlS5fw3HPPISYmBhqNBhEREUhISKixii3ZF9d2ISIit5WYmIjy8nK89dZbaNGiBfLz85Geno7Lly87+tJUjXc+iBRg+Bu9076B4kSr37HmQwWEAMpLlf+y4s9xYWEhDhw4gCVLluDBBx9E06ZN0bNnTyQnJ2P48OH6Mf/7v/+L8PBwaLVadOjQAZ9++qnsOGlpaWjXrh0CAgIwePBg5Obm6vfpdDq8+uqraNKkCTQaDbp06VJj2fqTJ0+if//+8PX1RVhYGJ555hlcu3atDk++8+OdDyJX4LSJhciEiuvA36OUP+9fcgAff4uGBgQEICAgADt37kTv3r2h0Whk+3U6HYYMGYKSkhJs3rwZLVu2xOnTp+Hp6akfc/36dSxbtgxvv/02PDw88MQTT2DmzJnYsmULAGDVqlV47bXX8MYbb6Br16548803MXz4cPzwww9o1aoVSktLkZCQgLi4OBw5cgQFBQV46qmnMGXKFJMr7qoBwweRg5haWM54zYdBbw/D7Rb3+bBgrLGeHnbp81HLMQzGc2E5UpKXlxc2bdqEp59+GuvWrUO3bt3Qr18/PP744+jUqRM+//xzHD58GGfOnEHr1q0BAC1atJAdo6KiAuvWrUPLli0BAFOmTMGrr76q379s2TLMmTMHjz/+OABgyZIl2LdvH1auXInU1FRs3boVN2/exH/+8x/4+98OTa+//jqGDRuGJUuWIDw8XImnQnEMH0REZH/efrfvQjjivFZITEzE0KFDceDAARw6dAi7d+/G0qVL8e9//xsFBQVo0qSJPngY4+fnpw8eABAZGYmCggIAQHFxMXJyctCnTx/ZY/r06YMTJ04AAM6cOYPOnTvrg0fVfp1Oh6ysLIYPIrKdEC6wtotT1XyQy5Mki9/+cDStVouBAwdi4MCBmDt3Lp566inMnz8fM2fOrPWx3t7esp8lSZL9fSfjWHBK5ALY5oNIOe3bt0dpaSk6deqE3377DWfPnrXpOEFBQYiKiqrxsd1vvvkG7du3BwC0a9cOJ06cQGlpqWy/h4cH2rRpY/sknBzDB5GD1ezzUZPpPh+WpRJJMn43oX76fJjZX2ufD6YsUs7ly5fRv39/bN68Gd9//z0uXLiA7du3Y+nSpRgxYgT69euH+++/H4mJidi7dy8uXLiA3bt31/i0ijmzZs3CkiVL8O677yIrKwsvvfQSjh8/jmnTpgEAxo4dC61WiwkTJuDUqVPYt28fpk6dinHjxqn2LReAb7sQEZGbCggIQK9evbBixQqcP38eFRUViI6OxtNPP42//OUvAIAPPvgAM2fOxJgxY1BaWoq7774bixcvtvgczz//PIqKivDiiy+ioKAA7du3x8cff4xWrVoBuF0zkpaWhmnTpqFHjx7w8/NDYmIili9fXi9zdhYMH0QKkPf5cNL3g53ofWqnfY5IVTQaDVJSUpCSkmJyTGhoKN58802j+yZOnIiJEyfKto0cOVJW8+Hh4YH58+dj/vz5Js/RsWNHfPHFFyb3q/Ejt3zbhcgFsOaDiNSE4YPIQUzVN9TW50Neh2HZuaTbi7sY337nB2MPNH7ealdn+jHV95tfX4b3O4jcA8MHkYOxyJKI3A3DB5ECDMspGDVqJ0HwLgiRijF8ENFtTlRwSq6JzbXUz17/jRk+iFyAOxacuuGUXVZVl8/r1687+EqovpWXlwOAbHE9W/CjtkQOYt3CcgbfW19vanKsbJvdmoyZKUA12mSMC8u5Ok9PT4SEhOjXNPHz85MXM5Mq6HQ6XLp0CX5+fvDyqlt8YPggIqI6i4iIAAB9ACF18vDwQExMTJ3DJcMHkRIMC04lJ31fnO/XUx1IkoTIyEg0btwYFRUVjr4cqic+Pj7w8Kh7xQbDB5Er4C1schGenp51rgcg9WPBKZGDmLrPUOvCcobfWxhKTPQYM72wXNVZ5EUhpo5u5qDV97PJGBExfBAREZHCGD6IFOASLbOcqOaDC8sRqRvDB5ELYMkHEakJwweRg1jX5+PONsM6D0szifTf/5k7ruP6fNzBPh9E7oHhg4iIiBTF8EGkAPnCck5az8CaDyJSCMMHkQtgzQcRqQnDB5GDWFPfYLrPh4WPt7nPh/keHUa316HPB5eTI3IPDB9EDsYiSyJyNwwfRAowrGBw2noGp6r5ICI1Y/ggcgGs+SAiNWH4IHIQ02u7GKuLMOzzAaPfmyPB+N0EebmFg/p8GGxznnsvRFSfGD6IHIwvuETkbhg+iBQgDOop+A5K7STXWA2HiGzE8EFERESKYvggchBTH7E1XvNh4nsL76OY6vNh+uHW9PkwcjKTJzF/DMO58w4RkXoxfBA5GPt8EJG7YfggIiIiRTF8ECmATcas47TPERHZBcMHkQuwtLaDiMgVMHwQOcztQFG95sPY7/ymikwtbzImGQ0wkqkmYoo2GbvzPetfiNwDwwcREREpiuGDSAGG5RROW8/gVDUfRKRmdQofixcvhiRJmD59un7bzZs3kZSUhLCwMAQEBCAxMRH5+fl1vU4it8YXYyJSE5vDx5EjR/DGG2+gU6dOsu0vvPACPvnkE2zfvh379+9HTk4OHnnkkTpfKJHaWLewnOEP1p9LMrGynPy4Rk5iUVGJmRqPGvvN1504z70XIqpPNoWPa9euYezYsVi/fj0aNGig315UVIQNGzZg+fLl6N+/P2JjY7Fx40Z8++23OHTokN0umkhN+IJLRO7GpvCRlJSEoUOHIj4+XrY9MzMTFRUVsu1t27ZFTEwMDh48aPRYZWVlKC4uln0RqY3hMmlO+xaKU9V8OM+1EJH9eVn7gG3btuG7777DkSNHauzLy8uDj48PQkJCZNvDw8ORl5dn9HgpKSl45ZVXrL0MIrfCPh9EpCZW3fnIzs7GtGnTsGXLFmi1WrtcQHJyMoqKivRf2dnZdjkukbOzeWE52XbLQ0mt68qxzwcRKcSq8JGZmYmCggJ069YNXl5e8PLywv79+7F69Wp4eXkhPDwc5eXlKCwslD0uPz8fERERRo+p0WgQFBQk+yJyJ3zBJSJ3Y9XbLgMGDMDJkydl2yZNmoS2bdtizpw5iI6Ohre3N9LT05GYmAgAyMrKwsWLFxEXF2e/qyZyNezzYSVnuhYisjerwkdgYCA6dOgg2+bv74+wsDD99smTJ2PGjBkIDQ1FUFAQpk6diri4OPTu3dt+V01EREQuy+qC09qsWLECHh4eSExMRFlZGRISErBmzRp7n4bI5Zmu+ajJsLZD9r2F55IkyWh9iGSyHsNYnw9TZ6trn487+BYUkXuoc/j48ssvZT9rtVqkpqYiNTW1rocmchN8wSUi98K1XYgUIMz8RDVJ4LNEpGYMH0QugPdGiEhNGD6IHMTWtV1MLsdihomlXUyHmnrr82H+4YbPCQMXkXoxfBA5GN9eICJ3w/BBREREimL4IFKAkDUZc1JO1GTMaRuxEZFdMHwQuQCnDSxERDZg+CByENMNtcw3A5PXb1oWSyTJeL2oZLJ6VckmY5acg4jUhOGDyMHY1ZOI3A3DB5EChEENg9PWM7Dmg4gUwvBB5AIs7edBROQKGD6IHMTkwnJGfumXV0UYr/8wRzIxVjLVRMyuTcbMja3eZIwpi8gdMHwQORhfcInI3TB8EClA3ufDSesZnKrmg4jUjOGDyBWw6IOIVIThg8hBrFpYzvB78+u0GSVJkvGeIE7Q58OQE918IaJ6xPBB5GB8vSUid8PwQaQAl1gq3oluOzhtXQwR2QXDB5ELYMkHEakJwweRg5js81HL+ie1tM0wyuq1XerU58PMfqN9Pu5s48eOidwDwweRg/EFl4jcDcMHkQKEcIG1XZyIBBbiEqkZwweRC2DNBxGpCcMHkZMx9hu/ZNkPJlnYoaPmHgX6fBhuESZqW4hIXRg+iIiISFEMH0QKMLe2C2sbapIg+LwQqRjDBxHd5kRNxohI3Rg+iFyAOxacuuGUidwGwweRg9jeZMzge8tXlpM187qz2UQDsDo1GTNTgGr0Gu58z3svRO6B4YPIwdhkjIjcDcMHkcKcNmo4Vc2HM10LEdkbwweRK3DaxEJEZD2GDyIHsa7mw+B7yfh2cyQTY+X9yozUZljU0Kx+mowRkXoxfBA5HF9wici9MHwQKUBeTuGk9QwOqvmotZ08EakOwweRCzD2MVkiIlfF8EHkIKbuM9R2J0Be/2HhwnKS8TYdkqliEmNnq68+HwbfO+k9ISKyM4YPIgfjCy4RuRuGDyIFGC6T5rRvoDhVzQcjGZGaMXwQuQCWfBCRmjB8EDmI6Z4W5tc/MazzsLzPhyRbE8Zwu9GTGN1W/30+nPi+EBHZEcMHkYOxsRYRuRuGDyIFGJZTsJ6hdpKsSoaI1Ibhg8gFGHvLhIjIVTF8EDmIVWu7mCjNsLQQ1aI+H8YfWfvgOvb5MGQ4d8YtIvVi+CAiIiJFMXwQKcCwfqF6zQcLTmuSwOZrRGrG8EFERESKYvggchCr1nYx0dvD4poP2FhDoUCfD0PyO0REpFYMH0RERKQohg8iB2NtAxG5G4YPIgUIwYXlTJ7WyDY2YiNSN4YPIhfgtIGFiMgGDB9EDmKXJmMWxhKLmowZrV6t/yZjhlv4sWMi98DwQeRgfMElInfD8EGkAHNNxpyGM9V8SE76HBGRXVgVPtauXYtOnTohKCgIQUFBiIuLw+7du/X7b968iaSkJISFhSEgIACJiYnIz8+3+0UTuRsuLEdEamJV+GjSpAkWL16MzMxMHD16FP3798eIESPwww8/AABeeOEFfPLJJ9i+fTv279+PnJwcPPLII/Vy4USuz1TNh5GRsqGS0W/Nn8l4mzGptpoOi5qMmXtM9cex5oOIAC9rBg8bNkz286JFi7B27VocOnQITZo0wYYNG7B161b0798fALBx40a0a9cOhw4dQu/eve131UQqwhdcInI3Ntd8VFZWYtu2bSgtLUVcXBwyMzNRUVGB+Ph4/Zi2bdsiJiYGBw8eNHmcsrIyFBcXy76I1MawnII1H9VOa2Qb4xiRulkdPk6ePImAgABoNBo8++yz2LFjB9q3b4+8vDz4+PggJCRENj48PBx5eXkmj5eSkoLg4GD9V3R0tNWTIFI7vhgTkZpYHT7atGmD48ePIyMjA8899xwmTJiA06dP23wBycnJKCoq0n9lZ2fbfCwiV2LqRoN1fT4sZJc+H6aObc8+H0TkDqyq+QAAHx8f3H333QCA2NhYHDlyBKtWrcLo0aNRXl6OwsJC2d2P/Px8REREmDyeRqOBRqOx/sqJiIjIJdW5z4dOp0NZWRliY2Ph7e2N9PR0/b6srCxcvHgRcXFxdT0NkYszvbYLf9s3RvB5IVIxq+58JCcnY8iQIYiJiUFJSQm2bt2KL7/8EmlpaQgODsbkyZMxY8YMhIaGIigoCFOnTkVcXBw/6UJUZ6z6ICL1sCp8FBQUYPz48cjNzUVwcDA6deqEtLQ0DBw4EACwYsUKeHh4IDExEWVlZUhISMCaNWvq5cKJXJ3Na7vItlu4tguMx5daH21Rnw8zNR419ps/o+HcGbeI1Muq8LFhwwaz+7VaLVJTU5GamlqniyIiIiL14touRAqQf7KlejUDf8evTmLNB5GqMXwQuQDGEyJSE4YPIgcxXfNRk2Fth+x7C88lSZLR+pDaa0ZqWfvF6Hbr+nwYYs0HkXtg+CAiIiJFMXwQORgXliMid8PwQaQAw7dSuLBctdMa2cY4RqRuDB9ELoAvxkSkJgwfRA5i+j6DNU3GLDuXRU3Gal95zszRzR3H/DEYrIjcD8MHkYM56N0OIiKHYfggUoBhwHDa3/SdquaDiYxIzRg+iFyApW+vEBG5AoYPIgexamE5w+ZbkvHt5kiSJSUdtVSF2KXJmNkz8GPHRG6C4YOIiIgUxfBBpADDZdKq1zM4zW/7rPkgIoUwfBC5AifJJ0RE9sDwQeQgVi0sJ/veeP2HOaZrPmqp6VC4z4fT3AUionrF8EFERESKYvggUoC8z4eT1nw4EQnmOsASkatj+CByARIbfRCRijB8EDmIqd/sjff5MPjefNsMo6T//s969ujzYW6snHz1XyJSK4YPIiIiUhTDB5ECzK3twtqGmiRZZxQiUhuGDyIXwJIPIlIThg8ih7FtbRcLDlFzmGT52JoPrO1kZmo8amyorebDoIdJLZdGRK6L4YOIiIgUxfBBpADDCgYPiX0+asM+H0TqxvBB5AJY80FEasLwQeQgNq/tIuvzYXkqsS2/2KPPhwXH+C/WfBC5B4YPIiIiUhTDBxERESmK4YNIAcJM9aTTFFaau8j6PK0VW4lIHRg+iFwBCyCISEUYPogcxLqF5STj31vcZEyqfWVcY/ttajJmrgDV/KJ5vN9B5B4YPoiIiEhRDB9Eiqrtg7QO5EQ1HxLvgRCpGsMHkQtgkzEiUhOGDyIHMd1kzHxdhLzJmGUsWlfOlpoQo9utazImr/lgyiJyBwwfREREpCiGDyIFVJVTGKtl4G/7NXFhOSJ1Y/ggcgG1fkyWiMiFMHwQOYg1v9mbKpuwNJRIko1Fqwr0+TA1lnGLSL0YPoiIiEhRDB9EChD/vc9h7Ld51jbUJOmfMSJSI4YPIhfAtyCISE0YPogcxKo+H7LeHjas7fLf/1nPfI8O49ut6/NhyPBuBwMXkXoxfBAREZGiGD6IFGCuzwfVxJoPInVj+CByMMuajPFNCCJSD4YPIgexuebDlrVdXKTPh2CfDyK3wPBBREREimL4ICIiIkUxfBApoKp40qkXlhOOKfE0dlYneUaIqJ4wfBC5AL4YE5GaMHwQOYip+wxG7wRIxgsxLW8yZkGAMXqw+m8yZriFH68lcg8MH0RERKQohg8iBQjhAgvLOVXNh9M8K0RUD6wKHykpKejRowcCAwPRuHFjjBw5EllZWbIxN2/eRFJSEsLCwhAQEIDExETk5+fb9aKJ3A1rPohITawKH/v370dSUhIOHTqEvXv3oqKiAoMGDUJpaal+zAsvvIBPPvkE27dvx/79+5GTk4NHHnnE7hdO5Prq3mTM0lgiWVL0YaymQ4EmY/KaD8YsInfgZc3gPXv2yH7etGkTGjdujMzMTNx///0oKirChg0bsHXrVvTv3x8AsHHjRrRr1w6HDh1C79697XflRERE5JLqVPNRVFQEAAgNDQUAZGZmoqKiAvHx8foxbdu2RUxMDA4ePGj0GGVlZSguLpZ9EamNS/T5cCJcWI5I3WwOHzqdDtOnT0efPn3QoUMHAEBeXh58fHwQEhIiGxseHo68vDyjx0lJSUFwcLD+Kzo62tZLIlItiQGFiFTE5vCRlJSEU6dOYdu2bXW6gOTkZBQVFem/srOz63Q8Ildh68JyhlUSli8WZ2t4qf8+H4a4sByRe7Cq5qPKlClT8Omnn+Krr75CkyZN9NsjIiJQXl6OwsJC2d2P/Px8REREGD2WRqOBRqOx5TKIiIjIBVl150MIgSlTpmDHjh344osv0Lx5c9n+2NhYeHt7Iz09Xb8tKysLFy9eRFxcnH2umMgFVbXQYP8Ky/GZIlIvq+58JCUlYevWrfjoo48QGBior+MIDg6Gr68vgoODMXnyZMyYMQOhoaEICgrC1KlTERcXx0+6EJnAglMicjdWhY+1a9cCAB544AHZ9o0bN2LixIkAgBUrVsDDwwOJiYkoKytDQkIC1qxZY5eLJVIT69Z2MfG9heeyvDbEhgdWH2Nlnw9DhnNnJCNSL6vCh7Cg/bJWq0VqaipSU1NtvigiIiJSL67tQqQIVjBYg30+iNSN4YPIBbDPBxGpCcMHkYPY2udDVkFhYTGH7dHF8h4dJs/GPh9EVA3DBxERESmK4YOIiIgUxfBBpABzTcYU6/NRWwWnBZ9mqw9GP1rMclMiVWP4IHIBNvfpICJyQgwfRA5i+o6HkYJT2WJy1hdlSpJU+2BjCUeytEGYuaJS88eQzPxEROrE8EFERESKYvggUkBVBQNrPoyc1sg23v8gUjeGDyIXwJoPIlIThg8iB7HmPoOpqglLQ4lU/YFGBxkbYGGDMHO1IbU0GTPcws+4ELkHhg8iIiJSFMMHkQLu9Pkwsk/RK3ENXFiOSN0YPohcAGs+iEhNGD6IHMTYp1x0ovbeH/JF5ixcWM7W8GL3Ph/mCYP5M28RqRfDBxERESmK4YNIAeK/RR9cs8QyrPkgUjeGDyIHs6TJGGs+iEhNGD6IHMRY6DD127688sJ4/Yc5toeX+u/zYcjwOWHeIlIvhg8iIiJSFMMHkQLMre1CNUlg/xMiNWP4IHIwxRaWIyJyEgwfRE7EVBCRJOvrPGSPtzXg2KXPh4lxRhje7WAkI1Ivhg8iIiJSFMMHkQLMre1CNbHPB5G6MXwQORhfZInI3TB8EDmI8T4fJmo+6noyRft8mNnHPh9EBIYPIiIiUhjDBxERESmK4YNIAVXlk8aajCnW56O24hLhmOoT42dlJQyRmjF8EBERkaIYPogcxKqF5Syv2TT+eP3/MTfIyAC7NBkzfwzDLez2SuQeGD6IiIhIUQwfREoQNb4hM7iwHJG6MXwQORzfaiAi98LwQeQgxn+zN9VkzHBhOevDii2PMXIQC/dJpvfV2mTM5FGISEUYPoiIiEhRDB9ECqj6jZ6/zVuGC8sRqRvDB5GD8UWWiNwNwweRg1i1sJxh2YQN57J9Xbn67/NhaizvEhGpF8MHERERKYrhg0gBVcumGFvbhWpinw8idWP4IHIwthQnInfD8EHkINas7WLIprVdbC/6sOwgduvzwZoPInfA8EFERESKYvggUkBV1wrWfFiGfT6I1I3hg8jB+CJLRO6G4YPIQYyFDsv6fNiwtoutFRQK9/ng2i5E7oHhg4iIiBTF8EFERESKYvggUsCdJmOOvIja9jum+sTYWVmYS6RuDB9EDsYmY0Tkbhg+iBzEuoXlDJpv2dpkrNY13YwNqP8mY4ZbGMSI3APDBxERESmK4YNIAVUVDKxlsIwksf8JkZpZHT6++uorDBs2DFFRUZAkCTt37pTtF0Jg3rx5iIyMhK+vL+Lj43Hu3Dl7XS+R6vCtBiJyN1aHj9LSUnTu3BmpqalG9y9duhSrV6/GunXrkJGRAX9/fyQkJODmzZt1vlgidbE8dFjepquuZ6r+QDYZIyL787L2AUOGDMGQIUOM7hNCYOXKlfjrX/+KESNGAAD+85//IDw8HDt37sTjjz9et6slIiIil2fXmo8LFy4gLy8P8fHx+m3BwcHo1asXDh48aPQxZWVlKC4uln0RqY0QVQvLkSW4sByRutk1fOTl5QEAwsPDZdvDw8P1+6pLSUlBcHCw/is6Otqel0Tk9PgiS0TuxuGfdklOTkZRUZH+Kzs729GXROQwliwsp3DRh4mLqD7M9j4fhgznz7tEROpl1/AREREBAMjPz5dtz8/P1++rTqPRICgoSPZFRERE6mXX8NG8eXNEREQgPT1dv624uBgZGRmIi4uz56mIXAr7fFiLNR9Eamb1p12uXbuGn376Sf/zhQsXcPz4cYSGhiImJgbTp0/H3/72N7Rq1QrNmzfH3LlzERUVhZEjR9rzuolUg30+iMjdWB0+jh49igcffFD/84wZMwAAEyZMwKZNmzB79myUlpbimWeeQWFhIfr27Ys9e/ZAq9Xa76qJXJxOGA8cJms+ZLUQ1ocVydbPjyje54M1H0TuwOrw8cADD+g/NmiMJEl49dVX8eqrr9bpwoiIiEidHP5pFyK3IGp8Q2ZI4DNFpGYMH0RERKQohg8iB5D/Vi+Z2G4wwvJWGbU+3spHGv3W3LC69fmw7HRE5NoYPoiIiEhRDB9ECqj6pAl/m7cM+6EQqRvDB5G7qO313Myn2OoTYwaR+2H4IHIwvvgSkbth+CByAFPNxISJgky7rCtX2wONnVuBJmOSmZ+ISJ0YPoiIiEhRDB9ECqgqp2AhpWVsbAZPRC6C4YPIwYSJdV6IiNSK4YPIAUyvZFt7EJFs6Bhmy2P++0jDg5g7gfHHVN9Xa5MxLixH5A4YPoiIiEhRDB9ECmDNh3W4sByRujF8EDmY6bdgiIjUieGDyAFM/VZvyW/7NvX5sLnkwx59PkyMM4ILyxG5B4YPIiIiUhTDB5ECqn6j52/zlmGfDyJ1Y/ggIiIiRTF8EDmAYZGpMLHdFFvqN2y/42KHPh8mx9XEPh9E7oHhg4iIiBTF8EGkAPHfRh/s82EZ1nwQqRvDBxERESmK4YPIIQxrPox/b/qRtqztYvVDjDzQHn0+zGPNB5F7YPggIiIiRTF8ECngTp8PVjJYgmu7EKkbwwcREREpiuGDyAFM9/aor0oHm4s+DL61Q5+PWnBtFyL3wPBBREREimL4ICIiIkUxfBAp4L89xuryKVQ3wyZjRGrG8EHkYHyRJSJ3w/BB5ACmmonVVxBRtsmYjecCm4wRuQuGDyIiIlIUwweRIkS1/0/mcGE5InVj+CAiIiJFMXwQOYCpxmKWLCxnC9uPqnSTMdZ8ELkDhg8iIiJSFMMHkQL0fT5YyWARLixHpG4MH0RERKQohg8iBzC1sFy91XzY2uhDMvmD6YF2auPKmg8i9WL4ICIiIkUxfBApoOpOB3+btwz7fBCpG8MHERERKYrhg8gB5HUe9Y99PojImTB8EBERkaIYPogUwD4f1mGfDyJ1Y/ggIiIiRTF8EDmAqd4e9dfnwx4PrP8+H6z5IHIPDB9ERESkKIYPIiIiUhTDB5ECqlpmseDUMmwyRqRuDB9EDsYXWSJyNwwfRA5gKnDUW8GpzcdVusmYPY5CRM6O4YOIiIgUxfBBpIA7TcbIEqz5IFI3hg8iIiJSVL2Fj9TUVDRr1gxarRa9evXC4cOH6+tURC6o/huLyc7GJmNE5ETqJXy8++67mDFjBubPn4/vvvsOnTt3RkJCAgoKCurjdERERORCvOrjoMuXL8fTTz+NSZMmAQDWrVuHzz77DG+++SZeeukl2diysjKUlZXpfy4uLq6PS8Ll/N9w7v0F9XJsd8EeFbbT3LiF+V43ECyVOvpSXEJb6SJmizeRkbrd0ZdC1qjDXS9SlvBrjN4T/+6w89s9fJSXlyMzMxPJycn6bR4eHoiPj8fBgwdrjE9JScErr7xi78uo4XrxZfS+xH/IyIEM/rYVw0//fYnB99c9/OHn44nr5ZWyh/pp7jxY433nhmWjQI1Fpw7QeCFAI//rHhGklQ9qfj9wcjvQsPWdbZqgO99rg2CSNgio+r1BU22c9535wavaOQG0jgjUf18ibo+N8biECUgDLpk+JRHZ7qLHXQBUFD7++OMPVFZWIjw8XLY9PDwcP/74Y43xycnJmDFjhv7n4uJiREdH2/uyEBDSGAfvmmT347obSVKmRkGNPCTAx8sTDQN88HNIH3zati+KblTg9K9N8U1OMDzLChHW/WH8X9NYvH3wV0SF+CLI1wstGgYgQOOFt57sCQmA1ttTf8zwIC3+Pb47KoXAr5dL0at5GE78Voiz+SUI0nojxM8bjQO1aNbQHwCw7olYeHpI+PnSNdzfupH8AhP+DoR3ANoMubPt3ucB3xDAyxdoP9L05EauBX787PbYjo/J92kCgCc+ACABPn41Hnpvy4ZYMbozTmQX4YGWHfFlpg9E6SVovFgP7yr4L4IL8gtDjANPLwkh7HovPScnB3fddRe+/fZbxMXF6bfPnj0b+/fvR0ZGhtnHFxcXIzg4GEVFRQgKMvObFhERETkNa16/7f6rRcOGDeHp6Yn8/HzZ9vz8fERERNj7dERERORi7B4+fHx8EBsbi/T0dP02nU6H9PR02Z0QIiIick/18mmXGTNmYMKECejevTt69uyJlStXorS0VP/pFyIiInJf9RI+Ro8ejUuXLmHevHnIy8tDly5dsGfPnhpFqEREROR+7F5wWlcsOCUiInI9Di04JSIiIjKH4YOIiIgUxfBBREREimL4ICIiIkUxfBAREZGiGD6IiIhIUQwfREREpCiGDyIiIlJUvXQ4rYuqnmfFxcUOvhIiIiKyVNXrtiW9S50ufJSUlAAAoqOjHXwlREREZK2SkhIEBwebHeN07dV1Oh1ycnIQGBgISZLseuzi4mJER0cjOzvb7Vq3u+vc3XXeAOfujnN313kDnLszzF0IgZKSEkRFRcHDw3xVh9Pd+fDw8ECTJk3q9RxBQUFu94ezirvO3V3nDXDu7jh3d503wLk7eu613fGowoJTIiIiUhTDBxERESnKrcKHRqPB/PnzodFoHH0pinPXubvrvAHO3R3n7q7zBjh3V5u70xWcEhERkbq51Z0PIiIicjyGDyIiIlIUwwcREREpiuGDiIiIFMXwQURERIpym/CRmpqKZs2aQavVolevXjh8+LCjL6lOUlJS0KNHDwQGBqJx48YYOXIksrKyZGNu3ryJpKQkhIWFISAgAImJicjPz5eNuXjxIoYOHQo/Pz80btwYs2bNwq1bt5ScSp0tXrwYkiRh+vTp+m1qnvvvv/+OJ554AmFhYfD19UXHjh1x9OhR/X4hBObNm4fIyEj4+voiPj4e586dkx3jypUrGDt2LIKCghASEoLJkyfj2rVrSk/FYpWVlZg7dy6aN28OX19ftGzZEgsXLpQtYKWWeX/11VcYNmwYoqKiIEkSdu7cKdtvr3l+//33uO+++6DVahEdHY2lS5fW99RqZW7uFRUVmDNnDjp27Ah/f39ERUVh/PjxyMnJkR1DjXOv7tlnn4UkSVi5cqVsu0vNXbiBbdu2CR8fH/Hmm2+KH374QTz99NMiJCRE5OfnO/rSbJaQkCA2btwoTp06JY4fPy7+9Kc/iZiYGHHt2jX9mGeffVZER0eL9PR0cfToUdG7d29x77336vffunVLdOjQQcTHx4tjx46JXbt2iYYNG4rk5GRHTMkmhw8fFs2aNROdOnUS06ZN029X69yvXLkimjZtKiZOnCgyMjLEzz//LNLS0sRPP/2kH7N48WIRHBwsdu7cKU6cOCGGDx8umjdvLm7cuKEfM3jwYNG5c2dx6NAhceDAAXH33XeLMWPGOGJKFlm0aJEICwsTn376qbhw4YLYvn27CAgIEKtWrdKPUcu8d+3aJV5++WXx4YcfCgBix44dsv32mGdRUZEIDw8XY8eOFadOnRLvvPOO8PX1FW+88YZS0zTK3NwLCwtFfHy8ePfdd8WPP/4oDh48KHr27CliY2Nlx1Dj3A19+OGHonPnziIqKkqsWLFCts+V5u4W4aNnz54iKSlJ/3NlZaWIiooSKSkpDrwq+yooKBAAxP79+4UQt/+ient7i+3bt+vHnDlzRgAQBw8eFELc/sPu4eEh8vLy9GPWrl0rgoKCRFlZmbITsEFJSYlo1aqV2Lt3r+jXr58+fKh57nPmzBF9+/Y1uV+n04mIiAjxj3/8Q7+tsLBQaDQa8c477wghhDh9+rQAII4cOaIfs3v3biFJkvj999/r7+LrYOjQoeLJJ5+UbXvkkUfE2LFjhRDqnXf1FyF7zXPNmjWiQYMGsj/rc+bMEW3atKnnGVnO3AtwlcOHDwsA4tdffxVCqH/uv/32m7jrrrvEqVOnRNOmTWXhw9Xmrvq3XcrLy5GZmYn4+Hj9Ng8PD8THx+PgwYMOvDL7KioqAgCEhoYCADIzM1FRUSGbd9u2bRETE6Of98GDB9GxY0eEh4frxyQkJKC4uBg//PCDgldvm6SkJAwdOlQ2R0Ddc//444/RvXt3PPbYY2jcuDG6du2K9evX6/dfuHABeXl5srkHBwejV69esrmHhISge/fu+jHx8fHw8PBARkaGcpOxwr333ov09HScPXsWAHDixAl8/fXXGDJkCAD1zrs6e83z4MGDuP/+++Hj46Mfk5CQgKysLFy9elWh2dRdUVERJElCSEgIAHXPXafTYdy4cZg1axbuueeeGvtdbe6qDx9//PEHKisrZS8yABAeHo68vDwHXZV96XQ6TJ8+HX369EGHDh0AAHl5efDx8dH/paxiOO+8vDyjz0vVPme2bds2fPfdd0hJSamxT81z//nnn7F27Vq0atUKaWlpeO655/D888/jrbfeAnDn2s39ec/Ly0Pjxo1l+728vBAaGuq0c3/ppZfw+OOPo23btvD29kbXrl0xffp0jB07FoB6512dvebpqn/+Dd28eRNz5szBmDFj9Cu5qnnuS5YsgZeXF55//nmj+11t7l6Kno3qRVJSEk6dOoWvv/7a0ZeiiOzsbEybNg179+6FVqt19OUoSqfToXv37vj73/8OAOjatStOnTqFdevWYcKECQ6+uvrz3nvvYcuWLdi6dSvuueceHD9+HNOnT0dUVJSq503GVVRUYNSoURBCYO3atY6+nHqXmZmJVatW4bvvvoMkSY6+HLtQ/Z2Phg0bwtPTs8YnHfLz8xEREeGgq7KfKVOm4NNPP8W+ffvQpEkT/faIiAiUl5ejsLBQNt5w3hEREUafl6p9ziozMxMFBQXo1q0bvLy84OXlhf3792P16tXw8vJCeHi4auceGRmJ9u3by7a1a9cOFy9eBHDn2s39eY+IiEBBQYFs/61bt3DlyhWnnfusWbP0dz86duyIcePG4YUXXtDf+VLrvKuz1zxd9c8/cCd4/Prrr9i7d6/+rgeg3rkfOHAABQUFiImJ0f+b9+uvv+LFF19Es2bNALje3FUfPnx8fBAbG4v09HT9Np1Oh/T0dMTFxTnwyupGCIEpU6Zgx44d+OKLL9C8eXPZ/tjYWHh7e8vmnZWVhYsXL+rnHRcXh5MnT8r+wFb9Za7+AudMBgwYgJMnT+L48eP6r+7du2Ps2LH679U69z59+tT4SPXZs2fRtGlTAEDz5s0REREhm3txcTEyMjJkcy8sLERmZqZ+zBdffAGdTodevXopMAvrXb9+HR4e8n+uPD09odPpAKh33tXZa55xcXH46quvUFFRoR+zd+9etGnTBg0aNFBoNtarCh7nzp3D559/jrCwMNl+tc593Lhx+P7772X/5kVFRWHWrFlIS0sD4IJzV7zE1QG2bdsmNBqN2LRpkzh9+rR45plnREhIiOyTDq7mueeeE8HBweLLL78Uubm5+q/r16/rxzz77LMiJiZGfPHFF+Lo0aMiLi5OxMXF6fdXfdx00KBB4vjx42LPnj2iUaNGTv9xU2MMP+0ihHrnfvjwYeHl5SUWLVokzp07J7Zs2SL8/PzE5s2b9WMWL14sQkJCxEcffSS+//57MWLECKMfxezatavIyMgQX3/9tWjVqpXTfeTU0IQJE8Rdd92l/6jthx9+KBo2bChmz56tH6OWeZeUlIhjx46JY8eOCQBi+fLl4tixY/pPdNhjnoWFhSI8PFyMGzdOnDp1Smzbtk34+fk5/OOm5uZeXl4uhg8fLpo0aSKOHz8u+3fP8NMbapy7MdU/7SKEa83dLcKHEEL885//FDExMcLHx0f07NlTHDp0yNGXVCcAjH5t3LhRP+bGjRviz3/+s2jQoIHw8/MTDz/8sMjNzZUd55dffhFDhgwRvr6+omHDhuLFF18UFRUVCs+m7qqHDzXP/ZNPPhEdOnQQGo1GtG3bVvzrX/+S7dfpdGLu3LkiPDxcaDQaMWDAAJGVlSUbc/nyZTFmzBgREBAggoKCxKRJk0RJSYmS07BKcXGxmDZtmoiJiRFarVa0aNFCvPzyy7IXHbXMe9++fUb/bk+YMEEIYb95njhxQvTt21doNBpx1113icWLFys1RZPMzf3ChQsm/93bt2+f/hhqnLsxxsKHK81dEsKgRSARERFRPVN9zQcRERE5F4YPIiIiUhTDBxERESmK4YOIiIgUxfBBREREimL4ICIiIkUxfBAREZGiGD6IiIhIUQwfREREpCiGDyIiIlIUwwcREREp6v8BEIaZ+oCLXj8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -225,20 +141,20 @@
     }
    ],
    "source": [
-    "for day_idx, day in enumerate(use_case.days):\n",
-    "    household_profile = household.generate_single_load_profile(\n",
-    "        prof_i=day_idx, day_type=get_day_type(day)\n",
-    "    )\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
+    "\n",
     "\n",
-    "    school_profile = school.generate_single_load_profile(\n",
-    "        prof_i=day_idx, day_type=get_day_type(day)\n",
-    "    )\n",
+    "i = 0\n",
+    "for name, df in dict(\n",
+    "    household_profiles=pd.DataFrame(household_profiles),\n",
+    "    school_profiles=pd.DataFrame(school_profiles),\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
+    "    axes[i].set_title(name)\n",
+    "    i += 1\n",
     "\n",
-    "    pd.DataFrame(\n",
-    "        data=[household_profile, school_profile],\n",
-    "        columns=range(1440),\n",
-    "        index=[household.user_name, school.user_name],\n",
-    "    ).T.plot(title=f\"day - {day}\")"
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -250,6 +166,12 @@
     "the school's appliance mix while, for the household, it is only occasionally \n",
     "present."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb28fd04",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -268,7 +190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/plot_class.ipynb b/docs/notebooks/plot_class.ipynb
index 6571fb6d..9a3ffc33 100644
--- a/docs/notebooks/plot_class.ipynb
+++ b/docs/notebooks/plot_class.ipynb
@@ -388532,7 +388532,7 @@
         }
        }
       },
-      "image/png": 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",
+      "image/png": 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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
diff --git a/docs/notebooks/random_seed.ipynb b/docs/notebooks/random_seed.ipynb
new file mode 100644
index 00000000..556af272
--- /dev/null
+++ b/docs/notebooks/random_seed.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Specifing the Random Seed\n",
+    "\n",
+    "When building a UseCase, you can provide a random_seed value. It is used to initialize the pseudo-random number generator.\n",
+    "This makes it possible to exactly reproduce the results of running a RAMP UseCase, for example to provide a code sample which exactly produces the presented data and plots in a paper."
+   ],
+   "id": "ce224d1e9b768f7a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate a UseCase without specified seed\n",
+    "- By default, the random_seed is None. In this case, the pseudo-random number generator uses the current system time as seed. (See https://docs.python.org/3/library/random.html#bookkeeping-functions for details). \n",
+    "- All UseCases in the following example are generated with the User_list from input_file_1 of the examples.\n",
+    "- The UseCase is initialized and a load profile is generated.\n"
+   ],
+   "id": "4f72c158e8d9fda0"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:52:38.256239Z",
+     "start_time": "2024-05-08T14:52:37.058279Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from ramp import UseCase\n",
+    "from copy import deepcopy"
+   ],
+   "id": "2a602befafcfae1f",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:54:08.983642Z",
+     "start_time": "2024-05-08T14:54:08.329458Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Import users list of example file 1\n",
+    "from ramp.example.input_file_1 import User_list\n",
+    "\n",
+    "# Build use case 1 with non-fixed seed\n",
+    "uc_1 = UseCase(users=deepcopy(User_list), random_seed=None)\n",
+    "# Initialize and generate load profile\n",
+    "uc_1.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_1_lp = uc_1.generate_daily_load_profiles()"
+   ],
+   "id": "441c5f7b2bf33409",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate two UseCases with fixed random seed\n",
+    "- The same, fixed random seed is specified for UseCases 2 and 3. \n",
+    "- The users and parameters of initialization are identical with UseCase 1\n",
+    "- A load profile is generated for each UseCase"
+   ],
+   "id": "1c07481527fd933c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:52:47.272310Z",
+     "start_time": "2024-05-08T14:52:45.855080Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Build use case 2 and fixed random seed\n",
+    "uc_2 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
+    "# Initialize and generate load profile\n",
+    "uc_2.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_2_lp = uc_2.generate_daily_load_profiles()\n",
+    "\n",
+    "# Build use case 3 and same fixed random seed as uc_2\n",
+    "uc_3 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
+    "\n",
+    "# Initialize and generate load profile\n",
+    "uc_3.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_3_lp = uc_3.generate_daily_load_profiles()"
+   ],
+   "id": "902bbd475ea04ac4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n",
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Comparing the results\n",
+    "The plot visualizes the effect of specifying the random seed:\n",
+    "- The load profiles of UseCase 2 and 3 are identical.\n",
+    "- The load profile of UseCase 1 is different, since the pseudo-random number generators seed was not specified.\n",
+    "If you rerun the above code and plot the new load profiles, you will notice that the load profile of UseCase 2 and 3 remain unchanged, while the result of UseCase 1 differs. "
+   ],
+   "id": "f72471c560cacc3c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:59:11.000826Z",
+     "start_time": "2024-05-08T14:59:10.766744Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "lp_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"UseCase_1: non_fixed_seed\": uc_1_lp,\n",
+    "        \"UseCase_2: fixed_seed\": uc_2_lp,\n",
+    "        \"UseCase_3: fixed_seed\": uc_3_lp,\n",
+    "        \"diff UseCase_2 - UseCase_1\": uc_2_lp\n",
+    "        - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
+    "        \"diff UseCase_2 - UseCase_3\": uc_2_lp\n",
+    "        - uc_3_lp,  # difference between uc_2 and uc_3 is zero\n",
+    "    }\n",
+    ")\n",
+    "lp_df.plot()\n",
+    "plt.show()"
+   ],
+   "id": "d65dc51db815e8b4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index 23d7da02..455f302e 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -5,7 +5,9 @@
    "id": "208fe9e9",
    "metadata": {},
    "source": [
-    "# Simple Appliances with multiple functioning windows"
+    "# Simple Appliances with multiple functioning windows\n",
+    "\n",
+    "In this example we are going to build a load simulation of a households category with only access to some indoor light bulbs."
    ]
   },
   {
@@ -16,7 +18,7 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
+    "from ramp import User, UseCase\n",
     "import pandas as pd"
    ]
   },
@@ -25,12 +27,14 @@
    "id": "a07be2a6",
    "metadata": {},
    "source": [
-    "### Creating a user category "
+    "### Creating a user category \n",
+    "\n",
+    "To represent a user category, which is a collection of users with similar consumption characteristics such as appliance ownership and consumption behavior, the class **User** should be used. Each User instance is initially characterized by the number of users assigned to the user category. For example, let's consider a household category comprising 10 households:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "77e6919a",
    "metadata": {},
    "outputs": [],
@@ -46,33 +50,68 @@
    "id": "8628f23a",
    "metadata": {},
    "source": [
-    "### Creating a simple appliance with two functioning time"
+    "### Creating a simple appliance with two functioning time\n",
+    "In the most basic use cases of RAMP, you can model appliances with random usage variability across different time windows throughout the day, as will be created in this example.\n",
+    "\n",
+    "To model appliances within a household category, parameters such as the number of appliances owned, power usage, number of usage windows, and total usage time need to be specified. Additionally, parameters like the minimum usage time after a switch-on event can enhance the realism of simulations. The randomness of appliance usage can be adjusted through parameters such as variability in the usage window or total usage time.\n",
+    "\n",
+    "For example, let's consider a household category where each household owns 6 \"Indoor Light Bulbs,\" each consuming 7 Watts. These light bulbs are used for 120 minutes in two time windows: from 00:00 to 00:30 and from 19:30 to 24:00. In RAMP simulations, time resolution is measured in minutes, with each simulation representing a single day from 00:00 (corresponding to minute 0) to 24:00 (corresponding to minute 1440).\n",
+    "\n",
+    "Assuming a minimum usage time of 10 minutes each time a light bulb is switched on, we can simulate this appliance by creating a new appliance using the **add_appliance** method of the User object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "76ff56d6",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# add_appliance is meth\n",
     "indoor_bulb = household.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\",\n",
-    "    number=6,\n",
-    "    power=7,\n",
-    "    num_windows=2,\n",
-    "    func_time=120,\n",
-    "    time_fraction_random_variability=0.2,\n",
-    "    func_cycle=10,\n",
-    "    window_1=[1170, 1440],  # from 19:30 to 24:00\n",
-    "    window_2=[0, 30],  # from 24 to 00:30\n",
-    "    random_var_w=0.35,\n",
+    "    name=\"Indoor Light Bulb\",  # the name of the appliance\n",
+    "    number=6,  # how many of this appliance each user has in this user category\n",
+    "    power=7,  # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
+    "    num_windows=2,  # how many usage time windows throughout the day?\n",
+    "    func_time=120,  # the total usage time of appliances\n",
+    "    func_cycle=10,  # the minimum usage time after a switch on event\n",
+    "    window_1=[0, 30],  # from 24 to 00:30\n",
+    "    window_2=[1170, 1440],  # from 19:30 to 24:00\n",
+    "    random_var_w=0.35,  # Variability of the windows in percentage\n",
+    "    time_fraction_random_variability=0.2,  # randomizes the total time the appliance is on (between 0 and 1)\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
+   "id": "7d02bfd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   user_name  num_users               name  number  power\n",
+      "0  Household         10  Indoor Light Bulb       6    7.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(household)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1363283c",
+   "metadata": {},
+   "source": [
+    "You can check the  **maximum theoretical profile** of the appliance and user category by calling the **maximum_profile** property of each user or appliance object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "id": "f07cd11c",
    "metadata": {
     "scrolled": true
@@ -84,13 +123,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -100,7 +139,7 @@
     },
     {
      "data": {
-      "image/png": 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hw3XBBRdozpw5GjVqlDZt2nTOne8MrBkBgNDjYnB2lKDCSEtLi6qqqpSdnf31C4SFKTs7W+Xl5X6fb4xRWVmZdu3apcsvv/y07Zqbm9XY2OjzAAAAzhRUGDl06JBaW1vldrt9jrvdbtXW1p72eQ0NDerdu7ciIyM1efJkPfTQQ/re97532vYlJSWKi4vzPpKTk4PpZodgyQgA2MdY7EydcjdNnz59VF1drbfeekv33nuvCgsLtXHjxtO2X7BggRoaGryPffv2dUY3AQCABRHBNE5ISFB4eLjq6up8jtfV1SkxMfG0zwsLC9OFF14oSRozZox27typkpISXXHFFW22j4qKUlRUVDBd60DMSwJAqGFkdpagroxERkZq3LhxKisr8x7zeDwqKytTVlZWwK/j8XjU3NwczFsDAACHCurKiCQVFhYqLy9P6enpysjIUGlpqY4cOaL8/HxJ0vTp0zVo0CCVlJRI+mr9R3p6ui644AI1NzfrhRde0J///GctX768fSvpYExTAoB9htHYkYIOI7m5uTp48KCKiopUW1urMWPGaN26dd5FrTU1NQoL+/qCy5EjRzRr1ix9/PHH6tmzp1JTU/Xkk08qNze3/aoAAABdVtBhRJIKCgpUUFDQ5rlTF6b+6le/0q9+9auzeRsAANAN8Nk0frCvDgCEHsZmZyGMAAAAqwgjAWKjHQAIAYzFjkQYAQAAVhFG/GBaEgBCj4vR2VEIIwAAwCrCSIDYaAcA7GMkdibCCAAAsIow4gf3sgNA6GFsdhbCCAAAsIowEiD2GQEA+xiLnYkwAgAArCKM+MG97AAAdCzCCAAAsIowEiCmKQHAPvZ8cibCCAAAsIow4gf3sgNA6HExODsKYQQAAFhFGAkUN7cDgHUMxc5EGAEAAFYRRvxgVhIAQg9js7MQRgAAgFWEEQAAYBVhJECsmQIA+xiLnYkwAgAArCKM+MHGOgAQehianYUwAgAArCKMBIiNdgDAPsNg7EiEEQAAYBVhBADQ5bBmxFkIIwAAwCrCSIAMd7cDgHWMxM5EGAEAAFYRRvxgXhIAQo+Lj8pzFMIIAACwijASIG5tB4AQwFjsSIQRAABgFWHED+YlASD0sJ7PWQgjAADAKsJIgJimBAD72PPJmQgjAADAKsKIH8xLAkDoYWh2FsIIAACwijACAACsIowEiE3PAMA+xmJnIowAAACrCCN+sEgKAEIQdxc4CmEEAABYRRgJEBvtAIB9rBlxJsIIAACwijDiB9OSABB6GJqdhTACAACsIowEinlKALCOodiZCCMAAMAqwogfLhaNAEDIYWh2FsIIAACwijASIOYpAcA+w0YjjkQYAQAAVhFG/GBaEgBCD2OzsxBGAACAVYSRADFPCQD2MRI7E2EEAABYRRjxh4lJAAg57AHlLIQRAABgFWEkQCwZAQD7GIudiTACAACsIoz44WLRCAAAHYowAgDocvhvorMQRgAAgFVnFUaWLVumlJQURUdHKzMzU5WVladt+8gjj+iyyy5T37591bdvX2VnZ5+xfahizRQAhAJGYycKOoysXr1ahYWFKi4u1pYtWzR69Gjl5OSovr6+zfYbN27UlClT9PLLL6u8vFzJycm68sor9cknn5xz5wEAQNcXdBhZunSpZs6cqfz8fI0YMUIrVqxQTEyMVq5c2Wb7p556SrNmzdKYMWOUmpqqRx99VB6PR2VlZefc+c7AvjoAEHoYm50lqDDS0tKiqqoqZWdnf/0CYWHKzs5WeXl5QK9x9OhRHTt2TP369Tttm+bmZjU2Nvo8AACAMwUVRg4dOqTW1la53W6f4263W7W1tQG9xvz585WUlOQTaE5VUlKiuLg47yM5OTmYbnYINtoBAPsYi52pU++mWbx4sVatWqU1a9YoOjr6tO0WLFighoYG72Pfvn2d2EsAANCZIoJpnJCQoPDwcNXV1fkcr6urU2Ji4hmfe//992vx4sV68cUXNWrUqDO2jYqKUlRUVDBd6zBMSwJA6GFDSmcJ6spIZGSkxo0b57P49MRi1KysrNM+77777tM999yjdevWKT09/ex7CwAAHCeoKyOSVFhYqLy8PKWnpysjI0OlpaU6cuSI8vPzJUnTp0/XoEGDVFJSIkn6zW9+o6KiIj399NNKSUnxri3p3bu3evfu3Y6ldCzDve0AYB0jsTMFHUZyc3N18OBBFRUVqba2VmPGjNG6deu8i1pramoUFvb1BZfly5erpaVFP/jBD3xep7i4WHfddde59R4AAHR5QYcRSSooKFBBQUGb5zZu3Ojz9717957NW4QM7mUHgBDE2OwofDYNAACwijASIO5tBwD7GIudiTACAACsIoz4wb3sABB6GJmdhTACAACsIowAALoM9nxyJsIIAACwijDiB/uMAEDoYWx2FsIIAACwijACAACsIowEyLDTDgBYx1DsTIQRAABgFWHED9ZIAUDoYUNKZyGMAAAAqwgjAWKaEgDsYyx2JsIIAACwijDiDzvrAEDIYWh2FsIIAACwijASIO5tBwD72PPJmQgjAADAKsKIH0xLAkDoYc2IsxBGAACAVYSRABnubgcAoEMQRgAAgFWEET+YlwSA0MNn0zgLYQQAAFhFGAkQt7YDgH2Mxc5EGAEAAFYRRvxgXhIAQg/r+ZyFMAIAAKwijAAAAKsIIwFizRQA2McGlM5EGAEAAFYRRvxgkRQAAB2LMAIAAKwijASIjXYAwD7GYmcijAAAAKsII36wZAQAQo+LBX2OQhgBAABWEUYCxkQlANjGmhFnIowAAACrCCN+MC0JAKGHodlZCCMAAMAqwkiAmKcEAPsYip2JMAIAAKwijPjBvewAEHoYmp2FMAIAAKwijASINSMAYJ9hMHYkwggAALCKMAIA6HJYMuIshBEAAGAVYSRAhrvbAcA6RmJnIowAAACrCCN+cC87AIQe9oByFsIIAACwijACAACsIowEiH12ACAEMBY7EmEEAABYRRjxw8XWOgAQchiZnYUwAgAArCKMBIhpSgCwjw0onYkwAgAArCKM+MG+OgAQehibnYUwAgAArCKMBIh9RgDAPsZiZyKMAAAAqwgjfjAtCQChiNHZSQgjAADAKsJIgLi3HQDsYyR2prMKI8uWLVNKSoqio6OVmZmpysrK07Z99913ddNNNyklJUUul0ulpaVn21cAAOBAQYeR1atXq7CwUMXFxdqyZYtGjx6tnJwc1dfXt9n+6NGjGjp0qBYvXqzExMRz7nBn4152AAg9jM3OEnQYWbp0qWbOnKn8/HyNGDFCK1asUExMjFauXNlm+/Hjx2vJkiX60Y9+pKioqHPuMAAAcJagwkhLS4uqqqqUnZ399QuEhSk7O1vl5eXt1qnm5mY1Njb6PKxjohIArGOfEWcKKowcOnRIra2tcrvdPsfdbrdqa2vbrVMlJSWKi4vzPpKTk9vttQEAQGgJybtpFixYoIaGBu9j37591vri4l52AAg5jMzOEhFM44SEBIWHh6uurs7neF1dXbsuTo2KimJ9CQAA3URQV0YiIyM1btw4lZWVeY95PB6VlZUpKyur3TsHAACcL6grI5JUWFiovLw8paenKyMjQ6WlpTpy5Ijy8/MlSdOnT9egQYNUUlIi6atFrzt27PD++ZNPPlF1dbV69+6tCy+8sB1L6VismQIA+9iA0pmCDiO5ubk6ePCgioqKVFtbqzFjxmjdunXeRa01NTUKC/v6gsv+/fs1duxY79/vv/9+3X///Zo4caI2btx47hUAAIAuLegwIkkFBQUqKCho89ypASMlJUWmC9+LxcY6ABB6GJudJSTvpgEAAN0HYSRAXfnqDgA4BUOxMxFGAACAVYQRAECXw4aUzkIYAQAAVhFGAsQ0JQDYx1jsTIQRAABgFWHEDxc3swNAyGFodhbCCAAAsIowEiDubQeAEMBg7EiEEQAAYBVhxA+mJQEg9LBmxFkIIwAAwCrCSICYpQQA+xiLnYkwAgAArCKM+MG8JACEHj6bxlkIIwAAwCrCCAAAsIowEiDDRjsAYB1DsTMRRgAAgFWEET9YIgUAIYjB2VEIIwAAwCrCSICYpgQA+1i/50yEEQAAYBVhxA8Xu54BQMhhZHYWwggAALCKMBIopikBwDqGYmcijAAAAKsII36wZAQAQg/r+ZyFMAIAAKwijATIMFMJANaxzYgzEUYAAIBVhBE/mJUEgNDD2OwshBEAAGAVYSRAzFMCgH0Mxc5EGAEAAFYRRvzhXnYACDkMzc5CGAEAAFYRRgLEmhEAsM8wGDsSYQQAAFhFGPGDaUkACD2Mzc5CGAEAAFYRRgAAgFWEkQDxQXkAAHQMwggAALCKMOIHG+sAQOhxMTg7CmEEAABYRRgJEPvsAIB9jMXORBgBAABWEUb8cLG1DgCEHEZmZyGMAAAAqwgjAWKaEgDsY88nZyKMAAAAqwgjfnArOwCEIMZmRyGMAAAAqwgjAeLedgCwj7HYmQgjAADAKsKIH0xLAkDoYQ8oZyGMAAAAqwgjAWOiEgBsYyR2JsIIAACwijDiB/uMAEDoYWx2FsIIAACwijACAACsIowEiI12AMA+xmJnIowAAACrCCN+sLEOAIQeRmZnIYwAAACrCCMBYpoSAOwzjMaOdFZhZNmyZUpJSVF0dLQyMzNVWVl5xvbPPvusUlNTFR0drbS0NL3wwgtn1VkAAOA8QYeR1atXq7CwUMXFxdqyZYtGjx6tnJwc1dfXt9n+jTfe0JQpUzRjxgxt3bpV119/va6//npt3779nDvfKZiYBICQw6ZnzhJ0GFm6dKlmzpyp/Px8jRgxQitWrFBMTIxWrlzZZvsHH3xQV111lebNm6fhw4frnnvu0SWXXKKHH374nDsPAAC6vohgGre0tKiqqkoLFizwHgsLC1N2drbKy8vbfE55ebkKCwt9juXk5Gjt2rWnfZ/m5mY1Nzd7/97Y2BhMNzvE+3VNWvS3d213AwC6tW0fN9juAjpAUGHk0KFDam1tldvt9jnudrv13nvvtfmc2traNtvX1tae9n1KSkq0aNGiYLrWYWKjv/oSffzZF3rs9b12OwMAkCT1juphuwtoR0GFkc6yYMECn6spjY2NSk5OttKX76YOUPE1I3To82b/jQEAHS46Ily54+38TkDHCCqMJCQkKDw8XHV1dT7H6+rqlJiY2OZzEhMTg2ovSVFRUYqKigqmax0mKiJc+ROG2O4GAACOFdQC1sjISI0bN05lZWXeYx6PR2VlZcrKymrzOVlZWT7tJWnDhg2nbQ8AALqXoKdpCgsLlZeXp/T0dGVkZKi0tFRHjhxRfn6+JGn69OkaNGiQSkpKJElz5szRxIkT9cADD2jy5MlatWqVNm/erD/84Q/tWwkAAOiSgg4jubm5OnjwoIqKilRbW6sxY8Zo3bp13kWqNTU1Cgv7+oLLpZdeqqefflq//OUv9Z//+Z8aNmyY1q5dq5EjR7ZfFQAAoMtyGRP6H8jc2NiouLg4NTQ0KDY21nZ3AABAAAL9/c1n0wAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrgt4O3oYTm8Q2NjZa7gkAAAjUid/b/jZ77xJhpKmpSZKUnJxsuScAACBYTU1NiouLO+35LvHZNB6PR/v371efPn3kcrna7XUbGxuVnJysffv2dbvPvKH27ld7d61bovbuWHt3rVsKrdqNMWpqalJSUpLPh+ieqktcGQkLC9N5553XYa8fGxtr/RtmC7V3v9q7a90StXfH2rtr3VLo1H6mKyInsIAVAABYRRgBAABWdeswEhUVpeLiYkVFRdnuSqej9u5Xe3etW6L27lh7d61b6pq1d4kFrAAAwLm69ZURAABgH2EEAABYRRgBAABWEUYAAIBV3TqMLFu2TCkpKYqOjlZmZqYqKyttd+mclJSUaPz48erTp48GDBig66+/Xrt27fJp8+WXX2r27Nnq37+/evfurZtuukl1dXU+bWpqajR58mTFxMRowIABmjdvno4fP96ZpZyTxYsXy+Vyae7cud5jTq77k08+0b//+7+rf//+6tmzp9LS0rR582bveWOMioqKNHDgQPXs2VPZ2dn64IMPfF7j008/1dSpUxUbG6v4+HjNmDFDn3/+eWeXEpTW1lbdeeedGjJkiHr27KkLLrhA99xzj89nYDil9ldffVXXXHONkpKS5HK5tHbtWp/z7VXntm3bdNlllyk6OlrJycm67777Orq0MzpT3ceOHdP8+fOVlpamXr16KSkpSdOnT9f+/ft9XqMr1i35/56f7NZbb5XL5VJpaanP8S5Vu+mmVq1aZSIjI83KlSvNu+++a2bOnGni4+NNXV2d7a6dtZycHPPYY4+Z7du3m+rqavP973/fDB482Hz++efeNrfeeqtJTk42ZWVlZvPmzeZf/uVfzKWXXuo9f/z4cTNy5EiTnZ1ttm7dal544QWTkJBgFixYYKOkoFVWVpqUlBQzatQoM2fOHO9xp9b96aefmvPPP9/cfPPNpqKiwnz44Ydm/fr1Zvfu3d42ixcvNnFxcWbt2rXm7bffNtdee60ZMmSI+eKLL7xtrrrqKjN69Gjz5ptvmtdee81ceOGFZsqUKTZKCti9995r+vfvb/7+97+bPXv2mGeffdb07t3bPPjgg942Tqn9hRdeMAsXLjTPPfeckWTWrFnjc7496mxoaDBut9tMnTrVbN++3fzlL38xPXv2NL///e87q8xvOFPdhw8fNtnZ2Wb16tXmvffeM+Xl5SYjI8OMGzfO5zW6Yt3G+P+en/Dcc8+Z0aNHm6SkJPPb3/7W51xXqr3bhpGMjAwze/Zs799bW1tNUlKSKSkpsdir9lVfX28kmVdeecUY89UPb48ePcyzzz7rbbNz504jyZSXlxtjvvoBCAsLM7W1td42y5cvN7Gxsaa5ublzCwhSU1OTGTZsmNmwYYOZOHGiN4w4ue758+eb73znO6c97/F4TGJiolmyZIn32OHDh01UVJT5y1/+YowxZseOHUaSeeutt7xt/vd//9e4XC7zySefdFznz9HkyZPNj3/8Y59jN954o5k6daoxxrm1n/qLqb3q/N3vfmf69u3r8+99/vz55tvf/nYHVxSYM/1CPqGystJIMh999JExxhl1G3P62j/++GMzaNAgs337dnP++ef7hJGuVnu3nKZpaWlRVVWVsrOzvcfCwsKUnZ2t8vJyiz1rXw0NDZKkfv36SZKqqqp07Ngxn7pTU1M1ePBgb93l5eVKS0uT2+32tsnJyVFjY6PefffdTux98GbPnq3Jkyf71Cc5u+7/+Z//UXp6uv7t3/5NAwYM0NixY/XII494z+/Zs0e1tbU+tcfFxSkzM9On9vj4eKWnp3vbZGdnKywsTBUVFZ1XTJAuvfRSlZWV6f3335ckvf3229q0aZOuvvpqSc6u/WTtVWd5ebkuv/xyRUZGetvk5ORo165d+uyzzzqpmnPT0NAgl8ul+Ph4Sc6u2+PxaNq0aZo3b54uvvjib5zvarV3yzBy6NAhtba2+vzikSS3263a2lpLvWpfHo9Hc+fO1YQJEzRy5EhJUm1trSIjI70/qCecXHdtbW2bX5cT50LVqlWrtGXLFpWUlHzjnJPr/vDDD7V8+XINGzZM69ev109/+lPddttt+tOf/iTp676f6d96bW2tBgwY4HM+IiJC/fr1C+na/+M//kM/+tGPlJqaqh49emjs2LGaO3eupk6dKsnZtZ+sversqj8DJ3z55ZeaP3++pkyZ4v1wOCfX/Zvf/EYRERG67bbb2jzf1WrvEp/ai+DNnj1b27dv16ZNm2x3pcPt27dPc+bM0YYNGxQdHW27O53K4/EoPT1dv/71ryVJY8eO1fbt27VixQrl5eVZ7l3HeuaZZ/TUU0/p6aef1sUXX6zq6mrNnTtXSUlJjq8dvo4dO6Yf/vCHMsZo+fLltrvT4aqqqvTggw9qy5YtcrlctrvTLrrllZGEhASFh4d/426Kuro6JSYmWupV+ykoKNDf//53vfzyyzrvvPO8xxMTE9XS0qLDhw/7tD+57sTExDa/LifOhaKqqirV19frkksuUUREhCIiIvTKK6/ov/7rvxQRESG32+3IuiVp4MCBGjFihM+x4cOHq6amRtLXfT/Tv/XExETV19f7nD9+/Lg+/fTTkK593rx53qsjaWlpmjZtmn7+8597r445ufaTtVedXfVn4EQQ+eijj7RhwwbvVRHJuXW/9tprqq+v1+DBg71j3kcffaTbb79dKSkpkrpe7d0yjERGRmrcuHEqKyvzHvN4PCorK1NWVpbFnp0bY4wKCgq0Zs0avfTSSxoyZIjP+XHjxqlHjx4+de/atUs1NTXeurOysvTOO+/4/CM+8QN+6i+9UDFp0iS98847qq6u9j7S09M1depU75+dWLckTZgw4Ru3b7///vs6//zzJUlDhgxRYmKiT+2NjY2qqKjwqf3w4cOqqqrytnnppZfk8XiUmZnZCVWcnaNHjyoszHcICw8Pl8fjkeTs2k/WXnVmZWXp1Vdf1bFjx7xtNmzYoG9/+9vq27dvJ1UTnBNB5IMPPtCLL76o/v37+5x3at3Tpk3Ttm3bfMa8pKQkzZs3T+vXr5fUBWvv9CWzIWLVqlUmKirKPP7442bHjh3mJz/5iYmPj/e5m6Kr+elPf2ri4uLMxo0bzYEDB7yPo0ePetvceuutZvDgweall14ymzdvNllZWSYrK8t7/sQtrldeeaWprq4269atM9/61rdC/hbXU518N40xzq27srLSREREmHvvvdd88MEH5qmnnjIxMTHmySef9LZZvHixiY+PN88//7zZtm2bue6669q87XPs2LGmoqLCbNq0yQwbNizkbm89VV5enhk0aJD31t7nnnvOJCQkmDvuuMPbxim1NzU1ma1bt5qtW7caSWbp0qVm69at3rtG2qPOw4cPG7fbbaZNm2a2b99uVq1aZWJiYqze4nqmultaWsy1115rzjvvPFNdXe0z5p18d0hXrNsY/9/zU516N40xXav2bhtGjDHmoYceMoMHDzaRkZEmIyPDvPnmm7a7dE4ktfl47LHHvG2++OILM2vWLNO3b18TExNjbrjhBnPgwAGf19m7d6+5+uqrTc+ePU1CQoK5/fbbzbFjxzq5mnNzahhxct1/+9vfzMiRI01UVJRJTU01f/jDH3zOezwec+eddxq3222ioqLMpEmTzK5du3za/N///Z+ZMmWK6d27t4mNjTX5+fmmqampM8sIWmNjo5kzZ44ZPHiwiY6ONkOHDjULFy70+UXklNpffvnlNn+28/LyjDHtV+fbb79tvvOd75ioqCgzaNAgs3jx4s4qsU1nqnvPnj2nHfNefvll72t0xbqN8f89P1VbYaQr1e4y5qTtCgEAADpZt1wzAgAAQgdhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFX/P50KofcMeeraAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -124,7 +163,11 @@
    "id": "91442f65",
    "metadata": {},
    "source": [
-    "### Whole year profile functionality"
+    "### Whole year profile functionality\n",
+    "\n",
+    "To generate profiles spanning more than a single day, the UseCase class in RAMP allows for specifying calendar days as the start and end dates of the simulation. The UseCase class serves as a collector of different user categories and provides user-friendly methods for generating profiles with various settings.\n",
+    "\n",
+    "For example, suppose you want to simulate the behavior of 10 households for the entire year 2020. Using the UseCase class, you can specify the calendar days for the start and end dates of the simulation, creating a comprehensive profile spanning the entire year:"
    ]
   },
   {
@@ -142,14 +185,48 @@
     }
    ],
    "source": [
-    "whole_year_profile = []\n",
-    "use_case = UseCase(users=[household], date_start=\"2020-01-01\", date_end=\"2020-12-31\")\n",
+    "use_case = UseCase(\n",
+    "    users=[\n",
+    "        household\n",
+    "    ],  # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
+    "    date_start=\"2020-01-01\",  # starting date of the simulation\n",
+    "    date_end=\"2020-12-31\",  # end date of the simulation\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2678cc9d",
+   "metadata": {},
+   "source": [
+    "To generate the profiles, you can use, **generate_daily_load_profiles** methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ef095488",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'use_case' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m whole_year_profile \u001b[38;5;241m=\u001b[39m \u001b[43muse_case\u001b[49m\u001b[38;5;241m.\u001b[39mgenerate_daily_load_profiles()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'use_case' is not defined"
+     ]
+    }
+   ],
+   "source": [
     "whole_year_profile = use_case.generate_daily_load_profiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "10d23827-9ea6-4a34-b00c-66f811b024bb",
    "metadata": {},
    "outputs": [
@@ -187,22 +264,33 @@
    "metadata": {},
    "source": [
     "### Generating a profile for a single day\n",
-    "provide day_type=0 for weekday and day_type=1 for weekends"
+    "\n",
+    "To generate daily profiles for specific user categories, the User class in RAMP offers various methods. One such method is **generate_single_load_profile**, which constructs a load profile for a single user, accounting for all their appliances, based on a specific day of the year or a designated day_type. In RAMP, day_types represent weekdays and weekends, with day_type=0 indicating weekdays and day_type=1 signifying weekends. When defining appliances using the add_appliance method, users can specify whether the appliance is utilized throughout the designated wd_we_type argument.\n",
+    "\n",
+    "For instance, let's consider the scenario where we aim to generate a single-day load profile for a weekday of the year for a single user within the household category:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "e4ea14ed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
+     ]
+    }
+   ],
    "source": [
     "single_profile = household.generate_single_load_profile(day_type=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "04545584",
    "metadata": {},
    "outputs": [
@@ -212,13 +300,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -239,22 +327,22 @@
    "source": [
     "### Generating aggregated_load_profile for the user category\n",
     "\n",
-    "Single daily profiles are aggregated for all the users defined within the User class"
+    "If instead of a single user from the categoy, you want to generate load profiles of the aggregated users of the category, for a single day, you can use the similar method of User class, named **generate_aggregated_load_profile**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "3ed3f527",
    "metadata": {},
    "outputs": [],
    "source": [
-    "aggregated_profile = household.generate_aggregated_load_profile()"
+    "aggregated_profile = household.generate_aggregated_load_profile(day_type=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "9bb8f29c",
    "metadata": {},
    "outputs": [
@@ -264,13 +352,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -283,14 +371,6 @@
     "aggregated_profile = pd.DataFrame(aggregated_profile, columns=[\"household\"])\n",
     "aggregated_profile.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "130f4d0e-15cd-44b0-9cde-5a95a6431405",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -309,7 +389,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb
index 6c59948e..39975b90 100644
--- a/docs/notebooks/thermal_app.ipynb
+++ b/docs/notebooks/thermal_app.ipynb
@@ -7,12 +7,7 @@
    "source": [
     "# Thermal loads\n",
     "\n",
-    "This example input file represents a single household user whose only load\n",
-    "is the \"shower\". The example showcases how to model thermal loads\n",
-    "by: \n",
-    "1) using a time-varying average `power` attribute, pre-calculated as a function of the average daily groundwater temperature; \n",
-    "and\n",
-    "2) using the `thermal_p_var` attribute to add further variability to the actual power absorbed by the appliance in each usage event, which reflects the randomness of user behaviour in preferring a slightly warmer or colder shower temperature."
+    "Some of the energy requirements of users are drived my technologies that have time variant power consumption. Hot water boilers are an example of technologies that can have variant powers throughout time. To show case how loads as such can be modelled in RAMP, we take the example of a household thermal load for generating shower hot water.\n"
    ]
   },
   {
@@ -23,8 +18,9 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, load_data, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase, load_data\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -37,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 2,
    "id": "70b0f3cb",
    "metadata": {},
    "outputs": [],
@@ -50,26 +46,14 @@
    "id": "808da9d5",
    "metadata": {},
    "source": [
-    "When the power varies across days of the year, for instance, as a function of the average daily groundwater temperature,\n",
-    "the “power” parameter can be passed as a `pd.DataFrame` or `np.array` with a daily profile (365 rows\n",
-    "of data). For this exercise, data can be loaded from the default examples in ramp:"
+    "RAMP allows the user to give variant power profile for an appliance with a daily resolution. In this case,the power property in an appliance should be given as a timeseries pd.DataFrame or np.array with daily profile (365 rows of data). For this exercise, we will use a built-in power profile for thermal load example that can be loaded using load_data util function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 3,
    "id": "41280681",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "shower_power = load_data(example=\"shower\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "1d5b6d41",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -77,13 +61,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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1VU2ON5Pj8ggor2/uk+chop5hkENEQc0ZIMiJCu/bTE6DT5ADALWNLHAlGogY5BBRUJODHL3Gu6y7r2tyfKerAAY5RAMVgxwiCmpKJidgTU7fT1cBQE2jo0+eh4h6hkEOEQU1u7ylg19NTt9mcloGObV99DxE1DMMcogoqDnd4uoqv8JjuU9Os7NPNmuUmwFGS7U/tQ3M5BANRAxyiCioyc0A/aarpEyO0y34Ne7rLXImJ8USBoCZHKKBikEOEQW1QIXH4XoNNFJjwL5YRt4yyGFNDtHAxCCHiIJaoD45KpXKu0lnHywjl4OcIVKQU1BUi3v/sRu3vJ6P/x4o6fXnI6Lu6dLeVUREA43cbdi34zEgNgSsbnD0SfGxvIQ8OcoIADhd2YDTlQ0AAJvdhfkTk3v9OYmo65jJIaKgFqjwGOi7rR0EQUCj03+6yhd75hANHAxyiCioeWtyWmZyxCCnrhcyOWXWZjTYxexNs9MDecFWoCCnvo968xBR1zHIIaKg5t3WQeV3e6S0f5Wtg6Djo33FePTdAjS3sQqrymbHFSs+x8z/3YJvzlQry8cBIMVibDW+vtkJj6f3l60TUdcxyCGioBao8BgAIgxikFNvbz/Ieejfe/Hh3gt4b/e5gMfPVDXA4fKgttGJH73xDUqt4macRp0a0eH6VuM9AtDgYDaHaCBgkENEQa2twuNIabqqs9NH1W009Ktp8E532ewunCy3AQDC9VoYdRrlWHKUUZky45QV0cDAIIeIglqgXcgBIKIT01W+3ZBbBkmylj1wyq12AECYT4ADALERemWKrK92PyeirmGQQ0RBTV5d1SqTI01X2dqZrmrw2YOqZeGyrGXhcoVNDHJMhhZBjsngXdHVBw0IiajrGOQQUVCTp6taFh7LmZz2po5qfbI0bW1x1TqTI9bkhOnFx79tejoAYFneaCWTU89MDtGAwGaARBTU2pqu6kzA4dvTpuXO4rKaFn1vyuvFTE64NF31m+sm4Im80bCE6727nzPIIRoQmMkhoqDWVuFxRCemq3ynohrbWBFV1yLIqaj3n65Sq1WwSKuslN3POV1FNCAwyCGioNZWM0ClT047QU7nMjnidJUcNMmZHHm6yu85DfKKLmZyiAYCBjlEFNTa2tYhQgo42ltdVdvkrbfpaLoqNVrsbixnfyJaFB4DPpkcLiEnGhAY5BBRUGurGWBkpwqPOzNdJQZCcpAjswRoBOjtzcNMDtFAwCCHiIJaW9s6yKurHG4P7K7AWRr/mpyOMjnhfrfHBAhyzEbW5BANJAxyiCioKTU5LQqPTT41M21NWfkuIW8KEOQ0O91okva0ap3J0bUa31c7nxNR9zDIIaKgpqyuajFdpVGrvPtXtRnk+GRynK3HyMc1ahUSzf6bcQbatypSWULOTA7RQMAgh4iCmqONwmOg42Xktb7TVfbWmRy5MNkSplNqfGTRpgCZHLkOqImZHKKBgEEOEQU1p9zxOMDeUx11Pa7rYAm5vDmnJVynBEyy9jM5DHKIBgIGOUQU1NoqPAY6k8nxXUIeaLpKyuSE62HqRJDDJeREAwuDHCIaUErrmtHsDLzSKRA5yDEEyOR0tLVDR80A5ZVV0S0yOSqVt8jYV5R0m8PlCRg0CYKA6gZHq9uJqG8wyCGiAeNUhQ0zntuM6/+0o9P3sbsC98kB2u963Ox0K/cFAJdHUIqYZfIWDvGRBr8gxxKmg0YdOHMkjyuubWp1fNXnJzHl15uw4WBJh9dFRD3HIIeIBoyP9xUDAI6UWDt9H3l5eMuaGd/bAtXknK9pBOC/9LzlMvJSacfxhEij33RVoKkqAFCpVBhiCZMev3WQ88LG4wCApe/ta+NqiKg3McghogHD3iKT0hGPR4BNmhaKMAYKcqStHQJkcgorxSBnZEKEUs/T0GKKqVwKcpKijNBr1coy9UA9cmRDpH46FwJkcmRtNR4kot7FIIeIBgy70xvkCILQ4fgGhwvyMLOxdeDRXk1OYaUNAJAZZ0KYTtyHqmXwUVYvBjmJZgMA787jMabAmRzA2zTwQoBMDhFdXAxyiGjAaPbZfqGpE8XH8jSUTqMKWHicFCU28Dtb1djqmJzJyYwzKVNRLaeryqxiTU5CpPg4crYo0L5VMnm6KlAmJ1zv3dSzM0EcEfUMgxwiGjB8g4z2dg9Xxti99TgqVetC4IlDogAA+8/XtQoqzlQ2AACGxpoQJgUfe8/VKI/pcntQaRODHLnbsbxVRHQnpqsC1eT41vKUS0XNRNR3GOQQ0YDhu7y6rd42vuRpqMgAU1UAMCoxEnqNGnVNTpyr9g86CqUgJzPepGRYlv/nEH659iAAoMJmhyCIWzrEStNTciFzpzI5AYIcebk74A2yiKjvMMghogHDN8hpCLDNQkty072WWy7I9Fo1xiRHAgAOXKhTbm90uJSVU5mxJoTrvPf/cO8FAL5TVQaopeXicsFxfIShzXOSMzll9c2tlqT7ZqrOVreeQiOi3sUgh4gGDN8gp97e8dYI7S0flylTVhdqldvOSPU4lnAdok16hBs0fvfxeASUWeWiY+/GnA/MHoEfzEhH3oSkNp8vPsIAg1YNQRAbG8oEQfBbvXW2ipkcor7GIIeIBgRBEJQaGKBzmZx6JZPTdo3MpFQxyDlw3pvJOVPlrccBgCqbfxfiC7VNOFFWD8C7sgoApmZE4zcLJyqdjQPx7ZVzrsabrbG7PPD4lAWdrmCQQ9TX2n77Q0R0kfz1y0K88Okxvz45DV2oyTG3MV0FABOHWACI01UejwC1WuWtx4kzKcd8zXlxGxxS/Yy8sqorhsaZcLqyAacrGzBzRByA1svTDxbXBborEfUiZnKIqFd8c6Ya817ejh2nKrt831+vO9xqybhceNzocOHJ9/dj46HSVvdTVle1E+SMTIyAXqtGfbMLv/r4EL7zxy+wq7AagDfI+dHMoX73cfgUCMvL0LtieLz4uKcrbMptLfeyOlfdhBruY0XUpxjkEFGv+ODb8zhaWo8Pv73QK48nBzArN5/Au7vP4b5/7mk1pr6DwmNA3NNqXLIZAPD3/LM4eMGK7ccrAIgZFwB4Im8M/nbXNNyQPcTvvjNHxGLBxOQun/vw+AgAwCmfKSk5kxMdrlOCq/0XmM0h6ksMcoioV5wsF7MWXV015Lus2pc8XbX9uDcz1HJ3cmsHS8hlcl1OS8OkYCNMr8HVYxIxPCFCOfa9qal4654ZSiDUFcOkIMc/kyOee7he6y2GPlfb5ccmos5jkENEvUIJcrq4ash3BZIvm92FRocLp3wChZbFup1ZXQV4V1i11DKASYsJVz6flhHd7mO2R56uulDbpARmjVLQFq7XKEHXvvPM5BD1pS4HOdu3b8e1116LlJQUqFQqrF271u+4zWbDkiVLkJqairCwMIwbNw6vvfaa35jm5mYsXrwYsbGxiIiIwI033oiysjK/MUVFRViwYAHCw8ORkJCAZcuWweXyn9PeunUrpkyZAoPBgBEjRuDNN9/s6uUQUS+obnCgplHMqpRZ7a22R2hPcRsbWTbYXfjiRKVfr5kT5fV+YzozXQUAEwNkctSq1sFRik/9zbShMe2feDtiTHpEhekgCN6mg0omx6BFVpoFAHCQ01VEfarLQU5DQwOysrKwatWqgMeXLl2KDRs24F//+heOHDmCRx55BEuWLMFHH32kjHn00Ufx8ccfY82aNdi2bRuKi4txww03KMfdbjcWLFgAh8OBHTt24O9//zvefPNNLF++XBlTWFiIBQsW4KqrrkJBQQEeeeQR3HPPPfj000+7eklE1ENyFkdW1IUpq+I6McgZkxSJO3My8MOcDABiJmdvUa3f2ONlLYIcu7y6qv3pqhHxEUiJMvot/fYE2DpqTLIZcREGjEyIULIx3aFSqZT7y5kouUdOuE6DkdK0WKm1uVVBMhH1ni4HOfPnz8dvfvMbXH/99QGP79ixA3feeSdmz56NoUOH4r777kNWVha+/vprAEBdXR3++te/4g9/+AOuvvpqTJ06FW+88QZ27NiBnTt3AgA2btyIw4cP41//+hcmT56M+fPn49e//jVWrVoFh0NcjfDaa68hMzMTL774IsaOHYslS5bge9/7Hl566aXu/l8QUTe1DHLOdGHKqrhWnK6aMCQKv7puAialWgAANrtbqbmRt1U4Xub/PHImp73VVQCg1aixdvFMrH/4CmUHcd/+N7IIgxZbHr8SaxfPDLgXVleMkAKZtXuLIQiCkt0yGTSwhOuVgKsrASERdU2v1+Rcdtll+Oijj3DhwgUIgoDPP/8cx48fx9y5cwEAe/bsgdPpRG5urnKfMWPGID09Hfn5+QCA/Px8TJw4EYmJicqYvLw8WK1WHDp0SBnj+xjyGPkxArHb7bBarX4fRNRzrTI5AXb9bou8W3eK1EAvQuo+3GB3KUHMFKk+ZtPhMlyxYotSx2Pr5HQVACSYjRhiCcO/752By4bH4q93XhJwnNmoU3Yl74kfzMiAXqPGZ0fK8MZXZ9AgBTlh0iafGbFi/U+gHdKJqHf0epDzxz/+EePGjUNqair0ej3mzZuHVatWYdasWQCA0tJS6PV6WCwWv/slJiaitLRUGeMb4MjH5WPtjbFarWhqCjzH/9xzzyEqKkr5SEtL6/H1EpF3SiZO2tMpUCan0eFqtToK8G5kOcQi1sNEGMQMhxjkiJmcaRnRkLaPwrnqJvy/b88D6FzH45ZGJ0Xi7XtnYEIbxci9ZVKqBY/njQIArD9QgiZpWsokbQaaIXVb5vYORH2nT4KcnTt34qOPPsKePXvw4osvYvHixfjss896+6m67KmnnkJdXZ3yce7cuf4+JaKQUNMoTiNfMlTMuLQMcmx2F2Y/vxXfe20HBMG/GEYuPB5iETMbJimTU9/szeRkxIbjpZsnY5hU5/LfgyVodrqVpn0dra7qL1nS1FtVg8MnkyNe31Bmcoj6XK++MjQ1NeFnP/sZPvzwQyxYsAAAMGnSJBQUFOCFF15Abm4ukpKS4HA4UFtb65fNKSsrQ1KSuOldUlKSUsPje1w+Jv/bckVWWVkZzGYzwsLCAp6fwWCAwdD27sFE1D1yT5upGdH478FS7DtXB6fbA51GfB+1/1wtyuvtKK+341SFDSMSxJ3BBUHwma6SMzniy1KDw+UzHaXDvBFxuGJkPC757Wc4eMGKZe/vV55/oAY5sVJmq9LmXXFmkqar0mMY5BD1tV7N5DidTjidTqjV/g+r0Wjg8YjvuKZOnQqdTofNmzcrx48dO4aioiLk5OQAAHJycnDgwAGUl5crYzZt2gSz2Yxx48YpY3wfQx4jPwYRXTzy8uhLhsYgOlwHm92FAp9Gd6crvZmd/NPVyueVNgcaHW6oVMCQaPHNiVwP4ztdJQcxMSY9ZgwTl3Z/vK8YADAl3QKNumdFwn0lXgpy6ptdSrZLyeRIPXq6UqRNRF3T5bc/NpsNJ0+eVL4uLCxEQUEBYmJikJ6ejiuvvBLLli1DWFgYMjIysG3bNvzjH//AH/7wBwBAVFQUFi1ahKVLlyImJgZmsxkPPvggcnJyMGPGDADA3LlzMW7cONxxxx1YsWIFSktL8Ytf/AKLFy9WMjH3338/XnnlFTzxxBO4++67sWXLFrz33nv45JNPeuP/hYi6oMFnD6nLR8bj433FuOm1fIxOjMQ/77lU2dEbAHaeqsIdM8Rl4kXV4h/4lKgwGLQa5TEAwOkWUCnt7eRbWPxo7ii43McwLsWMq8ckYHpmbN9fYDeZw7TQqlVweQSck1ZReWtyxExOcW0THC4P9Fr2ZiXqbV0Ocnbv3o2rrrpK+Xrp0qUAgDvvvBNvvvkm3nnnHTz11FO4/fbbUV1djYyMDPz2t7/F/fffr9znpZdeglqtxo033gi73Y68vDz86U9/Uo5rNBqsW7cODzzwAHJycmAymXDnnXfi2WefVcZkZmbik08+waOPPoqVK1ciNTUVq1evRl5eXrf+I4ioewRBUDI5Jr0WV4yMU7Isx8rqsWb3eb+l3ztPV0EQBKhUKmWqJt2n07A8nQNAaQToW1g8bWgM3v1xcGRsVSoVYiP0KLPaUVQtTsuFS9cXH2FAmE6DJqcbJXVNSiEyEfWeLgc5s2fPblU46CspKQlvvPFGu49hNBqxatWqNhsKAkBGRgbWr1/f4bns3bu3/RMmoj7lcHvgkjrrhRs0mDUyHioVIL9MfFRQjCqf3barGhw4eMGKialRSpAjZzUAQKNWITpcp3RQBjq3RHygijUZUGa1o9JmByD+HwFiAGQJ16Gpzo26Jmd7D0FE3cT8KBH1SKPduyw8XKdBUpQRr9w6Bb+7fiL0GjWOldUrf+DnjEkAALzxVSEAbyO8dJ8gBwDiI70LBHQaFQxBPJUTG6H3+zpcmq4CvMGbXGBNRL0reF85iGhAkLcrMGjV0EqrqRZMSsZt09NxtRTUAGKH4YdzRwIAPtpXjC9PVCpFtxkx/lM1vkFOpFHX4+7D/UnuHSQL95mOkwuq6+3eIOejfcX4186zF+fkiEJc8OaAiahfON0eXKhpUlYHKfU4AZZx/3zBWBTXNWH/+TpcmhmLSakWTM+Mwa7Cavzgr7uUcRktMzk+gcFAXR7eWfKWFDLffbYipM/lfkDNTjce+rc4BT93XCISzEYQUfcxk0NEXbL0vX2Y/cJWbDwkdh+XV1b5TsPI0mLCsfYnM/HBTy7D72+YCAD4v1uzceOUVL9xLaerfLMfwVyPA3h75QDi/9GoxAjla+90lViT47s9But0iHqOQQ4RdYm8cmrV52IricYWTe5aUqtVmJIerWR6Es1GvPj9LDx09QgAYkDTchdx/+mqYA9yvJmcqRnRypQeAERK/yc2KVD03WXdyjodoh4L7lcPIuo3TdI+VEomx9A6k9OeR3JHIcUShsy41kunfYMceS+rYBXnE+RMz4zxO6bU5EgBzTHfIIeZHKIeY5BDRN0iBzkdZXLaolarcMul6QGP+QY55mDP5Ji813Jpi8aFcv8fufD4eKlvJodBDlFPcbqKiLql2Sk26pNXVwWqyemuUKrJSfQpHp6U6r/zudzdWc7k+DZNZCaHqOeC+9WDiC4quQMxADRLGRy5T06g1VXd5ZvJMfZi8NQfkqKM+OOt2Ygx6WHU+V+Lb+FxfbNT2awUYE0OUW9gkENEneY7hdLkdEMQhD7J5ESHe+tYfAOrYHVtVkrA230Ljwsr/TfqZCaHqOc4XUVEnea7rNnlEVDd4Gi3T053+e4qLk+LhSLf6Sqb3T9zwyXkRD3HIIeIOq3lH94yq73dPjm9IZi3dOhIpE8zwJYZKxYeE/Vc6L56EFGvax3kNHd7dVVHnr52HEYnRuKB2cN79XEHEu8ScmfrIKeJNTlEPcUgh4g6rWWdSJm1udt9cjryo5mZ+PTRWX6rk0KNUnhsd8HezUxOpc2Of39dFBK1S0S9jYXHRBSQIAiwuzx+K4JaZnJK+zCTMxjIQY5HAGql/1uTXoMGh7vThce3/WUnjpfZUFLbhKVzR4uP5xFgc7hadZImGmyYySEahPacrcG8l7fjmzPVbY65/197MOO5zahpcCi3tfzDW1rX3CerqwaLMJ1GKbKuton/z3HS8vnOFh7LvXU2SHuJAcCz6w5j0jMbse9cbS+eLVHwYZBDNAjd+OoOHC2txyPvFAQ8LggCth2vQG2jE4eKrcrt8h9eeWft8zVNfdInZ7BQqVRKXU5Vgx2AtxGitdkFQRDavX+ZtVn53CItu69rcuLNHWcAAB9J+4wRDVYMcogGGZfbp6GftDVDS5U2h7J0u9TnD6kc5EwYInbuPVfTyExOD3mDHCmTI+115fYIylRgW/YW1SifV9nEIMk3sIkx6Vvdh2gwYZBDNMgU+Exh+G6f4OtcTaPyeVnAIMcMALhQ06RMYTGT0z1yXY4cpFjC9NBKU1gdFR9/W1SrfH6+pgkej4D3d59TbmvZe4dosGGQQzTIbD9RqXxeXt8ccMy56vaDnFGJkdBr1HB5BGX7AWZyukcOcqqlTI5eq4Y5TCwY7mgZuW8mx+7yoKy+2W960catIWiQY5BDNMh8U+gtNq5pdAacsjpf491DqbTON8gR/2hawvVIjQ5TblepvDUh1DVyBsw3yImSgpxAxcduj4D/HihBmbUZJ8ttfscKimrh8njreBqYyaFBjvllokGm1TLwumYMjTP53Xa+jekqeWrKbNQiLSYcp6X9lsanmJXaEuoaOQMmf18MWjXMUnZHvu2Xaw9Co1bhme+Ox4d7L+DxNfsQadQqu5dPGGLGwQtWfHOmxu+x6xnk0CDHTA7RINPo8P/D51tYLDtX7c3klFnFWhFBEFDTKGYbLOF6pMV4MzmXDY/ri1MdFIxaMchxusUMjF6rRrRUMFzT4EBxbRP+ufMs3txxBvXNTmw5WgYASoBj0mswPlksBG/ZEoDTVTTYMcghGmQapBU7lnBxSsR3OkrmW3hcYbPD7RFgbXIpq32So4xIjwlXxuQMj+3LUw5pxha1THqtGvFSQXh5fXOr1W1hOv+MWbIlTAk4D1yoAwDoNGLhMguPabBjkEM0yDRKf/iGx0cAAEpaBDluj4Di2ia/ryttdpyvFQOfWJMeRp3GbzXVJUNj+vq0Q5acyZHpNWokmMUgp6Lejgs+9VF1TU5l5ZUsOcqIYdL3UiZ/bxnk0GDHIIdoEPF4BDRKhcbDpDqcshbTVcW1TXC6Beg0KsRL3XdL65qVP7ZDpILjueOSYAnX4frsIazH6QGjzv9l2OCXybH7BZx1TU5lylCWHGXEmKRIv9sY5BCJ+MpENIg0u9yQm+gOT5AzOU1+Y+Qpj9FJkdCoVKiot6PM2qz8sU2JEoOc+EgD9v7yf9BBU17qQJiu9XRVpLTnVEW9HRd8ghxrk0tpGihLigpDRqwJRp1aaeAof29Zk0ODHTM5RINIg7QFg0oFZEqZnJbTVfJ+R1mpFmUH8DJrs/LHdojf0nEV1C2mT6hrjAGCHDmDVt5iusra5FSaBspSoozQqFUYnejN5oyQgpwmp9uvwzXRYMMgh2gQkVdWhes0GGIRgxXf6RAA2He+FoAY5CRFiUFOqbUZxbViMJRiCQP1nlaFxxoNEiJ9anJaTFdV2VpMV0nfjzFJZuW24fHelgByYEs0GDHIIRpE5D944Qat0sxP3KdKvN3tEXDgvDhdlZXmzeSU1tlxXs7kMMjpVUZtgJocKchpcrpxrKxeOVZhs7fqfZMsBaIZcd7Vbmkx4dBLj1tv79xu5kShiEEO0SAiZ3JMeg2iwnQwSVkEucPx6QobGhxuhOs1GJEQoQQ55fXewmPfTsfUc2EBlpCH67VKMbdvzdPpCv8Ox4A3yEmL9gY5kQYtIqX7M5NDgxmDHKJBRO6RE67XQqVSIVX6wyhPiRwuEfc9GpdshkatQpIU5BRVN6JSqgXhdFXvarWEXMrAyNkcX6crxA7TkQYtIo1aZMaZlCLlvPFJmDMmAQ/NGQmVSoUIqWuyjZkcGsS4uopoEJF75JgM4h/WIdFhOFZWr2Rp5CLkNKnRX6LUr+VsldgjJ0ynQbTURJB6R6DCY0AMcgqlbTNk8jYa6bHheONHl8DgEyDptWr89a5LlK9NevHlvZ4rrGgQYyaHaBDxzeQA3vqaC1KjP7lnjtyMLlGaCpFlxpmgUnE1VW8K0/u/DOs14tcmn2msq8ck+I2JjTAgIdKobOQZiDeTwyCHBi8GOUSDiFKT45PJAaBkcsrrxSmphEgxuIk0aJUNJAFgbLJ3BQ/1DkOL6SqDlMnRarwvz4suz/QbE2fqeMd3uSaHvXJoMON0FdEgoqyuapHJkQuPy6VMjjxNpVKJdTnyNMnYZP/OutRzgQqPAeDhOSNR3eDAE3mjYW6RsYmN6DjIYSaHiEEO0aDiu7oK8MnkSIXH8o7j8qoqQJy6koOccczk9Lq2anImDInC/3vgMgDw65UDABmxJnREXp3FIIcGM05XEQ0ivn1yAO9y8DJrM5qdbpTXSzU5Pit7IgzeLAKnq3pfy20dWk5fAWhVezNjWMe7vkdwuoqIQQ7RYNIykxMfYUBchAEeAfjqZKWy95FckwMAdU3eDrvRnagFoa5puUGnXtv6ZdnUYkrLt6NxW+Qgh6uraDBjkEM0iLRcXaVSqTAtIxoAsP5AKQDAbNT61YlMGBJ1kc9ycGnVJ0fT+mXZd0XbyISITq1wk1fIlbTYZZ5oMGFNDtEg0rJPDgBMzYjGhkOl+O/BEgBAgtl/2fgjc0ZBr1FjYfaQi3eig4harYJeq4bDJWbRdJr2A5jJaZZOPa7cAfl8TWOPzo8omDGTQzSINMgbdOq972+mDhUzOY1SlkdeWSWLCtfhqWvGsh6nD8n7V+m16jazNPdfORxDLGFYlje6U4+pdLOuaYLguzcE0SDCIIdoEJEDGd9MzvgUs18dSGKksdX9qG/J04OGAFNVsp/OH4Mvn7yqVaatLckWI9QqwO7yoELakoNosGGQQzSINNhbZ3IMWg3mjU9Svo43t94zifqWvIw8UNGxr650m9Zp1EiOElfPnatu6mA0UWhikEM0iCiZHL1/Od7zN03Ck/PGYEq6BddOSumPUxvU5OLjjoKcrpL7ILEuhwYrBjlEIa64tgmPr9mHQ8V13kyOoXVvlgdmD8cHP5nJ1VT9wChPV/VykOMtPmYmhwanLv9Gbd++Hddeey1SUlKgUqmwdu3aVmOOHDmC7373u4iKioLJZMIll1yCoqIi5XhzczMWL16M2NhYRERE4MYbb0RZWZnfYxQVFWHBggUIDw9HQkICli1bBpfLv9/D1q1bMWXKFBgMBowYMQJvvvlmVy+HKOS9vasI7+85j79+UdhmJof6l2/hcW9KZSaHBrku/0Y1NDQgKysLq1atCnj81KlTuPzyyzFmzBhs3boV+/fvxy9/+UsYjd5iuUcffRQff/wx1qxZg23btqG4uBg33HCDctztdmPBggVwOBzYsWMH/v73v+PNN9/E8uXLlTGFhYVYsGABrrrqKhQUFOCRRx7BPffcg08//bSrl0QU0o6V1QMACs7XwuURV9lYwtvevZouPrnwuLeDnLQYZnJocOvy27n58+dj/vz5bR7/+c9/jmuuuQYrVqxQbhs+fLjyeV1dHf7617/i7bffxtVXXw0AeOONNzB27Fjs3LkTM2bMwMaNG3H48GF89tlnSExMxOTJk/HrX/8aTz75JJ555hno9Xq89tpryMzMxIsvvggAGDt2LL788ku89NJLyMvL6+plEYWs41KQc7pC3H8qyWxstV8S9S+lJqed1VXdIW/A2nLvK6LBold/ozweDz755BOMGjUKeXl5SEhIwPTp0/2mtPbs2QOn04nc3FzltjFjxiA9PR35+fkAgPz8fEycOBGJiYnKmLy8PFitVhw6dEgZ4/sY8hj5MQKx2+2wWq1+H0Sh6r3d5/Dc+iM4W+U/VZEWE9ZPZ0Rt6atMjrznFbd2oMGqV3+jysvLYbPZ8Pvf/x7z5s3Dxo0bcf311+OGG27Atm3bAAClpaXQ6/WwWCx+901MTERpaakyxjfAkY/Lx9obY7Va0dQU+F3Lc889h6ioKOUjLS2tx9dMdDF9dbISO09XdTiuvtmJn31wAH/efrrVMbkYlQYOef8qfYDNOXtC3r+qgTuR0yDV65kcALjuuuvw6KOPYvLkyfjpT3+K73znO3jttdd686m65amnnkJdXZ3yce7cuf4+JaJOq2lw4PbVu3DL6zuVLQDa8tXJKqX+pqXUGAY5A42hj6ar5FV0jQ43PG38PBCFsl79jYqLi4NWq8W4ceP8bh87dqyyuiopKQkOhwO1tbV+Y8rKypCUlKSMabnaSv66ozFmsxlhYYHT8QaDAWaz2e+DKFicrfZOO9U2OtoZCWw7Xt7msXQGOQNOWB8tIZczOQDQ6HT36mMTBYNe/Y3S6/W45JJLcOzYMb/bjx8/joyMDADA1KlTodPpsHnzZuX4sWPHUFRUhJycHABATk4ODhw4gPJy7wv1pk2bYDablQAqJyfH7zHkMfJjEIUSt0fA2aoG5evqdoIcQRCw7VhFm8fTolmTM9DIhce9HeQYtGpo1GKX5N6YsnK6Pdh0uAz1zc4ePxbRxdDl1VU2mw0nT55Uvi4sLERBQQFiYmKQnp6OZcuW4eabb8asWbNw1VVXYcOGDfj444+xdetWAEBUVBQWLVqEpUuXIiYmBmazGQ8++CBycnIwY8YMAMDcuXMxbtw43HHHHVixYgVKS0vxi1/8AosXL4bBILacv//++/HKK6/giSeewN13340tW7bgvffewyeffNIL/y1EA8enh0rx8Dt7/WppqhvaDnLOVjWiuK4Zeq0a79w3A58dLoNapcIrn4u/t2nM5Aw4k9KioFYBk1J7txGjSqWCSa+BtdkFm92FxI7vglWfn8TO01X4yw+ntVqFt2LDUfzli0IsnJyCl2/J7tVzJeoLXX7bsHv3bmRnZyM7W/wBX7p0KbKzs5UeNtdffz1ee+01rFixAhMnTsTq1avx//7f/8Pll1+uPMZLL72E73znO7jxxhsxa9YsJCUl4YMPPlCOazQarFu3DhqNBjk5OfjBD36AH/7wh3j22WeVMZmZmfjkk0+wadMmZGVl4cUXX8Tq1au5fJxCzlMfHECz04MT5TbltpqGtt9JF9eJhfdp0WGYkh6NJ+aNwbB4EwBAp1EhsZMbPNLFc9XoBBx4Jg93zczs9cc2dbH4+K9fFuKLE5XYW1Tb6thfvigEAKwtKO618yPqS13O5MyePRuC0H4B291334277767zeNGoxGrVq1qs6EgAGRkZGD9+vUdnsvevXvbP2GiIGcMMIXR3nRVpU08Fhfh3WhzeHwEAGBYXIQyfUEDi8nQN12o5ce1dSLIaXa6lSxheX2z3zHf1/0w9lmiIMHe7kQDXILZiOI6/z84Ne1MV1XW2wEAcZHeIGdSahReuCkLY5Mj++YkacCSg5xGe8eFx2VW789ZhfRzJCvx+RnMjDP10tkR9S0GOUQDXF1T66mp9mpyKm3iH6d4n0yOSqXC96am9v7J0YAXIS0jb3D4Z3LkzIxK5c3s+QYyvgEPAL/pKzeXo1OQ4C7kRANcZYt31ABQ0+50lTg+1qTvs3Oi4BGubz1d5fYIuPHVHbj5zzv9+ueU+gQ55S1+7vYW1Sifd2bqi2ggYCaHaABrdrpRH+APSnuZnCq5JsdnuooGr0Bdj8/XNOJbKTNzobZJWXHXXibncIl3G5yWWSGigYqZHKIBrEoKZnQaFT78yWV44aYsAJ3L5PgWHtPgZZKmq2w+NTmFld6eS8dK65XPS+u8W+KUW/0zOedqvM0ouU0EBQsGOUQDxOfHynHvP3b7FXzKU1WxJgOy06MxIkFcJdXeEnLv6ipOV5Fv4bE3MPELcsq8QU5xG9NVLrcHxbXeY063ALuLHZRp4GOQQzRAvPHVGWw6XIbNR7zblVQ1yCulxIAlJlz8t63pKkEQUMFMDvkwSTU5vlNMvkHO8TLfTI43kLHZXUrtTUldM9weAVqf9gM27mxOQYBBDtEAUSdNQdU0erM0lfX+PW+iTToAQJPTjSZH63fSNrtL2byTQQ4Bvn1yOp6uKmnRqqBcqss5J+2blh4brvTIaejEknSi/sYgh2iAqJfeGfsuGW+ZlYkwaKHTiO+mA9XlyFNVJr1G2fSRBjdlCXkb01WnKxrgcnvgcHmUei5LuBhMy1NWRXKQExPepeaCRP2NQQ7RAGGVNj2saxIDlf3na7H+QAkAIFaqr1GpVIhuZ8pKKTrmyiqStAxKmp1uXKgVC4y1ahUcbg/OVDUoHY71GjVGJ4pNI+VNYeWi47To8Db77hANRAxyiAYAQRBgbfJmcpqdbtz2l104VCwu2/Vt7BcvBTDnfVa7yJRux5yqIolSeCwFJYeK6yAIQKRRi6kZ0QCA93afR5m0mirBbECqtBnsTz84gH/kn8G5amk/tJgwb9DEmhwKAgxyiAYAu8sDh1uspalrcuJoab3fdIBv0JKVZgEA7Dlbg5Yq2AiQWpALjw9esGLML/+L772WDwCYOCQK988eDgD4+44zOHihDgCQEGnAQ3NGYOaIWAgC8MG3F/ymqyI4XUVBhEEO0QBg9anDqW10Yv/5WuXrMUmRuGxErPL1NOnd9+4AQU5RlTStIDV3I5L75ABAs9MDQQAuHxGH398wCbNHxWNaRjTsLg9WfX4SAJBoNiIj1oRfXzcBAHC01Ioz0rRVanR4wOaCRAMVOx4TDQByPQ4gZnL2nRPfVT909QgsnTvab+wlQ2MAAAcv1KHJ4fYrMD4rvePOiGWQQ6KIFrub5z91NZKjwpSvrxqTgN1na5Qi4wRpOnRorAkmvQYNDjeanR4YdWqMTIxg4TEFFWZyiAaAuibvH4w6n0zOpFRLq7Gp0WFINBvgdAvY55PxAXyW+jKTQxKTT5BzzcQkvwAHaP2zkmA2AgDUahXGpZiV2y8ZGgODVqM8HpeQUzBgkEM0APhmcurtLpwotwEAJqVFtRqrUqkwLUPM5nzrs2miIAhK7URGrKkvT5eCiFyTAwAzhsW2Ot4y65fgszJvfIr35y9nuHjfSKOcyWm76zbRQMEgh2gA8K3JkSVHGZEQaQw4fpS8xLfSu8KqwmZHo8MNtQoYYgkLeD8afIw6tdLAL298UqvjbWVyAPhlcmYOjwPgDZpszORQEGBNDtEAYA2wHFcOZAJJixGDGN9NE+Wi4xRLGPRavn8hkUqlwsZHZ8Hu8iDR3DpojgrTIdKoVZpR+mZypqRbAIir9SYMEbM6pgDNBYkGKgY5RANAoEyOvBlnIPK7b3l6CgDOVrHomAJrb7WdSqVCeky40pPJNxAakRCJ1++YiqQoIzTSvlVcXUXBhG/3iAYA35oc2fD4joOc4tomOKX+OmeVomPW41DXyD9POo0K0dKWDrK545P8CuAjpJqcegY5FAQY5BANAPUBpqvay+TERxpg0KrhEYCSWrEd/+kKsViZmRzqKjnISYg0QqVStTvWxEwOBREGOUQDQKDpquHxbWdkVCqVMgUhT1ntLaoFIHayJeoK+WcpvhN7nkVKQU6gwJxooGGQQzQABCo8ju1g/ynfupzi2iZcqG2CRq3CZGnbB6LOmjM2AWOTzbj5krQOx8qBUHl9MwRB6OtTI+oRFh4TDQCBMjkdkYOcczWNyhYP45LNfs3fiDojOSoM/334ik6NlQuTm50eWJtciGpRw9OeJocb/9p5FnPHJ7KXE10UzOQQDQBy4bG8ZFfen6o9qdHiMvKiqkbsPlMNAMqu0kR9xajTwCIFNluOleG1bafgcHk6dd91+4vx2/VH8IdNx/vyFIkUfMtHNABYpW0dfvmdcSiqbsSskfEd3mdsstiobefpKsRIu47L+1oR9aUksxG1jU48+u4+AEC4XoMf5gzt8H4ldWKRfIW0TxZRX2OQQ9TPBEFAXZMDAJAUZUR2eueyMZdmxsASrkNVgwNVDQ7oNWql9T5RX0qKMuJoab3y9cELdZ26X3WD+HPOzT3pYuF0FVE/sza54HSLBZxyRqYzdBo15o5LVL6+NiulS/cn6q6kFp2Tw/Wde78sBzlcmUUXC4Mcon5W2SCm7s1GLQxaTZfuO39isvL5XZcN7c3TImpTy+0hahsdnbpfTSODHLq4OF1F1M+qbOILf1wHS8YDuXxEHL6blYL4SAMmprI/Dl0cSVH+QU6ZtXM1Nt7pKu5gThcHgxyiflZlE/9AxEZ0fapJp1Hj/27N7u1TImpXqyCnvrlT95ODnGanB063BzoNJxOob/EnjKifVUov/LGmrmdyiPpDy5qc8k5kcgRBUIIcALBxyoouAgY5RP2ssr77mRyi/tAyyLHZXR2umGpyumH36afDFVZ0MTDIIepnVQ1ykMNMDgUHS7iu1W7l5db2p6zk2jOZ3ACTqC8xyCHqZ97CY2ZyKDioVCp8/vhsfP3zORgmbSTbUfFxTYsVWJyuoouBQQ5RP5ODHNbkUDCxhOuREGlEYqQ4dVXeQfGxbz0OwOkqujgY5BD1s8oG1uRQ8Eo0i8F5WQfTVS2DHPbKoYuBQQ5RP+N0FQUzuTFgR9NVrYIcZnLoImCQQ9SPHC4P6prEAkxOV1EwSlCCnC5OVzGTQxcBgxyifiQXY2rUKkSF6ToYTTTwyNNVHfXKaVl4XM/VVXQRMMgh6kcXapsAiFNVarWqn8+GqOuU6aoOCo/lIEielmXhMV0MDHKI+tGB83UAgHHJ5n4+E6LukVdXlVmbIQhCm+NK6sQgaGRCJABOV9HFwSCHqB/tO18LAJiUaunX8yDqrgRpuqrZ6YG1ncClpE7MWo5KjACAdscS9RYGOUT9aL+UyclK4w7iFJyMOo1ST9ZW1+Nmpxs1jWINzshEKZPThzuRNznceH37qQ6LoSn0Mcgh6ic2uwunKmwAgIlDLP17MkQ94O2VE7j4WJ6qCtdrMMQSBqBva3L+9lUhfrf+KH7zyZE+ew4KDgxyiPrJwQt1EAQgJcqI+EguH6fgldjBMnJ5qiopyohIoxZA3zYD3FVYDQD48kQFPJ6264Qo9HU5yNm+fTuuvfZapKSkQKVSYe3atW2Ovf/++6FSqfDyyy/73V5dXY3bb78dZrMZFosFixYtgs1m8xuzf/9+XHHFFTAajUhLS8OKFStaPf6aNWswZswYGI1GTJw4EevXr+/q5RD1m71FtQBYj0PBLyGy/RVWJbXi7SlRYYg0ilNbfVV47PEI2Hu2BgBQ0+jE4RJrnzwPBYcuBzkNDQ3IysrCqlWr2h334YcfYufOnUhJSWl17Pbbb8ehQ4ewadMmrFu3Dtu3b8d9992nHLdarZg7dy4yMjKwZ88ePP/883jmmWfw+uuvK2N27NiBW2+9FYsWLcLevXuxcOFCLFy4EAcPHuzqJRH1i68LqwAAl2TG9POZEPVMR71ySqUMT1KUERFyJqePpqtOlNv8HnvHqco+eR4KDl0OcubPn4/f/OY3uP7669scc+HCBTz44IN46623oNP5Nzg7cuQINmzYgNWrV2P69Om4/PLL8cc//hHvvPMOiouLAQBvvfUWHA4H/va3v2H8+PG45ZZb8NBDD+EPf/iD8jgrV67EvHnzsGzZMowdOxa//vWvMWXKFLzyyitdvSSii87tEbD7jPhuczqDHApyvtNVq784jRtf3YG6Rm9hsTxdlRJlRIRBDHIcLg/sLnevn8u3RTV+X391sqrXn4OCR6/X5Hg8Htxxxx1YtmwZxo8f3+p4fn4+LBYLpk2bptyWm5sLtVqNXbt2KWNmzZoFvd67l09eXh6OHTuGmpoaZUxubq7fY+fl5SE/P7/Nc7Pb7bBarX4fRL1p46FSTPn1Jmw5WtbuuCMlVtTbXYg0aDGWPXIoyMmZnJK6Zvzli9PYc7YGX5ysUI7L01VJUWFKkAP0zZTVt9JU1RUj4wAAR0v5Oj+Y9XqQ87//+7/QarV46KGHAh4vLS1FQkKC321arRYxMTEoLS1VxiQmJvqNkb/uaIx8PJDnnnsOUVFRykdaWlrXLo6oHW6PgPv+uQfVDQ78YdPxdsfKhZHThkZDw07HFOQy48TeN4dLrMoKq1PlDcpxeXVVssUIjVoFk14DoG9WWMlTYznDYwGIG+Cy+Hjw6tUgZ8+ePVi5ciXefPNNqFQD74X7qaeeQl1dnfJx7ty5/j4lCiEbD3kD7EhD+/tQ7T4jBjmsx6FQMDzeBINWDYfLo9wmt0dwuj04XSl+nh4TDgDeupw+yOTIjzk01gQAcHkE1DZxn6zBqleDnC+++ALl5eVIT0+HVquFVqvF2bNn8dhjj2Ho0KEAgKSkJJSXl/vdz+Vyobq6GklJScqYsjL/dL/8dUdj5OOBGAwGmM1mvw+i3vKP/LPK542O9l+8952rBQBMSY/uy1Miuii0GjXGtJh2lQObY6X1aHZ6EGnUIlMKPOQVVn0R5MjZoehwPSzh4vNU2trfPJRCV68GOXfccQf279+PgoIC5SMlJQXLli3Dp59+CgDIyclBbW0t9uzZo9xvy5Yt8Hg8mD59ujJm+/btcDq90femTZswevRoREdHK2M2b97s9/ybNm1CTk5Ob14SUac02F3YfbZa+bqivu0X1XJrM4rrmqFWAROHsNMxhYYJKf5BzqnyBng8AgqkgH5ymkXZhFauy+mL6aoG6TEjDFrER4i1Qu39PlJo03Y8xJ/NZsPJkyeVrwsLC1FQUICYmBikp6cjNjbWb7xOp0NSUhJGjx4NABg7dizmzZuHe++9F6+99hqcTieWLFmCW265RVluftttt+FXv/oVFi1ahCeffBIHDx7EypUr8dJLLymP+/DDD+PKK6/Eiy++iAULFuCdd97B7t27/ZaZE10sXxdWw+kWoFYBHgGosNkhCELAadt90lYOIxMiYTJ0+VeQaEAan+IfsDc53Si1NvsFOTK5IWBfbO0gFzNHGLWIizDgRLmNmZxBrMuZnN27dyM7OxvZ2dkAgKVLlyI7OxvLly/v9GO89dZbGDNmDObMmYNrrrkGl19+uV9wEhUVhY0bN6KwsBBTp07FY489huXLl/v10rnsssvw9ttv4/XXX0dWVhbef/99rF27FhMmTOjqJRH12JcnxV4cC7OHAACcbgF1bdQByFNV3K+KQsmEId5MjkEr/mk5VWELGOTImZy2pqsOF1txx193KfftLI9HgM3hk8mJZCZnsOvy28jZs2dDEDpfqX7mzJlWt8XExODtt99u936TJk3CF1980e6Ym266CTfddFOnz4Wor3x5Qgxy5oxJxOYj5ahrcqKi3g5LuL7VWHnn8SyfF32iYDc6KRJDLGHQalQYHh+BLUfL8e3ZWqUAOVAmp60g56F39uJkuQ1fnKjEmd8v6PQ5NDrdkP88RRjETA7AIGcw495VRD1kd7lxrKweAHBJZnSH7x4PFYt9OyZxU04KIQatBusfvgIfP3g5sqStSlZ/eRqCAIxIiEBshHd/tghp9WFbNTnd3T1crsfRqFUw6tTe30VOVw1aDHKIeqhW6uyqUasQZzJ4ix0DvLDWNjpQ3eAAAAyLN128kyS6CKLCdDAbdcgdJ/ZCkzM1c8b690bzLiEPPKXru2GtxyPgna+L8MxHh/yWqAciP1+EQQuVSsXpKur6dBUR+auyiUFLdLgOanX7L6ynK8UGaUlmI4uOKWSNSzZjiCUMF2rF7RzmjPFv3GqWC4/bmK6KizDgdIX4u3LfP3fjsyNi25E5YxNwxcj4Np/X5rOySnwccbq4UvodpcGHmRyiHqpplIMc8QW13SBHeuFmFodCmUqlwtzxYmATFabDlHSL3/GOlpCH6TTK53KAA3SckbE1+wc5zOQQgxyiHpKnn2JMHQc5hVKDtMw4BjkU2m65JB2RBi3umJEBrcb/T408XWVtI5PT5Ai8caf8u9YWJZMjPb48dVzdYIebWzsMSsyXE/WQnMlRghzphbWsvhmCIMDu8sAovTMtlKarGORQqBudFIkDv8oLeEzueNzWdFWjM/Dt8u9aW1pOV8WY9FBJvauqGxx+tT40ODCTQ9RD8rvLaCnIGZkobla483Q1bnx1B6b95jOUShsUytNVw+Mj+uFMiQaGjqarGltkcqZliJ3uqxvabx5okwqZ5UyOVqNGpPRcdU2syxmMGOQQ9ZAyXSXV5ExKtWDuuES4PQK+LaqFze7C/vO18HgEZnKI4NsnJ3DQ0mj3D3LmjBXre2o6O12l905SyL2q2mrOSaGNQQ5RD7XM5ADA8mvHKS/kgLi640JtE+wuD3QaFVKjwy76eRINFN5tHVwBm8v6bnBrNmqV3cs7qsmpb1GTA4iFz4C31QMNLgxyiHpIrhOI9QlyUqPDsfmxK5EnrTCpstlxolxsGDgsLqJVISbRYCJPVzndYs0aABwvq0edFIg0OcVMzvPfm4RPH52FaJO0m3iDHRsOlmL1F6fxbVFNq8dtaFGTA0DZiZyZnMGJhcdEPSTXCfhmcgAgIdKIEQkR+PRQGSptdhwvE1dWyTU7RIOVyWc6qa7Jid+tP4J/5J/FpUNj8K97psPpFrM7c8clISpcpwQopysacP+/9gAQs0F7f/k/fm8Y5EJm3yyqmZmcQY1vJ4l6qKZFTY4vee+cygYHjktbP4xOjLx4J0c0AKnVKiUQeXbdYfwj/ywA4Osz1X7Lx8P04qrEQL9b9c0unKtp8rtNrsnxbbRpCWMmZzBjkEPUA4Ig+NTk6Fodl/frqay344SSyWGQQzRrlNi5+JP9JcptahXQINXjaNUq6KXdzANtdAsAJ6Q3DrL65tbTVVEMcgY1BjlEPdDgcMPhFmsKYk2te3DESVNYFT41OaM4XUWEO3OGKp/Ly7w9AlAqbc4pZ3EAQK9V+01ByT2pTpTb/B5TDpACFR4zyBmcGOQQ9YA8VWXUqf1elGVxUvOx0xUNaHZ6oNeqkRHL5eNElwyNxpgkMat518yhMEm/PxekKSjfuh3AGwgBwDUTkwAAJ1sEOUpNDguPScIgh6gHWvbIaSm2RTHyiPgIaNSqPj8vooFOpVJh5S3ZeHjOSPxk9ghlSqpY2tQzvMWbhlqfIOXyEeJUl5wdlQWqyfEuIWczwMGIq6uIekB+4Y1qI8iJDtdDLbWVB4CJQ6Iu1qkRDXijkyIxWsrmRJt0uFDbpAQ5LTOjvl2Q5Snfk+U2eDwC1GoVXG6P8qbD981FVBibAQ5mzOQQ9YD8whkVFvj9glqtQoxPrc6lmTEX5byIgk209EbhQhuZHF/pMeHQa9VodnpwXpreKq+3wyOIBcvyqkaANTmDHYMcoh6wSi+cZmPrlVWyCIP3xZpBDlFgcpAjBy3hLWpyVt4yGUadGn+9cxq0GjWGxopdkM9UiVullEj7wyWajVD7TAn71uQIgoD/HijBGWl7FQp9nK4i6gFrs5zJaTvI8e3lwe0ciAKLloKRtmpyrps8BN+ZlKLUtKVGh+N4mQ3nahoBACV14v1SLEa/+8m/m063gE8PleGBt75FVpoF/1k8s+8uhgYMZnKIekBOgZvbCXLcHu/ePCoVi46JApELj63SCqlAqxV9i/bTpDcMcuanVMrkJEX5v5EI12ug04j3W7e/GABw4Hxtm5uDUmhhkEPUA9Ym8QW5vemqF27KgkGrxt/umnaxToso6MS0WInYXk0OIGZyAG+QU1wrBjkpUf6ZHJVKpWRzNh0uAyAuBNh3rq7nJ00DHqeriHrA2kHhMQB8b2oqrs8ewqXjRO2Qa2dkLfvktJQWI2ZszlWL01WlVjHYSWoR5ABiprXS5lA2AwWAPWdrcPnIuB6dMw18zOQQ9YBck9PedBUABjhEHYhu0YYh0HSVr7YyOclRreveAvWxCrSLOYUeBjlEPeBdQt5+kENE7WsZ5HQ0XZUmBTmVNjvG/nIDCs7VAgCSA2RyFl2eqXwub5C7+0w19kn3odDFIIeoB6ydKDwmoo613OA2rIPpKnOYVtm+ocnpbRSYbGkd5MyfmIz/90AObsgegpdvmYxRiRFocLhx05/zlekuCk0Mcoh6QF4JwkwOUc+0zOSYOsjkqFQqNLvcrW6PC7BRLgBMzYjBH26ejLHJZrz34xyMSoyAw+XBN2equ3/SNOAxyCHqJkEQvEvI21ldRUQdC9drlAZ/ADq1ka1vZ+MksxG5YxP8GgG2xRKuR3ZaNADgbBUzOaGMq6uIuqnR4VZ64DCTQ9QzKpUKHz94Ofafr4NRp8GUdEuH93nhpiz8aetJPHvdBKTHhEPbhQL/jDgxoCridFVIY5BD1E1yFkenUcGoY1KUqKcijTrMHNH5Zd0zR8R1abyvjBgxU3S2ils8hDK+MhN1k7J83KhjJ2OiIJMRy0zOYMAgh6ib5G7HnKoiCj7psfISdAdsdlc/nw31FQY5RN0kT1dFMsghCjpmo07ZFLSIxcchi0EOUTdZ2QiQKKilSyu4iqpZlxOqGOQQdZN3+Tjr94mCUUaMOGV1hpmckMUgh6ibqhrsAFo3MSOi4DA6Sdzi4cB57kgeqhjkEHVTibQhYIql9YaARDTwTc0QGwJ+c6YagiD089lQX2CQQ9RNF2rF3Y9TAuyVQ0QDX1aqBVq1CuX1dmU3cwotDHKIuqm4TnxRHMJMDlFQCtNrMGFIFABg91nuYRWKGOQQdYPHI6C0TpyuSmaQQxS0pklTVrvP1PTzmVBfYJBD1A2VNjucbgFqFZAYGXjXYyIa+KZIQc6BCyw+DkUMcoi6Qa7HSTIbodXw14goWI1KFFdYnSy3sfg4BPHVmagbims5VUUUCjJixd3LGx1uFEtT0BQ6GOQQdUOxsrKKQQ5RMNNp1MiMEzsfnyy39fPZUG9jkEPUgeNl9Xhu/RFUNziU27h8nCh0jEyMAACcKKvv5zOh3sYgh6gFQRCw+UgZKm1iR+NffHgQf95+Gne/+Y0yRsnkRDGTQxTsRsSLQQ4zOaGny0HO9u3bce211yIlJQUqlQpr165VjjmdTjz55JOYOHEiTCYTUlJS8MMf/hDFxcV+j1FdXY3bb78dZrMZFosFixYtgs3m/8O1f/9+XHHFFTAajUhLS8OKFStancuaNWswZswYGI1GTJw4EevXr+/q5RC18tG+Yiz6+258949forCyAV+fEftnFJyrxY5TlQCAUxXiz+tQKc1NRMFrhFR8fIJBTsjpcpDT0NCArKwsrFq1qtWxxsZGfPvtt/jlL3+Jb7/9Fh988AGOHTuG7373u37jbr/9dhw6dAibNm3CunXrsH37dtx3333KcavVirlz5yIjIwN79uzB888/j2eeeQavv/66MmbHjh249dZbsWjRIuzduxcLFy7EwoULcfDgwa5eEoW4b4tq8MxHh2BtdnZq/Jrd5wEAxXXNuOqFrX7H3tpVBLvLrWzoN0pKcxNR8BqZ4J2u4gqr0KISevAdValU+PDDD7Fw4cI2x3zzzTe49NJLcfbsWaSnp+PIkSMYN24cvvnmG0ybNg0AsGHDBlxzzTU4f/48UlJS8Oqrr+LnP/85SktLodeLmx/+9Kc/xdq1a3H06FEAwM0334yGhgasW7dOea4ZM2Zg8uTJeO211zp1/larFVFRUairq4PZbO7m/wINdHf8dRe+OFGJFd+bhO9PS2t3bJm1GTOe24y2fiuy0iz43xsnYt7LXyDSoMX+Z+ZCpVL1wVkT0cVid7kx4elP4XQL+PLJq5AaHd7fp0Qd6Ozf7z6vyamrq4NKpYLFYgEA5Ofnw2KxKAEOAOTm5kKtVmPXrl3KmFmzZikBDgDk5eXh2LFjqKmpUcbk5ub6PVdeXh7y8/PbPBe73Q6r1er3QaHvTFUDACgditvz8b5iCIK4cd/S/xml3J47NkF6jCYcLxNT2iMTIxjgEIUAg1aj9Ms5eIF/F0JJnwY5zc3NePLJJ3HrrbcqkVZpaSkSEhL8xmm1WsTExKC0tFQZk5iY6DdG/rqjMfLxQJ577jlERUUpH2lp7b+rp+DncnuUnjZl1o6DnEPF4gvc1WMS8IMZGcrt12alAADK6+04UiKOkV8UiSj4TUgR97A6yM7HIaXPghyn04nvf//7EAQBr776al89TZc89dRTqKurUz7OnTvX36dEfaykrhlujzj3VF5v73B8eb3U5C/KiBiTHn+9cxp+On8Mrp2UAp1GBUEAvjopFh+PZJBDFDImDBHfiB8sZpATSrR98aBygHP27Fls2bLFb74sKSkJ5eXlfuNdLheqq6uRlJSkjCkrK/MbI3/d0Rj5eCAGgwEGA/cZGkzOVTcqn3cqyLGKYxIixf43c8YmYs5YMWOYaDbifE0T9p8XXwRZdEwUOsYP8WZyBEHgVHSI6PVMjhzgnDhxAp999hliY2P9jufk5KC2thZ79uxRbtuyZQs8Hg+mT5+ujNm+fTucTu9qmE2bNmH06NGIjo5WxmzevNnvsTdt2oScnJzeviQKYudqfIKcFtNVX52sxKwVn+PZjw+jrkn8WauQeuMkmFsHw8lR/o3/Ricxk0MUKsYmmaFWAZU2B8qsHb8houDQ5SDHZrOhoKAABQUFAIDCwkIUFBSgqKgITqcT3/ve97B792689dZbcLvdKC0tRWlpKRwOsVvs2LFjMW/ePNx77734+uuv8dVXX2HJkiW45ZZbkJIi1j3cdttt0Ov1WLRoEQ4dOoR3330XK1euxNKlS5XzePjhh7Fhwwa8+OKLOHr0KJ555hns3r0bS5Ys6YX/FgoVRT6ZnIp6Ozwe77Kp5z89hqLqRvztq0I8vmYf7C43ahvFYCc+onWQk+TT+G+IJUzJ9hBR8AvTa5ARK/a9Kqxs6Oezod7S5SBn9+7dyM7ORnZ2NgBg6dKlyM7OxvLly3HhwgV89NFHOH/+PCZPnozk5GTlY8eOHcpjvPXWWxgzZgzmzJmDa665BpdffrlfD5yoqChs3LgRhYWFmDp1Kh577DEsX77cr5fOZZddhrfffhuvv/46srKy8P7772Pt2rWYMGFCT/4/KMScq25SPnd5BFQ3isH2sdJ6FJyrVY4dL6tHhTSdpdeoYQnXtXos30zOlIzoPjpjIuovCZHimxu52zkFvy7X5MyePbvdZkmdabsTExODt99+u90xkyZNwhdffNHumJtuugk33XRTh89Hg5fvdBUg1tzERRjwzjdFAICxyWYcKbGizNqs1OzERxoCzscnmX2CnHRL3500EfWLeCnIqehE/R4FB+5dRSFNzuToNGLQUiatntp0WCxa/8ns4QCAZqcHhRViijouMnBxepJvJiedmRyiUKMEOczkhAwGORSy3B4BVQ3ii5VcJFxhteNcdSPO1zRBq1bh6jEJiDSKCU156WhCG0GOJcw7hTU2mR2yiUINMzmhp0+WkBMNBDWNDggCoFIBoxPNOHjBivL6ZuSfrgIATEqNgsmgRUKkAfXNLhySOp22FeRMHxaLm6amYsKQKOi1fH9AFGrkBQcMckIHgxwKWdUNYpGxJUyHFIs41VRS14zT0rRUznCxvUFCpBGnKhqUTE58G0GORq3C8zdl9fVpE1E/6U4m51x1I4ZYwqBWs6/OQMS3oxSyqmxikBNj0mNYvLg09GhpvZLJyRkWBwBIlHriNDrcAMCl4USDVFdrcv6RfwZXrPgcb+4404dnRT3BIIdClpzJiTUZMFHqZlpwrhYldc3QaVSYKi0DTzD7BzVtZXKIKLTJv/tVNruyHUxb3B4By/9zCADw7LrDfX5u1D0McihkyUXHMSY9MuMiEK7XKC9ck9MsCNNrALSuwRnNPamIBqVYkwFqFeARvK8fbdl23Ls9kU6jUhqNltY14383HEWN9CaL+heDHApZynRVhB4atUrZZRgAcoZ5txvxzdxkxpmQHht+8U6SiAYMjVqF2E4WH6/ZfV753OkWcKrCBgB4ceMxvLr1FFZ/ebrvTpQ6jUEOBaWvTlZi5Wcn/LZpaMk7XaUHAEwY4g1yZgz3BjmJPtNVs0bG9fapElEQ6ewKq5ZbP+yVOqjvPlsDQOyqTv2PQQ4FHYfLg9tX78JLnx3HpiNlbY6Tg5wYKciZmCr2ttFr1X7N/Hynq64cHd8Xp0xEQULO7JZ3EOTIWz9cIb0xKjhXiyqbXQl+Tpbb+vAsqbMY5FDQ2XLUG9i0t5GePKcup5+vHJWAYXEm3HZpOow6jTIu2WfjzRk+01hENPgMlaar395VBJfbE3CMy+1BlfQmKndsIgDg0IU6fFtUq4wpqm6E3eXu25OlDrFPDgUd37nw0xVtv1tqOV0VY9Jjy+OzW40L02uw5bEroVapEK7nrwTRYPbjK4fjg28voOBcLf6efxaLLs9sNaZaajSqVgHZ0j52F2qbsEeaqgLE4uUzlY1Kt3XqH8zkUFBpsLuw9XiF8vWJdlLCLaer2jMsPgJD40w9P0EiCmopljA8nDsSAPD50fKAYyrrva8tqdFi5qfS5sCuwiq/cZyy6n8MciioFFY2+PWvOFlmQ6Cd7z0eoVUmh4ioM8YkifV7pdbmgMflZoFxEQZEh+tgkLZ52ScVH8t72zHI6X8MciionKkSa3AmpUZBo1ah3u5CmbV1gWBtkxNyLBTNIIeIuiApSqzjK6sLHORUSkXJ8ZEGqFQqJEeJKzTl15zcsQkAgJPtTKfTxcEgh4LK2apGAMDIhEhkSAWCJ8ttOHihzq8IuVR6cTIbtdBp+GNORJ0nt5Wot7vQYHe1Ol7pk8kB/BcvxEXoldWbh6X98Kj/8NWfgoocyAyNDcfIhAgAQP7pStz46g7c8nq+MpW1/kAJAGCyz1JxIqLOiDTqEGEQFyEEmrKSe+jERYhZ4mSLt9dWRqwJk1LFnlynKhpQ1+Ts69OldjDIoaByVpquGhpnwsgEcdXCR/uKYXd5UGa143SFDW6PgPf3iCuwvj8ttd/OlYiCl7xxb6ApKzmTI/fUkaerACAjNhyxEQYl0yzX6bRHEAQ88f4+/OStPe02OKWuY5BDQaWwUpyuGhprwshEMZNzrrpJOb7vfB2+PFmJUmszLOE6/M+4xH45TyIKbklS4BIwk9POdNXQWHGV5uQ0CwCxSWBHvi2qwXu7z2P9gVKcrW7syWlTCwxyKGjY7C7lHVRGXDhGSNNVvvafr8Vnh8VmgddMTIZBq2k1hoioI3JdTqAgR15C7g1y/DM5gDfI2VtUg468tbNI+by0jWJn6h52PqOg4PYI+OBbcQoq1qSH2ajD8PgIqFSA7wryfefrUNcovgDNHsUtGoioe5KkIKe96ar2MjnZUj1gwblaCIIAlUoV8HlqGhxYJ9UQAkBZG8vWqXuYyaGg8MZXhVj+n0MAoHQQNeo0SIv23zF837lanKlqhFatQs5wbtFARN3jO13l24vrdIVN2dJBzuAMsbQOcsYmR0KrVqGm0YmSdrIzmw6XweHybh/RVm8e6h4GORQU5KZaV42Oxws3ZSm3j/SZsoo0ehOTU9KjEWnUXbwTJKKQIk9XfXqoDHNe3IZaKUP85o4zAIA5YxKUHlxR4Tos/844PHPtOESFi687Bq0Gw+PF16cjJVbUNDiwbM0+7Dlb7fc8Gw+X+n3N6arexSCHgoL8zmnO2ESk+LxrGiEVHyeaDfjd9ROV27mbOBH1hG+dzenKBry/5zzqmpzK3nkt97S6+/JM3DXT/7axyWLW+XCxFb/4z0Gs2XMeN76arxxvdLjwxYlKAMCtl6YB4HRVb2OQQ0GhrS0axknt08ckmXFtVgrWP3QFHvufUbjzsqEX+xSJKISMSzZjrs/qzLd2FeGT/SVocroxKjGiU9Ph8vYOR0qt2H6swu/YfwouYNzyT2F3eZAeE44rpRpCTlf1LhYeU5+raXDgQm0TJgyJ6vZjVEmFfi0325w/IRlV33EomZtxKWaMSzF3/2SJiABoNWq8/sNpsNldmPG7zSisbMDPPjwAAFiYPaTNQmJf8mvRkZJ61LfonPzO1+eUz2+amqpMj7W1lQR1DzM51Od+8ta3+M4fv8TxsvpuP4Y8XRUrrWaQ6bVq3H15pjL3TUTUmyIMWtx8SZrfbddOSunUfeVMju+WM4DY/K+4Tuzv9YfvZ2HJ1SOUQufyejsbAvYiBjnU5w5J+7ccKbF26/4Olwf1zeK7IO4oTkQX22NzR0GnETM3mXEmpMWEd3APUVyEQemc7Kum0YmSWjFjM31YLFQqFeIjDFCrAJdHQGVD602HqXsY5FCfsjY7YZUClOLa7qVha6RVDRq1ClFhXDFFRBdXuF6LdQ9egatGx/stcOiMmy9Jb3XbkRIrHG4P1CogUdoaQqtRK313yuoY5PQWBjnUpy7UeLdcKKlramdk2+TGW9HheqjVHc+DExH1ttFJkXjjR5d2uf/Wj2cNa3XbvvO1AMSGg1qN98+wPGVV3M3XSmqNQQ71qfM+QU5xbfd+cdtaWUVENNCZDFr8/e5LcdnwWKVpoLxpp287DAAYIdUWHi3pfv0i+WOQQ33qQo13s7nuTlfJQU7LlVVERMHgylHxePveGchOtwAA9p8X6xSHRPsHOfIK1AMX6nrleb88UYl1+4t75bGCFZeQU5/yy+R0e7pKCnIiGOQQUfCKl+pv5G0eWmZyJqaKQc7BXghyXG4P7vnHN2h2emBtcuG26a1rgwYDZnKoT13wmaKqbXSi0eFqZ3Rg1dJKgzhmcogoiMW1aIHRMsgZl2yGSiU2BKyo71nxcUldM5qd4p5Yz3x0CGerGjq4R2hikEN9yjeTA3Rvyso7XdV6KSYRUbCQMzmyIRaj39cmg1bp+dXTbE5RtbdUwOH2YMepqh49XrBikEN9Ss7kyD0murPCqorTVUQUAuJbZHLSolv325nYS3U553yCHMA/6BlMGORQn2l0uJQsjPyL250VVnK3Y05XEVEw883kLJiYjBEJrTu1Z8aZAHjfEH7w7Xk89O+9aHa6u/RcLYMaBjlEvUyemoo0aDE6yex3W1dwdRURhYIxSZGYNz4Jd8/MxMpbJgfc/8psFNcDWZtcqGt0Yul7+/DRvmKsP1DSpeeSg5pLh8YAaJ3ZGSy4uora5HR7cMvrO5FoNmDVbVM6tSGdr1JpBUFSlBEpcpOr7mRypGaAsZyuIqIgptWo8dodU9sdExUudnW3Njvx7u4i5fau7k5+TqqHvGxELL4+U81MDlFLZyobsOdsDdYfKMXxMluX7y8vGU+KMiqrCEq6uMOuw+VRtoWIZeExEYU4s1EMcuqanPj7jrPK7UVVXQtS5MzNzBFxAMTVrXVNzg7v53R78LcvC0Mm88Mgh9rU6PDOAX/SjYZSciYnJSoMyZbA7crLrM1Y9OY32HGyMuBjcN8qIhpMzNLr3JnKBr8WHC13Mm+Pze6thxydFIk4KQvemcDl/+05j2fXHcavPj7UldMesBjkUJtsdm9Pm08OlEAQhC7dv8RnukpuZ15c2+T3OI++W4DNR8tx2+pdAR9DXlkVHa7jvlVEFPLkTI6cwZad7UImR876WMJ1MBt1yq7pnQlyDhVbAQC7z9Z0+TV/IGKQQ22q9/klO1XRgDNdTJfKqwNSLEZl47lmpwe1jd6U6a7C6nYfo0pqBMiiYyIaDMxh/qWy0VKNTqm1GU2Ozq2wOlwiBiojpdVb6VKQ05m6nBPl4r5ZtY3OLgVWAxWDHGqTbyYH6PoSRG/hcRgMWo3S7VNOwTrdHrg93ncKdlfrX2Dv5pysxyGi0CdncmTD4iOUqfqz1Z2bstov7XI+KdUCwNuPpzOv4SfLvfWXBdJGosGMQQ61ydbsX6RW1sWiYWV/FimLkyLV5ci3H5HebcgCpVLZCJCIBpNwvQYan6n56HA9hsaKQcqZys690dwnbQA6SdoLS94ItKOFH9UNDmWvQIBBDoW4lpmcrixhbHS4lEp+eaoqucUy8j1na/zuc7pCfJey41Qlpv56E/5TcMEnk8Mgh4hCn0qlUnrlAECMSYehUoPAM53Yf8rh8uCIVFeTJWVyUnxqIttzoqze7+tBGeRs374d1157LVJSUqBSqbB27Vq/44IgYPny5UhOTkZYWBhyc3Nx4sQJvzHV1dW4/fbbYTabYbFYsGjRIths/kuU9+/fjyuuuAJGoxFpaWlYsWJFq3NZs2YNxowZA6PRiIkTJ2L9+vVdvRxqR30Pghz5HUOkQYtIKf0q/6IVVjbA7RHwVYsVVfLqgdv+sgtVDQ489t4+pSaH01VENFiYfVaSxpgMymtnaSey6cdK6+FwexAVpkOGlAGS98jqMMiRpqrkGp6WQU8w6nKQ09DQgKysLKxatSrg8RUrVuD//u//8Nprr2HXrl0wmUzIy8tDc7P3m3P77bfj0KFD2LRpE9atW4ft27fjvvvuU45brVbMnTsXGRkZ2LNnD55//nk888wzeP3115UxO3bswK233opFixZh7969WLhwIRYuXIiDBw929ZKoDTap8DjRLAYYXZmuKqn1rqySpUSJv6hv7jiDmb/fgs+OlAMQ25sD4rsU32WSLo/A6SoiGnR863JiTDolky1vcdOeAqUeJ0pp4JosvfZam12ob267V45cjzNrlNhbp8HhbpXRDzZdDnLmz5+P3/zmN7j++utbHRMEAS+//DJ+8Ytf4LrrrsOkSZPwj3/8A8XFxUrG58iRI9iwYQNWr16N6dOn4/LLL8cf//hHvPPOOyguFnuxvPXWW3A4HPjb3/6G8ePH45ZbbsFDDz2EP/zhD8pzrVy5EvPmzcOyZcswduxY/PrXv8aUKVPwyiuvdPO/glpqkH645f1VupLJKa8XxyaavUHOlIxo5XP5sSanWZA7LgGAOF317jfn/B7nmPROgtNVRDRY+PYEiw7XK3teVdbbO7zvztPibuNT0r2vtyaDVnnMd785h5c/O47395xvtUT8uPR6m5VqQYRBnDIr72Kn5YGmV2tyCgsLUVpaitzcXOW2qKgoTJ8+Hfn5+QCA/Px8WCwWTJs2TRmTm5sLtVqNXbt2KWNmzZoFvd77hy0vLw/Hjh1DTU2NMsb3eeQx8vMEYrfbYbVa/T6obXIEPyJeDHLKuvDDXiH9Mib4bEg3NSMaXzxxFZ69brxy2/empiIzTnz8E+U2bD1W7vc48hJGLiEnosHCdxl5bIRema6Xp+/b4vEIyD8lBjlyp2OZPOX1m0+O4OXPTuDxNfuw83Q1BEFQgh15umpkYqTy2l3eicBqIOvVIKe0tBQAkJiY6Hd7YmKicqy0tBQJCQl+x7VaLWJiYvzGBHoM3+doa4x8PJDnnnsOUVFRykdaWlpXL3FQkfvkyJmcSpsDDpenU/eVfzF8d90FgLSYcPwwZyjunpmJnGGxWJg9BGOSIqHXqlHd4MDRUvGdxPTMGL/7xXG6iogGCd/pquhwvbJvn+/Kp0COltajusGBcL0Gk9Msfsfkuhxf6/YXI+tXG/H8p8dQ2+hQ3pyOSIhAglym0MVMjtsjYN3+4gGzLcSg2qDzqaeewtKlS5WvrVYrA512yJmc1Jhw6DQqON0CyuubkSr1XKhvdqK6wYGMWFOr+1a0EeTIll87zu/r7DSL0hgwNToMs0cnKF9r1SokmFv/ghIRhSL/wmM9wvXin+qaRgdcbg+0msD5iR2nxMUcl2bGQK/1HyNncny9tUvcAPRPW09hXIoZADDEEoYIgxYJkeJrbkUXMzlfnqzEkrf3AgB+On8MTHoN8iYkKY93sfVqJicpKQkAUFZW5nd7WVmZciwpKQnl5f5TEi6XC9XV1X5jAj2G73O0NUY+HojBYIDZbPb7oLbJQY7Z6P2BL7M2o9npxi/XHsS033yGK5/fii9PtN53qqMgp6Xpw2KVz6dmRGPCEO/35mfXjG3VIIuIKFT5LyHXIzpcB5UKEASgurHtbM7O0+Ibw5nD41odk4uPAeDWS9NbHf/5h+KiHTlzn9jNTM6FGu8Krt//9yh++Z9DOF/T/qquvtSrQU5mZiaSkpKwefNm5Tar1Ypdu3YhJycHAJCTk4Pa2lrs2bNHGbNlyxZ4PB5Mnz5dGbN9+3Y4nd4q8E2bNmH06NGIjo5Wxvg+jzxGfh7qOXl1VYRBp6ySOlfdhFte34l/7jwLuzR1tS7A5p0Vtq4FOTN8pqempEdj5vA4PJI7En/54TTcfXlmj66DiCiYyJkcnUaFCIMWWo0aMeHSCqt2pqwOXhCbAGanW1od803+3BYgyJH7mslbQchvbLtak2P1Wb01f0IS5k9IgqUfN1fucpBjs9lQUFCAgoICAGKxcUFBAYqKiqBSqfDII4/gN7/5DT766CMcOHAAP/zhD5GSkoKFCxcCAMaOHYt58+bh3nvvxddff42vvvoKS5YswS233IKUlBQAwG233Qa9Xo9Fixbh0KFDePfdd7Fy5Uq/qaaHH34YGzZswIsvvoijR4/imWeewe7du7FkyZKe/68QAG+fnAijVkl1vrnjDArO1SLSqMWPZg4FAHxxorJVlb5ckZ/QySAnOz0aeum3cGpGNNRqFR7JHYX/GZfYwT2JiEKLnLmOMemVZeDeupzAQUelzY5SazNUKmBscutZioWThyAh0oC7Z2ZiVFKEX1flMUmRyucjE6Ugp5uZHDlY+tHMoXj1B1Px6g+mYpi0eKU/dDnI2b17N7Kzs5GdnQ0AWLp0KbKzs7F8+XIAwBNPPIEHH3wQ9913Hy655BLYbDZs2LABRqN3Pu6tt97CmDFjMGfOHFxzzTW4/PLL/XrgREVFYePGjSgsLMTUqVPx2GOPYfny5X69dC677DK8/fbbeP3115GVlYX3338fa9euxYQJE7r9n0FedpdbKTKOMGiVQmC5A+bCyUPwRN4Y6DVqXKhtwmmf/jbNTreyg258ROfmYcP0Grzw/Sz87JoxGJ/CaUQiGrzk1aS+mXB577+2Mjny7uGZcSaYDK3LbRPMRnz981wsv3YcDFqN0ihwWLwJf/mhd7XzhCHiVhDdzuRIQU5UP2ZvfHW58Hj27Nntbr+uUqnw7LPP4tlnn21zTExMDN5+++12n2fSpEn44osv2h1z00034aabbmr/hEPItuMVeHzNPqz43iRcNVpcoVZa14wvT1bimolJaHZ6YNSplSK1nmiwezfLjDBoMWdsAn6x1nt81qh4hOk1uCQzGl+drML24xUYHh+Bf+SfwR+3nAQA6LXqVjvqtue7WSk9Pm8iomA3Y1gs7rk8E1eOjldui5WCnFWfn0SYXoO88f71p/JU1YSUqE49x6iESJyuaEBWqgVpMeHY+OgsnK5owHjp/nImp9zatSBHzuQMlDpK7l0VRN74qhAV9Xb89YtCAMDmI2XIe3k7Hl+zD7f9ZRcu+/1m3PCnHb3yXHI9jrxZXHJUGMZJKVCdRoWc4WKh8OUjxF/CHVJvhuX/OeQtOo4wKKlWIiLqHL1WjV98ZxyuGOkNcuQ2GifKbfjxP/e06lx8WMrkdDYTfm1WCsJ0GlyfPQQAMCoxEvMmeAMnuZGrze5SGsN2hpzFHyiZHAY5feDnHx7AvJe3K5F1b2h2upVOljtPV+HTQ6W4/197lKi54Fwtmp0eHC2th9PduV427am3i48b4ZP2zB0rZo+mZkQrt18qTWPtOVsDV4vn7WzRMRERtU+erpJ9LbXYAIBvi2qU5ePydFNHFkxKxpFfz8OsUfEBj0cYtDDpNQCAIyWdb5yrZHIY5ISuE+U2HC2tV/YB6Q3fnKlGs1MMIlweAT/+5x443QLmT0jCr306CANAbWPbe5N0lrKyymcp4z2zhuGuy4Zi+Xe8zzdhiFlp5PdFiw03mxxuEBFRz+k0/lnxL6XX2xNl9bjlzztR0+hEZpzJbzuHnpKnxF7YeKzdMhVf9QOsJodBTh8YFic2x/Mtxu2Mc9WNShTc0rZjFQDExniySzNj8NLNk3FHzlB8+eRVSm+Fmnb6KHSW3CMn0ieTYzbq8Mx3xytNowDAoNVgcqoFAPD+7vN+j2F3McghIuoN10xMRqLZoKyEen/3eXx2uAyPvFsAh9uDnGGxWLt4JsKk7EtveCxvNPRaNXaerlaCqo54MzkDo9cwg5w+kCkFOYVdCHIOF1sx58VtmP/ydhTXtm6ctKdI3LPrgdnDodeqcfmIOLxx1yUw6sQf6NTocMRJ00PVndiptiM2e+tMTlumDRXfOXxyoASAGBilRofht9dP7PF5EBGR+Bq/62e5+Pe9MwCILT7u+cduHCq2QqtW4bfXT+j17MkQSxhunCLW7Hx+tKLD8YIgKH1ymMkJYXKQc7rChrV7L+BMB8GOIAj43fojcLg9KK5rxo/e+KZVXU1pndirIHdsIvYtn4t/Lrq01TJBuVlUTS8EOfVKI8COg5xLhvrvM3Xb9HR8+eTVrTaIIyKinok26ZE7NgEqlThroFYBP5k9vM960eRI3ZO/PlPV4dhmpwdOtzitNVBWVw2MfFKIkX/YDhVb8ci7BRibbMZ/H76izfH5p6vw5clK6DVqGLRqHCurx4ELdcrcqtsjKL0KEs3GNtOR0VJvhfbafneWkskxdPyDmjM8FtHhOtRItUBykEdERL3vz3dMg93lRrheC49HgFrdd6tYL5XexB4utqK+2Yk/bT2Fyno7VnxvUqvVs/JUlVatQngvTpv1BDM5fSA9Jhy+P3NHSqwoOFeL8zWBd2WV9xv5zqRkTJWmfnxXZlU12OH2CFCr2t+NuzczOXLhcWQnpquMOg2+P8270elQBjlERH1Go1Yp/dD6MsABgKQoI9JjwuERxAUwr249hTV7ziuNYX3JU1XmMN2AaR/CIKcP6LVqv83QAGDhqq8w+/mtOFFWr9zm8QhwuT1K7c7opEhMlJb/HTjvDXLkZkxxEYY2d58FfDI5Dd7i5ZPl9dh5ugp1XVxx5c3kdC7Zd9t0714ow+IZ5BARhQq5VcjmI97NtQNtulk3wFZWAZyu6jPl9a33+3B5BHx6qBQjE8Xq+B//aw/2FtXAoBXTeplxJsiL9A4We/sSyPU4cnOmtsSYxB8seXVVSV0T5q/8Ak63AEu4Dl8+eXWngxa5JidQe/BAMmJNWHnLZDhcHqUdOBERBT+5EazvDMPZqta1plal2/HACS2Yyekj8yYkAxA3PvPNbGw7Llao2+wufHakDJU2By5Iq6ky40xKI6cTZfVodopLsMvq5SCn/eZ60eFyJkcMcg5dsCpFYLWNzg4LoH3Z5GaAXfhhvW7yENzkM21FRETBL1p6A11U7S25CNQHbqA1AgSYyekzv/rueIxJisTt09NhNupwrqYRVz6/Fd8W1cLa7MTBC3Xw7a2kUgHpseHQa9SIMelR3eDA374qxI8uy0SZ1Vt03B55Uzc5k3OmRaTd1u61gQTqk0NERIOPPP1U41P2cLKidZBjZZAzeMSY9Fh81Qjl64xYE4bFm3C6ogE7TlaisNK/CHmIJUyZtpqSbsFnR8qxYsMxVNscytRRR0FOdIsgxzfqBtrevTYQWxeWkBMRUeiKCmu94OVUeUOrlV11TQNr3yqA01UX1ZXSHiHbjldgX4vK9OQobwDz7HUTcOOUVADi8vLOTld5V1eJ0fSZqhZBTkPnMzn1XWgGSEREocsS3jpoaXK6UVznX3w80HYgBxjkXFTyRmjbj1e2Wn4nZ2sAIMUShsfzRgEAjpbWK7U0nc3k2Owu2F1uFEnTVfKutF3J5DR0cXUVERGFprYyMyda1OXUSrMI0QGCov7CIOcimpEZC71WjQu1TSi1NkOt8u5zdd3kIX5jk6PCkBxlhNsjKBmZjoIcs1ELjZQ6rLQ5lCV+UzOilds68sn+Evxy7UFlk8/O9MkhIqLQ1VaQ8+3ZGr+vq6RFL3J96EDAv2AXUZheg6np0cg/LbbH/s6kFDzz3fHYdrwc105KaTU+O92CkgOlAMTC5JQWvXdaUqlUiA7Xo9Jmx96iGrg8AvRatTeTI01XCYKAn7z1LWx2F/7+o0uVOVWn24PFb3/r95jM5BARDW46jRomvQYNDnHFb1yEHpU2B3ZJjWxl8sre2Haa1l5szORcZLNHxyufL792HGJMelyfnRqwyV92WrTy+a2XpiOqEylAeYfwtXsvAAAyYsIRL23cKa+uqrDZ8d+DpfjiRKVfQ6fdZ2rQEmtyiIjIEu4NXHLHJgIACs7VKq1OAG+QE2Nqv370YmKQc5H9YEYGfpiTgfd+nIO4iPZ/EGaOiINKJa68+tk1Yzv1+NOlzpSfSZ0pM2JNiJV+4OSanNMV3qXlvvtcfXakzO+xxL20Bsb+I0RE1H98p6wmpkYh0WyAw+3Bt0U1aLC7YLO7vEFO+MDJ5PBt+kVmMmjx7HUTOjV2XIoZHy2+HCkWY6enjeT227KrxyQoqcMqmwOCIOCUT3+DCmnjT4fLg42HS/3uyywOEREB/kFOVJgO0zNj8dG+Ynx1shKPv7cPzS4PmqSsTgynq6izJqZGIbaDjI+vSalRfl9/d3KKkslxuD2ot7twqtybyamot8Pp9uCef+zGuWr/5YCsxyEiIsB/GbnZqMM0aTPpNbvPo7iuWcni6LVi/c5AwSAnxBi0GoTpxB+w7HQLIgxahOk1yg/dQ//e65exqai3Y/vxCmw/XoEwnQY/njVMOcYgh4iIAP9MjjlMh0mpFgBAeb1//7WYcP2A2YEcYJATkv561zTkjk3EK7dNUW6Ti8a2HqvwKzausDUru6BfPTYBN1/i3XuKQQ4REQHwW/hiNmoxNjkSOk3rYGYgLR8HWJMTki4bHofLhsf53VbSojOlrKLeDp20sis1OgzJHSxTJyKiwcfis7WDOUwHg1aD0UmROHjB6jduIC0fB5jJGTTuuWIYtGoVzC2KiSvq7UpmJ9UShjCfuVRrsxNERES+01Vyk1h5yspX9ABaWQUwyBk0npo/Bnt+8T/Y8MgsDI83YZrUBbnCZscFOciJDve7j+9WE0RENHjJhcdGnbe1SFaLhS7AwJuuYpAzSKhUKkSF65BiCcPmx2bjpZsnAwDKrXacrxG3jRgS7T9VVc9MDhERAbBImRzfzTf/Z1wSJg6Jwg3Z3m2JYhnk0EAgNyK0uzywShmbIRYxyLlipFjP8/1paYHvTEREg8rYZDNiTHrMHOGt94wx6fHxg5fjdzdMVG4bQAurALDweNAK02sQadCiXtptPDpcB5O0mmrV7VPw5YlKXD0moT9PkYiIBohokx67fjYHWnXrKMao89Zy2l2ei3laHWImZxCT97QC/OtxzEYdrpmY7PeDS0REg5tOo26zB84PZqQj0qDFrZemX+Szah+DnEFsVGKk8vlAKxYjIqLg8ZuFE/Ht8v9BimVgtSFhkDOIPfPd8UrqccIQcz+fDRERBTO559pAwpqcQSwpyoidP5uDdfuKcd3kIR3fgYiIKIgwyBnk4iIMuGtmZn+fBhERUa8beLklIiIiol7AIIeIiIhCEoMcIiIiCkkMcoiIiCgkMcghIiKikMQgh4iIiEISgxwiIiIKSQxyiIiIKCQxyCEiIqKQxCCHiIiIQhKDHCIiIgpJDHKIiIgoJDHIISIiopA0qHchFwQBAGC1Wvv5TIiIiKiz5L/b8t/xtgzqIKe+vh4AkJaW1s9nQkRERF1VX1+PqKioNo+rhI7CoBDm8XhQXFyMyMhIqFSqXntcq9WKtLQ0nDt3Dmazudced6DjdfO6Q91gvGaA183rHngEQUB9fT1SUlKgVrddeTOoMzlqtRqpqal99vhms3nA/oD0JV734DIYr3swXjPA6x5sBvp1t5fBkbHwmIiIiEISgxwiIiIKSQxy+oDBYMDTTz8Ng8HQ36dyUfG6ed2hbjBeM8Dr5nUHr0FdeExEREShi5kcIiIiCkkMcoiIiCgkMcghIiKikMQgh4iIiEISg5w+sGrVKgwdOhRGoxHTp0/H119/3d+n1GueeeYZqFQqv48xY8Yox5ubm7F48WLExsYiIiICN954I8rKyvrxjLtn+/btuPbaa5GSkgKVSoW1a9f6HRcEAcuXL0dycjLCwsKQm5uLEydO+I2prq7G7bffDrPZDIvFgkWLFsFms13Eq+i6jq77rrvuavX9nzdvnt+YYLvu5557DpdccgkiIyORkJCAhQsX4tixY35jOvNzXVRUhAULFiA8PBwJCQlYtmwZXC7XxbyULunMdc+ePbvV9/v+++/3GxNs1/3qq69i0qRJSqO7nJwc/Pe//1WOh+L3Guj4ukPxew0wyOl17777LpYuXYqnn34a3377LbKyspCXl4fy8vL+PrVeM378eJSUlCgfX375pXLs0Ucfxccff4w1a9Zg27ZtKC4uxg033NCPZ9s9DQ0NyMrKwqpVqwIeX7FiBf7v//4Pr732Gnbt2gWTyYS8vDw0NzcrY26//XYcOnQImzZtwrp167B9+3bcd999F+sSuqWj6waAefPm+X3///3vf/sdD7br3rZtGxYvXoydO3di06ZNcDqdmDt3LhoaGpQxHf1cu91uLFiwAA6HAzt27MDf//53vPnmm1i+fHl/XFKndOa6AeDee+/1+36vWLFCORaM152amorf//732LNnD3bv3o2rr74a1113HQ4dOgQgNL/XQMfXDYTe9xoAIFCvuvTSS4XFixcrX7vdbiElJUV47rnn+vGses/TTz8tZGVlBTxWW1sr6HQ6Yc2aNcptR44cEQAI+fn5F+kMex8A4cMPP1S+9ng8QlJSkvD8888rt9XW1goGg0H497//LQiCIBw+fFgAIHzzzTfKmP/+97+CSqUSLly4cNHOvSdaXrcgCMKdd94pXHfddW3eJxSuu7y8XAAgbNu2TRCEzv1cr1+/XlCr1UJpaaky5tVXXxXMZrNgt9sv7gV0U8vrFgRBuPLKK4WHH364zfuEwnULgiBER0cLq1evHjTfa5l83YIQut9rZnJ6kcPhwJ49e5Cbm6vcplarkZubi/z8/H48s9514sQJpKSkYNiwYbj99ttRVFQEANizZw+cTqff9Y8ZMwbp6ekhdf2FhYUoLS31u86oqChMnz5duc78/HxYLBZMmzZNGZObmwu1Wo1du3Zd9HPuTVu3bkVCQgJGjx6NBx54AFVVVcqxULjuuro6AEBMTAyAzv1c5+fnY+LEiUhMTFTG5OXlwWq1+r1THshaXrfsrbfeQlxcHCZMmICnnnoKjY2NyrFgv26324133nkHDQ0NyMnJGTTf65bXLQvF7/Wg3qCzt1VWVsLtdvv9EABAYmIijh492k9n1bumT5+ON998E6NHj0ZJSQl+9atf4YorrsDBgwdRWloKvV4Pi8Xid5/ExESUlpb2zwn3AflaAn2f5WOlpaVISEjwO67VahETExPU/xfz5s3DDTfcgMzMTJw6dQo/+9nPMH/+fOTn50Oj0QT9dXs8HjzyyCOYOXMmJkyYAACd+rkuLS0N+PMgHxvoAl03ANx2223IyMhASkoK9u/fjyeffBLHjh3DBx98ACB4r/vAgQPIyclBc3MzIiIi8OGHH2LcuHEoKCgI6e91W9cNhO73mkEOdcn8+fOVzydNmoTp06cjIyMD7733HsLCwvrxzOhiuOWWW5TPJ06ciEmTJmH48OHYunUr5syZ049n1jsWL16MgwcP+tWZDQZtXbdvLdXEiRORnJyMOXPm4NSpUxg+fPjFPs1eM3r0aBQUFKCurg7vv/8+7rzzTmzbtq2/T6vPtXXd48aNC9nvNaerelFcXBw0Gk2rSvyysjIkJSX101n1LYvFglGjRuHkyZNISkqCw+FAbW2t35hQu375Wtr7PiclJbUqNne5XKiurg6p/4thw4YhLi4OJ0+eBBDc171kyRKsW7cOn3/+OVJTU5XbO/NznZSUFPDnQT42kLV13YFMnz4dAPy+38F43Xq9HiNGjMDUqVPx3HPPISsrCytXrgz573Vb1x1IqHyvGeT0Ir1ej6lTp2Lz5s3KbR6PB5s3b/ab9wwlNpsNp06dQnJyMqZOnQqdTud3/ceOHUNRUVFIXX9mZiaSkpL8rtNqtWLXrl3Kdebk5KC2thZ79uxRxmzZsgUej0d58QgF58+fR1VVFZKTkwEE53ULgoAlS5bgww8/xJYtW5CZmel3vDM/1zk5OThw4IBfgLdp0yaYzWZlOmCg6ei6AykoKAAAv+93sF13IB6PB3a7PWS/122RrzuQkPle93flc6h55513BIPBILz55pvC4cOHhfvuu0+wWCx+FenB7LHHHhO2bt0qFBYWCl999ZWQm5srxMXFCeXl5YIgCML9998vpKenC1u2bBF2794t5OTkCDk5Of181l1XX18v7N27V9i7d68AQPjDH/4g7N27Vzh79qwgCILw+9//XrBYLMJ//vMfYf/+/cJ1110nZGZmCk1NTcpjzJs3T8jOzhZ27dolfPnll8LIkSOFW2+9tb8uqVPau+76+nrh8ccfF/Lz84XCwkLhs88+E6ZMmSKMHDlSaG5uVh4j2K77gQceEKKiooStW7cKJSUlykdjY6MypqOfa5fLJUyYMEGYO3euUFBQIGzYsEGIj48Xnnrqqf64pE7p6LpPnjwpPPvss8Lu3buFwsJC4T//+Y8wbNgwYdasWcpjBON1//SnPxW2bdsmFBYWCvv37xd++tOfCiqVSti4caMgCKH5vRaE9q87VL/XgiAIDHL6wB//+EchPT1d0Ov1wqWXXirs3Lmzv0+p19x8881CcnKyoNfrhSFDhgg333yzcPLkSeV4U1OT8JOf/ESIjo4WwsPDheuvv14oKSnpxzPuns8//1wA0OrjzjvvFARBXEb+y1/+UkhMTBQMBoMwZ84c4dixY36PUVVVJdx6661CRESEYDabhR/96EdCfX19P1xN57V33Y2NjcLcuXOF+Ph4QafTCRkZGcK9997bKoAPtusOdL0AhDfeeEMZ05mf6zNnzgjz588XwsLChLi4OOGxxx4TnE7nRb6azuvououKioRZs2YJMTExgsFgEEaMGCEsW7ZMqKur83ucYLvuu+++W8jIyBD0er0QHx8vzJkzRwlwBCE0v9eC0P51h+r3WhAEQSUIgnDx8kZEREREFwdrcoiIiCgkMcghIiKikMQgh4iIiEISgxwiIiIKSQxyiIiIKCQxyCEiIqKQxCCHiIiIQhKDHCIiIgpJDHKIiIgoJDHIISIiopDEIIeIiIhCEoMcIiIiCkn/H2vAwPTzCkcJAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -93,13 +77,14 @@
     }
    ],
    "source": [
+    "shower_power = load_data(example=\"shower\")\n",
     "# shower power distribution\n",
     "shower_power.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 4,
    "id": "e5e87e38",
    "metadata": {},
    "outputs": [],
@@ -107,14 +92,12 @@
     "shower = household.add_appliance(\n",
     "    name=\"Shower\",\n",
     "    number=1,\n",
-    "    power=shower_power,\n",
-    "    num_windows=2,\n",
-    "    func_time=15,\n",
-    "    time_fraction_random_variability=0.1,\n",
-    "    func_cycle=3,\n",
-    "    window_1=[390, 540],\n",
-    "    window_2=[1080, 1200],\n",
-    "    random_var_w=0.2,\n",
+    "    power=shower_power,  # pass the pd.DataFrame or np.array instead of a number\n",
+    "    num_windows=2,  # two possibe time window for shower\n",
+    "    func_time=15,  # each shower takes 15 minute\n",
+    "    func_cycle=3,  # every\n",
+    "    window_1=[390, 540],  #  morning shower from 6:30 to 9:00 AM\n",
+    "    window_2=[1080, 1200],  # evening shower from 18:00 to 20:00\n",
     ")"
    ]
   },
@@ -128,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 5,
    "id": "320b7296",
    "metadata": {},
    "outputs": [
@@ -138,6 +121,16 @@
      "text": [
       "You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.\n",
+      "  end=self.days[-1] + pd.Timedelta(1, \"d\") - pd.Timedelta(1, \"T\"),\n",
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  self.__datetimeindex = pd.date_range(\n"
+     ]
     }
    ],
    "source": [
@@ -145,25 +138,34 @@
     "usecase.initialize(num_days=365)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf52f91d",
+   "metadata": {},
+   "source": [
+    "As everyone has a different habit in the water temperature for taking shower, we can also consider a variability in the thermal power through the thermal_p_var property. Using the `thermal_p_var` attribute to add further variability to the actual power absorbed by the appliance in each usage event, which reflects the randomness of user behaviour in preferring a slightly warmer or colder shower temperature.To better understand the effect of this parameters, let's perform a sensitivity analysis on themal_p_var function. To do so, we use the following function **thermal_p_var_sensitivty**:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 6,
    "id": "285d7b2f-7d20-475d-847c-769810ae34db",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def thermal_p_var_sensitivity(values):\n",
+    "def thermal_p_var_sensitivity(sensitivity_values):\n",
     "    # buidling a pd.DataFrame for saving sensitivity results\n",
     "    results = pd.DataFrame(\n",
-    "        index=pd.date_range(start=\"2020-01-01\", periods=1440 * 365, freq=\"T\"),\n",
-    "        columns=[f\"p_var = {value}\" for value in values],\n",
+    "        index=range(0, 1440 * 365),\n",
+    "        columns=[f\"p_var = {value}\" for value in sensitivity_values],\n",
     "    )\n",
     "\n",
-    "    for value in values:\n",
+    "    for value in sensitivity_values:\n",
+    "\n",
     "        # changing the thermal_P_var\n",
     "        shower.thermal_p_var = value\n",
     "\n",
-    "        profiles = usecase.generate_daily_load_profiles(flat=True)\n",
+    "        profiles = usecase.generate_daily_load_profiles()\n",
     "\n",
     "        # assigning the yearly profile for a given sensitivity case\n",
     "        results[f\"p_var = {value}\"] = profiles\n",
@@ -173,213 +175,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "03cff922",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "2b544ef0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>p_var = 0</th>\n",
-       "      <th>p_var = 0.25</th>\n",
-       "      <th>p_var = 0.5</th>\n",
-       "      <th>p_var = 0.75</th>\n",
-       "      <th>p_var = 1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>525600 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     p_var = 0  p_var = 0.25  p_var = 0.5  p_var = 0.75  \\\n",
-       "2020-01-01 00:00:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:01:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:02:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:03:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:04:00        0.0           0.0          0.0           0.0   \n",
-       "...                        ...           ...          ...           ...   \n",
-       "2020-12-30 23:55:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:56:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:57:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:58:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:59:00        0.0           0.0          0.0           0.0   \n",
-       "\n",
-       "                     p_var = 1  \n",
-       "2020-01-01 00:00:00        0.0  \n",
-       "2020-01-01 00:01:00        0.0  \n",
-       "2020-01-01 00:02:00        0.0  \n",
-       "2020-01-01 00:03:00        0.0  \n",
-       "2020-01-01 00:04:00        0.0  \n",
-       "...                        ...  \n",
-       "2020-12-30 23:55:00        0.0  \n",
-       "2020-12-30 23:56:00        0.0  \n",
-       "2020-12-30 23:57:00        0.0  \n",
-       "2020-12-30 23:58:00        0.0  \n",
-       "2020-12-30 23:59:00        0.0  \n",
-       "\n",
-       "[525600 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sensitivity_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "78743706",
+   "execution_count": 7,
+   "id": "3b5450e6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# showing the daily average of the load profiles\n",
-    "average_daily_profiles = sensitivity_results.resample(\"1d\").mean()"
+    "# generating 5 senstivities on thermal_p_var\n",
+    "sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "29e7de24",
+   "execution_count": 8,
+   "id": "8c8ca6d2",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x1000 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -387,50 +202,32 @@
     }
    ],
    "source": [
-    "average_daily_profiles.plot()"
+    "days_to_plot = [1, 100, 200]  # which days of the year to plot\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)\n",
+    ")\n",
+    "\n",
+    "for j, day in enumerate(days_to_plot):\n",
+    "    for i, col in enumerate(sensitivity_results):\n",
+    "        sensitivity_results[col].iloc[1440 * (day - 1) : 1440 * (day)].plot(\n",
+    "            ax=axes[i, j]\n",
+    "        )  # just plot for the first day\n",
+    "        axes[i, j].set_title(f\"Day {day} - thermal_p_var = {col}\", fontsize=8)\n",
+    "        axes[i, j].set_ylim(0, 24000)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "ad0238d2-f90f-4df3-a0de-986f1de75d70",
+   "cell_type": "markdown",
+   "id": "03cff922",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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2FvX19Yp8fHz0/121prlKpVIBuBF0hYSEyNvLysrQs2dPvedOSUnBjh07sHfvXnTs2FFnWnU/pe+//75VQY702/+IiMg6GOQ4oYKCAsX3gwcPIioqSm8tDgB07NgRgwYNwpYtW1BbW4thw4Ypalv279+PNWvWYNSoUQCA8+fP49dffzWpnK1proqMjIRKpUJeXp4c1FRVVaGgoACzZs3SepwQAk8//TS2bduG3bt3IzIyUu+5jh07BgCKYMpQpi7r0LyjuCGdxomIqCUGOU6opKQEqampeOqpp1BUVIRVq1YhMzPT4OMTEhKwaNEi1NfXIysrS7EvKioK77zzDvr27YuqqirMmTMH3t7eJpWzNc1VkiRh9uzZeOmllxAVFYXIyEikp6cjNDQU48aNk9MNHToUjzzyCFJSUgDcaKLKycnBRx99hHbt2sn9d/z8/ODt7Y2zZ88iJycHo0aNQocOHXD8+HE899xzGDhwILp3725SWYmIyDY4hNwJPfHEE6itrUX//v2RnJyMZ599VtHPRJ8JEybg4sWLqKmpUQQMAPDmm2/i8uXL6N27N6ZMmYJnnnnGZv1q5s6di6effhpJSUno168fqqursXPnTnh5eclpzp49q6hpWrt2LSorKzF48GCEhITIn/fffx8A4OHhgS+//BLDhw9H586d8Ze//AXjx4/HJ5980urycvg4EZF1sSbHCbm7uyM7Oxtr16416Xh/f3+tE+r16tULhw8fVmybMGGC4ru1mlckScKSJUvkOXw0+fHHHxXf9ZUtLCwMe/bsMUfxiIjIxliTQ2RBpi/roOVnIiIyGIOc28i+ffvQtm1brR8iIiJnwuYqJ7N7926t+/r27SuPFCLrk8BlHYiIrIlBzm3E29tbnrmYiIjI2bG5isiCTJ0nR7F2FefJISIyCYMcIiIickoMcoishMs6EBFZF4McIgsyubmKiIhajUEOkV1inxwiotZikENkLRLYXEVEZEUMcshhCSGwcOFChISEwNvbG3FxcThz5ozOYxYvXgxJkhSfzp07W6nERERkTQxyyOIaGhosku+yZcuwcuVKrFu3DgUFBfDx8UF8fLzWdbfUunbtip9//ln+fP311xYpH6Bj+QZjWqDYXEVEZBIGOQYSQqCmocYmH2MWvBw8eDBSUlKQkpICPz8/BAYGIj093aA8FixYgJiYmBbbe/ToIS+CefjwYQwbNgyBgYHw8/PDoEGDUFRUpEgvSRLWrl2LMWPGwMfHBy+//LLB5TeUEALZ2dlIS0vD2LFj0b17d7z99tu4cOECtm/frvNYNzc3qFQq+RMYGGj28rUaAxsiolbjjMcGqr1ei5iclgGANRRMLkAb9zYGp3/rrbcwffp0HDp0CEeOHEFSUhLCw8MxY8YMncclJCQgIyMDZ8+eRadOnQAAp06dwvHjx/Gvf/0LAHDlyhUkJiZi1apVEEIgMzMTo0aNwpkzZ9CuXTs5r8WLF2Pp0qXIzs6Gm5vm/8xmzpyJzZs36yxTdXW1xu3nzp1DaWkp4uLi5G1+fn6IiYlBfn4+Jk6cqDXPM2fOIDQ0FF5eXoiNjUVGRgbCw8N1lsMcuKwDEZF1MchxQmFhYcjKyoIkSYiOjsaJEyeQlZWlN8jp2rUrevTogZycHKSnpwMAtmzZgpiYGHk5iCFDhiiOWb9+Pfz9/bFnzx489NBD8vbJkydj2rRpOs+3ZMkSPP/886ZcIkpLSwEAwcHBiu3BwcHyPk1iYmKwadMmREdH4+eff8YLL7yABx54ACdPnlQEaURE5PiMDnL27t2L1157DYWFhfj555+xbds2jBs3DsCNvhdpaWn47LPP8MMPP8DPzw9xcXFYunQpQkND5TwuXbqEp59+Gp988glcXFwwfvx4rFixQrES9vHjx5GcnIzDhw/jjjvuwNNPP425c+cqyrJ161akp6fjxx9/RFRUFF599VWMGjXKxL8K3bzdvFEwucAieRtybmMMGDBAUWMQGxuLzMxMNDY2wtXVVeexCQkJ2LBhg9zE9e677yI1NVXeX1ZWhrS0NOzevRvl5eVobGxETU0NSkpKFPn07dtXbzmDgoIQFBRk1LW11siRI+Wfu3fvjpiYGEREROCDDz7A9OnTrVYOo5Z14Jw6REQmMbpPztWrV9GjRw+sXr26xb6amhoUFRUhPT0dRUVF+PDDD1FcXIwxY8Yo0iUkJODUqVPIzc3Fjh07sHfvXiQlJcn7q6qqMHz4cERERKCwsBCvvfYaFi9ejPXr18tpDhw4gEmTJmH69Ok4evQoxo0bh3HjxuHkyZPGXpJBJElCG/c2NvlYs4lj0qRJKC4uRlFREQ4cOIDz58/j8ccfl/cnJibi2LFjWLFiBQ4cOIBjx46hQ4cOqK+vV+Tj4+Oj91wzZ85E27ZtdX60UalUAG4EXc2VlZXJ+wzh7++PP/zhD/j+++8NPoaIiByD0TU5I0eOVLwNN+fn54fc3FzFtjfeeAP9+/dHSUkJwsPD8e2332Lnzp04fPiw/La/atUqjBo1Cq+//jpCQ0OxZcsW1NfXY8OGDfDw8EDXrl1x7NgxLF++XA6GVqxYgREjRmDOnDkAgBdffBG5ubl44403sG7dOmMvy6kUFChrnA4ePIioqCi9tTgA0LFjRwwaNAhbtmxBbW0thg0bpqht2b9/P9asWSPXmJ0/fx6//vqrSeVsTXNVZGQkVCoV8vLy0LNnTwA3guOCggLMmjXL4Hyqq6tx9uxZTJkyxaRyGIPLOhARWZfFR1dVVlZCkiT4+/sDAPLz8+Hv769ozoiLi4OLi4v8yzk/Px8DBw6Eh4eHnCY+Ph7FxcW4fPmynKZ5p1N1mvz8fK1lqaurQ1VVleLjjEpKSpCamori4mK8++67WLVqFZ599lmDj09ISMB7772HrVu3IiEhQbEvKioK77zzDr799lsUFBQgISEB3t7GNaepBQUF4a677tL50UaSJMyePRsvvfQSPv74Y5w4cQJPPPEEQkND5eZTABg6dCjeeOMN+fvzzz+PPXv24Mcff8SBAwfwyCOPwNXVFZMmTTLpGvTRNqrNmBFzRERkGosGOdeuXcO8efMwadIk+Pr6ArjRYfTWfhhubm4ICAiQO4yWlpZq7FCq3qcrja5OpxkZGfDz85M/YWFhrbtAO/XEE0+gtrYW/fv3R3JyMp599llFc6A+EyZMwMWLF1FTU6MIGADgzTffxOXLl9G7d29MmTIFzzzzjNX71ajNnTsXTz/9NJKSktCvXz9UV1dj586d8PLyktOcPXtWUdP03//+F5MmTUJ0dDQee+wxdOjQAQcPHsQdd9xhi0vQqnkQxICIiMg0Fhtd1dDQgMceewxCCKxdu9ZSpzHK/PnzFZ1oq6qqnDLQcXd3R3Z2tsl/7/7+/lon1OvVqxcOHz6s2DZhwgTFd2v9UpYkCUuWLJHn8NHkxx9/VHx/7733LFwqIiKyFxYJctQBzk8//YRdu3bJtTjAjQ6j5eXlivTXr1/HpUuX5A6jKpVKY4dS9T5daXR1OvX09ISnp6fpF0bUCpIkgV1yiIisx+zNVeoA58yZM/jyyy/RoUMHxf7Y2FhUVFSgsLBQ3rZr1y40NTXJs+3GxsZi7969iuUAcnNzER0djfbt28tp8vLyFHnn5uYiNjbW3JfkNPbt22fSSCYynbah4nqHkBMRUasZXZNTXV2tGG577tw5HDt2DAEBAQgJCcGECRNQVFSEHTt2oLGxUe4jExAQAA8PD3Tp0gUjRozAjBkzsG7dOjQ0NCAlJQUTJ06U59KZPHkyXnjhBUyfPh3z5s3DyZMnsWLFCmRlZcnnffbZZzFo0CBkZmZi9OjReO+993DkyBHFMPPb0e7du7Xu69u3L44dO2a1slArNG/yY58cIiKTGB3kHDlyBA8++KD8Xd3HJTExEYsXL8bHH38MAPKwXrWvvvoKgwcPBnBjFt2UlBQMHTpUngxw5cqVclo/Pz988cUXSE5ORp8+fRAYGIiFCxcqOs/ee++9yMnJQVpaGhYsWICoqChs374d99xzj7GXdNvw9vbWOWKJLItDyImIrMvoIGfw4ME6O5Ya0uk0ICAAOTk5OtN0794d+/bt05nm0UcfxaOPPqr3fERERHT74SrkRBbEPjlERLbDIIfIHnHpKiKiVmOQQ2QlksQ+OURE1sQgh4iIiJwSgxwiCzLL2lUcQk5EZBIGOeSwhBBYuHAhQkJC4O3tjbi4OJw5c0bnMXfeeeeNZqNbPsnJyXKawYMHt9g/c+ZMS1/OLRjYEBG1FoMcsrjmM1eb07Jly7By5UqsW7cOBQUF8PHxQXx8vNZ1twDg8OHD+Pnnn+VPbm4uALSYimDGjBmKdMuWLTNLmSWJfXKIiKyFQY6BhBBoqqmxyceYpo3BgwcjJSUFKSkp8PPzQ2BgINLT0w3KY8GCBfLSGs316NFDXgTz8OHDGDZsGAIDA+Hn54dBgwahqKhIkV6SJKxduxZjxoyBj48PXn75ZYPLbyghBLKzs5GWloaxY8eie/fuePvtt3HhwgVs375d63F33HEHVCqV/NmxYwc6deqEQYMGKdK1adNGka75+mtGlZM1MkRENmOxVcidjaitRXHvPjY5d3RRIaQ2bQxO/9Zbb2H69Ok4dOgQjhw5gqSkJISHh2PGjBk6j0tISEBGRgbOnj2LTp06AQBOnTqF48eP41//+hcA4MqVK0hMTMSqVasghEBmZiZGjRqFM2fOoF27dnJeixcvxtKlS5GdnQ03N83/mc2cORObN2/WWabq6mqN28+dO4fS0lLExcXJ2/z8/BATE4P8/HxMnDhRZ74AUF9fj82bNyM1NbVFDcuWLVuwefNmqFQqPPzww0hPT0cbI/4NWo3LOhARtRqDHCcUFhaGrKwsSJKE6OhonDhxAllZWXqDnK5du6JHjx7IyclBeno6gBu/7GNiYuTlIIYMGaI4Zv369fD398eePXvw0EMPydsnT56MadOm6TzfkiVL8Pzzz5tyifKaaMHBwYrtwcHB8j59tm/fjoqKCkydOlWxffLkyYiIiEBoaCiOHz+OefPmobi4GB9++KFJZQUgDx3nEHIiIuthkGMgydsb0UWF+hNa6NzGGDBggKJmIjY2FpmZmWhsbISrq6vOYxMSErBhwwa5ievdd9+V1ycDgLKyMqSlpWH37t0oLy9HY2MjampqUFJSosinb9++essZFBSEoKAgo67NnN58802MHDlSXhhWrfkaad26dUNISAiGDh2qqOEiIiL7xyDHQJIkGdVk5KgmTZqEefPmoaioCLW1tTh//jwef/xxeX9iYiIuXryIFStWICIiAp6enoiNjUV9fb0iHx8fH73nak1zlUqlAnAj6AoJCZG3l5WVtVgcVpOffvoJX375pUG1M+p+St9//73Zghz21SEisjwGOU6ooKBA8f3gwYOIiorSW4sDAB07dsSgQYOwZcsW1NbWYtiwYYralv3792PNmjUYNWoUAOD8+fP49ddfTSpna5qrIiMjoVKpkJeXJwc1VVVVKCgowKxZs/Qev3HjRgQFBWH06NF60x47dgwAFMGUxQmu60BE1FoMcpxQSUkJUlNT8dRTT6GoqAirVq1CZmamwccnJCRg0aJFqK+vR1ZWlmJfVFQU3nnnHfTt2xdVVVWYM2cOvI1sTlNrTXOVJEmYPXs2XnrpJURFRSEyMhLp6ekIDQ3FuHHj5HRDhw7FI488gpSUFHlbU1MTNm7ciMTExBados+ePYucnByMGjUKHTp0wPHjx/Hcc89h4MCB6N69u0llVZeXiJzIzgXA0WY10b2nAPHmH0lKrcMh5E7oiSeeQG1tLfr374/k5GQ8++yzin4m+kyYMAEXL15ETU2NImAAbvRjuXz5Mnr37o0pU6bgmWeesVm/mrlz5+Lpp59GUlIS+vXrh+rqauzcuRNeXl5ymrNnz7aoafryyy9RUlKCJ598skWeHh4e+PLLLzF8+HB07twZf/nLXzB+/Hh88sknFr8eInIg37wL1FXe/Hzzrq1LRBqwJscJubu7Izs7G2vXrjXpeH9/f60T6vXq1QuHDx9WbJswYYLiu1FLFrSCJElYsmSJPIePJj/++GOLbcOHD9daxrCwMOzZs8dcRTTPsg5EZId+u4dHLgP+Pde2RSGtWJNDZIeaB0EMiIjsWDsr9tUjozHIuY3s27cPbdu21fohy5HnyWHfHCLncOvLB19G7BKbq5zM7t27te7r27evPFKIrEPbUHEOISdyEnxxsWsMcm4j3t7e8szFRERkDgxy7Bmbq4jsEdeuIrJzt96XvE/tEYMcIivg2lVETorNVXaNQQ6RBXEIOZGzY5BjzxjkEBERGatFaxVfXOwRgxwia/jtZc/gIeRcuorIMbC5yq4xyCEiIjIZgxx7xiCHHNaHH36I4cOHo0OHDpAkyS7nAOI8OUTOiqOrHAGDHLK4hoYGi+R79epV3H///Xj11Vctkr/dYFs/kf1ic5VdY5BjICEEGuoabfIxZiTO4MGDkZKSgpSUFPj5+SEwMBDp6ekG5bFgwQLExMS02N6jRw95EczDhw9j2LBhCAwMhJ+fHwYNGoSioiJFekmSsHbtWowZMwY+Pj54+eWXDS6/MaZMmYKFCxciLi7OIvmbkzyE/JfiGxvOfgUU/1v7AQxsiBwEgxx7xhmPDXS9vgnrnzXf6tTGSFoxCO6erganf+uttzB9+nQcOnQIR44cQVJSEsLDwzFjxgydxyUkJCAjIwNnz55Fp06dAACnTp3C8ePH8a9//QsAcOXKFSQmJmLVqlUQQiAzMxOjRo3CmTNn0K5dOzmvxYsXY+nSpcjOzoabm+b/zGbOnInNmzfrLFN1dbXB1+0QznwJ+LgCl84Cny8AokfaukREZIoWa1fZphikG4McJxQWFoasrCxIkoTo6GicOHECWVlZeoOcrl27okePHsjJyUF6ejoAYMuWLYiJiZGXgxgyZIjimPXr18Pf3x979uzBQw89JG+fPHkypk2bpvN8S5YswfPPP2/KJTqupusAfgtYr9fbtChEZAZsrrJrDHIM5ObhgqQVg2x2bmMMGDBAMVQ5NjYWmZmZaGxshKur7hqhhIQEbNiwQW7ievfdd5GamirvLysrQ1paGnbv3o3y8nI0NjaipqYGJSUlinz69u2rt5xBQUEICgoy6tpuH1zWgcgxMMixZwxyDCRJklFNRo5q0qRJmDdvHoqKilBbW4vz58/j8ccfl/cnJibi4sWLWLFiBSIiIuDp6YnY2FjU1ytrJXx8fPSe63Zqrrq5rAMnwCFyDhxd5QgY5DihgoICxfeDBw8iKipKby0OAHTs2BGDBg3Cli1bUFtbi2HDhilqW/bv3481a9Zg1KhRAIDz58/j119/Namct0NzldZlHaxcDiKyEFbk2DUGOU6opKQEqampeOqpp1BUVIRVq1YhMzPT4OMTEhKwaNEi1NfXIysrS7EvKioK77zzDvr27YuqqirMmTMH3t7eJpWztc1Vly5dQklJCS5cuAAAKC6+MXJJpVJBpVKZnC8RkeEY5dgzDiF3Qk888QRqa2vRv39/JCcn49lnn0VSUpLBx0+YMAEXL15ETU0Nxo0bp9j35ptv4vLly+jduzemTJmCZ555xmb9aj7++GP06tULo0ePBgBMnDgRvXr1wrp162xSHl3kPlKK1iod9TmCfXKI7FqL0VW8T+0Ra3KckLu7O7Kzs7F27VqTjvf398e1a9c07uvVqxcOHz6s2DZhwgTFd2utsD116lRMnTrVKuciItKIo6vsmtE1OXv37sXDDz+M0NBQSJKE7du3K/YLIbBw4UKEhITA29sbcXFxOHPmjCLNpUuXkJCQAF9fX/j7+2P69OktOpgeP34cDzzwALy8vBAWFoZly5a1KMvWrVvRuXNneHl5oVu3bvjss8+MvRwii2q5fINo8f9E5MgY5Ngzo4Ocq1evokePHli9erXG/cuWLcPKlSuxbt06FBQUwMfHB/Hx8YqagYSEBJw6dQq5ubnYsWMH9u7dq2hOqaqqwvDhwxEREYHCwkK89tprWLx4MdavXy+nOXDgACZNmoTp06fj6NGjGDduHMaNG4eTJ08ae0m3jX379qFt27ZaP0REZCiOrnIERjdXjRw5EiNHap6lVQiB7OxspKWlYezYsQCAt99+G8HBwdi+fTsmTpyIb7/9Fjt37sThw4fluVRWrVqFUaNG4fXXX0doaCi2bNmC+vp6bNiwAR4eHujatSuOHTuG5cuXy8HQihUrMGLECMyZMwcA8OKLLyI3NxdvvPGGXfbJsJbdu3dr3de3b1+7XMTydnBzCLmBBIeaEzkENlfZNbN2PD537hxKS0sVawn5+fkhJiYG+fn5AID8/Hz4+/srJouLi4uDi4uLPPQ5Pz8fAwcOhIeHh5wmPj4excXFuHz5spzm1jWL4uPj5fNoUldXh6qqKsXnduLt7Y277rpL64esjB0ViZwAgxx7ZtYgp7S0FAAQHBys2B4cHCzvKy0tbTEax83NDQEBAYo0mvJofg5tadT7NcnIyICfn5/8CQsL03tN1upES45L138jLfvkqLcTkUPj6CqHcFsNIZ8/fz4qKyvlz/nz57WmVU+cd+tMvkS3qqmpAXBjVBsR3WbYXGXXzDqEXD0BW1lZGUJCQuTtZWVl6Nmzp5ymvLxccdz169dx6dIl+XiVSoWysjJFGvV3fWl0TQLn6ekJT09Pg67Fzc0Nbdq0wS+//AJ3d3e4uNxW8SAZQAiBmpoalJeXw9/fX+eM0up5cgxd1qF57RBrE4nsGYMce2bWICcyMhIqlQp5eXlyUFNVVYWCggLMmjULwI3FIisqKlBYWIg+ffoAAHbt2oWmpibExMTIaf72t7+hoaFBfjvOzc1FdHQ02rdvL6fJy8vD7Nmz5fPn5uYiNjbWLNciSRJCQkJw7tw5/PTTT2bJk5yTv7+/1uBa+7IOfDASOTaOrnIERgc51dXV+P777+Xv586dw7FjxxAQEIDw8HDMnj0bL730EqKiohAZGYn09HSEhobKM+d26dIFI0aMwIwZM7Bu3To0NDQgJSUFEydORGhoKABg8uTJeOGFFzB9+nTMmzcPJ0+exIoVKxRLDDz77LMYNGgQMjMzMXr0aLz33ns4cuSIYph5a3l4eCAqKopNVqSVu7u7QWuCEZGTYnOVXTM6yDly5AgefPBB+XtqaiqAG6tTb9q0CXPnzsXVq1eRlJSEiooK3H///di5cye8vLzkY7Zs2YKUlBQMHToULi4uGD9+PFauXCnv9/PzwxdffIHk5GT06dMHgYGBWLhwoWIunXvvvRc5OTlIS0vDggULEBUVhe3bt+Oee+4x6S9CGxcXF0XZiVrF0GUdtB1DRHaGQY49MzrIGTx4sM4+ApIkYcmSJViyZInWNAEBAcjJydF5nu7du2Pfvn060zz66KN49NFHdReYyI4YPk+OJUtBdHv76Nj/sHH/j4rfZQM6dcD8kV0Mz+S3Y3cVl2MIgKampttrJI+D4NpVRBakbQg5X/6IbGf93h9w6oJynrRv/luJlAfvQjsv40ZJrt39A4Z4Ag1NAoYNayFrYuBJZFWcyZjI1hqbbtx7fxn2B6yf0kfe3mTCLak+hIMg7RNrcoisoFXLOvDpSWQRvcLbI+b3ATc3GHWrKRNLfGmxS6zJISIiMhGng7BvDHKILEnLVBoCYA0NkY1JUuu7xzHIsW8McoiI6Lai7f1C60ABAzJhc5V9YpBDZAXGLuug2McaHyIikzDIISKi25KEmy8gpmJzlX1jkENkQdqqv1k3Q2Q7Wu9Ljq5yOgxyiGyFzVBEDo81OfaNQQ6RFUjyg9DQ9arYJ4fIUuRb6pbRVabcabw77RuDHCILYnMVkZPiy4dDYJBDZDN8SBLZkgQJrex3zOYqO8cgh8gK5GUdGNcQ2Zy221CYUDvDIMe+McghskfC0Pl0iMg2OLrKETDIIbKglm+G4ub/s02fyCbU96UkKefJYcdj58Mgh4iIyERsrrJvDHKIrEFS/GEUU/oJEJGFce0qh8Agh8hmtD8UGdgQWU6zaXKU20267ViTY88Y5BBZEOfJIXJuvJftG4McIiK6Lak7HZs2Vw7DG0fAIIfICm7Ok9N8uQYDD+azlMi8tNxT2mpedWfF5ip7xiCHyIJMeWj+diAROQAGOfaNQQ6RzTCSIbIFuePxraMeW3FLcnSVfWKQQ2RVfBASORPe0faNQQ6RFcgdHA09QNF3h49RIktQ349SK1bpZHOVfWOQQ2RJWjs4SgxeiGxE2zxUBt+RGo53YZ2OXWKQQ2RNfA4SORXW5Ng3BjlEViAPIbdxOYjophYdj03A9xb7xiCHyGZ0PB7ZJ4fIYrTdUQbfarwnHQaDHCILajlPzo3vglU6RE6BzVX2jUEOERHdVm5WxCiXdTB58k6yWwxyiKxA0vS2xypvIgelbdgk72l7wyCHyC6xTw6Rpd3seMx5cpwVgxwiC2r1fBxEZHbamqVMeZ9gkGPfGOQQWUuLJyhDHSKHpC0aYq2r3WGQQ2QFRi/roMAHJ5E5qWMR+X6UOx6bkJcZykOWY/Ygp7GxEenp6YiMjIS3tzc6deqEF198UVFtL4TAwoULERISAm9vb8TFxeHMmTOKfC5duoSEhAT4+vrC398f06dPR3V1tSLN8ePH8cADD8DLywthYWFYtmyZuS+HqFW0VovrPZCPTiJHwOYq+2b2IOfVV1/F2rVr8cYbb+Dbb7/Fq6++imXLlmHVqlVymmXLlmHlypVYt24dCgoK4OPjg/j4eFy7dk1Ok5CQgFOnTiE3Nxc7duzA3r17kZSUJO+vqqrC8OHDERERgcLCQrz22mtYvHgx1q9fb+5LIjKPWwMXBjJEDkrrdIJWLQXp52buDA8cOICxY8di9OjRAIA777wT7777Lg4dOgTgRi1OdnY20tLSMHbsWADA22+/jeDgYGzfvh0TJ07Et99+i507d+Lw4cPo27cvAGDVqlUYNWoUXn/9dYSGhmLLli2or6/Hhg0b4OHhga5du+LYsWNYvny5Ihgisid85yOyPbm56pZmZG0DBXTmxbvarpm9Jufee+9FXl4e/vOf/wAAvvnmG3z99dcYOXIkAODcuXMoLS1FXFycfIyfnx9iYmKQn58PAMjPz4e/v78c4ABAXFwcXFxcUFBQIKcZOHAgPDw85DTx8fEoLi7G5cuXNZatrq4OVVVVig+R7Rj2QDXlwUtE1sG7076ZvSbnr3/9K6qqqtC5c2e4urqisbERL7/8MhISEgAApaWlAIDg4GDFccHBwfK+0tJSBAUFKQvq5oaAgABFmsjIyBZ5qPe1b9++RdkyMjLwwgsvmOEqiQyj7JMjNPyk5TgGNkQWp66DkYytjOHoKodh9pqcDz74AFu2bEFOTg6Kiorw1ltv4fXXX8dbb71l7lMZbf78+aisrJQ/58+ft3WRiIjITnCeHOdj9pqcOXPm4K9//SsmTpwIAOjWrRt++uknZGRkIDExESqVCgBQVlaGkJAQ+biysjL07NkTAKBSqVBeXq7I9/r167h06ZJ8vEqlQllZmSKN+rs6za08PT3h6enZ+oskMpJ6RlXF45BvfUROgEGOPTN7TU5NTQ1cXJTZurq6oqmpCQAQGRkJlUqFvLw8eX9VVRUKCgoQGxsLAIiNjUVFRQUKCwvlNLt27UJTUxNiYmLkNHv37kVDQ4OcJjc3F9HR0RqbqohszpigpnlaxkJEZqVuDjZ9WQeOrnIUZq/Jefjhh/Hyyy8jPDwcXbt2xdGjR7F8+XI8+eSTAG70Zp89ezZeeuklREVFITIyEunp6QgNDcW4ceMAAF26dMGIESMwY8YMrFu3Dg0NDUhJScHEiRMRGhoKAJg8eTJeeOEFTJ8+HfPmzcPJkyexYsUKZGVlmfuSiEzGvjVEzuHCmcs4+kUJmhoFVJE+6Pfbdt7i9s3sQc6qVauQnp6OP//5zygvL0doaCieeuopLFy4UE4zd+5cXL16FUlJSaioqMD999+PnTt3wsvLS06zZcsWpKSkYOjQoXBxccH48eOxcuVKeb+fnx+++OILJCcno0+fPggMDMTChQs5fJwcCJ+ORLYkNyMbUJFzNPc8fjxxEQBQcvoSugW1g5fLFfbJsXNmD3LatWuH7OxsZGdna00jSRKWLFmCJUuWaE0TEBCAnJwcnefq3r079u3bZ2pRiazmxnwcAhIDGyKb09rYpOP2bLrepPjeKFyNy4BsgmtXEdmA/mUdmv/MByeRrbW8C6XftrMmx54xyCGyFQYvRDZ1s+Ox6XgX2zcGOUTWwqCGyC5onctPV8hi0P3Le9zeMMghsgLjh6gSkSNgc5V9Y5BDZEHa3gxFs//XnEBo/pmIWu3W+1K9UKeuW+3WfYJ9chwCgxwiq2GwQuTU+EJidxjkEFmBPB8Hn4FEdqN1HY/VNTlkzxjkEFlLi/puAx+PfDskMivtHY9NweYqe8Ygh8iCtC3roP9hysCGyJ607JOjMZUVSkLGYJBDZDN8IBLZgvrOk0c9/vaHKWvNseOxfWOQQ2QNEsDHIZEzYZ8cR8Agh8jesU8OkUUY1/HYgPuQ96rdYZBDZEFa58mRJOMm5SAiszFnx2PWz9o3BjlE1sLAhchxaZ0MkOwZgxwiK5DnybFxOYjoJrm5SjLXncmQx94wyCGyIJ0L/hn4QNSdBxEZT0szsjEtyEL9B19d7BmDHCKrMTxYMWUoKxFZH4Mc+8Ygh8gK1NXhfBwS2Z76HUJuRpZvTGNeLjTczXw5sTsMcoiIiEzEsMa+McghsqTmT8Bmb3l6H4zN3wj5dkhkEcbMk3NrE/LNZirWz9ozBjlERHRb0fba0Pr3Cb6Q2BsGOURWoHUIOWtpiByUep4c1uTYMwY5RFbDgIbInsgNTq2YJ4d3tX1jkENkQVqXddB/oBGJicgY2qZo0HmrtZjxWGPGJpaILIVBDpEt8aFI5NDYXGXfGOQQWYEEPQtyEpHVqO/EW0dXGbdmLvvkOAIGOUQWZFK1eMtMzFIWIrI03qv2hkEOkU1peSgysCGygltnPDb+WN6p9o1BDpHV8HFIZA+0vUOYsqAum6vsG4McIiu4uXbVrUM0GPgQOSKOrnIMDHKILEjxZmjMsg6KTPjgJDIndV+5m81UvzU9GdHxuOJsGzQ2SKzJsXNuti4AEWnAwIbIrl0sbgc/F28gzNYlIV1Yk0NkRS3f+RjMENnKzRmPTTu+qeHWX6G8n+0Ngxwiq+EDkMgemG2BTsGOx/aOQQ6RBZm8rIMiMYMjIlvTPiKLQY49Y5BDZEtaAxgGNkSWdnPUo5nwhcTuMMghsgL1sg4Sn4FEtmfKPDktAhhOBugILBLk/O9//8P/+3//Dx06dIC3tze6deuGI0eOyPuFEFi4cCFCQkLg7e2NuLg4nDlzRpHHpUuXkJCQAF9fX/j7+2P69Omorq5WpDl+/DgeeOABeHl5ISwsDMuWLbPE5RAREWnE5ir7ZvYg5/Lly7jvvvvg7u6Of//73zh9+jQyMzPRvn17Oc2yZcuwcuVKrFu3DgUFBfDx8UF8fDyuXbsmp0lISMCpU6eQm5uLHTt2YO/evUhKSpL3V1VVYfjw4YiIiEBhYSFee+01LF68GOvXrzf3JRGZTNvaVc1SaDtQfxoiMom8QKf6z99+MKa1SUBi65QDMPs8Oa+++irCwsKwceNGeVtkZKT8sxAC2dnZSEtLw9ixYwEAb7/9NoKDg7F9+3ZMnDgR3377LXbu3InDhw+jb9++AIBVq1Zh1KhReP311xEaGootW7agvr4eGzZsgIeHB7p27Ypjx45h+fLlimCoubq6OtTV1cnfq6qqzH35RER0G2FNjn0ze03Oxx9/jL59++LRRx9FUFAQevXqhX/84x/y/nPnzqG0tBRxcXHyNj8/P8TExCA/Px8AkJ+fD39/fznAAYC4uDi4uLigoKBATjNw4EB4eHjIaeLj41FcXIzLly9rLFtGRgb8/PzkT1gYZ3Ei6zB7B0ciajV1DY5kwJ2ppUsO61ntnNmDnB9++AFr165FVFQUPv/8c8yaNQvPPPMM3nrrLQBAaWkpACA4OFhxXHBwsLyvtLQUQUFBiv1ubm4ICAhQpNGUR/Nz3Gr+/PmorKyUP+fPn2/l1RKZRphSP05EZqG/GdnkjC2TL5nM7M1VTU1N6Nu3L1555RUAQK9evXDy5EmsW7cOiYmJ5j6dUTw9PeHp6WnTMtBtzIgHYPOHsMUeyETUCqyXdQRmr8kJCQnB3XffrdjWpUsXlJSUAABUKhUAoKysTJGmrKxM3qdSqVBeXq7Yf/36dVy6dEmRRlMezc9BZC/U1eGmPBa/u/QdXjr4EhqaGsxbKKLbnHxfmhqvCC7Qae/MHuTcd999KC4uVmz7z3/+g4iICAA3OiGrVCrk5eXJ+6uqqlBQUIDY2FgAQGxsLCoqKlBYWCin2bVrF5qamhATEyOn2bt3Lxoabj74c3NzER0drRjJRWTf9NfSVNRV4v3i93H8l+NWKA+R8zPbsg4G50y2YvYg57nnnsPBgwfxyiuv4Pvvv0dOTg7Wr1+P5ORkADc6YM6ePRsvvfQSPv74Y5w4cQJPPPEEQkNDMW7cOAA3an5GjBiBGTNm4NChQ9i/fz9SUlIwceJEhIaGAgAmT54MDw8PTJ8+HadOncL777+PFStWIDU11dyXRGQy5eRiQsNPhmNNDpHtsNnYMZm9T06/fv2wbds2zJ8/H0uWLEFkZCSys7ORkJAgp5k7dy6uXr2KpKQkVFRU4P7778fOnTvh5eUlp9myZQtSUlIwdOhQuLi4YPz48Vi5cqW838/PD1988QWSk5PRp08fBAYGYuHChVqHjxPZJe0L4sg4SzKRealvu5ujq37bbsTrx81mKjZX2TOzBzkA8NBDD+Ghhx7Sul+SJCxZsgRLlizRmiYgIAA5OTk6z9O9e3fs27fP5HISWYtkcqP/TXyTJLJzvEftDteuIrIWPgCJ7JJJLyESZzx2BAxyiCxIW+2LMb1zTKlKJyLttN1LuoIWjfvMUENLlsUgh8geaXqiMsYhsnO8Se0NgxwiK7gxHwdn1CCyB7d2PJa36zzIUqUhS2KQQ2RLxsyCzKcskR2R5MCnSfD1xV4xyCGyIEVgIkycJ0doyIuIWk1eONegGMWA+489ke0Ogxwie8SHJZHFaJ/x2JiaVYBz5Ng/BjlEVtTykWjaop1EZF26xgLwzrRfDHKILEgZmLRuWQciMi95zmKzVcjwzrY3DHKI7Jz024OTfXKIzET/air6cY4ch8Agh8gK5A6Ot+7Q2gTFgIbI/t24ozk5hP1ikENkLexTQ2QX1LWiNxfo/C1YMXbG41u38R63OwxyiCzInE1M7HhMRGQcBjlENqWtc8DN7RLnySGyCHUNjmnda24exOYq+8Ugh8gKbi7rwECFyNa0V4oa215lxPFkEwxyiGzJmMnH2FxFZDdYe+MYGOQQ2QDDFSLbu9nx2OQcAPB+tmcMcoispZW1NuyTQ2Qe2pd1MCITTZERa1vtDoMcIitQd3Bs+XTlKuREjkDjsg68Je0egxwiC9LWj0awOZ/IZtT35c1lHUxpdpJw892FN7S9YpBDZDWmvfaph5CzIofIzrR4W+FNam8Y5BBZgdHLOmhc8ZgPUCKzanXHY7J3DHKIiOi2YkrHY91TODBMslcMcogsSFH70uwhaUqdDGtyiOyHxn447IlsdxjkENk5viMSmZc6FpFuaa/SWVvD+MUhudm6AES3DyPGYGiaJ4dviUT2Q5KgbzJAIQS+P1KOK5euAQDcPFzxh/7B8PJxt04ZiUEOkaNgcxWReRkz47Fhd58y1S8lV/DFm6cU22qr6xHz8O8Nyo1aj81VRBakLTARN+vHrVgaItLF6LtRzwHXqhsAAJ5t3NDhd20BAHVXrxtfMDIZgxwiazE1oBHqPxgQEdmMxqVW1H/qrgtq18ELkT0CLVAo0odBDpEVaJ0nRxuNc8ibrThEhJYzHrfaLfet+psif9beWhWDHCKb4gOPyJp0deA3Jv4waBgB31VsjkEOkQUpH6ite7yxuYrIPmkLeBT3vyQnJitikENkS1pfHW9ul5+NrOYmajVF3HFLM3LrXyQ0Hy9JjHFshUEOkRWoJx3jxH5Ejsmkd4zmx5ir3w8ZhUEOkQVxWQci+3Wz47EpB0vyKuR670xJunkO1shaFYMcIpviA4/ImnTecbp2GnKrahldZWw2ZD4McoiswOjmqmYPS4nz5BA5pt/uY0VNEW9jq7J4kLN06VJIkoTZs2fL265du4bk5GR06NABbdu2xfjx41FWVqY4rqSkBKNHj0abNm0QFBSEOXPm4Pp15UyRu3fvRu/eveHp6Ym77roLmzZtsvTlELVCK0dXsZqbyKxuLuug//VD10uG9tFVLc/F5irrsmiQc/jwYfz9739H9+7dFdufe+45fPLJJ9i6dSv27NmDCxcu4I9//KO8v7GxEaNHj0Z9fT0OHDiAt956C5s2bcLChQvlNOfOncPo0aPx4IMP4tixY5g9ezb+9Kc/4fPPP7fkJREZR8vzTN7MBx6RVemcJ8eYfDQGNtpHV3HYgW1YLMiprq5GQkIC/vGPf6B9+/by9srKSrz55ptYvnw5hgwZgj59+mDjxo04cOAADh48CAD44osvcPr0aWzevBk9e/bEyJEj8eKLL2L16tWor68HAKxbtw6RkZHIzMxEly5dkJKSggkTJiArK0trmerq6lBVVaX4EBER6dWq95GbY8j5WmNdFgtykpOTMXr0aMTFxSm2FxYWoqGhQbG9c+fOCA8PR35+PgAgPz8f3bp1Q3BwsJwmPj4eVVVVOHXqlJzm1rzj4+PlPDTJyMiAn5+f/AkLC2v1dRIZQpIko2ptNL1tsk8OUespRnWr+8qZtF6uBCGPrtJSS6OxucqYc1BrWSTIee+991BUVISMjIwW+0pLS+Hh4QF/f3/F9uDgYJSWlsppmgc46v3qfbrSVFVVoba2VmO55s+fj8rKSvlz/vx5k66PyHz4xCNyGi1GV7XseMw73rrczJ3h+fPn8eyzzyI3NxdeXl7mzr5VPD094enpaeti0G1EW+2LMGFiDnY8JjKz1nSTMeRYeYXO5tt4H1uT2WtyCgsLUV5ejt69e8PNzQ1ubm7Ys2cPVq5cCTc3NwQHB6O+vh4VFRWK48rKyqBSqQAAKpWqxWgr9Xd9aXx9feHt7W3uyyKyGYnPRCKz0RVj6BxBpfM4/ceYbaVzMorZg5yhQ4fixIkTOHbsmPzp27cvEhIS5J/d3d2Rl5cnH1NcXIySkhLExsYCAGJjY3HixAmUl5fLaXJzc+Hr64u7775bTtM8D3UadR5E9kRu+7/1Santyck+OUR2zvCgRWJ7lc2YvbmqXbt2uOeeexTbfHx80KFDB3n79OnTkZqaioCAAPj6+uLpp59GbGwsBgwYAAAYPnw47r77bkyZMgXLli1DaWkp0tLSkJycLDc3zZw5E2+88Qbmzp2LJ598Ert27cIHH3yATz/91NyXRGQyrc1VpuTFam6iVmt+T8rz5Pz2g85brLX3H0dX2YTZgxxDZGVlwcXFBePHj0ddXR3i4+OxZs0aeb+rqyt27NiBWbNmITY2Fj4+PkhMTMSSJUvkNJGRkfj000/x3HPPYcWKFejYsSP++c9/Ij4+3haXRKQfgxQip/Fbl+LfvmmbDFDDPc/HgFVZJcjZvXu34ruXlxdWr16N1atXaz0mIiICn332mc58Bw8ejKNHj5qjiERW0fJRqP+Jx5Z8IsvQHaIoaVyH6taNWl5kJOlmkzWbna2La1cRWY0RDzeNL4B8OBK1likdiJWJmn77wYDQqPnoKr6t2ASDHCIL0taPhss6EDkPraOrmkU5nAzQNhjkEDkIdjwmMi91h2ODRncbVs2jPwVvY6tikENkBeplHW4+S42ou+ZDkcgqDHmRkNQ3pCTpvzebJSXbYJBDZFOcJ4fIEYgWP0COXrStXaVxMkBW5VgVgxwiCzJnYMIgh6j1FIGH+k+j5rARGn9skXkzrMmxHQY5RFbDIIXIYckBjDGjJJu1V3EyQJtgkENkBfKyDrfuMGBZB3lQBqu5icxKnvHYmCUafvuzeROVtuYqTedilGNdDHKIiOi2obPZ15AARPfaDxq/NX/F4buKdTHIIbKWZk83wTZ6IuenjHJu2UjWwCCHyKYMX9aBzVVErafseKycJ0dXLc/N40SLbVonA5R3cDJAW2GQQ2QFWvvkaMUnIZE9ku9hTUOmWi5mpTUpWQeDHCILUta+NHsDNGEhGw4hJ7IheXCVafehccPUyVwY5BDZkhEPTAY5RK2nYS6/Zk3Cxuag3qJnMkAJjHJshEEOkRVIramv5kORyOYMe8nQMhlg8xTsW2dVDHKIrMWYWhtNyzrw4UhkGUa8hKjXrhIwfO0qdsqxHQY5RBak7e1PaPjJ1LyIyHC6XhYMmgJHkUbf2lXNJvVknGMTDHKIiIgMpnHym9926X8RYYWsdTHIIbKCG0PINbzvGfDE4wsgkfno7HhsdA6GkaRW9ssjkzHIIbJHfN0jsktSsx/0Ldmp8TbmvW1VDHKILEhb+78w4a2OHY+JzOvWGY8NYsTaVfJ5uAq5zTDIIbIWza91hh/OxyNRq+mKUYzrlGxAZKQ+iC1VNsMgh8gabmn7N+pQxjZE9kleaVfPZIAA166yEQY5RBakrH0x4ummISmbq4jM69aOxzoJfT1woLWaqPnKdbyNrYtBDpEtcVkHIuvSFaMYcLhJFTJS834/vI+tiUEOkRXIHRz5fCNySPKtq2k2cr3NVc328xlgVQxyiKzFmHpq1mkTWUTzGlG5R41kTFOSjskAW0Qw4rf8tSQni2OQQ2RB+pd1ICKHYMLgyOarkLPfsW242boARHRD/fnzqPjwQ6ChARff3NBiPzseE5mXugbHmEoW9QKdzSfXMejOVKfnfWxVDHKIrODmsg7aH3C/rFqFqo8/aXnsb4ew4zFR6+mOMXTMk6MhSYvUt2auYZocxjjWxeYqIltq9sRrulJtw4IQkWFMiFK4bpXNMMghsiQtz0ONm5uabvyp5YHImhyi1lMs0Kn+05CWJI07f+uwrHV0VbNOzoxzbIJBDpG16KmnloMYV1cthzPIIbI1dZOz5sBG29pVuLl2FW9jq2KQQ2QF2js4NnviNf023NRFeVvyBZDIDsgTHuvqlKOdxPFVNsEgh8heCD01OXw4ErWapiYkSW52MjFPfZMBNp8nh7exVTHIIbIgbWtXCU1117/1ybm1JoeI7IkRa1fJo6skrUnIssz+NM3IyEC/fv3Qrl07BAUFYdy4cSguLlakuXbtGpKTk9GhQwe0bdsW48ePR1lZmSJNSUkJRo8ejTZt2iAoKAhz5szB9evXFWl2796N3r17w9PTE3fddRc2bdpk7sshsiLdNTlEZF6SESt0tpjnWJL0TwaoaQw5WZXZg5w9e/YgOTkZBw8eRG5uLhoaGjB8+HBcvXpVTvPcc8/hk08+wdatW7Fnzx5cuHABf/zjH+X9jY2NGD16NOrr63HgwAG89dZb2LRpExYuXCinOXfuHEaPHo0HH3wQx44dw+zZs/GnP/0Jn3/+ubkviajVJAOeckJLnxz1c5Idj4laz+gBVLoSqStk9ZxMQrOAiqzK7JMB7ty5U/F906ZNCAoKQmFhIQYOHIjKykq8+eabyMnJwZAhQwAAGzduRJcuXXDw4EEMGDAAX3zxBU6fPo0vv/wSwcHB6NmzJ1588UXMmzcPixcvhoeHB9atW4fIyEhkZmYCALp06YKvv/4aWVlZiI+PN/dlEZlE0VzV7OEoNK1IrKdPDhHZ0C2zAQpoCor0R0l8WbEuizf+V1ZWAgACAgIAAIWFhWhoaEBcXJycpnPnzggPD0d+fj4AID8/H926dUNwcLCcJj4+HlVVVTh16pScpnke6jTqPDSpq6tDVVWV4kNkN/T0yWHHY6LW0xRj3OwTrP8eM6k+RuI8ObZi0SCnqakJs2fPxn333Yd77rkHAFBaWgoPDw/4+/sr0gYHB6O0tFRO0zzAUe9X79OVpqqqCrW1tRrLk5GRAT8/P/kTFhbW6mskMobO55y+0VV8AySymZsBUMtVyPVOBtg8wuFtbFUWDXKSk5Nx8uRJvPfee5Y8jcHmz5+PyspK+XP+/HlbF4luKxqebs2bsATnySGyluZxh1G1LCZ06pHk/yNrs9gCnSkpKdixYwf27t2Ljh07yttVKhXq6+tRUVGhqM0pKyuDSqWS0xw6dEiRn3r0VfM0t47IKisrg6+vL7y9vTWWydPTE56enq2+NiJDaat90biV8+QQWZyu+0j3sg63/mDAYIJmSeW5eHgbW5XZa3KEEEhJScG2bduwa9cuREZGKvb36dMH7u7uyMvLk7cVFxejpKQEsbGxAIDY2FicOHEC5eXlcprc3Fz4+vri7rvvltM0z0OdRp0Hkd3R93Bjnxwiu6d5MQctAY+mhbJ4H1uV2WtykpOTkZOTg48++gjt2rWT+9D4+fnB29sbfn5+mD59OlJTUxEQEABfX188/fTTiI2NxYABAwAAw4cPx913340pU6Zg2bJlKC0tRVpaGpKTk+WamJkzZ+KNN97A3Llz8eSTT2LXrl344IMP8Omnn5r7kohazaBlHTi6ishqJMXPRkyUo7u9Su+5WJNjXWavyVm7di0qKysxePBghISEyJ/3339fTpOVlYWHHnoI48ePx8CBA6FSqfDhhx/K+11dXbFjxw64uroiNjYW/+///T888cQTWLJkiZwmMjISn376KXJzc9GjRw9kZmbin//8J4ePk8OS++S43tIn59b+jkRkOl0TFhuTj2TIATc7HnN0lW2YvSbHkBEgXl5eWL16NVavXq01TUREBD777DOd+QwePBhHjx41uoxE1qJtWQeNld7qe8eFNTlE9uZmRY6G5Vm0HWN49x2yEC6SQ2RLGtaugiv75BBZihx3NKtaUf9ozDQNGgMcLYdLzU7C5irrYpBDZAXqNn9D5smRtNTkMMghsiH1/anoR2fQIb8dp2EjWRyDHCIL0rqsg6a0csdj3pZElqboeNzK5iQh9GQgSWyyshE+TYlsqmUQpLUmh2+ARK1m6m2kqU+OjlSKtBxdZTsMcoisQPsQ8mbYJ4fIARgxGaD6B9bi2AyDHCKr0b2sg7aaHImxDZHZqF8WFMs6GDEbsebJALWeTD5G4hhym2CQQ2RJWp5+mvvk6KnJYT03ke3cOhmgJLW8kXXdoyaM4KLWY5BDZC/Uz07Ok0Nkcc1nOTbbAp23Jm22CjnrcWyDQQ6RFUiQbrzltWjON3yeHCJqPV0VKYb1exPy/9+cjFzf6Co0q8kx4BRkNnyaEtkLbX1y1LvZ8ZjI5vTMcazxK2txbIdBDpEFaQtMdPfJYXMVkcUZEXko+9GYNrpKbrDiu4pVMcghshp9o6tu/CG5sOMxkaXonOXGoNaqlom0NldpXK6O97E1McghsoKbyzroeMA16a7JYXMVkY1ou/X0jq5STwYosU+OjTDIIbIJ7auQt6jJ4UORyGyEhlmIJSMWz9T5oqL9IPbLsREGOUQWJDRM9gc0j1ua/aSnTw5rcohsQ+Od12zcudaKnubddzgZoE0wyCGyF3r65BCR+ShnPDaCUWtXtcyfzVXWxacpkTVIij8009MnhxU5RK2ne54cww8SOg9omd/Nfse8ka2JQQ6RBSmbmDQ0Umlau+qWyQA5Tw6R/ZA0hC7aR1c1WwKCrVU2wSCHyE7IfXK4rAORxRmzrIPQ8U25S/O+5l1yWJFjXQxyiKxAvayDzmep+qWPC3QS2YRx95hxkwGyKsc2GOQQ2ZSGtau01OSwuYrIRjS3OuvvJ9estYpsg0EOkQUZFZho65PD2IbIbISGwONmvzeDcpCParlAp+bRVc1PwhpZ62KQQ2Q1eh5urMkhsnvGVMqIZgERV3WwDQY5RFZwc1mHW2iYIFBbnxwiMh9NMx5rJTR8MSbaadbzmDGOdfFpSmQv9NXksJqbqNV01oiauECn1n3N4iH2y7ENBjlEFmTMsg7yftbkENkVTYGRnrGSv6X5Dac8thk+TYnsxW81ORLnySGymJsdj5vNk6PeZ0BVjmIyQANHV5HtMMghsgL1A7XlY/Um+XmobZ4cPjGJbEPv8g3aRlf9NmKy2YzHrMixLgY5RFbTrJFK0zNRS02OnJRPRyKzUXY8NuJAI+5D5QhyqeVGsjgGOUT2gn1yiCxO5xriWnZqXGjzllWsdJJg3GgsMhs+TYmsQDLkCaenTw6bq4jsgRGdkDWMruJ9bF0McoisReNrooZxVqzJIbI8SeuXljTOkyO1jHduucc1hjOMcayKT1MiC9L21qb54fdbJ8Vb++QI9W4+HYlaS9d9ZNAdZsxtqGkNCbIqBjlE9kI9GSBHVxHZleaBkbonjiHrdMqzmEs3R1jyXcW6GOQQWYMEaGy51zBZoOTKeXKILOXmalI3SaYO726RnhGMvWGQQ2Qv1E9YF96WRPZLU5ikO6nUfHQVq3Ksik9TIgvS1v7fsr9is+pwLTU57JNDZD6aZjw2iIb7UPvoKk1pyZocPshZvXo17rzzTnh5eSEmJgaHDh2ydZGINNM1ukrdHwfQukAnEbWezvU1tYUgzTbrDIi0jq6SbvY9ZpRjVQ4d5Lz//vtITU3FokWLUFRUhB49eiA+Ph7l5eW2LhqRgnqeHElrD8XmNTnseExkv4y4DxUtW5KxR5MZOHSQs3z5csyYMQPTpk3D3XffjXXr1qFNmzbYsGGDrYtGZBzW5BBZVfNR3a0d4a1vRfIbo6tadw4yjSQctKG/vr4ebdq0wf/93/9h3Lhx8vbExERUVFTgo48+anFMXV0d6urq5O9VVVUICwtD5hP/B28PH2sUm0iv666AW6OtS0FEakKSIFzcAQB3/HIUv9zRC1JTIySh+0ZtcnEDJBdE/LgdAZdO4mjvNACAS2O9xcvs7Grrr+Ivb09AZWUlfH19taZzs2KZzOrXX39FY2MjgoODFduDg4Px3XffaTwmIyMDL7zwQovtTa4eaHL1sEg5iYzlAqCJlTlEdqdt9X/hV3kWv9zRC8LFFQL6b1RJNMK/8r9od/US3OuvoMGjHX/fmEGTa4NB6Rw2yDHF/PnzkZqaKn9X1+T0Gn0VPm0cskKLHICb5AI/D/WbhgtqPdvhakM1pNqLLRP7twPc3IBrdUDDdXh4+qBdnUD19RrU8e2PyGz8vd3h0qwNqepaA6436f494NnGD5L7SLT3ckdTTT3EdS9AktDOwxXVleUaezW7ugu4/79ENALocx1ouFZt7ku5LV2tqQE26k/nsEFOYGAgXF1dUVZWptheVlYGlUql8RhPT094enq22N5v2Cid1V1ERERkP6qqqgxK57Adjz08PNCnTx/k5eXJ25qampCXl4fY2FgbloyIiIjsgcPW5ABAamoqEhMT0bdvX/Tv3x/Z2dm4evUqpk2bZuuiERERkY05dJDz+OOP45dffsHChQtRWlqKnj17YufOnS06IxMREdHtx2GHkJtDVVUV/Pz89A5BIyIiIvth6O9vh+2TQ0RERKQLgxwiIiJySgxyiIiIyCkxyCEiIiKnxCCHiIiInBKDHCIiInJKDHKIiIjIKTHIISIiIqfEIIeIiIickkMv69Ba6smeDV3NlIiIiGxP/Xtb36INt3WQc/HiRQBAWFiYjUtCRERExrp48SL8/Py07r+tg5yAgAAAQElJic6/JGfUr18/HD582NbFsDpe9+2F13174XXfPiorKxEeHi7/Htfmtg5yXFxudEny8/O77RbodHV1ve2uGeB132543bcXXvftR/17XOt+K5WD7ExycrKti2ATvO7bC6/79sLrpltJQl+vHSdm6FLtREREZD8M/f19W9fkeHp6YtGiRfD09LR1UYiIiMhAhv7+vq1rcoiIiMh53dY1OUREROS8GOQQERGRU2KQ44BWr16NO++8E15eXoiJicGhQ4fkfU899RQ6deoEb29v3HHHHRg7diy+++47vXlu3boVnTt3hpeXF7p164bPPvtMsV8IgYULFyIkJATe3t6Ii4vDmTNnzH5tuui6bgDIz8/HkCFD4OPjA19fXwwcOBC1tbU689y9ezd69+4NT09P3HXXXdi0aZPR57U0Xec/e/YsHnnkEdxxxx3w9fXFY489hrKyMr152vN17927Fw8//DBCQ0MhSRK2b98u72toaMC8efPQrVs3+Pj4IDQ0FE888QQuXLigN197vmY1XdcOAFOnToUkSYrPiBEj9OZrz9eu75qrq6uRkpKCjh07wtvbG3fffTfWrVunN9/jx4/jgQcegJeXF8LCwrBs2bIWafQ99ywpIyMD/fr1Q7t27RAUFIRx48ahuLhYkWb9+vUYPHgwfH19IUkSKioqDMrbnv+9rU6QQ3nvvfeEh4eH2LBhgzh16pSYMWOG8Pf3F2VlZUIIIf7+97+LPXv2iHPnzonCwkLx8MMPi7CwMHH9+nWtee7fv1+4urqKZcuWidOnT4u0tDTh7u4uTpw4IadZunSp8PPzE9u3bxfffPONGDNmjIiMjBS1tbUWv2Yh9F/3gQMHhK+vr8jIyBAnT54U3333nXj//ffFtWvXtOb5ww8/iDZt2ojU1FRx+vRpsWrVKuHq6ip27txp8HktTdf5q6urxe9//3vxyCOPiOPHj4vjx4+LsWPHin79+onGxkatedr7dX/22Wfib3/7m/jwww8FALFt2zZ5X0VFhYiLixPvv/+++O6770R+fr7o37+/6NOnj8487f2a1XRduxBCJCYmihEjRoiff/5Z/ly6dElnnvZ+7fquecaMGaJTp07iq6++EufOnRN///vfhaurq/joo4+05llZWSmCg4NFQkKCOHnypHj33XeFt7e3+Pvf/y6nMeS5Z0nx8fFi48aN4uTJk+LYsWNi1KhRIjw8XFRXV8tpsrKyREZGhsjIyBAAxOXLl/Xma+//3tbm0EHOG2+8ISIiIoSnp6fo37+/KCgokPfV1taKP//5zyIgIED4+PiIP/7xj6K0tFRvnh988IGIjo4Wnp6e4p577hGffvqpYn9TU5NIT08XKpVKeHl5iaFDh4r//Oc/Zr82bfr37y+Sk5Pl742NjSI0NFRkZGRoTP/NN98IAOL777/Xmudjjz0mRo8erdgWExMjnnrqKSHEjWtWqVTitddek/dXVFQIT09P8e6777bmcgym77pjYmJEWlqaUXnOnTtXdO3aVbHt8ccfF/Hx8Qaf19J0nf/zzz8XLi4uorKyUt5fUVEhJEkSubm5WvN0hOtW0/RL71aHDh0SAMRPP/2kNY0jXbOatiBn7NixRuXjSNeu6Zq7du0qlixZotjWu3dv8be//U1rPmvWrBHt27cXdXV18rZ58+aJ6Oho+bu+5561lZeXCwBiz549LfZ99dVXBgc5jvTvbQ0O21z1/vvvIzU1FYsWLUJRURF69OiB+Ph4lJeXAwCee+45fPLJJ9i6dSv27NmDCxcu4I9//KPOPA8cOIBJkyZh+vTpOHr0KMaNG4dx48bh5MmTcpply5Zh5cqVWLduHQoKCuDj44P4+Hhcu3bNotcLAPX19SgsLERcXJy8zcXFBXFxccjPz2+R/urVq9i4cSMiIyMV63PdeeedWLx4sfw9Pz9fkScAxMfHy3meO3cOpaWlijR+fn6IiYnReF5z03fd5eXlKCgoQFBQEO69914EBwdj0KBB+PrrrxX5DB48GFOnTpW/67tuY/++zU3f+evq6iBJkmIIpZeXF1xcXBTX7mjXbazKykpIkgR/f395mzNf8+7duxEUFITo6GjMmjVLXoNPzdmu/d5778XHH3+M//3vfxBC4KuvvsJ//vMfDB8+XE4zdepUDB48WP6en5+PgQMHwsPDQ94WHx+P4uJiXL58WU6j6+/F2iorKwFA7zIFt3K2f29zc9ggZ/ny5ZgxYwamTZsmt9G2adMGGzZsQGVlJd58800sX74cQ4YMQZ8+fbBx40YcOHAABw8e1JrnihUrMGLECMyZMwddunTBiy++iN69e+ONN94AcKNfSnZ2NtLS0jB27Fh0794db7/9Ni5cuNCiHdkSfv31VzQ2NiI4OFixPTg4GKWlpfL3NWvWoG3btmjbti3+/e9/Izc3V3Gzd+rUCYGBgfL30tJSnXmq/9R3XkvRd90//PADAGDx4sWYMWMGdu7cid69e2Po0KGKfkPh4eEICQmRv2u77qqqKtTW1hr8920p+s4/YMAA+Pj4YN68eaipqcHVq1fx/PPPo7GxET///LOc3tGu2xjXrl3DvHnzMGnSJMWEYM56zSNGjMDbb7+NvLw8vPrqq9izZw9GjhyJxsZGOY2zXfuqVatw9913o2PHjvDw8MCIESOwevVqDBw4UE4TEhKC8PBw+bu2a1bv05XGFtfc1NSE2bNn47777sM999xj1LHO9u9tbg65dpU6Ep0/f768rXkk2r9/fzQ0NCgi1c6dOyM8PBz5+fkYMGAAgBs1GlOnTpVrNfLz85Gamqo4V3x8vBzA6KvRmDhxooWu2DgJCQkYNmwYfv75Z7z++ut47LHHsH//fnh5eQEA8vLybFxC82pqagJwo9P1tGnTAAC9evVCXl4eNmzYgIyMDADA22+/bbMyWsIdd9yBrVu3YtasWVi5ciVcXFwwadIk9O7dW7Gei7Ndt1pDQwMee+wxCCGwdu1axT5nvebmz5hu3bqhe/fu6NSpE3bv3o2hQ4cCcL5rX7VqFQ4ePIiPP/4YERER2Lt3L5KTkxEaGio/i9X3uKNKTk7GyZMnW9Q+G8LZ/r3NzSGDHF2R6HfffYfS0lJ4eHgoqq/V+5tHqo5WoxEYGAhXV9cWo2fKysqgUqnk735+fvDz80NUVBQGDBiA9u3bY9u2bZg0aZLGfFUqlc481X+WlZUp3hjKysrQs2dPc1yaTvquW12mu+++W7G/S5cuKCkp0Zqvtuv29fWFt7c3XF1dDfr7thRD/r2HDx+Os2fP4tdff4Wbmxv8/f2hUqnw+9//Xmu+9n7dhlAHOD/99BN27dqld1kWZ7hmTX7/+98jMDAQ33//vRzk3MqRr722thYLFizAtm3bMHr0aABA9+7dcezYMbz++ustmmXUtF2zep+uNNa+5pSUFOzYsQN79+5Fx44dW52fI/97W4LDNleZQ15eHlJSUmxdDIN5eHigT58+ipqYpqYm5OXlITY2VuMx4kbnctTV1WnNNzY2tkXtTm5urpxnZGQkVCqVIk1VVRUKCgq0ntec9F33nXfeidDQ0BbDL//zn/8gIiJCa776rtuUv29zMub8gYGB8Pf3x65du1BeXo4xY8Zozdfer1sfdYBz5swZfPnll+jQoYPeYxz9mrX573//i4sXLypePm7lyNfe0NCAhoaGFitNu7q6yjW4msTGxmLv3r1oaGiQt+Xm5iI6Ohrt27eX0+j6e7E0IQRSUlKwbds27Nq1C5GRkWbJ15H/vS3Clr2eTVVXVydcXV1b9MJ/4oknxJgxY0ReXp7Gnujh4eFi+fLlWvMNCwsTWVlZim0LFy4U3bt3F0IIcfbsWQFAHD16VJFm4MCB4plnnjH1cozy3nvvCU9PT7Fp0yZx+vRpkZSUJPz9/UVpaak4e/aseOWVV8SRI0fETz/9JPbv3y8efvhhERAQoBgaOGTIELFq1Sr5+/79+4Wbm5t4/fXXxbfffisWLVqkcQi5v7+/+Oijj+ShytYeQq7tuoW4MdTS19dXbN26VZw5c0akpaUJLy8vxaiyKVOmiL/+9a/yd/VQyzlz5ohvv/1WrF69WuNQS13ntfV1b9iwQeTn54vvv/9evPPOOyIgIECkpqYq8nC0675y5Yo4evSoOHr0qAAgli9fLo4ePSp++uknUV9fL8aMGSM6duwojh07phhK3XwkjaNdsyHXfuXKFfH888+L/Px8ce7cOfHll1+K3r17i6ioKMVUCY527bquWQghBg0aJLp27Sq++uor8cMPP4iNGzcKLy8vsWbNGjmPv/71r2LKlCny94qKChEcHCymTJkiTp48Kd577z3Rpk2bFkPI9T33LGnWrFnCz89P7N69W/HfcU1NjZzm559/FkePHhX/+Mc/BACxd+9ecfToUXHx4kU5jaP9e1ubQwY5QtwYApeSkiJ/b2xsFL/73e9ERkaGqKioEO7u7uL//u//5P3fffedACDy8/O15vnYY4+Jhx56SLEtNja2xVDq119/Xd5fWVlp1aHUQgixatUqER4eLjw8PET//v3FwYMHhRBC/O9//xMjR44UQUFBwt3dXXTs2FFMnjxZfPfdd4rjIyIixKJFixTbPvjgA/GHP/xBeHh4iK5du2odOh8cHCw8PT3F0KFDRXFxsUWv81barlstIyNDdOzYUbRp00bExsaKffv2KfYPGjRIJCYmKrZ99dVXomfPnsLDw0P8/ve/Fxs3bjT6vJam6/zz5s0TwcHBwt3dXURFRYnMzEzR1NSkON7Rrls9XPbWT2Jiojh37pzGfQDEV199JefhaNfcvIzarr2mpkYMHz5c3HHHHcLd3V1ERESIGTNmtPjF5GjXruuahbjxi37q1KkiNDRUeHl5iejo6Bb/nScmJopBgwYp8v3mm2/E/fffLzw9PcXvfvc7sXTp0hbn1vfcsyRt/x03/7dZtGiR3jSO9u9tbQ4b5OiLRGfOnCnCw8PFrl27xJEjR0RsbKyIjY1V5OGINRpERERkGIfseAwAjz/+OH755RcsXLgQpaWl6NmzJ3bu3Cl3Cs7KyoKLiwvGjx+Puro6xMfHY82aNYo81B021e69917k5OQgLS0NCxYsQFRUFLZv364Y0jd37lxcvXoVSUlJqKiowP3334+dO3fKI5eIiIjIPkhCCGHrQhARERGZ2209uoqIiIicF4McIiIickoMcoiIiMgpMcghIiIip8Qgh4iIiJwSgxwiIiJySg4X5KxevRp33nknvLy8EBMTg0OHDsn71q9fj8GDB8PX1xeSJKGiosKgPDdt2tRiMU8iIiJybA4V5Lz//vtITU3FokWLUFRUhB49eiA+Ph7l5eUAgJqaGowYMQILFiywcUmJiIjI1hwqyFm+fDlmzJiBadOm4e6778a6devQpk0bbNiwAQAwe/Zs/PWvf8WAAQNadZ6zZ89i7NixCA4ORtu2bdGvXz98+eWXijR33nknXnnlFTz55JNo164dwsPDsX79+ladl4iIiMzHYYKc+vp6FBYWIi4uTt7m4uKCuLg45Ofnm/Vc1dXVGDVqFPLy8nD06FGMGDECDz/8MEpKShTpMjMz0bdvXxw9ehR//vOfMWvWLBQXF5u1LERERGQahwlyfv31VzQ2NsprU6kFBwejtLTUrOfq0aMHnnrqKdxzzz2IiorCiy++iE6dOuHjjz9WpBs1ahT+/Oc/46677sK8efMQGBiIr776yqxlISIiItM4TJBjDiNHjkTbtm3Rtm1bdO3aVWu66upqPP/88+jSpQv8/f3Rtm1bfPvtty1qcrp37y7/LEkSVCqV3D+IiIiIbMthViEPDAyEq6srysrKFNvLysqgUqkMyuOf//wnamtrAQDu7u5a0z3//PPIzc3F66+/jrvuugve3t6YMGEC6uvrFeluzUOSJDQ1NRlUFiIiIrIshwlyPDw80KdPH+Tl5WHcuHEAgKamJuTl5SElJcWgPH73u98ZlG7//v2YOnUqHnnkEQA3anZ+/PFHU4pNRERENuIwQQ4ApKamIjExEX379kX//v2RnZ2Nq1evYtq0aQCA0tJSlJaW4vvvvwcAnDhxQh75FBAQYPB5oqKi8OGHH+Lhhx+GJElIT09nDQ0REZGDcagg5/HHH8cvv/yChQsXorS0FD179sTOnTvlzsjr1q3DCy+8IKcfOHAgAGDjxo2YOnWq1nybmprg5nbzr2L58uV48sknce+99yIwMBDz5s1DVVWVZS6KiIiILEISQghbF8LWli5dis2bN+PkyZO2LgoRERGZiUPV5JhbTU0NvvvuO2zcuBEjR460dXGIiIjIjG6rIeS3Wr9+PeLi4tCjRw8sXLjQ1sUhIiIiM2JzFRERETml27omh4iIiJwXgxwiIiJySg4d5GRkZKBfv35o164dgoKCMG7cuBYLZF67dg3Jycno0KED2rZti/HjxytmTf7mm28wadIkhIWFwdvbG126dMGKFStanGv37t3o3bs3PD09cdddd2HTpk2WvjwiIiJqBYcOcvbs2YPk5GQcPHgQubm5aGhowPDhw3H16lU5zXPPPYdPPvkEW7duxZ49e3DhwgX88Y9/lPcXFhYiKCgImzdvxqlTp/C3v/0N8+fPxxtvvCGnOXfuHEaPHo0HH3wQx44dw+zZs/GnP/0Jn3/+uVWvl4iIiAznVB2Pf/nlFwQFBWHPnj0YOHAgKisrcccddyAnJwcTJkwAAHz33Xfo0qUL8vPzMWDAAI35JCcn49tvv8WuXbsAAPPmzcOnn36qmEdn4sSJqKiowM6dOy1/YURERGQ0h67JuVVlZSUAyEs4FBYWoqGhAXFxcXKazp07Izw8HPn5+Trzab4MRH5+viIPAIiPj9eZBxEREdmW00wG2NTUhNmzZ+O+++7DPffcA+DGWlYeHh7w9/dXpA0ODkZpaanGfA4cOID3338fn376qbyttLRUXjqieR5VVVWora2Ft7e3eS+GiIiIWs1pgpzk5GScPHkSX3/9tcl5nDx5EmPHjsWiRYswfPhwM5aOiIiIrM0pmqtSUlKwY8cOfPXVV+jYsaO8XaVSob6+HhUVFYr0ZWVlUKlUim2nT5/G0KFDkZSUhLS0NMU+lUqlGJGlzsPX15e1OERERHbKoYMcIQRSUlKwbds27Nq1C5GRkYr9ffr0gbu7O/Ly8uRtxcXFKCkpQWxsrLzt1KlTePDBB5GYmIiXX365xXliY2MVeQBAbm6uIg8iIiKyLw49uurPf/4zcnJy8NFHHyE6Olre7ufnJ9ewzJo1C5999hk2bdoEX19fPP300wBu9L0BbjRRDRkyBPHx8XjttdfkPFxdXXHHHXcAuDGE/J577kFycjKefPJJ7Nq1C8888ww+/fRTxMfHW+tyiYiIyAgOHeRIkqRx+8aNGzF16lQANyYD/Mtf/oJ3330XdXV1iI+Px5o1a+TmqsWLF+OFF15okUdERAR+/PFH+fvu3bvx3HPP4fTp0+jYsSPS09PlcxAREZH9ceggh4iIiEgbh+6TQ0RERKQNgxwiIiJySgxyiIiIyCkxyCEiIiKnxCCHiIiInBKDHCIiInJKDHKIiIjIKTHIISIiIqfEIIeIiIicEoMcIiIickoMcoiIiMgp/X/ZMv806akZLgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "first_day = pd.date_range(\n",
-    "    start=\"2020-01-01 00:00:00\", freq=\"1min\", periods=24 * 60  # a full day\n",
-    ")\n",
-    "sensitivity_results.loc[first_day].plot()"
+    "As it can be observed, the power consumption of hot water supply technology varies across different days of the year, primarily due to fluctuations in the nominal power. When adjusting the parameter **thermal_p_var** from 0, indicating no variability in power consumption due to user preferences, to higher values, which signify the probability of changes in hot water temperature, the power consumption also varies accordingly."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da74d4b3-1dfb-460e-836a-a383e40265a6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -449,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks_convert.py b/docs/notebooks_convert.py
index 60a491c1..879ec4c4 100644
--- a/docs/notebooks_convert.py
+++ b/docs/notebooks_convert.py
@@ -1,13 +1,8 @@
 from nbconvert import RSTExporter
-from nbconvert.writers import FilesWriter
 import nbformat
 import os
 
-path = os.path.abspath(
-    os.path.join(
-        os.path.dirname(__file__),
-    )
-)
+DOC_PATH = os.path.abspath(os.path.dirname(__file__))
 
 
 def convert_notebook_to_rst(input_file, output_dir, file_name):
@@ -26,6 +21,9 @@ def convert_notebook_to_rst(input_file, output_dir, file_name):
 
     output_file = os.path.join(output_dir, f"{file_name}.rst")
 
+    # Add a link to the jupyter notebook file to the documentation
+    rst_content += f"\n:download:`Link to the jupyter notebook file </../notebooks/{file_name}.ipynb>`.\n"
+
     # Write RST content to the output file
     with open(output_file, "w", encoding="utf-8") as f:
         f.write(rst_content)
@@ -33,26 +31,32 @@ def convert_notebook_to_rst(input_file, output_dir, file_name):
     print(f"Conversion successful. RST file saved to: {output_file}")
 
     # Copy images to the output folder
-    image_dir = output_dir
     for image_filename, image_data in resources["outputs"].items():
         image_path = os.path.join(output_dir, image_filename)
         with open(image_path, "wb") as img_file:
             img_file.write(image_data)
 
 
-def get_all_ipynb_files(examples_path="notebooks"):
-    all_files = os.listdir(examples_path)
-    return [
-        (os.path.join(path, examples_path, file), file.split(".ipynb")[0])
-        for file in all_files
+def get_all_ipynb_files(doc_path=DOC_PATH, examples_path="notebooks"):
+    notebooks_path = os.path.join(doc_path, examples_path)
+    notebooks = [
+        (os.path.join(notebooks_path, file), file.split(".ipynb")[0])
+        for file in os.listdir(notebooks_path)
         if file.endswith("ipynb")
     ]
+    return notebooks
 
 
-if __name__ == "__main__":
-    files = get_all_ipynb_files()
+def update_notebooks_rst_files(doc_path=DOC_PATH):
+    files = get_all_ipynb_files(doc_path)
 
     for file, folder in files:
         convert_notebook_to_rst(
-            file, os.path.join(path, "source", "examples", folder), folder
+            file,
+            output_dir=os.path.join(doc_path, "source", "examples", folder),
+            file_name=folder,
         )
+
+
+if __name__ == "__main__":
+    update_notebooks_rst_files()
diff --git a/docs/source/api_references.rst b/docs/source/api_references.rst
index c19abb51..ffc68c09 100644
--- a/docs/source/api_references.rst
+++ b/docs/source/api_references.rst
@@ -76,6 +76,7 @@ Utilities
 
     load_data
     download_example
+    ramp_py2xlsx
 
 
 *************
diff --git a/docs/source/conf.py b/docs/source/conf.py
index b65416c9..1a2df393 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -12,20 +12,51 @@
 #
 import os
 import sys
-import sphinx_pdj_theme
+
 
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath(".."))
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../.."))
 
+from notebooks_convert import update_notebooks_rst_files
+
+update_notebooks_rst_files()
+
+
+def copy_readme():
+    with open("../../README.rst", "r", encoding="utf8") as fp:
+        data = fp.readlines()
+
+    # Replace the reference to contributing guidelines with an internal link
+    idx = data.index(
+        "To contribute changes please consult our `Contribution guidelines <https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md>`_\n"
+    )
+    data[idx] = (
+        "To contribute changes please consult our `Contribution guidelines <contributing.html>`_\n"
+    )
+    with open("readme.rst", "w") as fp:
+        fp.writelines(data)
+
+
+def copy_contributing():
+    with open("../../CONTRIBUTING.md", "r", encoding="utf8") as fp:
+        data = fp.readlines()
+
+    # Change the title of the file
+    data[0] = "# Contribute\n"
+    with open("contributing.md", "w") as fp:
+        fp.writelines(data)
+
+
+copy_readme()
+copy_contributing()
 # -- Project information -----------------------------------------------------
 
 project = "RAMP"
 copyright = "2022, Author List"
 author = "Author List"
 
-
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
@@ -43,8 +74,17 @@
     "sphinx.ext.doctest",
     "sphinx_copybutton",
     "sphinx.ext.autosectionlabel",
+    "sphinx_wagtail_theme",
+    "sphinx_copybutton",
+    "myst_parser",
 ]
 
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".txt": "markdown",
+    ".md": "markdown",
+}
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -59,8 +99,12 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-extensions.append("sphinxjp.themes.basicstrap")
-html_theme = "basicstrap"
+
+
+html_theme = "sphinx_wagtail_theme"
+html_theme_options = dict(
+    project_name="RAMP Documentation",
+)
 # html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
diff --git a/docs/source/examples/cooking_app/cooking_app.rst b/docs/source/examples/cooking_app/cooking_app.rst
index 670a09c1..530dba18 100644
--- a/docs/source/examples/cooking_app/cooking_app.rst
+++ b/docs/source/examples/cooking_app/cooking_app.rst
@@ -1,8 +1,13 @@
 Cooking Appliances
 ==================
 
-In this example, appliances with multiple preferences index and
-attributes are modelled.
+Some appliances load profiles, highly depend on the user choices and
+preferences. For example, electric stoves power usage, highly depends on
+the type of food and that a user wants to cook. This kind of appliances
+in RAMP are flagged by the user category consumption preferences.
+
+In this example, we will see how the electric cookstoves with multiple
+user preferences can be modelled in RAMP.
 
 To have a better understanding of RAMP features for modelling this
 category of appliances, two households are considered:
@@ -16,8 +21,9 @@ The number of user preferences can be specified through
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user category
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -27,18 +33,22 @@ Creating a user category
     user_1 = User(
         user_name="Household with single lunch habit",
         num_users=1,
-        user_preference=1,  # user_1 has only one lunch preference
+        user_preference=1,  # user_1 has only one consumption preference
     )
     
     user_2 = User(
         user_name="Household with different lunch habits",
         num_users=1,
-        user_preference=2,  # user_2 has two lunch preferences
+        user_preference=2,  # user_2 has two consumption preferences
     )
 
 Defining the cycles for cooking soup and rice
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+For cookstoves, it will be more realistic to have different operation
+cycles as cooking a food usually requires different levels of heat for
+the different parts of food processing:
+
 For cooking soup it is assumed that the user needs 25 minutes divided
 into two parts:
 
@@ -62,19 +72,20 @@ Cooking rice  600   10
 .. code:: ipython3
 
     # soup for lunch
+    lunch_window = [12 * 60, 12 * 60 + 26]
+    
     soup_1 = user_1.add_appliance(
         name="soup for lunch",
-        power=1200,
-        func_time=25,
-        func_cycle=25,
-        thermal_p_var=0.2,
-        fixed_cycle=1,
-        window_1=[12 * 60, 15 * 60],
+        power=1200,  # nominal power of appliance
+        func_time=25,  # the cooking time
+        func_cycle=25,  # we always need  25 minute for cooking
+        fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier
+        window_1=lunch_window,  # lunch is always prepared from 12
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=750,  # power of the second cycle
         t_12=20,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window,  # the time window of the working cycle
     )
 
 The second user has two different preferences for lunch. Accordingly, we
@@ -98,15 +109,14 @@ rice is the second one (pref_index = 2).
         power=1200,
         func_time=25,
         func_cycle=25,
-        thermal_p_var=0.2,
         fixed_cycle=1,
-        pref_index=1,  # the first preference
-        window_1=[12 * 60, 15 * 60],
+        window_1=lunch_window,
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=750,  # power of the second cycle
         t_12=20,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window,
+        pref_index=1,  # the first preference
     )
 
 .. code:: ipython3
@@ -117,419 +127,60 @@ rice is the second one (pref_index = 2).
         power=1200,
         func_time=15,
         func_cycle=15,
-        thermal_p_var=0.2,
-        pref_index=2,  # the second preference
         fixed_cycle=1,
-        window_1=[12 * 60, 15 * 60],
+        window_1=lunch_window,
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=600,  # power of the second cycle
         t_12=10,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window,
+        pref_index=2,  # the second preference
     )
 
 .. code:: ipython3
 
-    # you can have an overview of data inputs by usering User.export_to_dataframe method
-    user_lunch = UseCase(users=[user_1, user_2], date_start="2020-01-01")
-    user_lunch.export_to_dataframe().T
-
+    number_of_days = 5
+    user_1_profiles = {}
+    user_2_profiles = {}
+    for day in range(1, number_of_days + 1):
+        user_1_profiles[f"day {day}"] = pd.Series(user_1.generate_single_load_profile())
+        user_2_profiles[f"day {day}"] = pd.Series(user_2.generate_single_load_profile())
 
 
+.. parsed-literal::
 
-.. raw:: html
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
 
-    <div>
-    <style scoped>
-        .dataframe tbody tr th:only-of-type {
-            vertical-align: middle;
-        }
-    
-        .dataframe tbody tr th {
-            vertical-align: top;
-        }
-    
-        .dataframe thead th {
-            text-align: right;
-        }
-    </style>
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>0</th>
-          <th>1</th>
-          <th>2</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>user_name</th>
-          <td>Household with single lunch habit</td>
-          <td>Household with different lunch habit</td>
-          <td>Household with different lunch habit</td>
-        </tr>
-        <tr>
-          <th>num_users</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>user_preference</th>
-          <td>1</td>
-          <td>2</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>name</th>
-          <td>soup for lunch</td>
-          <td>soup for lunch</td>
-          <td>rice for lunch</td>
-        </tr>
-        <tr>
-          <th>number</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>power</th>
-          <td>1200.0</td>
-          <td>1200.0</td>
-          <td>1200.0</td>
-        </tr>
-        <tr>
-          <th>num_windows</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>func_time</th>
-          <td>25</td>
-          <td>25</td>
-          <td>15</td>
-        </tr>
-        <tr>
-          <th>time_fraction_random_variability</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>func_cycle</th>
-          <td>25</td>
-          <td>25</td>
-          <td>15</td>
-        </tr>
-        <tr>
-          <th>fixed</th>
-          <td>no</td>
-          <td>no</td>
-          <td>no</td>
-        </tr>
-        <tr>
-          <th>fixed_cycle</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>occasional_use</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>flat</th>
-          <td>no</td>
-          <td>no</td>
-          <td>no</td>
-        </tr>
-        <tr>
-          <th>thermal_p_var</th>
-          <td>0.2</td>
-          <td>0.2</td>
-          <td>0.2</td>
-        </tr>
-        <tr>
-          <th>pref_index</th>
-          <td>0</td>
-          <td>1</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>wd_we_type</th>
-          <td>2</td>
-          <td>2</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>p_11</th>
-          <td>1200</td>
-          <td>1200</td>
-          <td>1200</td>
-        </tr>
-        <tr>
-          <th>t_11</th>
-          <td>5</td>
-          <td>5</td>
-          <td>5</td>
-        </tr>
-        <tr>
-          <th>cw11_start</th>
-          <td>720</td>
-          <td>720</td>
-          <td>720</td>
-        </tr>
-        <tr>
-          <th>cw11_end</th>
-          <td>900</td>
-          <td>900</td>
-          <td>900</td>
-        </tr>
-        <tr>
-          <th>p_12</th>
-          <td>750</td>
-          <td>750</td>
-          <td>600</td>
-        </tr>
-        <tr>
-          <th>t_12</th>
-          <td>20</td>
-          <td>20</td>
-          <td>10</td>
-        </tr>
-        <tr>
-          <th>cw12_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw12_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c1</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_21</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_21</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw21_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw21_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_22</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_22</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw22_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw22_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c2</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_31</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_31</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw31_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw31_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_32</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_32</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw32_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw32_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c3</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_1_start</th>
-          <td>720</td>
-          <td>720</td>
-          <td>720</td>
-        </tr>
-        <tr>
-          <th>window_1_end</th>
-          <td>900</td>
-          <td>900</td>
-          <td>900</td>
-        </tr>
-        <tr>
-          <th>window_2_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_2_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_3_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_3_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>random_var_w</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-      </tbody>
-    </table>
-    </div>
-
-
-
-Generating a profile for some months
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    # number of days
-    n_days = 90
-    
-    user_lunch.initialize(num_days=n_days)
-    # storing all the profiles for all the users
-    profiles = pd.DataFrame(
-        index=pd.date_range(start="2020-01-01", periods=1440 * n_days, freq="T")
-    )
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
     
-    # here we need to use the
     
-    for user in user_lunch.users:
-        # storing daily profiles for a user
-        user_profiles = []
-        for day_idx, day in enumerate(user_lunch.days):
-            single_profile = user.generate_single_load_profile(
-                prof_i=day_idx,  # the day to generate the profile
-                day_type=get_day_type(day),
-            )
+    i = 0
+    for name, df in dict(
+        user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[
+            lunch_window[0] - 5 : lunch_window[1] + 5
+        ],  # take only the lunch window
+        user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[
+            lunch_window[0] - 5 : lunch_window[1] + 5
+        ],  # take only the lunch window
+    ).items():
+        df.plot(ax=axes[i], legend=True)
+        axes[i].set_title(name)
+        i += 1
     
-            user_profiles.extend(single_profile)
-    
-        profiles[user.user_name] = user_profiles
-
-
-.. parsed-literal::
-
-    You will simulate 90 day(s) from 2020-01-01 00:00:00 until 2020-03-31 00:00:00
-
-
-Considering that the second user has the possibility of cooking rice for
-lunch, which has a less energy-intensive cooking cycle, we expect to see
-a higher energy consumption for the user that only eats soup, in most of
-the cases.
-
-.. code:: ipython3
-
-    # daily energy consumption
-    profiles.resample("1d").sum().plot(title="daily energy consumption")
-
-
-
-
-.. parsed-literal::
-
-    <Axes: title={'center': 'daily energy consumption'}>
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_10_0.png
 
-.. image:: output_13_1.png
 
+As it can be observed, user_1 always have the same demand profile for
+lunch prepration while user_2 can have two different profiles (for
+example on day 3 and 4, the user cooks rice for lunch)!
 
+:download:`Link to the jupyter notebook file </../notebooks/cooking_app.ipynb>`.
diff --git a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
index 12f01860..2a9d1cfd 100644
--- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
+++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
@@ -1,11 +1,26 @@
 Fixed-Flat Appliance
 ====================
 
+Some appliances exhibit deterministic consumption patterns, such as
+security lights used for safety purposes, which often adhere to specific
+schedules. In RAMP, such appliances are categorized as **flat
+appliances** indicating they lack random variability in total usage
+time.
+
+When multiple appliances of the same type are consistently switched on
+together, they can be attributed with the ‘fixed’ flag in their
+definition. This synchronizes the switch-on and switch-off events for
+appliances of the same type.
+
+For instance, let’s model the security lights of a school to illustrate
+these characteristics:
+
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user
 ~~~~~~~~~~~~~~~
@@ -19,29 +34,34 @@ Adding an appliance with flat and fixed consumption
 
 .. code:: ipython3
 
-    indoor_bulb = school.add_appliance(
-        name="Indoor Light Bulb",
+    security_lights = school.add_appliance(
+        name="Security Light Bulb",
         number=10,
         power=25,
         num_windows=1,
         func_time=210,
-        time_fraction_random_variability=0.2,
         func_cycle=60,
-        fixed="yes", # This means all the 'n' appliances of this kind are always switched-on together
-        flat="yes", # This means the appliance is not subject to random variability in terms of total usage time
-    )
-    indoor_bulb.windows(
+        fixed="yes",  # This means all the 'n' appliances of this kind are always switched-on together
+        flat="yes",  # This means the appliance is not subject to random variability in terms of total usage time
         window_1=[1200, 1440],  # from 20:00 to 24:00
-        window_2=[0, 0],
-        random_var_w=0.35,
     )
 
-Initialize the usecase (it defines the peak time range and simulation time)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Generating the profiles using UseCase class
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Similar to previous example, you can generate the load profiles using
+the UseCase class. In this example, we use another functionality of the
+UseCase class by identifying the starting date of the simulation and the
+number of days for generating the profiles:
 
 .. code:: ipython3
 
-    school_case = UseCase(users=[school], date_start="2023-01-01")
+    school_case = UseCase(
+        users=[school],  # users of the usecase
+        date_start="2023-01-01",  # start date
+    )
+    
+    # when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days
     school_case.initialize(num_days=7)
 
 
@@ -50,45 +70,90 @@ Initialize the usecase (it defines the peak time range and simulation time)
     You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00
 
 
-Generating a profile for the 1st week of the year
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: ipython3
 
-From the usecase directly
+    fixed_flat = school_case.generate_daily_load_profiles()
+    fixed_flat = pd.DataFrame(fixed_flat, columns=["fixed_flat"])
+    fixed_flat.plot()
 
-.. code:: ipython3
 
-    first_week = school_case.generate_daily_load_profiles(flat=True)
 
-or from the user
 
-.. code:: ipython3
+.. parsed-literal::
 
-    first_week = []
-    
-    for day_idx, day in enumerate(school_case.days):
-        first_week.extend(
-            school.generate_single_load_profile(
-                prof_i=day_idx,  # the day to generate the profile
-                peak_time_range=school_case.peak_time_range,
-                day_type=get_day_type(day),
-            )
-        )
+    <Axes: >
 
-.. code:: ipython3
 
-    first_week = pd.DataFrame(first_week, columns=["household"])
-    first_week.plot()
 
 
+.. image:: output_8_1.png
 
 
-.. parsed-literal::
+As it can be seen, in each day, all the 10 lights with 25 Watt power
+(aggregated power of 250) are always switched on together on the
+specified schedule.
 
-    <Axes: >
+To clarify the impact of parameters, let’s examine how the profiles
+would vary under different combinations:
+
+1. no_fixed_no_flat consumption: when appliances are not synchronized
+   and they can randomly switch on during the window time
+
+2. fixed_no_flat consumption: when appliances are synchronized and they
+   can randomly switch on during the window time
+
+3. fixed_flat consumption (original example): when appliances are
+   synchronized and they are always switched on during the window time
+
+4. no_fixed_flat consumption: when appliances are not synchronized but
+   they are always switched on during the window time
+
+By observing the load profiles generated under these different
+combinations, we can gain insights into how each parameter influences
+overall energy consumption and demand patterns within simulation.
+
+.. code:: ipython3
+
+    # no fixed and no flat property
+    security_lights.fixed = "no"
+    security_lights.flat = "no"
+    no_fix_no_flat = school_case.generate_daily_load_profiles()
+    no_fix_no_flat = pd.DataFrame(no_fix_no_flat, columns=["no_fix_no_flat"])
+    
+    # not flat but fixed
+    security_lights.fixed = "yes"
+    fix_no_flat = school_case.generate_daily_load_profiles()
+    fix_no_flat = pd.DataFrame(fix_no_flat, columns=["fix_no_flat"])
+    
+    # not fixed but flat
+    security_lights.fixed = "no"
+    security_lights.flat = "yes"
+    no_fixed_flat = school_case.generate_daily_load_profiles()
+    no_fixed_flat = pd.DataFrame(no_fixed_flat, columns=["no_fixed_flat"])
+
+.. code:: ipython3
+
+    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))
+    
+    
+    for i, df in enumerate([no_fix_no_flat, fix_no_flat, fixed_flat, no_fixed_flat]):
+        df.plot(ax=axes[i], legend=False)
+        axes[i].set_title(df.columns[0])
+    
+    
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_12_0.png
 
-.. image:: output_12_1.png
 
+As it can be seen, in case of no flat and no fix, both scheduling of
+appliances and the switched on events of appliances are randomized.
+While when appliances are fixed, not flat, their scheduling is
+randomized keeping their switch on events synced as it can be observed
+that power usage is always kept at 250 Watt with more intermittent
+swithced on/off events.
 
+:download:`Link to the jupyter notebook file </../notebooks/fixed_flat_app.ipynb>`.
diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst
index f03e52e0..d281b3f6 100644
--- a/docs/source/examples/multi_cycle/multi_cycle.rst
+++ b/docs/source/examples/multi_cycle/multi_cycle.rst
@@ -1,14 +1,32 @@
-Appliances with multiple cycles
-===============================
-
-An example of an appliance with multiple cycle is a fridge. Fridges
-usually have different duty cycles, which can be estimated based on
-seasonal temperature trends and/or frequency of user interaction (e.g.,
-how often the door gets opened).
-
-In this example a fridge with 3 different duty cycles is modelled. The
-time windows are defined for 3 different cycles across 3 different
-season types:
+Appliances with duty cycles
+===========================
+
+Some appliances may operate based on fixed duty cycle. For instance, you
+may think of a dishwasher that follows a pre-determined program. This
+can be modelled by means of the ``fixed_cycle`` parameter, which allows
+to define a duty cycle consisting of up to two temporal operation
+segments with distinct power consumption levels.
+
+Furthermore, we acknowledge that some of the appliances that can operate
+based on duty cucles may still follow multiple, different duty cycles
+throughout a day or across seasons. An example of an appliance
+characterised by variable duty cycles is a fridge. Fridges tend to
+follow different duty cycles throughout the day, depending on the
+ambient temperature (which also varies across seasons) and/or frequency
+of user interaction (e.g., how often the door gets opened). Accordingly,
+RAMP allows modelling up to 3 different duty cycles per appliance,
+giving room for modulating the appliance’s behaviour depending on any
+given factor of interest.
+
+In this example, we model a fridge with 3 different duty cycles, which
+are representative of its operation during a ‘hot’ season. The time
+windows are defined for 3 different cycles throughout the day,
+corresponding to a different temperature and frequency of user
+interaction. For instance, between 08:00 and 20:00, we assume that the
+fridge operates more intensely due to the combined effects of
+temperature and frequent use. In the early morning, it operates at an
+intermediate intensity, and during the night, it operates based on its
+standard cycle.
 
 +--------+------------------------------+--------------+--------------+
 | season | Standard cycle               | Intermediate | Intensive    |
@@ -17,12 +35,6 @@ season types:
 | Hot    | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 08:00:00 -   |
 |        | 20:01:00 - 23:59:00          | 07:59:00     | 20:00:00     |
 +--------+------------------------------+--------------+--------------+
-| Warm   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 09:40:00 -   |
-|        | 18:01:00 - 23:59:00          | 09:39:00     | 18:00:00     |
-+--------+------------------------------+--------------+--------------+
-| Cold   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | -            |
-|        | 20:01:00 - 23:59:00          | 20:00:00     |              |
-+--------+------------------------------+--------------+--------------+
 
 Creating the user and appliance
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -41,7 +53,7 @@ Creating the user and appliance
 .. code:: ipython3
 
     # creating the appliance
-    fridge = household.Appliance(
+    fridge = household.add_appliance(
         name="Fridge",
         number=1,
         power=200,
@@ -56,49 +68,53 @@ Creating the user and appliance
 .. code:: ipython3
 
     # setting the functioning windows
-    fridge.windows([0, 1440])  # always on during the whole year
+    fridge.windows([0, 1440])  # always on during the whole day, for all days of the year
+
+Characterising the specific cycles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Assigining the specific cycles
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+As anticipated above, each cycle is characterised by two operation
+segments at different power levels. They can be defined as per the code
+below. In addition, it is possible to apply a random variability
+(``r_c1``, ``r_c2``, ``r_c3``, one for each specific cycle) to the exact
+duration of the two segments (see the API documentation). In this
+example, we do not apply such variability.
 
 .. code:: ipython3
 
     # assiging the specific cycles
     # first cycle: standard cycle
     fridge.specific_cycle_1(
-        p_11=200,
-        t_11=20,
-        p_12=5,
-        t_12=10,
+        p_11=200,  # power level for the first operation segment
+        t_11=10,  # duration of the first operation segment
+        p_12=5,  # power level for the second operation segment
+        t_12=20,  # duration of the second operation segment
+        r_c1=0,  # random variability assigned to the duration of each segment
     )
     
     # second cycle: intermediate cycle
-    fridge.specific_cycle_2(
-        p_21=200,
-        t_21=15,
-        p_22=5,
-        t_22=15,
-    )
+    fridge.specific_cycle_2(p_21=200, t_21=15, p_22=5, t_22=15, r_c2=0)
     
     # third cycle: intensive cycle
-    fridge.specific_cycle_3(
-        p_31=200,
-        t_31=10,
-        p_32=5,
-        t_32=20,
-    )
+    fridge.specific_cycle_3(p_31=200, t_31=20, p_32=5, t_32=10, r_c3=0)
 
 After defining the cycle power and duration parameters, the time windows
-of year at which the cycles happens should be specifid by:
+within a day at which the cycles occur should be specified by means of
+the ‘cycle window’ (``cw``). In fact, up to two time windows within a
+day can be specified for each of the 3 cycles. For instance, according
+to the table discussed earlier, for the standard ``specific_cycle_1`` we
+may define an occurrence in the early morning (``cw11`` in the code
+below) as well as one in the late evening (``cw12``).The windows defined
+across all cycles should not overlap.
 
 .. code:: ipython3
 
     # defining cycle behaviour
     fridge.cycle_behaviour(
-        cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]
+        cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]
     )
 
-Buidling the profiles
+Building the profiles
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
@@ -110,9 +126,7 @@ Buidling the profiles
 
     # days to build the profiles
     days = [
-        "2020-05-16",
-        "2020-08-16",
-        "2020-12-16",
+        "2020-12-16",  # a day in the 'Hot' season, assuming a location in the Southern hemisphere
     ]
     
     profiles = pd.DataFrame(index=range(0, 1440), columns=days)
@@ -133,36 +147,39 @@ Buidling the profiles
 
 .. parsed-literal::
 
-          2020-05-16  2020-08-16  2020-12-16
-    0          0.001       5.000       0.001
-    1          0.001       5.000       0.001
-    2          0.001       5.000       0.001
-    3          5.000       5.000       0.001
-    4          5.000       5.000       0.001
-    ...          ...         ...         ...
-    1435       0.001       0.001       0.001
-    1436       0.001       0.001       0.001
-    1437       0.001       0.001       0.001
-    1438       0.001       0.001       0.001
-    1439       0.001       0.001       0.001
+          2020-12-16
+    0          0.001
+    1          0.001
+    2          0.001
+    3          0.001
+    4          5.000
+    ...          ...
+    1435       0.001
+    1436       0.001
+    1437       0.001
+    1438       0.001
+    1439       0.001
     
-    [1440 rows x 3 columns]
+    [1440 rows x 1 columns]
 
 
 .. code:: ipython3
 
-    # plotting a part of the days
-    profiles.iloc[400:500].plot()
+    # plotting the whole day
+    profiles.plot()
 
 
 
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
 
 .. image:: output_15_1.png
 
+
+
+:download:`Link to the jupyter notebook file </../notebooks/multi_cycle.ipynb>`.
diff --git a/docs/source/examples/multi_cycle/output_15_1.png b/docs/source/examples/multi_cycle/output_15_1.png
index e74e5e89..0437a4a3 100644
Binary files a/docs/source/examples/multi_cycle/output_15_1.png and b/docs/source/examples/multi_cycle/output_15_1.png differ
diff --git a/docs/source/examples/occasional_use/occasional_use.rst b/docs/source/examples/occasional_use/occasional_use.rst
index ad9be5a6..9b17d129 100644
--- a/docs/source/examples/occasional_use/occasional_use.rst
+++ b/docs/source/examples/occasional_use/occasional_use.rst
@@ -8,8 +8,8 @@ iron, a stereo, printers, etc.
 Within RAMP, the user may specify the probability of using an appliance
 on the daily mix with a parameter called **occasional_use**.
 
-When ``occasional_use = 0``, the appliance is always present in the mix,
-and when ``occasional_use = 1``, the appliance is never present. Any
+When ``occasional_use = 1``, the appliance is always present in the mix,
+and when ``occasional_use = 0``, the appliance is never present. Any
 in-between values will lead to a probabilistic calculation to decide
 whether the appliance is used or not on a given day.
 
@@ -20,7 +20,8 @@ occasionally \* A school that uses the computer every day
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
+    import matplotlib.pyplot as plt
     import pandas as pd
 
 Creating user categories and appliances
@@ -38,9 +39,10 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210,
+        func_time=210,  # 3.5 hours
+        func_cycle=210,
         occasional_use=0.5,  # 50% chance of occasional use,
-        window_1=[510, 750],
+        window_1=[480, 750],  # start from 8AM
     )
 
 .. code:: ipython3
@@ -50,92 +52,62 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210,
-        time_fraction_random_variability=0.2,
-        func_cycle=10,
+        func_time=210,  # 3.5 hours
+        func_cycle=210,
         occasional_use=1,  # always present in the mix of appliances,
-        window_1=[510, 750],
+        window_1=[480, 750],  # start from 8AM
     )
 
-.. code:: ipython3
-
-    # Checking the maximum profile of the two appliances
-    
-    max_profile_c1 = pd.DataFrame(computer_0.maximum_profile, columns=[computer_0.name])
-    max_profile_c2 = pd.DataFrame(computer_1.maximum_profile, columns=[computer_1.name])
-    
-    max_profile_c1.plot()
-    max_profile_c2.plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
-
-
-
-
-.. image:: output_6_1.png
-
-
-
-.. image:: output_6_2.png
-
-
 Generating profiles
 ~~~~~~~~~~~~~~~~~~~
 
+As the profiles of each specific User category is important, we will use
+the User object profile genertor methods for 5 consecutive days:
+
 .. code:: ipython3
 
-    use_case = UseCase(users=[household, school])
-    use_case.initialize(5)
+    number_of_days = 5
+    household_profiles = []
+    school_profiles = []
+    
+    for day in range(1, number_of_days + 1):
+        household_profiles.extend(household.generate_single_load_profile(prof_i=day))
+    
+        school_profiles.extend(school.generate_single_load_profile(prof_i=day))
 
 
 .. parsed-literal::
 
-    You will simulate 5 day(s) from 2023-12-01 00:00:00 until 2023-12-06 00:00:00
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
 
 
 .. code:: ipython3
 
-    for day_idx, day in enumerate(use_case.days):
-        household_profile = household.generate_single_load_profile(
-            prof_i=day_idx, day_type=get_day_type(day)
-        )
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
     
-        school_profile = school.generate_single_load_profile(
-            prof_i=day_idx, day_type=get_day_type(day)
-        )
     
-        pd.DataFrame(
-            data=[household_profile, school_profile],
-            columns=range(1440),
-            index=[household.user_name, school.user_name],
-        ).T.plot(title=f"day - {day}")
-
-
-
-.. image:: output_9_0.png
-
-
-
-.. image:: output_9_1.png
-
-
-
-.. image:: output_9_2.png
-
-
-
-.. image:: output_9_3.png
+    i = 0
+    for name, df in dict(
+        household_profiles=pd.DataFrame(household_profiles),
+        school_profiles=pd.DataFrame(school_profiles),
+    ).items():
+        df.plot(ax=axes[i], legend=False)
+        axes[i].set_title(name)
+        i += 1
+    
+    plt.tight_layout()
+    plt.show()
 
 
 
-.. image:: output_9_4.png
+.. image:: output_8_0.png
 
 
 As it can be seen from the figures, the computer is always present in
 the school’s appliance mix while, for the household, it is only
 occasionally present.
+
+
+
+:download:`Link to the jupyter notebook file </../notebooks/occasional_use.ipynb>`.
diff --git a/docs/source/examples/parallel_processing/parallel_processing.rst b/docs/source/examples/parallel_processing/parallel_processing.rst
index 43571111..5c9d1ad9 100644
--- a/docs/source/examples/parallel_processing/parallel_processing.rst
+++ b/docs/source/examples/parallel_processing/parallel_processing.rst
@@ -154,3 +154,5 @@ You can also set the ``parallel_processing`` attribute of the
 .. image:: output_10_2.png
 
 
+
+:download:`Link to the jupyter notebook file </../notebooks/parallel_processing.ipynb>`.
diff --git a/docs/source/examples/plot_class/plot_class.rst b/docs/source/examples/plot_class/plot_class.rst
index 371b1688..f9676060 100644
--- a/docs/source/examples/plot_class/plot_class.rst
+++ b/docs/source/examples/plot_class/plot_class.rst
@@ -366,12 +366,12 @@ functions:
     2022-01-01 01:00:00  36.000000
     2022-01-01 02:00:00  36.000000
     2022-01-01 03:00:00  36.000000
-    2022-01-01 04:00:00  36.000008
-    2022-01-01 05:00:00  36.053523
-    2022-01-01 06:00:00  37.023682
-    2022-01-01 07:00:00   2.744088
-    2022-01-01 08:00:00   9.889124
-    2022-01-01 09:00:00  14.973023
+    2022-01-01 04:00:00  36.000009
+    2022-01-01 05:00:00  37.515430
+    2022-01-01 06:00:00  44.341832
+    2022-01-01 07:00:00   3.040070
+    2022-01-01 08:00:00   1.646964
+    2022-01-01 09:00:00   6.791291
      ......
 
 
@@ -391,12 +391,12 @@ functions:
     2022-01-01 01:00:00  396.000000
     2022-01-01 02:00:00  396.000000
     2022-01-01 03:00:00  396.000000
-    2022-01-01 04:00:00  396.000083
-    2022-01-01 05:00:00  396.588750
-    2022-01-01 06:00:00  407.260500
-    2022-01-01 07:00:00   30.184967
-    2022-01-01 08:00:00  108.780367
-    2022-01-01 09:00:00  164.703250
+    2022-01-01 04:00:00  396.000100
+    2022-01-01 05:00:00  412.669733
+    2022-01-01 06:00:00  487.760150
+    2022-01-01 07:00:00   33.440767
+    2022-01-01 08:00:00   18.116600
+    2022-01-01 09:00:00   74.704200
      ......
 
 
@@ -417,27 +417,27 @@ peak value for each column of your data using the ``peak`` property:
 
 .. parsed-literal::
 
-    {'case 1': 2022-10-28 06:00:00    146.00145
+    {'case 1': 2022-08-16 21:00:00    184.502267
      Freq: H, Name: case 1, dtype: float64,
-     'case 2': 2022-01-28 10:00:00    149.585283
+     'case 2': 2022-04-23 12:00:00    170.418517
      Freq: H, Name: case 2, dtype: float64,
-     'case 3': 2022-08-26 21:00:00    164.669633
+     'case 3': 2022-09-07 21:00:00    166.00135
      Freq: H, Name: case 3, dtype: float64,
-     'case 4': 2022-07-29 21:00:00    172.667983
+     'case 4': 2022-03-26 22:00:00    153.8342
      Freq: H, Name: case 4, dtype: float64,
-     'case 5': 2022-07-04 06:00:00    166.000917
+     'case 5': 2022-05-09 06:00:00    172.667167
      Freq: H, Name: case 5, dtype: float64,
-     'case 6': 2022-05-14 06:00:00    162.667917
+     'case 6': 2022-06-29 06:00:00    166.000667
      Freq: H, Name: case 6, dtype: float64,
-     'case 7': 2022-03-13 06:00:00    151.002083
+     'case 7': 2022-09-04 18:00:00    160.169433
      Freq: H, Name: case 7, dtype: float64,
-     'case 8': 2022-12-16 05:00:00    169.334517
+     'case 8': 2022-03-17 06:00:00    162.666883
      Freq: H, Name: case 8, dtype: float64,
-     'case 9': 2022-06-15 18:00:00    149.836033
+     'case 9': 2022-10-20 06:00:00    152.667083
      Freq: H, Name: case 9, dtype: float64,
-     'case 10': 2022-02-15 06:00:00    187.00175
+     'case 10': 2022-10-02 21:00:00    156.0024
      Freq: H, Name: case 10, dtype: float64,
-     'case 11': 2022-10-21 06:00:00    166.00235
+     'case 11': 2022-12-27 06:00:00    158.917483
      Freq: H, Name: case 11, dtype: float64}
 
 
@@ -469,7 +469,7 @@ your data to plot a simple line plot:
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -483,7 +483,34 @@ your data to plot a simple line plot:
     hourly_data.line(engine="plotly", plot_bgcolor="gray", title="My Beautiful Plot")
 
 
-.. image:: output_36_1.png
+
+.. raw:: html
+
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+    
+    var gd = document.getElementById('c3b793ca-e47b-4142-950f-ffefed07ef16');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
+            if (!display || display === 'none') {{
+                console.log([gd, 'removed!']);
+                Plotly.purge(gd);
+                observer.disconnect();
+            }}
+    }});
+    
+    // Listen for the removal of the full notebook cells
+    var notebookContainer = gd.closest('#notebook-container');
+    if (notebookContainer) {{
+        x.observe(notebookContainer, {childList: true});
+    }}
+    
+    // Listen for the clearing of the current output cell
+    var outputEl = gd.closest('.output');
+    if (outputEl) {{
+        x.observe(outputEl, {childList: true});
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+                            })                };                });            </script>        </div>
 
 
 Load duration curve
@@ -506,7 +533,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -524,7 +551,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -542,7 +569,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -561,7 +588,34 @@ the columns in your data.
     hourly_data.area(engine="plotly")
 
 
-.. image:: output_41_1.png
+
+.. raw:: html
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+    
+    var gd = document.getElementById('b1d1e3d6-e981-47cb-a9a5-08001e455fef');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
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+                console.log([gd, 'removed!']);
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+    // Listen for the removal of the full notebook cells
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 Shadow Plot
@@ -580,7 +634,35 @@ shadow plot.
         engine="plotly",
     )
 
-.. image:: output_42_1.png
+
+
+.. raw:: html
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                       {"responsive": true}                    ).then(function(){
+    
+    var gd = document.getElementById('f13fde2c-d783-49e8-abc1-6728a046762a');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
+            if (!display || display === 'none') {{
+                console.log([gd, 'removed!']);
+                Plotly.purge(gd);
+                observer.disconnect();
+            }}
+    }});
+    
+    // Listen for the removal of the full notebook cells
+    var notebookContainer = gd.closest('#notebook-container');
+    if (notebookContainer) {{
+        x.observe(notebookContainer, {childList: true});
+    }}
+    
+    // Listen for the clearing of the current output cell
+    var outputEl = gd.closest('.output');
+    if (outputEl) {{
+        x.observe(outputEl, {childList: true});
+    }}
+    
+                            })                };                });            </script>        </div>
 
 
 General plot function
@@ -599,7 +681,7 @@ pd.DataFrame.plot function offering some more plotting routines:
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -620,3 +702,6 @@ use the two save functions:
     
     # saving as xslx
     hourly_data.to_excel("path and name of excel file.xlsx")
+
+
+:download:`Link to the jupyter notebook file </../notebooks/plot_class.ipynb>`.
diff --git a/docs/source/examples/simple_bulb/output_13_1.png b/docs/source/examples/simple_bulb/output_13_1.png
index 3ffe73d8..f125a911 100644
Binary files a/docs/source/examples/simple_bulb/output_13_1.png and b/docs/source/examples/simple_bulb/output_13_1.png differ
diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst
index fd4ed059..ff57a1bc 100644
--- a/docs/source/examples/simple_bulb/simple_bulb.rst
+++ b/docs/source/examples/simple_bulb/simple_bulb.rst
@@ -1,15 +1,25 @@
 Simple Appliances with multiple functioning windows
 ===================================================
 
+In this example we are going to build a load simulation of a households
+category with only access to some indoor light bulbs.
+
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
 
 Creating a user category
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
+To represent a user category, which is a collection of users with
+similar consumption characteristics such as appliance ownership and
+consumption behavior, the class **User** should be used. Each User
+instance is initially characterized by the number of users assigned to
+the user category. For example, let’s consider a household category
+comprising 10 households:
+
 .. code:: ipython3
 
     household = User(
@@ -20,21 +30,59 @@ Creating a user category
 Creating a simple appliance with two functioning time
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+In the most basic use cases of RAMP, you can model appliances with
+random usage variability across different time windows throughout the
+day, as will be created in this example.
+
+To model appliances within a household category, parameters such as the
+number of appliances owned, power usage, number of usage windows, and
+total usage time need to be specified. Additionally, parameters like the
+minimum usage time after a switch-on event can enhance the realism of
+simulations. The randomness of appliance usage can be adjusted through
+parameters such as variability in the usage window or total usage time.
+
+For example, let’s consider a household category where each household
+owns 6 “Indoor Light Bulbs,” each consuming 7 Watts. These light bulbs
+are used for 120 minutes in two time windows: from 00:00 to 00:30 and
+from 19:30 to 24:00. In RAMP simulations, time resolution is measured in
+minutes, with each simulation representing a single day from 00:00
+(corresponding to minute 0) to 24:00 (corresponding to minute 1440).
+
+Assuming a minimum usage time of 10 minutes each time a light bulb is
+switched on, we can simulate this appliance by creating a new appliance
+using the **add_appliance** method of the User object:
+
 .. code:: ipython3
 
+    # add_appliance is meth
     indoor_bulb = household.add_appliance(
-        name="Indoor Light Bulb",
-        number=6,
-        power=7,
-        num_windows=2,
-        func_time=120,
-        time_fraction_random_variability=0.2,
-        func_cycle=10,
-        window_1=[1170, 1440],  # from 19:30 to 24:00
-        window_2=[0, 30],  # from 24 to 00:30
-        random_var_w=0.35,
+        name="Indoor Light Bulb",  # the name of the appliance
+        number=6,  # how many of this appliance each user has in this user category
+        power=7,  # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model
+        num_windows=2,  # how many usage time windows throughout the day?
+        func_time=120,  # the total usage time of appliances
+        func_cycle=10,  # the minimum usage time after a switch on event
+        window_1=[0, 30],  # from 24 to 00:30
+        window_2=[1170, 1440],  # from 19:30 to 24:00
+        random_var_w=0.35,  # Variability of the windows in percentage
+        time_fraction_random_variability=0.2,  # randomizes the total time the appliance is on (between 0 and 1)
     )
 
+.. code:: ipython3
+
+    print(household)
+
+
+.. parsed-literal::
+
+       user_name  num_users               name  number  power
+    0  Household         10  Indoor Light Bulb       6    7.0
+
+
+You can check the **maximum theoretical profile** of the appliance and
+user category by calling the **maximum_profile** property of each user
+or appliance object:
+
 .. code:: ipython3
 
     # Checking the maximum profile of the appliance and user
@@ -55,21 +103,36 @@ Creating a simple appliance with two functioning time
 
 
 
-.. image:: output_6_1.png
+.. image:: output_8_1.png
 
 
 
-.. image:: output_6_2.png
+.. image:: output_8_2.png
 
 
 Whole year profile functionality
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+To generate profiles spanning more than a single day, the UseCase class
+in RAMP allows for specifying calendar days as the start and end dates
+of the simulation. The UseCase class serves as a collector of different
+user categories and provides user-friendly methods for generating
+profiles with various settings.
+
+For example, suppose you want to simulate the behavior of 10 households
+for the entire year 2020. Using the UseCase class, you can specify the
+calendar days for the start and end dates of the simulation, creating a
+comprehensive profile spanning the entire year:
+
 .. code:: ipython3
 
-    whole_year_profile = []
-    use_case = UseCase(users=[household], date_start="2020-01-01", date_end="2020-12-31")
-    whole_year_profile = use_case.generate_daily_load_profiles()
+    use_case = UseCase(
+        users=[
+            household
+        ],  # A list of all the user categories to be included in the simulation. In this case, we only have household user category
+        date_start="2020-01-01",  # starting date of the simulation
+        date_end="2020-12-31",  # end date of the simulation
+    )
 
 
 .. parsed-literal::
@@ -77,6 +140,28 @@ Whole year profile functionality
     You will simulate 366 day(s) from 2020-01-01 00:00:00 until 2021-01-01 00:00:00
 
 
+To generate the profiles, you can use, **generate_daily_load_profiles**
+methods.
+
+.. code:: ipython3
+
+    whole_year_profile = use_case.generate_daily_load_profiles()
+
+
+::
+
+
+    ---------------------------------------------------------------------------
+
+    NameError                                 Traceback (most recent call last)
+
+    Cell In[16], line 1
+    ----> 1 whole_year_profile = use_case.generate_daily_load_profiles()
+
+
+    NameError: name 'use_case' is not defined
+
+
 .. code:: ipython3
 
     whole_year_profile = pd.DataFrame(
@@ -94,18 +179,36 @@ Whole year profile functionality
 
 
 
-.. image:: output_9_1.png
+.. image:: output_13_1.png
 
 
 Generating a profile for a single day
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-provide day_type=0 for weekday and day_type=1 for weekends
+To generate daily profiles for specific user categories, the User class
+in RAMP offers various methods. One such method is
+**generate_single_load_profile**, which constructs a load profile for a
+single user, accounting for all their appliances, based on a specific
+day of the year or a designated day_type. In RAMP, day_types represent
+weekdays and weekends, with day_type=0 indicating weekdays and
+day_type=1 signifying weekends. When defining appliances using the
+add_appliance method, users can specify whether the appliance is
+utilized throughout the designated wd_we_type argument.
+
+For instance, let’s consider the scenario where we aim to generate a
+single-day load profile for a weekday of the year for a single user
+within the household category:
 
 .. code:: ipython3
 
     single_profile = household.generate_single_load_profile(day_type=0)
 
+
+.. parsed-literal::
+
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+
+
 .. code:: ipython3
 
     single_profile = pd.DataFrame(single_profile, columns=["household"])
@@ -121,18 +224,20 @@ provide day_type=0 for weekday and day_type=1 for weekends
 
 
 
-.. image:: output_12_1.png
+.. image:: output_16_1.png
 
 
 Generating aggregated_load_profile for the user category
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Single daily profiles are aggregated for all the users defined within
-the User class
+If instead of a single user from the categoy, you want to generate load
+profiles of the aggregated users of the category, for a single day, you
+can use the similar method of User class, named
+**generate_aggregated_load_profile**:
 
 .. code:: ipython3
 
-    aggregated_profile = household.generate_aggregated_load_profile()
+    aggregated_profile = household.generate_aggregated_load_profile(day_type=0)
 
 .. code:: ipython3
 
@@ -149,6 +254,7 @@ the User class
 
 
 
-.. image:: output_15_1.png
+.. image:: output_19_1.png
 
 
+:download:`Link to the jupyter notebook file </../notebooks/simple_bulb.ipynb>`.
diff --git a/docs/source/examples/thermal_app/thermal_app.rst b/docs/source/examples/thermal_app/thermal_app.rst
index de36db0d..26903dc3 100644
--- a/docs/source/examples/thermal_app/thermal_app.rst
+++ b/docs/source/examples/thermal_app/thermal_app.rst
@@ -1,20 +1,18 @@
 Thermal loads
 =============
 
-This example input file represents a single household user whose only
-load is the “shower”. The example showcases how to model thermal loads
-by: 1) using a time-varying average ``power`` attribute, pre-calculated
-as a function of the average daily groundwater temperature; and 2) using
-the ``thermal_p_var`` attribute to add further variability to the actual
-power absorbed by the appliance in each usage event, which reflects the
-randomness of user behaviour in preferring a slightly warmer or colder
-shower temperature.
+Some of the energy requirements of users are drived my technologies that
+have time variant power consumption. Hot water boilers are an example of
+technologies that can have variant powers throughout time. To show case
+how loads as such can be modelled in RAMP, we take the example of a
+household thermal load for generating shower hot water.
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, load_data, get_day_type
+    from ramp import User, UseCase, load_data
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user category and appliances
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -23,18 +21,16 @@ Creating a user category and appliances
 
     household = User()
 
-When the power varies across days of the year, for instance, as a
-function of the average daily groundwater temperature, the “power”
-parameter can be passed as a ``pd.DataFrame`` or ``np.array`` with a
-daily profile (365 rows of data). For this exercise, data can be loaded
-from the default examples in ramp:
+RAMP allows the user to give variant power profile for an appliance with
+a daily resolution. In this case,the power property in an appliance
+should be given as a timeseries pd.DataFrame or np.array with daily
+profile (365 rows of data). For this exercise, we will use a built-in
+power profile for thermal load example that can be loaded using
+load_data util function:
 
 .. code:: ipython3
 
     shower_power = load_data(example="shower")
-
-.. code:: ipython3
-
     # shower power distribution
     shower_power.plot()
 
@@ -48,7 +44,7 @@ from the default examples in ramp:
 
 
 
-.. image:: output_6_1.png
+.. image:: output_5_1.png
 
 
 .. code:: ipython3
@@ -56,14 +52,12 @@ from the default examples in ramp:
     shower = household.add_appliance(
         name="Shower",
         number=1,
-        power=shower_power,
-        num_windows=2,
-        func_time=15,
-        time_fraction_random_variability=0.1,
-        func_cycle=3,
-        window_1=[390, 540],
-        window_2=[1080, 1200],
-        random_var_w=0.2,
+        power=shower_power,  # pass the pd.DataFrame or np.array instead of a number
+        num_windows=2,  # two possibe time window for shower
+        func_time=15,  # each shower takes 15 minute
+        func_cycle=3,  # every
+        window_1=[390, 540],  #  morning shower from 6:30 to 9:00 AM
+        window_2=[1080, 1200],  # evening shower from 18:00 to 20:00
     )
 
 Generating profiles for increasing degrees of ``thermal_p_var``
@@ -80,20 +74,39 @@ Generating profiles for increasing degrees of ``thermal_p_var``
     You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00
 
 
+.. parsed-literal::
+
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.
+      end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.
+      self.__datetimeindex = pd.date_range(
+
+
+As everyone has a different habit in the water temperature for taking
+shower, we can also consider a variability in the thermal power through
+the thermal_p_var property. Using the ``thermal_p_var`` attribute to add
+further variability to the actual power absorbed by the appliance in
+each usage event, which reflects the randomness of user behaviour in
+preferring a slightly warmer or colder shower temperature.To better
+understand the effect of this parameters, let’s perform a sensitivity
+analysis on themal_p_var function. To do so, we use the following
+function **thermal_p_var_sensitivty**:
+
 .. code:: ipython3
 
-    def thermal_p_var_sensitivity(values):
+    def thermal_p_var_sensitivity(sensitivity_values):
         # buidling a pd.DataFrame for saving sensitivity results
         results = pd.DataFrame(
-            index=pd.date_range(start="2020-01-01", periods=1440 * 365, freq="T"),
-            columns=[f"p_var = {value}" for value in values],
+            index=range(0, 1440 * 365),
+            columns=[f"p_var = {value}" for value in sensitivity_values],
         )
     
-        for value in values:
+        for value in sensitivity_values:
+    
             # changing the thermal_P_var
             shower.thermal_p_var = value
     
-            profiles = usecase.generate_daily_load_profiles(flat=True)
+            profiles = usecase.generate_daily_load_profiles()
     
             # assigning the yearly profile for a given sensitivity case
             results[f"p_var = {value}"] = profiles
@@ -102,177 +115,40 @@ Generating profiles for increasing degrees of ``thermal_p_var``
 
 .. code:: ipython3
 
-    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])
+    # generating 5 senstivities on thermal_p_var
+    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5])
 
 .. code:: ipython3
 
-    sensitivity_results
-
-
-
-
-.. raw:: html
-
-    <div>
-    <style scoped>
-        .dataframe tbody tr th:only-of-type {
-            vertical-align: middle;
-        }
-    
-        .dataframe tbody tr th {
-            vertical-align: top;
-        }
+    days_to_plot = [1, 100, 200]  # which days of the year to plot
     
-        .dataframe thead th {
-            text-align: right;
-        }
-    </style>
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>p_var = 0</th>
-          <th>p_var = 0.25</th>
-          <th>p_var = 0.5</th>
-          <th>p_var = 0.75</th>
-          <th>p_var = 1</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>2020-01-01 00:00:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:01:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:02:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:03:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:04:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>...</th>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:55:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:56:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:57:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:58:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:59:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-      </tbody>
-    </table>
-    <p>525600 rows × 5 columns</p>
-    </div>
-
-
-
-.. code:: ipython3
-
-    # showing the daily average of the load profiles
-    average_daily_profiles = sensitivity_results.resample("1d").mean()
-
-.. code:: ipython3
-
-    average_daily_profiles.plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
-
-
-
-
-.. image:: output_14_1.png
-
-
-.. code:: ipython3
-
-    first_day = pd.date_range(
-        start="2020-01-01 00:00:00", freq="1min", periods=24 * 60  # a full day
+    fig, axes = plt.subplots(
+        ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)
     )
-    sensitivity_results.loc[first_day].plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
+    
+    for j, day in enumerate(days_to_plot):
+        for i, col in enumerate(sensitivity_results):
+            sensitivity_results[col].iloc[1440 * (day - 1) : 1440 * (day)].plot(
+                ax=axes[i, j]
+            )  # just plot for the first day
+            axes[i, j].set_title(f"Day {day} - thermal_p_var = {col}", fontsize=8)
+            axes[i, j].set_ylim(0, 24000)
+    
+    
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_12_0.png
 
-.. image:: output_15_1.png
 
+As it can be observed, the power consumption of hot water supply
+technology varies across different days of the year, primarily due to
+fluctuations in the nominal power. When adjusting the parameter
+**thermal_p_var** from 0, indicating no variability in power consumption
+due to user preferences, to higher values, which signify the probability
+of changes in hot water temperature, the power consumption also varies
+accordingly.
 
+:download:`Link to the jupyter notebook file </../notebooks/thermal_app.ipynb>`.
diff --git a/docs/source/examples/using_excel/using_excel.rst b/docs/source/examples/using_excel/using_excel.rst
index a97dba6a..79efbef3 100644
--- a/docs/source/examples/using_excel/using_excel.rst
+++ b/docs/source/examples/using_excel/using_excel.rst
@@ -315,3 +315,5 @@ Generating load profiles for the single users of the usecase
 .. image:: output_19_1.png
 
 
+
+:download:`Link to the jupyter notebook file </../notebooks/using_excel.ipynb>`.
diff --git a/docs/source/examples/year_simulation/year_simulation.rst b/docs/source/examples/year_simulation/year_simulation.rst
index 43a1e373..1cb0903b 100644
--- a/docs/source/examples/year_simulation/year_simulation.rst
+++ b/docs/source/examples/year_simulation/year_simulation.rst
@@ -34,7 +34,7 @@ will generate a daily profile for 10 days of starting on 3rd January 2022
 
 .. code-block:: bash
 
-   ramp -i path-to-input-file --end-date 2022-02-09 -n
+   ramp -i path-to-input-file --end-date 2022-02-09 -n 10
 
 will generate a daily profile for 10 days ending on 9th Febuary 2022
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 4b9244eb..3041cea6 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,4 +1,6 @@
-
+******************
+RAMP Documentation
+******************
 .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/RAMP_logo_basic.png?raw=true
    :width: 300
 
@@ -11,6 +13,7 @@ User Guide:
    algorithm
    examples
    input_parameters
+   contributing
    api_references
 
 
diff --git a/docs/source/input_parameters.rst b/docs/source/input_parameters.rst
index ea8938b5..a0d489bd 100644
--- a/docs/source/input_parameters.rst
+++ b/docs/source/input_parameters.rst
@@ -108,6 +108,13 @@ The **"allowed values"** column provide information about the format one should
      - integer
      - no
      - 0
+  * - continuous_use_duty_cycle
+     - NA
+     - {0,1}
+     - Duty cycle mode, 0 triggers once per switch-on event, 1 let the duty cycle repeat during the entire switch-on event
+     - integer
+     - no
+     - 1
    * - occasional_use
      - %
      - in [0,1]
diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index ebbded96..90ae0dd2 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -1,7 +1,7 @@
 
-*******************
+*****************
 RAMP Introduction
-*******************
+*****************
 
-.. include:: ../../README.rst
+.. include:: readme.rst
 
diff --git a/environment.yml b/environment.yml
index 715b4764..7f00928d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,6 +1,6 @@
 name: ramp
 dependencies:
-- python=3.8
+- python=3.10
 - pip=21.0.1
 - pip:
    - -r requirements.txt
diff --git a/joss_paper/example_output.png b/joss_paper/example_output.png
new file mode 100644
index 00000000..5ae45a91
Binary files /dev/null and b/joss_paper/example_output.png differ
diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib
new file mode 100644
index 00000000..5d70bc27
--- /dev/null
+++ b/joss_paper/paper.bib
@@ -0,0 +1,275 @@
+
+@article{dimovski_holistic:2023,
+	title = {Holistic {MILP}-based approach for rural electrification planning},
+	volume = {49},
+	issn = {2211-467X},
+	url = {https://www.sciencedirect.com/science/article/pii/S2211467X23001219},
+	doi = {10.1016/j.esr.2023.101171},
+	urldate = {2023-10-31},
+	journal = {Energy Strategy Reviews},
+	author = {Dimovski, Aleksandar and Corigliano, Silvia and Edeme, Darlain and Merlo, Marco},
+	month = sep,
+	year = {2023},
+	keywords = {Geospatial planning, Least-cost electrification, MILP, Rural electrification, Sustainable development},
+	pages = {101171},
+}
+
+@article{baruah_modelling:2023,
+	title = {Modelling of an off-grid roof-top residential photovoltaic nano grid system for an urban locality in {India}},
+	volume = {74},
+	issn = {0973-0826},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082623000935},
+	doi = {10.1016/j.esd.2023.05.004},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Baruah, Abhinandan and Basu, Mousumi},
+	month = jun,
+	year = {2023},
+	keywords = {Correlation analysis, HOMER Pro, Off-grid rooftop PV, RAMP, Rooftop area feasibility, Sensitivity analysis},
+	pages = {471--498},
+}
+
+@article{villarroel-schneider_open-source:2023,
+	title = {Open-source model applied for techno-economic optimization of a hybrid solar {PV} biogas-based polygeneration plant: {The} case of a dairy farmers’ association in central {Bolivia}},
+	volume = {291},
+	issn = {0196-8904},
+	shorttitle = {Open-source model applied for techno-economic optimization of a hybrid solar {PV} biogas-based polygeneration plant},
+	url = {https://www.sciencedirect.com/science/article/pii/S0196890423005691},
+	doi = {10.1016/j.enconman.2023.117223},
+	urldate = {2023-10-31},
+	journal = {Energy Conversion and Management},
+	author = {Villarroel-Schneider, J. and Balderrama, Sergio and Sánchez, Claudia and Cardozo, Evelyn and Malmquist, Anders and Martin, Andrew},
+	month = sep,
+	year = {2023},
+	keywords = {Biogas, Cogeneration, Dairy, Hybrid solar-biogas, Optimization, Techno-economic},
+	pages = {117223},
+}
+
+@article{pasqui_new:2023,
+	title = {A new smart batteries management for {Renewable} {Energy} {Communities}},
+	volume = {34},
+	issn = {2352-4677},
+	url = {https://www.sciencedirect.com/science/article/pii/S2352467723000516},
+	doi = {10.1016/j.segan.2023.101043},
+	urldate = {2023-10-31},
+	journal = {Sustainable Energy, Grids and Networks},
+	author = {Pasqui, Mattia and Felice, Alex and Messagie, Maarten and Coosemans, Thierry and Bastianello, Tommaso Tiozzo and Baldi, Duccio and Lubello, Pietro and Carcasci, Carlo},
+	month = jun,
+	year = {2023},
+	keywords = {Battery management, Energy system simulation, Load forecasting, Photovoltaic systems, Renewable Energy Communities},
+	pages = {101043},
+}
+
+@article{lubello_assessment:2022,
+	title = {Assessment of hydrogen-based long term electrical energy storage in residential energy systems},
+	volume = {8},
+	issn = {2666-9552},
+	url = {https://www.sciencedirect.com/science/article/pii/S2666955222000260},
+	doi = {10.1016/j.segy.2022.100088},
+	urldate = {2023-10-31},
+	journal = {Smart Energy},
+	author = {Lubello, Pietro and Pasqui, Mattia and Mati, Alessandro and Carcasci, Carlo},
+	month = nov,
+	year = {2022},
+	keywords = {Energy forecasting, Energy systems, Hydrogen storage, Residential building, Self-consumption},
+	pages = {100088},
+}
+
+@article{mangipinto_impact:2022,
+	title = {Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all {European} countries},
+	volume = {312},
+	issn = {0306-2619},
+	url = {https://www.sciencedirect.com/science/article/pii/S0306261922001416},
+	doi = {10.1016/j.apenergy.2022.118676},
+	urldate = {2023-10-31},
+	journal = {Applied Energy},
+	author = {Mangipinto, Andrea and Lombardi, Francesco and Sanvito, Francesco Davide and Pavičević, Matija and Quoilin, Sylvain and Colombo, Emanuela},
+	month = apr,
+	year = {2022},
+	keywords = {Electric vehicles, Sector coupling, Smart charging, stochastic demand simulation, Time series},
+	pages = {118676},
+}
+
+@article{stevanato_rural:2021,
+	title = {Rural areas electrification strategies through shadow costs analysis - {Bolivian} {Highlands} case study},
+	volume = {65},
+	issn = {0973-0826},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082621001253},
+	doi = {10.1016/j.esd.2021.10.009},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Stevanato, Nicolò and Corigliano, Silvia and Petrelli, Marina and Tonini, Francesco and Merlo, Marco and Colombo, Emanuela},
+	month = dec,
+	year = {2021},
+	keywords = {Electricity demand, Electrification costs, Grid extension, Grid routing, Rural electrification, Solar home systems},
+	pages = {162--174},
+}
+
+@article{stevanato_long-term:2020,
+	title = {Long-term sizing of rural microgrids: {Accounting} for load evolution through multi-step investment plan and stochastic optimization},
+	volume = {58},
+	issn = {0973-0826},
+	shorttitle = {Long-term sizing of rural microgrids},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082620302635},
+	doi = {10.1016/j.esd.2020.07.002},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Stevanato, Nicolò and Lombardi, Francesco and Guidicini, Giulia and Rinaldi, Lorenzo and Balderrama, Sergio L. and Pavičević, Matija and Quoilin, Sylvain and Colombo, Emanuela},
+	month = oct,
+	year = {2020},
+	keywords = {Load evolution, Microgrid, Multi-step investment, Off-grid, Rural electrification},
+	pages = {16--29},
+}
+
+@article{lombardi_generating:2019,
+	title = {Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model},
+	volume = {177},
+	issn = {0360-5442},
+	url = {https://www.sciencedirect.com/science/article/pii/S0360544219307303},
+	doi = {10.1016/j.energy.2019.04.097},
+	urldate = {2023-10-31},
+	journal = {Energy},
+	author = {Lombardi, Francesco and Balderrama, Sergio and Quoilin, Sylvain and Colombo, Emanuela},
+	month = jun,
+	year = {2019},
+	keywords = {Energy demand, Load profile, Multi-energy system, Off-grid, Rural areas},
+	pages = {433--444},
+}
+
+
+@article{secchi_smart:2023,
+	title = {Smart electric vehicles charging with centralised vehicle-to-grid capability for net-load variance minimisation under increasing {EV} and {PV} penetration levels},
+	volume = {35},
+	issn = {2352-4677},
+	url = {https://www.sciencedirect.com/science/article/pii/S2352467723001285},
+	doi = {10.1016/j.segan.2023.101120},
+	urldate = {2023-10-31},
+	journal = {Sustainable Energy, Grids and Networks},
+	author = {Secchi, M. and Barchi, G. and Macii, D. and Petri, D.},
+	month = sep,
+	year = {2023},
+	keywords = {Electric vehicles (EV), Photovoltaic Generator, Smart EV charging, Vehicle-to-grid},
+	pages = {101120},
+}
+
+
+@article{pickering_diversity:2022,
+	title = {Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire {European} energy system},
+	volume = {6},
+	issn = {2542-4785, 2542-4351},
+	url = {https://www.cell.com/joule/abstract/S2542-4351(22)00236-7},
+	doi = {10.1016/j.joule.2022.05.009},
+	language = {English},
+	number = {6},
+	urldate = {2022-06-16},
+	journal = {Joule},
+	author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},
+	month = jun,
+	year = {2022},
+	note = {Publisher: Elsevier},
+	keywords = {near-optimal solutions, SPORES, flexibility, modeling to generate alternatives, Calliope, Carbon neutrality, energy self-sufficiency, energy system optimisation, renewable energy, sector coupling},
+	pages = {1253--1276},
+}
+
+
+@article{stevanato_modeling:2020,
+	title = {Modeling of a {Village}-{Scale} {Multi}-{Energy} {System} for the {Integrated} {Supply} of {Electric} and {Thermal} {Energy}},
+	volume = {10},
+	copyright = {http://creativecommons.org/licenses/by/3.0/},
+	issn = {2076-3417},
+	url = {https://www.mdpi.com/2076-3417/10/21/7445},
+	doi = {10.3390/app10217445},
+	language = {en},
+	number = {21},
+	urldate = {2023-11-06},
+	journal = {Applied Sciences},
+	author = {Stevanato, Nicolo and Rinaldi, Lorenzo and Pistolese, Stefano and Balderrama Subieta, Sergio Luis and Quoilin, Sylvain and Colombo, Emanuela},
+	month = jan,
+	year = {2020},
+	note = {Number: 21
+Publisher: Multidisciplinary Digital Publishing Institute},
+	keywords = {electric, energy system, modeling, off-grid, rural electrification, thermal},
+	pages = {7445},
+}
+
+
+@misc{ramp_github,
+	title = {{RAMP: stochastic multi-energy demand profiles}},
+	url = {https://github.com/RAMP-project},
+	year = {2019},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+}
+
+
+@article{hart_gui:2023,
+	title = {Sustainable {Energy} {System} {Planning} in {Developing} {Countries}: {Facilitating} {Load} {Profile} {Generation} in {Energy} {System} {Simulations}},
+	isbn = {978-0-9981331-6-4},
+	url = {https://hdl.handle.net/10125/102726},
+	author = {Hart, Maria C. G. and Eckhoff, Sarah and Breitner, Michael H.},
+	year = {2023},
+    journal = {Hawaii International Conference on System Sciences}
+}
+
+
+@misc{ramp_zenodo:2023,
+	title = {{RAMP}-project/{RAMP}: v0.5.0},
+	url = {https://doi.org/10.5281/zenodo.10275752},
+	publisher = {Zenodo},
+	author = {Lombardi, Francesco and Duc, Pierre-Francois and Tahavori, Mohammad Amin and Sanchez-Solis, Claudia and Eckhoff, Sarah and Hart, Maria C.G. and Sanvito, Francesco Davide and Ireland, Gregory and Balderrama, Sergio and Quoilin, Sylvain},
+	year = {2023},
+	doi = {10.5281/zenodo.10275752},
+}
+
+@article{crest_model:2015,
+	author = "McKenna, Eoghan and Thomson, Murray and Barton, John",
+	title = "{CREST Demand Model}",
+	year = "2015",
+	month = "9",
+	url = "https://repository.lboro.ac.uk/articles/dataset/CREST_Demand_Model_v2_0/2001129",
+	doi = "10.17028/rd.lboro.2001129.v8"
+}
+
+
+@article{mandelli_lpg:2016,
+	title = {Novel procedure to formulate load profiles for off-grid rural areas},
+	volume = {31},
+	issn = {0973-0826},
+	doi = {https://doi.org/10.1016/j.esd.2016.01.005},
+    journal = {Energy for Sustainable Development},
+	author = {Mandelli, Stefano and Merlo, Marco and Colombo, Emanuela},
+	year = {2016},
+	keywords = {Electric consumptions, Load model, Off-grid energy systems, Renewables, Rural electrification, Stochastic model},
+	pages = {130--142},
+}
+
+@article{barsanti_demod:2021,
+	title = {Socio-technical modeling of smart energy systems: a co-simulation design for domestic energy demand},
+	volume = {4},
+	issn = {2520-8942},
+	doi = {10.1186/s42162-021-00180-6},
+	number = {3},
+	urldate = {2024-05-22},
+	journal = {Energy Informatics},
+	author = {Barsanti, Matteo and Schwarz, Jan Sören and Gérard Constantin, Lionel Guy and Kasturi, Pranay and Binder, Claudia R. and Lehnhoff, Sebastian},
+	month = sep,
+	year = {2021},
+	keywords = {Co-simulation, Domestic energy demand, Granularity, Modularity, Scalability, Smart energy system, Socio-technical simulation, Transparency},
+	pages = {12},
+}
+
+
+@article{pflugradt_loadprofilegenerator:2022,
+	title = {{LoadProfileGenerator}: {An} {Agent}-{Based} {Behavior} {Simulation} for {Generating} {Residential} {Load} {Profiles}},
+	volume = {7},
+	issn = {2475-9066},
+	url = {https://joss.theoj.org/papers/10.21105/joss.03574},
+	doi = {10.21105/joss.03574},
+	number = {71},
+	urldate = {2024-05-22},
+	journal = {Journal of Open Source Software},
+	author = {Pflugradt, Noah and Stenzel, Peter and Kotzur, Leander and Stolten, Detlef},
+	month = mar,
+	year = {2022},
+	pages = {3574},
+}
\ No newline at end of file
diff --git a/joss_paper/paper.md b/joss_paper/paper.md
new file mode 100644
index 00000000..7f1d0f11
--- /dev/null
+++ b/joss_paper/paper.md
@@ -0,0 +1,87 @@
+---
+title: 'RAMP: stochastic simulation of user-driven energy demand time series'
+tags:
+  - Python
+  - energy demand
+  - stochastic
+  - time series
+  - synthetic data
+authors:
+  - name: Francesco Lombardi
+    orcid: 0000-0002-7624-5886
+    corresponding: true # (This is how to denote the corresponding author)
+    affiliation: 1
+  - name: Pierre-François Duc
+    orcid: 0000-0001-8698-8854
+    affiliation: 2
+  - name: Mohammad Amin Tahavori
+    orcid: 0000-0002-7753-0523
+    affiliation: 3
+  - name: Claudia Sanchez-Solis
+    orcid: 0000-0003-2385-7392
+    affiliation: "4,7"
+  - name: Sarah Eckhoff
+    orcid: 0000-0002-6168-4835
+    affiliation: 5
+  - name: Maria C.G. Hart
+    orcid: 0000-0002-1031-9782
+    affiliation: 5
+  - name: Francesco Sanvito
+    orcid: 0000-0002-9152-9684
+    affiliation: 1
+  - name: Gregory Ireland
+    orcid:
+    affiliation: "2,6"
+  - name: Sergio Balderrama
+    orcid:
+    affiliation: 7
+  - name: Johann Kraft
+    orcid:
+    affiliation: 2
+  - name: Gokarna Dhungel
+    orcid:
+    affiliation: "2,8"
+  - name: Sylvain Quoilin
+    orcid:
+    affiliation: 4
+affiliations:
+ - name: TU Delft, Faculty of Technology, Policy and Management, Delft, The Netherlands
+   index: 1
+ - name: Reiner Lemoine Institut, Berlin, Germany
+   index: 2
+ - name: VITO, Mol, Belgium
+   index: 3
+ - name: University of Liège, Integrated and Sustainable Energy Systems, Thermodynamics Laboratory, Liège, Belgium
+   index: 4
+ - name: Leibniz Universität Hannover, Information Systems Institute, Hannover, Germany
+   index: 5
+ - name: University of Cape Town, Cape Town, South Africa
+   index: 6
+ - name: Universidad Mayor de San Simon, Centro Universitario de Investigacion en Energias, Cochabamba, Bolivia
+   index: 7
+ - name: University of Applied Sciences Nordhausen, Nordhausen, Germany
+   index: 8
+date: 6 December 2023
+bibliography: paper.bib
+
+---
+
+# Summary
+
+The urgency of the energy transition is leading to a rapid evolution of energy system design worldwide. In areas with widespread energy infrastructure, existing electricity, heat and mobility networks are being re-designed for carbon neutrality and are increasingly interconnected. In areas where energy infrastructure is limited, instead, networks and systems are being rapidly expanded to ensure access to energy for all. In both cases, re-designing and expanding energy systems in these directions requires information on future user behaviour and associated energy demand, yet this type of data is often unavailable. In fact, historical data are often either entirely missing or poorly representative of future behaviour within transitioning systems. This results in reliance on inadequate demand data, which affects system design and its resilience to rapid behaviour evolution.
+
+# Statement of need
+
+RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, which are provided in tabular format (`.xlsx`). For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022].
+
+Reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [@crest_model:2015] and demod [@barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [@pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [@mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
+
+In fact, RAMP features several degrees of customisation, enabling modellers to switch on or off features tailored to the needs of specific energy uses. For example, to represent the highly variable load profile of cooking appliances, which changes completely depending on which meal is cooked, the software allows defining many possible meal types with an associated cooking cycle; then, it leverages stochasticity to diversity which meals are cooked when. For heat-related energy uses that may be influenced by weather parameters, such as outdoor or groundwater temperature, it is possible to provide a time series of such parameters and define how they affect the default energy consumption. These and many other customisation options allow users to explicitly simulate radically different but equally plausible behaviour scenarios, including behaviours that may happen in the future and that have no relationship with past data, as a key ingredient to robust system design.
+
+RAMP has already been used in many scientific publications, for instance, for the simulation of electricity [@dimovski_holistic:2023], heating [@stevanato_modeling:2020], cooking [@stevanato_long-term:2020] and electric mobility [@secchi_smart:2023] demand time series at scales ranging from districts [@pasqui_new:2023] or villages [@villarroel-schneider_open-source:2023] to continents [@pickering_diversity:2022]. It has dozens of users globally and has recently become a multi-institution software development effort, actively developed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover. The joint development process has brought major improvements to the code structure, syntax and efficiency, more extensive documentation, and a web-based graphical user interface for users with no Python experience [@hart_gui:2023].
+
+RAMP is developed openly on GitHub [@ramp_github] and each new release is archived on Zenodo [@ramp_zenodo:2023].
+
+![Example output (normalised by peak demand) for the simulation of the electricity load of three households in a small village over five days. The thick blue line represents the five-day average, while individual days are plotted in a lighter colour. \label{fig:example}](example_output.png)
+
+# References
diff --git a/joss_paper/paper_compiled.pdf b/joss_paper/paper_compiled.pdf
new file mode 100644
index 00000000..5e3bb37c
Binary files /dev/null and b/joss_paper/paper_compiled.pdf differ
diff --git a/ramp/__init__.py b/ramp/__init__.py
index 3f23af7e..235e2796 100644
--- a/ramp/__init__.py
+++ b/ramp/__init__.py
@@ -13,12 +13,14 @@
     - random
 """
 
-
 from ramp._version import __version__
 from ramp.core.core import UseCase, User, Appliance
 from ramp.core.utils import yearly_pattern, get_day_type
 from ramp.example.examples import load_data, download_example
 from ramp.post_process.post_process import Plot
+from ramp.ramp_convert_old_input_files import (
+    convert_old_user_input_file as ramp_py2xlsx,
+)
 
 __authors__ = "Listed in AUTHORS"
 __copyright__ = (
diff --git a/ramp/_version.py b/ramp/_version.py
index 3d187266..72251527 100644
--- a/ramp/_version.py
+++ b/ramp/_version.py
@@ -1 +1 @@
-__version__ = "0.5.0"
+__version__ = "0.5.2"
diff --git a/ramp/cli.py b/ramp/cli.py
index d4f9e94f..394f2387 100644
--- a/ramp/cli.py
+++ b/ramp/cli.py
@@ -9,7 +9,9 @@
 BASE_PATH = os.path.dirname(os.path.abspath(__file__))
 
 parser = argparse.ArgumentParser(
-    prog="python ramp_run.py", description="Execute RAMP code"
+    prog="ramp",
+    description="Execute RAMP code",
+    epilog="To convert '.py' input files into '.xlsx' input files use the command 'ramp_convert'",
 )
 parser.add_argument(
     "-i",
@@ -182,37 +184,53 @@ def main():
                 month_end = datetime.date(
                     year, i + 1, pd.Period(month_start, freq="D").days_in_month
                 )
-                run_usecase(
+                month_profiles = run_usecase(
                     fname=fname,
-                    ofname=ofnames[i],
+                    ofname=None,
                     num_days=num_days[i],
                     date_start=month_start,
                     date_end=month_end,
                     plot=False,
                     parallel=parallel_processing,
                 )
+                month_profiles = month_profiles.reshape(
+                    1, 1440 * month_profiles.shape[0]
+                ).squeeze()
+                year_profile.append(month_profiles)
 
             # Create a dataFrame to save the year profile with timestamps every minutes
             series_frame = pd.DataFrame(
                 np.hstack(year_profile),
                 index=pd.date_range(
-                    start=f"{year}-1-1", end=f"{year}-12-31 23:59", freq="T"
+                    start=f"{year}-1-1", end=f"{year}-12-31 23:59", freq="min"
                 ),
             )
             # Save to minute and hour resolution
-            # TODO let the user choose where to save the files/file_name, make sure the user wants to overwrite the file
-            # if it already exists
+            if len(ofnames) == 1:
+                ofname = ofnames[0]
+            else:
+                ofname = None
+
+            if ofname is None:
+                ofname = os.path.abspath(os.path.curdir)
+
+            if not os.path.exists(ofname):
+                os.mkdir(ofname)
+
             series_frame.to_csv(
-                os.path.join(BASE_PATH, "yearly_profile_min_resolution.csv")
+                os.path.join(ofname, "yearly_profile_min_resolution.csv")
             )
             resampled = pd.DataFrame()
 
-            resampled["mean"] = series_frame.resample("H").mean()
-            resampled["max"] = series_frame.resample("H").max()
-            resampled["min"] = series_frame.resample("H").min()
+            resampled["mean"] = series_frame.resample("h").mean()
+            resampled["max"] = series_frame.resample("h").max()
+            resampled["min"] = series_frame.resample("h").min()
             # TODO add more columns with other resampled functions (do this in Jupyter)
             resampled.to_csv(
-                os.path.join(BASE_PATH, "yearly_profile_hourly_resolution.csv")
+                os.path.join(ofname, "yearly_profile_hourly_resolution.csv")
+            )
+            print(
+                f"Results of the yearly RAMP simulation with seasonality are located in the folder {ofname}"
             )
 
 
diff --git a/ramp/core/constants.py b/ramp/core/constants.py
index d3433aff..846a8e96 100644
--- a/ramp/core/constants.py
+++ b/ramp/core/constants.py
@@ -73,6 +73,7 @@ def switch_on_parameters():
     "func_cycle",
     "fixed",
     "fixed_cycle",
+    "continuous_duty_cycle",
     "occasional_use",
     "flat",
     "thermal_p_var",
@@ -115,6 +116,7 @@ def switch_on_parameters():
     "func_cycle",
     "fixed",
     "fixed_cycle",
+    "continuous_duty_cycle",
     "occasional_use",
     "flat",
     "thermal_p_var",
diff --git a/ramp/core/core.py b/ramp/core/core.py
index e21047b2..1578c935 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -28,6 +28,7 @@
     random_choice,
     read_input_file,
     within_peak_time_window,
+    range_within_window,
 )
 from ramp.post_process.post_process import Plot
 
@@ -43,6 +44,9 @@ def single_appliance_daily_load_profile(args):
     return args[0], app.daily_use
 
 
+warnings.simplefilter("always", DeprecationWarning)
+
+
 class UseCase:
     def __init__(
         self,
@@ -52,6 +56,7 @@ def __init__(
         date_end: str = None,
         parallel_processing: bool = False,
         peak_enlarge: float = 0.15,
+        random_seed: int = None,
     ):
         """Creates a UseCase instance for gathering a list of User instances which own Appliance instances
 
@@ -69,6 +74,8 @@ def __init__(
             if set True, the profiles will be generated in parallel rather than sequencially
         peak_enlarge: float, optional
             percentage random enlargement or reduction of peak time range length, used in UseCase.calc_peak_time_range
+        random_seed: int, optional
+            specify seed for the random number generator to exactly reproduce results
 
         """
         self.name = name
@@ -89,6 +96,7 @@ def __init__(
         self.__num_days = None
         self.__datetimeindex = None
         self.daily_profiles = None
+        self.random_seed = random_seed
 
         self.appliances = []
         self.users = []
@@ -100,6 +108,10 @@ def __init__(
         if self.date_start is not None and self.date_end is not None:
             self.initialize()
 
+        # Set global random seed if it is specified
+        if self.random_seed:
+            random.seed(self.random_seed)
+
     @property
     def date_start(self):
         """Start date of the daily profiles generated by the UseCase instance"""
@@ -289,6 +301,11 @@ def initialize(
                 print(
                     f"You will simulate {self.__num_days} day(s) from {self.days[0]} until {self.days[-1]+datetime.timedelta(days=1)}"
                 )
+
+            # Verify that each appliance has long enough power data for the simulated time
+            for app in self.appliances:
+                app.check_power_values(self.__num_days)
+
             if peak_enlarge is not None:
                 self.peak_enlarge = peak_enlarge
             else:
@@ -296,8 +313,8 @@ def initialize(
             # format datetimeindex in minutes
             self.__datetimeindex = pd.date_range(
                 start=self.days[0],
-                end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
-                freq="T",
+                end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "min"),
+                freq="min",
             )
 
     @property
@@ -356,10 +373,15 @@ def calc_peak_time_range(self, peak_enlarge=None):
                 sigma=1 / 3 * (peak_window[-1] - peak_window[0]),
             )
         )
-        rand_peak_enlarge = round(
-            math.fabs(
-                peak_time - random.gauss(mu=peak_time, sigma=peak_enlarge * peak_time)
-            )
+        # Rand_peak_enlarge is rounded to be at least 1 -> if rounded to 0 peak_time_range would be empty
+        rand_peak_enlarge = max(
+            round(
+                math.fabs(
+                    peak_time
+                    - random.gauss(mu=peak_time, sigma=peak_enlarge * peak_time)
+                )
+            ),
+            1,
         )
         # The peak_time is randomly enlarged based on the calibration parameter peak_enlarge
         return np.arange(peak_time - rand_peak_enlarge, peak_time + rand_peak_enlarge)
@@ -584,7 +606,10 @@ def load(self, filename: str) -> None:
                 :, ~user_df.columns.isin(["user_name", "num_users", "user_preference"])
             ].to_dict(orient="records"):
                 # assign Appliance arguments
-                appliance_parameters = {k: row[k] for k in APPLIANCE_ARGS}
+                appliance_parameters = {}
+                for k in APPLIANCE_ARGS:
+                    if k in row:
+                        appliance_parameters[k] = row[k]
 
                 # assign windows arguments
                 for k in WINDOWS_PARAMETERS:
@@ -650,13 +675,81 @@ def __init__(
             []
         )  # each instance of User (i.e. each user class) has its own list of Appliances
 
+    def __str__(self):
+        try:
+            return self.save()[
+                ["user_name", "num_users", "name", "number", "power"]
+            ].to_string()
+
+        except Exception:
+            return f"""
+user_name: {self.user_name} \n
+num_users: {self.num_users} \n
+appliances: no appliances assigned to the user.
+                    """
+
     def __repr__(self):
-        return self.save()[
-            ["user_name", "num_users", "name", "number", "power"]
-        ].to_string()
+        return self.__str__()
+
+    def _add_appliance_instance(self, appliances):
+        if isinstance(appliances, Appliance):
+            appliances = [appliances]
+        for app in appliances:
+            if not isinstance(app, Appliance):
+                raise TypeError(
+                    f"You are trying to add an object of type {type(app)} as an appliance to the user {self.user_name}"
+                )
+            if app not in self.App_list:
+                if app.name == "":
+                    app.name = f"appliance_{len(self.App_list) + 1}"
+                self.App_list.append(app)
 
     def add_appliance(self, *args, **kwargs):
-        """adds an appliance to the user category with all the appliance characteristics in a single function
+        """Adds an appliance to the user category with all the appliance characteristics in a single function
+
+        Parameters
+        ----------
+        number : int, optional
+            number of appliances of the specified kind, by default 1
+
+        power : Union[float.pd.DataFrame], optional
+            Power rating of appliance (average). If the appliance has variant daily power, a series (with the size of 366) can be passed., by default 0
+
+        num_windows : int [1,2,3], optional
+            Number of distinct time windows, by default 1
+
+        func_time : int[0,1440], optional
+            total time (minutes) the appliance is on during the day (not dependant on windows). Acceptable values are in range 0 to 1440, by default 0
+
+        time_fraction_random_variability : Percentage, optional
+            percentage of total time of use that is subject to random variability. For time (not for windows), randomizes the total time the appliance is on, by default 0
+
+        func_cycle : int[0,1440], optional
+            minimum time(minutes) the appliance is kept on after switch-on event, by default 1
+
+        fixed : str, optional
+            if 'yes', all the 'n' appliances of this kind are always switched-on together, by default "no"
+
+        fixed_cycle : int{0,1,2,3,4}, optional
+            Number of duty cycle, 0 means continuous power, if not 0 you have to fill the cw (cycle window) parameter (you may define up to 3 cws), by default 0
+
+        occasional_use : Percentage, optional
+            Defines how often the appliance is used, e.g. every second day will be 0.5, by default 1
+
+        flat : str{'yes','no'}, optional
+            allows to model appliances that are not subject to any kind of random variability, such as public lighting, by default "no"
+
+        thermal_p_var : Percentage, optional
+            Range of change of the power of the appliance (e.g. shower not taken at same temparature) or for the power of duty cycles (e.g. for a cooker, AC, heater if external temperature is different…), by default 0
+
+        pref_index : int{0,1,2,3}, optional
+            defines preference index for association with random User daily preference behaviour.This number must be smaller or equal to the value input in user_preference, by default 0
+
+        wd_we_type : int{0,1,2}, optional
+            Specify whether the appliance is used only on weekdays (0), weekend (1) or the whole week (2), by default 2
+
+        name : str, optional
+            the name of the appliance, by default ""
 
 
         Returns
@@ -667,7 +760,13 @@ def add_appliance(self, *args, **kwargs):
 
         # parse the args into the kwargs
         if len(args) > 0:
+            if isinstance(args[0], Appliance):
+                # if the first argument is an Appliance instance, it is assumed all arguments are
+                # if this is not the case, error will be thrown by _add_appliance_instance method
+                self._add_appliance_instance(args)
+                return
             for a_name, a_val in zip(APPLIANCE_ARGS, args):
+                # TODO here we could do validation of the arguments
                 kwargs[a_name] = a_val
 
         # collects windows arguments
@@ -693,6 +792,8 @@ def add_appliance(self, *args, **kwargs):
         for i in duty_cycle_parameters:
             app.specific_cycle(i, **duty_cycle_parameters[i])
 
+        self._add_appliance_instance(app)
+
         return app
 
     @property
@@ -723,7 +824,7 @@ def num_days(self):
         return answer
 
     def save(self, filename: str = None) -> Union[pd.DataFrame, None]:
-        """Saves/returns the model databas including allappliances as a single pd.DataFrame or excel file.
+        """Saves/returns the model database including all appliances as a single pd.DataFrame or excel file.
 
         Parameters
         ----------
@@ -845,6 +946,12 @@ def Appliance(
         ------
         refer to Appliance class docs
         """
+
+        warnings.warn(
+            "This function is deprecated and not supported since version v0.4.0. Instead use the add_appliance method.",
+            DeprecationWarning,
+        )
+
         return self.add_appliance(
             number=number,
             power=power,
@@ -884,9 +991,6 @@ def generate_single_load_profile(
             load profile for the requested day
         """
 
-        if prof_i not in range(366):
-            raise ValueError(f"prof_i should be an integer in range of 0 to 364")
-
         if peak_time_range is None:
             if self.usecase is None:
                 # logging warning
@@ -941,9 +1045,6 @@ def generate_aggregated_load_profile(
         Each single load profile has its own separate randomisation
         """
 
-        if prof_i not in range(366):
-            raise ValueError(f"prof_i should be an integer in range of 0 to 364")
-
         self.load = np.zeros(1440)  # initialise empty load for User instance
         for _ in range(self.num_users):
             # iterates for every single user within a User class.
@@ -966,6 +1067,7 @@ def __init__(
         func_cycle: int = 1,
         fixed: str = "no",
         fixed_cycle: int = 0,
+        continuous_duty_cycle: int = 1,
         occasional_use: float = 1,
         flat: str = "no",
         thermal_p_var: int = 0,
@@ -998,6 +1100,13 @@ def __init__(
         func_cycle : int[0,1440], optional
             minimum time(minutes) the appliance is kept on after switch-on event, by default 1
 
+        continuous_duty_cycle : int[0,1], optional
+            if value is 0 : the duty cycle is executed once per switch on event (like a
+            welder, or other productive use appliances)
+            if value is 1 : whether the duty cycle are filling the whole switch on event of
+            the appliance (like a fridge or other continuous use appliance)
+            by default 1
+
         fixed : str, optional
             if 'yes', all the 'n' appliances of this kind are always switched-on together, by default "no"
 
@@ -1033,30 +1142,37 @@ def __init__(
         self.name = name
         self.number = number
         self.num_windows = num_windows
+
+        if func_time == 0:
+            warnings.warn(
+                UserWarning(
+                    f"Func_time of appliance '{self.name}' is defined as 0. Ignore if this is intended"
+                )
+            )
+
         self.func_time = func_time
         self.time_fraction_random_variability = time_fraction_random_variability
         self.func_cycle = func_cycle
         self.fixed = fixed
         self.fixed_cycle = fixed_cycle
+        self.continuous_duty_cycle = continuous_duty_cycle
         self.occasional_use = occasional_use
         self.flat = flat
         self.thermal_p_var = thermal_p_var
         self.pref_index = pref_index
         self.wd_we_type = wd_we_type
 
+        self.__constant_power = False
+
         if isinstance(power, pd.DataFrame):
-            if len(power) >= self.user.num_days:
-                power = power.values[:, 0]
-            else:
-                raise ValueError(
-                    f"Wrong number of power values for appliance '{self.name}'. Number should be at least {self.user.num_days} values or a constant value."
-                )
+            power = power.values[:, 0]
 
         elif isinstance(power, str):
             power = pd.read_json(power).values[:, 0]
 
         elif isinstance(power, (float, int)):
             power = power * np.ones(self.user.num_days + 1)
+            self.__constant_power = True
 
         else:
             raise ValueError(f"Wrong data type for power of appliance '{self.name}'.")
@@ -1071,7 +1187,7 @@ def __init__(
         self.random_var_1 = 0
         self.random_var_2 = 0
         self.random_var_3 = 0
-        self.daily_use = None
+        self.daily_use = np.zeros(1440)
         self.free_spots = None
 
         # attributes used for specific fixed and random cycles
@@ -1103,6 +1219,22 @@ def __init__(
         self.random_cycle2 = np.array([])
         self.random_cycle3 = np.array([])
 
+        # attribute used to know if a switch on event falls within a given duty cycle window
+        # if it is 0, then no switch on events happen within any duty cycle windows
+        self.current_duty_cycle_id = 0
+
+    def check_power_values(self, num_days):
+        if len(self.power) < num_days:
+            if self.__constant_power is True:
+                self.power = self.power[0] * np.ones(num_days + 1)
+                warnings.warn(
+                    f"Appliance {self.name} of user {self.user.user_name} of constant power had its power timeseries updated to match total number of days of the usecase {self.user.usecase.name}"
+                )
+            else:
+                raise ValueError(
+                    f"Wrong number of values for appliance '{self.name}''s power of user {self.user.user_name}: {len(self.power)}. Number of values should at least match the total number of days: {num_days}. Alternatively the power of the appliance can be set to a constant value."
+                )
+
     def save(self) -> pd.DataFrame:
         """returns a pd.DataFrame containing the appliance data
 
@@ -1185,7 +1317,7 @@ def __eq__(self, other_appliance) -> bool:
         answer = np.array([])
         for attribute in APPLIANCE_ATTRIBUTES:
             if hasattr(self, attribute) and hasattr(other_appliance, attribute):
-                np.append(
+                answer = np.append(
                     answer,
                     [getattr(self, attribute) == getattr(other_appliance, attribute)],
                 )
@@ -1193,7 +1325,7 @@ def __eq__(self, other_appliance) -> bool:
                 hasattr(self, attribute) is False
                 and hasattr(other_appliance, attribute) is False
             ):
-                np.append(answer, True)
+                answer = np.append(answer, [True])
             else:
                 if hasattr(self, attribute) is False:
                     print(f"{attribute} of appliance {self.name} is not assigned")
@@ -1201,7 +1333,7 @@ def __eq__(self, other_appliance) -> bool:
                     print(
                         f"{attribute} of appliance {other_appliance.name} is not assigned"
                     )
-                np.append(answer, False)
+                answer = np.append(answer, [False])
         return answer.all()
 
     def windows(
@@ -1299,13 +1431,17 @@ def windows(
         )  # same as above for window3
 
         self.random_var_1 = int(
-            random_var_w * np.diff(self.window_1)
+            random_var_w * np.diff(self.window_1)[0]
         )  # calculate the random variability of window1, i.e. the maximum range of time they can be enlarged or shortened
-        self.random_var_2 = int(random_var_w * np.diff(self.window_2))  # same as above
-        self.random_var_3 = int(random_var_w * np.diff(self.window_3))  # same as above
-        self.user.App_list.append(
-            self
-        )  # automatically appends the appliance to the user's appliance list
+        self.random_var_2 = int(
+            random_var_w * np.diff(self.window_2)[0]
+        )  # same as above
+        self.random_var_3 = int(
+            random_var_w * np.diff(self.window_3)[0]
+        )  # same as above
+
+        # automatically appends the appliance to the user's appliance list
+        self.user._add_appliance_instance(self)
 
         if self.fixed_cycle == 1:
             self.cw11 = self.window_1
@@ -1412,18 +1548,19 @@ def update_daily_use(self, coincidence, power, indexes):
         if (
             self.fixed_cycle > 0
         ):  # evaluates if the app has some duty cycles to be considered
-            evaluate = np.round(np.mean(indexes)) if indexes.size > 0 else 0
-            # selects the proper duty cycle and puts the corresponding power values in the indexes range
-            if evaluate in range(self.cw11[0], self.cw11[1]) or evaluate in range(
-                self.cw12[0], self.cw12[1]
-            ):
+            # the proper duty cycle was selected in self.rand_switch_on_window()
+            # now setting the corresponding power values in the indexes range
+            if self.current_duty_cycle_id == 1:
                 np.put(self.daily_use, indexes, (self.random_cycle1 * coincidence))
-            elif evaluate in range(self.cw21[0], self.cw21[1]) or evaluate in range(
-                self.cw22[0], self.cw22[1]
-            ):
+            elif self.current_duty_cycle_id == 2:
                 np.put(self.daily_use, indexes, (self.random_cycle2 * coincidence))
-            else:
+            elif self.current_duty_cycle_id == 3:
                 np.put(self.daily_use, indexes, (self.random_cycle3 * coincidence))
+            else:
+                print(
+                    f"The app {self.name} has duty cycle option on, however the switch on event fell outside the provided duty cycle windows"
+                )
+
         else:  # if no duty cycles are specified, a regular switch_on event is modelled
             # randomises also the App Power if thermal_p_var is on
             np.put(
@@ -1496,13 +1633,13 @@ def specific_cycle_1(
             Power rating for first part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_11 : int[0,1440], optional
-            Duration (minutes) of first part of first duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of first part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         p_12 : int, float, optional
             Power rating for second part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_12 : int[0,1440], optional
-            Duration (minutes) of second part of first duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of second part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         r_c1 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
@@ -1538,13 +1675,13 @@ def specific_cycle_2(
             Power rating for first part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_21 : int[0,1440], optional
-            Duration (minutes) of first part of second duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of first part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         p_22 : int, float, optional
             Power rating for second part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_22 : int[0,1440], optional
-            Duration (minutes) of second part of second duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of second part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         r_c2 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
@@ -1576,25 +1713,25 @@ def specific_cycle_3(
 
         Parameters
         ----------
-        p_21 : float, optional
+        p_31 : float, optional
             Power rating for first part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        t_21 : int[0,1440], optional
-            Duration (minutes) of first part of third duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+        t_31 : int[0,1440], optional
+            Duration (minutes) of first part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        p_22 : int, float, optional
+        p_32 : int, float, optional
             Power rating for second part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        t_22 : int[0,1440], optional
-            Duration (minutes) of second part of third duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+        t_32 : int[0,1440], optional
+            Duration (minutes) of second part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        r_c2 : Percentage [0,1], optional
+        r_c3 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
 
-        cw21 : Iterable, optional
+        cw31 : Iterable, optional
             Window time range for the first part of third duty cycle number (not neccessarily linked to the overall time window), by default None
 
-        cw22 : Iterable, optional
+        cw32 : Iterable, optional
             Window time range for the first part of third duty cycle number (not neccessarily linked to the overall time window), by default None, by default None
         """
         self.p_31 = p_31
@@ -1727,6 +1864,58 @@ def rand_switch_on_window(self, rand_time: int):
                 raise ValueError(
                     "There is something fishy with upper limit in switch on..."
                 )
+
+            if (
+                self.fixed_cycle > 0
+            ):  # evaluates if the app has some duty cycles to be considered
+                indexes_low = indexes[0]
+                indexes_high = indexes[-1]
+                # selects the proper duty cycle
+                if range_within_window(
+                    indexes_low, indexes_high, self.cw11
+                ) or range_within_window(indexes_low, indexes_high, self.cw12):
+                    self.current_duty_cycle_id = 1
+                    duty_cycle_duration = len(self.random_cycle1)
+                elif range_within_window(
+                    indexes_low, indexes_high, self.cw21
+                ) or range_within_window(indexes_low, indexes_high, self.cw22):
+                    self.current_duty_cycle_id = 2
+                    duty_cycle_duration = len(self.random_cycle2)
+                elif range_within_window(
+                    indexes_low, indexes_high, self.cw31
+                ) or range_within_window(indexes_low, indexes_high, self.cw32):
+                    self.current_duty_cycle_id = 3
+                    duty_cycle_duration = len(self.random_cycle3)
+                else:
+                    # previously duty_cycle3 was always considered as default if neither duty_cycle1 nor duty_cycle2
+                    # got selected. If the switch on window does not fall within any duty cycle we do not assign it
+                    # to duty_cycle3 by default, rather we pick another switch on event and we notify the user we
+                    # did so. That way, in case this warning is shown too often, it can indicate to the user there
+                    # is some peculiar behavior for this appliance
+                    warnings.warn(
+                        f"The app {self.name} has duty cycle option on (with {self.fixed_cycle} cycle(s)). However, the switch on window [{switch_on}, {switch_on + len(indexes)}] fell outside the provided duty cycle windows: "
+                        + "cw11 "
+                        + str(self.cw11)
+                        + ", cw12 "
+                        + str(self.cw12)
+                        + ", cw21 "
+                        + str(self.cw21)
+                        + ", cw22 "
+                        + str(self.cw22)
+                        + ", cw31 "
+                        + str(self.cw31)
+                        + ", cw32 "
+                        + str(self.cw32)
+                        + ". Picking another random switch on event. You probably see this warning because your window of use is the same as the duty cycle window and the random variability of the windows of use is greater than zero. If you see this warning only once, no need to worry, this is inherent to stochasticity."
+                    )
+                    return self.rand_switch_on_window(rand_time)
+
+                if (
+                    indexes.size > duty_cycle_duration
+                    and self.continuous_duty_cycle == 0
+                ):
+                    # Limit switch_on_window to duration of duty_cycle
+                    indexes = indexes[0:duty_cycle_duration]
         else:
             indexes = None
             # there are no available windows anymore
@@ -1754,7 +1943,7 @@ def calc_coincident_switch_on(self, inside_peak_window: bool = True):
                     1,
                     math.ceil(
                         random.gauss(
-                            mu=(self.number * mu_peak + 0.5),
+                            mu=(self.number * mu_peak),
                             sigma=(s_peak * self.number * mu_peak),
                         )
                     ),
@@ -1799,6 +1988,8 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
             or (self.pref_index != 0 and self.user.rand_daily_pref != self.pref_index)
             # checks if the app is allowed in the given yearly behaviour pattern
             or self.wd_we_type not in [day_type, 2]
+            # skip if the app has a func_time of 0
+            or self.func_time == 0
         ):
             return
 
@@ -1845,7 +2036,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
         ]
 
         tot_time = 0
-        while tot_time <= rand_time:
+        while tot_time <= rand_time and rand_time != 0:
             # one option could be to generate a lot of them at once
             indexes = self.rand_switch_on_window(
                 rand_time=rand_time,  # TODO maybe only consider rand_time-tot_time ...
diff --git a/ramp/core/utils.py b/ramp/core/utils.py
index 8274de85..cac6936d 100644
--- a/ramp/core/utils.py
+++ b/ramp/core/utils.py
@@ -138,6 +138,20 @@ def duty_cycle(var, t1, p1, t2, p2):
     )
 
 
+def range_within_window(range_low, range_high, window):
+    """Compare a range with a window to see if there is an overlap
+
+    The two cases where there is no overlap between two windows are when the
+    range boundaries are both lower than the lowest window value or both
+    higher than the highest window value
+
+    """
+    return not (
+        (range_low < window[0] and range_high < window[0])
+        or (range_low > window[1] and range_high > window[1])
+    )
+
+
 def random_choice(var, t1, p1, t2, p2):
     """Chooses one of two duty cycles randomly
 
diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index 8ff7cd6b..ae9cb1cd 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -19,7 +19,7 @@
 """
 
 # Create new user classes
-HI = User("high income", 11, 3)
+HI = User(user_name="high income", num_users=11, user_preference=3)
 User_list.append(HI)
 
 HMI = User("higher middle income", 38, 3)
@@ -46,43 +46,59 @@
 # Create new appliances
 
 # Church
-Ch_indoor_bulb = Church.Appliance(10, 26, 1, 210, 0.2, 60, "yes", flat="yes")
-Ch_indoor_bulb.windows([1200, 1440], [0, 0], 0.1)
+Ch_indoor_bulb = Church.add_appliance(
+    number=10,
+    power=26,
+    num_windows=1,
+    func_time=210,
+    time_fraction_random_variability=0.2,
+    func_cycle=60,
+    fixed="yes",
+    flat="yes",
+    name="indoor_bulb",
+)
+Ch_indoor_bulb.windows(window_1=[1200, 1440], window_2=[0, 0], random_var_w=0.1)
 
-Ch_outdoor_bulb = Church.Appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
+Ch_outdoor_bulb = Church.add_appliance(
+    7, 26, 1, 150, 0.2, 60, "yes", flat="yes", name="outdoor_bulb"
+)
 Ch_outdoor_bulb.windows([1200, 1440], [0, 0], 0.1)
 
-Ch_speaker = Church.Appliance(1, 100, 1, 150, 0.2, 60)
+Ch_speaker = Church.add_appliance(1, 100, 1, 150, 0.2, 60, name="speaker")
 Ch_speaker.windows([1200, 1350], [0, 0], 0.1)
 
 # Public lighting
-Pub_lights = Public_lighting.Appliance(12, 40, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights = Public_lighting.add_appliance(
+    12, 40, 2, 310, 0.1, 300, "yes", flat="yes", name="lights"
+)
 Pub_lights.windows([0, 336], [1110, 1440], 0.2)
 
-Pub_lights_2 = Public_lighting.Appliance(25, 150, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights_2 = Public_lighting.add_appliance(
+    25, 150, 2, 310, 0.1, 300, "yes", flat="yes", name="lights2"
+)
 Pub_lights_2.windows([0, 336], [1110, 1440], 0.2)
 
 
 # High-Income
-HI_indoor_bulb = HI.Appliance(6, 7, 2, 120, 0.2, 10)
+HI_indoor_bulb = HI.add_appliance(6, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 HI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HI_outdoor_bulb = HI.Appliance(2, 13, 2, 600, 0.2, 10)
+HI_outdoor_bulb = HI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 HI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HI_TV = HI.Appliance(2, 60, 3, 180, 0.1, 5)
+HI_TV = HI.add_appliance(2, 60, 3, 180, 0.1, 5, name="TV")
 HI_TV.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_DVD = HI.Appliance(1, 8, 3, 60, 0.1, 5)
+HI_DVD = HI.add_appliance(1, 8, 3, 60, 0.1, 5, name="DVD")
 HI_DVD.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Antenna = HI.Appliance(1, 8, 3, 120, 0.1, 5)
+HI_Antenna = HI.add_appliance(1, 8, 3, 120, 0.1, 5, name="antenna")
 HI_Antenna.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Phone_charger = HI.Appliance(5, 2, 2, 300, 0.2, 5)
+HI_Phone_charger = HI.add_appliance(5, 2, 2, 300, 0.2, 5, name="phone_charger")
 HI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HI_Freezer = HI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 HI_Freezer.windows([0, 1440], [0, 0])
 HI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -91,7 +107,7 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Freezer2 = HI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer2 = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer2")
 HI_Freezer2.windows([0, 1440], [0, 0])
 HI_Freezer2.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer2.specific_cycle_2(200, 15, 5, 15)
@@ -100,32 +116,32 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Mixer = HI.Appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HI_Mixer = HI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 HI_Mixer.windows([420, 480], [660, 750], 0.35, [1140, 1200])
 
 # Higher-Middle Income
-HMI_indoor_bulb = HMI.Appliance(5, 7, 2, 120, 0.2, 10)
+HMI_indoor_bulb = HMI.add_appliance(5, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 HMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HMI_outdoor_bulb = HMI.Appliance(2, 13, 2, 600, 0.2, 10)
+HMI_outdoor_bulb = HMI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 HMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HMI_TV = HMI.Appliance(1, 60, 2, 120, 0.1, 5)
+HMI_TV = HMI.add_appliance(1, 60, 2, 120, 0.1, 5, name="TV")
 HMI_TV.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_DVD = HMI.Appliance(1, 8, 2, 40, 0.1, 5)
+HMI_DVD = HMI.add_appliance(1, 8, 2, 40, 0.1, 5, name="DVD")
 HMI_DVD.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Antenna = HMI.Appliance(1, 8, 2, 80, 0.1, 5)
+HMI_Antenna = HMI.add_appliance(1, 8, 2, 80, 0.1, 5, name="antenna")
 HMI_Antenna.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Radio = HMI.Appliance(1, 36, 2, 60, 0.1, 5)
+HMI_Radio = HMI.add_appliance(1, 36, 2, 60, 0.1, 5, name="radio")
 HMI_Radio.windows([390, 450], [1140, 1260], 0.35)
 
-HMI_Phone_charger = HMI.Appliance(4, 2, 2, 300, 0.2, 5)
+HMI_Phone_charger = HMI.add_appliance(4, 2, 2, 300, 0.2, 5, name="phone_charger")
 HMI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HMI_Freezer = HMI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HMI_Freezer = HMI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 HMI_Freezer.windows([0, 1440], [0, 0])
 HMI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HMI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -134,61 +150,61 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HMI_Mixer = HMI.Appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HMI_Mixer = HMI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 HMI_Mixer.windows([420, 450], [660, 750], 0.35, [1020, 1170])
 
 # Lower-Midlle Income
-LMI_indoor_bulb = LMI.Appliance(3, 7, 2, 120, 0.2, 10)
+LMI_indoor_bulb = LMI.add_appliance(3, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 LMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LMI_outdoor_bulb = LMI.Appliance(2, 13, 2, 600, 0.2, 10)
+LMI_outdoor_bulb = LMI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 LMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LMI_TV = LMI.Appliance(1, 60, 3, 90, 0.1, 5)
+LMI_TV = LMI.add_appliance(1, 60, 3, 90, 0.1, 5, name="TV")
 LMI_TV.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_DVD = LMI.Appliance(1, 8, 3, 30, 0.1, 5)
+LMI_DVD = LMI.add_appliance(1, 8, 3, 30, 0.1, 5, name="DVD")
 LMI_DVD.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Antenna = LMI.Appliance(1, 8, 3, 60, 0.1, 5)
+LMI_Antenna = LMI.add_appliance(1, 8, 3, 60, 0.1, 5, name="antenna")
 LMI_Antenna.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Phone_charger = LMI.Appliance(4, 2, 1, 300, 0.2, 5)
+LMI_Phone_charger = LMI.add_appliance(4, 2, 1, 300, 0.2, 5, name="phone_charger")
 LMI_Phone_charger.windows([1020, 1440], [0, 0], 0.35)
 
-LMI_Mixer = LMI.Appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33)
+LMI_Mixer = LMI.add_appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 LMI_Mixer.windows([660, 750], [1110, 1200], 0.35)
 
 # Low Income
-LI_indoor_bulb = LI.Appliance(2, 7, 2, 120, 0.2, 10)
+LI_indoor_bulb = LI.add_appliance(2, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 LI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LI_outdoor_bulb = LI.Appliance(1, 13, 2, 600, 0.2, 10)
+LI_outdoor_bulb = LI.add_appliance(1, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 LI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LI_TV = LI.Appliance(1, 60, 3, 90, 0.1, 5)
+LI_TV = LI.add_appliance(1, 60, 3, 90, 0.1, 5, name="TV")
 LI_TV.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_DVD = LI.Appliance(1, 8, 3, 30, 0.1, 5)
+LI_DVD = LI.add_appliance(1, 8, 3, 30, 0.1, 5, name="DVD")
 LI_DVD.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Antenna = LI.Appliance(1, 8, 3, 60, 0.1, 5)
+LI_Antenna = LI.add_appliance(1, 8, 3, 60, 0.1, 5, name="antenna")
 LI_Antenna.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Phone_charger = LI.Appliance(2, 2, 1, 300, 0.2, 5)
+LI_Phone_charger = LI.add_appliance(2, 2, 1, 300, 0.2, 5, name="phone_charger")
 LI_Phone_charger.windows([1080, 1440], [0, 0], 0.35)
 
 # Hospital
-Ho_indoor_bulb = Hospital.Appliance(12, 7, 2, 690, 0.2, 10)
+Ho_indoor_bulb = Hospital.add_appliance(12, 7, 2, 690, 0.2, 10, name="indoor_bulb")
 Ho_indoor_bulb.windows([480, 720], [870, 1440], 0.35)
 
-Ho_outdoor_bulb = Hospital.Appliance(1, 13, 2, 690, 0.2, 10)
+Ho_outdoor_bulb = Hospital.add_appliance(1, 13, 2, 690, 0.2, 10, name="outdoor_bulb")
 Ho_outdoor_bulb.windows([0, 330], [1050, 1440], 0.35)
 
-Ho_Phone_charger = Hospital.Appliance(8, 2, 2, 300, 0.2, 5)
+Ho_Phone_charger = Hospital.add_appliance(8, 2, 2, 300, 0.2, 5, name="phone_charger")
 Ho_Phone_charger.windows([480, 720], [900, 1440], 0.35)
 
-Ho_Fridge = Hospital.Appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3, name="fridge")
 Ho_Fridge.windows([0, 1440], [0, 0])
 Ho_Fridge.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge.specific_cycle_2(150, 15, 5, 15)
@@ -197,47 +213,49 @@
     [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_Fridge2 = Hospital.Appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge2 = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3, name="fridge2")
 Ho_Fridge2.windows([0, 1440], [0, 0])
 Ho_Fridge2.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge2.specific_cycle_2(150, 15, 5, 15)
 Ho_Fridge2.specific_cycle_3(150, 10, 5, 20)
 Ho_Fridge2.cycle_behaviour(
-    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 299], [1201, 1440]
+    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_Fridge3 = Hospital.Appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3)
+Ho_Fridge3 = Hospital.add_appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3, name="fridge3")
 Ho_Fridge3.windows([0, 1440], [0, 0])
 Ho_Fridge3.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge3.specific_cycle_2(150, 15, 5, 15)
 Ho_Fridge3.specific_cycle_3(150, 10, 5, 20)
 Ho_Fridge3.cycle_behaviour(
-    [580, 1200], [0, 0], [420, 479], [0, 0], [0, 419], [1201, 1440]
+    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_PC = Hospital.Appliance(2, 50, 2, 300, 0.1, 10)
+Ho_PC = Hospital.add_appliance(2, 50, 2, 300, 0.1, 10, name="PC")
 Ho_PC.windows([480, 720], [1050, 1440], 0.35)
 
-Ho_Mixer = Hospital.Appliance(1, 50, 2, 60, 0.1, 1, occasional_use=0.33)
+Ho_Mixer = Hospital.add_appliance(
+    1, 50, 2, 60, 0.1, 1, occasional_use=0.33, name="mixer"
+)
 Ho_Mixer.windows([480, 720], [1050, 1440], 0.35)
 
 # School
-S_indoor_bulb = School.Appliance(8, 7, 1, 60, 0.2, 10)
+S_indoor_bulb = School.add_appliance(8, 7, 1, 60, 0.2, 10, name="indoor_bulb")
 S_indoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_outdoor_bulb = School.Appliance(6, 13, 1, 60, 0.2, 10)
+S_outdoor_bulb = School.add_appliance(6, 13, 1, 60, 0.2, 10, name="outdoor_bulb")
 S_outdoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_Phone_charger = School.Appliance(5, 2, 2, 180, 0.2, 5)
+S_Phone_charger = School.add_appliance(5, 2, 2, 180, 0.2, 5, name="phone_charger")
 S_Phone_charger.windows([510, 750], [810, 1080], 0.35)
 
-S_PC = School.Appliance(18, 50, 2, 210, 0.1, 10)
+S_PC = School.add_appliance(18, 50, 2, 210, 0.1, 10, name="PC")
 S_PC.windows([510, 750], [810, 1080], 0.35)
 
-S_Printer = School.Appliance(1, 20, 2, 30, 0.1, 5)
+S_Printer = School.add_appliance(1, 20, 2, 30, 0.1, 5, name="printer")
 S_Printer.windows([510, 750], [810, 1080], 0.35)
 
-S_Freezer = School.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+S_Freezer = School.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 S_Freezer.windows([0, 1440])
 S_Freezer.specific_cycle_1(200, 20, 5, 10)
 S_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -246,13 +264,15 @@
     [580, 1200], [0, 0], [510, 579], [0, 0], [0, 509], [1201, 1440]
 )
 
-S_TV = School.Appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5)
+S_TV = School.add_appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5, name="TV")
 S_TV.windows([510, 750], [810, 1080], 0.35)
 
-S_DVD = School.Appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5)
+S_DVD = School.add_appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5, name="DVD")
 S_DVD.windows([510, 750], [810, 1080], 0.35)
 
-S_Stereo = School.Appliance(1, 150, 2, 90, 0.1, 5, occasional_use=0.33)
+S_Stereo = School.add_appliance(
+    1, 150, 2, 90, 0.1, 5, occasional_use=0.33, name="stereo"
+)
 S_Stereo.windows([510, 750], [810, 1080], 0.35)
 
 if __name__ == "__main__":
diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index 4e738882..cbe59067 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -18,14 +18,21 @@
 """
 
 # Create new user classes
-HH = User("generic households", 1)
+HH = User(user_name="generic households", num_users=1)
 User_list.append(HH)
 
 HH_shower_P = pd.read_csv("ramp/example/shower_P.csv")
-
-# High-Income
-HH_shower = HH.Appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_P_var=0.2)
-HH_shower.windows([390, 540], [1080, 1200], 0.2)
+HH_shower = HH.add_appliance(
+    number=1,
+    power=HH_shower_P,
+    num_windows=2,
+    func_time=15,
+    time_fraction_random_variability=0.1,
+    func_cycle=3,
+    thermal_p_var=0.2,
+    name="shower",
+)
+HH_shower.windows(window_1=[390, 540], window_2=[1080, 1200], random_var_w=0.2)
 
 
 if __name__ == "__main__":
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index 1572e526..2414e4a9 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -17,77 +17,156 @@
 """
 
 # Create new user classes
-HH = User("generic household", 1, 3)
+HH = User(user_name="generic household", num_users=1, user_preference=3)
 User_list.append(HH)
 
 # Create new appliances
 
 # Create Cooking appliances
 
-HH_lunch1_soup = HH.Appliance(
-    1, 1800, 2, 70, 0.15, 60, thermal_P_var=0.2, pref_index=1, fixed_cycle=1
+HH_lunch1_soup = HH.add_appliance(
+    number=1,
+    power=1800,
+    num_windows=2,
+    func_time=70,
+    time_fraction_random_variability=0.15,
+    func_cycle=60,
+    thermal_p_var=0.2,
+    pref_index=1,
+    fixed_cycle=1,
+    name="lunch1_soup",
 )
-HH_lunch1_soup.windows([12 * 60, 15 * 60], [0, 0], 0.15)
-HH_lunch1_soup.specific_cycle_1(1800, 10, 750, 60, 0.15)
-HH_lunch1_soup.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
-
-HH_lunch2_rice = HH.Appliance(
-    1, 1800, 2, 25, 0.15, 20, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
+HH_lunch1_soup.windows(window_1=[12 * 60, 15 * 60], window_2=[0, 0], random_var_w=0.15)
+HH_lunch1_soup.specific_cycle_1(p_11=1800, t_11=10, p_12=750, t_12=60, r_c1=0.15)
+HH_lunch1_soup.cycle_behaviour(cw11=[12 * 60, 15 * 60], cw12=[0, 0])
+
+HH_lunch2_rice = HH.add_appliance(
+    1,
+    1800,
+    2,
+    25,
+    0.15,
+    20,
+    thermal_p_var=0.2,
+    pref_index=2,
+    fixed_cycle=1,
+    name="lunch2_rice",
 )
 HH_lunch2_rice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_rice.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch2_rice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_egg = HH.Appliance(1, 1200, 2, 3, 0.2, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_egg = HH.add_appliance(
+    1, 1200, 2, 3, 0.2, 3, thermal_p_var=0.2, pref_index=2, name="lunch2_egg"
+)
 HH_lunch2_egg.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch2_platano = HH.Appliance(
-    1, 1800, 2, 10, 0.15, 5, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
+HH_lunch2_platano = HH.add_appliance(
+    1,
+    1800,
+    2,
+    10,
+    0.15,
+    5,
+    thermal_p_var=0.2,
+    pref_index=2,
+    fixed_cycle=1,
+    name="lunch2_planato",
 )
 HH_lunch2_platano.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_platano.specific_cycle_1(1800, 5, 1200, 5, 0.15)
 HH_lunch2_platano.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_meat = HH.Appliance(1, 1200, 2, 7, 0.15, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_meat = HH.add_appliance(
+    1, 1200, 2, 7, 0.15, 3, thermal_p_var=0.2, pref_index=2, name="lunch2_meat"
+)
 HH_lunch2_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch3_beansnrice = HH.Appliance(
-    1, 1800, 2, 45, 0.2, 30, thermal_P_var=0.2, pref_index=3, fixed_cycle=1
+HH_lunch3_beansnrice = HH.add_appliance(
+    1,
+    1800,
+    2,
+    45,
+    0.2,
+    30,
+    thermal_p_var=0.2,
+    pref_index=3,
+    fixed_cycle=1,
+    name="lunch3_beansnrice",
 )
 HH_lunch3_beansnrice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch3_beansnrice.specific_cycle_1(1800, 10, 750, 35, 0.2)
 HH_lunch3_beansnrice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch3_meat = HH.Appliance(1, 1200, 2, 10, 0.2, 5, thermal_P_var=0.2, pref_index=3)
+HH_lunch3_meat = HH.add_appliance(
+    1, 1200, 2, 10, 0.2, 5, thermal_p_var=0.2, pref_index=3, name="lunch3_meat"
+)
 HH_lunch3_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch_yuca = HH.Appliance(
-    1, 1800, 1, 25, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+HH_lunch_yuca = HH.add_appliance(
+    1,
+    1800,
+    1,
+    25,
+    0.15,
+    10,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="lunch_yuca",
 )
 HH_lunch_yuca.windows([13 * 60, 14 * 60], [0, 0], 0.15)
 HH_lunch_yuca.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch_yuca.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_breakfast_huminta = HH.Appliance(
-    1, 1800, 1, 65, 0.15, 50, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+HH_breakfast_huminta = HH.add_appliance(
+    1,
+    1800,
+    1,
+    65,
+    0.15,
+    50,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="breakfast_huminta",
 )
 HH_breakfast_huminta.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_huminta.specific_cycle_1(1800, 5, 750, 60, 0.15)
 HH_breakfast_huminta.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
-HH_breakfast_bread = HH.Appliance(
-    1, 1800, 1, 15, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+HH_breakfast_bread = HH.add_appliance(
+    1,
+    1800,
+    1,
+    15,
+    0.15,
+    10,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="breakfast_bread",
 )
 HH_breakfast_bread.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_bread.specific_cycle_1(1800, 10, 1200, 5, 0.15)
 HH_breakfast_bread.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
-HH_breakfast_coffee = HH.Appliance(
-    1, 1800, 1, 5, 0.15, 2, thermal_P_var=0.2, pref_index=0
+HH_breakfast_coffee = HH.add_appliance(
+    1,
+    1800,
+    1,
+    5,
+    0.15,
+    2,
+    thermal_p_var=0.2,
+    pref_index=0,
+    name="break_fast_coffee",
 )
 HH_breakfast_coffee.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 
-HH_mate = HH.Appliance(1, 1800, 1, 30, 0.3, 2, thermal_P_var=0.2, pref_index=0)
+HH_mate = HH.add_appliance(
+    1, 1800, 1, 30, 0.3, 2, thermal_p_var=0.2, pref_index=0, name="mate"
+)
 HH_mate.windows([7 * 60, 20 * 60], [0, 0], 0.15)
 
 
diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 747e30e8..b8c45259 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -7,8 +7,7 @@
 from ramp.core.core import UseCase
 
 parser = argparse.ArgumentParser(
-    prog="python ramp_convert_old_input_files.py",
-    description="Convert old python input files to xlsx ones",
+    prog="ramp_convert", description="Convert RAMP python input files to xlsx ones"
 )
 parser.add_argument(
     "-i",
@@ -18,10 +17,7 @@
     help="path to the input file (including filename)",
 )
 parser.add_argument(
-    "-o",
-    dest="output_path",
-    type=str,
-    help="path where to save the converted filename",
+    "-o", dest="output_path", type=str, help="path where to save the converted filename"
 )
 parser.add_argument(
     "--suffix",
@@ -44,8 +40,25 @@
 def convert_old_user_input_file(
     fname_path, output_path=None, suffix="", keep_names=True
 ):
-    """
-    Imports an input file from a path and returns a processed User_list
+    """Convert old RAMP python input files to xlsx ones
+
+    The old (RAMP version < 0.5) .py input files defined all users and gathered them in a variable named User_list,
+    this variable must be defined in the .py file to be converted to .xlsx.
+
+    To convert a .py input file to an .xlsx using the UseCase objects, please refer to
+    https://rampdemand.readthedocs.io/en/latest/examples/using_excel/using_excel.html#exporting-the-database
+
+    Parameters
+    ----------
+    fname_path: path
+        path to a .py ramp input file containing a variable named User_list
+    output_path: path, optional
+        path to the converted .xlsx ramp input file, by default the same folder as the .py file
+    suffix: str, optional
+        suffix to be added to the converted .xlsx ramp input file name, default ''
+    keep_names: bool, optional
+        keep the variable names of the Appliance instances as their 'name' attribute, default True
+
     """
 
     line_to_change = -1
@@ -81,6 +94,7 @@ def convert_old_user_input_file(
     output_fname = fname_path.split(os.path.sep)[-1].replace(".py", suffix)
     output_fname = os.path.join(output_path, output_fname)
 
+    sys.path.insert(0, os.path.dirname(os.path.abspath(fname_path)))
     file_module = importlib.import_module(fname)
 
     user_list = file_module.User_list
@@ -88,7 +102,7 @@ def convert_old_user_input_file(
     UseCase(users=user_list).save(output_fname)
 
 
-if __name__ == "__main__":
+def cli():
     args = vars(parser.parse_args())
     fname = args["fname_path"]
     output_path = args.get("output_path")
@@ -105,3 +119,7 @@ def convert_old_user_input_file(
                 )
         else:
             convert_old_user_input_file(fname, output_path=output_path, suffix=suffix)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/ramp/ramp_run.py b/ramp/ramp_run.py
index 5484ef21..861f7e42 100644
--- a/ramp/ramp_run.py
+++ b/ramp/ramp_run.py
@@ -51,7 +51,7 @@ def run_usecase(
         )
         usecase.initialize(num_days=num_days)
         usecase.load(fname)
-        Profiles_list = usecase.generate_daily_load_profiles()
+        Profiles_list = usecase.generate_daily_load_profiles(flat=False)
 
         if plot is True:
             # TODO use new plotting
diff --git a/ramp/test/results/.gitkeep b/ramp/test/results/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index 9717fbb1..6135d75e 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -1,13 +1,15 @@
 # -*- coding: utf-8 -*-
 """
-Minimal qualitative test functionality. Compares outputs of the code to default outputs,
+Minimal qualitative test functionality. Compares the outputs of the code to default outputs,
 for the 3 reference input files. The judgement about whether the resulting changes are
-as expected or not is left to the developers
+as expected or not is left to the developer making the commit
 """
 
 # %% Import required modules
 import pandas as pd
 import matplotlib.pyplot as plt
+from ramp.core.core import UseCase
+from ramp.post_process import post_process as pp
 import os
 
 # %% Function to test the output against reference results
@@ -70,10 +72,10 @@ def series_to_average(profile_series, num_days):
             axes[n].set_xmargin(0)
             axes[n].set_ymargin(0)
 
-    axes[n].get_shared_x_axes().join(axes[n], axes[n - 1], axes[n - 2])
     axes[n - 1].legend()
     axes[n - 1].set_xticklabels([])
     axes[n - 2].set_xticklabels([])
+    plt.show()
 
 
 # %% Testing the output and providing visual result
@@ -81,8 +83,41 @@ def series_to_average(profile_series, num_days):
 Here the visual comparison between default and new/current results occurs.
 Besides the difference naturally occurring due to different realisations of stochastic
 parameters, the developers should check whether any other differences are brought by
-by the tested code changes. If any differences are there, the developers should 
+by the tested code changes. If any differences are there, the developers should
 evaluate whether these are as expected/designed or not
 """
+from ramp.example.input_file_1 import User_list as User_list1
+from ramp.example.input_file_2 import User_list as User_list2
+from ramp.example.input_file_3 import User_list as User_list3
 
-test_output("../results", "../test", num_input_files=3)
+TEST_OUTPUT_PATH = os.path.join(".", "ramp/test", "results")
+
+remove_old_tests = False
+for file in os.listdir(TEST_OUTPUT_PATH):
+    if file.endswith(".csv"):
+        if remove_old_tests is False:
+            answer = input(
+                "Some result file for the qualitative testing exists already, do you want to overwrite them? (y/n)"
+            )
+            if answer == "y" or answer == "yes":
+                remove_old_tests = True
+            else:
+                break
+        if remove_old_tests is True:
+            os.remove(os.path.join(TEST_OUTPUT_PATH, file))
+
+for i, ul in enumerate([User_list1, User_list2, User_list3]):
+    of_path = os.path.join(".", "ramp/test", "results", f"output_file_{i + 1}.csv")
+    if os.path.exists(of_path) is False:
+        uc = UseCase(users=ul, parallel_processing=False, random_seed=True)
+        uc.initialize(peak_enlarge=0.15, num_days=30)
+
+        Profiles_list = uc.generate_daily_load_profiles(flat=True)
+
+        pp.export_series(Profiles_list, ofname=of_path)
+
+test_output(
+    os.path.join(".", "ramp/test", "results"),
+    os.path.join(".", "ramp/test"),
+    num_input_files=3,
+)
diff --git a/setup.py b/setup.py
index 81211ae9..83fa52ff 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
     version=__version__,
     packages=find_packages(),
     license="European Union Public License 1.2",
-    python_requires=">=3.6.0",
+    python_requires="<3.12",
     package_data={"": ["*.txt", "*.dat", "*.doc", "*.rst", "*.xlsx", "*.csv"]},
     install_requires=[
         "pandas >= 1.3.3",
@@ -23,6 +23,7 @@
         "openpyxl >= 3.0.6",
         "tqdm",
         "plotly",
+        "multiprocess",
     ],
     # classifiers=[
     #     "Programming Language :: Python :: 3.7",
@@ -39,6 +40,7 @@
     entry_points={
         "console_scripts": [
             "ramp=ramp.cli:main",
+            "ramp_convert=ramp.ramp_convert_old_input_files:cli",
         ],
     },
 )
diff --git a/tests/__test_year_behavior.py b/tests/__test_year_behavior.py
new file mode 100644
index 00000000..52d00c4c
--- /dev/null
+++ b/tests/__test_year_behavior.py
@@ -0,0 +1,104 @@
+import os
+import pytest
+import numpy as np
+
+from ramp.core.core import User
+
+
+class TestApplianceClass:
+    def setup_method(self):
+        HI = User("high income", 1)
+        self.HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+        self.HI_Freezer.windows([0, 1440], [0, 0])
+        self.HI_Freezer.specific_cycle_1(200, 20, 5, 10)
+        self.HI_Freezer.specific_cycle_2(200, 15, 5, 15)
+        self.HI_Freezer.specific_cycle_3(200, 10, 5, 20)
+        self.HI_Freezer.cycle_behaviour(
+            [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
+        )
+        self.HI_Freezer.assign_random_cycles()
+
+        self.HI_Freezer_fixed = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+        self.HI_Freezer_fixed.windows([0, 1440], [0, 0])
+
+        self.HI_Freezer_var = HI.add_appliance(
+            1, 200, 1, 1440, 0, 30, "yes", 3, thermal_p_var=0.1
+        )
+        self.HI_Freezer_var.windows([0, 1440], [0, 0])
+
+    def teardown_method(self):
+        pass
+
+    def test_first_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        with pytest.raises(ValueError):
+            print(self.HI_Freezer.daily_use)
+            self.HI_Freezer.update_daily_use(
+                coincidence=1, power=1, indexes=np.array([1])
+            )
+            print(self.HI_Freezer.daily_use)
+        # print(self.HI_Freezer.random_cycle1)
+
+        # pytest.fail()
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_first_cycle_correctly_updated(self):
+        pass
+
+    def test_second_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=4, power=1, indexes=np.array([1]))
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_coincidence_number(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=2, power=1, indexes=np.array([1]))
+        print(self.HI_Freezer.random_cycle1)
+        print(self.HI_Freezer.daily_use)
+        assert self.HI_Freezer.daily_use[9] == 400.0
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_third_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=1, power=1, indexes=np.array([1]))
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_daily_use_without_duty_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer_fixed.update_daily_use(
+            coincidence=1, power=1, indexes=np.array([1])
+        )
+
+    def test_daily_use_without_duty_cycle_but_thermal_var_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer_var.update_daily_use(
+            coincidence=1, power=1, indexes=np.array([1])
+        )
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+
+def test_something():
+    """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+    HI = User("high income", 1)
+    HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+    HI_Freezer.windows([0, 1440], [0, 0])
+    HI_Freezer.specific_cycle_1(200, 20, 5, 10)
+    HI_Freezer.specific_cycle_2(200, 15, 5, 15)
+    HI_Freezer.specific_cycle_3(200, 10, 5, 20)
+    HI_Freezer.cycle_behaviour(
+        [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
+    )
+    HI_Freezer.assign_random_cycles()
+    # do somthing here for this particular usecase
+
+    # test that the daily use is updated by the value (random_cycle_1 * coincidence)
diff --git a/tests/requirements.txt b/tests/requirements.txt
index d3c960f9..8ba4d4e7 100644
--- a/tests/requirements.txt
+++ b/tests/requirements.txt
@@ -1,5 +1,8 @@
 pytest
-scipy
+mock
+scipy==1.12.0
 nbconvert
 ipykernel
+coverage==7.4.4
+coveralls==4.0.0
 -e .
\ No newline at end of file
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 00000000..4448355d
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,96 @@
+import os
+import shutil
+
+import mock
+import matplotlib.pyplot as plt
+import pytest
+from ramp.cli import parser as ramp_parser, main as ramp_main
+from .utils import TEST_PATH, TEST_OUTPUT_PATH
+
+
+class TestProcessUserArguments:
+    def setup_method(self):
+        if os.path.exists(TEST_OUTPUT_PATH):
+            shutil.rmtree(TEST_OUTPUT_PATH, ignore_errors=True)
+        if os.path.exists(TEST_OUTPUT_PATH) is False:
+            os.mkdir(TEST_OUTPUT_PATH)
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            ["--start-date", "2022-01-01", "-y", "2022"]
+        ),
+    )
+    def test_impossible_option_combinaison_start_date_year(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(["--end-date", "2022-01-01", "-y", "2022"]),
+    )
+    def test_impossible_option_combinaison_end_date_year(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_PATH, "test_inputs", "example_excel_usecase.xlsx"),
+                "-y",
+                "2022",
+                "2023",
+                "-o",
+                os.path.join(TEST_OUTPUT_PATH, "example_excel.csv"),
+            ]
+        ),
+    )
+    def test_multiple_year_is_possible(self, m_args, monkeypatch):
+        monkeypatch.setattr(
+            plt, "show", lambda: None
+        )  # prevents the test to output figure
+        ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_OUTPUT_PATH),
+                "-y",
+                "2022",
+                "-n",
+                "1",
+            ]
+        ),
+    )
+    def test_month_variation_without_month_files(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_OUTPUT_PATH),
+                "-y",
+                "2022",
+                "-n",
+                "1",
+            ]
+        ),
+    )
+    def test_month_variation_with_month_files(self, m_args):
+        for i in range(12):
+            shutil.copy(
+                os.path.join(TEST_PATH, "test_inputs", "example_excel_usecase.xlsx"),
+                os.path.join(TEST_OUTPUT_PATH, f"example_excel_usecase_{i}.xlsx"),
+            )
+        ramp_main()
+
+    def teardown_method(self):
+        if os.path.exists(TEST_OUTPUT_PATH):
+            shutil.rmtree(TEST_OUTPUT_PATH, ignore_errors=True)
diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
new file mode 100644
index 00000000..b9153d15
--- /dev/null
+++ b/tests/test_duty_cycle_repetition.py
@@ -0,0 +1,78 @@
+import os
+import pytest
+import numpy as np
+
+from ramp import User, UseCase
+import pandas as pd
+
+pd.options.plotting.backend = "plotly"
+import plotly.io as pio
+
+pio.renderers.default = "browser"
+
+
+@pytest.fixture
+def test_use_case():
+    # Create an instance of UseCase to test fix for the duty cycle repetition issue 78
+
+    # %% Create test user
+    test_user = User(user_name="test_user", num_users=1)
+
+    # Create test appliance
+    test_appliance = test_user.add_appliance(
+        name="test_appliance_with_duty_cycles",
+        func_time=4 * 60,  # runs for 2 hours per day
+        window_1=[6 * 60, 20 * 60],  # usage timeframe from 10:00 to 17:00
+        num_windows=1,
+        fixed_cycle=1,  # appliance uses duty cycles
+        # Duty cycle 1
+        p_11=8000,  # power of the first cycle
+        t_11=2,  # time needed for the first cycle
+        p_12=2000,  # power of the second cycle
+        t_12=18,  # time needed for the second cycle
+        continuous_duty_cycle=0,
+    )
+    # Create and initialize UseCase
+    duty_cycle_test_uc = UseCase(name="duty_cycle_test", users=[test_user])
+    duty_cycle_test_uc.initialize(num_days=3)
+
+    return duty_cycle_test_uc
+
+
+class TestUseCase:
+    @pytest.mark.usefixtures("test_use_case")
+    def test_daily_load_profile(self, test_use_case):
+        # Generate load profiles
+        daily_load_profile = pd.DataFrame(
+            test_use_case.generate_daily_load_profiles(),
+            index=test_use_case.datetimeindex,
+        )
+
+        # Count separate switch-on events -> whenever there is a jump in the load profile from smaller or equal 0.001
+        # to a value larger 0.001 (0.001 is used in the RAMP algorithm to "mark" available switch-on events, therefore
+        # load profiles are often not actually 0 even though there is no appliance use scheduled
+
+        # create a boolean mask for the condition
+        mask = (daily_load_profile[0] <= 0.001) & (
+            daily_load_profile[0].shift(-1) > 0.001
+        )
+        # Sum the number of switch on events
+        switch_on_count = mask.sum()
+
+        # Get the duration of the test duty cycle
+        test_duty_cycle_duration = (
+            test_use_case.appliances[0].t_11 + test_use_case.appliances[0].t_12
+        )
+
+        # Calculate how many switch-on events are expected
+        expected_switch_on_count = (
+            test_use_case.appliances[0].func_time / test_duty_cycle_duration
+        )
+
+        # In rare cases, it might happen that switch-on events are scheduled in direct succession
+        # Therefore, the test should not fail if the number of expected switch-on events is not fully reached.
+        # For the defined test, I allow 2 switch-on events less than expected
+
+        # The test fails, if there are 2 switch-on events less than expected
+
+        assert switch_on_count >= expected_switch_on_count - 2
diff --git a/tests/test_func_time_zero.py b/tests/test_func_time_zero.py
new file mode 100644
index 00000000..0cf1d0af
--- /dev/null
+++ b/tests/test_func_time_zero.py
@@ -0,0 +1,42 @@
+import numpy as np
+import pytest
+from ramp import UseCase, User
+
+
+def test_skip_when_func_time_zero():
+    # Generate test user and appliance
+    user = User(user_name="Test User", num_users=1)
+    appliance = user.add_appliance(
+        name="Test Appliance",
+        func_time=0,  # Set func_time to be 0
+        func_cycle=20,
+        time_fraction_random_variability=0.1,
+    )
+
+    # Add to use_case
+    use_case = UseCase(name="test_use_case", users=[user])
+
+    # Calculate peak time range for this use_case
+    peak_time_range = use_case.calc_peak_time_range()
+
+    # Generated one load profile of this appliance
+    power = 1000
+    appliance.generate_load_profile(
+        prof_i=0, peak_time_range=peak_time_range, day_type=0, power=power
+    )
+
+    # Check that no use of this appliance is simulated -> the appliances load profile is always smaller than it's power
+    # (Checking for the load_profile to always be 0 might be unreliable, since the RAMP core "marks" potential usage
+    # windows with small power values larger 0)
+    assert np.max(appliance.daily_use) < power
+
+
+def test_warning_when_func_time_zero():
+    user = User(user_name="Test User", num_users=1)
+    with pytest.warns():
+        appliance = user.add_appliance(
+            name="Test Appliance",
+            func_time=0,  # Set func_time to be 0
+            func_cycle=20,
+            time_fraction_random_variability=0.1,
+        )
diff --git a/tests/test_input_file_conversion.py b/tests/test_input_file_conversion.py
index a2f259e3..5c3b6674 100644
--- a/tests/test_input_file_conversion.py
+++ b/tests/test_input_file_conversion.py
@@ -69,11 +69,26 @@ def test_convert_py_to_xlsx(self):
                 if old_user != new_user:
                     pytest.fail()
 
+    def test_convert_py_to_xlsx_command_line(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+        for i, j in enumerate(self.input_files_to_run):
+            old_user_list = load_py_usecase(j=j)
+            output_path = os.path.join("ramp", "test")
+            os.system(
+                f"ramp_convert -i {self.py_fnames[i]} -o {output_path} --suffix {self.file_suffix}"
+            )
+            new_user_list = load_xlsx_usecase(fname=self.xlsx_fnames[i])
+            for old_user, new_user in zip(old_user_list, new_user_list):
+                if old_user != new_user:
+                    pytest.fail()
+
 
 def test_define_appliance_window_directly_equivalent_to_use_windows_method():
     user = User("test user", 1)
 
-    params = dict(number=1, power=200, num_windows=1, func_time=0)
+    params = dict(
+        number=1, power=200, num_windows=1, func_time=0, name="test_appliance"
+    )
     win_start = 390
     win_stop = 540
     appliance1 = user.add_appliance(**params)
@@ -95,6 +110,7 @@ def test_define_appliance_duty_cycle_directly_equivalent_to_use_specific_cycle_m
         func_time=0,
         window_1=[390, 540],
         fixed_cycle=1,
+        name="test_appliance",
     )
 
     appliance1 = user.add_appliance(**params)
@@ -136,7 +152,7 @@ def test_provide_no_appliance_window_when_declaring_one():
 def test_A():
     user = User("test user", 1)
 
-    old_params = dict(power=200, num_windows=1, func_time=0)
+    old_params = dict(power=200, num_windows=1, func_time=0, name="test_appliance")
     win_start = 390
     win_stop = 540
     appliance1 = user.Appliance(user, **old_params)
@@ -148,6 +164,7 @@ def test_A():
         num_windows=1,
         func_time=0,
         window_1=np.array([win_start, win_stop]),
+        name="test_appliance",
     )
     appliance2 = user.add_appliance(**params)
 
@@ -164,6 +181,7 @@ def test_B():
         func_time=0,
         window_1=[390, 540],
         fixed_cycle=1,
+        name="test_appliance",
     )
 
     appliance1 = user.add_appliance(**params)
diff --git a/tests/test_inputs/example_excel_usecase.xlsx b/tests/test_inputs/example_excel_usecase.xlsx
new file mode 100644
index 00000000..011cabc9
Binary files /dev/null and b/tests/test_inputs/example_excel_usecase.xlsx differ
diff --git a/tests/test_object_comparison.py b/tests/test_object_comparison.py
new file mode 100644
index 00000000..115cefef
--- /dev/null
+++ b/tests/test_object_comparison.py
@@ -0,0 +1,33 @@
+import pytest
+from ramp import User, Appliance
+
+
+@pytest.fixture
+def test_user():
+    # Create a User instance (you may need to provide the required arguments for User)
+    user = User(user_name="Test User", num_users=1)
+    return user
+
+
+@pytest.mark.usefixtures("test_user")
+def test_compare_two_appliances(test_user):
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    appliance2 = Appliance(
+        test_user,
+        name="test_appliance2",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    appliance3 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    assert appliance1 != appliance2
+    assert appliance1 == appliance3
diff --git a/tests/test_object_creation.py b/tests/test_object_creation.py
new file mode 100644
index 00000000..309ef569
--- /dev/null
+++ b/tests/test_object_creation.py
@@ -0,0 +1,44 @@
+import pytest
+
+from ramp import User, Appliance
+
+
+@pytest.fixture
+def test_user():
+    # Create a User instance (you may need to provide the required arguments for User)
+    user = User(user_name="Test User", num_users=1)
+    return user
+
+
+@pytest.mark.usefixtures("test_user")
+def test_add_several_appliances_to_user(test_user):
+    assert len(test_user.App_list) == 0
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    appliance2 = Appliance(
+        test_user,
+        name="test_appliance2",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    test_user.add_appliance(appliance1, appliance2)
+    assert len(test_user.App_list) == 2
+
+
+@pytest.mark.usefixtures("test_user")
+def test_skip_add_existing_appliances_to_user(test_user):
+    assert len(test_user.App_list) == 0
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    test_user.add_appliance(appliance1)
+
+    assert len(test_user.App_list) == 1
+
+    test_user.add_appliance(appliance1)
+
+    assert len(test_user.App_list) == 1
diff --git a/tests/test_switch_on.py b/tests/test_switch_on.py
index be7be8ee..1b51f71f 100644
--- a/tests/test_switch_on.py
+++ b/tests/test_switch_on.py
@@ -7,7 +7,10 @@
 """
 
 from ramp import User
-from scipy import stats
+import numpy as np
+import scipy.optimize as opt
+
+from ramp.core.constants import switch_on_parameters
 
 
 class TestRandSwitchOnWindow:
@@ -26,20 +29,64 @@ def test_all_appliances_switched_on_together(self):
     def test_coincidence_normality_on_peak(self):
         # Create an instance of the Appliance class with the desired parameters
 
-        appliance = self.user.add_appliance(number=10, fixed="no")
+        N = 100
+        appliance = self.user.add_appliance(number=N, fixed="no")
 
         # Generate a sample of 'coincidence' values
-        sample_size = 30
+        sample_size = N * 10
         coincidence_sample = []
         for _ in range(sample_size):
             coincidence = appliance.calc_coincident_switch_on(inside_peak_window=True)
             coincidence_sample.append(coincidence)
 
-        # Perform the Shapiro-Wilk test for normality
-        _, p_value = stats.shapiro(coincidence_sample)
+        def normed_dist(bins, mu, sigma):
+            return (
+                1
+                / (sigma * np.sqrt(2 * np.pi))
+                * np.exp(-((bins - mu) ** 2) / (2 * sigma**2))
+            )
+
+        # exclude the tail values i.e. only one appliance is switched on or all of them are, see https://github.com/RAMP-project/RAMP/issues/99 for illustrations
+        coincidence_sample = np.array(coincidence_sample)
+        max_val = np.max(coincidence_sample)
+        coincidence_sample_reduced = coincidence_sample[
+            np.where(coincidence_sample != 1)
+        ]
+        coincidence_sample_reduced = coincidence_sample_reduced[
+            np.where(coincidence_sample_reduced != max_val)
+        ]
+
+        # compute the experimental probability density function for appliance numbers from 2 to N-1
+        exp_pdf, bins = np.histogram(
+            coincidence_sample_reduced,
+            bins=[i for i in range(2, N + 1, 1)],
+            density=True,
+        )
 
-        # Assert that the p-value is greater than a chosen significance level
-        assert p_value > 0.05, "The 'coincidence' values are not normally distributed."
+        s_peak, mu_peak, op_factor = switch_on_parameters()
+        mu = mu_peak * N
+        sigma = s_peak * N * mu_peak
+
+        p0 = [mu, sigma]  # Inital guess of mean and std
+        errfunc = (
+            lambda p, x, y: normed_dist(x, *p) - y
+        )  # Distance to the target function
+        p1, success = opt.leastsq(errfunc, p0[:], args=(bins[:-1], exp_pdf))
+
+        # if not then the fit did not succeed
+        assert success in [1, 2, 3, 4]
+
+        fit_mu, fit_stdev = p1
+        tolerance_mu = 0.05  # arbitrary
+        tolerance_sigma = 0.1  # arbitrary
+        err_mu = np.abs(mu - fit_mu) / mu
+        err_sigma = np.abs(sigma - fit_stdev) / sigma
+        assert (
+            err_mu < tolerance_mu
+        ), f"The mean value of a normal fit onto the sampled coincidence histogram ({fit_mu}) divert more than {tolerance_mu*100} % of the provided gaussian mean ({mu})"
+        assert (
+            err_sigma < tolerance_sigma
+        ), f"The std value of a normal fit onto the sampled coincidence histogram ({fit_stdev}) divert more than {tolerance_sigma*100} % of the provided gaussian std ({sigma})"
 
     # Tests that the method returns a list of indexes within the available functioning windows when there are multiple available functioning windows and the random time is larger than the duration of the appliance's function cycle.
 
diff --git a/tests/utils.py b/tests/utils.py
new file mode 100644
index 00000000..0b65a076
--- /dev/null
+++ b/tests/utils.py
@@ -0,0 +1,4 @@
+import os
+
+TEST_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_OUTPUT_PATH = os.path.join(TEST_PATH, "test_outputs")