From 48db186936f77e62271c1cb6ea1bd8ae2b22206d Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Wed, 1 Mar 2023 11:58:54 +0100 Subject: [PATCH 01/47] Create the publish-pypi workflow (issue #71) --- .github/workflows/python-publish-pypi.yml | 35 +++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 .github/workflows/python-publish-pypi.yml diff --git a/.github/workflows/python-publish-pypi.yml b/.github/workflows/python-publish-pypi.yml new file mode 100644 index 00000000..c29d1e7a --- /dev/null +++ b/.github/workflows/python-publish-pypi.yml @@ -0,0 +1,35 @@ +name: Deploy to PyPi + +on: + release: + types: [created] + +jobs: + build_and_deploy: + name: Build the release and deploy to test pypi + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@master + + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + + - name: Install main dependencies + run: | + python -m pip install --upgrade pip twine setuptools wheel + - name: build distribution + run: | + python setup.py sdist bdist_wheel + - name: test install package + run: | + pip install -e . + - name: Publish distribution 📦 to PyPI + + uses: pypa/gh-action-pypi-publish@release/v1 + with: + verbose: true + password: ${{ secrets.RAMP_PYPI_PW }} + repository_url: https://upload.pypi.org/legacy/ \ No newline at end of file From 392588ffe72697556fe3a28c57fc64d4666228d6 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Wed, 1 Mar 2023 12:18:04 +0100 Subject: [PATCH 02/47] pypi test distribution workflow ( issue #71 ) --- .github/workflows/python-pypi-test.yml | 36 ++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .github/workflows/python-pypi-test.yml diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml new file mode 100644 index 00000000..66706c09 --- /dev/null +++ b/.github/workflows/python-pypi-test.yml @@ -0,0 +1,36 @@ +ame: Deploy to Test PyPi + +on: + push: + branches: + - development +jobs: + build_and_deploy: + name: Build the release and deploy to test pypi + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@master + + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + + - name: Install main dependencies + run: | + python -m pip install --upgrade pip twine setuptools wheel + - name: build distribution + run: | + python setup.py sdist bdist_wheel + - name: test install package + run: | + pip install -e . + - name: Publish distribution 📦 to Test PyPI + + uses: pypa/gh-action-pypi-publish@release/v1 + with: + skip_existing: true + verbose: true + password: ${{ secrets.SHAFT_PYPI_TEST }} + repository_url: https://test.pypi.org/legacy/ \ No newline at end of file From bd3fbcd5e0105d8ed6c909ee0a94348070a63e70 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:19:35 +0200 Subject: [PATCH 03/47] Fix installation guide and small typos --- README.rst | 34 +++++++++++++++++----------------- environment.yml | 4 ++-- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/README.rst b/README.rst index 628a6a7c..50400486 100644 --- a/README.rst +++ b/README.rst @@ -58,34 +58,35 @@ the following line in the *Anaconda Prompt* pip install rampdemand -Installing through source code +Installing through the source code ------------------------------ -You can also install RAMP from the source code! To do so, you first need to download the source code first: +You can also install RAMP from the source code! To do so, you first need to download the source code, which can be done in two ways: -1. you can use git to clone the repository using: +* You can use git to clone the repository via: .. code-block:: bash git clone https://github.com/RAMP-project/RAMP.git -2. you may download the source code directly from: +* Or, you may download the source code directly from: `"RAMP GitHub Repository" `_. -In this case, the source code will be downloaded as a zip file, so you need the extract the files. +In this second case, the source code will be downloaded as a zip file, so you'll need to extract the files. -After downloading the source code using any of abovementioned ways, you need to use your **anaconda prompt** to install the code. -You can follow the first two steps mentioned in **Installing through pip**. Then you need to change the directory of the promt to the folder where the source code is saved (where you can find the *setup.py* file). To install the RAMP software use: +After downloading the source code using any of abovem methods, you'll need to use your **anaconda prompt** to install it. There are two options again: + +* You may follow the first two steps mentioned in **Installing through pip**. Then, change the directory in the prompt to the folder where the source code is saved (where you can find the *setup.py* file). To install the RAMP software, you may then use: .. code-block:: bash python setup.py -Alternatively, you may use: +* Alternatively, without taking any prior action, simply change the directory in the prompt to the folder where the source code is saved and then use: .. code-block:: bash - conda env create -f requirements.yml + conda env create -f environment.yml Requirements ============ @@ -110,7 +111,7 @@ There are different ways to build a model using RAMP! Here, we provide a first e Example python input files -------------------------- Three different input files are provided as example representing three different categories of appliances that can be modelled with RAMP. -To have a look to the python files, you can download them using the "download_example" function: +To have a look to the python files, you can download them using the `download_example` function: .. code-block:: python @@ -153,15 +154,14 @@ To have a look to the python files, you can download them using the "download_ex Spreadsheet input files ----------------------- -It is also possible to use spreadsheets as input files. To do so you +It is also possible to use spreadsheets as input files. To do so, you need to run the ``ramp`` command with the option ``-i``: .. code-block:: bash ramp -i -If you already know -how many profile you want to simulate you can indicate it with the +If you already know how many profile you want to simulate, you can indicate it with the ``-n`` option: .. code-block:: bash @@ -173,7 +173,7 @@ providing a ``.xlsx`` input file with the ``-i`` option, this will then be equivalent to running ``python ramp_run.py`` from the ``ramp`` folder without being prompted for the number of profile within the console. -Other options are documented in the help of `ramp`, which you access with the ``-h`` option +Other options are documented in the helper of `ramp`, which you access with the ``-h`` option .. code-block:: bash @@ -181,13 +181,13 @@ Other options are documented in the help of `ramp`, which you access with the `` If you have existing python input files, you can convert them to -spreadsheet. To do so, go to ``ramp`` folder and run +spreadsheet. To do so, go to the ``\ramp`` folder and run .. code-block:: bash python ramp_convert_old_input_files.py -i -For other example of command lines options, such as setting date ranges, please visit `the dedicated section `_ of the documentation. +For other examples of command lines options, such as setting date ranges, please visit `the dedicated section `_ of the documentation. Building a model with a python script ------------------------------------- @@ -226,7 +226,7 @@ The use time frames can be specified using the 'window' method for each applianc window_2 = [1320,1380], # from 10 PM to 11 PM ) -Now you can generate your **stochastic Profiles**: +Now you can generate your **stochastic profiles**: .. code-block:: python diff --git a/environment.yml b/environment.yml index a556f539..2ae54e6d 100644 --- a/environment.yml +++ b/environment.yml @@ -1,5 +1,5 @@ -name: ramp_env +name: ramp dependencies: - python=3.8 - pip: - - -r requirements.txt \ No newline at end of file + - -r requirements.txt From d569ce902efecf51fd3b46058fbfc40b470a354d Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:21:29 +0200 Subject: [PATCH 04/47] Fix rst ordered list --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 50400486..6f3c67c1 100644 --- a/README.rst +++ b/README.rst @@ -38,20 +38,20 @@ You can install RAMP using pip, conda or from source code. Installing through pip ---------------------- -1. To install the RAMP software, we suggest to create a new environment by running the following command in the anaconda prompt: +#. To install the RAMP software, we suggest to create a new environment by running the following command in the anaconda prompt: .. code-block:: python conda create -n ramp python=3.8 -2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing +#. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the *Anaconda Prompt* .. code-block:: python conda activate ramp -3. Now you can use pip to install `rampdemand` on your environment as follow: +#. Now you can use pip to install `rampdemand` on your environment as follow: .. code-block:: python From fa20d26d613e433fee93a210c9ec33e7b8e129c0 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:29:26 +0200 Subject: [PATCH 05/47] Fix rst ordered list again --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 6f3c67c1..c39650be 100644 --- a/README.rst +++ b/README.rst @@ -43,7 +43,7 @@ Installing through pip .. code-block:: python conda create -n ramp python=3.8 - + #. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the *Anaconda Prompt* From 68a2d9c2e6d823384d0760068c4f966db21e9c8d Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:30:30 +0200 Subject: [PATCH 06/47] Fix rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index c39650be..7fdcbbe7 100644 --- a/README.rst +++ b/README.rst @@ -45,7 +45,7 @@ Installing through pip conda create -n ramp python=3.8 #. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing -the following line in the *Anaconda Prompt* +the following line in the *Anaconda Prompt*: .. code-block:: python From 9f0b9829296c2191ee4ce4b632d0708625b5a83a Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:32:08 +0200 Subject: [PATCH 07/47] Fix RST --- README.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 7fdcbbe7..59873487 100644 --- a/README.rst +++ b/README.rst @@ -40,20 +40,20 @@ Installing through pip ---------------------- #. To install the RAMP software, we suggest to create a new environment by running the following command in the anaconda prompt: -.. code-block:: python + .. code-block:: python conda create -n ramp python=3.8 #. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the *Anaconda Prompt*: -.. code-block:: python + .. code-block:: python conda activate ramp #. Now you can use pip to install `rampdemand` on your environment as follow: -.. code-block:: python + .. code-block:: python pip install rampdemand @@ -64,7 +64,7 @@ You can also install RAMP from the source code! To do so, you first need to down * You can use git to clone the repository via: -.. code-block:: bash + .. code-block:: bash git clone https://github.com/RAMP-project/RAMP.git @@ -78,13 +78,13 @@ After downloading the source code using any of abovem methods, you'll need to us * You may follow the first two steps mentioned in **Installing through pip**. Then, change the directory in the prompt to the folder where the source code is saved (where you can find the *setup.py* file). To install the RAMP software, you may then use: -.. code-block:: bash + .. code-block:: bash python setup.py * Alternatively, without taking any prior action, simply change the directory in the prompt to the folder where the source code is saved and then use: -.. code-block:: bash + .. code-block:: bash conda env create -f environment.yml From 0e33970dc97c4cdc2cfad62f138d9a6d2883b6a6 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:33:25 +0200 Subject: [PATCH 08/47] fix rst --- README.rst | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/README.rst b/README.rst index 59873487..61998a6b 100644 --- a/README.rst +++ b/README.rst @@ -38,22 +38,23 @@ You can install RAMP using pip, conda or from source code. Installing through pip ---------------------- -#. To install the RAMP software, we suggest to create a new environment by running the following command in the anaconda prompt: +#. To install the RAMP software, we suggest to create a new environment by running the following command in the *Anaconda prompt*: - .. code-block:: python +.. code-block:: python conda create -n ramp python=3.8 - + + #. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the *Anaconda Prompt*: - .. code-block:: python +.. code-block:: python conda activate ramp #. Now you can use pip to install `rampdemand` on your environment as follow: - .. code-block:: python +.. code-block:: python pip install rampdemand @@ -64,7 +65,7 @@ You can also install RAMP from the source code! To do so, you first need to down * You can use git to clone the repository via: - .. code-block:: bash +.. code-block:: bash git clone https://github.com/RAMP-project/RAMP.git @@ -78,13 +79,13 @@ After downloading the source code using any of abovem methods, you'll need to us * You may follow the first two steps mentioned in **Installing through pip**. Then, change the directory in the prompt to the folder where the source code is saved (where you can find the *setup.py* file). To install the RAMP software, you may then use: - .. code-block:: bash +.. code-block:: bash python setup.py * Alternatively, without taking any prior action, simply change the directory in the prompt to the folder where the source code is saved and then use: - .. code-block:: bash +.. code-block:: bash conda env create -f environment.yml From b6770a04b0f2430038b437d5a31c78b13a46259b Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:35:00 +0200 Subject: [PATCH 09/47] fix rst --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 61998a6b..1847f019 100644 --- a/README.rst +++ b/README.rst @@ -38,21 +38,21 @@ You can install RAMP using pip, conda or from source code. Installing through pip ---------------------- -#. To install the RAMP software, we suggest to create a new environment by running the following command in the *Anaconda prompt*: +1. To install the RAMP software, we suggest to create a new environment by running the following command in the *Anaconda prompt*: .. code-block:: python conda create -n ramp python=3.8 -#. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing +2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the *Anaconda Prompt*: .. code-block:: python conda activate ramp -#. Now you can use pip to install `rampdemand` on your environment as follow: +3. Now you can use pip to install `rampdemand` on your environment as follow: .. code-block:: python From 4654f6b3a866da410efbc2a5c18c848d01460ec4 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:39:38 +0200 Subject: [PATCH 10/47] Add pip and rampdemand to environment.yml --- condaenv.ntmw16k1.requirements.txt | 2 ++ environment.yml | 2 ++ 2 files changed, 4 insertions(+) create mode 100644 condaenv.ntmw16k1.requirements.txt diff --git a/condaenv.ntmw16k1.requirements.txt b/condaenv.ntmw16k1.requirements.txt new file mode 100644 index 00000000..62ae5c5d --- /dev/null +++ b/condaenv.ntmw16k1.requirements.txt @@ -0,0 +1,2 @@ +-r requirements.txt +rampdemand \ No newline at end of file diff --git a/environment.yml b/environment.yml index 2ae54e6d..6dd1df65 100644 --- a/environment.yml +++ b/environment.yml @@ -1,5 +1,7 @@ name: ramp dependencies: - python=3.8 +- pip=21.0.1 - pip: - -r requirements.txt + - rampdemand From 441d7d1b6f6b4a2559149f57a81cfc930bb00b9a Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:40:34 +0200 Subject: [PATCH 11/47] Remove unwanted file --- condaenv.ntmw16k1.requirements.txt | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 condaenv.ntmw16k1.requirements.txt diff --git a/condaenv.ntmw16k1.requirements.txt b/condaenv.ntmw16k1.requirements.txt deleted file mode 100644 index 62ae5c5d..00000000 --- a/condaenv.ntmw16k1.requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ --r requirements.txt -rampdemand \ No newline at end of file From 5292b91f673f460177f2b33cc87b9abcfa0f34d6 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Fri, 2 Jun 2023 17:48:18 +0200 Subject: [PATCH 12/47] wrong path in input_file_2.py --- ramp/example/input_file_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py index a0bed3b9..933924b9 100644 --- a/ramp/example/input_file_2.py +++ b/ramp/example/input_file_2.py @@ -20,7 +20,7 @@ HH = User("generic households",1) User_list.append(HH) -HH_shower_P = pd.read_csv('example/shower_P.csv') +HH_shower_P = pd.read_csv('shower_P.csv') #High-Income HH_shower = HH.Appliance(1,HH_shower_P,2,15,0.1,3, thermal_P_var = 0.2) From c3f3da94860debedc464b70892f9d7aab0959a79 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Fri, 2 Jun 2023 21:05:15 +0200 Subject: [PATCH 13/47] The parameter description are not included in the docs (issue #59) The old docs/input_parameters.md file was not included in the document. The new file with rst format is added to the documentations in: docs/source/input_parameters.rst --- docs/source/input_parameters.rst | 265 +++++++++++++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 docs/source/input_parameters.rst diff --git a/docs/source/input_parameters.rst b/docs/source/input_parameters.rst new file mode 100644 index 00000000..e07b821f --- /dev/null +++ b/docs/source/input_parameters.rst @@ -0,0 +1,265 @@ +###################################### +Excel input file parameter description +###################################### + +The table below displays the input parameters of RAMP. If **NA** is displayed in the table below, it means that the corresponding column is not applicable to the parameter. + +When filling .xlsx input file, you can simply leave the cell empty if the parameter is not mandatory and the default value will be automatically used. + +The **"allowed values"** column provide information about the format one should provide when: + +- {1,2,3} is a set and the allowed value are either 1, 2 or 3; +- in [0-1440] is a range and the allowed value must lie between 0 and 1440. 0 and 1440 are also possible values. + +.. list-table:: Parameters + :widths: 20 15 20 150 10 10 10 + :header-rows: 1 + + * - Name + - Unit + - Allowed values + - Description + - Coding type + - Is this value mandatory? + - Default value + * - user_name + - NA + - NA + - Name of user type + - string + - yes + - NA + * - num_users + - NA + - >=0 + - Number of users within the resprective user-type + - integer + - yes + - 0 + * - user_preference + - NA + - {0,1,2,3} + - Related to cooking behaviour, how many types of meal a user wants a day (number of user preferences has to be defined here and will be further specified with pref_index parameter) + - integer + - no + - 0 + * - name + - NA + - NA + - Appliance name + - string + - yes + - NA + * - number + - NA + - >=0 + - Number of appliances + - integer + - yes + - 0 + * - power + - Watt + - >=0 + - Power rating of appliance (average) + - Float or array + - yes + - 0 + * - num_windows + - NA + - {1,2,3} + - Number of distinct time windows, e.g. if an appliance is running 24 h the num_windows is 1. If num_windows is set to x then you have to fill in the window_x_start, window_x_end and random_var_w parameters) + - integer + - yes + - 1 + * - func_time + - minutes + - in [0,1440] + - Total time an appliance is running in a day (not dependant on windows) + - integer + - yes + - 0 + * - time_fraction_random_variability + - % + - in [0,1] + - For time (not for windows), randomizes the total time the appliance is on + - float + - no + - 0 + * - func_cycle + - minutes + - in [0,1440] + - Running time: time the appliance is on (after switching it on) + - float + - yes + - 1 + * - fixed + - NA + - {yes,no} + - All appliances of the same kind (e.g. street lights) are switched on at the same time (if fixed=yes) + - boolean + - no + - 1 + * - fixed_cycle + - NA + - {0,1,2,3} + - 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) + - integer + - no + - 0 + * - occasional_use + - % + - in [0,1] + - Defines how often the appliance is used, e.g. every second day will be 0.5 + - float + - no + - 1 + * - flat + - NA + - {yes,no} + - no variability in the time of usage, similar to fixed, but does not account for all appliances of the same type at the same time, no, if switched on and off at different times for different days + - boolean + - no + - no + * - thermal_p_var + - % + - in [0,1] + - 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…) + - float + - no + - 0 + * - pref_index + - NA + - {0,1,2,3} + - This number must be smaller or equal to the value input in user_preference + - integer + - no + - 0 + * - wd_we_type + - NA + - {0,1,2} + - Specify whether the appliance is used only on weekdays (0), weekend (1) or the whole week (2) + - integer + - no + - 2 + * - p_i1 + - Watt + - >=0 + - Power rating for first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater + - float + - no + - 0 + * - t_i1 + - minutes + - in [0,1440] + - Duration of first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater + - float + - no + - 0 + * - cwi1_start + - minutes + - in [0,1440] + - Window start time for the first part of ith specific duty cycle number (not neccessarily linked to the overall time window) + - float + - no + - 0 + * - cwi1_end + - minutes + - in [0,1440] + - Window end time for the first part of ith specific duty cycle number (not neccessarily linked to the overall time window) + - float + - no + - 0 + * - p_i2 + - Watt + - >=0 + - Power rating for second part of ith duty cycle number. Only necessary if fixed_cycle is set to i or greater + - float + - no + - 0 + * - t_i2 + - minutes + - in [0,1440] + - Duration second part of ith duty cycle number. Only necessary if fixed_cycle is set to I or greater + - float + - no + - 0 + * - cwi2_start + - minutes + - in [0,1440] + - Window start time for the second part of ith duty cycle number (not neccessarily linked to the overall time window) + - float + - no + - 0 + * - cwi2_end + - minutes + - in [0,1440] + - Window end time for the second part of ith duty cycle number (not neccessarily linked to the overall time window) + - float + - no + - 0 + * - r_ci + - % + - in [0,1] + - 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 + - float + - no + - 0 + * - window_j_start + - minutes + - in [0,1440] + - Start time of time-window j. Only necessary if num_windows is set to j or greater + - integer + - yes + - 0 + * - window_j_end + - minutes + - in [0,1440] + - End time of time-window j. Only necessary if num_windows is set to j or greater + - integer + - yes + - 0 + * - random_var_w + - % + - in [0,1] + - variability of the windows in percent, the same for all windows + - float + - no + - 0 + +Python input file parameter description +======================================= + +A new instance of class ``User`` need the parameters ``user_name``, +``num_users``, ``user_preference`` from the table above. To add an +appliance use the method ``add_appliance`` with at least the mandatory +parameters listed in the table above (except the 3 first parameters +which belong to the user class and are already assigned in this case) +and with any of the non-mandatory ones. + +If no window parameter (``window_j_start``, ``window_j_end``) is +provided to the ``add_appliance`` method of the user, then one must then +call the ``windows`` method of the appliance to provide up to 3 windows +: ``window_1``, ``window_2``, ``window_3`` as well as ``random_var_w`` +The parameters to describe a window of time should simply directly be +provided as a numpy array ( for example +``window_j = np.array([window_j_start, window_j_end])``) (where j is an +integer smaller or equal to the provided value of ``num_windows``). + +If no duty cycle parameter is provided to the ``add_appliance`` method +of the user, then one can then enable up to 3 duty cycle by calling the +method ``specific_cycle_i`` of the appliance (where i is an integer +smaller or equal to the provided value of ``fixed_cycle``) The +parameters to describe the ith duty cycle are the following: ``p_i1``, +``t_i1``, ``p_i2``, ``t_i2``, ``r_ci``, ``cwi1`` and ``cwi2`` It is also +possible to provide the parameters ``cwi1`` and ``cwi2`` using the +method ``cycle_behaviour`` of the appliance. + +The legacy way to create an appliance instance is by using the +``Appliance`` method of the user (note that the names of input +parameters are the old ones). This way of creating an appliance is to +keep a back compatibility of the legacy input files, using the +``add_appliance`` method of the user should be preferred Note that with +the legacy way, one must then call the ``windows`` method of the +appliance to provide at least one windows. And one can add duty cycles +only via the method ``specific_cycle_i`` of the appliance. + From 14af0a29dba916e86c1a2b99ef5762586fc29c04 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Fri, 2 Jun 2023 21:14:33 +0200 Subject: [PATCH 14/47] addin the input_parameters to index.rst Addine the new input_parameters to index.rst and removing the old file. --- docs/input_parameters.md | 59 ---------------------------------------- docs/source/index.rst | 1 + 2 files changed, 1 insertion(+), 59 deletions(-) delete mode 100644 docs/input_parameters.md diff --git a/docs/input_parameters.md b/docs/input_parameters.md deleted file mode 100644 index 3454b323..00000000 --- a/docs/input_parameters.md +++ /dev/null @@ -1,59 +0,0 @@ -## Excel input file parameter description -The table below displays the input parameters of RAMP. If NA is displayed in the table below, it means that the corresponding column is not applicable to the parameter. - -When filling .xlsx input file, you can simply leave the cell empty if the parameter is not mandatory and the default value will be automatically used. - -The "allowed values" column provide information about the format one should provide when: - -* {1,2,3} is a set and the allowed value are either 1, 2 or 3; -* in [0-1440] is a range and the allowed value must lie between 0 and 1440. 0 and 1440 are also possible values. - -| Name | Unit | Allowed values | Description | Coding type | Is this value mandatory? | Default value | -|:---------------------------------|:--------|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|:---------------------------|:----------------| -| user_name | NA | NA | Name of user type | string | yes | NA | -| num_users | NA | \>=0 | Number of users within the resprective user-type | integer | yes | 0 | -| user_preference | NA | {0,1,2,3} | Related to cooking behaviour, how many types of meal a user wants a day (number of user preferences has to be defined here and will be further specified with pref_index parameter) | integer | no | 0 | -| name | NA | NA | Appliance name | string | yes | NA | -| number | NA | \>=0 | Number of appliances | integer | yes | 0 | -| power | Watt | \>=0 | Power rating of appliance (average) | Float or array | yes | 0 | -| num_windows | NA | {1,2,3} | Number of distinct time windows, e.g. if an appliance is running 24 h the num_windows is 1. If num_windows is set to x then you have to fill in the window_x_start, window_x_end and random_var_w parameters) | integer | yes | 1 | -| func_time | minutes | in [0,1440] | Total time an appliance is running in a day (not dependant on windows) | integer | yes | 0 | -| time_fraction_random_variability | % | in [0,1] | For time (not for windows), randomizes the total time the appliance is on | float | no | 0 | -| func_cycle | minutes | in [0,1440] | Running time: time the appliance is on (after switching it on) | float | yes | 1 | -| fixed | NA | {yes,no} | All appliances of the same kind (e.g. street lights) are switched on at the same time (if fixed=yes) | boolean | no | no | -| fixed_cycle | NA | {0,1,2,3} | 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) | integer | no | 0 | -| occasional_use | % | in [0,1] | Defines how often the appliance is used, e.g. every second day will be 0.5 | float | no | 1 | -| flat | NA | {yes,no} | no variability in the time of usage, similar to fixed, but does not account for all appliances of the same type at the same time, no, if switched on and off at different times for different days | boolean | no | no | -| thermal_p_var | % | in [0,1] | 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…) | float | no | 0 | -| pref_index | NA | {0,1,2,3} | This number must be smaller or equal to the value input in user_preference | integer | no | 0 | -| wd_we_type | NA | {0,1,2} | Specify whether the appliance is used only on weekdays (0), weekend (1) or the whole week (2) | integer | no | 2 | -| p_i1 | Watt | \>=0 | Power rating for first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater | float | no | 0 | -| t_i1 | minutes | in [0,1440] | Duration of first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater | float | no | 0 | -| cwi1_start | minutes | in [0,1440] | Window start time for the first part of ith specific duty cycle number (not neccessarily linked to the overall time window) | float | no | 0 | -| cwi1_end | minutes | in [0,1440] | Window end time for the first part of ith specific duty cycle number (not neccessarily linked to the overall time window) | float | no | 0 | -| p_i2 | Watt | \>=0 | Power rating for second part of ith duty cycle number. Only necessary if fixed_cycle is set to i or greater | float | no | 0 | -| t_i2 | minutes | in [0,1440] | Duration second part of ith duty cycle number. Only necessary if fixed_cycle is set to I or greater | float | no | 0 | -| cwi2_start | minutes | in [0,1440] | Window start time for the second part of ith duty cycle number (not neccessarily linked to the overall time window) | float | no | 0 | -| cwi2_end | minutes | in [0,1440] | Window end time for the second part of ith duty cycle number (not neccessarily linked to the overall time window) | float | no | 0 | -| r_ci | % | in [0,1] | 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 | float | no | 0 | -| window_j_start | minutes | in [0,1440] | Start time of time-window j. Only necessary if num_windows is set to j or greater | integer | yes | 0 | -| window_j_end | minutes | in [0,1440] | End time of time-window j. Only necessary if num_windows is set to j or greater | integer | yes | 0 | -| random_var_w | % | in [0,1] | variability of the windows in percent, the same for all windows | float | no | 0 | - -## Python input file parameter description - -A new instance of class `User` need the parameters `user_name`, `num_users`, `user_preference` from the table above. -To add an appliance use the method `add_appliance` with at least the mandatory parameters listed in the table above (except the 3 first parameters which belong to the user class and are already assigned in this case) -and with any of the non-mandatory ones. - -If no window parameter (`window_j_start`, `window_j_end`) is provided to the `add_appliance` method of the user, then one must then call the `windows` method of the appliance to provide up to 3 windows : `window_1`, `window_2`, `window_3` as well as `random_var_w` -The parameters to describe a window of time should simply directly be provided as a numpy array ( for example `window_j = np.array([window_j_start, window_j_end])`) (where j is an integer smaller or equal to the provided value of `num_windows`). - -If no duty cycle parameter is provided to the `add_appliance` method of the user, then one can then enable up to 3 duty cycle by calling the method `specific_cycle_i` of the appliance (where i is an integer smaller or equal to the provided value of `fixed_cycle`) -The parameters to describe the ith duty cycle are the following: `p_i1`, `t_i1`, `p_i2`, `t_i2`, `r_ci`, `cwi1` and `cwi2` -It is also possible to provide the parameters `cwi1` and `cwi2` using the method `cycle_behaviour` of the appliance. - -The legacy way to create an appliance instance is by using the `Appliance` method of the user (note that the names of input parameters are the old ones). This way of creating an appliance is to keep a back compatibility of the legacy input files, using the `add_appliance` method of the user should be preferred -Note that with the legacy way, one must then call the `windows` method of the appliance to provide at least one windows. And one can add duty cycles only via the method `specific_cycle_i` of the appliance. - - diff --git a/docs/source/index.rst b/docs/source/index.rst index eeec91d8..2549a95c 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,6 +10,7 @@ User Guide: intro algorithm examples + input_parameters api_references From 96dbcaf349c6420d786a1dff0d12c35e9c6a1760 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Fri, 2 Jun 2023 21:35:59 +0200 Subject: [PATCH 15/47] Improving the test-pypi workflow pypi test release on push for main branch --- .github/workflows/python-pypi-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml index 66706c09..f1c26d79 100644 --- a/.github/workflows/python-pypi-test.yml +++ b/.github/workflows/python-pypi-test.yml @@ -4,6 +4,7 @@ on: push: branches: - development + - main jobs: build_and_deploy: name: Build the release and deploy to test pypi From c3292c80f7fd6b7ed9d2b75dfdfff2e85909d249 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Mon, 5 Jun 2023 14:59:16 +0200 Subject: [PATCH 16/47] Minor fix to README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 1847f019..2c828b41 100644 --- a/README.rst +++ b/README.rst @@ -81,7 +81,7 @@ After downloading the source code using any of abovem methods, you'll need to us .. code-block:: bash - python setup.py + python setup.py install * Alternatively, without taking any prior action, simply change the directory in the prompt to the folder where the source code is saved and then use: From 6da5b48c9186b294873eda0109a18c5774789704 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Mon, 5 Jun 2023 15:13:08 +0200 Subject: [PATCH 17/47] fix broken path input_file_2.py --- ramp/example/input_file_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py index 933924b9..6c3699e7 100644 --- a/ramp/example/input_file_2.py +++ b/ramp/example/input_file_2.py @@ -20,7 +20,7 @@ HH = User("generic households",1) User_list.append(HH) -HH_shower_P = pd.read_csv('shower_P.csv') +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) From b6aa104e375ae638edde001512696171d3bafc2c Mon Sep 17 00:00:00 2001 From: Francesco Lombardi Date: Mon, 5 Jun 2023 15:31:31 +0200 Subject: [PATCH 18/47] Minor edits to input_parameters.rst I have made minor edits to correct small typos or improve clarity --- docs/source/input_parameters.rst | 36 ++++++++++++++++---------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/source/input_parameters.rst b/docs/source/input_parameters.rst index e07b821f..c51736b8 100644 --- a/docs/source/input_parameters.rst +++ b/docs/source/input_parameters.rst @@ -4,9 +4,9 @@ Excel input file parameter description The table below displays the input parameters of RAMP. If **NA** is displayed in the table below, it means that the corresponding column is not applicable to the parameter. -When filling .xlsx input file, you can simply leave the cell empty if the parameter is not mandatory and the default value will be automatically used. +When filling an .xlsx input file, you can simply leave the cell empty if the parameter is not mandatory. In this case, the default value will be automatically used. -The **"allowed values"** column provide information about the format one should provide when: +The **"allowed values"** column provide information about the format one should provide. For example: - {1,2,3} is a set and the allowed value are either 1, 2 or 3; - in [0-1440] is a range and the allowed value must lie between 0 and 1440. 0 and 1440 are also possible values. @@ -116,7 +116,7 @@ The **"allowed values"** column provide information about the format one should * - flat - NA - {yes,no} - - no variability in the time of usage, similar to fixed, but does not account for all appliances of the same type at the same time, no, if switched on and off at different times for different days + - No variability in the time of usage, similar to fixed, and no variability in the power consumption - boolean - no - no @@ -144,14 +144,14 @@ The **"allowed values"** column provide information about the format one should * - p_i1 - Watt - >=0 - - Power rating for first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater + - Power rating for first part of ith duty cycle. Only necessary if fixed_cycle is set to 1 or greater - float - no - 0 * - t_i1 - minutes - in [0,1440] - - Duration of first part of ith duty cycle. Only necessary if fixed_cycle is set to I or greater + - Duration of first part of ith duty cycle. Only necessary if fixed_cycle is set to 1 or greater - float - no - 0 @@ -200,7 +200,7 @@ The **"allowed values"** column provide information about the format one should * - r_ci - % - in [0,1] - - 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 + - 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 - float - no - 0 @@ -221,7 +221,7 @@ The **"allowed values"** column provide information about the format one should * - random_var_w - % - in [0,1] - - variability of the windows in percent, the same for all windows + - Variability of the windows in percentage, the same for all windows - float - no - 0 @@ -229,37 +229,37 @@ The **"allowed values"** column provide information about the format one should Python input file parameter description ======================================= -A new instance of class ``User`` need the parameters ``user_name``, +A new instance of class ``User`` requires the parameters ``user_name``, ``num_users``, ``user_preference`` from the table above. To add an -appliance use the method ``add_appliance`` with at least the mandatory -parameters listed in the table above (except the 3 first parameters +appliance, use the method ``add_appliance`` with at least the mandatory +parameters listed in the table above (except the first three parameters which belong to the user class and are already assigned in this case) and with any of the non-mandatory ones. If no window parameter (``window_j_start``, ``window_j_end``) is -provided to the ``add_appliance`` method of the user, then one must then +provided to the ``add_appliance`` method of the user, then one must call the ``windows`` method of the appliance to provide up to 3 windows : ``window_1``, ``window_2``, ``window_3`` as well as ``random_var_w`` -The parameters to describe a window of time should simply directly be +The parameters to describe a window of time should directly be provided as a numpy array ( for example ``window_j = np.array([window_j_start, window_j_end])``) (where j is an integer smaller or equal to the provided value of ``num_windows``). If no duty cycle parameter is provided to the ``add_appliance`` method -of the user, then one can then enable up to 3 duty cycle by calling the -method ``specific_cycle_i`` of the appliance (where i is an integer +of the user, then one can enable up to 3 different duty cycles by calling +the method ``specific_cycle_i`` of the appliance (where i is an integer smaller or equal to the provided value of ``fixed_cycle``) The parameters to describe the ith duty cycle are the following: ``p_i1``, -``t_i1``, ``p_i2``, ``t_i2``, ``r_ci``, ``cwi1`` and ``cwi2`` It is also +``t_i1``, ``p_i2``, ``t_i2``, ``r_ci``, ``cwi1`` and ``cwi2``. It is also possible to provide the parameters ``cwi1`` and ``cwi2`` using the method ``cycle_behaviour`` of the appliance. The legacy way to create an appliance instance is by using the ``Appliance`` method of the user (note that the names of input parameters are the old ones). This way of creating an appliance is to -keep a back compatibility of the legacy input files, using the -``add_appliance`` method of the user should be preferred Note that with -the legacy way, one must then call the ``windows`` method of the +keep the backward compatibility of legacy input files: using the +``add_appliance`` method of the user should be preferred. Note that with +the legacy method, one must then call the ``windows`` method of the appliance to provide at least one windows. And one can add duty cycles only via the method ``specific_cycle_i`` of the appliance. From 9e81d2facff0ee1b23715ce9679cb9f558112f62 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Mon, 5 Jun 2023 15:35:15 +0200 Subject: [PATCH 19/47] update the changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 68be5e8a..e55c7bdc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,8 @@ Release History 0.4.1 (dev) ------------------ +**|fixed|** installation options have been debugged and the documentation updated accordingly + **|fixed|** automated download of example applications via the `download_example` functions now includes previously missing .csv files From 0edfd0b01dc6591d1ae461d1d004bddddd1e2aa0 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Tue, 6 Jun 2023 16:06:46 +0200 Subject: [PATCH 20/47] remove redundant requirements --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index 6dd1df65..157ca78c 100644 --- a/environment.yml +++ b/environment.yml @@ -3,5 +3,4 @@ dependencies: - python=3.8 - pip=21.0.1 - pip: - - -r requirements.txt - rampdemand From 091542c416f09e439df5cea70544ab74d9052960 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Wed, 23 Aug 2023 11:29:18 +0200 Subject: [PATCH 21/47] update environment.yml --- environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 157ca78c..715b4764 100644 --- a/environment.yml +++ b/environment.yml @@ -3,4 +3,5 @@ dependencies: - python=3.8 - pip=21.0.1 - pip: - - rampdemand + - -r requirements.txt + - -e . From d9929fe01763868bb568c93ba2c73e8a1486550c Mon Sep 17 00:00:00 2001 From: Pierre Francois Date: Mon, 4 Sep 2023 22:39:50 +0200 Subject: [PATCH 22/47] Update README.rst requirement section --- README.rst | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/README.rst b/README.rst index 2c828b41..d0011fab 100644 --- a/README.rst +++ b/README.rst @@ -30,6 +30,8 @@ The project aims to provide synthetic data wherever metered data does not exist, Recommended installation method =============================== +RAMP has been successfully installed and used on macOS, Windows and Linux. + The easiest way to make RAMP software working is to use the free conda package manager which can install the current and future RAMP depencies in an easy and user friendly way. @@ -89,22 +91,6 @@ After downloading the source code using any of abovem methods, you'll need to us conda env create -f environment.yml -Requirements -============ -RAMP has been tested on macOS, Windows and Linux. - -For running RAMP, you'll need a few packages: - -#. The Python programming language, version 3.6 or higher -#. A number of Python adds-on packages: - - * `Pandas `_ - * `Numpy `_ - * `Matplotlib `_ - * `Openpyxl `_ - -The requirements are specified in the `requirements.txt` file. - Quick start =========== There are different ways to build a model using RAMP! Here, we provide a first example but you can find more information in our `documentation `_. From 7b9222e1559350013c4184cfdba29177f0dc1309 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Wed, 4 Oct 2023 22:43:13 +0200 Subject: [PATCH 23/47] Add mandatory build-os parameter to RTD config Our previous settings are deprecated since September 25th 2023 https://blog.readthedocs.com/migrate-configuration-v2/ --- .readthedocs.yml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 3217092b..415a6dca 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -3,6 +3,13 @@ # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.7" + # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py @@ -15,6 +22,5 @@ sphinx: # - pdf # Optionally set the version of Python and requirements required to build your docs python: - version: 3.7 install: - requirements: docs/docs-requirements.txt \ No newline at end of file From 6c5541678a28696253603e2bb4b3c34779c6f1a8 Mon Sep 17 00:00:00 2001 From: JJ <103335846+computerscienceiscool@users.noreply.github.com> Date: Thu, 12 Oct 2023 01:29:53 -0700 Subject: [PATCH 24/47] Update README.rst Fixed a few small spelling errors. --- README.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 17f558cb..a2d32d1a 100644 --- a/README.rst +++ b/README.rst @@ -33,7 +33,7 @@ Recommended installation method RAMP has been successfully installed and used on macOS, Windows and Linux. The easiest way to make RAMP software working is to use the free conda package manager which can install the current and future RAMP -depencies in an easy and user friendly way. +dependencies in an easy and user friendly way. To get conda, `download and install "Anaconda Distribution" `_, or `"miniconda" `_ which is lighter. You can install RAMP using pip, conda or from source code. @@ -77,7 +77,7 @@ You can also install RAMP from the source code! To do so, you first need to down In this second case, the source code will be downloaded as a zip file, so you'll need to extract the files. -After downloading the source code using any of abovem methods, you'll need to use your **anaconda prompt** to install it. There are two options again: +After downloading the source code using any of abovementioned methods, you'll need to use your **anaconda prompt** to install it. There are two options again: * You may follow the first two steps mentioned in **Installing through pip**. Then, change the directory in the prompt to the folder where the source code is saved (where you can find the *setup.py* file). To install the RAMP software, you may then use: @@ -113,7 +113,7 @@ To have a look to the python files, you can download them using the `download_ex publication `__. - ``input_file_2.py``: shows how to model thermal loads, with the - example of a “shower” appliance. The peculiarity of thermal appiances + example of a “shower” appliance. The peculiarity of thermal appliances is that the nominal power can be provided as external input as a “csv” file (in this case, ``shower_P.csv``). For the example “shower” appliance, the varying nominal power accounts for the effect of @@ -239,7 +239,7 @@ Now you can generate your **stochastic profiles**: 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 contibute to its capabilities, computational-efficiency, formulation, etc. +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: @@ -250,7 +250,7 @@ To contribute changes: #. 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 `qualitativte testing `_ functionality. +When committing new changes, please also take care of checking code stability by means of the `qualitative testing `_ functionality. How to cite From 8c9c7e9a14c06a2f284896ce3a79156ec5489287 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Wed, 25 Oct 2023 21:50:17 +0200 Subject: [PATCH 25/47] Fix user_preference usage Move the randomized daily preference at the user level --- ramp/core/core.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ramp/core/core.py b/ramp/core/core.py index d28de45a..a2aac595 100644 --- a/ramp/core/core.py +++ b/ramp/core/core.py @@ -266,6 +266,7 @@ def __init__(self, user_name:str="", num_users:int=1, user_preference:int=0): self.user_name = user_name self.num_users = num_users self.user_preference = user_preference + self.rand_daily_pref = 0 self.load = None self.App_list = [] # each instance of User (i.e. each user class) has its own list of Appliances @@ -494,6 +495,7 @@ def generate_single_load_profile(self, prof_i:int, peak_time_range:np.array, day single_load = np.zeros(1440) + self.rand_daily_pref = 0 if self.user_preference == 0 else random.randint(1, self.user_preference) for App in self.App_list: # iterates for all the App types in the given User class @@ -1275,14 +1277,12 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power): # initialises variables for the cycle self.daily_use = np.zeros(1440) - rand_daily_pref = 0 if self.user.user_preference == 0 else random.randint(1, self.user.user_preference) - # skip this appliance in any of the following applies if ( # evaluates if occasional use happens or not (random.uniform(0, 1) > self.occasional_use # evaluates if daily preference coincides with the randomised daily preference number - or (self.pref_index != 0 and rand_daily_pref != self.pref_index) + 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]) ): From 724f4a48a8d2ef9fb1b7df2719f50b0e5500085a Mon Sep 17 00:00:00 2001 From: ClaudiaLSS Date: Thu, 24 Aug 2023 10:28:29 -0400 Subject: [PATCH 26/47] adding peak time range testing --- tests/test_peak_time_range.py | 39 +++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 tests/test_peak_time_range.py diff --git a/tests/test_peak_time_range.py b/tests/test_peak_time_range.py new file mode 100644 index 00000000..221cfc01 --- /dev/null +++ b/tests/test_peak_time_range.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed May 31 10:04:56 2023 + +@author: claudia +""" + +import os +import pytest +import numpy as np +import random +import math +from scipy.stats import norm + +from ramp.core.core import User, Appliance +from ramp.core.core import UseCase +from ramp.core.initialise import initialise_inputs +from ramp.core.stochastic_process import calc_peak_time_range + +def load_usecase(j=None, fname=None): + peak_enlarge, user_list, num_profiles = initialise_inputs( + j, fname, num_profiles=1 + ) + return user_list + +class TestPeakTime: + def test_calc_peak_time_range(self): + j=1 + peak_enlarge=0.15 + user_list=load_usecase(j,peak_enlarge ) + #call the function onder test + peak_time_range = calc_peak_time_range(user_list, peak_enlarge) + #perform assertions to validate the output + assert isinstance(peak_time_range, np.ndarray) # Check if the result is a NumPy array + assert peak_time_range.shape[0] > 0 # Check if the result array is not empty + assert peak_time_range.dtype == int # Check if the array contains integers + assert np.all(peak_time_range >= 0) # Check if all values are non-negative + \ No newline at end of file From 4708f7411b97b8c3c99e22fa94326ebff1f55fa5 Mon Sep 17 00:00:00 2001 From: ClaudiaLSS Date: Wed, 6 Sep 2023 06:52:24 -0400 Subject: [PATCH 27/47] New tests added --- tests/test_calc_peak_time_range.py | 91 ++++++++++++++++++++++++++++ tests/test_rand_total_time_of_use.py | 65 ++++++++++++++++++++ tests/test_switch_on.py | 56 +++++++++++++++++ 3 files changed, 212 insertions(+) create mode 100644 tests/test_calc_peak_time_range.py create mode 100644 tests/test_rand_total_time_of_use.py create mode 100644 tests/test_switch_on.py diff --git a/tests/test_calc_peak_time_range.py b/tests/test_calc_peak_time_range.py new file mode 100644 index 00000000..acc1e8cc --- /dev/null +++ b/tests/test_calc_peak_time_range.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 28 13:27:24 2023 + +@author: claudia +""" + +import numpy as np +from ramp.core.stochastic_process import calc_peak_time_range +from ramp.core.core import User +from ramp.core.initialise import initialise_inputs +import pytest + + +def load_usecase(j=None, fname=None): + peak_enlarge, user_list, num_profiles = initialise_inputs( + j, fname, num_profiles=1 + ) + return user_list + +class TestCalcPeakTimeRange: + + # Tests that the peak time range is calculated correctly for a single user class with the default peak_enlarge value + def test_single_user_class_default_peak_enlarge(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] + peak_time_range = calc_peak_time_range(user_list) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + # Tests that the peak time range is calculated correctly for multiple user classes with the default peak_enlarge value + def test_multiple_user_classes_default_peak_enlarge(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), + User(maximum_profile=np.array([2, 3, 4, 5, 6]))] + peak_time_range = calc_peak_time_range(user_list) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + # Tests that the peak time range is calculated correctly for a single user class with a non-default peak_enlarge value + def test_single_user_class_non_default_peak_enlarge(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] + peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0.2) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + # Tests that the peak time range is calculated correctly for multiple user classes with a non-default peak_enlarge value + def test_multiple_user_classes_non_default_peak_enlarge(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), + User(maximum_profile=np.array([2, 3, 4, 5, 6]))] + peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0.2) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + # Tests that the peak time range is calculated correctly for a single user class with peak_enlarge=0 + def test_single_user_class_peak_enlarge_0(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] + peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + # Tests that the peak time range is calculated correctly for multiple user classes with peak_enlarge=0 + def test_multiple_user_classes_peak_enlarge_0(self): + user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), + User(maximum_profile=np.array([2, 3, 4, 5, 6]))] + peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0) + assert isinstance(peak_time_range, np.ndarray) + assert peak_time_range.shape[0] > 0 + assert peak_time_range.dtype == int + assert np.all(peak_time_range >= 0) + + def test_calc_peak_time_range_with_inputs(self): + j=1 + peak_enlarge=0.15 + user_list=load_usecase(j,peak_enlarge ) + #call the function onder test + peak_time_range = calc_peak_time_range(user_list, peak_enlarge) + #perform assertions to validate the output + assert isinstance(peak_time_range, np.ndarray) # Check if the result is a NumPy array + assert peak_time_range.shape[0] > 0 # Check if the result array is not empty + assert peak_time_range.dtype == int # Check if the array contains integers + assert np.all(peak_time_range >= 0) # Check if all values are non-negative \ No newline at end of file diff --git a/tests/test_rand_total_time_of_use.py b/tests/test_rand_total_time_of_use.py new file mode 100644 index 00000000..fce1b8bb --- /dev/null +++ b/tests/test_rand_total_time_of_use.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 28 15:40:12 2023 + +@author: claudia +""" + +from ramp.core.utils import random_variation +from ramp.core.core import Appliance +from ramp.core.core import User + + +import pytest + + +@pytest.fixture +def appliance_instance(): + + # Create a User instance (you may need to provide the required arguments for User) + user = User(user_name="Test User", num_users=1) + appliance = Appliance( + user="Test User", + name="Test Appliance", + func_time=100, # Set an appropriate func_time + func_cycle=20, + time_fraction_random_variability=0.1 + ) + return appliance + + +# Define the test class for the Appliance class +class TestAppliance: + + #Test that the method returns an integer + @pytest.mark.usefixtures("appliance_instance") + def test_returns_integer_value(self, appliance_instance): + result = appliance_instance.rand_total_time_of_use([0, 480], [600, 1080], [1200, 1440]) + assert isinstance(result, int) + + #Tests that the method returns a value greater than func_cycle + @pytest.mark.usefixtures("appliance_instance") + def test_rand_time_equal_or_greater_than_func_cycle(self, appliance_instance): + # Define windows with total available time + rand_window_1 = [0, 100] + rand_window_2 = [200, 300] + rand_window_3 = [400, 500] + appliance_instance.func_cycle = 50 + # Call the method from the class + rand_time = appliance_instance.rand_total_time_of_use(rand_window_1, rand_window_2, rand_window_3) + assert rand_time >= appliance_instance.func_cycle, 'rand_time is not greater than func_cycle' + + # Tests that the method returns a value less than or equal to 0.99 * total_time + @pytest.mark.usefixtures("appliance_instance") + def test_rand_time_less_than_99_percent_total_time(self, appliance_instance): + rand_window_1 = [0, 100] + rand_window_2 = [200, 300] + rand_window_3 = [400, 500] + appliance_instance.func_time = 200 + appliance_instance.time_fraction_random_variability = 0.5 + # Call the method from the class + rand_time = appliance_instance.rand_total_time_of_use(rand_window_1, rand_window_2, rand_window_3) + total_time = (rand_window_1[1] - rand_window_1[0]) + (rand_window_2[1] - rand_window_2[0]) + (rand_window_3[1] - rand_window_3[0]) + assert rand_time <= 0.99 * total_time + diff --git a/tests/test_switch_on.py b/tests/test_switch_on.py new file mode 100644 index 00000000..96e914b9 --- /dev/null +++ b/tests/test_switch_on.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 5 10:47:58 2023 + +@author: claudia +""" + +from ramp.core.core import Appliance + + +import pytest + +class TestRandSwitchOnWindow: + + # 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. + def test_happy_path(self): + appliance = Appliance(user=None, func_cycle=2) + appliance.free_spots = [slice(0, 5), slice(10, 15)] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert all(index in range(0, 5) or index in range(10, 15) for index in indexes) + + # Tests that the method returns None when there are no available functioning windows. + def test_edge_case_no_windows(self): + appliance = Appliance(user=None, func_cycle=2) + appliance.free_spots = [] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert indexes is None + + # Tests that the method returns None when the available functioning window is smaller than the duration of the appliance's function cycle. + def test_edge_case_small_window(self): + appliance = Appliance(user=None, func_cycle=5) + appliance.free_spots = [slice(0, 3)] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert indexes is None + + # Tests that the method returns a list of indexes within the available functioning window when the available functioning window is equal to the duration of the appliance's function cycle. + def test_edge_case_equal_window(self): + appliance = Appliance(user=None, func_cycle=5) + appliance.free_spots = [slice(0, 5)] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert all(index in range(0, 5) for index in indexes) + + # Tests that the method returns a list of indexes within the available functioning window when the available functioning window is larger than the duration of the appliance's function cycle. + def test_edge_case_large_window(self): + appliance = Appliance(user=None, func_cycle=2) + appliance.free_spots = [slice(0, 10)] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert all(index in range(0, 10) for index in indexes) + + # Tests that the method returns a list of indexes within the available functioning windows when the appliance has multiple free spots. + def test_edge_case_multiple_free_spots(self): + appliance = Appliance(user=None, func_cycle=2) + appliance.free_spots = [slice(0, 5), slice(10, 15), slice(20, 25)] + indexes = appliance.rand_switch_on_window(rand_time=6) + assert all(index in range(0, 5) or index in range(10, 15) or index in range(20, 25) for index in indexes) \ No newline at end of file From 033f892be70b1195b9af5ff1d19ef934e67fce2a Mon Sep 17 00:00:00 2001 From: Claudia Sanchez-Solis <79788249+ClaudiaLSS@users.noreply.github.com> Date: Tue, 3 Oct 2023 15:41:09 -0400 Subject: [PATCH 28/47] Update tests/test_peak_time_range.py Co-authored-by: Pierre Francois --- tests/test_peak_time_range.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_peak_time_range.py b/tests/test_peak_time_range.py index 221cfc01..7a184001 100644 --- a/tests/test_peak_time_range.py +++ b/tests/test_peak_time_range.py @@ -28,7 +28,7 @@ class TestPeakTime: def test_calc_peak_time_range(self): j=1 peak_enlarge=0.15 - user_list=load_usecase(j,peak_enlarge ) + user_list=load_usecase(j) #call the function onder test peak_time_range = calc_peak_time_range(user_list, peak_enlarge) #perform assertions to validate the output From 07e3f592fdd96cb246335496436596f3881331a8 Mon Sep 17 00:00:00 2001 From: ClaudiaLSS Date: Tue, 3 Oct 2023 21:45:40 -0400 Subject: [PATCH 29/47] Address reviewer feedback: - test focused on peak time range values added. - at least 100 iteration of the method added to make sure none of these gives rand_time < func_cycle - new tests for switch on coincidence added --- tests/test_calc_peak_time_range.py | 119 +++++++++------------------ tests/test_rand_total_time_of_use.py | 12 ++- tests/test_switch_on.py | 32 ++++++- 3 files changed, 75 insertions(+), 88 deletions(-) diff --git a/tests/test_calc_peak_time_range.py b/tests/test_calc_peak_time_range.py index acc1e8cc..61612aef 100644 --- a/tests/test_calc_peak_time_range.py +++ b/tests/test_calc_peak_time_range.py @@ -1,91 +1,46 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Mon Aug 28 13:27:24 2023 +Created on Mon Jun 5 16:21:32 2023 @author: claudia """ import numpy as np +import scipy.stats as stats +from ramp.core.initialise import user_defined_inputs from ramp.core.stochastic_process import calc_peak_time_range -from ramp.core.core import User -from ramp.core.initialise import initialise_inputs -import pytest - - -def load_usecase(j=None, fname=None): - peak_enlarge, user_list, num_profiles = initialise_inputs( - j, fname, num_profiles=1 - ) - return user_list - -class TestCalcPeakTimeRange: - - # Tests that the peak time range is calculated correctly for a single user class with the default peak_enlarge value - def test_single_user_class_default_peak_enlarge(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] - peak_time_range = calc_peak_time_range(user_list) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - # Tests that the peak time range is calculated correctly for multiple user classes with the default peak_enlarge value - def test_multiple_user_classes_default_peak_enlarge(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), - User(maximum_profile=np.array([2, 3, 4, 5, 6]))] - peak_time_range = calc_peak_time_range(user_list) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - # Tests that the peak time range is calculated correctly for a single user class with a non-default peak_enlarge value - def test_single_user_class_non_default_peak_enlarge(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] - peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0.2) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - # Tests that the peak time range is calculated correctly for multiple user classes with a non-default peak_enlarge value - def test_multiple_user_classes_non_default_peak_enlarge(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), - User(maximum_profile=np.array([2, 3, 4, 5, 6]))] - peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0.2) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - # Tests that the peak time range is calculated correctly for a single user class with peak_enlarge=0 - def test_single_user_class_peak_enlarge_0(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5]))] - peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - # Tests that the peak time range is calculated correctly for multiple user classes with peak_enlarge=0 - def test_multiple_user_classes_peak_enlarge_0(self): - user_list = [User(maximum_profile=np.array([1, 2, 3, 4, 5])), - User(maximum_profile=np.array([2, 3, 4, 5, 6]))] - peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0) - assert isinstance(peak_time_range, np.ndarray) - assert peak_time_range.shape[0] > 0 - assert peak_time_range.dtype == int - assert np.all(peak_time_range >= 0) - - def test_calc_peak_time_range_with_inputs(self): - j=1 - peak_enlarge=0.15 - user_list=load_usecase(j,peak_enlarge ) - #call the function onder test - peak_time_range = calc_peak_time_range(user_list, peak_enlarge) - #perform assertions to validate the output - assert isinstance(peak_time_range, np.ndarray) # Check if the result is a NumPy array - assert peak_time_range.shape[0] > 0 # Check if the result array is not empty - assert peak_time_range.dtype == int # Check if the array contains integers - assert np.all(peak_time_range >= 0) # Check if all values are non-negative \ No newline at end of file + + +def test_peak_time_range_values(): + """ + Perform a statistical analysis on the peak time range values obtained from the calc_peak_time_range function. + + Inputs: + - user_list: A list containing all the user types. + - num_repetitions: The desired number of repetitions for the statistical analysis. + + Outputs: + - None (The function performs an assertion to check the normality of the mean values). + """ + + user_list = user_defined_inputs(j=1, fname=None) + + num_repetitions = 100 # Set the desired number of repetitions + results = [] + + for _ in range(num_repetitions): + peak_time_range = calc_peak_time_range(user_list, peak_enlarge=0.15) + results.append(peak_time_range) + + statistics_dict = {} + + # Performing statistical analysis on the results + for i, arr in enumerate(results): + statistics_dict[i] = {'mean': np.mean(arr)} + + # Extract the mean values + mean_sample = [inner_dict['mean'] for inner_dict in statistics_dict.values()] + # Perform the normality test (Shapiro-Wilk in this sample) + _, p_value = stats.shapiro(mean_sample) + assert p_value > 0.05 diff --git a/tests/test_rand_total_time_of_use.py b/tests/test_rand_total_time_of_use.py index fce1b8bb..e4e3c99f 100644 --- a/tests/test_rand_total_time_of_use.py +++ b/tests/test_rand_total_time_of_use.py @@ -6,7 +6,6 @@ @author: claudia """ -from ramp.core.utils import random_variation from ramp.core.core import Appliance from ramp.core.core import User @@ -46,9 +45,14 @@ def test_rand_time_equal_or_greater_than_func_cycle(self, appliance_instance): rand_window_2 = [200, 300] rand_window_3 = [400, 500] appliance_instance.func_cycle = 50 - # Call the method from the class - rand_time = appliance_instance.rand_total_time_of_use(rand_window_1, rand_window_2, rand_window_3) - assert rand_time >= appliance_instance.func_cycle, 'rand_time is not greater than func_cycle' + # Generate a sample of 'rand_time' values + sample_size = 100 + rand_time_sample = [] + for _ in range(sample_size): + rand_time = appliance_instance.rand_total_time_of_use(rand_window_1, rand_window_2, rand_window_3) + rand_time_sample.append(rand_time) + assert all(rand_time >= appliance_instance.func_cycle for rand_time in rand_time_sample) + # Tests that the method returns a value less than or equal to 0.99 * total_time @pytest.mark.usefixtures("appliance_instance") diff --git a/tests/test_switch_on.py b/tests/test_switch_on.py index 96e914b9..456bd35a 100644 --- a/tests/test_switch_on.py +++ b/tests/test_switch_on.py @@ -6,13 +6,39 @@ @author: claudia """ +import pytest from ramp.core.core import Appliance +from scipy import stats -import pytest class TestRandSwitchOnWindow: + # Test when self.fixed = yes, all the apps are switched on together (should return the total number of apps) + def test_all_appliances_switched_on_together(self): + appliance = Appliance(user=None, number=5, fixed='yes') + coincidence = appliance.calc_coincident_switch_on() + assert isinstance(coincidence, int) + assert coincidence == appliance.number + + # Test when self.fixed= no and the index value lies in peak_time_range (test whether the coincidence values are normally distributed or not) + def test_coincidence_normality_on_peak(self): + # Create an instance of the Appliance class with the desired parameters + appliance = Appliance(user=None, number=10, fixed='no') + + # Generate a sample of 'coincidence' values + sample_size = 30 + 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) + + # Assert that the p-value is greater than a chosen significance level + assert p_value > 0.05, "The 'coincidence' values are not normally distributed." + # 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. def test_happy_path(self): appliance = Appliance(user=None, func_cycle=2) @@ -53,4 +79,6 @@ def test_edge_case_multiple_free_spots(self): appliance = Appliance(user=None, func_cycle=2) appliance.free_spots = [slice(0, 5), slice(10, 15), slice(20, 25)] indexes = appliance.rand_switch_on_window(rand_time=6) - assert all(index in range(0, 5) or index in range(10, 15) or index in range(20, 25) for index in indexes) \ No newline at end of file + assert all(index in range(0, 5) or index in range(10, 15) or index in range(20, 25) for index in indexes) + + \ No newline at end of file From 9a99ecfefa8d7c2e2a31e584a1dcdbe376060b78 Mon Sep 17 00:00:00 2001 From: ClaudiaLSS Date: Tue, 3 Oct 2023 21:56:47 -0400 Subject: [PATCH 30/47] incorrect file deleted --- tests/test_peak_time_range.py | 39 ----------------------------------- 1 file changed, 39 deletions(-) delete mode 100644 tests/test_peak_time_range.py diff --git a/tests/test_peak_time_range.py b/tests/test_peak_time_range.py deleted file mode 100644 index 7a184001..00000000 --- a/tests/test_peak_time_range.py +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed May 31 10:04:56 2023 - -@author: claudia -""" - -import os -import pytest -import numpy as np -import random -import math -from scipy.stats import norm - -from ramp.core.core import User, Appliance -from ramp.core.core import UseCase -from ramp.core.initialise import initialise_inputs -from ramp.core.stochastic_process import calc_peak_time_range - -def load_usecase(j=None, fname=None): - peak_enlarge, user_list, num_profiles = initialise_inputs( - j, fname, num_profiles=1 - ) - return user_list - -class TestPeakTime: - def test_calc_peak_time_range(self): - j=1 - peak_enlarge=0.15 - user_list=load_usecase(j) - #call the function onder test - peak_time_range = calc_peak_time_range(user_list, peak_enlarge) - #perform assertions to validate the output - assert isinstance(peak_time_range, np.ndarray) # Check if the result is a NumPy array - assert peak_time_range.shape[0] > 0 # Check if the result array is not empty - assert peak_time_range.dtype == int # Check if the array contains integers - assert np.all(peak_time_range >= 0) # Check if all values are non-negative - \ No newline at end of file From 1b4f9d6ecaa2b9ef9ee54b1e7d9e0ee7af73e27a Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Tue, 31 Oct 2023 22:21:17 +0100 Subject: [PATCH 31/47] Fix typo in test file --- tests/test_input_file_conversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_input_file_conversion.py b/tests/test_input_file_conversion.py index 7e5ddba4..d685946a 100644 --- a/tests/test_input_file_conversion.py +++ b/tests/test_input_file_conversion.py @@ -4,7 +4,7 @@ from ramp.core.core import User, Appliance from ramp.core.initialise import initialise_inputs -e + from ramp.ramp_convert_old_input_files import convert_old_user_input_file From c46780ff0e3760236c3b72b2d46d3dbeafc8143e Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Tue, 31 Oct 2023 22:21:47 +0100 Subject: [PATCH 32/47] Add github action workflow for pytest --- .github/workflows/pytest.yml | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 .github/workflows/pytest.yml diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml new file mode 100644 index 00000000..6e2eb08f --- /dev/null +++ b/.github/workflows/pytest.yml @@ -0,0 +1,33 @@ +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: RAMP tests + +on: + push: + branches: [ "main", "development" ] + pull_request: + branches: [ "main", "development" ] + +permissions: + contents: read + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Test with pytest + run: | + pytest tests/ From 4d2a094fbfc9e3f8631de7a20988a5c1735334b6 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Tue, 31 Oct 2023 22:27:07 +0100 Subject: [PATCH 33/47] Add test dependencies --- .github/workflows/pytest.yml | 2 +- tests/requirements.txt | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 tests/requirements.txt diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 6e2eb08f..f804b1ba 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -26,8 +26,8 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install -r tests/requirements.txt - name: Test with pytest run: | pytest tests/ diff --git a/tests/requirements.txt b/tests/requirements.txt new file mode 100644 index 00000000..feb62d07 --- /dev/null +++ b/tests/requirements.txt @@ -0,0 +1,2 @@ +pytest +scipy \ No newline at end of file From 8af94845ebef90997d05e1a5a2e3672caf3e4dc7 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Tue, 31 Oct 2023 22:42:16 +0100 Subject: [PATCH 34/47] Fix failing test for file conversion --- tests/test_input_file_conversion.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/tests/test_input_file_conversion.py b/tests/test_input_file_conversion.py index d685946a..b327d9c7 100644 --- a/tests/test_input_file_conversion.py +++ b/tests/test_input_file_conversion.py @@ -19,15 +19,12 @@ class TestConversion: def setup_method(self): self.input_files_to_run = [1, 2, 3] self.file_suffix = "_test" - os.chdir( - "ramp" - ) # for legacy code to work the loading of the input file has to happen from the ramp folder self.py_fnames = [ - os.path.join("input_files", f"input_file_{i}.py") + os.path.join("ramp","example", f"input_file_{i}.py") for i in self.input_files_to_run ] self.xlsx_fnames = [ - os.path.join("test", f"input_file_{i}{self.file_suffix}.xlsx") + os.path.join("ramp","test", f"input_file_{i}{self.file_suffix}.xlsx") for i in self.input_files_to_run ] for fname in self.xlsx_fnames: @@ -56,7 +53,7 @@ def test_convert_py_to_xlsx(self): for i, j in enumerate(self.input_files_to_run): old_user_list = load_usecase(j=j) convert_old_user_input_file( - self.py_fnames[i], output_path="test", suffix=self.file_suffix + self.py_fnames[i], output_path=os.path.join("ramp","test"), suffix=self.file_suffix ) new_user_list = load_usecase(fname=self.xlsx_fnames[i]) for old_user, new_user in zip(old_user_list, new_user_list): @@ -130,7 +127,7 @@ def test_provide_no_appliance_window_when_declaring_one(): def test_A(): user = User("test user", 1) - old_params = dict(n=1, P=200, w=1, t=0) + old_params = dict(power=200, num_windows=1, func_time=0) win_start = 390 win_stop = 540 appliance1 = user.Appliance(user, **old_params) From 7cf59106435a102cf3ba4fb20ecbdf3e6bed99a1 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Tue, 31 Oct 2023 22:51:31 +0100 Subject: [PATCH 35/47] Update changelog --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4ec54f31..f5f773fd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,10 @@ Release History 0.4.2 (dev) ------------------ +**|new|** continuous integration setup + +**|new|** first automated tests + **|fixed|** installation options have been debugged and the documentation updated accordingly **|fixed|** automated download of example applications via the `download_example` functions now includes previously missing .csv files From 30cec11cacdd07501ac289be6b1423779b609b97 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Mon, 6 Nov 2023 11:50:18 +0100 Subject: [PATCH 36/47] update figure links from master to main --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index a2d32d1a..cebc9e91 100644 --- a/README.rst +++ b/README.rst @@ -11,7 +11,7 @@ :target: https://rampdemand.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status -.. image:: https://github.com/RAMP-project/RAMP/blob/documentation/docs/source/_static/RAMP_logo_basic.png?raw=true +.. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/source/_static/RAMP_logo_basic.png?raw=true :width: 300 @@ -24,7 +24,7 @@ RAMP is an open-source software suite for the stochastic simulation of any user- The project aims to provide synthetic data wherever metered data does not exist, such as when designing systems in remote areas. Check out the `documentation `_ and learn more on the RAMP world from our `website `_! -.. image:: https://github.com/RAMP-project/RAMP/blob/master/docs/figures/Example_output.jpg?raw=true +.. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/Example_output.jpg?raw=true :width: 600 Recommended installation method From 4c050064e91e043fe569c4a5db9d4e33cd573528 Mon Sep 17 00:00:00 2001 From: "F.L" Date: Mon, 6 Nov 2023 14:23:16 +0100 Subject: [PATCH 37/47] Fixed typos and minor edits to docs and notebooks --- docs/notebooks/cooking_app.ipynb | 30 +++++++++----- docs/notebooks/fixed_flat_app.ipynb | 4 +- docs/notebooks/multi_cycle.ipynb | 13 ++++-- docs/notebooks/occasional_use.ipynb | 40 +++++++++++-------- docs/notebooks/simple_bulb.ipynb | 10 ++--- docs/notebooks/thermal_app.ipynb | 13 +++--- docs/notebooks/using_excel.ipynb | 26 ++++++------ docs/source/algorithm.rst | 6 +-- .../examples/cooking_app/cooking_app.rst | 35 ++++++++-------- .../fixed_flat_app/fixed_flat_app.rst | 2 +- .../examples/multi_cycle/multi_cycle.rst | 9 +++-- .../occasional_use/occasional_use.rst | 15 ++++--- .../examples/simple_bulb/simple_bulb.rst | 2 +- .../examples/thermal_app/thermal_app.rst | 4 +- .../examples/using_excel/using_excel.rst | 33 ++++++--------- .../year_simulation/year_simulation.rst | 23 ++++++----- docs/source/index.rst | 2 +- 17 files changed, 147 insertions(+), 120 deletions(-) diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb index 7fd8c27a..d7cbb2a2 100644 --- a/docs/notebooks/cooking_app.ipynb +++ b/docs/notebooks/cooking_app.ipynb @@ -7,12 +7,14 @@ "source": [ "# Cooking Appliances\n", "\n", - "In this example, appliances with multiple preferences index and attributes are modeled.\n", + "In this example, appliances with multiple preferences index and\n", + "attributes are modelled.\n", "\n", - "To have a better understanding of RAMP features for modelling these category of appliances, two households are considered:\n", + "To have a better understanding of RAMP features for modelling these\n", + "category of appliances, two households are considered:\n", "\n", - "1. First household with a fixed lunch habit of eating soup everyday.\n", - "2. Second household with two lunch preferences: cooking soup or rice.\n", + "1. A household with a fixed lunch habit of eating soup everyday.\n", + "2. A household with two lunch preferences: cooking soup or rice.\n", "\n", "The number of user preferences can be specified through **\"user_preference\"** parameter when initializing a **User** instance." ] @@ -64,14 +66,14 @@ "source": [ "### Defining the cycles for cooking soup and rice \n", "\n", - "for cooking soup it is assumed that the user needs 25 minutes divided into two parts:\n", + "For cooking soup it is assumed that the user needs 25 minutes divided into two parts:\n", "\n", "| cycle | power | time |\n", "|---------------|-------|------|\n", "| Boiling Water | 1200 | 5 |\n", "| Cooking soup | 750 | 20 |\n", "\n", - "for cooking rice it is assumed that the user needs 15 minutes divided into two parts:\n", + "For cooking rice it is assumed that the user needs 15 minutes divided into two parts:\n", "\n", "| cycle | power | time |\n", "|---------------|-------|------|\n", @@ -109,11 +111,16 @@ "id": "b8293434", "metadata": {}, "source": [ - "As second user has two different preferences for lunch, we need to model this preferences and their characterisitics as two different appliances.\n", + "The second user has two different preferences for lunch. Accordingly, we need to model\n", + "these preferences and their characterisitics as two different appliances.\n", "\n", - "Each preference needs to be specified with the cooking energy needs like the power, functioning time and the duty cycles of the cooking process. \n", + "Each preference needs to be specified with its associated cooking energy needs, such as\n", + "the power, functioning time and the duty cycles of the cooking process.\n", "\n", - "More importantly, for each preference, the user needs to specify the index of preference by using **pref_index** parameter. In this example, soup is the first prefernce of the user (pref_index = 1), and rice is the second one (pref_index = 2)." + "More importantly, for each preference, the user needs to specify the\n", + "index of preference by using the **pref_index** parameter. In this example,\n", + "soup is the first preference of the user (pref_index = 1), and rice is\n", + "the second one (pref_index = 2)." ] }, { @@ -742,7 +749,8 @@ "id": "aa34afee", "metadata": {}, "source": [ - "As the second user has the probability of cooking rice for lunch, and lunch has a less energy intensive cooking cycle, it is expected to see that the user with fixed lunch meal has higher energy consumption in most of the cases." + "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 \n", + "consumption for the the user that only eats soup, in most of the cases." ] }, { @@ -763,7 +771,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb index 93718339..e882d089 100644 --- a/docs/notebooks/fixed_flat_app.ipynb +++ b/docs/notebooks/fixed_flat_app.ipynb @@ -101,7 +101,7 @@ "metadata": {}, "source": [ "### Defining the yearly patterns\n", - "by default the yearly_pattern function returns weekdays (defined by 0) /weekends (defined by 1) division" + "By default the yearly_pattern function returns weekdays (defined by 0) /weekends (defined by 1) division" ] }, { @@ -160,7 +160,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb index 2586c881..ba436a15 100644 --- a/docs/notebooks/multi_cycle.ipynb +++ b/docs/notebooks/multi_cycle.ipynb @@ -13,10 +13,15 @@ "id": "938ce72f", "metadata": {}, "source": [ - "An example of an appliance with multiple cycle is fridge.\n", - "Fridges usually have different duty cycles can be estimated based on seasonal temperature trends.\n", "\n", - "In this example a fridge with 3 different duty cycles is modelled. The time windows are defined for 3 different cycles for 3 different season types:\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", + "\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", "\n", "\n", "| season type | Standard cycle | Intermediate cycle | Intensive cycle |\n", @@ -250,7 +255,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb index 086de950..6246e1da 100644 --- a/docs/notebooks/occasional_use.ipynb +++ b/docs/notebooks/occasional_use.ipynb @@ -7,11 +7,17 @@ "source": [ "# Appliances with occasional use\n", "\n", - "There are some appliances that are occasionally included in the mix pf appliances that the user switches-on during the day. For example, iron, stereo, printers and ...\n", + "There are some appliances that are occasionally included in the mix pf\n", + "appliances that the user switches-on during the day. For example, an iron,\n", + "a stereo, printers, etc.\n", "\n", - "Within ramp, the user may specify the probability of using an appliance on the daily mix with a parameter called, **occasional_use**.\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", "\n", - "When occasional_use = 0, the appliance is always present in the mix and when occasional_use = 1, the appliance is never present.\n", + "When :code:`occasional_use = 0`, the appliance is always present in the mix, and\n", + "when :code:`occasional_use = 1`, the appliance is never present. Any in-between\n", + "values will lead to a probablistic calculation to decide whether the appliance\n", + "is used or not on a given day.\n", "\n", "The following example, investigates the effect of this parameter by modelling two user category:\n", "* A household that uses a computer with ocationally\n", @@ -106,7 +112,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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QSyZCzmdH7ElCUNBLpiJMekToQWJT0IuIBKegFymSiEtGJQYFvYhIcAp6yVSEpYkBWpDgFPQiIsEp6CUTEZdXRuxJYlDQi4gEp6CXTEWY3o7Qg8SmoJdMRFyKGK8jiUJBLyISnIJeMhVhaWKEJaISm4JeRCQ4Bb1kIuJSRI/YlISgoBcRCU5BL5mKML0doQeJTUEvIhKcgl4yEXE2O2JPEoOCXkQkOAW9ZMoCvIFA1+9AoksV9GY2xcy2mlmDmd1VYL+Z2YJk/0YzG51sH2pmK81si5ltMrNvFLsB6ZoiLkUM2JIE0WbQm1kZcD8wFRgOzDCz4S2GTQWqk49ZwAPJ9qPA7e7+SWAccFuBY0VEpBOlOaOvBRrcfbu7HwEeA+pajKkDHvGcNcAAM6tw913u/jKAu+8DtgBDili/dHUR5j20vlJKXJqgHwK8k3e/kY+GdZtjzGwYMApYW+hBzGyWmdWbWX1TU1OKskREJI00QV/odKXlbORJx5jZGcA/A//g7u8XehB3X+TuNe5eM3jw4BRlSVcWcTo74lsvSwxpgr4RGJp3vxLYmXaMmfUgF/K/dPdfd7xUERHpiDRBvw6oNrMqM+sJXA8sazFmGXBzsvpmHLDX3XdZ7v1bfw5scfd5Ra1cQogwux2hB4mte1sD3P2omc0GngHKgIfcfZOZ3ZrsXwgsB6YBDcBB4Jbk8AnATcAfzGxDsu2/ufvyonYhIiKtajPoAZJgXt5i28K82w7cVuC41eiERwoIueY8Yk8Sgl4ZK5mKcHWmAC1IcAp6EZHgFPSSkXjzHPE6kigU9CIiwSnoJVMRprcjvAOnxKagFxEJTkEvmYi4vDJiTxKDgl5EJDgFvWQqwhr0CD1IbAp6EZHgFPSSiYjT2XqbYilVCnrJVISliV2/A4lOQS8iEpyCXjIRcSlixJ4kBgW9iEhwCnrJVISliRF6kNgU9CIiwSnoJRMecEI7XkcShYJeRCQ4Bb1kKsL0doTXAkhsCnoRkeAU9JKJiPPZEZ93kBgU9JKtCLMeEXqQ0BT0IiLBKeglExFnOSL2JDEo6EVEglPQS6YiLE3s+h1IdAp6EZHgFPSSCV2NSeT0UdCLiASXKujNbIqZbTWzBjO7q8B+M7MFyf6NZjY6b99DZvaumb1WzMIlhghv8WsRmpDQ2gx6MysD7gemAsOBGWY2vMWwqUB18jELeCBv32JgSjGKFRGR9ktzRl8LNLj7dnc/AjwG1LUYUwc84jlrgAFmVgHg7s8Dfy5m0RJAwCl6raOXUpUm6IcA7+Tdb0y2tXfMSZnZLDOrN7P6pqam9hwqXViESY8IPUhsaYK+0M9xy3OXNGNOyt0XuXuNu9cMHjy4PYeKiMhJpAn6RmBo3v1KYGcHxog0izjLoSWjUqrSBP06oNrMqsysJ3A9sKzFmGXAzcnqm3HAXnffVeRaRUSkA9oMenc/CswGngG2AL9y901mdquZ3ZoMWw5sBxqAnwH/+cTxZrYEeBG40Mwazezvi9yDdGERliYGaEGC655mkLsvJxfm+dsW5t124LZWjp1xKgWKiMip0StjJRMRlyJG7EliUNCLiASnoJdMRZjfjtCDxKagl0xEXIoYryOJQkEvIhKcgl4yFWHWI8JVsiQ2Bb2ISHAKeslExKWIHrEpCUFBLyISnIJeMhVhaWKEHiQ2Bb2ISHAKeslExNnsiD1JDAp6EZHgFPSSMU1wi3Q2Bb1kIuJSxIAtSRAKehGR4BT0kqkISxMjXCVLYlPQi4gEp6CXTMSczo7ZlXR9CnoRkeAU9JKpCLPbEXqQ2BT0IiLBKeglGwGns7WOXkqVgl4yFWFpYoAWJDgFvYhIcAp6yYQHnLuJ15FEoaAXEQlOQS+ZijC9bSG6kMgU9CIiwSnoJRMRlyJG7EliUNCLiASXKujNbIqZbTWzBjO7q8B+M7MFyf6NZjY67bHyty3CGvQIPUhsbQa9mZUB9wNTgeHADDMb3mLYVKA6+ZgFPNCOY0VEpBN1TzGmFmhw9+0AZvYYUAdszhtTBzziuevDrTGzAWZWAQxLcWzRfOF/r+bQB8c640tLkR04fDTrEoruPz1ST6/umg2VjhvYtye/unV80b9umqAfAryTd78RGJtizJCUxwJgZrPI/TXA2WefnaKsjzpvcD+OHDveoWPl9Lusdw8u+ET/rMs4ZTXDBvKlUUM4dFQnGXJqzuzdo1O+bpqgLzQD2XJ9QWtj0hyb2+i+CFgEUFNT06H1C/OvH9WRw0ROycf792be9EuzLkOkVWmCvhEYmne/EtiZckzPFMeKiEgnSjOhuA6oNrMqM+sJXA8sazFmGXBzsvpmHLDX3XelPFZERDpRm2f07n7UzGYDzwBlwEPuvsnMbk32LwSWA9OABuAgcMvJju2UTkREpCDzEnw5X01NjdfX12ddhohIl2Fm6929ptA+rQUTEQlOQS8iEpyCXkQkOAW9iEhwJflkrJk1AW918PByYHcRy+ksqrP4ukqtqrP4ukqtnVnnOe4+uNCOkgz6U2Fm9a0981xKVGfxdZVaVWfxdZVas6pTUzciIsEp6EVEgosY9IuyLiAl1Vl8XaVW1Vl8XaXWTOoMN0cvIiIfFvGMXkRE8ijoRUSCCxP0pXQRcjMbamYrzWyLmW0ys28k2//OzH5rZtuSzwPzjvlWUvtWM/v3p7neMjN7xcyeLvE6B5jZP5nZ68m/7fhSrNXM/kvyfX/NzJaYWe9SqdPMHjKzd83stbxt7a7NzD5tZn9I9i0wK+4l0lup88fJ936jmT1pZgNKsc68fd80Mzez8qzrxN27/Ae5t0B+EziX3MVOXgWGZ1hPBTA6ud0feIPcxdH/B3BXsv0u4L7k9vCk5l5AVdJL2Wms978CjwJPJ/dLtc6HgZnJ7Z7AgFKrldzlM/8I9Enu/wr4WqnUCVwGjAZey9vW7tqAl4Dx5K4i9xtg6mmo8yqge3L7vlKtM9k+lNzbs78FlGddZ5Qz+uYLmLv7EeDERcgz4e673P3l5PY+YAu5AKgjF1Ykn69LbtcBj7n7YXf/I7n39a89HbWaWSVwNfBg3uZSrPNMcr9UPwdw9yPu/l4p1kruOg99zKw70JfcVdVKok53fx74c4vN7arNzCqAM939Rc+l1CN5x3Rane7+rLufuKr8GnJXrCu5OhP/C7iDD186NbM6owR9axcnz5yZDQNGAWuBT3juylsknz+eDMuy/vnkfiDzr6peinWeCzQBv0immR40s36lVqu7/wn4n8DbwC5yV1t7ttTqbKG9tQ1Jbrfcfjr9R3JnvlBidZrZtcCf3P3VFrsyqzNK0Ke+CPnpZGZnAP8M/IO7v3+yoQW2dXr9ZnYN8K67r097SIFtp+vfuTu5P5EfcPdRwAFy0wytyerfdCC5M7cq4Cygn5ndeLJDCmzL/Gc30VptmdZsZncDR4FfntjUSj2nvU4z6wvcDXyn0O5W6un0OqMEfZoLmJ9WZtaDXMj/0t1/nWz+t+TPNJLP7ybbs6p/AnCtme0gN911uZn9YwnWeeKxG919bXL/n8gFf6nVeiXwR3dvcvcPgF8DnynBOvO1t7ZG/jptkr+905nZV4FrgBuSaY5Sq/M8cv/Jv5r8XlUCL5vZv8uyzihBX1IXIU+eMf85sMXd5+XtWgZ8Nbn9VeCpvO3Xm1kvM6sCqsk9OdOp3P1b7l7p7sPI/Zv9i7vfWGp1JrX+K/COmV2YbLoC2FyCtb4NjDOzvsnPwRXknqMptTrztau2ZHpnn5mNS3q8Oe+YTmNmU4A7gWvd/WCL+kuiTnf/g7t/3N2HJb9XjeQWZvxrpnUW85ndLD/IXZz8DXLPZN+dcS2fJfen10ZgQ/IxDRgEPAdsSz7/Xd4xdye1b6XIz7inrHkSf111U5J1ApcC9cm/61JgYCnWCnwPeB14Dfg/5FZZlESdwBJyzx18QC6E/r4jtQE1SX9vAj8heZV9J9fZQG6O+8Tv1MJSrLPF/h0kq26yrFNvgSAiElyUqRsREWmFgl5EJDgFvYhIcAp6EZHgFPQiIsEp6EVEglPQi4gE9/8BCCDXQ6jj77EAAAAASUVORK5CYII=\n", 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", 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" ] @@ -168,7 +174,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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mYLdmYElkVYqISBfV9AO/CTjbzNYCZwePRbC09ZtWG7jEVFnzgbv7cmB5cL8VmBZ+SZIGntMlsDTZLRZlr0pftLmj/OYQU7u5xJxGYkrvoPZsSSEFuEQgBd0Is19ATSgSUwpwEZGEUoBL5HpiVfruzpp7W2x3dSOU5FCAS++gMJYUUoBL6FIxnWzWC2govcSVAlxEJKEU4BIJr2AoPTn9wMucTrbYUPqK2sD16yHxpk+ohC+OTQ5Vtd+0Fd9FpAYU4CIiCaUAl4iEOJS+lFXpi15iVzCUXh1XJOYU4NI7KI0lhRTgErpCsxGqG6FIuBTgIiIJpQCXSOROJ1v+ijxdVrWvRTdCTScrMacAl95BbeCSQgpwiUDK2ozVBi4xpQCXiBSYWbC7I/I0uwRPULwbYfhMIzEl5vQJFRFJKAW4hC6eixpXcY0ex7cjggJcRCSxFOASkUpWgf9A11XpixwbQSN48ZXuRWpLAS69g8JYUkgBLj2mqgj13Ds9uiq9ppOVmFKAi4gklAJcIuE590scSl9oRR7LbgMvMJQ+rJU4tSq9JIgCXHoJhbGkjwJcQhfPfuCV03SyElcKcIlGoWHx3R1SwSo+eV4uNOpGKHGnABcRSaiiAW5mDWb2n2b2RzN7wcy+HWwfYmaPmNna4HZw9OVKIsSxyaGqq+kYvh8RSrsCfx84y91PBsYC081sIjAXWObuxwLLgsciItJDiga4Z+wMHvYN/jkwA1gYbF8IzIyiQEmqalel7zydbM9TG7jEXUlt4GZWZ2ZrgE3AI+6+EjjM3TcCBLfDChx7mZm1mFnL5s2bQypbRERKCnB33+/uY4ERwAQzO6nUF3D3+e7e5O5NQ4cOrbBMSZJIVqWveih9ia+uVeklQcrqheLu24DlwHTgHTMbDhDcbgq7OBERKayUXihDzeyQ4H5/4BPAy8BDQHOwWzOwJKIaJYE8z5Jq5VzH5vYdL2EofcH26sqH0rvawCXm6kvYZziw0MzqyAT+fe6+1MxWAPeZ2SXAW8CFEdYpUh2FsaRQ0QB39+eAcXm2twLToihKJFbUBi4xpZGYEo0yhtJnTwD4wTH5z1XwHGWUVqrCMxyKxIMCXEQkoRTgErp4zkZYzar0cXw/IgpwEZHEUoBLJLyMofTWcWt5tna+X+AcUUwn20e/HhJv+oSKiCSUAlwiEMOh9KVeouddlV5t4BJPCnARkYRSgEvkig2lbx8GX92q9IVUsSq9+oFLzCnApZdQGEv6KMAldJa2ftNpez+SGgpwiUjpV7zW6RY6N7eU0o0w/CtsrcgjcacAFxFJKAW4hC6W161VXE27uhFKTCnARUQSSgEu0ahoOtkCx5QynWwUQ+nVBi4xpwAXEUkoBbhEIIZD6atYlV7dCCWuFOAiIgmlAJdI5Ewn60WG0rcPjy9wfCmr0hdW+VB6tYFL3CnAJXTxXJGncq4mFIkpBbj0DrqalhRSgEtESl+Rhw9aLfIfU6NV6WM6JEmkgwJcUkGNHNIbKcAlAimLU7WBS0wpwEVEEkoBLtEotLpON7sWHEpfq7ZoffEpMacAFxFJKAW4hK7QdatWpRcJlwJcRCShFOASka7t2YVXpe+6rdxV6Uupo7Tds4fS69dD4q3oJ9TMjjKz35nZS2b2gpldHWwfYmaPmNna4HZw9OVKMqSsyUHdCCWmSrnE2Ad81d2PByYCXzazE4C5wDJ3PxZYFjwWiSn1KJH0KRrg7r7R3VcH93cALwFHAjOAhcFuC4GZEdUoCeRlDKW3ouGqboQi+ZTVyGdmI4FxwErgMHffCJmQB4YVOOYyM2sxs5bNmzdXWa6IiLQrOcDNbCDwAPAVd99e6nHuPt/dm9y9aejQoZXUKAkTy+lkq7maVhu4xFRJAW5mfcmE9z3u/mCw+R0zGx48PxzYFE2JIiKSTym9UAz4KfCSu38/66mHgObgfjOwJPzyJLEqWJU+W7mr0kdBK/JI3NWXsM9kYBbwvJmtCbb9d+Am4D4zuwR4C7gwkgpFRCSvogHu7r+ncDeAaeGWI2lgsWwzrvxq2uPYpi+CRmJKZEqfTTDfs+XORhhFY4eaUCTuFOCSCrpGlt5IAS5STCybhEQU4NJbqDlEUkgBLpEoayh9nnAtd1X6KKgNXOJOAS4iklAKcAldLIfSVyVt70fSQgEuvYSaQyR9FOASjXKG0ufZVuyYnqA2cIk7BbiISEIpwCUCMWwz1nSykkIKcIlc0eaQmM5GqL7jEncKcBGRhFKAS+jS1o3Q1YQiMaUAl15CzSGSPgpwiUg5q9J35UX36An69ZB40ydURCShFOASuvQ1VrTVugCRvBTg0juoS6CkkAJcIuFVBmYc+oFbH4W+xJsCXEQkoRTgEoE49puuZih9eFWIhEkBLhGptvmhvFXpo2Ap/DpW0kUBLiKSUApwCZ2lbeh52t6PpIYCXHoHdSOUFFKAS0RCDMyadSPUr4fEmz6hIiIJpQCX0HW+XrYC29uV1MLsuXdKb5Yus/26/cQ5L6Ch9BJPCnDpJdQGLulTNMDN7E4z22Rmf8raNsTMHjGztcHt4GjLlMSxrtPJFroWLhStbZ517W65e5feLF5mcLef2NQLXOKvlCvwBcD0TtvmAsvc/VhgWfBYRER6UNEAd/cngK2dNs8AFgb3FwIzwy1Lki1l/aZT9nYkPSptAz/M3TcCBLfDCu1oZpeZWYuZtWzevLnCl5OkKboSfUnnCITRjbCCc5hlN+OIxE/kX2K6+3x3b3L3pqFDh0b9ciIivUalAf6OmQ0HCG43hVeSJJ1WpRfpGZUG+ENAc3C/GVgSTjkiUVFTiKRPKd0IFwErgOPMbL2ZXQLcBJxtZmuBs4PHIqHyokOAomWp+1tC0qa+2A7u/rkCT00LuRYRESmDRmJK6DpftyZ/KL2uwyWeFODSO2g6WUkhBbhEI4Sh9B1t4FajofQWTn92kagowEWKUTdCiSkFuIhIQinAJSIhDqUPpRmjsnOoCUXiTAEuIpJQCnAJXfqGv6Tt/UhaKMCld1A3QkkhBbhEI4TAzOlGWAOZboQi8aUAFxFJKAW4hC51Q+nVD1xiSgEuIpJQCnCJRHb/6UqH0udcu9dkVXpTP3CJNQW4SFFqQpF4UoCLiCSUAlyiEUo3wvDOVemq9FqKTeJMAS4iklAKcAld6obSqxuhxJQCXEQkoRTgEpEQh9LXaDpZDaWXuFOAi4gklAJcQpe2NnBP2fuR9FCAS2yFOhthJd0INRJTYk4BLiKSUApwCV3qrlnVjVBiSgEuIpJQCnCJhlX/0ar1qvSZboSp+3tCUkQBLiKSUApwiUDa2ozT9n4kLRTgIiIJVVWAm9l0M3vFzF4zs7lhFSUC2f3AQzhZRf3Ade0t8VZxgJtZHXAb8EngBOBzZnZCWIWJiEj36qs4dgLwmrv/GcDMfgnMAF4Mo7BsK+66juF/+U3Yp5WIHOU7cx63BdcJu2nIu/+wQfm317P/gwd9Mh/Vtrp+APSty732qOtT4Aq7Pv+5C2rf3+o6NjWu/RVv3vBoeecR6WT3uf/K8aedG+o5qwnwI4G/ZD1eD5zWeSczuwy4DODoo4+u6IXqBh3O1gNHVXSs9LytfIiDJ1zMrz59Kv949ypGN/0dT770Go/Wnc/0IYczZOABHDPkQH62Yh1vb9vNzZ8Z3XHs//jU8ax9ZydHHNKfNRsu55g9axk+5rNwzGQ482scfuql/NcVf+MfP/Yhmo4ZzPb39gJw7omHM/+JP/P3TUflFnPWPHj253D0JPgvP4GDR3Rf/PSbYNARcNx5jKQPjw2fw9CdL+fZUY0rUp5BDQNDP6d5haPMzOxC4Fx3/4fg8SxggrtfWeiYpqYmb2lpqej1RER6KzNb5e5NnbdX8yXmeiD7cmcEsKGK84mISBmqCfBngGPNbJSZHQB8FngonLJERKSYitvA3X2fmV0B/BaoA+509xdCq0xERLpVzZeYuPu/Af8WUi0iIlIGjcQUEUkoBbiISEIpwEVEEkoBLiKSUBUP5Knoxcw2A+sqPLwR2BJiOVFJSp2QnFpVZ/iSUqvqzDjG3Yd23tijAV4NM2vJNxIpbpJSJySnVtUZvqTUqjq7pyYUEZGEUoCLiCRUkgJ8fq0LKFFS6oTk1Ko6w5eUWlVnNxLTBi4iIrmSdAUuIiJZFOAiIgmViACP0+LJZnaUmf3OzF4ysxfM7Opg+xAze8TM1ga3g7OO+XpQ+ytmFu6aSsXrrTOzZ81saVzrNLNDzOx+M3s5+LlOimmd/y34b/4nM1tkZg1xqdPM7jSzTWb2p6xtZddmZqea2fPBcz80q2A16PLr/F/Bf/vnzOzXZnZIressVGvWc18zMzezxprW6u6x/kdmqtrXgQ8BBwB/BE6oYT3DgVOC+wcBr5JZ1Pl/AnOD7XOBm4P7JwQ19wNGBe+lrgfr/SfgF8DS4HHs6gQWAv8Q3D8AOCRudZJZQvANoH/w+D5gTlzqBKYApwB/ytpWdm3AfwKTAAP+HfhkD9R5DlAf3L85DnUWqjXYfhSZabTXAY21rDUJV+Adiye7+x6gffHkmnD3je6+Ori/A3iJzC/3DDJBRHA7M7g/A/ilu7/v7m8Ar5F5T5EzsxHAp4A7sjbHqk4zG0TmF+WnAO6+x923xa3OQD3Q38zqgQPJrEAVizrd/Qlga6fNZdVmZsOBQe6+wjPJ87OsYyKr090fdvd9wcOnyazuVdM6C9Ua+N/AteQujFqTWpMQ4PkWTz6yRrXkMLORwDhgJXCYu2+ETMgDw4Ldaln/D8h80NqytsWtzg8Bm4G7gqaeO8xsQNzqdPe3ge8BbwEbgXfd/eG41dlJubUdGdzvvL0nfYnMVSrEsE4zOx94293/2OmpmtSahADP115U876PZjYQeAD4irtv727XPNsir9/MPg1scvdVpR6SZ1tP/JzryfyZ+mN3Hwf8PzJ/7hdSq5/nYDJXWaOAI4ABZvaF7g7Js63mn9tAodpqWrOZzQP2Afe0bypQT60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", 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\n", 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", 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\n", 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", 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\n", 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HJJ1mYoqIBEoBLrFpz6r0yZpKD+kjZjWMUJJKAS4iEigFuMQq0zT15j3Qmf4Klngqvaf302sqvYRDAS4iEigFuMQmyavS5z6j+sAl+RTgIiKBUoBLrDKt+N6Zq9Jnul2sVqWXUCnApegauxySHYFtDyNsPtFHfSiSTApwEZFAKcCl6Jr3gOS74nupVqWPzmDpbVqVXsKkABcRCZQCXGKT/W5/CZhK7233gbuGEUoAFOAiIoFSgEuskrcqfetjZ1o1SMMIJQQKcBGRQCnA5SCVa0yMOr4l+RTgEqv0LwDzXpEn1mGEURdJ1mGEIuFQgIuIBEoBLkV3YCp9gq9ncwwjbH6/8PjLESmEAlxEJFAKcCm6Qqapl25V+ugUWevS8EEJhwJcRCRQCnCJTV6r0peog7lDq9InuS9fDmoKcBGRQCnAJVYFTaWPdVX61uPA02kqvYREAS4iEigFuMQm0dewnmvphgP93hoHLkmlAJdYZV7U+ICMdwmMdRihptJL16EAFxEJVM4AN7MKM/sPM/uTmb1tZr+O2geY2XNmtiZ67B9/uRKCLjeVPv5qRAqSzxX418AZ7n4iMBI418zGAbOBxe5+DLA4ei4iIiWSM8A9ZXv0tHv048AUYF7UPg+YGkeBEp5s/cttfa1ZutvJNp4h81R6rUovIcmrD9zMysxsNbAReM7dVwCD3H0DQPQ4MMt7Z5hZnZnVbdq0qUhli4hIXgHu7vvcfSQwBBhrZifkewJ3n+PuNe5eU1VVVWCZEqK8ptJ31qr0eI6zpq9Kr15wSaZ2jUJx963AUuBc4HMzGwwQPW4sdnEiIpJdPqNQqsysX7TdCzgTeA94GqiNdqsFFsZUowQs04rvZOzvjnccuLXYat5P33pbU+klBOV57DMYmGdmZaQC/3F3X2Rmy4HHzexK4BPgwhjrFBGRFnIGuLu/AYzK0N4ATI6jKOkaEn0Nm2MqfXr/vXrAJak0E1NiVsg09RJMpc/Q1nJbJOkU4CIigVKAS9EFMZWe/OvTKEJJKgW4iEigFOBSdEGsSl9AjSJJowAXEQmUAlxilHsqvXXWVPoct5NNn0qvgYSSVApwEZFAKcAlVrmm0meeXl98GZdua2prva2p9BICBbiISKAU4BKbpF/D5j2VXl3gklAKcIlVrhXfG4fzeabGGOspZNUgkaRRgIuIBEoBLkUXxFT6nKvS626EknwKcBGRQCnApegKm6Ze2lXps51bU+klJApwEZFAKcAlNnmtSu+dM5XeaLsP3DWMUAKgABcRCZQCXGKVc1V6a6uteFpNpbfM49M1lV5CogCXojswjDC5rD2LGqsPRRJKAS4iEigFuBRd9mnq2Xmz3eKfSp+tTcMIJSQKcBGRQCnApeiCmEqP09bgRU2llxAowEVEAqUAl6Jr31T60gwjbHVWrUovXYACXEQkUApwiU0+U+k7d1X6/N6hYeCSVApwEZFAKcClZNwz9HdnXJU+/qn0hqbSS/hyBriZfdPMXjCzd83sbTObFbUPMLPnzGxN9Ng//nIlBCFMpSfH3QibdaFoIKEkVD5X4HuBn7r7ccA44O/NbBgwG1js7scAi6PnIiJSIjkD3N03uPuqaHsb8C5wJDAFmBftNg+YGlONEpjsQ/Sy8xINI2zsxkk/g4YRSqja1QduZtXAKGAFMMjdN0Aq5IGBWd4zw8zqzKxu06ZNHSxXREQa5R3gZtYHeBL4ibt/le/73H2Ou9e4e01VVVUhNUpgDgy7S3Lfcf6r0if6Y8hBLa8AN7PupML7YXd/Kmr+3MwGR68PBjbGU6KIiGSSzygUA34PvOvud6S99DRQG23XAguLX56EqJBp6s0vcku7Kr36wCVU5XnsMwGYBrxpZqujtv8G3Ao8bmZXAp8AF8ZSoYiIZJQzwN39ZbJfEk0ubjnSFYQzDjy75qNURJJJMzElVplmOWZWomGEZBpGKBImBbiISKAU4FJ0jVPPk74iT77DCHU3QkkqBbiISKAU4BKr/IcRxns3wgPnic5gmevSMEIJiQJcRCRQCnApuiBWpfd29IEn+XPIQU0BLiISKAW4xKqgqfQlHgee6XWRECjARUQCpQCXousKU+mb3U1WXeCSUApwiVW+2Ve6YYTW6hT5rhokkjQKcBGRQCnApeiaJssk+XrWnbaut5sPIxRJJgW4iEigFOASq/ynqZfmdrIHzqap9BI+BbiISKAU4FJ07l3tdrJJ/hxyMFOAi4gESgEuMUvaqvRtL6mmPnAJiQJcRCRQCnCJTbKvZXONidE4cEk+BbjEKt9V6b1Ewwgzd6FoKr2ESQEuIhIoBbgU3YFRdwm+ns2xIk/zfeMtRaRQCnARkUApwCVWSV2VPvvtZJP91atIOgW4iEigFOBSdI2ruGfvY853bErxtD5Lrj7w/Wl7qhNckkkBLiISqJwBbmYPmNlGM3srrW2AmT1nZmuix/7xlimhyjxNPUd/dwzjwBuPeGAceNtjv0194RKAfK7A5wLntmibDSx292OAxdFzEREpoZwB7u7LgC0tmqcA86LtecDU4pYlIQtiVXpvx1R6dYFLQhXaBz7I3TcARI8Ds+1oZjPMrM7M6jZt2lTg6SRU+U5TL/VU+mxtGkYoIYn9S0x3n+PuNe5eU1VVFffpREQOGoUG+OdmNhggetxYvJIkdEGsSo9j1taKPGl7JvljyEGt0AB/GqiNtmuBhcUpR0RE8pXPMML5wHLgWDOrN7MrgVuBs8xsDXBW9FyklcKm0scn88V0Wo2uPnAJR3muHdz9kiwvTS5yLSIi0g6aiSlFl3tV+tZT6ePuZm55fPf9bfbRe7Op9CLJpAAXEQmUAlxilXm1+UxT6ePte25zKn1akQdG0KgvXJJPAS5FdyAEk6ztjpHmMzHViSLJpAAXEQmUAlxilbwV3zWVXroOBbiISKAU4FJ80aV2tzamqne6HKvSN5tKH381IgVRgIuIBEoBLjFL/lR69YFLqBTgIiKBUoBL0eXuM07AVPqc19ppU+nVCS4JpQAXEQmUAlxilf+q9KWeSp9eV+ttTaWXECjApehSU8+T3u/Q9jDCzLEukiwKcBGRQCnAJVbpK9y0eb1boh4LrUovXYkCXEQkUApwKTon6SvS076p9An/KHLwUoCLiARKAS6xyr9/ORlT6ZO+DIVIOgW4iEigFOBSdO5hXMe2/ftA2pJq8ZciUhAFuMQq80zMTPuVqgsl0zDCzNsiSacAFxEJlAJcis5zTlNPgJzDCNNXpS9FQSLtpwAXEQmUAlxile+q9KW6yM3c166p9BImBbiISKAU4FJ0qWGEye44tnb003vCP4scvBTgIiKB6lCAm9m5Zva+mX1oZrOLVZR0HflOpdeq9CLtV3CAm1kZcB/wfWAYcImZDStWYSIi0rbyDrx3LPChu38EYGaPAlOAd4pRWLrlD17HEX95ptiHlZg8uHc/1qONfuPeVU2b+yv6AdCzPN7evB4tjt9n6Q3MKt+Zdb/bdt/MX3uUAVD+lLFuga7MpWN2nHM7x518blGP2ZEAPxL4S9rzeuDkljuZ2QxgBsBRRx1V0InK+n6DhkO+XdB7pfQc2PDlLj7q9m1e6jaG075TxYsfbKJm1El476uwEy9u2rfXeTfxxsI7+dkFU2HjAPhibSw1/f3pR7PPnWEDj+fTd1/nyB47+WjtFp7e1puKfoNg6y4AfnJFLbz+Od2/+JK/frGD/Vlm8ehrTWmvw3r1LfoxzQucZmZmFwLnuPvfRc+nAWPdfWa299TU1HhdXV1B5xMROViZ2Up3r2nZ3pHfW+uBb6Y9HwKs78DxRESkHToS4K8Bx5jZUDPrAVwMPF2cskREJJeC+8Ddfa+ZXQ38P6AMeMDd3y5aZSIi0qaOfImJu/8r8K9FqkVERNpBMzFFRAKlABcRCZQCXEQkUApwEZFAFTyRp6CTmW0C1hX49kpgcxHLiUsodUI4tarO4gulVtWZ8i13r2rZWNIA7wgzq8s0EylpQqkTwqlVdRZfKLWqzrapC0VEJFAKcBGRQIUU4HM6u4A8hVInhFOr6iy+UGpVnW0Ipg9cRESaC+kKXERE0ijARUQCFUSAJ2nxZDP7ppm9YGbvmtnbZjYrah9gZs+Z2ZrosX/ae34R1f6+mZ1T4nrLzOx1M1uU1DrNrJ+ZPWFm70V/ruMTWuc/RP/N3zKz+WZWkZQ6zewBM9toZm+ltbW7NjM7yczejF6728yKupZcljr/Z/Tf/g0z+4OZ9evsOrPVmvbaz8zMzayyU2t190T/kLpV7Z+BbwM9gD8BwzqxnsHA6Gj7UOADUos6/w9gdtQ+G7gt2h4W1dwTGBp9lrIS1vtfgUeARdHzxNUJzAP+LtruAfRLWp2klhBcC/SKnj8OTE9KncBEYDTwVlpbu2sD/gMYDxjwb8D3S1Dn2UB5tH1bEurMVmvU/k1St9FeB1R2Zq0hXIE3LZ7s7ruBxsWTO4W7b3D3VdH2NuBdUv+4p5AKIqLHqdH2FOBRd//a3dcCH5L6TLEzsyHA3wD3pzUnqk4z60vqH8rvAdx9t7tvTVqdkXKgl5mVA4eQWoEqEXW6+zJgS4vmdtVmZoOBvu6+3FPJ889p74mtTnd/1t33Rk9fJbW6V6fWma3WyP8GrqX50qidUmsIAZ5p8eQjO6mWZsysGhgFrAAGufsGSIU8MDDarTPrv5PUX7T9aW1Jq/PbwCbgwair534z6520Ot39U+B24BNgA/Cluz+btDpbaG9tR0bbLdtL6QpSV6mQwDrN7HzgU3f/U4uXOqXWEAI8U39Rp499NLM+wJPAT9z9q7Z2zdAWe/1m9gNgo7uvzPctGdpK8edcTurX1H9091HAX0n9up9NZ/159id1lTUUOALobWY/bOstGdo6/e9tJFttnVqzmV0P7AUebmzKUk9n/R04BLge+GWmlzO0xV5rCAGeuMWTzaw7qfB+2N2fipo/j35dInrcGLV3Vv0TgPPN7GNS3U5nmNm/JLDOeqDe3VdEz58gFehJq/NMYK27b3L3PcBTwCkJrDNde2ur50D3RXp77MysFvgBcFnU1ZDEOr9D6n/gf4r+XQ0BVpnZNzqr1hACPFGLJ0ffIP8eeNfd70h76WmgNtquBRamtV9sZj3NbChwDKkvNWLl7r9w9yHuXk3qz2yJu/8wgXV+BvzFzI6NmiYD7yStTlJdJ+PM7JDo78BkUt9/JK3OdO2qLepm2WZm46LPeHnae2JjZucC1wHnu/uOFvUnpk53f9PdB7p7dfTvqp7UgIbPOq3WYn9zG8cPcB6p0R5/Bq7v5FpOJfUr0BvA6ujnPOBwYDGwJnockPae66Pa3yeGb8vzqHkSB0ahJK5OYCRQF/2ZLgD6J7TOXwPvAW8BD5EacZCIOoH5pPrm95AKlisLqQ2oiT7fn4F7iWZrx1znh6T6jxv/Pf2us+vMVmuL1z8mGoXSWbVqKr2ISKBC6EIREZEMFOAiIoFSgIuIBEoBLiISKAW4iEigFOAiIoFSgIuIBOr/A7tsv2z2neKcAAAAAElFTkSuQmCC", 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\n", 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", 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", 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RKmg+cHdfA6wJ7u8GpoZfkpSC1Io8Uc1GmMce+a5Kb4A3DiNs/oQupZe405WYIiIJpQCX0CVqGGEcShVpJwW4lKZY/BIRiZYCXCLRcmrWtAetdw59Otm2d242jNCyr0qvS+kl7hTgIiIJpQCXCMShfRGHGkSipQCX0qQeuHQBCnCJROupWRvvxmFV+kzTybY+TuPAJe4U4BK+WJz9hlhDLD6PSGsKcClNCl3pAhTgEomir0rfxs6Zjneseeanz0aoxZklphTgIiIJpQCX8MWie5FfEbEoVaSdFOBSmpTM0gUowKUTFGE62byfPTblbcuhjxpGKHGnABfJQvEtcacAlwgcLXYBqIciXYECXCLh2LFuSTGGETaOCcw4jDDzlZitFzXWObjEmwJcRCShFOASvlh0L0IoovEEXFd1SkwpwKU0KXSlC1CASySyz0aYae9Ce80dm40wE/dMq9I33lUvXOJJAS4iklAKcIlAHNoX+dYQh1pF2kcBLqUp3x648lsSTAEukSjuqvQFTCfbxqr0GgcucacAF8lC8S1xpwCX8MViCF8cahCJlgJcIhHtosZ57NH0SyRHC8WP3Wg2QkkaBbiISELlDHAz62lm/25mL5nZa2b2o2B7fzNbaWabg9t+0ZcryRCH9kWIwwhj0RISaS2fM/BPgQvc/SygGrjEzCYA84BV7j4MWBU8FomHPENX0SxJljPAPWV/8LB78MeB6cDiYPtiYEYUBUoyRbsqfY79cw0jzFCDY62GPupSeom7vHrgZlZmZpuAHcBKd18PDHT37QDB7YAsx15nZnVmVrdz586QyhYRkbwC3N2PuHs1MBgYb2Yj8n0Dd1/o7jXuXlNZWdnOMiVRYtEzzq+GvM6tY/F5RForaBSKu38IrAEuAT4ws0EAwe2OsIsTaTeFrnQB+YxCqTSzE4P7vYALgTeBJ4DaYLdaYHlENUoCOelnt8Vclb7tceDedGutjtM4cIm78jz2GQQsNrMyUoH/qLuvMLN1wKNmdg2wFbgiwjpFOp3iW+IuZ4C7+8vA6AzbdwNToyhKki4O7Ys41CASLV2JKZEobBhhYa/d4VXpMxyiVekliRTgIiIJpQCX8MViBEiINcTi84i0pgAXEUkoBbhEJMLpZHPunmsYYeYaWp5nqwcucacAFxFJKAW4RCA5PeP8zrGT83mka1GAi4gklAJcIhHpqvS5zptzTid7TPql9NnHrovEkwJcJCuFuMSbAlzCV2rjpkvt80jJUIBLJFrP7hcIYRhh7t0Lv3TfaflVZethhSJxowAXEUkoBbhEoNTOXUvt80ipUICLiCSUAlwi4ViWLnIYwwhz7ZD/MMJGmYYR6lJ6iTsFuIhIQinAJXzBsDtvPPuN9TC8PGqLdf3SlSnARUQSSgEukXDAMi1rFsp0sjnXVGvztdOPb+xzt1pSDdPl9BJ7CnCJQKm1HErt80ipUIBLdHQGKxIpBbhEIvsQvM4YRtj2a2cfRtj8OJ13S9wpwEVEEkoBLuErtWF3pfZ5pGQowEVEEkoBLpHwtP9tJhar0rc+ovX0t7qUXuJPAS4iklAKcIlA45l3/C+lb2usSfrlPiJxpAAXEUkoBbhEotl0srFblT6PS+k1nawkQM4AN7NTzexpM3vDzF4zsxuC7f3NbKWZbQ5u+0VfriRCjFsmreRTapI+j3Qp+ZyBHwa+4+5nABOAvzWz4cA8YJW7DwNWBY9FmugMViRaOQPc3be7+8bg/j7gDeAUYDqwONhtMTAjoholkbKsyBPTYYStv87UpfQSfwX1wM2sChgNrAcGuvt2SIU8MCDLMdeZWZ2Z1e3cubOD5YqISKO8A9zM+gBLgb9z9735HufuC929xt1rKisr21OjJFayWyjHvuzUubjEU14BbmbdSYX3g+7+eLD5AzMbFDw/CNgRTYkiIpJJPqNQDPgl8Ia735n21BNAbXC/FlgefnmSVFkvpY/BqvSZZF6VXiTeyvPYZxIwC3jFzDYF2/4ncBvwqJldA2wFroikQhERyShngLv7c2Q/6ZkabjlSErzFpfRJp3HgElO6ElMi4Vjm/M4yhK8gBQ0jzPBshrZKpkWNNY5d4k4BLtHRmatIpBTgEoHkBHd+lSbn80jXogCX6CR9VfqEly+lTwEukXAsSwslDrMRtpZ5VXoluMSbAlxEJKEU4BK+UvvystQ+j5QMBbhEJ+E98GRXL12BAlwi4YBlOnON6XSyrS6l13SykgAKcBGRhFKASwSCqawS3kJB08lKzCnAJRKOZW6hxGA2wswtlEyLGovEmwJcRCShFOASgRI7d9UwQokpBbhEJuk98GRXL12BAlwi4ZGuSp9r/xzDCDNsa33ZvC6ll/hTgIuIJJQCXMKnnrFIp1CAS4TUghCJkgJcIpH9HDyEceA5W+DtGQeeaVV6/QKSeFOAi4gklAJcQlcq1zA2nX+rpy8xpQCXiGRpP4QwNjz3K+QaRpiZVqWXpFGAi4gklAJcwldyLYdS+zxSKhTg0sUpnCW5FOASCc+6dnzxhxFm2pZxVXq1wCXmFOAiIgmlAJcItGhLJLQnbo1lJ7R+KX0KcJFs1EKRmMsZ4GZ2r5ntMLNX07b1N7OVZrY5uO0XbZmSNM1OWnP1pAtOyo5OJ3tM00l2xkvpReItnzPwRcAlLbbNA1a5+zBgVfBYREQ6Uc4Ad/e1wJ4Wm6cDi4P7i4EZ4ZYliVZyPeNS+zxSKtrbAx/o7tsBgtsB2XY0s+vMrM7M6nbu3NnOt5OkyX4ZevGHEea1Kj1qgUv8Rf4lprsvdPcad6+prKyM+u1ERLqM9gb4B2Y2CCC43RFeSZJ8CWo55FNqybWEpFS0N8CfAGqD+7XA8nDKEYkTNVEk3vIZRrgEWAecbmYNZnYNcBtwkZltBi4KHos0ydoDD2NV+oL2yG862VaX0oskQHmuHdz9yixPTQ25FhERKYCuxJToxbiH3NbZvKVd5iMSRwpwEZGEUoBLJFpOzZr2oPXOBY8Dz7F/s7fLNA782Lasl9LT1lh2kXhQgEv4YtwyaZdS+zxSMhTgIiIJpQCXSDiWZcRgXIcRFl6HSLEpwEVEEkoBLhEotZ5xqX0eKRUKcJEs1FCRuFOASySyn7PGdTpZxbUkjwJcRCShFOASOms5bjrG46jbPu8O6o5x/dK1KcClS/PCmj0isaIAl4hk6UOHMg68o6vS57i0P+AF9uZFOpsCXCJQai2HUvs8UioU4NKlKZolyRTgEok4r0qfqQQNI5QkUoCLiCSUAlwiUGKNCQ0jlJhSgIuIJJQCXCKR9Zw1hGGEubVjOllXD1ySRwEuIpJQCnAJX8n1jEvt80ipUIBLJAoalheL2QhFkkcBLiKSUApwiUCJnc+WXEtISoUCXEQkoRTgEgnPNmdgZ89GmKkHnuH4TD17DSyUuFOAi4gklAJcwldyLeOS+0BSIhTgIiIJ1aEAN7NLzOwtM3vbzOaFVZQkX2xWpc/zaU0nK0nU7gA3szJgAfBlYDhwpZkND6swERFpW3kHjh0PvO3u7wCY2cPAdOD1MApLt+6+mxj0x1+H/bISkVOPfgTA0fKeqQ0W307doTb+Chy041J3tr/Ee7eM6KSKpFR9PO0fGT7h4lBfsyMBfgrwx7THDcDZLXcys+uA6wCGDBnSrjcq6/tZ9hx/WruOlc63h9P484D/QvlXzoOX74dTxh57ctw10KsfVJ0L778Chz+Fqi8V9PqnVfbm6glD2P/JYc4dVtl6h/F/BX1Pgcr/lPH4WRM+R7/jj+P9vZ/wG/6G6oN1DDllGmN39qC+z98ytuokAN6f8o8cWTmVl3ufg8f4l5Akw2eO7xP6a5q38yozM7sCuNjd/zJ4PAsY7+5zsx1TU1PjdXV17Xo/EZGuyszq3b2m5faOnFY0AKemPR4MbOvA64mISAE6EuAbgGFmNtTMjgO+BjwRTlkiIpJLu3vg7n7YzK4H/g0oA+5199dCq0xERNrUkS8xcfd/Bf41pFpERKQA+mpdRCShFOAiIgmlABcRSSgFuIhIQrX7Qp52vZnZTmBLOw+vAHaFWE5UklInJKdW1Rm+pNSqOlM+5+6tLjvu1ADvCDOry3QlUtwkpU5ITq2qM3xJqVV1tk0tFBGRhFKAi4gkVJICfGGxC8hTUuqE5NSqOsOXlFpVZxsS0wMXEZHmknQGLiIiaRTgIiIJlYgAj9PiyWZ2qpk9bWZvmNlrZnZDsL2/ma00s83Bbb+0Y74X1P6WmYW7plLuesvM7EUzWxHXOs3sRDN7zMzeDH6uE2Na538L/j9/1cyWmFnPuNRpZvea2Q4zezVtW8G1mdlYM3sleO5nZgWuON2+Ov9X8P/9y2b2KzM7sdh1Zqs17bn/YWZuZhVFrdXdY/2H1FS1fwBOA44DXgKGF7GeQcCY4P4JwO9JLer8D8C8YPs84Pbg/vCg5h7A0OCzlHVivf8deAhYETyOXZ3AYuAvg/vHASfGrU5SSwi+C/QKHj8KzIlLncBkYAzwatq2gmsD/h2YCBjwG+DLnVDnNKA8uH97HOrMVmuw/VRS02hvASqKWWsSzsCbFk9294NA4+LJReHu2919Y3B/H/AGqb/c00kFEcHtjOD+dOBhd//U3d8F3ib1mSJnZoOBrwD3pG2OVZ1m1pfUX5RfArj7QXf/MG51BsqBXmZWDhxPagWqWNTp7muBPS02F1SbmQ0C+rr7Ok8lz/1px0RWp7s/6e6Hg4cvkFrdq6h1Zqs18L+BG4H0ESBFqTUJAZ5p8eRTilRLM2ZWBYwG1gMD3X07pEIeGBDsVsz6f0rqP7SjadviVudpwE7gvqDVc4+Z9Y5bne7+J+AOYCuwHfjI3Z+MW50tFFrbKcH9lts70zdJnaVCDOs0s8uAP7n7Sy2eKkqtSQjwTP2ioo99NLM+wFLg79x9b1u7ZtgWef1m9lVgh7vX53tIhm2d8XMuJ/XP1P/j7qOBP5P65342xfp59iN1ljUUOBnobWZXt3VIhm1F/+82kK22otZsZvOBw8CDjZuy1FOs/waOB+YDP8j0dIZtkdeahACP3eLJZtadVHg/6O6PB5s/CP65RHC7I9herPonAZeZ2Xuk2k4XmNm/xLDOBqDB3dcHjx8jFehxq/NC4F133+nuh4DHgXNiWGe6Qmtr4Fj7In175MysFvgq8PWg1RDHOj9P6hf4S8Hfq8HARjP7bLFqTUKAx2rx5OAb5F8Cb7j7nWlPPQHUBvdrgeVp279mZj3MbCgwjNSXGpFy9++5+2B3ryL1M1vt7lfHsM73gT+a2enBpqnA63Grk1TrZIKZHR/8NzCV1PcfcaszXUG1BW2WfWY2IfiMs9OOiYyZXQLcBFzm7h+3qD82dbr7K+4+wN2rgr9XDaQGNLxftFrD/uY2ij/ApaRGe/wBmF/kWs4l9U+gl4FNwZ9LgZOAVcDm4LZ/2jHzg9rfIoJvy/OoeQrHRqHErk6gGqgLfqbLgH4xrfNHwJvAq8ADpEYcxKJOYAmp3vwhUsFyTXtqA2qCz/cH4G6Cq7UjrvNtUv3jxr9P/1TsOrPV2uL59whGoRSrVl1KLyKSUElooYiISAYKcBGRhFKAi4gklAJcRCShFOAiIgmlABcRSSgFuIhIQv1/+Wio4uiu9F8AAAAASUVORK5CYII=\n", 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", 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", 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\n", 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", 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" ] @@ -318,7 +324,9 @@ "id": "5507c1ac", "metadata": {}, "source": [ - "As it can be seen from the figures, the computer is always present in the school appliance mix while for household, is occasionally present." + "As it can be seen from the figures, the computer is always present in\n", + "the school's appliance mix while, for the household, it is only occasionally \n", + "present." ] } ], diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb index f655fb7f..9bde66f6 100644 --- a/docs/notebooks/simple_bulb.ipynb +++ b/docs/notebooks/simple_bulb.ipynb @@ -90,7 +90,7 @@ }, { "data": { - "image/png": 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\n", 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", 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", 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\n", 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4yDtQRKRWoghwEZFSFZ+VvozhZCsrJXMKcBGRSEUZ4AM+SNW+IiJk11874G7gkQR4yHtQRIJSdDTCcq7EDLwfYRwBLiIig0Qf4BroSkQguxP1kBMmigDXrPQiIoNFEeAiIqUqOit9WesKmwJcRCRS0Qe4OqiICGTXVq3hZCuk4WRFRAaLIsBFREpV9ACvvCl5gqYAFxGJVJQBruFkRaRQZpfSZ7LWdEQR4LpYR0RKVbQFJcV1hSKKABcRkcGiD/CQu/iISPVklgQBR0wUAT4oo0M/rxERqYIoAlxEpFQaTlZERIIXfYAH3DwlIlWU3XCy4aZMFAGuJnARkcGiCHARkVIVa7cuNszssOuqtJiMKcBFRCIVZYDnf8KqG7iIQHZt1SFnTBQBHvIOFJE4lNeNMP060rTDADezO8xso5kty1s2ycweM7NVye3EbMsUEZFCpRyB/ydwcsGyi4Eud98f6Eoei4jUTGbdCANuAdhhgLv7k8DrBYvnAguT+wuBU9Mtq6CGgratwM9qRESqotw28L3dfT1AcrtXsRea2Tlm1m1m3Zs2bSpzcyIipSl6KX056wr8cDHzLzHd/VZ3b3f39qampqw3JyKy0yg3wDeY2VSA5HZjeiWNnIaUFZFiKo2HeryU/iGgM7nfCTyYTjlFhLv/RERqppRuhHcDzwAfM7NeMzsbuBo4wcxWASckj6sm9L6ZIlI7he3WfXlRj/3AR+3oBe7+hSJPdaRci4iIjEAUV2LuiJrARUT9wAM1aDjZwE9rRKR2incjrL/giCLARURksLoI8IDPcESkSjIbjTCTtaYjigAv7Oddj6dCIiIjFUWAi4iUqujhnWalFxGRUNRFgOtSehEpFgMVX0ofcLxEEeAh70ARkVqJIsALBd4sJSI1VJgP/ZfSl7OuiqvJVpQBXkgH6CKSXQ6EmzB1EeAiIjujKAI83M8/EQlNsetEyukSGHpzbRQBLiIig9VFgKuXiohk1Z045HyJIsALd2DoV0eJiFRDFAEuIlKqVGelD/xYUQEuIhKpugjwkGeNFpHqKJYClc9KH64oAlwBLSIyWBQBXijwZikRCUhFs9IHnjZRBnihkLv5iEh1ZDepcbgBUxcBLiKyM4oiwAf3A69NHSISvmLXiZTVhBJ41kQR4CIiMpgCXETqQ1Zt4NmsNhUKcBGRSEUZ4KF37RGR2imWDuXkRuhJE2WAi4hInQR4wN00RaRKsrpiO+R8iSLACzvSh961R0Rqp+hohHU4HGEUAS4iIoPVRYBrsCsRyexS+mxWm4q6CHARkZ1RFAFe+AkYdquUiNRSmvkQetZEEeAiIjJYXQR4yN18RKQ6sooBDSdboYD3n4hIzVQU4GZ2spm9ZGYvm9nFaRW14+1Wa0siEpviw8mWcSl94FlTdoCbWQNwE/ApYAbwBTObkVZhIiIyvFEVvPdw4GV3Xw1gZvcAc4EX0yhsOA27DPzc+ewPn2KX0D8qJVMht1NKdWzbPvTfwOhRlbUUd695gxOu+7+K1gFw5ef/nsNaJlW8nnyVBPg+wGt5j3uBIwpfZGbnAOcA7LfffmVt6OB99uD09mZ2GzOKXUc30PHxvTjvHz7KMQc0cdeza4v+4mQno8/wnV7bvhM49oCmAcu+fvxHmTJ+DEdMm8QNZ7QxZfyYktfXObuFR1/8Yyq1jWtsSGU9+azcIxczOw04yd2/nDyeBxzu7l8v9p729nbv7u4ua3siIjsrM+tx9/bC5ZWcW/QC++Y9bgb+UMH6RERkBCoJ8F8B+5vZNDMbDZwBPJROWSIisiNlt4G7+/tmdh7wv0ADcIe7L0+tMhERGVYlX2Li7o8Aj6RUi4iIjEAUV2KKiMhgCnARkUgpwEVEIqUAFxGJVNkX8pS1MbNNwNoy3z4F2JxiOVmJpU6Ip1bVmb5YalWdOR9296bChVUN8EqYWfdQVyKFJpY6IZ5aVWf6YqlVdQ5PTSgiIpFSgIuIRCqmAL+11gWUKJY6IZ5aVWf6YqlVdQ4jmjZwEREZKKYjcBERyaMAFxGJVBQBXqvJk4vUsq+Z/dzMVpjZcjO7IFk+ycweM7NVye3EvPd8K6n9JTM7qcr1NpjZb8zs4VDrNLMJZrbEzFYm+3V2oHX+c/I7X2Zmd5vZ2FDqNLM7zGyjmS3LWzbi2szsUDP7bfLcjVbOTMAjr/N7ye/+BTP7bzObUOs6i9Wa99wCM3Mzm1LTWt096H/khqp9BZgOjAaeB2bUsJ6pwCeS+7sDvyM3qfO1wMXJ8ouBa5L7M5KaxwDTkp+loYr1fhP4L+Dh5HFwdQILgS8n90cDE0Krk9wUgq8C45LH9wH/GEqdwDHAJ4BlectGXBvwS2A2uQnq/gf4VBXqPBEYldy/JoQ6i9WaLN+X3DDaa4Eptaw1hiPw/smT3f1doG/y5Jpw9/Xu/uvk/p+BFeT+c88lF0Qkt6cm9+cC97j739z9VeBlcj9T5sysGfgMcHve4qDqNLM9yP1H+RGAu7/r7ltDqzMxChhnZqOAXcnNQBVEne7+JPB6weIR1WZmU4E93P0ZzyXPj/Pek1md7v6ou7+fPHyW3OxeNa2zWK2J/wAuAvJ7gNSk1hgCfKjJk/epUS0DmFkLcAjwHLC3u6+HXMgDeyUvq2X915P7Q9uetyy0OqcDm4A7k6ae281st9DqdPffA98H1gHrgTfd/dHQ6iww0tr2Se4XLq+mfyJ3lAoB1mlmpwC/d/fnC56qSa0xBPhQ7UU17/toZuOB+4FvuPufhnvpEMsyr9/M5gAb3b2n1LcMsawa+3kUudPUm939EOBtcqf7xdRqf04kd5Q1DfgQsJuZfXG4twyxrOZ/t4litdW0ZjO7BHgfWNy3qEg9tfob2BW4BLh0qKeHWJZ5rTEEeHCTJ5tZI7nwXuzuP0kWb0hOl0huNybLa1X/UcApZraGXLPT8WZ2V4B19gK97v5c8ngJuUAPrc5PAq+6+yZ3fw/4CXBkgHXmG2ltvXzQfJG/PHNm1gnMAc5KmhpCrPMj5D7An0/+XzUDvzazv6tVrTEEeFCTJyffIP8IWOHu1+U99RDQmdzvBB7MW36GmY0xs2nA/uS+1MiUu3/L3ZvdvYXcPnvc3b8YYJ1/BF4zs48lizqAF0Ork1zTySwz2zX5G+gg9/1HaHXmG1FtSTPLn81sVvIzzs97T2bM7GTgX4FT3P0vBfUHU6e7/9bd93L3luT/VS+5Dg1/rFmtaX9zm8U/4NPkenu8AlxS41qOJncK9AKwNPn3aWAy0AWsSm4n5b3nkqT2l8jg2/ISaj6OD3qhBFcn0AZ0J/v0AWBioHVeDqwElgGLyPU4CKJO4G5ybfPvkQuWs8upDWhPfr5XgB+SXK2dcZ0vk2s/7vv/dEut6yxWa8Hza0h6odSqVl1KLyISqRiaUEREZAgKcBGRSCnARUQipQAXEYmUAlxEJFIKcBGRSCnARUQi9f9vrXCtHgjFgwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -268,7 +268,7 @@ "id": "91442f65", "metadata": {}, "source": [ - "### whole year profile can be generated" + "### Whole year profile functionality" ] }, { @@ -333,7 +333,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb index 9933e342..75269467 100644 --- a/docs/notebooks/thermal_app.ipynb +++ b/docs/notebooks/thermal_app.ipynb @@ -8,8 +8,8 @@ "# 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 by means of\n", - "the thermal_P_var attribute." + "is the \"shower\". The example showcases how to model thermal loads\n", + "by means of the `thermal_P_var` attribute." ] }, { @@ -48,7 +48,10 @@ "id": "808da9d5", "metadata": {}, "source": [ - "when the power is variant during the day, the \"power\" parameter needs to 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:" + "When the power is varying during the day, the “power” parameter needs to\n", + "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\n", + "examples in ramp:" ] }, { @@ -79,7 +82,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] diff --git a/docs/notebooks/using_excel.ipynb b/docs/notebooks/using_excel.ipynb index 62c015cf..561b6124 100644 --- a/docs/notebooks/using_excel.ipynb +++ b/docs/notebooks/using_excel.ipynb @@ -5,9 +5,12 @@ "id": "377ae9b9", "metadata": {}, "source": [ - "# Using excel to build a model\n", + "# Using tabular inputs to build a model\n", "\n", - "In some cases in which the number of users or appliances are high, it can be difficult to create a model using python scripts. In RAMP, there is the possibility to create inputs using excel. In this example we show a use case of this functionality.\n" + "When the number of users or appliances is high, it can be difficult \n", + "to create a model using python scripts. Therefore, RAMP allows you \n", + "to create inputs in tabular format (.xlsx). In this example we show\n", + "a use case of this functionality.\n" ] }, { @@ -97,7 +100,9 @@ "id": "2688824d", "metadata": {}, "source": [ - "Once the users and appliacnes are added to the use_case instance, the user can get a pd.DataFrame or an excel file of all the data with the default values:" + "Once the users and appliances are added to the `use_case` instance, the\n", + "user can get a pd.DataFrame or an .xlsx file of all the data with the\n", + "default values." ] }, { @@ -305,7 +310,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Printing out the database to an excel file\n", + "# Printing out the database to an .xlsx file\n", "use_case.save(\"path/name_of_file\")" ] }, @@ -314,12 +319,9 @@ "id": "4b6db372", "metadata": {}, "source": [ - "Once the function is used, an excel file will be created in the given path:\n", - "\n", - "![excel file image](excel_file.png)\n", - "\n", - "\n", - "Now the user can easily fill-out the information in the excel file and load the data into the model database as follow: " + "Once the function is used, an .xlsx file will be created in the given\n", + "path. Now you can easily fill-out the information in the .xlsx file and\n", + "load the data into the model database as detailed below." ] }, { @@ -381,7 +383,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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VmpezuqjWKeiVWQyqGHC/2h5aby5liw8nVNUeyvQAX1DVNSIyAVgtIo85731PVb+TXVhEzgMWAecDpwG/F5GzVbU3zMBNaWV/cSVl4LCg1/ZJ/EIKlZ2up4Lrmbuq7lPVNc7rI8BmYEaBWa4G7lHVTlVtBBqAhWEEa4wprThcxZhgfLW5i8gc4EJgpTPpRhFZJyJ3iMgkZ9oMYHfWbE3k+DIQkRtEpF5E6ltaWvxHbowxJi/PyV1ExgP3AZ9V1cPArcBZwHxgH/Dd/qI5Zh9xnaeqt6nqAlVdUFdX5zduY4wxBXhK7iJSQyax/0JV7wdQ1f2q2quqfcDtDDa9NAGzsmafCewNL+TC/HVzCrdtsSRdMkMdOCxgDMFmc19upAOHhbgsH4Fq3j/KqEw9ymLTky0G2yEWz1CVzJ20HwObVfWWrOnTs4q9D9jgvF4BLBKRWhE5A5gHrPIfWjSsDTEESbnhZtu6aAnZ0okW1W7qpbfMRcCHgfUistaZ9iXgOhGZT2b77wA+CaCqG0VkObCJTE+bxXHqKePv7CF5u3bwiJO3rq7icqYYsbKOCpngeoN4DBwWVRW6JndVfY7cXy4PF5hnCbCkiLhMnEV0+RP6M1TT+IVljEep+4WqMcWypjuTBpbcjcMyWj9L7iYNLLkbY0wKpSK5Dxl5r4zPUC0FfyMLRjT2XkR3lvx3D4um26trF1LPS/I3EmKplHNERq+ivF+S9vXrl4rk7oddchcvDgeHFxU/RkxASe8BkzRR7aUVl9xTf2YfOOiIzsbLWIvhXuXEVzkjT/oXQRzyQVTLrbjkboqXlFEhk554jCmGJXdjjEkhS+7GGJNCqUju2e2l5fw5cUmeSWoDhwUq7+vpUS7NQ0E/Ny7DWbj3BgrY/azIzx0WRGRisRn87usBPiIVyd0P6y2Tj/eKicXB4UHQbW29bEwpWW8ZY0zsJLmXUdpVXHKPzZjSEQl+sMXjh0nl+uwkbut+5WzuSXK9gd/nP0QVQzQqLrmb4kXVtBX+qJDGVC5L7saYIZJ+Nm4yLLkbY/KzHgiJlYrkrprvD5f5EnjhHmZvssCt81G1PUbYPSzcmIO108bljNjXfh9mV0hfw0FEJw7Hvd8IgtxXSUVy98NORIoXh4PDC9vUxUvGlk426woZEn9fgMnbteNydhgHgc/sE/atUNaBw8r42WlRtt4yIjJLRJ4Ukc0islFEPuNMnywij4nINuf/SVnz3CwiDSKyVUQuiyh2kwDl7QoZ7MMTltvLKi6/ug3MVwtVstbVy5l7D/AFVT0XeCuwWETOA24CHlfVecDjzt847y0CzgcuB34kIlVRBG/KI6pfcFqTmTG5SYCDwzW5q+o+VV3jvD4CbAZmAFcDy5xiy4BrnNdXA/eoaqeqNgINwELfkRljjAnMV5u7iMwBLgRWAtNUdR9kvgCAqU6xGcDurNmanGnDl3WDiNSLSH1LS0uA0AeV4mrJy03EksQR4qPlSlNvES67TCNRBb1vU85xs+LQojCk55CPslHGEWrZiGLIlI+wt4yIjAfuAz6rqocLFc0xbURkqnqbqi5Q1QV1dXVew3CV8vulsRBVb5k4JKBK4Ge0UNsmyeUpuYtIDZnE/gtVvd+ZvF9EpjvvTweanelNwKys2WcCe8MJt3g2UmDx/Bzw5ay1IO2UcRF25H6qohT5PClbxk+9BV2nsnWFlMwR8mNgs6rekvXWCuB65/X1wINZ0xeJSK2InAHMA1aFF3JxKmUwKeOzCSvCOCJXzh5J5fvo1IiqDqs9lLkI+DCwXkTWOtO+BCwFlovIx4FdwLUAqrpRRJYDm8j0tFmsqr1hB27ClbQR7+L+2WmR4IsfT2Ly/JBIuCZ3VX2O/FcO78kzzxJgSRFxmRhLyqiQQSW5Ocf4193dTVNTEx0dHSPee+/pyiWvmQ7AmGP72Ly5eUSZfpee1sNbrpo+8PfmzZvzlh3X3cvtTtnR1VKwLMDHLxjP0mdPcLizr2C5bF7O3E0FsHw2yKrCuzQ0XTY1NTFhwgTmzJkz4ou96eBx2o51AXBm3XjG1+ZPmXvbT3DgaOfA3+fOnJi37JGObqoOHANg7Ogq5k2dkLesqtJV08Snu3pY8kyrl1UCUjL8QMBxwxJ3mQUhDxwW8Mis9IHDgn5uXBKhn/2ibPUW4dE5fMkdHR2ceuqpsb1iExFqx53C6RNrfM2XiuTuR0y3X6LEJUm5sU1tvCpnYvfyySLiu8dexSV3Y4ypBBWX3FPfbBO7ppYyPt/TT9mkXI7kUN7Ik1tv4HO/j7BH2SOPPMI555zD3LlzWbp0aSjLrbjkboqXlN4yCc7XpoL09vayePFifvvb37Jp0ybuvvtuNm3aVPRyLbkbY0wZrVtTz9y5cznzzDMZPXo0ixYt4sEHH3Sf0UUqukKW4pLa08BhJbhEDfMXtklvtihXE1vQzy3nY+bisPmG9MJxLVuaOIb7119vZNPewaGzOnv66OnN9C0fU1NF1aj8l5ddPX109w72Qx/ndJs877ST+ep7z8873/5X9zFr1uCILTNnzmTlypXuK+IidWfuvg6gGOzwSRSXrpAmmDBHFjXFy1XHYfTeScWZux82cFjxknK4iyT3C6OcA4eVQszCGTD8DHvIj5imjGf8GO8/YnpDgR8xZZs+/TQe2j04SnpTUxOnnXaaj6hzS92ZuzHGJMnrL3wT27Zto7Gxka6uLu655x6uuuqqopdbcWfuaR8VMmjMCVxVV2nv9tqvrN1Nk1xx+L1PFs3KVlVX84Mf/IDLLruM3t5ePvaxj3H++fnb6L2quORuihfVJXXcmg6MKZUrrriCK664ItRlWrOMMcakUCqSe/CBwxJ4TRnicwSDd+mLhv+Bw8rTM8pfb5NoYiiGr8fshfm5EZX1KyabIXKpSO5+2KV/8ax7nEmbcu7TXlKSqvo+Ga245G6MCU8avubHjBlDa2trbE9aVJXOo4fY2d7taz67oWrMMHZ1V1lmzpxJU1MTLS0tI947eLyLY52Zp4T2tY2mtqYq73IOnejmSEfPwN+bj4zNW7aju5cDRzP950dXC92tYwrGuHHPMb6/8mDBMsNVXHJPe5t8VBEHzXcxPRkaKSvOpOX20IcqqKBfeatCTU0NZ5xxRs73b75/HXev2gfAXZ94C/PnTsm7rG/+ZhP/+9zgj5F2LL0yb9mnX27h7+5aBcAbZp7CihsvLBjn4oee8vWIPfDQLCMid4hIs4hsyJr2NRHZIyJrnX9XZL13s4g0iMhWEbnMVzQmEaJ6sIGdMRsTHi9t7j8FLs8x/XuqOt/59zCAiJwHLALOd+b5kYjkv44xxsSafd8ml2tyV9VngDaPy7sauEdVO1W1EWgAFhYRnzcBu26FfUlZikvUMC+Zg45SGNWNJ99dIcvUxJb00TTdRNYV0tevw6McFjK6RXsOwef6BQm5mN4yN4rIOqfZZpIzbQawO6tMkzNtBBG5QUTqRaQ+142MqNilvzEmTqJKSUGT+63AWcB8YB/wXWd6rjhzfumo6m2qukBVF9TV1QUMwxhjTC6Bkruq7lfVXlXtA25nsOmlCZiVVXQmsLe4EMMV5cBhMbjaS3zvhXJJYs+ofuXc5klsdoqbqGowUHIXkelZf74P6O9JswJYJCK1InIGMA9YVVyIphQia0dPcNI07pK+deMyJIKbIE03rv3cReRu4F3AFBFpAr4KvEtE5pNZ3x3AJwFUdaOILAc2AT3AYlXtDRCXqUBxuR8SVVdPY0rJNbmr6nU5Jv+4QPklwJJigvKrJM8u9fIZpegtE+YY5dk/3HFJaEn/EZOfONzWNfCAa34GHPP+Ed6WF4vtEJNnqEb0I60orwJK3VsmlirlAQ2lVoq6ikcCSr+0P7DGZKQuubuJ9BmqFXI17+ssuELqJGzlfIZqKYZ3Tspu4afegq5T3LpCGmOMibGKS+5pvyS13inBJHFb94tym4d5/8HkFquukCZ94jKkgEmWpJ9MBH2qVhJYcje+RdWObu3zxoQnFcl96EBHfr6J/XRNi0lXSD9lXQcOC7asqM5gIh04LMxnqAbsSheXEz+3+LOPi3INHBalWMThe1/3H3QqkrsfdnZYvKRfiruyfcSUkPWWMZFy/RFTBSU8T91ejYk5S+7GGJNCFZfcU/8M1YAhRzdwWDIkJc5cytqGnOSKIx75wLpCmtiIqtmikpp+jIlaKpJ7XMY9KckAZr765br1ivDzud7L+omhGP4eExhqd5lAn1vOcY/iMOaSn2ENIh04LKL93t/AYfF+zF48xWAnTqMhSSqqy9OYbJAkNsf54VrPdgylQvqSuwu79C+ev4HDrMKDKOfAYaUQs3DysoHDjDHGxIold2OGsX7uJg0qLrmnf1TIaARNeEl5gHJ2mHFrwnBT3gdkl++zSy2yYTeiWWzlJXdTPBs4zJj4c03uInKHiDSLyIasaZNF5DER2eb8PynrvZtFpEFEtorIZVEFni3w4Ewh9wooxVlMmM9pDNqV0AYOC1Y4Lr1w/A0YF17McfjBUGbZ5Rflvt7Py5n7T4HLh027CXhcVecBjzt/IyLnAYuA8515fiQiVf7Dio6dHRYvDgeHMWlRtt4yqvoM0DZs8tXAMuf1MuCarOn3qGqnqjYCDcDCcEI1xsRNXK5GzEhB29ynqeo+AOf/qc70GcDurHJNzjRjjDElFPYN1VxXGDm/2kXkBhGpF5H6lpaWkMMwxgRl5+LpEDS57xeR6QDO/83O9CZgVla5mcDeXAtQ1dtUdYGqLqirqwsYhn+p77oVcAVLMZ5InFnzQjBJP57iNuZOmIIm9xXA9c7r64EHs6YvEpFaETkDmAesKi5Ed6UZsMtLHNELs4dI0APT1w0gX0MV+Isjuj79Lp8bMCEkZeCwqAwdn8ilbKQDhwV7TKJrWV8x+ChMsBxX7VZARO4G3gVMEZEm4KvAUmC5iHwc2AVcC6CqG0VkObAJ6AEWq2qv76iKEPpzUSuEn7wa2RlMTDZH2ntUuY8WGpMNYYrimtxV9bo8b70nT/klwJJigopSlAduynPCAF8Hf6VUSszF7QsrZuHkZQOHGWOMiRVL7iGyi1ljTFykLrm7jR9uA4eZXJK4rfuVs408wdUWG3HrLWNSJrKD1I5+E2O+xldK2BlAKpL7kIHDIuvm5F64FBs/zOeGRtl1qxSi2tauy/KVELLn8/EZkT571vv7odZbDPqUR71sr/zGENXAYakSt14DSRSHg8OYtLDeMsaUSKU/iSmOV2nGP0vuBnA/e6ikK55KWleTXpbcjTF52RddclVcco/L02CiYpfUwSS52soZe9J6kMSRdYUsoCS7V0wGDgvzOXvZB2bSRrzzu+wwv6iDniCk8UHWQXtcuQ8cFmFvIc392rWs23J9xeBv/ay3DOn/kVK5BK0rO7OLHztGKkPqkrsbGzgsBL6G8a2YWom1oJshqqbJpOwVNnCYMcaYWLHkbowxKWTJ3ZhhrCXJpEHFJfe030wK2kZa8T9iGrKxk7WyZd1PE3iMxI11hSykJAN2hVOmWCH2hBw6QJRb2Rj2linXF3XwAdfKM9CZ3+VFdRPV1zNUI4lg5NJ97fdu3SYDRRCddCT3LOWs4CSe6XsVdNWSWSXJjNo7t2eo+llS2usquVKX3N2kvnmhBPwczlbd8RC3/T5m4eSV5K6Qrg/ILkREdgBHgF6gR1UXiMhk4F5gDrAD+ICqHiwuTGOMMX6Eceb+blWdr6oLnL9vAh5X1XnA487fxhhjSiiKZpmrgWXO62XANRF8hjHGmAKKTe4KPCoiq0XkBmfaNFXdB+D8PzXXjCJyg4jUi0h9S0tLkWF4F5dHfUUleK+WcOMYWG40iw1dUuLMpZw3NZPeicDXoxoj60UUjaLa3IGLVHWviEwFHhORLV5nVNXbgNsAFixYUNT6BX3mo79ncMZjLw61+188Vim4yL6oC9/i8pcQsufzHkGUCdvXSIgRPUM1qq63fuMoF78xBMk/RZ25q+pe5/9m4AFgIbBfRKYDOP83F/MZYYtbr4G48FMvcfmii4rtI6aUYjdwmIiME5EJ/a+BvwA2ACuA651i1wMPFhukMSaeUv49n2jFNMtMAx5whnStBu5S1UdE5EVguYh8HNgFXFt8mMYYY/wInNxVdTtwQY7prcB7ignKGGNMcSruF6pR3XCNi+RFHA8J3NQDyvr4vvJ9dGrYwGEFlGLnjsvB76cXhVvZUjzfM9JeD74G4grxGap+ymqwOi7nwGHZorq57B5PhL2FfHyKr+etBo4i7NIZqUju2fwlP+NV4P7zVsuxE3xUS5MkqUvubqybW/H8DRxmFR4HwZ+hWtmSPHBYxSV3Y4ypBJbcDeB+hlJJVzwVtKpFS2Kng0phyd0YY1Ko4pK7DRxWihhiEIRPSYy5nz1CNdmsK2QBpehuFpedOMz189XNK+BgUuUcAGtoHGF+cLCicenJ5b6to+kiO3QfCu9Rf8XFEWy+MGOI6rNTkdz9qKS2Y2NM/FlvGWNMScTlKtUUx5K7MTGR5HZ/Ez+W3I0xeVkzZnJZcjfGBGYXG/GViuQe1VgZUT1urBjuT87L6ungVjarQJg/YipVvYW53bPfdltXXz2AAvfM8DMoWjhlggk2kF05nwDpb8A5P8sNVtbLfEF6nRX7DNWSUFXqdx6kseUYr595CudOPzlv2cMd3TyyYR+1NVWMranizXMmUzUq99G6emcbJ4+pZtK40Rzp6OH1M05h7OiqnGX7VHn+lVZOdPdwpKOHt501hboJtXnjeGnXQXr6lGOdPbzx9EmcPKYmb9mdrcfY295BV28fb5w9kQkFyh481sWu1uN09fYxb+p4Tps4Nm9ZVeXFHW0cPNbFie5ernz9dKqrcn+fH+/sZV1TOwePd1NbPWpEvWXv5LvajvPU1mZOHVfLjEljmTxudIEY4OmXW+jq6aNuQi3zZ03MWxZg5fZWJoypoflIBxfOnsQpY/PXxYGjnaxramdsTRUnj60puF+samxj66tHmHRSDZe/7jVIgQze0HyUtbvbaT/exZxTxzFnyriCMf9i5U7eMGMir595SsFyfQrPvNxCQ/NRzqwbx+tmnMKU8fn3oebDHTy/vZXa6lHMnTqBuVPH5y17oquXlY2tqMLcqeOZNfmkvGVf2N7KgaOdVI0SaqpG8cbZE4fUR/a2rt/RRsO50zhwtJNRIlww6xRqq3MfI5Cpu1WNbUydUMvb5p7KSaNzp5jO7j6e3dZCT58ytqaKM+vGMXXCmLzL7ezppX7HQQ6d6Gbu1PGcPW1C3rIrt7cyadxo1uw8yLxpE3jT6ZPyln1+eyugbNx7mPG11Vzy2ql5941VO9o43t3LO+dN4ZWWY7xh5inU5DmeAB7ZsA8RYdzoai6cPZFxtbnroqdXB/a3g8e7eMe8uoL7xYY9h+hTpe1YV94ykJDk/tLudq797+cH/t789cvzJuH71+zh/jV7Bv5e9rGFXHx23cDf2dvt9mcbuf3ZxoG/P3XxWdz0l6/NudynX27hp3/cMWTajqVX5o35fT/648Drj75tDl+76vy8ZS/+j6cGXv/dO87gy1eel7fsJ3+2mj3tJwC4cPZEHviHi/KWXbu7nf/8/baBv4939XLdwtk5y67a0cZVP/jDwN93fmwh78yqt+E++pMXB14Xqoc97Se4/o5VA38/+0/vLph4PnjbCwOv//ats/nmNa/PW/bm+9ezeufBgb+3fONyxtTk3i++/cjgs9vv+/s/402nT8673Be2t3HNDzN1MXPSWJ774iV5ywJ8+YENAKz68nsKJqiH1u/lvjVNQ6YVqrtP3FnPuqZDnsre+lQD//VEAwDzpo7nsc9fPOT97P3+qys2Dnnvkc++g9e+JvcX4+83N/P7zYOPQV787rP4x8tyHyMAl97y9MDrj110Bv/y3tz7cldvHx/+8eB+cc60Cfzuc+/Mu9wH1uzhpvvXD/xdqC6y9yEonC/++Vcbhvw9vC6y6+22Z7YPKfu1957HRy86Y7DssGV/6udrBl6//8IZ3PLB+Tlj2LTv8MD+BnBW3Tge/8K7cpYF+KvvP5f3vWyJaJY5fKJ7yN8nuns9z3uko9u9kKPp4HHPMfjhZ94jHT0F3+9P7F6We/jE0GW9vP+I5zgO+6g3P9zWz0/Z4evf4XG/ePVQp+cY/Gy7oy7xdnT3eV4WZK6QvGo5OrhOh3zuq8P3k0IeWrfPc9ndBY6n4ba67JvF7I9+8oWfutjR6n39ntza7F7I8UrLMc9lC0lEcu/uHdre1NPr/SDp9lG2kK4iluN13upR4utzevoKt8MVs+5u847Nc4Zc7HL9lB2+/sVso/wxeG/r9FPWi+Fngl7b4Xtd9ovhwjpGolRM3Ya5zyVJZMldRC4Xka0i0iAiNxWzrOHJvNvHzhvWAddTxHK8zju2psrX57iV7ekrJrkXXna+y1w3fmJyi2H4gVjMNsrHX7zRJgavSdtvHElIaFGeqIT1OXETSXIXkSrgh8BfAucB14lI/oZkF8OTeXePj7NbHwd8oZLFJEqv844dXRVqMhmeHP3c+Xert+Bn7n6+vNzWL1hy99PzoLtXPZ8xu11J+TV8aYWWnx2i3zjCOkZGlA2xOnp6NW/HCC/zRlG23Kpd6iOqG6oLgQZV3Q4gIvcAVwObchXeffA4n793bd6F3TnsRuY3H9rMyWMGQ29oOZp33nvrd1O/o23g7+ybQ8OtamwbEkdr1t3oXEmpUMzZ1u855KnsSaOreGlXu+fltp/oHlF2/Z7BG3ArG1uHvHffmqYhbch7D51guNFVo+jq7RtRb/e/tGdIuZOyztyHx1Do8PjBEw0sf3F3gRKDNu49XLAuDh4f2g77jYc2MSFPj4Rsy/64gyeG7QeF9qHP3buWUVl31vYd6shZ7pbHXmbKsJ5D+cr2y16/7Hrbf7iTo51D23//8ZfrqMlxQB/u6OaF7YPb+kR374h6e3l//vW7/dntPLx+sC391cP5Y97ZOvRYzd7fhlu9c+jx1Ha8cO+O7LLD29gf3fQq1aNk4OrF6zECI/NF08GR+32/4XWxKusYGO53G18dcjw9s60lb9mDx4ceq03t+WOAkevXfnzkPYd8nQf6RZXcZwDZR3AT8JbsAiJyA3ADwNjXnMWLO4dWYk2VDCTU/Uc6qBolVI0SJo6tYev+w3k/eHxt9ZCDou1YJ23HBm82jRtdxbGuzA2WsTVVQ262jK4aNSKOfrMmj2Xc6Gq6evvY7tzwGF529uSThtwEO3XcaC6YNZFtzUfyLrffRXNP5XUzTuHh9ftGlM2Os8rZwU8/9SRUR8ZQyNiaKtfy17/tdNY1HWLvoRN56+01J4/huoWz+fpvMt/VhZZZN6GWliOdfOgts1mzq52dbcfY2Zb7htGU8bUcONrJmVPGcdbU8Wx59XDBZU87uZa5deN5cmvmoNryav79IturhzsKJrB+bzvrVPYd6mD1roMFy00ZX8uU8aNpPHCUxgP5y00eN3pE97V86zdpXA0TT6oZkojW7s4dx2tOHkOvc5o8ZXwt42tHbufhnz3n1JPo6O7j1cMd7D10IucXfb+pE2ppPjK4L2Qvu9CZ7kmjq/Ou3+jqUXRlXYHXVuc/9iDzhXXpudPYsPcQO1uP5y0rkrlimH7KmIEv1kL5Yji3uoDB/VQYWhejcnShnDV5LLvbTlBTJa7H3vsvnDFwEjW87LSTxwzJLdNOruWCmRMZ2u9pKIliPAsRuRa4TFU/4fz9YWChqn46V/kFCxZofX196HEYY0yaichqVV2Q672obqg2AbOy/p4J7I3os4wxxgwTVXJ/EZgnImeIyGhgEbAios8yxhgzTCRt7qraIyI3Ar8DqoA7VLVQ85AxxpgQRTb8gKo+DDwc1fKNMcbkl4hfqBpjjPHHkrsxxqSQJXdjjEkhS+7GGJNCkfyIyXcQIkeArR6LnwLk/81zceXTXDYuccShbFziiEPZuMSRtLJxieMcVc395BJVLfs/oN5H2dt8Lttz+TSXjUsccSgblzjiUDYucSStbFziKJQ7k9gs8+sIy6e5bFziiEPZuMQRh7JxiSNpZeMUR05xaZap1zzjIxhjjMmtUO6My5n7beUOwBhjEihv7ozFmbsxxphwxeXMPTXyPV5QRD7tTN8oIv9ezhjjSETuEJFmEdmQNe0bIrJORNaKyKMiclo5Y4wjEZklIk+KyGZn3/qMM32yiDwmItuc/yeVO9Y4KVBv9zr721oR2SEia8scamB25h4i5/GCLwN/TmbY4xeB64BpwJeBK1W1U0Smqqr3x6FXABF5J3AUuFNVX+dMO1lVDzuv/y9wnqp+qoxhxo6ITAemq+oaEZkArAauAT4KtKnqUuckY5KqfrF8kcZLvnpT1U1ZZb4LHFLVr5crzmJENnBYhcr3eMELgaWq2glgiX0kVX1GROYMm5b9CJ1x+HuEZ0VQ1X3APuf1ERHZTOZJaFcD73KKLQOeAiy5OwrU2yYAERHgA8AlZQuySNYsE65cjxecAZwNvENEVorI0yLy5rJEl0AiskREdgMfAv6l3PHEmfPleCGwEpjmJLD+RDa1jKHF2rB66/cOYL+qbitLUCGw5B6uXI8jVzJXSJOAtwL/CCx3zgyMC1X9sqrOAn4B3FjueOJKRMYD9wGfHXbFYwooUG/XAXeXJ6pwWHIPV77HCzYB92vGKqAPmFKG+JLsLuCvyx1EHIlIDZkE9QtVvd+ZvN9pV+5vX7amwGHy1BsiUg28H7i3XLGFwZJ7uPI9XvBXOG13InI2MBo4UK4gk0JE5mX9eRWwpVyxxJVzBfhjYLOq3pL11grgeuf19cCDpY4tzgrUG8ClwBZVbSp9ZOGx3jIhE5ErgP9k8PGCS5xEfwcwH+gC/p+qPlG2IGNIRO4mcwNwCrAf+CpwBXAOmSudncCnVHVPuWKMIxF5O/AssJ5MPQF8iUz78XJgNrALuFZV28oSZAzlqzdVfVhEfgq8oKr/Xa74wmDJ3RhjUsiaZYwxJoUsuRtjTApZcjfGmBSy5G6MMSlkyd0YY1LIkrsxxqSQJXdjjEkhS+7GGJNCltyNMSaFLLkbY0wKWXI3xpgUsuRujDEpZMndGGNSyJK7McakkCV3Y4xJIUvuxhiTQiVN7iJytJSfZ4wxlcrO3I0xJoVKntxFZLyIPC4ia0RkvYhc7UyfIyKbReR2EdkoIo+KyNhSx2eMMWlQ0meoOs0yE4GTVPWwiEwBXgDmAacDDcACVV0rIsuBFar685IFaIwxKVFdhs8U4Fsi8k4yTx2fAUxz3mtU1bXO69XAnJJHZ4wxKVCO5P4hoA54k6p2i8gOYIzzXmdWuV7AmmWMMSaActxQPQVodhL7u8k0xxhjjAlRyc7cRaSazJn5L4Bfi0g9sBbYUqoYjDGmUpTshqqIXADcrqoLS/KBxhhTwUrSLCMinwLuBr5Sis8zxphKV9KukMYYY0ojkjN3EZklIk86P0raKCKfcaZPFpHHRGSb8/8kZ/qfi8hq50dNq0XkkqxlvcmZ3iAi/yUiEkXMxhiTJlE1y/QAX1DVc4G3AotF5DzgJuBxVZ0HPO78DXAAeK+qvh64HvhZ1rJuBW4g80OnecDlEcVsjDGpEUlyV9V9qrrGeX0E2Ezmx0pXA8ucYsuAa5wyL6nqXmf6RmCMiNSKyHTgZFV9XjPtR3f2z2OMMSa/yG+oisgc4EJgJTBNVfdB5gsAmJpjlr8GXlLVTjJfCE1Z7zU504wxxhQQaT93ERkP3Ad81hlLxq38+cC3gb/on5SjmN0BNsYYF5GduYtIDZnE/gtVvd+ZvN9pasH5vzmr/EzgAeAjqvqKM7kJmJm12JnAXowxxhQUVW8ZAX4MbFbVW7LeWkHmhinO/w865ScCDwE3q+of+gs7TTdHROStzjI/0j+PMcaY/CLp5y4ibweeBdaTGfkR4Etk2t2XA7OBXcC1qtomIl8Bbga2ZS3mL1S1WUQWAD8lM4jYb4FPq3XON8aYguxHTMYYk0L2mD1jjEkhS+7GGJNCltyNMSaFLLkbY0wKWXI3xpgUsuRujDEpZMndGGNSyJK7Mcak0P8HmPSCrCIJIPIAAAAASUVORK5CYII=\n", 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", 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" ] diff --git a/docs/source/algorithm.rst b/docs/source/algorithm.rst index 6c44aea6..6a206605 100644 --- a/docs/source/algorithm.rst +++ b/docs/source/algorithm.rst @@ -2,7 +2,7 @@ RAMP algorithm **************** -An input file consists in a list of user type (i.e. Hospital, Low Income Household, School, etc.). There is a certain number of users from each of the user types (minimum is one). To each user type is associated a list of typical appliances. +An input file consists in a list of user types (i.e. Hospital, Low Income Household, School, etc.). There is a certain number of users from each of the user types (minimum is one). To each user type is associated a list of typical appliances. Almost all the usage parameters (specific power consumption, usage windows during a 24h period) are defined at the appliance level. A theoretical load profile is computed with the following steps: @@ -10,9 +10,9 @@ A theoretical load profile is computed with the following steps: #. for each type of appliance of each user of each user type, check if the appliance type is used based on a weekly frequency of use. If not, ignore the appliance type. Otherwise,compute: #. the randomised appliance type's total time of use - #. the randomised vector of time frames in which the appliance type is allowed to be switched on Subsequently, iterate over the following steps until the sum of the durations of all the switch-on events equals the randomised total time of use defined in step 2.1. + #. the randomised vector of time frames in which the appliance type is allowed to be switched on. Subsequently, iterate over the following steps until the sum of the durations of all the switch-on events equals the randomised total time of use defined in step 2.1. #. a random switch-on time frame within the allowed time frames defined in step 2.2 #. compute the randomised power required by the appliance type for the switch-on time frame defined in step 2.3 - #. compute the actual power absorbed by the appliances of the type under consideration during the switch-on event considering a random numerosity of appliancesRepeat then step 2. N times to get a stochastic variation of the appliances' usage + #. compute the actual power absorbed by the appliances of the type under consideration during the switch-on event considering a random numerosity of appliances. Repeat then step 2 for N times to get a stochastic variation of the appliances' usage #. Average the N profiles in the total load profile. diff --git a/docs/source/examples/cooking_app/cooking_app.rst b/docs/source/examples/cooking_app/cooking_app.rst index 8a3d56d7..1ccd5c6d 100644 --- a/docs/source/examples/cooking_app/cooking_app.rst +++ b/docs/source/examples/cooking_app/cooking_app.rst @@ -2,15 +2,15 @@ Cooking Appliances ================== In this example, appliances with multiple preferences index and -attributes are modeled. +attributes are modelled. To have a better understanding of RAMP features for modelling these category of appliances, two households are considered: -1. First household with a fixed lunch habit of eating soup everyday. -2. Second household with two lunch preferences: cooking soup or rice. +1. A household with a fixed lunch habit of eating soup everyday. +2. A household with two lunch preferences: cooking soup or rice. -The number of user preferences can be specified through +The number of user preferences can be specified through the **“user_preference”** parameter when initializing a **User** instance. .. code:: ipython3 @@ -25,7 +25,7 @@ Creating a user category .. code:: ipython3 user_1 = User( - user_name = "Household with single lunch habit", + user_name = "Household with a single lunch habit", num_users = 1, user_preference = 1, # user_1 has only one lunch preference ) @@ -39,7 +39,7 @@ Creating a user category Defining the cycles for cooking soup and rice ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -for cooking soup it is assumed that the user needs 25 minutes divided +For cooking soup, we assume the user needs 25 minutes divided into two parts: ============= ===== ==== @@ -49,7 +49,7 @@ Boiling Water 1200 5 Cooking soup 750 20 ============= ===== ==== -for cooking rice it is assumed that the user needs 15 minutes divided +For cooking rice, we assume the user needs 15 minutes divided into two parts: ============= ===== ==== @@ -78,15 +78,15 @@ Cooking rice 600 10 ) -As second user has two different preferences for lunch, we need to model -this preferences and their characterisitics as two different appliances. +The second user has two different preferences for lunch. Accordingly, we need to model +these preferences and their characterisitics as two different appliances. -Each preference needs to be specified with the cooking energy needs like +Each preference needs to be specified with its associated cooking energy needs, such as the power, functioning time and the duty cycles of the cooking process. More importantly, for each preference, the user needs to specify the -index of preference by using **pref_index** parameter. In this example, -soup is the first prefernce of the user (pref_index = 1), and rice is +index of preference by using the **pref_index** parameter. In this example, +soup is the first preference of the user (pref_index = 1), and rice is the second one (pref_index = 2). .. code:: ipython3 @@ -132,7 +132,7 @@ the second one (pref_index = 2). .. code:: ipython3 - # you can have an overview of data inputs by usering User.export_to_dataframe method + # you can have an overview of the just defined input data by using User.export_to_dataframe method user_lunch = UseCase(users=[user_1,user_2]) user_lunch.export_to_dataframe().T @@ -167,7 +167,7 @@ the second one (pref_index = 2). user_name - Household with single lunch habit + Household with a single lunch habit Household with different lunch habit Household with different lunch habit @@ -514,10 +514,9 @@ Generating a profile for some months -As the second user has the probability of cooking rice for lunch, and -lunch has a less energy intensive cooking cycle, it is expected to see -that the user with fixed lunch meal has higher energy consumption in -most of the cases. +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 the user that only eats soup, in most of the cases. .. code:: ipython3 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 11ebb468..1d624308 100644 --- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst +++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst @@ -52,7 +52,7 @@ Defining the peak time range using the calc_peak_time_range Defining the yearly patterns ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -by default the yearly_pattern function returns weekdays (defined by 0) +By default the :code:`yearly_pattern` function returns weekdays (defined by 0) /weekends (defined by 1) division .. code:: ipython3 diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst index b2e3c552..4e8d42d8 100644 --- a/docs/source/examples/multi_cycle/multi_cycle.rst +++ b/docs/source/examples/multi_cycle/multi_cycle.rst @@ -1,12 +1,13 @@ Appliances with multiple cycles =============================== -An example of an appliance with multiple cycle is fridge. Fridges -usually have different duty cycles can be estimated based on seasonal -temperature trends. +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 for 3 different season +time windows are defined for 3 different cycles across 3 different season types: +--------+------------------------------+--------------+--------------+ diff --git a/docs/source/examples/occasional_use/occasional_use.rst b/docs/source/examples/occasional_use/occasional_use.rst index e8a2d9cb..fda03849 100644 --- a/docs/source/examples/occasional_use/occasional_use.rst +++ b/docs/source/examples/occasional_use/occasional_use.rst @@ -2,14 +2,16 @@ Appliances with occasional use ============================== There are some appliances that are occasionally included in the mix pf -appliances that the user switches-on during the day. For example, iron, -stereo, printers and … +appliances that the user switches-on during the day. For example, an iron, +a stereo, printers, etc. -Within ramp, the user may specify the probability of using an appliance +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. +When :code:`occasional_use = 0`, the appliance is always present in the mix, and +when :code:`occasional_use = 1`, the appliance is never present. Any in-between +values will lead to a probablistic calculation to decide whether the appliance +is used or not on a given day. The following example, investigates the effect of this parameter by modelling two user category: \* A household that uses a computer with @@ -163,4 +165,5 @@ Generating profiles As it can be seen from the figures, the computer is always present in -the school appliance mix while for household, is occasionally present. +the school's appliance mix while, for the household, it is only occasionally +present. diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst index 84f4a298..84a0c534 100644 --- a/docs/source/examples/simple_bulb/simple_bulb.rst +++ b/docs/source/examples/simple_bulb/simple_bulb.rst @@ -153,7 +153,7 @@ Generating a profile for the ith day of the year .. image:: output_13_1.png -whole year profile can be generated +Whole year profile functionality ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 diff --git a/docs/source/examples/thermal_app/thermal_app.rst b/docs/source/examples/thermal_app/thermal_app.rst index b5b34310..84fca84a 100644 --- a/docs/source/examples/thermal_app/thermal_app.rst +++ b/docs/source/examples/thermal_app/thermal_app.rst @@ -3,7 +3,7 @@ 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 means of the thermal_P_var attribute. +by means of the :code:`thermal_P_var` attribute. .. code:: ipython3 @@ -19,7 +19,7 @@ Creating a user category and appliances household = User() -when the power is variant during the day, the “power” parameter needs to +When the power is varying during the day, the “power” parameter needs to 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: diff --git a/docs/source/examples/using_excel/using_excel.rst b/docs/source/examples/using_excel/using_excel.rst index d263d8f9..25209c9b 100644 --- a/docs/source/examples/using_excel/using_excel.rst +++ b/docs/source/examples/using_excel/using_excel.rst @@ -1,9 +1,9 @@ -Using excel to build a model +Using tabular inputs to build a model ============================ -In some cases in which the number of users or appliances are high, it -can be difficult to create a model using python scripts. In RAMP, there -is the possibility to create inputs using excel. In this example we show +When the number of users or appliances is high, it can be difficult +to create a model using python scripts. Therefore, RAMP allows you +to create inputs in tabular format (.xlsx). In this example we show a use case of this functionality. .. code:: ipython3 @@ -13,7 +13,7 @@ a use case of this functionality. import pandas as pd At the first step, user needs to creat User classes and assign -Appliances to the user class without assiging detaild appliance +Appliances to the user class without assiging any detailed appliance characteristics Buidling a model database @@ -49,13 +49,13 @@ Buidling a model database .. parsed-literal:: - /Users/mohammadamintahavori/Documents/GitHub/RAMP/ramp/core/core.py:738: UserWarning: No windows is declared, default window of 24 hours is selected + UserWarning: No windows is declared, default window of 24 hours is selected warnings.warn(UserWarning("No windows is declared, default window of 24 hours is selected")) -Once the users and appliacnes are added to the use_case instance, the -user can get a pd.DataFrame or an excel file of all the data with the -default values: +Once the users and appliances are added to the :code:`use_case` instance, the +user can get a pd.DataFrame or an .xlsx file of all the data with the +default values. Exporting the database ~~~~~~~~~~~~~~~~~~~~~~ @@ -217,19 +217,12 @@ Exporting the database .. code:: ipython3 - # Printing out the database to an excel file + # Printing out the database to an .xlsx file use_case.save("path/name_of_file") -Once the function is used, an excel file will be created in the given -path: - -.. figure:: excel_file.png - :alt: excel file image - - excel file image - -Now the user can easily fill-out the information in the excel file and -load the data into the model database as follow: +Once the function is used, an .xlsx file will be created in the given +path. Now you can easily fill-out the information in the .xlsx file and +load the data into the model database as detailed below. Loading the database ~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/examples/year_simulation/year_simulation.rst b/docs/source/examples/year_simulation/year_simulation.rst index 019673dc..43a1e373 100644 --- a/docs/source/examples/year_simulation/year_simulation.rst +++ b/docs/source/examples/year_simulation/year_simulation.rst @@ -19,7 +19,7 @@ It is possible to provide a date range by using ``--start-date`` and ``--end-dat ramp -i path-to-input-file --start-date 2022-01-01 --end-date 2022-01-31 -Will generate a daily profile for each day of january 2022. The type of day (weekday or weekend is automatically attributed) +Will generate a daily profile for each day of January 2022. The type of day (weekday or weekend) is automatically assigned. .. note:: order of inputs does not matter @@ -43,7 +43,7 @@ will generate a daily profile for 10 days ending on 9th Febuary 2022 ramp -i path-to-input-file --start-date 2022-03-01 -will generate a daily profile for all days between march 1st 2022 and december 31st 2022 +will generate a daily profile for all days between March 1st 2022 and December 31st 2022 .. code-block:: bash @@ -51,7 +51,7 @@ will generate a daily profile for all days between march 1st 2022 and december 3 ramp -i path-to-input-file --end-date 2022-04-25 -will generate a daily profile for all days from January 1st 2022 and april 25th 2022 +will generate a daily profile for all days from January 1st 2022 and April 25th 2022 @@ -85,15 +85,19 @@ To chain two or more years you simply provide them, separated by a single space will generate a daily profile for all days of 2022 and 2023 + Year simulation with different input parameters per month ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +It is possible to get a daily profile for each day of a whole year +having different parameters for appliances throughout the 12 months +of the year. The daily profiles are then concatenated in a long timeseries +for the year with 1-minute resolution. This functionality expects 12 +independent .xlsx input files located in a folder and sorted numerically +by month number (the sorted order is printed out at the execution of +the function for the user to check). -It is possible to get a daily profile for each day of a whole year having different parameters for appliances throughout the 12 months -of the year, the daily profiles are concatenated to a long timeseries for the year with minute resolution. -It expects 12 independent .xlsx input files located in a folder and sorted numerically by month number (the sorted order is printed out at the execution of the function for the user to check) - -For example +For example: .. code-block:: bash @@ -103,6 +107,7 @@ For example (for windows user use ``\`` instead of ``/`` for path separation) will simulate 1 daily profile for each day of the whole year 2022. -The results will be saved in the files ``'yearly_profile_min_resolution.csv'`` and ``'yearly_profile_hour_resolution.csv'`` for further data analysis for the time being. +The results will be saved in the files ``'yearly_profile_min_resolution.csv'`` +and ``'yearly_profile_hour_resolution.csv'`` for further data analysis. diff --git a/docs/source/index.rst b/docs/source/index.rst index 2549a95c..4b9244eb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,5 @@ -.. image:: https://github.com/RAMP-project/RAMP/blob/master/docs/figures/RAMP_logo_basic.png?raw=true +.. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/RAMP_logo_basic.png?raw=true :width: 300 User Guide: From 9ce7d7c9236fe0a8233555666f655be311ebb2cf Mon Sep 17 00:00:00 2001 From: Francesco Lombardi Date: Tue, 7 Nov 2023 10:58:55 +0100 Subject: [PATCH 38/47] Improve .rst title formatting Fixed the mismatch between a title length and the underlying line of "~" symbols, as requested by the reviewers --- docs/source/examples/simple_bulb/simple_bulb.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst index 84a0c534..4b6666da 100644 --- a/docs/source/examples/simple_bulb/simple_bulb.rst +++ b/docs/source/examples/simple_bulb/simple_bulb.rst @@ -154,7 +154,7 @@ Generating a profile for the ith day of the year Whole year profile functionality -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 From 2a524010b76f8fea54688091f27575a465c7723d Mon Sep 17 00:00:00 2001 From: Francesco Lombardi Date: Tue, 7 Nov 2023 15:57:49 +0100 Subject: [PATCH 39/47] Update README.rst to fix line-length issue This fixes the problem currently blocking PR #96 --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index cebc9e91..703734f6 100644 --- a/README.rst +++ b/README.rst @@ -62,7 +62,7 @@ the following line in the *Anaconda Prompt*: Installing through the source code ------------------------------- +---------------------------------- You can also install RAMP from the source code! To do so, you first need to download the source code, which can be done in two ways: * You can use git to clone the repository via: From 8b15d8d737d7f4df8ad7081a48a6b8735b152759 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Mon, 6 Nov 2023 19:29:32 +0100 Subject: [PATCH 40/47] solving the test.pypi release issue #96 updating the secrets for API Token for the automatic test release of software after Pushes and PRs to developemnt and main --- .github/workflows/python-pypi-test.yml | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml index f1c26d79..8533ed08 100644 --- a/.github/workflows/python-pypi-test.yml +++ b/.github/workflows/python-pypi-test.yml @@ -1,10 +1,15 @@ -ame: Deploy to Test PyPi +name: Deploy to Test PyPi -on: +on: push: branches: - development - main + + pull_request: + branches: + - development + - main jobs: build_and_deploy: name: Build the release and deploy to test pypi @@ -12,7 +17,7 @@ jobs: steps: - name: Checkout code uses: actions/checkout@master - + - name: Set up Python 3.10 uses: actions/setup-python@v3 with: @@ -33,5 +38,5 @@ jobs: with: skip_existing: true verbose: true - password: ${{ secrets.SHAFT_PYPI_TEST }} + password: ${{ secrets.RAMP_TEST }} repository_url: https://test.pypi.org/legacy/ \ No newline at end of file From cda75e3b683766502fcee0fd196eb8fa0f316ef6 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Tue, 7 Nov 2023 18:47:49 +0100 Subject: [PATCH 41/47] Update python-pypi-test.yml using kebab-case for skip-exisiting and repository-url --- .github/workflows/python-pypi-test.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml index 8533ed08..65df7566 100644 --- a/.github/workflows/python-pypi-test.yml +++ b/.github/workflows/python-pypi-test.yml @@ -32,11 +32,11 @@ jobs: - name: test install package run: | pip install -e . - - name: Publish distribution 📦 to Test PyPI + - name: Publish distribution 📦 to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - skip_existing: true + skip-existing: true verbose: true - password: ${{ secrets.RAMP_TEST }} - repository_url: https://test.pypi.org/legacy/ \ No newline at end of file + password: ${{ secrets.RAMP_TEST}} + repository-url: https://test.pypi.org/legacy/ \ No newline at end of file From 747295b48de457941471e328eff3428ab4d6eed1 Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Sun, 15 Oct 2023 18:39:52 +0200 Subject: [PATCH 42/47] Plot class for ramp visualization and data analysis routines (isse #63) The new features added will allow the user to perform some data analysis and visualization routines: changes ------ *. ramp/post_process/post_process.py - the class Plot is the main class carrying on the plotting and data analysis functionalities with custom APIs providing access to some pd.DataFrame features also easy to plot functions conencting to plotly and matplotlibe methods. - different plotting classes that include a specific plot type visualiztion using two different engines (plotly & matplotlib) *. ramp.__init__.py - adding the Plot class to the init. documentation ------------- - the functions API docuemntation is added to the API documentaiton - A full example titled "Plot class" is added to the example gallery describing the full potential of the new functionalities --- docs/notebooks/Plot class.ipynb | 183456 +++++++++++++++ docs/source/api_references.rst | 35 +- docs/source/examples.rst | 1 + .../source/examples/Plot_class/Plot class.rst | 1060 + .../examples/Plot_class/output_38_1.png | Bin 0 -> 12275 bytes .../examples/Plot_class/output_41_1.png | Bin 0 -> 8187 bytes .../examples/Plot_class/output_42_1.png | Bin 0 -> 8903 bytes .../examples/Plot_class/output_43_1.png | Bin 0 -> 9226 bytes .../examples/Plot_class/output_49_1.png | Bin 0 -> 10957 bytes ramp/__init__.py | 1 + ramp/post_process/post_process.py | 604 +- 11 files changed, 185144 insertions(+), 13 deletions(-) create mode 100644 docs/notebooks/Plot class.ipynb create mode 100644 docs/source/examples/Plot_class/Plot class.rst create mode 100644 docs/source/examples/Plot_class/output_38_1.png create mode 100644 docs/source/examples/Plot_class/output_41_1.png create mode 100644 docs/source/examples/Plot_class/output_42_1.png create mode 100644 docs/source/examples/Plot_class/output_43_1.png create mode 100644 docs/source/examples/Plot_class/output_49_1.png diff --git a/docs/notebooks/Plot class.ipynb b/docs/notebooks/Plot class.ipynb new file mode 100644 index 00000000..963a0cfb --- /dev/null +++ b/docs/notebooks/Plot class.ipynb @@ -0,0 +1,183456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5a0da146", + "metadata": {}, + "source": [ + "# Ramp Plotting Class\n", + "\n", + "Ramp Plotting class offers some functionalities for visualizing ramp outputs. A Plot class can be initialized by reading a pandas.DataFrame or reading results from a csv or excel file." + ] + }, + { + "cell_type": "markdown", + "id": "b07bde96", + "metadata": {}, + "source": [ + "## Initializing a Plot class\n", + "\n", + "### running a ramp using a python🐍 IDE " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "eb23109f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.4)\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n" + ] + } + ], + "source": [ + "from ramp import User,calc_peak_time_range,yearly_pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b3d4fb01", + "metadata": {}, + "outputs": [], + "source": [ + "# creating a user and adding some appliances\n", + "\n", + "Petrol_Station = User(\n", + " user_name = \"Petrol Station\",\n", + " num_users = 1,\n", + ")\n", + "\n", + "#add appliances\n", + "outside_bulb = Petrol_Station.add_appliance(\n", + " name = \"Security Light\",\n", + " number = 2,\n", + " power = 18, #watt\n", + " num_windows = 2, # security light has two functioning window: during night and during day\n", + " fixed_cycle = 0, # bulb has no duty cycle and always work with a single cycle\n", + " window_1 = [0,7*60], # from mid-night to 7 AM with opening\n", + " window_2 = [21*60,23*60+59], # from 21 to 23:59\n", + " fixed = \"yes\",\n", + " flat = \"yes\",\n", + "\n", + ")\n", + "\n", + "\n", + "inside_bulb = Petrol_Station.add_appliance(\n", + " name = \"Indoor Light\",\n", + " number = 1,\n", + " power = 75, #watt\n", + " num_windows = 1, # security light has two functioning window: during night and during day\n", + " fixed_cycle = 0, # bulb has no duty cycle and always work with a single cycle\n", + " window_1 = [7*60,21*60], # from mid-night to 7 AM,9PM with opening,\n", + " func_cycle = 1, # when the appliance is switched on, it remains on at least for a min,\n", + " occasional_use = 1, # it is always present in the energy mix\n", + " func_time = 30, #usually is used around 60 min during the whole day\n", + " time_fraction_random_variability = 0.9,\n", + " random_var_w = 0.15,\n", + ")\n", + "\n", + "petrol_pump = Petrol_Station.add_appliance(\n", + " name = \"Petrol Pump\",\n", + " number = 1,\n", + " power= 200,\n", + " num_windows = 1,\n", + " fixed_cycle = 0,\n", + " window_1 = [7*60,21*60],\n", + " func_cycle = 1,\n", + " occasional_use = 1,\n", + " func_time = 40,\n", + " time_fraction_random_variability = 0.15,\n", + " random_var_w = 0.15,\n", + ")\n", + "\n", + "# phone chargers\n", + "phone_charger = Petrol_Station.add_appliance(\n", + " name = \"Phone Charger\",\n", + " number = 1,\n", + " power= 5,\n", + " num_windows = 1,\n", + " fixed_cycle = 0,\n", + " window_1 = [7*60,21*60],\n", + " func_cycle = 30,\n", + " occasional_use = 0.7,\n", + " func_time = 60,\n", + " time_fraction_random_variability = 0.15,\n", + " random_var_w = 0.15,\n", + ")\n", + "\n", + "# Radio\n", + "radio = Petrol_Station.add_appliance(\n", + " name = \"Radio\",\n", + " number = 1,\n", + " power= 5,\n", + " num_windows = 1,\n", + " fixed_cycle = 0,\n", + " window_1 = [7*60,21*60],\n", + " func_cycle = 20,\n", + " occasional_use = 0.95,\n", + " func_time = 4*60,\n", + " time_fraction_random_variability = 0.15,\n", + " random_var_w = 0.15,\n", + ")\n", + "#%%\n", + "\n", + "peak_time_range = calc_peak_time_range(\n", + " user_list = [Petrol_Station]\n", + ")\n", + "year_behaviour = yearly_pattern()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9071d728", + "metadata": {}, + "outputs": [], + "source": [ + "# running the simulation for multiple cases to generate mutlipe simulations for a year = 2022\n", + "\n", + "import pandas as pd\n", + "\n", + "number_of_simulations = 10\n", + "number_of_days = 365\n", + "\n", + "results = {}\n", + "\n", + "idx = pd.date_range(start=f\"2022-01-01\",periods=number_of_days*60*24,freq=\"1min\",name = \"date\")\n", + "\n", + "for profile in range(1,number_of_simulations+1):\n", + " profiles = []\n", + " for prof_i in range(number_of_days): # 365 days of the year\n", + "\n", + " result = Petrol_Station.generate_aggregated_load_profile(\n", + " prof_i = prof_i,\n", + " day_type = 1,\n", + " peak_time_range = peak_time_range\n", + " )\n", + "\n", + " profiles.extend(result.tolist())\n", + "\n", + " results[f\"case {profile}\"] = pd.Series(index=idx,data=profiles)\n", + " \n", + "dataframe = pd.concat(results,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8844ac40", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 \\\n", + "date \n", + "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "... ... ... ... ... ... ... ... \n", + "2022-12-31 23:55:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-12-31 23:56:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-12-31 23:57:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-12-31 23:58:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 \n", + "2022-12-31 23:59:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " case 8 case 9 case 10 \n", + "date \n", + "2022-01-01 00:00:00 36.0 36.0 36.0 \n", + "2022-01-01 00:01:00 36.0 36.0 36.0 \n", + "2022-01-01 00:02:00 36.0 36.0 36.0 \n", + "2022-01-01 00:03:00 36.0 36.0 36.0 \n", + "2022-01-01 00:04:00 36.0 36.0 36.0 \n", + "... ... ... ... \n", + "2022-12-31 23:55:00 36.0 36.0 36.0 \n", + "2022-12-31 23:56:00 36.0 36.0 36.0 \n", + "2022-12-31 23:57:00 36.0 36.0 36.0 \n", + "2022-12-31 23:58:00 36.0 36.0 36.0 \n", + "2022-12-31 23:59:00 0.0 0.0 0.0 \n", + "\n", + "[525600 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f40014b0", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a Plot class\n", + "from ramp import Plot\n", + "\n", + "plot = Plot(dataframe)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "003bedb5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10\n", + "date \n", + "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + " ......" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot" + ] + }, + { + "cell_type": "markdown", + "id": "aeff5e48", + "metadata": {}, + "source": [ + "If the dateframe object did not have a pd.DatatimeIndex, the plot class could not be initialized. in this case, you can use the 'index' argument to pass a datetime index!" + ] + }, + { + "cell_type": "markdown", + "id": "3c345425", + "metadata": {}, + "source": [ + "### reading the ramp output from excel or csv\n", + "\n", + "If you run your ramp simulations from terminal, and saved your outputs into a file, you can read your saved results to perform your analysis. The structure of the saved results should be exactly as shown in previous line where the index is a date-time index and on the columns you have different cases, or even different users of your simulation case. To read the file:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c651b69f", + "metadata": {}, + "outputs": [], + "source": [ + "plot = Plot.from_file(file=\"path to csv or xlsx file\")" + ] + }, + { + "cell_type": "markdown", + "id": "bc7c7987", + "metadata": {}, + "source": [ + "If the data in file did not have a date-time index, the plot class could not be initialized! in this case, you can use the 'index' argument to pass a datetime index!" + ] + }, + { + "cell_type": "markdown", + "id": "ae868c41", + "metadata": {}, + "source": [ + "## Filtering data using loc & iloc functions\n", + "if you need to take only a part of your data, you can use the loc function. For example to take only the second day of your data:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0e8797dd", + "metadata": {}, + "outputs": [], + "source": [ + "second_day = pd.date_range(\n", + " start=\"2022-01-01 00:00:00\",\n", + " freq = \"1min\",\n", + " periods = 24*60 # a full day\n", + ")\n", + "\n", + "filtered_data = plot.loc(index=second_day)" + ] + }, + { + "cell_type": "markdown", + "id": "cc5f3375", + "metadata": {}, + "source": [ + "if you need to also filter on the columns, you can use the columns argument or use the **get item** method:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9f7d1159", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " case 1 case 2\n", + "date \n", + "2022-01-01 00:00:00 36.0 36.0\n", + "2022-01-01 00:01:00 36.0 36.0\n", + "2022-01-01 00:02:00 36.0 36.0\n", + "2022-01-01 00:03:00 36.0 36.0\n", + "2022-01-01 00:04:00 36.0 36.0\n", + "2022-01-01 00:05:00 36.0 36.0\n", + "2022-01-01 00:06:00 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0\n", + " ......" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using the function\n", + "filtered_data = plot.loc(index=second_day,columns=[\"case 1\",\"case 2\"])\n", + "filtered_data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b4e4b23a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " case 1\n", + "date \n", + "2022-01-01 00:00:00 36.0\n", + "2022-01-01 00:01:00 36.0\n", + "2022-01-01 00:02:00 36.0\n", + "2022-01-01 00:03:00 36.0\n", + "2022-01-01 00:04:00 36.0\n", + "2022-01-01 00:05:00 36.0\n", + "2022-01-01 00:06:00 36.0\n", + "2022-01-01 00:07:00 36.0\n", + "2022-01-01 00:08:00 36.0\n", + "2022-01-01 00:09:00 36.0\n", + " ......" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using the get item method\n", + "filtered_column = plot[\"case 1\"]\n", + "filtered_column" + ] + }, + { + "cell_type": "markdown", + "id": "877d8bdc", + "metadata": {}, + "source": [ + "To filter the data using the position of the data, you can use the iloc function. for example for filtering your data from the 6th row to 10th row:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "13191cdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10\n", + "date \n", + "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:10:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + " ......" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_rows = plot.iloc(index=slice(6,11))\n", + "filtered_rows" + ] + }, + { + "cell_type": "markdown", + "id": "3a8592c8", + "metadata": {}, + "source": [ + "## Adding new column to the data\n", + "In case, you want to add new columns to your data, you can use the **add_column** function:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e205e410", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 new column\n", + "date \n", + "2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + "2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0\n", + " ......" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets take a copy of the current plot object\n", + "\n", + "copy_plot = plot.copy()\n", + "\n", + "# lets take a column of the existing data\n", + "new_data = plot[\"case 1\"]\n", + "\n", + "# lets change the name of the column assuming that this is a new data\n", + "new_data.columns = [\"new column\"]\n", + "\n", + "copy_plot.add_column(new_data)\n", + "\n", + "copy_plot" + ] + }, + { + "cell_type": "markdown", + "id": "0cec30e0", + "metadata": {}, + "source": [ + "## Resampling data\n", + "\n", + "if you want to represent your data in a different time resolution, you need to resample the data. In this case the original data (plot object) has a minute time resolution:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4d5b93f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot.freq" + ] + }, + { + "cell_type": "markdown", + "id": "1066e40a", + "metadata": {}, + "source": [ + "Let's assume you want to resample your data to an hourly resolution. To do so, you can use **resample** function:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1f41870c", + "metadata": {}, + "outputs": [], + "source": [ + "hourly_data = plot.resample(\n", + " freq = \"1h\", # resampling freqency, similar pd.DataFrame rule\n", + " rule = \"sum\", # different options for resampling data like sum, mean, nearest\n", + " conversion = \"/60\", # resampling data from minute resolution to hour resolution has an assumption of dividing the minute date by 60 minute to sum to hourly data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "02eac015", + "metadata": {}, + "source": [ + "Let's check the resolution now:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e53de489", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hourly_data.freq" + ] + }, + { + "cell_type": "markdown", + "id": "0de4752e", + "metadata": {}, + "source": [ + "you can further apply all the Plot functions to the output of this resampling. For example, if you want to calculate the weekly consumption average based on the hourly data, you can use:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "de50f6a4", + "metadata": {}, + "outputs": [], + "source": [ + "weekly = hourly_data.resample(\n", + " freq = \"1w\",\n", + " rule = \"mean\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ee3a07e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weekly.freq" + ] + }, + { + "cell_type": "markdown", + "id": "e7bcdfba", + "metadata": {}, + "source": [ + "## Mean, Sum, and Peak:\n", + "\n", + "you can calculate the mean and the sum of all the columns of your ramp simulations stored in Plot object using the **mean** and **sum** functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5064b978", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Mean\n", + "date \n", + "2022-01-01 00:00:00 36.000000\n", + "2022-01-01 01:00:00 36.000000\n", + "2022-01-01 02:00:00 36.000000\n", + "2022-01-01 03:00:00 36.000000\n", + "2022-01-01 04:00:00 36.000012\n", + "2022-01-01 05:00:00 37.000557\n", + "2022-01-01 06:00:00 36.309570\n", + "2022-01-01 07:00:00 4.993772\n", + "2022-01-01 08:00:00 7.977883\n", + "2022-01-01 09:00:00 19.736763\n", + " ......" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = hourly_data.mean()\n", + "mean" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9628df92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Sum\n", + "date \n", + "2022-01-01 00:00:00 360.000000\n", + "2022-01-01 01:00:00 360.000000\n", + "2022-01-01 02:00:00 360.000000\n", + "2022-01-01 03:00:00 360.000000\n", + "2022-01-01 04:00:00 360.000117\n", + "2022-01-01 05:00:00 370.005567\n", + "2022-01-01 06:00:00 363.095700\n", + "2022-01-01 07:00:00 49.937717\n", + "2022-01-01 08:00:00 79.778833\n", + "2022-01-01 09:00:00 197.367633\n", + " ......" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summed = hourly_data.sum()\n", + "summed" + ] + }, + { + "cell_type": "markdown", + "id": "658eb6f4", + "metadata": {}, + "source": [ + "you can also find the the peak values and correspondoing hours to the peak value for each column of your data using the peak property:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c0297a1f", + "metadata": {}, + "outputs": [], + "source": [ + "peak = hourly_data.peak" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "00e89c9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'case 1': date\n", + " 2022-03-15 06:00:00 176.417483\n", + " Freq: H, Name: case 1, dtype: float64,\n", + " 'case 2': date\n", + " 2022-05-29 06:00:00 162.667033\n", + " Freq: H, Name: case 2, dtype: float64,\n", + " 'case 3': date\n", + " 2022-07-13 05:00:00 162.667667\n", + " Freq: H, Name: case 3, dtype: float64,\n", + " 'case 4': date\n", + " 2022-07-14 21:00:00 176.0013\n", + " Freq: H, Name: case 4, dtype: float64,\n", + " 'case 5': date\n", + " 2022-08-23 21:00:00 147.2522\n", + " Freq: H, Name: case 5, dtype: float64,\n", + " 'case 6': date\n", + " 2022-03-25 11:00:00 161.6685\n", + " Freq: H, Name: case 6, dtype: float64,\n", + " 'case 7': date\n", + " 2022-05-13 21:00:00 169.333667\n", + " Freq: H, Name: case 7, dtype: float64,\n", + " 'case 8': date\n", + " 2022-10-10 06:00:00 169.335567\n", + " Freq: H, Name: case 8, dtype: float64,\n", + " 'case 9': date\n", + " 2022-12-29 06:00:00 172.666983\n", + " Freq: H, Name: case 9, dtype: float64,\n", + " 'case 10': date\n", + " 2022-05-30 19:00:00 158.75205\n", + " Freq: H, Name: case 10, dtype: float64}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "peak" + ] + }, + { + "cell_type": "markdown", + "id": "e632eea1", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "ramp Plot object offers some **easy to use** plotting function for some routine visualizatoins. each plotting function allows you to choose the plotting engine that can be matplotlib or plotly. the output of the plot functions will be a matplotlib or plotly object, so you can customize your functions after plotting, with your specific taste and plotting style! you can also use the ** kwargs to pass plotly or matplotlib specific styling features." + ] + }, + { + "cell_type": "markdown", + "id": "9795ac08", + "metadata": {}, + "source": [ + "### line plot\n", + "with the line plot function, you can choose one or multiple columns of your data to plot a simple line plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "02e17fdf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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:-)" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "date" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "value" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plotting all columns using plotly engine, you can \n", + "hourly_data.line(engine=\"plotly\",plot_bgcolor=\"gray\",title=\"My Beautiful Plot
:-)\")" + ] + }, + { + "cell_type": "markdown", + "id": "04f08f05", + "metadata": {}, + "source": [ + "## Load duration curve\n", + "Load duration curve, also known as a duration curve or a load-duration profile, is the representation of the cumulative distribution of a electricity demand over a specified period of time. To plot this curve, you may use the load_duration_curve function in different modes like taking a specific column of your data calculating the mean or sum for this purpose first:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a4b3e4a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a column\n", + "hourly_data.load_duration_curve(column=\"case 1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f4366b66", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting for mean\n", + "hourly_data.mean().load_duration_curve(\"Mean\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "26e16e61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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{ + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# to have a more visible visualization, lets only take the first 5 days of the data using the iloc function\n", + "\n", + "hourly_data.iloc(index=slice(0,24*5)).shadow(\n", + " average=True, # plotting the average with a bold line and all the other solumns as cloud around it\n", + " engine = \"plotly\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8bc41b50", + "metadata": {}, + "source": [ + "## General plot fucntion\n", + "the Plot object has a function called **plot** that returns a pd.DataFrame.plot function offering some more plotting routines:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0f766ace", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting the yearly consumption of different cases as bar charts\n", + "hourly_data.resample(\"1y\",\"sum\").plot(kind=\"bar\")" + ] + }, + { + "cell_type": "markdown", + "id": "b03d77b6", + "metadata": {}, + "source": [ + "## Saving the to a file\n", + "\n", + "If you want to save the data into a csv or excel file, you can use the two save function:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "501aa0bb", + "metadata": {}, + "outputs": [], + "source": [ + "# saving as csv\n", + "hourly_data.to_csv(\"path and name of csv file.csv\")\n", + "\n", + "# saving as xslx\n", + "hourly_data.to_excel(\"path and name of excel file.xlsx\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/api_references.rst b/docs/source/api_references.rst index 4f15214d..ecfb99e8 100644 --- a/docs/source/api_references.rst +++ b/docs/source/api_references.rst @@ -55,7 +55,7 @@ Appliance class Appliance.__eq__ -**** + ********* Utilities @@ -67,4 +67,35 @@ Utilities yearly_pattern calc_peak_time_range load_data - download_example \ No newline at end of file + download_example + + +************* +Visualization +************* + +.. autosummary:: + :toctree: api_document/ + + Plot.__init__ + Plot.from_file + Plot.freq + Plot.columns + Plot.index + Plot.resample + Plot.line + Plot.shadow + Plot.area + Plot.load_duration_curve + Plot.validate_results + Plot.error + Plot.peak + Plot.DataFrame + Plot.add_column + Plot.loc + Plot.head + Plot.plot + Plot.to_excel + Plot.to_csv + Plot.mean + Plot.sum \ No newline at end of file diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 677c2940..ed3a5e64 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -13,3 +13,4 @@ RAMP examples examples/thermal_app/thermal_app examples/year_simulation/year_simulation examples/parallel_processing/parallel_processing + examples/Plot_class/Plot class diff --git a/docs/source/examples/Plot_class/Plot class.rst b/docs/source/examples/Plot_class/Plot class.rst new file mode 100644 index 00000000..4906d3ec --- /dev/null +++ b/docs/source/examples/Plot_class/Plot class.rst @@ -0,0 +1,1060 @@ +Ramp Plotting Class +=================== + +Ramp Plotting class offers some functionalities for visualizing ramp +outputs. A Plot class can be initialized by reading a pandas.DataFrame +or reading results from a csv or excel file. + +Initializing a Plot class +------------------------- + +running a ramp using a python🐍 IDE +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + from ramp import User,calc_peak_time_range,yearly_pattern + + +.. parsed-literal:: + + /opt/anaconda3/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.4) + warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of " + + +.. code:: ipython3 + + # creating a user and adding some appliances + + Petrol_Station = User( + user_name = "Petrol Station", + num_users = 1, + ) + + #add appliances + outside_bulb = Petrol_Station.add_appliance( + name = "Security Light", + number = 2, + power = 18, #watt + num_windows = 2, # security light has two functioning window: during night and during day + fixed_cycle = 0, # bulb has no duty cycle and always work with a single cycle + window_1 = [0,7*60], # from mid-night to 7 AM with opening + window_2 = [21*60,23*60+59], # from 21 to 23:59 + fixed = "yes", + flat = "yes", + + ) + + + inside_bulb = Petrol_Station.add_appliance( + name = "Indoor Light", + number = 1, + power = 75, #watt + num_windows = 1, # security light has two functioning window: during night and during day + fixed_cycle = 0, # bulb has no duty cycle and always work with a single cycle + window_1 = [7*60,21*60], # from mid-night to 7 AM,9PM with opening, + func_cycle = 1, # when the appliance is switched on, it remains on at least for a min, + occasional_use = 1, # it is always present in the energy mix + func_time = 30, #usually is used around 60 min during the whole day + time_fraction_random_variability = 0.9, + random_var_w = 0.15, + ) + + petrol_pump = Petrol_Station.add_appliance( + name = "Petrol Pump", + number = 1, + power= 200, + num_windows = 1, + fixed_cycle = 0, + window_1 = [7*60,21*60], + func_cycle = 1, + occasional_use = 1, + func_time = 40, + time_fraction_random_variability = 0.15, + random_var_w = 0.15, + ) + + # phone chargers + phone_charger = Petrol_Station.add_appliance( + name = "Phone Charger", + number = 1, + power= 5, + num_windows = 1, + fixed_cycle = 0, + window_1 = [7*60,21*60], + func_cycle = 30, + occasional_use = 0.7, + func_time = 60, + time_fraction_random_variability = 0.15, + random_var_w = 0.15, + ) + + # Radio + radio = Petrol_Station.add_appliance( + name = "Radio", + number = 1, + power= 5, + num_windows = 1, + fixed_cycle = 0, + window_1 = [7*60,21*60], + func_cycle = 20, + occasional_use = 0.95, + func_time = 4*60, + time_fraction_random_variability = 0.15, + random_var_w = 0.15, + ) + #%% + + peak_time_range = calc_peak_time_range( + user_list = [Petrol_Station] + ) + year_behaviour = yearly_pattern() + +.. code:: ipython3 + + # running the simulation for multiple cases to generate mutlipe simulations for a year = 2022 + + import pandas as pd + + number_of_simulations = 10 + number_of_days = 365 + + results = {} + + idx = pd.date_range(start=f"2022-01-01",periods=number_of_days*60*24,freq="1min",name = "date") + + for profile in range(1,number_of_simulations+1): + profiles = [] + for prof_i in range(number_of_days): # 365 days of the year + + result = Petrol_Station.generate_aggregated_load_profile( + prof_i = prof_i, + day_type = 1, + peak_time_range = peak_time_range + ) + + profiles.extend(result.tolist()) + + results[f"case {profile}"] = pd.Series(index=idx,data=profiles) + + dataframe = pd.concat(results,axis=1) + +.. code:: ipython3 + + dataframe + + + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
case 1case 2case 3case 4case 5case 6case 7case 8case 9case 10
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+

525600 rows × 10 columns

+
+ + + +.. code:: ipython3 + + # Creating a Plot class + from ramp import Plot + + plot = Plot(dataframe) + +.. code:: ipython3 + + plot + + + + +.. parsed-literal:: + + case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 + date + 2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + ...... + + + +If the dateframe object did not have a pd.DatatimeIndex, the plot class +could not be initialized. in this case, you can use the ‘index’ argument +to pass a datetime index! + +reading the ramp output from excel or csv +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you run your ramp simulations from terminal, and saved your outputs +into a file, you can read your saved results to perform your analysis. +The structure of the saved results should be exactly as shown in +previous line where the index is a date-time index and on the columns +you have different cases, or even different users of your simulation +case. To read the file: + +.. code:: ipython3 + + plot = Plot.from_file(file="path to csv or xlsx file") + +If the data in file did not have a date-time index, the plot class could +not be initialized! in this case, you can use the ‘index’ argument to +pass a datetime index! + +Filtering data using loc & iloc functions +----------------------------------------- + +if you need to take only a part of your data, you can use the loc +function. For example to take only the second day of your data: + +.. code:: ipython3 + + second_day = pd.date_range( + start="2022-01-01 00:00:00", + freq = "1min", + periods = 24*60 # a full day + ) + + filtered_data = plot.loc(index=second_day) + +if you need to also filter on the columns, you can use the columns +argument or use the **get item** method: + +.. code:: ipython3 + + # using the function + filtered_data = plot.loc(index=second_day,columns=["case 1","case 2"]) + filtered_data + + + + +.. parsed-literal:: + + case 1 case 2 + date + 2022-01-01 00:00:00 36.0 36.0 + 2022-01-01 00:01:00 36.0 36.0 + 2022-01-01 00:02:00 36.0 36.0 + 2022-01-01 00:03:00 36.0 36.0 + 2022-01-01 00:04:00 36.0 36.0 + 2022-01-01 00:05:00 36.0 36.0 + 2022-01-01 00:06:00 36.0 36.0 + 2022-01-01 00:07:00 36.0 36.0 + 2022-01-01 00:08:00 36.0 36.0 + 2022-01-01 00:09:00 36.0 36.0 + ...... + + + +.. code:: ipython3 + + # using the get item method + filtered_column = plot["case 1"] + filtered_column + + + + +.. parsed-literal:: + + case 1 + date + 2022-01-01 00:00:00 36.0 + 2022-01-01 00:01:00 36.0 + 2022-01-01 00:02:00 36.0 + 2022-01-01 00:03:00 36.0 + 2022-01-01 00:04:00 36.0 + 2022-01-01 00:05:00 36.0 + 2022-01-01 00:06:00 36.0 + 2022-01-01 00:07:00 36.0 + 2022-01-01 00:08:00 36.0 + 2022-01-01 00:09:00 36.0 + ...... + + + +To filter the data using the position of the data, you can use the iloc +function. for example for filtering your data from the 6th row to 10th +row: + +.. code:: ipython3 + + filtered_rows = plot.iloc(index=slice(6,11)) + filtered_rows + + + + +.. parsed-literal:: + + case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 + date + 2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:10:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + ...... + + + +Adding new column to the data +----------------------------- + +In case, you want to add new columns to your data, you can use the +**add_column** function: + +.. code:: ipython3 + + # lets take a copy of the current plot object + + copy_plot = plot.copy() + + # lets take a column of the existing data + new_data = plot["case 1"] + + # lets change the name of the column assuming that this is a new data + new_data.columns = ["new column"] + + copy_plot.add_column(new_data) + + copy_plot + + + + +.. parsed-literal:: + + case 1 case 2 case 3 case 4 case 5 case 6 case 7 case 8 case 9 case 10 new column + date + 2022-01-01 00:00:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:01:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:02:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:03:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:04:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:05:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:06:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:07:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:08:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + 2022-01-01 00:09:00 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 + ...... + + + +Resampling data +--------------- + +if you want to represent your data in a different time resolution, you +need to resample the data. In this case the original data (plot object) +has a minute time resolution: + +.. code:: ipython3 + + plot.freq + + + + +.. parsed-literal:: + + + + + +Let’s assume you want to resample your data to an hourly resolution. To +do so, you can use **resample** function: + +.. code:: ipython3 + + hourly_data = plot.resample( + freq = "1h", # resampling freqency, similar pd.DataFrame rule + rule = "sum", # different options for resampling data like sum, mean, nearest + conversion = "/60", # resampling data from minute resolution to hour resolution has an assumption of dividing the minute date by 60 minute to sum to hourly data + ) + +Let’s check the resolution now: + +.. code:: ipython3 + + hourly_data.freq + + + + +.. parsed-literal:: + + + + + +you can further apply all the Plot functions to the output of this +resampling. For example, if you want to calculate the weekly consumption +average based on the hourly data, you can use: + +.. code:: ipython3 + + weekly = hourly_data.resample( + freq = "1w", + rule = "mean" + ) + +.. code:: ipython3 + + weekly.freq + + + + +.. parsed-literal:: + + + + + +Mean, Sum, and Peak: +-------------------- + +you can calculate the mean and the sum of all the columns of your ramp +simulations stored in Plot object using the **mean** and **sum** +functions: + +.. code:: ipython3 + + mean = hourly_data.mean() + mean + + + + +.. parsed-literal:: + + Mean + date + 2022-01-01 00:00:00 36.000000 + 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 36.000012 + 2022-01-01 05:00:00 37.000557 + 2022-01-01 06:00:00 36.309570 + 2022-01-01 07:00:00 4.993772 + 2022-01-01 08:00:00 7.977883 + 2022-01-01 09:00:00 19.736763 + ...... + + + +.. code:: ipython3 + + summed = hourly_data.sum() + summed + + + + +.. parsed-literal:: + + Sum + date + 2022-01-01 00:00:00 360.000000 + 2022-01-01 01:00:00 360.000000 + 2022-01-01 02:00:00 360.000000 + 2022-01-01 03:00:00 360.000000 + 2022-01-01 04:00:00 360.000117 + 2022-01-01 05:00:00 370.005567 + 2022-01-01 06:00:00 363.095700 + 2022-01-01 07:00:00 49.937717 + 2022-01-01 08:00:00 79.778833 + 2022-01-01 09:00:00 197.367633 + ...... + + + +you can also find the the peak values and correspondoing hours to the +peak value for each column of your data using the peak property: + +.. code:: ipython3 + + peak = hourly_data.peak + +.. code:: ipython3 + + peak + + + + +.. parsed-literal:: + + {'case 1': date + 2022-03-15 06:00:00 176.417483 + Freq: H, Name: case 1, dtype: float64, + 'case 2': date + 2022-05-29 06:00:00 162.667033 + Freq: H, Name: case 2, dtype: float64, + 'case 3': date + 2022-07-13 05:00:00 162.667667 + Freq: H, Name: case 3, dtype: float64, + 'case 4': date + 2022-07-14 21:00:00 176.0013 + Freq: H, Name: case 4, dtype: float64, + 'case 5': date + 2022-08-23 21:00:00 147.2522 + Freq: H, Name: case 5, dtype: float64, + 'case 6': date + 2022-03-25 11:00:00 161.6685 + Freq: H, Name: case 6, dtype: float64, + 'case 7': date + 2022-05-13 21:00:00 169.333667 + Freq: H, Name: case 7, dtype: float64, + 'case 8': date + 2022-10-10 06:00:00 169.335567 + Freq: H, Name: case 8, dtype: float64, + 'case 9': date + 2022-12-29 06:00:00 172.666983 + Freq: H, Name: case 9, dtype: float64, + 'case 10': date + 2022-05-30 19:00:00 158.75205 + Freq: H, Name: case 10, dtype: float64} + + + +Visualization +------------- + +ramp Plot object offers some **easy to use** plotting function for some +routine visualizatoins. each plotting function allows you to choose the +plotting engine that can be matplotlib or plotly. the output of the plot +functions will be a matplotlib or plotly object, so you can customize +your functions after plotting, with your specific taste and plotting +style! you can also use the \*\* kwargs to pass plotly or matplotlib +specific styling features. + +line plot +~~~~~~~~~ + +with the line plot function, you can choose one or multiple columns of +your data to plot a simple line plot: + +.. code:: ipython3 + + # plotting one column using matplotlib engine (default engine) + hourly_data.line(columns = "case 1") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_38_1.png + + +.. code:: ipython3 + + # plotting all columns using plotly engine, you can + hourly_data.line(engine="plotly",plot_bgcolor="gray",title="My Beautiful Plot
:-)") + + + +.. raw:: html + + + + + + +.. raw:: html + +
+ + +Load duration curve +------------------- + +Load duration curve, also known as a duration curve or a load-duration +profile, is the representation of the cumulative distribution of a +electricity demand over a specified period of time. To plot this curve, +you may use the load_duration_curve function in different modes like +taking a specific column of your data calculating the mean or sum for +this purpose first: + +.. code:: ipython3 + + # Taking a column + hourly_data.load_duration_curve(column="case 1") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_41_1.png + + +.. code:: ipython3 + + # plotting for mean + hourly_data.mean().load_duration_curve("Mean") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_42_1.png + + +.. code:: ipython3 + + # Taking sum + hourly_data.sum().load_duration_curve("Sum") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_43_1.png + + +Area Plot +--------- + +using the area plot function, you can plot the cummulative sum of the +the columns in your data: + +.. code:: ipython3 + + weekly.area(engine="plotly") + + + +.. raw:: html + +
+ + +Shadow Plot +----------- + +in case you want to represent all the simulations together, to show the +variability of stochastic profiles generated by ramp, you may use the +shadow plot: + +.. code:: ipython3 + + # to have a more visible visualization, lets only take the first 5 days of the data using the iloc function + + hourly_data.iloc(index=slice(0,24*5)).shadow( + average=True, # plotting the average with a bold line and all the other solumns as cloud around it + engine = "plotly" + ) + + + +.. raw:: html + +
+ + +General plot fucntion +--------------------- + +the Plot object has a function called **plot** that returns a +pd.DataFrame.plot function offering some more plotting routines: + +.. code:: ipython3 + + # plotting the yearly consumption of different cases as bar charts + hourly_data.resample("1y","sum").plot(kind="bar") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_49_1.png + + +Saving the to a file +-------------------- + +If you want to save the data into a csv or excel file, you can use the +two save function: + +.. code:: ipython3 + + # saving as csv + hourly_data.to_csv("path and name of csv file.csv") + + # saving as xslx + hourly_data.to_excel("path and name of excel file.xlsx") diff --git a/docs/source/examples/Plot_class/output_38_1.png b/docs/source/examples/Plot_class/output_38_1.png new file mode 100644 index 0000000000000000000000000000000000000000..1b40f54d38992457e0c0f73bb547729403db4c3d GIT binary patch literal 12275 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+ return (self.DataFrame.head(10).to_string() + "\n ......") + + @property + def DataFrame(self): + """returns the data of the Plot object + """ + return self.df + + @DataFrame.setter + def DataFrame(self,var): + + self._validate_df(var,check_index=False) + + self.df = var + + def add_column(self,var): + """adds new column to the data + + Parameters + ---------- + var : pd.DataFrame + a pd.DataFrame with similar index to the main dataset + """ + if isinstance(var,Plot): + var = var.DataFrame + + self._validate_df(var) + + self.df[var.columns] = var.values + + + def __getitem__(self,key): + + if isinstance(key,str): + key = [key] + + return Plot(self.DataFrame[key]) + + def loc(self,index=slice(None),columns=slice(None)): + """loc method to filter the data + + Parameters + ---------- + index : str,list,tuple, optional + pd.DataFrame.loc index filtering input, by default slice(None) + columns : str,list,tuple, optional + pd.DataFrame.loc columns filtering input, by default slice(None) + + Returns + ------- + Plot + a Plot object using the index and columns filters + """ + + return Plot(self.DataFrame.loc[index,columns]) + + def iloc(self,index=slice(None),columns=slice(None)): + """iloc method to filter the data based on position index + + Parameters + ---------- + index : int,list,tuple, optional + pd.DataFrame.iloc index filtering input, by default slice(None) + columns : int,list,tuple, optional + pd.DataFrame.iloc columns filtering input, by default slice(None) + + Returns + ------- + Plot + a Plot object using the index and columns filters + """ + + return Plot(self.DataFrame.iloc[index,columns]) + + def head(self,var): + """returns the top var numbers of data + + Parameters + ---------- + var : int + numbers of rows of the data to show + + Returns + ------- + Plot + a Plot object with the numbers of rows specified + """ + return Plot(self.DataFrame.head(var)) + + def plot(self,**kwargs): + """returns a pd.DataFrame.plot object + + Returns + ------- + matplotlib.axes._axes.Axes + """ + return self.DataFrame.plot(**kwargs) + + def _validate_df(self,other,check_index=True): + if not isinstance(other,pd.DataFrame): + raise ValueError("only pd.DataFrame object is allowed") + + if not(isinstance,other.index,pd.DatetimeIndex): + raise ValueError("a valid dataframe shoud has only a pd.DatatimeIndex") + if check_index: + if not self.index.equals(other.index): + raise ValueError("the new column should have identical index with the existing DataFrame") + + + def to_excel(self,path): + """saves the data into excel + + Parameters + ---------- + path : str + path to save the file + """ + + with pd.ExcelWriter(path) as file: + self.DataFrame.to_excel(file) + + def to_csv(self,path,sep=','): + """saves the data into csv + + Parameters + ---------- + path : str + path to save the file + sep : str,optional + csv separator + """ + self.DataFrame.to_csv(path,sep=sep) + + def mean(self): + """returns the mean of the columns + + Returns + ------- + Plot + average of columns for each timestep + """ + return Plot(self.DataFrame.mean(1).to_frame("Mean")) + + def sum(self): + """returns the sum of the columns + + Returns + ------- + Plot + sum of columns for each timestep + """ + return Plot(self.DataFrame.sum(1).to_frame("Sum")) + + def copy(self): + """returns a copy of the existing object + """ + return Plot(self.df.copy()) + From da268748112486857a58e4116925f3b0e41290eb Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Sun, 15 Oct 2023 18:51:32 +0200 Subject: [PATCH 43/47] adding the new dependencies to the requirements --- requirements.txt | 3 ++- setup.py | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8416a795..53d8d8b4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,5 @@ numpy matplotlib openpyxl multiprocess -tqdm \ No newline at end of file +tqdm +plotly \ No newline at end of file diff --git a/setup.py b/setup.py index 053c4447..d4a6d502 100644 --- a/setup.py +++ b/setup.py @@ -22,6 +22,7 @@ "matplotlib >= 3.3.4", "openpyxl >= 3.0.6", "tqdm", + "plotly" ], # classifiers=[ # "Programming Language :: Python :: 3.7", From 0c015991f223d8bcb57a371a4b09e58f16dcf767 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Thu, 5 Oct 2023 00:19:11 +0200 Subject: [PATCH 44/47] Update RTD --- README.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.rst b/README.rst index 703734f6..68b3f024 100644 --- a/README.rst +++ b/README.rst @@ -22,7 +22,7 @@ What is RAMP ============ RAMP is an open-source software suite for the stochastic simulation of any user-driven energy demand time series based on few simple inputs. -The project aims to provide synthetic data wherever metered data does not exist, such as when designing systems in remote areas. Check out the `documentation `_ and learn more on the RAMP world from our `website `_! +The project aims to provide synthetic data wherever metered data does not exist, such as when designing systems in remote areas. Check out the `documentation `_ and learn more on the RAMP world from our `website `_! .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/Example_output.jpg?raw=true :width: 600 @@ -151,9 +151,7 @@ need to run the ``ramp`` command with the option ``-i``: .. note:: You can input several files, separated from each others by a single blank space -If you already know -how many profile you want to simulate you can indicate it with the -``-n`` option: +If you already know how many profile you want to simulate you can indicate it with the ``-n`` option: .. code-block:: bash From 511d90b7aa1a69b19c08c5f57dd19eea8e5de29c Mon Sep 17 00:00:00 2001 From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com> Date: Tue, 24 Oct 2023 17:13:32 +0200 Subject: [PATCH 45/47] PR #89 reviews - Improving the Plot class docstring - Improving the error handling with invalid inputs by user regarding the file extensions and plot engine - Improving the readability of the plotting scripts by merging the plot classes into Plot methods --- docs/notebooks/Plot class.ipynb | 121438 ++++++++------- .../source/examples/Plot_class/Plot class.rst | 106 +- ramp/post_process/post_process.py | 213 +- 3 files changed, 60897 insertions(+), 60860 deletions(-) diff --git a/docs/notebooks/Plot class.ipynb b/docs/notebooks/Plot class.ipynb index 963a0cfb..25cc4e5b 100644 --- a/docs/notebooks/Plot class.ipynb +++ b/docs/notebooks/Plot class.ipynb @@ -164,7 +164,7 @@ " profiles.extend(result.tolist())\n", "\n", " results[f\"case {profile}\"] = pd.Series(index=idx,data=profiles)\n", - " \n", + "\n", "dataframe = pd.concat(results,axis=1)" ] }, @@ -535,7 +535,6 @@ "data": { "text/plain": [ " case 1 case 2\n", - "date \n", "2022-01-01 00:00:00 36.0 36.0\n", "2022-01-01 00:01:00 36.0 36.0\n", "2022-01-01 00:02:00 36.0 36.0\n", @@ -735,7 +734,7 @@ "hourly_data = plot.resample(\n", " freq = \"1h\", # resampling freqency, similar pd.DataFrame rule\n", " rule = \"sum\", # different options for resampling data like sum, mean, nearest\n", - " conversion = \"/60\", # resampling data from minute resolution to hour resolution has an assumption of dividing the minute date by 60 minute to sum to hourly data\n", + " conversion = 60, # resampling data from minute resolution to hour resolution has an assumption of dividing the minute date by 60 minute to sum to hourly data\n", ")" ] }, @@ -835,12 +834,12 @@ "2022-01-01 01:00:00 36.000000\n", "2022-01-01 02:00:00 36.000000\n", "2022-01-01 03:00:00 36.000000\n", - "2022-01-01 04:00:00 36.000012\n", - "2022-01-01 05:00:00 37.000557\n", - "2022-01-01 06:00:00 36.309570\n", - "2022-01-01 07:00:00 4.993772\n", - "2022-01-01 08:00:00 7.977883\n", - "2022-01-01 09:00:00 19.736763\n", + "2022-01-01 04:00:00 36.000000\n", + "2022-01-01 05:00:00 36.333950\n", + "2022-01-01 06:00:00 36.826410\n", + "2022-01-01 07:00:00 4.776980\n", + "2022-01-01 08:00:00 23.261020\n", + "2022-01-01 09:00:00 17.369903\n", " ......" ] }, @@ -869,12 +868,12 @@ "2022-01-01 01:00:00 360.000000\n", "2022-01-01 02:00:00 360.000000\n", "2022-01-01 03:00:00 360.000000\n", - "2022-01-01 04:00:00 360.000117\n", - "2022-01-01 05:00:00 370.005567\n", - "2022-01-01 06:00:00 363.095700\n", - "2022-01-01 07:00:00 49.937717\n", - "2022-01-01 08:00:00 79.778833\n", - "2022-01-01 09:00:00 197.367633\n", + "2022-01-01 04:00:00 360.000000\n", + "2022-01-01 05:00:00 363.339500\n", + "2022-01-01 06:00:00 368.264100\n", + "2022-01-01 07:00:00 47.769800\n", + "2022-01-01 08:00:00 232.610200\n", + "2022-01-01 09:00:00 173.699033\n", " ......" ] }, @@ -916,34 +915,34 @@ "data": { "text/plain": [ "{'case 1': date\n", - " 2022-03-15 06:00:00 176.417483\n", + " 2022-03-16 21:00:00 153.917183\n", " Freq: H, Name: case 1, dtype: float64,\n", " 'case 2': date\n", - " 2022-05-29 06:00:00 162.667033\n", + " 2022-12-22 06:00:00 156.001167\n", " Freq: H, Name: case 2, dtype: float64,\n", " 'case 3': date\n", - " 2022-07-13 05:00:00 162.667667\n", + " 2022-05-25 05:00:00 166.001067\n", " Freq: H, Name: case 3, dtype: float64,\n", " 'case 4': date\n", - " 2022-07-14 21:00:00 176.0013\n", + " 2022-02-11 21:00:00 165.0009\n", " Freq: H, Name: case 4, dtype: float64,\n", " 'case 5': date\n", - " 2022-08-23 21:00:00 147.2522\n", + " 2022-10-16 06:00:00 165.251917\n", " Freq: H, Name: case 5, dtype: float64,\n", " 'case 6': date\n", - " 2022-03-25 11:00:00 161.6685\n", + " 2022-11-25 08:00:00 158.334717\n", " Freq: H, Name: case 6, dtype: float64,\n", " 'case 7': date\n", - " 2022-05-13 21:00:00 169.333667\n", + " 2022-08-24 06:00:00 162.668033\n", " Freq: H, Name: case 7, dtype: float64,\n", " 'case 8': date\n", - " 2022-10-10 06:00:00 169.335567\n", + " 2022-06-04 06:00:00 185.585083\n", " Freq: H, Name: case 8, dtype: float64,\n", " 'case 9': date\n", - " 2022-12-29 06:00:00 172.666983\n", + " 2022-03-25 10:00:00 177.918433\n", " Freq: H, Name: case 9, dtype: float64,\n", " 'case 10': date\n", - " 2022-05-30 19:00:00 158.75205\n", + " 2022-06-16 19:00:00 182.918433\n", " Freq: H, Name: case 10, dtype: float64}" ] }, @@ -993,7 +992,7 @@ }, { "data": { - "image/png": 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\n", 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36.001083333333334, - 35.4, + 0.004, + 0.004, + 15.003800000000004, + 0.08731666666666667, + 38.58523333333333, 36, + 35.4, 36, 36, 36, 36, 36, - 116.00245, - 0.004, + 36.00093333333333, + 36.00221666666666, + 0.0031333333333333335, + 2.4201833333333336, 0.004, - 50.25305, - 17.58591666666667, - 2.5035000000000003, - 27.502700000000004, + 70.66953333333333, 5.003, - 1.9202833333333333, - 0.004, - 0.004, + 12.5029, + 5.003, + 21.253716666666673, 0.004, + 22.25345, 0.004, - 0.0028, - 0.0014666666666666667, - 36.00086666666667, - 36, + 76.25333333333333, + 0.002916666666666667, + 0.0017833333333333334, + 36.001, + 36.0002, 35.4, 36, 36, 36, 36, 36, - 36.000166666666665, - 36.001933333333334, - 0.002616666666666667, - 93.33608333333333, - 40.0038, - 5.419583333333334, + 36.00143333333333, + 36.0023, + 0.004, + 1.0871166666666667, + 2.1702333333333335, + 3.58685, 3.1700333333333335, - 37.66966666666667, - 2.0869166666666668, - 0.33726666666666666, + 78.75301666666667, 0.004, - 3.6699333333333337, - 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6.503400000000001, - 0.004, - 0.6705333333333334, - 9.169066666666666, - 7.086566666666667, - 1.3366, - 0.0029833333333333335, - 149.33408333333333, 36.00046666666667, - 35.4 + 76.0008, + 0.0015500000000000002, + 8.75275, + 40.00280000000001, + 0.003, + 0.003, + 0.5028999999999999, + 2.0859166666666664, + 0.003, + 0.003, + 56.66938333333334, + 25.00266666666667, + 3.918883333333333, + 5.002, + 8.751949999999999, + 38.91808333333333, + 36.001, + 35.40005 ], "yaxis": "y" } @@ -177373,9 +177372,9 @@ } }, "text/html": [ - "
@@ -948,7 +942,7 @@ the columns in your data: .. raw:: html
@@ -984,7 +978,7 @@ shadow plot: .. code:: ipython3 # to have a more visible visualization, lets only take the first 5 days of the data using the iloc function - + hourly_data.iloc(index=slice(0,24*5)).shadow( average=True, # plotting the average with a bold line and all the other solumns as cloud around it engine = "plotly" @@ -995,7 +989,7 @@ shadow plot: .. raw:: html
@@ -1055,6 +1049,6 @@ two save function: # saving as csv hourly_data.to_csv("path and name of csv file.csv") - + # saving as xslx hourly_data.to_excel("path and name of excel file.xlsx") diff --git a/ramp/post_process/post_process.py b/ramp/post_process/post_process.py index 47dc1ba9..4b5bdc09 100644 --- a/ramp/post_process/post_process.py +++ b/ramp/post_process/post_process.py @@ -90,6 +90,7 @@ def export_series(stoch_profiles_series, j=None, fname=None, ofname=None): else: print("No path to a file was provided to write the results") + valid_units = ('kW',"W","MW","GW","TW") class Run: @@ -158,90 +159,25 @@ def generate_profiles(self,starting_day,n_days,peak_time_range,columns=["Baselin -class LinePlot: - """Line plot functions for Plotly and matplotlib engine - """ - def plotly(df,**layout): - - fig = px.line(df) - fig.update_layout(layout) - - return fig - - def matplotlib(df,**kwargs): - ax = df.plot(kind="line",**kwargs) - - return ax - - -class ShadowPlot: - """Shadow plot functions for Plotly and matplotlib engine +class Plot: """ - def plotly(line,other,**layout): - - fig = go.Figure() - - fig.add_trace( - go.Scatter( - x = line.index, - y = line.values.ravel(), - mode = "lines", - name = line.columns[0], - line = dict(color = "rgb(0,0,0)") - ) - ) - - for col,vals in other.iteritems(): - - fig.add_trace( - go.Scatter( - x = vals.index, - y = vals.values.ravel(), - mode = "lines", - showlegend=False, - line = dict(color = "rgba(0,0,0,0.15)") - ) - ) - - - - fig.update_layout(**layout) - - return fig - - def matplotlib(line,other,**kwargs): - - fig = plt.figure(**kwargs) - ax = fig.add_subplot(1, 1, 1) - - ax.plot(line.index, line.values,color="black",label=line.columns[0]) - - ax.plot(other.index,other.values,alpha=0.3,color="black") - - ax.legend() - - return fig,ax - -class AreaPlot: - """Area plot functions for Plotly and matplotlib engine + The Plot class will provide useful fucntionalities for analyzing and visualizing the results of one or multiple ramp simulations. + + The Plot class will store ramp simulation into a pd.DataFrame with timeseries index, representign the timeline of the simulation and the columns representing the simulated cases. + A Plot class can be initialized using a pd.DataFrame, or from a csv, or xlsx file. + + Parameters + ---------- + DataFrame : pd.DataFrame + the Plot object data storing the simulation cases + freq : pd.offsets + the frequency of the data based on the DataFrame index + index : Index + Index to use for resulting frame timeseries. + columns : Index or list-like + Column labels to use for resulting frame when representing the simulation cases """ - def matplotlib(df,**kwargs): - - ax = df.plot(kind="area") - - return ax - - def plotly(df,**layout): - fig = px.area(df) - fig.update_layout(**layout) - - return fig - - - -class Plot: - @classmethod def from_file(self,file,sheet_name=0,sep=",",index=None): """initializing a Plot object from a file results @@ -263,13 +199,16 @@ def from_file(self,file,sheet_name=0,sep=",",index=None): A Plot object """ - if file.split(".")[-1] == "csv": - df = pd.read_excel(file,sheet_name=sheet_name,index_col=0,header=0,sep=sep) + if file.endswith(".csv"): + df = pd.read_csv(file,sheet_name=sheet_name,index_col=0,header=0,sep=sep) - elif file.split(".")[-1] == "xlsx": + elif file.endswith(".xlsx"): df = pd.read_excel(file,sheet_name=sheet_name,index_col=0,header=0) + else: + raise ValueError("unkwnon format specified for the file. Only .csv or .xlsx formats are allowed.") + return Plot(df,index) @@ -317,7 +256,7 @@ def columns(self,var): def index(self): return self.df.index - def resample(self,freq,rule,conversion="*1",inplace=False): + def resample(self,freq,rule,conversion=1,inplace=False): """returns a resampled version of the data Parameters @@ -326,8 +265,8 @@ def resample(self,freq,rule,conversion="*1",inplace=False): pd.DataFrame.resample frequency, for example: "1h" for hourly resampling, "1w" for weekly data. Refer to https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html rule : str resampling rule. acceptable values are 'sum', 'mean', 'nearest', ... - conversion : str, optional - if resampling needs a conversion, can be done through this argument, by default "*1" + conversion : int, optional + if resampling needs a conversion, can be done through this argument. The value of the data will be divided by 'conversion', by default 1 inplace : bool, optional if True, implements the changes inplace otherwise, returns the resampled data as a new object, by default False @@ -338,11 +277,10 @@ def resample(self,freq,rule,conversion="*1",inplace=False): """ df = self.df.copy() - df = eval(f"df{conversion}") + df = df/conversion df = getattr(df.resample(freq),rule)() if inplace: - self.df = df else: @@ -365,14 +303,26 @@ def line(self,columns=None,engine="matplotlib",**kwargs): a matplotlib or plotly graph """ + engine = self._check_engine(engine) if columns is None: df = self.df else: df = self.df[columns] + if engine == "plotly": + + fig = px.line(df) + fig.update_layout(**kwargs) + + return fig + + elif engine == "matplotlib": + + ax = df.plot(kind="line",**kwargs) + + return ax - return getattr(LinePlot,engine)(df,**kwargs) def shadow(self,main_column=None,columns="all",average=True,engine="matplotlib",**kwargs): """creating a shadow plot @@ -394,6 +344,7 @@ def shadow(self,main_column=None,columns="all",average=True,engine="matplotlib", ------- a matplotlib or plotly graph """ + engine = self._check_engine(engine) if main_column is None and average == False: raise ValueError("one of columns should be passed as the main_column when the average = False") @@ -415,8 +366,48 @@ def shadow(self,main_column=None,columns="all",average=True,engine="matplotlib", if isinstance(df_other,pd.Series): df_other = df_other.to_frame(columns) + if engine == "matplotlib": + fig = plt.figure(**kwargs) + ax = fig.add_subplot(1, 1, 1) + + ax.plot(df_main.index, df_main.values,color="black",label=df_main.columns[0]) + + ax.plot(df_other.index,df_other.values,alpha=0.3,color="black") + + ax.legend() - return getattr(ShadowPlot,engine)(df_main,df_other,**kwargs) + return fig,ax + + elif engine == "plotly": + fig = go.Figure() + + fig.add_trace( + go.Scatter( + x = df_main.index, + y = df_main.values.ravel(), + mode = "lines", + name = df_main.columns[0], + line = dict(color = "rgb(0,0,0)") + ) + ) + + for col,vals in df_other.items(): + + fig.add_trace( + go.Scatter( + x = vals.index, + y = vals.values.ravel(), + mode = "lines", + showlegend=False, + line = dict(color = "rgba(0,0,0,0.15)") + ) + ) + + + + fig.update_layout(**kwargs) + + return fig def area(self,columns=None,engine="matplotlib",**kwargs): @@ -435,12 +426,23 @@ def area(self,columns=None,engine="matplotlib",**kwargs): ------- a matplotlib or plotly graph """ + engine = self._check_engine(engine) + if columns is None: df = self.df else: df = self.df[columns] - return getattr(AreaPlot,engine)(df,**kwargs) + if engine == "matplotlib": + return df.plot(kind = "area",**kwargs) + + elif engine == "plotly": + + fig = px.area(df) + fig.update_layout(**kwargs) + + return fig + def load_duration_curve(self,column,engine="matplotlib",**kwargs): """plots the load duration curve @@ -458,15 +460,26 @@ def load_duration_curve(self,column,engine="matplotlib",**kwargs): ------- a matplotlib or plotly graph """ + engine = self._check_engine(engine) + df = self.df[[column]] df = df.sort_values(by=column,ascending=False) df.index = [i for i in range(1,len(df.index)+1)] - return getattr(LinePlot,engine)(df,**kwargs) + if engine == "plotly": + fig = px.line(df) + fig.update_layout(**kwargs) + return fig + + elif engine == "matplotlib": + + ax = df.plot(kind="line",**kwargs) + + return ax def error(self,base_column,validated_data): """returns the error @@ -502,7 +515,7 @@ def peak(self): output = {} - for col,vals in self.df.iteritems(): + for col,vals in self.df.items(): max = vals.loc[vals == vals.max()] output[col] = max @@ -672,3 +685,15 @@ def copy(self): """ return Plot(self.df.copy()) + def _check_engine(self,engine): + + if engine.lower() == "matplotlib": + + return "matplotlib" + + elif engine.lower() == "plotly": + + return "plotly" + + raise ValueError(f"{engine} is not a valid plot engine. Only 'Plotly' and 'matplotlib are valid.'") + From 5766f8c4c405bea045d48a1ba18137f21435bac9 Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Wed, 8 Nov 2023 22:19:49 +0100 Subject: [PATCH 46/47] Update docs requirements --- docs/docs-requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/docs-requirements.txt b/docs/docs-requirements.txt index 317218e0..21d2eba4 100644 --- a/docs/docs-requirements.txt +++ b/docs/docs-requirements.txt @@ -2,6 +2,7 @@ pandas >= 1.3.3 numpy >= 1.21.2 multiprocess tqdm +plotly xlsxwriter <= 1.3.7 matplotlib >= 3.3.4 openpyxl >= 3.0.6 From 6815a4dd8a4945f262453009dc458a6b5299ae6f Mon Sep 17 00:00:00 2001 From: "pierre-francois.duc" Date: Wed, 8 Nov 2023 22:46:18 +0100 Subject: [PATCH 47/47] Update changelog --- CHANGELOG.md | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f5f773fd..3d7b7bad 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,18 +1,24 @@ Release History =============== -0.4.2 (dev) +0.4.3 (dev) ------------------ -**|new|** continuous integration setup +0.4.2 (2023-11-09) +------------------ +**|new|** classes to plot and save RAMP simulation results for post-processing (#89) + +**|new|** continuous integration setup (#94 and #96) + +**|new|** first automated tests (#93) -**|new|** first automated tests +**|fixed|** moved the randomization of daily preference from appliance to user level (#92) -**|fixed|** installation options have been debugged and the documentation updated accordingly +**|fixed|** installation options have been debugged and the documentation updated accordingly (#76, #95) -**|fixed|** automated download of example applications via the `download_example` functions now includes previously missing .csv files +**|fixed|** automated download of example applications via the `download_example` functions now includes previously missing .csv files (#69) -0.4.1 (2023-10-XX) +0.4.1 (2023-10-08) ------------------ **|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.