diff --git a/docs/source/api_document/ramp.calc_peak_time_range.rst b/docs/source/api_document/ramp.calc_peak_time_range.rst
deleted file mode 100644
index c0cc15a9..00000000
--- a/docs/source/api_document/ramp.calc_peak_time_range.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-ramp.calc\_peak\_time\_range
-============================
-
-.. currentmodule:: ramp
-
-.. autofunction:: calc_peak_time_range
\ No newline at end of file
diff --git a/docs/source/api_document/ramp.yearly_pattern.rst b/docs/source/api_document/ramp.yearly_pattern.rst
deleted file mode 100644
index 18ea068a..00000000
--- a/docs/source/api_document/ramp.yearly_pattern.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-ramp.yearly\_pattern
-====================
-
-.. currentmodule:: ramp
-
-.. autofunction:: yearly_pattern
\ No newline at end of file
diff --git a/docs/source/examples.rst b/docs/source/examples.rst
index ed3a5e64..9744dcd0 100644
--- a/docs/source/examples.rst
+++ b/docs/source/examples.rst
@@ -13,4 +13,4 @@ RAMP examples
examples/thermal_app/thermal_app
examples/year_simulation/year_simulation
examples/parallel_processing/parallel_processing
- examples/Plot_class/Plot class
+ 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
deleted file mode 100644
index 3e6f2821..00000000
--- a/docs/source/examples/Plot_class/Plot class.rst
+++ /dev/null
@@ -1,1054 +0,0 @@
-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
-
-
-.. 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 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 |
-
-
- | ... |
- ... |
- ... |
- ... |
- ... |
- ... |
- ... |
- ... |
- ... |
- ... |
- ... |
-
-
- | 2022-12-31 23:55:00 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
-
-
- | 2022-12-31 23:56:00 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
-
-
- | 2022-12-31 23:57:00 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
-
-
- | 2022-12-31 23:58:00 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
- 36.0 |
-
-
- | 2022-12-31 23:59:00 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
- 0.0 |
-
-
-
-
525600 rows × 10 columns
-
:-)")
-
-
-
-.. 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/plot_class.rst b/docs/source/examples/plot_class/plot_class.rst
new file mode 100644
index 00000000..3af95810
--- /dev/null
+++ b/docs/source/examples/plot_class/plot_class.rst
@@ -0,0 +1,707 @@
+Plotting functionalities
+========================
+
+RAMP’s plotting class offers some functionalities for visualizing
+outputs. A ``Plot`` class can be initialized by reading a
+``pandas.DataFrame`` or reading results from a ``.csv`` or ``.xlsx``
+file.
+
+Initializing a Plot class
+-------------------------
+
+Running RAMP interactively
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+ from ramp import User, UseCase
+
+.. 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,
+ )
+
+.. code:: ipython3
+
+ # running the simulation for multiple cases to generate mutlipe simulations for a year = 2022
+
+ number_of_simulations = 11
+ number_of_days = 365
+
+ uc = UseCase(users=[Petrol_Station], date_start="2022-01-01")
+ uc.initialize(num_days=number_of_days)
+
+ plot = uc.generate_daily_load_profiles(
+ cases=[profile for profile in range(1, number_of_simulations + 1)]
+ )
+
+
+.. parsed-literal::
+
+ You will simulate 365 day(s) from 2022-01-01 00:00:00 until 2023-01-01 00:00:00
+
+
+If the dataframe 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!
+
+Running via the CLI and reading back the outputs from ``.csv`` or ``.xlsx``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you run your RAMP simulations from a terminal, and save 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
+
+ # Uncomment the line below once you have replaced the placeholder with the path of an existing file
+ # plot = Plot.from_file(file="path to .csv or .xlsx file")
+
+If the data in the 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 use 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
+
+ import pandas as pd
+
+ 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
+ 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
+ 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 case 11
+ 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
+ 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 36.0
+ ......
+
+
+
+Adding new columns 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 case 11 new column
+ 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 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 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 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 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 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 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 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 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 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 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 the ``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 minutes 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::
+
+
+
+
+
+Compute Mean, Sum, and Peak
+---------------------------
+
+You can calculate the mean and the sum of all the columns of your RAMP
+simulations stored in a ``Plot`` object using the ``mean`` and ``sum``
+functions:
+
+.. code:: ipython3
+
+ mean = hourly_data.mean()
+ mean
+
+
+
+
+.. parsed-literal::
+
+ Mean
+ 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 36.000009
+ 2022-01-01 05:00:00 37.515430
+ 2022-01-01 06:00:00 44.341832
+ 2022-01-01 07:00:00 3.040070
+ 2022-01-01 08:00:00 1.646964
+ 2022-01-01 09:00:00 6.791291
+ ......
+
+
+
+.. code:: ipython3
+
+ summed = hourly_data.sum()
+ summed
+
+
+
+
+.. parsed-literal::
+
+ Sum
+ 2022-01-01 00:00:00 396.000000
+ 2022-01-01 01:00:00 396.000000
+ 2022-01-01 02:00:00 396.000000
+ 2022-01-01 03:00:00 396.000000
+ 2022-01-01 04:00:00 396.000100
+ 2022-01-01 05:00:00 412.669733
+ 2022-01-01 06:00:00 487.760150
+ 2022-01-01 07:00:00 33.440767
+ 2022-01-01 08:00:00 18.116600
+ 2022-01-01 09:00:00 74.704200
+ ......
+
+
+
+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': 2022-08-16 21:00:00 184.502267
+ Freq: H, Name: case 1, dtype: float64,
+ 'case 2': 2022-04-23 12:00:00 170.418517
+ Freq: H, Name: case 2, dtype: float64,
+ 'case 3': 2022-09-07 21:00:00 166.00135
+ Freq: H, Name: case 3, dtype: float64,
+ 'case 4': 2022-03-26 22:00:00 153.8342
+ Freq: H, Name: case 4, dtype: float64,
+ 'case 5': 2022-05-09 06:00:00 172.667167
+ Freq: H, Name: case 5, dtype: float64,
+ 'case 6': 2022-06-29 06:00:00 166.000667
+ Freq: H, Name: case 6, dtype: float64,
+ 'case 7': 2022-09-04 18:00:00 160.169433
+ Freq: H, Name: case 7, dtype: float64,
+ 'case 8': 2022-03-17 06:00:00 162.666883
+ Freq: H, Name: case 8, dtype: float64,
+ 'case 9': 2022-10-20 06:00:00 152.667083
+ Freq: H, Name: case 9, dtype: float64,
+ 'case 10': 2022-10-02 21:00:00 156.0024
+ Freq: H, Name: case 10, dtype: float64,
+ 'case 11': 2022-12-27 06:00:00 158.917483
+ Freq: H, Name: case 11, dtype: float64}
+
+
+
+Visualisation
+-------------
+
+A ``Plot`` object offers some easy-to-use plotting functions for routine
+visualisations. Each plotting function allows you to choose the plotting
+engine, which can be either ``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_35_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
+
+
+
+
+Load duration curve
+~~~~~~~~~~~~~~~~~~~
+
+A Load duration curve (LDC), also known as a duration curve or a
+load-duration profile, represents the cumulative distribution of a given
+energy 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 and calculating the mean or sum.
+To this end, go through the steps below.
+
+.. code:: ipython3
+
+ # Taking a column
+ hourly_data.load_duration_curve(column="case 1")
+
+
+
+
+.. parsed-literal::
+
+
+
+
+
+
+.. image:: output_38_1.png
+
+
+.. code:: ipython3
+
+ # plotting for mean
+ hourly_data.mean().load_duration_curve("Mean")
+
+
+
+
+.. parsed-literal::
+
+
+
+
+
+
+.. image:: output_39_1.png
+
+
+.. code:: ipython3
+
+ # Taking sum
+ hourly_data.sum().load_duration_curve("Sum")
+
+
+
+
+.. parsed-literal::
+
+
+
+
+
+
+.. image:: output_40_1.png
+
+
+Area Plot
+~~~~~~~~~
+
+Using the ``area`` plot function, you can plot the cumulative sum of the
+the columns in your data.
+
+.. code:: ipython3
+
+ hourly_data.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 visualisation, let's 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 columns as a cloud around it
+ engine="plotly",
+ )
+
+
+
+.. raw:: html
+
+
+
+
+General plot function
+~~~~~~~~~~~~~~~~~~~~~
+
+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_46_1.png
+
+
+Saving the data into a file
+---------------------------
+
+If you want to save the data into a ``.csv`` or ``.xlsx`` file, you can
+use the two save functions:
+
+.. 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/input_parameters.rst b/docs/source/input_parameters.rst
index c51736b8..ea8938b5 100644
--- a/docs/source/input_parameters.rst
+++ b/docs/source/input_parameters.rst
@@ -1,6 +1,8 @@
-######################################
+Model Parameters
+================
+
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.
@@ -227,7 +229,7 @@ The **"allowed values"** column provide information about the format one should
- 0
Python input file parameter description
-=======================================
+---------------------------------------
A new instance of class ``User`` requires the parameters ``user_name``,
``num_users``, ``user_preference`` from the table above. To add an
diff --git a/ramp/core/core.py b/ramp/core/core.py
index 7b45df83..e21047b2 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1461,7 +1461,7 @@ def maximum_profile(self):
return self.daily_use * np.mean(self.power) * self.number
def specific_cycle(self, cycle_num, **kwargs):
- """assigining specific duty cycle for the appliace (maximum of three cycles can be assigned)
+ """assigining specific duty cycle for the appliance (maximum of three cycles can be assigned)
Parameters
----------
@@ -1469,7 +1469,7 @@ def specific_cycle(self, cycle_num, **kwargs):
represents the number of the specific cycle to be assigned. acceptable values are [1,2,3]
**kwargs :
- additional features passed tp each specific cycle function. For example iff cycle_num = 1, **kwargs represents the arguments of function 'spefici_cycle_1' which are:
+ additional features passed tp each specific cycle function. For example iff cycle_num = 1, **kwargs represents the arguments of function 'specific_cycle_1' which are:
* p_11
* t_11
* p_12