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 1/5] 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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\n",
+ "
"
+ ],
+ "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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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting one column using matplotlib engine (default engine)\n",
+ "hourly_data.line(columns = \"case 1\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "9bef8400",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "hovertemplate": "variable=case 1 date=%{x} value=%{y} ",
+ "legendgroup": "case 1",
+ "line": {
+ "color": "#636efa",
+ "dash": "solid"
+ },
+ "marker": {
+ "symbol": "circle"
+ },
+ "mode": "lines",
+ "name": "case 1",
+ "showlegend": true,
+ "type": "scattergl",
+ "x": [
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+ },
+ {
+ "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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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Taking sum\n",
+ "hourly_data.sum().load_duration_curve(\"Sum\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3bb5e37",
+ "metadata": {},
+ "source": [
+ "## Area Plot\n",
+ "using the area plot function, you can plot the cummulative sum of the the columns in your data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "ae8f6f1f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "hovertemplate": "variable=case 1 date=%{x} value=%{y} ",
+ "legendgroup": "case 1",
+ "line": {
+ "color": "#636efa"
+ },
+ "marker": {
+ "symbol": "circle"
+ },
+ "mode": "lines",
+ "name": "case 1",
+ "orientation": "v",
+ "showlegend": true,
+ "stackgroup": "1",
+ "type": "scatter",
+ "x": [
+ "2022-01-02T00:00:00",
+ "2022-01-09T00:00:00",
+ "2022-01-16T00:00:00",
+ "2022-01-23T00:00:00",
+ "2022-01-30T00:00:00",
+ "2022-02-06T00:00:00",
+ "2022-02-13T00:00:00",
+ "2022-02-20T00:00:00",
+ "2022-02-27T00:00:00",
+ "2022-03-06T00:00:00",
+ "2022-03-13T00:00:00",
+ "2022-03-20T00:00:00",
+ "2022-03-27T00:00:00",
+ "2022-04-03T00:00:00",
+ "2022-04-10T00:00:00",
+ "2022-04-17T00:00:00",
+ "2022-04-24T00:00:00",
+ "2022-05-01T00:00:00",
+ "2022-05-08T00:00:00",
+ "2022-05-15T00:00:00",
+ "2022-05-22T00:00:00",
+ "2022-05-29T00:00:00",
+ "2022-06-05T00:00:00",
+ "2022-06-12T00:00:00",
+ "2022-06-19T00:00:00",
+ "2022-06-26T00:00:00",
+ "2022-07-03T00:00:00",
+ "2022-07-10T00:00:00",
+ "2022-07-17T00:00:00",
+ "2022-07-24T00:00:00",
+ "2022-07-31T00:00:00",
+ "2022-08-07T00:00:00",
+ "2022-08-14T00:00:00",
+ "2022-08-21T00:00:00",
+ "2022-08-28T00:00:00",
+ "2022-09-04T00:00:00",
+ "2022-09-11T00:00:00",
+ "2022-09-18T00:00:00",
+ "2022-09-25T00:00:00",
+ "2022-10-02T00:00:00",
+ "2022-10-09T00:00:00",
+ "2022-10-16T00:00:00",
+ "2022-10-23T00:00:00",
+ "2022-10-30T00:00:00",
+ "2022-11-06T00:00:00",
+ "2022-11-13T00:00:00",
+ "2022-11-20T00:00:00",
+ "2022-11-27T00:00:00",
+ "2022-12-04T00:00:00",
+ "2022-12-11T00:00:00",
+ "2022-12-18T00:00:00",
+ "2022-12-25T00:00:00",
+ "2023-01-01T00:00:00"
+ ],
+ "xaxis": "x",
+ "y": [
+ 23.140235763888892,
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+ 22.97284871031746,
+ 22.92040128968254,
+ 22.65841041666667,
+ 23.236260119047618,
+ 23.086467162698412,
+ 22.803317757936508,
+ 23.240684027777778,
+ 23.14117628968254,
+ 22.780860416666666,
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+ 22.68599761904762,
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+ 23.012166964285715,
+ 22.753407242063492,
+ 23.320685912698412,
+ 23.128423032407408
+ ],
+ "yaxis": "y"
+ },
+ {
+ "hovertemplate": "variable=case 2 date=%{x} value=%{y} ",
+ "legendgroup": "case 2",
+ "line": {
+ "color": "#EF553B"
+ },
+ "marker": {
+ "symbol": "circle"
+ },
+ "mode": "lines",
+ "name": "case 2",
+ "orientation": "v",
+ "showlegend": true,
+ "stackgroup": "1",
+ "type": "scatter",
+ "x": [
+ "2022-01-02T00:00:00",
+ "2022-01-09T00:00:00",
+ "2022-01-16T00:00:00",
+ "2022-01-23T00:00:00",
+ "2022-01-30T00:00:00",
+ "2022-02-06T00:00:00",
+ "2022-02-13T00:00:00",
+ "2022-02-20T00:00:00",
+ "2022-02-27T00:00:00",
+ "2022-03-06T00:00:00",
+ "2022-03-13T00:00:00",
+ "2022-03-20T00:00:00",
+ "2022-03-27T00:00:00",
+ "2022-04-03T00:00:00",
+ "2022-04-10T00:00:00",
+ "2022-04-17T00:00:00",
+ "2022-04-24T00:00:00",
+ "2022-05-01T00:00:00",
+ "2022-05-08T00:00:00",
+ "2022-05-15T00:00:00",
+ "2022-05-22T00:00:00",
+ "2022-05-29T00:00:00",
+ "2022-06-05T00:00:00",
+ "2022-06-12T00:00:00",
+ "2022-06-19T00:00:00",
+ "2022-06-26T00:00:00",
+ "2022-07-03T00:00:00",
+ "2022-07-10T00:00:00",
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+ "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,
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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 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
+
+
+
+
+.. 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
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