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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case 1case 2case 3case 4case 5case 6case 7case 8case 9case 10
date
2022-01-01 00:00:0036.036.036.036.036.036.036.036.036.036.0
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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 2/5] 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 3/5] 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 4/5] 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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18.752816666666668, - 147.08546666666666, - 5.586216666666666, + 36.00078333333333, + 36.001733333333334, + 0.0037166666666666667, + 1.2539833333333337, + 22.920416666666668, + 1.2537500000000001, + 5.003, + 121.66908333333333, + 8.5023, 4.419783333333333, - 0.33726666666666666, 0.004, 0.004, + 25.003666666666675, 0.004, - 0.003116666666666667, - 10.002266666666669, + 2.0024, + 0.002, 36.00128333333333, - 36, + 36.0009, 35.4, 36, 36, 36, 36, 36, - 37.50053333333333, - 136.0025, - 0.003, - 0.003, - 0.003, - 0.003, - 26.669533333333337, + 36.000299999999996, + 36.001666666666665, + 0.0025, + 57.08601666666667, + 36.00186666666667, + 6.251983333333333, + 2.0025999999999997, 0.003, - 0.7528499999999999, - 2.5025, + 50.002750000000006, + 31.002650000000003, + 5.002, + 4.918683333333333, 0.003, 0.003, - 10.002866666666668, - 37.50246666666667, - 0.002, - 0.0014, - 36.001, - 36.001, - 35.400083333333335, + 0.0021333333333333334, + 0.00075, + 36, 36, + 35.4, 36, 36, 36, 36, 36, - 36.00058333333333, - 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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 5/5] 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