I removed the outlier and the graph makes more sense now. which contains the four features, three classes/target (type of iris plant), and 150 observations. There are several chart types allowing to visualize the distribution of a combination of 2 numeric variables. If you want to specify the same RGB or RGBA value for among the variables. if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[250,250],'reneshbedre_com-large-mobile-banner-1','ezslot_9',122,'0','0'])};__ez_fad_position('div-gpt-ad-reneshbedre_com-large-mobile-banner-1-0'); For the vertical line, the position on the x-axis should be provided. Basic Scatter plot in python First, let's create artifical data using the np.random.randint(). Other keyword arguments are passed down to You can change this style by using one of several options. We visualize the numpy array by plotting the data on the graph or making a heat map using it. Asking for help, clarification, or responding to other answers. Create basic scatter plot (2D) A scatter plot is useful for displaying the correlation between two numerical data values or two data sets. The consent submitted will only be used for data processing originating from this website. Use the xlabel () function to add x-axis labels. In Python, the matplotlib is the most important package that to make a plot, you can have a look of the matplotlib gallery and get a sense of what could be done there. You then defined the variable sugar_content to classify each drink. An object that determines how sizes are chosen when size is used. We specify the shape of the resulting array we want. matplotlib.axes.Axes.scatter(). Connecting three parallel LED strips to the same power supply. For example to save plot, use the below command. It represents data points on a two-dimensional plane or on a Cartesian system. colormapped. What is a 2D density chart? hue semantic. Note: The default edgecolors Draw a scatter plot with possibility of several semantic groupings. Is this an at-all realistic configuration for a DHC-2 Beaver? The different orange drinks he sells come from different suppliers and have different profit margins. If you would like to change your settings or withdraw consent at any time, the link to do so is in our privacy policy accessible from our home page. rev2022.12.9.43105. Get tips for asking good questions and get answers to common questions in our support portal. To plot multiple lines in one chart, we can either use base R or install a fancier package like ggplot2. Save plot to image file instead of displaying it using Matplotlib, Concentration bounds for martingales with adaptive Gaussian steps. However, not all of these points are likely to be close to the reality that the commuter observed from the data she gathered and analyzed. You then create lists with the price and average sales per day for each of the six orange drinks sold. However, the drink that costs $4.02 is an outlier, which may show that its a particularly popular product. 20122022 RealPython Newsletter Podcast YouTube Twitter Facebook Instagram PythonTutorials Search Privacy Policy Energy Policy Advertise Contact Happy Pythoning! It can be created using the scatter () method of plotly.express between 0 (transparent) and 1 (opaque). In case Note that c should not be a single numeric RGB or RGBA sequence You can plot the distribution she obtained from the data with the simulated bus arrivals: To keep the simulation realistic, you need to make sure that the random bus arrivals match the data and the distribution obtained from those data. Otherwise, value- This cycle defaults to rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])). The normalization method used to scale scalar data to the [0, 1] range if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[300,250],'reneshbedre_com-leader-4','ezslot_14',128,'0','0'])};__ez_fad_position('div-gpt-ad-reneshbedre_com-leader-4-0'); This work is licensed under a Creative Commons Attribution 4.0 International License. There should be six orange drinks, but only five round markers can be seen in the figure. hue and style for the same variable) can be helpful for making Pre-existing axes for the plot. DataFrame.plot.scatter(x, y, s=None, c=None, **kwargs) [source] # Create a scatter plot with varying marker point size and color. You can also produce the scatter plot shown above using another function within matplotlib.pyplot. style is a circle (defined as o). By the end of this tutorial, youll have learned how to use Seaborn to: How to create scatter plots in Python with Seaborn This is necessary because the plot command returns a list of line objects. Grouping variable that will produce points with different markers. This parameter defines the size of the marker. 3D scatter plot is created by using ax.scatter3D() the function of the matplotlib libra. Being able to effectively create and customize scatter plots in Python will make your data analysis workflow much easier! The Python matplotlib pyplot scatter plot is a two-dimensional graphical representation of the data. To do this, you can create random times and random relative probabilities using the built-in random module. Additionally, ymin and ymax parameters can also be Example: # Import Library import numpy as np import matplotlib.pyplot as plt # Define Data x = np.array ( [ [2, 4, 6], [6, 8, 10]]) y = np.array ( [ [8, 10, 12], [14, 16, 18]]) # Plot plt.plot (x, y) # Display plt.show () function. Before you can start working with plt.scatter () , you'll need to install Matplotlib. If full, every group will get an entry in the legend. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. How to plot a graph in Python. Matplotlib provides a very versatile tool called plt.scatter() that allows you to create both basic and more complex scatter plots. figure axes, respectively. matching will have precedence in case of a size matching with x The coordinates of each point are defined by two dataframe columns and filled circles are used to represent each point. Change marker and intermediate, Recommended Video Course: Using plt.scatter() to Visualize Data in Python, Recommended Video CourseUsing plt.scatter() to Visualize Data in Python. It seems that you have an outlier row in the array with the first coordinate close to 2.5*10^6 (which gives the point close to the right margin of the plot), while other rows have their first coordinates smaller by a few orders of magnitude. Making a 3D scatterplot is very similar to creating a 2d scatter plot, only some minor differences. It helps in making 2D plots from arrays. list of available scales, call matplotlib.scale.get_scale_names(). Below are various examples which depict how to plot 2D data on 3D plot in Python: Example 1: Using Matplotlib.pyplot.gca () function. In this example, we add the 2D density layer to the scatter plot using the geom_density_2d . The plot you created with this code is identical to the plot you created earlier with plt.scatter(). In this tutorial, all the examples will be in the form of scripts and will include the call to plt.show(). entries show regular ticks with values that may or may not exist in the is 'face'. Using the parameter marker color to create a Scatter Plot . These parameters represent the two main variables and can be any array-like data types, such as lists or NumPy arrays. style variable. For starters, we will place sepalLength on the x-axis and petalLength on the y-axis. Not relevant when the Either a pair of values that set the normalization range in data units In that case, a suitable Normalize subclass is dynamically generated Finally, you create the scatter plot by using plt.scatter() with the two variables you wish to compare as input arguments. styles. How to draw a scatter plot in Python (matplotlib)? Grouping variable that will produce points with different colors. Input data structure. The linewidth of the marker edges. Here in the digits dataset we already know that the labels range from 0 to 9, so we have 10 classes (or clusters). Fundamentally, scatter works with 1D arrays; x, y, s, and c The rest of the code remains the same, but you can now choose the colormap to use. internally. Use the pcolor () method to create a two-dimensional colour surface plot. You can see the scatter plot created by this code below: The size of the marker indicates the profit margin for each product. I want to get a scatter plot such that all my positive examples are marked with 'o' and negative ones with 'x'. Usage The parameters x and y are required, but all other parameters are optional. min, max tuple. Get more in-built colormaps here. Grouping variable that will produce points with different sizes. I have my dataset that has multiple features and based on that the dependent variable is defined to be 0 or 1. It is generally used for data visualization and represent through the various graphs. marker-less lines. Almost there! Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Change the sizes of the data points using s parameter based on the additional variable of the same length as You need to specify the no. Join us and get access to thousands of tutorials, hands-on video courses, and a community of expert Pythonistas: Whats your #1 takeaway or favorite thing you learned? What happens if you score more than 99 points in volleyball? When using scatter plots in this way, close inspection can help you explore the relationship between variables. You then plot two separate scatter plots, one with the points that fall within the distribution and another for the points that fall outside the distribution. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. the complete value range of the supplied data. If you really have only one (or just a few) outliers, you can remove them from the array and possibly plot them separately. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. Fundamentally, scatter works with 1-D arrays; x, y, s, and c may be input as 2-D arrays, but within scatter they will be flattened. Find object by id in an array of JavaScript objects. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. The caf owner has found this exercise very useful, and he wants to investigate another product. This parameter is used to customize the shape of the marker. By default, a linear scaling is What's the simplest way to print a Java array? Data Visualization with Matplotlib and Python Scatterplot example Example: import numpy as np import matplotlib.pyplot as plt # Create data N = 500 x = np.random.rand (N) y = np.random.rand (N) colors = (0,0,0) area = np.pi*3 # Plot plt.scatter (x, y, s=area, c=colors, alpha=0.5) plt.title ('Scatter plot pythonspot.com') plt.xlabel ('x') Disclaimer. and instantiated. Matplotlib can create 3d plots. For horizontal lines, the position on the y-axis should be provided. In the scatter plots youve created so far, youve used three colors to represent low, medium, or high sugar content for the drinks and cereal bars. List or dict arguments should provide a size for each unique data value, A sequence of colors of length n. A single color format string. 3d scatter plot python. which forces a categorical interpretation. Cookie policy This kind of plot is useful to see complex correlations between two variables. Not relevant when the To represent a scatter plot, we will use the matplotlib library. Youve learned about the main input parameters to create scatter plots in the sections above. The possible values for marker color are: A single color format string. The alpha blending value, between 0 (transparent) and 1 (opaque). The parameter s denotes the size of the marker. 2022 Data science blog. one of "linear", "log", "symlog", "logit", etc. Python hosting: Host, run, and code Python in the cloud! Download Jupyter notebook: scatter.ipynb. You set the most likely arrival time to a value of 1 by dividing by the maximum value. plotted. A scalar or sequence of n numbers to be mapped to colors using Specified order for appearance of the size variable levels, if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[300,250],'reneshbedre_com-box-4','ezslot_7',117,'0','0'])};__ez_fad_position('div-gpt-ad-reneshbedre_com-box-4-0'); The plt.show() is necessary to visualize the plot. size variable is numeric. install python packages. It is open-source, cross-platform for making 2D plots for from data in array. Curated by the Real Python team. The relationship between x and y can be shown for different subsets If given, the following parameters also accept a string s, which is A scatter plot (also called an XY graph, or scatter diagram) is a two-dimensional chart that shows the relationship between two variables. A scale name, i.e. if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[336,280],'reneshbedre_com-large-leaderboard-2','ezslot_6',147,'0','0'])};__ez_fad_position('div-gpt-ad-reneshbedre_com-large-leaderboard-2-0');The colormap instance can be used to map data values to RGBA color for a given colormap. behave differently in latter case. Otherwise, call matplotlib.pyplot.gca() A caf sells six different types of bottled orange drinks. To create scatterplots in matplotlib, we use its scatter function, which requires two arguments: x: The horizontal values of the scatterplot data points. : Thanks for contributing an answer to Stack Overflow! may be input as N-D arrays, but within scatter they will be In later sections, youll learn how to further customize your plots to represent more complex data using more than two dimensions. It can be a, This parameter represents the color of the markers. @nilsinelabore Yes, you can use numpy in a similar way: Thank you. It is used for plotting various plots in Python like scatter plot, bar charts, pie charts, line plots, histograms, 3-D plots and many more. We can also generate arrays using NumPy's random number generator. otherwise they are determined from the data. The plots help in understanding trends, discovering patterns, and find relationships between data. The example scatter plot above shows the diameters and . Get a short & sweet Python Trick delivered to your inbox every couple of days. choose between brief or full representation based on number of levels. We will learn about the scatter plot from the matplotlib library. subsets. For non-filled markers, edgecolors is ignored. and y. and clustering analysis for exploring the relationship The tuples for low, medium, and high represent green, yellow, and red, respectively. You can do so using Pythons standard package manger, pip, by running the following command in the console : Now that you have Matplotlib installed, consider the following use case. 2D Plotting. This article introduces the use of matplotlib to draw different two-dimensional graphics. Manage SettingsContinue with Recommended Cookies. Find centralized, trusted content and collaborate around the technologies you use most. The default colormap is viridis. Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. The dots in the plot are the data values. Complete this form and click the button below to gain instant access: NumPy: The Best Learning Resources (A Free PDF Guide). Now that you know how to create and customize scatter plots using plt.scatter(), youre ready to start practicing with your own datasets and examples. In particular, numeric variables reshaped. In the United States, must state courts follow rulings by federal courts of appeals? int i, j, x, y; char plot[21][75] = {' 2) Resize blue rectangle to set ruler for axis scaling Interactive, free online graphing calculator from GeoGebra . How do you plot a scatter plot for an array result_array of shape (1087, 2) that looks like this: plt.scatter() has many addional options, see the documentation for details. You can find the list of all markers you can use in the documentation page on markers. h =plt.hist2d(x, y) plt.colorbar(h[3]) 3D plotting. Variables that specify positions on the x and y axes. Some of our partners may process your data as a part of their legitimate business interest without asking for consent. You dont need to be familiar with Matplotlib to follow this tutorial, but if youd like to learn more about the module, then check out Python Plotting With Matplotlib (Guide). Setting to False will draw marker-less lines. Set the linewidth and edgecolor to 2 and black, respectively. Basically, the scatter () method draws one dot for each observation. The exception is c, which will be flattened only if its But there is one problem with the last plot you created that youll explore in the next section. Using redundant semantics (i.e. interpret and is often ineffective. This allows grouping within additional categorical variables, and plotting them across multiple subplots. Markers are specified as in matplotlib. To run the app below, run pip install dash, click "Download" to get the code and run python app.py. No spam ever. One of the cereal bar data points is hiding an orange drink data point. Create a 3D scatter plot using three features from the iris dataset. used, mapping the lowest value to 0 and the highest to 1. In matplotlib, you can create a scatter plot using the pyplot's scatter () function. These examples will use the tips dataset, which has a mixture of numeric and categorical variables: Passing long-form data and assigning x and y will draw a scatter plot between two variables: Assigning a variable to hue will map its levels to the color of the points: Assigning the same variable to style will also vary the markers and create a more accessible plot: Assigning hue and style to different variables will vary colors and markers independently: If the variable assigned to hue is numeric, the semantic mapping will be quantitative and use a different default palette: Pass the name of a categorical palette or explicit colors (as a Python list of dictionary) to force categorical mapping of the hue variable: If there are a large number of unique numeric values, the legend will show a representative, evenly-spaced set: A numeric variable can also be assigned to size to apply a semantic mapping to the areas of the points: Control the range of marker areas with sizes, and set lengend="full" to force every unique value to appear in the legend: Pass a tuple of values or a matplotlib.colors.Normalize object to hue_norm to control the quantitative hue mapping: Control the specific markers used to map the style variable by passing a Python list or dictionary of marker codes: Additional keyword arguments are passed to matplotlib.axes.Axes.scatter(), allowing you to directly set the attributes of the plot that are not semantically mapped: The previous examples used a long-form dataset. You can change the shape of the marker for one of the scatter plots: You keep the default marker shape for the orange drink data. Default is rcParams['lines.markersize'] ** 2. The alpha takes a value Many of the customers of the caf like to read the labels carefully, especially to find out the sugar content of the drinks theyre buying. Learn Linux command lines for Bioinformatics analysis, Detailed introduction of survival analysis and its calculations in R, Perform differential gene expression analysis of RNA-seq data using EdgeR, Perform differential gene expression analysis of RNA-seq data using DESeq2. This parameter is ignored if c is RGB(A). Creating Scatter Plots With Pyplot, you can use the scatter () function to draw a scatter plot. 2 . To learn more, see our tips on writing great answers. Additionally, xmin and xmax parameters can also be Scatter plot needs arrays for the same length, one for the value of x-axis and other value for the y-axis. The default treatment of the hue (and to a lesser extent, size) Here, we are only plotting a single line, so we simply want the first (i.e., zeroth) object in the list of lines. y plot(x, y) #add line of best fit to scatter plot abline(lm(y ~ x)) Method 2: Plot Line of Best Fit in ggplot2. Here are the variables being represented in this example: The ability to represent more than two variables makes plt.scatter() a very powerful and versatile tool. Learn how to Scatterplots are an essential type of data visualization for exploring your data. You can show this additional information in the scatter plot by adjusting the size of the marker. the data range that the colormap covers. This sets up a line object with the desired attributes, which in this case are that it's coloured black and has a line weight of 2. This alias is generally used by convention to shorten the module and submodule names. You then plot both scatter plots in a single figure. Create Random Forests Plots in Python with scikit. because that is indistinguishable from an array of values to be We pass c parameter to set the variable represented by color and cmap parameter to set the colormap. The profit margin is given as a percentage in this example: You can notice a few changes from the first example. vmin/vmax when a norm instance is given (but using a str norm of points you require as the arguments. To scatter a 2D numpy array in matplotlib, we can take the following steps Steps Set the figure size and adjust the padding between and around the subplots. Setting to True will use default markers, or Numpy's np.random module contains rand, randn and randint functions that can be used to generate different random numbers from different distributions.. rand - generates random samples from uniform distribution between 0 and 1. I am using python and here is the code for the beginning.. "/> A scatter plot of y vs x with varying marker size and/or color. plt.scatter (cmap='Set2) Read: Matplotlib invert y axis. three (3D) numerical variables.if(typeof ez_ad_units!='undefined'){ez_ad_units.push([[728,90],'reneshbedre_com-box-3','ezslot_3',114,'0','0'])};__ez_fad_position('div-gpt-ad-reneshbedre_com-box-3-0'); Scatter plots are used in numerous applications such as correlation If brief, numeric hue and size not in relation to your actual location within the 3D environment.OpenGL and Glut $10-20 USD Freelancer Jobs OpenGL OpenGL and Glut I need someone expert in openGL and glut to create 3D object (python) Skills: OpenGL, Python About the Client: ( 11 reviews ) MORGANTOWN, United States Project ID: #28138825 . plt.scatter () has many addional options, see the documentation for details. Python3 # importing numpy package It offers a range of different plots and customizations. A convenient way to plot data from a table is to pass the table to the scatter function and specify the variables you want to plot. List or dict values Representation using 2D histograms. You can visualize this relationship as follows: In this Python script, you import the pyplot submodule from Matplotlib using the alias plt. don't vary in size or color. imply categorical mapping, while a colormap object implies numeric mapping. You can filter the randomly generated points by keeping only the ones that fall within the probability distribution. cmap and norm. You can add color to the markers in the scatter plot to show the sugar content of each drink: You define the variables low, medium, and high to be tuples, each containing three values that represent the red, green, and blue color components, in that order. Matplotlib library is used for making 2D plots from data in arrays. or the text shorthand for a particular marker. used for covering the portion of the figure. The edge color of the marker. Example Should he also stop stocking the cheapest of the drinks to boost the health credentials of the business, even though it sells well and has a good profit margin? One of the data points for the orange drinks has disappeared. Watch it together with the written tutorial to deepen your understanding: Using plt.scatter() to Visualize Data in Python. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. The retailer will pay the commission at no additional cost to you. Use the ylabel () function to add a y-axis label. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. You can visualize more than two variables on a two-dimensional scatter plot by customizing the markers. Is there any reason on passenger airliners not to have a physical lock between throttles? reneshbe@gmail.com, #buymecoffee{background-color:#ddeaff;width:600px;border:2px solid #ddeaff;padding:50px;margin:50px}. Terms and conditions Youll find the answer in the rest of this tutorial. The owner wants to understand the relationship between the price of the drinks and how many of each one he sells, so he keeps track of how many of each drink he sells every day. To display the figure, use show () method. This function can be used for quickly checking modeling. Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. I will use the example of the iris dataset Specify the order of processing and plotting for categorical levels of the both You can get the most out of visualization using plt.scatter() by learning more about all the features in Matplotlib and dealing with data using NumPy. Then use the plt.scatter() function to draw a scatter plot using matplotlib. This probability distribution can be represented using NumPy and np.linspace(): Youve created two normal distributions centered on 15 and 45 minutes past the hour and summed them. Heres the resulting scatter plot: All the plots youve plotted so far have been displayed in the native Matplotlib style. can be individually controlled or mapped to data.. Let's show this by creating a random scatter plot with points of many colors and sizes. In that case the marker color is determined size matches the size of x and y. Normalization in data units for scaling plot objects when the XKCD even has a comic about it. To view the purposes they believe they have legitimate interest for, or to object to this data processing use the vendor list link below. Answer to the updated question: It seems that you have an outlier row in the array with the first coordinate close to 2.5*10^6 (which gives the point close to the right margin of the plot), while other rows have their first coordinates smaller by a few orders of magnitude. In this section, youll explore how to mask data using NumPy arrays and scatter plots through an example. The scatter plot can be used for visualizing the multivariate data. Matplotlib scatter marker Matplotlib provides a pyplot module for data visualization. These are RGB color values. The basic scatter. Specified order for appearance of the style variable levels The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Since you have some points with negative first coordinates, you would need to use the symmetric logarithmic scale - which is logarithmic in both positive and negative directions of the x-axis. fit #only for illustration purposes; does not make real sense print (regression. Not the answer you're looking for? The independent variable or attribute is plotted on the X-axis, while the dependent variable is plotted on the Y-axis. You may want to change this as well. If you can create scatter plots using plt.plot(), and its also much faster, why should you ever use plt.scatter()? Minitab also draws a reference line at the overall mean. represent numeric or categorical data. Scatter plots in Dash Dash is the best way to build analytical apps in Python using Plotly figures. plt.scatter() offers even more flexibility in customizing scatter plots. When running the example above on my system, plt.plot() was over seven times faster. You can achieve this by creating a mask for the scatter plot: The variables in_region and out_region are NumPy arrays containing Boolean values based on whether the randomly generated likelihoods fall above or below the distribution y. If you like to save the plot to a file, you need to call pyplot.savefig() You can use any array-like data structure for the data, and NumPy arrays are commonly used in these types of applications since they enable element-wise operations that are performed efficiently. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Hi bb1, thanks for your answer but the plot returned looks kind of weird? using the cmap parameter. Since R2021b. - an alternative to plt.plot() which gives you more control on setting colours based on another variable. Instead of lists, youre now using NumPy arrays. If you have any questions, comments or recommendations, please email me at This article is written by A Aryan verma Author & Contributors Author A Updated - 21 Nov 2022 8 mins read Published : 21 Nov 2022 In Jupyter notebook, we could show the figure directly within the notebook and also have the interactive operations like . Plot 2D data on 3D plot; Demo of 3D bar charts; Create 2D bar graphs in different planes; . Heres the scatter plot produced by this code: The caf owner has already decided to remove the most expensive drink from the menu as this doesnt sell well and has a high sugar content. Import the matplotlib.pyplot library into your project. Example: Using the c parameter to depict scatter plot with different colors in Python. These Can be either categorical or numeric, although color mapping will The data points that fall above the distribution are not representative of the real data: Youve segmented the data points from the original scatter plot based on whether they fall within the distribution and used a different color and marker to identify the two sets of data. A commuter whos keen on collecting data has collated the arrival times for buses at her local bus stop over a six-month period. Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills. This function takes in 2 variables to plot - we'll use the first 2 columns of our xyz array: We take your privacy seriously. Each data is represented as a dot point, whose location is given by x and y columns. The scatter () function plots one dot for each observation. Using relplot() is safer than using FacetGrid directly, as it ensures synchronization of the semantic mappings across facets. I am using python and here is the code for the beginning. How to draw a scatter plot in Python (matplotlib)? Where does the idea of selling dragon parts come from? Python provides one of a most popular plotting library called Matplotlib. To create a scatter plot, we use scatter () method. Defaults to None. It is possible to show up to three dimensions independently by Matplotlib Library Matlplotlib is a library in python which is used for data visualization and plotting graphs. You can create two scatter plots (grid of subplots) within a same figure. In this example, you use the profit margin as a variable to determine the size of the marker and multiply it by 10 to display the size difference more clearly. 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