Getting bins from plots jl histogram

When working with Julia, it is common to encounter situations where we need to extract the bins from a histogram plot. This can be useful for further analysis or visualization. In this article, we will explore three different ways to solve this problem.

Option 1: Using the `edges` function

One straightforward way to obtain the bins from a histogram plot in Julia is by using the `edges` function. This function returns an array of bin edges, which can be used to define the bins.


using Plots

# Generate some data
data = randn(1000)

# Create a histogram plot
histogram(data)

To extract the bins from the plot, we can simply call the `edges` function on the histogram object:


# Get the bins
bins = edges(histogram(data))

Now, the variable `bins` contains an array of bin edges, which can be used for further analysis or visualization.

Option 2: Using the `hist` function

Another way to obtain the bins from a histogram plot is by using the `hist` function. This function returns the bin counts and bin edges as separate arrays.


using StatsBase

# Generate some data
data = randn(1000)

# Create a histogram plot
hist = fit(Histogram, data)

To extract the bins from the plot, we can access the `edges` field of the histogram object:


# Get the bins
bins = hist.edges

Now, the variable `bins` contains an array of bin edges, which can be used for further analysis or visualization.

Option 3: Using the `bin_edges` function

A third way to obtain the bins from a histogram plot is by using the `bin_edges` function from the `StatsPlots` package. This function returns an array of bin edges, similar to the `edges` function.


using StatsPlots

# Generate some data
data = randn(1000)

# Create a histogram plot
histogram(data)

To extract the bins from the plot, we can call the `bin_edges` function on the histogram object:


# Get the bins
bins = bin_edges(histogram(data))

Now, the variable `bins` contains an array of bin edges, which can be used for further analysis or visualization.

After exploring these three options, it is clear that the best approach depends on the specific requirements of your project. If you are already using the `Plots` package, option 1 might be the most convenient. However, if you prefer to work with the `StatsBase` or `StatsPlots` packages, options 2 and 3 respectively provide similar functionality. Ultimately, the choice between these options should be based on your familiarity with the packages and the overall structure of your code.

Rate this post

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents