Histograms¶

Histograms are an essential tool for visualizing the distribution of a variable within your tabular data. In this guide, we'll demonstrate how to create a histogram using MicPy and Pandas by analyzing data from a MICRESS grain growth simulation.

Overview of the Data¶

The dataset we'll be using is stored in a tabular file named T10_01_GrainGrowth_2D.TabGD. This file contains data from a grain growth simulation, with columns that include:

• Simulation time [s]: The elapsed time since the start of the simulation.
• Gr. Nb.: A unique identifier for each grain.
• Nb. of Neighbours: The number of neighboring grains for each grain.

Below is a sample structure of the dataset:

Step 1: Importing the Required Modules¶

We begin by importing the tab module from MicPy, which will allow us to read the tabular file and process the data.

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from micpy import tab

Step 2: Loading the Data¶

Next, we load the data from T10_01_GrainGrowth_2D.TabGD into a Pandas DataFrame.

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df = tab.read("T10_01_GrainGrowth_2D.TabGD")

Step 3: Filtering Data for the Initial Timestep¶

Since we are interested in analyzing the initial timestep, we filter the DataFrame to include only the rows where the simulation time is 0 seconds.

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timestep = df[df["Simulation time [s]"] == 0]

Step 4: Extracting the Relevant Column¶

From the filtered DataFrame, we extract the Nb. of Neighbours column, which contains the number of neighbors for each grain at the initial timestep.

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neighbors = timestep["Nb. of Neighbours"]

Step 5: Defining Histogram Bins¶

To ensure that each bin in our histogram is centered on an integer value, we define the bin edges by shifting the integer values by -0.5.

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bins = [x - 0.5 for x in range(neighbors.min(), neighbors.max() + 2)]

Step 6: Plotting the Histogram¶

Using the plot() method of the DataFrame object, we create a histogram to visualize the distribution of the number of neighbors.

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ax = neighbors.plot(kind="hist", bins=bins)

Step 7: Customizing the X-Axis¶

To enhance the readability of the histogram, we adjust the x-axis ticks to display integer values that correspond to the number of neighbors.

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ax.set_xticks(range(neighbors.min(), neighbors.max() + 1))

Step 8: Adding Titles and Labels¶

Finally, we add a title, labels for the axes, and a grid to the plot to make it more informative and easier to interpret.

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ax.set_title("Histogram of the number of grain neighbors")
ax.set_xlabel("Number of grain neighbors")
ax.set_ylabel("Frequency")
ax.grid(axis="y", linestyle=":")

The resulting histogram shows the distribution of the number of neighbors for grains at the initial timestep.

Conclusion¶

The resulting histogram provides a clear visual representation of the distribution of the number of neighboring grains at the initial timestep of the simulation.

Below is the complete code used to create the histogram:

from micpy import tab

# Load the data
df = tab.read("T10_01_GrainGrowth_2D.TabGD")

# Filter for the initial timestep
timestep = df[df["Simulation time [s]"] == 0]

# Extract the number of neighbors
neighbors = timestep["Nb. of Neighbours"]

# Define the bins
bins = [x - 0.5 for x in range(neighbors.min(), neighbors.max() + 2)]

# Plot the histogram
ax = neighbors.plot(kind="hist", bins=bins)

# Customize the x-axis
ax.set_xticks(range(neighbors.min(), neighbors.max() + 1))

# Add titles and labels
ax.set_title("Histogram of the Number of Grain Neighbors")
ax.set_xlabel("Number of Grain Neighbors")
ax.set_ylabel("Frequency")
ax.grid(axis="y", linestyle=":")