# Exploring ALMA Image Data with Viridis Colormap

• dcc
In summary, the code generates a 2D Gaussian with given x- and y-widths, convolves it with an image, and plots the resulting convolved image. It first imports the necessary libraries and defines a function to generate a 2D Gaussian. Then it creates a mesh grid for the x and y coordinates of the image and defines parameters for the Gaussian. The Gaussian is then calculated and plotted. Finally, the Gaussian is convolved with the image using the signal.convolve2d function
dcc
Homework Statement
I'm making an image from ALMA data for a presentation. My moment 0 image (fits file) is very faint when coded into python. How do I get it to be brighter than the background so it's more visible?

I've tried the vmin, vmax within the plt.imshow() with no change and also so many other things I can't even recall anymore.
Relevant Equations
image_data = fits.getdata(alma_image, ext=0)
plt.figure()
plt.imshow(image_data, cmap='viridis')
plt.xlim(540,640)
plt.ylim(540,640)
plt.colorbar(extend='both')
plt.clim(0, 1)
plt.show()
image_data = fits.getdata(alma_image, ext=0)
plt.figure()
plt.imshow(image_data, cmap='viridis', vmin=-4, vmax=4)
plt.xlim(540,640)
plt.ylim(540,640)
plt.colorbar(extend='both')
plt.clim(0, 1)
plt.show()

§ Output> § Markdown### 3.2.2§ Codedef gauss2D(x, y, sigma_x, sigma_y): """ Generate a 2D Gaussian with given x- and y-widths Parameters ---------- x: float x position of peak y: float y position of peak sigma_x: float width in x direction sigma_y: float width in y direction Returns ------- 2D array 2D Gaussian """ return np.exp(-(x**2/sigma_x**2 + y**2/sigma_y**2))# Generate mesh grid for x and y coordinatesx_y = np.mgrid[0:image_data.shape[0], 0:image_data.shape[1]]# Define parameters for Gaussianx_mean = image_data.shape[0] / 2y_mean = image_data.shape[1] / 2sigma_x = 10sigma_y = 10# Calculate the Gaussiangaussian_image = gauss2D(x_y[0] - x_mean, x_y[1] - y_mean, sigma_x, sigma_y)# Plot the Gaussianplt.figure()plt.imshow(gaussian_image, cmap='viridis', vmin=0, vmax=1)plt.colorbar()plt.clim(0, 1)plt.xlabel('$x$')plt.ylabel('$y$')plt.show()§ Output> § Markdown### 3.2.3§ Code# Convolve the Gaussian with the imageconvolved_image = signal.convolve2d(image_data, gaussian_image, boundary='wrap', mode='same')# Plot the convolved imageplt.figure()plt.imshow(

## 1. What is ALMA and why is it important?

ALMA (Atacama Large Millimeter/submillimeter Array) is a powerful radio telescope located in the Atacama Desert in Chile. It is used to study the universe at millimeter and submillimeter wavelengths, allowing scientists to observe objects that are invisible to optical telescopes. ALMA is important because it has revolutionized our understanding of the universe, providing new insights into the formation of stars, galaxies, and planets.

## 2. What is a Viridis colormap and how is it used?

A Viridis colormap is a color scheme that maps numerical values to colors, typically used in data visualization. It is designed to be perceptually uniform, meaning that the colors are evenly spaced and the changes in color correspond to changes in the data. In the context of exploring ALMA image data, the Viridis colormap can be used to represent different intensities of radiation emitted by astronomical objects.

## 3. How do I access ALMA image data?

ALMA image data is publicly available through the ALMA Science Archive (ASA). Users can search for specific data sets or browse through the archive to find data of interest. Once a data set is selected, it can be downloaded in various formats, including FITS (Flexible Image Transport System) files that can be opened and analyzed using specialized software.

## 4. What can I learn from exploring ALMA image data with a Viridis colormap?

Exploring ALMA image data with a Viridis colormap can reveal valuable information about the physical properties and structures of astronomical objects. By visualizing the data in different ways, patterns and trends can be identified, leading to new insights and discoveries. Additionally, the use of a Viridis colormap allows for a more intuitive understanding of the data, as colors can represent different intensities or wavelengths of radiation.

## 5. Are there any limitations to using a Viridis colormap with ALMA image data?

While the Viridis colormap is a powerful tool for visualizing ALMA image data, it is important to note that it may not be suitable for all types of data. For example, if the data has a large dynamic range or contains outliers, the use of a Viridis colormap may not accurately represent the data. It is always important to carefully consider the data and choose an appropriate color scheme for visualization.

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