spectral | Python module for hyperspectral image processing | Computer Vision library
kandi X-RAY | spectral Summary
kandi X-RAY | spectral Summary
Python module for hyperspectral image processing
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Top functions reviewed by kandi - BETA
- Calculate k - means clustering
- K - means clustering
- Display a percentage
- String representation of the table
- Display an image
- Set the range
- Get color metadata
- Get the color of the source
- Import spectrometer data files
- View a hypercube
- Create a spectral library
- Compute principal components
- Save an image
- Imports files from files
- Key event handler
- Get the spectrum for a given sample
- Classify an image
- Load the image
- Creates a sqlite database
- Compute a resampling matrix
- Open an ENVI file
- Handles keyboard events
- Train the model
- Load textures
- View an NDWindow
- Import ECOSTRESS files
spectral Key Features
spectral Examples and Code Snippets
Lines of Code : 125License : Non-SPDX (Apache License 2.0)
Copydef linear_to_mel_weight_matrix(num_mel_bins=20,
num_spectrogram_bins=129,
sample_rate=8000,
lower_edge_hertz=125.0,
upper def exponential_power_convolution_kernel(
grid_shape,
length_scale,
power=None,
divisor=None,
zero_inflation=None,
):
"""Make an exponentiated convolution kernel.
In signal processing, a [kernel]
(https://en.wikipedia.org/w def hamming_window(window_length, periodic=True, dtype=dtypes.float32,
name=None):
"""Generate a [Hamming][hamming] window.
Args:
window_length: A scalar `Tensor` indicating the window length to generate.
periodic: A b diag([1. / (1.+ w_k*i) ...])
linalg.inv(diag(1. + w_k*s2))
x = np.linspace(0,355/113, 101)
p = plt.get_cmap('plasma')
n = 23
for i in range(n):
plt.plot(x, np.sin(x-i/2/n), color=p(i/(n-1)))
X_small, y_small = make_circles(n_samples=(1000,2000), random_state=3,
noise=0.07, factor = 0.1)
X_large, y_large = make_circles(n_samples=(1000,2000), random_state=3,
noise=0.07, factor = 0.6)
y_large[y_large==1] = 2
df = pd.DataFrame(naggrnyl agsunset blackbody bluered blues blugrn bluyl brwnyl
bugn bupu burg burgyl cividis darkmint electric emrld
gnbu greens greys hot inferno xTrain = np.reshape(xTrain, (xTrain.shape[0], 1, xTrain.shape[1]))
xTest = np.reshape(xTest, (xTest.shape[0], 1, xTest.shape[1]))
xTrain = np.reshape(xTrain, (xTrain.shape[0], xTrain.shape[1], 1))
xTest = np.reshau1_norm = u1 / sum(u1)
u2_norm = u2 / sum(u2)
u1_norm = u1 / np.linalg.norm(u1)
# Make Graph
G = nx.Graph()
i = 0
for i in range(0, len(X)):
j = 0
for affinity in clustering.affinity_matrix_[i]:
G.add_edge(tuple(X[i]), tuple(X[j]), weight = affinity)
j += 1
i += 1
# Draw graph in moon shape
pos = {node_Community Discussions
Trending Discussions on spectral
QUESTION
I have created a confusion matrix using the following code:
...ANSWER
Answered 2021-Apr-27 at 18:43Try to add this line after the first geom_tile
QUESTION
This example code gives me everything I want except for the colorbar labels.
...ANSWER
Answered 2021-Jun-06 at 17:38Not a perfect solution but maybe it fits your needs:
- To show the endpoints add
show.limits=TRUEas I already suggested in my comment. - To get rid of the intermediate labels I make use of a custom labeller function. This function is called two times by the scale. Once for the default intermediate breaks (which in almost(!!) all cases is a vector of length > 2) and once for the limits (which is a vector of length 2). Hence I check for the length of the passed vector and keep only the labels for the "limits". But keep in mind that this is only a kind of heuristic which may fail in extreme special cases.
QUESTION
from my knowledge, Power Spectral Density (PSD) should stay relatively constant with the total time sampled (or aka. N-points sampled), however I have having trouble obtaining this result.
As I know from Discrete Fourier Transform (DFT), the amplitude normalization is 1/N. (e.g Amplitude Spectrum = DFT/N). However, from various sources, the PSD is defined as (DFT * DFT-conjugate / N).
How can this be possible? It is true that the Amplitude Spectrum has a 1/N normalization constant, then shouldn't the PSD have a 1/N^2 normalization constant (since DFT is proportional to N and so is its conjugate).
More specifically, I am trying to calcuated the PSD of a continuous electric field wave using the Eq. 9 of this paper. However I can't make sense of it's constants infront of the DFT since the factors of N's cancel out leaving behind only the summation of the window function squared. I tested this result and found that the PSD does not stay relatively constant with sampling size.
In summary, I have having troubles since my PSD varies with the amount of total time of the signal sampled. Any help would be great, thanks!
...ANSWER
Answered 2021-May-27 at 03:29I've found the PSD of a time-series does increase linearly with the number of points sampled, N, however, an appropriately FITTED function (or some sort of averaging) allows the PSD to remain constant with N. One would then take the PSD at a point on this fitted function.
This is a direct result of conserving the area of a curve, AKA Plancherel's theorem.
QUESTION
I am trying plot 8 ggplot objects with ggarrange such that it plots 4 ggplot objects in each row. The code works fine, but now I am wanting the plot to be plotted in a way that the the plots occupy the extra white space between the label and the plot, for example the plot occupies the white space between label A and that plot, and so on for the rest of the plots. Basically, I want to stretch the plots, so all the space available in the grid is used. How can I do this?
ANSWER
Answered 2021-May-12 at 07:36I found another way to reduce the margins, i.e. to minimize the space between the plot label and the plot itself. What I did was that once ggarrange displayed the plots in the plot section of RStudio.
I clicked on on the Zoom button, and the plots then got displayed in a popup window, which showed the plots with reduced space between the labels and the plots. And I believe, this can be further modified by adjusting the size of the popup window.
I used this approach, because I had a lot of plots in ggarrange, and one of these plots contained a raster with thousands of pixels, so each time I tried something different with the code, ggarrange took a lot of time, thus reducing the performance of my computer.
QUESTION
I'm trying to get the alt texts from class="css-cku98t" that are inside div (class_='css-8atqhb')[0], but I'm stuck, I tried almost everything.
ANSWER
Answered 2021-May-11 at 18:06Since 'alt' is an attribute of a img element, you can first search for all 'img' elements before querying the 'alt' attribute for each found element:
QUESTION
I'm new to python and trying to plot the PSD in separate plots for each electrode of my EEG dataset via a for loop. The title of the plot should include the respective electrode name.
Here is the code I use to load the data from a .txt file:
...ANSWER
Answered 2021-May-11 at 11:15The response from @Mr.T is really helpful!!
Use f-string formatting plt.title(f'PSD: power spectral density for {columns}')? You probably will also benefit from getting familiar with subplots and axis objects. – Mr. T
QUESTION
I have a stacked bar chart in Chart.js 2.9.4.
On hovering, the tooltip's tip is positioned at the top-center of each bar, but the tooltip itself is rendered to the right and blocks the view of other bars.
I'd like to draw the tooltip above the bar. Is there a way to do this?
(Here's the raw code as required by StackOverflow)
...ANSWER
Answered 2021-Apr-28 at 21:36QUESTION
I have a huge data frame with multiple columns that I would like to make multiple tsne plot based on two first columns and colored by other variables in each plot. Here is a sample of my data frame like this:
...ANSWER
Answered 2021-Apr-26 at 12:30You can easily do this with the wonderful patchwork package:
QUESTION
I am trying to convert a netCDF file from GOES Full disk to geotiff but have an error on last step.
Apparently the process of generating the tif from NC works and fulldisk.tif is generated but not georeferenced and I need it to overlay in a leaflet map.
...ANSWER
Answered 2021-Apr-25 at 09:52Just replaced
-dstnodata -999.0
with
-dstnodata -999
forcing the destination no data value to be integer, and got
QUESTION
If I have an image in the form of [556,556] PX and I would like to plot a certain range of them so say:
Image Size: [556,556]
Plot -> XPixels [224,300] YPixels [224,300]
This was my attempt and it does sort-of what I need it to do, I have the correct pixels selected but unfortunately this only labels the given range and doesn't actually plot the range. Understandably the next part of this would be to plot the new range given the image but how would I go about this?
...ANSWER
Answered 2021-Apr-19 at 19:17Just pass the array sub area to imshow.
plt.imshow(image[y_start:y_end, x_start:x_end])
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install spectral
You can use spectral like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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