downsample | several downsampling methods for time series | Time Series Database library
kandi X-RAY | downsample Summary
kandi X-RAY | downsample Summary
Downsampling methods for time series visualisation. downsample is useful when, not extremely surprisingly, you need to downsample a numeric time series before visualizing it without losing the visual characteristics of the data.
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QUESTION
I'm working with InfluxDB v2.1.
I have this task:
...ANSWER
Answered 2022-Mar-28 at 09:53Sometimes InfluxDB fails to write but doesn't respond with an error. In those cases, success status doesn't mean that the data was written. It means that data was accepted for writing but it might fail later. Try checking the _monitoring
bucket. That bucket stores rejected data points with the error message.
For example, in my case, it was a schema mismatch. One type was expected but another was received. I was using implicit schema which means that schema was decided by the InfluxDB itself based on the data I put there. I resolved this by making schema explicit. In this case, InfluxDB returns an error right away at the moment of writing.
QUESTION
I have no loop experience and need a loop to:
- downsample a data frame
- do a t test and store the results
- extract the confidence limits
- add confidence limits to a data frame
- and redo say 100 times and append each iteration to the list. Below is the code for one iteration. I would like a data frame with 100 rows and 2 columns i.e. the upper and lower limits
ANSWER
Answered 2022-Mar-28 at 23:04You can use lapply()
, and then use do.call(rbind,...)
to a matrix, set to data.frame, and rename columns:
QUESTION
I have a large dataframe which I would like to downsample from 120hz to 40hz. It's not consistent enough though that I could just take every third row from the dataframe.
I have something like the following dataframe:
...ANSWER
Answered 2022-Feb-23 at 15:09Are you looking for this?
QUESTION
I have two arrays (vel_y,vel_z) representing velocities in the y and z directions, respectively, that are both shaped as (512,512) that I am attempting to represent using a quiver plot. Here is how I plotted the quiver:
...ANSWER
Answered 2022-Feb-23 at 10:59The simple way to do this is simply to take one point over N in each vectors given to quiver.
For a given np.array
, you can do this using the following syntax: a[::N]
. If you have multiple dimensions, repeat this in each dimension (you can give different slip for each dimension): a[::N1, ::N2]
.
In your case:
QUESTION
I'm trying to train a 1D CNN to identify specific parts of a text string.
The inputs are arrays of shape (128,1)
containing 128 characters, and the aim is for the network to classify each of the characters into a particular class. For purposes of illustration, an input array could look like this:
ANSWER
Answered 2022-Feb-02 at 22:48I think when you use UpSampling1D
each value is repeated twice. Which means the input to the last step contains pair-wise duplicated value. It would then give the same predicted class for adjancent characters. If my guess is correct, you would always see the same prediction for the 2k and 2k+1 characters.
You could confirm by inspecting the input x
in
QUESTION
I have an array with many millions of elements (7201 x 7201 data points) where I am converting the data to a greyscale image.
...ANSWER
Answered 2022-Jan-26 at 17:25This is not a complete answer to your question, but I think it should give you a start on where to go. vDSP is part of Accelerate
, and it's built to speed up mathematical operations on arrays. This code uses multiple steps, so probably could be more optimised, and it's not taking any other filters than linear into account, but I don't have enough knowledge to make the steps more effective. However, on my machine, vDSP is 4x faster than map for the following processing:
QUESTION
I'd like to use the bucket expression with groupby, to downsample on monthly basis, as the downsampling function will be deprecated. Is there a easy way to do this, datetime.timedelta only works on days and lower.
...ANSWER
Answered 2021-Dec-14 at 21:55I found a solutuion for my problem using the round expression and a groupby operation on the date column after it.
Here is some Code exmaple:
QUESTION
Let's say I have some DataArray:
...ANSWER
Answered 2021-Dec-15 at 00:16One way to do this is through DataArrayCoarsen.construct
, which allows you to more easily operate on individual windows at a time:
QUESTION
I'm trying to bin (downsample) a time series based on its timestamps. For instance:
...ANSWER
Answered 2021-Nov-05 at 22:28As mentioned in the comments, if your primary concern for not using Pandas is speed, I'd actually recommend using it, because it's not written entirely in Python, but it has many internal portions written using Cython (basically C), so they're very, very fast.
QUESTION
I have a large set of 2D points that I've downsampled into a 44x2 numpy array (array defined later). I am trying to find the bounding shape of those points which are effectively a concave hull. In the 2nd image I've manually marked an approximate bounding shape that I am hoping to get.
I have tried using alphashape and the Delauney triangulation method from here, both methods providing the same answer.
Unfortunately, I don't seem to be able to achieve what I need, regardless of the alpha parameters. I've tried some manual settings and alphaoptimize, some examples of which are below.
Is there something critical I'm misunderstanding about alphashape? The documentation seems very clear, but obviously I'm missing something.
...ANSWER
Answered 2021-Sep-15 at 04:05The plots that you attached are misleading, since the scales on the x-axis and the y-axis are very different. If you set both axes to the same scale, you obtain the following plot:
Since differences between x-coordinates of points are on the average much larger than differences between y-coordinates, you cannot obtain an alpha shape resembling your desired result. For larger values of alpha points scattered along the x-axis will not be connected by edges, since alpha shape will use circles too small to connect these points. For values of alpha small enough that these points get connected you will obtain the long edges on the right-hand side of the plot.
You can fix this issue by rescaling y-coordinates of all points, effectively stretching the plot in the vertical direction. For example, multiplying y-coordinates by 7 and setting alpha = 0.4
gives the following picture:
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