Interpolation - Science topic

I found this error Error at k-point 33 ndimwin 30 num_wann 32.

First you remove the old version then upload new version, on line you can edit only title, once you upload you cannot edit.

Dear Alaleh

If you are sure of the 100% purity of your homemade standard sample and the absence of interfering impurity in it, and the dilution is also done with the special buffer of the kit, please show the linear equation of the two standard graphs. Is the difference between the two graphs only in the y-intercept or are the slope of the lines also different?

Your question is strictly related to the Nyquist theorem. Any wavenumber greater than pi/h cannot be resolved. Increasing the accuracy dose not solve the matter, you have to reduce h.

Any reconstruction between two nodes is arbitrary, this is the boundary of the discrete world of computation.

You can uses remapdis o remapbil for regular grids. The interpolation for both methods are very same. You can analized performance compared reanalysis with local meteorological data. The metrics are used common RSME, Bias, Taylor Diagram.

Best regards Axel

LAMMPS will be able to give you the intermediate configurations from the NEB calculations.

I've attached a PNG version of your file 'paradeep-imd.eps 99.61 KB' - 'EPS', ( Encapsulated PostScript ) is not a very common image format. I haven't sseen one for a long time, they were prevalent back in the 80s, and 90s ( https://www.adobe.com/creativecloud/file-types/image/vector/eps-file.html#history-of-the-eps-file ).

Differents authors, such as Torma et al., 2015 recommended used fir reggriding of precipitation inverse of distance weithing (idw). If you use Climate Data Operator (CDO) you can use its command:

cdo remapdis,gridfile infile.nc outfile.nc

Please, you read CDO User Manual.

Best regards,

Axel

please be sure that the physical model you defined particularly at tht interface of the boundaries are correct.

Gáspár Albert INRE:"idea', if you found it useful, 'recommend' the answer so that others down the line can discover it.

What follows has a huge caveat, the specifics of your work is way over my head, but my work does have an aspect of dealing efficiently with all sorts of coordinate systems and data representations, so the following are more for possibly sparking trains of thought which someone far more capable than me might find useful - somewhat inspired by the false dichotomy between vector and raster formats in a GIS.

The 'spherical' issue is as old as geography itself, and up until now has been addressed piecemeal mostly by projecting geographic coordinate systems, and the performance limitations of computational infrastructure available. Relatively recently, the notion of DDGs (Discrete Global Grids) is undergoing an explosion of refinement as a 'sphere' ( ... and 'sphere-ish) as a recursive n-dimensional data representation (cursory, rough overview at https://spatialparalysis.xyz/blog/dggs-eli5/ but a quick Google Scholar finds more detailed descriptions: https://scholar.google.com/scholar?lookup=0&q=A+Review+of+the+Research+on+Discrete+Global+Grid+Systems+in+Digital+Earth ). Although on first appearance it would seem complex to calculate through these fields, there are now methods which mitigate this. These are already making their way into weather modeling ( https://www.abccolumbia.com/2019/01/11/ibm-to-deliver-worlds-highest-resolution-weather-model/ ).

From my naive reading of your project, it seemed to be analogous to a sort of 'weather' model, except with 'rock' instead of 'gas'.

The other vauge 'hunch' is how modern GPUs use quaternion representations and maths to short circuit the need for Cartesian calculations and transformations (quaternion interpolation) - and not just the 'spatial' sorts of computation, but for physics engines ( collision detection, etc. like when there are two spinning irregular objects with different rotational axes).

Another factor for a smooth visualisation of an interpolated result, is the data used for the interpolation.

If the data used are "poor", i.e. unable to give high quality interpolation result, it is almost for sure to get an expected poor visualisation result as well.

BEFORE

1. Data verification:

2. Data preprocessing:

3. Model selection:

4. Validation and cross-validation:

5. Sensitivity analysis:

6. Error estimation:

After applying interpolation or extrapolation techniques, additional quality assurance measures can be taken:

1. Result validation

2. Sensitivity analysis

3. Result visualization:

By following these quality assurance measures, the accuracy and reliability of the interpolation or extrapolation results can be improved, ensuring that the derived values are as valid and useful as possible.

That is a vast topic (especially since the term 'robot' has thousands of variations), all with lots of dependencies. What references have you read, as a starting point?

Do you have a sample aerosol.data file? I don't known how to customize this file,thanks a lot

There is a point outside your study area (located in the northern part), you should consider either removing it or correct its location. I believe you will have a better luck using the 87 stations.

As for the Kriging with External Drift (KED), it suffers from expensive computational cost, as it computes Kriging weights locally. As you can understand, the bigger the study area, the higher the computational time required for the interpolation. Compared with KED, area-to-point regression Kriging (ATPRK) is more efficient and you can incorporate multiple covariates. This is something you need to consider.

the criterion is the dispersion less than 4A2

El método de INTERPOLACION de Krigin resulta más apropiado por su capacidad espacial.

Hi Shivani,

same here. It would be great to get Metashape running on a Jetson but as far as I can tell only the Mac version is also ARM compatible? Can you share some information on how you got it running?

Let's calculate the maximum ramp length for a 1:16.6 gradient using linear interpolation based on the two values provided: 1:14 (9 meters) and 1:20 (15 meters) before needing a landing.

First, let's assign the values:

x1 = 14 (corresponding to 1:14 gradient)

y1 = 9 meters (maximum ramp length for 1:14 gradient)

x2 = 20 (corresponding to 1:20 gradient)

y2 = 15 meters (maximum ramp length for 1:20 gradient)

x = 16.6 (corresponding to 1:16.6 gradient)

Now, we can use the linear interpolation formula:

Linear interpolation = y1 + (x - x1)*(y2-y1)/(x2-x1)

Substitute the values:

Linear interpolation = 9 + (16.6 - 14)(15-9)/(20-14)

Linear interpolation = 9 + 2.66/6

Linear interpolation = 9 + 2.6

Linear interpolation = 11.6 meters

So, according to the formula, the maximum ramp length for a 1:16.6 gradient before needing a landing is 11.6 meters. The website you mentioned provided the correct result. Linear interpolation is a useful method for estimating values between two known data points, and it's commonly used in various fields, including architecture and engineering.

In ArcMap, you can perform a regression analysis between land use types and PM 2.5 levels using the Geographically Weighted Regression (GWR) tool. GWR allows you to explore spatially varying relationships between variables, which is useful when analyzing how the relationship between land use and PM 2.5 levels changes across your study area.

Here's a step-by-step guide on how to conduct a GWR analysis in ArcMap:

It's important to note that the GWR tool assumes that the relationships between variables vary smoothly across space. Additionally, it's recommended to consider any relevant data preprocessing steps, such as data normalization or transformation, before conducting the regression analysis.

Remember to consult the ArcMap documentation or seek further assistance from spatial analysis experts if you encounter any specific issues or require more advanced analysis techniques.

Hi Danielle,

Because, as you say, there are so many inconsistencies in the literature as to how pupil preprocessing should go, I like to follow the pipeline suggested by Kret & Sjak-Shie:

They also published a code which you can use on your data.

Answering your questions briefly:

1. The blinks should be interpolated on continuos signal, not on a small window (like a baseline). However, if due to a blink you are missing too much data in the baseline, you may need to reject this trial, as you have no "real" signal to calculate the baseline from.

2. I'm not sure I understand what you mean "fall out form blinks" - is it maybe that due to the blink you have data loss in the time window of the baseline? If so, please see the point above.

3. This is a matter of discussion, of course, but again, I suggest you take a look at Kret & Sjak-Shie's paper. For a more elaborate discussion, take a look at (many) papers by Mathôt, maybe especially

About your last comment, I have a feeling that you run your preprocessing separately for the time windows of the stimuli and of the baselines - is that correct? Remember that you deal with a continuous signal, so things like interpolation should not be used on cut-out parts of that signal. If I misunderstood, sorry about it. Either way - the papers mentioned above should help a lot!

Good luck,

Lena

Gemma Abaunza

Google Scholar is your friend https://scholar.google.com/scholar?hl=en&as_sdt=0%2C48&q=interpol+cybercrime

Muhammad Amin Hasan

Yes, interpolation and extrapolation can affect the results of Gaussian Mixture Models (GMM).

Interpolation refers to predicting values within the range of the training data, while extrapolation refers to predicting values outside the range of the training data.

When GMM is used for interpolation, it assumes that the data within the range of the training data is generated from a mixture of Gaussian distributions. If this assumption is correct, then GMM can accurately predict the values within the range of the training data.

However, if the assumption is incorrect, then the predicted values may not accurately reflect the underlying distribution of the data.

Similarly, when GMM is used for extrapolation, it assumes that the data outside the range of the training data is generated from a mixture of Gaussian distributions. If this assumption is incorrect, then the predicted values may not be reliable.

Therefore, it is important to evaluate the assumptions made by GMM and carefully consider the implications of interpolation and extrapolation when interpreting the results.

Gerard Taulé Codinach

It is important to note that the choice of interpolation method depends on the specific characteristics of the data and the study area. However, in general, kriging is considered to be one of the most accurate methods for interpolating temperature data.

In your case, since you have a high spatial density of observation points in the downtown area and a lower density in rural areas, it might be useful to use a spatially adaptive interpolation method such as kriging with varying local means (KVL) or regression kriging (RK). These methods can account for the varying spatial patterns of temperature across the study area.

Here are some references that might be helpful:

Thank you.

Performing change detection of interpolated datasets involves comparing two or more raster layers to identify areas where changes have occurred. Here are the general steps you can follow:

It's important to note that change detection of interpolated datasets can be challenging because interpolation can introduce uncertainty and artifacts in the data. Therefore, it's important to use appropriate interpolation techniques and validate the interpolated datasets before performing change detection. Additionally, understanding the limitations of the data and the assumptions made during the interpolation process can help to interpret the results accurately.

Thanks for your responses.

I tried to complete the missing electrodes at 61 to have the same number of electrodes for each subject. Indeed I do not understand why the number 70 appears. I have the same error message with 46 channels for example.

The problem is that when I apply the Interpol.m function to an EEG structure with, for example, 50 channels (indicating a chanloc of 61 electrodes), I get a structure with more electrodes than 61.

You don't mention the specifics of how large your area of interest is (i.e. a 1 sq km, 10 sq km, 1000 sq km, 10000 sq km, etc.) or what exactly 'low' and 'high' mean, along with the baseline those are stating from, or where in the world it is - which would indicate the orography and general texture of the terrain relief. Perhaps extreme, but in my neighborhood, that can range from 2000 ft to 14,000 ft in 18 miles - 50 miles east that same distance has a range of about a 100ft, not 12,000 ft of elevation. Also you don't mention the chronological aspects and intervals.

Merely creating some sort of interpolated surface between your samples, depending on the above is self referential and artificial. The actual 'surface', again depending on the area of interest, is another 'DEM', grid data from the weather model of the region appropriate for the AOI which captures the general trends in the AOI.

Also, there is another correction, depending on your data source, it is not only the measured temperature on the interpolated surface, but the Density Altitude relationship correction, which will vary across the surface. Even very small variation in pressure, temperature, and altitude can have influences of a thousand feet - this life or death correction is done immediately before every airplane flight.

These aren't the only factors, and whether they matter of not depends on the extents and sampling of your area of interest, and your analytical goals.

PME interpolation order determines the number of grid points used to interpolate the long-range electrostatics in GROMACS simulations. The default value of 4 or 5 is sufficient for most systems. However, higher interpolation orders (up to 7) may be necessary for systems with larger unit cells or larger periodicity in the z-dimension, where the default values may not be accurate enough.

In terms of FEP simulations, higher interpolation orders may be useful to improve the accuracy of free energy calculations, especially for systems with a high degree of charge transfer or polarization, where the electrostatic energy plays a significant role. However, it should be noted that higher interpolation orders require more computational resources and longer simulation times. In general, it is recommended to use the default interpolation order unless it is necessary to increase it based on system-specific considerations.

Bearing in mind the above suggestions, It is simplest to plot the points on the maps. If you wish to interpolate the point data to create isohyets (lines of equal rainfall), you might wish to do some research on the best interpolation method, to give the accuracy that you need, for your circumstances. There are a number of interpolation algorithms, that will produce varying results.

The structural similarity index (SSIM) value is a measure of the structural similarity between two images. It compares the luminance, contrast, and structure of the images. When you decrease the image resolution, you are essentially reducing the number of pixels in the image. This can lead to a loss of information and detail, which can affect the SSIM value.

When you use bicubic interpolation to resize the image, you are essentially estimating the values of the pixels in the new resolution. These estimated values may not match the original high-resolution values exactly, leading to a decrease in the structural similarity between the two images.

Additionally, as the resolution decreases, the structural similarity will be less and less, as the change in pixel values is more pronounced for smaller images. This can also contribute to the decrease in SSIM values as the resolution decreases.

It's worth noting that SSIM is a relative measure and its value can vary depending on the image content, lighting conditions and the specific dataset. The value of 0.9544 for 1024 x 1024 resolution is considered as a high value but it does not provide a clear interpretation of the quality of the predicted mask compared to the ground truth mask.

It's me

To get the node pressure in a user-defined function (UDF) by interpolating the pressure on the faces surrounding the node, you can follow these steps:

Here is some example code in C that demonstrates how this could be done:

#include "udf.h"

/* define a function to interpolate the pressure at a node */

real interpolate_pressure(Node *node)

{

/* retrieve the adjacent faces */

Face *f1 = node->f[0];

Face *f2 = node->f[1];

Face *f3 = node->f[2];

/* retrieve the pressure values on the faces */

real p1 = C_P(f1, NULL);

real p2 = C_P(f2, NULL);

real p3 = C_P(f3, NULL);

/* interpolate the pressure at the node using linear interpolation */

real p_node = (p1 + p2 + p3) / 3;

return p_node;

}

DEFINE_ON_DEMAND(interpolate_pressure_udf)

{

/* retrieve the node for which we want to calculate the pressure */

Node *node = THREAD_NODE(t);

/* interpolate the pressure at the node */

real p_node = interpolate_pressure(node);

}

This example uses linear interpolation to calculate the node pressure, but you can use other interpolation methods if desired. You can also adjust the number of faces used in the interpolation depending on your specific needs.

I hope this helps! Let me know if you have any questions or need further assistance.

No, the ordinary spatio-temporal kriging predictor is not an exact interpolator. This is because the kriging model is based on the assumption that the sample data points are not perfectly representative of the underlying trend of the data. Thus, kriging will provide an estimate of the underlying trend, rather than an exact interpolation of the sample data points.

I'm not familiar with this sector but maybe this would help you (https://ch.mathworks.com/matlabcentral/answers/99955-how-do-i-compute-the-mean-and-median-frequency-of-a-power-spectrum-using-the-signal-processing-toolb).

So you want the code for Kriging using gstat. A simple Google search shows plenty of tutorials. You just need to apply the principles showed on these tutorials to your data.

I would advise you to start with Kriging and then move to co-Kriging.

Daniel Gaida hi, does DistMesh just generate mesh ? Is there any tools which can visualize the numerical results ?

Hello,

you derived the equation from the measurement values having uncertainties. For this reason, the coefficients of your functions are also uncertain. Several approaches are known to transfer the measurement uncertainties to the coefficients, for instance, propagation of uncertainty or the Monte-Carlo simulation. Please take a look to the "Guide to the Expression of Uncertainty in Measurement" to get further information, cf. https://www.bipm.org/en/committees/jc/jcgm/publications

All the best

Michael

Comsol 5.5

Ok what is the meaning of that?

Dear Dr. Li,

Technically, it requires at least five (5) data points with flow (q) and concentrations (c) to generate a C-Q hysteresis loop, including the initial data point, one in the rising limb, the peak flow, one in the falling limb, and the event ending point. The more data you have, the less uncertainties in your hysteresis analysis.

In your case, as long as you have more than two data points in your falling limb, I did not see a problem to do an interpolation for hysteresis analysis.

Best,

Wenlong

Dear Gatoto Placide

I think the "wrf_interp_3d_z" function can be helpful for you. It Interpolates WRF variables to a specified pressure/height level.

Based on your NCL script, I think you need to have your output at a specific location. In WRF version 4.4, there is a new capability for printing the WRF output time series at a given location (lon, lat). You can look at the following link for more information.

Best,

Morteza Babaei

And a worth reading article for further enlightenment;

So this has plagued me for a while. The short answer for anyone following this is to use linear interpolation or something like a 2-point COSINE interpolation (What we settled on).

The problem is, as intuitive as it may be, is that this assumes that the position you are interpolating will lie exactly on this line between point A and B. Secondly, interpolating wind speed might be easy but wind direction is not. A somewhat simple solution to this is to change it from direction and magnitude to just a vector and interpolate it that way.

The better interpolation method that we finally rested on was just to hit our wind server with more traffic in an effort to do 4 point interpolation.

Change dcdfreq 100 to dcdfreq 1000 or dcdfreq 5000

The character of the river changes as it may meander or sometimes straight sections as it flows downstream. The characteristics change with various factors as geology, substrate, sediment loading, vegetation, access to floodplain, gradient, watershed or basin size and land use patterns. The facet sequence of riffle, run, pool and glide, and whether the channel is braided (typical of too much sediment), or entrenched (lack of floodplain access) can make a difference. Many rivers adjust their pattern with time. The suggestions of Dr. Harris are helpful too. It may take some training and experience to successfully interpret between two cross sections, but this may be difficult nonetheless unless some effort in placement was made. If the river is relatively clear, green LiDAR coverage may be helpful in visualizing complex river patterns and depths. If I had to choose two measurement cross sections, I would probably choose a riffle (shoal) and a typical pool. Riffles typically are gradient controls, and pools are located at river bends, often have a pronounced point bar, which is suggestive of bankfull flow, and whether there is floodplain access. Besides geologic controls and substrate controls, many rivers have a history of hydrologic modification and upstream influences by land instability and/or accelerated erosion from land use practices, which can influence river character and adjustments. Rivers are more dynamic than we are likely to perceive. Using aerial photos (leaf off period may be best, and possibly select photos taken in dry and wet or flood periods if available, and the sequence through time may also help evaluate the river boundaries and frequency and extent of changes.

When dilutions work but the neat/pure sample don't work the causes are most likely to be matrix effects or user error. Dilution decreases concentration of interfering substances in the matrix. Depedent on your isolation method there may be something in your solution that is interfering with the assay. High detergent and alchohol concentration are common examples of this.

Examples of methods to resolve this would be some chromotography approaches(immobilize on column and exchange buffer) or much cheaper dialysis into another buffer. However, it would be suprirsing to see such a dramatic matrix effect with zero change in signal without this always occuring when you run the test. Is this the first time this assay was run by your group?

Second just to confirm the pure sample had no change in signal from your 0 ng sample?

Get your XRD data analyzed using this website!

Check out this website:

The service is not free but it's very affordable and definitely worth it.

The interesting thing about a Mollier chart (enthalpy vs. entropy): it's the only one with isotherms or isobars that are continuous in value and slope.

Hi Afifa Talukder ,

what Ahmed Albabily mentioned in the query looks like he is interested in downscaling the precipitation data. I seriously suggest against statistical downscaling, especially for precipitation. Though physical-based numerical models are better for downscaling, the present employed methods can not downscale the precipitation reliably.

Cheers,

Kishore

Hi,

check the recently released Copernicus DEM (30 m and 90 m versions available; see -->https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198) is accessible via OpenTopography (https://portal.opentopography.org/dataCatalog?group=global).

Regards

Hello Nasiru,

What's the reason for wanting these values? That may have more to do with what sort of approach, if any, might make sense.

At first glance, doing this doesn't sound like a good idea to me. Among the reasons:

Any method for taking adjacent weekly values and inserting some estimate for the 6 six "missing" daily values will only bias the estimates of day-to-day variance in scores/values as well as increase the serial correlation across a lag from 1 to 6 days. As well, the presumption of a (perfectly) predictable day to day change is unlikely to be realized in actual measurements.

Good luck with your work.

Thank you very much.

How should I draw the baseline?

we can see this:

Janak Tiwari suggest reading these papers:

1.

2. https://www.sciencedirect.com/science/article/am/pii/S2542529319301208

3.

The problem seemed to be subregional cropping while downloading the climate data from Copernicus website.

Hi Elijah,

Here are some suggestions:

If you use rainfall data you need to question what accumulation you employ. For instance, hourly accumulations have steeper gradients (are less smooth) than monthly accumulations, meaning that any prediction for hourly images is more difficult than for monthly ones, therefore what interpolation scheme and data you use matter the smaller the accumulation is.

The variability of the distances between the chosen points matters also, especially for kriging. Avoid choosing points all of which have similar distances from each other: if you do that the semivariogram is not computed correctly because this scheme exclude measurements in small distances. You need to inform the semivariogram on how the variability of the field changes in all different distances, including the short ones. Then you will catch microvariability (variability in short scales).

Choice of skill scores also matters. RMSE Bias and HK should be included. Also any measure for the scatter (in the scattergram measurements versus cross-validated