Questions related to Interpolation
Assume a mobile air pollution monitoring strategy using a network of sensors that move around the city, specifically a network of sensors that quantify PM2.5 at a height of 1.5 meters that lasts about 20 minutes. Clearly, using this strategy we would lose temporal resolution to gain spatial resolution.
If we would like to perform spatial interpolation to "fill" the empty spaces, what would you recommend? What do you think about it? What would be your approximations?
I have two points along a straight line path. Each of these points has associated with it weather forecast data in the form :
[forecast time, direction, speed]
I need to generate direction and speed predictions along a route between these points at regular intervals (e.g. 10m or 10s)
I have seen a lot of methods using four points for speed interpolation but these do not work well for only two points.
Dear, I am performing molecular dynamics through NAMD, and in the production stage (100ns), in my generated script, I can only get files > 80gb. How can I avoid producing a huge DCD file in the NAMD production step? Can anyone give a hint?
set temp 310;
outputName step5_production; # base name for output from this run
# NAMD writes two files at the end, final coord and vel
# in the format of first-dyn.coor and first-dyn.vel
set inputname step4_equilibration;
binCoordinates $inputname.coor; # coordinates from last run (binary)
binVelocities $inputname.vel; # velocities from last run (binary)
extendedSystem $inputname.xsc; # cell dimensions from last run (binary)
dcdUnitCell yes; # the file will contain unit cell info in the style of
# charmm dcd files. if yes, the dcd files will contain
# unit cell information in the style of charmm DCD files.
xstFreq 100; # XSTFreq: control how often the extended systen configuration
# will be appended to the XST file
outputEnergies 100; # 5000 steps = every 10ps
# The number of timesteps between each energy output of NAMD
outputTiming 100; # The number of timesteps between each timing output shows
# time per step and time to completion
restartfreq 100; # 5000 steps = every 10ps
# Force-Field Parameters
paraTypeCharmm on; # We're using charmm type parameter file(s)
# multiple definitions may be used but only one file per definition
parameters ../unk/unk.prm # Custom topology and parameter files for UNK
# Nonbonded Parameters
exclude scaled1-4 # non-bonded exclusion policy to use "none,1-2,1-3,1-4,or scaled1-4"
# 1-2: all atoms pairs that are bonded are going to be ignored
# 1-3: 3 consecutively bonded are excluded
# scaled1-4: include all the 1-3, and modified 1-4 interactions
# electrostatic scaled by 1-4scaling factor 1.0
# vdW special 1-4 parameters in charmm parameter file.
vdwForceSwitching on; # New option for force-based switching of vdW
# if both switching and vdwForceSwitching are on CHARMM force
# switching is used for vdW forces.
# You have some freedom choosing the cutoff
cutoff 12.0; # may use smaller, maybe 10., with PME
switchdist 10.0; # cutoff - 2.
# switchdist - where you start to switch
# cutoff - where you stop accounting for nonbond interactions.
# correspondence in charmm:
# (cutnb,ctofnb,ctonnb = pairlistdist,cutoff,switchdist)
pairlistdist 16.0; # stores the all the pairs with in the distance it should be larger
# than cutoff( + 2.)
stepspercycle 20; # 20 redo pairlists every ten steps
pairlistsPerCycle 2; # 2 is the default
# cycle represents the number of steps between atom reassignments
# this means every 20/2=10 steps the pairlist will be updated
# Integrator Parameters
timestep 2.0; # fs/step
rigidBonds all; # Bound constraint all bonds involving H are fixed in length
nonbondedFreq 1; # nonbonded forces every step
fullElectFrequency 1; # PME every step
wrapWater on; # wrap water to central cell
wrapAll on; # wrap other molecules too
wrapNearest off; # use for non-rectangular cells (wrap to the nearest image)
# PME (for full-system periodic electrostatics)
PMEInterpOrder 6; # interpolation order (spline order 6 in charmm)
PMEGridSpacing 1.0; # maximum PME grid space / used to calculate grid size
# Constant Pressure Control (variable volume)
useGroupPressure yes; # use a hydrogen-group based pseudo-molecular viral to calcualte pressure and
# has less fluctuation, is needed for rigid bonds (rigidBonds/SHAKE)
useFlexibleCell no; # yes for anisotropic system like membrane
useConstantRatio no; # keeps the ratio of the unit cell in the x-y plane constant A=B
# Constant Temperature Control
langevin on; # langevin dynamics
langevinDamping 1.0; # damping coefficient of 1/ps (keep low)
langevinTemp $temp; # random noise at this level
langevinHydrogen off; # don't couple bath to hydrogens
# Constant pressure
langevinPiston on; # Nose-Hoover Langevin piston pressure control
langevinPistonTarget 1.01325; # target pressure in bar 1atm = 1.01325bar
langevinPistonPeriod 50.0; # oscillation period in fs. correspond to pgamma T=50fs=0.05ps
langevinPistonDecay 25.0; # oscillation decay time. smaller value corresponds to larger random
# forces and increased coupling to the Langevin temp bath.
# Equal or smaller than piston period
langevinPistonTemp $temp; # coupled to heat bath
numsteps 500000; # run stops when this step is reached
run 10000000; # 1ns
We use the commercial eurofins abraxis kit's for the detection of anatoxin-a (toxin produced by cyanobacteria). The test is a direct competitive ELISA based on the recognition of Anatoxin-a by a monoclonal antibody. Anatoxin-a, when present in a sample, and an Anatoxin-a-enzyme conjugate compete for the binding sites of mouse anti-Anatoxin-a antibodies in solution.
The concentrations of the samples are determined by interpolation using the standard curve constructed with each run.
The sample was containing a large concentration of cyanobacteria. So we analysed the sample pur and diluted at 1/100 and 1/200 (to be ine the linearity zone of the standard range). The sample pur was negative. However the dilutions gave positive results and I don't know why.
Thank you for helping me understand.
I am working on kaolin clay as an adsorbent for wastewater treatment and also i have undergone XRD characteristics. Thus, the analyst send me two type of data. one is the scan of the sample in the ‘native’ detector resolution of 0.1313° 2Θ, the other contains the same data, but interpolated to a step size of 0.02° 2Θ. So, which one can be used for graphical analysis to interpret its diffraction pattern?
Hello, I am trying to run a simulation with real gas model from NIST using ch4a as working fluid. When I try to initialisate or run the simulation its not converging in Fluent,
in my simulation, inlet temp 110K, 0.02 kg/s ch4, pressure outlet 53 bar. just ıwant to see the phase change, ı try to understand the supercritical fluid, firstly ı tried to fit the curves at MATLAB, but there were wrongs with interpolations. ı reserach the rgp table from nist to CFX, ı couldnt, how do we deal with?
I am going to derive the precipitation data from NETCDF files of CMIP5 GCMs in order to forecast precipitation after doing Bias Correction with Quantile Mapping as a downscaling method. In the literature that some of the bests are attached, the nearest neighborhood and Inverse Distance Method are highly recommended.
The nearest neighbour give the average value of the grid to each point located in the grid as a simple method. According to the attached paper (drought-assessment-based-on-m...) the author claimed that the NN method is better than other methods such as IDM because:
"The major reason is that we intended to preserve the
original climate signal of the GCMs even though the large grid spacing.
Involving more GCM grid cell data on the interpolation procedure
(as in Inverse Distance Weighting–IDW) may result to significant
information dilution, or signal cancellation between two or more grid
cell data from GCM outputs."
But in my opinion maybe the IDM is a better choice because I think as the estimates of subgrid-scale values are generally not provided and the other attached paper (1-s2.0-S00221...) is a good instance for its efficiency.
I would appreciate if someone can answer this question with an evidence. Which interpolation method do you recommend for interpolation of GCM cmip5 outputs?
Thank you in advance.
I was wondering if there's any tutorial or scripts that can be used to directly downscale and correct the bias in a reanalysis data available as NetCDF such as those provided by CRU or GPCC?
Also, for the downscaling part, does this mean we're just interpolating to lower mesh or is there an actual algorithm used to downscale the data in CDO?
Thanks in advance!
Srtm 30m Dem data has significantly less data coverage in Nepal region; even NasaDEM modified version of SRTM is not precise in that region, I have tried a fill function by which via idw interpolation I filled the gaps of those dem in that region, but since the holes in dem data extend up to 10km it is not scientifically justified to interpolate in that large region. Even after this kind of dem gaps filling interpolation algorithm, further processing using that dem like Slope, Aspect, etc. map carry forward those errors... Can anyone suggest any solution regarding how to fill those gaps in the srtm data?
I am using heat transfer in solids and fluids model in Comsol Multiphysics. I want to add a heat source term in the solid domain, which is the function of solid domain temperature.
The source term is as follows:
where rho, Cp and Tad are density, specific heat and Adiabatic temperature of the solid domain, which is the function of solid domain temperature.
I have used interpolation for defining Cp and Tad properties of materials in Comsol Multiphysics.
Please see the attached files.
Then I defined the source as, Source= (rho*Cp(T)*Tad(T))/dt in the variables node and used this Source in the Heat transfer in solids and fluids section as a source term.
I am getting very less increment in temperature after simulation(only 1.5K). It should be approx 4.5-5K.
Can anyone tell me, where I am doing wrong?
I did baseline correction by Xpert high Score Plus software with manual setting and used cubic spline interpolation for bacterial cellulose (BC). Is this baseline correction good in xrd spectrum of cellulose?
I want to learn about Solving Differential Equation based on Barycentric Interpolation. I want to learn this method, if someone has hand notes it would be great to share with me. I need to learn that in 2 weeks. Thanks in advance.
Suppose I got Force constants at 500K, 1000K, 1500K, and 2000K using TDEP. How do I interpolate the force constants for the temperatures in between?
https://ollehellman.github.io/page/workflows/minimal_example_6.html and https://ollehellman.github.io/program/extract_forceconstants.html talk a bit about the interpolation.
I found it confusing when I first used it, so I am explaining the steps here in much detail.
I'm trying to apply the spatial interpolation process for my NetCDF file. However, I keep getting the "segmentation fault (core dumped)" error.
The screenshot of the error and my NetCDF file are in the attachment.
I'd be so glad if you could explain what goes wrong during the interpolation process.
Thanks in advance
Best of luck
I am setting up an experiment to estimate the accuracy of different interpolation algorithms for generating a spatially continuous rainfall data (gird) for a certain area. The data density (number of points versus the area) and the spatial arrangement of the data (i.e., random versus grid-based ), will vary for each run (attempt)
The objective is to understand how each algorithm performs under varying data density and spatial configuration.
Typically, different studies have done this using station data of varying data density and spatial configuration. In the current context, there are limited stations (just about 2)and the intent is to execute this experiment using data sampled (extracted) from existing regional rainfall grid, but varying the data density as well as the spatial configuration.
Note that I cannot generate random values because the kriging is to be implemented as a (multivariate stuff ) using some rainfall covariates....random values will mess up the relevance of such covariates.
I did a rapid test and found that, despite a wide difference in the density and configuration, there was no significant difference in the accuracy result, based on cross validation results. What's going on? It's not intuitive to me!!
Please, can you identify something potentially not correct with this design? Theoretically, is there anything about dependency that may affect the result negatively? Generally, what may not be fine with the design? how can we explain this in your view??
Thanks for your thoughts.
Sorry for many text..
I am using a python script to run Agisoft Metashape on Jetson TX2. It takes a lot of time when there are more images involved in the creation of the model. I want to increase the speed of the operation by running those on CUDA. Can someone please help me with this?
import os import Metashape doc = Metashape.app.document print("Srcipt started") doc = Metashape.app.document chunk = doc.addChunk() chunk.label = "New Chunk" path_photos = Metashape.app.getExistingDirectory("main folder:") # image_list = os.listdir(path_photos) # photo_list = list() sub_folders = os.listdir(path_photos) for folder in sub_folders: folder = os.path.join(path_photos, folder) if not os.path.isdir(folder): continue image_list = os.listdir(folder) photo_list = list() new_group = chunk.addCameraGroup() new_group.label = os.path.basename(folder) for photo in image_list: if photo.rsplit(".", 1).lower() in ["jpg", "jpeg", "tif", "tiff"]: # photo_list.append("/".join([path_photos, photo])) photo_list.append(os.path.join(folder, photo)) chunk.addPhotos(photo_list) for camera in chunk.cameras: if not camera.group: camera.group = new_group print("- Photos ajoutées") doc.chunk = chunk Metashape.app.update() # Processing: chunk.matchPhotos(downscale=1, generic_preselection=True, reference_preselection=False) chunk.alignCameras() chunk.buildDepthMaps(downscale=4) chunk.buildDenseCloud() chunk.buildModel(surface_type=Metashape.Arbitrary, interpolation=Metashape.EnabledInterpolation) chunk.buildUV() chunk.buildTexture(texture_size=4096) doc.save() # chunk.buildTexture(mapping = 'generic', blending = 'mosaic', size = 2048, count=1, frames = list(range(frames))) print("Script finished")
I have to prepare a spatial map of various soil properties for that I am confused about the semi-variogram is compulsory or not?
i want to do the interpolation of the weather data from mess station to my interest points. Before that i need the data from mess station. But the data are not distributed very well and some data were missed. So i think about it and i have the idea, first to do the interpolation of time sequences to get all the data from mess station in all time point and then from the data in mess station to get them of my interest points. Has anyone done it before or what do you think of this idea? Do you know some good methods to do the interpolation of time sequences. My Weather data are temperature, wind speed, wind direction, humidity and dew point. thanks.
I am utilizing Area-to-Area CoKriging (AtACoK) for raster downscaling in R using the atakrig package. For this purpose I have one dependent variable (let's say x) with pixel size of 400m and 2 covariates (c1 and c2) with 100m pixel size. When I perform AtACoK the resulting raster has pixel values from 0:Infinity. What might causes the problem? For comparison, when I perform AtAK, that is, using a single independent variable the downscaling works fine.
I am trying to compare the accuracy of different interpolation methods in GIS using Cross-Validation, but not sure what are the differences between the same methods in different toolboxes (e.g., IDW in Spatial Analyst Tools and Geostatistical Wizard).
Thanks in advance.
How to estimate FD wavefield at receiver position in case of FDFD method? Let's say incident wavefield is u at source position. Pls help & discuss in details.
Code in MATLAB for this will be well appreciated.
I have prepared these two maps of Electrical Conductivity (EC). I used both IDW and Kriging to prepare them. Which one should I choose? The sampling sites are also pointed in the map.
You can see that the Kirging map is quite strange!
I am in a fix. Please help!
Does anyone help me with how I can interpolate the water level record using elevation as a reference? My study area is very rugged topographically, and I need to consider surface elevation during interpolation of the measured hydraulic heads. I am looking for a technical answer pls.
Recently, our group collect some water samples at several rainfall events. And then I tried to analyze the c-Q relationships. The question is the amount of data in the falling part is less than the rising part. Could we use extending lines to interpolate the missing data? Or do I have some other choice to solve this problem?
I have 500 companies individual data for the period 2009-2021. There are so many missing values in the data. So I deleted those company whose data is missing greater than 5 percent. So with the remaining companies I waana replace these missing values. So linear interpolation is good or not. Or shall I use mice in r.
Does anyone have an idea how to reconstruct Nitrate profile either by interpolation or any other method.
I have a dataset with Nitrate concentrations at every 20-50 m resolution upto to 1000m
Is it possible to interpolate the profiles to get a higher resolution dataset say at every 5m.
If you have an idea, please let me know. I will appreciate your help.
An inquiry about Interpolation Model Validation.
I ran the interpolation of IDW, Ordinary kriging, and EBK. But the R(sq.) values for the all these models (including the semivariograms for OK) rarely exceed 0.1 (even sometimes 0.007 is the highest).
Is model with R(sq.) value of 0.007 good for publication? I think this value indicates too poor prediction, but none of these models is showing a decent R(sq.) value.
On the other hand, the RMSSE is really close to 1, and mean error is around 0.009.
What should I do know?
What can be the possible reason? Am I missing something? Or I should follow more complex models? Is the spatial distribution being controlled more by extrinsic factors (e.g. human interference)?
[120 samples were collected randomly from this study area].
I would gladly appreciate any suggestion.
I am looking for a reference (or a benchmark solution) to check my restriction and interpolation operators for a second order element in case of a geometric multigrid method using FEM descretization.
I've downloaded netCDF file (climatic variable ),measured monthly for 20 years over the country.
I need to extract the value of variable at all point locations for the entirety of the time series.
So for the result, I would like to obtain monthly data for 20 years for each province of country(interpolation from the grid into points).
I will be grateful for any help.
I'm working more with r and mathlab.
Thank you very much for your answers.
I am working on v-ADCP data of marine currents in a port. I have data along transects and I want to create a 2d map with interpolation of data which shows direction and intensity of currents. I use surfer 13 and I created two grid for velocity and direction and then created a 2-grid vector layer, resulting in arrows that show the direction of the current with a scale color for velocity. the problem is that the interpolation creates data inside a polygon delimtated but my pool of data, so there is new data even in areas where I don't have any, for example on land and docks..how do I set an interpolation method which allows me to interpolate data only in an area along the transect? I tried kriging and Triangulation with Linear Interpolation methods. thank you
I took interpolation techniques on one of my interesting research sites using R-programming to predict and estimate long year-based mean monthly and annual rainfall and temperature using KED, IDW, and OK methods. As a result, I found the predicted values, which were equal/same with the actual value for most of the months and annual, specifically for KED using DEM_90m as a covariate, and OK. The problem was more worsen in the case of OK, and I attached herewith the findings. So, I need your valuable recommendations on this one.
Thank you for your time!!
NB: I took the same procedure on the other catchment, and found good results using the same interpolation techniques.
I have some small periodic data (about 32 points), and I want to fit it in a Fourier transform function.
Actually I use the fast Fourier transform method using the python toolbox (numpy.fft.fft). At first I calculate all Fourier complex coefficients (32 coefficients), however I get a noisy function as shown in the attached figure.
My question, is what is the best method to smooth the results?
Should I reduce the number of points by interpolating other points (e.g. 16 or 8 points) based the 32 points?
Should I apply some filters, like low-pass filter? if yes how can I choose the frequencies threshold to apply this filter?
Should I apply an filter based on the power of the complex Fourier coefficients (power = ||Ci||^2), and keep only frequencies which correspond to high powers? if yes how can I choose the power threshold?
Or are there other methods?
Thanks in advance.
I have daily rainfall data for the year 2000 - 2020 for a rainfall station. How i want to find the suitable value to use in ArcGIS / QGIS to do the interpolation? Only 1 value is used to do the interpolation. Thanks in advance
For example, in surface window, I can define the specific depth of interested factors. And define the z factor as like the concentration of Si @ 10 meters depth, on the condition, even if I got only 10 points around 10 meters.
I am a novice researcher, and i'm working on a project which is busy analyzing water quality data from different water sources such as dams,rivers, and springs , and also from secondary sources such water treatment plants, and households, i have collected water GPS coordinates, which appear on the image* , hence im having difficult to find the right methodology to analyse these results spatially. The microbiological parameters that are going to analysed include bacteria such as E.coli, salmonella spp , Shigella spp, Giardia spp, Entamoba Histolytica spp.
please help and be kind :)
i have attached an image showing the sample locations , additionally the study area is in six Quaternary drainage basins
Dear scholars and reviewers
What is your thought about a journal whose analysis is based on interpolation due to missing values. In addition, sometimes it becomes imperative to convert low frequency data to high frequency data most especially when mixed frequency can't be used because the dependent variable is high frequency. How do reviewer judge estimate from interpolated data and converted data. Can it lead to rejection of the paper
We have ERA5 time series data which was used to extract maximum and minimum temperature. As the spatial resolution was not good therefore we downscaled it to 90 meters to increase the correlation coefficent value against in-situ data.
so i wanted to know whether this step is scientifically correct or not???
I am working on use of interpolated filter in channelizer of software defined radio .I wanted to know how ripples in stopband can affect the performance of digital filter?
I would like to interpolate S-values for calculating absorbed self-doses of tumor xenografts from a pre-clinical study. OLINDA or IDAC are my sources for the model spheres data. For now, I fitted the data with a Power-law equation y=a*x^b, and the fit is quite okay (figure attached).
I was wondering if anyone has a better approach to derive the s-values for various sphere masses.
Thanks a lot and greetings from Berlin,
I try to predict daily stock market movements of German DAX using the SVM. As input features in want to take the daily changes of several stock markets. The problem is that different stock markets have different holidays which results in missing data (on trading days of the DAX). I get the data from Yahoo Finance. For example the S&P500 has similar trading days as the DAX with just a few days missing (1-2 days in a row).
The trading days of SSE composite vary more significantly from the ones of the European markets, sometimes 5 days in a row are missing.
Studies use different approaches on that topic. Some take the linear interpolated data between two trading days. This seems problematic because in reality, investors do not have these informations of market trends when the SSE is closed. Other studies remove the missing data with the changes of the previous day. Here a sequence of days would have the same values which could cause difficulties.
What would you recommend to deal with this issue? Should I remove all data, where at least one stock market is missing, from the training and test set (~8% of data set) and use the changes to the previous trading day of every individual stock markets?
Thank you in advance!
I'm working on estimation with the Stochastic Frontier Analysis (fels) in the context of a panel data set and I've been having trouble with missing values in some spatial units. To handle this, I interpolated the variables I'm using in the model (nnipolate), and since some units persisted with missing values, I filtered the data, keeping only observations with information (keep if var!=.) and taking care that the panel is still balanced.
Is this a good approach? If not, what can you recommend?
I have a computational data of geometrical points and their instantaneous stress components. However based on the available data. I have to interpolate the stress values at points where the stress values are not available. Any inputs on the interpolation functions are highly appreciated. I did try interpolation function and scattered data function available in MATLAB. I am getting an error as the points are not monotonically increasing and the points are not under a convex hull.
I have question about resizing the complex arrays.
I need to resize the complex valued array with interpolation method.
I tried scikit-image but this one didn't support complex data type.
I also tried to resize by cv2, and also it didn't work.
even with the real value and imaginary value separately.
Is there any solution to this??
I have 90 point data of soil texture, for example sand loam, silty loam etc. Now i want to prepared soil texture by interpolation in GIS. Kindly help me if anyone has some solution, suggestions for this type of mapping.
Which method is being used for interpolation in DSM generation using Pix4D Mapper, Metashape and Inpho UASMaster?
I am generating a regression model using daily frequency data and most of the independent variables have missing values in non-working days but the dependent variable is daily frequency with no missing values. What approach should i take?
I found an educational attainment dataset that has values for every 5 years (1960, 1965, 1970....) and I want to interpolate the missing data by using stata. Anyone knows any method of how to do this? Also, what are the limitations of interpolating missing data ?(As I need to mention them in my paper)
Nonlinear mixed-effects models (may) consider data below the limit of quantification (BLQ) in parameter estimation. However, an evaluation of the goodness-of-fit plots (observations vs predictions in particular, using spline interpolation), displays a strong trend (of spline interpolation, but not of the data) in the region of censored data, as if the model disregarded BLQ data and the data were the lower limit of quantification itself, as structured in the database. I believe that the database is structured correctly and that the model considered the censored interval. Apparently this plot is the only one to exhibit this behavior.
Is spline interpolation adequately representing the competence/capability of the final model in this case? How to handle this situation?
I'm in a situation where I need to compare the accuracy of one stress-strain curve with respect to the other, with both curves having different x and y coordinates. If both curves have the same x-coordinates (independent variable) and varying y-coordinates (dependent variable), I could use the R squared value or the Weighted Least Squares (WLS) method.
I'm trying to avoid interpolation as there are many values and would be a very tedious task.
Any help is appreciated :)
I am hoping someone with experience in immunoassays will see this. The question I have is this: can one overcome the hook effect by interpolating high antigen concentration from the linear portion of a standard antigen curve?
Thanks in advance
How can we interpolate the age of marker microfossils according to new time scale.
Eg. if the previous research papers or standard zonation charts has used older timescale then how can we use that particular microfossil in the new time scale.
When we are working with multiple microfossils then we try to follow a single timescale (the most recent one) so this is required.
I know it might depend also in the distribution / behavior of the variable that we are studying. The sample spacing must be able to capture the spatial dependence .
But, since Kriging is very much dependent in the computed variance within lag distance, if we have few number of observations we might fail to capture the spatial dependence because we would have few pairs of points within a specific lag distance. We would also have few number of lags. Specially, when we have points with a very irregular distribution across the study area, with a lot of observation in a specific region and sparce observations in other region, this will also will affect the estimation of computed variance among lag (different accuracy).
Therefore, I think in such circumstances computing semivariogram seems useless. What is the best practices if iwe still want to use kriging instead of other interpolation methods?
Thank you in advance
I would like to interpolate hourly gridded data (e.g. spatial grid 10 x 10 km) to a non regular set of points. The dataset covers around 100,000 points and, within each point, 1 year of data (i.e. 8760 hourly time-steps). I have tried using QGIS but the system crashes when attempting to load a .txt file of 8762 coloumns (e.g. lon, lat, T1, T2, T3, ..., T8760).
Probably Climate Data Operators (CDO) commands could be more useful/efficient in managing such heavy datasets.
Do you have any advice or experience to share on how to load and interpolate heavy datasets?
Thank you in advance for your kind reply.
One of my research students (Ph.D. research) has done extension type of research on representation of numerical data on a pair of variables by mathematical curve. He derived some formulas from Newton's forward interpolation formula , Newton's backward interpolation formula, Newton's divided difference interpolation formula and Lagrange's interpolation formula. Accordingly, he mentioned these four formulas in his thesis to be submitted for the degree. It was necessary to mention them in the thesis since these are the sources from which he derived the new formulas. But, in the plagiarism checking done by the university, these have been treated as copied from the others and due to this reason the thesis has been treated as unfit for submission. Thus the question here arises -whether it is wrong or otherwise to mention these four formulas in the thesis. Therefore the question is
" Is it wrong to mention an existing formula in the thesis, containing a new formula, if the new one is derived from it ? "
I have used geolocation grid data and interpolated the values between to get the incidence angles, but I am not able to assign the incidence angles to specific pixels on the image. Is there any way to get a 2d matrix of the incidence angles or any other way that may help me get to this?
I had an XY table that I mapped, and I have successfully interpolated 26 other maps from the same data set. For some reason, a few of the maps are not showing the appropriate variation, rather it is only showing a single value for every point.
When dealing with historical single radar station data (default coords system is polar), when I convert the polar coords system into cartesian system (e.g. WGS84), there will be some NaN region left around four corners for converted data. How to deal with these NaN value regions by proper interpolating methods?
Thank you all.