I was useing Obspy spectrogram to look some Deep-ocean Assessment and Reporting of Tsunamis (DART) data.
The raw data is seafloor pressure[dbar].(The variation of pressure with Time)
The spectrogram x scale is time series, y scale is frequency.
I would like to get the data (The variation of amplitude of frequency with Time) which converted by spectrogram(FFT).
And I expect to plot like this picture.
enter image description here
What should I do? Is there have any way to get the converted data?
Could I output the data (The variation of amplitude of frequency with Time)?
Related
I have two sensors in the same place, but one of the sensors stopped working, and I therefore want to interpolate the missing values based on the pattern that exists in the other sensor that still works.
The Y axis is a value is degrees in celsius, and the X is aggregated data on 10 minute interval.
Picture of the graph and data I want to interpolate.
I am unsure how to do this, and which package and formula to use. Any ideas?
I'm using R and I have a raster stack of surface soil moisture measurements from a radiometer on fixed on an observation tower. These data are daily values going back 10 years.
I also have another raster stack of satellite microwave measurements of soil moisture over a larger area going back 25 years. Both sensors have similar frequencies.
On a per-pixel basis, I would like to use a linear cumulative distribution function matching that rescales the satellite data against the tower data so that it would result in a longer time series of rescale satellite data.
This point is to correct for systematic differences between the soil moisture values and extend the time series. This is similar to what was done in the figure below where they matched the AMSR-E (blue plot) and ASCAT (red plot) data to Noah data (black plot).
Does anyone know how to implement this in R? Or at the very least help me get started? I've scoured the Internet and this website without success.
I am relatively new to R studio and R in general, I am not even sure if this is the right place to ask this question. I was instructed to draw a graph showing seasonality using daily rainfall over a number of years. I need help more in interpreting the graph than in plotting it.
There is an example already in R using mscdata that I was able to replicate using my own data, the code for the example is as below. Any help with what this graph means or explains will be greatly appreciated.Thank you
install.packages(seas)
library(seas)
data(mscdata)
dat <- mksub(mscdata, id=1108447)
dat.ss <- seas.sum(dat, width="mon")
x<-mscdata
# Structure in R
str(dat.ss)
tail(mscdata)
# Annual data
dat.ss$ann
# Demonstrate how to slice through a cubic array
dat.ss$seas["1990",,]
dat.ss$seas[,2,] # or "Feb", if using English locale
dat.ss$seas[,,"precip"]
# Simple calculation on an array
(monthly.mean <- apply(dat.ss$seas[,,"precip"], 2, mean,na.rm=TRUE))
barplot(monthly.mean, ylab="Mean monthly total (mm/month)",
main="Un-normalized mean precipitation in Vancouver, BC")
text(6.5, 150, paste("Un-normalized rates given 'per month' should be",
"avoided since ~3-9% error is introduced",
"to the analysis between months", sep="\n"))
# Normalized precip
norm.monthly <- dat.ss$seas[,,"precip"] / dat.ss$days
norm.monthly.mean <- apply(norm.monthly, 2, mean,na.rm=TRUE)
print(round(norm.monthly, 2))
print(round(norm.monthly.mean, 2))
barplot(norm.monthly.mean,
ylab="Normalized mean monthly total (mm/day)",
main="Normalized mean precipitation in Vancouver, BC")
# Better graphics of data
dat.ss <- seas.sum(dat, width=11)
image(dat.ss)
This code gives a graph showing sample quartiles, annual rainfall but I don't really know what it means. Any help whatsoever will be appreciated
The Graph using the package seas is as below
Plot
I'll start with the top left graph :
You've probably guessed that each row is a year (as shown by the Y-axis) while day groups/months of the year are X-axis. The color of each box of the heatmap is proportionally darker according to the mm's worth of rain in that day group, with the scale being displayed on the far right. I assume the red X's mean missing values.
Top right is like a barplot with the sum of rainfall each year (row), just continuously plotted. The red bar should be the average precipitation overall (not sure about the orange one).
Bottom left is a bit more tricky. Think of it like you reordered the rows in each column to have the heaviest rainfall of the day group at the top (forgetting about the year info here). The Y-axis shows the quantiles. The quantiles' respective values change for each day group, so the lines you see on top of the plot indicate key rainfall values in mm (4,6,8,10,12). Indeed, If you look at the 2mm line (lowest one), you'll see that in January, about 20% of rainfalls (across all years) are below this threshold, while in the end of July, over 80% are below 2mm (expect less rainfall in the summer).
Lastly, bottom right is similar to the one above it. It's the sum of all rows, referring to the quantiles rather than years this time, resulting in the staircase pattern.
You'll notice that since the scale of the plot is the same as the one showing the average per year, the top of the staircase is outside of the plot...
Hope I made that clear enough.
I'm trying to find out the peak frequencies hidden in my data using the fft() method in R. While preparing the data, a more experienced user recommends to create a "mask" (more after explaining the details), that does give me the exact diagram I'm looking for. The problem is, I don't understand what it does or why it's needed.
To give some context, I'm working with .txt files with around 12000 entries each. It's voltage vs. time information, and the expected result is just a sinusoidal wave with a clear peak frequency that should be close to 1-2 Hz. This is an example of what one of those files look like:
I've been trying to use the Fast Fourier Transform method fft() implemented in R to find the peak frequencies and get a diagram that reflected them clearly. At first, I calculate some things that I understand are going to be useful, like the Nyquist frequency and the range of frequencies I'll show in the final graph:
n = length(variable)
dt = time[5]-time[4]
df = 1/(max(time)) #Find out the "unit" frequency
fnyquist = 1/(2*dt) #The Nyquist frequency
f = seq(-fnyquist, fnyquist-df, by=df) #These are the frequencies I'll plot
But when I plot the absolute value of what fft(data) calculates vs. the range of frequencies, I get this:
The peak frequency seems to be close to 50 Hz, but I know that's not the case. It should be close to 1 Hz. I'm a complete newbie in R and in Fourier analysis, so after researching a little, I found in a Swiss page that this can be solved by creating a "mask", which is actually just a vector with a repeatting patern (1, -1, 1, -1...) with the same length as my data vector itself:
mask=rep(c(1, -1),length.out=n)
Then if I multiply my data vector by this mask and plot the results:
results = mask*data
plot(f,abs(fft(results)),type="h")
I get what I was looking for. (This is the graph after limiting the x-axis to a reasonable scale).
So, what's the mask actually doing? I undestand it's changing my data point signs in an alternate manner, but I don't get why it would take the infered peak frequencies from ~50 Hz to the correct result of ~1 Hz.
Thanks in advance!
Your "mask" is one of two methods of performing an fftshift, which is commonly done to center the 0 Hz output of an FFT in the middle of a graph or plot (instead of at the left edge, with the negative frequencies wrapping around to the right edge).
To perform an fftshift, you can hetrodyne or modulate your data (by Fs/2) before the FFT, or simply do a circular shift by 50% after the FFT. Both produce the same result. They are the same due to the shift property of the DFT.
I have a time series dataset with spatial data (x,y coordinates). Each point is static in location, but its value varies over time, ie. each point has its own unique function. I want to assign these functions as a mark, so I can plot the point pattern with each individual time series as a plotting symbol.
This is an exploratory step to eventually perform some spatial functional data analysis.
As an example, I want something like Figure 2 published in this article:
*Delicado,P., R. Giraldo, C. Comas, and J. Mateu. 2010. Spatial Functional Data: Some Recent Contibutions. Environmetrics 21:224-239
I'm having trouble posting an image of the figure
1) Working in R with ggplot2, I can plot a line of change in quant of each id over time:
(Fake example dataset, where x and y are Carteian coordinates, id is an individual observation, and quant are values of id at each year):
x<-c(1,1,1,2,2,2,3,3,3)
y<-c(1,1,1,2,2,2,3,3,3)
year<-c(1,2,3,1,2,3,1,2,3)
id<-c("a","a","a","b","b","b","c","c","c")
quant<-c(5,2,4,2,4,2,4,4,6)
allData<-data.frame(x,y,year,id,quant)
ggplot(allData,aes(x=year,y=quant, group=id))+geom_line()
2) Or I can plot the geographic point pattern of id:
ggplot(allData,aes(x=x,y=y,color=id))+geom_point()
I want to plot the graph from (2), but use the line plots from (1) as the point symbols (marks). Any suggestions?