binning

Intro In gnuplot there's a solution to create histogram from file named hist.dat what likes 1 2 2 2 3 by using commands binwidth=1 set boxwidth binwidth bin(x,width)=width*floor(x/width) + binwidth/2.0 plot [0:5][0:*] "hist.dat" u (bin($1,binwidth)):(1.0) smooth freq with boxes that generates a histogram like this one from other SO page . Question How can I fit my function to this histogram? I defined a Gaussian function and initialized its values by f(x) = a*exp(-((x-m)/s)**2) a=3; m=2.5; s=1 and in the output the function follow the histogram well. Unfortunatelly I cannot fit to this

I have two data sets, one for crime in Chicago, labeled with longitude and latitude coords and a shapefile of census blocks also in Chicago. Is it possible in R to aggregate crimes within census blocks, given these two files? The purpose is to be able to map out the crimes by census block. Location for download of Chicago census tract data: https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Census-Blocks-2000/uktd-fzhd Location for download of crime data: https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2 Some code that I have pruned down from

I'm new to R. My data has 600k objects defined by three attributes: Id , Date and TimeOfCall . TimeofCall has a 00:00:00 format and range from 00:00:00 to 23:59:59 . I want to bin the TimeOfCall attribute, into 24 bins, each one representing hourly slot (first bin 00:00:00 to 00:59:59 and so on). Can someone talk me through how to do this? I tried using cut() but apparently my format is not numeric. Thanks in advance! While you could convert to a formal time representation, in this case it might be easier to just use substr : test <- c("00:00:01","02:07:01","22:30:15") as.numeric(substr(test,1

I have a bunch of geographical data as below. I would like to group the data by bins of .2 degrees in longitude AND .2 degree in latitude. While it is trivial to do for either latitude or longitude, what is the most appropriate of doing this for both variables? |User_ID |Latitude |Longitude|Datetime |u |v | |---------|----------|---------|-------------------|-----|-----| |222583401|41.4020375|2.1478710|2014-07-06 20:49:20|0.3 | 0.2 | |287280509|41.3671346|2.0793115|2013-01-30 09:25:47|0.2 | 0.7 | |329757763|41.5453577|2.1175164|2012-09-25 08:40:59|0.5 | 0.8 | |189757330|41.5844998|2.5621569

I have a set of data, and want to make an histogram of it. I need the bins to have the same size , by which I mean that they must contain the same number of objects, rather than the more common (numpy.histogram) problem of having equally spaced bins. This will naturally come at the expenses of the bins widths, which can - and in general will - be different. I will specify the number of desired bins and the data set, obtaining the bins edges in return. Example: data = numpy.array([1., 1.2, 1.3, 2.0, 2.1, 2.12]) bins_edges = somefunc(data, nbins=3) print(bins_edges) >> [1.,1.3,2.1,2.12] So the

I have a large two dimensional array arr which I would like to bin over the second axis using numpy. Because np.histogram flattens the array I'm currently using a for loop: import numpy as np arr = np.random.randn(100, 100) nbins = 10 binned = np.empty((arr.shape[0], nbins)) for i in range(arr.shape[0]): binned[i,:] = np.histogram(arr[i,:], bins=nbins)[0] I feel like there should be a more direct and more efficient way to do that within numpy but I failed to find one. You could use np.apply_along_axis : x = np.array([range(20), range(1, 21), range(2, 22)]) nbins = 2 >>> np.apply_along_axis

I am looking for a fast formulation to do a numerical binning of a 2D numpy array. By binning I mean calculate submatrix averages or cumulative values. For ex. x = numpy.arange(16).reshape(4, 4) would have been splitted in 4 submatrix of 2x2 each and gives numpy.array([[2.5,4.5],[10.5,12.5]]) where 2.5=numpy.average([0,1,4,5]) etc... How to perform such an operation in an efficient way... I don't have really any ideay how to perform this ... Many thanks... You can use a higher dimensional view of your array and take the average along the extra dimensions: In [12]: a = np.arange(36).reshape(6,