denss | Calculate electron density from a solution scattering | Data Manipulation library
kandi X-RAY | denss Summary
kandi X-RAY | denss Summary
DENSS is an algorithm used for calculating ab initio electron density maps directly from solution scattering data. DENSS implements a novel iterative structure factor retrieval algorithm to cycle between real space density and reciprocal space structure factors, applying appropriate restraints in each domain to obtain a set of structure factors whose intensities are consistent with experimental data and whose electron density is consistent with expected real space properties of particles. DENSS utilizes the NumPy Fast Fourier Transform for moving between real and reciprocal space domains. Each domain is represented by a grid of points (Cartesian), N x N x N. N is determined by the size of the system and the desired resolution. The real space size of the box is determined by the maximum dimension of the particle, D, and the desired sampling ratio. Larger sampling ratio results in a larger real space box and therefore a higher sampling in reciprocal space (i.e. distance between data points in q). Smaller voxel size in real space corresponds to higher spatial resolution and therefore to larger q values in reciprocal space. The core functions are stored in the saxstats.py module. The actual script to run DENSS is denss.py.
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- R Compute density of densities
- Compute the principal axes of a principal component
- Compute the inertia tensor
- Write atom numbers to file
- Aligns rho to the center of mass
- Parse arguments
- Calculate the chi2
- Return the parameter values as a string
- Check if raw data is raw
- Convert from FDB to FFT
- Calculate the form factor of an element
- Convert a PDB to support support
- Converts a PDB into a density map
- Compute the form factor of a realspace
- Convert u to b
- R Compute the density of densities
- Convert rho to rg
- Alignment for principal axes
- Calculate the average rho
- Save data to a file
- Write the molecule to a file
- Estimate the Vp for the scattering
- Centers the center of mass using the center of mass
- Select the best enantiomers for each rho
- Align rho to refactor
- Optimizes the alpha distribution
- Print the values of the store
denss Key Features
denss Examples and Code Snippets
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Trending Discussions on denss
QUESTION
I have a df
with 5
variables,
head(df,15)
...ANSWER
Answered 2019-May-11 at 03:26The ecdf follows the data exactly, without any smoothing. However, you can create a smoothed cumulative density by generating a kernel density estimate (basically a smoothed histogram) from the data and creating an "ecdf" from that. Here's an example with fake data:
First we generate a kernel density estimate using the density
function. This gives us, by default, a density estimate on a grid of 512 x-values. Then we use that as the "data" for calculating the ecdf, which is just the cumulative sum of the density (or, for any given point a along the x axis, the value of the ecdf at a is the area under the kernel density curve (that is, the integral from -Inf to a).
I've pacakaged the code into a function below so you can see how changing the adjust
parameter of the density function changes the smoothed ecdf. A smaller value of adjust
reduces the amount of smoothing, creating a density estimate that more closely follows the data. You can see in the plots below that setting adj=0.1
results in less smoothing of the smoothed ecdf so that it more closely follows the step in the original ecdf.
QUESTION
I have the following data file:
...ANSWER
Answered 2017-Aug-13 at 21:07I've discovered a way to do it. These two commands are equivalent:
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