Making CC plots

Utilites for plotting various CCs from careless output

rs.ccsym

Compute CCsym from careless output.

Note: This method currently assumes that careless has been called using both the reduced-symmetry and the parent spacegroup

usage: rs.ccsym [-h] [-s] [-i IMAGE] [-o OUTPUT] --op OP [-sg SPACEGROUP]
                [-m {spearman,pearson}] [--mod2]
                mtz [mtz ...]

mtz: MTZs containing crossvalidation data from careless

-h, --help: show this help message and exit

-s, --show: Make a plot of the results and display it using matplotlib.

-i <image>, --image <image>: Make a plot of the results and save it to this filename. The filetype will be determined from the filename. Any filetype supported by your matplotlib version will be available.

-o <output>, --output <output>: Optionally save results to this file in csv format instead of printing them to the terminal.

--op <op>: Symmetry operation to use to compute internal difference map. Can be given as ISYM if used with a spacegroup argumentSymops start counting at 0 (the identity), but CCsym for the identity are NaNs.Minus signs in symops are presently causing problems!

-sg <spacegroup>, --spacegroup <spacegroup>: Spacegroup to use for symmetry operation (only necessary if op specifies an ISYM).

-m {spearman,pearson}, --method {spearman,pearson}: Method for computing correlation coefficient (spearman or pearson)

--mod2: Use id mod 2 to assign delays (use when employing spacegroup hack)

rs.ccanom

Compute CCanom from careless output.

usage: rs.ccanom [-h] [-s] [-i IMAGE] [-o OUTPUT] [-m {spearman,pearson}]
                 mtz [mtz ...]

mtz: MTZs containing crossvalidation data from careless

-h, --help: show this help message and exit

-s, --show: Make a plot of the results and display it using matplotlib.

-i <image>, --image <image>: Make a plot of the results and save it to this filename. The filetype will be determined from the filename. Any filetype supported by your matplotlib version will be available.

-o <output>, --output <output>: Optionally save results to this file in csv format instead of printing them to the terminal.

-m {spearman,pearson}, --method {spearman,pearson}: Method for computing correlation coefficient (spearman or pearson)

rs.cchalf

Compute CChalf from careless output.

usage: rs.cchalf [-h] [-s] [-i IMAGE] [-o OUTPUT] [-m {spearman,pearson}]
                 mtz [mtz ...]

mtz: MTZs containing crossvalidation data from careless

-h, --help: show this help message and exit

-s, --show: Make a plot of the results and display it using matplotlib.

-i <image>, --image <image>: Make a plot of the results and save it to this filename. The filetype will be determined from the filename. Any filetype supported by your matplotlib version will be available.

-o <output>, --output <output>: Optionally save results to this file in csv format instead of printing them to the terminal.

-m {spearman,pearson}, --method {spearman,pearson}: Method for computing correlation coefficient (spearman or pearson)

rs.ccpred

Compute CCsym from careless output.

Note: If more than one -i argument is given, only overall CCpred is reported

usage: rs.ccpred [-h] [-s] [-i IMAGE] [-o OUTPUT] [-m {spearman,pearson}]
                 [--mod2] [--overall]
                 mtzs [mtzs ...]

mtzs: MTZs containing prediction data from careless

-h, --help: show this help message and exit

-s, --show: Make a plot of the results and display it using matplotlib.

-i <image>, --image <image>: Make a plot of the results and save it to this filename. The filetype will be determined from the filename. Any filetype supported by your matplotlib version will be available.

-o <output>, --output <output>: Optionally save results to this file in csv format instead of printing them to the terminal.

-m {spearman,pearson}, --method {spearman,pearson}: Method for computing correlation coefficient (spearman or pearson)

--mod2: Use (id mod 2) to assign delays (use when employing spacegroup hack)

--overall: Whether to report a single value for the entire dataset