#MAGIC MAPS GRID REFERENCE HOW TO#

We could use multiple seeds to obtain a single mask for all blobs together. The second argument of flood_fill_above() is a list of positions used as seeds. sum () # = number of masked points 62 > _ * grid. flood_fill_above ( grid, , threshold = 0.6 ) > _. To get a maskĬorresponding to a Blob we need to run the flood fill algorithm again,

Objects that contain a few statistics for each blob. The blob search from the previous section uses flood fill and returns Blob (or, alternatively, below) a given threshold value. Here, the criterion for grid points is having the value above The area of all connected points which match a certain condition. Is an algorithm that determines the area connected to a seed point, min_score = 0, min_peak = 0 ) > blobs > blobs. find_blobs_by_flood_fill ( grid, cutoff = 0.6, min_volume = 5. set_to_zero ( grid, st ) > # find the blob > blobs = gemmi. remove_waters () > # use SolventMasker to mask the models with zeros > masker = gemmi. read_structure ( './tests/5wkd.pdb' ) > st. No real blobs in this tiny example, > # so we remove water to get one water-sized blob. transform_f_phi_to_map ( 'FWT', 'PHWT', sample_rate = 3 ) > # Load model. read_mtz_file ( './tests/5wkd_' ) > grid = mtz.

#MAGIC MAPS GRID REFERENCE HOW TO#

Here is how to create a mask identical as phenix.mask: This step was added for compatibility with Refmac. (contiguous areas of 1’s) up to a certain volume. If it is not sufficient, we can explicitly remove islands The above procedure eliminates small solvent islands. Proposed r probe = 1.0 Å and r shrink = 1.1 Å. We shrink the non-solvent area by r shrink.Īll the 0’s in a distance r shrink from 1’s are changed to 1, The extra margin r probe is largely cancelled in the next step. r usually depends on the element,īut some programs use the same radius for all atoms. We mark the area in radius r + r probe of eachĪtom as non-solvent (0). It uses van der Waals (or similar) atomic radii r and two parameters: Gemmi implements a variant of the most popular method for calculating value != 0 : print ( point ) 0.125> 7> Solvent mask ¶ value for point in asu ) 7.125 > for point in asu. The primary use for MaskedGrid is working with asymmetric unit (asu) only: Of the grid is unmasked and is to be used. When an element of the mask is 0 (false), the corresponding element Positions and a reference to the original grid.

Sometimes we want to focus on a part of the grid only.įor this, we have class MaskedGrid that contains vector marking masked (If your points are not on a regular grid – get in touch – there might be interpolate_values ( arr, tr ) > arr # -> corresponds to Position(2, 3, 4) 2.0333264 fromlist () > # finally we calculate interpolated values > grid. float32 ) > # then we setup a transformation (array indices) -> (position ). > # first we create a numpy array of the same type as the grid > arr = numpy.

With two arguments: a 3D NumPy array (for storing the results)Īnd a Transform that relates indices of the array With our grid) call interpolate_values() (with s at the end) If these points are on a regular 3D grid (which may not be aligned If you have a large number of points, making a Python function call If you’d like to speed it up or to get derivatives, The latter method should be more efficient, but gemmi uses the former, Which involves 64圆4 matrix of integral coefficients The right plot – along a line parallel to one of the axes.Ĭan be implemented either as 21 cubic interpolations, or using method The left plot shows density along a line in a random direction, Trilinear interpolation is blue, tricubic – red. In a grid that was filled with random numbers from [0, 1). This is illustrated on the plots below, which shows density along two lines

The cubic interpolation is smoother than linear, but may amplify the noise. Position ( 2, 3, 4 )) 2.6075661737715046 > # calculate also derivatives in directions of unit cell axes > grid.

Magic maps grid reference