upxo.repqual.grain_network_repr_assesser module

Created on Fri Jun 7 10:10:42 2024

@author: Dr. Sunil Anandatheertha

class upxo.repqual.grain_network_repr_assesser.KREPR(**kwargs)

Bases: object

Grain-network representativeness assessor (2D K-topology / morphometrics).

Compares target and sample grain structures via neighbour networks (NetworkX / UPXO netops), morphological property tables, and distribution distances (Wasserstein, KS, energy distance, optional NetLSD). Used by repgen2d ranking and standalone R-field studies.

Import:

from upxo.repqual.grain_network_repr_assesser import KREPR

Parameters / attributes

upxogs_tgt, upxogs_smp

Single UPXO grain-structure objects when used in one-vs-one mode.

tgset, sgsetdict

Sets of target / sample grain structures (keys often tslices).

tkset, sksetdict

Neighbour-network graphs (or adjacency dicts) for target / sample, keyed by neighbour order ordern.

tnset, snsetdict

UPXO neighbour maps (gid → neighbour gids) by order.

tmpset, smpsetdict

Morphological property sets for target / sample.

tid, sidlist

Usable target / sample IDs (subset of set keys); default all keys.

ordernlist

Neighbour orders to analyse (default [1] if unset).

mprop2d_flags, mprop3d_flags, sprop2d_flags, sprop3d_flagsdict

Flags controlling which morphological properties are computed.

mprop2d, mprop3d, sprop2d, sprop3ddict

Computed morphological property stores.

rkfdict

Network-based R-field data; set before full representativeness runs.

_cim_: str

Class initiation method, not intended for user use.

Author: Dr. Sunil Anandatheertha

om jayanti man’gaLA kALi bhadrakALi kapAlini | durgA kshamA shivA dhAtri svAhA svadhA namOstute ||

mp_gspn_map

dim

gstype

tid

sid

upxogs_tgt

upxogs_smp

tgset

sgset

tkset

skset

tnset

snset

classmethod from_gs(*, upxogs_tgt=None, upxogs_smp=None, tgset=None, sgset=None, ordern=[1], tsid_source='from_gs', ssid_source='from_gs', tid=None, sid=None, _cim_='from_gs', gstype_tgt='mcgs2d', gstype_smp='mcgs3d')

Instantiate network based repr class using UPXO grain structure.

Parameters:

• upxogs_tgt

• upxogs_smp

• tgset (dict) – Target grain structures. Defaults to None.

• sgset (dict) – Sample grain structures. Defaults to None.

• ordern (list) – Neighbour order-n to be used. Defaults to [1].

• _cim_ (str) – Class initiation method. Defaults to ‘from_gs’. Not intended for user. Leave it alone.

• mcgs (from upxo.ggrowth.mcgs import)

• mcgs(study='independent' (tgt =)

• input_dashboard='input_dashboard.xls')

• tgt.simulate()

• tgt.detect_grains()

• tgt.char_morph_2d(tgt.m)

• {i (tgset =)

• KREPR (from upxo.repqual.grain_network_repr_assesser import)

• KREPR.from_gs(tgset=tgset (kr =)

• sgset=tgset

• ordern=[1

• 5])

• kr.creation_method

• kr.calculate_mprop2d()

• kr.set_rkf(js=True – btwcen=False, clscen=False, egnvcen=False)

• wd=True – btwcen=False, clscen=False, egnvcen=False)

• ksp=True – btwcen=False, clscen=False, egnvcen=False)

• ed=True – btwcen=False, clscen=False, egnvcen=False)

• nlsd=True – btwcen=False, clscen=False, egnvcen=False)

• degcen=False – btwcen=False, clscen=False, egnvcen=False)

:param : btwcen=False, clscen=False, egnvcen=False) :param kr.calculate_rkf(): :param kr.plot_rkf(neigh_orders=[1: xtick_incr=5, ytick_incr=5, lfs=7, tfs=8,

cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

‘rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

Parameters:

• 5] –

  xtick_incr=5, ytick_incr=5, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

  ’rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

• power=1 –

  xtick_incr=5, ytick_incr=5, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

  ’rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

• figsize=(7 –

  xtick_incr=5, ytick_incr=5, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

  ’rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

• 5) –

  xtick_incr=5, ytick_incr=5, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

  ’rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

• dpi=120 –

  xtick_incr=5, ytick_incr=5, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

  ’rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

:paramxtick_incr=5, ytick_incr=5, lfs=7, tfs=8,

cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation=’vertical’, flags={‘rkf_js’: False, ‘rkf_wd’: True,

‘rkf_ksp’: True, ‘rkf_ed’: True, ‘rkf_nlsd’: True, ‘rkf_degcen’: False, ‘rkf_btwcen’: False, ‘rkf_clscen’: False, ‘rkf_egnvcen’: False})

Parameters:

• measure (data_title = 'R-Field)

• 5 (n_bins =)

• AD – neigh_orders=[1, 5], n_bins=n_bins, data_title=data_title, throw=True, plot_ad=False)

• kr.calculate_uncertainty_angdist(rkf_measure='ed' (AX =) – neigh_orders=[1, 5], n_bins=n_bins, data_title=data_title, throw=True, plot_ad=False)

:paramneigh_orders=[1, 5],

n_bins=n_bins, data_title=data_title, throw=True, plot_ad=False)

Parameters:

• kr.plot_ang_dist(AD – figsize=(5, 5), dpi=150, data_title=data_title, cmap=’nipy_spectral’)

• neigh_orders=[1 – figsize=(5, 5), dpi=150, data_title=data_title, cmap=’nipy_spectral’)

• 5] – figsize=(5, 5), dpi=150, data_title=data_title, cmap=’nipy_spectral’)

• n_bins=n_bins – figsize=(5, 5), dpi=150, data_title=data_title, cmap=’nipy_spectral’)

:paramfigsize=(5, 5), dpi=150, data_title=data_title,

cmap=’nipy_spectral’)

Parameters:

kr.calculate_mprop2d()

classmethod from_neigh(*, tnset=None, snset=None, ordern=[1], tsid_source='from_neigh', ssid_source='from_neigh', tid=None, sid=None, _cim_='from_neigh')

# Assuming 20 tslices being available witrh increments of tslice=1, # we will go through the folloing example.

ordern = [1, 3]

from upxo.ggrowth.mcgs import mcgs tgt = mcgs(study=’independent’, input_dashboard=’mcgs2d_100x100_m50_q10_mcalg201.xls’) tgt.simulate() tgt.detect_grains() tslices = np.array(list(tgt.gs.keys()))[1::10] tnset = {no: {tslice: None for tslice in tslices} for no in ordern} for no in ordern:

for tslice in tslices:

_ = tgt.gs[tslice].get_upto_nth_order_neighbors_all_grains tnn = _(no, include_parent=True, output_type=’nparray’) tnset[no][tslice] = tnn

smp = mcgs(study=’independent’, input_dashboard=’mcgs2d_100x100_m50_q10_mcalg201.xls’) smp.simulate() smp.detect_grains() tslices = np.array(list(smp.gs.keys()))[1::10] snset = {no: {tslice: None for tslice in tslices} for no in ordern} for no in ordern:

for tslice in tslices:

_ = smp.gs[tslice].get_upto_nth_order_neighbors_all_grains snn = _(no, include_parent=True, output_type=’nparray’) snset[no][tslice] = snn

from upxo.repqual.grain_network_repr_assesser import KREPR kr = KREPR.from_neigh(tnset=tnset, snset=tnset,

tid=list(tnset.keys()), sid=list(snset.keys()), _cim_=’from_neigh’)

kr.snset.keys() kr.snset[3].keys() kr.snset[3][11] kr.snset[3][11][40] # <– O(3) Neigh gids of gid=40 of tslice = 11 kr.ordern kr.tid

classmethod from_k(*, tkset=None, skset=None, ordern=[1], tsid_source='from_k', ssid_source='from_k', tid=None, sid=None, _cim_='from_k')

# Assuming 20 tslices being available witrh increments of tslice=1, # we will go through the folloing example.

ordern = [1, 3]

from upxo.ggrowth.mcgs import mcgs tgt = mcgs(study=’independent’, input_dashboard=’mcgs2d_100x100_m50_q10_mcalg201.xls’) tgt.simulate() tgt.detect_grains() tslices = np.array(list(tgt.gs.keys()))[1::10] tkset = {no: {tslice: None for tslice in tslices} for no in ordern} for no in ordern:

for tslice in tslices:

_ = tgt.gs[tslice].get_upto_nth_order_neighbors_all_grains tnn = _(no, include_parent=True, output_type=’nparray’) tnn_k = kmake.create_grain_network_nx(tnn) tkset[no][tslice] = tnn_k

smp = mcgs(study=’independent’, input_dashboard=’mcgs2d_100x100_m50_q10_mcalg201.xls’) smp.simulate() smp.detect_grains() tslices = np.array(list(smp.gs.keys()))[1::10] skset = {no: {tslice: None for tslice in tslices} for no in ordern} for no in ordern:

for tslice in tslices:

_ = smp.gs[tslice].get_upto_nth_order_neighbors_all_grains snn = _(no, include_parent=True, output_type=’nparray’) snn_k = kmake.create_grain_network_nx(snn) skset[no][tslice] = snn_k

from upxo.repqual.grain_network_repr_assesser import KREPR kr = KREPR.from_k(tkset=tkset, skset=tkset,

tid=list(tkset.keys()), sid=list(skset.keys()), _cim_=’from_k’)

kr.tkset kr.ordern kr.tid

classmethod from_gsgen(gstype_tgt='mcgs2d', gstype_smp='mcgs3d', is_smp_same_as_tgt=False, characterize_tgt=True, characterize_smp=True, tgt_dashboard='input_dashboard_krepr1.xls', smp_dashboard='input_dashboard_krepr2.xls', ordern=[1], tsid_source='from_neigh', ssid_source='from_neigh', tid=None, sid=None, _cim_='from_gsgen')

Initiate KREPR by generating target and sample grain structure sets.

Parameters:

• gstype (str) – Type of grain structure needed. Could be deprecated later on. Defaults to ‘mcgs’.

• is_smp_same_as_tgt (bool) – Defaults to False.

• tgt_dashboard (str) – Defaults to ‘input_dashboard.xls’.

• smp_dashboard (str) – Defaults to ‘input_dashboard.xls’.

• _cim_ (str) – Defaults to ‘from_gsgen’.

• Explanations

• ------------

Example

from upxo.repqual.grain_network_repr_assesser import KREPR kr = KREPR.from_gsgen(gstype=’mcgs’,

is_smp_same_as_tgt = False, tgt_dashboard=’input_dashboard.xls’, smp_dashboard=’input_dashboard.xls’, _cim_=’from_gsgen’)

kr.tgset kr.ordern kr.tid

property creation_method

Creation method.

init_subdef_set_dim()

Init subdef set dim.

init_subdef_set_gsid(data)

Init subdef set gsid.

init_subdef_set_neighs(data)

Init subdef set neighs.

init_subdef_set_networks()

Init subdef set networks.

init_subdef_set_prop_flags()

Init subdef set prop flags.

set_ordern(ordern)

Set the n values in O(n).

Parametyers

ordern: list

O(n) values

rtype:

None

set_tid(from_gs=False, from_k=False, from_neigh=False, tid=None)

Set or update tid.

set_sid(from_gs=False, from_k=False, from_neigh=False, sid=None)

Set or update sid.

set_mprop2d_flags(area_pix=True, area_geo=False, gbl_pix=False, gbl_geo=False, eq_dia=False, ell_a=False, ell_b=False, inclination=False, aspect_ratio=False, roundness=False, circularity=False, solidity=False, formfactor=False, convexity=False, br=False, gbr=False, fractal_dimension=False)

Set flags for operational 2D morphological properties.

Parameters:

• area_pix (Pixel area of the grains. Defaults to True.)

• area_geo (Geometric area of the grains. Defaults to False.)

• gbl_pix (Pixel gb length of the grains. Defaults to False.)

• gbl_geo (Geometric gb length of the grains. Defaults to False.)

• eq_dia (Equivalent diameter of the grains. Defaults to False.)

• ell_a (Fit ellipse's major axis length of grains. Defaults to False.)

• ell_b (Fit ellipse's minor axis length of grains. Defaults to False.)

• inclination (Morphological inclination of grains. Defaults to False.)

• their (The input arguments for characterisaion module for mcgs 2d and)

• are (mapping with the above variables) – npixels: area_pix npixels_gb: gbl_pix area: gbl_pix (preferred as of now) eq_diameter: eq_dia perimeter: gbl_pix perimeter_crofton: not available yet compactness gb_length_px aspect_ratio solidity morph_ori circularity eccentricity feret_diameter major_axis_length minor_axis_length euler_number

• structures (Data)

• ---------------

• mprop2d_flags (dict)

set_mprop3d_flags(volume_vox=False, volume_geo=False, eq_dia=False, gba_vox=False, gba_geo=False, gbl_vox=False, gbl_geo=False, ell_a=False, ell_b=False, ell_c=False, inclination=False, sphericity=False, aspectratio_lw=False, aspectratio_lh=False, aspectratio_wh=False, vol_surfarea_ratio=False, flatness=False, compactness=False, iso_parametric_quotient=False, convextity=False, shape_entropy=False)

Set flags for operational 3D morphological properties.

Parameters:

• volume_vox (Voxellated volume of the grains. Defaults to True.)

• volume_geo (Geometric volume of the grains. Defaults to False.)

• gba_vox (Voxellated gb surface area of grains. Defaults to False.)

• gba_geo (Geometric gb surface area of grains. Defaults to False.)

• gbl_vox (Voxellated gb length of grains. Defaults to False.)

• gbl_geo (Geometric gb length of grains. Defaults to False.)

• eq_dia (Equivalent diameter of the grains. Defaults to False.)

• ell_a (Fit ellipsoid's 1st axis length of grains. Defaults to False.)

• ell_b (Fit ellipsoid's 2nd axis length of grains. Defaults to False.)

• ell_c (Fit ellipsoid's 3rd axis length of grains. Defaults to False.)

• inclination (Morphological inclination of grains. Defaults to False.)

• structures (Data)

• ---------------

• mprop3d_flags (dict)

set_prop_flag(propname, propflagvalue)

Set or update prop flag.

calculate_mprop2d(print_msg_tors=True, print_msg_prnm=True, print_msg_no=True, print_msg_gsid=False, print_msg_gid=False)

Data structure

kr.mprop2d: dict

kr.mprop2d[tors]: dict

kr.mprop2d[tors][prnm]: dict

kr.mprop2d[tors][prnm][no]: dict

kr.mprop2d[tors][prnm][no][gsid]: dict

kr.mprop2d[tors][prnm][no][gsid][gid]: np.array

kr.mprop2d[tors][prnm][no][gsid][gid][i]: float

Where,

mprop2d: 2d morphology properties tors: either ‘tgt’ or ‘smp’ prnm: property name no: neighbour order gsid: grain structure ID gid: grain ID i: prnm Property value of ith neighbour of gid grain of tors gid

for O(n) = on.

Data access

kr.mprop2d[‘tgt’][‘area_pix’][O(n)][gsid][GID]. This contains a

list of gids which are O(n) neighbours of GID grain.

Example

gid = 2 kr.mprop2d[‘tgt’][‘area_pix’][1.25][8][gid] The correspionding neighbour data is: kr.tnset[1.25][8][gid]

Note

len(kr.tnset[1.25][8][gid]) = kr.mprop2d[‘tgt’][‘area_pix’][1.25][8][gid].size

mpflags: local copy of morpho prop flag. reqprop: keys in mpflags with True values. tors: target or sample: self.mprop2d keys. prnm: property name in the list of values in reqprop. no: neighbour order in list kr.ordern. gsid: Grain structue ID in self.tid gid: Grain IDs in local neighbour network.

Author: Dr. Sunil Anandatheertha

estimate_upper_ordern_bycount(tors='tgt', gsid=1, on_start=1.0, on_max=10.0, on_incr=0.5, neigh_count_vf_max=0.8, include_parent=True, kdeplot=True, kdeplot_kwargs={'cmap': 'cividis', 'dpi': 120, 'figsize': (5, 5), 'fill': True, 'fs_legend': 10, 'fs_xlabel': 12, 'fs_xticks': 10, 'fs_ylabel': 12, 'fs_yticks': 10, 'legend_loc': 'best', 'legend_ncols': 2}, statplot=True, statplot_kwargs={'dpi': 120, 'figsize': (5, 5), 'stat': 'mean'}, gsplot=True, gsplot_kwargs={'dpi': 120, 'figsize': (5, 5)})

Estimate O(n) needed to reach neigh_count_vf_max.

Parameters:

• tors (str) – Specify ‘tgt’ for Target and ‘smp’ for Sample. Defaults to ‘tgt’.

• gsid (int) – Grain Structure ID. Defaults to 1.

• on_start (float) – Minimum O(n) value to start iterations from. on_start >= 1. Defaults to 1.0.

• on_max (float) – Maximum O(n) value to end iterating. on_max >= on_start. Defaults to 10.0.

• on_incr (float) – del(O(n)) increments to o(n) search space. on_incr >= 0.1. Defaults to 0.5.

• neigh_count_vf_max (float) – neigh_count_vf value to stop iterating. 0.11 < neigh_count_vf_max < 0.99, generally, although value may change depending on grain structure. Note: these bounds are not accurate. Defaults to 0.8.

• include_parent (bool) – Include gid in the neigh list of gid if True, else exclude. Defaults to True.

• plot_kde (bool) – Plot kdes of a list containing total number of neighbours of every gid in the grain structure for each O(n). Defaults to True.

Returns:

• LON (float) – Limiting Order-n

• neighn_stats (dict) – keys: on of every iteration. value: dict

  (key, value):

  ’mean’ neighn.min() ‘min’: neighn.min() ‘max’: neighn.max() ‘std’: neighn.std() ‘var’: neighn.var() ‘iqr’: stats.iqr(neighn): Inter-quartile range ‘sem’: stats.sem(neighn): Standard Error of the Mean

  Where,

  neighn = np.array([len(neighs) for neighs in ngh.values()]) ngh: dict: {gid: gid neighbours list}

• Ng (int) – Number of grains in the provided grain structure.

• Explanations

• ————

• As O(n) increases the number of order-n neighbours (N) for a gid

• increases. But, it cannot increase for ever. Its maximum value is

• the total number of grains in the grain structure. The ratio of N to

• total number of grains (i.e. neigh_count_vf) is then unity. However,

• for o(n) < O(n), neigh_count_vf < 1. This function helps determine

• o(n) for which neigh_count_vf < neigh_count_vf_max.

• The kde if plotted, will show the following trends –

  • Shift right as o(n) increases during iterations.

  • Peak drops initially as o(n) increases and as width increases.

  • Peak increase again as o(n) increases further and width decreases.

find_neigh_order_n(saa=True, throw=False)

Find neigh order n.

create_gid_network(dataid='tgt', neigh_order=1, gsid=1)

Create the network nx graph from the neighbours dictionary.

Parameters:

• dataid (str. Options: 'tgt' (default), 'smp'.)

• neigh_order (int. Order of the raw neighbours data-structure. Defaults) – to 1.

• gsid (int. ID of the grain structure. Defaults to 1.)

Returns:

nxg

Return type:

network nx graph.

create_tgt_networks(saa=True, throw=False)

Create networkx graphs for all target gs neighbours database.

Parameters:

• saa (bool.) – Save as attrbute of True. Defults to True.

• throw (bool.) – Return value if True. Defaults to False.

• structure (Data)

• --------------

• dict(no1 (dict(gsid1: dict(gid1: [12, 1, 16,..]))),) – no2: dict(gsid2: dict(gid2: [16, 15, 8,..]))),… noi: dict(gsidj: dict(gidk: [2, 86, 95,..]))),… noN: dict(gsidM: dict(gidG: [20, 15, 196,..]))),… )

• Where –

  noi: an element of ordern list of size N. gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

  neighbour network dictionary of the ith O(n) database.

:paramnoi: an element of ordern list of size N.

gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

neighbour network dictionary of the ith O(n) database.

create_smp_networks(saa=True, throw=False)

Create networkx graphs for all sample gs neighbours database.

Parameters:

• saa (bool.) – Save as attrbute of True. Defults to True.

• throw (bool.) – Return value if True. Defaults to False.

• structure (Data)

• --------------

• dict(no1 (dict(gsid1: dict(gid1: [12, 1, 16,..]))),) – no2: dict(gsid2: dict(gid2: [16, 15, 8,..]))),… noi: dict(gsidj: dict(gidk: [2, 86, 95,..]))),… noN: dict(gsidM: dict(gidG: [20, 15, 196,..]))),… )

• Where –

  noi: an element of ordern list of size N. gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

  neighbour network dictionary of the ith O(n) database.

:paramnoi: an element of ordern list of size N.

gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

neighbour network dictionary of the ith O(n) database.

create_tgt_smp_networks(saa=True, throw=False)

Create networkx graphs for all tgt and smp gs neighbours database.

Parameters:

• saa (bool.) – Save as attrbute of True. Defults to True.

• throw (bool.) – Return value if True. Defaults to False.

• structure (Data)

• --------------

• dict(no1 (dict(gsid1: dict(gid1: [12, 1, 16,..]))),) – no2: dict(gsid2: dict(gid2: [16, 15, 8,..]))),… noi: dict(gsidj: dict(gidk: [2, 86, 95,..]))),… noN: dict(gsidM: dict(gidG: [20, 15, 196,..]))),… )

• Where –

  noi: an element of ordern list of size N. gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

  neighbour network dictionary of the ith O(n) database.

:paramnoi: an element of ordern list of size N.

gsidj: jth grain structure’s ID of a toytal of M grain structes. gidk: kth grain ID of all G grains. noi-gsidj-gidk: kth grain ID in the jth grain structure’s

neighbour network dictionary of the ith O(n) database.

set_rkf(js=False, wd=False, ksp=False, ed=False, nlsd=False, degcen=False, btwcen=False, clscen=False, egnvcen=False)

Set rkf field calculation flags and initiate rkf dict accordingly.

Parameters:

• js (bool) – Jaccard similarity measure of representativeness. Defaults to True

• wd (bool) – Wasserstein distance measure of representativeness. Defaults to True

• ksp (bool) – K-S test P-value measure of representativeness. Defaults to False

• ed (bool) – Energy distance measure of representativeness. Defaults to True

• nlsd (bool) – NetLSD similarity measure of representativeness. Defaults to False

• degcen (bool) – Betweenness Centrality. How connected each grain is. Defaults to False.

• btwcen (bool) – Betweenness Centrality. How important a grain is in connecting others. Defaults to False.

• clscen (bool) – Closeness Centrality. How close a grain is to all other grains. Defaults to False.

• egnvcen (bool) – Eigenvector Centrality. How influential a grain is within the network. Defaults to False.

• structures (Data)

• ---------------

• {n (kr.rkf[RMNAME] =)

• Where – MNAME = Repr metric name in (‘js’, ‘wd’, ‘ksp’, ‘ed’, ‘nlsd’) ZEROS = np.zeros((len(kr.sid), len(kr.tid)))

:paramMNAME = Repr metric name in (‘js’, ‘wd’, ‘ksp’, ‘ed’, ‘nlsd’)

ZEROS = np.zeros((len(kr.sid), len(kr.tid)))

initiate_rk_dict(js=False, wd=False, ksp=False, ed=False, nlsd=False, degcen=False, btwcen=False, clscen=False, egnvcen=False)

Initiate dictionaries to store representativeness measures.

Parameters:

• js (bool) – Jaccard similarity measure of representativeness. Defaults to True

• wd (bool) – Wasserstein distance measure of representativeness. Defaults to True

• ksp (bool) – K-S test P-value measure of representativeness. Defaults to False

• ed (bool) – Energy distance measure of representativeness. Defaults to True

• nlsd (bool) – NetLSD similarity measure of representativeness. Defaults to False

• structures (Data)

• ---------------

• {n (kr.rkf[RMNAME] =)

• Where – MNAME = Repr metric name in (‘js’, ‘wd’, ‘ksp’, ‘ed’, ‘nlsd’) ZEROS = np.zeros((len(kr.sid), len(kr.tid)))

:paramMNAME = Repr metric name in (‘js’, ‘wd’, ‘ksp’, ‘ed’, ‘nlsd’)

ZEROS = np.zeros((len(kr.sid), len(kr.tid)))

calculate_kdeg(ktgt, ksmp)

Calculate the node degrees of target and sample gs O(n) networks.

Paramerters

ktgt: target grain structure O(n) neighbour network graph. ksmp: sample grain structure O(n) neighbour network graph.

returns:

• kd_tgt (node degrees of target gs O(n) neigh network graph.)

• kd_smp (node degrees of sample gs O(n) neigh network graph.)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• kd_tgt (list: nodal degres of ktgt)

• kd_smp (list: nodal degres of ksmp)

• Exzplanations

• ————-

• This def calls for calculate_kdegrees. Please refer to

• upxo.netops.kchar.calculate_kdegrees for complete documentaion.

calculate_kdeg_equal_binning(ktgt, ksmp)

Calculate the node degrees of T and S gs O(n) k’s and equally bin them.

Paramerters

ktgt: target grain structure O(n) neighbour network graph. ksmp: sample grain structure O(n) neighbour network graph.

returns:

• kd_tgt (node degrees of target gs O(n) neigh network graph.)

• kd_smp (node degrees of sample gs O(n) neigh network graph.)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• kd_tgt (list: nodal degres of ktgt)

• kd_smp (list: nodal degres of ksmp)

• Exzplanations

• ————-

• This def calls for calculate_kdegrees_equalbinning. Please refer to

• upxo.netops.kchar.calculate_kdegrees_equalbinning for complete

• documentaion.

• Data is binned as per global min and max in degree and the distribtuion

• is re-computed using histogram.

tmpset

smpset

ntid

nsid

ordern

rkf_flags

rkf

mprop2d_flags

mprop3d_flags

mprop2d

mprop3d

calculate_rkf_js_pairwise(ktgt, ksmp)

Calculate Jaccard similarity between ktgt and ksmp.

Parameters:

• ktgt (target grain structure O(n) neighbour network graph.)

• ksmp (sample grain structure O(n) neighbour network graph.)

Returns:

• r (representativeness level.)

• Explanations

• ————

• Refer to calculate_rkfield_js for complete documentation.

• Location (upxo.netops.kcmp.calculate_rkfield_js)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• r (int between 0 and 1. Higher the value, greater is) – the representativeness.

calculate_rkf_wd_pairwise(ktgt, ksmp, equal_bins=False)

Calculate Jaccard similarity between ktgt and ksmp.

Parameters:

• ktgt (target grain structure O(n) neighbour network graph.)

• ksmp (sample grain structure O(n) neighbour network graph.)

Returns:

• r (representativeness level.)

• Explanations

• ————

• Refer to calculate_rkfield_wd for complete documentation.

• Location (upxo.netops.kcmp.calculate_rkfield_wd)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• r (int between 0 and 1. Higher the value, greater is) – the representativeness.

calculate_rkf_ksp_pairwise(ktgt, ksmp, equal_bins=False)

Calculate Jaccard similarity between ktgt and ksmp.

Parameters:

• ktgt (target grain structure O(n) neighbour network graph.)

• ksmp (sample grain structure O(n) neighbour network graph.)

Returns:

• r (representativeness level.)

• Explanations

• ————

• Refer to calculate_rkfield_ksp for complete documentation.

• Location (upxo.netops.kcmp.calculate_rkfield_ksp)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• r (int between 0 and 1. Higher the value, greater is) – the representativeness.

calculate_rkf_ed_pairwise(ktgt, ksmp, equal_bins=False)

Calculate Jaccard similarity between ktgt and ksmp.

Parameters:

• ktgt (target grain structure O(n) neighbour network graph.)

• ksmp (sample grain structure O(n) neighbour network graph.)

Returns:

• r (representativeness level.)

• Explanations

• ————

• Refer to calculate_rkfield_ed for complete documentation.

• Location (upxo.netops.kcmp.calculate_rkfield_ed)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• r (int between 0 and 1. Higher the value, greater is) – the representativeness.

calculate_rkf_nlsd_pairwise(ktgt, ksmp, timescales=numpy.logspace, equal_bins=False)

Calculate Jaccard similarity between ktgt and ksmp.

Parameters:

• ktgt (target grain structure O(n) neighbour network graph.)

• ksmp (sample grain structure O(n) neighbour network graph.)

Returns:

• r (representativeness level.)

• Explanations

• ————

• Refer to calculate_rkfield_nlsd for complete documentation.

• Location (upxo.netops.kcmp.calculate_rkfield_nlsd)

• Data structures

• —————

• ktgt (networkx graph for target gs’s O(n) neighbour netwprk dict data.)

• ksmp (networkx graph for sample gs’s O(n) neighbour netwprk dict data.)

• r (int between 0 and 1. Higher the value, greater is) – the representativeness.

calculate_rkf_js_on(neigh_order=1)

Parameters:

• notgt (neighbour order of interest for target)

• nosmp (neighbour order of interest for sample)

calculate_rkf_wd_on(neigh_order=1, equal_bins=False)

Calculate rkf wd on.

calculate_rkf_wd_on_generalized(neigh_order_tgt=1, neigh_order_smp=1, equal_bins=False)

Calculate rkf wd on generalized.

calculate_rkf_ksp_on(neigh_order=1, equal_bins=False)

Calculate rkf ksp on.

calculate_rkf_ksp_on_generalized(neigh_order_tgt=1, neigh_order_smp=1, equal_bins=False)

Calculate rkf ksp on generalized.

calculate_rkf_ed_on(neigh_order=1, equal_bins=False)

Calculate rkf ed on.

calculate_rkf_ed_on_generalized(neigh_order_tgt=1, neigh_order_smp=1, equal_bins=False)

Calculate rkf ed on generalized.

calculate_rkf_nlsd_on(neigh_order=1, timescales=numpy.logspace, equal_bins=False)

Calculate rkf nlsd on.

calculate_rkf_ed_nlsd_generalized(neigh_order_tgt=1, neigh_order_smp=1, equal_bins=False)

Calculate rkf ed nlsd generalized.

calculate_rkf_js()

Calculate rkf js.

calculate_rkf_wd()

Calculate rkf wd.

calculate_rkf_ksp()

Calculate rkf ksp.

calculate_rkf_ed()

Calculate rkf ed.

calculate_rkf_nlsd(timescales=numpy.logspace, equal_bins=False)

Calculate rkf nlsd.

calculate_rkf_pairwise(neigh_order, idtgt, idsmp, prop='kdegree', printmsg=False)

Calculate rkf pairwise.

calculate_rkf_pairwise_generalized(neigh_order_tgt, neigh_order_smp, idtgt, idsmp, prop='kdegree', printmsg=False)

Calculate rkf pairwise generalized.

calculate_rkf_no(neigh_order, prop='kdegree')

Calculate rkf no.

calculate_rkf(prop='kdegree')

Calculate the network R-Field values for entire tgt and smp database.

Parmeters

prop: str

property name. Defaults to ‘kdegree’. Options include:

• ‘kdegree’

• ‘area_pixel’

• ‘volume_voxel’

• ‘gblength_pixel’

• ‘gblength_geom2’

• ‘gblength_voxel’

• ‘gblength_geom3’

• ‘gbarea_voxels’

• ‘gbarea_geom’

• ‘gbrough_r’

• ‘ntjp’

Explanations

User specified boolean flags in rkf_flags dictate which R-field metrics would be calculated.

calculate_uncertainty_angdist(rkf_measure='js', neigh_orders=[1], n_bins=30, data_title='Jaccard sim. measure', throw=False, plot_ad=True)

Calculate uncertainty angdist.

plot_ang_dist(ANG_DISTANCE, n_bins, neigh_orders=[1], figsize=(5, 5), dpi=150, data_title='DATA TITLE', cmap='nipy_spectral', throw_axis=True)

Visualise ang dist using Matplotlib or PyVista.

plot_rkf(neigh_orders=[1], power=1, figsize=(7, 5), dpi=120, xtick_incr=2, ytick_incr=2, lfs=7, tfs=8, cmap='nipy_spectral', cbarticks=numpy.arange, cbfs=10, cbtitle='Measure of representativeness R(S|T)', cbfraction=0.046, cbpad=0.04, cbaspect=30, shrink=0.5, cborientation='vertical', flags={'rkf_btwcen': False, 'rkf_clscen': False, 'rkf_degcen': False, 'rkf_ed': False, 'rkf_egnvcen': False, 'rkf_js': False, 'rkf_ksp': False, 'rkf_nlsd': False, 'rkf_wd': False}, xlabel='Target GS ID', ylabel='Sample GS ID')

Example

import numpy as np from upxo.repqual.grain_network_repr_assesser import KREPR import matplotlib.pyplot as plt kr = KREPR.from_gsgen(gstype=’mcgs’,

is_smp_same_as_tgt = False, tgt_dashboard=’input_dashboard.xls’, smp_dashboard=’input_dashboard.xls’, ordern=[1, 3, 5],

tsid_source=’from_gs’, ssid_source=’from_gs’,

tid=None, sid=None, _cim_=’from_gsgen’)

kr.set_rkf(js=True, wd=True, ksp=False, ed=True, nlsd=False) kr.calculate_rkf()

kr.plot_rkf(neigh_orders=[1, 3, 5], figsize=(7, 5), dpi=50,

xtick_incr=2, ytick_incr=2, lfs=7, tfs=8, cmap=’nipy_spectral’, cbarticks=np.arange(0, 1.1, 0.1), cbfs=10, cbtitle=’Measure of representativeness R(S|T)’, cbfraction=0.046, cbpad=0.04, cbaspect=15, shrink=0.4, cborientation=’vertical’, plot_rkf_js=False)