5. Function overview¶

Here we presente the function associated with the class aita

5.1. loadData_aita¶

AITAToolbox.loadData_aita.aita3col(data_adress, im_adress, micro_adress=0)[source]¶

Function to open data from AITA analyser using ‘cistodat’ which give output file with 3 column azi,col,qua) Require to load an image file to get the image dimension nx, ny

Parameters

data_adress (str) – orientation.dat file
im_adress (str) – any .bmp image file from AITA G50
micro_adress (str) – .bmp (24 bit) with black background and grains boundary in white

Returns

aita object

Return type

aita

AITAToolbox.loadData_aita.aita5col(data_adress, micro_adress=0)[source]¶

Function to open data from AITA analyser using ‘cistodat’ which give output file with 5 column (x,y,azi,col,qua)

Parameters

data_adress (str) – orientation.dat file
micro_adress (str) – .bmp (24 bit) with black background and grains boundary in white

Returns

aita object

Return type

aita

5.2. aita¶

Created on 3 juil. 2015 Toolbox for data obtained using G50 Automatique Ice Texture Analyser (AITA) provide by : Russell-Head, D.S., Wilson, C., 2001. Automated fabric analyser system for quartz and ice. J. Glaciol. 24, 117–130

@author: Thomas Chauve @contact: thomas.chauve@univ-grenoble-alpes.fr @license: CC-BY-CC

class AITAToolbox.aita.aita(phi1_field, phi_field, qua_field, micro_field, resolution=1)[source]¶

class aita¶

“aita” is a python class to analyse output from G50-AITA analyser. It provide an environnement to plot data, to create inpur for CraFT code,…

__init__(phi1_field, phi_field, qua_field, micro_field, resolution=1)[source]¶

Parameters

phi1_field – Euler angle phi1 map
phi_field – Euler angle phi map
qua_field – quality facteur map
resolution – spatial step size (mm); default = 1 mm
micro_field – microstructure (0 background, 1 grain boundary)

:type phi1_field np.array :type phi_field np.array :type qua_field: np.array :type resolution: float :type micro_adress: np.array

Returns: aita object output
Return type: aita

Note

Bunge Euler Angle convention is used (phi1,phi,phi2) ,phi2 is not compute as during optical measurement phi2 is not know.

__weakref__¶: list of weak references to the object (if defined)

addgrain(ori=0)[source]¶

add a grain inside the microstructure

Parameters

ori (array, float) – orienation of the new grain [phi1 phi] (default random value)

Returns

new_micro, object with the new grain include

Return type

aita

Exemple

>>> data.addgrain()      

craft(nameId)[source]¶

Create the inputs for craft

Parameters

nameId (str) – name of the prefixe used for craft files

Returns

create file : nameId_micro.vtk, nameId.phase, nameId.in, nameId.load, nameId.output

Exemple

>>> data.craft('manip01')

Note

nameId.load, nameId.output need to be rewrite to get a correct loading and the output wanted

Note

nameId.in need to be adapt depending of your folder structure used for craft

Note

NaN orientation value are removed by the closest orientation

crop(xmin=0, xmax=0, ymin=0, ymax=0, new=False)[source]¶

Crop function to select the area of interest

Returns

crop aita object

Return type

aita

Exemple

>>> data.crop()

Note

clic on the top left corner and bottom right corner to select the area

filter(value)[source]¶

Remove data of bad quality

Parameters

value (int) – limit quality value between 0 to 100

Returns

data object with no orientation with quality value under threshold

Return type

aita

Exemple

>>> data.filter(75)

fliplr()[source]¶

Applied an horizontal miror to the data

Returns

aita object with an horizontal miror

Return type

aita

Exemple

>>> data.fliplr()

grain_ori()[source]¶: Give the grain orientation output

grelon(posc=array([0, 0]))[source]¶

Compute the angle between the directions defined by the “center” and the pixel with the c-axis direction

Returns: angle (degree)
Return type: im2d.image2d

imresize(res)[source]¶

Resize the data

Parameters

res (float) – the new resolution wanted in millimeter (mm)

Returns

data with the resolution wanted

Return type

aita

Exemple

>>> data.imresize(0.25)

interactive_crop(new=False)[source]¶

out=data_aita.interactive_crop()

This function can be use to crop within a jupyter notebook It will crop the data and export the value of the crop in out.pos

Parameters: new (bool) – create a new data variable (default:False; erase input data)

Note

If you use new=True (out=interactive_crop(new=true)) you can find the cropped data in out.crop_data

Note

The position of the rectangle used for the cropping is in out.pos

interactive_grelon()[source]¶: Interactuve grelon function for jupyter notebook

interactive_misorientation_profile()[source]¶: Interactive misorientation profile for jupyter notebook

interactive_segmentation(val_scharr_init=1.5, use_scharr_init=True, val_canny_init=1.5, use_canny_init=True, val_qua_init=60, use_qua_init=False, inc_border_init=False, mask=False)[source]¶

This function allow you to performed grain segmentation on aita data. The intitial value of the segmenation function can be set-up initially

Parameters

val_scharr_init (float) – scharr filter usually between 0 and 10 (default : 1.5)
use_scharr_init (bool) – use scharr filter
val_canny_init (float) – canny filter usually between 0 and 10 (default : 1.5)
use_canny_init (bool) – use canny filter
val_qua_init (int) – quality filter usually between 0 and 100 (default : 60)
use_qua_init (bool) – use quality filter
inc_border_init (bool) – add image border to grain boundaries

Note

on data with holes such as snow, using quality filter is not recommended

mask(mask)[source]¶

Applied mask on aita data

Parameters: mask (im2d.mask2d) –
Returns: aita object with the mask applied
Return type: aita

mean_grain()[source]¶

Compute the mean orientation inside the grain

Returns

data with only one orientation per grains, the mean orientation

Return type

aita

Exemple

>>> data.mean_orientation()

mesh(name, resGB=1, resInG=5, DistMin=5)[source]¶

Create mesh in vtk format resInG _______

/

/

/ |

/

resGB ________/ |: DistMin

Parameters

name (str) – output file name without extension
resGB (float) – resolution on the Grains Boundaries (in pixel)
resInG (float) – resolution within the Grains (in pixel)
DistMin – starting distance for the transition between resGB and resInG

misorientation(random=False, filter_angle=0.017453292519943295)[source]¶: Compute the misorientation with the neighbouring grain :param random: suffle the image and compute the angle :type random: bool :param filter_angle: threshold angle for removing small value in radians (default : pi/180) :type filter_angle: float

misorientation_extractor(pos)[source]¶

Compute the misorientation profile along a line

Parameters: pos (array) – first point and last point of the line
Returns: x - coordinate along the line
Return type: array, float
Returns: mis2o,mis2p - misorientation angle to the origin, and misorientation angle to the previous pixel
Return type: array, float

misorientation_profile(ploton=True, plot='all', orientation=False)[source]¶

Compute the misorientation profile along a line

Parameters

plot (str) – option for to misorientation profile plot, ‘all’ (default), ‘mis2o’, ‘mis2p’
orientation (bool) – option for the color code used for the map, False (default) use phi1 and True use colorwheel (take time)
pos (array) – coordinate of the profile line - 0 (default) click on the map to select the 2 points

Returns

x - coordinate along the line

Return type

array, float

Returns

mis2o,mis2p - misorientation angle to the origin, and misorientation angle to the previous pixel

Return type

array, float

Returns

h - matplotlib image with line draw on the orientation map, subplot with mis2o and/or mis2p profile

Returns

pos - coordinate of the profile line

Exemple

>>> [x,mis2o,mis2p,h,pos]=data.misorientation_profile()
>>> rpos = pos[::-1]
>>> [x,mis2o,mis2p,hr,pos]=data.misorientation_profile(pos=rpos)
>>> plt.show()

new_ori_TJ(mask, mean=True)[source]¶: Extract orientation to compare with CraFT simulation

plot(nlut=512, semi=False)[source]¶

Plot the data using a 2d lut

Parameters

nlut (int) – number of pixel tou want for the 2d LUT (default 512)

Returns

figure of orientation mapping

Return type

matplotlib figure

Exemple

>>> data.plot()
>>> plt.show()
>>> # print the associated color wheel
>>> lut=lut()
>>> plt.show()

Note

It takes time to build the colormap

plotpdf(peigen=True, grainlist=[], nbp=10000, contourf=True, cm2=<matplotlib.colors.ListedColormap object>, bw=0.1, projz=1, angle=array([30., 60.]), cline=15, n_jobs=-1)[source]¶

Plot pole figure for c-axis (0001)

Parameters

peigen (bool) – Plot the eigenvalues and eigenvectors on the pole figure (default = False)
grainlist (list) – give the list of the grainId you want to plot
nbp (int) – number of pixel plotted
contourf (bool) – Do you want to add contouring to your pole figure ? (Default : False)
cm2 (cm) – colorbar (default : cm.viridis)
bw (float) – bandwidth to compute kernel density (default : 0.1) bw=0 mean find the best fit between 0.01 and 1
projz (int) – 0 or 1. It choose the type of projection. 0 (1) means projection in the plane z=0 (1).
angle (np.array) – plot circle for this angle value (default : np.array([30.,60.])) 0 if you don’t want inner circle.
cline (int) – Number of line in contourf (default 15) Used only when contourf=True.
n_jobs (int) – number of job in parellel (CPU). Only use when bw=0 (best fit) (default : -1 mean all processor)

Returns

pole figure image

Return type

matplotlib figure

Returns

eigenvalue

Return type

float

Exemple

>>> eigenvalue = data.plotpdf(peigen=True)

project_end2init(self_e, image, mask=0, min_win=20, apply_morph=True)[source]¶: Project the final microstructure on the initial microstrucure :param self_e: final AITA :type self_e: aita :param image: Binairy image to project instead of final microstructure. :type image: im2d.image2d :return: image projected on aita initial :rtype: im2d.image2d

Note

It works better if you pass aita with mean_grain function applied before.

rectangular_mask()[source]¶

out=data_aita.mask()

This function can be use to crop within a jupyter notebook It will crop the data and export the value of the crop in out.pos

Parameters: new (bool) – create a new data variable (default:False; erase input data)

Note

If you use new=True (out=interactive_crop(new=true)) you can find the cropped data in out.crop_data

Note

The position of the rectangle used for the cropping is in out.pos

rot180()[source]¶

Rotate the data of 180 degree

Returns

crop aita object

Return type

aita

Exemple

>>> data.rot180()

AITAToolbox.aita.cart2pol(x, y)[source]¶

Convert cartesien coordinate x,y into polar coordinate rho, theta

Parameters

x (float) – x cartesian coordinate
y (float) – y cartesian coordinate

Returns

rho (radius), theta (angle)

Return type

float

Exemple

>>> rho,theta=cart2pol(x,y)

AITAToolbox.aita.euler2azi(phi1, phi)[source]¶: Convert Euler angle to azimuth and colatitude :param phi1: :type phi1: array :param phi: :type phi: array :return: azi :rtype: array :return: col :rtype: array

AITAToolbox.aita.isInsideTriangle(P, p1, p2, p3)[source]¶

test if P is inside the triangle define by p1 p2 p3

Parameters

P (array) – point you want test
p1 (array) – one submit of the triangle
p2 (array) – one submit of the triangle
p3 (array) – one submit of the triangle

Returns

isIn

Return type

bool

Exemple

>>> isInsideTriangle([0,0],[-1,0],[0,1],[1,0])
>>> isInsideTriangle([0,-0.1],[-1,0],[0,1],[1,0])

AITAToolbox.aita.lut(nx=512, semi=False, circle=True)[source]¶

Create a 2D colorwheel

Parameters

nx (int) – number of pixel for the colorwheel
circle (bool) – do you want create a black circle around
semi (bool) – do you want a semi LUT

Returns

lut

Return type

array of size [nx,nx,3]

Exemple

>>> lut2d=lut()
>>> plt.imshow(lut)
>>> plt.show()