Setup

The setup module contains functions that setup arguments for the main modules.

Argument Parser (setup.args)

Contains functions for creating a consistent set of argparse arguments for the entire couponmlactivation suite.

Default file paths execute the example code for the tensorflow coupon models

fns.setup.args.argument(parser, dft)

Example function to create the argument “–ARGUMENT”

Parameters

• parser (argparse.ArgumentParser) – pre-existing parser

• dft (type varies, optional) – default value, should be same type as the argument being defined

Returns

No Return Objects

Show All Arguments

usage: module [-h] [--PACKAGE] [--EXPERIMENT] [--MODEL] [--INPUT_FIELD] [--INPUT_NPZ]
              [--INPUT_DIR] [--FILE_LIST] [--DESIGN_FILE] [--NORM_FILE]
              [--PRINT_LAYERS] [--PRINT_FEATURES] [--PRINT_FIELDS] [--PRINT_KEYS]
              [--PRINT_SAMPLES] [--LAYER] [--FEATURES  [...]] [--D_SCALE] [--MAT_NORM]
              [--SCLR_NORM] [--FIELDS  [...]] [--FIXED_KEY] [--NUM_SAMPLES] [--ALPHA1]
              [--ALPHA2] [--COLOR1] [--COLOR2] [--SAVE_FIG]

Named Arguments

--PACKAGE, -P

Possible choices: tensorflow, pytorch

Which python package was used to create the model

Default: “tensorflow”

--EXPERIMENT, -E

Possible choices: coupon, nestedcylinder

Which experiment the model was trained on

Default: “coupon”

--MODEL, -M

Model file

Default: “../examples/tf_coupon/trained_pRad2TePla_model.h5”

--INPUT_FIELD, -IF

The radiographic/hydrodynamic field the model is trained on

Default: “pRad”

--INPUT_NPZ, -IN

The .npz file with an input image to the model

Default: “../examples/tf_coupon/data/r60um_tpl112_complete_idx00110.npz”

--INPUT_DIR, -ID

Directory path where all of the .npz files are stored

Default: “../examples/tf_coupon/data/”

--FILE_LIST, -FL

The .txt file containing a list of .npz file paths; use “MAKE” to generate a file list given an input directory (passed with -ID) and a number of samples (passed with -NS).

Default: “MAKE”

--DESIGN_FILE, -DF

The .csv file with master design study parameters

Default: “../examples/tf_coupon/coupon_design_file.csv”

--NORM_FILE, -NF

The .npz file normalization values

Default: “../examples/tf_coupon/coupon_normalization.npz”

--PRINT_LAYERS, -PL

Prints list of layer names in a model (passed with -M) and quits program

Default: False

--PRINT_FEATURES, -PT

Prints number of features extracted by a layer (passed with -L) and quits program

Default: False

--PRINT_FIELDS, -PF

Prints list of hydrodynamic/radiographic fields present in a given .npz file (passed with -IN) and quits program

Default: False

--PRINT_KEYS, -PK

Prints list of choices for the fixed key avialable in a given input dirrectory (passed with -ID) and quits program

Default: False

--PRINT_SAMPLES, -PS

Prints number of samples in a directory (passed with -ID) matching a fixed key (passed with -XK) and quits program

Default: False

--LAYER, -L

Name of model layer that features will be extracted from

Default: “None”

--FEATURES, -T

List of features to include; “Grid” plots all features in one figure using subplots; “All” plots all features each in a new figure; A list of integers can be passed to plot those features each in a new figure. Integer convention starts at 1.

Default: [‘Grid’]

--D_SCALE, -DS

Scaling factor used in feature derivatives.

Default: 0.001

--MAT_NORM, -NM

Possible choices: ft01, all01, none

How the extracted features will be normalized, resulting in a scaled matrix; “ft01” normalizes by the min and max of each feature separately; “all01” normalizes by the min and max of all extracted features; “none” does not normalize features.

Default: “ft01”

--SCLR_NORM, -NR

Possible choices: fro, nuc, inf, -inf, 0, 1, -1, 2, -2

How the extracted features will be normalized, resulting in a scalar value; for choices, see numpy.linalg.norm documentation.

Default: “2”

--FIELDS, -F

List of fields to be included; pass “none” to use an all-zero field; pass “All” to use all valid fields.

Default: [‘rho’, ‘eqps’, ‘eqps_rate’, ‘eff_stress’, ‘porosity’]

--FIXED_KEY, -XK

The identifying string for some subset of all data samples; pass “None” to consider all samples

Default: “None”

--NUM_SAMPLES, -NS

Number of samples to use; pass “All” to use all samples in a given input dirrectory (passed with -ID)

Default: “All”

--ALPHA1, -A1

Opacity of colormap at value 0

Default: 0.25

--ALPHA2, -A2

Opacity of colormap at value 1

Default: 1.0

--COLOR1, -C1

Possible choices: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgreen, darkgrey, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, green, greenyellow, grey, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgreen, lightgrey, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, rebeccapurple, red, rosybrown, royalblue, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen

Color of colormap at value 0; Choose from matplotlib CSS4 color list.

Default: “yellow”

--COLOR2, -C2

Possible choices: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgreen, darkgrey, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, green, greenyellow, grey, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgreen, lightgrey, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, rebeccapurple, red, rosybrown, royalblue, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen

Color of colormap at value 1; Choose from matplotlib CSS4 color list.

Default: “red”

--SAVE_FIG, -S

Directory to save the outputs to.

Default: “../examples/tf_coupon/figures/”

Data Prints (setup.data_prints)

Contains functions to print out lists of options for data-related input arguments

fns.setup.data_prints.print_fields(npz)

Function that prints a list of radiographic/hydrodynamic fields in an npz file

Parameters

npz (np.lib.npyio.NpzFile) – a loaded .npz file

Returns

No Return Objects

fns.setup.data_prints.print_samples(search_dir)

Function that prints how many samples (npz files) in a directory

Parameters

search_dir (str) – file path to directory to search for samples in; include any restrictions for file name

Returns

No Return Objects

Data Checks (setup.data_checks)

Contains functions to check that the data-related input arguments passed are valid

fns.setup.data_checks.check_fields(npz, fields)

Function that checks if all fields in a list exist in a npz file

Parameters

• npz (np.lib.npyio.NpzFile) – a loaded .npz file

• fields (list[str]) – list of the fields to test

Returns

No Return Objects

fns.setup.data_checks.check_key(fixed_key, search_dir)

Function that prints a list of unique variable indexes for a given fixed variable

Parameters

• fixed_key (str) – the key for samples that is being checked

• search_dir (str) – file path to directory to search for samples in

Returns

No Return Objects

fns.setup.data_checks.check_samples(num_samples, search_dir)

Function that checks if the number of samples requested are aviailable in a directory

Parameters

• num_samples (str) – should be ‘All’, or an integer; number of samples the script will use

• search_dir (str) – path to directory of .npz files

Returns

No Return Objects