Scripts (overview)

All scripts are located in ./rft1d/examples, and below are listed alphabetically, and also bundled into categories. Details regarding particular concepts are available in the main Examples section.

Categories:

1. Random field generation

2. Validation — field smoothness estimation

3. Validation — conjunction analysis

4. Validation — field maxima

5. Validation — upcrossing extent

6. Example application — weather dataset

Random field generation

• random_fields_0.py — verbose random field generation

• random_fields_1.py — using rft1d.randn1d

• random_fields_2.py — using rft1d.random.Generator1D

Broken random field generation

• random_fields_broken_0.py — verbose broken random field generation

• random_fields_broken_1.py — using rft1d.randn1d

• random_fields_broken_2.py — using rft1d.random.Generator1D

Field smoothness estimation

• smoothness_estimation.py — continous field FWHM estimation validation

• smoothness_estimation_broken.py — piecewise continuous field FWHM estimation validation

Validation (conjunction analysis)

• val_conj_0_gauss — standard normal distribution

• val_conj_1_t — Student’s t distribution

• val_conj_1_F — Fisher-Snedecor F

• val_conj_1_T2 — Hotelling’s T-squared

• val_conj_1_X2 — chi-squared

Validation (field maxima)

• val_max_0_gaussian_0d.py — standard normal distribution

• val_max_0_gaussian_1d.py — standard normal 1D Gaussian fields

• val_max_1_onesampleT_0d.py — Student’s t distribution, from one-sample statistic

• val_max_1_onesampleT_1d.py — Student’s t distribution (1D), from one-sample statistic

• val_max_2_twosampleT_0d.py — Student’s t distribution, from one-sample statistic

• val_max_2_twosampleT_1d.py — Student’s t distribution (1D), from two-sample statistic

• val_max_3_regress_0d.py — Student’s t distribution, from linear regression

• val_max_3_regress_1d.py — Student’s t distribution (1D), from linear regression

• val_max_4_anova1_0d.py — Fisher-Snedecor F distribution, from one-way design

• val_max_4_anova1_1d.py — Fisher-Snedecor F distribution (1D), from one-way design

• val_max_5_onesampleT2_0d.py — Hotelling’s T-squared distribution, from one-way design

• val_max_5_onesampleT2_1d.py — Hotelling’s T-squared distribution (1D), from one-way design

• val_max_6_twosampleT2_0d.py — Hotelling’s T-squared distribution, from two-way design

• val_max_6_twosampleT2_1d.py — Hotelling’s T-squared distribution (1D), from two-way design

• val_max_7_cca_0d.py — chi-squared distribution, from CCA

• val_max_7_cca_1d.py — chi-squared distribution (1D), from CCA

• val_max_8_manova1_0d.py — chi-squared distribution, from one-way MANOVA

• val_max_8_manova1_1d.py — chi-squared distribution (1D), from one-way MANOVA

Validation (upcrossing extents)

• val_upx_0_gauss_cluster.py — cluster-level inference (Gaussian fields)

• val_upx_0_gauss_set.py — set-level inference (Gaussian fields)

• val_upx_1_t_cluster.py — cluster-level inference (t fields)

• val_upx_1_t_set.py — set-level inference (t fields)

• val_upx_2_F_cluster.py — cluster-level inference (F fields)

• val_upx_2_F_set.py — set-level inference (F fields)

• val_upx_3_T2_cluster.py — cluster-level inference (T-squared fields)

• val_upx_3_T2_set.py — set-level inference (T-squared fields)

• val_upx_4_X2_cluster.py — cluster-level inference (chi-squared fields)

• val_upx_4_X2_set.py — set-level inference (chi-squared fields)

Example application

• weather_0_plotdata.py — plot of all experimental temperature fields

• weather_1_rft.py — parametric inference using RFT

• weater_2_nonparam.py — non-parametric inference using permutation

• weater_3_wrapped.py — parametric inference, assuming a circular field