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:

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