rm fake-gisas1 script and data

Examples/data/README.md

 # Test data for fit examples
 
-### faked_gisas1
-
-Created by devtools/fakeData/fake-gisas1.py
-
 ### MAFO-Saturated
 
 From https://www.nist.gov/document/spinelfilmzip,

devtools/fakeData/fake-gisas1.py

-#!/usr/bin/env python3
-"""
-Fake GISAS data for testing and benchmarking 2D fits.
-The sample model basically consists of dilute cylinders on a substrate.
-There are, however, some systematic distortions:
-  * Refractive indices changed, and absorption strongly enhanced;
-  * Cylinders replaced by a mixture of cones and segmented spheroids;
-  * Beam wavelength and alpha_i slightly changed.
-  * Detector x-axis skewed;
-"""
-
-import bornagain as ba
-from bornagain import deg, nm
-import numpy as np
-
-mat_vacuum = ba.HomogeneousMaterial("Vacuum", 0, 0)
-# nominal material constants are (6e-6, 2e-8) and (6e-4, 2e-8)
-mat_substrate = ba.HomogeneousMaterial("Substrate", 6.3e-6, 4e-7)
-mat_particle = ba.HomogeneousMaterial("Particle", 5.8e-4, 2e-7)
-
-
-def get_sample(params):
-    h = params["cylinder_height"]
-    r = params["cylinder_radius"]
-
-    ff1 = ba.FormFactorCone(1.1*r, h, 80*deg)
-    ff2 = ba.FormFactorTruncatedSpheroid(r, 3.3*h, 2.5*h/r, 2.2*h)
-
-    layout = ba.ParticleLayout()
-    layout.addParticle(ba.Particle(mat_particle, ff1), .4)
-    layout.addParticle(ba.Particle(mat_particle, ff2), .6)
-
-    layer_1 = ba.Layer(mat_vacuum)
-    layer_1.addLayout(layout)
-    layer_2 = ba.Layer(mat_substrate)
-
-    sample = ba.MultiLayer()
-    sample.addLayer(layer_1)
-    sample.addLayer(layer_2)
-    return sample
-
-
-def get_simulation(params):
-    # nominal beam parameters are 0.1*nm, 0.2*deg
-    beam = ba.Beam(10**params['lg(intensity)'], 0.0993*nm,
-                   ba.Direction(0.202*deg, 0))
-
-    # nominal detector x-axis starts from -1.5*deg
-    det = ba.SphericalDetector(100, -1.49*deg, 1.5*deg, 100, 0, 3*deg)
-
-    sample = get_sample(params)
-
-    simulation = ba.GISASSimulation(beam, sample, det)
-    if 'lg(background)' in params:
-        simulation.setBackground(
-            ba.ConstantBackground(10**params['lg(background)']))
-
-    return simulation
-
-
-def model_parameters():
-    return {
-        'lg(intensity)': 6,
-        'lg(background)': 0.5,
-        'cylinder_height': 4*nm,
-        'cylinder_radius': 5*nm,
-    }
-
-
-def fake_data():
-    """
-    Generate fake "experimental" data, and save them as numpy array.
-    """
-
-    params = model_parameters()
-
-    # Compute model distribution
-    simulation = get_simulation(params)
-    simulation.runSimulation()
-    theory = simulation.result().array()
-
-    # Draw noisy data
-    data = np.random.poisson(theory)
-
-    # Save to numpy
-    np.savetxt("faked-gisas1.txt.gz", data)
-
-
-if __name__ == '__main__':
-    np.random.seed(1)
-    fake_data()