def test_glitch_divref(self):
"""
Testing based on fail2 test case
"""
x = '-1.36768994867991128'
y = '0.00949048853859240532'
dx = '2.477633848347765e-8'
precision = 18
nx = 1600
test_name = self.test_glitch_divref.__name__
prefix="fail2"
black = np.array([0, 0, 0]) / 255.
citrus2 = np.array([103, 189, 0]) / 255.
colors1 = np.vstack((citrus2[np.newaxis, :]))
colors2 = np.vstack((black[np.newaxis, :]))
colormap = fscolors.Fractal_colormap(kinds="Lch", colors1=colors1,
colors2=colors2, n=200, funcs=None, extent="mirror")
grey_layer_key = ("DEM_shade", {"kind": "potential",
"theta_LS": 30.,
"phi_LS": 70.,
"shininess": 300.,
"ratio_specular": 15000.})
test_file = self.make_M2_img(x, y, dx, precision, nx,
np.complex128, test_name, prefix, interior_detect=True,
mask_codes=[2], antialiasing=True, colormap=colormap,
probes_val=[0.25, 0.75], grey_layer_key=grey_layer_key,
blur_ranges=[[0.8, 0.95, 1.0]], hardness=0.9, intensity=0.8,
glitch_max_attempt=10)
ref_file = os.path.join(self.image_dir_ref, test_name + ".png")
err = compare_png(ref_file, test_file)
self.assertTrue(err < 0.02)
def setUp(self):
image_dir = os.path.join(test_config.test_dir, "_images_comparison")
fsutils.mkdir_p(image_dir)
self.image_dir = image_dir
image_dir_ref = os.path.join(test_config.test_dir, "images_REF")
fsutils.mkdir_p(image_dir_ref)
self.image_dir_ref = image_dir_ref
purple = np.array([181, 40, 99]) / 255.
gold = np.array([255, 210, 66]) / 255.
colors1 = np.vstack((purple[np.newaxis, :]))
colors2 = np.vstack((gold[np.newaxis, :]))
self.colormap = fscolors.Fractal_colormap(kinds="Lch", colors1=colors1,
colors2=colors2, n=200, funcs=None, extent="mirror")
def test_M2_antialias_E0(self):
"""
Testing field lines, and antialiasing. Full Mandelbrot
"""
with self.subTest(zoom=1):
x, y = "-0.75", "0."
dx = "5.e0"
precision = 10
nx = 1600
test_name = self.test_M2_antialias_E0.__name__
complex_type = np.complex128
prefix = "antialiasing"
gold = np.array([255, 210, 66]) / 255.
black = np.array([0, 0, 0]) / 255.
colors1 = np.vstack((gold[np.newaxis, :]))
colors2 = np.vstack((black[np.newaxis, :]))
colormap = fscolors.Fractal_colormap(kinds="Lch", colors1=colors1,
colors2=colors2, n=200, funcs=None, extent="clip")
# color_gradient = fscolors.Color_tools.Lch_gradient(gold, black, 200)
# colormap = fscolors.Fractal_colormap(color_gradient)
test_file = self.make_M2_img(x, y, dx, precision, nx,
complex_type, test_name, prefix, interior_detect=True,
mask_codes=[2], antialiasing=True, colormap=colormap,
probes_val=[0., 0.1], grey_layer_key=
("field_lines", {"n_iter": 10, "swirl": 1.}),
blur_ranges=[[0.8, 0.95, 1.0]], hardness=0.9, intensity=0.8)
ref_file = os.path.join(self.image_dir_ref, test_name + ".png")
err = compare_png(ref_file, test_file)
self.assertTrue(err < 0.02)
with self.subTest(zoom=2):
x, y = "-0.1", "0.975"
dx = "0.8e0"
prefix = "antialiasing_2"
test_file = self.make_M2_img(x, y, dx, precision, nx,
complex_type, test_name, prefix, interior_detect=True,
mask_codes=[2], antialiasing=True, colormap=colormap,
probes_val=[0., 0.1], grey_layer_key=("field_lines", {}),
blur_ranges=[[0.8, 0.95, 1.0]], hardness=0.9, intensity=0.8)
ref_file = os.path.join(self.image_dir_ref, test_name + "_2.png")
err = compare_png(ref_file, test_file)
self.assertTrue(err < 0.01)
def test_cmap_widget():
gold = np.array([255, 210, 66]) / 255.
black = np.array([0, 0, 0]) / 255.
colors1 = np.vstack((gold[np.newaxis, :],
black[np.newaxis, :]))
colors2 = np.vstack((black[np.newaxis, :],
gold[np.newaxis, :]))
kinds = ["Lch", "Lch"]
n = 100
funcs = [lambda x: x**6, lambda x: 1.- (1. - x)**6]
colormap = fscolors.Fractal_colormap(
kinds, colors1, colors2, n, funcs, extent="clip")
print("probes", colormap._probes)
class Mywindow(QMainWindow):
def __init__(self):
super().__init__(parent=None)
self.setWindowTitle('Testing inspector')
tb = QToolBar(self)
self.addToolBar(tb)
# print_dict = QAction("print dict")
tb.addAction("print_dict")
tb.actionTriggered[QAction].connect(self.on_tb_action)
#self.setWindowState(Qt.WindowMaximized)
# And don't forget to call setCentralWidget to your main layout widget.
icone = Qcmap_image(self, colormap) # fsgui.
self.setCentralWidget(icone)
self._icone = icone
def on_tb_action(self, qa):
print("ON ACTION qa", qa)
app = getapp()
win = Mywindow()
win.show()
app.exec()
def test_glitch_dyn(self):
"""
Testing based on Dinkydau "Flake" test case
"""
x = "-1.99996619445037030418434688506350579675531241540724851511761922944801584242342684381376129778868913812287046406560949864353810575744772166485672496092803920095332"
y = "0.00000000000000000000000000000000030013824367909383240724973039775924987346831190773335270174257280120474975614823581185647299288414075519224186504978181625478529"
dx = "1.8e-157"
precision = 200
nx = 1600
test_name = self.test_glitch_dyn.__name__
prefix="flake"
black = np.array([0, 0, 0]) / 255.
purple = np.array([181, 40, 99]) / 255.
colors1 = np.vstack((black[np.newaxis, :]))
colors2 = np.vstack((purple[np.newaxis, :]))
colormap = fscolors.Fractal_colormap(kinds="Lab", colors1=colors1,
colors2=colors2, n=200, funcs=None, extent="mirror")
grey_layer_key = ("DEM_shade", {"kind": "potential",
"theta_LS": 30.,
"phi_LS": 70.,
"shininess": 300.,
"ratio_specular": 15000.})
SA_params={"cutdeg": 8,
"cutdeg_glitch": 8,
"SA_err": 1.e-4,
"use_Taylor_shift": True}
test_file = self.make_M2_img(x, y, dx, precision, nx,
np.complex128, test_name, prefix, interior_detect=True,
mask_codes=[2], antialiasing=True, colormap=colormap,
probes_val=[0.3, 0.5], grey_layer_key=grey_layer_key,
blur_ranges=[[0.98, 0.995, 1.0]], hardness=0.9, intensity=0.8,
glitch_max_attempt=10, xy_ratio=0.5,SA_params=SA_params)
ref_file = os.path.join(self.image_dir_ref, test_name + ".png")
err = compare_png(ref_file, test_file)
self.assertTrue(err < 0.02)
def plot():
"""
Example plot of "Dinkydau flake" location, classic test case for
perturbation techinque and glitch correction.
"""
directory = "./perturb3"
# A simple showcas using perturbation technique
x, y = "-0.75", "0."
dx = "5.e0"
precision = 150
nx = 3200
x = '-1.993101465346811633595616349779370960719700854263460744227885419412572351324445321874004442403503011725492641453441484329872421586639050539267910275781311721259641283288898121495962960511188360859446141741747448336162741635241659807342073817485606900204314068415477260531866235822220430486119843399542682686903115170284286744427789259769672374264750048282753697939441835784223761880144973743249562058785490789121881765822494487680713802365108655723804325265573559505557675274602687698535315326824126504568612493712586162172913902182849175957355201038749736221172166381630971780664574945186600702295814202276821096082583371646391752258136082934974808859285874633438821365019578751567557825904520349615083013659980914914027970434909527071583051834592117828848476162531653958895351112086988431145593103631584906268842017812275327407852982198464690863881669210429524886644612378383194829443312845510612712652609161824242961337428831821452985753354390486290596759804822656081435972288149493458837417345622327424314356121019642859410599076344584439053'
y = '-0.0000000000000000000001023710512431589570005203798273240286470771650981763139330824251367527539704908652681677255846096018123604758184274291544039306788236186699886780634842327075201516856958710101981983627439650646801970216473720458108184168523830340517163870169919249638047451318365933557979031258177237717753794769207546631778925762244933996868229775839453423976148160460368950010744615992543567659336441278697767936754177402161193387335896316048958999044786173769537636937690325372184302039387809032377492478883386867909568081378343026465718279702082155350634045451662043279571130329807181205168575035218579446441743895034500494162054542465662499561872308796389299543485971550119965555406797513540955180015002773089512902159298036751555750537198269153512054314778106379999904973319311775913118876664658896447125394230182911598486994660593785101070940474199338173235671779027464278763584882649334715308568951689930703319182855450455829071681801598812078546949506703052822064075723064430117087174494686425795962484572598182905448268661576794867514394048126125843131'
dx = '0.000000000000000000000000000000000000000000000000000001611311677165311992036964188953416400357306950103215815939935672793028078345666262795353854791128193417180028322502563016371152356567374462535929127041725333256467561733290384009069197316210881426462964928434585521650089769009585599250595741136222346428026621232359069818161715344902647536565294138377179149230247747809792678090166161270842118179873716803137049807896171897484336423552754428273143300120233210420317384023816622150052145902931298541283952210479589483643604612244327904646956347253652556190458708451251053597744280720211750536552494355053069572018017158283906744473170534917744052363527030185810701281751448157641033852978421572439410367749824884649311769407121646490987840830692921695850309271975840408355787675238627661695949205488539553336065242972661368264433343340961667491522236015169240810915606280026794402643082165872947330057146892300382601081535552273108633947461317572323488134065825603499355505743581891071053495091972381402991134813796575186487618387985500615976611029133681690931704603523636857668558756510416666667'
# Set to True if you only want to rerun the post-processing part
settings.skip_calc = False
# Set to True to enable multi-processing
settings.enable_multiprocessing = True
# xy_ratio = 1.0
# theta_deg = 0.
# complex_type = np.complex128
mandelbrot = fsm.Perturbation_mandelbrot(directory)
mandelbrot.zoom(precision=precision,
x=x,
y=y,
dx=dx,
nx=nx,
xy_ratio=1.0,
theta_deg=0.,
projection="cartesian",
antialiasing=True)
mandelbrot.calc_std_div(
complex_type=np.complex128,
file_prefix="dev",
subset=None,
max_iter=50000,
M_divergence=1.e3,
epsilon_stationnary=1.e-3,
pc_threshold=0.1,
SA_params={
"cutdeg": 64,
"cutdeg_glitch": 8,
"SA_err": 1.e-4,
"use_Taylor_shift": True
},
glitch_eps=1.e-6,
interior_detect=False, #True,
glitch_max_attempt=20)
mandelbrot.run()
mask_codes = [0, 2, 3, 4]
mask = fs.Fractal_Data_array(
mandelbrot,
file_prefix="dev",
postproc_keys=('stop_reason', lambda x: np.isin(x, mask_codes)),
mode="r+raw")
# cv = fs.Fractal_Data_array(mandelbrot, file_prefix="dev",
# postproc_keys=('stop_reason', lambda x: x != 2), mode="r+raw")
# potential_data_key = ("potential", {})
# gold = np.array([255, 210, 66]) / 255.
# black = np.array([0, 0, 0]) / 255.
# color_gradient = fs.Color_tools.Lch_gradient(gold, black, 200)
# colormap = fs.Fractal_colormap(color_gradient)
gold = np.array([255, 210, 66]) / 255.
black = np.array([0, 0, 0]) / 255.
purple = np.array([181, 40, 99]) / 255.
colors1 = np.vstack((purple[np.newaxis, :]))
colors2 = np.vstack((gold[np.newaxis, :]))
colormap = fscolors.Fractal_colormap(kinds="Lch",
colors1=colors1,
colors2=colors2,
n=200,
funcs=None,
extent="mirror")
plotter = fs.Fractal_plotter(
fractal=mandelbrot,
base_data_key=("potential",
{}), # potential_data_key, #glitch_sort_key,
base_data_prefix="dev",
base_data_function=lambda x: x, # np.sin(x*0.0001),
colormap=colormap,
probes_val=[
0., 0.55
], # 200. + 200, #* 428 - 00.,#[0., 0.5, 1.], #phi * k * 2. + k * np.array([0., 1., 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) / 3.5,
probes_kind="qt", #"z", "qt"
mask=mask)
#plotter.add_calculation_layer(postproc_key=potential_data_key)
# layer1_key = ("DEM_shade", {"kind": "potential",
# "theta_LS": 30.,
# "phi_LS": 50.,
# "shininess": 3.,
# "ratio_specular": 15000.})
# plotter.add_grey_layer(postproc_key=layer1_key, intensity=0.75,
# blur_ranges=[],#[[0.99, 0.999, 1.0]],
# disp_layer=False, #skewness=0.2,
# normalized=False, hardness=0.35,
# skewness=0.0, shade_type={"Lch": 1.0, "overlay": 1., "pegtop": 4.})
# layer2_key = ("field_lines", {})
# plotter.add_grey_layer(postproc_key=layer2_key,
# hardness=1.0, intensity=0.68, skewness=0.0,
## blur_ranges=[[0.50, 0.60, 1.0]],
# shade_type={"Lch": 0., "overlay": 2., "pegtop": 1.})
plotter.add_grey_layer(postproc_key=("DEM_shade", {
"kind": "potential",
"theta_LS": 30.,
"phi_LS": 50.,
"shininess": 30.,
"ratio_specular": 15000.
}),
blur_ranges=[],
hardness=0.9,
intensity=0.8,
shade_type={
"Lch": 1.0,
"overlay": 0.,
"pegtop": 4.
})
plotter.plot("dev", mask_color=(0., 0., 0.))
def plot():
"""
Example plot of "Dinkydau flake" location, classic test case for
perturbation techinque and glitch correction.
"""
directory = "./perturb2"
# A simple showcas using perturbation technique
x, y = "-1.74920463345912691e+00", "-2.8684660237361114e-04"
dx = "5e-12"
precision = 16
nx = 800
# Set to True if you only want to rerun the post-processing part
settings.skip_calc = False
# Set to True to enable multi-processing
settings.enable_multiprocessing = True
# xy_ratio = 1.0
# theta_deg = 0.
# complex_type = np.complex128
mandelbrot = fsm.Perturbation_mandelbrot(directory)
mandelbrot.zoom(precision=precision,
x=x,
y=y,
dx=dx,
nx=nx,
xy_ratio=1.0,
theta_deg=0.,
projection="cartesian",
antialiasing=False)
mandelbrot.calc_std_div(complex_type=np.complex128,
file_prefix="dev",
subset=None,
max_iter=50000,
M_divergence=1.e3,
epsilon_stationnary=1.e-3,
pc_threshold=0.1,
SA_params={
"cutdeg": 8,
"cutdeg_glitch": 8,
"SA_err": 1.e-4,
"use_Taylor_shift": True
},
glitch_eps=1.e-6,
interior_detect=True,
glitch_max_attempt=20)
mandelbrot.run()
cv = fs.Fractal_Data_array(mandelbrot,
file_prefix="dev",
postproc_keys=('stop_reason', lambda x: x == 1),
mode="r+raw")
potential_data_key = ("potential", {})
citrus2 = np.array([103, 189, 0]) / 255.
citrus_white = np.array([252, 251, 226]) / 255.
wheat1 = np.array([244, 235, 158]) / 255.
wheat2 = np.array([246, 207, 106]) / 255.
wheat3 = np.array([191, 156, 96]) / 255.
lavender1 = np.array([154, 121, 144]) / 255.
lavender2 = np.array([140, 94, 134]) / 255.
lavender3 = np.array([18, 16, 58]) / 255.
def wave(x):
return 0.5 + (0.4 * (x - 0.5) - 0.6 * 0.5 * np.cos(x * np.pi * 3.))
colormap = fscolors.Fractal_colormap(
kinds="Lch",
colors1=np.vstack(
(citrus_white, wheat2, wheat1, wheat2, wheat1, wheat2, wheat3,
wheat1, lavender2, wheat1, wheat2, wheat3, wheat1, lavender2,
wheat1, lavender3, lavender2, lavender3, lavender1, lavender3,
lavender2)),
colors2=None,
n=100,
funcs=lambda x: wave(x),
extent="mirror")
plotter = fs.Fractal_plotter(
fractal=mandelbrot,
base_data_key=
potential_data_key, # potential_data_key, #glitch_sort_key,
base_data_prefix="dev",
base_data_function=lambda x: x, # np.sin(x*0.0001),
colormap=colormap,
probes_val=np.linspace(0., 1., 22) *
0.5, # 200. + 200, #* 428 - 00.,#[0., 0.5, 1.], #phi * k * 2. + k * np.array([0., 1., 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) / 3.5,
probes_kind="qt", #"z", "qt"
mask=~cv)
#plotter.add_calculation_layer(postproc_key=potential_data_key)
layer1_key = ("DEM_shade", {
"kind": "potential",
"theta_LS": 30.,
"phi_LS": 50.,
"shininess": 3.,
"ratio_specular": 15000.
})
plotter.add_grey_layer(
postproc_key=layer1_key,
intensity=0.75,
blur_ranges=[], #[[0.99, 0.999, 1.0]],
disp_layer=False, #skewness=0.2,
normalized=False,
hardness=0.35,
skewness=0.0,
shade_type={
"Lch": 1.0,
"overlay": 1.,
"pegtop": 4.
})
layer2_key = ("field_lines", {})
plotter.add_grey_layer(postproc_key=layer2_key,
hardness=1.0,
intensity=0.68,
skewness=0.4,
blur_ranges=[[0.50, 0.60, 1.0]],
shade_type={
"Lch": 0.,
"overlay": 2.,
"pegtop": 1.
})
plotter.plot("dev", mask_color=(0., 0., 1.))
def plot():
"""
Example plot of "Dinkydau flake" location, classic test case for
perturbation technique and glitch correction.
"""
directory = "./flake"
# Dinkydau flake
# http://www.fractalforums.com/announcements-and-news/pertubation-theory-glitches-improvement/msg73027/#msg73027
# Ball method 1 found period: 7884
x = "-1.99996619445037030418434688506350579675531241540724851511761922944801584242342684381376129778868913812287046406560949864353810575744772166485672496092803920095332"
y = "0.00000000000000000000000000000000030013824367909383240724973039775924987346831190773335270174257280120474975614823581185647299288414075519224186504978181625478529"
dx = "1.8e-157"
precision = 200
# Set to True if you only want to rerun the post-processing part
settings.skip_calc = False
# Set to True to enable multi-processing
settings.enable_multiprocessing = True
nx = 600
xy_ratio = 0.5
theta_deg = 0.
complex_type = np.complex128
mandelbrot = fsm.Perturbation_mandelbrot(directory)
mandelbrot.zoom(precision=precision,
x=x,
y=y,
dx=dx,
nx=nx,
xy_ratio=xy_ratio,
theta_deg=theta_deg,
projection="cartesian",
antialiasing=False)
mandelbrot.calc_std_div(complex_type=complex_type,
file_prefix="dev",
subset=None,
max_iter=50000,
M_divergence=1.e3,
epsilon_stationnary=1.e-3,
pc_threshold=0.1,
SA_params={
"cutdeg": 8,
"cutdeg_glitch": 8,
"SA_err": 1.e-4,
"use_Taylor_shift": True
},
glitch_eps=1.e-6,
interior_detect=False,
glitch_max_attempt=20)
mandelbrot.run()
glitched = fs.Fractal_Data_array(mandelbrot,
file_prefix="dev",
postproc_keys=('stop_reason',
lambda x: x == 3),
mode="r+raw")
potential_data_key = ("potential", {})
citrus2 = np.array([103, 189, 0]) / 255.
citrus_white = np.array([252, 251, 226]) / 255.
wheat1 = np.array([244, 235, 158]) / 255.
wheat2 = np.array([246, 207, 106]) / 255.
wheat3 = np.array([191, 156, 96]) / 255.
lavender1 = np.array([154, 121, 144]) / 255.
lavender2 = np.array([140, 94, 134]) / 255.
lavender3 = np.array([18, 16, 58]) / 255.
def wave(x):
return 0.5 + (0.4 * (x - 0.5) - 0.6 * 0.5 * np.cos(x * np.pi * 3.))
colormap = fscolors.Fractal_colormap(
kinds="Lch",
colors1=np.vstack(
(citrus_white, wheat2, wheat1, wheat2, wheat1, wheat2, wheat3,
wheat1, lavender2, wheat1, wheat2, wheat3, wheat1, lavender2,
wheat1, lavender3, lavender2, lavender3, lavender1, lavender3,
lavender2)),
colors2=None,
n=100,
funcs=lambda x: wave(x),
extent="mirror")
# colormap.extent = "mirror" #"repeat"
plotter = fs.Fractal_plotter(
fractal=mandelbrot,
base_data_key=
potential_data_key, # potential_data_key, #glitch_sort_key,
base_data_prefix="dev",
base_data_function=lambda x: x, # np.sin(x*0.0001),
colormap=colormap,
probes_val=np.linspace(0., 1., 22)**
0.2, # 200. + 200, #* 428 - 00.,#[0., 0.5, 1.], #phi * k * 2. + k * np.array([0., 1., 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) / 3.5,
probes_kind="qt", #"z", "qt"
mask=glitched)
#plotter.add_calculation_layer(postproc_key=potential_data_key)
layer1_key = ("DEM_shade", {
"kind": "potential",
"theta_LS": 30.,
"phi_LS": 50.,
"shininess": 30.,
"ratio_specular": 15000.
})
plotter.add_grey_layer(
postproc_key=layer1_key,
intensity=0.75,
blur_ranges=[], #[[0.99, 0.999, 1.0]],
disp_layer=False, #skewness=0.2,
normalized=False,
hardness=0.35,
skewness=0.0,
shade_type={
"Lch": 1.0,
"overlay": 1.,
"pegtop": 4.
})
layer2_key = ("field_lines", {})
plotter.add_grey_layer(postproc_key=layer2_key,
hardness=1.0,
intensity=0.68,
skewness=0.4,
blur_ranges=[[0.50, 0.60, 1.0]],
shade_type={
"Lch": 0.,
"overlay": 2.,
"pegtop": 1.
})
plotter.plot("dev", mask_color=(0., 0., 1.))
def func(fractal: fsm.Perturbation_mandelbrot = fractal,
file_prefix: str = "test",
x: mpmath.mpf = x,
y: mpmath.mpf = y,
dx: mpmath.mpf = dx,
xy_ratio: float = xy_ratio,
dps: int = dps,
max_iter: int = max_iter,
nx: int = nx): #
# interior_detect: bool=True):
interior_detect = False # True
# fractal.clean_up(file_prefix)
fractal.zoom(precision=dps,
x=x,
y=y,
dx=dx,
nx=nx,
xy_ratio=xy_ratio,
theta_deg=0.,
projection="cartesian",
antialiasing=False)
fractal.calc_std_div(
complex_type=np.complex128,
file_prefix=file_prefix,
subset=None,
max_iter=max_iter,
M_divergence=1.e3,
epsilon_stationnary=1.e-4,
pc_threshold=0.1,
SA_params={
"cutdeg": 8,
"cutdeg_glitch": 8,
"SA_err": 1.e-20, # "SA_err" 1e-6, "cutdeg": 8 fails
# "SA_err" 1e-20, "cutdeg": 8 fails
# "SA_err" 1e-20, "cutdeg": 64 fails
# "SA_err" 1e-50, "cutdeg": 64 fails
# "SA_err" 1e-200, "cutdeg": 64
"use_Taylor_shift": True
},
glitch_eps=1.e-6,
interior_detect=interior_detect,
glitch_max_attempt=4)
fractal.run()
gold = np.array([255, 210, 66]) / 255.
black = np.array([0, 0, 0]) / 255.
purple = np.array([181, 40, 99]) / 255.
citrus2 = np.array([103, 189, 0]) / 255.
colors1 = np.vstack((citrus2[np.newaxis, :]))
colors2 = np.vstack((black[np.newaxis, :]))
colormap = fscolors.Fractal_colormap(kinds="Lch",
colors1=colors1,
colors2=colors2,
n=200,
funcs=None,
extent="mirror")
mask_codes = [2] #, 3, 4]
mask = fs.Fractal_Data_array(
fractal,
file_prefix=file_prefix,
postproc_keys=('stop_reason', lambda x: np.isin(x, mask_codes)),
mode="r+raw")
plotter = fs.Fractal_plotter(
fractal=fractal,
base_data_key=("potential",
{}), # ("field_lines", {"n_iter": 10, "swirl": 1.}), ,
base_data_prefix=file_prefix,
base_data_function=lambda x: x,
colormap=colormap,
probes_val=[0.25, 0.75],
probes_kind="qt",
mask=mask)
# plotter.add_calculation_layer(("potential", {}))
plotter.add_grey_layer(postproc_key=("DEM_shade", {
"kind": "potential",
"theta_LS": 30.,
"phi_LS": 70.,
"shininess": 300.,
"ratio_specular": 15000.
}),
blur_ranges=[],
hardness=0.9,
intensity=0.8,
shade_type={
"Lch": 1.0,
"overlay": 0.,
"pegtop": 4.
})
plotter.plot(file_prefix, mask_color=(0., 0., 1.))
