def lpm_models():
a0, b0, c0 = [2.2e-3, 1.1e-1, 6.8e-3]
dlog = 0.1
a = FloatLogSlider(value=a0,
base=10,
description=r'$a$',
min=np.log10(a0) - dlog,
max=np.log10(a0) + dlog,
step=dlog / 10,
continuous_update=False)
b = FloatLogSlider(value=b0,
base=10,
description=r'$b$',
min=np.log10(b0) - dlog,
max=np.log10(b0) + dlog,
step=dlog / 10,
continuous_update=False)
dlog *= 5
c = FloatLogSlider(value=c0,
base=10,
description=r'$c$',
min=np.log10(c0) - dlog,
max=np.log10(c0) + dlog,
step=dlog / 10,
continuous_update=False)
io = interactive_output(plot_lpm_models, {'a': a, 'b': b, 'c': c})
return VBox([HBox([a, b, c]), io])
def interactive_res_model():
app = interactive(
plot_res_model,
lay1=FloatSlider(
min=zmin,
max=zmax,
step=1.,
value=30.,
continuous_update=True,
description="$layer2_{top}$",
),
lay2=FloatSlider(
min=zmin,
max=zmax,
value=100.,
step=1.,
continuous_update=True,
description="$layer2_{bottom}$",
),
rho_half=FloatLogSlider(
base=10,
min=np.log10(rhomin),
max=np.log10(rhomax),
value=50.,
step=0.1,
continuous_update=True,
description=r"$\rho_{background}$",
),
rho1=FloatLogSlider(
base=10,
min=np.log10(rhomin),
max=np.log10(rhomax),
value=100.,
step=0.1,
continuous_update=True,
description=r"$\rho_{1}$",
),
rho2=FloatLogSlider(
base=10,
min=np.log10(rhomin),
max=np.log10(rhomax),
value=10.,
step=0.1,
continuous_update=True,
description=r"$\rho_{2}$",
)
)
return app
def recharge_source():
Q = FloatSlider(value=1000,
description=r'$Q$ [m$^3$/day]',
min=500,
max=1500,
step=500,
continuous_update=False)
t = FloatLogSlider(value=1.0,
description=r'$t$ [day]',
base=10,
min=-1,
max=2,
step=0.2,
continuous_update=False)
r = FloatSlider(value=200,
description=r'$r$ [m]',
min=100,
max=500,
step=100,
continuous_update=False)
T = FloatSlider(value=300,
description=r'$T$ [m$^2$/day]',
min=100,
max=500,
step=100,
continuous_update=False)
image = Checkbox(value=False, description='image well')
io = interactive_output(show_theis_image2, {
'Q': Q,
't': t,
'r': r,
'T': T,
'image': image
})
return VBox([HBox([Q, t, image]), HBox([T, r]), io])
def show(x, y):
C = FloatLogSlider(value=0,
base=10,
min=-1,
max=2,
step=1,
readout_format='.3f',
description='$C$',
continuous_update=False)
gamma = FloatLogSlider(value=0,
base=10,
min=-3,
max=2,
step=0.5,
readout_format='.3f',
description='$\gamma$',
continuous_update=False)
interact(plot, C=C, gamma=gamma, x=fixed(x), y=fixed(y))
def show(x, y):
C = FloatLogSlider(value=0,
base=10,
min=-0.5,
max=3,
step=0.5,
description='$C$',
readout_format='.1f',
continuous_update=False)
interact(plot, C=C, x=fixed(x), y=fixed(y))
def add_controls(volume, name, color=None):
"""
"""
level = FloatLogSlider(
base=10, min=-0.5, max=0, step=0.0002, description=f"{name} level:"
)
opacity = FloatLogSlider(
base=10, min=-2, max=1.0, step=0.01, description=f"{name} opacity:"
)
jslink((volume.tf, "level1"), (level, "value"))
jslink((volume.tf, "level2"), (level, "value"))
jslink((volume.tf, "level3"), (level, "value"))
jslink((volume, "opacity_scale"), (opacity, "value"))
button = Button(description=name)
if color is not None:
button.style.button_color = color
controls = HBox([button, level, opacity])
widgets.append(controls)
def leaky_aquifer():
Q = FloatSlider(value=1000,
description=r'$Q$ [m$^3$/day]',
min=500,
max=1500,
step=500,
continuous_update=False)
t = FloatLogSlider(value=1.0,
description=r'$t$ [day]',
base=10,
min=-1,
max=2,
step=0.2,
continuous_update=False)
r = FloatSlider(value=200,
description=r'$r$ [m]',
min=100,
max=500,
step=100,
continuous_update=False)
T = FloatSlider(value=300,
description=r'$T$ [m$^2$/day]',
min=100,
max=500,
step=100,
continuous_update=False)
c = FloatLogSlider(value=1.e5,
description=r'$c$ [day]',
base=10,
min=2,
max=6,
step=1,
continuous_update=False)
io = interactive_output(show_hantushjacob, {
'Q': Q,
't': t,
'r': r,
'T': T,
'c': c
})
return VBox([HBox([Q, t, c]), HBox([T, r]), io])
def show():
style = dict(description_width='initial')
interact(plot,
lambda_=FloatLogSlider(value=0.5,
base=10,
min=-1,
max=1,
step=0.25,
description='単位時間あたり平均回数：',
continuous_update=False,
readout_format='.1f',
style=style))
def build_widget(self):
widget = []
# dropdown for system selection
# checkbox for showing/hiding unmodified data
self.select = Dropdown(options=self.df_all.index.values,description='System:')
# slider array for trimming datapoints
vbox1 = [HBox([Label('Trim-Lo')],layout={'justify_content':'center'}) ]
vbox2 = [HBox([Label('Trim-Hi')],layout={'justify_content':'center'}) ]
self.apply_bg = Checkbox(value=False,description='Subtract BG?')
vbox3 = [HBox([self.apply_bg],layout={'justify_content':'center'}) ]
for config in self.df_all.columns:
sl1 = IntSlider(min=0,max=25,value=7,description='{}'.format(config))
sl1.style.handle_color = self.df_colors.loc[config]
vbox1.append(sl1)
sl2 = IntSlider(min=0,max=100,value=15,description='{}'.format(config))
sl2.style.handle_color = self.df_colors.loc[config]
vbox2.append(sl2)
# sl3 = FloatSlider(min=0.001,max=1,value=0.5,description='{}'.format(config))
sl3 = FloatLogSlider(min=-3,max=0,value=0.5,description='{}'.format(config))
sl3.style.handle_color = self.df_colors.loc[config]
vbox3.append(sl3)
widget.append(HBox([VBox(vbox1), VBox(vbox2)]))
## store slider objects in dataframe
self.df_slider = pd.DataFrame(np.transpose([vbox1[1:],vbox2[1:],vbox3[1:]]),index=self.df_all.columns,columns=['Lo','Hi','bgLoc'])
# self.df_bgslider = pd.DataFrame(np.transpose(vbox3[1:]),index=self.df_all.columns,columns=['Lo','Hi'])
# dropdown for shift-configuration selection
ops = ['None']
ops += [str(i) for i in self.df_all.columns.values]
self.shift_config = Dropdown(options=ops,description='Shift-To:')
self.shift_factors_out = Output()
self.show_original = Checkbox(value=True,description='Show Original Data')
vbox4 = [VBox([self.shift_config,self.show_original]),self.shift_factors_out]
# widget.append(HBox([VBox(vbox3),VBox(vbox4)]))
self.bg_out = Output()
widget.append(HBox([VBox(vbox3),self.bg_out]))
widget.append(HBox(vbox4))
tabs = Tab(widget)
tabs.set_title(0,'Data Trimming')
tabs.set_title(1,'Subtract BG')
tabs.set_title(2,'Curve Shift')
self.debug_out = Output()
return VBox([self.select,tabs,self.debug_out])
def show(x, y):
kernel = Dropdown(options={
'線形カーネル': 'linear',
'ガウシアンカーネル': 'rbf'
},
value='linear',
description='カーネル')
C = FloatLogSlider(value=0,
base=10,
min=-1,
max=2,
step=1,
readout_format='.3f',
description='$C$',
continuous_update=False)
gamma = FloatLogSlider(value=0,
base=10,
min=-3,
max=2,
step=0.5,
readout_format='.3f',
description='$\gamma$ (gamma)',
continuous_update=False)
interact(plot, kernel=kernel, C=C, gamma=gamma, x=fixed(x), y=fixed(y))
def create_widget(A, G, default_horizon=1000):
from ipywidgets import FloatLogSlider, interactive, fixed
slider = FloatLogSlider(
value=default_horizon,
base=10,
min=1, # max exponent of base
max=5, # min exponent of base
step=1, # exponent step
description='time-horizon ($n$)',
readout_format='d'
)
return interactive(run_widget, {'manual' : True, 'manual_name' : 'Run Simulation'}, A=fixed(A), g=G, horizon=slider)
def show():
interact(plot,
mu=FloatSlider(value=0,
min=-1,
max=1,
step=0.5,
description='平均：',
continuous_update=False,
readout_format='.1f'),
sigma=FloatLogSlider(value=1,
base=10,
min=-0.25,
max=0.25,
step=0.125,
description='標準偏差：',
continuous_update=False,
readout_format='.1f'))
def lpm_prediction(sa, sb, sc):
Nmods = FloatLogSlider(value=64,
base=4,
description='samples',
min=0,
max=5,
step=1,
continuous_update=False)
reveal = Checkbox(value=False, description='reveal future!')
io = interactive_output(
plot_lpm_prediction, {
'Nmods': Nmods,
'reveal': reveal,
'sa': fixed(sa),
'sb': fixed(sb),
'sc': fixed(sc)
})
return VBox([HBox([Nmods, reveal]), io])
def setup_lift_slider(self):
name = "lift"
setattr(
self,
f"slider_{name}",
FloatLogSlider(
value=getattr(self, f"min_{name}"),
min=-0.5,
max=1.5,
step=0.05,
base=10,
description=f"min_{name}",
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
readout_format=".3f",
),
)
getattr(self, f"slider_{name}").observe(self.set_slider_value, "value")
def streamflow_depletion():
Q = FloatSlider(value=1000,
description=r'$Q$ [m$^3$/day]',
min=500,
max=1500,
step=500,
continuous_update=False)
t = FloatLogSlider(value=1.0,
description=r'$t$ [day]',
base=10,
min=-1,
max=2,
step=0.2,
continuous_update=False)
r = FloatSlider(value=200,
description=r'$r$ [m]',
min=100,
max=500,
step=100,
continuous_update=False)
T = FloatSlider(value=300,
description=r'$T$ [m$^2$/day]',
min=100,
max=500,
step=100,
continuous_update=False)
H = FloatSlider(value=2,
description=r'$b$ [m]',
min=2,
max=5,
step=1.5,
continuous_update=False)
io = interactive_output(show_hunt2003, {
'Q': Q,
't': t,
'r': r,
'T': T,
'H': H
})
return VBox([HBox([Q, t, H]), HBox([T, r]), io])
def confined_aquifer(analysis=False):
approx = Checkbox(value=True, description='approx.')
semilog = Checkbox(value=False, description='SemiLog')
Q = FloatSlider(value=1000,
description=r'$Q$ [m$^3$/day]',
min=500,
max=1500,
step=500,
continuous_update=False)
t = FloatLogSlider(value=1.0,
description=r'$t$ [day]',
base=10,
min=-1,
max=2,
step=0.2,
continuous_update=False)
r = FloatSlider(value=200,
description=r'$r$ [m]',
min=100,
max=500,
step=100,
continuous_update=False)
T = FloatSlider(value=300,
description=r'$T$ [m$^2$/day]',
min=100,
max=500,
step=100,
continuous_update=False)
io = interactive_output(
show_theis, {
'Q': Q,
't': t,
'r': r,
'T': T,
'approx': approx,
'semilog': semilog,
'analysis': fixed(analysis)
})
return VBox([HBox([Q, t, approx]), HBox([T, r, semilog]), io])
def observation_error():
#out = Output()
seed = 13
rolldice = Button(description='ROLL THE DICE',
tooltip='randomise the random number generator')
Nsldr = IntSlider(value=5,
description='$N_{obs}$',
min=2,
max=10,
step=1,
continuous_update=False)
trueModel = Checkbox(value=False, description='True Process')
bestModel = Checkbox(value=False, description='Best (LSQ) Model')
varsldr = FloatLogSlider(value=0.1,
base=10,
description='$\sigma_i^2$',
min=-2,
max=0,
step=1,
continuous_update=False)
sdf = fixed(seed)
np.random.seed(13)
def on_button_clicked(b):
sdf.value = int(time.time())
rolldice.on_click(on_button_clicked)
io = interactive_output(
plot_observations, {
'N_obs': Nsldr,
'trueModel': trueModel,
'bestModel': bestModel,
'var': varsldr,
'seed': sdf
})
return VBox([HBox([Nsldr, bestModel, trueModel, rolldice, varsldr]), io])
def lpm_posterior():
a0, b0, c0 = [2.2e-3, 1.1e-1, 6.8e-3]
dlog = 0.1
sa = FloatSlider(value=dlog * a0 / 2,
description=r'$\sigma_a$',
min=0.,
max=dlog * a0,
step=dlog * a0 / 10.,
continuous_update=False)
sb = FloatSlider(value=dlog * b0 / 2,
description=r'$\sigma_b$',
min=0.,
max=dlog * b0,
step=dlog * b0 / 10.,
continuous_update=False)
dlog *= 5
sc = FloatSlider(value=dlog * c0 / 2,
description=r'$\sigma_c$',
min=0.,
max=dlog * c0,
step=dlog * c0 / 10.,
continuous_update=False)
Nmods = FloatLogSlider(value=4,
base=2,
description='samples',
min=0,
max=8,
step=1,
continuous_update=False)
io = interactive_output(plot_lpm_posterior, {
'sa': sa,
'sb': sb,
'sc': sc,
'Nmods': Nmods
})
return VBox([HBox([sa, sb, sc, Nmods]), io])
def prediction(var):
mtrue, ctrue = [2, 3]
cmin, c0, cmax = [2.55, 3.05, 3.55]
mmin, m0, mmax = [1.3, 2.1, 2.9]
cmin, c0, cmax = [1.55, 3.05, 4.55]
mmin, m0, mmax = [0.3, 2.1, 4.9]
p = Posterior(cmin=cmin,
cmax=cmax,
Nc=101,
mmin=mmin,
mmax=mmax,
Nm=101,
ctrue=ctrue,
mtrue=mtrue,
var=var)
zoom = Checkbox(value=False, description='zoom')
Nsamples = FloatLogSlider(value=16,
base=4,
description='samples',
min=0,
max=5,
step=1,
continuous_update=False)
xf = FloatSlider(value=3,
description=r'$x_f$',
min=2,
max=5,
step=0.5,
continuous_update=False)
io = interactive_output(plot_predictions, {
'zoom': zoom,
'N': Nsamples,
'xf': xf,
'p': fixed(p)
})
return VBox([HBox([zoom, Nsamples, xf]), io])
class CostWidget(object):
def __init__(self, model):
self._model = model
@property
def app(self):
app_params = self._model._params.copy()
cost = self._model.compute().values["Total Cost [$/tCO2]"]
rsliders = {}
esliders = {}
all_sliders = {}
labels = {
"Scale [tCO2/year]": "Scale [tCO2/year]",
"DAC Capacity Factor": "DAC Capacity Factor",
"DAC Section Lead Time [years]": "DAC Section Lead Time [years]",
"Total Capex [$]": "Overnight Capex [M$] *",
"Electric Power Requierement [MW]":
"Electric Power Requierement [MW] *",
"Thermal [GJ/tCO2]": "Thermal [GJ/tCO2] *",
"Fixed O+M Costs [$/tCO2]": "Fixed O+M Costs [$/tCO2]*",
"Varible O+M Cost [$/tCO2]": "Varible O+M Cost [$/tCO2] *",
"Economic Lifetime [years]": "Economic Lifetime [years]",
"WACC [%]": "WACC [%]",
"Natural Gas Cost [$/mmBTU]": "Natural Gas Cost [$/mmBTU]",
}
# --------- callbacks --------- #
def update_cost(app_params):
cost = self._model.compute().values["Total Cost [$/tCO2]"]
result.value = f"<h1>${cost:0.2f}<h1/>"
def on_value_change(param, change):
app_params[param] = change["new"]
update_cost(app_params)
def set_defaults(change):
case = change["new"]
p = default_params(case.lower()) # TODO
app_params.update(p)
for k, v in app_params.items():
if k in all_sliders:
all_sliders[k].value = v
# --------- callbacks --------- #
header = HTML("""
<h1> DAC Cost Estimator </h1>
<b>By NOAH MCQUEEN and JOE HAMMAN</b>
<div style="width:800px"
<p>
How much does it cost to build a Direct Air Capture facility? To help answer this question, we've built a calculator that takes the most important variables that drive the cost of building and operating a DAC plant. To find out more about the fundementals and assumptions in the calcuator, check out Noah's paper...
</p>
</div>
""")
# presets
presets = Dropdown(description="Preset Scenario",
options=["Low", "High"],
value="Low")
presets.observe(set_defaults, names="value")
# report data
result = HTML(value=f"<h1>${cost:0.2f}<h1/>")
right = VBox(
[HTML("<b>You can build this DAC plant for ... </b>"), result])
rsliders["Scale [tCO2/year]"] = FloatLogSlider(
min=1, max=12, step=0.1, value=app_params["Scale [tCO2/year]"])
rsliders["DAC Capacity Factor"] = FloatSlider(
min=0,
max=1,
step=0.01,
readout_format=".2%",
value=app_params["DAC Capacity Factor"])
rsliders["DAC Section Lead Time [years]"] = IntSlider(
min=1, max=6, value=app_params["DAC Section Lead Time [years]"])
rsliders["Total Capex [$]"] = FloatSlider(
value=app_params["Total Capex [$]"])
rsliders["Electric Power Requierement [MW]"] = FloatSlider(
value=app_params["Electric Power Requierement [MW]"])
rsliders["Thermal [GJ/tCO2]"] = FloatSlider(
value=app_params["Thermal [GJ/tCO2]"])
rsliders["Fixed O+M Costs [$/tCO2]"] = FloatSlider(
value=app_params["Fixed O+M Costs [$/tCO2]"])
rsliders["Varible O+M Cost [$/tCO2]"] = FloatSlider(
value=app_params["Varible O+M Cost [$/tCO2]"])
for key, slider in rsliders.items():
slider.observe(functools.partial(on_value_change, key),
names="value")
details = HTML("""
<h2>Report Data</h2>
<p>Parameters from the <em>Report Data</em> worksheet...</p>
""")
report_data = VBox([details] + [
HBox([Label(labels[k], layout={"width": "250px"}), s])
for k, s in rsliders.items()
])
details = HTML("""
<h2>Economic Data</h2>
<p>Parameters from the <em>Economic Parameters</em> worksheet...</p>
""")
# economic parameters
esliders["Economic Lifetime [years]"] = IntSlider(
min=1, max=50, value=app_params["Economic Lifetime [years]"])
esliders["WACC [%]"] = FloatSlider(min=0,
max=1,
step=0.01,
readout_format=".2%",
value=app_params["WACC [%]"])
esliders["Natural Gas Cost [$/mmBTU]"] = FloatSlider(
min=0,
max=10,
step=0.1,
value=app_params["Natural Gas Cost [$/mmBTU]"])
for key, slider in esliders.items():
slider.observe(functools.partial(on_value_change, key),
names="value")
econ_data = VBox([details] + [
HBox([Label(labels[k], layout={"width": "250px"}), s])
for k, s in esliders.items()
])
all_sliders = {**rsliders, **esliders}
center = VBox([presets, report_data, econ_data])
return AppLayout(header=header,
center=center,
right_sidebar=right,
width="900px")
def all_options(analysis=True):
options = Dropdown(options={
'confined': 1,
'leaky aquifer': 2,
'flow barrier': 3,
'recharge source': 4,
'unconfined': 5
},
value=1,
description='Aquifer type')
approx = Checkbox(value=True, description='approx.')
semilog = Checkbox(value=False, description='SemiLog')
image = Checkbox(value=False, description='image')
Q = FloatSlider(value=1000,
description=r'$Q$ [m$^3$/day]',
min=500,
max=1500,
step=500,
continuous_update=False)
t = FloatLogSlider(value=1.0,
description=r'$t$ [day]',
base=10,
min=-1,
max=2,
step=0.2,
continuous_update=False)
r = FloatSlider(value=200,
description=r'$r$ [m]',
min=100,
max=500,
step=100,
continuous_update=False)
T = FloatSlider(value=300,
description=r'$T$ [m$^2$/day]',
min=100,
max=500,
step=100,
continuous_update=False)
c = FloatLogSlider(value=1.e5,
description=r'$c$ [day]',
base=10,
min=2,
max=6,
step=1,
continuous_update=False)
H = FloatSlider(value=2,
description=r'$b$ [m]',
min=2,
max=5,
step=1.5,
continuous_update=False)
io = interactive_output(
plot_theis, {
'Q': Q,
't': t,
'r': r,
'T': T,
'approx': approx,
'semilog': semilog,
'itest': options,
'image': image,
'c': c,
'H': H,
'analysis': fixed(analysis)
})
return VBox([
HBox([options]),
HBox([Q, t, approx]),
HBox([T, r, semilog]),
HBox([H, c, image]), io
])
def draw_roaming_ui():
global iter_slider, reset_button, color_it_button, juliabrot_button, canvases
global drawing, uly_select, ulx_select, color_list, picker1, picker2, bump_ud_slider, hue_slider, sat_slider, val_slider
global lry_select, lrx_select, color_it, modulo_slider, picker3, bump_lr_slider, zoom_slider, save_button
# This establishes the size of the preview gui
drawing = False
color_it = True
uly_select = 0
ulx_select = 0
lry_select = jgrid.settings.sizeY
lrx_select = jgrid.settings.sizeX
canvases = MultiCanvas(3,
width=jgrid.settings.sizeX * 2.5,
height=jgrid.settings.sizeY + 75)
canvases[drawing_layer].font = '25px serif'
canvases[drawing_layer].fill_style = '#aaaaaa'
canvases[drawing_layer].line_width = 3
canvases[interaction_layer].font = '35px serif'
canvases[interaction_layer].fill_style = '#eee800'
canvases[interaction_layer].stroke_style = '#ffffff'
canvases[interaction_layer].line_width = 3
iter_slider = FloatLogSlider(description='Iterations:',
base=10,
value=jgrid.settings.max_iterations,
min=1,
max=7,
step=.01,
continuous_update=False)
iter_slider.observe(handler=iter_slider_handler, names='value')
max_lr_bump = jgrid.settings.sizeX
max_ud_bump = jgrid.settings.sizeY
bump_ud_slider = IntSlider(description='Bump UD pix:',
value=1,
min=0,
max=max_ud_bump,
step=1,
continuous_update=False)
bump_lr_slider = IntSlider(description='Bump LR pix:',
value=1,
min=0,
max=max_lr_bump,
step=1,
continuous_update=False)
zoom_slider = FloatSlider(description='Zoom:',
value=2.0,
min=0.0,
max=1000.0,
step=.001,
continuous_update=False)
#zoom_slider.observe(handler=zoom_button_handler, names='value')
hue_slider = FloatSlider(description='Hue :',
value=jgrid.settings.hue,
min=0.0,
max=1.0,
step=.001,
continuous_update=False)
sat_slider = FloatSlider(description='Sat:',
value=jgrid.settings.sat,
min=0.0,
max=1.0,
step=.01,
continuous_update=False)
val_slider = FloatSlider(description='Val:',
value=jgrid.settings.val,
min=0.0,
max=1.0,
step=.02,
continuous_update=False)
hue_slider.observe(handler=hue_slider_handler, names='value')
sat_slider.observe(handler=sat_slider_handler, names='value')
val_slider.observe(handler=val_slider_handler, names='value')
modulo_slider = IntSlider(description='Modulo:',
value=jgrid.settings.modulo,
min=1,
max=1000000,
step=1,
continuous_update=False)
modulo_slider.observe(handler=modulo_slider_handler, names='value')
canvases[interaction_layer].on_mouse_down(on_mouse_down)
canvases[interaction_layer].on_mouse_move(on_mouse_move)
reset_button = Button(description='Zoom',
disabled=False,
button_style='',
tooltip='Click to use zoom slider setting for zoom',
icon='')
reset_button.on_click(zoom_button_handler)
save_button = Button(description='Save',
disabled=False,
button_style='',
tooltip='Click to save as JSON settings file',
icon='')
save_button.on_click(save_button_handler)
color_it_button = Button(description='Color/BW',
disabled=False,
button_style='',
tooltip='Click for BW or Color',
icon='')
color_it_button.on_click(color_button_handler)
juliabrot_button = Button(description='JM Mode',
disabled=False,
button_style='',
tooltip='Click for Julia or Mandelbrot',
icon='')
juliabrot_button.on_click(juliabrot_button_handler)
undo_button = Button(description='Undo',
disabled=False,
button_style='',
tooltip='Click to revert to last view',
icon='')
undo_button.on_click(undo_button_handler)
bleft_button = Button(description='Bump L',
disabled=False,
button_style='',
tooltip='Click to nudge left num bump LR pixels',
icon='')
bleft_button.on_click(bleft_button_handler)
bright_button = Button(description='Bump R',
disabled=False,
button_style='',
tooltip='Click to nudge right num bump LR pixels',
icon='')
bright_button.on_click(bright_button_handler)
bup_button = Button(description='Bump U',
disabled=False,
button_style='',
tooltip='Click to nudge up num bump UD pixels',
icon='')
bup_button.on_click(bup_button_handler)
bdown_button = Button(description='Bump D',
disabled=False,
button_style='',
tooltip='Click to nudge down bump UD pixels',
icon='')
bdown_button.on_click(bdown_button_handler)
picker1 = ColorPicker(description='M Color:', value=jgrid.settings.m_color)
#picker2 = ColorPicker(description='Color 1:', value='#fff800')
#picker3 = ColorPicker(description='Color 2:', value='#fff800')
picker1.observe(color_picker1_handler, names='value')
#picker2.observe(color_picker2_handler, names='value')
#picker3.observe(color_picker3_handler, names='value')
color_list = Dropdown(disabled=False,
options=[('Rainbow', 1), ('Classic', 2), ('Log', 3),
('RGB Max Iter', 4), ('Rainbow 2', 5)],
value=jgrid.settings.color_mode,
description='Color Mode:',
tooltip='Select built-in coloring options')
color_list.observe(color_select_handler, names='value')
draw_fractal(canvases, jgrid.tile_list)
display_info(canvases, jgrid)
return AppLayout(center=canvases,
header=HBox((iter_slider, bump_ud_slider, bump_lr_slider,
zoom_slider)),
right_sidebar=VBox(
(picker1, color_list, hue_slider, sat_slider,
val_slider, modulo_slider)),
footer=HBox(
(bleft_button, bright_button, bup_button,
bdown_button, color_it_button, juliabrot_button,
reset_button, undo_button, save_button)))
rho=IntSlider(value=3300.,
min=1000.,
max=3700.,
step=100.,
description='Density of Planet (kg/m$^3$)',
continuous_update=True,
readout=True,
readout_format='.0f',
style=style,
layout=layout),
M_Earth=FloatLogSlider(value=5.97219e24,
base=10,
min=22,
max=27,
step=1,
description='Mass of Planet (kg)',
continuous_update=False,
readout=True,
readout_format='.1e',
style=style,
layout=layout))
output = interactive_plot.children[-1]
interactive_plot
# <i>Caption</i>. Earth-mass, uniformly-dense silicate body. Many of the finer details about a body's interior structure are complicated, so this calculation is simplified and assumes we have a "homogeneous" spherical body [same material (no separation of a mantle and core), density, and temperature throughout]. For reference, Earth's present-day mass is 6.0 x 10$^{24}$ kg, and the average density of a rock (silicate) under standard conditions is 3300 kg/m$^3$.
# ## Weighing Dependencies of the Corotation Limit on Planet Density and Mass
# In addition to increasing the rotation rate of a body, decreasing the density of a material also helps a body exceed the corotation limit. Decreasing the density of the material can indicate that the material is hotter. It is easier to exceed the corotation limit when the body is warmer.
#
# However, it is easiest to cross the threshold between planet and synestia when decreasing the length of day for the planet (the time it takes for a planet to complete one rotation) as opposed to changing the planet's mass or density. The corotation limit is most sensitive to the rate of rotation of a body. The faster a planet spins, the more likely it will transition into a synestia. This is why it is easier for planets with greater angular momentum, particularly those involved in giant impacts, to become synestias.
def __init__(self, layer_state):
self.state = layer_state
self.widget_lighting = Checkbox(description='lighting',
value=self.state.lighting)
link((self.state, 'lighting'), (self.widget_lighting, 'value'))
render_methods = 'NORMAL MAX_INTENSITY'.split()
self.widget_render_method = Dropdown(options=render_methods,
value=self.state.render_method,
description='method')
link((self.state, 'render_method'),
(self.widget_render_method, 'value'))
self.size_options = [32, 64, 128, 128 + 64, 256, 256 + 128, 512]
options = [(str(k), k) for k in self.size_options]
self.widget_max_resolution = Dropdown(options=options,
value=128,
description='max resolution')
link((self.state, 'max_resolution'),
(self.widget_max_resolution, 'value'))
if self.state.vmin is None:
self.state.vmin = 0
self.widget_data_min = FloatSlider(description='min',
min=0,
max=1,
value=self.state.vmin,
step=0.001)
link((self.state, 'vmin'), (self.widget_data_min, 'value'))
dlink((self.state, 'data_min'), (self.widget_data_min, 'min'))
dlink((self.state, 'data_max'), (self.widget_data_min, 'max'))
if self.state.vmax is None:
self.state.vmax = 1
self.widget_data_max = FloatSlider(description='max',
min=0,
max=1,
value=self.state.vmax,
step=0.001)
link((self.state, 'vmax'), (self.widget_data_max, 'value'))
dlink((self.state, 'data_min'), (self.widget_data_max, 'min'))
dlink((self.state, 'data_max'), (self.widget_data_max, 'max'))
self.widget_clamp_min = Checkbox(description='clamp minimum',
value=self.state.clamp_min)
link((self.state, 'clamp_min'), (self.widget_clamp_min, 'value'))
self.widget_clamp_max = Checkbox(description='clamp maximum',
value=self.state.clamp_max)
link((self.state, 'clamp_max'), (self.widget_clamp_max, 'value'))
self.widget_color = ColorPicker(value=self.state.color,
description='color')
link((self.state, 'color'), (self.widget_color, 'value'), color2hex)
if self.state.alpha is None:
self.state.alpha = 1
self.widget_opacity = FloatSlider(description='opacity',
min=0,
max=1,
value=self.state.alpha,
step=0.001)
link((self.state, 'alpha'), (self.widget_opacity, 'value'))
self.widget_opacity_scale = FloatLogSlider(
description='opacity scale',
base=10,
min=-3,
max=3,
value=self.state.opacity_scale,
step=0.01)
link((self.state, 'opacity_scale'),
(self.widget_opacity_scale, 'value'))
# FIXME: this should be fixed
# self.widget_reset_zoom = Button(description="Reset zoom")
# self.widget_reset_zoom.on_click(self.state.viewer_state.reset_limits)
super().__init__([
self.widget_render_method,
self.widget_lighting,
self.widget_data_min,
self.widget_data_max,
self.widget_clamp_min,
self.widget_clamp_max,
self.widget_max_resolution, # self.widget_reset_zoom,
self.widget_color,
self.widget_opacity,
self.widget_opacity_scale
])
label_dict = {
0: 'threshold',
1: 'prior strength',
2: '1 star ratings (#1)',
3: '2 star ratings (#1)',
4: '3 star ratings (#1)',
5: '4 star ratings (#1)',
6: '5 star ratings (#1)',
7: '1 star ratings (#2)',
8: '2 star ratings (#2)',
9: '3 star ratings (#2)',
10: '4 star ratings (#2)',
11: '5 star ratings (#2)'
}
interactive_plot.children = [
i if k != 1 else FloatLogSlider(value=1, base=10, min=-2, max=2)
for k, i in enumerate(interactive_plot.children)
]
style = {'description_width': 'initial'}
for k, child in enumerate(interactive_plot.children):
if k == 12:
continue
child.description = label_dict[k]
child.style = style
#help(child)
#print(k,child)
order = [0, 1, 2, 7, 3, 8, 4, 9, 5, 10, 6, 11, 12]
interactive_plot.children = [interactive_plot.children[k] for k in order]
g = GridBox(children=interactive_plot.children[:-1],
layout=Layout(width='100%',
