def __init__(self, lineFig):
"""
"""
self.lineFig = lineFig
self.marks = self.lineFig.marks
y_ord = bq.OrdinalScale()
x_sc = bq.LinearScale()
legendLabels = []
self.colours = []
for mark in self.marks:
legendLabels += mark.labels
self.colours += mark.colors[:len(mark.labels)]
self.bars = bq.Bars(
y=[1] *
len(legendLabels), # all bars have a amplitude of 1 ## ?
x=legendLabels,
scales={
'y': x_sc,
'x': y_ord
},
colors=self.colours,
padding=0.6,
orientation='horizontal',
stroke='white') ## remove the black border around the bar
self.bars.opacities = [ScatLegend.OPAC_BRIGHT] * len(self.bars.x)
ax_y = bq.Axis(scale=y_ord, orientation="vertical")
ax_x = bq.Axis(scale=x_sc)
ax_x.visible = False
margin = dict(top=40, bottom=0, left=110, right=5)
self.fig = bq.Figure(marks=[self.bars],
axes=[ax_y, ax_x],
fig_margin=margin)
# Variable height depending on number of bars in legend
self.fig.layout.height = str(45 + 20 * len(legendLabels)) + 'px'
self.fig.layout.width = '170px'
self.fig.min_aspect_ratio = 0.000000000001 # effectively remove aspect ratio constraint
self.fig.max_aspect_ratio = 999999999999999 # effectively remove aspect ratio constraint
self.fig.background_style = {'fill': 'White'}
self.bars.on_element_click(self.changeOpacity)
def create_plot():
index = pd.date_range(start='2000-06-01', end='2001-06-01',
freq='30min') + timedelta(minutes=15)
s = pd.Series(np.full(len(index), np.nan), index=index)
x = index.values
y = s
x_sc = bq.DateScale()
y_sc = bq.LinearScale(min=0)
line = bq.Lines(
x=x,
y=y,
scales={
'x': x_sc,
'y': y_sc
},
#display_legend=True, labels=["line 1"],
#fill='bottom', # opacity does work with this option
#fill_opacities = [0.5] *len(x)
)
panzoom = bq.PanZoom(scales={'x': [x_sc], 'y': [y_sc]})
#p = bq.Scatter(x=x, y=y, scales= {'x': x_sc, 'y': y_sc})
ax_x = bq.Axis(scale=x_sc)
ax_y = bq.Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
#fig = bq.Figure(marks=[line, p], axes=[ax_x, ax_y])
fig = bq.Figure(marks=[line], axes=[ax_x, ax_y], interaction=panzoom)
fig.layout.width = '95%'
#p.interactions = {'click': 'select', 'hover': 'tooltip'}
#p.selected_style = {'opacity': 1.0, 'fill': 'DarkOrange', 'stroke': 'Red'}
#p.unselected_style = {'opacity': 0.5}
#p.tooltip = bq.Tooltip(fields=['x', 'y'], formats=['', '.2f'])
#sel = bq.interacts.IndexSelector(scale=p.scales['x'])
#
#def update_range(*args):
# if sel.selected:
# print(sel.selected[0])
#
#sel.observe(update_range, 'selected')
#fig.interaction = sel
return fig, line
def gui_tips (self) :
sc_r = bq.LinearScale(min=0, max=np.pi)
sc_c = bq.ColorScale(colors=Palette.mkpal(["#000000", "#FFFFFF"]))
tips = {}
for shape in self._tips_shapes :
for end in ("src", "dst") :
edges = self.edges[self.edges["_shape_" + end] == shape]
scatt = bq.Scatter(x=edges["_x_" + end],
y=edges["_y_" + end],
marker=shape,
rotation=edges["_angle_" + end],
color=edges["_color_" + end],
stroke="#000000",
scales={"x" : self._x_scale,
"y" : self._y_scale,
"color" : sc_c,
"rotation" : sc_r,
})
tips[end,shape] = scatt
return tips
def scale_x():
return bqplot.LinearScale(min=0, max=1, allow_padding=False)
def scale_y():
return bqplot.LinearScale(min=2, max=3, allow_padding=False)
class Plot_interface(object):
"""Class to handle map and plot interaction"""
# Declare class attributes
pyccd_flag = False
pyccd_flag2 = False
current_band = ''
band_index1 = 4
band_index2 = 4
click_col = ''
point_color = ['#43a2ca']
click_df = pd.DataFrame()
sample_col = ''
sample_df = pd.DataFrame()
PyCCDdf = pd.DataFrame()
table = pd.DataFrame()
band_list = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2']
year_range = [1986, 2018]
doy_range = [1, 365]
step = 1 #in years
# Create widget controls
next_pt = Button(value=False, description='Next point', disabled=False)
previous_pt = Button(value=False,
description='Previous point',
disabled=False)
pyccd_button = Button(value=False,
description='Run PyCCD 1',
disabled=False)
pyccd_button2 = Button(value=False,
description='Run PyCCD 2',
disabled=False)
band_selector1 = Dropdown(
options=['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
description='Select band',
value=None)
band_selector2 = Dropdown(
options=['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
description='Select band',
value=None)
image_band_1 = Dropdown(
options=['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
description='Red:',
value='SWIR1')
image_band_2 = Dropdown(
options=['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
description='Green:',
value='NIR')
image_band_3 = Dropdown(
options=['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
description='Blue:',
value='RED')
stretch_min = FloatText(value=0, description='Min:', disabled=False)
stretch_max = FloatText(value=6000, description='Min:', disabled=False)
# Clear layers on map
clear_layers = Button(value=False, description='Clear Map', disabled=False)
# Color points by DOY
color_check = widgets.Checkbox(value=False,
description='Color DOY',
disabled=False)
idBox = widgets.Text(value='0', description='ID:', disabled=False)
ylim = widgets.IntRangeSlider(value=[0, 4000],
min=0,
max=10000,
step=500,
description='YLim:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d')
xlim = widgets.IntRangeSlider(value=[2000, 2018],
min=1986,
max=2018,
step=1,
description='XLim:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d')
ylim2 = widgets.IntRangeSlider(value=[0, 4000],
min=0,
max=10000,
step=500,
description='YLim:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d')
xlim2 = widgets.IntRangeSlider(value=[2000, 2018],
min=1986,
max=2018,
step=1,
description='XLim:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d')
coords_label = Label()
pt_message = HTML("Current ID: ")
time_label = HTML(value='')
selected_label = HTML("ID of selected point")
hover_label = HTML("test value")
text_brush = HTML(value='Selected year range:')
kml_link = HTML(value='KML:')
error_label = HTML(value='Load a point')
# Create map including streets and satellite and controls
m = ipyleaflet.Map(zoom=5,
layout={'height': '400px'},
center=(3.3890701010382958, -67.32297252983098),
dragging=True,
close_popup_on_click=False,
basemap=ipyleaflet.basemaps.Esri.WorldStreetMap)
streets = ipyleaflet.basemap_to_tiles(
ipyleaflet.basemaps.Esri.WorldImagery)
m.add_layer(streets)
dc = ipyleaflet.DrawControl(marker={'shapeOptions': {
'color': '#ff0000'
}},
polygon={},
circle={},
circlemarker={},
polyline={})
# Table widget
table_widget = qgrid.show_grid(table, show_toolbar=False)
# Set plots
# Plot scales. HERE
lc1_x = bqplot.DateScale(min=datetime.date(xlim.value[0], 2, 1),
max=datetime.date(xlim.value[1], 1, 1))
# DOY scale
lc1_x3 = bqplot.LinearScale(min=0, max=365)
lc2_y = bqplot.LinearScale(min=ylim.value[0], max=ylim.value[1])
lc1_x2 = bqplot.DateScale(min=datetime.date(xlim.value[0], 2, 1),
max=datetime.date(xlim.value[1], 1, 1))
lc2_y2 = bqplot.LinearScale(min=ylim.value[0], max=ylim.value[1])
# Main scatter plot for samples
lc2 = bqplot.Scatter(x=[],
y=[],
scales={
'x': lc1_x,
'y': lc2_y
},
size=[1, 1],
interactions={
'click': 'select',
'hover': 'tooltip'
},
selected_style={
'opacity': 1.0,
'fill': 'DarkOrange',
'stroke': 'Red'
},
unselected_style={'opacity': 0.5},
display_legend=True,
labels=['Sample point'])
# Pyccd model fit
lc4 = bqplot.Lines(
x=[],
y=[],
colors=['black'],
stroke_width=3,
scales={
'x': lc1_x,
'y': lc2_y
},
size=[1, 1],
)
# Pyccd model break
lc5 = bqplot.Scatter(x=[],
y=[],
marker='triangle-up',
colors=['red'],
scales={
'x': lc1_x,
'y': lc2_y
},
size=[1, 1],
display_legend=False,
labels=['Model Endpoint'])
# Scatter plot for clicked points in map
lc3 = bqplot.Scatter(x=[],
y=[],
scales={
'x': lc1_x2,
'y': lc2_y2
},
size=[1, 1],
colors=['gray'],
interactions={
'click': 'select',
'hover': 'tooltip'
},
selected_style={
'opacity': 1.0,
'fill': 'DarkOrange',
'stroke': 'Red'
},
unselected_style={'opacity': 0.5},
display_legend=True,
labels=['Clicked point'])
# Pyccd model fit for clicked point
lc6 = bqplot.Lines(
x=[],
y=[],
colors=['black'],
stroke_width=3,
scales={
'x': lc1_x2,
'y': lc2_y2
},
size=[1, 1],
)
# Pyccd model break for clicked point
lc7 = bqplot.Scatter(x=[],
y=[],
marker='triangle-up',
colors=['red'],
scales={
'x': lc1_x2,
'y': lc2_y2
},
size=[1, 1],
display_legend=False,
labels=['Model Endpoint'])
# Scatter for sample DOY
lc8 = bqplot.Scatter(x=[],
y=[],
scales={
'x': lc1_x3,
'y': lc2_y
},
size=[1, 1],
interactions={
'click': 'select',
'hover': 'tooltip'
},
selected_style={
'opacity': 1.0,
'fill': 'DarkOrange',
'stroke': 'Red'
},
unselected_style={'opacity': 0.5},
display_legend=True,
labels=['Sample point'])
# Plot axes.
x_ax1 = bqplot.Axis(label='Date',
scale=lc1_x,
num_ticks=6,
tick_format='%Y')
x_ax2 = bqplot.Axis(label='Date',
scale=lc1_x2,
num_ticks=6,
tick_format='%Y')
x_ax3 = bqplot.Axis(label='DOY', scale=lc1_x3, num_ticks=6)
x_ay1 = bqplot.Axis(label='SWIR1', scale=lc2_y, orientation='vertical')
x_ay2 = bqplot.Axis(label='SWIR1', scale=lc2_y2, orientation='vertical')
# Create a figure for sample points.
fig = bqplot.Figure(marks=[lc2, lc4, lc5],
axes=[x_ax1, x_ay1],
layout={
'height': '300px',
'width': '100%'
},
title="Sample TS")
# Create a figure for clicked points.
fig2 = bqplot.Figure(marks=[lc3, lc6, lc7],
axes=[x_ax2, x_ay2],
layout={
'height': '300px',
'width': '100%'
},
title="Clicked TS")
# Create a figure for sample DOY.
fig3 = bqplot.Figure(marks=[lc8],
axes=[x_ax3, x_ay1],
layout={
'height': '300px',
'width': '100%'
},
title="Clicked TS")
def __init__(self, navigate):
Plot_interface.navigate = navigate
Plot_interface.band_index1 = 4
Plot_interface.band_index2 = 4
Plot_interface.pyccd_flag = False
Plot_interface.pyccd_flag2 = False
Plot_interface.table = None
# Set up database
conn = sqlite3.connect(Plot_interface.navigate.dbPath)
Plot_interface.current_id = Plot_interface.navigate.current_id
Plot_interface.c = conn.cursor()
Plot_interface.minv = 0
Plot_interface.maxv = 6000
Plot_interface.b1 = 'SWIR1'
Plot_interface.b2 = 'NIR'
Plot_interface.b3 = 'RED'
@classmethod
def map_point(self):
gjson = ipyleaflet.GeoJSON(
data=Plot_interface.navigate.fc_df['geometry'][
Plot_interface.current_id],
name="Sample point")
Plot_interface.m.center = gjson.data['coordinates'][::-1]
Plot_interface.m.zoom = 12
Plot_interface.m.add_layer(gjson)
kmlstr = ee.FeatureCollection(
ee.Geometry.Point(Plot_interface.navigate.fc_df['geometry'][
Plot_interface.current_id]['coordinates'])).getDownloadURL(
"kml")
Plot_interface.kml_link.value = "<a '_blank' rel='noopener noreferrer' href={}>KML Link</a>".format(
kmlstr)
@classmethod
def get_ts(self):
#try:
Plot_interface.error_label.value = 'Loading'
Plot_interface.current_band = Plot_interface.band_list[
Plot_interface.band_index1]
Plot_interface.sample_col = get_full_collection(
Plot_interface.navigate.fc_df['geometry'][
Plot_interface.current_id]['coordinates'],
Plot_interface.year_range, Plot_interface.doy_range)
Plot_interface.sample_df = get_df_full(
Plot_interface.sample_col,
Plot_interface.navigate.fc_df['geometry'][
Plot_interface.current_id]['coordinates']).dropna()
Plot_interface.error_label.value = 'Point loaded!'
#except:
# Plot_interface.error_label.value = 'Point could not be loaded!'
def clear_map(b):
Plot_interface.m.clear_layers()
Plot_interface.m.add_layer(Plot_interface.streets)
Plot_interface.map_point()
@classmethod
def plot_ts(self):
current_band = Plot_interface.band_list[Plot_interface.band_index1]
Plot_interface.lc2.x = Plot_interface.sample_df['datetime']
if Plot_interface.color_check.value == False:
Plot_interface.lc2.colors = list(Plot_interface.point_color)
Plot_interface.lc8.colors = list(Plot_interface.point_color)
else:
Plot_interface.lc2.colors = list(
Plot_interface.sample_df['color'].values)
Plot_interface.lc8.colors = list(
Plot_interface.sample_df['color'].values)
Plot_interface.lc2.y = Plot_interface.sample_df[current_band]
Plot_interface.x_ay1.label = current_band
Plot_interface.lc4.x = []
Plot_interface.lc4.y = []
Plot_interface.lc5.x = []
Plot_interface.lc5.y = []
Plot_interface.lc8.x = Plot_interface.sample_df['doy']
Plot_interface.lc8.y = Plot_interface.sample_df[current_band]
#if pyccd_flag:
if Plot_interface.pyccd_flag:
Plot_interface.run_pyccd(0)
# Go back or forth between sample points
def advance(b):
# Plot point in map
Plot_interface.lc4.x = []
Plot_interface.lc4.y = []
Plot_interface.lc5.x = []
Plot_interface.lc5.y = []
Plot_interface.lc5.display_legend = False
Plot_interface.pyccd_flag = False
Plot_interface.current_id += 1
Plot_interface.pt_message.value = "Point ID: {}".format(
Plot_interface.current_id)
Plot_interface.map_point()
Plot_interface.get_ts()
Plot_interface.plot_ts()
Plot_interface.change_table(0)
Plot_interface.navigate.valid.value = False
Plot_interface.navigate.description = 'Not Saved'
def decrease(b):
# Plot point in map
Plot_interface.lc4.x = []
Plot_interface.lc4.y = []
Plot_interface.lc5.x = []
Plot_interface.lc5.y = []
Plot_interface.lc5.display_legend = False
Plot_interface.pyccd_flag = False
Plot_interface.current_id -= 1
Plot_interface.pt_message.value = "Point ID: {}".format(
Plot_interface.current_id)
Plot_interface.map_point()
Plot_interface.get_ts()
Plot_interface.plot_ts()
Plot_interface.change_table(0)
Plot_interface.navigate.valid.value = False
Plot_interface.navigate.description = 'Not Saved'
def change_table(b):
# Update the table based on current ID
# Get header
cursor = Plot_interface.c.execute('select * from measures')
names = list(map(lambda x: x[0], cursor.description))
previous_inputs = pd.DataFrame()
for i, row in enumerate(
Plot_interface.c.execute(
"SELECT * FROM measures WHERE id = '%s'" %
Plot_interface.current_id)):
previous_inputs[i] = row
previous_inputs = previous_inputs.T
if previous_inputs.shape[0] > 0:
previous_inputs.columns = names
Plot_interface.table_widget.df = previous_inputs
# Functions for changing image stretch
def change_image_band1(change):
new_band = change['new']
Plot_interface.b1 = new_band
def change_image_band2(change):
new_band = change['new']
Plot_interface.b2 = new_band
def change_image_band3(change):
new_band = change['new']
Plot_interface.b3 = new_band
# Band selection for sample point
def on_band_selection1(change):
new_band = change['new']
#global band_index
band_index = change['owner'].index
Plot_interface.band_index1 = band_index
Plot_interface.plot_ts()
# Band selection for clicked point
def on_band_selection2(change):
new_band = change['new']
band_index = change['owner'].index
Plot_interface.band_index2 = band_index
Plot_interface.lc3.x = Plot_interface.click_df['datetime']
Plot_interface.lc3.y = Plot_interface.click_df[new_band]
Plot_interface.x_ay2.label = new_band
if Plot_interface.pyccd_flag2:
Plot_interface.run_pyccd2(0)
def change_yaxis(value):
Plot_interface.lc2_y.min = Plot_interface.ylim.value[0]
Plot_interface.lc2_y.max = Plot_interface.ylim.value[1]
def change_xaxis(value):
Plot_interface.lc1_x.min = datetime.date(Plot_interface.xlim.value[0],
2, 1)
Plot_interface.lc1_x.max = datetime.date(Plot_interface.xlim.value[1],
2, 1)
Plot_interface.year_range = [
Plot_interface.xlim.value[0], Plot_interface.xlim.value[1]
]
def change_yaxis2(value):
Plot_interface.lc2_y2.min = Plot_interface.ylim2.value[0]
Plot_interface.lc2_y2.max = Plot_interface.ylim2.value[1]
def change_xaxis2(value):
Plot_interface.lc1_x2.min = datetime.date(
Plot_interface.xlim2.value[0], 2, 1)
Plot_interface.lc1_x2.max = datetime.date(
Plot_interface.xlim2.value[1], 2, 1)
def hover_event(self, target):
Plot_interface.hover_label.value = str(target['data']['x'])
# Add layer from clicked point in sample TS figure
def click_event(self, target):
pt_index = target['data']['index']
current_band = Plot_interface.band_list[Plot_interface.band_index1]
image_id = Plot_interface.sample_df['id'].values[pt_index]
selected_image = ee.Image(
Plot_interface.sample_col.filterMetadata('system:index', 'equals',
image_id).first())
tile_url = GetTileLayerUrl(
selected_image.visualize(min=Plot_interface.stretch_min.value,
max=Plot_interface.stretch_max.value,
bands=[
Plot_interface.b1, Plot_interface.b2,
Plot_interface.b3
]))
Plot_interface.m.add_layer(
ipyleaflet.TileLayer(url=tile_url, name=image_id))
# Add layer from clicked point in clicked TS figure
def click_event2(self, target):
pt_index = target['data']['index']
current_band = Plot_interface.band_list[Plot_interface.band_index2]
#Find clicked image. .values needed to access the nth element of that list instead of indexing by ID
image_id = Plot_interface.click_df['id'].values[pt_index]
selected_image = ee.Image(
Plot_interface.click_col.filterMetadata('system:index', 'equals',
image_id).first())
tile_url = GetTileLayerUrl(
selected_image.visualize(min=Plot_interface.minv,
max=Plot_interface.maxv,
bands=[
Plot_interface.b1, Plot_interface.b2,
Plot_interface.b3
]))
Plot_interface.m.add_layer(
ipyleaflet.TileLayer(url=tile_url, name=image_id))
# Plot TS from clicked point
def handle_draw(self, action, geo_json):
# Get the selected coordinates from the map's drawing control.
current_band = Plot_interface.band_list[Plot_interface.band_index2]
coords = geo_json['geometry']['coordinates']
Plot_interface.click_col = get_full_collection(
coords, Plot_interface.year_range, Plot_interface.doy_range)
Plot_interface.click_df = get_df_full(Plot_interface.click_col,
coords).dropna()
Plot_interface.lc6.x = []
Plot_interface.lc6.y = []
Plot_interface.lc7.x = []
Plot_interface.lc7.y = []
Plot_interface.lc3.x = Plot_interface.click_df['datetime']
Plot_interface.lc3.y = Plot_interface.click_df[current_band]
if Plot_interface.color_check.value == False:
Plot_interface.lc3.colors = list(Plot_interface.point_color)
else:
Plot_interface.lc3.colors = list(
Plot_interface.click_df['color'].values)
# Plotting pyccd
def plot_pyccd(results, band, plotband, dates, yl, ylabel, ts_type):
mask = np.array(results['processing_mask']).astype(np.bool_)
predicted_values = []
prediction_dates = []
break_dates = []
start_dates = []
for num, result in enumerate(results['change_models']):
days = np.arange(result['start_day'], result['end_day'] + 1)
prediction_dates.append(days)
break_dates.append(result['break_day'])
start_dates.append(result['start_day'])
intercept = result[list(result.keys())[6 + band]]['intercept']
coef = result[list(result.keys())[6 + band]]['coefficients']
predicted_values.append(
intercept + coef[0] * days +
coef[1] * np.cos(days * 1 * 2 * np.pi / 365.25) +
coef[2] * np.sin(days * 1 * 2 * np.pi / 365.25) +
coef[3] * np.cos(days * 2 * 2 * np.pi / 365.25) +
coef[4] * np.sin(days * 2 * 2 * np.pi / 365.25) +
coef[5] * np.cos(days * 3 * 2 * np.pi / 365.25) +
coef[6] * np.sin(days * 3 * 2 * np.pi / 365.25))
num_breaks = len(break_dates)
break_y = [plotband[dates == i][0] for i in break_dates]
#break_y = [0] * num_breaks
break_dates_plot = [
datetime.datetime.fromordinal(i).strftime('%Y-%m-%d %H:%M:%S.%f')
for i in break_dates
]
plot_dates = np.array(
[datetime.datetime.fromordinal(i) for i in (dates)])
# Predicted curves
all_dates = []
all_preds = []
for _preddate, _predvalue in zip(prediction_dates, predicted_values):
all_dates.append(_preddate)
all_preds.append(_predvalue)
all_preds = [item for sublist in all_preds for item in sublist]
all_dates = [item for sublist in all_dates for item in sublist]
date_ord = [
datetime.datetime.fromordinal(i).strftime('%Y-%m-%d %H:%M:%S.%f')
for i in all_dates
]
_x = np.array(date_ord, dtype='datetime64')
_y = all_preds
if ts_type == 'sample_ts':
Plot_interface.lc4.x = _x
Plot_interface.lc4.y = _y
Plot_interface.lc5.x = np.array(break_dates_plot,
dtype='datetime64')
Plot_interface.lc5.y = break_y
elif ts_type == 'clicked_ts':
Plot_interface.lc6.x = _x
Plot_interface.lc6.y = _y
Plot_interface.lc7.x = np.array(break_dates_plot,
dtype='datetime64')
Plot_interface.lc7.y = break_y
# Go to a specific sample
def go_to_sample(b):
# Plot point in map
Plot_interface.lc4.x = []
Plot_interface.lc4.y = []
Plot_interface.lc5.x = []
Plot_interface.lc5.y = []
Plot_interface.lc5.display_legend = False
Plot_interface.pyccd_flag = False
Plot_interface.current_id = int(b.value)
Plot_interface.pt_message.value = "Point ID: {}".format(
Plot_interface.current_id)
Plot_interface.navigate.valid.value = False
Plot_interface.navigate.description = 'Not Saved'
Plot_interface.map_point()
Plot_interface.get_ts()
Plot_interface.plot_ts()
# Run pyccd
def run_pyccd(b):
# Run pyCCD on current point
Plot_interface.pyccd_flag = True
Plot_interface.lc5.display_legend = True
dfPyCCD = Plot_interface.sample_df
dfPyCCD['pixel_qa'][dfPyCCD['pixel_qa'] > 4] = 0
#TODO: Paramaterize everything
params = {
'QA_BITPACKED': False,
'QA_FILL': 255,
'QA_CLEAR': 0,
'QA_WATER': 1,
'QA_SHADOW': 2,
'QA_SNOW': 3,
'QA_CLOUD': 4
}
dates = np.array(dfPyCCD['ord_time'])
blues = np.array(dfPyCCD['BLUE'])
greens = np.array(dfPyCCD['GREEN'])
reds = np.array(dfPyCCD['RED'])
nirs = np.array(dfPyCCD['NIR'])
swir1s = np.array(dfPyCCD['SWIR1'])
swir2s = np.array(dfPyCCD['SWIR2'])
thermals = np.array(dfPyCCD['THERMAL'])
qas = np.array(dfPyCCD['pixel_qa'])
results = ccd.detect(dates,
blues,
greens,
reds,
nirs,
swir1s,
swir2s,
thermals,
qas,
params=params)
band_names = [
'Blue SR', 'Green SR', 'Red SR', 'NIR SR', 'SWIR1 SR', 'SWIR2 SR',
'THERMAL'
]
plotlabel = band_names[Plot_interface.band_index1]
plot_arrays = [blues, greens, reds, nirs, swir1s, swir2s]
plotband = plot_arrays[Plot_interface.band_index1]
Plot_interface.plot_pyccd(results, Plot_interface.band_index1,
plotband, dates, (0, 4000), 'PyCCD Results',
'sample_ts')
def run_pyccd2(b):
# Run pyCCD on current point
Plot_interface.pyccd_flag2 = True
# Display the legend
Plot_interface.lc7.display_legend = True
dfPyCCD = Plot_interface.click_df
# First two lines no longer required bc we are removing NA's when we load the TS
dfPyCCD['pixel_qa'][dfPyCCD['pixel_qa'] > 4] = 0
#TODO: Paramaterize everything
params = {
'QA_BITPACKED': False,
'QA_FILL': 255,
'QA_CLEAR': 0,
'QA_WATER': 1,
'QA_SHADOW': 2,
'QA_SNOW': 3,
'QA_CLOUD': 4
}
dates = np.array(dfPyCCD['ord_time'])
blues = np.array(dfPyCCD['BLUE'])
greens = np.array(dfPyCCD['GREEN'])
reds = np.array(dfPyCCD['RED'])
nirs = np.array(dfPyCCD['NIR'])
swir1s = np.array(dfPyCCD['SWIR1'])
swir2s = np.array(dfPyCCD['SWIR2'])
thermals = np.array(dfPyCCD['THERMAL'])
qas = np.array(dfPyCCD['pixel_qa'])
results = ccd.detect(dates,
blues,
greens,
reds,
nirs,
swir1s,
swir2s,
thermals,
qas,
params=params)
band_names = [
'Blue SR', 'Green SR', 'Red SR', 'NIR SR', 'SWIR1 SR', 'SWIR2 SR',
'THERMAL'
]
plotlabel = band_names[Plot_interface.band_index2]
plot_arrays = [blues, greens, reds, nirs, swir1s, swir2s]
plotband = plot_arrays[Plot_interface.band_index2]
Plot_interface.plot_pyccd(results, Plot_interface.band_index2,
plotband, dates, (0, 4000), 'PyCCD Results',
'clicked_ts')
ylim.observe(change_yaxis)
xlim.observe(change_xaxis)
ylim2.observe(change_yaxis2)
xlim2.observe(change_xaxis2)
next_pt.on_click(advance)
previous_pt.on_click(decrease)
pyccd_button.on_click(run_pyccd)
pyccd_button2.on_click(run_pyccd2)
clear_layers.on_click(clear_map)
band_selector1.observe(on_band_selection1, names='value')
band_selector2.observe(on_band_selection2, names='value')
image_band_1.observe(change_image_band1, names='value')
image_band_2.observe(change_image_band2, names='value')
image_band_3.observe(change_image_band3, names='value')
lc2.on_element_click(click_event)
lc2.tooltip = hover_label
lc2.on_hover(hover_event)
lc3.on_element_click(click_event2)
lc3.tooltip = hover_label
lc3.on_hover(hover_event)
idBox.on_submit(go_to_sample)
dc.on_draw(handle_draw)
m.add_control(dc)
m.add_control(ipyleaflet.LayersControl())
def __init__(self, zoom_y=True, **kwargs):
super().__init__(**kwargs)
self.x_scale = bqplot.LinearScale(allow_padding=False)
self.y_scale = bqplot.LinearScale(allow_padding=False)
widgets.link((self, 'x_min'), (self.x_scale, 'min'))
widgets.link((self, 'x_max'), (self.x_scale, 'max'))
widgets.link((self, 'y_min'), (self.y_scale, 'min'))
widgets.link((self, 'y_max'), (self.y_scale, 'max'))
self.x_axis = bqplot.Axis(scale=self.x_scale)
self.y_axis = bqplot.Axis(scale=self.y_scale, orientation='vertical')
widgets.link((self, 'x_label'), (self.x_axis, 'label'))
widgets.link((self, 'y_label'), (self.y_axis, 'label'))
self.x_axis.color = blackish
self.y_axis.color = blackish
self.x_axis.label_color = blackish
self.y_axis.label_color = blackish
# self.y_axis.tick_style = {'fill': blackish, 'stroke':'none'}
self.y_axis.grid_color = blackish
self.x_axis.grid_color = blackish
self.x_axis.label_offset = "2em"
self.y_axis.label_offset = "3em"
self.x_axis.grid_lines = 'none'
self.y_axis.grid_lines = 'none'
self.axes = [self.x_axis, self.y_axis]
self.scales = {'x': self.x_scale, 'y': self.y_scale}
self.figure = bqplot.Figure(axes=self.axes)
self.figure.background_style = {'fill': 'none'}
self.figure.padding_y = 0
self.figure.fig_margin = {'bottom': 40, 'left': 60, 'right': 10, 'top': 10}
self.interacts = {}
self.interacts['pan-zoom'] = bqplot.PanZoom(scales={'x': [self.x_scale], 'y': [self.y_scale] if zoom_y else []})
self.interacts['select-rect'] = bqplot.interacts.BrushSelector(x_scale=self.x_scale, y_scale=self.y_scale, color="green")
self.interacts['select-x'] = bqplot.interacts.BrushIntervalSelector(scale=self.x_scale, color="green")
self._brush = self.interacts['select-rect']
self._brush_interval = self.interacts['select-x']
# TODO: put the debounce in the presenter?
@vaex.jupyter.debounced(DEBOUNCE_SELECT)
def update_brush(*args):
with self.output:
if not self._brush.brushing: # if we ended _brushing, reset it
self.figure.interaction = None
if self._brush.selected is not None:
x1, x2 = self._brush.selected_x
y1, y2 = self._brush.selected_y
# (x1, y1), (x2, y2) = self._brush.selected
# mode = self.modes_names[self.modes_labels.index(self.button_selection_mode.value)]
self.presenter.select_rectangle(x1, x2, y1, y2)
else:
self.presenter.select_nothing()
if not self._brush.brushing: # but then put it back again so the rectangle is gone,
self.figure.interaction = self._brush
self._brush.observe(update_brush, ["selected", "selected_x"])
@vaex.jupyter.debounced(DEBOUNCE_SELECT)
def update_brush(*args):
with self.output:
if not self._brush_interval.brushing: # if we ended _brushing, reset it
self.figure.interaction = None
if self._brush_interval.selected is not None and len(self._brush_interval.selected):
x1, x2 = self._brush_interval.selected
self.presenter.select_x_range(x1, x2)
else:
self.presenter.select_nothing()
if not self._brush_interval.brushing: # but then put it back again so the rectangle is gone,
self.figure.interaction = self._brush_interval
self._brush_interval.observe(update_brush, ["selected"])
def tool_change(change=None):
self.figure.interaction = self.interacts.get(self.tool, None)
self.observe(tool_change, 'tool')
self.widget = self.figure
def line_profile(
image,
order=2,
plotter=None, # noqa: C901
comparisons=None,
**viewer_kwargs):
"""View the image with a line profile.
Creates and returns an ipywidget to visualize the image along with a line
profile.
The image can be 2D or 3D.
Parameters
----------
image : array_like, itk.Image, or vtk.vtkImageData
The 2D or 3D image to visualize.
order : int, optional
Spline order for line profile interpolation. The order has to be in the
range 0-5.
plotter : 'plotly', 'bqplot', or 'ipympl', optional
Plotting library to use. If not defined, use plotly if available,
otherwise bqplot if available, otherwise ipympl.
comparisons: dict, optional
A dictionary whose keys are legend labels and whose values are other
images whose intensities to plot over the same line.
viewer_kwargs : optional
Keyword arguments for the viewer. See help(itkwidgets.view).
"""
profiler = LineProfiler(image=image, order=order, **viewer_kwargs)
if not plotter:
try:
import plotly.graph_objs as go
plotter = 'plotly'
except ImportError:
pass
if not plotter:
try:
import bqplot
plotter = 'bqplot'
except ImportError:
pass
if not plotter:
plotter = 'ipympl'
if plotter == 'plotly':
import plotly.graph_objs as go
layout = go.Layout(xaxis=dict(title='Distance'),
yaxis=dict(title='Intensity'))
fig = go.FigureWidget(layout=layout)
elif plotter == 'bqplot':
import bqplot
x_scale = bqplot.LinearScale()
y_scale = bqplot.LinearScale()
x_axis = bqplot.Axis(scale=x_scale,
grid_lines='solid',
label='Distance')
y_axis = bqplot.Axis(scale=y_scale,
orientation='vertical',
grid_lines='solid',
label='Intensity')
labels = ['Reference']
display_legend = False
if comparisons:
display_legend = True
labels += [label for label in comparisons.keys()]
lines = [
bqplot.Lines(scales={
'x': x_scale,
'y': y_scale
},
labels=labels,
display_legend=display_legend,
enable_hover=True)
]
fig = bqplot.Figure(marks=lines, axes=[x_axis, y_axis])
elif plotter == 'ipympl':
ipython = IPython.get_ipython()
ipython.enable_matplotlib('widget')
matplotlib.interactive(False)
fig, ax = plt.subplots()
else:
raise ValueError('Invalid plotter: ' + plotter)
def update_plot():
if plotter == 'plotly':
distance, intensity = profiler.get_profile(image)
fig.data[0]['x'] = distance
fig.data[0]['y'] = intensity
if comparisons:
for ii, image_ in enumerate(comparisons.values()):
distance, intensity = profiler.get_profile(image_)
fig.data[ii + 1]['x'] = distance
fig.data[ii + 1]['y'] = intensity
elif plotter == 'bqplot':
distance, intensity = profiler.get_profile(image)
if comparisons:
for image_ in comparisons.values():
distance_, intensity_ = profiler.get_profile(image_)
distance = np.vstack((distance, distance_))
intensity = np.vstack((intensity, intensity_))
fig.marks[0].x = distance
fig.marks[0].y = intensity
elif plotter == 'ipympl':
ax.plot(*profiler.get_profile(image))
if comparisons:
ax.plot(*profiler.get_profile(image), label='Reference')
for label, image_ in comparisons.items():
ax.plot(*profiler.get_profile(image_), label=label)
ax.legend()
else:
ax.plot(*profiler.get_profile(image))
ax.set_xlabel('Distance')
ax.set_ylabel('Intensity')
fig.canvas.draw()
fig.canvas.flush_events()
def update_profile(change):
if plotter == 'plotly':
update_plot()
elif plotter == 'bqplot':
update_plot()
elif plotter == 'ipympl':
is_interactive = matplotlib.is_interactive()
matplotlib.interactive(False)
ax.clear()
update_plot()
matplotlib.interactive(is_interactive)
if plotter == 'plotly':
distance, intensity = profiler.get_profile(image)
trace = go.Scattergl(x=distance, y=intensity, name='Reference')
fig.add_trace(trace)
if comparisons:
for label, image_ in comparisons.items():
distance, intensity = profiler.get_profile(image_)
trace = go.Scattergl(x=distance, y=intensity, name=label)
fig.add_trace(trace)
widget = widgets.VBox([profiler, fig])
elif plotter == 'bqplot':
update_plot()
widget = widgets.VBox([profiler, fig])
elif plotter == 'ipympl':
update_plot()
widget = widgets.VBox([profiler, fig.canvas])
profiler.observe(update_profile, names=['point1', 'point2'])
return widget
shift_label2.layout.width = label_width
shift_label3.layout.width = label_width
shift_label4.layout.width = label_width
shift_label = widgets.HTML(value='Shift: ')
shift_text_width = '50px'
shift_label.layout.width = shift_text_width
units = widgets.Label(value='nm')
unit_text_width = '30px'
units.layout.width = unit_text_width
## Create spectral lines display for both figures
# Axes for spectra
# Set the scales
x_sc_fig1 = bq.LinearScale(min=float(min(wavelengths)), max=float(max(wavelengths)))
y_sc_fig1 = bq.LinearScale()
x_sc = bq.LinearScale(min=float(min(wavelengths)), max=float(max(wavelengths)))
y_sc = bq.LinearScale()
# Define the axes
ax_x_fig1 = bq.Axis(label='Wavelength (nm)', scale=x_sc_fig1, grid_lines='none', num_ticks=12)
ax_y_fig1 = bq.Axis(scale=y_sc_fig1, orientation='vertical', grid_lines='none', num_ticks=0)
ax_x = bq.Axis(label='Wavelength (nm)', scale=x_sc, grid_lines='none', num_ticks=12)
ax_y = bq.Axis(scale=y_sc, orientation='vertical', grid_lines='none', num_ticks=0)
# Arrays for the spectral lines. one for the stars (0.5 to 1) and one for
# laboratory measurements (0 to 0.5).
height1 = np.array([[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],
[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5],[0.0,0.5]])
height2 = np.array([[0.5,1.0],[0.5,1.0],[0.5,1.0],[0.5,1.0],[0.5,1.0],[0.5,1.0],
def __init__(self, session):
super(BqplotBaseView, self).__init__(session)
# session.hub.subscribe(self, SubsetCreateMessage,
# handler=self.receive_message)
self.state = self._state_cls()
# if we allow padding, we sometimes get odd behaviour with the interacts
self.scale_x = bqplot.LinearScale(min=0, max=1, allow_padding=False)
self.scale_y = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y}
self.axis_x = bqplot.Axis(
scale=self.scale_x, grid_lines='solid', label='x')
self.axis_y = bqplot.Axis(scale=self.scale_y, orientation='vertical', tick_format='0.2f',
grid_lines='solid', label='y')
def update_axes(*ignore):
self.axis_x.label = str(self.state.x_att)
if self.is2d:
self.axis_y.label = str(self.state.y_att)
self.state.add_callback('x_att', update_axes)
if self.is2d:
self.state.add_callback('y_att', update_axes)
self.figure = bqplot.Figure(scales=self.scales, animation_duration=0, axes=[
self.axis_x, self.axis_y])
self.figure.padding_y = 0
actions = ['move']
self.interact_map = {}
self.panzoom = bqplot.PanZoom(scales={'x': [self.scale_x], 'y': [self.scale_y]})
self.interact_map['move'] = self.panzoom
if self.is2d:
self.brush = bqplot.interacts.BrushSelector(x_scale=self.scale_x, y_scale=self.scale_y, color="green")
self.interact_map['brush'] = self.brush
self.brush.observe(self.update_brush, "brushing")
actions.append('brush')
self.brush_x = bqplot.interacts.BrushIntervalSelector(scale=self.scale_x, color="green" )
self.interact_map['brush x'] = self.brush_x
self.brush_x.observe(self.update_brush_x, "brushing")
actions.append('brush x')
if self.is2d:
self.brush_y = bqplot.interacts.BrushIntervalSelector(scale=self.scale_y, color="green", orientation='vertical')
self.interact_map['brush y'] = self.brush_y
self.brush_y.observe(self.update_brush_y, "brushing")
actions.append('brush y')
self.button_action = widgets.ToggleButtons(description='Mode: ', options=[(action, action) for action in actions],
icons=["arrows", "pencil-square-o"])
self.button_action.observe(self.change_action, "value")
self.change_action() # 'fire' manually for intial value
# self.state.add_callback('y_att', self._update_axes)
# self.state.add_callback('x_log', self._update_axes)
# self.state.add_callback('y_log', self._update_axes)
self.state.add_callback('x_min', self.limits_to_scales)
self.state.add_callback('x_max', self.limits_to_scales)
self.state.add_callback('y_min', self.limits_to_scales)
self.state.add_callback('y_max', self.limits_to_scales)
self.create_tab()
self.output_widget = widgets.Output()
self.main_widget = widgets.VBox(
children=[self.tab, self.figure, self.output_widget])
def run_zonal_computation(aoi, output):
aoi_model = aoi.view.model
list_zones = get_ecozones()
#get the aoi name
aoi_name = aoi_model.name
#create the result folder
resultDir = os.path.join(os.path.expanduser('~'), 'zonal_results',
aoi_name, '')
os.makedirs(resultDir, exist_ok=True)
#create the map
Map = sm.SepalMap(['CartoDB.Positron'])
###################################
### placer sur la map ####
###################################
output.add_live_msg('visualize data')
aoi = aoi_model.feature_collection
Map.addLayer(aoi, {}, 'aoi')
Map.zoom_ee_object(aoi.geometry())
#add dataset
dataset = ee.ImageCollection('NASA/MEASURES/GFCC/TC/v3').filter(
ee.Filter.date('2010-01-01', '2010-12-31'))
treeCanopyCover = dataset.select('tree_canopy_cover')
treeCanopyCoverVis = {
'min': 0.0,
'max': 100.0,
'palette': ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800'],
}
country_gfcc_2010 = treeCanopyCover.mean().clip(aoi)
Map.addLayer(country_gfcc_2010, treeCanopyCoverVis, 'Tree Canopy Cover')
#load the ecozones
gez_2010 = ee.Image('users/bornToBeAlive/gez_2010_wgs84')
country_gez_2010 = gez_2010.select('b1').clip(aoi)
Map.addLayer(country_gez_2010.sldStyle(getSldStyle()), {},
'gez 2010 raster')
#show the 0 values
cdl = country_gez_2010.select('b1')
masked = ee.Image(cdl).updateMask(cdl.eq(0))
Map.addLayer(masked, {'palette': 'red'}, 'masked 0 data')
#mask these values from the results
country_gez_2010 = ee.Image(cdl).updateMask(cdl.neq(0))
#Get the list of values from the mosaic image
freqHist = country_gez_2010.reduceRegions(
**{
'reducer': ee.Reducer.frequencyHistogram(),
'collection': aoi,
'scale': 100,
})
#rewrite the dictionary
values = ee.List(
freqHist.map(getVal).aggregate_array('vals')).flatten().distinct()
#Run reduceToVectors per class by masking all other classes.
classes = country_gez_2010.reduceToVectors(
**{
'reducer': ee.Reducer.countEvery(),
'geometry': aoi,
'scale': 100,
'maxPixels': 1e10
})
country_gez_2010_vector = ee.FeatureCollection(classes)
#add the borders on the map
empty = ee.Image().byte()
outline = empty.paint(**{
'featureCollection': country_gez_2010_vector,
'color': 1,
'width': 1
})
Map.addLayer(outline, {'palette': '000000'}, 'gez 2010 borders')
#export raw data
out_dir = os.path.join(os.path.expanduser('~'), 'downloads')
raw_stats = os.path.join(resultDir, aoi_name + '_raw.csv')
if not os.path.isfile(raw_stats):
output.add_live_msg('compute the zonal analysis')
#compute the zonal analysis
computation = False
cpt = 0
while not computation:
f = io.StringIO()
with redirect_stdout(f):
geemap.zonal_statistics(in_value_raster=country_gfcc_2010,
in_zone_vector=country_gez_2010_vector,
out_file_path=raw_stats,
statistics_type='FIXED_HIST',
scale=100,
crs=getConformProj(),
hist_min=0,
hist_max=100,
hist_steps=100,
tile_scale=2**cpt)
output.add_live_msg(f.getvalue())
#check if the computation have finished on a file
if not os.path.isfile(raw_stats):
#cannot use tile_scale > 16
if cpt == 3:
raise Exception("Aoi is to big")
#change the tile_scale and relaunch the process
output.add_live_msg(f.getvalue(), 'error')
time.sleep(2)
cpt += 1
output.add_live_msg(f'Increasing tile_scale ({2**cpt})',
'warning')
time.sleep(2)
else:
computation = True
#######################################
### lire et concatener les données ###
#######################################
out_stats = os.path.join(resultDir, aoi_name + '_stats.csv')
if not os.path.isfile(out_stats):
output.add_live_msg('read and concatenate the data')
data = pd.read_csv(raw_stats, sep=',')
output.add_live_msg('remove no_data')
#enlever les lignes Nan
#(vérifier dans le tableau d'avant qu'elles ne sont pas trop grandes)
data = data.dropna()
#recuperer les valeurs de label
ecozones = data.label.unique()
output.add_live_msg('merge')
#aggreger les lignes avec les même valeurs
stats = pd.DataFrame([i for i in range(100)], columns=['treecover'])
stats = stats.set_index('treecover')
for _, ecozoneId in enumerate(ecozones):
patches = data[data.label == ecozoneId]
ecozone = get_ecozones()[ecozoneId]
stats[ecozone] = 0
for index, patch in patches.iterrows():
tmp = pd.DataFrame(
[val[1] for val in json.loads(patch['histogram'])])
stats[ecozone] = stats[ecozone] + tmp[0]
stats = stats * 100 * 100 / 1e6
#exporter
stats.to_csv(out_stats, index=True)
#############################
## create the layout ##
#############################
output.add_live_msg('create the layout')
stats = pd.read_csv(out_stats, index_col='treecover')
#recuperer les noms de label
ecozones = stats.columns
x_sc = bq.LinearScale()
ax_x = bq.Axis(label='treecover (%)', scale=x_sc)
x = []
for i in range(100):
x.append(i)
figs = []
zones_id = []
for ecozone in ecozones:
#get the # of the ecozone
for index, value in get_ecozones().items():
if value == ecozone:
zone_index = index
zones_id.append(index)
break
y_sc = bq.LinearScale(max=stats[ecozone].max())
ax_y = bq.Axis(label='surface (Km\u00B2)',
scale=y_sc,
orientation='vertical') #, tick_format='.2e')
y = []
for index, row in stats.iterrows():
y.append(row[ecozone])
mark = bq.Bars(x=x,
y=y,
scales={
'x': x_sc,
'y': y_sc
},
colors=[get_colors()[zone_index]],
stroke='black',
padding=0.1)
fig_hist = bq.Figure(title=ecozone, marks=[mark], axes=[ax_x, ax_y])
figs.append(fig_hist)
#add a coherent legend
legend_keys = [get_ecozones()[i] for i in zones_id]
legend_colors = [get_colors()[i] for i in zones_id]
Map.add_legend(legend_keys=legend_keys,
legend_colors=legend_colors,
position='topleft')
#create the partial layout
children = [
v.Flex(xs12=True,
class_='pa-0',
children=[sw.DownloadBtn('Download .csv', path=out_stats)]),
v.Flex(xs12=True, class_='pa-0', children=[Map]),
v.Flex(xs12=True, class_='pa-0', children=figs)
]
output.add_live_msg('complete', 'success')
return children
return "Tuples are not the same length!"
else:
summation = 0.0
for i in range(n):
summation += (x[i] - y[i])**2
return np.sqrt(summation)
a = np.random.rand(100, 2)
b = np.random.rand(100, 2)
a = a * 700
b = b * 30
x_sc = bqplot.LinearScale()
y_sc = bqplot.LinearScale()
ax_x = bqplot.Axis(scale=x_sc, label="X-value")
ax_y = bqplot.Axis(scale=y_sc, label="Y-value", orientation='vertical')
scatters1 = bqplot.Scatter(x=a[:, 0],
y=a[:, 1],
scales={
'x': x_sc,
'y': y_sc
},
default_size=5,
colors=["navy"],
sizes=[10])
scatters2 = bqplot.Scatter(x=b[:, 0],
def __init__(self):
self.episode_play = widgets.Play(
value=0,
min=0,
max=100,
step=1,
description="Press play",
disabled=False,
interval=10,
)
self.update_interval_slider = widgets.IntSlider(
min=1,
max=300,
value=30,
)
self.update_interval_box = widgets.HBox([
widgets.Label("Animation update interval:"),
self.update_interval_slider
])
self.episode_slider = widgets.IntSlider()
self.episode_hbox = widgets.HBox(
[self.episode_play, self.episode_slider])
self.episode_vbox = widgets.VBox(
[self.episode_hbox, self.update_interval_box])
widgets.jslink((self.episode_play, 'value'),
(self.episode_slider, 'value'))
widgets.jslink((self.episode_play, 'max'),
(self.episode_slider, 'max'))
widgets.jslink((self.episode_play, 'min'),
(self.episode_slider, 'min'))
widgets.jslink((self.update_interval_slider, 'value'),
(self.episode_play, 'interval'))
self.episode_gradient_image = widgets.Image(
format='png',
width=120,
height=600,
)
self.episode_image = widgets.Image(
format='png',
width=120,
height=600,
)
x_sc = bq.LinearScale()
# x_sc.max = size * 1.3
y_sc = bq.LinearScale()
y_sc.max = 1
y_sc2 = bq.LinearScale()
self.x_sc = x_sc
ax_x = bq.Axis(label='step', scale=x_sc, grid_lines='none')
ax_x.min = 0
ax_x.max = 100
ax_y = bq.Axis(label='costs',
scale=y_sc,
orientation='vertical',
grid_lines='solid')
ax_y2 = bq.Axis(label='speed',
scale=y_sc2,
orientation='vertical',
side='right',
grid_lines='none')
self.costs_plot_lines_costs = Lines(scales={
'x': x_sc,
'y': y_sc
},
display_legend=True,
stroke_width=1)
self.costs_plot_lines_speed = Lines(scales={
'x': x_sc,
'y': y_sc2
},
colors=['red'],
display_legend=True,
stroke_width=1)
self.costs_plot_progress = Lines(scales={'x': x_sc, 'y': y_sc})
pan_zoom = bq.interacts.PanZoom(scales={'x': [x_sc], 'y': []})
self.costs_plot_figure = Figure(marks=[
self.costs_plot_lines_costs, self.costs_plot_lines_speed,
self.costs_plot_progress
],
axes=[ax_x, ax_y, ax_y2],
title='Costs and speed',
legend_location='top-left',
interaction=pan_zoom,
layout=widgets.Layout(width='100%',
height='100%'))
self.follow_present = widgets.ToggleButton(
value=False,
description='Follow present',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
)
self.reset_scale = widgets.Button(
description='Reset scale',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
)
def reset_scale_callback(b):
self.reset_costs_plot_scale()
self.reset_scale.on_click(reset_scale_callback)
self.plot_controls_hbox = widgets.HBox(
[self.follow_present, self.reset_scale],
layout=widgets.Layout(width='100%', justify_content='center'))
self.costs_plot_box = widgets.VBox(
[self.costs_plot_figure, self.plot_controls_hbox],
layout=widgets.Layout(width='100%'))
self.images_hbox = widgets.HBox([
self.episode_gradient_image, self.episode_image,
self.costs_plot_box
],
layout=widgets.Layout(width='100%'))
def episode_slider_callback(change):
if change.name == 'value' and change.new is not None:
gradient_shift = max(
0,
len(self.images) - len(self.gradient_images))
if change.new >= gradient_shift:
self.episode_gradient_image.value = self.gradient_images[
change.new - gradient_shift]
self.episode_image.value = self.images[change.new]
self.update_timestamp_line(change.new)
self.episode_slider.observe(episode_slider_callback, type='change')
super(EpisodeReview,
self).__init__([self.episode_vbox, self.images_hbox])
def __init__(self):
self.episode_play = widgets.Play(
value=0,
min=0,
max=100,
step=1,
description="Press play",
disabled=False,
interval=10,
)
self.update_interval_slider = widgets.IntSlider(
min=1, max=300, value=30,
)
self.update_interval_box = widgets.HBox(
[
widgets.Label("Animation update interval:"),
self.update_interval_slider,
]
)
self.episode_slider = widgets.IntSlider()
self.episode_hbox = widgets.HBox(
[self.episode_play, self.episode_slider]
)
self.episode_vbox = widgets.VBox(
[self.episode_hbox, self.update_interval_box]
)
widgets.jslink(
(self.episode_play, "value"), (self.episode_slider, "value")
)
widgets.jslink(
(self.episode_play, "max"), (self.episode_slider, "max")
)
widgets.jslink(
(self.episode_play, "min"), (self.episode_slider, "min")
)
widgets.jslink(
(self.update_interval_slider, "value"),
(self.episode_play, "interval"),
)
self.episode_gradient_image = widgets.Image(
format="png", width=120, height=600,
)
self.episode_image = widgets.Image(
format="png", width=120, height=600,
)
x_sc = bq.LinearScale()
# x_sc.max = size * 1.3
y_sc = bq.LinearScale()
y_sc.max = 1
y_sc2 = bq.LinearScale()
self.x_sc = x_sc
ax_x = bq.Axis(label="step", scale=x_sc, grid_lines="none")
ax_x.min = 0
ax_x.max = 100
ax_y = bq.Axis(
label="costs",
scale=y_sc,
orientation="vertical",
grid_lines="solid",
)
ax_y2 = bq.Axis(
label="speed",
scale=y_sc2,
orientation="vertical",
side="right",
grid_lines="none",
)
self.costs_plot_lines_costs = Lines(
scales={"x": x_sc, "y": y_sc}, display_legend=True, stroke_width=1
)
self.costs_plot_lines_speed = Lines(
scales={"x": x_sc, "y": y_sc2},
colors=["red"],
display_legend=True,
stroke_width=1,
)
self.costs_plot_progress = Lines(scales={"x": x_sc, "y": y_sc})
pan_zoom = bq.interacts.PanZoom(scales={"x": [x_sc], "y": []})
self.costs_plot_figure = Figure(
marks=[
self.costs_plot_lines_costs,
self.costs_plot_lines_speed,
self.costs_plot_progress,
],
axes=[ax_x, ax_y, ax_y2],
title="Costs and speed",
legend_location="top-left",
interaction=pan_zoom,
layout=widgets.Layout(width="100%", height="100%"),
)
self.follow_present = widgets.ToggleButton(
value=False,
description="Follow present",
disabled=False,
button_style="", # 'success', 'info', 'warning', 'danger' or ''
)
self.reset_scale = widgets.Button(
description="Reset scale",
disabled=False,
button_style="", # 'success', 'info', 'warning', 'danger' or ''
)
def reset_scale_callback(b):
self.reset_costs_plot_scale()
self.reset_scale.on_click(reset_scale_callback)
self.plot_controls_hbox = widgets.HBox(
[self.follow_present, self.reset_scale],
layout=widgets.Layout(width="100%", justify_content="center"),
)
self.costs_plot_box = widgets.VBox(
[self.costs_plot_figure, self.plot_controls_hbox],
layout=widgets.Layout(width="100%"),
)
self.images_hbox = widgets.HBox(
[
self.episode_gradient_image,
self.episode_image,
self.costs_plot_box,
],
layout=widgets.Layout(width="100%"),
)
def episode_slider_callback(change):
if change.name == "value" and change.new is not None:
gradient_shift = max(
0, len(self.images) - len(self.gradient_images)
)
if change.new >= gradient_shift:
if len(self.gradient_images) > change.new - gradient_shift:
self.episode_gradient_image.value = self.gradient_images[
change.new - gradient_shift
]
self.episode_image.value = self.images[change.new]
self.update_timestamp_line(change.new)
self.episode_slider.observe(episode_slider_callback, type="change")
super(EpisodeReview, self).__init__(
[self.episode_vbox, self.images_hbox]
)
def get_scales(self):
return {"x": bq.DateScale(), "y": bq.LinearScale()}
def __init__(self, video_stream=None, img_path=None, img=None, interactors=None, size=0.5, vision_system=None, frame=None, \
filter_fn=None, apply_filter_to_vision_system_input=False, update_frame_cbs=None, display_colorspace=ColorSpaces.BGR.value, **kwargs):
if not (video_stream is not None) ^ (img is not None) ^ (
frame is not None) ^ (img_path is not None):
raise Exception(
"either path, img or frame must be defined, and not both")
self.bq_img = None
self.raw_frame = None
self.size = size or 1
self.video_stream = video_stream
self.togglebutton_group = []
self.interactors = interactors or []
self.vision_system = vision_system
self.filter_fn = filter_fn
self.apply_filter_to_vision_system_input = apply_filter_to_vision_system_input
self.image_plot_scales = {'x': bq.LinearScale(), 'y': bq.LinearScale()}
self.hidden = False
if display_colorspace in ColorSpaces:
self.display_colorspace = display_colorspace.value
else:
self.display_colorspace = display_colorspace
self.update_frame_cbs = update_frame_cbs or []
# read the data from a file to display
if img is None:
if frame is not None and type(frame is Frame):
self.raw_frame = frame
elif img_path is not None:
self.raw_frame = Frame(cv2.imread(img_path))
else:
self.raw_frame = next(self.video_stream)
else:
self.raw_frame = Frame(img)
self.filtered_frame = Frame.copy_of(self.raw_frame)
self.labelled_frame = Frame.copy_of(self.filtered_frame)
self.update_data_and_display()
# link all required interactors
for interactor in self.interactors:
interactor.link_with(self)
self.image_plot = self.make_image_plot()
interactors_with_controls = [
interactor for interactor in self.interactors
if interactor.ipy_controls is not None
]
panel_controls = [
interactor.ipy_controls for interactor in interactors_with_controls
if interactor.is_panel
]
toolbar_controls = [
interactor.ipy_controls for interactor in interactors_with_controls
if not interactor.is_panel
]
display_pane = ipy.VBox(
[self.image_plot,
self.make_image_tools(self.image_plot)] + toolbar_controls)
display_pane.layout.width = str(
self.size * self.FULL_EXTERNAL_WIDTH) + 'px'
# fill accross 1/size times before filling downwards
hbox_list = [display_pane]
vbox_list = []
for controls in (c for c in panel_controls if c is not None):
hbox_list.append(controls)
if len(hbox_list) == int(1 / size):
vbox_list.append(ipy.HBox(hbox_list))
hbox_list = []
# add the remainder
vbox_list += hbox_list
super().__init__(vbox_list, **kwargs)
search_min_time, search_max_time)
dataset_id = datasets[0]
feature_layer = ipyl.GeoJSON(data=features)
feature_layer.on_click(map_click_handler)
map.layers = [map.layers[0], feature_layer]
# In[32]:
widget_dsnames = ipyw.Dropdown(options=datasets, value=dataset_id)
# This defines the intitial `bqplot` time series plot
# In[33]:
dt_x = bq.DateScale()
sc_y = bq.LinearScale()
constraints = {'time>=': search_min_time, 'time<=': search_max_time}
df, var = get_data(dataset=dataset_id,
standard_name=standard_name,
constraints=constraints)
def_tt = bq.Tooltip(fields=['y'], formats=['.2f'], labels=['value'])
time_series = bq.Lines(x=df.index,
y=df[var],
scales={
'x': dt_x,
'y': sc_y
},
tooltip=def_tt)
ax_x = bq.Axis(scale=dt_x, label='Time')
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
def fix(v):
# bqplot is picky about float and numpy scalars
if hasattr(v, 'item'):
return v.item()
else:
return v
self.scale_x = bqplot.LinearScale(min=fix(limits[0][0]),
max=fix(limits[0][1]),
allow_padding=False)
self.scale_y = bqplot.LinearScale(min=fix(limits[1][0]),
max=fix(limits[1][1]),
allow_padding=False)
self.scales = {'x': self.scale_x, 'y': self.scale_y}
self.figure = plt.figure(self.figure_key,
fig=self.figure,
scales=self.scales)
self.figure.layout.width = 'calc(100% - 400px)'
self.figure.layout.min_height = '800px'
plt.figure(fig=self.figure)
#self.figure.padding_y = 0
x = np.arange(0, 10)
y = x**2
self.core_image = widgets.Image(format='png')
self.core_image_fix = widgets.Image(format='png')
self.image = bqplot.Image(scales=self.scales, image=self.core_image)
# triggered by regular mouse click not a brush selector
self.image.on_element_click(self.click_to_zoom)
self.figure.marks = self.figure.marks + [self.image]
self.scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales,
size=x,
marker="arrow")
self.panzoom = bqplot.PanZoom(scales={
'x': [self.scale_x],
'y': [self.scale_y]
})
self.figure.interaction = self.panzoom
for axes in self.figure.axes:
axes.grid_lines = 'none'
axes.color = axes.grid_color = axes.label_color = blackish
self.figure.axes[0].label = plot.x_label
self.figure.axes[1].label = plot.y_label
self.figure.axes[1].scale = bqplot.LinearScale(min=0,
max=self.scale_y.max -
self.scale_y.min,
allow_padding=False)
self.stuck_ctr = 0
self.base_address = widgets.Label(
value=f"Base address: 0x{int(self.limits[1][0]):X}")
self.zoom_args = widgets.Text(description="Zoom Args",
value=self.zoom_args_string(),
disabled=True,
style={'description_width': 'initial'},
layout=widgets.Layout(width='50%'))
self.curr_action = Action.other
self.undo_actions = list()
self.redo_actions = list()
self.counter = 2
self.scale_x.observe(self._update_limits)
self.scale_y.observe(self._update_limits)
self.widget = widgets.VBox(
[self.figure, self.base_address, self.zoom_args])
self.create_tools()
def test_astro_image():
data = np.zeros((2, 3))
color_scale = bqplot.ColorScale()
scale_x = bqplot.LinearScale()
scale_y = bqplot.LinearScale()
ImageGL(image=data, scales={'image': color_scale, 'x': scale_x, 'y': scale_y})
def __init__(self, view, viewer_state, layer_state=None, layer=None):
super(BqplotScatterLayerArtist, self).__init__(viewer_state,
layer_state=layer_state,
layer=layer)
self.view = view
self.scale_size = bqplot.LinearScale()
self.scale_color = bqplot.ColorScale()
self.scale_size_quiver = bqplot.LinearScale(min=0, max=1)
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scales = dict(self.view.scales,
size=self.scale_size,
rotation=self.scale_rotation,
color=self.scale_color)
self.scale_image = bqplot.ColorScale()
self.scales_quiver = dict(self.view.scales,
size=self.scale_size_quiver,
rotation=self.scale_rotation)
self.scales_image = dict(self.view.scales, image=self.scale_image)
self.scatter = bqplot.ScatterGL(scales=self.scales, x=[0, 1], y=[0, 1])
self.quiver = bqplot.ScatterGL(scales=self.scales_quiver,
x=[0, 1],
y=[0, 1],
visible=False,
marker='arrow')
self.counts = None
self.image = AstroImage(scales=self.scales_image)
on_change([(self.state, 'density_map')])(self._on_change_density_map)
on_change([(self.state, 'bins')])(self._update_scatter)
self._viewer_state.add_global_callback(self._update_scatter)
self.view.figure.marks = list(
self.view.figure.marks) + [self.image, self.scatter, self.quiver]
dlink((self.state, 'color'), (self.scatter, 'colors'), lambda x: [x])
dlink((self.state, 'color'), (self.quiver, 'colors'), lambda x: [x])
self.scatter.observe(self._workaround_unselected_style, 'colors')
self.quiver.observe(self._workaround_unselected_style, 'colors')
on_change([(self.state, 'cmap_mode', 'cmap_att')
])(self._on_change_cmap_mode_or_att)
on_change([(self.state, 'cmap')])(self._on_change_cmap)
dlink((self.state, 'cmap_vmin'), (self.scale_color, 'min'),
float_or_none)
dlink((self.state, 'cmap_vmax'), (self.scale_color, 'max'),
float_or_none)
on_change([(self.state, 'size', 'size_scaling', 'size_mode',
'size_vmin', 'size_vmax')])(self._update_size)
viewer_state.add_callback('x_att', self._update_xy_att)
viewer_state.add_callback('y_att', self._update_xy_att)
self._update_size()
# set initial values for the colormap
self._on_change_cmap()
self.state.add_callback('visible', self._update_visibility)
self.state.add_callback('vector_visible', self._update_visibility)
self.state.add_callback('density_map', self._update_visibility)
dlink((self.state, 'visible'), (self.scatter, 'visible'))
dlink((self.state, 'visible'), (self.image, 'visible'))
dlink((self.state, 'alpha'), (self.scatter, 'default_opacities'),
lambda x: [x])
dlink((self.state, 'alpha'), (self.quiver, 'default_opacities'),
lambda x: [x])
dlink((self.state, 'alpha'), (self.image, 'opacity'))
on_change([(self.state, 'vector_visible', 'vx_att', 'vy_att')
])(self._update_quiver)
dlink((self.state, 'vector_visible'), (self.quiver, 'visible'))
def __init__(self,
y,
x=None,
xlim=None,
ylim=None,
xlabel=None,
ylabel=None,
title=None):
super().__init__()
self.x = x
self.xlim = xlim or [0, 1]
self.ylim = ylim or [0, 1]
self.xlabel = xlabel or 'x'
self.ylabel = ylabel or 'y'
self.title = title or "{} vs {}".format(self.ylabel, self.xlabel)
if isinstance(y, list):
self.y = y
else:
self.y = [y]
self.colors = [
'blue', 'red', 'green', 'orange', 'black', 'purple', 'gray'
]
self.xscale = bq.LinearScale(min=self.xlim[0], max=self.xlim[1])
self.yscale = bq.LinearScale(min=self.ylim[0], max=self.ylim[1])
if isinstance(self.ylabel, list):
ylabel = ''
else:
ylabel = self.ylabel
self.xax = bq.Axis(
scale=self.xscale,
label=self.xlabel,
grid_lines='none',
)
self.yax = bq.Axis(
scale=self.yscale,
label=ylabel,
orientation='vertical',
grid_lines='none',
)
self.num_lines = 0
self.lines = []
self.scatters = []
self.labels = []
if isinstance(self.y, list):
for y in self.y:
self.create_line(y, display_legend=True)
else:
self.create_line(self.y)
self.fig = bq.Figure(marks=self.lines + self.scatters,
axes=[self.xax, self.yax],
layout=ipw.Layout(height='550px', width='100%'),
title=self.title)
self.debug = ipw.Output(
layout=ipw.Layout(height='100px', overflow_y='scroll'))
self.children = [self.fig]
])
]), header_rel_html
] #, VBox(list_relative_controls), VBox([floattext_confidence])]
def updatecontrolinui():
UI_model.children[0].children = UI_viewcontrol
updateviewcontrol()
# END OF ************************************************************************************* GUI portion of the code that contains various labels,checkboxes etc. ***********************
# START OF ************************************************************************************************************** Build bar charts (use of bqp) ***********************
x_ord = bqp.OrdinalScale()
y_sc = bqp.LinearScale()
y_sc.max = 1
#Plot #1 i.e. Creation of the bar plot
bar = bqp.Bars(
x=[],
y=[],
scales={
'x': x_ord,
'y': y_sc
},
orientation="horizontal",
display_legend=True,
labels=[
'Initial Weights', 'Mkt Efficient Portfolio (Max Sharpe)',
def metrics(id,
y_true,
y_pred,
protected,
desc,
x_ticks,
pname,
backend="bqplot"):
"""Plot a stack of figures for the three metrics."""
figure_name = f"Fairness_{id}_{desc}"
dis = protected.astype(bool)
tn_adv, fp_adv, fn_adv, tp_adv = confusion_matrix(y_true[~dis],
y_pred[~dis]).ravel()
tn_dis, fp_dis, fn_dis, tp_dis = confusion_matrix(y_true[dis],
y_pred[dis]).ravel()
# Also just count all positives and negatives
pos_adv = tp_adv + fp_adv # all positives
pos_dis = tp_dis + fp_dis
neg_adv = tn_adv + fn_adv # all negatives
neg_dis = tn_dis + fn_dis
# Demographic Parity
dem_par = (pos_dis / (pos_dis + neg_dis)) / (pos_adv / (pos_adv + neg_adv))
# Equal Opportunity
eq_opp = (tp_dis / (tp_dis + fn_dis)) / (tp_adv / (tp_adv + fn_adv))
acc_adv = np.mean(y_true[~dis] == y_pred[~dis])
acc_dis = np.mean(y_true[dis] == y_pred[dis])
print("Acc adv: {}".format(acc_adv))
print("Acc dis: {}".format(acc_dis))
def normalise(nd):
numer, denom = nd
numer = np.asarray(numer, float)
denom = np.asarray(denom, float)
total = numer + denom
total /= 100. # make percentage
numer = numer / total
denom = denom / total
return [numer, denom]
def describe(rate):
rate = np.round(rate, 2) # make a percent
if rate < 0.5:
return f"{rate:.0%} as often"
elif rate == 0.5:
return "half as often"
elif rate < 1.:
return f"{1. - rate:.0%} less often"
elif rate == 1.:
return "equally often"
elif rate < 2.:
return f"{rate - 1.:.0%} more often"
elif rate == 2.:
return "twice as often"
elif rate < 4:
return f"{rate:.1f}x as often"
elif rate < 10:
return f"{rate:.0f}x as often"
else:
return "significantly more often"
# if eo > 0.995:
# stake = f"Suitable {groups[0].capitalize()} and {groups[1]} are selected at equal rates."
def bqbars(values, colors, title, stake):
fig = plt.figure()
eo_bars = plt.bar(x_ticks, values, colors=colors)
plt.ylabel("Fraction (%)")
plt.xlabel(stake)
fig.title = title
return fig
def matplotbars(ax, values, colors, title, stake):
b0 = ax.bar(x_ticks, values[0], width=0.5, color=colors[0])
b1 = ax.bar(x_ticks,
values[1],
bottom=values[0],
width=0.5,
color=colors[1])
ax.set_ylabel("Fraction (%)")
ax.set_xlabel(stake)
ax.set_title(title)
return b0, b1
eov = normalise([[tp_adv, tp_dis], [fn_adv, fn_dis]])
eov_title = "Opportunity{}".format(desc)
eov_stake = f"Suitable {pname} are selected {describe(eq_opp)}."
eov_colors = ["green", "lightgreen"]
dpv = normalise([[pos_adv, pos_dis], [neg_adv, neg_dis]])
dpv_title = "Selection Rates{}".format(desc)
dpv_stake = f"{pname} are selected {describe(dem_par)}."
dpv_colors = ["black", "gray"]
ppv = normalise([[tp_adv, tp_dis], [fp_adv, fp_dis]])
# error rates (false Discovery Rate parity)
prec_par = (tp_dis / pos_dis) / (tp_adv / pos_adv)
ppv_title = "Precision{}".format(desc)
#ppv_stake = f"{pname.capitalize()} are selected {describe(prec_par)}.".replace("often", "precisely")
ppv_stake = f"Selected {pname} are profitable {describe(prec_par)}."
ppv_colors = ["green", "red"]
print("Selection: {}".format(dpv))
print("Opportunity: {}".format(eov))
print("Precision: {}".format(ppv))
if backend == "bqplot":
scales = {
"x": bq.OrdinalScale(),
"y": bq.LinearScale(),
}
eo_fig = bqbars(eov, eov_colors, eov_title, eov_stake)
dp_fig = bqbars(dpv, dpv_colors, dpv_title, dpv_stake)
pp_fig = bqbars(ppv, ppv_colors, ppv_title, ppv_stake)
fairbox = widgets.HBox((
dp_fig,
eo_fig,
pp_fig,
))
fairbox.layout.width = "99%"
display(fairbox)
eo_fig.axes[0].color = eo_fig.axes[1].color = "Black"
dp_fig.axes[0].color = dp_fig.axes[1].color = "Black"
pp_fig.axes[0].color = pp_fig.axes[1].color = "Black"
else:
fig, ax = mplt.subplots(1, 3, figsize=(9, 3), dpi=DPI)
def clean(strv):
# We have a bit more room for better sentences
val = strv.replace("SE Asian",
"SE Asians").replace("x ", " times ")
# split the line
s = len(val) // 2
a = val[s:].find(' ')
b = val[:s][::-1].find(' ')
split = s + a
if (b >= 0 and b < a) or (a < 0):
split = s - b - 1
val = val[:split] + "\n" + val[split + 1:]
return val
sa, na = matplotbars(ax[0], dpv, dpv_colors, dpv_title,
clean(dpv_stake))
es, en = matplotbars(ax[1], eov, eov_colors, eov_title,
clean(eov_stake))
_, ii = matplotbars(ax[2], ppv, ppv_colors, ppv_title,
clean(ppv_stake))
mplt.legend(
(es, en, sa, na, ii),
(
"Profitable (Selected)",
"Profitable (Rejected)",
"Selected (All)",
"Rejected (All)",
"Not Profitable (Selected)",
),
bbox_to_anchor=(1.25, 0.75),
)
mplt.tight_layout()
fig.savefig("images/" + figure_name + ".png", bbox_inches="tight")
##
## Set up counts versus time plot
##
# Set up Species to be Generic
init_species_ind = 0
# Set up initial time
init_time = 0
init_time_idx = 0
# Set up axes
x_time = bq.LinearScale(min = 0, max=max_half_lifes)
y_number = bq.LinearScale(min = 0, max=N_parent)
y_fraction = bq.LinearScale(min = 0, max=1)
# Labels and scales for Axes
ax_x_time = bq.Axis(label=species['timeunits'][init_species_ind], scale=x_time, num_ticks = time_ticks,
tick_values = half_life_ticks )
ax_y_number = bq.Axis(label='Number of atoms', scale=y_number, orientation='vertical')
ax_y_fraction = bq.Axis(label='Fraction of atoms', scale=y_fraction, orientation='vertical')
# Define tooltip (Bug: doesn't allow relabeling Tooltips, also needs to apply to Scatter, not Lines)
#def_tt_parent = bq.Tooltip(fields=['x', 'y'], formats=['.2f', '3.0f'], labels=['time', species['parent_short'][init_species_ind]])
#def_tt_daughter = bq.Tooltip(fields=['x', 'y'], formats=['.2f', '3.0f'], labels=['time', species['daughter_short'][init_species_ind]])
def_tt_parent = bq.Tooltip(fields=['x', 'y'], formats=['.2f', '.3f'], labels=['time', 'amount of parent isotope'])
def_tt_daughter = bq.Tooltip(fields=['x', 'y'], formats=['.2f', '.3f'], labels=['time', 'amount of daughter isotope'])
def interactive_body(body,
angles,
bodies,
r_arm=True,
l_arm=True,
r_leg=True,
l_leg=True,
neck_p=True):
"""Plot an interactive body with which we can play. Only call this function on a complete body (for now)"""
def refresh(_):
i = 0
to_update = []
if l_arm:
left_arm.x, left_arm.y = [[p[2][0], scat.x[i], scat.x[i + 1]],
[p[2][1], scat.y[i], scat.y[i + 1]]]
to_update.append(3)
to_update.append(4)
i += 2
if r_arm:
right_arm.x, right_arm.y = [[p[6][0], scat.x[i], scat.x[i + 1]],
[p[6][1], scat.y[i], scat.y[i + 1]]]
to_update.append(7)
to_update.append(8)
i += 2
if l_leg:
left_leg.x, left_leg.y = [[p[9][0], scat.x[i], scat.x[i + 1]],
[p[9][1], scat.y[i], scat.y[i + 1]]]
to_update.append(10)
to_update.append(11)
i += 2
if r_leg:
right_leg.x, right_leg.y = [[p[13][0], scat.x[i], scat.x[i + 1]],
[p[13][1], scat.y[i], scat.y[i + 1]]]
to_update.append(14)
to_update.append(15)
i += 2
if neck_p:
to_update.append(0)
neck.x, neck.y = [[p[1][0], scat.x[i]], [p[1][1], scat.y[i]]]
head.x = np.cos(np.linspace(0, 2 * np.pi, 100)) * 60 + neck.x[1]
head.y = np.sin(np.linspace(0, 2 * np.pi, 100)) * 65 + neck.y[1]
#update body
for i in range(len(to_update)):
body[0][to_update[i]] = [scat.x[i], scat.y[i]]
body_angles = []
b_angles = member_relative_angles(body)
if l_arm:
body_angles.append(b_angles[0])
body_angles.append(b_angles[1])
if r_arm:
body_angles.append(b_angles[2])
body_angles.append(b_angles[3])
if l_leg:
body_angles.append(b_angles[4])
body_angles.append(b_angles[5])
if r_leg:
body_angles.append(b_angles[6])
body_angles.append(b_angles[7])
if neck_p:
body_angles.append(b_angles[8])
b = get_nearest_neighbor(np.transpose(all_angles),
np.transpose(body_angles), bodies)[0]
p2 = b[0]
nchest.x, nchest.y = np.transpose(
[p2[1], p2[2], p2[9], p2[13], p2[6], p2[1], p2[0]])
nhead.x = np.cos(np.linspace(0, 2 * np.pi, 100)) * 60 + p2[0][0]
nhead.y = np.sin(np.linspace(0, 2 * np.pi, 100)) * 65 + p2[0][1]
nleft_arm.x, nleft_arm.y = [[p2[2][0], p2[3][0], p2[4][0]],
[p2[2][1], p2[3][1], p2[4][1]]]
nright_arm.x, nright_arm.y = [[p2[6][0], p2[7][0], p2[8][0]],
[p2[6][1], p2[7][1], p2[8][1]]]
nleft_leg.x, nleft_leg.y = [[p2[9][0], p2[10][0], p2[11][0]],
[p2[9][1], p2[10][1], p2[11][1]]]
nright_leg.x, nright_leg.y = [[p2[13][0], p2[14][0], p2[15][0]],
[p2[13][1], p2[14][1], p2[15][1]]]
scales = {
'x': bqp.LinearScale(min=0, max=1000),
'y': bqp.LinearScale(min=1000, max=0)
}
#initialization of the nearest neighbor of the interactive body.
body_angles = []
all_angles = []
b_angles = member_relative_angles(body)
a = np.transpose(angles)
if l_arm:
body_angles.append(b_angles[0])
body_angles.append(b_angles[1])
all_angles.append(a[0])
all_angles.append(a[1])
if r_arm:
body_angles.append(b_angles[2])
body_angles.append(b_angles[3])
all_angles.append(a[2])
all_angles.append(a[3])
if l_leg:
body_angles.append(b_angles[4])
body_angles.append(b_angles[5])
all_angles.append(a[4])
all_angles.append(a[5])
if r_leg:
body_angles.append(b_angles[6])
body_angles.append(b_angles[7])
all_angles.append(a[6])
all_angles.append(a[7])
if neck_p:
body_angles.append(b_angles[8])
all_angles.append(a[8])
nbody = get_nearest_neighbor(np.transpose(all_angles),
np.transpose(body_angles), bodies)[0]
#points of the interactive and neighbor bodies
p = body[0]
p2 = nbody[0]
marks = []
#Constructions of the two bodies: the interactive one and its nearest neighbor. body part beginnig with n... are part of the neighbor.
#draw the chest
chest = bqp.Lines(scales=scales)
chest.x, chest.y = np.transpose([p[1], p[2], p[9], p[13], p[6], p[1]])
marks.append(chest)
nchest = bqp.Lines(scales=scales, colors=['red'])
nchest.x, nchest.y = np.transpose(
[p2[1], p2[2], p2[9], p2[13], p2[6], p2[1], p2[0]])
marks.append(nchest)
#draw the head
head = bqp.Lines(scales=scales)
head.x = np.cos(np.linspace(0, 2 * np.pi, 100)) * 60 + p[0][0]
head.y = np.sin(np.linspace(0, 2 * np.pi, 100)) * 65 + p[0][1]
marks.append(head)
nhead_x = np.cos(np.linspace(0, 2 * np.pi, 100)) * 60 + p2[0][0]
nhead_y = np.sin(np.linspace(0, 2 * np.pi, 100)) * 65 + p2[0][1]
nhead = bqp.Lines(x=nhead_x, y=nhead_y, scales=scales, colors=['red'])
marks.append(nhead)
to_keep = list()
if l_arm:
to_keep.append(p[3])
to_keep.append(p[4])
if r_arm:
to_keep.append(p[7])
to_keep.append(p[8])
if l_leg:
to_keep.append(p[10])
to_keep.append(p[11])
if r_leg:
to_keep.append(p[14])
to_keep.append(p[15])
if neck_p:
to_keep.append(p[0])
#movable points: arms first, left side first
scat = bqp.Scatter(scales=scales,
enable_move=True,
update_on_move=True,
stroke_width=7)
scat.x, scat.y = np.transpose(to_keep)
marks.append(scat)
i = 0
left_arm = bqp.Lines(scales=scales)
if l_arm:
left_arm.x, left_arm.y = [[p[2][0], scat.x[i], scat.x[i + 1]],
[p[2][1], scat.y[i], scat.y[i + 1]]]
i += 2
else:
left_arm.x, left_arm.y = [[p[2][0], p[3][0], p[4][0]],
[p[2][1], p[3][1], p[4][1]]]
marks.append(left_arm)
nleft_arm = bqp.Lines(scales=scales, colors=['red'])
nleft_arm.x, nleft_arm.y = [[p2[2][0], p2[3][0], p2[4][0]],
[p2[2][1], p2[3][1], p2[4][1]]]
marks.append(nleft_arm)
right_arm = bqp.Lines(scales=scales)
if r_arm:
right_arm.x, right_arm.y = [[p[6][0], scat.x[i], scat.x[i + 1]],
[p[6][1], scat.y[i], scat.y[i + 1]]]
i += 2
else:
right_arm.x, right_arm.y = [[p[6][0], p[7][0], p[8][0]],
[p[6][1], p[7][1], p[8][1]]]
marks.append(right_arm)
nright_arm = bqp.Lines(scales=scales, colors=['red'])
nright_arm.x, nright_arm.y = [[p2[6][0], p2[7][0], p2[8][0]],
[p2[6][1], p2[7][1], p2[8][1]]]
marks.append(nright_arm)
left_leg = bqp.Lines(scales=scales)
if l_leg:
left_leg.x, left_leg.y = [[p[9][0], scat.x[i], scat.x[i + 1]],
[p[9][1], scat.y[i], scat.y[i + 1]]]
i += 2
else:
left_leg.x, left_leg.y = [[p[9][0], p[10][0], p[11][0]],
[p[9][1], p[10][1], p[11][1]]]
marks.append(left_leg)
nleft_leg = bqp.Lines(scales=scales, colors=['red'])
nleft_leg.x, nleft_leg.y = [[p2[9][0], p2[10][0], p2[11][0]],
[p2[9][1], p2[10][1], p2[11][1]]]
marks.append(nleft_leg)
right_leg = bqp.Lines(scales=scales)
if r_leg:
right_leg.x, right_leg.y = [[p[13][0], scat.x[i], scat.x[i + 1]],
[p[13][1], scat.y[i], scat.y[i + 1]]]
i += 2
else:
right_leg.x, right_leg.y = [[p[13][0], p[14][0], p[15][0]],
[p[13][1], p[14][1], p[15][1]]]
marks.append(right_leg)
nright_leg = bqp.Lines(scales=scales, colors=['red'])
nright_leg.x, nright_leg.y = [[p2[13][0], p2[14][0], p2[15][0]],
[p2[13][1], p2[14][1], p2[15][1]]]
marks.append(nright_leg)
neck = bqp.Lines(scales=scales)
if neck_p:
neck.x, neck.y = [[p[1][0], scat.x[i]], [p[1][1], scat.y[i]]]
i += 1
else:
neck.x, neck.y = [[p[1][0], p[0][0]], [p[1][1], p[0][1]]]
marks.append(neck)
scat.observe(refresh, names=['x', 'y'])
return bqp.Figure(marks=marks, padding_y=0., min_height=750, min_width=750)
def _init_grid(self):
# Set up scales for the spatial x, spatial y, spectral, and flux
self.scale_x = bqplot.LinearScale(min=0, max=1)
self.scale_y = bqplot.LinearScale(min=0, max=1)
self.scale_spec = bqplot.LinearScale(min=0, max=1)
self.scale_flux = bqplot.LinearScale(min=0, max=1)
# Set up colorscale
self.scale_cutout_image = ColorScale(colors=['black', 'white'])
self.scale_spec2d_image = ColorScale(colors=['black', 'white'])
# Set up axes
self.axis_x = bqplot.Axis(scale=self.scale_x,
grid_lines='solid',
label='x')
self.axis_y = bqplot.Axis(scale=self.scale_y,
grid_lines='solid',
label='y',
orientation='vertical')
self.axis_spec = bqplot.Axis(scale=self.scale_spec,
grid_lines='solid',
label='spec')
self.axis_flux = bqplot.Axis(scale=self.scale_flux,
grid_lines='solid',
label='flux',
orientation='vertical')
# Set up bqplot viewers
# =====================
# Cutout
# ------
self.fig_cutout = bqplot.Figure(scales={
'x': self.scale_x,
'y': self.scale_y
},
axes=[self.axis_x, self.axis_y],
layout={
'width': '500px',
'height': '400px'
})
self.fig_cutout.interaction = PanZoom(scales={
'x': [self.scale_x],
'y': [self.scale_y]
})
# Spec 2d
# -------
self.fig_spec2d = bqplot.Figure(scales={
'x': self.scale_spec,
'y': self.scale_y
},
axes=[self.axis_spec, self.axis_y],
layout={
'width': '500px',
'height': '400px'
})
self.fig_spec2d.interaction = PanZoom(scales={
'x': [self.scale_spec],
'y': [self.scale_y]
})
# Spec 1d
# -------
self.fig_spec1d = bqplot.Figure(scales={
'x': self.scale_spec,
'y': self.scale_flux
},
axes=[self.axis_spec, self.axis_flux],
layout={
'width': '500px',
'height': '400px'
})
self.fig_spec1d.interaction = PanZoom(scales={
'x': [self.scale_spec],
'y': [self.scale_flux]
})
# info box
# --------
self.info_box = Textarea(value='Hello World')
self.info_box.layout.height = '100%'
self.info_box.layout.width = '100%'
self.info_box.layout.align_self = 'flex-end'
# Set up content of figures
# =========================
self.cutout_mark = AstroImage(scales={
'x': self.scale_x,
'y': self.scale_y,
'image': self.scale_cutout_image
})
self.fig_cutout.marks = [self.cutout_mark]
self.spec2d_mark = AstroImage(
scales={
'x': self.scale_spec,
'y': self.scale_y,
'image': self.scale_spec2d_image
})
self.fig_spec2d.marks = [self.spec2d_mark]
self.spec1d_mark = bqplot.Lines(scales={
'x': self.scale_spec,
'y': self.scale_flux
},
x=[],
y=[])
self.fig_spec1d.marks = [self.spec1d_mark]
GridBox.__init__(self, [
self.fig_cutout,
HTML(), self.fig_spec2d,
HTML(),
HTML(),
HTML(), self.info_box,
HTML(), self.fig_spec1d
],
layout=Layout(width='100%',
grid_template_columns='35% 5% 35%',
grid_template_rows='30% 5% 30%',
grid_gap='30px 30px'))
self.layout.justify_content = "center"
self.layout.align_items = "center"
def square_orbit(x0, y0):
sc_x = bqplot.LinearScale(min=-1.2, max=1.2)
sc_y = bqplot.LinearScale(min=-1.2, max=1.2)
z0 = x0 + y0 * 1j
z_point = bqplot.Scatter(x=[z0.real],
y=[z0.imag],
scales={
'x': sc_x,
'y': sc_y
},
colors=['green'],
enable_move=True,
default_size=200)
z_point.update_on_move = True
z_label = bqplot.Label(x=[z0.real + .05],
y=[z0.imag + .05],
scales={
'x': sc_x,
'y': sc_y
},
colors=['green'],
text=['z0'],
default_size=26,
font_weight='bolder')
scatt = bqplot.Scatter(x=[],
y=[],
scales={
'x': sc_x,
'y': sc_y
},
colors=['black'],
default_size=20)
theta = np.linspace(0, 2. * np.pi, 1000)
x = np.cos(theta)
y = np.sin(theta)
#circle = bqplot.Lines(x=x, y=y, scales={'x': sc_x, 'y': sc_y}, colors=['black'])
lin = bqplot.Lines(x=[],
y=[],
scales={
'x': sc_x,
'y': sc_y
},
colors=['black'],
stroke_width=1)
def update_line(change=None):
out = orbit(z_point.x + 1j * z_point.y)
z_label.x = z_point.x + 0.05
z_label.y = z_point.y + 0.05
lin.x = out.real
lin.y = out.imag
scatt.x = out.real.flatten()
scatt.y = out.imag.flatten()
update_line()
# update line on change of x or y of scatter
z_point.observe(update_line, names=['x'])
z_point.observe(update_line, names=['y'])
ax_x = bqplot.Axis(scale=sc_x, offset=dict(value=0.5), grid_lines='none')
ax_y = bqplot.Axis(scale=sc_y,
orientation='vertical',
offset=dict(value=0.5),
grid_lines='none')
#fig = bqplot.Figure(marks=[scatt, lin, circle, z_point, z_label], axes=[ax_x, ax_y],
#min_aspect_ratio=1, max_aspect_ratio=1)
fig = bqplot.Figure(marks=[scatt, lin, z_point, z_label],
axes=[ax_x, ax_y],
min_aspect_ratio=1,
max_aspect_ratio=1)
fig.layout.height = '800px'
return fig
def __init__(self, rope):
super().__init__()
title = ipw.HTML("<h3>Rope Parameters</h3>")
self.rope = rope
grid = rope.grid
z_quants = ['water_speeds', 'water_angles', 'frond_lengths', 'frond_stds', 'num_fronds']
z_scale = bq.LinearScale(min=grid.z.minval, max=grid.z.maxval)
z_ax = bq.Axis(scale=z_scale, label='Depth (z)', grid_lines='none')
mins = {
'water_speeds': 0,
'water_angles': 0,
'frond_lengths': 0,
'frond_stds': 0,
'num_fronds': 0,
}
maxs = {
'water_speeds': 50,
'water_angles': 2*np.pi,
'frond_lengths': 3,
'frond_stds': .5,
'num_fronds': 5000
}
colors = {
'water_speeds': 'red',
'water_angles': 'green',
'frond_lengths': 'blue',
'frond_stds': 'yellow',
'num_fronds': 'purple'
}
ylabels = {
'water_speeds': 'Water current velocity',
'water_angles': 'Water current angle',
'frond_lengths': 'Frond length mean',
'frond_stds': 'Frond length std. dev.',
'num_fronds': 'Number of fronds'
}
values = {}
figs = {}
for quant in z_quants:
values[quant] = getattr(rope, quant)
ylim = np.array([mins[quant], maxs[quant]], dtype=float)
labels = {'xlabel': 'z', 'ylabel': '', 'title': ylabels[quant]}
figs[quant] = HandDrawFigure(rope, quant, grid.z, ylim, labels, color=colors[quant])
self.children = [
title,
ipw.HBox([
figs['water_speeds'],
figs['water_angles'],
]),
ipw.HBox([
figs['frond_lengths'],
figs['frond_stds']
]),
ipw.HBox([
figs['num_fronds']
])
]
def __init__(self, session, state=None):
# if we allow padding, we sometimes get odd behaviour with the interacts
self.scale_x = bqplot.LinearScale(min=0, max=1, allow_padding=False)
self.scale_y = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y}
self.axis_x = bqplot.Axis(scale=self.scale_x,
grid_lines='none',
label='x')
self.axis_y = bqplot.Axis(scale=self.scale_y,
orientation='vertical',
tick_format='0.2f',
grid_lines='none',
label='y')
self.figure = bqplot.Figure(scales=self.scales,
animation_duration=0,
axes=[self.axis_x, self.axis_y],
fig_margin={
'left': 60,
'bottom': 60,
'top': 10,
'right': 10
})
self.figure.padding_y = 0
self._fig_margin_default = self.figure.fig_margin
self._fig_margin_zero = dict(self.figure.fig_margin)
self._fig_margin_zero['left'] = 0
self._fig_margin_zero['bottom'] = 0
super(BqplotBaseView, self).__init__(session, state=state)
# Remove the following two lines once glue v0.16 is required - see
# https://github.com/glue-viz/glue/pull/2099/files for more information.
self.state.remove_callback('layers', self._sync_layer_artist_container)
self.state.add_callback('layers',
self._sync_layer_artist_container,
priority=10000)
def update_axes(*ignore):
try:
# Extract units from data
x_unit = self.state.reference_data.get_component(
self.state.x_att_world).units
except AttributeError:
# If no data loaded yet, ignore units
x_unit = ""
finally:
# Append units to axis label
self.axis_x.label = str(self.state.x_att) + " " + str(x_unit)
if self.is2d:
self.axis_y.label = str(self.state.y_att)
try:
y_unit = self.state.reference_data.get_component(
self.state.y_att_world).units
except AttributeError:
y_unit = ""
finally:
self.axis_y.label = str(
self.state.y_att) + " " + str(y_unit)
self.state.add_callback('x_att', update_axes)
if self.is2d:
self.state.add_callback('y_att', update_axes)
self.scale_x.observe(self.update_glue_scales, names=['min', 'max'])
self.scale_y.observe(self.update_glue_scales, names=['min', 'max'])
dlink((self.state, 'x_min'), (self.scale_x, 'min'), float_or_none)
dlink((self.state, 'x_max'), (self.scale_x, 'max'), float_or_none)
dlink((self.state, 'y_min'), (self.scale_y, 'min'), float_or_none)
dlink((self.state, 'y_max'), (self.scale_y, 'max'), float_or_none)
on_change([(self.state, 'show_axes')])(self._sync_show_axes)
self.create_layout()
def _gen_series_tab(self):
"""Creates widgets for selecting timeseries data.
- Dropdown to select column from dataframe.
- Interactive bqplot with BrushIntervalSelector.
Returns
-------
widget : tab content widget
"""
scales = {
'x': bqp.DateScale(),
'y': bqp.LinearScale(),
'color': bqp.ColorScale(scheme='oranges')
}
axes = [
bqp.Axis(scale=scales['x'],
label='Date',
num_ticks=int(len(self._data.index) / 2)),
bqp.Axis(scale=scales['y'], label='Value', orientation='vertical')
]
mark = bqp.Lines(scales=scales)
feature_selector = widgets.Dropdown(options=self._data.columns,
description='Feature:')
def feature_selection_callback(change):
"""Callback to update graph when new data has been selected
Parameters
----------
change : dict
Widget and changed values.
"""
self._selected_feature = change.new
fig.title = f'Feature: {change.new}'
series = self._data[change.new]
mark.x = series.index
mark.y = series.values
# register callback for dropdown value-change events
feature_selector.observe(feature_selection_callback, ['value'])
def brush_sel_dt_callback(change):
"""Callback to update graph when new data has been selected
Parameters
----------
change : dict
Widget and changed values.
"""
tstamps = selector.selected
if isinstance(tstamps, np.ndarray):
tstamps = tstamps.tolist()
if not selector.brushing and tstamps:
# extract year and month from timestamp string: e.g. 2000-03-11T06:51:50.089000 -> 2000-03
dates = [
pd.to_datetime(re.split(r'-\d\dT', str(ts))[0])
for ts in tstamps
]
self._selected_dates = dates
selector = bqpi.BrushIntervalSelector(scale=scales['x'], color='blue')
# register callback for BrushIntervalSelector brushing events
selector.observe(brush_sel_dt_callback, ['brushing'])
fig = bqp.Figure(marks=[mark],
axes=axes,
interaction=selector,
layout=widgets.Layout(width='880px', height='380px'),
animation_duration=1000)
def reset_btn_click(b):
"""Reset selector and update instance properties accordingly.
Parameters
----------
b : Button
Button instance.
"""
selector.reset()
self.reset()
reset_btn = widgets.Button(description='reset')
reset_btn.on_click(reset_btn_click)
return widgets.VBox(children=[
widgets.HBox(children=[feature_selector, reset_btn]), fig
])