def _make_date_slider(dates):
options = [pandas.Timestamp(d).strftime('%m/%d') for d in dates]
return ipywidgets.SelectionRangeSlider(
index=(0, len(dates) - 1),
options=options,
description='Dates',
disabled=False,
layout=ipywidgets.Layout(width='400px'),
continuous_update=False)
def SearchDatesSlider():
options = [(' 1951 ', '1951-01-01'), (' SARS 2003 ', '2002-11-01'),
(' H1N1 2009 ', '2009-04-01'), (' COVID 19 ', '2019-11-30'),
(' 2020 ', '2020-12-31')]
return widgets.SelectionRangeSlider(options=options,
description='Dates',
disabled=False,
value=('2002-11-01', '2020-12-31'),
layout={'width': '480px'})
def generate_slider(value, range_steps):
slider = widgets.SelectionRangeSlider(options=[
i for i in range(range_steps[0], range_steps[1], range_steps[2])
],
index=(0, 30),
description=value,
disabled=False,
continuous_update=False)
return slider
def basic_buttons(hdbdata):
"""
Creates a widget box containing basic buttons to filter HDB results
"""
# https://blog.jupyter.org/introducing-templates-for-jupyter-widget-layouts-f72bcb35a662
# https://github.com/jupyter-widgets/ipywidgets/issues/1853#issuecomment-349201240
# Configure layout and set up widgets for plotly
filterdict = {
'flat_model':
widgets.SelectMultiple(options=hdbdata['flat_model'].unique(),
value=list(hdbdata['flat_model'].unique()),
description='Flat Model'),
'flat_type':
widgets.SelectMultiple(options=hdbdata['flat_type'].unique(),
value=list(hdbdata['flat_type'].unique()),
description='Flat Type'),
'storey_range':
widgets.SelectMultiple(options=hdbdata['storey_range'].unique(),
value=list(hdbdata['storey_range'].unique()),
description='Storeys'),
'floor_area_sqm':
widgets.FloatRangeSlider(value=[
hdbdata['floor_area_sqm'].min(), hdbdata['floor_area_sqm'].max()
],
min=hdbdata['floor_area_sqm'].min(),
max=hdbdata['floor_area_sqm'].max(),
step=1,
description='Floor Area (sqm):',
layout=Layout(width='100%')),
'remaining_lease':
widgets.FloatRangeSlider(value=[
hdbdata['remaining_lease'].min(), hdbdata['remaining_lease'].max()
],
min=hdbdata['remaining_lease'].min(),
max=hdbdata['remaining_lease'].max(),
step=1,
description='Remaining Lease (years):',
layout=Layout(width='100%')),
'month':
widgets.SelectionRangeSlider(
options=[(i, i) for i in hdbdata['month'].unique()],
index=(0, hdbdata['month'].unique().size - 1),
description='Date of transaction',
layout=Layout(width='100%'))
}
h1 = HBox([
filterdict['flat_model'], filterdict['flat_type'],
filterdict['storey_range']
])
h2 = filterdict['floor_area_sqm']
h3 = filterdict['remaining_lease']
h4 = filterdict['month']
filter_widget = VBox([h1, h2, h3, h4])
return filterdict, filter_widget
def slider(self, multiplier=None, **kwargs):
"""Slider for interactive plotting.
Based on the values in the ``r`` column,
``ipywidgets.SelectionRangeSlider`` is returned for navigating
interactive plots.
This method is based on ``ipywidgets.SelectionRangeSlider``, so any
keyword argument accepted by it can be passed.
Parameters
----------
multiplier : numbers.Real, optional
``multiplier`` can be passed as :math:`10^{n}`, where :math:`n` is
a multiple of 3 (..., -6, -3, 0, 3, 6,...). According to that
value, the values will be scaled and appropriate units shown. For
instance, if ``multiplier=1e-9`` is passed, the slider points will
be divided by :math:`1\\,\\text{nm}` and :math:`\\text{nm}` units
will be used in the description. If ``multiplier`` is not passed,
the optimum one is computed internally. Defaults to ``None``.
Returns
-------
ipywidgets.SelectionRangeSlider
``r`` range slider.
Example
-------
1. Get the slider for the horizontal axis.
>>> import discretisedfield as df
...
>>> points = [(0, 0, 0), (2, 0, 0), (4, 0, 0)]
>>> values = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # vector values
>>> line = df.Line(points=points, values=values)
>>> line.slider()
SelectionRangeSlider(...)
"""
if multiplier is None:
multiplier = uu.si_multiplier(self.length)
values = self.data['r'].to_numpy()
labels = np.around(values/multiplier, decimals=2)
options = list(zip(labels, values))
slider_description = f'r ({uu.rsi_prefixes[multiplier]}m):'
return ipywidgets.SelectionRangeSlider(options=options,
value=(values[0], values[-1]),
description=slider_description,
**kwargs)
def __init__(self, list_of_metrics):
self.metric_dropdown = widgets.Dropdown(options=list_of_metrics,
description='Metric')
self.plot_toggle = widgets.ToggleButtons(
options=['Count', 'Change'],
value='Count',
description='',
disabled=False,
button_style='',
tooltips=['Display absolute counts', 'Display change over time'])
options = [(" Q{} {} ".format(qtr, year), (qtr, year))
for year in range(2005, 2016) for qtr in range(1, 5)]
self.time_slider = widgets.SelectionSlider(options=options,
description='Quarter',
disabled=False,
continuous_update=False,
layout=Layout(width='60%'))
self.time_range_slider = widgets.SelectionRangeSlider(
options=options,
index=(0, len(options) - 1),
description='Time Range',
disabled=False,
layout=Layout(width='60%'))
self.time_range_slider.layout.visibility = 'hidden'
self.generate_button = widgets.Button(description='Generate Plot',
disabled=False,
tooltip='Generate Plot')
self.panel_items = [
HBox([self.metric_dropdown]),
HBox([self.plot_toggle]),
VBox([self.time_slider, self.time_range_slider]),
HBox([self.generate_button])
]
self.input_panel = VBox(self.panel_items)
self.output = Output()
def on_value_change(change):
if change['new'] == 'Count':
self.time_range_slider.layout.visibility = 'hidden'
self.time_slider.layout.visibility = 'visible'
elif change['new'] == 'Change':
self.time_slider.layout.visibility = 'hidden'
self.time_range_slider.layout.visibility = 'visible'
self.plot_toggle.observe(on_value_change, names='value')
def dates_slider_control(properties):
slider_dates = [
(date.strftime(' %Y-%m-%d '), date) for date in pd.date_range(
properties['start_date'], properties['end_date'], freq='D')
]
slider_index = (0, len(slider_dates) - 1)
date_slider_control = widgets.SelectionRangeSlider(
options=slider_dates,
index=slider_index,
description='Date Range',
orientation='horizontal',
layout={'width': '100%'})
return date_slider_control
def date_slider(start_date, end_date):
dates = pd.date_range(start_date, end_date, freq='D')
options = [(date.strftime(' %d/%m/%y '), date) for date in dates]
index = (0, len(options) - 1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='Dates',
orientation='horizontal',
layout={'width': '500px'})
return selection_range_slider
def get_time_slider(df, timestamp_column='_timestamp'):
def get_date_range():
timestamps = pd.to_datetime(df[timestamp_column]).apply(
pd.Timestamp.date)
vmin, vmax = timestamps.min(), timestamps.max()
return pd.Series(pd.date_range(vmin, vmax)).apply(pd.Timestamp.date)
date_range = get_date_range()
return widgets.SelectionRangeSlider(options=date_range.values,
description='Date Range',
continuous_update=False,
index=(0, len(date_range) - 1),
values=(0, len(date_range)),
layout=Layout(width='500px'))
def gui_date_picker(dates: list) -> widgets.SelectionRangeSlider:
start_date = datetime.strptime(min(dates), '%Y%m%d')
end_date = datetime.strptime(max(dates), '%Y%m%d')
date_range = pd.date_range(start_date, end_date, freq='D')
options = [(date.strftime(' %m/%d/%Y '), date) for date in date_range]
index = (0, len(options) - 1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='Dates',
orientation='horizontal',
layout={'width': '500px'})
return (selection_range_slider)
def get_dates_widgets(options, index):
monitoring_period_chooser = widgets.interactive(g,
i=widgets.SelectionRangeSlider(
options=options,
index=index,
description='Select the monitoring date range: ',
style = {'description_width': 'initial'},
orientation='horizontal',
layout={'width': '800px',"height":"50px"}))
history_period_chooser = widgets.interactive(g,
i=widgets.SelectionSlider(
description="Start history period:",
options = options,
style = {'description_width': 'initial'}))
return monitoring_period_chooser, history_period_chooser
def period_form_field(datetime_index, fmt="%d %b", value=None, **kwargs):
kwargs = {"continuous_update": False, **kwargs}
if value is not None:
series = datetime_index.to_series()
start, end = value
start = series.loc[start][0] if start else datetime_index[0]
end = series.loc[end][0] if end else datetime_index[-1]
kwargs["value"] = (start, end)
elif "index" not in kwargs:
kwargs["index"] = (0, len(datetime_index) - 1)
return (
"Periodo",
widgets.SelectionRangeSlider(options=list(
zip(datetime_index.strftime(fmt), datetime_index)),
**kwargs),
)
def __init__(self, start_datetime, end_datetime):
self.start_datetime = start_datetime
self.end_datetime = end_datetime
dates = pd.date_range(start_datetime, end_datetime, freq='H')
options = [(date.strftime('%m/%d %H:00'), date) for date in dates]
index = (0, len(options) - 1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='',
orientation='horizontal',
layout={'width': '100%'})
selection_range_slider.observe(self.on_change)
self.widget = selection_range_slider
def __make_widgets__(self):
self.time_widget = widgets.SelectionRangeSlider(
options=[0], continuous_update=False, layout={'width': '400px'})
#TODO rewrite to use just index not Begin Date
self.date_widget = widgets.Dropdown(options=self.df_subset['Begin Date'].unique())
self.type_widget = widgets.ToggleButtons(
options=['Detailled', 'Aggregate', 'Aggregate (percent)'],
description='Plot Type',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltips=['A detailled plot of space occupancy over time.\
Only available for single-day selections.',
'A summary plot showing the total number of activities and total\
amount of time for each user type in the selection interval.',
'A summary plot showing the percentage of activities and percentage\
of time for each user type in the selection interval.'],
)
self.weekday_widget = widgets.Select(
options=['Specific Date/Time', 'Monday', 'Tuesday', 'Wednesday',
'Thursday', 'Friday', 'Saturday', 'Sunday', 'Weekdays', 'Weekends',
'All Data'],
value='Specific Date/Time',
style={'description_width': 'initial'},
description='Aggregation Type:',
disabled=False
)
self.download_button = widgets.Button(
description='Download Data',
button_style='',
tooltip='Download Data',
icon='download'
)
def update(*args):
"""allows date selection to define time selection range"""
#TODO rewrite to use just index not Begin Date
index = self.df_subset[
self.df_subset['Begin Date'] == self.date_widget.value].index
minutes = index.strftime('%I:%M%p').unique()
self.time_widget.options = minutes
self.time_widget.value = (minutes[0], minutes[-1])
self.date_widget.observe(update)
def crop_model():
start_date = dt.datetime(2014, 2, 1)
end_date = dt.datetime(2014, 12, 1)
dates = pd.date_range(start_date, end_date, freq='D')
options = [(date.strftime(' %d %b '), date) for date in dates]
index = (0, len(options) - 1)
integration_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='Sowing & Harvest',
orientation='horizontal',
layout={'width': '600px'}
)
year_widget = widgets.IntSlider(min=2010, max=2018, value=2015)
widgets.interact(crop_model_func, year=year_widget,
integration_time = integration_slider,
epsilon = widgets.fixed(0.33)
)
def cummulative_lai_plots(df):
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
dates = pd.date_range(start_date, end_date, freq="D")
options = [(date.strftime(" %d %b %Y "), date) for date in dates]
index = (0, len(options) - 1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description="Sowing & Harvest",
orientation="horizontal",
layout={"width": "600px"},
)
def plot_aggr_meteo(sowing_harvesting):
sowing, harvesting = sowing_harvesting
accum_lai(df, sowing, harvesting)
widgets.interact_manual(plot_aggr_meteo,
sowing_harvesting=selection_range_slider)
def make_viz(self):
# Create controls
# Initialize column type controller with demand
fields = []
for k in list(self.KEY.keys()):
if len([el for el in self.data.df.columns if self._matches(k, el)
]) > 0:
fields.append(k)
self.ctl_filter = widgets.Dropdown(options=fields + ["ALL"])
self.ctl_cols = widgets.Dropdown(options=sorted(
[col for col in self.data.df.columns if self._matches("D", col)]))
dates = pd.date_range(self.data.df.index[0],
self.data.df.index[-1],
freq="D")
options = [(date.strftime(" %d/%m/%Y "), date) for date in dates]
index = (0, len(options) - 1)
self.ctl_date = widgets.SelectionRangeSlider(
options=options,
index=index,
description="Dates",
orientation="horizontal",
continuous_update=False,
layout=widgets.Layout(width="80%"),
)
self.ctl_filter.observe(self._ctl_filter_handler, names=["value"])
self.ctl_cols.observe(self._ctl_cols_handler, names=["value"])
self.ctl_date.observe(self._ctl_date_handler, names=["value"])
self.input_widgets = widgets.VBox(
[widgets.HBox([self.ctl_filter, self.ctl_cols]), self.ctl_date])
self.output = widgets.Output()
# initalize plot before making it
self._make_plot(self.ctl_cols.value, self.ctl_date.value)
def make_date_range_slider(start_date, end_date, freq='D'):
from ipywidgets import interact, interactive, interaction, Label, Box, Layout
import ipywidgets as iw
from datetime import datetime
# specify the date range from user input
dates = pd.date_range(start_date, end_date, freq=freq)
# specify formatting based on frequency code
date_format_lib = {'D': '%m/%d/%Y', 'H': '%m/%d/%Y: %T'}
freq_format = date_format_lib[freq]
# creat options list and index for SelectionRangeSlider
options = [(date.strftime(date_format_lib[freq]), date) for date in dates]
index = (0, len(options) - 1)
# # Create out function to display outputs (not needed?)
# out = iw.Output(layout={'border': '1px solid black'})
# # @out.capture()
# Instantiate the date_range_slider
date_range_slider = iw.SelectionRangeSlider(
options=options,
index=index,
description='Date Range',
orientation='horizontal',
layout={
'width': '500px',
'grid_area': 'main'
}, #layout=Layout(grid_area='main'),
readout=True)
# Save the labels for the date_range_slider as separate items
date_list = [date_range_slider.label[0], date_range_slider.label[-1]]
date_label = iw.Label(f'{date_list[0]} -- {date_list[1]}',
layout=Layout(grid_area='header'))
def aggregate_plots():
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
dates = pd.date_range(start_date, end_date, freq='D')
options = [(date.strftime(' %d %b %Y '), date) for date in dates]
index = (0, len(options)-1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='Sowing & Harvest',
orientation='horizontal',
layout={'width': '600px'}
)
def plot_aggr_meteo(sowing_harvesting, region_name, selected_years):
sowing, harvesting = sowing_harvesting
meteo_files = get_region_data_func(region_name, selected_years, do_plot=False)
data = aggregate_meteo(meteo_files, sowing, harvesting, aggr=np.sum)
fig, axs = plt.subplots(nrows=2, ncols=3, sharex=True,
figsize=(12,12), squeeze=True)
axs = axs.flatten()
for i in range(6):
axs[i].plot(data[:, 0], data[:, i + 1], '-o')
axs[i].set_title(parameters[i])
fig.suptitle(region_name)
widgets.interact_manual(
plot_aggr_meteo,
sowing_harvesting=selection_range_slider,
region_name=widgets.Dropdown(
options=regions, value='Central', description='Region:',
disabled=False,),
selected_years=widgets.IntRangeSlider(min=2010, max=2018, value=(2015,2016)))
dc.on_draw(handle_draw)
m.add_control(dc)
start_date = date(1950, 1, 1)
end_date = date(2020, 1, 1)
days = (end_date - start_date).days
dates = []
for n_day in range(days):
dates.append(start_date + timedelta(days=n_day))
dates = widgets.SelectionRangeSlider(
value=(start_date, end_date - timedelta(days=1)),
options=dates,
description='Starting date',
layout=widgets.Layout(width='90%')
)
def on_button_clicked(_):
global kw, df
if coords:
kw = get_data()
df = run.ui_query(kw)
if df.shape[0] == 0:
msg = 'No data returned... select different filter criteria'
else:
df.dropna(subset=['tabledap'], inplace=True)
msg = 'Found {} datasets'.format(df.shape[0])
def trade_review_in_notebook2(symbol=None,
mkdata=None,
period=1,
executions: list = None,
account=None,
date_from=None,
date_to=None,
expand_offset=120,
source='IB'):
import ipywidgets as widgets
from IPython.display import display
from dateutil import parser
import datetime as dt
import talib
import pandas as pd
import mpl_finance as mpf
if isinstance(expand_offset, tuple):
s_offset = expand_offset[0]
e_offset = expand_offset[1]
else:
s_offset = e_offset = expand_offset
if account and date_from and symbol:
# 在只填写accout与date_from的情况下，找出date_from当天，account中的所有交易记录
from KRData.IBData import IBTrade
ibt = IBTrade(account)
date_from = parser.parse(date_from) if isinstance(date_from,
str) else date_from
date_to = date_from + dt.timedelta(days=1) if date_to is None else (
parser.parse(date_to) if isinstance(date_to, str) else date_to)
fills = ibt[date_from:date_to:symbol]
if fills.count() < 1:
raise Exception(f'账户：{account}在{date_from}-{date_to}不存在交易记录')
conId = fills[0].contract.conId
executions = [{
'datetime':
f.time.replace(second=0) + dt.timedelta(hours=8),
'price':
f.execution.price,
'size':
f.execution.shares,
'direction':
'long' if f.execution.side == 'BOT' else 'short'
} for f in fills]
executions.sort(key=lambda e: e['datetime'])
start = executions[0]['datetime'] - dt.timedelta(minutes=s_offset)
end = executions[-1]['datetime'] + dt.timedelta(minutes=e_offset)
if source == 'IB':
barTypeMap = {
1: '1 min',
5: '5 mins',
15: '15 mins',
30: '30 mins',
60: '1 hour'
}
mkdata = ibt._ib_market.get_bars_from_ib(conId,
barType=barTypeMap.get(
period, '1 min'),
start=start,
end=end)
elif source == 'HK':
from KRData.HKData import HKFuture
hf = HKFuture()
mkdata = hf.get_bars(symbol,
start=start,
end=end,
ktype=f'{period}min',
queryByDate=False)
del hf
del ibt
elif mkdata is None and symbol:
# 在只填写execution的情况下，自动根据source获取数据
start = executions[0]['datetime'] - dt.timedelta(minutes=s_offset)
end = executions[-1]['datetime'] + dt.timedelta(minutes=e_offset)
if source == 'IB':
from KRData.IBData import IBMarket
ibm = IBMarket()
barTypeMap = {
1: '1 min',
5: '5 mins',
15: '15 mins',
30: '30 mins',
60: '1 hour'
}
mkdata = ibm.get_bars_from_ib(symbol,
barType=barTypeMap.get(
period, '1 min'),
start=start,
end=end)
del ibm
elif source == 'HK':
from KRData.HKData import HKFuture
hf = HKFuture()
mkdata = hf.get_bars(symbol,
start=start,
end=end,
ktype=f'{period}min',
queryByDate=False)
del hf
mkdata = _concat_executions(mkdata, executions)
mkdata['ma5'] = talib.MA(mkdata['close'].values, timeperiod=5)
mkdata['ma10'] = talib.MA(mkdata['close'].values, timeperiod=10)
mkdata['ma30'] = talib.MA(mkdata['close'].values, timeperiod=30)
mkdata['ma60'] = talib.MA(mkdata['close'].values, timeperiod=60)
symbolWidget = widgets.Text(value=str(symbol),
description='symbol',
disable=True)
fromWidget = widgets.Text(value='',
placeholder='yyyymmdd HH:MM:SS',
description='From:',
disabled=True)
toWidget = widgets.Text(value='',
placeholder='yyyymmdd HH:MM:SS',
description='To:',
disabled=True)
time_range_box = widgets.HBox([fromWidget, toWidget])
annotate = widgets.Checkbox(value=False, description='annotate')
offsetWidget = widgets.IntSlider(min=0,
max=500,
step=10,
value=60,
description='expand_offset')
executionSelection = widgets.SelectionRangeSlider(
options=[(str(_e['datetime']), _e['datetime']) for _e in executions],
index=(0, len(executions) - 1),
description='execution',
disabled=False,
continuous_update=False,
)
fig = None
def save_fig(b):
if fig is not None:
try:
fig.savefig('fig.png')
except Exception as e:
print('errSaveFig:', e)
saveFigButton = widgets.Button(description='保存图片')
saveFigButton.on_click(save_fig)
def show_data(e_select, offset, annotate):
nonlocal fig
s = e_select[0] - dt.timedelta(minutes=offset)
e = e_select[-1] + dt.timedelta(minutes=offset)
fromWidget.value = str(s)
toWidget.value = str(e)
temp_mkdata = mkdata[s:e]
fig = draw_klines(temp_mkdata, annotate=annotate)
params = {
'e_select': executionSelection,
'offset': offsetWidget,
'annotate': annotate
}
out = widgets.interactive_output(show_data, params)
display(symbolWidget, time_range_box, annotate, executionSelection,
offsetWidget, saveFigButton, out)
widgets.SelectionSlider(
options=['scrambled', 'sunny side up', 'poached', 'over easy'],
value='sunny side up',
description='I like my eggs ...',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True)
# SelectionRangeSlider
import datetime
dates = [datetime.date(2015, i, 1) for i in range(1, 13)]
options = [(i.strftime('%b'), i) for i in dates]
widgets.SelectionRangeSlider(options=options,
index=(0, 11),
description='Months (2015)',
disabled=False)
# ToggleButtons
widgets.ToggleButtons(
options=['Slow', 'Regular', 'Fast'],
description='Speed:',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltips=[
'Description of slow', 'Description of regular', 'Description of fast'
],
# icons=['check'] * 3
)
# SelectMultiple
def slider(self, x=None, multiplier=None, description=None, **kwargs):
"""Slider for interactive plotting.
Based on the values in the independent variable column,
``ipywidgets.SelectionRangeSlider`` is returned for navigating
interactive plots. This method is based on
``ipywidgets.SelectionRangeSlider``, so any keyword argument accepted
by it can be passed.
Parameters
----------
x : str, optional
Independent variable. Defaults to ``None``.
multiplier : numbers.Real, optional
``multiplier`` can be passed as :math:`10^{n}`, where :math:`n` is
a multiple of 3 (..., -6, -3, 0, 3, 6,...). According to that
value, the values will be scaled and appropriate units shown. For
instance, if ``multiplier=1e-9`` is passed, the slider points will
be divided by :math:`1\\,\\text{ns}` and :math:`\\text{ns}` units
will be used in the description. If ``multiplier`` is not passed,
the optimum one is computed internally. Defaults to ``None``.
description : str
Slider description. Defaults to ``None``.
Returns
-------
ipywidgets.SelectionRangeSlider
Independent variable slider.
Example
-------
1. Get slider for the independent variable.
>>> import os
>>> import ubermagtable as ut
...
>>> odtfile = os.path.join(os.path.dirname(__file__),
... 'tests', 'test_sample',
... 'oommf-old-file1.odt')
>>> table = ut.Table.fromfile(odtfile, x='t')
>>> table.slider()
SelectionRangeSlider(...)
"""
if x is None and self.x is not None:
x = self.x
if x not in self.data.columns:
msg = f"Independent variable {x=} is not in table."
raise ValueError(msg)
if multiplier is None:
multiplier = ubermagutil.units.si_multiplier(self.xmax)
values = self.data[self.x].to_numpy()
labels = np.around(values / multiplier, decimals=2)
options = list(zip(labels, values))
if x == "t":
units = f" ({ubermagutil.units.rsi_prefixes[multiplier]}s)"
else:
units = ""
if description is None:
description = f"{x}{units}:"
return ipywidgets.SelectionRangeSlider(
options=options,
value=(values[0], values[-1]),
description=description,
**kwargs,
)
max=hdbdata['floor_area_sqm'].max(),
step=1,
description='Floor Area (sqm):',
layout=Layout(width='100%')
),
'remaining_lease': widgets.FloatRangeSlider(
value=[hdbdata['remaining_lease'].min(), hdbdata['remaining_lease'].max()],
min=hdbdata['remaining_lease'].min(),
max=hdbdata['remaining_lease'].max(),
step=1,
description='Remaining Lease (years):',
layout=Layout(width='100%')
),
'year': widgets.SelectionRangeSlider(
options=[(i,i) for i in hdbdata['year'].unique()],
index=(0, hdbdata['year'].unique().size-1),
description='Year of transaction',
layout=Layout(width='100%')
)
}
# https://blog.jupyter.org/introducing-templates-for-jupyter-widget-layouts-f72bcb35a662
# https://github.com/jupyter-widgets/ipywidgets/issues/1853#issuecomment-349201240
# Configure layout and set up widgets for plotly
h1 = HBox([filterdict['flat_model'], filterdict['flat_type'], filterdict['storey_range']])
h2 = filterdict['floor_area_sqm']
h3 = filterdict['remaining_lease']
h4 = filterdict['year']
filter_widget = VBox([h1,h2,h3,h4])
# Filter data
def filter_HDB(flat_model, flat_type, storey_range, floor_area_sqm, remaining_lease, year):
'''
display(Markdown(description))
states = list(datadic.state_to_abbrev.keys())
state_w = widgets.Dropdown(
options=states,
value='Maine',
description='State',
disabled=False
)
options = list(range(100))
age_w = widgets.SelectionRangeSlider(
options=options,
index=(18,75),
description='Age',
disabled=False
)
fulltime_w = widgets.IntSlider(description='Fulltime' , value = 40)
min_hours_w = widgets.IntSlider(description='Min hours' , value = 15)
options = ['Abilities', 'Skills','Knowledge', 'Interests', 'Work Activities', 'Technology Skills',
'Education, Training, and Experience','Tools and Technology', 'Task Ratings',
'Work Context', 'Tools Used','Work Styles','Work Values']
X_label_w = widgets.Dropdown(
options=options,
value = options[0],
description='Variables',
disabled=False,
)
def __init__(self, statefp, list_of_metrics, count_qry, change_qry):
self.engine = create_engine('postgresql://@10.10.2.10/appliedda')
qry = "SELECT countyfp, name, ST_Transform(geom, 102698) geom FROM tl_2016_us_county WHERE statefp = '{}'".format(statefp)
self.counties = gpd.read_postgis(qry, self.engine, geom_col='geom')
self.counties['coords'] = self.counties.geometry.apply(lambda x: x.representative_point().coords[0])
self.count_qry = count_qry
self.change_qry = change_qry
self.metric_dropdown = widgets.Dropdown(
options=list_of_metrics,
description='Metric'
)
self.plot_toggle = widgets.ToggleButtons(
options=['Count', 'Change'],
value='Count',
description='',
disabled=False,
button_style='',
tooltips=['Display absolute counts', 'Display change over time']
)
options = [(" Q{} {} ".format(qtr, year), (qtr,year)) for year in range(2005,2016) for qtr in range(1,5)]
self.time_slider = widgets.SelectionSlider(
options=options,
description='Quarter',
disabled=False,
continuous_update=False,
layout=Layout(width='60%')
)
self.time_range_slider = widgets.SelectionRangeSlider(
options=options,
index=(0,len(options)-1),
description='Time Range',
disabled=False,
layout=Layout(width='60%')
)
self.time_range_slider.layout.visibility = 'hidden'
self.generate_button = widgets.Button(
description='Generate Plot',
disabled=False,
tooltip='Generate Plot'
)
self.panel_items = [
HBox([self.metric_dropdown]),
HBox([self.plot_toggle]),
VBox([self.time_slider, self.time_range_slider]),
HBox([self.generate_button])
]
self.input_panel = VBox(self.panel_items)
self.output = Output()
def on_value_change(change):
if change['new'] == 'Count':
self.time_range_slider.layout.visibility = 'hidden'
self.time_slider.layout.visibility = 'visible'
elif change['new'] == 'Change':
self.time_slider.layout.visibility = 'hidden'
self.time_range_slider.layout.visibility = 'visible'
self.plot_toggle.observe(on_value_change, names='value')
def run_query():
# Get toggle value to determine which plot type to use
plot_type = self.plot_toggle.value
# Set query for either Count or Change
if plot_type == 'Count':
qry = self.count_qry.format(q=self.time_slider.value[0],
y=self.time_slider.value[1])
elif plot_type == 'Change':
qry = self.change_qry.format(q0=self.time_range_slider.value[0][0],
y0=self.time_range_slider.value[0][1],
q1=self.time_range_slider.value[1][0],
y1=self.time_range_slider.value[1][1])
# Run the query and return the Dataframe
return pd.read_sql(qry, self.engine)
def generate_plot(button_obj):
self.output.clear_output()
with self.output:
# Configure plot settings
sns.set_style('white')
f, ax = plt.subplots(1, figsize=(6,8))
ax.axis('off')
# Plot basemap so counties with no data appear hatched
self.counties.plot(ax=ax, edgecolor='black', color='lightgray', hatch='//')
# Query and merge data with county shapefile
df = run_query()
cnty_df = pd.merge(self.counties, df, left_on=['countyfp'], right_on=['cnty'])
# Get metric of interest from dropdown
metric = self.metric_dropdown.value
# Exception for zero counts
if self.plot_toggle.value == 'Count':
cnty_df = cnty_df[cnty_df[metric] > 0]
# If no counties data, show empty map and return
if len(cnty_df) == 0:
plt.show()
return
# Plot county data
if self.plot_toggle.value == 'Count':
colmap = sns.cubehelix_palette(8, start=2.9, rot=0, dark=.1, light=.95, as_cmap=True)
cnty_df.plot(metric, ax=ax, legend=True, edgecolor='black', cmap=colmap)
elif self.plot_toggle.value == 'Change':
metric = 'change_in_{}_pct'.format(metric)
bound = cnty_df[metric].abs().max()
colmap = sns.diverging_palette(10, 150, center='light', as_cmap=True)
cnty_df.plot(metric, ax=ax, legend=True, edgecolor='black', cmap=colmap, vmax=bound, vmin=bound*-1)
# Show plot
plt.show()
self.generate_button.on_click(generate_plot)
]
exp_name_ = widgets.ToggleButtons(options=exp_names_,
description='exp name:',
disabled=False)
domain_ = widgets.ToggleButtons(options=['H36M', 'MPII'],
description='domain:',
disabled=False)
mode_ = widgets.ToggleButtons(options=['train', 'val'],
description='mode:',
disabled=False)
loss_map_ = {'Pose Regression': 'pr', 'Domain Confusion': 'dd'}
loss_type_ = widgets.ToggleButtons(
options=['Pose Regression', 'Domain Confusion'],
description='loss type:',
disabled=False)
max_x = 100000
step = 50
slices_ = widgets.SelectionRangeSlider(options=list(
(range(0, max_x + step)))[0::step],
index=[0, max_x // step],
description='x range: ',
disabled=False)
do_aver_ = widgets.ToggleButtons(options=['do averaging', 'raw batch losses'],
description='aver:',
disabled=False)
def __init__(self):
self.out = widgets.Output(layout={'border': '1px solid black'})
self.iceflow = IceflowClient()
self.last_orders = None
self.current_projection = 'north'
self.clear = True
self.controls = []
self.credentials = None
self.start_date = datetime(1993, 1, 1)
self.end_date = datetime.now()
slider_dates = [(date.strftime(' %Y-%m-%d '), date) for date in
pd.date_range(datetime(1993, 1, 1),
datetime.now(),
freq='D')]
slider_index = (0, len(slider_dates) - 1)
self.username = widgets.Text(
value='',
description='User:'******'Your NASA EarthData username ',
disabled=False
)
self.password = widgets.Password(
value='',
placeholder='Enter password',
description='Password:'******'',
description='Email:',
placeholder='Your EarthData e-mail',
disabled=False
)
self.credentials_button = widgets.Button(description='Set Credentials', )
self.projection = widgets.Dropdown(
options=['global', 'south', 'north'],
description='Hemisphere:',
disabled=False,
value='north'
)
self.dataset = widgets.SelectMultiple(
options=['ATM1B', 'GLAH06', 'ILVIS2'],
value=['ATM1B'],
rows=4,
description='Datasets',
disabled=False
)
self.itrf = widgets.Dropdown(
options=[None, 'ITRF2000', 'ITRF2008', 'ITRF2014'],
disabled=False,
description='ITRF:',
layout={'width': 'max-content',
'display': 'flex',
'description_width': 'initial'}
)
self.epoch = widgets.Text(
value='',
description='Epoch:',
placeholder='i.e. 2008.1',
disabled=False
)
self.is2 = widgets.Dropdown(
options=['None', 'ATL03', 'ATL06', 'ATL07', 'ATL08'],
description='ICESat 2:',
disabled=False,
)
self.dates_range = widgets.SelectionRangeSlider(
options=slider_dates,
index=slider_index,
continuous_update=False,
description='Date Range',
orientation='horizontal',
layout={'width': '90%'})
self.granule_count = widgets.Button(description="Get Raw Granule Count",
display='flex',
flex_flow='column',
align_items='stretch', )
self.granule_count.style.button_color = 'lightgreen'
self.granule_count.layout.width = 'auto'
self.print_parameters = widgets.Button(description="Print Current Parameters",
display='flex',
flex_flow='column',
align_items='stretch', )
self.print_parameters.style.button_color = 'lightgreen'
self.print_parameters.layout.width = 'auto'
self.post_order = widgets.Button(description="Place Data Order",
display='flex',
flex_flow='column',
align_items='stretch', )
self.post_order.style.button_color = 'lightblue'
self.post_order.layout.width = 'auto'
self.check_order_status = widgets.Button(description="Order status",
display='flex',
flex_flow='column',
align_items='stretch', )
self.check_order_status.style.button_color = 'lightblue'
self.check_order_status.layout.width = 'auto'
self.download_button = widgets.Button(description="Download completed orders",
display='flex',
flex_flow='column',
align_items='stretch', )
self.download_button.style.button_color = 'lightblue'
self.download_button.layout.width = 'auto'
self.selection_buttons = widgets.HBox([self.granule_count,
self.print_parameters,
self.post_order,
self.check_order_status,
self.download_button])
self.selection_controls = widgets.VBox([self.projection,
self.dataset,
self.itrf,
self.epoch,
self.is2,
self.dates_range,
self.selection_buttons])
self.controls.append(self.selection_controls)
self.layers_control = LayersControl(position='topright')
# Map Components
place_marker = Marker(icon=AwesomeIcon(name="check", marker_color='green', icon_color='darkgreen'))
self.search_control = SearchControl(
position="topleft",
url='https://nominatim.openstreetmap.org/search?format=json&q={s}',
zoom=5,
marker=place_marker
)
self.dc = DrawControl(
circlemarker={},
polyline={},
rectangle={
"shapeOptions": {
"fillColor": "#cc00cc",
"color": "#cc00cc",
"fillOpacity": 0.5
}
})
self.file_upload = widgets.FileUpload(
accept='.json,.geojson,.shp',
multiple=False # True to accept multiple files upload else False
)
# Now we are going to link the controls to the relevant events
self.dates_range.observe(self.dates_changed, 'value')
self.projection.observe(self.hemisphere_change)
self.credentials_button.on_click(self.set_credentials)
self.granule_count.on_click(self.query_cmr)
self.print_parameters.on_click(self.get_parameters)
self.post_order.on_click(self.place_data_orders)
self.check_order_status.on_click(self.order_statuses)
self.download_button.on_click(self.download_orders)
# Nodeidt listaan
nodeIds = []
nodeIds.append("All")
for i in range(32):
nodeIds.append(i + 1)
start_date = widgets.DatePicker(value=pd.to_datetime('2020-05-01'),
description='Starting Date',
layout=Layout(margin='0px 0px 0px 0px'))
end_date = widgets.DatePicker(value=pd.to_datetime('2020-11-01 23:00:00'),
description='Ending Date')
tunnit = widgets.SelectionRangeSlider(options=list(range(8, 23)),
index=[0, 14],
description='Tunnit',
disabled=False,
value=[8, 22])
def sort_by_widgets(df,
start_date=start_date,
end_date=end_date,
tunnit=tunnit):
# Sortaa pvm:s
df = df.loc[df["timestamp"] >= pd.to_datetime(start_date.value)]
df = df.loc[df["timestamp"] < pd.to_datetime(end_date.value)]
# Sorttaa tunnit
df = df.loc[
]
# date_time_obj = [datetime.datetime.strptime(date, '%d/%m/%Y').date() for date in date_list]
from datetime import datetime
start_date = datetime(2012, 1, 1)
end_date = datetime(2012, 10, 31)
dates = pd.date_range(start_date, end_date, freq='D')
options = [(date.strftime(' %d %b %Y '), date) for date in dates]
index = (0, len(options) - 1)
selection_range_slider = widgets.SelectionRangeSlider(
options=options,
index=index,
description='Date',
orientation='horizontal',
layout={'width': '500px'})
# selection_range_slider
import datetime
def print_date_range(date_range):
time_stamp = date_range[1]
day_time = time_stamp.to_pydatetime()
day = day_time.date()
