class TestSet(param.Parameterized):
numpy_params = ['r']
pandas_params = ['s','t','u']
conditionally_unsafe = ['f', 'o']
a = param.Integer(default=5, doc='Example doc', bounds=(2,30), inclusive_bounds=(True, False))
b = param.Number(default=4.3, allow_None=True)
c = param.String(default='foo')
d = param.Boolean(default=False)
e = param.List([1,2,3], class_=int)
f = param.List([1,2,3])
g = param.Date(default=datetime.datetime.now())
h = param.Tuple(default=(1,2,3), length=3)
i = param.NumericTuple(default=(1,2,3,4))
j = param.XYCoordinates(default=(32.1, 51.5))
k = param.Integer(default=1)
l = param.Range(default=(1.1,2.3), bounds=(1,3))
m = param.String(default='baz', allow_None=True)
n = param.ObjectSelector(default=3, objects=[3,'foo'], allow_None=False)
o = param.ObjectSelector(default=simple_list, objects=[simple_list], allow_None=False)
p = param.ListSelector(default=[1,4,5], objects=[1,2,3,4,5,6])
q = param.CalendarDate(default=datetime.date.today())
r = None if np is None else param.Array(default=ndarray)
s = None if pd is None else param.DataFrame(default=df1, columns=2)
t = None if pd is None else param.DataFrame(default=pd.DataFrame(
{'A':[1,2,3], 'B':[1.1,2.2,3.3]}), columns=(1,4), rows=(2,5))
u = None if pd is None else param.DataFrame(default=df2, columns=['A', 'B'])
v = param.Dict({'1':2})
class _BigDumbParams(param.Parameterized):
action = param.Action(default_action, allow_None=True)
array = param.Array(np.array([1.0, 2.0]))
boolean = param.Boolean(True, allow_None=True)
callable = param.Callable(default_action, allow_None=True)
class_selector = param.ClassSelector(int, is_instance=False, allow_None=True)
color = param.Color("#FFFFFF", allow_None=True)
composite = param.Composite(["action", "array"], allow_None=True)
try:
data_frame = param.DataFrame(
pd.DataFrame({"A": 1.0, "B": np.arange(5)}), allow_None=True
)
except TypeError:
data_frame = param.DataFrame(pd.DataFrame({"A": 1.0, "B": np.arange(5)}))
date = param.Date(datetime.now(), allow_None=True)
date_range = param.DateRange((datetime.min, datetime.max), allow_None=True)
dict_ = param.Dict({"foo": "bar"}, allow_None=True, doc="dict means dictionary")
dynamic = param.Dynamic(default=default_action, allow_None=True)
file_selector = param.FileSelector(
os.path.join(FILE_DIR_DIR, "LICENSE"),
path=os.path.join(FILE_DIR_DIR, "*"),
allow_None=True,
)
filename = param.Filename(
os.path.join(FILE_DIR_DIR, "LICENSE"), allow_None=True
)
foldername = param.Foldername(os.path.join(FILE_DIR_DIR), allow_None=True)
hook_list = param.HookList(
[CallableObject(), CallableObject()], class_=CallableObject, allow_None=True
)
integer = param.Integer(10, allow_None=True)
list_ = param.List([1, 2, 3], allow_None=True, class_=int)
list_selector = param.ListSelector([2, 2], objects=[1, 2, 3], allow_None=True)
magnitude = param.Magnitude(0.5, allow_None=True)
multi_file_selector = param.MultiFileSelector(
[],
path=os.path.join(FILE_DIR_DIR, "*"),
allow_None=True,
check_on_set=True,
)
number = param.Number(-10.0, allow_None=True, doc="here is a number")
numeric_tuple = param.NumericTuple((5.0, 10.0), allow_None=True)
object_selector = param.ObjectSelector(
False, objects={"False": False, "True": 1}, allow_None=True
)
path = param.Path(os.path.join(FILE_DIR_DIR, "LICENSE"), allow_None=True)
range_ = param.Range((-1.0, 2.0), allow_None=True)
series = param.Series(pd.Series(range(5)), allow_None=True)
string = param.String("foo", allow_None=True, doc="this is a string")
tuple_ = param.Tuple((3, 4, "fi"), allow_None=True)
x_y_coordinates = param.XYCoordinates((1.0, 2.0), allow_None=True)
class MyParameterized(param.Parameterized):
enable = param.Boolean(True,
doc="A sample Boolean parameter",
allow_None=True)
what_proportion = param.Magnitude(default=0.9)
age = param.Number(49,
bounds=(0, 100),
doc="Any Number between 0 to 100")
how_many = param.Integer()
favorite_quote = param.String(default="Hello, world!")
choose_file_or_folder = param.Path(search_paths='./')
choose_folder = param.Foldername(search_paths="./")
choose_file = param.Filename(search_paths="./")
select_a_file = param.FileSelector(path='./*')
select_multiple_files = param.MultiFileSelector(path='./*')
favorite_color = param.ObjectSelector(
default="green", objects=["red", "yellow", "green"])
favorite_fruit = param.Selector(default="Apple",
objects=["Orange", "Apple", "Mango"])
select_multiple = param.ListSelector(default=[3, 5],
objects=[1, 2, 3, 4, 5])
birthday = param.CalendarDate(dt.date(2017, 1, 1),
bounds=(dt.date(2017, 1,
1), dt.date(2017, 2, 1)))
appointment = param.Date(dt.datetime(2017, 1, 1),
bounds=(dt.datetime(2017, 1, 1),
dt.datetime(2017, 2, 1)))
least_favorite_color = param.Color(default='#FF0000')
dataset = param.DataFrame(pd.util.testing.makeDataFrame().iloc[:3])
this_strange_thing = param.Tuple(default=(False, ), allow_None=True)
some_numbers = param.NumericTuple(default=(1, 2, 3.0, 4.0))
home_city = param.XYCoordinates(default=(-111.65, 40.23))
bounds = param.Range(default=(-10, 10))
class ObservationsExplorer(param.Parameterized):
"""Param interface for inspecting observations"""
observation_df = param.DataFrame(
doc='The DataFrame for the observations.',
precedence=-1 # Don't show widget
)
images_df = param.DataFrame(
doc='The DataFrame for the images from the selected observations.',
precedence=-1 # Don't show widget
)
show_recent = param.Boolean(label='Show recent observations',
doc='Show recent observations',
default=True)
search_name = param.String(
label='Coordinates for object',
doc='Field name for coordinate lookup',
)
coords = param.XYCoordinates(label='RA/Dec Coords [deg]',
doc='RA/Dec Coords [degrees]',
default=(0, 0))
radius = param.Number(label='Search radius [deg]',
doc='Search radius [degrees]',
default=15.,
bounds=(0, 180),
softbounds=(0, 25))
time = param.DateRange(
label='Date Range',
default=(pendulum.parse('2016-01-01').replace(tzinfo=None), now),
bounds=(pendulum.parse('2016-01-01').replace(tzinfo=None), now))
min_num_images = param.Integer(doc='Minimum number of images.',
default=1,
bounds=(1, 50),
softbounds=(1, 10))
unit_id = param.ListSelector(
doc='Unit IDs',
label='Unit IDs',
)
def __init__(self, **kwargs):
super().__init__(**kwargs)
logger.debug(f'Getting recent stats from {BASE_URL}')
self._observations_path = download_file(f'{BASE_URL}',
cache='update',
show_progress=False,
pkgname='panoptes')
self._observations_df = pd.read_csv(
self._observations_path).convert_dtypes()
# Setup up widgets
# Set some default for the params now that we have data.
units = sorted(self._observations_df.unit_id.unique())
units.insert(0, 'The Whole World! 🌎')
self.param.unit_id.objects = units
self.unit_id = [units[0]]
# Create the source objects.
self.update_dataset()
@param.depends('coords', 'radius', 'time', 'min_num_images', 'unit_id',
'search_name')
def update_dataset(self):
if self.show_recent:
# Get just the recent result on initial load
df = search_observations(ra=180,
dec=0,
radius=180,
start_date=now.subtract(months=1),
end_date=now,
min_num_images=1,
source=self._observations_df).sort_values(
by=['time', 'unit_id', 'camera_id'],
ascending=False)
else:
# If using the default unit_ids option, then search for all.
unit_ids = self.unit_id
if unit_ids == self.param.unit_id.objects[0:1]:
unit_ids = self.param.unit_id.objects[1:]
if self.search_name != '':
coords = SkyCoord.from_name(self.search_name)
self.coords = (round(coords.ra.value,
3), round(coords.dec.value, 3))
# Search for the observations given the current params.
df = search_observations(ra=self.coords[0],
dec=self.coords[1],
radius=self.radius,
start_date=self.time[0],
end_date=self.time[1],
min_num_images=self.min_num_images,
unit_id=unit_ids).sort_values(
by=['time', 'unit_id', 'camera_id'],
ascending=False)
df.time = pd.to_datetime(df.time)
cds = ColumnDataSource(data=df, name='observations_source')
def obs_row_selected(attrname, old_row_index, new_row_index):
# We only lookup one even if they select multiple rows.
newest_index = new_row_index[-1]
row = df.iloc[newest_index]
print(f'Looking up sequence_id={row.sequence_id}')
self.images_df = get_metadata(sequence_id=row.sequence_id)
if self.images_df is not None:
self.images_df = self.images_df.dropna()
cds.selected.on_change('indices', obs_row_selected)
return cds
@param.depends("images_df")
def selected_title(self):
try:
sequence_id = self.images_df.sequence_id.iloc[0]
except AttributeError:
sequence_id = ''
return pn.panel(f'<h5>{sequence_id}</h5>')
@param.depends('images_df')
def image_table(self):
columns = [('time', 'Time [UTC]')]
try:
images_table = self.images_df.hvplot.table(columns=columns).opts(
width=250,
height=100,
title=f'Images ({len(self.images_df)})',
)
except AttributeError:
images_table = self.images_df
return images_table
@param.depends('images_df')
def image_preview(self):
try:
image_url = self.images_df.public_url.dropna().iloc[0].replace(
'.fits.fz', '.jpg')
return pn.pane.HTML(f'''
<div class="media" style="width: 300px; height: 200px">
<a href="{image_url}" target="_blank">
<img src="{image_url}" class="card-img-top" alt="Observation Image">
</a>
</div>
''')
except AttributeError:
return ''
@param.depends('observation_df')
def fits_file_list_to_csv_cb(self):
""" Generates a CSV file from current image list."""
df = self.images_df.public_url.dropna()
sio = StringIO()
df.to_csv(sio, index=False, header=False)
sio.seek(0)
return sio
def table_download_button(self):
""" A button for downloading the images CSV."""
try:
sequence_id = self.images_df.sequence_id.iloc[0]
return pn.widgets.FileDownload(
callback=self.fits_file_list_to_csv_cb,
filename=f'fits-list-{sequence_id}.txt',
label='Download FITS List (.txt)',
)
except AttributeError:
return ''
def sources_download_button(self):
try:
sequence_id = self.images_df.sequence_id.iloc[0]
parquet_url = f'{OBSERVATIONS_BASE_URL}/{sequence_id}-sources.parquet'
source_btn = pn.widgets.Button(
name='Download sources list (.parquet)', )
source_btn.js_on_click(args=dict(url=parquet_url),
code='''
window.open(url, '_blank')
''')
return source_btn
except AttributeError:
return ''
def table(self):
columns = [
TableColumn(
field="unit_id",
title="Unit ID",
width=60,
),
TableColumn(
field="camera_id",
title="Camera ID",
width=60,
),
TableColumn(
field="time",
title="Time [UTC]",
formatter=DateFormatter(format='%Y-%m-%d %H:%M'),
width=130,
),
TableColumn(
field="field_name",
title="Field Name",
width=240,
),
TableColumn(
field="ra",
title="RA [deg]",
formatter=NumberFormatter(format="0.000"),
width=70,
),
TableColumn(
field="dec",
title="Dec [deg]",
formatter=NumberFormatter(format="0.000"),
width=70,
),
TableColumn(
field="num_images",
title="Images",
width=40,
),
TableColumn(
field="status",
title="Status",
width=75,
),
TableColumn(
field="exptime",
title="Exptime [sec]",
formatter=NumberFormatter(format="0.00"),
width=60,
),
TableColumn(
field="total_minutes_exptime",
title="Total Minutes",
formatter=NumberFormatter(format="0.0"),
width=60,
),
]
cds = self.update_dataset()
data_table = DataTable(
source=cds,
name='observations_table',
columns=columns,
index_position=None,
min_width=1100,
fit_columns=True,
sizing_mode='stretch_both',
)
return data_table
class MyParamXYCoordinates(param.Parameterized):
xy_coordinates = param.XYCoordinates(default=(-111.65, 40.23))
class MyParamXYCoordinates(param.Parameterized):
home_town = param.XYCoordinates(default=(-111.65, 40.23))
my_numeric_tuples = param.NumericTuple(default=(1, 2, 3))