partition stringclasses 3
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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | FloatEntry.set_bind | Sets key bindings. | hcam_widgets/widgets.py | def set_bind(self):
"""
Sets key bindings.
"""
self.bind('<Button-1>', lambda e: self.add(0.1))
self.bind('<Button-3>', lambda e: self.sub(0.1))
self.bind('<Up>', lambda e: self.add(0.1))
self.bind('<Down>', lambda e: self.sub(0.1))
self.bind('<Shift-Up>',... | def set_bind(self):
"""
Sets key bindings.
"""
self.bind('<Button-1>', lambda e: self.add(0.1))
self.bind('<Button-3>', lambda e: self.sub(0.1))
self.bind('<Up>', lambda e: self.add(0.1))
self.bind('<Down>', lambda e: self.sub(0.1))
self.bind('<Shift-Up>',... | [
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train | FloatEntry.set_unbind | Unsets key bindings. | hcam_widgets/widgets.py | def set_unbind(self):
"""
Unsets key bindings.
"""
self.unbind('<Button-1>')
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self.unbind('<Up>')
self.unbind('<Down>')
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"""
Unsets key bindings.
"""
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self.unbind('<Up>')
self.unbind('<Down>')
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train | RangedFloat.set_bind | Sets key bindings -- we need this more than once | hcam_widgets/widgets.py | def set_bind(self):
"""
Sets key bindings -- we need this more than once
"""
FloatEntry.set_bind(self)
self.bind('<Next>', lambda e: self.set(self.fmin))
self.bind('<Prior>', lambda e: self.set(self.fmax)) | def set_bind(self):
"""
Sets key bindings -- we need this more than once
"""
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train | RangedFloat.validate | Applies the validation criteria.
Returns value, new value, or None if invalid.
Overload this in derived classes. | hcam_widgets/widgets.py | def validate(self, value):
"""
Applies the validation criteria.
Returns value, new value, or None if invalid.
Overload this in derived classes.
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try:
# trap blank fields here
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Applies the validation criteria.
Returns value, new value, or None if invalid.
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train | RangedFloat.add | Adds num to the current value | hcam_widgets/widgets.py | def add(self, num):
"""
Adds num to the current value
"""
try:
val = self.value() + num
except:
val = num
self.set(min(self.fmax, max(self.fmin, val))) | def add(self, num):
"""
Adds num to the current value
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train | RangedFloat.ok | Returns True if OK to use, else False | hcam_widgets/widgets.py | def ok(self):
"""
Returns True if OK to use, else False
"""
try:
v = float(self._value)
if v < self.fmin or v > self.fmax:
return False
else:
return True
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return False | def ok(self):
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Returns True if OK to use, else False
"""
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train | Expose.validate | This prevents setting any value more precise than 0.00001 | hcam_widgets/widgets.py | def validate(self, value):
"""
This prevents setting any value more precise than 0.00001
"""
try:
# trap blank fields here
if value:
v = float(value)
if (v != 0 and v < self.fmin) or v > self.fmax:
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... | def validate(self, value):
"""
This prevents setting any value more precise than 0.00001
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try:
# trap blank fields here
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train | Expose.set_min | Updates minimum value | hcam_widgets/widgets.py | def set_min(self, fmin):
"""
Updates minimum value
"""
if round(100000*fmin) != 100000*fmin:
raise DriverError('utils.widgets.Expose.set_min: ' +
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self.fmin = fmin
self.set(self.fmin) | def set_min(self, fmin):
"""
Updates minimum value
"""
if round(100000*fmin) != 100000*fmin:
raise DriverError('utils.widgets.Expose.set_min: ' +
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self.fmin = fmin
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train | ActButton.disable | Disable the button, if in non-expert mode;
unset its activity flag come-what-may. | hcam_widgets/widgets.py | def disable(self):
"""
Disable the button, if in non-expert mode;
unset its activity flag come-what-may.
"""
if not self._expert:
self.config(state='disable')
self._active = False | def disable(self):
"""
Disable the button, if in non-expert mode;
unset its activity flag come-what-may.
"""
if not self._expert:
self.config(state='disable')
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train | ActButton.setNonExpert | Turns off 'expert' status whereby to allow a button to be disabled | hcam_widgets/widgets.py | def setNonExpert(self):
"""
Turns off 'expert' status whereby to allow a button to be disabled
"""
self._expert = False
if self._active:
self.enable()
else:
self.disable() | def setNonExpert(self):
"""
Turns off 'expert' status whereby to allow a button to be disabled
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train | Sexagesimal.validate | Applies the validation criteria.
Returns value, new value, or None if invalid. | hcam_widgets/widgets.py | def validate(self, value):
"""
Applies the validation criteria.
Returns value, new value, or None if invalid.
"""
try:
coord.Angle(value, unit=self.unit)
return value
except ValueError:
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"""
Applies the validation criteria.
Returns value, new value, or None if invalid.
"""
try:
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train | Sexagesimal.set | Sets the current value equal to num | hcam_widgets/widgets.py | def set(self, num):
"""
Sets the current value equal to num
"""
self._value = coord.Angle(num, unit=u.deg)
self._variable.set(self.as_string()) | def set(self, num):
"""
Sets the current value equal to num
"""
self._value = coord.Angle(num, unit=u.deg)
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train | Sexagesimal.add | Adds an angle to the value | hcam_widgets/widgets.py | def add(self, quantity):
"""
Adds an angle to the value
"""
newvalue = self._value + quantity
self.set(newvalue.deg) | def add(self, quantity):
"""
Adds an angle to the value
"""
newvalue = self._value + quantity
self.set(newvalue.deg) | [
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train | Sexagesimal.sub | Subtracts an angle from the value | hcam_widgets/widgets.py | def sub(self, quantity):
"""
Subtracts an angle from the value
"""
newvalue = self._value - quantity
self.set(newvalue.deg) | def sub(self, quantity):
"""
Subtracts an angle from the value
"""
newvalue = self._value - quantity
self.set(newvalue.deg) | [
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train | Sexagesimal.ok | Returns True if OK to use, else False | hcam_widgets/widgets.py | def ok(self):
"""
Returns True if OK to use, else False
"""
try:
coord.Angle(self._value, unit=u.deg)
return True
except ValueError:
return False | def ok(self):
"""
Returns True if OK to use, else False
"""
try:
coord.Angle(self._value, unit=u.deg)
return True
except ValueError:
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train | Sexagesimal._callback | This gets called on any attempt to change the value | hcam_widgets/widgets.py | def _callback(self, *dummy):
"""
This gets called on any attempt to change the value
"""
# retrieve the value from the Entry
value = self._variable.get()
# run the validation. Returns None if no good
newvalue = self.validate(value)
if newvalue is None:
... | def _callback(self, *dummy):
"""
This gets called on any attempt to change the value
"""
# retrieve the value from the Entry
value = self._variable.get()
# run the validation. Returns None if no good
newvalue = self.validate(value)
if newvalue is None:
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train | Stop.act | Carries out the action associated with Stop button | hcam_widgets/widgets.py | def act(self):
"""
Carries out the action associated with Stop button
"""
g = get_root(self).globals
g.clog.debug('Stop pressed')
# Stop exposure meter
# do this first, so timer doesn't also try to enable idle mode
g.info.timer.stop()
def stop_in... | def act(self):
"""
Carries out the action associated with Stop button
"""
g = get_root(self).globals
g.clog.debug('Stop pressed')
# Stop exposure meter
# do this first, so timer doesn't also try to enable idle mode
g.info.timer.stop()
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train | Stop.check | Checks the status of the stop exposure command
This is run in background and can take a few seconds | hcam_widgets/widgets.py | def check(self):
"""
Checks the status of the stop exposure command
This is run in background and can take a few seconds
"""
g = get_root(self).globals
if self.stopped_ok:
# Exposure stopped OK; modify buttons
self.disable()
# try and ... | def check(self):
"""
Checks the status of the stop exposure command
This is run in background and can take a few seconds
"""
g = get_root(self).globals
if self.stopped_ok:
# Exposure stopped OK; modify buttons
self.disable()
# try and ... | [
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train | Target.modver | Switches colour of verify button | hcam_widgets/widgets.py | def modver(self, *args):
"""
Switches colour of verify button
"""
g = get_root(self).globals
if self.ok():
tname = self.val.get()
if tname in self.successes:
# known to be in simbad
self.verify.config(bg=g.COL['start'])
... | def modver(self, *args):
"""
Switches colour of verify button
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train | Target.act | Carries out the action associated with Verify button | hcam_widgets/widgets.py | def act(self):
"""
Carries out the action associated with Verify button
"""
tname = self.val.get()
g = get_root(self).globals
g.clog.info('Checking ' + tname + ' in simbad')
try:
ret = checkSimbad(g, tname)
if len(ret) == 0:
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"""
Carries out the action associated with Verify button
"""
tname = self.val.get()
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g.clog.info('Checking ' + tname + ' in simbad')
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train | PowerOn.act | Power on action | hcam_widgets/widgets.py | def act(self):
"""
Power on action
"""
g = get_root(self).globals
g.clog.debug('Power on pressed')
if execCommand(g, 'online'):
g.clog.info('ESO server online')
g.cpars['eso_server_online'] = True
if not isPoweredOn(g):
... | def act(self):
"""
Power on action
"""
g = get_root(self).globals
g.clog.debug('Power on pressed')
if execCommand(g, 'online'):
g.clog.info('ESO server online')
g.cpars['eso_server_online'] = True
if not isPoweredOn(g):
... | [
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train | InstSetup.setExpertLevel | Set expert level | hcam_widgets/widgets.py | def setExpertLevel(self):
"""
Set expert level
"""
g = get_root(self).globals
level = g.cpars['expert_level']
# first define which buttons are visible
if level == 0:
# simple layout
for button in self.all_buttons:
button.gr... | def setExpertLevel(self):
"""
Set expert level
"""
g = get_root(self).globals
level = g.cpars['expert_level']
# first define which buttons are visible
if level == 0:
# simple layout
for button in self.all_buttons:
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train | Switch.setExpertLevel | Modifies widget according to expertise level, which in this
case is just matter of hiding or revealing the button to
set CCD temps | hcam_widgets/widgets.py | def setExpertLevel(self):
"""
Modifies widget according to expertise level, which in this
case is just matter of hiding or revealing the button to
set CCD temps
"""
g = get_root(self).globals
level = g.cpars['expert_level']
if level == 0:
if se... | def setExpertLevel(self):
"""
Modifies widget according to expertise level, which in this
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g = get_root(self).globals
level = g.cpars['expert_level']
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train | Timer.start | Starts the timer from zero | hcam_widgets/widgets.py | def start(self):
"""
Starts the timer from zero
"""
self.startTime = time.time()
self.configure(text='{0:<d} s'.format(0))
self.update() | def start(self):
"""
Starts the timer from zero
"""
self.startTime = time.time()
self.configure(text='{0:<d} s'.format(0))
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train | Timer.update | Updates @ 10Hz to give smooth running clock, checks
run status @0.2Hz to reduce load on servers. | hcam_widgets/widgets.py | def update(self):
"""
Updates @ 10Hz to give smooth running clock, checks
run status @0.2Hz to reduce load on servers.
"""
g = get_root(self).globals
try:
self.count += 1
delta = int(round(time.time() - self.startTime))
self.configure(t... | def update(self):
"""
Updates @ 10Hz to give smooth running clock, checks
run status @0.2Hz to reduce load on servers.
"""
g = get_root(self).globals
try:
self.count += 1
delta = int(round(time.time() - self.startTime))
self.configure(t... | [
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train | InfoFrame.dumpJSON | Return dictionary of data for FITS headers. | hcam_widgets/widgets.py | def dumpJSON(self):
"""
Return dictionary of data for FITS headers.
"""
g = get_root(self).globals
return dict(
RA=self.ra['text'],
DEC=self.dec['text'],
tel=g.cpars['telins_name'],
alt=self._getVal(self.alt),
az=self._g... | def dumpJSON(self):
"""
Return dictionary of data for FITS headers.
"""
g = get_root(self).globals
return dict(
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tel=g.cpars['telins_name'],
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train | InfoFrame.update_tcs_table | Periodically update a table of info from the TCS.
Only works at GTC | hcam_widgets/widgets.py | def update_tcs_table(self):
"""
Periodically update a table of info from the TCS.
Only works at GTC
"""
g = get_root(self).globals
if not g.cpars['tcs_on'] or not g.cpars['telins_name'].lower() == 'gtc':
self.after(60000, self.update_tcs_table)
re... | def update_tcs_table(self):
"""
Periodically update a table of info from the TCS.
Only works at GTC
"""
g = get_root(self).globals
if not g.cpars['tcs_on'] or not g.cpars['telins_name'].lower() == 'gtc':
self.after(60000, self.update_tcs_table)
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train | InfoFrame.update_tcs | Periodically update TCS info.
A long running process, so run in a thread and fill a queue | hcam_widgets/widgets.py | def update_tcs(self):
"""
Periodically update TCS info.
A long running process, so run in a thread and fill a queue
"""
g = get_root(self).globals
if not g.cpars['tcs_on']:
self.after(20000, self.update_tcs)
return
if g.cpars['telins_nam... | def update_tcs(self):
"""
Periodically update TCS info.
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train | InfoFrame.update_slidepos | Periodically update the slide position.
Also farmed out to a thread to avoid hanging GUI main thread | hcam_widgets/widgets.py | def update_slidepos(self):
"""
Periodically update the slide position.
Also farmed out to a thread to avoid hanging GUI main thread
"""
g = get_root(self).globals
if not g.cpars['focal_plane_slide_on']:
self.after(20000, self.update_slidepos)
retu... | def update_slidepos(self):
"""
Periodically update the slide position.
Also farmed out to a thread to avoid hanging GUI main thread
"""
g = get_root(self).globals
if not g.cpars['focal_plane_slide_on']:
self.after(20000, self.update_slidepos)
retu... | [
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train | InfoFrame.update | Updates run & tel status window. Runs
once every 2 seconds. | hcam_widgets/widgets.py | def update(self):
"""
Updates run & tel status window. Runs
once every 2 seconds.
"""
g = get_root(self).globals
if g.astro is None or g.fpslide is None:
self.after(100, self.update)
return
try:
if g.cpars['tcs_on']:
... | def update(self):
"""
Updates run & tel status window. Runs
once every 2 seconds.
"""
g = get_root(self).globals
if g.astro is None or g.fpslide is None:
self.after(100, self.update)
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if g.cpars['tcs_on']:
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train | AstroFrame.update | Updates @ 10Hz to give smooth running clock. | hcam_widgets/widgets.py | def update(self):
"""
Updates @ 10Hz to give smooth running clock.
"""
try:
# update counter
self.counter += 1
g = get_root(self).globals
# current time
now = Time.now()
# configure times
self.utc.con... | def update(self):
"""
Updates @ 10Hz to give smooth running clock.
"""
try:
# update counter
self.counter += 1
g = get_root(self).globals
# current time
now = Time.now()
# configure times
self.utc.con... | [
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train | WinPairs.check | Checks the values of the window pairs. If any problems are found, it
flags them by changing the background colour.
Returns (status, synced)
status : flag for whether parameters are viable at all
synced : flag for whether the windows are synchronised. | hcam_widgets/widgets.py | def check(self):
"""
Checks the values of the window pairs. If any problems are found, it
flags them by changing the background colour.
Returns (status, synced)
status : flag for whether parameters are viable at all
synced : flag for whether the windows are synchron... | def check(self):
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Checks the values of the window pairs. If any problems are found, it
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synced : flag for whether the windows are synchron... | [
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"""
# needs some mods for ultracam ??
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train | WinQuads.enable | Enables WinQuad setting | hcam_widgets/widgets.py | def enable(self):
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train | Windows.check | Checks the values of the windows. If any problems are found,
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train | Windows.freeze | Freeze all settings so they can't be altered | hcam_widgets/widgets.py | def freeze(self):
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train | Windows.enable | Enables all settings | hcam_widgets/widgets.py | def enable(self):
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train | APlugin.check_download | Check if the download of the given dict was successful. No proving if the content of the file is correct too.
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"""
Check if the download of the given dict was successful. No proving if the content of the file is correct too.
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train | APlugin.delete_data | Delete everything which is related to the plugin. **Do not use if you do not know what you do!** | unidown/plugin/a_plugin.py | def delete_data(self):
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Delete everything which is related to the plugin. **Do not use if you do not know what you do!**
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train | APlugin.download_as_file | Download the given url to the given target folder.
:param url: link
:type url: str
:param folder: target folder
:type folder: ~pathlib.Path
:param name: target file name
:type name: str
:param delay: after download wait in seconds
:type delay: float
... | unidown/plugin/a_plugin.py | def download_as_file(self, url: str, folder: Path, name: str, delay: float = 0) -> str:
"""
Download the given url to the given target folder.
:param url: link
:type url: str
:param folder: target folder
:type folder: ~pathlib.Path
:param name: target file name
... | def download_as_file(self, url: str, folder: Path, name: str, delay: float = 0) -> str:
"""
Download the given url to the given target folder.
:param url: link
:type url: str
:param folder: target folder
:type folder: ~pathlib.Path
:param name: target file name
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train | APlugin.download | .. warning::
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.. warning::
The parameters may change in future versions. (e.g. change order and accept another host)
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"""
.. warning::
The parameters may change in future versions. (e.g. change order and accept another host)
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train | APlugin._create_save_state | Create protobuf savestate of the module and the given data.
:param link_item_dict: data
:type link_item_dict: Dict[str, ~unidown.plugin.link_item.LinkItem]
:return: the savestate
:rtype: ~unidown.plugin.save_state.SaveState | unidown/plugin/a_plugin.py | def _create_save_state(self, link_item_dict: Dict[str, LinkItem]) -> SaveState:
"""
Create protobuf savestate of the module and the given data.
:param link_item_dict: data
:type link_item_dict: Dict[str, ~unidown.plugin.link_item.LinkItem]
:return: the savestate
:rtype: ... | def _create_save_state(self, link_item_dict: Dict[str, LinkItem]) -> SaveState:
"""
Create protobuf savestate of the module and the given data.
:param link_item_dict: data
:type link_item_dict: Dict[str, ~unidown.plugin.link_item.LinkItem]
:return: the savestate
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train | APlugin.save_save_state | Save meta data about the downloaded things and the plugin to file.
:param data_dict: data
:type data_dict: Dict[link, ~unidown.plugin.link_item.LinkItem] | unidown/plugin/a_plugin.py | def save_save_state(self, data_dict: Dict[str, LinkItem]): # TODO: add progressbar
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Save meta data about the downloaded things and the plugin to file.
:param data_dict: data
:type data_dict: Dict[link, ~unidown.plugin.link_item.LinkItem]
"""
json_data = json_format.M... | def save_save_state(self, data_dict: Dict[str, LinkItem]): # TODO: add progressbar
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Save meta data about the downloaded things and the plugin to file.
:param data_dict: data
:type data_dict: Dict[link, ~unidown.plugin.link_item.LinkItem]
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train | APlugin.load_save_state | Load the savestate of the plugin.
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:raises ~unidown.plugin.exceptions.PluginException: broken savestate json
:raises ~unidown.plugin.exceptions.PluginException: different savestate versions
:raises ~unidown.plugin.ex... | unidown/plugin/a_plugin.py | def load_save_state(self) -> SaveState:
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Load the savestate of the plugin.
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:raises ~unidown.plugin.exceptions.PluginException: broken savestate json
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Load the savestate of the plugin.
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train | APlugin.get_updated_data | Get links who needs to be downloaded by comparing old and the new data.
:param old_data: old data
:type old_data: Dict[str, ~unidown.plugin.link_item.LinkItem]
:return: data which is newer or dont exist in the old one
:rtype: Dict[str, ~unidown.plugin.link_item.LinkItem] | unidown/plugin/a_plugin.py | def get_updated_data(self, old_data: Dict[str, LinkItem]) -> Dict[str, LinkItem]:
"""
Get links who needs to be downloaded by comparing old and the new data.
:param old_data: old data
:type old_data: Dict[str, ~unidown.plugin.link_item.LinkItem]
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Get links who needs to be downloaded by comparing old and the new data.
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train | APlugin.update_dict | Use for updating save state dicts and get the new save state dict. Provides a debug option at info level.
Updates the base dict. Basically executes `base.update(new)`.
:param base: base dict **gets overridden!**
:type base: Dict[str, ~unidown.plugin.link_item.LinkItem]
:param new: data ... | unidown/plugin/a_plugin.py | def update_dict(self, base: Dict[str, LinkItem], new: Dict[str, LinkItem]):
"""
Use for updating save state dicts and get the new save state dict. Provides a debug option at info level.
Updates the base dict. Basically executes `base.update(new)`.
:param base: base dict **gets overridde... | def update_dict(self, base: Dict[str, LinkItem], new: Dict[str, LinkItem]):
"""
Use for updating save state dicts and get the new save state dict. Provides a debug option at info level.
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train | APlugin._get_options_dic | Convert the option list to a dictionary where the key is the option and the value is the related option.
Is called in the init.
:param options: options given to the plugin.
:type options: List[str]
:return: dictionary which contains the option key as str related to the option string
... | unidown/plugin/a_plugin.py | def _get_options_dic(self, options: List[str]) -> Dict[str, str]:
"""
Convert the option list to a dictionary where the key is the option and the value is the related option.
Is called in the init.
:param options: options given to the plugin.
:type options: List[str]
:re... | def _get_options_dic(self, options: List[str]) -> Dict[str, str]:
"""
Convert the option list to a dictionary where the key is the option and the value is the related option.
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train | APlugin.get_plugins | Get all available plugins for unidown.
:return: plugin name list
:rtype: Dict[str, ~pkg_resources.EntryPoint] | unidown/plugin/a_plugin.py | def get_plugins() -> Dict[str, pkg_resources.EntryPoint]:
"""
Get all available plugins for unidown.
:return: plugin name list
:rtype: Dict[str, ~pkg_resources.EntryPoint]
"""
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Get all available plugins for unidown.
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train | _equation_of_time | Find the difference between apparent and mean solar time
Parameters
----------
t : `~astropy.time.Time`
times (array)
Returns
----------
ret1 : `~astropy.units.Quantity`
the equation of time | hcam_widgets/astro.py | def _equation_of_time(t):
"""
Find the difference between apparent and mean solar time
Parameters
----------
t : `~astropy.time.Time`
times (array)
Returns
----------
ret1 : `~astropy.units.Quantity`
the equation of time
"""
# Julian centuries since J2000.0
... | def _equation_of_time(t):
"""
Find the difference between apparent and mean solar time
Parameters
----------
t : `~astropy.time.Time`
times (array)
Returns
----------
ret1 : `~astropy.units.Quantity`
the equation of time
"""
# Julian centuries since J2000.0
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train | _astropy_time_from_LST | Convert a Local Sidereal Time to an astropy Time object.
The local time is related to the LST through the RA of the Sun.
This routine uses this relationship to convert a LST to an astropy
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ret1 : `~astropy.time.Time`
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Convert a Local Sidereal Time to an astropy Time object.
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ret1 : `... | def _astropy_time_from_LST(t, LST, location, prev_next):
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Convert a Local Sidereal Time to an astropy Time object.
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train | _rise_set_trig | Crude time at next rise/set of ``target`` using spherical trig.
This method is ~15 times faster than `_calcriseset`,
and inherently does *not* take the atmosphere into account.
The time returned should not be used in calculations; the purpose
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... | hcam_widgets/astro.py | def _rise_set_trig(t, target, location, prev_next, rise_set):
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Crude time at next rise/set of ``target`` using spherical trig.
This method is ~15 times faster than `_calcriseset`,
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Crude time at next rise/set of ``target`` using spherical trig.
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train | calc_riseset | Time at next rise/set of ``target``.
Parameters
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t : `~astropy.time.Time` or other (see below)
Time of observation. This will be passed in as the first argument to
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"""
Time at next rise/set of ``target``.
Parameters
----------
t : `~astropy.time.Time` or other (see below)
Time of observation. This will be passed in as the first argument to
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Time at next rise/set of ``target``.
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t : `~astropy.time.Time` or other (see below)
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train | _horiz_cross | Find time ``t`` when values in array ``a`` go from
negative to positive or positive to negative (exclude endpoints)
``return_limits`` will return nearest times to zero-crossing.
Parameters
----------
t : `~astropy.time.Time`
Grid of times
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"""
Find time ``t`` when values in array ``a`` go from
negative to positive or positive to negative (exclude endpoints)
``return_limits`` will return nearest times to zero-crossing.
Parameters
----------
t : `~astropy.time.Time`
... | def _horiz_cross(t, alt, rise_set, horizon=0*u.degree):
"""
Find time ``t`` when values in array ``a`` go from
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``return_limits`` will return nearest times to zero-crossing.
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t : `~astropy.time.Time`
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train | _two_point_interp | Do linear interpolation between two ``altitudes`` at
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times : `~astropy.time.Time`
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Do linear interpolation between two ``altitudes`` at
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Do linear interpolation between two ``altitudes`` at
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train | init_dirs | Initialize the main directories.
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:type main_dir: ~pathlib.Path
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:type logfilepath: ~pathlib.Path
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Initialize the main directories.
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train | reset | Reset all dynamic variables to the default values. | unidown/dynamic_data.py | def reset():
"""
Reset all dynamic variables to the default values.
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"""
Reset all dynamic variables to the default values.
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train | check_dirs | Check the directories if they exist.
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"""
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train | parse_hstring | Parse a single item from the telescope server into name, value, comment. | hcam_widgets/gtc/headers.py | def parse_hstring(hs):
"""
Parse a single item from the telescope server into name, value, comment.
"""
# split the string on = and /, also stripping whitespace and annoying quotes
name, value, comment = yield_three(
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Parse a single item from the telescope server into name, value, comment.
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Arguments
---------
telpars : list
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Create a list of fits header items from GTC telescope pars.
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train | add_gtc_header_table_row | Add a row with current values to GTC table
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t : `~astropy.table.Table`
The table to append row to
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list returned by server call to getTelescopeParams | hcam_widgets/gtc/headers.py | def add_gtc_header_table_row(t, telpars):
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Add a row with current values to GTC table
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t : `~astropy.table.Table`
The table to append row to
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Add a row with current values to GTC table
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train | PluginInfo.from_protobuf | Constructor from protobuf.
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:return: the PluginInfo
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train | PluginInfo.to_protobuf | Create protobuf item.
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train | SaveState.from_protobuf | Constructor from protobuf. Can raise ValueErrors from called from_protobuf() parsers.
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train | SaveState.to_protobuf | Create protobuf item.
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train | definite_article | Returns the definite article (der/die/das/die) for a given word. | textblob_de/ext/_pattern/text/de/inflect.py | def definite_article(word, gender=MALE, role=SUBJECT):
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"""
return article_definite.get((gender[:1].lower(), role[:3].lower())) | def definite_article(word, gender=MALE, role=SUBJECT):
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train | indefinite_article | Returns the indefinite article (ein) for a given word. | textblob_de/ext/_pattern/text/de/inflect.py | def indefinite_article(word, gender=MALE, role=SUBJECT):
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train | article | Returns the indefinite (ein) or definite (der/die/das/die) article for the given word. | textblob_de/ext/_pattern/text/de/inflect.py | def article(word, function=INDEFINITE, gender=MALE, role=SUBJECT):
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train | referenced | Returns a string with the article + the word. | textblob_de/ext/_pattern/text/de/inflect.py | def referenced(word, article=INDEFINITE, gender=MALE, role=SUBJECT):
""" Returns a string with the article + the word.
"""
return "%s %s" % (_article(word, article, gender, role), word) | def referenced(word, article=INDEFINITE, gender=MALE, role=SUBJECT):
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train | gender | Returns the gender (MALE, FEMALE or NEUTRAL) for nouns (majority vote).
Returns None for words that are not nouns. | textblob_de/ext/_pattern/text/de/inflect.py | def gender(word, pos=NOUN):
""" Returns the gender (MALE, FEMALE or NEUTRAL) for nouns (majority vote).
Returns None for words that are not nouns.
"""
w = word.lower()
if pos == NOUN:
# Default rules (baseline = 32%).
if w.endswith(gender_masculine):
return MASCULINE
... | def gender(word, pos=NOUN):
""" Returns the gender (MALE, FEMALE or NEUTRAL) for nouns (majority vote).
Returns None for words that are not nouns.
"""
w = word.lower()
if pos == NOUN:
# Default rules (baseline = 32%).
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train | pluralize | Returns the plural of a given word.
The inflection is based on probability rather than gender and role. | textblob_de/ext/_pattern/text/de/inflect.py | def pluralize(word, pos=NOUN, gender=MALE, role=SUBJECT, custom={}):
""" Returns the plural of a given word.
The inflection is based on probability rather than gender and role.
"""
w = word.lower().capitalize()
if word in custom:
return custom[word]
if pos == NOUN:
for a, b i... | def pluralize(word, pos=NOUN, gender=MALE, role=SUBJECT, custom={}):
""" Returns the plural of a given word.
The inflection is based on probability rather than gender and role.
"""
w = word.lower().capitalize()
if word in custom:
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train | singularize | Returns the singular of a given word.
The inflection is based on probability rather than gender and role. | textblob_de/ext/_pattern/text/de/inflect.py | def singularize(word, pos=NOUN, gender=MALE, role=SUBJECT, custom={}):
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The inflection is based on probability rather than gender and role.
"""
w = word.lower().capitalize()
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r... | def singularize(word, pos=NOUN, gender=MALE, role=SUBJECT, custom={}):
""" Returns the singular of a given word.
The inflection is based on probability rather than gender and role.
"""
w = word.lower().capitalize()
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train | attributive | For a predicative adjective, returns the attributive form (lowercase).
In German, the attributive is formed with -e, -em, -en, -er or -es,
depending on gender (masculine, feminine, neuter or plural) and role
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""" For a predicative adjective, returns the attributive form (lowercase).
In German, the attributive is formed with -e, -em, -en, -er or -es,
depending on gender (masculine, feminine, neuter or plural) and role
(nominative... | def attributive(adjective, gender=MALE, role=SUBJECT, article=None):
""" For a predicative adjective, returns the attributive form (lowercase).
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train | predicative | Returns the predicative adjective (lowercase).
In German, the attributive form preceding a noun is always used:
"ein kleiner Junge" => strong, masculine, nominative,
"eine schöne Frau" => mixed, feminine, nominative,
"der kleine Prinz" => weak, masculine, nominative, etc.
The pre... | textblob_de/ext/_pattern/text/de/inflect.py | def predicative(adjective):
""" Returns the predicative adjective (lowercase).
In German, the attributive form preceding a noun is always used:
"ein kleiner Junge" => strong, masculine, nominative,
"eine schöne Frau" => mixed, feminine, nominative,
"der kleine Prinz" => weak, masculi... | def predicative(adjective):
""" Returns the predicative adjective (lowercase).
In German, the attributive form preceding a noun is always used:
"ein kleiner Junge" => strong, masculine, nominative,
"eine schöne Frau" => mixed, feminine, nominative,
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train | grade | Returns the comparative or superlative form of the given (inflected) adjective. | textblob_de/ext/_pattern/text/de/inflect.py | def grade(adjective, suffix=COMPARATIVE):
""" Returns the comparative or superlative form of the given (inflected) adjective.
"""
b = predicative(adjective)
# groß => großt, schön => schönst
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suffix = suffix[1:]
# große => großere, sch... | def grade(adjective, suffix=COMPARATIVE):
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"""
b = predicative(adjective)
# groß => großt, schön => schönst
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train | Verbs.find_lemma | Returns the base form of the given inflected verb, using a rule-based approach. | textblob_de/ext/_pattern/text/de/inflect.py | def find_lemma(self, verb):
""" Returns the base form of the given inflected verb, using a rule-based approach.
"""
v = verb.lower()
# Common prefixes: be-finden and emp-finden probably inflect like finden.
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... | def find_lemma(self, verb):
""" Returns the base form of the given inflected verb, using a rule-based approach.
"""
v = verb.lower()
# Common prefixes: be-finden and emp-finden probably inflect like finden.
if not (v.startswith("ge") and v.endswith("t")): # Probably gerund.
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train | Verbs.find_lexeme | For a regular verb (base form), returns the forms using a rule-based approach. | textblob_de/ext/_pattern/text/de/inflect.py | def find_lexeme(self, verb):
""" For a regular verb (base form), returns the forms using a rule-based approach.
"""
v = verb.lower()
# Stem = infinitive minus -en, -ln, -rn.
b = b0 = re.sub("en$", "", re.sub("ln$", "l", re.sub("rn$", "r", v)))
# Split common prefixes.
... | def find_lexeme(self, verb):
""" For a regular verb (base form), returns the forms using a rule-based approach.
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v = verb.lower()
# Stem = infinitive minus -en, -ln, -rn.
b = b0 = re.sub("en$", "", re.sub("ln$", "l", re.sub("rn$", "r", v)))
# Split common prefixes.
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train | Verbs.tenses | Returns a list of possible tenses for the given inflected verb. | textblob_de/ext/_pattern/text/de/inflect.py | def tenses(self, verb, parse=True):
""" Returns a list of possible tenses for the given inflected verb.
"""
tenses = _Verbs.tenses(self, verb, parse)
if len(tenses) == 0:
# auswirkte => wirkte aus
for prefix in prefix_separable:
if verb.startswith(... | def tenses(self, verb, parse=True):
""" Returns a list of possible tenses for the given inflected verb.
"""
tenses = _Verbs.tenses(self, verb, parse)
if len(tenses) == 0:
# auswirkte => wirkte aus
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train | _get_words_from_dataset | Return a set of all words in a dataset.
:param dataset: A list of tuples of the form ``(words, label)`` where
``words`` is either a string of a list of tokens. | textblob_de/classifiers.py | def _get_words_from_dataset(dataset):
"""Return a set of all words in a dataset.
:param dataset: A list of tuples of the form ``(words, label)`` where
``words`` is either a string of a list of tokens.
"""
# Words may be either a string or a list of tokens. Return an iterator
# of tokens ac... | def _get_words_from_dataset(dataset):
"""Return a set of all words in a dataset.
:param dataset: A list of tuples of the form ``(words, label)`` where
``words`` is either a string of a list of tokens.
"""
# Words may be either a string or a list of tokens. Return an iterator
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:param document: The text to extract features from. Can be a string or an iterable.
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"""A basic document feature extractor that returns a dict indicating what
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"""A basic document feature extractor that returns a dict indicating what
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train | contains_extractor | A basic document feature extractor that returns a dict of words that the
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"""A basic document feature extractor that returns a dict of words that the
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tokens = _get_document_tokens(document)
features = dict((u'contains({0})'.format(w), True) for w in tokens)
return features | def contains_extractor(document):
"""A basic document feature extractor that returns a dict of words that the
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tokens = _get_document_tokens(document)
features = dict((u'contains({0})'.format(w), True) for w in tokens)
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train | BaseClassifier._read_data | Reads a data file and returns and iterable that can be used as
testing or training data. | textblob_de/classifiers.py | def _read_data(self, dataset, format=None):
"""Reads a data file and returns and iterable that can be used as
testing or training data."""
# Attempt to detect file format if "format" isn't specified
if not format:
format_class = formats.detect(dataset)
else:
... | def _read_data(self, dataset, format=None):
"""Reads a data file and returns and iterable that can be used as
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# Attempt to detect file format if "format" isn't specified
if not format:
format_class = formats.detect(dataset)
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train | BaseClassifier.extract_features | Extracts features from a body of text.
:rtype: dictionary of features | textblob_de/classifiers.py | def extract_features(self, text):
"""Extracts features from a body of text.
:rtype: dictionary of features
"""
# Feature extractor may take one or two arguments
try:
return self.feature_extractor(text, self.train_set)
except (TypeError, AttributeError):
... | def extract_features(self, text):
"""Extracts features from a body of text.
:rtype: dictionary of features
"""
# Feature extractor may take one or two arguments
try:
return self.feature_extractor(text, self.train_set)
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train | NLTKClassifier.train | Train the classifier with a labeled feature set and return the
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methods and is included only to allow passing in arguments to the
``train`` method of ... | textblob_de/classifiers.py | def train(self, *args, **kwargs):
"""Train the classifier with a labeled feature set and return the
classifier. Takes the same arguments as the wrapped NLTK class. This
method is implicitly called when calling ``classify`` or ``accuracy``
methods and is included only to allow passing in ... | def train(self, *args, **kwargs):
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train | NLTKClassifier.classify | Classifies the text.
:param str text: A string of text. | textblob_de/classifiers.py | def classify(self, text):
"""Classifies the text.
:param str text: A string of text.
"""
text_features = self.extract_features(text)
return self.classifier.classify(text_features) | def classify(self, text):
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:param str text: A string of text.
"""
text_features = self.extract_features(text)
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train | NLTKClassifier.accuracy | Compute the accuracy on a test set.
:param test_set: A list of tuples of the form ``(text, label)``, or a
filename.
:param format: If ``test_set`` is a filename, the file format, e.g.
``"csv"`` or ``"json"``. If ``None``, will attempt to detect the
file format. | textblob_de/classifiers.py | def accuracy(self, test_set, format=None):
"""Compute the accuracy on a test set.
:param test_set: A list of tuples of the form ``(text, label)``, or a
filename.
:param format: If ``test_set`` is a filename, the file format, e.g.
``"csv"`` or ``"json"``. If ``None``, wil... | def accuracy(self, test_set, format=None):
"""Compute the accuracy on a test set.
:param test_set: A list of tuples of the form ``(text, label)``, or a
filename.
:param format: If ``test_set`` is a filename, the file format, e.g.
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train | NLTKClassifier.update | Update the classifier with new training data and re-trains the
classifier.
:param new_data: New data as a list of tuples of the form
``(text, label)``. | textblob_de/classifiers.py | def update(self, new_data, *args, **kwargs):
'''Update the classifier with new training data and re-trains the
classifier.
:param new_data: New data as a list of tuples of the form
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'''
self.train_set += new_data
self.train_features = [(self... | def update(self, new_data, *args, **kwargs):
'''Update the classifier with new training data and re-trains the
classifier.
:param new_data: New data as a list of tuples of the form
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'''
self.train_set += new_data
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train | NaiveBayesClassifier.prob_classify | Return the label probability distribution for classifying a string
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Example:
::
>>> classifier = NaiveBayesClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
... | textblob_de/classifiers.py | def prob_classify(self, text):
"""Return the label probability distribution for classifying a string
of text.
Example:
::
>>> classifier = NaiveBayesClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> pro... | def prob_classify(self, text):
"""Return the label probability distribution for classifying a string
of text.
Example:
::
>>> classifier = NaiveBayesClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> pro... | [
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train | PositiveNaiveBayesClassifier.train | Train the classifier with a labeled and unlabeled feature sets and
return the classifier. Takes the same arguments as the wrapped NLTK
class. This method is implicitly called when calling ``classify`` or
``accuracy`` methods and is included only to allow passing in arguments
to the ``tra... | textblob_de/classifiers.py | def train(self, *args, **kwargs):
"""Train the classifier with a labeled and unlabeled feature sets and
return the classifier. Takes the same arguments as the wrapped NLTK
class. This method is implicitly called when calling ``classify`` or
``accuracy`` methods and is included only to al... | def train(self, *args, **kwargs):
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train | PositiveNaiveBayesClassifier.update | Update the classifier with new data and re-trains the
classifier.
:param new_positive_data: List of new, labeled strings.
:param new_unlabeled_data: List of new, unlabeled strings. | textblob_de/classifiers.py | def update(self, new_positive_data=None,
new_unlabeled_data=None, positive_prob_prior=0.5,
*args, **kwargs):
'''Update the classifier with new data and re-trains the
classifier.
:param new_positive_data: List of new, labeled strings.
:param new_unlabeled_da... | def update(self, new_positive_data=None,
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'''Update the classifier with new data and re-trains the
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:param new_positive_data: List of new, labeled strings.
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train | MaxEntClassifier.prob_classify | Return the label probability distribution for classifying a string
of text.
Example:
::
>>> classifier = MaxEntClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
... | textblob_de/classifiers.py | def prob_classify(self, text):
"""Return the label probability distribution for classifying a string
of text.
Example:
::
>>> classifier = MaxEntClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_di... | def prob_classify(self, text):
"""Return the label probability distribution for classifying a string
of text.
Example:
::
>>> classifier = MaxEntClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_di... | [
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train | PatternParserLemmatizer.lemmatize | Return a list of (lemma, tag) tuples.
:param str text: A string. | textblob_de/lemmatizers.py | def lemmatize(self, text):
"""Return a list of (lemma, tag) tuples.
:param str text: A string.
"""
#: Do not process empty strings (Issue #3)
if text.strip() == "":
return []
parsed_sentences = self._parse_text(text)
_lemmalist = []
for s in ... | def lemmatize(self, text):
"""Return a list of (lemma, tag) tuples.
:param str text: A string.
"""
#: Do not process empty strings (Issue #3)
if text.strip() == "":
return []
parsed_sentences = self._parse_text(text)
_lemmalist = []
for s in ... | [
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train | PatternParserLemmatizer._parse_text | Parse text (string) and return list of parsed sentences (strings).
Each sentence consists of space separated token elements and the
token format returned by the PatternParser is WORD/TAG/PHRASE/ROLE/LEMMA
(separated by a forward slash '/')
:param str text: A string. | textblob_de/lemmatizers.py | def _parse_text(self, text):
"""Parse text (string) and return list of parsed sentences (strings).
Each sentence consists of space separated token elements and the
token format returned by the PatternParser is WORD/TAG/PHRASE/ROLE/LEMMA
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:param... | def _parse_text(self, text):
"""Parse text (string) and return list of parsed sentences (strings).
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train | _match | Returns True if the pattern matches the given word string.
The pattern can include a wildcard (*front, back*, *both*, in*side),
or it can be a compiled regular expression. | textblob_de/ext/_pattern/text/search.py | def _match(string, pattern):
""" Returns True if the pattern matches the given word string.
The pattern can include a wildcard (*front, back*, *both*, in*side),
or it can be a compiled regular expression.
"""
p = pattern
try:
if p[:1] == WILDCARD and (p[-1:] == WILDCARD and p[1:-... | def _match(string, pattern):
""" Returns True if the pattern matches the given word string.
The pattern can include a wildcard (*front, back*, *both*, in*side),
or it can be a compiled regular expression.
"""
p = pattern
try:
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