Datasets:

calum

/

the-stack-smol-python-docstrings

like 6

@property def available(self) -> bool: 'Return if bulb is available.' return self._available

Return if bulb is available.

homeassistant/components/light/yeelight.py

available

DevRGT/home-assistant

python

@property def available(self) -> bool: return self._available

@property def supported_features(self) -> int: 'Flag supported features.' return self._supported_features

Flag supported features.

homeassistant/components/light/yeelight.py

supported_features

DevRGT/home-assistant

python

@property def supported_features(self) -> int: return self._supported_features

@property def effect_list(self): 'Return the list of supported effects.' return YEELIGHT_EFFECT_LIST

Return the list of supported effects.

homeassistant/components/light/yeelight.py

effect_list

DevRGT/home-assistant

python

@property def effect_list(self): return YEELIGHT_EFFECT_LIST

@property def color_temp(self) -> int: 'Return the color temperature.' return self._color_temp

Return the color temperature.

homeassistant/components/light/yeelight.py

color_temp

DevRGT/home-assistant

python

@property def color_temp(self) -> int: return self._color_temp

@property def name(self) -> str: 'Return the name of the device if any.' return self._name

Return the name of the device if any.

homeassistant/components/light/yeelight.py

name

DevRGT/home-assistant

python

@property def name(self) -> str: return self._name

@property def is_on(self) -> bool: 'Return true if device is on.' return self._is_on

Return true if device is on.

homeassistant/components/light/yeelight.py

is_on

DevRGT/home-assistant

python

@property def is_on(self) -> bool: return self._is_on

@property def brightness(self) -> int: 'Return the brightness of this light between 1..255.' return self._brightness

Return the brightness of this light between 1..255.

homeassistant/components/light/yeelight.py

brightness

DevRGT/home-assistant

python

@property def brightness(self) -> int: return self._brightness

@property def min_mireds(self): 'Return minimum supported color temperature.' if (self.supported_features & SUPPORT_COLOR_TEMP): return kelvin_to_mired(YEELIGHT_RGB_MAX_KELVIN) return kelvin_to_mired(YEELIGHT_MAX_KELVIN)

Return minimum supported color temperature.

homeassistant/components/light/yeelight.py

min_mireds

DevRGT/home-assistant

python

@property def min_mireds(self): if (self.supported_features & SUPPORT_COLOR_TEMP): return kelvin_to_mired(YEELIGHT_RGB_MAX_KELVIN) return kelvin_to_mired(YEELIGHT_MAX_KELVIN)

@property def max_mireds(self): 'Return maximum supported color temperature.' if (self.supported_features & SUPPORT_COLOR_TEMP): return kelvin_to_mired(YEELIGHT_RGB_MIN_KELVIN) return kelvin_to_mired(YEELIGHT_MIN_KELVIN)

Return maximum supported color temperature.

homeassistant/components/light/yeelight.py

max_mireds

DevRGT/home-assistant

python

@property def max_mireds(self): if (self.supported_features & SUPPORT_COLOR_TEMP): return kelvin_to_mired(YEELIGHT_RGB_MIN_KELVIN) return kelvin_to_mired(YEELIGHT_MIN_KELVIN)

@property def hs_color(self) -> tuple: 'Return the color property.' return self._hs

Return the color property.

homeassistant/components/light/yeelight.py

hs_color

DevRGT/home-assistant

python

@property def hs_color(self) -> tuple: return self._hs

def set_music_mode(self, mode) -> None: 'Set the music mode on or off.' if mode: self._bulb.start_music() else: self._bulb.stop_music()

Set the music mode on or off.

homeassistant/components/light/yeelight.py

set_music_mode

DevRGT/home-assistant

python

def set_music_mode(self, mode) -> None: if mode: self._bulb.start_music() else: self._bulb.stop_music()

def update(self) -> None: 'Update properties from the bulb.' import yeelight try: self._bulb.get_properties() if (self._bulb_device.bulb_type == yeelight.BulbType.Color): self._supported_features = SUPPORT_YEELIGHT_RGB self._is_on = (self._properties.get('power') == 'on') bright = self._properties.get('bright', None) if bright: self._brightness = round((255 * (int(bright) / 100))) temp_in_k = self._properties.get('ct', None) if temp_in_k: self._color_temp = kelvin_to_mired(int(temp_in_k)) self._hs = self._get_hs_from_properties() self._available = True except yeelight.BulbException as ex: if self._available: _LOGGER.error('Unable to update bulb status: %s', ex) self._available = False

Update properties from the bulb.

homeassistant/components/light/yeelight.py

update

DevRGT/home-assistant

python

def update(self) -> None: import yeelight try: self._bulb.get_properties() if (self._bulb_device.bulb_type == yeelight.BulbType.Color): self._supported_features = SUPPORT_YEELIGHT_RGB self._is_on = (self._properties.get('power') == 'on') bright = self._properties.get('bright', None) if bright: self._brightness = round((255 * (int(bright) / 100))) temp_in_k = self._properties.get('ct', None) if temp_in_k: self._color_temp = kelvin_to_mired(int(temp_in_k)) self._hs = self._get_hs_from_properties() self._available = True except yeelight.BulbException as ex: if self._available: _LOGGER.error('Unable to update bulb status: %s', ex) self._available = False

@_cmd def set_brightness(self, brightness, duration) -> None: 'Set bulb brightness.' if brightness: _LOGGER.debug('Setting brightness: %s', brightness) self._bulb.set_brightness(((brightness / 255) * 100), duration=duration)

Set bulb brightness.

homeassistant/components/light/yeelight.py

set_brightness

DevRGT/home-assistant

python

@_cmd def set_brightness(self, brightness, duration) -> None: if brightness: _LOGGER.debug('Setting brightness: %s', brightness) self._bulb.set_brightness(((brightness / 255) * 100), duration=duration)

@_cmd def set_rgb(self, rgb, duration) -> None: "Set bulb's color." if (rgb and (self.supported_features & SUPPORT_COLOR)): _LOGGER.debug('Setting RGB: %s', rgb) self._bulb.set_rgb(rgb[0], rgb[1], rgb[2], duration=duration)

Set bulb's color.

homeassistant/components/light/yeelight.py

set_rgb

DevRGT/home-assistant

python

@_cmd def set_rgb(self, rgb, duration) -> None: if (rgb and (self.supported_features & SUPPORT_COLOR)): _LOGGER.debug('Setting RGB: %s', rgb) self._bulb.set_rgb(rgb[0], rgb[1], rgb[2], duration=duration)

@_cmd def set_colortemp(self, colortemp, duration) -> None: "Set bulb's color temperature." if (colortemp and (self.supported_features & SUPPORT_COLOR_TEMP)): temp_in_k = mired_to_kelvin(colortemp) _LOGGER.debug('Setting color temp: %s K', temp_in_k) self._bulb.set_color_temp(temp_in_k, duration=duration)

Set bulb's color temperature.

homeassistant/components/light/yeelight.py

set_colortemp

DevRGT/home-assistant

python

@_cmd def set_colortemp(self, colortemp, duration) -> None: if (colortemp and (self.supported_features & SUPPORT_COLOR_TEMP)): temp_in_k = mired_to_kelvin(colortemp) _LOGGER.debug('Setting color temp: %s K', temp_in_k) self._bulb.set_color_temp(temp_in_k, duration=duration)

@_cmd def set_default(self) -> None: 'Set current options as default.' self._bulb.set_default()

Set current options as default.

homeassistant/components/light/yeelight.py

set_default

DevRGT/home-assistant

python

@_cmd def set_default(self) -> None: self._bulb.set_default()

@_cmd def set_flash(self, flash) -> None: 'Activate flash.' if flash: from yeelight import RGBTransition, SleepTransition, Flow, BulbException if (self._bulb.last_properties['color_mode'] != 1): _LOGGER.error('Flash supported currently only in RGB mode.') return transition = int(self.config[CONF_TRANSITION]) if (flash == FLASH_LONG): count = 1 duration = (transition * 5) if (flash == FLASH_SHORT): count = 1 duration = (transition * 2) (red, green, blue) = color_util.color_hs_to_RGB(*self._hs) transitions = list() transitions.append(RGBTransition(255, 0, 0, brightness=10, duration=duration)) transitions.append(SleepTransition(duration=transition)) transitions.append(RGBTransition(red, green, blue, brightness=self.brightness, duration=duration)) flow = Flow(count=count, transitions=transitions) try: self._bulb.start_flow(flow) except BulbException as ex: _LOGGER.error('Unable to set flash: %s', ex)

Activate flash.

homeassistant/components/light/yeelight.py

set_flash

DevRGT/home-assistant

python

@_cmd def set_flash(self, flash) -> None: if flash: from yeelight import RGBTransition, SleepTransition, Flow, BulbException if (self._bulb.last_properties['color_mode'] != 1): _LOGGER.error('Flash supported currently only in RGB mode.') return transition = int(self.config[CONF_TRANSITION]) if (flash == FLASH_LONG): count = 1 duration = (transition * 5) if (flash == FLASH_SHORT): count = 1 duration = (transition * 2) (red, green, blue) = color_util.color_hs_to_RGB(*self._hs) transitions = list() transitions.append(RGBTransition(255, 0, 0, brightness=10, duration=duration)) transitions.append(SleepTransition(duration=transition)) transitions.append(RGBTransition(red, green, blue, brightness=self.brightness, duration=duration)) flow = Flow(count=count, transitions=transitions) try: self._bulb.start_flow(flow) except BulbException as ex: _LOGGER.error('Unable to set flash: %s', ex)

@_cmd def set_effect(self, effect) -> None: 'Activate effect.' if effect: from yeelight import Flow, BulbException from yeelight.transitions import disco, temp, strobe, pulse, strobe_color, alarm, police, police2, christmas, rgb, randomloop, slowdown if (effect == EFFECT_STOP): self._bulb.stop_flow() return if (effect == EFFECT_DISCO): flow = Flow(count=0, transitions=disco()) if (effect == EFFECT_TEMP): flow = Flow(count=0, transitions=temp()) if (effect == EFFECT_STROBE): flow = Flow(count=0, transitions=strobe()) if (effect == EFFECT_STROBE_COLOR): flow = Flow(count=0, transitions=strobe_color()) if (effect == EFFECT_ALARM): flow = Flow(count=0, transitions=alarm()) if (effect == EFFECT_POLICE): flow = Flow(count=0, transitions=police()) if (effect == EFFECT_POLICE2): flow = Flow(count=0, transitions=police2()) if (effect == EFFECT_CHRISTMAS): flow = Flow(count=0, transitions=christmas()) if (effect == EFFECT_RGB): flow = Flow(count=0, transitions=rgb()) if (effect == EFFECT_RANDOM_LOOP): flow = Flow(count=0, transitions=randomloop()) if (effect == EFFECT_FAST_RANDOM_LOOP): flow = Flow(count=0, transitions=randomloop(duration=250)) if (effect == EFFECT_SLOWDOWN): flow = Flow(count=0, transitions=slowdown()) if (effect == EFFECT_WHATSAPP): flow = Flow(count=2, transitions=pulse(37, 211, 102)) if (effect == EFFECT_FACEBOOK): flow = Flow(count=2, transitions=pulse(59, 89, 152)) if (effect == EFFECT_TWITTER): flow = Flow(count=2, transitions=pulse(0, 172, 237)) try: self._bulb.start_flow(flow) except BulbException as ex: _LOGGER.error('Unable to set effect: %s', ex)

Activate effect.

homeassistant/components/light/yeelight.py

set_effect

DevRGT/home-assistant

python

@_cmd def set_effect(self, effect) -> None: if effect: from yeelight import Flow, BulbException from yeelight.transitions import disco, temp, strobe, pulse, strobe_color, alarm, police, police2, christmas, rgb, randomloop, slowdown if (effect == EFFECT_STOP): self._bulb.stop_flow() return if (effect == EFFECT_DISCO): flow = Flow(count=0, transitions=disco()) if (effect == EFFECT_TEMP): flow = Flow(count=0, transitions=temp()) if (effect == EFFECT_STROBE): flow = Flow(count=0, transitions=strobe()) if (effect == EFFECT_STROBE_COLOR): flow = Flow(count=0, transitions=strobe_color()) if (effect == EFFECT_ALARM): flow = Flow(count=0, transitions=alarm()) if (effect == EFFECT_POLICE): flow = Flow(count=0, transitions=police()) if (effect == EFFECT_POLICE2): flow = Flow(count=0, transitions=police2()) if (effect == EFFECT_CHRISTMAS): flow = Flow(count=0, transitions=christmas()) if (effect == EFFECT_RGB): flow = Flow(count=0, transitions=rgb()) if (effect == EFFECT_RANDOM_LOOP): flow = Flow(count=0, transitions=randomloop()) if (effect == EFFECT_FAST_RANDOM_LOOP): flow = Flow(count=0, transitions=randomloop(duration=250)) if (effect == EFFECT_SLOWDOWN): flow = Flow(count=0, transitions=slowdown()) if (effect == EFFECT_WHATSAPP): flow = Flow(count=2, transitions=pulse(37, 211, 102)) if (effect == EFFECT_FACEBOOK): flow = Flow(count=2, transitions=pulse(59, 89, 152)) if (effect == EFFECT_TWITTER): flow = Flow(count=2, transitions=pulse(0, 172, 237)) try: self._bulb.start_flow(flow) except BulbException as ex: _LOGGER.error('Unable to set effect: %s', ex)

def turn_on(self, **kwargs) -> None: 'Turn the bulb on.' import yeelight brightness = kwargs.get(ATTR_BRIGHTNESS) colortemp = kwargs.get(ATTR_COLOR_TEMP) hs_color = kwargs.get(ATTR_HS_COLOR) rgb = (color_util.color_hs_to_RGB(*hs_color) if hs_color else None) flash = kwargs.get(ATTR_FLASH) effect = kwargs.get(ATTR_EFFECT) duration = int(self.config[CONF_TRANSITION]) if (ATTR_TRANSITION in kwargs): duration = int((kwargs.get(ATTR_TRANSITION) * 1000)) try: self._bulb.turn_on(duration=duration) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn the bulb on: %s', ex) return if (self.config[CONF_MODE_MUSIC] and (not self._bulb.music_mode)): try: self.set_music_mode(self.config[CONF_MODE_MUSIC]) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn on music mode,consider disabling it: %s', ex) try: self.set_rgb(rgb, duration) self.set_colortemp(colortemp, duration) self.set_brightness(brightness, duration) self.set_flash(flash) self.set_effect(effect) except yeelight.BulbException as ex: _LOGGER.error('Unable to set bulb properties: %s', ex) return if (self.config[CONF_SAVE_ON_CHANGE] and (brightness or colortemp or rgb)): try: self.set_default() except yeelight.BulbException as ex: _LOGGER.error('Unable to set the defaults: %s', ex) return

Turn the bulb on.

homeassistant/components/light/yeelight.py

turn_on

DevRGT/home-assistant

python

def turn_on(self, **kwargs) -> None: import yeelight brightness = kwargs.get(ATTR_BRIGHTNESS) colortemp = kwargs.get(ATTR_COLOR_TEMP) hs_color = kwargs.get(ATTR_HS_COLOR) rgb = (color_util.color_hs_to_RGB(*hs_color) if hs_color else None) flash = kwargs.get(ATTR_FLASH) effect = kwargs.get(ATTR_EFFECT) duration = int(self.config[CONF_TRANSITION]) if (ATTR_TRANSITION in kwargs): duration = int((kwargs.get(ATTR_TRANSITION) * 1000)) try: self._bulb.turn_on(duration=duration) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn the bulb on: %s', ex) return if (self.config[CONF_MODE_MUSIC] and (not self._bulb.music_mode)): try: self.set_music_mode(self.config[CONF_MODE_MUSIC]) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn on music mode,consider disabling it: %s', ex) try: self.set_rgb(rgb, duration) self.set_colortemp(colortemp, duration) self.set_brightness(brightness, duration) self.set_flash(flash) self.set_effect(effect) except yeelight.BulbException as ex: _LOGGER.error('Unable to set bulb properties: %s', ex) return if (self.config[CONF_SAVE_ON_CHANGE] and (brightness or colortemp or rgb)): try: self.set_default() except yeelight.BulbException as ex: _LOGGER.error('Unable to set the defaults: %s', ex) return

def turn_off(self, **kwargs) -> None: 'Turn off.' import yeelight duration = int(self.config[CONF_TRANSITION]) if (ATTR_TRANSITION in kwargs): duration = int((kwargs.get(ATTR_TRANSITION) * 1000)) try: self._bulb.turn_off(duration=duration) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn the bulb off: %s', ex)

Turn off.

homeassistant/components/light/yeelight.py

turn_off

DevRGT/home-assistant

python

def turn_off(self, **kwargs) -> None: import yeelight duration = int(self.config[CONF_TRANSITION]) if (ATTR_TRANSITION in kwargs): duration = int((kwargs.get(ATTR_TRANSITION) * 1000)) try: self._bulb.turn_off(duration=duration) except yeelight.BulbException as ex: _LOGGER.error('Unable to turn the bulb off: %s', ex)

def set_mode(self, mode: str): 'Set a power mode.' import yeelight try: self._bulb.set_power_mode(yeelight.enums.PowerMode[mode.upper()]) except yeelight.BulbException as ex: _LOGGER.error('Unable to set the power mode: %s', ex)

Set a power mode.

homeassistant/components/light/yeelight.py

set_mode

DevRGT/home-assistant

python

def set_mode(self, mode: str): import yeelight try: self._bulb.set_power_mode(yeelight.enums.PowerMode[mode.upper()]) except yeelight.BulbException as ex: _LOGGER.error('Unable to set the power mode: %s', ex)

def addSymbol(self, char): 'Displays the inputted char onto the display' self.stringContents += char self.displayStr.set(self.stringContents)

Displays the inputted char onto the display

ProgrammingInPython/proj08_daniel_campos.py

addSymbol

spacemanidol/RPICS

python

def addSymbol(self, char): self.stringContents += char self.displayStr.set(self.stringContents)

def addKeyboardSymbol(self, event): 'Displays the inputted char onto the display' self.stringContents += str(repr(event.char))[1:(- 1)] self.displayStr.set(self.stringContents)

Displays the inputted char onto the display

ProgrammingInPython/proj08_daniel_campos.py

addKeyboardSymbol

spacemanidol/RPICS

python

def addKeyboardSymbol(self, event): self.stringContents += str(repr(event.char))[1:(- 1)] self.displayStr.set(self.stringContents)

def evaluate(self, evt=None): 'Evalutes the expression' try: self.displayStr.set(eval(self.stringContents)) self.stringContents = str(eval(self.stringContents)) except Exception as e: self.displayStr.set('Error') self.stringContents = ''

Evalutes the expression

ProgrammingInPython/proj08_daniel_campos.py

evaluate

spacemanidol/RPICS

python

def evaluate(self, evt=None): try: self.displayStr.set(eval(self.stringContents)) self.stringContents = str(eval(self.stringContents)) except Exception as e: self.displayStr.set('Error') self.stringContents =

def clear(self, evt=None): 'Clears the expression' self.stringContents = '' self.displayStr.set(self.stringContents)

Clears the expression

ProgrammingInPython/proj08_daniel_campos.py

clear

spacemanidol/RPICS

python

def clear(self, evt=None): self.stringContents = self.displayStr.set(self.stringContents)

def backSpace(self, evt=None): 'Backspace on expression' self.stringContents = self.stringContents[:(- 1)] self.displayStr.set(self.stringContents)

Backspace on expression

ProgrammingInPython/proj08_daniel_campos.py

backSpace

spacemanidol/RPICS

python

def backSpace(self, evt=None): self.stringContents = self.stringContents[:(- 1)] self.displayStr.set(self.stringContents)

def count_samples(ns_run, **kwargs): 'Number of samples in run.\n\n Unlike most estimators this does not require log weights, but for\n convenience will not throw an error if they are specified.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n\n Returns\n -------\n int\n ' kwargs.pop('logw', None) kwargs.pop('simulate', None) if kwargs: raise TypeError('Unexpected **kwargs: {0}'.format(kwargs)) return ns_run['logl'].shape[0]

Number of samples in run. Unlike most estimators this does not require log weights, but for convenience will not throw an error if they are specified. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). Returns ------- int

nestcheck/estimators.py

count_samples

ThomasEdwardRiley/nestcheck

python

def count_samples(ns_run, **kwargs): 'Number of samples in run.\n\n Unlike most estimators this does not require log weights, but for\n convenience will not throw an error if they are specified.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n\n Returns\n -------\n int\n ' kwargs.pop('logw', None) kwargs.pop('simulate', None) if kwargs: raise TypeError('Unexpected **kwargs: {0}'.format(kwargs)) return ns_run['logl'].shape[0]

def logz(ns_run, logw=None, simulate=False): 'Natural log of Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) return scipy.special.logsumexp(logw)

Natural log of Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. Returns ------- float

nestcheck/estimators.py

logz

ThomasEdwardRiley/nestcheck

python

def logz(ns_run, logw=None, simulate=False): 'Natural log of Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) return scipy.special.logsumexp(logw)

def evidence(ns_run, logw=None, simulate=False): 'Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) return np.exp(scipy.special.logsumexp(logw))

Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. Returns ------- float

nestcheck/estimators.py

evidence

ThomasEdwardRiley/nestcheck

python

def evidence(ns_run, logw=None, simulate=False): 'Bayesian evidence :math:`\\log \\mathcal{Z}`.\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) return np.exp(scipy.special.logsumexp(logw))

def param_mean(ns_run, logw=None, simulate=False, param_ind=0, handle_indexerror=False): "Mean of a single parameter (single component of theta).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n param_ind: int, optional\n Index of parameter for which the mean should be calculated. This\n corresponds to the column of ns_run['theta'] which contains the\n parameter.\n handle_indexerror: bool, optional\n Make the function function return nan rather than raising an\n IndexError if param_ind >= ndim. This is useful when applying\n the same list of estimators to data sets of different dimensions.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) try: return (np.sum((w_relative * ns_run['theta'][:, param_ind])) / np.sum(w_relative)) except IndexError: if handle_indexerror: return np.nan else: raise

Mean of a single parameter (single component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. param_ind: int, optional Index of parameter for which the mean should be calculated. This corresponds to the column of ns_run['theta'] which contains the parameter. handle_indexerror: bool, optional Make the function function return nan rather than raising an IndexError if param_ind >= ndim. This is useful when applying the same list of estimators to data sets of different dimensions. Returns ------- float

nestcheck/estimators.py

param_mean

ThomasEdwardRiley/nestcheck

python

def param_mean(ns_run, logw=None, simulate=False, param_ind=0, handle_indexerror=False): "Mean of a single parameter (single component of theta).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n param_ind: int, optional\n Index of parameter for which the mean should be calculated. This\n corresponds to the column of ns_run['theta'] which contains the\n parameter.\n handle_indexerror: bool, optional\n Make the function function return nan rather than raising an\n IndexError if param_ind >= ndim. This is useful when applying\n the same list of estimators to data sets of different dimensions.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) try: return (np.sum((w_relative * ns_run['theta'][:, param_ind])) / np.sum(w_relative)) except IndexError: if handle_indexerror: return np.nan else: raise

def param_cred(ns_run, logw=None, simulate=False, probability=0.5, param_ind=0): "One-tailed credible interval on the value of a single parameter\n (component of theta).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n probability: float, optional\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile. Passed to weighted_quantile.\n param_ind: int, optional\n Index of parameter for which the credible interval should be\n calculated. This corresponds to the column of ns_run['theta']\n which contains the parameter.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) return weighted_quantile(probability, ns_run['theta'][:, param_ind], w_relative)

One-tailed credible interval on the value of a single parameter (component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. probability: float, optional Quantile to estimate - must be in open interval (0, 1). For example, use 0.5 for the median and 0.84 for the upper 84% quantile. Passed to weighted_quantile. param_ind: int, optional Index of parameter for which the credible interval should be calculated. This corresponds to the column of ns_run['theta'] which contains the parameter. Returns ------- float

nestcheck/estimators.py

param_cred

ThomasEdwardRiley/nestcheck

python

def param_cred(ns_run, logw=None, simulate=False, probability=0.5, param_ind=0): "One-tailed credible interval on the value of a single parameter\n (component of theta).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n probability: float, optional\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile. Passed to weighted_quantile.\n param_ind: int, optional\n Index of parameter for which the credible interval should be\n calculated. This corresponds to the column of ns_run['theta']\n which contains the parameter.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) return weighted_quantile(probability, ns_run['theta'][:, param_ind], w_relative)

def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0): "Mean of the square of single parameter (second moment of its\n posterior distribution).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n param_ind: int, optional\n Index of parameter for which the second moment should be\n calculated. This corresponds to the column of ns_run['theta']\n which contains the parameter.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) w_relative /= np.sum(w_relative) return np.sum((w_relative * (ns_run['theta'][:, param_ind] ** 2)))

Mean of the square of single parameter (second moment of its posterior distribution). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. param_ind: int, optional Index of parameter for which the second moment should be calculated. This corresponds to the column of ns_run['theta'] which contains the parameter. Returns ------- float

nestcheck/estimators.py

param_squared_mean

ThomasEdwardRiley/nestcheck

python

def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0): "Mean of the square of single parameter (second moment of its\n posterior distribution).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n param_ind: int, optional\n Index of parameter for which the second moment should be\n calculated. This corresponds to the column of ns_run['theta']\n which contains the parameter.\n\n Returns\n -------\n float\n " if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) w_relative /= np.sum(w_relative) return np.sum((w_relative * (ns_run['theta'][:, param_ind] ** 2)))

def r_mean(ns_run, logw=None, simulate=False): 'Mean of the radial coordinate (magnitude of theta vector).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) r = np.sqrt(np.sum((ns_run['theta'] ** 2), axis=1)) return (np.sum((w_relative * r)) / np.sum(w_relative))

Mean of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. Returns ------- float

nestcheck/estimators.py

r_mean

ThomasEdwardRiley/nestcheck

python

def r_mean(ns_run, logw=None, simulate=False): 'Mean of the radial coordinate (magnitude of theta vector).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) r = np.sqrt(np.sum((ns_run['theta'] ** 2), axis=1)) return (np.sum((w_relative * r)) / np.sum(w_relative))

def r_cred(ns_run, logw=None, simulate=False, probability=0.5): 'One-tailed credible interval on the value of the radial coordinate\n (magnitude of theta vector).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n probability: float, optional\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile. Passed to weighted_quantile.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) r = np.sqrt(np.sum((ns_run['theta'] ** 2), axis=1)) return weighted_quantile(probability, r, w_relative)

One-tailed credible interval on the value of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of samples. simulate: bool, optional Passed to ns_run_utils.get_logw if logw needs to be calculated. probability: float, optional Quantile to estimate - must be in open interval (0, 1). For example, use 0.5 for the median and 0.84 for the upper 84% quantile. Passed to weighted_quantile. Returns ------- float

nestcheck/estimators.py

r_cred

ThomasEdwardRiley/nestcheck

python

def r_cred(ns_run, logw=None, simulate=False, probability=0.5): 'One-tailed credible interval on the value of the radial coordinate\n (magnitude of theta vector).\n\n Parameters\n ----------\n ns_run: dict\n Nested sampling run dict (see the data_processing module\n docstring for more details).\n logw: None or 1d numpy array, optional\n Log weights of samples.\n simulate: bool, optional\n Passed to ns_run_utils.get_logw if logw needs to be\n calculated.\n probability: float, optional\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile. Passed to weighted_quantile.\n\n Returns\n -------\n float\n ' if (logw is None): logw = nestcheck.ns_run_utils.get_logw(ns_run, simulate=simulate) w_relative = np.exp((logw - logw.max())) r = np.sqrt(np.sum((ns_run['theta'] ** 2), axis=1)) return weighted_quantile(probability, r, w_relative)

def get_latex_name(func_in, **kwargs): '\n Produce a latex formatted name for each function for use in labelling\n results.\n\n Parameters\n ----------\n func_in: function\n kwargs: dict, optional\n Kwargs for function.\n\n Returns\n -------\n latex_name: str\n Latex formatted name for the function.\n ' if isinstance(func_in, functools.partial): func = func_in.func assert (not (set(func_in.keywords) & set(kwargs))), 'kwargs={0} and func_in.keywords={1} contain repeated keys'.format(kwargs, func_in.keywords) kwargs.update(func_in.keywords) else: func = func_in param_ind = kwargs.pop('param_ind', 0) probability = kwargs.pop('probability', 0.5) kwargs.pop('handle_indexerror', None) if kwargs: raise TypeError('Unexpected **kwargs: {0}'.format(kwargs)) ind_str = (('{\\hat{' + str((param_ind + 1))) + '}}') latex_name_dict = {'count_samples': 'samples', 'logz': '$\\mathrm{log} \\mathcal{Z}$', 'evidence': '$\\mathcal{Z}$', 'r_mean': '$\\overline{|\\theta|}$', 'param_mean': (('$\\overline{\\theta_' + ind_str) + '}$'), 'param_squared_mean': (('$\\overline{\\theta^2_' + ind_str) + '}$')} if (probability == 0.5): cred_str = '$\\mathrm{median}(' else: percent_str = ('%f' % (probability * 100)).rstrip('0').rstrip('.') cred_str = (('$\\mathrm{C.I.}_{' + percent_str) + '\\%}(') latex_name_dict['param_cred'] = (((cred_str + '\\theta_') + ind_str) + ')$') latex_name_dict['r_cred'] = (cred_str + '|\\theta|)$') try: return latex_name_dict[func.__name__] except KeyError as err: err.args = (err.args + (('get_latex_name not yet set up for ' + func.__name__),)) raise

Produce a latex formatted name for each function for use in labelling results. Parameters ---------- func_in: function kwargs: dict, optional Kwargs for function. Returns ------- latex_name: str Latex formatted name for the function.

nestcheck/estimators.py

get_latex_name

ThomasEdwardRiley/nestcheck

python

def get_latex_name(func_in, **kwargs): '\n Produce a latex formatted name for each function for use in labelling\n results.\n\n Parameters\n ----------\n func_in: function\n kwargs: dict, optional\n Kwargs for function.\n\n Returns\n -------\n latex_name: str\n Latex formatted name for the function.\n ' if isinstance(func_in, functools.partial): func = func_in.func assert (not (set(func_in.keywords) & set(kwargs))), 'kwargs={0} and func_in.keywords={1} contain repeated keys'.format(kwargs, func_in.keywords) kwargs.update(func_in.keywords) else: func = func_in param_ind = kwargs.pop('param_ind', 0) probability = kwargs.pop('probability', 0.5) kwargs.pop('handle_indexerror', None) if kwargs: raise TypeError('Unexpected **kwargs: {0}'.format(kwargs)) ind_str = (('{\\hat{' + str((param_ind + 1))) + '}}') latex_name_dict = {'count_samples': 'samples', 'logz': '$\\mathrm{log} \\mathcal{Z}$', 'evidence': '$\\mathcal{Z}$', 'r_mean': '$\\overline{|\\theta|}$', 'param_mean': (('$\\overline{\\theta_' + ind_str) + '}$'), 'param_squared_mean': (('$\\overline{\\theta^2_' + ind_str) + '}$')} if (probability == 0.5): cred_str = '$\\mathrm{median}(' else: percent_str = ('%f' % (probability * 100)).rstrip('0').rstrip('.') cred_str = (('$\\mathrm{C.I.}_{' + percent_str) + '\\%}(') latex_name_dict['param_cred'] = (((cred_str + '\\theta_') + ind_str) + ')$') latex_name_dict['r_cred'] = (cred_str + '|\\theta|)$') try: return latex_name_dict[func.__name__] except KeyError as err: err.args = (err.args + (('get_latex_name not yet set up for ' + func.__name__),)) raise

def weighted_quantile(probability, values, weights): '\n Get quantile estimate for input probability given weighted samples using\n linear interpolation.\n\n Parameters\n ----------\n probability: float\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile.\n values: 1d numpy array\n Sample values.\n weights: 1d numpy array\n Corresponding sample weights (same shape as values).\n\n Returns\n -------\n quantile: float\n ' assert (1 > probability > 0), (('credible interval prob= ' + str(probability)) + ' not in (0, 1)') assert (values.shape == weights.shape) assert (values.ndim == 1) assert (weights.ndim == 1) sorted_inds = np.argsort(values) quantiles = (np.cumsum(weights[sorted_inds]) - (0.5 * weights[sorted_inds])) quantiles /= np.sum(weights) return np.interp(probability, quantiles, values[sorted_inds])

Get quantile estimate for input probability given weighted samples using linear interpolation. Parameters ---------- probability: float Quantile to estimate - must be in open interval (0, 1). For example, use 0.5 for the median and 0.84 for the upper 84% quantile. values: 1d numpy array Sample values. weights: 1d numpy array Corresponding sample weights (same shape as values). Returns ------- quantile: float

nestcheck/estimators.py

weighted_quantile

ThomasEdwardRiley/nestcheck

python

def weighted_quantile(probability, values, weights): '\n Get quantile estimate for input probability given weighted samples using\n linear interpolation.\n\n Parameters\n ----------\n probability: float\n Quantile to estimate - must be in open interval (0, 1).\n For example, use 0.5 for the median and 0.84 for the upper\n 84% quantile.\n values: 1d numpy array\n Sample values.\n weights: 1d numpy array\n Corresponding sample weights (same shape as values).\n\n Returns\n -------\n quantile: float\n ' assert (1 > probability > 0), (('credible interval prob= ' + str(probability)) + ' not in (0, 1)') assert (values.shape == weights.shape) assert (values.ndim == 1) assert (weights.ndim == 1) sorted_inds = np.argsort(values) quantiles = (np.cumsum(weights[sorted_inds]) - (0.5 * weights[sorted_inds])) quantiles /= np.sum(weights) return np.interp(probability, quantiles, values[sorted_inds])

def _is_sqlite_json1_enabled(): 'Check if SQLite implementation includes JSON1 extension.' con = sqlite3.connect(':memory:') try: con.execute("SELECT json_valid('123')") except sqlite3.OperationalError: return False finally: con.close() return True

Check if SQLite implementation includes JSON1 extension.

toron/_node_schema.py

_is_sqlite_json1_enabled

shawnbrown/gpn

python

def _is_sqlite_json1_enabled(): con = sqlite3.connect(':memory:') try: con.execute("SELECT json_valid('123')") except sqlite3.OperationalError: return False finally: con.close() return True

def _is_wellformed_json(x): 'Return 1 if *x* is well-formed JSON or return 0 if *x* is not\n well-formed. This function should be registered with SQLite (via\n the create_function() method) when the JSON1 extension is not\n available.\n\n This function mimics the JSON1 json_valid() function, see:\n https://www.sqlite.org/json1.html#jvalid\n ' try: _loads(x) except (ValueError, TypeError): return 0 return 1

Return 1 if *x* is well-formed JSON or return 0 if *x* is not well-formed. This function should be registered with SQLite (via the create_function() method) when the JSON1 extension is not available. This function mimics the JSON1 json_valid() function, see: https://www.sqlite.org/json1.html#jvalid

toron/_node_schema.py

_is_wellformed_json

shawnbrown/gpn

python

def _is_wellformed_json(x): 'Return 1 if *x* is well-formed JSON or return 0 if *x* is not\n well-formed. This function should be registered with SQLite (via\n the create_function() method) when the JSON1 extension is not\n available.\n\n This function mimics the JSON1 json_valid() function, see:\n https://www.sqlite.org/json1.html#jvalid\n ' try: _loads(x) except (ValueError, TypeError): return 0 return 1

def _make_trigger_for_json(insert_or_update, table, column): 'Return a SQL statement for creating a temporary trigger. The\n trigger is used to validate the contents of TEXT_JSON type columns.\n The trigger will pass without error if the JSON is wellformed.\n ' if (insert_or_update.upper() not in {'INSERT', 'UPDATE'}): msg = f"expected 'INSERT' or 'UPDATE', got {insert_or_update!r}" raise ValueError(msg) if SQLITE_JSON1_ENABLED: when_clause = f''' NEW.{column} IS NOT NULL AND json_valid(NEW.{column}) = 0 '''.rstrip() else: when_clause = f''' NEW.{column} IS NOT NULL AND is_wellformed_json(NEW.{column}) = 0 '''.rstrip() return f''' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN{when_clause} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be wellformed JSON'); END; '''

Return a SQL statement for creating a temporary trigger. The trigger is used to validate the contents of TEXT_JSON type columns. The trigger will pass without error if the JSON is wellformed.

toron/_node_schema.py

_make_trigger_for_json

shawnbrown/gpn

python

def _make_trigger_for_json(insert_or_update, table, column): 'Return a SQL statement for creating a temporary trigger. The\n trigger is used to validate the contents of TEXT_JSON type columns.\n The trigger will pass without error if the JSON is wellformed.\n ' if (insert_or_update.upper() not in {'INSERT', 'UPDATE'}): msg = f"expected 'INSERT' or 'UPDATE', got {insert_or_update!r}" raise ValueError(msg) if SQLITE_JSON1_ENABLED: when_clause = f' NEW.{column} IS NOT NULL AND json_valid(NEW.{column}) = 0 '.rstrip() else: when_clause = f' NEW.{column} IS NOT NULL AND is_wellformed_json(NEW.{column}) = 0 '.rstrip() return f' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN{when_clause} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be wellformed JSON'); END; '

def _is_wellformed_user_properties(x): "Check if *x* is a wellformed TEXT_USERPROPERTIES value.\n A wellformed TEXT_USERPROPERTIES value is a string containing\n a JSON formatted object. Returns 1 if *x* is valid or 0 if\n it's not.\n\n This function should be registered as an application-defined\n SQL function and used in queries when SQLite's JSON1 extension\n is not enabled.\n " try: obj = _loads(x) except (ValueError, TypeError): return 0 if isinstance(obj, dict): return 1 return 0

Check if *x* is a wellformed TEXT_USERPROPERTIES value. A wellformed TEXT_USERPROPERTIES value is a string containing a JSON formatted object. Returns 1 if *x* is valid or 0 if it's not. This function should be registered as an application-defined SQL function and used in queries when SQLite's JSON1 extension is not enabled.

toron/_node_schema.py

_is_wellformed_user_properties

shawnbrown/gpn

python

def _is_wellformed_user_properties(x): "Check if *x* is a wellformed TEXT_USERPROPERTIES value.\n A wellformed TEXT_USERPROPERTIES value is a string containing\n a JSON formatted object. Returns 1 if *x* is valid or 0 if\n it's not.\n\n This function should be registered as an application-defined\n SQL function and used in queries when SQLite's JSON1 extension\n is not enabled.\n " try: obj = _loads(x) except (ValueError, TypeError): return 0 if isinstance(obj, dict): return 1 return 0

def _make_trigger_for_user_properties(insert_or_update, table, column): 'Return a CREATE TRIGGER statement to check TEXT_USERPROPERTIES\n values. This trigger is used to check values before they are saved\n in the database.\n\n A wellformed TEXT_USERPROPERTIES value is a string containing\n a JSON formatted object.\n\n The trigger will pass without error if the value is wellformed.\n ' if SQLITE_JSON1_ENABLED: user_properties_check = f"(json_valid(NEW.{column}) = 0 OR json_type(NEW.{column}) != 'object')" else: user_properties_check = f'is_wellformed_user_properties(NEW.{column}) = 0' return f''' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN NEW.{column} IS NOT NULL AND {user_properties_check} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be wellformed JSON object'); END; '''

Return a CREATE TRIGGER statement to check TEXT_USERPROPERTIES values. This trigger is used to check values before they are saved in the database. A wellformed TEXT_USERPROPERTIES value is a string containing a JSON formatted object. The trigger will pass without error if the value is wellformed.

toron/_node_schema.py

_make_trigger_for_user_properties

shawnbrown/gpn

python

def _make_trigger_for_user_properties(insert_or_update, table, column): 'Return a CREATE TRIGGER statement to check TEXT_USERPROPERTIES\n values. This trigger is used to check values before they are saved\n in the database.\n\n A wellformed TEXT_USERPROPERTIES value is a string containing\n a JSON formatted object.\n\n The trigger will pass without error if the value is wellformed.\n ' if SQLITE_JSON1_ENABLED: user_properties_check = f"(json_valid(NEW.{column}) = 0 OR json_type(NEW.{column}) != 'object')" else: user_properties_check = f'is_wellformed_user_properties(NEW.{column}) = 0' return f' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN NEW.{column} IS NOT NULL AND {user_properties_check} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be wellformed JSON object'); END; '

def _is_wellformed_attributes(x): 'Returns 1 if *x* is a wellformed TEXT_ATTRIBUTES column\n value else returns 0. TEXT_ATTRIBUTES should be flat, JSON\n object strings. This function should be registered with SQLite\n (via the create_function() method) when the JSON1 extension\n is not available.\n ' try: obj = _loads(x) except (ValueError, TypeError): return 0 if (not isinstance(obj, dict)): return 0 for value in obj.values(): if (not isinstance(value, str)): return 0 return 1

Returns 1 if *x* is a wellformed TEXT_ATTRIBUTES column value else returns 0. TEXT_ATTRIBUTES should be flat, JSON object strings. This function should be registered with SQLite (via the create_function() method) when the JSON1 extension is not available.

toron/_node_schema.py

_is_wellformed_attributes

shawnbrown/gpn

python

def _is_wellformed_attributes(x): 'Returns 1 if *x* is a wellformed TEXT_ATTRIBUTES column\n value else returns 0. TEXT_ATTRIBUTES should be flat, JSON\n object strings. This function should be registered with SQLite\n (via the create_function() method) when the JSON1 extension\n is not available.\n ' try: obj = _loads(x) except (ValueError, TypeError): return 0 if (not isinstance(obj, dict)): return 0 for value in obj.values(): if (not isinstance(value, str)): return 0 return 1

def _make_trigger_for_attributes(insert_or_update, table, column): 'Return a SQL statement for creating a temporary trigger. The\n trigger is used to validate the contents of TEXT_ATTRIBUTES\n type columns.\n\n The trigger will pass without error if the JSON is a wellformed\n "object" containing "text" values.\n\n The trigger will raise an error if the value is:\n\n * not wellformed JSON\n * not an "object" type\n * an "object" type that contains one or more "integer", "real",\n "true", "false", "null", "object" or "array" types\n ' if (insert_or_update.upper() not in {'INSERT', 'UPDATE'}): msg = f"expected 'INSERT' or 'UPDATE', got {insert_or_update!r}" raise ValueError(msg) if SQLITE_JSON1_ENABLED: when_clause = f''' NEW.{column} IS NOT NULL AND (json_valid(NEW.{column}) = 0 OR json_type(NEW.{column}) != 'object' OR (SELECT COUNT(*) FROM json_each(NEW.{column}) WHERE json_each.type != 'text') != 0) '''.rstrip() else: when_clause = f''' NEW.{column} IS NOT NULL AND is_wellformed_attributes(NEW.{column}) = 0 '''.rstrip() return f''' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN{when_clause} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be a JSON object with text values'); END; '''

Return a SQL statement for creating a temporary trigger. The trigger is used to validate the contents of TEXT_ATTRIBUTES type columns. The trigger will pass without error if the JSON is a wellformed "object" containing "text" values. The trigger will raise an error if the value is: * not wellformed JSON * not an "object" type * an "object" type that contains one or more "integer", "real", "true", "false", "null", "object" or "array" types

toron/_node_schema.py

_make_trigger_for_attributes

shawnbrown/gpn

python

def _make_trigger_for_attributes(insert_or_update, table, column): 'Return a SQL statement for creating a temporary trigger. The\n trigger is used to validate the contents of TEXT_ATTRIBUTES\n type columns.\n\n The trigger will pass without error if the JSON is a wellformed\n "object" containing "text" values.\n\n The trigger will raise an error if the value is:\n\n * not wellformed JSON\n * not an "object" type\n * an "object" type that contains one or more "integer", "real",\n "true", "false", "null", "object" or "array" types\n ' if (insert_or_update.upper() not in {'INSERT', 'UPDATE'}): msg = f"expected 'INSERT' or 'UPDATE', got {insert_or_update!r}" raise ValueError(msg) if SQLITE_JSON1_ENABLED: when_clause = f' NEW.{column} IS NOT NULL AND (json_valid(NEW.{column}) = 0 OR json_type(NEW.{column}) != 'object' OR (SELECT COUNT(*) FROM json_each(NEW.{column}) WHERE json_each.type != 'text') != 0) '.rstrip() else: when_clause = f' NEW.{column} IS NOT NULL AND is_wellformed_attributes(NEW.{column}) = 0 '.rstrip() return f' CREATE TEMPORARY TRIGGER IF NOT EXISTS trigger_check_{insert_or_update.lower()}_{table}_{column} BEFORE {insert_or_update.upper()} ON main.{table} FOR EACH ROW WHEN{when_clause} BEGIN SELECT RAISE(ABORT, '{table}.{column} must be a JSON object with text values'); END; '

def _add_functions_and_triggers(connection): 'Create triggers and application-defined functions *connection*.\n\n Note: This function must not be executed on an empty connection.\n The table schema must exist before triggers can be created.\n ' if (not SQLITE_JSON1_ENABLED): try: connection.create_function('is_wellformed_json', 1, _is_wellformed_json, deterministic=True) connection.create_function('is_wellformed_user_properties', 1, _is_wellformed_user_properties, deterministic=True) connection.create_function('is_wellformed_attributes', 1, _is_wellformed_attributes, deterministic=True) except TypeError: connection.create_function('is_wellformed_json', 1, _is_wellformed_json) connection.create_function('is_wellformed_user_properties', 1, _is_wellformed_user_properties) connection.create_function('is_wellformed_attributes', 1, _is_wellformed_attributes) connection.execute(_make_trigger_for_json('INSERT', 'property', 'value')) connection.execute(_make_trigger_for_json('UPDATE', 'property', 'value')) connection.execute(_make_trigger_for_user_properties('INSERT', 'edge', 'user_properties')) connection.execute(_make_trigger_for_user_properties('UPDATE', 'edge', 'user_properties')) jsonflatobj_columns = [('edge', 'type_info'), ('quantity', 'attributes'), ('weight', 'type_info')] for (table, column) in jsonflatobj_columns: connection.execute(_make_trigger_for_attributes('INSERT', table, column)) connection.execute(_make_trigger_for_attributes('UPDATE', table, column))

Create triggers and application-defined functions *connection*. Note: This function must not be executed on an empty connection. The table schema must exist before triggers can be created.

toron/_node_schema.py

_add_functions_and_triggers

shawnbrown/gpn

python

def _add_functions_and_triggers(connection): 'Create triggers and application-defined functions *connection*.\n\n Note: This function must not be executed on an empty connection.\n The table schema must exist before triggers can be created.\n ' if (not SQLITE_JSON1_ENABLED): try: connection.create_function('is_wellformed_json', 1, _is_wellformed_json, deterministic=True) connection.create_function('is_wellformed_user_properties', 1, _is_wellformed_user_properties, deterministic=True) connection.create_function('is_wellformed_attributes', 1, _is_wellformed_attributes, deterministic=True) except TypeError: connection.create_function('is_wellformed_json', 1, _is_wellformed_json) connection.create_function('is_wellformed_user_properties', 1, _is_wellformed_user_properties) connection.create_function('is_wellformed_attributes', 1, _is_wellformed_attributes) connection.execute(_make_trigger_for_json('INSERT', 'property', 'value')) connection.execute(_make_trigger_for_json('UPDATE', 'property', 'value')) connection.execute(_make_trigger_for_user_properties('INSERT', 'edge', 'user_properties')) connection.execute(_make_trigger_for_user_properties('UPDATE', 'edge', 'user_properties')) jsonflatobj_columns = [('edge', 'type_info'), ('quantity', 'attributes'), ('weight', 'type_info')] for (table, column) in jsonflatobj_columns: connection.execute(_make_trigger_for_attributes('INSERT', table, column)) connection.execute(_make_trigger_for_attributes('UPDATE', table, column))

def _path_to_sqlite_uri(path): "Convert a path into a SQLite compatible URI.\n\n Unlike pathlib's URI handling, SQLite accepts relative URI paths.\n For details, see:\n\n https://www.sqlite.org/uri.html#the_uri_path\n " if (os.name == 'nt'): if re.match('^[a-zA-Z]:', path): path = os.path.abspath(path) drive_prefix = f'/{path[:2]}' path = path[2:] else: drive_prefix = '' path = path.replace('\\', '/') path = urllib_parse_quote(path) path = f'{drive_prefix}{path}' else: path = urllib_parse_quote(path) path = re.sub('/+', '/', path) return f'file:{path}'

Convert a path into a SQLite compatible URI. Unlike pathlib's URI handling, SQLite accepts relative URI paths. For details, see: https://www.sqlite.org/uri.html#the_uri_path

toron/_node_schema.py

_path_to_sqlite_uri

shawnbrown/gpn

python

def _path_to_sqlite_uri(path): "Convert a path into a SQLite compatible URI.\n\n Unlike pathlib's URI handling, SQLite accepts relative URI paths.\n For details, see:\n\n https://www.sqlite.org/uri.html#the_uri_path\n " if (os.name == 'nt'): if re.match('^[a-zA-Z]:', path): path = os.path.abspath(path) drive_prefix = f'/{path[:2]}' path = path[2:] else: drive_prefix = path = path.replace('\\', '/') path = urllib_parse_quote(path) path = f'{drive_prefix}{path}' else: path = urllib_parse_quote(path) path = re.sub('/+', '/', path) return f'file:{path}'

def connect(path, mode='rwc'): 'Returns a sqlite3 connection to a Toron node file.' uri_path = _path_to_sqlite_uri(path) uri_path = f'{uri_path}?mode={mode}' try: get_connection = (lambda : sqlite3.connect(database=uri_path, detect_types=sqlite3.PARSE_DECLTYPES, isolation_level=None, uri=True)) if os.path.exists(path): con = get_connection() else: con = get_connection() con.executescript(_schema_script) except sqlite3.OperationalError as err: msg = str(err).replace('database file', f'node file {path!r}') raise ToronError(msg) try: _add_functions_and_triggers(con) except (sqlite3.OperationalError, sqlite3.DatabaseError): con.close() raise ToronError(f'Path is not a Toron node: {path!r}') cur = con.execute("SELECT value FROM property WHERE key='schema_version'") (schema_version, *_) = (cur.fetchone() or (None,)) cur.close() if (schema_version != 1): msg = f'Unsupported Toron node format: schema version {schema_version!r}' raise ToronError(msg) return con

Returns a sqlite3 connection to a Toron node file.

toron/_node_schema.py

connect

shawnbrown/gpn

python

def connect(path, mode='rwc'): uri_path = _path_to_sqlite_uri(path) uri_path = f'{uri_path}?mode={mode}' try: get_connection = (lambda : sqlite3.connect(database=uri_path, detect_types=sqlite3.PARSE_DECLTYPES, isolation_level=None, uri=True)) if os.path.exists(path): con = get_connection() else: con = get_connection() con.executescript(_schema_script) except sqlite3.OperationalError as err: msg = str(err).replace('database file', f'node file {path!r}') raise ToronError(msg) try: _add_functions_and_triggers(con) except (sqlite3.OperationalError, sqlite3.DatabaseError): con.close() raise ToronError(f'Path is not a Toron node: {path!r}') cur = con.execute("SELECT value FROM property WHERE key='schema_version'") (schema_version, *_) = (cur.fetchone() or (None,)) cur.close() if (schema_version != 1): msg = f'Unsupported Toron node format: schema version {schema_version!r}' raise ToronError(msg) return con

@contextmanager def transaction(path_or_connection, mode=None): 'A context manager that yields a cursor that runs in an\n isolated transaction. If the context manager exits without\n errors, the transaction is committed. If an exception is\n raised, all changes are rolled-back.\n ' if isinstance(path_or_connection, sqlite3.Connection): connection = path_or_connection connection_close = (lambda : None) else: connection = connect(path_or_connection, mode=mode) connection_close = connection.close cursor = connection.cursor() try: with savepoint(cursor): (yield cursor) finally: cursor.close() connection_close()

A context manager that yields a cursor that runs in an isolated transaction. If the context manager exits without errors, the transaction is committed. If an exception is raised, all changes are rolled-back.

toron/_node_schema.py

transaction

shawnbrown/gpn

python

@contextmanager def transaction(path_or_connection, mode=None): 'A context manager that yields a cursor that runs in an\n isolated transaction. If the context manager exits without\n errors, the transaction is committed. If an exception is\n raised, all changes are rolled-back.\n ' if isinstance(path_or_connection, sqlite3.Connection): connection = path_or_connection connection_close = (lambda : None) else: connection = connect(path_or_connection, mode=mode) connection_close = connection.close cursor = connection.cursor() try: with savepoint(cursor): (yield cursor) finally: cursor.close() connection_close()

def run(cmd, *args, **kwargs): 'Echo a command before running it' log.info(('> ' + list2cmdline(cmd))) kwargs['shell'] = (sys.platform == 'win32') return check_call(cmd, *args, **kwargs)

Echo a command before running it

setupbase.py

run

bualpha/jupyterlab

python

def run(cmd, *args, **kwargs): log.info(('> ' + list2cmdline(cmd))) kwargs['shell'] = (sys.platform == 'win32') return check_call(cmd, *args, **kwargs)

def find_packages(): '\n Find all of the packages.\n ' packages = [] for (dir, subdirs, files) in os.walk('jupyterlab'): if ('node_modules' in subdirs): subdirs.remove('node_modules') package = dir.replace(osp.sep, '.') if ('__init__.py' not in files): continue packages.append(package) return packages

Find all of the packages.

setupbase.py

find_packages

bualpha/jupyterlab

python

def find_packages(): '\n \n ' packages = [] for (dir, subdirs, files) in os.walk('jupyterlab'): if ('node_modules' in subdirs): subdirs.remove('node_modules') package = dir.replace(osp.sep, '.') if ('__init__.py' not in files): continue packages.append(package) return packages

def find_package_data(): '\n Find package_data.\n ' theme_dirs = [] for (dir, subdirs, files) in os.walk(pjoin('jupyterlab', 'themes')): slice_len = len(('jupyterlab' + os.sep)) theme_dirs.append(pjoin(dir[slice_len:], '*')) schema_dirs = [] for (dir, subdirs, files) in os.walk(pjoin('jupyterlab', 'schemas')): slice_len = len(('jupyterlab' + os.sep)) schema_dirs.append(pjoin(dir[slice_len:], '*')) return {'jupyterlab': ((['build/*', '*.js', 'package.app.json', 'yarn.lock', 'yarn.app.lock', '.yarnrc'] + theme_dirs) + schema_dirs)}

Find package_data.

setupbase.py

find_package_data

bualpha/jupyterlab

python

def find_package_data(): '\n \n ' theme_dirs = [] for (dir, subdirs, files) in os.walk(pjoin('jupyterlab', 'themes')): slice_len = len(('jupyterlab' + os.sep)) theme_dirs.append(pjoin(dir[slice_len:], '*')) schema_dirs = [] for (dir, subdirs, files) in os.walk(pjoin('jupyterlab', 'schemas')): slice_len = len(('jupyterlab' + os.sep)) schema_dirs.append(pjoin(dir[slice_len:], '*')) return {'jupyterlab': ((['build/*', '*.js', 'package.app.json', 'yarn.lock', 'yarn.app.lock', '.yarnrc'] + theme_dirs) + schema_dirs)}

def find_data_files(): '\n Find data_files.\n ' if (not os.path.exists(pjoin('jupyterlab', 'build'))): return [] files = [] static_files = os.listdir(pjoin('jupyterlab', 'build')) files.append(('share/jupyter/lab/static', [('jupyterlab/build/%s' % f) for f in static_files])) for (dir, subdirs, fnames) in os.walk(pjoin('jupyterlab', 'schemas')): dir = dir.replace(os.sep, '/') schema_files = [] for fname in fnames: schema_files.append(('%s/%s' % (dir, fname))) slice_len = len('jupyterlab/') files.append((('share/jupyter/lab/%s' % dir[slice_len:]), schema_files)) for (dir, subdirs, fnames) in os.walk(pjoin('jupyterlab', 'themes')): dir = dir.replace(os.sep, '/') themes_files = [] for fname in fnames: themes_files.append(('%s/%s' % (dir, fname))) slice_len = len('jupyterlab/') files.append((('share/jupyter/lab/%s' % dir[slice_len:]), themes_files)) return files

Find data_files.

setupbase.py

find_data_files

bualpha/jupyterlab

python

def find_data_files(): '\n \n ' if (not os.path.exists(pjoin('jupyterlab', 'build'))): return [] files = [] static_files = os.listdir(pjoin('jupyterlab', 'build')) files.append(('share/jupyter/lab/static', [('jupyterlab/build/%s' % f) for f in static_files])) for (dir, subdirs, fnames) in os.walk(pjoin('jupyterlab', 'schemas')): dir = dir.replace(os.sep, '/') schema_files = [] for fname in fnames: schema_files.append(('%s/%s' % (dir, fname))) slice_len = len('jupyterlab/') files.append((('share/jupyter/lab/%s' % dir[slice_len:]), schema_files)) for (dir, subdirs, fnames) in os.walk(pjoin('jupyterlab', 'themes')): dir = dir.replace(os.sep, '/') themes_files = [] for fname in fnames: themes_files.append(('%s/%s' % (dir, fname))) slice_len = len('jupyterlab/') files.append((('share/jupyter/lab/%s' % dir[slice_len:]), themes_files)) return files

def js_prerelease(command, strict=False): 'decorator for building minified js/css prior to another command' class DecoratedCommand(command): def run(self): jsdeps = self.distribution.get_command_obj('jsdeps') if ((not is_repo) and all((osp.exists(t) for t in jsdeps.targets))): command.run(self) return try: self.distribution.run_command('jsdeps') except Exception as e: missing = [t for t in jsdeps.targets if (not osp.exists(t))] if (strict or missing): log.warn('js check failed') if missing: log.error(('missing files: %s' % missing)) raise e else: log.warn('js check failed (not a problem)') log.warn(str(e)) command.run(self) return DecoratedCommand

decorator for building minified js/css prior to another command

setupbase.py

js_prerelease

bualpha/jupyterlab

python

def js_prerelease(command, strict=False): class DecoratedCommand(command): def run(self): jsdeps = self.distribution.get_command_obj('jsdeps') if ((not is_repo) and all((osp.exists(t) for t in jsdeps.targets))): command.run(self) return try: self.distribution.run_command('jsdeps') except Exception as e: missing = [t for t in jsdeps.targets if (not osp.exists(t))] if (strict or missing): log.warn('js check failed') if missing: log.error(('missing files: %s' % missing)) raise e else: log.warn('js check failed (not a problem)') log.warn(str(e)) command.run(self) return DecoratedCommand

def update_package_data(distribution): 'update build_py options to get package_data changes' build_py = distribution.get_command_obj('build_py') build_py.finalize_options()

update build_py options to get package_data changes

setupbase.py

update_package_data

bualpha/jupyterlab

python

def update_package_data(distribution): build_py = distribution.get_command_obj('build_py') build_py.finalize_options()

@classmethod def _find_playlist_info(cls, response): '\n Finds playlist info (type, id) in HTTP response.\n\n :param response: Response object.\n :returns: Dictionary with type and id.\n ' values = {} matches = cls._playlist_info_re.search(response.text) if matches: values['type'] = matches.group(1) values['id'] = matches.group(2) return values

Finds playlist info (type, id) in HTTP response. :param response: Response object. :returns: Dictionary with type and id.

src/streamlink/plugins/ceskatelevize.py

_find_playlist_info

Erk-/streamlink

python

@classmethod def _find_playlist_info(cls, response): '\n Finds playlist info (type, id) in HTTP response.\n\n :param response: Response object.\n :returns: Dictionary with type and id.\n ' values = {} matches = cls._playlist_info_re.search(response.text) if matches: values['type'] = matches.group(1) values['id'] = matches.group(2) return values

@classmethod def _find_player_url(cls, response): '\n Finds embedded player url in HTTP response.\n\n :param response: Response object.\n :returns: Player url (str).\n ' url = '' matches = cls._player_re.search(response.text) if matches: tmp_url = matches.group(0).replace('&', '&') if ('hash' not in tmp_url): matches = cls._hash_re.search(response.text) if matches: url = ((tmp_url + '&hash=') + matches.group(1)) else: url = tmp_url return ('http://ceskatelevize.cz/' + url)

Finds embedded player url in HTTP response. :param response: Response object. :returns: Player url (str).

src/streamlink/plugins/ceskatelevize.py

_find_player_url

Erk-/streamlink

python

@classmethod def _find_player_url(cls, response): '\n Finds embedded player url in HTTP response.\n\n :param response: Response object.\n :returns: Player url (str).\n ' url = matches = cls._player_re.search(response.text) if matches: tmp_url = matches.group(0).replace('&', '&') if ('hash' not in tmp_url): matches = cls._hash_re.search(response.text) if matches: url = ((tmp_url + '&hash=') + matches.group(1)) else: url = tmp_url return ('http://ceskatelevize.cz/' + url)

def main(): 'Main routine' debug = False try: argparser = ArgumentParser(description=modules[__name__].__doc__) argparser.add_argument('device', nargs='?', default='ftdi:///?', help='serial port device name') argparser.add_argument('-x', '--hexdump', action='store_true', help='dump EEPROM content as ASCII') argparser.add_argument('-X', '--hexblock', type=int, help='dump EEPROM as indented hexa blocks') argparser.add_argument('-i', '--input', type=FileType('rt'), help='input ini file to load EEPROM content') argparser.add_argument('-l', '--load', default='all', choices=('all', 'raw', 'values'), help='section(s) to load from input file') argparser.add_argument('-o', '--output', type=FileType('wt'), help='output ini file to save EEPROM content') argparser.add_argument('-s', '--serial-number', help='set serial number') argparser.add_argument('-m', '--manufacturer', help='set manufacturer name') argparser.add_argument('-p', '--product', help='set product name') argparser.add_argument('-c', '--config', action='append', help='change/configure a property as key=value pair') argparser.add_argument('-e', '--erase', action='store_true', help='erase the whole EEPROM content') argparser.add_argument('-u', '--update', action='store_true', help='perform actual update, use w/ care') argparser.add_argument('-P', '--vidpid', action='append', help='specify a custom VID:PID device ID, may be repeated') argparser.add_argument('-V', '--virtual', type=FileType('r'), help='use a virtual device, specified as YaML') argparser.add_argument('-v', '--verbose', action='count', default=0, help='increase verbosity') argparser.add_argument('-d', '--debug', action='store_true', help='enable debug mode') args = argparser.parse_args() debug = args.debug if (not args.device): argparser.error('Serial device not specified') loglevel = max(DEBUG, (ERROR - (10 * args.verbose))) loglevel = min(ERROR, loglevel) if debug: formatter = Formatter('%(asctime)s.%(msecs)03d %(name)-20s %(message)s', '%H:%M:%S') else: formatter = Formatter('%(message)s') FtdiLogger.set_formatter(formatter) FtdiLogger.set_level(loglevel) FtdiLogger.log.addHandler(StreamHandler(stderr)) if args.virtual: from pyftdi.usbtools import UsbTools UsbTools.BACKENDS = ('pyftdi.tests.backend.usbvirt',) backend = UsbTools.find_backend() loader = backend.create_loader()() loader.load(args.virtual) try: add_custom_devices(Ftdi, args.vidpid, force_hex=True) except ValueError as exc: argparser.error(str(exc)) eeprom = FtdiEeprom() eeprom.open(args.device) if args.erase: eeprom.erase() if args.input: eeprom.load_config(args.input, args.load) if args.serial_number: eeprom.set_serial_number(args.serial_number) if args.manufacturer: eeprom.set_manufacturer_name(args.manufacturer) if args.product: eeprom.set_product_name(args.product) for conf in (args.config or []): if (conf == '?'): helpstr = ', '.join(sorted(eeprom.properties)) print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) for sep in ':=': if (sep in conf): (name, value) = conf.split(sep, 1) if (not value): argparser.error(('Configuration %s without value' % conf)) helpio = StringIO() eeprom.set_property(name, value, helpio) helpstr = helpio.getvalue() if helpstr: print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) break else: argparser.error(('Missing name:value separator in %s' % conf)) if args.hexdump: print(hexdump(eeprom.data)) if (args.hexblock is not None): indent = (' ' * args.hexblock) for pos in range(0, len(eeprom.data), 16): hexa = ' '.join([('%02x' % x) for x in eeprom.data[pos:(pos + 16)]]) print(indent, hexa, sep='') if args.update: if eeprom.commit(False): eeprom.reset_device() if (args.verbose > 0): eeprom.dump_config() if args.output: eeprom.save_config(args.output) except (ImportError, IOError, NotImplementedError, ValueError) as exc: print(('\nError: %s' % exc), file=stderr) if debug: print(format_exc(chain=False), file=stderr) exit(1) except KeyboardInterrupt: exit(2)

Main routine

bin/ftconf.py

main

andrario/API_Estacao

python

def main(): debug = False try: argparser = ArgumentParser(description=modules[__name__].__doc__) argparser.add_argument('device', nargs='?', default='ftdi:///?', help='serial port device name') argparser.add_argument('-x', '--hexdump', action='store_true', help='dump EEPROM content as ASCII') argparser.add_argument('-X', '--hexblock', type=int, help='dump EEPROM as indented hexa blocks') argparser.add_argument('-i', '--input', type=FileType('rt'), help='input ini file to load EEPROM content') argparser.add_argument('-l', '--load', default='all', choices=('all', 'raw', 'values'), help='section(s) to load from input file') argparser.add_argument('-o', '--output', type=FileType('wt'), help='output ini file to save EEPROM content') argparser.add_argument('-s', '--serial-number', help='set serial number') argparser.add_argument('-m', '--manufacturer', help='set manufacturer name') argparser.add_argument('-p', '--product', help='set product name') argparser.add_argument('-c', '--config', action='append', help='change/configure a property as key=value pair') argparser.add_argument('-e', '--erase', action='store_true', help='erase the whole EEPROM content') argparser.add_argument('-u', '--update', action='store_true', help='perform actual update, use w/ care') argparser.add_argument('-P', '--vidpid', action='append', help='specify a custom VID:PID device ID, may be repeated') argparser.add_argument('-V', '--virtual', type=FileType('r'), help='use a virtual device, specified as YaML') argparser.add_argument('-v', '--verbose', action='count', default=0, help='increase verbosity') argparser.add_argument('-d', '--debug', action='store_true', help='enable debug mode') args = argparser.parse_args() debug = args.debug if (not args.device): argparser.error('Serial device not specified') loglevel = max(DEBUG, (ERROR - (10 * args.verbose))) loglevel = min(ERROR, loglevel) if debug: formatter = Formatter('%(asctime)s.%(msecs)03d %(name)-20s %(message)s', '%H:%M:%S') else: formatter = Formatter('%(message)s') FtdiLogger.set_formatter(formatter) FtdiLogger.set_level(loglevel) FtdiLogger.log.addHandler(StreamHandler(stderr)) if args.virtual: from pyftdi.usbtools import UsbTools UsbTools.BACKENDS = ('pyftdi.tests.backend.usbvirt',) backend = UsbTools.find_backend() loader = backend.create_loader()() loader.load(args.virtual) try: add_custom_devices(Ftdi, args.vidpid, force_hex=True) except ValueError as exc: argparser.error(str(exc)) eeprom = FtdiEeprom() eeprom.open(args.device) if args.erase: eeprom.erase() if args.input: eeprom.load_config(args.input, args.load) if args.serial_number: eeprom.set_serial_number(args.serial_number) if args.manufacturer: eeprom.set_manufacturer_name(args.manufacturer) if args.product: eeprom.set_product_name(args.product) for conf in (args.config or []): if (conf == '?'): helpstr = ', '.join(sorted(eeprom.properties)) print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) for sep in ':=': if (sep in conf): (name, value) = conf.split(sep, 1) if (not value): argparser.error(('Configuration %s without value' % conf)) helpio = StringIO() eeprom.set_property(name, value, helpio) helpstr = helpio.getvalue() if helpstr: print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) break else: argparser.error(('Missing name:value separator in %s' % conf)) if args.hexdump: print(hexdump(eeprom.data)) if (args.hexblock is not None): indent = (' ' * args.hexblock) for pos in range(0, len(eeprom.data), 16): hexa = ' '.join([('%02x' % x) for x in eeprom.data[pos:(pos + 16)]]) print(indent, hexa, sep=) if args.update: if eeprom.commit(False): eeprom.reset_device() if (args.verbose > 0): eeprom.dump_config() if args.output: eeprom.save_config(args.output) except (ImportError, IOError, NotImplementedError, ValueError) as exc: print(('\nError: %s' % exc), file=stderr) if debug: print(format_exc(chain=False), file=stderr) exit(1) except KeyboardInterrupt: exit(2)

def _analyze_result_columns(self, query: Query): 'Given info on a list of SELECTs, determine whether to warn.' if (not query.selectables): return for selectable in query.selectables: self.logger.debug(f'Analyzing query: {selectable.selectable.raw}') for wildcard in selectable.get_wildcard_info(): if wildcard.tables: for wildcard_table in wildcard.tables: self.logger.debug(f'Wildcard: {wildcard.segment.raw} has target {{wildcard_table}}') alias_info = selectable.find_alias(wildcard_table) if alias_info: self._handle_alias(selectable, alias_info, query) else: cte = query.lookup_cte(wildcard_table) if cte: self._analyze_result_columns(cte) else: self.logger.debug(f'Query target {wildcard_table} is external. Generating warning.') raise RuleFailure(selectable.selectable) else: query_list = SelectCrawler.get(query, query.selectables[0].selectable) for o in query_list: if isinstance(o, Query): self._analyze_result_columns(o) return self.logger.debug(f'Query target "{query.selectables[0].selectable.raw}" has no targets. Generating warning.') raise RuleFailure(query.selectables[0].selectable)

Given info on a list of SELECTs, determine whether to warn.

src/sqlfluff/rules/L044.py

_analyze_result_columns

R7L208/sqlfluff

python

def _analyze_result_columns(self, query: Query): if (not query.selectables): return for selectable in query.selectables: self.logger.debug(f'Analyzing query: {selectable.selectable.raw}') for wildcard in selectable.get_wildcard_info(): if wildcard.tables: for wildcard_table in wildcard.tables: self.logger.debug(f'Wildcard: {wildcard.segment.raw} has target {{wildcard_table}}') alias_info = selectable.find_alias(wildcard_table) if alias_info: self._handle_alias(selectable, alias_info, query) else: cte = query.lookup_cte(wildcard_table) if cte: self._analyze_result_columns(cte) else: self.logger.debug(f'Query target {wildcard_table} is external. Generating warning.') raise RuleFailure(selectable.selectable) else: query_list = SelectCrawler.get(query, query.selectables[0].selectable) for o in query_list: if isinstance(o, Query): self._analyze_result_columns(o) return self.logger.debug(f'Query target "{query.selectables[0].selectable.raw}" has no targets. Generating warning.') raise RuleFailure(query.selectables[0].selectable)

def _eval(self, context: RuleContext) -> Optional[LintResult]: 'Outermost query should produce known number of columns.' start_types = ['select_statement', 'set_expression', 'with_compound_statement'] if (context.segment.is_type(*start_types) and (not context.functional.parent_stack.any(sp.is_type(*start_types)))): crawler = SelectCrawler(context.segment, context.dialect) if crawler.query_tree: try: return self._analyze_result_columns(crawler.query_tree) except RuleFailure as e: return LintResult(anchor=e.anchor) return None

Outermost query should produce known number of columns.

src/sqlfluff/rules/L044.py

_eval

R7L208/sqlfluff

python

def _eval(self, context: RuleContext) -> Optional[LintResult]: start_types = ['select_statement', 'set_expression', 'with_compound_statement'] if (context.segment.is_type(*start_types) and (not context.functional.parent_stack.any(sp.is_type(*start_types)))): crawler = SelectCrawler(context.segment, context.dialect) if crawler.query_tree: try: return self._analyze_result_columns(crawler.query_tree) except RuleFailure as e: return LintResult(anchor=e.anchor) return None

def imread(filename, *args, **kwargs): 'Read and decode an image to an NDArray.\n\n Note: `imread` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.\n\n Parameters\n ----------\n filename : str\n Name of the image file to be loaded.\n flag : {0, 1}, default 1\n 1 for three channel color output. 0 for grayscale output.\n to_rgb : bool, default True\n True for RGB formatted output (MXNet default).\n False for BGR formatted output (OpenCV default).\n out : NDArray, optional\n Output buffer. Use `None` for automatic allocation.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the image.\n\n Example\n -------\n >>> mx.img.imread("flower.jpg")\n <NDArray 224x224x3 @cpu(0)>\n\n Set `flag` parameter to 0 to get grayscale output\n\n >>> mx.img.imread("flower.jpg", flag=0)\n <NDArray 224x224x1 @cpu(0)>\n\n Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)\n\n >>> mx.img.imread("flower.jpg", to_rgb=0)\n <NDArray 224x224x3 @cpu(0)>\n ' return _internal._cvimread(filename, *args, **kwargs)

Read and decode an image to an NDArray. Note: `imread` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- filename : str Name of the image file to be loaded. flag : {0, 1}, default 1 1 for three channel color output. 0 for grayscale output. to_rgb : bool, default True True for RGB formatted output (MXNet default). False for BGR formatted output (OpenCV default). out : NDArray, optional Output buffer. Use `None` for automatic allocation. Returns ------- NDArray An `NDArray` containing the image. Example ------- >>> mx.img.imread("flower.jpg") <NDArray 224x224x3 @cpu(0)> Set `flag` parameter to 0 to get grayscale output >>> mx.img.imread("flower.jpg", flag=0) <NDArray 224x224x1 @cpu(0)> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR) >>> mx.img.imread("flower.jpg", to_rgb=0) <NDArray 224x224x3 @cpu(0)>

python/mxnet/image/image.py

imread

Vikas89/private-mxnet

python

def imread(filename, *args, **kwargs): 'Read and decode an image to an NDArray.\n\n Note: `imread` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.\n\n Parameters\n ----------\n filename : str\n Name of the image file to be loaded.\n flag : {0, 1}, default 1\n 1 for three channel color output. 0 for grayscale output.\n to_rgb : bool, default True\n True for RGB formatted output (MXNet default).\n False for BGR formatted output (OpenCV default).\n out : NDArray, optional\n Output buffer. Use `None` for automatic allocation.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the image.\n\n Example\n -------\n >>> mx.img.imread("flower.jpg")\n <NDArray 224x224x3 @cpu(0)>\n\n Set `flag` parameter to 0 to get grayscale output\n\n >>> mx.img.imread("flower.jpg", flag=0)\n <NDArray 224x224x1 @cpu(0)>\n\n Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)\n\n >>> mx.img.imread("flower.jpg", to_rgb=0)\n <NDArray 224x224x3 @cpu(0)>\n ' return _internal._cvimread(filename, *args, **kwargs)

def imdecode(buf, *args, **kwargs): 'Decode an image to an NDArray.\n\n Note: `imdecode` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.\n\n Parameters\n ----------\n buf : str/bytes or numpy.ndarray\n Binary image data as string or numpy ndarray.\n flag : int, optional, default=1\n 1 for three channel color output. 0 for grayscale output.\n to_rgb : int, optional, default=1\n 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).\n out : NDArray, optional\n Output buffer. Use `None` for automatic allocation.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the image.\n\n Example\n -------\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image)\n >>> image\n <NDArray 224x224x3 @cpu(0)>\n\n Set `flag` parameter to 0 to get grayscale output\n\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image, flag=0)\n >>> image\n <NDArray 224x224x1 @cpu(0)>\n\n Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)\n\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image, to_rgb=0)\n >>> image\n <NDArray 224x224x3 @cpu(0)>\n ' if (not isinstance(buf, nd.NDArray)): buf = nd.array(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8) return _internal._cvimdecode(buf, *args, **kwargs)

Decode an image to an NDArray. Note: `imdecode` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- buf : str/bytes or numpy.ndarray Binary image data as string or numpy ndarray. flag : int, optional, default=1 1 for three channel color output. 0 for grayscale output. to_rgb : int, optional, default=1 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default). out : NDArray, optional Output buffer. Use `None` for automatic allocation. Returns ------- NDArray An `NDArray` containing the image. Example ------- >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 224x224x3 @cpu(0)> Set `flag` parameter to 0 to get grayscale output >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image, flag=0) >>> image <NDArray 224x224x1 @cpu(0)> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR) >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image, to_rgb=0) >>> image <NDArray 224x224x3 @cpu(0)>

python/mxnet/image/image.py

imdecode

Vikas89/private-mxnet

python

def imdecode(buf, *args, **kwargs): 'Decode an image to an NDArray.\n\n Note: `imdecode` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.\n\n Parameters\n ----------\n buf : str/bytes or numpy.ndarray\n Binary image data as string or numpy ndarray.\n flag : int, optional, default=1\n 1 for three channel color output. 0 for grayscale output.\n to_rgb : int, optional, default=1\n 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).\n out : NDArray, optional\n Output buffer. Use `None` for automatic allocation.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the image.\n\n Example\n -------\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image)\n >>> image\n <NDArray 224x224x3 @cpu(0)>\n\n Set `flag` parameter to 0 to get grayscale output\n\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image, flag=0)\n >>> image\n <NDArray 224x224x1 @cpu(0)>\n\n Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)\n\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image, to_rgb=0)\n >>> image\n <NDArray 224x224x3 @cpu(0)>\n ' if (not isinstance(buf, nd.NDArray)): buf = nd.array(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8) return _internal._cvimdecode(buf, *args, **kwargs)

def scale_down(src_size, size): "Scales down crop size if it's larger than image size.\n\n If width/height of the crop is larger than the width/height of the image,\n sets the width/height to the width/height of the image.\n\n Parameters\n ----------\n src_size : tuple of int\n Size of the image in (width, height) format.\n size : tuple of int\n Size of the crop in (width, height) format.\n\n Returns\n -------\n tuple of int\n A tuple containing the scaled crop size in (width, height) format.\n\n Example\n --------\n >>> src_size = (640,480)\n >>> size = (720,120)\n >>> new_size = mx.img.scale_down(src_size, size)\n >>> new_size\n (640,106)\n " (w, h) = size (sw, sh) = src_size if (sh < h): (w, h) = ((float((w * sh)) / h), sh) if (sw < w): (w, h) = (sw, (float((h * sw)) / w)) return (int(w), int(h))

Scales down crop size if it's larger than image size. If width/height of the crop is larger than the width/height of the image, sets the width/height to the width/height of the image. Parameters ---------- src_size : tuple of int Size of the image in (width, height) format. size : tuple of int Size of the crop in (width, height) format. Returns ------- tuple of int A tuple containing the scaled crop size in (width, height) format. Example -------- >>> src_size = (640,480) >>> size = (720,120) >>> new_size = mx.img.scale_down(src_size, size) >>> new_size (640,106)

python/mxnet/image/image.py

scale_down

Vikas89/private-mxnet

python

def scale_down(src_size, size): "Scales down crop size if it's larger than image size.\n\n If width/height of the crop is larger than the width/height of the image,\n sets the width/height to the width/height of the image.\n\n Parameters\n ----------\n src_size : tuple of int\n Size of the image in (width, height) format.\n size : tuple of int\n Size of the crop in (width, height) format.\n\n Returns\n -------\n tuple of int\n A tuple containing the scaled crop size in (width, height) format.\n\n Example\n --------\n >>> src_size = (640,480)\n >>> size = (720,120)\n >>> new_size = mx.img.scale_down(src_size, size)\n >>> new_size\n (640,106)\n " (w, h) = size (sw, sh) = src_size if (sh < h): (w, h) = ((float((w * sh)) / h), sh) if (sw < w): (w, h) = (sw, (float((h * sw)) / w)) return (int(w), int(h))

def _get_interp_method(interp, sizes=()): 'Get the interpolation method for resize functions.\n The major purpose of this function is to wrap a random interp method selection\n and a auto-estimation method.\n\n Parameters\n ----------\n interp : int\n interpolation method for all resizing operations\n\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n More details can be found in the documentation of OpenCV, please refer to\n http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.\n sizes : tuple of int\n (old_height, old_width, new_height, new_width), if None provided, auto(9)\n will return Area(2) anyway.\n\n Returns\n -------\n int\n interp method from 0 to 4\n ' if (interp == 9): if sizes: assert (len(sizes) == 4) (oh, ow, nh, nw) = sizes if ((nh > oh) and (nw > ow)): return 2 elif ((nh < oh) and (nw < ow)): return 3 else: return 1 else: return 2 if (interp == 10): return random.randint(0, 4) if (interp not in (0, 1, 2, 3, 4)): raise ValueError(('Unknown interp method %d' % interp)) return interp

Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp method selection and a auto-estimation method. Parameters ---------- interp : int interpolation method for all resizing operations Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. sizes : tuple of int (old_height, old_width, new_height, new_width), if None provided, auto(9) will return Area(2) anyway. Returns ------- int interp method from 0 to 4

python/mxnet/image/image.py

_get_interp_method

Vikas89/private-mxnet

python

def _get_interp_method(interp, sizes=()): 'Get the interpolation method for resize functions.\n The major purpose of this function is to wrap a random interp method selection\n and a auto-estimation method.\n\n Parameters\n ----------\n interp : int\n interpolation method for all resizing operations\n\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n More details can be found in the documentation of OpenCV, please refer to\n http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.\n sizes : tuple of int\n (old_height, old_width, new_height, new_width), if None provided, auto(9)\n will return Area(2) anyway.\n\n Returns\n -------\n int\n interp method from 0 to 4\n ' if (interp == 9): if sizes: assert (len(sizes) == 4) (oh, ow, nh, nw) = sizes if ((nh > oh) and (nw > ow)): return 2 elif ((nh < oh) and (nw < ow)): return 3 else: return 1 else: return 2 if (interp == 10): return random.randint(0, 4) if (interp not in (0, 1, 2, 3, 4)): raise ValueError(('Unknown interp method %d' % interp)) return interp

def resize_short(src, size, interp=2): 'Resizes shorter edge to size.\n\n Note: `resize_short` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with OpenCV for `resize_short` to work.\n\n Resizes the original image by setting the shorter edge to size\n and setting the longer edge accordingly.\n Resizing function is called from OpenCV.\n\n Parameters\n ----------\n src : NDArray\n The original image.\n size : int\n The length to be set for the shorter edge.\n interp : int, optional, default=2\n Interpolation method used for resizing the image.\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n More details can be found in the documentation of OpenCV, please refer to\n http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.\n\n Returns\n -------\n NDArray\n An \'NDArray\' containing the resized image.\n\n Example\n -------\n >>> with open("flower.jpeg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image)\n >>> image\n <NDArray 2321x3482x3 @cpu(0)>\n >>> size = 640\n >>> new_image = mx.img.resize_short(image, size)\n >>> new_image\n <NDArray 2321x3482x3 @cpu(0)>\n ' (h, w, _) = src.shape if (h > w): (new_h, new_w) = (((size * h) // w), size) else: (new_h, new_w) = (size, ((size * w) // h)) return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w)))

Resizes shorter edge to size. Note: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been built with OpenCV for `resize_short` to work. Resizes the original image by setting the shorter edge to size and setting the longer edge accordingly. Resizing function is called from OpenCV. Parameters ---------- src : NDArray The original image. size : int The length to be set for the shorter edge. interp : int, optional, default=2 Interpolation method used for resizing the image. Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. Returns ------- NDArray An 'NDArray' containing the resized image. Example ------- >>> with open("flower.jpeg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> size = 640 >>> new_image = mx.img.resize_short(image, size) >>> new_image <NDArray 2321x3482x3 @cpu(0)>

python/mxnet/image/image.py

resize_short

Vikas89/private-mxnet

python

def resize_short(src, size, interp=2): 'Resizes shorter edge to size.\n\n Note: `resize_short` uses OpenCV (not the CV2 Python library).\n MXNet must have been built with OpenCV for `resize_short` to work.\n\n Resizes the original image by setting the shorter edge to size\n and setting the longer edge accordingly.\n Resizing function is called from OpenCV.\n\n Parameters\n ----------\n src : NDArray\n The original image.\n size : int\n The length to be set for the shorter edge.\n interp : int, optional, default=2\n Interpolation method used for resizing the image.\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n More details can be found in the documentation of OpenCV, please refer to\n http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.\n\n Returns\n -------\n NDArray\n An \'NDArray\' containing the resized image.\n\n Example\n -------\n >>> with open("flower.jpeg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.img.imdecode(str_image)\n >>> image\n <NDArray 2321x3482x3 @cpu(0)>\n >>> size = 640\n >>> new_image = mx.img.resize_short(image, size)\n >>> new_image\n <NDArray 2321x3482x3 @cpu(0)>\n ' (h, w, _) = src.shape if (h > w): (new_h, new_w) = (((size * h) // w), size) else: (new_h, new_w) = (size, ((size * w) // h)) return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w)))

def fixed_crop(src, x0, y0, w, h, size=None, interp=2): 'Crop src at fixed location, and (optionally) resize it to size.\n\n Parameters\n ----------\n src : NDArray\n Input image\n x0 : int\n Left boundary of the cropping area\n y0 : int\n Top boundary of the cropping area\n w : int\n Width of the cropping area\n h : int\n Height of the cropping area\n size : tuple of (w, h)\n Optional, resize to new size after cropping\n interp : int, optional, default=2\n Interpolation method. See resize_short for details.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n ' out = nd.crop(src, begin=(y0, x0, 0), end=((y0 + h), (x0 + w), int(src.shape[2]))) if ((size is not None) and ((w, h) != size)): sizes = (h, w, size[1], size[0]) out = imresize(out, *size, interp=_get_interp_method(interp, sizes)) return out

Crop src at fixed location, and (optionally) resize it to size. Parameters ---------- src : NDArray Input image x0 : int Left boundary of the cropping area y0 : int Top boundary of the cropping area w : int Width of the cropping area h : int Height of the cropping area size : tuple of (w, h) Optional, resize to new size after cropping interp : int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image.

python/mxnet/image/image.py

fixed_crop

Vikas89/private-mxnet

python

def fixed_crop(src, x0, y0, w, h, size=None, interp=2): 'Crop src at fixed location, and (optionally) resize it to size.\n\n Parameters\n ----------\n src : NDArray\n Input image\n x0 : int\n Left boundary of the cropping area\n y0 : int\n Top boundary of the cropping area\n w : int\n Width of the cropping area\n h : int\n Height of the cropping area\n size : tuple of (w, h)\n Optional, resize to new size after cropping\n interp : int, optional, default=2\n Interpolation method. See resize_short for details.\n\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n ' out = nd.crop(src, begin=(y0, x0, 0), end=((y0 + h), (x0 + w), int(src.shape[2]))) if ((size is not None) and ((w, h) != size)): sizes = (h, w, size[1], size[0]) out = imresize(out, *size, interp=_get_interp_method(interp, sizes)) return out

def random_crop(src, size, interp=2): 'Randomly crop `src` with `size` (width, height).\n Upsample result if `src` is smaller than `size`.\n\n Parameters\n ----------\n src: Source image `NDArray`\n size: Size of the crop formatted as (width, height). If the `size` is larger\n than the image, then the source image is upsampled to `size` and returned.\n interp: int, optional, default=2\n Interpolation method. See resize_short for details.\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n Tuple\n A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the\n original image and (width, height) are the dimensions of the cropped image.\n\n Example\n -------\n >>> im = mx.nd.array(cv2.imread("flower.jpg"))\n >>> cropped_im, rect = mx.image.random_crop(im, (100, 100))\n >>> print cropped_im\n <NDArray 100x100x1 @cpu(0)>\n >>> print rect\n (20, 21, 100, 100)\n ' (h, w, _) = src.shape (new_w, new_h) = scale_down((w, h), size) x0 = random.randint(0, (w - new_w)) y0 = random.randint(0, (h - new_h)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h))

Randomly crop `src` with `size` (width, height). Upsample result if `src` is smaller than `size`. Parameters ---------- src: Source image `NDArray` size: Size of the crop formatted as (width, height). If the `size` is larger than the image, then the source image is upsampled to `size` and returned. interp: int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image. Tuple A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the original image and (width, height) are the dimensions of the cropped image. Example ------- >>> im = mx.nd.array(cv2.imread("flower.jpg")) >>> cropped_im, rect = mx.image.random_crop(im, (100, 100)) >>> print cropped_im <NDArray 100x100x1 @cpu(0)> >>> print rect (20, 21, 100, 100)

python/mxnet/image/image.py

random_crop

Vikas89/private-mxnet

python

def random_crop(src, size, interp=2): 'Randomly crop `src` with `size` (width, height).\n Upsample result if `src` is smaller than `size`.\n\n Parameters\n ----------\n src: Source image `NDArray`\n size: Size of the crop formatted as (width, height). If the `size` is larger\n than the image, then the source image is upsampled to `size` and returned.\n interp: int, optional, default=2\n Interpolation method. See resize_short for details.\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n Tuple\n A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the\n original image and (width, height) are the dimensions of the cropped image.\n\n Example\n -------\n >>> im = mx.nd.array(cv2.imread("flower.jpg"))\n >>> cropped_im, rect = mx.image.random_crop(im, (100, 100))\n >>> print cropped_im\n <NDArray 100x100x1 @cpu(0)>\n >>> print rect\n (20, 21, 100, 100)\n ' (h, w, _) = src.shape (new_w, new_h) = scale_down((w, h), size) x0 = random.randint(0, (w - new_w)) y0 = random.randint(0, (h - new_h)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h))

def center_crop(src, size, interp=2): 'Crops the image `src` to the given `size` by trimming on all four\n sides and preserving the center of the image. Upsamples if `src` is smaller\n than `size`.\n\n .. note:: This requires MXNet to be compiled with USE_OPENCV.\n\n Parameters\n ----------\n src : NDArray\n Binary source image data.\n size : list or tuple of int\n The desired output image size.\n interp : int, optional, default=2\n Interpolation method. See resize_short for details.\n\n Returns\n -------\n NDArray\n The cropped image.\n Tuple\n (x, y, width, height) where x, y are the positions of the crop in the\n original image and width, height the dimensions of the crop.\n\n Example\n -------\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.image.imdecode(str_image)\n >>> image\n <NDArray 2321x3482x3 @cpu(0)>\n >>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500))\n >>> cropped_image\n <NDArray 500x1000x3 @cpu(0)>\n >>> x, y, width, height\n (1241, 910, 1000, 500)\n ' (h, w, _) = src.shape (new_w, new_h) = scale_down((w, h), size) x0 = int(((w - new_w) / 2)) y0 = int(((h - new_h) / 2)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h))

Crops the image `src` to the given `size` by trimming on all four sides and preserving the center of the image. Upsamples if `src` is smaller than `size`. .. note:: This requires MXNet to be compiled with USE_OPENCV. Parameters ---------- src : NDArray Binary source image data. size : list or tuple of int The desired output image size. interp : int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray The cropped image. Tuple (x, y, width, height) where x, y are the positions of the crop in the original image and width, height the dimensions of the crop. Example ------- >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.image.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500)) >>> cropped_image <NDArray 500x1000x3 @cpu(0)> >>> x, y, width, height (1241, 910, 1000, 500)

python/mxnet/image/image.py

center_crop

Vikas89/private-mxnet

python

def center_crop(src, size, interp=2): 'Crops the image `src` to the given `size` by trimming on all four\n sides and preserving the center of the image. Upsamples if `src` is smaller\n than `size`.\n\n .. note:: This requires MXNet to be compiled with USE_OPENCV.\n\n Parameters\n ----------\n src : NDArray\n Binary source image data.\n size : list or tuple of int\n The desired output image size.\n interp : int, optional, default=2\n Interpolation method. See resize_short for details.\n\n Returns\n -------\n NDArray\n The cropped image.\n Tuple\n (x, y, width, height) where x, y are the positions of the crop in the\n original image and width, height the dimensions of the crop.\n\n Example\n -------\n >>> with open("flower.jpg", \'rb\') as fp:\n ... str_image = fp.read()\n ...\n >>> image = mx.image.imdecode(str_image)\n >>> image\n <NDArray 2321x3482x3 @cpu(0)>\n >>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500))\n >>> cropped_image\n <NDArray 500x1000x3 @cpu(0)>\n >>> x, y, width, height\n (1241, 910, 1000, 500)\n ' (h, w, _) = src.shape (new_w, new_h) = scale_down((w, h), size) x0 = int(((w - new_w) / 2)) y0 = int(((h - new_h) / 2)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h))

def color_normalize(src, mean, std=None): 'Normalize src with mean and std.\n\n Parameters\n ----------\n src : NDArray\n Input image\n mean : NDArray\n RGB mean to be subtracted\n std : NDArray\n RGB standard deviation to be divided\n\n Returns\n -------\n NDArray\n An `NDArray` containing the normalized image.\n ' if (mean is not None): src -= mean if (std is not None): src /= std return src

Normalize src with mean and std. Parameters ---------- src : NDArray Input image mean : NDArray RGB mean to be subtracted std : NDArray RGB standard deviation to be divided Returns ------- NDArray An `NDArray` containing the normalized image.

python/mxnet/image/image.py

color_normalize

Vikas89/private-mxnet

python

def color_normalize(src, mean, std=None): 'Normalize src with mean and std.\n\n Parameters\n ----------\n src : NDArray\n Input image\n mean : NDArray\n RGB mean to be subtracted\n std : NDArray\n RGB standard deviation to be divided\n\n Returns\n -------\n NDArray\n An `NDArray` containing the normalized image.\n ' if (mean is not None): src -= mean if (std is not None): src /= std return src

def random_size_crop(src, size, area, ratio, interp=2, **kwargs): 'Randomly crop src with size. Randomize area and aspect ratio.\n\n Parameters\n ----------\n src : NDArray\n Input image\n size : tuple of (int, int)\n Size of the crop formatted as (width, height).\n area : float in (0, 1] or tuple of (float, float)\n If tuple, minimum area and maximum area to be maintained after cropping\n If float, minimum area to be maintained after cropping, maximum area is set to 1.0\n ratio : tuple of (float, float)\n Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)\n interp: int, optional, default=2\n Interpolation method. See resize_short for details.\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n Tuple\n A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the\n original image and (width, height) are the dimensions of the cropped image.\n\n ' (h, w, _) = src.shape src_area = (h * w) if ('min_area' in kwargs): warnings.warn('`min_area` is deprecated. Please use `area` instead.', DeprecationWarning) area = kwargs.pop('min_area') assert (not kwargs), 'unexpected keyword arguments for `random_size_crop`.' if isinstance(area, numeric_types): area = (area, 1.0) for _ in range(10): target_area = (random.uniform(area[0], area[1]) * src_area) new_ratio = random.uniform(*ratio) new_w = int(round(np.sqrt((target_area * new_ratio)))) new_h = int(round(np.sqrt((target_area / new_ratio)))) if (random.random() < 0.5): (new_h, new_w) = (new_w, new_h) if ((new_w <= w) and (new_h <= h)): x0 = random.randint(0, (w - new_w)) y0 = random.randint(0, (h - new_h)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h)) return center_crop(src, size, interp)

Randomly crop src with size. Randomize area and aspect ratio. Parameters ---------- src : NDArray Input image size : tuple of (int, int) Size of the crop formatted as (width, height). area : float in (0, 1] or tuple of (float, float) If tuple, minimum area and maximum area to be maintained after cropping If float, minimum area to be maintained after cropping, maximum area is set to 1.0 ratio : tuple of (float, float) Aspect ratio range as (min_aspect_ratio, max_aspect_ratio) interp: int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image. Tuple A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the original image and (width, height) are the dimensions of the cropped image.

python/mxnet/image/image.py

random_size_crop

Vikas89/private-mxnet

python

def random_size_crop(src, size, area, ratio, interp=2, **kwargs): 'Randomly crop src with size. Randomize area and aspect ratio.\n\n Parameters\n ----------\n src : NDArray\n Input image\n size : tuple of (int, int)\n Size of the crop formatted as (width, height).\n area : float in (0, 1] or tuple of (float, float)\n If tuple, minimum area and maximum area to be maintained after cropping\n If float, minimum area to be maintained after cropping, maximum area is set to 1.0\n ratio : tuple of (float, float)\n Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)\n interp: int, optional, default=2\n Interpolation method. See resize_short for details.\n Returns\n -------\n NDArray\n An `NDArray` containing the cropped image.\n Tuple\n A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the\n original image and (width, height) are the dimensions of the cropped image.\n\n ' (h, w, _) = src.shape src_area = (h * w) if ('min_area' in kwargs): warnings.warn('`min_area` is deprecated. Please use `area` instead.', DeprecationWarning) area = kwargs.pop('min_area') assert (not kwargs), 'unexpected keyword arguments for `random_size_crop`.' if isinstance(area, numeric_types): area = (area, 1.0) for _ in range(10): target_area = (random.uniform(area[0], area[1]) * src_area) new_ratio = random.uniform(*ratio) new_w = int(round(np.sqrt((target_area * new_ratio)))) new_h = int(round(np.sqrt((target_area / new_ratio)))) if (random.random() < 0.5): (new_h, new_w) = (new_w, new_h) if ((new_w <= w) and (new_h <= h)): x0 = random.randint(0, (w - new_w)) y0 = random.randint(0, (h - new_h)) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return (out, (x0, y0, new_w, new_h)) return center_crop(src, size, interp)

def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2): 'Creates an augmenter list.\n\n Parameters\n ----------\n data_shape : tuple of int\n Shape for output data\n resize : int\n Resize shorter edge if larger than 0 at the begining\n rand_crop : bool\n Whether to enable random cropping other than center crop\n rand_resize : bool\n Whether to enable random sized cropping, require rand_crop to be enabled\n rand_gray : float\n [0, 1], probability to convert to grayscale for all channels, the number\n of channels will not be reduced to 1\n rand_mirror : bool\n Whether to apply horizontal flip to image with probability 0.5\n mean : np.ndarray or None\n Mean pixel values for [r, g, b]\n std : np.ndarray or None\n Standard deviations for [r, g, b]\n brightness : float\n Brightness jittering range (percent)\n contrast : float\n Contrast jittering range (percent)\n saturation : float\n Saturation jittering range (percent)\n hue : float\n Hue jittering range (percent)\n pca_noise : float\n Pca noise level (percent)\n inter_method : int, default=2(Area-based)\n Interpolation method for all resizing operations\n\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n\n Examples\n --------\n >>> # An example of creating multiple augmenters\n >>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True,\n ... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05,\n ... saturation=0.125, pca_noise=0.05, inter_method=10)\n >>> # dump the details\n >>> for aug in augs:\n ... aug.dumps()\n ' auglist = [] if (resize > 0): auglist.append(ResizeAug(resize, inter_method)) crop_size = (data_shape[2], data_shape[1]) if rand_resize: assert rand_crop auglist.append(RandomSizedCropAug(crop_size, 0.08, ((3.0 / 4.0), (4.0 / 3.0)), inter_method)) elif rand_crop: auglist.append(RandomCropAug(crop_size, inter_method)) else: auglist.append(CenterCropAug(crop_size, inter_method)) if rand_mirror: auglist.append(HorizontalFlipAug(0.5)) auglist.append(CastAug()) if (brightness or contrast or saturation): auglist.append(ColorJitterAug(brightness, contrast, saturation)) if hue: auglist.append(HueJitterAug(hue)) if (pca_noise > 0): eigval = np.array([55.46, 4.794, 1.148]) eigvec = np.array([[(- 0.5675), 0.7192, 0.4009], [(- 0.5808), (- 0.0045), (- 0.814)], [(- 0.5836), (- 0.6948), 0.4203]]) auglist.append(LightingAug(pca_noise, eigval, eigvec)) if (rand_gray > 0): auglist.append(RandomGrayAug(rand_gray)) if (mean is True): mean = nd.array([123.68, 116.28, 103.53]) elif (mean is not None): assert (isinstance(mean, (np.ndarray, nd.NDArray)) and (mean.shape[0] in [1, 3])) if (std is True): std = nd.array([58.395, 57.12, 57.375]) elif (std is not None): assert (isinstance(std, (np.ndarray, nd.NDArray)) and (std.shape[0] in [1, 3])) if ((mean is not None) or (std is not None)): auglist.append(ColorNormalizeAug(mean, std)) return auglist

Creates an augmenter list. Parameters ---------- data_shape : tuple of int Shape for output data resize : int Resize shorter edge if larger than 0 at the begining rand_crop : bool Whether to enable random cropping other than center crop rand_resize : bool Whether to enable random sized cropping, require rand_crop to be enabled rand_gray : float [0, 1], probability to convert to grayscale for all channels, the number of channels will not be reduced to 1 rand_mirror : bool Whether to apply horizontal flip to image with probability 0.5 mean : np.ndarray or None Mean pixel values for [r, g, b] std : np.ndarray or None Standard deviations for [r, g, b] brightness : float Brightness jittering range (percent) contrast : float Contrast jittering range (percent) saturation : float Saturation jittering range (percent) hue : float Hue jittering range (percent) pca_noise : float Pca noise level (percent) inter_method : int, default=2(Area-based) Interpolation method for all resizing operations Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). Examples -------- >>> # An example of creating multiple augmenters >>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True, ... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05, ... saturation=0.125, pca_noise=0.05, inter_method=10) >>> # dump the details >>> for aug in augs: ... aug.dumps()

python/mxnet/image/image.py

CreateAugmenter

Vikas89/private-mxnet

python

def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2): 'Creates an augmenter list.\n\n Parameters\n ----------\n data_shape : tuple of int\n Shape for output data\n resize : int\n Resize shorter edge if larger than 0 at the begining\n rand_crop : bool\n Whether to enable random cropping other than center crop\n rand_resize : bool\n Whether to enable random sized cropping, require rand_crop to be enabled\n rand_gray : float\n [0, 1], probability to convert to grayscale for all channels, the number\n of channels will not be reduced to 1\n rand_mirror : bool\n Whether to apply horizontal flip to image with probability 0.5\n mean : np.ndarray or None\n Mean pixel values for [r, g, b]\n std : np.ndarray or None\n Standard deviations for [r, g, b]\n brightness : float\n Brightness jittering range (percent)\n contrast : float\n Contrast jittering range (percent)\n saturation : float\n Saturation jittering range (percent)\n hue : float\n Hue jittering range (percent)\n pca_noise : float\n Pca noise level (percent)\n inter_method : int, default=2(Area-based)\n Interpolation method for all resizing operations\n\n Possible values:\n 0: Nearest Neighbors Interpolation.\n 1: Bilinear interpolation.\n 2: Area-based (resampling using pixel area relation). It may be a\n preferred method for image decimation, as it gives moire-free\n results. But when the image is zoomed, it is similar to the Nearest\n Neighbors method. (used by default).\n 3: Bicubic interpolation over 4x4 pixel neighborhood.\n 4: Lanczos interpolation over 8x8 pixel neighborhood.\n 9: Cubic for enlarge, area for shrink, bilinear for others\n 10: Random select from interpolation method metioned above.\n Note:\n When shrinking an image, it will generally look best with AREA-based\n interpolation, whereas, when enlarging an image, it will generally look best\n with Bicubic (slow) or Bilinear (faster but still looks OK).\n\n Examples\n --------\n >>> # An example of creating multiple augmenters\n >>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True,\n ... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05,\n ... saturation=0.125, pca_noise=0.05, inter_method=10)\n >>> # dump the details\n >>> for aug in augs:\n ... aug.dumps()\n ' auglist = [] if (resize > 0): auglist.append(ResizeAug(resize, inter_method)) crop_size = (data_shape[2], data_shape[1]) if rand_resize: assert rand_crop auglist.append(RandomSizedCropAug(crop_size, 0.08, ((3.0 / 4.0), (4.0 / 3.0)), inter_method)) elif rand_crop: auglist.append(RandomCropAug(crop_size, inter_method)) else: auglist.append(CenterCropAug(crop_size, inter_method)) if rand_mirror: auglist.append(HorizontalFlipAug(0.5)) auglist.append(CastAug()) if (brightness or contrast or saturation): auglist.append(ColorJitterAug(brightness, contrast, saturation)) if hue: auglist.append(HueJitterAug(hue)) if (pca_noise > 0): eigval = np.array([55.46, 4.794, 1.148]) eigvec = np.array([[(- 0.5675), 0.7192, 0.4009], [(- 0.5808), (- 0.0045), (- 0.814)], [(- 0.5836), (- 0.6948), 0.4203]]) auglist.append(LightingAug(pca_noise, eigval, eigvec)) if (rand_gray > 0): auglist.append(RandomGrayAug(rand_gray)) if (mean is True): mean = nd.array([123.68, 116.28, 103.53]) elif (mean is not None): assert (isinstance(mean, (np.ndarray, nd.NDArray)) and (mean.shape[0] in [1, 3])) if (std is True): std = nd.array([58.395, 57.12, 57.375]) elif (std is not None): assert (isinstance(std, (np.ndarray, nd.NDArray)) and (std.shape[0] in [1, 3])) if ((mean is not None) or (std is not None)): auglist.append(ColorNormalizeAug(mean, std)) return auglist

def dumps(self): 'Saves the Augmenter to string\n\n Returns\n -------\n str\n JSON formatted string that describes the Augmenter.\n ' return json.dumps([self.__class__.__name__.lower(), self._kwargs])

Saves the Augmenter to string Returns ------- str JSON formatted string that describes the Augmenter.

python/mxnet/image/image.py

dumps

Vikas89/private-mxnet

python

def dumps(self): 'Saves the Augmenter to string\n\n Returns\n -------\n str\n JSON formatted string that describes the Augmenter.\n ' return json.dumps([self.__class__.__name__.lower(), self._kwargs])

def __call__(self, src): 'Abstract implementation body' raise NotImplementedError('Must override implementation.')

Abstract implementation body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): raise NotImplementedError('Must override implementation.')

def dumps(self): 'Override the default to avoid duplicate dump.' return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]

Override the default to avoid duplicate dump.

python/mxnet/image/image.py

dumps

Vikas89/private-mxnet

python

def dumps(self): return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]

def __call__(self, src): 'Augmenter body' for aug in self.ts: src = aug(src) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): for aug in self.ts: src = aug(src) return src

def __call__(self, src): 'Augmenter body' return resize_short(src, self.size, self.interp)

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): return resize_short(src, self.size, self.interp)

def __call__(self, src): 'Augmenter body' sizes = (src.shape[0], src.shape[1], self.size[1], self.size[0]) return imresize(src, *self.size, interp=_get_interp_method(self.interp, sizes))

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): sizes = (src.shape[0], src.shape[1], self.size[1], self.size[0]) return imresize(src, *self.size, interp=_get_interp_method(self.interp, sizes))

def __call__(self, src): 'Augmenter body' return random_crop(src, self.size, self.interp)[0]

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): return random_crop(src, self.size, self.interp)[0]

def __call__(self, src): 'Augmenter body' return random_size_crop(src, self.size, self.area, self.ratio, self.interp)[0]

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): return random_size_crop(src, self.size, self.area, self.ratio, self.interp)[0]

def __call__(self, src): 'Augmenter body' return center_crop(src, self.size, self.interp)[0]

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): return center_crop(src, self.size, self.interp)[0]

def dumps(self): 'Override the default to avoid duplicate dump.' return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]

Override the default to avoid duplicate dump.

python/mxnet/image/image.py

dumps

Vikas89/private-mxnet

python

def dumps(self): return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]]

def __call__(self, src): 'Augmenter body' random.shuffle(self.ts) for t in self.ts: src = t(src) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): random.shuffle(self.ts) for t in self.ts: src = t(src) return src

def __call__(self, src): 'Augmenter body' alpha = (1.0 + random.uniform((- self.brightness), self.brightness)) src *= alpha return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): alpha = (1.0 + random.uniform((- self.brightness), self.brightness)) src *= alpha return src

def __call__(self, src): 'Augmenter body' alpha = (1.0 + random.uniform((- self.contrast), self.contrast)) gray = (src * self.coef) gray = (((3.0 * (1.0 - alpha)) / gray.size) * nd.sum(gray)) src *= alpha src += gray return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): alpha = (1.0 + random.uniform((- self.contrast), self.contrast)) gray = (src * self.coef) gray = (((3.0 * (1.0 - alpha)) / gray.size) * nd.sum(gray)) src *= alpha src += gray return src

def __call__(self, src): 'Augmenter body' alpha = (1.0 + random.uniform((- self.saturation), self.saturation)) gray = (src * self.coef) gray = nd.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): alpha = (1.0 + random.uniform((- self.saturation), self.saturation)) gray = (src * self.coef) gray = nd.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src

def __call__(self, src): 'Augmenter body.\n Using approximate linear transfomation described in:\n https://beesbuzz.biz/code/hsv_color_transforms.php\n ' alpha = random.uniform((- self.hue), self.hue) u = np.cos((alpha * np.pi)) w = np.sin((alpha * np.pi)) bt = np.array([[1.0, 0.0, 0.0], [0.0, u, (- w)], [0.0, w, u]]) t = np.dot(np.dot(self.ityiq, bt), self.tyiq).T src = nd.dot(src, nd.array(t)) return src

Augmenter body. Using approximate linear transfomation described in: https://beesbuzz.biz/code/hsv_color_transforms.php

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): 'Augmenter body.\n Using approximate linear transfomation described in:\n https://beesbuzz.biz/code/hsv_color_transforms.php\n ' alpha = random.uniform((- self.hue), self.hue) u = np.cos((alpha * np.pi)) w = np.sin((alpha * np.pi)) bt = np.array([[1.0, 0.0, 0.0], [0.0, u, (- w)], [0.0, w, u]]) t = np.dot(np.dot(self.ityiq, bt), self.tyiq).T src = nd.dot(src, nd.array(t)) return src

def __call__(self, src): 'Augmenter body' alpha = np.random.normal(0, self.alphastd, size=(3,)) rgb = np.dot((self.eigvec * alpha), self.eigval) src += nd.array(rgb) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): alpha = np.random.normal(0, self.alphastd, size=(3,)) rgb = np.dot((self.eigvec * alpha), self.eigval) src += nd.array(rgb) return src

def __call__(self, src): 'Augmenter body' return color_normalize(src, self.mean, self.std)

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): return color_normalize(src, self.mean, self.std)

def __call__(self, src): 'Augmenter body' if (random.random() < self.p): src = nd.dot(src, self.mat) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): if (random.random() < self.p): src = nd.dot(src, self.mat) return src

def __call__(self, src): 'Augmenter body' if (random.random() < self.p): src = nd.flip(src, axis=1) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): if (random.random() < self.p): src = nd.flip(src, axis=1) return src

def __call__(self, src): 'Augmenter body' src = src.astype(self.typ) return src

Augmenter body

python/mxnet/image/image.py

__call__

Vikas89/private-mxnet

python

def __call__(self, src): src = src.astype(self.typ) return src

def reset(self): 'Resets the iterator to the beginning of the data.' if ((self.seq is not None) and self.shuffle): random.shuffle(self.seq) if ((self.last_batch_handle != 'roll_over') or (self._cache_data is None)): if (self.imgrec is not None): self.imgrec.reset() self.cur = 0 if (self._allow_read is False): self._allow_read = True

Resets the iterator to the beginning of the data.

python/mxnet/image/image.py

reset

Vikas89/private-mxnet

python

def reset(self): if ((self.seq is not None) and self.shuffle): random.shuffle(self.seq) if ((self.last_batch_handle != 'roll_over') or (self._cache_data is None)): if (self.imgrec is not None): self.imgrec.reset() self.cur = 0 if (self._allow_read is False): self._allow_read = True

def hard_reset(self): 'Resets the iterator and ignore roll over data' if ((self.seq is not None) and self.shuffle): random.shuffle(self.seq) if (self.imgrec is not None): self.imgrec.reset() self.cur = 0 self._allow_read = True self._cache_data = None self._cache_label = None self._cache_idx = None

Resets the iterator and ignore roll over data

python/mxnet/image/image.py

hard_reset

Vikas89/private-mxnet

python

def hard_reset(self): if ((self.seq is not None) and self.shuffle): random.shuffle(self.seq) if (self.imgrec is not None): self.imgrec.reset() self.cur = 0 self._allow_read = True self._cache_data = None self._cache_label = None self._cache_idx = None

def next_sample(self): 'Helper function for reading in next sample.' if (self._allow_read is False): raise StopIteration if (self.seq is not None): if (self.cur < self.num_image): idx = self.seq[self.cur] else: if (self.last_batch_handle != 'discard'): self.cur = 0 raise StopIteration self.cur += 1 if (self.imgrec is not None): s = self.imgrec.read_idx(idx) (header, img) = recordio.unpack(s) if (self.imglist is None): return (header.label, img) else: return (self.imglist[idx][0], img) else: (label, fname) = self.imglist[idx] return (label, self.read_image(fname)) else: s = self.imgrec.read() if (s is None): if (self.last_batch_handle != 'discard'): self.imgrec.reset() raise StopIteration (header, img) = recordio.unpack(s) return (header.label, img)

Helper function for reading in next sample.

python/mxnet/image/image.py

next_sample

Vikas89/private-mxnet

python

def next_sample(self): if (self._allow_read is False): raise StopIteration if (self.seq is not None): if (self.cur < self.num_image): idx = self.seq[self.cur] else: if (self.last_batch_handle != 'discard'): self.cur = 0 raise StopIteration self.cur += 1 if (self.imgrec is not None): s = self.imgrec.read_idx(idx) (header, img) = recordio.unpack(s) if (self.imglist is None): return (header.label, img) else: return (self.imglist[idx][0], img) else: (label, fname) = self.imglist[idx] return (label, self.read_image(fname)) else: s = self.imgrec.read() if (s is None): if (self.last_batch_handle != 'discard'): self.imgrec.reset() raise StopIteration (header, img) = recordio.unpack(s) return (header.label, img)

def _batchify(self, batch_data, batch_label, start=0): 'Helper function for batchifying data' i = start batch_size = self.batch_size try: while (i < batch_size): (label, s) = self.next_sample() data = self.imdecode(s) try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue data = self.augmentation_transform(data) assert (i < batch_size), 'Batch size must be multiples of augmenter output length' batch_data[i] = self.postprocess_data(data) batch_label[i] = label i += 1 except StopIteration: if (not i): raise StopIteration return i

Helper function for batchifying data

python/mxnet/image/image.py

_batchify

Vikas89/private-mxnet

python

def _batchify(self, batch_data, batch_label, start=0): i = start batch_size = self.batch_size try: while (i < batch_size): (label, s) = self.next_sample() data = self.imdecode(s) try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue data = self.augmentation_transform(data) assert (i < batch_size), 'Batch size must be multiples of augmenter output length' batch_data[i] = self.postprocess_data(data) batch_label[i] = label i += 1 except StopIteration: if (not i): raise StopIteration return i

def next(self): 'Returns the next batch of data.' batch_size = self.batch_size (c, h, w) = self.data_shape if (self._cache_data is not None): assert (self._cache_label is not None), "_cache_label didn't have values" assert (self._cache_idx is not None), "_cache_idx didn't have values" batch_data = self._cache_data batch_label = self._cache_label i = self._cache_idx else: batch_data = nd.empty((batch_size, c, h, w)) batch_label = nd.empty(self.provide_label[0][1]) i = self._batchify(batch_data, batch_label) pad = (batch_size - i) if (pad != 0): if (self.last_batch_handle == 'discard'): raise StopIteration elif ((self.last_batch_handle == 'roll_over') and (self._cache_data is None)): self._cache_data = batch_data self._cache_label = batch_label self._cache_idx = i raise StopIteration else: _ = self._batchify(batch_data, batch_label, i) if (self.last_batch_handle == 'pad'): self._allow_read = False else: self._cache_data = None self._cache_label = None self._cache_idx = None return io.DataBatch([batch_data], [batch_label], pad=pad)

Returns the next batch of data.

python/mxnet/image/image.py

next

Vikas89/private-mxnet

python

def next(self): batch_size = self.batch_size (c, h, w) = self.data_shape if (self._cache_data is not None): assert (self._cache_label is not None), "_cache_label didn't have values" assert (self._cache_idx is not None), "_cache_idx didn't have values" batch_data = self._cache_data batch_label = self._cache_label i = self._cache_idx else: batch_data = nd.empty((batch_size, c, h, w)) batch_label = nd.empty(self.provide_label[0][1]) i = self._batchify(batch_data, batch_label) pad = (batch_size - i) if (pad != 0): if (self.last_batch_handle == 'discard'): raise StopIteration elif ((self.last_batch_handle == 'roll_over') and (self._cache_data is None)): self._cache_data = batch_data self._cache_label = batch_label self._cache_idx = i raise StopIteration else: _ = self._batchify(batch_data, batch_label, i) if (self.last_batch_handle == 'pad'): self._allow_read = False else: self._cache_data = None self._cache_label = None self._cache_idx = None return io.DataBatch([batch_data], [batch_label], pad=pad)

def check_data_shape(self, data_shape): 'Checks if the input data shape is valid' if (not (len(data_shape) == 3)): raise ValueError('data_shape should have length 3, with dimensions CxHxW') if (not (data_shape[0] == 3)): raise ValueError('This iterator expects inputs to have 3 channels.')

Checks if the input data shape is valid

python/mxnet/image/image.py

check_data_shape

Vikas89/private-mxnet

python

def check_data_shape(self, data_shape): if (not (len(data_shape) == 3)): raise ValueError('data_shape should have length 3, with dimensions CxHxW') if (not (data_shape[0] == 3)): raise ValueError('This iterator expects inputs to have 3 channels.')

def check_valid_image(self, data): 'Checks if the input data is valid' if (len(data[0].shape) == 0): raise RuntimeError('Data shape is wrong')

Checks if the input data is valid

python/mxnet/image/image.py

check_valid_image

Vikas89/private-mxnet

python

def check_valid_image(self, data): if (len(data[0].shape) == 0): raise RuntimeError('Data shape is wrong')

def imdecode(self, s): 'Decodes a string or byte string to an NDArray.\n See mx.img.imdecode for more details.' def locate(): 'Locate the image file/index if decode fails.' if (self.seq is not None): idx = self.seq[((self.cur % self.num_image) - 1)] else: idx = ((self.cur % self.num_image) - 1) if (self.imglist is not None): (_, fname) = self.imglist[idx] msg = 'filename: {}'.format(fname) else: msg = 'index: {}'.format(idx) return ('Broken image ' + msg) try: img = imdecode(s) except Exception as e: raise RuntimeError('{}, {}'.format(locate(), e)) return img

Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.

python/mxnet/image/image.py

imdecode

Vikas89/private-mxnet

python

def imdecode(self, s): 'Decodes a string or byte string to an NDArray.\n See mx.img.imdecode for more details.' def locate(): 'Locate the image file/index if decode fails.' if (self.seq is not None): idx = self.seq[((self.cur % self.num_image) - 1)] else: idx = ((self.cur % self.num_image) - 1) if (self.imglist is not None): (_, fname) = self.imglist[idx] msg = 'filename: {}'.format(fname) else: msg = 'index: {}'.format(idx) return ('Broken image ' + msg) try: img = imdecode(s) except Exception as e: raise RuntimeError('{}, {}'.format(locate(), e)) return img

def read_image(self, fname): "Reads an input image `fname` and returns the decoded raw bytes.\n Example usage:\n ----------\n >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.\n " with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img

Reads an input image `fname` and returns the decoded raw bytes. Example usage: ---------- >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.

python/mxnet/image/image.py

read_image

Vikas89/private-mxnet

python

def read_image(self, fname): "Reads an input image `fname` and returns the decoded raw bytes.\n Example usage:\n ----------\n >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.\n " with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img

def augmentation_transform(self, data): 'Transforms input data with specified augmentation.' for aug in self.auglist: data = aug(data) return data

Transforms input data with specified augmentation.

python/mxnet/image/image.py

augmentation_transform

Vikas89/private-mxnet

python

def augmentation_transform(self, data): for aug in self.auglist: data = aug(data) return data

def postprocess_data(self, datum): 'Final postprocessing step before image is loaded into the batch.' return nd.transpose(datum, axes=(2, 0, 1))

Final postprocessing step before image is loaded into the batch.

python/mxnet/image/image.py

postprocess_data

Vikas89/private-mxnet

python

def postprocess_data(self, datum): return nd.transpose(datum, axes=(2, 0, 1))