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nirum/tableprint
tableprint/printer.py
top
def top(n, width=WIDTH, style=STYLE): """Prints the top row of a table""" return hrule(n, width, linestyle=STYLES[style].top)
python
def top(n, width=WIDTH, style=STYLE): """Prints the top row of a table""" return hrule(n, width, linestyle=STYLES[style].top)
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Prints the top row of a table
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train
https://github.com/nirum/tableprint/blob/50ab4b96706fce8ee035a4d48cb456e3271eab3d/tableprint/printer.py#L245-L247
nirum/tableprint
tableprint/printer.py
banner
def banner(message, width=30, style='banner', out=sys.stdout): """Prints a banner message Parameters ---------- message : string The message to print in the banner width : int The minimum width of the banner (Default: 30) style : string A line formatting style (Default: 'banner') out : writer An object that has write() and flush() methods (Default: sys.stdout) """ out.write(header([message], width=max(width, len(message)), style=style) + '\n') out.flush()
python
def banner(message, width=30, style='banner', out=sys.stdout): """Prints a banner message Parameters ---------- message : string The message to print in the banner width : int The minimum width of the banner (Default: 30) style : string A line formatting style (Default: 'banner') out : writer An object that has write() and flush() methods (Default: sys.stdout) """ out.write(header([message], width=max(width, len(message)), style=style) + '\n') out.flush()
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Prints a banner message Parameters ---------- message : string The message to print in the banner width : int The minimum width of the banner (Default: 30) style : string A line formatting style (Default: 'banner') out : writer An object that has write() and flush() methods (Default: sys.stdout)
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train
https://github.com/nirum/tableprint/blob/50ab4b96706fce8ee035a4d48cb456e3271eab3d/tableprint/printer.py#L255-L273
nirum/tableprint
tableprint/printer.py
dataframe
def dataframe(df, **kwargs): """Print table with data from the given pandas DataFrame Parameters ---------- df : DataFrame A pandas DataFrame with the table to print """ table(df.values, list(df.columns), **kwargs)
python
def dataframe(df, **kwargs): """Print table with data from the given pandas DataFrame Parameters ---------- df : DataFrame A pandas DataFrame with the table to print """ table(df.values, list(df.columns), **kwargs)
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Print table with data from the given pandas DataFrame Parameters ---------- df : DataFrame A pandas DataFrame with the table to print
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train
https://github.com/nirum/tableprint/blob/50ab4b96706fce8ee035a4d48cb456e3271eab3d/tableprint/printer.py#L276-L284
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_address
def set_address(self, address): """ Set the address. :param address: """ self._query_params += str(QueryParam.ADVANCED) + str(QueryParam.ADDRESS) + address.replace(" ", "+").lower()
python
def set_address(self, address): """ Set the address. :param address: """ self._query_params += str(QueryParam.ADVANCED) + str(QueryParam.ADDRESS) + address.replace(" ", "+").lower()
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Set the address. :param address:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L42-L47
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_min_lease
def set_min_lease(self, min_lease): """ Set the minimum lease period in months. :param min_lease: int """ self._query_params += str(QueryParam.MIN_LEASE) + str(min_lease)
python
def set_min_lease(self, min_lease): """ Set the minimum lease period in months. :param min_lease: int """ self._query_params += str(QueryParam.MIN_LEASE) + str(min_lease)
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Set the minimum lease period in months. :param min_lease: int
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L49-L54
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_added_since
def set_added_since(self, added): """ Set this to retrieve ads that are a given number of days old. For example to retrieve listings that have been been added a week ago: set_added_since(7) :param added: int """ self._query_params += str(QueryParam.DAYS_OLD) + str(added)
python
def set_added_since(self, added): """ Set this to retrieve ads that are a given number of days old. For example to retrieve listings that have been been added a week ago: set_added_since(7) :param added: int """ self._query_params += str(QueryParam.DAYS_OLD) + str(added)
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Set this to retrieve ads that are a given number of days old. For example to retrieve listings that have been been added a week ago: set_added_since(7) :param added: int
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L56-L62
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_max_lease
def set_max_lease(self, max_lease): """ Set the maximum lease period in months. :param max_lease: int """ self._query_params += str(QueryParam.MAX_LEASE) + str(max_lease)
python
def set_max_lease(self, max_lease): """ Set the maximum lease period in months. :param max_lease: int """ self._query_params += str(QueryParam.MAX_LEASE) + str(max_lease)
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Set the maximum lease period in months. :param max_lease: int
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L64-L69
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_availability
def set_availability(self, availability): """ Set the maximum lease period in months. :param availability: """ if availability >= 5: availability = '5%2B' self._query_params += str(QueryParam.AVALIABILITY) + str(availability)
python
def set_availability(self, availability): """ Set the maximum lease period in months. :param availability: """ if availability >= 5: availability = '5%2B' self._query_params += str(QueryParam.AVALIABILITY) + str(availability)
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Set the maximum lease period in months. :param availability:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L71-L78
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_room_type
def set_room_type(self, room_type): """ Set the room type. :param room_type: """ if not isinstance(room_type, RoomType): raise DaftException("room_type should be an instance of RoomType.") self._query_params += str(QueryParam.ROOM_TYPE) + str(room_type)
python
def set_room_type(self, room_type): """ Set the room type. :param room_type: """ if not isinstance(room_type, RoomType): raise DaftException("room_type should be an instance of RoomType.") self._query_params += str(QueryParam.ROOM_TYPE) + str(room_type)
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Set the room type. :param room_type:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L106-L113
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_keywords
def set_keywords(self, keywords): """ Pass an array to filter the result by keywords. :param keywords """ self._query_params += str(QueryParam.KEYWORDS) + '+'.join(keywords)
python
def set_keywords(self, keywords): """ Pass an array to filter the result by keywords. :param keywords """ self._query_params += str(QueryParam.KEYWORDS) + '+'.join(keywords)
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Pass an array to filter the result by keywords. :param keywords
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L123-L128
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_area
def set_area(self, area): """ The area to retrieve listings from. Use an array to search multiple areas. :param area: :return: """ self._area = area.replace(" ", "-").lower() if isinstance(area, str) else ','.join( map(lambda x: x.lower().replace(' ', '-'), area))
python
def set_area(self, area): """ The area to retrieve listings from. Use an array to search multiple areas. :param area: :return: """ self._area = area.replace(" ", "-").lower() if isinstance(area, str) else ','.join( map(lambda x: x.lower().replace(' ', '-'), area))
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The area to retrieve listings from. Use an array to search multiple areas. :param area: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L139-L146
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_open_viewing
def set_open_viewing(self, open_viewing): """ Set to True to only search for properties that have upcoming 'open for viewing' dates. :param open_viewing: :return: """ if open_viewing: self._open_viewing = open_viewing self._query_params += str(QueryParam.OPEN_VIEWING)
python
def set_open_viewing(self, open_viewing): """ Set to True to only search for properties that have upcoming 'open for viewing' dates. :param open_viewing: :return: """ if open_viewing: self._open_viewing = open_viewing self._query_params += str(QueryParam.OPEN_VIEWING)
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Set to True to only search for properties that have upcoming 'open for viewing' dates. :param open_viewing: :return:
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https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L156-L164
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_offset
def set_offset(self, offset): """ The page number which is in increments of 10. The default page number is 0. :param offset: :return: """ if not isinstance(offset, int) or offset < 0: raise DaftException("Offset should be a positive integer.") self._offset = str(offset)
python
def set_offset(self, offset): """ The page number which is in increments of 10. The default page number is 0. :param offset: :return: """ if not isinstance(offset, int) or offset < 0: raise DaftException("Offset should be a positive integer.") self._offset = str(offset)
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The page number which is in increments of 10. The default page number is 0. :param offset: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L166-L176
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_min_price
def set_min_price(self, min_price): """ The minimum price. :param min_price: :return: """ if not isinstance(min_price, int): raise DaftException("Min price should be an integer.") self._min_price = str(min_price) self._price += str(QueryParam.MIN_PRICE) + self._min_price
python
def set_min_price(self, min_price): """ The minimum price. :param min_price: :return: """ if not isinstance(min_price, int): raise DaftException("Min price should be an integer.") self._min_price = str(min_price) self._price += str(QueryParam.MIN_PRICE) + self._min_price
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The minimum price. :param min_price: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L178-L189
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_max_price
def set_max_price(self, max_price): """ The maximum price. :param max_price: :return: """ if not isinstance(max_price, int): raise DaftException("Max price should be an integer.") self._max_price = str(max_price) self._price += str(QueryParam.MAX_PRICE) + self._max_price
python
def set_max_price(self, max_price): """ The maximum price. :param max_price: :return: """ if not isinstance(max_price, int): raise DaftException("Max price should be an integer.") self._max_price = str(max_price) self._price += str(QueryParam.MAX_PRICE) + self._max_price
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The maximum price. :param max_price: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L191-L202
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_listing_type
def set_listing_type(self, listing_type): """ The listings you'd like to scrape i.e houses, properties, auction, commercial or apartments. Use the SaleType or RentType enum to select the listing type. i.e set_listing_type(SaleType.PROPERTIES) :param listing_type: :return: """ if not isinstance(listing_type, SaleType) and not isinstance(listing_type, RentType): raise DaftException("listing_type should be an instance of SaleType or RentType.") self._listing_type = listing_type
python
def set_listing_type(self, listing_type): """ The listings you'd like to scrape i.e houses, properties, auction, commercial or apartments. Use the SaleType or RentType enum to select the listing type. i.e set_listing_type(SaleType.PROPERTIES) :param listing_type: :return: """ if not isinstance(listing_type, SaleType) and not isinstance(listing_type, RentType): raise DaftException("listing_type should be an instance of SaleType or RentType.") self._listing_type = listing_type
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The listings you'd like to scrape i.e houses, properties, auction, commercial or apartments. Use the SaleType or RentType enum to select the listing type. i.e set_listing_type(SaleType.PROPERTIES) :param listing_type: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L204-L216
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_min_beds
def set_min_beds(self, min_beds): """ The minimum number of beds. :param min_beds: :return: """ if not isinstance(min_beds, int): raise DaftException("Minimum number of beds should be an integer.") self._min_beds = str(min_beds) self._query_params += str(QueryParam.MIN_BEDS) + self._min_beds
python
def set_min_beds(self, min_beds): """ The minimum number of beds. :param min_beds: :return: """ if not isinstance(min_beds, int): raise DaftException("Minimum number of beds should be an integer.") self._min_beds = str(min_beds) self._query_params += str(QueryParam.MIN_BEDS) + self._min_beds
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The minimum number of beds. :param min_beds: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L226-L237
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_max_beds
def set_max_beds(self, max_beds): """ The maximum number of beds. :param max_beds: :return: """ if not isinstance(max_beds, int): raise DaftException("Maximum number of beds should be an integer.") self._max_beds = str(max_beds) self._query_params += str(QueryParam.MAX_BEDS) + self._max_beds
python
def set_max_beds(self, max_beds): """ The maximum number of beds. :param max_beds: :return: """ if not isinstance(max_beds, int): raise DaftException("Maximum number of beds should be an integer.") self._max_beds = str(max_beds) self._query_params += str(QueryParam.MAX_BEDS) + self._max_beds
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The maximum number of beds. :param max_beds: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L239-L249
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_sort_by
def set_sort_by(self, sort_by): """ Use this method to sort by price, distance, upcoming viewing or date using the SortType object. :param sort_by: :return: """ if not isinstance(sort_by, SortType): raise DaftException("sort_by should be an instance of SortType.") self._sort_by = str(sort_by)
python
def set_sort_by(self, sort_by): """ Use this method to sort by price, distance, upcoming viewing or date using the SortType object. :param sort_by: :return: """ if not isinstance(sort_by, SortType): raise DaftException("sort_by should be an instance of SortType.") self._sort_by = str(sort_by)
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Use this method to sort by price, distance, upcoming viewing or date using the SortType object. :param sort_by: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L251-L260
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_sort_order
def set_sort_order(self, sort_order): """ Use the SortOrder object to sort the listings descending or ascending. :param sort_order: :return: """ if not isinstance(sort_order, SortOrder): raise DaftException("sort_order should be an instance of SortOrder.") self._sort_order = str(sort_order)
python
def set_sort_order(self, sort_order): """ Use the SortOrder object to sort the listings descending or ascending. :param sort_order: :return: """ if not isinstance(sort_order, SortOrder): raise DaftException("sort_order should be an instance of SortOrder.") self._sort_order = str(sort_order)
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Use the SortOrder object to sort the listings descending or ascending. :param sort_order: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L262-L272
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_commercial_property_type
def set_commercial_property_type(self, commercial_property_type): """ Use the CommercialType object to set the commercial property type. :param commercial_property_type: :return: """ if not isinstance(commercial_property_type, CommercialType): raise DaftException("commercial_property_type should be an instance of CommercialType.") self._commercial_property_type = str(commercial_property_type)
python
def set_commercial_property_type(self, commercial_property_type): """ Use the CommercialType object to set the commercial property type. :param commercial_property_type: :return: """ if not isinstance(commercial_property_type, CommercialType): raise DaftException("commercial_property_type should be an instance of CommercialType.") self._commercial_property_type = str(commercial_property_type)
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Use the CommercialType object to set the commercial property type. :param commercial_property_type: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L274-L284
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_commercial_min_size
def set_commercial_min_size(self, commercial_min_size): """ The minimum size in sq ft. :param commercial_min_size: :return: """ if not isinstance(commercial_min_size, int): raise DaftException("commercial_min_size should be an integer.") self._commercial_min_size = str(commercial_min_size) self._query_params += str(QueryParam.COMMERCIAL_MIN) + self._commercial_min_size
python
def set_commercial_min_size(self, commercial_min_size): """ The minimum size in sq ft. :param commercial_min_size: :return: """ if not isinstance(commercial_min_size, int): raise DaftException("commercial_min_size should be an integer.") self._commercial_min_size = str(commercial_min_size) self._query_params += str(QueryParam.COMMERCIAL_MIN) + self._commercial_min_size
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The minimum size in sq ft. :param commercial_min_size: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L286-L296
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_commercial_max_size
def set_commercial_max_size(self, commercial_max_size): """ The maximum size in sq ft. :param commercial_max_size: :return: """ if not isinstance(commercial_max_size, int): raise DaftException("commercial_max_size should be an integer.") self._commercial_max_size = str(commercial_max_size) self._query_params += str(QueryParam.COMMERCIAL_MAX) + self._commercial_max_size
python
def set_commercial_max_size(self, commercial_max_size): """ The maximum size in sq ft. :param commercial_max_size: :return: """ if not isinstance(commercial_max_size, int): raise DaftException("commercial_max_size should be an integer.") self._commercial_max_size = str(commercial_max_size) self._query_params += str(QueryParam.COMMERCIAL_MAX) + self._commercial_max_size
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The maximum size in sq ft. :param commercial_max_size: :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L298-L308
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_student_accommodation_type
def set_student_accommodation_type(self, student_accommodation_type): """ Set the student accomodation type. :param student_accommodation_type: StudentAccomodationType """ if not isinstance(student_accommodation_type, StudentAccommodationType): raise DaftException("student_accommodation_type should be an instance of StudentAccommodationType.") self._student_accommodation_type = str(student_accommodation_type)
python
def set_student_accommodation_type(self, student_accommodation_type): """ Set the student accomodation type. :param student_accommodation_type: StudentAccomodationType """ if not isinstance(student_accommodation_type, StudentAccommodationType): raise DaftException("student_accommodation_type should be an instance of StudentAccommodationType.") self._student_accommodation_type = str(student_accommodation_type)
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Set the student accomodation type. :param student_accommodation_type: StudentAccomodationType
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L318-L326
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_num_occupants
def set_num_occupants(self, num_occupants): """ Set the max number of occupants living in the property for rent. :param num_occupants: int """ self._query_params += str(QueryParam.NUM_OCCUPANTS) + str(num_occupants)
python
def set_num_occupants(self, num_occupants): """ Set the max number of occupants living in the property for rent. :param num_occupants: int """ self._query_params += str(QueryParam.NUM_OCCUPANTS) + str(num_occupants)
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Set the max number of occupants living in the property for rent. :param num_occupants: int
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L328-L333
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_public_transport_route
def set_public_transport_route(self, public_transport_route): """ Set the public transport route. :param public_transport_route: TransportRoute """ self._query_params += str(QueryParam.ROUTE_ID) + str(public_transport_route)
python
def set_public_transport_route(self, public_transport_route): """ Set the public transport route. :param public_transport_route: TransportRoute """ self._query_params += str(QueryParam.ROUTE_ID) + str(public_transport_route)
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Set the public transport route. :param public_transport_route: TransportRoute
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L342-L347
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.set_property_type
def set_property_type(self, property_types): """ Set the property type for rents. :param property_types: Array of Enum PropertyType """ query_add = '' for property_type in property_types: if not isinstance(property_type, PropertyType): raise DaftException("property_types should be an instance of PropertyType.") query_add += str(property_type) self._query_params += query_add
python
def set_property_type(self, property_types): """ Set the property type for rents. :param property_types: Array of Enum PropertyType """ query_add = '' for property_type in property_types: if not isinstance(property_type, PropertyType): raise DaftException("property_types should be an instance of PropertyType.") query_add += str(property_type) self._query_params += query_add
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Set the property type for rents. :param property_types: Array of Enum PropertyType
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L357-L368
AnthonyBloomer/daftlistings
daftlistings/daft.py
Daft.search
def search(self): """ The search function returns an array of Listing objects. :return: Listing object """ self.set_url() listings = [] request = Request(debug=self._debug) url = self.get_url() soup = request.get(url) divs = soup.find_all("div", {"class": "box"}) [listings.append(Listing(div, debug=self._debug)) for div in divs] return listings
python
def search(self): """ The search function returns an array of Listing objects. :return: Listing object """ self.set_url() listings = [] request = Request(debug=self._debug) url = self.get_url() soup = request.get(url) divs = soup.find_all("div", {"class": "box"}) [listings.append(Listing(div, debug=self._debug)) for div in divs] return listings
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The search function returns an array of Listing objects. :return: Listing object
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/daft.py#L424-L436
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.price_change
def price_change(self): """ This method returns any price change. :return: """ try: if self._data_from_search: return self._data_from_search.find('div', {'class': 'price-changes-sr'}).text else: return self._ad_page_content.find('div', {'class': 'price-changes-sr'}).text except Exception as e: if self._debug: logging.error( "Error getting price_change. Error message: " + e.args[0]) return
python
def price_change(self): """ This method returns any price change. :return: """ try: if self._data_from_search: return self._data_from_search.find('div', {'class': 'price-changes-sr'}).text else: return self._ad_page_content.find('div', {'class': 'price-changes-sr'}).text except Exception as e: if self._debug: logging.error( "Error getting price_change. Error message: " + e.args[0]) return
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This method returns any price change. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L104-L118
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.upcoming_viewings
def upcoming_viewings(self): """ Returns an array of upcoming viewings for a property. :return: """ upcoming_viewings = [] try: if self._data_from_search: viewings = self._data_from_search.find_all( 'div', {'class': 'smi-onview-text'}) else: viewings = [] except Exception as e: if self._debug: logging.error( "Error getting upcoming_viewings. Error message: " + e.args[0]) return for viewing in viewings: upcoming_viewings.append(viewing.text.strip()) return upcoming_viewings
python
def upcoming_viewings(self): """ Returns an array of upcoming viewings for a property. :return: """ upcoming_viewings = [] try: if self._data_from_search: viewings = self._data_from_search.find_all( 'div', {'class': 'smi-onview-text'}) else: viewings = [] except Exception as e: if self._debug: logging.error( "Error getting upcoming_viewings. Error message: " + e.args[0]) return for viewing in viewings: upcoming_viewings.append(viewing.text.strip()) return upcoming_viewings
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Returns an array of upcoming viewings for a property. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L121-L140
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.facilities
def facilities(self): """ This method returns the properties facilities. :return: """ facilities = [] try: list_items = self._ad_page_content.select("#facilities li") except Exception as e: if self._debug: logging.error( "Error getting facilities. Error message: " + e.args[0]) return for li in list_items: facilities.append(li.text) return facilities
python
def facilities(self): """ This method returns the properties facilities. :return: """ facilities = [] try: list_items = self._ad_page_content.select("#facilities li") except Exception as e: if self._debug: logging.error( "Error getting facilities. Error message: " + e.args[0]) return for li in list_items: facilities.append(li.text) return facilities
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This method returns the properties facilities. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L143-L159
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.overviews
def overviews(self): """ This method returns the properties overviews. :return: """ overviews = [] try: list_items = self._ad_page_content.select("#overview li") except Exception as e: if self._debug: logging.error( "Error getting overviews. Error message: " + e.args[0]) return for li in list_items: overviews.append(li.text) return overviews
python
def overviews(self): """ This method returns the properties overviews. :return: """ overviews = [] try: list_items = self._ad_page_content.select("#overview li") except Exception as e: if self._debug: logging.error( "Error getting overviews. Error message: " + e.args[0]) return for li in list_items: overviews.append(li.text) return overviews
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This method returns the properties overviews. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L162-L178
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.features
def features(self): """ This method returns the properties features. :return: """ features = [] try: list_items = self._ad_page_content.select("#features li") except Exception as e: if self._debug: logging.error( "Error getting features. Error message: " + e.args[0]) return for li in list_items: features.append(li.text) return features
python
def features(self): """ This method returns the properties features. :return: """ features = [] try: list_items = self._ad_page_content.select("#features li") except Exception as e: if self._debug: logging.error( "Error getting features. Error message: " + e.args[0]) return for li in list_items: features.append(li.text) return features
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This method returns the properties features. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L181-L197
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.formalised_address
def formalised_address(self): """ This method returns the formalised address. :return: """ try: if self._data_from_search: t = self._data_from_search.find('a').contents[0] else: t = self._ad_page_content.find( 'div', {'class': 'smi-object-header'}).find( 'h1').text.strip() except Exception as e: if self._debug: logging.error( "Error getting formalised_address. Error message: " + e.args[0]) return s = t.split('-') a = s[0].strip() if 'SALE AGREED' in a: a = a.split() a = a[3:] a = ' '.join([str(x) for x in a]) return a.lower().title().strip()
python
def formalised_address(self): """ This method returns the formalised address. :return: """ try: if self._data_from_search: t = self._data_from_search.find('a').contents[0] else: t = self._ad_page_content.find( 'div', {'class': 'smi-object-header'}).find( 'h1').text.strip() except Exception as e: if self._debug: logging.error( "Error getting formalised_address. Error message: " + e.args[0]) return s = t.split('-') a = s[0].strip() if 'SALE AGREED' in a: a = a.split() a = a[3:] a = ' '.join([str(x) for x in a]) return a.lower().title().strip()
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This method returns the formalised address. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L200-L224
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.address_line_1
def address_line_1(self): """ This method returns the first line of the address. :return: """ formalised_address = self.formalised_address if formalised_address is None: return try: address = formalised_address.split(',') except Exception as e: if self._debug: logging.error( "Error getting address_line_1. Error message: " + e.args[0]) return return address[0].strip()
python
def address_line_1(self): """ This method returns the first line of the address. :return: """ formalised_address = self.formalised_address if formalised_address is None: return try: address = formalised_address.split(',') except Exception as e: if self._debug: logging.error( "Error getting address_line_1. Error message: " + e.args[0]) return return address[0].strip()
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This method returns the first line of the address. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L227-L243
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.images
def images(self): """ This method returns the listing image. :return: """ try: uls = self._ad_page_content.find( "ul", {"class": "smi-gallery-list"}) except Exception as e: if self._debug: logging.error( "Error getting images. Error message: " + e.args[0]) return images = [] if uls is None: return for li in uls.find_all('li'): if li.find('img')['src']: images.append(li.find('img')['src']) return images
python
def images(self): """ This method returns the listing image. :return: """ try: uls = self._ad_page_content.find( "ul", {"class": "smi-gallery-list"}) except Exception as e: if self._debug: logging.error( "Error getting images. Error message: " + e.args[0]) return images = [] if uls is None: return for li in uls.find_all('li'): if li.find('img')['src']: images.append(li.find('img')['src']) return images
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This method returns the listing image. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L266-L286
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.agent
def agent(self): """ This method returns the agent name. :return: """ try: if self._data_from_search: agent = self._data_from_search.find( 'ul', {'class': 'links'}).text return agent.split(':')[1].strip() else: return self._ad_page_content.find('a', {'id': 'smi-link-branded'}).text.strip() except Exception as e: if self._debug: logging.error( "Error getting agent. Error message: " + e.args[0]) return
python
def agent(self): """ This method returns the agent name. :return: """ try: if self._data_from_search: agent = self._data_from_search.find( 'ul', {'class': 'links'}).text return agent.split(':')[1].strip() else: return self._ad_page_content.find('a', {'id': 'smi-link-branded'}).text.strip() except Exception as e: if self._debug: logging.error( "Error getting agent. Error message: " + e.args[0]) return
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This method returns the agent name. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L311-L327
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.agent_url
def agent_url(self): """ This method returns the agent's url. :return: """ try: if self._data_from_search: agent = self._data_from_search.find('ul', {'class': 'links'}) links = agent.find_all('a') return links[1]['href'] else: return self._ad_page_content.find('a', {'id': 'smi-link-branded'})['href'] except Exception as e: if self._debug: logging.error( "Error getting agent_url. Error message: " + e.args[0]) return
python
def agent_url(self): """ This method returns the agent's url. :return: """ try: if self._data_from_search: agent = self._data_from_search.find('ul', {'class': 'links'}) links = agent.find_all('a') return links[1]['href'] else: return self._ad_page_content.find('a', {'id': 'smi-link-branded'})['href'] except Exception as e: if self._debug: logging.error( "Error getting agent_url. Error message: " + e.args[0]) return
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This method returns the agent's url. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L330-L346
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.contact_number
def contact_number(self): """ This method returns the contact phone number. :return: """ try: number = self._ad_page_content.find( 'button', {'class': 'phone-number'}) return (base64.b64decode(number.attrs['data-p'])).decode('ascii') except Exception as e: if self._debug: logging.error( "Error getting contact_number. Error message: " + e.args[0]) return 'N/A'
python
def contact_number(self): """ This method returns the contact phone number. :return: """ try: number = self._ad_page_content.find( 'button', {'class': 'phone-number'}) return (base64.b64decode(number.attrs['data-p'])).decode('ascii') except Exception as e: if self._debug: logging.error( "Error getting contact_number. Error message: " + e.args[0]) return 'N/A'
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This method returns the contact phone number. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L349-L362
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.daft_link
def daft_link(self): """ This method returns the url of the listing. :return: """ try: if self._data_from_search: link = self._data_from_search.find('a', href=True) return 'http://www.daft.ie' + link['href'] else: return self._ad_page_content.find('link', {'rel': 'canonical'})['href'] except Exception as e: if self._debug: logging.error( "Error getting daft_link. Error message: " + e.args[0]) return
python
def daft_link(self): """ This method returns the url of the listing. :return: """ try: if self._data_from_search: link = self._data_from_search.find('a', href=True) return 'http://www.daft.ie' + link['href'] else: return self._ad_page_content.find('link', {'rel': 'canonical'})['href'] except Exception as e: if self._debug: logging.error( "Error getting daft_link. Error message: " + e.args[0]) return
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This method returns the url of the listing. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L365-L380
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.shortcode
def shortcode(self): """ This method returns the shortcode url of the listing. :return: """ try: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Shortcode' in str(s)][0] + 1 return div.contents[index]['href'] except Exception as e: if self._debug: logging.error( "Error getting shortcode. Error message: " + e.args[0]) return 'N/A'
python
def shortcode(self): """ This method returns the shortcode url of the listing. :return: """ try: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Shortcode' in str(s)][0] + 1 return div.contents[index]['href'] except Exception as e: if self._debug: logging.error( "Error getting shortcode. Error message: " + e.args[0]) return 'N/A'
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This method returns the shortcode url of the listing. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L383-L398
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.views
def views(self): """ This method returns the "Property Views" from listing. :return: """ try: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Property Views' in str(s)][0] + 1 return int(''.join(list(filter(str.isdigit, div.contents[index])))) except Exception as e: if self._debug: logging.error( "Error getting views. Error message: " + e.args[0]) return 'N/A'
python
def views(self): """ This method returns the "Property Views" from listing. :return: """ try: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Property Views' in str(s)][0] + 1 return int(''.join(list(filter(str.isdigit, div.contents[index])))) except Exception as e: if self._debug: logging.error( "Error getting views. Error message: " + e.args[0]) return 'N/A'
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This method returns the "Property Views" from listing. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L419-L434
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.dwelling_type
def dwelling_type(self): """ This method returns the dwelling type. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'ul', {"class": "info"}).text s = info.split('|') return s[0].strip() else: return self._ad_page_content.find( 'div', {'id': 'smi-summary-items'} ).find('span', {'class': 'header_text'}).text except Exception as e: if self._debug: logging.error( "Error getting dwelling_type. Error message: " + e.args[0]) return
python
def dwelling_type(self): """ This method returns the dwelling type. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'ul', {"class": "info"}).text s = info.split('|') return s[0].strip() else: return self._ad_page_content.find( 'div', {'id': 'smi-summary-items'} ).find('span', {'class': 'header_text'}).text except Exception as e: if self._debug: logging.error( "Error getting dwelling_type. Error message: " + e.args[0]) return
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This method returns the dwelling type. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L437-L457
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.posted_since
def posted_since(self): """ This method returns the date the listing was entered. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'div', {"class": "date_entered"}).text s = info.split(':') return s[-1].strip() else: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Entered/Renewed' in str(s)][0] + 1 return re.search("([0-9]{1,2}/[0-9]{1,2}/[0-9]{4})", str(div.contents[index]))[0] except Exception as e: if self._debug: logging.error( "Error getting posted_since. Error message: " + e.args[0]) return
python
def posted_since(self): """ This method returns the date the listing was entered. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'div', {"class": "date_entered"}).text s = info.split(':') return s[-1].strip() else: div = self._ad_page_content.find( 'div', {'class': 'description_extras'}) index = [i for i, s in enumerate( div.contents) if 'Entered/Renewed' in str(s)][0] + 1 return re.search("([0-9]{1,2}/[0-9]{1,2}/[0-9]{4})", str(div.contents[index]))[0] except Exception as e: if self._debug: logging.error( "Error getting posted_since. Error message: " + e.args[0]) return
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This method returns the date the listing was entered. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L460-L481
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.bedrooms
def bedrooms(self): """ This method gets the number of bedrooms. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'ul', {"class": "info"}).text s = info.split('|') nb = s[1].strip() return int(nb.split()[0]) else: div = self._ad_page_content.find( 'div', {'id': 'smi-summary-items'}) spans = div.find_all('span', {'class': 'header_text'}) for span in spans: # print(span.text) if 'bed' in span.text.lower(): return int(''.join([n for n in span.text if n.isdigit()])) return except Exception as e: if self._debug: logging.error( "Error getting bedrooms. Error message: " + e.args[0]) return 'N/A'
python
def bedrooms(self): """ This method gets the number of bedrooms. :return: """ try: if self._data_from_search: info = self._data_from_search.find( 'ul', {"class": "info"}).text s = info.split('|') nb = s[1].strip() return int(nb.split()[0]) else: div = self._ad_page_content.find( 'div', {'id': 'smi-summary-items'}) spans = div.find_all('span', {'class': 'header_text'}) for span in spans: # print(span.text) if 'bed' in span.text.lower(): return int(''.join([n for n in span.text if n.isdigit()])) return except Exception as e: if self._debug: logging.error( "Error getting bedrooms. Error message: " + e.args[0]) return 'N/A'
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This method gets the number of bedrooms. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L484-L509
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.city_center_distance
def city_center_distance(self): """ This method gets the distance to city center, in km. :return: """ try: infos = self._ad_page_content.find_all( 'div', {"class": "map_info_box"}) for info in infos: if 'Distance to City Centre' in info.text: distance_list = re.findall( 'Distance to City Centre: (.*) km', info.text) return distance_list[0] return None except Exception as e: if self._debug: logging.error(e.args[0]) print(e) return 'N/A'
python
def city_center_distance(self): """ This method gets the distance to city center, in km. :return: """ try: infos = self._ad_page_content.find_all( 'div', {"class": "map_info_box"}) for info in infos: if 'Distance to City Centre' in info.text: distance_list = re.findall( 'Distance to City Centre: (.*) km', info.text) return distance_list[0] return None except Exception as e: if self._debug: logging.error(e.args[0]) print(e) return 'N/A'
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This method gets the distance to city center, in km. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L541-L559
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.transport_routes
def transport_routes(self): """ This method gets a dict of routes listed in Daft. :return: """ routes = {} try: big_div = self._ad_page_content.find( 'div', {"class": "half_area_box_right"}) uls = big_div.find("ul") if uls is None: return None for li in uls.find_all('li'): route_li = li.text.split(':') routes[route_li[0]] = [x.strip() for x in route_li[1].split(',')] return routes except Exception as e: if self._debug: logging.error(e.args[0]) return 'N/A'
python
def transport_routes(self): """ This method gets a dict of routes listed in Daft. :return: """ routes = {} try: big_div = self._ad_page_content.find( 'div', {"class": "half_area_box_right"}) uls = big_div.find("ul") if uls is None: return None for li in uls.find_all('li'): route_li = li.text.split(':') routes[route_li[0]] = [x.strip() for x in route_li[1].split(',')] return routes except Exception as e: if self._debug: logging.error(e.args[0]) return 'N/A'
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This method gets a dict of routes listed in Daft. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L562-L582
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.longitude
def longitude(self): """ This method gets a dict of routes listed in Daft. :return: """ try: scripts = self._ad_page_content.find_all('script') for script in scripts: if 'longitude' in script.text: find_list = re.findall( r'"longitude":"([\-]?[0-9.]*[0-9]+)"', script.text) if len(find_list) >= 1: return find_list[0] return None except Exception as e: if self._debug: logging.error( "Error getting longitude. Error message: " + e.args[0]) return None
python
def longitude(self): """ This method gets a dict of routes listed in Daft. :return: """ try: scripts = self._ad_page_content.find_all('script') for script in scripts: if 'longitude' in script.text: find_list = re.findall( r'"longitude":"([\-]?[0-9.]*[0-9]+)"', script.text) if len(find_list) >= 1: return find_list[0] return None except Exception as e: if self._debug: logging.error( "Error getting longitude. Error message: " + e.args[0]) return None
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This method gets a dict of routes listed in Daft. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L606-L624
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.ber_code
def ber_code(self): """ This method gets ber code listed in Daft. :return: """ try: alt_text = self._ad_page_content.find( 'span', {'class': 'ber-hover'} ).find('img')['alt'] if ('exempt' in alt_text): return 'exempt' else: alt_arr = alt_text.split() if 'ber' in alt_arr[0].lower(): return alt_arr[1].lower() else: return None except Exception as e: if self._debug: logging.error( "Error getting the Ber Code. Error message: " + e.args[0]) return None
python
def ber_code(self): """ This method gets ber code listed in Daft. :return: """ try: alt_text = self._ad_page_content.find( 'span', {'class': 'ber-hover'} ).find('img')['alt'] if ('exempt' in alt_text): return 'exempt' else: alt_arr = alt_text.split() if 'ber' in alt_arr[0].lower(): return alt_arr[1].lower() else: return None except Exception as e: if self._debug: logging.error( "Error getting the Ber Code. Error message: " + e.args[0]) return None
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This method gets ber code listed in Daft. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L627-L649
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.contact_advertiser
def contact_advertiser(self, name, email, contact_number, message): """ This method allows you to contact the advertiser of a listing. :param name: Your name :param email: Your email address. :param contact_number: Your contact number. :param message: Your message. :return: """ req = Request(debug=self._debug) ad_search_type = self.search_type agent_id = self.agent_id ad_id = self.id response = req.post('https://www.daft.ie/ajax_endpoint.php?', params={ 'action': 'daft_contact_advertiser', 'from': name, 'email': email, 'message': message, 'contact_number': contact_number, 'type': ad_search_type, 'agent_id': agent_id, 'id': ad_id }) if self._debug: logging.info("Status code: %d" % response.status_code) logging.info("Response: %s" % response.content) if response.status_code != 200: logging.error("Status code: %d" % response.status_code) logging.error("Response: %s" % response.content) return response.status_code == 200
python
def contact_advertiser(self, name, email, contact_number, message): """ This method allows you to contact the advertiser of a listing. :param name: Your name :param email: Your email address. :param contact_number: Your contact number. :param message: Your message. :return: """ req = Request(debug=self._debug) ad_search_type = self.search_type agent_id = self.agent_id ad_id = self.id response = req.post('https://www.daft.ie/ajax_endpoint.php?', params={ 'action': 'daft_contact_advertiser', 'from': name, 'email': email, 'message': message, 'contact_number': contact_number, 'type': ad_search_type, 'agent_id': agent_id, 'id': ad_id }) if self._debug: logging.info("Status code: %d" % response.status_code) logging.info("Response: %s" % response.content) if response.status_code != 200: logging.error("Status code: %d" % response.status_code) logging.error("Response: %s" % response.content) return response.status_code == 200
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This method allows you to contact the advertiser of a listing. :param name: Your name :param email: Your email address. :param contact_number: Your contact number. :param message: Your message. :return:
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L671-L704
AnthonyBloomer/daftlistings
daftlistings/listing.py
Listing.as_dict
def as_dict(self): """ Return a Listing object as Dictionary :return: dict """ return { 'search_type': self.search_type, 'agent_id': self.agent_id, 'id': self.id, 'price': self.price, 'price_change': self.price_change, 'viewings': self.upcoming_viewings, 'facilities': self.facilities, 'overviews': self.overviews, 'formalised_address': self.formalised_address, 'address_line_1': self.address_line_1, 'county': self.county, 'listing_image': self.images, 'listing_hires_image': self.hires_images, 'agent': self.agent, 'agent_url': self.agent_url, 'contact_number': self.contact_number, 'daft_link': self.daft_link, 'shortcode': self.shortcode, 'date_insert_update': self.date_insert_update, 'views': self.views, 'description': self.description, 'dwelling_type': self.dwelling_type, 'posted_since': self.posted_since, 'num_bedrooms': self.bedrooms, 'num_bathrooms': self.bathrooms, 'city_center_distance': self.city_center_distance, 'transport_routes': self.transport_routes, 'latitude': self.latitude, 'longitude': self.longitude, 'ber_code': self.ber_code, 'commercial_area_size': self.commercial_area_size }
python
def as_dict(self): """ Return a Listing object as Dictionary :return: dict """ return { 'search_type': self.search_type, 'agent_id': self.agent_id, 'id': self.id, 'price': self.price, 'price_change': self.price_change, 'viewings': self.upcoming_viewings, 'facilities': self.facilities, 'overviews': self.overviews, 'formalised_address': self.formalised_address, 'address_line_1': self.address_line_1, 'county': self.county, 'listing_image': self.images, 'listing_hires_image': self.hires_images, 'agent': self.agent, 'agent_url': self.agent_url, 'contact_number': self.contact_number, 'daft_link': self.daft_link, 'shortcode': self.shortcode, 'date_insert_update': self.date_insert_update, 'views': self.views, 'description': self.description, 'dwelling_type': self.dwelling_type, 'posted_since': self.posted_since, 'num_bedrooms': self.bedrooms, 'num_bathrooms': self.bathrooms, 'city_center_distance': self.city_center_distance, 'transport_routes': self.transport_routes, 'latitude': self.latitude, 'longitude': self.longitude, 'ber_code': self.ber_code, 'commercial_area_size': self.commercial_area_size }
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Return a Listing object as Dictionary :return: dict
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train
https://github.com/AnthonyBloomer/daftlistings/blob/f6c1b52425bc740f443b5efe6632a4bf18ee997f/daftlistings/listing.py#L706-L743
fralau/mkdocs_macros_plugin
macros/plugin.py
MacrosPlugin.on_config
def on_config(self, config): "Fetch the variables and functions" #print("Here is the config:", config) # fetch variables from YAML file: self._variables = config.get(YAML_SUBSET) # add variables and functions from the module: module_reader.load_variables(self._variables, config) print("Variables:", self.variables)
python
def on_config(self, config): "Fetch the variables and functions" #print("Here is the config:", config) # fetch variables from YAML file: self._variables = config.get(YAML_SUBSET) # add variables and functions from the module: module_reader.load_variables(self._variables, config) print("Variables:", self.variables)
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Fetch the variables and functions
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train
https://github.com/fralau/mkdocs_macros_plugin/blob/8a02189395adae3acd2d18d9edcf0790ff7b4904/macros/plugin.py#L41-L51
fralau/mkdocs_macros_plugin
macros/plugin.py
MacrosPlugin.on_page_markdown
def on_page_markdown(self, markdown, page, config, site_navigation=None, **kwargs): "Provide a hook for defining functions from an external module" # the site_navigation argument has been made optional # (deleted in post 1.0 mkdocs, but maintained here # for backward compatibility) if not self.variables: return markdown else: # Create templae and get the variables md_template = Template(markdown) # Execute the jinja2 template and return return md_template.render(**self.variables)
python
def on_page_markdown(self, markdown, page, config, site_navigation=None, **kwargs): "Provide a hook for defining functions from an external module" # the site_navigation argument has been made optional # (deleted in post 1.0 mkdocs, but maintained here # for backward compatibility) if not self.variables: return markdown else: # Create templae and get the variables md_template = Template(markdown) # Execute the jinja2 template and return return md_template.render(**self.variables)
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Provide a hook for defining functions from an external module
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train
https://github.com/fralau/mkdocs_macros_plugin/blob/8a02189395adae3acd2d18d9edcf0790ff7b4904/macros/plugin.py#L54-L71
fralau/mkdocs_macros_plugin
macros/module_reader.py
load_variables
def load_variables(variables, config): """ Add the template functions, via the python module located in the same directory as the Yaml config file. The python module must contain the following hook: declare_variables(variables, macro): variables['a'] = 5 @macro def bar(x): .... @macro def baz(x): .... """ def macro(v, name=''): """ Registers a variable as a macro in the template, i.e. in the variables dictionary: macro(myfunc) Optionally, you can assign a different name: macro(myfunc, 'funcname') You can also use it as a decorator: @macro def foo(a): return a ** 2 More info: https://stackoverflow.com/questions/6036082/call-a-python-function-from-jinja2 """ name = name or v.__name__ variables[name] = v return v # determine the package name, from the filename: python_module = config.get('python_module') or DEFAULT_MODULE_NAME # get the directory of the yaml file: config_file = config['config_file_path'] yaml_dir = os.path.dirname(config_file) # print("Found yaml directory: %s" % yaml_dir) # that's the directory of the package: repackage.add(yaml_dir) try: module = importlib.import_module(python_module) print("Found module '%s'" % python_module) # execute the hook, passing the template decorator function module.declare_variables(variables, macro) except ModuleNotFoundError: print("No module found.")
python
def load_variables(variables, config): """ Add the template functions, via the python module located in the same directory as the Yaml config file. The python module must contain the following hook: declare_variables(variables, macro): variables['a'] = 5 @macro def bar(x): .... @macro def baz(x): .... """ def macro(v, name=''): """ Registers a variable as a macro in the template, i.e. in the variables dictionary: macro(myfunc) Optionally, you can assign a different name: macro(myfunc, 'funcname') You can also use it as a decorator: @macro def foo(a): return a ** 2 More info: https://stackoverflow.com/questions/6036082/call-a-python-function-from-jinja2 """ name = name or v.__name__ variables[name] = v return v # determine the package name, from the filename: python_module = config.get('python_module') or DEFAULT_MODULE_NAME # get the directory of the yaml file: config_file = config['config_file_path'] yaml_dir = os.path.dirname(config_file) # print("Found yaml directory: %s" % yaml_dir) # that's the directory of the package: repackage.add(yaml_dir) try: module = importlib.import_module(python_module) print("Found module '%s'" % python_module) # execute the hook, passing the template decorator function module.declare_variables(variables, macro) except ModuleNotFoundError: print("No module found.")
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Add the template functions, via the python module located in the same directory as the Yaml config file. The python module must contain the following hook: declare_variables(variables, macro): variables['a'] = 5 @macro def bar(x): .... @macro def baz(x): ....
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train
https://github.com/fralau/mkdocs_macros_plugin/blob/8a02189395adae3acd2d18d9edcf0790ff7b4904/macros/module_reader.py#L18-L84
mayeut/pybase64
pybase64/_fallback.py
b64decode
def b64decode(s, altchars=None, validate=False): """Decode bytes encoded with the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` or ASCII string to decode. Optional ``altchars`` must be a :term:`bytes-like object` or ASCII string of length 2 which specifies the alternative alphabet used instead of the '+' and '/' characters. If ``validate`` is ``False`` (the default), characters that are neither in the normal base-64 alphabet nor the alternative alphabet are discarded prior to the padding check. If ``validate`` is ``True``, these non-alphabet characters in the input result in a :exc:`binascii.Error`. The result is returned as a :class:`bytes` object. A :exc:`binascii.Error` is raised if ``s`` is incorrectly padded. """ if version_info < (3, 0) or validate: if validate and len(s) % 4 != 0: raise BinAsciiError('Incorrect padding') s = _get_bytes(s) if altchars is not None: altchars = _get_bytes(altchars) assert len(altchars) == 2, repr(altchars) if version_info < (3, 0): map = maketrans(altchars, b'+/') else: map = bytes.maketrans(altchars, b'+/') s = s.translate(map) try: result = builtin_decode(s, altchars) except TypeError as e: raise BinAsciiError(str(e)) if validate: # check length of result vs length of input padding = 0 if len(s) > 1 and s[-2] in (b'=', 61): padding = padding + 1 if len(s) > 0 and s[-1] in (b'=', 61): padding = padding + 1 if 3 * (len(s) / 4) - padding != len(result): raise BinAsciiError('Non-base64 digit found') return result return builtin_decode(s, altchars)
python
def b64decode(s, altchars=None, validate=False): """Decode bytes encoded with the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` or ASCII string to decode. Optional ``altchars`` must be a :term:`bytes-like object` or ASCII string of length 2 which specifies the alternative alphabet used instead of the '+' and '/' characters. If ``validate`` is ``False`` (the default), characters that are neither in the normal base-64 alphabet nor the alternative alphabet are discarded prior to the padding check. If ``validate`` is ``True``, these non-alphabet characters in the input result in a :exc:`binascii.Error`. The result is returned as a :class:`bytes` object. A :exc:`binascii.Error` is raised if ``s`` is incorrectly padded. """ if version_info < (3, 0) or validate: if validate and len(s) % 4 != 0: raise BinAsciiError('Incorrect padding') s = _get_bytes(s) if altchars is not None: altchars = _get_bytes(altchars) assert len(altchars) == 2, repr(altchars) if version_info < (3, 0): map = maketrans(altchars, b'+/') else: map = bytes.maketrans(altchars, b'+/') s = s.translate(map) try: result = builtin_decode(s, altchars) except TypeError as e: raise BinAsciiError(str(e)) if validate: # check length of result vs length of input padding = 0 if len(s) > 1 and s[-2] in (b'=', 61): padding = padding + 1 if len(s) > 0 and s[-1] in (b'=', 61): padding = padding + 1 if 3 * (len(s) / 4) - padding != len(result): raise BinAsciiError('Non-base64 digit found') return result return builtin_decode(s, altchars)
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Decode bytes encoded with the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` or ASCII string to decode. Optional ``altchars`` must be a :term:`bytes-like object` or ASCII string of length 2 which specifies the alternative alphabet used instead of the '+' and '/' characters. If ``validate`` is ``False`` (the default), characters that are neither in the normal base-64 alphabet nor the alternative alphabet are discarded prior to the padding check. If ``validate`` is ``True``, these non-alphabet characters in the input result in a :exc:`binascii.Error`. The result is returned as a :class:`bytes` object. A :exc:`binascii.Error` is raised if ``s`` is incorrectly padded.
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train
https://github.com/mayeut/pybase64/blob/861c48675fd6e37c129e1d7a1233074f8d54449e/pybase64/_fallback.py#L40-L86
mayeut/pybase64
pybase64/_fallback.py
b64encode
def b64encode(s, altchars=None): """Encode bytes using the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` to encode. Optional ``altchars`` must be a byte string of length 2 which specifies an alternative alphabet for the '+' and '/' characters. This allows an application to e.g. generate url or filesystem safe Base64 strings. The result is returned as a :class:`bytes` object. """ if altchars is not None: altchars = _get_bytes(altchars) assert len(altchars) == 2, repr(altchars) if version_info < (3, 0): if isinstance(s, text_type): raise TypeError('a bytes-like object is required, not \'' + type(s).__name__ + '\'') return builtin_encode(s, altchars)
python
def b64encode(s, altchars=None): """Encode bytes using the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` to encode. Optional ``altchars`` must be a byte string of length 2 which specifies an alternative alphabet for the '+' and '/' characters. This allows an application to e.g. generate url or filesystem safe Base64 strings. The result is returned as a :class:`bytes` object. """ if altchars is not None: altchars = _get_bytes(altchars) assert len(altchars) == 2, repr(altchars) if version_info < (3, 0): if isinstance(s, text_type): raise TypeError('a bytes-like object is required, not \'' + type(s).__name__ + '\'') return builtin_encode(s, altchars)
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Encode bytes using the standard Base64 alphabet. Argument ``s`` is a :term:`bytes-like object` to encode. Optional ``altchars`` must be a byte string of length 2 which specifies an alternative alphabet for the '+' and '/' characters. This allows an application to e.g. generate url or filesystem safe Base64 strings. The result is returned as a :class:`bytes` object.
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train
https://github.com/mayeut/pybase64/blob/861c48675fd6e37c129e1d7a1233074f8d54449e/pybase64/_fallback.py#L89-L107
mayeut/pybase64
setup.py
CleanExtensionCommand.run
def run(self): """Run command.""" for ext in ['*.so', '*.pyd']: for file in glob.glob('./pybase64/' + ext): log.info("removing '%s'", file) if self.dry_run: continue os.remove(file)
python
def run(self): """Run command.""" for ext in ['*.so', '*.pyd']: for file in glob.glob('./pybase64/' + ext): log.info("removing '%s'", file) if self.dry_run: continue os.remove(file)
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Run command.
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train
https://github.com/mayeut/pybase64/blob/861c48675fd6e37c129e1d7a1233074f8d54449e/setup.py#L204-L211
kalbhor/MusicTools
musictools/musictools.py
improve_name
def improve_name(song_name): """ Improves file name by removing words such as HD, Official,etc eg : Hey Jude (Official HD) lyrics -> Hey Jude This helps in better searching of metadata since a spotify search of 'Hey Jude (Official HD) lyrics' fetches 0 results """ try: song_name = os.path.splitext(song_name)[0] except IndexError: pass song_name = song_name.partition('ft')[0] # Replace characters to filter with spaces song_name = ''.join( map(lambda c: " " if c in chars_filter else c, song_name)) # Remove crap words song_name = re.sub('|'.join(re.escape(key) for key in words_filter), "", song_name, flags=re.IGNORECASE) # Remove duplicate spaces song_name = re.sub(' +', ' ', song_name) return song_name.strip()
python
def improve_name(song_name): """ Improves file name by removing words such as HD, Official,etc eg : Hey Jude (Official HD) lyrics -> Hey Jude This helps in better searching of metadata since a spotify search of 'Hey Jude (Official HD) lyrics' fetches 0 results """ try: song_name = os.path.splitext(song_name)[0] except IndexError: pass song_name = song_name.partition('ft')[0] # Replace characters to filter with spaces song_name = ''.join( map(lambda c: " " if c in chars_filter else c, song_name)) # Remove crap words song_name = re.sub('|'.join(re.escape(key) for key in words_filter), "", song_name, flags=re.IGNORECASE) # Remove duplicate spaces song_name = re.sub(' +', ' ', song_name) return song_name.strip()
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Improves file name by removing words such as HD, Official,etc eg : Hey Jude (Official HD) lyrics -> Hey Jude This helps in better searching of metadata since a spotify search of 'Hey Jude (Official HD) lyrics' fetches 0 results
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L18-L44
kalbhor/MusicTools
musictools/musictools.py
get_song_urls
def get_song_urls(song_input): """ Gather all urls, titles for a search query from youtube """ YOUTUBECLASS = 'spf-prefetch' html = requests.get("https://www.youtube.com/results", params={'search_query': song_input}) soup = BeautifulSoup(html.text, 'html.parser') soup_section = soup.findAll('a', {'rel': YOUTUBECLASS}) # Use generator over list, since storage isn't important song_urls = ('https://www.youtube.com' + i.get('href') for i in soup_section) song_titles = (i.get('title') for i in soup_section) youtube_list = list(zip(song_urls, song_titles)) del song_urls del song_titles return youtube_list
python
def get_song_urls(song_input): """ Gather all urls, titles for a search query from youtube """ YOUTUBECLASS = 'spf-prefetch' html = requests.get("https://www.youtube.com/results", params={'search_query': song_input}) soup = BeautifulSoup(html.text, 'html.parser') soup_section = soup.findAll('a', {'rel': YOUTUBECLASS}) # Use generator over list, since storage isn't important song_urls = ('https://www.youtube.com' + i.get('href') for i in soup_section) song_titles = (i.get('title') for i in soup_section) youtube_list = list(zip(song_urls, song_titles)) del song_urls del song_titles return youtube_list
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Gather all urls, titles for a search query from youtube
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L46-L69
kalbhor/MusicTools
musictools/musictools.py
download_song
def download_song(song_url, song_title): """ Download a song using youtube url and song title """ outtmpl = song_title + '.%(ext)s' ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': outtmpl, 'postprocessors': [ {'key': 'FFmpegExtractAudio','preferredcodec': 'mp3', 'preferredquality': '192', }, {'key': 'FFmpegMetadata'}, ], } with youtube_dl.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(song_url, download=True)
python
def download_song(song_url, song_title): """ Download a song using youtube url and song title """ outtmpl = song_title + '.%(ext)s' ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': outtmpl, 'postprocessors': [ {'key': 'FFmpegExtractAudio','preferredcodec': 'mp3', 'preferredquality': '192', }, {'key': 'FFmpegMetadata'}, ], } with youtube_dl.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(song_url, download=True)
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Download a song using youtube url and song title
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L72-L90
kalbhor/MusicTools
musictools/musictools.py
get_metadata
def get_metadata(file_name, client_id, client_secret): """ Tries finding metadata through Spotify """ song_name = improve_name(file_name) # Remove useless words from title client_credentials_manager = SpotifyClientCredentials(client_id, client_secret) spotify = spotipy.Spotify(client_credentials_manager=client_credentials_manager) results = spotify.search(song_name, limit=1) results = results['tracks']['items'][0] # Find top result album = results['album']['name'] # Parse json dictionary artist = results['album']['artists'][0]['name'] song_title = results['name'] album_art = results['album']['images'][0]['url'] return artist, album, song_title, album_art
python
def get_metadata(file_name, client_id, client_secret): """ Tries finding metadata through Spotify """ song_name = improve_name(file_name) # Remove useless words from title client_credentials_manager = SpotifyClientCredentials(client_id, client_secret) spotify = spotipy.Spotify(client_credentials_manager=client_credentials_manager) results = spotify.search(song_name, limit=1) results = results['tracks']['items'][0] # Find top result album = results['album']['name'] # Parse json dictionary artist = results['album']['artists'][0]['name'] song_title = results['name'] album_art = results['album']['images'][0]['url'] return artist, album, song_title, album_art
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Tries finding metadata through Spotify
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L93-L110
kalbhor/MusicTools
musictools/musictools.py
add_album_art
def add_album_art(file_name, album_art): """ Add album_art in .mp3's tags """ img = requests.get(album_art, stream=True) # Gets album art from url img = img.raw audio = EasyMP3(file_name, ID3=ID3) try: audio.add_tags() except _util.error: pass audio.tags.add( APIC( encoding=3, # UTF-8 mime='image/png', type=3, # 3 is for album art desc='Cover', data=img.read() # Reads and adds album art ) ) audio.save() return album_art
python
def add_album_art(file_name, album_art): """ Add album_art in .mp3's tags """ img = requests.get(album_art, stream=True) # Gets album art from url img = img.raw audio = EasyMP3(file_name, ID3=ID3) try: audio.add_tags() except _util.error: pass audio.tags.add( APIC( encoding=3, # UTF-8 mime='image/png', type=3, # 3 is for album art desc='Cover', data=img.read() # Reads and adds album art ) ) audio.save() return album_art
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Add album_art in .mp3's tags
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L113-L139
kalbhor/MusicTools
musictools/musictools.py
add_metadata
def add_metadata(file_name, title, artist, album): """ As the method name suggests """ tags = EasyMP3(file_name) if title: tags["title"] = title if artist: tags["artist"] = artist if album: tags["album"] = album tags.save() return file_name
python
def add_metadata(file_name, title, artist, album): """ As the method name suggests """ tags = EasyMP3(file_name) if title: tags["title"] = title if artist: tags["artist"] = artist if album: tags["album"] = album tags.save() return file_name
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As the method name suggests
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L142-L156
kalbhor/MusicTools
musictools/musictools.py
revert_metadata
def revert_metadata(files): """ Removes all tags from a mp3 file """ for file_path in files: tags = EasyMP3(file_path) tags.delete() tags.save()
python
def revert_metadata(files): """ Removes all tags from a mp3 file """ for file_path in files: tags = EasyMP3(file_path) tags.delete() tags.save()
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Removes all tags from a mp3 file
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train
https://github.com/kalbhor/MusicTools/blob/324159448553033173bb050458c6a56e3cfa2738/musictools/musictools.py#L166-L173
Tivix/django-braintree
django_braintree/models.py
UserVaultManager.get_user_vault_instance_or_none
def get_user_vault_instance_or_none(self, user): """Returns a vault_id string or None""" qset = self.filter(user=user) if not qset: return None if qset.count() > 1: raise Exception('This app does not currently support multiple vault ids') return qset.get()
python
def get_user_vault_instance_or_none(self, user): """Returns a vault_id string or None""" qset = self.filter(user=user) if not qset: return None if qset.count() > 1: raise Exception('This app does not currently support multiple vault ids') return qset.get()
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Returns a vault_id string or None
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train
https://github.com/Tivix/django-braintree/blob/7beb2c8392c2a454c36b353818f3e1db20511ef9/django_braintree/models.py#L11-L20
Tivix/django-braintree
django_braintree/models.py
UserVaultManager.charge
def charge(self, user, vault_id=None): """If vault_id is not passed this will assume that there is only one instane of user and vault_id in the db.""" assert self.is_in_vault(user) if vault_id: user_vault = self.get(user=user, vault_id=vault_id) else: user_vault = self.get(user=user)
python
def charge(self, user, vault_id=None): """If vault_id is not passed this will assume that there is only one instane of user and vault_id in the db.""" assert self.is_in_vault(user) if vault_id: user_vault = self.get(user=user, vault_id=vault_id) else: user_vault = self.get(user=user)
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If vault_id is not passed this will assume that there is only one instane of user and vault_id in the db.
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train
https://github.com/Tivix/django-braintree/blob/7beb2c8392c2a454c36b353818f3e1db20511ef9/django_braintree/models.py#L25-L31
Tivix/django-braintree
django_braintree/models.py
UserVault.charge
def charge(self, amount): """ Charges the users credit card, with he passed $amount, if they are in the vault. Returns the payment_log instance or None (if charge fails etc.) """ try: result = Transaction.sale( { 'amount': amount.quantize(Decimal('.01')), 'customer_id': self.vault_id, "options": { "submit_for_settlement": True } } ) if result.is_success: # create a payment log payment_log = PaymentLog.objects.create(user=self.user, amount=amount, transaction_id=result.transaction.id) return payment_log else: raise Exception('Logical error in CC transaction') except Exception: logging.error('Failed to charge $%s to user: %s with vault_id: %s' % (amount, self.user, self.vault_id)) return None
python
def charge(self, amount): """ Charges the users credit card, with he passed $amount, if they are in the vault. Returns the payment_log instance or None (if charge fails etc.) """ try: result = Transaction.sale( { 'amount': amount.quantize(Decimal('.01')), 'customer_id': self.vault_id, "options": { "submit_for_settlement": True } } ) if result.is_success: # create a payment log payment_log = PaymentLog.objects.create(user=self.user, amount=amount, transaction_id=result.transaction.id) return payment_log else: raise Exception('Logical error in CC transaction') except Exception: logging.error('Failed to charge $%s to user: %s with vault_id: %s' % (amount, self.user, self.vault_id)) return None
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Charges the users credit card, with he passed $amount, if they are in the vault. Returns the payment_log instance or None (if charge fails etc.)
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train
https://github.com/Tivix/django-braintree/blob/7beb2c8392c2a454c36b353818f3e1db20511ef9/django_braintree/models.py#L43-L67
Tivix/django-braintree
django_braintree/forms.py
UserCCDetailsForm.save
def save(self, prepend_vault_id=''): """ Adds or updates a users CC to the vault. @prepend_vault_id: any string to prepend all vault id's with in case the same braintree account is used by multiple projects/apps. """ assert self.is_valid() cc_details_map = { # cc details 'number': self.cleaned_data['cc_number'], 'cardholder_name': self.cleaned_data['name'], 'expiration_date': '%s/%s' %\ (self.cleaned_data['expiration_month'], self.cleaned_data['expiration_year']), 'cvv': self.cleaned_data['cvv'], 'billing_address': { 'postal_code': self.cleaned_data['zip_code'], } } if self.__user_vault: try: # get customer info, its credit card and then update that credit card response = Customer.find(self.__user_vault.vault_id) cc_info = response.credit_cards[0] return CreditCard.update(cc_info.token, params=cc_details_map) except Exception, e: logging.error('Was not able to get customer from vault. %s' % e) self.__user_vault.delete() # delete the stale instance from our db # in case the above updating fails or user was never in the vault new_customer_vault_id = '%s%s' % (prepend_vault_id, md5_hash()[:24]) respone = Customer.create({ # creating a customer, but we really just want to store their CC details 'id': new_customer_vault_id, # vault id, uniquely identifies customer. We're not caring about tokens (used for storing multiple CC's per user) 'credit_card': cc_details_map }) if respone.is_success: # save a new UserVault instance UserVault.objects.create(user=self.__user, vault_id=new_customer_vault_id) return respone
python
def save(self, prepend_vault_id=''): """ Adds or updates a users CC to the vault. @prepend_vault_id: any string to prepend all vault id's with in case the same braintree account is used by multiple projects/apps. """ assert self.is_valid() cc_details_map = { # cc details 'number': self.cleaned_data['cc_number'], 'cardholder_name': self.cleaned_data['name'], 'expiration_date': '%s/%s' %\ (self.cleaned_data['expiration_month'], self.cleaned_data['expiration_year']), 'cvv': self.cleaned_data['cvv'], 'billing_address': { 'postal_code': self.cleaned_data['zip_code'], } } if self.__user_vault: try: # get customer info, its credit card and then update that credit card response = Customer.find(self.__user_vault.vault_id) cc_info = response.credit_cards[0] return CreditCard.update(cc_info.token, params=cc_details_map) except Exception, e: logging.error('Was not able to get customer from vault. %s' % e) self.__user_vault.delete() # delete the stale instance from our db # in case the above updating fails or user was never in the vault new_customer_vault_id = '%s%s' % (prepend_vault_id, md5_hash()[:24]) respone = Customer.create({ # creating a customer, but we really just want to store their CC details 'id': new_customer_vault_id, # vault id, uniquely identifies customer. We're not caring about tokens (used for storing multiple CC's per user) 'credit_card': cc_details_map }) if respone.is_success: # save a new UserVault instance UserVault.objects.create(user=self.__user, vault_id=new_customer_vault_id) return respone
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Adds or updates a users CC to the vault. @prepend_vault_id: any string to prepend all vault id's with in case the same braintree account is used by multiple projects/apps.
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train
https://github.com/Tivix/django-braintree/blob/7beb2c8392c2a454c36b353818f3e1db20511ef9/django_braintree/forms.py#L93-L133
Tivix/django-braintree
django_braintree/views.py
payments_billing
def payments_billing(request, template='django_braintree/payments_billing.html'): """ Renders both the past payments that have occurred on the users credit card, but also their CC information on file (if any) """ d = {} if request.method == 'POST': # Credit Card is being changed/updated by the user form = UserCCDetailsForm(request.user, True, request.POST) if form.is_valid(): response = form.save() if response.is_success: messages.add_message(request, messages.SUCCESS, 'Your credit card information has been securely saved.') return JsonResponse() else: return JsonResponse(success=False, errors=[BAD_CC_ERROR_MSG]) return JsonResponse(success=False, data={'form': form_errors_serialize(form)}) else: if UserVault.objects.is_in_vault(request.user): try: response = Customer.find(UserVault.objects.get_user_vault_instance_or_none(request.user).vault_id) d['current_cc_info'] = response.credit_cards[0] except Exception, e: logging.error('Unable to get vault information for user from braintree. %s' % e) d['cc_form'] = UserCCDetailsForm(request.user) return render(request, template, d)
python
def payments_billing(request, template='django_braintree/payments_billing.html'): """ Renders both the past payments that have occurred on the users credit card, but also their CC information on file (if any) """ d = {} if request.method == 'POST': # Credit Card is being changed/updated by the user form = UserCCDetailsForm(request.user, True, request.POST) if form.is_valid(): response = form.save() if response.is_success: messages.add_message(request, messages.SUCCESS, 'Your credit card information has been securely saved.') return JsonResponse() else: return JsonResponse(success=False, errors=[BAD_CC_ERROR_MSG]) return JsonResponse(success=False, data={'form': form_errors_serialize(form)}) else: if UserVault.objects.is_in_vault(request.user): try: response = Customer.find(UserVault.objects.get_user_vault_instance_or_none(request.user).vault_id) d['current_cc_info'] = response.credit_cards[0] except Exception, e: logging.error('Unable to get vault information for user from braintree. %s' % e) d['cc_form'] = UserCCDetailsForm(request.user) return render(request, template, d)
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Renders both the past payments that have occurred on the users credit card, but also their CC information on file (if any)
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train
https://github.com/Tivix/django-braintree/blob/7beb2c8392c2a454c36b353818f3e1db20511ef9/django_braintree/views.py#L20-L48
dropbox/pygerduty
pygerduty/v2.py
Incident.snooze
def snooze(self, requester, duration): """Snooze incident. :param requester: The email address of the individual requesting snooze. """ path = '{0}/{1}/{2}'.format(self.collection.name, self.id, 'snooze') data = {"duration": duration} extra_headers = {"From": requester} return self.pagerduty.request('POST', path, data=_json_dumper(data), extra_headers=extra_headers)
python
def snooze(self, requester, duration): """Snooze incident. :param requester: The email address of the individual requesting snooze. """ path = '{0}/{1}/{2}'.format(self.collection.name, self.id, 'snooze') data = {"duration": duration} extra_headers = {"From": requester} return self.pagerduty.request('POST', path, data=_json_dumper(data), extra_headers=extra_headers)
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Snooze incident. :param requester: The email address of the individual requesting snooze.
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/v2.py#L465-L472
dropbox/pygerduty
pygerduty/v2.py
Incident.reassign
def reassign(self, user_ids, requester): """Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids :param requester: The email address of individual requesting reassign """ path = '{0}'.format(self.collection.name) assignments = [] if not user_ids: raise Error('Must pass at least one user id') for user_id in user_ids: ref = { "assignee": { "id": user_id, "type": "user_reference" } } assignments.append(ref) data = { "incidents": [ { "id": self.id, "type": "incident_reference", "assignments": assignments } ] } extra_headers = {"From": requester} return self.pagerduty.request('PUT', path, data=_json_dumper(data), extra_headers=extra_headers)
python
def reassign(self, user_ids, requester): """Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids :param requester: The email address of individual requesting reassign """ path = '{0}'.format(self.collection.name) assignments = [] if not user_ids: raise Error('Must pass at least one user id') for user_id in user_ids: ref = { "assignee": { "id": user_id, "type": "user_reference" } } assignments.append(ref) data = { "incidents": [ { "id": self.id, "type": "incident_reference", "assignments": assignments } ] } extra_headers = {"From": requester} return self.pagerduty.request('PUT', path, data=_json_dumper(data), extra_headers=extra_headers)
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Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids :param requester: The email address of individual requesting reassign
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/v2.py#L478-L506
dropbox/pygerduty
pygerduty/events.py
Events.resolve_incident
def resolve_incident(self, incident_key, description=None, details=None): """ Causes the referenced incident to enter resolved state. Send a resolve event when the problem that caused the initial trigger has been fixed. """ return self.create_event(description, "resolve", details, incident_key)
python
def resolve_incident(self, incident_key, description=None, details=None): """ Causes the referenced incident to enter resolved state. Send a resolve event when the problem that caused the initial trigger has been fixed. """ return self.create_event(description, "resolve", details, incident_key)
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Causes the referenced incident to enter resolved state. Send a resolve event when the problem that caused the initial trigger has been fixed.
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/events.py#L57-L65
dropbox/pygerduty
pygerduty/common.py
clean_response
def clean_response(response): '''Recurse through dictionary and replace any keys "self" with "self_"''' if type(response) is list: for elem in response: clean_response(elem) elif type(response) is dict: for key, val in response.items(): if key == 'self': val = response.pop('self') response['self_'] = val clean_response(val) else: clean_response(response[key]) return response
python
def clean_response(response): '''Recurse through dictionary and replace any keys "self" with "self_"''' if type(response) is list: for elem in response: clean_response(elem) elif type(response) is dict: for key, val in response.items(): if key == 'self': val = response.pop('self') response['self_'] = val clean_response(val) else: clean_response(response[key]) return response
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Recurse through dictionary and replace any keys "self" with "self_"
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/common.py#L55-L69
dropbox/pygerduty
pygerduty/common.py
_lower
def _lower(string): """Custom lower string function. Examples: FooBar -> foo_bar """ if not string: return "" new_string = [string[0].lower()] for char in string[1:]: if char.isupper(): new_string.append("_") new_string.append(char.lower()) return "".join(new_string)
python
def _lower(string): """Custom lower string function. Examples: FooBar -> foo_bar """ if not string: return "" new_string = [string[0].lower()] for char in string[1:]: if char.isupper(): new_string.append("_") new_string.append(char.lower()) return "".join(new_string)
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Custom lower string function. Examples: FooBar -> foo_bar
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/common.py#L72-L86
dropbox/pygerduty
pygerduty/__init__.py
Incident.reassign
def reassign(self, user_ids, requester_id): """Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids """ if not user_ids: raise Error('Must pass at least one user id') self._do_action('reassign', requester_id=requester_id, assigned_to_user=','.join(user_ids))
python
def reassign(self, user_ids, requester_id): """Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids """ if not user_ids: raise Error('Must pass at least one user id') self._do_action('reassign', requester_id=requester_id, assigned_to_user=','.join(user_ids))
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Reassign this incident to a user or list of users :param user_ids: A non-empty list of user ids
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/__init__.py#L391-L398
dropbox/pygerduty
pygerduty/__init__.py
PagerDuty.acknowledge_incident
def acknowledge_incident(self, service_key, incident_key, description=None, details=None): """ Causes the referenced incident to enter the acknowledged state. Send an acknowledge event when someone is presently working on the incident. """ return self.create_event(service_key, description, "acknowledge", details, incident_key)
python
def acknowledge_incident(self, service_key, incident_key, description=None, details=None): """ Causes the referenced incident to enter the acknowledged state. Send an acknowledge event when someone is presently working on the incident. """ return self.create_event(service_key, description, "acknowledge", details, incident_key)
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Causes the referenced incident to enter the acknowledged state. Send an acknowledge event when someone is presently working on the incident.
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/__init__.py#L573-L581
dropbox/pygerduty
pygerduty/__init__.py
PagerDuty.trigger_incident
def trigger_incident(self, service_key, description, incident_key=None, details=None, client=None, client_url=None, contexts=None): """ Report a new or ongoing problem. When PagerDuty receives a trigger, it will either open a new incident, or add a new log entry to an existing incident. """ return self.create_event(service_key, description, "trigger", details, incident_key, client=client, client_url=client_url, contexts=contexts)
python
def trigger_incident(self, service_key, description, incident_key=None, details=None, client=None, client_url=None, contexts=None): """ Report a new or ongoing problem. When PagerDuty receives a trigger, it will either open a new incident, or add a new log entry to an existing incident. """ return self.create_event(service_key, description, "trigger", details, incident_key, client=client, client_url=client_url, contexts=contexts)
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Report a new or ongoing problem. When PagerDuty receives a trigger, it will either open a new incident, or add a new log entry to an existing incident.
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train
https://github.com/dropbox/pygerduty/blob/11b28bfb66306aa7fc2b95ab9df65eb97ea831cf/pygerduty/__init__.py#L583-L593
tanyaschlusser/array2gif
array2gif/core.py
check_dataset
def check_dataset(dataset): """Confirm shape (3 colors x rows x cols) and values [0 to 255] are OK.""" if isinstance(dataset, numpy.ndarray) and not len(dataset.shape) == 4: check_dataset_shape(dataset) check_dataset_range(dataset) else: # must be a list of arrays or a 4D NumPy array for i, d in enumerate(dataset): if not isinstance(d, numpy.ndarray): raise ValueError( 'Requires a NumPy array (rgb x rows x cols) ' 'with integer values in the range [0, 255].' ) try: check_dataset_shape(d) check_dataset_range(d) except ValueError as err: raise ValueError( '{}\nAt position {} in the list of arrays.' .format(err, i) )
python
def check_dataset(dataset): """Confirm shape (3 colors x rows x cols) and values [0 to 255] are OK.""" if isinstance(dataset, numpy.ndarray) and not len(dataset.shape) == 4: check_dataset_shape(dataset) check_dataset_range(dataset) else: # must be a list of arrays or a 4D NumPy array for i, d in enumerate(dataset): if not isinstance(d, numpy.ndarray): raise ValueError( 'Requires a NumPy array (rgb x rows x cols) ' 'with integer values in the range [0, 255].' ) try: check_dataset_shape(d) check_dataset_range(d) except ValueError as err: raise ValueError( '{}\nAt position {} in the list of arrays.' .format(err, i) )
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Confirm shape (3 colors x rows x cols) and values [0 to 255] are OK.
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L55-L74
tanyaschlusser/array2gif
array2gif/core.py
try_fix_dataset
def try_fix_dataset(dataset): """Transpose the image data if it's in PIL format.""" if isinstance(dataset, numpy.ndarray): if len(dataset.shape) == 3: # NumPy 3D if dataset.shape[-1] == 3: return dataset.transpose((2, 0, 1)) elif len(dataset.shape) == 4: # NumPy 4D if dataset.shape[-1] == 3: return dataset.transpose((0, 3, 1, 2)) # Otherwise couldn't fix it. return dataset # List of Numpy 3D arrays. for i, d in enumerate(dataset): if not isinstance(d, numpy.ndarray): return dataset if not (len(d.shape) == 3 and d.shape[-1] == 3): return dataset dataset[i] = d.transpose() return dataset
python
def try_fix_dataset(dataset): """Transpose the image data if it's in PIL format.""" if isinstance(dataset, numpy.ndarray): if len(dataset.shape) == 3: # NumPy 3D if dataset.shape[-1] == 3: return dataset.transpose((2, 0, 1)) elif len(dataset.shape) == 4: # NumPy 4D if dataset.shape[-1] == 3: return dataset.transpose((0, 3, 1, 2)) # Otherwise couldn't fix it. return dataset # List of Numpy 3D arrays. for i, d in enumerate(dataset): if not isinstance(d, numpy.ndarray): return dataset if not (len(d.shape) == 3 and d.shape[-1] == 3): return dataset dataset[i] = d.transpose() return dataset
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tanyaschlusser/array2gif
array2gif/core.py
get_image
def get_image(dataset): """Convert the NumPy array to two nested lists with r,g,b tuples.""" dim, nrow, ncol = dataset.shape uint8_dataset = dataset.astype('uint8') if not (uint8_dataset == dataset).all(): message = ( "\nYour image was cast to a `uint8` (`<img>.astype(uint8)`), " "but some information was lost.\nPlease check your gif and " "convert to uint8 beforehand if the gif looks wrong." ) warnings.warn(message) image = [[ struct.pack( 'BBB', uint8_dataset[0, i, j], uint8_dataset[1, i, j], uint8_dataset[2, i, j] ) for j in range(ncol)] for i in range(nrow)] return image
python
def get_image(dataset): """Convert the NumPy array to two nested lists with r,g,b tuples.""" dim, nrow, ncol = dataset.shape uint8_dataset = dataset.astype('uint8') if not (uint8_dataset == dataset).all(): message = ( "\nYour image was cast to a `uint8` (`<img>.astype(uint8)`), " "but some information was lost.\nPlease check your gif and " "convert to uint8 beforehand if the gif looks wrong." ) warnings.warn(message) image = [[ struct.pack( 'BBB', uint8_dataset[0, i, j], uint8_dataset[1, i, j], uint8_dataset[2, i, j] ) for j in range(ncol)] for i in range(nrow)] return image
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L98-L118
tanyaschlusser/array2gif
array2gif/core.py
get_color_table_size
def get_color_table_size(num_colors): """Total values in the color table is 2**(1 + int(result, base=2)). The result is a three-bit value (represented as a string with ones or zeros) that will become part of a packed byte encoding various details about the color table, used in the Logical Screen Descriptor block. """ nbits = max(math.ceil(math.log(num_colors, 2)), 2) return '{:03b}'.format(int(nbits - 1))
python
def get_color_table_size(num_colors): """Total values in the color table is 2**(1 + int(result, base=2)). The result is a three-bit value (represented as a string with ones or zeros) that will become part of a packed byte encoding various details about the color table, used in the Logical Screen Descriptor block. """ nbits = max(math.ceil(math.log(num_colors, 2)), 2) return '{:03b}'.format(int(nbits - 1))
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Total values in the color table is 2**(1 + int(result, base=2)). The result is a three-bit value (represented as a string with ones or zeros) that will become part of a packed byte encoding various details about the color table, used in the Logical Screen Descriptor block.
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train
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tanyaschlusser/array2gif
array2gif/core.py
get_colors
def get_colors(image): """Return a Counter containing each color and how often it appears. """ colors = Counter(pixel for row in image for pixel in row) if len(colors) > 256: msg = ( "The maximum number of distinct colors in a GIF is 256 but " "this image has {} colors and can't be encoded properly." ) raise RuntimeError(msg.format(len(colors))) return colors
python
def get_colors(image): """Return a Counter containing each color and how often it appears. """ colors = Counter(pixel for row in image for pixel in row) if len(colors) > 256: msg = ( "The maximum number of distinct colors in a GIF is 256 but " "this image has {} colors and can't be encoded properly." ) raise RuntimeError(msg.format(len(colors))) return colors
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Return a Counter containing each color and how often it appears.
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L167-L177
tanyaschlusser/array2gif
array2gif/core.py
_get_global_color_table
def _get_global_color_table(colors): """Return a color table sorted in descending order of count. """ global_color_table = b''.join(c[0] for c in colors.most_common()) full_table_size = 2**(1+int(get_color_table_size(len(colors)), 2)) repeats = 3 * (full_table_size - len(colors)) zeros = struct.pack('<{}x'.format(repeats)) return global_color_table + zeros
python
def _get_global_color_table(colors): """Return a color table sorted in descending order of count. """ global_color_table = b''.join(c[0] for c in colors.most_common()) full_table_size = 2**(1+int(get_color_table_size(len(colors)), 2)) repeats = 3 * (full_table_size - len(colors)) zeros = struct.pack('<{}x'.format(repeats)) return global_color_table + zeros
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L180-L187
tanyaschlusser/array2gif
array2gif/core.py
_get_image_data
def _get_image_data(image, colors): """Performs the LZW compression as described by Matthew Flickinger. This isn't fast, but it works. http://www.matthewflickinger.com/lab/whatsinagif/lzw_image_data.asp """ lzw_code_size, coded_bits = _lzw_encode(image, colors) coded_bytes = ''.join( '{{:0{}b}}'.format(nbits).format(val) for val, nbits in coded_bits) coded_bytes = '0' * ((8 - len(coded_bytes)) % 8) + coded_bytes coded_data = list( reversed([ int(coded_bytes[8*i:8*(i+1)], 2) for i in range(len(coded_bytes) // 8) ]) ) output = [struct.pack('<B', lzw_code_size)] # Must output the data in blocks of length 255 block_length = min(255, len(coded_data)) while block_length > 0: block = struct.pack( '<{}B'.format(block_length + 1), block_length, *coded_data[:block_length] ) output.append(block) coded_data = coded_data[block_length:] block_length = min(255, len(coded_data)) return b''.join(output)
python
def _get_image_data(image, colors): """Performs the LZW compression as described by Matthew Flickinger. This isn't fast, but it works. http://www.matthewflickinger.com/lab/whatsinagif/lzw_image_data.asp """ lzw_code_size, coded_bits = _lzw_encode(image, colors) coded_bytes = ''.join( '{{:0{}b}}'.format(nbits).format(val) for val, nbits in coded_bits) coded_bytes = '0' * ((8 - len(coded_bytes)) % 8) + coded_bytes coded_data = list( reversed([ int(coded_bytes[8*i:8*(i+1)], 2) for i in range(len(coded_bytes) // 8) ]) ) output = [struct.pack('<B', lzw_code_size)] # Must output the data in blocks of length 255 block_length = min(255, len(coded_data)) while block_length > 0: block = struct.pack( '<{}B'.format(block_length + 1), block_length, *coded_data[:block_length] ) output.append(block) coded_data = coded_data[block_length:] block_length = min(255, len(coded_data)) return b''.join(output)
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Performs the LZW compression as described by Matthew Flickinger. This isn't fast, but it works. http://www.matthewflickinger.com/lab/whatsinagif/lzw_image_data.asp
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L311-L339
tanyaschlusser/array2gif
array2gif/core.py
write_gif
def write_gif(dataset, filename, fps=10): """Write a NumPy array to GIF 89a format. Or write a list of NumPy arrays to an animation (GIF 89a format). - Positional arguments:: :param dataset: A NumPy arrayor list of arrays with shape rgb x rows x cols and integer values in [0, 255]. :param filename: The output file that will contain the GIF image. :param fps: The (integer) frames/second of the animation (default 10). :type dataset: a NumPy array or list of NumPy arrays. :return: None - Example: a minimal array, with one red pixel, would look like this:: import numpy as np one_red_pixel = np.array([[[255]], [[0]], [[0]]]) write_gif(one_red_pixel, 'red_pixel.gif') ..raises:: ValueError """ try: check_dataset(dataset) except ValueError as e: dataset = try_fix_dataset(dataset) check_dataset(dataset) delay_time = 100 // int(fps) def encode(d): four_d = isinstance(dataset, numpy.ndarray) and len(dataset.shape) == 4 if four_d or not isinstance(dataset, numpy.ndarray): return _make_animated_gif(d, delay_time=delay_time) else: return _make_gif(d) with open(filename, 'wb') as outfile: outfile.write(HEADER) for block in encode(dataset): outfile.write(block) outfile.write(TRAILER)
python
def write_gif(dataset, filename, fps=10): """Write a NumPy array to GIF 89a format. Or write a list of NumPy arrays to an animation (GIF 89a format). - Positional arguments:: :param dataset: A NumPy arrayor list of arrays with shape rgb x rows x cols and integer values in [0, 255]. :param filename: The output file that will contain the GIF image. :param fps: The (integer) frames/second of the animation (default 10). :type dataset: a NumPy array or list of NumPy arrays. :return: None - Example: a minimal array, with one red pixel, would look like this:: import numpy as np one_red_pixel = np.array([[[255]], [[0]], [[0]]]) write_gif(one_red_pixel, 'red_pixel.gif') ..raises:: ValueError """ try: check_dataset(dataset) except ValueError as e: dataset = try_fix_dataset(dataset) check_dataset(dataset) delay_time = 100 // int(fps) def encode(d): four_d = isinstance(dataset, numpy.ndarray) and len(dataset.shape) == 4 if four_d or not isinstance(dataset, numpy.ndarray): return _make_animated_gif(d, delay_time=delay_time) else: return _make_gif(d) with open(filename, 'wb') as outfile: outfile.write(HEADER) for block in encode(dataset): outfile.write(block) outfile.write(TRAILER)
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train
https://github.com/tanyaschlusser/array2gif/blob/b229da6c8e979314810f59ed0a15ea0f16f71243/array2gif/core.py#L386-L426
mstuttgart/qdarkgraystyle
example/example_pyqt5.py
main
def main(): """ Application entry point """ logging.basicConfig(level=logging.DEBUG) # create the application and the main window app = QtWidgets.QApplication(sys.argv) window = QtWidgets.QMainWindow() # setup ui ui = example_ui.Ui_MainWindow() ui.setupUi(window) ui.bt_delay_popup.addActions([ ui.actionAction, ui.actionAction_C ]) ui.bt_instant_popup.addActions([ ui.actionAction, ui.actionAction_C ]) ui.bt_menu_button_popup.addActions([ ui.actionAction, ui.actionAction_C ]) window.setWindowTitle('QDarkGrayStyle example') # tabify dock widgets to show bug #6 window.tabifyDockWidget(ui.dockWidget1, ui.dockWidget2) # setup stylesheet app.setStyleSheet(qdarkgraystyle.load_stylesheet()) # auto quit after 2s when testing on travis-ci if '--travis' in sys.argv: QtCore.QTimer.singleShot(2000, app.exit) # run window.show() app.exec_()
python
def main(): """ Application entry point """ logging.basicConfig(level=logging.DEBUG) # create the application and the main window app = QtWidgets.QApplication(sys.argv) window = QtWidgets.QMainWindow() # setup ui ui = example_ui.Ui_MainWindow() ui.setupUi(window) ui.bt_delay_popup.addActions([ ui.actionAction, ui.actionAction_C ]) ui.bt_instant_popup.addActions([ ui.actionAction, ui.actionAction_C ]) ui.bt_menu_button_popup.addActions([ ui.actionAction, ui.actionAction_C ]) window.setWindowTitle('QDarkGrayStyle example') # tabify dock widgets to show bug #6 window.tabifyDockWidget(ui.dockWidget1, ui.dockWidget2) # setup stylesheet app.setStyleSheet(qdarkgraystyle.load_stylesheet()) # auto quit after 2s when testing on travis-ci if '--travis' in sys.argv: QtCore.QTimer.singleShot(2000, app.exit) # run window.show() app.exec_()
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train
https://github.com/mstuttgart/qdarkgraystyle/blob/d65182cfe4510fc507e80bfe974dd7faa5429cf0/example/example_pyqt5.py#L51-L89
mstuttgart/qdarkgraystyle
qdarkgraystyle/__init__.py
load_stylesheet
def load_stylesheet(): """ Loads the stylesheet for use in a pyqt5 application. :return the stylesheet string """ # Smart import of the rc file f = QtCore.QFile(':qdarkgraystyle/style.qss') if not f.exists(): _logger().error('Unable to load stylesheet, file not found in ' 'resources') return '' else: f.open(QtCore.QFile.ReadOnly | QtCore.QFile.Text) ts = QtCore.QTextStream(f) stylesheet = ts.readAll() if platform.system().lower() == 'darwin': # see issue #12 on github mac_fix = ''' QDockWidget::title { background-color: #31363b; text-align: center; height: 12px; } ''' stylesheet += mac_fix return stylesheet
python
def load_stylesheet(): """ Loads the stylesheet for use in a pyqt5 application. :return the stylesheet string """ # Smart import of the rc file f = QtCore.QFile(':qdarkgraystyle/style.qss') if not f.exists(): _logger().error('Unable to load stylesheet, file not found in ' 'resources') return '' else: f.open(QtCore.QFile.ReadOnly | QtCore.QFile.Text) ts = QtCore.QTextStream(f) stylesheet = ts.readAll() if platform.system().lower() == 'darwin': # see issue #12 on github mac_fix = ''' QDockWidget::title { background-color: #31363b; text-align: center; height: 12px; } ''' stylesheet += mac_fix return stylesheet
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Loads the stylesheet for use in a pyqt5 application. :return the stylesheet string
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train
https://github.com/mstuttgart/qdarkgraystyle/blob/d65182cfe4510fc507e80bfe974dd7faa5429cf0/qdarkgraystyle/__init__.py#L48-L74
hovren/crisp
crisp/timesync.py
sync_camera_gyro
def sync_camera_gyro(image_sequence_or_flow, image_timestamps, gyro_data, gyro_timestamps, levels=6, full_output=False): """Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using zero-mean cross correlation of the gyroscope data magnitude and the optical flow magnitude, calculated from the image sequence. ZNCC is performed using pyramids to make it quick. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence_or_flow : sequence of image data, or ndarray This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data levels : int Number of pyramid levels full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude """ # If input is not flow, then create from iamge sequence try: assert image_sequence_or_flow.ndim == 1 flow_org = image_sequence_or_flow except AssertionError: flow_org = tracking.optical_flow_magnitude(image_sequence_or_flow) # Gyro from gyro data gyro_mag = np.sum(gyro_data**2, axis=0) flow_timestamps = image_timestamps[:-2] # Resample to match highest rate = lambda ts: len(ts) / (ts[-1] - ts[0]) freq_gyro = rate(gyro_timestamps) freq_image = rate(flow_timestamps) if freq_gyro > freq_image: rel_rate = freq_gyro / freq_image flow_mag = znccpyr.upsample(flow_org, rel_rate) else: flow_mag = flow_org rel_rate = freq_image / freq_gyro gyro_mag = znccpyr.upsample(gyro_mag, rel_rate) ishift = znccpyr.find_shift_pyr(flow_mag, gyro_mag, levels) if freq_gyro > freq_image: flow_shift = int(-ishift / rel_rate) else: flow_shift = int(-ishift) time_offset = flow_timestamps[flow_shift] if full_output: return time_offset, flow_org # Return the orginal flow, not the upsampled version else: return time_offset
python
def sync_camera_gyro(image_sequence_or_flow, image_timestamps, gyro_data, gyro_timestamps, levels=6, full_output=False): """Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using zero-mean cross correlation of the gyroscope data magnitude and the optical flow magnitude, calculated from the image sequence. ZNCC is performed using pyramids to make it quick. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence_or_flow : sequence of image data, or ndarray This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data levels : int Number of pyramid levels full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude """ # If input is not flow, then create from iamge sequence try: assert image_sequence_or_flow.ndim == 1 flow_org = image_sequence_or_flow except AssertionError: flow_org = tracking.optical_flow_magnitude(image_sequence_or_flow) # Gyro from gyro data gyro_mag = np.sum(gyro_data**2, axis=0) flow_timestamps = image_timestamps[:-2] # Resample to match highest rate = lambda ts: len(ts) / (ts[-1] - ts[0]) freq_gyro = rate(gyro_timestamps) freq_image = rate(flow_timestamps) if freq_gyro > freq_image: rel_rate = freq_gyro / freq_image flow_mag = znccpyr.upsample(flow_org, rel_rate) else: flow_mag = flow_org rel_rate = freq_image / freq_gyro gyro_mag = znccpyr.upsample(gyro_mag, rel_rate) ishift = znccpyr.find_shift_pyr(flow_mag, gyro_mag, levels) if freq_gyro > freq_image: flow_shift = int(-ishift / rel_rate) else: flow_shift = int(-ishift) time_offset = flow_timestamps[flow_shift] if full_output: return time_offset, flow_org # Return the orginal flow, not the upsampled version else: return time_offset
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Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using zero-mean cross correlation of the gyroscope data magnitude and the optical flow magnitude, calculated from the image sequence. ZNCC is performed using pyramids to make it quick. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence_or_flow : sequence of image data, or ndarray This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data levels : int Number of pyramid levels full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/timesync.py#L36-L120
hovren/crisp
crisp/timesync.py
sync_camera_gyro_manual
def sync_camera_gyro_manual(image_sequence, image_timestamps, gyro_data, gyro_timestamps, full_output=False): """Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using correlation. The parts of the signals to use are chosen by the user by picking points in a plot window. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence : sequence of image data This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude frame_pair : (int, int) The frame pair that was picked for synchronization """ flow = tracking.optical_flow_magnitude(image_sequence) flow_timestamps = image_timestamps[:-2] # Let user select points in both pieces of data (frame_pair, gyro_idx) = manual_sync_pick(flow, gyro_timestamps, gyro_data) # Normalize data gyro_abs_max = np.max(np.abs(gyro_data), axis=0) gyro_normalized = (gyro_abs_max / np.max(gyro_abs_max)).flatten() flow_normalized = (flow / np.max(flow)).flatten() rate = lambda ts: len(ts) / (ts[-1] - ts[0]) # Resample to match highest freq_gyro = rate(gyro_timestamps) freq_image = rate(flow_timestamps) logger.debug("Gyro sampling frequency: %.2f Hz, Image sampling frequency: %.2f Hz", freq_gyro, freq_image) gyro_part = gyro_normalized[gyro_idx[0]:gyro_idx[1]+1] # only largest flow_part = flow_normalized[frame_pair[0]:frame_pair[1]+1] N = flow_part.size * freq_gyro / freq_image flow_part_resampled = ssig.resample(flow_part, N).flatten() # ) Cross correlate the two signals and find time diff corr = ssig.correlate(gyro_part, flow_part_resampled, 'full') # Find the flow in gyro data i = np.argmax(corr) t_0_f = flow_timestamps[frame_pair[0]] t_1_f = flow_timestamps[frame_pair[1]] t_off_g = gyro_timestamps[gyro_idx[0] + i] t_off_f = t_1_f time_offset = t_off_g - t_off_f if full_output: return time_offset, flow, frame_pair else: return time_offset
python
def sync_camera_gyro_manual(image_sequence, image_timestamps, gyro_data, gyro_timestamps, full_output=False): """Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using correlation. The parts of the signals to use are chosen by the user by picking points in a plot window. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence : sequence of image data This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude frame_pair : (int, int) The frame pair that was picked for synchronization """ flow = tracking.optical_flow_magnitude(image_sequence) flow_timestamps = image_timestamps[:-2] # Let user select points in both pieces of data (frame_pair, gyro_idx) = manual_sync_pick(flow, gyro_timestamps, gyro_data) # Normalize data gyro_abs_max = np.max(np.abs(gyro_data), axis=0) gyro_normalized = (gyro_abs_max / np.max(gyro_abs_max)).flatten() flow_normalized = (flow / np.max(flow)).flatten() rate = lambda ts: len(ts) / (ts[-1] - ts[0]) # Resample to match highest freq_gyro = rate(gyro_timestamps) freq_image = rate(flow_timestamps) logger.debug("Gyro sampling frequency: %.2f Hz, Image sampling frequency: %.2f Hz", freq_gyro, freq_image) gyro_part = gyro_normalized[gyro_idx[0]:gyro_idx[1]+1] # only largest flow_part = flow_normalized[frame_pair[0]:frame_pair[1]+1] N = flow_part.size * freq_gyro / freq_image flow_part_resampled = ssig.resample(flow_part, N).flatten() # ) Cross correlate the two signals and find time diff corr = ssig.correlate(gyro_part, flow_part_resampled, 'full') # Find the flow in gyro data i = np.argmax(corr) t_0_f = flow_timestamps[frame_pair[0]] t_1_f = flow_timestamps[frame_pair[1]] t_off_g = gyro_timestamps[gyro_idx[0] + i] t_off_f = t_1_f time_offset = t_off_g - t_off_f if full_output: return time_offset, flow, frame_pair else: return time_offset
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Get time offset that aligns image timestamps with gyro timestamps. Given an image sequence, and gyroscope data, with their respective timestamps, calculate the offset that aligns the image data with the gyro data. The timestamps must only differ by an offset, not a scale factor. This function finds an approximation of the offset *d* that makes this transformation t_gyro = t_camera + d i.e. your new image timestamps should be image_timestamps_aligned = image_timestamps + d The offset is calculated using correlation. The parts of the signals to use are chosen by the user by picking points in a plot window. The offset is accurate up to about +/- 2 frames, so you should run *refine_time_offset* if you need better accuracy. Parameters --------------- image_sequence : sequence of image data This must be either a list or generator that provides a stream of images that are used for optical flow calculations. image_timestamps : ndarray Timestamps of the images in image_sequence gyro_data : (3, N) ndarray Gyroscope measurements (angular velocity) gyro_timestamps : ndarray Timestamps of data in gyro_data full_output : bool If False, only return the offset, otherwise return extra data Returns -------------- time_offset : float The time offset to add to image_timestamps to align the image data with the gyroscope data flow : ndarray (Only if full_output=True) The calculated optical flow magnitude frame_pair : (int, int) The frame pair that was picked for synchronization
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/timesync.py#L124-L210
hovren/crisp
crisp/timesync.py
refine_time_offset
def refine_time_offset(image_list, frame_timestamps, rotation_sequence, rotation_timestamps, camera_matrix, readout_time): """Refine a time offset between camera and IMU using rolling shutter aware optimization. To refine the time offset using this function, you must meet the following constraints 1) The data must already be roughly aligned. Only a few image frames of error is allowed. 2) The images *must* have been captured by a *rolling shutter* camera. This function finds a refined offset using optimization. Points are first tracked from the start to the end of the provided images. Then an optimization function looks at the reprojection error of the tracked points given the IMU-data and the refined offset. The found offset *d* is such that you want to perform the following time update new_frame_timestamps = frame_timestamps + d Parameters ------------ image_list : list of ndarray A list of images to perform tracking on. High quality tracks are required, so make sure the sequence you choose is easy to track in. frame_timestamps : ndarray Timestamps of image_list rotation_sequence : (4, N) ndarray Absolute rotations as a sequence of unit quaternions (first element is scalar). rotation_timestamps : ndarray Timestamps of rotation_sequence camera_matrix : (3,3) ndarray The internal camera calibration matrix of the camera. readout_time : float The readout time of the camera. Returns ------------ offset : float A refined offset that aligns the image data with the rotation data. """ # ) Track points max_corners = 200 quality_level = 0.07 min_distance = 5 max_tracks = 20 initial_points = cv2.goodFeaturesToTrack(image_list[0], max_corners, quality_level, min_distance) (points, status) = tracking.track_retrack(image_list, initial_points) # Prune to at most max_tracks number of tracks, choose randomly track_id_list = np.random.permutation(points.shape[0])[:max_tracks] rows, cols = image_list[0].shape[:2] row_delta_time = readout_time / rows num_tracks, num_frames, _ = points.shape K = np.matrix(camera_matrix) def func_to_optimize(td, *args): res = 0.0 N = 0 for frame_idx in range(num_frames-1): for track_id in track_id_list: p1 = points[track_id, frame_idx, :].reshape((-1,1)) p2 = points[track_id, frame_idx + 1, :].reshape((-1,1)) t1 = frame_timestamps[frame_idx] + (p1[1] - 1) * row_delta_time + td t2 = frame_timestamps[frame_idx + 1] + (p2[1] - 1) * row_delta_time +td t1 = float(t1) t2 = float(t2) q1 = IMU.rotation_at_time(t1, rotation_timestamps, rotation_sequence) q2 = IMU.rotation_at_time(t2, rotation_timestamps, rotation_sequence) R1 = rotations.quat_to_rotation_matrix(q1) R2 = rotations.quat_to_rotation_matrix(q2) p1_rec = K.dot(R1.T).dot(R2).dot(K.I).dot(np.vstack((p2, 1))) if p1_rec[2] == 0: continue else: p1_rec /= p1_rec[2] res += np.sum((p1 - np.array(p1_rec[0:2]))**2) N += 1 return res / N # Bounded Brent optimizer t0 = time.time() tolerance = 1e-4 # one tenth millisecond (refined_offset, fval, ierr, numfunc) = scipy.optimize.fminbound(func_to_optimize, -0.12, 0.12, xtol=tolerance, full_output=True) t1 = time.time() if ierr == 0: logger.info("Time offset found by brent optimizer: %.4f. Elapsed: %.2f seconds (%d function calls)", refined_offset, t1-t0, numfunc) else: logger.error("Brent optimizer did not converge. Aborting!") raise Exception("Brent optimizer did not converge, when trying to refine offset.") return refined_offset
python
def refine_time_offset(image_list, frame_timestamps, rotation_sequence, rotation_timestamps, camera_matrix, readout_time): """Refine a time offset between camera and IMU using rolling shutter aware optimization. To refine the time offset using this function, you must meet the following constraints 1) The data must already be roughly aligned. Only a few image frames of error is allowed. 2) The images *must* have been captured by a *rolling shutter* camera. This function finds a refined offset using optimization. Points are first tracked from the start to the end of the provided images. Then an optimization function looks at the reprojection error of the tracked points given the IMU-data and the refined offset. The found offset *d* is such that you want to perform the following time update new_frame_timestamps = frame_timestamps + d Parameters ------------ image_list : list of ndarray A list of images to perform tracking on. High quality tracks are required, so make sure the sequence you choose is easy to track in. frame_timestamps : ndarray Timestamps of image_list rotation_sequence : (4, N) ndarray Absolute rotations as a sequence of unit quaternions (first element is scalar). rotation_timestamps : ndarray Timestamps of rotation_sequence camera_matrix : (3,3) ndarray The internal camera calibration matrix of the camera. readout_time : float The readout time of the camera. Returns ------------ offset : float A refined offset that aligns the image data with the rotation data. """ # ) Track points max_corners = 200 quality_level = 0.07 min_distance = 5 max_tracks = 20 initial_points = cv2.goodFeaturesToTrack(image_list[0], max_corners, quality_level, min_distance) (points, status) = tracking.track_retrack(image_list, initial_points) # Prune to at most max_tracks number of tracks, choose randomly track_id_list = np.random.permutation(points.shape[0])[:max_tracks] rows, cols = image_list[0].shape[:2] row_delta_time = readout_time / rows num_tracks, num_frames, _ = points.shape K = np.matrix(camera_matrix) def func_to_optimize(td, *args): res = 0.0 N = 0 for frame_idx in range(num_frames-1): for track_id in track_id_list: p1 = points[track_id, frame_idx, :].reshape((-1,1)) p2 = points[track_id, frame_idx + 1, :].reshape((-1,1)) t1 = frame_timestamps[frame_idx] + (p1[1] - 1) * row_delta_time + td t2 = frame_timestamps[frame_idx + 1] + (p2[1] - 1) * row_delta_time +td t1 = float(t1) t2 = float(t2) q1 = IMU.rotation_at_time(t1, rotation_timestamps, rotation_sequence) q2 = IMU.rotation_at_time(t2, rotation_timestamps, rotation_sequence) R1 = rotations.quat_to_rotation_matrix(q1) R2 = rotations.quat_to_rotation_matrix(q2) p1_rec = K.dot(R1.T).dot(R2).dot(K.I).dot(np.vstack((p2, 1))) if p1_rec[2] == 0: continue else: p1_rec /= p1_rec[2] res += np.sum((p1 - np.array(p1_rec[0:2]))**2) N += 1 return res / N # Bounded Brent optimizer t0 = time.time() tolerance = 1e-4 # one tenth millisecond (refined_offset, fval, ierr, numfunc) = scipy.optimize.fminbound(func_to_optimize, -0.12, 0.12, xtol=tolerance, full_output=True) t1 = time.time() if ierr == 0: logger.info("Time offset found by brent optimizer: %.4f. Elapsed: %.2f seconds (%d function calls)", refined_offset, t1-t0, numfunc) else: logger.error("Brent optimizer did not converge. Aborting!") raise Exception("Brent optimizer did not converge, when trying to refine offset.") return refined_offset
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Refine a time offset between camera and IMU using rolling shutter aware optimization. To refine the time offset using this function, you must meet the following constraints 1) The data must already be roughly aligned. Only a few image frames of error is allowed. 2) The images *must* have been captured by a *rolling shutter* camera. This function finds a refined offset using optimization. Points are first tracked from the start to the end of the provided images. Then an optimization function looks at the reprojection error of the tracked points given the IMU-data and the refined offset. The found offset *d* is such that you want to perform the following time update new_frame_timestamps = frame_timestamps + d Parameters ------------ image_list : list of ndarray A list of images to perform tracking on. High quality tracks are required, so make sure the sequence you choose is easy to track in. frame_timestamps : ndarray Timestamps of image_list rotation_sequence : (4, N) ndarray Absolute rotations as a sequence of unit quaternions (first element is scalar). rotation_timestamps : ndarray Timestamps of rotation_sequence camera_matrix : (3,3) ndarray The internal camera calibration matrix of the camera. readout_time : float The readout time of the camera. Returns ------------ offset : float A refined offset that aligns the image data with the rotation data.
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/timesync.py#L243-L333
hovren/crisp
crisp/timesync.py
good_sequences_to_track
def good_sequences_to_track(flow, motion_threshold=1.0): """Get list of good frames to do tracking in. Looking at the optical flow, this function chooses a span of frames that fulfill certain criteria. These include * not being too short or too long * not too low or too high mean flow magnitude * a low max value (avoids motion blur) Currently, the cost function for a sequence is hard coded. Sorry about that. Parameters ------------- flow : ndarray The optical flow magnitude motion_threshold : float The maximum amount of motion to consider for sequence endpoints. Returns ------------ sequences : list Sorted list of (a, b, score) elements (highest scpre first) of sequences where a sequence is frames with frame indices in the span [a, b]. """ endpoints = [] in_low = False for i, val in enumerate(flow): if val < motion_threshold: if not in_low: endpoints.append(i) in_low = True else: if in_low: endpoints.append(i-1) # Previous was last in a low spot in_low = False def mean_score_func(m): mu = 15 sigma = 8 top_val = normpdf(mu, mu, sigma) return normpdf(m, mu, sigma) / top_val def max_score_func(m): mu = 40 sigma = 8 if m <= mu: return 1. else: top_val = normpdf(mu, mu, sigma) return normpdf(m, mu, sigma) / top_val def length_score_func(l): mu = 30 sigma = 10 top_val = normpdf(mu, mu, sigma) return normpdf(l, mu, sigma) / top_val min_length = 5 # frames sequences = [] for k, i in enumerate(endpoints[:-1]): for j in endpoints[k+1:]: length = j - i if length < min_length: continue seq = flow[i:j+1] m_score = mean_score_func(np.mean(seq)) mx_score = max_score_func(np.max(seq)) l_score = length_score_func(length) logger.debug("%d, %d scores: (mean=%.5f, max=%.5f, length=%.5f)" % (i,j,m_score, mx_score, l_score)) if min(m_score, mx_score, l_score) < 0.2: continue score = m_score + mx_score + l_score sequences.append((i, j, score)) return sorted(sequences, key=lambda x: x[2], reverse=True)
python
def good_sequences_to_track(flow, motion_threshold=1.0): """Get list of good frames to do tracking in. Looking at the optical flow, this function chooses a span of frames that fulfill certain criteria. These include * not being too short or too long * not too low or too high mean flow magnitude * a low max value (avoids motion blur) Currently, the cost function for a sequence is hard coded. Sorry about that. Parameters ------------- flow : ndarray The optical flow magnitude motion_threshold : float The maximum amount of motion to consider for sequence endpoints. Returns ------------ sequences : list Sorted list of (a, b, score) elements (highest scpre first) of sequences where a sequence is frames with frame indices in the span [a, b]. """ endpoints = [] in_low = False for i, val in enumerate(flow): if val < motion_threshold: if not in_low: endpoints.append(i) in_low = True else: if in_low: endpoints.append(i-1) # Previous was last in a low spot in_low = False def mean_score_func(m): mu = 15 sigma = 8 top_val = normpdf(mu, mu, sigma) return normpdf(m, mu, sigma) / top_val def max_score_func(m): mu = 40 sigma = 8 if m <= mu: return 1. else: top_val = normpdf(mu, mu, sigma) return normpdf(m, mu, sigma) / top_val def length_score_func(l): mu = 30 sigma = 10 top_val = normpdf(mu, mu, sigma) return normpdf(l, mu, sigma) / top_val min_length = 5 # frames sequences = [] for k, i in enumerate(endpoints[:-1]): for j in endpoints[k+1:]: length = j - i if length < min_length: continue seq = flow[i:j+1] m_score = mean_score_func(np.mean(seq)) mx_score = max_score_func(np.max(seq)) l_score = length_score_func(length) logger.debug("%d, %d scores: (mean=%.5f, max=%.5f, length=%.5f)" % (i,j,m_score, mx_score, l_score)) if min(m_score, mx_score, l_score) < 0.2: continue score = m_score + mx_score + l_score sequences.append((i, j, score)) return sorted(sequences, key=lambda x: x[2], reverse=True)
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Get list of good frames to do tracking in. Looking at the optical flow, this function chooses a span of frames that fulfill certain criteria. These include * not being too short or too long * not too low or too high mean flow magnitude * a low max value (avoids motion blur) Currently, the cost function for a sequence is hard coded. Sorry about that. Parameters ------------- flow : ndarray The optical flow magnitude motion_threshold : float The maximum amount of motion to consider for sequence endpoints. Returns ------------ sequences : list Sorted list of (a, b, score) elements (highest scpre first) of sequences where a sequence is frames with frame indices in the span [a, b].
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/timesync.py#L336-L411
hovren/crisp
crisp/calibration.py
AutoCalibrator.initialize
def initialize(self, gyro_rate, slices=None, skip_estimation=False): """Prepare calibrator for calibration This method does three things: 1. Create slices from the video stream, if not already provided 2. Estimate time offset 3. Estimate rotation between camera and gyroscope Parameters ------------------ gyro_rate : float Estimated gyroscope sample rate slices : list of Slice, optional Slices to use for optimization skip_estimation : bool Do not estimate initial time offset and rotation. Raises -------------------- InitializationError If the initialization fails """ self.params['user']['gyro_rate'] = gyro_rate for p in ('gbias_x', 'gbias_y', 'gbias_z'): self.params['initialized'][p] = 0.0 if slices is not None: self.slices = slices if self.slices is None: self.slices = videoslice.Slice.from_stream_randomly(self.video) logger.debug("Number of slices: {:d}".format(len(self.slices))) if len(self.slices) < 2: logger.error("Calibration requires at least 2 video slices to proceed, got %d", len(self.slices)) raise InitializationError("Calibration requires at least 2 video slices to proceed, got {:d}".format(len(self.slices))) if not skip_estimation: time_offset = self.find_initial_offset() # TODO: Detect when time offset initialization fails, and raise InitializationError R = self.find_initial_rotation() if R is None: raise InitializationError("Failed to calculate initial rotation")
python
def initialize(self, gyro_rate, slices=None, skip_estimation=False): """Prepare calibrator for calibration This method does three things: 1. Create slices from the video stream, if not already provided 2. Estimate time offset 3. Estimate rotation between camera and gyroscope Parameters ------------------ gyro_rate : float Estimated gyroscope sample rate slices : list of Slice, optional Slices to use for optimization skip_estimation : bool Do not estimate initial time offset and rotation. Raises -------------------- InitializationError If the initialization fails """ self.params['user']['gyro_rate'] = gyro_rate for p in ('gbias_x', 'gbias_y', 'gbias_z'): self.params['initialized'][p] = 0.0 if slices is not None: self.slices = slices if self.slices is None: self.slices = videoslice.Slice.from_stream_randomly(self.video) logger.debug("Number of slices: {:d}".format(len(self.slices))) if len(self.slices) < 2: logger.error("Calibration requires at least 2 video slices to proceed, got %d", len(self.slices)) raise InitializationError("Calibration requires at least 2 video slices to proceed, got {:d}".format(len(self.slices))) if not skip_estimation: time_offset = self.find_initial_offset() # TODO: Detect when time offset initialization fails, and raise InitializationError R = self.find_initial_rotation() if R is None: raise InitializationError("Failed to calculate initial rotation")
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Prepare calibrator for calibration This method does three things: 1. Create slices from the video stream, if not already provided 2. Estimate time offset 3. Estimate rotation between camera and gyroscope Parameters ------------------ gyro_rate : float Estimated gyroscope sample rate slices : list of Slice, optional Slices to use for optimization skip_estimation : bool Do not estimate initial time offset and rotation. Raises -------------------- InitializationError If the initialization fails
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L90-L134
hovren/crisp
crisp/calibration.py
AutoCalibrator.video_time_to_gyro_sample
def video_time_to_gyro_sample(self, t): """Convert video time to gyroscope sample index and interpolation factor Parameters ------------------- t : float Video timestamp Returns -------------------- n : int Sample index that precedes t tau : float Interpolation factor [0.0-1.0]. If tau=0, then t falls on exactly n. If tau=1 then t falls exactly on n+1 """ f_g = self.parameter['gyro_rate'] d_c = self.parameter['time_offset'] n = f_g * (t + d_c) n0 = int(np.floor(n)) tau = n - n0 return n0, tau
python
def video_time_to_gyro_sample(self, t): """Convert video time to gyroscope sample index and interpolation factor Parameters ------------------- t : float Video timestamp Returns -------------------- n : int Sample index that precedes t tau : float Interpolation factor [0.0-1.0]. If tau=0, then t falls on exactly n. If tau=1 then t falls exactly on n+1 """ f_g = self.parameter['gyro_rate'] d_c = self.parameter['time_offset'] n = f_g * (t + d_c) n0 = int(np.floor(n)) tau = n - n0 return n0, tau
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L137-L157
hovren/crisp
crisp/calibration.py
AutoCalibrator.parameter
def parameter(self): """Return the current best value of a parameter""" D = {} for source in PARAM_SOURCE_ORDER: D.update(self.params[source]) return D
python
def parameter(self): """Return the current best value of a parameter""" D = {} for source in PARAM_SOURCE_ORDER: D.update(self.params[source]) return D
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Return the current best value of a parameter
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L160-L165
hovren/crisp
crisp/calibration.py
AutoCalibrator.calibrate
def calibrate(self, max_tracks=MAX_OPTIMIZATION_TRACKS, max_eval=MAX_OPTIMIZATION_FEV, norm_c=DEFAULT_NORM_C): """Perform calibration Parameters ---------------------- max_eval : int Maximum number of function evaluations Returns --------------------- dict Optimization result Raises ----------------------- CalibrationError If calibration fails """ x0 = np.array([self.parameter[param] for param in PARAM_ORDER]) available_tracks = np.sum([len(s.inliers) for s in self.slices]) if available_tracks < max_tracks: warnings.warn("Could not use the requested {} tracks, since only {} were available in the slice data.".format(max_tracks, available_tracks)) max_tracks = available_tracks # Get subset of available tracks such that all slices are still used slice_sample_idxs = videoslice.fill_sampling(self.slices, max_tracks) func_args = (self.slices, slice_sample_idxs, self.video.camera_model, self.gyro, norm_c) self.slice_sample_idxs = slice_sample_idxs logger.debug("Starting optimization on {:d} slices and {:d} tracks".format(len(self.slices), max_tracks)) start_time = time.time() # TODO: Check what values of ftol and xtol are required for good results. The current setting is probably pessimistic. leastsq_result = scipy.optimize.leastsq(optimization_func, x0, args=func_args, full_output=True, ftol=1e-10, xtol=1e-10, maxfev=max_eval) elapsed = time.time() - start_time x, covx, infodict, mesg, ier = leastsq_result self.__debug_leastsq = leastsq_result logger.debug("Optimization completed in {:.1f} seconds and {:d} function evaluations. ier={}, mesg='{}'".format(elapsed, infodict['nfev'], ier, mesg)) if ier in (1,2,3,4): for pname, val in zip(PARAM_ORDER, x): self.params['calibrated'][pname] = val return self.parameter else: raise CalibrationError(mesg)
python
def calibrate(self, max_tracks=MAX_OPTIMIZATION_TRACKS, max_eval=MAX_OPTIMIZATION_FEV, norm_c=DEFAULT_NORM_C): """Perform calibration Parameters ---------------------- max_eval : int Maximum number of function evaluations Returns --------------------- dict Optimization result Raises ----------------------- CalibrationError If calibration fails """ x0 = np.array([self.parameter[param] for param in PARAM_ORDER]) available_tracks = np.sum([len(s.inliers) for s in self.slices]) if available_tracks < max_tracks: warnings.warn("Could not use the requested {} tracks, since only {} were available in the slice data.".format(max_tracks, available_tracks)) max_tracks = available_tracks # Get subset of available tracks such that all slices are still used slice_sample_idxs = videoslice.fill_sampling(self.slices, max_tracks) func_args = (self.slices, slice_sample_idxs, self.video.camera_model, self.gyro, norm_c) self.slice_sample_idxs = slice_sample_idxs logger.debug("Starting optimization on {:d} slices and {:d} tracks".format(len(self.slices), max_tracks)) start_time = time.time() # TODO: Check what values of ftol and xtol are required for good results. The current setting is probably pessimistic. leastsq_result = scipy.optimize.leastsq(optimization_func, x0, args=func_args, full_output=True, ftol=1e-10, xtol=1e-10, maxfev=max_eval) elapsed = time.time() - start_time x, covx, infodict, mesg, ier = leastsq_result self.__debug_leastsq = leastsq_result logger.debug("Optimization completed in {:.1f} seconds and {:d} function evaluations. ier={}, mesg='{}'".format(elapsed, infodict['nfev'], ier, mesg)) if ier in (1,2,3,4): for pname, val in zip(PARAM_ORDER, x): self.params['calibrated'][pname] = val return self.parameter else: raise CalibrationError(mesg)
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Perform calibration Parameters ---------------------- max_eval : int Maximum number of function evaluations Returns --------------------- dict Optimization result Raises ----------------------- CalibrationError If calibration fails
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L167-L209
hovren/crisp
crisp/calibration.py
AutoCalibrator.find_initial_offset
def find_initial_offset(self, pyramids=6): """Estimate time offset This sets and returns the initial time offset estimation. Parameters --------------- pyramids : int Number of pyramids to use for ZNCC calculations. If initial estimation of time offset fails, try lowering this value. Returns --------------- float Estimated time offset """ flow = self.video.flow gyro_rate = self.parameter['gyro_rate'] frame_times = np.arange(len(flow)) / self.video.frame_rate gyro_times = np.arange(self.gyro.num_samples) / gyro_rate time_offset = timesync.sync_camera_gyro(flow, frame_times, self.gyro.data.T, gyro_times, levels=pyramids) logger.debug("Initial time offset: {:.4f}".format(time_offset)) self.params['initialized']['time_offset'] = time_offset return time_offset
python
def find_initial_offset(self, pyramids=6): """Estimate time offset This sets and returns the initial time offset estimation. Parameters --------------- pyramids : int Number of pyramids to use for ZNCC calculations. If initial estimation of time offset fails, try lowering this value. Returns --------------- float Estimated time offset """ flow = self.video.flow gyro_rate = self.parameter['gyro_rate'] frame_times = np.arange(len(flow)) / self.video.frame_rate gyro_times = np.arange(self.gyro.num_samples) / gyro_rate time_offset = timesync.sync_camera_gyro(flow, frame_times, self.gyro.data.T, gyro_times, levels=pyramids) logger.debug("Initial time offset: {:.4f}".format(time_offset)) self.params['initialized']['time_offset'] = time_offset return time_offset
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Estimate time offset This sets and returns the initial time offset estimation. Parameters --------------- pyramids : int Number of pyramids to use for ZNCC calculations. If initial estimation of time offset fails, try lowering this value. Returns --------------- float Estimated time offset
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L211-L236
hovren/crisp
crisp/calibration.py
AutoCalibrator.find_initial_rotation
def find_initial_rotation(self): """Estimate rotation between camera and gyroscope This sets and returns the initial rotation estimate. Note that the initial time offset must have been estimated before calling this function! Returns -------------------- (3,3) ndarray Estimated rotation between camera and gyroscope """ if 'time_offset' not in self.parameter: raise InitializationError("Can not estimate rotation without an estimate of time offset. Please estimate the offset and try again.") dt = float(1.0 / self.parameter['gyro_rate']) # Must be python float for fastintegrate q = self.gyro.integrate(dt) video_axes = [] gyro_axes = [] for _slice in self.slices: # Estimate rotation here _slice.estimate_rotation(self.video.camera_model, ransac_threshold=7.0) # sets .axis and .angle memebers if _slice.axis is None: continue assert _slice.angle > 0 t1 = _slice.start / self.video.frame_rate n1, _ = self.video_time_to_gyro_sample(t1) t2 = _slice.end / self.video.frame_rate n2, _ = self.video_time_to_gyro_sample(t2) try: qx = q[n1] qy = q[n2] except IndexError: continue # No gyro data -> nothing to do with this slice Rx = rotations.quat_to_rotation_matrix(qx) Ry = rotations.quat_to_rotation_matrix(qy) R = np.dot(Rx.T, Ry) v, theta = rotations.rotation_matrix_to_axis_angle(R) if theta < 0: v = -v gyro_axes.append(v) video_axes.append(_slice.axis) if len(gyro_axes) < 2: logger.warning("Rotation estimation requires at least 2 rotation axes, got {}".format(len(gyro_axes))) return None logger.debug("Using {:d} slices (from initial {:d} for rotation estimation".format(len(gyro_axes), len(self.slices))) model_func = lambda data: rotations.procrustes(data[:3], data[3:6], remove_mean=False)[0] def eval_func(model, data): X = data[:3].reshape(3,-1) Y = data[3:6].reshape(3,-1) R = model Xhat = np.dot(R, Y) costheta = np.sum(Xhat*X, axis=0) theta = np.arccos(costheta) return theta inlier_selection_prob = 0.99999 model_points = 2 # Set to 3 to use non-minimal case inlier_ratio = 0.5 threshold = np.deg2rad(10.0) ransac_iterations = int(np.log(1 - inlier_selection_prob) / np.log(1-inlier_ratio**model_points)) data = np.vstack((np.array(video_axes).T, np.array(gyro_axes).T)) assert data.shape == (6, len(gyro_axes)) R, ransac_conseus_idx = ransac.RANSAC(model_func, eval_func, data, model_points, ransac_iterations, threshold, recalculate=True) n, theta = rotations.rotation_matrix_to_axis_angle(R) logger.debug("Found rotation: n={} theta={}; r={}".format(n, theta, n*theta)) logger.debug(R) rx, ry, rz = theta * n self.params['initialized']['rot_x'] = rx self.params['initialized']['rot_y'] = ry self.params['initialized']['rot_z'] = rz return R
python
def find_initial_rotation(self): """Estimate rotation between camera and gyroscope This sets and returns the initial rotation estimate. Note that the initial time offset must have been estimated before calling this function! Returns -------------------- (3,3) ndarray Estimated rotation between camera and gyroscope """ if 'time_offset' not in self.parameter: raise InitializationError("Can not estimate rotation without an estimate of time offset. Please estimate the offset and try again.") dt = float(1.0 / self.parameter['gyro_rate']) # Must be python float for fastintegrate q = self.gyro.integrate(dt) video_axes = [] gyro_axes = [] for _slice in self.slices: # Estimate rotation here _slice.estimate_rotation(self.video.camera_model, ransac_threshold=7.0) # sets .axis and .angle memebers if _slice.axis is None: continue assert _slice.angle > 0 t1 = _slice.start / self.video.frame_rate n1, _ = self.video_time_to_gyro_sample(t1) t2 = _slice.end / self.video.frame_rate n2, _ = self.video_time_to_gyro_sample(t2) try: qx = q[n1] qy = q[n2] except IndexError: continue # No gyro data -> nothing to do with this slice Rx = rotations.quat_to_rotation_matrix(qx) Ry = rotations.quat_to_rotation_matrix(qy) R = np.dot(Rx.T, Ry) v, theta = rotations.rotation_matrix_to_axis_angle(R) if theta < 0: v = -v gyro_axes.append(v) video_axes.append(_slice.axis) if len(gyro_axes) < 2: logger.warning("Rotation estimation requires at least 2 rotation axes, got {}".format(len(gyro_axes))) return None logger.debug("Using {:d} slices (from initial {:d} for rotation estimation".format(len(gyro_axes), len(self.slices))) model_func = lambda data: rotations.procrustes(data[:3], data[3:6], remove_mean=False)[0] def eval_func(model, data): X = data[:3].reshape(3,-1) Y = data[3:6].reshape(3,-1) R = model Xhat = np.dot(R, Y) costheta = np.sum(Xhat*X, axis=0) theta = np.arccos(costheta) return theta inlier_selection_prob = 0.99999 model_points = 2 # Set to 3 to use non-minimal case inlier_ratio = 0.5 threshold = np.deg2rad(10.0) ransac_iterations = int(np.log(1 - inlier_selection_prob) / np.log(1-inlier_ratio**model_points)) data = np.vstack((np.array(video_axes).T, np.array(gyro_axes).T)) assert data.shape == (6, len(gyro_axes)) R, ransac_conseus_idx = ransac.RANSAC(model_func, eval_func, data, model_points, ransac_iterations, threshold, recalculate=True) n, theta = rotations.rotation_matrix_to_axis_angle(R) logger.debug("Found rotation: n={} theta={}; r={}".format(n, theta, n*theta)) logger.debug(R) rx, ry, rz = theta * n self.params['initialized']['rot_x'] = rx self.params['initialized']['rot_y'] = ry self.params['initialized']['rot_z'] = rz return R
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Estimate rotation between camera and gyroscope This sets and returns the initial rotation estimate. Note that the initial time offset must have been estimated before calling this function! Returns -------------------- (3,3) ndarray Estimated rotation between camera and gyroscope
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L238-L326
hovren/crisp
crisp/calibration.py
AutoCalibrator.print_params
def print_params(self): """Print the current best set of parameters""" print("Parameters") print("--------------------") for param in PARAM_ORDER: print(' {:>11s} = {}'.format(param, self.parameter[param]))
python
def print_params(self): """Print the current best set of parameters""" print("Parameters") print("--------------------") for param in PARAM_ORDER: print(' {:>11s} = {}'.format(param, self.parameter[param]))
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Print the current best set of parameters
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/calibration.py#L328-L333
hovren/crisp
crisp/remove_slp.py
remove_slp
def remove_slp(img, gstd1=GSTD1, gstd2=GSTD2, gstd3=GSTD3, ksize=KSIZE, w=W): """Remove the SLP from kinect IR image The input image should be a float32 numpy array, and should NOT be a square root image Parameters ------------------ img : (M, N) float ndarray Kinect NIR image with SLP pattern gstd1 : float Standard deviation of gaussian kernel 1 gstd2 : float Standard deviation of gaussian kernel 2 gstd3 : float Standard deviation of gaussian kernel 3 ksize : int Size of kernel (should be odd) w : float Weighting factor Returns ------------------ img_noslp : (M,N) float ndarray Input image with SLP removed """ gf1 = cv2.getGaussianKernel(ksize, gstd1) gf2 = cv2.getGaussianKernel(ksize, gstd2) gf3 = cv2.getGaussianKernel(ksize, gstd3) sqrtimg = cv2.sqrt(img) p1 = cv2.sepFilter2D(sqrtimg, -1, gf1, gf1) p2 = cv2.sepFilter2D(sqrtimg, -1, gf2, gf2) maxarr = np.maximum(0, (p1 - p2) / p2) minarr = np.minimum(w * maxarr, 1) p = 1 - minarr nc = cv2.sepFilter2D(p, -1, gf3, gf3) + EPS output = cv2.sepFilter2D(p*sqrtimg, -1, gf3, gf3) output = (output / nc) ** 2 # Since input is sqrted return output
python
def remove_slp(img, gstd1=GSTD1, gstd2=GSTD2, gstd3=GSTD3, ksize=KSIZE, w=W): """Remove the SLP from kinect IR image The input image should be a float32 numpy array, and should NOT be a square root image Parameters ------------------ img : (M, N) float ndarray Kinect NIR image with SLP pattern gstd1 : float Standard deviation of gaussian kernel 1 gstd2 : float Standard deviation of gaussian kernel 2 gstd3 : float Standard deviation of gaussian kernel 3 ksize : int Size of kernel (should be odd) w : float Weighting factor Returns ------------------ img_noslp : (M,N) float ndarray Input image with SLP removed """ gf1 = cv2.getGaussianKernel(ksize, gstd1) gf2 = cv2.getGaussianKernel(ksize, gstd2) gf3 = cv2.getGaussianKernel(ksize, gstd3) sqrtimg = cv2.sqrt(img) p1 = cv2.sepFilter2D(sqrtimg, -1, gf1, gf1) p2 = cv2.sepFilter2D(sqrtimg, -1, gf2, gf2) maxarr = np.maximum(0, (p1 - p2) / p2) minarr = np.minimum(w * maxarr, 1) p = 1 - minarr nc = cv2.sepFilter2D(p, -1, gf3, gf3) + EPS output = cv2.sepFilter2D(p*sqrtimg, -1, gf3, gf3) output = (output / nc) ** 2 # Since input is sqrted return output
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Remove the SLP from kinect IR image The input image should be a float32 numpy array, and should NOT be a square root image Parameters ------------------ img : (M, N) float ndarray Kinect NIR image with SLP pattern gstd1 : float Standard deviation of gaussian kernel 1 gstd2 : float Standard deviation of gaussian kernel 2 gstd3 : float Standard deviation of gaussian kernel 3 ksize : int Size of kernel (should be odd) w : float Weighting factor Returns ------------------ img_noslp : (M,N) float ndarray Input image with SLP removed
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/remove_slp.py#L37-L74
hovren/crisp
crisp/camera.py
AtanCameraModel.from_hdf
def from_hdf(cls, filename): """Load camera model params from a HDF5 file The HDF5 file should contain the following datasets: wc : (2,) float with distortion center lgamma : float distortion parameter readout : float readout value size : (2,) int image size fps : float frame rate K : (3, 3) float camera matrix Parameters -------------------- filename : str Path to file with parameters Returns --------------------- AtanCameraModel Camera model instance """ import h5py with h5py.File(filename, 'r') as f: wc = f["wc"].value lgamma = f["lgamma"].value K = f["K"].value readout = f["readout"].value image_size = f["size"].value fps = f["fps"].value instance = cls(image_size, fps, readout, K, wc, lgamma) return instance
python
def from_hdf(cls, filename): """Load camera model params from a HDF5 file The HDF5 file should contain the following datasets: wc : (2,) float with distortion center lgamma : float distortion parameter readout : float readout value size : (2,) int image size fps : float frame rate K : (3, 3) float camera matrix Parameters -------------------- filename : str Path to file with parameters Returns --------------------- AtanCameraModel Camera model instance """ import h5py with h5py.File(filename, 'r') as f: wc = f["wc"].value lgamma = f["lgamma"].value K = f["K"].value readout = f["readout"].value image_size = f["size"].value fps = f["fps"].value instance = cls(image_size, fps, readout, K, wc, lgamma) return instance
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Load camera model params from a HDF5 file The HDF5 file should contain the following datasets: wc : (2,) float with distortion center lgamma : float distortion parameter readout : float readout value size : (2,) int image size fps : float frame rate K : (3, 3) float camera matrix Parameters -------------------- filename : str Path to file with parameters Returns --------------------- AtanCameraModel Camera model instance
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train
https://github.com/hovren/crisp/blob/65cae19e7cfae5a397859096c9ef666e0f4e7f1b/crisp/camera.py#L128-L158