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Komnomnomnom/swigibpy | swigibpy.py | EWrapper.scannerData | def scannerData(self, reqId, rank, contractDetails, distance, benchmark, projection, legsStr):
"""scannerData(EWrapper self, int reqId, int rank, ContractDetails contractDetails, IBString const & distance, IBString const & benchmark, IBString const & projection, IBString const & legsStr)"""
return _swigibpy.EWrapper_scannerData(self, reqId, rank, contractDetails, distance, benchmark, projection, legsStr) | python | def scannerData(self, reqId, rank, contractDetails, distance, benchmark, projection, legsStr):
"""scannerData(EWrapper self, int reqId, int rank, ContractDetails contractDetails, IBString const & distance, IBString const & benchmark, IBString const & projection, IBString const & legsStr)"""
return _swigibpy.EWrapper_scannerData(self, reqId, rank, contractDetails, distance, benchmark, projection, legsStr) | [
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Komnomnomnom/swigibpy | swigibpy.py | EWrapper.realtimeBar | def realtimeBar(self, reqId, time, open, high, low, close, volume, wap, count):
"""realtimeBar(EWrapper self, TickerId reqId, long time, double open, double high, double low, double close, long volume, double wap, int count)"""
return _swigibpy.EWrapper_realtimeBar(self, reqId, time, open, high, low, close, volume, wap, count) | python | def realtimeBar(self, reqId, time, open, high, low, close, volume, wap, count):
"""realtimeBar(EWrapper self, TickerId reqId, long time, double open, double high, double low, double close, long volume, double wap, int count)"""
return _swigibpy.EWrapper_realtimeBar(self, reqId, time, open, high, low, close, volume, wap, count) | [
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Komnomnomnom/swigibpy | swigibpy.py | EWrapper.position | def position(self, account, contract, position, avgCost):
"""position(EWrapper self, IBString const & account, Contract contract, int position, double avgCost)"""
return _swigibpy.EWrapper_position(self, account, contract, position, avgCost) | python | def position(self, account, contract, position, avgCost):
"""position(EWrapper self, IBString const & account, Contract contract, int position, double avgCost)"""
return _swigibpy.EWrapper_position(self, account, contract, position, avgCost) | [
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Komnomnomnom/swigibpy | swigibpy.py | EWrapper.accountSummary | def accountSummary(self, reqId, account, tag, value, curency):
"""accountSummary(EWrapper self, int reqId, IBString const & account, IBString const & tag, IBString const & value, IBString const & curency)"""
return _swigibpy.EWrapper_accountSummary(self, reqId, account, tag, value, curency) | python | def accountSummary(self, reqId, account, tag, value, curency):
"""accountSummary(EWrapper self, int reqId, IBString const & account, IBString const & tag, IBString const & value, IBString const & curency)"""
return _swigibpy.EWrapper_accountSummary(self, reqId, account, tag, value, curency) | [
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Komnomnomnom/swigibpy | swigibpy.py | EWrapper.pyError | def pyError(self, type, value, traceback):
'''Handles an error thrown during invocation of an EWrapper method.
Arguments are those provided by sys.exc_info()
'''
sys.stderr.write("Exception thrown during EWrapper method dispatch:\n")
print_exception(type, value, traceback) | python | def pyError(self, type, value, traceback):
'''Handles an error thrown during invocation of an EWrapper method.
Arguments are those provided by sys.exc_info()
'''
sys.stderr.write("Exception thrown during EWrapper method dispatch:\n")
print_exception(type, value, traceback) | [
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HdrHistogram/HdrHistogram_py | hdrh/codec.py | HdrPayload.init_counts | def init_counts(self, counts_len):
'''Called after instantiating with a compressed payload
Params:
counts_len counts size to use based on decoded settings in the header
'''
assert self._data and counts_len and self.counts_len == 0
self.counts_len = counts_len
self._init_counts()
results = decode(self._data, payload_header_size, addressof(self.counts),
counts_len, self.word_size)
# no longer needed
self._data = None
return results | python | def init_counts(self, counts_len):
'''Called after instantiating with a compressed payload
Params:
counts_len counts size to use based on decoded settings in the header
'''
assert self._data and counts_len and self.counts_len == 0
self.counts_len = counts_len
self._init_counts()
results = decode(self._data, payload_header_size, addressof(self.counts),
counts_len, self.word_size)
# no longer needed
self._data = None
return results | [
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HdrHistogram/HdrHistogram_py | hdrh/codec.py | HdrPayload._decompress | def _decompress(self, compressed_payload):
'''Decompress a compressed payload into this payload wrapper.
Note that the decompressed buffer is saved in self._data and the
counts array is not yet allocated.
Args:
compressed_payload (string) a payload in zlib compressed form
Exception:
HdrCookieException:
the compressed payload has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
HdrHistogramSettingsException:
mismatch in the significant figures, lowest and highest
trackable value
'''
# make sure this instance is pristine
if self._data:
raise RuntimeError('Cannot decompress to an instance with payload')
# Here it is important to keep a reference to the decompressed
# string so that it does not get garbage collected
self._data = zlib.decompress(compressed_payload)
len_data = len(self._data)
counts_size = len_data - payload_header_size
if payload_header_size > counts_size > MAX_COUNTS_SIZE:
raise HdrLengthException('Invalid size:' + str(len_data))
# copy the first bytes for the header
self.payload = PayloadHeader.from_buffer_copy(self._data)
cookie = self.payload.cookie
if get_cookie_base(cookie) != V2_ENCODING_COOKIE_BASE:
raise HdrCookieException('Invalid cookie: %x' % cookie)
word_size = get_word_size_in_bytes_from_cookie(cookie)
if word_size != V2_MAX_WORD_SIZE_IN_BYTES:
raise HdrCookieException('Invalid V2 cookie: %x' % cookie) | python | def _decompress(self, compressed_payload):
'''Decompress a compressed payload into this payload wrapper.
Note that the decompressed buffer is saved in self._data and the
counts array is not yet allocated.
Args:
compressed_payload (string) a payload in zlib compressed form
Exception:
HdrCookieException:
the compressed payload has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
HdrHistogramSettingsException:
mismatch in the significant figures, lowest and highest
trackable value
'''
# make sure this instance is pristine
if self._data:
raise RuntimeError('Cannot decompress to an instance with payload')
# Here it is important to keep a reference to the decompressed
# string so that it does not get garbage collected
self._data = zlib.decompress(compressed_payload)
len_data = len(self._data)
counts_size = len_data - payload_header_size
if payload_header_size > counts_size > MAX_COUNTS_SIZE:
raise HdrLengthException('Invalid size:' + str(len_data))
# copy the first bytes for the header
self.payload = PayloadHeader.from_buffer_copy(self._data)
cookie = self.payload.cookie
if get_cookie_base(cookie) != V2_ENCODING_COOKIE_BASE:
raise HdrCookieException('Invalid cookie: %x' % cookie)
word_size = get_word_size_in_bytes_from_cookie(cookie)
if word_size != V2_MAX_WORD_SIZE_IN_BYTES:
raise HdrCookieException('Invalid V2 cookie: %x' % cookie) | [
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HdrHistogram/HdrHistogram_py | hdrh/codec.py | HdrPayload.compress | def compress(self, counts_limit):
'''Compress this payload instance
Args:
counts_limit how many counters should be encoded
starting from index 0 (can be 0),
Return:
the compressed payload (python string)
'''
if self.payload:
# worst case varint encoded length is when each counter is at the maximum value
# in this case 1 more byte per counter is needed due to the more bits
varint_len = counts_limit * (self.word_size + 1)
# allocate enough space to fit the header and the varint string
encode_buf = (c_byte * (payload_header_size + varint_len))()
# encode past the payload header
varint_len = encode(addressof(self.counts), counts_limit,
self.word_size,
addressof(encode_buf) + payload_header_size,
varint_len)
# copy the header after updating the varint stream length
self.payload.payload_len = varint_len
ctypes.memmove(addressof(encode_buf), addressof(self.payload), payload_header_size)
cdata = zlib.compress(ctypes.string_at(encode_buf, payload_header_size + varint_len))
return cdata
# can't compress if no payload
raise RuntimeError('No payload to compress') | python | def compress(self, counts_limit):
'''Compress this payload instance
Args:
counts_limit how many counters should be encoded
starting from index 0 (can be 0),
Return:
the compressed payload (python string)
'''
if self.payload:
# worst case varint encoded length is when each counter is at the maximum value
# in this case 1 more byte per counter is needed due to the more bits
varint_len = counts_limit * (self.word_size + 1)
# allocate enough space to fit the header and the varint string
encode_buf = (c_byte * (payload_header_size + varint_len))()
# encode past the payload header
varint_len = encode(addressof(self.counts), counts_limit,
self.word_size,
addressof(encode_buf) + payload_header_size,
varint_len)
# copy the header after updating the varint stream length
self.payload.payload_len = varint_len
ctypes.memmove(addressof(encode_buf), addressof(self.payload), payload_header_size)
cdata = zlib.compress(ctypes.string_at(encode_buf, payload_header_size + varint_len))
return cdata
# can't compress if no payload
raise RuntimeError('No payload to compress') | [
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HdrHistogram/HdrHistogram_py | hdrh/codec.py | HdrHistogramEncoder.encode | def encode(self):
'''Compress the associated encodable payload,
prepend the header then encode with base64 if requested
Returns:
the b64 encoded wire encoding of the histogram (as a string)
or the compressed payload (as a string, if b64 wrappinb is disabled)
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# only compress the first non zero buckets
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else:
relevant_length = 0
cpayload = self.payload.compress(relevant_length)
if self.b64_wrap:
self.header.length = len(cpayload)
header_str = ctypes.string_at(addressof(self.header), ext_header_size)
return base64.b64encode(header_str + cpayload)
return cpayload | python | def encode(self):
'''Compress the associated encodable payload,
prepend the header then encode with base64 if requested
Returns:
the b64 encoded wire encoding of the histogram (as a string)
or the compressed payload (as a string, if b64 wrappinb is disabled)
'''
# only compress the first non zero buckets
# if histogram is empty we do not encode any counter
if self.histogram.total_count:
relevant_length = \
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else:
relevant_length = 0
cpayload = self.payload.compress(relevant_length)
if self.b64_wrap:
self.header.length = len(cpayload)
header_str = ctypes.string_at(addressof(self.header), ext_header_size)
return base64.b64encode(header_str + cpayload)
return cpayload | [
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the b64 encoded wire encoding of the histogram (as a string)
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HdrHistogram/HdrHistogram_py | hdrh/codec.py | HdrHistogramEncoder.decode | def decode(encoded_histogram, b64_wrap=True):
'''Decode a wire histogram encoding into a read-only Hdr Payload instance
Args:
encoded_histogram a string containing the wire encoding of a histogram
such as one returned from encode()
Returns:
an hdr_payload instance with all the decoded/uncompressed fields
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
HdrHistogramSettingsException:
mismatch in the significant figures, lowest and highest
trackable value
zlib.error:
in case of zlib decompression error
'''
if b64_wrap:
b64decode = base64.b64decode(encoded_histogram)
# this string has 2 parts in it: the header (raw) and the payload (compressed)
b64dec_len = len(b64decode)
if b64dec_len < ext_header_size:
raise HdrLengthException('Base64 decoded message too short')
header = ExternalHeader.from_buffer_copy(b64decode)
if get_cookie_base(header.cookie) != V2_COMPRESSION_COOKIE_BASE:
raise HdrCookieException()
if header.length != b64dec_len - ext_header_size:
raise HdrLengthException('Decoded length=%d buffer length=%d' %
(header.length, b64dec_len - ext_header_size))
# this will result in a copy of the compressed payload part
# could not find a way to do otherwise since zlib.decompress()
# expects a string (and does not like a buffer or a memoryview object)
cpayload = b64decode[ext_header_size:]
else:
cpayload = encoded_histogram
hdr_payload = HdrPayload(8, compressed_payload=cpayload)
return hdr_payload | python | def decode(encoded_histogram, b64_wrap=True):
'''Decode a wire histogram encoding into a read-only Hdr Payload instance
Args:
encoded_histogram a string containing the wire encoding of a histogram
such as one returned from encode()
Returns:
an hdr_payload instance with all the decoded/uncompressed fields
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
HdrHistogramSettingsException:
mismatch in the significant figures, lowest and highest
trackable value
zlib.error:
in case of zlib decompression error
'''
if b64_wrap:
b64decode = base64.b64decode(encoded_histogram)
# this string has 2 parts in it: the header (raw) and the payload (compressed)
b64dec_len = len(b64decode)
if b64dec_len < ext_header_size:
raise HdrLengthException('Base64 decoded message too short')
header = ExternalHeader.from_buffer_copy(b64decode)
if get_cookie_base(header.cookie) != V2_COMPRESSION_COOKIE_BASE:
raise HdrCookieException()
if header.length != b64dec_len - ext_header_size:
raise HdrLengthException('Decoded length=%d buffer length=%d' %
(header.length, b64dec_len - ext_header_size))
# this will result in a copy of the compressed payload part
# could not find a way to do otherwise since zlib.decompress()
# expects a string (and does not like a buffer or a memoryview object)
cpayload = b64decode[ext_header_size:]
else:
cpayload = encoded_histogram
hdr_payload = HdrPayload(8, compressed_payload=cpayload)
return hdr_payload | [
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HdrLengthException:
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.record_value | def record_value(self, value, count=1):
'''Record a new value into the histogram
Args:
value: the value to record (must be in the valid range)
count: incremental count (defaults to 1)
'''
if value < 0:
return False
counts_index = self._counts_index_for(value)
if (counts_index < 0) or (self.counts_len <= counts_index):
return False
self.counts[counts_index] += count
self.total_count += count
self.min_value = min(self.min_value, value)
self.max_value = max(self.max_value, value)
return True | python | def record_value(self, value, count=1):
'''Record a new value into the histogram
Args:
value: the value to record (must be in the valid range)
count: incremental count (defaults to 1)
'''
if value < 0:
return False
counts_index = self._counts_index_for(value)
if (counts_index < 0) or (self.counts_len <= counts_index):
return False
self.counts[counts_index] += count
self.total_count += count
self.min_value = min(self.min_value, value)
self.max_value = max(self.max_value, value)
return True | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.record_corrected_value | def record_corrected_value(self, value, expected_interval, count=1):
'''Record a new value into the histogram and correct for
coordinated omission if needed
Args:
value: the value to record (must be in the valid range)
expected_interval: the expected interval between 2 value samples
count: incremental count (defaults to 1)
'''
while True:
if not self.record_value(value, count):
return False
if value <= expected_interval or expected_interval <= 0:
return True
value -= expected_interval | python | def record_corrected_value(self, value, expected_interval, count=1):
'''Record a new value into the histogram and correct for
coordinated omission if needed
Args:
value: the value to record (must be in the valid range)
expected_interval: the expected interval between 2 value samples
count: incremental count (defaults to 1)
'''
while True:
if not self.record_value(value, count):
return False
if value <= expected_interval or expected_interval <= 0:
return True
value -= expected_interval | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.get_value_at_percentile | def get_value_at_percentile(self, percentile):
'''Get the value for a given percentile
Args:
percentile: a float in [0.0..100.0]
Returns:
the value for the given percentile
'''
count_at_percentile = self.get_target_count_at_percentile(percentile)
total = 0
for index in range(self.counts_len):
total += self.get_count_at_index(index)
if total >= count_at_percentile:
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if percentile:
return self.get_highest_equivalent_value(value_at_index)
return self.get_lowest_equivalent_value(value_at_index)
return 0 | python | def get_value_at_percentile(self, percentile):
'''Get the value for a given percentile
Args:
percentile: a float in [0.0..100.0]
Returns:
the value for the given percentile
'''
count_at_percentile = self.get_target_count_at_percentile(percentile)
total = 0
for index in range(self.counts_len):
total += self.get_count_at_index(index)
if total >= count_at_percentile:
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return 0 | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.get_percentile_to_value_dict | def get_percentile_to_value_dict(self, percentile_list):
'''A faster alternative to query values for a list of percentiles.
Args:
percentile_list: a list of percentiles in any order, dups will be ignored
each element in the list must be a float value in [0.0 .. 100.0]
Returns:
a dict of percentile values indexed by the percentile
'''
result = {}
total = 0
percentile_list_index = 0
count_at_percentile = 0
# remove dups and sort
percentile_list = list(set(percentile_list))
percentile_list.sort()
for index in range(self.counts_len):
total += self.get_count_at_index(index)
while True:
# recalculate target based on next requested percentile
if not count_at_percentile:
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return result
percentile = percentile_list[percentile_list_index]
percentile_list_index += 1
if percentile > 100:
return result
count_at_percentile = self.get_target_count_at_percentile(percentile)
if total >= count_at_percentile:
value_at_index = self.get_value_from_index(index)
if percentile:
result[percentile] = self.get_highest_equivalent_value(value_at_index)
else:
result[percentile] = self.get_lowest_equivalent_value(value_at_index)
count_at_percentile = 0
else:
break
return result | python | def get_percentile_to_value_dict(self, percentile_list):
'''A faster alternative to query values for a list of percentiles.
Args:
percentile_list: a list of percentiles in any order, dups will be ignored
each element in the list must be a float value in [0.0 .. 100.0]
Returns:
a dict of percentile values indexed by the percentile
'''
result = {}
total = 0
percentile_list_index = 0
count_at_percentile = 0
# remove dups and sort
percentile_list = list(set(percentile_list))
percentile_list.sort()
for index in range(self.counts_len):
total += self.get_count_at_index(index)
while True:
# recalculate target based on next requested percentile
if not count_at_percentile:
if percentile_list_index == len(percentile_list):
return result
percentile = percentile_list[percentile_list_index]
percentile_list_index += 1
if percentile > 100:
return result
count_at_percentile = self.get_target_count_at_percentile(percentile)
if total >= count_at_percentile:
value_at_index = self.get_value_from_index(index)
if percentile:
result[percentile] = self.get_highest_equivalent_value(value_at_index)
else:
result[percentile] = self.get_lowest_equivalent_value(value_at_index)
count_at_percentile = 0
else:
break
return result | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.values_are_equivalent | def values_are_equivalent(self, val1, val2):
'''Check whether 2 values are equivalent (meaning they
are in the same bucket/range)
Returns:
true if the 2 values are equivalent
'''
return self.get_lowest_equivalent_value(val1) == self.get_lowest_equivalent_value(val2) | python | def values_are_equivalent(self, val1, val2):
'''Check whether 2 values are equivalent (meaning they
are in the same bucket/range)
Returns:
true if the 2 values are equivalent
'''
return self.get_lowest_equivalent_value(val1) == self.get_lowest_equivalent_value(val2) | [
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] | cb99981b0564a62e1aa02bd764efa6445923f8f7 | https://github.com/HdrHistogram/HdrHistogram_py/blob/cb99981b0564a62e1aa02bd764efa6445923f8f7/hdrh/histogram.py#L335-L342 | train |
HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.reset | def reset(self):
'''Reset the histogram to a pristine state
'''
for index in range(self.counts_len):
self.counts[index] = 0
self.total_count = 0
self.min_value = sys.maxsize
self.max_value = 0
self.start_time_stamp_msec = sys.maxsize
self.end_time_stamp_msec = 0 | python | def reset(self):
'''Reset the histogram to a pristine state
'''
for index in range(self.counts_len):
self.counts[index] = 0
self.total_count = 0
self.min_value = sys.maxsize
self.max_value = 0
self.start_time_stamp_msec = sys.maxsize
self.end_time_stamp_msec = 0 | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.adjust_internal_tacking_values | def adjust_internal_tacking_values(self,
min_non_zero_index,
max_index,
total_added):
'''Called during decoding and add to adjust the new min/max value and
total count
Args:
min_non_zero_index min nonzero index of all added counts (-1 if none)
max_index max index of all added counts (-1 if none)
'''
if max_index >= 0:
max_value = self.get_highest_equivalent_value(self.get_value_from_index(max_index))
self.max_value = max(self.max_value, max_value)
if min_non_zero_index >= 0:
min_value = self.get_value_from_index(min_non_zero_index)
self.min_value = min(self.min_value, min_value)
self.total_count += total_added | python | def adjust_internal_tacking_values(self,
min_non_zero_index,
max_index,
total_added):
'''Called during decoding and add to adjust the new min/max value and
total count
Args:
min_non_zero_index min nonzero index of all added counts (-1 if none)
max_index max index of all added counts (-1 if none)
'''
if max_index >= 0:
max_value = self.get_highest_equivalent_value(self.get_value_from_index(max_index))
self.max_value = max(self.max_value, max_value)
if min_non_zero_index >= 0:
min_value = self.get_value_from_index(min_non_zero_index)
self.min_value = min(self.min_value, min_value)
self.total_count += total_added | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.set_internal_tacking_values | def set_internal_tacking_values(self,
min_non_zero_index,
max_index,
total_added):
'''Called during decoding and add to adjust the new min/max value and
total count
Args:
min_non_zero_index min nonzero index of all added counts (-1 if none)
max_index max index of all added counts (-1 if none)
'''
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if min_non_zero_index >= 0:
self.min_value = self.get_value_from_index(min_non_zero_index)
self.total_count = total_added | python | def set_internal_tacking_values(self,
min_non_zero_index,
max_index,
total_added):
'''Called during decoding and add to adjust the new min/max value and
total count
Args:
min_non_zero_index min nonzero index of all added counts (-1 if none)
max_index max index of all added counts (-1 if none)
'''
if max_index >= 0:
self.max_value = self.get_highest_equivalent_value(self.get_value_from_index(max_index))
if min_non_zero_index >= 0:
self.min_value = self.get_value_from_index(min_non_zero_index)
self.total_count = total_added | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.get_counts_array_index | def get_counts_array_index(self, value):
'''Return the index in the counts array for a given value
'''
if value < 0:
raise ValueError("Histogram recorded value cannot be negative.")
bucket_index = self._get_bucket_index(value)
sub_bucket_index = self._get_sub_bucket_index(value, bucket_index)
# Calculate the index for the first entry in the bucket:
bucket_base_index = (bucket_index + 1) << self.sub_bucket_half_count_magnitude
# The following is the equivalent of ((bucket_index + 1) * sub_bucket_half_count)
# Calculate the offset in the bucket (can be negative for first bucket):
offset_in_bucket = sub_bucket_index - self.sub_bucket_half_count
# The following is the equivalent of
# ((sub_bucket_index - sub_bucket_half_count) + bucket_base_index
return bucket_base_index + offset_in_bucket | python | def get_counts_array_index(self, value):
'''Return the index in the counts array for a given value
'''
if value < 0:
raise ValueError("Histogram recorded value cannot be negative.")
bucket_index = self._get_bucket_index(value)
sub_bucket_index = self._get_sub_bucket_index(value, bucket_index)
# Calculate the index for the first entry in the bucket:
bucket_base_index = (bucket_index + 1) << self.sub_bucket_half_count_magnitude
# The following is the equivalent of ((bucket_index + 1) * sub_bucket_half_count)
# Calculate the offset in the bucket (can be negative for first bucket):
offset_in_bucket = sub_bucket_index - self.sub_bucket_half_count
# The following is the equivalent of
# ((sub_bucket_index - sub_bucket_half_count) + bucket_base_index
return bucket_base_index + offset_in_bucket | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.decode_and_add | def decode_and_add(self, encoded_histogram):
'''Decode an encoded histogram and add it to this histogram
Args:
encoded_histogram (string) an encoded histogram
following the V1 format, such as one returned by the encode() method
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
zlib.error:
in case of zlib decompression error
'''
other_hist = HdrHistogram.decode(encoded_histogram, self.b64_wrap)
self.add(other_hist) | python | def decode_and_add(self, encoded_histogram):
'''Decode an encoded histogram and add it to this histogram
Args:
encoded_histogram (string) an encoded histogram
following the V1 format, such as one returned by the encode() method
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
zlib.error:
in case of zlib decompression error
'''
other_hist = HdrHistogram.decode(encoded_histogram, self.b64_wrap)
self.add(other_hist) | [
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HdrHistogram/HdrHistogram_py | hdrh/histogram.py | HdrHistogram.decode | def decode(encoded_histogram, b64_wrap=True):
'''Decode an encoded histogram and return a new histogram instance that
has been initialized with the decoded content
Return:
a new histogram instance representing the decoded content
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
zlib.error:
in case of zlib decompression error
'''
hdr_payload = HdrHistogramEncoder.decode(encoded_histogram, b64_wrap)
payload = hdr_payload.payload
histogram = HdrHistogram(payload.lowest_trackable_value,
payload.highest_trackable_value,
payload.significant_figures,
hdr_payload=hdr_payload)
return histogram | python | def decode(encoded_histogram, b64_wrap=True):
'''Decode an encoded histogram and return a new histogram instance that
has been initialized with the decoded content
Return:
a new histogram instance representing the decoded content
Exception:
TypeError in case of base64 decode error
HdrCookieException:
the main header has an invalid cookie
the compressed payload header has an invalid cookie
HdrLengthException:
the decompressed size is too small for the HdrPayload structure
or is not aligned or is too large for the passed payload class
zlib.error:
in case of zlib decompression error
'''
hdr_payload = HdrHistogramEncoder.decode(encoded_histogram, b64_wrap)
payload = hdr_payload.payload
histogram = HdrHistogram(payload.lowest_trackable_value,
payload.highest_trackable_value,
payload.significant_figures,
hdr_payload=hdr_payload)
return histogram | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator._validate_json | def _validate_json(self, checked_json, schema):
""" Validate JSON according to JSONSchema
*Args*:\n
_checked_json_: validated JSON.
_schema_: schema that used for validation.
"""
try:
jsonschema.validate(checked_json, schema)
except jsonschema.ValidationError as e:
print("""Failed validating '{0}'
in schema {1}:
{2}
On instance {3}:
{4}""".format(e.validator,
list(e.relative_schema_path)[:-1], pprint.pformat(e.schema),
"[%s]" % "][".join(repr(index) for index in e.absolute_path),
pprint.pformat(e.instance).encode('utf-8')))
raise JsonValidatorError("Failed validating json by schema")
except jsonschema.SchemaError as e:
raise JsonValidatorError('Json-schema error') | python | def _validate_json(self, checked_json, schema):
""" Validate JSON according to JSONSchema
*Args*:\n
_checked_json_: validated JSON.
_schema_: schema that used for validation.
"""
try:
jsonschema.validate(checked_json, schema)
except jsonschema.ValidationError as e:
print("""Failed validating '{0}'
in schema {1}:
{2}
On instance {3}:
{4}""".format(e.validator,
list(e.relative_schema_path)[:-1], pprint.pformat(e.schema),
"[%s]" % "][".join(repr(index) for index in e.absolute_path),
pprint.pformat(e.instance).encode('utf-8')))
raise JsonValidatorError("Failed validating json by schema")
except jsonschema.SchemaError as e:
raise JsonValidatorError('Json-schema error') | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.validate_jsonschema_from_file | def validate_jsonschema_from_file(self, json_string, path_to_schema):
"""
Validate JSON according to schema, loaded from a file.
*Args:*\n
_json_string_ - JSON string;\n
_path_to_schema_ - path to file with JSON schema;
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Simple | Validate jsonschema from file | {"foo":bar} | ${CURDIR}${/}schema.json |
"""
schema = open(path_to_schema).read()
load_input_json = self.string_to_json(json_string)
try:
load_schema = json.loads(schema)
except ValueError as e:
raise JsonValidatorError('Error in schema: {}'.format(e))
self._validate_json(load_input_json, load_schema) | python | def validate_jsonschema_from_file(self, json_string, path_to_schema):
"""
Validate JSON according to schema, loaded from a file.
*Args:*\n
_json_string_ - JSON string;\n
_path_to_schema_ - path to file with JSON schema;
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Simple | Validate jsonschema from file | {"foo":bar} | ${CURDIR}${/}schema.json |
"""
schema = open(path_to_schema).read()
load_input_json = self.string_to_json(json_string)
try:
load_schema = json.loads(schema)
except ValueError as e:
raise JsonValidatorError('Error in schema: {}'.format(e))
self._validate_json(load_input_json, load_schema) | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.validate_jsonschema | def validate_jsonschema(self, json_string, input_schema):
"""
Validate JSON according to schema.
*Args:*\n
_json_string_ - JSON string;\n
_input_schema_ - schema in string format;
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Simple | ${schema}= | OperatingSystem.Get File | ${CURDIR}${/}schema_valid.json |
| | Validate jsonschema | {"foo":bar} | ${schema} |
"""
load_input_json = self.string_to_json(json_string)
try:
load_schema = json.loads(input_schema)
except ValueError as e:
raise JsonValidatorError('Error in schema: {}'.format(e))
self._validate_json(load_input_json, load_schema) | python | def validate_jsonschema(self, json_string, input_schema):
"""
Validate JSON according to schema.
*Args:*\n
_json_string_ - JSON string;\n
_input_schema_ - schema in string format;
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Simple | ${schema}= | OperatingSystem.Get File | ${CURDIR}${/}schema_valid.json |
| | Validate jsonschema | {"foo":bar} | ${schema} |
"""
load_input_json = self.string_to_json(json_string)
try:
load_schema = json.loads(input_schema)
except ValueError as e:
raise JsonValidatorError('Error in schema: {}'.format(e))
self._validate_json(load_input_json, load_schema) | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.string_to_json | def string_to_json(self, source):
"""
Deserialize string into JSON structure.
*Args:*\n
_source_ - JSON string
*Returns:*\n
JSON structure
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| String to json | ${json_string}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json}= | String to json | ${json_string} |
| | Log | ${json["store"]["book"][0]["price"]} |
=>\n
8.95
"""
try:
load_input_json = json.loads(source)
except ValueError as e:
raise JsonValidatorError("Could not parse '%s' as JSON: %s" % (source, e))
return load_input_json | python | def string_to_json(self, source):
"""
Deserialize string into JSON structure.
*Args:*\n
_source_ - JSON string
*Returns:*\n
JSON structure
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| String to json | ${json_string}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json}= | String to json | ${json_string} |
| | Log | ${json["store"]["book"][0]["price"]} |
=>\n
8.95
"""
try:
load_input_json = json.loads(source)
except ValueError as e:
raise JsonValidatorError("Could not parse '%s' as JSON: %s" % (source, e))
return load_input_json | [
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| | Log | ${json["store"]["book"][0]["price"]} |
=>\n
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.json_to_string | def json_to_string(self, source):
"""
Serialize JSON structure into string.
*Args:*\n
_source_ - JSON structure
*Returns:*\n
JSON string
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Json to string | ${json_string}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json}= | String to json | ${json_string} |
| | ${string}= | Json to string | ${json} |
| | ${pretty_string}= | Pretty print json | ${string} |
| | Log to console | ${pretty_string} |
"""
try:
load_input_json = json.dumps(source)
except ValueError as e:
raise JsonValidatorError("Could serialize '%s' to JSON: %s" % (source, e))
return load_input_json | python | def json_to_string(self, source):
"""
Serialize JSON structure into string.
*Args:*\n
_source_ - JSON structure
*Returns:*\n
JSON string
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Json to string | ${json_string}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json}= | String to json | ${json_string} |
| | ${string}= | Json to string | ${json} |
| | ${pretty_string}= | Pretty print json | ${string} |
| | Log to console | ${pretty_string} |
"""
try:
load_input_json = json.dumps(source)
except ValueError as e:
raise JsonValidatorError("Could serialize '%s' to JSON: %s" % (source, e))
return load_input_json | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.get_elements | def get_elements(self, json_string, expr):
"""
Get list of elements from _json_string_, matching [http://goessner.net/articles/JsonPath/|JSONPath] expression.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONPath expression;
*Returns:*\n
List of found elements or ``None`` if no elements were found
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Get json elements | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_elements}= | Get elements | ${json_example} | $.store.book[*].author |
=>\n
| [u'Nigel Rees', u'Evelyn Waugh', u'Herman Melville', u'J. R. R. Tolkien']
"""
load_input_json = self.string_to_json(json_string)
# parsing jsonpath
jsonpath_expr = parse(expr)
# list of returned elements
value_list = []
for match in jsonpath_expr.find(load_input_json):
value_list.append(match.value)
if not value_list:
return None
else:
return value_list | python | def get_elements(self, json_string, expr):
"""
Get list of elements from _json_string_, matching [http://goessner.net/articles/JsonPath/|JSONPath] expression.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONPath expression;
*Returns:*\n
List of found elements or ``None`` if no elements were found
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Get json elements | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_elements}= | Get elements | ${json_example} | $.store.book[*].author |
=>\n
| [u'Nigel Rees', u'Evelyn Waugh', u'Herman Melville', u'J. R. R. Tolkien']
"""
load_input_json = self.string_to_json(json_string)
# parsing jsonpath
jsonpath_expr = parse(expr)
# list of returned elements
value_list = []
for match in jsonpath_expr.find(load_input_json):
value_list.append(match.value)
if not value_list:
return None
else:
return value_list | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.select_elements | def select_elements(self, json_string, expr):
"""
Return list of elements from _json_string_, matching [ http://jsonselect.org/ | JSONSelect] expression.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONSelect expression;
*Returns:*\n
List of found elements or ``None`` if no elements were found
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Select json elements | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_elements}= | Select elements | ${json_example} | .author:contains("Evelyn Waugh")~.price |
=>\n
| 12.99
"""
load_input_json = self.string_to_json(json_string)
# parsing jsonselect
match = jsonselect.match(sel=expr, obj=load_input_json)
ret = list(match)
return ret if ret else None | python | def select_elements(self, json_string, expr):
"""
Return list of elements from _json_string_, matching [ http://jsonselect.org/ | JSONSelect] expression.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONSelect expression;
*Returns:*\n
List of found elements or ``None`` if no elements were found
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Select json elements | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_elements}= | Select elements | ${json_example} | .author:contains("Evelyn Waugh")~.price |
=>\n
| 12.99
"""
load_input_json = self.string_to_json(json_string)
# parsing jsonselect
match = jsonselect.match(sel=expr, obj=load_input_json)
ret = list(match)
return ret if ret else None | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.select_objects | def select_objects(self, json_string, expr):
"""
Return list of elements from _json_string_, matching [ http://objectpath.org// | ObjectPath] expression.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - ObjectPath expression;
*Returns:*\n
List of found elements. If no elements were found, empty list will be returned
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
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| | ${json_objectss}= | Select objects | ${json_example} | $..book[@.author.name is "Evelyn Waugh"].price |
=>\n
| [12.99]
"""
load_input_json = self.string_to_json(json_string)
# parsing objectpath
tree = objectpath.Tree(load_input_json)
values = tree.execute(expr)
return list(values) | python | def select_objects(self, json_string, expr):
"""
Return list of elements from _json_string_, matching [ http://objectpath.org// | ObjectPath] expression.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - ObjectPath expression;
*Returns:*\n
List of found elements. If no elements were found, empty list will be returned
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Select json objects | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_objectss}= | Select objects | ${json_example} | $..book[@.author.name is "Evelyn Waugh"].price |
=>\n
| [12.99]
"""
load_input_json = self.string_to_json(json_string)
# parsing objectpath
tree = objectpath.Tree(load_input_json)
values = tree.execute(expr)
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| | ${json_objectss}= | Select objects | ${json_example} | $..book[@.author.name is "Evelyn Waugh"].price |
=>\n
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.element_should_exist | def element_should_exist(self, json_string, expr):
"""
Check the existence of one or more elements, matching [ http://jsonselect.org/ | JSONSelect] expression.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
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JsonValidatorError
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| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Check element | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | Element should exist | ${json_example} | .author:contains("Evelyn Waugh") |
| | Element should exist | ${json_example} | .store .book .price:expr(x=8.95) |
"""
value = self.select_elements(json_string, expr)
if value is None:
raise JsonValidatorError('Elements %s does not exist' % expr) | python | def element_should_exist(self, json_string, expr):
"""
Check the existence of one or more elements, matching [ http://jsonselect.org/ | JSONSelect] expression.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONSelect expression;\n
*Raises:*\n
JsonValidatorError
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Check element | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | Element should exist | ${json_example} | .author:contains("Evelyn Waugh") |
| | Element should exist | ${json_example} | .store .book .price:expr(x=8.95) |
"""
value = self.select_elements(json_string, expr)
if value is None:
raise JsonValidatorError('Elements %s does not exist' % expr) | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.element_should_not_exist | def element_should_not_exist(self, json_string, expr):
"""
Check that one or more elements, matching [ http://jsonselect.org/ | JSONSelect] expression, don't exist.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONSelect expression;\n
*Raises:*\n
JsonValidatorError
"""
value = self.select_elements(json_string, expr)
if value is not None:
raise JsonValidatorError('Elements %s exist but should not' % expr) | python | def element_should_not_exist(self, json_string, expr):
"""
Check that one or more elements, matching [ http://jsonselect.org/ | JSONSelect] expression, don't exist.
*DEPRECATED* JSON Select query language is outdated and not supported any more.
Use other keywords of this library to query JSON.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONSelect expression;\n
*Raises:*\n
JsonValidatorError
"""
value = self.select_elements(json_string, expr)
if value is not None:
raise JsonValidatorError('Elements %s exist but should not' % expr) | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator._json_path_search | def _json_path_search(self, json_dict, expr):
"""
Scan JSON dictionary with using json-path passed sting of the format of $.element..element1[index] etc.
*Args:*\n
_json_dict_ - JSON dictionary;\n
_expr_ - string of fuzzy search for items within the directory;\n
*Returns:*\n
List of DatumInContext objects:
``[DatumInContext(value=..., path=..., context=[DatumInContext])]``
- value - found value
- path - value selector inside context.value (in implementation of jsonpath-rw: class Index or Fields)
*Raises:*\n
JsonValidatorError
"""
path = parse(expr)
results = path.find(json_dict)
if len(results) is 0:
raise JsonValidatorError("Nothing found in the dictionary {0} using the given path {1}".format(
str(json_dict), str(expr)))
return results | python | def _json_path_search(self, json_dict, expr):
"""
Scan JSON dictionary with using json-path passed sting of the format of $.element..element1[index] etc.
*Args:*\n
_json_dict_ - JSON dictionary;\n
_expr_ - string of fuzzy search for items within the directory;\n
*Returns:*\n
List of DatumInContext objects:
``[DatumInContext(value=..., path=..., context=[DatumInContext])]``
- value - found value
- path - value selector inside context.value (in implementation of jsonpath-rw: class Index or Fields)
*Raises:*\n
JsonValidatorError
"""
path = parse(expr)
results = path.find(json_dict)
if len(results) is 0:
raise JsonValidatorError("Nothing found in the dictionary {0} using the given path {1}".format(
str(json_dict), str(expr)))
return results | [
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.update_json | def update_json(self, json_string, expr, value, index=0):
"""
Replace the value in the JSON string.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONPath expression for determining the value to be replaced;\n
_value_ - the value to be replaced with;\n
_index_ - index for selecting item within a match list, default value is 0;\n
*Returns:*\n
Changed JSON in dictionary format.
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Update element | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_update}= | Update_json | ${json_example} | $..color | changed |
"""
load_input_json = self.string_to_json(json_string)
matches = self._json_path_search(load_input_json, expr)
datum_object = matches[int(index)]
if not isinstance(datum_object, DatumInContext):
raise JsonValidatorError("Nothing found by the given json-path")
path = datum_object.path
# Edit the directory using the received data
# If the user specified a list
if isinstance(path, Index):
datum_object.context.value[datum_object.path.index] = value
# If the user specified a value of type (string, bool, integer or complex)
elif isinstance(path, Fields):
datum_object.context.value[datum_object.path.fields[0]] = value
return load_input_json | python | def update_json(self, json_string, expr, value, index=0):
"""
Replace the value in the JSON string.
*Args:*\n
_json_string_ - JSON string;\n
_expr_ - JSONPath expression for determining the value to be replaced;\n
_value_ - the value to be replaced with;\n
_index_ - index for selecting item within a match list, default value is 0;\n
*Returns:*\n
Changed JSON in dictionary format.
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Update element | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
| | ${json_update}= | Update_json | ${json_example} | $..color | changed |
"""
load_input_json = self.string_to_json(json_string)
matches = self._json_path_search(load_input_json, expr)
datum_object = matches[int(index)]
if not isinstance(datum_object, DatumInContext):
raise JsonValidatorError("Nothing found by the given json-path")
path = datum_object.path
# Edit the directory using the received data
# If the user specified a list
if isinstance(path, Index):
datum_object.context.value[datum_object.path.index] = value
# If the user specified a value of type (string, bool, integer or complex)
elif isinstance(path, Fields):
datum_object.context.value[datum_object.path.fields[0]] = value
return load_input_json | [
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_value_ - the value to be replaced with;\n
_index_ - index for selecting item within a match list, default value is 0;\n
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| *Settings* | *Value* |
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| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Update element | ${json_example}= | OperatingSystem.Get File | ${CURDIR}${/}json_example.json |
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peterservice-rnd/robotframework-jsonvalidator | src/JsonValidator.py | JsonValidator.pretty_print_json | def pretty_print_json(self, json_string):
"""
Return formatted JSON string _json_string_.\n
Using method json.dumps with settings: _indent=2, ensure_ascii=False_.
*Args:*\n
_json_string_ - JSON string.
*Returns:*\n
Formatted JSON string.
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Check element | ${pretty_json}= | Pretty print json | {a:1,foo:[{b:2,c:3},{d:"baz",e:4}]} |
| | Log | ${pretty_json} |
=>\n
| {
| "a": 1,
| "foo": [
| {
| "c": 3,
| "b": 2
| },
| {
| "e": 4,
| "d": "baz"
| }
| ]
| }
"""
return json.dumps(self.string_to_json(json_string), indent=2, ensure_ascii=False) | python | def pretty_print_json(self, json_string):
"""
Return formatted JSON string _json_string_.\n
Using method json.dumps with settings: _indent=2, ensure_ascii=False_.
*Args:*\n
_json_string_ - JSON string.
*Returns:*\n
Formatted JSON string.
*Example:*\n
| *Settings* | *Value* |
| Library | JsonValidator |
| Library | OperatingSystem |
| *Test Cases* | *Action* | *Argument* | *Argument* |
| Check element | ${pretty_json}= | Pretty print json | {a:1,foo:[{b:2,c:3},{d:"baz",e:4}]} |
| | Log | ${pretty_json} |
=>\n
| {
| "a": 1,
| "foo": [
| {
| "c": 3,
| "b": 2
| },
| {
| "e": 4,
| "d": "baz"
| }
| ]
| }
"""
return json.dumps(self.string_to_json(json_string), indent=2, ensure_ascii=False) | [
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HdrHistogram/HdrHistogram_py | hdrh/log.py | HistogramLogWriter.output_interval_histogram | def output_interval_histogram(self,
histogram,
start_time_stamp_sec=0,
end_time_stamp_sec=0,
max_value_unit_ratio=1000000.0):
'''Output an interval histogram, with the given timestamp and a
configurable maxValueUnitRatio.
(note that the specified timestamp will be used, and the timestamp in
the actual histogram will be ignored).
The max value reported with the interval line will be scaled by the
given max_value_unit_ratio.
The histogram start and end timestamps are assumed to be in msec units.
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The default base time is 0.
By covention, histogram start/end time are generally stamped with
absolute times in msec since the epoch. For logging with absolute time
stamps, the base time would remain zero. For
logging with relative time stamps (time since a start point),
Params:
histogram The interval histogram to log.
start_time_stamp_sec The start timestamp to log with the
interval histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
end_time_stamp_sec The end timestamp to log with the interval
histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
max_value_unit_ratio The ratio by which to divide the histogram's max
value when reporting on it.
default: 1,000,000 (which is the msec : nsec ratio
'''
if not start_time_stamp_sec:
start_time_stamp_sec = \
(histogram.get_start_time_stamp() - self.base_time) / 1000.0
if not end_time_stamp_sec:
end_time_stamp_sec = (histogram.get_end_time_stamp() - self.base_time) / 1000.0
cpayload = histogram.encode()
self.log.write("%f,%f,%f,%s\n" %
(start_time_stamp_sec,
end_time_stamp_sec - start_time_stamp_sec,
histogram.get_max_value() // max_value_unit_ratio,
cpayload.decode('utf-8'))) | python | def output_interval_histogram(self,
histogram,
start_time_stamp_sec=0,
end_time_stamp_sec=0,
max_value_unit_ratio=1000000.0):
'''Output an interval histogram, with the given timestamp and a
configurable maxValueUnitRatio.
(note that the specified timestamp will be used, and the timestamp in
the actual histogram will be ignored).
The max value reported with the interval line will be scaled by the
given max_value_unit_ratio.
The histogram start and end timestamps are assumed to be in msec units.
Logging will be in seconds, realtive by a base time
The default base time is 0.
By covention, histogram start/end time are generally stamped with
absolute times in msec since the epoch. For logging with absolute time
stamps, the base time would remain zero. For
logging with relative time stamps (time since a start point),
Params:
histogram The interval histogram to log.
start_time_stamp_sec The start timestamp to log with the
interval histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
end_time_stamp_sec The end timestamp to log with the interval
histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
max_value_unit_ratio The ratio by which to divide the histogram's max
value when reporting on it.
default: 1,000,000 (which is the msec : nsec ratio
'''
if not start_time_stamp_sec:
start_time_stamp_sec = \
(histogram.get_start_time_stamp() - self.base_time) / 1000.0
if not end_time_stamp_sec:
end_time_stamp_sec = (histogram.get_end_time_stamp() - self.base_time) / 1000.0
cpayload = histogram.encode()
self.log.write("%f,%f,%f,%s\n" %
(start_time_stamp_sec,
end_time_stamp_sec - start_time_stamp_sec,
histogram.get_max_value() // max_value_unit_ratio,
cpayload.decode('utf-8'))) | [
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The max value reported with the interval line will be scaled by the
given max_value_unit_ratio.
The histogram start and end timestamps are assumed to be in msec units.
Logging will be in seconds, realtive by a base time
The default base time is 0.
By covention, histogram start/end time are generally stamped with
absolute times in msec since the epoch. For logging with absolute time
stamps, the base time would remain zero. For
logging with relative time stamps (time since a start point),
Params:
histogram The interval histogram to log.
start_time_stamp_sec The start timestamp to log with the
interval histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
end_time_stamp_sec The end timestamp to log with the interval
histogram, in seconds.
default: using the start/end timestamp indicated in the histogram
max_value_unit_ratio The ratio by which to divide the histogram's max
value when reporting on it.
default: 1,000,000 (which is the msec : nsec ratio | [
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HdrHistogram/HdrHistogram_py | hdrh/log.py | HistogramLogWriter.output_start_time | def output_start_time(self, start_time_msec):
'''Log a start time in the log.
Params:
start_time_msec time (in milliseconds) since the absolute start time (the epoch)
'''
self.log.write("#[StartTime: %f (seconds since epoch), %s]\n" %
(float(start_time_msec) / 1000.0,
datetime.fromtimestamp(start_time_msec).iso_format(' '))) | python | def output_start_time(self, start_time_msec):
'''Log a start time in the log.
Params:
start_time_msec time (in milliseconds) since the absolute start time (the epoch)
'''
self.log.write("#[StartTime: %f (seconds since epoch), %s]\n" %
(float(start_time_msec) / 1000.0,
datetime.fromtimestamp(start_time_msec).iso_format(' '))) | [
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HdrHistogram/HdrHistogram_py | hdrh/log.py | HistogramLogReader._decode_next_interval_histogram | def _decode_next_interval_histogram(self,
dest_histogram,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range.
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
dest_histogram if None, created a new histogram, else adds
the new interval histogram to it
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
while 1:
line = self.input_file.readline()
if not line:
return None
if line[0] == '#':
match_res = re_start_time.match(line)
if match_res:
self.start_time_sec = float(match_res.group(1))
self.observed_start_time = True
continue
match_res = re_base_time.match(line)
if match_res:
self.base_time_sec = float(match_res.group(1))
self.observed_base_time = True
continue
match_res = re_histogram_interval.match(line)
if not match_res:
# probably a legend line that starts with "\"StartTimestamp"
continue
# Decode: startTimestamp, intervalLength, maxTime, histogramPayload
# Timestamp is expected to be in seconds
log_time_stamp_in_sec = float(match_res.group(1))
interval_length_sec = float(match_res.group(2))
cpayload = match_res.group(4)
if not self.observed_start_time:
# No explicit start time noted. Use 1st observed time:
self.start_time_sec = log_time_stamp_in_sec
self.observed_start_time = True
if not self.observed_base_time:
# No explicit base time noted.
# Deduce from 1st observed time (compared to start time):
if log_time_stamp_in_sec < self.start_time_sec - (365 * 24 * 3600.0):
# Criteria Note: if log timestamp is more than a year in
# the past (compared to StartTime),
# we assume that timestamps in the log are not absolute
self.base_time_sec = self.start_time_sec
else:
# Timestamps are absolute
self.base_time_sec = 0.0
self.observed_base_time = True
absolute_start_time_stamp_sec = \
log_time_stamp_in_sec + self.base_time_sec
offset_start_time_stamp_sec = \
absolute_start_time_stamp_sec - self.start_time_sec
# Timestamp length is expect to be in seconds
absolute_end_time_stamp_sec = \
absolute_start_time_stamp_sec + interval_length_sec
if absolute:
start_time_stamp_to_check_range_on = absolute_start_time_stamp_sec
else:
start_time_stamp_to_check_range_on = offset_start_time_stamp_sec
if start_time_stamp_to_check_range_on < range_start_time_sec:
continue
if start_time_stamp_to_check_range_on > range_end_time_sec:
return None
if dest_histogram:
# add the interval histogram to the destination histogram
histogram = dest_histogram
histogram.decode_and_add(cpayload)
else:
histogram = HdrHistogram.decode(cpayload)
histogram.set_start_time_stamp(absolute_start_time_stamp_sec * 1000.0)
histogram.set_end_time_stamp(absolute_end_time_stamp_sec * 1000.0)
return histogram | python | def _decode_next_interval_histogram(self,
dest_histogram,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range.
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
dest_histogram if None, created a new histogram, else adds
the new interval histogram to it
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
while 1:
line = self.input_file.readline()
if not line:
return None
if line[0] == '#':
match_res = re_start_time.match(line)
if match_res:
self.start_time_sec = float(match_res.group(1))
self.observed_start_time = True
continue
match_res = re_base_time.match(line)
if match_res:
self.base_time_sec = float(match_res.group(1))
self.observed_base_time = True
continue
match_res = re_histogram_interval.match(line)
if not match_res:
# probably a legend line that starts with "\"StartTimestamp"
continue
# Decode: startTimestamp, intervalLength, maxTime, histogramPayload
# Timestamp is expected to be in seconds
log_time_stamp_in_sec = float(match_res.group(1))
interval_length_sec = float(match_res.group(2))
cpayload = match_res.group(4)
if not self.observed_start_time:
# No explicit start time noted. Use 1st observed time:
self.start_time_sec = log_time_stamp_in_sec
self.observed_start_time = True
if not self.observed_base_time:
# No explicit base time noted.
# Deduce from 1st observed time (compared to start time):
if log_time_stamp_in_sec < self.start_time_sec - (365 * 24 * 3600.0):
# Criteria Note: if log timestamp is more than a year in
# the past (compared to StartTime),
# we assume that timestamps in the log are not absolute
self.base_time_sec = self.start_time_sec
else:
# Timestamps are absolute
self.base_time_sec = 0.0
self.observed_base_time = True
absolute_start_time_stamp_sec = \
log_time_stamp_in_sec + self.base_time_sec
offset_start_time_stamp_sec = \
absolute_start_time_stamp_sec - self.start_time_sec
# Timestamp length is expect to be in seconds
absolute_end_time_stamp_sec = \
absolute_start_time_stamp_sec + interval_length_sec
if absolute:
start_time_stamp_to_check_range_on = absolute_start_time_stamp_sec
else:
start_time_stamp_to_check_range_on = offset_start_time_stamp_sec
if start_time_stamp_to_check_range_on < range_start_time_sec:
continue
if start_time_stamp_to_check_range_on > range_end_time_sec:
return None
if dest_histogram:
# add the interval histogram to the destination histogram
histogram = dest_histogram
histogram.decode_and_add(cpayload)
else:
histogram = HdrHistogram.decode(cpayload)
histogram.set_start_time_stamp(absolute_start_time_stamp_sec * 1000.0)
histogram.set_end_time_stamp(absolute_end_time_stamp_sec * 1000.0)
return histogram | [
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range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
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Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
dest_histogram if None, created a new histogram, else adds
the new interval histogram to it
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields | [
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HdrHistogram/HdrHistogram_py | hdrh/log.py | HistogramLogReader.get_next_interval_histogram | def get_next_interval_histogram(self,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range.
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
return self._decode_next_interval_histogram(None,
range_start_time_sec,
range_end_time_sec,
absolute) | python | def get_next_interval_histogram(self,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range.
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
return self._decode_next_interval_histogram(None,
range_start_time_sec,
range_end_time_sec,
absolute) | [
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] | Read the next interval histogram from the log, if interval falls
within an absolute or relative time range.
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns an histogram object if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or null if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields | [
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HdrHistogram/HdrHistogram_py | hdrh/log.py | HistogramLogReader.add_next_interval_histogram | def add_next_interval_histogram(self,
dest_histogram=None,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range, and add it to the destination
histogram (or to the reference histogram if dest_histogram is None)
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
dest_histogram where to add the next interval histogram, if None
the interal histogram will be added to the reference
histogram passed in the constructor
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns the destination histogram if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or None if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
if not dest_histogram:
dest_histogram = self.reference_histogram
return self._decode_next_interval_histogram(dest_histogram,
range_start_time_sec,
range_end_time_sec,
absolute) | python | def add_next_interval_histogram(self,
dest_histogram=None,
range_start_time_sec=0.0,
range_end_time_sec=sys.maxsize,
absolute=False):
'''Read the next interval histogram from the log, if interval falls
within an absolute or relative time range, and add it to the destination
histogram (or to the reference histogram if dest_histogram is None)
Timestamps are assumed to appear in order in the log file, and as such
this method will return a null upon encountering a timestamp larger than
range_end_time_sec.
Relative time range:
the range is assumed to be in seconds relative to
the actual timestamp value found in each interval line in the log
Absolute time range:
Absolute timestamps are calculated by adding the timestamp found
with the recorded interval to the [latest, optional] start time
found in the log. The start time is indicated in the log with
a "#[StartTime: " followed by the start time in seconds.
Params:
dest_histogram where to add the next interval histogram, if None
the interal histogram will be added to the reference
histogram passed in the constructor
range_start_time_sec The absolute or relative start of the expected
time range, in seconds.
range_start_time_sec The absolute or relative end of the expected
time range, in seconds.
absolute Defines if the passed range is absolute or relative
Return:
Returns the destination histogram if an interval line was found with an
associated start timestamp value that falls between start_time_sec and
end_time_sec,
or None if no such interval line is found.
Upon encountering any unexpected format errors in reading the next
interval from the file, this method will return None.
The histogram returned will have it's timestamp set to the absolute
timestamp calculated from adding the interval's indicated timestamp
value to the latest [optional] start time found in the log.
Exceptions:
ValueError if there is a syntax error in one of the float fields
'''
if not dest_histogram:
dest_histogram = self.reference_histogram
return self._decode_next_interval_histogram(dest_histogram,
range_start_time_sec,
range_end_time_sec,
absolute) | [
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range_start_time_sec The absolute or relative end of the expected
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ui/django-html_sanitizer | sanitizer/templatetags/sanitizer.py | sanitize | def sanitize(value):
'''
Sanitizes strings according to SANITIZER_ALLOWED_TAGS,
SANITIZER_ALLOWED_ATTRIBUTES and SANITIZER_ALLOWED_STYLES variables in
settings.
Example usage:
{% load sanitizer %}
{{ post.content|escape_html }}
'''
if isinstance(value, basestring):
value = bleach.clean(value, tags=ALLOWED_TAGS,
attributes=ALLOWED_ATTRIBUTES,
styles=ALLOWED_STYLES, strip=False)
return value | python | def sanitize(value):
'''
Sanitizes strings according to SANITIZER_ALLOWED_TAGS,
SANITIZER_ALLOWED_ATTRIBUTES and SANITIZER_ALLOWED_STYLES variables in
settings.
Example usage:
{% load sanitizer %}
{{ post.content|escape_html }}
'''
if isinstance(value, basestring):
value = bleach.clean(value, tags=ALLOWED_TAGS,
attributes=ALLOWED_ATTRIBUTES,
styles=ALLOWED_STYLES, strip=False)
return value | [
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Example usage:
{% load sanitizer %}
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ui/django-html_sanitizer | sanitizer/templatetags/sanitizer.py | strip_filter | def strip_filter(value):
'''
Strips HTML tags from strings according to SANITIZER_ALLOWED_TAGS,
SANITIZER_ALLOWED_ATTRIBUTES and SANITIZER_ALLOWED_STYLES variables in
settings.
Example usage:
{% load sanitizer %}
{{ post.content|strip_html }}
'''
if isinstance(value, basestring):
value = bleach.clean(value, tags=ALLOWED_TAGS,
attributes=ALLOWED_ATTRIBUTES,
styles=ALLOWED_STYLES, strip=True)
return value | python | def strip_filter(value):
'''
Strips HTML tags from strings according to SANITIZER_ALLOWED_TAGS,
SANITIZER_ALLOWED_ATTRIBUTES and SANITIZER_ALLOWED_STYLES variables in
settings.
Example usage:
{% load sanitizer %}
{{ post.content|strip_html }}
'''
if isinstance(value, basestring):
value = bleach.clean(value, tags=ALLOWED_TAGS,
attributes=ALLOWED_ATTRIBUTES,
styles=ALLOWED_STYLES, strip=True)
return value | [
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ui/django-html_sanitizer | sanitizer/templatetags/sanitizer.py | sanitize_allow | def sanitize_allow(value, args=''):
'''
Strip HTML tags other than provided tags and attributes.
Example usage:
{% load sanitizer %}
{{ post.body|sanitize_allow:'a, strong, img; href, src'}}
'''
if isinstance(value, basestring):
allowed_tags = []
allowed_attributes = []
allowed_styles = []
args = args.strip().split(';')
if len(args) > 0:
allowed_tags = [tag.strip() for tag in args[0].split(',')]
if len(args) > 1:
allowed_attributes = [attr.strip() for attr in args[1].split(',')]
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes, strip=True)
return value | python | def sanitize_allow(value, args=''):
'''
Strip HTML tags other than provided tags and attributes.
Example usage:
{% load sanitizer %}
{{ post.body|sanitize_allow:'a, strong, img; href, src'}}
'''
if isinstance(value, basestring):
allowed_tags = []
allowed_attributes = []
allowed_styles = []
args = args.strip().split(';')
if len(args) > 0:
allowed_tags = [tag.strip() for tag in args[0].split(',')]
if len(args) > 1:
allowed_attributes = [attr.strip() for attr in args[1].split(',')]
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes, strip=True)
return value | [
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ui/django-html_sanitizer | sanitizer/templatetags/sanitizer.py | escape_html | def escape_html(value, allowed_tags=[], allowed_attributes=[],
allowed_styles=[]):
"""
Template tag to sanitize string values. It accepts lists of
allowed tags, attributes or styles in comma separated string or list format.
For example:
{% load sanitizer %}
{% escape_html '<a href="">bar</a> <script>alert('baz')</script>' "a,img' 'href',src' %}
Will output:
<a href="">bar</a> <cript>alert('baz')</script>
On django 1.4 you could also use keyword arguments:
{% escape_html '<a href="">bar</a>' allowed_tags="a,img' allowed_attributes='href',src' %}
"""
if isinstance(value, basestring):
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes,
styles=allowed_styles, strip=False)
return value | python | def escape_html(value, allowed_tags=[], allowed_attributes=[],
allowed_styles=[]):
"""
Template tag to sanitize string values. It accepts lists of
allowed tags, attributes or styles in comma separated string or list format.
For example:
{% load sanitizer %}
{% escape_html '<a href="">bar</a> <script>alert('baz')</script>' "a,img' 'href',src' %}
Will output:
<a href="">bar</a> <cript>alert('baz')</script>
On django 1.4 you could also use keyword arguments:
{% escape_html '<a href="">bar</a>' allowed_tags="a,img' allowed_attributes='href',src' %}
"""
if isinstance(value, basestring):
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes,
styles=allowed_styles, strip=False)
return value | [
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ui/django-html_sanitizer | sanitizer/templatetags/sanitizer.py | strip_html | def strip_html(value, allowed_tags=[], allowed_attributes=[],
allowed_styles=[]):
"""
Template tag to strip html from string values. It accepts lists of
allowed tags, attributes or stylesin comma separated string or list format.
For example:
{% load sanitizer %}
{% strip_html '<a href="">bar</a> <script>alert('baz')</script>' "a,img' 'href',src' %}
Will output:
<a href="">bar</a> alert('baz');
On django 1.4 you could also use keyword arguments:
{% strip_html '<a href="">bar</a>' allowed_tags="a,img' allowed_attributes='href',src' %}
"""
if isinstance(value, basestring):
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes,
styles=allowed_styles, strip=True)
return value | python | def strip_html(value, allowed_tags=[], allowed_attributes=[],
allowed_styles=[]):
"""
Template tag to strip html from string values. It accepts lists of
allowed tags, attributes or stylesin comma separated string or list format.
For example:
{% load sanitizer %}
{% strip_html '<a href="">bar</a> <script>alert('baz')</script>' "a,img' 'href',src' %}
Will output:
<a href="">bar</a> alert('baz');
On django 1.4 you could also use keyword arguments:
{% strip_html '<a href="">bar</a>' allowed_tags="a,img' allowed_attributes='href',src' %}
"""
if isinstance(value, basestring):
value = bleach.clean(value, tags=allowed_tags,
attributes=allowed_attributes,
styles=allowed_styles, strip=True)
return value | [
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<a href="">bar</a> alert('baz');
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ben174/profanity | profanity/profanity.py | get_censor_char | def get_censor_char():
"""Plucks a letter out of the censor_pool. If the censor_pool is empty,
replenishes it. This is done to ensure all censor chars are used before
grabbing more (avoids ugly duplicates).
"""
global _censor_pool
if not _censor_pool:
# censor pool is empty. fill it back up.
_censor_pool = list(_censor_chars)
return _censor_pool.pop(random.randrange(len(_censor_pool))) | python | def get_censor_char():
"""Plucks a letter out of the censor_pool. If the censor_pool is empty,
replenishes it. This is done to ensure all censor chars are used before
grabbing more (avoids ugly duplicates).
"""
global _censor_pool
if not _censor_pool:
# censor pool is empty. fill it back up.
_censor_pool = list(_censor_chars)
return _censor_pool.pop(random.randrange(len(_censor_pool))) | [
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ben174/profanity | profanity/profanity.py | censor | def censor(input_text):
""" Returns the input string with profanity replaced with a random string
of characters plucked from the censor_characters pool.
"""
ret = input_text
words = get_words()
for word in words:
curse_word = re.compile(re.escape(word), re.IGNORECASE)
cen = "".join(get_censor_char() for i in list(word))
ret = curse_word.sub(cen, ret)
return ret | python | def censor(input_text):
""" Returns the input string with profanity replaced with a random string
of characters plucked from the censor_characters pool.
"""
ret = input_text
words = get_words()
for word in words:
curse_word = re.compile(re.escape(word), re.IGNORECASE)
cen = "".join(get_censor_char() for i in list(word))
ret = curse_word.sub(cen, ret)
return ret | [
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ben174/profanity | profanity/profanity.py | load_words | def load_words(wordlist=None):
""" Loads and caches the profanity word list. Input file (if provided)
should be a flat text file with one profanity entry per line.
"""
global words
if not wordlist:
# no wordlist was provided, load the wordlist from the local store
filename = get_data('wordlist.txt')
f = open(filename)
wordlist = f.readlines()
wordlist = [w.strip() for w in wordlist if w]
words = wordlist | python | def load_words(wordlist=None):
""" Loads and caches the profanity word list. Input file (if provided)
should be a flat text file with one profanity entry per line.
"""
global words
if not wordlist:
# no wordlist was provided, load the wordlist from the local store
filename = get_data('wordlist.txt')
f = open(filename)
wordlist = f.readlines()
wordlist = [w.strip() for w in wordlist if w]
words = wordlist | [
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oplatek/csv2json | csv2json/__init__.py | convert | def convert(csv, json, **kwargs):
'''Convert csv to json.
csv: filename or file-like object
json: filename or file-like object
if csv is '-' or None:
stdin is used for input
if json is '-' or None:
stdout is used for output
'''
csv_local, json_local = None, None
try:
if csv == '-' or csv is None:
csv = sys.stdin
elif isinstance(csv, str):
csv = csv_local = open(csv, 'r')
if json == '-' or json is None:
json = sys.stdout
elif isinstance(json, str):
json = json_local = open(json, 'w')
data = load_csv(csv, **kwargs)
save_json(data, json, **kwargs)
finally:
if csv_local is not None:
csv_local.close()
if json_local is not None:
json_local.close() | python | def convert(csv, json, **kwargs):
'''Convert csv to json.
csv: filename or file-like object
json: filename or file-like object
if csv is '-' or None:
stdin is used for input
if json is '-' or None:
stdout is used for output
'''
csv_local, json_local = None, None
try:
if csv == '-' or csv is None:
csv = sys.stdin
elif isinstance(csv, str):
csv = csv_local = open(csv, 'r')
if json == '-' or json is None:
json = sys.stdout
elif isinstance(json, str):
json = json_local = open(json, 'w')
data = load_csv(csv, **kwargs)
save_json(data, json, **kwargs)
finally:
if csv_local is not None:
csv_local.close()
if json_local is not None:
json_local.close() | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/wrapper.py | WordCharEmbd.update_dicts | def update_dicts(self, sentence):
"""Add new sentence to generate dictionaries.
:param sentence: A list of strings representing the sentence.
"""
self.dict_generator(sentence=sentence)
self.word_dict, self.char_dict = None, None | python | def update_dicts(self, sentence):
"""Add new sentence to generate dictionaries.
:param sentence: A list of strings representing the sentence.
"""
self.dict_generator(sentence=sentence)
self.word_dict, self.char_dict = None, None | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/wrapper.py | WordCharEmbd.set_dicts | def set_dicts(self, word_dict, char_dict):
"""Set with custom dictionaries.
:param word_dict: The word dictionary.
:param char_dict: The character dictionary.
"""
self.word_dict = word_dict
self.char_dict = char_dict | python | def set_dicts(self, word_dict, char_dict):
"""Set with custom dictionaries.
:param word_dict: The word dictionary.
:param char_dict: The character dictionary.
"""
self.word_dict = word_dict
self.char_dict = char_dict | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/wrapper.py | WordCharEmbd.get_dicts | def get_dicts(self):
"""Get word and character dictionaries.
:return word_dict, char_dict:
"""
if self.word_dict is None:
self.word_dict, self.char_dict, self.max_word_len = self.dict_generator(return_dict=True)
return self.word_dict, self.char_dict | python | def get_dicts(self):
"""Get word and character dictionaries.
:return word_dict, char_dict:
"""
if self.word_dict is None:
self.word_dict, self.char_dict, self.max_word_len = self.dict_generator(return_dict=True)
return self.word_dict, self.char_dict | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/wrapper.py | WordCharEmbd.get_embedding_layer | def get_embedding_layer(self,
word_embd_dim=300,
char_embd_dim=30,
char_hidden_dim=150,
char_hidden_layer_type='lstm',
word_embd_weights=None,
word_embd_file_path=None,
char_embd_weights=None,
char_embd_file_path=None,
word_embd_trainable=None,
char_embd_trainable=None,
word_mask_zero=True,
char_mask_zero=True,):
"""Get the merged embedding layer.
:param word_embd_dim: The dimensions of the word embedding.
:param char_embd_dim: The dimensions of the character embedding
:param char_hidden_dim: The dimensions of the hidden states of RNN in one direction.
:param word_embd_weights: A numpy array representing the pre-trained embeddings for words.
:param word_embd_file_path: The file that contains the word embeddings.
:param char_embd_weights: A numpy array representing the pre-trained embeddings for characters.
:param char_embd_file_path: The file that contains the character embeddings.
:param word_embd_trainable: Whether the word embedding layer is trainable.
:param char_embd_trainable: Whether the character embedding layer is trainable.
:param char_hidden_layer_type: The type of the recurrent layer, 'lstm' or 'gru'.
:param word_mask_zero: Whether enable the mask for words.
:param char_mask_zero: Whether enable the mask for characters.
:return inputs, embd_layer: The keras layer.
"""
if word_embd_file_path is not None:
word_embd_weights = get_embedding_weights_from_file(word_dict=self.get_word_dict(),
file_path=word_embd_file_path,
ignore_case=self.word_ignore_case)
if char_embd_file_path is not None:
char_embd_weights = get_embedding_weights_from_file(word_dict=self.get_char_dict(),
file_path=char_embd_file_path,
ignore_case=self.char_ignore_case)
return get_embedding_layer(word_dict_len=len(self.get_word_dict()),
char_dict_len=len(self.get_char_dict()),
max_word_len=self.max_word_len,
word_embd_dim=word_embd_dim,
char_embd_dim=char_embd_dim,
char_hidden_dim=char_hidden_dim,
char_hidden_layer_type=char_hidden_layer_type,
word_embd_weights=word_embd_weights,
char_embd_weights=char_embd_weights,
word_embd_trainable=word_embd_trainable,
char_embd_trainable=char_embd_trainable,
word_mask_zero=word_mask_zero,
char_mask_zero=char_mask_zero) | python | def get_embedding_layer(self,
word_embd_dim=300,
char_embd_dim=30,
char_hidden_dim=150,
char_hidden_layer_type='lstm',
word_embd_weights=None,
word_embd_file_path=None,
char_embd_weights=None,
char_embd_file_path=None,
word_embd_trainable=None,
char_embd_trainable=None,
word_mask_zero=True,
char_mask_zero=True,):
"""Get the merged embedding layer.
:param word_embd_dim: The dimensions of the word embedding.
:param char_embd_dim: The dimensions of the character embedding
:param char_hidden_dim: The dimensions of the hidden states of RNN in one direction.
:param word_embd_weights: A numpy array representing the pre-trained embeddings for words.
:param word_embd_file_path: The file that contains the word embeddings.
:param char_embd_weights: A numpy array representing the pre-trained embeddings for characters.
:param char_embd_file_path: The file that contains the character embeddings.
:param word_embd_trainable: Whether the word embedding layer is trainable.
:param char_embd_trainable: Whether the character embedding layer is trainable.
:param char_hidden_layer_type: The type of the recurrent layer, 'lstm' or 'gru'.
:param word_mask_zero: Whether enable the mask for words.
:param char_mask_zero: Whether enable the mask for characters.
:return inputs, embd_layer: The keras layer.
"""
if word_embd_file_path is not None:
word_embd_weights = get_embedding_weights_from_file(word_dict=self.get_word_dict(),
file_path=word_embd_file_path,
ignore_case=self.word_ignore_case)
if char_embd_file_path is not None:
char_embd_weights = get_embedding_weights_from_file(word_dict=self.get_char_dict(),
file_path=char_embd_file_path,
ignore_case=self.char_ignore_case)
return get_embedding_layer(word_dict_len=len(self.get_word_dict()),
char_dict_len=len(self.get_char_dict()),
max_word_len=self.max_word_len,
word_embd_dim=word_embd_dim,
char_embd_dim=char_embd_dim,
char_hidden_dim=char_hidden_dim,
char_hidden_layer_type=char_hidden_layer_type,
word_embd_weights=word_embd_weights,
char_embd_weights=char_embd_weights,
word_embd_trainable=word_embd_trainable,
char_embd_trainable=char_embd_trainable,
word_mask_zero=word_mask_zero,
char_mask_zero=char_mask_zero) | [
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:param char_embd_dim: The dimensions of the character embedding
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] | cca6ddff01b6264dd0d12613bb9ed308e1367b8c | https://github.com/CyberZHG/keras-word-char-embd/blob/cca6ddff01b6264dd0d12613bb9ed308e1367b8c/keras_wc_embd/wrapper.py#L72-L122 | train |
CyberZHG/keras-word-char-embd | keras_wc_embd/wrapper.py | WordCharEmbd.get_batch_input | def get_batch_input(self, sentences):
"""Convert sentences to desired input tensors.
:param sentences: A list of lists representing the input sentences.
:return word_embd_input, char_embd_input: The desired inputs.
"""
return get_batch_input(sentences,
max_word_len=self.max_word_len,
word_dict=self.get_word_dict(),
char_dict=self.get_char_dict(),
word_ignore_case=self.word_ignore_case,
char_ignore_case=self.char_ignore_case) | python | def get_batch_input(self, sentences):
"""Convert sentences to desired input tensors.
:param sentences: A list of lists representing the input sentences.
:return word_embd_input, char_embd_input: The desired inputs.
"""
return get_batch_input(sentences,
max_word_len=self.max_word_len,
word_dict=self.get_word_dict(),
char_dict=self.get_char_dict(),
word_ignore_case=self.word_ignore_case,
char_ignore_case=self.char_ignore_case) | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/word_char_embd.py | get_batch_input | def get_batch_input(sentences,
max_word_len,
word_dict,
char_dict,
word_unknown=1,
char_unknown=1,
word_ignore_case=False,
char_ignore_case=False):
"""Convert sentences to desired input tensors.
:param sentences: A list of lists representing the input sentences.
:param max_word_len: The maximum allowed length of word.
:param word_dict: Map a word to an integer. (0 and 1 should be preserved)
:param char_dict: Map a character to an integer. (0 and 1 should be preserved)
:param word_unknown: An integer representing the unknown word.
:param char_unknown: An integer representing the unknown character.
:param word_ignore_case: Word will be transformed to lower case before mapping.
:param char_ignore_case: Character will be transformed to lower case before mapping.
:return word_embd_input, char_embd_input: The desired inputs.
"""
sentence_num = len(sentences)
max_sentence_len = max(map(len, sentences))
word_embd_input = [[0] * max_sentence_len for _ in range(sentence_num)]
char_embd_input = [[[0] * max_word_len for _ in range(max_sentence_len)] for _ in range(sentence_num)]
for sentence_index, sentence in enumerate(sentences):
for word_index, word in enumerate(sentence):
if word_ignore_case:
word_key = word.lower()
else:
word_key = word
word_embd_input[sentence_index][word_index] = word_dict.get(word_key, word_unknown)
for char_index, char in enumerate(word):
if char_index >= max_word_len:
break
if char_ignore_case:
char = char.lower()
char_embd_input[sentence_index][word_index][char_index] = char_dict.get(char, char_unknown)
return [numpy.asarray(word_embd_input), numpy.asarray(char_embd_input)] | python | def get_batch_input(sentences,
max_word_len,
word_dict,
char_dict,
word_unknown=1,
char_unknown=1,
word_ignore_case=False,
char_ignore_case=False):
"""Convert sentences to desired input tensors.
:param sentences: A list of lists representing the input sentences.
:param max_word_len: The maximum allowed length of word.
:param word_dict: Map a word to an integer. (0 and 1 should be preserved)
:param char_dict: Map a character to an integer. (0 and 1 should be preserved)
:param word_unknown: An integer representing the unknown word.
:param char_unknown: An integer representing the unknown character.
:param word_ignore_case: Word will be transformed to lower case before mapping.
:param char_ignore_case: Character will be transformed to lower case before mapping.
:return word_embd_input, char_embd_input: The desired inputs.
"""
sentence_num = len(sentences)
max_sentence_len = max(map(len, sentences))
word_embd_input = [[0] * max_sentence_len for _ in range(sentence_num)]
char_embd_input = [[[0] * max_word_len for _ in range(max_sentence_len)] for _ in range(sentence_num)]
for sentence_index, sentence in enumerate(sentences):
for word_index, word in enumerate(sentence):
if word_ignore_case:
word_key = word.lower()
else:
word_key = word
word_embd_input[sentence_index][word_index] = word_dict.get(word_key, word_unknown)
for char_index, char in enumerate(word):
if char_index >= max_word_len:
break
if char_ignore_case:
char = char.lower()
char_embd_input[sentence_index][word_index][char_index] = char_dict.get(char, char_unknown)
return [numpy.asarray(word_embd_input), numpy.asarray(char_embd_input)] | [
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:param sentences: A list of lists representing the input sentences.
:param max_word_len: The maximum allowed length of word.
:param word_dict: Map a word to an integer. (0 and 1 should be preserved)
:param char_dict: Map a character to an integer. (0 and 1 should be preserved)
:param word_unknown: An integer representing the unknown word.
:param char_unknown: An integer representing the unknown character.
:param word_ignore_case: Word will be transformed to lower case before mapping.
:param char_ignore_case: Character will be transformed to lower case before mapping.
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] | cca6ddff01b6264dd0d12613bb9ed308e1367b8c | https://github.com/CyberZHG/keras-word-char-embd/blob/cca6ddff01b6264dd0d12613bb9ed308e1367b8c/keras_wc_embd/word_char_embd.py#L39-L81 | train |
CyberZHG/keras-word-char-embd | keras_wc_embd/word_char_embd.py | get_embedding_layer | def get_embedding_layer(word_dict_len,
char_dict_len,
max_word_len,
word_embd_dim=300,
char_embd_dim=30,
char_hidden_dim=150,
char_hidden_layer_type='lstm',
word_embd_weights=None,
char_embd_weights=None,
word_embd_trainable=None,
char_embd_trainable=None,
word_mask_zero=True,
char_mask_zero=True):
"""Get the merged embedding layer.
:param word_dict_len: The number of words in the dictionary including the ones mapped to 0 or 1.
:param char_dict_len: The number of characters in the dictionary including the ones mapped to 0 or 1.
:param max_word_len: The maximum allowed length of word.
:param word_embd_dim: The dimensions of the word embedding.
:param char_embd_dim: The dimensions of the character embedding
:param char_hidden_dim: The dimensions of the hidden states of RNN in one direction.
:param word_embd_weights: A numpy array representing the pre-trained embeddings for words.
:param char_embd_weights: A numpy array representing the pre-trained embeddings for characters.
:param word_embd_trainable: Whether the word embedding layer is trainable.
:param char_embd_trainable: Whether the character embedding layer is trainable.
:param char_hidden_layer_type: The type of the recurrent layer, 'lstm' or 'gru'.
:param word_mask_zero: Whether enable the mask for words.
:param char_mask_zero: Whether enable the mask for characters.
:return inputs, embd_layer: The keras layer.
"""
if word_embd_weights is not None:
word_embd_weights = [word_embd_weights]
if word_embd_trainable is None:
word_embd_trainable = word_embd_weights is None
if char_embd_weights is not None:
char_embd_weights = [char_embd_weights]
if char_embd_trainable is None:
char_embd_trainable = char_embd_weights is None
word_input_layer = keras.layers.Input(
shape=(None,),
name='Input_Word',
)
char_input_layer = keras.layers.Input(
shape=(None, max_word_len),
name='Input_Char',
)
word_embd_layer = keras.layers.Embedding(
input_dim=word_dict_len,
output_dim=word_embd_dim,
mask_zero=word_mask_zero,
weights=word_embd_weights,
trainable=word_embd_trainable,
name='Embedding_Word',
)(word_input_layer)
char_embd_layer = keras.layers.Embedding(
input_dim=char_dict_len,
output_dim=char_embd_dim,
mask_zero=char_mask_zero,
weights=char_embd_weights,
trainable=char_embd_trainable,
name='Embedding_Char_Pre',
)(char_input_layer)
if char_hidden_layer_type == 'lstm':
char_hidden_layer = keras.layers.Bidirectional(
keras.layers.LSTM(
units=char_hidden_dim,
input_shape=(max_word_len, char_dict_len),
return_sequences=False,
return_state=False,
),
name='Bi-LSTM_Char',
)
elif char_hidden_layer_type == 'gru':
char_hidden_layer = keras.layers.Bidirectional(
keras.layers.GRU(
units=char_hidden_dim,
input_shape=(max_word_len, char_dict_len),
return_sequences=False,
return_state=False,
),
name='Bi-GRU_Char',
)
elif char_hidden_layer_type == 'cnn':
char_hidden_layer = [
MaskedConv1D(
filters=max(1, char_hidden_dim // 5),
kernel_size=3,
activation='relu',
),
MaskedFlatten(),
keras.layers.Dense(
units=char_hidden_dim,
name='Dense_Char',
),
]
elif isinstance(char_hidden_layer_type, list) or isinstance(char_hidden_layer_type, keras.layers.Layer):
char_hidden_layer = char_hidden_layer_type
else:
raise NotImplementedError('Unknown character hidden layer type: %s' % char_hidden_layer_type)
if not isinstance(char_hidden_layer, list):
char_hidden_layer = [char_hidden_layer]
for i, layer in enumerate(char_hidden_layer):
if i == len(char_hidden_layer) - 1:
name = 'Embedding_Char'
else:
name = 'Embedding_Char_Pre_%d' % (i + 1)
char_embd_layer = keras.layers.TimeDistributed(layer=layer, name=name)(char_embd_layer)
embd_layer = keras.layers.Concatenate(
name='Embedding',
)([word_embd_layer, char_embd_layer])
return [word_input_layer, char_input_layer], embd_layer | python | def get_embedding_layer(word_dict_len,
char_dict_len,
max_word_len,
word_embd_dim=300,
char_embd_dim=30,
char_hidden_dim=150,
char_hidden_layer_type='lstm',
word_embd_weights=None,
char_embd_weights=None,
word_embd_trainable=None,
char_embd_trainable=None,
word_mask_zero=True,
char_mask_zero=True):
"""Get the merged embedding layer.
:param word_dict_len: The number of words in the dictionary including the ones mapped to 0 or 1.
:param char_dict_len: The number of characters in the dictionary including the ones mapped to 0 or 1.
:param max_word_len: The maximum allowed length of word.
:param word_embd_dim: The dimensions of the word embedding.
:param char_embd_dim: The dimensions of the character embedding
:param char_hidden_dim: The dimensions of the hidden states of RNN in one direction.
:param word_embd_weights: A numpy array representing the pre-trained embeddings for words.
:param char_embd_weights: A numpy array representing the pre-trained embeddings for characters.
:param word_embd_trainable: Whether the word embedding layer is trainable.
:param char_embd_trainable: Whether the character embedding layer is trainable.
:param char_hidden_layer_type: The type of the recurrent layer, 'lstm' or 'gru'.
:param word_mask_zero: Whether enable the mask for words.
:param char_mask_zero: Whether enable the mask for characters.
:return inputs, embd_layer: The keras layer.
"""
if word_embd_weights is not None:
word_embd_weights = [word_embd_weights]
if word_embd_trainable is None:
word_embd_trainable = word_embd_weights is None
if char_embd_weights is not None:
char_embd_weights = [char_embd_weights]
if char_embd_trainable is None:
char_embd_trainable = char_embd_weights is None
word_input_layer = keras.layers.Input(
shape=(None,),
name='Input_Word',
)
char_input_layer = keras.layers.Input(
shape=(None, max_word_len),
name='Input_Char',
)
word_embd_layer = keras.layers.Embedding(
input_dim=word_dict_len,
output_dim=word_embd_dim,
mask_zero=word_mask_zero,
weights=word_embd_weights,
trainable=word_embd_trainable,
name='Embedding_Word',
)(word_input_layer)
char_embd_layer = keras.layers.Embedding(
input_dim=char_dict_len,
output_dim=char_embd_dim,
mask_zero=char_mask_zero,
weights=char_embd_weights,
trainable=char_embd_trainable,
name='Embedding_Char_Pre',
)(char_input_layer)
if char_hidden_layer_type == 'lstm':
char_hidden_layer = keras.layers.Bidirectional(
keras.layers.LSTM(
units=char_hidden_dim,
input_shape=(max_word_len, char_dict_len),
return_sequences=False,
return_state=False,
),
name='Bi-LSTM_Char',
)
elif char_hidden_layer_type == 'gru':
char_hidden_layer = keras.layers.Bidirectional(
keras.layers.GRU(
units=char_hidden_dim,
input_shape=(max_word_len, char_dict_len),
return_sequences=False,
return_state=False,
),
name='Bi-GRU_Char',
)
elif char_hidden_layer_type == 'cnn':
char_hidden_layer = [
MaskedConv1D(
filters=max(1, char_hidden_dim // 5),
kernel_size=3,
activation='relu',
),
MaskedFlatten(),
keras.layers.Dense(
units=char_hidden_dim,
name='Dense_Char',
),
]
elif isinstance(char_hidden_layer_type, list) or isinstance(char_hidden_layer_type, keras.layers.Layer):
char_hidden_layer = char_hidden_layer_type
else:
raise NotImplementedError('Unknown character hidden layer type: %s' % char_hidden_layer_type)
if not isinstance(char_hidden_layer, list):
char_hidden_layer = [char_hidden_layer]
for i, layer in enumerate(char_hidden_layer):
if i == len(char_hidden_layer) - 1:
name = 'Embedding_Char'
else:
name = 'Embedding_Char_Pre_%d' % (i + 1)
char_embd_layer = keras.layers.TimeDistributed(layer=layer, name=name)(char_embd_layer)
embd_layer = keras.layers.Concatenate(
name='Embedding',
)([word_embd_layer, char_embd_layer])
return [word_input_layer, char_input_layer], embd_layer | [
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:param word_dict_len: The number of words in the dictionary including the ones mapped to 0 or 1.
:param char_dict_len: The number of characters in the dictionary including the ones mapped to 0 or 1.
:param max_word_len: The maximum allowed length of word.
:param word_embd_dim: The dimensions of the word embedding.
:param char_embd_dim: The dimensions of the character embedding
:param char_hidden_dim: The dimensions of the hidden states of RNN in one direction.
:param word_embd_weights: A numpy array representing the pre-trained embeddings for words.
:param char_embd_weights: A numpy array representing the pre-trained embeddings for characters.
:param word_embd_trainable: Whether the word embedding layer is trainable.
:param char_embd_trainable: Whether the character embedding layer is trainable.
:param char_hidden_layer_type: The type of the recurrent layer, 'lstm' or 'gru'.
:param word_mask_zero: Whether enable the mask for words.
:param char_mask_zero: Whether enable the mask for characters.
:return inputs, embd_layer: The keras layer. | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/word_char_embd.py | get_dicts_generator | def get_dicts_generator(word_min_freq=4,
char_min_freq=2,
word_ignore_case=False,
char_ignore_case=False):
"""Get word and character dictionaries from sentences.
:param word_min_freq: The minimum frequency of a word.
:param char_min_freq: The minimum frequency of a character.
:param word_ignore_case: Word will be transformed to lower case before saving to dictionary.
:param char_ignore_case: Character will be transformed to lower case before saving to dictionary.
:return gen: A closure that accepts sentences and returns the dictionaries.
"""
word_count, char_count = {}, {}
def get_dicts(sentence=None,
return_dict=False):
"""Update and return dictionaries for each sentence.
:param sentence: A list of strings representing the sentence.
:param return_dict: Returns the dictionaries if it is True.
:return word_dict, char_dict, max_word_len:
"""
if sentence is not None:
for word in sentence:
if not word:
continue
if word_ignore_case:
word_key = word.lower()
else:
word_key = word
word_count[word_key] = word_count.get(word_key, 0) + 1
for char in word:
if char_ignore_case:
char_key = char.lower()
else:
char_key = char
char_count[char_key] = char_count.get(char_key, 0) + 1
if not return_dict:
return None
word_dict, char_dict = {'': 0, '<UNK>': 1}, {'': 0, '<UNK>': 1}
max_word_len = 0
for word, count in word_count.items():
if count >= word_min_freq:
word_dict[word] = len(word_dict)
max_word_len = max(max_word_len, len(word))
for char, count in char_count.items():
if count >= char_min_freq:
char_dict[char] = len(char_dict)
return word_dict, char_dict, max_word_len
return get_dicts | python | def get_dicts_generator(word_min_freq=4,
char_min_freq=2,
word_ignore_case=False,
char_ignore_case=False):
"""Get word and character dictionaries from sentences.
:param word_min_freq: The minimum frequency of a word.
:param char_min_freq: The minimum frequency of a character.
:param word_ignore_case: Word will be transformed to lower case before saving to dictionary.
:param char_ignore_case: Character will be transformed to lower case before saving to dictionary.
:return gen: A closure that accepts sentences and returns the dictionaries.
"""
word_count, char_count = {}, {}
def get_dicts(sentence=None,
return_dict=False):
"""Update and return dictionaries for each sentence.
:param sentence: A list of strings representing the sentence.
:param return_dict: Returns the dictionaries if it is True.
:return word_dict, char_dict, max_word_len:
"""
if sentence is not None:
for word in sentence:
if not word:
continue
if word_ignore_case:
word_key = word.lower()
else:
word_key = word
word_count[word_key] = word_count.get(word_key, 0) + 1
for char in word:
if char_ignore_case:
char_key = char.lower()
else:
char_key = char
char_count[char_key] = char_count.get(char_key, 0) + 1
if not return_dict:
return None
word_dict, char_dict = {'': 0, '<UNK>': 1}, {'': 0, '<UNK>': 1}
max_word_len = 0
for word, count in word_count.items():
if count >= word_min_freq:
word_dict[word] = len(word_dict)
max_word_len = max(max_word_len, len(word))
for char, count in char_count.items():
if count >= char_min_freq:
char_dict[char] = len(char_dict)
return word_dict, char_dict, max_word_len
return get_dicts | [
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:param char_min_freq: The minimum frequency of a character.
:param word_ignore_case: Word will be transformed to lower case before saving to dictionary.
:param char_ignore_case: Character will be transformed to lower case before saving to dictionary.
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CyberZHG/keras-word-char-embd | keras_wc_embd/word_char_embd.py | get_word_list_eng | def get_word_list_eng(text):
"""A naive function that extracts English words from raw texts.
:param text: The raw text.
:return words: A list of strings.
"""
words, index = [''], 0
while index < len(text):
while index < len(text) and ('a' <= text[index] <= 'z' or 'A' <= text[index] <= 'Z'):
words[-1] += text[index]
index += 1
if words[-1]:
words.append('')
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if text[index] != ' ':
words[-1] += text[index]
index += 1
if words[-1]:
words.append('')
if not words[-1]:
words.pop()
return words | python | def get_word_list_eng(text):
"""A naive function that extracts English words from raw texts.
:param text: The raw text.
:return words: A list of strings.
"""
words, index = [''], 0
while index < len(text):
while index < len(text) and ('a' <= text[index] <= 'z' or 'A' <= text[index] <= 'Z'):
words[-1] += text[index]
index += 1
if words[-1]:
words.append('')
while index < len(text) and not ('a' <= text[index] <= 'z' or 'A' <= text[index] <= 'Z'):
if text[index] != ' ':
words[-1] += text[index]
index += 1
if words[-1]:
words.append('')
if not words[-1]:
words.pop()
return words | [
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CyberZHG/keras-word-char-embd | keras_wc_embd/word_char_embd.py | get_embedding_weights_from_file | def get_embedding_weights_from_file(word_dict, file_path, ignore_case=False):
"""Load pre-trained embeddings from a text file.
Each line in the file should look like this:
word feature_dim_1 feature_dim_2 ... feature_dim_n
The `feature_dim_i` should be a floating point number.
:param word_dict: A dict that maps words to indice.
:param file_path: The location of the text file containing the pre-trained embeddings.
:param ignore_case: Whether ignoring the case of the words.
:return weights: A numpy array.
"""
pre_trained = {}
with codecs.open(file_path, 'r', 'utf8') as reader:
for line in reader:
line = line.strip()
if not line:
continue
parts = line.split()
if ignore_case:
parts[0] = parts[0].lower()
pre_trained[parts[0]] = list(map(float, parts[1:]))
embd_dim = len(next(iter(pre_trained.values())))
weights = [[0.0] * embd_dim for _ in range(max(word_dict.values()) + 1)]
for word, index in word_dict.items():
if not word:
continue
if ignore_case:
word = word.lower()
if word in pre_trained:
weights[index] = pre_trained[word]
else:
weights[index] = numpy.random.random((embd_dim,)).tolist()
return numpy.asarray(weights) | python | def get_embedding_weights_from_file(word_dict, file_path, ignore_case=False):
"""Load pre-trained embeddings from a text file.
Each line in the file should look like this:
word feature_dim_1 feature_dim_2 ... feature_dim_n
The `feature_dim_i` should be a floating point number.
:param word_dict: A dict that maps words to indice.
:param file_path: The location of the text file containing the pre-trained embeddings.
:param ignore_case: Whether ignoring the case of the words.
:return weights: A numpy array.
"""
pre_trained = {}
with codecs.open(file_path, 'r', 'utf8') as reader:
for line in reader:
line = line.strip()
if not line:
continue
parts = line.split()
if ignore_case:
parts[0] = parts[0].lower()
pre_trained[parts[0]] = list(map(float, parts[1:]))
embd_dim = len(next(iter(pre_trained.values())))
weights = [[0.0] * embd_dim for _ in range(max(word_dict.values()) + 1)]
for word, index in word_dict.items():
if not word:
continue
if ignore_case:
word = word.lower()
if word in pre_trained:
weights[index] = pre_trained[word]
else:
weights[index] = numpy.random.random((embd_dim,)).tolist()
return numpy.asarray(weights) | [
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Each line in the file should look like this:
word feature_dim_1 feature_dim_2 ... feature_dim_n
The `feature_dim_i` should be a floating point number.
:param word_dict: A dict that maps words to indice.
:param file_path: The location of the text file containing the pre-trained embeddings.
:param ignore_case: Whether ignoring the case of the words.
:return weights: A numpy array. | [
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pygobject/pgi | pgi/codegen/backend.py | init_backends | def init_backends():
"""Loads all backends"""
global _BACKENDS, _ACTIVE_BACKENDS
try:
from .cffi_backend import CFFIBackend
except ImportError:
pass
else:
_BACKENDS.append(CFFIBackend)
from .ctypes_backend import CTypesBackend
from .null_backend import NullBackend
_BACKENDS.append(CTypesBackend)
_ACTIVE_BACKENDS = _BACKENDS[:]
# null isn't active by default
_BACKENDS.append(NullBackend) | python | def init_backends():
"""Loads all backends"""
global _BACKENDS, _ACTIVE_BACKENDS
try:
from .cffi_backend import CFFIBackend
except ImportError:
pass
else:
_BACKENDS.append(CFFIBackend)
from .ctypes_backend import CTypesBackend
from .null_backend import NullBackend
_BACKENDS.append(CTypesBackend)
_ACTIVE_BACKENDS = _BACKENDS[:]
# null isn't active by default
_BACKENDS.append(NullBackend) | [
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pygobject/pgi | pgi/codegen/backend.py | get_backend | def get_backend(name):
"""Returns the backend by name or raises KeyError"""
for backend in _BACKENDS:
if backend.NAME == name:
return backend
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"""Returns the backend by name or raises KeyError"""
for backend in _BACKENDS:
if backend.NAME == name:
return backend
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pygobject/pgi | pgi/codegen/backend.py | set_backend | def set_backend(name=None):
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set_backend() -- default
set_backend("cffi") -- cffi first, others as fallback
set_backend("ctypes") -- ctypes first, others as fallback
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"""Set a prefered ffi backend (cffi, ctypes).
set_backend() -- default
set_backend("cffi") -- cffi first, others as fallback
set_backend("ctypes") -- ctypes first, others as fallback
"""
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names = []
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if backend.NAME == name:
possible.remove(backend)
possible.insert(0, backend)
break
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raise LookupError("Unkown backend: %r" % name)
# only add null as fallback it explicitly specified
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pygobject/pgi | pgi/debug.py | pprint | def pprint(obj, file_=None):
"""Prints debug information for various public objects like methods,
functions, constructors etc.
"""
if file_ is None:
file_ = sys.stdout
# functions, methods
if callable(obj) and hasattr(obj, "_code"):
obj._code.pprint(file_)
return
# classes
if isinstance(obj, type) and hasattr(obj, "_constructors"):
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raise TypeError("unkown type") | python | def pprint(obj, file_=None):
"""Prints debug information for various public objects like methods,
functions, constructors etc.
"""
if file_ is None:
file_ = sys.stdout
# functions, methods
if callable(obj) and hasattr(obj, "_code"):
obj._code.pprint(file_)
return
# classes
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pygobject/pgi | pgi/codegen/fieldgen.py | get_field_type | def get_field_type(info):
"""A field python type"""
type_ = info.get_type()
cls = get_field_class(type_)
field = cls(info, type_, None)
field.setup()
return field.py_type | python | def get_field_type(info):
"""A field python type"""
type_ = info.get_type()
cls = get_field_class(type_)
field = cls(info, type_, None)
field.setup()
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pygobject/pgi | pgi/overrides/Gdk.py | Color.from_floats | def from_floats(red, green, blue):
"""Return a new Color object from red/green/blue values from 0.0 to 1.0."""
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"""Return a new Color object from red/green/blue values from 0.0 to 1.0."""
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pygobject/pgi | pgi/overrides/Gtk.py | _construct_target_list | def _construct_target_list(targets):
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The list can also contain existing TargetEntry items in which case the existing entry
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target_entries = []
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return target_entries | python | def _construct_target_list(targets):
"""Create a list of TargetEntry items from a list of tuples in the form (target, flags, info)
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is re-used in the return list.
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pygobject/pgi | pgi/overrides/Gtk.py | Widget.style_get_property | def style_get_property(self, property_name, value=None):
"""style_get_property(property_name, value=None)
:param property_name:
the name of a style property
:type property_name: :obj:`str`
:param value:
Either :obj:`None` or a correctly initialized :obj:`GObject.Value`
:type value: :obj:`GObject.Value` or :obj:`None`
:returns: The Python value of the style property
{{ docs }}
"""
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value = GObject.Value(prop.value_type)
Gtk.Widget.style_get_property(self, property_name, value)
return value.get_value() | python | def style_get_property(self, property_name, value=None):
"""style_get_property(property_name, value=None)
:param property_name:
the name of a style property
:type property_name: :obj:`str`
:param value:
Either :obj:`None` or a correctly initialized :obj:`GObject.Value`
:type value: :obj:`GObject.Value` or :obj:`None`
:returns: The Python value of the style property
{{ docs }}
"""
if value is None:
prop = self.find_style_property(property_name)
if prop is None:
raise ValueError('Class "%s" does not contain style property "%s"' %
(self, property_name))
value = GObject.Value(prop.value_type)
Gtk.Widget.style_get_property(self, property_name, value)
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pygobject/pgi | pgi/overrides/Gtk.py | Container.child_get_property | def child_get_property(self, child, property_name, value=None):
"""child_get_property(child, property_name, value=None)
:param child:
a widget which is a child of `self`
:type child: :obj:`Gtk.Widget`
:param property_name:
the name of the property to get
:type property_name: :obj:`str`
:param value:
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:type value: :obj:`GObject.Value` or :obj:`None`
:returns: The Python value of the child property
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if value is None:
prop = self.find_child_property(property_name)
if prop is None:
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value = GObject.Value(prop.value_type)
Gtk.Container.child_get_property(self, child, property_name, value)
return value.get_value() | python | def child_get_property(self, child, property_name, value=None):
"""child_get_property(child, property_name, value=None)
:param child:
a widget which is a child of `self`
:type child: :obj:`Gtk.Widget`
:param property_name:
the name of the property to get
:type property_name: :obj:`str`
:param value:
Either :obj:`None` or a correctly initialized :obj:`GObject.Value`
:type value: :obj:`GObject.Value` or :obj:`None`
:returns: The Python value of the child property
{{ docs }}
"""
if value is None:
prop = self.find_child_property(property_name)
if prop is None:
raise ValueError('Class "%s" does not contain child property "%s"' %
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value = GObject.Value(prop.value_type)
Gtk.Container.child_get_property(self, child, property_name, value)
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pygobject/pgi | pgi/overrides/Gtk.py | Container.child_get | def child_get(self, child, *prop_names):
"""Returns a list of child property values for the given names."""
return [self.child_get_property(child, name) for name in prop_names] | python | def child_get(self, child, *prop_names):
"""Returns a list of child property values for the given names."""
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pygobject/pgi | pgi/overrides/Gtk.py | Container.child_set | def child_set(self, child, **kwargs):
"""Set a child properties on the given child to key/value pairs."""
for name, value in kwargs.items():
name = name.replace('_', '-')
self.child_set_property(child, name, value) | python | def child_set(self, child, **kwargs):
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pygobject/pgi | pgi/overrides/Gtk.py | Editable.insert_text | def insert_text(self, text, position):
"""insert_text(self, text, position)
:param new_text:
the text to append
:type new_text: :obj:`str`
:param position:
location of the position text will be inserted at
:type position: :obj:`int`
:returns:
location of the position text will be inserted at
:rtype: :obj:`int`
Inserts `new_text` into the contents of the
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return super(Editable, self).insert_text(text, -1, position) | python | def insert_text(self, text, position):
"""insert_text(self, text, position)
:param new_text:
the text to append
:type new_text: :obj:`str`
:param position:
location of the position text will be inserted at
:type position: :obj:`int`
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location of the position text will be inserted at
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:param new_text:
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:type new_text: :obj:`str`
:param position:
location of the position text will be inserted at
:type position: :obj:`int`
:returns:
location of the position text will be inserted at
:rtype: :obj:`int`
Inserts `new_text` into the contents of the
widget, at position `position`.
Note that the position is in characters, not in bytes. | [
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pygobject/pgi | pgi/overrides/Gtk.py | ActionGroup.add_actions | def add_actions(self, entries, user_data=None):
"""
The add_actions() method is a convenience method that creates a number
of gtk.Action objects based on the information in the list of action
entry tuples contained in entries and adds them to the action group.
The entry tuples can vary in size from one to six items with the
following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
gtk.accelerator_parse() function. Optional with a default value of
None.
* The tooltip for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None.
* The callback function invoked when the action is activated.
Optional with a default value of None.
The "activate" signals of the actions are connected to the callbacks and
their accel paths are set to <Actions>/group-name/action-name.
"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
def _process_action(name, stock_id=None, label=None, accelerator=None, tooltip=None, callback=None):
action = Action(name=name, label=label, tooltip=tooltip, stock_id=stock_id)
if callback is not None:
if user_data is None:
action.connect('activate', callback)
else:
action.connect('activate', callback, user_data)
self.add_action_with_accel(action, accelerator)
for e in entries:
# using inner function above since entries can leave out optional arguments
_process_action(*e) | python | def add_actions(self, entries, user_data=None):
"""
The add_actions() method is a convenience method that creates a number
of gtk.Action objects based on the information in the list of action
entry tuples contained in entries and adds them to the action group.
The entry tuples can vary in size from one to six items with the
following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
gtk.accelerator_parse() function. Optional with a default value of
None.
* The tooltip for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None.
* The callback function invoked when the action is activated.
Optional with a default value of None.
The "activate" signals of the actions are connected to the callbacks and
their accel paths are set to <Actions>/group-name/action-name.
"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
def _process_action(name, stock_id=None, label=None, accelerator=None, tooltip=None, callback=None):
action = Action(name=name, label=label, tooltip=tooltip, stock_id=stock_id)
if callback is not None:
if user_data is None:
action.connect('activate', callback)
else:
action.connect('activate', callback, user_data)
self.add_action_with_accel(action, accelerator)
for e in entries:
# using inner function above since entries can leave out optional arguments
_process_action(*e) | [
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* The tooltip for the action. This field should typically be marked
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pygobject/pgi | pgi/overrides/Gtk.py | ActionGroup.add_toggle_actions | def add_toggle_actions(self, entries, user_data=None):
"""
The add_toggle_actions() method is a convenience method that creates a
number of gtk.ToggleAction objects based on the information in the list
of action entry tuples contained in entries and adds them to the action
group. The toggle action entry tuples can vary in size from one to seven
items with the following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
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* The tooltip for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None.
* The callback function invoked when the action is activated.
Optional with a default value of None.
* A flag indicating whether the toggle action is active. Optional
with a default value of False.
The "activate" signals of the actions are connected to the callbacks and
their accel paths are set to <Actions>/group-name/action-name.
"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
def _process_action(name, stock_id=None, label=None, accelerator=None, tooltip=None, callback=None, is_active=False):
action = Gtk.ToggleAction(name=name, label=label, tooltip=tooltip, stock_id=stock_id)
action.set_active(is_active)
if callback is not None:
if user_data is None:
action.connect('activate', callback)
else:
action.connect('activate', callback, user_data)
self.add_action_with_accel(action, accelerator)
for e in entries:
# using inner function above since entries can leave out optional arguments
_process_action(*e) | python | def add_toggle_actions(self, entries, user_data=None):
"""
The add_toggle_actions() method is a convenience method that creates a
number of gtk.ToggleAction objects based on the information in the list
of action entry tuples contained in entries and adds them to the action
group. The toggle action entry tuples can vary in size from one to seven
items with the following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
gtk.accelerator_parse() function. Optional with a default value of
None.
* The tooltip for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None.
* The callback function invoked when the action is activated.
Optional with a default value of None.
* A flag indicating whether the toggle action is active. Optional
with a default value of False.
The "activate" signals of the actions are connected to the callbacks and
their accel paths are set to <Actions>/group-name/action-name.
"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
def _process_action(name, stock_id=None, label=None, accelerator=None, tooltip=None, callback=None, is_active=False):
action = Gtk.ToggleAction(name=name, label=label, tooltip=tooltip, stock_id=stock_id)
action.set_active(is_active)
if callback is not None:
if user_data is None:
action.connect('activate', callback)
else:
action.connect('activate', callback, user_data)
self.add_action_with_accel(action, accelerator)
for e in entries:
# using inner function above since entries can leave out optional arguments
_process_action(*e) | [
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pygobject/pgi | pgi/overrides/Gtk.py | ActionGroup.add_radio_actions | def add_radio_actions(self, entries, value=None, on_change=None, user_data=None):
"""
The add_radio_actions() method is a convenience method that creates a
number of gtk.RadioAction objects based on the information in the list
of action entry tuples contained in entries and adds them to the action
group. The entry tuples can vary in size from one to six items with the
following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
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with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
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* The tooltip for the action. This field should typically be marked
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"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
first_action = None
def _process_action(group_source, name, stock_id=None, label=None, accelerator=None, tooltip=None, entry_value=0):
action = RadioAction(name=name, label=label, tooltip=tooltip, stock_id=stock_id, value=entry_value)
# FIXME: join_group is a patch to Gtk+ 3.0
# otherwise we can't effectively add radio actions to a
# group. Should we depend on 3.0 and error out here
# or should we offer the functionality via a compat
# C module?
if hasattr(action, 'join_group'):
action.join_group(group_source)
if value == entry_value:
action.set_active(True)
self.add_action_with_accel(action, accelerator)
return action
for e in entries:
# using inner function above since entries can leave out optional arguments
action = _process_action(first_action, *e)
if first_action is None:
first_action = action
if first_action is not None and on_change is not None:
if user_data is None:
first_action.connect('changed', on_change)
else:
first_action.connect('changed', on_change, user_data) | python | def add_radio_actions(self, entries, value=None, on_change=None, user_data=None):
"""
The add_radio_actions() method is a convenience method that creates a
number of gtk.RadioAction objects based on the information in the list
of action entry tuples contained in entries and adds them to the action
group. The entry tuples can vary in size from one to six items with the
following information:
* The name of the action. Must be specified.
* The stock id for the action. Optional with a default value of None
if a label is specified.
* The label for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None if a stock id is specified.
* The accelerator for the action, in the format understood by the
gtk.accelerator_parse() function. Optional with a default value of
None.
* The tooltip for the action. This field should typically be marked
for translation, see the set_translation_domain() method. Optional
with a default value of None.
* The value to set on the radio action. Optional with a default
value of 0. Should be specified in applications.
The value parameter specifies the radio action that should be set
active. The "changed" signal of the first radio action is connected to
the on_change callback (if specified and not None) and the accel paths
of the actions are set to <Actions>/group-name/action-name.
"""
try:
iter(entries)
except (TypeError):
raise TypeError('entries must be iterable')
first_action = None
def _process_action(group_source, name, stock_id=None, label=None, accelerator=None, tooltip=None, entry_value=0):
action = RadioAction(name=name, label=label, tooltip=tooltip, stock_id=stock_id, value=entry_value)
# FIXME: join_group is a patch to Gtk+ 3.0
# otherwise we can't effectively add radio actions to a
# group. Should we depend on 3.0 and error out here
# or should we offer the functionality via a compat
# C module?
if hasattr(action, 'join_group'):
action.join_group(group_source)
if value == entry_value:
action.set_active(True)
self.add_action_with_accel(action, accelerator)
return action
for e in entries:
# using inner function above since entries can leave out optional arguments
action = _process_action(first_action, *e)
if first_action is None:
first_action = action
if first_action is not None and on_change is not None:
if user_data is None:
first_action.connect('changed', on_change)
else:
first_action.connect('changed', on_change, user_data) | [
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pygobject/pgi | pgi/overrides/Gtk.py | UIManager.add_ui_from_string | def add_ui_from_string(self, buffer, length=-1):
"""add_ui_from_string(buffer, length=-1)
{{ all }}
"""
return Gtk.UIManager.add_ui_from_string(self, buffer, length) | python | def add_ui_from_string(self, buffer, length=-1):
"""add_ui_from_string(buffer, length=-1)
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"""
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pygobject/pgi | pgi/overrides/Gtk.py | Builder.add_from_string | def add_from_string(self, buffer, length=-1):
"""add_from_string(buffer, length=-1)
{{ all }}
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pygobject/pgi | pgi/overrides/Gtk.py | Builder.add_objects_from_string | def add_objects_from_string(self, buffer, object_ids):
"""add_objects_from_string(buffer, object_ids)
:param buffer: the string to parse
:type buffer: :obj:`str`
:param object_ids: array of objects to build
:type object_ids: [:obj:`str`]
:raises: :class:`GLib.Error`
:returns: A positive value on success, 0 if an error occurred
:rtype: :obj:`int`
{{ docs }}
"""
length = -1
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"""add_objects_from_string(buffer, object_ids)
:param buffer: the string to parse
:type buffer: :obj:`str`
:param object_ids: array of objects to build
:type object_ids: [:obj:`str`]
:raises: :class:`GLib.Error`
:returns: A positive value on success, 0 if an error occurred
:rtype: :obj:`int`
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length = -1
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pygobject/pgi | pgi/overrides/Gtk.py | Dialog.add_buttons | def add_buttons(self, *args):
"""add_buttons(*args)
The add_buttons() method adds several buttons to the Gtk.Dialog using
the button data passed as arguments to the method. This method is the
same as calling the Gtk.Dialog.add_button() repeatedly. The button data
pairs - button text (or stock ID) and a response ID integer are passed
individually. For example:
.. code-block:: python
dialog.add_buttons(Gtk.STOCK_OPEN, 42, "Close", Gtk.ResponseType.CLOSE)
will add "Open" and "Close" buttons to dialog.
"""
def _button(b):
while b:
t, r = b[0:2]
b = b[2:]
yield t, r
try:
for text, response in _button(args):
self.add_button(text, response)
except (IndexError):
raise TypeError('Must pass an even number of arguments') | python | def add_buttons(self, *args):
"""add_buttons(*args)
The add_buttons() method adds several buttons to the Gtk.Dialog using
the button data passed as arguments to the method. This method is the
same as calling the Gtk.Dialog.add_button() repeatedly. The button data
pairs - button text (or stock ID) and a response ID integer are passed
individually. For example:
.. code-block:: python
dialog.add_buttons(Gtk.STOCK_OPEN, 42, "Close", Gtk.ResponseType.CLOSE)
will add "Open" and "Close" buttons to dialog.
"""
def _button(b):
while b:
t, r = b[0:2]
b = b[2:]
yield t, r
try:
for text, response in _button(args):
self.add_button(text, response)
except (IndexError):
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pygobject/pgi | pgi/overrides/Gtk.py | TextBuffer.create_tag | def create_tag(self, tag_name=None, **properties):
"""Creates a tag and adds it to the tag table of the TextBuffer.
:param str tag_name:
Name of the new tag, or None
:param **properties:
Keyword list of properties and their values
:returns:
A new tag.
This is equivalent to creating a Gtk.TextTag and then adding the
tag to the buffer's tag table. The returned tag is owned by
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If ``tag_name`` is None, the tag is anonymous.
If ``tag_name`` is not None, a tag called ``tag_name`` must not already
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Properties are passed as a keyword list of names and values (e.g.
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tag = Gtk.TextTag(name=tag_name, **properties)
self._get_or_create_tag_table().add(tag)
return tag | python | def create_tag(self, tag_name=None, **properties):
"""Creates a tag and adds it to the tag table of the TextBuffer.
:param str tag_name:
Name of the new tag, or None
:param **properties:
Keyword list of properties and their values
:returns:
A new tag.
This is equivalent to creating a Gtk.TextTag and then adding the
tag to the buffer's tag table. The returned tag is owned by
the buffer's tag table.
If ``tag_name`` is None, the tag is anonymous.
If ``tag_name`` is not None, a tag called ``tag_name`` must not already
exist in the tag table for this buffer.
Properties are passed as a keyword list of names and values (e.g.
foreground='DodgerBlue', weight=Pango.Weight.BOLD)
"""
tag = Gtk.TextTag(name=tag_name, **properties)
self._get_or_create_tag_table().add(tag)
return tag | [
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pygobject/pgi | pgi/overrides/Gtk.py | TextBuffer.set_text | def set_text(self, text, length=-1):
"""set_text(text, length=-1)
{{ all }}
"""
Gtk.TextBuffer.set_text(self, text, length) | python | def set_text(self, text, length=-1):
"""set_text(text, length=-1)
{{ all }}
"""
Gtk.TextBuffer.set_text(self, text, length) | [
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pygobject/pgi | pgi/overrides/Gtk.py | TextBuffer.insert | def insert(self, iter, text, length=-1):
"""insert(iter, text, length=-1)
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Gtk.TextBuffer.insert(self, iter, text, length) | python | def insert(self, iter, text, length=-1):
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{{ all }}
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel.get_iter | def get_iter(self, path):
"""
:param path: the :obj:`Gtk.TreePath`-struct
:type path: :obj:`Gtk.TreePath`
:raises: :class:`ValueError` if `path` doesn't exist
:returns: a :obj:`Gtk.TreeIter`
:rtype: :obj:`Gtk.TreeIter`
Returns an iterator pointing to `path`. If `path` does not exist
:class:`ValueError` is raised.
"""
path = self._coerce_path(path)
success, aiter = super(TreeModel, self).get_iter(path)
if not success:
raise ValueError("invalid tree path '%s'" % path)
return aiter | python | def get_iter(self, path):
"""
:param path: the :obj:`Gtk.TreePath`-struct
:type path: :obj:`Gtk.TreePath`
:raises: :class:`ValueError` if `path` doesn't exist
:returns: a :obj:`Gtk.TreeIter`
:rtype: :obj:`Gtk.TreeIter`
Returns an iterator pointing to `path`. If `path` does not exist
:class:`ValueError` is raised.
"""
path = self._coerce_path(path)
success, aiter = super(TreeModel, self).get_iter(path)
if not success:
raise ValueError("invalid tree path '%s'" % path)
return aiter | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel.iter_next | def iter_next(self, iter):
"""
:param iter: the :obj:`Gtk.TreeIter`-struct
:type iter: :obj:`Gtk.TreeIter`
:returns: a :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: :obj:`Gtk.TreeIter` or :obj:`None`
Returns an iterator pointing to the node following `iter` at the
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If there is no next `iter`, :obj:`None` is returned.
"""
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return next_iter | python | def iter_next(self, iter):
"""
:param iter: the :obj:`Gtk.TreeIter`-struct
:type iter: :obj:`Gtk.TreeIter`
:returns: a :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: :obj:`Gtk.TreeIter` or :obj:`None`
Returns an iterator pointing to the node following `iter` at the
current level.
If there is no next `iter`, :obj:`None` is returned.
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next_iter = iter.copy()
success = super(TreeModel, self).iter_next(next_iter)
if success:
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel.iter_previous | def iter_previous(self, iter):
"""
:param iter: the :obj:`Gtk.TreeIter`-struct
:type iter: :obj:`Gtk.TreeIter`
:returns: a :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: :obj:`Gtk.TreeIter` or :obj:`None`
Returns an iterator pointing to the previous node at the current level.
If there is no previous `iter`, :obj:`None` is returned.
"""
prev_iter = iter.copy()
success = super(TreeModel, self).iter_previous(prev_iter)
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return prev_iter | python | def iter_previous(self, iter):
"""
:param iter: the :obj:`Gtk.TreeIter`-struct
:type iter: :obj:`Gtk.TreeIter`
:returns: a :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: :obj:`Gtk.TreeIter` or :obj:`None`
Returns an iterator pointing to the previous node at the current level.
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prev_iter = iter.copy()
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel.set_row | def set_row(self, treeiter, row):
"""
:param treeiter: the :obj:`Gtk.TreeIter`
:type treeiter: :obj:`Gtk.TreeIter`
:param row: a list of values for each column
:type row: [:obj:`object`]
Sets all values of a row pointed to by `treeiter` from a list of
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:obj:`Gtk.TreeStore.set_value`\\()
"""
converted_row, columns = self._convert_row(row)
for column in columns:
value = row[column]
if value is None:
continue # None means skip this row
self.set_value(treeiter, column, value) | python | def set_row(self, treeiter, row):
"""
:param treeiter: the :obj:`Gtk.TreeIter`
:type treeiter: :obj:`Gtk.TreeIter`
:param row: a list of values for each column
:type row: [:obj:`object`]
Sets all values of a row pointed to by `treeiter` from a list of
values passes as `row`. The length of the row has to match the number
of columns of the model. :obj:`None` in `row` means the value will be
skipped and not set.
Also see :obj:`Gtk.ListStore.set_value`\\() and
:obj:`Gtk.TreeStore.set_value`\\()
"""
converted_row, columns = self._convert_row(row)
for column in columns:
value = row[column]
if value is None:
continue # None means skip this row
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:param row: a list of values for each column
:type row: [:obj:`object`]
Sets all values of a row pointed to by `treeiter` from a list of
values passes as `row`. The length of the row has to match the number
of columns of the model. :obj:`None` in `row` means the value will be
skipped and not set.
Also see :obj:`Gtk.ListStore.set_value`\\() and
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel._convert_value | def _convert_value(self, column, value):
'''Convert value to a GObject.Value of the expected type'''
if isinstance(value, GObject.Value):
return value
return GObject.Value(self.get_column_type(column), value) | python | def _convert_value(self, column, value):
'''Convert value to a GObject.Value of the expected type'''
if isinstance(value, GObject.Value):
return value
return GObject.Value(self.get_column_type(column), value) | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModel.get | def get(self, treeiter, *columns):
"""
:param treeiter: the :obj:`Gtk.TreeIter`
:type treeiter: :obj:`Gtk.TreeIter`
:param \\*columns: a list of column indices to fetch
:type columns: (:obj:`int`)
Returns a tuple of all values specified by their indices in `columns`
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Also see :obj:`Gtk.TreeStore.get_value`\\()
"""
n_columns = self.get_n_columns()
values = []
for col in columns:
if not isinstance(col, int):
raise TypeError("column numbers must be ints")
if col < 0 or col >= n_columns:
raise ValueError("column number is out of range")
values.append(self.get_value(treeiter, col))
return tuple(values) | python | def get(self, treeiter, *columns):
"""
:param treeiter: the :obj:`Gtk.TreeIter`
:type treeiter: :obj:`Gtk.TreeIter`
:param \\*columns: a list of column indices to fetch
:type columns: (:obj:`int`)
Returns a tuple of all values specified by their indices in `columns`
in the order the indices are contained in `columns`
Also see :obj:`Gtk.TreeStore.get_value`\\()
"""
n_columns = self.get_n_columns()
values = []
for col in columns:
if not isinstance(col, int):
raise TypeError("column numbers must be ints")
if col < 0 or col >= n_columns:
raise ValueError("column number is out of range")
values.append(self.get_value(treeiter, col))
return tuple(values) | [
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pygobject/pgi | pgi/overrides/Gtk.py | ListStore.append | def append(self, row=None):
"""append(row=None)
:param row: a list of values to apply to the newly append row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` of the appended row
:rtype: :obj:`Gtk.TreeIter`
If `row` is :obj:`None` the appended row will be empty and to fill in
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If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row .
"""
if row:
return self._do_insert(-1, row)
# gtk_list_store_insert() does not know about the "position == -1"
# case, so use append() here
else:
return Gtk.ListStore.append(self) | python | def append(self, row=None):
"""append(row=None)
:param row: a list of values to apply to the newly append row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` of the appended row
:rtype: :obj:`Gtk.TreeIter`
If `row` is :obj:`None` the appended row will be empty and to fill in
values you need to call :obj:`Gtk.ListStore.set`\\() or
:obj:`Gtk.ListStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row .
"""
if row:
return self._do_insert(-1, row)
# gtk_list_store_insert() does not know about the "position == -1"
# case, so use append() here
else:
return Gtk.ListStore.append(self) | [
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pygobject/pgi | pgi/overrides/Gtk.py | ListStore.insert_before | def insert_before(self, sibling, row=None):
"""insert_before(sibling, row=None)
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row before `sibling`. If `sibling` is :obj:`None`, then
the row will be appended to the end of the list.
The row will be empty if `row` is :obj:`None. To fill in values, you
need to call :obj:`Gtk.ListStore.set`\\() or
:obj:`Gtk.ListStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.ListStore.insert_before(self, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | python | def insert_before(self, sibling, row=None):
"""insert_before(sibling, row=None)
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row before `sibling`. If `sibling` is :obj:`None`, then
the row will be appended to the end of the list.
The row will be empty if `row` is :obj:`None. To fill in values, you
need to call :obj:`Gtk.ListStore.set`\\() or
:obj:`Gtk.ListStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.ListStore.insert_before(self, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | [
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pygobject/pgi | pgi/overrides/Gtk.py | ListStore.insert_after | def insert_after(self, sibling, row=None):
"""insert_after(sibling, row=None)
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row after `sibling`. If `sibling` is :obj:`None`, then
the row will be prepended to the beginning of the list.
The row will be empty if `row` is :obj:`None. To fill in values, you
need to call :obj:`Gtk.ListStore.set`\\() or
:obj:`Gtk.ListStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.ListStore.insert_after(self, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | python | def insert_after(self, sibling, row=None):
"""insert_after(sibling, row=None)
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row after `sibling`. If `sibling` is :obj:`None`, then
the row will be prepended to the beginning of the list.
The row will be empty if `row` is :obj:`None. To fill in values, you
need to call :obj:`Gtk.ListStore.set`\\() or
:obj:`Gtk.ListStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.ListStore.insert_after(self, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | [
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pygobject/pgi | pgi/overrides/Gtk.py | ListStore.set_value | def set_value(self, treeiter, column, value):
"""
{{ all }}
`value` can also be a Python value and will be converted to a
:obj:`GObject.Value` using the corresponding column type (See
:obj:`Gtk.ListStore.set_column_types`\\()).
"""
value = self._convert_value(column, value)
Gtk.ListStore.set_value(self, treeiter, column, value) | python | def set_value(self, treeiter, column, value):
"""
{{ all }}
`value` can also be a Python value and will be converted to a
:obj:`GObject.Value` using the corresponding column type (See
:obj:`Gtk.ListStore.set_column_types`\\()).
"""
value = self._convert_value(column, value)
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModelRow.get_next | def get_next(self):
"""Returns the next :obj:`Gtk.TreeModelRow` or None"""
next_iter = self.model.iter_next(self.iter)
if next_iter:
return TreeModelRow(self.model, next_iter) | python | def get_next(self):
"""Returns the next :obj:`Gtk.TreeModelRow` or None"""
next_iter = self.model.iter_next(self.iter)
if next_iter:
return TreeModelRow(self.model, next_iter) | [
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"""Returns the previous :obj:`Gtk.TreeModelRow` or None"""
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"""Returns the previous :obj:`Gtk.TreeModelRow` or None"""
prev_iter = self.model.iter_previous(self.iter)
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pygobject/pgi | pgi/overrides/Gtk.py | TreeModelRow.get_parent | def get_parent(self):
"""Returns the parent :obj:`Gtk.TreeModelRow` or htis row or None"""
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if parent_iter:
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"""Returns the parent :obj:`Gtk.TreeModelRow` or htis row or None"""
parent_iter = self.model.iter_parent(self.iter)
if parent_iter:
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"""Returns a :obj:`Gtk.TreeModelRowIter` for the row's children"""
child_iter = self.model.iter_children(self.iter)
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"""Returns a :obj:`Gtk.TreeModelRowIter` for the row's children"""
child_iter = self.model.iter_children(self.iter)
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pygobject/pgi | pgi/overrides/Gtk.py | TreeStore.insert | def insert(self, parent, position, row=None):
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:param parent:
A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type parent: :obj:`Gtk.TreeIter` or :obj:`None`
:param position:
position to insert the new row, or -1 for last
:type position: :obj:`int`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: a :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Creates a new row at `position`. If parent is not :obj:`None`, then
the row will be made a child of `parent`. Otherwise, the row will be
created at the toplevel. If `position` is -1 or is larger than the
number of rows at that level, then the new row will be inserted to the
end of the list.
The returned iterator will point to the newly inserted row. The row
will be empty after this function is called if `row` is :obj:`None`.
To fill in values, you need to call :obj:`Gtk.TreeStore.set`\\() or
:obj:`Gtk.TreeStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
return self._do_insert(parent, position, row) | python | def insert(self, parent, position, row=None):
"""insert(parent, position, row=None)
:param parent:
A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type parent: :obj:`Gtk.TreeIter` or :obj:`None`
:param position:
position to insert the new row, or -1 for last
:type position: :obj:`int`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: a :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Creates a new row at `position`. If parent is not :obj:`None`, then
the row will be made a child of `parent`. Otherwise, the row will be
created at the toplevel. If `position` is -1 or is larger than the
number of rows at that level, then the new row will be inserted to the
end of the list.
The returned iterator will point to the newly inserted row. The row
will be empty after this function is called if `row` is :obj:`None`.
To fill in values, you need to call :obj:`Gtk.TreeStore.set`\\() or
:obj:`Gtk.TreeStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
return self._do_insert(parent, position, row) | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeStore.insert_before | def insert_before(self, parent, sibling, row=None):
"""insert_before(parent, sibling, row=None)
:param parent: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type parent: :obj:`Gtk.TreeIter` or :obj:`None`
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: a :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row before `sibling`. If `sibling` is :obj:`None`, then
the row will be appended to `parent` 's children. If `parent` and
`sibling` are :obj:`None`, then the row will be appended to the
toplevel. If both `sibling` and `parent` are set, then `parent` must
be the parent of `sibling`. When `sibling` is set, `parent` is
optional.
The returned iterator will point to this new row. The row will be
empty after this function is called if `row` is :obj:`None`. To fill
in values, you need to call :obj:`Gtk.TreeStore.set`\\() or
:obj:`Gtk.TreeStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.TreeStore.insert_before(self, parent, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | python | def insert_before(self, parent, sibling, row=None):
"""insert_before(parent, sibling, row=None)
:param parent: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type parent: :obj:`Gtk.TreeIter` or :obj:`None`
:param sibling: A valid :obj:`Gtk.TreeIter`, or :obj:`None`
:type sibling: :obj:`Gtk.TreeIter` or :obj:`None`
:param row: a list of values to apply to the newly inserted row or :obj:`None`
:type row: [:obj:`object`] or :obj:`None`
:returns: a :obj:`Gtk.TreeIter` pointing to the new row
:rtype: :obj:`Gtk.TreeIter`
Inserts a new row before `sibling`. If `sibling` is :obj:`None`, then
the row will be appended to `parent` 's children. If `parent` and
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toplevel. If both `sibling` and `parent` are set, then `parent` must
be the parent of `sibling`. When `sibling` is set, `parent` is
optional.
The returned iterator will point to this new row. The row will be
empty after this function is called if `row` is :obj:`None`. To fill
in values, you need to call :obj:`Gtk.TreeStore.set`\\() or
:obj:`Gtk.TreeStore.set_value`\\().
If `row` isn't :obj:`None` it has to be a list of values which will be
used to fill the row.
"""
treeiter = Gtk.TreeStore.insert_before(self, parent, sibling)
if row is not None:
self.set_row(treeiter, row)
return treeiter | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeStore.set_value | def set_value(self, treeiter, column, value):
"""
{{ all }}
`value` can also be a Python value and will be converted to a
:obj:`GObject.Value` using the corresponding column type (See
:obj:`Gtk.ListStore.set_column_types`\\()).
"""
value = self._convert_value(column, value)
Gtk.TreeStore.set_value(self, treeiter, column, value) | python | def set_value(self, treeiter, column, value):
"""
{{ all }}
`value` can also be a Python value and will be converted to a
:obj:`GObject.Value` using the corresponding column type (See
:obj:`Gtk.ListStore.set_column_types`\\()).
"""
value = self._convert_value(column, value)
Gtk.TreeStore.set_value(self, treeiter, column, value) | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeView.insert_column_with_attributes | def insert_column_with_attributes(self, position, title, cell, **kwargs):
"""
:param position: The position to insert the new column in
:type position: :obj:`int`
:param title: The title to set the header to
:type title: :obj:`str`
:param cell: The :obj:`Gtk.CellRenderer`
:type cell: :obj:`Gtk.CellRenderer`
{{ docs }}
"""
column = TreeViewColumn()
column.set_title(title)
column.pack_start(cell, False)
self.insert_column(column, position)
column.set_attributes(cell, **kwargs) | python | def insert_column_with_attributes(self, position, title, cell, **kwargs):
"""
:param position: The position to insert the new column in
:type position: :obj:`int`
:param title: The title to set the header to
:type title: :obj:`str`
:param cell: The :obj:`Gtk.CellRenderer`
:type cell: :obj:`Gtk.CellRenderer`
{{ docs }}
"""
column = TreeViewColumn()
column.set_title(title)
column.pack_start(cell, False)
self.insert_column(column, position)
column.set_attributes(cell, **kwargs) | [
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pygobject/pgi | pgi/overrides/Gtk.py | TreeViewColumn.set_attributes | def set_attributes(self, cell_renderer, **attributes):
"""
:param cell_renderer: the :obj:`Gtk.CellRenderer` we're setting the attributes of
:type cell_renderer: :obj:`Gtk.CellRenderer`
{{ docs }}
"""
Gtk.CellLayout.clear_attributes(self, cell_renderer)
for (name, value) in attributes.items():
Gtk.CellLayout.add_attribute(self, cell_renderer, name, value) | python | def set_attributes(self, cell_renderer, **attributes):
"""
:param cell_renderer: the :obj:`Gtk.CellRenderer` we're setting the attributes of
:type cell_renderer: :obj:`Gtk.CellRenderer`
{{ docs }}
"""
Gtk.CellLayout.clear_attributes(self, cell_renderer)
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pygobject/pgi | pgi/overrides/Gtk.py | TreeSelection.get_selected | def get_selected(self):
"""
:returns:
:model: the :obj:`Gtk.TreeModel`
:iter: The :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: (**model**: :obj:`Gtk.TreeModel`, **iter**: :obj:`Gtk.TreeIter` or :obj:`None`)
{{ docs }}
"""
success, model, aiter = super(TreeSelection, self).get_selected()
if success:
return (model, aiter)
else:
return (model, None) | python | def get_selected(self):
"""
:returns:
:model: the :obj:`Gtk.TreeModel`
:iter: The :obj:`Gtk.TreeIter` or :obj:`None`
:rtype: (**model**: :obj:`Gtk.TreeModel`, **iter**: :obj:`Gtk.TreeIter` or :obj:`None`)
{{ docs }}
"""
success, model, aiter = super(TreeSelection, self).get_selected()
if success:
return (model, aiter)
else:
return (model, None) | [
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