hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
56eb1c7306fe729c0ed76152de5031669b58ac0e | 230 | py | Python | mmseg/models/segmentors/__init__.py | JZTgentle/-mm-salient-detection | 3e9550529620d3e4f86824152ec9d1a2f1d668e6 | [
"Apache-2.0"
] | 2 | 2021-01-22T10:10:53.000Z | 2021-03-22T12:40:27.000Z | mmseg/models/segmentors/__init__.py | JZTgentle/mm-salient-detection | 3e9550529620d3e4f86824152ec9d1a2f1d668e6 | [
"Apache-2.0"
] | null | null | null | mmseg/models/segmentors/__init__.py | JZTgentle/mm-salient-detection | 3e9550529620d3e4f86824152ec9d1a2f1d668e6 | [
"Apache-2.0"
] | null | null | null | from .cascade_encoder_decoder import CascadeEncoderDecoder
from .encoder_decoder import EncoderDecoder
from .sod_encoder_decoder import SODEncoderDecoder
__all__ = ['EncoderDecoder', 'CascadeEncoderDecoder', 'SODEncoderDecoder']
| 38.333333 | 74 | 0.86087 | 21 | 230 | 9 | 0.47619 | 0.222222 | 0.31746 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.078261 | 230 | 5 | 75 | 46 | 0.891509 | 0 | 0 | 0 | 0 | 0 | 0.226087 | 0.091304 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
710e5d85af6a2a2691cdff1ba558b988ded9adb8 | 28,600 | py | Python | modules/FASTX_toolkit.py | tyrmi/STAPLER | fd83eee4be0bb78c67a111fd1c1c1dff4c16aefe | [
"BSD-3-Clause"
] | 4 | 2017-07-17T07:45:39.000Z | 2021-01-12T00:33:10.000Z | modules/FASTX_toolkit.py | tyrmi/STAPLER | fd83eee4be0bb78c67a111fd1c1c1dff4c16aefe | [
"BSD-3-Clause"
] | null | null | null | modules/FASTX_toolkit.py | tyrmi/STAPLER | fd83eee4be0bb78c67a111fd1c1c1dff4c16aefe | [
"BSD-3-Clause"
] | null | null | null | import os
import directory
from STAPLERerror import STAPLERerror
from STAPLERerror import VirtualIOError
from GenericBase import GenericBase
import utils
class fastx_toolkit_generic_compressable(GenericBase):
"""Generic class with method for parsing fastx toolkit IO parameters.
Only single type of output file is expected (but can be
uncompressed or compressed if -z parameter is present).
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
def _select_IO(self, out_cmd, in_dir, out_dir):
"""Infers the input and output file paths.
This method must keep the directory objects up to date of the file
edits!
Parameters:
in_cmd: A dict containing the command line.
in_dir: Input directory (instance of filetypes.Directory).
out_dir: Output directory (instance of filetypes.Directory).
Returns:
out_cmd: Dict containing the output commands
command_identifier: Input file name based identifier for the current command
Raises:
VirtualIOError: No valid input file can be found.
"""
IO_files = {}
file_names = set()
for fl in in_dir.files:
if self.name not in fl.users:
if utils.splitext(fl.name)[-1] in self.input_types:
IO_files['-i'] = os.path.join(in_dir.path, fl.name)
command_ids = [utils.infer_path_id(IO_files['-i'])]
in_dir.use_file(fl.name, self.name)
assert len(self.output_types) < 2, 'Several output ' \
'types, override ' \
'this method!'
# If -z parameter is present in the input, output file will
# be compressed
if '-z' in out_cmd:
output_name = utils.splitext(fl.name)[0] + \
self.output_types[0] + '.gz'
else:
output_name = utils.splitext(fl.name)[0] + \
self.output_types[0]
output_path = os.path.join(out_dir.path, output_name)
IO_files['-o'] = output_path
file_names.add(output_name)
out_dir.add_file(output_name)
break
if not IO_files:
raise VirtualIOError('No more unused input files')
out_cmd.update(IO_files)
return out_cmd, command_ids
class fastx_toolkit_generic_compressable_fastx(GenericBase):
"""Generic class with method for parsing fastx toolkit IO parameters.
Similar to fastx_toolkit_generic_compressable, but fasta or fastq output
is expected (either can be compressed).
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
def _select_IO(self, out_cmd, in_dir, out_dir):
"""Infers the input and output file paths.
This method must keep the directory objects up to date of the file
edits!
Parameters:
in_cmd: A dict containing the command line.
in_dir: Input directory (instance of filetypes.Directory).
out_dir: Output directory (instance of filetypes.Directory).
Returns:
out_cmd: Dict containing the output commands
command_identifier: Input file name based identifier for the current command
Raises:
VirtualIOError: No valid input file can be found.
"""
IO_files = {}
file_names = set()
for fl in in_dir.files:
if self.name not in fl.users:
if utils.splitext(fl.name)[-1] in self.input_types:
IO_files['-i'] = os.path.join(in_dir.path, fl.name)
command_ids = [utils.infer_path_id(IO_files['-i'])]
in_dir.use_file(fl.name, self.name)
# Output filename extension is the same as input filename
# extension
output_file_extension = utils.splitext(IO_files['-i'])
# If -z parameter is present in the input, output file will
# be compressed
if '-z' in out_cmd:
output_name = utils.splitext(fl.name)[0] + \
output_file_extension + '.gz'
else:
output_name = utils.splitext(fl.name)[0] + \
output_file_extension
output_path = os.path.join(out_dir.path, output_name)
IO_files['-o'] = output_path
file_names.add(output_name)
out_dir.add_file(output_name)
break
if not IO_files:
raise VirtualIOError('No more unused input files')
out_cmd.update(IO_files)
return out_cmd, command_ids
class fasta_formatter(GenericBase):
"""Class for using FASTX-toolkit command fasta_formatter.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fasta_formatter'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq', '.tab']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = ['-w', '-t', '-e']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
def _select_IO(self, out_cmd, in_dir, out_dir):
"""Infers the input and output file paths.
This method must keep the directory objects up to date of the file
edits!
Parameters:
in_cmd: A dict containing the command line.
in_dir: Input directory (instance of filetypes.Directory).
out_dir: Output directory (instance of filetypes.Directory).
Returns:
out_cmd: Dict containing the output commands
command_identifier: Input file name based identifier for the current command
Raises:
VirtualIOError: No valid input file can be found.
"""
IO_files = {}
file_names = set()
for fl in in_dir.files:
if self.name not in fl.users:
if utils.splitext(fl.name)[-1] in self.input_types:
IO_files['-i'] = os.path.join(in_dir.path, fl.name)
command_ids = [utils.infer_path_id(IO_files['-i'])]
in_dir.use_file(fl.name, self.name)
# Output filename extension is the same as input filename
# extension, except when -t parameter is included the
# output format is .tab
if '-t' in out_cmd:
output_file_extension = '.tab'
else:
output_file_extension = utils.splitext(IO_files['-i'])
# If -z parameter is present in the input, output file will
# be compressed
if '-z' in out_cmd:
output_name = utils.splitext(fl.name)[0] + \
output_file_extension + '.gz'
else:
output_name = utils.splitext(fl.name)[0] + \
output_file_extension
output_path = os.path.join(out_dir.path, output_name)
IO_files['-o'] = output_path
file_names.add(output_name)
out_dir.add_file(output_name)
break
if not IO_files:
raise VirtualIOError('No more unused input files')
out_cmd.update(IO_files)
return out_cmd, command_ids
class fasta_nucleotide_changer(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fasta_nucleotide_changer.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fasta_nucleotide_changer'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = ['-r', '-d', '-z', '-v']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastq_quality_boxplot_graph(GenericBase):
"""Class for using FASTX-toolkit command fastq_quality_boxplot_graph.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastq_quality_boxplot_graph.sh'
input_types = {'.fastx_quality_stats'}
output_types = ['.png']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = []
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
def _format_cmd(self, cmd):
"""Determines the output file name and type.
Also adds the -t argument which based on file name.
Parameters:
cmd: Parsed command line.
Returns:
cmd: Command line with -o set to point to a file in a dir instead a dir
file_names: Name(s) of the files this command outputs
"""
assert len(self.output_types) == 1, 'Many output types, override this function'
file_name = cmd['-i']
file_name = utils.splitext(file_name)[0] + self.output_types[0]
cmd['-o'] = os.path.join(cmd['-o'], file_name)
cmd['-t'] = file_name
file_names = [os.path.basename(file_name)]
return cmd, file_names
class fastq_quality_filter(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastq_quality_filter.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastq_quality_filter'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o', '-q', '-p']
user_optional_args = ['-v', '-z']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastq_quality_trimmer(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastq_quality_trimmer.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastq_quality_trimmer'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o', '-t']
user_optional_args = ['-l', '-v', '-Q']
help_description = """
Tested with fastx-tookit v. 0.0.14
"""
class fastq_to_fasta(fastx_toolkit_generic_compressable):
"""Class for parallelizing fastq_to_fasta.
Parameters:
in_cmd: String containing a command line
in_dir: Directory object containing input files
out_dir: Directory object containing output files
NOTICE! Keep the directory objects up to date about file edits!
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
output_types: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
user_mandatory_args: Args the user must provide.
remove_user_args: Args that will be removed from the final command.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
file_names: Names of output files.
command_ids: File names of input file(s) with no file extensions.
Methods:
get_cmd: Method for getting the final cmd line string for output.
"""
name = 'stapler_fastx_toolkit_fastq_to_fasta'
input_types = {'.fastq'}
output_types = ['.fasta']
hidden_mandatory_args = ['-i', '-o']
user_mandatory_args = []
remove_user_args = user_mandatory_args
user_optional_args = ['-r', '-n', '-v', '-z']
parallelizable = True
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_artifacts_filter(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_artifacts_filter.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_artifacts_filter'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_mandatory_args = []
user_optional_args = ['-z']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_clipper(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_clipper.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_clipper'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = ['-a', '-l', '-d', '-c', '-C', '-k', '-n', '-v', '-z',
'-D']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_collapser(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_collapser.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_collapser'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_mandatory_args = []
user_optional_args = []
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_nucleotide_distribution_graph(fastq_quality_boxplot_graph):
"""Class for FASTX-toolkit command fastx_nucleotide_distribution_graph.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_nucleotide_distribution_graph.sh'
class fastx_quality_stats(GenericBase):
"""Class for using FASTX-toolkit command fastx_quality_stats.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_quality_stats'
input_types = {'.fasta', '.fastq'}
output_types = ['.fastx_quality_stats']
hidden_mandatory_args = ['-i', '-o']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_renamer(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_renamer.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_trimmer'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_mandatory_args = ['-n']
user_optional_args = ['-z']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_reverse_complement(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_reverse_complement.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_reverse_complement'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = ['-z']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
class fastx_trimmer(fastx_toolkit_generic_compressable_fastx):
"""Class for using FASTX-toolkit command fastx_trimmer.
Attributes:
name: Name of the function.
input_type: Input types accepted by this application.
_output_type: List of output types produced by the application.
mandatory_args: Args the user be provided in in_cmd when initializing.
optional_args: Args that may be part of the command line.
in_cmd: Command entered by user.
parsed_cmd: Final output command as option:value dict.
id: Bare name of input file (without the possible ending)
file_names: Names of output files
Methods:
_cmd_parse: Turns a command line into argument-value pairs.
_validate: Classmethod for validating command lines.
get_cmd: Method for getting the final cmd line string for output.
_set_IO: Determines the output file name and type.
_parse_id: Returns the bare input file name
"""
name = 'stapler_fastx_toolkit_fastx_trimmer'
input_types = {'.fasta', '.fastq'}
output_types = ['.fasta', '.fastq']
hidden_mandatory_args = ['-i', '-o']
user_optional_args = ['-f', '-l', '-z']
help_description = '''
Tested with fastx-tookit v. 0.0.14
'''
def _validate(self, parsed_cmd):
"""Validates the command line.
Raises STAPLERerror if validation is unsuccessfull
Args:
parsed_cmd: Dict of arguments entered by user
Raises:
STAPLERerror if validation is unsuccessful
"""
for ma in self.hidden_mandatory_args:
if ma not in parsed_cmd:
raise STAPLERerror('The command line does not contain '
'all the mandatory arguments '
'{0}:\n{1}'.format(self.hidden_mandatory_args,
' '.join(parsed_cmd)))
for cmd in parsed_cmd:
if cmd not in self.hidden_mandatory_args and cmd not in self.user_optional_args:
raise STAPLERerror('Unknown option: {0}\n'
'on command line:\n{1}'.format(cmd, self.in_cmd))
| 40.567376 | 93 | 0.653566 | 3,773 | 28,600 | 4.764909 | 0.060429 | 0.028034 | 0.016353 | 0.01691 | 0.881744 | 0.8635 | 0.85727 | 0.847425 | 0.837357 | 0.837023 | 0 | 0.003423 | 0.274825 | 28,600 | 704 | 94 | 40.625 | 0.863404 | 0.547517 | 0 | 0.650862 | 0 | 0 | 0.172222 | 0.055613 | 0 | 0 | 0 | 0 | 0.008621 | 1 | 0.021552 | false | 0 | 0.025862 | 0 | 0.49569 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7133176081a0893b30c17bb5af1161f4bc57d2bf | 33 | py | Python | src/interface/menu_options.py | KaiAPaulhus/GifExtract | 75028e6b0f2ddcfc07bf5a1a738c24b2a9912a3c | [
"MIT"
] | 1 | 2016-10-26T01:42:59.000Z | 2016-10-26T01:42:59.000Z | src/interface/menu_options.py | KaiAPaulhus/GifExtract | 75028e6b0f2ddcfc07bf5a1a738c24b2a9912a3c | [
"MIT"
] | null | null | null | src/interface/menu_options.py | KaiAPaulhus/GifExtract | 75028e6b0f2ddcfc07bf5a1a738c24b2a9912a3c | [
"MIT"
] | null | null | null | def saveButtonPressed():
pass | 16.5 | 24 | 0.727273 | 3 | 33 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 33 | 2 | 25 | 16.5 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
854078ee57fa5cfd262900e7b00db6ee5f98aeb7 | 111 | py | Python | fathom/autoenc/__init__.py | Aetf/fathom | 1f0dafa9fe3b7988708522d93ecda7f282cb2090 | [
"Apache-2.0"
] | 1 | 2021-06-30T04:59:22.000Z | 2021-06-30T04:59:22.000Z | fathom/autoenc/__init__.py | Aetf/fathom | 1f0dafa9fe3b7988708522d93ecda7f282cb2090 | [
"Apache-2.0"
] | null | null | null | fathom/autoenc/__init__.py | Aetf/fathom | 1f0dafa9fe3b7988708522d93ecda7f282cb2090 | [
"Apache-2.0"
] | null | null | null | from __future__ import absolute_import, print_function, division
from .variational import Autoenc, AutoencFwd
| 27.75 | 64 | 0.855856 | 13 | 111 | 6.846154 | 0.769231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 111 | 3 | 65 | 37 | 0.89899 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0.5 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
85644509dfa376fb58ddf2270cb0c55214fbb546 | 77 | py | Python | tokopedia/shop/api.py | hexatester/tokopedia | 20e46c3ec2c70de6b24460634b7c185ffdb15691 | [
"MIT"
] | 5 | 2021-07-01T05:09:20.000Z | 2022-03-06T10:53:07.000Z | tokopedia/shop/api.py | hexatester/tokopedia | 20e46c3ec2c70de6b24460634b7c185ffdb15691 | [
"MIT"
] | null | null | null | tokopedia/shop/api.py | hexatester/tokopedia | 20e46c3ec2c70de6b24460634b7c185ffdb15691 | [
"MIT"
] | 1 | 2022-02-14T01:20:34.000Z | 2022-02-14T01:20:34.000Z | from tokopedia import BaseTokopedia
class ShopApi(BaseTokopedia):
pass
| 12.833333 | 35 | 0.792208 | 8 | 77 | 7.625 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.168831 | 77 | 5 | 36 | 15.4 | 0.953125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
859cd25dcfde675efc2771f6cb10f8d763aafc39 | 213 | py | Python | rkn/exceptions.py | OlefirenkoK/monitoring | ad5c4f1445083d04c967acaedfad025ecc6573b7 | [
"Apache-2.0"
] | null | null | null | rkn/exceptions.py | OlefirenkoK/monitoring | ad5c4f1445083d04c967acaedfad025ecc6573b7 | [
"Apache-2.0"
] | null | null | null | rkn/exceptions.py | OlefirenkoK/monitoring | ad5c4f1445083d04c967acaedfad025ecc6573b7 | [
"Apache-2.0"
] | null | null | null | class RepositoryAlreadyExistsError(Exception):
"""Raise if repository already exists for given path"""
class RepositoryIsNotExistsError(Exception):
"""Raise if repository is not exists for given path"""
| 30.428571 | 59 | 0.769953 | 23 | 213 | 7.130435 | 0.608696 | 0.170732 | 0.195122 | 0.317073 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.14554 | 213 | 6 | 60 | 35.5 | 0.901099 | 0.460094 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
85acc246ba71d0cc63786e9940b1356156aea6f0 | 34 | py | Python | fetcher/__init__.py | Avalanche-FR-community/apr-fetcher | 25b12e8fe3da4a7ee678017b80dabc07990144f8 | [
"MIT"
] | null | null | null | fetcher/__init__.py | Avalanche-FR-community/apr-fetcher | 25b12e8fe3da4a7ee678017b80dabc07990144f8 | [
"MIT"
] | null | null | null | fetcher/__init__.py | Avalanche-FR-community/apr-fetcher | 25b12e8fe3da4a7ee678017b80dabc07990144f8 | [
"MIT"
] | null | null | null | from .apr_fetchers import fetchers | 34 | 34 | 0.882353 | 5 | 34 | 5.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 34 | 1 | 34 | 34 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a415f0205837f34fa8d22ade1cd2c94bebd471b6 | 1,623 | py | Python | nexpose_rest/nexpose_scan_engine.py | Patralos/nexpose-rest | c03431a408afd1528b0ca5a00859467574953ea0 | [
"MIT"
] | null | null | null | nexpose_rest/nexpose_scan_engine.py | Patralos/nexpose-rest | c03431a408afd1528b0ca5a00859467574953ea0 | [
"MIT"
] | null | null | null | nexpose_rest/nexpose_scan_engine.py | Patralos/nexpose-rest | c03431a408afd1528b0ca5a00859467574953ea0 | [
"MIT"
] | null | null | null | from nexpose_rest.nexpose import _GET
def getScanEngines(config):
getParameters=[]
code, data = _GET('/api/3/scan_engines', config, getParameters=getParameters)
return data
def getScanEnginePoolSites(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engine_pools/' + str(id) + '/sites', config, getParameters=getParameters)
return data
def getEnginePool(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engine_pools/' + str(id) + '', config, getParameters=getParameters)
return data
def getAssignedEnginePools(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engines/' + str(id) + '/scan_engine_pools', config, getParameters=getParameters)
return data
def getScanEngineScans(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engines/' + str(id) + '/scans', config, getParameters=getParameters)
return data
def getScanEngine(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engines/' + str(id) + '', config, getParameters=getParameters)
return data
def getScanEnginePoolScanEngines(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engine_pools/' + str(id) + '/engines', config, getParameters=getParameters)
return data
def getScanEngineSites(config, id):
getParameters=[]
code, data = _GET('/api/3/scan_engines/' + str(id) + '/sites', config, getParameters=getParameters)
return data
def getScanEnginePools(config):
getParameters=[]
code, data = _GET('/api/3/scan_engine_pools', config, getParameters=getParameters)
return data
| 28.982143 | 115 | 0.699322 | 182 | 1,623 | 6.093407 | 0.164835 | 0.188458 | 0.170424 | 0.19477 | 0.815149 | 0.815149 | 0.771867 | 0.6844 | 0.467989 | 0.339044 | 0 | 0.006623 | 0.162662 | 1,623 | 55 | 116 | 29.509091 | 0.809419 | 0 | 0 | 0.486486 | 0 | 0 | 0.149107 | 0.060998 | 0 | 0 | 0 | 0 | 0 | 1 | 0.243243 | false | 0 | 0.027027 | 0 | 0.513514 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
a433eac1e79a11c191297555fe1050a4a98967eb | 175 | py | Python | src/pynwb/io/icephys.py | VBaratham/pynwb | a9429c93f29763b9ebe9022b099afcffbc6be493 | [
"BSD-3-Clause-LBNL"
] | 1 | 2021-04-13T20:47:36.000Z | 2021-04-13T20:47:36.000Z | src/pynwb/io/icephys.py | VBaratham/pynwb | a9429c93f29763b9ebe9022b099afcffbc6be493 | [
"BSD-3-Clause-LBNL"
] | null | null | null | src/pynwb/io/icephys.py | VBaratham/pynwb | a9429c93f29763b9ebe9022b099afcffbc6be493 | [
"BSD-3-Clause-LBNL"
] | null | null | null | from .. import register_map
from pynwb.icephys import SweepTable
from .core import DynamicTableMap
@register_map(SweepTable)
class SweepTableMap(DynamicTableMap):
pass
| 17.5 | 37 | 0.811429 | 20 | 175 | 7 | 0.6 | 0.157143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.131429 | 175 | 9 | 38 | 19.444444 | 0.921053 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.166667 | 0.5 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
a49d7053bfb95389ebe87a78c03881030b051957 | 107 | py | Python | lib/__init__.py | RRyan2447/discord-ticket-bot | c90ae89f634f958d2b1868debfe81b25407f2643 | [
"MIT"
] | null | null | null | lib/__init__.py | RRyan2447/discord-ticket-bot | c90ae89f634f958d2b1868debfe81b25407f2643 | [
"MIT"
] | null | null | null | lib/__init__.py | RRyan2447/discord-ticket-bot | c90ae89f634f958d2b1868debfe81b25407f2643 | [
"MIT"
] | null | null | null | from .lib import time_now, load_JSON, check_integer, check_JSON_files, check_version, setup_change, config
| 53.5 | 106 | 0.841121 | 17 | 107 | 4.882353 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093458 | 107 | 1 | 107 | 107 | 0.85567 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a4a72c08ef66d9d1450a31edee92b168e79b2175 | 40,024 | py | Python | pymatex/grammar/MatexLexer.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | 1 | 2019-03-05T09:45:04.000Z | 2019-03-05T09:45:04.000Z | pymatex/grammar/MatexLexer.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | null | null | null | pymatex/grammar/MatexLexer.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | null | null | null | # Generated from .\pymatex\grammar\MatexLexer.g4 by ANTLR 4.7.2
from antlr4 import *
from io import StringIO
from typing.io import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2K")
buf.write("\u03df\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7")
buf.write("\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r")
buf.write("\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22\4\23")
buf.write("\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4\30\t\30")
buf.write("\4\31\t\31\4\32\t\32\4\33\t\33\4\34\t\34\4\35\t\35\4\36")
buf.write("\t\36\4\37\t\37\4 \t \4!\t!\4\"\t\"\4#\t#\4$\t$\4%\t%")
buf.write("\4&\t&\4\'\t\'\4(\t(\4)\t)\4*\t*\4+\t+\4,\t,\4-\t-\4.")
buf.write("\t.\4/\t/\4\60\t\60\4\61\t\61\4\62\t\62\4\63\t\63\4\64")
buf.write("\t\64\4\65\t\65\4\66\t\66\4\67\t\67\48\t8\49\t9\4:\t:")
buf.write("\4;\t;\4<\t<\4=\t=\4>\t>\4?\t?\4@\t@\4A\tA\4B\tB\4C\t")
buf.write("C\4D\tD\4E\tE\4F\tF\4G\tG\4H\tH\4I\tI\4J\tJ\4K\tK\4L\t")
buf.write("L\4M\tM\4N\tN\4O\tO\4P\tP\4Q\tQ\4R\tR\4S\tS\4T\tT\4U\t")
buf.write("U\4V\tV\4W\tW\4X\tX\4Y\tY\4Z\tZ\4[\t[\4\\\t\\\4]\t]\4")
buf.write("^\t^\4_\t_\4`\t`\4a\ta\4b\tb\4c\tc\4d\td\4e\te\4f\tf\4")
buf.write("g\tg\4h\th\4i\ti\4j\tj\4k\tk\3\2\3\2\3\3\3\3\3\4\3\4\3")
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buf.write("\u03cc\u03db\3\b\2\2")
return buf.getvalue()
class MatexLexer(Lexer):
atn = ATNDeserializer().deserialize(serializedATN())
decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ]
PLUS = 1
MINUS = 2
MUL = 3
DIV = 4
L_PAREN = 5
R_PAREN = 6
L_BRACE = 7
R_BRACE = 8
L_BRACKET = 9
R_BRACKET = 10
COMMA = 11
DOT = 12
SEMICOLON = 13
BAR = 14
UNDERSCORE = 15
CARET = 16
COLON = 17
FUNC_LIM = 18
LIM_APPROACH_SYM = 19
FUNC_FRAC = 20
FUNC_INT = 21
FUNC_SUM = 22
FUNC_PROD = 23
FUNC_LOG = 24
FUNC_LN = 25
FUNC_SIN = 26
FUNC_COS = 27
FUNC_TAN = 28
FUNC_CSC = 29
FUNC_SEC = 30
FUNC_COT = 31
FUNC_ARCSIN = 32
FUNC_ARCCOS = 33
FUNC_ARCTAN = 34
FUNC_ARCCSC = 35
FUNC_ARCSEC = 36
FUNC_ARCCOT = 37
FUNC_SINH = 38
FUNC_COSH = 39
FUNC_TANH = 40
FUNC_ARCSINH = 41
FUNC_ARCCOSH = 42
FUNC_ARCTANH = 43
FUNC_ECOS = 44
FUNC_ESIN = 45
FUNC_EDELTAAMPLITUDE = 46
FUNC_ARCECOS = 47
FUNC_ARCESIN = 48
FUNC_ARCEDELTAAMPLITUDE = 49
FUNC_SQRT = 50
FUNC_BINOMIAL = 51
CMD_TIMES = 52
CMD_CDOT = 53
CMD_DIV = 54
CMD_FRAC = 55
NUMBER = 56
DERIVATIVE = 57
VARIABLE = 58
MIXNUMBER = 59
WORD = 60
INFINITY = 61
EQ = 62
INEQUALITIES = 63
BANG = 64
GREEKLETTER = 65
LETTERFUNCTIONBRACE = 66
LETTERFUNCTIONPAREN = 67
GREEKFUNCTIONBRACE = 68
GREEKFUNCTIONPAREN = 69
SET_IN = 70
SET_LIKE = 71
SET_DIFFERENCE = 72
WS = 73
channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ]
modeNames = [ "DEFAULT_MODE" ]
literalNames = [ "<INVALID>",
"'+'", "'-'", "'*'", "'/'", "'('", "')'", "'{'", "'}'", "'['",
"']'", "','", "'.'", "';'", "'|'", "'_'", "'^'", "':'", "'\\lim'",
"'K'", "'\\int'", "'\\sum'", "'\\prod'", "'\\times'", "'\\cdot'",
"'\\div'", "'\\frac'", "'\\infty'", "'='", "'!'", "'\\in'" ]
symbolicNames = [ "<INVALID>",
"PLUS", "MINUS", "MUL", "DIV", "L_PAREN", "R_PAREN", "L_BRACE",
"R_BRACE", "L_BRACKET", "R_BRACKET", "COMMA", "DOT", "SEMICOLON",
"BAR", "UNDERSCORE", "CARET", "COLON", "FUNC_LIM", "LIM_APPROACH_SYM",
"FUNC_FRAC", "FUNC_INT", "FUNC_SUM", "FUNC_PROD", "FUNC_LOG",
"FUNC_LN", "FUNC_SIN", "FUNC_COS", "FUNC_TAN", "FUNC_CSC", "FUNC_SEC",
"FUNC_COT", "FUNC_ARCSIN", "FUNC_ARCCOS", "FUNC_ARCTAN", "FUNC_ARCCSC",
"FUNC_ARCSEC", "FUNC_ARCCOT", "FUNC_SINH", "FUNC_COSH", "FUNC_TANH",
"FUNC_ARCSINH", "FUNC_ARCCOSH", "FUNC_ARCTANH", "FUNC_ECOS",
"FUNC_ESIN", "FUNC_EDELTAAMPLITUDE", "FUNC_ARCECOS", "FUNC_ARCESIN",
"FUNC_ARCEDELTAAMPLITUDE", "FUNC_SQRT", "FUNC_BINOMIAL", "CMD_TIMES",
"CMD_CDOT", "CMD_DIV", "CMD_FRAC", "NUMBER", "DERIVATIVE", "VARIABLE",
"MIXNUMBER", "WORD", "INFINITY", "EQ", "INEQUALITIES", "BANG",
"GREEKLETTER", "LETTERFUNCTIONBRACE", "LETTERFUNCTIONPAREN",
"GREEKFUNCTIONBRACE", "GREEKFUNCTIONPAREN", "SET_IN", "SET_LIKE",
"SET_DIFFERENCE", "WS" ]
ruleNames = [ "PLUS", "MINUS", "MUL", "DIV", "L_PAREN", "R_PAREN", "L_BRACE",
"R_BRACE", "L_BRACKET", "R_BRACKET", "COMMA", "DOT", "SEMICOLON",
"BAR", "UNDERSCORE", "CARET", "COLON", "BACKSLASH", "FUNC_LIM",
"LIM_APPROACH_SYM", "FUNC_FRAC", "FUNC_INT", "FUNC_SUM",
"FUNC_PROD", "ARC", "FUNC_LOG", "FUNC_LN", "FUNC_SIN",
"FUNC_COS", "FUNC_TAN", "FUNC_CSC", "FUNC_SEC", "FUNC_COT",
"FUNC_ARCSIN", "FUNC_ARCCOS", "FUNC_ARCTAN", "FUNC_ARCCSC",
"FUNC_ARCSEC", "FUNC_ARCCOT", "FUNC_SINH", "FUNC_COSH",
"FUNC_TANH", "FUNC_ARCSINH", "FUNC_ARCCOSH", "FUNC_ARCTANH",
"FUNC_ECOS", "FUNC_ESIN", "FUNC_EDELTAAMPLITUDE", "FUNC_ARCECOS",
"FUNC_ARCESIN", "FUNC_ARCEDELTAAMPLITUDE", "FUNC_SQRT",
"FUNC_BINOMIAL", "CMD_TIMES", "CMD_CDOT", "CMD_DIV", "CMD_FRAC",
"LETTER", "DIGIT", "NUMBER", "DERIVATIVE", "VARIABLE",
"MIXNUMBER", "WORD", "INFINITY", "EQ", "LT", "LTE", "GT",
"GTE", "INEQUALITIES", "BANG", "ALPHA", "BETA", "GAMMA",
"DELTA", "EPSILON", "ZETA", "ETA", "THETA", "IOTA", "KAPPA",
"LAMBDA", "MU", "NU", "XI", "OMICRON", "PI", "RHO", "SIGMA",
"TAU", "UPSILON", "PHI", "CHI", "PSI", "OMEGA", "GREEKLETTER",
"INDICED", "LETTERFUNCTIONBRACE", "LETTERFUNCTIONPAREN",
"GREEKFUNCTIONBRACE", "GREEKFUNCTIONPAREN", "SET_IN",
"SET_LIKE", "SET_DIFFERENCE", "WS" ]
grammarFileName = "MatexLexer.g4"
def __init__(self, input=None, output:TextIO = sys.stdout):
super().__init__(input, output)
self.checkVersion("4.7.2")
self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache())
self._actions = None
self._predicates = None
| 66.264901 | 103 | 0.586523 | 8,770 | 40,024 | 2.656556 | 0.15667 | 0.124646 | 0.064898 | 0.075715 | 0.244399 | 0.177998 | 0.118165 | 0.096532 | 0.090866 | 0.084127 | 0 | 0.353047 | 0.140291 | 40,024 | 603 | 104 | 66.374793 | 0.324043 | 0.001524 | 0 | 0.010256 | 1 | 0.203419 | 0.657924 | 0.613429 | 0 | 0 | 0 | 0 | 0 | 1 | 0.003419 | false | 0 | 0.006838 | 0 | 0.152137 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f188bae93b95717644a9b150a2c4cda521a63ce7 | 244,363 | py | Python | directdm/wilson_coefficients.py | DirectDM/directdm-py | 9e940703bc4e5b2266ce2c93c27abee755c0cbaf | [
"MIT"
] | 5 | 2017-09-09T16:22:00.000Z | 2021-11-17T07:31:11.000Z | directdm/wilson_coefficients.py | DirectDM/directdm-py | 9e940703bc4e5b2266ce2c93c27abee755c0cbaf | [
"MIT"
] | 2 | 2018-04-17T16:43:27.000Z | 2018-04-19T12:34:54.000Z | directdm/wilson_coefficients.py | DirectDM/directdm-py | 9e940703bc4e5b2266ce2c93c27abee755c0cbaf | [
"MIT"
] | 2 | 2018-05-10T17:39:57.000Z | 2018-09-19T16:40:07.000Z | #!/usr/bin/env python3
import sys
import numpy as np
import scipy.integrate as spint
import warnings
import os.path
from directdm.run import adm
from directdm.run import rge
from directdm.num.num_input import Num_input
from directdm.match.dim4_gauge_contribution import Higgspenguin
from directdm.num.single_nucleon_form_factors import *
#----------------------------------------------#
# convert dictionaries to lists and vice versa #
#----------------------------------------------#
def dict_to_list(dictionary, order_list):
""" Create a list from dictionary, according to ordering in order_list """
#assert sorted(order_list) == sorted(dictionary.keys())
wc_list = []
for wc_name in order_list:
wc_list.append(dictionary[wc_name])
return wc_list
def list_to_dict(wc_list, order_list):
""" Create a dictionary from a list wc_list, using keys in order_list """
#assert len(order_list) == len(wc_list)
wc_dict = {}
for wc_ind in range(len(order_list)):
wc_dict[order_list[wc_ind]] = wc_list[wc_ind]
return wc_dict
#---------------------------------------------------#
# Classes for Wilson coefficients at various scales #
#---------------------------------------------------#
class WC_3flavor(object):
def __init__(self, coeff_dict, DM_type, input_dict):
""" Class for Wilson coefficients in 3 flavor QCD x QED plus DM.
The first argument should be a dictionary for the initial conditions
of the 2 + 24 + 4 + 36 + 4 + 48 + 6 + 1 + 12 = 137
dimension-five to dimension-eight three-flavor-QCD Wilson coefficients of the form
{'C51' : value, 'C52' : value, ...}.
An arbitrary number of them can be given; the default values are zero.
The second argument is the DM type; it can take the following values:
"D" (Dirac fermion)
"M" (Majorana fermion)
"C" (Complex scalar)
"R" (Real scalar)
The possible names are (with an hopefully obvious notation):
Dirac fermion: 'C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723e', 'C723mu', 'C723tau',
'C725',
'C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s'
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s'
Majorana fermion: 'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C82u', 'C82d', 'C82s', 'C84u', 'C84d', 'C84s'
Complex Scalar: 'C61u', 'C61d', 'C61s', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C65', 'C66', 'C67', 'C68'
'C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610'
Real Scalar: 'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C65', 'C66', 'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610'
(the notation corresponds to the numbering in 1707.06998, 1801.04240).
The Wilson coefficients should be specified in the MS-bar scheme at 2 GeV.
For completeness, the default initial conditions at MZ for the corresponding
leptonic operator Wilson coefficients are defined as the SM values
(note that these operators have vanishing QCD anomalous dimension):
'D63eu', 'D63muu', 'D63tauu', 'D63ed', 'D63mud', 'D63taud', 'D63es', 'D63mus', 'D63taus',
'D62ue', 'D62umu', 'D62utau', 'D62de', 'D62dmu', 'D62dtau', 'D62se', 'D62smu', 'D62stau'
The third argument is a dictionary with all input parameters.
The class has three methods:
run
---
Run the Wilson coefficients from mu = 2 GeV to mu_low [GeV; default 2 GeV], with 3 active quark flavors
cNR
---
Calculate the cNR coefficients as defined in 1308.6288
The class has two mandatory arguments: The DM mass in GeV and the momentum transfer in GeV
The effects of double insertion [arxiv:1801.04240] are included also for leptons;
for couplings to electrons and muons, there are other contributions that are neglected.
If the relevant initial conditions are set to non-zero values, a user warning is issued
upon creation of the class instance.
write_mma
---------
Write an output file that can be loaded into mathematica,
to be used in the DMFormFactor package [1308.6288].
"""
self.DM_type = DM_type
self.sm_lepton_name_list = ['D63eu', 'D63muu', 'D63tauu', 'D63ed', 'D63mud',
'D63taud', 'D63es', 'D63mus', 'D63taus',
'D62ue', 'D62umu', 'D62utau', 'D62de', 'D62dmu',
'D62dtau', 'D62se', 'D62smu', 'D62stau']
if self.DM_type == "D":
self.wc_name_list = ['C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61e', 'C61mu',
'C61tau', 'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau', 'C64u',
'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s',
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s']
if self.DM_type == "M":
self.wc_name_list = ['C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C82u', 'C82d', 'C82s', 'C84u', 'C84d', 'C84s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = np.r_[np.s_[0:8], np.s_[14:20], np.s_[54:66], np.s_[94:118]]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
if self.DM_type == "C":
self.wc_name_list = ['C61u', 'C61d', 'C61s', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = np.r_[np.s_[0:2], np.s_[8:14], np.s_[20:26], np.s_[27:28], np.s_[29:30],\
np.s_[36:42], np.s_[48:66], np.s_[67:68], np.s_[69:70], np.s_[70:118]]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
if self.DM_type == "R":
self.wc_name_list = ['C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = []
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = np.r_[np.s_[0:26], np.s_[27:28], np.s_[29:30], np.s_[36:42],\
np.s_[48:66], np.s_[67:68], np.s_[69:70], np.s_[70:118]]
self.coeff_dict = {}
# Issue a user warning if a key is not defined:
for wc_name in coeff_dict.keys():
if wc_name in self.wc_name_list:
pass
elif wc_name in self.wc8_name_list:
pass
else:
warnings.warn('The key ' + wc_name + ' is not a valid key. Typo?')
# Create the dictionary.
for wc_name in self.wc_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
for wc_name in self.wc8_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
# The dictionary of input parameters
self.ip = input_dict
# The default values for the SM lepton operators:
# Input for lepton contribution
sw = np.sqrt(self.ip['sw2_MSbar'])
cw = np.sqrt(1-sw**2)
vd = (-1/2 - 2*sw**2*(-1/3))/(2*sw*cw)
vu = (1/2 - 2*sw**2*(2/3))/(2*sw*cw)
ad = -(-1/2)/(2*sw*cw)
au = -(1/2)/(2*sw*cw)
vl = (-1/2 - 2*sw**2*(-1))/(2*sw*cw)
al = -(-1/2)/(2*sw*cw)
self.coeff_dict['D62ue'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62umu'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62utau'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62de'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62dmu'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62dtau'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62se'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62smu'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62stau'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D63eu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63muu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63tauu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63ed'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63mud'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63taud'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63es'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63mus'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63taus'] = al*vd * 4*sw**2*cw**2
for wc_name in self.sm_lepton_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
pass
# Issue a user warning if certain electron / muon Wilson coefficients are non-zero:
for wc_name in self.coeff_dict.keys():
if DM_type == "D":
for wc_name in ['C63e', 'C63mu', 'C64e', 'C64mu']:
if self.coeff_dict[wc_name] != 0.:
warnings.warn('The RG result for ' + wc_name + ' is incomplete, expect large uncertainties!')
else:
pass
elif DM_type == "M":
for wc_name in ['C64e', 'C64mu']:
if self.coeff_dict[wc_name] != 0.:
warnings.warn('The RG result for ' + wc_name + ' is incomplete, expect large uncertainties!')
else:
pass
elif DM_type == "C":
for wc_name in ['C62e', 'C62mu']:
if self.coeff_dict[wc_name] != 0.:
warnings.warn('The RG result for ' + wc_name + ' is incomplete, expect large uncertainties!')
else:
pass
elif DM_type == "R":
pass
# Create the np.array of coefficients:
self.coeff_list_dm_dim5_dim6_dim7 = np.array(dict_to_list(self.coeff_dict, self.wc_name_list))
self.coeff_list_dm_dim8 = np.array(dict_to_list(self.coeff_dict, self.wc8_name_list))
self.coeff_list_sm_lepton_dim6 = np.array(dict_to_list(self.coeff_dict, self.sm_lepton_name_list))
#---------------------------#
# The anomalous dimensions: #
#---------------------------#
if self.DM_type == "D":
self.gamma_QED = adm.ADM_QED(3)
self.gamma_QED2 = adm.ADM_QED2(3)
self.gamma_QCD = adm.ADM_QCD(3)
self.gamma_QCD2 = adm.ADM_QCD2(3)
self.gamma_QCD_dim8 = adm.ADM_QCD_dim8(3)
if self.DM_type == "M":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(3), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(3), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(3), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
if self.DM_type == "C":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(3), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(3), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(3), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
if self.DM_type == "R":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(3), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(3), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(3), del_ind_list, 1), del_ind_list, 2)
def run(self, mu_low=None):
""" Running of 3-flavor Wilson coefficients
Calculate the running from 2 GeV to mu_low [GeV; default 2 GeV] in the three-flavor theory.
Return a dictionary of Wilson coefficients for the three-flavor Lagrangian
at scale mu_low (this is the default).
"""
if mu_low is None:
mu_low=2
#-------------#
# The running #
#-------------#
alpha_at_mu = 1/self.ip['amtauinv']
as31 = rge.AlphaS(self.ip['asMZ'], self.ip['Mz'])
as31_high = as31.run({'mbmb': self.ip['mb_at_mb'], 'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'], 'muc': self.ip['mc_at_mc']}, 2, 3, 1)
as31_low = as31.run({'mbmb': self.ip['mb_at_mb'], 'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'], 'muc': self.ip['mc_at_mc']}, mu_low, 3, 1)
evolve1 = rge.RGE(self.gamma_QCD, 3)
evolve2 = rge.RGE(self.gamma_QCD2, 3)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
evolve8 = rge.RGE([self.gamma_QCD_dim8], 3)
else:
pass
C_at_mu_QCD = np.dot(evolve2.U0_as2(as31_high, as31_low),\
np.dot(evolve1.U0(as31_high, as31_low),\
self.coeff_list_dm_dim5_dim6_dim7))
C_at_mu_QED = np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED)\
* np.log(mu_low/2) * alpha_at_mu/(4*np.pi)\
+ np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED2)\
* np.log(mu_low/2) * (alpha_at_mu/(4*np.pi))**2
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
C_dim8_at_mu = np.dot(evolve8.U0(as31_high, as31_low), self.coeff_list_dm_dim8)
else:
pass
# Revert back to dictionary
dict_coeff_mu = list_to_dict(C_at_mu_QCD + C_at_mu_QED, self.wc_name_list)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
dict_dm_dim8 = list_to_dict(C_dim8_at_mu, self.wc8_name_list)
dict_coeff_mu.update(dict_dm_dim8)
dict_sm_lepton_dim6 = list_to_dict(self.coeff_list_sm_lepton_dim6, self.sm_lepton_name_list)
dict_coeff_mu.update(dict_sm_lepton_dim6)
else:
pass
return dict_coeff_mu
def _my_cNR(self, DM_mass, RGE=None, NLO=None, DOUBLE_WEAK=None):
"""Calculate the coefficients of the NR operators, with momentum dependence factored out.
DM_mass is the DM mass in GeV
RGE is a flag to turn RGE running on (True) or off (False). (Default True)
If NLO is set to True, the coherently enhanced NLO terms for Q_9^(7) are added. (Default False)
If DOUBLE_WEAK is set to False, the weak mixing below the weak scale is set to zero. (Default True)
Returns a dictionary of coefficients for the NR Lagrangian,
as in 1308.6288, plus coefficients c13 -- c23, c100 for "spurious" long-distance operators
The possible names are
['cNR1p', 'cNR1n', 'cNR2p', 'cNR2n', 'cNR3p', 'cNR3n', 'cNR4p', 'cNR4n', 'cNR5p', 'cNR5n',
'cNR6p', 'cNR6n', 'cNR7p', 'cNR7n', 'cNR8p', 'cNR8n', 'cNR9p', 'cNR9n', 'cNR10p', 'cNR10n',
'cNR11p', 'cNR11n', 'cNR12p', 'cNR12n', 'cNR13p', 'cNR13n', 'cNR14p', 'cNR14n', 'cNR15p', 'cNR15n',
'cNR16p', 'cNR16n', 'cNR17p', 'cNR17n', 'cNR18p', 'cNR18n', 'cNR19p', 'cNR19n', 'cNR20p', 'cNR20n',
'cNR21p', 'cNR21n', 'cNR22p', 'cNR22n', 'cNR23p', 'cNR23n', 'cNR100p', 'cNR100n', 'cNR104p', 'cNR104n']
"""
if RGE is None:
RGE = True
if NLO is None:
NLO = False
if DOUBLE_WEAK is None:
DOUBLE_WEAK = True
if DOUBLE_WEAK:
wmws = 1.
else:
wmws = 0.
### Input parameters ####
mpi = self.ip['mpi0']
mp = self.ip['mproton']
mn = self.ip['mneutron']
mN = (mp+mn)/2
alpha = 1/self.ip['alowinv']
GF = self.ip['GF']
as_2GeV = rge.AlphaS(self.ip['asMZ'],\
self.ip['Mz']).run({'mbmb': self.ip['mb_at_mb'], 'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'], 'muc': self.ip['mc_at_mc']}, 2, 3, 1)
gs2_2GeV = 4*np.pi*as_2GeV
# Quark masses at 2GeV
mu = self.ip['mu_at_2GeV']
md = self.ip['md_at_2GeV']
ms = self.ip['ms_at_2GeV']
mtilde = 1/(1/mu + 1/md + 1/ms)
# Lepton masses
me = self.ip['me']
mmu = self.ip['mmu']
mtau = self.ip['mtau']
# Z boson mass
MZ = self.ip['Mz']
### Numerical constants
mproton = self.ip['mproton']
mneutron = self.ip['mneutron']
F1up = F1('u', 'p', self.ip).value_zero_mom()
F1dp = F1('d', 'p', self.ip).value_zero_mom()
F1sp = F1('s', 'p', self.ip).value_zero_mom()
F1un = F1('u', 'n', self.ip).value_zero_mom()
F1dn = F1('d', 'n', self.ip).value_zero_mom()
F1sn = F1('s', 'n', self.ip).value_zero_mom()
F1spslope = F1('s', 'p', self.ip).first_deriv_zero_mom()
F1snslope = F1('s', 'n', self.ip).first_deriv_zero_mom()
F2up = F2('u', 'p', self.ip).value_zero_mom()
F2dp = F2('d', 'p', self.ip).value_zero_mom()
F2sp = F2('s', 'p', self.ip).value_zero_mom()
F2un = F2('u', 'n', self.ip).value_zero_mom()
F2dn = F2('d', 'n', self.ip).value_zero_mom()
F2sn = F2('s', 'n', self.ip).value_zero_mom()
FAup = FA('u', 'p', self.ip).value_zero_mom()
FAdp = FA('d', 'p', self.ip).value_zero_mom()
FAsp = FA('s', 'p', self.ip).value_zero_mom()
FAun = FA('u', 'n', self.ip).value_zero_mom()
FAdn = FA('d', 'n', self.ip).value_zero_mom()
FAsn = FA('s', 'n', self.ip).value_zero_mom()
FPpup_pion = FPprimed('u', 'p', self.ip).value_pion_pole()
FPpdp_pion = FPprimed('d', 'p', self.ip).value_pion_pole()
FPpsp_pion = FPprimed('s', 'p', self.ip).value_pion_pole()
FPpun_pion = FPprimed('u', 'n', self.ip).value_pion_pole()
FPpdn_pion = FPprimed('d', 'n', self.ip).value_pion_pole()
FPpsn_pion = FPprimed('s', 'n', self.ip).value_pion_pole()
FPpup_eta = FPprimed('u', 'p', self.ip).value_eta_pole()
FPpdp_eta = FPprimed('d', 'p', self.ip).value_eta_pole()
FPpsp_eta = FPprimed('s', 'p', self.ip).value_eta_pole()
FPpun_eta = FPprimed('u', 'n', self.ip).value_eta_pole()
FPpdn_eta = FPprimed('d', 'n', self.ip).value_eta_pole()
FPpsn_eta = FPprimed('s', 'n', self.ip).value_eta_pole()
FSup = FS('u', 'p', self.ip).value_zero_mom()
FSdp = FS('d', 'p', self.ip).value_zero_mom()
FSsp = FS('s', 'p', self.ip).value_zero_mom()
FSun = FS('u', 'n', self.ip).value_zero_mom()
FSdn = FS('d', 'n', self.ip).value_zero_mom()
FSsn = FS('s', 'n', self.ip).value_zero_mom()
FPup_pion = FP('u', 'p', self.ip).value_pion_pole()
FPdp_pion = FP('d', 'p', self.ip).value_pion_pole()
FPsp_pion = FP('s', 'p', self.ip).value_pion_pole()
FPun_pion = FP('u', 'n', self.ip).value_pion_pole()
FPdn_pion = FP('d', 'n', self.ip).value_pion_pole()
FPsn_pion = FP('s', 'n', self.ip).value_pion_pole()
FPup_eta = FP('u', 'p', self.ip).value_eta_pole()
FPdp_eta = FP('d', 'p', self.ip).value_eta_pole()
FPsp_eta = FP('s', 'p', self.ip).value_eta_pole()
FPun_eta = FP('u', 'n', self.ip).value_eta_pole()
FPdn_eta = FP('d', 'n', self.ip).value_eta_pole()
FPsn_eta = FP('s', 'n', self.ip).value_eta_pole()
FGp = FG('p', self.ip).value_zero_mom()
FGn = FG('n', self.ip).value_zero_mom()
FGtildep = FGtilde('p', self.ip).value_zero_mom()
FGtilden = FGtilde('n', self.ip).value_zero_mom()
FGtildep_pion = FGtilde('p', self.ip).value_pion_pole()
FGtilden_pion = FGtilde('n', self.ip).value_pion_pole()
FGtildep_eta = FGtilde('p', self.ip).value_eta_pole()
FGtilden_eta = FGtilde('n', self.ip).value_eta_pole()
FT0up = FT0('u', 'p', self.ip).value_zero_mom()
FT0dp = FT0('d', 'p', self.ip).value_zero_mom()
FT0sp = FT0('s', 'p', self.ip).value_zero_mom()
FT0un = FT0('u', 'n', self.ip).value_zero_mom()
FT0dn = FT0('d', 'n', self.ip).value_zero_mom()
FT0sn = FT0('s', 'n', self.ip).value_zero_mom()
FT1up = FT1('u', 'p', self.ip).value_zero_mom()
FT1dp = FT1('d', 'p', self.ip).value_zero_mom()
FT1sp = FT1('s', 'p', self.ip).value_zero_mom()
FT1un = FT1('u', 'n', self.ip).value_zero_mom()
FT1dn = FT1('d', 'n', self.ip).value_zero_mom()
FT1sn = FT1('s', 'n', self.ip).value_zero_mom()
FTW2up = FTwist2('u', 'p', self.ip).value_zero_mom()
FTW2dp = FTwist2('d', 'p', self.ip).value_zero_mom()
FTW2sp = FTwist2('s', 'p', self.ip).value_zero_mom()
FTW2gp = FTwist2('g', 'p', self.ip).value_zero_mom()
FTW2un = FTwist2('u', 'n', self.ip).value_zero_mom()
FTW2dn = FTwist2('d', 'n', self.ip).value_zero_mom()
FTW2sn = FTwist2('s', 'n', self.ip).value_zero_mom()
FTW2gn = FTwist2('g', 'n', self.ip).value_zero_mom()
### The coefficients ###
#
# Note that all dependence on 1/q^2, 1/(m^2-q^2), q^2/(m^2-q^2) is taken care of
# by defining spurious operators.
#
# Therefore, we need to split some of the coefficients
# into the "pion part" etc. with the q-dependence factored out,
# and introduce a few spurious "long-distance" operators.
#
# The coefficients cNR1 -- cNR12 correspond to the operators in 1611.00368 and 1308.6288
#
# Therefore, we define O13 = O6/(mpi^2+q^2);
# O14 = O6/(meta^2+q^2);
# O15 = O6*q^2/(mpi^2+q^2);
# O16 = O6*q^2/(meta^2+q^2);
# O17 = O10/(mpi^2+q^2);
# O18 = O10/(meta^2+q^2);
# O19 = O10*q^2/(mpi^2+q^2);
# O20 = O10*q^2/(meta^2+q^2);
#
# For the dipole interactions, these are the ones that have c2p1, c1N2, c2p2 as coefficients.
# Therefore, we define O21 = O5/q^2;
# O22 = O6/q^2.
# O23 = O11/q^2.
#
# For the tensors, O4 * q^2 appears as a leading contribution.
# Therefore, we define O104 = O4 * q^2
#
# For the tensors, O1 * q^2 appears as a subleading contribution.
# Therefore, we define O100 = O1 * q^2
#
# q^2 is here always the spatial part!!!
#
if RGE:
c3mu_dict = self.run(2)
else:
c3mu_dict = self.coeff_dict
if self.DM_type == "D":
my_cNR_dict = {
'cNR1p' : F1up*(c3mu_dict['C61u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C81u'])\
+ F1dp*(c3mu_dict['C61d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C81d'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D63eu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D63ed'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D63muu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D63mud'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D63tauu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D63taud'])\
+ FGp*c3mu_dict['C71']\
+ FSup*c3mu_dict['C75u'] + FSdp*c3mu_dict['C75d'] + FSsp*c3mu_dict['C75s']\
- alpha/(2*np.pi*DM_mass)*c3mu_dict['C51']\
+ 2*DM_mass * (F1up*c3mu_dict['C715u'] + F1dp*c3mu_dict['C715d'] + F1sp*c3mu_dict['C715s'])\
+ FTW2up*c3mu_dict['C723u']\
+ FTW2dp*c3mu_dict['C723d']\
+ FTW2sp*c3mu_dict['C723s']\
+ FTW2gp*c3mu_dict['C725'],
'cNR2p' : 0,
'cNR3p' : F2sp * c3mu_dict['C61s'],
'cNR4p' : - 4*( FAup*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FAdp*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FAsp*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau']))\
- 2*alpha/np.pi * self.ip['mup']/mN * c3mu_dict['C51']\
+ 8*(FT0up*c3mu_dict['C79u'] + FT0dp*c3mu_dict['C79d'] + FT0sp*c3mu_dict['C79s']),
'cNR5p' : - 2*mN * (F1up*c3mu_dict['C719u'] + F1dp*c3mu_dict['C719d'] + F1sp*c3mu_dict['C719s']),
'cNR6p' : mN/DM_mass * FGtildep * c3mu_dict['C74']\
-2*mN*((F1up+F2up)*c3mu_dict['C719u']\
+ (F1dp+F2dp)*c3mu_dict['C719d']\
+ (F1sp+F2dp)*c3mu_dict['C719s'])\
+ mN/DM_mass * F2sp * c3mu_dict['C61s'],
'cNR7p' : - 2*( FAup*(c3mu_dict['C63u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C83u'])\
+ FAdp*(c3mu_dict['C63d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C83d'])\
+ FAsp*(c3mu_dict['C63s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C83s'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62ue'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62de'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62se'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62umu'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62dmu'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62smu'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62utau'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62dtau'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62stau']))\
- 4*DM_mass * (FAup*c3mu_dict['C717u'] + FAdp*c3mu_dict['C717d'] + FAsp*c3mu_dict['C717s']),
'cNR8p' : 2*( F1up*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ F1dp*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])),
'cNR9p' : 2*( (F1up+F2up)*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ (F1dp+F2dp)*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ (F1sp+F2sp)*(c3mu_dict['C62s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63es'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mus'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taus']))
+ 2*mN*( FAup*(c3mu_dict['C63u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C83u'])\
+ FAdp*(c3mu_dict['C63d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C83d'])\
+ FAsp*(c3mu_dict['C63s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C83s'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62ue'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62de'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62se'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62umu'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62dmu'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62smu'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62utau'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62dtau'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62stau']))/DM_mass\
- 4*mN * (FAup*c3mu_dict['C721u'] + FAdp*c3mu_dict['C721d'] + FAsp*c3mu_dict['C721s']),
'cNR10p' : FGtildep * c3mu_dict['C73']\
-2*mN/DM_mass * (FT0up*c3mu_dict['C710u']\
+ FT0dp*c3mu_dict['C710d']\
+ FT0sp*c3mu_dict['C710s']),
'cNR11p' : - mN/DM_mass * (FSup*c3mu_dict['C76u']\
+ FSdp*c3mu_dict['C76d']\
+ FSsp*c3mu_dict['C76s'])\
- mN/DM_mass * FGp * c3mu_dict['C72']\
+ 2*((FT0up-FT1up)*c3mu_dict['C710u']\
+ (FT0dp-FT1dp)*c3mu_dict['C710d']\
+ (FT0sp-FT1sp)*c3mu_dict['C710s'])\
- 2*mN * ( F1up*(c3mu_dict['C716u']+c3mu_dict['C720u'])\
+ F1dp*(c3mu_dict['C716d']+c3mu_dict['C720d'])\
+ F1sp*(c3mu_dict['C716s']+c3mu_dict['C720s'])),
'cNR12p' : -8*(FT0up*c3mu_dict['C710u'] + FT0dp*c3mu_dict['C710d'] + FT0sp*c3mu_dict['C710s']),
'cNR13p' : mN/DM_mass * (FPup_pion*c3mu_dict['C78u'] + FPdp_pion*c3mu_dict['C78d'])\
+ FPpup_pion*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdp_pion*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau']),
'cNR14p' : mN/DM_mass * (FPup_eta*c3mu_dict['C78u']\
+ FPdp_eta*c3mu_dict['C78d']\
+ FPsp_eta*c3mu_dict['C78s'])\
+ FPpup_eta*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdp_eta*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpsp_eta*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])\
+ 4*mN * ( FAup*(c3mu_dict['C718u']+c3mu_dict['C722u'])\
+ FAdp*(c3mu_dict['C718d']+c3mu_dict['C722d'])\
+ FAsp*(c3mu_dict['C718s']+c3mu_dict['C722s'])),
'cNR15p' : mN/DM_mass * FGtildep_pion * c3mu_dict['C74'],
'cNR16p' : mN/DM_mass * FGtildep_eta * c3mu_dict['C74'],
'cNR17p' : FPup_pion*c3mu_dict['C77u'] + FPdp_pion*c3mu_dict['C77d'],
'cNR18p' : FPup_eta*c3mu_dict['C77u'] + FPdp_eta*c3mu_dict['C77d'] + FPsp_eta*c3mu_dict['C77s'],
'cNR19p' : FGtildep_pion * c3mu_dict['C73'],
'cNR20p' : FGtildep_eta * c3mu_dict['C73'],
'cNR21p' : mN* (2*alpha/np.pi*c3mu_dict['C51']),
'cNR22p' : -mN**2* (- 2*alpha/np.pi * self.ip['mup']/mN * c3mu_dict['C51']),
'cNR23p' : mN* (2*alpha/np.pi*c3mu_dict['C52']),
'cNR100p' : ( F1up*c3mu_dict['C719u']\
+ F1dp*c3mu_dict['C719d']\
+ F1sp*c3mu_dict['C719s'])/(2*DM_mass)\
+ (F1spslope - F2sp / mN**2/4) * c3mu_dict['C61s'],
'cNR104p' : 2*((F1up+F2up)*c3mu_dict['C719u']\
+ (F1dp+F2dp)*c3mu_dict['C719d']\
+ (F1sp+F2dp)*c3mu_dict['C719s'])/mN\
- 1/mN/DM_mass * F2sp * c3mu_dict['C61s'],
'cNR1n' : F1un*(c3mu_dict['C61u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C81u'])\
+ F1dn*(c3mu_dict['C61d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C81d'])\
+ FGn*c3mu_dict['C71']\
+ FSun*c3mu_dict['C75u'] + FSdn*c3mu_dict['C75d'] + FSsn*c3mu_dict['C75s']\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D63eu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D63ed'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D63muu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D63mud'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D63tauu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D63taud'])\
+ 2*DM_mass * (F1un*c3mu_dict['C715u'] + F1dn*c3mu_dict['C715d'] + F1sn*c3mu_dict['C715s'])\
+ FTW2un*c3mu_dict['C723u']\
+ FTW2dn*c3mu_dict['C723d']\
+ FTW2sn*c3mu_dict['C723s']\
+ FTW2gn*c3mu_dict['C725'],
'cNR2n' : 0,
'cNR3n' : F2sn * c3mu_dict['C61s'],
'cNR4n' : - 4*( FAun*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FAdn*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FAsn*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau']))\
- 2*alpha/np.pi * self.ip['mun']/mN * c3mu_dict['C51']\
+ 8*(FT0un*c3mu_dict['C79u'] + FT0dn*c3mu_dict['C79d'] + FT0sn*c3mu_dict['C79s']),
'cNR5n' : - 2*mN * (F1un*c3mu_dict['C719u'] + F1dn*c3mu_dict['C719d'] + F1sn*c3mu_dict['C719s']),
'cNR6n' : mN/DM_mass * FGtilden * c3mu_dict['C74']\
-2*mN*((F1un+F2un)*c3mu_dict['C719u']\
+ (F1dn+F2dn)*c3mu_dict['C719d']\
+ (F1sn+F2dn)*c3mu_dict['C719s'])\
+ mN/DM_mass * F2sn * c3mu_dict['C61s'],
'cNR7n' : - 2*( FAun*(c3mu_dict['C63u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C83u'])\
+ FAdn*(c3mu_dict['C63d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C83d'])\
+ FAsn*(c3mu_dict['C63s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C83s'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62ue'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62de'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62se'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62umu'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62dmu'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62smu'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62utau'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62dtau'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62stau']))\
- 4*DM_mass * (FAun*c3mu_dict['C717u'] + FAdn*c3mu_dict['C717d']+ FAsn*c3mu_dict['C717s']),
'cNR8n' : 2*( F1un*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ F1dn*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])),
'cNR9n' : 2*( (F1un+F2un)*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ (F1dn+F2dn)*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ (F1sn+F2sn)*(c3mu_dict['C62s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ (F1un+F2un)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ (F1dn+F2dn)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ (F1sn+F2sn)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63es'])\
+ (F1un+F2un)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ (F1dn+F2dn)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ (F1sn+F2sn)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mus'])\
+ (F1un+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ (F1dn+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taus']))
+ 2*mN*( FAun*(c3mu_dict['C63u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C83u'])\
+ FAdn*(c3mu_dict['C63d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C83d'])\
+ FAsn*(c3mu_dict['C63s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C83s'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62ue'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62de'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C63e'] * c3mu_dict['D62se'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62umu'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62dmu'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C63mu'] * c3mu_dict['D62smu'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62utau'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62dtau'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C63tau'] * c3mu_dict['D62stau']))/DM_mass\
- 4*mN * (FAun*c3mu_dict['C721u']\
+ FAdn*c3mu_dict['C721d']\
+ FAsn*c3mu_dict['C721s']),
'cNR10n' : FGtilden * c3mu_dict['C73']\
-2*mN/DM_mass * (FT0un*c3mu_dict['C710u']\
+ FT0dn*c3mu_dict['C710d']\
+ FT0sn*c3mu_dict['C710s']),
'cNR11n' : - mN/DM_mass * (FSun*c3mu_dict['C76u']\
+ FSdn*c3mu_dict['C76d']\
+ FSsn*c3mu_dict['C76s'])\
- mN/DM_mass * FGn * c3mu_dict['C72']\
+ 2*((FT0un-FT1un)*c3mu_dict['C710u']\
+ (FT0dn-FT1dn)*c3mu_dict['C710d']\
+ (FT0sn-FT1sn)*c3mu_dict['C710s'])\
- 2*mN * ( F1un*(c3mu_dict['C716u']+c3mu_dict['C720u'])\
+ F1dn*(c3mu_dict['C716d']+c3mu_dict['C720d'])\
+ F1sn*(c3mu_dict['C716s']+c3mu_dict['C720s'])),
'cNR12n' : -8*(FT0un*c3mu_dict['C710u'] + FT0dn*c3mu_dict['C710d'] + FT0sn*c3mu_dict['C710s']),
'cNR13n' : mN/DM_mass * (FPun_pion*c3mu_dict['C78u'] + FPdn_pion*c3mu_dict['C78d'])\
+ FPpun_pion*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdn_pion*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau']),
'cNR14n' : mN/DM_mass * (FPun_eta*c3mu_dict['C78u']\
+ FPdn_eta*c3mu_dict['C78d']\
+ FPsn_eta*c3mu_dict['C78s'])\
+ FPpun_eta*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdn_eta*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpsn_eta*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])\
+ 4*mN * ( FAun*(c3mu_dict['C718u']+c3mu_dict['C722u'])\
+ FAdn*(c3mu_dict['C718d']+c3mu_dict['C722d'])\
+ FAsn*(c3mu_dict['C718s']+c3mu_dict['C722s'])),
'cNR15n' : mN/DM_mass * FGtilden_pion * c3mu_dict['C74'],
'cNR16n' : mN/DM_mass * FGtilden_eta * c3mu_dict['C74'],
'cNR17n' : FPun_pion*c3mu_dict['C77u'] + FPdn_pion*c3mu_dict['C77d'],
'cNR18n' : FPun_eta*c3mu_dict['C77u'] + FPdn_eta*c3mu_dict['C77d'] + FPsn_eta*c3mu_dict['C77s'],
'cNR19n' : FGtilden_pion * c3mu_dict['C73'],
'cNR20n' : FGtilden_eta * c3mu_dict['C73'],
'cNR21n' : 0,
'cNR22n' : -mN**2 * (- 2*alpha/np.pi * self.ip['mun']/mN * c3mu_dict['C51']),
'cNR23n' : 0,
'cNR100n' : ( F1un*c3mu_dict['C719u']\
+ F1dn*c3mu_dict['C719d']\
+ F1sn*c3mu_dict['C719s'])/(2*DM_mass)\
+ (F1snslope - F2sn / mN**2/4) * c3mu_dict['C61s'],
'cNR104n' : 2*((F1un+F2un)*c3mu_dict['C719u']\
+ (F1dn+F2dn)*c3mu_dict['C719d']\
+ (F1sn+F2dn)*c3mu_dict['C719s'])/mN\
- 1/mN/DM_mass * F2sn * c3mu_dict['C61s']
}
if NLO:
my_cNR_dict['cNR5p'] = - 2*mN * (F1un*c3mu_dict['C719u']\
+ F1dn*c3mu_dict['C719d']\
+ F1sn*c3mu_dict['C719s'])\
+ 2*((FT0up-FT1up)*c3mu_dict['C79u']\
+ (FT0dp-FT1dp)*c3mu_dict['C79d']\
+ (FT0sp-FT1sp)*c3mu_dict['C79s'])
my_cNR_dict['cNR100p'] = - ((FT0up-FT1up)*c3mu_dict['C79u']\
+ (FT0dp-FT1dp)*c3mu_dict['C79d']\
+ (FT0sp-FT1sp)*c3mu_dict['C79s'])/(2*DM_mass*mN)
my_cNR_dict['cNR5n'] = - 2*mN * (F1un*c3mu_dict['C719u']\
+ F1dn*c3mu_dict['C719d'] + F1sn*c3mu_dict['C719s'])\
+ 2*((FT0un-FT1un)*c3mu_dict['C79u']\
+ (FT0dn-FT1dn)*c3mu_dict['C79d']\
+ (FT0sn-FT1sn)*c3mu_dict['C79s'])
my_cNR_dict['cNR100n'] = - ((FT0un-FT1un)*c3mu_dict['C79u']\
+ (FT0dn-FT1dn)*c3mu_dict['C79d']\
+ (FT0sn-FT1sn)*c3mu_dict['C79s'])/(2*DM_mass*mN)
if self.DM_type == "M":
my_cNR_dict = {
'cNR1p' : FGp*c3mu_dict['C71']\
+ FSup*c3mu_dict['C75u'] + FSdp*c3mu_dict['C75d'] + FSsp*c3mu_dict['C75s']\
+ 2*DM_mass * (F1up*c3mu_dict['C715u'] + F1dp*c3mu_dict['C715d'] + F1sp*c3mu_dict['C715s']),
'cNR2p' : 0,
'cNR3p' : 0,
'cNR4p' : - 4*( FAup*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FAdp*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FAsp*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])),
'cNR5p' : 0,
'cNR6p' : mN/DM_mass * FGtildep * c3mu_dict['C74'],
'cNR7p' : - 4*DM_mass * (FAup*c3mu_dict['C717u'] + FAdp*c3mu_dict['C717d'] + FAsp*c3mu_dict['C717s']),
'cNR8p' : 2*( F1up*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ F1dp*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])),
'cNR9p' : 2*( (F1up+F2up)*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ (F1dp+F2dp)*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ (F1sp+F2sp)*(c3mu_dict['C62s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63es'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mus'])\
+ (F1up+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ (F1dp+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taus'])),
'cNR10p' : FGtildep * c3mu_dict['C73'],
'cNR11p' : - mN/DM_mass * (FSup*c3mu_dict['C76u']\
+ FSdp*c3mu_dict['C76d']\
+ FSsp*c3mu_dict['C76s'])\
- mN/DM_mass * FGp * c3mu_dict['C72']\
- 2*mN * ( F1up*c3mu_dict['C716u']\
+ F1dp*c3mu_dict['C716d']\
+ F1sp*c3mu_dict['C716s']),
'cNR12p' : 0,
'cNR13p' : mN/DM_mass * (FPup_pion*c3mu_dict['C78u'] + FPdp_pion*c3mu_dict['C78d'])\
+ FPpup_pion*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdp_pion*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpup_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdp_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau']),
'cNR14p' : mN/DM_mass * (FPup_eta*c3mu_dict['C78u']\
+ FPdp_eta*c3mu_dict['C78d']\
+ FPsp_eta*c3mu_dict['C78s'])\
+ FPpup_eta*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdp_eta*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpsp_eta*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FPpup_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FPpsp_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])\
+ 4*mN * (FAup*c3mu_dict['C718u']\
+ FAdp*c3mu_dict['C718d']\
+ FAsp*c3mu_dict['C718s']),
'cNR15p' : mN/DM_mass * FGtildep_pion * c3mu_dict['C74'],
'cNR16p' : mN/DM_mass * FGtildep_eta * c3mu_dict['C74'],
'cNR17p' : FPup_pion*c3mu_dict['C77u'] + FPdp_pion*c3mu_dict['C77d'],
'cNR18p' : FPup_eta*c3mu_dict['C77u'] + FPdp_eta*c3mu_dict['C77d'] + FPsp_eta*c3mu_dict['C77s'],
'cNR19p' : FGtildep_pion * c3mu_dict['C73'],
'cNR20p' : FGtildep_eta * c3mu_dict['C73'],
'cNR21p' : 0,
'cNR22p' : 0,
'cNR23p' : 0,
'cNR100p' : 0,
'cNR104p' : 0,
'cNR1n' : FGn*c3mu_dict['C71']\
+ FSun*c3mu_dict['C75u'] + FSdn*c3mu_dict['C75d'] + FSsn*c3mu_dict['C75s']\
+ 2*DM_mass * (F1un*c3mu_dict['C715u'] + F1dn*c3mu_dict['C715d'] + F1sn*c3mu_dict['C715s']),
'cNR2n' : 0,
'cNR3n' : 0,
'cNR4n' : - 4*( FAun*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FAdn*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FAsn*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])),
'cNR5n' : 0,
'cNR6n' : mN/DM_mass * FGtilden * c3mu_dict['C74'],
'cNR7n' : - 4*DM_mass * (FAun*c3mu_dict['C717u'] + FAdn*c3mu_dict['C717d'] + FAsn*c3mu_dict['C717s']),
'cNR8n' : 2*( F1un*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ F1dn*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])),
'cNR9n' : 2*( (F1un+F2un)*(c3mu_dict['C62u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ (F1dn+F2dn)*(c3mu_dict['C62d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ (F1sn+F2sn)*(c3mu_dict['C62s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ (F1un+F2un)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63eu'])\
+ (F1dn+F2dn)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63ed'])\
+ (F1sn+F2sn)*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D63es'])\
+ (F1un+F2un)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63muu'])\
+ (F1dn+F2dn)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mud'])\
+ (F1sn+F2sn)*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D63mus'])\
+ (F1un+F2up)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63tauu'])\
+ (F1dn+F2dp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taud'])\
+ (F1sp+F2sp)*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D63taus'])),
'cNR10n' : FGtilden * c3mu_dict['C73'],
'cNR11n' : - mN/DM_mass * (FSun*c3mu_dict['C76u']\
+ FSdn*c3mu_dict['C76d']\
+ FSsn*c3mu_dict['C76s'])\
- mN/DM_mass * FGn * c3mu_dict['C72']\
- 2*mN * ( F1un*c3mu_dict['C716u']\
+ F1dn*c3mu_dict['C716d']\
+ F1sn*c3mu_dict['C716s']),
'cNR12n' : 0,
'cNR13n' : mN/DM_mass * (FPun_pion*c3mu_dict['C78u'] + FPdn_pion*c3mu_dict['C78d'])\
+ FPpun_pion*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdn_pion*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpun_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdn_pion*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau']),
'cNR14n' : mN/DM_mass * (FPun_eta*c3mu_dict['C78u']\
+ FPdn_eta*c3mu_dict['C78d']\
+ FPsn_eta*c3mu_dict['C78s'])\
+ FPpun_eta*(c3mu_dict['C64u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C84u'])\
+ FPpdn_eta*(c3mu_dict['C64d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C84d'])\
+ FPpsn_eta*(c3mu_dict['C64s'] - np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C84s'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62ue'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62de'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C64e'] * c3mu_dict['D62se'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62umu'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62dmu'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C64mu'] * c3mu_dict['D62smu'])\
+ FPpun_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62utau'])\
+ FPpdn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62dtau'])\
+ FPpsn_eta*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C64tau'] * c3mu_dict['D62stau'])\
+ 4*mN * (FAun*c3mu_dict['C718u']\
+ FAdn*c3mu_dict['C718d']\
+ FAsn*c3mu_dict['C718s']),
'cNR15n' : mN/DM_mass * FGtilden_pion * c3mu_dict['C74'],
'cNR16n' : mN/DM_mass * FGtilden_eta * c3mu_dict['C74'],
'cNR17n' : FPun_pion*c3mu_dict['C77u'] + FPdn_pion*c3mu_dict['C77d'],
'cNR18n' : FPun_eta*c3mu_dict['C77u'] + FPdn_eta*c3mu_dict['C77d'] + FPsn_eta*c3mu_dict['C77s'],
'cNR19n' : FGtilden_pion * c3mu_dict['C73'],
'cNR20n' : FGtilden_eta * c3mu_dict['C73'],
'cNR21n' : 0,
'cNR22n' : 0,
'cNR23n' : 0,
'cNR100n' : 0,
'cNR104n' : 0
}
if self.DM_type == "C":
my_cNR_dict = {
'cNR1p' : F1up * (c3mu_dict['C61u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C81u'])\
+ F1dp * (c3mu_dict['C61d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C81d'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D63eu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D63ed'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D63muu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D63mud'])\
+ F1up*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D63tauu'])\
+ F1dp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D63taud'])\
+ FGp*c3mu_dict['C65']/2/DM_mass\
+ (FSup*c3mu_dict['C63u'] + FSdp*c3mu_dict['C63d'] + FSsp*c3mu_dict['C63s'])/2/DM_mass,
'cNR2p' : 0,
'cNR3p' : F2sp * c3mu_dict['C61s'],
'cNR4p' : 0,
'cNR5p' : 0,
'cNR6p' : 0,
'cNR7p' : -2*( FAup * (c3mu_dict['C62u']\
- np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ FAdp * (c3mu_dict['C62d']\
- np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ FAsp * (c3mu_dict['C62s']\
- np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62ue'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62de'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62se'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62umu'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62dmu'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62smu'])\
+ FAup*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62utau'])\
+ FAdp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62dtau'])\
+ FAsp*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62stau'])),
'cNR8p' : 0,
'cNR9p' : 0,
'cNR10p' : FGtildep * c3mu_dict['C66']/2/DM_mass,
'cNR11p' : 0,
'cNR12p' : 0,
'cNR13p' : 0,
'cNR14p' : 0,
'cNR15p' : 0,
'cNR16p' : 0,
'cNR17p' : (FPup_pion*c3mu_dict['C64u'] + FPdp_pion*c3mu_dict['C64d'])/2/DM_mass,
'cNR18p' : ( FPup_eta*c3mu_dict['C64u']\
+ FPdp_eta*c3mu_dict['C64d']\
+ FPsp_eta*c3mu_dict['C64s'])/2/DM_mass,
'cNR19p' : FGtildep_pion * c3mu_dict['C66']/2/DM_mass,
'cNR20p' : FGtildep_eta * c3mu_dict['C66']/2/DM_mass,
'cNR21p' : 0,
'cNR22p' : 0,
'cNR23p' : 0,
'cNR100p' : (F1spslope - 1/mN**2/4 * F2sp) * c3mu_dict['C61s'],
'cNR104p' : 0,
'cNR1n' : F1un * (c3mu_dict['C61u'] - np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C81u'])\
+ F1dn * (c3mu_dict['C61d'] - np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C81d'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D63eu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D63ed'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D63muu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D63mud'])\
+ F1un*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D63tauu'])\
+ F1dn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D63taud'])\
+ FGn*c3mu_dict['C65']/2/DM_mass\
+ (FSun*c3mu_dict['C63u'] + FSdn*c3mu_dict['C63d'] + FSsn*c3mu_dict['C63s'])/2/DM_mass,
'cNR2n' : 0,
'cNR3n' : F2sp * c3mu_dict['C61s'],
'cNR4n' : 0,
'cNR5n' : 0,
'cNR6n' : 0,
'cNR7n' : -2*( FAun * (c3mu_dict['C62u']\
- np.sqrt(2)*GF*wmws*mu**2 / gs2_2GeV * c3mu_dict['C82u'])\
+ FAdn * (c3mu_dict['C62d']\
- np.sqrt(2)*GF*wmws*md**2 / gs2_2GeV * c3mu_dict['C82d'])\
+ FAsn * (c3mu_dict['C62s']\
- np.sqrt(2)*GF*wmws*ms**2 / gs2_2GeV * c3mu_dict['C82s'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62ue'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62de'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * me**2 * np.log(2/MZ)\
* c3mu_dict['C62e'] * c3mu_dict['D62se'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62umu'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62dmu'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mmu**2 * np.log(2/MZ)\
* c3mu_dict['C62mu'] * c3mu_dict['D62smu'])\
+ FAun*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62utau'])\
+ FAdn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62dtau'])\
+ FAsn*(np.sqrt(2)*GF*wmws/np.pi**2 * mtau**2 * np.log(2/MZ)\
* c3mu_dict['C62tau'] * c3mu_dict['D62stau'])),
'cNR8n' : 0,
'cNR9n' : 0,
'cNR10n' : FGtilden * c3mu_dict['C66']/2/DM_mass,
'cNR11n' : 0,
'cNR12n' : 0,
'cNR13n' : 0,
'cNR14n' : 0,
'cNR15n' : 0,
'cNR16n' : 0,
'cNR17n' : (FPun_pion*c3mu_dict['C64u'] + FPdn_pion*c3mu_dict['C64d'])/2/DM_mass,
'cNR18n' : ( FPun_eta*c3mu_dict['C64u']\
+ FPdn_eta*c3mu_dict['C64d']\
+ FPsn_eta*c3mu_dict['C64s'])/2/DM_mass,
'cNR19n' : FGtilden_pion * c3mu_dict['C66']/2/DM_mass,
'cNR20n' : FGtilden_eta * c3mu_dict['C66']/2/DM_mass,
'cNR21n' : 0,
'cNR22n' : 0,
'cNR23n' : 0,
'cNR100n' : (F1snslope - 1/mN**2/4 * F2sn) * c3mu_dict['C61s'],
'cNR104n' : 0
}
if self.DM_type == "R":
my_cNR_dict = {
'cNR1p' : FSup*c3mu_dict['C63u']/2/DM_mass\
+ FSdp*c3mu_dict['C63d']/2/DM_mass\
+ FSsp*c3mu_dict['C63s']/2/DM_mass\
+ FGp*c3mu_dict['C65']/2/DM_mass,
'cNR2p' : 0,
'cNR3p' : 0,
'cNR4p' : 0,
'cNR5p' : 0,
'cNR6p' : 0,
'cNR7p' : 0,
'cNR8p' : 0,
'cNR9p' : 0,
'cNR10p' : FGtildep * c3mu_dict['C66']/2/DM_mass,
'cNR11p' : 0,
'cNR12p' : 0,
'cNR13p' : 0,
'cNR14p' : 0,
'cNR15p' : 0,
'cNR16p' : 0,
'cNR17p' : (FPup_pion*c3mu_dict['C64u'] + FPdp_pion*c3mu_dict['C64d'])/2/DM_mass,
'cNR18p' : FPup_eta*c3mu_dict['C64u']/2/DM_mass\
+ FPdp_eta*c3mu_dict['C64d']/2/DM_mass\
+ FPsp_eta*c3mu_dict['C64s']/2/DM_mass,
'cNR19p' : FGtildep_pion * c3mu_dict['C66']/2/DM_mass,
'cNR20p' : FGtildep_eta * c3mu_dict['C66']/2/DM_mass,
'cNR21p' : 0,
'cNR22p' : 0,
'cNR23p' : 0,
'cNR100p' : 0,
'cNR104p' : 0,
'cNR1n' : FSun*c3mu_dict['C63u']/2/DM_mass\
+ FSdn*c3mu_dict['C63d']/2/DM_mass\
+ FSsn*c3mu_dict['C63s']/2/DM_mass\
+ FGn*c3mu_dict['C65']/2/DM_mass,
'cNR2n' : 0,
'cNR3n' : 0,
'cNR4n' : 0,
'cNR5n' : 0,
'cNR6n' : 0,
'cNR7n' : 0,
'cNR8n' : 0,
'cNR9n' : 0,
'cNR10n' : FGtilden * c3mu_dict['C66']/2/DM_mass,
'cNR11n' : 0,
'cNR12n' : 0,
'cNR13n' : 0,
'cNR14n' : 0,
'cNR15n' : 0,
'cNR16n' : 0,
'cNR17n' : (FPun_pion*c3mu_dict['C64u'] + FPdn_pion*c3mu_dict['C64d'])/2/DM_mass,
'cNR18n' : FPun_eta*c3mu_dict['C64u']/2/DM_mass\
+ FPdn_eta*c3mu_dict['C64d']/2/DM_mass\
+ FPsn_eta*c3mu_dict['C64s']/2/DM_mass,
'cNR19n' : FGtilden_pion * c3mu_dict['C66']/2/DM_mass,
'cNR20n' : FGtilden_eta * c3mu_dict['C66']/2/DM_mass,
'cNR21n' : 0,
'cNR22n' : 0,
'cNR23n' : 0,
'cNR100n' : 0,
'cNR104n' : 0
}
return my_cNR_dict
def cNR(self, DM_mass, q, RGE=None, NLO=None, DOUBLE_WEAK=None):
""" The operator coefficients of O_1^N -- O_12^N as in 1308.6288
(multiply by propagators and sum up contributions)
DM_mass is the DM mass in GeV
RGE is an optional argument to turn RGE running on (True) or off (False). (Default True)
If NLO is set to True, the coherently enhanced NLO terms for Q_9^(7) are added. (Default False)
Returns a dictionary of coefficients for the NR Lagrangian,
cNR1 -- cNR12, as in 1308.6288
The possible names are
['cNR1p', 'cNR1n', 'cNR2p', 'cNR2n', 'cNR3p', 'cNR3n', 'cNR4p', 'cNR4n', 'cNR5p', 'cNR5n',
'cNR6p', 'cNR6n', 'cNR7p', 'cNR7n', 'cNR8p', 'cNR8n', 'cNR9p', 'cNR9n', 'cNR10p', 'cNR10n',
'cNR11p', 'cNR11n', 'cNR12p', 'cNR12n']
"""
if RGE is None:
RGE = True
if NLO is None:
NLO = False
if DOUBLE_WEAK is None:
DOUBLE_WEAK = True
meta = self.ip['meta']
mpi = self.ip['mpi0']
qsq = q**2
# The traditional coefficients, where different from above
cNR_dict = {}
my_cNR = self._my_cNR(DM_mass, RGE, NLO, DOUBLE_WEAK)
# Add meson- / photon-pole contributions
cNR_dict['cNR1p'] = my_cNR['cNR1p'] + qsq * my_cNR['cNR100p']
cNR_dict['cNR2p'] = my_cNR['cNR2p']
cNR_dict['cNR3p'] = my_cNR['cNR3p']
cNR_dict['cNR4p'] = my_cNR['cNR4p'] + qsq * my_cNR['cNR104p']
cNR_dict['cNR5p'] = my_cNR['cNR5p'] + 1/qsq * my_cNR['cNR21p']
cNR_dict['cNR6p'] = my_cNR['cNR6p']\
+ 1/(mpi**2 + qsq) * my_cNR['cNR13p']\
+ 1/(meta**2 + qsq) * my_cNR['cNR14p']\
+ qsq/(mpi**2 + qsq) * my_cNR['cNR15p']\
+ qsq/(meta**2 + qsq) * my_cNR['cNR16p']\
+ 1/qsq * my_cNR['cNR22p']
cNR_dict['cNR7p'] = my_cNR['cNR7p']
cNR_dict['cNR8p'] = my_cNR['cNR8p']
cNR_dict['cNR9p'] = my_cNR['cNR9p']
cNR_dict['cNR10p'] = my_cNR['cNR10p']\
+ 1/(mpi**2 + qsq) * my_cNR['cNR17p']\
+ 1/(meta**2 + qsq) * my_cNR['cNR18p']\
+ qsq/(mpi**2 + qsq) * my_cNR['cNR19p']\
+ qsq/(meta**2 + qsq) * my_cNR['cNR20p']
cNR_dict['cNR11p'] = my_cNR['cNR11p'] + 1/qsq * my_cNR['cNR23p']
cNR_dict['cNR12p'] = my_cNR['cNR12p']
cNR_dict['cNR1n'] = my_cNR['cNR1n'] + qsq * my_cNR['cNR100n']
cNR_dict['cNR2n'] = my_cNR['cNR2n']
cNR_dict['cNR3n'] = my_cNR['cNR3n']
cNR_dict['cNR4n'] = my_cNR['cNR4n'] + qsq * my_cNR['cNR104n']
cNR_dict['cNR5n'] = my_cNR['cNR5n'] + 1/qsq * my_cNR['cNR21n']
cNR_dict['cNR6n'] = my_cNR['cNR6n']\
+ 1/(mpi**2 + qsq) * my_cNR['cNR13n']\
+ 1/(meta**2 + qsq) * my_cNR['cNR14n']\
+ qsq/(mpi**2 + qsq) * my_cNR['cNR15n']\
+ qsq/(meta**2 + qsq) * my_cNR['cNR16n']\
+ 1/qsq * my_cNR['cNR22n']
cNR_dict['cNR7n'] = my_cNR['cNR7n']
cNR_dict['cNR8n'] = my_cNR['cNR8n']
cNR_dict['cNR9n'] = my_cNR['cNR9n']
cNR_dict['cNR10n'] = my_cNR['cNR10n']\
+ 1/(mpi**2 + qsq) * my_cNR['cNR17n']\
+ 1/(meta**2 + qsq) * my_cNR['cNR18n']\
+ qsq/(mpi**2 + qsq) * my_cNR['cNR19n']\
+ qsq/(meta**2 + qsq) * my_cNR['cNR20n']
cNR_dict['cNR11n'] = my_cNR['cNR11n'] + 1/qsq * my_cNR['cNR23n']
cNR_dict['cNR12n'] = my_cNR['cNR12n']
return cNR_dict
def write_mma(self, DM_mass, qvec, RGE=None, NLO=None, DOUBLE_WEAK=None, path=None, filename=None):
""" Write a text file with the NR coefficients that can be read into DMFormFactor
The order is {cNR1p, cNR2p, ... , cNR1n, cNR2n, ... }
Mandatory arguments are the DM mass DM_mass (in GeV) and the spatial momentum transfer qvec (in GeV)
<path> should be a string with the path (including the trailing "/") where the file should be saved
(default is './')
<filename> is the filename (default 'cNR.m')
"""
if RGE is None:
RGE=True
if NLO is None:
NLO=False
if DOUBLE_WEAK is None:
DOUBLE_WEAK = True
if path is None:
path = './'
assert type(path) is str
if path.endswith('/'):
pass
else:
path += '/'
if filename is None:
filename = 'cNR.m'
val = self.cNR(DM_mass, qvec, RGE, NLO)
self.cNR_list_mma = '{' + str(val['cNR1p']) + ', '\
+ str(val['cNR2p']) + ', '\
+ str(val['cNR3p']) + ', '\
+ str(val['cNR4p']) + ', '\
+ str(val['cNR5p']) + ', '\
+ str(val['cNR6p']) + ', '\
+ str(val['cNR7p']) + ', '\
+ str(val['cNR8p']) + ', '\
+ str(val['cNR9p']) + ', '\
+ str(val['cNR10p']) + ', '\
+ str(val['cNR11p']) + ', '\
+ str(val['cNR12p']) + ', '\
+ str(val['cNR1n']) + ', '\
+ str(val['cNR2n']) + ', '\
+ str(val['cNR3n']) + ', '\
+ str(val['cNR4n']) + ', '\
+ str(val['cNR5n']) + ', '\
+ str(val['cNR6n']) + ', '\
+ str(val['cNR7n']) + ', '\
+ str(val['cNR8n']) + ', '\
+ str(val['cNR9n']) + ', '\
+ str(val['cNR10n']) + ', '\
+ str(val['cNR11n']) + ', '\
+ str(val['cNR12n']) + '}' + '\n'
output_file = str(os.path.expanduser(path)) + filename
with open(output_file,'w') as f:
f.write(self.cNR_list_mma)
class WC_4flavor(object):
def __init__(self, coeff_dict, DM_type, input_dict):
""" Class for Wilson coefficients in 4 flavor QCD x QED plus DM.
The argument should be a dictionary for the initial conditions
of the 2 + 28 + 4 + 42 + 4 + 56 + 7 + 1 + 6 = 150 dimension-five to dimension-eight
four-flavor-QCD Wilson coefficients (for Dirac DM) of the form
{'C51' : value, 'C52' : value, ...}. For other DM types there are less coefficients.
An arbitrary number of them can be given; the default values are zero.
The second argument is the DM type; it can take the following values:
"D" (Dirac fermion)
"M" (Majorana fermion)
"C" (Complex scalar)
"R" (Real scalar)
The possible name are (with an hopefully obvious notation):
Dirac fermion: 'C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79c', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710c', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719c', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720c', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721c', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722c', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725',
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s'
Majorana fermion: 'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725',
Complex Scalar: 'C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610'
Real Scalar: 'C65', 'C66'
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610'
(the notation corresponds to the numbering in 1707.06998).
The Wilson coefficients should be specified in the MS-bar scheme at mb = 4.18 GeV.
In order to calculate consistently to dim.8 in the EFT, we need also the dim.6 SM operators.
The following subset of 6*8 + 4*4 = 64 operators is sufficient for our purposes:
'D61ud', 'D62ud', 'D63ud', 'D63du', 'D64ud', 'D65ud', 'D66ud', 'D66du',
'D61us', 'D62us', 'D63us', 'D63su', 'D64us', 'D65us', 'D66us', 'D66su',
'D61uc', 'D62uc', 'D63uc', 'D63cu', 'D64uc', 'D65uc', 'D66uc', 'D66cu',
'D61ds', 'D62ds', 'D63ds', 'D63sd', 'D64ds', 'D65ds', 'D66ds', 'D66sd',
'D61dc', 'D62dc', 'D63dc', 'D63cd', 'D64dc', 'D65dc', 'D66dc', 'D66cd',
'D61sc', 'D62sc', 'D63sc', 'D63cs', 'D64sc', 'D65sc', 'D66sc', 'D66cs',
'D61u', 'D62u', 'D63u', 'D64u',
'D61d', 'D62d', 'D63d', 'D64d',
'D61s', 'D62s', 'D63s', 'D64s',
'D61c', 'D62c', 'D63c', 'D64c'
The initial conditions at scale mb have to given; e.g. using WC_5f
The third argument is a dictionary with all input parameters.
The class has four methods:
run
---
Run the Wilson from mb(mb) to mu_low [GeV; default 2 GeV], with 4 active quark flavors
match
-----
Match the Wilson coefficients from 4-flavor to 3-flavor QCD, at scale mu [GeV; default 2 GeV]
cNR
---
Calculate the cNR coefficients as defined in 1308.6288
It has two mandatory arguments: The DM mass in GeV and the momentum transfer in GeV
write_mma
---------
Writes an output file that can be loaded into mathematica,
to be used in the DMFormFactor package [1308.6288].
"""
self.DM_type = DM_type
# First, we define a standard ordering for the Wilson coefficients, so that we can use arrays
self.sm_name_list = ['D61ud', 'D62ud', 'D63ud', 'D63du', 'D64ud', 'D65ud', 'D66ud', 'D66du',
'D61us', 'D62us', 'D63us', 'D63su', 'D64us', 'D65us', 'D66us', 'D66su',
'D61uc', 'D62uc', 'D63uc', 'D63cu', 'D64uc', 'D65uc', 'D66uc', 'D66cu',
'D61ds', 'D62ds', 'D63ds', 'D63sd', 'D64ds', 'D65ds', 'D66ds', 'D66sd',
'D61dc', 'D62dc', 'D63dc', 'D63cd', 'D64dc', 'D65dc', 'D66dc', 'D66cd',
'D61sc', 'D62sc', 'D63sc', 'D63cs', 'D64sc', 'D65sc', 'D66sc', 'D66cs',
'D61u', 'D62u', 'D63u', 'D64u',
'D61d', 'D62d', 'D63d', 'D64d',
'D61s', 'D62s', 'D63s', 'D64s',
'D61c', 'D62c', 'D63c', 'D64c']
self.sm_lepton_name_list = ['D63eu', 'D63muu', 'D63tauu', 'D63ed', 'D63mud',\
'D63taud', 'D63es', 'D63mus', 'D63taus',
'D62ue', 'D62umu', 'D62utau', 'D62de', 'D62dmu',\
'D62dtau', 'D62se', 'D62smu', 'D62stau']
if self.DM_type == "D":
self.wc_name_list = ['C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79c', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710c', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719c', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720c', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721c', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722c', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s',\
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s']
# The 3-flavor list for matching only
self.wc_name_list_3f = ['C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723e', 'C723mu', 'C723tau',
'C725']
if self.DM_type == "M":
self.wc_name_list = ['C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C82u', 'C82d', 'C82s', 'C84u', 'C84d', 'C84s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = np.r_[np.s_[0:9], np.s_[16:23], np.s_[62:76], np.s_[108:136]]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
# The list of indices to be deleted from the ADT because of less operators (dim.6 part)
del_ind_list_adt_quark = np.r_[np.s_[0:4]]
# The 3-flavor list for matching only
self.wc_name_list_3f = ['C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723e', 'C723mu', 'C723tau',
'C725']
if self.DM_type == "C":
self.wc_name_list = ['C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = [0,1] + [i for i in range(9,16)] + [i for i in range(23,30)]\
+ [31] + [33] + [i for i in range(41,48)]\
+ [i for i in range(55,76)] + [77] + [79] + [i for i in range(80,136)]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
# The list of indices to be deleted from the ADT because of less operators (dim.6 part)
del_ind_list_adt_quark = np.r_[np.s_[0:4]]
# The 3-flavor list for matching only
self.wc_name_list_3f = ['C61u', 'C61d', 'C61s', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610']
if self.DM_type == "R":
self.wc_name_list = ['C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = []
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = [i for i in range(0,30)] + [31] + [33] + [i for i in range(41,48)]\
+ [i for i in range(55,76)]\
+ [77] + [79] + [i for i in range(80,136)]
# The 3-flavor list for matching only
self.wc_name_list_3f = ['C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69e', 'C69mu', 'C69tau',
'C610']
self.coeff_dict = {}
# Issue a user warning if a key is not defined:
for wc_name in coeff_dict.keys():
if wc_name in self.wc_name_list:
pass
elif wc_name in self.wc8_name_list:
pass
elif wc_name in self.sm_name_list:
pass
elif wc_name in self.sm_lepton_name_list:
pass
else:
warnings.warn('The key ' + wc_name + ' is not a valid key. Typo?')
# Create the dictionary.
for wc_name in self.wc_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
for wc_name in self.wc8_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
for wc_name in self.sm_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
for wc_name in self.sm_lepton_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
# Create the np.array of coefficients:
self.coeff_list_dm_dim5_dim6_dim7 = np.array(dict_to_list(self.coeff_dict, self.wc_name_list))
self.coeff_list_dm_dim8 = np.array(dict_to_list(self.coeff_dict, self.wc8_name_list))
self.coeff_list_sm_dim6 = np.array(dict_to_list(self.coeff_dict, self.sm_name_list))
self.coeff_list_sm_lepton_dim6 = np.array(dict_to_list(self.coeff_dict, self.sm_lepton_name_list))
# The dictionary of input parameters
self.ip = input_dict
#---------------------------#
# The anomalous dimensions: #
#---------------------------#
if self.DM_type == "D":
self.gamma_QED = adm.ADM_QED(4)
self.gamma_QED2 = adm.ADM_QED2(4)
self.gamma_QCD = adm.ADM_QCD(4)
self.gamma_QCD2 = adm.ADM_QCD2(4)
self.gamma_QCD_dim8 = adm.ADM_QCD_dim8(4)
self.gamma_hat = adm.ADT_QCD(4, self.ip)
if self.DM_type == "M":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(4), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(4), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(4), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
self.gamma_hat = np.delete(np.delete(adm.ADT_QCD(4, self.ip), del_ind_list_dim_8, 0),\
del_ind_list_adt_quark, 2)
if self.DM_type == "C":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(4), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(4), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(4), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
self.gamma_hat = np.delete(np.delete(adm.ADT_QCD(4, self.ip), del_ind_list_dim_8, 0),\
del_ind_list_adt_quark, 2)
if self.DM_type == "R":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(4), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(4), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(4), del_ind_list, 1), del_ind_list, 2)
self.ADM_SM = adm.ADM_SM_QCD(4)
#------------------------------------------------------------------------------#
# The effective anomalous dimension for mixing into dimension eight -- quarks: #
#------------------------------------------------------------------------------#
# We need to contract the ADT with a subset of the dim.-6 DM Wilson coefficients
if self.DM_type == "D":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7,\
np.r_[np.s_[0:16], np.s_[20:23], np.s_[27:144]])
elif self.DM_type == "M":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7, np.r_[np.s_[0:7], np.s_[11:86]])
elif self.DM_type == "C":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7, np.r_[np.s_[0:7], np.s_[11:40]])
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
# The columns of ADM_eff correspond to SM6 operators; the rows of ADM_eff correspond to DM8 operators;
C6_dot_ADM_hat = np.transpose(np.tensordot(DM_dim6_init, self.gamma_hat, (0,2)))
# The effective ADM
#
# Note that the mixing of the SM operators with four equal flavors
# does not contribute if we neglect yu, yd, ys!
self.ADM_eff = [np.vstack((np.hstack((self.ADM_SM,\
np.vstack((C6_dot_ADM_hat,\
np.zeros((16, len(self.gamma_QCD_dim8))))))),\
np.hstack((np.zeros((len(self.gamma_QCD_dim8),\
len(self.coeff_list_sm_dim6))),\
self.gamma_QCD_dim8))))]
if self.DM_type == "R":
pass
def run(self, mu_low=None, double_QCD=None):
""" Running of 4-flavor Wilson coefficients
Calculate the running from mb(mb) to mu_low [GeV; default 2 GeV] in the four-flavor theory.
Return a dictionary of Wilson coefficients for the four-flavor Lagrangian
at scale mu_low.
"""
if mu_low is None:
mu_low=2
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
if double_QCD is None:
double_QCD=True
else:
double_QCD=False
#-------------#
# The running #
#-------------#
mb = self.ip['mb_at_mb']
alpha_at_mc = 1/self.ip['aMZinv']
as_2GeV = rge.AlphaS(self.ip['asMZ'],\
self.ip['Mz']).run({'mbmb': self.ip['mb_at_mb'],\
'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'],\
'muc': self.ip['mc_at_mc']}, 2, 3, 1)
gs2_2GeV = 4*np.pi*as_2GeV
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
if double_QCD:
adm_eff = self.ADM_eff
else:
projector = np.vstack((np.hstack((np.zeros((64,64)),\
np.ones((64,12)))),\
np.zeros((12,76))))
adm_eff = [np.multiply(projector, self.ADM_eff[0])]
else:
pass
as41 = rge.AlphaS(self.ip['asMZ'], self.ip['Mz'])
as41_high = as41.run({'mbmb': self.ip['mb_at_mb'], 'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'], 'muc': self.ip['mc_at_mc']}, mb, 4, 1)
as41_low = as41.run({'mbmb': self.ip['mb_at_mb'], 'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'], 'muc': self.ip['mc_at_mc']}, mu_low, 4, 1)
evolve1 = rge.RGE(self.gamma_QCD, 4)
evolve2 = rge.RGE(self.gamma_QCD2, 4)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
evolve8 = rge.RGE(adm_eff, 4)
else:
pass
# Mixing in the dim.6 DM-SM sector
#
C_at_mc_QCD = np.dot(evolve2.U0_as2(as41_high, as41_low),\
np.dot(evolve1.U0(as41_high, as41_low),\
self.coeff_list_dm_dim5_dim6_dim7))
C_at_mc_QED = np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED)\
* np.log(mu_low/mb) * alpha_at_mc/(4*np.pi)\
+ np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED2)\
* np.log(mu_low/mb) * (alpha_at_mc/(4*np.pi))**2
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
# Mixing in the dim.6 SM-SM and dim.8 DM-SM sector
DIM6_DIM8_init = np.hstack((self.coeff_list_sm_dim6, self.coeff_list_dm_dim8))
DIM6_DIM8_at_mb = np.dot(evolve8.U0(as41_high, as41_low), DIM6_DIM8_init)
# Revert back to dictionary
dict_coeff_mc = list_to_dict(C_at_mc_QCD + C_at_mc_QED, self.wc_name_list)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
dict_dm_dim8 = list_to_dict(np.delete(DIM6_DIM8_at_mb, np.s_[0:64]), self.wc8_name_list)
dict_sm_dim6 = list_to_dict(np.delete(DIM6_DIM8_at_mb, np.s_[64:70]), self.sm_name_list)
dict_sm_lepton_dim6 = list_to_dict(self.coeff_list_sm_lepton_dim6, self.sm_lepton_name_list)
dict_coeff_mc.update(dict_dm_dim8)
dict_coeff_mc.update(dict_sm_dim6)
dict_coeff_mc.update(dict_sm_lepton_dim6)
return dict_coeff_mc
def match(self, RGE=None, double_QCD=None, mu=None):
""" Match from four-flavor to three-flavor QCD
Calculate the matching at mu [GeV; default 2 GeV].
Returns a dictionary of Wilson coefficients for the three-flavor Lagrangian
at scale mu. The SM-SM Wilson coefficients are NOT returned.
RGE is an optional argument to turn RGE running on (True) or off (False). (Default True)
"""
if mu is None:
mu=2
if RGE is None:
RGE=True
if double_QCD is None:
double_QCD=True
# The new coefficients
cdict3f = {}
if RGE:
cdold = self.run(mu, double_QCD)
else:
cdold = self.coeff_dict
if self.DM_type == "D" or self.DM_type == "M":
for wcn in self.wc_name_list_3f:
cdict3f[wcn] = cdold[wcn]
for wcn in self.wc8_name_list:
cdict3f[wcn] = cdold[wcn]
cdict3f['C71'] = cdold['C71'] - cdold['C75c']
cdict3f['C72'] = cdold['C72'] - cdold['C76c']
cdict3f['C73'] = cdold['C73'] + cdold['C77c']
cdict3f['C74'] = cdold['C74'] + cdold['C78c']
if self.DM_type == "C":
for wcn in self.wc_name_list_3f:
cdict3f[wcn] = cdold[wcn]
for wcn in self.wc8_name_list:
cdict3f[wcn] = cdold[wcn]
cdict3f['C65'] = cdold['C65'] - cdold['C63c']
cdict3f['C66'] = cdold['C66'] + cdold['C64c']
if self.DM_type == "R":
for wcn in self.wc_name_list_3f:
cdict3f[wcn] = cdold[wcn]
cdict3f['C65'] = cdold['C65'] - cdold['C63c']
cdict3f['C66'] = cdold['C66'] + cdold['C64c']
# return the 3-flavor coefficients
return cdict3f
def _my_cNR(self, DM_mass, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None):
""" Calculate the NR coefficients from four-flavor theory with meson contributions split off
(mainly for internal use)
"""
return WC_3flavor(self.match(RGE, double_QCD, DOUBLE_WEAK), self.DM_type, self.ip)._my_cNR(DM_mass, RGE, NLO)
def cNR(self, DM_mass, qvec, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None):
""" Calculate the NR coefficients from four-flavor theory """
return WC_3flavor(self.match(RGE, double_QCD), self.DM_type, self.ip).cNR(DM_mass, qvec, RGE, NLO, DOUBLE_WEAK)
def write_mma(self, DM_mass, qvec, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None, path=None, filename=None):
""" Write a text file with the NR coefficients that can be read into DMFormFactor
The order is {cNR1p, cNR2p, ... , cNR1n, cNR2n, ... }
Mandatory arguments are the DM mass DM_mass (in GeV) and the spatial momentum transfer qvec (in GeV)
<path> should be a string with the path (including the trailing "/") where the file should be saved
(default is './')
<filename> is the filename (default 'cNR.m')
"""
WC_3flavor(self.match(RGE, double_QCD), self.DM_type,\
self.ip).write_mma(DM_mass, qvec, RGE, NLO, DOUBLE_WEAK, path, filename)
class WC_5flavor(object):
def __init__(self, coeff_dict, DM_type, input_dict=None):
# def __init__(self, coeff_dict, DM_type):
""" Class for Wilson coefficients in 5 flavor QCD x QED plus DM.
The argument should be a dictionary for the initial conditions of the
2 + 32 + 4 + 48 + 4 + 64 + 8 + 1 + 12 = 175
dimension-five to dimension-eight five-flavor-QCD Wilson coefficients (for Dirac DM) of the form
{'C51' : value, 'C52' : value, ...}. For other DM types there are less coefficients.
A subset of twist-two operators is currently only included for Dirac DM.
An arbitrary number of them can be given; the default values are zero.
The possible name are (with an hopefully obvious notation):
The second argument is the DM type; it can take the following values:
"D" (Dirac fermion)
"M" (Majorana fermion)
"C" (Complex scalar)
"R" (Real scalar)
Dirac fermion: 'C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61b', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75b', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76b', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77b', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78b', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79c', 'C79b', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710c', 'C710b', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715b', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716b', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717b', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718b', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719c', 'C719b', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720c', 'C720b', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721c', 'C721b', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722c', 'C722b', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723b', 'C723e', 'C723mu', 'C723tau',
'C725',
'C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s'
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s'
Majorana fermion: 'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75b', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76b', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77b', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78b', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715b', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716b', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717b', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718b', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723b', 'C723e', 'C723mu', 'C723tau',
'C725',
Complex Scalar: 'C61u', 'C61d', 'C61s', 'C61c', 'C61b', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C65', 'C66', 'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69b', 'C69e', 'C69mu', 'C69tau',
'C610'
Real Scalar: 'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C65', 'C66', 'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69b', 'C69e', 'C69mu', 'C69tau',
'C610'
(the notation corresponds to the numbering in 1707.06998, 1801.04240, and 1809.03506).
The Wilson coefficients should be specified in the MS-bar scheme at MZ = 91.1876 GeV.
In order to calculate consistently to dim.8 in the EFT, we need also the dim.6 SM operators.
The following subset of 10*8 + 5*4 = 100 operator coefficients are sufficient for our purposes:
'D61ud', 'D62ud', 'D63ud', 'D63du', 'D64ud', 'D65ud', 'D66ud', 'D66du',
'D61us', 'D62us', 'D63us', 'D63su', 'D64us', 'D65us', 'D66us', 'D66su',
'D61uc', 'D62uc', 'D63uc', 'D63cu', 'D64uc', 'D65uc', 'D66uc', 'D66cu',
'D61ub', 'D62ub', 'D63ub', 'D63bu', 'D64ub', 'D65ub', 'D66ub', 'D66bu',
'D61ds', 'D62ds', 'D63ds', 'D63sd', 'D64ds', 'D65ds', 'D66ds', 'D66sd',
'D61dc', 'D62dc', 'D63dc', 'D63cd', 'D64dc', 'D65dc', 'D66dc', 'D66cd',
'D61db', 'D62db', 'D63db', 'D63bd', 'D64db', 'D65db', 'D66db', 'D66bd',
'D61sc', 'D62sc', 'D63sc', 'D63cs', 'D64sc', 'D65sc', 'D66sc', 'D66cs',
'D61sb', 'D62sb', 'D63sb', 'D63bs', 'D64sb', 'D65sb', 'D66sb', 'D66bs',
'D61cb', 'D62cb', 'D63cb', 'D63bc', 'D64cb', 'D65cb', 'D66cb', 'D66bc',
'D61u', 'D62u', 'D63u', 'D64u',
'D61d', 'D62d', 'D63d', 'D64d',
'D61s', 'D62s', 'D63s', 'D64s',
'D61c', 'D62c', 'D63c', 'D64c',
'D61b', 'D62b', 'D63b', 'D64b'
Unless specified otherwise by the user, the tree-level SM initial conditions at MZ are provided be default.
For completeness, the default initial conditions at MZ for the corresponding
leptonic operator Wilson coefficients are also given:
'D63eu', 'D63muu', 'D63tauu', 'D63ed', 'D63mud', 'D63taud', 'D63es', 'D63mus', 'D63taus',
'D62ue', 'D62umu', 'D62utau', 'D62de', 'D62dmu', 'D62dtau', 'D62se', 'D62smu', 'D62stau'
The third argument is a dictionary with all input parameters.
The class has four methods:
run
---
Run the Wilson from MZ = 91.1876 GeV to mu_low [GeV; default mb(mb)], with 5 active quark flavors
match
-----
Match the Wilson coefficients from 5-flavor to 4-flavor QCD, at scale mu [GeV; default mu = mb(mb)]
cNR
---
Calculate the cNR coefficients as defined in 1308.6288
It has two mandatory arguments: The DM mass in GeV and the momentum transfer in GeV
write_mma
---------
Write an output file that can be loaded into mathematica,
to be used in the DMFormFactor package [1308.6288].
"""
self.DM_type = DM_type
# First, we define a standard ordering for the Wilson coefficients, so that we can use arrays
self.sm_lepton_name_list = ['D63eu', 'D63muu', 'D63tauu', 'D63ed', 'D63mud',\
'D63taud', 'D63es', 'D63mus', 'D63taus',
'D62ue', 'D62umu', 'D62utau', 'D62de', 'D62dmu',\
'D62dtau', 'D62se', 'D62smu', 'D62stau']
self.sm_name_list = ['D61ud', 'D62ud', 'D63ud', 'D63du', 'D64ud', 'D65ud', 'D66ud', 'D66du',
'D61us', 'D62us', 'D63us', 'D63su', 'D64us', 'D65us', 'D66us', 'D66su',
'D61uc', 'D62uc', 'D63uc', 'D63cu', 'D64uc', 'D65uc', 'D66uc', 'D66cu',
'D61ub', 'D62ub', 'D63ub', 'D63bu', 'D64ub', 'D65ub', 'D66ub', 'D66bu',
'D61ds', 'D62ds', 'D63ds', 'D63sd', 'D64ds', 'D65ds', 'D66ds', 'D66sd',
'D61dc', 'D62dc', 'D63dc', 'D63cd', 'D64dc', 'D65dc', 'D66dc', 'D66cd',
'D61db', 'D62db', 'D63db', 'D63bd', 'D64db', 'D65db', 'D66db', 'D66bd',
'D61sc', 'D62sc', 'D63sc', 'D63cs', 'D64sc', 'D65sc', 'D66sc', 'D66cs',
'D61sb', 'D62sb', 'D63sb', 'D63bs', 'D64sb', 'D65sb', 'D66sb', 'D66bs',
'D61cb', 'D62cb', 'D63cb', 'D63bc', 'D64cb', 'D65cb', 'D66cb', 'D66bc',
'D61u', 'D62u', 'D63u', 'D64u',
'D61d', 'D62d', 'D63d', 'D64d',
'D61s', 'D62s', 'D63s', 'D64s',
'D61c', 'D62c', 'D63c', 'D64c',
'D61b', 'D62b', 'D63b', 'D64b']
self.sm_name_list_4f = ['D61ud', 'D62ud', 'D63ud', 'D63du', 'D64ud', 'D65ud', 'D66ud', 'D66du',
'D61us', 'D62us', 'D63us', 'D63su', 'D64us', 'D65us', 'D66us', 'D66su',
'D61uc', 'D62uc', 'D63uc', 'D63cu', 'D64uc', 'D65uc', 'D66uc', 'D66cu',
'D61ds', 'D62ds', 'D63ds', 'D63sd', 'D64ds', 'D65ds', 'D66ds', 'D66sd',
'D61dc', 'D62dc', 'D63dc', 'D63cd', 'D64dc', 'D65dc', 'D66dc', 'D66cd',
'D61sc', 'D62sc', 'D63sc', 'D63cs', 'D64sc', 'D65sc', 'D66sc', 'D66cs',
'D61u', 'D62u', 'D63u', 'D64u',
'D61d', 'D62d', 'D63d', 'D64d',
'D61s', 'D62s', 'D63s', 'D64s',
'D61c', 'D62c', 'D63c', 'D64c']
if self.DM_type == "D":
self.wc_name_list = ['C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61b', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75b', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76b', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77b', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78b', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79c', 'C79b', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710c', 'C710b', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715b', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716b', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717b', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718b', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719c', 'C719b', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720c', 'C720b', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721c', 'C721b', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722c', 'C722b', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723b', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s',\
'C83u', 'C83d', 'C83s', 'C84u', 'C84d', 'C84s']
self.wc_name_list_4f = ['C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C79c', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C710c', 'C710e', 'C710mu', 'C710tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C719u', 'C719d', 'C719s', 'C719c', 'C719e', 'C719mu', 'C719tau',
'C720u', 'C720d', 'C720s', 'C720c', 'C720e', 'C720mu', 'C720tau',
'C721u', 'C721d', 'C721s', 'C721c', 'C721e', 'C721mu', 'C721tau',
'C722u', 'C722d', 'C722s', 'C722c', 'C722e', 'C722mu', 'C722tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725']
if self.DM_type == "M":
self.wc_name_list = ['C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75b', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76b', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77b', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78b', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715b', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716b', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717b', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718b', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723b', 'C723e', 'C723mu', 'C723tau',
'C725']
self.wc8_name_list = ['C82u', 'C82d', 'C82s', 'C84u', 'C84d', 'C84s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = [i for i in range(0,10)] + [i for i in range(18,26)]\
+ [i for i in range(70,86)] + [i for i in range(122,154)]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
# The list of indices to be deleted from the ADT because of less operators (dim.6 part)
del_ind_list_adt_quark = np.r_[np.s_[0:5]]
# The 4-flavor list for matching only
self.wc_name_list_4f = ['C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C75c', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C76c', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C77c', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C78c', 'C78e', 'C78mu', 'C78tau',
'C711', 'C712', 'C713', 'C714',
'C715u', 'C715d', 'C715s', 'C715c', 'C715e', 'C715mu', 'C715tau',
'C716u', 'C716d', 'C716s', 'C716c', 'C716e', 'C716mu', 'C716tau',
'C717u', 'C717d', 'C717s', 'C717c', 'C717e', 'C717mu', 'C717tau',
'C718u', 'C718d', 'C718s', 'C718c', 'C718e', 'C718mu', 'C718tau',
'C723u', 'C723d', 'C723s', 'C723c', 'C723e', 'C723mu', 'C723tau',
'C725']
if self.DM_type == "C":
self.wc_name_list = ['C61u', 'C61d', 'C61s', 'C61c', 'C61b', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69b', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = ['C81u', 'C81d', 'C81s', 'C82u', 'C82d', 'C82s']
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = [0,1] + [i for i in range(10,18)] + [i for i in range(26,34)]\
+ [35] + [37] + [i for i in range(46,54)]\
+ [i for i in range(62,86)] + [87] + [89] + [i for i in range(90,154)]
# The list of indices to be deleted from the dim.8 ADM because of less operators
del_ind_list_dim_8 = np.r_[np.s_[0:3], np.s_[6:9]]
# The list of indices to be deleted from the ADT because of less operators (dim.6 part)
del_ind_list_adt_quark = np.r_[np.s_[0:5]]
# The 4-flavor list for matching only
self.wc_name_list_4f = ['C61u', 'C61d', 'C61s', 'C61c', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62e', 'C62mu', 'C62tau',
'C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610']
if self.DM_type == "R":
self.wc_name_list = ['C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69b', 'C69e', 'C69mu', 'C69tau',
'C610']
self.wc8_name_list = []
# The list of indices to be deleted from the QCD/QED ADM because of less operators
del_ind_list = [i for i in range(0,34)] + [35] + [37] + [i for i in range(46,54)]\
+ [i for i in range(62,86)]\
+ [87] + [89] + [i for i in range(90,154)]
# The 4-flavor list for matching only
self.wc_name_list_4f = ['C65', 'C66',
'C63u', 'C63d', 'C63s', 'C63c', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64e', 'C64mu', 'C64tau',
'C67', 'C68',
'C69u', 'C69d', 'C69s', 'C69c', 'C69e', 'C69mu', 'C69tau',
'C610']
self.coeff_dict = {}
# Issue a user warning if a key is not defined:
for wc_name in coeff_dict.keys():
if wc_name in self.wc_name_list:
pass
elif wc_name in self.wc8_name_list:
pass
elif wc_name in self.sm_name_list:
pass
elif wc_name in self.sm_lepton_name_list:
pass
else:
warnings.warn('The key ' + wc_name + ' is not a valid key. Typo?')
# The dictionary of input parameters
self.ip = input_dict
# if input_dict is None:
# self.ip = Num_input().input_parameters
# else:
# self.ip = Num_input(input_dict).input_parameters
# Create the dictionary of Wilson coefficients.
#
# First, the default values (0 for DM operators, SM values for SM operators):
#
# This is actually conceptually not so good.
# The SM initial conditions should be moved to a matching method above the e/w scale?
for wc_name in self.wc_name_list:
self.coeff_dict[wc_name] = 0.
for wc_name in self.wc8_name_list:
self.coeff_dict[wc_name] = 0.
sw = np.sqrt(self.ip['sw2_MSbar'])
cw = np.sqrt(1-sw**2)
vd = (-1/2 - 2*sw**2*(-1/3))/(2*sw*cw)
vu = (1/2 - 2*sw**2*(2/3))/(2*sw*cw)
ad = -(-1/2)/(2*sw*cw)
au = -(1/2)/(2*sw*cw)
vl = (-1/2 - 2*sw**2*(-1))/(2*sw*cw)
al = -(-1/2)/(2*sw*cw)
self.coeff_dict['D61ud'] = vu*vd * 4*sw**2*cw**2 + 1/6
self.coeff_dict['D62ud'] = au*ad * 4*sw**2*cw**2 + 1/6
self.coeff_dict['D63ud'] = au*vd * 4*sw**2*cw**2 - 1/6
self.coeff_dict['D63du'] = ad*vu * 4*sw**2*cw**2 - 1/6
self.coeff_dict['D64ud'] = 1
self.coeff_dict['D65ud'] = 1
self.coeff_dict['D66ud'] = -1
self.coeff_dict['D66du'] = -1
self.coeff_dict['D61us'] = vu*vd * 4*sw**2*cw**2
self.coeff_dict['D62us'] = au*ad * 4*sw**2*cw**2
self.coeff_dict['D63us'] = au*vd * 4*sw**2*cw**2
self.coeff_dict['D63su'] = ad*vu * 4*sw**2*cw**2
self.coeff_dict['D64us'] = 0
self.coeff_dict['D65us'] = 0
self.coeff_dict['D66us'] = 0
self.coeff_dict['D66su'] = 0
self.coeff_dict['D61uc'] = vu*vu * 4*sw**2*cw**2
self.coeff_dict['D62uc'] = au*au * 4*sw**2*cw**2
self.coeff_dict['D63uc'] = au*vu * 4*sw**2*cw**2
self.coeff_dict['D63cu'] = au*vu * 4*sw**2*cw**2
self.coeff_dict['D64uc'] = 0
self.coeff_dict['D65uc'] = 0
self.coeff_dict['D66uc'] = 0
self.coeff_dict['D66cu'] = 0
self.coeff_dict['D61ub'] = vu*vd * 4*sw**2*cw**2
self.coeff_dict['D62ub'] = au*ad * 4*sw**2*cw**2
self.coeff_dict['D63ub'] = au*vd * 4*sw**2*cw**2
self.coeff_dict['D63bu'] = ad*vu * 4*sw**2*cw**2
self.coeff_dict['D64ub'] = 0
self.coeff_dict['D65ub'] = 0
self.coeff_dict['D66ub'] = 0
self.coeff_dict['D66bu'] = 0
self.coeff_dict['D61ds'] = vd*vd * 4*sw**2*cw**2
self.coeff_dict['D62ds'] = ad*ad * 4*sw**2*cw**2
self.coeff_dict['D63ds'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D63sd'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D64ds'] = 0
self.coeff_dict['D65ds'] = 0
self.coeff_dict['D66ds'] = 0
self.coeff_dict['D66sd'] = 0
self.coeff_dict['D61dc'] = vd*vu * 4*sw**2*cw**2
self.coeff_dict['D62dc'] = ad*au * 4*sw**2*cw**2
self.coeff_dict['D63dc'] = ad*vu * 4*sw**2*cw**2
self.coeff_dict['D63cd'] = au*vd * 4*sw**2*cw**2
self.coeff_dict['D64dc'] = 0
self.coeff_dict['D65dc'] = 0
self.coeff_dict['D66dc'] = 0
self.coeff_dict['D66cd'] = 0
self.coeff_dict['D61db'] = vd*vd * 4*sw**2*cw**2
self.coeff_dict['D62db'] = ad*ad * 4*sw**2*cw**2
self.coeff_dict['D63db'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D63bd'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D64db'] = 0
self.coeff_dict['D65db'] = 0
self.coeff_dict['D66db'] = 0
self.coeff_dict['D66bd'] = 0
self.coeff_dict['D61sc'] = vd*vu * 4*sw**2*cw**2 + 1/6
self.coeff_dict['D62sc'] = ad*au * 4*sw**2*cw**2 + 1/6
self.coeff_dict['D63sc'] = ad*vu * 4*sw**2*cw**2 - 1/6
self.coeff_dict['D63cs'] = au*vd * 4*sw**2*cw**2 - 1/6
self.coeff_dict['D64sc'] = 1
self.coeff_dict['D65sc'] = 1
self.coeff_dict['D66sc'] = -1
self.coeff_dict['D66cs'] = -1
self.coeff_dict['D61sb'] = vd*vd * 4*sw**2*cw**2
self.coeff_dict['D62sb'] = ad*ad * 4*sw**2*cw**2
self.coeff_dict['D63sb'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D63bs'] = ad*vd * 4*sw**2*cw**2
self.coeff_dict['D64sb'] = 0
self.coeff_dict['D65sb'] = 0
self.coeff_dict['D66sb'] = 0
self.coeff_dict['D66bs'] = 0
self.coeff_dict['D61cb'] = vu*vd * 4*sw**2*cw**2
self.coeff_dict['D62cb'] = au*ad * 4*sw**2*cw**2
self.coeff_dict['D63cb'] = au*vd * 4*sw**2*cw**2
self.coeff_dict['D63bc'] = ad*vu * 4*sw**2*cw**2
self.coeff_dict['D64cb'] = 0
self.coeff_dict['D65cb'] = 0
self.coeff_dict['D66cb'] = 0
self.coeff_dict['D66bc'] = 0
self.coeff_dict['D61u'] = vu**2 * 2*sw**2*cw**2
self.coeff_dict['D62u'] = au**2 * 2*sw**2*cw**2
self.coeff_dict['D63u'] = vu*au * 4*sw**2*cw**2
self.coeff_dict['D64u'] = 0
self.coeff_dict['D61d'] = vd**2 * 2*sw**2*cw**2
self.coeff_dict['D62d'] = ad**2 * 2*sw**2*cw**2
self.coeff_dict['D63d'] = vd*ad * 4*sw**2*cw**2
self.coeff_dict['D64d'] = 0
self.coeff_dict['D61s'] = vd**2 * 2*sw**2*cw**2
self.coeff_dict['D62s'] = ad**2 * 2*sw**2*cw**2
self.coeff_dict['D63s'] = vd*ad * 4*sw**2*cw**2
self.coeff_dict['D64s'] = 0
self.coeff_dict['D61c'] = vu**2 * 2*sw**2*cw**2
self.coeff_dict['D62c'] = au**2 * 2*sw**2*cw**2
self.coeff_dict['D63c'] = vu*au * 4*sw**2*cw**2
self.coeff_dict['D64c'] = 0
self.coeff_dict['D61b'] = vd**2 * 2*sw**2*cw**2
self.coeff_dict['D62b'] = ad**2 * 2*sw**2*cw**2
self.coeff_dict['D63b'] = vd*ad * 4*sw**2*cw**2
self.coeff_dict['D64b'] = 0
# Leptons
self.coeff_dict['D62ue'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62umu'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62utau'] = au*al * 4*sw**2*cw**2
self.coeff_dict['D62de'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62dmu'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62dtau'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62se'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62smu'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D62stau'] = ad*al * 4*sw**2*cw**2
self.coeff_dict['D63eu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63muu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63tauu'] = al*vu * 4*sw**2*cw**2
self.coeff_dict['D63ed'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63mud'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63taud'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63es'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63mus'] = al*vd * 4*sw**2*cw**2
self.coeff_dict['D63taus'] = al*vd * 4*sw**2*cw**2
# Now update with the user-specified values, if defined
for wc_name in self.wc_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
pass
for wc_name in self.wc8_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
pass
for wc_name in self.sm_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
pass
for wc_name in self.sm_lepton_name_list:
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
pass
# Create the np.array of coefficients:
self.coeff_list_dm_dim5_dim6_dim7 = np.array(dict_to_list(self.coeff_dict, self.wc_name_list))
self.coeff_list_dm_dim8 = np.array(dict_to_list(self.coeff_dict, self.wc8_name_list))
self.coeff_list_sm_dim6 = np.array(dict_to_list(self.coeff_dict, self.sm_name_list))
self.coeff_list_sm_lepton_dim6 = np.array(dict_to_list(self.coeff_dict, self.sm_lepton_name_list))
#---------------------------#
# The anomalous dimensions: #
#---------------------------#
if self.DM_type == "D":
self.gamma_QED = adm.ADM_QED(5)
self.gamma_QED2 = adm.ADM_QED2(5)
self.gamma_QCD = adm.ADM_QCD(5)
self.gamma_QCD2 = adm.ADM_QCD2(5)
self.gamma_QCD_dim8 = adm.ADM_QCD_dim8(5)
self.gamma_hat = adm.ADT_QCD(5, self.ip)
if self.DM_type == "M":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(5), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(5), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(5), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
self.gamma_hat = np.delete(np.delete(adm.ADT_QCD(5, self.ip), del_ind_list_dim_8, 0),\
del_ind_list_adt_quark, 2)
if self.DM_type == "C":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(5), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(5), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD_dim8 = np.delete(np.delete(adm.ADM_QCD_dim8(5), del_ind_list_dim_8, 0),\
del_ind_list_dim_8, 1)
self.gamma_hat = np.delete(np.delete(adm.ADT_QCD(5, self.ip), del_ind_list_dim_8, 0),\
del_ind_list_adt_quark, 2)
if self.DM_type == "R":
self.gamma_QED = np.delete(np.delete(adm.ADM_QED(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QED2 = np.delete(np.delete(adm.ADM_QED2(5), del_ind_list, 0), del_ind_list, 1)
self.gamma_QCD = np.delete(np.delete(adm.ADM_QCD(5), del_ind_list, 1), del_ind_list, 2)
self.gamma_QCD2 = np.delete(np.delete(adm.ADM_QCD2(5), del_ind_list, 1), del_ind_list, 2)
self.ADM_SM = adm.ADM_SM_QCD(5)
#--------------------------------------------------------------------#
# The effective anomalous dimension for mixing into dimension eight: #
#--------------------------------------------------------------------#
# We need to contract the ADT with a subset of the dim.-6 Wilson coefficients
if self.DM_type == "D":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7,\
np.r_[np.s_[0:18], np.s_[23:26], np.s_[31:163]])
elif self.DM_type == "M":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7, np.r_[np.s_[0:8], np.s_[13:97]])
elif self.DM_type == "C":
DM_dim6_init = np.delete(self.coeff_list_dm_dim5_dim6_dim7, np.r_[np.s_[0:8], np.s_[13:45]])
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
# The columns of ADM_eff correspond to SM6 operators;
# the rows of ADM_eff correspond to DM8 operators:
C6_dot_ADM_hat = np.transpose(np.tensordot(DM_dim6_init, self.gamma_hat, (0,2)))
# The effective ADM
#
# Note that the mixing of the SM operators with four equal flavors
# does not contribute if we neglect yu, yd, ys!
self.ADM_eff = [np.vstack((np.hstack((self.ADM_SM,\
np.vstack((C6_dot_ADM_hat,\
np.zeros((20, len(self.gamma_QCD_dim8))))))),\
np.hstack((np.zeros((len(self.gamma_QCD_dim8),\
len(self.coeff_list_sm_dim6))), self.gamma_QCD_dim8))))]
if self.DM_type == "R":
pass
def run(self, mu_low=None, double_QCD=None):
""" Running of 5-flavor Wilson coefficients
Calculate the running from MZ to mu_low [GeV; default mb(mb)] in the five-flavor theory.
Return a dictionary of Wilson coefficients for the five-flavor Lagrangian
at scale mu_low.
"""
if mu_low is None:
mu_low=self.ip['mb_at_mb']
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
if double_QCD is None:
double_QCD=True
else:
double_QCD=False
#-------------#
# The running #
#-------------#
MZ = self.ip['Mz']
alpha_at_mb = 1/self.ip['aMZinv']
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
if double_QCD:
adm_eff = self.ADM_eff
else:
projector = np.vstack((np.hstack((np.zeros((100,100)),\
np.ones((100,12)))), np.zeros((12,112))))
adm_eff = [np.multiply(projector, self.ADM_eff[0])]
else:
double_QCD=False
as51 = rge.AlphaS(self.ip['asMZ'], self.ip['Mz'])
as51_high = as51.run({'mbmb': self.ip['mb_at_mb'],\
'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'],\
'muc': self.ip['mc_at_mc']}, MZ, 5, 1)
as51_low = as51.run({'mbmb': self.ip['mb_at_mb'],\
'mcmc': self.ip['mc_at_mc']},\
{'mub': self.ip['mb_at_mb'],\
'muc': self.ip['mc_at_mc']}, mu_low, 5, 1)
evolve1 = rge.RGE(self.gamma_QCD, 5)
evolve2 = rge.RGE(self.gamma_QCD2, 5)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
evolve8 = rge.RGE(adm_eff, 5)
else:
pass
# Mixing in the dim.6 DM-SM sector
#
# Strictly speaking, MZ and mb should be defined at the same scale
# (however, this is a higher-order difference)
C_at_mb_QCD = np.dot(evolve2.U0_as2(as51_high, as51_low),\
np.dot(evolve1.U0(as51_high, as51_low),\
self.coeff_list_dm_dim5_dim6_dim7))
C_at_mb_QED = np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED)\
* np.log(mu_low/MZ) * alpha_at_mb/(4*np.pi)\
+ np.dot(self.coeff_list_dm_dim5_dim6_dim7, self.gamma_QED2)\
* np.log(mu_low/MZ) * (alpha_at_mb/(4*np.pi))**2
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
# Mixing in the dim.6 SM-SM and dim.8 DM-SM sector
DIM6_DIM8_init = np.hstack((self.coeff_list_sm_dim6, self.coeff_list_dm_dim8))
DIM6_DIM8_at_mb = np.dot(evolve8.U0(as51_high, as51_low), DIM6_DIM8_init)
# Revert back to dictionary
dict_coeff_mb = list_to_dict(C_at_mb_QCD + C_at_mb_QED, self.wc_name_list)
if self.DM_type == "D" or self.DM_type == "M" or self.DM_type == "C":
dict_dm_dim8 = list_to_dict(np.delete(DIM6_DIM8_at_mb, np.s_[0:100]), self.wc8_name_list)
dict_sm_dim6 = list_to_dict(np.delete(DIM6_DIM8_at_mb, np.s_[100:112]), self.sm_name_list)
dict_sm_lepton_dim6 = list_to_dict(self.coeff_list_sm_lepton_dim6, self.sm_lepton_name_list)
dict_coeff_mb.update(dict_dm_dim8)
dict_coeff_mb.update(dict_sm_dim6)
dict_coeff_mb.update(dict_sm_lepton_dim6)
return dict_coeff_mb
def match(self, RGE=None, double_QCD=None, mu=None):
""" Match from five-flavor to four-flavor QCD
Calculate the matching at mu [GeV; default 4.18 GeV].
Returns a dictionary of Wilson coefficients for the four-flavor Lagrangian
at scale mu.
RGE is an optional argument to turn RGE running on (True) or off (False). (Default True)
"""
if RGE is None:
RGE=True
if mu is None:
mu=self.ip['mb_at_mb']
if double_QCD is None:
double_QCD=True
# The new coefficients
cdict4f = {}
if RGE:
cdold = self.run(mu, double_QCD)
else:
cdold = self.coeff_dict
if self.DM_type == "D" or self.DM_type == "M":
for wcn in self.wc_name_list_4f:
cdict4f[wcn] = cdold[wcn]
for wcn in self.wc8_name_list:
cdict4f[wcn] = cdold[wcn]
for wcn in self.sm_name_list_4f:
cdict4f[wcn] = cdold[wcn]
for wcn in self.sm_lepton_name_list:
cdict4f[wcn] = cdold[wcn]
cdict4f['C71'] = cdold['C71'] - cdold['C75b']
cdict4f['C72'] = cdold['C72'] - cdold['C76b']
cdict4f['C73'] = cdold['C73'] + cdold['C77b']
cdict4f['C74'] = cdold['C74'] + cdold['C78b']
if self.DM_type == "C":
for wcn in self.wc_name_list_4f:
cdict4f[wcn] = cdold[wcn]
for wcn in self.wc8_name_list:
cdict4f[wcn] = cdold[wcn]
for wcn in self.sm_name_list_4f:
cdict4f[wcn] = cdold[wcn]
for wcn in self.sm_lepton_name_list:
cdict4f[wcn] = cdold[wcn]
cdict4f['C65'] = cdold['C65'] - cdold['C63b']
cdict4f['C66'] = cdold['C66'] + cdold['C64b']
if self.DM_type == "R":
for wcn in self.wc_name_list_4f:
cdict4f[wcn] = cdold[wcn]
cdict4f['C65'] = cdold['C65'] - cdold['C63b']
cdict4f['C66'] = cdold['C66'] + cdold['C64b']
# return the 4-flavor coefficients
return cdict4f
def _my_cNR(self, DM_mass, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None):
""" Calculate the NR coefficients from four-flavor theory with meson contributions split off
(mainly for internal use)
"""
return WC_4flavor(self.match(RGE, double_QCD), self.DM_type,\
self.ip)._my_cNR(DM_mass, RGE, NLO, double_QCD, DOUBLE_WEAK)
def cNR(self, DM_mass, qvec, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None):
""" Calculate the NR coefficients from four-flavor theory """
return WC_4flavor(self.match(RGE, double_QCD), self.DM_type,\
self.ip).cNR(DM_mass, qvec, RGE, NLO, double_QCD, DOUBLE_WEAK)
def write_mma(self, DM_mass, qvec, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None, path=None, filename=None):
""" Write a text file with the NR coefficients that can be read into DMFormFactor
The order is {cNR1p, cNR2p, ... , cNR1n, cNR2n, ... }
Mandatory arguments are the DM mass DM_mass (in GeV) and the spatial momentum transfer qvec (in GeV)
<path> should be a string with the path (including the trailing "/") where the file should be saved
(default is './')
<filename> is the filename (default 'cNR.m')
"""
WC_4flavor(self.match(RGE, double_QCD), self.DM_type,\
self.ip).write_mma(DM_mass, qvec, RGE, NLO, double_QCD, DOUBLE_WEAK, path, filename)
#-----------------------------#
# The e/w Wilson coefficients #
#-----------------------------#
class WilCo_EW(object):
def __init__(self, coeff_dict, Ychi, dchi, DM_type, input_dict):
""" Class for DM Wilson coefficients in the SM unbroken phase
The first argument should be a dictionary for the initial conditions of the 8
dimension-five Wilson coefficients of the form
{'C51' : value, 'C52' : value, ...};
the 46 dimension-six Wilson coefficients of the form
{'C611' : value, 'C621' : value, ...};
and the dimension-seven Wilson coefficient (currently not yet implemented).
An arbitrary number of them can be given; the default values are zero.
The possible keys are, for dchi != 1:
'C51', 'C52', 'C53', 'C54', 'C55', 'C56', 'C57', 'C58',
'C611', 'C621', 'C631', 'C641', 'C651', 'C661', 'C671',
'C681', 'C691', 'C6101', 'C6111', 'C6121', 'C6131', 'C6141',
'C612', 'C622', 'C632', 'C642', 'C652', 'C662', 'C672',
'C682', 'C692', 'C6102', 'C6112', 'C6122', 'C6132', 'C6142',
'C613', 'C623', 'C633', 'C643', 'C653', 'C663', 'C673',
'C683', 'C693', 'C6103', 'C6113', 'C6123', 'C6133', 'C6143',
'C615', 'C616', 'C617', 'C618'
The possible keys are, for dchi = 1:
'C51', 'C53', 'C55', 'C57',
'C621', 'C631', 'C641', 'C661', 'C671', 'C681', 'C6101', 'C6111', 'C6131', 'C6141',
'C622', 'C632', 'C642', 'C662', 'C672', 'C682', 'C6102', 'C6112', 'C6132', 'C6142',
'C623', 'C633', 'C643', 'C663', 'C673', 'C683', 'C6103', 'C6113', 'C6133', 'C6143',
'C616', 'C618'
The following set of 3*17 + 3*6 + 6*11 + 3*7 + 1 = 157 SM operator coefficients
are also taken into account.
They are generated, from the DM-SM operators, through mixing via penguin insertions.
Note that any mixing within the SM sector is neglected.
For this reason, the values after running are not returned.
The possible keys are
'SM6111', 'SM6211', 'SM6311', 'SM6411', 'SM6511', 'SM6611', 'SM6711', 'SM6811', 'SM6911', 'SM61011',
'SM61111', 'SM61211', 'SM61311', 'SM61411', 'SM61511', 'SM61611', 'SM617711',
'SM6122', 'SM6222', 'SM6322', 'SM6422', 'SM6522', 'SM6622', 'SM6722', 'SM6822', 'SM6922', 'SM61022',
'SM61122', 'SM61222', 'SM61322', 'SM61422', 'SM61522', 'SM61622', 'SM617722',
'SM6133', 'SM6233', 'SM6333', 'SM6433', 'SM6533', 'SM6633', 'SM6733', 'SM6833', 'SM6933', 'SM61033',
'SM61133', 'SM61233', 'SM61333', 'SM61433', 'SM61533', 'SM61633', 'SM617733',
'SM6112', 'SM6212', 'SM6312', 'SM6321', 'SM6412', 'SM6421', 'SM6512', 'SM6612', 'SM6621', 'SM6712',
'SM6812', 'SM6912', 'SM6921', 'SM61012', 'SM61112', 'SM61121', 'SM61212', 'SM61221', 'SM61312', 'SM61321',
'SM61412', 'SM61421', 'SM61512', 'SM61521', 'SM61612', 'SM61621', 'SM617712', 'SM617721',
'SM6113', 'SM6213', 'SM6313', 'SM6331', 'SM6413', 'SM6431', 'SM6513', 'SM6613', 'SM6631', 'SM6713',
'SM6813', 'SM6913', 'SM6931', 'SM61013', 'SM61113', 'SM61131', 'SM61213', 'SM61231', 'SM61313', 'SM61331',
'SM61413', 'SM61431', 'SM61513', 'SM61531', 'SM61613', 'SM61631', 'SM617713', 'SM617731',
'SM6123', 'SM6223', 'SM6323', 'SM6332', 'SM6423', 'SM6432', 'SM6523', 'SM6623', 'SM6632', 'SM6723',
'SM6823', 'SM6923', 'SM6932', 'SM61023', 'SM61123', 'SM61132', 'SM61223', 'SM61232', 'SM61323', 'SM61332',
'SM61423', 'SM61432', 'SM61523', 'SM61532', 'SM61623', 'SM61632', 'SM617723', 'SM617732',
'SM6181', 'SM6191', 'SM6201', 'SM6211', 'SM6221', 'SM6231', 'SM6241',
'SM6182', 'SM6192', 'SM6202', 'SM6212', 'SM6222', 'SM6232', 'SM6242',
'SM6183', 'SM6193', 'SM6203', 'SM6213', 'SM6223', 'SM6233', 'SM6243',
'SM625'
Unless specified otherwise by the user, the tree-level initial conditions are set to zero.
Finally, four DM operator coefficients are also taken into account.
They are generated, from the DM-SM operators, through mixing via penguin insertions.
Note that any mixing within the DM sector is neglected.
For this reason, the values after running are not returned.
The possible keys are
'DM61', 'DM62', 'DM63', 'DM64'
Unless specified otherwise by the user, the tree-level initial conditions are set to zero.
Note that the numbering scheme for these coefficients is likely to change in the future.
dchi is the dimension of the DM SU2 representation.
Ychi is the DM hypercharge such that Q = I^3 + Y/2
The second-to-last argument is the DM type; it can only take the following value:
"D" (Dirac fermion)
Other DM types might be implemented in the future.
The last argument is a dictionary with all input parameters.
"""
self.DM_type = DM_type
self.Ychi = Ychi
self.dchi = dchi
if self.DM_type == "D":
if self.dchi == 1:
self.wc_name_list_dim_5 = ['C51', 'C53', 'C55', 'C57']
self.wc_name_list_dim_6 = ['C621', 'C631', 'C641', 'C661', 'C671',\
'C681', 'C6101', 'C6111', 'C6131', 'C6141',\
'C622', 'C632', 'C642', 'C662', 'C672',\
'C682', 'C6102', 'C6112', 'C6132', 'C6142',\
'C623', 'C633', 'C643', 'C663', 'C673',\
'C683', 'C6103', 'C6113', 'C6133', 'C6143',\
'C616', 'C618']
self.dm_name_list_dim_6 = ['DM61', 'DM62']
else:
self.wc_name_list_dim_5 = ['C51', 'C52', 'C53', 'C54', 'C55', 'C56', 'C57', 'C58']
self.wc_name_list_dim_6 = ['C611', 'C621', 'C631', 'C641', 'C651', 'C661', 'C671',\
'C681', 'C691', 'C6101', 'C6111', 'C6121', 'C6131', 'C6141',\
'C612', 'C622', 'C632', 'C642', 'C652', 'C662', 'C672',\
'C682', 'C692', 'C6102', 'C6112', 'C6122', 'C6132', 'C6142',\
'C613', 'C623', 'C633', 'C643', 'C653', 'C663', 'C673',\
'C683', 'C693', 'C6103', 'C6113', 'C6123', 'C6133', 'C6143',\
'C615', 'C616', 'C617', 'C618']
self.dm_name_list_dim_6 = ['DM61', 'DM62', 'DM63', 'DM64']
self.sm_name_list_dim_6 = ['SM6111', 'SM6211', 'SM6311', 'SM6411', 'SM6511',\
'SM6611', 'SM6711', 'SM6811', 'SM6911', 'SM61011',\
'SM61111', 'SM61211', 'SM61311', 'SM61411',\
'SM61511', 'SM61611', 'SM617711',\
'SM6122', 'SM6222', 'SM6322', 'SM6422', 'SM6522',\
'SM6622', 'SM6722', 'SM6822', 'SM6922', 'SM61022',\
'SM61122', 'SM61222', 'SM61322', 'SM61422',\
'SM61522', 'SM61622', 'SM617722',\
'SM6133', 'SM6233', 'SM6333', 'SM6433', 'SM6533',\
'SM6633', 'SM6733', 'SM6833', 'SM6933', 'SM61033',\
'SM61133', 'SM61233', 'SM61333', 'SM61433',\
'SM61533', 'SM61633', 'SM617733',\
'SM6112', 'SM6212', 'SM6312', 'SM6321', 'SM6412',\
'SM6421', 'SM6512', 'SM6612', 'SM6621', 'SM6712',\
'SM6812', 'SM6912', 'SM6921', 'SM61012', 'SM61112',\
'SM61121', 'SM61212', 'SM61221', 'SM61312', 'SM61321',\
'SM61412', 'SM61421', 'SM61512', 'SM61521',\
'SM61612', 'SM61621', 'SM617712', 'SM617721',\
'SM6113', 'SM6213', 'SM6313', 'SM6331', 'SM6413',\
'SM6431', 'SM6513', 'SM6613', 'SM6631', 'SM6713',\
'SM6813', 'SM6913', 'SM6931', 'SM61013', 'SM61113',\
'SM61131', 'SM61213', 'SM61231', 'SM61313', 'SM61331',\
'SM61413', 'SM61431', 'SM61513', 'SM61531',\
'SM61613', 'SM61631', 'SM617713', 'SM617731',\
'SM6123', 'SM6223', 'SM6323', 'SM6332', 'SM6423',\
'SM6432', 'SM6523', 'SM6623', 'SM6632', 'SM6723',\
'SM6823', 'SM6923', 'SM6932', 'SM61023', 'SM61123',\
'SM61132', 'SM61223', 'SM61232', 'SM61323', 'SM61332',\
'SM61423', 'SM61432', 'SM61523', 'SM61532',\
'SM61623', 'SM61632', 'SM617723', 'SM617732',\
'SM6181', 'SM6191', 'SM6201', 'SM6211',\
'SM6221', 'SM6231', 'SM6241',\
'SM6182', 'SM6192', 'SM6202', 'SM6212',\
'SM6222', 'SM6232', 'SM6242',\
'SM6183', 'SM6193', 'SM6203', 'SM6213',\
'SM6223', 'SM6233', 'SM6243', 'SM625']
else: raise Exception("Only Dirac fermion DM is implemented at the moment.")
# Issue a user warning if a key is not defined or belongs to a redundant operator:
for wc_name in coeff_dict.keys():
if wc_name in self.wc_name_list_dim_5:
pass
elif wc_name in self.wc_name_list_dim_6:
pass
elif wc_name in self.sm_name_list_dim_6:
pass
elif wc_name in self.dm_name_list_dim_6:
pass
else:
if self.dchi == 1:
warnings.warn('The key ' + wc_name + ' is not a valid key. Typo; or belongs to an operator that is redundant for dchi = 1?')
else:
warnings.warn('The key ' + wc_name + ' is not a valid key. Typo?')
self.coeff_dict = {}
# Create the dictionary:
for wc_name in (self.wc_name_list_dim_5 + self.wc_name_list_dim_6\
+ self.sm_name_list_dim_6 + self.dm_name_list_dim_6):
if wc_name in coeff_dict.keys():
self.coeff_dict[wc_name] = coeff_dict[wc_name]
else:
self.coeff_dict[wc_name] = 0.
# Create the np.array of coefficients:
self.coeff_list_dim_5 = np.array(dict_to_list(self.coeff_dict, self.wc_name_list_dim_5))
self.coeff_list_dim_6 = np.array(dict_to_list(self.coeff_dict, self.wc_name_list_dim_6))
self.coeff_list_sm_dim_6 = np.array(dict_to_list(self.coeff_dict, self.sm_name_list_dim_6))
self.coeff_list_dm_dim_6 = np.array(dict_to_list(self.coeff_dict, self.dm_name_list_dim_6))
# The dictionary of input parameters
self.ip = input_dict
#---------#
# Running #
#---------#
def run(self, mu_Lambda, muz=None):
"""Calculate the e/w running from scale mu_Lambda (to be given in GeV) to scale muz [muz = MZ by default].
"""
if muz is None:
muz = self.ip['Mz']+0.01
# Define the dictionary of initial condictions for gauge / Yukawa couplings
# at the scale mu = MZ (MSbar):
#
# (In the future, implement also the "running and matching" of mtop to mu = MZ)
# The quark masses at MZ:
def mb(mu, mub, muc, nf, loop):
return rge.M_Quark_MSbar('b', self.ip['mb_at_mb'], self.ip['mb_at_mb'], self.ip['asMZ'],\
self.ip['Mz']).run(mu, {'mbmb': self.ip['mb_at_mb'],\
'mcmc': self.ip['mc_at_mc']},\
{'mub': mub, 'muc': muc}, nf, loop)
def mc(mu, mub, muc, nf, loop):
return rge.M_Quark_MSbar('c', self.ip['mc_at_mc'], self.ip['mc_at_mc'], self.ip['asMZ'],\
self.ip['Mz']).run(mu, {'mbmb': self.ip['mb_at_mb'],\
'mcmc': self.ip['mc_at_mc']},\
{'mub': mub, 'muc': muc}, nf, loop)
self.mb_at_MZ = mb(self.ip['Mz'], self.ip['mb_at_mb'], self.ip['mc_at_mc'], 5, 1)
self.mc_at_MZ = mc(self.ip['Mz'], self.ip['mb_at_mb'], self.ip['mc_at_mc'], 5, 1)
self.g2_at_MZ = np.sqrt(4*np.pi/self.ip['aMZinv']/self.ip['sw2_MSbar'])
self.g1_at_MZ = np.sqrt(self.g2_at_MZ**2/(1/self.ip['sw2_MSbar'] - 1))
self.g3_at_MZ = np.sqrt(4*np.pi*self.ip['asMZ'])
self.yc_at_MZ = np.sqrt(np.sqrt(2)*self.ip['GF'])*np.sqrt(2) * self.mc_at_MZ
self.yb_at_MZ = np.sqrt(np.sqrt(2)*self.ip['GF'])*np.sqrt(2) * self.mb_at_MZ
self.ytau_at_MZ = np.sqrt(np.sqrt(2)*self.ip['GF'])*np.sqrt(2) * self.ip['mtau']
self.yt_at_MZ = np.sqrt(np.sqrt(2)*self.ip['GF'])*np.sqrt(2) * self.ip['mt_at_MZ']
self.lam_at_MZ = 2*np.sqrt(2) * self.ip['GF'] * self.ip['Mh']**2
self.coupl_init_dict = {'g1': self.g1_at_MZ,\
'g2': self.g2_at_MZ,\
'gs': self.g3_at_MZ,\
'ytau': self.ytau_at_MZ,\
'yc': self.yc_at_MZ,\
'yb': self.yb_at_MZ,\
'yt': self.yt_at_MZ,\
'lam': self.lam_at_MZ}
# The full vector of dim.-6 Wilson coefficients
C6_at_Lambda = np.concatenate((self.coeff_list_dim_6, self.coeff_list_sm_dim_6,\
self.coeff_list_dm_dim_6))
# The vector of rescaled dim.-5 Wilson coefficients
#
# The e/w dipole operators are defined with a prefactor g_{1,2}/(8*pi^2).
# The ADM are calculated in a basis with prefactors 1/g_{1,2}.
# Therefore, we need to rescale the Wilson coefficients by g_{1,2}(Lambda)^2/(8*pi^2) at mu=Lambda,
# and then again by (8*pi^2)/g_{1,2}(MZ)^2 at mu=MZ.
alpha1_at_Lambda = rge.CmuEW([], [], self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi,\
self.dchi)._alphai(rge.CmuEW([], [],\
self.coupl_init_dict,\
self.ip['Mz'],\
mu_Lambda, muz,\
self.Ychi, self.dchi).ginit,\
self.ip['Mz'], mu_Lambda, self.Ychi, self.dchi)[0]
alpha2_at_Lambda = rge.CmuEW([], [], self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi,\
self.dchi)._alphai(rge.CmuEW([], [],\
self.coupl_init_dict,\
self.ip['Mz'],\
mu_Lambda, muz,\
self.Ychi, self.dchi).ginit,\
self.ip['Mz'], mu_Lambda, self.Ychi, self.dchi)[1]
if self.dchi == 1:
C5_at_Lambda_rescaled = self.coeff_list_dim_5 * np.array([alpha1_at_Lambda/(2*np.pi), 1,\
alpha1_at_Lambda/(2*np.pi), 1])
else:
C5_at_Lambda_rescaled = self.coeff_list_dim_5 * np.array([alpha1_at_Lambda/(2*np.pi),\
alpha2_at_Lambda/(2*np.pi), 1, 1,\
alpha1_at_Lambda/(2*np.pi),\
alpha2_at_Lambda/(2*np.pi), 1, 1])
# The actual running
C5_at_muz = rge.CmuEW(C5_at_Lambda_rescaled, adm.ADM5(self.Ychi, self.dchi),\
self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi, self.dchi).run()
C6_at_muz = rge.CmuEW(C6_at_Lambda, adm.ADM6(self.Ychi, self.dchi),\
self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi, self.dchi).run()
# Rescaling back to original normalization of dim.-5 Wilson coefficients
alpha1_at_muz = rge.CmuEW([], [], self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi,\
self.dchi)._alphai(rge.CmuEW([], [],\
self.coupl_init_dict,\
self.ip['Mz'],\
mu_Lambda, muz,\
self.Ychi, self.dchi).ginit,\
self.ip['Mz'], muz, self.Ychi, self.dchi)[0]
alpha2_at_muz = rge.CmuEW([], [], self.coupl_init_dict, self.ip['Mz'],\
mu_Lambda, muz, self.Ychi,\
self.dchi)._alphai(rge.CmuEW([], [],\
self.coupl_init_dict,\
self.ip['Mz'],\
mu_Lambda, muz,\
self.Ychi, self.dchi).ginit,\
self.ip['Mz'], muz, self.Ychi, self.dchi)[1]
if self.dchi == 1:
C5_at_muz_rescaled = C5_at_muz * np.array([(2*np.pi)/alpha1_at_muz, 1,\
(2*np.pi)/alpha1_at_muz, 1])
else:
C5_at_muz_rescaled = C5_at_muz * np.array([(2*np.pi)/alpha1_at_muz,\
(2*np.pi)/alpha2_at_muz, 1, 1,\
(2*np.pi)/alpha1_at_muz,\
(2*np.pi)/alpha2_at_muz, 1, 1])
# Convert arrays to dictionaries
C5_at_muz_dict = list_to_dict(C5_at_muz_rescaled, self.wc_name_list_dim_5)
C6_at_muz_dict = list_to_dict(C6_at_muz, self.wc_name_list_dim_6)
C_at_muz_dict = {}
for wc_name in self.wc_name_list_dim_5:
C_at_muz_dict[wc_name] = C5_at_muz_dict[wc_name]
for wc_name in self.wc_name_list_dim_6:
C_at_muz_dict[wc_name] = C6_at_muz_dict[wc_name]
return C_at_muz_dict
#----------#
# Matching #
#----------#
def match(self, DM_mass, mu_Lambda, DM_mass_threshold=None, RUN_EW=None, DIM4=None):
"""Calculate the matching from the relativistic theory to the five-flavor theory at scale MZ
DM_mass is the DM mass, as it appears in the UV Lagrangian. It is not the physical DM mass after EWSB.
mu_Lambda (to be given in GeV) is the starting scale of the RG evolution
DM_mass_threshold is the DM mass below which DM is treated as "light" [default is 40 GeV]
RUN_EW can have three values:
- RUN_EW = True does the full leading-logarithmic resummation (this is the default)
- RUN_EW = False -- no electroweak running
DIM4 multiplies the dimension-four matching contributions.
To be considered as an "checking tool", will be removed in the future
Return a dictionary of Wilson coefficients for the five-flavor Lagrangian,
with the following keys (only Dirac DM is implemented so far):
Dirac fermion: 'C51', 'C52', 'C61u', 'C61d', 'C61s', 'C61c', 'C61b', 'C61e', 'C61mu', 'C61tau',
'C62u', 'C62d', 'C62s', 'C62c', 'C62b', 'C62e', 'C62mu', 'C62tau',
'C63u', 'C63d', 'C63s', 'C63c', 'C63b', 'C63e', 'C63mu', 'C63tau',
'C64u', 'C64d', 'C64s', 'C64c', 'C64b', 'C64e', 'C64mu', 'C64tau',
'C71', 'C72', 'C73', 'C74',
'C75u', 'C75d', 'C75s', 'C65c', 'C65b', 'C75e', 'C75mu', 'C75tau',
'C76u', 'C76d', 'C76s', 'C66c', 'C66b', 'C76e', 'C76mu', 'C76tau',
'C77u', 'C77d', 'C77s', 'C67c', 'C67b', 'C77e', 'C77mu', 'C77tau',
'C78u', 'C78d', 'C78s', 'C68c', 'C68b', 'C78e', 'C78mu', 'C78tau',
'C79u', 'C79d', 'C79s', 'C69c', 'C69b', 'C79e', 'C79mu', 'C79tau',
'C710u', 'C710d', 'C710s', 'C610c', 'C610b', 'C710e', 'C710mu', 'C710tau'
"""
if RUN_EW is None:
RUN_EW = True
self.RUN_EW = RUN_EW
if DM_mass_threshold is None:
DM_mass_threshold = 40 # GeV
self.DM_mass_threshold = DM_mass_threshold
if DIM4 is None:
DIM4 = 1
else:
DIM4 = 0
# Issue a user warning that matching results are not complete for Ychi != 0
if self.Ychi != 0:
warnings.warn('Matching contributions from gauge interactions are not complete for Ychi != 0')
# Some input parameters:
vev = 1/np.sqrt(np.sqrt(2)*self.ip['GF'])
alpha = 1/self.ip['aMZinv']
MW = self.ip['Mw']
MZ = self.ip['Mz']
Mh = self.ip['Mh']
sw = np.sqrt(self.ip['sw2_MSbar'])
cw = np.sqrt(1-sw**2)
# The Wilson coefficients in the "UV" EFT at scale MZ
if RUN_EW:
wcew_dict = self.run(mu_Lambda, muz=self.ip['Mz'])
else:
wcew_dict = self.coeff_dict
# Calculate the physical DM mass in terms of DM_mass and the Wilson coefficients,
# and the corresponding shift in the dimension-five Wilson coefficients.
if DM_mass > DM_mass_threshold:
if self.dchi == 1:
self.DM_mass_phys = DM_mass - vev**2/2 * wcew_dict['C53']
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = wcew_dict['C51']\
+ vev**2/2/DM_mass * wcew_dict['C57'] * wcew_dict['C55']
wc5_dict_shifted['C53'] = wcew_dict['C53']\
+ vev**2/2/DM_mass * wcew_dict['C57'] * wcew_dict['C57']
wc5_dict_shifted['C55'] = wcew_dict['C55']\
- vev**2/2/DM_mass * wcew_dict['C57'] * wcew_dict['C51']
wc5_dict_shifted['C57'] = wcew_dict['C57']\
- vev**2/2/DM_mass * wcew_dict['C57'] * wcew_dict['C53']
else:
self.DM_mass_phys = DM_mass - vev**2/2 * (wcew_dict['C53'] + self.Ychi/4 * wcew_dict['C54'])
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = wcew_dict['C51']\
+ vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C55']
wc5_dict_shifted['C52'] = wcew_dict['C52']\
+ vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C56']
wc5_dict_shifted['C53'] = wcew_dict['C53']\
+ vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C57']
wc5_dict_shifted['C54'] = wcew_dict['C54']\
+ vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C58']
wc5_dict_shifted['C55'] = wcew_dict['C55']\
- vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C51']
wc5_dict_shifted['C56'] = wcew_dict['C56']\
- vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C52']
wc5_dict_shifted['C57'] = wcew_dict['C57']\
- vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C53']
wc5_dict_shifted['C58'] = wcew_dict['C58']\
- vev**2/2/DM_mass * (wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58']) * wcew_dict['C54']
else:
if self.dchi == 1:
cosphi = np.sqrt((wcew_dict['C53'] - 2*DM_mass/vev**2)**2/\
((wcew_dict['C53'] - 2*DM_mass/vev**2)**2 + wcew_dict['C57']**2))
sinphi = np.sqrt((wcew_dict['C57'])**2/\
((wcew_dict['C53'] - 2*DM_mass/vev**2)**2 + wcew_dict['C57']**2))
pre_DM_mass_phys = DM_mass*cosphi + vev**2/2\
* (wcew_dict['C57'] * sinphi - wcew_dict['C53'] * cosphi)
if pre_DM_mass_phys > 0:
self.DM_mass_phys = pre_DM_mass_phys
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = cosphi * wcew_dict['C51'] + sinphi * wcew_dict['C55']
wc5_dict_shifted['C53'] = cosphi * wcew_dict['C53'] + sinphi * wcew_dict['C57']
wc5_dict_shifted['C55'] = cosphi * wcew_dict['C55'] - sinphi * wcew_dict['C51']
wc5_dict_shifted['C57'] = cosphi * wcew_dict['C57'] - sinphi * wcew_dict['C53']
else:
self.DM_mass_phys = - pre_DM_mass_phys
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = cosphi * wcew_dict['C51'] - sinphi * wcew_dict['C55']
wc5_dict_shifted['C53'] = cosphi * wcew_dict['C53'] - sinphi * wcew_dict['C57']
wc5_dict_shifted['C55'] = cosphi * wcew_dict['C55'] + sinphi * wcew_dict['C51']
wc5_dict_shifted['C57'] = cosphi * wcew_dict['C57'] + sinphi * wcew_dict['C53']
else:
cosphi = np.sqrt((wcew_dict['C53'] + self.Ychi/4 * wcew_dict['C54'] - 2*DM_mass/vev**2)**2/\
((wcew_dict['C53'] + self.Ychi/4 * wcew_dict['C54'] - 2*DM_mass/vev**2)**2\
+(wcew_dict['C57'] + self.Ychi/4 * wcew_dict['C58'])**2))
sinphi = np.sqrt((wcew_dict['C57'] + self.Ychi/4 * wcew_dict['C58'])**2/\
((wcew_dict['C53'] + self.Ychi/4 * wcew_dict['C54'] - 2*DM_mass/vev**2)**2\
+(wcew_dict['C57'] + self.Ychi/4 * wcew_dict['C58'])**2))
pre_DM_mass_phys = DM_mass*cosphi + vev**2/2 * ((wcew_dict['C57'] + self.Ychi/4\
* wcew_dict['C58'])*sinphi\
- (wcew_dict['C53'] + self.Ychi/4\
* wcew_dict['C54'])*cosphi)
if pre_DM_mass_phys > 0:
self.DM_mass_phys = pre_DM_mass_phys
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = cosphi * wcew_dict['C51'] + sinphi * wcew_dict['C55']
wc5_dict_shifted['C52'] = cosphi * wcew_dict['C52'] + sinphi * wcew_dict['C56']
wc5_dict_shifted['C53'] = cosphi * wcew_dict['C53'] + sinphi * wcew_dict['C57']
wc5_dict_shifted['C54'] = cosphi * wcew_dict['C54'] + sinphi * wcew_dict['C58']
wc5_dict_shifted['C55'] = cosphi * wcew_dict['C55'] - sinphi * wcew_dict['C51']
wc5_dict_shifted['C56'] = cosphi * wcew_dict['C56'] - sinphi * wcew_dict['C52']
wc5_dict_shifted['C57'] = cosphi * wcew_dict['C57'] - sinphi * wcew_dict['C53']
wc5_dict_shifted['C58'] = cosphi * wcew_dict['C58'] - sinphi * wcew_dict['C54']
else:
self.DM_mass_phys = - pre_DM_mass_phys
wc5_dict_shifted = {}
wc5_dict_shifted['C51'] = cosphi * wcew_dict['C51'] - sinphi * wcew_dict['C55']
wc5_dict_shifted['C52'] = cosphi * wcew_dict['C52'] - sinphi * wcew_dict['C56']
wc5_dict_shifted['C53'] = cosphi * wcew_dict['C53'] - sinphi * wcew_dict['C57']
wc5_dict_shifted['C54'] = cosphi * wcew_dict['C54'] - sinphi * wcew_dict['C58']
wc5_dict_shifted['C55'] = cosphi * wcew_dict['C55'] + sinphi * wcew_dict['C51']
wc5_dict_shifted['C56'] = cosphi * wcew_dict['C56'] + sinphi * wcew_dict['C52']
wc5_dict_shifted['C57'] = cosphi * wcew_dict['C57'] + sinphi * wcew_dict['C53']
wc5_dict_shifted['C58'] = cosphi * wcew_dict['C58'] + sinphi * wcew_dict['C54']
# The redefinitions of the dim.-5 Wilson coefficients resulting from the mass shift:
coeff_dict_shifted = wcew_dict
coeff_dict_shifted.update(wc5_dict_shifted)
# The Higgs penguin function.
# The result is valid for all input values and gives (in principle) a real output.
# Note that currently there is no distinction between e/w and light DM,
# as the two-loop function for light DM is unknown.
# Note also that we set the DM hypercharge to zero, Y=0,
# since for Y!=0 tree-level Z exchange dominates.
def higgs_penguin_fermion(dchi):
return Higgspenguin(dchi, self.ip).f_q_hisano(self.DM_mass_phys)
def twist_two_fermion(dchi):
return Higgspenguin(dchi, self.ip).g_q_1_hisano(self.DM_mass_phys)\
+ Higgspenguin(dchi, self.ip).g_q_2_hisano(self.DM_mass_phys)
def W_box_fermion(dchi):
return Higgspenguin(dchi, self.ip).d_q_hisano(self.DM_mass_phys)
def higgs_penguin_gluon(dchi):
return Higgspenguin(dchi, self.ip).hisano_fa(self.DM_mass_phys)\
+ Higgspenguin(dchi, self.ip).hisano_fbc(self.DM_mass_phys)
#-----------------------#
# The new coefficients: #
#-----------------------#
coeff_dict_5f = {}
if self.dchi == 1:
coeff_dict_5f['C51'] = coeff_dict_shifted['C51']
coeff_dict_5f['C52'] = coeff_dict_shifted['C55']
coeff_dict_5f['C61u'] = coeff_dict_shifted['C621']/2 + coeff_dict_shifted['C631']/2\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-8*sw**2) * DIM4
coeff_dict_5f['C61d'] = coeff_dict_shifted['C621']/2 + coeff_dict_shifted['C641']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61s'] = coeff_dict_shifted['C622']/2 + coeff_dict_shifted['C642']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61c'] = coeff_dict_shifted['C622']/2 + coeff_dict_shifted['C632']/2\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-8*sw**2) * DIM4
coeff_dict_5f['C61b'] = coeff_dict_shifted['C623']/2 + coeff_dict_shifted['C643']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61e'] = coeff_dict_shifted['C6101']/2 + coeff_dict_shifted['C6111']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C61mu'] = coeff_dict_shifted['C6102']/2 + coeff_dict_shifted['C6112']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C61tau'] = coeff_dict_shifted['C6103']/2 + coeff_dict_shifted['C6113']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C62u'] = coeff_dict_shifted['C661']/2 + coeff_dict_shifted['C671']/2\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62d'] = coeff_dict_shifted['C661']/2 + coeff_dict_shifted['C681']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62s'] = coeff_dict_shifted['C662']/2 + coeff_dict_shifted['C682']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62c'] = coeff_dict_shifted['C662']/2 + coeff_dict_shifted['C672']/2\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62b'] = coeff_dict_shifted['C663']/2 + coeff_dict_shifted['C683']/2\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62e'] = coeff_dict_shifted['C6131']/2 + coeff_dict_shifted['C6141']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C62mu'] = coeff_dict_shifted['C6132']/2 + coeff_dict_shifted['C6142']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C62tau'] = coeff_dict_shifted['C6133']/2 + coeff_dict_shifted['C6143']/2\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C63u'] = - coeff_dict_shifted['C621']/2 + coeff_dict_shifted['C631']/2\
- 1/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63d'] = - coeff_dict_shifted['C621']/2 + coeff_dict_shifted['C641']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63s'] = - coeff_dict_shifted['C622']/2 + coeff_dict_shifted['C642']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63c'] = - coeff_dict_shifted['C622']/2 + coeff_dict_shifted['C632']/2\
- 1/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63b'] = - coeff_dict_shifted['C623']/2 + coeff_dict_shifted['C643']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63e'] = - coeff_dict_shifted['C6101']/2 + coeff_dict_shifted['C6111']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63mu'] = - coeff_dict_shifted['C6102']/2 + coeff_dict_shifted['C6112']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63tau'] = - coeff_dict_shifted['C6103']/2 + coeff_dict_shifted['C6113']/2\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C64u'] = - coeff_dict_shifted['C661']/2 + coeff_dict_shifted['C671']/2\
- 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64d'] = - coeff_dict_shifted['C661']/2 + coeff_dict_shifted['C681']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64s'] = - coeff_dict_shifted['C662']/2 + coeff_dict_shifted['C682']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64c'] = - coeff_dict_shifted['C662']/2 + coeff_dict_shifted['C672']/2\
- 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64b'] = - coeff_dict_shifted['C663']/2 + coeff_dict_shifted['C683']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64e'] = - coeff_dict_shifted['C6131']/2 + coeff_dict_shifted['C6141']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64mu'] = - coeff_dict_shifted['C6132']/2 + coeff_dict_shifted['C6142']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64tau'] = - coeff_dict_shifted['C6133']/2 + coeff_dict_shifted['C6143']/2\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C71'] = (1/Mh**2 * (coeff_dict_shifted['C53']))\
+ higgs_penguin_gluon(self.dchi) * DIM4
coeff_dict_5f['C72'] = (1/Mh**2 * (coeff_dict_shifted['C57']))
coeff_dict_5f['C75u'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75d'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75s'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75c'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75b'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75e'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75mu'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75tau'] = - 1/Mh**2 * (coeff_dict_shifted['C53'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C76u'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76d'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76s'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76c'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76b'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76e'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76mu'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C76tau'] = - 1/Mh**2 * coeff_dict_shifted['C57']
coeff_dict_5f['C723u'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723d'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723s'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723c'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723b'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723e'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723mu'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723tau'] = twist_two_fermion(self.dchi) * DIM4
else:
coeff_dict_5f['C51'] = coeff_dict_shifted['C51'] + self.Ychi/2 * coeff_dict_shifted['C52']
coeff_dict_5f['C52'] = coeff_dict_shifted['C55'] + self.Ychi/2 * coeff_dict_shifted['C56']
coeff_dict_5f['C61u'] = - self.Ychi/8 * coeff_dict_shifted['C611']\
+ coeff_dict_shifted['C621']/2\
+ coeff_dict_shifted['C631']/2\
+ self.Ychi * (3-8*sw**2)/24 * coeff_dict_shifted['C615']\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-8*sw**2) * DIM4
coeff_dict_5f['C61d'] = self.Ychi/8*coeff_dict_shifted['C611']\
+ coeff_dict_shifted['C621']/2 + coeff_dict_shifted['C641']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C615']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61s'] = self.Ychi/8*coeff_dict_shifted['C612']\
+ coeff_dict_shifted['C622']/2\
+ coeff_dict_shifted['C642']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C615']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61c'] = - self.Ychi/8*coeff_dict_shifted['C612']\
+ coeff_dict_shifted['C622']/2\
+ coeff_dict_shifted['C632']/2\
+ self.Ychi * (3-8*sw**2)/24 * coeff_dict_shifted['C615']\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-8*sw**2) * DIM4
coeff_dict_5f['C61b'] = self.Ychi/8*coeff_dict_shifted['C613']\
+ coeff_dict_shifted['C623']/2\
+ coeff_dict_shifted['C643']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C615']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(6*sw**2*cw**2) * (3-4*sw**2) * DIM4
coeff_dict_5f['C61e'] = self.Ychi/8*coeff_dict_shifted['C691']\
+ coeff_dict_shifted['C6101']/2\
+ coeff_dict_shifted['C6111']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C615']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C61mu'] = self.Ychi/8*coeff_dict_shifted['C692']\
+ coeff_dict_shifted['C6102']/2\
+ coeff_dict_shifted['C6112']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C615']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C61tau'] = self.Ychi/8*coeff_dict_shifted['C693']\
+ coeff_dict_shifted['C6103']/2\
+ coeff_dict_shifted['C6113']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C615']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * (1-4*sw**2) * DIM4
coeff_dict_5f['C62u'] = - self.Ychi/8*coeff_dict_shifted['C651']\
+ coeff_dict_shifted['C661']/2\
+ coeff_dict_shifted['C671']/2\
+ self.Ychi * (3-8*sw**2)/24 * coeff_dict_shifted['C617']\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62d'] = self.Ychi/8*coeff_dict_shifted['C651']\
+ coeff_dict_shifted['C661']/2\
+ coeff_dict_shifted['C681']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C617']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62s'] = self.Ychi/8*coeff_dict_shifted['C652']\
+ coeff_dict_shifted['C662']/2\
+ coeff_dict_shifted['C682']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C617']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62c'] = - self.Ychi/8*coeff_dict_shifted['C652']\
+ coeff_dict_shifted['C662']/2\
+ coeff_dict_shifted['C672']/2\
+ self.Ychi * (3-8*sw**2)/24 * coeff_dict_shifted['C617']\
+ (3-8*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62b'] = self.Ychi/8*coeff_dict_shifted['C653']\
+ coeff_dict_shifted['C663']/2\
+ coeff_dict_shifted['C683']/2\
- self.Ychi * (3-4*sw**2)/24 * coeff_dict_shifted['C617']\
- (3-4*sw**2)/6 * coeff_dict_shifted['C618']
coeff_dict_5f['C62e'] = self.Ychi/8*coeff_dict_shifted['C6121']\
+ coeff_dict_shifted['C6131']/2\
+ coeff_dict_shifted['C6141']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C617']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C62mu'] = self.Ychi/8*coeff_dict_shifted['C6122']\
+ coeff_dict_shifted['C6132']/2\
+ coeff_dict_shifted['C6142']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C617']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C62tau'] = self.Ychi/8*coeff_dict_shifted['C6123']\
+ coeff_dict_shifted['C6133']/2\
+ coeff_dict_shifted['C6143']/2\
- self.Ychi * (1-4*sw**2)/8 * coeff_dict_shifted['C617']\
- (1-4*sw**2)/2 * coeff_dict_shifted['C618']
coeff_dict_5f['C63u'] = self.Ychi/8*coeff_dict_shifted['C611']\
- coeff_dict_shifted['C621']/2\
+ coeff_dict_shifted['C631']/2\
- self.Ychi/8 * coeff_dict_shifted['C615']\
- 1/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63d'] = - self.Ychi/8*coeff_dict_shifted['C611']\
- coeff_dict_shifted['C621']/2\
+ coeff_dict_shifted['C641']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63s'] = - self.Ychi/8*coeff_dict_shifted['C612']\
- coeff_dict_shifted['C622']/2\
+ coeff_dict_shifted['C642']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63c'] = self.Ychi/8*coeff_dict_shifted['C612']\
- coeff_dict_shifted['C622']/2\
+ coeff_dict_shifted['C632']/2\
- self.Ychi/8 * coeff_dict_shifted['C615']\
- 1/2 * coeff_dict_shifted['C616']\
- 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63b'] = - self.Ychi/8*coeff_dict_shifted['C613']\
- coeff_dict_shifted['C623']/2\
+ coeff_dict_shifted['C643']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63e'] = - self.Ychi/8*coeff_dict_shifted['C691']\
- coeff_dict_shifted['C6101']/2\
+ coeff_dict_shifted['C6111']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63mu'] = - self.Ychi/8*coeff_dict_shifted['C692']\
- coeff_dict_shifted['C6102']/2\
+ coeff_dict_shifted['C6112']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C63tau'] = - self.Ychi/8*coeff_dict_shifted['C693']\
- coeff_dict_shifted['C6103']/2\
+ coeff_dict_shifted['C6113']/2\
+ self.Ychi/8 * coeff_dict_shifted['C615']\
+ 1/2 * coeff_dict_shifted['C616']\
+ 1/MZ**2 * (np.pi*alpha*self.Ychi)/(2*sw**2*cw**2) * DIM4
coeff_dict_5f['C64u'] = self.Ychi/8*coeff_dict_shifted['C651']\
- coeff_dict_shifted['C661']/2\
+ coeff_dict_shifted['C671']/2\
- self.Ychi/8 * coeff_dict_shifted['C617']\
- 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64d'] = - self.Ychi/8*coeff_dict_shifted['C651']\
- coeff_dict_shifted['C661']/2\
+ coeff_dict_shifted['C681']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64s'] = - self.Ychi/8*coeff_dict_shifted['C652']\
- coeff_dict_shifted['C662']/2\
+ coeff_dict_shifted['C682']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64c'] = self.Ychi/8*coeff_dict_shifted['C652']\
- coeff_dict_shifted['C662']/2\
+ coeff_dict_shifted['C672']/2\
- self.Ychi/8 * coeff_dict_shifted['C617']\
- 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64b'] = - self.Ychi/8*coeff_dict_shifted['C653']\
- coeff_dict_shifted['C663']/2\
+ coeff_dict_shifted['C683']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64e'] = - self.Ychi/8*coeff_dict_shifted['C6121']\
- coeff_dict_shifted['C6131']/2\
+ coeff_dict_shifted['C6141']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64mu'] = - self.Ychi/8*coeff_dict_shifted['C6122']\
- coeff_dict_shifted['C6132']/2\
+ coeff_dict_shifted['C6142']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C64tau'] = - self.Ychi/8*coeff_dict_shifted['C6123']\
- coeff_dict_shifted['C6133']/2\
+ coeff_dict_shifted['C6143']/2\
+ self.Ychi/8 * coeff_dict_shifted['C617']\
+ 1/2 * coeff_dict_shifted['C618']\
+ W_box_fermion(self.dchi) * DIM4
coeff_dict_5f['C71'] = 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_gluon(self.dchi) * DIM4
coeff_dict_5f['C72'] = 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C75u'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75d'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75s'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75c'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75b'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75e'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75mu'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C75tau'] = - 1/Mh**2 * (coeff_dict_shifted['C53'] + self.Ychi/4\
* coeff_dict_shifted['C54'])\
+ higgs_penguin_fermion(self.dchi) * DIM4
coeff_dict_5f['C76u'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76d'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76s'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76c'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76b'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76e'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76mu'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C76tau'] = - 1/Mh**2 * (coeff_dict_shifted['C57'] + self.Ychi/4\
* coeff_dict_shifted['C58'])
coeff_dict_5f['C723u'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723d'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723s'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723c'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723b'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723e'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723mu'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C723tau'] = twist_two_fermion(self.dchi) * DIM4
coeff_dict_5f['C73'] = 0
coeff_dict_5f['C74'] = 0
coeff_dict_5f['C77u'] = 0
coeff_dict_5f['C77d'] = 0
coeff_dict_5f['C77s'] = 0
coeff_dict_5f['C77c'] = 0
coeff_dict_5f['C77b'] = 0
coeff_dict_5f['C77e'] = 0
coeff_dict_5f['C77mu'] = 0
coeff_dict_5f['C77tau'] = 0
coeff_dict_5f['C78u'] = 0
coeff_dict_5f['C78d'] = 0
coeff_dict_5f['C78s'] = 0
coeff_dict_5f['C78c'] = 0
coeff_dict_5f['C78b'] = 0
coeff_dict_5f['C78e'] = 0
coeff_dict_5f['C78mu'] = 0
coeff_dict_5f['C78tau'] = 0
coeff_dict_5f['C79u'] = 0
coeff_dict_5f['C79d'] = 0
coeff_dict_5f['C79s'] = 0
coeff_dict_5f['C79c'] = 0
coeff_dict_5f['C79b'] = 0
coeff_dict_5f['C79e'] = 0
coeff_dict_5f['C79mu'] = 0
coeff_dict_5f['C79tau'] = 0
coeff_dict_5f['C710u'] = 0
coeff_dict_5f['C710d'] = 0
coeff_dict_5f['C710s'] = 0
coeff_dict_5f['C710c'] = 0
coeff_dict_5f['C710b'] = 0
coeff_dict_5f['C710e'] = 0
coeff_dict_5f['C710mu'] = 0
coeff_dict_5f['C710tau'] = 0
coeff_dict_5f['C711'] = 0
coeff_dict_5f['C712'] = 0
coeff_dict_5f['C713'] = 0
coeff_dict_5f['C714'] = 0
coeff_dict_5f['C715u'] = 0
coeff_dict_5f['C715d'] = 0
coeff_dict_5f['C715s'] = 0
coeff_dict_5f['C715c'] = 0
coeff_dict_5f['C715b'] = 0
coeff_dict_5f['C715e'] = 0
coeff_dict_5f['C715mu'] = 0
coeff_dict_5f['C715tau'] = 0
coeff_dict_5f['C716u'] = 0
coeff_dict_5f['C716d'] = 0
coeff_dict_5f['C716s'] = 0
coeff_dict_5f['C716c'] = 0
coeff_dict_5f['C716b'] = 0
coeff_dict_5f['C716e'] = 0
coeff_dict_5f['C716mu'] = 0
coeff_dict_5f['C716tau'] = 0
coeff_dict_5f['C717u'] = 0
coeff_dict_5f['C717d'] = 0
coeff_dict_5f['C717s'] = 0
coeff_dict_5f['C717c'] = 0
coeff_dict_5f['C717b'] = 0
coeff_dict_5f['C717e'] = 0
coeff_dict_5f['C717mu'] = 0
coeff_dict_5f['C717tau'] = 0
coeff_dict_5f['C718u'] = 0
coeff_dict_5f['C718d'] = 0
coeff_dict_5f['C718s'] = 0
coeff_dict_5f['C718c'] = 0
coeff_dict_5f['C718b'] = 0
coeff_dict_5f['C718e'] = 0
coeff_dict_5f['C718mu'] = 0
coeff_dict_5f['C718tau'] = 0
coeff_dict_5f['C719u'] = 0
coeff_dict_5f['C719d'] = 0
coeff_dict_5f['C719s'] = 0
coeff_dict_5f['C719c'] = 0
coeff_dict_5f['C719b'] = 0
coeff_dict_5f['C719e'] = 0
coeff_dict_5f['C719mu'] = 0
coeff_dict_5f['C719tau'] = 0
coeff_dict_5f['C720u'] = 0
coeff_dict_5f['C720d'] = 0
coeff_dict_5f['C720s'] = 0
coeff_dict_5f['C720c'] = 0
coeff_dict_5f['C720b'] = 0
coeff_dict_5f['C720e'] = 0
coeff_dict_5f['C720mu'] = 0
coeff_dict_5f['C720tau'] = 0
coeff_dict_5f['C721u'] = 0
coeff_dict_5f['C721d'] = 0
coeff_dict_5f['C721s'] = 0
coeff_dict_5f['C721c'] = 0
coeff_dict_5f['C721b'] = 0
coeff_dict_5f['C721e'] = 0
coeff_dict_5f['C721mu'] = 0
coeff_dict_5f['C721tau'] = 0
coeff_dict_5f['C722u'] = 0
coeff_dict_5f['C722d'] = 0
coeff_dict_5f['C722s'] = 0
coeff_dict_5f['C722c'] = 0
coeff_dict_5f['C722b'] = 0
coeff_dict_5f['C722e'] = 0
coeff_dict_5f['C722mu'] = 0
coeff_dict_5f['C722tau'] = 0
coeff_dict_5f['C725'] = 0
return coeff_dict_5f
def _my_cNR(self, DM_mass, mu_Lambda, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None, DM_mass_threshold=None, RUN_EW=None, DIM4=None):
""" Calculate the NR coefficients from four-flavor theory
with meson contributions split off
(mainly for internal use)
"""
return WC_5flavor(self.match(DM_mass, mu_Lambda, DM_mass_threshold, RUN_EW, DIM4),\
self.DM_type, self.ip)._my_cNR(self.DM_mass_phys, RGE, NLO, double_QCD, DOUBLE_WEAK)
def cNR(self, DM_mass, qvec, mu_Lambda, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None, DM_mass_threshold=None, RUN_EW=None, DIM4=None):
""" Calculate the NR coefficients from four-flavor theory """
return WC_5flavor(self.match(DM_mass, mu_Lambda, DM_mass_threshold, RUN_EW, DIM4),\
self.DM_type, self.ip).cNR(DM_mass, qvec, RGE, NLO, double_QCD, DOUBLE_WEAK)
def write_mma(self, DM_mass, qvec, mu_Lambda, RGE=None, NLO=None, double_QCD=None, DOUBLE_WEAK=None,\
DM_mass_threshold=None, RUN_EW=None, DIM4=None, path=None, filename=None):
""" Write a text file with the NR coefficients that can be read into DMFormFactor
The order is {cNR1p, cNR2p, ... , cNR1n, cNR2n, ... }
Mandatory arguments are the DM mass DM_mass (in GeV) and the momentum transfer qvec (in GeV)
<path> should be a string with the path (including the trailing "/") where the file should be saved
(default is '.')
<filename> is the filename (default 'cNR.m')
"""
WC_5flavor(self.match(DM_mass, mu_Lambda, DM_mass_threshold, RUN_EW, DIM4),\
self.DM_type, self.ip).write_mma(DM_mass, qvec, RGE, NLO, double_QCD, DOUBLE_WEAK, path, filename)
| 57.864788 | 147 | 0.456808 | 29,524 | 244,363 | 3.599106 | 0.045827 | 0.069942 | 0.021212 | 0.026426 | 0.869706 | 0.858216 | 0.835216 | 0.807002 | 0.797704 | 0.78706 | 0 | 0.129211 | 0.372147 | 244,363 | 4,222 | 148 | 57.878494 | 0.56338 | 0.156603 | 0 | 0.62702 | 0 | 0.000344 | 0.105874 | 0 | 0 | 0 | 0 | 0 | 0.000344 | 1 | 0.010657 | false | 0.011 | 0.003438 | 0.002063 | 0.023376 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
74c01f5abe2afcd29106124bf83fdc950c518ad3 | 22,889 | py | Python | python/ks_api_client/api/mis_order_api.py | ashwinkp/ksapi | c348765cefb4d51fd90febcbfa9ff890b67bdc7d | [
"Apache-2.0"
] | 7 | 2022-02-05T16:20:37.000Z | 2022-02-27T16:48:28.000Z | python/ks_api_client/api/mis_order_api.py | ashwinkp/ksapi | c348765cefb4d51fd90febcbfa9ff890b67bdc7d | [
"Apache-2.0"
] | 19 | 2022-02-03T12:40:08.000Z | 2022-03-30T09:12:46.000Z | python/ks_api_client/api/mis_order_api.py | ashwinkp/ksapi | c348765cefb4d51fd90febcbfa9ff890b67bdc7d | [
"Apache-2.0"
] | 12 | 2021-12-23T06:14:21.000Z | 2022-03-28T07:47:19.000Z | # coding: utf-8
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from ks_api_client.api_client import ApiClient
from ks_api_client.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class MISOrderApi(object):
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def cancel_mis_order(self, consumerKey, sessionToken, orderId, **kwargs): # noqa: E501
"""Cancel a MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.cancel_mis_order(consumerKey, sessionToken, orderId, async_req=True)
>>> result = thread.get()
:param consumerKey: (required)
:type consumerKey: str
:param sessionToken: (required)
:type sessionToken: str
:param orderId: Order ID to cancel. (required)
:type orderId: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.cancel_mis_order_with_http_info(consumerKey, sessionToken, orderId, **kwargs) # noqa: E501
def cancel_mis_order_with_http_info(self, consumerKey, sessionToken, orderId, **kwargs): # noqa: E501
"""Cancel a MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.cancel_mis_order_with_http_info(consumerKey, sessionToken, orderId, async_req=True)
>>> result = thread.get()
:param consumerKey: (required)
:type consumerKey: str
:param sessionToken: (required)
:type sessionToken: str
:param orderId: Order ID to cancel. (required)
:type orderId: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'consumerKey',
'sessionToken',
'orderId'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method cancel_mis_order" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'consumerKey' is set
if self.api_client.client_side_validation and ('consumerKey' not in local_var_params or # noqa: E501
local_var_params['consumerKey'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `consumerKey` when calling `cancel_mis_order`") # noqa: E501
# verify the required parameter 'sessionToken' is set
if self.api_client.client_side_validation and ('sessionToken' not in local_var_params or # noqa: E501
local_var_params['sessionToken'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sessionToken` when calling `cancel_mis_order`") # noqa: E501
# verify the required parameter 'orderId' is set
if self.api_client.client_side_validation and ('orderId' not in local_var_params or # noqa: E501
local_var_params['orderId'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `orderId` when calling `cancel_mis_order`") # noqa: E501
collection_formats = {}
path_params = {}
if 'orderId' in local_var_params:
path_params['orderId'] = local_var_params['orderId'] # noqa: E501
query_params = []
header_params = {}
if 'consumerKey' in local_var_params:
header_params['consumerKey'] = local_var_params['consumerKey'] # noqa: E501
if 'sessionToken' in local_var_params:
header_params['sessionToken'] = local_var_params['sessionToken'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['bearerAuth'] # noqa: E501
return self.api_client.call_api(
'/orders/1.0/order/mis/{orderId}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def modify_mis_order(self, consumerKey, sessionToken, ExistingMISOrder, **kwargs): # noqa: E501
"""Modify an existing MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_mis_order(consumerKey, sessionToken, ExistingMISOrder, async_req=True)
>>> result = thread.get()
:param consumerKey: Unique ID for your application (required)
:type consumerKey: str
:param sessionToken: Session ID for your application (required)
:type sessionToken: str
:param ExistingMISOrder: (required)
:type ExistingMISOrder: ExistingMISOrder
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.modify_mis_order_with_http_info(consumerKey, sessionToken, ExistingMISOrder, **kwargs) # noqa: E501
def modify_mis_order_with_http_info(self, consumerKey, sessionToken, ExistingMISOrder, **kwargs): # noqa: E501
"""Modify an existing MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_mis_order_with_http_info(consumerKey, sessionToken, ExistingMISOrder, async_req=True)
>>> result = thread.get()
:param consumerKey: Unique ID for your application (required)
:type consumerKey: str
:param sessionToken: Session ID for your application (required)
:type sessionToken: str
:param ExistingMISOrder: (required)
:type ExistingMISOrder: ExistingMISOrder
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'consumerKey',
'sessionToken',
'ExistingMISOrder'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method modify_mis_order" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'consumerKey' is set
if self.api_client.client_side_validation and ('consumerKey' not in local_var_params or # noqa: E501
local_var_params['consumerKey'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `consumerKey` when calling `modify_mis_order`") # noqa: E501
# verify the required parameter 'sessionToken' is set
if self.api_client.client_side_validation and ('sessionToken' not in local_var_params or # noqa: E501
local_var_params['sessionToken'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sessionToken` when calling `modify_mis_order`") # noqa: E501
# verify the required parameter 'ExistingMISOrder' is set
if self.api_client.client_side_validation and ('ExistingMISOrder' not in local_var_params or # noqa: E501
local_var_params['ExistingMISOrder'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `ExistingMISOrder` when calling `modify_mis_order`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'consumerKey' in local_var_params:
header_params['consumerKey'] = local_var_params['consumerKey'] # noqa: E501
if 'sessionToken' in local_var_params:
header_params['sessionToken'] = local_var_params['sessionToken'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'ExistingMISOrder' in local_var_params:
body_params = local_var_params['ExistingMISOrder']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['bearerAuth'] # noqa: E501
return self.api_client.call_api(
'/orders/1.0/order/mis', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def place_new_mis_order(self, consumerKey, sessionToken, NewMISOrder, **kwargs): # noqa: E501
"""Place a New MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.place_new_mis_order(consumerKey, sessionToken, NewMISOrder, async_req=True)
>>> result = thread.get()
:param consumerKey: Unique ID for your application (required)
:type consumerKey: str
:param sessionToken: Session ID Generated with successful login. (required)
:type sessionToken: str
:param NewMISOrder: (required)
:type NewMISOrder: NewMISOrder
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.place_new_mis_order_with_http_info(consumerKey, sessionToken, NewMISOrder, **kwargs) # noqa: E501
def place_new_mis_order_with_http_info(self, consumerKey, sessionToken, NewMISOrder, **kwargs): # noqa: E501
"""Place a New MIS order # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.place_new_mis_order_with_http_info(consumerKey, sessionToken, NewMISOrder, async_req=True)
>>> result = thread.get()
:param consumerKey: Unique ID for your application (required)
:type consumerKey: str
:param sessionToken: Session ID Generated with successful login. (required)
:type sessionToken: str
:param NewMISOrder: (required)
:type NewMISOrder: NewMISOrder
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'consumerKey',
'sessionToken',
'NewMISOrder'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method place_new_mis_order" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'consumerKey' is set
if self.api_client.client_side_validation and ('consumerKey' not in local_var_params or # noqa: E501
local_var_params['consumerKey'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `consumerKey` when calling `place_new_mis_order`") # noqa: E501
# verify the required parameter 'sessionToken' is set
if self.api_client.client_side_validation and ('sessionToken' not in local_var_params or # noqa: E501
local_var_params['sessionToken'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sessionToken` when calling `place_new_mis_order`") # noqa: E501
# verify the required parameter 'NewMISOrder' is set
if self.api_client.client_side_validation and ('NewMISOrder' not in local_var_params or # noqa: E501
local_var_params['NewMISOrder'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `NewMISOrder` when calling `place_new_mis_order`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'consumerKey' in local_var_params:
header_params['consumerKey'] = local_var_params['consumerKey'] # noqa: E501
if 'sessionToken' in local_var_params:
header_params['sessionToken'] = local_var_params['sessionToken'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'NewMISOrder' in local_var_params:
body_params = local_var_params['NewMISOrder']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['bearerAuth'] # noqa: E501
return self.api_client.call_api(
'/orders/1.0/order/mis', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
| 47.193814 | 130 | 0.605793 | 2,459 | 22,889 | 5.400163 | 0.074421 | 0.041569 | 0.066421 | 0.024399 | 0.949996 | 0.94736 | 0.943294 | 0.940131 | 0.930944 | 0.914 | 0 | 0.013935 | 0.322819 | 22,889 | 484 | 131 | 47.291322 | 0.842774 | 0.441085 | 0 | 0.696833 | 0 | 0 | 0.203643 | 0.029534 | 0 | 0 | 0 | 0 | 0 | 1 | 0.031674 | false | 0 | 0.022624 | 0 | 0.085973 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
74dc4c1e38276504181b16d9db0458c55ad83426 | 377 | py | Python | Python-BlockLetters.py | H2oPtic/Codecademy-Education | 454ecff36a055fa17b4b338e1c6f1e9b3b94ef66 | [
"MIT"
] | null | null | null | Python-BlockLetters.py | H2oPtic/Codecademy-Education | 454ecff36a055fa17b4b338e1c6f1e9b3b94ef66 | [
"MIT"
] | null | null | null | Python-BlockLetters.py | H2oPtic/Codecademy-Education | 454ecff36a055fa17b4b338e1c6f1e9b3b94ef66 | [
"MIT"
] | null | null | null | #Gal Conradi
#Im buying a Catamaran
print(" BB BB")
print(" BB BB")
print(" BBBBBBBBBBBBBB)")
print(" BBB BB)")
print(" BBB BB)")
print(" BBBBBBBBBBBB)")
print(" BBB BB)")
print(" BBB BB)")
print(" BBBBBBBBBBBBBB)")
print(" BB BB")
print(" BB BB")
print("")
print("Dont Trust, Verify!") | 25.133333 | 29 | 0.488064 | 41 | 377 | 4.487805 | 0.341463 | 0.304348 | 0.195652 | 0.304348 | 0.521739 | 0.521739 | 0.521739 | 0 | 0 | 0 | 0 | 0 | 0.344828 | 377 | 15 | 30 | 25.133333 | 0.744939 | 0.087533 | 0 | 0.769231 | 0 | 0 | 0.598784 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
2d3084bd835a35161918df9acce7c7d2ba4b5431 | 19 | py | Python | main.py | nizarabdouss/tradingbotV2 | 6e141b7f229c1a86f0dec3c0aa895d2bbf8fc845 | [
"MIT"
] | null | null | null | main.py | nizarabdouss/tradingbotV2 | 6e141b7f229c1a86f0dec3c0aa895d2bbf8fc845 | [
"MIT"
] | null | null | null | main.py | nizarabdouss/tradingbotV2 | 6e141b7f229c1a86f0dec3c0aa895d2bbf8fc845 | [
"MIT"
] | null | null | null | import getCharts
| 4.75 | 16 | 0.789474 | 2 | 19 | 7.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.210526 | 19 | 3 | 17 | 6.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
74609c753f3c42f2ff01ab774f133e3197fc1a42 | 42 | py | Python | cubework/module/metric/__init__.py | kurisusnowdeng/Cubework | 56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa | [
"Apache-2.0"
] | null | null | null | cubework/module/metric/__init__.py | kurisusnowdeng/Cubework | 56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa | [
"Apache-2.0"
] | null | null | null | cubework/module/metric/__init__.py | kurisusnowdeng/Cubework | 56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa | [
"Apache-2.0"
] | null | null | null | from .metrics import Accuracy, Perplexity
| 21 | 41 | 0.833333 | 5 | 42 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119048 | 42 | 1 | 42 | 42 | 0.945946 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
74744763e1250002b3ef808ea7a1ad9a9c1131ee | 8,912 | py | Python | tests/integration/test_worker.py | illinois-cs241/broadway-api | 5a2ac7e5542ca4bb7aa40a9f6a2e6afed314e2ee | [
"NCSA"
] | 10 | 2018-11-27T17:07:38.000Z | 2020-02-10T19:59:04.000Z | tests/integration/test_worker.py | illinois-cs241/broadway-api | 5a2ac7e5542ca4bb7aa40a9f6a2e6afed314e2ee | [
"NCSA"
] | 75 | 2018-10-27T07:08:55.000Z | 2019-09-29T01:52:53.000Z | tests/integration/test_worker.py | illinois-cs241/broadway-api | 5a2ac7e5542ca4bb7aa40a9f6a2e6afed314e2ee | [
"NCSA"
] | 2 | 2019-07-11T03:10:22.000Z | 2019-12-22T01:16:11.000Z | import logging
import time
import json
import websockets
from tests.base import BaseTest
from tests._fixtures.config import HEARTBEAT_INTERVAL
import tornado.testing
logging.disable(logging.WARNING)
class RegisterGraderEndpointsTest(BaseTest):
def test_register(self):
self.assertIsNotNone(self.register_worker(self.get_header()))
def test_duplicate_id(self):
worker_id = "duplicate"
self.register_worker(self.get_header(), worker_id=worker_id, expected_code=200)
self.register_worker(self.get_header(), worker_id=worker_id, expected_code=400)
def test_reregister_id(self):
worker_id = self.register_worker(self.get_header(), expected_code=200)
time.sleep(HEARTBEAT_INTERVAL * 2 + 1)
self.register_worker(self.get_header(), worker_id=worker_id, expected_code=200)
def test_unauthorized(self):
self.register_worker(None, 401)
def test_wrong_token(self):
self.register_worker(self.get_header("invalid"), 401)
class PollGradingJobEndpointsTest(BaseTest):
def test_unauthorized(self):
worker_id = self.register_worker(self.get_header())
self.assertEqual(self.poll_job(worker_id, None), 401)
def test_wrong_token(self):
worker_id = self.register_worker(self.get_header())
self.assertEqual(self.poll_job(worker_id, self.get_header("invalid")), 401)
def test_invalid_worker_id(self):
self.assertEqual(self.poll_job("1234", self.get_header()), 401)
def test_empty_poll(self):
worker_id = self.register_worker(self.get_header())
self.assertEqual(self.poll_job(worker_id, self.get_header()), 498)
class UpdateGradingJobEndpointsTest(BaseTest):
def test_unauthorized(self):
worker_id = self.register_worker(self.get_header())
self.post_job_result(worker_id, None, "1234", True, 401)
def test_wrong_token(self):
worker_id = self.register_worker(self.get_header())
self.post_job_result(worker_id, self.get_header("invalid"), "1234", True, 401)
def test_invalid_worker_id(self):
self.post_job_result("1234", self.get_header(), "1234", True, 401)
class HeartBeatEndpointsTest(BaseTest):
def test_unauthorized(self):
worker_id = self.register_worker(self.get_header())
self.send_heartbeat(worker_id, None, 401)
def test_wrong_token(self):
worker_id = self.register_worker(self.get_header())
self.send_heartbeat(worker_id, self.get_header("fake"), 401)
def test_invalid_worker_id(self):
self.send_heartbeat("1234", self.get_header(), 401)
def test_valid_heartbeat(self):
worker_id = self.register_worker(self.get_header())
self.send_heartbeat(worker_id, self.get_header())
class WorkerWSEndpointTest(BaseTest):
@tornado.testing.gen_test
async def test_decode_error(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn:
try:
await conn.send("i'm not json")
except Exception as e:
self.assertEqual(e.code, 1011)
# submit job result before registering
@tornado.testing.gen_test
async def test_bad_job_result(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn:
try:
await conn.send(
json.dumps(
{
"type": "job_result",
"args": {
"grading_job_id": "someid",
"success": True,
"results": [{"res": "spoof"}],
"logs": {"stdout": "stdout", "stderr": "stderr"},
},
}
)
)
await conn.recv()
except Exception as e:
self.assertEqual(e.code, 1002)
@tornado.testing.gen_test
async def test_register(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn:
await conn.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
ack = json.loads(await conn.recv())
self.assertTrue(ack["success"])
@tornado.testing.gen_test
async def test_pong(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn:
await conn.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
ack = json.loads(await conn.recv())
self.assertTrue(ack["success"])
await conn.pong()
@tornado.testing.gen_test
async def test_no_token(self):
async with self.worker_ws_conn(worker_id="test_worker", headers=None) as conn:
try:
await conn.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
ack = json.loads(await conn.recv())
self.assertFalse(ack["success"])
except websockets.exceptions.ConnectionClosed as e:
self.assertEqual(e.code, 1008)
@tornado.testing.gen_test
async def test_wrong_token(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header("invalid")
) as conn:
try:
await conn.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
ack = json.loads(await conn.recv())
self.assertFalse(ack["success"])
except websockets.exceptions.ConnectionClosed as e:
self.assertEqual(e.code, 1008)
@tornado.testing.gen_test
async def test_duplicate_token(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn1:
await conn1.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
# worker 1 should successfully register
ack = json.loads(await conn1.recv())
self.assertTrue(ack["success"])
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn2:
try:
await conn2.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
# worker 2 should fail
ack = json.loads(await conn2.recv())
self.assertFalse(ack["success"])
except websockets.exceptions.ConnectionClosed as e:
self.assertEqual(e.code, 1002)
@tornado.testing.gen_test
async def test_reregister(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn1:
await conn1.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
# worker 1 should succeed
ack = json.loads(await conn1.recv())
self.assertTrue(ack["success"])
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn2:
await conn2.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
# worker 2 should also succeed
ack = json.loads(await conn2.recv())
self.assertTrue(ack["success"])
@tornado.testing.gen_test
async def test_wrong_job_id(self):
async with self.worker_ws_conn(
worker_id="test_worker", headers=self.get_header()
) as conn:
await conn.send(
json.dumps({"type": "register", "args": {"hostname": "eniac"}})
)
ack = json.loads(await conn.recv())
self.assertTrue(ack["success"])
try:
await conn.send(
json.dumps(
{
"type": "job_result",
"args": {
"grading_job_id": "no_such_id",
"success": True,
"results": [{"res": "spoof"}],
"logs": {"stdout": "stdout", "stderr": "stderr"},
},
}
)
)
except websockets.exceptions.ConnectionClosed as e:
self.assertEqual(e.code, 1002)
| 35.365079 | 87 | 0.559919 | 969 | 8,912 | 4.94324 | 0.117647 | 0.063466 | 0.086848 | 0.064301 | 0.834029 | 0.821086 | 0.802505 | 0.755532 | 0.721712 | 0.712735 | 0 | 0.018956 | 0.32518 | 8,912 | 251 | 88 | 35.505976 | 0.777519 | 0.016607 | 0 | 0.636364 | 0 | 0 | 0.080155 | 0 | 0 | 0 | 0 | 0 | 0.10101 | 1 | 0.080808 | false | 0 | 0.035354 | 0 | 0.141414 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
749097d54938b5e4c3f688adbcc8e702f396dc3e | 33 | py | Python | addons14/storage_image/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | 1 | 2021-06-10T14:59:13.000Z | 2021-06-10T14:59:13.000Z | addons14/storage_image/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | null | null | null | addons14/storage_image/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | 1 | 2021-04-09T09:44:44.000Z | 2021-04-09T09:44:44.000Z | from . import test_storage_image
| 16.5 | 32 | 0.848485 | 5 | 33 | 5.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.896552 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
74943f3669a0411e8b4b035647fd6cb8806ad85a | 106 | py | Python | terrascript/newrelic/d.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | terrascript/newrelic/d.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | terrascript/newrelic/d.py | vfoucault/python-terrascript | fe82b3d7e79ffa72b7871538f999828be0a115d0 | [
"BSD-2-Clause"
] | null | null | null | from terrascript import _data
class newrelic_application(_data): pass
application = newrelic_application
| 21.2 | 39 | 0.858491 | 12 | 106 | 7.25 | 0.666667 | 0.436782 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103774 | 106 | 4 | 40 | 26.5 | 0.915789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
7789901d8d2248e3ad1577139dd99bb783130021 | 25,059 | py | Python | STowl_multi.py | yiwangz/LS_learning | 18e876035de368d5a4db3ed0532bfd113e282812 | [
"MIT"
] | null | null | null | STowl_multi.py | yiwangz/LS_learning | 18e876035de368d5a4db3ed0532bfd113e282812 | [
"MIT"
] | null | null | null | STowl_multi.py | yiwangz/LS_learning | 18e876035de368d5a4db3ed0532bfd113e282812 | [
"MIT"
] | null | null | null | import numpy as np
from sklearn import svm
#from sklearn import linear_model
#from statsmodels.sandbox.regression.predstd import wls_prediction_std
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from pygam import GAM, LinearGAM, s, l
#from patsy import dmatrix
#import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
########################################################################################################################
########################################################################################################################
## calculate propensity score
class propensityScore:
def __init__(self, Xall, Aall, uniqueIndex, dataLabel):
self.Xall = Xall
self.Aall = Aall
self.uniqueIndex = uniqueIndex
self.dataLabel = dataLabel
def p(self, obsSetting = 'trial'):
if obsSetting == 'trial':
pall = np.full(self.Aall.shape[0], 0.5)
elif obsSetting == 'observational':
pall = []
for i in self.uniqueIndex:
index = [item for sublist in np.where(self.dataLabel == i) for item in sublist] #[l for l, x in enumerate(self.dataLabel) if x == i]
pvec = np.zeros(len(index))
Xtemp = self.Xall[index,:]
Atemp = self.Aall[index]
logReg = LogisticRegression()
logReg.fit(Xtemp[:,:], Atemp)
index1 = [item for sublist in np.where(Atemp == 1) for item in sublist]
index0 = [item for sublist in np.where(Atemp == -1) for item in sublist]
pvec[index1] = logReg.predict_proba(Xtemp)[index1, 1]
pvec[index0] = logReg.predict_proba(Xtemp)[index0, 0]
pall = np.concatenate((pall, pvec))
return pall
########################################################################################################################
########################################################################################################################
## Model selection (parameter tuning).
class tuneStat:
def __init__(self, Xall, Aall, Ball, m, uniqueIndex, dataLabel, model):
self.Xall = Xall
self.Aall = Aall
self.Ball = Ball
self.m = m
self.uniqueIndex = uniqueIndex
self.dataLabel = dataLabel
self.model = model
def tsSSE(self, model='linear'):
sse = 0
for i in self.uniqueIndex:
index = [item for sublist in np.where(self.dataLabel == i) for item in sublist] #[l for l, x in enumerate(self.dataLabel) if x == i]
Xfit = self.Xall[index,:]
Afit = self.Aall[index]
Bfit = self.Ball[index]
Af = Afit * self.model.decision_function(Xfit)
Xmat = np.column_stack((Xfit, Af))
if model == 'linear':
Xmat = sm.add_constant(Xmat)
BModel = sm.OLS(Bfit, Xmat)
res = BModel.fit()
pred = res.predict()
elif model == 'GAM':
BModel = LinearGAM(fit_intercept=True)
res = BModel.fit(Xmat, Bfit)
pred = res.predict(Xmat)
sse = sse + sum([(Bfit[elem]-pred[elem])**2 for elem in range(len(Bfit))])
return sse
class tuneStat_longitudinal:
def __init__(self, Xall, Aall, Ball, B_mat, miss_mat, n_valid, m, uniqueIndex, dataLable, model):
self.Xall = Xall
self.Aall = Aall
self.Ball = Ball
self.B_mat = B_mat
self.miss_mat = miss_mat
self.n_valid = n_valid
self.m = m
self.uniqueIndex = uniqueIndex
self.dataLabel = dataLable
self.model = model
def tsSSE_multiple(self):
d = self.Xall.shape[1]
fit_mat = np.full([int(self.n_valid*self.m-np.sum(self.miss_mat)),int(5+d*2)], np.nan)
B_obs = np.copy(self.B_mat)
B_obs[self.miss_mat == 1] = np.nan
fit_index = 0
for i in range(self.n_valid):
for j in range(self.m):
if not np.isnan(B_obs[i,j]):
fit_mat[fit_index,0] = i
fit_mat[fit_index,1] = 1
fit_mat[fit_index,2] = (j+1)/self.m
fit_mat[fit_index,3:(3+d)] = self.Xall[i,:]
fit_mat[fit_index,(3+d):(3+d*2)] = self.Xall[i,:]*(j+1)/self.m
fit_mat[fit_index,(3+d*2)] = self.Aall[i]*self.model.decision_function(self.Xall[i,:])
fit_mat[fit_index,(4+d*2)] = B_obs[i,j]
fit_index = fit_index+1
mod = sm.NominalGEE(endog=fit_mat[:,-1],exog=fit_mat[:,1:(4+d*2)],groups=fit_mat[:,0])
res = mod.fit()
pred = res.predict(fit_mat[:,1:(4+d*2)])
sse = sum([(fit_mat[elem,-1] - pred[elem])**2 for elem in range(len(pred))])
return sse
def tsSSE_single(self):
sse = 0
for i in self.uniqueIndex:
index = [item for sublist in np.where(self.dataLabel == i) for item in sublist]
Xfit = self.Xall[index, :]
Afit = self.Aall[index]
Bfit = self.Ball[index]
time = np.repeat((i+1)/self.m,len(index))
Xt = Xfit*((i+1)/self.m)
Af = Afit*self.model.decision_function(Xfit)
Xmat = np.column_stack((Xfit,time,Xt,Af))
BModel = LinearGAM(l(0)+l(1)+l(2)+l(3)+l(4)+l(5)+l(6)+l(7)+l(8)+l(9)+
s(10)+s(11)+s(12)+s(13)+s(14)+s(15)+s(16)+s(17)+s(18)+s(19)+s(20)+l(21),fit_intercept=True)
res = BModel.fit(Xmat, Bfit) ##the GAM model can be specified differently
pred = res.predict(Xmat)
sse = sse + sum([(Bfit[elem]-pred[elem])**2 for elem in range(len(index))])
return sse
########################################################################################################################
########################################################################################################################
## calculate the estimated value function separately
class EVF:
def evfCal(self, Xall, Aall, Ball, uniqueIndex, dataLabel, model):
labelall = model.predict(Xall)
evfSeq = np.zeros(len(uniqueIndex))
for i in uniqueIndex:
index = [item for sublist in np.where(dataLabel == i) for item in sublist] #[l for l, x in enumerate(dataLabel) if x == i]
Acal = Aall[index]
Bcal = Ball[index]
labelcal = labelall[index]
evfSeq[i] = sum(Bcal*(Acal==labelcal))/sum((Acal==labelcal))
self.evfSeq = evfSeq
########################################################################################################################
########################################################################################################################
## Self training OWL
class STowlLinear:
def __init__(self, Xall, Aall, Ball, B_mat, miss_mat, n_valid, m, uniqueIndex, dataLabel, pall):
self.Xall = Xall
self.Aall = Aall
self.Ball = Ball
self.B_mat = B_mat
self.miss_mat = miss_mat
self.n_valid = n_valid
self.m = m
self.uniqueIndex = uniqueIndex
self.dataLabel = dataLabel
self.pall = pall
def iniFit(self, cost, study='single'):
if study == 'single':
index = []
for i in range(self.m):
index = np.concatenate((index, [item for sublist in np.where(self.dataLabel == i) for item in sublist]))
index1 = [item for sublist in np.where(self.dataLabel == 0) for item in sublist]
indexRemain = index[len(index1):]
indexRemain = [int(indexRemain[elem]) for elem in range(len(indexRemain))]
elif study == 'longitudinal':
index = []
for i in self.uniqueIndex:
index = np.concatenate((index, [item for sublist in np.where(self.dataLabel == i) for item in sublist]))
index1 = [item for sublist in np.where(self.dataLabel == self.m-1) for item in sublist]
indexRemain = index[:(len(index)-len(index1))]
indexRemain = [int(indexRemain[elem]) for elem in range(len(indexRemain))]
X1 = self.Xall[index1, :]
A1 = self.Aall[index1]
B1 = self.Ball[index1]
p1 = self.pall[index1]
dataLabel1 = self.dataLabel[index1]
Xremain = self.Xall[indexRemain, :] ##ordered
Aremain = self.Aall[indexRemain] ##ordered
Bremain = self.Ball[indexRemain] ##ordered
premain = self.pall[indexRemain] ##ordered
dataLabelremain = self.dataLabel[indexRemain]
model_ini = svm.SVC(kernel='linear', C=cost, decision_function_shape="ovo")
model_ini.fit(X1, A1, sample_weight=B1/p1)
pred_ini = model_ini.predict(Xremain)
self.pred_ini = pred_ini
self.cost = cost
self.X1 = X1
self.A1 = A1
self.B1 = B1
self.p1 = p1
self.Xremain = Xremain
self.Aremain = Aremain
self.Bremain = Bremain
self.premain = premain
self.study = study
self.dataLabel1 = dataLabel1
self.dataLabelremain = dataLabelremain
## set itertol=0, track=True to perform convergence analysis
def fit(self, lam, itermax=50, itertol=1e-4, track=True):
if not hasattr(self, 'pred_ini'):
print('Run iniFit() for initial predicted labels!')
else:
iter = 0
rel_obj = 1
obj_old = 1
if self.m == 2:
lam = [lam]
predRemainK = np.copy(self.pred_ini)
X_aux = np.concatenate((self.X1, self.Xremain, self.Xremain), axis=0)
B_aux = np.concatenate((self.B1/self.p1, self.Bremain/self.premain))
wts_aux = np.repeat(1, self.A1.shape[0])
if self.study == 'single':
index = []
for i in range(1,self.m):
index.append([item for sublist in np.where(self.dataLabelremain == i) for item in sublist]) #[l for l, x in enumerate(self.dataLabel) if x == i]
for j in range(self.m-1):
Btemp = self.Bremain[index[j]]
ptemp = self.premain[index[j]]
B_aux = np.concatenate((B_aux, np.repeat(np.average(Btemp/ptemp, axis=0), len(index[j]))))
wts_aux = np.concatenate((wts_aux, np.repeat(lam[j], len(index[j]))))
for k in range(self.m-1):
wts_aux = np.concatenate((wts_aux, np.repeat(1-lam[j], len(index[j]))))
wts_aux = wts_aux*B_aux
elif self.study == 'longitudinal':
uniqueIndex_new = self.uniqueIndex.copy()
uniqueIndex_new.remove(self.m-1)
index = []
for i in uniqueIndex_new:
index.append([item for sublist in np.where(self.dataLabelremain == i) for item in sublist]) #[l for l, x in enumerate(self.dataLabel) if x == i]
for j in range(len(uniqueIndex_new)):
Btemp = self.Bremain[index[j]]
ptemp = self.premain[index[j]]
B_aux = np.concatenate((B_aux, np.repeat(np.average(Btemp/ptemp, axis=0), len(index[j]))))
wts_aux = np.concatenate((wts_aux, np.repeat(lam[j], len(index[j]))))
for k in range(len(uniqueIndex_new)):
wts_aux = np.concatenate((wts_aux, np.repeat(1-lam[k], len(index[k]))))
wts_aux = wts_aux*B_aux
if track:
obj_path = np.full(itermax, np.nan)
pred_path = np.full([itermax, (self.Aall.shape[0])], np.nan)
model_path = []
ts_path = np.full(itermax, np.nan)
while (iter < itermax and rel_obj >= itertol):
A_aux = np.concatenate((self.A1, self.Aremain, predRemainK)) ##this is wrong
modelk = svm.SVC(kernel='linear', C=self.cost, decision_function_shape="ovo")
modelk.fit(X_aux, A_aux, sample_weight=wts_aux) ##this is wrong
xi_vec = 1-A_aux*modelk.decision_function(X_aux)
xi_vec[xi_vec < 0] = 0
obj_new = np.sum(np.power(modelk.coef_,2))*(1/2)+np.sum(xi_vec*wts_aux)*self.cost
rel_obj = abs(obj_new-obj_old)/obj_old
obj_old = np.copy(obj_new)
predRemainK = modelk.predict(self.Xremain)
tuning_stat = tuneStat_longitudinal(self.Xall, self.Aall, self.Ball, self.B_mat, self.miss_mat,
self.n_valid, self.m, self.uniqueIndex, self.dataLabel, modelk)
ts_stat = tuning_stat.tsSSE_single()
predk = modelk.predict(self.Xall)
if track:
obj_path[iter] = obj_new
pred_path[iter,:] = predk
model_path.append(modelk)
ts_path[iter] = ts_stat
iter += 1
if iter >= itermax:
conv = 99
else:
conv = iter
self.conv = conv
self.objConv = obj_new
self.predConv = predk
self.modelConv = modelk
self.tsConv = ts_stat
if track:
self.objPath = obj_path
self.predPath = pred_path
self.modelPath = model_path
self.tsPath = ts_path
## predictions on new datasets
def predict(self, Xindpt, track):
## results based on final model
if not track:
if not hasattr(self, 'modelConv'):
print('Please run fit() first.')
else:
return self.modelConv.predict(Xindpt) ##nindpt*1
## results based on the whole path of models (multi-D predicted labels)
else:
if not hasattr(self, 'modelPath'):
print('Please run fit() with track = True.')
else:
predOut = []
for model in self.modelPath:
predOut.append(model.predict(Xindpt))
return np.asarray(predOut) ##nindpt*conv
########################################################################################################################
########################################################################################################################
## calculate the norm of RKHS for nonlinear kernel
class RKHSnorm:
def __init__(self, sv, svIndex, lagMulti, gamma):
self.sv = sv ##support vectors
self.svIndex = svIndex ##index of support vectors
self.lagMulti = lagMulti ##Lagrange Multiplier
self.gamma = gamma
def omegaNorm(self):
omega_penalty = np.zeros(len(self.svIndex)**2)
for i in range(len(self.svIndex)):
for j in range(len(self.svIndex)):
omega_penalty[i*len(self.svIndex)+j] = self.lagMulti[0,i]*self.lagMulti[0,j]*np.exp(-self.gamma*np.sum(np.power(self.sv[i]-self.sv[j],2)))
norm_result = 0.5*np.sum(omega_penalty)
return norm_result
########################################################################################################################
########################################################################################################################
## Self training OWL
class STowlNonlinear:
def __init__(self, Xall, Aall, Ball, B_mat, miss_mat, n_valid, m, uniqueIndex, dataLabel, pall):
self.Xall = Xall
self.Aall = Aall
self.Ball = Ball
self.B_mat = B_mat
self.miss_mat = miss_mat
self.n_valid = n_valid
self.m = m
self.uniqueIndex = uniqueIndex
self.dataLabel = dataLabel
self.pall = pall
def iniFit(self, cost, gamma, study='single'):
if study == 'single':
index = []
for i in range(self.m):
index = np.concatenate((index, [item for sublist in np.where(self.dataLabel == i) for item in sublist]))
index1 = [item for sublist in np.where(self.dataLabel == 0) for item in sublist]
indexRemain = index[len(index1):]
indexRemain = [int(indexRemain[elem]) for elem in range(len(indexRemain))]
elif study == 'longitudinal':
index = []
for i in self.uniqueIndex:
index = np.concatenate((index, [item for sublist in np.where(self.dataLabel == i) for item in sublist]))
index1 = [item for sublist in np.where(self.dataLabel == self.m-1) for item in sublist]
indexRemain = index[:(len(index)-len(index1))]
indexRemain = [int(indexRemain[elem]) for elem in range(len(indexRemain))]
X1 = self.Xall[index1, :]
A1 = self.Aall[index1]
B1 = self.Ball[index1]
p1 = self.pall[index1]
dataLabel1 = self.dataLabel[index1]
Xremain = self.Xall[indexRemain, :] ##ordered
Aremain = self.Aall[indexRemain] ##ordered
Bremain = self.Ball[indexRemain] ##ordered
premain = self.pall[indexRemain] ##ordered
dataLabelremain = self.dataLabel[indexRemain]
model_ini = svm.SVC(kernel='rbf', C=cost, gamma=gamma, decision_function_shape="ovo")
model_ini.fit(X1, A1, sample_weight=B1/p1)
pred_ini = model_ini.predict(Xremain)
self.pred_ini = pred_ini
self.cost = cost
self.gamma = gamma
self.X1 = X1
self.A1 = A1
self.B1 = B1
self.p1 = p1
self.Xremain = Xremain
self.Aremain = Aremain
self.Bremain = Bremain
self.premain = premain
self.study = study
self.dataLabel1 = dataLabel1
self.dataLabelremain = dataLabelremain
## set itertol=0, track=True to perform convergence analysis
def fit(self, lam, itermax=50, itertol=1e-4, track=True):
if not hasattr(self, 'pred_ini'):
print('Run iniFit() for initial predicted labels!')
else:
predRemainK = np.copy(self.pred_ini)
X_aux = np.concatenate((self.X1, self.Xremain, self.Xremain), axis=0)
B_aux = np.concatenate((self.B1 / self.p1, self.Bremain / self.premain))
wts_aux = np.repeat(1, self.A1.shape[0])
if self.study == 'single':
index = []
for i in range(1, self.m):
index.append([item for sublist in np.where(self.dataLabelremain == i) for item in
sublist]) # [l for l, x in enumerate(self.dataLabel) if x == i]
for j in range(self.m - 1):
Btemp = self.Bremain[index[j]]
ptemp = self.premain[index[j]]
B_aux = np.concatenate((B_aux, np.repeat(np.average(Btemp / ptemp, axis=0), len(index[j]))))
wts_aux = np.concatenate((wts_aux, np.repeat(lam[j], len(index[j]))))
for k in range(self.m - 1):
wts_aux = np.concatenate((wts_aux, np.repeat(1 - lam[j], len(index[j]))))
wts_aux = wts_aux * B_aux
elif self.study == 'longitudinal':
uniqueIndex_new = self.uniqueIndex.copy()
uniqueIndex_new.remove(self.m - 1)
index = []
for i in uniqueIndex_new:
index.append([item for sublist in np.where(self.dataLabelremain == i) for item in
sublist]) # [l for l, x in enumerate(self.dataLabel) if x == i]
for j in range(len(uniqueIndex_new)):
Btemp = self.Bremain[index[j]]
ptemp = self.premain[index[j]]
B_aux = np.concatenate((B_aux, np.repeat(np.average(Btemp / ptemp, axis=0), len(index[j]))))
wts_aux = np.concatenate((wts_aux, np.repeat(lam[j], len(index[j]))))
for k in range(len(uniqueIndex_new)):
wts_aux = np.concatenate((wts_aux, np.repeat(1 - lam[k], len(index[k]))))
wts_aux = wts_aux * B_aux
if track:
obj_path = np.full(itermax, np.nan)
pred_path = np.full([itermax, (self.Aall.shape[0])], np.nan)
model_path = []
ts_path = np.full(itermax, np.nan)
while (iter < itermax and rel_obj >= itertol):
A_aux = np.concatenate((self.A1, self.Aremain, predRemainK))
modelk = svm.SVC(kernel='rbf', C=self.cost, gamma=self.gamma, decision_function_shape="ovo")
modelk.fit(X_aux, A_aux, sample_weight=wts_aux)
lagMulti = modelk.dual_coef_ ## Lagrange multiplier times label y. Summation = 0.
svIndex = modelk.support_
sv = modelk.support_vectors_
baseObj = RKHSnorm(sv, svIndex, lagMulti, self.gamma)
rkhsNorm = baseObj.omegaNorm()
xi_vec = 1-A_aux*modelk.decision_function(X_aux)
xi_vec[xi_vec < 0] = 0
obj_new = rkhsNorm+np.sum(xi_vec*wts_aux)*self.cost
rel_obj = abs(obj_new - obj_old)/obj_old
obj_old = np.copy(obj_new)
predRemainK = modelk.predict(self.Xremain)
tuning_stat = tuneStat_longitudinal(self.Xall, self.Aall, self.Ball, self.B_mat, self.miss_mat,
self.n_valid, self.m, self.uniqueIndex, self.dataLabel, modelk)
ts_stat = tuning_stat.tsSSE_single()
predk = modelk.predict(self.Xall)
if track:
obj_path[iter] = obj_new
pred_path[iter,:] = predk
model_path.append(modelk)
ts_path[iter] = ts_stat
iter += 1
if iter >= itermax:
conv = 99
else:
conv = iter
self.conv = conv
self.objConv = obj_new
self.predConv = predk
self.modelConv = modelk
self.tsConv = ts_stat
if track:
self.objPath = obj_path
self.predPath = pred_path
self.modelPath = model_path
self.tsPath = ts_path
## predictions on new datasets
def predict(self, Xindpt, track):
## results based on final model
if not track:
if not hasattr(self, 'modelConv'):
print('Please run fit() first.')
else:
return self.modelConv.predict(Xindpt) ##nindpt*1
## results based on the whole path of models (multi-D predicted labels)
else:
if not hasattr(self, 'modelPath'):
print('Please run fit() with track = True.')
else:
predOut = []
for model in self.modelPath:
predOut.append(model.predict(Xindpt))
return np.asarray(predOut) ##nindpt*conv
########################################################################################################################
########################################################################################################################
## Evaluate prediction performance (accuracy).
class evalPred:
def __init__(self, predLabel, trueLabel):
self.pred = predLabel
self.true = trueLabel
def acc(self):
# only one set of predicted labels (e.g., conv)
if self.pred.ndim == 1:
return np.sum(self.pred == self.true)/self.true.shape[0]
# multiple rows of predicted labels (e.g. path)
else:
acc_vec = np.full(self.pred.shape[0], np.nan)
for ii in np.arange(self.pred.shape[0]):
acc_vec[ii] = np.sum(self.pred[ii, :] == self.true)/ self.true.shape[0]
return(acc_vec)
########################################################################################################################
########################################################################################################################
## Evaluate tuning process.
class evalTune:
def maxTune(self, ts_array, acc_array, method='max'):
dim = len(ts_array.shape)
opt_idx = []
tune_idx = []
for i in range(dim):
opt_idx.append([np.where(acc_array == np.amax(acc_array))[i][0]])
if method == 'max':
for j in range(dim):
tune_idx.append([np.where(ts_array == np.amax(ts_array))[j][0]])
elif method == 'min':
for j in range(dim):
tune_idx.append([np.where(ts_array == np.amin(ts_array))[j][0]])
self.tune_idx = tune_idx
self.opt_idx = opt_idx
| 39.339089 | 164 | 0.507283 | 2,952 | 25,059 | 4.202575 | 0.107724 | 0.012897 | 0.020313 | 0.023215 | 0.755118 | 0.733355 | 0.721264 | 0.714815 | 0.703772 | 0.700226 | 0 | 0.01292 | 0.308153 | 25,059 | 636 | 165 | 39.400943 | 0.702659 | 0.059659 | 0 | 0.703297 | 0 | 0 | 0.019157 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.046154 | false | 0 | 0.015385 | 0 | 0.103297 | 0.013187 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
77aca5e18627d0aeef946e48603b6c66ea63a597 | 33 | py | Python | Attack_SplitNN/build/lib/attacksplitnn/defense/__init__.py | EduardaChagas/Cybersecurity-FL | 52db1a31e71e021f2ce57231a27341c4c080ad3b | [
"MIT"
] | 10 | 2021-04-16T00:38:34.000Z | 2022-02-11T01:28:52.000Z | Attack_SplitNN/build/lib/attacksplitnn/defense/__init__.py | EduardaChagas/Cybersecurity-FL | 52db1a31e71e021f2ce57231a27341c4c080ad3b | [
"MIT"
] | 7 | 2021-03-27T04:49:06.000Z | 2021-06-20T06:16:32.000Z | Attack_SplitNN/build/lib/attacksplitnn/defense/__init__.py | EduardaChagas/Cybersecurity-FL | 52db1a31e71e021f2ce57231a27341c4c080ad3b | [
"MIT"
] | 3 | 2021-07-07T21:55:52.000Z | 2022-03-17T21:17:51.000Z | from .noisedgrad import max_norm
| 16.5 | 32 | 0.848485 | 5 | 33 | 5.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.931034 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
77b346d1798928f0b5ca5b0ae6aacbe97bead2ae | 76,844 | py | Python | pybind/slxos/v16r_1_00b/brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v16r_1_00b/brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v16r_1_00b/brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | 1 | 2021-11-05T22:15:42.000Z | 2021-11-05T22:15:42.000Z |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class show_fibrechannel_info(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-fabric-service - based on the path /brocade_fabric_service_rpc/show-fibrechannel-interface-info/output/show-fibrechannel-interface/show-fibrechannel-info. Each member element of
the container is represented as a class variable - with a specific
YANG type.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__port_interface','__port_index','__port_type','__port_wwn','__remote_port_wwn','__remote_node_wwn','__port_state','__port_status','__port_status_message','__port_health','__port_trunked','__port_trunk_master','__port_actual_distance','__port_desired_credit','__port_buffer_allocated','__port_licensed','__port_address','__port_fec','__port_configured_speed','__port_actual_speed',)
_yang_name = 'show-fibrechannel-info'
_rest_name = 'show-fibrechannel-info'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__port_desired_credit = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-desired-credit", rest_name="port-desired-credit", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides Fibre Channel port Desired Credit\nInformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
self.__port_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="port-wwn", rest_name="port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Local Fibre Channel port WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
self.__port_health = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-health", rest_name="port-health", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port health\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_buffer_allocated = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-buffer-allocated", rest_name="port-buffer-allocated", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Buffer\nAllocation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
self.__port_fec = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-fec", rest_name="port-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide FEC operational status on a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_interface = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([0-9]|[1][0-6]))/([1-9]|[1-9][0-9]|[1-9][0-9][0-9])(:[1-4])?)', 'length': [u'3..16']}), is_leaf=True, yang_name="port-interface", rest_name="port-interface", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port\nInterface[rbridge-id/slot/port]'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='interface:interface-type', is_config=True)
self.__port_actual_distance = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-actual-distance", rest_name="port-actual-distance", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Actual\nDistance Information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
self.__port_trunked = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-trunked", rest_name="port-trunked", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
self.__remote_port_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-port-wwn", rest_name="remote-port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port remotePort WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
self.__port_actual_speed = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-actual-speed", rest_name="port-actual-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the actual speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_state = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-state", rest_name="port-state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port State -\nOnline/Offline'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_index = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-index", rest_name="port-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port index'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
self.__remote_node_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-node-wwn", rest_name="remote-node-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel ports remoteNode WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
self.__port_licensed = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-licensed", rest_name="port-licensed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides if the port is\nlicensed or not. Set to TRUE\nfor licenced ports'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
self.__port_type = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'E-Port|U-Port|G-Port|F-Port', 'length': [u'6']}), is_leaf=True, yang_name="port-type", rest_name="port-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port type'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='porttype-type', is_config=True)
self.__port_address = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F]*', 'length': [u'6']}), is_leaf=True, yang_name="port-address", rest_name="port-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'24 bit PID of a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='fabric-portid-type', is_config=True)
self.__port_status = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status", rest_name="port-status", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port Status -\nOnline/No_Module/No_Light/Disabled\nIn_Sync/No_Sync'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_configured_speed = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-configured-speed", rest_name="port-configured-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the configured speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
self.__port_trunk_master = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-trunk-master", rest_name="port-trunk-master", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\nmaster information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
self.__port_status_message = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status-message", rest_name="port-status-message", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port status\nmessages'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'brocade_fabric_service_rpc', u'show-fibrechannel-interface-info', u'output', u'show-fibrechannel-interface', u'show-fibrechannel-info']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'show-fibrechannel-interface-info', u'output', u'show-fibrechannel-interface', u'show-fibrechannel-info']
def _get_port_interface(self):
"""
Getter method for port_interface, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_interface (interface:interface-type)
YANG Description: The Fibre Channel port interface.
It is represented in the format
rbridge-id/slot/port.
"""
return self.__port_interface
def _set_port_interface(self, v, load=False):
"""
Setter method for port_interface, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_interface (interface:interface-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_interface is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_interface() directly.
YANG Description: The Fibre Channel port interface.
It is represented in the format
rbridge-id/slot/port.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([0-9]|[1][0-6]))/([1-9]|[1-9][0-9]|[1-9][0-9][0-9])(:[1-4])?)', 'length': [u'3..16']}), is_leaf=True, yang_name="port-interface", rest_name="port-interface", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port\nInterface[rbridge-id/slot/port]'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='interface:interface-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_interface must be of a type compatible with interface:interface-type""",
'defined-type': "interface:interface-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([0-9]|[1][0-6]))/([1-9]|[1-9][0-9]|[1-9][0-9][0-9])(:[1-4])?)', 'length': [u'3..16']}), is_leaf=True, yang_name="port-interface", rest_name="port-interface", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port\nInterface[rbridge-id/slot/port]'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='interface:interface-type', is_config=True)""",
})
self.__port_interface = t
if hasattr(self, '_set'):
self._set()
def _unset_port_interface(self):
self.__port_interface = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([0-9]|[1][0-6]))/([1-9]|[1-9][0-9]|[1-9][0-9][0-9])(:[1-4])?)', 'length': [u'3..16']}), is_leaf=True, yang_name="port-interface", rest_name="port-interface", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port\nInterface[rbridge-id/slot/port]'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='interface:interface-type', is_config=True)
def _get_port_index(self):
"""
Getter method for port_index, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_index (uint32)
YANG Description: The Fibre Channel port index of the
RBridge.
"""
return self.__port_index
def _set_port_index(self, v, load=False):
"""
Setter method for port_index, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_index (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_index is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_index() directly.
YANG Description: The Fibre Channel port index of the
RBridge.
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-index", rest_name="port-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port index'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_index must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-index", rest_name="port-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port index'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)""",
})
self.__port_index = t
if hasattr(self, '_set'):
self._set()
def _unset_port_index(self):
self.__port_index = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-index", rest_name="port-index", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'The Fibre Channel port index'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
def _get_port_type(self):
"""
Getter method for port_type, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_type (porttype-type)
"""
return self.__port_type
def _set_port_type(self, v, load=False):
"""
Setter method for port_type, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_type (porttype-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_type is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_type() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'E-Port|U-Port|G-Port|F-Port', 'length': [u'6']}), is_leaf=True, yang_name="port-type", rest_name="port-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port type'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='porttype-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_type must be of a type compatible with porttype-type""",
'defined-type': "brocade-fabric-service:porttype-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'E-Port|U-Port|G-Port|F-Port', 'length': [u'6']}), is_leaf=True, yang_name="port-type", rest_name="port-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port type'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='porttype-type', is_config=True)""",
})
self.__port_type = t
if hasattr(self, '_set'):
self._set()
def _unset_port_type(self):
self.__port_type = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'E-Port|U-Port|G-Port|F-Port', 'length': [u'6']}), is_leaf=True, yang_name="port-type", rest_name="port-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port type'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='porttype-type', is_config=True)
def _get_port_wwn(self):
"""
Getter method for port_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_wwn (common-def:wwn-type)
YANG Description: Local Fibre Channel port WWN.
"""
return self.__port_wwn
def _set_port_wwn(self, v, load=False):
"""
Setter method for port_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_wwn (common-def:wwn-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_wwn is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_wwn() directly.
YANG Description: Local Fibre Channel port WWN.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="port-wwn", rest_name="port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Local Fibre Channel port WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_wwn must be of a type compatible with common-def:wwn-type""",
'defined-type': "common-def:wwn-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="port-wwn", rest_name="port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Local Fibre Channel port WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)""",
})
self.__port_wwn = t
if hasattr(self, '_set'):
self._set()
def _unset_port_wwn(self):
self.__port_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="port-wwn", rest_name="port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Local Fibre Channel port WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
def _get_remote_port_wwn(self):
"""
Getter method for remote_port_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/remote_port_wwn (common-def:wwn-type)
YANG Description: WWN of the remote port that connects
to this Fibre Channel port.
"""
return self.__remote_port_wwn
def _set_remote_port_wwn(self, v, load=False):
"""
Setter method for remote_port_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/remote_port_wwn (common-def:wwn-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_remote_port_wwn is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_remote_port_wwn() directly.
YANG Description: WWN of the remote port that connects
to this Fibre Channel port.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-port-wwn", rest_name="remote-port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port remotePort WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """remote_port_wwn must be of a type compatible with common-def:wwn-type""",
'defined-type': "common-def:wwn-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-port-wwn", rest_name="remote-port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port remotePort WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)""",
})
self.__remote_port_wwn = t
if hasattr(self, '_set'):
self._set()
def _unset_remote_port_wwn(self):
self.__remote_port_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-port-wwn", rest_name="remote-port-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port remotePort WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
def _get_remote_node_wwn(self):
"""
Getter method for remote_node_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/remote_node_wwn (common-def:wwn-type)
YANG Description: WWN of the remote switch that connects
to this Fibre Channel port.
"""
return self.__remote_node_wwn
def _set_remote_node_wwn(self, v, load=False):
"""
Setter method for remote_node_wwn, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/remote_node_wwn (common-def:wwn-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_remote_node_wwn is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_remote_node_wwn() directly.
YANG Description: WWN of the remote switch that connects
to this Fibre Channel port.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-node-wwn", rest_name="remote-node-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel ports remoteNode WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """remote_node_wwn must be of a type compatible with common-def:wwn-type""",
'defined-type': "common-def:wwn-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-node-wwn", rest_name="remote-node-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel ports remoteNode WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)""",
})
self.__remote_node_wwn = t
if hasattr(self, '_set'):
self._set()
def _unset_remote_node_wwn(self):
self.__remote_node_wwn = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]:[0-9a-fA-F][0-9a-fA-F]'}), is_leaf=True, yang_name="remote-node-wwn", rest_name="remote-node-wwn", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel ports remoteNode WWN'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='common-def:wwn-type', is_config=True)
def _get_port_state(self):
"""
Getter method for port_state, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_state (string)
YANG Description: This specifies the Fibre Channel port
State. The state can be
Online or Offline.
"""
return self.__port_state
def _set_port_state(self, v, load=False):
"""
Setter method for port_state, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_state (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_state() directly.
YANG Description: This specifies the Fibre Channel port
State. The state can be
Online or Offline.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-state", rest_name="port-state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port State -\nOnline/Offline'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_state must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-state", rest_name="port-state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port State -\nOnline/Offline'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_state = t
if hasattr(self, '_set'):
self._set()
def _unset_port_state(self):
self.__port_state = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-state", rest_name="port-state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port State -\nOnline/Offline'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_status(self):
"""
Getter method for port_status, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_status (string)
YANG Description: Provides the Fibre Channel port
status. The Fibre Channel port status
can be Online, No Module, No Light,
In Sync, No Sync etc.
"""
return self.__port_status
def _set_port_status(self, v, load=False):
"""
Setter method for port_status, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_status (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_status is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_status() directly.
YANG Description: Provides the Fibre Channel port
status. The Fibre Channel port status
can be Online, No Module, No Light,
In Sync, No Sync etc.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-status", rest_name="port-status", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port Status -\nOnline/No_Module/No_Light/Disabled\nIn_Sync/No_Sync'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_status must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status", rest_name="port-status", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port Status -\nOnline/No_Module/No_Light/Disabled\nIn_Sync/No_Sync'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_status = t
if hasattr(self, '_set'):
self._set()
def _unset_port_status(self):
self.__port_status = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status", rest_name="port-status", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Fibre Channel port Status -\nOnline/No_Module/No_Light/Disabled\nIn_Sync/No_Sync'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_status_message(self):
"""
Getter method for port_status_message, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_status_message (string)
YANG Description: Provides the Fibre Channel port status
messages. It consists of messages like
down stream, trunk port, trunk
master etc.
"""
return self.__port_status_message
def _set_port_status_message(self, v, load=False):
"""
Setter method for port_status_message, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_status_message (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_status_message is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_status_message() directly.
YANG Description: Provides the Fibre Channel port status
messages. It consists of messages like
down stream, trunk port, trunk
master etc.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-status-message", rest_name="port-status-message", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port status\nmessages'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_status_message must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status-message", rest_name="port-status-message", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port status\nmessages'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_status_message = t
if hasattr(self, '_set'):
self._set()
def _unset_port_status_message(self):
self.__port_status_message = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-status-message", rest_name="port-status-message", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port status\nmessages'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_health(self):
"""
Getter method for port_health, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_health (string)
YANG Description: Provides the Fibre Channel port health
information. It consists of information
like fabric watch licence details, etc.
"""
return self.__port_health
def _set_port_health(self, v, load=False):
"""
Setter method for port_health, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_health (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_health is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_health() directly.
YANG Description: Provides the Fibre Channel port health
information. It consists of information
like fabric watch licence details, etc.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-health", rest_name="port-health", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port health\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_health must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-health", rest_name="port-health", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port health\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_health = t
if hasattr(self, '_set'):
self._set()
def _unset_port_health(self):
self.__port_health = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-health", rest_name="port-health", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port health\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_trunked(self):
"""
Getter method for port_trunked, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_trunked (boolean)
YANG Description: Provides the Fibre Channel port trunk
information. This parameter is set to
'true' for trunked ports and 'false' for
non trunked ports.
"""
return self.__port_trunked
def _set_port_trunked(self, v, load=False):
"""
Setter method for port_trunked, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_trunked (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_trunked is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_trunked() directly.
YANG Description: Provides the Fibre Channel port trunk
information. This parameter is set to
'true' for trunked ports and 'false' for
non trunked ports.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="port-trunked", rest_name="port-trunked", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_trunked must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-trunked", rest_name="port-trunked", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)""",
})
self.__port_trunked = t
if hasattr(self, '_set'):
self._set()
def _unset_port_trunked(self):
self.__port_trunked = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-trunked", rest_name="port-trunked", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\ninformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
def _get_port_trunk_master(self):
"""
Getter method for port_trunk_master, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_trunk_master (uint32)
YANG Description: Provides the Fibre Channel port trunk
master information. This parameter value
is set to '1' for trunk master and
'0' for slave ports.
"""
return self.__port_trunk_master
def _set_port_trunk_master(self, v, load=False):
"""
Setter method for port_trunk_master, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_trunk_master (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_trunk_master is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_trunk_master() directly.
YANG Description: Provides the Fibre Channel port trunk
master information. This parameter value
is set to '1' for trunk master and
'0' for slave ports.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-trunk-master", rest_name="port-trunk-master", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\nmaster information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_trunk_master must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-trunk-master", rest_name="port-trunk-master", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\nmaster information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)""",
})
self.__port_trunk_master = t
if hasattr(self, '_set'):
self._set()
def _unset_port_trunk_master(self):
self.__port_trunk_master = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-trunk-master", rest_name="port-trunk-master", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port trunk\nmaster information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
def _get_port_actual_distance(self):
"""
Getter method for port_actual_distance, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_actual_distance (uint32)
YANG Description: Provides the Fibre Channel port Actual
Distance Information.
"""
return self.__port_actual_distance
def _set_port_actual_distance(self, v, load=False):
"""
Setter method for port_actual_distance, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_actual_distance (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_actual_distance is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_actual_distance() directly.
YANG Description: Provides the Fibre Channel port Actual
Distance Information.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-actual-distance", rest_name="port-actual-distance", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Actual\nDistance Information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_actual_distance must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-actual-distance", rest_name="port-actual-distance", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Actual\nDistance Information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)""",
})
self.__port_actual_distance = t
if hasattr(self, '_set'):
self._set()
def _unset_port_actual_distance(self):
self.__port_actual_distance = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-actual-distance", rest_name="port-actual-distance", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Actual\nDistance Information'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
def _get_port_desired_credit(self):
"""
Getter method for port_desired_credit, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_desired_credit (uint32)
YANG Description: Provides the Fibre Channel port Desired
Credit Information.
"""
return self.__port_desired_credit
def _set_port_desired_credit(self, v, load=False):
"""
Setter method for port_desired_credit, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_desired_credit (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_desired_credit is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_desired_credit() directly.
YANG Description: Provides the Fibre Channel port Desired
Credit Information.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-desired-credit", rest_name="port-desired-credit", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides Fibre Channel port Desired Credit\nInformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_desired_credit must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-desired-credit", rest_name="port-desired-credit", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides Fibre Channel port Desired Credit\nInformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)""",
})
self.__port_desired_credit = t
if hasattr(self, '_set'):
self._set()
def _unset_port_desired_credit(self):
self.__port_desired_credit = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-desired-credit", rest_name="port-desired-credit", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides Fibre Channel port Desired Credit\nInformation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
def _get_port_buffer_allocated(self):
"""
Getter method for port_buffer_allocated, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_buffer_allocated (uint32)
YANG Description: Provides the Fibre Channel port Buffer
Allocation.
"""
return self.__port_buffer_allocated
def _set_port_buffer_allocated(self, v, load=False):
"""
Setter method for port_buffer_allocated, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_buffer_allocated (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_buffer_allocated is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_buffer_allocated() directly.
YANG Description: Provides the Fibre Channel port Buffer
Allocation.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-buffer-allocated", rest_name="port-buffer-allocated", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Buffer\nAllocation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_buffer_allocated must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-buffer-allocated", rest_name="port-buffer-allocated", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Buffer\nAllocation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)""",
})
self.__port_buffer_allocated = t
if hasattr(self, '_set'):
self._set()
def _unset_port_buffer_allocated(self):
self.__port_buffer_allocated = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="port-buffer-allocated", rest_name="port-buffer-allocated", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides the Fibre Channel port Buffer\nAllocation'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='uint32', is_config=True)
def _get_port_licensed(self):
"""
Getter method for port_licensed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_licensed (boolean)
YANG Description: Indicates if the port is
licensed or not. This is set to 'true'
for licenced ports and 'false' for
non licenced ports.
"""
return self.__port_licensed
def _set_port_licensed(self, v, load=False):
"""
Setter method for port_licensed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_licensed (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_licensed is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_licensed() directly.
YANG Description: Indicates if the port is
licensed or not. This is set to 'true'
for licenced ports and 'false' for
non licenced ports.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="port-licensed", rest_name="port-licensed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides if the port is\nlicensed or not. Set to TRUE\nfor licenced ports'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_licensed must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-licensed", rest_name="port-licensed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides if the port is\nlicensed or not. Set to TRUE\nfor licenced ports'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)""",
})
self.__port_licensed = t
if hasattr(self, '_set'):
self._set()
def _unset_port_licensed(self):
self.__port_licensed = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="port-licensed", rest_name="port-licensed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provides if the port is\nlicensed or not. Set to TRUE\nfor licenced ports'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='boolean', is_config=True)
def _get_port_address(self):
"""
Getter method for port_address, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_address (fabric-portid-type)
YANG Description: Fibre Channel address (24 bit
address PID).
Uses hexadecimal format.
"""
return self.__port_address
def _set_port_address(self, v, load=False):
"""
Setter method for port_address, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_address (fabric-portid-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_address is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_address() directly.
YANG Description: Fibre Channel address (24 bit
address PID).
Uses hexadecimal format.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F]*', 'length': [u'6']}), is_leaf=True, yang_name="port-address", rest_name="port-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'24 bit PID of a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='fabric-portid-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_address must be of a type compatible with fabric-portid-type""",
'defined-type': "brocade-fabric-service:fabric-portid-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F]*', 'length': [u'6']}), is_leaf=True, yang_name="port-address", rest_name="port-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'24 bit PID of a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='fabric-portid-type', is_config=True)""",
})
self.__port_address = t
if hasattr(self, '_set'):
self._set()
def _unset_port_address(self):
self.__port_address = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'[0-9a-fA-F]*', 'length': [u'6']}), is_leaf=True, yang_name="port-address", rest_name="port-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'24 bit PID of a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='fabric-portid-type', is_config=True)
def _get_port_fec(self):
"""
Getter method for port_fec, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_fec (string)
YANG Description: Provide port FEC operational status of a port.
Active - When FEC enabled and operational on a port.
Inactive - When FEC is not operational on a port.
Not Supported - When the platform doesn't support FEC feature.
"""
return self.__port_fec
def _set_port_fec(self, v, load=False):
"""
Setter method for port_fec, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_fec (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_fec is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_fec() directly.
YANG Description: Provide port FEC operational status of a port.
Active - When FEC enabled and operational on a port.
Inactive - When FEC is not operational on a port.
Not Supported - When the platform doesn't support FEC feature.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-fec", rest_name="port-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide FEC operational status on a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_fec must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-fec", rest_name="port-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide FEC operational status on a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_fec = t
if hasattr(self, '_set'):
self._set()
def _unset_port_fec(self):
self.__port_fec = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-fec", rest_name="port-fec", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide FEC operational status on a port'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_configured_speed(self):
"""
Getter method for port_configured_speed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_configured_speed (string)
YANG Description: Provides the Fibre Channel port Configured
Speed Information.
"""
return self.__port_configured_speed
def _set_port_configured_speed(self, v, load=False):
"""
Setter method for port_configured_speed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_configured_speed (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_configured_speed is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_configured_speed() directly.
YANG Description: Provides the Fibre Channel port Configured
Speed Information.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-configured-speed", rest_name="port-configured-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the configured speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_configured_speed must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-configured-speed", rest_name="port-configured-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the configured speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_configured_speed = t
if hasattr(self, '_set'):
self._set()
def _unset_port_configured_speed(self):
self.__port_configured_speed = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-configured-speed", rest_name="port-configured-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the configured speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
def _get_port_actual_speed(self):
"""
Getter method for port_actual_speed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_actual_speed (string)
YANG Description: Provides the Fibre Channel port Actual
speed Information.
"""
return self.__port_actual_speed
def _set_port_actual_speed(self, v, load=False):
"""
Setter method for port_actual_speed, mapped from YANG variable /brocade_fabric_service_rpc/show_fibrechannel_interface_info/output/show_fibrechannel_interface/show_fibrechannel_info/port_actual_speed (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_port_actual_speed is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_port_actual_speed() directly.
YANG Description: Provides the Fibre Channel port Actual
speed Information.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="port-actual-speed", rest_name="port-actual-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the actual speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """port_actual_speed must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="port-actual-speed", rest_name="port-actual-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the actual speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)""",
})
self.__port_actual_speed = t
if hasattr(self, '_set'):
self._set()
def _unset_port_actual_speed(self):
self.__port_actual_speed = YANGDynClass(base=unicode, is_leaf=True, yang_name="port-actual-speed", rest_name="port-actual-speed", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'info': u'Provide the actual speed of the port.'}}, namespace='urn:brocade.com:mgmt:brocade-fabric-service', defining_module='brocade-fabric-service', yang_type='string', is_config=True)
port_interface = __builtin__.property(_get_port_interface, _set_port_interface)
port_index = __builtin__.property(_get_port_index, _set_port_index)
port_type = __builtin__.property(_get_port_type, _set_port_type)
port_wwn = __builtin__.property(_get_port_wwn, _set_port_wwn)
remote_port_wwn = __builtin__.property(_get_remote_port_wwn, _set_remote_port_wwn)
remote_node_wwn = __builtin__.property(_get_remote_node_wwn, _set_remote_node_wwn)
port_state = __builtin__.property(_get_port_state, _set_port_state)
port_status = __builtin__.property(_get_port_status, _set_port_status)
port_status_message = __builtin__.property(_get_port_status_message, _set_port_status_message)
port_health = __builtin__.property(_get_port_health, _set_port_health)
port_trunked = __builtin__.property(_get_port_trunked, _set_port_trunked)
port_trunk_master = __builtin__.property(_get_port_trunk_master, _set_port_trunk_master)
port_actual_distance = __builtin__.property(_get_port_actual_distance, _set_port_actual_distance)
port_desired_credit = __builtin__.property(_get_port_desired_credit, _set_port_desired_credit)
port_buffer_allocated = __builtin__.property(_get_port_buffer_allocated, _set_port_buffer_allocated)
port_licensed = __builtin__.property(_get_port_licensed, _set_port_licensed)
port_address = __builtin__.property(_get_port_address, _set_port_address)
port_fec = __builtin__.property(_get_port_fec, _set_port_fec)
port_configured_speed = __builtin__.property(_get_port_configured_speed, _set_port_configured_speed)
port_actual_speed = __builtin__.property(_get_port_actual_speed, _set_port_actual_speed)
_pyangbind_elements = {'port_interface': port_interface, 'port_index': port_index, 'port_type': port_type, 'port_wwn': port_wwn, 'remote_port_wwn': remote_port_wwn, 'remote_node_wwn': remote_node_wwn, 'port_state': port_state, 'port_status': port_status, 'port_status_message': port_status_message, 'port_health': port_health, 'port_trunked': port_trunked, 'port_trunk_master': port_trunk_master, 'port_actual_distance': port_actual_distance, 'port_desired_credit': port_desired_credit, 'port_buffer_allocated': port_buffer_allocated, 'port_licensed': port_licensed, 'port_address': port_address, 'port_fec': port_fec, 'port_configured_speed': port_configured_speed, 'port_actual_speed': port_actual_speed, }
| 82.010672 | 710 | 0.75324 | 11,179 | 76,844 | 4.933447 | 0.025315 | 0.048322 | 0.074341 | 0.021323 | 0.930083 | 0.912041 | 0.897772 | 0.888887 | 0.882232 | 0.875542 | 0 | 0.012452 | 0.111694 | 76,844 | 936 | 711 | 82.098291 | 0.795491 | 0.238002 | 0 | 0.502119 | 0 | 0.067797 | 0.443925 | 0.246352 | 0 | 0 | 0 | 0 | 0 | 1 | 0.133475 | false | 0 | 0.016949 | 0 | 0.258475 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
77b34b15d526d8d96ffd13eda1c8a4ffbcdab3d2 | 252 | py | Python | ps2_census/utils.py | spascou/ps2-census | 4edce4b9bfe8af9aca2f28244cb4f70cad67dc93 | [
"MIT"
] | 4 | 2020-05-19T16:20:32.000Z | 2020-10-13T06:09:01.000Z | ps2_census/utils.py | spascou/ps2-census | 4edce4b9bfe8af9aca2f28244cb4f70cad67dc93 | [
"MIT"
] | null | null | null | ps2_census/utils.py | spascou/ps2-census | 4edce4b9bfe8af9aca2f28244cb4f70cad67dc93 | [
"MIT"
] | 1 | 2021-03-08T06:14:53.000Z | 2021-03-08T06:14:53.000Z | from .constants import COMMAND_PREFIX, SERVICE_ID_PREFIX, Command
def command_key(command: Command, prefix: str = COMMAND_PREFIX) -> str:
return f"{prefix}{command}"
def bool2str(arg: bool) -> str:
return "true" if arg is True else "false"
| 25.2 | 71 | 0.722222 | 36 | 252 | 4.916667 | 0.555556 | 0.220339 | 0.180791 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004762 | 0.166667 | 252 | 9 | 72 | 28 | 0.838095 | 0 | 0 | 0 | 0 | 0 | 0.103175 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.2 | 0.4 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
77d74782eadf8fcdb67a0a9c378e7f18358d040b | 2,566 | py | Python | stars/test/test_tech.py | therussellhome/inherit_the_stars | aa498103e1683625f762afee8c95ffcebb77ca03 | [
"Unlicense"
] | null | null | null | stars/test/test_tech.py | therussellhome/inherit_the_stars | aa498103e1683625f762afee8c95ffcebb77ca03 | [
"Unlicense"
] | null | null | null | stars/test/test_tech.py | therussellhome/inherit_the_stars | aa498103e1683625f762afee8c95ffcebb77ca03 | [
"Unlicense"
] | null | null | null | import unittest
from .. import *
class _TestPlayer:
pass
class TechTestCase(unittest.TestCase):
def test_is_available1(self):
t = tech.Tech(level=tech_level.TechLevel(
energy=1,
weapons=2,
propulsion=3,
construction=4,
electonics=5,
biotechnology=6
))
l = tech_level.TechLevel()
self.assertFalse(t.is_available(level=l))
def test_is_available2(self):
t = tech.Tech(level=tech_level.TechLevel(
energy=1,
weapons=2,
propulsion=3,
construction=4,
electonics=5,
biotechnology=6
))
l = tech_level.TechLevel(
energy=6,
weapons=5,
propulsion=4,
construction=4,
electonics=5,
biotechnology=6
)
self.assertTrue(t.is_available(level=l))
def test_is_available3(self):
t = tech.Tech(race_requirements='Kender')
r = race.Race()
self.assertFalse(t.is_available(race=r))
def test_is_available4(self):
t = tech.Tech(race_requirements='Kender')
r = race.Race(primary_race_trait='Kender')
self.assertTrue(t.is_available(race=r))
def test_is_available5(self):
t = tech.Tech()
r = race.Race()
self.assertTrue(t.is_available(race=r))
def test_is_available6(self):
t = tech.Tech(race_requirements='Kender 2ndSight')
r = race.Race(lrt_2ndSight=True)
self.assertFalse(t.is_available(race=r))
def test_is_available7(self):
t = tech.Tech(race_requirements='Kender 2ndSight')
r = race.Race(primary_race_trait='Kender', lrt_2ndSight=True)
self.assertTrue(t.is_available(race=r))
def test_is_available8(self):
t = tech.Tech(race_requirements='-Akultan')
r = race.Race()
self.assertTrue(t.is_available(race=r))
def test_is_available9(self):
t = tech.Tech(race_requirements='Kender', level=tech_level.TechLevel(
energy=1,
weapons=2,
propulsion=3,
construction=4,
electonics=5,
biotechnology=6
))
l = tech_level.TechLevel(
energy=6,
weapons=5,
propulsion=4,
construction=4,
electonics=5,
biotechnology=6
)
r = race.Race(primary_race_trait='Kender')
self.assertTrue(t.is_available(level=l, race=r))
| 28.831461 | 77 | 0.56742 | 291 | 2,566 | 4.838488 | 0.175258 | 0.044744 | 0.057528 | 0.083097 | 0.836648 | 0.825994 | 0.805398 | 0.75071 | 0.712358 | 0.712358 | 0 | 0.024841 | 0.325409 | 2,566 | 88 | 78 | 29.159091 | 0.788562 | 0 | 0 | 0.675325 | 0 | 0 | 0.028839 | 0 | 0 | 0 | 0 | 0 | 0.116883 | 1 | 0.116883 | false | 0.012987 | 0.025974 | 0 | 0.168831 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
77f93e7c4975db428dd03488c385716b36bf8692 | 20 | py | Python | rugosa/emulation/call_hooks/stdlib/__init__.py | Defense-Cyber-Crime-Center/rugosa | 70f5b1db7e3f02ecccb0495fe1c0c77930769276 | [
"MIT"
] | 1 | 2022-03-13T03:03:31.000Z | 2022-03-13T03:03:31.000Z | rugosa/emulation/call_hooks/stdlib/__init__.py | Defense-Cyber-Crime-Center/rugosa | 70f5b1db7e3f02ecccb0495fe1c0c77930769276 | [
"MIT"
] | null | null | null | rugosa/emulation/call_hooks/stdlib/__init__.py | Defense-Cyber-Crime-Center/rugosa | 70f5b1db7e3f02ecccb0495fe1c0c77930769276 | [
"MIT"
] | null | null | null | from .libc import *
| 10 | 19 | 0.7 | 3 | 20 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 20 | 1 | 20 | 20 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bb27dad713e1ac8f23bbae157a68fdf5d5a72e4d | 33 | py | Python | dridex/__init__.py | futex/mwcfg-modules | a65bc69fd302215485f2ea62c4fd35c6b8f04397 | [
"BSD-3-Clause"
] | 23 | 2021-05-01T19:19:36.000Z | 2022-03-19T22:10:25.000Z | dridex/__init__.py | futex/mwcfg-modules | a65bc69fd302215485f2ea62c4fd35c6b8f04397 | [
"BSD-3-Clause"
] | 3 | 2021-05-01T22:10:32.000Z | 2021-05-15T13:35:22.000Z | dridex/__init__.py | futex/mwcfg-modules | a65bc69fd302215485f2ea62c4fd35c6b8f04397 | [
"BSD-3-Clause"
] | 8 | 2021-05-01T21:13:54.000Z | 2021-11-22T06:53:54.000Z | from .loader import DridexLoader
| 16.5 | 32 | 0.848485 | 4 | 33 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 33 | 1 | 33 | 33 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bb3882aa51f453a1b0c233be7f666b3992f2d5a7 | 113 | py | Python | api/app.py | NamrataSitlani/medhavi | d4d93289fca53c199bc9ba5810604386ea643910 | [
"Apache-2.0"
] | 1 | 2019-09-29T05:33:16.000Z | 2019-09-29T05:33:16.000Z | api/app.py | NamrataSitlani/medhavi | d4d93289fca53c199bc9ba5810604386ea643910 | [
"Apache-2.0"
] | 1 | 2019-09-09T05:10:21.000Z | 2019-09-09T05:10:21.000Z | api/app.py | NamrataSitlani/medhavi | d4d93289fca53c199bc9ba5810604386ea643910 | [
"Apache-2.0"
] | 8 | 2019-08-01T11:43:01.000Z | 2019-10-03T05:35:50.000Z | from flask import Blueprint
from flask_restful import Api
api_bp = Blueprint('api', __name__)
api = Api(api_bp)
| 18.833333 | 35 | 0.778761 | 18 | 113 | 4.5 | 0.444444 | 0.222222 | 0.197531 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.141593 | 113 | 5 | 36 | 22.6 | 0.835052 | 0 | 0 | 0 | 0 | 0 | 0.026549 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
bb58f423dced24533b159e36a8fe516d175059a7 | 25,641 | py | Python | server/resources/porting/prop.py | mirokymac/propAPIne | b6bc952ac1996644653c50c3336b12c8d9ee6d86 | [
"MIT"
] | null | null | null | server/resources/porting/prop.py | mirokymac/propAPIne | b6bc952ac1996644653c50c3336b12c8d9ee6d86 | [
"MIT"
] | null | null | null | server/resources/porting/prop.py | mirokymac/propAPIne | b6bc952ac1996644653c50c3336b12c8d9ee6d86 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 9 11:17:03 2019
@author: Zihao.MAI
porting of cool prop gas properties
"""
__all__ = ["mProp", "flasher"]
REFPROP_READY = False
from difflib import SequenceMatcher
from functools import reduce
from json import load as load_json
import logging
#from os.path import isfile, getsize
#import pickle
import re
import CoolProp.CoolProp as cp
from ..util.load_csv import Load_csv, Key
# 初始化环境
logger = logging.getLogger(__name__)
# 输入输出变量列表
# 不建议H和S作为intype，因为使用的reference不一样
loader = Load_csv(r'./common/CoolProp.AbstractState.IO.csv')
tmp_SIcal_types = loader(Key(0))
tmp_io_flags = loader(Key(2))
tmp_qc_flags = loader(Key(3))
tmp_ABcal_types = loader(Key(4))
OTYPE = dict(zip(tmp_SIcal_types, tmp_ABcal_types))
ITYPE = frozenset([i for i, j in zip(tmp_SIcal_types, tmp_io_flags) if "I" in j])
QCTYPE = frozenset([i for i, j in zip(tmp_SIcal_types, tmp_qc_flags) if j])
OTYPE_MIX = {
"VAPFRAC": "mole_fractions_vapor",
"LIQFRAC": "mole_fractions_liquid"
}
OTYPE_COMBO = {
"CRIT": frozenset(("TCRIT", "PCRIT", "ACENTRIC", "M")),
"FLOW": frozenset(("D", "V", "M", "Z")),
"HEAT": frozenset(("S", "H", "U", "CP"))
}
# 构建ITYPE_AB
def input_construct(type1:str, type2:str, value1:float, value2:float):
type1 = type1[0] + type1[1:].lower()
type2 = type2[0] + type2[1:].lower()
if type1 in "DHSU":
type1 += "mass"
if type2 in "DHSU":
type2 += "mass"
res = None
if type1[0] < type2[0]:
res = {
"ipair": getattr(cp, type1 + type2 + "_INPUTS"),
"Value1": value1,
"Value2": value2
}
else:
res = {
"ipair": getattr(cp, type2 + type1 + "_INPUTS"),
"Value1": value2,
"Value2": value1
}
return res
TYPE_PATTERN = r"[A-Za-z0-9]+"
MIXTURE_PATTERN = r"[A-Z0-9]+\s?\:\s?\d+\.?\d*"
del loader, tmp_ABcal_types, tmp_io_flags, tmp_qc_flags, tmp_SIcal_types
# 物料字典
loader = './common/fluidlist.json'
with open(loader, "r") as loader:
SUBS_P = load_json(loader)
#SUBS_M_CP = dict()
#SUBS_M_REFPROP = dict()
# 尝试载入REFPROP
PATH_TO_REFPROP = "./refprop/"
cp.set_config_string(5, PATH_TO_REFPROP)
try:
cp.PropsSI('H', 'P', 101325, 'Q', 1, 'REFPROP::Water')
REFPROP_READY = True
logger.critical("Refprop loaded.")
except:
REFPROP_READY = False
logger.critical("Refprop unable to load.")
#def prop(sub:str,
# outType:list,
# inputType1:str,
# inputValue1:float,
# inputType2:str,
# inputValue2:float,
# backend:str="CP")-> dict:
#
# pass
#
## 废弃代码： mProp/AbstractState可以完全覆盖这部分的内容。
#def pProp(sub:str,
# outType:list,
# inputType1:str,
# inputValue1:float,
# inputType2:str,
# inputValue2:float,
# backend:str="CP")-> dict:
#
# res = dict()
# errtext = ""
# mode_refprop = False
# extend_backend_req = False
# # 检查REFPROP可用性
# if backend == "REFPROP":
# if REFPROP_READY:
# mode_refprop = True
# else:
# errtext += "Warning: No REFPROP support on server!\n"
# res.update({"warning": True,
# "message": errtext})
# mode_refprop = False
# # 转换输出关键字为list
# if type(outType) != list:
# if type(outType) == str:
# outType = [outType]
# else:
# errtext += "Input Error: Invalid output type: " + str(outType)
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
# # 将所有文本输入转化为大写
# sub = sub.upper()
# backend = backend.upper()
# inputType1 = inputType1.upper()
# inputType2 = inputType2.upper()
# outType = set(list(map(str.upper, outType)))
#
# # 检查纯物质名字是否受到支持
# if sub in SUBS_P:
# if mode_refprop:
# if SUBS_P[sub][1] != "N/A":
# errtext += "Input Error: Not a REFPROP supported substance" + str(sub)
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
# else:
# sub = "REFPROP::" + SUBS_P[sub][1]
# else:
# # 检查使用的backend类型
# if backend in ("CP", "COOLPROP"):
# sub = SUBS_P[sub][0]
# elif backend in ("PENGROBINSON", "PENG-ROBINSON", "PR"):
# sub = "PR::" + SUBS_P[sub][0]
# else:
# errtext += "Input Error: Not supported backend [%s] requested." % backend
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
# else:
# extend_backend_req = True
#
# if inputType1 == inputType2 and not set(outType).issubset(QCTYPE):
# errtext += "Input Error. Only one input Type [%s] is given." % inputType1
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
#
# if SequenceMatcher(None, inputType1, inputType2).find_longest_match(0, len(inputType1), 0, len(inputType2)).size and not set(outType).issubset(QCTYPE):
# errtext += "Input Error. Input Type [%s&%s] is the same." % (inputType1, inputType2)
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
#
# if {inputType1, inputType2}.issubset(ITYPE) and not set(outType).issubset(QCTYPE):
# errtext += "Input Error. Invalid input type combo [%s&%s]." % (inputType1, inputType2)
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
#
# for item in ("H", "S"):
# if item in inputType1 + inputType2:
# errtext += "Warning: Unexpected result due to [%s] using difference Reference point on different backend.\n" % item
# logger.warning(errtext)
# res.update({"warning": True,
# "message": errtext})
#
# if type(inputValue1) == float and type(inputValue2) == float:
# errtext += "Input Error. Invalid values are not numbers."
# logger.error(errtext)
# res.update({"error": True,
# "message": errtext})
# return {"result": res}
#
# # 处理输出项
# for item in set(OTYPE_COMBO.keys()).intersection(outType):
# outType.remove(item)
# outType.union(OTYPE_COMBO[item])
#
# if outType - set(OTYPE.keys()):
# errtext += "Warning: Unsupported Output Type keywords: %s \n" % list(outType - OTYPE)
# logger.warning(errtext)
# res.update({"warning": True,
# "message": errtext})
#
# outType = outType.intersection(OTYPE.keys())
#
# for item in outType:
# try:
# if extend_backend_req:
# # todo: impletementing of pProp_extend()
# res.update(pProp_extend())
# else:
# res.update({item: cp.PropsSI(item, inputType1, inputValue1, inputType2, inputValue2, sub)})
# except Exception as e:
# errtext += "Calculation Error: Fail to cal [%s]:\n" % item
# errtext += str(e)
# res.update({"warning": True,
# "message": errtext})
#
# if {"message", "warning"} == set(res.keys()):
# errtext += "Fatal Calculation Error: No result calculated!"
# res.update({"warning": True,
# "message": errtext})
#
# return {"result": res}
def mProp(sub:str,
outType:list,
inputType1:str,
inputValue1:float,
inputType2:str,
inputValue2:float,
backend:str="CP")-> dict:
res = dict()
errtext = ""
mode_refprop = False
extend_backend_req = False
mixture = None
flash = []
# 检查REFPROP可用性
if backend == "REFPROP":
if REFPROP_READY:
mode_refprop = True
else:
errtext += "Warning: No REFPROP support on server!\n"
res.update({"warning": True,
"message": errtext})
mode_refprop = False
# 转换输出关键字为list
if type(outType) != list:
if type(outType) == str:
outType = re.findall(TYPE_PATTERN, outType)
else:
errtext += "Input Error: Invalid output type: " + str(outType)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
# 将所有文本输入转化为大写
sub = sub.upper()
backend = backend.upper()
inputType1 = inputType1.upper()
inputType2 = inputType2.upper()
outType = set(list(map(str.upper, outType)))
# 检查纯物质名字是否受到支持
# 1-检查REFPROPbackend可用性
if sub in SUBS_P:
if mode_refprop:
if SUBS_P[sub][1] == "N/A":
errtext += "Input Error: Not a REFPROP supported substance " + str(sub)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
sub = SUBS_P[sub][1]
else:
# 检查使用的backend类型
if backend in ("CP", "COOLPROP") or (not mode_refprop and backend == "REFPROP"):
sub_ = SUBS_P[sub][0]
elif backend in ("PENGROBINSON", "PENG-ROBINSON", "PR"):
sub_ = SUBS_P[sub][0]
else:
errtext += "Input Error: Not supported backend [%s] requested." % backend
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
# todo: make extend pure backend avaible
if sub_ == "N/A":
# extend_backend_req = True
errtext += "Input Error: Not a CoolProp supported substance " + str(sub)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
sub = sub_
del sub_
else:
# 2-检查混合物可用性
# 2.1-检查是否输入了混合物组分
# 2.2-检查是否在组成自定义混合物的物质名中包含了混合物名称
# 2.3-检查组成自定义混合物的物质是否都是物性数据库支持的物质
# 2.4-检查各物质份额是否均为正数
# 2.5-归一化混合物组成
mixture = re.findall(MIXTURE_PATTERN, sub)
if mixture:
# get substance name
sub = list(map(lambda x: x.split(":")[0], mixture))
# get substance fraction
mixture = list(map(lambda x: float(x.split(":")[1]), mixture))
if set(sub) - set(SUBS_P.keys()):
errtext += "Input Error: Not supported substance for mixture: " + ", ".join(sub)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
if reduce(lambda acc, x: SUBS_P[x][2] or acc, sub, False):
errtext += "Input Error: Building a mixture with a mixture..."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
if backend in ("CP", "COOLPROP"):
sub = "&".join([SUBS_P[i][0] for i in sub])
elif mode_refprop:
sub = "&".join([SUBS_P[i][1] for i in sub])
if "N/A" in sub:
errtext += "Input Error: Not REFPROP supported substance is detected."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
# 检查混合组分输入是否均为正数
if not reduce(lambda acc, x: acc and (x > 0), mixture, True):
errtext += "Input Error: Building a mixture with a mixture..."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
mixture = [i/sum(mixture) for i in mixture]
else:
extend_backend_req = True
if not outType.issubset(QCTYPE):
if inputType1 == inputType2:
errtext += "Input Error. Only one input Type [%s] is given." % inputType1
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if SequenceMatcher(None, inputType1, inputType2).find_longest_match(0, len(inputType1), 0, len(inputType2)).size:
errtext += "Input Error. Input Type [%s&%s] is the same." % (inputType1, inputType2)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if not {inputType1, inputType2}.issubset(ITYPE):
errtext += "Input Error. Invalid input type combo [%s&%s]." % (inputType1, inputType2)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
for item in ("H", "S"):
if item in inputType1 + inputType2:
errtext += "Warning: Unexpected result due to [%s] using difference Reference point on different backend.\n" % item
logger.warning(errtext)
res.update({"warning": True,
"message": errtext})
try:
inputValue1 = float(inputValue1)
inputValue2 = float(inputValue2)
except:
errtext += "Input Error. Invalid values are not numbers."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if backend in ("PENGROBINSON", "PENG-ROBINSON", "PR"):
backend = "PR"
elif backend in ("CP", "COOLPROP") or not mode_refprop:
backend = "HEOS"
elif backend == "REFPROP" and mode_refprop:
backend = "REFPROP"
# 处理输出项
# 构建闪蒸计算输出
flash = set(OTYPE_MIX.keys())
flash &= outType
outType -= flash
# 如果将进行闪蒸计算而目标组成不是混合物
if not mixture and flash:
flash = []
errtext += "Input Error. Not support flash calculation for predefine mixture or pure component."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
# 构建组合输出
for item in set(OTYPE_COMBO.keys()).intersection(outType):
outType.remove(item)
outType = outType.union(OTYPE_COMBO[item])
# 构建普通输出
if outType - set(OTYPE.keys()):
errtext += "Warning: Unsupported Output Type keywords: %s \n" % list(outType - set(OTYPE.keys()))
logger.warning(errtext)
res.update({"warning": True,
"message": errtext})
outType = outType.intersection(OTYPE.keys())
if extend_backend_req:
# todo: impletementing of mProp_extend()
res.update(mProp_extend())
else:
sub = cp.AbstractState(backend, sub)
if mixture:
sub.set_mole_fractions(mixture)
sub.update(
**input_construct(
inputType1,
inputType2,
inputValue1,
inputValue2
)
)
for item in outType:
try:
res.update({
item: sub.keyed_output(getattr(cp, OTYPE[item]))
})
except Exception as e:
errtext += "Calculation Error: Fail to cal [%s]:\n" % item
errtext += str(e)
res.update({"warning": True,
"message": errtext})
for item in flash:
try:
res.update({
item: getattr(sub, OTYPE_MIX[item])()
})
except Exception as e:
errtext += "Calculation Error: Fail to cal [%s]:\n" % item
errtext += str(e)
res.update({"warning": True,
"message": errtext})
if {"message", "warning"} == set(res.keys()):
errtext += "Fatal Calculation Error: No result calculated!"
res.update({"warning": True,
"message": errtext})
return {"result": res}
#
#def pProp_extend():
# pass
def mProp_extend():
errtext = ""
errtext += "Extended component database not impletemented."
logger.warning(errtext)
return {"error": True, "message": errtext}
def flasher(sub:str,
inputType1:str,
inputValue1:float,
inputType2:str,
inputValue2:float,
backend:str="CP")-> dict:
res = dict()
errtext = ""
extend_backend_req = False
mode_refprop = False
mixture = None
outType = ("D", "H", "M", "S", "U", "V", "Z")
# 检查REFPROP可用性
if backend == "REFPROP":
if REFPROP_READY:
mode_refprop = True
else:
errtext += "Warning: No REFPROP support on server!\n"
res.update({"warning": True,
"message": errtext})
mode_refprop = False
# 将所有文本输入转化为大写
sub = sub.upper()
backend = backend.upper()
inputType1 = inputType1.upper()
inputType2 = inputType2.upper()
# 检查纯物质名字是否受到支持
# 2-检查混合物可用性
mixture = re.findall(MIXTURE_PATTERN, sub)
if mixture:
# get substance name
sub = list(map(lambda x: x.split(":")[0], mixture))
# get substance fraction
mixture = list(map(lambda x: float(x.split(":")[1]), mixture))
if set(sub) - set(SUBS_P.keys()):
errtext += "Input Error: Not supported substance for mixture: " + ", ".join(sub)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
if reduce(lambda acc, x: SUBS_P[x][2] or acc, sub, False):
errtext += "Input Error: Building a mixture with a mixture..."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
if backend in ("CP", "COOLPROP"):
sub = "&".join([SUBS_P[i][0] for i in sub])
elif mode_refprop:
sub = "&".join([SUBS_P[i][1] for i in sub])
if "N/A" in sub:
errtext += "Input Error: Not supported substance for REFPROP is detected."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
else:
extend_backend_req = True
# 检查混合组分输入是否均为正数
if not reduce(lambda acc, x: acc and (x > 0), mixture, True):
errtext += "Input Error: Building a mixture with a mixture..."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
mixture = [i/sum(mixture) for i in mixture]
else:
extend_backend_req = True
if not mixture:
errtext += "Input Error: Can not calculate flashing for pure component."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if type(sub) == str:
res.update({"components": sub.split("&")})
if inputType1 == inputType2:
errtext += "Input Error. Only one input Type [%s] is given." % inputType1
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if SequenceMatcher(None, inputType1, inputType2).find_longest_match(0, len(inputType1), 0, len(inputType2)).size:
errtext += "Input Error. Input Type [%s&%s] is the same." % (inputType1, inputType2)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if not {inputType1, inputType2}.issubset(ITYPE):
errtext += "Input Error. Invalid input type combo [%s&%s]." % (inputType1, inputType2)
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
for item in ("H", "S"):
if item in inputType1 + inputType2:
errtext += "Warning: Unexpected result due to [%s] using difference Reference point on different backend.\n" % item
logger.warning(errtext)
res.update({"warning": True,
"message": errtext})
try:
inputValue1 = float(inputValue1)
inputValue2 = float(inputValue2)
except:
errtext += "Input Error. Invalid values are not numbers."
logger.error(errtext)
res.update({"error": True,
"message": errtext})
return {"result": res}
if backend in ("PENGROBINSON", "PENG-ROBINSON", "PR"):
backend = "PR"
elif backend in ("CP", "COOLPROP") or not mode_refprop:
backend = "HEOS"
elif backend == "REFPROP" and mode_refprop:
backend = "REFPROP"
if extend_backend_req:
# todo: impletementing of mProp_extend()
res.update(mProp_extend())
else:
sub = cp.AbstractState(backend, sub)
sub.set_mole_fractions(mixture)
sub.update(
**input_construct(
inputType1,
inputType2,
inputValue1,
inputValue2
)
)
#
Q = sub.keyed_output(getattr(cp, "iQ"))
T = sub.T()
P = sub.p()
rtn = dict()
for item in outType:
try:
rtn.update({
item: sub.keyed_output(getattr(cp, OTYPE[item]))
})
except Exception as e:
errtext += "Calculation Error: Fail to cal [%s] for INFLOW:\n" % item
errtext += str(e)
res.update({"warning": True,
"message": errtext})
rtn.update({"Fraction": mixture})
res.update({
"IN": rtn,
"T": T,
"P": P,
"Q": Q
})
if not (0 < Q < 1):
errtext += "Input Error: INFLOW is single phase"
res.update({"warning": True,
"message": errtext})
return {"result": res}
for mix in OTYPE_MIX:
_mixture = None
sub.set_mole_fractions(mixture)
try:
_mixture = getattr(sub, OTYPE_MIX[mix])()
except Exception as e:
errtext += "Calculation Error: Fail to cal [%s]:\n" % mix
errtext += str(e)
res.update({"warning": True,
"message": errtext})
continue
sub.set_mole_fractions(_mixture)
sub.specify_phase(cp.iphase_gas if mix == "VAPFRAC" else cp.iphase_liquid)
sub.update(cp.PT_INPUTS, P, T)
rtn = dict()
for item in outType:
try:
rtn.update({
item: sub.keyed_output(getattr(cp, OTYPE[item]))
})
except Exception as e:
errtext += "Calculation Error: Fail to cal [%s] for INFLOW:\n" % item
errtext += str(e)
res.update({"warning": True,
"message": errtext})
rtn.update({"Fraction": _mixture})
res.update({mix: rtn})
if {"message", "warning"} == set(res.keys()):
errtext += "Fatal Calculation Error: No result calculated!"
res.update({"warning": True,
"message": errtext})
return {"result": res}
| 36.421875 | 157 | 0.495339 | 2,534 | 25,641 | 4.9412 | 0.11247 | 0.040252 | 0.069004 | 0.061337 | 0.778612 | 0.764875 | 0.762399 | 0.757288 | 0.75601 | 0.741315 | 0 | 0.012497 | 0.382083 | 25,641 | 704 | 158 | 36.421875 | 0.777771 | 0.230997 | 0 | 0.723214 | 0 | 0 | 0.161597 | 0.005743 | 0.004464 | 0 | 0 | 0.00142 | 0 | 1 | 0.008929 | false | 0 | 0.015625 | 0 | 0.082589 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
24a21fa009a7772f27568fd2474c9f50e89e1d5e | 35 | py | Python | bitinfer/wrappers/__init__.py | piero2c/BitInfer | f265870ce374dccc78a425402caa5a9f36dfe99f | [
"MIT"
] | 1 | 2021-09-11T18:30:34.000Z | 2021-09-11T18:30:34.000Z | bitinfer/wrappers/__init__.py | piero2c/BitInfer | f265870ce374dccc78a425402caa5a9f36dfe99f | [
"MIT"
] | null | null | null | bitinfer/wrappers/__init__.py | piero2c/BitInfer | f265870ce374dccc78a425402caa5a9f36dfe99f | [
"MIT"
] | null | null | null | from .bert_modeling import BitModel | 35 | 35 | 0.885714 | 5 | 35 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085714 | 35 | 1 | 35 | 35 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
24c9e8c77128e00a5b67538223f3c1adeb86f767 | 5,394 | py | Python | mozurestsdk/commerce/catalog/admin/attributedefinition/producttypes/producttypeoption.py | Mozu/mozu-python-sdk | 9cc918aca7db3639264184e5266e8e508a08a7dd | [
"Apache-2.0"
] | 1 | 2021-03-22T12:38:42.000Z | 2021-03-22T12:38:42.000Z | mozurestsdk/commerce/catalog/admin/attributedefinition/producttypes/producttypeoption.py | Mozu/mozu-python-sdk | 9cc918aca7db3639264184e5266e8e508a08a7dd | [
"Apache-2.0"
] | null | null | null | mozurestsdk/commerce/catalog/admin/attributedefinition/producttypes/producttypeoption.py | Mozu/mozu-python-sdk | 9cc918aca7db3639264184e5266e8e508a08a7dd | [
"Apache-2.0"
] | 2 | 2015-09-30T19:49:00.000Z | 2015-09-30T19:51:03.000Z |
"""
This code was generated by Codezu.
Changes to this file may cause incorrect behavior and will be lost if
the code is regenerated.
"""
from mozurestsdk.mozuclient import default as default_client
from mozurestsdk.mozuurl import MozuUrl;
from mozurestsdk.urllocation import UrlLocation
from mozurestsdk.apicontext import ApiContext;
class ProductTypeOption(object):
def __init__(self, apiContext: ApiContext = None, dataViewMode="Live", mozuClient = None):
if (apiContext is not None and apiContext.dataViewMode is None):
apiContext.dataViewMode = dataViewMode;
else:
apiContext = ApiContext(dataViewMode = dataViewMode);
self.client = mozuClient or default_client();
self.client.withApiContext(apiContext);
def getOptions(self,productTypeId):
""" Retrieves a list of option product attributes defined for the specified product type.
Args:
| productTypeId (int) - Identifier of the product type.
Returns:
| array of AttributeInProductType
Raises:
| ApiException
"""
url = MozuUrl("/api/commerce/catalog/admin/attributedefinition/producttypes/{productTypeId}/Options", "GET", UrlLocation.TenantPod, False);
url.formatUrl("productTypeId", productTypeId);
self.client.withResourceUrl(url).execute();
return self.client.result();
def getOption(self,productTypeId, attributeFQN, responseFields = None):
""" Retrieves the details of an option attribute defined for the specified product type.
Args:
| productTypeId (int) - Identifier of the product type.
| attributeFQN (string) - The fully qualified name of the attribute, which is a user defined attribute identifier.
| responseFields (string) - Use this field to include those fields which are not included by default.
Returns:
| AttributeInProductType
Raises:
| ApiException
"""
url = MozuUrl("/api/commerce/catalog/admin/attributedefinition/producttypes/{productTypeId}/Options/{attributeFQN}?responseFields={responseFields}", "GET", UrlLocation.TenantPod, False);
url.formatUrl("attributeFQN", attributeFQN);
url.formatUrl("productTypeId", productTypeId);
url.formatUrl("responseFields", responseFields);
self.client.withResourceUrl(url).execute();
return self.client.result();
def addOption(self,attributeInProductType, productTypeId, responseFields = None):
""" Assigns an option attribute to the product type based on the information supplied in the request.
Args:
| attributeInProductType(attributeInProductType) - Properties of an attribute definition associated with a specific product type. When an attribute is applied to a product type, each product of that type maintains the same set of attributes.
| productTypeId (int) - Identifier of the product type.
| responseFields (string) - Use this field to include those fields which are not included by default.
Returns:
| AttributeInProductType
Raises:
| ApiException
"""
url = MozuUrl("/api/commerce/catalog/admin/attributedefinition/producttypes/{productTypeId}/Options?responseFields={responseFields}", "POST", UrlLocation.TenantPod, False);
url.formatUrl("productTypeId", productTypeId);
url.formatUrl("responseFields", responseFields);
self.client.withResourceUrl(url).withBody(attributeInProductType).execute();
return self.client.result();
def updateOption(self,attributeInProductType, productTypeId, attributeFQN, responseFields = None):
""" Updates an option attribute definition for the specified product type.
Args:
| attributeInProductType(attributeInProductType) - Properties of an attribute definition associated with a specific product type. When an attribute is applied to a product type, each product of that type maintains the same set of attributes.
| productTypeId (int) - Identifier of the product type.
| attributeFQN (string) - The fully qualified name of the attribute, which is a user defined attribute identifier.
| responseFields (string) - Use this field to include those fields which are not included by default.
Returns:
| AttributeInProductType
Raises:
| ApiException
"""
url = MozuUrl("/api/commerce/catalog/admin/attributedefinition/producttypes/{productTypeId}/Options/{attributeFQN}?responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False);
url.formatUrl("attributeFQN", attributeFQN);
url.formatUrl("productTypeId", productTypeId);
url.formatUrl("responseFields", responseFields);
self.client.withResourceUrl(url).withBody(attributeInProductType).execute();
return self.client.result();
def deleteOption(self,productTypeId, attributeFQN):
""" Removes an option attribute definition for the specified product type.
Args:
| productTypeId (int) - Identifier of the product type.
| attributeFQN (string) - The fully qualified name of the attribute, which is a user defined attribute identifier.
Raises:
| ApiException
"""
url = MozuUrl("/api/commerce/catalog/admin/attributedefinition/producttypes/{productTypeId}/Options/{attributeFQN}", "DELETE", UrlLocation.TenantPod, False);
url.formatUrl("attributeFQN", attributeFQN);
url.formatUrl("productTypeId", productTypeId);
self.client.withResourceUrl(url).execute();
| 38.805755 | 245 | 0.740082 | 567 | 5,394 | 7.029982 | 0.22575 | 0.038635 | 0.021074 | 0.035123 | 0.730557 | 0.730557 | 0.729052 | 0.716508 | 0.716508 | 0.698194 | 0 | 0 | 0.169633 | 5,394 | 139 | 246 | 38.805755 | 0.889931 | 0.453281 | 0 | 0.47619 | 1 | 0.071429 | 0.259272 | 0.200071 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0.095238 | 0 | 0.357143 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7038a784ccc14a979a4c039a81cee5f6d1c276f1 | 66 | py | Python | OptimalTransportBalancing/__init__.py | MGIMM/Optimal_Transport_Balancing | 99af9b7fe5d19db422178e612917bfdcb0e5b3f1 | [
"MIT"
] | null | null | null | OptimalTransportBalancing/__init__.py | MGIMM/Optimal_Transport_Balancing | 99af9b7fe5d19db422178e612917bfdcb0e5b3f1 | [
"MIT"
] | null | null | null | OptimalTransportBalancing/__init__.py | MGIMM/Optimal_Transport_Balancing | 99af9b7fe5d19db422178e612917bfdcb0e5b3f1 | [
"MIT"
] | null | null | null | from .OptimalTransportBalancing import OptimalTransportBalancing
| 22 | 64 | 0.909091 | 4 | 66 | 15 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.075758 | 66 | 2 | 65 | 33 | 0.983607 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5615a6a300acb00199089d290949779970fb89f6 | 28 | py | Python | app/auth/google/__init__.py | alcarruth/fullstack-p3-item-catalog | 5f0311675719ea1788495ada672eb3da21e44922 | [
"MIT"
] | 1 | 2019-07-19T15:36:58.000Z | 2019-07-19T15:36:58.000Z | app/auth/google/__init__.py | alcarruth/fullstack-p3-item-catalog | 5f0311675719ea1788495ada672eb3da21e44922 | [
"MIT"
] | null | null | null | app/auth/google/__init__.py | alcarruth/fullstack-p3-item-catalog | 5f0311675719ea1788495ada672eb3da21e44922 | [
"MIT"
] | null | null | null |
from google_auth import *
| 7 | 25 | 0.75 | 4 | 28 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 28 | 3 | 26 | 9.333333 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5659dbde96a1bc8bcabd1d652d8d7bc7b5476483 | 2,633 | py | Python | tests/test_cli.py | json-transformations/jsoncat | 56f3a42f01487ba778b39d5c1399a247e09a7194 | [
"MIT"
] | 1 | 2020-10-09T02:02:04.000Z | 2020-10-09T02:02:04.000Z | tests/test_cli.py | json-transformations/jsoncat | 56f3a42f01487ba778b39d5c1399a247e09a7194 | [
"MIT"
] | null | null | null | tests/test_cli.py | json-transformations/jsoncat | 56f3a42f01487ba778b39d5c1399a247e09a7194 | [
"MIT"
] | 1 | 2020-10-09T02:02:08.000Z | 2020-10-09T02:02:08.000Z | import json
import pytest
import click.testing
from jsoncat import cli
D1 = {"asteroid": "433 Eros"}
D2 = {"asteroid": "951 Gaspra"}
J1 = json.dumps(D1)
J2 = json.dumps(D2)
JE = '{Invalid JSON'
@pytest.fixture
def runner():
return click.testing.CliRunner()
def test_no_args_with_stdin(runner):
result = runner.invoke(cli.jsoncat, args=[], input=J1)
assert result.exit_code == 0
assert eval(result.output) == D1
def test_stdin_arg(runner):
result = runner.invoke(cli.jsoncat, args=['-'], input=J1)
assert result.exit_code == 0
assert eval(result.output) == D1
def test_single_filename(runner):
with runner.isolated_filesystem():
with open('t1.json', 'w') as f:
f.write(J1)
result = runner.invoke(cli.jsoncat, args=['t1.json'])
assert result.exit_code == 0
assert eval(result.output) == D1
def test_stdin_arg_plus_filename(runner):
with runner.isolated_filesystem():
with open('t2.json', 'w') as f:
f.write(J2)
result = runner.invoke(cli.jsoncat, args=['-', 't2.json'],
input=J1)
assert result.exit_code == 0
assert eval(result.output) == [D1, D2]
def test_stdin_as_second_arg(runner):
with runner.isolated_filesystem():
with open('t1.json', 'w') as f:
f.write(J1)
result = runner.invoke(cli.jsoncat, args=['t1.json', '-'],
input=J2)
assert result.exit_code == 0
assert eval(result.output) == [D1, D2]
def test_two_filenames(runner):
with runner.isolated_filesystem():
with open('t1.json', 'w') as f:
f.write(J1)
with open('t2.json', 'w') as f:
f.write(J2)
result = runner.invoke(cli.jsoncat, args=['t1.json', 't2.json'])
assert result.exit_code == 0
assert eval(result.output) == [D1, D2]
def test_two_filenames_reversed(runner):
with runner.isolated_filesystem():
with open('t1.json', 'w') as f:
f.write(J1)
with open('t2.json', 'w') as f:
f.write(J2)
result = runner.invoke(cli.jsoncat, args=['t2.json', 't1.json'])
assert result.exit_code == 0
assert eval(result.output) == [D2, D1]
def test_indented_json(runner):
result = runner.invoke(cli.jsoncat, args=[], input=J1)
assert result.exit_code == 0
assert len(result.output.splitlines()) == 3
def test_compact_json(runner):
result = runner.invoke(cli.jsoncat, args=['--indent=0'], input=J1)
assert result.exit_code == 0
assert len(result.output.splitlines()) == 1
| 28.311828 | 72 | 0.606912 | 358 | 2,633 | 4.354749 | 0.175978 | 0.040411 | 0.103913 | 0.121232 | 0.813342 | 0.813342 | 0.813342 | 0.813342 | 0.747915 | 0.747915 | 0 | 0.031171 | 0.244588 | 2,633 | 92 | 73 | 28.619565 | 0.75264 | 0 | 0 | 0.521739 | 0 | 0 | 0.062666 | 0 | 0 | 0 | 0 | 0 | 0.26087 | 1 | 0.144928 | false | 0 | 0.057971 | 0.014493 | 0.217391 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
569eb0336204d0b12c7b406621b9c2c0a970f544 | 36 | py | Python | bertopic/__init__.py | ashishpatel26/BERTopic | d38cef4edffb3ab2565baeccdb7b816a5d41e5dd | [
"MIT"
] | null | null | null | bertopic/__init__.py | ashishpatel26/BERTopic | d38cef4edffb3ab2565baeccdb7b816a5d41e5dd | [
"MIT"
] | null | null | null | bertopic/__init__.py | ashishpatel26/BERTopic | d38cef4edffb3ab2565baeccdb7b816a5d41e5dd | [
"MIT"
] | 2 | 2020-10-28T07:51:50.000Z | 2021-01-03T12:51:40.000Z | from bertopic.model import BERTopic
| 18 | 35 | 0.861111 | 5 | 36 | 6.2 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 36 | 1 | 36 | 36 | 0.96875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3b26cf38eb309de4101ec628afe39ff8702e3c45 | 260,256 | py | Python | instances/passenger_demand/pas-20210422-1717-int18e/14.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int18e/14.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int18e/14.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 34512
passenger_arriving = (
(9, 6, 6, 9, 6, 3, 4, 6, 5, 1, 0, 0, 0, 5, 8, 8, 7, 6, 1, 6, 1, 3, 3, 2, 0, 0), # 0
(10, 6, 7, 9, 10, 4, 3, 9, 3, 1, 0, 0, 0, 9, 8, 4, 4, 9, 5, 6, 6, 3, 1, 2, 1, 0), # 1
(12, 8, 12, 11, 13, 3, 6, 4, 7, 4, 0, 2, 0, 11, 11, 8, 9, 9, 7, 6, 3, 3, 3, 3, 2, 0), # 2
(12, 10, 9, 11, 8, 9, 8, 3, 2, 1, 3, 0, 0, 8, 12, 4, 9, 10, 7, 2, 3, 8, 3, 2, 2, 0), # 3
(17, 11, 6, 10, 11, 6, 3, 4, 4, 3, 0, 4, 0, 11, 13, 7, 6, 19, 6, 5, 5, 2, 1, 3, 0, 0), # 4
(13, 18, 14, 10, 11, 5, 7, 5, 5, 3, 1, 3, 0, 11, 20, 9, 8, 8, 6, 3, 5, 5, 2, 0, 2, 0), # 5
(18, 13, 11, 13, 8, 7, 11, 2, 9, 3, 1, 2, 0, 17, 7, 11, 3, 11, 1, 9, 2, 3, 2, 4, 0, 0), # 6
(11, 23, 13, 13, 9, 5, 4, 9, 6, 1, 6, 0, 0, 11, 13, 7, 7, 5, 12, 3, 2, 2, 4, 3, 1, 0), # 7
(16, 14, 9, 11, 14, 3, 5, 9, 4, 2, 0, 0, 0, 9, 10, 6, 11, 4, 6, 4, 1, 5, 2, 1, 0, 0), # 8
(20, 13, 12, 9, 8, 5, 7, 7, 8, 4, 2, 0, 0, 16, 14, 15, 8, 15, 9, 7, 3, 5, 5, 5, 1, 0), # 9
(9, 18, 16, 20, 10, 6, 5, 6, 7, 2, 1, 1, 0, 13, 10, 5, 9, 10, 7, 9, 6, 5, 2, 3, 0, 0), # 10
(16, 10, 6, 14, 12, 6, 4, 6, 9, 3, 1, 2, 0, 15, 10, 12, 10, 6, 6, 3, 4, 9, 6, 3, 1, 0), # 11
(15, 20, 23, 9, 14, 9, 2, 3, 10, 2, 2, 1, 0, 15, 17, 11, 14, 12, 10, 8, 3, 4, 6, 4, 3, 0), # 12
(18, 17, 10, 18, 18, 3, 3, 9, 11, 6, 4, 0, 0, 19, 11, 16, 8, 15, 6, 6, 0, 6, 1, 3, 2, 0), # 13
(6, 21, 14, 16, 8, 4, 5, 5, 9, 3, 3, 3, 0, 29, 11, 8, 6, 17, 10, 6, 3, 5, 10, 3, 2, 0), # 14
(6, 30, 20, 19, 15, 9, 7, 10, 6, 2, 2, 1, 0, 13, 17, 4, 10, 19, 12, 14, 7, 3, 5, 5, 1, 0), # 15
(21, 18, 10, 21, 18, 6, 14, 8, 5, 5, 1, 2, 0, 18, 13, 10, 13, 27, 12, 10, 3, 5, 7, 1, 1, 0), # 16
(22, 23, 6, 15, 9, 9, 5, 3, 13, 3, 2, 2, 0, 16, 17, 10, 17, 13, 10, 4, 2, 3, 3, 1, 2, 0), # 17
(19, 15, 15, 26, 12, 7, 3, 9, 7, 1, 2, 1, 0, 24, 15, 11, 20, 11, 9, 11, 6, 6, 7, 0, 1, 0), # 18
(26, 12, 14, 22, 6, 8, 10, 6, 9, 7, 1, 1, 0, 19, 17, 20, 7, 12, 9, 10, 4, 3, 5, 7, 1, 0), # 19
(20, 15, 15, 17, 15, 8, 8, 7, 7, 0, 3, 2, 0, 18, 13, 12, 18, 12, 7, 5, 5, 9, 7, 4, 3, 0), # 20
(18, 10, 10, 19, 20, 6, 5, 7, 11, 3, 4, 0, 0, 21, 20, 12, 12, 10, 15, 8, 7, 8, 2, 4, 2, 0), # 21
(17, 19, 11, 13, 19, 7, 6, 5, 5, 1, 2, 2, 0, 21, 19, 15, 8, 15, 7, 6, 5, 12, 3, 2, 2, 0), # 22
(18, 18, 16, 13, 16, 6, 7, 3, 4, 3, 5, 2, 0, 15, 19, 15, 8, 14, 5, 5, 7, 7, 3, 2, 2, 0), # 23
(17, 15, 14, 17, 12, 10, 6, 8, 12, 2, 4, 2, 0, 19, 18, 12, 12, 13, 9, 6, 2, 6, 16, 5, 2, 0), # 24
(20, 18, 18, 12, 14, 6, 8, 4, 4, 3, 4, 0, 0, 26, 15, 10, 5, 8, 9, 7, 6, 7, 9, 3, 0, 0), # 25
(16, 13, 17, 23, 12, 8, 6, 9, 7, 4, 7, 0, 0, 17, 13, 13, 9, 18, 17, 6, 0, 7, 3, 4, 1, 0), # 26
(8, 24, 8, 20, 16, 8, 13, 13, 7, 10, 4, 1, 0, 25, 13, 14, 6, 19, 13, 13, 10, 7, 6, 3, 1, 0), # 27
(12, 17, 16, 22, 12, 5, 7, 5, 14, 6, 4, 1, 0, 22, 21, 16, 14, 17, 6, 6, 4, 2, 7, 2, 1, 0), # 28
(14, 14, 6, 11, 12, 7, 8, 11, 6, 3, 2, 2, 0, 15, 13, 9, 6, 10, 6, 3, 4, 9, 3, 1, 3, 0), # 29
(14, 21, 17, 10, 17, 2, 4, 2, 12, 3, 3, 0, 0, 19, 11, 7, 14, 20, 7, 7, 1, 10, 7, 2, 1, 0), # 30
(19, 13, 25, 17, 14, 6, 5, 10, 4, 3, 4, 3, 0, 17, 9, 12, 8, 11, 10, 15, 5, 2, 4, 1, 0, 0), # 31
(21, 19, 11, 19, 20, 7, 2, 10, 6, 3, 1, 1, 0, 9, 15, 10, 10, 10, 11, 3, 6, 10, 4, 2, 1, 0), # 32
(22, 13, 17, 20, 9, 4, 8, 8, 9, 2, 1, 3, 0, 17, 14, 12, 9, 10, 7, 8, 6, 6, 3, 2, 0, 0), # 33
(24, 14, 21, 20, 17, 5, 9, 3, 7, 4, 3, 1, 0, 16, 10, 12, 14, 9, 15, 5, 4, 8, 7, 2, 0, 0), # 34
(20, 15, 19, 15, 12, 5, 6, 10, 4, 2, 1, 3, 0, 17, 21, 12, 6, 12, 13, 14, 7, 5, 6, 2, 1, 0), # 35
(20, 21, 14, 14, 13, 6, 3, 3, 3, 1, 2, 2, 0, 11, 7, 19, 10, 15, 10, 7, 11, 14, 6, 7, 2, 0), # 36
(16, 16, 13, 12, 11, 4, 6, 7, 9, 5, 4, 0, 0, 22, 14, 13, 8, 25, 8, 5, 10, 6, 8, 1, 3, 0), # 37
(17, 18, 14, 12, 11, 5, 5, 6, 7, 4, 5, 1, 0, 27, 19, 15, 10, 10, 6, 6, 3, 13, 9, 5, 2, 0), # 38
(17, 21, 13, 16, 14, 5, 10, 3, 5, 3, 1, 1, 0, 18, 12, 17, 6, 18, 7, 8, 4, 2, 4, 1, 4, 0), # 39
(21, 18, 11, 15, 14, 6, 7, 11, 9, 0, 1, 0, 0, 21, 15, 18, 15, 11, 4, 7, 3, 11, 11, 2, 1, 0), # 40
(25, 19, 18, 17, 16, 12, 7, 10, 3, 3, 5, 1, 0, 13, 9, 9, 10, 15, 10, 8, 6, 6, 7, 4, 3, 0), # 41
(22, 20, 17, 17, 9, 7, 9, 8, 11, 5, 1, 0, 0, 19, 19, 9, 13, 18, 7, 5, 4, 9, 4, 0, 1, 0), # 42
(22, 9, 13, 12, 18, 6, 7, 7, 6, 2, 4, 2, 0, 29, 17, 10, 15, 19, 4, 7, 3, 11, 5, 2, 1, 0), # 43
(25, 21, 15, 16, 15, 5, 6, 7, 7, 4, 3, 1, 0, 17, 17, 12, 9, 17, 5, 5, 4, 6, 4, 3, 0, 0), # 44
(20, 16, 19, 23, 13, 3, 5, 8, 4, 1, 4, 2, 0, 16, 23, 12, 9, 14, 13, 3, 6, 3, 8, 2, 3, 0), # 45
(19, 16, 16, 18, 20, 4, 7, 7, 5, 3, 2, 1, 0, 18, 23, 12, 9, 15, 5, 12, 3, 9, 4, 3, 1, 0), # 46
(22, 16, 18, 18, 15, 9, 7, 5, 8, 1, 2, 1, 0, 21, 14, 10, 8, 16, 14, 9, 2, 12, 10, 2, 3, 0), # 47
(22, 16, 10, 17, 18, 5, 6, 6, 5, 4, 2, 2, 0, 22, 21, 15, 11, 11, 8, 3, 3, 6, 4, 3, 1, 0), # 48
(23, 16, 16, 22, 14, 5, 6, 5, 6, 3, 2, 0, 0, 13, 15, 10, 11, 11, 9, 9, 7, 8, 6, 5, 1, 0), # 49
(25, 15, 20, 14, 10, 4, 8, 4, 5, 1, 2, 2, 0, 12, 14, 13, 7, 22, 5, 2, 5, 9, 2, 6, 1, 0), # 50
(21, 12, 16, 13, 15, 7, 7, 7, 9, 4, 0, 1, 0, 13, 11, 12, 8, 17, 8, 3, 2, 7, 12, 1, 4, 0), # 51
(21, 18, 6, 26, 13, 7, 6, 7, 5, 2, 2, 0, 0, 18, 24, 16, 14, 12, 3, 4, 8, 4, 7, 4, 2, 0), # 52
(18, 27, 16, 16, 8, 4, 7, 7, 7, 4, 2, 0, 0, 15, 10, 7, 14, 14, 10, 10, 7, 6, 7, 1, 1, 0), # 53
(12, 8, 16, 18, 20, 8, 4, 12, 4, 1, 2, 1, 0, 25, 17, 7, 6, 16, 7, 20, 3, 9, 5, 4, 2, 0), # 54
(22, 16, 15, 20, 16, 8, 4, 5, 12, 4, 0, 2, 0, 9, 16, 18, 6, 14, 8, 8, 3, 6, 6, 2, 1, 0), # 55
(22, 19, 14, 13, 20, 6, 7, 3, 6, 3, 3, 0, 0, 17, 14, 12, 11, 14, 11, 5, 4, 8, 4, 4, 2, 0), # 56
(18, 15, 14, 19, 11, 3, 4, 4, 7, 5, 1, 2, 0, 22, 20, 5, 12, 9, 7, 6, 6, 10, 6, 4, 2, 0), # 57
(21, 21, 17, 19, 11, 9, 4, 4, 8, 4, 2, 1, 0, 19, 13, 15, 6, 9, 8, 14, 4, 4, 3, 2, 0, 0), # 58
(14, 18, 8, 10, 12, 5, 3, 7, 7, 4, 3, 1, 0, 26, 20, 14, 10, 19, 8, 10, 6, 11, 6, 4, 2, 0), # 59
(20, 21, 12, 13, 7, 5, 8, 5, 4, 2, 1, 2, 0, 17, 18, 10, 9, 18, 8, 6, 3, 7, 3, 1, 1, 0), # 60
(18, 16, 15, 19, 17, 2, 6, 3, 10, 8, 3, 1, 0, 15, 10, 13, 7, 16, 8, 5, 3, 7, 5, 5, 0, 0), # 61
(23, 12, 15, 9, 12, 6, 5, 4, 2, 3, 4, 1, 0, 19, 16, 15, 8, 18, 5, 8, 6, 7, 2, 4, 1, 0), # 62
(26, 13, 17, 8, 9, 10, 7, 7, 5, 5, 2, 0, 0, 17, 14, 7, 11, 18, 7, 8, 2, 4, 7, 2, 4, 0), # 63
(20, 19, 10, 23, 16, 0, 8, 9, 12, 2, 2, 0, 0, 17, 15, 14, 8, 10, 8, 12, 5, 5, 3, 4, 0, 0), # 64
(18, 11, 13, 11, 18, 8, 11, 2, 6, 3, 6, 2, 0, 19, 10, 14, 9, 20, 7, 2, 3, 5, 6, 2, 1, 0), # 65
(30, 21, 13, 17, 14, 9, 2, 3, 6, 4, 1, 1, 0, 18, 8, 14, 8, 16, 6, 7, 8, 8, 4, 7, 1, 0), # 66
(23, 14, 14, 15, 20, 7, 2, 12, 6, 1, 2, 1, 0, 24, 17, 11, 8, 11, 7, 4, 1, 6, 9, 2, 2, 0), # 67
(13, 16, 12, 18, 17, 5, 11, 6, 5, 5, 2, 1, 0, 12, 15, 10, 17, 14, 9, 8, 5, 11, 6, 4, 4, 0), # 68
(17, 30, 17, 14, 15, 6, 3, 6, 9, 6, 1, 2, 0, 18, 20, 14, 14, 21, 7, 7, 3, 5, 3, 5, 0, 0), # 69
(15, 17, 16, 17, 18, 8, 6, 5, 9, 1, 6, 0, 0, 20, 17, 17, 15, 15, 4, 5, 4, 11, 4, 2, 1, 0), # 70
(20, 18, 14, 21, 11, 4, 9, 4, 6, 4, 5, 4, 0, 23, 13, 14, 13, 16, 6, 10, 3, 5, 2, 3, 1, 0), # 71
(13, 20, 18, 11, 13, 9, 9, 3, 6, 4, 1, 3, 0, 14, 11, 19, 12, 12, 10, 9, 5, 7, 3, 4, 1, 0), # 72
(20, 17, 11, 24, 12, 7, 5, 4, 10, 3, 1, 0, 0, 14, 11, 5, 13, 13, 13, 8, 3, 9, 3, 4, 2, 0), # 73
(17, 22, 13, 26, 14, 6, 9, 3, 7, 4, 5, 1, 0, 17, 17, 8, 5, 14, 5, 8, 5, 8, 5, 2, 2, 0), # 74
(23, 16, 9, 16, 10, 8, 5, 4, 4, 7, 3, 1, 0, 19, 11, 10, 8, 9, 5, 5, 6, 6, 6, 5, 1, 0), # 75
(19, 16, 18, 19, 13, 6, 11, 5, 8, 3, 5, 3, 0, 16, 15, 15, 5, 17, 8, 4, 3, 5, 10, 3, 2, 0), # 76
(23, 20, 13, 15, 7, 9, 4, 5, 9, 3, 2, 1, 0, 29, 12, 12, 8, 14, 5, 7, 8, 6, 13, 5, 1, 0), # 77
(11, 14, 15, 14, 16, 10, 5, 4, 7, 3, 1, 1, 0, 12, 15, 5, 6, 9, 9, 9, 10, 4, 6, 3, 1, 0), # 78
(19, 11, 20, 14, 12, 8, 7, 3, 8, 4, 2, 2, 0, 20, 18, 6, 3, 17, 12, 10, 4, 14, 6, 1, 1, 0), # 79
(21, 15, 25, 12, 16, 5, 3, 5, 9, 4, 3, 1, 0, 11, 14, 12, 11, 13, 12, 6, 2, 8, 4, 2, 0, 0), # 80
(20, 22, 12, 11, 11, 7, 3, 1, 9, 1, 4, 3, 0, 18, 19, 10, 8, 11, 12, 6, 11, 10, 12, 1, 0, 0), # 81
(10, 12, 15, 9, 13, 6, 5, 5, 5, 3, 4, 2, 0, 26, 9, 12, 4, 24, 7, 5, 2, 5, 6, 2, 1, 0), # 82
(18, 14, 9, 16, 12, 10, 9, 5, 8, 3, 1, 1, 0, 20, 12, 17, 7, 19, 9, 4, 2, 8, 3, 1, 0, 0), # 83
(16, 14, 20, 19, 14, 6, 9, 4, 8, 3, 2, 0, 0, 29, 17, 16, 8, 13, 4, 4, 2, 8, 6, 3, 1, 0), # 84
(16, 16, 19, 17, 10, 3, 5, 5, 4, 3, 2, 0, 0, 19, 15, 6, 12, 13, 9, 4, 3, 6, 8, 6, 2, 0), # 85
(15, 12, 17, 11, 20, 1, 5, 4, 8, 4, 1, 2, 0, 11, 16, 13, 11, 14, 3, 5, 1, 10, 4, 3, 0, 0), # 86
(16, 20, 19, 12, 15, 6, 3, 7, 10, 0, 6, 2, 0, 14, 12, 11, 15, 11, 7, 8, 2, 8, 4, 3, 1, 0), # 87
(23, 20, 13, 21, 11, 4, 8, 6, 4, 3, 4, 0, 0, 20, 14, 10, 6, 13, 6, 6, 3, 9, 5, 3, 1, 0), # 88
(17, 13, 18, 19, 9, 5, 6, 4, 7, 3, 6, 0, 0, 26, 10, 9, 10, 12, 8, 4, 5, 7, 6, 5, 1, 0), # 89
(25, 12, 10, 15, 7, 8, 6, 6, 9, 3, 0, 4, 0, 19, 16, 8, 7, 16, 7, 5, 2, 6, 2, 3, 2, 0), # 90
(24, 17, 9, 12, 9, 6, 6, 6, 4, 5, 3, 0, 0, 19, 13, 10, 3, 10, 3, 7, 3, 5, 6, 2, 1, 0), # 91
(18, 16, 10, 10, 12, 2, 7, 6, 13, 6, 4, 1, 0, 19, 12, 8, 8, 24, 8, 12, 7, 6, 6, 4, 3, 0), # 92
(12, 17, 14, 14, 10, 10, 5, 3, 6, 1, 1, 1, 0, 24, 13, 15, 11, 18, 14, 1, 7, 7, 6, 2, 1, 0), # 93
(20, 17, 11, 18, 11, 4, 5, 9, 11, 5, 0, 0, 0, 10, 14, 10, 10, 18, 4, 9, 2, 4, 5, 9, 0, 0), # 94
(10, 19, 14, 15, 10, 5, 4, 4, 5, 2, 3, 0, 0, 18, 8, 13, 5, 18, 6, 6, 2, 3, 4, 6, 2, 0), # 95
(26, 10, 13, 13, 7, 8, 3, 3, 12, 3, 1, 1, 0, 16, 14, 9, 11, 13, 5, 6, 4, 8, 2, 3, 1, 0), # 96
(24, 22, 15, 19, 13, 6, 8, 2, 9, 2, 2, 1, 0, 26, 14, 11, 5, 13, 7, 6, 3, 8, 3, 2, 2, 0), # 97
(20, 12, 15, 18, 21, 5, 3, 7, 4, 6, 5, 3, 0, 22, 17, 8, 9, 6, 10, 3, 3, 7, 6, 1, 2, 0), # 98
(20, 11, 14, 13, 16, 6, 5, 5, 11, 1, 2, 2, 0, 17, 15, 17, 6, 11, 7, 4, 5, 5, 5, 0, 3, 0), # 99
(23, 12, 17, 6, 22, 6, 5, 5, 6, 2, 2, 0, 0, 25, 15, 10, 9, 12, 6, 6, 4, 3, 3, 1, 1, 0), # 100
(21, 8, 7, 13, 17, 5, 5, 3, 10, 3, 4, 2, 0, 28, 20, 9, 9, 9, 5, 4, 3, 7, 3, 2, 2, 0), # 101
(25, 20, 14, 22, 11, 6, 4, 3, 9, 3, 2, 0, 0, 20, 18, 7, 8, 13, 6, 10, 4, 8, 8, 1, 1, 0), # 102
(9, 10, 11, 17, 14, 6, 2, 4, 7, 4, 0, 0, 0, 10, 14, 12, 9, 13, 8, 6, 2, 3, 5, 2, 0, 0), # 103
(15, 21, 22, 23, 12, 6, 3, 5, 5, 2, 2, 1, 0, 18, 14, 12, 11, 17, 7, 7, 6, 6, 6, 3, 0, 0), # 104
(21, 19, 15, 12, 13, 4, 7, 5, 8, 2, 2, 2, 0, 10, 16, 9, 6, 13, 4, 5, 4, 11, 5, 8, 0, 0), # 105
(18, 16, 16, 13, 7, 6, 6, 2, 3, 6, 2, 1, 0, 20, 11, 19, 5, 16, 5, 3, 4, 5, 7, 2, 2, 0), # 106
(17, 15, 10, 16, 8, 7, 8, 6, 6, 2, 1, 2, 0, 24, 12, 15, 9, 10, 6, 4, 6, 8, 11, 4, 5, 0), # 107
(9, 14, 14, 16, 15, 4, 5, 5, 6, 3, 3, 3, 0, 30, 12, 5, 6, 14, 4, 3, 5, 11, 8, 1, 1, 0), # 108
(11, 14, 10, 13, 15, 6, 4, 5, 8, 1, 2, 1, 0, 20, 19, 11, 3, 13, 7, 6, 6, 3, 6, 2, 2, 0), # 109
(17, 8, 15, 20, 18, 1, 4, 5, 6, 3, 3, 1, 0, 22, 14, 11, 11, 14, 11, 4, 2, 9, 4, 1, 1, 0), # 110
(22, 12, 11, 12, 10, 5, 7, 4, 10, 0, 5, 2, 0, 15, 3, 11, 5, 12, 6, 5, 5, 7, 6, 1, 0, 0), # 111
(11, 13, 11, 13, 11, 6, 4, 9, 7, 4, 1, 2, 0, 19, 10, 6, 11, 12, 8, 7, 8, 5, 3, 3, 2, 0), # 112
(15, 7, 12, 12, 11, 4, 1, 5, 7, 3, 0, 2, 0, 17, 13, 9, 7, 13, 6, 7, 2, 4, 5, 2, 1, 0), # 113
(18, 12, 13, 17, 12, 6, 4, 3, 6, 4, 4, 0, 0, 23, 14, 7, 12, 11, 7, 6, 4, 8, 5, 2, 4, 0), # 114
(14, 10, 13, 12, 10, 4, 6, 4, 7, 1, 0, 0, 0, 14, 15, 6, 10, 11, 7, 7, 6, 4, 1, 3, 0, 0), # 115
(14, 17, 15, 15, 11, 6, 6, 4, 8, 1, 0, 3, 0, 23, 16, 12, 10, 13, 7, 3, 12, 4, 5, 2, 2, 0), # 116
(16, 12, 12, 11, 15, 10, 6, 5, 5, 2, 0, 2, 0, 27, 11, 9, 9, 10, 4, 5, 4, 8, 7, 0, 0, 0), # 117
(14, 17, 12, 12, 14, 4, 4, 3, 4, 4, 3, 1, 0, 13, 12, 11, 6, 15, 7, 10, 6, 5, 3, 4, 0, 0), # 118
(13, 16, 15, 13, 6, 7, 1, 7, 3, 2, 1, 2, 0, 13, 15, 15, 4, 11, 5, 4, 4, 7, 3, 8, 2, 0), # 119
(16, 15, 17, 19, 11, 5, 5, 5, 4, 3, 2, 2, 0, 16, 11, 12, 2, 8, 11, 4, 4, 4, 7, 1, 0, 0), # 120
(15, 15, 6, 15, 10, 9, 3, 4, 5, 1, 0, 1, 0, 18, 9, 13, 4, 13, 7, 3, 4, 6, 8, 3, 2, 0), # 121
(13, 14, 16, 16, 19, 6, 2, 0, 8, 2, 3, 4, 0, 17, 10, 9, 5, 11, 9, 7, 4, 4, 2, 3, 1, 0), # 122
(24, 7, 23, 16, 15, 6, 3, 4, 5, 3, 1, 2, 0, 15, 10, 9, 11, 8, 2, 2, 4, 8, 6, 3, 1, 0), # 123
(16, 15, 13, 10, 11, 7, 6, 8, 6, 2, 2, 0, 0, 22, 14, 9, 6, 13, 10, 7, 8, 9, 5, 1, 2, 0), # 124
(15, 18, 13, 10, 15, 9, 4, 7, 8, 3, 1, 0, 0, 18, 8, 15, 6, 20, 10, 2, 5, 5, 7, 2, 0, 0), # 125
(12, 14, 5, 14, 17, 6, 4, 4, 3, 6, 1, 1, 0, 20, 9, 12, 15, 13, 6, 4, 7, 10, 8, 1, 2, 0), # 126
(21, 15, 17, 11, 12, 8, 4, 10, 7, 3, 0, 1, 0, 16, 7, 5, 7, 13, 6, 6, 1, 5, 2, 3, 1, 0), # 127
(21, 10, 8, 24, 12, 4, 6, 4, 10, 3, 3, 0, 0, 15, 7, 11, 10, 9, 7, 5, 9, 6, 4, 3, 0, 0), # 128
(18, 12, 13, 16, 6, 6, 3, 6, 10, 2, 0, 2, 0, 16, 14, 12, 9, 15, 6, 4, 5, 3, 4, 2, 3, 0), # 129
(16, 6, 15, 12, 17, 4, 11, 5, 4, 3, 1, 2, 0, 9, 16, 6, 8, 7, 4, 8, 2, 4, 3, 4, 3, 0), # 130
(12, 16, 14, 17, 9, 4, 5, 2, 3, 1, 0, 2, 0, 11, 14, 4, 10, 15, 4, 3, 3, 2, 7, 6, 1, 0), # 131
(19, 13, 16, 14, 5, 7, 6, 1, 12, 2, 1, 2, 0, 17, 12, 19, 8, 7, 8, 8, 1, 4, 7, 3, 2, 0), # 132
(12, 15, 10, 12, 15, 6, 4, 3, 8, 3, 3, 1, 0, 13, 11, 7, 4, 13, 5, 2, 5, 6, 2, 3, 0, 0), # 133
(13, 13, 10, 13, 14, 3, 2, 5, 2, 2, 1, 3, 0, 18, 15, 11, 15, 13, 4, 7, 2, 7, 4, 2, 2, 0), # 134
(17, 17, 11, 12, 18, 4, 5, 5, 8, 1, 2, 1, 0, 13, 11, 12, 10, 11, 7, 5, 4, 6, 1, 1, 0, 0), # 135
(12, 8, 18, 17, 13, 3, 4, 3, 6, 3, 1, 0, 0, 16, 9, 9, 11, 11, 3, 3, 2, 8, 5, 2, 1, 0), # 136
(14, 23, 12, 16, 7, 7, 4, 7, 2, 4, 0, 0, 0, 18, 9, 17, 6, 14, 9, 7, 2, 6, 11, 3, 0, 0), # 137
(9, 13, 11, 16, 22, 3, 4, 7, 7, 1, 1, 0, 0, 11, 17, 16, 8, 11, 4, 5, 6, 7, 4, 4, 1, 0), # 138
(15, 16, 12, 10, 14, 8, 8, 3, 5, 4, 0, 2, 0, 13, 10, 12, 8, 9, 6, 5, 5, 3, 4, 0, 1, 0), # 139
(17, 13, 21, 12, 5, 3, 6, 6, 5, 4, 2, 1, 0, 10, 17, 8, 11, 11, 7, 8, 1, 9, 4, 3, 1, 0), # 140
(14, 12, 23, 8, 15, 7, 7, 5, 5, 2, 0, 1, 0, 8, 12, 12, 5, 17, 6, 6, 7, 6, 9, 1, 1, 0), # 141
(21, 6, 11, 16, 14, 4, 8, 6, 6, 1, 3, 2, 0, 12, 8, 5, 11, 10, 6, 3, 6, 12, 9, 2, 3, 0), # 142
(19, 14, 14, 13, 8, 6, 3, 6, 8, 4, 3, 3, 0, 15, 22, 6, 4, 15, 6, 2, 5, 8, 3, 2, 2, 0), # 143
(14, 18, 14, 3, 11, 10, 7, 1, 6, 4, 3, 0, 0, 16, 15, 6, 8, 10, 7, 1, 5, 9, 5, 4, 1, 0), # 144
(10, 8, 11, 12, 13, 6, 3, 5, 9, 1, 1, 1, 0, 18, 12, 9, 5, 12, 7, 5, 5, 3, 8, 4, 1, 0), # 145
(20, 14, 11, 13, 9, 5, 6, 2, 3, 1, 2, 0, 0, 23, 26, 6, 6, 10, 7, 8, 6, 8, 8, 3, 2, 0), # 146
(13, 8, 5, 9, 13, 5, 6, 6, 7, 1, 3, 2, 0, 17, 11, 8, 3, 18, 4, 5, 6, 6, 5, 1, 1, 0), # 147
(20, 14, 16, 8, 11, 9, 3, 6, 8, 3, 0, 1, 0, 16, 4, 7, 6, 11, 4, 3, 2, 6, 3, 1, 0, 0), # 148
(9, 9, 6, 10, 9, 7, 6, 1, 5, 2, 1, 3, 0, 17, 21, 9, 6, 9, 9, 6, 3, 4, 2, 4, 0, 0), # 149
(16, 13, 16, 14, 8, 5, 7, 2, 8, 3, 2, 1, 0, 13, 11, 10, 14, 10, 5, 3, 1, 7, 6, 2, 1, 0), # 150
(12, 11, 9, 11, 12, 6, 5, 4, 6, 1, 2, 0, 0, 15, 15, 9, 9, 11, 5, 6, 3, 6, 1, 0, 2, 0), # 151
(16, 7, 15, 11, 8, 10, 3, 0, 7, 1, 1, 0, 0, 10, 8, 10, 14, 9, 4, 4, 7, 5, 6, 2, 1, 0), # 152
(22, 13, 14, 11, 10, 2, 5, 2, 6, 1, 0, 0, 0, 16, 11, 8, 7, 10, 2, 6, 3, 2, 6, 5, 0, 0), # 153
(14, 11, 12, 12, 14, 7, 4, 4, 5, 0, 2, 0, 0, 21, 10, 6, 5, 11, 7, 6, 5, 3, 1, 2, 1, 0), # 154
(18, 12, 15, 14, 9, 5, 3, 4, 10, 1, 2, 2, 0, 11, 12, 6, 4, 10, 5, 8, 3, 4, 3, 2, 1, 0), # 155
(15, 8, 7, 12, 12, 2, 3, 4, 6, 2, 5, 1, 0, 22, 13, 6, 9, 12, 9, 4, 0, 8, 3, 5, 2, 0), # 156
(15, 10, 13, 14, 17, 5, 6, 4, 6, 2, 2, 1, 0, 18, 11, 8, 6, 12, 6, 1, 0, 9, 5, 4, 2, 0), # 157
(11, 7, 11, 6, 12, 9, 10, 5, 6, 2, 0, 0, 0, 15, 13, 4, 7, 10, 8, 2, 2, 3, 3, 3, 0, 0), # 158
(14, 8, 13, 13, 9, 5, 3, 7, 6, 4, 1, 1, 0, 9, 14, 8, 4, 14, 3, 3, 3, 7, 3, 0, 0, 0), # 159
(16, 12, 12, 9, 14, 9, 1, 3, 9, 5, 2, 1, 0, 14, 11, 8, 7, 14, 7, 4, 1, 3, 5, 5, 2, 0), # 160
(5, 6, 6, 11, 8, 4, 5, 6, 5, 3, 3, 0, 0, 18, 10, 6, 8, 18, 8, 5, 7, 4, 1, 1, 3, 0), # 161
(12, 11, 13, 15, 9, 5, 5, 3, 4, 5, 3, 2, 0, 11, 15, 13, 0, 15, 5, 4, 0, 7, 4, 1, 0, 0), # 162
(12, 7, 9, 12, 12, 6, 2, 2, 8, 2, 3, 2, 0, 17, 5, 8, 4, 7, 9, 4, 5, 4, 3, 1, 0, 0), # 163
(12, 5, 16, 11, 12, 2, 3, 4, 5, 0, 0, 1, 0, 10, 11, 7, 5, 8, 6, 4, 3, 6, 4, 2, 1, 0), # 164
(14, 7, 6, 9, 11, 1, 4, 6, 6, 1, 2, 0, 0, 9, 11, 5, 4, 11, 5, 5, 4, 4, 4, 2, 1, 0), # 165
(12, 10, 12, 4, 13, 6, 4, 1, 3, 0, 3, 0, 0, 9, 5, 12, 4, 10, 4, 3, 6, 6, 4, 3, 1, 0), # 166
(10, 8, 13, 12, 9, 3, 3, 5, 8, 2, 3, 1, 0, 14, 11, 11, 4, 9, 4, 2, 2, 2, 2, 4, 1, 0), # 167
(12, 2, 10, 9, 16, 2, 1, 2, 6, 2, 1, 1, 0, 13, 11, 5, 8, 7, 8, 7, 4, 6, 2, 5, 0, 0), # 168
(7, 8, 13, 8, 15, 2, 5, 2, 5, 1, 2, 0, 0, 5, 12, 10, 2, 11, 8, 4, 1, 5, 1, 2, 1, 0), # 169
(10, 10, 9, 14, 6, 6, 3, 7, 4, 3, 1, 4, 0, 16, 11, 6, 4, 8, 4, 6, 2, 4, 3, 0, 0, 0), # 170
(9, 6, 6, 14, 7, 4, 3, 2, 4, 2, 0, 0, 0, 12, 9, 6, 5, 7, 3, 4, 4, 6, 5, 3, 2, 0), # 171
(14, 9, 8, 10, 6, 1, 4, 4, 3, 1, 1, 0, 0, 7, 10, 7, 6, 12, 3, 2, 3, 4, 2, 1, 2, 0), # 172
(12, 5, 10, 6, 12, 5, 4, 6, 1, 0, 1, 2, 0, 13, 7, 5, 7, 7, 3, 5, 3, 6, 2, 3, 0, 0), # 173
(9, 2, 8, 11, 11, 6, 0, 5, 0, 2, 2, 1, 0, 7, 6, 4, 3, 10, 2, 2, 3, 4, 0, 1, 0, 0), # 174
(6, 5, 8, 5, 3, 5, 2, 6, 2, 1, 1, 1, 0, 10, 4, 8, 5, 10, 4, 1, 0, 6, 2, 0, 0, 0), # 175
(10, 3, 3, 9, 7, 3, 1, 2, 6, 1, 1, 0, 0, 11, 5, 4, 3, 5, 5, 4, 1, 4, 3, 0, 0, 0), # 176
(4, 6, 7, 5, 7, 2, 5, 4, 2, 2, 1, 1, 0, 7, 11, 4, 1, 4, 5, 4, 3, 3, 4, 1, 1, 0), # 177
(8, 5, 7, 8, 5, 2, 5, 4, 3, 1, 1, 0, 0, 5, 7, 8, 4, 7, 5, 0, 2, 3, 1, 1, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(9.037558041069182, 9.9455194074477, 9.380309813302512, 11.18640199295418, 9.998434093697302, 5.64957887766721, 7.462864107673047, 8.375717111362961, 10.962178311902413, 7.124427027940266, 7.569477294994085, 8.816247140951113, 9.150984382641052), # 0
(9.637788873635953, 10.602109249460566, 9.999623864394273, 11.925259655897909, 10.660482607453627, 6.0227704512766005, 7.955044094274649, 8.927124701230275, 11.686041587399236, 7.59416524609887, 8.069573044721038, 9.398189989465838, 9.755624965391739), # 1
(10.236101416163518, 11.256093307603763, 10.616476113985344, 12.66117786839663, 11.320133352749538, 6.3944732061224006, 8.445273314329269, 9.476325446227955, 12.407016252379588, 8.062044795036982, 8.567681667797364, 9.9778187736955, 10.357856690777442), # 2
(10.830164027663812, 11.904876903485604, 11.228419564775738, 13.391237533557733, 11.974791016803424, 6.763213120653203, 8.93160655496632, 10.021142083490112, 13.122243289657968, 8.526208857167125, 9.061827141289289, 10.55283423287483, 10.955291051257605), # 3
(11.417645067148767, 12.545865358714394, 11.833007219465467, 14.112519554488625, 12.621860286833686, 7.127516173317602, 9.412098603315226, 10.559397350150848, 13.828863682048873, 8.984800614901822, 9.550033442263036, 11.120937106238575, 11.54553953929167), # 4
(11.996212893630318, 13.176463994898459, 12.427792080754532, 14.822104834296708, 13.258745850058704, 7.485908342564186, 9.884804246505404, 11.088913983344266, 14.524018412366805, 9.435963250653593, 10.030324547784838, 11.679828133021466, 12.126213647339089), # 5
(12.5635358661204, 13.794078133646101, 13.010327151342958, 15.517074276089375, 13.882852393696878, 7.836915606841555, 10.347778271666273, 11.60751472020448, 15.204848463426268, 9.877839946834966, 10.500724434920908, 12.227208052458254, 12.694924867859292), # 6
(13.117282343630944, 14.396113096565637, 13.578165433930742, 16.194508782974033, 14.491584604966597, 8.179063944598298, 10.799075465927253, 12.113022297865593, 15.868494818041759, 10.308573885858456, 10.959257080737483, 12.760777603783673, 13.249284693311735), # 7
(13.655120685173882, 14.979974205265378, 14.128859931217914, 16.85148925805807, 15.082347171086255, 8.510879334283002, 11.236750616417757, 12.603259453461705, 16.512098459027772, 10.726308250136594, 11.403946462300778, 13.278237526232465, 13.786904616155851), # 8
(14.174719249761154, 15.543066781353641, 14.659963645904467, 17.485096604448906, 15.652544779274237, 8.830887754344271, 11.658858510267216, 13.076048924126933, 17.132800369198815, 11.129186222081895, 11.83281655667702, 13.777288559039365, 14.305396128851092), # 9
(14.673746396404677, 16.082796146438728, 15.169029580690424, 18.092411725253918, 16.199582116748942, 9.137615183230693, 12.063453934605038, 13.52921344699538, 17.727741531369386, 11.515350984106886, 12.243891340932432, 14.255631441439114, 14.802370723856898), # 10
(15.149870484116411, 16.596567622128973, 15.653610738275788, 18.670515523580516, 16.72086387072876, 9.429587599390864, 12.44859167656065, 13.960575759201147, 18.294062928353988, 11.882945718624095, 12.635194792133248, 14.710966912666459, 15.2754398936327), # 11
(15.600759871908263, 17.081786530032655, 16.111260121360573, 19.216488902536103, 17.21379472843208, 9.705330981273365, 12.812326523263462, 14.367958597878339, 18.82890554296712, 12.23011360804603, 13.004750887345683, 15.140995711956123, 15.722215130637963), # 12
(16.02408291879218, 17.535858191758116, 16.539530732644792, 19.727412765228078, 17.675779377077284, 9.963371307326803, 13.152713261842901, 14.749184700161067, 19.329410358023278, 12.554997834785228, 13.350583603635965, 15.543418578542857, 16.140307927332124), # 13
(16.41750798378009, 17.95618792891366, 16.935975574828465, 20.20036801476383, 18.10422250388278, 10.202234555999762, 13.46780667942839, 15.102076803183444, 19.79271835633696, 12.855741581254202, 13.670716918070312, 15.915936251661408, 16.527329776174614), # 14
(16.77870342588394, 18.34018106310759, 17.298147650611575, 20.632435554250776, 18.496528796066954, 10.420446705740842, 13.755661563149326, 15.424457644079562, 20.215970520722674, 13.130488029865482, 13.963174807714955, 16.256249470546507, 16.880892169624886), # 15
(17.10533760411564, 18.685242915948237, 17.623599962694165, 21.02069628679629, 18.8501029408482, 10.616533734998628, 14.014332700135158, 15.71414995998353, 20.596307833994917, 13.377380363031593, 14.225981249636122, 16.56205897443289, 17.198606600142384), # 16
(17.395078877487137, 18.988778809043904, 17.909885513776235, 21.362231115507804, 19.162349625444907, 10.789021622221714, 14.24187487751528, 15.968976488029472, 20.930871278968173, 13.594561763165041, 14.457160220900038, 16.8310655025553, 17.47808456018655), # 17
(17.645595605010367, 19.248194064002895, 18.154557306557784, 21.654120943492703, 19.43067353707546, 10.936436345858706, 14.436342882419133, 16.18675996535147, 21.216801838456973, 13.780175412678366, 14.654735698572916, 17.060969794148487, 17.716937542216822), # 18
(17.85455614569726, 19.46089400243354, 18.355168343738843, 21.893446673858367, 19.65247936295826, 11.057303884358175, 14.59579150197611, 16.36532312908364, 21.4512404952758, 13.93236449398409, 14.81673165972098, 17.249472588447173, 17.912777038692653), # 19
(18.01962885855975, 19.624283945944132, 18.509271628019405, 22.077289209712237, 19.8251717903117, 11.150150216168733, 14.718275523315652, 16.50248871636009, 21.631328232239156, 14.049272189494726, 14.94117208141047, 17.394274624686105, 18.063214542073485), # 20
(18.13848210260976, 19.735769216143005, 18.614420162099496, 22.202729454161673, 19.94615550635416, 11.213501319738963, 14.801849733567167, 16.596079464314922, 21.754206032161537, 14.1290416816228, 15.026080940707608, 17.49307664210003, 18.165861544818743), # 21
(18.20878423685924, 19.792755134638462, 18.668166948679115, 22.266848310314106, 20.012835198304035, 11.245883173517461, 14.844568919860079, 16.643918110082247, 21.81701487785745, 14.169816152780836, 15.069482214678613, 17.54357937992368, 18.218329539387888), # 22
(18.23470805401675, 19.799502469135803, 18.674861728395065, 22.274875462962967, 20.029917700858675, 11.25, 14.84964720406681, 16.64908888888889, 21.824867222222224, 14.17462609053498, 15.074924466891131, 17.549815637860082, 18.225), # 23
(18.253822343461476, 19.79556666666667, 18.673766666666666, 22.273887500000004, 20.039593704506736, 11.25, 14.8468568627451, 16.6419, 21.823815, 14.17167111111111, 15.074324242424245, 17.548355555555556, 18.225), # 24
(18.272533014380844, 19.78780864197531, 18.671604938271606, 22.27193287037037, 20.049056902070106, 11.25, 14.841358024691358, 16.62777777777778, 21.82173611111111, 14.16585390946502, 15.073134118967452, 17.545473251028806, 18.225), # 25
(18.290838634286462, 19.776346913580248, 18.668406172839507, 22.269033796296295, 20.05830696315799, 11.25, 14.833236092955698, 16.60698888888889, 21.81865722222222, 14.157271275720165, 15.07136487093154, 17.54120823045268, 18.225), # 26
(18.308737770689945, 19.7613, 18.6642, 22.265212499999997, 20.067343557379587, 11.25, 14.822576470588237, 16.579800000000002, 21.814605, 14.146019999999998, 15.069027272727272, 17.535600000000002, 18.225), # 27
(18.3262289911029, 19.742786419753084, 18.659016049382718, 22.260491203703705, 20.076166354344124, 11.25, 14.809464560639071, 16.54647777777778, 21.809606111111112, 14.132196872427985, 15.066132098765433, 17.528688065843625, 18.225), # 28
(18.34331086303695, 19.720924691358025, 18.652883950617287, 22.25489212962963, 20.084775023660796, 11.25, 14.793985766158318, 16.507288888888887, 21.803687222222223, 14.115898683127574, 15.06269012345679, 17.520511934156378, 18.225), # 29
(18.359981954003697, 19.695833333333333, 18.645833333333332, 22.2484375, 20.093169234938827, 11.25, 14.776225490196078, 16.4625, 21.796875, 14.097222222222223, 15.058712121212121, 17.51111111111111, 18.225), # 30
(18.376240831514746, 19.667630864197534, 18.637893827160497, 22.241149537037035, 20.101348657787415, 11.25, 14.756269135802471, 16.412377777777778, 21.78919611111111, 14.07626427983539, 15.054208866442199, 17.500525102880662, 18.225), # 31
(18.392086063081717, 19.636435802469137, 18.629095061728393, 22.233050462962964, 20.10931296181577, 11.25, 14.734202106027599, 16.357188888888892, 21.780677222222224, 14.053121646090535, 15.0491911335578, 17.48879341563786, 18.225), # 32
(18.407516216216216, 19.602366666666665, 18.619466666666668, 22.2241625, 20.117061816633115, 11.25, 14.710109803921569, 16.2972, 21.771345, 14.027891111111112, 15.043669696969696, 17.475955555555554, 18.225), # 33
(18.422529858429858, 19.56554197530864, 18.609038271604938, 22.21450787037037, 20.12459489184864, 11.25, 14.684077632534496, 16.232677777777777, 21.761226111111114, 14.000669465020577, 15.037655331088663, 17.462051028806584, 18.225), # 34
(18.437125557234253, 19.52608024691358, 18.597839506172843, 22.204108796296293, 20.131911857071568, 11.25, 14.656190994916486, 16.163888888888888, 21.750347222222224, 13.971553497942386, 15.031158810325476, 17.447119341563788, 18.225), # 35
(18.45130188014101, 19.484099999999998, 18.5859, 22.192987499999997, 20.139012381911105, 11.25, 14.626535294117646, 16.0911, 21.738735, 13.94064, 15.024190909090908, 17.431200000000004, 18.225), # 36
(18.46505739466174, 19.43971975308642, 18.57324938271605, 22.181166203703704, 20.145896135976457, 11.25, 14.595195933188089, 16.014577777777777, 21.72641611111111, 13.908025761316873, 15.016762401795738, 17.414332510288066, 18.225), # 37
(18.47839066830806, 19.39305802469136, 18.559917283950615, 22.168667129629632, 20.152562788876843, 11.25, 14.562258315177923, 15.934588888888891, 21.713417222222223, 13.873807572016462, 15.00888406285073, 17.396556378600824, 18.225), # 38
(18.491300268591576, 19.34423333333333, 18.545933333333334, 22.1555125, 20.159012010221467, 11.25, 14.527807843137257, 15.8514, 21.699765000000003, 13.838082222222223, 15.000566666666668, 17.37791111111111, 18.225), # 39
(18.503784763023894, 19.293364197530863, 18.531327160493827, 22.14172453703704, 20.165243469619533, 11.25, 14.491929920116196, 15.765277777777781, 21.685486111111114, 13.800946502057615, 14.99182098765432, 17.358436213991773, 18.225), # 40
(18.51584271911663, 19.24056913580247, 18.51612839506173, 22.127325462962965, 20.171256836680264, 11.25, 14.454709949164851, 15.67648888888889, 21.67060722222222, 13.76249720164609, 14.982657800224468, 17.338171193415636, 18.225), # 41
(18.527472704381402, 19.18596666666667, 18.500366666666668, 22.112337500000002, 20.177051781012857, 11.25, 14.416233333333333, 15.5853, 21.655155000000004, 13.72283111111111, 14.97308787878788, 17.317155555555555, 18.225), # 42
(18.538673286329807, 19.12967530864198, 18.484071604938272, 22.096782870370372, 20.182627972226527, 11.25, 14.37658547567175, 15.491977777777779, 21.63915611111111, 13.682045020576133, 14.96312199775533, 17.295428806584365, 18.225), # 43
(18.54944303247347, 19.071813580246914, 18.467272839506176, 22.0806837962963, 20.18798507993048, 11.25, 14.335851779230211, 15.396788888888892, 21.62263722222222, 13.64023572016461, 14.952770931537597, 17.2730304526749, 18.225), # 44
(18.55978051032399, 19.0125, 18.45, 22.064062500000002, 20.193122773733933, 11.25, 14.294117647058824, 15.3, 21.605625, 13.597500000000002, 14.942045454545454, 17.25, 18.225), # 45
(18.569684287392985, 18.951853086419753, 18.432282716049382, 22.046941203703703, 20.198040723246088, 11.25, 14.251468482207699, 15.20187777777778, 21.588146111111108, 13.553934650205761, 14.930956341189674, 17.226376954732512, 18.225), # 46
(18.579152931192063, 18.88999135802469, 18.41415061728395, 22.02934212962963, 20.202738598076163, 11.25, 14.207989687726945, 15.102688888888888, 21.570227222222226, 13.50963646090535, 14.919514365881032, 17.20220082304527, 18.225), # 47
(18.588185009232834, 18.827033333333333, 18.395633333333333, 22.0112875, 20.20721606783336, 11.25, 14.163766666666668, 15.0027, 21.551895000000002, 13.464702222222222, 14.907730303030302, 17.177511111111112, 18.225), # 48
(18.596779089026917, 18.763097530864197, 18.376760493827163, 21.99279953703704, 20.211472802126895, 11.25, 14.118884822076978, 14.902177777777778, 21.53317611111111, 13.419228724279836, 14.895614927048262, 17.152347325102884, 18.225), # 49
(18.604933738085908, 18.698302469135808, 18.357561728395066, 21.973900462962963, 20.21550847056597, 11.25, 14.073429557007989, 14.801388888888889, 21.514097222222222, 13.373312757201646, 14.883179012345678, 17.126748971193418, 18.225), # 50
(18.61264752392144, 18.63276666666667, 18.338066666666666, 21.9546125, 20.219322742759797, 11.25, 14.027486274509805, 14.7006, 21.494685000000004, 13.32705111111111, 14.870433333333335, 17.10075555555556, 18.225), # 51
(18.619919014045102, 18.56660864197531, 18.318304938271606, 21.934957870370372, 20.222915288317584, 11.25, 13.981140377632535, 14.600077777777777, 21.47496611111111, 13.280540576131688, 14.857388664421999, 17.074406584362144, 18.225), # 52
(18.626746775968517, 18.49994691358025, 18.29830617283951, 21.914958796296297, 20.226285776848552, 11.25, 13.93447726942629, 14.50008888888889, 21.454967222222226, 13.233877942386831, 14.844055780022448, 17.04774156378601, 18.225), # 53
(18.63312937720329, 18.432900000000004, 18.2781, 21.8946375, 20.229433877961906, 11.25, 13.887582352941177, 14.400899999999998, 21.434715, 13.18716, 14.830445454545453, 17.0208, 18.225), # 54
(18.63906538526104, 18.365586419753086, 18.25771604938272, 21.874016203703704, 20.232359261266843, 11.25, 13.840541031227307, 14.302777777777777, 21.414236111111112, 13.140483539094651, 14.816568462401795, 16.993621399176956, 18.225), # 55
(18.64455336765337, 18.298124691358026, 18.237183950617286, 21.85311712962963, 20.235061596372585, 11.25, 13.793438707334786, 14.20598888888889, 21.393557222222224, 13.09394534979424, 14.802435578002246, 16.96624526748971, 18.225), # 56
(18.649591891891887, 18.230633333333333, 18.216533333333334, 21.8319625, 20.23754055288834, 11.25, 13.746360784313726, 14.110800000000001, 21.372705, 13.047642222222223, 14.788057575757577, 16.93871111111111, 18.225), # 57
(18.654179525488225, 18.163230864197534, 18.195793827160493, 21.810574537037034, 20.239795800423316, 11.25, 13.699392665214235, 14.017477777777778, 21.35170611111111, 13.001670946502058, 14.773445230078567, 16.91105843621399, 18.225), # 58
(18.658314835953966, 18.096035802469135, 18.174995061728396, 21.788975462962963, 20.24182700858672, 11.25, 13.65261975308642, 13.92628888888889, 21.330587222222224, 12.956128312757203, 14.758609315375981, 16.883326748971193, 18.225), # 59
(18.661996390800738, 18.02916666666667, 18.154166666666665, 21.767187500000002, 20.243633846987766, 11.25, 13.606127450980392, 13.8375, 21.309375000000003, 12.911111111111111, 14.743560606060607, 16.855555555555558, 18.225), # 60
(18.665222757540146, 17.962741975308646, 18.13333827160494, 21.74523287037037, 20.24521598523566, 11.25, 13.560001161946259, 13.751377777777778, 21.288096111111113, 12.866716131687244, 14.728309876543209, 16.82778436213992, 18.225), # 61
(18.66799250368381, 17.89688024691358, 18.112539506172844, 21.7231337962963, 20.246573092939624, 11.25, 13.514326289034132, 13.66818888888889, 21.266777222222224, 12.823040164609054, 14.712867901234567, 16.80005267489712, 18.225), # 62
(18.670304196743327, 17.831699999999998, 18.0918, 21.7009125, 20.24770483970884, 11.25, 13.469188235294117, 13.5882, 21.245445, 12.78018, 14.697245454545456, 16.7724, 18.225), # 63
(18.672156404230314, 17.767319753086422, 18.071149382716047, 21.678591203703704, 20.24861089515255, 11.25, 13.424672403776325, 13.511677777777779, 21.22412611111111, 12.738232427983538, 14.681453310886642, 16.7448658436214, 18.225), # 64
(18.67354769365639, 17.703858024691357, 18.05061728395062, 21.65619212962963, 20.24929092887994, 11.25, 13.380864197530865, 13.438888888888888, 21.202847222222225, 12.697294238683126, 14.665502244668913, 16.717489711934153, 18.225), # 65
(18.674476632533153, 17.641433333333335, 18.030233333333335, 21.6337375, 20.249744610500233, 11.25, 13.337849019607843, 13.3701, 21.181635000000004, 12.657462222222222, 14.649403030303029, 16.690311111111114, 18.225), # 66
(18.674941788372227, 17.580164197530863, 18.010027160493827, 21.611249537037036, 20.249971609622634, 11.25, 13.29571227305737, 13.30557777777778, 21.16051611111111, 12.618833168724281, 14.633166442199778, 16.6633695473251, 18.225), # 67
(18.674624906065485, 17.519847550776582, 17.989930709876543, 21.588555132850242, 20.249780319535223, 11.24979122085048, 13.254327350693364, 13.245018930041153, 21.13935812757202, 12.5813167949649, 14.616514779372677, 16.636554039419536, 18.22477527006173), # 68
(18.671655072463768, 17.458641935483872, 17.969379166666666, 21.564510326086953, 20.248039215686273, 11.248140740740741, 13.212482726423904, 13.185177777777778, 21.11723611111111, 12.543851503267971, 14.597753110047847, 16.608994152046783, 18.222994791666668), # 69
(18.665794417606012, 17.39626642771804, 17.948283179012343, 21.538956823671498, 20.244598765432098, 11.244890260631001, 13.169988242210465, 13.125514403292183, 21.09402520576132, 12.506255144032922, 14.576667995746943, 16.580560970327056, 18.219478202160495), # 70
(18.657125389157272, 17.332758303464754, 17.92665015432099, 21.51193230676329, 20.239502541757446, 11.240092455418381, 13.12686298717018, 13.066048559670783, 21.06975997942387, 12.46852864681675, 14.553337267410951, 16.551275286982886, 18.21427179783951), # 71
(18.64573043478261, 17.268154838709677, 17.9044875, 21.48347445652174, 20.23279411764706, 11.2338, 13.083126050420168, 13.0068, 21.044475000000002, 12.43067294117647, 14.527838755980863, 16.52115789473684, 18.207421875), # 72
(18.631692002147076, 17.20249330943847, 17.88180262345679, 21.45362095410628, 20.224517066085692, 11.226065569272976, 13.038796521077565, 12.947788477366256, 21.01820483539095, 12.392688956669087, 14.50025029239766, 16.490229586311454, 18.198974729938275), # 73
(18.61509253891573, 17.1358109916368, 17.858602932098762, 21.42240948067633, 20.214714960058096, 11.216941838134431, 12.9938934882595, 12.889033744855967, 20.990984053497943, 12.354577622851611, 14.470649707602341, 16.45851115442928, 18.18897665895062), # 74
(18.59601449275362, 17.06814516129032, 17.83489583333333, 21.389877717391304, 20.203431372549023, 11.206481481481482, 12.9484360410831, 12.830555555555556, 20.96284722222222, 12.316339869281046, 14.439114832535884, 16.426023391812866, 18.177473958333334), # 75
(18.57454031132582, 16.99953309438471, 17.8106887345679, 21.35606334541063, 20.19070987654321, 11.19473717421125, 12.902443268665492, 12.772373662551441, 20.93382890946502, 12.277976625514404, 14.405723498139285, 16.392787091184747, 18.164512924382716), # 76
(18.55075244229737, 16.93001206690562, 17.785989043209874, 21.32100404589372, 20.176594045025414, 11.18176159122085, 12.855934260123803, 12.714507818930043, 20.90396368312757, 12.239488821108692, 14.370553535353537, 16.358823045267492, 18.150139853395064), # 77
(18.524733333333334, 16.859619354838713, 17.760804166666667, 21.2847375, 20.16112745098039, 11.167607407407406, 12.808928104575164, 12.65697777777778, 20.87328611111111, 12.200877385620915, 14.333682775119618, 16.324152046783627, 18.134401041666667), # 78
(18.496565432098766, 16.788392234169656, 17.735141512345677, 21.24730138888889, 20.144353667392885, 11.152327297668037, 12.761443891136702, 12.59980329218107, 20.84183076131687, 12.162143248608086, 14.29518904837852, 16.28879488845571, 18.117342785493825), # 79
(18.466331186258724, 16.71636798088411, 17.70900848765432, 21.208733393719807, 20.126316267247642, 11.135973936899862, 12.713500708925546, 12.543004115226339, 20.809632201646092, 12.123287339627208, 14.255150186071239, 16.252772363006283, 18.09901138117284), # 80
(18.434113043478263, 16.643583870967742, 17.682412499999998, 21.169071195652176, 20.10705882352941, 11.118599999999999, 12.665117647058823, 12.486600000000001, 20.776725, 12.084310588235295, 14.213644019138757, 16.216105263157896, 18.079453124999997), # 81
(18.399993451422436, 16.570077180406216, 17.655360956790126, 21.12835247584541, 20.086624909222948, 11.10025816186557, 12.616313794653665, 12.430610699588478, 20.743143724279836, 12.045213923989348, 14.170748378522063, 16.178814381633096, 18.058714313271608), # 82
(18.364054857756308, 16.495885185185184, 17.6278612654321, 21.086614915458934, 20.065058097313, 11.08100109739369, 12.567108240827196, 12.37505596707819, 20.70892294238683, 12.00599827644638, 14.12654109516215, 16.14092051115443, 18.036841242283952), # 83
(18.326379710144927, 16.421045161290323, 17.599920833333332, 21.043896195652174, 20.042401960784314, 11.060881481481482, 12.517520074696545, 12.319955555555556, 20.674097222222223, 11.9666645751634, 14.0811, 16.102444444444444, 18.013880208333333), # 84
(18.287050456253354, 16.345594384707287, 17.571547067901232, 21.000233997584544, 20.01870007262164, 11.039951989026063, 12.467568385378843, 12.265329218106997, 20.63870113168724, 11.92721374969741, 14.034502923976609, 16.06340697422569, 17.989877507716052), # 85
(18.246149543746643, 16.269570131421744, 17.54274737654321, 20.955666002415462, 19.99399600580973, 11.018265294924555, 12.417272261991217, 12.21119670781893, 20.60276923868313, 11.887646729605423, 13.986827698032961, 16.02382889322071, 17.964879436728395), # 86
(18.203759420289852, 16.193009677419354, 17.513529166666665, 20.910229891304347, 19.968333333333337, 10.995874074074074, 12.366650793650793, 12.157577777777778, 20.566336111111116, 11.847964444444443, 13.938152153110048, 15.983730994152046, 17.938932291666667), # 87
(18.159962533548043, 16.11595029868578, 17.483899845679012, 20.86396334541063, 19.941755628177198, 10.972831001371743, 12.315723069474704, 12.104492181069958, 20.52943631687243, 11.808167823771482, 13.888554120148857, 15.943134069742257, 17.912082368827164), # 88
(18.11484133118626, 16.03842927120669, 17.453866820987656, 20.81690404589372, 19.91430646332607, 10.94918875171468, 12.264508178580074, 12.051959670781894, 20.492104423868312, 11.76825779714355, 13.838111430090379, 15.902058912713883, 17.884375964506173), # 89
(18.068478260869565, 15.960483870967742, 17.423437500000002, 20.769089673913047, 19.886029411764707, 10.925, 12.213025210084034, 12.0, 20.454375000000002, 11.728235294117647, 13.786901913875598, 15.860526315789475, 17.855859375), # 90
(18.020955770263015, 15.8821513739546, 17.392619290123456, 20.720557910628024, 19.85696804647785, 10.900317421124829, 12.161293253103711, 11.9486329218107, 20.41628261316873, 11.688101244250786, 13.735003402445509, 15.818557071691574, 17.826578896604936), # 91
(17.97235630703167, 15.80346905615293, 17.361419598765433, 20.671346437198068, 19.827165940450254, 10.875193689986283, 12.109331396756236, 11.897878189300412, 20.377861831275723, 11.647856577099976, 13.682493726741095, 15.776171973142736, 17.796580825617283), # 92
(17.92276231884058, 15.724474193548389, 17.329845833333334, 20.621492934782612, 19.796666666666667, 10.84968148148148, 12.057158730158731, 11.847755555555556, 20.339147222222223, 11.607502222222221, 13.62945071770335, 15.733391812865497, 17.76591145833333), # 93
(17.872256253354806, 15.645204062126643, 17.29790540123457, 20.571035084541062, 19.765513798111837, 10.823833470507545, 12.00479434242833, 11.798284773662553, 20.300173353909464, 11.567039109174534, 13.575952206273259, 15.690237383582414, 17.734617091049383), # 94
(17.820920558239397, 15.56569593787336, 17.265605709876546, 20.52001056763285, 19.733750907770517, 10.797702331961592, 11.95225732268216, 11.749485596707821, 20.260974794238685, 11.526468167513919, 13.522076023391813, 15.646729478016026, 17.70274402006173), # 95
(17.76883768115942, 15.485987096774197, 17.23295416666667, 20.468457065217393, 19.701421568627453, 10.77134074074074, 11.899566760037347, 11.701377777777779, 20.221586111111108, 11.485790326797385, 13.4679, 15.602888888888891, 17.67033854166667), # 96
(17.716090069779927, 15.406114814814819, 17.199958179012345, 20.416412258454105, 19.668569353667394, 10.744801371742112, 11.846741743611025, 11.65398106995885, 20.182041872427984, 11.445006516581941, 13.413501967038808, 15.558736408923545, 17.637446952160495), # 97
(17.66276017176597, 15.326116367980884, 17.166625154320986, 20.363913828502415, 19.635237835875095, 10.718136899862827, 11.793801362520316, 11.607315226337448, 20.142376646090533, 11.404117666424595, 13.35895975544923, 15.514292830842535, 17.604115547839505), # 98
(17.608930434782607, 15.246029032258065, 17.1329625, 20.31099945652174, 19.601470588235298, 10.6914, 11.740764705882354, 11.5614, 20.102625, 11.363124705882353, 13.304351196172249, 15.469578947368422, 17.570390625), # 99
(17.5546833064949, 15.165890083632016, 17.09897762345679, 20.257706823671498, 19.567311183732752, 10.664643347050754, 11.687650862814262, 11.516255144032922, 20.062821502057616, 11.322028564512225, 13.249754120148857, 15.42461555122374, 17.536318479938274), # 100
(17.500101234567904, 15.085736798088412, 17.064677932098768, 20.204073611111113, 19.532803195352216, 10.637919615912208, 11.634478922433171, 11.471900411522633, 20.02300072016461, 11.280830171871218, 13.195246358320043, 15.379423435131034, 17.501945408950615), # 101
(17.44526666666667, 15.005606451612904, 17.030070833333333, 20.1501375, 19.497990196078433, 10.611281481481482, 11.58126797385621, 11.428355555555555, 19.98319722222222, 11.239530457516341, 13.140905741626794, 15.334023391812867, 17.467317708333336), # 102
(17.390262050456254, 14.92553632019116, 16.9951637345679, 20.095936171497584, 19.462915758896152, 10.584781618655693, 11.528037106200506, 11.385640329218107, 19.943445576131687, 11.1981303510046, 13.086810101010101, 15.28843621399177, 17.432481674382714), # 103
(17.335169833601718, 14.845563679808842, 16.959964043209876, 20.041507306763286, 19.427623456790123, 10.558472702331962, 11.474805408583187, 11.343774485596708, 19.90378034979424, 11.156630781893005, 13.03303726741095, 15.242682694390297, 17.397483603395063), # 104
(17.280072463768114, 14.765725806451613, 16.924479166666668, 19.98688858695652, 19.392156862745097, 10.532407407407408, 11.421591970121383, 11.302777777777779, 19.86423611111111, 11.115032679738563, 12.979665071770334, 15.196783625730996, 17.362369791666666), # 105
(17.225052388620504, 14.686059976105138, 16.888716512345678, 19.932117693236716, 19.356559549745825, 10.50663840877915, 11.36841587993222, 11.262669958847736, 19.82484742798354, 11.07333697409828, 12.92677134502924, 15.15075980073641, 17.327186535493826), # 106
(17.17019205582394, 14.606603464755079, 16.852683487654325, 19.877232306763286, 19.32087509077705, 10.48121838134431, 11.31529622713283, 11.223470781893006, 19.78564886831276, 11.03154459452917, 12.874433918128654, 15.104632012129088, 17.29198013117284), # 107
(17.11557391304348, 14.5273935483871, 16.8163875, 19.822270108695655, 19.28514705882353, 10.4562, 11.262252100840335, 11.185200000000002, 19.746675000000003, 10.989656470588237, 12.82273062200957, 15.05842105263158, 17.256796875000003), # 108
(17.061280407944178, 14.448467502986858, 16.779835956790127, 19.767268780193234, 19.249419026870008, 10.431635939643346, 11.209302590171871, 11.147877366255145, 19.707960390946504, 10.947673531832486, 12.771739287612972, 15.012147714966428, 17.221683063271605), # 109
(17.007393988191087, 14.369862604540026, 16.743036265432103, 19.71226600241546, 19.213734567901238, 10.407578875171467, 11.15646678424456, 11.111522633744855, 19.669539609053498, 10.90559670781893, 12.72153774587985, 14.965832791856185, 17.18668499228395), # 110
(16.953997101449275, 14.29161612903226, 16.705995833333336, 19.65729945652174, 19.178137254901962, 10.384081481481482, 11.103763772175537, 11.076155555555555, 19.631447222222224, 10.863426928104575, 12.672203827751195, 14.919497076023394, 17.151848958333336), # 111
(16.90117219538379, 14.213765352449222, 16.66872206790124, 19.602406823671497, 19.142670660856936, 10.361196433470509, 11.051212643081925, 11.041795884773663, 19.593717798353907, 10.821165122246429, 12.623815364167996, 14.873161360190599, 17.11722125771605), # 112
(16.84890760266548, 14.136477513814715, 16.631312090853726, 19.547700988485673, 19.10731622431267, 10.338965584586125, 10.998946734582185, 11.00853462380509, 19.556483060265517, 10.778948525902914, 12.57646303107516, 14.826947285707972, 17.0827990215178), # 113
(16.796665616220118, 14.060514930345965, 16.594282215038913, 19.493620958299207, 19.071708038219388, 10.317338295353823, 10.947632775139043, 10.976780267109216, 19.52031426428351, 10.73756730224301, 12.530239806803754, 14.781441909803354, 17.048295745488062), # 114
(16.744292825407193, 13.985904957629483, 16.55765447887317, 19.440152109327204, 19.035733820199482, 10.296258322497776, 10.89730737034481, 10.946524777701677, 19.485224961603823, 10.697085590378538, 12.485078120568769, 14.736667648605932, 17.013611936988678), # 115
(16.691723771827743, 13.912538906325063, 16.521357941970972, 19.38719907047953, 18.999339347490803, 10.275675979116777, 10.847888671550209, 10.917684563218188, 19.451126410610094, 10.657428045209185, 12.440890676288666, 14.692541755477222, 16.978693067560602), # 116
(16.63889299708279, 13.840308087092497, 16.485321663946774, 19.33466647066604, 18.9624703973312, 10.255541578309604, 10.799294830105955, 10.890176031294454, 19.417929869685967, 10.618519321634633, 12.39759017788191, 14.64898148377875, 16.943484608744804), # 117
(16.58573504277338, 13.769103810591583, 16.44947470441506, 19.2824589387966, 18.925072746958516, 10.235805433175049, 10.751443997362767, 10.863915589566174, 19.385546597215082, 10.580284074554568, 12.355089329266963, 14.60590408687203, 16.907932032082243), # 118
(16.532184450500534, 13.698817387482112, 16.413746122990304, 19.23048110378107, 18.887092173610597, 10.2164178568119, 10.70425432467136, 10.838819645669062, 19.353887851581078, 10.54264695886867, 12.31330083436229, 14.563226818118581, 16.87198080911388), # 119
(16.47817576186529, 13.629340128423884, 16.37806497928697, 19.17863759452931, 18.848474454525295, 10.197329162318939, 10.657643963382455, 10.814804607238818, 19.322864891167605, 10.50553262947663, 12.272137397086349, 14.520866930879935, 16.835576411380675), # 120
(16.423643518468683, 13.560563344076693, 16.342360332919537, 19.12683303995118, 18.809165366940455, 10.178489662794956, 10.611531064846766, 10.791786881911152, 19.2923889743583, 10.468865741278133, 12.23151172135761, 14.4787416785176, 16.79866431042359), # 121
(16.36852226191174, 13.49237834510033, 16.30656124350248, 19.07497206895654, 18.76911068809392, 10.159849671338735, 10.565833780415012, 10.769682877321769, 19.2623713595368, 10.43257094917286, 12.191336511094532, 14.436768314393102, 16.761189977783587), # 122
(16.312746533795494, 13.424676442154594, 16.270596770650265, 19.02295931045525, 18.728256195223544, 10.141359501049065, 10.52047026143791, 10.74840900110637, 19.232723305086758, 10.396572908060497, 12.151524470215579, 14.394864091867959, 16.72309888500163), # 123
(16.256250875720976, 13.357348945899277, 16.234395973977367, 18.970699393357176, 18.68654766556717, 10.12296946502473, 10.475358659266176, 10.727881660900668, 19.20335606939181, 10.36079627284073, 12.111988302639215, 14.352946264303695, 16.68433650361868), # 124
(16.198969829289226, 13.290287166994178, 16.197887913098263, 18.91809694657217, 18.643930876362642, 10.104629876364521, 10.43041712525053, 10.708017264340365, 19.174180910835588, 10.32516569841324, 12.072640712283903, 14.310932085061827, 16.644848305175692), # 125
(16.14083793610127, 13.22338241609909, 16.16100164762742, 18.8650565990101, 18.60035160484781, 10.086291048167222, 10.385563810741687, 10.688732219061166, 19.145109087801753, 10.289605839677717, 12.033394403068103, 14.268738807503881, 16.604579761213643), # 126
(16.08178973775815, 13.156526003873804, 16.123666237179307, 18.81148297958082, 18.555755628260517, 10.067903293531618, 10.34071686709037, 10.669942932698781, 19.116051858673934, 10.254041351533843, 11.994162078910282, 14.226283684991369, 16.56347634327348), # 127
(16.021759775860883, 13.089609240978122, 16.08581074136841, 18.7572807171942, 18.51008872383862, 10.0494169255565, 10.295794445647289, 10.651565812888913, 19.086920481835772, 10.218396888881303, 11.954856443728904, 14.183483970885819, 16.521483522896165), # 128
(15.960682592010507, 13.022523438071834, 16.047364219809193, 18.702354440760086, 18.46329666881996, 10.03078225734065, 10.250714697763163, 10.633517267267269, 19.057626215670915, 10.182597106619781, 11.915390201442428, 14.140256918548745, 16.478546771622668), # 129
(15.89849272780806, 12.955159905814739, 16.008255732116123, 18.646608779188355, 18.415325240442385, 10.011949601982854, 10.205395774788713, 10.61571370346955, 19.028080318563003, 10.146566659648963, 11.87567605596932, 14.096519781341675, 16.434611560993947), # 130
(15.83512472485457, 12.887409954866628, 15.968414337903685, 18.589948361388856, 18.36612021594374, 9.992869272581904, 10.159755828074656, 10.59807152913147, 18.998194048895677, 10.110230202868534, 11.835626711228041, 14.052189812626125, 16.38962336255096), # 131
(15.770513124751067, 12.8191648958873, 15.927769096786342, 18.532277816271456, 18.315627372561877, 9.973491582236585, 10.113713008971706, 10.580507151888732, 18.967878665052577, 10.073512391178177, 11.795154871137056, 14.007184265763614, 16.343527647834676), # 132
(15.704592469098595, 12.750316039536544, 15.88624906837857, 18.473501772746012, 18.263792487534637, 9.95376684404568, 10.06718546883058, 10.562936979377039, 18.93704542541735, 10.036337879477578, 11.754173239614829, 13.961420394115667, 16.296269888386057), # 133
(15.63729729949817, 12.68075469647416, 15.843783312294848, 18.413524859722386, 18.210561338099865, 9.933645371107978, 10.020091359002002, 10.545277419232098, 18.905605588373632, 9.998631322666423, 11.712594520579822, 13.914815451043799, 16.24779555574605), # 134
(15.568562157550836, 12.610372177359944, 15.800300888149636, 18.352251706110444, 18.15587970149542, 9.913077476522266, 9.972348830836681, 10.527444879089616, 18.873470412305064, 9.960317375644397, 11.670331417950496, 13.867286689909534, 16.198050121455637), # 135
(15.498321584857623, 12.539059792853687, 15.755730855557415, 18.28958694082003, 18.09969335495913, 9.892013473387332, 9.923876035685343, 10.509355766585298, 18.840551155595293, 9.92132069331118, 11.627296635645319, 13.818751364074394, 16.146979057055766), # 136
(15.426510123019561, 12.466708853615184, 15.710002274132659, 18.225435192761026, 18.04194807572886, 9.870403674801956, 9.8745911248987, 10.490926489354854, 18.80675907662796, 9.881565930566463, 11.583402877582751, 13.769126726899895, 16.094527834087398), # 137
(15.353062313637686, 12.393210670304235, 15.66304420348983, 18.159701090843274, 17.982589641042455, 9.848198393864935, 9.824412249827468, 10.472073455033982, 18.772005433786706, 9.840977742309924, 11.538562847681254, 13.718330031747561, 16.040641924091503), # 138
(15.277912698313022, 12.31845655358063, 15.614785703243411, 18.092289263976646, 17.921563828137746, 9.825347943675048, 9.773257561822367, 10.452713071258394, 18.73620148545517, 9.799480783441254, 11.492689249859293, 13.66627853197891, 15.985266798609034), # 139
(15.200995818646616, 12.242337814104165, 15.565155833007877, 18.023104341071, 17.858816414252605, 9.801802637331082, 9.721045212234115, 10.432761745663793, 18.699258490016998, 9.756999708860134, 11.445694788035329, 13.612889480955465, 15.928347929180966), # 140
(15.122246216239494, 12.164745762534638, 15.514083652397689, 17.952050951036195, 17.794293176624855, 9.777512787931828, 9.667693352413432, 10.412135885885887, 18.661087705855824, 9.713459173466253, 11.39749216612783, 13.558080132038745, 15.869830787348244), # 141
(15.041598432692682, 12.08557170953184, 15.461498221027327, 17.879033722782097, 17.727939892492355, 9.752428708576069, 9.613120133711027, 10.39075189956038, 18.621600391355297, 9.66878383215929, 11.347994088055255, 13.50176773859027, 15.80966084465184), # 142
(14.958987009607215, 12.004706965755565, 15.407328598511267, 17.803957285218555, 17.659702339092952, 9.726500712362592, 9.557243707477623, 10.368526194322978, 18.580707804899063, 9.622898339838935, 11.297113257736068, 13.443869553971561, 15.747783572632711), # 143
(14.874346488584132, 11.922042841865615, 15.35150384446397, 17.72672626725544, 17.58952629366449, 9.699679112390184, 9.499982225063938, 10.34537517780939, 18.53832120487076, 9.575727351404868, 11.244762379088732, 13.384302831544138, 15.684144442831826), # 144
(14.787611411224459, 11.837470648521778, 15.29395301849992, 17.64724529780261, 17.51735753344482, 9.671914221757634, 9.441253837820689, 10.321215257655316, 18.494351849654016, 9.527195521756779, 11.190854156031712, 13.322984824669524, 15.618688926790139), # 145
(14.69871631912923, 11.750881696383855, 15.23460518023359, 17.565419005769925, 17.443141835671785, 9.643156353563725, 9.380976697098594, 10.295962841496468, 18.448710997632492, 9.477227505794348, 11.135301292483467, 13.259832786709236, 15.551362496048613), # 146
(14.607595753899481, 11.662167296111635, 15.173389389279437, 17.481152020067245, 17.36682497758323, 9.613355820907245, 9.319068954248365, 10.269534336968547, 18.401309907189823, 9.425747958417263, 11.078016492362465, 13.194763971024798, 15.482110622148213), # 147
(14.51418425713624, 11.571218758364918, 15.11023470525195, 17.394348969604433, 17.28835273641701, 9.582462936886982, 9.255448760620729, 10.241846151707264, 18.352059836709653, 9.372681534525205, 11.018912459587169, 13.127695630977726, 15.410878776629895), # 148
(14.418416370440541, 11.477927393803494, 15.045070187765598, 17.304914483291345, 17.207670889410966, 9.550428014601719, 9.190034267566393, 10.21281469334832, 18.30087204457561, 9.317952889017864, 10.957901898076038, 13.058545019929545, 15.337612431034628), # 149
(14.320226635413416, 11.382184513087163, 14.97782489643485, 17.212753190037848, 17.124725213802947, 9.517201367150248, 9.122743626436081, 10.182356369527422, 18.247657789171353, 9.261486676794918, 10.894897511747537, 12.987229391241772, 15.262257056903364), # 150
(14.219549593655895, 11.283881426875716, 14.908427890874176, 17.117769718753795, 17.0394614868308, 9.48273330763135, 9.05349498858051, 10.150387587880278, 18.19232832888052, 9.20320755275606, 10.829812004520129, 12.91366599827593, 15.184758125777073), # 151
(14.116319786769019, 11.182909445828951, 14.836808230698063, 17.019868698349054, 16.951825485732364, 9.446974149143815, 8.982206505350396, 10.116824756042595, 18.134794922086748, 9.143040171800969, 10.762558080312278, 12.837772094393538, 15.105061109196717), # 152
(14.010471756353809, 11.079159880606662, 14.762894975520963, 16.91895475773348, 16.8617629877455, 9.409874204786428, 8.908796328096455, 10.081584281650072, 18.07496882717368, 9.080909188829333, 10.693048443042448, 12.759464932956115, 15.02311147870325), # 153
(13.901940044011312, 10.972524041868644, 14.686617184957365, 16.81493252581694, 16.769219770108045, 9.371383787657978, 8.83318260816941, 10.044582572338422, 18.01276130252496, 9.016739258740834, 10.6211957966291, 12.678661767325185, 14.938854705837642), # 154
(13.790659191342543, 10.86289324027469, 14.607903918621735, 16.707706631509282, 16.674141610057855, 9.331453210857248, 8.75528349691997, 10.005736035743345, 17.948083606524232, 8.950455036435159, 10.5469128449907, 12.595279850862267, 14.852236262140847), # 155
(13.676563739948545, 10.750158786484597, 14.526684236128547, 16.597181703720377, 16.576474284832766, 9.29003278748303, 8.67501714569886, 9.964961079500554, 17.88084699755513, 8.88198117681199, 10.470112292045709, 12.50923643692888, 14.763201619153833), # 156
(13.559588231430352, 10.634211991158162, 14.442887197092272, 16.483262371360087, 16.476163571670632, 9.247072830634105, 8.592301705856794, 9.922174111245749, 17.8109627340013, 8.811242334771014, 10.39070684171259, 12.420448778886547, 14.671696248417557), # 157
(13.43642570352943, 10.512815617390064, 14.352465517024239, 16.36158524697224, 16.368625990567796, 9.199844057370798, 8.505192097670143, 9.87443451422887, 17.732991764878374, 8.73605864932406, 10.306072354570096, 12.32567921554981, 14.573674546947622), # 158
(13.288116180561124, 10.37351757527906, 14.232128073125379, 16.207158885819215, 16.22734435760693, 9.132641366412786, 8.40278297409429, 9.804984358975888, 17.61556907019986, 8.644105789377742, 10.20135048411419, 12.206452542629595, 14.445769764456351), # 159
(13.112769770827757, 10.215174111373285, 14.0794577243206, 16.017439518735948, 16.04955623642423, 9.043814332885832, 8.284038747090811, 9.712078541149223, 17.455365409011574, 8.534170173353209, 10.075067115497172, 12.060903507998123, 14.285557096008445), # 160
(12.911799698254727, 10.038817562544844, 13.896084549438555, 15.79423050676211, 15.837107623707803, 8.934439034826566, 8.149826602812377, 9.596880959597605, 17.254493580598233, 8.407184747707687, 9.928334978279473, 11.890381444033627, 14.094673280674375), # 161
(12.686619186767443, 9.84548026566583, 13.683638627307893, 15.539335210937388, 15.591844516145768, 8.80559155027162, 8.001013727411657, 9.460555513169764, 17.015066384244545, 8.264082458898416, 9.762266802021516, 11.696235683114327, 13.874755057524599), # 162
(12.438641460291295, 9.636194557608343, 13.443750036757264, 15.254556992301481, 15.315612910426239, 8.65834795725763, 7.838467307041322, 9.304266100714425, 16.73919661923523, 8.105796253382625, 9.577975316283736, 11.479815557618458, 13.627439165629584), # 163
(12.16927974275169, 9.411992775244478, 13.178048856615318, 14.941699211894072, 15.01025880323734, 8.493784333821234, 7.663054527854039, 9.129176621080324, 16.428997084855002, 7.933259077617543, 9.376573250626553, 11.242470399924246, 13.35436234405979), # 164
(11.879947258074031, 9.173907255446338, 12.888165165710705, 14.602565230754854, 14.677628191267182, 8.312976757999055, 7.475642576002479, 8.936450973116184, 16.086580580388564, 7.747403878060404, 9.1591733346104, 10.985549542409915, 13.057161331885686), # 165
(11.572057230183715, 8.922970335086019, 12.57572904287207, 14.238958409923503, 14.319567071203886, 8.117001307827735, 7.277098637639315, 8.727253055670738, 15.714059905120632, 7.549163601168441, 8.926888297795703, 10.710402317453703, 12.737472868177733), # 166
(11.24702288300614, 8.660214351035616, 12.242370566928068, 13.852682110439718, 13.937921439735565, 7.906934061343905, 7.0682898989172145, 8.502746767592717, 15.31354785833592, 7.339471193398886, 8.680830869742888, 10.418378057433825, 12.396933692006392), # 167
(10.906257440466712, 8.386671640167231, 11.889719816707347, 13.445539693343184, 13.534537293550335, 7.683851096584198, 6.850083545988848, 8.264096007730847, 14.887157239319139, 7.11925960120897, 8.422113780012385, 10.11082609472852, 12.037180542442131), # 168
(10.551174126490828, 8.103374539352963, 11.519406871038555, 13.019334519673588, 13.111260629336316, 7.4488284915852505, 6.623346765006885, 8.012464674933861, 14.437000847355009, 6.889461771055926, 8.151849758164623, 9.78909576171601, 11.659850158555415), # 169
(10.18318616500389, 7.811355385464907, 11.133061808750343, 12.575869950470615, 12.66993744378162, 7.2029423243836925, 6.388946742123995, 7.749016668050485, 13.96519148172823, 6.6510106493969845, 7.871151533760029, 9.454536390774527, 11.2665792794167), # 170
(9.8037067799313, 7.511646515375161, 10.73231470867136, 12.116949346773964, 12.21241373357437, 6.947268673016157, 6.147750663492849, 7.47491588592945, 13.47384194172352, 6.404839182689379, 7.581131836359027, 9.108497314282296, 10.859004644096458), # 171
(9.414149195198457, 7.205280265955825, 10.318795649630257, 11.644376069623315, 11.740535495402677, 6.682883615519281, 5.900625715266118, 7.191326227419487, 12.965065026625595, 6.151880317390344, 7.282903395522049, 8.752327864617548, 10.438762991665145), # 172
(9.015926634730764, 6.893288974078996, 9.894134710455681, 11.159953480058356, 11.256148725954663, 6.410863229929695, 5.64843908359647, 6.899411591369322, 12.440973535719161, 5.893066999957107, 6.97757894080952, 8.387377374158506, 10.007491061193234), # 173
(8.610452322453618, 6.576704976616772, 9.459961969976282, 10.665484939118773, 10.76109942191844, 6.132283594284034, 5.3920579546365754, 6.600335876627689, 11.903680268288936, 5.629332176846904, 6.66627120178187, 8.014995175283403, 9.566825591751181), # 174
(8.19913948229242, 6.256560610441251, 9.017907507020714, 10.162773807844262, 10.257233579982124, 5.848220786618931, 5.132349514539104, 6.295262982043313, 11.35529802361963, 5.361608794516964, 6.3500929079995245, 7.636530600370466, 9.118403322409455), # 175
(7.783401338172574, 5.933888212424531, 8.569601400417621, 9.653623447274505, 9.746397196833835, 5.55975088497102, 4.870180949456727, 5.985356806464928, 10.797939600995955, 5.090829799424521, 6.0301567890229135, 7.253332981797922, 8.663860992238513), # 176
(7.364651114019479, 5.6097201194387125, 8.116673728995655, 9.13983721844919, 9.230436269161691, 5.267949967376934, 4.606419445542112, 5.671781248741259, 10.233717799702626, 4.817928138026804, 5.7075755744124645, 6.866751651944002, 8.204835340308824), # 177
(6.944302033758534, 5.285088668355891, 7.660754571583465, 8.623218482408008, 8.711196793653805, 4.973894111873309, 4.341932188947932, 5.355700207721038, 9.664745419024355, 4.54383675678105, 5.383461993728603, 6.478135943186929, 7.742963105690853), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(9, 6, 6, 9, 6, 3, 4, 6, 5, 1, 0, 0, 0, 5, 8, 8, 7, 6, 1, 6, 1, 3, 3, 2, 0, 0), # 0
(19, 12, 13, 18, 16, 7, 7, 15, 8, 2, 0, 0, 0, 14, 16, 12, 11, 15, 6, 12, 7, 6, 4, 4, 1, 0), # 1
(31, 20, 25, 29, 29, 10, 13, 19, 15, 6, 0, 2, 0, 25, 27, 20, 20, 24, 13, 18, 10, 9, 7, 7, 3, 0), # 2
(43, 30, 34, 40, 37, 19, 21, 22, 17, 7, 3, 2, 0, 33, 39, 24, 29, 34, 20, 20, 13, 17, 10, 9, 5, 0), # 3
(60, 41, 40, 50, 48, 25, 24, 26, 21, 10, 3, 6, 0, 44, 52, 31, 35, 53, 26, 25, 18, 19, 11, 12, 5, 0), # 4
(73, 59, 54, 60, 59, 30, 31, 31, 26, 13, 4, 9, 0, 55, 72, 40, 43, 61, 32, 28, 23, 24, 13, 12, 7, 0), # 5
(91, 72, 65, 73, 67, 37, 42, 33, 35, 16, 5, 11, 0, 72, 79, 51, 46, 72, 33, 37, 25, 27, 15, 16, 7, 0), # 6
(102, 95, 78, 86, 76, 42, 46, 42, 41, 17, 11, 11, 0, 83, 92, 58, 53, 77, 45, 40, 27, 29, 19, 19, 8, 0), # 7
(118, 109, 87, 97, 90, 45, 51, 51, 45, 19, 11, 11, 0, 92, 102, 64, 64, 81, 51, 44, 28, 34, 21, 20, 8, 0), # 8
(138, 122, 99, 106, 98, 50, 58, 58, 53, 23, 13, 11, 0, 108, 116, 79, 72, 96, 60, 51, 31, 39, 26, 25, 9, 0), # 9
(147, 140, 115, 126, 108, 56, 63, 64, 60, 25, 14, 12, 0, 121, 126, 84, 81, 106, 67, 60, 37, 44, 28, 28, 9, 0), # 10
(163, 150, 121, 140, 120, 62, 67, 70, 69, 28, 15, 14, 0, 136, 136, 96, 91, 112, 73, 63, 41, 53, 34, 31, 10, 0), # 11
(178, 170, 144, 149, 134, 71, 69, 73, 79, 30, 17, 15, 0, 151, 153, 107, 105, 124, 83, 71, 44, 57, 40, 35, 13, 0), # 12
(196, 187, 154, 167, 152, 74, 72, 82, 90, 36, 21, 15, 0, 170, 164, 123, 113, 139, 89, 77, 44, 63, 41, 38, 15, 0), # 13
(202, 208, 168, 183, 160, 78, 77, 87, 99, 39, 24, 18, 0, 199, 175, 131, 119, 156, 99, 83, 47, 68, 51, 41, 17, 0), # 14
(208, 238, 188, 202, 175, 87, 84, 97, 105, 41, 26, 19, 0, 212, 192, 135, 129, 175, 111, 97, 54, 71, 56, 46, 18, 0), # 15
(229, 256, 198, 223, 193, 93, 98, 105, 110, 46, 27, 21, 0, 230, 205, 145, 142, 202, 123, 107, 57, 76, 63, 47, 19, 0), # 16
(251, 279, 204, 238, 202, 102, 103, 108, 123, 49, 29, 23, 0, 246, 222, 155, 159, 215, 133, 111, 59, 79, 66, 48, 21, 0), # 17
(270, 294, 219, 264, 214, 109, 106, 117, 130, 50, 31, 24, 0, 270, 237, 166, 179, 226, 142, 122, 65, 85, 73, 48, 22, 0), # 18
(296, 306, 233, 286, 220, 117, 116, 123, 139, 57, 32, 25, 0, 289, 254, 186, 186, 238, 151, 132, 69, 88, 78, 55, 23, 0), # 19
(316, 321, 248, 303, 235, 125, 124, 130, 146, 57, 35, 27, 0, 307, 267, 198, 204, 250, 158, 137, 74, 97, 85, 59, 26, 0), # 20
(334, 331, 258, 322, 255, 131, 129, 137, 157, 60, 39, 27, 0, 328, 287, 210, 216, 260, 173, 145, 81, 105, 87, 63, 28, 0), # 21
(351, 350, 269, 335, 274, 138, 135, 142, 162, 61, 41, 29, 0, 349, 306, 225, 224, 275, 180, 151, 86, 117, 90, 65, 30, 0), # 22
(369, 368, 285, 348, 290, 144, 142, 145, 166, 64, 46, 31, 0, 364, 325, 240, 232, 289, 185, 156, 93, 124, 93, 67, 32, 0), # 23
(386, 383, 299, 365, 302, 154, 148, 153, 178, 66, 50, 33, 0, 383, 343, 252, 244, 302, 194, 162, 95, 130, 109, 72, 34, 0), # 24
(406, 401, 317, 377, 316, 160, 156, 157, 182, 69, 54, 33, 0, 409, 358, 262, 249, 310, 203, 169, 101, 137, 118, 75, 34, 0), # 25
(422, 414, 334, 400, 328, 168, 162, 166, 189, 73, 61, 33, 0, 426, 371, 275, 258, 328, 220, 175, 101, 144, 121, 79, 35, 0), # 26
(430, 438, 342, 420, 344, 176, 175, 179, 196, 83, 65, 34, 0, 451, 384, 289, 264, 347, 233, 188, 111, 151, 127, 82, 36, 0), # 27
(442, 455, 358, 442, 356, 181, 182, 184, 210, 89, 69, 35, 0, 473, 405, 305, 278, 364, 239, 194, 115, 153, 134, 84, 37, 0), # 28
(456, 469, 364, 453, 368, 188, 190, 195, 216, 92, 71, 37, 0, 488, 418, 314, 284, 374, 245, 197, 119, 162, 137, 85, 40, 0), # 29
(470, 490, 381, 463, 385, 190, 194, 197, 228, 95, 74, 37, 0, 507, 429, 321, 298, 394, 252, 204, 120, 172, 144, 87, 41, 0), # 30
(489, 503, 406, 480, 399, 196, 199, 207, 232, 98, 78, 40, 0, 524, 438, 333, 306, 405, 262, 219, 125, 174, 148, 88, 41, 0), # 31
(510, 522, 417, 499, 419, 203, 201, 217, 238, 101, 79, 41, 0, 533, 453, 343, 316, 415, 273, 222, 131, 184, 152, 90, 42, 0), # 32
(532, 535, 434, 519, 428, 207, 209, 225, 247, 103, 80, 44, 0, 550, 467, 355, 325, 425, 280, 230, 137, 190, 155, 92, 42, 0), # 33
(556, 549, 455, 539, 445, 212, 218, 228, 254, 107, 83, 45, 0, 566, 477, 367, 339, 434, 295, 235, 141, 198, 162, 94, 42, 0), # 34
(576, 564, 474, 554, 457, 217, 224, 238, 258, 109, 84, 48, 0, 583, 498, 379, 345, 446, 308, 249, 148, 203, 168, 96, 43, 0), # 35
(596, 585, 488, 568, 470, 223, 227, 241, 261, 110, 86, 50, 0, 594, 505, 398, 355, 461, 318, 256, 159, 217, 174, 103, 45, 0), # 36
(612, 601, 501, 580, 481, 227, 233, 248, 270, 115, 90, 50, 0, 616, 519, 411, 363, 486, 326, 261, 169, 223, 182, 104, 48, 0), # 37
(629, 619, 515, 592, 492, 232, 238, 254, 277, 119, 95, 51, 0, 643, 538, 426, 373, 496, 332, 267, 172, 236, 191, 109, 50, 0), # 38
(646, 640, 528, 608, 506, 237, 248, 257, 282, 122, 96, 52, 0, 661, 550, 443, 379, 514, 339, 275, 176, 238, 195, 110, 54, 0), # 39
(667, 658, 539, 623, 520, 243, 255, 268, 291, 122, 97, 52, 0, 682, 565, 461, 394, 525, 343, 282, 179, 249, 206, 112, 55, 0), # 40
(692, 677, 557, 640, 536, 255, 262, 278, 294, 125, 102, 53, 0, 695, 574, 470, 404, 540, 353, 290, 185, 255, 213, 116, 58, 0), # 41
(714, 697, 574, 657, 545, 262, 271, 286, 305, 130, 103, 53, 0, 714, 593, 479, 417, 558, 360, 295, 189, 264, 217, 116, 59, 0), # 42
(736, 706, 587, 669, 563, 268, 278, 293, 311, 132, 107, 55, 0, 743, 610, 489, 432, 577, 364, 302, 192, 275, 222, 118, 60, 0), # 43
(761, 727, 602, 685, 578, 273, 284, 300, 318, 136, 110, 56, 0, 760, 627, 501, 441, 594, 369, 307, 196, 281, 226, 121, 60, 0), # 44
(781, 743, 621, 708, 591, 276, 289, 308, 322, 137, 114, 58, 0, 776, 650, 513, 450, 608, 382, 310, 202, 284, 234, 123, 63, 0), # 45
(800, 759, 637, 726, 611, 280, 296, 315, 327, 140, 116, 59, 0, 794, 673, 525, 459, 623, 387, 322, 205, 293, 238, 126, 64, 0), # 46
(822, 775, 655, 744, 626, 289, 303, 320, 335, 141, 118, 60, 0, 815, 687, 535, 467, 639, 401, 331, 207, 305, 248, 128, 67, 0), # 47
(844, 791, 665, 761, 644, 294, 309, 326, 340, 145, 120, 62, 0, 837, 708, 550, 478, 650, 409, 334, 210, 311, 252, 131, 68, 0), # 48
(867, 807, 681, 783, 658, 299, 315, 331, 346, 148, 122, 62, 0, 850, 723, 560, 489, 661, 418, 343, 217, 319, 258, 136, 69, 0), # 49
(892, 822, 701, 797, 668, 303, 323, 335, 351, 149, 124, 64, 0, 862, 737, 573, 496, 683, 423, 345, 222, 328, 260, 142, 70, 0), # 50
(913, 834, 717, 810, 683, 310, 330, 342, 360, 153, 124, 65, 0, 875, 748, 585, 504, 700, 431, 348, 224, 335, 272, 143, 74, 0), # 51
(934, 852, 723, 836, 696, 317, 336, 349, 365, 155, 126, 65, 0, 893, 772, 601, 518, 712, 434, 352, 232, 339, 279, 147, 76, 0), # 52
(952, 879, 739, 852, 704, 321, 343, 356, 372, 159, 128, 65, 0, 908, 782, 608, 532, 726, 444, 362, 239, 345, 286, 148, 77, 0), # 53
(964, 887, 755, 870, 724, 329, 347, 368, 376, 160, 130, 66, 0, 933, 799, 615, 538, 742, 451, 382, 242, 354, 291, 152, 79, 0), # 54
(986, 903, 770, 890, 740, 337, 351, 373, 388, 164, 130, 68, 0, 942, 815, 633, 544, 756, 459, 390, 245, 360, 297, 154, 80, 0), # 55
(1008, 922, 784, 903, 760, 343, 358, 376, 394, 167, 133, 68, 0, 959, 829, 645, 555, 770, 470, 395, 249, 368, 301, 158, 82, 0), # 56
(1026, 937, 798, 922, 771, 346, 362, 380, 401, 172, 134, 70, 0, 981, 849, 650, 567, 779, 477, 401, 255, 378, 307, 162, 84, 0), # 57
(1047, 958, 815, 941, 782, 355, 366, 384, 409, 176, 136, 71, 0, 1000, 862, 665, 573, 788, 485, 415, 259, 382, 310, 164, 84, 0), # 58
(1061, 976, 823, 951, 794, 360, 369, 391, 416, 180, 139, 72, 0, 1026, 882, 679, 583, 807, 493, 425, 265, 393, 316, 168, 86, 0), # 59
(1081, 997, 835, 964, 801, 365, 377, 396, 420, 182, 140, 74, 0, 1043, 900, 689, 592, 825, 501, 431, 268, 400, 319, 169, 87, 0), # 60
(1099, 1013, 850, 983, 818, 367, 383, 399, 430, 190, 143, 75, 0, 1058, 910, 702, 599, 841, 509, 436, 271, 407, 324, 174, 87, 0), # 61
(1122, 1025, 865, 992, 830, 373, 388, 403, 432, 193, 147, 76, 0, 1077, 926, 717, 607, 859, 514, 444, 277, 414, 326, 178, 88, 0), # 62
(1148, 1038, 882, 1000, 839, 383, 395, 410, 437, 198, 149, 76, 0, 1094, 940, 724, 618, 877, 521, 452, 279, 418, 333, 180, 92, 0), # 63
(1168, 1057, 892, 1023, 855, 383, 403, 419, 449, 200, 151, 76, 0, 1111, 955, 738, 626, 887, 529, 464, 284, 423, 336, 184, 92, 0), # 64
(1186, 1068, 905, 1034, 873, 391, 414, 421, 455, 203, 157, 78, 0, 1130, 965, 752, 635, 907, 536, 466, 287, 428, 342, 186, 93, 0), # 65
(1216, 1089, 918, 1051, 887, 400, 416, 424, 461, 207, 158, 79, 0, 1148, 973, 766, 643, 923, 542, 473, 295, 436, 346, 193, 94, 0), # 66
(1239, 1103, 932, 1066, 907, 407, 418, 436, 467, 208, 160, 80, 0, 1172, 990, 777, 651, 934, 549, 477, 296, 442, 355, 195, 96, 0), # 67
(1252, 1119, 944, 1084, 924, 412, 429, 442, 472, 213, 162, 81, 0, 1184, 1005, 787, 668, 948, 558, 485, 301, 453, 361, 199, 100, 0), # 68
(1269, 1149, 961, 1098, 939, 418, 432, 448, 481, 219, 163, 83, 0, 1202, 1025, 801, 682, 969, 565, 492, 304, 458, 364, 204, 100, 0), # 69
(1284, 1166, 977, 1115, 957, 426, 438, 453, 490, 220, 169, 83, 0, 1222, 1042, 818, 697, 984, 569, 497, 308, 469, 368, 206, 101, 0), # 70
(1304, 1184, 991, 1136, 968, 430, 447, 457, 496, 224, 174, 87, 0, 1245, 1055, 832, 710, 1000, 575, 507, 311, 474, 370, 209, 102, 0), # 71
(1317, 1204, 1009, 1147, 981, 439, 456, 460, 502, 228, 175, 90, 0, 1259, 1066, 851, 722, 1012, 585, 516, 316, 481, 373, 213, 103, 0), # 72
(1337, 1221, 1020, 1171, 993, 446, 461, 464, 512, 231, 176, 90, 0, 1273, 1077, 856, 735, 1025, 598, 524, 319, 490, 376, 217, 105, 0), # 73
(1354, 1243, 1033, 1197, 1007, 452, 470, 467, 519, 235, 181, 91, 0, 1290, 1094, 864, 740, 1039, 603, 532, 324, 498, 381, 219, 107, 0), # 74
(1377, 1259, 1042, 1213, 1017, 460, 475, 471, 523, 242, 184, 92, 0, 1309, 1105, 874, 748, 1048, 608, 537, 330, 504, 387, 224, 108, 0), # 75
(1396, 1275, 1060, 1232, 1030, 466, 486, 476, 531, 245, 189, 95, 0, 1325, 1120, 889, 753, 1065, 616, 541, 333, 509, 397, 227, 110, 0), # 76
(1419, 1295, 1073, 1247, 1037, 475, 490, 481, 540, 248, 191, 96, 0, 1354, 1132, 901, 761, 1079, 621, 548, 341, 515, 410, 232, 111, 0), # 77
(1430, 1309, 1088, 1261, 1053, 485, 495, 485, 547, 251, 192, 97, 0, 1366, 1147, 906, 767, 1088, 630, 557, 351, 519, 416, 235, 112, 0), # 78
(1449, 1320, 1108, 1275, 1065, 493, 502, 488, 555, 255, 194, 99, 0, 1386, 1165, 912, 770, 1105, 642, 567, 355, 533, 422, 236, 113, 0), # 79
(1470, 1335, 1133, 1287, 1081, 498, 505, 493, 564, 259, 197, 100, 0, 1397, 1179, 924, 781, 1118, 654, 573, 357, 541, 426, 238, 113, 0), # 80
(1490, 1357, 1145, 1298, 1092, 505, 508, 494, 573, 260, 201, 103, 0, 1415, 1198, 934, 789, 1129, 666, 579, 368, 551, 438, 239, 113, 0), # 81
(1500, 1369, 1160, 1307, 1105, 511, 513, 499, 578, 263, 205, 105, 0, 1441, 1207, 946, 793, 1153, 673, 584, 370, 556, 444, 241, 114, 0), # 82
(1518, 1383, 1169, 1323, 1117, 521, 522, 504, 586, 266, 206, 106, 0, 1461, 1219, 963, 800, 1172, 682, 588, 372, 564, 447, 242, 114, 0), # 83
(1534, 1397, 1189, 1342, 1131, 527, 531, 508, 594, 269, 208, 106, 0, 1490, 1236, 979, 808, 1185, 686, 592, 374, 572, 453, 245, 115, 0), # 84
(1550, 1413, 1208, 1359, 1141, 530, 536, 513, 598, 272, 210, 106, 0, 1509, 1251, 985, 820, 1198, 695, 596, 377, 578, 461, 251, 117, 0), # 85
(1565, 1425, 1225, 1370, 1161, 531, 541, 517, 606, 276, 211, 108, 0, 1520, 1267, 998, 831, 1212, 698, 601, 378, 588, 465, 254, 117, 0), # 86
(1581, 1445, 1244, 1382, 1176, 537, 544, 524, 616, 276, 217, 110, 0, 1534, 1279, 1009, 846, 1223, 705, 609, 380, 596, 469, 257, 118, 0), # 87
(1604, 1465, 1257, 1403, 1187, 541, 552, 530, 620, 279, 221, 110, 0, 1554, 1293, 1019, 852, 1236, 711, 615, 383, 605, 474, 260, 119, 0), # 88
(1621, 1478, 1275, 1422, 1196, 546, 558, 534, 627, 282, 227, 110, 0, 1580, 1303, 1028, 862, 1248, 719, 619, 388, 612, 480, 265, 120, 0), # 89
(1646, 1490, 1285, 1437, 1203, 554, 564, 540, 636, 285, 227, 114, 0, 1599, 1319, 1036, 869, 1264, 726, 624, 390, 618, 482, 268, 122, 0), # 90
(1670, 1507, 1294, 1449, 1212, 560, 570, 546, 640, 290, 230, 114, 0, 1618, 1332, 1046, 872, 1274, 729, 631, 393, 623, 488, 270, 123, 0), # 91
(1688, 1523, 1304, 1459, 1224, 562, 577, 552, 653, 296, 234, 115, 0, 1637, 1344, 1054, 880, 1298, 737, 643, 400, 629, 494, 274, 126, 0), # 92
(1700, 1540, 1318, 1473, 1234, 572, 582, 555, 659, 297, 235, 116, 0, 1661, 1357, 1069, 891, 1316, 751, 644, 407, 636, 500, 276, 127, 0), # 93
(1720, 1557, 1329, 1491, 1245, 576, 587, 564, 670, 302, 235, 116, 0, 1671, 1371, 1079, 901, 1334, 755, 653, 409, 640, 505, 285, 127, 0), # 94
(1730, 1576, 1343, 1506, 1255, 581, 591, 568, 675, 304, 238, 116, 0, 1689, 1379, 1092, 906, 1352, 761, 659, 411, 643, 509, 291, 129, 0), # 95
(1756, 1586, 1356, 1519, 1262, 589, 594, 571, 687, 307, 239, 117, 0, 1705, 1393, 1101, 917, 1365, 766, 665, 415, 651, 511, 294, 130, 0), # 96
(1780, 1608, 1371, 1538, 1275, 595, 602, 573, 696, 309, 241, 118, 0, 1731, 1407, 1112, 922, 1378, 773, 671, 418, 659, 514, 296, 132, 0), # 97
(1800, 1620, 1386, 1556, 1296, 600, 605, 580, 700, 315, 246, 121, 0, 1753, 1424, 1120, 931, 1384, 783, 674, 421, 666, 520, 297, 134, 0), # 98
(1820, 1631, 1400, 1569, 1312, 606, 610, 585, 711, 316, 248, 123, 0, 1770, 1439, 1137, 937, 1395, 790, 678, 426, 671, 525, 297, 137, 0), # 99
(1843, 1643, 1417, 1575, 1334, 612, 615, 590, 717, 318, 250, 123, 0, 1795, 1454, 1147, 946, 1407, 796, 684, 430, 674, 528, 298, 138, 0), # 100
(1864, 1651, 1424, 1588, 1351, 617, 620, 593, 727, 321, 254, 125, 0, 1823, 1474, 1156, 955, 1416, 801, 688, 433, 681, 531, 300, 140, 0), # 101
(1889, 1671, 1438, 1610, 1362, 623, 624, 596, 736, 324, 256, 125, 0, 1843, 1492, 1163, 963, 1429, 807, 698, 437, 689, 539, 301, 141, 0), # 102
(1898, 1681, 1449, 1627, 1376, 629, 626, 600, 743, 328, 256, 125, 0, 1853, 1506, 1175, 972, 1442, 815, 704, 439, 692, 544, 303, 141, 0), # 103
(1913, 1702, 1471, 1650, 1388, 635, 629, 605, 748, 330, 258, 126, 0, 1871, 1520, 1187, 983, 1459, 822, 711, 445, 698, 550, 306, 141, 0), # 104
(1934, 1721, 1486, 1662, 1401, 639, 636, 610, 756, 332, 260, 128, 0, 1881, 1536, 1196, 989, 1472, 826, 716, 449, 709, 555, 314, 141, 0), # 105
(1952, 1737, 1502, 1675, 1408, 645, 642, 612, 759, 338, 262, 129, 0, 1901, 1547, 1215, 994, 1488, 831, 719, 453, 714, 562, 316, 143, 0), # 106
(1969, 1752, 1512, 1691, 1416, 652, 650, 618, 765, 340, 263, 131, 0, 1925, 1559, 1230, 1003, 1498, 837, 723, 459, 722, 573, 320, 148, 0), # 107
(1978, 1766, 1526, 1707, 1431, 656, 655, 623, 771, 343, 266, 134, 0, 1955, 1571, 1235, 1009, 1512, 841, 726, 464, 733, 581, 321, 149, 0), # 108
(1989, 1780, 1536, 1720, 1446, 662, 659, 628, 779, 344, 268, 135, 0, 1975, 1590, 1246, 1012, 1525, 848, 732, 470, 736, 587, 323, 151, 0), # 109
(2006, 1788, 1551, 1740, 1464, 663, 663, 633, 785, 347, 271, 136, 0, 1997, 1604, 1257, 1023, 1539, 859, 736, 472, 745, 591, 324, 152, 0), # 110
(2028, 1800, 1562, 1752, 1474, 668, 670, 637, 795, 347, 276, 138, 0, 2012, 1607, 1268, 1028, 1551, 865, 741, 477, 752, 597, 325, 152, 0), # 111
(2039, 1813, 1573, 1765, 1485, 674, 674, 646, 802, 351, 277, 140, 0, 2031, 1617, 1274, 1039, 1563, 873, 748, 485, 757, 600, 328, 154, 0), # 112
(2054, 1820, 1585, 1777, 1496, 678, 675, 651, 809, 354, 277, 142, 0, 2048, 1630, 1283, 1046, 1576, 879, 755, 487, 761, 605, 330, 155, 0), # 113
(2072, 1832, 1598, 1794, 1508, 684, 679, 654, 815, 358, 281, 142, 0, 2071, 1644, 1290, 1058, 1587, 886, 761, 491, 769, 610, 332, 159, 0), # 114
(2086, 1842, 1611, 1806, 1518, 688, 685, 658, 822, 359, 281, 142, 0, 2085, 1659, 1296, 1068, 1598, 893, 768, 497, 773, 611, 335, 159, 0), # 115
(2100, 1859, 1626, 1821, 1529, 694, 691, 662, 830, 360, 281, 145, 0, 2108, 1675, 1308, 1078, 1611, 900, 771, 509, 777, 616, 337, 161, 0), # 116
(2116, 1871, 1638, 1832, 1544, 704, 697, 667, 835, 362, 281, 147, 0, 2135, 1686, 1317, 1087, 1621, 904, 776, 513, 785, 623, 337, 161, 0), # 117
(2130, 1888, 1650, 1844, 1558, 708, 701, 670, 839, 366, 284, 148, 0, 2148, 1698, 1328, 1093, 1636, 911, 786, 519, 790, 626, 341, 161, 0), # 118
(2143, 1904, 1665, 1857, 1564, 715, 702, 677, 842, 368, 285, 150, 0, 2161, 1713, 1343, 1097, 1647, 916, 790, 523, 797, 629, 349, 163, 0), # 119
(2159, 1919, 1682, 1876, 1575, 720, 707, 682, 846, 371, 287, 152, 0, 2177, 1724, 1355, 1099, 1655, 927, 794, 527, 801, 636, 350, 163, 0), # 120
(2174, 1934, 1688, 1891, 1585, 729, 710, 686, 851, 372, 287, 153, 0, 2195, 1733, 1368, 1103, 1668, 934, 797, 531, 807, 644, 353, 165, 0), # 121
(2187, 1948, 1704, 1907, 1604, 735, 712, 686, 859, 374, 290, 157, 0, 2212, 1743, 1377, 1108, 1679, 943, 804, 535, 811, 646, 356, 166, 0), # 122
(2211, 1955, 1727, 1923, 1619, 741, 715, 690, 864, 377, 291, 159, 0, 2227, 1753, 1386, 1119, 1687, 945, 806, 539, 819, 652, 359, 167, 0), # 123
(2227, 1970, 1740, 1933, 1630, 748, 721, 698, 870, 379, 293, 159, 0, 2249, 1767, 1395, 1125, 1700, 955, 813, 547, 828, 657, 360, 169, 0), # 124
(2242, 1988, 1753, 1943, 1645, 757, 725, 705, 878, 382, 294, 159, 0, 2267, 1775, 1410, 1131, 1720, 965, 815, 552, 833, 664, 362, 169, 0), # 125
(2254, 2002, 1758, 1957, 1662, 763, 729, 709, 881, 388, 295, 160, 0, 2287, 1784, 1422, 1146, 1733, 971, 819, 559, 843, 672, 363, 171, 0), # 126
(2275, 2017, 1775, 1968, 1674, 771, 733, 719, 888, 391, 295, 161, 0, 2303, 1791, 1427, 1153, 1746, 977, 825, 560, 848, 674, 366, 172, 0), # 127
(2296, 2027, 1783, 1992, 1686, 775, 739, 723, 898, 394, 298, 161, 0, 2318, 1798, 1438, 1163, 1755, 984, 830, 569, 854, 678, 369, 172, 0), # 128
(2314, 2039, 1796, 2008, 1692, 781, 742, 729, 908, 396, 298, 163, 0, 2334, 1812, 1450, 1172, 1770, 990, 834, 574, 857, 682, 371, 175, 0), # 129
(2330, 2045, 1811, 2020, 1709, 785, 753, 734, 912, 399, 299, 165, 0, 2343, 1828, 1456, 1180, 1777, 994, 842, 576, 861, 685, 375, 178, 0), # 130
(2342, 2061, 1825, 2037, 1718, 789, 758, 736, 915, 400, 299, 167, 0, 2354, 1842, 1460, 1190, 1792, 998, 845, 579, 863, 692, 381, 179, 0), # 131
(2361, 2074, 1841, 2051, 1723, 796, 764, 737, 927, 402, 300, 169, 0, 2371, 1854, 1479, 1198, 1799, 1006, 853, 580, 867, 699, 384, 181, 0), # 132
(2373, 2089, 1851, 2063, 1738, 802, 768, 740, 935, 405, 303, 170, 0, 2384, 1865, 1486, 1202, 1812, 1011, 855, 585, 873, 701, 387, 181, 0), # 133
(2386, 2102, 1861, 2076, 1752, 805, 770, 745, 937, 407, 304, 173, 0, 2402, 1880, 1497, 1217, 1825, 1015, 862, 587, 880, 705, 389, 183, 0), # 134
(2403, 2119, 1872, 2088, 1770, 809, 775, 750, 945, 408, 306, 174, 0, 2415, 1891, 1509, 1227, 1836, 1022, 867, 591, 886, 706, 390, 183, 0), # 135
(2415, 2127, 1890, 2105, 1783, 812, 779, 753, 951, 411, 307, 174, 0, 2431, 1900, 1518, 1238, 1847, 1025, 870, 593, 894, 711, 392, 184, 0), # 136
(2429, 2150, 1902, 2121, 1790, 819, 783, 760, 953, 415, 307, 174, 0, 2449, 1909, 1535, 1244, 1861, 1034, 877, 595, 900, 722, 395, 184, 0), # 137
(2438, 2163, 1913, 2137, 1812, 822, 787, 767, 960, 416, 308, 174, 0, 2460, 1926, 1551, 1252, 1872, 1038, 882, 601, 907, 726, 399, 185, 0), # 138
(2453, 2179, 1925, 2147, 1826, 830, 795, 770, 965, 420, 308, 176, 0, 2473, 1936, 1563, 1260, 1881, 1044, 887, 606, 910, 730, 399, 186, 0), # 139
(2470, 2192, 1946, 2159, 1831, 833, 801, 776, 970, 424, 310, 177, 0, 2483, 1953, 1571, 1271, 1892, 1051, 895, 607, 919, 734, 402, 187, 0), # 140
(2484, 2204, 1969, 2167, 1846, 840, 808, 781, 975, 426, 310, 178, 0, 2491, 1965, 1583, 1276, 1909, 1057, 901, 614, 925, 743, 403, 188, 0), # 141
(2505, 2210, 1980, 2183, 1860, 844, 816, 787, 981, 427, 313, 180, 0, 2503, 1973, 1588, 1287, 1919, 1063, 904, 620, 937, 752, 405, 191, 0), # 142
(2524, 2224, 1994, 2196, 1868, 850, 819, 793, 989, 431, 316, 183, 0, 2518, 1995, 1594, 1291, 1934, 1069, 906, 625, 945, 755, 407, 193, 0), # 143
(2538, 2242, 2008, 2199, 1879, 860, 826, 794, 995, 435, 319, 183, 0, 2534, 2010, 1600, 1299, 1944, 1076, 907, 630, 954, 760, 411, 194, 0), # 144
(2548, 2250, 2019, 2211, 1892, 866, 829, 799, 1004, 436, 320, 184, 0, 2552, 2022, 1609, 1304, 1956, 1083, 912, 635, 957, 768, 415, 195, 0), # 145
(2568, 2264, 2030, 2224, 1901, 871, 835, 801, 1007, 437, 322, 184, 0, 2575, 2048, 1615, 1310, 1966, 1090, 920, 641, 965, 776, 418, 197, 0), # 146
(2581, 2272, 2035, 2233, 1914, 876, 841, 807, 1014, 438, 325, 186, 0, 2592, 2059, 1623, 1313, 1984, 1094, 925, 647, 971, 781, 419, 198, 0), # 147
(2601, 2286, 2051, 2241, 1925, 885, 844, 813, 1022, 441, 325, 187, 0, 2608, 2063, 1630, 1319, 1995, 1098, 928, 649, 977, 784, 420, 198, 0), # 148
(2610, 2295, 2057, 2251, 1934, 892, 850, 814, 1027, 443, 326, 190, 0, 2625, 2084, 1639, 1325, 2004, 1107, 934, 652, 981, 786, 424, 198, 0), # 149
(2626, 2308, 2073, 2265, 1942, 897, 857, 816, 1035, 446, 328, 191, 0, 2638, 2095, 1649, 1339, 2014, 1112, 937, 653, 988, 792, 426, 199, 0), # 150
(2638, 2319, 2082, 2276, 1954, 903, 862, 820, 1041, 447, 330, 191, 0, 2653, 2110, 1658, 1348, 2025, 1117, 943, 656, 994, 793, 426, 201, 0), # 151
(2654, 2326, 2097, 2287, 1962, 913, 865, 820, 1048, 448, 331, 191, 0, 2663, 2118, 1668, 1362, 2034, 1121, 947, 663, 999, 799, 428, 202, 0), # 152
(2676, 2339, 2111, 2298, 1972, 915, 870, 822, 1054, 449, 331, 191, 0, 2679, 2129, 1676, 1369, 2044, 1123, 953, 666, 1001, 805, 433, 202, 0), # 153
(2690, 2350, 2123, 2310, 1986, 922, 874, 826, 1059, 449, 333, 191, 0, 2700, 2139, 1682, 1374, 2055, 1130, 959, 671, 1004, 806, 435, 203, 0), # 154
(2708, 2362, 2138, 2324, 1995, 927, 877, 830, 1069, 450, 335, 193, 0, 2711, 2151, 1688, 1378, 2065, 1135, 967, 674, 1008, 809, 437, 204, 0), # 155
(2723, 2370, 2145, 2336, 2007, 929, 880, 834, 1075, 452, 340, 194, 0, 2733, 2164, 1694, 1387, 2077, 1144, 971, 674, 1016, 812, 442, 206, 0), # 156
(2738, 2380, 2158, 2350, 2024, 934, 886, 838, 1081, 454, 342, 195, 0, 2751, 2175, 1702, 1393, 2089, 1150, 972, 674, 1025, 817, 446, 208, 0), # 157
(2749, 2387, 2169, 2356, 2036, 943, 896, 843, 1087, 456, 342, 195, 0, 2766, 2188, 1706, 1400, 2099, 1158, 974, 676, 1028, 820, 449, 208, 0), # 158
(2763, 2395, 2182, 2369, 2045, 948, 899, 850, 1093, 460, 343, 196, 0, 2775, 2202, 1714, 1404, 2113, 1161, 977, 679, 1035, 823, 449, 208, 0), # 159
(2779, 2407, 2194, 2378, 2059, 957, 900, 853, 1102, 465, 345, 197, 0, 2789, 2213, 1722, 1411, 2127, 1168, 981, 680, 1038, 828, 454, 210, 0), # 160
(2784, 2413, 2200, 2389, 2067, 961, 905, 859, 1107, 468, 348, 197, 0, 2807, 2223, 1728, 1419, 2145, 1176, 986, 687, 1042, 829, 455, 213, 0), # 161
(2796, 2424, 2213, 2404, 2076, 966, 910, 862, 1111, 473, 351, 199, 0, 2818, 2238, 1741, 1419, 2160, 1181, 990, 687, 1049, 833, 456, 213, 0), # 162
(2808, 2431, 2222, 2416, 2088, 972, 912, 864, 1119, 475, 354, 201, 0, 2835, 2243, 1749, 1423, 2167, 1190, 994, 692, 1053, 836, 457, 213, 0), # 163
(2820, 2436, 2238, 2427, 2100, 974, 915, 868, 1124, 475, 354, 202, 0, 2845, 2254, 1756, 1428, 2175, 1196, 998, 695, 1059, 840, 459, 214, 0), # 164
(2834, 2443, 2244, 2436, 2111, 975, 919, 874, 1130, 476, 356, 202, 0, 2854, 2265, 1761, 1432, 2186, 1201, 1003, 699, 1063, 844, 461, 215, 0), # 165
(2846, 2453, 2256, 2440, 2124, 981, 923, 875, 1133, 476, 359, 202, 0, 2863, 2270, 1773, 1436, 2196, 1205, 1006, 705, 1069, 848, 464, 216, 0), # 166
(2856, 2461, 2269, 2452, 2133, 984, 926, 880, 1141, 478, 362, 203, 0, 2877, 2281, 1784, 1440, 2205, 1209, 1008, 707, 1071, 850, 468, 217, 0), # 167
(2868, 2463, 2279, 2461, 2149, 986, 927, 882, 1147, 480, 363, 204, 0, 2890, 2292, 1789, 1448, 2212, 1217, 1015, 711, 1077, 852, 473, 217, 0), # 168
(2875, 2471, 2292, 2469, 2164, 988, 932, 884, 1152, 481, 365, 204, 0, 2895, 2304, 1799, 1450, 2223, 1225, 1019, 712, 1082, 853, 475, 218, 0), # 169
(2885, 2481, 2301, 2483, 2170, 994, 935, 891, 1156, 484, 366, 208, 0, 2911, 2315, 1805, 1454, 2231, 1229, 1025, 714, 1086, 856, 475, 218, 0), # 170
(2894, 2487, 2307, 2497, 2177, 998, 938, 893, 1160, 486, 366, 208, 0, 2923, 2324, 1811, 1459, 2238, 1232, 1029, 718, 1092, 861, 478, 220, 0), # 171
(2908, 2496, 2315, 2507, 2183, 999, 942, 897, 1163, 487, 367, 208, 0, 2930, 2334, 1818, 1465, 2250, 1235, 1031, 721, 1096, 863, 479, 222, 0), # 172
(2920, 2501, 2325, 2513, 2195, 1004, 946, 903, 1164, 487, 368, 210, 0, 2943, 2341, 1823, 1472, 2257, 1238, 1036, 724, 1102, 865, 482, 222, 0), # 173
(2929, 2503, 2333, 2524, 2206, 1010, 946, 908, 1164, 489, 370, 211, 0, 2950, 2347, 1827, 1475, 2267, 1240, 1038, 727, 1106, 865, 483, 222, 0), # 174
(2935, 2508, 2341, 2529, 2209, 1015, 948, 914, 1166, 490, 371, 212, 0, 2960, 2351, 1835, 1480, 2277, 1244, 1039, 727, 1112, 867, 483, 222, 0), # 175
(2945, 2511, 2344, 2538, 2216, 1018, 949, 916, 1172, 491, 372, 212, 0, 2971, 2356, 1839, 1483, 2282, 1249, 1043, 728, 1116, 870, 483, 222, 0), # 176
(2949, 2517, 2351, 2543, 2223, 1020, 954, 920, 1174, 493, 373, 213, 0, 2978, 2367, 1843, 1484, 2286, 1254, 1047, 731, 1119, 874, 484, 223, 0), # 177
(2957, 2522, 2358, 2551, 2228, 1022, 959, 924, 1177, 494, 374, 213, 0, 2983, 2374, 1851, 1488, 2293, 1259, 1047, 733, 1122, 875, 485, 223, 0), # 178
(2957, 2522, 2358, 2551, 2228, 1022, 959, 924, 1177, 494, 374, 213, 0, 2983, 2374, 1851, 1488, 2293, 1259, 1047, 733, 1122, 875, 485, 223, 0), # 179
)
passenger_arriving_rate = (
(9.037558041069182, 9.116726123493724, 7.81692484441876, 8.389801494715634, 6.665622729131535, 3.295587678639206, 3.7314320538365235, 3.4898821297345672, 3.654059437300804, 1.781106756985067, 1.261579549165681, 0.7346872617459261, 0.0, 9.150984382641052, 8.081559879205185, 6.307897745828405, 5.3433202709552, 7.308118874601608, 4.885834981628395, 3.7314320538365235, 2.3539911990280045, 3.3328113645657673, 2.7966004982385453, 1.5633849688837522, 0.828793283953975, 0.0), # 0
(9.637788873635953, 9.718600145338852, 8.333019886995228, 8.943944741923431, 7.106988404969084, 3.5132827632446837, 3.9775220471373247, 3.7196352921792815, 3.8953471957997454, 1.8985413115247178, 1.3449288407868398, 0.7831824991221532, 0.0, 9.755624965391739, 8.615007490343684, 6.724644203934198, 5.695623934574153, 7.790694391599491, 5.207489409050994, 3.9775220471373247, 2.509487688031917, 3.553494202484542, 2.9813149139744777, 1.6666039773990458, 0.883509104121714, 0.0), # 1
(10.236101416163518, 10.318085531970116, 8.847063428321121, 9.495883401297473, 7.546755568499692, 3.7301093702380674, 4.222636657164634, 3.948468935928315, 4.135672084126529, 2.015511198759246, 1.4279469446328943, 0.8314848978079584, 0.0, 10.357856690777442, 9.14633387588754, 7.13973472316447, 6.046533596277737, 8.271344168253059, 5.527856510299641, 4.222636657164634, 2.6643638358843336, 3.773377784249846, 3.1652944670991583, 1.7694126856642243, 0.938007775633647, 0.0), # 2
(10.830164027663812, 10.912803828195138, 9.357016303979782, 10.0434281501683, 7.983194011202283, 3.9452076537143688, 4.46580327748316, 4.175475868120881, 4.374081096552656, 2.1315522142917818, 1.5103045235482149, 0.8794028527395692, 0.0, 10.955291051257605, 9.67343138013526, 7.551522617741075, 6.3946566428753435, 8.748162193105312, 5.845666215369232, 4.46580327748316, 2.818005466938835, 3.9915970056011414, 3.3478093833894342, 1.8714032607959565, 0.9920730752904672, 0.0), # 3
(11.417645067148767, 11.500376578821527, 9.860839349554556, 10.584389665866468, 8.41457352455579, 4.1577177677686015, 4.706049301657613, 4.399748895896186, 4.609621227349624, 2.246200153725456, 1.5916722403771728, 0.9267447588532147, 0.0, 11.54553953929167, 10.19419234738536, 7.958361201885864, 6.738600461176366, 9.219242454699248, 6.159648454254661, 4.706049301657613, 2.969798405549001, 4.207286762277895, 3.528129888622157, 1.9721678699109113, 1.0454887798928663, 0.0), # 4
(11.996212893630318, 12.07842532865692, 10.356493400628777, 11.11657862572253, 8.839163900039136, 4.366779866495776, 4.942402123252702, 4.620380826393444, 4.841339470788935, 2.3589908126633987, 1.67172075796414, 0.9733190110851223, 0.0, 12.126213647339089, 10.706509121936344, 8.358603789820698, 7.076972437990195, 9.68267894157787, 6.468533156950822, 4.942402123252702, 3.119128476068411, 4.419581950019568, 3.705526208574178, 2.071298680125756, 1.0980386662415385, 0.0), # 5
(12.5635358661204, 12.644571622508925, 10.8419392927858, 11.63780570706703, 9.255234929131252, 4.571534103990907, 5.173889135833137, 4.836464466751867, 5.068282821142089, 2.469459986708742, 1.750120739153485, 1.0189340043715214, 0.0, 12.694924867859292, 11.208274048086732, 8.750603695767424, 7.408379960126224, 10.136565642284179, 6.771050253452613, 5.173889135833137, 3.265381502850648, 4.627617464565626, 3.8792685690223445, 2.16838785855716, 1.1495065111371752, 0.0), # 6
(13.117282343630944, 13.196437005185167, 11.315137861608953, 12.145881587230525, 9.661056403311065, 4.771120634349007, 5.399537732963626, 5.047092624110664, 5.289498272680586, 2.5771434714646144, 1.8265428467895808, 1.0633981336486396, 0.0, 13.249284693311735, 11.697379470135033, 9.132714233947903, 7.7314304143938415, 10.578996545361171, 7.06592967375493, 5.399537732963626, 3.4079433102492906, 4.830528201655532, 4.048627195743509, 2.2630275723217905, 1.1996760913804698, 0.0), # 7
(13.655120685173882, 13.731643021493262, 11.774049942681595, 12.638616943543553, 10.054898114057503, 4.964679611665085, 5.618375308208878, 5.251358105609044, 5.504032819675924, 2.681577062534149, 1.9006577437167966, 1.1065197938527056, 0.0, 13.786904616155851, 12.171717732379758, 9.503288718583983, 8.044731187602444, 11.008065639351848, 7.351901347852662, 5.618375308208878, 3.5461997226179176, 5.027449057028751, 4.212872314514518, 2.3548099885363194, 1.248331183772115, 0.0), # 8
(14.174719249761154, 14.247811216240837, 12.216636371587056, 13.11382245333668, 10.43502985284949, 5.151351190034158, 5.829429255133608, 5.4483537183862225, 5.710933456399605, 2.782296555520474, 1.9721360927795035, 1.1481073799199473, 0.0, 14.305396128851092, 12.629181179119417, 9.860680463897518, 8.34688966656142, 11.42186691279921, 7.627695205740712, 5.829429255133608, 3.679536564310113, 5.217514926424745, 4.371274151112227, 2.4433272743174115, 1.2952555651128035, 0.0), # 9
(14.673746396404677, 14.7425631342355, 12.640857983908687, 13.569308793940438, 10.799721411165962, 5.330275523551238, 6.031726967302519, 5.637172269581408, 5.909247177123128, 2.878837746026722, 2.0406485568220725, 1.187969286786593, 0.0, 14.802370723856898, 13.06766215465252, 10.20324278411036, 8.636513238080164, 11.818494354246257, 7.892041177413972, 6.031726967302519, 3.8073396596794558, 5.399860705582981, 4.52310293131348, 2.5281715967817378, 1.3402330122032275, 0.0), # 10
(15.149870484116411, 15.213520320284891, 13.044675615229824, 14.002886642685386, 11.14724258048584, 5.500592766311337, 6.224295838280325, 5.816906566333811, 6.098020976117995, 2.970736429656024, 2.105865798688875, 1.2259139093888718, 0.0, 15.2754398936327, 13.485053003277587, 10.529328993444373, 8.912209288968072, 12.19604195223599, 8.143669192867335, 6.224295838280325, 3.9289948330795266, 5.57362129024292, 4.66762888089513, 2.6089351230459648, 1.3830473018440812, 0.0), # 11
(15.600759871908263, 15.6583043191966, 13.42605010113381, 14.412366676902078, 11.475863152288053, 5.6614430724094635, 6.406163261631731, 5.986649415782641, 6.276301847655707, 3.0575284020115086, 2.1674584812242808, 1.2617496426630104, 0.0, 15.722215130637963, 13.879246069293112, 10.837292406121403, 9.172585206034523, 12.552603695311413, 8.381309182095698, 6.406163261631731, 4.043887908863902, 5.737931576144026, 4.804122225634027, 2.6852100202267626, 1.4234822108360548, 0.0), # 12
(16.02408291879218, 16.074536675778273, 13.782942277203993, 14.795559573921057, 11.783852918051522, 5.8119665959406355, 6.576356630921451, 6.145493625067111, 6.443136786007759, 3.138749458696308, 2.225097267272661, 1.2952848815452382, 0.0, 16.140307927332124, 14.248133696997618, 11.125486336363304, 9.416248376088921, 12.886273572015519, 8.603691075093955, 6.576356630921451, 4.151404711386168, 5.891926459025761, 4.93185319130702, 2.756588455440799, 1.4613215159798432, 0.0), # 13
(16.41750798378009, 16.45983893483752, 14.113312979023721, 15.150276011072872, 12.069481669255186, 5.9513034909998614, 6.733903339714195, 6.292532001326435, 6.597572785445653, 3.2139353953135514, 2.2784528196783858, 1.3263280209717843, 0.0, 16.527329776174614, 14.589608230689624, 11.392264098391927, 9.641806185940652, 13.195145570891306, 8.80954480185701, 6.733903339714195, 4.250931064999901, 6.034740834627593, 5.050092003690958, 2.8226625958047444, 1.4963489940761385, 0.0), # 14
(16.77870342588394, 16.811832641181958, 14.415123042176313, 15.474326665688082, 12.33101919737797, 6.078593911682158, 6.877830781574663, 6.426857351699818, 6.738656840240891, 3.2826220074663714, 2.3271958012858263, 1.3546874558788757, 0.0, 16.880892169624886, 14.90156201466763, 11.63597900642913, 9.847866022399112, 13.477313680481782, 8.997600292379746, 6.877830781574663, 4.341852794058684, 6.165509598688985, 5.158108888562695, 2.883024608435263, 1.5283484219256327, 0.0), # 15
(17.10533760411564, 17.128139339619217, 14.686333302245139, 15.765522215097217, 12.566735293898798, 6.192978012082533, 7.007166350067579, 6.547562483326471, 6.865435944664972, 3.344345090757899, 2.370996874939354, 1.380171581202741, 0.0, 17.198606600142384, 15.181887393230149, 11.85498437469677, 10.033035272273695, 13.730871889329944, 9.16658747665706, 7.007166350067579, 4.423555722916095, 6.283367646949399, 5.255174071699074, 2.9372666604490276, 1.55710357632902, 0.0), # 16
(17.395078877487137, 17.406380574956913, 14.92490459481353, 16.021673336630855, 12.774899750296605, 6.2935959462960005, 7.12093743875764, 6.653740203345614, 6.976957092989391, 3.398640440791261, 2.40952670348334, 1.4025887918796085, 0.0, 17.47808456018655, 15.428476710675692, 12.047633517416699, 10.195921322373781, 13.953914185978782, 9.31523628468386, 7.12093743875764, 4.4954256759257145, 6.387449875148302, 5.340557778876952, 2.984980918962706, 1.5823982340869922, 0.0), # 17
(17.645595605010367, 17.644177892002652, 15.12879775546482, 16.24059070761953, 12.953782358050306, 6.379587868417579, 7.2181714412095666, 6.744483318896446, 7.072267279485658, 3.4450438531695924, 2.4424559497621527, 1.4217474828457075, 0.0, 17.716937542216822, 15.63922231130278, 12.212279748810763, 10.335131559508774, 14.144534558971316, 9.442276646455024, 7.2181714412095666, 4.556848477441128, 6.476891179025153, 5.413530235873177, 3.0257595510929645, 1.6040161720002415, 0.0), # 18
(17.85455614569726, 17.83915283556408, 15.29597361978237, 16.420085005393776, 13.10165290863884, 6.450093932542269, 7.297895750988055, 6.818884637118185, 7.150413498425267, 3.4830911234960236, 2.4694552766201636, 1.4374560490372645, 0.0, 17.912777038692653, 15.812016539409907, 12.347276383100818, 10.449273370488068, 14.300826996850533, 9.546438491965459, 7.297895750988055, 4.607209951815906, 6.55082645431942, 5.473361668464593, 3.059194723956474, 1.621741166869462, 0.0), # 19
(18.01962885855975, 17.988926950448786, 15.424393023349506, 16.55796690728418, 13.216781193541133, 6.504254292765094, 7.359137761657826, 6.876036965150038, 7.210442744079718, 3.5123180473736824, 2.490195346901745, 1.4495228853905089, 0.0, 18.063214542073485, 15.944751739295596, 12.450976734508725, 10.536954142121044, 14.420885488159437, 9.626451751210054, 7.359137761657826, 4.645895923403639, 6.608390596770566, 5.51932230242806, 3.084878604669901, 1.6353569954953444, 0.0), # 20
(18.13848210260976, 18.09112178146442, 15.51201680174958, 16.652047090621256, 13.297437004236105, 6.541209103181062, 7.400924866783583, 6.915033110131218, 7.251402010720512, 3.532260420405701, 2.5043468234512685, 1.4577563868416692, 0.0, 18.165861544818743, 16.03532025525836, 12.52173411725634, 10.5967812612171, 14.502804021441024, 9.681046354183705, 7.400924866783583, 4.672292216557902, 6.648718502118053, 5.550682363540419, 3.1024033603499164, 1.644647434678584, 0.0), # 21
(18.20878423685924, 18.143358873418588, 15.55680579056593, 16.70013623273558, 13.341890132202689, 6.560098517885186, 7.422284459930039, 6.934965879200936, 7.27233829261915, 3.54245403819521, 2.5115803691131027, 1.4619649483269737, 0.0, 18.218329539387888, 16.08161443159671, 12.557901845565512, 10.627362114585626, 14.5446765852383, 9.70895223088131, 7.422284459930039, 4.6857846556322755, 6.6709450661013445, 5.5667120775785275, 3.111361158113186, 1.649396261219872, 0.0), # 22
(18.23470805401675, 18.14954393004115, 15.562384773662554, 16.706156597222225, 13.353278467239116, 6.5625, 7.424823602033405, 6.937120370370371, 7.274955740740741, 3.543656522633746, 2.512487411148522, 1.4624846364883404, 0.0, 18.225, 16.08733100137174, 12.56243705574261, 10.630969567901236, 14.549911481481482, 9.71196851851852, 7.424823602033405, 4.6875, 6.676639233619558, 5.568718865740743, 3.1124769547325113, 1.6499585390946503, 0.0), # 23
(18.253822343461476, 18.145936111111112, 15.561472222222221, 16.705415625000004, 13.359729136337823, 6.5625, 7.42342843137255, 6.934125, 7.274604999999999, 3.5429177777777783, 2.5123873737373743, 1.462362962962963, 0.0, 18.225, 16.085992592592593, 12.561936868686871, 10.628753333333332, 14.549209999999999, 9.707775, 7.42342843137255, 4.6875, 6.679864568168911, 5.568471875000002, 3.1122944444444447, 1.649630555555556, 0.0), # 24
(18.272533014380844, 18.138824588477366, 15.559670781893006, 16.70394965277778, 13.366037934713404, 6.5625, 7.420679012345679, 6.928240740740742, 7.273912037037037, 3.541463477366256, 2.512189019827909, 1.4621227709190674, 0.0, 18.225, 16.08335048010974, 12.560945099139545, 10.624390432098766, 14.547824074074073, 9.69953703703704, 7.420679012345679, 4.6875, 6.683018967356702, 5.567983217592594, 3.1119341563786014, 1.6489840534979427, 0.0), # 25
(18.290838634286462, 18.128318004115226, 15.557005144032923, 16.70177534722222, 13.372204642105325, 6.5625, 7.416618046477849, 6.919578703703704, 7.27288574074074, 3.539317818930042, 2.511894145155257, 1.4617673525377233, 0.0, 18.225, 16.079440877914955, 12.559470725776283, 10.617953456790124, 14.54577148148148, 9.687410185185186, 7.416618046477849, 4.6875, 6.686102321052663, 5.567258449074075, 3.111401028806585, 1.648028909465021, 0.0), # 26
(18.308737770689945, 18.114524999999997, 15.553500000000001, 16.698909375, 13.378229038253057, 6.5625, 7.411288235294118, 6.908250000000002, 7.271535, 3.5365050000000005, 2.5115045454545455, 1.4613000000000003, 0.0, 18.225, 16.0743, 12.557522727272728, 10.609514999999998, 14.54307, 9.671550000000002, 7.411288235294118, 4.6875, 6.689114519126528, 5.566303125, 3.1107000000000005, 1.646775, 0.0), # 27
(18.3262289911029, 18.097554218106993, 15.549180041152265, 16.695368402777778, 13.384110902896083, 6.5625, 7.404732280319536, 6.894365740740742, 7.269868703703704, 3.533049218106997, 2.5110220164609056, 1.4607240054869688, 0.0, 18.225, 16.067964060356655, 12.555110082304529, 10.599147654320989, 14.539737407407408, 9.652112037037039, 7.404732280319536, 4.6875, 6.6920554514480415, 5.565122800925927, 3.1098360082304533, 1.6452322016460905, 0.0), # 28
(18.34331086303695, 18.077514300411522, 15.54406995884774, 16.69116909722222, 13.389850015773863, 6.5625, 7.396992883079159, 6.8780370370370365, 7.267895740740741, 3.5289746707818943, 2.510448353909465, 1.4600426611796984, 0.0, 18.225, 16.06046927297668, 12.552241769547326, 10.58692401234568, 14.535791481481482, 9.629251851851851, 7.396992883079159, 4.6875, 6.694925007886932, 5.563723032407409, 3.1088139917695483, 1.6434103909465023, 0.0), # 29
(18.359981954003697, 18.054513888888888, 15.538194444444445, 16.686328125000003, 13.395446156625884, 6.5625, 7.388112745098039, 6.859375, 7.265625, 3.5243055555555567, 2.509785353535354, 1.4592592592592593, 0.0, 18.225, 16.05185185185185, 12.548926767676768, 10.572916666666668, 14.53125, 9.603125, 7.388112745098039, 4.6875, 6.697723078312942, 5.562109375000001, 3.107638888888889, 1.6413194444444446, 0.0), # 30
(18.376240831514746, 18.028661625514406, 15.531578189300415, 16.680862152777777, 13.400899105191609, 6.5625, 7.378134567901236, 6.838490740740741, 7.26306537037037, 3.5190660699588485, 2.5090348110737, 1.458377091906722, 0.0, 18.225, 16.04214801097394, 12.5451740553685, 10.557198209876542, 14.52613074074074, 9.573887037037037, 7.378134567901236, 4.6875, 6.7004495525958045, 5.56028738425926, 3.106315637860083, 1.638969238683128, 0.0), # 31
(18.392086063081717, 18.000066152263376, 15.524245884773661, 16.674787847222223, 13.406208641210513, 6.5625, 7.3671010530137995, 6.815495370370372, 7.260225740740741, 3.5132804115226346, 2.5081985222596335, 1.4573994513031552, 0.0, 18.225, 16.031393964334704, 12.540992611298167, 10.539841234567902, 14.520451481481482, 9.541693518518521, 7.3671010530137995, 4.6875, 6.703104320605257, 5.558262615740742, 3.1048491769547324, 1.6363696502057616, 0.0), # 32
(18.407516216216216, 17.96883611111111, 15.516222222222224, 16.668121874999997, 13.411374544422076, 6.5625, 7.355054901960784, 6.790500000000001, 7.257115, 3.506972777777779, 2.507278282828283, 1.4563296296296298, 0.0, 18.225, 16.019625925925926, 12.536391414141413, 10.520918333333334, 14.51423, 9.5067, 7.355054901960784, 4.6875, 6.705687272211038, 5.5560406250000005, 3.103244444444445, 1.6335305555555555, 0.0), # 33
(18.422529858429858, 17.93508014403292, 15.507531893004115, 16.660880902777777, 13.41639659456576, 6.5625, 7.342038816267248, 6.7636157407407405, 7.253742037037037, 3.500167366255145, 2.5062758885147773, 1.4551709190672155, 0.0, 18.225, 16.006880109739367, 12.531379442573886, 10.500502098765432, 14.507484074074075, 9.469062037037038, 7.342038816267248, 4.6875, 6.70819829728288, 5.553626967592593, 3.1015063786008232, 1.6304618312757202, 0.0), # 34
(18.437125557234253, 17.898906893004114, 15.49819958847737, 16.65308159722222, 13.421274571381044, 6.5625, 7.328095497458243, 6.734953703703703, 7.250115740740741, 3.4928883744855974, 2.5051931350542462, 1.4539266117969825, 0.0, 18.225, 15.993192729766804, 12.52596567527123, 10.47866512345679, 14.500231481481482, 9.428935185185185, 7.328095497458243, 4.6875, 6.710637285690522, 5.551027199074074, 3.099639917695474, 1.627173353909465, 0.0), # 35
(18.45130188014101, 17.860424999999996, 15.488249999999999, 16.644740624999997, 13.426008254607403, 6.5625, 7.313267647058823, 6.704625000000001, 7.246244999999999, 3.485160000000001, 2.504031818181818, 1.4526000000000006, 0.0, 18.225, 15.978600000000004, 12.520159090909091, 10.45548, 14.492489999999998, 9.386475, 7.313267647058823, 4.6875, 6.7130041273037016, 5.548246875, 3.0976500000000002, 1.623675, 0.0), # 36
(18.46505739466174, 17.819743106995883, 15.477707818930043, 16.63587465277778, 13.430597423984304, 6.5625, 7.2975979665940445, 6.672740740740741, 7.242138703703703, 3.477006440329219, 2.502793733632623, 1.451194375857339, 0.0, 18.225, 15.963138134430727, 12.513968668163116, 10.431019320987655, 14.484277407407406, 9.341837037037038, 7.2975979665940445, 4.6875, 6.715298711992152, 5.545291550925927, 3.0955415637860084, 1.619976646090535, 0.0), # 37
(18.47839066830806, 17.776969855967078, 15.466597736625513, 16.626500347222226, 13.435041859251228, 6.5625, 7.281129157588961, 6.639412037037038, 7.237805740740741, 3.4684518930041164, 2.5014806771417883, 1.4497130315500688, 0.0, 18.225, 15.946843347050754, 12.507403385708942, 10.405355679012347, 14.475611481481481, 9.295176851851854, 7.281129157588961, 4.6875, 6.717520929625614, 5.542166782407409, 3.0933195473251027, 1.61608816872428, 0.0), # 38
(18.491300268591576, 17.732213888888886, 15.454944444444445, 16.616634375, 13.439341340147644, 6.5625, 7.2639039215686285, 6.60475, 7.233255000000001, 3.4595205555555566, 2.500094444444445, 1.4481592592592594, 0.0, 18.225, 15.92975185185185, 12.500472222222223, 10.378561666666666, 14.466510000000001, 9.24665, 7.2639039215686285, 4.6875, 6.719670670073822, 5.538878125000001, 3.0909888888888895, 1.6120194444444444, 0.0), # 39
(18.503784763023894, 17.685583847736623, 15.442772633744857, 16.60629340277778, 13.443495646413021, 6.5625, 7.245964960058098, 6.568865740740742, 7.228495370370371, 3.4502366255144046, 2.49863683127572, 1.4465363511659812, 0.0, 18.225, 15.911899862825791, 12.4931841563786, 10.350709876543212, 14.456990740740743, 9.196412037037039, 7.245964960058098, 4.6875, 6.721747823206511, 5.535431134259261, 3.0885545267489714, 1.6077803497942387, 0.0), # 40
(18.51584271911663, 17.637188374485596, 15.430106995884776, 16.595494097222222, 13.447504557786843, 6.5625, 7.2273549745824255, 6.531870370370371, 7.22353574074074, 3.4406243004115233, 2.4971096333707448, 1.4448475994513033, 0.0, 18.225, 15.893323593964332, 12.485548166853723, 10.321872901234567, 14.44707148148148, 9.14461851851852, 7.2273549745824255, 4.6875, 6.723752278893421, 5.531831365740742, 3.0860213991769556, 1.6033807613168727, 0.0), # 41
(18.527472704381402, 17.587136111111114, 15.416972222222224, 16.584253125000004, 13.45136785400857, 6.5625, 7.208116666666666, 6.493875, 7.218385000000001, 3.4307077777777786, 2.4955146464646467, 1.4430962962962963, 0.0, 18.225, 15.874059259259258, 12.477573232323234, 10.292123333333333, 14.436770000000003, 9.091425000000001, 7.208116666666666, 4.6875, 6.725683927004285, 5.5280843750000015, 3.083394444444445, 1.598830555555556, 0.0), # 42
(18.538673286329807, 17.53553569958848, 15.403393004115227, 16.57258715277778, 13.455085314817683, 6.5625, 7.188292737835875, 6.454990740740741, 7.213052037037036, 3.420511255144034, 2.4938536662925554, 1.4412857338820306, 0.0, 18.225, 15.854143072702334, 12.469268331462775, 10.2615337654321, 14.426104074074072, 9.036987037037038, 7.188292737835875, 4.6875, 6.727542657408842, 5.524195717592594, 3.080678600823046, 1.5941396090534983, 0.0), # 43
(18.54944303247347, 17.482495781893004, 15.389394032921814, 16.560512847222224, 13.458656719953654, 6.5625, 7.1679258896151055, 6.415328703703706, 7.2075457407407395, 3.4100589300411532, 2.4921284885895996, 1.439419204389575, 0.0, 18.225, 15.833611248285322, 12.460642442947998, 10.230176790123457, 14.415091481481479, 8.981460185185188, 7.1679258896151055, 4.6875, 6.729328359976827, 5.520170949074076, 3.077878806584363, 1.5893177983539097, 0.0), # 44
(18.55978051032399, 17.428124999999998, 15.375, 16.548046875, 13.462081849155954, 6.5625, 7.147058823529412, 6.375000000000001, 7.201874999999999, 3.3993750000000014, 2.4903409090909094, 1.4375000000000002, 0.0, 18.225, 15.8125, 12.451704545454545, 10.198125000000001, 14.403749999999999, 8.925, 7.147058823529412, 4.6875, 6.731040924577977, 5.516015625000001, 3.075, 1.584375, 0.0), # 45
(18.569684287392985, 17.372531995884774, 15.360235596707819, 16.535205902777776, 13.465360482164058, 6.5625, 7.125734241103849, 6.334115740740741, 7.196048703703703, 3.388483662551441, 2.4884927235316128, 1.4355314128943761, 0.0, 18.225, 15.790845541838134, 12.442463617658062, 10.16545098765432, 14.392097407407405, 8.86776203703704, 7.125734241103849, 4.6875, 6.732680241082029, 5.511735300925927, 3.072047119341564, 1.5793210905349795, 0.0), # 46
(18.579152931192063, 17.31582541152263, 15.345125514403293, 16.522006597222223, 13.46849239871744, 6.5625, 7.103994843863473, 6.292787037037037, 7.190075740740742, 3.3774091152263384, 2.486585727646839, 1.4335167352537728, 0.0, 18.225, 15.768684087791497, 12.432928638234193, 10.132227345679013, 14.380151481481484, 8.809901851851851, 7.103994843863473, 4.6875, 6.73424619935872, 5.507335532407408, 3.069025102880659, 1.5741659465020577, 0.0), # 47
(18.588185009232834, 17.258113888888886, 15.329694444444444, 16.508465625, 13.471477378555573, 6.5625, 7.081883333333334, 6.251125000000001, 7.183965000000001, 3.3661755555555564, 2.4846217171717173, 1.4314592592592594, 0.0, 18.225, 15.746051851851853, 12.423108585858586, 10.098526666666666, 14.367930000000001, 8.751575, 7.081883333333334, 4.6875, 6.735738689277786, 5.502821875000001, 3.065938888888889, 1.5689194444444445, 0.0), # 48
(18.596779089026917, 17.199506069958847, 15.313967078189304, 16.49459965277778, 13.47431520141793, 6.5625, 7.059442411038489, 6.209240740740741, 7.17772537037037, 3.35480718106996, 2.4826024878413775, 1.4293622770919072, 0.0, 18.225, 15.722985048010976, 12.413012439206886, 10.064421543209878, 14.35545074074074, 8.692937037037037, 7.059442411038489, 4.6875, 6.737157600708965, 5.498199884259261, 3.0627934156378607, 1.5635914609053498, 0.0), # 49
(18.604933738085908, 17.140110596707824, 15.297968106995889, 16.480425347222223, 13.477005647043978, 6.5625, 7.0367147785039945, 6.16724537037037, 7.1713657407407405, 3.3433281893004123, 2.480529835390947, 1.427229080932785, 0.0, 18.225, 15.699519890260632, 12.402649176954732, 10.029984567901234, 14.342731481481481, 8.634143518518519, 7.0367147785039945, 4.6875, 6.738502823521989, 5.4934751157407415, 3.059593621399178, 1.5581918724279842, 0.0), # 50
(18.61264752392144, 17.080036111111113, 15.281722222222223, 16.465959375, 13.479548495173198, 6.5625, 7.013743137254902, 6.12525, 7.164895000000001, 3.3317627777777785, 2.478405555555556, 1.4250629629629634, 0.0, 18.225, 15.675692592592595, 12.392027777777779, 9.995288333333333, 14.329790000000003, 8.57535, 7.013743137254902, 4.6875, 6.739774247586599, 5.488653125000001, 3.0563444444444445, 1.552730555555556, 0.0), # 51
(18.619919014045102, 17.019391255144033, 15.26525411522634, 16.45121840277778, 13.481943525545056, 6.5625, 6.9905701888162675, 6.08336574074074, 7.158322037037037, 3.320135144032923, 2.4762314440703332, 1.4228672153635122, 0.0, 18.225, 15.651539368998632, 12.381157220351666, 9.960405432098767, 14.316644074074073, 8.516712037037037, 6.9905701888162675, 4.6875, 6.740971762772528, 5.483739467592594, 3.0530508230452678, 1.547217386831276, 0.0), # 52
(18.626746775968517, 16.958284670781893, 15.248588477366258, 16.43621909722222, 13.484190517899034, 6.5625, 6.967238634713145, 6.041703703703704, 7.1516557407407415, 3.3084694855967087, 2.4740092966704084, 1.4206451303155008, 0.0, 18.225, 15.627096433470507, 12.37004648335204, 9.925408456790123, 14.303311481481483, 8.458385185185186, 6.967238634713145, 4.6875, 6.742095258949517, 5.478739699074075, 3.049717695473252, 1.5416622427983542, 0.0), # 53
(18.63312937720329, 16.896825000000003, 15.23175, 16.420978125, 13.486289251974604, 6.5625, 6.943791176470588, 6.000374999999999, 7.144905, 3.296790000000001, 2.4717409090909093, 1.4184000000000003, 0.0, 18.225, 15.602400000000001, 12.358704545454545, 9.89037, 14.28981, 8.400525, 6.943791176470588, 4.6875, 6.743144625987302, 5.473659375000001, 3.04635, 1.5360750000000005, 0.0), # 54
(18.63906538526104, 16.835120884773662, 15.2147633744856, 16.405512152777778, 13.488239507511228, 6.5625, 6.9202705156136535, 5.9594907407407405, 7.1380787037037035, 3.2851208847736637, 2.4694280770669663, 1.4161351165980798, 0.0, 18.225, 15.577486282578874, 12.34714038533483, 9.855362654320988, 14.276157407407407, 8.343287037037037, 6.9202705156136535, 4.6875, 6.744119753755614, 5.468504050925927, 3.04295267489712, 1.530465534979424, 0.0), # 55
(18.64455336765337, 16.77328096707819, 15.197653292181073, 16.389837847222225, 13.49004106424839, 6.5625, 6.896719353667393, 5.9191620370370375, 7.131185740740741, 3.2734863374485608, 2.467072596333708, 1.4138537722908093, 0.0, 18.225, 15.5523914951989, 12.335362981668538, 9.82045901234568, 14.262371481481482, 8.286826851851853, 6.896719353667393, 4.6875, 6.745020532124195, 5.463279282407409, 3.0395306584362145, 1.5248437242798356, 0.0), # 56
(18.649591891891887, 16.711413888888888, 15.180444444444445, 16.373971875, 13.49169370192556, 6.5625, 6.873180392156863, 5.879500000000001, 7.124235, 3.2619105555555565, 2.4646762626262633, 1.4115592592592594, 0.0, 18.225, 15.527151851851851, 12.323381313131314, 9.785731666666667, 14.24847, 8.231300000000001, 6.873180392156863, 4.6875, 6.74584685096278, 5.457990625000001, 3.0360888888888895, 1.5192194444444447, 0.0), # 57
(18.654179525488225, 16.64962829218107, 15.163161522633745, 16.357930902777774, 13.49319720028221, 6.5625, 6.849696332607118, 5.840615740740741, 7.11723537037037, 3.2504177366255154, 2.4622408716797612, 1.4092548696844995, 0.0, 18.225, 15.501803566529492, 12.311204358398806, 9.751253209876543, 14.23447074074074, 8.176862037037038, 6.849696332607118, 4.6875, 6.746598600141105, 5.4526436342592595, 3.032632304526749, 1.5136025720164612, 0.0), # 58
(18.658314835953966, 16.58803281893004, 15.145829218106996, 16.34173159722222, 13.494551339057814, 6.5625, 6.82630987654321, 5.802620370370371, 7.110195740740741, 3.2390320781893016, 2.4597682192293306, 1.4069438957475995, 0.0, 18.225, 15.476382853223592, 12.298841096146651, 9.717096234567903, 14.220391481481482, 8.12366851851852, 6.82630987654321, 4.6875, 6.747275669528907, 5.447243865740742, 3.0291658436213997, 1.5080029835390947, 0.0), # 59
(18.661996390800738, 16.526736111111113, 15.128472222222221, 16.325390625, 13.495755897991843, 6.5625, 6.803063725490196, 5.765625, 7.103125, 3.2277777777777787, 2.4572601010101014, 1.40462962962963, 0.0, 18.225, 15.450925925925928, 12.286300505050505, 9.683333333333334, 14.20625, 8.071875, 6.803063725490196, 4.6875, 6.747877948995922, 5.441796875000001, 3.0256944444444445, 1.502430555555556, 0.0), # 60
(18.665222757540146, 16.465846810699592, 15.111115226337452, 16.308924652777776, 13.496810656823772, 6.5625, 6.780000580973129, 5.729740740740741, 7.0960320370370376, 3.216679032921812, 2.4547183127572016, 1.40231536351166, 0.0, 18.225, 15.425468998628258, 12.273591563786008, 9.650037098765434, 14.192064074074075, 8.021637037037038, 6.780000580973129, 4.6875, 6.748405328411886, 5.436308217592593, 3.0222230452674905, 1.496895164609054, 0.0), # 61
(18.66799250368381, 16.40547355967078, 15.093782921810703, 16.292350347222225, 13.497715395293081, 6.5625, 6.757163144517066, 5.695078703703705, 7.088925740740741, 3.2057600411522644, 2.4521446502057613, 1.4000043895747603, 0.0, 18.225, 15.40004828532236, 12.260723251028807, 9.61728012345679, 14.177851481481483, 7.973110185185186, 6.757163144517066, 4.6875, 6.748857697646541, 5.430783449074076, 3.018756584362141, 1.4914066872427985, 0.0), # 62
(18.670304196743327, 16.345724999999998, 15.0765, 16.275684375, 13.498469893139227, 6.5625, 6.734594117647059, 5.6617500000000005, 7.081815, 3.195045000000001, 2.4495409090909095, 1.3977000000000002, 0.0, 18.225, 15.3747, 12.247704545454548, 9.585135, 14.16363, 7.926450000000001, 6.734594117647059, 4.6875, 6.749234946569613, 5.425228125000001, 3.0153000000000003, 1.485975, 0.0), # 63
(18.672156404230314, 16.286709773662555, 15.059291152263373, 16.258943402777778, 13.499073930101698, 6.5625, 6.712336201888163, 5.629865740740741, 7.0747087037037035, 3.1845581069958855, 2.446908885147774, 1.3954054869684502, 0.0, 18.225, 15.34946035665295, 12.23454442573887, 9.553674320987653, 14.149417407407407, 7.881812037037038, 6.712336201888163, 4.6875, 6.749536965050849, 5.419647800925927, 3.011858230452675, 1.4806099794238687, 0.0), # 64
(18.67354769365639, 16.228536522633743, 15.042181069958849, 16.242144097222223, 13.49952728591996, 6.5625, 6.690432098765433, 5.599537037037037, 7.067615740740742, 3.1743235596707824, 2.4442503741114856, 1.3931241426611796, 0.0, 18.225, 15.324365569272972, 12.221251870557428, 9.522970679012344, 14.135231481481483, 7.839351851851852, 6.690432098765433, 4.6875, 6.74976364295998, 5.4140480324074085, 3.00843621399177, 1.4753215020576131, 0.0), # 65
(18.674476632533153, 16.17131388888889, 15.025194444444447, 16.225303125, 13.499829740333489, 6.5625, 6.668924509803921, 5.570875000000001, 7.060545000000001, 3.1643655555555563, 2.4415671717171716, 1.3908592592592597, 0.0, 18.225, 15.299451851851854, 12.207835858585858, 9.493096666666666, 14.121090000000002, 7.799225000000001, 6.668924509803921, 4.6875, 6.749914870166744, 5.408434375000001, 3.0050388888888895, 1.4701194444444448, 0.0), # 66
(18.674941788372227, 16.11515051440329, 15.00835596707819, 16.208437152777776, 13.499981073081756, 6.5625, 6.647856136528685, 5.543990740740742, 7.05350537037037, 3.154708292181071, 2.438861073699963, 1.3886141289437586, 0.0, 18.225, 15.274755418381341, 12.194305368499816, 9.464124876543211, 14.10701074074074, 7.761587037037039, 6.647856136528685, 4.6875, 6.749990536540878, 5.40281238425926, 3.001671193415638, 1.465013683127572, 0.0), # 67
(18.674624906065485, 16.059860254878533, 14.99160892489712, 16.19141634963768, 13.499853546356814, 6.56237821216278, 6.627163675346682, 5.518757887517148, 7.046452709190673, 3.145329198741226, 2.436085796562113, 1.3863795032849615, 0.0, 18.22477527006173, 15.250174536134574, 12.180428982810565, 9.435987596223676, 14.092905418381346, 7.726261042524007, 6.627163675346682, 4.6874130086877, 6.749926773178407, 5.3971387832125615, 2.998321784979424, 1.4599872958980487, 0.0), # 68
(18.671655072463768, 16.00375510752688, 14.974482638888889, 16.173382744565217, 13.498692810457515, 6.561415432098766, 6.606241363211952, 5.493824074074074, 7.039078703703703, 3.1359628758169937, 2.4329588516746417, 1.3840828460038987, 0.0, 18.222994791666668, 15.224911306042884, 12.164794258373206, 9.407888627450978, 14.078157407407407, 7.6913537037037045, 6.606241363211952, 4.686725308641976, 6.749346405228757, 5.391127581521739, 2.994896527777778, 1.4548868279569895, 0.0), # 69
(18.665794417606012, 15.946577558741536, 14.956902649176953, 16.154217617753623, 13.496399176954732, 6.559519318701418, 6.5849941211052325, 5.468964334705077, 7.031341735253773, 3.1265637860082314, 2.429444665957824, 1.3817134141939216, 0.0, 18.219478202160495, 15.198847556133135, 12.147223329789119, 9.379691358024692, 14.062683470507546, 7.656550068587107, 6.5849941211052325, 4.685370941929584, 6.748199588477366, 5.384739205917875, 2.9913805298353906, 1.4496888689765035, 0.0), # 70
(18.657125389157272, 15.888361778176023, 14.938875128600824, 16.133949230072467, 13.493001694504963, 6.556720598994056, 6.56343149358509, 5.444186899862826, 7.023253326474624, 3.1171321617041885, 2.425556211235159, 1.3792729405819073, 0.0, 18.21427179783951, 15.172002346400978, 12.127781056175793, 9.351396485112563, 14.046506652949247, 7.621861659807958, 6.56343149358509, 4.683371856424325, 6.746500847252482, 5.377983076690823, 2.987775025720165, 1.4443965252887296, 0.0), # 71
(18.64573043478261, 15.82914193548387, 14.92040625, 16.112605842391304, 13.488529411764706, 6.553050000000001, 6.541563025210084, 5.4195, 7.014825, 3.1076682352941183, 2.421306459330144, 1.376763157894737, 0.0, 18.207421875, 15.144394736842104, 12.10653229665072, 9.323004705882353, 14.02965, 7.587300000000001, 6.541563025210084, 4.680750000000001, 6.744264705882353, 5.370868614130436, 2.98408125, 1.4390129032258066, 0.0), # 72
(18.631692002147076, 15.768952200318596, 14.90150218621399, 16.09021571557971, 13.483011377390461, 6.548538248742569, 6.519398260538782, 5.394911865569274, 7.006068278463649, 3.0981722391672726, 2.4167083820662767, 1.374185798859288, 0.0, 18.198974729938275, 15.116043787452165, 12.083541910331384, 9.294516717501814, 14.012136556927299, 7.552876611796983, 6.519398260538782, 4.677527320530407, 6.741505688695231, 5.363405238526571, 2.9803004372427986, 1.4335411091198726, 0.0), # 73
(18.61509253891573, 15.707826742333731, 14.882169110082302, 16.06680711050725, 13.47647664003873, 6.543216072245086, 6.49694674412975, 5.37043072702332, 6.996994684499314, 3.0886444057129037, 2.411774951267057, 1.3715425962024403, 0.0, 18.18897665895062, 15.086968558226841, 12.058874756335285, 9.26593321713871, 13.993989368998628, 7.518603017832648, 6.49694674412975, 4.673725765889347, 6.738238320019365, 5.355602370169083, 2.976433822016461, 1.4279842493030668, 0.0), # 74
(18.59601449275362, 15.645799731182793, 14.862413194444443, 16.04240828804348, 13.468954248366014, 6.537114197530865, 6.47421802054155, 5.346064814814815, 6.98761574074074, 3.0790849673202625, 2.406519138755981, 1.3688352826510723, 0.0, 18.177473958333334, 15.057188109161793, 12.032595693779903, 9.237254901960785, 13.97523148148148, 7.484490740740742, 6.47421802054155, 4.669367283950618, 6.734477124183007, 5.347469429347827, 2.9724826388888888, 1.422345430107527, 0.0), # 75
(18.57454031132582, 15.582905336519316, 14.842240612139918, 16.01704750905797, 13.460473251028805, 6.53026335162323, 6.451221634332746, 5.321822359396434, 6.977942969821673, 3.069494156378602, 2.400953916356548, 1.3660655909320625, 0.0, 18.164512924382716, 15.026721500252684, 12.004769581782737, 9.208482469135802, 13.955885939643347, 7.450551303155008, 6.451221634332746, 4.664473822588021, 6.730236625514403, 5.339015836352658, 2.9684481224279837, 1.4166277578653925, 0.0), # 76
(18.55075244229737, 15.519177727996816, 14.821657536008228, 15.99075303442029, 13.451062696683609, 6.522694261545496, 6.4279671300619015, 5.2977115912208514, 6.967987894375857, 3.059872205277174, 2.3950922558922563, 1.3632352537722912, 0.0, 18.150139853395064, 14.9955877914952, 11.975461279461282, 9.179616615831518, 13.935975788751714, 7.416796227709193, 6.4279671300619015, 4.659067329675354, 6.725531348341804, 5.330251011473431, 2.964331507201646, 1.4108343389088016, 0.0), # 77
(18.524733333333334, 15.45465107526882, 14.80067013888889, 15.963553124999999, 13.440751633986928, 6.514437654320987, 6.404464052287582, 5.273740740740742, 6.957762037037036, 3.0502193464052296, 2.388947129186603, 1.3603460038986357, 0.0, 18.134401041666667, 14.963806042884991, 11.944735645933015, 9.150658039215687, 13.915524074074073, 7.383237037037039, 6.404464052287582, 4.653169753086419, 6.720375816993464, 5.3211843750000005, 2.960134027777778, 1.404968279569893, 0.0), # 78
(18.496565432098766, 15.389359547988851, 14.779284593621398, 15.935476041666668, 13.429569111595256, 6.505524256973022, 6.380721945568351, 5.249918038408779, 6.947276920438957, 3.0405358121520223, 2.382531508063087, 1.3573995740379758, 0.0, 18.117342785493825, 14.931395314417731, 11.912657540315433, 9.121607436456063, 13.894553840877913, 7.349885253772292, 6.380721945568351, 4.646803040695016, 6.714784555797628, 5.311825347222223, 2.95585691872428, 1.399032686180805, 0.0), # 79
(18.466331186258724, 15.323337315810434, 14.757507073045266, 15.906550045289855, 13.417544178165095, 6.49598479652492, 6.356750354462773, 5.226251714677641, 6.9365440672153635, 3.030821834906803, 2.375858364345207, 1.3543976969171905, 0.0, 18.09901138117284, 14.898374666089092, 11.879291821726033, 9.092465504720405, 13.873088134430727, 7.316752400548698, 6.356750354462773, 4.639989140374943, 6.708772089082547, 5.302183348429953, 2.9515014146090537, 1.3930306650736761, 0.0), # 80
(18.434113043478263, 15.256618548387095, 14.735343749999998, 15.876803396739131, 13.404705882352939, 6.48585, 6.3325588235294115, 5.202750000000001, 6.925574999999999, 3.0210776470588248, 2.36894066985646, 1.3513421052631582, 0.0, 18.079453124999997, 14.864763157894737, 11.844703349282298, 9.063232941176471, 13.851149999999999, 7.283850000000001, 6.3325588235294115, 4.63275, 6.7023529411764695, 5.292267798913045, 2.94706875, 1.3869653225806453, 0.0), # 81
(18.399993451422436, 15.189237415372364, 14.712800797325105, 15.846264356884058, 13.391083272815298, 6.475150594421583, 6.308156897326833, 5.179421124828533, 6.914381241426612, 3.011303480997338, 2.3617913964203443, 1.3482345318027582, 0.0, 18.058714313271608, 14.830579849830338, 11.80895698210172, 9.03391044299201, 13.828762482853223, 7.2511895747599455, 6.308156897326833, 4.625107567443988, 6.695541636407649, 5.2820881189613536, 2.9425601594650215, 1.3808397650338515, 0.0), # 82
(18.364054857756308, 15.121228086419752, 14.689884387860083, 15.8149611865942, 13.376705398208665, 6.463917306812986, 6.283554120413598, 5.156273319615913, 6.902974314128944, 3.001499569111596, 2.3544235158603586, 1.3450767092628693, 0.0, 18.036841242283952, 14.79584380189156, 11.772117579301792, 9.004498707334786, 13.805948628257887, 7.218782647462278, 6.283554120413598, 4.617083790580704, 6.688352699104333, 5.2716537288647345, 2.9379768775720168, 1.374657098765432, 0.0), # 83
(18.326379710144927, 15.052624731182796, 14.666600694444444, 15.78292214673913, 13.361601307189542, 6.452180864197532, 6.258760037348273, 5.133314814814815, 6.89136574074074, 2.9916661437908503, 2.3468500000000003, 1.3418703703703705, 0.0, 18.013880208333333, 14.760574074074073, 11.73425, 8.97499843137255, 13.78273148148148, 7.186640740740741, 6.258760037348273, 4.608700617283951, 6.680800653594771, 5.260974048913044, 2.933320138888889, 1.3684204301075271, 0.0), # 84
(18.287050456253354, 14.983461519315012, 14.642955889917694, 15.750175498188408, 13.345800048414427, 6.439971993598538, 6.233784192689422, 5.110553840877915, 6.879567043895747, 2.981803437424353, 2.3390838206627684, 1.338617247852141, 0.0, 17.989877507716052, 14.724789726373547, 11.69541910331384, 8.945410312273058, 13.759134087791494, 7.154775377229082, 6.233784192689422, 4.5999799954275264, 6.672900024207213, 5.250058499396137, 2.928591177983539, 1.362132865392274, 0.0), # 85
(18.246149543746643, 14.913772620469931, 14.618956147119343, 15.716749501811597, 13.32933067053982, 6.427321422039324, 6.208636130995608, 5.087998628257887, 6.86758974622771, 2.9719116824013563, 2.3311379496721605, 1.3353190744350594, 0.0, 17.964879436728395, 14.68850981878565, 11.655689748360802, 8.915735047204068, 13.73517949245542, 7.123198079561043, 6.208636130995608, 4.590943872885232, 6.66466533526991, 5.2389165006038665, 2.923791229423869, 1.3557975109518121, 0.0), # 86
(18.203759420289852, 14.843592204301075, 14.594607638888888, 15.68267241847826, 13.312222222222225, 6.41425987654321, 6.1833253968253965, 5.065657407407408, 6.855445370370372, 2.9619911111111112, 2.323025358851675, 1.3319775828460039, 0.0, 17.938932291666667, 14.651753411306041, 11.615126794258373, 8.885973333333332, 13.710890740740744, 7.091920370370371, 6.1833253968253965, 4.581614197530865, 6.656111111111112, 5.227557472826088, 2.9189215277777776, 1.3494174731182798, 0.0), # 87
(18.159962533548043, 14.772954440461966, 14.569916538065844, 15.647972509057974, 13.294503752118132, 6.400818084133517, 6.157861534737352, 5.043538408779149, 6.843145438957476, 2.952041955942871, 2.31475902002481, 1.328594505811855, 0.0, 17.912082368827164, 14.614539563930402, 11.573795100124048, 8.856125867828611, 13.686290877914953, 7.06095377229081, 6.157861534737352, 4.572012917238227, 6.647251876059066, 5.215990836352659, 2.913983307613169, 1.3429958582238153, 0.0), # 88
(18.11484133118626, 14.701893498606132, 14.544889017489714, 15.612678034420288, 13.276204308884047, 6.387026771833563, 6.132254089290037, 5.0216498628257895, 6.830701474622771, 2.942064449285888, 2.3063519050150636, 1.3251715760594904, 0.0, 17.884375964506173, 14.576887336654393, 11.531759525075316, 8.826193347857663, 13.661402949245542, 7.0303098079561055, 6.132254089290037, 4.562161979881116, 6.638102154442024, 5.2042260114734304, 2.908977803497943, 1.3365357726005578, 0.0), # 89
(18.068478260869565, 14.630443548387097, 14.519531250000002, 15.576817255434786, 13.257352941176471, 6.372916666666668, 6.106512605042017, 5.0, 6.818125, 2.9320588235294123, 2.2978169856459334, 1.3217105263157898, 0.0, 17.855859375, 14.538815789473684, 11.489084928229666, 8.796176470588236, 13.63625, 7.0, 6.106512605042017, 4.552083333333334, 6.6286764705882355, 5.192272418478263, 2.903906250000001, 1.3300403225806454, 0.0), # 90
(18.020955770263015, 14.558638759458383, 14.493849408436214, 15.540418432971018, 13.237978697651899, 6.35851849565615, 6.0806466265518555, 4.978597050754459, 6.80542753772291, 2.922025311062697, 2.2891672337409186, 1.3182130893076314, 0.0, 17.826578896604936, 14.500343982383942, 11.445836168704592, 8.76607593318809, 13.61085507544582, 6.9700358710562424, 6.0806466265518555, 4.541798925468679, 6.6189893488259495, 5.180139477657007, 2.898769881687243, 1.3235126144962168, 0.0), # 91
(17.97235630703167, 14.486513301473519, 14.467849665637862, 15.50350982789855, 13.218110626966835, 6.343862985825332, 6.054665698378118, 4.957449245541839, 6.7926206104252405, 2.9119641442749944, 2.2804156211235163, 1.3146809977618947, 0.0, 17.796580825617283, 14.46149097538084, 11.40207810561758, 8.735892432824983, 13.585241220850481, 6.940428943758574, 6.054665698378118, 4.531330704160951, 6.609055313483418, 5.167836609299518, 2.8935699331275724, 1.3169557546794108, 0.0), # 92
(17.92276231884058, 14.414101344086022, 14.441538194444446, 15.46611970108696, 13.197777777777777, 6.328980864197531, 6.0285793650793655, 4.936564814814815, 6.779715740740741, 2.9018755555555558, 2.2715751196172254, 1.3111159844054583, 0.0, 17.76591145833333, 14.422275828460037, 11.357875598086125, 8.705626666666666, 13.559431481481482, 6.911190740740742, 6.0285793650793655, 4.520700617283951, 6.598888888888888, 5.155373233695654, 2.888307638888889, 1.3103728494623659, 0.0), # 93
(17.872256253354806, 14.341437056949422, 14.414921167695475, 15.428276313405796, 13.177009198741224, 6.313902857796068, 6.002397171214165, 4.915951989026064, 6.766724451303155, 2.891759777293634, 2.2626587010455435, 1.3075197819652014, 0.0, 17.734617091049383, 14.382717601617212, 11.313293505227715, 8.675279331880901, 13.53344890260631, 6.88233278463649, 6.002397171214165, 4.509930612711477, 6.588504599370612, 5.1427587711352665, 2.882984233539095, 1.3037670051772203, 0.0), # 94
(17.820920558239397, 14.268554609717246, 14.388004758230455, 15.390007925724635, 13.155833938513677, 6.298659693644262, 5.97612866134108, 4.895618998628259, 6.753658264746228, 2.88161704187848, 2.253679337231969, 1.3038941231680024, 0.0, 17.70274402006173, 14.342835354848022, 11.268396686159845, 8.644851125635439, 13.507316529492456, 6.853866598079563, 5.97612866134108, 4.49904263831733, 6.577916969256838, 5.130002641908213, 2.8776009516460914, 1.2971413281561135, 0.0), # 95
(17.76883768115942, 14.195488172043014, 14.360795138888891, 15.351342798913045, 13.134281045751635, 6.283282098765432, 5.9497833800186735, 4.875574074074075, 6.740528703703703, 2.8714475816993468, 2.2446500000000005, 1.300240740740741, 0.0, 17.67033854166667, 14.30264814814815, 11.22325, 8.614342745098039, 13.481057407407405, 6.825803703703705, 5.9497833800186735, 4.488058641975309, 6.5671405228758175, 5.117114266304349, 2.8721590277777787, 1.2904989247311833, 0.0), # 96
(17.716090069779927, 14.12227191358025, 14.333298482510289, 15.31230919384058, 13.112379569111596, 6.267800800182899, 5.9233708718055125, 4.855825445816188, 6.727347290809328, 2.8612516291454857, 2.235583661173135, 1.2965613674102956, 0.0, 17.637446952160495, 14.262175041513249, 11.177918305865674, 8.583754887436456, 13.454694581618655, 6.798155624142662, 5.9233708718055125, 4.477000571559214, 6.556189784555798, 5.104103064613527, 2.8666596965020577, 1.2838429012345685, 0.0), # 97
(17.66276017176597, 14.048940003982477, 14.305520961934155, 15.27293537137681, 13.090158557250064, 6.252246524919983, 5.896900681260158, 4.83638134430727, 6.714125548696844, 2.851029416606149, 2.226493292574872, 1.2928577359035447, 0.0, 17.604115547839505, 14.22143509493899, 11.13246646287436, 8.553088249818446, 13.428251097393687, 6.770933882030178, 5.896900681260158, 4.465890374942845, 6.545079278625032, 5.090978457125605, 2.8611041923868314, 1.277176363998407, 0.0), # 98
(17.608930434782607, 13.975526612903225, 14.277468750000002, 15.233249592391303, 13.067647058823532, 6.23665, 5.870382352941177, 4.8172500000000005, 6.700875, 2.8407811764705886, 2.2173918660287084, 1.2891315789473687, 0.0, 17.570390625, 14.180447368421053, 11.086959330143541, 8.522343529411764, 13.40175, 6.744150000000001, 5.870382352941177, 4.45475, 6.533823529411766, 5.0777498641304355, 2.8554937500000004, 1.2705024193548389, 0.0), # 99
(17.5546833064949, 13.902065909996015, 14.249148019547325, 15.193280117753623, 13.044874122488501, 6.2210419524462734, 5.843825431407131, 4.798439643347051, 6.687607167352539, 2.8305071411280567, 2.2082923533581433, 1.285384629268645, 0.0, 17.536318479938274, 14.139230921955095, 11.041461766790714, 8.49152142338417, 13.375214334705078, 6.717815500685871, 5.843825431407131, 4.443601394604481, 6.522437061244251, 5.064426705917875, 2.8498296039094653, 1.2638241736360014, 0.0), # 100
(17.500101234567904, 13.828592064914377, 14.22056494341564, 15.153055208333335, 13.021868796901476, 6.205453109282122, 5.817239461216586, 4.7799585048010975, 6.674333573388203, 2.820207542967805, 2.1992077263866743, 1.281618619594253, 0.0, 17.501945408950615, 14.097804815536781, 10.99603863193337, 8.460622628903414, 13.348667146776407, 6.691941906721536, 5.817239461216586, 4.432466506630087, 6.510934398450738, 5.051018402777779, 2.8441129886831282, 1.2571447331740344, 0.0), # 101
(17.44526666666667, 13.755139247311828, 14.191725694444445, 15.112603125, 12.998660130718955, 6.189914197530865, 5.790633986928105, 4.761814814814815, 6.66106574074074, 2.809882614379086, 2.1901509569377993, 1.2778352826510724, 0.0, 17.467317708333336, 14.056188109161795, 10.950754784688995, 8.429647843137257, 13.32213148148148, 6.666540740740741, 5.790633986928105, 4.421367283950618, 6.499330065359477, 5.037534375000001, 2.838345138888889, 1.2504672043010754, 0.0), # 102
(17.390262050456254, 13.681741626841896, 14.16263644547325, 15.071952128623188, 12.975277172597433, 6.174455944215821, 5.764018553100253, 4.7440168038408785, 6.647815192043895, 2.7995325877511505, 2.181135016835017, 1.2740363511659811, 0.0, 17.432481674382714, 14.014399862825789, 10.905675084175085, 8.39859776325345, 13.29563038408779, 6.64162352537723, 5.764018553100253, 4.410325674439872, 6.487638586298717, 5.023984042874397, 2.8325272890946502, 1.2437946933492634, 0.0), # 103
(17.335169833601718, 13.608433373158105, 14.133303369341563, 15.031130480072465, 12.951748971193414, 6.159109076360311, 5.737402704291593, 4.7265727023319615, 6.634593449931413, 2.7891576954732518, 2.1721728779018252, 1.2702235578658583, 0.0, 17.397483603395063, 13.972459136524439, 10.860864389509127, 8.367473086419754, 13.269186899862826, 6.617201783264746, 5.737402704291593, 4.399363625971651, 6.475874485596707, 5.010376826690822, 2.826660673868313, 1.237130306650737, 0.0), # 104
(17.280072463768114, 13.535248655913978, 14.103732638888891, 14.99016644021739, 12.928104575163397, 6.143904320987655, 5.710795985060692, 4.709490740740741, 6.621412037037037, 2.7787581699346413, 2.1632775119617227, 1.2663986354775831, 0.0, 17.362369791666666, 13.930384990253412, 10.816387559808613, 8.336274509803923, 13.242824074074074, 6.5932870370370384, 5.710795985060692, 4.388503086419754, 6.464052287581699, 4.996722146739131, 2.820746527777778, 1.2304771505376346, 0.0), # 105
(17.225052388620504, 13.462221644763043, 14.073930426954732, 14.949088269927536, 12.904373033163882, 6.128872405121171, 5.68420793996611, 4.6927791495198905, 6.608282475994512, 2.7683342435245706, 2.1544618908382067, 1.2625633167280343, 0.0, 17.327186535493826, 13.888196484008375, 10.772309454191033, 8.30500273057371, 13.216564951989024, 6.5698908093278465, 5.68420793996611, 4.377766003657979, 6.452186516581941, 4.98302942330918, 2.8147860853909465, 1.223838331342095, 0.0), # 106
(17.17019205582394, 13.389386509358822, 14.043902906378605, 14.907924230072464, 12.880583393851367, 6.114044055784181, 5.657648113566415, 4.6764461591220865, 6.595216289437586, 2.7578861486322928, 2.145738986354776, 1.2587193343440908, 0.0, 17.29198013117284, 13.845912677784996, 10.728694931773878, 8.273658445896878, 13.190432578875171, 6.547024622770921, 5.657648113566415, 4.367174325560129, 6.440291696925684, 4.969308076690822, 2.808780581275721, 1.2172169553962566, 0.0), # 107
(17.11557391304348, 13.31677741935484, 14.013656250000002, 14.866702581521741, 12.856764705882352, 6.099450000000001, 5.631126050420168, 4.660500000000001, 6.582225000000001, 2.7474141176470597, 2.1371217703349283, 1.2548684210526317, 0.0, 17.256796875000003, 13.803552631578947, 10.685608851674642, 8.242242352941178, 13.164450000000002, 6.524700000000001, 5.631126050420168, 4.356750000000001, 6.428382352941176, 4.955567527173915, 2.8027312500000003, 1.2106161290322583, 0.0), # 108
(17.061280407944178, 13.24442854440462, 13.983196630658439, 14.825451585144926, 12.832946017913338, 6.085120964791952, 5.604651295085936, 4.644948902606311, 6.569320130315501, 2.736918382958122, 2.1286232146021624, 1.2510123095805359, 0.0, 17.221683063271605, 13.761135405385891, 10.64311607301081, 8.210755148874364, 13.138640260631002, 6.502928463648835, 5.604651295085936, 4.346514974851394, 6.416473008956669, 4.941817195048309, 2.796639326131688, 1.2040389585822384, 0.0), # 109
(17.007393988191087, 13.17237405416169, 13.95253022119342, 14.784199501811596, 12.809156378600825, 6.071087677183356, 5.57823339212228, 4.62980109739369, 6.556513203017833, 2.726399176954733, 2.120256290979975, 1.2471527326546823, 0.0, 17.18668499228395, 13.718680059201501, 10.601281454899876, 8.179197530864197, 13.113026406035665, 6.4817215363511655, 5.57823339212228, 4.336491197988112, 6.404578189300413, 4.928066500603866, 2.790506044238684, 1.1974885503783357, 0.0), # 110
(16.953997101449275, 13.10064811827957, 13.921663194444447, 14.742974592391306, 12.785424836601308, 6.0573808641975315, 5.551881886087768, 4.615064814814815, 6.543815740740741, 2.715856732026144, 2.1120339712918663, 1.2432914230019496, 0.0, 17.151848958333336, 13.676205653021444, 10.56016985645933, 8.147570196078432, 13.087631481481482, 6.461090740740741, 5.551881886087768, 4.326700617283951, 6.392712418300654, 4.914324864130436, 2.78433263888889, 1.1909680107526885, 0.0), # 111
(16.90117219538379, 13.029284906411787, 13.890601723251033, 14.701805117753622, 12.76178044057129, 6.044031252857797, 5.5256063215409625, 4.60074828532236, 6.531239266117969, 2.7052912805616076, 2.103969227361333, 1.2394301133492167, 0.0, 17.11722125771605, 13.633731246841382, 10.519846136806663, 8.115873841684822, 13.062478532235938, 6.441047599451304, 5.5256063215409625, 4.3171651806127125, 6.380890220285645, 4.900601705917875, 2.778120344650207, 1.1844804460374354, 0.0), # 112
(16.84890760266548, 12.958437720996821, 13.859426742378105, 14.660775741364255, 12.738210816208445, 6.03106325767524, 5.499473367291093, 4.586889426585454, 6.518827686755172, 2.694737131475729, 2.0960771718458604, 1.2355789404756645, 0.0, 17.0827990215178, 13.591368345232306, 10.480385859229301, 8.084211394427186, 13.037655373510344, 6.421645197219636, 5.499473367291093, 4.307902326910885, 6.369105408104223, 4.886925247121419, 2.7718853484756214, 1.178039792817893, 0.0), # 113
(16.796665616220118, 12.888805352817133, 13.828568512532428, 14.620215718724406, 12.71447202547959, 6.018447338956397, 5.473816387569522, 4.57365844462884, 6.506771421427836, 2.684391825560753, 2.0883733011339594, 1.2317868258169462, 0.0, 17.048295745488062, 13.549655083986407, 10.441866505669795, 8.053175476682258, 13.013542842855673, 6.403121822480377, 5.473816387569522, 4.298890956397426, 6.357236012739795, 4.873405239574803, 2.7657137025064857, 1.1717095775288306, 0.0), # 114
(16.744292825407193, 12.820412877827026, 13.798045399060976, 14.580114081995404, 12.690489213466321, 6.006150688123703, 5.448653685172405, 4.561051990709032, 6.495074987201274, 2.674271397594635, 2.0808463534281283, 1.2280556373838278, 0.0, 17.013611936988678, 13.508612011222104, 10.404231767140642, 8.022814192783905, 12.990149974402549, 6.385472786992645, 5.448653685172405, 4.290107634374073, 6.345244606733161, 4.860038027331802, 2.7596090798121957, 1.165492079802457, 0.0), # 115
(16.691723771827743, 12.753160664131308, 13.767798284975811, 14.540399302859647, 12.666226231660534, 5.994144321151453, 5.423944335775104, 4.549035234674245, 6.483708803536698, 2.6643570113022967, 2.0734817793814444, 1.224378479623102, 0.0, 16.978693067560602, 13.46816327585412, 10.367408896907222, 7.9930710339068884, 12.967417607073395, 6.368649328543944, 5.423944335775104, 4.281531657965324, 6.333113115830267, 4.846799767619883, 2.7535596569951624, 1.1593782421937553, 0.0), # 116
(16.63889299708279, 12.686949079834788, 13.73776805328898, 14.50099985299953, 12.641646931554131, 5.982399254013936, 5.399647415052978, 4.537573346372689, 6.472643289895322, 2.6546298304086586, 2.0662650296469853, 1.2207484569815625, 0.0, 16.943484608744804, 13.428233026797187, 10.331325148234924, 7.963889491225975, 12.945286579790643, 6.352602684921765, 5.399647415052978, 4.2731423242956685, 6.320823465777066, 4.833666617666511, 2.747553610657796, 1.1533590072577082, 0.0), # 117
(16.58573504277338, 12.621678493042284, 13.707895587012551, 14.461844204097451, 12.616715164639011, 5.970886502685445, 5.375721998681383, 4.526631495652572, 6.461848865738361, 2.6450710186386424, 2.0591815548778274, 1.2171586739060027, 0.0, 16.907932032082243, 13.388745412966028, 10.295907774389137, 7.935213055915925, 12.923697731476722, 6.337284093913602, 5.375721998681383, 4.264918930489604, 6.3083575823195055, 4.820614734699151, 2.74157911740251, 1.1474253175492988, 0.0), # 118
(16.532184450500534, 12.557249271858602, 13.678121769158587, 14.422860827835802, 12.591394782407065, 5.9595770831402755, 5.35212716233568, 4.516174852362109, 6.451295950527026, 2.6356617397171678, 2.0522168057270487, 1.2136022348432152, 0.0, 16.87198080911388, 13.349624583275366, 10.261084028635242, 7.906985219151502, 12.902591901054052, 6.322644793306953, 5.35212716233568, 4.256840773671625, 6.295697391203532, 4.807620275945268, 2.7356243538317178, 1.1415681156235096, 0.0), # 119
(16.47817576186529, 12.49356178438856, 13.648387482739144, 14.383978195896983, 12.565649636350196, 5.948442011352714, 5.3288219816912274, 4.506168586349507, 6.440954963722534, 2.626383157369158, 2.045356232847725, 1.2100722442399947, 0.0, 16.835576411380675, 13.31079468663994, 10.226781164238623, 7.879149472107472, 12.881909927445069, 6.308636020889311, 5.3288219816912274, 4.248887150966224, 6.282824818175098, 4.794659398632328, 2.7296774965478288, 1.1357783440353237, 0.0), # 120
(16.423643518468683, 12.430516398736968, 13.618633610766281, 14.345124779963385, 12.539443577960302, 5.937452303297058, 5.305765532423383, 4.49657786746298, 6.430796324786099, 2.6172164353195337, 2.038585286892935, 1.2065618065431336, 0.0, 16.79866431042359, 13.272179871974467, 10.192926434464676, 7.8516493059586, 12.861592649572199, 6.295209014448172, 5.305765532423383, 4.2410373594978985, 6.269721788980151, 4.781708259987796, 2.7237267221532564, 1.1300469453397246, 0.0), # 121
(16.36852226191174, 12.368013483008635, 13.588801036252066, 14.306229051717406, 12.51274045872928, 5.926578974947596, 5.282916890207506, 4.487367865550737, 6.420790453178933, 2.6081427372932153, 2.0318894185157554, 1.2030640261994254, 0.0, 16.761189977783587, 13.233704288193676, 10.159447092578777, 7.824428211879645, 12.841580906357866, 6.282315011771032, 5.282916890207506, 4.2332706963911395, 6.25637022936464, 4.768743017239136, 2.7177602072504135, 1.1243648620916942, 0.0), # 122
(16.312746533795494, 12.305953405308378, 13.558830642208555, 14.267219482841437, 12.485504130149028, 5.915793042278621, 5.260235130718955, 4.478503750460988, 6.410907768362252, 2.5991432270151247, 2.0252540783692634, 1.1995720076556633, 0.0, 16.72309888500163, 13.195292084212294, 10.126270391846315, 7.797429681045372, 12.821815536724504, 6.269905250645383, 5.260235130718955, 4.225566458770444, 6.242752065074514, 4.755739827613813, 2.711766128441711, 1.1187230368462162, 0.0), # 123
(16.256250875720976, 12.244236533741004, 13.528663311647806, 14.228024545017881, 12.457698443711445, 5.905065521264426, 5.237679329633088, 4.469950692041945, 6.401118689797269, 2.590199068210183, 2.018664717106536, 1.1960788553586414, 0.0, 16.68433650361868, 13.156867408945052, 10.09332358553268, 7.770597204630548, 12.802237379594539, 6.257930968858723, 5.237679329633088, 4.217903943760304, 6.2288492218557225, 4.742674848339295, 2.7057326623295617, 1.1131124121582732, 0.0), # 124
(16.198969829289226, 12.18276323641133, 13.498239927581887, 14.188572709929128, 12.429287250908427, 5.894367427879304, 5.215208562625265, 4.461673860141818, 6.391393636945196, 2.5812914246033105, 2.012106785380651, 1.1925776737551523, 0.0, 16.644848305175692, 13.118354411306674, 10.060533926903252, 7.74387427380993, 12.782787273890392, 6.246343404198546, 5.215208562625265, 4.210262448485217, 6.2146436254542134, 4.7295242366430434, 2.6996479855163775, 1.1075239305828484, 0.0), # 125
(16.14083793610127, 12.121433881424165, 13.46750137302285, 14.148792449257574, 12.400234403231872, 5.883669778097547, 5.192781905370843, 4.453638424608819, 6.381703029267251, 2.57240145991943, 2.005565733844684, 1.1890615672919902, 0.0, 16.604579761213643, 13.079677240211891, 10.02782866922342, 7.717204379758288, 12.763406058534501, 6.235093794452347, 5.192781905370843, 4.202621270069677, 6.200117201615936, 4.716264149752526, 2.69350027460457, 1.1019485346749243, 0.0), # 126
(16.08178973775815, 12.06014883688432, 13.436388530982757, 14.108612234685616, 12.370503752173677, 5.872943587893444, 5.170358433545185, 4.445809555291159, 6.3720172862246445, 2.563510337883461, 1.9990270131517138, 1.1855236404159475, 0.0, 16.56347634327348, 13.040760044575421, 9.99513506575857, 7.690531013650382, 12.744034572449289, 6.224133377407623, 5.170358433545185, 4.194959705638174, 6.185251876086839, 4.702870744895206, 2.6872777061965514, 1.0963771669894837, 0.0), # 127
(16.021759775860883, 11.998808470896611, 13.404842284473675, 14.06796053789565, 12.340059149225747, 5.862159873241292, 5.147897222823644, 4.438152422037048, 6.362306827278591, 2.554599222220326, 1.9924760739548175, 1.1819569975738184, 0.0, 16.521483522896165, 13.001526973312, 9.962380369774086, 7.663797666660978, 12.724613654557182, 6.2134133908518665, 5.147897222823644, 4.187257052315209, 6.170029574612873, 4.689320179298551, 2.680968456894735, 1.0908007700815103, 0.0), # 128
(15.960682592010507, 11.937313151565847, 13.37280351650766, 14.026765830570064, 12.308864445879973, 5.85128965011538, 5.125357348881582, 4.430632194694696, 6.352542071890305, 2.5456492766549457, 1.9858983669070716, 1.1783547432123955, 0.0, 16.478546771622668, 12.96190217533635, 9.929491834535357, 7.636947829964836, 12.70508414378061, 6.202885072572574, 5.125357348881582, 4.179492607225272, 6.154432222939986, 4.675588610190022, 2.6745607033015326, 1.0852102865059863, 0.0), # 129
(15.89849272780806, 11.875563246996844, 13.34021311009677, 13.984956584391266, 12.276883493628256, 5.840303934489999, 5.102697887394356, 4.423214043112313, 6.342693439521001, 2.536641664912241, 1.9792793426615536, 1.174709981778473, 0.0, 16.434611560993947, 12.921809799563201, 9.896396713307768, 7.609924994736723, 12.685386879042001, 6.192499660357238, 5.102697887394356, 4.171645667492856, 6.138441746814128, 4.66165219479709, 2.668042622019354, 1.0795966588178951, 0.0), # 130
(15.83512472485457, 11.81345912529441, 13.307011948253072, 13.942461271041642, 12.244080143962494, 5.829173742339445, 5.079877914037328, 4.415863137138113, 6.332731349631892, 2.527557550717134, 1.9726044518713404, 1.1710158177188439, 0.0, 16.38962336255096, 12.88117399490728, 9.863022259356702, 7.5826726521514, 12.665462699263784, 6.182208391993358, 5.079877914037328, 4.16369553024246, 6.122040071981247, 4.647487090347215, 2.6614023896506143, 1.073950829572219, 0.0), # 131
(15.770513124751067, 11.750901154563357, 13.27314091398862, 13.899208362203591, 12.210418248374584, 5.817870089638008, 5.056856504485853, 4.408544646620305, 6.322626221684192, 2.5183780977945447, 1.9658591451895095, 1.1672653554803014, 0.0, 16.343527647834676, 12.839918910283313, 9.829295725947548, 7.555134293383633, 12.645252443368385, 6.171962505268427, 5.056856504485853, 4.155621492598577, 6.105209124187292, 4.633069454067865, 2.654628182797724, 1.0682637413239418, 0.0), # 132
(15.704592469098595, 11.687789702908498, 13.238540890315475, 13.855126329559509, 12.175861658356425, 5.80636399235998, 5.03359273441529, 4.4012237414071, 6.312348475139116, 2.509084469869395, 1.9590288732691383, 1.1634516995096391, 0.0, 16.296269888386057, 12.797968694606027, 9.795144366345692, 7.527253409608184, 12.624696950278231, 6.1617132379699395, 5.03359273441529, 4.1474028516857, 6.087930829178212, 4.618375443186504, 2.647708178063095, 1.0625263366280455, 0.0), # 133
(15.63729729949817, 11.624025138434646, 13.203152760245707, 13.81014364479179, 12.14037422539991, 5.794626466479654, 5.010045679501001, 4.3938655913467075, 6.301868529457877, 2.499657830666606, 1.952099086763304, 1.1595679542536501, 0.0, 16.24779555574605, 12.755247496790147, 9.76049543381652, 7.498973491999817, 12.603737058915755, 6.151411827885391, 5.010045679501001, 4.139018904628324, 6.070187112699955, 4.6033812149305975, 2.6406305520491418, 1.0567295580395135, 0.0), # 134
(15.568562157550836, 11.559507829246614, 13.166917406791363, 13.764188779582833, 12.103919800996945, 5.7826285279713225, 4.986174415418341, 4.3864353662873405, 6.291156804101687, 2.4900793439110998, 1.945055236325083, 1.155607224159128, 0.0, 16.198050121455637, 12.711679465750406, 9.725276181625414, 7.470238031733298, 12.582313608203375, 6.141009512802277, 4.986174415418341, 4.130448948550945, 6.051959900498472, 4.588062926527612, 2.633383481358273, 1.0508643481133288, 0.0), # 135
(15.498321584857623, 11.494138143449213, 13.129775712964513, 13.717190205615022, 12.066462236639419, 5.770341192809277, 4.961938017842671, 4.378898236077208, 6.280183718531764, 2.4803301733277956, 1.9378827726075534, 1.1515626136728663, 0.0, 16.146979057055766, 12.667188750401527, 9.689413863037766, 7.4409905199833855, 12.560367437063528, 6.130457530508091, 4.961938017842671, 4.121672280578055, 6.033231118319709, 4.572396735205008, 2.6259551425929026, 1.044921649404474, 0.0), # 136
(15.426510123019561, 11.427816449147253, 13.091668561777217, 13.66907639457077, 12.02796538381924, 5.757735476967808, 4.93729556244935, 4.371219370564522, 6.2689196922093195, 2.4703914826416162, 1.930567146263792, 1.1474272272416581, 0.0, 16.094527834087398, 12.621699499658236, 9.652835731318959, 7.411174447924847, 12.537839384418639, 6.119707118790331, 4.93729556244935, 4.112668197834148, 6.01398269190962, 4.556358798190257, 2.6183337123554433, 1.0388924044679322, 0.0), # 137
(15.353062313637686, 11.360443114445548, 13.052536836241526, 13.619775818132457, 11.988393094028304, 5.744782396421213, 4.912206124913734, 4.363363939597493, 6.257335144595569, 2.4602444355774815, 1.9230938079468758, 1.143194169312297, 0.0, 16.040641924091503, 12.575135862435264, 9.615469039734378, 7.380733306732443, 12.514670289191137, 6.10870951543649, 4.912206124913734, 4.103415997443723, 5.994196547014152, 4.5399252727108195, 2.6105073672483052, 1.0327675558586864, 0.0), # 138
(15.277912698313022, 11.29191850744891, 13.01232141936951, 13.569216947982484, 11.947709218758497, 5.731452967143778, 4.886628780911184, 4.355297113024331, 6.245400495151722, 2.449870195860314, 1.9154482083098823, 1.1388565443315761, 0.0, 15.985266798609034, 12.527421987647335, 9.577241041549412, 7.3496105875809405, 12.490800990303445, 6.0974159582340635, 4.886628780911184, 4.093894976531271, 5.973854609379249, 4.523072315994162, 2.602464283873902, 1.0265380461317193, 0.0), # 139
(15.200995818646616, 11.22214299626215, 12.970963194173232, 13.51732825580325, 11.905877609501736, 5.717718205109798, 4.860522606117057, 4.346984060693248, 6.233086163338999, 2.439249927215034, 1.9076157980058883, 1.134407456746289, 0.0, 15.928347929180966, 12.478482024209175, 9.538078990029442, 7.3177497816451, 12.466172326677999, 6.085777684970546, 4.860522606117057, 4.084084432221284, 5.952938804750868, 4.505776085267751, 2.5941926388346466, 1.020194817842014, 0.0), # 140
(15.122246216239494, 11.151016948990085, 12.92840304366474, 13.464038213277146, 11.862862117749902, 5.7035491262935665, 4.833846676206716, 4.338389952452453, 6.220362568618608, 2.4283647933665637, 1.8995820276879718, 1.129840011003229, 0.0, 15.869830787348244, 12.428240121035515, 9.497910138439858, 7.2850943800996895, 12.440725137237216, 6.073745933433434, 4.833846676206716, 4.0739636616382615, 5.931431058874951, 4.48801273775905, 2.5856806087329485, 1.0137288135445532, 0.0), # 141
(15.041598432692682, 11.07844073373752, 12.884581850856106, 13.409275292086573, 11.818626594994903, 5.688916746669374, 4.806560066855513, 4.329479958150158, 6.207200130451765, 2.417195958039823, 1.8913323480092095, 1.1251473115491895, 0.0, 15.80966084465184, 12.37662042704108, 9.456661740046046, 7.251587874119467, 12.41440026090353, 6.061271941410222, 4.806560066855513, 4.063511961906696, 5.909313297497452, 4.469758430695525, 2.5769163701712214, 1.00713097579432, 0.0), # 142
(14.958987009607215, 11.004314718609267, 12.839440498759389, 13.352967963913915, 11.773134892728635, 5.673792082211512, 4.778621853738811, 4.320219247634575, 6.1935692682996875, 2.405724584959734, 1.8828522096226783, 1.1203224628309636, 0.0, 15.747783572632711, 12.323547091140597, 9.41426104811339, 7.217173754879202, 12.387138536599375, 6.048306946688404, 4.778621853738811, 4.05270863015108, 5.886567446364317, 4.45098932130464, 2.5678880997518783, 1.0003922471462972, 0.0), # 143
(14.874346488584132, 10.928539271710147, 12.792919870386642, 13.29504470044158, 11.726350862442994, 5.658146148894274, 4.749991112531969, 4.310572990753912, 6.1794404016235855, 2.3939318378512175, 1.8741270631814555, 1.115358569295345, 0.0, 15.684144442831826, 12.268944262248793, 9.370635315907277, 7.181795513553651, 12.358880803247171, 6.034802187055478, 4.749991112531969, 4.04153296349591, 5.863175431221497, 4.431681566813861, 2.5585839740773286, 0.993503570155468, 0.0), # 144
(14.787611411224459, 10.851014761144963, 12.744960848749933, 13.235433973351956, 11.67823835562988, 5.641949962691953, 4.7206269189103445, 4.300506357356382, 6.164783949884672, 2.381798880439195, 1.865142359338619, 1.110248735389127, 0.0, 15.618688926790139, 12.212736089280396, 9.325711796693094, 7.145396641317584, 12.329567899769344, 6.020708900298935, 4.7206269189103445, 4.029964259065681, 5.83911917781494, 4.411811324450653, 2.548992169749987, 0.986455887376815, 0.0), # 145
(14.69871631912923, 10.771641555018533, 12.695504316861326, 13.174064254327444, 11.62876122378119, 5.62517453957884, 4.690488348549297, 4.289984517290195, 6.1495703325441635, 2.3693068764485874, 1.8558835487472447, 1.104986065559103, 0.0, 15.551362496048613, 12.154846721150133, 9.279417743736223, 7.107920629345761, 12.299140665088327, 6.005978324206273, 4.690488348549297, 4.0179818139848855, 5.814380611890595, 4.391354751442482, 2.539100863372265, 0.9792401413653213, 0.0), # 146
(14.607595753899481, 10.690320021435666, 12.644491157732865, 13.110864015050435, 11.577883318388821, 5.607790895529226, 4.659534477124183, 4.278972640403562, 6.133769969063274, 2.3564369896043162, 1.846336082060411, 1.0995636642520668, 0.0, 15.482110622148213, 12.095200306772732, 9.231680410302054, 7.069310968812948, 12.267539938126548, 5.990561696564987, 4.659534477124183, 4.005564925378019, 5.7889416591944105, 4.370288005016812, 2.5288982315465733, 0.9718472746759697, 0.0), # 147
(14.51418425713624, 10.606950528501175, 12.591862254376625, 13.045761727203324, 11.525568490944673, 5.5897700465174065, 4.627724380310364, 4.2674358965446935, 6.1173532789032175, 2.3431703836313016, 1.836485409931195, 1.0939746359148106, 0.0, 15.410878776629895, 12.033720995062914, 9.182427049655974, 7.029511150893903, 12.234706557806435, 5.974410255162571, 4.627724380310364, 3.9926928903695758, 5.762784245472337, 4.348587242401109, 2.5183724508753254, 0.9642682298637433, 0.0), # 148
(14.418416370440541, 10.52143344431987, 12.537558489804665, 12.97868586246851, 11.471780592940643, 5.57108300851767, 4.595017133783196, 4.255339455561801, 6.100290681525203, 2.3294882222544664, 1.8263169830126733, 1.0882120849941288, 0.0, 15.337612431034628, 11.970332934935415, 9.131584915063366, 6.988464666763398, 12.200581363050405, 5.957475237786521, 4.595017133783196, 3.9793450060840496, 5.735890296470322, 4.326228620822837, 2.507511697960933, 0.9564939494836247, 0.0), # 149
(14.320226635413416, 10.433669136996565, 12.481520747029043, 12.909564892528387, 11.416483475868631, 5.551700797504312, 4.561371813218041, 4.242648487303093, 6.0825525963904505, 2.31537166919873, 1.815816251957923, 1.0822691159368145, 0.0, 15.262257056903364, 11.904960275304958, 9.079081259789614, 6.946115007596189, 12.165105192780901, 5.93970788222433, 4.561371813218041, 3.9655005696459367, 5.7082417379343156, 4.303188297509463, 2.4963041494058085, 0.948515376090597, 0.0), # 150
(14.219549593655895, 10.343557974636072, 12.423689909061814, 12.838327289065347, 11.359640991220532, 5.531594429451621, 4.526747494290255, 4.229328161616783, 6.064109442960174, 2.3008018881890155, 1.8049686674200216, 1.0761388331896609, 0.0, 15.184758125777073, 11.837527165086268, 9.024843337100108, 6.902405664567045, 12.128218885920347, 5.921059426263496, 4.526747494290255, 3.951138878179729, 5.679820495610266, 4.27944242968845, 2.484737981812363, 0.9403234522396431, 0.0), # 151
(14.116319786769019, 10.251000325343204, 12.364006858915053, 12.76490152376179, 11.301216990488243, 5.510734920333892, 4.491103252675198, 4.215343648351081, 6.044931640695582, 2.2857600429502427, 1.7937596800520466, 1.0698143411994616, 0.0, 15.105061109196717, 11.767957753194075, 8.968798400260232, 6.857280128850727, 12.089863281391164, 5.901481107691514, 4.491103252675198, 3.936239228809923, 5.650608495244121, 4.254967174587264, 2.4728013717830106, 0.931909120485746, 0.0), # 152
(14.010471756353809, 10.155896557222773, 12.302412479600802, 12.68921606830011, 11.241175325163667, 5.489093286125417, 4.454398164048228, 4.200660117354197, 6.024989609057894, 2.2702272972073336, 1.782174740507075, 1.0632887444130097, 0.0, 15.02311147870325, 11.696176188543106, 8.910873702535374, 6.810681891622, 12.049979218115787, 5.880924164295876, 4.454398164048228, 3.920780918661012, 5.620587662581833, 4.229738689433371, 2.4604824959201608, 0.9232633233838886, 0.0), # 153
(13.901940044011312, 10.05814703837959, 12.238847654131138, 12.611199394362703, 11.179479846738696, 5.466640542800487, 4.416591304084705, 4.185242738474343, 6.00425376750832, 2.254184814685209, 1.7701992994381837, 1.0565551472770989, 0.0, 14.938854705837642, 11.622106620048086, 8.850996497190918, 6.762554444055626, 12.00850753501664, 5.85933983386408, 4.416591304084705, 3.904743244857491, 5.589739923369348, 4.203733131454236, 2.447769530826228, 0.9143770034890537, 0.0), # 154
(13.790659191342543, 9.957652136918465, 12.173253265518113, 12.530779973631962, 11.116094406705237, 5.443347706333395, 4.377641748459985, 4.169056681559727, 5.982694535508077, 2.23761375910879, 1.7578188074984502, 1.0496066542385225, 0.0, 14.852236262140847, 11.545673196623744, 8.789094037492251, 6.712841277326369, 11.965389071016155, 5.836679354183619, 4.377641748459985, 3.8881055045238533, 5.5580472033526185, 4.176926657877321, 2.4346506531036227, 0.9052411033562243, 0.0), # 155
(13.676563739948545, 9.854312220944214, 12.10557019677379, 12.447886277790282, 11.050982856555176, 5.419185792698435, 4.33750857284943, 4.152067116458564, 5.960282332518376, 2.220495294202998, 1.7450187153409518, 1.0424363697440735, 0.0, 14.763201619153833, 11.466800067184806, 8.725093576704758, 6.661485882608993, 11.920564665036752, 5.81289396304199, 4.33750857284943, 3.870846994784596, 5.525491428277588, 4.149295425930095, 2.4211140393547583, 0.8958465655403832, 0.0), # 156
(13.559588231430352, 9.748027658561648, 12.035739330910227, 12.362446778520066, 10.984109047780422, 5.394125817869895, 4.296150852928397, 4.134239213019062, 5.9369875780004335, 2.202810583692754, 1.731784473618765, 1.0350373982405456, 0.0, 14.671696248417557, 11.385411380646001, 8.658922368093824, 6.60843175107826, 11.873975156000867, 5.787934898226687, 4.296150852928397, 3.8529470127642105, 5.492054523890211, 4.120815592840023, 2.407147866182046, 0.8861843325965136, 0.0), # 157
(13.43642570352943, 9.636747649274225, 11.960387930853534, 12.27118893522918, 10.912417327045198, 5.366575700132966, 4.252596048835072, 4.1143477142620295, 5.910997254959458, 2.1840146623310153, 1.717678725761683, 1.027139934629151, 0.0, 14.573674546947622, 11.298539280920659, 8.588393628808413, 6.552043986993045, 11.821994509918916, 5.7600867999668415, 4.252596048835072, 3.833268357237833, 5.456208663522599, 4.090396311743061, 2.3920775861707066, 0.8760679681158388, 0.0), # 158
(13.288116180561124, 9.509057777339137, 11.860106727604483, 12.155369164364412, 10.818229571737954, 5.327374130407459, 4.201391487047145, 4.085410149573287, 5.871856356733287, 2.161026447344436, 1.7002250806856987, 1.0172043785524665, 0.0, 14.445769764456351, 11.189248164077128, 8.501125403428492, 6.483079342033307, 11.743712713466573, 5.719574209402602, 4.201391487047145, 3.8052672360053275, 5.409114785868977, 4.051789721454805, 2.372021345520897, 0.8644597979399218, 0.0), # 159
(13.112769770827757, 9.363909602092178, 11.732881436933834, 12.013079639051961, 10.699704157616154, 5.275558360850069, 4.142019373545406, 4.04669939214551, 5.818455136337191, 2.1335425433383026, 1.6791778525828622, 1.0050752923331772, 0.0, 14.285557096008445, 11.055828215664945, 8.39588926291431, 6.400627630014906, 11.636910272674381, 5.665379149003714, 4.142019373545406, 3.7682559720357633, 5.349852078808077, 4.004359879683988, 2.346576287386767, 0.8512645092811072, 0.0), # 160
(12.911799698254727, 9.202249432332774, 11.580070457865464, 11.845672880071582, 10.558071749138534, 5.21175610364883, 4.0749133014061885, 3.9987003998323356, 5.751497860199411, 2.101796186926922, 1.6547224963799123, 0.9908651203361357, 0.0, 14.094673280674375, 10.899516323697492, 8.273612481899562, 6.305388560780765, 11.502995720398822, 5.59818055976527, 4.0749133014061885, 3.722682931177736, 5.279035874569267, 3.9485576266905285, 2.3160140915730927, 0.8365681302120704, 0.0), # 161
(12.686619186767443, 9.025023576860344, 11.403032189423245, 11.654501408203041, 10.394563010763845, 5.1365950709917785, 4.000506863705828, 3.941898130487402, 5.6716887947481816, 2.0660206147246045, 1.6270444670035862, 0.9746863069261941, 0.0, 13.874755057524599, 10.721549376188133, 8.13522233501793, 6.198061844173813, 11.343377589496363, 5.518657382682362, 4.000506863705828, 3.668996479279842, 5.197281505381922, 3.884833802734348, 2.280606437884649, 0.8204566888054858, 0.0), # 162
(12.438641460291295, 8.833178344474314, 11.203125030631053, 11.44091774422611, 10.210408606950825, 5.050702975066952, 3.919233653520661, 3.876777541964344, 5.579732206411743, 2.0264490633456567, 1.5963292193806227, 0.956651296468205, 0.0, 13.627439165629584, 10.523164261150253, 7.9816460969031136, 6.079347190036969, 11.159464412823485, 5.427488558750082, 3.919233653520661, 3.6076449821906795, 5.105204303475412, 3.813639248075371, 2.2406250061262107, 0.8030162131340287, 0.0), # 163
(12.16927974275169, 8.627660043974105, 10.981707380512765, 11.206274408920553, 10.006839202158226, 4.954707528062387, 3.8315272639270197, 3.8038235921168018, 5.476332361618334, 1.9833147694043862, 1.562762208437759, 0.9368725333270206, 0.0, 13.35436234405979, 10.305597866597225, 7.813811042188794, 5.949944308213158, 10.952664723236667, 5.325353028963523, 3.8315272639270197, 3.5390768057588473, 5.003419601079113, 3.735424802973519, 2.1963414761025533, 0.7843327312703733, 0.0), # 164
(11.879947258074031, 8.409414984159142, 10.740137638092254, 10.95192392306614, 9.785085460844787, 4.849236442166116, 3.7378212880012396, 3.7235212387984102, 5.3621935267961875, 1.9368509695151015, 1.5265288891017337, 0.915462461867493, 0.0, 13.057161331885686, 10.070087080542422, 7.632644445508667, 5.810552908545303, 10.724387053592375, 5.2129297343177745, 3.7378212880012396, 3.4637403158329394, 4.892542730422393, 3.6506413076887143, 2.148027527618451, 0.7644922712871949, 0.0), # 165
(11.572057230183715, 8.17938947382885, 10.479774202393392, 10.679218807442627, 9.546378047469258, 4.734917429566179, 3.6385493188196576, 3.636355439862808, 5.2380199683735436, 1.8872909002921108, 1.4878147162992839, 0.8925335264544754, 0.0, 12.737472868177733, 9.817868790999228, 7.4390735814964195, 5.661872700876331, 10.476039936747087, 5.090897615807931, 3.6385493188196576, 3.3820838782615565, 4.773189023734629, 3.5597396024808767, 2.0959548404786785, 0.7435808612571683, 0.0), # 166
(11.24702288300614, 7.938529821782648, 10.201975472440058, 10.389511582829789, 9.291947626490376, 4.6123782024506115, 3.5341449494586072, 3.542811153163632, 5.104515952778639, 1.834867798349722, 1.4468051449571482, 0.8681981714528189, 0.0, 12.396933692006392, 9.550179885981006, 7.23402572478574, 5.504603395049164, 10.209031905557278, 4.959935614429085, 3.5341449494586072, 3.2945558588932937, 4.645973813245188, 3.4631705276099303, 2.040395094488012, 0.7216845292529681, 0.0), # 167
(10.906257440466712, 7.687782336819962, 9.908099847256123, 10.084154770007387, 9.023024862366888, 4.482246473007449, 3.425041772994424, 3.44337333655452, 4.962385746439713, 1.779814900302243, 1.4036856300020644, 0.8425688412273767, 0.0, 12.037180542442131, 9.268257253501142, 7.018428150010321, 5.339444700906728, 9.924771492879426, 4.820722671176328, 3.425041772994424, 3.2016046235767495, 4.511512431183444, 3.361384923335797, 1.9816199694512246, 0.6988893033472693, 0.0), # 168
(10.551174126490828, 7.428093327740216, 9.599505725865463, 9.76450088975519, 8.740840419557543, 4.3451499534247295, 3.3116733825034426, 3.338526947889109, 4.812333615785002, 1.7223654427639818, 1.3586416263607706, 0.8157579801430009, 0.0, 11.659850158555415, 8.97333778157301, 6.793208131803853, 5.167096328291944, 9.624667231570005, 4.673937727044753, 3.3116733825034426, 3.103678538160521, 4.370420209778771, 3.254833629918398, 1.9199011451730927, 0.675281211612747, 0.0), # 169
(10.18318616500389, 7.160409103342831, 9.277551507291953, 9.43190246285296, 8.44662496252108, 4.201716355890488, 3.1944733710619975, 3.228756945021036, 4.655063827242743, 1.6627526623492466, 1.311858588960005, 0.7878780325645439, 0.0, 11.2665792794167, 8.666658358209983, 6.559292944800025, 4.988257987047739, 9.310127654485486, 4.52025972302945, 3.1944733710619975, 3.0012259684932054, 4.22331248126054, 3.1439674876176547, 1.8555103014583907, 0.6509462821220756, 0.0), # 170
(9.8037067799313, 6.88567597242723, 8.943595590559468, 9.087712010080473, 8.141609155716246, 4.052573392592758, 3.0738753317464247, 3.1145482858039375, 4.491280647241173, 1.6012097956723452, 1.2635219727265048, 0.759041442856858, 0.0, 10.859004644096458, 8.349455871425437, 6.317609863632523, 4.803629387017034, 8.982561294482347, 4.360367600125513, 3.0738753317464247, 2.8946952804233987, 4.070804577858123, 3.029237336693492, 1.7887191181118935, 0.6259705429479302, 0.0), # 171
(9.414149195198457, 6.604840243792839, 8.59899637469188, 8.733282052217486, 7.827023663601784, 3.898348775719581, 2.950312857633059, 2.996385928091453, 4.321688342208532, 1.5379700793475863, 1.2138172325870082, 0.7293606553847958, 0.0, 10.438762991665145, 8.022967209232752, 6.069086162935041, 4.613910238042758, 8.643376684417063, 4.194940299328034, 2.950312857633059, 2.7845348397997007, 3.913511831800892, 2.911094017405829, 1.7197992749383764, 0.6004400221629854, 0.0), # 172
(9.015926634730764, 6.31884822623908, 8.245112258713068, 8.369965110043767, 7.504099150636442, 3.739670217458989, 2.824219541798235, 2.874754829737218, 4.146991178573053, 1.4732667499892769, 1.1629298234682535, 0.6989481145132089, 0.0, 10.007491061193234, 7.6884292596452966, 5.8146491173412675, 4.41980024996783, 8.293982357146106, 4.024656761632105, 2.824219541798235, 2.6711930124707064, 3.752049575318221, 2.7899883700145893, 1.6490224517426137, 0.5744407478399164, 0.0), # 173
(8.610452322453618, 6.028646228565374, 7.883301641646902, 7.99911370433908, 7.174066281278959, 3.57716542999902, 2.6960289773182877, 2.7501399485948705, 3.9678934227629785, 1.4073330442117262, 1.1110452002969786, 0.6679162646069503, 0.0, 9.566825591751181, 7.347078910676452, 5.555226001484892, 4.221999132635178, 7.935786845525957, 3.850195928032819, 2.6960289773182877, 2.5551181642850143, 3.5870331406394795, 2.6663712347796937, 1.5766603283293805, 0.5480587480513978, 0.0), # 174
(8.19913948229242, 5.7351805595711465, 7.514922922517262, 7.622080355883197, 6.838155719988082, 3.41146212552771, 2.566174757269552, 2.623026242518047, 3.7850993412065432, 1.3404021986292411, 1.058348817999921, 0.6363775500308723, 0.0, 9.118403322409455, 7.000153050339593, 5.291744089999604, 4.021206595887723, 7.5701986824130865, 3.6722367395252657, 2.566174757269552, 2.4367586610912215, 3.419077859994041, 2.540693451961066, 1.5029845845034526, 0.5213800508701043, 0.0), # 175
(7.783401338172574, 5.43939752805582, 7.141334500348018, 7.240217585455879, 6.497598131222556, 3.2431880162330953, 2.4350904747283635, 2.493898669360387, 3.5993132003319848, 1.2727074498561304, 1.0050261315038191, 0.6044444151498269, 0.0, 8.663860992238513, 6.648888566648095, 5.025130657519095, 3.8181223495683905, 7.1986264006639695, 3.4914581371045417, 2.4350904747283635, 2.3165628687379254, 3.248799065611278, 2.4134058618186267, 1.4282669000696038, 0.49449068436871096, 0.0), # 176
(7.364651114019479, 5.1422434428188195, 6.763894774163046, 6.8548779138368925, 6.1536241794411275, 3.0729708143032117, 2.303209722771056, 2.3632421869755245, 3.411239266567542, 1.2044820345067013, 0.9512625957354108, 0.5722293043286669, 0.0, 8.204835340308824, 6.2945223476153345, 4.756312978677054, 3.6134461035201033, 6.822478533135084, 3.3085390617657344, 2.303209722771056, 2.1949791530737226, 3.0768120897205637, 2.284959304612298, 1.3527789548326095, 0.4674766766198928, 0.0), # 177
(6.944302033758534, 4.8446646126595665, 6.383962142986221, 6.467413861806007, 5.807464529102536, 2.901438231926097, 2.170966094473966, 2.2315417532170994, 3.2215818063414514, 1.1359591891952627, 0.897243665621434, 0.5398446619322442, 0.0, 7.742963105690853, 5.938291281254685, 4.486218328107169, 3.4078775675857873, 6.443163612682903, 3.1241584545039394, 2.170966094473966, 2.072455879947212, 2.903732264551268, 2.1558046206020025, 1.2767924285972443, 0.44042405569632426, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 134
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 136
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 137
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
13, # 1
)
| 278.348663 | 490 | 0.771287 | 32,987 | 260,256 | 6.084852 | 0.235396 | 0.35512 | 0.340772 | 0.645672 | 0.366927 | 0.361327 | 0.36059 | 0.36059 | 0.36059 | 0.36059 | 0 | 0.851054 | 0.095037 | 260,256 | 934 | 491 | 278.646681 | 0.001185 | 0.015412 | 0 | 0.200873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.005459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3b30c1ecc7c8601f82245da25ceb6f1834a945ef | 105 | py | Python | src/hkserror/__init__.py | huykingsofm/hkserror | a15b80fe9e8b3c2a37f901d82358db032fb32ec9 | [
"MIT"
] | null | null | null | src/hkserror/__init__.py | huykingsofm/hkserror | a15b80fe9e8b3c2a37f901d82358db032fb32ec9 | [
"MIT"
] | null | null | null | src/hkserror/__init__.py | huykingsofm/hkserror | a15b80fe9e8b3c2a37f901d82358db032fb32ec9 | [
"MIT"
] | null | null | null | from hkserror.hkserror import HKSError, HTypeError, HFormatError
from hkserror.version import __version__ | 52.5 | 64 | 0.87619 | 12 | 105 | 7.333333 | 0.5 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085714 | 105 | 2 | 65 | 52.5 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3b47789fe4104f1d9fd6eb67649c660fda0a7566 | 32,957 | py | Python | SUPPLEMENTAL_FIGURES.py | tortugar/Schott_etal_2022 | 5cccec4d59184397df39f0bae3544b9c8294ffe2 | [
"MIT"
] | null | null | null | SUPPLEMENTAL_FIGURES.py | tortugar/Schott_etal_2022 | 5cccec4d59184397df39f0bae3544b9c8294ffe2 | [
"MIT"
] | null | null | null | SUPPLEMENTAL_FIGURES.py | tortugar/Schott_etal_2022 | 5cccec4d59184397df39f0bae3544b9c8294ffe2 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 10 18:30:46 2021
@author: fearthekraken
"""
import AS
import pwaves
import sleepy
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats as stats
from statsmodels.stats.anova import AnovaRM
from statsmodels.stats.multicomp import MultiComparison
#%%
### Supp. FIGURE 1D - FISH quantification ###
df = pd.read_csv('/home/fearthekraken/Documents/Data/sleepRec_processed/FISH_counts.csv')
plt.figure()
sns.boxplot(x='MARKER LABEL', y='%CRH + MARKER', order=['VGLUT1','VGLUT2','GAD2'], data=df, whis=np.inf, color='white', fliersize=0)
sns.stripplot(x='MARKER LABEL', y='%CRH + MARKER', hue='Mouse', order=['VGLUT1','VGLUT2','GAD2'], data=df,
palette={'Marlin':'lightgreen', 'SERT1':'lightblue', 'Nemo':'lightgray'}, size=10, linewidth=1, edgecolor='black')
plt.show()
print('')
for marker_label in ['VGLUT1', 'VGLUT2', 'GAD2']:
p = df['%CRH + MARKER'].iloc[np.where(df['MARKER LABEL']==marker_label)[0]]
print(f'{round(p.mean(),2)}% of CRH+ neurons co-express {marker_label} (+/-{round(p.std(),2)}%)')
#%%
### Supp. FIGURE 2B - time-normalized DF/F activity across brain state transitions ###
ppath = '/home/fearthekraken/Documents/Data/photometry'
recordings = sleepy.load_recordings(ppath, 'crh_photometry.txt')[1]
sequence=[3,4,1,2]; state_thres=[(0,10000)]*len(sequence); nstates=[20,20,20,20]; vm=[0.2, 1.9] # NREM --> IS --> REM --> WAKE
_, mx_pwave, _ = pwaves.stateseq(ppath, recordings, sequence=sequence, nstates=nstates, state_thres=state_thres, ma_thr=20, ma_state=3,
flatten_tnrem=4, fmax=25, pnorm=1, vm=vm, psmooth=[2,2], mode='dff', mouse_avg='mouse', print_stats=False)
#%%
### Supp. FIGURE 2C,D,E - DF/F activity at brain state transitions ###
ppath = '/home/fearthekraken/Documents/Data/photometry'
recordings = sleepy.load_recordings(ppath, 'crh_photometry.txt')[1]
transitions = [(3,4)]; pre=40; post=15; vm=[0.3, 1.9]; tr_label = 'NtN' # NREM --> IS
#transitions = [(4,1)]; pre=15; post=40; vm=[0.1, 2.1]; tr_label = 'tNR' # IS --> REM
#transitions = [(1,2)]; pre=40; post=15; vm=[0.1, 2.1]; tr_label = 'RW' # REM --> WAKE
si_threshold = [pre]*6; sj_threshold = [post]*6
mice, tr_act, tr_spe = pwaves.activity_transitions(ppath, recordings, transitions=transitions, pre=pre, post=post, si_threshold=si_threshold,
sj_threshold=sj_threshold, ma_thr=20, ma_state=3, flatten_tnrem=4, vm=vm, fmax=25, pnorm=1,
psmooth=[3,3], mode='dff', mouse_avg='trials', base_int=5, print_stats=True)
#%%
### Supp. FIGURE 2F - DF/F activity following single & cluster P-waves ###
ppath = '/home/fearthekraken/Documents/Data/photometry'
recordings = sleepy.load_recordings(ppath, 'pwaves_photometry.txt')[1]
# get DF/F timecourse data, store in dataframe
pzscore=[0,0,0]; p_iso=0.8; pcluster=0
mice, iso_mx = pwaves.dff_timecourse(ppath, recordings, istate=0, plotMode='', dff_win=[0,2], pzscore=pzscore, mouse_avg='mouse', # single P-waves
p_iso=p_iso, pcluster=pcluster, clus_event='waves', psmooth=(8,15), print_stats=False)
pzscore=[0,0,0]; p_iso=0; pcluster=0.5
mice, clus_mx = pwaves.dff_timecourse(ppath, recordings, istate=0, plotMode='', dff_win=[0,2], pzscore=pzscore, mouse_avg='mouse', # clustered P-waves
p_iso=p_iso, pcluster=pcluster, clus_event='waves', psmooth=(8,15), print_stats=False)
df = pd.DataFrame({'Mouse' : np.tile(mice,2),
'Event' : np.repeat(['single', 'cluster'], len(mice)),
'DFF' : np.concatenate((iso_mx[2].mean(axis=1), clus_mx[2].mean(axis=1))) })
# bar plot
plt.figure(); sns.barplot(x='Event', y='DFF', data=df, ci=68, palette={'single':'salmon', 'cluster':'mediumslateblue'})
sns.pointplot(x='Event', y='DFF', hue='Mouse', data=df, ci=None, markers='', color='black'); plt.gca().get_legend().remove(); plt.show()
# stats
p = stats.ttest_rel(df['DFF'].iloc[np.where(df['Event'] == 'single')[0]], df['DFF'].iloc[np.where(df['Event'] == 'cluster')[0]])
print(f'single vs cluster P-waves -- T={round(p.statistic,3)}, p-value={round(p.pvalue,5)}')
#%%
### Supp. FIGURE 2G -averaged DF/F surrounding P-waves in each brain state ###
ppath = '/home/fearthekraken/Documents/Data/photometry'
recordings = sleepy.load_recordings(ppath, 'pwaves_photometry.txt')[1]
for s in [1,2,3,4]:
pwaves.dff_timecourse(ppath, recordings, istate=s, dff_win=[2,2], plotMode='03', pzscore=[0,0,0], mouse_avg='mouse',
ma_thr=20, ma_state=3, flatten_tnrem=4, p_iso=0, pcluster=0)
#%%
### Supp. FIGURE 3B,C,D - P-wave frequency at brain state transitions ###
ppath ='/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
recordings = sleepy.load_recordings(ppath, 'pwaves_mice.txt')[0]
transitions = [(3,4)]; pre=40; post=15; vm=[0.4,2.0]; tr_label = 'NtN' # NREM --> IS
#transitions = [(4,1)]; pre=15; post=40; vm=[0.1, 2.0]; tr_label = 'tNR' # IS --> REM
#transitions = [(1,2)]; pre=40; post=15; vm=[0.1, 2.0]; tr_label = 'RW' # REM --> WAKE
si_threshold = [pre]*6; sj_threshold = [post]*6
mice, tr_act, tr_spe = pwaves.activity_transitions(ppath, recordings, transitions=transitions, pre=pre, post=post, si_threshold=si_threshold,
sj_threshold=sj_threshold, ma_thr=20, ma_state=3, flatten_tnrem=4, vm=vm, fmax=25, pnorm=1,
psmooth=[3,3], mode='pwaves', mouse_avg='trials', base_int=5, print_stats=True)
#%%
### Supp. FIGURE 3E - time-normalized frequency of single & clustered P-waves across brain state transitions ###
ppath ='/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
recordings = sleepy.load_recordings(ppath, 'pwaves_mice.txt')[0]
sequence=[3,4,1,2]; state_thres=[(0,10000)]*len(sequence); nstates=[20,20,20,20]; vm=[0.2,2.0] # NREM --> IS --> REM --> WAKE
mice,smx,sspe = pwaves.stateseq(ppath, recordings, sequence=sequence, nstates=nstates, state_thres=state_thres, fmax=25, # single P-waves
pnorm=1, vm=vm, psmooth=[2,2], mode='pwaves', mouse_avg='mouse', p_iso=0.8, pcluster=0,
clus_event='waves', pplot=False, print_stats=False)
mice,cmx,cspe = pwaves.stateseq(ppath, recordings, sequence=sequence, nstates=nstates, state_thres=state_thres, fmax=25, # clustered P-waves
pnorm=1, vm=vm, psmooth=[2,2], mode='pwaves', mouse_avg='mouse', p_iso=0, pcluster=0.5,
clus_event='waves', pplot=False, print_stats=False)
# plot timecourses
pwaves.plot_activity_transitions([smx, cmx], [mice, mice], plot_id=['salmon', 'mediumslateblue'], group_labels=['single', 'cluster'],
xlim=nstates, xlabel='Time (normalized)', ylabel='P-waves/s', title='NREM-->tNREM-->REM-->Wake')
#%%
### Supp. FIGURE 3F - average single & cluster P-wave frequency in each brain state ###
ppath ='/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
recordings = sleepy.load_recordings(ppath, 'pwaves_mice.txt')[0]
mice,x,sfreq,swf = pwaves.state_freq(ppath, recordings, istate=[1,2,3,4], p_iso=0.8, pcluster=0, # single P-waves
clus_event='waves', pplot=False, print_stats=False)
mice,x,cfreq,cwf = pwaves.state_freq(ppath, recordings, istate=[1,2,3,4], p_iso=0, pcluster=0.5, # clustered P-waves
clus_event='waves', pplot=False, print_stats=False)
# bar plot
pwaves.plot_state_freq(x, [mice,mice], [sfreq,cfreq], [swf,cwf], group_colors=['salmon', 'mediumslateblue'], group_labels=['single','cluster'],
legend='groups', title='Avg. P-wave frequency - single vs clustered waves')
# stats
df = pd.DataFrame(columns=['Mouse', 'State', 'Event', 'Freq'])
for i,s in enumerate(['REM', 'Wake', 'NREM', 'IS']):
df = df.append(pd.DataFrame({'Mouse' : np.tile(mice,2),
'State' : [s]*len(mice)*2,
'Event' : np.repeat(['single', 'cluster'], len(mice)),
'Freq' : np.concatenate((sfreq[:,i], cfreq[:,i])) }))
# two-way repeated measures ANOVA
res_anova = AnovaRM(data=df, depvar='Freq', subject='Mouse', within=['Event', 'State']).fit()
print(res_anova); print(' ### P-values ###'); print(res_anova.anova_table['Pr > F'])
# post hocs - single and cluster P-wave frequency compared between each pair of brain states
single_df = df.iloc[np.where(df['Event'] == 'single')[0], :]; clus_df = df.iloc[np.where(df['Event'] == 'cluster')[0], :]
mc_single = MultiComparison(single_df['Freq'], single_df['State']).allpairtest(stats.ttest_rel, method='bonf'); mc_clus = MultiComparison(clus_df['Freq'], clus_df['State']).allpairtest(stats.ttest_rel, method='bonf')
print('\nSingle P-waves\n'); print(mc_single[0]); print('\nCluster P-waves\n'); print(mc_clus[0]);
# post hocs - for each brain state, single compared to cluster P-wave frequency
print('\nSingle vs cluster P-waves\n')
for s in ['REM', 'Wake', 'NREM', 'IS']:
p = stats.ttest_rel(single_df['Freq'].iloc[np.where(single_df['State'] == s)[0]], clus_df['Freq'].iloc[np.where(clus_df['State'] == s)[0]])
print(f'{s} -- T={round(p.statistic,3)}, p-value={round(p.pvalue,5)}, sig={"yes" if p.pvalue < 0.05 else "no"}')
#%%
### Supp. FIGURE 3G - average spectral power surrounding single & cluster P-waves ###
ppath ='/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
recordings = sleepy.load_recordings(ppath, 'pwaves_mice.txt')[0]
# top - averaged spectrograms
filename = 'sp_win3_single'; win=[-3,3]; pnorm=2; p_iso=0.8; pcluster=0
pwaves.avg_SP(ppath, recordings, istate=[1], win=win, mouse_avg='mouse', plaser=False, pnorm=pnorm, psmooth=[2,2], # single P-waves
fmax=25, vm=[0.6,2.0], p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename)
filename = 'sp_win3_cluster'; win=[-3,3]; pnorm=2; p_iso=0; pcluster=0.5
pwaves.avg_SP(ppath, recordings, istate=[1], win=win, mouse_avg='mouse', plaser=False, pnorm=pnorm, psmooth=[2,2], # clustered P-waves
fmax=25, vm=[0.6,2.0], p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename)
# bottom - average high theta power
filename = 'sp_win3_single'; win=[-3,3]; pnorm=2; p_iso=0.8; pcluster=0
mice, sdict, t = pwaves.avg_band_power(ppath, recordings, istate=[1], win=win, mouse_avg='mouse', plaser=False, pnorm=pnorm, # single P-waves
psmooth=0, bands=[(8,15)], band_colors=['green'], p_iso=p_iso, pcluster=pcluster,
clus_event='waves', ylim=[0.6,1.8], pload=filename, psave=filename)
filename = 'sp_win3_cluster'; win=[-3,3]; pnorm=2; p_iso=0; pcluster=0.5
mice, cdict, t = pwaves.avg_band_power(ppath, recordings, istate=[1], win=win, mouse_avg='mouse', plaser=False, pnorm=pnorm, # clustered P-waves
psmooth=0, bands=[(8,15)], band_colors=['green'], p_iso=p_iso, pcluster=pcluster,
clus_event='waves', ylim=[0.6,1.8], pload=filename, psave=filename)
# right - mean power in 1 s time window
x = np.intersect1d(np.where(t>=-0.5)[0], np.where(t<=0.5)[0]) # get columns between -0.5 s and +0.5 s
df = pd.DataFrame({'Mouse' : np.tile(mice,2),
'Event' : np.repeat(['single', 'cluster'], len(mice)),
'Pwr' : np.concatenate((sdict[(8,15)][:,x].mean(axis=1), cdict[(8,15)][:,x].mean(axis=1))) })
fig = plt.figure(); sns.barplot(x='Event', y='Pwr', data=df, ci=68, palette={'single':'salmon', 'cluster':'mediumslateblue'})
sns.pointplot(x='Event', y='Pwr', hue='Mouse', data=df, ci=None, markers='', color='black'); plt.gca().get_legend().remove()
plt.title('Single vs Clustered P-waves'); plt.show()
# stats
p = stats.ttest_rel(df['Pwr'].iloc[np.where(df['Event'] == 'single')[0]], df['Pwr'].iloc[np.where(df['Event'] == 'cluster')[0]])
print(f'single vs cluster P-waves -- T={round(p.statistic,3)}, p-value={round(p.pvalue,5)}')
#%%
### Supp. FIGURE 4A - % time in each brain state before and during the laser ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_chr2_ol.txt')[1]
BS, t, df = AS.laser_brainstate(ppath, recordings, pre=400, post=520, flatten_tnrem=4, ma_state=3, ma_thr=20, edge=10, sf=0, ci='sem')
#%%
### Supp. FIGURE 4B- averaged spectral band power before and during the laser ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_chr2_ol.txt')[1]
bands=[(0.5,4), (6,10), (11,15), (55,99)]; band_labels=['delta', 'theta', 'sigma', 'gamma']; band_colors=['firebrick', 'limegreen', 'cyan', 'purple']
AS.laser_triggered_eeg_avg(ppath, recordings, pre=400, post=520, fmax=100, laser_dur=120, pnorm=1, psmooth=3, harmcs=10,
iplt_level=2, vm=[0.6,1.4], sf=7, bands=bands, band_labels=band_labels, band_colors=band_colors, ci=95)
#%%
### Supp. FIGURE 4C - laser-triggered change in REM transition probability ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_chr2_ol.txt')[1]
AS.laser_transition_probability(ppath, recordings, pre=400, post=520, ma_state=3, ma_thr=20, sf=10)
#%%
### Supp. FIGURE 4D, spectral power during NREM-->REM transitions ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_chr2_ol.txt')[1]
pre=40; post=40; si_threshold=[pre]*6; sj_threshold=[post]*6
bands=[(0.5,4), (6,12), (13,20), (50,100)]; band_labels=['delta', 'theta', 'sigma', 'gamma']; band_colors=['firebrick', 'limegreen', 'cyan', 'purple']
AS.avg_sp_transitions(ppath, recordings, transitions=[(3,1)], pre=pre, post=post, si_threshold=si_threshold, sj_threshold=sj_threshold,
laser=1, bands=bands, band_labels=band_labels, band_colors=band_colors, flatten_tnrem=3, ma_thr=20, ma_state=3,
fmax=100, pnorm=1, psmooth=[3,3], vm=[(0.1,2.5),(0.1,2.5)], mouse_avg='mouse', sf=0)
#%%
### Supp. FIGURE 4E - power spectrum for each brain state, ChR2 & eYFP mice ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_chr2_ol.txt')[1]
_ = AS.sleep_spectrum_simple(ppath, recordings, istate=[1,2,3,4], pmode=1, pnorm=0, fmax=30, ma_thr=20, # ChR2
ma_state=3, flatten_tnrem=4, harmcs=10)
recordings = sleepy.load_recordings(ppath, 'crh_yfp_chr2_ol.txt')[1]
_ = AS.sleep_spectrum_simple(ppath, recordings, istate=[1,2,3,4], pmode=1, pnorm=0, fmax=30, ma_thr=20, # eYFP
ma_state=3, flatten_tnrem=4, harmcs=10)
#%%
### Supp. FIGURE 4G,H - eYFP percent time spent in each brain state surrounding laser ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_yfp_chr2_ol.txt')[1]
BS, t, df = AS.laser_brainstate(ppath, recordings, pre=400, post=520, flatten_tnrem=4, ma_state=3, ma_thr=20, edge=10, sf=0, ci='sem', ylim=[0,80])
#%%
### Supp. FIGURE 4I - eYFP averaged SPs and frequency band power surrounding laser ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
recordings = sleepy.load_recordings(ppath, 'crh_yfp_chr2_ol.txt')[1]
bands=[(0.5,4), (6,10), (11,15), (55,99)]; band_labels=['delta', 'theta', 'sigma', 'gamma']; band_colors=['firebrick', 'limegreen', 'cyan', 'purple']
AS.laser_triggered_eeg_avg(ppath, recordings, pre=400, post=520, fmax=100, laser_dur=120, pnorm=1, psmooth=3, harmcs=10, iplt_level=2,
vm=[0.6,1.4], sf=7, bands=bands, band_labels=band_labels, band_colors=band_colors, ci=95, ylim=[0.6,1.3])
#%%
### Supp. FIGURE 4J - closed loop overall REM duration ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
ctr_rec = sleepy.load_recordings(ppath, 'crh_yfp_chr2_cl.txt')[1]; exp_rec = sleepy.load_recordings(ppath, 'crh_chr2_cl.txt')[1]
AS.compare_online_analysis(ppath, ctr_rec, exp_rec, istate=1, stat='dur', mouse_avg='mouse', group_colors=['gray','blue'], ylim=[0,120]) # eYFP vs ChR2
ctr_rec = sleepy.load_recordings(ppath, 'crh_yfp_ic_cl.txt')[1]; exp_rec = sleepy.load_recordings(ppath, 'crh_ic_cl.txt')[1]
AS.compare_online_analysis(ppath, ctr_rec, exp_rec, istate=1, stat='dur', mouse_avg='mouse', group_colors=['gray','red'], ylim=[0,120]) # eYFP vs iC++
#%%
### Supp. FIGURE 4K - closed loop total % time in REM ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed/'
ctr_rec = sleepy.load_recordings(ppath, 'crh_yfp_chr2_cl.txt')[1]; exp_rec = sleepy.load_recordings(ppath, 'crh_chr2_cl.txt')[1]
AS.compare_online_analysis(ppath, ctr_rec, exp_rec, istate=1, stat='perc', mouse_avg='mouse', group_colors=['gray','blue'], ylim=[0,12]) # eYFP vs ChR2
ctr_rec = sleepy.load_recordings(ppath, 'crh_yfp_ic_cl.txt')[1]; exp_rec = sleepy.load_recordings(ppath, 'crh_ic_cl.txt')[1]
AS.compare_online_analysis(ppath, ctr_rec, exp_rec, istate=1, stat='perc', mouse_avg='mouse', group_colors=['gray','red'], ylim=[0,12]) # eYFP vs iC++
#%%
### Supp. FIGURE 5B - average amplitude and half-width of spontaneous & laser-triggered P-waves ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed'
recordings = sleepy.load_recordings(ppath, 'lsr_pwaves.txt')[1]
filename = 'lsr_stats'
df = pwaves.get_lsr_stats(ppath, recordings, istate=[1,2,3,4], post_stim=0.1, flatten_tnrem=4, ma_thr=20, ma_state=3, psave=filename)
pwaves.lsr_pwave_size(df, stat='amp2', plotMode='03', istate=1, mouse_avg='mouse')
pwaves.lsr_pwave_size(df, stat='halfwidth', plotMode='03', istate=1, mouse_avg='mouse')
#%%
### Supp. FIGURE 5C - average spectral power surrounding single & cluster P-waves ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed'
recordings = sleepy.load_recordings(ppath, 'lsr_pwaves.txt')[1]
# top - averaged spectrograms
filename = 'sp_win3_single_lsr'; win=[-3,3]; pnorm=2; p_iso=0.8; pcluster=0
pwaves.avg_SP(ppath, recordings, istate=[1], mode='pwaves', win=win, plaser=True, post_stim=0.1, mouse_avg='mouse', # single lsr P-waves
pnorm=pnorm, psmooth=[(3,3),(5,5)], vm=[(0.6,1.65),(0.8,1.5)], fmax=25, recalc_highres=False,
p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename)
filename = 'sp_win3_cluster_lsr'; win=[-3,3]; pnorm=2; p_iso=0; pcluster=0.5
pwaves.avg_SP(ppath, recordings, istate=[1], mode='pwaves', win=win, plaser=True, post_stim=0.1, mouse_avg='mouse', # clustered lsr P-waves
pnorm=pnorm, psmooth=[(7,7),(5,5)], vm=[(0.6,1.65),(0.8,1.5)], fmax=25, recalc_highres=False,
p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename)
# bottom - averaged high theta power
filename = 'sp_win3_single_lsr'; win=[-3,3]; pnorm=2; p_iso=0.8; pcluster=0
mice,lsr_iso,spon_iso,t = pwaves.avg_band_power(ppath, recordings, istate=[1], mode='pwaves', win=win, plaser=True, post_stim=0.1, # single ls P-waves
mouse_avg='mouse', bands=[(8,15)], band_colors=[('green')], pnorm=pnorm, psmooth=(4,4),
fmax=25, p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename, ylim=[0.5,2])
filename = 'sp_win3_cluster_lsr'; win=[-3,3]; pnorm=2; p_iso=0; pcluster=0.5
mice,lsr_clus,spon_clus,t = pwaves.avg_band_power(ppath, recordings, istate=[1], mode='pwaves', win=win, plaser=True, post_stim=0.1, # clustered lsr P-waves
mouse_avg='mouse', bands=[(8,15)], band_colors=[('green')], pnorm=pnorm, psmooth=(4,4),
fmax=25, p_iso=p_iso, pcluster=pcluster, clus_event='waves', pload=filename, psave=filename, ylim=[0.5,2])
# right - mean power in 1 s time window
x = np.intersect1d(np.where(t>=-0.5)[0], np.where(t<=0.5)[0]) # get columns between -0.5 s and +0.5 s
df = pd.DataFrame({'Mouse' : np.tile(mice,2),
'Event' : np.repeat(['single', 'cluster'], len(mice)),
'Pwr' : np.concatenate((lsr_iso[(8,15)][:,x].mean(axis=1), lsr_clus[(8,15)][:,x].mean(axis=1))) })
fig = plt.figure(); sns.barplot(x='Event', y='Pwr', data=df, ci=68, palette={'single':'salmon', 'cluster':'mediumslateblue'})
sns.pointplot(x='Event', y='Pwr', hue='Mouse', data=df, ci=None, markers='', color='black'); plt.gca().get_legend().remove()
plt.title('Laser-triggered Single vs Clustered P-waves'); plt.show()
# stats
p = stats.ttest_rel(df['Pwr'].iloc[np.where(df['Event'] == 'single')[0]], df['Pwr'].iloc[np.where(df['Event'] == 'cluster')[0]])
print(f'single vs cluster laser P-waves -- T={round(p.statistic,3)}, p-value={round(p.pvalue,5)}')
#%%
### Supp. FIGURE 5D,E,F - spectral power preceding successful & failed laser pulses ###
ppath = '/home/fearthekraken/Documents/Data/sleepRec_processed'
recordings = sleepy.load_recordings(ppath, 'lsr_pwaves.txt')[1]
# D - normalized power spectrum
filename = 'sp_win3pre_pnorm1'; win=[-3,0]; pnorm=1
pwaves.lsr_prev_theta_success(ppath, recordings, win=win, mode='spectrum', theta_band=[0,20], post_stim=0.1, pnorm=pnorm, psmooth=3,
ci='sem', nbins=14, prange1=(), prange2=(), mouse_avg='trials', pload=filename, psave=filename)
# E - mean theta power
filename = 'sp_win3pre_pnorm1'; win=[-3,0]; pnorm=1
pwaves.lsr_prev_theta_success(ppath, recordings, win=win, mode='power', theta_band=[6,12], post_stim=0.1, pnorm=pnorm, psmooth=0,
ci='sem', nbins=14, prange1=(), prange2=(0,4), mouse_avg='trials', pload=filename, psave=filename)
# F - mean theta frequency
filename = 'sp_win3pre_pnorm0'; win=[-3,0]; pnorm=0
pwaves.lsr_prev_theta_success(ppath, recordings, win=win, mode='mean freq', theta_band=[6,12], post_stim=0.1, pnorm=pnorm, psmooth=0,
ci='sem', nbins=14, prange1=(), prange2=(6.5,9.5), mouse_avg='trials', pload=filename, psave=filename)
#%%
### Supp. FIGURE 6A - hm3dq power spectrums, saline vs cno ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
(c, e) = AS.load_recordings(ppath, 'crh_hm3dq_tnrem.txt', dose=True, pwave_channel=False); e=e['0.25']
AS.compare_power_spectrums(ppath, [c, e], ['hm3dq-saline', 'hm3dq-cno'], istate=[1,2,3,4], pmode=0, pnorm=0,
fmax=30, flatten_tnrem=4, ma_thr=20, ma_state=3, colors=['gray', 'blue'])
#%%
### Supp. FIGURE 6B - hm4di power spectrums, saline vs cno ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/nrem_transitions/'
(c, e) = AS.load_recordings(ppath, 'crh_hm4di_tnrem.txt', dose=True, pwave_channel=False); e=e['5']
AS.compare_power_spectrums(ppath, [c, e], ['hm4di-saline', 'hm4di-cno'], istate=[1,2,3,4], pmode=0, pnorm=0,
fmax=30, flatten_tnrem=4, ma_thr=20, ma_state=3, colors=['gray', 'red'])
#%%
### Supp. FIGURE 6D - mCherry percent time spent in REM ##
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
m1, T1 = pwaves.sleep_timecourse(ppath, c, istate=[1], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # saline
m2, T2 = pwaves.sleep_timecourse(ppath, hm3dq, istate=[1], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # 0.25 mg/kg CNO
m3, T3 = pwaves.sleep_timecourse(ppath, hm4di, istate=[1], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # 5.0 mg/kg CNO
pwaves.plot_sleep_timecourse([T1,T2,T3], [m1,m2,m3], tstart=0, tbin=18000, stats='perc', plotMode='03',
group_colors=['gray', 'orangered', 'brown'], group_labels=['saline','0.25 mg/kg cno', '5.0 mg/kg cno'])
# stats
df = pwaves.df_from_timecourse_dict([T1,T2,T3], [m1,m2,m3], ['0','0.25', '5'])
res_anova = AnovaRM(data=df, depvar='t0', subject='Mouse', within=['dose']).fit()
print('\n\n ### REM PERCENTAGE ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6E - mCherry mean REM duration ##
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
m1, T1 = pwaves.sleep_timecourse(ppath, c, istate=[1], tbin=18000, n=1, stats='dur', flatten_tnrem=4, pplot=False) # saline
m2, T2 = pwaves.sleep_timecourse(ppath, hm3dq, istate=[1], tbin=18000, n=1, stats='dur', flatten_tnrem=4, pplot=False) # 0.25 mg/kg CNO
m3, T3 = pwaves.sleep_timecourse(ppath, hm4di, istate=[1], tbin=18000, n=1, stats='dur', flatten_tnrem=4, pplot=False) # 5.0 mg/kg CNO
pwaves.plot_sleep_timecourse([T1,T2,T3], [m1,m2,m3], tstart=0, tbin=18000, stats='dur', plotMode='03',
group_colors=['gray', 'orangered', 'brown'], group_labels=['saline','0.25 mg/kg cno', '5.0 mg/kg cno'])
# stats
df = pwaves.df_from_timecourse_dict([T1,T2,T3], [m1,m2,m3], ['0','0.25', '5'])
res_anova = AnovaRM(data=df, depvar='t0', subject='Mouse', within=['dose']).fit()
print('\n\n ### REM DURATION ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6F - mCherry mean REM frequency ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
m1, T1 = pwaves.sleep_timecourse(ppath, c, istate=[1], tbin=18000, n=1, stats='freq', flatten_tnrem=4, pplot=False) # saline
m2, T2 = pwaves.sleep_timecourse(ppath, hm3dq, istate=[1], tbin=18000, n=1, stats='freq', flatten_tnrem=4, pplot=False) # 0.25 mg/kg CNO
m3, T3 = pwaves.sleep_timecourse(ppath, hm4di, istate=[1], tbin=18000, n=1, stats='freq', flatten_tnrem=4, pplot=False) # 5.0 mg/kg CNO
pwaves.plot_sleep_timecourse([T1,T2,T3], [m1,m2,m3], tstart=0, tbin=18000, stats='freq', plotMode='03',
group_colors=['gray', 'orangered', 'brown'], group_labels=['saline','0.25 mg/kg cno', '5.0 mg/kg cno'])
# stats
df = pwaves.df_from_timecourse_dict([T1,T2,T3], [m1,m2,m3], ['0','0.25', '5'])
res_anova = AnovaRM(data=df, depvar='t0', subject='Mouse', within=['dose']).fit()
print('\n\n ### REM FREQUENCY ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6G - mCherry percent time spent in Wake/NREM/IS ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
m1, T1 = pwaves.sleep_timecourse(ppath, c, istate=[2,3,4], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # saline
m2, T2 = pwaves.sleep_timecourse(ppath, hm3dq, istate=[2,3,4], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # 0.25 mg/kg CNO
m3, T3 = pwaves.sleep_timecourse(ppath, hm4di, istate=[2,3,4], tbin=18000, n=1, stats='perc', flatten_tnrem=4, pplot=False) # 5.0 mg/kg CNO
pwaves.plot_sleep_timecourse([T1,T2,T3], [m1,m2,m3], tstart=0, tbin=18000, stats='perc', plotMode='03',
group_colors=['gray', 'orangered', 'brown'], group_labels=['saline','0.25 mg/kg cno', '5.0 mg/kg cno'])
# stats
df = pwaves.df_from_timecourse_dict([T1,T2,T3], [m1,m2,m3], ['0','0.25', '5'])
for s in [2,3,4]:
sdf = df.iloc[np.where(df['state']==s)[0],:]
res_anova = AnovaRM(data=sdf, depvar='t0', subject='Mouse', within=['dose']).fit()
print(f'\n\n ### STATE = {s} ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6H - mCherry REM sleep transition probability ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
m1, T1 = pwaves.sleep_timecourse(ppath, c, istate=[1], tbin=18000, n=1, stats='transition probability', flatten_tnrem=False, pplot=False) # saline
m2, T2 = pwaves.sleep_timecourse(ppath, hm3dq, istate=[1], tbin=18000, n=1, stats='transition probability', flatten_tnrem=False, pplot=False) # 0.25 mg/kg CNO
m3, T3 = pwaves.sleep_timecourse(ppath, hm4di, istate=[1], tbin=18000, n=1, stats='transition probability', flatten_tnrem=False, pplot=False) # 5.0 mg/kg CNO
pwaves.plot_sleep_timecourse([T1,T2,T3], [m1,m2,m3], tstart=0, tbin=18000, stats='transition probability', plotMode='03',
group_colors=['gray', 'orangered', 'brown'], group_labels=['saline','0.25 mg/kg cno', '5.0 mg/kg cno'])
df = pwaves.df_from_timecourse_dict([T1,T2,T3], [m1,m2,m3], ['0','0.25', '5'])
res_anova = AnovaRM(data=df, depvar='t0', subject='Mouse', within=['dose']).fit()
print('\n\n ### REM TRANSITION PROBABILITY ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6I - mCherry time-normalized P-wave frequency across brain state transitions ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=True); hm3dq=e['0.25']; hm4di=e['5']
c = [i[0] for i in c if i[1] != 'X']; hm3dq = [i[0] for i in hm3dq if i[1] != 'X']; hm4di = [i[0] for i in hm4di if i[1] != 'X']
sequence=[3,4,1,2]; state_thres=[(0,10000)]*len(sequence); nstates=[20,20,20,20] # NREM --> IS --> REM --> WAKE
m1,mx1,spe1 = pwaves.stateseq(ppath, c, sequence=sequence, nstates=nstates, state_thres=state_thres, fmax=25, pnorm=1, # saline
psmooth=[2,2], mode='pwaves', mouse_avg='mouse', pplot=False, print_stats=False)
m2,mx2,spe2 = pwaves.stateseq(ppath, hm3dq, sequence=sequence, nstates=nstates, state_thres=state_thres, fmax=25, pnorm=1, # 0.25 mg/kg CNO
psmooth=[2,2], mode='pwaves', mouse_avg='mouse', pplot=False, print_stats=False)
m3,mx3,spe3 = pwaves.stateseq(ppath, hm4di, sequence=sequence, nstates=nstates, state_thres=state_thres, fmax=25, pnorm=1, # 5.0 mg/kg CNO
psmooth=[2,2], mode='pwaves', mouse_avg='mouse', pplot=False, print_stats=False)
mx_list = [mx1,mx2,mx2]
# plot timecourses
pwaves.plot_activity_transitions(mx_list, [m1,m2,m3], plot_id=['gray', 'orangered', 'brown'], xlim=nstates,
group_labels=['mCherry-saline', 'mCherry-0.25mg/kg cno', 'mCherry-5.0mg/kg cno'],
xlabel='Time (normalized', ylabel='P-waves/s', title='NREM-->tNREM-->REM-->Wake', sem=True)
#%%
### Supp. FIGURE 6J - mCherry average P-wave frequency in each brain state ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=True); hm3dq=e['0.25']; hm4di=e['5']
c = [i[0] for i in c if i[1] != 'X']; hm3dq = [i[0] for i in hm3dq if i[1] != 'X']; hm4di = [i[0] for i in hm4di if i[1] != 'X']
m1, x, f1, w1 = pwaves.state_freq(ppath, c, istate=[1,2,3,4], flatten_tnrem=4, pplot=False, print_stats=False) # saline
m2, x, f2, w2 = pwaves.state_freq(ppath, hm3dq, istate=[1,2,3,4], flatten_tnrem=4, pplot=False, print_stats=False) # 0.25 mg/kg CNO
m3, x, f3, w3 = pwaves.state_freq(ppath, hm4di, istate=[1,2,3,4], flatten_tnrem=4, pplot=False, print_stats=False) # 5.0 mg/kg CNO
f_list = [f1,f2,f3]; w_list = [w1,w2,w3]
pwaves.plot_state_freq(x, [m1, m2, m3], f_list, w_list, group_colors=['gray', 'orangered', 'brown'],
group_labels=['mCherry-saline', 'mCherry-0.25mg/kg cno', 'mCherry-5.0mg/kg cno'])
# stats
df = pd.DataFrame(columns=['Mouse', 'State', 'Dose', 'Freq'])
for i,s in enumerate([1,2,3,4]):
df = df.append(pd.DataFrame({'Mouse' : m1 + m2 + m3,
'State' : [s]*len(m1) + [s]*len(m2) + [s]*len(m3),
'Dose' : ['saline']*len(m1) + ['0.25']*len(m2) + ['5']*len(m3),
'Freq' : np.concatenate((f1[:,i], f2[:,i], f3[:,i])) }))
for s in [1,2,3,4]:
sdf = df.iloc[np.where(df['State']==s)[0],:]
res_anova = AnovaRM(data=sdf, depvar='Freq', subject='Mouse', within=['Dose']).fit()
print(f'\n\n ### STATE = {s} ###\n'); print(res_anova)
#%%
### Supp. FIGURE 6K - mCherry power spectrums ###
ppath = '/media/fearthekraken/Mandy_HardDrive1/dreadds_processed/'
(c, e) = AS.load_recordings(ppath, 'mCherry_all.txt', dose=True, pwave_channel=False); hm3dq=e['0.25']; hm4di=e['5']
AS.compare_power_spectrums(ppath, [c, hm3dq, hm4di], ['mCherry-saline', 'mCherry-0.25mg/kg cno', 'mCherry-5.0mg/kg cno'],
istate=[1,2,3,4], pmode=0, pnorm=0, fmax=30, flatten_tnrem=4, ma_thr=20, ma_state=3, colors=['gray', 'orangered', 'brown']) | 69.824153 | 216 | 0.651546 | 5,079 | 32,957 | 4.102973 | 0.091357 | 0.024857 | 0.033735 | 0.032391 | 0.82384 | 0.8093 | 0.768703 | 0.737751 | 0.721148 | 0.69845 | 0 | 0.051675 | 0.15681 | 32,957 | 472 | 217 | 69.824153 | 0.698226 | 0.12489 | 0 | 0.486755 | 0 | 0.016556 | 0.19707 | 0.069989 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.033113 | 0 | 0.033113 | 0.099338 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3b59c10ca9c76cb4989a98f725276e8695a70520 | 4,719 | py | Python | mongosync/progress_logger.py | caosiyang/py-mongo-sync | 980a37c6f2b012025d43878a315e2869af397dd5 | [
"MIT"
] | 98 | 2016-05-26T07:45:08.000Z | 2022-03-15T03:52:22.000Z | mongosync/progress_logger.py | caosiyang/py-mongo-sync | 980a37c6f2b012025d43878a315e2869af397dd5 | [
"MIT"
] | 31 | 2016-08-27T06:46:20.000Z | 2021-07-27T11:52:58.000Z | mongosync/progress_logger.py | caosiyang/py-mongo-sync | 980a37c6f2b012025d43878a315e2869af397dd5 | [
"MIT"
] | 51 | 2016-05-05T05:47:41.000Z | 2021-09-09T10:53:11.000Z | import sys
import time
import multiprocessing
import threading
import Queue
from mongosync.logger import Logger
log = Logger.get()
class Message(object):
""" Progress change message.
"""
def __init__(self, ns, cnt, done):
self.ns = ns
self.cnt = cnt
self.done = done
class Progress(object):
""" Progress attibutes.
"""
def __init__(self, ns, total):
self.ns = ns
self.curr = 0
self.total = total
self.start_time = time.time()
self.done = False
class LoggerThread(threading.Thread):
""" Logger thread.
"""
def __init__(self, n_colls, **kwargs):
self._n_colls = n_colls
self._q = Queue.Queue()
self._ns_map = {}
super(LoggerThread, self).__init__(**kwargs)
def run(self):
n_colls_done = 0
while n_colls_done < self._n_colls:
m = self._q.get()
if m.ns not in self._ns_map:
raise Exception('missing namespace: %s' % m.ns)
self._ns_map[m.ns].curr += m.cnt
prog = self._ns_map[m.ns]
s = '\t%s\t%d/%d\t[%.2f%%]' % (
prog.ns,
prog.curr,
prog.total,
float(prog.curr)/prog.total*100 if prog.total > 0 else float(prog.curr+1)/(prog.total+1)*100)
if not m.done:
log.info(s)
else:
log.info('[ OK ] ' + s)
n_colls_done += 1
time_used = time.time() - prog.start_time
sys.stdout.write('\r\33[K')
sys.stdout.write('\r[\033[32m OK \033[0m]\t[%d/%d]\t%s\t%d/%d\t%.1fs\n' % (n_colls_done, self._n_colls, m.ns, prog.curr, prog.total, time_used))
sys.stdout.flush()
del self._ns_map[m.ns]
# s = ''
# for ns, prog in self._ns_map.iteritems():
# s += '|| %s %d/%d %.1f%% ' % (ns, prog.curr, prog.total, float(prog.curr)/prog.total*100)
# if len(s) > 0:
# s += '||'
# sys.stdout.write('\r%s' % s)
# sys.stdout.flush()
log.info('ProgressLogger thread %s exit' % threading.currentThread().name)
def register(self, ns, total):
""" Register collection.
"""
if ns in self._ns_map:
raise Exception('duplicate collection %s' % ns)
self._ns_map[ns] = Progress(ns, total)
def add(self, ns, count, done=False):
""" Update progress.
"""
self._q.put(Message(ns, count, done))
class LoggerProcess(multiprocessing.Process):
""" Logger progress.
"""
def __init__(self, n_colls, **kwargs):
self._n_colls = n_colls
self._q = multiprocessing.Queue()
self._ns_map = multiprocessing.Manager().dict()
super(LoggerProcess, self).__init__(**kwargs)
def run(self):
n_colls_done = 0
while n_colls_done < self._n_colls:
m = self._q.get()
if m.ns not in self._ns_map:
raise Exception('missing namespace: %s' % m.ns)
self._ns_map[m.ns].curr += m.cnt
prog = self._ns_map[m.ns]
s = '\t%s\t%d/%d\t[%.2f%%]' % (
prog.ns,
prog.curr,
prog.total,
float(prog.curr)/prog.total*100 if prog.total > 0 else float(prog.curr+1)/(prog.total+1)*100)
if not m.done:
log.info(s)
else:
log.info('[ OK ] ' + s)
n_colls_done += 1
time_used = time.time() - prog.start_time
sys.stdout.write('\r\33[K')
sys.stdout.write('\r[\033[32m OK \033[0m]\t[%d/%d]\t%s\t%d/%d\t%.1fs\n' % (n_colls_done, self._n_colls, m.ns, prog.curr, prog.total, time_used))
sys.stdout.flush()
del self._ns_map[m.ns]
# s = ''
# for ns, prog in self._ns_map.iteritems():
# s += '|| %s %d/%d %.1f%% ' % (ns, prog.curr, prog.total, float(prog.curr)/prog.total*100)
# if len(s) > 0:
# s += '||'
# sys.stdout.write('\r%s' % s)
# sys.stdout.flush()
log.info('ProgressLogger process %s exit' % multiprocessing.current_process().name)
def register(self, ns, total):
""" Register collection.
"""
if ns in self._ns_map:
raise Exception('duplicate collection %s' % ns)
self._ns_map[ns] = Progress(ns, total)
def add(self, ns, count, done=False):
""" Update progress.
"""
self._q.put(Message(ns, count, done))
| 33 | 160 | 0.503073 | 612 | 4,719 | 3.712418 | 0.145425 | 0.06338 | 0.06338 | 0.074824 | 0.736796 | 0.736796 | 0.736796 | 0.736796 | 0.736796 | 0.736796 | 0 | 0.019162 | 0.347531 | 4,719 | 142 | 161 | 33.232394 | 0.71874 | 0.141767 | 0 | 0.703297 | 0 | 0.021978 | 0.080491 | 0.029087 | 0 | 0 | 0 | 0 | 0 | 1 | 0.10989 | false | 0 | 0.065934 | 0 | 0.21978 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8e6d874c2b16b50924a57fc1144d3932da922efa | 4,505 | py | Python | 2019/Day 5/main.py | edgorman/Advent-of-Code | 7c354dfdfd0c072c59b15bcac265c19e6a088440 | [
"MIT"
] | null | null | null | 2019/Day 5/main.py | edgorman/Advent-of-Code | 7c354dfdfd0c072c59b15bcac265c19e6a088440 | [
"MIT"
] | null | null | null | 2019/Day 5/main.py | edgorman/Advent-of-Code | 7c354dfdfd0c072c59b15bcac265c19e6a088440 | [
"MIT"
] | null | null | null | import os
from copy import deepcopy
def part_one_helper(intcode, position, mode):
if mode == "1": return intcode[position]
else: return intcode[intcode[position]]
def part_one(intcode, input_):
index = 0
output_ = 0
# Define custom op code functions
def add(a, b):
return a + b
def mult(a, b):
return a * b
def store(v, p):
intcode[p] = v
def output(p):
return intcode[p]
# Iterate until reach the stop code
while intcode[index] != 99:
opcode = int(str(intcode[index])[-2:])
modes = str(intcode[index])[:-2][::-1] + '000'
# Addition
if opcode == 1:
intcode[intcode[index + 3]] = add(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
)
index += 4
# Multiplication
elif opcode == 2:
intcode[intcode[index + 3]] = mult(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
)
index += 4
# Storage
elif opcode == 3:
store(input_, intcode[index + 1])
index += 2
# Output
elif opcode == 4:
output_ = output(intcode[index + 1])
index += 2
# Unknown
else:
raise Exception("Unknown op code")
return output_
def part_two(intcode, input_):
index = 0
output_ = 0
# Define custom op code functions
def add(a, b):
return a + b
def mult(a, b):
return a * b
def store(v, p):
intcode[p] = v
def output(p):
return intcode[p]
def lessthan(a, b):
return a < b
def equals(a, b):
return a == b
# Iterate until reach the stop code
while intcode[index] != 99:
opcode = int(str(intcode[index])[-2:])
modes = str(intcode[index])[:-2][::-1] + '000'
# Addition
if opcode == 1:
intcode[intcode[index + 3]] = add(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
)
index += 4
# Multiplication
elif opcode == 2:
intcode[intcode[index + 3]] = mult(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
)
index += 4
# Storage
elif opcode == 3:
store(input_, intcode[index + 1])
index += 2
# Output
elif opcode == 4:
output_ = output(intcode[index + 1])
index += 2
# Jump if true
elif opcode == 5:
if part_one_helper(intcode, index + 1, modes[0]) != 0:
index = part_one_helper(intcode, index + 2, modes[1])
else:
index += 3
# Jump if false
elif opcode == 6:
if part_one_helper(intcode, index + 1, modes[0]) == 0:
index = part_one_helper(intcode, index + 2, modes[1])
else:
index += 3
# Less than
elif opcode == 7:
value = 0
position = intcode[index + 3]
if lessthan(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
):
value = 1
store(value, position)
index += 4
# Equals
elif opcode == 8:
value = 0
position = intcode[index + 3]
if equals(
part_one_helper(intcode, index + 1, modes[0]),
part_one_helper(intcode, index + 2, modes[1])
):
value = 1
store(value, position)
index += 4
# Unknown
else:
raise Exception("Unknown op code")
return output_
if __name__ == "__main__":
# Get input from txt file
with open(os.getcwd() + '\\2019\\Day 5\\input.txt', 'r') as file_obj:
file_input = file_obj.readlines()
# Clean input
entries = []
for entry in file_input:
for number in entry.rstrip().split(','):
entries.append(int(number))
# Part one
print(part_one(deepcopy(entries), 1))
# Part two
print(part_two(deepcopy(entries), 5))
| 27.469512 | 73 | 0.48768 | 523 | 4,505 | 4.086042 | 0.173996 | 0.179691 | 0.103416 | 0.159102 | 0.763219 | 0.75854 | 0.752457 | 0.725316 | 0.725316 | 0.678521 | 0 | 0.036928 | 0.39889 | 4,505 | 163 | 74 | 27.638037 | 0.752216 | 0.071698 | 0 | 0.728814 | 0 | 0 | 0.017071 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.110169 | false | 0 | 0.016949 | 0.067797 | 0.211864 | 0.016949 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8e7328dd71593da7df05ff8beb9a5d391abf90f7 | 38,520 | py | Python | pic/utils.py | ratnania/mhd | 89f383695075aa26fc805ffbf6ba458f0cdd6f4e | [
"MIT"
] | 5 | 2018-04-13T15:35:21.000Z | 2019-10-29T09:39:13.000Z | pic/utils.py | ratnania/mhd | 89f383695075aa26fc805ffbf6ba458f0cdd6f4e | [
"MIT"
] | null | null | null | pic/utils.py | ratnania/mhd | 89f383695075aa26fc805ffbf6ba458f0cdd6f4e | [
"MIT"
] | 1 | 2018-10-19T10:53:15.000Z | 2018-10-19T10:53:15.000Z | import numpy as np
import scipy.special as sp
from scipy.interpolate import splev
from scipy.sparse import csr_matrix
from bsplines import find_span, basis_funs
def borisPush(particles, dt, B, E, qe, me, Lz, bcs = 1):
'''Pushes particles by a time step dt in the electromagnetic fields E and B by using the Boris method.
Parameters:
particles : ndarray
2D-arrray (Np x 5) containing the particle information (z,vx,vy,vz,wk).
dt : float
The size of the time step.
B : ndarray
2D-array (Np x 3) with the magnetic field (Bx, By, Bz) at the particle positions.
E : ndarray
2D-array (Np x 3) with the electric field (Ex, Ey, Ez) at the particle positions.
qe : float
The particles' electric charge.
me : float
The particles' mass.
Lz : float
The length of the compuational domain (important for boundary treatment).
bcs : int
The boundary conditions. 1: periodic, 2: reflecting
Returns:
znew : ndarray
1D-array (Np) with the updated particle positions.
vnew : ndarray
2D-array (Np x 3) with the updated particle velocities.
'''
if bcs == 1:
qprime = dt*qe/(2*me)
H = qprime*B
S = 2*H/(1 + np.linalg.norm(H, axis = 1)**2)[:, None]
u = particles[:, 1:4] + qprime*E
uprime = u + np.cross(u + np.cross(u, H), S)
vnew = uprime + qprime*E
znew = (particles[:, 0] + dt*vnew[:, 2])%Lz
return znew, vnew
elif bcs == 2:
qprime = dt*qe/(2*me)
H = qprime*B
S = 2*H/(1 + np.linalg.norm(H, axis = 1)**2)[:, None]
u = particles[:, 1:4] + qprime*E
uprime = u + np.cross(u + np.cross(u, H), S)
vnew = uprime + qprime*E
znew = particles[:, 0] + dt*vnew[:, 2]
indices_right = np.where(znew > Lz)[0]
indices_left = np.where(znew < 0)[0]
H_right = qprime*B[indices_right]
S_right = 2*H_right/(1 + np.linalg.norm(H_right, axis = 1)**2)[:, None]
H_left = qprime*B[indices_left]
S_left = 2*H_left/(1 + np.linalg.norm(H_left, axis = 1)**2)[:, None]
u_right = -particles[indices_right, 1:4] + qprime*E[indices_right]
u_left = -particles[indices_left, 1:4] + qprime*E[indices_left]
uprime_right = u_right + np.cross(u_right + np.cross(u_right, H_right), S_right)
uprime_left = u_left + np.cross(u_left + np.cross(u_left, H_left), S_left)
vnew[indices_right] = uprime_right + qprime*E[indices_right]
vnew[indices_left] = uprime_left + qprime*E[indices_left]
znew[indices_right] = particles[indices_right, 0] + dt*vnew[indices_right, 2]
znew[indices_left] = particles[indices_left, 0] + dt*vnew[indices_left, 2]
return znew, vnew
def borisPushRelativistic(particles, dt, B, E, qe, me, Lz, c, bcs = 1):
'''Pushes relativistic particles by a time step dt in the electromagnetic fields E and B using the Boris method.
Parameters:
particles : ndarray
2D-arrray (Np x 5) containing the particle information (z,ux,uy,uz,wk).
dt : float
The size of the time step.
B : ndarray
2D-array (Np x 3) with the magnetic field (Bx, By, Bz) at the particle positions.
E : ndarray
2D-array (Np x 3) with the electric field (Ex, Ey, Ez) at the particle positions.
qe : float
The particles' electric charge.
me : float
The particles' mass.
Lz : float
The length of the compuational domain (important for boundary treatment).
c : float
The speed of light.
bcs : int
The boundary conditions. 1: periodic, 2: reflecting
Returns:
znew : ndarray
1D-array (Np) with the updated particle positions.
unew : ndarray
2D-array (Np x 3) with the updated particle momenta.
'''
if bcs == 1:
qprime = dt*qe/(2*me)
u = particles[:, 1:4] + qprime*E
H = qprime*B/np.sqrt(1 + np.linalg.norm(u, axis = 1)**2/c**2)[:, None]
S = 2*H/(1 + np.linalg.norm(H, axis = 1)**2)[:, None]
uprime = u + np.cross(u + np.cross(u, H), S)
unew = uprime + qprime*E
vnew = unew[:, 2]/np.sqrt(1 + np.linalg.norm(unew, axis = 1)**2/c**2)
znew = (particles[:, 0] + dt*vnew)%Lz
return znew, unew
elif bcs == 2:
qprime = dt*qe/(2*me)
u = particles[:, 1:4] + qprime*E
H = qprime*B/np.sqrt(1 + np.linalg.norm(u, axis = 1)**2/c**2)[:, None]
S = 2*H/(1 + np.linalg.norm(H, axis = 1)**2)[:, None]
uprime = u + np.cross(u + np.cross(u, H), S)
unew = uprime + qprime*E
vnew = unew[:, 2]/np.sqrt(1 + np.linalg.norm(unew, axis = 1)**2/c**2)
znew = particles[:, 0] + dt*vnew
indices_right = np.where(znew > Lz)[0]
indices_left = np.where(znew < 0)[0]
u_right = particles[indices_right, 1:4] + qprime*E[indices_right]
H_right = qprime*B[indices_right]/np.sqrt(1 + np.linalg.norm(u_right, axis = 1)**2/c**2)[:, None]
S_right = 2*H_right/(1 + np.linalg.norm(H_right, axis = 1)**2)[:, None]
u_left = particles[indices_left, 1:4] + qprime*E[indices_left]
H_left = qprime*B[indices_left]/np.sqrt(1 + np.linalg.norm(u_left, axis = 1)**2/c**2)[:, None]
S_left = 2*H_left/(1 + np.linalg.norm(H_left, axis = 1)**2)[:, None]
uprime_right = u_right + np.cross(u_right + np.cross(u_right, H_right), S_right)
uprime_left = u_left + np.cross(u_left + np.cross(u_left, H_left), S_left)
unew[indices_right] = uprime_right + qprime*E[indices_right]
unew[indices_left] = uprime_left + qprime*E[indices_left]
vnew[indices_right] = unew[indices_right, 2]/np.sqrt(1 + np.linalg.norm(unew[indices_right], axis = 1)**2/c**2)
vnew[indices_left] = unew[indices_left, 2]/np.sqrt(1 + np.linalg.norm(unew[indices_left], axis = 1)**2/c**2)
znew[indices_right] = particles[indices_right, 0] + dt*vnew[indices_right]
znew[indices_left] = particles[indices_left, 0] + dt*vnew[indices_left]
return znew, unew
class Bspline(object):
def __init__(self, T, p):
"""
initialize splines for given knot sequence t and degree p
"""
self.T = T
self.p = p
self.N = len(T) - p - 1
self.c = np.zeros(self.N)
def __call__(self, x, i = None, n_deriv = 0):
"""
evaluate b-spline starting at node i at x
"""
if i is not None:
c = np.zeros_like(self.T)
if i < 0:
c[self.N + i] = 1.
else:
c[i] = 1.
else:
c = self.c
tck = (self.T, c, self.p)
return splev(x, tck, der = n_deriv)
def grevilleIga(self):
"""
Returns the Greville points
"""
p = self.p
T = self.T
# TODO implement a pure python function and not use igakit
#from igakit import igalib
#return igalib.bsp.Greville(p, T)
def greville(self):
"""
Returns the Greville points
"""
p = self.p
T = self.T
N = self.N
grev = np.zeros(N)
for i in range(N):
grev[i] = 1/p*sum(T[i+1:p+i+1])
return grev
def plot(self, nx = 100):
"""
Plots all splines constructed from a knot sequence
"""
T = self.T
p = self.p
N = self.N
x = np.linspace(0.0, 1.0, nx)
y = np.zeros((N, nx), dtype = np.double)
for i in range(0, N):
y[i] = self(x, i = i)
plt.plot(x, y[i])
def createBasis(L, Nel, p, bcs = 1):
'''Creates a B-spline basis and the corresponding quadrature/weights grid for exact Gauss-Legendre quadrature.
Parameters:
L : float
Length of the domain.
Nel : int
The number of elements.
p : int
The degree of the basis.
bcs : int
The boundary conditions. 1: periodic, 2: Dirichlet.
Returns:
bsp : B-spline object.
The basis functions. They can be called via bsp(x,j,d), where x is the evaluation point, j the j-th B-spline and d the d-th derivative.
N : int
The number of basis functions.
quad_points : ndarray
1D- array with the quadrature points.
weights : ndarray
1D- array with the corresponding weights.
'''
if bcs == 1:
dz = L/Nel
zj = np.linspace(0, L, Nel + 1)
left = np.linspace(-p*dz, -dz, p)
right = np.linspace(L + dz, L + p*dz, p)
Tbsp = np.array(list(left) + list(zj) + list(right))
bsp = Bspline(Tbsp, p)
N = len(Tbsp) - p - 1
xi, wi = np.polynomial.legendre.leggauss(p + 1)
quad_points = np.zeros((p + 1)*Nel)
weights = np.zeros((p + 1)*Nel)
for i in range(0, Nel):
a1 = zj[i]
a2 = zj[i + 1]
xis = (a2 - a1)/2*xi + (a1 + a2)/2
quad_points[(p + 1)*i:(p + 1)*i + (p + 1)] = xis
wis = (a2 - a1)/2*wi
weights[(p + 1)*i:(p + 1)*i + (p + 1)] = wis
return bsp, N, quad_points, weights
elif bcs == 2:
dz = L/Nel
zj = np.linspace(0, L, Nel + 1)
Tbsp = np.array([0]*p + list(zj) + [L]*p)
bsp = Bspline(Tbsp, p)
N = len(Tbsp) - p - 1
xi,wi = np.polynomial.legendre.leggauss(p + 1)
quad_points = np.zeros((p + 1)*Nel)
weights = np.zeros((p + 1)*Nel)
for i in range(0, Nel):
a1 = zj[i]
a2 = zj[i + 1]
xis = (a2 - a1)/2*xi + (a1 + a2)/2
quad_points[(p + 1)*i:(p + 1)*i + (p + 1)] = xis
wis = (a2 - a1)/2*wi
weights[(p + 1)*i:(p + 1)*i + (p + 1)] = wis
return bsp, N, quad_points, weights
def fieldInterpolation(particles_pos, nodes, basis, uj, bcs = 1):
'''Computes the electromagnetic fields E and B at the particle positions using the basis functions.
Parameters:
particles_pos : ndarray
1D-array containing the particles positions.
nodes : ndarray
1D-array containing the element boundaries.
basis : B-spline object
The B-spline basis functions.
uj : ndarray
1D-array with the FEM coefficients.
bcs : int
The boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
Returns:
Ep : ndarray
2D-array (Np x 2) with the electric fields at the particle positions.
Bp : ndarray
2D-array (Np x 2) with the magnetic fields at the particle positions.
'''
if bcs == 1:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel
Ep = np.zeros((len(particles_pos), 2))
Bp = np.zeros((len(particles_pos), 2))
Zbin = np.digitize(particles_pos, nodes) - 1
ex = uj[0::6]
ey = uj[1::6]
bx = uj[2::6]
by = uj[3::6]
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
Ep[indices, 0] += ex[i%Nb]*bi
Ep[indices, 1] += ey[i%Nb]*bi
Bp[indices, 0] += bx[i%Nb]*bi
Bp[indices, 1] += by[i%Nb]*bi
return Ep, Bp
elif bcs == 2:
Nel = len(nodes) - 1
p = basis.p
Ep = np.zeros((len(particles_pos), 2))
Bp = np.zeros((len(particles_pos), 2))
Zbin = np.digitize(particles_pos, nodes) - 1
ex = np.array([0] + list(uj[0::6]) + [0])
ey = np.array([0] + list(uj[1::6]) + [0])
bx = np.array([0] + list(uj[2::6]) + [0])
by = np.array([0] + list(uj[3::6]) + [0])
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
Ep[indices, 0] += ex[i]*bi
Ep[indices, 1] += ey[i]*bi
Bp[indices, 0] += bx[i]*bi
Bp[indices, 1] += by[i]*bi
return Ep,Bp
def fieldInterpolationFull(particles_pos, nodes, basis, uj, bcs = 1):
'''Computes the electromagnetic fields E and B at the particle positions using the basis functions.
Parameters:
particles_pos : ndarray
1D-array containing the particles positions.
nodes : ndarray
1D-array containing the element boundaries.
basis : B-spline object
The B-spline basis functions.
uj : ndarray
1D-array with the FEM coefficients.
bcs : int
The boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
Returns:
Ep : ndarray
2D-array (Np x 2) with the electric fields at the particle positions.
Bp : ndarray
2D-array (Np x 2) with the magnetic fields at the particle positions.
'''
if bcs == 1:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel
Ep = np.zeros((len(particles_pos), 2))
Bp = np.zeros((len(particles_pos), 2))
Zbin = np.digitize(particles_pos, nodes) - 1
ex = uj[0::6]
ey = uj[1::6]
bx = uj[2::6]
by = uj[3::6]
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
Ep[indices, 0] += ex[i%Nb]*bi
Ep[indices, 1] += ey[i%Nb]*bi
Bp[indices, 0] += bx[i%Nb]*bi
Bp[indices, 1] += by[i%Nb]*bi
return Ep, Bp
elif bcs == 2:
Nel = len(nodes) - 1
p = basis.p
Ep = np.zeros((len(particles_pos), 2))
Bp = np.zeros((len(particles_pos), 2))
Zbin = np.digitize(particles_pos, nodes) - 1
ex = uj[0::6]
ey = uj[1::6]
bx = uj[2::6]
by = uj[3::6]
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
Ep[indices, 0] += ex[i]*bi
Ep[indices, 1] += ey[i]*bi
Bp[indices, 0] += bx[i]*bi
Bp[indices, 1] += by[i]*bi
return Ep, Bp
def hotCurrent(particles_vel, particles_pos, particles_wk, nodes, basis, qe, c, bcs = 1, rel = 1):
'''Computes the hot current density in terms of the weak formulation.
Parameters:
particles_vel : ndarray
2D-array (Np x 2) with the particle velocities (vx, vy).
particles_pos : ndarray
1D-array (Np x 1) with the particle positions (z).
particles_wk : ndarray
1D-array (Np x 1) with the particle weights.
nodes : ndarray
1D-array containing the element boundaries.
basis : B-spline object
B-spline basis functions.
qe : float
The particles' charge.
c : foat
The speed of light
bcs : int
The boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
rel : int
Nonrelativistic (1) or relativistic computation (2).
Returns:
jh : ndarray
1D-array with the hot current densities (jhx, jhy).
'''
if bcs == 1:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel
Np = len(particles_pos)
jh = np.zeros(2*Nb)
if rel == 2:
gamma = np.sqrt(1 + np.sqrt(1 + np.linalg.norm(particles_vel, axis = 1)**2/c**2))
particles_vel[:, 0] = particles_vel[:, 0]/gamma
particles_vel[:, 1] = particles_vel[:, 1]/gamma
Zbin = np.digitize(particles_pos, nodes) - 1
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
wk = particles_wk[indices]
vx = particles_vel[indices, 0]
vy = particles_vel[indices, 1]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
jh[2*(i%Nb)] += np.einsum('i,i,i', vx, wk, bi)
jh[2*(i%Nb) + 1] += np.einsum('i,i,i', vy, wk, bi)
return qe*1/Np*jh
elif bcs == 2:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel + p
Np = len(particles_pos)
jh = np.zeros(2*Nb)
if rel == 2:
gamma = np.sqrt(1 + np.sqrt(1 + np.linalg.norm(particles_vel, axis = 1)**2/c**2))
particles_vel[:, 0] = particles_vel[:, 0]/gamma
particles_vel[:, 1] = particles_vel[:, 1]/gamma
Zbin = np.digitize(particles_pos, nodes) - 1
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
wk = particles_wk[indices]
vx = particles_vel[indices, 0]
vy = particles_vel[indices, 1]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
jh[2*i] += np.einsum('i,i,i', vx, wk, bi)
jh[2*i + 1] += np.einsum('i,i,i', vy, wk, bi)
return qe*1/Np*jh[2:2*(Nb - 1)]
def hotCurrentFull(particles_vel, particles_pos, particles_wk, nodes, basis, qe, c, bcs = 1, rel = 1):
'''Computes the hot current density in terms of the weak formulation.
Parameters:
particles_vel : ndarray
2D-array (Np x 2) with the particle velocities (vx, vy).
particles_pos : ndarray
1D-array (Np x 1) with the particle positions (z).
particles_wk : ndarray
1D-array (Np x 1) with the particle weights.
nodes : ndarray
1D-array containing the element boundaries.
basis : B-spline object
B-spline basis functions.
qe : float
The particles' charge.
c : foat
The speed of light
bcs : int
The boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
rel : int
Nonrelativistic (1) or relativistic computation (2).
Returns:
jh : ndarray
1D-array with the hot current densities (jhx, jhy).
'''
if bcs == 1:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel
Np = len(particles_pos)
jh = np.zeros(2*Nb)
if rel == 2:
gamma = np.sqrt(1 + np.sqrt(1 + np.linalg.norm(particles_vel, axis = 1)**2/c**2))
particles_vel[:, 0] = particles_vel[:, 0]/gamma
particles_vel[:, 1] = particles_vel[:, 1]/gamma
Zbin = np.digitize(particles_pos, nodes) - 1
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
wk = particles_wk[indices]
vx = particles_vel[indices, 0]
vy = particles_vel[indices, 1]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
jh[2*(i%Nb)] += np.einsum('i,i,i', vx, wk, bi)
jh[2*(i%Nb) + 1] += np.einsum('i,i,i', vy, wk, bi)
return qe*1/Np*jh
elif bcs == 2:
Nel = len(nodes) - 1
p = basis.p
Nb = Nel + p
Np = len(particles_pos)
jh = np.zeros(2*Nb)
if rel == 2:
gamma = np.sqrt(1 + np.sqrt(1 + np.linalg.norm(particles_vel, axis = 1)**2/c**2))
particles_vel[:, 0] = particles_vel[:, 0]/gamma
particles_vel[:, 1] = particles_vel[:, 1]/gamma
Zbin = np.digitize(particles_pos, nodes) - 1
for ie in range(0, Nel):
indices = np.where(Zbin == ie)[0]
wk = particles_wk[indices]
vx = particles_vel[indices, 0]
vy = particles_vel[indices, 1]
for il in range(0, p + 1):
i = il + ie
bi = basis(particles_pos[indices], i)
jh[2*i] += np.einsum('i,i,i', vx, wk, bi)
jh[2*i + 1] += np.einsum('i,i,i', vy, wk, bi)
return qe*1/Np*jh
def IC(z,ini,amp,k,omega):
'''Defines the initial conditions of the simulation.
Parameters:
z : ndarray
Positions to be evaluated.
ini : int
Type of inital conditions.
amp : float
Amplitude of the initial perturbations.
k : float
Wavenumber of initial perturbations.
omega : float
Frequency of initial perturbations.
Returns:
initial : ndarray
2D-array (6 x len(z)) with the initial values.
'''
if ini == 1:
eps0 = 1.0
wce = -1.0
wpe = 2.0
Ex0 = +amp*np.cos(k*z)
Ey0 = -amp*np.sin(k*z)
Bx0 = -Ey0*k/omega
By0 = +Ex0*k/omega
Dj = eps0*wpe**2*(omega - wce)/(wce**2 - omega**2)
jx0 = -Ey0*Dj
jy0 = +Ex0*Dj
return np.array([Ex0, Ey0, Bx0, By0, jx0, jy0])
elif ini == 2:
Ex0 = +amp*np.real(np.exp(1j*k*z))
Ey0 = -amp*np.imag(np.exp(1j*k*z))
Bx0 = k*amp*np.imag(1/omega*np.exp(1j*k*z))
By0 = k*amp*np.real(1/omega*np.exp(1j*k*z))
Dj = eps0*wpe**2*(omega - wce)/(wce**2 - omega**2)
jx0 = amp*np.imag(Dj*np.exp(1j*k*z))
jy0 = amp*np.real(Dj*np.exp(1j*k*z))
return np.array([Ex0, Ey0, Bx0, By0, Bz0, jx0, jy0])
elif ini == 3:
Ex0 = 0*z
Ey0 = 0*z
Bx0 = amp*np.sin(k*z)
By0 = 0*z
jx0 = 0*z
jy0 = 0*z
return np.array([Ex0, Ey0, Bx0, By0, jx0, jy0])
elif ini == 4:
Ex0 = 0*z
Ey0 = 0*z
Bx0 = amp*np.random.randn()
By0 = amp*np.random.randn()
jx0 = 0*z
jy0 = 0*z
return np.array([Ex0, Ey0, Bx0, By0, jx0, jy0])
elif ini == 5:
Ex0 = amp*np.random.randn()
Ey0 = amp*np.random.randn()
Bx0 = amp*np.random.randn()
By0 = amp*np.random.randn()
jx0 = amp*np.random.randn()
jy0 = amp*np.random.randn()
return np.array([Ex0, Ey0, Bx0, By0, jx0, jy0])
elif ini == 6:
Ex0 = 0*z
Ey0 = 0*z
Bx0 = 0*z
By0 = 0*z
jx0 = 0*z
jy0 = 0*z
return np.array([Ex0, Ey0, Bx0, By0, jx0, jy0])
def L2proj(basis, L, quad_points, weights, mass, fun, bcs = 1):
'''Computes the coefficients of some given function in the B-spline basis using the L2-projection.
Parameters:
basis : B-spline object
The B-spline basis functions.
L : float
The length of the computational domain.
quad_points : ndarray
Gauss-Legendre quadrature points.
weights : ndarray
The corresponding weights.
mass : ndarry
The mass matrix of the B-spline basis.
fun : function
Function to be projected.
bcs : int
Boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
Returns:
fj : ndarray
The coefficients of the projected function.
'''
if bcs == 1:
p = basis.p
Nel = basis.N - p
Nb = Nel
f = np.zeros(Nb)
for ie in range(0, Nel):
for il in range(0, p + 1):
i = il + ie
value_f = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_f += weights[gl]*fun(quad_points[gl])*basis(quad_points[gl], i, 0)
f[i%Nb] += value_f
fj = np.linalg.solve(mass, f)
return fj
elif bcs == 2:
p = basis.p
Nel = basis.N - p
Nb = basis.N
Ua = fun(0)
Ub = fun(L)
f = np.zeros(Nb)
fj = np.zeros(Nb)
for ie in range(0, Nel):
for il in range(0, p + 1):
i = ie + il
value_f = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_f += weights[gl]*(fun(quad_points[gl])*basis(quad_points[gl], i, 0) - Ua*basis(quad_points[gl], 0, 0)*basis(quad_points[gl], i, 0) - Ub*basis(quad_points[gl], Nb - 1, 0)*basis(quad_points[gl], i, 0))
f[i] += value_f
fj[1:Nb - 1] = np.linalg.solve(mass[1:Nb - 1, 1:Nb - 1],f[1:Nb - 1])
fj[0] = Ua
fj[-1] = Ub
return fj
def matrixAssembly(basis, weights, quad_points, B0, bcs):
'''Assembles the mass, convection and field matrix of a given B-spline Finite Element basis.
Parameters:
basis : B-spline object
The B-spline basis functions.
weights: ndarray
1D-array containing the weights of the Gauss-Legendre quadrature.
quad_points : ndarray
1D-array containing the evaluation points of the Gauss-Legendre quadrature.
B0 : function
The background magnetic field.
bcs : int
Boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
Returns:
M : ndarray
The mass matrix phi_i*phi_j (2D-array).
C : ndarray
The convection matrix phi_i*phi_j^' (2D-array).
D : ndarray
The field matrix B0*phi_i*phi_j (2D-array).
'''
if bcs == 1:
N = basis.N
p = basis.p
Nb = N - p
Nel = Nb
M = np.zeros((Nb, Nb))
C = np.zeros((Nb, Nb))
D = np.zeros((Nb, Nb))
for ie in range(0, Nel):
for il in range(0, p + 1):
for jl in range(0, p + 1):
i = il + ie
j = jl + ie
value_m = 0.0
value_c = 0.0
value_d = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_m += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j ,0)
value_c += weights[gl]*basis(quad_points[gl], i ,0)*basis(quad_points[gl], j, 1)
value_d += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)*B0(quad_points[gl])
M[i%Nb, j%Nb] += value_m
C[i%Nb, j%Nb] += value_c
D[i%Nb, j%Nb] += value_d
return M, C, D
elif bcs == 2:
N = basis.N
p = basis.p
Nel = N - p
M = np.zeros((N, N))
C = np.zeros((N, N))
D = np.zeros((N, N))
for ie in range(0, Nel):
for il in range(0, p + 1):
for jl in range(0, p + 1):
i = il + ie
j = jl + ie
value_m = 0.0
value_c = 0.0
value_d = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_m += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)
value_c += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 1)
value_d += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)*B0(quad_points[gl])
M[i,j] += value_m
C[i,j] += value_c
D[i,j] += value_d
return M, C, D
def matrixAssemblySparse(basis, weights, quad_points, B0, bcs):
'''Assembles the mass, convection and field matrix of a given B-spline Finite Element basis.
Parameters:
basis : B-spline object
The B-spline basis functions.
weights: ndarray
1D-array containing the weights of the Gauss-Legendre quadrature.
quad_points : ndarray
1D-array containing the evaluation points of the Gauss-Legendre quadrature.
B0 : function
The background magnetic field.
bcs : int
Boundary conditions. 1: periodic, 2: homogeneous Dirichlet.
Returns:
M : ndarray
The mass matrix phi_i*phi_j (2D-array).
C : ndarray
The convection matrix phi_i*phi_j^' (2D-array).
D : ndarray
The field matrix B0*phi_i*phi_j (2D-array).
'''
if bcs == 1:
N = basis.N
p = basis.p
Nb = N - p
Nel = Nb
M = np.zeros((Nb, Nb))
C = np.zeros((Nb, Nb))
D = np.zeros((Nb, Nb))
for ie in range(0, Nel):
for il in range(0, p + 1):
for jl in range(0, p + 1):
i = il + ie
j = jl + ie
value_m = 0.0
value_c = 0.0
value_d = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_m += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j ,0)
value_c += weights[gl]*basis(quad_points[gl], i ,0)*basis(quad_points[gl], j, 1)
value_d += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)*B0(quad_points[gl])
M[i%Nb, j%Nb] += value_m
C[i%Nb, j%Nb] += value_c
D[i%Nb, j%Nb] += value_d
return M, C, D
elif bcs == 2:
N = basis.N
p = basis.p
Nel = N - p
row = np.array([])
col = np.array([])
dat_M = np.array([])
dat_C = np.array([])
dat_D = np.array([])
for ie in range(0, Nel):
for il in range(0, p + 1):
for jl in range(0, p + 1):
i = il + ie
j = jl + ie
row = np.append(row, i)
col = np.append(col, j)
value_m = 0.0
value_c = 0.0
value_d = 0.0
for g in range(0, p + 1):
gl = ie*(p + 1) + g
value_m += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)
value_c += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 1)
value_d += weights[gl]*basis(quad_points[gl], i, 0)*basis(quad_points[gl], j, 0)*B0(quad_points[gl])
dat_M = np.append(dat_M, value_m)
dat_C = np.append(dat_C, value_c)
dat_D = np.append(dat_D, value_d)
M = csr_matrix((dat_M, (row, col)))
C = csr_matrix((dat_C, (row, col)))
D = csr_matrix((dat_D, (row, col)))
return M, C, D
def dampingAssembly(basis, damp, meth):
'''Assembles the damping matrix.
Parameters:
basis : B-spline object
The B-spline basis functions.
damp : function
The masking function.
meth : int
The used method for the assembly. 1: only evaluation on support. 2: conventional method.
Returns:
DAMP : ndarray
The damping matrix (2D-array).
'''
if meth == 1:
p = basis.p
Nbase = basis.N
grev = basis.greville()
gi = np.zeros(Nbase)
Bij = np.zeros((Nbase,Nbase))
p_boundary_l = p
p_boundary_r = p + 3
counter = 2
for ie in range(p+1):
for il in range(p_boundary_l):
Bij[il,ie] = basis(grev[il],ie)
p_boundary_l += 1
for ie in range(p+1,Nbase-p-1):
for il in range(p+2):
i = il + counter
Bij[i,ie] = basis(grev[i],ie)
for ie in range(Nbase-p-1,Nbase):
for il in range(p_boundary_r):
i = Nbase - p_boundary_r + il
Bij[i,ie] = basis(grev[i],ie)
p_boundary_r -= 1
for i in range(Nbase):
gi[i] = damp(grev[i])
G = np.diag(gi[1:Nbase-1])
Bijinv = np.linalg.inv(Bij[1:Nbase-1,1:Nbase-1])
DAMP = np.dot(np.dot(Bijinv,G),Bij[1:Nbase-1,1:Nbase-1])
return DAMP
elif meth == 2:
p = basis.p
Nbase = basis.N
grev = basis.greville()
gi = np.zeros(Nbase)
Bij = np.zeros((Nbase,Nbase))
for i in range(Nbase):
for j in range(Nbase):
Bij[i,j] = basis(grev[i],j)
for i in range(Nbase):
gi[i] = damp(grev[i])
G = np.diag(gi[1:Nbase-1])
Bijinv = np.linalg.inv(Bij[1:Nbase-1,1:Nbase-1])
DAMP = np.dot(np.dot(Bijinv,G),Bij[1:Nbase-1,1:Nbase-1])
return DAMP
def evaluation(uj, basis , nodes, x, bcs = 1):
''' Given a coefficient vector, this function computes the value at some position in space spanned by a B-spline basis
Parameters:
uj : ndarray
Coefficient vector.
basis : B-spline object
Object of B-spline basis functions.
nodes : ndarray.
The element boundaries.
x : ndarray
Positions to be evaluated.
bcs : int
Boundary conditions. DEFAULT = 1 (periodic), bcs = 2 (Dirichlet).
Returns:
u : ndarray
Vector with values at positions x.
'''
if bcs == 1:
p = basis.p
Nb = basis.N - p
Nel = Nb
u = np.zeros(len(x))
Xbin = np.digitize(x, nodes) - 1
for ie in range(Nel):
indices = np.where(ie == Xbin)[0]
if len(indices) != 0:
for il in range(0, p + 1):
i = ie + il
u[indices] += uj[i%Nb]*basis(x[indices], i)
return u
elif bcs == 2:
p = basis.p
N = basis.N
Nel = N - p
u = np.zeros(len(x))
Xbin = np.digitize(x, nodes) - 1
for ie in range(Nel):
indices = np.where(ie == Xbin)[0]
if len(indices) != 0:
for il in range(0, p + 1):
i = ie + il
u[indices] += uj[i]*basis(x[indices], i)
return u
def solveDispersionHybrid(k, pol, c, wce, wpe, wpar, wperp, nuh, initial_guess, tol, max_it = 100):
Taniso = 1 - wperp**2/wpar**2
def Z(xi):
return np.sqrt(np.pi)*np.exp(-xi**2)*(1j - sp.erfi(xi))
def Zprime(xi):
return -2*(1 + xi*Z(xi))
def Dhybrid(k, w, pol):
xi = (w + pol*wce)/(k*np.sqrt(2)*wpar)
return 1 - k**2*c**2/w**2 - wpe**2/(w*(w + pol*wce)) + nuh*wpe**2/w**2*(w/(k*np.sqrt(2)*wpar)*Z(xi) - Taniso*(1 + xi*Z(xi)))
def Dhybridprime(k, w, pol):
xi = (w + pol*wce)/(k*np.sqrt(2)*wpar)
xip = 1/(k*np.sqrt(2)*wpar)
return 2*k**2/w**3 + wpe**2*(2*w + pol*wce)/(w**2*(w + pol*wce)**2) - 2*nuh*wpe**2/w**3*(w/(np.sqrt(2)*k*wpar)*Z(xi) - Taniso*(1 + xi*Z(xi))) + nuh*wpe**2/w**2*(1/(np.sqrt(2)*k*wpar)*Z(xi) + w/(np.sqrt(2)*k*wpar)*Zprime(xi)*xip - Taniso*(xip*Z(xi) + xi*Zprime(xi)*xip))
w = initial_guess
counter = 0
while True:
wnew = w - Dhybrid(k, w, pol)/Dhybridprime(k, w, pol)
if np.abs(wnew - w) < tol or counter == max_it:
w = wnew
break
w = wnew
counter += 1
return w, counter
def solveDispersionHybridExplicit(k, pol, c, wce, wpe, wpar, wperp, nuh, initial_guess, tol, max_it = 100):
def Dcold(k, w, pol):
return 1 - k**2*c**2/w**2 - wpe**2/(w*(w + pol*wce))
def Dcoldprime(k, w, pol):
return 2*k**2/w**3 + wpe**2*(2*w + pol*wce)/(w**2*(w + pol*wce)**2)
wr = initial_guess
counter = 0
while True:
wnew = wr - Dcold(k, wr, pol)/Dcoldprime(k, wr, pol)
if np.abs(wnew - wr) < tol or counter == max_it:
wr = wnew
break
wr = wnew
counter += 1
vR = (wr + pol*wce)/k
wi = 1/(2*wr - pol*wpe**2*wce/(wr + pol*wce)**2)*np.sqrt(2*np.pi)*wpe**2*nuh*vR/wpar*np.exp(-vR**2/(2*wpar**2))*(wr/(2*(-pol*wce - wr)) + 1/2*(1 - wperp**2/wpar**2))
return wr, wi, counter
def solveDispersionCold(k, pol, c, wce, wpe, initial_guess, tol, max_it = 100):
def Dcold(k, w, pol):
return 1 - k**2*c**2/w**2 - wpe**2/(w*(w + pol*wce))
def Dcoldprime(k, w, pol):
return 2*k**2/w**3 + wpe**2*(2*w + pol*wce)/(w**2*(w + pol*wce)**2)
wr = initial_guess
counter = 0
while True:
wnew = wr - Dcold(k, wr, pol)/Dcoldprime(k, wr, pol)
if np.abs(wnew - wr) < tol or counter == max_it:
wr = wnew
break
wr = wnew
counter += 1
return wr, counter
def solveDispersionArtificial(k, pol, c, wce, wpe, c1, c2, mu0, initial_guess, tol, max_it = 100):
def Dart(k, w, pol):
return 1 - k**2*c**2/w**2 - wpe**2/(w*(w + pol*wce)) + 1/w*(1j*c1 - pol*c2)
def Dartprime(k, w, pol):
return 2*k**2/w**3 + wpe**2*(2*w + pol*wce)/(w**2*(w + pol*wce)**2) - 1/w**2*(1j*c1 - pol*c2)
w = initial_guess
counter = 0
while True:
wnew = w - Dart(k, w, pol)/Dartprime(k, w, pol)
if np.abs(wnew - w) < tol or counter == max_it:
w = wnew
break
w = wnew
counter += 1
return w, counter
| 28.596882 | 277 | 0.487617 | 5,487 | 38,520 | 3.36304 | 0.069984 | 0.006178 | 0.018642 | 0.027638 | 0.778898 | 0.759389 | 0.741451 | 0.726223 | 0.707148 | 0.698477 | 0 | 0.037345 | 0.382009 | 38,520 | 1,346 | 278 | 28.618128 | 0.737828 | 0.240395 | 0 | 0.727685 | 0 | 0 | 0.001473 | 0 | 0 | 0 | 0 | 0.000743 | 0 | 1 | 0.048412 | false | 0 | 0.007564 | 0.012103 | 0.12708 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8e925766c6f65c8446ac4b086be847e10d2cc29a | 28 | py | Python | hrm_api/community/views/__init__.py | unknowncoder05/HRM | 2a0ad62373fdaefafe533727b2d586d8f6327e87 | [
"MIT"
] | null | null | null | hrm_api/community/views/__init__.py | unknowncoder05/HRM | 2a0ad62373fdaefafe533727b2d586d8f6327e87 | [
"MIT"
] | null | null | null | hrm_api/community/views/__init__.py | unknowncoder05/HRM | 2a0ad62373fdaefafe533727b2d586d8f6327e87 | [
"MIT"
] | null | null | null | from .social_media import *
| 14 | 27 | 0.785714 | 4 | 28 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8eca35c76136fe155221570dc0149eefe1f56f9a | 104 | py | Python | kyokigo/admin.py | seoworks0/docker_test2 | 9ea4a62a8a669d8b1e4be40979b53c0c26ad052f | [
"Apache-2.0"
] | null | null | null | kyokigo/admin.py | seoworks0/docker_test2 | 9ea4a62a8a669d8b1e4be40979b53c0c26ad052f | [
"Apache-2.0"
] | null | null | null | kyokigo/admin.py | seoworks0/docker_test2 | 9ea4a62a8a669d8b1e4be40979b53c0c26ad052f | [
"Apache-2.0"
] | null | null | null | from django.contrib import admin
from .models import Kyokigo_input
admin.site.register(Kyokigo_input)
| 17.333333 | 34 | 0.836538 | 15 | 104 | 5.666667 | 0.666667 | 0.282353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105769 | 104 | 5 | 35 | 20.8 | 0.913978 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d9409011482998f57b1cd2c31f8b30bec82b2380 | 145 | py | Python | gestionPlanificacion_y_Desarrollo_Proyectos_Nuevos/views.py | Juan-Manuel-Diaz/UniNeuroLab | 7cc3f7e67f5d4d7d96f75cf3b13d5f32644a280d | [
"Apache-2.0"
] | 1 | 2021-01-09T01:20:45.000Z | 2021-01-09T01:20:45.000Z | gestionPlanificacion_y_Desarrollo_Proyectos_Nuevos/views.py | Juan-Manuel-Diaz/UniNeuroLab | 7cc3f7e67f5d4d7d96f75cf3b13d5f32644a280d | [
"Apache-2.0"
] | 1 | 2021-01-09T00:53:55.000Z | 2021-01-09T00:53:55.000Z | gestionPlanificacion_y_Desarrollo_Proyectos_Nuevos/views.py | Juan-Manuel-Diaz/UniNeuroLab | 7cc3f7e67f5d4d7d96f75cf3b13d5f32644a280d | [
"Apache-2.0"
] | 1 | 2021-01-07T23:57:28.000Z | 2021-01-07T23:57:28.000Z | from django.shortcuts import render
# Create your views here.
def nuevosProyectos(request):
return render(request, "nuevosProyectos.html")
| 16.111111 | 47 | 0.77931 | 17 | 145 | 6.647059 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 145 | 8 | 48 | 18.125 | 0.904 | 0.158621 | 0 | 0 | 0 | 0 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
d95f1571772395d23ad4fa1b5c95b5a83bed29b4 | 79 | py | Python | pyslam/thirdparty/disk/disk/geom/__init__.py | dysdsyd/VO_benchmark | a7602edab934419c1ec73618ee655e18026f834f | [
"Apache-2.0"
] | 2 | 2021-09-11T09:13:31.000Z | 2021-11-03T01:39:56.000Z | pyslam/thirdparty/disk/disk/geom/__init__.py | dysdsyd/VO_benchmark | a7602edab934419c1ec73618ee655e18026f834f | [
"Apache-2.0"
] | null | null | null | pyslam/thirdparty/disk/disk/geom/__init__.py | dysdsyd/VO_benchmark | a7602edab934419c1ec73618ee655e18026f834f | [
"Apache-2.0"
] | null | null | null | from .distance_matrix import distance_matrix
from .pose import Pose, PoseError
| 26.333333 | 44 | 0.848101 | 11 | 79 | 5.909091 | 0.545455 | 0.430769 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.113924 | 79 | 2 | 45 | 39.5 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d96c2432f040bc29cd1574ca47317b5dcf6150db | 19,039 | py | Python | Final Project/new_clrs/clrs/_src/algorithms/strings.py | mohammedElfatihSalah/string-experiments | e43bb8db323d2d6da702697d052e8c9dac9782de | [
"Apache-2.0"
] | null | null | null | Final Project/new_clrs/clrs/_src/algorithms/strings.py | mohammedElfatihSalah/string-experiments | e43bb8db323d2d6da702697d052e8c9dac9782de | [
"Apache-2.0"
] | null | null | null | Final Project/new_clrs/clrs/_src/algorithms/strings.py | mohammedElfatihSalah/string-experiments | e43bb8db323d2d6da702697d052e8c9dac9782de | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Strings algorithm generators.
Currently implements the following:
- Naive string matching
- Knuth-Morris-Pratt string matching (Knuth et al., 1977)
See "Introduction to Algorithms" 3ed (CLRS3) for more information.
"""
# pylint: disable=invalid-name
from typing import Tuple
import chex
from clrs._src import probing
from clrs._src import specs
import numpy as np
from clrs._src.algorithms.strings_graph_structures import get_seq_mat, get_seq_mat_i_j,get_graph_struct,get_predecessor
_Array = np.ndarray
_Out = Tuple[int, probing.ProbesDict]
_ALPHABET_SIZE = 4
def get_from_i_to_j(T,P,i,j):
N = T.shape[0]
M = P.shape[0]
mat = np.zeros((N+M, N+M), dtype=np.int)
mat[i, j+N] = 1
return mat
# def naive_string_matcher(T: _Array, P: _Array) -> _Out:
# """Naive string matching."""
# chex.assert_rank([T, P], 1)
# probes = probing.initialize(specs.SPECS['naive_string_matcher'])
# T_pos = np.arange(T.shape[0])
# P_pos = np.arange(P.shape[0])
# predecessor = get_predecessor(T,P)
# probing.push(
# probes,
# specs.Stage.INPUT,
# next_probe={
# 'string':
# probing.strings_id(T_pos, P_pos),
# 'pos':
# probing.strings_pos(T_pos, P_pos),
# 'key':
# probing.array_cat(
# np.concatenate([np.copy(T), np.copy(P)]), _ALPHABET_SIZE),
# 'pattern_start':probing.mask_one(T.shape[0], T.shape[0] + P.shape[0]),
# 'predecessor':predecessor,
# })
# s = 0
# while s <= T.shape[0] - P.shape[0]:
# i = s
# j = 0
# # mat_i_j = get_seq_mat_i_j(np.copy(T), np.copy(P), i, j, s)
# # from_i_to_j = get_from_i_to_j(T,P, i, j)
# adj = get_graph_struct(T,P,i,j,s)
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'adj': adj,
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# })
# while True:
# if T[i] != P[j]:
# break
# elif j == P.shape[0] - 1:
# probing.push(
# probes,
# specs.Stage.OUTPUT,
# next_probe={'match': probing.mask_one(s, T.shape[0] + P.shape[0])
# })
# probing.finalize(probes)
# return s, probes
# else:
# i += 1
# j += 1
# adj = get_graph_struct(T,P,i,j,s)
# # from_i_to_j = get_from_i_to_j(T,P, i, j)
# # mat_i_j = get_seq_mat_i_j(np.copy(T), np.copy(P), i, j, s)
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'adj': adj,
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# })
# s += 1
# # By convention, set probe to head of needle if no match is found
# probing.push(
# probes,
# specs.Stage.OUTPUT,
# next_probe={
# 'match': probing.mask_one(T.shape[0], T.shape[0] + P.shape[0])
# })
# return T.shape[0], probes
def get_i_j_mat(T,P, i,j):
T = np.copy(T)
P = np.copy(P)
T_len = T.shape[0]
P_len = P.shape[0]
mat = np.zeros((T_len + P_len, T_len + P_len))
mat[i, j + T_len] = 1
mat[j + T_len, i] = 1
return mat
# hope is here
# def naive_string_matcher(T: _Array, P: _Array) -> _Out:
# """Naive string matching."""
# #print(f"T {T}")
# #print(f"P {P}")
# chex.assert_rank([T, P], 1)
# probes = probing.initialize(specs.SPECS['naive_string_matcher'])
# T_pos = np.arange(T.shape[0])
# P_pos = np.arange(P.shape[0])
# adj = np.full((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]), 1)
# # debug
# debug_advance = []
# probing.push(
# probes,
# specs.Stage.INPUT,
# next_probe={
# 'string':
# probing.strings_id(T_pos, P_pos),
# 'pos':
# probing.strings_pos(T_pos, P_pos),
# 'key':
# probing.array_cat(
# np.concatenate([np.copy(T), np.copy(P)]), _ALPHABET_SIZE),
# 'adj': adj
# })
# s = 0
# while s <= T.shape[0] - P.shape[0]:
# i = s
# j = 0
# # debug
# #print("check: ", T[i], T[j])
# debug_advance.append(int(T[i] != P[j]))
# # if s == 0:
# # if T[i] == P[j]:
# # debug_advance.append(0)
# # else:
# # debug_advance.append(1)
# # else:
# # debug_advance.append(1)
# i_j_mat = get_i_j_mat(T,P,i,j)
# adj2 = np.zeros((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]))
# adj2[i,T.shape[0]:] = 1
# adj2[T.shape[0]:, i] = 1
# adj2[:T.shape[0], j + T.shape[0]] = 1
# adj2[j + T.shape[0], :T.shape[0]] = 1
# # if s == 0:
# # probing.push(
# # probes,
# # specs.Stage.HINT,
# # next_probe={
# # 'pred_h': probing.strings_pred(T_pos, P_pos),
# # 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# # 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# # 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# # 'advance':1,
# # 'i_g':0,
# # 'j_g':0,
# # 's_g':0,
# # 'i_j_mat': i_j_mat,
# # 'adj2':adj2,
# # })
# # else:
# # probing.push(
# # probes,
# # specs.Stage.HINT,
# # next_probe={
# # 'pred_h': probing.strings_pred(T_pos, P_pos),
# # 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# # 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# # 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# # 'advance':1,
# # 'i_g':0,
# # 'j_g':0,
# # 's_g':1,
# # 'i_j_mat': i_j_mat,
# # 'adj2':adj2
# # })
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'advance': int(T[i] != P[j]),
# 'i_g':0,
# 'j_g':0,
# 's_g':1,
# 'i_j_mat': i_j_mat,
# 'adj2':adj2
# })
# while True:
# if T[i] != P[j]:
# break
# elif j == P.shape[0] - 1:
# probing.push(
# probes,
# specs.Stage.OUTPUT,
# next_probe={'match': 0,
# #probing.mask_one(s, T.shape[0] + P.shape[0])
# })
# probing.finalize(probes)
# #debug
# #print("debug advance >> ", debug_advance)
# return s, probes
# else:
# i += 1
# j += 1
# i_j_mat = get_i_j_mat(T,P,i,j)
# adj2 = np.zeros((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]))
# adj2[i,T.shape[0]:] = 1
# adj2[T.shape[0]:, i] = 1
# adj2[:T.shape[0], j + T.shape[0]] = 1
# adj2[j + T.shape[0], :T.shape[0]] = 1
# #debug
# debug_advance.append(int(T[i] != P[j]))
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'advance':int(T[i] != P[j]),
# 'i_g':1,
# 'j_g':1,
# 's_g':0,
# 'i_j_mat': i_j_mat,
# 'adj2':adj2
# })
# s += 1
# # By convention, set probe to head of needle if no match is found
# probing.push(
# probes,
# specs.Stage.OUTPUT,
# next_probe={
# 'match': probing.mask_one(T.shape[0], T.shape[0] + P.shape[0])
# })
# #debug
# #print("debug advance >> ", debug_advance)
# return T.shape[0], probes
def get_predecessor(T, P):
nb_text = T.shape[0]
nb_pattern = P.shape[0]
predecessor = np.eye(nb_pattern + nb_text)
for i in range(1,nb_text):
predecessor[i-1,i] = 1
#predecessor[i,i-1] = 1
for j in range(1 + nb_text, nb_pattern + nb_text):
predecessor[j-1, j] = 1
#predecessor[j, j-1] = 1
return predecessor
def naive_string_matcher(T: _Array, P: _Array) -> _Out:
"""Naive string matching."""
chex.assert_rank([T, P], 1)
probes = probing.initialize(specs.SPECS['naive_string_matcher'])
T_pos = np.arange(T.shape[0])
P_pos = np.arange(P.shape[0])
adj = np.full((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]), 1)
predecessor = get_predecessor(T, P)
# debug
debug_advance = []
probing.push(
probes,
specs.Stage.INPUT,
next_probe={
'predecessor':predecessor,
'string':
probing.strings_id(T_pos, P_pos),
'pos':
probing.strings_pos(T_pos, P_pos),
'key':
probing.array_cat(
np.concatenate([np.copy(T), np.copy(P)]), _ALPHABET_SIZE),
'adj': adj
})
s = 0
while s <= T.shape[0] - P.shape[0]:
i = s
j = 0
# debug
#print("check: ", T[i], T[j])
debug_advance.append(int(T[i] != P[j]))
# if s == 0:
# if T[i] == P[j]:
# debug_advance.append(0)
# else:
# debug_advance.append(1)
# else:
# debug_advance.append(1)
i_j_mat = get_i_j_mat(T,P,i,j)
adj2 = np.zeros((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]))
adj2[i,T.shape[0]:] = 1
adj2[T.shape[0]:, i] = 1
adj2[:T.shape[0], j + T.shape[0]] = 1
adj2[j + T.shape[0], :T.shape[0]] = 1
# if s == 0:
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'advance':1,
# 'i_g':0,
# 'j_g':0,
# 's_g':0,
# 'i_j_mat': i_j_mat,
# 'adj2':adj2,
# })
# else:
# probing.push(
# probes,
# specs.Stage.HINT,
# next_probe={
# 'pred_h': probing.strings_pred(T_pos, P_pos),
# 's': probing.mask_one(s, T.shape[0] + P.shape[0]),
# 'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
# 'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
# 'advance':1,
# 'i_g':0,
# 'j_g':0,
# 's_g':1,
# 'i_j_mat': i_j_mat,
# 'adj2':adj2
# })
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
's': probing.mask_one(s, T.shape[0] + P.shape[0]),
'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
'advance': int(T[i] != P[j]),
## equal: one - not equal: 0
'advance_i':int(T[i] == P[j]), # advance i if they are not equal
'advance_j':int(T[i] == P[j]), # advance j if they are not equal
'advance_s':int(T[i] != P[j]), # advacne s if they are not equal
'i_j_mat': i_j_mat,
#'adj2':adj2
})
while True:
if T[i] != P[j]:
break
elif j == P.shape[0] - 1:
probing.push(
probes,
specs.Stage.OUTPUT,
next_probe={'match': probing.mask_one(s, T.shape[0] + P.shape[0])
})
probing.finalize(probes)
#debug
#print("debug advance >> ", debug_advance)
return s, probes
else:
i += 1
j += 1
i_j_mat = get_i_j_mat(T,P,i,j)
adj2 = np.zeros((T.shape[0] + P.shape[0] , T.shape[0] + P.shape[0]))
adj2[i,T.shape[0]:] = 1
adj2[T.shape[0]:, i] = 1
adj2[:T.shape[0], j + T.shape[0]] = 1
adj2[j + T.shape[0], :T.shape[0]] = 1
#debug
debug_advance.append(int(T[i] != P[j]))
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
's': probing.mask_one(s, T.shape[0] + P.shape[0]),
'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
'j': probing.mask_one(T.shape[0] + j, T.shape[0] + P.shape[0]),
'advance':int(T[i] != P[j]),
## equal: one - not equal: 0
'advance_i':int(T[i] == P[j]), # advance i if they are not equal
'advance_j':int(T[i] == P[j]), # advance j if they are not equal
'advance_s':int(T[i] != P[j]), # advacne s if they are not equal
'i_j_mat': i_j_mat,
#'adj2':adj2
})
s += 1
# By convention, set probe to head of needle if no match is found
probing.push(
probes,
specs.Stage.OUTPUT,
next_probe={
'match': probing.mask_one(T.shape[0], T.shape[0] + P.shape[0])
})
#debug
#print("debug advance >> ", debug_advance)
return T.shape[0], probes
def kmp_matcher(T: _Array, P: _Array) -> _Out:
"""Knuth-Morris-Pratt string matching (Knuth et al., 1977)."""
chex.assert_rank([T, P], 1)
probes = probing.initialize(specs.SPECS['kmp_matcher'])
T_pos = np.arange(T.shape[0])
P_pos = np.arange(P.shape[0])
probing.push(
probes,
specs.Stage.INPUT,
next_probe={
'string':
probing.strings_id(T_pos, P_pos),
'pos':
probing.strings_pos(T_pos, P_pos),
'key':
probing.array_cat(
np.concatenate([np.copy(T), np.copy(P)]), _ALPHABET_SIZE),
})
pi = np.arange(P.shape[0])
k = 0
# Cover the edge case where |P| = 1, and the first half is not executed.
delta = 1 if P.shape[0] > 1 else 0
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
'pi': probing.strings_pi(T_pos, P_pos, pi),
'k': probing.mask_one(T.shape[0], T.shape[0] + P.shape[0]),
'q': probing.mask_one(T.shape[0] + delta, T.shape[0] + P.shape[0]),
's': probing.mask_one(0, T.shape[0] + P.shape[0]),
'i': probing.mask_one(0, T.shape[0] + P.shape[0]),
'phase': 0
})
for q in range(1, P.shape[0]):
while k != pi[k] and P[k] != P[q]:
k = pi[k]
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
'pi': probing.strings_pi(T_pos, P_pos, pi),
'k': probing.mask_one(T.shape[0] + k, T.shape[0] + P.shape[0]),
'q': probing.mask_one(T.shape[0] + q, T.shape[0] + P.shape[0]),
's': probing.mask_one(0, T.shape[0] + P.shape[0]),
'i': probing.mask_one(0, T.shape[0] + P.shape[0]),
'phase': 0
})
if P[k] == P[q]:
k += 1
pi[q] = k
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
'pi': probing.strings_pi(T_pos, P_pos, pi),
'k': probing.mask_one(T.shape[0] + k, T.shape[0] + P.shape[0]),
'q': probing.mask_one(T.shape[0] + q, T.shape[0] + P.shape[0]),
's': probing.mask_one(0, T.shape[0] + P.shape[0]),
'i': probing.mask_one(0, T.shape[0] + P.shape[0]),
'phase': 0
})
q = 0
s = 0
for i in range(T.shape[0]):
if i >= P.shape[0]:
s += 1
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
'pi': probing.strings_pi(T_pos, P_pos, pi),
'k': probing.mask_one(T.shape[0] + k, T.shape[0] + P.shape[0]),
'q': probing.mask_one(T.shape[0] + q, T.shape[0] + P.shape[0]),
's': probing.mask_one(s, T.shape[0] + P.shape[0]),
'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
'phase': 1
})
while q != pi[q] and P[q] != T[i]:
q = pi[q]
probing.push(
probes,
specs.Stage.HINT,
next_probe={
'pred_h': probing.strings_pred(T_pos, P_pos),
'pi': probing.strings_pi(T_pos, P_pos, pi),
'k': probing.mask_one(T.shape[0] + k, T.shape[0] + P.shape[0]),
'q': probing.mask_one(T.shape[0] + q, T.shape[0] + P.shape[0]),
's': probing.mask_one(s, T.shape[0] + P.shape[0]),
'i': probing.mask_one(i, T.shape[0] + P.shape[0]),
'phase': 1
})
if P[q] == T[i]:
if q == P.shape[0] - 1:
probing.push(
probes,
specs.Stage.OUTPUT,
next_probe={'match': probing.mask_one(s, T.shape[0] + P.shape[0])})
probing.finalize(probes)
return s, probes
q += 1
# By convention, set probe to head of needle if no match is found
probing.push(
probes,
specs.Stage.OUTPUT,
next_probe={
'match': probing.mask_one(T.shape[0], T.shape[0] + P.shape[0])
})
probing.finalize(probes)
return T.shape[0], probes
| 30.807443 | 119 | 0.49241 | 2,804 | 19,039 | 3.197218 | 0.072397 | 0.149916 | 0.10541 | 0.070496 | 0.841049 | 0.826436 | 0.820078 | 0.817289 | 0.815059 | 0.799777 | 0 | 0.0304 | 0.326173 | 19,039 | 617 | 120 | 30.857374 | 0.668408 | 0.523767 | 0 | 0.695279 | 0 | 0 | 0.031509 | 0 | 0 | 0 | 0 | 0 | 0.008584 | 1 | 0.021459 | false | 0 | 0.025751 | 0 | 0.077253 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d96d452c5603ccdf6b8f834c75aa5d955cf1b275 | 128 | py | Python | cart/views.py | thtorun/simple-ecommerce | f93a9161c2e25e872dc1ccb71fa21ef79dc403e1 | [
"MIT"
] | null | null | null | cart/views.py | thtorun/simple-ecommerce | f93a9161c2e25e872dc1ccb71fa21ef79dc403e1 | [
"MIT"
] | null | null | null | cart/views.py | thtorun/simple-ecommerce | f93a9161c2e25e872dc1ccb71fa21ef79dc403e1 | [
"MIT"
] | null | null | null | from django.shortcuts import render
# Create your views here.
def cart(request):
return render(request,'cart.html')
| 18.285714 | 39 | 0.710938 | 17 | 128 | 5.352941 | 0.823529 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195313 | 128 | 6 | 40 | 21.333333 | 0.883495 | 0.179688 | 0 | 0 | 0 | 0 | 0.092784 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
7947882f7992a2e92a5534fbedad35e26f9dd60d | 377 | py | Python | authz/controller/apiv1/user.py | amnshzd/authz | 63a60bd185bd8119b0aac48fa5d0e19754b8d03b | [
"Apache-2.0"
] | null | null | null | authz/controller/apiv1/user.py | amnshzd/authz | 63a60bd185bd8119b0aac48fa5d0e19754b8d03b | [
"Apache-2.0"
] | null | null | null | authz/controller/apiv1/user.py | amnshzd/authz | 63a60bd185bd8119b0aac48fa5d0e19754b8d03b | [
"Apache-2.0"
] | null | null | null | from authz.util.jsonify import jsonify
class UserController:
def creat_user():
return jsonify(status=501)
def get_user_list():
return jsonify(status=501)
def get_user(user_id):
return jsonify(status=501)
def update_user(user_id):
return jsonify(status=501)
def delete_user(user_id):
return jsonify(status=501)
| 18.85 | 38 | 0.665782 | 50 | 377 | 4.84 | 0.36 | 0.268595 | 0.392562 | 0.454545 | 0.669421 | 0.669421 | 0.669421 | 0.289256 | 0 | 0 | 0 | 0.052817 | 0.246684 | 377 | 19 | 39 | 19.842105 | 0.799296 | 0 | 0 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.416667 | false | 0 | 0.083333 | 0.416667 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
7951897dd6c9c5ac7f0c52e19439b1b89eed3b0e | 282 | py | Python | dtamg_py/build_datapackages.py | transparencia-mg/dtamg-py | 6007d33a1f33f01f504f82a52ed298cc9f466f61 | [
"MIT"
] | null | null | null | dtamg_py/build_datapackages.py | transparencia-mg/dtamg-py | 6007d33a1f33f01f504f82a52ed298cc9f466f61 | [
"MIT"
] | 15 | 2021-12-20T15:25:23.000Z | 2022-03-07T21:55:57.000Z | dtamg_py/build_datapackages.py | transparencia-mg/dtamg-py | 6007d33a1f33f01f504f82a52ed298cc9f466f61 | [
"MIT"
] | null | null | null | import click
from dtamg_py.utils import build_datapackages
@click.command(name='build-datapackages')
def build_datapackages_cli():
"""
Função responsável pela construção dos conjuntos derivados de todo conjunto AGE7. Constroi pasta build_datasets.
"""
build_datapackages()
| 28.2 | 114 | 0.797872 | 35 | 282 | 6.257143 | 0.742857 | 0.310502 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004049 | 0.124113 | 282 | 9 | 115 | 31.333333 | 0.882591 | 0.397163 | 0 | 0 | 0 | 0 | 0.113924 | 0 | 0 | 0 | 0 | 0.111111 | 0 | 1 | 0.2 | true | 0 | 0.4 | 0 | 0.6 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
796e90fae2d4fa00e66c39c6bbad04e28be37d95 | 26 | py | Python | app/resources/__init__.py | M4cs/SimplyBlog | 0b3b5e7605df269d71a556c70f705f65bed14664 | [
"MIT"
] | 10 | 2019-09-17T13:29:42.000Z | 2021-08-13T22:40:58.000Z | app/resources/__init__.py | M4cs/SimplyBlog | 0b3b5e7605df269d71a556c70f705f65bed14664 | [
"MIT"
] | null | null | null | app/resources/__init__.py | M4cs/SimplyBlog | 0b3b5e7605df269d71a556c70f705f65bed14664 | [
"MIT"
] | 1 | 2020-08-06T02:22:49.000Z | 2020-08-06T02:22:49.000Z | from .upload import Upload | 26 | 26 | 0.846154 | 4 | 26 | 5.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 26 | 1 | 26 | 26 | 0.956522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7990a15a874abc3db07b46a2752c70ad9d69a961 | 143 | py | Python | two_qubit_simulator/quantum_gates/cnot.py | maggiecorrigan/two-qubit-simulator | b4fbf88dfefcac135ac08303af3e6e57f33a0cf4 | [
"MIT"
] | null | null | null | two_qubit_simulator/quantum_gates/cnot.py | maggiecorrigan/two-qubit-simulator | b4fbf88dfefcac135ac08303af3e6e57f33a0cf4 | [
"MIT"
] | null | null | null | two_qubit_simulator/quantum_gates/cnot.py | maggiecorrigan/two-qubit-simulator | b4fbf88dfefcac135ac08303af3e6e57f33a0cf4 | [
"MIT"
] | 22 | 2019-01-30T02:49:42.000Z | 2020-04-04T11:02:47.000Z | """
Contains the CNOT gate
"""
from .quantum_gate import QuantumGate
class CNOT(QuantumGate):
""" Implements the CNOT gate """
pass
| 13 | 37 | 0.678322 | 17 | 143 | 5.647059 | 0.647059 | 0.145833 | 0.229167 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.20979 | 143 | 10 | 38 | 14.3 | 0.849558 | 0.335664 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
799f2e0e0ed0672969a496a40d6ace3773065704 | 1,893 | py | Python | B2G/gecko/dom/bindings/parser/tests/test_duplicate_qualifiers.py | wilebeast/FireFox-OS | 43067f28711d78c429a1d6d58c77130f6899135f | [
"Apache-2.0"
] | 3 | 2015-08-31T15:24:31.000Z | 2020-04-24T20:31:29.000Z | B2G/gecko/dom/bindings/parser/tests/test_duplicate_qualifiers.py | wilebeast/FireFox-OS | 43067f28711d78c429a1d6d58c77130f6899135f | [
"Apache-2.0"
] | null | null | null | B2G/gecko/dom/bindings/parser/tests/test_duplicate_qualifiers.py | wilebeast/FireFox-OS | 43067f28711d78c429a1d6d58c77130f6899135f | [
"Apache-2.0"
] | 3 | 2015-07-29T07:17:15.000Z | 2020-11-04T06:55:37.000Z | def WebIDLTest(parser, harness):
threw = False
try:
parser.parse("""
interface DuplicateQualifiers1 {
getter getter byte foo(unsigned long index);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
threw = False
try:
parser.parse("""
interface DuplicateQualifiers2 {
setter setter byte foo(unsigned long index, byte value);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
threw = False
try:
parser.parse("""
interface DuplicateQualifiers3 {
creator creator byte foo(unsigned long index, byte value);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
threw = False
try:
parser.parse("""
interface DuplicateQualifiers4 {
deleter deleter byte foo(unsigned long index);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
threw = False
try:
parser.parse("""
interface DuplicateQualifiers5 {
getter deleter getter byte foo(unsigned long index);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
threw = False
try:
results = parser.parse("""
interface DuplicateQualifiers6 {
creator setter creator byte foo(unsigned long index, byte value);
};
""")
results = parser.finish()
except:
threw = True
harness.ok(threw, "Should have thrown.")
| 22.270588 | 79 | 0.526677 | 169 | 1,893 | 5.899408 | 0.195266 | 0.091274 | 0.078235 | 0.114343 | 0.761284 | 0.761284 | 0.728185 | 0.728185 | 0.728185 | 0.728185 | 0 | 0.005063 | 0.37401 | 1,893 | 84 | 80 | 22.535714 | 0.836287 | 0 | 0 | 0.791045 | 0 | 0 | 0.496038 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.014925 | false | 0 | 0 | 0 | 0.014925 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
79d3e5c084d27a42e42920b068f4477512405d96 | 293 | py | Python | pyrsa/model/__init__.py | PeerHerholz/pyrsa | 994007086c59de93d86b982f1fff73fe6a8ea929 | [
"MIT"
] | 4 | 2015-08-10T18:34:21.000Z | 2018-05-15T20:43:15.000Z | pyrsa/model/__init__.py | PeerHerholz/pyrsa | 994007086c59de93d86b982f1fff73fe6a8ea929 | [
"MIT"
] | null | null | null | pyrsa/model/__init__.py | PeerHerholz/pyrsa | 994007086c59de93d86b982f1fff73fe6a8ea929 | [
"MIT"
] | 2 | 2018-03-26T03:02:07.000Z | 2021-11-10T21:09:48.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Model definitions and handling
"""
from .model import Model, ModelFixed, ModelSelect, ModelWeighted
from .model import ModelInterpolate
from .model import model_from_dict
from .fitter import fit_mock, fit_optimize, fit_select, fit_interpolate
| 32.555556 | 71 | 0.78157 | 39 | 293 | 5.717949 | 0.615385 | 0.121076 | 0.201794 | 0.179372 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007722 | 0.116041 | 293 | 8 | 72 | 36.625 | 0.853282 | 0.25256 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8dd3b0a44fa42a28bbec4009bc21896b8359100c | 357 | py | Python | polyaxon/notifier/events/experiment_group.py | elyase/polyaxon | 1c19f059a010a6889e2b7ea340715b2bcfa382a0 | [
"MIT"
] | null | null | null | polyaxon/notifier/events/experiment_group.py | elyase/polyaxon | 1c19f059a010a6889e2b7ea340715b2bcfa382a0 | [
"MIT"
] | null | null | null | polyaxon/notifier/events/experiment_group.py | elyase/polyaxon | 1c19f059a010a6889e2b7ea340715b2bcfa382a0 | [
"MIT"
] | null | null | null | import notifier
from event_manager.events import experiment_group
notifier.subscribe_event(experiment_group.ExperimentGroupStoppedEvent)
notifier.subscribe_event(experiment_group.ExperimentGroupDoneEvent)
# notifier.subscribe_event(experiment_group.ExperimentGroupNewStatusEvent)
# notifier.subscribe_event(experiment_group.ExperimentGroupIterationEvent)
| 39.666667 | 74 | 0.907563 | 33 | 357 | 9.515152 | 0.393939 | 0.238854 | 0.280255 | 0.407643 | 0.471338 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.039216 | 357 | 8 | 75 | 44.625 | 0.915452 | 0.406162 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
8dfcdb36faacf361900cfd774142c91385cf36a5 | 7,156 | py | Python | src/CLI/actioner/sonic-cli-sag.py | project-arlo/sonic-mgmt-framework | 562cd84ff3fec9ca705c7df621742f2daa61ce71 | [
"Apache-2.0"
] | 7 | 2019-10-17T06:12:02.000Z | 2021-09-08T11:16:19.000Z | src/CLI/actioner/sonic-cli-sag.py | noolex/sonic-mgmt-framework | 5493889adc47fc584b04dca1a0cc0a2007211df4 | [
"Apache-2.0"
] | 207 | 2019-06-24T04:48:11.000Z | 2020-05-06T05:51:37.000Z | src/CLI/actioner/sonic-cli-sag.py | noolex/sonic-mgmt-framework | 5493889adc47fc584b04dca1a0cc0a2007211df4 | [
"Apache-2.0"
] | 20 | 2019-06-27T19:24:45.000Z | 2021-07-15T21:12:30.000Z | #!/usr/bin/python
###########################################################################
#
# Copyright 2019 Dell, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
###########################################################################
import sys
import json
import collections
import re
import cli_client as cc
from rpipe_utils import pipestr
from scripts.render_cli import show_cli_output
def invoke(func, args):
body = None
aa = cc.ApiClient()
# SAG delete ipv4 anycast gateway address
if func == 'del_llist_openconfig_interfaces_ext_interfaces_interface_subinterfaces_subinterface_ipv4_sag_ipv4_config_static_anycast_gateway' :
sag_key = collections.defaultdict(dict)
sag_key = {
"name": args[0],
"index": "0",
"static-anycast-gateway": args[1]
}
keypath = cc.Path('/restconf/data/openconfig-interfaces:interfaces/interface={name}/subinterfaces/subinterface={index}/openconfig-if-ip:ipv4/openconfig-interfaces-ext:sag-ipv4/config/static-anycast-gateway={static-anycast-gateway}',
**sag_key)
return aa.delete(keypath)
# SAG configure ipv4 anycast gateway address
if func == 'patch_openconfig_interfaces_ext_interfaces_interface_subinterfaces_subinterface_ipv4_sag_ipv4_config_static_anycast_gateway' :
keypath = cc.Path('/restconf/data/openconfig-interfaces:interfaces/interface={name}/subinterfaces/subinterface={index}/openconfig-if-ip:ipv4/openconfig-interfaces-ext:sag-ipv4/config/static-anycast-gateway',
name=args[0], index="0")
body = collections.defaultdict(dict)
body = {
"openconfig-interfaces-ext:static-anycast-gateway": [args[1]]
}
return aa.patch(keypath, body)
# SAG delete ipv6 anycast gateway address
if func == 'del_llist_openconfig_interfaces_ext_interfaces_interface_subinterfaces_subinterface_ipv6_sag_ipv6_config_static_anycast_gateway' :
sag_key = collections.defaultdict(dict)
sag_key = {
"name": args[0],
"index": "0",
"static-anycast-gateway": args[1]
}
keypath = cc.Path('/restconf/data/openconfig-interfaces:interfaces/interface={name}/subinterfaces/subinterface={index}/openconfig-if-ip:ipv6/openconfig-interfaces-ext:sag-ipv6/config/static-anycast-gateway={static-anycast-gateway}',
**sag_key)
return aa.delete(keypath)
# SAG configure ipv6 anycast gateway address
if func == 'patch_openconfig_interfaces_ext_interfaces_interface_subinterfaces_subinterface_ipv6_sag_ipv6_config_static_anycast_gateway' :
keypath = cc.Path('/restconf/data/openconfig-interfaces:interfaces/interface={name}/subinterfaces/subinterface={index}/openconfig-if-ip:ipv6/openconfig-interfaces-ext:sag-ipv6/config/static-anycast-gateway',
name=args[0], index="0")
body = collections.defaultdict(dict)
body = {
"openconfig-interfaces-ext:static-anycast-gateway": [args[1]]
}
return aa.patch(keypath, body)
# SAG delete global mac
if func == 'delete_openconfig_network_instance_ext_network_instances_network_instance_global_sag_config_anycast_mac' :
keypath = cc.Path('/restconf/data/openconfig-network-instance:network-instances/network-instance={name}/openconfig-network-instance-ext:global-sag/config/anycast-mac',
name="default")
return aa.delete(keypath)
# SAG configure global mac
if func == 'patch_openconfig_network_instance_ext_network_instances_network_instance_global_sag_config_anycast_mac' :
keypath = cc.Path('/restconf/data/openconfig-network-instance:network-instances/network-instance={name}/openconfig-network-instance-ext:global-sag/config/anycast-mac',
name="default")
body = collections.defaultdict(dict)
body = {
"openconfig-network-instance-ext:anycast-mac": args[0]
}
return aa.patch(keypath, body)
# SAG IPv4 enable/disable
if func == 'patch_openconfig_network_instance_ext_network_instances_network_instance_global_sag_config_ipv4_enable' :
keypath = cc.Path('/restconf/data/openconfig-network-instance:network-instances/network-instance={name}/openconfig-network-instance-ext:global-sag/config/ipv4-enable',
name="default")
if args[0] == "True":
body = { "openconfig-network-instance-ext:ipv4-enable": True }
elif args[0] == "False":
body = { "openconfig-network-instance-ext:ipv4-enable": False }
return aa.patch(keypath, body)
# SAG IPv6 enable/disable
if func == 'patch_openconfig_network_instance_ext_network_instances_network_instance_global_sag_config_ipv6_enable' :
keypath = cc.Path('/restconf/data/openconfig-network-instance:network-instances/network-instance={name}/openconfig-network-instance-ext:global-sag/config/ipv6-enable',
name="default")
if args[0] == "True":
body = { "openconfig-network-instance-ext:ipv6-enable": True }
elif args[0] == "False":
body = { "openconfig-network-instance-ext:ipv6-enable": False }
return aa.patch(keypath, body)
def run(func, args):
try:
api_response = invoke(func, args)
if api_response.ok():
response = api_response.content
if response is None:
pass
elif 'openconfig-interfaces:config' in response.keys():
value = response['openconfig-interfaces:config']
if value is None:
return
show_cli_output(args[2], value)
elif 'openconfig-network-instance:config' in response.keys():
value = response['openconfig-interfaces:config']
if value is None:
return
show_cli_output(args[2], value)
else:
#error response
print(api_response.error_message())
except:
# system/network error
raise
if __name__ == '__main__':
pipestr().write(sys.argv)
#pdb.set_trace()
run(sys.argv[1], sys.argv[2:])
| 47.078947 | 241 | 0.628144 | 780 | 7,156 | 5.584615 | 0.197436 | 0.089532 | 0.103306 | 0.083563 | 0.778237 | 0.770432 | 0.742195 | 0.722452 | 0.722452 | 0.722452 | 0 | 0.011047 | 0.253633 | 7,156 | 151 | 242 | 47.390728 | 0.804531 | 0.122275 | 0 | 0.489796 | 0 | 0.081633 | 0.476559 | 0.463788 | 0 | 0 | 0 | 0 | 0 | 1 | 0.020408 | false | 0.010204 | 0.071429 | 0 | 0.193878 | 0.010204 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5c0d033d6b901a5c705fb7dc8c7fc3bec8a869fb | 17,576 | py | Python | lib/JsonDIF.py | joser1945/cmr-metadata-review | df0bb24dd06f981af907569f1a97966753053a99 | [
"Apache-2.0"
] | 15 | 2018-06-26T19:58:44.000Z | 2022-03-01T21:19:34.000Z | lib/JsonDIF.py | joser1945/cmr-metadata-review | df0bb24dd06f981af907569f1a97966753053a99 | [
"Apache-2.0"
] | 61 | 2018-06-27T15:15:41.000Z | 2022-03-08T15:39:32.000Z | lib/JsonDIF.py | vbjayanti/cmr-metadata-review | 1c7ac12ef26f144289e3004588a2e2b305d4f940 | [
"Apache-2.0"
] | 9 | 2019-01-22T15:48:48.000Z | 2021-10-01T18:38:30.000Z | '''This file is for get JSON output for Collection DIF data'''
class DIFOutputJSON():
def __init__(self,checkerRules,wrap):
self.checkerRules = checkerRules
self.wrap = wrap
def checkAll(self, metadata):
result = {}
#=======================================
str = 'Entry_Title'
try:
result[str] = self.checkerRules.check_Entry_Title(metadata)
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Citation.Dataset_Release_Date'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Dataset_Citation_Dataset_Release_Date,str)
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Citation.Persistent_Identifier.Type'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Dataset_Citation_Persistent_Identifier_Type,'Dataset_Citation.Persistent_Identifier.Type')
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Citation.Persistent_Identifier.Identifier'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Dataset_Citation_Persistent_Identifier_Identifier,str)
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Citation.Online_Resource'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Dataset_Citation_Online_Resource,'Dataset_Citation.Online_Resource')
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Role'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Personnel_Role_item,str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Person.Email'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Personnel_Contact_Person_Email_item, str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Person.Phone.Number'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Personnel_Contact_Person_phone_item, str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Person.Phone.Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Personnel_Contact_Person_Phone_Type_item,str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Group.Email'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Personnel_Contact_Group_Email_item,str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Group.Phone.Number'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Personnel_Contact_Group_Phone_item,str)
except:
result[str] = 'np'
# ======================================
str = 'Personnel.Contact_Group.Phone.Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Personnel_Contact_Group_Phone_Type_item,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Category'
try:
result[str] = self.wrap(metadata,self.checkerRules.science_Keywords_item_Category,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Topic'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_science_Keywords_item_topic,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Term'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_science_Keywords_item_Term,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Variable_Level_1'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_science_Keywords_item_Variable_1,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Variable_Level_2'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_science_Keywords_item_Variable_2,str)
except:
result[str] = 'np'
# ======================================
str = 'Science_Keywords.Variable_Level_3'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_science_Keywords_item_Variable_3,str)
except:
result[str] = 'np'
# ======================================
str = 'ISO_Topic_Category'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_ISO_Topic_Category,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Type'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Platform_item_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Short_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Short_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Long_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Long_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Instrument.Short_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Instrument_item_shortname,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Instrument.Long_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Instrument_item_longname,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Instrument'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Instrument_sensor_shortname,str)
except:
result[str] = 'np'
# ======================================
str = 'Platform.Instrument'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Platform_item_Instrument_sensor_longname,str)
except:
result[str] = 'np'
# ======================================
str = 'Temporal_Coverage.Range_DateTime.Beginning_Date_Time'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Temporal_Coverage_item_Begin_Date_Time,str)
except:
result[str] = 'np'
# ======================================
str = 'Temporal_Coverage.Range_DateTime.Ending_Date_Time'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Temporal_Coverage_item_end_Date_Time,str)
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Progress'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_dataset_progress,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Granule_Spatial_Representation'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Granule_Spatial_Representation,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Geometry.Coordinate_System'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Geometry_Coordinate_System,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Geometry.Bounding_Rectangle'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Geometry_Bounding_Rectangle_Southernmost_Latitude,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Geometry.Bounding_Rectangle'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Geometry_Bounding_Rectangle_Northernmost_Latitude,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Geometry.Bounding_Rectangle'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Geometry_Bounding_Rectangle_Westernmost_Longitude,str)
except:
result[str] = 'np'
# ======================================
str = 'Spatial_Coverage.Geometry.Bounding_Rectangle'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Spatial_Coverage_Geometry_Bounding_Rectangle_Easternmost_Longitude,str)
except:
result[str] = 'np'
# ======================================
str = 'Location.Location_Category'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Location_Location_Category,str)
except:
result[str] = 'np'
# ======================================
str = 'Location.Location_Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Location_Location_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Location.Location_Subregion1'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Location_Subregion1,str)
except:
result[str] = 'np'
# ======================================
str = 'Location.Location_Subregion2'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Location_Subregion2,str)
except:
result[str] = 'np'
# ======================================
str = 'Location.Location_Subregion3'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Location_Subregion3, str)
except:
result[str] = 'np'
# ======================================
str = 'Data_Resolution.Horizontal_Resolution_Range'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Horizontal_Resolution_Range,str)
except:
result[str] = 'np'
# ======================================
str = 'Data_Resolution.Vertical_Resolution_Range'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Vertical_Resolution_Range,str)
except:
result[str] = 'np'
# ======================================
str = 'Data_Resolution.Temporal_Resolution_Range'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Temporal_Resolution_Range,str)
except:
result[str] = 'np'
# ======================================
str = 'Project.Short_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Project_Short_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Project.Long_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Project_Long_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Quality'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Quality,str)
except:
result[str] = 'np'
# ======================================
str = 'Dataset_Language'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Dataset_Language,str)
except:
result[str] = 'np'
# ======================================
str = 'Organization.Organization_Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Organization_Organization_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Organization.Organization_Name.Short_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Organization_Name_Short_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Organization.Organization_Name.Long_Name'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Organization_Name_Long_Name,str)
except:
result[str] = 'np'
# ======================================
str = 'Organization.Personnel.Contact_Person.Phone.Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Organization_Personnel_Contact_Person_Phone_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Organization.Personnel.Contact_Group.Phone.Type'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Organization_Personnel_Contact_Person_Phone_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Distribution.Distribution_Format'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Distribution_Distribution_Format,str)
except:
result[str] = 'np'
# ======================================
str = 'Multimedia_Sample.URL'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Multimedia_Sample_URL,str)
except:
result[str] = 'np'
# ======================================
str = 'Summary.Abstract'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_summary_abstract,str)
except:
result[str] = 'np'
# ======================================
str = 'Related_URL.URL_Content_Type.Type'
try:
temp = self.wrap(metadata,self.checkerRules.check_Related_URL_item_Content_Type,str)
result[str] = self.checkerRules.check_Related_URL_Content_Type(temp)
except:
result[str] = 'np'
# ======================================
str = 'Related_URL.URL_Content_Type.Subtype'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Related_URL_Content_Type_SubType,str)
except:
result[str] = 'np'
# ======================================
str = 'Related_URL.Description'
try:
temp = self.wrap(metadata,self.checkerRules.check_Related_URL_Description_Item,str)
result[str] += self.checkerRules.check_Related_URL_Description(temp)
except:
result[str] = 'np'
# ======================================
str = 'Related_URL'
try:
temp = self.wrap(metadata,self.checkerRules.check_Related_URL_Mime_Type,str)
result[str] = self.checkerRules.convertMimeType(temp)
except:
result[str] = 'np'
# ======================================
str = 'Product_Level_Id'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Product_Level_ID,str)
except:
result[str] = 'np'
# ======================================
str = 'Collection_Data_Type'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Collection_Data_Type,str)
except:
result[str] = 'np'
# ======================================
str = 'Metadata_Dates.Metadata_Creation'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Metadata_Dates_Creation,str)
except:
result[str] = 'np'
# ======================================
str = 'Metadata_Dates.Metadata_Last_Revision'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Metadata_last_revision,str)
except:
result[str] = 'np'
# ======================================
str = 'Metadata_Dates.Data_Creation'
try:
result[str] = self.wrap(metadata, self.checkerRules.check_Metadata_data_creation,str)
except:
result[str] = 'np'
# ======================================
str = 'Metadata_Dates.Data_Last_Revision'
try:
result[str] = self.wrap(metadata,self.checkerRules.check_Metadata_data_latest_revision,str)
except:
result[str] = 'np'
return result | 43.184275 | 160 | 0.511664 | 1,555 | 17,576 | 5.535691 | 0.077814 | 0.136966 | 0.161013 | 0.128369 | 0.904391 | 0.879182 | 0.851417 | 0.823536 | 0.722584 | 0.614661 | 0 | 0.000926 | 0.262517 | 17,576 | 407 | 161 | 43.184275 | 0.663169 | 0.147474 | 0 | 0.60597 | 0 | 0 | 0.145223 | 0.118538 | 0 | 0 | 0 | 0 | 0 | 1 | 0.00597 | false | 0 | 0 | 0 | 0.01194 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5c16bc03fef547b26441dd335b7e7456cabe6074 | 22 | py | Python | code_py/Linear_Optimization_1.py | Moises9/orToolsOptimization | 497468a43871607f45b6ac47953cf084ce183943 | [
"MIT"
] | null | null | null | code_py/Linear_Optimization_1.py | Moises9/orToolsOptimization | 497468a43871607f45b6ac47953cf084ce183943 | [
"MIT"
] | null | null | null | code_py/Linear_Optimization_1.py | Moises9/orToolsOptimization | 497468a43871607f45b6ac47953cf084ce183943 | [
"MIT"
] | null | null | null |
import ortools as ot | 7.333333 | 20 | 0.772727 | 4 | 22 | 4.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.227273 | 22 | 3 | 20 | 7.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5c4621cb9ed77af336580dd91e9c10df289e9ecc | 1,260 | py | Python | src/dwiprep/utils/inputs.py | GalBenZvi/dwiprep | b19340533813f7dcbfb8ab471d57a2a3407358af | [
"Apache-2.0"
] | 1 | 2021-10-30T12:55:27.000Z | 2021-10-30T12:55:27.000Z | src/dwiprep/utils/inputs.py | GalBenZvi/dwiprep | b19340533813f7dcbfb8ab471d57a2a3407358af | [
"Apache-2.0"
] | 18 | 2021-02-21T10:46:11.000Z | 2021-11-29T20:42:04.000Z | src/dwiprep/utils/inputs.py | GalBenZvi/dwiprep | b19340533813f7dcbfb8ab471d57a2a3407358af | [
"Apache-2.0"
] | 3 | 2021-02-21T10:46:20.000Z | 2021-10-30T12:55:32.000Z | import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
INPUT_FIELDS = [
"output_dir",
# DWI
"dwi",
"in_bvec",
"in_bval",
"in_json",
# fmap
"fmap_ap",
"fmap_ap_json",
"fmap_pa",
"fmap_pa_json",
# From anatomical
"t1w_preproc",
"t1w_mask",
"t1w_dseg",
"t1w_aseg",
"t1w_aparc",
"t1w_tpms",
"template",
"anat2std_xfm",
"std2anat_xfm",
"subjects_dir",
"subject_id",
"t1w2fsnative_xfm",
"fsnative2t1w_xfm",
]
INPUTNODE = pe.Node(
niu.IdentityInterface(
fields=[
"output_dir",
# DWI
"dwi",
"in_bvec",
"in_bval",
"in_json",
# fmap
"fmap_ap",
"fmap_ap_json",
"fmap_pa",
"fmap_pa_json",
# From anatomical
"t1w_preproc",
"t1w_mask",
"t1w_dseg",
"t1w_aseg",
"t1w_aparc",
"t1w_tpms",
"template",
"anat2std_xfm",
"std2anat_xfm",
"subjects_dir",
"subject_id",
"t1w2fsnative_xfm",
"fsnative2t1w_xfm",
]
),
name="inputnode",
)
| 20 | 39 | 0.469841 | 118 | 1,260 | 4.635593 | 0.364407 | 0.058501 | 0.054845 | 0.065814 | 0.786106 | 0.786106 | 0.786106 | 0.786106 | 0.786106 | 0.786106 | 0 | 0.031915 | 0.403175 | 1,260 | 62 | 40 | 20.322581 | 0.695479 | 0.038889 | 0 | 0.8 | 0 | 0 | 0.356312 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.036364 | 0 | 0.036364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a50dbc6309d1f44efd26e99a8a3b5cb2067866df | 173 | py | Python | tests/segmentation/pspnet/pspnet_test.py | indiradutta/PyVision | cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf | [
"BSD-3-Clause"
] | 31 | 2020-05-03T07:03:01.000Z | 2022-01-29T15:29:22.000Z | tests/segmentation/pspnet/pspnet_test.py | indiradutta/PyVision | cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf | [
"BSD-3-Clause"
] | 13 | 2020-05-25T14:23:46.000Z | 2021-08-04T10:38:02.000Z | tests/segmentation/pspnet/pspnet_test.py | indiradutta/PyVision | cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf | [
"BSD-3-Clause"
] | 12 | 2020-05-24T22:26:59.000Z | 2021-08-03T18:30:51.000Z | from pyvision.segmentation.pspnet import PSPNet
m = PSPNet(model="pspnet-resnet50-ade20k")
m.inference("pyvision/segmentation/pspnet/examples/ade20k.jpg", save="ade20k")
| 28.833333 | 78 | 0.791908 | 22 | 173 | 6.227273 | 0.590909 | 0.291971 | 0.379562 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.049689 | 0.069364 | 173 | 5 | 79 | 34.6 | 0.801242 | 0 | 0 | 0 | 0 | 0 | 0.439306 | 0.404624 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
a519e3434901731bd0935ab21ff108f6a3d4a4d7 | 42 | py | Python | discloud/__init__.py | DiogoMarques2003/python-discloud-status | 0f476fab01d397457a05721ed09428b5593bca9f | [
"Apache-2.0"
] | 8 | 2019-11-07T00:28:10.000Z | 2022-03-30T00:03:21.000Z | discloud/__init__.py | DiogoMarques2003/python-discloud-status | 0f476fab01d397457a05721ed09428b5593bca9f | [
"Apache-2.0"
] | null | null | null | discloud/__init__.py | DiogoMarques2003/python-discloud-status | 0f476fab01d397457a05721ed09428b5593bca9f | [
"Apache-2.0"
] | 1 | 2022-01-12T13:20:28.000Z | 2022-01-12T13:20:28.000Z | from .ram import ram, total_ram, using_ram | 42 | 42 | 0.809524 | 8 | 42 | 4 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119048 | 42 | 1 | 42 | 42 | 0.864865 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
eb5eb0de0cfc110299bcbd5804cb3b27859de120 | 30 | py | Python | readthedocs/tastyapi/__init__.py | ludia/readthedocs.org | 636c2bd57b417c4d73657d2517efaf4258dd75c1 | [
"MIT"
] | 1 | 2021-11-12T23:52:23.000Z | 2021-11-12T23:52:23.000Z | readthedocs/tastyapi/__init__.py | titilambert/readthedocs.org | 774611db90fea94c3ae4d7de4726f010ab01ddab | [
"MIT"
] | null | null | null | readthedocs/tastyapi/__init__.py | titilambert/readthedocs.org | 774611db90fea94c3ae4d7de4726f010ab01ddab | [
"MIT"
] | null | null | null | from .slum import api # noqa
| 15 | 29 | 0.7 | 5 | 30 | 4.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.233333 | 30 | 1 | 30 | 30 | 0.913043 | 0.133333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
eb87af8042c1df5eaaf859413d6ced9c70a91f26 | 3,525 | py | Python | Python/count-of-range-sum.py | bssrdf/LeetCode-5 | 746df5cff523361145a74d9d429dc541a7b99910 | [
"MIT"
] | 68 | 2018-01-13T07:15:37.000Z | 2022-02-20T12:58:24.000Z | Python/count-of-range-sum.py | bssrdf/LeetCode-5 | 746df5cff523361145a74d9d429dc541a7b99910 | [
"MIT"
] | 2 | 2021-12-10T01:43:37.000Z | 2021-12-14T21:48:53.000Z | Python/count-of-range-sum.py | bssrdf/LeetCode-5 | 746df5cff523361145a74d9d429dc541a7b99910 | [
"MIT"
] | 63 | 2017-04-10T03:38:25.000Z | 2022-03-17T23:24:51.000Z | # Time: O(nlogn)
# Space: O(n)
# Given an integer array nums, return the number of range
# sums that lie in [lower, upper] inclusive.
# Range sum S(i, j) is defined as the sum of the elements
# in nums between indices i and j (i <= j), inclusive.
#
# Note:
# A naive algorithm of O(n^2) is trivial. You MUST do better than that.
#
# Example:
# Given nums = [-2, 5, -1], lower = -2, upper = 2,
# Return 3.
# The three ranges are : [0, 0], [2, 2], [0, 2] and
# their respective sums are: -2, -1, 2.
# Divide and Conquer solution.
class Solution(object):
def countRangeSum(self, nums, lower, upper):
"""
:type nums: List[int]
:type lower: int
:type upper: int
:rtype: int
"""
def countAndMergeSort(sums, start, end, lower, upper):
if end - start <= 1: # The size of range [start, end) less than 2 is always with count 0.
return 0
mid = start + (end - start) / 2
count = countAndMergeSort(sums, start, mid, lower, upper) + \
countAndMergeSort(sums, mid, end, lower, upper)
j, k, r = mid, mid, mid
tmp = []
for i in xrange(start, mid):
# Count the number of range sums that lie in [lower, upper].
while k < end and sums[k] - sums[i] < lower:
k += 1
while j < end and sums[j] - sums[i] <= upper:
j += 1
count += j - k
# Merge the two sorted arrays into tmp.
while r < end and sums[r] < sums[i]:
tmp.append(sums[r])
r += 1
tmp.append(sums[i])
# Copy tmp back to sums.
sums[start:start+len(tmp)] = tmp
return count
sums = [0] * (len(nums) + 1)
for i in xrange(len(nums)):
sums[i + 1] = sums[i] + nums[i]
return countAndMergeSort(sums, 0, len(sums), lower, upper)
# Divide and Conquer solution.
class Solution2(object):
def countRangeSum(self, nums, lower, upper):
"""
:type nums: List[int]
:type lower: int
:type upper: int
:rtype: int
"""
def countAndMergeSort(sums, start, end, lower, upper):
if end - start <= 0: # The size of range [start, end] less than 2 is always with count 0.
return 0
mid = start + (end - start) / 2
count = countAndMergeSort(sums, start, mid, lower, upper) + \
countAndMergeSort(sums, mid + 1, end, lower, upper)
j, k, r = mid + 1, mid + 1, mid + 1
tmp = []
for i in xrange(start, mid + 1):
# Count the number of range sums that lie in [lower, upper].
while k <= end and sums[k] - sums[i] < lower:
k += 1
while j <= end and sums[j] - sums[i] <= upper:
j += 1
count += j - k
# Merge the two sorted arrays into tmp.
while r <= end and sums[r] < sums[i]:
tmp.append(sums[r])
r += 1
tmp.append(sums[i])
# Copy tmp back to sums
sums[start:start+len(tmp)] = tmp
return count
sums = [0] * (len(nums) + 1)
for i in xrange(len(nums)):
sums[i + 1] = sums[i] + nums[i]
return countAndMergeSort(sums, 0, len(sums) - 1, lower, upper)
| 35.969388 | 102 | 0.489362 | 464 | 3,525 | 3.717672 | 0.200431 | 0.075362 | 0.034783 | 0.027826 | 0.806377 | 0.772754 | 0.772754 | 0.724058 | 0.724058 | 0.724058 | 0 | 0.021973 | 0.393191 | 3,525 | 97 | 103 | 36.340206 | 0.784479 | 0.293617 | 0 | 0.653846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.076923 | false | 0 | 0 | 0 | 0.230769 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cce62feff0b13ed7f9f1dec6b2d06c7b53a00237 | 34 | py | Python | nih_mpd_lib/__init__.py | s-kostyuk/sockets_samples | d0c876c8a4ec4a5890f22c6b5a1091ee167d968e | [
"MIT"
] | 1 | 2018-02-20T09:16:46.000Z | 2018-02-20T09:16:46.000Z | nih_mpd_lib/__init__.py | s-kostyuk/sockets_samples | d0c876c8a4ec4a5890f22c6b5a1091ee167d968e | [
"MIT"
] | null | null | null | nih_mpd_lib/__init__.py | s-kostyuk/sockets_samples | d0c876c8a4ec4a5890f22c6b5a1091ee167d968e | [
"MIT"
] | null | null | null | from .mpd_client import MPDClient
| 17 | 33 | 0.852941 | 5 | 34 | 5.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 34 | 1 | 34 | 34 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ccf0b58b4f0a0d484efd601b2bd243d4e56de1dc | 94 | py | Python | Shared_Files/Custom_Exception.py | EricCacciavillani/LyreBird | 858657faef39d1adcba19ff0213210ba490b4afa | [
"MIT"
] | 1 | 2019-05-04T02:34:20.000Z | 2019-05-04T02:34:20.000Z | Shared_Files/Custom_Exception.py | EricCacciavillani/LyreBird | 858657faef39d1adcba19ff0213210ba490b4afa | [
"MIT"
] | null | null | null | Shared_Files/Custom_Exception.py | EricCacciavillani/LyreBird | 858657faef39d1adcba19ff0213210ba490b4afa | [
"MIT"
] | 1 | 2019-04-04T19:14:09.000Z | 2019-04-04T19:14:09.000Z | class Midi_Reading_Error(Exception):
pass
class Model_Pathing_Error(Exception):
pass | 15.666667 | 37 | 0.776596 | 12 | 94 | 5.75 | 0.666667 | 0.405797 | 0.521739 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159574 | 94 | 6 | 38 | 15.666667 | 0.873418 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
69032d390087f9288099369d83523d38fd24f7ff | 5,031 | py | Python | generate_tsp_pomo.py | dschaub95/pyconcorde | 8b4a7af120071e939445050efdf613412475cb26 | [
"BSD-3-Clause"
] | null | null | null | generate_tsp_pomo.py | dschaub95/pyconcorde | 8b4a7af120071e939445050efdf613412475cb26 | [
"BSD-3-Clause"
] | null | null | null | generate_tsp_pomo.py | dschaub95/pyconcorde | 8b4a7af120071e939445050efdf613412475cb26 | [
"BSD-3-Clause"
] | null | null | null | from TSP_utils import TSP_solver, TSP_plotter, TSP_generator, TSP_loader
import numpy as np
import os
import networkx as nx
import argparse
import json
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--save_folder", type=str, default='./test_sets')
parser.add_argument("--num_nodes", type=int, default=100)
parser.add_argument("--num_samples", type=int, default=10000)
parser.add_argument("--start_index", type=int, default=0)
parser.add_argument("--scale_factor", type=int, default=8)
opts = parser.parse_known_args()[0]
# generate directory and subdirs based on opts
save_path = f'{opts.save_folder}/uniform_n_{opts.num_nodes}_{opts.num_samples}'
if not os.path.exists(save_path):
os.mkdir(save_path)
# problem_folder = f'{save_path}/problems'
# solution_folder = f'{save_path}/solutions'
# heatmap_folder = f'{save_path}/heatmaps'
generator = TSP_generator(g_type='tsp_2d', num_min=opts.num_nodes, num_max=opts.num_nodes)
solver = TSP_solver()
for i in tqdm(range(opts.start_index, opts.num_samples)):
# generate instance
graph = generator.gen_graph()
# solve instance
length, sol_time, solution = solver.calc_opt_tour_from_nx(graph, scale=opts.scale_factor)
# delete temp files
solver.del_tmp_files()
# save everything
idx_str = f'{i}'.zfill(len(str(opts.num_samples)))
# problem_name = f'TSP_Problem_{idx_str}'
problem_name = f'tsp_{idx_str}'
instance_path = f'{save_path}/{problem_name}'
if not os.path.exists(instance_path):
os.mkdir(instance_path)
# save instance data to folder
# once just save the networkx graph
# convert numpy arrays to list
for i in range(graph.number_of_nodes()):
graph.nodes[i].update({'coord': list(graph.nodes[i]['coord'])})
nx.write_gml(graph, path=f'{instance_path}/nx_graph.gml', stringizer=str)
# extract and save node_feats
node_feats = np.array([graph.nodes[i]['coord'] for i in range(graph.number_of_nodes())])
np.savetxt(f'{instance_path}/node_feats.txt', node_feats)
# extract and save edge weights#
edge_weights = nx.convert_matrix.to_numpy_array(graph)
np.savetxt(f'{instance_path}/edge_weights.txt', edge_weights)
# save solution data to folder
solution_data = {'problem_name': problem_name,
'opt_tour_length': length,
'opt_tour': solution.tolist()}
with open(f"{instance_path}/solution.json", 'w') as f:
# indent=2 is not needed but makes the file human-readable
json.dump(solution_data, f, indent=2)
################################ OLD VERSION #######################################
# if __name__ == "__main__":
# parser = argparse.ArgumentParser()
# parser.add_argument("--save_folder", type=str, default='./test_sets')
# parser.add_argument("--num_nodes", type=int, default=100)
# parser.add_argument("--num_samples", type=int, default=10000)
# parser.add_argument("--start_index", type=int, default=0)
# parser.add_argument("--scale_factor", type=int, default=8)
# opts = parser.parse_known_args()[0]
# # generate directory and subdirs based on opts
# save_path = f'{opts.save_folder}/uniform_n_{opts.num_nodes}_{opts.num_samples}'
# if not os.path.exists(save_path):
# os.mkdir(save_path)
# # problem_folder = f'{save_path}/problems'
# # solution_folder = f'{save_path}/solutions'
# # heatmap_folder = f'{save_path}/heatmaps'
# generator = TSP_generator(g_type='tsp_2d', num_min=opts.num_nodes, num_max=opts.num_nodes)
# solver = TSP_solver()
# for i in tqdm(range(opts.start_index, opts.num_samples)):
# # generate instance
# graph = generator.gen_graph()
# # solve instance
# length, sol_time, solution = solver.calc_opt_tour_from_nx(graph, scale=8)
# # delete temp files
# solver.del_tmp_files()
# # save everything
# idx_str = f'{i}'.zfill(len(str(opts.num_samples)))
# # problem_name = f'TSP_Problem_{idx_str}'
# problem_name = f'problem_{idx_str}'
# instance_path = f'{save_path}/{problem_name}'
# if not os.path.exists(instance_path):
# os.mkdir(instance_path)
# # save instance data to folder
# generator.save_nx_as_tsp_single(graph, save_path=instance_path, problem_name=problem_name, scale=8)
# # save solution data to folder
# solution_data = {'problem_name': problem_name,
# 'opt_tour_length': length,
# 'opt_tour': solution.tolist()}
# with open(f"{instance_path}/solution.txt", 'w') as f:
# # indent=2 is not needed but makes the file human-readable
# json.dump(solution_data, f, indent=2) | 42.277311 | 109 | 0.640032 | 672 | 5,031 | 4.516369 | 0.199405 | 0.039539 | 0.056013 | 0.029654 | 0.808567 | 0.794069 | 0.794069 | 0.794069 | 0.774959 | 0.774959 | 0 | 0.007675 | 0.223017 | 5,031 | 119 | 110 | 42.277311 | 0.768739 | 0.507255 | 0 | 0 | 0 | 0 | 0.15605 | 0.089354 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.170732 | 0 | 0.170732 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6934c3863f99e48f4f17fd1fdf4ad670b7595b9d | 77 | py | Python | tests/test_app.py | IraaSrivastava/medical_record | 0561d62a520b11b6578ba8c26c5c766d74174ec0 | [
"MIT"
] | null | null | null | tests/test_app.py | IraaSrivastava/medical_record | 0561d62a520b11b6578ba8c26c5c766d74174ec0 | [
"MIT"
] | null | null | null | tests/test_app.py | IraaSrivastava/medical_record | 0561d62a520b11b6578ba8c26c5c766d74174ec0 | [
"MIT"
] | null | null | null | from app import hello
def test_hello():
assert hello() == "Hello World!"
| 11 | 33 | 0.675325 | 11 | 77 | 4.636364 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.194805 | 77 | 6 | 34 | 12.833333 | 0.822581 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d6690c27ba3feb1775fa17c21fa7366a8dee84b3 | 15,508 | py | Python | functions/cursor_functions.py | mtasa-typescript/mtasa-wiki-dump | edea1746850fb6c99d6155d1d7891e2cceb33a5c | [
"MIT"
] | null | null | null | functions/cursor_functions.py | mtasa-typescript/mtasa-wiki-dump | edea1746850fb6c99d6155d1d7891e2cceb33a5c | [
"MIT"
] | 1 | 2021-02-24T21:50:18.000Z | 2021-02-24T21:50:18.000Z | functions/cursor_functions.py | mtasa-typescript/mtasa-wiki-dump | edea1746850fb6c99d6155d1d7891e2cceb33a5c | [
"MIT"
] | null | null | null | # Autogenerated file. ANY CHANGES WILL BE OVERWRITTEN
from to_python.core.types import FunctionType, \
FunctionArgument, \
FunctionArgumentValues, \
FunctionReturnTypes, \
FunctionSignature, \
FunctionDoc, \
FunctionData, \
CompoundFunctionData
DUMP_PARTIAL = [
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getCursorAlpha',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['int'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to get alpha (transparency) from the clients cursor.' ,
arguments={
},
result='returns a int, 0-255, where 255 is fully opaque and 0 is fully transparent.' ,
),
url='getCursorAlpha',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getCursorPosition',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function gets the current position of the mouse cursor. Note that for performance reasons, the world position returned is always 300 units away. If you want the exact world point (similar to onClientClick), use processLineOfSight between the camera position and the worldX/Y/Z result of this function. (See example below)' ,
arguments={
},
result='returns 5 values: cursorx, cursory, worldx, worldy, worldz. the first two values are the 2d relative screen coordinates of the cursor: cursorx goes from 0 (left side of the screen) to 1 (right side), cursory goes from 0 (top) to 1 (bottom). the 3 values that follow are the 3d world map coordinates that the cursor points at. if the cursor isnt showing, returns false as the first value.' ,
),
url='getCursorPosition',
)
],
),
CompoundFunctionData(
server=[
FunctionData(
signature=FunctionSignature(
name='isCursorShowing',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='thePlayer',
argument_type=FunctionType(
names=['player'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to determine whether or not a players cursor is showing.' ,
arguments={
"thePlayer": """The player you want to get cursor state of. """
},
result='returns true if the players cursor is showing, false if it isnt or if invalid parameters were passed.' ,
),
url='isCursorShowing',
)
],
client=[
FunctionData(
signature=FunctionSignature(
name='isCursorShowing',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to determine whether or not a players cursor is showing.' ,
arguments={
},
result='returns true if the players cursor is showing, false if it isnt.' ,
),
url='isCursorShowing',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='setCursorAlpha',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='alpha',
argument_type=FunctionType(
names=['int'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to change alpha (transparency) from the clients cursor.' ,
arguments={
"alpha": """: The alpha value to set. Value can be 0-255, where 255 is fully opaque and 0 is fully transparent. """
},
result='returns true if the new alpha value was set, or false otherwise.' ,
),
url='setCursorAlpha',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='setCursorPosition',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='cursorX',
argument_type=FunctionType(
names=['int'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='cursorY',
argument_type=FunctionType(
names=['int'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function sets the current position of the mouse cursor.' ,
arguments={
"cursorX": """Position over the X axis """,
"cursorY": """Position over the Y axis """
},
result='returns true if the position has been successfully set, false otherwise.' ,
),
url='setCursorPosition',
)
],
),
CompoundFunctionData(
server=[
FunctionData(
signature=FunctionSignature(
name='showCursor',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='thePlayer',
argument_type=FunctionType(
names=['player'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='show',
argument_type=FunctionType(
names=['bool'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='toggleControls',
argument_type=FunctionType(
names=['bool'],
is_optional=True,
),
default_value='true',
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to show or hide a players cursor.' ,
arguments={
"thePlayer": """The player you want to show or hide the cursor of. """,
"show": """A boolean value determining whether to show (true) or hide (false) the cursor. """,
"toggleControls": """A boolean value determining whether to disable controls whilst the cursor is showing. true implies controls are disabled, false implies controls remain enabled. """
},
result='' ,
),
url='showCursor',
)
],
client=[
FunctionData(
signature=FunctionSignature(
name='showCursor',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='show',
argument_type=FunctionType(
names=['bool'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='toggleControls',
argument_type=FunctionType(
names=['bool'],
is_optional=True,
),
default_value='true',
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function is used to show or hide a players cursor.' ,
arguments={
"show": """A boolean value determining whether to show (true) or hide (false) the cursor. """,
"toggleControls": """A boolean value determining whether to disable controls whilst the cursor is showing. true implies controls are disabled, false implies controls remain enabled. """
},
result='' ,
),
url='showCursor',
)
],
)
]
| 38.197044 | 414 | 0.3622 | 867 | 15,508 | 6.385236 | 0.199539 | 0.064487 | 0.051481 | 0.041546 | 0.781611 | 0.771496 | 0.761561 | 0.651553 | 0.647941 | 0.647941 | 0 | 0.004074 | 0.572672 | 15,508 | 405 | 415 | 38.291358 | 0.831296 | 0.003289 | 0 | 0.781818 | 1 | 0.012987 | 0.178583 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.002597 | 0.002597 | 0 | 0.002597 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d6ce528c9f4441a8b2c7b3e4f590f8c1cf8c952b | 204 | py | Python | VKActivityAnalisys/__init__.py | Rig0ur/VKAnalysis | 23bfedc490c99d488078039b9aab1c7cd3defce9 | [
"Apache-2.0"
] | 54 | 2018-03-04T10:18:59.000Z | 2022-03-24T20:47:13.000Z | VKActivityAnalisys/__init__.py | Lukmora/VKAnalysis | 23bfedc490c99d488078039b9aab1c7cd3defce9 | [
"Apache-2.0"
] | 7 | 2020-09-30T10:17:20.000Z | 2021-12-27T01:53:52.000Z | VKActivityAnalisys/__init__.py | Lukmora/VKAnalysis | 23bfedc490c99d488078039b9aab1c7cd3defce9 | [
"Apache-2.0"
] | 12 | 2019-11-29T15:54:39.000Z | 2021-12-13T22:33:20.000Z | # from .TimeActivityAnalysis import TimeActivityAnalysisWidget
from .InterestingActivityAnalysis import InterestingActivityAnalysisWidget
from .FriendsActivityAnalysis import FriendsActivityAnalysisWidget | 68 | 74 | 0.921569 | 12 | 204 | 15.666667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058824 | 204 | 3 | 75 | 68 | 0.979167 | 0.294118 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d6ded53f1cd4fcb7360d1d590f46faf42994105e | 83 | py | Python | run_prediction.py | Rhoana/soma_segmentation | 82d3e170c7e5b53d3e0fdc507f3430e5a3bc9a2e | [
"MIT"
] | null | null | null | run_prediction.py | Rhoana/soma_segmentation | 82d3e170c7e5b53d3e0fdc507f3430e5a3bc9a2e | [
"MIT"
] | null | null | null | run_prediction.py | Rhoana/soma_segmentation | 82d3e170c7e5b53d3e0fdc507f3430e5a3bc9a2e | [
"MIT"
] | null | null | null | from main import Predict
from parameters import ckpt_restore
Predict(ckpt_restore) | 20.75 | 35 | 0.86747 | 12 | 83 | 5.833333 | 0.583333 | 0.314286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108434 | 83 | 4 | 36 | 20.75 | 0.945946 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d6ed2db60e823268a6bdcbf69f0400c0a91e459d | 7,601 | py | Python | tests/test_date_helper.py | yabov/hermes_fix | 0a5e89fd15903a7ee0929e82b39879362e2e1008 | [
"Apache-2.0"
] | 2 | 2020-02-20T15:00:35.000Z | 2020-02-21T19:27:53.000Z | tests/test_date_helper.py | yabov/hermes_fix | 0a5e89fd15903a7ee0929e82b39879362e2e1008 | [
"Apache-2.0"
] | 3 | 2020-02-21T03:25:35.000Z | 2020-02-21T18:37:42.000Z | tests/test_date_helper.py | yabov/hermes_fix | 0a5e89fd15903a7ee0929e82b39879362e2e1008 | [
"Apache-2.0"
] | null | null | null | import unittest
from datetime import datetime, timedelta
from dateutil import tz
from hermes_fix.utils import date_helper
class Test(unittest.TestCase):
def setUp(self):
pass
"""Daily: time < start < end"""
def test_daily_t_s_e(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10,0,0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(dt, self.tz, "11:00:00", "12:00:00")
self.assertEqual(dt.date(), start.date())
self.assertEqual(dt.date(), end.date())
self.assertEqual(increment, timedelta(days=1))
"""Daily: start < time < end"""
def test_daily_s_t_e(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "09:00:00", "12:00:00")
self.assertEqual(dt.date(), start.date())
self.assertEqual(dt.date(), end.date())
self.assertEqual(increment, timedelta(days=1))
"""Daily: start < end < time"""
def test_daily_s_e_t(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "08:00:00", "09:00:00")
self.assertEqual(dt.date(), start.date())
self.assertEqual(dt.date(), end.date())
self.assertEqual(increment, timedelta(days=1))
"""Daily: time < end < start"""
def test_daily_t_e_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "12:00:00", "11:00:00")
self.assertEqual(start, datetime(2020, 2, 1, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 2, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=1))
"""Daily: end < time < start"""
def test_daily_e_t_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "12:00:00", "09:00:00")
self.assertEqual(dt.date(), start.date())
self.assertEqual(dt.date(), end.date() - timedelta(days=1))
self.assertEqual(increment, timedelta(days=1))
"""Daily: end < start < time"""
def test_daily_e_s_t(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 2, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "09:00:00", "08:00:00")
self.assertEqual(dt.date(), start.date())
self.assertEqual(dt.date(), end.date() - timedelta(days=1))
self.assertEqual(increment, timedelta(days=1))
"""Weekly: time < start < end"""
def test_weekly_t_s_e(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 4, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Wednesday 11:00:00", "Friday 12:00:00")
self.assertEqual(start, datetime(2020, 2, 5, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 7, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly: start < time < end"""
def test_weekly_s_t_e(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 5, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Monday 11:00:00", "Friday 12:00:00")
self.assertEqual(start, datetime(2020, 2, 3, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 7, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly: start < end < time """
def test_weekly_s_e_t(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 7, 15, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Monday 11:00:00", "Friday 12:00:00")
self.assertEqual(start, datetime(2020, 2, 10, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 14, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly: time < end < start"""
def test_weekly_t_e_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 4, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Friday 11:00:00", "Wednesday 12:00:00")
self.assertEqual(start, datetime(2020, 1, 31, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 5, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly: end < time < start"""
def test_weekly_e_t_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 4, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Friday 11:00:00", "Monday 12:00:00")
self.assertEqual(start, datetime(2020, 2, 7, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 10, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly: end < start < time"""
def test_weekly_e_s_t(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 7, 10, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Thursday 11:00:00", "Monday 12:00:00")
self.assertEqual(start, datetime(2020, 2, 6, 11, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 10, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly Same day: time < end < start"""
def test_weekly_sd_t_e_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 7, 9, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Friday 12:00:00", "Friday 10:00:00")
self.assertEqual(start, datetime(
2020, 1, 31, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 7, 10, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly Same day: end < time < start"""
def test_weekly_sd_e_t_s(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 7, 11, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Friday 12:00:00", "Friday 10:00:00")
self.assertEqual(start, datetime(
2020, 2, 7, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 14, 10, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
"""Weekly Same day: end < start < time"""
def test_weekly_sd_e_s_t(self):
self.tz = tz.gettz("UTC")
dt = datetime(2020, 2, 7, 14, 0, 0, tzinfo=self.tz)
start, end, increment = date_helper.get_session_times(
dt, self.tz, "Friday 12:00:00", "Friday 10:00:00")
self.assertEqual(start, datetime(
2020, 2, 7, 12, 0, 0, tzinfo=self.tz))
self.assertEqual(end, datetime(2020, 2, 14, 10, 0, 0, tzinfo=self.tz))
self.assertEqual(increment, timedelta(days=7))
def tearDown(self):
pass
if __name__ == "__main__":
unittest.main()
| 37.628713 | 98 | 0.595843 | 1,138 | 7,601 | 3.876098 | 0.058875 | 0.088415 | 0.063478 | 0.095217 | 0.933802 | 0.899569 | 0.868284 | 0.868284 | 0.855135 | 0.845613 | 0 | 0.096147 | 0.241942 | 7,601 | 201 | 99 | 37.81592 | 0.669386 | 0 | 0 | 0.609023 | 0 | 0 | 0.060149 | 0 | 0 | 0 | 0 | 0 | 0.338346 | 1 | 0.12782 | false | 0.015038 | 0.030075 | 0 | 0.165414 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ba516b55a3397f92a0f58bd7b84021e2b6f82f2d | 122 | py | Python | server/__init__.py | mica-framework/cli | a5a851a73d7b9bd0431e9c8bb0c8fca401b32ccf | [
"MIT"
] | 5 | 2019-06-14T12:32:56.000Z | 2022-03-17T20:55:48.000Z | server/__init__.py | mica-framework/cli | a5a851a73d7b9bd0431e9c8bb0c8fca401b32ccf | [
"MIT"
] | null | null | null | server/__init__.py | mica-framework/cli | a5a851a73d7b9bd0431e9c8bb0c8fca401b32ccf | [
"MIT"
] | null | null | null | # import the modules
from .mica_server import * #FIXME we could make that dynamically editable within the config.yml file! | 61 | 101 | 0.803279 | 19 | 122 | 5.105263 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.147541 | 122 | 2 | 101 | 61 | 0.932692 | 0.745902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bab1c666630f1b7286568dcc3bf7a5216a8eaae5 | 23,770 | py | Python | hydrogen/Selector.py | belre/EMNumerics | b44fde7685adfc3b1477ec5064cade0ad4accedf | [
"MIT"
] | 1 | 2021-05-16T14:00:07.000Z | 2021-05-16T14:00:07.000Z | hydrogen/Selector.py | belre/EMNumerics | b44fde7685adfc3b1477ec5064cade0ad4accedf | [
"MIT"
] | null | null | null | hydrogen/Selector.py | belre/EMNumerics | b44fde7685adfc3b1477ec5064cade0ad4accedf | [
"MIT"
] | null | null | null |
import numpy as np
import tkinter as tk
import tkinter.ttk as ttk
from tkinter import font
from functools import partial
from Function import SphericalSurface, GraphView
# https://stackoverflow.com/questions/49888623/tkinter-hovering-over-button-color-change
class MarkerButton(tk.Button):
def __init__(self, master, **kwargs):
tk.Button.__init__(self,master=master,**kwargs)
self.turn_off()
def turn_on(self):
self['highlightbackground'] = "yellow"
self['fg'] = "black"
def turn_off(self):
self['highlightbackground'] = "gray"
self['fg'] = "black"
class Application(tk.Frame):
_before_action_button = None
def __init__(self, master=None):
super().__init__(master)
self.master = master
self.pack()
self.create_widgets()
self._graph_object = None
def R_1s(self, r):
return 2 * np.exp(-r)
def R_2s(self, r):
return (1/np.sqrt(2)) * (1-r/2) * np.exp(-r/2)
def R_2p(self, r):
return (1/(2*np.sqrt(6))) * r * np.exp(-r/2)
def R_3s(self, r):
return (2/(3*np.sqrt(3))) * (1-2*r/3+2*r*r/27) * np.exp(-r/3)
def R_3p(self, r):
return (8 / (27 * np.sqrt(6))) * r * (1 - r / 6) * np.exp(-r/3)
def R_3d(self, r):
return (4 / (81 * np.sqrt(30))) * (r**2) * np.exp(-r/3)
def R_4s(self, r):
return (1 / 4) * (1 - 3 * r / 4 + r ** 2 / 8 - r ** 3 / 192) * np.exp(-r/4)
def R_4p(self, r):
return (np.sqrt(5) / (16 * np.sqrt(3))) * r * (1 - r / 4 + r ** 2 / 80) * np.exp(-r/4)
def R_4d(self, r):
return (1 / (64 * np.sqrt(5))) * (r ** 2) * (1 - r / 12) * np.exp(-r/4)
def R_4f(self, r):
return (1 / (768 * np.sqrt(35))) * (r ** 3) * np.exp(-r/4)
def Y_0_0(self, theta, phi):
return 1 / np.sqrt(4 * np.pi)
def Y_1_0(self, theta, phi):
return np.sqrt(3 / (4*np.pi)) * np.cos(theta)
def Y_1_1(self, theta, phi, pm):
return - pm * np.sqrt(3 / (8*np.pi)) * np.sin(theta) * np.exp(pm * 1j * phi)
def Yc_100(self, theta, phi):
return (-self.Y_1_1(theta, phi, 1) + self.Y_1_1(theta, phi, -1)) / np.sqrt(2)
def Yc_010(self, theta, phi):
return 1j * (self.Y_1_1(theta, phi, 1) + self.Y_1_1(theta, phi, -1)) / np.sqrt(2)
def Y_2_0(self, theta, phi):
return np.sqrt(5 / (16*np.pi)) * (3 * np.cos(theta) ** 2 - 1)
def Y_2_1(self, theta, phi, pm):
return - pm * np.sqrt(15 / (8*np.pi)) * (np.sin(theta) * np.cos(theta)) * np.exp(pm * 1j * phi)
def Y_2_2(self, theta, phi, pm):
return - pm * np.sqrt(15 / (32*np.pi)) * (np.sin(theta) ** 2) * np.exp(pm * 2j * phi)
def Y_3_0(self, theta, phi):
return np.sqrt(7 / (16 * np.pi)) * (5 * np.cos(theta) ** 3 - 3 * np.cos(theta))
def Y_3_1(self, theta, phi, pm):
return - pm * np.sqrt(21 / (64 * np.pi)) * (5 * np.cos(theta) ** 2 - 1) * np.sin(theta) * np.exp(pm * 1j * phi)
def Y_3_2(self, theta, phi, pm):
return np.sqrt(105 / (32 * np.pi)) * (np.cos(theta) * np.sin(theta) ** 2) * np.exp(pm * 2j * phi)
def Y_3_3(self, theta, phi, pm):
return - pm * np.sqrt(35 / (64 * np.pi)) * (np.sin(theta) ** 3) * np.exp(pm * 3j * phi)
def create_widgets(self):
my_font = font.Font(root,family="System",size=16,weight="bold")
self.parent_panel = ttk.Frame(root, padding=5)
self.parent_panel.pack(anchor=tk.NW, side=tk.LEFT)
label = tk.Label(self.parent_panel)
label["text"] = "×"
label.grid(row=0, column=1)
label.configure(font=my_font)
label = tk.Label(self.parent_panel)
label["text"] = "球面調和関数"
label.grid(row=0, column=2)
label.configure(font=my_font)
cnt = 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "s軌道(l0,m0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( self.Y_0_0(theta, phi) )))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "p軌道(l1,m0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi : np.abs(np.real(self.Y_1_0(theta, phi)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "p軌道(l1,m1)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs(np.real(self.Y_1_1(theta, phi, 1) ))))
tk_btn.configure(font=my_font)
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "調和振動子-x合成"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( self.Yc_100(theta, phi)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "調和振動子-y合成"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( 1j * (self.Y_1_1(theta, phi, 1) + self.Y_1_1(theta, phi, -1)) / np.sqrt(2) ))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "調和振動子-z合成"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( self.Y_1_0(theta, phi)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
cnt += 3
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "d軌道(l2,m0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( self.Y_2_0(theta, phi)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "d軌道(l2,m1)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( self.Y_2_1(theta, phi, 1)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "d軌道(l2,m2)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real( self.Y_2_2(theta, phi, 1)))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
cnt += 6
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "f軌道(l3,m0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real(self.Y_3_0(theta, phi)) )))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "f軌道(l3,m1)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real(self.Y_3_1(theta, phi, 1) ))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "f軌道(l3,m2)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real(self.Y_3_2(theta, phi, 1) ))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "f軌道(l3,m3)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d',
lambda theta, phi: np.abs( np.real(self.Y_3_3(theta, phi, 1) ))))
tk_btn.grid(row=cnt, column=2, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
label = tk.Label(self.parent_panel)
label["text"] = "="
label.grid(row=0, column=3)
label.configure(font=my_font)
label = tk.Label(self.parent_panel)
label["text"] = "電子分布\nφnlm"
label.grid(row=0, column=4)
label.configure(font=my_font)
cnt = 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ100"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: ( self.R_1s(r) * self.Y_0_0(theta, phi) )],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ200"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: ( self.R_2s(r) * self.Y_0_0(theta, phi) )]))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ210"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_2p(r) * self.Y_1_0(theta, phi)]))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ211"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_2p(r) * self.Y_1_1(theta, phi, 1)]))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φx"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_2p(r) * self.Yc_100(theta, phi)]))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φy"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_2p(r) * self.Yc_010(theta, phi)],
lambda axes, fig: axes.view_init(elev=0,azim=0)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φz"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_2p(r) * self.Y_1_0(theta, phi)]))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ300"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3s(r) * self.Y_0_0(theta, phi) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ310"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3p(r) * self.Y_1_0(theta, phi) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ311"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3p(r) * self.Y_1_1(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ320"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3d(r) * self.Y_2_0(theta, phi)],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ321"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3d(r) * self.Y_2_1(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ322"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_3d(r) * self.Y_2_2(theta, phi, 1)],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ400"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4s(r) * self.Y_0_0(theta, phi)],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ410"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4p(r) * self.Y_1_0(theta, phi)],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ411"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4p(r) * self.Y_1_1(theta, phi, 1)],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ420"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4d(r)* self.Y_2_0(theta, phi) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ421"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4d(r)* self.Y_2_1(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ422"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4d(r)* self.Y_2_2(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ430"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4f(r) * self.Y_3_0(theta, phi) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ431"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4f(r) * self.Y_3_1(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ432"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4f(r) * self.Y_3_2(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "φ433"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('3d_field',
[lambda r, theta, phi: self.R_4f(r) * self.Y_3_3(theta, phi, 1) ],
lambda axes, fig: axes.view_init(elev=0,azim=90)))
tk_btn.grid(row=cnt, column=4, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
cnt = cnt + 1
label = tk.Label(self.parent_panel)
label["text"] = "動径関数"
label.grid(row=0, column=0)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "1s(n1,l0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r : ((r * self.R_1s(r)) ** 2)))
tk_btn.grid(row=1, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "2s(n2,l0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_2s(r)) ** 2)) )
tk_btn.grid(row=2, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "2p(n2,l0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_2p(r)) ** 2)) )
tk_btn.grid(row=3, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "3s(n3,l0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_3s(r)) ** 2)) )
tk_btn.grid(row=8, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "3p(n3,l1)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_3p(r)) ** 2)) )
tk_btn.grid(row=9, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "3d(n3, l2)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_3d(r)) ** 2)) )
tk_btn.grid(row=10, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "4s(n4, l0)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_4s(r)) ** 2)) )
tk_btn.grid(row=14, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "4p(n4, l1)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_4p(r)) ** 2)) )
tk_btn.grid(row=15, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "4d(n4, l2)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_4d(r)) ** 2)) )
tk_btn.grid(row=16, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
tk_btn = MarkerButton(self.parent_panel)
tk_btn["text"] = "4f(n4, l3)"
tk_btn["command"] = partial(self.press_for_graph, tk_btn, GraphView('2d',
lambda r: np.abs((r * self.R_4f(r)) ** 2)) )
tk_btn.grid(row=17, column=0, sticky=tk.W+tk.E+tk.N+tk.S)
tk_btn.configure(font=my_font)
tk_btn = MarkerButton(self.parent_panel)
def select(self, sender):
if self._before_action_button != None:
self._before_action_button.turn_off()
sender.turn_on()
self._before_action_button = sender
def press_for_graph(self, sender, graph_func, graph_object=None):
self.select(sender)
#graph_obj = GraphView('3d', lambda : "")
if self._graph_object == None:
self._graph_object = SphericalSurface(self.master)
self._graph_object.plot(graph_func, graph_object)
root = tk.Tk()
root.title("Spherical Surface Demonstration")
root.attributes('-fullscreen', True)
app = Application(master=root)
app.mainloop()
| 42.220249 | 127 | 0.576441 | 3,821 | 23,770 | 3.39754 | 0.054436 | 0.108997 | 0.069327 | 0.085734 | 0.867817 | 0.851949 | 0.828686 | 0.820983 | 0.812895 | 0.795563 | 0 | 0.036812 | 0.260581 | 23,770 | 562 | 128 | 42.295374 | 0.701752 | 0.005301 | 0 | 0.5625 | 0 | 0 | 0.050761 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.064732 | false | 0 | 0.013393 | 0.049107 | 0.133929 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
bab47803c81513579eb51a8aafb59a2a5f6f410d | 3,086 | py | Python | tests/fields/test_render_as_radio.py | jpsca/pforms | 77c9da93e5224e79bb147aa873f28951e972bb21 | [
"MIT"
] | 2 | 2019-10-11T03:13:10.000Z | 2019-11-12T10:31:54.000Z | tests/fields/test_render_as_radio.py | jpsca/hyperform | d5c450ad8684a853fed26f8c2606877151125a9e | [
"MIT"
] | 2 | 2021-11-18T18:01:28.000Z | 2021-11-18T18:03:29.000Z | tests/fields/test_render_as_radio.py | jpsca/hyperform | d5c450ad8684a853fed26f8c2606877151125a9e | [
"MIT"
] | null | null | null | import proper_forms.fields as f
def test_text_as_radio():
field = f.Text(name="name")
assert field.as_radio() == '<input name="name" type="radio">'
def test_text_as_radio_with_label():
field = f.Text(name="name")
assert (
field.as_radio(label="I have read the TOS")
== '<label class="radio"><input name="name" type="radio">'
" I have read the TOS</label>"
)
def test_text_as_radio_checked():
field = f.Text(name="name")
field.input_values = ["hello"]
assert field.as_radio() == '<input name="name" type="radio" checked>'
def test_boolean_as_radio():
field = f.Boolean(name="name")
assert field.as_radio() == '<input name="name" type="radio">'
def test_boolean_as_radio_checked():
field = f.Boolean(name="name")
field.object_value = True
assert field.as_radio() == '<input name="name" type="radio" checked>'
def test_boolean_as_radio_force_checked():
field = f.Boolean(name="name")
assert field.as_radio(checked=True) == '<input name="name" type="radio" checked>'
def test_boolean_as_radio_custom_value():
field = f.Boolean(name="name")
assert (
field.as_radio(value="newsletter")
== '<input name="name" type="radio" value="newsletter">'
)
def test_boolean_as_radio_custom_value_checked():
field = f.Boolean(name="name")
field.input_values = ["newsletter"]
assert (
field.as_radio(value="newsletter")
== '<input name="name" type="radio" value="newsletter" checked>'
)
def test_boolean_as_radio_custom_str_value_checked():
field = f.Boolean(name="name")
field.input_values = [5]
assert (
field.as_radio(value="5")
== '<input name="name" type="radio" value="5" checked>'
)
def test_boolean_as_radio_custom_str_reverse_value_checked():
field = f.Boolean(name="name")
field.input_values = ["5"]
assert (
field.as_radio(value=5) == '<input name="name" type="radio" value="5" checked>'
)
def test_boolean_as_radio_custom_values_checked():
field = f.Boolean(name="name", multiple=True)
field.input_values = ["alerts", "newsletter", "replies"]
assert (
field.as_radio(value="newsletter")
== '<input name="name" type="radio" value="newsletter" checked>'
)
def test_boolean_as_radio_custom_value_unchecked():
field = f.Boolean(name="name")
field.input_values = ["newsletter"]
assert (
field.as_radio(value="direct")
== '<input name="name" type="radio" value="direct">'
)
def test_boolean_as_radio_custom_values_unchecked():
field = f.Boolean(name="name", multiple=True)
field.input_values = ["alerts", "newsletter", "replies"]
assert (
field.as_radio(value="direct")
== '<input name="name" type="radio" value="direct">'
)
def test_boolean_as_radio_custom_value_object_unchecked():
field = f.Boolean(name="name")
field.object_value = True
assert (
field.as_radio(value="newsletter")
== '<input name="name" type="radio" value="newsletter">'
)
| 28.574074 | 87 | 0.650032 | 405 | 3,086 | 4.708642 | 0.101235 | 0.102779 | 0.095438 | 0.132145 | 0.958574 | 0.905087 | 0.857892 | 0.821709 | 0.810173 | 0.747771 | 0 | 0.002405 | 0.19151 | 3,086 | 107 | 88 | 28.841122 | 0.761924 | 0 | 0 | 0.56962 | 0 | 0 | 0.284835 | 0 | 0 | 0 | 0 | 0 | 0.177215 | 1 | 0.177215 | false | 0 | 0.012658 | 0 | 0.189873 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
baf2cd39103117544e114c833995848cfc9c7b78 | 44 | py | Python | model/__init__.py | xionluhnis/neural_inverse_knitting | da4edc3ccbc470db77f97e7776946ff0369b8c68 | [
"MIT"
] | 29 | 2019-06-20T20:38:15.000Z | 2021-10-08T06:12:45.000Z | model/__init__.py | xionluhnis/neural_inverse_knitting | da4edc3ccbc470db77f97e7776946ff0369b8c68 | [
"MIT"
] | 8 | 2020-01-28T22:12:52.000Z | 2022-01-13T01:19:46.000Z | model/__init__.py | xionluhnis/neural_inverse_knitting | da4edc3ccbc470db77f97e7776946ff0369b8c68 | [
"MIT"
] | 4 | 2019-08-06T16:45:30.000Z | 2021-01-03T01:31:27.000Z | from .m_feedforw import FeedForwardNetworks
| 22 | 43 | 0.886364 | 5 | 44 | 7.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 44 | 1 | 44 | 44 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
24524014c4c441dcf01ac5c743244dc4b3cca703 | 1,570 | py | Python | common/archive.py | LordKBX/EbookCollection | 3e6ba33fb012b1dbb371704094b02cece66a7e80 | [
"MIT"
] | 1 | 2021-06-03T01:44:50.000Z | 2021-06-03T01:44:50.000Z | common/archive.py | LordKBX/eBookCollection | 3e6ba33fb012b1dbb371704094b02cece66a7e80 | [
"MIT"
] | null | null | null | common/archive.py | LordKBX/eBookCollection | 3e6ba33fb012b1dbb371704094b02cece66a7e80 | [
"MIT"
] | null | null | null | import os, sys
import subprocess
sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)))
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import common.vars
def inflate(src: str, dest: str):
list_args = list() # create list argument for external command execution
list_args.append(common.vars.load_path_archiver() + os.sep + common.vars.env_vars['tools']['archiver'][os.name]['exe']) # insert executable path
temp_args = common.vars.env_vars['tools']['archiver'][os.name]['params_inflate'].split(' ') # create table of raw command arguments
for var in temp_args: # parse table of raw command arguments
# insert parsed param
list_args.append(var.replace('%input%', src).replace('%output%', dest))
# print(list_args)
return subprocess.call(list_args, shell=True) # execute the command
def deflate(src: str, dest: str):
list_args = list() # create list argument for external command execution
list_args.append(common.vars.load_path_archiver() + os.sep + common.vars.env_vars['tools']['archiver'][os.name]['exe']) # insert executable path
temp_args = common.vars.env_vars['tools']['archiver'][os.name]['params_deflate'].split(' ') # create table of raw command arguments
for var in temp_args: # parse table of raw command arguments
# insert parsed param
list_args.append(var.replace('%input%', src).replace('%output%', dest))
print(list_args)
ret = subprocess.call(list_args, shell=True) # execute the command
print(ret)
return ret
| 52.333333 | 149 | 0.705732 | 225 | 1,570 | 4.782222 | 0.257778 | 0.074349 | 0.052045 | 0.063197 | 0.904275 | 0.904275 | 0.904275 | 0.869888 | 0.869888 | 0.719331 | 0 | 0.001506 | 0.15414 | 1,570 | 29 | 150 | 54.137931 | 0.808735 | 0.252229 | 0 | 0.363636 | 0 | 0 | 0.101724 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090909 | false | 0 | 0.136364 | 0 | 0.318182 | 0.090909 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
245ecfc1a9012bc0664a7d439cc591484bca36fc | 350 | py | Python | textformer/models/__init__.py | gugarosa/textformer | cccc670d48995fa0bfbdf9fc8013d13a90ea5e84 | [
"Apache-2.0"
] | 3 | 2020-07-26T03:51:56.000Z | 2020-10-04T18:42:18.000Z | textformer/models/__init__.py | gugarosa/textformer | cccc670d48995fa0bfbdf9fc8013d13a90ea5e84 | [
"Apache-2.0"
] | null | null | null | textformer/models/__init__.py | gugarosa/textformer | cccc670d48995fa0bfbdf9fc8013d13a90ea5e84 | [
"Apache-2.0"
] | null | null | null | """A package contaning all models (networks) for all common textformer modules.
"""
from textformer.models.att_seq2seq import AttSeq2Seq
from textformer.models.conv_seq2seq import ConvSeq2Seq
from textformer.models.joint_seq2seq import JointSeq2Seq
from textformer.models.seq2seq import Seq2Seq
from textformer.models.transformer import Transformer
| 38.888889 | 79 | 0.851429 | 44 | 350 | 6.704545 | 0.454545 | 0.237288 | 0.338983 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025237 | 0.094286 | 350 | 8 | 80 | 43.75 | 0.905363 | 0.217143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0377883e3bfc629defce7af51a69cec2ffe564ee | 10,519 | py | Python | 11 - Extra-- sonos snips voice app/tests/services/node/test_device_transport_control_service.py | RedaMastouri/marvis | 75e90a66d0746f12ba6231a4cab16ab40b42928e | [
"MIT"
] | 1 | 2021-12-29T08:44:34.000Z | 2021-12-29T08:44:34.000Z | 11 - Extra-- sonos snips voice app/tests/services/node/test_device_transport_control_service.py | RedaMastouri/marvis | 75e90a66d0746f12ba6231a4cab16ab40b42928e | [
"MIT"
] | null | null | null | 11 - Extra-- sonos snips voice app/tests/services/node/test_device_transport_control_service.py | RedaMastouri/marvis | 75e90a66d0746f12ba6231a4cab16ab40b42928e | [
"MIT"
] | null | null | null | import pytest
import requests
from mock import mock, patch
from snipssonos.services.node.device_transport_control import NodeDeviceTransportControlService
from snipssonos.exceptions import NoReachableDeviceException
from snipssonos.use_cases.volume.up import VolumeUpUseCase
from snipssonos.entities.device import Device
@pytest.fixture
def node_configuration():
return {
'global' : {
'node_device_transport_control_port' : 5005 ,
'node_device_transport_control_host' : 'localhost'
}
}
@pytest.fixture
def connected_device():
return Device.from_dict(
{
'identifier': 'RINCON_XXXX',
'name': 'Antho',
'volume': 10
}
)
def test_transport_control_service_initialization():
transport_service = NodeDeviceTransportControlService()
assert transport_service.PORT == NodeDeviceTransportControlService.PORT
assert transport_service.HOST == NodeDeviceTransportControlService.HOST
assert transport_service.PROTOCOL == NodeDeviceTransportControlService.PROTOCOL
def test_generate_url_query_for_volume_up(connected_device, node_configuration):
PROTOCOL = NodeDeviceTransportControlService.PROTOCOL
HOST = NodeDeviceTransportControlService.HOST
PORT = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
volume_level = 10
transport_service = NodeDeviceTransportControlService(node_configuration)
assert transport_service._generate_volume_query(room_name, volume_level) == "http://localhost:5005/Antho/volume/10"
def test_generate_url_query_for_resume(connected_device, node_configuration):
PROTOCOL = NodeDeviceTransportControlService.PROTOCOL
HOST = NodeDeviceTransportControlService.HOST
PORT = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
volume_level = 10
transport_service = NodeDeviceTransportControlService(node_configuration)
assert transport_service._generate_resume_query(room_name) == "http://localhost:5005/Antho/play"
def test_generate_url_query_for_mute(connected_device, node_configuration):
PROTOCOL = NodeDeviceTransportControlService.PROTOCOL
HOST = NodeDeviceTransportControlService.HOST
PORT = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
volume_level = 10
transport_service = NodeDeviceTransportControlService(node_configuration)
assert transport_service._generate_mute_query(room_name) == "http://localhost:5005/Antho/mute"
def test_generate_url_query_for_next_track(connected_device, node_configuration):
PROTOCOL = NodeDeviceTransportControlService.PROTOCOL
HOST = NodeDeviceTransportControlService.HOST
PORT = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
volume_level = 10
transport_service = NodeDeviceTransportControlService(node_configuration)
assert transport_service._generate_next_track_query(room_name) == "http://localhost:5005/Antho/next"
def test_generate_url_query_for_previous_track(connected_device):
protocol = NodeDeviceTransportControlService.PROTOCOL
host = NodeDeviceTransportControlService.HOST
port = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
expected_query = "{}{}:{}/{}/previous".format(protocol, host, port, room_name)
transport_service = NodeDeviceTransportControlService()
assert transport_service._generate_previous_track_query(room_name) == expected_query
def test_generate_url_query_for_state(connected_device):
protocol = NodeDeviceTransportControlService.PROTOCOL
host = NodeDeviceTransportControlService.HOST
port = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
expected_query = "{}{}:{}/{}/trackseek/1".format(protocol, host, port, room_name)
transport_service = NodeDeviceTransportControlService()
assert transport_service._generate_track_seek_query(room_name, 1) == expected_query
def test_generate_url_query_for_track_seek_query(connected_device):
protocol = NodeDeviceTransportControlService.PROTOCOL
host = NodeDeviceTransportControlService.HOST
port = NodeDeviceTransportControlService.PORT
room_name = connected_device.name
expected_query = "{}{}:{}/{}/state".format(protocol, host, port, room_name)
transport_service = NodeDeviceTransportControlService()
assert transport_service._generate_state_query(room_name) == expected_query
@mock.patch('snipssonos.services.node.device_transport_control.requests.Response')
def test_extract_state_track_number(mock_response):
mock_response.json.return_value = {
'trackNo': 1
}
transport_service = NodeDeviceTransportControlService()
assert transport_service._extract_state_track_number(mock_response) == 1
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_get_track_number(mock_request, connected_device):
mocked_response = mock.create_autospec(requests.Response)
mocked_response.ok = True
mocked_response.json.return_value = {
'trackNo': 1
}
mock_request.get.return_value = mocked_response
transport_service = NodeDeviceTransportControlService()
assert transport_service.get_track_number(connected_device.name) == 1
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_volume_up_method_performs_correct_api_query(mocked_requests, connected_device):
transport_service = NodeDeviceTransportControlService()
volume_increment = VolumeUpUseCase.DEFAULT_VOLUME_INCREMENT
connected_device.increase_volume(volume_increment)
transport_service.volume_up(connected_device)
mocked_requests.get.assert_called_with(
transport_service._generate_volume_query(connected_device.name, connected_device.volume))
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_volume_up_method_failure_raises_exception(mocked_requests, connected_device):
transport_service = NodeDeviceTransportControlService()
volume_increment = VolumeUpUseCase.DEFAULT_VOLUME_INCREMENT
connected_device.increase_volume(volume_increment)
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = False
mocked_requests.get.return_value = mocked_response_object
with pytest.raises(NoReachableDeviceException):
transport_service.volume_up(connected_device)
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_mute_method_performs_correct_api_query(mocked_requests, connected_device):
transport_service = NodeDeviceTransportControlService()
transport_service.mute(connected_device)
mocked_requests.get.assert_called_with(
transport_service._generate_mute_query(connected_device.name))
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_volume_up_method_failure_raises_exception(mocked_requests, connected_device):
transport_service = NodeDeviceTransportControlService()
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = False
mocked_requests.get.return_value = mocked_response_object
with pytest.raises(NoReachableDeviceException):
transport_service.mute(connected_device)
@mock.patch('snipssonos.services.node.device_transport_control.requests')
@patch.object(NodeDeviceTransportControlService, 'get_track_number')
def test_transport_service_previous_track_correct_api_query_for_not_first_track_in_queue(mock_get_track_number,
mock_request, connected_device):
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = True
mock_request.get.return_value = mocked_response_object
mock_get_track_number.return_value = 3
transport_service = NodeDeviceTransportControlService()
transport_service.previous_track(connected_device)
mock_request.get.assert_called_with(
transport_service._generate_previous_track_query(connected_device.name))
@mock.patch('snipssonos.services.node.device_transport_control.requests')
@patch.object(NodeDeviceTransportControlService, 'get_track_number')
def test_transport_service_previous_track_correct_api_query_for_first_track_in_queue(mock_get_track_number,
mock_request, connected_device):
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = True
mock_request.get.return_value = mocked_response_object
mock_get_track_number.return_value = 1
transport_service = NodeDeviceTransportControlService()
transport_service.previous_track(connected_device)
mock_request.get.assert_called_with(
transport_service._generate_track_seek_query(connected_device.name, 1))
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_transport_service_get_info_does_correct_api_query(mock_request, connected_device):
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = True
mocked_response_object.json.return_value = {
'currentTrack': {
'title': 'Teenage Fantasy',
'artist': 'Jorja Smith'
}
}
mock_request.get.return_value = mocked_response_object
transport_service = NodeDeviceTransportControlService()
transport_service.get_track_info(connected_device)
mock_request.get.assert_called_with(
transport_service._generate_state_query(connected_device.name))
@mock.patch('snipssonos.services.node.device_transport_control.requests')
def test_transport_service_get_info_return_title_and_artist(mock_request, connected_device):
mocked_response_object = mock.create_autospec(requests.Response)
mocked_response_object.ok = True
mocked_response_object.json.return_value = {
'currentTrack': {
'title': 'Teenage Fantasy',
'artist': 'Jorja Smith'
}
}
mock_request.get.return_value = mocked_response_object
transport_service = NodeDeviceTransportControlService()
transport_service.get_track_info(connected_device)
track, artist = transport_service.get_track_info(connected_device)
assert track.name == "Teenage Fantasy"
assert artist.name == "Jorja Smith"
| 38.250909 | 121 | 0.789048 | 1,096 | 10,519 | 7.158759 | 0.094891 | 0.097884 | 0.050981 | 0.043079 | 0.874713 | 0.856232 | 0.778486 | 0.748534 | 0.738593 | 0.723936 | 0 | 0.004535 | 0.140603 | 10,519 | 274 | 122 | 38.390511 | 0.863385 | 0 | 0 | 0.603175 | 0 | 0 | 0.10154 | 0.064556 | 0 | 0 | 0 | 0 | 0.100529 | 1 | 0.10582 | false | 0 | 0.037037 | 0.010582 | 0.153439 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3044c87d459ddb26a651b0306d43230727851480 | 2,297 | py | Python | epytope/Data/pssms/smmpmbec/mat/A_24_03_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 7 | 2021-02-01T18:11:28.000Z | 2022-01-31T19:14:07.000Z | epytope/Data/pssms/smmpmbec/mat/A_24_03_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 22 | 2021-01-02T15:25:23.000Z | 2022-03-14T11:32:53.000Z | epytope/Data/pssms/smmpmbec/mat/A_24_03_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 4 | 2021-05-28T08:50:38.000Z | 2022-03-14T11:45:32.000Z | A_24_03_9 = {0: {'A': -0.269, 'C': -0.177, 'E': 0.695, 'D': 1.059, 'G': 0.041, 'F': -0.283, 'I': 0.289, 'H': -0.195, 'K': -0.196, 'M': -0.31, 'L': 0.004, 'N': 0.12, 'Q': -0.014, 'P': 0.716, 'S': -0.19, 'R': -0.498, 'T': 0.053, 'W': -0.2, 'V': 0.026, 'Y': -0.67}, 1: {'A': 0.349, 'C': -0.165, 'E': 0.594, 'D': 0.567, 'G': 0.138, 'F': -1.371, 'I': 0.361, 'H': 0.12, 'K': 0.926, 'M': -0.224, 'L': 0.246, 'N': 0.204, 'Q': 0.25, 'P': 0.606, 'S': 0.301, 'R': 0.672, 'T': -0.053, 'W': -1.483, 'V': 0.095, 'Y': -2.133}, 2: {'A': 0.018, 'C': 0.063, 'E': 0.356, 'D': 0.541, 'G': -0.083, 'F': -0.064, 'I': -0.013, 'H': -0.148, 'K': 0.308, 'M': -0.291, 'L': -0.382, 'N': -0.211, 'Q': -0.237, 'P': -0.446, 'S': 0.047, 'R': 0.184, 'T': 0.151, 'W': 0.041, 'V': 0.153, 'Y': 0.014}, 3: {'A': -0.139, 'C': -0.01, 'E': 0.079, 'D': 0.178, 'G': -0.1, 'F': 0.03, 'I': 0.114, 'H': -0.018, 'K': -0.039, 'M': 0.164, 'L': 0.116, 'N': -0.047, 'Q': -0.12, 'P': -0.213, 'S': -0.101, 'R': -0.064, 'T': -0.025, 'W': 0.061, 'V': 0.025, 'Y': 0.109}, 4: {'A': -0.018, 'C': -0.029, 'E': -0.053, 'D': 0.003, 'G': -0.034, 'F': -0.044, 'I': 0.061, 'H': 0.048, 'K': 0.154, 'M': 0.009, 'L': -0.05, 'N': -0.127, 'Q': -0.037, 'P': 0.0, 'S': -0.004, 'R': 0.212, 'T': -0.015, 'W': -0.024, 'V': -0.053, 'Y': -0.0}, 5: {'A': 0.096, 'C': 0.039, 'E': 0.113, 'D': 0.225, 'G': 0.068, 'F': -0.301, 'I': -0.181, 'H': 0.008, 'K': 0.08, 'M': -0.087, 'L': -0.123, 'N': 0.155, 'Q': 0.13, 'P': 0.22, 'S': 0.085, 'R': 0.043, 'T': -0.065, 'W': -0.093, 'V': -0.089, 'Y': -0.323}, 6: {'A': 0.12, 'C': 0.029, 'E': 0.012, 'D': -0.025, 'G': 0.103, 'F': -0.11, 'I': 0.101, 'H': -0.019, 'K': 0.267, 'M': -0.24, 'L': -0.036, 'N': -0.108, 'Q': -0.049, 'P': 0.003, 'S': 0.082, 'R': 0.119, 'T': 0.144, 'W': -0.206, 'V': -0.02, 'Y': -0.167}, 7: {'A': 0.072, 'C': 0.024, 'E': -0.037, 'D': 0.027, 'G': 0.039, 'F': -0.051, 'I': -0.056, 'H': 0.039, 'K': 0.04, 'M': -0.069, 'L': -0.022, 'N': 0.038, 'Q': -0.017, 'P': 0.029, 'S': 0.057, 'R': 0.085, 'T': -0.013, 'W': -0.048, 'V': -0.04, 'Y': -0.097}, 8: {'A': 0.257, 'C': -0.139, 'E': 0.286, 'D': -0.003, 'G': 0.145, 'F': -1.377, 'I': -0.701, 'H': 0.331, 'K': 0.828, 'M': -0.394, 'L': -0.671, 'N': 0.034, 'Q': 0.656, 'P': 0.434, 'S': 0.249, 'R': 0.99, 'T': -0.079, 'W': -1.001, 'V': -0.027, 'Y': 0.18}, -1: {'con': 4.37328}} | 2,297 | 2,297 | 0.39138 | 557 | 2,297 | 1.608618 | 0.310592 | 0.020089 | 0.011161 | 0.013393 | 0.044643 | 0 | 0 | 0 | 0 | 0 | 0 | 0.370062 | 0.162386 | 2,297 | 1 | 2,297 | 2,297 | 0.095634 | 0 | 0 | 0 | 0 | 0 | 0.079634 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
305e296150749a15df14e3389aa18bf57478548f | 3,352 | py | Python | src/sgd.py | ntueclab/SpecTrain-PyTorch | b93355d6b8f74864ce1d6eb6978e24fbbf62fc54 | [
"MIT"
] | 3 | 2021-02-10T13:46:31.000Z | 2022-01-24T20:46:14.000Z | src/sgd.py | ntueclab/SpecTrain-PyTorch | b93355d6b8f74864ce1d6eb6978e24fbbf62fc54 | [
"MIT"
] | null | null | null | src/sgd.py | ntueclab/SpecTrain-PyTorch | b93355d6b8f74864ce1d6eb6978e24fbbf62fc54 | [
"MIT"
] | null | null | null | # Copyright (c) Microsoft Corporation.
# (c) I-Ching Tseng
# Licensed under the MIT license.
from torch.optim.optimizer import required
from optimizer import OptimizerWithWeightStashing
from optimizer import OptimizerWithWeightPrediction
class SGDWithWeightStashing(OptimizerWithWeightStashing):
"""
SGD optimizer with weight stashing.
"""
def __init__(self,
modules,
master_parameters,
model_parameters,
loss_scale,
num_versions,
lr=required,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
verbose_freq=0,
macrobatch=False):
super(SGDWithWeightStashing, self).__init__(
optim_name='SGD',
modules=modules,
master_parameters=master_parameters,
model_parameters=model_parameters,
loss_scale=loss_scale,
num_versions=num_versions,
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
verbose_freq=verbose_freq,
macrobatch=macrobatch,
)
class SGDWithSpectrain(OptimizerWithWeightStashing):
"""
SGD optimizer with spectrain.
"""
def __init__(self,
modules,
master_parameters,
model_parameters,
loss_scale,
num_versions,
lr=required,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
verbose_freq=0,
macrobatch=False):
super(SGDWithSpectrain, self).__init__(
optim_name='Spectrain',
modules=modules,
master_parameters=master_parameters,
model_parameters=model_parameters,
loss_scale=loss_scale,
num_versions=num_versions,
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
verbose_freq=verbose_freq,
macrobatch=macrobatch,
)
class SGDWithSpectrainCHC(OptimizerWithWeightPrediction):
"""
SGD optimizer with spectrain.
"""
def __init__(self,
modules,
master_parameters,
model_parameters,
loss_scale,
num_versions,
lr=required,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
verbose_freq=0,
macrobatch=False):
super(SGDWithSpectrainCHC, self).__init__(
optim_name='SGD',
modules=modules,
master_parameters=master_parameters,
model_parameters=model_parameters,
loss_scale=loss_scale,
num_versions=num_versions,
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
verbose_freq=verbose_freq,
macrobatch=macrobatch,
)
| 29.403509 | 57 | 0.537888 | 262 | 3,352 | 6.572519 | 0.198473 | 0.083624 | 0.130662 | 0.108014 | 0.735772 | 0.735772 | 0.735772 | 0.735772 | 0.735772 | 0.735772 | 0 | 0.005961 | 0.399463 | 3,352 | 113 | 58 | 29.663717 | 0.849478 | 0.057578 | 0 | 0.855556 | 0 | 0 | 0.004822 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.033333 | false | 0 | 0.033333 | 0 | 0.1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
069cc0057199e1587ef347e4d3c49e1b322ae72b | 124 | py | Python | prettyqt/qml/qmlparserstatus.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 7 | 2019-05-01T01:34:36.000Z | 2022-03-08T02:24:14.000Z | prettyqt/qml/qmlparserstatus.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 141 | 2019-04-16T11:22:01.000Z | 2021-04-14T15:12:36.000Z | prettyqt/qml/qmlparserstatus.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 5 | 2019-04-17T11:48:19.000Z | 2021-11-21T10:30:19.000Z | from __future__ import annotations
from prettyqt.qt import QtQml
class QmlParserStatus(QtQml.QQmlParserStatus):
pass
| 15.5 | 46 | 0.814516 | 14 | 124 | 6.928571 | 0.785714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.145161 | 124 | 7 | 47 | 17.714286 | 0.915094 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.25 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
069d6e218d474dd6b46b1c34e5a7122aeea9362f | 83 | py | Python | YTV/mywidgets/custom_textfield/mytext.py | Eghosa-Osayande/ytv-series-downloader | 6b0efa69ca002279226ac6063e1bbc3eae9b2c97 | [
"MIT"
] | 1 | 2020-11-22T20:30:28.000Z | 2020-11-22T20:30:28.000Z | YTV/mywidgets/custom_textfield/mytext.py | yande-eghosa/ytv-series-downloader | 6b0efa69ca002279226ac6063e1bbc3eae9b2c97 | [
"MIT"
] | null | null | null | YTV/mywidgets/custom_textfield/mytext.py | yande-eghosa/ytv-series-downloader | 6b0efa69ca002279226ac6063e1bbc3eae9b2c97 | [
"MIT"
] | null | null | null | from .mytextfield import MDTextField as MYText
class MYTextField(MYText):
pass | 20.75 | 47 | 0.795181 | 10 | 83 | 6.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.156627 | 83 | 4 | 48 | 20.75 | 0.942857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
23095c1f34c714fb24ba726ef2025d27cbe5e27a | 275 | py | Python | docs/doxygen/doxyxml/generated/__init__.py | cbdonohue/gr-equalizers | 3b5f86238afff7becfb9988dc6a27a35468d179f | [
"BSD-3-Clause"
] | 15 | 2022-01-26T23:09:23.000Z | 2022-02-17T15:50:32.000Z | docs/doxygen/doxyxml/generated/__init__.py | cbdonohue/gr-equalizers | 3b5f86238afff7becfb9988dc6a27a35468d179f | [
"BSD-3-Clause"
] | 7 | 2020-02-06T11:18:58.000Z | 2021-02-05T13:20:05.000Z | docs/doxygen/doxyxml/generated/__init__.py | cbdonohue/gr-equalizers | 3b5f86238afff7becfb9988dc6a27a35468d179f | [
"BSD-3-Clause"
] | 4 | 2020-01-21T14:47:10.000Z | 2022-03-09T08:39:06.000Z | """
Contains generated files produced by generateDS.py.
These do the real work of parsing the doxygen xml files but the
resultant classes are not very friendly to navigate so the rest of the
doxyxml module processes them further.
"""
from __future__ import unicode_literals
| 30.555556 | 70 | 0.807273 | 43 | 275 | 5.046512 | 0.860465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 275 | 8 | 71 | 34.375 | 0.939394 | 0.821818 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
235068faad0865f98f09af638b8dc0fa6af41cdd | 193 | py | Python | pair-up.py | drtnf/cits3403-pair-up | 886cb45cb401f1f54706a40cad1986d23f593e68 | [
"MIT"
] | 9 | 2019-05-07T06:33:40.000Z | 2021-04-20T12:19:52.000Z | pair-up.py | drtnf/cits3403-pair-up | 886cb45cb401f1f54706a40cad1986d23f593e68 | [
"MIT"
] | 2 | 2020-04-20T04:52:27.000Z | 2020-04-20T04:52:27.000Z | pair-up.py | drtnf/cits3403-pair-up | 886cb45cb401f1f54706a40cad1986d23f593e68 | [
"MIT"
] | 31 | 2019-05-17T10:42:14.000Z | 2021-06-02T14:31:52.000Z | from app import app, db
from app.models import Student, Project, Lab
@app.shell_context_processor
def make_shell_context():
return {'db':db, 'Student':Student, "Project":Project, 'Lab':Lab}
| 27.571429 | 67 | 0.751295 | 29 | 193 | 4.862069 | 0.482759 | 0.099291 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11399 | 193 | 6 | 68 | 32.166667 | 0.824561 | 0 | 0 | 0 | 0 | 0 | 0.098446 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.4 | 0.2 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
237700744d64a116def487cd77c7600c99e00d8e | 124 | py | Python | AudioChessSite/apps/notifications/views.py | saisree27/audio-chess-site | 97c4bd70fd96cd21c76c777aa41c849ccbe1db71 | [
"MIT"
] | 1 | 2021-01-27T14:38:57.000Z | 2021-01-27T14:38:57.000Z | AudioChessSite/apps/notifications/views.py | saisree27/audio-chess-site | 97c4bd70fd96cd21c76c777aa41c849ccbe1db71 | [
"MIT"
] | null | null | null | AudioChessSite/apps/notifications/views.py | saisree27/audio-chess-site | 97c4bd70fd96cd21c76c777aa41c849ccbe1db71 | [
"MIT"
] | null | null | null | from django.shortcuts import render
def user_list(request):
return render(request, 'templates/example/user_list.html') | 24.8 | 62 | 0.790323 | 17 | 124 | 5.647059 | 0.764706 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.112903 | 124 | 5 | 62 | 24.8 | 0.872727 | 0 | 0 | 0 | 0 | 0 | 0.256 | 0.256 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
88b98242dc2f6a0c3820100699d5636210dbcab5 | 25,794 | py | Python | tests/ut/python/dataset/test_datasets_imdb.py | PowerOlive/mindspore | bda20724a94113cedd12c3ed9083141012da1f15 | [
"Apache-2.0"
] | 1 | 2022-02-23T09:13:43.000Z | 2022-02-23T09:13:43.000Z | tests/ut/python/dataset/test_datasets_imdb.py | PowerOlive/mindspore | bda20724a94113cedd12c3ed9083141012da1f15 | [
"Apache-2.0"
] | null | null | null | tests/ut/python/dataset/test_datasets_imdb.py | PowerOlive/mindspore | bda20724a94113cedd12c3ed9083141012da1f15 | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import pytest
import mindspore.dataset as ds
from mindspore import log as logger
DATA_DIR = "../data/dataset/testIMDBDataset"
def test_imdb_basic():
"""
Feature: Test IMDB Dataset.
Description: read data from all file.
Expectation: the data is processed successfully.
"""
logger.info("Test Case basic")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, shuffle=False)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 8
content = ["train_pos_0.txt", "train_pos_1.txt", "train_neg_0.txt", "train_neg_1.txt",
"test_pos_0.txt", "test_pos_1.txt", "test_neg_0.txt", "test_neg_1.txt"]
label = [1, 1, 0, 0, 1, 1, 0, 0]
num_iter = 0
for index, item in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
# each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
strs = item["text"].item().decode("utf8")
logger.info("text is {}".format(strs))
logger.info("label is {}".format(item["label"]))
assert strs == content[index]
assert label[index] == int(item["label"])
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 8
def test_imdb_test():
"""
Feature: Test IMDB Dataset.
Description: read data from test file.
Expectation: the data is processed successfully.
"""
logger.info("Test Case test")
# define parameters
repeat_count = 1
usage = "test"
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, usage=usage, shuffle=False)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 4
content = ["test_pos_0.txt", "test_pos_1.txt", "test_neg_0.txt", "test_neg_1.txt"]
label = [1, 1, 0, 0]
num_iter = 0
for index, item in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
# each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
strs = item["text"].item().decode("utf8")
logger.info("text is {}".format(strs))
logger.info("label is {}".format(item["label"]))
assert strs == content[index]
assert label[index] == int(item["label"])
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 4
def test_imdb_train():
"""
Feature: Test IMDB Dataset.
Description: read data from train file.
Expectation: the data is processed successfully.
"""
logger.info("Test Case train")
# define parameters
repeat_count = 1
usage = "train"
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, usage=usage, shuffle=False)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 4
content = ["train_pos_0.txt", "train_pos_1.txt", "train_neg_0.txt", "train_neg_1.txt"]
label = [1, 1, 0, 0]
num_iter = 0
for index, item in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
# each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
strs = item["text"].item().decode("utf8")
logger.info("text is {}".format(strs))
logger.info("label is {}".format(item["label"]))
assert strs == content[index]
assert label[index] == int(item["label"])
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 4
def test_imdb_num_samples():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with num_samples=10 and num_parallel_workers=2.
Expectation: the data is processed successfully.
"""
logger.info("Test Case numSamples")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, num_samples=6, num_parallel_workers=2)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 6
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 6
random_sampler = ds.RandomSampler(num_samples=3, replacement=True)
data1 = ds.IMDBDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
num_iter = 0
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
assert num_iter == 3
random_sampler = ds.RandomSampler(num_samples=3, replacement=False)
data1 = ds.IMDBDataset(DATA_DIR, num_parallel_workers=2, sampler=random_sampler)
num_iter = 0
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
assert num_iter == 3
def test_imdb_num_shards():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with num_shards=2 and shard_id=1.
Expectation: the data is processed successfully.
"""
logger.info("Test Case numShards")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, num_shards=2, shard_id=1)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 4
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 4
def test_imdb_shard_id():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with num_shards=4 and shard_id=1.
Expectation: the data is processed successfully.
"""
logger.info("Test Case withShardID")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, num_shards=2, shard_id=0)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 4
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 4
def test_imdb_no_shuffle():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with shuffle=False.
Expectation: the data is processed successfully.
"""
logger.info("Test Case noShuffle")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, shuffle=False)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 8
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 8
def test_imdb_true_shuffle():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with shuffle=True.
Expectation: the data is processed successfully.
"""
logger.info("Test Case extraShuffle")
# define parameters
repeat_count = 2
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, shuffle=True)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 16
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 16
def test_random_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with sampler=ds.RandomSampler().
Expectation: the data is processed successfully.
"""
logger.info("Test Case RandomSampler")
# define parameters
repeat_count = 1
# apply dataset operations
sampler = ds.RandomSampler()
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 8
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 8
def test_distributed_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with sampler=ds.DistributedSampler().
Expectation: the data is processed successfully.
"""
logger.info("Test Case DistributedSampler")
# define parameters
repeat_count = 1
# apply dataset operations
sampler = ds.DistributedSampler(4, 1)
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 2
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 2
def test_pk_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with sampler=ds.PKSampler().
Expectation: the data is processed successfully.
"""
logger.info("Test Case PKSampler")
# define parameters
repeat_count = 1
# apply dataset operations
sampler = ds.PKSampler(3)
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 6
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 6
def test_subset_random_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with sampler=ds.SubsetRandomSampler().
Expectation: the data is processed successfully.
"""
logger.info("Test Case SubsetRandomSampler")
# define parameters
repeat_count = 1
# apply dataset operations
indices = [0, 3, 1, 2, 5, 4]
sampler = ds.SubsetRandomSampler(indices)
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 6
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 6
def test_weighted_random_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with sampler=ds.WeightedRandomSampler().
Expectation: the data is processed successfully.
"""
logger.info("Test Case WeightedRandomSampler")
# define parameters
repeat_count = 1
# apply dataset operations
weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05]
sampler = ds.WeightedRandomSampler(weights, 6)
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 6
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 6
def test_weighted_random_sampler_exception():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with random sampler exception.
Expectation: the data is processed successfully.
"""
logger.info("Test error cases for WeightedRandomSampler")
error_msg_1 = "type of weights element must be number"
with pytest.raises(TypeError, match=error_msg_1):
weights = ""
ds.WeightedRandomSampler(weights)
error_msg_2 = "type of weights element must be number"
with pytest.raises(TypeError, match=error_msg_2):
weights = (0.9, 0.8, 1.1)
ds.WeightedRandomSampler(weights)
error_msg_3 = "WeightedRandomSampler: weights vector must not be empty"
with pytest.raises(RuntimeError, match=error_msg_3):
weights = []
sampler = ds.WeightedRandomSampler(weights)
sampler.parse()
error_msg_4 = "WeightedRandomSampler: weights vector must not contain negative numbers, got: "
with pytest.raises(RuntimeError, match=error_msg_4):
weights = [1.0, 0.1, 0.02, 0.3, -0.4]
sampler = ds.WeightedRandomSampler(weights)
sampler.parse()
error_msg_5 = "WeightedRandomSampler: elements of weights vector must not be all zero"
with pytest.raises(RuntimeError, match=error_msg_5):
weights = [0, 0, 0, 0, 0]
sampler = ds.WeightedRandomSampler(weights)
sampler.parse()
def test_chained_sampler_with_random_sequential_repeat():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with Random and Sequential, with repeat.
Expectation: the data is processed successfully.
"""
logger.info("Test Case Chained Sampler - Random and Sequential, with repeat")
# Create chained sampler, random and sequential
sampler = ds.RandomSampler()
child_sampler = ds.SequentialSampler()
sampler.add_child(child_sampler)
# Create IMDBDataset with sampler
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.repeat(count=3)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 24
# Verify number of iterations
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 24
def test_chained_sampler_with_distribute_random_batch_then_repeat():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with Distributed and Random, with batch then repeat.
Expectation: the data is processed successfully.
"""
logger.info("Test Case Chained Sampler - Distributed and Random, with batch then repeat")
# Create chained sampler, distributed and random
sampler = ds.DistributedSampler(num_shards=4, shard_id=3)
child_sampler = ds.RandomSampler()
sampler.add_child(child_sampler)
# Create IMDBDataset with sampler
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
data1 = data1.batch(batch_size=5, drop_remainder=True)
data1 = data1.repeat(count=3)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 0
# Verify number of iterations
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
# Note: Each of the 4 shards has 44/4=11 samples
# Note: Number of iterations is (11/5 = 2) * 3 = 6
assert num_iter == 0
def test_chained_sampler_with_weighted_random_pk_sampler():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with WeightedRandom and PKSampler.
Expectation: the data is processed successfully.
"""
logger.info("Test Case Chained Sampler - WeightedRandom and PKSampler")
# Create chained sampler, WeightedRandom and PKSampler
weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05]
sampler = ds.WeightedRandomSampler(weights=weights, num_samples=6)
child_sampler = ds.PKSampler(num_val=3) # Number of elements per class is 3 (and there are 4 classes)
sampler.add_child(child_sampler)
# Create IMDBDataset with sampler
data1 = ds.IMDBDataset(DATA_DIR, sampler=sampler)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 6
# Verify number of iterations
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
# Note: WeightedRandomSampler produces 12 samples
# Note: Child PKSampler produces 12 samples
assert num_iter == 6
def test_imdb_rename():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with rename.
Expectation: the data is processed successfully.
"""
logger.info("Test Case rename")
# define parameters
repeat_count = 1
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, num_samples=8)
data1 = data1.repeat(repeat_count)
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 8
data1 = data1.rename(input_columns=["text"], output_columns="text2")
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text2"]))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 8
def test_imdb_zip():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with zip.
Expectation: the data is processed successfully.
"""
logger.info("Test Case zip")
# define parameters
repeat_count = 2
# apply dataset operations
data1 = ds.IMDBDataset(DATA_DIR, num_samples=4)
data2 = ds.IMDBDataset(DATA_DIR, num_samples=4)
data1 = data1.repeat(repeat_count)
# rename dataset2 for no conflict
data2 = data2.rename(input_columns=["text", "label"], output_columns=["text1", "label1"])
data3 = ds.zip((data1, data2))
num_iter = 0
for item in data3.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
# in this example, each dictionary has keys "text" and "label"
logger.info("text is {}".format(item["text"].item().decode("utf8")))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 4
def test_imdb_exception():
"""
Feature: Test IMDB Dataset.
Description: read data from all file with exception.
Expectation: the data is processed successfully.
"""
logger.info("Test imdb exception")
def exception_func(item):
raise Exception("Error occur!")
def exception_func2(text, label):
raise Exception("Error occur!")
try:
data = ds.IMDBDataset(DATA_DIR)
data = data.map(operations=exception_func, input_columns=["text"], num_parallel_workers=1)
for _ in data.__iter__():
pass
assert False
except RuntimeError as e:
assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)
try:
data = ds.IMDBDataset(DATA_DIR)
data = data.map(operations=exception_func2, input_columns=["text", "label"],
output_columns=["text", "label", "label1"],
column_order=["text", "label", "label1"], num_parallel_workers=1)
for _ in data.__iter__():
pass
assert False
except RuntimeError as e:
assert "map operation: [PyFunc] failed. The corresponding data files" in str(e)
data_dir_invalid = "../data/dataset/IMDBDATASET"
try:
data = ds.IMDBDataset(data_dir_invalid)
for _ in data.__iter__():
pass
assert False
except ValueError as e:
assert "does not exist or is not a directory or permission denied" in str(e)
if __name__ == '__main__':
test_imdb_basic()
test_imdb_test()
test_imdb_train()
test_imdb_num_samples()
test_random_sampler()
test_distributed_sampler()
test_pk_sampler()
test_subset_random_sampler()
test_weighted_random_sampler()
test_weighted_random_sampler_exception()
test_chained_sampler_with_random_sequential_repeat()
test_chained_sampler_with_distribute_random_batch_then_repeat()
test_chained_sampler_with_weighted_random_pk_sampler()
test_imdb_num_shards()
test_imdb_shard_id()
test_imdb_no_shuffle()
test_imdb_true_shuffle()
test_imdb_rename()
test_imdb_zip()
test_imdb_exception()
| 35.189632 | 106 | 0.670078 | 3,489 | 25,794 | 4.791344 | 0.072227 | 0.055632 | 0.025124 | 0.028713 | 0.858647 | 0.832745 | 0.814141 | 0.795657 | 0.764611 | 0.75821 | 0 | 0.023217 | 0.213499 | 25,794 | 732 | 107 | 35.237705 | 0.800808 | 0.259091 | 0 | 0.67581 | 0 | 0 | 0.15595 | 0.008955 | 0 | 0 | 0 | 0 | 0.122195 | 1 | 0.054863 | false | 0.007481 | 0.007481 | 0 | 0.062344 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
001ac898cc6042edbce108d0ccf435e54334a789 | 106 | py | Python | email_config.py | ZhenyueQin/Training-Progress-Email-Notifier | 2a108ac50112681af18d31b0ba2ff9d33c461263 | [
"CC-BY-4.0"
] | 24 | 2021-07-02T13:08:47.000Z | 2021-12-06T17:26:09.000Z | email_config.py | ZhenyueQin/Training-Progress-Email-Notifier | 2a108ac50112681af18d31b0ba2ff9d33c461263 | [
"CC-BY-4.0"
] | 1 | 2021-07-08T19:18:02.000Z | 2021-07-08T22:13:05.000Z | email_config.py | ZhenyueQin/Training-Progress-Email-Notifier | 2a108ac50112681af18d31b0ba2ff9d33c461263 | [
"CC-BY-4.0"
] | null | null | null | EMAIL_S_ADDRESS = 'zylytech@gmail.com'
PASSWORD = 'zylytech123456'
EMAIL_R_ADDRESS = 'zylytech@gmail.com'
| 26.5 | 38 | 0.792453 | 14 | 106 | 5.714286 | 0.642857 | 0.375 | 0.5 | 0.575 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.061856 | 0.084906 | 106 | 3 | 39 | 35.333333 | 0.762887 | 0 | 0 | 0 | 0 | 0 | 0.471698 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.333333 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
cc9b9ce3b6ca5e304fc209e6bd148831fa90eecf | 8,131 | py | Python | tests/dataset/test_sparkdfdataset.py | lcorneliussen/great_expectations | 00a94d9dd7397b726e951baf290f4b5d18101b4d | [
"Apache-2.0"
] | 1 | 2020-09-29T18:19:35.000Z | 2020-09-29T18:19:35.000Z | tests/dataset/test_sparkdfdataset.py | lcorneliussen/great_expectations | 00a94d9dd7397b726e951baf290f4b5d18101b4d | [
"Apache-2.0"
] | 21 | 2020-08-05T07:15:47.000Z | 2021-04-29T05:35:52.000Z | tests/dataset/test_sparkdfdataset.py | lcorneliussen/great_expectations | 00a94d9dd7397b726e951baf290f4b5d18101b4d | [
"Apache-2.0"
] | null | null | null | import importlib.util
from unittest import mock
import pandas as pd
import pytest
from great_expectations.dataset.sparkdf_dataset import SparkDFDataset
def test_sparkdfdataset_persist(spark_session):
df = pd.DataFrame({"a": [1, 2, 3]})
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf, persist=True)
sdf.persist.assert_called_once()
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf, persist=False)
sdf.persist.assert_not_called()
sdf = spark_session.createDataFrame(df)
sdf.persist = mock.MagicMock()
_ = SparkDFDataset(sdf)
sdf.persist.assert_called_once()
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
@pytest.fixture
def test_dataframe(spark_session):
from pyspark.sql.types import IntegerType, StringType, StructField, StructType
schema = StructType(
[
StructField("name", StringType(), True),
StructField("age", IntegerType(), True),
StructField(
"address",
StructType(
[
StructField("street", StringType(), True),
StructField("city", StringType(), True),
StructField("house_number", IntegerType(), True),
]
),
False,
),
StructField("name_duplicate", StringType(), True),
StructField("non.nested", StringType(), True),
]
)
rows = [
("Alice", 1, ("Street 1", "Alabama", 10), "Alice", "a"),
("Bob", 2, ("Street 2", "Brooklyn", 11), "Bob", "b"),
("Charlie", 3, ("Street 3", "Alabama", 12), "Charlie", "c"),
]
rdd = spark_session.sparkContext.parallelize(rows)
df = spark_session.createDataFrame(rdd, schema)
return SparkDFDataset(df, persist=True)
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_values_to_be_of_type(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_of_type(
"address.street", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"`non.nested`", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"name", "StringType"
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_values_to_be_of_type(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_of_type(
"address.street", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"`non.nested`", "StringType"
).success
assert test_dataframe.expect_column_values_to_be_of_type(
"name", "StringType"
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_values_to_be_in_type_list(spark_session, test_dataframe):
"""
data asset expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_in_type_list(
"address.street", ["StringType", "IntegerType"]
).success
assert test_dataframe.expect_column_values_to_be_in_type_list(
"`non.nested`", ["StringType", "IntegerType"]
).success
assert test_dataframe.expect_column_values_to_be_in_type_list(
"name", ["StringType", "IntegerType"]
).success
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_of_type("non.nested", "StringType")
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_pair_values_to_be_equal(spark_session, test_dataframe):
"""
column_pair_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_pair_values_to_be_equal(
"name", "name_duplicate"
).success
assert not test_dataframe.expect_column_pair_values_to_be_equal(
"name", "address.street"
).success
assert not test_dataframe.expect_column_pair_values_to_be_equal(
"name", "`non.nested`"
).success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_column_pair_values_to_be_equal("name", "non.nested")
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_pair_values_A_to_be_greater_than_B(
spark_session, test_dataframe
):
"""
column_pair_map_expectation
"""
assert test_dataframe.expect_column_pair_values_A_to_be_greater_than_B(
"address.house_number", "age"
).success
assert test_dataframe.expect_column_pair_values_A_to_be_greater_than_B(
"age", "age", or_equal=True
).success
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_multicolumn_values_to_be_unique(spark_session, test_dataframe):
"""
multicolumn_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_multicolumn_values_to_be_unique(
["name", "age"]
).success
assert test_dataframe.expect_multicolumn_values_to_be_unique(
["address.street", "name"]
).success
assert test_dataframe.expect_multicolumn_values_to_be_unique(
["address.street", "`non.nested`"]
).success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_multicolumn_values_to_be_unique(
["address.street", "non.nested"]
)
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_values_to_be_unique(spark_session, test_dataframe):
"""
column_map_expectation
"""
from pyspark.sql.utils import AnalysisException
assert test_dataframe.expect_column_values_to_be_unique("name").success
assert not test_dataframe.expect_column_values_to_be_unique("address.city").success
assert test_dataframe.expect_column_values_to_be_unique("`non.nested`").success
# Expectation should fail when no `` surround a non-nested column with dot notation
with pytest.raises(AnalysisException):
test_dataframe.expect_column_values_to_be_unique("non.nested")
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_value_lengths_to_be_between(spark_session, test_dataframe):
"""
column_map_expectation
"""
assert test_dataframe.expect_column_value_lengths_to_be_between(
"name", 3, 7
).success
assert test_dataframe.expect_column_value_lengths_to_be_between(
"address.street", 1, 10
).success
@pytest.mark.skipif(
importlib.util.find_spec("pyspark") is None, reason="requires the Spark library"
)
def test_expect_column_value_lengths_to_equal(spark_session, test_dataframe):
"""
column_map_expectation
"""
assert test_dataframe.expect_column_value_lengths_to_equal("age", 1).success
assert test_dataframe.expect_column_value_lengths_to_equal(
"address.street", 8
).success
| 33.460905 | 87 | 0.709507 | 986 | 8,131 | 5.505071 | 0.117647 | 0.0958 | 0.055269 | 0.119749 | 0.819086 | 0.806743 | 0.8014 | 0.794031 | 0.755895 | 0.734709 | 0 | 0.003331 | 0.187677 | 8,131 | 242 | 88 | 33.599174 | 0.818471 | 0.057558 | 0 | 0.522989 | 0 | 0 | 0.13103 | 0 | 0 | 0 | 0 | 0 | 0.155172 | 1 | 0.063218 | false | 0 | 0.126437 | 0 | 0.195402 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ccc448b73f9b0a1cb8ec171f82b55b4e3fd5e415 | 62 | py | Python | rubin_sim/scheduler/schedulers/__init__.py | RileyWClarke/flarubin | eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a | [
"MIT"
] | null | null | null | rubin_sim/scheduler/schedulers/__init__.py | RileyWClarke/flarubin | eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a | [
"MIT"
] | null | null | null | rubin_sim/scheduler/schedulers/__init__.py | RileyWClarke/flarubin | eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a | [
"MIT"
] | null | null | null | from .core_scheduler import *
from .filter_scheduler import *
| 20.666667 | 31 | 0.806452 | 8 | 62 | 6 | 0.625 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 62 | 2 | 32 | 31 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ccd00bd665d4645552ba2b41962db83e0be38a1e | 98 | py | Python | src/module.py | k-styles/Python-Template | bb90e33db3093a8e3e6a45a0c0cbb85c01982ff7 | [
"MIT"
] | null | null | null | src/module.py | k-styles/Python-Template | bb90e33db3093a8e3e6a45a0c0cbb85c01982ff7 | [
"MIT"
] | null | null | null | src/module.py | k-styles/Python-Template | bb90e33db3093a8e3e6a45a0c0cbb85c01982ff7 | [
"MIT"
] | null | null | null | class dummy_module():
def __init__(self):
pass
def dummy_func(self):
pass | 16.333333 | 25 | 0.581633 | 12 | 98 | 4.25 | 0.666667 | 0.313725 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.326531 | 98 | 6 | 26 | 16.333333 | 0.772727 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0.4 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
4e131f2fc0fd4f12b2ce6ea93905b7f248b4ccc7 | 8,981 | py | Python | panda/tests/safety/test_honda.py | nfralick/openpilot | a2f4d6b5ad1471f26dac707a82ef666ac32d77d9 | [
"MIT"
] | 3 | 2019-01-18T16:48:15.000Z | 2019-03-03T15:43:52.000Z | panda/tests/safety/test_honda.py | nfralick/openpilot | a2f4d6b5ad1471f26dac707a82ef666ac32d77d9 | [
"MIT"
] | 2 | 2021-03-25T22:42:16.000Z | 2021-10-12T22:57:08.000Z | panda/tests/safety/test_honda.py | nfralick/openpilot | a2f4d6b5ad1471f26dac707a82ef666ac32d77d9 | [
"MIT"
] | 1 | 2019-08-14T14:56:15.000Z | 2019-08-14T14:56:15.000Z | #!/usr/bin/env python2
import unittest
import numpy as np
import libpandasafety_py
MAX_BRAKE = 255
class TestHondaSafety(unittest.TestCase):
@classmethod
def setUp(cls):
cls.safety = libpandasafety_py.libpandasafety
cls.safety.honda_init(0)
cls.safety.init_tests_honda()
def _speed_msg(self, speed):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0x158 << 21
to_send[0].RDLR = speed
return to_send
def _button_msg(self, buttons, msg):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = msg << 21
to_send[0].RDLR = buttons << 5
return to_send
def _brake_msg(self, brake):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0x17C << 21
to_send[0].RDHR = 0x200000 if brake else 0
return to_send
def _alt_brake_msg(self, brake):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0x1BE << 21
to_send[0].RDLR = 0x10 if brake else 0
return to_send
def _gas_msg(self, gas):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0x17C << 21
to_send[0].RDLR = 1 if gas else 0
return to_send
def _send_brake_msg(self, brake):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0x1FA << 21
to_send[0].RDLR = ((brake & 0x3) << 8) | ((brake & 0x3FF) >> 2)
return to_send
def _send_interceptor_msg(self, gas, addr):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = addr << 21
to_send[0].RDTR = 6
to_send[0].RDLR = ((gas & 0xff) << 8) | ((gas & 0xff00) >> 8)
return to_send
def _send_steer_msg(self, steer):
to_send = libpandasafety_py.ffi.new('CAN_FIFOMailBox_TypeDef *')
to_send[0].RIR = 0xE4 << 21
to_send[0].RDLR = steer
return to_send
def test_default_controls_not_allowed(self):
self.assertFalse(self.safety.get_controls_allowed())
def test_resume_button(self):
RESUME_BTN = 4
self.safety.set_controls_allowed(0)
self.safety.honda_rx_hook(self._button_msg(RESUME_BTN, 0x1A6))
self.assertTrue(self.safety.get_controls_allowed())
def test_set_button(self):
SET_BTN = 3
self.safety.set_controls_allowed(0)
self.safety.honda_rx_hook(self._button_msg(SET_BTN, 0x1A6))
self.assertTrue(self.safety.get_controls_allowed())
def test_cancel_button(self):
CANCEL_BTN = 2
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._button_msg(CANCEL_BTN, 0x1A6))
self.assertFalse(self.safety.get_controls_allowed())
def test_sample_speed(self):
self.assertEqual(0, self.safety.get_honda_ego_speed())
self.safety.honda_rx_hook(self._speed_msg(100))
self.assertEqual(100, self.safety.get_honda_ego_speed())
def test_prev_brake(self):
self.assertFalse(self.safety.get_honda_brake_prev())
self.safety.honda_rx_hook(self._brake_msg(True))
self.assertTrue(self.safety.get_honda_brake_prev())
def test_disengage_on_brake(self):
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._brake_msg(1))
self.assertFalse(self.safety.get_controls_allowed())
def test_alt_disengage_on_brake(self):
self.safety.set_honda_alt_brake_msg(1)
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._alt_brake_msg(1))
self.assertFalse(self.safety.get_controls_allowed())
self.safety.set_honda_alt_brake_msg(0)
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._alt_brake_msg(1))
self.assertTrue(self.safety.get_controls_allowed())
def test_allow_brake_at_zero_speed(self):
# Brake was already pressed
self.safety.honda_rx_hook(self._brake_msg(True))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._brake_msg(True))
self.assertTrue(self.safety.get_controls_allowed())
self.safety.honda_rx_hook(self._brake_msg(False)) # reset no brakes
def test_not_allow_brake_when_moving(self):
# Brake was already pressed
self.safety.honda_rx_hook(self._brake_msg(True))
self.safety.honda_rx_hook(self._speed_msg(100))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._brake_msg(True))
self.assertFalse(self.safety.get_controls_allowed())
def test_prev_gas(self):
self.safety.honda_rx_hook(self._gas_msg(False))
self.assertFalse(self.safety.get_honda_gas_prev())
self.safety.honda_rx_hook(self._gas_msg(True))
self.assertTrue(self.safety.get_honda_gas_prev())
def test_prev_gas_interceptor(self):
self.safety.honda_rx_hook(self._send_interceptor_msg(0x0, 0x201))
self.assertFalse(self.safety.get_gas_interceptor_prev())
self.safety.honda_rx_hook(self._send_interceptor_msg(0x1000, 0x201))
self.assertTrue(self.safety.get_gas_interceptor_prev())
self.safety.honda_rx_hook(self._send_interceptor_msg(0x0, 0x201))
self.safety.set_gas_interceptor_detected(False)
def test_disengage_on_gas(self):
for long_controls_allowed in [0, 1]:
self.safety.set_long_controls_allowed(long_controls_allowed)
self.safety.honda_rx_hook(self._gas_msg(0))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._gas_msg(1))
if long_controls_allowed:
self.assertFalse(self.safety.get_controls_allowed())
else:
self.assertTrue(self.safety.get_controls_allowed())
self.safety.set_long_controls_allowed(True)
def test_allow_engage_with_gas_pressed(self):
self.safety.honda_rx_hook(self._gas_msg(1))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._gas_msg(1))
self.assertTrue(self.safety.get_controls_allowed())
def test_disengage_on_gas_interceptor(self):
for long_controls_allowed in [0, 1]:
self.safety.set_long_controls_allowed(long_controls_allowed)
self.safety.honda_rx_hook(self._send_interceptor_msg(0, 0x201))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._send_interceptor_msg(0x1000, 0x201))
if long_controls_allowed:
self.assertFalse(self.safety.get_controls_allowed())
else:
self.assertTrue(self.safety.get_controls_allowed())
self.safety.honda_rx_hook(self._send_interceptor_msg(0, 0x201))
self.safety.set_gas_interceptor_detected(False)
self.safety.set_long_controls_allowed(True)
def test_allow_engage_with_gas_interceptor_pressed(self):
self.safety.honda_rx_hook(self._send_interceptor_msg(0x1000, 0x201))
self.safety.set_controls_allowed(1)
self.safety.honda_rx_hook(self._send_interceptor_msg(0x1000, 0x201))
self.assertTrue(self.safety.get_controls_allowed())
self.safety.honda_rx_hook(self._send_interceptor_msg(0, 0x201))
self.safety.set_gas_interceptor_detected(False)
def test_brake_safety_check(self):
for long_controls_allowed in [0, 1]:
self.safety.set_long_controls_allowed(long_controls_allowed)
for brake in np.arange(0, MAX_BRAKE + 10, 1):
for controls_allowed in [True, False]:
self.safety.set_controls_allowed(controls_allowed)
if controls_allowed and long_controls_allowed:
send = MAX_BRAKE >= brake >= 0
else:
send = brake == 0
self.assertEqual(send, self.safety.honda_tx_hook(self._send_brake_msg(brake)))
self.safety.set_long_controls_allowed(True)
def test_gas_interceptor_safety_check(self):
for long_controls_allowed in [0, 1]:
self.safety.set_long_controls_allowed(long_controls_allowed)
for gas in np.arange(0, 4000, 100):
for controls_allowed in [True, False]:
self.safety.set_controls_allowed(controls_allowed)
if controls_allowed and long_controls_allowed:
send = True
else:
send = gas == 0
self.assertEqual(send, self.safety.honda_tx_hook(self._send_interceptor_msg(gas, 0x200)))
self.safety.set_long_controls_allowed(True)
def test_steer_safety_check(self):
self.safety.set_controls_allowed(0)
self.assertTrue(self.safety.honda_tx_hook(self._send_steer_msg(0x0000)))
self.assertFalse(self.safety.honda_tx_hook(self._send_steer_msg(0x1000)))
def test_spam_cancel_safety_check(self):
RESUME_BTN = 4
SET_BTN = 3
CANCEL_BTN = 2
BUTTON_MSG = 0x296
self.safety.set_honda_bosch_hardware(1)
self.safety.set_controls_allowed(0)
self.assertTrue(self.safety.honda_tx_hook(self._button_msg(CANCEL_BTN, BUTTON_MSG)))
self.assertFalse(self.safety.honda_tx_hook(self._button_msg(RESUME_BTN, BUTTON_MSG)))
self.assertFalse(self.safety.honda_tx_hook(self._button_msg(SET_BTN, BUTTON_MSG)))
# do not block resume if we are engaged already
self.safety.set_controls_allowed(1)
self.assertTrue(self.safety.honda_tx_hook(self._button_msg(RESUME_BTN, BUTTON_MSG)))
if __name__ == "__main__":
unittest.main()
| 37.577406 | 99 | 0.7419 | 1,345 | 8,981 | 4.579182 | 0.098141 | 0.147751 | 0.090112 | 0.080045 | 0.817016 | 0.790388 | 0.757103 | 0.738269 | 0.707745 | 0.654652 | 0 | 0.030943 | 0.150763 | 8,981 | 238 | 100 | 37.735294 | 0.776583 | 0.015032 | 0 | 0.534031 | 0 | 0 | 0.023527 | 0.020812 | 0 | 0 | 0.019342 | 0 | 0.162304 | 1 | 0.151832 | false | 0 | 0.015707 | 0 | 0.21466 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9dd0462026b9f420ab4b4fc998eea9d0ebf6f3fb | 24 | py | Python | app/eSignature/examples/eg030_brands_apply_to_template/__init__.py | olegliubimov/code-examples-python | 7af8c58138a9dd0f3b0be12eff1768ae23e449d3 | [
"MIT"
] | 21 | 2020-05-13T21:08:44.000Z | 2022-02-18T01:32:16.000Z | app/eSignature/examples/eg030_brands_apply_to_template/__init__.py | olegliubimov/code-examples-python | 7af8c58138a9dd0f3b0be12eff1768ae23e449d3 | [
"MIT"
] | 8 | 2020-11-23T09:28:04.000Z | 2022-02-02T12:04:08.000Z | app/eSignature/examples/eg030_brands_apply_to_template/__init__.py | olegliubimov/code-examples-python | 7af8c58138a9dd0f3b0be12eff1768ae23e449d3 | [
"MIT"
] | 26 | 2020-05-12T22:20:01.000Z | 2022-03-09T10:57:27.000Z | from .views import eg030 | 24 | 24 | 0.833333 | 4 | 24 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 0.125 | 24 | 1 | 24 | 24 | 0.809524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9df04b83aa89b479d5457f3fa0495941027b3634 | 327 | py | Python | main_project/accounts/views.py | mayc2/MStream | f5ec884a69499ecf90414c36dadb6c17f7fd6b95 | [
"MIT"
] | null | null | null | main_project/accounts/views.py | mayc2/MStream | f5ec884a69499ecf90414c36dadb6c17f7fd6b95 | [
"MIT"
] | null | null | null | main_project/accounts/views.py | mayc2/MStream | f5ec884a69499ecf90414c36dadb6c17f7fd6b95 | [
"MIT"
] | null | null | null | from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
def index(request):
return HttpResponse("Account says hello! <br/> <a href='/accounts/about'>About</a>")
def about(request):
return HttpResponse("This is the about page <br/> <a href='/accounts/'>Accounts</a>") | 36.333333 | 89 | 0.70948 | 45 | 327 | 5.155556 | 0.6 | 0.086207 | 0.215517 | 0.12931 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.152905 | 327 | 9 | 90 | 36.333333 | 0.837545 | 0.070336 | 0 | 0 | 0 | 0 | 0.416949 | 0.210169 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
d17e2cbd1228112ed16488c5e9afb97f26aa8258 | 5,184 | py | Python | test/printing_test.py | raphaelahrens/doto | e1a8db42a29a9844322ecc58576a18d4782bfa1d | [
"BSD-2-Clause"
] | 1 | 2016-05-03T12:38:30.000Z | 2016-05-03T12:38:30.000Z | test/printing_test.py | raphaelahrens/doto | e1a8db42a29a9844322ecc58576a18d4782bfa1d | [
"BSD-2-Clause"
] | null | null | null | test/printing_test.py | raphaelahrens/doto | e1a8db42a29a9844322ecc58576a18d4782bfa1d | [
"BSD-2-Clause"
] | null | null | null |
"""Unitests for the cli.printing module"""
import unittest
import datetime
import doto.cli.printing
import doto.model
class TestStrFromTimeSpan(unittest.TestCase):
"""Unittest for the Printing Module."""
now = doto.model.now_with_tz()
def test_one_sec(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(0, 1, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "soon")
def test_two_secs(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(0, 2, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "soon")
def test_one_min(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(0, 60, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "1m")
def test_two_min(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(0, 120, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "2m")
def test_one_day(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(1, 0, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "1d 0h 0m")
def test_two_days(self):
now_plus_x = TestStrFromTimeSpan.now + datetime.timedelta(2, 120, 0)
span = doto.model.TimeSpan(TestStrFromTimeSpan.now, now_plus_x)
result = doto.cli.printing.str_from_time_delta(span.time_delta())
self.assertEqual(result, "2d 0h 2m")
class TestUnicodeFromatter(unittest.TestCase):
def test_empty(self):
formatter = doto.cli.printing.UnicodeFormatter()
self.assertEqual('', formatter.format(''))
def test_int(self):
formatter = doto.cli.printing.UnicodeFormatter()
self.assertEqual('11', formatter.format('{}', 11))
def test_float(self):
formatter = doto.cli.printing.UnicodeFormatter()
self.assertEqual('11.0', formatter.format('{}', 11.0))
def test_simple_inster(self):
formatter = doto.cli.printing.UnicodeFormatter()
format_spec = '{}'
test_str = 'Testa̲'
self.assertEqual(test_str, formatter.format(format_spec, test_str))
test_str = 'test'
self.assertEqual(format_spec.format(test_str), formatter.format(format_spec, test_str))
def test_left_pad(self):
formatter = doto.cli.printing.UnicodeFormatter()
format_spec = '{:<10}'
test_str = 'test'
self.assertEqual(format_spec.format(test_str), formatter.format(format_spec, test_str))
utest_str = 'Testa̲'
self.assertEqual(format_spec.format(utest_str) + ' ', formatter.format(format_spec, utest_str))
def test_right_pad(self):
formatter = doto.cli.printing.UnicodeFormatter()
format_spec = '{:>10}'
test_str = "test"
self.assertEqual(format_spec.format(test_str), formatter.format(format_spec, test_str))
utest_str = 'Testa̲'
self.assertEqual(' ' + format_spec.format(utest_str), formatter.format(format_spec, utest_str))
def test_center_pad(self):
formatter = doto.cli.printing.UnicodeFormatter()
format_spec = '{:^10}'
test_str = "test"
self.assertEqual(format_spec.format(test_str), formatter.format(format_spec, test_str))
utest_str = 'Testa̲'
self.assertEqual(format_spec.format(utest_str) + ' ', formatter.format(format_spec, utest_str))
def test_center_pad_fill(self):
formatter = doto.cli.printing.UnicodeFormatter()
format_spec = '{:^10}'
test_str = "test"
self.assertEqual(format_spec.format(test_str), formatter.format(format_spec, test_str))
utest_str = 'Testa̲'
self.assertEqual(format_spec.format(utest_str) + ' ', formatter.format(format_spec, utest_str))
def test_unicode_pad(self):
formatter = doto.cli.printing.UnicodeFormatter()
utest_str = 'Tet̲a̲r̲st'
format_specs = (('{:<10}', utest_str + ' '),
('{:>10}', ' ' + utest_str),
('{:^10}', ' ' + utest_str + ' ')
)
for spec, t_str in format_specs:
self.assertEqual(t_str, formatter.format(spec, utest_str))
def test_zero_pad(self):
formatter = doto.cli.printing.UnicodeFormatter()
utest_str = 'Tet̲a̲r̲st'
format_specs = (('{:<010}', utest_str + '000'),
('{:>010}', '000' + utest_str),
('{:^010}', '0' + utest_str + '00')
)
for spec, t_str in format_specs:
self.assertEqual(t_str, formatter.format(spec, utest_str))
| 41.472 | 103 | 0.64892 | 642 | 5,184 | 5.020249 | 0.127726 | 0.08067 | 0.079119 | 0.062054 | 0.848278 | 0.840832 | 0.829352 | 0.829352 | 0.750543 | 0.680422 | 0 | 0.018114 | 0.222608 | 5,184 | 124 | 104 | 41.806452 | 0.778908 | 0.013503 | 0 | 0.494949 | 0 | 0 | 0.03822 | 0 | 0 | 0 | 0 | 0 | 0.212121 | 1 | 0.161616 | false | 0 | 0.040404 | 0 | 0.232323 | 0.171717 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d1aadb325f65ce205d9ff7657485f37c1e69fbaf | 83 | py | Python | src/RobotFrameworkElasticSearchLibrary/__init__.py | chilispa/robotframework-elasticsearch | fa416cc6314a02cf5299ecbeadc2b4dc966464e6 | [
"MIT"
] | 3 | 2020-03-04T14:17:24.000Z | 2020-03-04T15:20:48.000Z | src/RobotFrameworkElasticSearchLibrary/__init__.py | chilispa/robotframework-elasticsearch | fa416cc6314a02cf5299ecbeadc2b4dc966464e6 | [
"MIT"
] | null | null | null | src/RobotFrameworkElasticSearchLibrary/__init__.py | chilispa/robotframework-elasticsearch | fa416cc6314a02cf5299ecbeadc2b4dc966464e6 | [
"MIT"
] | 1 | 2019-12-09T13:55:31.000Z | 2019-12-09T13:55:31.000Z | from .RobotFrameworkElasticSearchLibrary import RobotFrameworkElasticSearchLibrary
| 41.5 | 82 | 0.939759 | 4 | 83 | 19.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.048193 | 83 | 1 | 83 | 83 | 0.987342 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d1b6afc98e667d171147c397be3b22504cf42650 | 31 | py | Python | nertk/__init__.py | johnsmithm/nertk | d57b10b1de10d10425616b70d1a0cf7df4a9fc8b | [
"MIT"
] | 3 | 2021-04-12T08:48:24.000Z | 2021-04-20T09:56:49.000Z | nertk/__init__.py | johnsmithm/nertk | d57b10b1de10d10425616b70d1a0cf7df4a9fc8b | [
"MIT"
] | 1 | 2021-04-19T15:47:14.000Z | 2021-04-20T08:38:54.000Z | nertk/__init__.py | johnsmithm/nertk | d57b10b1de10d10425616b70d1a0cf7df4a9fc8b | [
"MIT"
] | 2 | 2021-08-10T17:16:52.000Z | 2021-08-20T08:28:40.000Z | from .annotator import Entator
| 15.5 | 30 | 0.83871 | 4 | 31 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 31 | 1 | 31 | 31 | 0.962963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ae3cf20da26bd04f1d112b3e36acb2784d93e832 | 12,419 | py | Python | CGATPipelines/pipeline_docs/pipeline_proj007/trackers/macs_replicated_shared_intervals.py | cdrakesmith/CGATPipelines | 3c94ae4f9d87d51108255dc405c4b95af7c8b694 | [
"MIT"
] | 49 | 2015-04-13T16:49:25.000Z | 2022-03-29T10:29:14.000Z | CGATPipelines/pipeline_docs/pipeline_proj007/trackers/macs_replicated_shared_intervals.py | cdrakesmith/CGATPipelines | 3c94ae4f9d87d51108255dc405c4b95af7c8b694 | [
"MIT"
] | 252 | 2015-04-08T13:23:34.000Z | 2019-03-18T21:51:29.000Z | CGATPipelines/pipeline_docs/pipeline_proj007/trackers/macs_replicated_shared_intervals.py | cdrakesmith/CGATPipelines | 3c94ae4f9d87d51108255dc405c4b95af7c8b694 | [
"MIT"
] | 22 | 2015-05-21T00:37:52.000Z | 2019-09-25T05:04:27.000Z |
from CGATReport.Tracker import *
from cpgReport import *
##########################################################################
class replicatedSharedIntervals(cpgTracker):
"""Summary stats of intervals called by the peak finder. """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getFirstRow(
"SELECT COUNT(*) as number, round(AVG(stop-start),0) as length FROM %(track)s_replicated_shared_intervals" % locals())
return odict(list(zip(("Shared intervals", "mean_interval_length"), data)))
##########################################################################
class replicatedsharedIntervalLengths(cpgTracker):
"""Distribution of interval length. """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getValues(
"SELECT (stop-start) FROM %(track)s_replicated_shared_intervals" % locals())
return {"length": data}
##########################################################################
class replicatedSharedIntervalPeakValues(cpgTracker):
"""Distribution of maximum interval coverage (the number of reads at peak). """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getValues( '''SELECT i.peakval FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return {"peakval": data}
##########################################################################
class replicatedSharedIntervalAverageValues(cpgTracker):
"""Distribution of average coverage (the average number of reads within the interval) """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getValues( '''SELECT avgval FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return {"avgval": data}
##########################################################################
class replicatedSharedIntervalFoldChange(cpgTracker):
"""Distribution of fold change """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getValues( '''SELECT i.fold FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict([("Fold Change", data)])
##########################################################################
class replicatedSharedIntervalTSS(cpgTracker):
"""Distribution of distance to closest TSS """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
ANNOTATIONS_NAME = P['annotations_name']
data = self.getValues( '''SELECT closest_dist FROM %(track)s_replicated_shared_intervals u,
%(track)s_replicated_intervals i, %(track)s_replicated_%(ANNOTATIONS_NAME)s_transcript_tss_distance t
WHERE u.contig=i.contig
AND u.start=i.start
AND t.gene_id=i.interval_id''' % locals() )
return {"distance": data}
##########################################################################
class replicatedSharedIntervalCpGDensity(cpgTracker):
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getAll( '''SELECT pCpG FROM %(track)s_replicated_shared_intervals u,
%(track)s_replicated_intervals i,%(track)s_replicated_capseq_composition c
WHERE u.contig=i.contig
AND u.start=i.start
AND c.gene_id=i.interval_id''' % locals() )
return data
##########################################################################
class replicatedSharedIntervalCpGObsExp(cpgTracker):
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getAll( '''SELECT CpG_ObsExp FROM %(track)s_replicated_shared_intervals u,
%(track)s_replicated_intervals i,%(track)s_replicated_capseq_composition c
WHERE u.contig=i.contig
AND u.start=i.start
AND c.gene_id=i.interval_id''' % locals() )
return data
##########################################################################
class replicatedSharedIntervalCpGNumber(cpgTracker):
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getAll( '''SELECT nCpG FROM %(track)s_replicated_shared_intervals u,
%(track)s_replicated_intervals i,%(track)s_replicated_capseq_composition c
WHERE u.contig=i.contig
AND u.start=i.start
AND c.gene_id=i.interval_id''' % locals() )
return data
##########################################################################
class replicatedSharedIntervalGCContent(cpgTracker):
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.getAll( '''SELECT pGC FROM %(track)s_replicated_shared_intervals u,
%(track)s_replicated_intervals i,%(track)s_replicated_capseq_composition c
WHERE u.contig=i.contig
AND u.start=i.start
AND c.gene_id=i.interval_id''' % locals() )
return data
##########################################################################
##########################################################################
##########################################################################
class replicatedSharedIntervalLengthVsAverageValue(cpgTracker):
"""Length vs average value. """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT length, avgval FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("length", "avgval"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalLengthVsPeakValue(cpgTracker):
"""Length vs peak value """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT length, peakval FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("length", "peakval"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalLengthVsFoldChange(cpgTracker):
"""Length vs fold change"""
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT length, fold FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("length", "foldchange"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalAvgValVsPeakVal(cpgTracker):
"""average value vs peak value """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT avgval, peakval FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("avgval", "peakval"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalAvgValVsFoldChange(cpgTracker):
"""average value vs fold change """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT avgval, fold FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("avgval", "foldchange"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalPeakValVsFoldChange(cpgTracker):
"""Peak value vs fold change """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
data = self.get( '''SELECT peakval, fold FROM %(track)s_replicated_shared_intervals u, %(track)s_replicated_intervals i
WHERE u.contig=i.contig
AND u.start=i.start''' % locals() )
return odict(list(zip(("peakval", "foldchange"), list(zip(*data)))))
##########################################################################
class replicatedSharedIntervalTranscriptOverlap(featureOverlap):
"""return overlap of interval with protein-coding transcripts """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
ANNOTATIONS_NAME = P['annotations_name']
data = self.getValues( """ SELECT count(distinct gene_id) as intervals FROM (
SELECT gene_id,
CASE WHEN tss_transcript_extended_pover1 > 0 THEN 'TSS'
WHEN genes_pover1 > 0 THEN 'Gene'
WHEN upstream_flank_pover1 >0 THEN 'Upstream'
WHEN downstream_flank_pover1 >0 THEN 'Downstream'
ELSE 'Intergenic'
END AS feature_class
FROM %(track)s_replicated_%(ANNOTATIONS_NAME)s_overlap o, %(track)s_replicated_shared_intervals u
WHERE u.interval_id=o.gene_id)
group by feature_class
order by feature_class asc""" % locals() )
return odict(list(zip(("Downstream", "Gene", "Intergenic", "TSS", "Upstream"), data)))
##########################################################################
class replicatedSharedIntervalGeneOverlap(featureOverlap):
"""return overlap of interval with protein-coding genes """
mPattern = "_replicated_shared_intervals$"
def __call__(self, track, slice=None):
ANNOTATIONS_NAME = P['annotations_name']
data = self.getValues( """ SELECT count(distinct gene_id) as intervals FROM (
SELECT gene_id,
CASE WHEN tss_gene_extended_pover1 > 0 THEN 'TSS'
WHEN genes_pover1 > 0 THEN 'Gene'
WHEN upstream_flank_pover1 >0 THEN 'Upstream'
WHEN downstream_flank_pover1 >0 THEN 'Downstream'
ELSE 'Intergenic'
END AS feature_class
FROM %(track)s_replicated_%(ANNOTATIONS_NAME)s_overlap o, %(track)s_replicated_shared_intervals u
WHERE u.interval_id=o.gene_id)
group by feature_class
order by feature_class asc""" % locals() )
return odict(list(zip(("Downstream", "Gene", "Intergenic", "TSS", "Upstream"), data)))
| 42.385666 | 135 | 0.523231 | 1,121 | 12,419 | 5.543265 | 0.115076 | 0.037657 | 0.100418 | 0.095591 | 0.769714 | 0.747827 | 0.745253 | 0.740586 | 0.708079 | 0.708079 | 0 | 0.001876 | 0.270151 | 12,419 | 292 | 136 | 42.530822 | 0.683694 | 0.047266 | 0 | 0.656051 | 0 | 0.006369 | 0.60181 | 0.220255 | 0 | 0 | 0 | 0 | 0 | 1 | 0.11465 | false | 0 | 0.012739 | 0 | 0.471338 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ae5c5e80f3aba1cea62df25be98f3d557d11419e | 5,776 | py | Python | tests/unit_tests/test_component/marketplace/simulator_tcp_server_cyclic_tm_sim/test_cyclic_tm_tcp_mock.py | ismaelJimenez/mamba_server | e6e2343291a0df24f226bde0d13e5bfa74cc3650 | [
"MIT"
] | null | null | null | tests/unit_tests/test_component/marketplace/simulator_tcp_server_cyclic_tm_sim/test_cyclic_tm_tcp_mock.py | ismaelJimenez/mamba_server | e6e2343291a0df24f226bde0d13e5bfa74cc3650 | [
"MIT"
] | null | null | null | tests/unit_tests/test_component/marketplace/simulator_tcp_server_cyclic_tm_sim/test_cyclic_tm_tcp_mock.py | ismaelJimenez/mamba_server | e6e2343291a0df24f226bde0d13e5bfa74cc3650 | [
"MIT"
] | null | null | null | import socket
import time
from mamba.core.context import Context
from mamba.marketplace.components.simulator.tcp_server_cyclic_tm_sim import CyclicTmTcpMock
class TestClass:
def test_simple_tmtc(self):
self.mock = CyclicTmTcpMock(
Context(),
local_config={'instrument': {
'port': {
'tc': 9200,
'tm': 9201
}
}})
self.mock.initialize()
# Create a socket (SOCK_STREAM means a TCP socket)
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
# Connect to server and send data
sock.connect(("localhost", 9200))
# Create a socket (SOCK_STREAM means a TCP socket)
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock_tm:
# Connect to server and send data
sock_tm.connect(("localhost", 9201))
# Test cyclic telemetry reception
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 1\nPARAMETER_2 2\nPARAMETER_3 3\n'
sock.sendall(bytes('*IDN?\r\n', "utf-8"))
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock.recv(1024), "utf-8")
assert received == 'Mamba Framework,Cyclic Telemetry TCP Mock,1.0\n'
sock.sendall(bytes('*CLS\r\n', "utf-8"))
sock.sendall(
bytes(
'PARAMETER_1 4\r\nPARAMETER_2 5\r\nPARAMETER_3 6\r\n',
"utf-8"))
# Test cyclic telemetry reception
time.sleep(5)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 4\nPARAMETER_2 5\nPARAMETER_3 6\n'
# Create a socket (SOCK_STREAM means a TCP socket)
with socket.socket(socket.AF_INET,
socket.SOCK_STREAM) as sock_2_tm:
# Connect to server and send data
sock_2_tm.connect(("localhost", 9201))
# Test cyclic telemetry reception
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock_2_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 4\nPARAMETER_2 5\nPARAMETER_3 6\n'
self.mock._close()
def test_error_handling(self):
self.mock = CyclicTmTcpMock(
Context(),
local_config={'instrument': {
'port': {
'tc': 9401,
'tm': 9402
}
}})
self.mock.initialize()
# Create a socket (SOCK_STREAM means a TCP socket)
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
# Connect to server and send data
sock.connect(("localhost", 9401))
# Create a socket (SOCK_STREAM means a TCP socket)
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock_tm:
# Connect to server and send data
sock_tm.connect(("localhost", 9402))
sock.sendall(bytes('SYST:ERR?\r\n', "utf-8"))
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock.recv(1024), "utf-8")
assert received == '0,_No_Error\n'
# Test cyclic telemetry reception
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 1\nPARAMETER_2 2\nPARAMETER_3 3\n'
# Test wrong message ending
sock.sendall(bytes('PARAMETER_1 4\n', "utf-8"))
time.sleep(.1)
# Test cyclic telemetry reception
time.sleep(5)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 1\nPARAMETER_2 2\nPARAMETER_3 3\n'
# Test wrong number or parameters
sock.sendall(bytes('PARAMETER_1\n', "utf-8"))
# Test cyclic telemetry reception
time.sleep(5)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 1\nPARAMETER_2 2\nPARAMETER_3 3\n'
# Test wrong number or parameters
sock.sendall(bytes('PARAMETER_5 1234\r\n', "utf-8"))
# Test cyclic telemetry reception
time.sleep(5)
# Receive data from the server and shut down
received = str(sock_tm.recv(1024), "utf-8")
assert received == 'PARAMETER_1 1\nPARAMETER_2 2\nPARAMETER_3 3\n'
sock.sendall(bytes('SYST:ERR?\r\n', "utf-8"))
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock.recv(1024), "utf-8")
assert received == '1,_Command_Error\n'
sock.sendall(bytes('SYST:ERR?\r\n', "utf-8"))
time.sleep(.1)
# Receive data from the server and shut down
received = str(sock.recv(1024), "utf-8")
assert received == '0,_No_Error\n'
self.mock._close()
| 37.025641 | 91 | 0.524758 | 680 | 5,776 | 4.345588 | 0.136765 | 0.027073 | 0.055838 | 0.067005 | 0.87445 | 0.874112 | 0.850761 | 0.850761 | 0.839932 | 0.839932 | 0 | 0.049066 | 0.378982 | 5,776 | 155 | 92 | 37.264516 | 0.774742 | 0.206198 | 0 | 0.619048 | 0 | 0 | 0.162969 | 0 | 0 | 0 | 0 | 0 | 0.130952 | 1 | 0.02381 | false | 0 | 0.047619 | 0 | 0.083333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ae96deee933ff17ccb2a724334de1cff1e6f83e5 | 252 | py | Python | array/monotonic_array.py | elenaborisova/LeetCode-Solutions | 98376aab7fd150a724e316357ae5ea46988d9eac | [
"MIT"
] | null | null | null | array/monotonic_array.py | elenaborisova/LeetCode-Solutions | 98376aab7fd150a724e316357ae5ea46988d9eac | [
"MIT"
] | null | null | null | array/monotonic_array.py | elenaborisova/LeetCode-Solutions | 98376aab7fd150a724e316357ae5ea46988d9eac | [
"MIT"
] | null | null | null | def is_monotonic(nums):
return nums == sorted(nums) or nums == sorted(nums)[::-1]
print(is_monotonic([1, 2, 2, 3]))
print(is_monotonic([6, 5, 4, 4]))
print(is_monotonic([1, 3, 2]))
print(is_monotonic([1, 2, 4, 5]))
print(is_monotonic([1, 1, 1]))
| 25.2 | 61 | 0.630952 | 46 | 252 | 3.326087 | 0.304348 | 0.431373 | 0.522876 | 0.444444 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 0.087156 | 0.134921 | 252 | 9 | 62 | 28 | 0.614679 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0 | 0.142857 | 0.285714 | 0.714286 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 6 |
889fe8b0ab8c7d14c0b182208802fe3f284a0f65 | 19 | py | Python | __init__.py | dshirle7/euler | ee0ad73a9cc250674aa0cea30e4179aac1effccb | [
"MIT"
] | null | null | null | __init__.py | dshirle7/euler | ee0ad73a9cc250674aa0cea30e4179aac1effccb | [
"MIT"
] | null | null | null | __init__.py | dshirle7/euler | ee0ad73a9cc250674aa0cea30e4179aac1effccb | [
"MIT"
] | null | null | null | from euler import * | 19 | 19 | 0.789474 | 3 | 19 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 19 | 1 | 19 | 19 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ee46a36fef86f32bbc1b293dc0cfb73292b9aad8 | 11,645 | py | Python | shard/src/ext/log_cog.py | architus/aut-bot | 391b98533f0ed9ce235487c27559fe0aad037644 | [
"MIT"
] | 27 | 2019-07-06T07:04:26.000Z | 2022-02-12T12:10:54.000Z | shard/src/ext/log_cog.py | architus/aut-bot | 391b98533f0ed9ce235487c27559fe0aad037644 | [
"MIT"
] | 65 | 2019-07-06T07:12:45.000Z | 2021-10-31T22:45:07.000Z | shard/src/ext/log_cog.py | architus/aut-bot | 391b98533f0ed9ce235487c27559fe0aad037644 | [
"MIT"
] | 17 | 2019-07-06T06:59:04.000Z | 2021-06-26T13:42:06.000Z | from discord.ext import commands
from discord import ChannelType
class LogCog(commands.Cog):
def __init__(self, bot):
self.bot = bot
self.hoarfrost = bot.hoarfrost_gen
@commands.Cog.listener()
async def on_guild_update(self, before, after):
await self.bot.emitter.emit(
'gateway.on_guild_update',
{
'id': self.hoarfrost.generate(),
'guild_id': before.id,
'discord_object': before.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 1,
'agent_id': None,
'subject_id': before.id,
'data': {
'before': {
'name': before.name,
# ...
},
'after': {
'name': after.name,
# ...
},
},
}
)
@commands.Cog.listener()
async def on_guild_available(self, guild):
await self.bot.emitter.emit(
'gateway.on_guild_available',
{
'id': self.hoarfrost.generate(),
'guild_id': guild.id,
'discord_object': guild.id,
'private': False,
'reversible': False,
'action_number': -1,
'agent_id': None,
'subject_id': guild.id,
'data': {
'availible': True,
},
}
)
@commands.Cog.listener()
async def on_guild_unavailable(self, guild):
await self.bot.emitter.emit(
'gateway.on_guild_unavailable',
{
'id': self.hoarfrost.generate(),
'guild_id': guild.id,
'discord_object': guild.id,
'private': False,
'reversible': False,
'action_number': -1,
'agent_id': None,
'subject_id': guild.id,
'data': {
'availible': False,
},
}
)
@commands.Cog.listener()
async def on_guild_role_create(self, role):
await self.bot.emitter.emit(
'gateway.on_guild_role_create',
{
'id': self.hoarfrost.generate(),
'guild_id': role.guild.id,
'discord_object': role.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 30,
'agent_id': None,
'subject_id': role.id,
'data': {
'name': role.name,
# ...
},
}
)
@commands.Cog.listener()
async def on_guild_role_delete(self, role):
await self.bot.emitter.emit(
'gateway.on_guild_role_delete',
{
'id': self.hoarfrost.generate(),
'guild_id': role.guild.id,
'discord_object': role.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 32,
'agent_id': None,
'subject_id': role.id,
'data': {
'name': role.name,
# ...
},
}
)
@commands.Cog.listener()
async def on_guild_role_update(self, before, after):
await self.bot.emitter.emit(
'gateway.on_guild_role_update',
{
'id': self.hoarfrost.generate(),
'guild_id': before.guild.id,
'discord_object': before.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 31,
'agent_id': None,
'subject_id': before.id,
'data': {
'before': {
'name': before.name,
# ...
},
'after': {
'name': after.name,
# ...
},
},
}
)
@commands.Cog.listener()
async def on_guild_emojis_update(self, guild, before, after):
pass
@commands.Cog.listener()
async def on_guild_channel_create(self, channel):
await self.bot.emitter.emit(
'gateway.on_guild_channel_create',
{
'id': self.hoarfrost.generate(),
'guild_id': channel.guild.id,
'discord_object': channel.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 10,
'agent_id': None,
'subject_id': channel.id,
'data': {
'name': channel.name,
# ...
},
}
)
@commands.Cog.listener()
async def on_guild_channel_delete(self, channel):
await self.bot.emitter.emit(
'gateway.on_guild_channel_delete',
{
'id': self.hoarfrost.generate(),
'guild_id': channel.guild.id,
'discord_object': channel.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 12,
'agent_id': None,
'subject_id': channel.id,
'data': {
'name': channel.name,
# ...
},
}
)
@commands.Cog.listener()
async def on_guild_channel_update(self, before, after):
await self.bot.emitter.emit(
'gateway.on_guild_channel_update',
{
'id': self.hoarfrost.generate(),
'guild_id': before.guild.id,
'discord_object': before.id,
'private': False,
'reversible': False, # should be possible eventually
'action_number': 11,
'agent_id': None,
'subject_id': before.id,
'data': {
'before': {
'name': before.name,
# ...
},
'after': {
'name': after.name,
# ...
},
},
}
)
@commands.Cog.listener()
async def on_bulk_message_delete(self, messages):
pass
@commands.Cog.listener()
async def on_message_edit(self, before, after):
await self.bot.emitter.emit(
'gateway.on_message_edit',
{
'id': self.hoarfrost.generate(),
'guild_id': before.guild.id,
'discord_object': before.id,
'private': False,
'reversible': False,
'action_number': 3002,
'agent_id': None,
'subject_id': before.id,
'data': {
'before': {
'content': before.content,
# ...
},
'after': {
'content': after.content,
# ...
},
},
}
)
@commands.Cog.listener()
async def on_message(self, msg):
await self.bot.emitter.emit(
'gateway.on_message',
{
'id': self.hoarfrost.generate(),
'guild_id': msg.channel.guild.id,
'discord_object': msg.id,
'private': msg.channel.type == ChannelType.private,
'reversible': True,
'action_number': 3001,
'agent_id': msg.author.id,
'subject_id': msg.channel.id,
'data': {
'content': msg.content,
'created_at': msg.created_at.isoformat(),
},
}
)
@commands.Cog.listener()
async def on_message_delete(self, msg):
await self.bot.emitter.emit(
'gateway.on_message_delete',
{
'id': self.hoarfrost.generate(),
'guild_id': msg.channel.guild.id,
'discord_object': msg.id,
'private': msg.channel.type == ChannelType.private,
'reversible': True,
'action_number': 3003,
'agent_id': msg.author.id,
'subject_id': msg.id,
'data': None,
}
)
@commands.Cog.listener()
async def on_reaction_add(self, react, user):
await self.bot.emitter.emit(
'gateway.on_reation_add',
{
'id': self.hoarfrost.generate(),
'guild_id': react.message.channel.guild.id,
'discord_object': None,
'private': react.message.channel.type == ChannelType.private,
'reversible': True,
'action_number': 3100,
'agent_id': user.id,
'subject_id': react.message.id,
'data': {
'emoji': str(react.emoji),
},
}
)
@commands.Cog.listener()
async def on_reaction_remove(self, react, user):
await self.bot.emitter.emit(
'gateway.on_reation_remove',
{
'id': self.hoarfrost.generate(),
'guild_id': react.message.channel.guild.id,
'discord_object': None,
'private': react.message.channel.type == ChannelType.private,
'reversible': False,
'action_number': 3101,
'agent_id': user.id,
'subject_id': react.message.id,
'data': {
'emoji': str(react.emoji),
},
}
)
@commands.Cog.listener()
async def on_reaction_clear(self, msg, react):
pass
@commands.Cog.listener()
async def on_webhooks_update(self, channel):
pass
@commands.Cog.listener()
async def on_member_join(self, member):
await self.bot.emitter.emit(
'gateway.on_member_join',
{
'id': self.hoarfrost.generate(),
'guild_id': member.guild.id,
'discord_object': member.id,
'private': False,
'reversible': False,
'action_number': -1,
'agent_id': None,
'subject_id': member.id,
'data': {
'name': member.name,
# ...
},
}
)
@commands.Cog.listener()
async def on_member_remove(self, member):
pass
@commands.Cog.listener()
async def on_member_update(self, before, after):
pass
@commands.Cog.listener()
async def on_member_ban(self, guild, user):
pass
@commands.Cog.listener()
async def on_member_unban(self, guild, user):
pass
@commands.Cog.listener()
async def on_voice_state_update(self, member, before, after):
pass
def setup(bot):
pass
# bot.add_cog(LogCog(bot))
| 31.644022 | 77 | 0.438729 | 990 | 11,645 | 4.984848 | 0.093939 | 0.046809 | 0.092401 | 0.116717 | 0.863019 | 0.863019 | 0.85694 | 0.783384 | 0.729483 | 0.701722 | 0 | 0.005575 | 0.445513 | 11,645 | 367 | 78 | 31.730245 | 0.758711 | 0.02456 | 0 | 0.570988 | 0 | 0 | 0.147835 | 0.032725 | 0 | 0 | 0 | 0 | 0 | 1 | 0.006173 | false | 0.030864 | 0.006173 | 0 | 0.015432 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ee4ea8af14aa875e203a1d3b7657948f660bd35d | 230 | py | Python | ehr_functions/models/types/_base.py | fdabek1/EHR-Functions | e6bd0b6fa213930358c4a19be31c459ac7430ca9 | [
"MIT"
] | null | null | null | ehr_functions/models/types/_base.py | fdabek1/EHR-Functions | e6bd0b6fa213930358c4a19be31c459ac7430ca9 | [
"MIT"
] | null | null | null | ehr_functions/models/types/_base.py | fdabek1/EHR-Functions | e6bd0b6fa213930358c4a19be31c459ac7430ca9 | [
"MIT"
] | null | null | null | class Model:
def __init__(self, multiple_output=False):
self.multiple_output = multiple_output
def train(self, x, y):
raise NotImplementedError
def predict(self, x):
raise NotImplementedError
| 23 | 46 | 0.678261 | 26 | 230 | 5.730769 | 0.538462 | 0.281879 | 0.241611 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.247826 | 230 | 9 | 47 | 25.555556 | 0.861272 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.428571 | false | 0 | 0 | 0 | 0.571429 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
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