body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def check(self):
' Checks parameters and paths\n '
if ('UUID' not in PAR):
setattr(PAR, 'UUID', str(uuid4()))
if ('SCRATCH' not in PATH):
setattr(PATH, 'SCRATCH', join('/scratch/gpfs', getuser(), 'seisflows', PAR.UUID))
if ('LOCAL' not in PATH):
setattr(PATH, 'LOCAL', '')
... | -5,435,384,274,526,499,000 | Checks parameters and paths | seisflows/system/tiger_md.py | check | chukren/seisflows | python | def check(self):
' \n '
if ('UUID' not in PAR):
setattr(PAR, 'UUID', str(uuid4()))
if ('SCRATCH' not in PATH):
setattr(PATH, 'SCRATCH', join('/scratch/gpfs', getuser(), 'seisflows', PAR.UUID))
if ('LOCAL' not in PATH):
setattr(PATH, 'LOCAL', )
super(tiger_md, self).che... |
def submit(self, *args, **kwargs):
' Submits job\n '
if (not exists(((PATH.SUBMIT + '/') + 'scratch'))):
unix.ln(PATH.SCRATCH, ((PATH.SUBMIT + '/') + 'scratch'))
super(tiger_md, self).submit(*args, **kwargs) | -6,969,412,769,645,984,000 | Submits job | seisflows/system/tiger_md.py | submit | chukren/seisflows | python | def submit(self, *args, **kwargs):
' \n '
if (not exists(((PATH.SUBMIT + '/') + 'scratch'))):
unix.ln(PATH.SCRATCH, ((PATH.SUBMIT + '/') + 'scratch'))
super(tiger_md, self).submit(*args, **kwargs) |
def cltv_lock_to_height(node, tx, to_address, amount, height=(- 1)):
'Modify the scriptPubKey to add an OP_CHECKLOCKTIMEVERIFY, and make\n a transaction that spends it.\n\n This transforms the output script to anyone can spend (OP_TRUE) if the\n lock time condition is valid.\n\n Default height is -1 whi... | -6,207,897,528,851,743,000 | Modify the scriptPubKey to add an OP_CHECKLOCKTIMEVERIFY, and make
a transaction that spends it.
This transforms the output script to anyone can spend (OP_TRUE) if the
lock time condition is valid.
Default height is -1 which leads CLTV to fail
TODO: test more ways that transactions using CLTV could be invalid (eg
lo... | test/functional/feature_cltv.py | cltv_lock_to_height | ComputerCraftr/devault | python | def cltv_lock_to_height(node, tx, to_address, amount, height=(- 1)):
'Modify the scriptPubKey to add an OP_CHECKLOCKTIMEVERIFY, and make\n a transaction that spends it.\n\n This transforms the output script to anyone can spend (OP_TRUE) if the\n lock time condition is valid.\n\n Default height is -1 whi... |
def expand_dims(var, dim=0):
' Is similar to [numpy.expand_dims](https://docs.scipy.org/doc/numpy/reference/generated/numpy.expand_dims.html).\n var = torch.range(0, 9).view(-1, 2)\n torch.expand_dims(var, 0).size()\n # (1, 5, 2)\n '
sizes = list(var.size())
sizes.insert(dim, 1)
... | 123,622,040,983,809,650 | Is similar to [numpy.expand_dims](https://docs.scipy.org/doc/numpy/reference/generated/numpy.expand_dims.html).
var = torch.range(0, 9).view(-1, 2)
torch.expand_dims(var, 0).size()
# (1, 5, 2) | losses/magnet_loss.py | expand_dims | jiajunhua/HaydenFaulkner-pytorch.repmet | python | def expand_dims(var, dim=0):
' Is similar to [numpy.expand_dims](https://docs.scipy.org/doc/numpy/reference/generated/numpy.expand_dims.html).\n var = torch.range(0, 9).view(-1, 2)\n torch.expand_dims(var, 0).size()\n # (1, 5, 2)\n '
sizes = list(var.size())
sizes.insert(dim, 1)
... |
def comparison_mask(a_labels, b_labels):
'Computes boolean mask for distance comparisons'
return torch.eq(expand_dims(a_labels, 1), expand_dims(b_labels, 0)) | 1,893,867,106,700,745,200 | Computes boolean mask for distance comparisons | losses/magnet_loss.py | comparison_mask | jiajunhua/HaydenFaulkner-pytorch.repmet | python | def comparison_mask(a_labels, b_labels):
return torch.eq(expand_dims(a_labels, 1), expand_dims(b_labels, 0)) |
def dynamic_partition(X, partitions, n_clusters):
'Partitions the data into the number of cluster bins'
cluster_bin = torch.chunk(X, n_clusters)
return cluster_bin | 9,038,985,545,027,217,000 | Partitions the data into the number of cluster bins | losses/magnet_loss.py | dynamic_partition | jiajunhua/HaydenFaulkner-pytorch.repmet | python | def dynamic_partition(X, partitions, n_clusters):
cluster_bin = torch.chunk(X, n_clusters)
return cluster_bin |
def bool_from_env(var, default: bool=False) -> bool:
"Helper for converting env string into boolean.\n\n Returns bool True for string values: '1' or 'true', False otherwise.\n "
def str_to_bool(s: str) -> bool:
return (s.lower() in ('1', 'true'))
os_var = os.environ.get(var)
if (os_var is... | 5,744,022,469,535,834,000 | Helper for converting env string into boolean.
Returns bool True for string values: '1' or 'true', False otherwise. | src/ralph/settings/base.py | bool_from_env | p-bo/ralph | python | def bool_from_env(var, default: bool=False) -> bool:
"Helper for converting env string into boolean.\n\n Returns bool True for string values: '1' or 'true', False otherwise.\n "
def str_to_bool(s: str) -> bool:
return (s.lower() in ('1', 'true'))
os_var = os.environ.get(var)
if (os_var is... |
def _crypted_transfer(self, load, tries=3, timeout=60):
'\n In case of authentication errors, try to renegotiate authentication\n and retry the method.\n Indeed, we can fail too early in case of a master restart during a\n minion state execution call\n '
def _do_transfer():
... | 4,214,069,522,247,119,400 | In case of authentication errors, try to renegotiate authentication
and retry the method.
Indeed, we can fail too early in case of a master restart during a
minion state execution call | salt/transport/__init__.py | _crypted_transfer | otrempe/salt | python | def _crypted_transfer(self, load, tries=3, timeout=60):
'\n In case of authentication errors, try to renegotiate authentication\n and retry the method.\n Indeed, we can fail too early in case of a master restart during a\n minion state execution call\n '
def _do_transfer():
... |
def _identifier_split(identifier):
'Return (name, start, end) string tuple from an identifier (PRIVATE).'
(id, loc, strand) = identifier.split(':')
(start, end) = map(int, loc.split('-'))
start -= 1
return (id, start, end, strand) | -346,816,607,895,191,600 | Return (name, start, end) string tuple from an identifier (PRIVATE). | Bio/AlignIO/MauveIO.py | _identifier_split | BioinfoCat/biopython | python | def _identifier_split(identifier):
(id, loc, strand) = identifier.split(':')
(start, end) = map(int, loc.split('-'))
start -= 1
return (id, start, end, strand) |
def __init__(self, *args, **kwargs):
'Initialize.'
super(MauveWriter, self).__init__(*args, **kwargs)
self._wrote_header = False
self._wrote_first = False | 6,127,116,914,066,891,000 | Initialize. | Bio/AlignIO/MauveIO.py | __init__ | BioinfoCat/biopython | python | def __init__(self, *args, **kwargs):
super(MauveWriter, self).__init__(*args, **kwargs)
self._wrote_header = False
self._wrote_first = False |
def write_alignment(self, alignment):
'Use this to write (another) single alignment to an open file.\n\n Note that sequences and their annotation are recorded\n together (rather than having a block of annotation followed\n by a block of aligned sequences).\n '
count = len(alignment)
... | 7,175,577,788,833,652,000 | Use this to write (another) single alignment to an open file.
Note that sequences and their annotation are recorded
together (rather than having a block of annotation followed
by a block of aligned sequences). | Bio/AlignIO/MauveIO.py | write_alignment | BioinfoCat/biopython | python | def write_alignment(self, alignment):
'Use this to write (another) single alignment to an open file.\n\n Note that sequences and their annotation are recorded\n together (rather than having a block of annotation followed\n by a block of aligned sequences).\n '
count = len(alignment)
... |
def _write_record(self, record, record_idx=0):
'Write a single SeqRecord to the file (PRIVATE).'
if (self._length_of_sequences != len(record.seq)):
raise ValueError('Sequences must all be the same length')
seq_name = record.name
try:
seq_name = str(int(record.name))
except ValueError... | 5,108,774,003,558,236,000 | Write a single SeqRecord to the file (PRIVATE). | Bio/AlignIO/MauveIO.py | _write_record | BioinfoCat/biopython | python | def _write_record(self, record, record_idx=0):
if (self._length_of_sequences != len(record.seq)):
raise ValueError('Sequences must all be the same length')
seq_name = record.name
try:
seq_name = str(int(record.name))
except ValueError:
seq_name = str((record_idx + 1))
if... |
def __next__(self):
'Parse the next alignment from the handle.'
handle = self.handle
line = handle.readline()
if (not line):
raise StopIteration
while (line and line.strip().startswith('#')):
line = handle.readline()
seqs = {}
seq_regions = {}
passed_end_alignment = False... | -5,048,572,972,203,061,000 | Parse the next alignment from the handle. | Bio/AlignIO/MauveIO.py | __next__ | BioinfoCat/biopython | python | def __next__(self):
handle = self.handle
line = handle.readline()
if (not line):
raise StopIteration
while (line and line.strip().startswith('#')):
line = handle.readline()
seqs = {}
seq_regions = {}
passed_end_alignment = False
latest_id = None
while True:
... |
def run_gaussian_dataset_montecarlo(iterations: int=30, m: int=10000, n: int=128, param_list=None, epochs: int=300, batch_size: int=100, display_freq: int=1, optimizer='sgd', validation_split: float=0.2, shape_raw: List[int]=None, activation: t_activation='cart_relu', verbose: bool=False, do_all: bool=True, tensorboard... | -1,363,922,818,580,274,200 | This function is used to compare CVNN vs RVNN performance over statistical non-circular data.
1. Generates a complex-valued gaussian correlated noise with the characteristics given by the inputs.
2. It then runs a monte carlo simulation of several iterations of both CVNN and an equivalent RVNN model.
3. Sav... | cvnn/montecarlo.py | run_gaussian_dataset_montecarlo | NEGU93/cvnn | python | def run_gaussian_dataset_montecarlo(iterations: int=30, m: int=10000, n: int=128, param_list=None, epochs: int=300, batch_size: int=100, display_freq: int=1, optimizer='sgd', validation_split: float=0.2, shape_raw: List[int]=None, activation: t_activation='cart_relu', verbose: bool=False, do_all: bool=True, tensorboard... |
def run_montecarlo(models: List[Model], dataset: cvnn.dataset.Dataset, open_dataset: Optional[t_path]=None, iterations: int=30, epochs: int=300, batch_size: int=100, display_freq: int=1, validation_split: float=0.2, validation_data: Optional[Union[(Tuple, data.Dataset)]]=None, verbose: Union[(bool, int)]=False, do_conf... | 8,390,725,974,427,718,000 | This function is used to compare different neural networks performance.
1. Runs simulation and compares them.
2. Saves several files into ./log/montecarlo/date/of/run/
2.1. run_summary.txt: Summary of the run models and data
2.2. run_data.csv: Full information of performance of iteration of each model at each e... | cvnn/montecarlo.py | run_montecarlo | NEGU93/cvnn | python | def run_montecarlo(models: List[Model], dataset: cvnn.dataset.Dataset, open_dataset: Optional[t_path]=None, iterations: int=30, epochs: int=300, batch_size: int=100, display_freq: int=1, validation_split: float=0.2, validation_data: Optional[Union[(Tuple, data.Dataset)]]=None, verbose: Union[(bool, int)]=False, do_conf... |
def mlp_run_real_comparison_montecarlo(dataset: cvnn.dataset.Dataset, open_dataset: Optional[t_path]=None, iterations: int=30, epochs: int=300, batch_size: int=100, display_freq: int=1, optimizer='adam', shape_raw=None, activation: t_activation='cart_relu', output_activation: t_activation=DEFAULT_OUTPUT_ACT, verbose: U... | -8,105,493,941,948,592,000 | This function is used to compare CVNN vs RVNN performance over any dataset.
1. Automatically creates two Multi-Layer Perceptrons (MLP), one complex and one real.
2. Runs simulation and compares them.
3. Saves several files into ./log/montecarlo/date/of/run/
3.1. run_summary.txt: Summary of the run models and data
... | cvnn/montecarlo.py | mlp_run_real_comparison_montecarlo | NEGU93/cvnn | python | def mlp_run_real_comparison_montecarlo(dataset: cvnn.dataset.Dataset, open_dataset: Optional[t_path]=None, iterations: int=30, epochs: int=300, batch_size: int=100, display_freq: int=1, optimizer='adam', shape_raw=None, activation: t_activation='cart_relu', output_activation: t_activation=DEFAULT_OUTPUT_ACT, verbose: U... |
def __init__(self):
'\n Class that allows the statistical comparison of several models on the same dataset\n '
self.models = []
self.pandas_full_data = pd.DataFrame()
self.monte_carlo_analyzer = MonteCarloAnalyzer()
self.verbose = 1
self.output_config = {'plot_all': False, 'confusi... | 6,956,332,109,359,251,000 | Class that allows the statistical comparison of several models on the same dataset | cvnn/montecarlo.py | __init__ | NEGU93/cvnn | python | def __init__(self):
'\n \n '
self.models = []
self.pandas_full_data = pd.DataFrame()
self.monte_carlo_analyzer = MonteCarloAnalyzer()
self.verbose = 1
self.output_config = {'plot_all': False, 'confusion_matrix': False, 'excel_summary': True, 'summary_of_run': True, 'tensorboard': F... |
def add_model(self, model: Type[Model]):
'\n Adds a cvnn.CvnnModel to the list to then compare between them\n '
self.models.append(model) | -5,031,837,254,433,821,000 | Adds a cvnn.CvnnModel to the list to then compare between them | cvnn/montecarlo.py | add_model | NEGU93/cvnn | python | def add_model(self, model: Type[Model]):
'\n \n '
self.models.append(model) |
def run(self, x, y, data_summary: str='', real_cast_modes: Optional[Union[(str, List[Optional[str]], Tuple[Optional[str]])]]=None, validation_split: float=0.2, validation_data: Optional[Union[(Tuple[(np.ndarray, np.ndarray)], data.Dataset)]]=None, test_data: Optional[Union[(Tuple[(np.ndarray, np.ndarray)], data.Dataset... | 3,118,653,797,893,880,300 | This function is used to compare all models added with `self.add_model` method.
Runs the iteration dataset (x, y).
1. It then runs a monte carlo simulation of several iterations of both CVNN and an equivalent RVNN model.
2. Saves several files into ./log/montecarlo/date/of/run/
2.1. run_summary.txt: Summary of the ... | cvnn/montecarlo.py | run | NEGU93/cvnn | python | def run(self, x, y, data_summary: str=, real_cast_modes: Optional[Union[(str, List[Optional[str]], Tuple[Optional[str]])]]=None, validation_split: float=0.2, validation_data: Optional[Union[(Tuple[(np.ndarray, np.ndarray)], data.Dataset)]]=None, test_data: Optional[Union[(Tuple[(np.ndarray, np.ndarray)], data.Dataset)]... |
def _save_summary_of_run(self, run_summary, data_summary):
'\n Saves 2 files:\n - run_summary.txt: A user-friendly resume of the monte carlo run.\n - models_details.json: A full serialized version of the models.\n Contains info that lacks in the txt file like the loss or ... | 2,147,652,564,802,560,800 | Saves 2 files:
- run_summary.txt: A user-friendly resume of the monte carlo run.
- models_details.json: A full serialized version of the models.
Contains info that lacks in the txt file like the loss or optimizer. | cvnn/montecarlo.py | _save_summary_of_run | NEGU93/cvnn | python | def _save_summary_of_run(self, run_summary, data_summary):
'\n Saves 2 files:\n - run_summary.txt: A user-friendly resume of the monte carlo run.\n - models_details.json: A full serialized version of the models.\n Contains info that lacks in the txt file like the loss or ... |
def __init__(self, complex_model: Type[Model], capacity_equivalent: bool=True, equiv_technique: str='ratio'):
"\n :param complex_model: Complex keras model (ex: sequential)\n :param capacity_equivalent: An equivalent model can be equivalent in terms of layer neurons or\n trainab... | -8,439,983,612,307,735,000 | :param complex_model: Complex keras model (ex: sequential)
:param capacity_equivalent: An equivalent model can be equivalent in terms of layer neurons or
trainable parameters (capacity equivalent according to: https://arxiv.org/abs/1811.12351)
- True, it creates a capacity-equivalent model in terms ... | cvnn/montecarlo.py | __init__ | NEGU93/cvnn | python | def __init__(self, complex_model: Type[Model], capacity_equivalent: bool=True, equiv_technique: str='ratio'):
"\n :param complex_model: Complex keras model (ex: sequential)\n :param capacity_equivalent: An equivalent model can be equivalent in terms of layer neurons or\n trainab... |
def require_collection_playable(handler):
'Decorator that checks if the user can play the given collection.'
def test_can_play(self, collection_id, **kwargs):
'Check if the current user can play the collection.'
actor = rights_manager.Actor(self.user_id)
can_play = actor.can_play(rights... | 9,035,655,413,190,753,000 | Decorator that checks if the user can play the given collection. | core/controllers/collection_viewer.py | require_collection_playable | Himanshu1495/oppia | python | def require_collection_playable(handler):
def test_can_play(self, collection_id, **kwargs):
'Check if the current user can play the collection.'
actor = rights_manager.Actor(self.user_id)
can_play = actor.can_play(rights_manager.ACTIVITY_TYPE_COLLECTION, collection_id)
can_view... |
def test_can_play(self, collection_id, **kwargs):
'Check if the current user can play the collection.'
actor = rights_manager.Actor(self.user_id)
can_play = actor.can_play(rights_manager.ACTIVITY_TYPE_COLLECTION, collection_id)
can_view = actor.can_view(rights_manager.ACTIVITY_TYPE_COLLECTION, collectio... | 8,139,909,462,468,710,000 | Check if the current user can play the collection. | core/controllers/collection_viewer.py | test_can_play | Himanshu1495/oppia | python | def test_can_play(self, collection_id, **kwargs):
actor = rights_manager.Actor(self.user_id)
can_play = actor.can_play(rights_manager.ACTIVITY_TYPE_COLLECTION, collection_id)
can_view = actor.can_view(rights_manager.ACTIVITY_TYPE_COLLECTION, collection_id)
if (can_play and can_view):
return... |
@require_collection_playable
def get(self, collection_id):
'Handles GET requests.'
try:
collection = collection_services.get_collection_by_id(collection_id)
except Exception as e:
raise self.PageNotFoundException(e)
whitelisted_usernames = config_domain.WHITELISTED_COLLECTION_EDITOR_USER... | -5,600,260,206,156,374,000 | Handles GET requests. | core/controllers/collection_viewer.py | get | Himanshu1495/oppia | python | @require_collection_playable
def get(self, collection_id):
try:
collection = collection_services.get_collection_by_id(collection_id)
except Exception as e:
raise self.PageNotFoundException(e)
whitelisted_usernames = config_domain.WHITELISTED_COLLECTION_EDITOR_USERNAMES.value
self.va... |
def get(self, collection_id):
'Populates the data on the individual collection page.'
allow_invalid_explorations = bool(self.request.get('allow_invalid_explorations'))
try:
collection_dict = collection_services.get_learner_collection_dict_by_id(collection_id, self.user_id, allow_invalid_explorations... | -5,690,707,837,412,497,000 | Populates the data on the individual collection page. | core/controllers/collection_viewer.py | get | Himanshu1495/oppia | python | def get(self, collection_id):
allow_invalid_explorations = bool(self.request.get('allow_invalid_explorations'))
try:
collection_dict = collection_services.get_learner_collection_dict_by_id(collection_id, self.user_id, allow_invalid_explorations=allow_invalid_explorations)
except Exception as e:... |
def to_getdist(nested_samples):
'Convert from anesthetic to getdist samples.\n\n Parameters\n ----------\n nested_samples: MCMCSamples or NestedSamples\n anesthetic samples to be converted\n\n Returns\n -------\n getdist_samples: getdist.mcsamples.MCSamples\n getdist equivalent sampl... | 3,120,332,844,846,308,000 | Convert from anesthetic to getdist samples.
Parameters
----------
nested_samples: MCMCSamples or NestedSamples
anesthetic samples to be converted
Returns
-------
getdist_samples: getdist.mcsamples.MCSamples
getdist equivalent samples | anesthetic/convert.py | to_getdist | Stefan-Heimersheim/anesthetic | python | def to_getdist(nested_samples):
'Convert from anesthetic to getdist samples.\n\n Parameters\n ----------\n nested_samples: MCMCSamples or NestedSamples\n anesthetic samples to be converted\n\n Returns\n -------\n getdist_samples: getdist.mcsamples.MCSamples\n getdist equivalent sampl... |
def __init__(self, cfg):
'\n model: torch.nn.Module\n cfg: model-agnostic experiment configs\n '
super().__init__()
self.cfg = cfg
self.image = ('MAGNETOGRAM' in cfg.DATA.FEATURES)
self.model = build_model(cfg)
self.save_hyperparameters() | 5,289,503,828,256,731,000 | model: torch.nn.Module
cfg: model-agnostic experiment configs | arnet/modeling/learner.py | __init__ | ZeyuSun/flare-prediction-smarp | python | def __init__(self, cfg):
'\n model: torch.nn.Module\n cfg: model-agnostic experiment configs\n '
super().__init__()
self.cfg = cfg
self.image = ('MAGNETOGRAM' in cfg.DATA.FEATURES)
self.model = build_model(cfg)
self.save_hyperparameters() |
def grad_norm(self, norm_type: Union[(float, int, str)]) -> Dict[(str, float)]:
"Compute each parameter's gradient's norm and their overall norm.\n\n The overall norm is computed over all gradients together, as if they\n were concatenated into a single vector.\n\n Args:\n norm_type: ... | 6,898,759,577,125,780,000 | Compute each parameter's gradient's norm and their overall norm.
The overall norm is computed over all gradients together, as if they
were concatenated into a single vector.
Args:
norm_type: The type of the used p-norm, cast to float if necessary.
Can be ``'inf'`` for infinity norm.
Return:
norms: Th... | arnet/modeling/learner.py | grad_norm | ZeyuSun/flare-prediction-smarp | python | def grad_norm(self, norm_type: Union[(float, int, str)]) -> Dict[(str, float)]:
"Compute each parameter's gradient's norm and their overall norm.\n\n The overall norm is computed over all gradients together, as if they\n were concatenated into a single vector.\n\n Args:\n norm_type: ... |
def _set_by_path(tree, keys, value):
'Set a value in a nested object in tree by sequence of keys.'
keys = keys.split(';')
_get_by_path(tree, keys[:(- 1)])[keys[(- 1)]] = value | 2,666,638,307,653,452,000 | Set a value in a nested object in tree by sequence of keys. | code/utils/parse_config.py | _set_by_path | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def _set_by_path(tree, keys, value):
keys = keys.split(';')
_get_by_path(tree, keys[:(- 1)])[keys[(- 1)]] = value |
def _get_by_path(tree, keys):
'Access a nested object in tree by sequence of keys.'
return reduce(getitem, keys, tree) | 320,196,700,010,566,400 | Access a nested object in tree by sequence of keys. | code/utils/parse_config.py | _get_by_path | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def _get_by_path(tree, keys):
return reduce(getitem, keys, tree) |
def __init__(self, config, resume=None, modification=None, run_id=None):
'\n class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving\n and logging module.\n :param config: Dict containing configurations, hyperparameters for tra... | -4,933,217,559,418,165,000 | class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving
and logging module.
:param config: Dict containing configurations, hyperparameters for training. contents of `config.json` file for example.
:param resume: String, path to the checkpoint being loa... | code/utils/parse_config.py | __init__ | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def __init__(self, config, resume=None, modification=None, run_id=None):
'\n class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving\n and logging module.\n :param config: Dict containing configurations, hyperparameters for tra... |
@classmethod
def from_args(cls, args, options='', updates=dict()):
'\n Initialize this class from some cli arguments. Used in train, test.\n '
for opt in options:
args.add_argument(*opt.flags, default=None, type=opt.type)
if (not isinstance(args, tuple)):
args = args.parse_args... | 1,624,221,919,907,510,500 | Initialize this class from some cli arguments. Used in train, test. | code/utils/parse_config.py | from_args | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | @classmethod
def from_args(cls, args, options=, updates=dict()):
'\n \n '
for opt in options:
args.add_argument(*opt.flags, default=None, type=opt.type)
if (not isinstance(args, tuple)):
args = args.parse_args()
if (args.device is not None):
os.environ['CUDA_VISIBLE... |
def init_obj(self, name, module, *args, **kwargs):
"\n Finds a function handle with the name given as 'type' in config, and returns the\n instance initialized with corresponding arguments given.\n\n `object = config.init_obj('name', module, a, b=1)`\n is equivalent to\n `object = ... | 2,826,001,643,421,151,000 | Finds a function handle with the name given as 'type' in config, and returns the
instance initialized with corresponding arguments given.
`object = config.init_obj('name', module, a, b=1)`
is equivalent to
`object = module.name(a, b=1)` | code/utils/parse_config.py | init_obj | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def init_obj(self, name, module, *args, **kwargs):
"\n Finds a function handle with the name given as 'type' in config, and returns the\n instance initialized with corresponding arguments given.\n\n `object = config.init_obj('name', module, a, b=1)`\n is equivalent to\n `object = ... |
def init_ftn(self, name, module, *args, **kwargs):
"\n Finds a function handle with the name given as 'type' in config, and returns the\n function with given arguments fixed with functools.partial.\n\n `function = config.init_ftn('name', module, a, b=1)`\n is equivalent to\n `func... | 706,491,489,085,046,500 | Finds a function handle with the name given as 'type' in config, and returns the
function with given arguments fixed with functools.partial.
`function = config.init_ftn('name', module, a, b=1)`
is equivalent to
`function = lambda *args, **kwargs: module.name(a, *args, b=1, **kwargs)`. | code/utils/parse_config.py | init_ftn | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def init_ftn(self, name, module, *args, **kwargs):
"\n Finds a function handle with the name given as 'type' in config, and returns the\n function with given arguments fixed with functools.partial.\n\n `function = config.init_ftn('name', module, a, b=1)`\n is equivalent to\n `func... |
def __getitem__(self, name):
'Access items like ordinary dict.'
return self.config[name] | -3,024,819,929,258,913,300 | Access items like ordinary dict. | code/utils/parse_config.py | __getitem__ | weinajin/evaluate_multimodal_medical_image_heatmap_explanation | python | def __getitem__(self, name):
return self.config[name] |
def _get_sorted_box_lims(boxes, box_init):
'Sort the uncertainties for each box in boxes based on a\n normalization given box_init. Unrestricted dimensions are dropped.\n The sorting is based on the normalization of the first box in boxes.\n\n Parameters\n ----------\n boxes : list of numpy structure... | -9,012,174,965,740,264,000 | Sort the uncertainties for each box in boxes based on a
normalization given box_init. Unrestricted dimensions are dropped.
The sorting is based on the normalization of the first box in boxes.
Parameters
----------
boxes : list of numpy structured arrays
box_init : numpy structured array
Returns
-------
tuple
with... | ema_workbench/analysis/scenario_discovery_util.py | _get_sorted_box_lims | brodderickrodriguez/EMA_lite | python | def _get_sorted_box_lims(boxes, box_init):
'Sort the uncertainties for each box in boxes based on a\n normalization given box_init. Unrestricted dimensions are dropped.\n The sorting is based on the normalization of the first box in boxes.\n\n Parameters\n ----------\n boxes : list of numpy structure... |
def _make_box(x):
'\n Make a box that encompasses all the data\n\n Parameters\n ----------\n x : DataFrame\n\n Returns\n -------\n DataFrame\n\n\n '
def limits(x):
if (pd.api.types.is_integer_dtype(x.dtype) or pd.api.types.is_float_dtype(x.dtype)):
return pd.Series([... | -2,165,776,446,668,643,600 | Make a box that encompasses all the data
Parameters
----------
x : DataFrame
Returns
-------
DataFrame | ema_workbench/analysis/scenario_discovery_util.py | _make_box | brodderickrodriguez/EMA_lite | python | def _make_box(x):
'\n Make a box that encompasses all the data\n\n Parameters\n ----------\n x : DataFrame\n\n Returns\n -------\n DataFrame\n\n\n '
def limits(x):
if (pd.api.types.is_integer_dtype(x.dtype) or pd.api.types.is_float_dtype(x.dtype)):
return pd.Series([... |
def _normalize(box_lim, box_init, uncertainties):
'Normalize the given box lim to the unit interval derived\n from box init for the specified uncertainties.\n\n Categorical uncertainties are normalized based on fractionated. So\n value specifies the fraction of categories in the box_lim.\n\n Parameters\... | -522,221,167,959,005,700 | Normalize the given box lim to the unit interval derived
from box init for the specified uncertainties.
Categorical uncertainties are normalized based on fractionated. So
value specifies the fraction of categories in the box_lim.
Parameters
----------
box_lim : DataFrame
box_init : DataFrame
uncertainties : list of ... | ema_workbench/analysis/scenario_discovery_util.py | _normalize | brodderickrodriguez/EMA_lite | python | def _normalize(box_lim, box_init, uncertainties):
'Normalize the given box lim to the unit interval derived\n from box init for the specified uncertainties.\n\n Categorical uncertainties are normalized based on fractionated. So\n value specifies the fraction of categories in the box_lim.\n\n Parameters\... |
def _determine_restricted_dims(box_limits, box_init):
'returns a list of dimensions that is restricted\n\n Parameters\n ----------\n box_limits : pd.DataFrame\n box_init : pd.DataFrame\n\n Returns\n -------\n list of str\n\n '
cols = box_init.columns.values
restricted_dims = cols[(np... | 4,333,435,090,552,701,000 | returns a list of dimensions that is restricted
Parameters
----------
box_limits : pd.DataFrame
box_init : pd.DataFrame
Returns
-------
list of str | ema_workbench/analysis/scenario_discovery_util.py | _determine_restricted_dims | brodderickrodriguez/EMA_lite | python | def _determine_restricted_dims(box_limits, box_init):
'returns a list of dimensions that is restricted\n\n Parameters\n ----------\n box_limits : pd.DataFrame\n box_init : pd.DataFrame\n\n Returns\n -------\n list of str\n\n '
cols = box_init.columns.values
restricted_dims = cols[(np... |
def _determine_nr_restricted_dims(box_lims, box_init):
'\n\n determine the number of restriced dimensions of a box given\n compared to the inital box that contains all the data\n\n Parameters\n ----------\n box_lims : structured numpy array\n a specific box limit\n box_init : structu... | -6,357,786,457,148,202,000 | determine the number of restriced dimensions of a box given
compared to the inital box that contains all the data
Parameters
----------
box_lims : structured numpy array
a specific box limit
box_init : structured numpy array
the initial box containing all data points
Returns
-------
int | ema_workbench/analysis/scenario_discovery_util.py | _determine_nr_restricted_dims | brodderickrodriguez/EMA_lite | python | def _determine_nr_restricted_dims(box_lims, box_init):
'\n\n determine the number of restriced dimensions of a box given\n compared to the inital box that contains all the data\n\n Parameters\n ----------\n box_lims : structured numpy array\n a specific box limit\n box_init : structu... |
def _compare(a, b):
'compare two boxes, for each dimension return True if the\n same and false otherwise'
dtypesDesc = a.dtype.descr
logical = np.ones(len(dtypesDesc), dtype=np.bool)
for (i, entry) in enumerate(dtypesDesc):
name = entry[0]
logical[i] = ((logical[i] & (a[name][0] == b[... | 7,529,231,224,578,261,000 | compare two boxes, for each dimension return True if the
same and false otherwise | ema_workbench/analysis/scenario_discovery_util.py | _compare | brodderickrodriguez/EMA_lite | python | def _compare(a, b):
'compare two boxes, for each dimension return True if the\n same and false otherwise'
dtypesDesc = a.dtype.descr
logical = np.ones(len(dtypesDesc), dtype=np.bool)
for (i, entry) in enumerate(dtypesDesc):
name = entry[0]
logical[i] = ((logical[i] & (a[name][0] == b[... |
def _in_box(x, boxlim):
'\n\n returns the a boolean index indicated which data points are inside\n and which are outside of the given box_lims\n\n Parameters\n ----------\n x : pd.DataFrame\n boxlim : pd.DataFrame\n\n Returns\n -------\n ndarray\n boolean 1D array\n\n Raises\n ... | -4,604,904,357,187,681,000 | returns the a boolean index indicated which data points are inside
and which are outside of the given box_lims
Parameters
----------
x : pd.DataFrame
boxlim : pd.DataFrame
Returns
-------
ndarray
boolean 1D array
Raises
------
Attribute error if not numbered columns are not pandas
category dtype | ema_workbench/analysis/scenario_discovery_util.py | _in_box | brodderickrodriguez/EMA_lite | python | def _in_box(x, boxlim):
'\n\n returns the a boolean index indicated which data points are inside\n and which are outside of the given box_lims\n\n Parameters\n ----------\n x : pd.DataFrame\n boxlim : pd.DataFrame\n\n Returns\n -------\n ndarray\n boolean 1D array\n\n Raises\n ... |
def _setup(results, classify, incl_unc=[]):
'helper function for setting up CART or PRIM\n\n Parameters\n ----------\n results : tuple of DataFrame and dict with numpy arrays\n the return from :meth:`perform_experiments`.\n classify : string, function or callable\n either a st... | 6,892,984,485,061,205,000 | helper function for setting up CART or PRIM
Parameters
----------
results : tuple of DataFrame and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : string, function or callable
either a string denoting the outcome of interest to
use or a function.
incl_unc ... | ema_workbench/analysis/scenario_discovery_util.py | _setup | brodderickrodriguez/EMA_lite | python | def _setup(results, classify, incl_unc=[]):
'helper function for setting up CART or PRIM\n\n Parameters\n ----------\n results : tuple of DataFrame and dict with numpy arrays\n the return from :meth:`perform_experiments`.\n classify : string, function or callable\n either a st... |
def _calculate_quasip(x, y, box, Hbox, Tbox):
'\n\n Parameters\n ----------\n x : DataFrame\n y : np.array\n box : DataFrame\n Hbox : int\n Tbox : int\n\n '
logical = _in_box(x, box)
yi = y[logical]
Tj = yi.shape[0]
Hj = np.sum(yi)
p = (Hj / Tj)
Hbox = int(Hbox)
T... | 5,761,812,687,022,758,000 | Parameters
----------
x : DataFrame
y : np.array
box : DataFrame
Hbox : int
Tbox : int | ema_workbench/analysis/scenario_discovery_util.py | _calculate_quasip | brodderickrodriguez/EMA_lite | python | def _calculate_quasip(x, y, box, Hbox, Tbox):
'\n\n Parameters\n ----------\n x : DataFrame\n y : np.array\n box : DataFrame\n Hbox : int\n Tbox : int\n\n '
logical = _in_box(x, box)
yi = y[logical]
Tj = yi.shape[0]
Hj = np.sum(yi)
p = (Hj / Tj)
Hbox = int(Hbox)
T... |
def plot_pair_wise_scatter(x, y, boxlim, box_init, restricted_dims):
' helper function for pair wise scatter plotting\n\n Parameters\n ----------\n x : DataFrame\n the experiments\n y : numpy array\n the outcome of interest\n box_lim : DataFrame\n a boxlim\n box_init : D... | -772,414,657,591,447,600 | helper function for pair wise scatter plotting
Parameters
----------
x : DataFrame
the experiments
y : numpy array
the outcome of interest
box_lim : DataFrame
a boxlim
box_init : DataFrame
restricted_dims : collection of strings
list of uncertainties that define the boxlims | ema_workbench/analysis/scenario_discovery_util.py | plot_pair_wise_scatter | brodderickrodriguez/EMA_lite | python | def plot_pair_wise_scatter(x, y, boxlim, box_init, restricted_dims):
' helper function for pair wise scatter plotting\n\n Parameters\n ----------\n x : DataFrame\n the experiments\n y : numpy array\n the outcome of interest\n box_lim : DataFrame\n a boxlim\n box_init : D... |
def _setup_figure(uncs):
'\n\n helper function for creating the basic layout for the figures that\n show the box lims.\n\n '
nr_unc = len(uncs)
fig = plt.figure()
ax = fig.add_subplot(111)
rect = mpl.patches.Rectangle((0, (- 0.5)), 1, (nr_unc + 1.5), alpha=0.25, facecolor='#C0C0C0', edgecol... | 6,302,861,723,883,544,000 | helper function for creating the basic layout for the figures that
show the box lims. | ema_workbench/analysis/scenario_discovery_util.py | _setup_figure | brodderickrodriguez/EMA_lite | python | def _setup_figure(uncs):
'\n\n helper function for creating the basic layout for the figures that\n show the box lims.\n\n '
nr_unc = len(uncs)
fig = plt.figure()
ax = fig.add_subplot(111)
rect = mpl.patches.Rectangle((0, (- 0.5)), 1, (nr_unc + 1.5), alpha=0.25, facecolor='#C0C0C0', edgecol... |
def plot_box(boxlim, qp_values, box_init, uncs, coverage, density, ticklabel_formatter='{} ({})', boxlim_formatter='{: .2g}', table_formatter='{:.3g}'):
'Helper function for parallel coordinate style visualization\n of a box\n\n Parameters\n ----------\n boxlim : DataFrame\n qp_values : dict\n box... | 3,736,344,928,917,715,000 | Helper function for parallel coordinate style visualization
of a box
Parameters
----------
boxlim : DataFrame
qp_values : dict
box_init : DataFrame
uncs : list
coverage : float
density : float
ticklabel_formatter : str
boxlim_formatter : str
table_formatter : str
Returns
-------
a Figure instance | ema_workbench/analysis/scenario_discovery_util.py | plot_box | brodderickrodriguez/EMA_lite | python | def plot_box(boxlim, qp_values, box_init, uncs, coverage, density, ticklabel_formatter='{} ({})', boxlim_formatter='{: .2g}', table_formatter='{:.3g}'):
'Helper function for parallel coordinate style visualization\n of a box\n\n Parameters\n ----------\n boxlim : DataFrame\n qp_values : dict\n box... |
def plot_ppt(peeling_trajectory):
'show the peeling and pasting trajectory in a figure'
ax = host_subplot(111)
ax.set_xlabel('peeling and pasting trajectory')
par = ax.twinx()
par.set_ylabel('nr. restricted dimensions')
ax.plot(peeling_trajectory['mean'], label='mean')
ax.plot(peeling_trajec... | -7,503,041,594,958,456,000 | show the peeling and pasting trajectory in a figure | ema_workbench/analysis/scenario_discovery_util.py | plot_ppt | brodderickrodriguez/EMA_lite | python | def plot_ppt(peeling_trajectory):
ax = host_subplot(111)
ax.set_xlabel('peeling and pasting trajectory')
par = ax.twinx()
par.set_ylabel('nr. restricted dimensions')
ax.plot(peeling_trajectory['mean'], label='mean')
ax.plot(peeling_trajectory['mass'], label='mass')
ax.plot(peeling_traje... |
def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis):
'Visualize the trade off between coverage and density. Color\n is used to denote the number of restricted dimensions.\n\n Parameters\n ----------\n cmap : valid matplotlib colormap\n\n Returns\n -------\n a Figure instance\n\n '
... | -840,138,350,086,765,300 | Visualize the trade off between coverage and density. Color
is used to denote the number of restricted dimensions.
Parameters
----------
cmap : valid matplotlib colormap
Returns
-------
a Figure instance | ema_workbench/analysis/scenario_discovery_util.py | plot_tradeoff | brodderickrodriguez/EMA_lite | python | def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis):
'Visualize the trade off between coverage and density. Color\n is used to denote the number of restricted dimensions.\n\n Parameters\n ----------\n cmap : valid matplotlib colormap\n\n Returns\n -------\n a Figure instance\n\n '
... |
def plot_unc(box_init, xi, i, j, norm_box_lim, box_lim, u, ax, color=sns.color_palette()[0]):
'\n\n Parameters:\n ----------\n xi : int\n the row at which to plot\n i : int\n the index of the uncertainty being plotted\n j : int\n the index of the box being plotted\n u : strin... | -4,350,063,930,766,987,000 | Parameters:
----------
xi : int
the row at which to plot
i : int
the index of the uncertainty being plotted
j : int
the index of the box being plotted
u : string
the uncertainty being plotted:
ax : axes instance
the ax on which to plot | ema_workbench/analysis/scenario_discovery_util.py | plot_unc | brodderickrodriguez/EMA_lite | python | def plot_unc(box_init, xi, i, j, norm_box_lim, box_lim, u, ax, color=sns.color_palette()[0]):
'\n\n Parameters:\n ----------\n xi : int\n the row at which to plot\n i : int\n the index of the uncertainty being plotted\n j : int\n the index of the box being plotted\n u : strin... |
def plot_boxes(x, boxes, together):
'Helper function for plotting multiple boxlims\n\n Parameters\n ----------\n x : pd.DataFrame\n boxes : list of pd.DataFrame\n together : bool\n\n '
box_init = _make_box(x)
(box_lims, uncs) = _get_sorted_box_lims(boxes, box_init)
norm_box_lims = [_no... | 6,861,939,631,656,800,000 | Helper function for plotting multiple boxlims
Parameters
----------
x : pd.DataFrame
boxes : list of pd.DataFrame
together : bool | ema_workbench/analysis/scenario_discovery_util.py | plot_boxes | brodderickrodriguez/EMA_lite | python | def plot_boxes(x, boxes, together):
'Helper function for plotting multiple boxlims\n\n Parameters\n ----------\n x : pd.DataFrame\n boxes : list of pd.DataFrame\n together : bool\n\n '
box_init = _make_box(x)
(box_lims, uncs) = _get_sorted_box_lims(boxes, box_init)
norm_box_lims = [_no... |
@abc.abstractproperty
def boxes(self):
'Property for getting a list of box limits'
raise NotImplementedError | -4,880,963,140,910,533,000 | Property for getting a list of box limits | ema_workbench/analysis/scenario_discovery_util.py | boxes | brodderickrodriguez/EMA_lite | python | @abc.abstractproperty
def boxes(self):
raise NotImplementedError |
@abc.abstractproperty
def stats(self):
'property for getting a list of dicts containing the statistics\n for each box'
raise NotImplementedError | 5,963,327,774,905,103,000 | property for getting a list of dicts containing the statistics
for each box | ema_workbench/analysis/scenario_discovery_util.py | stats | brodderickrodriguez/EMA_lite | python | @abc.abstractproperty
def stats(self):
'property for getting a list of dicts containing the statistics\n for each box'
raise NotImplementedError |
def boxes_to_dataframe(self):
'convert boxes to pandas dataframe'
boxes = self.boxes
(box_lims, uncs) = _get_sorted_box_lims(boxes, _make_box(self.x))
nr_boxes = len(boxes)
dtype = float
index = ['box {}'.format((i + 1)) for i in range(nr_boxes)]
for value in box_lims[0].dtypes:
if (... | 2,196,040,324,089,710,600 | convert boxes to pandas dataframe | ema_workbench/analysis/scenario_discovery_util.py | boxes_to_dataframe | brodderickrodriguez/EMA_lite | python | def boxes_to_dataframe(self):
boxes = self.boxes
(box_lims, uncs) = _get_sorted_box_lims(boxes, _make_box(self.x))
nr_boxes = len(boxes)
dtype = float
index = ['box {}'.format((i + 1)) for i in range(nr_boxes)]
for value in box_lims[0].dtypes:
if (value == object):
dtype... |
def stats_to_dataframe(self):
'convert stats to pandas dataframe'
stats = self.stats
index = pd.Index(['box {}'.format((i + 1)) for i in range(len(stats))])
return pd.DataFrame(stats, index=index) | 1,509,923,910,514,162,200 | convert stats to pandas dataframe | ema_workbench/analysis/scenario_discovery_util.py | stats_to_dataframe | brodderickrodriguez/EMA_lite | python | def stats_to_dataframe(self):
stats = self.stats
index = pd.Index(['box {}'.format((i + 1)) for i in range(len(stats))])
return pd.DataFrame(stats, index=index) |
def show_boxes(self, together=False):
'display boxes\n\n Parameters\n ----------\n together : bool, otional\n\n '
plot_boxes(self.x, self.boxes, together=together) | -1,717,583,655,820,089,600 | display boxes
Parameters
----------
together : bool, otional | ema_workbench/analysis/scenario_discovery_util.py | show_boxes | brodderickrodriguez/EMA_lite | python | def show_boxes(self, together=False):
'display boxes\n\n Parameters\n ----------\n together : bool, otional\n\n '
plot_boxes(self.x, self.boxes, together=together) |
def read_code(filename):
'读取code编码文件并输出为字典格式\n 1、支持json格式\n 2、支持本包规定的xlsx格式\n see alse to_code\n '
file_type = os.path.splitext(filename)[1][1:]
if (file_type == 'json'):
import json
code = json.load(filename)
return code
d = pd.read_excel(filename, header=None)
d... | 2,747,203,315,166,959,600 | 读取code编码文件并输出为字典格式
1、支持json格式
2、支持本包规定的xlsx格式
see alse to_code | reportgen/questionnaire/questionnaire.py | read_code | brightgeng/reportgen | python | def read_code(filename):
'读取code编码文件并输出为字典格式\n 1、支持json格式\n 2、支持本包规定的xlsx格式\n see alse to_code\n '
file_type = os.path.splitext(filename)[1][1:]
if (file_type == 'json'):
import json
code = json.load(filename)
return code
d = pd.read_excel(filename, header=None)
d... |
def save_code(code, filename='code.xlsx'):
'code本地输出\n 1、输出为json格式，根据文件名自动识别\n 2、输出为Excel格式\n see also read_code\n '
save_type = os.path.splitext(filename)[1][1:]
if (save_type == 'json'):
code = pd.DataFrame(code)
code.to_json(filename, force_ascii=False)
return
tmp ... | -741,657,556,108,953,100 | code本地输出
1、输出为json格式，根据文件名自动识别
2、输出为Excel格式
see also read_code | reportgen/questionnaire/questionnaire.py | save_code | brightgeng/reportgen | python | def save_code(code, filename='code.xlsx'):
'code本地输出\n 1、输出为json格式，根据文件名自动识别\n 2、输出为Excel格式\n see also read_code\n '
save_type = os.path.splitext(filename)[1][1:]
if (save_type == 'json'):
code = pd.DataFrame(code)
code.to_json(filename, force_ascii=False)
return
tmp ... |
def dataText_to_code(df, sep, qqlist=None):
'编码文本数据\n\n '
if (sep in [';', '┋']):
qtype = '多选题'
elif (sep in ['-->', '→']):
qtype = '排序题'
if (not qqlist):
qqlist = df.columns
code = {}
for qq in qqlist:
tmp = df[qq].map((lambda x: (x.split(sep) if isinstance(x,... | -3,276,474,069,112,958,000 | 编码文本数据 | reportgen/questionnaire/questionnaire.py | dataText_to_code | brightgeng/reportgen | python | def dataText_to_code(df, sep, qqlist=None):
'\n\n '
if (sep in [';', '┋']):
qtype = '多选题'
elif (sep in ['-->', '→']):
qtype = '排序题'
if (not qqlist):
qqlist = df.columns
code = {}
for qq in qqlist:
tmp = df[qq].map((lambda x: (x.split(sep) if isinstance(x, str) ... |
def dataCode_to_text(df, code=None):
'将按序号数据转换成文本\n\n '
if (df.max().max() > 1):
sep = '→'
else:
sep = '┋'
if code:
df = df.rename(code)
qlist = list(df.columns)
df['text'] = np.nan
if (sep in ['┋']):
for i in df.index:
w = (df.loc[i, :] == 1)
... | 2,083,806,469,545,990,400 | 将按序号数据转换成文本 | reportgen/questionnaire/questionnaire.py | dataCode_to_text | brightgeng/reportgen | python | def dataCode_to_text(df, code=None):
'\n\n '
if (df.max().max() > 1):
sep = '→'
else:
sep = '┋'
if code:
df = df.rename(code)
qlist = list(df.columns)
df['text'] = np.nan
if (sep in ['┋']):
for i in df.index:
w = (df.loc[i, :] == 1)
... |
def var_combine(data, code, qq1, qq2, sep=',', qnum_new=None, qname_new=None):
"将两个变量组合成一个变量\n 例如：\n Q1:'性别',Q2: 年龄\n 组合后生成：\n 1、男_16~19岁\n 2、男_20岁~40岁\n 3、女_16~19岁\n 4、女_20~40岁\n "
if (qnum_new is None):
if ('Q' == qq2[0]):
qnum_new = ((qq1 + '_') + qq2[1:])
... | -7,023,054,160,902,175,000 | 将两个变量组合成一个变量
例如：
Q1:'性别',Q2: 年龄
组合后生成：
1、男_16~19岁
2、男_20岁~40岁
3、女_16~19岁
4、女_20~40岁 | reportgen/questionnaire/questionnaire.py | var_combine | brightgeng/reportgen | python | def var_combine(data, code, qq1, qq2, sep=',', qnum_new=None, qname_new=None):
"将两个变量组合成一个变量\n 例如：\n Q1:'性别',Q2: 年龄\n 组合后生成：\n 1、男_16~19岁\n 2、男_20岁~40岁\n 3、女_16~19岁\n 4、女_20~40岁\n "
if (qnum_new is None):
if ('Q' == qq2[0]):
qnum_new = ((qq1 + '_') + qq2[1:])
... |
def wenjuanwang(filepath='.\\data', encoding='gbk'):
'问卷网数据导入和编码\n 输入：\n filepath:\n 列表，[0]为按文本数据路径，[1]为按序号文本，[2]为编码文件\n 文件夹路径，函数会自动在文件夹下搜寻相关数据\n 输出：\n (data,code):\n data为按序号的数据，题目都替换成了Q_n\n code为数据编码，可利用函数to_code()导出为json格式或者Excel格式数据\n '
if isinstance(filepath, list... | 575,949,123,954,226,600 | 问卷网数据导入和编码
输入：
filepath:
列表，[0]为按文本数据路径，[1]为按序号文本，[2]为编码文件
文件夹路径，函数会自动在文件夹下搜寻相关数据
输出：
(data,code):
data为按序号的数据，题目都替换成了Q_n
code为数据编码，可利用函数to_code()导出为json格式或者Excel格式数据 | reportgen/questionnaire/questionnaire.py | wenjuanwang | brightgeng/reportgen | python | def wenjuanwang(filepath='.\\data', encoding='gbk'):
'问卷网数据导入和编码\n 输入：\n filepath:\n 列表，[0]为按文本数据路径，[1]为按序号文本，[2]为编码文件\n 文件夹路径，函数会自动在文件夹下搜寻相关数据\n 输出：\n (data,code):\n data为按序号的数据，题目都替换成了Q_n\n code为数据编码，可利用函数to_code()导出为json格式或者Excel格式数据\n '
if isinstance(filepath, list... |
def wenjuanxing(filepath='.\\data', headlen=6):
'问卷星数据导入和编码\n 输入：\n filepath:\n 列表, filepath[0]: (23_22_0.xls)为按文本数据路径，filepath[1]: (23_22_2.xls)为按序号文本\n 文件夹路径，函数会自动在文件夹下搜寻相关数据，优先为\\d+_\\d+_0.xls和\\d+_\\d+_2.xls\n headlen: 问卷星数据基础信息的列数\n 输出：\n (data,code):\n data为按序号的数据，题目都替换成了Q_... | -4,022,388,977,035,539,000 | 问卷星数据导入和编码
输入：
filepath:
列表, filepath[0]: (23_22_0.xls)为按文本数据路径，filepath[1]: (23_22_2.xls)为按序号文本
文件夹路径，函数会自动在文件夹下搜寻相关数据，优先为\d+_\d+_0.xls和\d+_\d+_2.xls
headlen: 问卷星数据基础信息的列数
输出：
(data,code):
data为按序号的数据，题目都替换成了Q_n
code为数据编码，可利用函数to_code()导出为json格式或者Excel格式数据 | reportgen/questionnaire/questionnaire.py | wenjuanxing | brightgeng/reportgen | python | def wenjuanxing(filepath='.\\data', headlen=6):
'问卷星数据导入和编码\n 输入：\n filepath:\n 列表, filepath[0]: (23_22_0.xls)为按文本数据路径，filepath[1]: (23_22_2.xls)为按序号文本\n 文件夹路径，函数会自动在文件夹下搜寻相关数据，优先为\\d+_\\d+_0.xls和\\d+_\\d+_2.xls\n headlen: 问卷星数据基础信息的列数\n 输出：\n (data,code):\n data为按序号的数据，题目都替换成了Q_... |
def load_data(method='filedialog', **kwargs):
'导入问卷数据\n # 暂时只支持已编码的和问卷星数据\n 1、支持路径搜寻\n 2、支持自由选择文件\n method:\n -filedialog: 打开文件窗口选择\n -pathsearch：自带搜索路径，需提供filepath\n '
if (method == 'filedialog'):
import tkinter as tk
from tkinter.filedialog import askopenfilenames
... | 4,276,380,133,315,128,000 | 导入问卷数据
# 暂时只支持已编码的和问卷星数据
1、支持路径搜寻
2、支持自由选择文件
method:
-filedialog: 打开文件窗口选择
-pathsearch：自带搜索路径，需提供filepath | reportgen/questionnaire/questionnaire.py | load_data | brightgeng/reportgen | python | def load_data(method='filedialog', **kwargs):
'导入问卷数据\n # 暂时只支持已编码的和问卷星数据\n 1、支持路径搜寻\n 2、支持自由选择文件\n method:\n -filedialog: 打开文件窗口选择\n -pathsearch：自带搜索路径，需提供filepath\n '
if (method == 'filedialog'):
import tkinter as tk
from tkinter.filedialog import askopenfilenames
... |
def levenshtein(s, t):
"'' From Wikipedia article; Iterative with two matrix rows. "
if (s == t):
return 0
elif (len(s) == 0):
return len(t)
elif (len(t) == 0):
return len(s)
v0 = ([None] * (len(t) + 1))
v1 = ([None] * (len(t) + 1))
for i in range(len(v0)):
v0... | -6,058,263,141,906,959,000 | '' From Wikipedia article; Iterative with two matrix rows. | reportgen/questionnaire/questionnaire.py | levenshtein | brightgeng/reportgen | python | def levenshtein(s, t):
" "
if (s == t):
return 0
elif (len(s) == 0):
return len(t)
elif (len(t) == 0):
return len(s)
v0 = ([None] * (len(t) + 1))
v1 = ([None] * (len(t) + 1))
for i in range(len(v0)):
v0[i] = i
for i in range(len(s)):
v1[0] = (i + 1... |
def code_similar(code1, code2):
'\n 题目内容相似度用最小编辑距离来度量\n 选项相似度分为几种\n 1、完全相同：1\n 2、单选题：暂时只考虑序号和值都相等的，且共同变量超过一半:2\n 2、多选题/排序题：不考虑序号，共同变量超过一半即可：3\n 3、矩阵单选题：code_r 暂时只考虑完全匹配\n 4、其他情况为0\n\n '
code_distance_min = pd.DataFrame(index=code1.keys(), columns=['qnum', 'similar_content', 'similar_code... | 2,684,696,960,743,818,000 | 题目内容相似度用最小编辑距离来度量
选项相似度分为几种
1、完全相同：1
2、单选题：暂时只考虑序号和值都相等的，且共同变量超过一半:2
2、多选题/排序题：不考虑序号，共同变量超过一半即可：3
3、矩阵单选题：code_r 暂时只考虑完全匹配
4、其他情况为0 | reportgen/questionnaire/questionnaire.py | code_similar | brightgeng/reportgen | python | def code_similar(code1, code2):
'\n 题目内容相似度用最小编辑距离来度量\n 选项相似度分为几种\n 1、完全相同：1\n 2、单选题：暂时只考虑序号和值都相等的，且共同变量超过一半:2\n 2、多选题/排序题：不考虑序号，共同变量超过一半即可：3\n 3、矩阵单选题：code_r 暂时只考虑完全匹配\n 4、其他情况为0\n\n '
code_distance_min = pd.DataFrame(index=code1.keys(), columns=['qnum', 'similar_content', 'similar_code... |
def data_merge(ques1, ques2, qlist1=None, qlist2=None, name1='ques1', name2='ques2', mergeqnum='Q0', similar_threshold=70):
'合并两份数据\n ques1: 列表,[data1,code1]\n ques2: 列表,[data2,code2]\n '
(data1, code1) = ques1
(data2, code2) = ques2
if ((qlist1 is None) or (qlist2 is None)):
qlist1 = [... | 6,126,437,658,393,545,000 | 合并两份数据
ques1: 列表,[data1,code1]
ques2: 列表,[data2,code2] | reportgen/questionnaire/questionnaire.py | data_merge | brightgeng/reportgen | python | def data_merge(ques1, ques2, qlist1=None, qlist2=None, name1='ques1', name2='ques2', mergeqnum='Q0', similar_threshold=70):
'合并两份数据\n ques1: 列表,[data1,code1]\n ques2: 列表,[data2,code2]\n '
(data1, code1) = ques1
(data2, code2) = ques2
if ((qlist1 is None) or (qlist2 is None)):
qlist1 = [... |
def clean_ftime(ftime, cut_percent=0.25):
'\n ftime 是完成问卷的秒数\n 思路：\n 1、只考虑截断问卷完成时间较小的样本\n 2、找到完成时间变化的拐点，即需要截断的时间点\n 返回：r\n 建议截断<r的样本\n '
t_min = int(ftime.min())
t_cut = int(ftime.quantile(cut_percent))
x = np.array(range(t_min, t_cut))
y = np.array([len(ftime[(ftime <= i)]) for... | -6,333,484,139,126,520,000 | ftime 是完成问卷的秒数
思路：
1、只考虑截断问卷完成时间较小的样本
2、找到完成时间变化的拐点，即需要截断的时间点
返回：r
建议截断<r的样本 | reportgen/questionnaire/questionnaire.py | clean_ftime | brightgeng/reportgen | python | def clean_ftime(ftime, cut_percent=0.25):
'\n ftime 是完成问卷的秒数\n 思路：\n 1、只考虑截断问卷完成时间较小的样本\n 2、找到完成时间变化的拐点，即需要截断的时间点\n 返回：r\n 建议截断<r的样本\n '
t_min = int(ftime.min())
t_cut = int(ftime.quantile(cut_percent))
x = np.array(range(t_min, t_cut))
y = np.array([len(ftime[(ftime <= i)]) for... |
def data_auto_code(data):
'智能判断问卷数据\n 输入\n data: 数据框，列名需要满足Qi或者Qi_\n 输出：\n code: 自动编码\n '
data = pd.DataFrame(data)
columns = data.columns
columns = [c for c in columns if re.match('Q\\d+', c)]
code = {}
for cc in columns:
if ('_' not in cc):
key = cc
e... | 7,994,973,602,825,367,000 | 智能判断问卷数据
输入
data: 数据框，列名需要满足Qi或者Qi_
输出：
code: 自动编码 | reportgen/questionnaire/questionnaire.py | data_auto_code | brightgeng/reportgen | python | def data_auto_code(data):
'智能判断问卷数据\n 输入\n data: 数据框，列名需要满足Qi或者Qi_\n 输出：\n code: 自动编码\n '
data = pd.DataFrame(data)
columns = data.columns
columns = [c for c in columns if re.match('Q\\d+', c)]
code = {}
for cc in columns:
if ('_' not in cc):
key = cc
e... |
def save_data(data, filename=u'data.xlsx', code=None):
'保存问卷数据到本地\n 根据filename后缀选择相应的格式保存\n 如果有code,则保存按文本数据\n '
savetype = os.path.splitext(filename)[1][1:]
data1 = data.copy()
if code:
for qq in code.keys():
qtype = code[qq]['qtype']
qlist = code[qq]['qlist']
... | 5,844,349,184,456,264,000 | 保存问卷数据到本地
根据filename后缀选择相应的格式保存
如果有code,则保存按文本数据 | reportgen/questionnaire/questionnaire.py | save_data | brightgeng/reportgen | python | def save_data(data, filename=u'data.xlsx', code=None):
'保存问卷数据到本地\n 根据filename后缀选择相应的格式保存\n 如果有code,则保存按文本数据\n '
savetype = os.path.splitext(filename)[1][1:]
data1 = data.copy()
if code:
for qq in code.keys():
qtype = code[qq]['qtype']
qlist = code[qq]['qlist']
... |
def sa_to_ma(data):
'单选题数据转换成多选题数据\n data是单选题数据, 要求非有效列别为nan\n 可以使用内置函数pd.get_dummies()代替\n '
if isinstance(data, pd.core.frame.DataFrame):
data = data[data.columns[0]]
categorys = data[data.notnull()].unique()
try:
categorys = sorted(categorys)
except:
pass
data... | -8,025,656,272,193,248,000 | 单选题数据转换成多选题数据
data是单选题数据, 要求非有效列别为nan
可以使用内置函数pd.get_dummies()代替 | reportgen/questionnaire/questionnaire.py | sa_to_ma | brightgeng/reportgen | python | def sa_to_ma(data):
'单选题数据转换成多选题数据\n data是单选题数据, 要求非有效列别为nan\n 可以使用内置函数pd.get_dummies()代替\n '
if isinstance(data, pd.core.frame.DataFrame):
data = data[data.columns[0]]
categorys = data[data.notnull()].unique()
try:
categorys = sorted(categorys)
except:
pass
data... |
def to_dummpy(data, code, qqlist=None, qtype_new='多选题', ignore_open=True):
'转化成哑变量\n 将数据中所有的单选题全部转化成哑变量，另外剔除掉开放题和填空题\n 返回一个很大的只有0和1的数据\n '
if (qqlist is None):
qqlist = sorted(code, key=(lambda x: int(re.findall('\\d+', x)[0])))
bdata = pd.DataFrame()
bcode = {}
for qq in qqlist:
... | 840,683,149,439,387,100 | 转化成哑变量
将数据中所有的单选题全部转化成哑变量，另外剔除掉开放题和填空题
返回一个很大的只有0和1的数据 | reportgen/questionnaire/questionnaire.py | to_dummpy | brightgeng/reportgen | python | def to_dummpy(data, code, qqlist=None, qtype_new='多选题', ignore_open=True):
'转化成哑变量\n 将数据中所有的单选题全部转化成哑变量，另外剔除掉开放题和填空题\n 返回一个很大的只有0和1的数据\n '
if (qqlist is None):
qqlist = sorted(code, key=(lambda x: int(re.findall('\\d+', x)[0])))
bdata = pd.DataFrame()
bcode = {}
for qq in qqlist:
... |
def qdata_flatten(data, code, quesid=None, userid_begin=None):
'将问卷数据展平，字段如下\n userid: 用户ID\n quesid: 问卷ID\n qnum: 题号\n qname: 题目内容\n qtype: 题目类型\n samplelen:题目的样本数\n itemnum: 选项序号\n itemname: 选项内容\n code: 用户的选择\n codename: 用户选择的具体值\n count: 计数\n percent(%): 计数占比（百分比）\n '
... | 8,205,630,172,292,489,000 | 将问卷数据展平，字段如下
userid: 用户ID
quesid: 问卷ID
qnum: 题号
qname: 题目内容
qtype: 题目类型
samplelen:题目的样本数
itemnum: 选项序号
itemname: 选项内容
code: 用户的选择
codename: 用户选择的具体值
count: 计数
percent(%): 计数占比（百分比） | reportgen/questionnaire/questionnaire.py | qdata_flatten | brightgeng/reportgen | python | def qdata_flatten(data, code, quesid=None, userid_begin=None):
'将问卷数据展平，字段如下\n userid: 用户ID\n quesid: 问卷ID\n qnum: 题号\n qname: 题目内容\n qtype: 题目类型\n samplelen:题目的样本数\n itemnum: 选项序号\n itemname: 选项内容\n code: 用户的选择\n codename: 用户选择的具体值\n count: 计数\n percent(%): 计数占比（百分比）\n '
... |
def sample_size_cal(interval, N, alpha=0.05):
'调研样本量的计算\n 参考：https://www.surveysystem.com/sscalc.htm\n sample_size_cal(interval,N,alpha=0.05)\n 输入：\n interval: 误差范围,例如0.03\n N: 总体的大小,一般1万以上就没啥差别啦\n alpha：置信水平，默认95%\n '
import scipy.stats as stats
p = stats.norm.ppf((1 - (alpha / 2)))
... | 906,193,507,839,740,700 | 调研样本量的计算
参考：https://www.surveysystem.com/sscalc.htm
sample_size_cal(interval,N,alpha=0.05)
输入：
interval: 误差范围,例如0.03
N: 总体的大小,一般1万以上就没啥差别啦
alpha：置信水平，默认95% | reportgen/questionnaire/questionnaire.py | sample_size_cal | brightgeng/reportgen | python | def sample_size_cal(interval, N, alpha=0.05):
'调研样本量的计算\n 参考：https://www.surveysystem.com/sscalc.htm\n sample_size_cal(interval,N,alpha=0.05)\n 输入：\n interval: 误差范围,例如0.03\n N: 总体的大小,一般1万以上就没啥差别啦\n alpha：置信水平，默认95%\n '
import scipy.stats as stats
p = stats.norm.ppf((1 - (alpha / 2)))
... |
def gof_test(fo, fe=None, alpha=0.05):
'拟合优度检验\n 输入：\n fo:观察频数\n fe:期望频数，缺省为平均数\n 返回：\n 1: 样本与总体有差异\n 0：样本与总体无差异\n 例子：\n gof_test(np.array([0.3,0.4,0.3])*222)\n '
import scipy.stats as stats
fo = np.array(fo).flatten()
C = len(fo)
if (not fe):
N = fo.sum()
... | -1,421,774,208,672,722,700 | 拟合优度检验
输入：
fo:观察频数
fe:期望频数，缺省为平均数
返回：
1: 样本与总体有差异
0：样本与总体无差异
例子：
gof_test(np.array([0.3,0.4,0.3])*222) | reportgen/questionnaire/questionnaire.py | gof_test | brightgeng/reportgen | python | def gof_test(fo, fe=None, alpha=0.05):
'拟合优度检验\n 输入：\n fo:观察频数\n fe:期望频数，缺省为平均数\n 返回：\n 1: 样本与总体有差异\n 0：样本与总体无差异\n 例子：\n gof_test(np.array([0.3,0.4,0.3])*222)\n '
import scipy.stats as stats
fo = np.array(fo).flatten()
C = len(fo)
if (not fe):
N = fo.sum()
... |
def fisher_exact(fo, alpha=0.05):
'fisher_exact 显著性检验函数\n 此处采用的是调用R的解决方案，需要安装包 pyper\n python解决方案参见\n https://mrnoutahi.com/2016/01/03/Fisher-exac-test-for-mxn-table/\n 但还有些问题，所以没用.\n '
import pyper as pr
r = pr.R(use_pandas=True, use_numpy=True)
r.assign('fo', fo)
r('b<-fisher.test(f... | 8,313,948,059,721,519,000 | fisher_exact 显著性检验函数
此处采用的是调用R的解决方案，需要安装包 pyper
python解决方案参见
https://mrnoutahi.com/2016/01/03/Fisher-exac-test-for-mxn-table/
但还有些问题，所以没用. | reportgen/questionnaire/questionnaire.py | fisher_exact | brightgeng/reportgen | python | def fisher_exact(fo, alpha=0.05):
'fisher_exact 显著性检验函数\n 此处采用的是调用R的解决方案，需要安装包 pyper\n python解决方案参见\n https://mrnoutahi.com/2016/01/03/Fisher-exac-test-for-mxn-table/\n 但还有些问题，所以没用.\n '
import pyper as pr
r = pr.R(use_pandas=True, use_numpy=True)
r.assign('fo', fo)
r('b<-fisher.test(f... |
def anova(data, formula):
'方差分析\n 输入\n --data： DataFrame格式，包含数值型变量和分类型变量\n --formula：变量之间的关系，如：数值型变量~C(分类型变量1)[+C(分类型变量1)[+C(分类型变量1):(分类型变量1)]\n\n 返回[方差分析表]\n [总体的方差来源于组内方差和组间方差，通过比较组间方差和组内方差的比来推断两者的差异]\n --df:自由度\n --sum_sq：误差平方和\n --mean_sq：误差平方和/对应的自由度\n --F：mean_sq之比\n --PR(>F)：p值，... | 3,996,825,466,765,998,600 | 方差分析
输入
--data： DataFrame格式，包含数值型变量和分类型变量
--formula：变量之间的关系，如：数值型变量~C(分类型变量1)[+C(分类型变量1)[+C(分类型变量1):(分类型变量1)]
返回[方差分析表]
[总体的方差来源于组内方差和组间方差，通过比较组间方差和组内方差的比来推断两者的差异]
--df:自由度
--sum_sq：误差平方和
--mean_sq：误差平方和/对应的自由度
--F：mean_sq之比
--PR(>F)：p值，比如<0.05则代表有显著性差异 | reportgen/questionnaire/questionnaire.py | anova | brightgeng/reportgen | python | def anova(data, formula):
'方差分析\n 输入\n --data： DataFrame格式，包含数值型变量和分类型变量\n --formula：变量之间的关系，如：数值型变量~C(分类型变量1)[+C(分类型变量1)[+C(分类型变量1):(分类型变量1)]\n\n 返回[方差分析表]\n [总体的方差来源于组内方差和组间方差，通过比较组间方差和组内方差的比来推断两者的差异]\n --df:自由度\n --sum_sq：误差平方和\n --mean_sq：误差平方和/对应的自由度\n --F：mean_sq之比\n --PR(>F)：p值，... |
def mca(X, N=2):
"对应分析函数，暂时支持双因素\n X：观察频数表\n N：返回的维数，默认2维\n 可以通过scatter函数绘制：\n fig=scatter([pr,pc])\n fig.savefig('mca.png')\n "
from scipy.linalg import diagsvd
S = X.sum().sum()
Z = (X / S)
r = Z.sum(axis=1)
c = Z.sum()
D_r = np.diag((1 / np.sqrt(r)))
Z_c = (Z - np.ou... | 8,680,992,238,391,971,000 | 对应分析函数，暂时支持双因素
X：观察频数表
N：返回的维数，默认2维
可以通过scatter函数绘制：
fig=scatter([pr,pc])
fig.savefig('mca.png') | reportgen/questionnaire/questionnaire.py | mca | brightgeng/reportgen | python | def mca(X, N=2):
"对应分析函数，暂时支持双因素\n X：观察频数表\n N：返回的维数，默认2维\n 可以通过scatter函数绘制：\n fig=scatter([pr,pc])\n fig.savefig('mca.png')\n "
from scipy.linalg import diagsvd
S = X.sum().sum()
Z = (X / S)
r = Z.sum(axis=1)
c = Z.sum()
D_r = np.diag((1 / np.sqrt(r)))
Z_c = (Z - np.ou... |
def cluster(data, code, cluster_qq, n_clusters='auto', max_clusters=7):
'对态度题进行聚类\n '
from sklearn.cluster import KMeans
from sklearn import metrics
qq_max = sorted(code, key=(lambda x: int(re.findall('\\d+', x)[0])))[(- 1)]
new_cluster = 'Q{}'.format((int(re.findall('\\d+', qq_max)[0]) + 1))
... | 1,234,331,575,149,067,300 | 对态度题进行聚类 | reportgen/questionnaire/questionnaire.py | cluster | brightgeng/reportgen | python | def cluster(data, code, cluster_qq, n_clusters='auto', max_clusters=7):
'\n '
from sklearn.cluster import KMeans
from sklearn import metrics
qq_max = sorted(code, key=(lambda x: int(re.findall('\\d+', x)[0])))[(- 1)]
new_cluster = 'Q{}'.format((int(re.findall('\\d+', qq_max)[0]) + 1))
qlist =... |
def scatter(data, legend=False, title=None, font_ch=None, find_path=None):
'\n 绘制带数据标签的散点图\n '
import matplotlib.font_manager as fm
if (font_ch is None):
fontlist = ['calibri.ttf', 'simfang.ttf', 'simkai.ttf', 'simhei.ttf', 'simsun.ttc', 'msyh.ttf', 'msyh.ttc']
myfont = ''
if (... | -3,198,626,937,060,132,000 | 绘制带数据标签的散点图 | reportgen/questionnaire/questionnaire.py | scatter | brightgeng/reportgen | python | def scatter(data, legend=False, title=None, font_ch=None, find_path=None):
'\n \n '
import matplotlib.font_manager as fm
if (font_ch is None):
fontlist = ['calibri.ttf', 'simfang.ttf', 'simkai.ttf', 'simhei.ttf', 'simsun.ttc', 'msyh.ttf', 'msyh.ttc']
myfont =
if (not find_path... |
def sankey(df, filename=None):
'SanKey图绘制\n df的列是左节点，行是右节点\n 注:暂时没找到好的Python方法，所以只生成R语言所需数据\n 返回links 和 nodes\n # R code 参考\n library(networkD3)\n dd=read.csv(\'price_links.csv\')\n links<-data.frame(source=dd$from,target=dd$to,value=dd$value)\n nodes=read.csv(\'price_nodes.csv\',encoding = ... | -9,141,936,434,518,142,000 | SanKey图绘制
df的列是左节点，行是右节点
注:暂时没找到好的Python方法，所以只生成R语言所需数据
返回links 和 nodes
# R code 参考
library(networkD3)
dd=read.csv('price_links.csv')
links<-data.frame(source=dd$from,target=dd$to,value=dd$value)
nodes=read.csv('price_nodes.csv',encoding = 'UTF-8')
nodes<-nodes['name']
Energy=c(links=links,nodes=nodes)
sankeyNetwork(Li... | reportgen/questionnaire/questionnaire.py | sankey | brightgeng/reportgen | python | def sankey(df, filename=None):
'SanKey图绘制\n df的列是左节点，行是右节点\n 注:暂时没找到好的Python方法，所以只生成R语言所需数据\n 返回links 和 nodes\n # R code 参考\n library(networkD3)\n dd=read.csv(\'price_links.csv\')\n links<-data.frame(source=dd$from,target=dd$to,value=dd$value)\n nodes=read.csv(\'price_nodes.csv\',encoding = ... |
def table(data, code, total=True):
"\n 单个题目描述统计\n code是data的编码，列数大于1\n 返回字典格式数据：\n 'fop'：百分比, 对于单选题和为1，多选题分母为样本数\n 'fo'： 观察频数表，其中添加了合计项\n 'fw': 加权频数表，可实现平均值、T2B等功能，仅当code中存在关键词'weight'时才有\n "
qtype = code['qtype']
index = code['qlist']
data = pd.DataFrame(data)
sample_len = dat... | -7,405,818,918,458,051,000 | 单个题目描述统计
code是data的编码，列数大于1
返回字典格式数据：
'fop'：百分比, 对于单选题和为1，多选题分母为样本数
'fo'： 观察频数表，其中添加了合计项
'fw': 加权频数表，可实现平均值、T2B等功能，仅当code中存在关键词'weight'时才有 | reportgen/questionnaire/questionnaire.py | table | brightgeng/reportgen | python | def table(data, code, total=True):
"\n 单个题目描述统计\n code是data的编码，列数大于1\n 返回字典格式数据：\n 'fop'：百分比, 对于单选题和为1，多选题分母为样本数\n 'fo'： 观察频数表，其中添加了合计项\n 'fw': 加权频数表，可实现平均值、T2B等功能，仅当code中存在关键词'weight'时才有\n "
qtype = code['qtype']
index = code['qlist']
data = pd.DataFrame(data)
sample_len = dat... |
def crosstab(data_index, data_column, code_index=None, code_column=None, qtype=None, total=True):
"适用于问卷数据的交叉统计\n 输入参数：\n data_index: 因变量，放在行中\n data_column:自变量，放在列中\n code_index: dict格式，指定data_index的编码等信息\n code_column: dict格式，指定data_column的编码等信息\n qtype: 给定两个数据的题目类型，若为字符串则给定data_index，若为列表，则给定两个... | -7,027,066,733,633,316,000 | 适用于问卷数据的交叉统计
输入参数：
data_index: 因变量，放在行中
data_column:自变量，放在列中
code_index: dict格式，指定data_index的编码等信息
code_column: dict格式，指定data_column的编码等信息
qtype: 给定两个数据的题目类型，若为字符串则给定data_index，若为列表，则给定两个的
返回字典格式数据
'fop'：默认的百分比表，行是data_index,列是data_column
'fo'：原始频数表，且添加了总体项
'fw': 加权平均值
简要说明：
因为要处理各类题型，这里将单选题处理为多选题
fo：观察频数表
nij是同时选择了R... | reportgen/questionnaire/questionnaire.py | crosstab | brightgeng/reportgen | python | def crosstab(data_index, data_column, code_index=None, code_column=None, qtype=None, total=True):
"适用于问卷数据的交叉统计\n 输入参数：\n data_index: 因变量，放在行中\n data_column:自变量，放在列中\n code_index: dict格式，指定data_index的编码等信息\n code_column: dict格式，指定data_column的编码等信息\n qtype: 给定两个数据的题目类型，若为字符串则给定data_index，若为列表，则给定两个... |
def qtable(data, *args, **kwargs):
"简易频数统计函数\n 输入\n data：数据框，可以是所有的数据\n code:数据编码\n q1: 题目序号\n q2: 题目序号\n # 单个变量的频数统计\n qtable(data,code,'Q1')\n # 两个变量的交叉统计\n qtable(data,code,'Q1','Q2')\n\n "
code = None
q1 = None
q2 = None
for a in args:
if (isinstance(a, str)... | -5,886,488,093,965,830,000 | 简易频数统计函数
输入
data：数据框，可以是所有的数据
code:数据编码
q1: 题目序号
q2: 题目序号
# 单个变量的频数统计
qtable(data,code,'Q1')
# 两个变量的交叉统计
qtable(data,code,'Q1','Q2') | reportgen/questionnaire/questionnaire.py | qtable | brightgeng/reportgen | python | def qtable(data, *args, **kwargs):
"简易频数统计函数\n 输入\n data：数据框，可以是所有的数据\n code:数据编码\n q1: 题目序号\n q2: 题目序号\n # 单个变量的频数统计\n qtable(data,code,'Q1')\n # 两个变量的交叉统计\n qtable(data,code,'Q1','Q2')\n\n "
code = None
q1 = None
q2 = None
for a in args:
if (isinstance(a, str)... |
def association_rules(df, minSup=0.08, minConf=0.4, Y=None):
'关联规则分析\n df： DataFrame，bool 类型。是一个类似购物篮数据 \n\n '
try:
df = df.astype(bool)
except:
print('df 必须为 bool 类型')
return (None, None, None)
columns = np.array(df.columns)
gen = associate.frequent_itemsets(np.array... | -8,448,162,522,908,191,000 | 关联规则分析
df： DataFrame，bool 类型。是一个类似购物篮数据 | reportgen/questionnaire/questionnaire.py | association_rules | brightgeng/reportgen | python | def association_rules(df, minSup=0.08, minConf=0.4, Y=None):
'关联规则分析\n df： DataFrame，bool 类型。是一个类似购物篮数据 \n\n '
try:
df = df.astype(bool)
except:
print('df 必须为 bool 类型')
return (None, None, None)
columns = np.array(df.columns)
gen = associate.frequent_itemsets(np.array... |
def contingency(fo, alpha=0.05):
" 列联表分析：(观察频数表分析)\n # 预增加一个各类别之间的距离\n 1、生成TGI指数、TWI指数、CHI指数\n 2、独立性检验\n 3、当两个变量不显著时，考虑单个之间的显著性\n 返回字典格式\n chi_test: 卡方检验结果，1:显著；0:不显著；-1：期望值不满足条件\n coef: 包含chi2、p值、V相关系数\n log: 记录一些异常情况\n FO: 观察频数\n FE: 期望频数\n TGI：fo/fe\n TWI：fo-fe\n CHI：sqrt((... | -9,149,343,796,111,427,000 | 列联表分析：(观察频数表分析)
# 预增加一个各类别之间的距离
1、生成TGI指数、TWI指数、CHI指数
2、独立性检验
3、当两个变量不显著时，考虑单个之间的显著性
返回字典格式
chi_test: 卡方检验结果，1:显著；0:不显著；-1：期望值不满足条件
coef: 包含chi2、p值、V相关系数
log: 记录一些异常情况
FO: 观察频数
FE: 期望频数
TGI：fo/fe
TWI：fo-fe
CHI：sqrt((fo-fe)(fo/fe-1))*sign(fo-fe)
significant:{
.'result': 显著性结果[1(显著),0(不显著),-1(fe小于5的过多)]
.'pvalue':
.'meth... | reportgen/questionnaire/questionnaire.py | contingency | brightgeng/reportgen | python | def contingency(fo, alpha=0.05):
" 列联表分析：(观察频数表分析)\n # 预增加一个各类别之间的距离\n 1、生成TGI指数、TWI指数、CHI指数\n 2、独立性检验\n 3、当两个变量不显著时，考虑单个之间的显著性\n 返回字典格式\n chi_test: 卡方检验结果，1:显著；0:不显著；-1：期望值不满足条件\n coef: 包含chi2、p值、V相关系数\n log: 记录一些异常情况\n FO: 观察频数\n FE: 期望频数\n TGI：fo/fe\n TWI：fo-fe\n CHI：sqrt((... |
def pre_cross_qlist(data, code):
'自适应给出可以进行交叉分析的变量和相应选项\n 满足以下条件的将一键交叉分析：\n 1、单选题\n 2、如果选项是文本，则平均长度应小于10\n ...\n 返回：\n cross_qlist: [[题目序号,变量选项],]\n '
cross_qlist = []
for qq in code:
qtype = code[qq]['qtype']
qlist = code[qq]['qlist']
content = code[qq]['content... | 8,495,738,686,203,361,000 | 自适应给出可以进行交叉分析的变量和相应选项
满足以下条件的将一键交叉分析：
1、单选题
2、如果选项是文本，则平均长度应小于10
...
返回：
cross_qlist: [[题目序号,变量选项],] | reportgen/questionnaire/questionnaire.py | pre_cross_qlist | brightgeng/reportgen | python | def pre_cross_qlist(data, code):
'自适应给出可以进行交叉分析的变量和相应选项\n 满足以下条件的将一键交叉分析：\n 1、单选题\n 2、如果选项是文本，则平均长度应小于10\n ...\n 返回：\n cross_qlist: [[题目序号,变量选项],]\n '
cross_qlist = []
for qq in code:
qtype = code[qq]['qtype']
qlist = code[qq]['qlist']
content = code[qq]['content... |
def cross_chart(data, code, cross_class, filename=u'交叉分析报告', cross_qlist=None, delclass=None, plt_dstyle=None, cross_order=None, reverse_display=False, total_display=True, max_column_chart=20, save_dstyle=None, template=None):
"使用帮助\n data: 问卷数据，包含交叉变量和所有的因变量\n code: 数据编码\n cross_class: 交叉变量，单选题或者多选题，例如：Q1... | -7,219,620,354,715,659,000 | 使用帮助
data: 问卷数据，包含交叉变量和所有的因变量
code: 数据编码
cross_class: 交叉变量，单选题或者多选题，例如：Q1
filename：文件名,用于PPT和保存相关数据
cross_list: 需要进行交叉分析的变量，缺省为code中的所有变量
delclass: 交叉变量中需要删除的单个变量，缺省空
plt_dstyle: 绘制图表需要用的数据类型，默认为百分比表，可以选择['TGI'、'CHI'、'TWI']等
save_dstyle: 需要保存的数据类型，格式为列表。
cross_order: 交叉变量中各个类别的顺序，可以缺少
total_display: PPT绘制图表中是否显示总体情况
ma... | reportgen/questionnaire/questionnaire.py | cross_chart | brightgeng/reportgen | python | def cross_chart(data, code, cross_class, filename=u'交叉分析报告', cross_qlist=None, delclass=None, plt_dstyle=None, cross_order=None, reverse_display=False, total_display=True, max_column_chart=20, save_dstyle=None, template=None):
"使用帮助\n data: 问卷数据，包含交叉变量和所有的因变量\n code: 数据编码\n cross_class: 交叉变量，单选题或者多选题，例如：Q1... |
def onekey_gen(data, code, filename=u'reprotgen 报告自动生成', template=None):
'一键生成所有可能需要的报告\n 包括\n 描述统计报告\n 单选题的交叉分析报告\n '
try:
summary_chart(data, code, filename=filename, template=template)
except:
print('整体报告生成过程中出现错误，将跳过..')
pass
print(('已生成 ' + filename))
cross_q... | -4,211,264,381,394,803,000 | 一键生成所有可能需要的报告
包括
描述统计报告
单选题的交叉分析报告 | reportgen/questionnaire/questionnaire.py | onekey_gen | brightgeng/reportgen | python | def onekey_gen(data, code, filename=u'reprotgen 报告自动生成', template=None):
'一键生成所有可能需要的报告\n 包括\n 描述统计报告\n 单选题的交叉分析报告\n '
try:
summary_chart(data, code, filename=filename, template=template)
except:
print('整体报告生成过程中出现错误，将跳过..')
pass
print(('已生成 ' + filename))
cross_q... |
def scorpion(data, code, filename='scorpion'):
'天蝎X计划\n 返回一个excel文件\n 1、索引\n 2、各个题目的频数表\n 3、所有可能的交叉分析\n '
if (not os.path.exists('.\\out')):
os.mkdir('.\\out')
Writer = pd.ExcelWriter((('.\\out\\' + filename) + '.xlsx'))
try:
qqlist = list(sorted(code, key=(lambda c: int(r... | -6,475,010,946,498,080,000 | 天蝎X计划
返回一个excel文件
1、索引
2、各个题目的频数表
3、所有可能的交叉分析 | reportgen/questionnaire/questionnaire.py | scorpion | brightgeng/reportgen | python | def scorpion(data, code, filename='scorpion'):
'天蝎X计划\n 返回一个excel文件\n 1、索引\n 2、各个题目的频数表\n 3、所有可能的交叉分析\n '
if (not os.path.exists('.\\out')):
os.mkdir('.\\out')
Writer = pd.ExcelWriter((('.\\out\\' + filename) + '.xlsx'))
try:
qqlist = list(sorted(code, key=(lambda c: int(r... |
def timer_callback(self):
' Calculate Mx1, My1, ...... Mx6, My6 '
if (self.t == 0):
self.Phix1 = 0
self.Phiy1 = 0
self.Phix3 = 0
self.Phiy3 = 0
self.t += 1
Mx1 = (self.x3 - self.x1)
My1 = (self.y3 - self.y1)
Mx3 = (self.x1 - self.x3)
My3 = (self.y1 - self.... | -1,594,632,542,043,036,000 | Calculate Mx1, My1, ...... Mx6, My6 | Real Topology Graph/GNN Model 2/Cyclic Graph/test_n2_robot3.py | timer_callback | HusseinLezzaik/Consensus-Algorithm-for-2-Mobile-Robots | python | def timer_callback(self):
' '
if (self.t == 0):
self.Phix1 = 0
self.Phiy1 = 0
self.Phix3 = 0
self.Phiy3 = 0
self.t += 1
Mx1 = (self.x3 - self.x1)
My1 = (self.y3 - self.y1)
Mx3 = (self.x1 - self.x3)
My3 = (self.y1 - self.y3)
' Use MLP to Predict contro... |
def get_samplesheet(self):
'Return path of an annotation samplesheet.'
files_path = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'files'))
samplesheet_name = 'annotation_spreadsheet.xlsm'
return os.path.join(files_path, samplesheet_name) | -6,796,047,194,800,837,000 | Return path of an annotation samplesheet. | resdk/tests/functional/data_upload/e2e_upload.py | get_samplesheet | tristanbrown/resolwe-bio-py | python | def get_samplesheet(self):
files_path = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'files'))
samplesheet_name = 'annotation_spreadsheet.xlsm'
return os.path.join(files_path, samplesheet_name) |
def r2(self):
'Calculate R2 for either the train model or the test model'
sse_sst = (self.sse() / self.sst())
return (1 - sse_sst) | 9,113,696,639,111,318,000 | Calculate R2 for either the train model or the test model | metrics/__init__.py | r2 | nathan-bennett/skellam | python | def r2(self):
sse_sst = (self.sse() / self.sst())
return (1 - sse_sst) |
def adjusted_r2(self):
'Calculate adjusted R2 for either the train model or the test model'
r2 = self.r2()
return (1 - (((1 - r2) * (self.train_length - 1)) / ((self.train_length - self.coeff_size) - 1))) | -1,238,627,807,371,270,000 | Calculate adjusted R2 for either the train model or the test model | metrics/__init__.py | adjusted_r2 | nathan-bennett/skellam | python | def adjusted_r2(self):
r2 = self.r2()
return (1 - (((1 - r2) * (self.train_length - 1)) / ((self.train_length - self.coeff_size) - 1))) |
def log_likelihood(self):
'Returns the maximum of the log likelihood function'
return self.max_ll | -512,140,616,830,212,860 | Returns the maximum of the log likelihood function | metrics/__init__.py | log_likelihood | nathan-bennett/skellam | python | def log_likelihood(self):
return self.max_ll |
def _calculate_lambda(self):
'Create arrays for our predictions of the two Poisson distributions\n '
_lambda0 = ArrayUtils.convert_to_array(np.exp(np.squeeze((self._x0 @ self.lambda_0_coefficients))))
_lambda1 = ArrayUtils.convert_to_array(np.exp(np.squeeze((self._x1 @ self.lambda_1_coefficients))))
... | 1,492,520,975,505,939,200 | Create arrays for our predictions of the two Poisson distributions | metrics/__init__.py | _calculate_lambda | nathan-bennett/skellam | python | def _calculate_lambda(self):
'\n '
_lambda0 = ArrayUtils.convert_to_array(np.exp(np.squeeze((self._x0 @ self.lambda_0_coefficients))))
_lambda1 = ArrayUtils.convert_to_array(np.exp(np.squeeze((self._x1 @ self.lambda_1_coefficients))))
return (_lambda0, _lambda1) |
def _calculate_v(self):
'Create diagonal matrix consisting of our predictions of the Poisson distributions\n '
(_lambda0, _lambda1) = self._calculate_lambda()
_v0 = np.diagflat(_lambda0)
_v1 = np.diagflat(_lambda1)
return (_v0, _v1) | -1,923,027,407,107,608,300 | Create diagonal matrix consisting of our predictions of the Poisson distributions | metrics/__init__.py | _calculate_v | nathan-bennett/skellam | python | def _calculate_v(self):
'\n '
(_lambda0, _lambda1) = self._calculate_lambda()
_v0 = np.diagflat(_lambda0)
_v1 = np.diagflat(_lambda1)
return (_v0, _v1) |
def _calculate_w(self):
'Create a diagonal matrix consisting of the difference between our predictions of the 2 Poisson distributions\n with their observed values\n '
(_lambda0, _lambda1) = self._calculate_lambda()
_w0 = np.diagflat(((self.l0 - _lambda0.reshape((- 1), 1)) ** 2))
_w1 = np.d... | -9,050,369,303,904,403,000 | Create a diagonal matrix consisting of the difference between our predictions of the 2 Poisson distributions
with their observed values | metrics/__init__.py | _calculate_w | nathan-bennett/skellam | python | def _calculate_w(self):
'Create a diagonal matrix consisting of the difference between our predictions of the 2 Poisson distributions\n with their observed values\n '
(_lambda0, _lambda1) = self._calculate_lambda()
_w0 = np.diagflat(((self.l0 - _lambda0.reshape((- 1), 1)) ** 2))
_w1 = np.d... |
def _calculate_robust_covariance(self):
'Calculate robust variance covariance matrices for our two sets of coefficients\n '
(_v0, _v1) = self._calculate_v()
(_w0, _w1) = self._calculate_w()
_robust_cov0 = ((np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0),... | -5,582,450,034,621,296,000 | Calculate robust variance covariance matrices for our two sets of coefficients | metrics/__init__.py | _calculate_robust_covariance | nathan-bennett/skellam | python | def _calculate_robust_covariance(self):
'\n '
(_v0, _v1) = self._calculate_v()
(_w0, _w1) = self._calculate_w()
_robust_cov0 = ((np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0), self._x0)) * np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)))
_r... |
def _calculate_robust_standard_errors(self):
'Calculate robust standard errors for our two sets of coefficients by taking the square root of the diagonal\n values in the variance covariance matrices\n '
(_robust_cov0, _robust_cov1) = self._calculate_robust_covariance()
_std_error0 = np.sqrt(np... | -3,722,754,812,175,301,600 | Calculate robust standard errors for our two sets of coefficients by taking the square root of the diagonal
values in the variance covariance matrices | metrics/__init__.py | _calculate_robust_standard_errors | nathan-bennett/skellam | python | def _calculate_robust_standard_errors(self):
'Calculate robust standard errors for our two sets of coefficients by taking the square root of the diagonal\n values in the variance covariance matrices\n '
(_robust_cov0, _robust_cov1) = self._calculate_robust_covariance()
_std_error0 = np.sqrt(np... |
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