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train | CubatureKalmanFilter.update | Update the CKF with the given measurements. On return,
self.x and self.P contain the new mean and covariance of the filter.
Parameters
----------
z : numpy.array of shape (dim_z)
measurement vector
R : numpy.array((dim_z, dim_z)), optional
Measurement n... | filterpy/kalman/CubatureKalmanFilter.py | def update(self, z, R=None, hx_args=()):
""" Update the CKF with the given measurements. On return,
self.x and self.P contain the new mean and covariance of the filter.
Parameters
----------
z : numpy.array of shape (dim_z)
measurement vector
R : numpy.arra... | def update(self, z, R=None, hx_args=()):
""" Update the CKF with the given measurements. On return,
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Parameters
----------
z : numpy.array of shape (dim_z)
measurement vector
R : numpy.arra... | [
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train | InformationFilter.update | Add a new measurement (z) to the kalman filter. If z is None, nothing
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z : np.array
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"""
Add a new measurement (z) to the kalman filter. If z is None, nothing
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Parameters
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z : np.array
measurement for this update.
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train | InformationFilter.predict | Predict next position.
Parameters
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u : ndarray
Optional control vector. If non-zero, it is multiplied by B
to create the control input into the system. | filterpy/kalman/information_filter.py | def predict(self, u=0):
""" Predict next position.
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Optional control vector. If non-zero, it is multiplied by B
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"""
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Parameters
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u : ndarray
Optional control vector. If non-zero, it is multiplied by B
to create the control input into the system.
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train | InformationFilter.batch_filter | Batch processes a sequences of measurements.
Parameters
----------
zs : list-like
list of measurements at each time step `self.dt` Missing
measurements must be represented by 'None'.
Rs : list-like, optional
optional list of values to use for the me... | filterpy/kalman/information_filter.py | def batch_filter(self, zs, Rs=None, update_first=False, saver=None):
""" Batch processes a sequences of measurements.
Parameters
----------
zs : list-like
list of measurements at each time step `self.dt` Missing
measurements must be represented by 'None'.
... | def batch_filter(self, zs, Rs=None, update_first=False, saver=None):
""" Batch processes a sequences of measurements.
Parameters
----------
zs : list-like
list of measurements at each time step `self.dt` Missing
measurements must be represented by 'None'.
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train | InformationFilter.F | State Transition matrix | filterpy/kalman/information_filter.py | def F(self, value):
"""State Transition matrix"""
self._F = value
self._F_inv = self.inv(self._F) | def F(self, value):
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self._F = value
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train | runge_kutta4 | computes 4th order Runge-Kutta for dy/dx.
Parameters
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y : scalar
Initial/current value for y
x : scalar
Initial/current value for x
dx : scalar
difference in x (e.g. the time step)
f : ufunc(y,x)
Callable function (y, x) that you supply to compute dy/d... | filterpy/common/helpers.py | def runge_kutta4(y, x, dx, f):
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Parameters
----------
y : scalar
Initial/current value for y
x : scalar
Initial/current value for x
dx : scalar
difference in x (e.g. the time step)
f : ufunc(y,x)
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Initial/current value for y
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train | pretty_str | Generates a pretty printed NumPy array with an assignment. Optionally
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train | reshape_z | ensure z is a (dim_z, 1) shaped vector | filterpy/common/helpers.py | def reshape_z(z, dim_z, ndim):
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train | inv_diagonal | Computes the inverse of a diagonal NxN np.array S. In general this will
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train | outer_product_sum | Computes the sum of the outer products of the rows in A and B
P = \Sum {A[i] B[i].T} for i in 0..N
Notionally:
P = 0
for y in A:
P += np.outer(y, y)
This is a standard computation for sigma points used in the UKF, ensemble
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"""
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train | Saver.save | save the current state of the Kalman filter | filterpy/common/helpers.py | def save(self):
""" save the current state of the Kalman filter"""
kf = self._kf
# force all attributes to be computed. this is only necessary
# if the class uses properties that compute data only when
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for prop in self.properties:
self._DL[prop[0]... | def save(self):
""" save the current state of the Kalman filter"""
kf = self._kf
# force all attributes to be computed. this is only necessary
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train | Saver.to_array | Convert all saved attributes from a list to np.array.
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This can also happen if the default initializa... | filterpy/common/helpers.py | def to_array(self):
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train | IMMEstimator.update | Add a new measurement (z) to the Kalman filter. If z is None, nothing
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Parameters
----------
z : np.array
measurement for this update. | filterpy/kalman/IMM.py | def update(self, z):
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Add a new measurement (z) to the Kalman filter. If z is None, nothing
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Parameters
----------
z : np.array
measurement for this update.
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# run update on each filter, and save the likelihood
for i... | def update(self, z):
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Add a new measurement (z) to the Kalman filter. If z is None, nothing
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z : np.array
measurement for this update.
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train | UnscentedKalmanFilter.predict | r"""
Performs the predict step of the UKF. On return, self.x and
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Parameters
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dt : double, optional
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Performs the predict step of the UKF. On return, self.x and
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Performs the predict step of the UKF. On return, self.x and
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z : numpy.array of shape (dim_z)
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Update the UKF with the given measurements. On return,
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train | UnscentedKalmanFilter.compute_process_sigmas | computes the values of sigmas_f. Normally a user would not call
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measurements), so the sigmas correctly reflect the updated state
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train | UnscentedKalmanFilter.batch_filter | Performs the UKF filter over the list of measurement in `zs`.
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zs : list-like
list of measurements at each time step `self._dt` Missing
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Rs : None, np.array or list-like, default=None
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Performs the UKF filter over the list of measurement in `zs`.
Parameters
----------
zs : list-like
list of measurements at each time step `self._dt` Missing
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train | UnscentedKalmanFilter.rts_smoother | Runs the Rauch-Tung-Striebal Kalman smoother on a set of
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Xs : numpy.array
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----------
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u : np.array
Optional control vector. If non-zero, it is multiplied by B
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u : np.array
Optional control vector. If non-zero, it is multiplied by B
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measurement for this update.
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train | residual_resample | Performs the residual resampling algorithm used by particle filters.
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z : ndarray
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z : ndarray
measurement for this update.
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train | HInfinityFilter.predict | Predict next position.
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Optional control vector. If non-zero, it is multiplied by `B`
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Zs : list-like
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update_first : bool, default=False, optional,
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""" Batch processes a sequences of measurements.
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Zs : list-like
list of measurements at each time step `self.dt` Missing
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u : ndarray
optional control input
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x : ndarray
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u : ndarray
optional control input
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x : ndarray
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z : np.array
measurement for this update.
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z : np.array
measurement for this update.
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train | WebSocketProtocol.run_asgi | Wrapper around the ASGI callable, handling exceptions and unexpected
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Wrapper around the ASGI callable, handling exceptions and unexpected
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"""
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result = await self.app(self.scope, self.asgi_receive, self.asgi_send)
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Return an ASGI message, with any body-type content omitted and replaced
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train | setup_logger | setup root logger with ColoredFormatter. | httprunner/logger.py | def setup_logger(log_level, log_file=None):
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level = getattr(logging, log_level.upper(), None)
if not level:
color_print("Invalid log level: %s" % log_level, "RED")
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train | log_with_color | log with color by different level | httprunner/logger.py | def log_with_color(level):
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train | Runner._handle_skip_feature | handle skip feature for test
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train | Runner.extract_output | extract output variables | httprunner/runner.py | def extract_output(self, output_variables_list):
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train | HttpRunner._run_suite | run tests in test_suite
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test_suite: unittest.TestSuite()
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train | HttpRunner._aggregate | aggregate results
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tests_results (list): list of (testcase, result) | httprunner/api.py | def _aggregate(self, tests_results):
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tests_results (list): list of (testcase, result)
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train | HttpRunner.run_path | run testcase/testsuite file or folder.
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path (str): testcase/testsuite file/foler path.
dot_env_path (str): specified .env file path.
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path (str): testcase/testsuite file/foler path.
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train | HttpRunner.run | main interface.
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train | set_os_environ | set variables mapping to os.environ | httprunner/utils.py | def set_os_environ(variables_mapping):
""" set variables mapping to os.environ
"""
for variable in variables_mapping:
os.environ[variable] = variables_mapping[variable]
logger.log_debug("Set OS environment variable: {}".format(variable)) | def set_os_environ(variables_mapping):
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train | unset_os_environ | set variables mapping to os.environ | httprunner/utils.py | def unset_os_environ(variables_mapping):
""" set variables mapping to os.environ
"""
for variable in variables_mapping:
os.environ.pop(variable)
logger.log_debug("Unset OS environment variable: {}".format(variable)) | def unset_os_environ(variables_mapping):
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train | build_url | prepend url with base_url unless it's already an absolute URL | httprunner/utils.py | def build_url(base_url, path):
""" prepend url with base_url unless it's already an absolute URL """
if absolute_http_url_regexp.match(path):
return path
elif base_url:
return "{}/{}".format(base_url.rstrip("/"), path.lstrip("/"))
else:
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elif base_url:
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train | query_json | Do an xpath-like query with json_content.
Args:
json_content (dict/list/string): content to be queried.
query (str): query string.
delimiter (str): delimiter symbol.
Returns:
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Examples:
>>> json_content = {
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... | httprunner/utils.py | def query_json(json_content, query, delimiter='.'):
""" Do an xpath-like query with json_content.
Args:
json_content (dict/list/string): content to be queried.
query (str): query string.
delimiter (str): delimiter symbol.
Returns:
str: queried result.
Examples:
... | def query_json(json_content, query, delimiter='.'):
""" Do an xpath-like query with json_content.
Args:
json_content (dict/list/string): content to be queried.
query (str): query string.
delimiter (str): delimiter symbol.
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train | deep_update_dict | update origin dict with override dict recursively
e.g. origin_dict = {'a': 1, 'b': {'c': 2, 'd': 4}}
override_dict = {'b': {'c': 3}}
return: {'a': 1, 'b': {'c': 3, 'd': 4}} | httprunner/utils.py | def deep_update_dict(origin_dict, override_dict):
""" update origin dict with override dict recursively
e.g. origin_dict = {'a': 1, 'b': {'c': 2, 'd': 4}}
override_dict = {'b': {'c': 3}}
return: {'a': 1, 'b': {'c': 3, 'd': 4}}
"""
if not override_dict:
return origin_dict
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""" update origin dict with override dict recursively
e.g. origin_dict = {'a': 1, 'b': {'c': 2, 'd': 4}}
override_dict = {'b': {'c': 3}}
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train | convert_dict_to_params | convert dict to params string
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src_dict (dict): source mapping data structure
Returns:
str: string params data
Examples:
>>> src_dict = {
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"b": 2
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>>> convert_dict_to_params(src_dict)
>>> "a=1&b=2" | httprunner/utils.py | def convert_dict_to_params(src_dict):
""" convert dict to params string
Args:
src_dict (dict): source mapping data structure
Returns:
str: string params data
Examples:
>>> src_dict = {
"a": 1,
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""" convert dict to params string
Args:
src_dict (dict): source mapping data structure
Returns:
str: string params data
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>>> src_dict = {
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train | lower_dict_keys | convert keys in dict to lower case
Args:
origin_dict (dict): mapping data structure
Returns:
dict: mapping with all keys lowered.
Examples:
>>> origin_dict = {
"Name": "",
"Request": "",
"URL": "",
"METHOD": "",
"Headers"... | httprunner/utils.py | def lower_dict_keys(origin_dict):
""" convert keys in dict to lower case
Args:
origin_dict (dict): mapping data structure
Returns:
dict: mapping with all keys lowered.
Examples:
>>> origin_dict = {
"Name": "",
"Request": "",
"URL": "",
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""" convert keys in dict to lower case
Args:
origin_dict (dict): mapping data structure
Returns:
dict: mapping with all keys lowered.
Examples:
>>> origin_dict = {
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"URL": "",
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train | deepcopy_dict | deepcopy dict data, ignore file object (_io.BufferedReader)
Args:
data (dict): dict data structure
{
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'b': [2, 4],
'c': lambda x: x+1,
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""" deepcopy dict data, ignore file object (_io.BufferedReader)
Args:
data (dict): dict data structure
{
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'b': [2, 4],
'c': lambda x: x+1,
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Args:
data (dict): dict data structure
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train | ensure_mapping_format | ensure variables are in mapping format.
Args:
variables (list/dict): original variables
Returns:
dict: ensured variables in dict format
Examples:
>>> variables = [
{"a": 1},
{"b": 2}
]
>>> print(ensure_mapping_format(variables))
... | httprunner/utils.py | def ensure_mapping_format(variables):
""" ensure variables are in mapping format.
Args:
variables (list/dict): original variables
Returns:
dict: ensured variables in dict format
Examples:
>>> variables = [
{"a": 1},
{"b": 2}
]
... | def ensure_mapping_format(variables):
""" ensure variables are in mapping format.
Args:
variables (list/dict): original variables
Returns:
dict: ensured variables in dict format
Examples:
>>> variables = [
{"a": 1},
{"b": 2}
]
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train | extend_variables | extend raw_variables with override_variables.
override_variables will merge and override raw_variables.
Args:
raw_variables (list):
override_variables (list):
Returns:
dict: extended variables mapping
Examples:
>>> raw_variables = [{"var1": "val1"}, {"var2": "val2"... | httprunner/utils.py | def extend_variables(raw_variables, override_variables):
""" extend raw_variables with override_variables.
override_variables will merge and override raw_variables.
Args:
raw_variables (list):
override_variables (list):
Returns:
dict: extended variables mapping
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""" extend raw_variables with override_variables.
override_variables will merge and override raw_variables.
Args:
raw_variables (list):
override_variables (list):
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dict: extended variables mapping
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train | print_info | print info in mapping.
Args:
info_mapping (dict): input(variables) or output mapping.
Examples:
>>> info_mapping = {
"var_a": "hello",
"var_b": "world"
}
>>> info_mapping = {
"status_code": 500
}
>>> print_... | httprunner/utils.py | def print_info(info_mapping):
""" print info in mapping.
Args:
info_mapping (dict): input(variables) or output mapping.
Examples:
>>> info_mapping = {
"var_a": "hello",
"var_b": "world"
}
>>> info_mapping = {
"status_code"... | def print_info(info_mapping):
""" print info in mapping.
Args:
info_mapping (dict): input(variables) or output mapping.
Examples:
>>> info_mapping = {
"var_a": "hello",
"var_b": "world"
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>>> info_mapping = {
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train | create_scaffold | create scaffold with specified project name. | httprunner/utils.py | def create_scaffold(project_name):
""" create scaffold with specified project name.
"""
if os.path.isdir(project_name):
logger.log_warning(u"Folder {} exists, please specify a new folder name.".format(project_name))
return
logger.color_print("Start to create new project: {}".format(proj... | def create_scaffold(project_name):
""" create scaffold with specified project name.
"""
if os.path.isdir(project_name):
logger.log_warning(u"Folder {} exists, please specify a new folder name.".format(project_name))
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logger.color_print("Start to create new project: {}".format(proj... | [
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train | gen_cartesian_product | generate cartesian product for lists
Args:
args (list of list): lists to be generated with cartesian product
Returns:
list: cartesian product in list
Examples:
>>> arg1 = [{"a": 1}, {"a": 2}]
>>> arg2 = [{"x": 111, "y": 112}, {"x": 121, "y": 122}]
>>> args = [arg1... | httprunner/utils.py | def gen_cartesian_product(*args):
""" generate cartesian product for lists
Args:
args (list of list): lists to be generated with cartesian product
Returns:
list: cartesian product in list
Examples:
>>> arg1 = [{"a": 1}, {"a": 2}]
>>> arg2 = [{"x": 111, "y": 112}, {"x"... | def gen_cartesian_product(*args):
""" generate cartesian product for lists
Args:
args (list of list): lists to be generated with cartesian product
Returns:
list: cartesian product in list
Examples:
>>> arg1 = [{"a": 1}, {"a": 2}]
>>> arg2 = [{"x": 111, "y": 112}, {"x"... | [
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train | prettify_json_file | prettify JSON testcase format | httprunner/utils.py | def prettify_json_file(file_list):
""" prettify JSON testcase format
"""
for json_file in set(file_list):
if not json_file.endswith(".json"):
logger.log_warning("Only JSON file format can be prettified, skip: {}".format(json_file))
continue
logger.color_print("Start ... | def prettify_json_file(file_list):
""" prettify JSON testcase format
"""
for json_file in set(file_list):
if not json_file.endswith(".json"):
logger.log_warning("Only JSON file format can be prettified, skip: {}".format(json_file))
continue
logger.color_print("Start ... | [
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train | omit_long_data | omit too long str/bytes | httprunner/utils.py | def omit_long_data(body, omit_len=512):
""" omit too long str/bytes
"""
if not isinstance(body, basestring):
return body
body_len = len(body)
if body_len <= omit_len:
return body
omitted_body = body[0:omit_len]
appendix_str = " ... OMITTED {} CHARACTORS ...".format(body_le... | def omit_long_data(body, omit_len=512):
""" omit too long str/bytes
"""
if not isinstance(body, basestring):
return body
body_len = len(body)
if body_len <= omit_len:
return body
omitted_body = body[0:omit_len]
appendix_str = " ... OMITTED {} CHARACTORS ...".format(body_le... | [
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train | dump_json_file | dump json data to file | httprunner/utils.py | def dump_json_file(json_data, pwd_dir_path, dump_file_name):
""" dump json data to file
"""
class PythonObjectEncoder(json.JSONEncoder):
def default(self, obj):
try:
return super().default(self, obj)
except TypeError:
return str(obj)
logs_... | def dump_json_file(json_data, pwd_dir_path, dump_file_name):
""" dump json data to file
"""
class PythonObjectEncoder(json.JSONEncoder):
def default(self, obj):
try:
return super().default(self, obj)
except TypeError:
return str(obj)
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train | _prepare_dump_info | prepare dump file info. | httprunner/utils.py | def _prepare_dump_info(project_mapping, tag_name):
""" prepare dump file info.
"""
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pwd_dir_path = project_mapping.get("PWD") or os.getcwd()
file_name, file_suffix = os.path.splitext(os.path.basename(test_path.rstrip("/")))
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train | dump_logs | dump tests data to json file.
the dumped file is located in PWD/logs folder.
Args:
json_data (list/dict): json data to dump
project_mapping (dict): project info
tag_name (str): tag name, loaded/parsed/summary | httprunner/utils.py | def dump_logs(json_data, project_mapping, tag_name):
""" dump tests data to json file.
the dumped file is located in PWD/logs folder.
Args:
json_data (list/dict): json data to dump
project_mapping (dict): project info
tag_name (str): tag name, loaded/parsed/summary
"""
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json_data (list/dict): json data to dump
project_mapping (dict): project info
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train | _check_format | check testcase format if valid | httprunner/loader.py | def _check_format(file_path, content):
""" check testcase format if valid
"""
# TODO: replace with JSON schema validation
if not content:
# testcase file content is empty
err_msg = u"Testcase file content is empty: {}".format(file_path)
logger.log_error(err_msg)
raise exc... | def _check_format(file_path, content):
""" check testcase format if valid
"""
# TODO: replace with JSON schema validation
if not content:
# testcase file content is empty
err_msg = u"Testcase file content is empty: {}".format(file_path)
logger.log_error(err_msg)
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train | load_yaml_file | load yaml file and check file content format | httprunner/loader.py | def load_yaml_file(yaml_file):
""" load yaml file and check file content format
"""
with io.open(yaml_file, 'r', encoding='utf-8') as stream:
yaml_content = yaml.load(stream)
_check_format(yaml_file, yaml_content)
return yaml_content | def load_yaml_file(yaml_file):
""" load yaml file and check file content format
"""
with io.open(yaml_file, 'r', encoding='utf-8') as stream:
yaml_content = yaml.load(stream)
_check_format(yaml_file, yaml_content)
return yaml_content | [
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train | load_json_file | load json file and check file content format | httprunner/loader.py | def load_json_file(json_file):
""" load json file and check file content format
"""
with io.open(json_file, encoding='utf-8') as data_file:
try:
json_content = json.load(data_file)
except exceptions.JSONDecodeError:
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""" load json file and check file content format
"""
with io.open(json_file, encoding='utf-8') as data_file:
try:
json_content = json.load(data_file)
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train | load_csv_file | load csv file and check file content format
Args:
csv_file (str): csv file path, csv file content is like below:
Returns:
list: list of parameters, each parameter is in dict format
Examples:
>>> cat csv_file
username,password
test1,111111
test2,222222
... | httprunner/loader.py | def load_csv_file(csv_file):
""" load csv file and check file content format
Args:
csv_file (str): csv file path, csv file content is like below:
Returns:
list: list of parameters, each parameter is in dict format
Examples:
>>> cat csv_file
username,password
te... | def load_csv_file(csv_file):
""" load csv file and check file content format
Args:
csv_file (str): csv file path, csv file content is like below:
Returns:
list: list of parameters, each parameter is in dict format
Examples:
>>> cat csv_file
username,password
te... | [
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train | load_folder_files | load folder path, return all files endswith yml/yaml/json in list.
Args:
folder_path (str): specified folder path to load
recursive (bool): load files recursively if True
Returns:
list: files endswith yml/yaml/json | httprunner/loader.py | def load_folder_files(folder_path, recursive=True):
""" load folder path, return all files endswith yml/yaml/json in list.
Args:
folder_path (str): specified folder path to load
recursive (bool): load files recursively if True
Returns:
list: files endswith yml/yaml/json
"""
... | def load_folder_files(folder_path, recursive=True):
""" load folder path, return all files endswith yml/yaml/json in list.
Args:
folder_path (str): specified folder path to load
recursive (bool): load files recursively if True
Returns:
list: files endswith yml/yaml/json
"""
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train | load_dot_env_file | load .env file.
Args:
dot_env_path (str): .env file path
Returns:
dict: environment variables mapping
{
"UserName": "debugtalk",
"Password": "123456",
"PROJECT_KEY": "ABCDEFGH"
}
Raises:
exceptions.FileFormat... | httprunner/loader.py | def load_dot_env_file(dot_env_path):
""" load .env file.
Args:
dot_env_path (str): .env file path
Returns:
dict: environment variables mapping
{
"UserName": "debugtalk",
"Password": "123456",
"PROJECT_KEY": "ABCDEFGH"
... | def load_dot_env_file(dot_env_path):
""" load .env file.
Args:
dot_env_path (str): .env file path
Returns:
dict: environment variables mapping
{
"UserName": "debugtalk",
"Password": "123456",
"PROJECT_KEY": "ABCDEFGH"
... | [
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train | locate_file | locate filename and return absolute file path.
searching will be recursive upward until current working directory.
Args:
start_path (str): start locating path, maybe file path or directory path
Returns:
str: located file path. None if file not found.
Raises:
exceptions.Fil... | httprunner/loader.py | def locate_file(start_path, file_name):
""" locate filename and return absolute file path.
searching will be recursive upward until current working directory.
Args:
start_path (str): start locating path, maybe file path or directory path
Returns:
str: located file path. None if fil... | def locate_file(start_path, file_name):
""" locate filename and return absolute file path.
searching will be recursive upward until current working directory.
Args:
start_path (str): start locating path, maybe file path or directory path
Returns:
str: located file path. None if fil... | [
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train | load_module_functions | load python module functions.
Args:
module: python module
Returns:
dict: functions mapping for specified python module
{
"func1_name": func1,
"func2_name": func2
} | httprunner/loader.py | def load_module_functions(module):
""" load python module functions.
Args:
module: python module
Returns:
dict: functions mapping for specified python module
{
"func1_name": func1,
"func2_name": func2
}
"""
module_functions ... | def load_module_functions(module):
""" load python module functions.
Args:
module: python module
Returns:
dict: functions mapping for specified python module
{
"func1_name": func1,
"func2_name": func2
}
"""
module_functions ... | [
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train | __extend_with_api_ref | extend with api reference
Raises:
exceptions.ApiNotFound: api not found | httprunner/loader.py | def __extend_with_api_ref(raw_testinfo):
""" extend with api reference
Raises:
exceptions.ApiNotFound: api not found
"""
api_name = raw_testinfo["api"]
# api maybe defined in two types:
# 1, individual file: each file is corresponding to one api definition
# 2, api sets file: one ... | def __extend_with_api_ref(raw_testinfo):
""" extend with api reference
Raises:
exceptions.ApiNotFound: api not found
"""
api_name = raw_testinfo["api"]
# api maybe defined in two types:
# 1, individual file: each file is corresponding to one api definition
# 2, api sets file: one ... | [
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train | load_folder_content | load api/testcases/testsuites definitions from folder.
Args:
folder_path (str): api/testcases/testsuites files folder.
Returns:
dict: api definition mapping.
{
"tests/api/basic.yml": [
{"api": {"def": "api_login", "request": {}, "validate": []}}... | httprunner/loader.py | def load_folder_content(folder_path):
""" load api/testcases/testsuites definitions from folder.
Args:
folder_path (str): api/testcases/testsuites files folder.
Returns:
dict: api definition mapping.
{
"tests/api/basic.yml": [
{"api": {"def"... | def load_folder_content(folder_path):
""" load api/testcases/testsuites definitions from folder.
Args:
folder_path (str): api/testcases/testsuites files folder.
Returns:
dict: api definition mapping.
{
"tests/api/basic.yml": [
{"api": {"def"... | [
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train | load_api_folder | load api definitions from api folder.
Args:
api_folder_path (str): api files folder.
api file should be in the following format:
[
{
"api": {
"def": "api_login",
"request": {},
... | httprunner/loader.py | def load_api_folder(api_folder_path):
""" load api definitions from api folder.
Args:
api_folder_path (str): api files folder.
api file should be in the following format:
[
{
"api": {
"def": "api_login",
... | def load_api_folder(api_folder_path):
""" load api definitions from api folder.
Args:
api_folder_path (str): api files folder.
api file should be in the following format:
[
{
"api": {
"def": "api_login",
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train | main_hrun | API test: parse command line options and run commands. | httprunner/cli.py | def main_hrun():
""" API test: parse command line options and run commands.
"""
import argparse
from httprunner import logger
from httprunner.__about__ import __description__, __version__
from httprunner.api import HttpRunner
from httprunner.compat import is_py2
from httprunner.validator... | def main_hrun():
""" API test: parse command line options and run commands.
"""
import argparse
from httprunner import logger
from httprunner.__about__ import __description__, __version__
from httprunner.api import HttpRunner
from httprunner.compat import is_py2
from httprunner.validator... | [
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train | main_locust | Performance test with locust: parse command line options and run commands. | httprunner/cli.py | def main_locust():
""" Performance test with locust: parse command line options and run commands.
"""
# monkey patch ssl at beginning to avoid RecursionError when running locust.
from gevent import monkey; monkey.patch_ssl()
import multiprocessing
import sys
from httprunner import logger
... | def main_locust():
""" Performance test with locust: parse command line options and run commands.
"""
# monkey patch ssl at beginning to avoid RecursionError when running locust.
from gevent import monkey; monkey.patch_ssl()
import multiprocessing
import sys
from httprunner import logger
... | [
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train | is_var_or_func_exist | check if variable or function exist | httprunner/parser.py | def is_var_or_func_exist(content):
""" check if variable or function exist
"""
if not isinstance(content, basestring):
return False
try:
match_start_position = content.index("$", 0)
except ValueError:
return False
while match_start_position < len(content):
dolla... | def is_var_or_func_exist(content):
""" check if variable or function exist
"""
if not isinstance(content, basestring):
return False
try:
match_start_position = content.index("$", 0)
except ValueError:
return False
while match_start_position < len(content):
dolla... | [
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] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L34-L59 | [
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train | regex_findall_variables | extract all variable names from content, which is in format $variable
Args:
content (str): string content
Returns:
list: variables list extracted from string content
Examples:
>>> regex_findall_variables("$variable")
["variable"]
>>> regex_findall_variables("/blog... | httprunner/parser.py | def regex_findall_variables(content):
""" extract all variable names from content, which is in format $variable
Args:
content (str): string content
Returns:
list: variables list extracted from string content
Examples:
>>> regex_findall_variables("$variable")
["variable... | def regex_findall_variables(content):
""" extract all variable names from content, which is in format $variable
Args:
content (str): string content
Returns:
list: variables list extracted from string content
Examples:
>>> regex_findall_variables("$variable")
["variable... | [
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train | parse_parameters | parse parameters and generate cartesian product.
Args:
parameters (list) parameters: parameter name and value in list
parameter value may be in three types:
(1) data list, e.g. ["iOS/10.1", "iOS/10.2", "iOS/10.3"]
(2) call built-in parameterize function, "${param... | httprunner/parser.py | def parse_parameters(parameters, variables_mapping=None, functions_mapping=None):
""" parse parameters and generate cartesian product.
Args:
parameters (list) parameters: parameter name and value in list
parameter value may be in three types:
(1) data list, e.g. ["iOS/10.1",... | def parse_parameters(parameters, variables_mapping=None, functions_mapping=None):
""" parse parameters and generate cartesian product.
Args:
parameters (list) parameters: parameter name and value in list
parameter value may be in three types:
(1) data list, e.g. ["iOS/10.1",... | [
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train | get_mapping_variable | get variable from variables_mapping.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping variable value.
Raises:
exceptions.VariableNotFound: variable is not found. | httprunner/parser.py | def get_mapping_variable(variable_name, variables_mapping):
""" get variable from variables_mapping.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping variable value.
Raises:
exceptions.VariableNotFound: variable is n... | def get_mapping_variable(variable_name, variables_mapping):
""" get variable from variables_mapping.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping variable value.
Raises:
exceptions.VariableNotFound: variable is n... | [
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train | get_mapping_function | get function from functions_mapping,
if not found, then try to check if builtin function.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping function object.
Raises:
exceptions.FunctionNotFound: function is neither... | httprunner/parser.py | def get_mapping_function(function_name, functions_mapping):
""" get function from functions_mapping,
if not found, then try to check if builtin function.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping function object.
... | def get_mapping_function(function_name, functions_mapping):
""" get function from functions_mapping,
if not found, then try to check if builtin function.
Args:
variable_name (str): variable name
variables_mapping (dict): variables mapping
Returns:
mapping function object.
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train | parse_function_params | parse function params to args and kwargs.
Args:
params (str): function param in string
Returns:
dict: function meta dict
{
"args": [],
"kwargs": {}
}
Examples:
>>> parse_function_params("")
{'args': [], 'kwargs': {}}... | httprunner/parser.py | def parse_function_params(params):
""" parse function params to args and kwargs.
Args:
params (str): function param in string
Returns:
dict: function meta dict
{
"args": [],
"kwargs": {}
}
Examples:
>>> parse_function_pa... | def parse_function_params(params):
""" parse function params to args and kwargs.
Args:
params (str): function param in string
Returns:
dict: function meta dict
{
"args": [],
"kwargs": {}
}
Examples:
>>> parse_function_pa... | [
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] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L289-L338 | [
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... | f259551bf9c8ba905eae5c1afcf2efea20ae0871 |
train | prepare_lazy_data | make string in content as lazy object with functions_mapping
Raises:
exceptions.VariableNotFound: if any variable undefined in check_variables_set | httprunner/parser.py | def prepare_lazy_data(content, functions_mapping=None, check_variables_set=None, cached=False):
""" make string in content as lazy object with functions_mapping
Raises:
exceptions.VariableNotFound: if any variable undefined in check_variables_set
"""
# TODO: refactor type check
if content ... | def prepare_lazy_data(content, functions_mapping=None, check_variables_set=None, cached=False):
""" make string in content as lazy object with functions_mapping
Raises:
exceptions.VariableNotFound: if any variable undefined in check_variables_set
"""
# TODO: refactor type check
if content ... | [
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] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L556-L610 | [
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"... | f259551bf9c8ba905eae5c1afcf2efea20ae0871 |
train | parse_lazy_data | parse lazy data with evaluated variables mapping.
Notice: variables_mapping should not contain any variable or function. | httprunner/parser.py | def parse_lazy_data(content, variables_mapping=None):
""" parse lazy data with evaluated variables mapping.
Notice: variables_mapping should not contain any variable or function.
"""
# TODO: refactor type check
if content is None or isinstance(content, (numeric_types, bool, type)):
retur... | def parse_lazy_data(content, variables_mapping=None):
""" parse lazy data with evaluated variables mapping.
Notice: variables_mapping should not contain any variable or function.
"""
# TODO: refactor type check
if content is None or isinstance(content, (numeric_types, bool, type)):
retur... | [
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] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L613-L640 | [
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"... | f259551bf9c8ba905eae5c1afcf2efea20ae0871 |
train | eval_lazy_data | evaluate data instantly.
Notice: variables_mapping should not contain any variable or function. | httprunner/parser.py | def eval_lazy_data(content, variables_mapping=None, functions_mapping=None):
""" evaluate data instantly.
Notice: variables_mapping should not contain any variable or function.
"""
variables_mapping = variables_mapping or {}
check_variables_set = set(variables_mapping.keys())
return parse_la... | def eval_lazy_data(content, variables_mapping=None, functions_mapping=None):
""" evaluate data instantly.
Notice: variables_mapping should not contain any variable or function.
"""
variables_mapping = variables_mapping or {}
check_variables_set = set(variables_mapping.keys())
return parse_la... | [
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] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L643-L656 | [
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train | extract_variables | extract all variables in content recursively. | httprunner/parser.py | def extract_variables(content):
""" extract all variables in content recursively.
"""
if isinstance(content, (list, set, tuple)):
variables = set()
for item in content:
variables = variables | extract_variables(item)
return variables
elif isinstance(content, dict):
... | def extract_variables(content):
""" extract all variables in content recursively.
"""
if isinstance(content, (list, set, tuple)):
variables = set()
for item in content:
variables = variables | extract_variables(item)
return variables
elif isinstance(content, dict):
... | [
"extract",
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"recursively",
"."
] | HttpRunner/HttpRunner | python | https://github.com/HttpRunner/HttpRunner/blob/f259551bf9c8ba905eae5c1afcf2efea20ae0871/httprunner/parser.py#L659-L677 | [
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train | parse_variables_mapping | eval each prepared variable and function in variables_mapping.
Args:
variables_mapping (dict):
{
"varA": LazyString(123$varB),
"varB": LazyString(456$varC),
"varC": LazyString(${sum_two($a, $b)}),
"a": 1,
"b": 2,
... | httprunner/parser.py | def parse_variables_mapping(variables_mapping, ignore=False):
""" eval each prepared variable and function in variables_mapping.
Args:
variables_mapping (dict):
{
"varA": LazyString(123$varB),
"varB": LazyString(456$varC),
"varC": LazyString($... | def parse_variables_mapping(variables_mapping, ignore=False):
""" eval each prepared variable and function in variables_mapping.
Args:
variables_mapping (dict):
{
"varA": LazyString(123$varB),
"varB": LazyString(456$varC),
"varC": LazyString($... | [
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... | f259551bf9c8ba905eae5c1afcf2efea20ae0871 |
train | _extend_with_api | extend test with api definition, test will merge and override api definition.
Args:
test_dict (dict): test block, this will override api_def_dict
api_def_dict (dict): api definition
Examples:
>>> api_def_dict = {
"name": "get token 1",
"request": {...},
... | httprunner/parser.py | def _extend_with_api(test_dict, api_def_dict):
""" extend test with api definition, test will merge and override api definition.
Args:
test_dict (dict): test block, this will override api_def_dict
api_def_dict (dict): api definition
Examples:
>>> api_def_dict = {
"n... | def _extend_with_api(test_dict, api_def_dict):
""" extend test with api definition, test will merge and override api definition.
Args:
test_dict (dict): test block, this will override api_def_dict
api_def_dict (dict): api definition
Examples:
>>> api_def_dict = {
"n... | [
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