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def calc_humidity(temp, dewpoint):
'''
calculates the humidity via the formula from weatherwise.org
return the relative humidity
'''
t = fahrenheit_to_celsius(temp)
td = fahrenheit_to_celsius(dewpoint)
num = 112 - (0.1 * t) + td
denom = 112 + (0.9 * t)
rh = math.pow((num / denom),... |
def calc_dewpoint(temp, hum):
'''
calculates the dewpoint via the formula from weatherwise.org
return the dewpoint in degrees F.
'''
c = fahrenheit_to_celsius(temp)
x = 1 - 0.01 * hum;
dewpoint = (14.55 + 0.114 * c) * x;
dewpoint = dewpoint + ((2.5 + 0.007 * c) * x) ** 3;
dewpoint ... |
def publish(self):
'''
Perform HTTP session to transmit defined weather values.
'''
return self._publish( self.args, self.server, self.URI) |
def get(data):
'''
return CRC calc value from raw serial data
'''
crc = 0
for byte in array('B', data):
crc = (VProCRC.CRC_TABLE[(crc >> 8) ^ byte] ^ ((crc & 0xFF) << 8))
return crc |
def verify(data):
'''
perform CRC check on raw serial data, return true if valid.
a valid CRC == 0.
'''
if len(data) == 0:
return False
crc = VProCRC.get(data)
if crc:
log.info("CRC Bad")
else:
log.debug("CRC OK")
... |
def _unpack_storm_date(date):
'''
given a packed storm date field, unpack and return 'YYYY-MM-DD' string.
'''
year = (date & 0x7f) + 2000 # 7 bits
day = (date >> 7) & 0x01f # 5 bits
month = (date >> 12) & 0x0f # 4 bits
return "%s-%s-%s" % (year, month, day) |
def _use_rev_b_archive(self, records, offset):
'''
return True if weather station returns Rev.B archives
'''
# if pre-determined, return result
if type(self._ARCHIVE_REV_B) is bool:
return self._ARCHIVE_REV_B
# assume, B and check 'RecType' field
data ... |
def _wakeup(self):
'''
issue wakeup command to device to take out of standby mode.
'''
log.info("send: WAKEUP")
for i in xrange(3):
self.port.write('\n') # wakeup device
ack = self.port.read(len(self.WAKE_ACK)) # read wakeup string
log_raw('r... |
def _cmd(self, cmd, *args, **kw):
'''
write a single command, with variable number of arguments. after the
command, the device must return ACK
'''
ok = kw.setdefault('ok', False)
self._wakeup()
if args:
cmd = "%s %s" % (cmd, ' '.join(str(a) for a in a... |
def _loop_cmd(self):
'''
reads a raw string containing data read from the device
provided (in /dev/XXX) format. all reads are non-blocking.
'''
self._cmd('LOOP', 1)
raw = self.port.read(LoopStruct.size) # read data
log_raw('read', raw)
return raw |
def _dmpaft_cmd(self, time_fields):
'''
issue a command to read the archive records after a known time stamp.
'''
records = []
# convert time stamp fields to buffer
tbuf = struct.pack('2H', *time_fields)
# 1. send 'DMPAFT' cmd
self._cmd('DMPAFT')
... |
def _get_new_archive_fields(self):
'''
returns a dictionary of fields from the newest archive record in the
device. return None when no records are new.
'''
for i in xrange(3):
records = self._dmpaft_cmd(self._archive_time)
if records is not None: break
... |
def _calc_derived_fields(self, fields):
'''
calculates the derived fields (those fields that are calculated)
'''
# convenience variables for the calculations below
temp = fields['TempOut']
hum = fields['HumOut']
wind = fields['WindSpeed']
wind10min = field... |
def parse(self):
'''
read and parse a set of data read from the console. after the
data is parsed it is available in the fields variable.
'''
fields = self._get_loop_fields()
fields['Archive'] = self._get_new_archive_fields()
self._calc_derived_fields(fields)
... |
def unpack_from(self, buf, offset=0 ):
'''
unpacks data from 'buf' and returns a dication of named fields. the
fields can be post-processed by extending the _post_unpack() method.
'''
data = super(Struct,self).unpack_from( buf, offset)
items = dict(zip(self.fields,data))
... |
def weather_update(station, pub_sites, interval):
'''
main execution loop. query weather data and post to online service.
'''
station.parse() # read weather data
# santity check weather data
if station.fields['TempOut'] > 200:
raise NoSensorException(
'Out of range temperature ... |
def init_log( quiet, debug ):
'''
setup system logging to desired verbosity.
'''
from logging.handlers import SysLogHandler
fmt = logging.Formatter( os.path.basename(sys.argv[0]) +
".%(name)s %(levelname)s - %(message)s")
facility = SysLogHandler.LOG_DAEMON
syslog = SysLogHandler(address='... |
def get_pub_services(opts):
'''
use values in opts data to generate instances of publication services.
'''
sites = []
for p_key in vars(opts).keys():
args = getattr(opts,p_key)
if p_key in PUB_SERVICES and args:
if isinstance(args,tuple):
ps = PUB_SERVICES[p_key](*args)
... |
def get_options(parser):
'''
read command line options to configure program behavior.
'''
# station services
# publication services
pub_g = optparse.OptionGroup( parser, "Publication Services",
'''One or more publication service must be specified to enable upload
of weather data.''',... |
def get( self, station, interval ):
'''
return gust data, if above threshold value and current time is inside
reporting window period
'''
rec = station.fields['Archive']
# process new data
if rec:
threshold = station.fields['WindSpeed10Min'] + GUST_MPH_MIN
if ... |
def set( self, pressure='NA', dewpoint='NA', humidity='NA', tempf='NA',
rainin='NA', rainday='NA', dateutc='NA', windgust='NA',
windgustdir='NA', windspeed='NA', winddir='NA',
clouds='NA', weather='NA', *args, **kw):
'''
Useful for defining weather data published to t... |
def set( self, **kw):
'''
Store keyword args to be written to output file.
'''
self.args = kw
log.debug( self.args ) |
def publish(self):
'''
Write output file.
'''
with open( self.file_name, 'w') as fh:
for k,v in self.args.iteritems():
buf = StringIO.StringIO()
buf.write(k)
self._append_vals(buf,v)
fh.write(buf.getvalue() + '\n')
buf.close() |
def requires(*requirements, **opts):
"""
Standalone decorator to apply requirements to routes, either function
handlers or class based views::
@requires(MyRequirement())
def a_view():
pass
class AView(View):
decorators = [requires(MyRequirement())]
:par... |
def guard_entire(requirements, identity=None, throws=None, on_fail=None):
"""
Used to protect an entire blueprint with a set of requirements. If a route
handler inside the blueprint should be exempt, then it may be decorated
with the :func:`~flask_allows.views.exempt_from_requirements` decorator.
T... |
def wants_request(f):
"""
Helper decorator for transitioning to user-only requirements, this aids
in situations where the request may be marked optional and causes an
incorrect flow into user-only requirements.
This decorator causes the requirement to look like a user-only requirement
but passe... |
def And(cls, *requirements):
"""
Short cut helper to construct a combinator that uses
:meth:`operator.and_` to reduce requirement results and stops
evaluating on the first False.
This is also exported at the module level as ``And``
"""
return cls(*requirements, o... |
def Or(cls, *requirements):
"""
Short cut helper to construct a combinator that uses
:meth:`operator.or_` to reduce requirement results and stops evaluating
on the first True.
This is also exported at the module level as ``Or``
"""
return cls(*requirements, op=op... |
def init_app(self, app):
"""
Initializes the Flask-Allows object against the provided application
"""
if not hasattr(app, "extensions"): # pragma: no cover
app.extensions = {}
app.extensions["allows"] = self
@app.before_request
def start_context(*a, ... |
def fulfill(self, requirements, identity=None):
"""
Checks that the provided or current identity meets each requirement
passed to this method.
This method takes into account both additional and overridden
requirements, with overridden requirements taking precedence::
... |
def run(
self,
requirements,
identity=None,
throws=None,
on_fail=None,
f_args=(),
f_kwargs=ImmutableDict(), # noqa: B008
use_on_fail_return=True,
):
"""
Used to preform a full run of the requirements and the options given,
this... |
def push(self, override, use_parent=False):
"""
Binds an override to the current context, optionally use the
current overrides in conjunction with this override
If ``use_parent`` is true, a new override is created from the
parent and child overrides rather than manipulating eith... |
def pop(self):
"""
Pops the latest override context.
If the override context was pushed by a different override manager,
a ``RuntimeError`` is raised.
"""
rv = _override_ctx_stack.pop()
if rv is None or rv[0] is not self:
raise RuntimeError(
... |
def override(self, override, use_parent=False):
"""
Allows temporarily pushing an override context, yields the new context
into the following block.
"""
self.push(override, use_parent)
yield self.current
self.pop() |
def push(self, additional, use_parent=False):
"""
Binds an additional to the current context, optionally use the
current additionals in conjunction with this additional
If ``use_parent`` is true, a new additional is created from the
parent and child additionals rather than manip... |
def pop(self):
"""
Pops the latest additional context.
If the additional context was pushed by a different additional manager,
a ``RuntimeError`` is raised.
"""
rv = _additional_ctx_stack.pop()
if rv is None or rv[0] is not self:
raise RuntimeError(
... |
def additional(self, additional, use_parent=False):
"""
Allows temporarily pushing an additional context, yields the new context
into the following block.
"""
self.push(additional, use_parent)
yield self.current
self.pop() |
def unduplicate_field_names(field_names):
"""Append a number to duplicate field names to make them unique. """
res = []
for k in field_names:
if k in res:
i = 1
while k + '_' + str(i) in res:
i += 1
k += '_' + str(i)
res.append(k)
retur... |
def interpret_stats(results):
"""Generates the string to be shown as updates after the execution of a
Cypher query
:param results: ``ResultSet`` with the raw results of the execution of
the Cypher query
"""
stats = results.stats
contains_updates = stats.pop("contains_updates... |
def extract_params_from_query(query, user_ns):
"""Generates a dictionary with safe keys and values to pass onto Neo4j
:param query: string with the Cypher query to execute
:param user_ns: dictionary with the IPython user space
"""
# TODO: Optmize this function
params = {}
for k, v in user_n... |
def run(query, params=None, config=None, conn=None, **kwargs):
"""Executes a query and depending on the options of the extensions will
return raw data, a ``ResultSet``, a Pandas ``DataFrame`` or a
NetworkX graph.
:param query: string with the Cypher query
:param params: dictionary with parameters f... |
def get_dataframe(self):
"""Returns a Pandas DataFrame instance built from the result set."""
if pd is None:
raise ImportError("Try installing Pandas first.")
frame = pd.DataFrame(self[:], columns=(self and self.keys) or [])
return frame |
def get_graph(self, directed=True):
"""Returns a NetworkX multi-graph instance built from the result set
:param directed: boolean, optional (default=`True`).
Whether to create a direted or an undirected graph.
"""
if nx is None:
raise ImportError("Try installing ... |
def draw(self, directed=True, layout="spring",
node_label_attr=None, show_node_labels=True,
edge_label_attr=None, show_edge_labels=True,
node_size=1600, node_color='blue', node_alpha=0.3,
node_text_size=12,
edge_color='blue', edge_alpha=0.3, edge_tickness... |
def pie(self, key_word_sep=" ", title=None, **kwargs):
"""Generates a pylab pie chart from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
Values (pie slice sizes) are taken from the
rightmost ... |
def plot(self, title=None, **kwargs):
"""Generates a pylab plot from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
The first and last columns are taken as the X and Y
values. Any columns bet... |
def bar(self, key_word_sep=" ", title=None, **kwargs):
"""Generates a pylab bar plot from the result set.
``matplotlib`` must be installed, and in an
IPython Notebook, inlining must be on::
%%matplotlib inline
The last quantitative column is taken as the Y values;
... |
def csv(self, filename=None, **format_params):
"""Generates results in comma-separated form. Write to ``filename``
if given. Any other parameter will be passed on to ``csv.writer``.
:param filename: if given, the CSV will be written to filename.
Any additional keyword arguments will b... |
def permission_required(perm, login_url=None, raise_exception=False):
"""
Re-implementation of the permission_required decorator, honors settings.
If ``DASHBOARD_REQUIRE_LOGIN`` is False, this decorator will always return
``True``, otherwise it will check for the permission as usual.
"""
def c... |
def get_context_data(self, **kwargs):
"""
Adds ``is_rendered`` to the context and the widget's context data.
``is_rendered`` signals that the AJAX view has been called and that
we are displaying the full widget now. When ``is_rendered`` is not
found in the widget template it mea... |
def get_widgets_sorted(self):
"""Returns the widgets sorted by position."""
result = []
for widget_name, widget in self.get_widgets().items():
result.append((widget_name, widget, widget.position))
result.sort(key=lambda x: x[2])
return result |
def get_widgets_that_need_update(self):
"""
Returns all widgets that need an update.
This should be scheduled every minute via crontab.
"""
result = []
for widget_name, widget in self.get_widgets().items():
if widget.should_update():
result.a... |
def register_widget(self, widget_cls, **widget_kwargs):
"""
Registers the given widget.
Widgets must inherit ``DashboardWidgetBase`` and you cannot register
the same widget twice.
:widget_cls: A class that inherits ``DashboardWidgetBase``.
"""
if not issubclass... |
def unregister_widget(self, widget_cls):
"""Unregisters the given widget."""
if widget_cls.__name__ in self.widgets:
del self.widgets[widget_cls().get_name()] |
def get_last_update(self):
"""Gets or creates the last update object for this widget."""
instance, created = \
models.DashboardWidgetLastUpdate.objects.get_or_create(
widget_name=self.get_name())
return instance |
def get_setting(self, setting_name, default=None):
"""
Returns the setting for this widget from the database.
:setting_name: The name of the setting.
:default: Optional default value if the setting cannot be found.
"""
try:
setting = models.DashboardWidgetSe... |
def save_setting(self, setting_name, value):
"""Saves the setting value into the database."""
setting = self.get_setting(setting_name)
if setting is None:
setting = models.DashboardWidgetSettings.objects.create(
widget_name=self.get_name(),
setting_nam... |
def should_update(self):
"""
Checks if an update is needed.
Checks against ``self.update_interval`` and this widgets
``DashboardWidgetLastUpdate`` instance if an update is overdue.
This should be called by
``DashboardWidgetPool.get_widgets_that_need_update()``, which in... |
def getCityDetails(self, **kwargs):
"""
:param q: query by city name
:param lat: latitude
:param lon: longitude
:param city_ids: comma separated city_id values
:param count: number of max results to display
Find the Zomato ID and other details for a city . You ca... |
def getCollectionsViaCityId(self, city_id, **kwargs):
"""
:param city_id: id of the city for which collections are needed
:param lat: latitude
:param lon: longitude
:param count: number of max results to display
Returns Zomato Restaurant Collections in a City. The locatio... |
def getEstablishments(self, city_id, **kwargs):
"""
:param city_id: id of the city for which collections are needed
:param lat: latitude
:param lon: longitude
Get a list of restaurant types in a city. The location/City input can be provided in the following ways
- Using Z... |
def getByGeocode(self, lat, lon):
"""
:param lat: latitude
:param lon: longitude
Get Foodie and Nightlife Index, list of popular cuisines and nearby restaurants around the given coordinates
"""
params = {"lat": lat, "lon": lon}
response = self.api.get("/geocode", ... |
def getLocationDetails(self, entity_id, entity_type):
"""
:param entity_id: location id obtained from locations api
:param entity_type: location type obtained from locations api
:return:
Get Foodie Index, Nightlife Index, Top Cuisines and Best rated restaurants in a given locatio... |
def getLocations(self, query, **kwargs):
"""
:param query: suggestion for location name
:param lat: latitude
:param lon: longitude
:param count: number of max results to display
:return: json response
Search for Zomato locations by keyword. Provide coordinates to ... |
def getDailyMenu(self, restaurant_id):
"""
:param restaurant_id: id of restaurant whose details are requested
:return: json response
Get daily menu using Zomato restaurant ID.
"""
params = {"res_id": restaurant_id}
daily_menu = self.api.get("/dailymenu", params)
... |
def getRestaurantDetails(self, restaurant_id):
"""
:param restaurant_id: id of restaurant whose details are requested
:return: json response
Get detailed restaurant information using Zomato restaurant ID.
Partner Access is required to access photos and reviews.
"""
... |
def getRestaurantReviews(self, restaurant_id, **kwargs):
"""
:param restaurant_id: id of restaurant whose details are requested
:param start: fetch results after this offset
:param count: max number of results to retrieve
:return: json response
Get restaurant reviews usin... |
def search(self, **kwargs):
"""
:param entity_id: location id
:param entity_type: location type (city, subzone, zone, lanmark, metro , group)
:param q: search keyword
:param start: fetch results after offset
:param count: max number of results to display
:param la... |
def array(a, context=None, axis=(0,), dtype=None, npartitions=None):
"""
Create a spark bolt array from a local array.
Parameters
----------
a : array-like
An array, any object exposing the array interface, an
object whose __array__ method returns an arra... |
def ones(shape, context=None, axis=(0,), dtype=float64, npartitions=None):
"""
Create a spark bolt array of ones.
Parameters
----------
shape : tuple
The desired shape of the array.
context : SparkContext
A context running Spark. (see pyspark)
... |
def concatenate(arrays, axis=0):
"""
Join two bolt arrays together, at least one of which is in spark.
Parameters
----------
arrays : tuple
A pair of arrays. At least one must be a spark array,
the other can be a local bolt array, a local numpy array,
... |
def _argcheck(*args, **kwargs):
"""
Check that arguments are consistent with spark array construction.
Conditions are:
(1) a positional argument is a SparkContext
(2) keyword arg 'context' is a SparkContext
(3) an argument is a BoltArraySpark, or
(4) an argument ... |
def _format_axes(axes, shape):
"""
Format target axes given an array shape
"""
if isinstance(axes, int):
axes = (axes,)
elif isinstance(axes, list) or hasattr(axes, '__iter__'):
axes = tuple(axes)
if not isinstance(axes, tuple):
raise V... |
def _wrap(func, shape, context=None, axis=(0,), dtype=None, npartitions=None):
"""
Wrap an existing numpy constructor in a parallelized construction
"""
if isinstance(shape, int):
shape = (shape,)
key_shape, value_shape = get_kv_shape(shape, ConstructSpark._format_axe... |
def _align(self, axes, key_shape=None):
"""
Align local bolt array so that axes for iteration are in the keys.
This operation is applied before most functional operators.
It ensures that the specified axes are valid, and might transpose/reshape
the underlying array so that the f... |
def filter(self, func, axis=(0,)):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the
keys, which may require a transpose/reshape.
... |
def map(self, func, axis=(0,)):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may require a transpose/reshape.
Parameters
----------
func : function
Function of a single array to app... |
def reduce(self, func, axis=0):
"""
Reduce an array along an axis.
Applies an associative/commutative function of two arguments
cumulatively to all arrays along an axis. Array will be aligned
so that the desired set of axes are in the keys, which may
require a transpose/... |
def concatenate(self, arry, axis=0):
"""
Join this array with another array.
Paramters
---------
arry : ndarray or BoltArrayLocal
Another array to concatenate with
axis : int, optional, default=0
The axis along which arrays will be joined.
... |
def tospark(self, sc, axis=0):
"""
Converts a BoltArrayLocal into a BoltArraySpark
Parameters
----------
sc : SparkContext
The SparkContext which will be used to create the BoltArraySpark
axis : tuple or int, optional, default=0
The axis (or axes... |
def tordd(self, sc, axis=0):
"""
Converts a BoltArrayLocal into an RDD
Parameters
----------
sc : SparkContext
The SparkContext which will be used to create the BoltArraySpark
axis : tuple or int, optional, default=0
The axis (or axes) across whi... |
def stack(self, size):
"""
Make an intermediate RDD where all records are combined into a
list of keys and larger ndarray along a new 0th dimension.
"""
def tostacks(partition):
keys = []
arrs = []
for key, arr in partition:
key... |
def unstack(self):
"""
Unstack array and return a new BoltArraySpark via flatMap().
"""
from bolt.spark.array import BoltArraySpark
if self._rekeyed:
rdd = self._rdd
else:
rdd = self._rdd.flatMap(lambda kv: zip(kv[0], list(kv[1])))
return... |
def map(self, func):
"""
Apply a function on each subarray.
Parameters
----------
func : function
This is applied to each value in the intermediate RDD.
Returns
-------
StackedArray
"""
vshape = self.shape[self.split:]
... |
def _chunk(self, size="150", axis=None, padding=None):
"""
Split values of distributed array into chunks.
Transforms an underlying pair RDD of (key, value) into
records of the form: (key, chunk id), (chunked value).
Here, chunk id is a tuple identifying the chunk and
chu... |
def unchunk(self):
"""
Convert a chunked array back into a full array with (key,value) pairs
where key is a tuple of indices, and value is an ndarray.
"""
plan, padding, vshape, split = self.plan, self.padding, self.vshape, self.split
nchunks = self.getnumber(plan, vshape... |
def keys_to_values(self, axes, size=None):
"""
Move indices in the keys into the values.
Padding on these new value-dimensions is not currently supported and is set to 0.
Parameters
----------
axes : tuple
Axes from keys to move to values.
size : tu... |
def map(self, func, value_shape=None, dtype=None):
"""
Apply an array -> array function on each subarray.
The function can change the shape of the subarray, but only along
dimensions that are not chunked.
Parameters
----------
func : function
Functio... |
def map_generic(self, func):
"""
Apply a generic array -> object to each subarray
The resulting object is a BoltArraySpark of dtype object where the
blocked dimensions are replaced with indices indication block ID.
"""
def process_record(val):
newval = empty(... |
def getplan(self, size="150", axes=None, padding=None):
"""
Identify a plan for chunking values along each dimension.
Generates an ndarray with the size (in number of elements) of chunks
in each dimension. If provided, will estimate chunks for only a
subset of axes, leaving all ... |
def removepad(idx, value, number, padding, axes=None):
"""
Remove the padding from chunks.
Given a chunk and its corresponding index, use the plan and padding to remove any
padding from the chunk along with specified axes.
Parameters
----------
idx: tuple or arr... |
def getnumber(plan, shape):
"""
Obtain number of chunks for the given dimensions and chunk sizes.
Given a plan for the number of chunks along each dimension,
calculate the number of chunks that this will lead to.
Parameters
----------
plan: tuple or array-like
... |
def getslices(plan, padding, shape):
"""
Obtain slices for the given dimensions, padding, and chunks.
Given a plan for the number of chunks along each dimension and the amount of padding,
calculate a list of slices required to generate those chunks.
Parameters
---------... |
def getmask(inds, n):
"""
Obtain a binary mask by setting a subset of entries to true.
Parameters
----------
inds : array-like
Which indices to set as true.
n : int
The length of the target mask.
"""
inds = asarray(inds, 'int')
... |
def repartition(self, npartitions):
"""
Repartitions the underlying RDD
Parameters
----------
npartitions : int
Number of partitions to repartion the underlying RDD to
"""
rdd = self._rdd.repartition(npartitions)
return self._constructor(rdd,... |
def stack(self, size=None):
"""
Aggregates records of a distributed array.
Stacking should improve the performance of vectorized operations,
but the resulting StackedArray object only exposes a restricted set
of operations (e.g. map, reduce). The unstack method can be used
... |
def _align(self, axis):
"""
Align spark bolt array so that axes for iteration are in the keys.
This operation is applied before most functional operators.
It ensures that the specified axes are valid, and swaps
key/value axes so that functional operators can be applied
o... |
def first(self):
"""
Return the first element of an array
"""
from bolt.local.array import BoltArrayLocal
rdd = self._rdd if self._ordered else self._rdd.sortByKey()
return BoltArrayLocal(rdd.values().first()) |
def map(self, func, axis=(0,), value_shape=None, dtype=None, with_keys=False):
"""
Apply a function across an axis.
Array will be aligned so that the desired set of axes
are in the keys, which may incur a swap.
Parameters
----------
func : function
F... |
def filter(self, func, axis=(0,), sort=False):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the keys,
which may incur a swap.
... |
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