BoghdadyJR/codesearch_python · Data Studio

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def pop_assign_tracking(self, frame): """Pops the topmost level for assignment tracking and updates the context variables if necessary. """ vars = self._assign_stack.pop() if not frame.toplevel or not vars: return public_names = [x for x in vars if x[:1] != '_...
def visit_Block(self, node, frame): """Call a block and register it for the template.""" level = 0 if frame.toplevel: # if we know that we are a child template, there is no need to # check if we are one if self.has_known_extends: return ...
def visit_Extends(self, node, frame): """Calls the extender.""" if not frame.toplevel: self.fail('cannot use extend from a non top-level scope', node.lineno) # if the number of extends statements in general is zero so # far, we don't have to add a check...
def visit_Include(self, node, frame): """Handles includes.""" if node.ignore_missing: self.writeline('try:') self.indent() func_name = 'get_or_select_template' if isinstance(node.template, nodes.Const): if isinstance(node.template.value, string_types)...
def visit_Import(self, node, frame): """Visit regular imports.""" self.writeline('%s = ' % frame.symbols.ref(node.target), node) if frame.toplevel: self.write('context.vars[%r] = ' % node.target) if self.environment.is_async: self.write('await ') self.writ...
def visit_FromImport(self, node, frame): """Visit named imports.""" self.newline(node) self.write('included_template = %senvironment.get_template(' % (self.environment.is_async and 'await ' or '')) self.visit(node.template, frame) self.write(', %r).' % self.nam...
def detach(self): """If alive then mark as dead and return (obj, func, args, kwargs); otherwise return None""" info = self._registry.get(self) obj = info and info.weakref() if obj is not None and self._registry.pop(self, None): return (obj, info.func, info.args, info....
def peek(self): """If alive then return (obj, func, args, kwargs); otherwise return None""" info = self._registry.get(self) obj = info and info.weakref() if obj is not None: return (obj, info.func, info.args, info.kwargs or {})
def atexit(self): """Whether finalizer should be called at exit""" info = self._registry.get(self) return bool(info) and info.atexit
def tostring(element): """Serialize an element and its child nodes to a string""" rv = [] def serializeElement(element): if not hasattr(element, "tag"): if element.docinfo.internalDTD: if element.docinfo.doctype: dtd_str = element.docinfo.doctype ...
def get_visitor(self, node): """Return the visitor function for this node or `None` if no visitor exists for this node. In that case the generic visit function is used instead. """ method = 'visit_' + node.__class__.__name__ return getattr(self, method, None)
def visit(self, node, args, kwargs): """Visit a node.""" f = self.get_visitor(node) if f is not None: return f(node, args, *kwargs) return self.generic_visit(node, args, **kwargs)
def generic_visit(self, node, args, kwargs): """Called if no explicit visitor function exists for a node.""" for node in node.iter_child_nodes(): self.visit(node, args, **kwargs)
def visit_list(self, node, args, kwargs): """As transformers may return lists in some places this method can be used to enforce a list as return value. """ rv = self.visit(node, args, **kwargs) if not isinstance(rv, list): rv = [rv] return rv
def default_subprocess_runner(cmd, cwd=None, extra_environ=None): """The default method of calling the wrapper subprocess.""" env = os.environ.copy() if extra_environ: env.update(extra_environ) check_call(cmd, cwd=cwd, env=env)
def build_wheel( self, wheel_directory, config_settings=None, metadata_directory=None): """Build a wheel from this project. Returns the name of the newly created file. In general, this will call the 'build_wheel' hook in the backend. However, if that was previou...
def bninception(num_classes=1000, pretrained='imagenet'): r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper. """ model = BNInception(num_classes=num_classes) if pretrained is not None: settings = pretrained_settings['bninception'][pretrained] assert n...
def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def resnet18(pretrained=False, kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])...
def resnet50(pretrained=False, kwargs): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 6, 3], kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])...
def nasnetamobile(num_classes=1000, pretrained='imagenet'): r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper. """ if pretrained: settings = pretrained_settings['nasnetamobile'][pretrained] assert num_classes == settings['num_classes'], \ ...
def cafferesnet101(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes) if pretrained is not None: settings = pretrained...
def fbresnet152(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes) if pretrained is not None: settings = pretra...
def alexnet(num_classes=1000, pretrained='imagenet'): r"""AlexNet model architecture from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper. """ # https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py model = models.alexnet(pretrained=False) if pretrained ...
def densenet121(num_classes=1000, pretrained='imagenet'): r"""Densenet-121 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>` """ model = models.densenet121(pretrained=False) if pretrained is not None: settings = pretrained_settings['densenet121'][...
def inceptionv3(num_classes=1000, pretrained='imagenet'): r"""Inception v3 model architecture from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_. """ model = models.inception_v3(pretrained=False) if pretrained is not None: settings = pretrai...
def resnet50(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-50 model. """ model = models.resnet50(pretrained=False) if pretrained is not None: settings = pretrained_settings['resnet50'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify...
def squeezenet1_0(num_classes=1000, pretrained='imagenet'): r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. """ model = models.squeezenet1_0(pretrained=False) if pretraine...
def vgg11(num_classes=1000, pretrained='imagenet'): """VGG 11-layer model (configuration "A") """ model = models.vgg11(pretrained=False) if pretrained is not None: settings = pretrained_settings['vgg11'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify...
def adjust_learning_rate(optimizer, epoch): """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" lr = args.lr * (0.1 ** (epoch // 30)) for param_group in optimizer.param_groups: param_group['lr'] = lr
def nasnetalarge(num_classes=1001, pretrained='imagenet'): r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper. """ if pretrained: settings = pretrained_settings['nasnetalarge'][pretrained] assert num_classes == settings['num_classes'], \ ...
def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False): """Selectable global pooling function with dynamic input kernel size """ if pool_type == 'avgmaxc': x = torch.cat([ F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, co...
def download_url(url, destination=None, progress_bar=True): """Download a URL to a local file. Parameters ---------- url : str The URL to download. destination : str, None The destination of the file. If None is given the file is saved to a temporary directory. progress_bar : bo...
def add(self, output, target): """ Args: output (Tensor): NxK tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. The probabilities should sum to one over a...
def value(self): """Returns the model's average precision for each class Return: ap (FloatTensor): 1xK tensor, with avg precision for each class k """ if self.scores.numel() == 0: return 0 ap = torch.zeros(self.scores.size(1)) rg = torch.arange(1,...
def polynet(num_classes=1000, pretrained='imagenet'): """PolyNet architecture from the paper 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks' https://arxiv.org/abs/1611.05725 """ if pretrained: settings = pretrained_settings['polynet'][pretrained] assert num_classes...
def unwrap(self, dt): """ Get the cached value. Returns ------- value : object The cached value. Raises ------ Expired Raised when `dt` is greater than self.expires. """ expires = self._expires if expires i...
def get(self, key, dt): """Get the value of a cached object. Parameters ---------- key : any The key to lookup. dt : datetime The time of the lookup. Returns ------- result : any The value for ``key``. Raises ...
def set(self, key, value, expiration_dt): """Adds a new key value pair to the cache. Parameters ---------- key : any The key to use for the pair. value : any The value to store under the name ``key``. expiration_dt : datetime When shou...
def ensure_dir(self, path_parts): """Ensures a subdirectory of the working directory. Parameters ---------- path_parts : iterable[str] The parts of the path after the working directory. """ path = self.getpath(path_parts) ensure_directory(path) ...
def verify_frames_aligned(frames, calendar): """ Verify that DataFrames in ``frames`` have the same indexing scheme and are aligned to ``calendar``. Parameters ---------- frames : list[pd.DataFrame] calendar : trading_calendars.TradingCalendar Raises ------ ValueError I...
def get_value(self, sid, dt, field): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. field : string The price field. e.g. ('open', 'high', 'low', 'close', ...
def get_last_traded_dt(self, asset, dt): """ Parameters ---------- asset : zipline.asset.Asset The asset identifier. dt : datetime64-like Midnight of the day for which data is requested. Returns ------- pd.Timestamp : The last know...
def same(*values): """ Check if all values in a sequence are equal. Returns True on empty sequences. Examples -------- >>> same(1, 1, 1, 1) True >>> same(1, 2, 1) False >>> same() True """ if not values: return True first, rest = values[0], values[1:] ...
def dzip_exact(dicts): """ Parameters ---------- dicts : iterable[dict] A sequence of dicts all sharing the same keys. Returns ------- zipped : dict A dict whose keys are the union of all keys in *dicts, and whose values are tuples of length len(dicts) containing t...
def _gen_unzip(it, elem_len): """Helper for unzip which checks the lengths of each element in it. Parameters ---------- it : iterable[tuple] An iterable of tuples. ``unzip`` should map ensure that these are already tuples. elem_len : int or None The expected element length. I...
def unzip(seq, elem_len=None): """Unzip a length n sequence of length m sequences into m seperate length n sequences. Parameters ---------- seq : iterable[iterable] The sequence to unzip. elem_len : int, optional The expected length of each element of ``seq``. If not provided thi...
def getattrs(value, attrs, default=_no_default): """ Perform a chained application of ``getattr`` on ``value`` with the values in ``attrs``. If ``default`` is supplied, return it if any of the attribute lookups fail. Parameters ---------- value : object Root of the lookup chain. ...
def set_attribute(name, value): """ Decorator factory for setting attributes on a function. Doesn't change the behavior of the wrapped function. Examples -------- >>> @set_attribute('__name__', 'foo') ... def bar(): ... return 3 ... >>> bar() 3 >>> bar.__name__ ...
def foldr(f, seq, default=_no_default): """Fold a function over a sequence with right associativity. Parameters ---------- f : callable[any, any] The function to reduce the sequence with. The first argument will be the element of the sequence; the second argument will be the acc...
def invert(d): """ Invert a dictionary into a dictionary of sets. >>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP {1: {'a', 'c'}, 2: {'b'}} """ out = {} for k, v in iteritems(d): try: out[v].add(k) except KeyError: out[v] = {k} return out
def simplex_projection(v, b=1): r"""Projection vectors to the simplex domain Implemented according to the paper: Efficient projections onto the l1-ball for learning in high dimensions, John Duchi, et al. ICML 2008. Implementation Time: 2011 June 17 by Bin@libin AT pmail.ntu.edu.sg Optimization Prob...
def run_example(example_name, environ): """ Run an example module from zipline.examples. """ mod = EXAMPLE_MODULES[example_name] register_calendar("YAHOO", get_calendar("NYSE"), force=True) return run_algorithm( initialize=getattr(mod, 'initialize', None), handle_data=getattr(m...
def vectorized_beta(dependents, independent, allowed_missing, out=None): """ Compute slopes of linear regressions between columns of ``dependents`` and ``independent``. Parameters ---------- dependents : np.array[N, M] Array with columns of data to be regressed against ``independent``. ...
def _format_url(instrument_type, instrument_ids, start_date, end_date, earliest_allowed_date): """ Format a URL for loading data from Bank of Canada. """ return ( "http://www.bankofcanada.ca/stats/results/csv" "?lP=lookup_{i...
def load_frame(url, skiprows): """ Load a DataFrame of data from a Bank of Canada site. """ return pd.read_csv( url, skiprows=skiprows, skipinitialspace=True, na_values=["Bank holiday", "Not available"], parse_dates=["Date"], index_col="Date", ).dropna...
def check_known_inconsistencies(bill_data, bond_data): """ There are a couple quirks in the data provided by Bank of Canada. Check that no new quirks have been introduced in the latest download. """ inconsistent_dates = bill_data.index.sym_diff(bond_data.index) known_inconsistencies = [ ...
def earliest_possible_date(): """ The earliest date for which we can load data from this module. """ today = pd.Timestamp('now', tz='UTC').normalize() # Bank of Canada only has the last 10 years of data at any given time. return today.replace(year=today.year - 10)
def fill_price_worse_than_limit_price(fill_price, order): """ Checks whether the fill price is worse than the order's limit price. Parameters ---------- fill_price: float The price to check. order: zipline.finance.order.Order The order whose limit price to check. Returns ...
def _get_window_data(self, data, asset, window_length): """ Internal utility method to return the trailing mean volume over the past 'window_length' days, and volatility of close prices for a specific asset. Parameters ---------- data : The BarData from which to ...
def validate_dtype(termname, dtype, missing_value): """ Validate a `dtype` and `missing_value` passed to Term.__new__. Ensures that we know how to represent ``dtype``, and that missing_value is specified for types without default missing values. Returns ------- validated_dtype, validated_m...
def _assert_valid_categorical_missing_value(value): """ Check that value is a valid categorical missing_value. Raises a TypeError if the value is cannot be used as the missing_value for a categorical_dtype Term. """ label_types = LabelArray.SUPPORTED_SCALAR_TYPES if not isinstance(value, la...
def _pop_params(cls, kwargs): """ Pop entries from the `kwargs` passed to cls.__new__ based on the values in `cls.params`. Parameters ---------- kwargs : dict The kwargs passed to cls.__new__. Returns ------- params : list[(str, objec...
def _static_identity(cls, domain, dtype, missing_value, window_safe, ndim, params): """ Return the identity of the Term that would be constructed from the...
def _init(self, domain, dtype, missing_value, window_safe, ndim, params): """ Parameters ---------- domain : zipline.pipeline.domain.Domain The domain of this term. dtype : np.dtype Dtype of this term's output. missing_value : object Mi...
def dependencies(self): """ The number of extra rows needed for each of our inputs to compute this term. """ extra_input_rows = max(0, self.window_length - 1) out = {} for term in self.inputs: out[term] = extra_input_rows out[self.mask] = 0 ...
def to_workspace_value(self, result, assets): """ Called with a column of the result of a pipeline. This needs to put the data into a format that can be used in a workspace to continue doing computations. Parameters ---------- result : pd.Series A mul...
def earn_stock_dividend(self, stock_dividend): """ Register the number of shares we held at this dividend's ex date so that we can pay out the correct amount on the dividend's pay date. """ return { 'payment_asset': stock_dividend.payment_asset, 'share_cou...
def handle_split(self, asset, ratio): """ Update the position by the split ratio, and return the resulting fractional share that will be converted into cash. Returns the unused cash. """ if self.asset != asset: raise Exception("updating split with the wrong a...
def adjust_commission_cost_basis(self, asset, cost): """ A note about cost-basis in zipline: all positions are considered to share a cost basis, even if they were executed in different transactions with different commission costs, different prices, etc. Due to limitations about ...
def to_dict(self): """ Creates a dictionary representing the state of this position. Returns a dict object of the form: """ return { 'sid': self.asset, 'amount': self.amount, 'cost_basis': self.cost_basis, 'last_sale_price': self.la...
def _make_bundle_core(): """Create a family of data bundle functions that read from the same bundle mapping. Returns ------- bundles : mappingproxy The mapping of bundles to bundle payloads. register : callable The function which registers new bundles in the ``bundles`` mapping....
def deprecated(msg=None, stacklevel=2): """ Used to mark a function as deprecated. Parameters ---------- msg : str The message to display in the deprecation warning. stacklevel : int How far up the stack the warning needs to go, before showing the relevant calling lines....
def load_pricing_adjustments(self, columns, dts, assets): """ Returns ------- adjustments : list[dict[int -> Adjustment]] A list, where each element corresponds to the `columns`, of mappings from index to adjustment objects to apply at that index. """ ...
def _get_adjustments_in_range(self, asset, dts, field): """ Get the Float64Multiply objects to pass to an AdjustedArrayWindow. For the use of AdjustedArrayWindow in the loader, which looks back from current simulation time back to a window of data the dictionary is structured wi...
def get(self, end_ix): """ Returns ------- out : A np.ndarray of the equity pricing up to end_ix after adjustments and rounding have been applied. """ if self.most_recent_ix == end_ix: return self.current target = end_ix - self.cal_start...
def _ensure_sliding_windows(self, assets, dts, field, is_perspective_after): """ Ensure that there is a Float64Multiply window for each asset that can provide data for the given parameters. If the corresponding window for the (assets, len(dts), field) does...
def history(self, assets, dts, field, is_perspective_after): """ A window of pricing data with adjustments applied assuming that the end of the window is the day before the current simulation time. Parameters ---------- assets : iterable of Assets The assets ...
def parse_date_str_series(format_str, tz, date_str_series, data_frequency, trading_day): """ Efficient parsing for a 1d Pandas/numpy object containing string representations of dates. Note: pd.to_datetime is significantly faster when no format string is ...
def _lookup_unconflicted_symbol(self, symbol): """ Attempt to find a unique asset whose symbol is the given string. If multiple assets have held the given symbol, return a 0. If no asset has held the given symbol, return a NaN. """ try: uppered = symbol.upp...
def transform(self): """ Main generator work loop. """ algo = self.algo metrics_tracker = algo.metrics_tracker emission_rate = metrics_tracker.emission_rate def every_bar(dt_to_use, current_data=self.current_data, handle_data=algo.event_mana...
def _cleanup_expired_assets(self, dt, position_assets): """ Clear out any assets that have expired before starting a new sim day. Performs two functions: 1. Finds all assets for which we have open orders and clears any orders whose assets are on or after their auto_close_dat...
def _get_daily_message(self, dt, algo, metrics_tracker): """ Get a perf message for the given datetime. """ perf_message = metrics_tracker.handle_market_close( dt, self.data_portal, ) perf_message['daily_perf']['recorded_vars'] = algo.recorded_vars...
def _get_minute_message(self, dt, algo, metrics_tracker): """ Get a perf message for the given datetime. """ rvars = algo.recorded_vars minute_message = metrics_tracker.handle_minute_close( dt, self.data_portal, ) minute_message['minute_p...
def load_adjustments(self, dates, assets, should_include_splits, should_include_mergers, should_include_dividends, adjustment_type): """ Load collection o...
def unpack_db_to_component_dfs(self, convert_dates=False): """Returns the set of known tables in the adjustments file in DataFrame form. Parameters ---------- convert_dates : bool, optional By default, dates are returned in seconds since EPOCH. If convert...
def _df_dtypes(self, table_name, convert_dates): """Get dtypes to use when unpacking sqlite tables as dataframes. """ out = self._raw_table_dtypes[table_name] if convert_dates: out = out.copy() for date_column in self._datetime_int_cols[table_name]: ...
def calc_dividend_ratios(self, dividends): """ Calculate the ratios to apply to equities when looking back at pricing history so that the price is smoothed over the ex_date, when the market adjusts to the change in equity value due to upcoming dividend. Returns ------- ...
def write_dividend_data(self, dividends, stock_dividends=None): """ Write both dividend payouts and the derived price adjustment ratios. """ # First write the dividend payouts. self._write_dividends(dividends) self._write_stock_dividends(stock_dividends) # Secon...
def write(self, splits=None, mergers=None, dividends=None, stock_dividends=None): """ Writes data to a SQLite file to be read by SQLiteAdjustmentReader. Parameters ---------- splits : pandas.DataFrame, optional ...
def compute(self, today, assets, out, *arrays): """ Override this method with a function that writes a value into `out`. """ raise NotImplementedError( "{name} must define a compute method".format( name=type(self).__name__ ) )
def _allocate_output(self, windows, shape): """ Allocate an output array whose rows should be passed to `self.compute`. The resulting array must have a shape of ``shape``. If we have standard outputs (i.e. self.outputs is NotSpecified), the default is an empty ndarray whose dty...
def _compute(self, windows, dates, assets, mask): """ Call the user's `compute` function on each window with a pre-built output array. """ format_inputs = self._format_inputs compute = self.compute params = self.params ndim = self.ndim shape = (le...
def make_aliased_type(cls, other_base): """ Factory for making Aliased{Filter,Factor,Classifier}. """ docstring = dedent( """ A {t} that names another {t}. Parameters ---------- term : {t} {{name}} """ ...
def compute_extra_rows(self, all_dates, start_date, end_date, min_extra_rows): """ Ensure that min_extra_rows pushes us back to a computation date. Parameters ---------- a...
def _compute(self, inputs, dates, assets, mask): """ Compute by delegating to self._wrapped_term._compute on sample dates. On non-sample dates, forward-fill from previously-computed samples. """ to_sample = dates[select_sampling_indices(dates, self._frequency)] assert to...
def make_downsampled_type(cls, other_base): """ Factory for making Downsampled{Filter,Factor,Classifier}. """ docstring = dedent( """ A {t} that defers to another {t} at lower-than-daily frequency. Parameters ---------- term : ...
def preprocess(_unused, processors): """ Decorator that applies pre-processors to the arguments of a function before calling the function. Parameters ---------- *processors : dict Map from argument name -> processor function. A processor function takes three arguments: (fun...
def call(f): """ Wrap a function in a processor that calls `f` on the argument before passing it along. Useful for creating simple arguments to the `@preprocess` decorator. Parameters ---------- f : function Function accepting a single argument and returning a replacement. Exa...
def _build_preprocessed_function(func, processors, args_defaults, varargs, varkw): """ Build a preprocessed function with the same signature as `func`. Uses `exec` internally ...

def pop_assign_tracking(self, frame): """Pops the topmost level for assignment tracking and updates the context variables if necessary. """ vars = self._assign_stack.pop() if not frame.toplevel or not vars: return public_names = [x for x in vars if x[:1] != '_...

def visit_Block(self, node, frame): """Call a block and register it for the template.""" level = 0 if frame.toplevel: # if we know that we are a child template, there is no need to # check if we are one if self.has_known_extends: return ...

def visit_Extends(self, node, frame): """Calls the extender.""" if not frame.toplevel: self.fail('cannot use extend from a non top-level scope', node.lineno) # if the number of extends statements in general is zero so # far, we don't have to add a check...

def visit_Include(self, node, frame): """Handles includes.""" if node.ignore_missing: self.writeline('try:') self.indent() func_name = 'get_or_select_template' if isinstance(node.template, nodes.Const): if isinstance(node.template.value, string_types)...

def visit_Import(self, node, frame): """Visit regular imports.""" self.writeline('%s = ' % frame.symbols.ref(node.target), node) if frame.toplevel: self.write('context.vars[%r] = ' % node.target) if self.environment.is_async: self.write('await ') self.writ...

def visit_FromImport(self, node, frame): """Visit named imports.""" self.newline(node) self.write('included_template = %senvironment.get_template(' % (self.environment.is_async and 'await ' or '')) self.visit(node.template, frame) self.write(', %r).' % self.nam...

def detach(self): """If alive then mark as dead and return (obj, func, args, kwargs); otherwise return None""" info = self._registry.get(self) obj = info and info.weakref() if obj is not None and self._registry.pop(self, None): return (obj, info.func, info.args, info....

def peek(self): """If alive then return (obj, func, args, kwargs); otherwise return None""" info = self._registry.get(self) obj = info and info.weakref() if obj is not None: return (obj, info.func, info.args, info.kwargs or {})

def atexit(self): """Whether finalizer should be called at exit""" info = self._registry.get(self) return bool(info) and info.atexit

def tostring(element): """Serialize an element and its child nodes to a string""" rv = [] def serializeElement(element): if not hasattr(element, "tag"): if element.docinfo.internalDTD: if element.docinfo.doctype: dtd_str = element.docinfo.doctype ...

def get_visitor(self, node): """Return the visitor function for this node or `None` if no visitor exists for this node. In that case the generic visit function is used instead. """ method = 'visit_' + node.__class__.__name__ return getattr(self, method, None)

def visit(self, node, *args, **kwargs): """Visit a node.""" f = self.get_visitor(node) if f is not None: return f(node, *args, **kwargs) return self.generic_visit(node, *args, **kwargs)

def generic_visit(self, node, *args, **kwargs): """Called if no explicit visitor function exists for a node.""" for node in node.iter_child_nodes(): self.visit(node, *args, **kwargs)

def visit_list(self, node, *args, **kwargs): """As transformers may return lists in some places this method can be used to enforce a list as return value. """ rv = self.visit(node, *args, **kwargs) if not isinstance(rv, list): rv = [rv] return rv

def default_subprocess_runner(cmd, cwd=None, extra_environ=None): """The default method of calling the wrapper subprocess.""" env = os.environ.copy() if extra_environ: env.update(extra_environ) check_call(cmd, cwd=cwd, env=env)

def build_wheel( self, wheel_directory, config_settings=None, metadata_directory=None): """Build a wheel from this project. Returns the name of the newly created file. In general, this will call the 'build_wheel' hook in the backend. However, if that was previou...

def bninception(num_classes=1000, pretrained='imagenet'): r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper. """ model = BNInception(num_classes=num_classes) if pretrained is not None: settings = pretrained_settings['bninception'][pretrained] assert n...

def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)

def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])...

def resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])...

def nasnetamobile(num_classes=1000, pretrained='imagenet'): r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper. """ if pretrained: settings = pretrained_settings['nasnetamobile'][pretrained] assert num_classes == settings['num_classes'], \ ...

def cafferesnet101(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes) if pretrained is not None: settings = pretrained...

def fbresnet152(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes) if pretrained is not None: settings = pretra...

def alexnet(num_classes=1000, pretrained='imagenet'): r"""AlexNet model architecture from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper. """ # https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py model = models.alexnet(pretrained=False) if pretrained ...

def densenet121(num_classes=1000, pretrained='imagenet'): r"""Densenet-121 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>` """ model = models.densenet121(pretrained=False) if pretrained is not None: settings = pretrained_settings['densenet121'][...

def inceptionv3(num_classes=1000, pretrained='imagenet'): r"""Inception v3 model architecture from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_. """ model = models.inception_v3(pretrained=False) if pretrained is not None: settings = pretrai...

def resnet50(num_classes=1000, pretrained='imagenet'): """Constructs a ResNet-50 model. """ model = models.resnet50(pretrained=False) if pretrained is not None: settings = pretrained_settings['resnet50'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify...

def squeezenet1_0(num_classes=1000, pretrained='imagenet'): r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. """ model = models.squeezenet1_0(pretrained=False) if pretraine...

def vgg11(num_classes=1000, pretrained='imagenet'): """VGG 11-layer model (configuration "A") """ model = models.vgg11(pretrained=False) if pretrained is not None: settings = pretrained_settings['vgg11'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify...

def adjust_learning_rate(optimizer, epoch): """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" lr = args.lr * (0.1 ** (epoch // 30)) for param_group in optimizer.param_groups: param_group['lr'] = lr

def nasnetalarge(num_classes=1001, pretrained='imagenet'): r"""NASNetALarge model architecture from the `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper. """ if pretrained: settings = pretrained_settings['nasnetalarge'][pretrained] assert num_classes == settings['num_classes'], \ ...

def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False): """Selectable global pooling function with dynamic input kernel size """ if pool_type == 'avgmaxc': x = torch.cat([ F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, co...

def download_url(url, destination=None, progress_bar=True): """Download a URL to a local file. Parameters ---------- url : str The URL to download. destination : str, None The destination of the file. If None is given the file is saved to a temporary directory. progress_bar : bo...

def add(self, output, target): """ Args: output (Tensor): NxK tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. The probabilities should sum to one over a...

def value(self): """Returns the model's average precision for each class Return: ap (FloatTensor): 1xK tensor, with avg precision for each class k """ if self.scores.numel() == 0: return 0 ap = torch.zeros(self.scores.size(1)) rg = torch.arange(1,...

def polynet(num_classes=1000, pretrained='imagenet'): """PolyNet architecture from the paper 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks' https://arxiv.org/abs/1611.05725 """ if pretrained: settings = pretrained_settings['polynet'][pretrained] assert num_classes...

def unwrap(self, dt): """ Get the cached value. Returns ------- value : object The cached value. Raises ------ Expired Raised when `dt` is greater than self.expires. """ expires = self._expires if expires i...

def get(self, key, dt): """Get the value of a cached object. Parameters ---------- key : any The key to lookup. dt : datetime The time of the lookup. Returns ------- result : any The value for ``key``. Raises ...

def set(self, key, value, expiration_dt): """Adds a new key value pair to the cache. Parameters ---------- key : any The key to use for the pair. value : any The value to store under the name ``key``. expiration_dt : datetime When shou...

def ensure_dir(self, *path_parts): """Ensures a subdirectory of the working directory. Parameters ---------- path_parts : iterable[str] The parts of the path after the working directory. """ path = self.getpath(*path_parts) ensure_directory(path) ...

def verify_frames_aligned(frames, calendar): """ Verify that DataFrames in ``frames`` have the same indexing scheme and are aligned to ``calendar``. Parameters ---------- frames : list[pd.DataFrame] calendar : trading_calendars.TradingCalendar Raises ------ ValueError I...

def get_value(self, sid, dt, field): """ Parameters ---------- sid : int The asset identifier. day : datetime64-like Midnight of the day for which data is requested. field : string The price field. e.g. ('open', 'high', 'low', 'close', ...

def get_last_traded_dt(self, asset, dt): """ Parameters ---------- asset : zipline.asset.Asset The asset identifier. dt : datetime64-like Midnight of the day for which data is requested. Returns ------- pd.Timestamp : The last know...

def same(*values): """ Check if all values in a sequence are equal. Returns True on empty sequences. Examples -------- >>> same(1, 1, 1, 1) True >>> same(1, 2, 1) False >>> same() True """ if not values: return True first, rest = values[0], values[1:] ...

def dzip_exact(*dicts): """ Parameters ---------- *dicts : iterable[dict] A sequence of dicts all sharing the same keys. Returns ------- zipped : dict A dict whose keys are the union of all keys in *dicts, and whose values are tuples of length len(dicts) containing t...

def _gen_unzip(it, elem_len): """Helper for unzip which checks the lengths of each element in it. Parameters ---------- it : iterable[tuple] An iterable of tuples. ``unzip`` should map ensure that these are already tuples. elem_len : int or None The expected element length. I...

def unzip(seq, elem_len=None): """Unzip a length n sequence of length m sequences into m seperate length n sequences. Parameters ---------- seq : iterable[iterable] The sequence to unzip. elem_len : int, optional The expected length of each element of ``seq``. If not provided thi...

def getattrs(value, attrs, default=_no_default): """ Perform a chained application of ``getattr`` on ``value`` with the values in ``attrs``. If ``default`` is supplied, return it if any of the attribute lookups fail. Parameters ---------- value : object Root of the lookup chain. ...

def set_attribute(name, value): """ Decorator factory for setting attributes on a function. Doesn't change the behavior of the wrapped function. Examples -------- >>> @set_attribute('__name__', 'foo') ... def bar(): ... return 3 ... >>> bar() 3 >>> bar.__name__ ...

def foldr(f, seq, default=_no_default): """Fold a function over a sequence with right associativity. Parameters ---------- f : callable[any, any] The function to reduce the sequence with. The first argument will be the element of the sequence; the second argument will be the acc...

def invert(d): """ Invert a dictionary into a dictionary of sets. >>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP {1: {'a', 'c'}, 2: {'b'}} """ out = {} for k, v in iteritems(d): try: out[v].add(k) except KeyError: out[v] = {k} return out

def simplex_projection(v, b=1): r"""Projection vectors to the simplex domain Implemented according to the paper: Efficient projections onto the l1-ball for learning in high dimensions, John Duchi, et al. ICML 2008. Implementation Time: 2011 June 17 by Bin@libin AT pmail.ntu.edu.sg Optimization Prob...

def run_example(example_name, environ): """ Run an example module from zipline.examples. """ mod = EXAMPLE_MODULES[example_name] register_calendar("YAHOO", get_calendar("NYSE"), force=True) return run_algorithm( initialize=getattr(mod, 'initialize', None), handle_data=getattr(m...

def vectorized_beta(dependents, independent, allowed_missing, out=None): """ Compute slopes of linear regressions between columns of ``dependents`` and ``independent``. Parameters ---------- dependents : np.array[N, M] Array with columns of data to be regressed against ``independent``. ...

def _format_url(instrument_type, instrument_ids, start_date, end_date, earliest_allowed_date): """ Format a URL for loading data from Bank of Canada. """ return ( "http://www.bankofcanada.ca/stats/results/csv" "?lP=lookup_{i...

def load_frame(url, skiprows): """ Load a DataFrame of data from a Bank of Canada site. """ return pd.read_csv( url, skiprows=skiprows, skipinitialspace=True, na_values=["Bank holiday", "Not available"], parse_dates=["Date"], index_col="Date", ).dropna...

def check_known_inconsistencies(bill_data, bond_data): """ There are a couple quirks in the data provided by Bank of Canada. Check that no new quirks have been introduced in the latest download. """ inconsistent_dates = bill_data.index.sym_diff(bond_data.index) known_inconsistencies = [ ...

def earliest_possible_date(): """ The earliest date for which we can load data from this module. """ today = pd.Timestamp('now', tz='UTC').normalize() # Bank of Canada only has the last 10 years of data at any given time. return today.replace(year=today.year - 10)

def fill_price_worse_than_limit_price(fill_price, order): """ Checks whether the fill price is worse than the order's limit price. Parameters ---------- fill_price: float The price to check. order: zipline.finance.order.Order The order whose limit price to check. Returns ...

def _get_window_data(self, data, asset, window_length): """ Internal utility method to return the trailing mean volume over the past 'window_length' days, and volatility of close prices for a specific asset. Parameters ---------- data : The BarData from which to ...

def validate_dtype(termname, dtype, missing_value): """ Validate a `dtype` and `missing_value` passed to Term.__new__. Ensures that we know how to represent ``dtype``, and that missing_value is specified for types without default missing values. Returns ------- validated_dtype, validated_m...

def _assert_valid_categorical_missing_value(value): """ Check that value is a valid categorical missing_value. Raises a TypeError if the value is cannot be used as the missing_value for a categorical_dtype Term. """ label_types = LabelArray.SUPPORTED_SCALAR_TYPES if not isinstance(value, la...

def _pop_params(cls, kwargs): """ Pop entries from the `kwargs` passed to cls.__new__ based on the values in `cls.params`. Parameters ---------- kwargs : dict The kwargs passed to cls.__new__. Returns ------- params : list[(str, objec...

def _static_identity(cls, domain, dtype, missing_value, window_safe, ndim, params): """ Return the identity of the Term that would be constructed from the...

def _init(self, domain, dtype, missing_value, window_safe, ndim, params): """ Parameters ---------- domain : zipline.pipeline.domain.Domain The domain of this term. dtype : np.dtype Dtype of this term's output. missing_value : object Mi...

def dependencies(self): """ The number of extra rows needed for each of our inputs to compute this term. """ extra_input_rows = max(0, self.window_length - 1) out = {} for term in self.inputs: out[term] = extra_input_rows out[self.mask] = 0 ...

def to_workspace_value(self, result, assets): """ Called with a column of the result of a pipeline. This needs to put the data into a format that can be used in a workspace to continue doing computations. Parameters ---------- result : pd.Series A mul...

def earn_stock_dividend(self, stock_dividend): """ Register the number of shares we held at this dividend's ex date so that we can pay out the correct amount on the dividend's pay date. """ return { 'payment_asset': stock_dividend.payment_asset, 'share_cou...

def handle_split(self, asset, ratio): """ Update the position by the split ratio, and return the resulting fractional share that will be converted into cash. Returns the unused cash. """ if self.asset != asset: raise Exception("updating split with the wrong a...

def adjust_commission_cost_basis(self, asset, cost): """ A note about cost-basis in zipline: all positions are considered to share a cost basis, even if they were executed in different transactions with different commission costs, different prices, etc. Due to limitations about ...

def to_dict(self): """ Creates a dictionary representing the state of this position. Returns a dict object of the form: """ return { 'sid': self.asset, 'amount': self.amount, 'cost_basis': self.cost_basis, 'last_sale_price': self.la...

def _make_bundle_core(): """Create a family of data bundle functions that read from the same bundle mapping. Returns ------- bundles : mappingproxy The mapping of bundles to bundle payloads. register : callable The function which registers new bundles in the ``bundles`` mapping....

def deprecated(msg=None, stacklevel=2): """ Used to mark a function as deprecated. Parameters ---------- msg : str The message to display in the deprecation warning. stacklevel : int How far up the stack the warning needs to go, before showing the relevant calling lines....

def load_pricing_adjustments(self, columns, dts, assets): """ Returns ------- adjustments : list[dict[int -> Adjustment]] A list, where each element corresponds to the `columns`, of mappings from index to adjustment objects to apply at that index. """ ...

def _get_adjustments_in_range(self, asset, dts, field): """ Get the Float64Multiply objects to pass to an AdjustedArrayWindow. For the use of AdjustedArrayWindow in the loader, which looks back from current simulation time back to a window of data the dictionary is structured wi...

def get(self, end_ix): """ Returns ------- out : A np.ndarray of the equity pricing up to end_ix after adjustments and rounding have been applied. """ if self.most_recent_ix == end_ix: return self.current target = end_ix - self.cal_start...

def _ensure_sliding_windows(self, assets, dts, field, is_perspective_after): """ Ensure that there is a Float64Multiply window for each asset that can provide data for the given parameters. If the corresponding window for the (assets, len(dts), field) does...

def history(self, assets, dts, field, is_perspective_after): """ A window of pricing data with adjustments applied assuming that the end of the window is the day before the current simulation time. Parameters ---------- assets : iterable of Assets The assets ...

def parse_date_str_series(format_str, tz, date_str_series, data_frequency, trading_day): """ Efficient parsing for a 1d Pandas/numpy object containing string representations of dates. Note: pd.to_datetime is significantly faster when no format string is ...

def _lookup_unconflicted_symbol(self, symbol): """ Attempt to find a unique asset whose symbol is the given string. If multiple assets have held the given symbol, return a 0. If no asset has held the given symbol, return a NaN. """ try: uppered = symbol.upp...

def transform(self): """ Main generator work loop. """ algo = self.algo metrics_tracker = algo.metrics_tracker emission_rate = metrics_tracker.emission_rate def every_bar(dt_to_use, current_data=self.current_data, handle_data=algo.event_mana...

def _cleanup_expired_assets(self, dt, position_assets): """ Clear out any assets that have expired before starting a new sim day. Performs two functions: 1. Finds all assets for which we have open orders and clears any orders whose assets are on or after their auto_close_dat...

def _get_daily_message(self, dt, algo, metrics_tracker): """ Get a perf message for the given datetime. """ perf_message = metrics_tracker.handle_market_close( dt, self.data_portal, ) perf_message['daily_perf']['recorded_vars'] = algo.recorded_vars...

def _get_minute_message(self, dt, algo, metrics_tracker): """ Get a perf message for the given datetime. """ rvars = algo.recorded_vars minute_message = metrics_tracker.handle_minute_close( dt, self.data_portal, ) minute_message['minute_p...

def load_adjustments(self, dates, assets, should_include_splits, should_include_mergers, should_include_dividends, adjustment_type): """ Load collection o...

def unpack_db_to_component_dfs(self, convert_dates=False): """Returns the set of known tables in the adjustments file in DataFrame form. Parameters ---------- convert_dates : bool, optional By default, dates are returned in seconds since EPOCH. If convert...

def _df_dtypes(self, table_name, convert_dates): """Get dtypes to use when unpacking sqlite tables as dataframes. """ out = self._raw_table_dtypes[table_name] if convert_dates: out = out.copy() for date_column in self._datetime_int_cols[table_name]: ...

def calc_dividend_ratios(self, dividends): """ Calculate the ratios to apply to equities when looking back at pricing history so that the price is smoothed over the ex_date, when the market adjusts to the change in equity value due to upcoming dividend. Returns ------- ...

def write_dividend_data(self, dividends, stock_dividends=None): """ Write both dividend payouts and the derived price adjustment ratios. """ # First write the dividend payouts. self._write_dividends(dividends) self._write_stock_dividends(stock_dividends) # Secon...

def write(self, splits=None, mergers=None, dividends=None, stock_dividends=None): """ Writes data to a SQLite file to be read by SQLiteAdjustmentReader. Parameters ---------- splits : pandas.DataFrame, optional ...

def compute(self, today, assets, out, *arrays): """ Override this method with a function that writes a value into `out`. """ raise NotImplementedError( "{name} must define a compute method".format( name=type(self).__name__ ) )

def _allocate_output(self, windows, shape): """ Allocate an output array whose rows should be passed to `self.compute`. The resulting array must have a shape of ``shape``. If we have standard outputs (i.e. self.outputs is NotSpecified), the default is an empty ndarray whose dty...

def _compute(self, windows, dates, assets, mask): """ Call the user's `compute` function on each window with a pre-built output array. """ format_inputs = self._format_inputs compute = self.compute params = self.params ndim = self.ndim shape = (le...

def make_aliased_type(cls, other_base): """ Factory for making Aliased{Filter,Factor,Classifier}. """ docstring = dedent( """ A {t} that names another {t}. Parameters ---------- term : {t} {{name}} """ ...

def compute_extra_rows(self, all_dates, start_date, end_date, min_extra_rows): """ Ensure that min_extra_rows pushes us back to a computation date. Parameters ---------- a...

def _compute(self, inputs, dates, assets, mask): """ Compute by delegating to self._wrapped_term._compute on sample dates. On non-sample dates, forward-fill from previously-computed samples. """ to_sample = dates[select_sampling_indices(dates, self._frequency)] assert to...

def make_downsampled_type(cls, other_base): """ Factory for making Downsampled{Filter,Factor,Classifier}. """ docstring = dedent( """ A {t} that defers to another {t} at lower-than-daily frequency. Parameters ---------- term : ...

def preprocess(*_unused, **processors): """ Decorator that applies pre-processors to the arguments of a function before calling the function. Parameters ---------- **processors : dict Map from argument name -> processor function. A processor function takes three arguments: (fun...

def call(f): """ Wrap a function in a processor that calls `f` on the argument before passing it along. Useful for creating simple arguments to the `@preprocess` decorator. Parameters ---------- f : function Function accepting a single argument and returning a replacement. Exa...

def _build_preprocessed_function(func, processors, args_defaults, varargs, varkw): """ Build a preprocessed function with the same signature as `func`. Uses `exec` internally ...