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paypalrestsdk.notifications
get_event_types
Get the list of events types that are subscribed to a webhook
def get_event_types(self, api=None): """Get the list of events types that are subscribed to a webhook """ api = api or default_api() url = util.join_url(self.path, str(self['id']), 'event-types') return Resource(self.api.get(url), api=api)
(self, api=None)
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22,359
paypalrestsdk.notifications
WebhookEvent
Exposes REST endpoints for working with subscribed webhooks events
class WebhookEvent(Find, List, Post): """Exposes REST endpoints for working with subscribed webhooks events """ path = "/v1/notifications/webhooks-events/" __root_cert_path = "data/DigiCertHighAssuranceEVRootCA.crt.pem" __intermediate_cert_path = "data/DigiCertSHA2ExtendedValidationServerCA.crt.pem" __cert_chain_path = [__root_cert_path, __intermediate_cert_path] def resend(self): """Specify a received webhook event-id to resend the event notification """ return self.post('resend', {}, self) def get_resource(self): """Get the resource sent via the webhook event, e.g. Authorization, conveniently wrapped in the corresponding paypalrestsdk class """ webhook_resource_type = self.resource_type klass = util.get_member(webhook_resource_type) resource = klass(self.resource.__dict__) return resource @staticmethod def _get_expected_sig(transmission_id, timestamp, webhook_id, event_body): """Get the input string to generate the HMAC signature """ if sys.version_info[0] == 2: data = str(binascii.crc32(event_body.decode('utf-8').encode('utf-8')) & 0xffffffff) else: data = str(binascii.crc32(event_body.encode('utf-8')) & 0xffffffff) expected_sig = transmission_id + "|" + timestamp + "|" + webhook_id + "|" + data return expected_sig @staticmethod def _is_common_name_valid(cert): """Check that the common name in the certificate refers to paypal""" from OpenSSL import crypto if cert.get_subject().commonName.lower().endswith(".paypal.com"): return True else: return False @classmethod def _get_certificate_store(cls): """Returns a certificate store with the trust chain loaded """ from OpenSSL import crypto store = crypto.X509Store() try: for cert_path in cls.__cert_chain_path: cert_str = open(os.path.join(os.path.dirname(__file__), cert_path)).read() cert = crypto.load_certificate(crypto.FILETYPE_PEM, cert_str) store.add_cert(cert) return store except Exception as e: print(e) @classmethod def _verify_certificate_chain(cls, cert): """Verify certificate using chain of trust shipped with sdk """ from OpenSSL import crypto store = cls._get_certificate_store() try: store_ctx = crypto.X509StoreContext(store, cert) store_ctx.verify_certificate() return True except Exception as e: print(e) return False @classmethod def _verify_certificate(cls, cert): """Verify that certificate is unexpired, has valid common name and is trustworthy """ if cls._verify_certificate_chain(cert) and cls._is_common_name_valid(cert) and not cert.has_expired(): return True else: return False @staticmethod def _get_cert(cert_url): """Fetches the paypal certificate used to sign the webhook event payload """ from OpenSSL import crypto try: r = requests.get(cert_url) cert = crypto.load_certificate(crypto.FILETYPE_PEM, str(r.text)) return cert except requests.exceptions.RequestException as e: print("Error retrieving PayPal certificate with url " + cert_url) print(e) @classmethod def _verify_signature(cls, transmission_id, timestamp, webhook_id, event_body, cert, actual_sig, auth_algo): """Verify that the webhook payload received is from PayPal, unaltered and targeted towards correct recipient """ from OpenSSL import crypto expected_sig = WebhookEvent._get_expected_sig(transmission_id, timestamp, webhook_id, event_body) try: crypto.verify(cert, b64decode(actual_sig), expected_sig.encode('utf-8'), auth_algo) return True except Exception as e: print(e) return False @classmethod def verify(cls, transmission_id, timestamp, webhook_id, event_body, cert_url, actual_sig, auth_algo='sha256'): """Verify certificate and payload """ __auth_algo_map = { 'SHA256withRSA': 'sha256WithRSAEncryption', 'SHA1withRSA': 'sha1WithRSAEncryption' } try: if auth_algo != 'sha256' and auth_algo not in __auth_algo_map.values(): auth_algo = __auth_algo_map[auth_algo] except KeyError as e: print('Authorization algorithm mapping not found in verify method.') return False cert = WebhookEvent._get_cert(cert_url) return WebhookEvent._verify_certificate(cert) and WebhookEvent._verify_signature(transmission_id, timestamp, webhook_id, event_body, cert, actual_sig, auth_algo)
(attributes=None, api=None)
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22,368
paypalrestsdk.notifications
_get_cert
Fetches the paypal certificate used to sign the webhook event payload
@staticmethod def _get_cert(cert_url): """Fetches the paypal certificate used to sign the webhook event payload """ from OpenSSL import crypto try: r = requests.get(cert_url) cert = crypto.load_certificate(crypto.FILETYPE_PEM, str(r.text)) return cert except requests.exceptions.RequestException as e: print("Error retrieving PayPal certificate with url " + cert_url) print(e)
(cert_url)
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22,369
paypalrestsdk.notifications
_get_expected_sig
Get the input string to generate the HMAC signature
@staticmethod def _get_expected_sig(transmission_id, timestamp, webhook_id, event_body): """Get the input string to generate the HMAC signature """ if sys.version_info[0] == 2: data = str(binascii.crc32(event_body.decode('utf-8').encode('utf-8')) & 0xffffffff) else: data = str(binascii.crc32(event_body.encode('utf-8')) & 0xffffffff) expected_sig = transmission_id + "|" + timestamp + "|" + webhook_id + "|" + data return expected_sig
(transmission_id, timestamp, webhook_id, event_body)
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22,370
paypalrestsdk.notifications
_is_common_name_valid
Check that the common name in the certificate refers to paypal
@staticmethod def _is_common_name_valid(cert): """Check that the common name in the certificate refers to paypal""" from OpenSSL import crypto if cert.get_subject().commonName.lower().endswith(".paypal.com"): return True else: return False
(cert)
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22,373
paypalrestsdk.notifications
get_resource
Get the resource sent via the webhook event, e.g. Authorization, conveniently wrapped in the corresponding paypalrestsdk class
def get_resource(self): """Get the resource sent via the webhook event, e.g. Authorization, conveniently wrapped in the corresponding paypalrestsdk class """ webhook_resource_type = self.resource_type klass = util.get_member(webhook_resource_type) resource = klass(self.resource.__dict__) return resource
(self)
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22,377
paypalrestsdk.notifications
resend
Specify a received webhook event-id to resend the event notification
def resend(self): """Specify a received webhook event-id to resend the event notification """ return self.post('resend', {}, self)
(self)
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22,380
paypalrestsdk.notifications
WebhookEventType
Exposes REST endpoint for listing available event types for webhooks
class WebhookEventType(List): """Exposes REST endpoint for listing available event types for webhooks """ path = "/v1/notifications/webhooks-event-types/"
(attributes=None, api=None)
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22,397
paypalrestsdk.api
set_config
Create new default api object with given configuration
def set_config(options=None, **config): """Create new default api object with given configuration """ global __api__ __api__ = Api(options or {}, **config) return __api__
(options=None, **config)
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22,409
copulas
NotFittedError
NotFittedError class.
class NotFittedError(Exception): """NotFittedError class."""
null
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-0.03618154674768448, 0.034725967794656754, -0.009981116279959679, 0.02306400239467621 ]
22,410
copulas
_find_addons
Find and load all copulas add-ons.
def _find_addons(): """Find and load all copulas add-ons.""" group = 'copulas_modules' try: eps = entry_points(group=group) except TypeError: # Load-time selection requires Python >= 3.10 or importlib_metadata >= 3.6 eps = entry_points().get(group, []) for entry_point in eps: try: addon = entry_point.load() except Exception: # pylint: disable=broad-exception-caught msg = f'Failed to load "{entry_point.name}" from "{entry_point.module_name}".' warnings.warn(msg) continue try: addon_target, addon_name = _get_addon_target(entry_point.name) except AttributeError as error: msg = f"Failed to set '{entry_point.name}': {error}." warnings.warn(msg) continue setattr(addon_target, addon_name, addon)
()
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22,411
copulas
_get_addon_target
Find the target object for the add-on. Args: addon_path_name (str): The add-on's name. The add-on's name should be the full path of valid Python identifiers (i.e. importable.module:object.attr). Returns: tuple: * object: The base module or object the add-on should be added to. * str: The name the add-on should be added to under the module or object.
def _get_addon_target(addon_path_name): """Find the target object for the add-on. Args: addon_path_name (str): The add-on's name. The add-on's name should be the full path of valid Python identifiers (i.e. importable.module:object.attr). Returns: tuple: * object: The base module or object the add-on should be added to. * str: The name the add-on should be added to under the module or object. """ module_path, _, object_path = addon_path_name.partition(':') module_path = module_path.split('.') if module_path[0] != __name__: msg = f"expected base module to be '{__name__}', found '{module_path[0]}'" raise AttributeError(msg) target_base = sys.modules[__name__] for submodule in module_path[1:-1]: target_base = getattr(target_base, submodule) addon_name = module_path[-1] if object_path: if len(module_path) > 1 and not hasattr(target_base, module_path[-1]): msg = f"cannot add '{object_path}' to unknown submodule '{'.'.join(module_path)}'" raise AttributeError(msg) if len(module_path) > 1: target_base = getattr(target_base, module_path[-1]) split_object = object_path.split('.') addon_name = split_object[-1] if len(split_object) > 1: target_base = attrgetter('.'.join(split_object[:-1]))(target_base) return target_base, addon_name
(addon_path_name)
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22,412
operator
attrgetter
attrgetter(attr, ...) --> attrgetter object Return a callable object that fetches the given attribute(s) from its operand. After f = attrgetter('name'), the call f(r) returns r.name. After g = attrgetter('name', 'date'), the call g(r) returns (r.name, r.date). After h = attrgetter('name.first', 'name.last'), the call h(r) returns (r.name.first, r.name.last).
class attrgetter: """ Return a callable object that fetches the given attribute(s) from its operand. After f = attrgetter('name'), the call f(r) returns r.name. After g = attrgetter('name', 'date'), the call g(r) returns (r.name, r.date). After h = attrgetter('name.first', 'name.last'), the call h(r) returns (r.name.first, r.name.last). """ __slots__ = ('_attrs', '_call') def __init__(self, attr, *attrs): if not attrs: if not isinstance(attr, str): raise TypeError('attribute name must be a string') self._attrs = (attr,) names = attr.split('.') def func(obj): for name in names: obj = getattr(obj, name) return obj self._call = func else: self._attrs = (attr,) + attrs getters = tuple(map(attrgetter, self._attrs)) def func(obj): return tuple(getter(obj) for getter in getters) self._call = func def __call__(self, obj): return self._call(obj) def __repr__(self): return '%s.%s(%s)' % (self.__class__.__module__, self.__class__.__qualname__, ', '.join(map(repr, self._attrs))) def __reduce__(self): return self.__class__, self._attrs
null
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22,413
copulas
check_valid_values
Raise an exception if the given values are not supported. Args: function(callable): Method whose unique argument is a numpy.array-like object. Returns: callable: Decorated function Raises: ValueError: If there are missing or invalid values or if the dataset is empty.
def check_valid_values(function): """Raise an exception if the given values are not supported. Args: function(callable): Method whose unique argument is a numpy.array-like object. Returns: callable: Decorated function Raises: ValueError: If there are missing or invalid values or if the dataset is empty. """ def decorated(self, X, *args, **kwargs): if isinstance(X, pd.DataFrame): W = X.to_numpy() else: W = X if not len(W): raise ValueError('Your dataset is empty.') if not (np.issubdtype(W.dtype, np.floating) or np.issubdtype(W.dtype, np.integer)): raise ValueError('There are non-numerical values in your data.') if np.isnan(W).any().any(): raise ValueError('There are nan values in your data.') return function(self, X, *args, **kwargs) return decorated
(function)
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22,416
importlib.metadata
entry_points
Return EntryPoint objects for all installed packages. Pass selection parameters (group or name) to filter the result to entry points matching those properties (see EntryPoints.select()). For compatibility, returns ``SelectableGroups`` object unless selection parameters are supplied. In the future, this function will return ``EntryPoints`` instead of ``SelectableGroups`` even when no selection parameters are supplied. For maximum future compatibility, pass selection parameters or invoke ``.select`` with parameters on the result. :return: EntryPoints or SelectableGroups for all installed packages.
def entry_points(**params) -> Union[EntryPoints, SelectableGroups]: """Return EntryPoint objects for all installed packages. Pass selection parameters (group or name) to filter the result to entry points matching those properties (see EntryPoints.select()). For compatibility, returns ``SelectableGroups`` object unless selection parameters are supplied. In the future, this function will return ``EntryPoints`` instead of ``SelectableGroups`` even when no selection parameters are supplied. For maximum future compatibility, pass selection parameters or invoke ``.select`` with parameters on the result. :return: EntryPoints or SelectableGroups for all installed packages. """ norm_name = operator.attrgetter('_normalized_name') unique = functools.partial(unique_everseen, key=norm_name) eps = itertools.chain.from_iterable( dist.entry_points for dist in unique(distributions()) ) return SelectableGroups.load(eps).select(**params)
(**params) -> Union[importlib.metadata.EntryPoints, importlib.metadata.SelectableGroups]
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22,417
copulas
get_instance
Create new instance of the ``obj`` argument. Args: obj (str, type, instance):
def get_instance(obj, **kwargs): """Create new instance of the ``obj`` argument. Args: obj (str, type, instance): """ instance = None if isinstance(obj, str): package, name = obj.rsplit('.', 1) instance = getattr(importlib.import_module(package), name)(**kwargs) elif isinstance(obj, type): instance = obj(**kwargs) else: if kwargs: instance = obj.__class__(**kwargs) else: args = getattr(obj, '__args__', ()) kwargs = getattr(obj, '__kwargs__', {}) instance = obj.__class__(*args, **kwargs) return instance
(obj, **kwargs)
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22,418
copulas
get_qualified_name
Return the Fully Qualified Name from an instance or class.
def get_qualified_name(_object): """Return the Fully Qualified Name from an instance or class.""" module = _object.__module__ if hasattr(_object, '__name__'): _class = _object.__name__ else: _class = _object.__class__.__name__ return module + '.' + _class
(_object)
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22,422
copulas
random_state
Set the random state before calling the function. Args: function (Callable): The function to wrap around.
def random_state(function): """Set the random state before calling the function. Args: function (Callable): The function to wrap around. """ def wrapper(self, *args, **kwargs): if self.random_state is None: return function(self, *args, **kwargs) else: with set_random_state(self.random_state, self.set_random_state): return function(self, *args, **kwargs) return wrapper
(function)
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22,423
copulas
scalarize
Allow methods that only accepts 1-d vectors to work with scalars. Args: function(callable): Function that accepts and returns vectors. Returns: callable: Decorated function that accepts and returns scalars.
def scalarize(function): """Allow methods that only accepts 1-d vectors to work with scalars. Args: function(callable): Function that accepts and returns vectors. Returns: callable: Decorated function that accepts and returns scalars. """ def decorated(self, X, *args, **kwargs): scalar = not isinstance(X, np.ndarray) if scalar: X = np.array([X]) result = function(self, X, *args, **kwargs) if scalar: result = result[0] return result decorated.__doc__ = function.__doc__ return decorated
(function)
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22,424
copulas
set_random_state
Context manager for managing the random state. Args: random_state (int or np.random.RandomState): The random seed or RandomState. set_model_random_state (function): Function to set the random state on the model.
def _get_addon_target(addon_path_name): """Find the target object for the add-on. Args: addon_path_name (str): The add-on's name. The add-on's name should be the full path of valid Python identifiers (i.e. importable.module:object.attr). Returns: tuple: * object: The base module or object the add-on should be added to. * str: The name the add-on should be added to under the module or object. """ module_path, _, object_path = addon_path_name.partition(':') module_path = module_path.split('.') if module_path[0] != __name__: msg = f"expected base module to be '{__name__}', found '{module_path[0]}'" raise AttributeError(msg) target_base = sys.modules[__name__] for submodule in module_path[1:-1]: target_base = getattr(target_base, submodule) addon_name = module_path[-1] if object_path: if len(module_path) > 1 and not hasattr(target_base, module_path[-1]): msg = f"cannot add '{object_path}' to unknown submodule '{'.'.join(module_path)}'" raise AttributeError(msg) if len(module_path) > 1: target_base = getattr(target_base, module_path[-1]) split_object = object_path.split('.') addon_name = split_object[-1] if len(split_object) > 1: target_base = attrgetter('.'.join(split_object[:-1]))(target_base) return target_base, addon_name
(random_state, set_model_random_state)
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22,425
copulas
store_args
Save ``*args`` and ``**kwargs`` used in the ``__init__`` of a copula. Args: __init__(callable): ``__init__`` function to store their arguments. Returns: callable: Decorated ``__init__`` function.
def store_args(__init__): """Save ``*args`` and ``**kwargs`` used in the ``__init__`` of a copula. Args: __init__(callable): ``__init__`` function to store their arguments. Returns: callable: Decorated ``__init__`` function. """ def new__init__(self, *args, **kwargs): args_copy = deepcopy(args) kwargs_copy = deepcopy(kwargs) __init__(self, *args, **kwargs) self.__args__ = args_copy self.__kwargs__ = kwargs_copy return new__init__
(__init__)
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22,427
copulas
validate_random_state
Validate random state argument. Args: random_state (int, numpy.random.RandomState, tuple, or None): Seed or RandomState for the random generator. Output: numpy.random.RandomState
def validate_random_state(random_state): """Validate random state argument. Args: random_state (int, numpy.random.RandomState, tuple, or None): Seed or RandomState for the random generator. Output: numpy.random.RandomState """ if random_state is None: return None if isinstance(random_state, int): return np.random.RandomState(seed=random_state) elif isinstance(random_state, np.random.RandomState): return random_state else: raise TypeError( f'`random_state` {random_state} expected to be an int ' 'or `np.random.RandomState` object.')
(random_state)
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22,428
copulas
vectorize
Allow a method that only accepts scalars to accept vectors too. This decorator has two different behaviors depending on the dimensionality of the array passed as an argument: **1-d array** It will work under the assumption that the `function` argument is a callable with signature:: function(self, X, *args, **kwargs) where X is an scalar magnitude. In this case the arguments of the input array will be given one at a time, and both the input and output of the decorated function will have shape (n,). **2-d array** It will work under the assumption that the `function` argument is a callable with signature:: function(self, X0, ..., Xj, *args, **kwargs) where `Xi` are scalar magnitudes. It will pass the contents of each row unpacked on each call. The input is espected to have shape (n, j), the output a shape of (n,) It will return a function that is guaranteed to return a `numpy.array`. Args: function(callable): Function that only accept and return scalars. Returns: callable: Decorated function that can accept and return :attr:`numpy.array`.
def vectorize(function): """Allow a method that only accepts scalars to accept vectors too. This decorator has two different behaviors depending on the dimensionality of the array passed as an argument: **1-d array** It will work under the assumption that the `function` argument is a callable with signature:: function(self, X, *args, **kwargs) where X is an scalar magnitude. In this case the arguments of the input array will be given one at a time, and both the input and output of the decorated function will have shape (n,). **2-d array** It will work under the assumption that the `function` argument is a callable with signature:: function(self, X0, ..., Xj, *args, **kwargs) where `Xi` are scalar magnitudes. It will pass the contents of each row unpacked on each call. The input is espected to have shape (n, j), the output a shape of (n,) It will return a function that is guaranteed to return a `numpy.array`. Args: function(callable): Function that only accept and return scalars. Returns: callable: Decorated function that can accept and return :attr:`numpy.array`. """ def decorated(self, X, *args, **kwargs): if not isinstance(X, np.ndarray): return function(self, X, *args, **kwargs) if len(X.shape) == 1: X = X.reshape([-1, 1]) if len(X.shape) == 2: return np.fromiter( (function(self, *x, *args, **kwargs) for x in X), np.dtype('float64') ) else: raise ValueError('Arrays of dimensionality higher than 2 are not supported.') decorated.__doc__ = function.__doc__ return decorated
(function)
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22,430
coca_pytorch.coca_pytorch
CoCa
null
class CoCa(nn.Module): def __init__( self, *, dim, num_tokens, unimodal_depth, multimodal_depth, dim_latents = None, image_dim = None, num_img_queries=256, dim_head=64, heads=8, ff_mult=4, img_encoder=None, caption_loss_weight=1., contrastive_loss_weight=1., pad_id=0 ): super().__init__() self.dim = dim self.pad_id = pad_id self.caption_loss_weight = caption_loss_weight self.contrastive_loss_weight = contrastive_loss_weight # token embeddings self.token_emb = nn.Embedding(num_tokens, dim) self.text_cls_token = nn.Parameter(torch.randn(dim)) # image encoder self.img_encoder = img_encoder # attention pooling for image tokens self.img_queries = nn.Parameter(torch.randn(num_img_queries + 1, dim)) # num image queries for multimodal, but 1 extra CLS for contrastive learning self.img_attn_pool = CrossAttention(dim=dim, context_dim=image_dim, dim_head=dim_head, heads=heads, norm_context=True) self.img_attn_pool_norm = LayerNorm(dim) self.text_cls_norm = LayerNorm(dim) # to latents dim_latents = default(dim_latents, dim) self.img_to_latents = EmbedToLatents(dim, dim_latents) self.text_to_latents = EmbedToLatents(dim, dim_latents) # contrastive learning temperature self.temperature = nn.Parameter(torch.Tensor([1.])) # unimodal layers self.unimodal_layers = nn.ModuleList([]) for ind in range(unimodal_depth): self.unimodal_layers.append( Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)), ) # multimodal layers self.multimodal_layers = nn.ModuleList([]) for ind in range(multimodal_depth): self.multimodal_layers.append(nn.ModuleList([ Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)), Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)) ])) # to logits self.to_logits = nn.Sequential( LayerNorm(dim), nn.Linear(dim, num_tokens, bias=False) ) # they used embedding weight tied projection out to logits, not common, but works self.to_logits[-1].weight = self.token_emb.weight nn.init.normal_(self.token_emb.weight, std=0.02) # whether in data parallel setting self.is_distributed = dist.is_initialized() and dist.get_world_size() > 1 def embed_text(self, text): batch, device = text.shape[0], text.device seq = text.shape[1] text_tokens = self.token_emb(text) # append text cls tokens text_cls_tokens = repeat(self.text_cls_token, 'd -> b 1 d', b=batch) text_tokens = torch.cat((text_tokens, text_cls_tokens), dim=-2) # create specific mask for text cls token at the end # to prevent it from attending to padding cls_mask = rearrange(text!=self.pad_id, 'b j -> b 1 j') attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True) # go through unimodal layers for attn_ff in self.unimodal_layers: text_tokens = attn_ff(text_tokens, attn_mask=attn_mask) # get text cls token text_tokens, text_cls_tokens = text_tokens[:, :-1], text_tokens[:, -1] text_embeds = self.text_cls_norm(text_cls_tokens) return text_embeds, text_tokens def embed_image(self, images=None, image_tokens=None): # encode images into embeddings # with the img_encoder passed in at init # it can also accept precomputed image tokens assert not (exists(images) and exists(image_tokens)) if exists(images): assert exists(self.img_encoder), 'img_encoder must be passed in for automatic image encoding' image_tokens = self.img_encoder(images) # attention pool image tokens img_queries = repeat(self.img_queries, 'n d -> b n d', b=image_tokens.shape[0]) img_queries = self.img_attn_pool(img_queries, image_tokens) img_queries = self.img_attn_pool_norm(img_queries) return img_queries[:, 0], img_queries[:, 1:] def forward( self, text, images=None, image_tokens=None, labels=None, return_loss=False, return_embeddings=False ): batch, device = text.shape[0], text.device if return_loss and not exists(labels): text, labels = text[:, :-1], text[:, 1:] text_embeds, text_tokens = self.embed_text(text) image_embeds, image_tokens = self.embed_image(images=images, image_tokens=image_tokens) # return embeddings if that is what the researcher wants if return_embeddings: return text_embeds, image_embeds # go through multimodal layers for attn_ff, cross_attn in self.multimodal_layers: text_tokens = attn_ff(text_tokens) text_tokens = cross_attn(text_tokens, image_tokens) logits = self.to_logits(text_tokens) if not return_loss: return logits # shorthand ce = F.cross_entropy # calculate caption loss (cross entropy loss) logits = rearrange(logits, 'b n c -> b c n') caption_loss = ce(logits, labels, ignore_index=self.pad_id) caption_loss = caption_loss * self.caption_loss_weight # embedding to latents text_latents = self.text_to_latents(text_embeds) image_latents = self.img_to_latents(image_embeds) # maybe distributed all gather if self.is_distributed: latents = torch.stack((text_latents, image_latents), dim = 1) latents = all_gather(latents) text_latents, image_latents = latents.unbind(dim = 1) # calculate contrastive loss sim = einsum('i d, j d -> i j', text_latents, image_latents) sim = sim * self.temperature.exp() contrastive_labels = torch.arange(batch, device=device) contrastive_loss = (ce(sim, contrastive_labels) + ce(sim.t(), contrastive_labels)) * 0.5 contrastive_loss = contrastive_loss * self.contrastive_loss_weight return caption_loss + contrastive_loss
(*, dim, num_tokens, unimodal_depth, multimodal_depth, dim_latents=None, image_dim=None, num_img_queries=256, dim_head=64, heads=8, ff_mult=4, img_encoder=None, caption_loss_weight=1.0, contrastive_loss_weight=1.0, pad_id=0)
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22,433
torch.nn.modules.module
__dir__
null
def __dir__(self): module_attrs = dir(self.__class__) attrs = list(self.__dict__.keys()) parameters = list(self._parameters.keys()) modules = list(self._modules.keys()) buffers = list(self._buffers.keys()) keys = module_attrs + attrs + parameters + modules + buffers # Eliminate attrs that are not legal Python variable names keys = [key for key in keys if not key[0].isdigit()] return sorted(keys)
(self)
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0.017135143280029297, 0.004873956553637981, -0.06114436686038971 ]
22,436
coca_pytorch.coca_pytorch
__init__
null
def __init__( self, *, dim, num_tokens, unimodal_depth, multimodal_depth, dim_latents = None, image_dim = None, num_img_queries=256, dim_head=64, heads=8, ff_mult=4, img_encoder=None, caption_loss_weight=1., contrastive_loss_weight=1., pad_id=0 ): super().__init__() self.dim = dim self.pad_id = pad_id self.caption_loss_weight = caption_loss_weight self.contrastive_loss_weight = contrastive_loss_weight # token embeddings self.token_emb = nn.Embedding(num_tokens, dim) self.text_cls_token = nn.Parameter(torch.randn(dim)) # image encoder self.img_encoder = img_encoder # attention pooling for image tokens self.img_queries = nn.Parameter(torch.randn(num_img_queries + 1, dim)) # num image queries for multimodal, but 1 extra CLS for contrastive learning self.img_attn_pool = CrossAttention(dim=dim, context_dim=image_dim, dim_head=dim_head, heads=heads, norm_context=True) self.img_attn_pool_norm = LayerNorm(dim) self.text_cls_norm = LayerNorm(dim) # to latents dim_latents = default(dim_latents, dim) self.img_to_latents = EmbedToLatents(dim, dim_latents) self.text_to_latents = EmbedToLatents(dim, dim_latents) # contrastive learning temperature self.temperature = nn.Parameter(torch.Tensor([1.])) # unimodal layers self.unimodal_layers = nn.ModuleList([]) for ind in range(unimodal_depth): self.unimodal_layers.append( Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)), ) # multimodal layers self.multimodal_layers = nn.ModuleList([]) for ind in range(multimodal_depth): self.multimodal_layers.append(nn.ModuleList([ Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult)), Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)) ])) # to logits self.to_logits = nn.Sequential( LayerNorm(dim), nn.Linear(dim, num_tokens, bias=False) ) # they used embedding weight tied projection out to logits, not common, but works self.to_logits[-1].weight = self.token_emb.weight nn.init.normal_(self.token_emb.weight, std=0.02) # whether in data parallel setting self.is_distributed = dist.is_initialized() and dist.get_world_size() > 1
(self, *, dim, num_tokens, unimodal_depth, multimodal_depth, dim_latents=None, image_dim=None, num_img_queries=256, dim_head=64, heads=8, ff_mult=4, img_encoder=None, caption_loss_weight=1.0, contrastive_loss_weight=1.0, pad_id=0)
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22,463
coca_pytorch.coca_pytorch
embed_image
null
def embed_image(self, images=None, image_tokens=None): # encode images into embeddings # with the img_encoder passed in at init # it can also accept precomputed image tokens assert not (exists(images) and exists(image_tokens)) if exists(images): assert exists(self.img_encoder), 'img_encoder must be passed in for automatic image encoding' image_tokens = self.img_encoder(images) # attention pool image tokens img_queries = repeat(self.img_queries, 'n d -> b n d', b=image_tokens.shape[0]) img_queries = self.img_attn_pool(img_queries, image_tokens) img_queries = self.img_attn_pool_norm(img_queries) return img_queries[:, 0], img_queries[:, 1:]
(self, images=None, image_tokens=None)
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22,464
coca_pytorch.coca_pytorch
embed_text
null
def embed_text(self, text): batch, device = text.shape[0], text.device seq = text.shape[1] text_tokens = self.token_emb(text) # append text cls tokens text_cls_tokens = repeat(self.text_cls_token, 'd -> b 1 d', b=batch) text_tokens = torch.cat((text_tokens, text_cls_tokens), dim=-2) # create specific mask for text cls token at the end # to prevent it from attending to padding cls_mask = rearrange(text!=self.pad_id, 'b j -> b 1 j') attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True) # go through unimodal layers for attn_ff in self.unimodal_layers: text_tokens = attn_ff(text_tokens, attn_mask=attn_mask) # get text cls token text_tokens, text_cls_tokens = text_tokens[:, :-1], text_tokens[:, -1] text_embeds = self.text_cls_norm(text_cls_tokens) return text_embeds, text_tokens
(self, text)
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0.004611885640770197, 0.011948772706091404, 0.06611714512109756 ]
22,468
coca_pytorch.coca_pytorch
forward
null
def forward( self, text, images=None, image_tokens=None, labels=None, return_loss=False, return_embeddings=False ): batch, device = text.shape[0], text.device if return_loss and not exists(labels): text, labels = text[:, :-1], text[:, 1:] text_embeds, text_tokens = self.embed_text(text) image_embeds, image_tokens = self.embed_image(images=images, image_tokens=image_tokens) # return embeddings if that is what the researcher wants if return_embeddings: return text_embeds, image_embeds # go through multimodal layers for attn_ff, cross_attn in self.multimodal_layers: text_tokens = attn_ff(text_tokens) text_tokens = cross_attn(text_tokens, image_tokens) logits = self.to_logits(text_tokens) if not return_loss: return logits # shorthand ce = F.cross_entropy # calculate caption loss (cross entropy loss) logits = rearrange(logits, 'b n c -> b c n') caption_loss = ce(logits, labels, ignore_index=self.pad_id) caption_loss = caption_loss * self.caption_loss_weight # embedding to latents text_latents = self.text_to_latents(text_embeds) image_latents = self.img_to_latents(image_embeds) # maybe distributed all gather if self.is_distributed: latents = torch.stack((text_latents, image_latents), dim = 1) latents = all_gather(latents) text_latents, image_latents = latents.unbind(dim = 1) # calculate contrastive loss sim = einsum('i d, j d -> i j', text_latents, image_latents) sim = sim * self.temperature.exp() contrastive_labels = torch.arange(batch, device=device) contrastive_loss = (ce(sim, contrastive_labels) + ce(sim.t(), contrastive_labels)) * 0.5 contrastive_loss = contrastive_loss * self.contrastive_loss_weight return caption_loss + contrastive_loss
(self, text, images=None, image_tokens=None, labels=None, return_loss=False, return_embeddings=False)
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22,503
salem.sio
DataArrayAccessor
null
class DataArrayAccessor(_XarrayAccessorBase): def quick_map(self, ax=None, interp='nearest', **kwargs): """Make a plot of the DataArray.""" return self._quick_map(self._obj, ax=ax, interp=interp, **kwargs) def deacc(self, as_rate=True): """De-accumulates the variable (i.e. compute the variable's rate). The returned variable has one element less over the time dimension. The default is to return in units per hour. Parameters ---------- as_rate: bool set to false if you don't want units per hour, but units per given data timestep """ out = self._obj[{self.t_dim: slice(1, len(self._obj[self.t_dim]))}] diff = self._obj[{self.t_dim: slice(0, len(self._obj[self.t_dim])-1)}] out.values = out.values - diff.values out.attrs['description'] = out.attrs['description'].replace('ACCUMULATED ', '') if as_rate: dth = self._obj.time[1].values - self._obj.time[0].values dth = dth.astype('timedelta64[h]').astype(float) out.values = out.values / dth out.attrs['units'] += ' h-1' else: out.attrs['units'] += ' step-1' return out def interpz(self, zcoord, levels, dim_name='', fill_value=np.NaN, use_multiprocessing=True): """Interpolates the array along the vertical dimension Parameters ---------- zcoord: DataArray the z coordinates of the variable. Must be of same dimensions levels: 1dArray the levels at which to interpolate dim_name: str the name of the new dimension fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- a new DataArray with the interpolated data """ if self.z_dim is None: raise RuntimeError('zdimension not recognized') data = wrftools.interp3d(self._obj.values, zcoord.values, np.atleast_1d(levels), fill_value=fill_value, use_multiprocessing=use_multiprocessing) dims = list(self._obj.dims) zd = np.nonzero([self.z_dim == d for d in dims])[0][0] dims[zd] = dim_name coords = dict(self._obj.coords) coords.pop(self.z_dim, None) coords[dim_name] = np.atleast_1d(levels) out = xr.DataArray(data, name=self._obj.name, dims=dims, coords=coords) out.attrs['pyproj_srs'] = self.grid.proj.srs if not np.asarray(levels).shape: out = out.isel(**{dim_name: 0}) return out
(xarray_obj)
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22,504
salem.sio
__init__
null
def __init__(self, xarray_obj): self._obj = xarray_obj if isinstance(xarray_obj, xr.DataArray): xarray_obj = xarray_obj.to_dataset(name='var') try: # maybe there was already some georef xarray_obj.attrs['pyproj_srs'] = xarray_obj['var'].pyproj_srs except: pass self.grid = grid_from_dataset(xarray_obj) if self.grid is None: raise RuntimeError('dataset Grid not understood.') dn = xarray_obj.sizes.keys() self.x_dim = utils.str_in_list(dn, utils.valid_names['x_dim'])[0] self.y_dim = utils.str_in_list(dn, utils.valid_names['y_dim'])[0] dim = utils.str_in_list(dn, utils.valid_names['t_dim']) self.t_dim = dim[0] if dim else None dim = utils.str_in_list(dn, utils.valid_names['z_dim']) self.z_dim = dim[0] if dim else None
(self, xarray_obj)
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22,505
salem.sio
_apply_transform
Common transform mixin
def _apply_transform(self, transform, grid, other, return_lut=False): """Common transform mixin""" was_dataarray = False if not isinstance(other, xr.Dataset): try: other = other.to_dataset(name=other.name) was_dataarray = True except AttributeError: # must be a ndarray if return_lut: rdata, lut = transform(other, grid=grid, return_lut=True) else: rdata = transform(other, grid=grid) # let's guess sh = rdata.shape nd = len(sh) if nd == 2: dims = (self.y_dim, self.x_dim) elif nd == 3: newdim = 'new_dim' if self.t_dim and sh[0] == self._obj.sizes[self.t_dim]: newdim = self.t_dim if self.z_dim and sh[0] == self._obj.sizes[self.z_dim]: newdim = self.z_dim dims = (newdim, self.y_dim, self.x_dim) else: raise NotImplementedError('more than 3 dims not ok yet.') coords = {} for d in dims: if d in self._obj: coords[d] = self._obj[d] out = xr.DataArray(rdata, coords=coords, dims=dims) out.attrs['pyproj_srs'] = self.grid.proj.srs if return_lut: return out, lut else: return out # go out = xr.Dataset() for v in other.data_vars: var = other[v] if return_lut: rdata, lut = transform(var, return_lut=True) else: rdata = transform(var) # remove old coords dims = [d for d in var.dims] coords = {} for c in var.coords: n = utils.str_in_list([c], utils.valid_names['x_dim']) if n: dims = [self.x_dim if x in n else x for x in dims] continue n = utils.str_in_list([c], utils.valid_names['y_dim']) if n: dims = [self.y_dim if x in n else x for x in dims] continue coords[c] = var.coords[c] # add new ones coords[self.x_dim] = self._obj[self.x_dim] coords[self.y_dim] = self._obj[self.y_dim] rdata = xr.DataArray(rdata, coords=coords, attrs=var.attrs, dims=dims) rdata.attrs['pyproj_srs'] = self.grid.proj.srs out[v] = rdata if was_dataarray: out = out[v] else: out.attrs['pyproj_srs'] = self.grid.proj.srs if return_lut: return out, lut else: return out
(self, transform, grid, other, return_lut=False)
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22,506
salem.sio
_quick_map
Make a plot of a data array.
def _quick_map(self, obj, ax=None, interp='nearest', **kwargs): """Make a plot of a data array.""" # some metadata? title = obj.name or '' if obj._title_for_slice(): title += ' (' + obj._title_for_slice() + ')' cb = obj.attrs['units'] if 'units' in obj.attrs else '' smap = self.get_map(**kwargs) smap.set_data(obj.values, interp=interp) smap.visualize(ax=ax, title=title, cbar_title=cb) return smap
(self, obj, ax=None, interp='nearest', **kwargs)
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22,507
salem.sio
cartopy
Get a cartopy.crs.Projection for this dataset.
def cartopy(self): """Get a cartopy.crs.Projection for this dataset.""" return proj_to_cartopy(self.grid.proj)
(self)
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22,508
salem.sio
deacc
De-accumulates the variable (i.e. compute the variable's rate). The returned variable has one element less over the time dimension. The default is to return in units per hour. Parameters ---------- as_rate: bool set to false if you don't want units per hour, but units per given data timestep
def deacc(self, as_rate=True): """De-accumulates the variable (i.e. compute the variable's rate). The returned variable has one element less over the time dimension. The default is to return in units per hour. Parameters ---------- as_rate: bool set to false if you don't want units per hour, but units per given data timestep """ out = self._obj[{self.t_dim: slice(1, len(self._obj[self.t_dim]))}] diff = self._obj[{self.t_dim: slice(0, len(self._obj[self.t_dim])-1)}] out.values = out.values - diff.values out.attrs['description'] = out.attrs['description'].replace('ACCUMULATED ', '') if as_rate: dth = self._obj.time[1].values - self._obj.time[0].values dth = dth.astype('timedelta64[h]').astype(float) out.values = out.values / dth out.attrs['units'] += ' h-1' else: out.attrs['units'] += ' step-1' return out
(self, as_rate=True)
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22,509
salem.sio
get_map
Make a salem.Map out of the dataset. All keywords are passed to :py:class:salem.Map
def get_map(self, **kwargs): """Make a salem.Map out of the dataset. All keywords are passed to :py:class:salem.Map """ from salem.graphics import Map return Map(self.grid, **kwargs)
(self, **kwargs)
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22,510
salem.sio
interpz
Interpolates the array along the vertical dimension Parameters ---------- zcoord: DataArray the z coordinates of the variable. Must be of same dimensions levels: 1dArray the levels at which to interpolate dim_name: str the name of the new dimension fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- a new DataArray with the interpolated data
def interpz(self, zcoord, levels, dim_name='', fill_value=np.NaN, use_multiprocessing=True): """Interpolates the array along the vertical dimension Parameters ---------- zcoord: DataArray the z coordinates of the variable. Must be of same dimensions levels: 1dArray the levels at which to interpolate dim_name: str the name of the new dimension fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- a new DataArray with the interpolated data """ if self.z_dim is None: raise RuntimeError('zdimension not recognized') data = wrftools.interp3d(self._obj.values, zcoord.values, np.atleast_1d(levels), fill_value=fill_value, use_multiprocessing=use_multiprocessing) dims = list(self._obj.dims) zd = np.nonzero([self.z_dim == d for d in dims])[0][0] dims[zd] = dim_name coords = dict(self._obj.coords) coords.pop(self.z_dim, None) coords[dim_name] = np.atleast_1d(levels) out = xr.DataArray(data, name=self._obj.name, dims=dims, coords=coords) out.attrs['pyproj_srs'] = self.grid.proj.srs if not np.asarray(levels).shape: out = out.isel(**{dim_name: 0}) return out
(self, zcoord, levels, dim_name='', fill_value=nan, use_multiprocessing=True)
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22,511
salem.sio
lookup_transform
Reprojects an other Dataset or DataArray onto self using the forward tranform lookup. See : :py:meth:`Grid.lookup_transform` Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info method : function, default: np.mean the aggregation method. Possibilities: np.std, np.median, np.sum, and more. Use ``len`` to count the number of grid points! lut : ndarray, optional computing the lookup table can be expensive. If you have several operations to do with the same grid, set ``lut`` to an existing table obtained from a previous call to :py:meth:`Grid.grid_lookup` return_lut : bool, optional set to True if you want to return the lookup table for later use. in this case, returns a tuple Returns ------- a dataset or a dataarray If ``return_lut==True``, also return the lookup table
def lookup_transform(self, other, grid=None, method=np.mean, lut=None, return_lut=False): """Reprojects an other Dataset or DataArray onto self using the forward tranform lookup. See : :py:meth:`Grid.lookup_transform` Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info method : function, default: np.mean the aggregation method. Possibilities: np.std, np.median, np.sum, and more. Use ``len`` to count the number of grid points! lut : ndarray, optional computing the lookup table can be expensive. If you have several operations to do with the same grid, set ``lut`` to an existing table obtained from a previous call to :py:meth:`Grid.grid_lookup` return_lut : bool, optional set to True if you want to return the lookup table for later use. in this case, returns a tuple Returns ------- a dataset or a dataarray If ``return_lut==True``, also return the lookup table """ transform = partial(self.grid.lookup_transform, method=method, lut=lut) return self._apply_transform(transform, grid, other, return_lut=return_lut)
(self, other, grid=None, method=<function mean at 0x7f9b3c8b9c70>, lut=None, return_lut=False)
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22,512
salem.sio
quick_map
Make a plot of the DataArray.
def quick_map(self, ax=None, interp='nearest', **kwargs): """Make a plot of the DataArray.""" return self._quick_map(self._obj, ax=ax, interp=interp, **kwargs)
(self, ax=None, interp='nearest', **kwargs)
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22,513
salem.sio
roi
roi(self, shape=None, geometry=None, grid=None, corners=None, crs=wgs84, roi=None, all_touched=False, other=None) Make a region of interest (ROI) for the dataset. All grid points outside the ROI will be masked out. Parameters ---------- ds : Dataset or DataArray form the ROI from the extent of the Dataset or DataArray shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray if you have a mask ready, you can give it here all_touched : boolean pass-through argument for rasterio.features.rasterize, indicating that all grid cells which are clipped by the shapefile defining the region of interest should be included (default=False) other : scalar, DataArray or Dataset, optional Value to use for locations in this object where cond is False. By default, these locations filled with NA. As in http://xarray.pydata.org/en/stable/generated/xarray.DataArray.where.html
def roi(self, ds=None, **kwargs): """roi(self, shape=None, geometry=None, grid=None, corners=None, crs=wgs84, roi=None, all_touched=False, other=None) Make a region of interest (ROI) for the dataset. All grid points outside the ROI will be masked out. Parameters ---------- ds : Dataset or DataArray form the ROI from the extent of the Dataset or DataArray shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray if you have a mask ready, you can give it here all_touched : boolean pass-through argument for rasterio.features.rasterize, indicating that all grid cells which are clipped by the shapefile defining the region of interest should be included (default=False) other : scalar, DataArray or Dataset, optional Value to use for locations in this object where cond is False. By default, these locations filled with NA. As in http://xarray.pydata.org/en/stable/generated/xarray.DataArray.where.html """ other = kwargs.pop('other', dtypes.NA) if ds is not None: grid = ds.salem.grid kwargs.setdefault('grid', grid) mask = self.grid.region_of_interest(**kwargs) out = self._obj.where(mask, other=other) # keep attrs and encoding out.attrs = self._obj.attrs out.encoding = self._obj.encoding if isinstance(out, xr.Dataset): for v in self._obj.variables: out[v].encoding = self._obj[v].encoding if isinstance(out, xr.DataArray): out.name = self._obj.name # add pyproj string everywhere out.attrs['pyproj_srs'] = self.grid.proj.srs if isinstance(out, xr.Dataset): for v in out.data_vars: out.variables[v].attrs = self._obj.variables[v].attrs out.variables[v].attrs['pyproj_srs'] = self.grid.proj.srs return out
(self, ds=None, **kwargs)
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22,514
salem.sio
subset
subset(self, margin=0, shape=None, geometry=None, grid=None, corners=None, crs=wgs84, roi=None) Get a subset of the dataset. Accepts all keywords of :py:func:`~Grid.roi` Parameters ---------- ds : Dataset or DataArray form the ROI from the extent of the Dataset or DataArray shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray a mask for the region of interest to subset the dataset onto margin : int add a margin to the region to subset (can be negative!). Can be used a single keyword, too: set_subset(margin=-5) will remove five pixels from each boundary of the dataset.
def subset(self, margin=0, ds=None, **kwargs): """subset(self, margin=0, shape=None, geometry=None, grid=None, corners=None, crs=wgs84, roi=None) Get a subset of the dataset. Accepts all keywords of :py:func:`~Grid.roi` Parameters ---------- ds : Dataset or DataArray form the ROI from the extent of the Dataset or DataArray shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray a mask for the region of interest to subset the dataset onto margin : int add a margin to the region to subset (can be negative!). Can be used a single keyword, too: set_subset(margin=-5) will remove five pixels from each boundary of the dataset. """ if ds is not None: grid = ds.salem.grid kwargs.setdefault('grid', grid) mask = self.grid.region_of_interest(**kwargs) if not kwargs: # user just wants a margin mask[:] = 1 ids = np.nonzero(mask) sub_x = [np.min(ids[1]) - margin, np.max(ids[1]) + margin] sub_y = [np.min(ids[0]) - margin, np.max(ids[0]) + margin] out_ds = self._obj[{self.x_dim: slice(sub_x[0], sub_x[1]+1), self.y_dim: slice(sub_y[0], sub_y[1]+1)} ] return out_ds
(self, margin=0, ds=None, **kwargs)
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22,515
salem.sio
transform
Reprojects an other Dataset or DataArray onto self. The returned object has the same data structure as ``other`` (i.e. variables names, attributes), but is defined on the new grid (``self.grid``). Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. Returns ------- a dataset or a dataarray
def transform(self, other, grid=None, interp='nearest', ks=3): """Reprojects an other Dataset or DataArray onto self. The returned object has the same data structure as ``other`` (i.e. variables names, attributes), but is defined on the new grid (``self.grid``). Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. Returns ------- a dataset or a dataarray """ transform = partial(self.grid.map_gridded_data, interp=interp, ks=ks) return self._apply_transform(transform, grid, other)
(self, other, grid=None, interp='nearest', ks=3)
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22,516
salem
DataLevels
null
def DataLevels(): raise ImportError('requires matplotlib')
()
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22,517
salem.sio
DatasetAccessor
null
class DatasetAccessor(_XarrayAccessorBase): def quick_map(self, varname, ax=None, interp='nearest', **kwargs): """Make a plot of a variable of the DataSet.""" return self._quick_map(self._obj[varname], ax=ax, interp=interp, **kwargs) def transform_and_add(self, other, grid=None, interp='nearest', ks=3, name=None): """Reprojects an other Dataset and adds it's content to the current one. Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. name: str or dict-like how to name to new variables in self. Per default the new variables are going to keep their name (it will raise an error in case of conflict). Set to a str to to rename the variable (if unique) or set to a dict for mapping the old names to the new names for datasets. """ out = self.transform(other, grid=grid, interp=interp, ks=ks) if isinstance(out, xr.DataArray): new_name = name or out.name if new_name is None: raise ValueError('You need to set name') self._obj[new_name] = out else: for v in out.data_vars: try: new_name = name[v] except (KeyError, TypeError): new_name = v self._obj[new_name] = out[v] def wrf_zlevel(self, varname, levels=None, fill_value=np.NaN, use_multiprocessing=True): """Interpolates to a specified height above sea level. Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray """ if levels is None: levels = np.array([10, 20, 30, 50, 75, 100, 200, 300, 500, 750, 1000, 2000, 3000, 5000, 7500, 10000]) zcoord = self._obj['Z'] out = self._obj[varname].salem.interpz(zcoord, levels, dim_name='z', fill_value=fill_value, use_multiprocessing=use_multiprocessing) out['z'].attrs['description'] = 'height above sea level' out['z'].attrs['units'] = 'm' return out def wrf_plevel(self, varname, levels=None, fill_value=np.NaN, use_multiprocessing=True): """Interpolates to a specified pressure level (hPa). Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray """ if levels is None: levels = np.array([1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 300, 200, 100]) zcoord = self._obj['PRESSURE'] out = self._obj[varname].salem.interpz(zcoord, levels, dim_name='p', fill_value=fill_value, use_multiprocessing=use_multiprocessing) out['p'].attrs['description'] = 'pressure' out['p'].attrs['units'] = 'hPa' return out
(xarray_obj)
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22,524
salem.sio
quick_map
Make a plot of a variable of the DataSet.
def quick_map(self, varname, ax=None, interp='nearest', **kwargs): """Make a plot of a variable of the DataSet.""" return self._quick_map(self._obj[varname], ax=ax, interp=interp, **kwargs)
(self, varname, ax=None, interp='nearest', **kwargs)
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22,528
salem.sio
transform_and_add
Reprojects an other Dataset and adds it's content to the current one. Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. name: str or dict-like how to name to new variables in self. Per default the new variables are going to keep their name (it will raise an error in case of conflict). Set to a str to to rename the variable (if unique) or set to a dict for mapping the old names to the new names for datasets.
def transform_and_add(self, other, grid=None, interp='nearest', ks=3, name=None): """Reprojects an other Dataset and adds it's content to the current one. Parameters ---------- other: Dataset, DataArray or ndarray the data to project onto self grid: salem.Grid in case the input dataset does not carry georef info interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. name: str or dict-like how to name to new variables in self. Per default the new variables are going to keep their name (it will raise an error in case of conflict). Set to a str to to rename the variable (if unique) or set to a dict for mapping the old names to the new names for datasets. """ out = self.transform(other, grid=grid, interp=interp, ks=ks) if isinstance(out, xr.DataArray): new_name = name or out.name if new_name is None: raise ValueError('You need to set name') self._obj[new_name] = out else: for v in out.data_vars: try: new_name = name[v] except (KeyError, TypeError): new_name = v self._obj[new_name] = out[v]
(self, other, grid=None, interp='nearest', ks=3, name=None)
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22,529
salem.sio
wrf_plevel
Interpolates to a specified pressure level (hPa). Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray
def wrf_plevel(self, varname, levels=None, fill_value=np.NaN, use_multiprocessing=True): """Interpolates to a specified pressure level (hPa). Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray """ if levels is None: levels = np.array([1000, 975, 950, 925, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 300, 200, 100]) zcoord = self._obj['PRESSURE'] out = self._obj[varname].salem.interpz(zcoord, levels, dim_name='p', fill_value=fill_value, use_multiprocessing=use_multiprocessing) out['p'].attrs['description'] = 'pressure' out['p'].attrs['units'] = 'hPa' return out
(self, varname, levels=None, fill_value=nan, use_multiprocessing=True)
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22,530
salem.sio
wrf_zlevel
Interpolates to a specified height above sea level. Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray
def wrf_zlevel(self, varname, levels=None, fill_value=np.NaN, use_multiprocessing=True): """Interpolates to a specified height above sea level. Parameters ---------- varname: str the name of the variable to interpolate levels: 1d array levels at which to interpolate (default: some levels I thought of) fill_value : np.NaN or 'extrapolate', optional how to handle levels below the topography. Default is to mark them as invalid, but you might want the have them extrapolated. use_multiprocessing: bool set to false if, for some reason, you don't want to use mp Returns ------- an interpolated DataArray """ if levels is None: levels = np.array([10, 20, 30, 50, 75, 100, 200, 300, 500, 750, 1000, 2000, 3000, 5000, 7500, 10000]) zcoord = self._obj['Z'] out = self._obj[varname].salem.interpz(zcoord, levels, dim_name='z', fill_value=fill_value, use_multiprocessing=use_multiprocessing) out['z'].attrs['description'] = 'height above sea level' out['z'].attrs['units'] = 'm' return out
(self, varname, levels=None, fill_value=nan, use_multiprocessing=True)
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22,531
salem.datasets
EsriITMIX
Open ITMIX geolocalised Esri ASCII images (needs rasterio).
class EsriITMIX(GeoDataset): """Open ITMIX geolocalised Esri ASCII images (needs rasterio).""" def __init__(self, file): """Open the file. Parameters ---------- file: path to the file """ bname = os.path.basename(file).split('.')[0] pok = bname.find('UTM') if pok == -1: raise ValueError(file + ' does not seem to be an ITMIX file.') zone = int(bname[pok+3:]) south = False if zone < 0: south = True zone = -zone proj = pyproj.Proj(proj='utm', zone=zone, ellps='WGS84', south=south) # brutally efficient with rasterio.Env(): with rasterio.open(file) as src: nxny = (src.width, src.height) ul_corner = (src.bounds.left, src.bounds.top) dxdy = (src.res[0], -src.res[1]) grid = Grid(x0y0=ul_corner, nxny=nxny, dxdy=dxdy, pixel_ref='corner', proj=proj) # done self.file = file GeoDataset.__init__(self, grid) def get_vardata(self, var_id=1): """Read the geotiff band. Parameters ---------- var_id: the variable name (here the band number) """ wx = (self.sub_x[0], self.sub_x[1]+1) wy = (self.sub_y[0], self.sub_y[1]+1) with rasterio.Env(): with rasterio.open(self.file) as src: band = src.read(var_id, window=(wy, wx)) return band
(file)
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22,532
salem.datasets
__init__
Open the file. Parameters ---------- file: path to the file
def __init__(self, file): """Open the file. Parameters ---------- file: path to the file """ bname = os.path.basename(file).split('.')[0] pok = bname.find('UTM') if pok == -1: raise ValueError(file + ' does not seem to be an ITMIX file.') zone = int(bname[pok+3:]) south = False if zone < 0: south = True zone = -zone proj = pyproj.Proj(proj='utm', zone=zone, ellps='WGS84', south=south) # brutally efficient with rasterio.Env(): with rasterio.open(file) as src: nxny = (src.width, src.height) ul_corner = (src.bounds.left, src.bounds.top) dxdy = (src.res[0], -src.res[1]) grid = Grid(x0y0=ul_corner, nxny=nxny, dxdy=dxdy, pixel_ref='corner', proj=proj) # done self.file = file GeoDataset.__init__(self, grid)
(self, file)
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22,533
salem.datasets
get_vardata
Read the geotiff band. Parameters ---------- var_id: the variable name (here the band number)
def get_vardata(self, var_id=1): """Read the geotiff band. Parameters ---------- var_id: the variable name (here the band number) """ wx = (self.sub_x[0], self.sub_x[1]+1) wy = (self.sub_y[0], self.sub_y[1]+1) with rasterio.Env(): with rasterio.open(self.file) as src: band = src.read(var_id, window=(wy, wx)) return band
(self, var_id=1)
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22,534
salem.datasets
set_period
Set a period of interest for the dataset. This will be remembered at later calls to time() or GeoDataset's getvardata implementations. Parameters ---------- t0: anything that represents a time. Could be a string (e.g '2012-01-01'), a DateTime, or an index in the dataset's time t1: same as t0 (inclusive)
def set_period(self, t0=0, t1=-1): """Set a period of interest for the dataset. This will be remembered at later calls to time() or GeoDataset's getvardata implementations. Parameters ---------- t0: anything that represents a time. Could be a string (e.g '2012-01-01'), a DateTime, or an index in the dataset's time t1: same as t0 (inclusive) """ if self._time is not None: self.sub_t = [0, -1] # we dont check for what t0 or t1 is, we let Pandas do the job try: self.sub_t[0] = self._time.loc[t0] except KeyError: self.sub_t[0] = self._time.iloc[t0] try: self.sub_t[1] = self._time.loc[t1] except KeyError: self.sub_t[1] = self._time.iloc[t1] self.t0 = self._time.index[self.sub_t[0]] self.t1 = self._time.index[self.sub_t[1]]
(self, t0=0, t1=-1)
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22,535
salem.datasets
set_roi
Set a region of interest for the dataset. If set succesfully, a ROI is simply a mask of the same size as the dataset's grid, obtained with the .roi attribute. I haven't decided yet if the data should be masekd out when a ROI has been set. Parameters ---------- shape: path to a shapefile geometry: a shapely geometry crs: the crs of the geometry grid: a Grid object corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword noerase: set to true in order to add the new ROI to the previous one
def set_roi(self, shape=None, geometry=None, crs=wgs84, grid=None, corners=None, noerase=False): """Set a region of interest for the dataset. If set succesfully, a ROI is simply a mask of the same size as the dataset's grid, obtained with the .roi attribute. I haven't decided yet if the data should be masekd out when a ROI has been set. Parameters ---------- shape: path to a shapefile geometry: a shapely geometry crs: the crs of the geometry grid: a Grid object corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword noerase: set to true in order to add the new ROI to the previous one """ # The rois are always defined on the original grids, but of course # we take that into account when a subset is set (see roi # decorator below) ogrid = self._ogrid # Initial mask if noerase and (self.roi is not None): mask = self.roi else: mask = np.zeros((ogrid.ny, ogrid.nx), dtype=np.int16) # Several cases if shape is not None: if isinstance(shape, pd.DataFrame): gdf = shape else: gdf = sio.read_shapefile(shape) gis.transform_geopandas(gdf, to_crs=ogrid.corner_grid, inplace=True) if rasterio is None: raise ImportError('This feature needs rasterio') from rasterio.features import rasterize with rasterio.Env(): mask = rasterize(gdf.geometry, out=mask) if geometry is not None: geom = gis.transform_geometry(geometry, crs=crs, to_crs=ogrid.corner_grid) if rasterio is None: raise ImportError('This feature needs rasterio') from rasterio.features import rasterize with rasterio.Env(): mask = rasterize(np.atleast_1d(geom), out=mask) if grid is not None: _tmp = np.ones((grid.ny, grid.nx), dtype=np.int16) mask = ogrid.map_gridded_data(_tmp, grid, out=mask).filled(0) if corners is not None: cgrid = self._ogrid.center_grid xy0, xy1 = corners x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True) x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True) mask[np.min([y0, y1]):np.max([y0, y1])+1, np.min([x0, x1]):np.max([x0, x1])+1] = 1 self.roi = mask
(self, shape=None, geometry=None, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, grid=None, corners=None, noerase=False)
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22,536
salem.datasets
set_subset
Set a subset for the dataset. This will be remembered at later calls to GeoDataset's getvardata implementations. Parameters ---------- corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword crs: the coordinates of the corner coordinates toroi: set to true to generate the smallest possible subset arond the region of interest set with set_roi() margin: when doing the subset, add a margin (can be negative!). Can be used alone: set_subset(margin=-5) will remove five pixels from each boundary of the dataset. TODO: shouldnt we make the toroi stuff easier to use?
def set_subset(self, corners=None, crs=wgs84, toroi=False, margin=0): """Set a subset for the dataset. This will be remembered at later calls to GeoDataset's getvardata implementations. Parameters ---------- corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword crs: the coordinates of the corner coordinates toroi: set to true to generate the smallest possible subset arond the region of interest set with set_roi() margin: when doing the subset, add a margin (can be negative!). Can be used alone: set_subset(margin=-5) will remove five pixels from each boundary of the dataset. TODO: shouldnt we make the toroi stuff easier to use? """ # Useful variables mx = self._ogrid.nx-1 my = self._ogrid.ny-1 cgrid = self._ogrid.center_grid # Three possible cases if toroi: if self.roi is None or np.max(self.roi) == 0: raise RuntimeError('roi is empty.') ids = np.nonzero(self.roi) sub_x = [np.min(ids[1])-margin, np.max(ids[1])+margin] sub_y = [np.min(ids[0])-margin, np.max(ids[0])+margin] elif corners is not None: xy0, xy1 = corners x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True) x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True) sub_x = [np.min([x0, x1])-margin, np.max([x0, x1])+margin] sub_y = [np.min([y0, y1])-margin, np.max([y0, y1])+margin] else: # Reset sub_x = [0-margin, mx+margin] sub_y = [0-margin, my+margin] # Some necessary checks if (np.max(sub_x) < 0) or (np.min(sub_x) > mx) or \ (np.max(sub_y) < 0) or (np.min(sub_y) > my): raise RuntimeError('subset not valid') if (sub_x[0] < 0) or (sub_x[1] > mx): warnings.warn('x0 out of bounds', RuntimeWarning) if (sub_y[0] < 0) or (sub_y[1] > my): warnings.warn('y0 out of bounds', RuntimeWarning) # Make the new grid sub_x = np.clip(sub_x, 0, mx) sub_y = np.clip(sub_y, 0, my) nxny = (sub_x[1] - sub_x[0] + 1, sub_y[1] - sub_y[0] + 1) dxdy = (self._ogrid.dx, self._ogrid.dy) xy0 = (self._ogrid.x0 + sub_x[0] * self._ogrid.dx, self._ogrid.y0 + sub_y[0] * self._ogrid.dy) self.grid = Grid(proj=self._ogrid.proj, nxny=nxny, dxdy=dxdy, x0y0=xy0) # If we arrived here, we can safely set the subset self.sub_x = sub_x self.sub_y = sub_y
(self, corners=None, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, toroi=False, margin=0)
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22,537
salem.datasets
GeoDataset
Interface for georeferenced datasets. A GeoDataset is a formalism for gridded data arrays, which are usually stored in geotiffs or netcdf files. It provides an interface to realise subsets, compute regions of interest and more. A GeoDataset makes more sense if it is subclassed for real files, such as GeoTiff or GeoNetCDF. In that case, the implemetations must make use of the subset indexes provided in the sub_x, sub_y and sub_t properties.
class GeoDataset(object): """Interface for georeferenced datasets. A GeoDataset is a formalism for gridded data arrays, which are usually stored in geotiffs or netcdf files. It provides an interface to realise subsets, compute regions of interest and more. A GeoDataset makes more sense if it is subclassed for real files, such as GeoTiff or GeoNetCDF. In that case, the implemetations must make use of the subset indexes provided in the sub_x, sub_y and sub_t properties. """ def __init__(self, grid, time=None): """Set-up the georeferencing, time is optional. Parameters: grid: a salem.Grid object which represents the underlying data time: if the data has a time dimension """ # The original grid, for always stored self._ogrid = grid # The current grid (changes if set_subset() is called) self.grid = grid # Default indexes to get in the underlying data (BOTH inclusive, # i.e [, ], not [,[ as in numpy) self.sub_x = [0, grid.nx-1] self.sub_y = [0, grid.ny-1] # Roi is a ny, nx array if set self.roi = None self.set_roi() # _time is a pd.Series because it's so nice to misuse the series.loc # flexibility (see set_period) if time is not None: if isinstance(time, pd.Series): time = pd.Series(np.arange(len(time)), index=time.index) else: try: time = pd.Series(np.arange(len(time)), index=time) except AttributeError: # https://github.com/pandas-dev/pandas/issues/23419 for t in time: setattr(t, 'nanosecond', 0) time = pd.Series(np.arange(len(time)), index=time) self._time = time # set_period() will set those self.t0 = None self.t1 = None self.sub_t = None self.set_period() @property def time(self): """Time array""" if self._time is None: return None return self._time[self.t0:self.t1].index def set_period(self, t0=0, t1=-1): """Set a period of interest for the dataset. This will be remembered at later calls to time() or GeoDataset's getvardata implementations. Parameters ---------- t0: anything that represents a time. Could be a string (e.g '2012-01-01'), a DateTime, or an index in the dataset's time t1: same as t0 (inclusive) """ if self._time is not None: self.sub_t = [0, -1] # we dont check for what t0 or t1 is, we let Pandas do the job try: self.sub_t[0] = self._time.loc[t0] except KeyError: self.sub_t[0] = self._time.iloc[t0] try: self.sub_t[1] = self._time.loc[t1] except KeyError: self.sub_t[1] = self._time.iloc[t1] self.t0 = self._time.index[self.sub_t[0]] self.t1 = self._time.index[self.sub_t[1]] def set_subset(self, corners=None, crs=wgs84, toroi=False, margin=0): """Set a subset for the dataset. This will be remembered at later calls to GeoDataset's getvardata implementations. Parameters ---------- corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword crs: the coordinates of the corner coordinates toroi: set to true to generate the smallest possible subset arond the region of interest set with set_roi() margin: when doing the subset, add a margin (can be negative!). Can be used alone: set_subset(margin=-5) will remove five pixels from each boundary of the dataset. TODO: shouldnt we make the toroi stuff easier to use? """ # Useful variables mx = self._ogrid.nx-1 my = self._ogrid.ny-1 cgrid = self._ogrid.center_grid # Three possible cases if toroi: if self.roi is None or np.max(self.roi) == 0: raise RuntimeError('roi is empty.') ids = np.nonzero(self.roi) sub_x = [np.min(ids[1])-margin, np.max(ids[1])+margin] sub_y = [np.min(ids[0])-margin, np.max(ids[0])+margin] elif corners is not None: xy0, xy1 = corners x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True) x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True) sub_x = [np.min([x0, x1])-margin, np.max([x0, x1])+margin] sub_y = [np.min([y0, y1])-margin, np.max([y0, y1])+margin] else: # Reset sub_x = [0-margin, mx+margin] sub_y = [0-margin, my+margin] # Some necessary checks if (np.max(sub_x) < 0) or (np.min(sub_x) > mx) or \ (np.max(sub_y) < 0) or (np.min(sub_y) > my): raise RuntimeError('subset not valid') if (sub_x[0] < 0) or (sub_x[1] > mx): warnings.warn('x0 out of bounds', RuntimeWarning) if (sub_y[0] < 0) or (sub_y[1] > my): warnings.warn('y0 out of bounds', RuntimeWarning) # Make the new grid sub_x = np.clip(sub_x, 0, mx) sub_y = np.clip(sub_y, 0, my) nxny = (sub_x[1] - sub_x[0] + 1, sub_y[1] - sub_y[0] + 1) dxdy = (self._ogrid.dx, self._ogrid.dy) xy0 = (self._ogrid.x0 + sub_x[0] * self._ogrid.dx, self._ogrid.y0 + sub_y[0] * self._ogrid.dy) self.grid = Grid(proj=self._ogrid.proj, nxny=nxny, dxdy=dxdy, x0y0=xy0) # If we arrived here, we can safely set the subset self.sub_x = sub_x self.sub_y = sub_y def set_roi(self, shape=None, geometry=None, crs=wgs84, grid=None, corners=None, noerase=False): """Set a region of interest for the dataset. If set succesfully, a ROI is simply a mask of the same size as the dataset's grid, obtained with the .roi attribute. I haven't decided yet if the data should be masekd out when a ROI has been set. Parameters ---------- shape: path to a shapefile geometry: a shapely geometry crs: the crs of the geometry grid: a Grid object corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to. The coordinates are not expressed in wgs84, set the crs keyword noerase: set to true in order to add the new ROI to the previous one """ # The rois are always defined on the original grids, but of course # we take that into account when a subset is set (see roi # decorator below) ogrid = self._ogrid # Initial mask if noerase and (self.roi is not None): mask = self.roi else: mask = np.zeros((ogrid.ny, ogrid.nx), dtype=np.int16) # Several cases if shape is not None: if isinstance(shape, pd.DataFrame): gdf = shape else: gdf = sio.read_shapefile(shape) gis.transform_geopandas(gdf, to_crs=ogrid.corner_grid, inplace=True) if rasterio is None: raise ImportError('This feature needs rasterio') from rasterio.features import rasterize with rasterio.Env(): mask = rasterize(gdf.geometry, out=mask) if geometry is not None: geom = gis.transform_geometry(geometry, crs=crs, to_crs=ogrid.corner_grid) if rasterio is None: raise ImportError('This feature needs rasterio') from rasterio.features import rasterize with rasterio.Env(): mask = rasterize(np.atleast_1d(geom), out=mask) if grid is not None: _tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)
(grid, time=None)
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22,538
salem.datasets
__init__
Set-up the georeferencing, time is optional. Parameters: grid: a salem.Grid object which represents the underlying data time: if the data has a time dimension
def __init__(self, grid, time=None): """Set-up the georeferencing, time is optional. Parameters: grid: a salem.Grid object which represents the underlying data time: if the data has a time dimension """ # The original grid, for always stored self._ogrid = grid # The current grid (changes if set_subset() is called) self.grid = grid # Default indexes to get in the underlying data (BOTH inclusive, # i.e [, ], not [,[ as in numpy) self.sub_x = [0, grid.nx-1] self.sub_y = [0, grid.ny-1] # Roi is a ny, nx array if set self.roi = None self.set_roi() # _time is a pd.Series because it's so nice to misuse the series.loc # flexibility (see set_period) if time is not None: if isinstance(time, pd.Series): time = pd.Series(np.arange(len(time)), index=time.index) else: try: time = pd.Series(np.arange(len(time)), index=time) except AttributeError: # https://github.com/pandas-dev/pandas/issues/23419 for t in time: setattr(t, 'nanosecond', 0) time = pd.Series(np.arange(len(time)), index=time) self._time = time # set_period() will set those self.t0 = None self.t1 = None self.sub_t = None self.set_period()
(self, grid, time=None)
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22,539
salem.datasets
get_vardata
Interface to implement by subclasses, taking sub_x, sub_y and sub_t into account.
def get_vardata(self, var_id=None): """Interface to implement by subclasses, taking sub_x, sub_y and sub_t into account.""" raise NotImplementedError()
(self, var_id=None)
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22,543
salem.datasets
GeoNetcdf
NetCDF files with geolocalisation info. GeoNetcdf will try hard to understand the geoloc and time of the file, but if it can't you can still provide the time and grid at instantiation.
class GeoNetcdf(GeoDataset): """NetCDF files with geolocalisation info. GeoNetcdf will try hard to understand the geoloc and time of the file, but if it can't you can still provide the time and grid at instantiation. """ def __init__(self, file, grid=None, time=None, monthbegin=False): """Open the file and try to understand it. Parameters ---------- file: path to the netcdf file grid: a Grid object. This will override the normal behavior of GeoNetcdf, which is to try to understand the grid automatically. time: a time array. This will override the normal behavior of GeoNetcdf, which is to try to understand the time automatically. monthbegin: set to true if you are sure that your data is monthly and that the data provider decided to tag the date as the center of the month (stupid) """ self._nc = netCDF4.Dataset(file) self._nc.set_auto_mask(False) self.variables = self._nc.variables if grid is None: grid = sio.grid_from_dataset(self._nc) if grid is None: raise RuntimeError('File grid not understood') if time is None: time = sio.netcdf_time(self._nc, monthbegin=monthbegin) dn = self._nc.dimensions.keys() try: self.x_dim = utils.str_in_list(dn, utils.valid_names['x_dim'])[0] self.y_dim = utils.str_in_list(dn, utils.valid_names['y_dim'])[0] except IndexError: raise RuntimeError('File coordinates not understood') dim = utils.str_in_list(dn, utils.valid_names['t_dim']) self.t_dim = dim[0] if dim else None dim = utils.str_in_list(dn, utils.valid_names['z_dim']) self.z_dim = dim[0] if dim else None GeoDataset.__init__(self, grid, time=time) def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): self.close() def close(self): self._nc.close() def get_vardata(self, var_id=0, as_xarray=False): """Reads the data out of the netCDF file while taking into account time and spatial subsets. Parameters ---------- var_id: the name of the variable (must be available in self.variables) as_xarray: returns a DataArray object """ v = self.variables[var_id] # Make the slices item = [] for d in v.dimensions: it = slice(None) if d == self.t_dim and self.sub_t is not None: it = slice(self.sub_t[0], self.sub_t[1]+1) elif d == self.y_dim: it = slice(self.sub_y[0], self.sub_y[1]+1) elif d == self.x_dim: it = slice(self.sub_x[0], self.sub_x[1]+1) item.append(it) with np.errstate(invalid='ignore'): # This is due to some numpy warnings out = v[tuple(item)] if as_xarray: # convert to xarray dims = v.dimensions coords = dict() x, y = self.grid.x_coord, self.grid.y_coord for d in dims: if d == self.t_dim: coords[d] = self.time elif d == self.y_dim: coords[d] = y elif d == self.x_dim: coords[d] = x attrs = v.__dict__.copy() bad_keys = ['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'ncvars'] _ = [attrs.pop(b, None) for b in bad_keys] out = xr.DataArray(out, dims=dims, coords=coords, attrs=attrs) return out
(file, grid=None, time=None, monthbegin=False)
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22,545
salem.datasets
__exit__
null
def __exit__(self, exception_type, exception_value, traceback): self.close()
(self, exception_type, exception_value, traceback)
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22,546
salem.datasets
__init__
Open the file and try to understand it. Parameters ---------- file: path to the netcdf file grid: a Grid object. This will override the normal behavior of GeoNetcdf, which is to try to understand the grid automatically. time: a time array. This will override the normal behavior of GeoNetcdf, which is to try to understand the time automatically. monthbegin: set to true if you are sure that your data is monthly and that the data provider decided to tag the date as the center of the month (stupid)
def __init__(self, file, grid=None, time=None, monthbegin=False): """Open the file and try to understand it. Parameters ---------- file: path to the netcdf file grid: a Grid object. This will override the normal behavior of GeoNetcdf, which is to try to understand the grid automatically. time: a time array. This will override the normal behavior of GeoNetcdf, which is to try to understand the time automatically. monthbegin: set to true if you are sure that your data is monthly and that the data provider decided to tag the date as the center of the month (stupid) """ self._nc = netCDF4.Dataset(file) self._nc.set_auto_mask(False) self.variables = self._nc.variables if grid is None: grid = sio.grid_from_dataset(self._nc) if grid is None: raise RuntimeError('File grid not understood') if time is None: time = sio.netcdf_time(self._nc, monthbegin=monthbegin) dn = self._nc.dimensions.keys() try: self.x_dim = utils.str_in_list(dn, utils.valid_names['x_dim'])[0] self.y_dim = utils.str_in_list(dn, utils.valid_names['y_dim'])[0] except IndexError: raise RuntimeError('File coordinates not understood') dim = utils.str_in_list(dn, utils.valid_names['t_dim']) self.t_dim = dim[0] if dim else None dim = utils.str_in_list(dn, utils.valid_names['z_dim']) self.z_dim = dim[0] if dim else None GeoDataset.__init__(self, grid, time=time)
(self, file, grid=None, time=None, monthbegin=False)
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22,547
salem.datasets
close
null
def close(self): self._nc.close()
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22,548
salem.datasets
get_vardata
Reads the data out of the netCDF file while taking into account time and spatial subsets. Parameters ---------- var_id: the name of the variable (must be available in self.variables) as_xarray: returns a DataArray object
def get_vardata(self, var_id=0, as_xarray=False): """Reads the data out of the netCDF file while taking into account time and spatial subsets. Parameters ---------- var_id: the name of the variable (must be available in self.variables) as_xarray: returns a DataArray object """ v = self.variables[var_id] # Make the slices item = [] for d in v.dimensions: it = slice(None) if d == self.t_dim and self.sub_t is not None: it = slice(self.sub_t[0], self.sub_t[1]+1) elif d == self.y_dim: it = slice(self.sub_y[0], self.sub_y[1]+1) elif d == self.x_dim: it = slice(self.sub_x[0], self.sub_x[1]+1) item.append(it) with np.errstate(invalid='ignore'): # This is due to some numpy warnings out = v[tuple(item)] if as_xarray: # convert to xarray dims = v.dimensions coords = dict() x, y = self.grid.x_coord, self.grid.y_coord for d in dims: if d == self.t_dim: coords[d] = self.time elif d == self.y_dim: coords[d] = y elif d == self.x_dim: coords[d] = x attrs = v.__dict__.copy() bad_keys = ['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'ncvars'] _ = [attrs.pop(b, None) for b in bad_keys] out = xr.DataArray(out, dims=dims, coords=coords, attrs=attrs) return out
(self, var_id=0, as_xarray=False)
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22,552
salem.datasets
GeoTiff
Geolocalised tiff images (needs rasterio).
class GeoTiff(GeoDataset): """Geolocalised tiff images (needs rasterio).""" def __init__(self, file): """Open the file. Parameters ---------- file: path to the file """ if rasterio is None: raise ImportError('This feature needs rasterio to be insalled') # brutally efficient with rasterio.Env(): with rasterio.open(file) as src: nxny = (src.width, src.height) ul_corner = (src.bounds.left, src.bounds.top) proj = pyproj.Proj(src.crs) dxdy = (src.res[0], -src.res[1]) grid = Grid(x0y0=ul_corner, nxny=nxny, dxdy=dxdy, pixel_ref='corner', proj=proj) # done self.file = file GeoDataset.__init__(self, grid) def get_vardata(self, var_id=1): """Read the geotiff band. Parameters ---------- var_id: the variable name (here the band number) """ wx = (self.sub_x[0], self.sub_x[1]+1) wy = (self.sub_y[0], self.sub_y[1]+1) with rasterio.Env(): with rasterio.open(self.file) as src: band = src.read(var_id, window=(wy, wx)) return band
(file)
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22,553
salem.datasets
__init__
Open the file. Parameters ---------- file: path to the file
def __init__(self, file): """Open the file. Parameters ---------- file: path to the file """ if rasterio is None: raise ImportError('This feature needs rasterio to be insalled') # brutally efficient with rasterio.Env(): with rasterio.open(file) as src: nxny = (src.width, src.height) ul_corner = (src.bounds.left, src.bounds.top) proj = pyproj.Proj(src.crs) dxdy = (src.res[0], -src.res[1]) grid = Grid(x0y0=ul_corner, nxny=nxny, dxdy=dxdy, pixel_ref='corner', proj=proj) # done self.file = file GeoDataset.__init__(self, grid)
(self, file)
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22,558
salem.datasets
GoogleCenterMap
Google static map centered on a point. Needs motionless.
class GoogleCenterMap(GeoDataset): """Google static map centered on a point. Needs motionless. """ def __init__(self, center_ll=(11.38, 47.26), size_x=640, size_y=640, scale=1, zoom=12, maptype='satellite', use_cache=True, **kwargs): """Initialize Parameters ---------- center_ll : tuple tuple of lon, lat center of the map size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download zoom : int google zoom level (https://developers.google.com/maps/documentation/ static-maps/intro#Zoomlevels). 1 (world) - 20 (buildings) maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API) """ global API_KEY # Google grid grid = gis.googlestatic_mercator_grid(center_ll=center_ll, nx=size_x, ny=size_y, zoom=zoom, scale=scale) if 'key' not in kwargs: if API_KEY is None: with open(utils.get_demo_file('.api_key'), 'r') as f: API_KEY = f.read().replace('\n', '') kwargs['key'] = API_KEY # Motionless import motionless googleurl = motionless.CenterMap(lon=center_ll[0], lat=center_ll[1], size_x=size_x, size_y=size_y, maptype=maptype, zoom=zoom, scale=scale, **kwargs) # done self.googleurl = googleurl self.use_cache = use_cache GeoDataset.__init__(self, grid) @lazy_property def _img(self): """Download the image.""" if self.use_cache: return utils.joblib_read_img_url(self.googleurl.generate_url()) else: from matplotlib.image import imread fd = urlopen(self.googleurl.generate_url()) return imread(io.BytesIO(fd.read())) def get_vardata(self, var_id=0): """Return and subset the image.""" return self._img[self.sub_y[0]:self.sub_y[1]+1, self.sub_x[0]:self.sub_x[1]+1, :]
(center_ll=(11.38, 47.26), size_x=640, size_y=640, scale=1, zoom=12, maptype='satellite', use_cache=True, **kwargs)
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22,559
salem.datasets
__init__
Initialize Parameters ---------- center_ll : tuple tuple of lon, lat center of the map size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download zoom : int google zoom level (https://developers.google.com/maps/documentation/ static-maps/intro#Zoomlevels). 1 (world) - 20 (buildings) maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API)
def __init__(self, center_ll=(11.38, 47.26), size_x=640, size_y=640, scale=1, zoom=12, maptype='satellite', use_cache=True, **kwargs): """Initialize Parameters ---------- center_ll : tuple tuple of lon, lat center of the map size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download zoom : int google zoom level (https://developers.google.com/maps/documentation/ static-maps/intro#Zoomlevels). 1 (world) - 20 (buildings) maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API) """ global API_KEY # Google grid grid = gis.googlestatic_mercator_grid(center_ll=center_ll, nx=size_x, ny=size_y, zoom=zoom, scale=scale) if 'key' not in kwargs: if API_KEY is None: with open(utils.get_demo_file('.api_key'), 'r') as f: API_KEY = f.read().replace('\n', '') kwargs['key'] = API_KEY # Motionless import motionless googleurl = motionless.CenterMap(lon=center_ll[0], lat=center_ll[1], size_x=size_x, size_y=size_y, maptype=maptype, zoom=zoom, scale=scale, **kwargs) # done self.googleurl = googleurl self.use_cache = use_cache GeoDataset.__init__(self, grid)
(self, center_ll=(11.38, 47.26), size_x=640, size_y=640, scale=1, zoom=12, maptype='satellite', use_cache=True, **kwargs)
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22,560
salem.datasets
get_vardata
Return and subset the image.
def get_vardata(self, var_id=0): """Return and subset the image.""" return self._img[self.sub_y[0]:self.sub_y[1]+1, self.sub_x[0]:self.sub_x[1]+1, :]
(self, var_id=0)
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22,564
salem.datasets
GoogleVisibleMap
Google static map automatically sized and zoomed to a selected region. It's usually more practical to use than GoogleCenterMap.
class GoogleVisibleMap(GoogleCenterMap): """Google static map automatically sized and zoomed to a selected region. It's usually more practical to use than GoogleCenterMap. """ def __init__(self, x, y, crs=wgs84, size_x=640, size_y=640, scale=1, maptype='satellite', use_cache=True, **kwargs): """Initialize Parameters ---------- x : array x coordinates of the points to include on the map y : array y coordinates of the points to include on the map crs : proj or Grid coordinate reference system of x, y size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API) Notes ----- To obtain the exact domain specified in `x` and `y` you may have to play with the `size_x` and `size_y` kwargs. """ global API_KEY if 'zoom' in kwargs or 'center_ll' in kwargs: raise ValueError('incompatible kwargs.') # Transform to lonlat crs = gis.check_crs(crs) if isinstance(crs, pyproj.Proj): lon, lat = gis.transform_proj(crs, wgs84, x, y) elif isinstance(crs, Grid): lon, lat = crs.ij_to_crs(x, y, crs=wgs84) else: raise NotImplementedError() # surely not the smartest way to do but should be enough for now mc = (np.mean(lon), np.mean(lat)) zoom = 20 while zoom >= 0: grid = gis.googlestatic_mercator_grid(center_ll=mc, nx=size_x, ny=size_y, zoom=zoom, scale=scale) dx, dy = grid.transform(lon, lat, maskout=True) if np.any(dx.mask): zoom -= 1 else: break if 'key' not in kwargs: if API_KEY is None: with open(utils.get_demo_file('.api_key'), 'r') as f: API_KEY = f.read().replace('\n', '') kwargs['key'] = API_KEY GoogleCenterMap.__init__(self, center_ll=mc, size_x=size_x, size_y=size_y, zoom=zoom, scale=scale, maptype=maptype, use_cache=use_cache, **kwargs)
(x, y, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, size_x=640, size_y=640, scale=1, maptype='satellite', use_cache=True, **kwargs)
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22,565
salem.datasets
__init__
Initialize Parameters ---------- x : array x coordinates of the points to include on the map y : array y coordinates of the points to include on the map crs : proj or Grid coordinate reference system of x, y size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API) Notes ----- To obtain the exact domain specified in `x` and `y` you may have to play with the `size_x` and `size_y` kwargs.
def __init__(self, x, y, crs=wgs84, size_x=640, size_y=640, scale=1, maptype='satellite', use_cache=True, **kwargs): """Initialize Parameters ---------- x : array x coordinates of the points to include on the map y : array y coordinates of the points to include on the map crs : proj or Grid coordinate reference system of x, y size_x : int image size size_y : int image size scale : int image scaling factor. 1, 2. 2 is higher resolution but takes longer to download maptype : str, default: 'satellite' 'roadmap', 'satellite', 'hybrid', 'terrain' use_cache : bool, default: True store the downloaded image in the cache to avoid future downloads kwargs : ** any keyword accepted by motionless.CenterMap (e.g. `key` for the API) Notes ----- To obtain the exact domain specified in `x` and `y` you may have to play with the `size_x` and `size_y` kwargs. """ global API_KEY if 'zoom' in kwargs or 'center_ll' in kwargs: raise ValueError('incompatible kwargs.') # Transform to lonlat crs = gis.check_crs(crs) if isinstance(crs, pyproj.Proj): lon, lat = gis.transform_proj(crs, wgs84, x, y) elif isinstance(crs, Grid): lon, lat = crs.ij_to_crs(x, y, crs=wgs84) else: raise NotImplementedError() # surely not the smartest way to do but should be enough for now mc = (np.mean(lon), np.mean(lat)) zoom = 20 while zoom >= 0: grid = gis.googlestatic_mercator_grid(center_ll=mc, nx=size_x, ny=size_y, zoom=zoom, scale=scale) dx, dy = grid.transform(lon, lat, maskout=True) if np.any(dx.mask): zoom -= 1 else: break if 'key' not in kwargs: if API_KEY is None: with open(utils.get_demo_file('.api_key'), 'r') as f: API_KEY = f.read().replace('\n', '') kwargs['key'] = API_KEY GoogleCenterMap.__init__(self, center_ll=mc, size_x=size_x, size_y=size_y, zoom=zoom, scale=scale, maptype=maptype, use_cache=use_cache, **kwargs)
(self, x, y, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, size_x=640, size_y=640, scale=1, maptype='satellite', use_cache=True, **kwargs)
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22,570
salem.gis
Grid
A structured grid on a map projection. Central class in the library, taking over user concerns about the gridded representation of georeferenced data. It adds a level of abstraction by defining a new coordinate system. A grid requires georeferencing information at instantiation and is immutable *in principle*. I didn't implement barriers: if you want to mess with it, you can (not recommended). Note that in most cases, users won't have to define the grid themselves: most georeferenced datasets contain enough metadata for Salem to determine the data's grid automatically. A grid is defined by a projection, a reference point in this projection, a grid spacing and a number of grid points. The grid can be defined in a "upper left" convention (reference point at the top left corner, dy negative - always -). This is the python convention, but not that of all datasets (for example, the output of the WRF model follows a down-left corner convention). Therefore, grids can also be defined in a "lower left" corner convention (dy positive). The use of one or the other convention depends on the data, so the user should take care of what he is doing. The reference points of the grid points might be located at the corner of the pixel (upper left corner for example if dy is negative) or the center of the pixel (most atmospheric datasets follow this convention). The two concepts are truly equivalent and each grid instance gives access to one representation or another ("center_grid" and "corner_grid" properties). Under the hood, Salem uses the representation it needs to do the job by accessing either one or the other of these parameters. The user should know which convention he needs for his purposes: some grid functions and properties are representation dependant (transform, ll_coordinates, ...) while some are not (extent, corner_ll_coordinates ...). Attributes ---------- proj nx ny dx dy x0 y0 origin pixel_ref x_coord y_coord xy_coordinates ll_coordinates xstagg_xy_coordinates ystagg_xy_coordinates xstagg_ll_coordinates ystagg_ll_coordinates center_grid corner_grid extent
class Grid(object): """A structured grid on a map projection. Central class in the library, taking over user concerns about the gridded representation of georeferenced data. It adds a level of abstraction by defining a new coordinate system. A grid requires georeferencing information at instantiation and is immutable *in principle*. I didn't implement barriers: if you want to mess with it, you can (not recommended). Note that in most cases, users won't have to define the grid themselves: most georeferenced datasets contain enough metadata for Salem to determine the data's grid automatically. A grid is defined by a projection, a reference point in this projection, a grid spacing and a number of grid points. The grid can be defined in a "upper left" convention (reference point at the top left corner, dy negative - always -). This is the python convention, but not that of all datasets (for example, the output of the WRF model follows a down-left corner convention). Therefore, grids can also be defined in a "lower left" corner convention (dy positive). The use of one or the other convention depends on the data, so the user should take care of what he is doing. The reference points of the grid points might be located at the corner of the pixel (upper left corner for example if dy is negative) or the center of the pixel (most atmospheric datasets follow this convention). The two concepts are truly equivalent and each grid instance gives access to one representation or another ("center_grid" and "corner_grid" properties). Under the hood, Salem uses the representation it needs to do the job by accessing either one or the other of these parameters. The user should know which convention he needs for his purposes: some grid functions and properties are representation dependant (transform, ll_coordinates, ...) while some are not (extent, corner_ll_coordinates ...). Attributes ---------- proj nx ny dx dy x0 y0 origin pixel_ref x_coord y_coord xy_coordinates ll_coordinates xstagg_xy_coordinates ystagg_xy_coordinates xstagg_ll_coordinates ystagg_ll_coordinates center_grid corner_grid extent """ def __init__(self, proj=wgs84, nxny=None, dxdy=None, x0y0=None, pixel_ref='center', corner=None, ul_corner=None, ll_corner=None): """ Parameters ---------- proj : pyproj.Proj instance defines the grid's map projection. Defaults to 'PlateCarree' (wgs84) nxny : (int, int) (nx, ny) number of grid points dxdy : (float, float) (dx, dy) grid spacing in proj coordinates. dx must be positive, while dy can be positive or negative depending on the origin grid point's lecation (upper-left or lower-left) x0y0 : (float, float) (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy pixel_ref : str either 'center' or 'corner' (default: 'center'). Tells the Grid object where the (x0, y0) is located in the grid point. If ``pixel_ref`` is set to 'corner' and dy < 0, the ``x0y0`` kwarg specifies the **grid point's upper left** corner coordinates. Equivalently, if dy > 0, x0y0 specifies the **grid point's lower left** coordinate. corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy ul_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left corner ll_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the lower left corner Examples -------- >>> g = Grid(nxny=(3, 2), dxdy=(1, 1), x0y0=(0, 0), proj=wgs84) >>> lon, lat = g.ll_coordinates >>> lon array([[ 0., 1., 2.], [ 0., 1., 2.]]) >>> lat array([[ 0., 0., 0.], [ 1., 1., 1.]]) >>> lon, lat = g.corner_grid.ll_coordinates >>> lon array([[-0.5, 0.5, 1.5], [-0.5, 0.5, 1.5]]) >>> lat array([[-0.5, -0.5, -0.5], [ 0.5, 0.5, 0.5]]) >>> g.corner_grid == g.center_grid # the two reprs are equivalent True """ # Check for coordinate system proj = check_crs(proj) if proj is None: raise ValueError('proj must be of type pyproj.Proj') self._proj = proj # deprecations if corner is not None: warnings.warn('The `corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) x0y0 = corner if ul_corner is not None: warnings.warn('The `ul_corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) if dxdy[1] > 0.: raise ValueError('dxdy and input params not compatible') x0y0 = ul_corner if ll_corner is not None: warnings.warn('The `ll_corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) if dxdy[1] < 0.: raise ValueError('dxdy and input params not compatible') x0y0 = ll_corner # Check for shortcut if dxdy[1] < 0.: ul_corner = x0y0 else: ll_corner = x0y0 # Initialise the rest self._check_input(nxny=nxny, dxdy=dxdy, ul_corner=ul_corner, ll_corner=ll_corner, pixel_ref=pixel_ref) def _check_input(self, **kwargs): """See which parameter combination we have and set everything.""" combi_a = ['nxny', 'dxdy', 'ul_corner'] combi_b = ['nxny', 'dxdy', 'll_corner'] if all(kwargs[k] is not None for k in combi_a): nx, ny = kwargs['nxny'] dx, dy = kwargs['dxdy'] x0, y0 = kwargs['ul_corner'] if (dx <= 0.) or (dy >= 0.): raise ValueError('dxdy and input params not compatible') origin = 'upper-left' elif all(kwargs[k] is not None for k in combi_b): nx, ny = kwargs['nxny'] dx, dy = kwargs['dxdy'] x0, y0 = kwargs['ll_corner'] if (dx <= 0.) or (dy <= 0.): raise ValueError('dxdy and input params not compatible') origin = 'lower-left' else: raise ValueError('Input params not compatible') self._nx = int(nx) self._ny = int(ny) if (self._nx <= 0) or (self._ny <= 0): raise ValueError('nxny not valid') self._dx = float(dx) self._dy = float(dy) self._x0 = float(x0) self._y0 = float(y0) self._origin = origin # Check for pixel ref self._pixel_ref = kwargs['pixel_ref'].lower() if self._pixel_ref not in ['corner', 'center']: raise ValueError('pixel_ref not recognized') def __eq__(self, other): """Two grids are considered equal when their defining coordinates and projection are equal. Note: equality also means floating point equality, with all the problems that come with it. (independent of the grid's cornered or centered representation.) """ # Attributes defining the instance ckeys = ['x0', 'y0', 'nx', 'ny', 'dx', 'dy', 'origin'] a = dict((k, getattr(self.corner_grid, k)) for k in ckeys) b = dict((k, getattr(other.corner_grid, k)) for k in ckeys) p1 = self.corner_grid.proj p2 = other.corner_grid.proj return (a == b) and proj_is_same(p1, p2)
(proj=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, nxny=None, dxdy=None, x0y0=None, pixel_ref='center', corner=None, ul_corner=None, ll_corner=None)
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22,571
salem.gis
__eq__
Two grids are considered equal when their defining coordinates and projection are equal. Note: equality also means floating point equality, with all the problems that come with it. (independent of the grid's cornered or centered representation.)
def __eq__(self, other): """Two grids are considered equal when their defining coordinates and projection are equal. Note: equality also means floating point equality, with all the problems that come with it. (independent of the grid's cornered or centered representation.) """ # Attributes defining the instance ckeys = ['x0', 'y0', 'nx', 'ny', 'dx', 'dy', 'origin'] a = dict((k, getattr(self.corner_grid, k)) for k in ckeys) b = dict((k, getattr(other.corner_grid, k)) for k in ckeys) p1 = self.corner_grid.proj p2 = other.corner_grid.proj return (a == b) and proj_is_same(p1, p2)
(self, other)
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22,572
salem.gis
__init__
Parameters ---------- proj : pyproj.Proj instance defines the grid's map projection. Defaults to 'PlateCarree' (wgs84) nxny : (int, int) (nx, ny) number of grid points dxdy : (float, float) (dx, dy) grid spacing in proj coordinates. dx must be positive, while dy can be positive or negative depending on the origin grid point's lecation (upper-left or lower-left) x0y0 : (float, float) (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy pixel_ref : str either 'center' or 'corner' (default: 'center'). Tells the Grid object where the (x0, y0) is located in the grid point. If ``pixel_ref`` is set to 'corner' and dy < 0, the ``x0y0`` kwarg specifies the **grid point's upper left** corner coordinates. Equivalently, if dy > 0, x0y0 specifies the **grid point's lower left** coordinate. corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy ul_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left corner ll_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the lower left corner Examples -------- >>> g = Grid(nxny=(3, 2), dxdy=(1, 1), x0y0=(0, 0), proj=wgs84) >>> lon, lat = g.ll_coordinates >>> lon array([[ 0., 1., 2.], [ 0., 1., 2.]]) >>> lat array([[ 0., 0., 0.], [ 1., 1., 1.]]) >>> lon, lat = g.corner_grid.ll_coordinates >>> lon array([[-0.5, 0.5, 1.5], [-0.5, 0.5, 1.5]]) >>> lat array([[-0.5, -0.5, -0.5], [ 0.5, 0.5, 0.5]]) >>> g.corner_grid == g.center_grid # the two reprs are equivalent True
def __init__(self, proj=wgs84, nxny=None, dxdy=None, x0y0=None, pixel_ref='center', corner=None, ul_corner=None, ll_corner=None): """ Parameters ---------- proj : pyproj.Proj instance defines the grid's map projection. Defaults to 'PlateCarree' (wgs84) nxny : (int, int) (nx, ny) number of grid points dxdy : (float, float) (dx, dy) grid spacing in proj coordinates. dx must be positive, while dy can be positive or negative depending on the origin grid point's lecation (upper-left or lower-left) x0y0 : (float, float) (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy pixel_ref : str either 'center' or 'corner' (default: 'center'). Tells the Grid object where the (x0, y0) is located in the grid point. If ``pixel_ref`` is set to 'corner' and dy < 0, the ``x0y0`` kwarg specifies the **grid point's upper left** corner coordinates. Equivalently, if dy > 0, x0y0 specifies the **grid point's lower left** coordinate. corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left or lower left corner, depending on the sign of dy ul_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the upper left corner ll_corner : (float, float) DEPRECATED in favor of ``x0y0`` (x0, y0) cartesian coordinates (in proj) of the lower left corner Examples -------- >>> g = Grid(nxny=(3, 2), dxdy=(1, 1), x0y0=(0, 0), proj=wgs84) >>> lon, lat = g.ll_coordinates >>> lon array([[ 0., 1., 2.], [ 0., 1., 2.]]) >>> lat array([[ 0., 0., 0.], [ 1., 1., 1.]]) >>> lon, lat = g.corner_grid.ll_coordinates >>> lon array([[-0.5, 0.5, 1.5], [-0.5, 0.5, 1.5]]) >>> lat array([[-0.5, -0.5, -0.5], [ 0.5, 0.5, 0.5]]) >>> g.corner_grid == g.center_grid # the two reprs are equivalent True """ # Check for coordinate system proj = check_crs(proj) if proj is None: raise ValueError('proj must be of type pyproj.Proj') self._proj = proj # deprecations if corner is not None: warnings.warn('The `corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) x0y0 = corner if ul_corner is not None: warnings.warn('The `ul_corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) if dxdy[1] > 0.: raise ValueError('dxdy and input params not compatible') x0y0 = ul_corner if ll_corner is not None: warnings.warn('The `ll_corner` kwarg is deprecated: ' 'use `x0y0` instead.', DeprecationWarning) if dxdy[1] < 0.: raise ValueError('dxdy and input params not compatible') x0y0 = ll_corner # Check for shortcut if dxdy[1] < 0.: ul_corner = x0y0 else: ll_corner = x0y0 # Initialise the rest self._check_input(nxny=nxny, dxdy=dxdy, ul_corner=ul_corner, ll_corner=ll_corner, pixel_ref=pixel_ref)
(self, proj=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, nxny=None, dxdy=None, x0y0=None, pixel_ref='center', corner=None, ul_corner=None, ll_corner=None)
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22,573
salem.gis
__repr__
null
def __repr__(self): srs = '+'.join(sorted(self.proj.srs.split('+'))).strip() summary = ['<salem.Grid>'] summary += [' proj: ' + srs] summary += [' pixel_ref: ' + self.pixel_ref] summary += [' origin: ' + str(self.origin)] summary += [' (nx, ny): (' + str(self.nx) + ', ' + str(self.ny) + ')'] summary += [' (dx, dy): (' + str(self.dx) + ', ' + str(self.dy) + ')'] summary += [' (x0, y0): (' + str(self.x0) + ', ' + str(self.y0) + ')'] return '\n'.join(summary) + '\n'
(self)
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22,574
salem.gis
_check_input
See which parameter combination we have and set everything.
def _check_input(self, **kwargs): """See which parameter combination we have and set everything.""" combi_a = ['nxny', 'dxdy', 'ul_corner'] combi_b = ['nxny', 'dxdy', 'll_corner'] if all(kwargs[k] is not None for k in combi_a): nx, ny = kwargs['nxny'] dx, dy = kwargs['dxdy'] x0, y0 = kwargs['ul_corner'] if (dx <= 0.) or (dy >= 0.): raise ValueError('dxdy and input params not compatible') origin = 'upper-left' elif all(kwargs[k] is not None for k in combi_b): nx, ny = kwargs['nxny'] dx, dy = kwargs['dxdy'] x0, y0 = kwargs['ll_corner'] if (dx <= 0.) or (dy <= 0.): raise ValueError('dxdy and input params not compatible') origin = 'lower-left' else: raise ValueError('Input params not compatible') self._nx = int(nx) self._ny = int(ny) if (self._nx <= 0) or (self._ny <= 0): raise ValueError('nxny not valid') self._dx = float(dx) self._dy = float(dy) self._x0 = float(x0) self._y0 = float(y0) self._origin = origin # Check for pixel ref self._pixel_ref = kwargs['pixel_ref'].lower() if self._pixel_ref not in ['corner', 'center']: raise ValueError('pixel_ref not recognized')
(self, **kwargs)
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22,575
salem.gis
almost_equal
A less strict comparison between grids. Two grids are considered equal when their defining coordinates and projection are equal. grid1 == grid2 uses floating point equality, which is very strict; here we uses numpy's is close instead. (independent of the grid's cornered or centered representation.)
def almost_equal(self, other, rtol=1e-05, atol=1e-08): """A less strict comparison between grids. Two grids are considered equal when their defining coordinates and projection are equal. grid1 == grid2 uses floating point equality, which is very strict; here we uses numpy's is close instead. (independent of the grid's cornered or centered representation.) """ # float attributes defining the instance fkeys = ['x0', 'y0', 'dx', 'dy'] # unambiguous attributes ckeys = ['nx', 'ny', 'origin'] ok = True for k in fkeys: ok = ok and np.isclose(getattr(self.corner_grid, k), getattr(other.corner_grid, k), rtol=rtol, atol=atol) for k in ckeys: _ok = getattr(self.corner_grid, k) == getattr(other.corner_grid, k) ok = ok and _ok p1 = self.corner_grid.proj p2 = other.corner_grid.proj return ok and proj_is_same(p1, p2)
(self, other, rtol=1e-05, atol=1e-08)
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0.023011256009340286, -0.0028470559045672417, 0.08713210374116898 ]
22,576
salem.gis
extent_as_polygon
Get the extent of the grid in a shapely.Polygon and desired crs. Parameters ---------- crs : crs the target coordinate reference system. Returns ------- [left, right, bottom, top] boundaries of the grid.
def extent_as_polygon(self, crs=wgs84): """Get the extent of the grid in a shapely.Polygon and desired crs. Parameters ---------- crs : crs the target coordinate reference system. Returns ------- [left, right, bottom, top] boundaries of the grid. """ from shapely.geometry import Polygon # this is not so trivial # for optimisation we will transform the boundaries only _i = np.hstack([np.arange(self.nx+1), np.ones(self.ny+1)*self.nx, np.arange(self.nx+1)[::-1], np.zeros(self.ny+1)]).flatten() _j = np.hstack([np.zeros(self.nx+1), np.arange(self.ny+1), np.ones(self.nx+1)*self.ny, np.arange(self.ny+1)[::-1]]).flatten() _i, _j = self.corner_grid.ij_to_crs(_i, _j, crs=crs) return Polygon(zip(_i, _j))
(self, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined)
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22,577
salem.gis
extent_in_crs
Get the extent of the grid in a desired crs. Parameters ---------- crs : crs the target coordinate reference system. Returns ------- [left, right, bottom, top] boundaries of the grid.
def extent_in_crs(self, crs=wgs84): """Get the extent of the grid in a desired crs. Parameters ---------- crs : crs the target coordinate reference system. Returns ------- [left, right, bottom, top] boundaries of the grid. """ # this is not so trivial # for optimisation we will transform the boundaries only poly = self.extent_as_polygon(crs=crs) _i, _j = poly.exterior.xy return [np.min(_i), np.max(_i), np.min(_j), np.max(_j)]
(self, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined)
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22,578
salem.gis
grid_lookup
Performs forward transformation of any other grid into self. The principle of forward transform is to obtain, for each grid point of ``self`` , all the indices of ``other`` that are located into the given grid point. This transformation makes sense ONLY if ``other`` has a higher resolution than the object grid. If ``other`` has a similar or coarser resolution than ``self`` , choose the more general (and much faster) :py:meth:`Grid.map_gridded_data` method. Parameters ---------- other : salem.Grid the grid that needs to be transformed into self Returns ------- a dict: each key (j, i) contains an array of shape (n, 2) where n is the number of ``other`` 's grid points found within the grid point (j, i)
def grid_lookup(self, other): """Performs forward transformation of any other grid into self. The principle of forward transform is to obtain, for each grid point of ``self`` , all the indices of ``other`` that are located into the given grid point. This transformation makes sense ONLY if ``other`` has a higher resolution than the object grid. If ``other`` has a similar or coarser resolution than ``self`` , choose the more general (and much faster) :py:meth:`Grid.map_gridded_data` method. Parameters ---------- other : salem.Grid the grid that needs to be transformed into self Returns ------- a dict: each key (j, i) contains an array of shape (n, 2) where n is the number of ``other`` 's grid points found within the grid point (j, i) """ # Input checks other = check_crs(other) if not isinstance(other, Grid): raise ValueError('`other` should be a Grid instance') # Transform the other grid into the local grid (forward transform) # Work in center grid cause that's what we need i, j = other.center_grid.ij_coordinates i, j = i.flatten(), j.flatten() oi, oj = self.center_grid.transform(i, j, crs=other.center_grid, nearest=True, maskout=True) # keep only valid values oi, oj, i, j = oi[~oi.mask], oj[~oi.mask], i[~oi.mask], j[~oi.mask] out_inds = oi.flatten() + self.nx * oj.flatten() # find the links ris = np.digitize(out_inds, bins=np.arange(self.nx*self.ny+1)) # some optim based on the fact that ris has many duplicates sort_idx = np.argsort(ris) unq_items, unq_count = np.unique(ris[sort_idx], return_counts=True) unq_idx = np.split(sort_idx, np.cumsum(unq_count)) # lets go out = dict() for idx, ri in zip(unq_idx, unq_items): ij = divmod(ri-1, self.nx) out[ij] = np.stack((j[idx], i[idx]), axis=1) return out
(self, other)
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22,579
salem.gis
ij_to_crs
Converts local i, j to cartesian coordinates in a specified crs Parameters ---------- i : array of floats the grid coordinates of the point(s) you want to convert j : array of floats the grid coordinates of the point(s) you want to convert crs: crs the target crs (default: self.proj) nearest: bool (for Grid crs only) convert to the nearest grid point Returns ------- (x, y) coordinates of the points in the specified crs.
def ij_to_crs(self, i, j, crs=None, nearest=False): """Converts local i, j to cartesian coordinates in a specified crs Parameters ---------- i : array of floats the grid coordinates of the point(s) you want to convert j : array of floats the grid coordinates of the point(s) you want to convert crs: crs the target crs (default: self.proj) nearest: bool (for Grid crs only) convert to the nearest grid point Returns ------- (x, y) coordinates of the points in the specified crs. """ # Default if crs is None: crs = self.proj # Convert i, j to x, y try: x = i * self.dx + self.x0 y = j * self.dy + self.y0 except TypeError: x = np.asarray(i) * self.dx + self.x0 y = np.asarray(j) * self.dy + self.y0 # Convert x, y to crs _crs = check_crs(crs, raise_on_error=True) if isinstance(_crs, pyproj.Proj): ret = transform_proj(self.proj, _crs, x, y) elif isinstance(_crs, Grid): ret = _crs.transform(x, y, crs=self.proj, nearest=nearest) return ret
(self, i, j, crs=None, nearest=False)
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22,580
salem.gis
lookup_transform
Performs the forward transformation of gridded data into self. This method is suitable when the data grid is of higher resolution than ``self``. ``lookup_transform`` performs aggregation of data according to a user given rule (e.g. ``np.mean``, ``len``, ``np.std``), applied to all grid points found below a grid point in ``self``. See also :py:meth:`Grid.grid_lookup` and examples in the docs Parameters ---------- data : ndarray an ndarray of dimensions 2, 3, or 4, the two last ones being y, x. grid : Grid a Grid instance matching the data method : function, default: np.mean the aggregation method. Possibilities: np.std, np.median, np.sum, and more. Use ``len`` to count the number of grid points! lut : ndarray, optional computing the lookup table can be expensive. If you have several operations to do with the same grid, set ``lut`` to an existing table obtained from a previous call to :py:meth:`Grid.grid_lookup` return_lut : bool, optional set to True if you want to return the lookup table for later use. in this case, returns a tuple Returns ------- An aggregated ndarray of the data, in ``self`` coordinates. If ``return_lut==True``, also return the lookup table
def lookup_transform(self, data, grid=None, method=np.mean, lut=None, return_lut=False): """Performs the forward transformation of gridded data into self. This method is suitable when the data grid is of higher resolution than ``self``. ``lookup_transform`` performs aggregation of data according to a user given rule (e.g. ``np.mean``, ``len``, ``np.std``), applied to all grid points found below a grid point in ``self``. See also :py:meth:`Grid.grid_lookup` and examples in the docs Parameters ---------- data : ndarray an ndarray of dimensions 2, 3, or 4, the two last ones being y, x. grid : Grid a Grid instance matching the data method : function, default: np.mean the aggregation method. Possibilities: np.std, np.median, np.sum, and more. Use ``len`` to count the number of grid points! lut : ndarray, optional computing the lookup table can be expensive. If you have several operations to do with the same grid, set ``lut`` to an existing table obtained from a previous call to :py:meth:`Grid.grid_lookup` return_lut : bool, optional set to True if you want to return the lookup table for later use. in this case, returns a tuple Returns ------- An aggregated ndarray of the data, in ``self`` coordinates. If ``return_lut==True``, also return the lookup table """ # Input checks if grid is None: grid = check_crs(data) # xarray if not isinstance(grid, Grid): raise ValueError('grid should be a Grid instance') if hasattr(data, 'values'): data = data.values # xarray # dimensional check in_shape = data.shape ndims = len(in_shape) if (ndims < 2) or (ndims > 4): raise ValueError('data dimension not accepted') if (in_shape[-1] != grid.nx) or (in_shape[-2] != grid.ny): raise ValueError('data dimension not compatible') if lut is None: lut = self.grid_lookup(grid) # Prepare the output out_shape = list(in_shape) out_shape[-2:] = [self.ny, self.nx] if data.dtype.kind == 'i': out_data = np.zeros(out_shape, dtype=float) * np.NaN else: out_data = np.zeros(out_shape, dtype=data.dtype) * np.NaN def _2d_trafo(ind, outd): for ji, l in lut.items(): outd[ji] = method(ind[l[:, 0], l[:, 1]]) return outd if ndims == 2: _2d_trafo(data, out_data) if ndims == 3: for dimi, cdata in enumerate(data): out_data[dimi, ...] = _2d_trafo(cdata, out_data[dimi, ...]) if ndims == 4: for dimj, cdata in enumerate(data): for dimi, ccdata in enumerate(cdata): tmp = _2d_trafo(ccdata, out_data[dimj, dimi, ...]) out_data[dimj, dimi, ...] = tmp # prepare output if method is len: out_data[~np.isfinite(out_data)] = 0 out_data = out_data.astype(int) else: out_data = np.ma.masked_invalid(out_data) if return_lut: return out_data, lut else: return out_data
(self, data, grid=None, method=<function mean at 0x7f9b3c8b9c70>, lut=None, return_lut=False)
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22,581
salem.gis
map_gridded_data
Reprojects any structured data onto the local grid. The z and time dimensions of the data (if provided) are conserved, but the projected data will have the x, y dimensions of the local grid. Currently, nearest neighbor, linear, and spline interpolation are available. The dtype of the input data is guaranteed to be conserved, except for int which will be converted to floats if non nearest neighbor interpolation is asked. Parameters ---------- data : ndarray an ndarray of dimensions 2, 3, or 4, the two last ones being y, x. grid : Grid a Grid instance matching the data interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. missing : int integer value to attribute to invalid data (for integer data only, floats invalids are forced to NaNs) out : ndarray output array to fill instead of creating a new one (useful for overwriting stuffs) Returns ------- A projected ndarray of the data, in ``self`` coordinates.
def map_gridded_data(self, data, grid=None, interp='nearest', ks=3, out=None): """Reprojects any structured data onto the local grid. The z and time dimensions of the data (if provided) are conserved, but the projected data will have the x, y dimensions of the local grid. Currently, nearest neighbor, linear, and spline interpolation are available. The dtype of the input data is guaranteed to be conserved, except for int which will be converted to floats if non nearest neighbor interpolation is asked. Parameters ---------- data : ndarray an ndarray of dimensions 2, 3, or 4, the two last ones being y, x. grid : Grid a Grid instance matching the data interp : str 'nearest' (default), 'linear', or 'spline' ks : int Degree of the bivariate spline. Default is 3. missing : int integer value to attribute to invalid data (for integer data only, floats invalids are forced to NaNs) out : ndarray output array to fill instead of creating a new one (useful for overwriting stuffs) Returns ------- A projected ndarray of the data, in ``self`` coordinates. """ if grid is None: try: grid = data.salem.grid # try xarray except AttributeError: pass # Input checks if not isinstance(grid, Grid): raise ValueError('grid should be a Grid instance') try: # in case someone gave an xarray dataarray data = data.values except AttributeError: pass try: # in case someone gave a masked array (won't work with scipy) data = data.filled(np.nan) except AttributeError: pass if data.dtype == np.float32: # New in scipy - issue with float32 data = data.astype(np.float64) in_shape = data.shape ndims = len(in_shape) if (ndims < 2) or (ndims > 4): raise ValueError('data dimension not accepted') if (in_shape[-1] != grid.nx) or (in_shape[-2] != grid.ny): raise ValueError('data dimension not compatible') interp = interp.lower() use_nn = False if interp == 'nearest': use_nn = True # Transform the local grid into the input grid (backwards transform) # Work in center grid cause that's what we need # TODO: this stage could be optimized when many variables need transfo i, j = self.center_grid.ij_coordinates oi, oj = grid.center_grid.transform(i, j, crs=self.center_grid, nearest=use_nn, maskout=False) pv = np.nonzero((oi >= 0) & (oi < grid.nx) & (oj >= 0) & (oj < grid.ny)) # Prepare the output if out is not None: out_data = np.ma.asarray(out) else: out_shape = list(in_shape) out_shape[-2:] = [self.ny, self.nx] if (data.dtype.kind == 'i') and (interp == 'nearest'): # We dont do integer arithmetics other than nearest out_data = np.ma.masked_all(out_shape, dtype=data.dtype) elif data.dtype.kind == 'i': out_data = np.ma.masked_all(out_shape, dtype=float) else: out_data = np.ma.masked_all(out_shape, dtype=data.dtype) # Spare us the trouble if len(pv[0]) == 0: return out_data i, j, oi, oj = i[pv], j[pv], oi[pv], oj[pv] # Interpolate if interp == 'nearest': if out is not None: if ndims > 2: raise ValueError('Need 2D for now.') vok = np.isfinite(data[oj, oi]) out_data[j[vok], i[vok]] = data[oj[vok], oi[vok]] else: out_data[..., j, i] = data[..., oj, oi] elif interp == 'linear': points = (np.arange(grid.ny), np.arange(grid.nx)) if ndims == 2: f = RegularGridInterpolator(points, data, bounds_error=False) if out is not None: tmp = f((oj, oi)) vok = np.isfinite(tmp) out_data[j[vok], i[vok]] = tmp[vok] else: out_data[j, i] = f((oj, oi)) if ndims == 3: for dimi, cdata in enumerate(data): f = RegularGridInterpolator(points, cdata, bounds_error=False) if out is not None: tmp = f((oj, oi)) vok = np.isfinite(tmp) out_data[dimi, j[vok], i[vok]] = tmp[vok] else: out_data[dimi, j, i] = f((oj, oi)) if ndims == 4: for dimj, cdata in enumerate(data): for dimi, ccdata in enumerate(cdata): f = RegularGridInterpolator(points, ccdata, bounds_error=False) if out is not None: tmp = f((oj, oi)) vok = np.isfinite(tmp) out_data[dimj, dimi, j[vok], i[vok]] = tmp[vok] else: out_data[dimj, dimi, j, i] = f((oj, oi)) elif interp == 'spline': px, py = np.arange(grid.ny), np.arange(grid.nx) if ndims == 2: f = RectBivariateSpline(px, py, data, kx=ks, ky=ks) if out is not None: tmp = f(oj, oi, grid=False) vok = np.isfinite(tmp) out_data[j[vok], i[vok]] = tmp[vok] else: out_data[j, i] = f(oj, oi, grid=False) if ndims == 3: for dimi, cdata in enumerate(data): f = RectBivariateSpline(px, py, cdata, kx=ks, ky=ks) if out is not None: tmp = f(oj, oi, grid=False) vok = np.isfinite(tmp) out_data[dimi, j[vok], i[vok]] = tmp[vok] else: out_data[dimi, j, i] = f(oj, oi, grid=False) if ndims == 4: for dimj, cdata in enumerate(data): for dimi, ccdata in enumerate(cdata): f = RectBivariateSpline(px, py, ccdata, kx=ks, ky=ks) if out is not None: tmp = f(oj, oi, grid=False) vok = np.isfinite(tmp) out_data[dimj, dimi, j[vok], i[vok]] = tmp[vok] else: out_data[dimj, dimi, j, i] = f(oj, oi, grid=False) else: msg = 'interpolation not understood: {}'.format(interp) raise ValueError(msg) # we have to catch a warning for an unexplained reason with warnings.catch_warnings(): mess = "invalid value encountered in isfinite" warnings.filterwarnings("ignore", message=mess) out_data = np.ma.masked_invalid(out_data) return out_data
(self, data, grid=None, interp='nearest', ks=3, out=None)
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22,582
salem.gis
region_of_interest
Computes a region of interest (ROI). A ROI is simply a mask of the same size as the grid. Parameters ---------- shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray add the new region_of_interest to a previous one (useful for multiple geometries for example) all_touched : boolean pass-through argument for rasterio.features.rasterize, indicating that all grid cells which are clipped by the shapefile defining the region of interest should be included (default=False)
def region_of_interest(self, shape=None, geometry=None, grid=None, corners=None, crs=wgs84, roi=None, all_touched=False): """Computes a region of interest (ROI). A ROI is simply a mask of the same size as the grid. Parameters ---------- shape : str path to a shapefile geometry : geometry a shapely geometry (don't forget the crs keyword) grid : Grid a Grid object which extent will form the ROI corners : tuple a ((x0, y0), (x1, y1)) tuple of the corners of the square to subset the dataset to (don't forget the crs keyword) crs : crs, default wgs84 coordinate reference system of the geometry and corners roi : ndarray add the new region_of_interest to a previous one (useful for multiple geometries for example) all_touched : boolean pass-through argument for rasterio.features.rasterize, indicating that all grid cells which are clipped by the shapefile defining the region of interest should be included (default=False) """ # Initial mask if roi is not None: mask = np.array(roi, dtype=np.int16) else: mask = np.zeros((self.ny, self.nx), dtype=np.int16) # Collect keyword arguments, overriding anything the user # inadvertently added rasterize_kws = dict(out=mask, all_touched=all_touched) # Several cases if shape is not None: import pandas as pd inplace = False if not isinstance(shape, pd.DataFrame): from salem.sio import read_shapefile shape = read_shapefile(shape) inplace = True # corner grid is needed for rasterio shape = transform_geopandas(shape, to_crs=self.corner_grid, inplace=inplace) import rasterio from rasterio.features import rasterize with rasterio.Env(): mask = rasterize(shape.geometry, **rasterize_kws) if geometry is not None: import rasterio from rasterio.features import rasterize # corner grid is needed for rasterio geom = transform_geometry(geometry, crs=crs, to_crs=self.corner_grid) with rasterio.Env(): mask = rasterize(np.atleast_1d(geom), **rasterize_kws) if grid is not None: _tmp = np.ones((grid.ny, grid.nx), dtype=np.int16) mask = self.map_gridded_data(_tmp, grid, out=mask).filled(0) if corners is not None: cgrid = self.center_grid xy0, xy1 = corners x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True) x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True) mask[np.min([y0, y1]):np.max([y0, y1]) + 1, np.min([x0, x1]):np.max([x0, x1]) + 1] = 1 return mask
(self, shape=None, geometry=None, grid=None, corners=None, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, roi=None, all_touched=False)
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22,583
salem.gis
regrid
Make a copy of the grid with an updated spatial resolution. The keyword parameters are mutually exclusive, because the x/y ratio of the grid has to be preserved. Parameters ---------- nx : int the new number of x pixels nx : int the new number of y pixels factor : int multiplication factor (factor=3 will generate a grid with a spatial resolution 3 times finer) Returns ------- a new Grid object.
def regrid(self, nx=None, ny=None, factor=1): """Make a copy of the grid with an updated spatial resolution. The keyword parameters are mutually exclusive, because the x/y ratio of the grid has to be preserved. Parameters ---------- nx : int the new number of x pixels nx : int the new number of y pixels factor : int multiplication factor (factor=3 will generate a grid with a spatial resolution 3 times finer) Returns ------- a new Grid object. """ if nx is not None: factor = nx / self.nx if ny is not None: factor = ny / self.ny nx = self.nx * factor ny = self.ny * factor dx = self.dx / factor dy = self.dy / factor x0 = self.corner_grid.x0 y0 = self.corner_grid.y0 args = dict(nxny=(nx, ny), dxdy=(dx, dy), x0y0=(x0, y0), proj=self.proj, pixel_ref='corner') g = Grid(**args) if self.pixel_ref == 'center': g = g.center_grid return g
(self, nx=None, ny=None, factor=1)
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22,584
salem.gis
to_dataset
Creates an empty dataset based on the Grid's geolocalisation. Returns ------- An xarray.Dataset object ready to be filled with data
def to_dataset(self): """Creates an empty dataset based on the Grid's geolocalisation. Returns ------- An xarray.Dataset object ready to be filled with data """ import xarray as xr ds = xr.Dataset(coords={'x': (['x', ], self.center_grid.x_coord), 'y': (['y', ], self.center_grid.y_coord)} ) ds.attrs['pyproj_srs'] = self.proj.srs return ds
(self)
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22,585
salem.gis
to_dict
Serialize this grid to a dictionary. Returns ------- a grid dictionary See Also -------- from_dict : create a Grid from a dict
def to_dict(self): """Serialize this grid to a dictionary. Returns ------- a grid dictionary See Also -------- from_dict : create a Grid from a dict """ return dict(proj=self.proj.srs, x0y0=(self.x0, self.y0), nxny=(self.nx, self.ny), dxdy=(self.dx, self.dy), pixel_ref=self.pixel_ref)
(self)
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22,586
salem.gis
to_geometry
Makes a geometrical representation of the grid (e.g. for drawing). This can come also handy when doing shape-to-raster operations. TODO: currently returns one polygon for each grid point, but this could do more. Returns ------- a geopandas.GeoDataFrame
def to_geometry(self, to_crs=None): """Makes a geometrical representation of the grid (e.g. for drawing). This can come also handy when doing shape-to-raster operations. TODO: currently returns one polygon for each grid point, but this could do more. Returns ------- a geopandas.GeoDataFrame """ from geopandas import GeoDataFrame from shapely.geometry import Polygon out = GeoDataFrame() geoms = [] ii = [] jj = [] xx = self.corner_grid.x0 + np.arange(self.nx+1) * self.dx yy = self.corner_grid.y0 + np.arange(self.ny+1) * self.dy for j, (y0, y1) in enumerate(zip(yy[:-1], yy[1:])): for i, (x0, x1) in enumerate(zip(xx[:-1], xx[1:])): coords = [(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)] geoms.append(Polygon(coords)) jj.append(j) ii.append(i) out['j'] = jj out['i'] = ii out.set_geometry(geoms, crs=self.proj.srs, inplace=True) if check_crs(to_crs): transform_geopandas(out, to_crs=to_crs, inplace=True) return out
(self, to_crs=None)
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22,587
salem.gis
to_json
Serialize this grid to a json file. Parameters ---------- fpath : str, required the path to the file to create See Also -------- from_json : read a json file
def to_json(self, fpath): """Serialize this grid to a json file. Parameters ---------- fpath : str, required the path to the file to create See Also -------- from_json : read a json file """ import json with open(fpath, 'w') as fp: json.dump(self.to_dict(), fp)
(self, fpath)
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22,588
salem.gis
transform
Converts any coordinates into the local grid. Parameters ---------- x : ndarray the grid coordinates of the point(s) you want to convert y : ndarray the grid coordinates of the point(s) you want to convert z : None ignored (but necessary since some shapes have a z dimension) crs : crs reference system of x, y. Could be a pyproj.Proj instance or a Grid instance. In the latter case (x, y) are actually (i, j). (Default: lonlat in wgs84). nearest : bool set to True if you wish to return the closest i, j coordinates instead of subpixel coords. maskout : bool set to true if you want to mask out the transformed coordinates that are not within the grid. Returns ------- (i, j) coordinates of the points in the local grid.
def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False): """Converts any coordinates into the local grid. Parameters ---------- x : ndarray the grid coordinates of the point(s) you want to convert y : ndarray the grid coordinates of the point(s) you want to convert z : None ignored (but necessary since some shapes have a z dimension) crs : crs reference system of x, y. Could be a pyproj.Proj instance or a Grid instance. In the latter case (x, y) are actually (i, j). (Default: lonlat in wgs84). nearest : bool set to True if you wish to return the closest i, j coordinates instead of subpixel coords. maskout : bool set to true if you want to mask out the transformed coordinates that are not within the grid. Returns ------- (i, j) coordinates of the points in the local grid. """ x, y = np.ma.array(x), np.ma.array(y) # First to local proj _crs = check_crs(crs, raise_on_error=True) if isinstance(_crs, pyproj.Proj): x, y = transform_proj(_crs, self.proj, x, y) elif isinstance(_crs, Grid): x, y = _crs.ij_to_crs(x, y, crs=self.proj) # Then to local grid x = (x - self.x0) / self.dx y = (y - self.y0) / self.dy # See if we need to round if nearest: f = np.rint if self.pixel_ref == 'center' else np.floor x = f(x).astype(int) y = f(y).astype(int) # Mask? if maskout: if self.pixel_ref == 'center': mask = ~((x >= -0.5) & (x < self.nx-0.5) & (y >= -0.5) & (y < self.ny-0.5)) else: mask = ~((x >= 0) & (x < self.nx) & (y >= 0) & (y < self.ny)) x = np.ma.array(x, mask=mask) y = np.ma.array(y, mask=mask) return x, y
(self, x, y, z=None, crs=<Other Coordinate Operation Transformer: longlat> Description: PROJ-based coordinate operation Area of Use: - undefined, nearest=False, maskout=False)
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22,589
salem
Map
null
def Map(): raise ImportError('requires matplotlib')
()
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22,590
scipy.interpolate._fitpack2
RectBivariateSpline
Bivariate spline approximation over a rectangular mesh. Can be used for both smoothing and interpolating data. Parameters ---------- x,y : array_like 1-D arrays of coordinates in strictly ascending order. Evaluated points outside the data range will be extrapolated. z : array_like 2-D array of data with shape (x.size,y.size). bbox : array_like, optional Sequence of length 4 specifying the boundary of the rectangular approximation domain, which means the start and end spline knots of each dimension are set by these values. By default, ``bbox=[min(x), max(x), min(y), max(y)]``. kx, ky : ints, optional Degrees of the bivariate spline. Default is 3. s : float, optional Positive smoothing factor defined for estimation condition: ``sum((z[i]-f(x[i], y[i]))**2, axis=0) <= s`` where f is a spline function. Default is ``s=0``, which is for interpolation. See Also -------- BivariateSpline : a base class for bivariate splines. UnivariateSpline : a smooth univariate spline to fit a given set of data points. SmoothBivariateSpline : a smoothing bivariate spline through the given points LSQBivariateSpline : a bivariate spline using weighted least-squares fitting RectSphereBivariateSpline : a bivariate spline over a rectangular mesh on a sphere SmoothSphereBivariateSpline : a smoothing bivariate spline in spherical coordinates LSQSphereBivariateSpline : a bivariate spline in spherical coordinates using weighted least-squares fitting bisplrep : a function to find a bivariate B-spline representation of a surface bisplev : a function to evaluate a bivariate B-spline and its derivatives Notes ----- If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating.
class RectBivariateSpline(BivariateSpline): """ Bivariate spline approximation over a rectangular mesh. Can be used for both smoothing and interpolating data. Parameters ---------- x,y : array_like 1-D arrays of coordinates in strictly ascending order. Evaluated points outside the data range will be extrapolated. z : array_like 2-D array of data with shape (x.size,y.size). bbox : array_like, optional Sequence of length 4 specifying the boundary of the rectangular approximation domain, which means the start and end spline knots of each dimension are set by these values. By default, ``bbox=[min(x), max(x), min(y), max(y)]``. kx, ky : ints, optional Degrees of the bivariate spline. Default is 3. s : float, optional Positive smoothing factor defined for estimation condition: ``sum((z[i]-f(x[i], y[i]))**2, axis=0) <= s`` where f is a spline function. Default is ``s=0``, which is for interpolation. See Also -------- BivariateSpline : a base class for bivariate splines. UnivariateSpline : a smooth univariate spline to fit a given set of data points. SmoothBivariateSpline : a smoothing bivariate spline through the given points LSQBivariateSpline : a bivariate spline using weighted least-squares fitting RectSphereBivariateSpline : a bivariate spline over a rectangular mesh on a sphere SmoothSphereBivariateSpline : a smoothing bivariate spline in spherical coordinates LSQSphereBivariateSpline : a bivariate spline in spherical coordinates using weighted least-squares fitting bisplrep : a function to find a bivariate B-spline representation of a surface bisplev : a function to evaluate a bivariate B-spline and its derivatives Notes ----- If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating. """ def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0): x, y, bbox = ravel(x), ravel(y), ravel(bbox) z = np.asarray(z) if not np.all(diff(x) > 0.0): raise ValueError('x must be strictly increasing') if not np.all(diff(y) > 0.0): raise ValueError('y must be strictly increasing') if not x.size == z.shape[0]: raise ValueError('x dimension of z must have same number of ' 'elements as x') if not y.size == z.shape[1]: raise ValueError('y dimension of z must have same number of ' 'elements as y') if not bbox.shape == (4,): raise ValueError('bbox shape should be (4,)') if s is not None and not s >= 0.0: raise ValueError("s should be s >= 0.0") z = ravel(z) xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(x, y, z, xb, xe, yb, ye, kx, ky, s) if ier not in [0, -1, -2]: msg = _surfit_messages.get(ier, 'ier=%s' % (ier)) raise ValueError(msg) self.fp = fp self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)] self.degrees = kx, ky
(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)
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22,591
scipy.interpolate._fitpack2
__call__
Evaluate the spline or its derivatives at given positions. Parameters ---------- x, y : array_like Input coordinates. If `grid` is False, evaluate the spline at points ``(x[i], y[i]), i=0, ..., len(x)-1``. Standard Numpy broadcasting is obeyed. If `grid` is True: evaluate spline at the grid points defined by the coordinate arrays x, y. The arrays must be sorted to increasing order. The ordering of axes is consistent with ``np.meshgrid(..., indexing="ij")`` and inconsistent with the default ordering ``np.meshgrid(..., indexing="xy")``. dx : int Order of x-derivative .. versionadded:: 0.14.0 dy : int Order of y-derivative .. versionadded:: 0.14.0 grid : bool Whether to evaluate the results on a grid spanned by the input arrays, or at points specified by the input arrays. .. versionadded:: 0.14.0 Examples -------- Suppose that we want to bilinearly interpolate an exponentially decaying function in 2 dimensions. >>> import numpy as np >>> from scipy.interpolate import RectBivariateSpline We sample the function on a coarse grid. Note that the default indexing="xy" of meshgrid would result in an unexpected (transposed) result after interpolation. >>> xarr = np.linspace(-3, 3, 100) >>> yarr = np.linspace(-3, 3, 100) >>> xgrid, ygrid = np.meshgrid(xarr, yarr, indexing="ij") The function to interpolate decays faster along one axis than the other. >>> zdata = np.exp(-np.sqrt((xgrid / 2) ** 2 + ygrid**2)) Next we sample on a finer grid using interpolation (kx=ky=1 for bilinear). >>> rbs = RectBivariateSpline(xarr, yarr, zdata, kx=1, ky=1) >>> xarr_fine = np.linspace(-3, 3, 200) >>> yarr_fine = np.linspace(-3, 3, 200) >>> xgrid_fine, ygrid_fine = np.meshgrid(xarr_fine, yarr_fine, indexing="ij") >>> zdata_interp = rbs(xgrid_fine, ygrid_fine, grid=False) And check that the result agrees with the input by plotting both. >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(1, 2, 1, aspect="equal") >>> ax2 = fig.add_subplot(1, 2, 2, aspect="equal") >>> ax1.imshow(zdata) >>> ax2.imshow(zdata_interp) >>> plt.show()
def __call__(self, x, y, dx=0, dy=0, grid=True): """ Evaluate the spline or its derivatives at given positions. Parameters ---------- x, y : array_like Input coordinates. If `grid` is False, evaluate the spline at points ``(x[i], y[i]), i=0, ..., len(x)-1``. Standard Numpy broadcasting is obeyed. If `grid` is True: evaluate spline at the grid points defined by the coordinate arrays x, y. The arrays must be sorted to increasing order. The ordering of axes is consistent with ``np.meshgrid(..., indexing="ij")`` and inconsistent with the default ordering ``np.meshgrid(..., indexing="xy")``. dx : int Order of x-derivative .. versionadded:: 0.14.0 dy : int Order of y-derivative .. versionadded:: 0.14.0 grid : bool Whether to evaluate the results on a grid spanned by the input arrays, or at points specified by the input arrays. .. versionadded:: 0.14.0 Examples -------- Suppose that we want to bilinearly interpolate an exponentially decaying function in 2 dimensions. >>> import numpy as np >>> from scipy.interpolate import RectBivariateSpline We sample the function on a coarse grid. Note that the default indexing="xy" of meshgrid would result in an unexpected (transposed) result after interpolation. >>> xarr = np.linspace(-3, 3, 100) >>> yarr = np.linspace(-3, 3, 100) >>> xgrid, ygrid = np.meshgrid(xarr, yarr, indexing="ij") The function to interpolate decays faster along one axis than the other. >>> zdata = np.exp(-np.sqrt((xgrid / 2) ** 2 + ygrid**2)) Next we sample on a finer grid using interpolation (kx=ky=1 for bilinear). >>> rbs = RectBivariateSpline(xarr, yarr, zdata, kx=1, ky=1) >>> xarr_fine = np.linspace(-3, 3, 200) >>> yarr_fine = np.linspace(-3, 3, 200) >>> xgrid_fine, ygrid_fine = np.meshgrid(xarr_fine, yarr_fine, indexing="ij") >>> zdata_interp = rbs(xgrid_fine, ygrid_fine, grid=False) And check that the result agrees with the input by plotting both. >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(1, 2, 1, aspect="equal") >>> ax2 = fig.add_subplot(1, 2, 2, aspect="equal") >>> ax1.imshow(zdata) >>> ax2.imshow(zdata_interp) >>> plt.show() """ x = np.asarray(x) y = np.asarray(y) tx, ty, c = self.tck[:3] kx, ky = self.degrees if grid: if x.size == 0 or y.size == 0: return np.zeros((x.size, y.size), dtype=self.tck[2].dtype) if (x.size >= 2) and (not np.all(np.diff(x) >= 0.0)): raise ValueError("x must be strictly increasing when `grid` is True") if (y.size >= 2) and (not np.all(np.diff(y) >= 0.0)): raise ValueError("y must be strictly increasing when `grid` is True") if dx or dy: z, ier = dfitpack.parder(tx, ty, c, kx, ky, dx, dy, x, y) if not ier == 0: raise ValueError("Error code returned by parder: %s" % ier) else: z, ier = dfitpack.bispev(tx, ty, c, kx, ky, x, y) if not ier == 0: raise ValueError("Error code returned by bispev: %s" % ier) else: # standard Numpy broadcasting if x.shape != y.shape: x, y = np.broadcast_arrays(x, y) shape = x.shape x = x.ravel() y = y.ravel() if x.size == 0 or y.size == 0: return np.zeros(shape, dtype=self.tck[2].dtype) if dx or dy: z, ier = dfitpack.pardeu(tx, ty, c, kx, ky, dx, dy, x, y) if not ier == 0: raise ValueError("Error code returned by pardeu: %s" % ier) else: z, ier = dfitpack.bispeu(tx, ty, c, kx, ky, x, y) if not ier == 0: raise ValueError("Error code returned by bispeu: %s" % ier) z = z.reshape(shape) return z
(self, x, y, dx=0, dy=0, grid=True)
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22,592
scipy.interpolate._fitpack2
__init__
null
def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0): x, y, bbox = ravel(x), ravel(y), ravel(bbox) z = np.asarray(z) if not np.all(diff(x) > 0.0): raise ValueError('x must be strictly increasing') if not np.all(diff(y) > 0.0): raise ValueError('y must be strictly increasing') if not x.size == z.shape[0]: raise ValueError('x dimension of z must have same number of ' 'elements as x') if not y.size == z.shape[1]: raise ValueError('y dimension of z must have same number of ' 'elements as y') if not bbox.shape == (4,): raise ValueError('bbox shape should be (4,)') if s is not None and not s >= 0.0: raise ValueError("s should be s >= 0.0") z = ravel(z) xb, xe, yb, ye = bbox nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(x, y, z, xb, xe, yb, ye, kx, ky, s) if ier not in [0, -1, -2]: msg = _surfit_messages.get(ier, 'ier=%s' % (ier)) raise ValueError(msg) self.fp = fp self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)] self.degrees = kx, ky
(self, x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)
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22,593
scipy.interpolate._fitpack2
_validate_input
null
@staticmethod def _validate_input(x, y, z, w, kx, ky, eps): x, y, z = np.asarray(x), np.asarray(y), np.asarray(z) if not x.size == y.size == z.size: raise ValueError('x, y, and z should have a same length') if w is not None: w = np.asarray(w) if x.size != w.size: raise ValueError('x, y, z, and w should have a same length') elif not np.all(w >= 0.0): raise ValueError('w should be positive') if (eps is not None) and (not 0.0 < eps < 1.0): raise ValueError('eps should be between (0, 1)') if not x.size >= (kx + 1) * (ky + 1): raise ValueError('The length of x, y and z should be at least' ' (kx+1) * (ky+1)') return x, y, z, w
(x, y, z, w, kx, ky, eps)
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22,594
scipy.interpolate._fitpack2
ev
Evaluate the spline at points Returns the interpolated value at ``(xi[i], yi[i]), i=0,...,len(xi)-1``. Parameters ---------- xi, yi : array_like Input coordinates. Standard Numpy broadcasting is obeyed. The ordering of axes is consistent with ``np.meshgrid(..., indexing="ij")`` and inconsistent with the default ordering ``np.meshgrid(..., indexing="xy")``. dx : int, optional Order of x-derivative .. versionadded:: 0.14.0 dy : int, optional Order of y-derivative .. versionadded:: 0.14.0 Examples -------- Suppose that we want to bilinearly interpolate an exponentially decaying function in 2 dimensions. >>> import numpy as np >>> from scipy.interpolate import RectBivariateSpline >>> def f(x, y): ... return np.exp(-np.sqrt((x / 2) ** 2 + y**2)) We sample the function on a coarse grid and set up the interpolator. Note that the default ``indexing="xy"`` of meshgrid would result in an unexpected (transposed) result after interpolation. >>> xarr = np.linspace(-3, 3, 21) >>> yarr = np.linspace(-3, 3, 21) >>> xgrid, ygrid = np.meshgrid(xarr, yarr, indexing="ij") >>> zdata = f(xgrid, ygrid) >>> rbs = RectBivariateSpline(xarr, yarr, zdata, kx=1, ky=1) Next we sample the function along a diagonal slice through the coordinate space on a finer grid using interpolation. >>> xinterp = np.linspace(-3, 3, 201) >>> yinterp = np.linspace(3, -3, 201) >>> zinterp = rbs.ev(xinterp, yinterp) And check that the interpolation passes through the function evaluations as a function of the distance from the origin along the slice. >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(1, 1, 1) >>> ax1.plot(np.sqrt(xarr**2 + yarr**2), np.diag(zdata), "or") >>> ax1.plot(np.sqrt(xinterp**2 + yinterp**2), zinterp, "-b") >>> plt.show()
def ev(self, xi, yi, dx=0, dy=0): """ Evaluate the spline at points Returns the interpolated value at ``(xi[i], yi[i]), i=0,...,len(xi)-1``. Parameters ---------- xi, yi : array_like Input coordinates. Standard Numpy broadcasting is obeyed. The ordering of axes is consistent with ``np.meshgrid(..., indexing="ij")`` and inconsistent with the default ordering ``np.meshgrid(..., indexing="xy")``. dx : int, optional Order of x-derivative .. versionadded:: 0.14.0 dy : int, optional Order of y-derivative .. versionadded:: 0.14.0 Examples -------- Suppose that we want to bilinearly interpolate an exponentially decaying function in 2 dimensions. >>> import numpy as np >>> from scipy.interpolate import RectBivariateSpline >>> def f(x, y): ... return np.exp(-np.sqrt((x / 2) ** 2 + y**2)) We sample the function on a coarse grid and set up the interpolator. Note that the default ``indexing="xy"`` of meshgrid would result in an unexpected (transposed) result after interpolation. >>> xarr = np.linspace(-3, 3, 21) >>> yarr = np.linspace(-3, 3, 21) >>> xgrid, ygrid = np.meshgrid(xarr, yarr, indexing="ij") >>> zdata = f(xgrid, ygrid) >>> rbs = RectBivariateSpline(xarr, yarr, zdata, kx=1, ky=1) Next we sample the function along a diagonal slice through the coordinate space on a finer grid using interpolation. >>> xinterp = np.linspace(-3, 3, 201) >>> yinterp = np.linspace(3, -3, 201) >>> zinterp = rbs.ev(xinterp, yinterp) And check that the interpolation passes through the function evaluations as a function of the distance from the origin along the slice. >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(1, 1, 1) >>> ax1.plot(np.sqrt(xarr**2 + yarr**2), np.diag(zdata), "or") >>> ax1.plot(np.sqrt(xinterp**2 + yinterp**2), zinterp, "-b") >>> plt.show() """ return self.__call__(xi, yi, dx=dx, dy=dy, grid=False)
(self, xi, yi, dx=0, dy=0)
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22,595
scipy.interpolate._fitpack2
get_coeffs
Return spline coefficients.
def get_coeffs(self): """ Return spline coefficients.""" return self.tck[2]
(self)
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22,596
scipy.interpolate._fitpack2
get_knots
Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. The position of interior and additional knots are given as t[k+1:-k-1] and t[:k+1]=b, t[-k-1:]=e, respectively.
def get_knots(self): """ Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. The position of interior and additional knots are given as t[k+1:-k-1] and t[:k+1]=b, t[-k-1:]=e, respectively. """ return self.tck[:2]
(self)
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22,597
scipy.interpolate._fitpack2
get_residual
Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0)
def get_residual(self): """ Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) """ return self.fp
(self)
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22,598
scipy.interpolate._fitpack2
integral
Evaluate the integral of the spline over area [xa,xb] x [ya,yb]. Parameters ---------- xa, xb : float The end-points of the x integration interval. ya, yb : float The end-points of the y integration interval. Returns ------- integ : float The value of the resulting integral.
def integral(self, xa, xb, ya, yb): """ Evaluate the integral of the spline over area [xa,xb] x [ya,yb]. Parameters ---------- xa, xb : float The end-points of the x integration interval. ya, yb : float The end-points of the y integration interval. Returns ------- integ : float The value of the resulting integral. """ tx, ty, c = self.tck[:3] kx, ky = self.degrees return dfitpack.dblint(tx, ty, c, kx, ky, xa, xb, ya, yb)
(self, xa, xb, ya, yb)
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22,599
scipy.interpolate._fitpack2
partial_derivative
Construct a new spline representing a partial derivative of this spline. Parameters ---------- dx, dy : int Orders of the derivative in x and y respectively. They must be non-negative integers and less than the respective degree of the original spline (self) in that direction (``kx``, ``ky``). Returns ------- spline : A new spline of degrees (``kx - dx``, ``ky - dy``) representing the derivative of this spline. Notes ----- .. versionadded:: 1.9.0
def partial_derivative(self, dx, dy): """Construct a new spline representing a partial derivative of this spline. Parameters ---------- dx, dy : int Orders of the derivative in x and y respectively. They must be non-negative integers and less than the respective degree of the original spline (self) in that direction (``kx``, ``ky``). Returns ------- spline : A new spline of degrees (``kx - dx``, ``ky - dy``) representing the derivative of this spline. Notes ----- .. versionadded:: 1.9.0 """ if dx == 0 and dy == 0: return self else: kx, ky = self.degrees if not (dx >= 0 and dy >= 0): raise ValueError("order of derivative must be positive or" " zero") if not (dx < kx and dy < ky): raise ValueError("order of derivative must be less than" " degree of spline") tx, ty, c = self.tck[:3] newc, ier = dfitpack.pardtc(tx, ty, c, kx, ky, dx, dy) if ier != 0: # This should not happen under normal conditions. raise ValueError("Unexpected error code returned by" " pardtc: %d" % ier) nx = len(tx) ny = len(ty) newtx = tx[dx:nx - dx] newty = ty[dy:ny - dy] newkx, newky = kx - dx, ky - dy newclen = (nx - dx - kx - 1) * (ny - dy - ky - 1) return _DerivedBivariateSpline._from_tck((newtx, newty, newc[:newclen], newkx, newky))
(self, dx, dy)
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22,600
scipy.interpolate._rgi
RegularGridInterpolator
Interpolator on a regular or rectilinear grid in arbitrary dimensions. The data must be defined on a rectilinear grid; that is, a rectangular grid with even or uneven spacing. Linear, nearest-neighbor, spline interpolations are supported. After setting up the interpolator object, the interpolation method may be chosen at each evaluation. Parameters ---------- points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ) The points defining the regular grid in n dimensions. The points in each dimension (i.e. every elements of the points tuple) must be strictly ascending or descending. values : array_like, shape (m1, ..., mn, ...) The data on the regular grid in n dimensions. Complex data is accepted. .. deprecated:: 1.13.0 Complex data is deprecated with ``method="pchip"`` and will raise an error in SciPy 1.15.0. This is because ``PchipInterpolator`` only works with real values. If you are trying to use the real components of the passed array, use ``np.real`` on ``values``. method : str, optional The method of interpolation to perform. Supported are "linear", "nearest", "slinear", "cubic", "quintic" and "pchip". This parameter will become the default for the object's ``__call__`` method. Default is "linear". bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then `fill_value` is used. Default is True. fill_value : float or None, optional The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Default is ``np.nan``. solver : callable, optional Only used for methods "slinear", "cubic" and "quintic". Sparse linear algebra solver for construction of the NdBSpline instance. Default is the iterative solver `scipy.sparse.linalg.gcrotmk`. .. versionadded:: 1.13 solver_args: dict, optional Additional arguments to pass to `solver`, if any. .. versionadded:: 1.13 Methods ------- __call__ Attributes ---------- grid : tuple of ndarrays The points defining the regular grid in n dimensions. This tuple defines the full grid via ``np.meshgrid(*grid, indexing='ij')`` values : ndarray Data values at the grid. method : str Interpolation method. fill_value : float or ``None`` Use this value for out-of-bounds arguments to `__call__`. bounds_error : bool If ``True``, out-of-bounds argument raise a ``ValueError``. Notes ----- Contrary to `LinearNDInterpolator` and `NearestNDInterpolator`, this class avoids expensive triangulation of the input data by taking advantage of the regular grid structure. In other words, this class assumes that the data is defined on a *rectilinear* grid. .. versionadded:: 0.14 The 'slinear'(k=1), 'cubic'(k=3), and 'quintic'(k=5) methods are tensor-product spline interpolators, where `k` is the spline degree, If any dimension has fewer points than `k` + 1, an error will be raised. .. versionadded:: 1.9 If the input data is such that dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating. **Choosing a solver for spline methods** Spline methods, "slinear", "cubic" and "quintic" involve solving a large sparse linear system at instantiation time. Depending on data, the default solver may or may not be adequate. When it is not, you may need to experiment with an optional `solver` argument, where you may choose between the direct solver (`scipy.sparse.linalg.spsolve`) or iterative solvers from `scipy.sparse.linalg`. You may need to supply additional parameters via the optional `solver_args` parameter (for instance, you may supply the starting value or target tolerance). See the `scipy.sparse.linalg` documentation for the full list of available options. Alternatively, you may instead use the legacy methods, "slinear_legacy", "cubic_legacy" and "quintic_legacy". These methods allow faster construction but evaluations will be much slower. Examples -------- **Evaluate a function on the points of a 3-D grid** As a first example, we evaluate a simple example function on the points of a 3-D grid: >>> from scipy.interpolate import RegularGridInterpolator >>> import numpy as np >>> def f(x, y, z): ... return 2 * x**3 + 3 * y**2 - z >>> x = np.linspace(1, 4, 11) >>> y = np.linspace(4, 7, 22) >>> z = np.linspace(7, 9, 33) >>> xg, yg ,zg = np.meshgrid(x, y, z, indexing='ij', sparse=True) >>> data = f(xg, yg, zg) ``data`` is now a 3-D array with ``data[i, j, k] = f(x[i], y[j], z[k])``. Next, define an interpolating function from this data: >>> interp = RegularGridInterpolator((x, y, z), data) Evaluate the interpolating function at the two points ``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``: >>> pts = np.array([[2.1, 6.2, 8.3], ... [3.3, 5.2, 7.1]]) >>> interp(pts) array([ 125.80469388, 146.30069388]) which is indeed a close approximation to >>> f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1) (125.54200000000002, 145.894) **Interpolate and extrapolate a 2D dataset** As a second example, we interpolate and extrapolate a 2D data set: >>> x, y = np.array([-2, 0, 4]), np.array([-2, 0, 2, 5]) >>> def ff(x, y): ... return x**2 + y**2 >>> xg, yg = np.meshgrid(x, y, indexing='ij') >>> data = ff(xg, yg) >>> interp = RegularGridInterpolator((x, y), data, ... bounds_error=False, fill_value=None) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(projection='3d') >>> ax.scatter(xg.ravel(), yg.ravel(), data.ravel(), ... s=60, c='k', label='data') Evaluate and plot the interpolator on a finer grid >>> xx = np.linspace(-4, 9, 31) >>> yy = np.linspace(-4, 9, 31) >>> X, Y = np.meshgrid(xx, yy, indexing='ij') >>> # interpolator >>> ax.plot_wireframe(X, Y, interp((X, Y)), rstride=3, cstride=3, ... alpha=0.4, color='m', label='linear interp') >>> # ground truth >>> ax.plot_wireframe(X, Y, ff(X, Y), rstride=3, cstride=3, ... alpha=0.4, label='ground truth') >>> plt.legend() >>> plt.show() Other examples are given :ref:`in the tutorial <tutorial-interpolate_regular_grid_interpolator>`. See Also -------- NearestNDInterpolator : Nearest neighbor interpolator on *unstructured* data in N dimensions LinearNDInterpolator : Piecewise linear interpolator on *unstructured* data in N dimensions interpn : a convenience function which wraps `RegularGridInterpolator` scipy.ndimage.map_coordinates : interpolation on grids with equal spacing (suitable for e.g., N-D image resampling) References ---------- .. [1] Python package *regulargrid* by Johannes Buchner, see https://pypi.python.org/pypi/regulargrid/ .. [2] Wikipedia, "Trilinear interpolation", https://en.wikipedia.org/wiki/Trilinear_interpolation .. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear and multilinear table interpolation in many dimensions." MATH. COMPUT. 50.181 (1988): 189-196. https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf :doi:`10.1090/S0025-5718-1988-0917826-0`
class RegularGridInterpolator: """ Interpolator on a regular or rectilinear grid in arbitrary dimensions. The data must be defined on a rectilinear grid; that is, a rectangular grid with even or uneven spacing. Linear, nearest-neighbor, spline interpolations are supported. After setting up the interpolator object, the interpolation method may be chosen at each evaluation. Parameters ---------- points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ) The points defining the regular grid in n dimensions. The points in each dimension (i.e. every elements of the points tuple) must be strictly ascending or descending. values : array_like, shape (m1, ..., mn, ...) The data on the regular grid in n dimensions. Complex data is accepted. .. deprecated:: 1.13.0 Complex data is deprecated with ``method="pchip"`` and will raise an error in SciPy 1.15.0. This is because ``PchipInterpolator`` only works with real values. If you are trying to use the real components of the passed array, use ``np.real`` on ``values``. method : str, optional The method of interpolation to perform. Supported are "linear", "nearest", "slinear", "cubic", "quintic" and "pchip". This parameter will become the default for the object's ``__call__`` method. Default is "linear". bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then `fill_value` is used. Default is True. fill_value : float or None, optional The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Default is ``np.nan``. solver : callable, optional Only used for methods "slinear", "cubic" and "quintic". Sparse linear algebra solver for construction of the NdBSpline instance. Default is the iterative solver `scipy.sparse.linalg.gcrotmk`. .. versionadded:: 1.13 solver_args: dict, optional Additional arguments to pass to `solver`, if any. .. versionadded:: 1.13 Methods ------- __call__ Attributes ---------- grid : tuple of ndarrays The points defining the regular grid in n dimensions. This tuple defines the full grid via ``np.meshgrid(*grid, indexing='ij')`` values : ndarray Data values at the grid. method : str Interpolation method. fill_value : float or ``None`` Use this value for out-of-bounds arguments to `__call__`. bounds_error : bool If ``True``, out-of-bounds argument raise a ``ValueError``. Notes ----- Contrary to `LinearNDInterpolator` and `NearestNDInterpolator`, this class avoids expensive triangulation of the input data by taking advantage of the regular grid structure. In other words, this class assumes that the data is defined on a *rectilinear* grid. .. versionadded:: 0.14 The 'slinear'(k=1), 'cubic'(k=3), and 'quintic'(k=5) methods are tensor-product spline interpolators, where `k` is the spline degree, If any dimension has fewer points than `k` + 1, an error will be raised. .. versionadded:: 1.9 If the input data is such that dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating. **Choosing a solver for spline methods** Spline methods, "slinear", "cubic" and "quintic" involve solving a large sparse linear system at instantiation time. Depending on data, the default solver may or may not be adequate. When it is not, you may need to experiment with an optional `solver` argument, where you may choose between the direct solver (`scipy.sparse.linalg.spsolve`) or iterative solvers from `scipy.sparse.linalg`. You may need to supply additional parameters via the optional `solver_args` parameter (for instance, you may supply the starting value or target tolerance). See the `scipy.sparse.linalg` documentation for the full list of available options. Alternatively, you may instead use the legacy methods, "slinear_legacy", "cubic_legacy" and "quintic_legacy". These methods allow faster construction but evaluations will be much slower. Examples -------- **Evaluate a function on the points of a 3-D grid** As a first example, we evaluate a simple example function on the points of a 3-D grid: >>> from scipy.interpolate import RegularGridInterpolator >>> import numpy as np >>> def f(x, y, z): ... return 2 * x**3 + 3 * y**2 - z >>> x = np.linspace(1, 4, 11) >>> y = np.linspace(4, 7, 22) >>> z = np.linspace(7, 9, 33) >>> xg, yg ,zg = np.meshgrid(x, y, z, indexing='ij', sparse=True) >>> data = f(xg, yg, zg) ``data`` is now a 3-D array with ``data[i, j, k] = f(x[i], y[j], z[k])``. Next, define an interpolating function from this data: >>> interp = RegularGridInterpolator((x, y, z), data) Evaluate the interpolating function at the two points ``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``: >>> pts = np.array([[2.1, 6.2, 8.3], ... [3.3, 5.2, 7.1]]) >>> interp(pts) array([ 125.80469388, 146.30069388]) which is indeed a close approximation to >>> f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1) (125.54200000000002, 145.894) **Interpolate and extrapolate a 2D dataset** As a second example, we interpolate and extrapolate a 2D data set: >>> x, y = np.array([-2, 0, 4]), np.array([-2, 0, 2, 5]) >>> def ff(x, y): ... return x**2 + y**2 >>> xg, yg = np.meshgrid(x, y, indexing='ij') >>> data = ff(xg, yg) >>> interp = RegularGridInterpolator((x, y), data, ... bounds_error=False, fill_value=None) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(projection='3d') >>> ax.scatter(xg.ravel(), yg.ravel(), data.ravel(), ... s=60, c='k', label='data') Evaluate and plot the interpolator on a finer grid >>> xx = np.linspace(-4, 9, 31) >>> yy = np.linspace(-4, 9, 31) >>> X, Y = np.meshgrid(xx, yy, indexing='ij') >>> # interpolator >>> ax.plot_wireframe(X, Y, interp((X, Y)), rstride=3, cstride=3, ... alpha=0.4, color='m', label='linear interp') >>> # ground truth >>> ax.plot_wireframe(X, Y, ff(X, Y), rstride=3, cstride=3, ... alpha=0.4, label='ground truth') >>> plt.legend() >>> plt.show() Other examples are given :ref:`in the tutorial <tutorial-interpolate_regular_grid_interpolator>`. See Also -------- NearestNDInterpolator : Nearest neighbor interpolator on *unstructured* data in N dimensions LinearNDInterpolator : Piecewise linear interpolator on *unstructured* data in N dimensions interpn : a convenience function which wraps `RegularGridInterpolator` scipy.ndimage.map_coordinates : interpolation on grids with equal spacing (suitable for e.g., N-D image resampling) References ---------- .. [1] Python package *regulargrid* by Johannes Buchner, see https://pypi.python.org/pypi/regulargrid/ .. [2] Wikipedia, "Trilinear interpolation", https://en.wikipedia.org/wiki/Trilinear_interpolation .. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear and multilinear table interpolation in many dimensions." MATH. COMPUT. 50.181 (1988): 189-196. https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf :doi:`10.1090/S0025-5718-1988-0917826-0` """ # this class is based on code originally programmed by Johannes Buchner,
(points, values, method='linear', bounds_error=True, fill_value=nan, *, solver=None, solver_args=None)
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