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# Default implementation data = self.pack(namedstruct) return stream.write(data)
def packto(self, namedstruct, stream)
Pack a struct to a stream :param namedstruct: struct to pack :param stream: a buffered stream :return: appended bytes size
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''' Unpack the struct from specified bytes. If the struct is sub-classed, definitions from the sub type is not unpacked. :param data: bytes of the struct, including fields of sub type and "extra" data. :param namedstruct: a NamedStruct object of this type ...
def unpack(self, data, namedstruct)
Unpack the struct from specified bytes. If the struct is sub-classed, definitions from the sub type is not unpacked. :param data: bytes of the struct, including fields of sub type and "extra" data. :param namedstruct: a NamedStruct object of this type :returns:...
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''' Pack the struct and return the packed bytes. :param namedstruct: a NamedStruct of this type. :returns: packed bytes, only contains fields of definitions in this type, not the sub type and "extra" data. ''' elements = [] t = namedstruct._targe...
def pack(self, namedstruct)
Pack the struct and return the packed bytes. :param namedstruct: a NamedStruct of this type. :returns: packed bytes, only contains fields of definitions in this type, not the sub type and "extra" data.
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''' Compatible to Parser.parse() ''' if len(buffer) < self.struct.size: return None try: return (self.struct.unpack(buffer[:self.struct.size])[0], self.struct.size) except struct.error as exc: raise BadFormatError(exc)
def parse(self, buffer, inlineparent = None)
Compatible to Parser.parse()
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''' Compatible to Parser.create() ''' try: return self.struct.unpack(data)[0] except struct.error as exc: raise BadFormatError(exc)
def create(self, data, inlineparent = None)
Compatible to Parser.create()
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''' Compatible to Parser.parse() ''' size = 0 v = [] for i in range(0, self.size): # @UnusedVariable r = self.innerparser.parse(buffer[size:], None) if r is None: return None v.append(r[0]) size += r[1] ...
def parse(self, buffer, inlineparent = None)
Compatible to Parser.parse()
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''' Compatible to Parser.new() ''' v = list(range(0, self.size)) for i in range(0, self.size): v[i] = self.innerparser.new() return v
def new(self, inlineparent = None)
Compatible to Parser.new()
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''' Compatible to Parser.create() ''' if self.size > 0: r = self.parse(data) if r is None: raise ParseError('data is not enough to create an array of size ' + self.size) else: return r[0] else: v = []...
def create(self, data, inlineparent = None)
Compatible to Parser.create()
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''' Compatible to Parser.sizeof() ''' size = 0 arraysize = self.size if arraysize == 0: arraysize = len(prim) for i in range(0, arraysize): if i >= len(prim): tp = self.innerparser.new() if hasattr(self.inner...
def sizeof(self, prim)
Compatible to Parser.sizeof()
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''' Compatible to Parser.tobytes() ''' stream = BytesIO() self.tostream(prim, stream, skipprepack=skipprepack) return stream.getvalue()
def tobytes(self, prim, skipprepack = False)
Compatible to Parser.tobytes()
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''' Compatible to Parser.create() ''' if self.cstr: return _copy(data).rstrip(b'\x00') else: return _copy(data)
def create(self, data, inlineparent = None)
Compatible to Parser.create()
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''' Get parser for this type. Create the parser on first call. ''' if not hasattr(self, '_parser'): self._parser = self._compile() return self._parser
def parser(self)
Get parser for this type. Create the parser on first call.
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''' Create a new object of this type. It is also available as __call__, so you can create a new object just like creating a class instance: a = mytype(a=1,b=2) :param args: Replace the embedded struct type. Each argument is a tuple (name, newtype). It is equ...
def new(self, *args, **kwargs)
Create a new object of this type. It is also available as __call__, so you can create a new object just like creating a class instance: a = mytype(a=1,b=2) :param args: Replace the embedded struct type. Each argument is a tuple (name, newtype). It is equivalent to call _rep...
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''' Get the enumerate name of a specified value. :param value: the enumerate value :param defaultName: returns if the enumerate value is not defined :returns: the corresponding enumerate value or *defaultName* if not found ''' for k,v in self._values.items(): ...
def getName(self, value, defaultName = None)
Get the enumerate name of a specified value. :param value: the enumerate value :param defaultName: returns if the enumerate value is not defined :returns: the corresponding enumerate value or *defaultName* if not found
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''' Import all the enumerate values from this enumerate to *gs* :param gs: usually globals(), a dictionary. At lease __setitem__ should be implemented if not a dictionary. ''' for k,v in self._values.items(): gs[k] = v
def importAll(self, gs)
Import all the enumerate values from this enumerate to *gs* :param gs: usually globals(), a dictionary. At lease __setitem__ should be implemented if not a dictionary.
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''' Create a new enumerate with current values merged with new enumerate values :param namespace: same as __init__ :param name: same as __init__ :param kwargs: same as __init__ :returns: a new enumerate type ''' if name is None: name = self._re...
def extend(self, namespace = None, name = None, **kwargs)
Create a new enumerate with current values merged with new enumerate values :param namespace: same as __init__ :param name: same as __init__ :param kwargs: same as __init__ :returns: a new enumerate type
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''' Format a enumerate value to enumerate names if possible. Used to generate human readable dump result. ''' if not self._bitwise: n = self.getName(value) if n is None: return value else: return n else: ...
def formatter(self, value)
Format a enumerate value to enumerate names if possible. Used to generate human readable dump result.
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if hasattr(namedstruct, self.name): return _tostream(self.basetypeparser, getattr(namedstruct, self.name), stream, True) else: return 0
def packto(self, namedstruct, stream)
Pack a struct to a stream
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''' Run prepack ''' if not skip_sub and hasattr(namedstruct, self.name) and hasattr(self.basetypeparser, 'fullprepack'): self.basetypeparser.fullprepack(getattr(namedstruct, self.name)) Parser.prepack(self, namedstruct, skip_self, skip_sub)
def prepack(self, namedstruct, skip_self=False, skip_sub=False)
Run prepack
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''' Run prepack ''' if not skip_sub and hasattr(self.innertypeparser, 'fullprepack'): for v in getattr(namedstruct, self.name): self.innertypeparser.fullprepack(v) Parser.prepack(self, namedstruct, skip_self, skip_sub)
def prepack(self, namedstruct, skip_self=False, skip_sub=False)
Run prepack
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''' Run prepack ''' if not skip_sub and self.header is not None and hasattr(self.header, 'fullprepack'): self.header.fullprepack(namedstruct._seqs[0]) Parser.prepack(self, namedstruct, skip_self, skip_sub)
def prepack(self, namedstruct, skip_self=False, skip_sub=False)
Run prepack
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r results_error = 'No results were found matching your query' auth_error = 'The token or API key is not valid, please contact Josh Clark at joshua.m.clark@utah.edu to ' \ 'resolve this' rule_error = 'This request violates a rule of the API. Please check the guidelin...
def _checkresponse(response)
r""" Returns the data requested by the other methods assuming the response from the API is ok. If not, provides error handling for all possible API errors. HTTP errors are handled in the get_response() function. Arguments: ---------- None. Returns: -------- ...
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http_error = 'Could not connect to the API. This could be because you have no internet connection, a parameter' \ ' was input incorrectly, or the API is currently down. Please try again.' json_error = 'Could not retrieve JSON values. Try again with a s...
def _get_response(self, endpoint, request_dict)
Returns a dictionary of data requested by each function. Arguments: ---------- endpoint: string, mandatory Set in all other methods, this is the API endpoint specific to each function. request_dict: string, mandatory A dictionary of parameters that are formatted ...
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r geo_func = lambda a, b: any(i in b for i in a) check = geo_func(self.geo_criteria, arg_list) if check is False: raise MesoPyError('No stations or geographic search criteria specified. Please provide one of the ' 'following: stid, state, county...
def _check_geo_param(self, arg_list)
r""" Checks each function call to make sure that the user has provided at least one of the following geographic parameters: 'stid', 'state', 'country', 'county', 'radius', 'bbox', 'cwa', 'nwsfirezone', 'gacc', or 'subgacc'. Arguments: ---------- arg_list: list, mandatory A l...
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count = self.count() if count: return self.sum(key) / count
def avg(self, key=None)
Get the average value of a given key. :param key: The key to get the average for :type key: mixed :rtype: float or int
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chunks = self._chunk(size) return self.__class__(list(map(self.__class__, chunks)))
def chunk(self, size)
Chunk the underlying collection. :param size: The chunk size :type size: int :rtype: Collection
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items = self.items return [items[i:i + size] for i in range(0, len(items), size)]
def _chunk(self, size)
Chunk the underlying collection. :param size: The chunk size :type size: int :rtype: Collection
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if value is not None: return self.contains(lambda x: data_get(x, key) == value) if self._use_as_callable(key): return self.first(key) is not None return key in self.items
def contains(self, key, value=None)
Determine if an element is in the collection :param key: The element :type key: int or str or callable :param value: The value of the element :type value: mixed :return: Whether the element is in the collection :rtype: bool
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results = [] items = self.items for values in items: if isinstance(values, BaseCollection): values = values.all() results += values return self.__class__(results)
def collapse(self)
Collapse the collection items into a single element (list) :return: A new Collection instance with collapsed items :rtype: Collection
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return self.__class__([i for i in self.items if i not in items])
def diff(self, items)
Diff the collections with the given items :param items: The items to diff with :type items: mixed :return: A Collection instance :rtype: Collection
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items = self.items for item in items: if callback(item) is False: break return self
def each(self, callback)
Execute a callback over each item. .. code:: collection = Collection([1, 2, 3]) collection.each(lambda x: x + 3) .. warning:: It only applies the callback but does not modify the collection's items. Use the `transform() <#backpack.Collection.transform>...
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new = [] for position, item in enumerate(self.items): if position % step == offset: new.append(item) return self.__class__(new)
def every(self, step, offset=0)
Create a new collection consisting of every n-th element. :param step: The step size :type step: int :param offset: The start offset :type offset: int :rtype: Collection
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items = copy(self.items) keys = reversed(sorted(keys)) for key in keys: del items[key] return self.__class__(items)
def without(self, *keys)
Get all items except for those with the specified keys. :param keys: The keys to remove :type keys: tuple :rtype: Collection
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items = [] for key, value in enumerate(self.items): if key in keys: items.append(value) return self.__class__(items)
def only(self, *keys)
Get the items with the specified keys. :param keys: The keys to keep :type keys: tuple :rtype: Collection
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if callback: return self.__class__(list(filter(callback, self.items))) return self.__class__(list(filter(None, self.items)))
def filter(self, callback=None)
Run a filter over each of the items. :param callback: The filter callback :type callback: callable or None :rtype: Collection
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return self.filter(lambda item: data_get(item, key) == value)
def where(self, key, value)
Filter items by the given key value pair. :param key: The key to filter by :type key: str :param value: The value to filter by :type value: mixed :rtype: Collection
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if callback is not None: for val in self.items: if callback(val): return val return value(default) if len(self.items) > 0: return self.items[0] else: return default
def first(self, callback=None, default=None)
Get the first item of the collection. :param default: The default value :type default: mixed
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def _flatten(d): if isinstance(d, dict): for v in d.values(): for nested_v in _flatten(v): yield nested_v elif isinstance(d, list): for list_v in d: for nested_v in _flatten(list_v):...
def flatten(self)
Get a flattened list of the items in the collection. :rtype: Collection
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keys = reversed(sorted(keys)) for key in keys: del self[key] return self
def forget(self, *keys)
Remove an item from the collection by key. :param keys: The keys to remove :type keys: tuple :rtype: Collection
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try: return self.items[key] except IndexError: return value(default)
def get(self, key, default=None)
Get an element of the collection. :param key: The index of the element :type key: mixed :param default: The default value to return :type default: mixed :rtype: mixed
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first = self.first() if not isinstance(first, (basestring)): return glue.join(self.pluck(value).all()) return value.join(self.items)
def implode(self, value, glue='')
Concatenate values of a given key as a string. :param value: The value :type value: str :param glue: The glue :type glue: str :rtype: str
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if callback is not None: for val in reversed(self.items): if callback(val): return val return value(default) if len(self.items) > 0: return self.items[-1] else: return default
def last(self, callback=None, default=None)
Get the last item of the collection. :param default: The default value :type default: mixed
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if key: return dict(map(lambda x: (data_get(x, key), data_get(x, value)), self.items)) else: results = list(map(lambda x: data_get(x, value), self.items)) return self.__class__(results)
def pluck(self, value, key=None)
Get a list with the values of a given key. :rtype: Collection
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return self.__class__(list(map(callback, self.items)))
def map(self, callback)
Run a map over each of the item. :param callback: The map function :type callback: callable :rtype: Collection
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def _max(result, item): val = data_get(item, key) if result is None or val > result: return value return result return self.reduce(_max)
def max(self, key=None)
Get the max value of a given key. :param key: The key :type key: str or None :rtype: mixed
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def _min(result, item): val = data_get(item, key) if result is None or val < result: return value return result return self.reduce(_min)
def min(self, key=None)
Get the min value of a given key. :param key: The key :type key: str or None :rtype: mixed
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start = (page - 1) * per_page return self[start:start + per_page]
def for_page(self, page, per_page)
"Paginate" the collection by slicing it into a smaller collection. :param page: The current page :type page: int :param per_page: Number of items by slice :type per_page: int :rtype: Collection
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val = self.get(key, default) self.forget(key) return val
def pull(self, key, default=None)
Pulls an item from the collection. :param key: The key :type key: mixed :param default: The default value :type default: mixed :rtype: mixed
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if self._use_as_callable(callback): return self.filter(lambda item: not callback(item)) return self.filter(lambda item: item != callback)
def reject(self, callback)
Create a collection of all elements that do not pass a given truth test. :param callback: The truth test :type callback: callable :rtype: Collection
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items = self.items if callback: return self.__class__(sorted(items, key=callback)) else: return self.__class__(sorted(items))
def sort(self, callback=None)
Sort through each item with a callback. :param callback: The callback :type callback: callable or None :rtype: Collection
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if callback is None: return sum(self.items) callback = self._value_retriever(callback) return self.reduce(lambda result, item: (result or 0) + callback(item))
def sum(self, callback=None)
Get the sum of the given values. :param callback: The callback :type callback: callable or string or None :rtype: mixed
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if key is None: seen = set() seen_add = seen.add return self.__class__([x for x in self.items if not (x in seen or seen_add(x))]) key = self._value_retriever(key) exists = [] def _check(item): id_ = key(item) if id_...
def unique(self, key=None)
Return only unique items from the collection list. :param key: The key to chech uniqueness on :type key: mixed :rtype: Collection
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return self.__class__(list(zip(self.items, *items)))
def zip(self, *items)
Zip the collection together with one or more arrays. :param items: The items to zip :type items: list :rtype: Collection
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if isinstance(items, BaseCollection): items = items.all() if not isinstance(items, list): raise ValueError('Unable to merge uncompatible types') self._items += items return self
def merge(self, items)
Merge the collection with the given items. :param items: The items to merge :type items: list or Collection :rtype: Collection
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self._items = self.map(callback).all() return self
def transform(self, callback)
Transform each item in the collection using a callback. :param callback: The callback :type callback: callable :rtype: Collection
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if self._use_as_callable(value): return value return lambda item: data_get(item, value)
def _value_retriever(self, value)
Get a value retrieving callback. :type value: mixed :rtype: callable
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def _serialize(value): if hasattr(value, 'serialize'): return value.serialize() elif hasattr(value, 'to_dict'): return value.to_dict() else: return value return list(map(_serialize, self.items))
def serialize(self)
Get the collection of items as a serialized object (ready to be json encoded). :rtype: dict or list
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# Schemes validation interface if is_scheme(self.validator): params = getcallargs(self.function, *args, **kwargs) params.update(kwargs) validator = self.validator(data=params, request=None) if validator.is_valid(): return ...
def validate(self, *args, **kwargs)
Step 4 (6 for invariant). Process contract (validator)
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self.validate(*args, **kwargs) return self.function(*args, **kwargs)
def patched_function(self, *args, **kwargs)
Step 3. Wrapped function calling.
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result = self.function(*args, **kwargs) self.validate(result) return result
def patched_function(self, *args, **kwargs)
Step 3. Wrapped function calling.
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# disable methods matching before validation self._disable_patching = True # validation by Invariant.validate self._validate_base(self) # enable methods matching after validation self._disable_patching = False
def _validate(self)
Step 5 (1st flow) or Step 4 (2nd flow). Process contract for object.
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self._validate() result = method(*args, **kwargs) self._validate() return result
def _patched_method(self, method, *args, **kwargs)
Step 4 (1st flow). Call method
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# create destination directory if not Path(output_file).parent.exists(): Path(output_file).parent.mkdir(parents=True, exist_ok=True) # make sure we have absolute paths and strings since BuildVRT does not like something else input_file_list = [str(Path(p).absolute()) for p in input_file_li...
def buildvrt(input_file_list, output_file, relative=True, **kwargs)
Build a VRT See also: https://www.gdal.org/gdalbuildvrt.html You can find the possible BuildVRTOptions (**kwargs**) here: https://github.com/nextgis/pygdal/blob/78a793057d2162c292af4f6b240e19da5d5e52e2/2.1.0/osgeo/gdal.py#L1051 Arguments: input_file_list {list of str or Path objects} -- List ...
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if not overwrite and Path(dst_file).exists(): print("Processing skipped. Destination file exists.") return 0 GDAL_RESAMPLING_ALGORITHMS = { "bilinear": "GRA_Bilinear", "cubic": "GRA_Cubic", "cubicspline": "GRA_CubicSpline", "lanczos": "GRA_Lanczos",...
def reproject_on_template_raster(src_file, dst_file, template_file, resampling="near", compress=None, overwrite=False)
Reproject a one-band raster to fit the projection, extend, pixel size etc. of a template raster. Function based on https://stackoverflow.com/questions/10454316/how-to-project-and-resample-a-grid-to-match-another-grid-with-gdal-python Arguments: src_file {str} -- Filename of the source one-band r...
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data = gdal.Open(str(src_raster_template), # str for the case that a Path instance arrives here gdalconst.GA_ReadOnly) geo_transform = data.GetGeoTransform() #source_layer = data.GetLayer() # x_max = x_min + geo_transform[1] * data.RasterXSize # y_min = y_max + geo_transf...
def rasterize(src_vector: str, burn_attribute: str, src_raster_template: str, dst_rasterized: str, gdal_dtype: int = 4)
Rasterize the values of a spatial vector file. Arguments: src_vector {str}} -- A OGR vector file (e.g. GeoPackage, ESRI Shapefile) path containing the data to be rasterized. burn_attribute {str} -- The attribute of the vector data to be burned in the raster. src_raster_template ...
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if Path(dst_raster).exists() and not overwrite: print(f"Returning 0 - File exists: {dst_raster}") return 0 with rasterio.open(template_raster) as tmp: crs = tmp.crs dst_raster = Path(dst_raster) dst_raster.parent.mkdir(exist_ok=True, parents=True) tempdir = Pa...
def calc_distance_to_border(polygons, template_raster, dst_raster, overwrite=False, keep_interim_files=False)
Calculate the distance of each raster cell (in and outside the polygons) to the next polygon border. Arguments: polygons {str} -- Filename to a geopandas-readable file with polygon features. template_raster {[type]} -- Filename to a rasterio-readable file. dst_raster {[type]} -- Destin...
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gdf = gpd.read_file(src_polygons) geom_coords = gdf["geometry"] # featureset.get(5)["geometry"]["coordinates"] lines = [] row_ids = [] for i_row, pol in tqdm(enumerate(geom_coords), total=len(geom_coords)): boundary = pol.boundary if boundary.type == 'MultiLineString': ...
def convert_polygons_to_lines(src_polygons, dst_lines, crs=None, add_allone_col=False)
Convert polygons to lines. Arguments: src_polygons {path to geopandas-readable file} -- Filename of the the polygon vector dataset to be converted to lines. dst_lines {[type]} -- Filename where to write the line vector dataset to. Keyword Arguments: crs {dict or str} -- Ou...
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dtype_range = dtype_ranges[dtype] df_out_of_range = (df < dtype_range[0]) | (df > dtype_range[1]) | (~np.isfinite(df)) if df_out_of_range.any().any(): if return_== "colsums": df_out_of_range = df_out_of_range.apply(sum, axis=0) # column elif return_== "rowsums": ...
def dtype_checker_df(df, dtype, return_=None)
Check if there are NaN values of values outside of a given datatype range. Arguments: df {dataframe} -- A dataframe. dtype {str} -- The datatype to check for. Keyword Arguments: return_ {str} -- Returns a boolean dataframe with the values not in the range of the dtype ('all'), ...
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df = self.df_layers.copy() df["index"] = range(df.shape[0]) idx_layers = [] if isinstance(band, str) and isinstance(date, str): idx_layers = df[(df["date"] == date) & (df["band"] == band)]["index"].values[0] if isinstance(band, list) and isinstance(date, s...
def get_df_ilocs(self, band, date)
Get positions of rows matching specific band(s) and date(s). The method supports three typical queries: * one band and one date (both given as strings) * one band and of several dates (band given as strings, date as list of strings) * several band and of one date (date given as strin...
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# This should be a MultiRasterIO method with rasterio.open(self._mrio._get_template_for_given_resolution(self._mrio.dst_res, "path")) as src_layer: pass # later we need src_layer for src_layer.window_transform(win) win_transform = src_layer.window_transform(self._window) ...
def _get_spatial_bounds(self)
Get the spatial bounds of the chunk.
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if self._data_structure != "DataFrame": raise Exception(f"Data is not a DataFrame but {self._data_structure}.") self._data = self._convert_to_ndarray(self._data) self._update_data_structure() return self
def convert_data_to_ndarray(self)
Converts the data from dataframe to ndarray format. Assumption: df-columns are ndarray-layers (3rd dim.)
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if data.__class__.__name__ != "DataFrame": raise Exception(f"data is not a DataFrame but {data.__class__.__name__}.") shape_ndarray = (self._height, self._width, data.shape[1]) data_ndarray = data.values.reshape(shape_ndarray) return data_ndarray
def _convert_to_ndarray(self, data)
Converts data from dataframe to ndarray format. Assumption: df-columns are ndarray-layers (3rd dim.)
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result = self._convert_to_ndarray(result) self.write_ndarray(result, dst_paths, nodata=nodata, compress=compress)
def write_dataframe(self, result, dst_paths, nodata=None, compress='lzw')
Write results (dataframe) to disc.
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assert len(dst_paths) == result.shape[2] assert result.shape[0] == self._height assert result.shape[1] == self._width assert result.shape[2] == len(dst_paths) with rasterio.open(self._mrio._get_template_for_given_resolution(self._mrio.dst_res, "path")) as src_layer: ...
def write_ndarray(self, result, dst_paths, nodata=None, compress='lzw')
Write results (ndarray) to disc.
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# from the seaborn code # https://github.com/mwaskom/seaborn/blob/3a3ec75befab52c02650c62772a90f8c23046038/seaborn/matrix.py#L201 def _get_vmin_vmax(arr2d, vmin=None, vmax=None): if vmin is None: vmin = np.percentile(arr2d, 2) if robust else arr2d.min() ...
def robust_data_range(arr, robust=False, vmin=None, vmax=None)
Get a robust data range, i.e. 2nd and 98th percentile for vmin, vmax parameters.
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eocubewin = EOCubeChunk(ji, eocube.df_layers, eocube.chunksize, eocube.wdir) return eocubewin
def from_eocube(eocube, ji)
Create a EOCubeChunk object from an EOCube object.
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return EOCubeSceneCollectionChunk(ji=ji, df_layers=self.df_layers, chunksize=self.chunksize, variables=self.variables, qa=self.qa, ...
def get_chunk(self, ji)
Get a EOCubeChunk
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def print_elapsed_time(start, last_stopped, prefix): # print(f"{prefix} - Elapsed time [s] since start / last stopped: \ # {(int(time.time() - start_time))} / {(int(time.time() - last_stopped))}") return time.time() start_time = time.time() last_s...
def read_data_by_variable(self, mask=True)
Reads and masks (if desired) the data and converts it in one dataframe per variable.
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if dataset == "s2l1c": search_string = os.path.join(DIR_DATA, dataset, "**", "*_B??.jp2") files = glob.glob(search_string, recursive=True) if not files: raise IOError(f"Could not find raster files of the s2l1c dataset. Search string: {search_string}") basename_splitt...
def get_dataset(dataset="s2l1c")
Get a specific sampledata to play around. So far the following sampledata exist: * 's2l1c': One Sentinel-2 Level 1C scene with a reference dataset. * 'lsts': A time series of 105 Landsat scenes each with the bands b3 (red), b4 (nir), b5 (swir1) and fmask. Keyword Arguments: dataset {str} -- T...
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# checks the blocksize input value_error_msg = "'blocksize must be an integer or a list of two integers.'" if isinstance(blocksize_xy, int): blockxsize, blockysize = (blocksize_xy, blocksize_xy) elif isinstance(blocksize_xy, list): if len(blocksize_xy) != 2: raise Value...
def windows_from_blocksize(blocksize_xy, width, height)
Create rasterio.windows.Window instances with given size which fully cover a raster. Arguments: blocksize_xy {int or list of two int} -- [description] width {int} -- With of the raster for which to create the windows. height {int} -- Heigth of the raster for which to create the windows. ...
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if dst_res is None: dst_res = min(self._res_indices.keys()) return dst_res
def _get_dst_resolution(self, dst_res=None)
Get default resolution, i.e. the highest resolution or smallest cell size.
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res = max(self._res_indices.keys()) self._windows_res = res a_file_index_given_res = self._res_indices[res][0] with rasterio.open(self._layer_files[a_file_index_given_res]) as src: wins_of_first_dst_res_layer = tuple(src.block_windows()) self.windows = np.array([w...
def block_windows(self, res=None): # setter and getter ? if res is None
Load windows for chunks-wise processing from raster internal tiling (first raster of given resolution). Arguments: res {numeric} -- Resolution determining the raster (1st of resolution group) from which to take the tiling.
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meta = self._get_template_for_given_resolution(self.dst_res, "meta") width = meta["width"] height = meta["height"] blocksize_wins = windows_from_blocksize(blocksize_xy, width, height) self.windows = np.array([win[1] for win in blocksize_wins]) self.windows_row =...
def windows_from_blocksize(self, blocksize_xy=512)
Create rasterio.windows.Window instances with given size which fully cover the raster. Arguments: blocksize_xy {int or list of two int} -- Size of the window. If one integer is given it defines the width and height of the window. If a list of two integers if given the first defines ...
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path = self._layer_files[self._res_indices[res][0]] if return_ == "path": return_value = path else: with rasterio.open(str(path)) as src: if return_ == "meta": return_value = src.meta elif return_ == "windows": ...
def _get_template_for_given_resolution(self, res, return_)
Given specified resolution ('res') return template layer 'path', 'meta' or 'windows'.
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import pandas as pd if self.windows is None: raise Exception("You need to call the block_windows or windows before.") df_wins = [] for row, col, win in zip(self.windows_row, self.windows_col, self.windows): df_wins.append(pd.DataFrame({"row":[row], "col"...
def windows_df(self)
Get Windows (W) W-row, W-col and W-index of windows e.g. loaded with :meth:`block_windows` as a dataframe. Returns: [dataframe] -- A dataframe with the window information and indices (row, col, index).
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transform_dst = self._layer_meta[self._res_indices[res][0]]["transform"] ji_windows[res] = window_from_window(window_src=self.windows[ij_win], transform_src=transform_src, transform_dst=transform_ds...
def ji_windows(self, ij_win): # what can be given to ij_win NOT intuitive/right name by now!!! ji_windows = {} transform_src = self._layer_meta[self._res_indices[self._windows_res][0]]["transform"] for res in self._res_indices
For a given specific window, i.e. an element of :attr:`windows`, get the windows of all resolutions. Arguments: ij_win {int} -- The index specifying the window for which to return the resolution-windows.
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if isinstance(ji_win, dict): ji_windows = ji_win else: ji_windows = self.ji_windows(ji_win) arrays = [] for filename, res in zip(self._layer_files, self._layer_resolution): with rasterio.open(filename) as src: arr = src.read(1...
def get_arrays(self, ji_win)
Get the data of the a window given the ji_windows derived with :method:`ji_windows`. Arguments: ji_win {[type]} -- The index of the window or the (multi-resolution) windows returned by :meth:`ji_window`. Returns: (list of) array(s) -- List of 2D arrays in native resolution in c...
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# get a destination array template win_dst = ji_windows[self.dst_res] aff_dst = self._layer_meta[self._res_indices[self.dst_res][0]]["transform"] arrays_dst = list() for i, array in enumerate(arrays): arr_dst = np.zeros((int(win_dst.height), int(win_dst.width...
def _resample(self, arrays, ji_windows)
Resample all arrays with potentially different resolutions to a common resolution.
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ji_results = self._process_windows(func, **kwargs) for idx_layer in range(len(ji_results[0])): # this is the number of output layers for j in np.unique(self.windows_row): win_indices_j = np.where(self.windows_row == j)[0] layer_merged_j = np.hstack([...
def _process_windows_merge_stack(self, func, **kwargs)
Load (resampled) array of all windows, apply custom function on it, merge and stack results to one array.
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ji_results = [] for ji_win in range(len(self.windows)): ji_results.append(self._process_window(ji_win, func, **kwargs)) return ji_results
def _process_windows(self, func, **kwargs)
Load (resampled) array of all windows and apply custom function on it.
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arr = self.get_arrays(ji_win) result = func(arr, **kwargs) return result
def _process_window(self, ji_win, func, **kwargs)
Load (resampled) array of window ji_win and apply custom function on it.
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a_transform = self._get_template_for_given_resolution(res=self.dst_res, return_="meta")["transform"] row, col = transform.rowcol(a_transform, xy[0], xy[1]) ij_containing_xy = None for ji, win in enumerate(self.windows): (row_start, row_end), (col_start, col_end) = ra...
def get_window_from_xy(self, xy)
Get the window index given a coordinate (raster CRS).
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def _load(path, index): if index is None: arr = np.load(str(path)) else: arr = np.load(str(path), mmap_mode="r")[index] return arr src_dir = Path(src_dir) paths = [] if isinstance(patterns, str): patterns = [patterns] for pat in patterns:...
def load_extracted(src_dir: str, patterns="*.npy", vars_in_cols: bool = True, index: pd.Series = None)
Load data extracted and stored by :py:func:`extract` Arguments: src_dir {str} -- The directory where the data is stored. Keyword Arguments: patterns {str, or list of str} -- A pattern (str) or list of patterns (list) to identify the variables to be loaded. The default l...
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minimum = 9223372036854775807 maximum = 0 for y in range(y0, y0 + h): for x in range(x0, x0 + w): value = self[x, y] if value != self.filler: minimum = min(minimum, value) maximum = max(maximum, value) ...
def extrema(self, x0, y0, w, h)
Returns the minimum and maximum values contained in a given area. :param x0: Starting x index. :param y0: Starting y index. :param w: Width of the area to scan. :param h: Height of the area to scan. :return: Tuple containing the minimum and maximum values of the given area.
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incs = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff) # Break 6-char RGB code into 3 integer vals. parts = [ int(h, 16) for h in re.split(r'(..)(..)(..)', rgb)[1:4] ] res = [] for part in parts: i = 0 while i < len(incs)-1: s, b = incs[i], incs[i+1] # smaller, bigger ...
def rgb2short(rgb)
Find the closest xterm-256 approximation to the given RGB value. @param rgb: Hex code representing an RGB value, eg, 'abcdef' @returns: String between 0 and 255, compatible with xterm. >>> rgb2short('123456') ('23', '005f5f') >>> rgb2short('ffffff') ('231', 'ffffff') >>> rgb2short('0DADD6') ...
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curses.curs_set(1) self.screen.move(y, x)
def set_cursor(self, x, y)
Sets the cursor to the desired position. :param x: X position :param y: Y position
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if x < self.width and y < self.height: try: self.screen.addstr(y, x, symbols.encode(text), self.pairs[fg, bg]) except curses.error: # Ignore out of bounds error pass
def put(self, x, y, text, fg, bg)
Puts a string at the desired coordinates using the provided colors. :param x: X position :param y: Y position :param text: Text to write :param fg: Foreground color number :param bg: Background color number
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# Flush all inputs before this one that were done since last poll curses.flushinp() ch = self.screen.getch() if ch == 27: return EVENT_ESC elif ch == -1 or ch == curses.KEY_RESIZE: return EVENT_RESIZE elif ch == 10 or ch == curses.KEY_EN...
def poll_event(self)
Waits for an event to happen and returns a string related to the event. If the event is a normal (letter) key press, the letter is returned (case sensitive) :return: Event type
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x, y, params = args return x, y, mandelbrot(x, y, params)
def compute(args)
Callable function for the multiprocessing pool.
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x, y, w, h, params = args return x, y, mandelbrot_capture(x, y, w, h, params)
def compute_capture(args)
Callable function for the multiprocessing pool.
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