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fhs/pyhdf
pyhdf/V.py
VG.tagrefs
def tagrefs(self): """Get the tags and reference numbers of all the vgroup members. Args:: no argument Returns:: list of (tag,ref) tuples, one for each vgroup member C library equivalent : Vgettagrefs """ n = self._nmembers ret = [] if n: tags = _C.array_int32(n) refs = _C.array_int32(n) k = _C.Vgettagrefs(self._id, tags, refs, n) _checkErr('tagrefs', k, "error getting tags and refs") for m in xrange(k): ret.append((tags[m], refs[m])) return ret
python
def tagrefs(self): """Get the tags and reference numbers of all the vgroup members. Args:: no argument Returns:: list of (tag,ref) tuples, one for each vgroup member C library equivalent : Vgettagrefs """ n = self._nmembers ret = [] if n: tags = _C.array_int32(n) refs = _C.array_int32(n) k = _C.Vgettagrefs(self._id, tags, refs, n) _checkErr('tagrefs', k, "error getting tags and refs") for m in xrange(k): ret.append((tags[m], refs[m])) return ret
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Get the tags and reference numbers of all the vgroup members. Args:: no argument Returns:: list of (tag,ref) tuples, one for each vgroup member C library equivalent : Vgettagrefs
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1100-L1124
4,501
fhs/pyhdf
pyhdf/V.py
VG.inqtagref
def inqtagref(self, tag, ref): """Determines if an object identified by its tag and reference number belongs to the vgroup. Args:: tag tag of the object to check ref reference number of the object to check Returns:: False (0) if the object does not belong to the vgroup, True (1) otherwise C library equivalent : Vinqtagref """ return _C.Vinqtagref(self._id, tag, ref)
python
def inqtagref(self, tag, ref): """Determines if an object identified by its tag and reference number belongs to the vgroup. Args:: tag tag of the object to check ref reference number of the object to check Returns:: False (0) if the object does not belong to the vgroup, True (1) otherwise C library equivalent : Vinqtagref """ return _C.Vinqtagref(self._id, tag, ref)
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Determines if an object identified by its tag and reference number belongs to the vgroup. Args:: tag tag of the object to check ref reference number of the object to check Returns:: False (0) if the object does not belong to the vgroup, True (1) otherwise C library equivalent : Vinqtagref
[ "Determines", "if", "an", "object", "identified", "by", "its", "tag", "and", "reference", "number", "belongs", "to", "the", "vgroup", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1126-L1143
4,502
fhs/pyhdf
pyhdf/V.py
VG.nrefs
def nrefs(self, tag): """Determine the number of tags of a given type in a vgroup. Args:: tag tag type to look for in the vgroup Returns:: number of members identified by this tag type C library equivalent : Vnrefs """ n = _C.Vnrefs(self._id, tag) _checkErr('nrefs', n, "bad arguments") return n
python
def nrefs(self, tag): """Determine the number of tags of a given type in a vgroup. Args:: tag tag type to look for in the vgroup Returns:: number of members identified by this tag type C library equivalent : Vnrefs """ n = _C.Vnrefs(self._id, tag) _checkErr('nrefs', n, "bad arguments") return n
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Determine the number of tags of a given type in a vgroup. Args:: tag tag type to look for in the vgroup Returns:: number of members identified by this tag type C library equivalent : Vnrefs
[ "Determine", "the", "number", "of", "tags", "of", "a", "given", "type", "in", "a", "vgroup", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1145-L1161
4,503
fhs/pyhdf
pyhdf/V.py
VG.attrinfo
def attrinfo(self): """Return info about all the vgroup attributes. Args:: no argument Returns:: dictionnary describing each vgroup attribute; for each attribute, a (name,data) pair is added to the dictionary, where 'data' is a tuple holding: - attribute data type (one of HC.xxx constants) - attribute order - attribute value - attribute size in bytes C library equivalent : no equivalent """ dic = {} for n in range(self._nattrs): att = self.attr(n) name, type, order, size = att.info() dic[name] = (type, order, att.get(), size) return dic
python
def attrinfo(self): """Return info about all the vgroup attributes. Args:: no argument Returns:: dictionnary describing each vgroup attribute; for each attribute, a (name,data) pair is added to the dictionary, where 'data' is a tuple holding: - attribute data type (one of HC.xxx constants) - attribute order - attribute value - attribute size in bytes C library equivalent : no equivalent """ dic = {} for n in range(self._nattrs): att = self.attr(n) name, type, order, size = att.info() dic[name] = (type, order, att.get(), size) return dic
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Return info about all the vgroup attributes. Args:: no argument Returns:: dictionnary describing each vgroup attribute; for each attribute, a (name,data) pair is added to the dictionary, where 'data' is a tuple holding: - attribute data type (one of HC.xxx constants) - attribute order - attribute value - attribute size in bytes C library equivalent : no equivalent
[ "Return", "info", "about", "all", "the", "vgroup", "attributes", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1218-L1245
4,504
fhs/pyhdf
pyhdf/V.py
VG.findattr
def findattr(self, name): """Search the vgroup for a given attribute. Args:: name attribute name Returns:: if found, VGAttr instance describing the attribute None otherwise C library equivalent : Vfindattr """ try: att = self.attr(name) if att._index is None: att = None except HDF4Error: att = None return att
python
def findattr(self, name): """Search the vgroup for a given attribute. Args:: name attribute name Returns:: if found, VGAttr instance describing the attribute None otherwise C library equivalent : Vfindattr """ try: att = self.attr(name) if att._index is None: att = None except HDF4Error: att = None return att
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Search the vgroup for a given attribute. Args:: name attribute name Returns:: if found, VGAttr instance describing the attribute None otherwise C library equivalent : Vfindattr
[ "Search", "the", "vgroup", "for", "a", "given", "attribute", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/V.py#L1248-L1269
4,505
fhs/pyhdf
pyhdf/SD.py
SDAttr.index
def index(self): """Retrieve the attribute index number. Args:: no argument Returns:: attribute index number (starting at 0) C library equivalent : SDfindattr """ self._index = _C.SDfindattr(self._obj._id, self._name) _checkErr('find', self._index, 'illegal attribute name') return self._index
python
def index(self): """Retrieve the attribute index number. Args:: no argument Returns:: attribute index number (starting at 0) C library equivalent : SDfindattr """ self._index = _C.SDfindattr(self._obj._id, self._name) _checkErr('find', self._index, 'illegal attribute name') return self._index
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Retrieve the attribute index number. Args:: no argument Returns:: attribute index number (starting at 0) C library equivalent : SDfindattr
[ "Retrieve", "the", "attribute", "index", "number", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1178-L1194
4,506
fhs/pyhdf
pyhdf/SD.py
SD.end
def end(self): """End access to the SD interface and close the HDF file. Args:: no argument Returns:: None The instance should not be used afterwards. The 'end()' method is implicitly called when the SD instance is deleted. C library equivalent : SDend """ status = _C.SDend(self._id) _checkErr('end', status, "cannot execute") self._id = None
python
def end(self): """End access to the SD interface and close the HDF file. Args:: no argument Returns:: None The instance should not be used afterwards. The 'end()' method is implicitly called when the SD instance is deleted. C library equivalent : SDend """ status = _C.SDend(self._id) _checkErr('end', status, "cannot execute") self._id = None
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End access to the SD interface and close the HDF file. Args:: no argument Returns:: None The instance should not be used afterwards. The 'end()' method is implicitly called when the SD instance is deleted. C library equivalent : SDend
[ "End", "access", "to", "the", "SD", "interface", "and", "close", "the", "HDF", "file", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1457-L1477
4,507
fhs/pyhdf
pyhdf/SD.py
SD.info
def info(self): """Retrieve information about the SD interface. Args:: no argument Returns:: 2-element tuple holding: number of datasets inside the file number of file attributes C library equivalent : SDfileinfo """ status, n_datasets, n_file_attrs = _C.SDfileinfo(self._id) _checkErr('info', status, "cannot execute") return n_datasets, n_file_attrs
python
def info(self): """Retrieve information about the SD interface. Args:: no argument Returns:: 2-element tuple holding: number of datasets inside the file number of file attributes C library equivalent : SDfileinfo """ status, n_datasets, n_file_attrs = _C.SDfileinfo(self._id) _checkErr('info', status, "cannot execute") return n_datasets, n_file_attrs
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Retrieve information about the SD interface. Args:: no argument Returns:: 2-element tuple holding: number of datasets inside the file number of file attributes C library equivalent : SDfileinfo
[ "Retrieve", "information", "about", "the", "SD", "interface", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1479-L1497
4,508
fhs/pyhdf
pyhdf/SD.py
SD.nametoindex
def nametoindex(self, sds_name): """Return the index number of a dataset given the dataset name. Args:: sds_name : dataset name Returns:: index number of the dataset C library equivalent : SDnametoindex """ sds_idx = _C.SDnametoindex(self._id, sds_name) _checkErr('nametoindex', sds_idx, 'non existent SDS') return sds_idx
python
def nametoindex(self, sds_name): """Return the index number of a dataset given the dataset name. Args:: sds_name : dataset name Returns:: index number of the dataset C library equivalent : SDnametoindex """ sds_idx = _C.SDnametoindex(self._id, sds_name) _checkErr('nametoindex', sds_idx, 'non existent SDS') return sds_idx
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Return the index number of a dataset given the dataset name. Args:: sds_name : dataset name Returns:: index number of the dataset C library equivalent : SDnametoindex
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1499-L1515
4,509
fhs/pyhdf
pyhdf/SD.py
SD.reftoindex
def reftoindex(self, sds_ref): """Returns the index number of a dataset given the dataset reference number. Args:: sds_ref : dataset reference number Returns:: dataset index number C library equivalent : SDreftoindex """ sds_idx = _C.SDreftoindex(self._id, sds_ref) _checkErr('reftoindex', sds_idx, 'illegal SDS ref number') return sds_idx
python
def reftoindex(self, sds_ref): """Returns the index number of a dataset given the dataset reference number. Args:: sds_ref : dataset reference number Returns:: dataset index number C library equivalent : SDreftoindex """ sds_idx = _C.SDreftoindex(self._id, sds_ref) _checkErr('reftoindex', sds_idx, 'illegal SDS ref number') return sds_idx
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Returns the index number of a dataset given the dataset reference number. Args:: sds_ref : dataset reference number Returns:: dataset index number C library equivalent : SDreftoindex
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1517-L1534
4,510
fhs/pyhdf
pyhdf/SD.py
SD.setfillmode
def setfillmode(self, fill_mode): """Set the fill mode for all the datasets in the file. Args:: fill_mode : fill mode; one of : SDC.FILL write the fill value to all the datasets of the file by default SDC.NOFILL do not write fill values to all datasets of the file by default Returns:: previous fill mode value C library equivalent: SDsetfillmode """ if not fill_mode in [SDC.FILL, SDC.NOFILL]: raise HDF4Error("bad fill mode") old_mode = _C.SDsetfillmode(self._id, fill_mode) _checkErr('setfillmode', old_mode, 'cannot execute') return old_mode
python
def setfillmode(self, fill_mode): """Set the fill mode for all the datasets in the file. Args:: fill_mode : fill mode; one of : SDC.FILL write the fill value to all the datasets of the file by default SDC.NOFILL do not write fill values to all datasets of the file by default Returns:: previous fill mode value C library equivalent: SDsetfillmode """ if not fill_mode in [SDC.FILL, SDC.NOFILL]: raise HDF4Error("bad fill mode") old_mode = _C.SDsetfillmode(self._id, fill_mode) _checkErr('setfillmode', old_mode, 'cannot execute') return old_mode
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Set the fill mode for all the datasets in the file. Args:: fill_mode : fill mode; one of : SDC.FILL write the fill value to all the datasets of the file by default SDC.NOFILL do not write fill values to all datasets of the file by default Returns:: previous fill mode value C library equivalent: SDsetfillmode
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1536-L1558
4,511
fhs/pyhdf
pyhdf/SD.py
SD.select
def select(self, name_or_index): """Locate a dataset. Args:: name_or_index dataset name or index number Returns:: SDS instance for the dataset C library equivalent : SDselect """ if isinstance(name_or_index, type(1)): idx = name_or_index else: try: idx = self.nametoindex(name_or_index) except HDF4Error: raise HDF4Error("select: non-existent dataset") id = _C.SDselect(self._id, idx) _checkErr('select', id, "cannot execute") return SDS(self, id)
python
def select(self, name_or_index): """Locate a dataset. Args:: name_or_index dataset name or index number Returns:: SDS instance for the dataset C library equivalent : SDselect """ if isinstance(name_or_index, type(1)): idx = name_or_index else: try: idx = self.nametoindex(name_or_index) except HDF4Error: raise HDF4Error("select: non-existent dataset") id = _C.SDselect(self._id, idx) _checkErr('select', id, "cannot execute") return SDS(self, id)
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Locate a dataset. Args:: name_or_index dataset name or index number Returns:: SDS instance for the dataset C library equivalent : SDselect
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1603-L1626
4,512
fhs/pyhdf
pyhdf/SD.py
SD.attributes
def attributes(self, full=0): """Return a dictionnary describing every global attribute attached to the SD interface. Args:: full true to get complete info about each attribute false to report only each attribute value Returns:: Empty dictionnary if no global attribute defined Otherwise, dictionnary where each key is the name of a global attribute. If parameter 'full' is false, key value is the attribute value. If 'full' is true, key value is a tuple with the following elements: - attribute value - attribute index number - attribute type - attribute length C library equivalent : no equivalent """ # Get the number of global attributes. nsds, natts = self.info() # Inquire each attribute res = {} for n in range(natts): a = self.attr(n) name, aType, nVal = a.info() if full: res[name] = (a.get(), a.index(), aType, nVal) else: res[name] = a.get() return res
python
def attributes(self, full=0): """Return a dictionnary describing every global attribute attached to the SD interface. Args:: full true to get complete info about each attribute false to report only each attribute value Returns:: Empty dictionnary if no global attribute defined Otherwise, dictionnary where each key is the name of a global attribute. If parameter 'full' is false, key value is the attribute value. If 'full' is true, key value is a tuple with the following elements: - attribute value - attribute index number - attribute type - attribute length C library equivalent : no equivalent """ # Get the number of global attributes. nsds, natts = self.info() # Inquire each attribute res = {} for n in range(natts): a = self.attr(n) name, aType, nVal = a.info() if full: res[name] = (a.get(), a.index(), aType, nVal) else: res[name] = a.get() return res
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Return a dictionnary describing every global attribute attached to the SD interface. Args:: full true to get complete info about each attribute false to report only each attribute value Returns:: Empty dictionnary if no global attribute defined Otherwise, dictionnary where each key is the name of a global attribute. If parameter 'full' is false, key value is the attribute value. If 'full' is true, key value is a tuple with the following elements: - attribute value - attribute index number - attribute type - attribute length C library equivalent : no equivalent
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1651-L1689
4,513
fhs/pyhdf
pyhdf/SD.py
SD.datasets
def datasets(self): """Return a dictionnary describing all the file datasets. Args:: no argument Returns:: Empty dictionnary if no dataset is defined. Otherwise, dictionnary whose keys are the file dataset names, and values are tuples describing the corresponding datasets. Each tuple holds the following elements in order: - tuple holding the names of the dimensions defining the dataset coordinate axes - tuple holding the dataset shape (dimension lengths); if a dimension is unlimited, the reported length corresponds to the dimension current length - dataset type - dataset index number C library equivalent : no equivalent """ # Get number of datasets nDs = self.info()[0] # Inquire each var res = {} for n in range(nDs): # Get dataset info. v = self.select(n) vName, vRank, vLen, vType, vAtt = v.info() if vRank < 2: # need a sequence vLen = [vLen] # Get dimension info. dimNames = [] dimLengths = [] for dimNum in range(vRank): d = v.dim(dimNum) dimNames.append(d.info()[0]) dimLengths.append(vLen[dimNum]) res[vName] = (tuple(dimNames), tuple(dimLengths), vType, n) return res
python
def datasets(self): """Return a dictionnary describing all the file datasets. Args:: no argument Returns:: Empty dictionnary if no dataset is defined. Otherwise, dictionnary whose keys are the file dataset names, and values are tuples describing the corresponding datasets. Each tuple holds the following elements in order: - tuple holding the names of the dimensions defining the dataset coordinate axes - tuple holding the dataset shape (dimension lengths); if a dimension is unlimited, the reported length corresponds to the dimension current length - dataset type - dataset index number C library equivalent : no equivalent """ # Get number of datasets nDs = self.info()[0] # Inquire each var res = {} for n in range(nDs): # Get dataset info. v = self.select(n) vName, vRank, vLen, vType, vAtt = v.info() if vRank < 2: # need a sequence vLen = [vLen] # Get dimension info. dimNames = [] dimLengths = [] for dimNum in range(vRank): d = v.dim(dimNum) dimNames.append(d.info()[0]) dimLengths.append(vLen[dimNum]) res[vName] = (tuple(dimNames), tuple(dimLengths), vType, n) return res
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Return a dictionnary describing all the file datasets. Args:: no argument Returns:: Empty dictionnary if no dataset is defined. Otherwise, dictionnary whose keys are the file dataset names, and values are tuples describing the corresponding datasets. Each tuple holds the following elements in order: - tuple holding the names of the dimensions defining the dataset coordinate axes - tuple holding the dataset shape (dimension lengths); if a dimension is unlimited, the reported length corresponds to the dimension current length - dataset type - dataset index number C library equivalent : no equivalent
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1691-L1736
4,514
fhs/pyhdf
pyhdf/SD.py
SDS.endaccess
def endaccess(self): """Terminates access to the SDS. Args:: no argument Returns:: None. The SDS instance should not be used afterwards. The 'endaccess()' method is implicitly called when the SDS instance is deleted. C library equivalent : SDendaccess """ status = _C.SDendaccess(self._id) _checkErr('endaccess', status, "cannot execute") self._id = None
python
def endaccess(self): """Terminates access to the SDS. Args:: no argument Returns:: None. The SDS instance should not be used afterwards. The 'endaccess()' method is implicitly called when the SDS instance is deleted. C library equivalent : SDendaccess """ status = _C.SDendaccess(self._id) _checkErr('endaccess', status, "cannot execute") self._id = None
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Terminates access to the SDS. Args:: no argument Returns:: None. The SDS instance should not be used afterwards. The 'endaccess()' method is implicitly called when the SDS instance is deleted. C library equivalent : SDendaccess
[ "Terminates", "access", "to", "the", "SDS", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1817-L1837
4,515
fhs/pyhdf
pyhdf/SD.py
SDS.dim
def dim(self, dim_index): """Get an SDim instance given a dimension index number. Args:: dim_index index number of the dimension (numbering starts at 0) C library equivalent : SDgetdimid """ id = _C.SDgetdimid(self._id, dim_index) _checkErr('dim', id, 'invalid SDS identifier or dimension index') return SDim(self, id, dim_index)
python
def dim(self, dim_index): """Get an SDim instance given a dimension index number. Args:: dim_index index number of the dimension (numbering starts at 0) C library equivalent : SDgetdimid """ id = _C.SDgetdimid(self._id, dim_index) _checkErr('dim', id, 'invalid SDS identifier or dimension index') return SDim(self, id, dim_index)
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Get an SDim instance given a dimension index number. Args:: dim_index index number of the dimension (numbering starts at 0) C library equivalent : SDgetdimid
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1840-L1851
4,516
fhs/pyhdf
pyhdf/SD.py
SDS.get
def get(self, start=None, count=None, stride=None): """Read data from the dataset. Args:: start : indices where to start reading in the data array; default to 0 on all dimensions count : number of values to read along each dimension; default to the current length of all dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to read the whole dataset contents, one should simply call the method with no argument. Returns:: numpy array initialized with the data. C library equivalent : SDreaddata The dataset can also be read using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access". """ # Obtain SDS info. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() if isinstance(dim_sizes, type(1)): dim_sizes = [dim_sizes] except HDF4Error: raise HDF4Error('get : cannot execute') # Validate args. if start is None: start = [0] * rank elif isinstance(start, type(1)): start = [start] if count is None: count = dim_sizes if count[0] == 0: count[0] = 1 elif isinstance(count, type(1)): count = [count] if stride is None: stride = [1] * rank elif isinstance(stride, type(1)): stride = [stride] if len(start) != rank or len(count) != rank or len(stride) != rank: raise HDF4Error('get : start, stride or count ' \ 'do not match SDS rank') for n in range(rank): if start[n] < 0 or start[n] + \ (abs(count[n]) - 1) * stride[n] >= dim_sizes[n]: raise HDF4Error('get arguments violate ' \ 'the size (%d) of dimension %d' \ % (dim_sizes[n], n)) if not data_type in SDC.equivNumericTypes: raise HDF4Error('get cannot currrently deal with '\ 'the SDS data type') return _C._SDreaddata_0(self._id, data_type, start, count, stride)
python
def get(self, start=None, count=None, stride=None): """Read data from the dataset. Args:: start : indices where to start reading in the data array; default to 0 on all dimensions count : number of values to read along each dimension; default to the current length of all dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to read the whole dataset contents, one should simply call the method with no argument. Returns:: numpy array initialized with the data. C library equivalent : SDreaddata The dataset can also be read using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access". """ # Obtain SDS info. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() if isinstance(dim_sizes, type(1)): dim_sizes = [dim_sizes] except HDF4Error: raise HDF4Error('get : cannot execute') # Validate args. if start is None: start = [0] * rank elif isinstance(start, type(1)): start = [start] if count is None: count = dim_sizes if count[0] == 0: count[0] = 1 elif isinstance(count, type(1)): count = [count] if stride is None: stride = [1] * rank elif isinstance(stride, type(1)): stride = [stride] if len(start) != rank or len(count) != rank or len(stride) != rank: raise HDF4Error('get : start, stride or count ' \ 'do not match SDS rank') for n in range(rank): if start[n] < 0 or start[n] + \ (abs(count[n]) - 1) * stride[n] >= dim_sizes[n]: raise HDF4Error('get arguments violate ' \ 'the size (%d) of dimension %d' \ % (dim_sizes[n], n)) if not data_type in SDC.equivNumericTypes: raise HDF4Error('get cannot currrently deal with '\ 'the SDS data type') return _C._SDreaddata_0(self._id, data_type, start, count, stride)
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Read data from the dataset. Args:: start : indices where to start reading in the data array; default to 0 on all dimensions count : number of values to read along each dimension; default to the current length of all dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to read the whole dataset contents, one should simply call the method with no argument. Returns:: numpy array initialized with the data. C library equivalent : SDreaddata The dataset can also be read using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access".
[ "Read", "data", "from", "the", "dataset", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1853-L1920
4,517
fhs/pyhdf
pyhdf/SD.py
SDS.set
def set(self, data, start=None, count=None, stride=None): """Write data to the dataset. Args:: data : array of data to write; can be given as a numpy array, or as Python sequence (whose elements can be imbricated sequences) start : indices where to start writing in the dataset; default to 0 on all dimensions count : number of values to write along each dimension; default to the current length of dataset dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to write the whole dataset at once, one has simply to call the method with the dataset values in parameter 'data', omitting all other parameters. Returns:: None. C library equivalent : SDwritedata The dataset can also be written using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access". """ # Obtain SDS info. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() if isinstance(dim_sizes, type(1)): dim_sizes = [dim_sizes] except HDF4Error: raise HDF4Error('set : cannot execute') # Validate args. if start is None: start = [0] * rank elif isinstance(start, type(1)): start = [start] if count is None: count = dim_sizes if count[0] == 0: count[0] = 1 elif isinstance(count, type(1)): count = [count] if stride is None: stride = [1] * rank elif isinstance(stride, type(1)): stride = [stride] if len(start) != rank or len(count) != rank or len(stride) != rank: raise HDF4Error('set : start, stride or count '\ 'do not match SDS rank') unlimited = self.isrecord() for n in range(rank): ok = 1 if start[n] < 0: ok = 0 elif n > 0 or not unlimited: if start[n] + (abs(count[n]) - 1) * stride[n] >= dim_sizes[n]: ok = 0 if not ok: raise HDF4Error('set arguments violate '\ 'the size (%d) of dimension %d' \ % (dim_sizes[n], n)) # ??? Check support for UINT16 if not data_type in SDC.equivNumericTypes: raise HDF4Error('set cannot currrently deal '\ 'with the SDS data type') _C._SDwritedata_0(self._id, data_type, start, count, data, stride)
python
def set(self, data, start=None, count=None, stride=None): """Write data to the dataset. Args:: data : array of data to write; can be given as a numpy array, or as Python sequence (whose elements can be imbricated sequences) start : indices where to start writing in the dataset; default to 0 on all dimensions count : number of values to write along each dimension; default to the current length of dataset dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to write the whole dataset at once, one has simply to call the method with the dataset values in parameter 'data', omitting all other parameters. Returns:: None. C library equivalent : SDwritedata The dataset can also be written using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access". """ # Obtain SDS info. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() if isinstance(dim_sizes, type(1)): dim_sizes = [dim_sizes] except HDF4Error: raise HDF4Error('set : cannot execute') # Validate args. if start is None: start = [0] * rank elif isinstance(start, type(1)): start = [start] if count is None: count = dim_sizes if count[0] == 0: count[0] = 1 elif isinstance(count, type(1)): count = [count] if stride is None: stride = [1] * rank elif isinstance(stride, type(1)): stride = [stride] if len(start) != rank or len(count) != rank or len(stride) != rank: raise HDF4Error('set : start, stride or count '\ 'do not match SDS rank') unlimited = self.isrecord() for n in range(rank): ok = 1 if start[n] < 0: ok = 0 elif n > 0 or not unlimited: if start[n] + (abs(count[n]) - 1) * stride[n] >= dim_sizes[n]: ok = 0 if not ok: raise HDF4Error('set arguments violate '\ 'the size (%d) of dimension %d' \ % (dim_sizes[n], n)) # ??? Check support for UINT16 if not data_type in SDC.equivNumericTypes: raise HDF4Error('set cannot currrently deal '\ 'with the SDS data type') _C._SDwritedata_0(self._id, data_type, start, count, data, stride)
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Write data to the dataset. Args:: data : array of data to write; can be given as a numpy array, or as Python sequence (whose elements can be imbricated sequences) start : indices where to start writing in the dataset; default to 0 on all dimensions count : number of values to write along each dimension; default to the current length of dataset dimensions stride : sampling interval along each dimension; default to 1 on all dimensions For n-dimensional datasets, those 3 parameters are entered using lists. For one-dimensional datasets, integers can also be used. Note that, to write the whole dataset at once, one has simply to call the method with the dataset values in parameter 'data', omitting all other parameters. Returns:: None. C library equivalent : SDwritedata The dataset can also be written using the familiar indexing and slicing notation, like ordinary python sequences. See "High level variable access".
[ "Write", "data", "to", "the", "dataset", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L1922-L2001
4,518
fhs/pyhdf
pyhdf/SD.py
SDS.info
def info(self): """Retrieves information about the dataset. Args:: no argument Returns:: 5-element tuple holding: - dataset name - dataset rank (number of dimensions) - dataset shape, that is a list giving the length of each dataset dimension; if the first dimension is unlimited, then the first value of the list gives the current length of the unlimited dimension - data type (one of the SDC.xxx values) - number of attributes defined for the dataset C library equivalent : SDgetinfo """ buf = _C.array_int32(_C.H4_MAX_VAR_DIMS) status, sds_name, rank, data_type, n_attrs = \ _C.SDgetinfo(self._id, buf) _checkErr('info', status, "cannot execute") dim_sizes = _array_to_ret(buf, rank) return sds_name, rank, dim_sizes, data_type, n_attrs
python
def info(self): """Retrieves information about the dataset. Args:: no argument Returns:: 5-element tuple holding: - dataset name - dataset rank (number of dimensions) - dataset shape, that is a list giving the length of each dataset dimension; if the first dimension is unlimited, then the first value of the list gives the current length of the unlimited dimension - data type (one of the SDC.xxx values) - number of attributes defined for the dataset C library equivalent : SDgetinfo """ buf = _C.array_int32(_C.H4_MAX_VAR_DIMS) status, sds_name, rank, data_type, n_attrs = \ _C.SDgetinfo(self._id, buf) _checkErr('info', status, "cannot execute") dim_sizes = _array_to_ret(buf, rank) return sds_name, rank, dim_sizes, data_type, n_attrs
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Retrieves information about the dataset. Args:: no argument Returns:: 5-element tuple holding: - dataset name - dataset rank (number of dimensions) - dataset shape, that is a list giving the length of each dataset dimension; if the first dimension is unlimited, then the first value of the list gives the current length of the unlimited dimension - data type (one of the SDC.xxx values) - number of attributes defined for the dataset C library equivalent : SDgetinfo
[ "Retrieves", "information", "about", "the", "dataset", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2102-L2130
4,519
fhs/pyhdf
pyhdf/SD.py
SDS.checkempty
def checkempty(self): """Determine whether the dataset is empty. Args:: no argument Returns:: True(1) if dataset is empty, False(0) if not C library equivalent : SDcheckempty """ status, emptySDS = _C.SDcheckempty(self._id) _checkErr('checkempty', status, 'invalid SDS identifier') return emptySDS
python
def checkempty(self): """Determine whether the dataset is empty. Args:: no argument Returns:: True(1) if dataset is empty, False(0) if not C library equivalent : SDcheckempty """ status, emptySDS = _C.SDcheckempty(self._id) _checkErr('checkempty', status, 'invalid SDS identifier') return emptySDS
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Determine whether the dataset is empty. Args:: no argument Returns:: True(1) if dataset is empty, False(0) if not C library equivalent : SDcheckempty
[ "Determine", "whether", "the", "dataset", "is", "empty", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2132-L2148
4,520
fhs/pyhdf
pyhdf/SD.py
SDS.ref
def ref(self): """Get the reference number of the dataset. Args:: no argument Returns:: dataset reference number C library equivalent : SDidtoref """ sds_ref = _C.SDidtoref(self._id) _checkErr('idtoref', sds_ref, 'illegal SDS identifier') return sds_ref
python
def ref(self): """Get the reference number of the dataset. Args:: no argument Returns:: dataset reference number C library equivalent : SDidtoref """ sds_ref = _C.SDidtoref(self._id) _checkErr('idtoref', sds_ref, 'illegal SDS identifier') return sds_ref
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Get the reference number of the dataset. Args:: no argument Returns:: dataset reference number C library equivalent : SDidtoref
[ "Get", "the", "reference", "number", "of", "the", "dataset", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2150-L2166
4,521
fhs/pyhdf
pyhdf/SD.py
SDS.getcal
def getcal(self): """Retrieve the SDS calibration coefficients. Args:: no argument Returns:: 5-element tuple holding: - cal: calibration factor (attribute 'scale_factor') - cal_error : calibration factor error (attribute 'scale_factor_err') - offset: calibration offset (attribute 'add_offset') - offset_err : offset error (attribute 'add_offset_err') - data_type : type of the data resulting from applying the calibration formula to the dataset values (attribute 'calibrated_nt') An exception is raised if no calibration data are defined. Original dataset values 'orival' are converted to calibrated values 'calval' through the formula:: calval = cal * (orival - offset) The calibration coefficients are part of the so-called "standard" SDS attributes. The values inside the tuple returned by 'getcal' are those of the following attributes, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDgetcal() """ status, cal, cal_error, offset, offset_err, data_type = \ _C.SDgetcal(self._id) _checkErr('getcal', status, 'no calibration record') return cal, cal_error, offset, offset_err, data_type
python
def getcal(self): """Retrieve the SDS calibration coefficients. Args:: no argument Returns:: 5-element tuple holding: - cal: calibration factor (attribute 'scale_factor') - cal_error : calibration factor error (attribute 'scale_factor_err') - offset: calibration offset (attribute 'add_offset') - offset_err : offset error (attribute 'add_offset_err') - data_type : type of the data resulting from applying the calibration formula to the dataset values (attribute 'calibrated_nt') An exception is raised if no calibration data are defined. Original dataset values 'orival' are converted to calibrated values 'calval' through the formula:: calval = cal * (orival - offset) The calibration coefficients are part of the so-called "standard" SDS attributes. The values inside the tuple returned by 'getcal' are those of the following attributes, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDgetcal() """ status, cal, cal_error, offset, offset_err, data_type = \ _C.SDgetcal(self._id) _checkErr('getcal', status, 'no calibration record') return cal, cal_error, offset, offset_err, data_type
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Retrieve the SDS calibration coefficients. Args:: no argument Returns:: 5-element tuple holding: - cal: calibration factor (attribute 'scale_factor') - cal_error : calibration factor error (attribute 'scale_factor_err') - offset: calibration offset (attribute 'add_offset') - offset_err : offset error (attribute 'add_offset_err') - data_type : type of the data resulting from applying the calibration formula to the dataset values (attribute 'calibrated_nt') An exception is raised if no calibration data are defined. Original dataset values 'orival' are converted to calibrated values 'calval' through the formula:: calval = cal * (orival - offset) The calibration coefficients are part of the so-called "standard" SDS attributes. The values inside the tuple returned by 'getcal' are those of the following attributes, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDgetcal()
[ "Retrieve", "the", "SDS", "calibration", "coefficients", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2206-L2246
4,522
fhs/pyhdf
pyhdf/SD.py
SDS.getdatastrs
def getdatastrs(self): """Retrieve the dataset standard string attributes. Args:: no argument Returns:: 4-element tuple holding: - dataset label string (attribute 'long_name') - dataset unit (attribute 'units') - dataset output format (attribute 'format') - dataset coordinate system (attribute 'coordsys') The values returned by 'getdatastrs' are part of the so-called "standard" SDS attributes. Those 4 values correspond respectively to the following attributes:: long_name, units, format, coordsys . C library equivalent: SDgetdatastrs """ status, label, unit, format, coord_system = \ _C.SDgetdatastrs(self._id, 128) _checkErr('getdatastrs', status, 'cannot execute') return label, unit, format, coord_system
python
def getdatastrs(self): """Retrieve the dataset standard string attributes. Args:: no argument Returns:: 4-element tuple holding: - dataset label string (attribute 'long_name') - dataset unit (attribute 'units') - dataset output format (attribute 'format') - dataset coordinate system (attribute 'coordsys') The values returned by 'getdatastrs' are part of the so-called "standard" SDS attributes. Those 4 values correspond respectively to the following attributes:: long_name, units, format, coordsys . C library equivalent: SDgetdatastrs """ status, label, unit, format, coord_system = \ _C.SDgetdatastrs(self._id, 128) _checkErr('getdatastrs', status, 'cannot execute') return label, unit, format, coord_system
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Retrieve the dataset standard string attributes. Args:: no argument Returns:: 4-element tuple holding: - dataset label string (attribute 'long_name') - dataset unit (attribute 'units') - dataset output format (attribute 'format') - dataset coordinate system (attribute 'coordsys') The values returned by 'getdatastrs' are part of the so-called "standard" SDS attributes. Those 4 values correspond respectively to the following attributes:: long_name, units, format, coordsys . C library equivalent: SDgetdatastrs
[ "Retrieve", "the", "dataset", "standard", "string", "attributes", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2248-L2276
4,523
fhs/pyhdf
pyhdf/SD.py
SDS.getrange
def getrange(self): """Retrieve the dataset min and max values. Args:: no argument Returns:: (min, max) tuple (attribute 'valid_range') Note that those are the values as stored by the 'setrange' method. 'getrange' does *NOT* compute the min and max from the current dataset contents. An exception is raised if the range is not set. The range returned by 'getrange' is part of the so-called "standard" SDS attributes. It corresponds to the following attribute:: valid_range C library equivalent: SDgetrange """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = \ self.info() except HDF4Error: raise HDF4Error('getrange : invalid SDS identifier') n_values = 1 convert = _array_to_ret if data_type == SDC.CHAR8: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) convert = _array_to_str elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type == SDC.INT8: buf1 = _C.array_int8(n_values) buf2 = _C.array_int8(n_values) elif data_type == SDC.INT16: buf1 = _C.array_int16(n_values) buf2 = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf1 = _C.array_uint16(n_values) buf2 = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf1 = _C.array_int32(n_values) buf2 = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf1 = _C.array_uint32(n_values) buf2 = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf1 = _C.array_float32(n_values) buf2 = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf1 = _C.array_float64(n_values) buf2 = _C.array_float64(n_values) else: raise HDF4Error("getrange: SDS has an illegal or " \ "unsupported type %d" % data) # Note: The C routine returns the max in buf1 and the min # in buf2. We swap the values returned by the Python # interface, since it is more natural to return # min first, then max. status = _C.SDgetrange(self._id, buf1, buf2) _checkErr('getrange', status, 'range not set') return convert(buf2, n_values), convert(buf1, n_values)
python
def getrange(self): """Retrieve the dataset min and max values. Args:: no argument Returns:: (min, max) tuple (attribute 'valid_range') Note that those are the values as stored by the 'setrange' method. 'getrange' does *NOT* compute the min and max from the current dataset contents. An exception is raised if the range is not set. The range returned by 'getrange' is part of the so-called "standard" SDS attributes. It corresponds to the following attribute:: valid_range C library equivalent: SDgetrange """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = \ self.info() except HDF4Error: raise HDF4Error('getrange : invalid SDS identifier') n_values = 1 convert = _array_to_ret if data_type == SDC.CHAR8: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) convert = _array_to_str elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type == SDC.INT8: buf1 = _C.array_int8(n_values) buf2 = _C.array_int8(n_values) elif data_type == SDC.INT16: buf1 = _C.array_int16(n_values) buf2 = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf1 = _C.array_uint16(n_values) buf2 = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf1 = _C.array_int32(n_values) buf2 = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf1 = _C.array_uint32(n_values) buf2 = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf1 = _C.array_float32(n_values) buf2 = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf1 = _C.array_float64(n_values) buf2 = _C.array_float64(n_values) else: raise HDF4Error("getrange: SDS has an illegal or " \ "unsupported type %d" % data) # Note: The C routine returns the max in buf1 and the min # in buf2. We swap the values returned by the Python # interface, since it is more natural to return # min first, then max. status = _C.SDgetrange(self._id, buf1, buf2) _checkErr('getrange', status, 'range not set') return convert(buf2, n_values), convert(buf1, n_values)
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Retrieve the dataset min and max values. Args:: no argument Returns:: (min, max) tuple (attribute 'valid_range') Note that those are the values as stored by the 'setrange' method. 'getrange' does *NOT* compute the min and max from the current dataset contents. An exception is raised if the range is not set. The range returned by 'getrange' is part of the so-called "standard" SDS attributes. It corresponds to the following attribute:: valid_range C library equivalent: SDgetrange
[ "Retrieve", "the", "dataset", "min", "and", "max", "values", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2344-L2426
4,524
fhs/pyhdf
pyhdf/SD.py
SDS.setcal
def setcal(self, cal, cal_error, offset, offset_err, data_type): """Set the dataset calibration coefficients. Args:: cal the calibraton factor (attribute 'scale_factor') cal_error calibration factor error (attribute 'scale_factor_err') offset offset value (attribute 'add_offset') offset_err offset error (attribute 'add_offset_err') data_type data type of the values resulting from applying the calibration formula to the dataset values (one of the SDC.xxx constants) (attribute 'calibrated_nt') Returns:: None See method 'getcal' for the definition of the calibration formula. Calibration coefficients are part of the so-called standard SDS attributes. Calling 'setcal' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDsetcal """ status = _C.SDsetcal(self._id, cal, cal_error, offset, offset_err, data_type) _checkErr('setcal', status, 'cannot execute')
python
def setcal(self, cal, cal_error, offset, offset_err, data_type): """Set the dataset calibration coefficients. Args:: cal the calibraton factor (attribute 'scale_factor') cal_error calibration factor error (attribute 'scale_factor_err') offset offset value (attribute 'add_offset') offset_err offset error (attribute 'add_offset_err') data_type data type of the values resulting from applying the calibration formula to the dataset values (one of the SDC.xxx constants) (attribute 'calibrated_nt') Returns:: None See method 'getcal' for the definition of the calibration formula. Calibration coefficients are part of the so-called standard SDS attributes. Calling 'setcal' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDsetcal """ status = _C.SDsetcal(self._id, cal, cal_error, offset, offset_err, data_type) _checkErr('setcal', status, 'cannot execute')
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Set the dataset calibration coefficients. Args:: cal the calibraton factor (attribute 'scale_factor') cal_error calibration factor error (attribute 'scale_factor_err') offset offset value (attribute 'add_offset') offset_err offset error (attribute 'add_offset_err') data_type data type of the values resulting from applying the calibration formula to the dataset values (one of the SDC.xxx constants) (attribute 'calibrated_nt') Returns:: None See method 'getcal' for the definition of the calibration formula. Calibration coefficients are part of the so-called standard SDS attributes. Calling 'setcal' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: scale_factor, scale_factor_err, add_offset, add_offset_err, calibrated_nt C library equivalent: SDsetcal
[ "Set", "the", "dataset", "calibration", "coefficients", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2428-L2463
4,525
fhs/pyhdf
pyhdf/SD.py
SDS.setdatastrs
def setdatastrs(self, label, unit, format, coord_sys): """Set the dataset standard string type attributes. Args:: label dataset label (attribute 'long_name') unit dataset unit (attribute 'units') format dataset format (attribute 'format') coord_sys dataset coordinate system (attribute 'coordsys') Returns:: None Those strings are part of the so-called standard SDS attributes. Calling 'setdatastrs' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: long_name, units, format, coordsys C library equivalent: SDsetdatastrs """ status = _C.SDsetdatastrs(self._id, label, unit, format, coord_sys) _checkErr('setdatastrs', status, 'cannot execute')
python
def setdatastrs(self, label, unit, format, coord_sys): """Set the dataset standard string type attributes. Args:: label dataset label (attribute 'long_name') unit dataset unit (attribute 'units') format dataset format (attribute 'format') coord_sys dataset coordinate system (attribute 'coordsys') Returns:: None Those strings are part of the so-called standard SDS attributes. Calling 'setdatastrs' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: long_name, units, format, coordsys C library equivalent: SDsetdatastrs """ status = _C.SDsetdatastrs(self._id, label, unit, format, coord_sys) _checkErr('setdatastrs', status, 'cannot execute')
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Set the dataset standard string type attributes. Args:: label dataset label (attribute 'long_name') unit dataset unit (attribute 'units') format dataset format (attribute 'format') coord_sys dataset coordinate system (attribute 'coordsys') Returns:: None Those strings are part of the so-called standard SDS attributes. Calling 'setdatastrs' is equivalent to setting the following attributes, which correspond to the method parameters, in order:: long_name, units, format, coordsys C library equivalent: SDsetdatastrs
[ "Set", "the", "dataset", "standard", "string", "type", "attributes", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2465-L2490
4,526
fhs/pyhdf
pyhdf/SD.py
SDS.setfillvalue
def setfillvalue(self, fill_val): """Set the dataset fill value. Args:: fill_val dataset fill value (attribute '_FillValue') Returns:: None The fill value is part of the so-called "standard" SDS attributes. Calling 'setfillvalue' is equivalent to setting the following attribute:: _FillValue C library equivalent: SDsetfillvalue """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() except HDF4Error: raise HDF4Error('setfillvalue : cannot execute') n_values = 1 # Fill value stands for 1 value. if data_type == SDC.CHAR8: buf = _C.array_byte(n_values) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(n_values) elif data_type == SDC.INT8: buf = _C.array_int8(n_values) elif data_type == SDC.INT16: buf = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("setfillvalue: SDS has an illegal or " \ "unsupported type %d" % data_type) buf[0] = fill_val status = _C.SDsetfillvalue(self._id, buf) _checkErr('setfillvalue', status, 'cannot execute')
python
def setfillvalue(self, fill_val): """Set the dataset fill value. Args:: fill_val dataset fill value (attribute '_FillValue') Returns:: None The fill value is part of the so-called "standard" SDS attributes. Calling 'setfillvalue' is equivalent to setting the following attribute:: _FillValue C library equivalent: SDsetfillvalue """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() except HDF4Error: raise HDF4Error('setfillvalue : cannot execute') n_values = 1 # Fill value stands for 1 value. if data_type == SDC.CHAR8: buf = _C.array_byte(n_values) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(n_values) elif data_type == SDC.INT8: buf = _C.array_int8(n_values) elif data_type == SDC.INT16: buf = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("setfillvalue: SDS has an illegal or " \ "unsupported type %d" % data_type) buf[0] = fill_val status = _C.SDsetfillvalue(self._id, buf) _checkErr('setfillvalue', status, 'cannot execute')
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Set the dataset fill value. Args:: fill_val dataset fill value (attribute '_FillValue') Returns:: None The fill value is part of the so-called "standard" SDS attributes. Calling 'setfillvalue' is equivalent to setting the following attribute:: _FillValue C library equivalent: SDsetfillvalue
[ "Set", "the", "dataset", "fill", "value", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2492-L2552
4,527
fhs/pyhdf
pyhdf/SD.py
SDS.setrange
def setrange(self, min, max): """Set the dataset min and max values. Args:: min dataset minimum value (attribute 'valid_range') max dataset maximum value (attribute 'valid_range') Returns:: None The data range is part of the so-called "standard" SDS attributes. Calling method 'setrange' is equivalent to setting the following attribute with a 2-element [min,max] array:: valid_range C library equivalent: SDsetrange """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() except HDF4Error: raise HDF4Error('setrange : cannot execute') n_values = 1 if data_type == SDC.CHAR8: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type == SDC.INT8: buf1 = _C.array_int8(n_values) buf2 = _C.array_int8(n_values) elif data_type == SDC.INT16: buf1 = _C.array_int16(n_values) buf2 = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf1 = _C.array_uint16(n_values) buf2 = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf1 = _C.array_int32(n_values) buf2 = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf1 = _C.array_uint32(n_values) buf2 = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf1 = _C.array_float32(n_values) buf2 = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf1 = _C.array_float64(n_values) buf2 = _C.array_float64(n_values) else: raise HDF4Error("SDsetrange: SDS has an illegal or " \ "unsupported type %d" % data_type) buf1[0] = max buf2[0] = min status = _C.SDsetrange(self._id, buf1, buf2) _checkErr('setrange', status, 'cannot execute')
python
def setrange(self, min, max): """Set the dataset min and max values. Args:: min dataset minimum value (attribute 'valid_range') max dataset maximum value (attribute 'valid_range') Returns:: None The data range is part of the so-called "standard" SDS attributes. Calling method 'setrange' is equivalent to setting the following attribute with a 2-element [min,max] array:: valid_range C library equivalent: SDsetrange """ # Obtain SDS data type. try: sds_name, rank, dim_sizes, data_type, n_attrs = self.info() except HDF4Error: raise HDF4Error('setrange : cannot execute') n_values = 1 if data_type == SDC.CHAR8: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf1 = _C.array_byte(n_values) buf2 = _C.array_byte(n_values) elif data_type == SDC.INT8: buf1 = _C.array_int8(n_values) buf2 = _C.array_int8(n_values) elif data_type == SDC.INT16: buf1 = _C.array_int16(n_values) buf2 = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf1 = _C.array_uint16(n_values) buf2 = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf1 = _C.array_int32(n_values) buf2 = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf1 = _C.array_uint32(n_values) buf2 = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf1 = _C.array_float32(n_values) buf2 = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf1 = _C.array_float64(n_values) buf2 = _C.array_float64(n_values) else: raise HDF4Error("SDsetrange: SDS has an illegal or " \ "unsupported type %d" % data_type) buf1[0] = max buf2[0] = min status = _C.SDsetrange(self._id, buf1, buf2) _checkErr('setrange', status, 'cannot execute')
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Set the dataset min and max values. Args:: min dataset minimum value (attribute 'valid_range') max dataset maximum value (attribute 'valid_range') Returns:: None The data range is part of the so-called "standard" SDS attributes. Calling method 'setrange' is equivalent to setting the following attribute with a 2-element [min,max] array:: valid_range C library equivalent: SDsetrange
[ "Set", "the", "dataset", "min", "and", "max", "values", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2555-L2629
4,528
fhs/pyhdf
pyhdf/SD.py
SDS.getcompress
def getcompress(self): """Retrieves info about dataset compression type and mode. Args:: no argument Returns:: tuple holding: - compression type (one of the SDC.COMP_xxx constants) - optional values, depending on the compression type COMP_NONE 0 value no additional value COMP_SKPHUFF 1 value : skip size COMP_DEFLATE 1 value : gzip compression level (1 to 9) COMP_SZIP 5 values : options mask, pixels per block (2 to 32) pixels per scanline, bits per pixel (number of bits in the SDS datatype) pixels (number of elements in the SDS) Note: in the context of an SDS, the word "pixel" should really be understood as meaning "data element", eg a cell value inside a multidimensional grid. Test the options mask against constants SDC.COMP_SZIP_NN and SDC.COMP_SZIP_EC, eg : if optionMask & SDC.COMP_SZIP_EC: print "EC encoding scheme used" An exception is raised if dataset is not compressed. .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. C library equivalent: SDgetcompress """ status, comp_type, value, v2, v3, v4, v5 = _C._SDgetcompress(self._id) _checkErr('getcompress', status, 'no compression') if comp_type == SDC.COMP_NONE: return (comp_type,) elif comp_type == SDC.COMP_SZIP: return comp_type, value, v2, v3, v4, v5 else: return comp_type, value
python
def getcompress(self): """Retrieves info about dataset compression type and mode. Args:: no argument Returns:: tuple holding: - compression type (one of the SDC.COMP_xxx constants) - optional values, depending on the compression type COMP_NONE 0 value no additional value COMP_SKPHUFF 1 value : skip size COMP_DEFLATE 1 value : gzip compression level (1 to 9) COMP_SZIP 5 values : options mask, pixels per block (2 to 32) pixels per scanline, bits per pixel (number of bits in the SDS datatype) pixels (number of elements in the SDS) Note: in the context of an SDS, the word "pixel" should really be understood as meaning "data element", eg a cell value inside a multidimensional grid. Test the options mask against constants SDC.COMP_SZIP_NN and SDC.COMP_SZIP_EC, eg : if optionMask & SDC.COMP_SZIP_EC: print "EC encoding scheme used" An exception is raised if dataset is not compressed. .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. C library equivalent: SDgetcompress """ status, comp_type, value, v2, v3, v4, v5 = _C._SDgetcompress(self._id) _checkErr('getcompress', status, 'no compression') if comp_type == SDC.COMP_NONE: return (comp_type,) elif comp_type == SDC.COMP_SZIP: return comp_type, value, v2, v3, v4, v5 else: return comp_type, value
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Retrieves info about dataset compression type and mode. Args:: no argument Returns:: tuple holding: - compression type (one of the SDC.COMP_xxx constants) - optional values, depending on the compression type COMP_NONE 0 value no additional value COMP_SKPHUFF 1 value : skip size COMP_DEFLATE 1 value : gzip compression level (1 to 9) COMP_SZIP 5 values : options mask, pixels per block (2 to 32) pixels per scanline, bits per pixel (number of bits in the SDS datatype) pixels (number of elements in the SDS) Note: in the context of an SDS, the word "pixel" should really be understood as meaning "data element", eg a cell value inside a multidimensional grid. Test the options mask against constants SDC.COMP_SZIP_NN and SDC.COMP_SZIP_EC, eg : if optionMask & SDC.COMP_SZIP_EC: print "EC encoding scheme used" An exception is raised if dataset is not compressed. .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. C library equivalent: SDgetcompress
[ "Retrieves", "info", "about", "dataset", "compression", "type", "and", "mode", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2631-L2677
4,529
fhs/pyhdf
pyhdf/SD.py
SDS.setcompress
def setcompress(self, comp_type, value=0, v2=0): """Compresses the dataset using a specified compression method. Args:: comp_type compression type, identified by one of the SDC.COMP_xxx constants value,v2 auxiliary value(s) needed by some compression types SDC.COMP_SKPHUFF Skipping-Huffman; compression value=data size in bytes, v2 is ignored SDC.COMP_DEFLATE Gzip compression; value=deflate level (1 to 9), v2 is ignored SDC.COMP_SZIP Szip compression; value=encoding scheme (SDC.COMP_SZIP_EC or SDC.COMP_SZIP_NN), v2=pixels per block (2 to 32) Returns:: None .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. SDC.COMP_DEFLATE applies the GZIP compression to the dataset, and the value varies from 1 to 9, according to the level of compression desired. SDC.COMP_SZIP compresses the dataset using the SZIP algorithm. See the HDF User's Guide for details about the encoding scheme and the number of pixels per block. SZIP is new with HDF 4.2. 'setcompress' must be called before writing to the dataset. The dataset must be written all at once, unless it is appendable (has an unlimited dimension). Updating the dataset in not allowed. Refer to the HDF user's guide for more details on how to use data compression. C library equivalent: SDsetcompress """ status = _C._SDsetcompress(self._id, comp_type, value, v2) _checkErr('setcompress', status, 'cannot execute')
python
def setcompress(self, comp_type, value=0, v2=0): """Compresses the dataset using a specified compression method. Args:: comp_type compression type, identified by one of the SDC.COMP_xxx constants value,v2 auxiliary value(s) needed by some compression types SDC.COMP_SKPHUFF Skipping-Huffman; compression value=data size in bytes, v2 is ignored SDC.COMP_DEFLATE Gzip compression; value=deflate level (1 to 9), v2 is ignored SDC.COMP_SZIP Szip compression; value=encoding scheme (SDC.COMP_SZIP_EC or SDC.COMP_SZIP_NN), v2=pixels per block (2 to 32) Returns:: None .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. SDC.COMP_DEFLATE applies the GZIP compression to the dataset, and the value varies from 1 to 9, according to the level of compression desired. SDC.COMP_SZIP compresses the dataset using the SZIP algorithm. See the HDF User's Guide for details about the encoding scheme and the number of pixels per block. SZIP is new with HDF 4.2. 'setcompress' must be called before writing to the dataset. The dataset must be written all at once, unless it is appendable (has an unlimited dimension). Updating the dataset in not allowed. Refer to the HDF user's guide for more details on how to use data compression. C library equivalent: SDsetcompress """ status = _C._SDsetcompress(self._id, comp_type, value, v2) _checkErr('setcompress', status, 'cannot execute')
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Compresses the dataset using a specified compression method. Args:: comp_type compression type, identified by one of the SDC.COMP_xxx constants value,v2 auxiliary value(s) needed by some compression types SDC.COMP_SKPHUFF Skipping-Huffman; compression value=data size in bytes, v2 is ignored SDC.COMP_DEFLATE Gzip compression; value=deflate level (1 to 9), v2 is ignored SDC.COMP_SZIP Szip compression; value=encoding scheme (SDC.COMP_SZIP_EC or SDC.COMP_SZIP_NN), v2=pixels per block (2 to 32) Returns:: None .. note:: Starting with v0.8, an exception is always raised if pyhdf was installed with the NOCOMPRESS macro set. SDC.COMP_DEFLATE applies the GZIP compression to the dataset, and the value varies from 1 to 9, according to the level of compression desired. SDC.COMP_SZIP compresses the dataset using the SZIP algorithm. See the HDF User's Guide for details about the encoding scheme and the number of pixels per block. SZIP is new with HDF 4.2. 'setcompress' must be called before writing to the dataset. The dataset must be written all at once, unless it is appendable (has an unlimited dimension). Updating the dataset in not allowed. Refer to the HDF user's guide for more details on how to use data compression. C library equivalent: SDsetcompress
[ "Compresses", "the", "dataset", "using", "a", "specified", "compression", "method", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2679-L2718
4,530
fhs/pyhdf
pyhdf/SD.py
SDS.setexternalfile
def setexternalfile(self, filename, offset=0): """Store the dataset data in an external file. Args:: filename external file name offset offset in bytes where to start writing in the external file Returns:: None C library equivalent : SDsetexternalfile """ status = _C.SDsetexternalfile(self._id, filename, offset) _checkErr('setexternalfile', status, 'execution error')
python
def setexternalfile(self, filename, offset=0): """Store the dataset data in an external file. Args:: filename external file name offset offset in bytes where to start writing in the external file Returns:: None C library equivalent : SDsetexternalfile """ status = _C.SDsetexternalfile(self._id, filename, offset) _checkErr('setexternalfile', status, 'execution error')
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Store the dataset data in an external file. Args:: filename external file name offset offset in bytes where to start writing in the external file Returns:: None C library equivalent : SDsetexternalfile
[ "Store", "the", "dataset", "data", "in", "an", "external", "file", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2721-L2738
4,531
fhs/pyhdf
pyhdf/SD.py
SDS.dimensions
def dimensions(self, full=0): """Return a dictionnary describing every dataset dimension. Args:: full true to get complete info about each dimension false to report only each dimension length Returns:: Dictionnary where each key is a dimension name. If no name has been given to the dimension, the key is set to 'fakeDimx' where 'x' is the dimension index number. If parameter 'full' is false, key value is the dimension length. If 'full' is true, key value is a 5-element tuple with the following elements: - dimension length; for an unlimited dimension, the reported length is the current dimension length - dimension index number - 1 if the dimension is unlimited, 0 otherwise - dimension scale type, or 0 if no scale is defined for the dimension - number of attributes defined on the dimension C library equivalent : no equivalent """ # Get the number of dimensions and their lengths. nDims, dimLen = self.info()[1:3] if isinstance(dimLen, int): # need a sequence dimLen = [dimLen] # Check if the dataset is appendable. unlim = self.isrecord() # Inquire each dimension res = {} for n in range(nDims): d = self.dim(n) # The length reported by info() is 0 for an unlimited dimension. # Rather use the lengths reported by SDS.info() name, k, scaleType, nAtt = d.info() length = dimLen[n] if full: res[name] = (length, n, unlim and n == 0, scaleType, nAtt) else: res[name] = length return res
python
def dimensions(self, full=0): """Return a dictionnary describing every dataset dimension. Args:: full true to get complete info about each dimension false to report only each dimension length Returns:: Dictionnary where each key is a dimension name. If no name has been given to the dimension, the key is set to 'fakeDimx' where 'x' is the dimension index number. If parameter 'full' is false, key value is the dimension length. If 'full' is true, key value is a 5-element tuple with the following elements: - dimension length; for an unlimited dimension, the reported length is the current dimension length - dimension index number - 1 if the dimension is unlimited, 0 otherwise - dimension scale type, or 0 if no scale is defined for the dimension - number of attributes defined on the dimension C library equivalent : no equivalent """ # Get the number of dimensions and their lengths. nDims, dimLen = self.info()[1:3] if isinstance(dimLen, int): # need a sequence dimLen = [dimLen] # Check if the dataset is appendable. unlim = self.isrecord() # Inquire each dimension res = {} for n in range(nDims): d = self.dim(n) # The length reported by info() is 0 for an unlimited dimension. # Rather use the lengths reported by SDS.info() name, k, scaleType, nAtt = d.info() length = dimLen[n] if full: res[name] = (length, n, unlim and n == 0, scaleType, nAtt) else: res[name] = length return res
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Return a dictionnary describing every dataset dimension. Args:: full true to get complete info about each dimension false to report only each dimension length Returns:: Dictionnary where each key is a dimension name. If no name has been given to the dimension, the key is set to 'fakeDimx' where 'x' is the dimension index number. If parameter 'full' is false, key value is the dimension length. If 'full' is true, key value is a 5-element tuple with the following elements: - dimension length; for an unlimited dimension, the reported length is the current dimension length - dimension index number - 1 if the dimension is unlimited, 0 otherwise - dimension scale type, or 0 if no scale is defined for the dimension - number of attributes defined on the dimension C library equivalent : no equivalent
[ "Return", "a", "dictionnary", "describing", "every", "dataset", "dimension", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2800-L2849
4,532
fhs/pyhdf
pyhdf/SD.py
SDim.info
def info(self): """Return info about the dimension instance. Args:: no argument Returns:: 4-element tuple holding: - dimension name; 'fakeDimx' is returned if the dimension has not been named yet, where 'x' is the dimension index number - dimension length; 0 is returned if the dimension is unlimited; call the SDim.length() or SDS.info() methods to obtain the current dimension length - scale data type (one of the SDC.xxx constants); 0 is returned if no scale has been set on the dimension - number of attributes attached to the dimension C library equivalent : SDdiminfo """ status, dim_name, dim_size, data_type, n_attrs = \ _C.SDdiminfo(self._id) _checkErr('info', status, 'cannot execute') return dim_name, dim_size, data_type, n_attrs
python
def info(self): """Return info about the dimension instance. Args:: no argument Returns:: 4-element tuple holding: - dimension name; 'fakeDimx' is returned if the dimension has not been named yet, where 'x' is the dimension index number - dimension length; 0 is returned if the dimension is unlimited; call the SDim.length() or SDS.info() methods to obtain the current dimension length - scale data type (one of the SDC.xxx constants); 0 is returned if no scale has been set on the dimension - number of attributes attached to the dimension C library equivalent : SDdiminfo """ status, dim_name, dim_size, data_type, n_attrs = \ _C.SDdiminfo(self._id) _checkErr('info', status, 'cannot execute') return dim_name, dim_size, data_type, n_attrs
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Return info about the dimension instance. Args:: no argument Returns:: 4-element tuple holding: - dimension name; 'fakeDimx' is returned if the dimension has not been named yet, where 'x' is the dimension index number - dimension length; 0 is returned if the dimension is unlimited; call the SDim.length() or SDS.info() methods to obtain the current dimension length - scale data type (one of the SDC.xxx constants); 0 is returned if no scale has been set on the dimension - number of attributes attached to the dimension C library equivalent : SDdiminfo
[ "Return", "info", "about", "the", "dimension", "instance", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2892-L2918
4,533
fhs/pyhdf
pyhdf/SD.py
SDim.setname
def setname(self, dim_name): """Set the dimension name. Args:: dim_name dimension name; setting 2 dimensions to the same name make the dimensions "shared"; in order to be shared, the dimesions must be deined similarly. Returns:: None C library equivalent : SDsetdimname """ status = _C.SDsetdimname(self._id, dim_name) _checkErr('setname', status, 'cannot execute')
python
def setname(self, dim_name): """Set the dimension name. Args:: dim_name dimension name; setting 2 dimensions to the same name make the dimensions "shared"; in order to be shared, the dimesions must be deined similarly. Returns:: None C library equivalent : SDsetdimname """ status = _C.SDsetdimname(self._id, dim_name) _checkErr('setname', status, 'cannot execute')
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Set the dimension name. Args:: dim_name dimension name; setting 2 dimensions to the same name make the dimensions "shared"; in order to be shared, the dimesions must be deined similarly. Returns:: None C library equivalent : SDsetdimname
[ "Set", "the", "dimension", "name", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2939-L2956
4,534
fhs/pyhdf
pyhdf/SD.py
SDim.getscale
def getscale(self): """Obtain the scale values along a dimension. Args:: no argument Returns:: list with the scale values; the list length is equal to the dimension length; the element type is equal to the dimension data type, as set when the 'setdimscale()' method was called. C library equivalent : SDgetdimscale """ # Get dimension info. If data_type is 0, no scale have been set # on the dimension. status, dim_name, dim_size, data_type, n_attrs = _C.SDdiminfo(self._id) _checkErr('getscale', status, 'cannot execute') if data_type == 0: raise HDF4Error("no scale set on that dimension") # dim_size is 0 for an unlimited dimension. The actual length is # obtained through SDgetinfo. if dim_size == 0: dim_size = self._sds.info()[2][self._index] # Get scale values. if data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(dim_size) elif data_type == SDC.INT8: buf = _C.array_int8(dim_size) elif data_type == SDC.INT16: buf = _C.array_int16(dim_size) elif data_type == SDC.UINT16: buf = _C.array_uint16(dim_size) elif data_type == SDC.INT32: buf = _C.array_int32(dim_size) elif data_type == SDC.UINT32: buf = _C.array_uint32(dim_size) elif data_type == SDC.FLOAT32: buf = _C.array_float32(dim_size) elif data_type == SDC.FLOAT64: buf = _C.array_float64(dim_size) else: raise HDF4Error("getscale: dimension has an "\ "illegal or unsupported type %d" % data_type) status = _C.SDgetdimscale(self._id, buf) _checkErr('getscale', status, 'cannot execute') return _array_to_ret(buf, dim_size)
python
def getscale(self): """Obtain the scale values along a dimension. Args:: no argument Returns:: list with the scale values; the list length is equal to the dimension length; the element type is equal to the dimension data type, as set when the 'setdimscale()' method was called. C library equivalent : SDgetdimscale """ # Get dimension info. If data_type is 0, no scale have been set # on the dimension. status, dim_name, dim_size, data_type, n_attrs = _C.SDdiminfo(self._id) _checkErr('getscale', status, 'cannot execute') if data_type == 0: raise HDF4Error("no scale set on that dimension") # dim_size is 0 for an unlimited dimension. The actual length is # obtained through SDgetinfo. if dim_size == 0: dim_size = self._sds.info()[2][self._index] # Get scale values. if data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(dim_size) elif data_type == SDC.INT8: buf = _C.array_int8(dim_size) elif data_type == SDC.INT16: buf = _C.array_int16(dim_size) elif data_type == SDC.UINT16: buf = _C.array_uint16(dim_size) elif data_type == SDC.INT32: buf = _C.array_int32(dim_size) elif data_type == SDC.UINT32: buf = _C.array_uint32(dim_size) elif data_type == SDC.FLOAT32: buf = _C.array_float32(dim_size) elif data_type == SDC.FLOAT64: buf = _C.array_float64(dim_size) else: raise HDF4Error("getscale: dimension has an "\ "illegal or unsupported type %d" % data_type) status = _C.SDgetdimscale(self._id, buf) _checkErr('getscale', status, 'cannot execute') return _array_to_ret(buf, dim_size)
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Obtain the scale values along a dimension. Args:: no argument Returns:: list with the scale values; the list length is equal to the dimension length; the element type is equal to the dimension data type, as set when the 'setdimscale()' method was called. C library equivalent : SDgetdimscale
[ "Obtain", "the", "scale", "values", "along", "a", "dimension", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L2959-L3018
4,535
fhs/pyhdf
pyhdf/SD.py
SDim.setscale
def setscale(self, data_type, scale): """Initialize the scale values along the dimension. Args:: data_type data type code (one of the SDC.xxx constants) scale sequence holding the scale values; the number of values must match the current length of the dataset along that dimension C library equivalent : SDsetdimscale Setting a scale on a dimension generates what HDF calls a "coordinate variable". This is a rank 1 dataset similar to any other dataset, which is created to hold the scale values. The dataset name is identical to that of the dimension on which setscale() is called, and the data type passed in 'data_type' determines the type of the dataset. To distinguish between such a dataset and a "normal" dataset, call the iscoordvar() method of the dataset instance. """ try: n_values = len(scale) except: n_values = 1 # Validate args info = self._sds.info() if info[1] == 1: dim_size = info[2] else: dim_size = info[2][self._index] if n_values != dim_size: raise HDF4Error('number of scale values (%d) does not match ' \ 'dimension size (%d)' % (n_values, dim_size)) if data_type == SDC.CHAR8: buf = _C.array_byte(n_values) # Allow a string as the scale argument. # Becomes a noop if already a list. scale = list(scale) for n in range(n_values): scale[n] = ord(scale[n]) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(n_values) elif data_type == SDC.INT8: buf = _C.array_int8(n_values) elif data_type == SDC.INT16: buf = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("setscale: illegal or usupported data_type") if n_values == 1: buf[0] = scale else: for n in range(n_values): buf[n] = scale[n] status = _C.SDsetdimscale(self._id, n_values, data_type, buf) _checkErr('setscale', status, 'cannot execute')
python
def setscale(self, data_type, scale): """Initialize the scale values along the dimension. Args:: data_type data type code (one of the SDC.xxx constants) scale sequence holding the scale values; the number of values must match the current length of the dataset along that dimension C library equivalent : SDsetdimscale Setting a scale on a dimension generates what HDF calls a "coordinate variable". This is a rank 1 dataset similar to any other dataset, which is created to hold the scale values. The dataset name is identical to that of the dimension on which setscale() is called, and the data type passed in 'data_type' determines the type of the dataset. To distinguish between such a dataset and a "normal" dataset, call the iscoordvar() method of the dataset instance. """ try: n_values = len(scale) except: n_values = 1 # Validate args info = self._sds.info() if info[1] == 1: dim_size = info[2] else: dim_size = info[2][self._index] if n_values != dim_size: raise HDF4Error('number of scale values (%d) does not match ' \ 'dimension size (%d)' % (n_values, dim_size)) if data_type == SDC.CHAR8: buf = _C.array_byte(n_values) # Allow a string as the scale argument. # Becomes a noop if already a list. scale = list(scale) for n in range(n_values): scale[n] = ord(scale[n]) elif data_type in [SDC.UCHAR8, SDC.UINT8]: buf = _C.array_byte(n_values) elif data_type == SDC.INT8: buf = _C.array_int8(n_values) elif data_type == SDC.INT16: buf = _C.array_int16(n_values) elif data_type == SDC.UINT16: buf = _C.array_uint16(n_values) elif data_type == SDC.INT32: buf = _C.array_int32(n_values) elif data_type == SDC.UINT32: buf = _C.array_uint32(n_values) elif data_type == SDC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == SDC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("setscale: illegal or usupported data_type") if n_values == 1: buf[0] = scale else: for n in range(n_values): buf[n] = scale[n] status = _C.SDsetdimscale(self._id, n_values, data_type, buf) _checkErr('setscale', status, 'cannot execute')
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Initialize the scale values along the dimension. Args:: data_type data type code (one of the SDC.xxx constants) scale sequence holding the scale values; the number of values must match the current length of the dataset along that dimension C library equivalent : SDsetdimscale Setting a scale on a dimension generates what HDF calls a "coordinate variable". This is a rank 1 dataset similar to any other dataset, which is created to hold the scale values. The dataset name is identical to that of the dimension on which setscale() is called, and the data type passed in 'data_type' determines the type of the dataset. To distinguish between such a dataset and a "normal" dataset, call the iscoordvar() method of the dataset instance.
[ "Initialize", "the", "scale", "values", "along", "the", "dimension", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L3020-L3098
4,536
fhs/pyhdf
pyhdf/SD.py
SDim.getstrs
def getstrs(self): """Retrieve the dimension standard string attributes. Args:: no argument Returns:: 3-element tuple holding: -dimension label (attribute 'long_name') -dimension unit (attribute 'units') -dimension format (attribute 'format') An exception is raised if the standard attributes have not been set. C library equivalent: SDgetdimstrs """ status, label, unit, format = _C.SDgetdimstrs(self._id, 128) _checkErr('getstrs', status, 'cannot execute') return label, unit, format
python
def getstrs(self): """Retrieve the dimension standard string attributes. Args:: no argument Returns:: 3-element tuple holding: -dimension label (attribute 'long_name') -dimension unit (attribute 'units') -dimension format (attribute 'format') An exception is raised if the standard attributes have not been set. C library equivalent: SDgetdimstrs """ status, label, unit, format = _C.SDgetdimstrs(self._id, 128) _checkErr('getstrs', status, 'cannot execute') return label, unit, format
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Retrieve the dimension standard string attributes. Args:: no argument Returns:: 3-element tuple holding: -dimension label (attribute 'long_name') -dimension unit (attribute 'units') -dimension format (attribute 'format') An exception is raised if the standard attributes have not been set. C library equivalent: SDgetdimstrs
[ "Retrieve", "the", "dimension", "standard", "string", "attributes", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L3100-L3122
4,537
fhs/pyhdf
pyhdf/SD.py
SDim.setstrs
def setstrs(self, label, unit, format): """Set the dimension standard string attributes. Args:: label dimension label (attribute 'long_name') unit dimension unit (attribute 'units') format dimension format (attribute 'format') Returns:: None C library equivalent: SDsetdimstrs """ status = _C.SDsetdimstrs(self._id, label, unit, format) _checkErr('setstrs', status, 'cannot execute')
python
def setstrs(self, label, unit, format): """Set the dimension standard string attributes. Args:: label dimension label (attribute 'long_name') unit dimension unit (attribute 'units') format dimension format (attribute 'format') Returns:: None C library equivalent: SDsetdimstrs """ status = _C.SDsetdimstrs(self._id, label, unit, format) _checkErr('setstrs', status, 'cannot execute')
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Set the dimension standard string attributes. Args:: label dimension label (attribute 'long_name') unit dimension unit (attribute 'units') format dimension format (attribute 'format') Returns:: None C library equivalent: SDsetdimstrs
[ "Set", "the", "dimension", "standard", "string", "attributes", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/SD.py#L3124-L3141
4,538
fhs/pyhdf
pyhdf/VS.py
VS.attach
def attach(self, num_name, write=0): """Locate an existing vdata or create a new vdata in the HDF file, returning a VD instance. Args:: num_name Name or reference number of the vdata. An existing vdata can be specified either through its reference number or its name. Use -1 to create a new vdata. Note that uniqueness is not imposed on vdatas names, whereas refnums are guaranteed to be unique. Thus knowledge of its reference number may be the only way to get at a wanted vdata. write Set to 0 to open the vdata in read-only mode, set to 1 to open it in write mode Returns:: VD instance representing the vdata C library equivalent : VSattach After creating a new vdata (num_name == -1), fields must be defined using method fdefine() of the VD instance, and those fields must be allocated to the vdata with method setfields(). Same results can be achieved, but more simply, by calling the create() method of the VS instance. """ mode = write and 'w' or 'r' if isinstance(num_name, str): num = self.find(num_name) else: num = num_name vd = _C.VSattach(self._hdf_inst._id, num, mode) if vd < 0: _checkErr('attach', vd, 'cannot attach vdata') return VD(self, vd)
python
def attach(self, num_name, write=0): """Locate an existing vdata or create a new vdata in the HDF file, returning a VD instance. Args:: num_name Name or reference number of the vdata. An existing vdata can be specified either through its reference number or its name. Use -1 to create a new vdata. Note that uniqueness is not imposed on vdatas names, whereas refnums are guaranteed to be unique. Thus knowledge of its reference number may be the only way to get at a wanted vdata. write Set to 0 to open the vdata in read-only mode, set to 1 to open it in write mode Returns:: VD instance representing the vdata C library equivalent : VSattach After creating a new vdata (num_name == -1), fields must be defined using method fdefine() of the VD instance, and those fields must be allocated to the vdata with method setfields(). Same results can be achieved, but more simply, by calling the create() method of the VS instance. """ mode = write and 'w' or 'r' if isinstance(num_name, str): num = self.find(num_name) else: num = num_name vd = _C.VSattach(self._hdf_inst._id, num, mode) if vd < 0: _checkErr('attach', vd, 'cannot attach vdata') return VD(self, vd)
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Locate an existing vdata or create a new vdata in the HDF file, returning a VD instance. Args:: num_name Name or reference number of the vdata. An existing vdata can be specified either through its reference number or its name. Use -1 to create a new vdata. Note that uniqueness is not imposed on vdatas names, whereas refnums are guaranteed to be unique. Thus knowledge of its reference number may be the only way to get at a wanted vdata. write Set to 0 to open the vdata in read-only mode, set to 1 to open it in write mode Returns:: VD instance representing the vdata C library equivalent : VSattach After creating a new vdata (num_name == -1), fields must be defined using method fdefine() of the VD instance, and those fields must be allocated to the vdata with method setfields(). Same results can be achieved, but more simply, by calling the create() method of the VS instance.
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L872-L911
4,539
fhs/pyhdf
pyhdf/VS.py
VS.create
def create(self, name, fields): """Create a new vdata, setting its name and allocating its fields. Args:: name Name to assign to the vdata fields Sequence of field definitions. Each field definition is a sequence with the following elements in order: - field name - field type (one of HC.xxx constants) - field order (number of values) Fields are allocated to the vdata in the given order Returns:: VD instance representing the created vdata Calling the create() method is equivalent to the following calls: - vd = attach(-1,1), to create a new vdata and open it in write mode - vd._name = name, to set the vdata name - vd.fdefine(...), to define the name, type and order of each field - vd.setfields(...), to allocate fields to the vdata C library equivalent : no equivalent """ try: # Create new vdata (-1), open in write mode (1) vd = self.attach(-1, 1) # Set vdata name vd._name = name # Define fields allNames = [] for name, type, order in fields: vd.fdefine(name, type, order) allNames.append(name) # Allocate fields to the vdata vd.setfields(*allNames) return vd except HDF4Error as msg: raise HDF4Error("error creating vdata (%s)" % msg)
python
def create(self, name, fields): """Create a new vdata, setting its name and allocating its fields. Args:: name Name to assign to the vdata fields Sequence of field definitions. Each field definition is a sequence with the following elements in order: - field name - field type (one of HC.xxx constants) - field order (number of values) Fields are allocated to the vdata in the given order Returns:: VD instance representing the created vdata Calling the create() method is equivalent to the following calls: - vd = attach(-1,1), to create a new vdata and open it in write mode - vd._name = name, to set the vdata name - vd.fdefine(...), to define the name, type and order of each field - vd.setfields(...), to allocate fields to the vdata C library equivalent : no equivalent """ try: # Create new vdata (-1), open in write mode (1) vd = self.attach(-1, 1) # Set vdata name vd._name = name # Define fields allNames = [] for name, type, order in fields: vd.fdefine(name, type, order) allNames.append(name) # Allocate fields to the vdata vd.setfields(*allNames) return vd except HDF4Error as msg: raise HDF4Error("error creating vdata (%s)" % msg)
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Create a new vdata, setting its name and allocating its fields. Args:: name Name to assign to the vdata fields Sequence of field definitions. Each field definition is a sequence with the following elements in order: - field name - field type (one of HC.xxx constants) - field order (number of values) Fields are allocated to the vdata in the given order Returns:: VD instance representing the created vdata Calling the create() method is equivalent to the following calls: - vd = attach(-1,1), to create a new vdata and open it in write mode - vd._name = name, to set the vdata name - vd.fdefine(...), to define the name, type and order of each field - vd.setfields(...), to allocate fields to the vdata C library equivalent : no equivalent
[ "Create", "a", "new", "vdata", "setting", "its", "name", "and", "allocating", "its", "fields", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L913-L959
4,540
fhs/pyhdf
pyhdf/VS.py
VS.next
def next(self, vRef): """Get the reference number of the vdata following a given vdata. Args:: vRef Reference number of the vdata preceding the one we require. Set to -1 to get the first vdata in the HDF file. Knowing its reference number, the vdata can then be opened (attached) by passing this reference number to the attach() method. Returns:: Reference number of the vdata following the one given by argument vref An exception is raised if no vdata follows the one given by vRef. C library equivalent : VSgetid """ num = _C.VSgetid(self._hdf_inst._id, vRef) _checkErr('next', num, 'cannot get next vdata') return num
python
def next(self, vRef): """Get the reference number of the vdata following a given vdata. Args:: vRef Reference number of the vdata preceding the one we require. Set to -1 to get the first vdata in the HDF file. Knowing its reference number, the vdata can then be opened (attached) by passing this reference number to the attach() method. Returns:: Reference number of the vdata following the one given by argument vref An exception is raised if no vdata follows the one given by vRef. C library equivalent : VSgetid """ num = _C.VSgetid(self._hdf_inst._id, vRef) _checkErr('next', num, 'cannot get next vdata') return num
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Get the reference number of the vdata following a given vdata. Args:: vRef Reference number of the vdata preceding the one we require. Set to -1 to get the first vdata in the HDF file. Knowing its reference number, the vdata can then be opened (attached) by passing this reference number to the attach() method. Returns:: Reference number of the vdata following the one given by argument vref An exception is raised if no vdata follows the one given by vRef. C library equivalent : VSgetid
[ "Get", "the", "reference", "number", "of", "the", "vdata", "following", "a", "given", "vdata", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L984-L1008
4,541
fhs/pyhdf
pyhdf/VS.py
VS.vdatainfo
def vdatainfo(self, listAttr=0): """Return info about all the file vdatas. Args:: listAttr Set to 0 to ignore vdatas used to store attribute values, 1 to list them (see the VD._isattr readonly attribute) Returns:: List of vdata descriptions. Each vdata is described as a 9-element tuple, composed of the following: - vdata name - vdata class - vdata reference number - vdata number of records - vdata number of fields - vdata number of attributes - vdata record size in bytes - vdata tag number - vdata interlace mode C library equivalent : no equivalent """ lst = [] ref = -1 # start at beginning while True: try: nxtRef = self.next(ref) except HDF4Error: # no vdata left break # Attach the vdata and check for an "attribute" vdata. ref = nxtRef vdObj = self.attach(ref) if listAttr or not vdObj._isattr: # Append a list of vdata properties. lst.append((vdObj._name, vdObj._class, vdObj._refnum, vdObj._nrecs, vdObj._nfields, vdObj._nattrs, vdObj._recsize, vdObj._tag, vdObj._interlace)) vdObj.detach() return lst
python
def vdatainfo(self, listAttr=0): """Return info about all the file vdatas. Args:: listAttr Set to 0 to ignore vdatas used to store attribute values, 1 to list them (see the VD._isattr readonly attribute) Returns:: List of vdata descriptions. Each vdata is described as a 9-element tuple, composed of the following: - vdata name - vdata class - vdata reference number - vdata number of records - vdata number of fields - vdata number of attributes - vdata record size in bytes - vdata tag number - vdata interlace mode C library equivalent : no equivalent """ lst = [] ref = -1 # start at beginning while True: try: nxtRef = self.next(ref) except HDF4Error: # no vdata left break # Attach the vdata and check for an "attribute" vdata. ref = nxtRef vdObj = self.attach(ref) if listAttr or not vdObj._isattr: # Append a list of vdata properties. lst.append((vdObj._name, vdObj._class, vdObj._refnum, vdObj._nrecs, vdObj._nfields, vdObj._nattrs, vdObj._recsize, vdObj._tag, vdObj._interlace)) vdObj.detach() return lst
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Return info about all the file vdatas. Args:: listAttr Set to 0 to ignore vdatas used to store attribute values, 1 to list them (see the VD._isattr readonly attribute) Returns:: List of vdata descriptions. Each vdata is described as a 9-element tuple, composed of the following: - vdata name - vdata class - vdata reference number - vdata number of records - vdata number of fields - vdata number of attributes - vdata record size in bytes - vdata tag number - vdata interlace mode C library equivalent : no equivalent
[ "Return", "info", "about", "all", "the", "file", "vdatas", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1010-L1060
4,542
fhs/pyhdf
pyhdf/VS.py
VS.storedata
def storedata(self, fieldName, values, data_type, vName, vClass): """Create and initialize a single field vdata, returning the vdata reference number. Args:: fieldName Name of the single field in the vadata to create values Sequence of values to store in the field;. Each value can itself be a sequence, in which case the field will be multivalued (all second-level sequences must be of the same length) data_type Values type (one of HC.xxx constants). All values must be of the same type vName Name of the vdata to create vClass Vdata class (string) Returns:: vdata reference number C library equivalent : VHstoredata / VHstoredatam """ # See if the field is multi-valued. nrecs = len(values) if type(values[0]) in [list, tuple]: order = len(values[0]) # Replace input list with a flattened list. newValues = [] for el in values: for e in el: newValues.append(e) values = newValues else: order = 1 n_values = nrecs * order if data_type == HC.CHAR8: buf = _C.array_byte(n_values) # Allow values to be passed as a string. # Noop if a list is passed. values = list(values) for n in range(n_values): values[n] = ord(values[n]) elif data_type in [HC.UCHAR8, HC.UINT8]: buf = _C.array_byte(n_values) elif data_type == HC.INT8: # SWIG refuses negative values here. We found that if we # pass them as byte values, it will work. buf = _C.array_int8(n_values) values = list(values) for n in range(n_values): v = values[n] if v >= 0: v &= 0x7f else: v = abs(v) & 0x7f if v: v = 256 - v else: v = 128 # -128 in 2s complement values[n] = v elif data_type == HC.INT16: buf = _C.array_int16(n_values) elif data_type == HC.UINT16: buf = _C.array_uint16(n_values) elif data_type == HC.INT32: buf = _C.array_int32(n_values) elif data_type == HC.UINT32: buf = _C.array_uint32(n_values) elif data_type == HC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == HC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("storedata: illegal or unimplemented data_type") for n in range(n_values): buf[n] = values[n] if order == 1: vd = _C.VHstoredata(self._hdf_inst._id, fieldName, buf, nrecs, data_type, vName, vClass) else: vd = _C.VHstoredatam(self._hdf_inst._id, fieldName, buf, nrecs, data_type, vName, vClass, order) _checkErr('storedata', vd, 'cannot create vdata') return vd
python
def storedata(self, fieldName, values, data_type, vName, vClass): """Create and initialize a single field vdata, returning the vdata reference number. Args:: fieldName Name of the single field in the vadata to create values Sequence of values to store in the field;. Each value can itself be a sequence, in which case the field will be multivalued (all second-level sequences must be of the same length) data_type Values type (one of HC.xxx constants). All values must be of the same type vName Name of the vdata to create vClass Vdata class (string) Returns:: vdata reference number C library equivalent : VHstoredata / VHstoredatam """ # See if the field is multi-valued. nrecs = len(values) if type(values[0]) in [list, tuple]: order = len(values[0]) # Replace input list with a flattened list. newValues = [] for el in values: for e in el: newValues.append(e) values = newValues else: order = 1 n_values = nrecs * order if data_type == HC.CHAR8: buf = _C.array_byte(n_values) # Allow values to be passed as a string. # Noop if a list is passed. values = list(values) for n in range(n_values): values[n] = ord(values[n]) elif data_type in [HC.UCHAR8, HC.UINT8]: buf = _C.array_byte(n_values) elif data_type == HC.INT8: # SWIG refuses negative values here. We found that if we # pass them as byte values, it will work. buf = _C.array_int8(n_values) values = list(values) for n in range(n_values): v = values[n] if v >= 0: v &= 0x7f else: v = abs(v) & 0x7f if v: v = 256 - v else: v = 128 # -128 in 2s complement values[n] = v elif data_type == HC.INT16: buf = _C.array_int16(n_values) elif data_type == HC.UINT16: buf = _C.array_uint16(n_values) elif data_type == HC.INT32: buf = _C.array_int32(n_values) elif data_type == HC.UINT32: buf = _C.array_uint32(n_values) elif data_type == HC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == HC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("storedata: illegal or unimplemented data_type") for n in range(n_values): buf[n] = values[n] if order == 1: vd = _C.VHstoredata(self._hdf_inst._id, fieldName, buf, nrecs, data_type, vName, vClass) else: vd = _C.VHstoredatam(self._hdf_inst._id, fieldName, buf, nrecs, data_type, vName, vClass, order) _checkErr('storedata', vd, 'cannot create vdata') return vd
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Create and initialize a single field vdata, returning the vdata reference number. Args:: fieldName Name of the single field in the vadata to create values Sequence of values to store in the field;. Each value can itself be a sequence, in which case the field will be multivalued (all second-level sequences must be of the same length) data_type Values type (one of HC.xxx constants). All values must be of the same type vName Name of the vdata to create vClass Vdata class (string) Returns:: vdata reference number C library equivalent : VHstoredata / VHstoredatam
[ "Create", "and", "initialize", "a", "single", "field", "vdata", "returning", "the", "vdata", "reference", "number", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1062-L1159
4,543
fhs/pyhdf
pyhdf/VS.py
VD.field
def field(self, name_index): """Get a VDField instance representing a field of the vdata. Args:: name_index name or index number of the field Returns:: VDfield instance representing the field C library equivalent : no equivalent """ # Transform a name to an index number if isinstance(name_index, str): status, index = _C.VSfindex(self._id, name_index) _checkErr('field', status, "illegal field name: %s" % name_index) else: n = _C.VFnfields(self._id) _checkErr('field', n, 'cannot execute') index = name_index if index >= n: raise HDF4Error("field: illegal index number") return VDField(self, index)
python
def field(self, name_index): """Get a VDField instance representing a field of the vdata. Args:: name_index name or index number of the field Returns:: VDfield instance representing the field C library equivalent : no equivalent """ # Transform a name to an index number if isinstance(name_index, str): status, index = _C.VSfindex(self._id, name_index) _checkErr('field', status, "illegal field name: %s" % name_index) else: n = _C.VFnfields(self._id) _checkErr('field', n, 'cannot execute') index = name_index if index >= n: raise HDF4Error("field: illegal index number") return VDField(self, index)
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Get a VDField instance representing a field of the vdata. Args:: name_index name or index number of the field Returns:: VDfield instance representing the field C library equivalent : no equivalent
[ "Get", "a", "VDField", "instance", "representing", "a", "field", "of", "the", "vdata", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1480-L1504
4,544
fhs/pyhdf
pyhdf/VS.py
VD.seek
def seek(self, recIndex): """Seek to the beginning of the record identified by its record index. A succeeding read will load this record in memory. Args:: recIndex index of the record in the vdata; numbering starts at 0. Legal values range from 0 (start of vdata) to the current number of records (at end of vdata). Returns:: record index An exception is raised if an attempt is made to seek beyond the last record. The C API prohibits seeking past the next-to-last record, forcing one to read the last record to advance to the end of the vdata. The python API removes this limitation. Seeking to the end of the vdata can also be done by calling method ``seekend()``. C library equivalent : VSseek """ if recIndex > self._nrecs - 1: if recIndex == self._nrecs: return self.seekend() else: raise HDF4Error("attempt to seek past last record") n = _C.VSseek(self._id, recIndex) _checkErr('seek', n, 'cannot seek') self._offset = n return n
python
def seek(self, recIndex): """Seek to the beginning of the record identified by its record index. A succeeding read will load this record in memory. Args:: recIndex index of the record in the vdata; numbering starts at 0. Legal values range from 0 (start of vdata) to the current number of records (at end of vdata). Returns:: record index An exception is raised if an attempt is made to seek beyond the last record. The C API prohibits seeking past the next-to-last record, forcing one to read the last record to advance to the end of the vdata. The python API removes this limitation. Seeking to the end of the vdata can also be done by calling method ``seekend()``. C library equivalent : VSseek """ if recIndex > self._nrecs - 1: if recIndex == self._nrecs: return self.seekend() else: raise HDF4Error("attempt to seek past last record") n = _C.VSseek(self._id, recIndex) _checkErr('seek', n, 'cannot seek') self._offset = n return n
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Seek to the beginning of the record identified by its record index. A succeeding read will load this record in memory. Args:: recIndex index of the record in the vdata; numbering starts at 0. Legal values range from 0 (start of vdata) to the current number of records (at end of vdata). Returns:: record index An exception is raised if an attempt is made to seek beyond the last record. The C API prohibits seeking past the next-to-last record, forcing one to read the last record to advance to the end of the vdata. The python API removes this limitation. Seeking to the end of the vdata can also be done by calling method ``seekend()``. C library equivalent : VSseek
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dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1507-L1544
4,545
fhs/pyhdf
pyhdf/VS.py
VD.inquire
def inquire(self): """Retrieve info about the vdata. Args:: no argument Returns:: 5-element tuple with the following elements: -number of records in the vdata -interlace mode -list of vdata field names -size in bytes of the vdata record -name of the vdata C library equivalent : VSinquire """ status, nRecs, interlace, fldNames, size, vName = \ _C.VSinquire(self._id) _checkErr('inquire', status, "cannot query vdata info") return nRecs, interlace, fldNames.split(','), size, vName
python
def inquire(self): """Retrieve info about the vdata. Args:: no argument Returns:: 5-element tuple with the following elements: -number of records in the vdata -interlace mode -list of vdata field names -size in bytes of the vdata record -name of the vdata C library equivalent : VSinquire """ status, nRecs, interlace, fldNames, size, vName = \ _C.VSinquire(self._id) _checkErr('inquire', status, "cannot query vdata info") return nRecs, interlace, fldNames.split(','), size, vName
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Retrieve info about the vdata. Args:: no argument Returns:: 5-element tuple with the following elements: -number of records in the vdata -interlace mode -list of vdata field names -size in bytes of the vdata record -name of the vdata C library equivalent : VSinquire
[ "Retrieve", "info", "about", "the", "vdata", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1861-L1883
4,546
fhs/pyhdf
pyhdf/VS.py
VD.fieldinfo
def fieldinfo(self): """Retrieve info about all vdata fields. Args:: no argument Returns:: list where each element describes a field of the vdata; each field is described by an 7-element tuple containing the following elements: - field name - field data type (one of HC.xxx constants) - field order - number of attributes attached to the field - field index number - field external size - field internal size C library equivalent : no equivalent """ lst = [] for n in range(self._nfields): fld = self.field(n) lst.append((fld._name, fld._type, fld._order, fld._nattrs, fld._index, fld._esize, fld._isize)) return lst
python
def fieldinfo(self): """Retrieve info about all vdata fields. Args:: no argument Returns:: list where each element describes a field of the vdata; each field is described by an 7-element tuple containing the following elements: - field name - field data type (one of HC.xxx constants) - field order - number of attributes attached to the field - field index number - field external size - field internal size C library equivalent : no equivalent """ lst = [] for n in range(self._nfields): fld = self.field(n) lst.append((fld._name, fld._type, fld._order, fld._nattrs, fld._index, fld._esize, fld._isize)) return lst
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Retrieve info about all vdata fields. Args:: no argument Returns:: list where each element describes a field of the vdata; each field is described by an 7-element tuple containing the following elements: - field name - field data type (one of HC.xxx constants) - field order - number of attributes attached to the field - field index number - field external size - field internal size C library equivalent : no equivalent
[ "Retrieve", "info", "about", "all", "vdata", "fields", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1886-L1921
4,547
fhs/pyhdf
pyhdf/VS.py
VD.sizeof
def sizeof(self, fields): """Retrieve the size in bytes of the given fields. Args:: fields sequence of field names to query Returns:: total size of the fields in bytes C library equivalent : VSsizeof """ if type(fields) in [tuple, list]: str = ','.join(fields) else: str = fields n = _C.VSsizeof(self._id, str) _checkErr('sizeof', n, "cannot retrieve field sizes") return n
python
def sizeof(self, fields): """Retrieve the size in bytes of the given fields. Args:: fields sequence of field names to query Returns:: total size of the fields in bytes C library equivalent : VSsizeof """ if type(fields) in [tuple, list]: str = ','.join(fields) else: str = fields n = _C.VSsizeof(self._id, str) _checkErr('sizeof', n, "cannot retrieve field sizes") return n
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Retrieve the size in bytes of the given fields. Args:: fields sequence of field names to query Returns:: total size of the fields in bytes C library equivalent : VSsizeof
[ "Retrieve", "the", "size", "in", "bytes", "of", "the", "given", "fields", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1923-L1943
4,548
fhs/pyhdf
pyhdf/VS.py
VD.fexist
def fexist(self, fields): """Check if a vdata contains a given set of fields. Args:: fields sequence of field names whose presence in the vdata must be checked Returns:: true (1) if the given fields are present false (0) otherwise C library equivalent : VSfexist """ if type(fields) in [tuple, list]: str = ','.join(fields) else: str = fields ret = _C.VSfexist(self._id, str) if ret < 0: return 0 else: return 1
python
def fexist(self, fields): """Check if a vdata contains a given set of fields. Args:: fields sequence of field names whose presence in the vdata must be checked Returns:: true (1) if the given fields are present false (0) otherwise C library equivalent : VSfexist """ if type(fields) in [tuple, list]: str = ','.join(fields) else: str = fields ret = _C.VSfexist(self._id, str) if ret < 0: return 0 else: return 1
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Check if a vdata contains a given set of fields. Args:: fields sequence of field names whose presence in the vdata must be checked Returns:: true (1) if the given fields are present false (0) otherwise C library equivalent : VSfexist
[ "Check", "if", "a", "vdata", "contains", "a", "given", "set", "of", "fields", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L1945-L1969
4,549
fhs/pyhdf
pyhdf/VS.py
VDField.find
def find(self, name): """Search the field for a given attribute. Args:: name attribute name Returns:: if found, VDAttr instance describing the attribute None otherwise C library equivalent : VSfindattr """ try: att = self.attr(name) if att._index is None: att = None except HDF4Error: att = None return att
python
def find(self, name): """Search the field for a given attribute. Args:: name attribute name Returns:: if found, VDAttr instance describing the attribute None otherwise C library equivalent : VSfindattr """ try: att = self.attr(name) if att._index is None: att = None except HDF4Error: att = None return att
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Search the field for a given attribute. Args:: name attribute name Returns:: if found, VDAttr instance describing the attribute None otherwise C library equivalent : VSfindattr
[ "Search", "the", "field", "for", "a", "given", "attribute", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L2236-L2257
4,550
fhs/pyhdf
pyhdf/VS.py
VDAttr.set
def set(self, data_type, values): """Set the attribute value. Args:: data_type : attribute data type (see constants HC.xxx) values : attribute value(s); specify a list to create a multi-valued attribute; a string valued attribute can be created by setting 'data_type' to HC.CHAR8 and 'values' to the corresponding string If the attribute already exists, it will be updated. However, it is illegal to try to change its data type or its order (number of values). Returns:: None C library equivalent : VSsetattr """ try: n_values = len(values) except: values = [values] n_values = 1 if data_type == HC.CHAR8: buf = _C.array_byte(n_values) # Allow values to be passed as a string. # Noop if a list is passed. values = list(values) for n in range(n_values): if not isinstance(values[n], int): values[n] = ord(values[n]) elif data_type in [HC.UCHAR8, HC.UINT8]: buf = _C.array_byte(n_values) elif data_type == HC.INT8: # SWIG refuses negative values here. We found that if we # pass them as byte values, it will work. buf = _C.array_int8(n_values) values = list(values) for n in range(n_values): v = values[n] if v >= 0: v &= 0x7f else: v = abs(v) & 0x7f if v: v = 256 - v else: v = 128 # -128 in 2s complement values[n] = v elif data_type == HC.INT16: buf = _C.array_int16(n_values) elif data_type == HC.UINT16: buf = _C.array_uint16(n_values) elif data_type == HC.INT32: buf = _C.array_int32(n_values) elif data_type == HC.UINT32: buf = _C.array_uint32(n_values) elif data_type == HC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == HC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("set: illegal or unimplemented data_type") for n in range(n_values): buf[n] = values[n] status = _C.VSsetattr(self._vd_inst._id, self._fIndex, self._name, data_type, n_values, buf) _checkErr('attr', status, 'cannot execute') # Update the attribute index self._index = _C.VSfindattr(self._vd_inst._id, self._fIndex, self._name); if self._index < 0: raise HDF4Error("set: error retrieving attribute index")
python
def set(self, data_type, values): """Set the attribute value. Args:: data_type : attribute data type (see constants HC.xxx) values : attribute value(s); specify a list to create a multi-valued attribute; a string valued attribute can be created by setting 'data_type' to HC.CHAR8 and 'values' to the corresponding string If the attribute already exists, it will be updated. However, it is illegal to try to change its data type or its order (number of values). Returns:: None C library equivalent : VSsetattr """ try: n_values = len(values) except: values = [values] n_values = 1 if data_type == HC.CHAR8: buf = _C.array_byte(n_values) # Allow values to be passed as a string. # Noop if a list is passed. values = list(values) for n in range(n_values): if not isinstance(values[n], int): values[n] = ord(values[n]) elif data_type in [HC.UCHAR8, HC.UINT8]: buf = _C.array_byte(n_values) elif data_type == HC.INT8: # SWIG refuses negative values here. We found that if we # pass them as byte values, it will work. buf = _C.array_int8(n_values) values = list(values) for n in range(n_values): v = values[n] if v >= 0: v &= 0x7f else: v = abs(v) & 0x7f if v: v = 256 - v else: v = 128 # -128 in 2s complement values[n] = v elif data_type == HC.INT16: buf = _C.array_int16(n_values) elif data_type == HC.UINT16: buf = _C.array_uint16(n_values) elif data_type == HC.INT32: buf = _C.array_int32(n_values) elif data_type == HC.UINT32: buf = _C.array_uint32(n_values) elif data_type == HC.FLOAT32: buf = _C.array_float32(n_values) elif data_type == HC.FLOAT64: buf = _C.array_float64(n_values) else: raise HDF4Error("set: illegal or unimplemented data_type") for n in range(n_values): buf[n] = values[n] status = _C.VSsetattr(self._vd_inst._id, self._fIndex, self._name, data_type, n_values, buf) _checkErr('attr', status, 'cannot execute') # Update the attribute index self._index = _C.VSfindattr(self._vd_inst._id, self._fIndex, self._name); if self._index < 0: raise HDF4Error("set: error retrieving attribute index")
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Set the attribute value. Args:: data_type : attribute data type (see constants HC.xxx) values : attribute value(s); specify a list to create a multi-valued attribute; a string valued attribute can be created by setting 'data_type' to HC.CHAR8 and 'values' to the corresponding string If the attribute already exists, it will be updated. However, it is illegal to try to change its data type or its order (number of values). Returns:: None C library equivalent : VSsetattr
[ "Set", "the", "attribute", "value", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/VS.py#L2406-L2493
4,551
fhs/pyhdf
pyhdf/HDF.py
getlibversion
def getlibversion(): """Get the library version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion """ status, major_v, minor_v, release, info = _C.Hgetlibversion() _checkErr('getlibversion', status, "cannot get lib version") return major_v, minor_v, release, info
python
def getlibversion(): """Get the library version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion """ status, major_v, minor_v, release, info = _C.Hgetlibversion() _checkErr('getlibversion', status, "cannot get lib version") return major_v, minor_v, release, info
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Get the library version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion
[ "Get", "the", "library", "version", "info", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/HDF.py#L99-L116
4,552
fhs/pyhdf
pyhdf/HDF.py
HDF.getfileversion
def getfileversion(self): """Get file version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion """ status, major_v, minor_v, release, info = _C.Hgetfileversion(self._id) _checkErr('getfileversion', status, "cannot get file version") return major_v, minor_v, release, info
python
def getfileversion(self): """Get file version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion """ status, major_v, minor_v, release, info = _C.Hgetfileversion(self._id) _checkErr('getfileversion', status, "cannot get file version") return major_v, minor_v, release, info
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Get file version info. Args: no argument Returns: 4-element tuple with the following components: -major version number (int) -minor version number (int) -complete library version number (int) -additional information (string) C library equivalent : Hgetlibversion
[ "Get", "file", "version", "info", "." ]
dbdc1810a74a38df50dcad81fe903e239d2b388d
https://github.com/fhs/pyhdf/blob/dbdc1810a74a38df50dcad81fe903e239d2b388d/pyhdf/HDF.py#L244-L261
4,553
mattmakai/underwear
underwear/run_underwear.py
colorize
def colorize(lead, num, color): """ Print 'lead' = 'num' in 'color' """ if num != 0 and ANSIBLE_COLOR and color is not None: return "%s%s%-15s" % (stringc(lead, color), stringc("=", color), stringc(str(num), color)) else: return "%s=%-4s" % (lead, str(num))
python
def colorize(lead, num, color): """ Print 'lead' = 'num' in 'color' """ if num != 0 and ANSIBLE_COLOR and color is not None: return "%s%s%-15s" % (stringc(lead, color), stringc("=", color), stringc(str(num), color)) else: return "%s=%-4s" % (lead, str(num))
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Print 'lead' = 'num' in 'color'
[ "Print", "lead", "=", "num", "in", "color" ]
7c484c7937d2df86dc569d411249ba366ed43ead
https://github.com/mattmakai/underwear/blob/7c484c7937d2df86dc569d411249ba366ed43ead/underwear/run_underwear.py#L24-L29
4,554
zapier/django-drip
drip/admin.py
DripAdmin.timeline
def timeline(self, request, drip_id, into_past, into_future): """ Return a list of people who should get emails. """ from django.shortcuts import render, get_object_or_404 drip = get_object_or_404(Drip, id=drip_id) shifted_drips = [] seen_users = set() for shifted_drip in drip.drip.walk(into_past=int(into_past), into_future=int(into_future)+1): shifted_drip.prune() shifted_drips.append({ 'drip': shifted_drip, 'qs': shifted_drip.get_queryset().exclude(id__in=seen_users) }) seen_users.update(shifted_drip.get_queryset().values_list('id', flat=True)) return render(request, 'drip/timeline.html', locals())
python
def timeline(self, request, drip_id, into_past, into_future): """ Return a list of people who should get emails. """ from django.shortcuts import render, get_object_or_404 drip = get_object_or_404(Drip, id=drip_id) shifted_drips = [] seen_users = set() for shifted_drip in drip.drip.walk(into_past=int(into_past), into_future=int(into_future)+1): shifted_drip.prune() shifted_drips.append({ 'drip': shifted_drip, 'qs': shifted_drip.get_queryset().exclude(id__in=seen_users) }) seen_users.update(shifted_drip.get_queryset().values_list('id', flat=True)) return render(request, 'drip/timeline.html', locals())
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Return a list of people who should get emails.
[ "Return", "a", "list", "of", "people", "who", "should", "get", "emails", "." ]
ffbef6927a1a20f4c353ecb108c1b484502d2b29
https://github.com/zapier/django-drip/blob/ffbef6927a1a20f4c353ecb108c1b484502d2b29/drip/admin.py#L33-L51
4,555
zapier/django-drip
drip/drips.py
DripBase.walk
def walk(self, into_past=0, into_future=0): """ Walk over a date range and create new instances of self with new ranges. """ walked_range = [] for shift in range(-into_past, into_future): kwargs = dict(drip_model=self.drip_model, name=self.name, now_shift_kwargs={'days': shift}) walked_range.append(self.__class__(**kwargs)) return walked_range
python
def walk(self, into_past=0, into_future=0): """ Walk over a date range and create new instances of self with new ranges. """ walked_range = [] for shift in range(-into_past, into_future): kwargs = dict(drip_model=self.drip_model, name=self.name, now_shift_kwargs={'days': shift}) walked_range.append(self.__class__(**kwargs)) return walked_range
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Walk over a date range and create new instances of self with new ranges.
[ "Walk", "over", "a", "date", "range", "and", "create", "new", "instances", "of", "self", "with", "new", "ranges", "." ]
ffbef6927a1a20f4c353ecb108c1b484502d2b29
https://github.com/zapier/django-drip/blob/ffbef6927a1a20f4c353ecb108c1b484502d2b29/drip/drips.py#L146-L156
4,556
zapier/django-drip
drip/drips.py
DripBase.run
def run(self): """ Get the queryset, prune sent people, and send it. """ if not self.drip_model.enabled: return None self.prune() count = self.send() return count
python
def run(self): """ Get the queryset, prune sent people, and send it. """ if not self.drip_model.enabled: return None self.prune() count = self.send() return count
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Get the queryset, prune sent people, and send it.
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ffbef6927a1a20f4c353ecb108c1b484502d2b29
https://github.com/zapier/django-drip/blob/ffbef6927a1a20f4c353ecb108c1b484502d2b29/drip/drips.py#L194-L204
4,557
zapier/django-drip
drip/drips.py
DripBase.prune
def prune(self): """ Do an exclude for all Users who have a SentDrip already. """ target_user_ids = self.get_queryset().values_list('id', flat=True) exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(), drip=self.drip_model, user__id__in=target_user_ids)\ .values_list('user_id', flat=True) self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)
python
def prune(self): """ Do an exclude for all Users who have a SentDrip already. """ target_user_ids = self.get_queryset().values_list('id', flat=True) exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(), drip=self.drip_model, user__id__in=target_user_ids)\ .values_list('user_id', flat=True) self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)
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Do an exclude for all Users who have a SentDrip already.
[ "Do", "an", "exclude", "for", "all", "Users", "who", "have", "a", "SentDrip", "already", "." ]
ffbef6927a1a20f4c353ecb108c1b484502d2b29
https://github.com/zapier/django-drip/blob/ffbef6927a1a20f4c353ecb108c1b484502d2b29/drip/drips.py#L206-L215
4,558
zapier/django-drip
drip/drips.py
DripBase.send
def send(self): """ Send the message to each user on the queryset. Create SentDrip for each user that gets a message. Returns count of created SentDrips. """ if not self.from_email: self.from_email = getattr(settings, 'DRIP_FROM_EMAIL', settings.DEFAULT_FROM_EMAIL) MessageClass = message_class_for(self.drip_model.message_class) count = 0 for user in self.get_queryset(): message_instance = MessageClass(self, user) try: result = message_instance.message.send() if result: SentDrip.objects.create( drip=self.drip_model, user=user, from_email=self.from_email, from_email_name=self.from_email_name, subject=message_instance.subject, body=message_instance.body ) count += 1 except Exception as e: logging.error("Failed to send drip %s to user %s: %s" % (self.drip_model.id, user, e)) return count
python
def send(self): """ Send the message to each user on the queryset. Create SentDrip for each user that gets a message. Returns count of created SentDrips. """ if not self.from_email: self.from_email = getattr(settings, 'DRIP_FROM_EMAIL', settings.DEFAULT_FROM_EMAIL) MessageClass = message_class_for(self.drip_model.message_class) count = 0 for user in self.get_queryset(): message_instance = MessageClass(self, user) try: result = message_instance.message.send() if result: SentDrip.objects.create( drip=self.drip_model, user=user, from_email=self.from_email, from_email_name=self.from_email_name, subject=message_instance.subject, body=message_instance.body ) count += 1 except Exception as e: logging.error("Failed to send drip %s to user %s: %s" % (self.drip_model.id, user, e)) return count
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Send the message to each user on the queryset. Create SentDrip for each user that gets a message. Returns count of created SentDrips.
[ "Send", "the", "message", "to", "each", "user", "on", "the", "queryset", "." ]
ffbef6927a1a20f4c353ecb108c1b484502d2b29
https://github.com/zapier/django-drip/blob/ffbef6927a1a20f4c353ecb108c1b484502d2b29/drip/drips.py#L217-L248
4,559
ladybug-tools/ladybug
ladybug/euclid.py
Vector2.angle
def angle(self, other): """Return the angle to the vector other""" return math.acos(self.dot(other) / (self.magnitude() * other.magnitude()))
python
def angle(self, other): """Return the angle to the vector other""" return math.acos(self.dot(other) / (self.magnitude() * other.magnitude()))
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Return the angle to the vector other
[ "Return", "the", "angle", "to", "the", "vector", "other" ]
c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/euclid.py#L298-L300
4,560
ladybug-tools/ladybug
ladybug/euclid.py
Vector2.project
def project(self, other): """Return one vector projected on the vector other""" n = other.normalized() return self.dot(n) * n
python
def project(self, other): """Return one vector projected on the vector other""" n = other.normalized() return self.dot(n) * n
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Return one vector projected on the vector other
[ "Return", "one", "vector", "projected", "on", "the", "vector", "other" ]
c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/euclid.py#L302-L305
4,561
ladybug-tools/ladybug
ladybug/euclid.py
Vector3.rotate_around
def rotate_around(self, axis, theta): """Return the vector rotated around axis through angle theta. Right hand rule applies. """ # Adapted from equations published by Glenn Murray. # http://inside.mines.edu/~gmurray/ArbitraryAxisRotation/ArbitraryAxisRotation.html x, y, z = self.x, self.y, self.z u, v, w = axis.x, axis.y, axis.z # Extracted common factors for simplicity and efficiency r2 = u**2 + v**2 + w**2 r = math.sqrt(r2) ct = math.cos(theta) st = math.sin(theta) / r dt = (u * x + v * y + w * z) * (1 - ct) / r2 return Vector3((u * dt + x * ct + (-w * y + v * z) * st), (v * dt + y * ct + (w * x - u * z) * st), (w * dt + z * ct + (-v * x + u * y) * st))
python
def rotate_around(self, axis, theta): """Return the vector rotated around axis through angle theta. Right hand rule applies. """ # Adapted from equations published by Glenn Murray. # http://inside.mines.edu/~gmurray/ArbitraryAxisRotation/ArbitraryAxisRotation.html x, y, z = self.x, self.y, self.z u, v, w = axis.x, axis.y, axis.z # Extracted common factors for simplicity and efficiency r2 = u**2 + v**2 + w**2 r = math.sqrt(r2) ct = math.cos(theta) st = math.sin(theta) / r dt = (u * x + v * y + w * z) * (1 - ct) / r2 return Vector3((u * dt + x * ct + (-w * y + v * z) * st), (v * dt + y * ct + (w * x - u * z) * st), (w * dt + z * ct + (-v * x + u * y) * st))
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Return the vector rotated around axis through angle theta. Right hand rule applies.
[ "Return", "the", "vector", "rotated", "around", "axis", "through", "angle", "theta", "." ]
c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/euclid.py#L588-L607
4,562
ladybug-tools/ladybug
ladybug/futil.py
preparedir
def preparedir(target_dir, remove_content=True): """Prepare a folder for analysis. This method creates the folder if it is not created, and removes the file in the folder if the folder already existed. """ if os.path.isdir(target_dir): if remove_content: nukedir(target_dir, False) return True else: try: os.makedirs(target_dir) return True except Exception as e: print("Failed to create folder: %s\n%s" % (target_dir, e)) return False
python
def preparedir(target_dir, remove_content=True): """Prepare a folder for analysis. This method creates the folder if it is not created, and removes the file in the folder if the folder already existed. """ if os.path.isdir(target_dir): if remove_content: nukedir(target_dir, False) return True else: try: os.makedirs(target_dir) return True except Exception as e: print("Failed to create folder: %s\n%s" % (target_dir, e)) return False
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Prepare a folder for analysis. This method creates the folder if it is not created, and removes the file in the folder if the folder already existed.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L20-L36
4,563
ladybug-tools/ladybug
ladybug/futil.py
nukedir
def nukedir(target_dir, rmdir=False): """Delete all the files inside target_dir. Usage: nukedir("c:/ladybug/libs", True) """ d = os.path.normpath(target_dir) if not os.path.isdir(d): return files = os.listdir(d) for f in files: if f == '.' or f == '..': continue path = os.path.join(d, f) if os.path.isdir(path): nukedir(path) else: try: os.remove(path) except Exception: print("Failed to remove %s" % path) if rmdir: try: os.rmdir(d) except Exception: print("Failed to remove %s" % d)
python
def nukedir(target_dir, rmdir=False): """Delete all the files inside target_dir. Usage: nukedir("c:/ladybug/libs", True) """ d = os.path.normpath(target_dir) if not os.path.isdir(d): return files = os.listdir(d) for f in files: if f == '.' or f == '..': continue path = os.path.join(d, f) if os.path.isdir(path): nukedir(path) else: try: os.remove(path) except Exception: print("Failed to remove %s" % path) if rmdir: try: os.rmdir(d) except Exception: print("Failed to remove %s" % d)
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Delete all the files inside target_dir. Usage: nukedir("c:/ladybug/libs", True)
[ "Delete", "all", "the", "files", "inside", "target_dir", "." ]
c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L39-L69
4,564
ladybug-tools/ladybug
ladybug/futil.py
write_to_file_by_name
def write_to_file_by_name(folder, fname, data, mkdir=False): """Write a string of data to file by filename and folder. Args: folder: Target folder (e.g. c:/ladybug). fname: File name (e.g. testPts.pts). data: Any data as string. mkdir: Set to True to create the directory if doesn't exist (Default: False). """ if not os.path.isdir(folder): if mkdir: preparedir(folder) else: created = preparedir(folder, False) if not created: raise ValueError("Failed to find %s." % folder) file_path = os.path.join(folder, fname) with open(file_path, writemode) as outf: try: outf.write(str(data)) return file_path except Exception as e: raise IOError("Failed to write %s to file:\n\t%s" % (fname, str(e)))
python
def write_to_file_by_name(folder, fname, data, mkdir=False): """Write a string of data to file by filename and folder. Args: folder: Target folder (e.g. c:/ladybug). fname: File name (e.g. testPts.pts). data: Any data as string. mkdir: Set to True to create the directory if doesn't exist (Default: False). """ if not os.path.isdir(folder): if mkdir: preparedir(folder) else: created = preparedir(folder, False) if not created: raise ValueError("Failed to find %s." % folder) file_path = os.path.join(folder, fname) with open(file_path, writemode) as outf: try: outf.write(str(data)) return file_path except Exception as e: raise IOError("Failed to write %s to file:\n\t%s" % (fname, str(e)))
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Write a string of data to file by filename and folder. Args: folder: Target folder (e.g. c:/ladybug). fname: File name (e.g. testPts.pts). data: Any data as string. mkdir: Set to True to create the directory if doesn't exist (Default: False).
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L72-L96
4,565
ladybug-tools/ladybug
ladybug/futil.py
copy_files_to_folder
def copy_files_to_folder(files, target_folder, overwrite=True): """Copy a list of files to a new target folder. Returns: A list of fullpath of the new files. """ if not files: return [] for f in files: target = os.path.join(target_folder, os.path.split(f)[-1]) if target == f: # both file path are the same! return target if os.path.exists(target): if overwrite: # remove the file before copying try: os.remove(target) except Exception: raise IOError("Failed to remove %s" % f) else: shutil.copy(f, target) else: continue else: print('Copying %s to %s' % (os.path.split(f)[-1], os.path.normpath(target_folder))) shutil.copy(f, target) return [os.path.join(target_folder, os.path.split(f)[-1]) for f in files]
python
def copy_files_to_folder(files, target_folder, overwrite=True): """Copy a list of files to a new target folder. Returns: A list of fullpath of the new files. """ if not files: return [] for f in files: target = os.path.join(target_folder, os.path.split(f)[-1]) if target == f: # both file path are the same! return target if os.path.exists(target): if overwrite: # remove the file before copying try: os.remove(target) except Exception: raise IOError("Failed to remove %s" % f) else: shutil.copy(f, target) else: continue else: print('Copying %s to %s' % (os.path.split(f)[-1], os.path.normpath(target_folder))) shutil.copy(f, target) return [os.path.join(target_folder, os.path.split(f)[-1]) for f in files]
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Copy a list of files to a new target folder. Returns: A list of fullpath of the new files.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L111-L143
4,566
ladybug-tools/ladybug
ladybug/futil.py
bat_to_sh
def bat_to_sh(file_path): """Convert honeybee .bat file to .sh file. WARNING: This is a very simple function and doesn't handle any edge cases. """ sh_file = file_path[:-4] + '.sh' with open(file_path, 'rb') as inf, open(sh_file, 'wb') as outf: outf.write('#!/usr/bin/env bash\n\n') for line in inf: # pass the path lines, etc to get to the commands if line.strip(): continue else: break for line in inf: if line.startswith('echo'): continue modified_line = line.replace('c:\\radiance\\bin\\', '').replace('\\', '/') outf.write(modified_line) print('bash file is created at:\n\t%s' % sh_file) # Heroku - Make command.sh executable st = os.stat(sh_file) os.chmod(sh_file, st.st_mode | 0o111) return sh_file
python
def bat_to_sh(file_path): """Convert honeybee .bat file to .sh file. WARNING: This is a very simple function and doesn't handle any edge cases. """ sh_file = file_path[:-4] + '.sh' with open(file_path, 'rb') as inf, open(sh_file, 'wb') as outf: outf.write('#!/usr/bin/env bash\n\n') for line in inf: # pass the path lines, etc to get to the commands if line.strip(): continue else: break for line in inf: if line.startswith('echo'): continue modified_line = line.replace('c:\\radiance\\bin\\', '').replace('\\', '/') outf.write(modified_line) print('bash file is created at:\n\t%s' % sh_file) # Heroku - Make command.sh executable st = os.stat(sh_file) os.chmod(sh_file, st.st_mode | 0o111) return sh_file
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Convert honeybee .bat file to .sh file. WARNING: This is a very simple function and doesn't handle any edge cases.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L146-L171
4,567
ladybug-tools/ladybug
ladybug/futil.py
_download_py2
def _download_py2(link, path, __hdr__): """Download a file from a link in Python 2.""" try: req = urllib2.Request(link, headers=__hdr__) u = urllib2.urlopen(req) except Exception as e: raise Exception(' Download failed with the error:\n{}'.format(e)) with open(path, 'wb') as outf: for l in u: outf.write(l) u.close()
python
def _download_py2(link, path, __hdr__): """Download a file from a link in Python 2.""" try: req = urllib2.Request(link, headers=__hdr__) u = urllib2.urlopen(req) except Exception as e: raise Exception(' Download failed with the error:\n{}'.format(e)) with open(path, 'wb') as outf: for l in u: outf.write(l) u.close()
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Download a file from a link in Python 2.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L174-L185
4,568
ladybug-tools/ladybug
ladybug/futil.py
_download_py3
def _download_py3(link, path, __hdr__): """Download a file from a link in Python 3.""" try: req = urllib.request.Request(link, headers=__hdr__) u = urllib.request.urlopen(req) except Exception as e: raise Exception(' Download failed with the error:\n{}'.format(e)) with open(path, 'wb') as outf: for l in u: outf.write(l) u.close()
python
def _download_py3(link, path, __hdr__): """Download a file from a link in Python 3.""" try: req = urllib.request.Request(link, headers=__hdr__) u = urllib.request.urlopen(req) except Exception as e: raise Exception(' Download failed with the error:\n{}'.format(e)) with open(path, 'wb') as outf: for l in u: outf.write(l) u.close()
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Download a file from a link in Python 3.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L188-L199
4,569
ladybug-tools/ladybug
ladybug/futil.py
download_file_by_name
def download_file_by_name(url, target_folder, file_name, mkdir=False): """Download a file to a directory. Args: url: A string to a valid URL. target_folder: Target folder for download (e.g. c:/ladybug) file_name: File name (e.g. testPts.zip). mkdir: Set to True to create the directory if doesn't exist (Default: False) """ # headers to "spoof" the download as coming from a browser (needed for E+ site) __hdr__ = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 ' '(KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11', 'Accept': 'text/html,application/xhtml+xml,' 'application/xml;q=0.9,*/*;q=0.8', 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3', 'Accept-Encoding': 'none', 'Accept-Language': 'en-US,en;q=0.8', 'Connection': 'keep-alive'} # create the target directory. if not os.path.isdir(target_folder): if mkdir: preparedir(target_folder) else: created = preparedir(target_folder, False) if not created: raise ValueError("Failed to find %s." % target_folder) file_path = os.path.join(target_folder, file_name) if (sys.version_info < (3, 0)): _download_py2(url, file_path, __hdr__) else: _download_py3(url, file_path, __hdr__)
python
def download_file_by_name(url, target_folder, file_name, mkdir=False): """Download a file to a directory. Args: url: A string to a valid URL. target_folder: Target folder for download (e.g. c:/ladybug) file_name: File name (e.g. testPts.zip). mkdir: Set to True to create the directory if doesn't exist (Default: False) """ # headers to "spoof" the download as coming from a browser (needed for E+ site) __hdr__ = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 ' '(KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11', 'Accept': 'text/html,application/xhtml+xml,' 'application/xml;q=0.9,*/*;q=0.8', 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3', 'Accept-Encoding': 'none', 'Accept-Language': 'en-US,en;q=0.8', 'Connection': 'keep-alive'} # create the target directory. if not os.path.isdir(target_folder): if mkdir: preparedir(target_folder) else: created = preparedir(target_folder, False) if not created: raise ValueError("Failed to find %s." % target_folder) file_path = os.path.join(target_folder, file_name) if (sys.version_info < (3, 0)): _download_py2(url, file_path, __hdr__) else: _download_py3(url, file_path, __hdr__)
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Download a file to a directory. Args: url: A string to a valid URL. target_folder: Target folder for download (e.g. c:/ladybug) file_name: File name (e.g. testPts.zip). mkdir: Set to True to create the directory if doesn't exist (Default: False)
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L202-L234
4,570
ladybug-tools/ladybug
ladybug/futil.py
unzip_file
def unzip_file(source_file, dest_dir=None, mkdir=False): """Unzip a compressed file. Args: source_file: Full path to a valid compressed file (e.g. c:/ladybug/testPts.zip) dest_dir: Target folder to extract to (e.g. c:/ladybug). Default is set to the same directory as the source file. mkdir: Set to True to create the directory if doesn't exist (Default: False) """ # set default dest_dir and create it if need be. if dest_dir is None: dest_dir, fname = os.path.split(source_file) elif not os.path.isdir(dest_dir): if mkdir: preparedir(dest_dir) else: created = preparedir(dest_dir, False) if not created: raise ValueError("Failed to find %s." % dest_dir) # extract files to destination with zipfile.ZipFile(source_file) as zf: for member in zf.infolist(): words = member.filename.split('\\') for word in words[:-1]: drive, word = os.path.splitdrive(word) head, word = os.path.split(word) if word in (os.curdir, os.pardir, ''): continue dest_dir = os.path.join(dest_dir, word) zf.extract(member, dest_dir)
python
def unzip_file(source_file, dest_dir=None, mkdir=False): """Unzip a compressed file. Args: source_file: Full path to a valid compressed file (e.g. c:/ladybug/testPts.zip) dest_dir: Target folder to extract to (e.g. c:/ladybug). Default is set to the same directory as the source file. mkdir: Set to True to create the directory if doesn't exist (Default: False) """ # set default dest_dir and create it if need be. if dest_dir is None: dest_dir, fname = os.path.split(source_file) elif not os.path.isdir(dest_dir): if mkdir: preparedir(dest_dir) else: created = preparedir(dest_dir, False) if not created: raise ValueError("Failed to find %s." % dest_dir) # extract files to destination with zipfile.ZipFile(source_file) as zf: for member in zf.infolist(): words = member.filename.split('\\') for word in words[:-1]: drive, word = os.path.splitdrive(word) head, word = os.path.split(word) if word in (os.curdir, os.pardir, ''): continue dest_dir = os.path.join(dest_dir, word) zf.extract(member, dest_dir)
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Unzip a compressed file. Args: source_file: Full path to a valid compressed file (e.g. c:/ladybug/testPts.zip) dest_dir: Target folder to extract to (e.g. c:/ladybug). Default is set to the same directory as the source file. mkdir: Set to True to create the directory if doesn't exist (Default: False)
[ "Unzip", "a", "compressed", "file", "." ]
c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L249-L279
4,571
ladybug-tools/ladybug
ladybug/futil.py
csv_to_matrix
def csv_to_matrix(csv_file_path): """Load a CSV file into a Python matrix of strings. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv) """ mtx = [] with open(csv_file_path) as csv_data_file: for row in csv_data_file: mtx.append(row.split(',')) return mtx
python
def csv_to_matrix(csv_file_path): """Load a CSV file into a Python matrix of strings. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv) """ mtx = [] with open(csv_file_path) as csv_data_file: for row in csv_data_file: mtx.append(row.split(',')) return mtx
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Load a CSV file into a Python matrix of strings. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv)
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L282-L292
4,572
ladybug-tools/ladybug
ladybug/futil.py
csv_to_num_matrix
def csv_to_num_matrix(csv_file_path): """Load a CSV file consisting only of numbers into a Python matrix of floats. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv) """ mtx = [] with open(csv_file_path) as csv_data_file: for row in csv_data_file: mtx.append([float(val) for val in row.split(',')]) return mtx
python
def csv_to_num_matrix(csv_file_path): """Load a CSV file consisting only of numbers into a Python matrix of floats. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv) """ mtx = [] with open(csv_file_path) as csv_data_file: for row in csv_data_file: mtx.append([float(val) for val in row.split(',')]) return mtx
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Load a CSV file consisting only of numbers into a Python matrix of floats. Args: csv_file_path: Full path to a valid CSV file (e.g. c:/ladybug/test.csv)
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/futil.py#L295-L305
4,573
ladybug-tools/ladybug
ladybug/stat.py
STAT.from_json
def from_json(cls, data): """ Create STAT from json dictionary. Args: data: { 'location': {} , // ladybug location schema 'ashrae_climate_zone': str, 'koppen_climate_zone': str, 'extreme_cold_week': {}, // ladybug analysis period schema 'extreme_hot_week': {}, // ladybug analysis period schema 'typical_weeks': {}, // dict of ladybug analysis period schemas 'heating_dict': {}, // dict containing heating design conditions 'cooling_dict': {}, // dict containing cooling design conditions "monthly_db_50": [], // list of 12 float values for each month "monthly_wb_50": [], // list of 12 float values for each month "monthly_db_range_50": [], // list of 12 float values for each month "monthly_wb_range_50": [], // list of 12 float values for each month "monthly_db_100": [], // list of 12 float values for each month "monthly_wb_100": [], // list of 12 float values for each month "monthly_db_20": [], // list of 12 float values for each month "monthly_wb_20": [], // list of 12 float values for each month "monthly_db_04": [], // list of 12 float values for each month "monthly_wb_04": [], // list of 12 float values for each month "monthly_wind": [], // list of 12 float values for each month "monthly_wind_dirs": [], // matrix with 12 cols for months of the year and 8 rows for the cardinal directions. "standard_pressure_at_elev": float, // float value for pressure in Pa "monthly_tau_beam":[], // list of 12 float values for each month "monthly_tau_diffuse": [] // list of 12 float values for each month } """ # Initialize the class with all data missing stat_ob = cls(None) # Check required and optional keys option_keys_none = ('ashrae_climate_zone', 'koppen_climate_zone', 'extreme_cold_week', 'extreme_hot_week', 'standard_pressure_at_elev') option_keys_list = ('monthly_db_50', 'monthly_wb_50', 'monthly_db_range_50', 'monthly_wb_range_50', 'monthly_db_100', 'monthly_wb_100', 'monthly_db_20', 'monthly_wb_20', 'monthly_db_04', 'monthly_wb_04', 'monthly_wind', 'monthly_wind_dirs', 'monthly_tau_beam', 'monthly_tau_diffuse') option_keys_dict = ('typical_weeks', 'heating_dict', 'cooling_dict') assert 'location' in data, 'Required key "location" is missing!' for key in option_keys_none: if key not in data: data[key] = None for key in option_keys_list: if key not in data: data[key] = [] for key in option_keys_dict: if key not in data: data[key] = {} # assign the properties of the dictionary to the stat object. stat_ob._location = Location.from_json(data['location']) stat_ob._ashrae_climate_zone = data['ashrae_climate_zone'] stat_ob._koppen_climate_zone = data['koppen_climate_zone'] stat_ob._extreme_cold_week = AnalysisPeriod.from_json(data['extreme_cold_week'])\ if data['extreme_cold_week'] else None stat_ob._extreme_hot_week = AnalysisPeriod.from_json(data['extreme_hot_week'])\ if data['extreme_hot_week'] else None stat_ob._typical_weeks = {} for key, val in data['typical_weeks'].items(): if isinstance(val, list): stat_ob._typical_weeks[key] = [AnalysisPeriod.from_json(v) for v in val] else: stat_ob._typical_weeks[key] = AnalysisPeriod.from_json(val) stat_ob._winter_des_day_dict = data['heating_dict'] stat_ob._summer_des_day_dict = data['cooling_dict'] stat_ob._monthly_db_50 = data['monthly_db_50'] stat_ob._monthly_wb_50 = data['monthly_wb_50'] stat_ob._monthly_db_range_50 = data['monthly_db_range_50'] stat_ob._monthly_wb_range_50 = data['monthly_wb_range_50'] stat_ob._monthly_db_100 = data['monthly_db_100'] stat_ob._monthly_wb_100 = data['monthly_wb_100'] stat_ob._monthly_db_20 = data['monthly_db_20'] stat_ob._monthly_wb_20 = data['monthly_wb_20'] stat_ob._monthly_db_04 = data['monthly_db_04'] stat_ob._monthly_wb_04 = data['monthly_wb_04'] stat_ob._monthly_wind = data['monthly_wind'] stat_ob._monthly_wind_dirs = data['monthly_wind_dirs'] stat_ob._stand_press_at_elev = data['standard_pressure_at_elev'] stat_ob._monthly_tau_beam = data['monthly_tau_beam'] stat_ob._monthly_tau_diffuse = data['monthly_tau_diffuse'] return stat_ob
python
def from_json(cls, data): """ Create STAT from json dictionary. Args: data: { 'location': {} , // ladybug location schema 'ashrae_climate_zone': str, 'koppen_climate_zone': str, 'extreme_cold_week': {}, // ladybug analysis period schema 'extreme_hot_week': {}, // ladybug analysis period schema 'typical_weeks': {}, // dict of ladybug analysis period schemas 'heating_dict': {}, // dict containing heating design conditions 'cooling_dict': {}, // dict containing cooling design conditions "monthly_db_50": [], // list of 12 float values for each month "monthly_wb_50": [], // list of 12 float values for each month "monthly_db_range_50": [], // list of 12 float values for each month "monthly_wb_range_50": [], // list of 12 float values for each month "monthly_db_100": [], // list of 12 float values for each month "monthly_wb_100": [], // list of 12 float values for each month "monthly_db_20": [], // list of 12 float values for each month "monthly_wb_20": [], // list of 12 float values for each month "monthly_db_04": [], // list of 12 float values for each month "monthly_wb_04": [], // list of 12 float values for each month "monthly_wind": [], // list of 12 float values for each month "monthly_wind_dirs": [], // matrix with 12 cols for months of the year and 8 rows for the cardinal directions. "standard_pressure_at_elev": float, // float value for pressure in Pa "monthly_tau_beam":[], // list of 12 float values for each month "monthly_tau_diffuse": [] // list of 12 float values for each month } """ # Initialize the class with all data missing stat_ob = cls(None) # Check required and optional keys option_keys_none = ('ashrae_climate_zone', 'koppen_climate_zone', 'extreme_cold_week', 'extreme_hot_week', 'standard_pressure_at_elev') option_keys_list = ('monthly_db_50', 'monthly_wb_50', 'monthly_db_range_50', 'monthly_wb_range_50', 'monthly_db_100', 'monthly_wb_100', 'monthly_db_20', 'monthly_wb_20', 'monthly_db_04', 'monthly_wb_04', 'monthly_wind', 'monthly_wind_dirs', 'monthly_tau_beam', 'monthly_tau_diffuse') option_keys_dict = ('typical_weeks', 'heating_dict', 'cooling_dict') assert 'location' in data, 'Required key "location" is missing!' for key in option_keys_none: if key not in data: data[key] = None for key in option_keys_list: if key not in data: data[key] = [] for key in option_keys_dict: if key not in data: data[key] = {} # assign the properties of the dictionary to the stat object. stat_ob._location = Location.from_json(data['location']) stat_ob._ashrae_climate_zone = data['ashrae_climate_zone'] stat_ob._koppen_climate_zone = data['koppen_climate_zone'] stat_ob._extreme_cold_week = AnalysisPeriod.from_json(data['extreme_cold_week'])\ if data['extreme_cold_week'] else None stat_ob._extreme_hot_week = AnalysisPeriod.from_json(data['extreme_hot_week'])\ if data['extreme_hot_week'] else None stat_ob._typical_weeks = {} for key, val in data['typical_weeks'].items(): if isinstance(val, list): stat_ob._typical_weeks[key] = [AnalysisPeriod.from_json(v) for v in val] else: stat_ob._typical_weeks[key] = AnalysisPeriod.from_json(val) stat_ob._winter_des_day_dict = data['heating_dict'] stat_ob._summer_des_day_dict = data['cooling_dict'] stat_ob._monthly_db_50 = data['monthly_db_50'] stat_ob._monthly_wb_50 = data['monthly_wb_50'] stat_ob._monthly_db_range_50 = data['monthly_db_range_50'] stat_ob._monthly_wb_range_50 = data['monthly_wb_range_50'] stat_ob._monthly_db_100 = data['monthly_db_100'] stat_ob._monthly_wb_100 = data['monthly_wb_100'] stat_ob._monthly_db_20 = data['monthly_db_20'] stat_ob._monthly_wb_20 = data['monthly_wb_20'] stat_ob._monthly_db_04 = data['monthly_db_04'] stat_ob._monthly_wb_04 = data['monthly_wb_04'] stat_ob._monthly_wind = data['monthly_wind'] stat_ob._monthly_wind_dirs = data['monthly_wind_dirs'] stat_ob._stand_press_at_elev = data['standard_pressure_at_elev'] stat_ob._monthly_tau_beam = data['monthly_tau_beam'] stat_ob._monthly_tau_diffuse = data['monthly_tau_diffuse'] return stat_ob
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Create STAT from json dictionary. Args: data: { 'location': {} , // ladybug location schema 'ashrae_climate_zone': str, 'koppen_climate_zone': str, 'extreme_cold_week': {}, // ladybug analysis period schema 'extreme_hot_week': {}, // ladybug analysis period schema 'typical_weeks': {}, // dict of ladybug analysis period schemas 'heating_dict': {}, // dict containing heating design conditions 'cooling_dict': {}, // dict containing cooling design conditions "monthly_db_50": [], // list of 12 float values for each month "monthly_wb_50": [], // list of 12 float values for each month "monthly_db_range_50": [], // list of 12 float values for each month "monthly_wb_range_50": [], // list of 12 float values for each month "monthly_db_100": [], // list of 12 float values for each month "monthly_wb_100": [], // list of 12 float values for each month "monthly_db_20": [], // list of 12 float values for each month "monthly_wb_20": [], // list of 12 float values for each month "monthly_db_04": [], // list of 12 float values for each month "monthly_wb_04": [], // list of 12 float values for each month "monthly_wind": [], // list of 12 float values for each month "monthly_wind_dirs": [], // matrix with 12 cols for months of the year and 8 rows for the cardinal directions. "standard_pressure_at_elev": float, // float value for pressure in Pa "monthly_tau_beam":[], // list of 12 float values for each month "monthly_tau_diffuse": [] // list of 12 float values for each month }
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L87-L175
4,574
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_cooling_design_days_050
def monthly_cooling_design_days_050(self): """A list of 12 objects representing monthly 5.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_50 == [] \ or self._monthly_wb_50 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_50, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_50] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '5% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
python
def monthly_cooling_design_days_050(self): """A list of 12 objects representing monthly 5.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_50 == [] \ or self._monthly_wb_50 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_50, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_50] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '5% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
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A list of 12 objects representing monthly 5.0% cooling design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L497-L513
4,575
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_cooling_design_days_100
def monthly_cooling_design_days_100(self): """A list of 12 objects representing monthly 10.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_100 == [] \ or self._monthly_wb_100 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_100, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_100] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '10% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
python
def monthly_cooling_design_days_100(self): """A list of 12 objects representing monthly 10.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_100 == [] \ or self._monthly_wb_100 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_100, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_100] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '10% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
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A list of 12 objects representing monthly 10.0% cooling design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L516-L532
4,576
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_cooling_design_days_020
def monthly_cooling_design_days_020(self): """A list of 12 objects representing monthly 2.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_20 == [] \ or self._monthly_wb_20 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_20, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_20] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '2% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
python
def monthly_cooling_design_days_020(self): """A list of 12 objects representing monthly 2.0% cooling design days.""" if self.monthly_found is False or self._monthly_db_20 == [] \ or self._monthly_wb_20 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_20, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_20] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '2% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
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A list of 12 objects representing monthly 2.0% cooling design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L535-L551
4,577
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_cooling_design_days_004
def monthly_cooling_design_days_004(self): """A list of 12 objects representing monthly 0.4% cooling design days.""" if self.monthly_found is False or self._monthly_db_04 == [] \ or self._monthly_wb_04 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_04, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_04] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '0.4% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
python
def monthly_cooling_design_days_004(self): """A list of 12 objects representing monthly 0.4% cooling design days.""" if self.monthly_found is False or self._monthly_db_04 == [] \ or self._monthly_wb_04 == []: return [] else: db_conds = [DryBulbCondition(x, y) for x, y in zip( self._monthly_db_04, self._monthly_db_range_50)] hu_conds = [HumidityCondition( 'Wetbulb', x, self._stand_press_at_elev) for x in self._monthly_wb_04] ws_conds = self.monthly_wind_conditions sky_conds = self.monthly_clear_sky_conditions return [DesignDay( '0.4% Cooling Design Day for {}'.format(self._months[i]), 'SummerDesignDay', self._location, db_conds[i], hu_conds[i], ws_conds[i], sky_conds[i]) for i in xrange(12)]
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A list of 12 objects representing monthly 0.4% cooling design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L554-L570
4,578
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_wind_conditions
def monthly_wind_conditions(self): """A list of 12 monthly wind conditions that are used on the design days.""" return [WindCondition(x, y) for x, y in zip( self._monthly_wind, self.monthly_wind_dirs)]
python
def monthly_wind_conditions(self): """A list of 12 monthly wind conditions that are used on the design days.""" return [WindCondition(x, y) for x, y in zip( self._monthly_wind, self.monthly_wind_dirs)]
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A list of 12 monthly wind conditions that are used on the design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L598-L601
4,579
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_wind_dirs
def monthly_wind_dirs(self): """A list of prevailing wind directions for each month.""" mwd = zip(*self._monthly_wind_dirs) return [self._wind_dirs[mon.index(max(mon))] for mon in mwd]
python
def monthly_wind_dirs(self): """A list of prevailing wind directions for each month.""" mwd = zip(*self._monthly_wind_dirs) return [self._wind_dirs[mon.index(max(mon))] for mon in mwd]
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A list of prevailing wind directions for each month.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L609-L612
4,580
ladybug-tools/ladybug
ladybug/stat.py
STAT.monthly_clear_sky_conditions
def monthly_clear_sky_conditions(self): """A list of 12 monthly clear sky conditions that are used on the design days.""" if self._monthly_tau_diffuse is [] or self._monthly_tau_beam is []: return [OriginalClearSkyCondition(i, 21) for i in xrange(1, 13)] return [RevisedClearSkyCondition(i, 21, x, y) for i, x, y in zip( list(xrange(1, 13)), self._monthly_tau_beam, self._monthly_tau_diffuse)]
python
def monthly_clear_sky_conditions(self): """A list of 12 monthly clear sky conditions that are used on the design days.""" if self._monthly_tau_diffuse is [] or self._monthly_tau_beam is []: return [OriginalClearSkyCondition(i, 21) for i in xrange(1, 13)] return [RevisedClearSkyCondition(i, 21, x, y) for i, x, y in zip( list(xrange(1, 13)), self._monthly_tau_beam, self._monthly_tau_diffuse)]
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A list of 12 monthly clear sky conditions that are used on the design days.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L615-L620
4,581
ladybug-tools/ladybug
ladybug/stat.py
STAT.to_json
def to_json(self): """Convert the STAT object to a dictionary.""" def jsonify_dict(base_dict): new_dict = {} for key, val in base_dict.items(): if isinstance(val, list): new_dict[key] = [v.to_json() for v in val] else: new_dict[key] = val.to_json() return new_dict return { 'location': self.location.to_json(), 'ashrae_climate_zone': self.ashrae_climate_zone, 'koppen_climate_zone': self.koppen_climate_zone, 'extreme_cold_week': self.extreme_cold_week.to_json() if self.extreme_cold_week else None, 'extreme_hot_week': self.extreme_hot_week.to_json() if self.extreme_cold_week else None, 'typical_weeks': jsonify_dict(self._typical_weeks), 'heating_dict': self._winter_des_day_dict, 'cooling_dict': self._summer_des_day_dict, "monthly_db_50": self._monthly_db_50, "monthly_wb_50": self._monthly_wb_50, "monthly_db_range_50": self._monthly_db_range_50, "monthly_wb_range_50": self._monthly_wb_range_50, "monthly_db_100": self._monthly_db_100, "monthly_wb_100": self._monthly_wb_100, "monthly_db_20": self._monthly_db_20, "monthly_wb_20": self._monthly_wb_20, "monthly_db_04": self._monthly_db_04, "monthly_wb_04": self._monthly_wb_04, "monthly_wind": self._monthly_wind, "monthly_wind_dirs": self._monthly_wind_dirs, "standard_pressure_at_elev": self.standard_pressure_at_elev, "monthly_tau_beam": self.monthly_tau_beam, "monthly_tau_diffuse": self.monthly_tau_diffuse }
python
def to_json(self): """Convert the STAT object to a dictionary.""" def jsonify_dict(base_dict): new_dict = {} for key, val in base_dict.items(): if isinstance(val, list): new_dict[key] = [v.to_json() for v in val] else: new_dict[key] = val.to_json() return new_dict return { 'location': self.location.to_json(), 'ashrae_climate_zone': self.ashrae_climate_zone, 'koppen_climate_zone': self.koppen_climate_zone, 'extreme_cold_week': self.extreme_cold_week.to_json() if self.extreme_cold_week else None, 'extreme_hot_week': self.extreme_hot_week.to_json() if self.extreme_cold_week else None, 'typical_weeks': jsonify_dict(self._typical_weeks), 'heating_dict': self._winter_des_day_dict, 'cooling_dict': self._summer_des_day_dict, "monthly_db_50": self._monthly_db_50, "monthly_wb_50": self._monthly_wb_50, "monthly_db_range_50": self._monthly_db_range_50, "monthly_wb_range_50": self._monthly_wb_range_50, "monthly_db_100": self._monthly_db_100, "monthly_wb_100": self._monthly_wb_100, "monthly_db_20": self._monthly_db_20, "monthly_wb_20": self._monthly_wb_20, "monthly_db_04": self._monthly_db_04, "monthly_wb_04": self._monthly_wb_04, "monthly_wind": self._monthly_wind, "monthly_wind_dirs": self._monthly_wind_dirs, "standard_pressure_at_elev": self.standard_pressure_at_elev, "monthly_tau_beam": self.monthly_tau_beam, "monthly_tau_diffuse": self.monthly_tau_diffuse }
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Convert the STAT object to a dictionary.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/stat.py#L642-L678
4,582
ladybug-tools/ladybug
ladybug/datatype/base.py
DataTypeBase.from_json
def from_json(cls, data): """Create a data type from a dictionary. Args: data: Data as a dictionary. { "name": data type name of the data type as a string "data_type": the class name of the data type as a string "base_unit": the base unit of the data type } """ assert 'name' in data, 'Required keyword "name" is missing!' assert 'data_type' in data, 'Required keyword "data_type" is missing!' if cls._type_enumeration is None: cls._type_enumeration = _DataTypeEnumeration(import_modules=False) if data['data_type'] == 'GenericType': assert 'base_unit' in data, \ 'Keyword "base_unit" is missing and is required for GenericType.' return cls._type_enumeration._GENERICTYPE(data['name'], data['base_unit']) elif data['data_type'] in cls._type_enumeration._TYPES: clss = cls._type_enumeration._TYPES[data['data_type']] if data['data_type'] == data['name'].title().replace(' ', ''): return clss() else: instance = clss() instance._name = data['name'] return instance else: raise ValueError( 'Data Type {} could not be recognized'.format(data['data_type']))
python
def from_json(cls, data): """Create a data type from a dictionary. Args: data: Data as a dictionary. { "name": data type name of the data type as a string "data_type": the class name of the data type as a string "base_unit": the base unit of the data type } """ assert 'name' in data, 'Required keyword "name" is missing!' assert 'data_type' in data, 'Required keyword "data_type" is missing!' if cls._type_enumeration is None: cls._type_enumeration = _DataTypeEnumeration(import_modules=False) if data['data_type'] == 'GenericType': assert 'base_unit' in data, \ 'Keyword "base_unit" is missing and is required for GenericType.' return cls._type_enumeration._GENERICTYPE(data['name'], data['base_unit']) elif data['data_type'] in cls._type_enumeration._TYPES: clss = cls._type_enumeration._TYPES[data['data_type']] if data['data_type'] == data['name'].title().replace(' ', ''): return clss() else: instance = clss() instance._name = data['name'] return instance else: raise ValueError( 'Data Type {} could not be recognized'.format(data['data_type']))
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Create a data type from a dictionary. Args: data: Data as a dictionary. { "name": data type name of the data type as a string "data_type": the class name of the data type as a string "base_unit": the base unit of the data type }
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/base.py#L70-L100
4,583
ladybug-tools/ladybug
ladybug/datatype/base.py
DataTypeBase.is_unit_acceptable
def is_unit_acceptable(self, unit, raise_exception=True): """Check if a certain unit is acceptable for the data type. Args: unit: A text string representing the abbreviated unit. raise_exception: Set to True to raise an exception if not acceptable. """ _is_acceptable = unit in self.units if _is_acceptable or raise_exception is False: return _is_acceptable else: raise ValueError( '{0} is not an acceptable unit type for {1}. ' 'Choose from the following: {2}'.format( unit, self.__class__.__name__, self.units ) )
python
def is_unit_acceptable(self, unit, raise_exception=True): """Check if a certain unit is acceptable for the data type. Args: unit: A text string representing the abbreviated unit. raise_exception: Set to True to raise an exception if not acceptable. """ _is_acceptable = unit in self.units if _is_acceptable or raise_exception is False: return _is_acceptable else: raise ValueError( '{0} is not an acceptable unit type for {1}. ' 'Choose from the following: {2}'.format( unit, self.__class__.__name__, self.units ) )
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Check if a certain unit is acceptable for the data type. Args: unit: A text string representing the abbreviated unit. raise_exception: Set to True to raise an exception if not acceptable.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/base.py#L102-L119
4,584
ladybug-tools/ladybug
ladybug/datatype/base.py
DataTypeBase._is_numeric
def _is_numeric(self, values): """Check to be sure values are numbers before doing numerical operations.""" if len(values) > 0: assert isinstance(values[0], (float, int)), \ "values must be numbers to perform math operations. Got {}".format( type(values[0])) return True
python
def _is_numeric(self, values): """Check to be sure values are numbers before doing numerical operations.""" if len(values) > 0: assert isinstance(values[0], (float, int)), \ "values must be numbers to perform math operations. Got {}".format( type(values[0])) return True
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Check to be sure values are numbers before doing numerical operations.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/base.py#L188-L194
4,585
ladybug-tools/ladybug
ladybug/datatype/base.py
DataTypeBase._to_unit_base
def _to_unit_base(self, base_unit, values, unit, from_unit): """Return values in a given unit given the input from_unit.""" self._is_numeric(values) namespace = {'self': self, 'values': values} if not from_unit == base_unit: self.is_unit_acceptable(from_unit, True) statement = '[self._{}_to_{}(val) for val in values]'.format( self._clean(from_unit), self._clean(base_unit)) values = eval(statement, namespace) namespace['values'] = values if not unit == base_unit: self.is_unit_acceptable(unit, True) statement = '[self._{}_to_{}(val) for val in values]'.format( self._clean(base_unit), self._clean(unit)) values = eval(statement, namespace) return values
python
def _to_unit_base(self, base_unit, values, unit, from_unit): """Return values in a given unit given the input from_unit.""" self._is_numeric(values) namespace = {'self': self, 'values': values} if not from_unit == base_unit: self.is_unit_acceptable(from_unit, True) statement = '[self._{}_to_{}(val) for val in values]'.format( self._clean(from_unit), self._clean(base_unit)) values = eval(statement, namespace) namespace['values'] = values if not unit == base_unit: self.is_unit_acceptable(unit, True) statement = '[self._{}_to_{}(val) for val in values]'.format( self._clean(base_unit), self._clean(unit)) values = eval(statement, namespace) return values
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Return values in a given unit given the input from_unit.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/base.py#L196-L211
4,586
ladybug-tools/ladybug
ladybug/datatype/base.py
DataTypeBase.name
def name(self): """The data type name.""" if self._name is None: return re.sub(r"(?<=\w)([A-Z])", r" \1", self.__class__.__name__) else: return self._name
python
def name(self): """The data type name.""" if self._name is None: return re.sub(r"(?<=\w)([A-Z])", r" \1", self.__class__.__name__) else: return self._name
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The data type name.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/base.py#L222-L227
4,587
ladybug-tools/ladybug
ladybug/header.py
Header.from_json
def from_json(cls, data): """Create a header from a dictionary. Args: data: { "data_type": {}, //Type of data (e.g. Temperature) "unit": string, "analysis_period": {} // A Ladybug AnalysisPeriod "metadata": {}, // A dictionary of metadata } """ # assign default values assert 'data_type' in data, 'Required keyword "data_type" is missing!' keys = ('data_type', 'unit', 'analysis_period', 'metadata') for key in keys: if key not in data: data[key] = None data_type = DataTypeBase.from_json(data['data_type']) ap = AnalysisPeriod.from_json(data['analysis_period']) return cls(data_type, data['unit'], ap, data['metadata'])
python
def from_json(cls, data): """Create a header from a dictionary. Args: data: { "data_type": {}, //Type of data (e.g. Temperature) "unit": string, "analysis_period": {} // A Ladybug AnalysisPeriod "metadata": {}, // A dictionary of metadata } """ # assign default values assert 'data_type' in data, 'Required keyword "data_type" is missing!' keys = ('data_type', 'unit', 'analysis_period', 'metadata') for key in keys: if key not in data: data[key] = None data_type = DataTypeBase.from_json(data['data_type']) ap = AnalysisPeriod.from_json(data['analysis_period']) return cls(data_type, data['unit'], ap, data['metadata'])
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Create a header from a dictionary. Args: data: { "data_type": {}, //Type of data (e.g. Temperature) "unit": string, "analysis_period": {} // A Ladybug AnalysisPeriod "metadata": {}, // A dictionary of metadata }
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/header.py#L58-L78
4,588
ladybug-tools/ladybug
ladybug/header.py
Header.duplicate
def duplicate(self): """Return a copy of the header.""" a_per = self.analysis_period.duplicate() if self.analysis_period else None return self.__class__(self.data_type, self.unit, a_per, deepcopy(self.metadata))
python
def duplicate(self): """Return a copy of the header.""" a_per = self.analysis_period.duplicate() if self.analysis_period else None return self.__class__(self.data_type, self.unit, a_per, deepcopy(self.metadata))
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Return a copy of the header.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/header.py#L105-L109
4,589
ladybug-tools/ladybug
ladybug/header.py
Header.to_tuple
def to_tuple(self): """Return Ladybug header as a list.""" return ( self.data_type, self.unit, self.analysis_period, self.metadata )
python
def to_tuple(self): """Return Ladybug header as a list.""" return ( self.data_type, self.unit, self.analysis_period, self.metadata )
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Return Ladybug header as a list.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/header.py#L111-L118
4,590
ladybug-tools/ladybug
ladybug/header.py
Header.to_json
def to_json(self): """Return a header as a dictionary.""" a_per = self.analysis_period.to_json() if self.analysis_period else None return {'data_type': self.data_type.to_json(), 'unit': self.unit, 'analysis_period': a_per, 'metadata': self.metadata}
python
def to_json(self): """Return a header as a dictionary.""" a_per = self.analysis_period.to_json() if self.analysis_period else None return {'data_type': self.data_type.to_json(), 'unit': self.unit, 'analysis_period': a_per, 'metadata': self.metadata}
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Return a header as a dictionary.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/header.py#L124-L130
4,591
ladybug-tools/ladybug
ladybug/skymodel.py
ashrae_clear_sky
def ashrae_clear_sky(altitudes, month, sky_clearness=1): """Calculate solar flux for an original ASHRAE Clear Sky Args: altitudes: A list of solar altitudes in degrees month: An integer (1-12) indicating the month the altitudes belong to sky_clearness: A factor that will be multiplied by the output of the model. This is to help account for locations where clear, dry skies predominate (e.g., at high elevations) or, conversely, where hazy and humid conditions are frequent. See Threlkeld and Jordan (1958) for recommended values. Typical values range from 0.95 to 1.05 and are usually never more than 1.2. Default is set to 1.0. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2. """ # apparent solar irradiation at air mass m = 0 MONTHLY_A = [1202, 1187, 1164, 1130, 1106, 1092, 1093, 1107, 1136, 1166, 1190, 1204] # atmospheric extinction coefficient MONTHLY_B = [0.141, 0.142, 0.149, 0.164, 0.177, 0.185, 0.186, 0.182, 0.165, 0.152, 0.144, 0.141] dir_norm_rad = [] dif_horiz_rad = [] for i, alt in enumerate(altitudes): if alt > 0: try: dir_norm = MONTHLY_A[month - 1] / (math.exp( MONTHLY_B[month - 1] / (math.sin(math.radians(alt))))) diff_horiz = 0.17 * dir_norm * math.sin(math.radians(alt)) dir_norm_rad.append(dir_norm * sky_clearness) dif_horiz_rad.append(diff_horiz * sky_clearness) except OverflowError: # very small altitude values dir_norm_rad.append(0) dif_horiz_rad.append(0) else: # night time dir_norm_rad.append(0) dif_horiz_rad.append(0) return dir_norm_rad, dif_horiz_rad
python
def ashrae_clear_sky(altitudes, month, sky_clearness=1): """Calculate solar flux for an original ASHRAE Clear Sky Args: altitudes: A list of solar altitudes in degrees month: An integer (1-12) indicating the month the altitudes belong to sky_clearness: A factor that will be multiplied by the output of the model. This is to help account for locations where clear, dry skies predominate (e.g., at high elevations) or, conversely, where hazy and humid conditions are frequent. See Threlkeld and Jordan (1958) for recommended values. Typical values range from 0.95 to 1.05 and are usually never more than 1.2. Default is set to 1.0. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2. """ # apparent solar irradiation at air mass m = 0 MONTHLY_A = [1202, 1187, 1164, 1130, 1106, 1092, 1093, 1107, 1136, 1166, 1190, 1204] # atmospheric extinction coefficient MONTHLY_B = [0.141, 0.142, 0.149, 0.164, 0.177, 0.185, 0.186, 0.182, 0.165, 0.152, 0.144, 0.141] dir_norm_rad = [] dif_horiz_rad = [] for i, alt in enumerate(altitudes): if alt > 0: try: dir_norm = MONTHLY_A[month - 1] / (math.exp( MONTHLY_B[month - 1] / (math.sin(math.radians(alt))))) diff_horiz = 0.17 * dir_norm * math.sin(math.radians(alt)) dir_norm_rad.append(dir_norm * sky_clearness) dif_horiz_rad.append(diff_horiz * sky_clearness) except OverflowError: # very small altitude values dir_norm_rad.append(0) dif_horiz_rad.append(0) else: # night time dir_norm_rad.append(0) dif_horiz_rad.append(0) return dir_norm_rad, dif_horiz_rad
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Calculate solar flux for an original ASHRAE Clear Sky Args: altitudes: A list of solar altitudes in degrees month: An integer (1-12) indicating the month the altitudes belong to sky_clearness: A factor that will be multiplied by the output of the model. This is to help account for locations where clear, dry skies predominate (e.g., at high elevations) or, conversely, where hazy and humid conditions are frequent. See Threlkeld and Jordan (1958) for recommended values. Typical values range from 0.95 to 1.05 and are usually never more than 1.2. Default is set to 1.0. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/skymodel.py#L11-L57
4,592
ladybug-tools/ladybug
ladybug/skymodel.py
zhang_huang_solar
def zhang_huang_solar(alt, cloud_cover, relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed, irr_0=1355): """Calculate global horizontal solar irradiance using the Zhang-Huang model. Note: [1] Zhang, Q.Y. and Huang, Y.J. 2002. "Development of typical year weather files for Chinese locations", LBNL-51436, ASHRAE Transactions, Vol. 108, Part 2. Args: alt: A solar altitude in degrees. cloud_cover: A float value between 0 and 10 that represents the sky cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A float value between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A float value that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A float value that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A float value that represents the wind speed in m/s. irr_0 = Optional extraterrestrial solar constant (W/m2). Default is to use the average value over the earth's orbit (1355). Returns: glob_ir: A global horizontall radiation value in W/m2. """ # zhang-huang solar model regression constants C0, C1, C2, C3, C4, C5, D_COEFF, K_COEFF = 0.5598, 0.4982, \ -0.6762, 0.02842, -0.00317, 0.014, -17.853, 0.843 # start assuming night time glob_ir = 0 if alt > 0: # get sin of the altitude sin_alt = math.sin(math.radians(alt)) # shortened and converted versions of the input parameters cc, rh, n_temp, n3_temp, w_spd = cloud_cover / 10.0, \ relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed # calculate zhang-huang global radiation glob_ir = ((irr_0 * sin_alt * (C0 + (C1 * cc) + (C2 * cc**2) + (C3 * (n_temp - n3_temp)) + (C4 * rh) + (C5 * w_spd))) + D_COEFF) / K_COEFF if glob_ir < 0: glob_ir = 0 return glob_ir
python
def zhang_huang_solar(alt, cloud_cover, relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed, irr_0=1355): """Calculate global horizontal solar irradiance using the Zhang-Huang model. Note: [1] Zhang, Q.Y. and Huang, Y.J. 2002. "Development of typical year weather files for Chinese locations", LBNL-51436, ASHRAE Transactions, Vol. 108, Part 2. Args: alt: A solar altitude in degrees. cloud_cover: A float value between 0 and 10 that represents the sky cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A float value between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A float value that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A float value that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A float value that represents the wind speed in m/s. irr_0 = Optional extraterrestrial solar constant (W/m2). Default is to use the average value over the earth's orbit (1355). Returns: glob_ir: A global horizontall radiation value in W/m2. """ # zhang-huang solar model regression constants C0, C1, C2, C3, C4, C5, D_COEFF, K_COEFF = 0.5598, 0.4982, \ -0.6762, 0.02842, -0.00317, 0.014, -17.853, 0.843 # start assuming night time glob_ir = 0 if alt > 0: # get sin of the altitude sin_alt = math.sin(math.radians(alt)) # shortened and converted versions of the input parameters cc, rh, n_temp, n3_temp, w_spd = cloud_cover / 10.0, \ relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed # calculate zhang-huang global radiation glob_ir = ((irr_0 * sin_alt * (C0 + (C1 * cc) + (C2 * cc**2) + (C3 * (n_temp - n3_temp)) + (C4 * rh) + (C5 * w_spd))) + D_COEFF) / K_COEFF if glob_ir < 0: glob_ir = 0 return glob_ir
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Calculate global horizontal solar irradiance using the Zhang-Huang model. Note: [1] Zhang, Q.Y. and Huang, Y.J. 2002. "Development of typical year weather files for Chinese locations", LBNL-51436, ASHRAE Transactions, Vol. 108, Part 2. Args: alt: A solar altitude in degrees. cloud_cover: A float value between 0 and 10 that represents the sky cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A float value between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A float value that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A float value that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A float value that represents the wind speed in m/s. irr_0 = Optional extraterrestrial solar constant (W/m2). Default is to use the average value over the earth's orbit (1355). Returns: glob_ir: A global horizontall radiation value in W/m2.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/skymodel.py#L112-L161
4,593
ladybug-tools/ladybug
ladybug/skymodel.py
zhang_huang_solar_split
def zhang_huang_solar_split(altitudes, doys, cloud_cover, relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed, atm_pressure, use_disc=False): """Calculate direct and diffuse solar irradiance using the Zhang-Huang model. By default, this function uses the DIRINT method (aka. Perez split) to split global irradiance into direct and diffuse. This is the same method used by EnergyPlus. Args: altitudes: A list of solar altitudes in degrees. doys: A list of days of the year that correspond to the altitudes. cloud_cover: A list of float values between 0 and 10 that represents cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A list of float values between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A list of float values that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A list of float values that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A list of float values that represents the wind speed in m/s. atm_pressure: A list of float values that represent the atmospheric pressure in Pa. use_disc: Set to True to use the original DISC model as opposed to the newer and more accurate DIRINT model. Default is False. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2. """ # Calculate global horizontal irradiance using the original zhang-huang model glob_ir = [] for i in range(len(altitudes)): ghi = zhang_huang_solar(altitudes[i], cloud_cover[i], relative_humidity[i], dry_bulb_present[i], dry_bulb_t3_hrs[i], wind_speed[i]) glob_ir.append(ghi) if use_disc is False: # Calculate dew point temperature to improve the splitting of direct + diffuse temp_dew = [dew_point_from_db_rh(dry_bulb_present[i], relative_humidity[i]) for i in range(len(glob_ir))] # Split global rad into direct + diffuse using dirint method (aka. Perez split) dir_norm_rad = dirint(glob_ir, altitudes, doys, atm_pressure, use_delta_kt_prime=True, temp_dew=temp_dew) # Calculate diffuse horizontal from dni and ghi. dif_horiz_rad = [glob_ir[i] - (dir_norm_rad[i] * math.sin(math.radians(altitudes[i]))) for i in range(len(glob_ir))] else: dir_norm_rad = [] dif_horiz_rad = [] for i in range(len(glob_ir)): dni, kt, am = disc(glob_ir[i], altitudes[i], doys[i], atm_pressure[i]) dhi = glob_ir[i] - (dni * math.sin(math.radians(altitudes[i]))) dir_norm_rad.append(dni) dif_horiz_rad.append(dhi) return dir_norm_rad, dif_horiz_rad
python
def zhang_huang_solar_split(altitudes, doys, cloud_cover, relative_humidity, dry_bulb_present, dry_bulb_t3_hrs, wind_speed, atm_pressure, use_disc=False): """Calculate direct and diffuse solar irradiance using the Zhang-Huang model. By default, this function uses the DIRINT method (aka. Perez split) to split global irradiance into direct and diffuse. This is the same method used by EnergyPlus. Args: altitudes: A list of solar altitudes in degrees. doys: A list of days of the year that correspond to the altitudes. cloud_cover: A list of float values between 0 and 10 that represents cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A list of float values between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A list of float values that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A list of float values that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A list of float values that represents the wind speed in m/s. atm_pressure: A list of float values that represent the atmospheric pressure in Pa. use_disc: Set to True to use the original DISC model as opposed to the newer and more accurate DIRINT model. Default is False. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2. """ # Calculate global horizontal irradiance using the original zhang-huang model glob_ir = [] for i in range(len(altitudes)): ghi = zhang_huang_solar(altitudes[i], cloud_cover[i], relative_humidity[i], dry_bulb_present[i], dry_bulb_t3_hrs[i], wind_speed[i]) glob_ir.append(ghi) if use_disc is False: # Calculate dew point temperature to improve the splitting of direct + diffuse temp_dew = [dew_point_from_db_rh(dry_bulb_present[i], relative_humidity[i]) for i in range(len(glob_ir))] # Split global rad into direct + diffuse using dirint method (aka. Perez split) dir_norm_rad = dirint(glob_ir, altitudes, doys, atm_pressure, use_delta_kt_prime=True, temp_dew=temp_dew) # Calculate diffuse horizontal from dni and ghi. dif_horiz_rad = [glob_ir[i] - (dir_norm_rad[i] * math.sin(math.radians(altitudes[i]))) for i in range(len(glob_ir))] else: dir_norm_rad = [] dif_horiz_rad = [] for i in range(len(glob_ir)): dni, kt, am = disc(glob_ir[i], altitudes[i], doys[i], atm_pressure[i]) dhi = glob_ir[i] - (dni * math.sin(math.radians(altitudes[i]))) dir_norm_rad.append(dni) dif_horiz_rad.append(dhi) return dir_norm_rad, dif_horiz_rad
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Calculate direct and diffuse solar irradiance using the Zhang-Huang model. By default, this function uses the DIRINT method (aka. Perez split) to split global irradiance into direct and diffuse. This is the same method used by EnergyPlus. Args: altitudes: A list of solar altitudes in degrees. doys: A list of days of the year that correspond to the altitudes. cloud_cover: A list of float values between 0 and 10 that represents cloud cover in tenths (0 = clear; 10 = completely overcast) relative_humidity: A list of float values between 0 and 100 that represents the relative humidity in percent. dry_bulb_present: A list of float values that represents the dry bulb temperature at the time of interest (in degrees C). dry_bulb_t3_hrs: A list of float values that represents the dry bulb temperature at three hours before the time of interest (in degrees C). wind_speed: A list of float values that represents the wind speed in m/s. atm_pressure: A list of float values that represent the atmospheric pressure in Pa. use_disc: Set to True to use the original DISC model as opposed to the newer and more accurate DIRINT model. Default is False. Returns: dir_norm_rad: A list of direct normal radiation values for each of the connected altitudes in W/m2. dif_horiz_rad: A list of diffuse horizontall radiation values for each of the connected altitudes in W/m2.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/skymodel.py#L164-L224
4,594
ladybug-tools/ladybug
ladybug/skymodel.py
calc_horizontal_infrared
def calc_horizontal_infrared(sky_cover, dry_bulb, dew_point): """Calculate horizontal infrared radiation intensity. See EnergyPlus Enrineering Reference for more information: https://bigladdersoftware.com/epx/docs/8-9/engineering-reference/climate-calculations.html#sky-radiation-modeling Note: [1] Walton, G. N. 1983. Thermal Analysis Research Program Reference Manual. NBSSIR 83-2655. National Bureau of Standards, p. 21. [2] Clark, G. and C. Allen, “The Estimation of Atmospheric Radiation for Clear and Cloudy Skies,” Proceedings 2nd National Passive Solar Conference (AS/ISES), 1978, pp. 675-678. Args: sky_cover: A float value between 0 and 10 that represents the opaque sky cover in tenths (0 = clear; 10 = completely overcast) dry_bulb: A float value that represents the dry bulb temperature in degrees C. dew_point: A float value that represents the dew point temperature in degrees C. Returns: horiz_ir: A horizontal infrared radiation intensity value in W/m2. """ # stefan-boltzmann constant SIGMA = 5.6697e-8 # convert to kelvin db_k = dry_bulb + 273.15 dp_k = dew_point + 273.15 # calculate sky emissivity and horizontal ir sky_emiss = (0.787 + (0.764 * math.log(dp_k / 273.15))) * \ (1 + (0.022 * sky_cover) - (0.0035 * (sky_cover ** 2)) + (0.00028 * (sky_cover ** 3))) horiz_ir = sky_emiss * SIGMA * (db_k ** 4) return horiz_ir
python
def calc_horizontal_infrared(sky_cover, dry_bulb, dew_point): """Calculate horizontal infrared radiation intensity. See EnergyPlus Enrineering Reference for more information: https://bigladdersoftware.com/epx/docs/8-9/engineering-reference/climate-calculations.html#sky-radiation-modeling Note: [1] Walton, G. N. 1983. Thermal Analysis Research Program Reference Manual. NBSSIR 83-2655. National Bureau of Standards, p. 21. [2] Clark, G. and C. Allen, “The Estimation of Atmospheric Radiation for Clear and Cloudy Skies,” Proceedings 2nd National Passive Solar Conference (AS/ISES), 1978, pp. 675-678. Args: sky_cover: A float value between 0 and 10 that represents the opaque sky cover in tenths (0 = clear; 10 = completely overcast) dry_bulb: A float value that represents the dry bulb temperature in degrees C. dew_point: A float value that represents the dew point temperature in degrees C. Returns: horiz_ir: A horizontal infrared radiation intensity value in W/m2. """ # stefan-boltzmann constant SIGMA = 5.6697e-8 # convert to kelvin db_k = dry_bulb + 273.15 dp_k = dew_point + 273.15 # calculate sky emissivity and horizontal ir sky_emiss = (0.787 + (0.764 * math.log(dp_k / 273.15))) * \ (1 + (0.022 * sky_cover) - (0.0035 * (sky_cover ** 2)) + (0.00028 * (sky_cover ** 3))) horiz_ir = sky_emiss * SIGMA * (db_k ** 4) return horiz_ir
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Calculate horizontal infrared radiation intensity. See EnergyPlus Enrineering Reference for more information: https://bigladdersoftware.com/epx/docs/8-9/engineering-reference/climate-calculations.html#sky-radiation-modeling Note: [1] Walton, G. N. 1983. Thermal Analysis Research Program Reference Manual. NBSSIR 83-2655. National Bureau of Standards, p. 21. [2] Clark, G. and C. Allen, “The Estimation of Atmospheric Radiation for Clear and Cloudy Skies,” Proceedings 2nd National Passive Solar Conference (AS/ISES), 1978, pp. 675-678. Args: sky_cover: A float value between 0 and 10 that represents the opaque sky cover in tenths (0 = clear; 10 = completely overcast) dry_bulb: A float value that represents the dry bulb temperature in degrees C. dew_point: A float value that represents the dew point temperature in degrees C. Returns: horiz_ir: A horizontal infrared radiation intensity value in W/m2.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/skymodel.py#L230-L267
4,595
ladybug-tools/ladybug
ladybug/legendparameters.py
LegendParameters.set_domain
def set_domain(self, values): """Set domain of the colors based on min and max of a list of values.""" _flattenedList = sorted(flatten(values)) self.domain = tuple(_flattenedList[0] if d == 'min' else d for d in self.domain) self.domain = tuple(_flattenedList[-1] if d == 'max' else d for d in self.domain)
python
def set_domain(self, values): """Set domain of the colors based on min and max of a list of values.""" _flattenedList = sorted(flatten(values)) self.domain = tuple(_flattenedList[0] if d == 'min' else d for d in self.domain) self.domain = tuple(_flattenedList[-1] if d == 'max' else d for d in self.domain)
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Set domain of the colors based on min and max of a list of values.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/legendparameters.py#L80-L84
4,596
ladybug-tools/ladybug
ladybug/datacollection.py
HourlyDiscontinuousCollection.timestep_text
def timestep_text(self): """Return a text string representing the timestep of the collection.""" if self.header.analysis_period.timestep == 1: return 'Hourly' else: return '{} Minute'.format(int(60 / self.header.analysis_period.timestep))
python
def timestep_text(self): """Return a text string representing the timestep of the collection.""" if self.header.analysis_period.timestep == 1: return 'Hourly' else: return '{} Minute'.format(int(60 / self.header.analysis_period.timestep))
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Return a text string representing the timestep of the collection.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datacollection.py#L96-L101
4,597
ladybug-tools/ladybug
ladybug/datacollection.py
HourlyDiscontinuousCollection.moys_dict
def moys_dict(self): """Return a dictionary of this collection's values where the keys are the moys. This is useful for aligning the values with another list of datetimes. """ moy_dict = {} for val, dt in zip(self.values, self.datetimes): moy_dict[dt.moy] = val return moy_dict
python
def moys_dict(self): """Return a dictionary of this collection's values where the keys are the moys. This is useful for aligning the values with another list of datetimes. """ moy_dict = {} for val, dt in zip(self.values, self.datetimes): moy_dict[dt.moy] = val return moy_dict
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Return a dictionary of this collection's values where the keys are the moys. This is useful for aligning the values with another list of datetimes.
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datacollection.py#L104-L112
4,598
ladybug-tools/ladybug
ladybug/datacollection.py
HourlyDiscontinuousCollection.filter_by_analysis_period
def filter_by_analysis_period(self, analysis_period): """ Filter a Data Collection based on an analysis period. Args: analysis period: A Ladybug analysis period Return: A new Data Collection with filtered data """ self._check_analysis_period(analysis_period) _filtered_data = self.filter_by_moys(analysis_period.moys) _filtered_data.header._analysis_period = analysis_period return _filtered_data
python
def filter_by_analysis_period(self, analysis_period): """ Filter a Data Collection based on an analysis period. Args: analysis period: A Ladybug analysis period Return: A new Data Collection with filtered data """ self._check_analysis_period(analysis_period) _filtered_data = self.filter_by_moys(analysis_period.moys) _filtered_data.header._analysis_period = analysis_period return _filtered_data
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Filter a Data Collection based on an analysis period. Args: analysis period: A Ladybug analysis period Return: A new Data Collection with filtered data
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datacollection.py#L114-L127
4,599
ladybug-tools/ladybug
ladybug/datacollection.py
HourlyDiscontinuousCollection.group_by_month_per_hour
def group_by_month_per_hour(self): """Return a dictionary of this collection's values grouped by each month per hour. Key values are tuples of 2 integers: The first represents the month of the year between 1-12. The first represents the hour of the day between 0-24. (eg. (12, 23) for December at 11 PM) """ data_by_month_per_hour = OrderedDict() for m in xrange(1, 13): for h in xrange(0, 24): data_by_month_per_hour[(m, h)] = [] for v, dt in zip(self.values, self.datetimes): data_by_month_per_hour[(dt.month, dt.hour)].append(v) return data_by_month_per_hour
python
def group_by_month_per_hour(self): """Return a dictionary of this collection's values grouped by each month per hour. Key values are tuples of 2 integers: The first represents the month of the year between 1-12. The first represents the hour of the day between 0-24. (eg. (12, 23) for December at 11 PM) """ data_by_month_per_hour = OrderedDict() for m in xrange(1, 13): for h in xrange(0, 24): data_by_month_per_hour[(m, h)] = [] for v, dt in zip(self.values, self.datetimes): data_by_month_per_hour[(dt.month, dt.hour)].append(v) return data_by_month_per_hour
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Return a dictionary of this collection's values grouped by each month per hour. Key values are tuples of 2 integers: The first represents the month of the year between 1-12. The first represents the hour of the day between 0-24. (eg. (12, 23) for December at 11 PM)
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c08b7308077a48d5612f644943f92d5b5dade583
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datacollection.py#L214-L228