Difference between revisions of "ViSUSpy"

Latest revision as of 21:06, 28 January 2020

The OpenVisus library is also available in python through a series of packages.

You can install the library using python pip:

python -m pip install OpenVisus
python -m OpenVisus configure

In the following sections we provide some examples on how to use it.

Library imports

To import and use the library in your python application you need to use the following imports:

from OpenVisus import *

These modules allow the basic interactions with IDX datasets, which can be either local or remote (served by a ViSUS Server).

Important: your code that interacts with IDX datasets has always to attach the DbModule first, as following:

DbModule.attach()
# your program code
DbModule.detach()

Read data into a NumPy array

In order to facilitate interoperation with existing python scientific libraries as NumPy we provide two function to convert data arrays from/to ViSUS Array an NumPy array.

import numpy

# load a remote dataset
dataset=LoadDataset("http://atlantis.sci.utah.edu/mod_visus?dataset=BlueMarble")

# get the bounding box of the dataset 
# the box is defined by a set of 2 points (p1 and p2)
# which define the two corners of a bounding box
box=dataset.getLogicBox()

# get the default field of a dataset
field=dataset.getDefaultField()

access=dataset.createAccess()

# select the resolution we need
resolution = 21

# initialize query
query=BoxQuery(dataset, field, time,ord('r'))

query.logic_box=dataset.getLogicBox()
query.end_resolutions.push_back(resolution)

# execute the query
dataset.beginQuery(query)
dataset.executeQuery(access,query)

# convert our buffer to a numpy array.
# This is useful when we want to read data from 
# and IDX dataset and use this array with other libraries
data=Array.toNumPy(query.buffer,bSqueeze=True,bShareMem=False)

@@ Line 1: / Line 1: @@
-ViSUSpy is a library that allows the user to use the ViSUS framework from python.
+The OpenVisus library is also available in python through a series of packages.
+You can install the library using python pip:
+<syntaxhighlight lang="python">
+python -m pip install OpenVisus
+python -m OpenVisus configure
+</syntaxhighlight>
 In the following sections we provide some examples on how to use it.
@@ Line 8: / Line 14: @@
 <syntaxhighlight lang="python">
-from visuspy import *
+from OpenVisus import *
-from VisusKernelPy import *
-from VisusIdxPy    import *
-from VisusDbPy     import *
 </syntaxhighlight>
 These modules allow the basic interactions with IDX datasets, which can be either local or remote (served by a [[ViSUS Server]]).
-'''Important''': your code that interacts with IDX datasets has always to attach the ''IdxModule'' first, as following:
+'''Important''': your code that interacts with IDX datasets has always to attach the ''DbModule'' first, as following:
 <syntaxhighlight lang="python">
-IdxModule.attach()
+DbModule.attach()
 # your program code
-IdxModule.detach()
+DbModule.detach()
 </syntaxhighlight>
-=== NumPy utils ===
+=== Read data into a NumPy array ===
 In order to facilitate interoperation with existing python scientific libraries as NumPy we provide two function to convert data arrays from/to ViSUS Array an NumPy array.
@@ Line 31: / Line 34: @@
 import numpy
-# convert our buffer to a numpy array.
+# load a remote dataset
-# This is useful when we want to read data from
+dataset=LoadDataset("http://atlantis.sci.utah.edu/mod_visus?dataset=BlueMarble")
-# and IDX dataset and use this array with other libraries
-numpy_array=convertToNumPyArray(in_visus_array)
-# convert a numpy array to a ViSUS array
+# get the bounding box of the dataset
-visus_array = convertToVisusArray(numpy_array)
+# the box is defined by a set of 2 points (p1 and p2)
+# which define the two corners of a bounding box
+box=dataset.getLogicBox()
-</syntaxhighlight>
+# get the default field of a dataset
+field=dataset.getDefaultField()
-== Write data in IDX format ==
+access=dataset.createAccess()
-<syntaxhighlight lang="python" line='line'>
+# select the resolution we need
-IdxModule.attach()
+resolution = 21
-# define a box which defines the bounding box
+# initialize query
-# of the grid where our data will be stored
+query=BoxQuery(dataset, field, time,ord('r'))
-# a box is defined by two points (p1 and p2)
-# which define the two opposite corners of the box
-# in this case the box has size 16x16x16
-dataset_box=NdBox(NdPoint(0,0,0),NdPoint.one(16,16,16))
-# create and IDX file where to to store the data
-idxfile=IdxFile();
-# set dataset box
+query.logic_box=dataset.getLogicBox()
-idxfile.box=NdBox(dataset_box)
+query.end_resolutions.push_back(resolution)
-# add integer field to the dataset
+# execute the query
-idxfile.fields.push_back(Field("myfield",DType.fromString("uint32")))
+dataset.beginQuery(query)
+dataset.executeQuery(access,query)
-# save the metadata on disk (no data are saved yet)
+# convert our buffer to a numpy array.
-bSaved=idxfile.save(self.filename)
+# This is useful when we want to read data from
-self.assertTrue(bSaved)
+# and IDX dataset and use this array with other libraries
+data=Array.toNumPy(query.buffer,bSqueeze=True,bShareMem=False)
-# load the dataset we just created
-dataset=Dataset.loadDataset(self.filename)
-self.assertIsNotNone(dataset)
-# create access to write the data
-access=dataset.get().createAccess()
-# write data slice by slice
-sampleid=0
-for Z in range(0,16):
-  # create box to hold the Z slice of the data
-  slice_box=dataset.get().getBox().getZSlab(Z,Z+1)
-  # create write query
-  query=QueryPtr(Query(dataset.get(),ord('w')))
-  # set query box
-  query.get().position=Position(slice_box)
-  # initialize query
-  self.assertTrue(dataset.get().beginQuery(query))
-  # check if size of the query is equal to the dimension of our dataset
-  self.assertEqual(query.get().nsamples.innerProduct(),16*16)
-  # create a Visus Array to store the data
-  buffer=Array(query.get().nsamples,query.get().field.dtype)
-  # set the output buffer of the query to the array we just created
-  query.get().buffer=buffer
-  # convert our buffer to a numpy array
-  fill=convertToNumPyArray(buffer)
-  # fill up the array with some values
-  for Y in range(16):
-    for X in range(16):
-      fill[Y,X]=sampleid
-      sampleid+=1
-# execute query
-success = dataset.get().executeQuery(access,query)
-IdxModule.detach()
-</syntaxhighlight>
-== Read from IDX file ==
-<syntaxhighlight lang="python" line='line'>
-IdxModule.attach()
-# open an IDX dataset
-dataset=Dataset_loadDataset(self.filename)
-# check it is valid
-self.assertIsNotNone(dataset)
-# get the bounding box of the dataset
-# the box is defined by a set of 2 points (p1 and p2)
-# which define the two corners of a bounding box
-box=dataset.get().getBox()
-# get the default field of a dataset
-field=dataset.get().getDefaultField()
-#create access
-access=dataset.get().createAccess()
-# here we read the datasets slice by slice
-# making box queries
-sampleid=0
-for Z in range(0,16):
-  slice_box=box.getZSlab(Z,Z+1)
-  # define a read query
-  query=QueryPtr(Query(dataset.get(),ord('r')))
-  # set the box for the query
-  query.get().position=Position(slice_box)
-  self.assertTrue(dataset.get().beginQuery(query))
-  # check the size of the output of the query
-  print("query size", query.get().nsamples.innerProduct())
-  # execute query
-  self.assertTrue(dataset.get().executeQuery(access,query))
-  # convert output buffer to a numpy array
-  my_numpy_array=convertToNumPyArray(query.get().buffer)
-IdxModule.detach()
 </syntaxhighlight>