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https://github.com/esheldon/numpydb
A numerical python interface to the Berkeley db
https://github.com/esheldon/numpydb
Last synced: 3 days ago
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A numerical python interface to the Berkeley db
- Host: GitHub
- URL: https://github.com/esheldon/numpydb
- Owner: esheldon
- Created: 2015-07-02T13:39:49.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2019-06-11T18:56:36.000Z (over 5 years ago)
- Last Synced: 2024-10-19T20:52:31.777Z (19 days ago)
- Language: C++
- Homepage:
- Size: 78.1 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
A numerical python interface to the berkeley database. This most useful in
combination with a higher level manager such as pycolumns. pycolumns has a
higher level interface that allows querying and comparison of "columns", each
of which is indexed using numpydb.
Examples of Construction and Creation
```python
# creating a database with keys strings of length 20
# and data values that are 32-bit integers (4-byte).
# this is effectively an index on the strings.
# use the create helper function for creation.
import numpydb
key_dtype = 'S20'
data_dtype = 'i4'
numpydb.create(dbfile, key_dtype, data_dtype)
# constructing the NumpyBD and opening database for
# reading and updating
db=numpydb.NumpyDB(dbfile, mode='r+')
# construction without opening a file
db=numpydb.NumpyDB()
# now open the database for updating and reading.
db.open(dbfile, 'r+')
# show a representation of the database
# print(db) or just type db at an interactive prompt
print(db)
Filename: 'test-S20.db'
key dtype: 'S20'
data dtype: 'i4'
```
Putting Records:
```python
# keys and values can be scalar, sequence, or array. They
# must be convertible to the same numpy data type as the keys
# and values in the database
db.put(keys, values)
```
Query Examples:
```python
import numpydb
db = numpydb.NumpyDB('somefile.db')
# Extract exact key matches from the database. Can
# be scalar, sequence, or array
values = db.match(values)
# Extract from a range of keys, using default inclusive interval
# [low,high]. Note between and range are synonyms
values = db.between(low, high)
values = db.range(low, high)
# Extract from different types of intervals. Again, note range()
# is synonymous with between
values = db.between(low, high,'[]')
values = db.between(low, high,'[)')
values = db.between(low, high,'(]')
values = db.between(low, high,'()')
values = db.range(low, high,'()')
# one sided range queries
values = db.range1(low, '>')
values = db.range1(low, '>=')
values = db.range1(high,'<')
values = db.range1(high,'<=')
values = db.range1(key2match,'=')
# Can also return keys,values, e.g.
keys,values = db.match(values, select='both')
keys,values = db.between(low, high, select='both')
# Just counting the matches
count = db.match(values,select='count')
count = db.between(low, high,select='count')
# just getting the keys
keys = db.match(values, select='keys')
keys = db.between(low,high,select='keys')
```