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A collection of Python convenience functions for matplotlib, networkx and mysql

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mtools

A collection of Python convenience functions

Usage

from mtools import *
depth = mgraph.directed.depth(my_networkx_graph)

mplot, mlang and mdb must be manually imported

from random import random
from mtools import mplot
mplot.one.p("test.eps", [random() for i in range(0, 100)], sliding=10)

Modules

  • mdb - MySQL convenience functions (depends on python-mysqldb)
  • mfile - File management functions
  • mgraph - Graph functions to use with networkx (depends on networkx)
  • mgroup - List and dictionary convenience functions
  • mlang - NLP convenience functions (depends on nltk)
  • mplot - Chart plotting convenience functions (depends on matplotlib)
  • mstring - String manipulation functions

MDb

Supports ActiveRecord-style chained queries

from mtools import mdb
db = mdb.mysql.MMySQL('my_database', host='my_host', username='my_username', password='my_password')

# Get a single row
print db.users_table.select('name').where(id=10).first()

# Get multiple rows
for user in db.users_table.select('id', 'location').where(location='SF').order_by('age DESC').limit(100):
  print user

# Get everything
for user in db.users_table:
  print user

# Count the number of distinct ids in a table
print db.users_table.count('DISTINCT id').where(location='NY').first()

# Chain whichever way you want
db.users_table.limit(2).select('id').order_by('name', 'id DESC').where(location='SF').select('name', 'location')

# Insert without duplicates
db.users_table.insert_if_not_exists(username='john', email='john@doe.corp')

Regular queries are also supported

# Use db.q if you want to get everything at once
users = db.q('SELECT * FROM users WHERE location = %s AND age > %s', (some_location, some_age))

# Use db.i if you want an iterator instead
for user in db.i('SELECT * FROM users'):
  print user

Remember to commit your changes if you modify the database!

db.commit()

MPlot

Plot two lines with different scales

from mtools import mplot
xylist1 = [0, 1, 2]
xylist2 = [100, 200, 300]
mplot.two.scales('my_output_file.eps', xylist1, xylist2, xlabel='X Axis', ylabel='Y Axis', labels['First Plot', 'Second Plot'])

MPlot understands several input formats

xylist = [0, 1, 2, 3]  # MPlot assumes these are y values and will add indices for the x values
xylist = {1: 500, 2: 600, 3: 900}
xylist = [(1, 500), (2, 600), (3, 900)]
xylist = [[1, 2, 3], [500, 600, 900]]

MPlot can also do sliding window averages

mplot.one.loglog('my_output_file.png', xylist, sliding=10)

The MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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A collection of Python convenience functions for matplotlib, networkx and mysql

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