hanneshapke

In [1]: data_point = WindData.objects.raw('SELECT * FROM meteo_winddata ORDER BY geom <-> ST_SetSRID(ST_MakePoint(%s, %s),4326) LIMIT 1', [foo.location.x, foo.location.y])[0]
DEBUG (0.282) SELECT * FROM meteo_winddata ORDER BY geom <-> ST_SetSRID(ST_MakePoint( -0.12574, 51.50853),4326) LIMIT 1; args=[-0.12574, 51.50853]
 
In [2]: data_point = WindDataPolygon.objects.get(geom__intersects = foo.location)
DEBUG (0.003) SELECT "meteo_winddatapolygon"."id", "meteo_winddatapolygon"."lat", "meteo_winddatapolygon"."lng", "meteo_winddatapolygon"."windspeed", "meteo_winddatapolygon"."geom" FROM "meteo_winddatapolygon" WHERE ST_Intersects("meteo_winddatapolygon"."geom", ST_GeomFromEWKB('\x0101000020e6100000f146e6913f18c0bf7784d38217c14940'::bytea)); args=(<django.contrib.gis.db.backends.postgis.adapter.PostGISAdapter object at 0x11080f810>,)

hanneshapke

data_point = WindData.objects.raw('SELECT * FROM meteo_winddata ORDER BY location <-> ST_SetSRID(ST_MakePoint(%s, %s),4326) LIMIT 1', [location.x, location.y])[0]

hanneshapke

# Some good references are:
# http://russbrooks.com/2010/11/25/install-postgresql-9-on-os-x
# http://www.paolocorti.net/2008/01/30/installing-postgis-on-ubuntu/
# http://postgis.refractions.net/documentation/manual-1.5/ch02.html#id2630392

#1. Install PostgreSQL postgis and postgres

brew install postgis
initdb /usr/local/var/postgres
pg_ctl -D /usr/local/var/postgres -l /usr/local/var/postgres/server.log start

hanneshapke

'''
This code example is a little gist example of the m2m problem
I am facing with flask and sqlalchemy. The purpose of this short
code sample is to select one or many grocery lists (List) based
on the grocery items (Grocery).

All query examples return either an empty list or all lists which contain
any of the items (OR selection instead of AND)

Below I have listed three approaches I have tried without any luck.

hanneshapke

packages: 
  yum:
    gcc-c++: [] 
    make: []
sources:
  /home/ec2-user: http://download.redis.io/releases/redis-2.8.4.tar.gz
commands:
  redis_build:
    command: make
    cwd: /home/ec2-user/redis-2.8.4

hanneshapke

###  Try it out with JSBin

(JSBin)[http://jsbin.com/dijefajalo/edit?html,js,console,output]

hanneshapke

import time
from memory_profiler import memory_usage

import logging
celery_logger = logging.getLogger('celery')


def track_celery(method):

    """

hanneshapke

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

hanneshapke

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

hanneshapke

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf