projection

I am trying to get a QueryOver working using a Projection on a many-to-one . The class "Post" has a property many-to-one "Creator". Using session.QueryOver(Of Post). Select(Projections. Property(of Post)(Function(x) x.Creator). WithAlias(Function() postAlias.Creator)). TransformUsing(Transformers.AliasToBean(Of Post)()). List() works BUT each creator is retrieved by a single query rather than using a join like it is done when not using a select/projection. So if there are 5 posts with 5 different creators, 6 queries will be run 1 for the list of posts and 5 for the creators. I tried to get it

I have a @Entity called Order in this I have a field or a member variable called orderEmails as show below. @Entity @Table(name = "order") public class Order { @Id @Column(name = "order_int") private Long id; @OneToMany(cascade = CascadeType.ALL, fetch = FetchType.LAZY) @JoinColumn(name = "order_int", insertable = false, updatable = false) private List<OrderEmail> orderEmails; ... } I am trying to create Projections on this Order , meaning I want to select some specific columns from the Order entity and a column from the OrderEmail entity But, when I create a projection on the orderEmails

I'm following the Slick documentation example for autoincrementing fields and I'm having trouble creating a mapped projection that ... well, only has one column. case class UserRole(id: Option[Int], role: String) object UserRoles extends Table[UserRole]("userRole") { def id = column[Int]("ID", O.PrimaryKey, O.AutoInc) def role = column[String]("ROLE") // ... def * = id.? ~ role <> (UserRole, UserRole.unapply _) // NEXT LINE ERRORS OUT def forInsert = role <> ({t => UserRole(None, t._1)}, {(r: UserRole) => Some((r.role))}) returning id } The error is "value <> is not a member of scala.slick

I use orthographic projection to plot maps. I use this programm: from mpl_toolkits.basemap import Basemap import numpy as np import matplotlib.pyplot as plt import os, sys from sys import argv import pylab from mpl_toolkits.basemap import Basemap, shiftgrid from matplotlib import mpl from matplotlib import rcParams import matplotlib.pyplot as plt import matplotlib.mlab as mlab import matplotlib.patches as patches import matplotlib.path as path import matplotlib.dates as dt from numpy import linalg import netCDF4 import time import datetime as d import sys import math from mpl_toolkits.axes

I have a fits file about an astronomical object. I can plot it like this: from astropy.io import fits from astropy.wcs import WCS hdul = fits.open(fitsfilename)[0] wcs = WCS(hdul.header) fig = plt.figure(figsize=(12,12)) fig.add_subplot(111, projection=wcs) plt.imshow(hdul.data) This works, and produces a nice pic: I would like to add some additional features to this plot, which doesn't work. For example lets try to add a circle to 119°, -67°30'. I expand the code by: plt.scatter([119],[-67.5],c='r',s=500) What I get is: Which is really not what we wanted, the circle is around 118°5', -67°5',

My Question is exactly like Grails Projections not returning all properties and not grouped I have a following criteria def sharedDocumentsInstanceList SharedDocuments.createCriteria().list(params){ createAlias('receiver', 'r') createAlias('author', 'a') eq("r.id",session.uid) projections{ groupProperty("a.id") property("a.firstName","firstName") property("a.lastName","lastName") property("a.emailAddress","email") } } Where sharedDocuments is defined as follows class SharedDocuments { Users author Users receiver Documents file } What I have seen is that sharedDocumentsInstanceList always has

I am reading through Residual learning , and I have a question. What is "linear projection" mentioned in 3.2? Looks pretty simple once got this but could not get the idea... I am basically not a computer science person, so I would very appreciate if someone provide me a simple example. First up, it's important to understand what x , y and F are and why they need any projection at all. I'll try explain in simple terms, but basic understanding of ConvNets is required. x is an input data (called tensor ) of the layer, in case of ConvNets it's rank is 4. You can think of it as a 4-dimensional