I don't use raw SQL very often so when I do I usually end up checking the manual for the correct syntax. One query I've wanted to run a couple of times recently and always struggled to find the correct statement for is checking the amount of disk space used by a table or database.
For the ease of future reference here they are.
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I'm currently working on a project which centres around pulling in data from an external website, "mashing" it up with some additional content, and then displaying it on a website.
The website is going to be interactive and reasonably complex so I decided to use django. To acquire the external data there isn't a webservice so I'm stuck parsing html (and excel spreadsheets but that's a separate story). Scrapy seemed ideal for this and although I wish I had used some other approach than xpath it largely has been.
Having set up my database models in django and built my spider in scrapy the next step was putting the data from the spider in the database. There are plenty of posts detailing how to use the django ORM from outside a django project, even some specific to scrapy but they didn't seem to be working for me.
The issue was the way I handled development and production environment settings.
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Back in December I gave a talk introducing Numpy to the PyNorthwest group. The slides are available as a pdf.
Although I frequently use Numpy I'm far from an expert and the content of my talk reflected this. I started with a general introduction to the array object and then expanded the scope of the talk to highlight some of the projects that use Numpy. I gave an example of using MDP and matplotlib.
The talk was followed by some excellent discussion. We went through some of the code on slide 6 in a lot of detail.
The PyNorthwest group meets at Madlab in Manchester city centre on the third Thursday of each month. If you're in the area check it out. The January event is on the 19th, starting at 7pm.
At BarcampNortheast4 last weekend and at the Python Northwest meetup on Thursday I gave a presentation on the work I've been doing generating full text visualisations of PDF document libraries.
This was the third BarcampNortheast event I have attended. Each has been slightly different but they have all been a weekend well spent. This year felt a little smaller than previous years but that may have partly been because we were in a bigger space.
I have been attending the python Edinburgh meetups for a while. They have always been interesting and the Northwest meetup this Thursday was the first since I moved back to the Northwest. The format, alternating talks and coding sessions, is different to Edinburgh, regular pub meetups with irregular talks, coding sessions and miniconferences. It was an interesting crowd and the other talks, on Apache Thrift and teaching programming to GCSE students (15-16 year olds), gave a really good variety of subjects to discuss later.
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Last weekend the Python Edinburgh users group hosted a mini-conference. Saturday morning was kicked off with a series of talks followed by sessions introducing and then focusing on contributing to django prior to sprints which really got going on the Sunday.
The slides for my talk on, "Images and Vision in Python" are now available in pdf format here.
The slide deck I used is relatively lightweight with my focus being on demonstrating using the different packages available. The code I went through is below.
from PIL import Image
#Open an image and show it
pil1 = Image.open('filename')
#Get its size
pil1s = pil1.resize((100,100))
#or - thumbnail
bg = Image.new('RGB', (500,500), '#ffffff')
#Two ways of accessing the pixels
#getpixel/putpixel and load
#load is faster
pix = bg.load()
for a in range(100, 200):
for b in range(100,110):
pix[a,b] = (0,0,255)
#Drawing shapes is slightly more involved
from PIL import ImageDraw
draw = ImageDraw.Draw(bg)
from PIL import ImageFont
font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeSerif.ttf", 72)
draw.text((10,10), "Hello", font=font, fill='#00ff00')
#Demo's for vision
from scipy import ndimage
#Create a sample image
v1 = np.zeros((10,10), bool)
v1[1:4,1:4] = True
v1[4:7,2:6] = True
#Opening, closing and top-hat as combinations of dilate and erode
#Latest version of mahotas has a label func
v1[8:,8:] = True
labeled, nr_obj = ndimage.label(v1)
#Convert a grayscale image to a binary image
v2 = mahotas.imread("/home/jonathan/openplaques/blueness_images/1.jpg")
T = mahotas.otsu(v2)
imshow(v2 > T)
dist = mahotas.distance(v2 > T)