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not show all method for response
pycharm version: PyCharm Community Edition 2016.1
python version: 2.7.1
Pycharm cannot show you autocomplete methods for undefined type values. How could pycharm know that response in this function will be a scrapy.http.Response object?
Python <3.5
For this PyCharm would need an explicit functionality to track whether the project is a scrapy spider and assume that response is a scrapy.http.Response object which I believe what it does with django framework. At the moment this doesn't exist for scrapy.
If you really want autocomplete you can override the response object temporarily:
from scrapy.http import Response
def parse(self, response):
response = response or Response()
response. # autocompletes now
# continue code as you would
This will give you autocomplete since pycharm will have a hint now what response might be. It's also shouldn't change your response to an empty object since response will never be empty(or in other words bool(response) is never False) to fail the first part of the evaluation.
Python >=3.5
as of python 3.6 type hints can be used:
from scrapy.http import Response
def parse(self, response:Response):
...
Related
I am adding profiler on my custom method on Odoo v15.0
I have referred below doc for code profiling
https://www.odoo.com/documentation/15.0/developer/howtos/profilecode.html
Below is the syntax i am using
from odoo.tools.profiler import profile
#profile
#api.model
def mymethod(...):
My code
But on execution of code i am getting below error on terminal
"ImportError: cannot import name 'profile' from 'odoo.tools.profiler'"
To debug the issue i have deep dived in base code of "/odoo/tool/profiler.py".
But unable to locate any wrapper or function called profiler.
What is correct way to use profiling using "Log a method" strategy on odoo v15.0?
Goto path and make sure you have this file for line-by-line code profiling looks like you don't have this file
From the front end in debug mode open enable profiling, this will give you all the information per user
I am using Scrapy-Splash to scrape real-time data from JavaScript websites. I am using Docker to run Splash. The Spider works completely fine, and I'm getting the required data from the website. However, the Spider crawls once and finishes the process, so I get the data of a particular time. I want to continuously collect the data and store it in a database(i.e MySQL) since the data is updated every second. The crawl needs to be continued and show the data in real-time using plotting libraries(i.e Matplotlib, Plotly). Is there any way to keep the Spider running as the Splash renders the updated data(I'm not sure if Splash updates the data like a normal browser)? Here's my code,
import scrapy
import pandas as pd
from scrapy_splash import SplashRequest
from scrapy.http import Request
class MandalaSpider(scrapy.Spider):
name = 'mandala'
def start_requests(self):
link="website link"
yield SplashRequest(url=link, callback=self.parse)
def parse(self,response):
products=response.css('a.abhead::text').getall()
nub=[]
for prod in products:
prod=prod.strip()
prod=prod.split()
nub.extend(prod)
data=pd.DataFrame({
'Name' : nub[0:len(nub):4],
'Price' : nub[1:len(nub):4],
'Change' : nub[2:len(nub):4],
'Change_percent' : nub[3:len(nub)+1:4],
})
# For single data monitoring
sub=[]
sub=sub.extend(data.iloc[3,1])
yield sub
yield Request(response.url, callback=self.parse, dont_filter=True)
I am completely a newbie in web scraping, so any additional information is greatly appreciated. I have searched other posts from this website but unfortunately couldn't get the solid info that I needed. This type of problem is solved usually using Selenium and BeautifulSoup. But I wanted to use Scrapy.
Thanks in advance.
Looking at the documentation for scrapy and scrapyD it appears the only way you can write the result of a scrape is to write the code in the pipeline of the spider itself. I am being told by my colleagues that there is an additional way whereby I can intercept the result of the scrape from within scrapyD!!
Has anyone heard of this and if so can someone shed some light on this for me please?
Thanks
item exporters
feed exports
scrapyd config
Scrapyd is indeed a service that can be used to schedule crawling processes of your Scrapy application through JSON API. It also permits the integration of Scrapy with different frameworks such as Django, see this guide in case you are interested.
Here is the documentation of Scrapyd.
However if your doubt is about saving the result of your scraping, the standard way is to do so in the pipelines.py file of your Scrapy applicaiton.
An example:
class Pipeline(object):
def __init__(self):
#initialization of your pipeline, maybe connecting to a database or creating a file
def process_item(self, item, spider):
# specify here what it needs to be done with the scraping result of a single page
Remember to define which pipeline are you using in your Scrapy application settings.py:
ITEM_PIPELINES = {
'scrapy_application.pipelines.Pipeline': 100,
}
Source: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
I have a Tornado web server that exposes some endpoints in its API.
I want to be able to document my handlers (endpoints) in-code, including description, parameters, example, response structure, etc., and afterwards generate an interactive documentation that enables one to "play" with my API, easily make requests and experience the response on a sandbox environment.
I know Swagger, and particularly their SwaggerUI solution is one of the best tools for that, but I get confused how it works. I understand that I need to feed the SwaggerUI engine some .yaml that defines my API, but how do I generate it from my code?
Many github libraries I found aren't good enough or only support Flask...
Thanks
To my understanding, SwaggerUI is dependent on swagger specification.
So, it boils down to generating the Swagger Specification in a clean and elegant manner.
Did you get a chance to look at apispec?
I am finding this to be an active project with a plugin for tornado.
Here's how we are doing it in our project. We made our own module and we are still actively developing this. For more info: https://pypi.org/project/tornado-swirl/
import tornado.web
import tornado_swirl as swirl
#swirl.restapi('/item/(?P<itemid>\d+)')
class ItemHandler(tornado.web.RequestHandler):
def get(self, itemid):
"""Get Item data.
Gets Item data from database.
Path Parameter:
itemid (int) -- The item id
"""
pass
#swirl.schema
class User(object):
"""This is the user class
Your usual long description.
Properties:
name (string) -- required. Name of user
age (int) -- Age of user
"""
pass
def make_app():
return swirl.Application(swirl.api_routes())
if __name__ == "__main__":
app = make_app()
app.listen(8888)
tornado.ioloop.IOLoop.current().start()
Scrapy has ImagesPipeline that helps download image. the process is
Spider: start a link and parse all image urls in response, and save
image urls to items.
ImagesPipeline: items['image_urls'] are processed by ImagesPipeline.
But what if I don't need spider parts and have 100k images URLs ready to be downloaded, for example read URLs from redis, how do I call ImagePipeline directly to download the image?
I know I could simply make Request in spider and save response, but I'd like to see if there is way use default ImagesPipeline to save images directly.
I don't think that the use case you describe is the best fit for Scrapy. Wget would work fine for such a constrained problem.
If you really need to use Scrapy for this, make a dummy request to some URL:
def start_requests(self):
request = Request('http://example.com')
# load from redis
redis_img_urls = ...
request.meta['redis_img_urls'] = redis_img_urls
yield request
Then on the parse() method return:
def parse(self, response):
return {'image_urls':request.meta['redis_img_urls'] }
This is ugly but it should work fine...
P.S. I'm not aware of any easy way to bypass the dummy request and inject and Item directly. I'm sure there's one but it's such an unusual thing to do.
The idea behind a scrapy Pipeline is to process the items the spider generates explained here.
Now scrapy isn't about "downloading" staff, but a way to create crawlers, spiders, so if you have a list with urls to "download", then just use for loop and download them.
If you still want to use a scrapy Pipeline, then you'll have to return an item with that list inside the image_urls field.
def start_requests(self):
yield Request('http://httpbin.org/ip', callback=self.parse)
def parse(self, response):
...
yield {'image_urls': [your list]}
Then enable the pipeline on settings.