An early ripening variety with a friendly and uniform yield - cucumber Green Stream F1: description, reviews on cultivation

Green stream F1 can be successfully cultivated in greenhouse and open conditions. The parthenocarpic cucumber is adapted to various climatic conditions and successfully copes with cold spells, shade, and heat. It has good disease resistance and is rarely affected by pests.

Description and characteristics of the variety

Green stream F1 from “Gavrish” stands out among other hybrids due to its uniform fruiting.
The harvest begins early - 42-45 days from germination. The variety belongs to the early ripening species. The bushes grow strong, powerful, and bloom according to the female type. The central trunk grows indeterminately and reaches 3 m, the lateral stems are compact and shortened.

Bunches of even and beautiful cucumbers, 4-6 pcs each, are formed in the nodes. in everyone. The leaves are small and sparse and do not shade the shoots.

Description of greens:

• length 11-13 cm;
• weight 80-100 g;
• diameter 3cm;
• oval-cylindrical shape;
• color bright green;
• the tubercles are small and sparsely located;
• pubescence is white;
• the pulp is juicy, crispy, not watery;
• the skin is tender and durable;
• long fruit stem.

When outgrown, the fruits reach 30 cm and weigh 300 g, but at the same time retain their marketable shape, do not taste bitter and do not form voids in the pulp.

Ripe greens are universally used for preparing a variety of culinary dishes.

They make excellent salads, delicious lightly salted cucumbers, vigorous pickled cucumbers in jars with a hint of spice.

Take turns

In each program, lines of code are executed one at a time. For example, if you have a line of code that requests something from the server, then this means that your program does nothing while waiting for a response. In some cases this is acceptable, but in many it is not. One solution to this problem is threads.

Threads enable your program to perform a number of tasks simultaneously. Of course, threads have a number of disadvantages. Multithreaded programs are more complex and tend to be more error prone. They include the following problems: race condition, deadlock and livelock, resource starvation.

Landing

To get an early harvest, seeds for seedlings are sown 25 days before transplanting into greenhouse beds. Prepare fertile soil with the following ingredients:

• 2 parts compost;
• 2 parts humus;
• 1 part peat;
• 1 part sand.

The seeds are buried 1-2 cm into the moistened mixture. The boxes or cassettes are covered with film on top and kept warm at 25-27 degrees.

With the emergence of shoots, the film is removed and the temperature is reduced to 20 degrees. At the stage of 2 leaves, fertilizing is carried out with an organic solution - diluted mullein or dung. When 4 leaves appear on the bushes, they are transplanted to a permanent place.

They are planted in beds according to a pattern of 30 by 70 cm. It is convenient to plant bushes in compact areas, maintaining a distance between holes of 40 by 40 cm.

Context switching

While asynchronous programming can work around threading bottlenecks, it was designed for a completely different purpose—processor context switching. When you have multiple threads, each CPU core can only run one thread at a time. To ensure that all threads/processes can share resources, the processor switches context very frequently. To simplify operation, the processor stores all thread context information at random intervals and switches to another thread.

Asynchronous programming is software/user space thread processing where the application, rather than the processor, manages threads and context switching. In asynchronous programming, the context is switched only at specified switch points, rather than at a frequency determined by the CPU.

Growing and care

The soil for seedlings is prepared in advance. For this purpose, per 1 m2 in the fall:

• 2-3 buckets of compost;
• 100 g nitrophoska;
• 1 cup wood ash.

In spring, the ground is plowed to a depth of 20 cm and a fertile substrate is laid. It is advisable to choose loose loams and sandy loams with a neutral pH within 7 for planting.

Good predecessors for the variety are cabbage and tomato species, potatoes, peas, and beans. It is not advisable to plant a hybrid after pumpkins or squash crops.

If the land has remained unprepared since the fall, then in the spring the following is added to it per 1 m2:

• 2 tbsp. superphosphate;
• 2 buckets of rotted humus;
• 1 tbsp. potassium sulfate.

Seeds are sown in an open bed only when the soil at a depth of 3-5 cm warms up to 12-14 degrees (for areas with a temperate climate, this time occurs at the end of May, the first week of June).

Sow in furrows, in holes with added organic fertilizers. Agronomists advise growing outdoor cucumbers in warm trenches and beds for early ripening and massive fruiting.

The optimal temperature is 22-27 degrees, humidity within 70-90%. Watering is carried out only with warm water 20-22 degrees, water is poured into the grooves, trying not to get on the foliage.

In greenhouses and greenhouses, it is recommended to keep a bush with 1 stem for better ventilation and lighting. Using twine, strong stems are fixed to a trellis or a stretched mesh.

Callback function

Python has many libraries for asynchronous programming, the most popular being Tornado, Asyncio and Gevent. Let's see how Tornado works. It uses callback style for asynchronous network I/O. A callback is a function that means, “Once this is done, execute this function.” In other words, you call customer service and leave your number so that when they are available they will call you back instead of waiting for them to answer. Let's see how to do the same as above using Tornado:

import tornado.ioloop from tornado.httpclient import AsyncHTTPClient urls = ['https://www.google.com', 'https://www.yandex.ru', 'https://www.python.org'] def handle_response(response): if response.error: print("Error:", response.error) else: url = response.request.url data = response.body print('{}: {} bytes: {}'.format (url, len(data), data)) http_client = AsyncHTTPClient() for url in urls: http_client.fetch(url, handle_response) tornado.ioloop.IOLoop.instance().start()

The second to last line of code calls the AsyncHTTPClient.fetch method, which fetches the data from the URL in a non-blocking manner. This method executes and returns immediately. Because each subsequent line will be executed before the response to the URL is received, it is not possible to retrieve the object as the result of the method execution. The solution to this problem is that the fetch method, instead of returning an object, calls a function with the result or a callback. The callback in this example is handle_response.

In the example, you may notice that the first line of the handle_response function checks for an error. This is necessary because it is not possible to handle the exception. If an exception was thrown, it will not be processed in the code due to the event loop. When fetch is executed, it fires an HTTP request and then processes the response in an event loop. By the time the error occurs, the call stack will contain only the event loop and the current function, and no exception will be thrown anywhere in the code. Thus, any exceptions thrown in the callback function interrupt the event loop and stop the execution of the program. Therefore, all errors should be passed as objects and not handled as exceptions. This means that if you haven't checked for errors, they won't be processed. Another problem with callbacks is that in asynchronous programming, the only way to avoid blocking is with a callback. This can lead to a very long chain: callback after callback after callback. Since access to the stack and variables is lost, you end up passing large objects to all your callbacks, but if you use third party APIs, you can't pass anything to the callback if it can't accept it. This also becomes a problem because each callback acts as a thread. For example, you would like to call three APIs and wait for all three to return a result before you can summarize it. In Gevent you can do this, but not with callbacks. You'll have to do a little magic by storing the result in a global variable and checking in the callback whether the result is final.

Asynchronous Python Applications and Their Components

Now, asynchronous Python applications use coroutines as a core ingredient. They use the Asyncio library to run them. But there are other important elements that can also be considered key for asynchronous applications:

• Event loops.
  Asyncio creates and manages event loops. Loops start coroutines before they complete. For ease of process tracking, only one event loop can be executed at a time.
• Tasks.
  When running a coroutine in an event loop, it is possible to return a
  Task . It controls the behavior of the coroutine outside of the event loop. Do you want to cancel a running task? Call the .cancel() .

Example.

Parser script with event loops and task objects in action.
import asyncio from web_scraping_library import read_from_site_async tasks = [] async def main(url_list): for n in url_list: tasks.append(asyncio.create_task(read_from_site_async(n))) print (tasks) return await asyncio.gather(*tasks) urls = ['https://site1.com','https://othersite.com','https://newsite.com'] loop = asyncio.get_event_loop() results = loop.run_until_complete(main(urls)) print(results)
.get_event_loop() method provides an object that allows you to control the event loop. All asynchronous functions are passed through .run_until_complete() , which runs assigned tasks until they are all completed.

The .create_task() method gives a Task to run and accepts a function. Each URL is sent as a separate Task to the event loop. Task objects are stored in a list. It's worth noting that all this can be done inside an asynchronous function or, more simply, inside an event loop.

Control over the event loop and tasks directly depends on the complexity of the application. In the example with the site parser script, there is no need for close monitoring. Here it is quite simple to collect the final data obtained as a result of running jobs. All fixed jobs will be executed here simultaneously without any problem.

However, if, for example, you need to create a framework, the level of control over the event loop and the behavior of coroutines will be much greater. Most likely, if the application crashes here, the event loop may need to be terminated gracefully, or threaded tasks can be started in safe mode while the event loop is called from another thread.

Async and await

Due to its versatility and power, Asyncio has become a foundational library in Python. Here, for universality, a more precise understanding of asynchronous code and separation of methods and generators, the keywords async (show the asynchrony of a method) and await (waiting for the completion of a coroutine) are used.

Example.

Accessing three URLs simultaneously using the Asyncio library using async.
The method returns the coroutine and it is waiting. import asyncio import aiohttp urls = ['https://www.google.com', 'https://www.yandex.ru', 'https://www.python.org'] async def call_url(url): print('Starting {}'.format(url)) response = await aiohttp.get(url) data = await response.text() print('{}: {} bytes: {}'.format(url, len( data), data)) return data futures = [call_url(url) for url in urls] loop = asyncio.get_event_loop() loop.run_until_complete(asyncio.wait(futures))