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all.
I have a question on how to add missing values to a dataset object.
I'm currently working on crop growth modeling, and employ NASA Power API as a weather dataset.
However, the NASA Power dataset has missing days.
enter image description here
I used pcse library in order to extract NASA Power dataset.
My question is, how to add the missing day's data.
I tried
wdp(date) = wdp(date-timedelta(days=1))
but it gives me back 'can't assign to function call'
anyhow, it seems that the data for the missing date does not exist in the object and I am not allowed to make it.
You have the right idea, but the wrong syntax. In Python, list and dict access uses square brackets ([]), see the docs.
To add to that, pcse’s WeatherDataProvider object does not support this style access. Checking out the code in this link, it appears there is a method you can call named _store_WeatherDataContainer, where the leading _ indicates it is not intended for public use, but that doesn’t mean you can’t :-)
It should look like this:
wdp._store_WeatherDataContainer(wdp(date-timedelta(days=1)), date)
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As we all know in python, there is one type of data - complex . I read the doc and then cannot find the use case, when to use it, and then what's its characters?
a = 20
b = 328
c = complex(a, b)
print(type(c))
Complex data types are used for advanced calculation in Electronics and applied or astrophysics or such type, just like we use complex numbers in the real world.
Complex numbers are used in electronics and electromagnetism. A single complex number puts together two real quantities, making the numbers easier to work with. For example, in electronics, the state of a circuit element is defined by the voltage (V) and the current (I).
In python, some libraries are used these things like:-
SkiDL
PySpice
Numpy
...etc
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I want to play a bunch of movies on a grid let's say of dimension (n, n), where each movie m_ij is an element of the grid. I would like to decide which movie is positioned where on the grid at each second, i.e. movies can switch positions over time. How would one start to do this? I'd preferably use Python, but am open to use another language if appropriate. Thanks a lot for any help, it's for an art project.
I know you prefer Python but first think I can think of to achieve something like that easily would be through using this JavaScript library called p5js or processing which is same library written in Java. It is very good for doing art with code kinda thing.
Here is a video to give you the idea.
Anyways hope this helps. Cheers.
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What I am trying to do is to create a Multiple Choice Question (MCQ) generation to our fill in the gap style question generator. I need to generate distracters (Wrong answers) from the Key (correct answer). The MCQ is generated from educational texts that users input. We're trying to tackle this through combining Contextual similarity, similarity of the sentences in which the keys and the distractors occur in and Difference in term frequencies Any help? I was thinking of using big data datasets to generate related distractors such as the ones provided by google vision, I have no clue how to achieve this in python.
This question is way too broad to be answered, though I would do my best to give you some pointers.
If you have a closed set of potential distractors, I would use word/phrase embedding to find the closest distractor to the right answer.
Gensim's word2vec is a good starting point in python
If you want your distractors to follow a template, for example replace a certain word from the right answer with its opposite, I would use nltk's wordnet implementation to find antonyns / synonyms.
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Hello dear Python experts:)
From a simulation I got data (course of energy over the time) which I have to fit. When I plot the energy it has a non-periodic oscillating course. There are a bunch of helping function like curve_fit from scipy etc. But you always have to specify a function with which the fit should take place. But I don't know a proper function a priori.
I need something like a Fourier fit to get a function representing the data (like it is possible in MatLab) to later use this function to determine its maxima. Has anyone an idea how to deal with such a problem?
Here is an example course: 2
If you like, you can have a look at the data in a .csv-file: https://1drv.ms/u/s!AuQAmr8-QRJSdzNTzyvWPhUaEnw
I would be very delighted to get some help:-)
Many thanks:-)
Using the Fourier fit in MATLAB you also specify a model (how many sin/cos you want).
For instance "Fourier 2" is:
f(x) = a0 + a1*cos(x*w) + b1*sin(x*w) +
a2*cos(2*x*w) + b2*sin(2*x*w)
Check http://exnumerus.blogspot.nl/2010/04/how-to-fit-sine-wave-example-in-python.html to see how to fit for "Fourier1".
If you really want no model you need to use something like "eureqa", which is free for academic use (http://www.nutonian.com/download/eureqa-desktop-download/).
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I want to write a Python 3 script to manage my expenses, and I'm going to have a rules filter that says 'if the description contains a particular string, categorize it as x', and these rules will be read in from a text file.
The only way I can think of doing this is to apply str.find() for each rule on the description of each transaction, and break if one is found - but this is a O^2 solution, is there a better way of doing this?
Strip punctuation from the description, and split it into words. Make the words in the description into a set, and the categories into another set.
Since sets use dictionaries internally and dictionaries are built on hash-tables, average membership checking is O(1).
Only when a transaction is entered (or changed), intersect both sets to find the categories that apply (if any), and add the categories to your transaction record (dict, namedtuple, whatever).