Is there a way to get at all the paths with matplotlib1.3.0?
I am using hexbin and create the following output: "hex31mm", which is a:
In [42]: type(hex31mm)
Out[42]: matplotlib.collections.PolyCollection
My aim is to use the method "get_paths" as is in "matplotlib 1.1.0" for the function linked below but with the newer version of "matplotlib 3.0.1"
Interestingly: "get_paths" under matplotlib 3.0.1, yields "802" distinct paths as below:
In [41]: len(hex31mm.get_paths())
Out[41]: 802
Yet "get_paths" under matplotlib 1.3.0, for this same object "hex31mm" yields only one path as below:
In[1] len(hex31mm.get_paths())
Out[1]: 1
Please check link below for more details, any help much appreciated!
NOTE:
I am sure the information for all paths are part of the object in both cases because the hexbin figure that plots up onto the screen is the same under both matplotlib versions, however I require the hexbin centres, hence my insistance of use on the "get_path" method for the linked function.
Sorry to sound repetitive but the function works fine in matplotlib1.1.0 but not under matplotlib1.3.0 and is supposed to return an array (n,2), and each element of that array is the centre (x,y) of n hexbins:
any hints, would be much appreciated...
I think in the newer versions of matplotlib the method: "get_offsets()" does the trick: "hex31mm.get_offsets()" returns the centres which is the output of the function ...
Related
I am trying to use sns.histplot() instead of sns.distplot() since I got the following message in colab:
FutureWarning: distplot is a deprecated function and will be removed
in a future version. Please adapt your code to use either displot (a
figure-level function with similar flexibility) or histplot (an axes-level function for histograms).
Code:
import pandas as pd
import seaborn as sns
df = sns.load_dataset('tips')
sns.histplot(df['tip'], kde=True, kde_kws={'fill' : True});
I got an error when passing kde_kws parameters inside sns.histplot():
TypeError: init() got an unexpected keyword argument 'fill'
From the documentation kde_kws= is intended to pass arguments "that control the KDE computation, as in kdeplot()." It is not entirely explicit which arguments those are, but they seem to be the ones like bw_method= and bw_adjust= that change the way the KDE is computed, rather than displayed. If you want to change the appearance of the KDE plot, the you can use line_kws=, but, as the name implies, the KDE is represented only by a line and therefore cannot be filled.
If you want both a histogram and a filled KDE, you need to combine histplot() and kdeplot() on the same axes
sns.histplot(df['tip'], stat='density')
sns.kdeplot(df['tip'], fill=True)
I'm working on the C++ version of Matt Zucker's Page dewarping. So far everything works fine, but I have a problem with optimization. In line 748 of Github repo Matt uses optimize function from Scipy. My C++ equivalent is find_min_bobyqa from dlib.net. The code is:
auto f = [&](const column_vector& ppts) { return objective( dstpoints, ppts, keypoint_index); };
dlib::find_min_bobyqa(f,
params,
2 * params.nr() + 1, // npt - number of interpolation points: x.size() + 2 <= npt && npt <= (x.size()+1)*(x.size()+2)/2
dlib::uniform_matrix<double>(params.nr(), 1, -2), // lower bound constraint
dlib::uniform_matrix<double>(params.nr(), 1, 2), // upper bound constraint
1, // initial trust region radius
1e-5, // stopping trust region radius
4000 // max number of objective function evaluations
);
In my concrete example params is a dlib::column_vector with double values and length = 189. Every element of params is less than 2.0 and greater than -2.0. Function objective() returns double value and "alone" it works properly because I get the same value as in the Python version. But after running fin_min_bobyqa function I usually get the message:
Terminate called after throwing an instance of 'dlib:bobyqa_failure', return from BOBYQA because the objective function has been called max_f_evals times.
I set max_f_evals to quite big value to see if it optimizes at all, but it doesn't. I did some tweaking with parameters but without good results. How should I set the parameters of find_min_bobyqa to get the right solution?
I am very interested in this issue as well. Zucker's work, with very minor tweaks, is ideal for straightening sheet music images, and I was looking for ways to implement it in a mobile platform when I came across your question.
My research so far suggests that BOBYQA is not the equivalent of Powell's method in scipy. BOBYQA is constrained, and the one in scipy is not.
See these links for more information, and a possible way to compile the right supporting library - I would try UOBYQA or NEWUOA.
https://github.com/jacobwilliams/PowellOpt
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#rdd2e1855725e-3
(See the Notes section)
EDIT: see C version here:
https://github.com/emmt/Algorithms/tree/master/newuoa
I wanted to post this as a comment, but I don't have enough points for that.
I am very interested in your progress. If you're willing, please keep me posted.
I finally solved this problem. I used PRAXIS library, because it doesn't need derivative information and is fast.
I modified the code a little to my needs and now it is faster around few seconds than original version written in Python.
Doing this example in Matlab Image Category Classification I have found an error trying to encode an image into a feature vector.
categoryClassifier = trainImageCategoryClassifier(trainingSet, bag);
Error using imageCategoryClassifier (line 436)
You need at least two image categories. That means that the number of elements in input array of imageSet
objects, imSets, must be at least two.
Error in imageCategoryClassifier.create (line 328)
this = imageCategoryClassifier(imgSet, bag, varargin{:});
Error in trainImageCategoryClassifier (line 82)
classifier = imageCategoryClassifier.create(imgSet, bag, varargin{:});
i have 3 categories But it says i have one category in trainingSet. what should i do?!
Which version of MATLAB are you using? This documentation is for the 2017a version. If you have an older version then running this code from the document gives you a 3x1 vector of categories.That is why the classifier treats it as one category.For 3 categories you'll need a 1x3 vector.
You can go to the command window and open the reference page on imageCategoryClassifier or on bagOfFeatures.This will give you the documentation of the version that you're running. There'll also be a link for the example MATLAB program. That's what worked for me.
Hope this helps!
An example of detectSURFFeatures in comparison of 2 image is in below. I couldn't make detectSURFFeatures function work in my MATLAB. no help or doc detectSURFFeatures gives any clue. the error says " > UncalibratedSterio
Undefined function 'detectSURFFeatures' for input arguments of type 'uint8'." but the function itself can cover uint8 as i know. what should i do?
%Rectified Sterio Image Uncalibrated
% There is no calibration of cameras
I1 = rgb2gray(imread('right_me.jpg'));
I2 = rgb2gray(imread('left_me.jpg'));
Value = 2000.0;
blobs1 = detectSURFFeatures(I1, 'MetricThreshold', Value);
blobs2 = detectSURFFeatures(I2, 'MetricThreshold', Value);
figure;
imshow(I1);
hold on;
plot(selectStrongest(blobs1, 30));
title('Thirty strongest SURF features in I1');
figure;
imshow(I2);
hold on;
plot(selectStrongest(blobs2, 30));
title('Thirty strongest SURF features in I2');
You are getting that error because detectSURFFeatures does not exist in your MATLAB distribution. You must have at least R2011b, as that was when detectSURFFeatures was available: http://www.mathworks.com/help/vision/release-notes.html#R2011b
I suspect you have an older version of MATLAB than R2011b and so if you want to make it easy on yourself, you need to upgrade your version of MATLAB. However, if I may make a suggestion, I suggest the mexopencv project by Kota Yamaguchi: http://kyamagu.github.io/mexopencv/
He wrote OpenCV wrappers that can directly interface with MATLAB and so you can actually call OpenCV's SURF feature detection and matching methods from MATLAB but you will need to install OpenCV to do that. It will be a bit of work to get it working, but this is one solution I can provide if you don't want to upgrade your version of MATLAB.
Good luck!
I was learning how to do machine learning on mldata.org and I was watching a video on Youtube on how to use the data (https://www.youtube.com/watch?v=zY0UhXPy8fM) (2:50). Using the same data, I tried to follow exactly what he did and create a scatterplot of the dataset. However when he used the scatterplot command, it worked perfectly on his side, but I cannot do it on myside.
Can anyone explain what's wrong and what I should do?
octave:2> load banana_data.octave
octave:3> pkg load communications
octave:4> whos
Variables in the current scope:
Attr Name Size Bytes Class
==== ==== ==== ===== =====
data 2x5300 84800 double
label 1x5300 42400 double
Total is 15900 elements using 127200 bytes
octave:5> scatterplot(data, label)
error: scatterplot: real X must be a vector or a 2-column matrix
error: called from:
error: /home/anthony/octave/communications-1.2.0/scatterplot.m at line 69, column 7
The error message says it all. Your data is a 2-row matrix, and not a 2-column matrix as it should be. Just transpose it with .'.
scatterplot(data.')
I dropped the label argument since it is not compatible with the communications toolbox, either in matlab or in octave.
Update:
According to news('communications'),
The plotting functions eyediagram' andscatterplot' have improved Matlab compatibility
This may be why the behaviour is different. Be ready to find other glitches, as the octave 3.2.4 used in this course is about 5 years old.
In order to use the label, you should rather use the standard octave scatter function.
Colors could be changed by choosing another colormap.
colormap(cool(256))
scatter(data(1,:), data(2,:), 6, label, "filled")