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,189,199,202,238,240,246,247,251],yearli:186,yearly_temp:186,years_nb:[193,194,199,202],yellow:75,yep:52,yes:[64,83],yet:[9,10,67,74,238],yield:[2,9,10,52,252],yinit:[167,179],ylabel:[40,41,47,60,160,165,173,180,184,186,189,193,194,195,196,197,198,199,201,222,223,227,228,229,231,244],ylim:[40,43,47,48,50,86,87,88,89,90,91,92,93,94,96,98,102,104,111,112,116,118,124,128,129,135,141,143,145,146,148,222],ymax:[40,203,205,222],ymin:[40,43,48,50,203,205,222],yml:[2,252],yolk:84,yosh:[0,252],you:[2,4,8,10,36,38,51,52,67,71,74,75,76,77,78,79,80,83,84,148,149,150,161,162,165,166,167,188,201,202,203,204,219,238,249,250,251,252],young:74,your:[4,8,9,10,36,37,38,52,67,70,71,73,74,75,76,77,78,79,82,84,148,150,162,167,190,203,219,238,249],yourself:[10,38,78,83],ypo:[167,180],ytest:238,ytick:[12,26,41,47,86,87,88,90,91,92,93,94,96,97,98,99,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,120,121,122,123,124,126,128,129,130,131,132,133,134,135,136,137,139,141,142,143,145,146,147,148,173,221,222,225,226,235,238],ytrain:238,yvec:[167,179,180],z_in:8,z_out:8,zachari:[0,1,252],zbigniew:[0,1,9,252],zdir:[125,142],zero:[1,8,10,12,15,16,21,22,24,25,26,28,30,34,36,38,45,46,51,52,58,60,61,72,76,77,112,136,150,161,165,167,177,187,188,211,215,219,226,237,238,240,249,251,252],zerodivisionerror:77,zip:[6,10,11,36,39,41,51,65,95,116,127,144,148,151,163,167,168,192,207,219,220,232,236,238,239,249],zipfil:9,zmax:[203,205],zmin:[203,205],zoom:[36,72,167,175],zoomed_fac:[72,167,175],zorder:[135,136,137,138,139,140,141,143,145,146,147,221,235]},titles:["4.1.1. Authors","What\u2019s new","Contributing","License","Scipy-Lecture-Notes","4. About the SciPy lecture notes","Examples for the advanced numpy chapter","Example: Masked statistics","2.2. Advanced NumPy","2.1. Advanced Python Constructs","2.3. Debugging code","Examples for the image processing chapter","2.6.8.21. Segmentation with Gaussian mixture models","2.6.8.5. Plot the block mean of an image","2.6.8.8. Blurring of images","2.6.8.20. Cleaning segmentation with mathematical morphology","2.6.8.15. Denoising an image with the median filter","2.6.8.6. Display a Racoon Face","2.6.8.1. Displaying a Racoon Face","2.6.8.11. Image denoising","2.6.8.18. Total Variation denoising","2.6.8.17. Finding edges with Sobel filters","2.6.8.12. Find the bounding box of an object","2.6.8.14. Geometrical transformations","2.6.8.23. Granulometry","2.6.8.19. Greyscale dilation","2.6.8.16. Histogram segmentation","2.6.8.2. Image interpolation","2.6.8.13. Measurements from images","2.6.8.3. Image manipulation and numpy arrays","2.6.8.10. Opening, erosion, and propagation","2.6.8.4. Radial mean","2.6.8.7. Image sharpening","2.6.8.24. Segmentation with spectral clustering","2.6.8.9. Synthetic data","2.6.8.22. Watershed segmentation","2.6. Image manipulation and processing using Numpy and Scipy","2. Advanced topics","2.8. Interfacing with C","Examples for the mathematical optimization chapter","2.7.4.7. Brent\u2019s method","2.7.4.9. Plotting the comparison of optimizers","2.7.4.8. Constraint optimization: visualizing the geometry","2.7.4.4. Convex function","2.7.4.3. Curve fitting","2.7.4.5. Finding a minimum in a flat neighborhood","2.7.4.10. Alternating optimization","2.7.4.11. Gradient descent","2.7.4.1. Noisy optimization problem","2.7.4.6. Optimization with constraints","2.7.4.2. Smooth vs non-smooth","2.7. Mathematical optimization: finding minima of functions","2.4. Optimizing code","Block Compressed Row Format (BSR)","Coordinate Format (COO)","Compressed Sparse Column Format (CSC)","Compressed Sparse Row Format (CSR)","Diagonal Format (DIA)","Dictionary of Keys Format (DOK)","2.5. Sparse Matrices in SciPy","2.5.1. Introduction","List of Lists Format (LIL)","2.5.4. Other Interesting Packages","2.5.3. Linear System Solvers","2.5.2. Storage Schemes","Examples for the contribution guide","A simple example","How to contribute","<no title>","<no title>","Scipy Lecture Notes","1.7. Getting help and finding documentation","Geometrical transformations on images","1. Getting started with Python for science","1.1. Python scientific computing ecosystem","1.2.2. Basic types","1.2.3. Control Flow","1.2.8. Exception handling in Python","1.2.1. First steps","1.2.4. Defining functions","1.2.6. Input and Output","1.2.9. Object-oriented programming (OOP)","1.2. The Python language","1.2.5. Reusing code: scripts and modules","1.2.7. Standard Library","Exercise 1","Exercise","Exercise 2","Exercise 3","Exercise 4","Exercise 5","Exercise 6","Exercise 7","Exercise 8","Exercise 9","Code samples for Matplotlib","Aliased versus anti-aliased","Alpha: transparency","Aliased versus anti-aliased","The colors matplotlib line plots","Colormaps","Dash capstyle","Dash join style","Linestyles","Linewidth","Markers","Marker edge color","Marker edge width","Marker face color","Marker size","Solid cap style","Solid joint style","Locators for tick on axis","Simple axes example","Axes","A simple plotting example","Bar plots","Displaying the contours of a function","A simple, good-looking plot","Grid","GridSpec","Imshow elaborate","Subplots","Pie chart","Plot and filled plots","3D plotting","3D plotting","Plotting in polar coordinnates","Plotting a vector field: quiver","Plotting a scatter of points","Subplot grid","Horizontal arrangement of subplots","Subplot plot arrangement vertical","Demo text printing","A example of plotting not quite right","Bar plot advanced","Boxplot with matplotlib","Display the contours of a function","Grid elaborate","Imshow demo","Multiple plots vignette","Pie chart vignette","3D plotting vignette","Plot example vignette","Plotting in polar, decorated","Plotting quiver decorated","Plot scatter decorated","Text printing decorated","1.5. Matplotlib: plotting","1.4.4. Advanced operations","1.4.1. The NumPy array object","Full code examples for the numpy chapter","1D plotting","2D plotting","Fitting in Chebyshev basis","Distances exercise","Reading and writing an elephant","Mandelbrot set","Fitting to polynomial","Population exercise","Random walk exercise","1.4.3. More elaborate arrays","1.4.5. Some exercises","1.4.6. Full code examples","1.4. NumPy: creating and manipulating numerical data","1.4.2. Numerical operations on arrays","1.3. Python 2 and Python 3","1.6. Scipy : high-level scientific computing","Full code examples for the scipy chapter","1.6.12.16. Optimization of a two-parameter function","1.6.12.13. Demo connected components","1.6.12.8. Curve fitting","1.6.12.2. Detrending a signal","1.6.12.17. Plotting and manipulating FFTs for filtering","1.6.12.15. Plot filtering on images","1.6.12.12. Plot geometrical transformations on images","1.6.12.10. A demo of 1D interpolation","1.6.12.11. Demo mathematical morphology","1.6.12.7. Normal distribution: histogram and PDF","1.6.12.6. Integrate the Damped spring-mass oscillator","1.6.12.4. Integrating a simple ODE","1.6.12.1. Finding the minimum of a smooth function","1.6.12.14. Minima and roots of a function","1.6.12.3. Resample a signal with scipy.signal.resample","1.6.12.9. Spectrogram, power spectral density","1.6.12.5. Comparing 2 sets of samples from Gaussians","Curve fitting: temperature as a function of month of the year","Image denoising by FFT","Simple image blur by convolution with a Gaussian kernel","Crude periodicity finding","Solutions","1.6.11.4. Example of solution for the image processing exercise: unmolten grains in glass","Examples for the summary excercices","Cumulative wind speed prediction","The Gumbell distribution","The lidar system, data (2 of 2 datasets)","The lidar system, data and fit (2 of 2 datasets)","The lidar system, data (1 of 2 datasets)","The lidar system, data and fit (1 of 2 datasets)","The Gumbell distribution, results","1.6.11.3. Image processing application: counting bubbles and unmolten grains","1.6.11.2. Non linear least squares curve fitting: application to point extraction in topographical lidar data","1.6.11.1. Maximum wind speed prediction at the Sprog\u00f8 station","3.5. 3D plotting with Mayavi","Interactive work","Mlab: the scripting interface","3. Packages and applications","Examples for the scikit-image chapter","3.3.9.6. Segmentation contours","3.3.9.2. Displaying a simple image","3.3.9.3. Integers can overflow","3.3.9.1. Creating an image","3.3.9.4. Equalizing the histogram of an image","3.3.9.9. Affine transform","3.3.9.10. Various denoising filters","3.3.9.8. Labelling connected components of an image","3.3.9.11. Watershed and random walker for segmentation","3.3.9.5. Computing horizontal gradients with the Sobel filter","3.3.9.7. Otsu thresholding","3.3. Scikit-image: image processing","Examples for the scikit-learn chapter","3.6.10.17. Tutorial Diagrams","3.6.10.16. Bias and variance of polynomial fit","3.6.10.11. A simple regression analysis on the California housing data","3.6.10.9. Compare classifiers on the digits data","3.6.10.13. Simple visualization and classification of the digits dataset","3.6.10.14. The eigenfaces example: chaining PCA and SVMs","3.6.10.12. Nearest-neighbor prediction on iris","3.6.10.4. Plot 2D views of the iris dataset","3.6.10.6. Use the RidgeCV and LassoCV to set the regularization parameter","3.6.10.3. A simple linear regression","3.6.10.1. Measuring Decision Tree performance","3.6.10.2. Demo PCA in 2D","3.6.10.10. Plot fitting a 9th order polynomial","3.6.10.8. Simple picture of the formal problem of machine learning","3.6.10.15. Example of linear and non-linear models","3.6.10.5. tSNE to visualize digits","3.6.10.7. Plot variance and regularization in linear models","3.6. scikit-learn: machine learning in Python","Full code for the figures","3.1.6.8. Air fares before and after 9/11","3.1.6.3. Analysis of Iris petal and sepal sizes","3.1.6.1. Boxplots and paired differences","3.1.6.2. Plotting simple quantities of a pandas dataframe","3.1.6.4. Simple Regression","3.1.6.5. Multiple Regression","3.1.6.7. Visualizing factors influencing wages","3.1.6.6. Test for an education/gender interaction in wages","3.1.7.1. Relating Gender and IQ","3.1. Statistics in Python","3.2. Sympy : Symbolic Mathematics in Python","3.4. Traits: building interactive dialogs","About the scipy lecture notes","<no title>"],titleterms:{"2nd":51,"9th":233,"boolean":150,"break":[76,166],"case":51,"catch":[9,77],"class":[9,63,64],"default":148,"final":[77,187],"float":67,"function":[8,9,43,51,74,79,84,117,137,150,166,167,169,181,182,186,188,203,205],"import":[83,150],"long":161,"new":[1,166,252],"return":79,"short":166,"sprog\u00f8":202,"static":10,"try":77,"while":[9,76],Adding:[148,165],Axes:[114,148],Doing:[226,238],Going:201,ODE:180,One:70,The:[8,63,74,82,99,150,167,186,188,190,194,195,196,197,198,199,203,204,226,238,249],Use:229,Using:[9,10,67,150,238,250],__main__:83,__next__:9,about:[51,252],acknowledg:238,across:249,adapt:51,addit:[0,52,252],admonit:67,advanc:[6,8,9,37,76,135,149,251],affin:213,after:240,air:240,algebra:[52,167,250],algorithm:[51,52,165],alias:[96,98],all:[173,251],alpha:97,altern:[10,46],analysi:[10,223,226,238,241,249],ani:76,anim:203,annot:148,anova:249,anti:[96,98],api:38,applic:[60,200,201,206,238],approach:[202,238],approxim:[51,162],apr:[1,252],arbitrari:[203,205],arrai:[8,29,36,150,161,162,165,249],arrang:[131,132],ask:77,assign:75,attribut:9,audienc:251,august:[1,252],author:[0,252],automat:238,avoid:10,awar:8,axes:113,axi:[112,148,225],background:219,bar:[116,135,148],base:[51,149,219],basi:154,basic:[36,75,150,165,238],bay:[225,238],befor:[74,240],best:10,better:187,between:[166,249],beyond:148,bia:[222,238],bidirect:9,binari:219,block:[8,13,53],blur:[14,36,188],boost:238,bound:[22,51,167],box:[22,51],boxplot:[136,242],brent:40,brian:10,broadcast:[8,165],brute:51,bsr:53,bubbl:200,buffer:8,bug:[8,10],build:[2,4,8,251,252],built:238,button:[203,204],cach:8,calcul:250,calculu:250,california:223,can:210,cap:110,capstyl:101,cast:[8,161],categor:249,categori:249,caution:238,chain:[9,162,226,238],chang:[148,166,203,205,251],channel:156,chapter:[0,2,6,8,9,10,11,36,38,39,51,52,67,95,148,151,163,167,168,203,207,219,220,238,239,249,250,252],chararrai:8,chart:[123,141,148],chebyshev:154,checker:10,chirp:184,choic:[2,95,148,252],choos:51,classif:[225,238],classifi:[224,225,238],clean:15,clear:67,cluster:33,code:[10,36,51,52,67,83,95,148,151,163,167,168,219,238,239,249],collaps:67,color:[99,106,108,148],colormap:[100,148],colorspac:219,column:55,command:[10,84],common:[63,64],commun:9,compar:[74,167,185,224,249],comparison:[41,219],compil:74,complex:[238,251],compon:[72,167,170,215,219,226,238],comprehens:76,compress:[53,55,56],comput:[51,52,74,82,165,167,173,184,187,202,217,219],concept:219,concis:67,condit:[51,76],conjug:51,connect:[72,167,170,215,219],constraint:[42,49,51],construct:[9,150],contain:75,content:[8,9,10,36,38,51,52,67,148,149,150,161,165,167,203,219,238,249,250,251],context:9,continu:[76,219],contour:[117,137,148,208],contribut:[0,2,4,8,65,67,252],control:76,conveni:8,convent:150,convert:249,convex:[43,51],convolut:188,coo:54,coordin:54,coordinn:127,copi:[9,150],correct:238,cost:51,count:200,cprofil:52,cpu:8,creat:[83,150,164,211,249],creation:150,cross:238,crude:[162,189],csc:55,csr:56,ctype:38,cumul:[193,202],current:10,curv:[44,51,167,171,186,201,222,238],cython:38,damp:179,dash:[101,102],data:[8,34,70,149,150,161,162,164,165,167,186,189,190,195,196,197,198,201,203,205,219,223,224,225,238,249],datafram:[243,249],dataset:[195,196,197,198,225,228,238],deal:161,debian:[2,252],debug:10,debugg:10,decis:231,decor:[9,144,145,146,147,203,205],deferr:251,defin:[9,79,182],definit:79,demo:[95,133,139,148,170,176,177,232],denois:[16,19,20,36,167,187,214,219],densiti:[167,184],deprec:9,descent:[47,51],descriptor:8,design:[2,252],detail:148,detect:36,detrend:172,devil:148,dia:57,diagon:[8,57],diagram:221,dialog:[203,204,251],dice:203,dictionari:[58,75,76],differ:[51,161,203,242,249],differenti:[51,167,250],diffus:165,digit:[224,225,236,238],dilat:25,dimens:[8,165,238],dimension:[51,238],direct:63,directori:[84,190],discov:75,discret:219,displai:[17,18,36,117,137,156,209],dissect:8,distanc:155,distribut:[4,167,178,194,199,202],divis:166,docstr:[9,79,203,205],document:[8,67,70,71,148,150,251],doe:74,dok:58,domain:8,donald:52,download:70,earlier:219,easi:84,easier:[8,77,187],ecosystem:[74,219],edg:[21,36,106,107],edit:10,editor:[0,74,252],educ:247,effect:8,eigen:63,eigenfac:[226,238],eigenvalu:63,elabor:[74,121,138,161],elementwis:165,eleph:156,elev:[203,205],elif:76,els:76,embed:238,enhanc:219,enumer:76,environ:[74,84],equal:212,equat:[167,250],eros:30,error:10,estim:238,etc:74,event:203,exact:51,exampl:[2,6,7,8,9,11,36,38,39,51,52,53,54,55,56,57,58,61,63,65,66,67,95,113,115,134,143,148,150,151,163,165,167,168,191,192,203,205,207,219,220,226,235,238,251,252],except:[9,10,77],excercic:[192,203],execut:10,exercic:[51,203],exercis:[8,10,36,38,51,72,76,79,84,85,86,87,88,89,90,91,92,93,94,95,148,149,150,155,159,160,162,165,167,168,191,202,203,219,238,239,249,250],expand:250,expans:250,explor:249,express:[9,76],extern:84,extract:[36,201,219],face:[17,18,108,162],facial:226,factor:[246,249],fake:8,fanci:150,fare:240,fast:[10,167],faster:[51,52],fault:10,featur:[8,36,166,219],feb:[1,252],feel:249,few:63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