a c q u i r e t e m p e r a t u r e () >>> name exp = ’ convection ’ >>> c o n t r o l p a r a m e t e r () >>> ... and o t h e r magical s p e l l s x
a c q u i r e t e m p e r a t u r e () >>> name exp = ’ convection ’ >>> c o n t r o l p a r a m e t e r () >>> ... and o t h e r magical s p e l l s x
that scale Several weeks of tutorials! Beginners: the core of Scientific Python Advanced: learn more tricks Packages: specific applications and packages Developed and used for Euroscipy conferences Curated and enriched over the years
i n i t i o n : np. d i f f (a, n=1, a x i s =-1) D o c s t r i n g : C a l c u l a t e the n- th o r d e r d i s c r e t e d i f f e r e n c e along g i v e n a x i s . x
t i o n : np. d i f f (a, n=1, a x i s =-1) Docstring : C a l c u l a t e the n- th o r d e r d i s c r e t e d i f f e r e n c e along given a x i s . The f i r s t o r d e r d i f f e r e n c e i s given by ‘‘ out [n] = a[n+1] - a[n]‘‘ along the given axis , h i g h e r o r d e r d i f f e r e n c e s are c a l c u l a t e d by using ‘ d i f f ‘ r e c u r s i v e l y . Parameters ---------- a : a r r a y l i k e Input a r r a y n : int , o p t i o n a l The number of times v a l u e s are d i f f e r e n c e d . a x i s : int , o p t i o n a l The a x i s along which the d i f f e r e n c e i s taken , d e f a u l t i s the l a s t a x i s . Returns ------- d i f f : ndarray The ‘n‘ o r d e r d i f f e r e n c e s . The shape of the output i s the same as ‘a‘ except along ‘ axis ‘ where the dimension i s s m a l l e r by ‘n‘. See Also -------- gradient , e d i f f 1 d , cumsum Examples -------- >>> x = np. a r r a y ([1, 2, 4, 7, 0]) >>> np. d i f f (x) a r r a y ([ 1, 2, 3, -7]) >>> np. d i f f (x , n=2) a r r a y ([ 1, 1, -10]) much better now! Parameters and their type Suggestion of other functions Simple example
enthusiastic cheering from my side Documentation effort led by St´ efan van der Walt Easy as Wikipedia A wiki to improve the docs We didn’t have Github!
, long var name = ’ hi ’) : r”””A one−line summary that does not use variable names or the function name. Several sentences providing an extended description . Refer to variables using back−ticks , e . g . ‘var ‘ . Parameters − − − − − − − − − − var1 : array like Array like means all those objects − − lists , nested lists , etc . − − that can be converted to an array . We can also refer to variables like ‘var1 ‘ . var2 : int The type above can either refer to an actual Python type (e . g . ‘ ‘ int ‘ ‘) , or describe the type of the variable in more detail , e . g . ‘ ‘(N,) ndarray ‘ ‘ or ‘ ‘ array like ‘ ‘ . Long variable name : {’ hi ’ , ’ho ’} , optional Choices in brackets , default f i r s t when optional . Returns − − − − − − − type Explanation of anonymous return value of type ‘ ‘type ‘ ‘ . describe : type Explanation of return value named ‘ describe ‘ . out : type Explanation of ‘out ‘ . Other Parameters − − − − − − − − − − − − − − − − only seldom used keywords : type Explanation common parameters listed above : type Explanation
Numpy reference: 8600 → 140,000 New contributors: 250 accounts Lower entry barrier to contribute Increased the standard for other packages Made people proud about docs
Numpy reference: 8600 → 140,000 New contributors: 250 accounts Lower entry barrier to contribute Increased the standard for other packages Made people proud about docs
for scientific experiment http://sdo.gsfc.nasa.gov/ Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning
outputs >>> from skimage import io , f i l t e r s >>> c a m e r a a r r a y = i o . imread ( ’ camera image . png ’ ) >>> type( c a m e r a a r r a y ) <type ’numpy . ndarray ’ > >>> c a m e r a a r r a y . dtype dtype ( ’ uint8 ’ ) >>> f i l t e r e d a r r a y = f i l t e r s . g a u s s i a n f i l t e r ( camera array , sigma =5) >>> type( f i l t e r e d a r r a y ) <type ’numpy . ndarray ’ > >>> import m a t p l o t l i b . p y p l o t as p l t >>> p l t .imshow( f i l t e r e d a r r a y , cmap= ’ gray ’ ) x
l t e r s . g a u s s i a n f i l t e r (image , sigma , output = None, mode= ’ n e a r e st ’ , c v a l =0, m u l t i c h a n n e l =None) Multi - d i m e n s i o n a l Gaussian filter Parameters ---------- image : array - l i k e input image ( g r a y s c a l e or c o l o r ) to filter. sigma : s c a l a r or sequence of s c a l a r s st and ard d e v i a t i o n f o r Gaussian k e r n e l . The st and ard d e v i a t i o n s of the Gaussian filter are g i v e n f o r each a x i s as a sequence , or as a s i n g l e number , in which case i t i s equal f o r all axes . output : array , o p t i o n a l The ‘‘ output ‘‘ parameter p a s s e s an a r r a y in which to s t o r e the filter output . mode : { ’ r e f l e c t ’ , ’ constant ’ , ’ n ea re st ’ , ’ mirror ’ , ’ wrap ’ }, o p t i o n a l One filter = one function Use keyword argument for parameter tuning
features (skimage.feature) Pixels intensity values (R, G, B) Local gradients More advanced descriptors: HOGs, Gabor, ... Train classifier with known regions here, random forest classifier Classify pixels
per year) Among 1000 best ranked packages on PyPi Development model Mature algorithms Only Python + Cython code for easier maintainability Focus on good practices: testing, documentation, version control Hosted on GitHub: thorough code reivew + continuous integration Core team of 5 − 10 persons (close to applications)
and smooth learning curve vs cool factor and bleeding-edge tools Feature development should not be faster than quality improvement Documentation and training for users Low entry barriers for contributors
use GPUs, Spark, etc. scikit-image uses NumPy! I/O: large images might not fit into memory use memory mapping of different file formats (raw binary with NumPy, hdf5 with pytables). Divide into blocks: use util.view as blocks to iterate conveniently over blocks Parallel processing: use joblib or dask Better integration desirable
skimage import data <>> hubble = data . h u b b l e d e e p f i e l d () >>> width = 10 >>> p i c s = u t i l . view as windows ( hubble , ( width , hubble . shape [1], hubble . shape [2]) , s t e p = width ) >>> from j o b l i b import P a r a l l e l , d e l a y e d >>> # task is an image processing function >>> P a r a l l e l ( n j o b s =4)( d e l a y e d ( t a s k )( p i c ) f o r p i c in p i c s ) x
this mess? from skimage import f i l t e r s # Comment to save some time # filter_im = filters.median(im) # binary_im = filters. threshold_otsu (filter_im) v a l u e s = np. unique (im) x
this mess? from skimage import f i l t e r s # Comment to save some time # filter_im = filters.median(im) # binary_im = filters. threshold_otsu (filter_im) v a l u e s = np. unique (im) x >>> from j o b l i b import Memory >>> mem = Memory( c a c h e d i r = ’ /tmp/ j o b l i b ’ ) >>> square = mem. cache (np. square ) >>> b = square (a) [Memory] C a l l i n g square ... square ( a r r a y ([[ 0., 0., 1.], [ 1., 1., 1.], [ 4., 2., 1.]])) s q u a r e - 0... s , 0.0 min >>> c = square (a) >>> # The above call did not trigger an evaluation x
OSS Social and pedagogical skills useful and welcome You will learn a lot and make friends. P. Hintjens Try it out! http://scikit-image.org/ Feedback welcome github.com/scikit-image/scikit-image Please cite the paper Let’s talk about scikit-image @EGouillart
emmanuelle
jbusecke
brainpadpr
wemeet
kazh
sonod
dscs
hayataka88
konakalab
ynakano
takehikoihayashi
yamahiro
kanojikajino
morganepeng
mraible
keavy
vanstee
samanthasiow
nonsquared
chriscoyier
jnunemaker
holman
addyosmani
pauljervisheath
