repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15 values |
|---|---|---|---|---|---|
deapplegate/wtgpipeline | CRNitschke/StarStripper.py | 1 | 14983 | #! /usr/bin/env python
import sys ; sys.path.append('/u/ki/awright/InstallingSoftware/pythons/')
import imagetools
from import_tools import *
import numpy
numpy.warnings.filterwarnings('ignore') #adam-tmp#
warnings.simplefilter("ignore", DeprecationWarning)
fl=sys.argv[-1]
ending=""
#fl='/nfs/slac/g/ki/ki18/anja/SUBARU/MACS0416-24/W-S-Z+_2010-11-04/SCIENCE/SUPA0125892_7OCF.fits'
#crfl='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/data_SCIENCE_cosmics/SEGMENTATION_BB_CRN-cosmics_MACS0416-24_W-S-Z+.SUPA0125892_7.fits'
header=astropy.io.fits.open(fl)[0].header
OBJECT=header['MYOBJ']
FILTER=header['FILTER']
CCDnum=header['IMAGEID']
flname=os.path.basename(fl).split('.')[0]
if 'OCF' in fl:
BASE=os.path.basename(fl).split('OCF')[0]
else:
BASE=flname
OFB='%s_%s_%s' % (OBJECT,FILTER,BASE,)
image=imagetools.GetImage(fl)
compare_dir='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/data_SCIENCE_compare/'
plot_dir='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/plot_SCIENCE_SS/'
BBCRfl='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/data_SCIENCE_cosmics/SEGMENTATION_BB_CRN-cosmics_%s_%s.%s%s.fits' % (OBJECT,FILTER,BASE,ending)
CR_segfl='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/data_SCIENCE_cosmics/SEGMENTATION_CRN-cosmics_%s_%s.%s%s.fits' % (OBJECT,FILTER,BASE,ending)
CR_filtfl='/nfs/slac/g/ki/ki18/anja/SUBARU/eyes/CRNitschke_output/data_SCIENCE_cosmics/FILTERED_CRN-cosmics_%s_%s.%s%s.fits' % (OBJECT,FILTER,BASE,ending)
#fl_original=compare_dir+'BBout_ORIGINAL_%s_%s.%s.fits' % (OBJECT,FILTER,BASE)
fl_woblend=compare_dir+'BBout_WOblend_%s_%s.%s%s.fits' % (OBJECT,FILTER,BASE,ending)
fl_revised=compare_dir+'BBrevised_*_BBCR_%s_%s.%s%s.fits' % (OBJECT,FILTER,BASE,ending)
#fl_erase=compare_dir+'BB_ERASED_'+bthresh1_tag+'_BBCR_%s_%s.%s.fits' % (OBJECT,FILTER,BASE)
#fl_revised=compare_dir+'BBrevised_'+bthresh1_tag+'_BBCR_%s_%s.%s.fits' % (OBJECT,FILTER,BASE)
#im_erased=astropy.io.fits.open(fl_erased)[0].data
#im_erased.max()
BBCRseg=imagetools.GetImage(BBCRfl)
BBCRseg=asarray(BBCRseg,dtype=int)
crheader=astropy.io.fits.open(BBCRfl)[0].header
SEEING=crheader['MYSEEING']
SEEING_str=('%.3f' % (SEEING)).replace('.','pt')
OFB_SEEING=OFB.replace('SUPA',SEEING_str+'_SUPA')
filtim=imagetools.GetImage(CR_filtfl)
#put SEEING and FILTER dependent kmax cut in here:
import os
CONFIG=os.environ['config']
if CONFIG=="10_3":
if FILTER=="W-C-RC":
if SEEING<0.50: kmax=3.50 #based on GUESSING
elif SEEING<0.60: kmax=2.85 #based on GUESSING
elif SEEING<0.70: kmax=2.70 #based on MACS1226 data
else: kmax=2.70
elif FILTER=="W-S-I+":
if SEEING<0.50: kmax=3.50 #based on GUESSING
elif SEEING<0.60: kmax=2.85 #based on GUESSING
elif SEEING<0.77: kmax=2.70 #based on MACS1226 data
elif SEEING<0.90: kmax=2.55 #based on MACS1226 data
else: kmax=2.55
elif FILTER=="W-C-IC":
if SEEING<0.50: kmax=3.50 #based on GUESSING
elif SEEING<0.60: kmax=2.85 #based on GUESSING
elif SEEING<0.80: kmax=2.70 #based (loosely) on MACS1226 data
else: kmax=2.70
elif FILTER=="W-S-Z+":
if SEEING<0.60: kmax=4.10 #based on MACS0416 data
elif SEEING<0.70: kmax=3.30 #based on GUESSING
elif SEEING<0.85: kmax=2.75 #based on MACS1226 data
elif SEEING<1.2: kmax=2.55 #based (loosely) on MACS1226 data
else: kmax=2.55
else:
kmax=3.00
elif CONFIG=="10_2":
if SEEING<0.50: kmax=4.10 #based on GUESSING
elif SEEING<0.70: kmax=3.30 #based on GUESSING
elif SEEING<1.00: kmax=2.85 #based on MACS1226 data
else: kmax=2.85
else:
raise Exception("SUBARU configuration isn't defined")
## get properties of the masks
import skimage
from skimage import measure
cr_regs=skimage.measure.regionprops(label_image=BBCRseg, intensity_image=image)
cr_labels=arange(BBCRseg.max(),dtype=int)+1
cr_e=asarray([cr_regs[i-1].eccentricity for i in cr_labels])
cr_diam=asarray([cr_regs[i-1].equivalent_diameter for i in cr_labels])
cr_solidity=asarray([cr_regs[i-1].solidity for i in cr_labels])
cr_max=asarray([cr_regs[i-1].max_intensity for i in cr_labels])
cr_mean=asarray([cr_regs[i-1].mean_intensity for i in cr_labels])
cr_area=asarray([cr_regs[i-1].area for i in cr_labels])
conn8=ones((3,3),dtype=bool)
CRslices=scipy.ndimage.find_objects(BBCRseg)
def cr_any_label(labels):
boolim=zeros(BBCRseg.shape,dtype=bool)
for l in labels:
boolim+=BBCRseg==l
return boolim
## see if skewness and kurtosis does anything
def skew_kurt_2D(Z):
h,w = np.shape(Z)
x = range(w)
y = range(h)
X,Y = np.meshgrid(x,y)
#Centroid (mean)
cx = np.sum(Z*X)/np.sum(Z)
cy = np.sum(Z*Y)/np.sum(Z)
###Standard deviation
x2 = (range(w) - cx)**2
y2 = (range(h) - cy)**2
X2,Y2 = np.meshgrid(x2,y2)
#Find the variance
vx = np.sum(Z*X2)/np.sum(Z)
vy = np.sum(Z*Y2)/np.sum(Z)
#SD is the sqrt of the variance
sx,sy = np.sqrt(vx),np.sqrt(vy)
###Skewness
x3 = (range(w) - cx)**3
y3 = (range(h) - cy)**3
X3,Y3 = np.meshgrid(x3,y3)
#Find the thid central moment
m3x = np.sum(Z*X3)/np.sum(Z)
m3y = np.sum(Z*Y3)/np.sum(Z)
#Skewness is the third central moment divided by SD cubed
skx = m3x/sx**3
sky = m3y/sy**3
###Kurtosis
x4 = (range(w) - cx)**4
y4 = (range(h) - cy)**4
X4,Y4 = np.meshgrid(x4,y4)
#Find the fourth central moment
m4x = np.sum(Z*X4)/np.sum(Z)
m4y = np.sum(Z*Y4)/np.sum(Z)
#Kurtosis is the fourth central moment divided by SD to the fourth power
kx = m4x/sx**4
ky = m4y/sy**4
#Centroid x: cx #Centroid y: cy
#StdDev x: sx #StdDev y: sy
#Skewness x: skx #Skewness y: sky
#Kurtosis x: kx #Kurtosis y: ky
return skx,sky,kx,ky
cr_skxs,cr_skys,cr_kxs,cr_kys=[],[],[],[]
## make the final cuts
MaxInside8s=[]
removed_labels=[]
for i,sl in enumerate(CRslices):
l=i+1
spots=BBCRseg[sl]==l
patch=image[sl]
max_pos_pt=scipy.ndimage.measurements.maximum_position(patch,spots)
max_spot=zeros(patch.shape,dtype=bool)
max_spot[max_pos_pt]=1
#now make sure max isn't on the edge and is in an open8 portion
insides_spots=scipy.ndimage.binary_erosion(spots,conn4)
open8_spots=scipy.ndimage.binary_opening(spots,conn8)
MaxInside8=(max_spot*insides_spots*open8_spots).any()
MaxInside8s.append(MaxInside8)
#now get clipped eccentricity
clip_spots=binary_propagation(max_spot,mask=spots)
clip_spots=asarray(clip_spots,dtype=int16)
try:
reg=skimage.measure.regionprops(clip_spots)[0]
except TypeError:
if 1 in clip_spots.shape:
cr_skxs.append(nan);cr_skys.append(nan);cr_kxs.append(nan);cr_kys.append(nan)
continue
e_clip=reg.eccentricity
e_orig=cr_e[i]
#now get skewness and kurtosis
skx,sky,kx,ky=skew_kurt_2D(patch-patch.min())
cr_skxs.append(skx);cr_skys.append(sky);cr_kxs.append(kx);cr_kys.append(ky)
if e_clip>e_orig:
cr_e[i]=e_clip
if e_clip>.8 and e_orig<.8:
removed_labels.append(l)
#######Xspots_starlike=cr_any_label(cr_labels[starlike])
#######Xspots_not_starlike=cr_any_label(cr_labels[logical_not(starlike)])
#######CLIPseg,CLIPseg_Nlabels=scipy.ndimage.label(Xspots_starlike,conn8)
#######CLIPslices=scipy.ndimage.find_objects(CLIPseg)
########f=figure()
########skx,sky,kx,ky=skew_kurt_2D(sp);f.add_subplot(321);title('sp: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(sp,interpolation='nearest',origin='lower left')
########skx,sky,kx,ky=skew_kurt_2D(sp);f.add_subplot(322);title('sp: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(sp,interpolation='nearest',origin='lower left')
########skx,sky,kx,ky=skew_kurt_2D(p);f.add_subplot(323);title('p: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(p,interpolation='nearest',origin='lower left')
########skx,sky,kx,ky=skew_kurt_2D(pppp);f.add_subplot(324);title('pppp: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(pppp,interpolation='nearest',origin='lower left')
########skx,sky,kx,ky=skew_kurt_2D(pp);f.add_subplot(325);title('pp: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(pp,interpolation='nearest',origin='lower left')
########skx,sky,kx,ky=skew_kurt_2D(ppp);f.add_subplot(326);title('ppp: skx=%.2f,sky=%.2f,kx=%.2f,ky=%.2f' % (skx,sky,kx,ky));imshow(ppp,interpolation='nearest',origin='lower left')
########show()
cr_skxs=asarray(cr_skxs).__abs__();cr_skys=asarray(cr_skys).__abs__();cr_kxs=asarray(cr_kxs).__abs__();cr_kys=asarray(cr_kys).__abs__()
cr_kmax=asarray([max(ky,kx) for ky,kx in zip(cr_kys,cr_kxs)])
cr_skmax=asarray([max(sky,skx) for sky,skx in zip(cr_skys,cr_skxs)])
MaxInside8s=asarray(MaxInside8s)
removed_labels=asarray(removed_labels)
starlike=(cr_e<.8)*(cr_area>9)*(cr_area<50)*(cr_max<30000)*MaxInside8s*(cr_kmax<kmax)*(cr_skmax<.88)
starlike_labels=cr_labels[starlike]
Xspots_starlike=cr_any_label(starlike_labels)
Xspots_not_starlike=cr_any_label(cr_labels[logical_not(starlike)])
## save plots of star postage stamps and things that missed the cut
#params=['e=%.2f , area=%i' % (cr_e[i],cr_area[i]) for i in cr_labels-1]
#params=['skxy=%.2f/%.2f|kxy=%.2f/%.2f' % (cr_skxs[i],cr_skys[i],cr_kxs[i],cr_kys[i]) for i in cr_labels-1]
params=['sk=%.2f|k=%.2f' % (cr_skmax[i],cr_kmax[i]) for i in cr_labels-1]
import img_scale
zmin,zmax,ziter=img_scale.range_from_zscale(image,contrast=.25)
fig=imagetools.plotlabels(ll=starlike_labels,segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Possible Stars Picked from blocked_blender.2.2.py Masks\neccentricity<.8 & 9<area<50 & max intensity<30,000 & 3x3 inside mask shape')
fig.savefig(plot_dir+'pltSS_Stars_'+OFB_SEEING)
if len(removed_labels):
fig=imagetools.plotlabels(ll=removed_labels,segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Not Starlike: eccentricity<.8 & when clipped eccentricity>.8')
fig.savefig(plot_dir+'pltSS_NotStars-Clip_e_raise_'+OFB_SEEING)
starlike_not8=(cr_e<.8)*(cr_area>5)*(cr_area<50)*(cr_max<30000)*logical_not(MaxInside8s)*(cr_kmax<kmax)*(cr_skmax<.88)
if starlike_not8.any():
fig=imagetools.plotlabels(cr_labels[starlike_not8],segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Not Starlike: Would be starlike, but no conn8 in the shape')
fig.savefig(plot_dir+'pltSS_NotStars-open8_'+OFB_SEEING)
kmax_ul=5.0
starlike_k=(cr_e<.8)*(cr_area>9)*(cr_area<50)*(cr_max<30000)*MaxInside8s*(cr_kmax>kmax)*(cr_kmax<kmax_ul)*(cr_skmax<.88)
if starlike_k.sum()>40:
kmax_ul=4.1
starlike_k=(cr_e<.8)*(cr_area>9)*(cr_area<50)*(cr_max<30000)*MaxInside8s*(cr_kmax>kmax)*(cr_kmax<kmax_ul)*(cr_skmax<.88)
if starlike_k.sum()>40:
kmax_ul=max(kmax+.3,3.0)
starlike_k=(cr_e<.8)*(cr_area>9)*(cr_area<50)*(cr_max<30000)*MaxInside8s*(cr_kmax>kmax)*(cr_kmax<kmax_ul)*(cr_skmax<.88)
if starlike_k.any():
fig=imagetools.plotlabels(ll=cr_labels[starlike_k],segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Not Starlike: k>kmax=%.2f' % kmax)
fig.savefig(plot_dir+'pltSS_NotStars-k_gt_kmax_'+OFB_SEEING)
starlike_gt30000=(cr_e<.8)*(cr_area>9)*(cr_area<50)*MaxInside8s*(cr_max>30000)*(cr_kmax<kmax)*(cr_skmax<.88)
if starlike_gt30000.any():
fig=imagetools.plotlabels(ll=cr_labels[starlike_gt30000],segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Not Starlike: Greater than 30,000')
fig.savefig(plot_dir+'pltSS_NotStars-gt_than_30000_'+OFB_SEEING)
starlike_skew_kurt=(cr_e<.8)*(cr_area>9)*(cr_area<50)*(cr_max<30000)*MaxInside8s*((cr_kmax>=kmax)+(cr_skmax>=.88))
if starlike_skew_kurt.any():
print 'skewness and kurtosis cut removed: ',starlike_skew_kurt.sum()
fig=imagetools.plotlabels(ll=cr_labels[starlike_skew_kurt],segments=BBCRseg,slices=CRslices,params=params,background=image,zscale=(zmin,zmax))
fig.suptitle('Not Starlike: too skewed or large kurtosis')
fig.savefig(plot_dir+'pltSS_NotStars-skew_kurt'+OFB_SEEING)
#f=imagetools.ImageWithSpots([image,filtim],Xspots_starlike,name1='image',name2='filtered image',nameX='Possible Stars',ignore_scale=True,mode='box')
#f.savefig(plot_dir+'pltSS_Star_Candidates-full_image_'+OFB_SEEING)
## Save KeepOrRM image and the final image with masks included
KeepOrRM=zeros(Xspots_starlike.shape,dtype=int)
KeepOrRM[Xspots_starlike]=-1
KeepOrRM[Xspots_not_starlike]=1
hdu=astropy.io.fits.PrimaryHDU(asarray(KeepOrRM,dtype=int))
hdu.header=crheader
fl_KeepOrRM=BBCRfl.replace('SEGMENTATION_BB_CRN-cosmics','SEGMENTATION_KeepOrRM-starlike_cosmics')
hdu.writeto(fl_KeepOrRM,overwrite=True)
final_im=image.copy()
final_im[Xspots_not_starlike]=0
hdu=astropy.io.fits.PrimaryHDU(asarray(final_im,dtype=float))
hdu.header=crheader
fl_final=BBCRfl.replace('SEGMENTATION_BB_CRN-cosmics','StarRMout_KeepOrRM-purified_cosmics')
hdu.writeto(fl_final,overwrite=True)
files2check=[fl,fl_woblend,fl_revised,fl_KeepOrRM,fl_final]
print '\nds9 -zscale -tile mode column '+' '.join(files2check)+' -zscale -lock frame image -lock crosshair image -geometry 2000x2000 &'
## plot star column in eccentricity vs. diameter space
from matplotlib import collections
fig, ax_d = subplots(figsize=(14,11))
ax_d.plot(cr_e, cr_diam, 'b.')
ax_d.plot(cr_e[starlike], cr_diam[starlike], 'bo')
star_e,star_diam,star_area=(cr_e[starlike], cr_diam[starlike], cr_area[starlike])
median_star_area=median(star_area)
median_star_diam=median(star_diam)
fwhm=SEEING/.202 #convert to pixels
star_diam_fwhm_ratio=median_star_diam/fwhm
fig.suptitle('Plot of eccentricity vs. effective diameter (blue) or vs. area (red) \n SEEING=%.2f" & FWHM Star = %.2f pixels & Median Diameter = %.2f & Ratio FWHM/Median(Diam)=%.2f' % (SEEING,fwhm,median_star_diam,star_diam_fwhm_ratio))
ax_d.set_xlabel('eccentricity')
# Make the y-axis label and tick labels match the line color.
ax_d.set_ylabel(r'Effective Diameter = $\sqrt{4/\pi \times area}$', color='b')
for tl in ax_d.get_yticklabels():
tl.set_color('b')
ax_a = ax_d.twinx()
ax_a.plot(cr_e, cr_area, 'r.')
ax_a.plot(cr_e[starlike], cr_area[starlike], 'ro')
ax_a.set_ylabel('Area [pixels]', color='r')
for tl in ax_a.get_yticklabels():
tl.set_color('r')
collection_a = collections.BrokenBarHCollection(xranges=[(0,.8)],yrange=[9,50],facecolor='red', alpha=0.5)
collection_d = collections.BrokenBarHCollection(xranges=[(0,.8)],yrange=[sqrt(4/pi*9),sqrt(4/pi*50)],facecolor='blue', alpha=0.5)
ax_a.add_collection(collection_a)
ax_d.add_collection(collection_d)
fig.savefig(plot_dir+'pltSS_e_vs_diam_and_area_'+OFB_SEEING)
## print stats
print "\nfor fl: %s \n\tSEEING=%.2f" % (fl,SEEING)
CRseg_tot_num=BBCRseg.max()
CRseg_removed_stars=starlike.sum()
print "\t# CR masks started with: %s\n\t# CR masks finished with: %s\n\t# CR masks deleted/starlike: %s" % (CRseg_tot_num,CRseg_tot_num-CRseg_removed_stars,CRseg_removed_stars)
print "\nBBSSCR_stats-SS",BASE,CRseg_removed_stars
## save SEGMENTATION_BBSS_CRN-cosmics (the new file that replaces SEGMENTATION_BB_CRN-cosmics in the pipeline)
BBSSCRseg,BBSSCRseg_Nlabels=scipy.ndimage.label(Xspots_not_starlike,conn8)
hdu=astropy.io.fits.PrimaryHDU(data=BBSSCRseg,header=crheader)
BBSSCRfl=BBCRfl.replace('SEGMENTATION_BB_CRN-cosmics','SEGMENTATION_BBSS_CRN-cosmics')
hdu.writeto(BBSSCRfl,overwrite=True)
| mit |
pylayers/pylayers | pylayers/em/openems/nf2ff.py | 3 | 1578 | import h5py
import numpy as np
import matplotlib.pyplot as plt
f = h5py.File('nf2ff.h5','r')
hdf_mesh = f['Mesh']
r = np.array(hdf_mesh['r'])
theta = np.array(hdf_mesh['theta'])
phi = np.array(hdf_mesh['phi'])
nf2ff = f['nf2ff']
attrs = nf2ff.attrs.items()
freq = attrs[0][1]
Prad = attrs[1][1]
Dmax = attrs[2][1]
try:
Eps_r = nf2ff['Eps_r']
except:
Eps_r = np.ones(freq.shape)
try:
Mue_r = nf2ff['Mue_r']
except:
Mue_r = np.ones(freq.shape)
for k,f in enumerate(freq):
Er_theta = np.array(nf2ff['E_theta']['FD']['f'+str(k)+'_real'])
Ei_theta = np.array(nf2ff['E_theta']['FD']['f'+str(k)+'_imag'])
Er_phi = np.array(nf2ff['E_phi']['FD']['f'+str(k)+'_real'])
Ei_phi = np.array(nf2ff['E_phi']['FD']['f'+str(k)+'_imag'])
E_theta = Er_theta+1j*Ei_theta
E_phi = Er_phi+1j*Ei_phi
E_norm = np.sqrt(E_theta*np.conj(E_theta)+E_phi*np.conj(E_phi))
P_rad = np.array(nf2ff['P_rad']['FD']['f'+str(k)])
plt.ion()
plt.polar(theta,Er_theta[0,:],'r')
plt.polar(theta,Ei_theta[0,:],'r')
plt.polar(theta,Er_theta[1,:],'b')
plt.polar(theta,Ei_theta[1,:],'b')
plt.figure()
plt.ion()
plt.polar(theta,E_norm[0,:],'b')
plt.polar(theta,E_norm[1,:],'r')
plt.show()
## Calculation of right- and left-handed circular polarization
## adopted from
## 2012, Tim Pegg <teepegg@gmail.com>
##
##% Setup vectors for converting to LHCP and RHCP polarization senses
#for k,f in enumerate(freq):
# E_cprh[k] = (cos(phi)+1j*sin(phi))*(E_theta[k]+1j*E_phi[k])/np.sqrt(2);
# E_cplh[k] = (cos(phi)-1j*sin(phi))*(E_theta[k]-1j*E_phi[k])/np.sqrt(2);
| mit |
akrherz/iem | htdocs/plotting/auto/scripts/p67.py | 1 | 3664 | """Wind Speed by Temperature"""
import datetime
import calendar
import matplotlib.patheffects as PathEffects
import psycopg2.extras
import pandas as pd
from pyiem.util import get_autoplot_context, get_dbconn
from pyiem.plot.use_agg import plt
from pyiem.exceptions import NoDataFound
def get_description():
""" Return a dict describing how to call this plotter """
desc = dict()
desc["data"] = True
desc["cache"] = 86400
desc[
"description"
] = """This plot displays the frequency of having a
reported wind speed be above a given threshold by reported temperature
and by month."""
desc["arguments"] = [
dict(
type="zstation",
name="zstation",
default="DSM",
network="IA_ASOS",
label="Select Station:",
),
dict(
type="int",
name="threshold",
default=10,
label="Wind Speed Threshold (knots)",
),
dict(type="month", name="month", default="3", label="Select Month:"),
]
return desc
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
threshold = ctx["threshold"]
month = ctx["month"]
cursor.execute(
"""
WITH data as (
SELECT tmpf::int as t, sknt from alldata where station = %s
and extract(month from valid) = %s and tmpf is not null
and sknt >= 0
)
SELECT t, sum(case when sknt >= %s then 1 else 0 end), count(*)
from data GROUP by t ORDER by t ASC
""",
(station, month, threshold),
)
if cursor.rowcount == 0:
raise NoDataFound("No Data was Found.")
tmpf = []
events = []
total = []
hits = 0
cnt = 0
for row in cursor:
if row[2] < 3:
continue
tmpf.append(row[0])
hits += row[1]
cnt += row[2]
events.append(row[1])
total.append(row[2])
df = pd.DataFrame(
dict(
tmpf=pd.Series(tmpf),
events=pd.Series(events),
total=pd.Series(total),
)
)
(fig, ax) = plt.subplots(1, 1)
ax.bar(
tmpf,
df["events"] / df["total"] * 100.0,
width=1.1,
ec="green",
fc="green",
)
avgval = hits / float(cnt) * 100.0
ax.axhline(avgval, lw=2, zorder=2)
txt = ax.text(
tmpf[10],
avgval + 1,
f"Average: {avgval:.1f}%",
va="bottom",
zorder=2,
color="yellow",
fontsize=14,
)
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground="k")])
ax.grid(zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
(
"%s [%s]\nFrequency of %s+ knot Wind Speeds by Temperature "
"for %s (%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
)
% (
ctx["_nt"].sts[station]["name"],
station,
threshold,
calendar.month_name[month],
ab.year,
datetime.datetime.now().year,
),
size=10,
)
ax.set_ylabel("Frequency [%]")
ax.set_ylim(0, 100)
ax.set_xlim(min(tmpf) - 3, max(tmpf) + 3)
ax.set_xlabel(r"Air Temperature $^\circ$F")
ax.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
return fig, df
if __name__ == "__main__":
plotter(dict())
| mit |
hlin117/scikit-learn | sklearn/tests/test_kernel_approximation.py | 78 | 7586 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
from sklearn.metrics.pairwise import kernel_metrics
from sklearn.kernel_approximation import RBFSampler
from sklearn.kernel_approximation import AdditiveChi2Sampler
from sklearn.kernel_approximation import SkewedChi2Sampler
from sklearn.kernel_approximation import Nystroem
from sklearn.metrics.pairwise import polynomial_kernel, rbf_kernel
# generate data
rng = np.random.RandomState(0)
X = rng.random_sample(size=(300, 50))
Y = rng.random_sample(size=(300, 50))
X /= X.sum(axis=1)[:, np.newaxis]
Y /= Y.sum(axis=1)[:, np.newaxis]
def test_additive_chi2_sampler():
# test that AdditiveChi2Sampler approximates kernel on random data
# compute exact kernel
# abbreviations for easier formula
X_ = X[:, np.newaxis, :]
Y_ = Y[np.newaxis, :, :]
large_kernel = 2 * X_ * Y_ / (X_ + Y_)
# reduce to n_samples_x x n_samples_y by summing over features
kernel = (large_kernel.sum(axis=2))
# approximate kernel mapping
transform = AdditiveChi2Sampler(sample_steps=3)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
X_sp_trans = transform.fit_transform(csr_matrix(X))
Y_sp_trans = transform.transform(csr_matrix(Y))
assert_array_equal(X_trans, X_sp_trans.A)
assert_array_equal(Y_trans, Y_sp_trans.A)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
# test error on invalid sample_steps
transform = AdditiveChi2Sampler(sample_steps=4)
assert_raises(ValueError, transform.fit, X)
# test that the sample interval is set correctly
sample_steps_available = [1, 2, 3]
for sample_steps in sample_steps_available:
# test that the sample_interval is initialized correctly
transform = AdditiveChi2Sampler(sample_steps=sample_steps)
assert_equal(transform.sample_interval, None)
# test that the sample_interval is changed in the fit method
transform.fit(X)
assert_not_equal(transform.sample_interval_, None)
# test that the sample_interval is set correctly
sample_interval = 0.3
transform = AdditiveChi2Sampler(sample_steps=4,
sample_interval=sample_interval)
assert_equal(transform.sample_interval, sample_interval)
transform.fit(X)
assert_equal(transform.sample_interval_, sample_interval)
def test_skewed_chi2_sampler():
# test that RBFSampler approximates kernel on random data
# compute exact kernel
c = 0.03
# abbreviations for easier formula
X_c = (X + c)[:, np.newaxis, :]
Y_c = (Y + c)[np.newaxis, :, :]
# we do it in log-space in the hope that it's more stable
# this array is n_samples_x x n_samples_y big x n_features
log_kernel = ((np.log(X_c) / 2.) + (np.log(Y_c) / 2.) + np.log(2.) -
np.log(X_c + Y_c))
# reduce to n_samples_x x n_samples_y by summing over features in log-space
kernel = np.exp(log_kernel.sum(axis=2))
# approximate kernel mapping
transform = SkewedChi2Sampler(skewedness=c, n_components=1000,
random_state=42)
X_trans = transform.fit_transform(X)
Y_trans = transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
assert_array_almost_equal(kernel, kernel_approx, 1)
# test error is raised on negative input
Y_neg = Y.copy()
Y_neg[0, 0] = -1
assert_raises(ValueError, transform.transform, Y_neg)
def test_rbf_sampler():
# test that RBFSampler approximates kernel on random data
# compute exact kernel
gamma = 10.
kernel = rbf_kernel(X, Y, gamma=gamma)
# approximate kernel mapping
rbf_transform = RBFSampler(gamma=gamma, n_components=1000, random_state=42)
X_trans = rbf_transform.fit_transform(X)
Y_trans = rbf_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
error = kernel - kernel_approx
assert_less_equal(np.abs(np.mean(error)), 0.01) # close to unbiased
np.abs(error, out=error)
assert_less_equal(np.max(error), 0.1) # nothing too far off
assert_less_equal(np.mean(error), 0.05) # mean is fairly close
def test_input_validation():
# Regression test: kernel approx. transformers should work on lists
# No assertions; the old versions would simply crash
X = [[1, 2], [3, 4], [5, 6]]
AdditiveChi2Sampler().fit(X).transform(X)
SkewedChi2Sampler().fit(X).transform(X)
RBFSampler().fit(X).transform(X)
X = csr_matrix(X)
RBFSampler().fit(X).transform(X)
def test_nystroem_approximation():
# some basic tests
rnd = np.random.RandomState(0)
X = rnd.uniform(size=(10, 4))
# With n_components = n_samples this is exact
X_transformed = Nystroem(n_components=X.shape[0]).fit_transform(X)
K = rbf_kernel(X)
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
trans = Nystroem(n_components=2, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
# test callable kernel
linear_kernel = lambda X, Y: np.dot(X, Y.T)
trans = Nystroem(n_components=2, kernel=linear_kernel, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
# test that available kernels fit and transform
kernels_available = kernel_metrics()
for kern in kernels_available:
trans = Nystroem(n_components=2, kernel=kern, random_state=rnd)
X_transformed = trans.fit(X).transform(X)
assert_equal(X_transformed.shape, (X.shape[0], 2))
def test_nystroem_singular_kernel():
# test that nystroem works with singular kernel matrix
rng = np.random.RandomState(0)
X = rng.rand(10, 20)
X = np.vstack([X] * 2) # duplicate samples
gamma = 100
N = Nystroem(gamma=gamma, n_components=X.shape[0]).fit(X)
X_transformed = N.transform(X)
K = rbf_kernel(X, gamma=gamma)
assert_array_almost_equal(K, np.dot(X_transformed, X_transformed.T))
assert_true(np.all(np.isfinite(Y)))
def test_nystroem_poly_kernel_params():
# Non-regression: Nystroem should pass other parameters beside gamma.
rnd = np.random.RandomState(37)
X = rnd.uniform(size=(10, 4))
K = polynomial_kernel(X, degree=3.1, coef0=.1)
nystroem = Nystroem(kernel="polynomial", n_components=X.shape[0],
degree=3.1, coef0=.1)
X_transformed = nystroem.fit_transform(X)
assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
def test_nystroem_callable():
# Test Nystroem on a callable.
rnd = np.random.RandomState(42)
n_samples = 10
X = rnd.uniform(size=(n_samples, 4))
def logging_histogram_kernel(x, y, log):
"""Histogram kernel that writes to a log."""
log.append(1)
return np.minimum(x, y).sum()
kernel_log = []
X = list(X) # test input validation
Nystroem(kernel=logging_histogram_kernel,
n_components=(n_samples - 1),
kernel_params={'log': kernel_log}).fit(X)
assert_equal(len(kernel_log), n_samples * (n_samples - 1) / 2)
| bsd-3-clause |
stylianos-kampakis/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`` function, then
shuffled and passed to the Spectral Biclustering algorithm. The rows
and columns of the shuffled matrix are rearranged to show the
biclusters found by the algorithm.
The outer product of the row and column label vectors shows a
representation of the checkerboard structure.
"""
print(__doc__)
# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import make_checkerboard
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralBiclustering
from sklearn.metrics import consensus_score
n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
shape=(300, 300), n_clusters=n_clusters, noise=10,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralBiclustering(n_clusters=n_clusters, method='log',
random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.1f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
np.sort(model.column_labels_) + 1),
cmap=plt.cm.Blues)
plt.title("Checkerboard structure of rearranged data")
plt.show()
| bsd-3-clause |
squilter/MAVProxy | MAVProxy/modules/lib/live_graph.py | 3 | 2920 | #!/usr/bin/env python
"""
MAVProxy realtime graphing module, partly based on the wx graphing
demo by Eli Bendersky (eliben@gmail.com)
http://eli.thegreenplace.net/files/prog_code/wx_mpl_dynamic_graph.py.txt
"""
from MAVProxy.modules.lib import mp_util
class LiveGraph():
'''
a live graph object using wx and matplotlib
All of the GUI work is done in a child process to provide some insulation
from the parent mavproxy instance and prevent instability in the GCS
New data is sent to the LiveGraph instance via a pipe
'''
def __init__(self,
fields,
title='MAVProxy: LiveGraph',
timespan=20.0,
tickresolution=0.2,
colors=[ 'red', 'green', 'blue', 'orange', 'olive', 'cyan', 'magenta', 'brown', 'dark green',
'violet', 'purple', 'grey', 'black']):
import multiprocessing
self.fields = fields
self.colors = colors
self.title = title
self.timespan = timespan
self.tickresolution = tickresolution
self.values = [None]*len(self.fields)
self.parent_pipe,self.child_pipe = multiprocessing.Pipe()
self.close_graph = multiprocessing.Event()
self.close_graph.clear()
self.child = multiprocessing.Process(target=self.child_task)
self.child.start()
def child_task(self):
'''child process - this holds all the GUI elements'''
mp_util.child_close_fds()
import matplotlib
import wx_processguard
from wx_loader import wx
from live_graph_ui import GraphFrame
matplotlib.use('WXAgg')
app = wx.App(False)
app.frame = GraphFrame(state=self)
app.frame.Show()
app.MainLoop()
def add_values(self, values):
'''add some data to the graph'''
if self.child.is_alive():
self.parent_pipe.send(values)
def close(self):
'''close the graph'''
self.close_graph.set()
if self.is_alive():
self.child.join(2)
def is_alive(self):
'''check if graph is still going'''
return self.child.is_alive()
if __name__ == "__main__":
# test the graph
import time, math
livegraph = LiveGraph(['sin(t)', 'cos(t)', 'sin(t+1)',
'cos(t+1)', 'sin(t+2)', 'cos(t+2)',
'cos(t+1)', 'sin(t+2)', 'cos(t+2)', 'x'],
timespan=30,
title='Graph Test')
while livegraph.is_alive():
t = time.time()
livegraph.add_values([math.sin(t), math.cos(t),
math.sin(t+1), math.cos(t+1),
math.sin(t+1), math.cos(t+1),
math.sin(t+1), math.cos(t+1),
math.sin(t+2), math.cos(t+2)])
time.sleep(0.05)
| gpl-3.0 |
mjzmjz/swift | swift/common/middleware/x_profile/html_viewer.py | 25 | 21039 | # Copyright (c) 2010-2012 OpenStack, LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cgi
import os
import random
import re
import string
import tempfile
from swift import gettext_ as _
from exceptions import PLOTLIBNotInstalled, ODFLIBNotInstalled,\
NotFoundException, MethodNotAllowed, DataLoadFailure, ProfileException
from profile_model import Stats2
PLOTLIB_INSTALLED = True
try:
import matplotlib
# use agg backend for writing to file, not for rendering in a window.
# otherwise some platform will complain "no display name and $DISPLAY
# environment variable"
matplotlib.use('agg')
import matplotlib.pyplot as plt
except ImportError:
PLOTLIB_INSTALLED = False
empty_description = """
The default profile of current process or the profile you requested is
empty. <input type="submit" name="refresh" value="Refresh"/>
"""
profile_tmpl = """
<select name="profile">
<option value="current">current</option>
<option value="all">all</option>
${profile_list}
</select>
"""
sort_tmpl = """
<select name="sort">
<option value="time">time</option>
<option value="cumulative">cumulative</option>
<option value="calls">calls</option>
<option value="pcalls">pcalls</option>
<option value="name">name</option>
<option value="file">file</option>
<option value="module">module</option>
<option value="line">line</option>
<option value="nfl">nfl</option>
<option value="stdname">stdname</option>
</select>
"""
limit_tmpl = """
<select name="limit">
<option value="-1">all</option>
<option value="0.1">10%</option>
<option value="0.2">20%</option>
<option value="0.3">30%</option>
<option value="10">10</option>
<option value="20">20</option>
<option value="30">30</option>
<option value="50">50</option>
<option value="100">100</option>
<option value="200">200</option>
<option value="300">300</option>
<option value="400">400</option>
<option value="500">500</option>
</select>
"""
fulldirs_tmpl = """
<input type="checkbox" name="fulldirs" value="1"
${fulldir_checked}/>
"""
mode_tmpl = """
<select name="mode">
<option value="stats">stats</option>
<option value="callees">callees</option>
<option value="callers">callers</option>
</select>
"""
nfl_filter_tmpl = """
<input type="text" name="nfl_filter" value="${nfl_filter}"
placeholder="filename part" />
"""
formelements_tmpl = """
<div>
<table>
<tr>
<td>
<strong>Profile</strong>
<td>
<strong>Sort</strong>
</td>
<td>
<strong>Limit</strong>
</td>
<td>
<strong>Full Path</strong>
</td>
<td>
<strong>Filter</strong>
</td>
<td>
</td>
<td>
<strong>Plot Metric</strong>
</td>
<td>
<strong>Plot Type</strong>
<td>
</td>
<td>
<strong>Format</strong>
</td>
<td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<td>
${profile}
<td>
${sort}
</td>
<td>
${limit}
</td>
<td>
${fulldirs}
</td>
<td>
${nfl_filter}
</td>
<td>
<input type="submit" name="query" value="query"/>
</td>
<td>
<select name='metric'>
<option value='nc'>call count</option>
<option value='cc'>primitive call count</option>
<option value='tt'>total time</option>
<option value='ct'>cumulative time</option>
</select>
</td>
<td>
<select name='plottype'>
<option value='bar'>bar</option>
<option value='pie'>pie</option>
</select>
<td>
<input type="submit" name="plot" value="plot"/>
</td>
<td>
<select name='format'>
<option value='default'>binary</option>
<option value='json'>json</option>
<option value='csv'>csv</option>
<option value='ods'>ODF.ods</option>
</select>
</td>
<td>
<input type="submit" name="download" value="download"/>
</td>
<td>
<input type="submit" name="clear" value="clear"/>
</td>
</tr>
</table>
</div>
"""
index_tmpl = """
<html>
<head>
<title>profile results</title>
<style>
<!--
tr.normal { background-color: #ffffff }
tr.hover { background-color: #88eeee }
//-->
</style>
</head>
<body>
<form action="${action}" method="POST">
<div class="form-text">
${description}
</div>
<hr />
${formelements}
</form>
<pre>
${profilehtml}
</pre>
</body>
</html>
"""
class HTMLViewer(object):
format_dict = {'default': 'application/octet-stream',
'json': 'application/json',
'csv': 'text/csv',
'ods': 'application/vnd.oasis.opendocument.spreadsheet',
'python': 'text/html'}
def __init__(self, app_path, profile_module, profile_log):
self.app_path = app_path
self.profile_module = profile_module
self.profile_log = profile_log
def _get_param(self, query_dict, key, default=None, multiple=False):
value = query_dict.get(key, default)
if value is None or value == '':
return default
if multiple:
return value
if isinstance(value, list):
return eval(value[0]) if isinstance(default, int) else value[0]
else:
return value
def render(self, url, method, path_entry, query_dict, clear_callback):
plot = self._get_param(query_dict, 'plot', None)
download = self._get_param(query_dict, 'download', None)
clear = self._get_param(query_dict, 'clear', None)
action = plot or download or clear
profile_id = self._get_param(query_dict, 'profile', 'current')
sort = self._get_param(query_dict, 'sort', 'time')
limit = self._get_param(query_dict, 'limit', -1)
fulldirs = self._get_param(query_dict, 'fulldirs', 0)
nfl_filter = self._get_param(query_dict, 'nfl_filter', '').strip()
metric_selected = self._get_param(query_dict, 'metric', 'cc')
plot_type = self._get_param(query_dict, 'plottype', 'bar')
download_format = self._get_param(query_dict, 'format', 'default')
content = ''
# GET /__profile, POST /__profile
if len(path_entry) == 2 and method in ['GET', 'POST']:
log_files = self.profile_log.get_logfiles(profile_id)
if action == 'plot':
content, headers = self.plot(log_files, sort, limit,
nfl_filter, metric_selected,
plot_type)
elif action == 'download':
content, headers = self.download(log_files, sort, limit,
nfl_filter, download_format)
else:
if action == 'clear':
self.profile_log.clear(profile_id)
clear_callback and clear_callback()
content, headers = self.index_page(log_files, sort, limit,
fulldirs, nfl_filter,
profile_id, url)
# GET /__profile__/all
# GET /__profile__/current
# GET /__profile__/profile_id
# GET /__profile__/profile_id/
# GET /__profile__/profile_id/account.py:50(GETorHEAD)
# GET /__profile__/profile_id/swift/proxy/controllers
# /account.py:50(GETorHEAD)
# with QUERY_STRING: ?format=[default|json|csv|ods]
elif len(path_entry) > 2 and method == 'GET':
profile_id = path_entry[2]
log_files = self.profile_log.get_logfiles(profile_id)
pids = self.profile_log.get_all_pids()
# return all profiles in a json format by default.
# GET /__profile__/
if profile_id == '':
content = '{"profile_ids": ["' + '","'.join(pids) + '"]}'
headers = [('content-type', self.format_dict['json'])]
else:
if len(path_entry) > 3 and path_entry[3] != '':
nfl_filter = '/'.join(path_entry[3:])
if path_entry[-1].find(':0') == -1:
nfl_filter = '/' + nfl_filter
content, headers = self.download(log_files, sort, -1,
nfl_filter, download_format)
headers.append(('Access-Control-Allow-Origin', '*'))
else:
raise MethodNotAllowed(_('method %s is not allowed.') % method)
return content, headers
def index_page(self, log_files=None, sort='time', limit=-1,
fulldirs=0, nfl_filter='', profile_id='current', url='#'):
headers = [('content-type', 'text/html')]
if len(log_files) == 0:
return empty_description, headers
try:
stats = Stats2(*log_files)
except (IOError, ValueError):
raise DataLoadFailure(_('Can not load profile data from %s.')
% log_files)
if not fulldirs:
stats.strip_dirs()
stats.sort_stats(sort)
nfl_filter_esc =\
nfl_filter.replace('(', '\(').replace(')', '\)')
amount = [nfl_filter_esc, limit] if nfl_filter_esc else [limit]
profile_html = self.generate_stats_html(stats, self.app_path,
profile_id, *amount)
description = "Profiling information is generated by using\
'%s' profiler." % self.profile_module
sort_repl = '<option value="%s">' % sort
sort_selected = '<option value="%s" selected>' % sort
sort = sort_tmpl.replace(sort_repl, sort_selected)
plist = ''.join(['<option value="%s">%s</option>' % (p, p)
for p in self.profile_log.get_all_pids()])
profile_element = string.Template(profile_tmpl).substitute(
{'profile_list': plist})
profile_repl = '<option value="%s">' % profile_id
profile_selected = '<option value="%s" selected>' % profile_id
profile_element = profile_element.replace(profile_repl,
profile_selected)
limit_repl = '<option value="%s">' % limit
limit_selected = '<option value="%s" selected>' % limit
limit = limit_tmpl.replace(limit_repl, limit_selected)
fulldirs_checked = 'checked' if fulldirs else ''
fulldirs_element = string.Template(fulldirs_tmpl).substitute(
{'fulldir_checked': fulldirs_checked})
nfl_filter_element = string.Template(nfl_filter_tmpl).\
substitute({'nfl_filter': nfl_filter})
form_elements = string.Template(formelements_tmpl).substitute(
{'description': description,
'action': url,
'profile': profile_element,
'sort': sort,
'limit': limit,
'fulldirs': fulldirs_element,
'nfl_filter': nfl_filter_element,
}
)
content = string.Template(index_tmpl).substitute(
{'formelements': form_elements,
'action': url,
'description': description,
'profilehtml': profile_html,
})
return content, headers
def download(self, log_files, sort='time', limit=-1, nfl_filter='',
output_format='default'):
if len(log_files) == 0:
raise NotFoundException(_('no log file found'))
try:
nfl_esc = nfl_filter.replace('(', '\(').replace(')', '\)')
# remove the slash that is intentionally added in the URL
# to avoid failure of filtering stats data.
if nfl_esc.startswith('/'):
nfl_esc = nfl_esc[1:]
stats = Stats2(*log_files)
stats.sort_stats(sort)
if output_format == 'python':
data = self.format_source_code(nfl_filter)
elif output_format == 'json':
data = stats.to_json(nfl_esc, limit)
elif output_format == 'csv':
data = stats.to_csv(nfl_esc, limit)
elif output_format == 'ods':
data = stats.to_ods(nfl_esc, limit)
else:
data = stats.print_stats()
return data, [('content-type', self.format_dict[output_format])]
except ODFLIBNotInstalled as ex:
raise ex
except Exception as ex:
raise ProfileException(_('Data download error: %s') % ex)
def plot(self, log_files, sort='time', limit=10, nfl_filter='',
metric_selected='cc', plot_type='bar'):
if not PLOTLIB_INSTALLED:
raise PLOTLIBNotInstalled(_('python-matplotlib not installed.'))
if len(log_files) == 0:
raise NotFoundException(_('no log file found'))
try:
stats = Stats2(*log_files)
stats.sort_stats(sort)
stats_dict = stats.stats
__, func_list = stats.get_print_list([nfl_filter, limit])
nfls = []
performance = []
names = {'nc': 'Total Call Count', 'cc': 'Primitive Call Count',
'tt': 'Total Time', 'ct': 'Cumulative Time'}
for func in func_list:
cc, nc, tt, ct, __ = stats_dict[func]
metric = {'cc': cc, 'nc': nc, 'tt': tt, 'ct': ct}
nfls.append(func[2])
performance.append(metric[metric_selected])
y_pos = range(len(nfls))
error = [random.random() for __ in y_pos]
plt.clf()
if plot_type == 'pie':
plt.pie(x=performance, explode=None, labels=nfls,
autopct='%1.1f%%')
else:
plt.barh(y_pos, performance, xerr=error, align='center',
alpha=0.4)
plt.yticks(y_pos, nfls)
plt.xlabel(names[metric_selected])
plt.title('Profile Statistics (by %s)' % names[metric_selected])
# plt.gcf().tight_layout(pad=1.2)
with tempfile.TemporaryFile() as profile_img:
plt.savefig(profile_img, format='png', dpi=300)
profile_img.seek(0)
data = profile_img.read()
return data, [('content-type', 'image/jpg')]
except Exception as ex:
raise ProfileException(_('plotting results failed due to %s') % ex)
def format_source_code(self, nfl):
nfls = re.split('[:()]', nfl)
file_path = nfls[0]
try:
lineno = int(nfls[1])
except (TypeError, ValueError, IndexError):
lineno = 0
# for security reason, this need to be fixed.
if not file_path.endswith('.py'):
return _('The file type are forbidden to access!')
try:
data = []
i = 0
with open(file_path) as f:
lines = f.readlines()
max_width = str(len(str(len(lines))))
fmt = '<span id="L%d" rel="#L%d">%' + max_width\
+ 'd|<code>%s</code></span>'
for line in lines:
l = cgi.escape(line, quote=None)
i = i + 1
if i == lineno:
fmt2 = '<span id="L%d" style="background-color: \
rgb(127,255,127)">%' + max_width +\
'd|<code>%s</code></span>'
data.append(fmt2 % (i, i, l))
else:
data.append(fmt % (i, i, i, l))
data = ''.join(data)
except Exception:
return _('Can not access the file %s.') % file_path
return '<pre>%s</pre>' % data
def generate_stats_html(self, stats, app_path, profile_id, *selection):
html = []
for filename in stats.files:
html.append('<p>%s</p>' % filename)
try:
for func in stats.top_level:
html.append('<p>%s</p>' % func[2])
html.append('%s function calls' % stats.total_calls)
if stats.total_calls != stats.prim_calls:
html.append("(%d primitive calls)" % stats.prim_calls)
html.append('in %.3f seconds' % stats.total_tt)
if stats.fcn_list:
stat_list = stats.fcn_list[:]
msg = "<p>Ordered by: %s</p>" % stats.sort_type
else:
stat_list = stats.stats.keys()
msg = '<p>Random listing order was used</p>'
for sel in selection:
stat_list, msg = stats.eval_print_amount(sel, stat_list, msg)
html.append(msg)
html.append('<table style="border-width: 1px">')
if stat_list:
html.append('<tr><th>#</th><th>Call Count</th>\
<th>Total Time</th><th>Time/Call</th>\
<th>Cumulative Time</th>\
<th>Cumulative Time/Call</th>\
<th>Filename:Lineno(Function)</th>\
<th>JSON</th>\
</tr>')
count = 0
for func in stat_list:
count = count + 1
html.append('<tr onMouseOver="this.className=\'hover\'"\
onMouseOut="this.className=\'normal\'">\
<td>%d)</td>' % count)
cc, nc, tt, ct, __ = stats.stats[func]
c = str(nc)
if nc != cc:
c = c + '/' + str(cc)
html.append('<td>%s</td>' % c)
html.append('<td>%f</td>' % tt)
if nc == 0:
html.append('<td>-</td>')
else:
html.append('<td>%f</td>' % (float(tt) / nc))
html.append('<td>%f</td>' % ct)
if cc == 0:
html.append('<td>-</td>')
else:
html.append('<td>%f</td>' % (float(ct) / cc))
nfls = cgi.escape(stats.func_std_string(func))
if nfls.split(':')[0] not in ['', 'profile'] and\
os.path.isfile(nfls.split(':')[0]):
html.append('<td><a href="%s/%s%s?format=python#L%d">\
%s</a></td>' % (app_path, profile_id,
nfls, func[1], nfls))
else:
html.append('<td>%s</td>' % nfls)
if not nfls.startswith('/'):
nfls = '/' + nfls
html.append('<td><a href="%s/%s%s?format=json">\
--></a></td></tr>' % (app_path,
profile_id, nfls))
except Exception as ex:
html.append("Exception:" % ex.message)
return ''.join(html)
| apache-2.0 |
nowls/gnuradio | gr-digital/examples/example_costas.py | 49 | 5316 | #!/usr/bin/env python
#
# Copyright 2011-2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from gnuradio import gr, digital, filter
from gnuradio import blocks
from gnuradio import channels
from gnuradio import eng_notation
from gnuradio.eng_option import eng_option
from optparse import OptionParser
import sys
try:
import scipy
except ImportError:
print "Error: could not import scipy (http://www.scipy.org/)"
sys.exit(1)
try:
import pylab
except ImportError:
print "Error: could not import pylab (http://matplotlib.sourceforge.net/)"
sys.exit(1)
class example_costas(gr.top_block):
def __init__(self, N, sps, rolloff, ntaps, bw, noise, foffset, toffset, poffset):
gr.top_block.__init__(self)
rrc_taps = filter.firdes.root_raised_cosine(
sps, sps, 1.0, rolloff, ntaps)
data = 2.0*scipy.random.randint(0, 2, N) - 1.0
data = scipy.exp(1j*poffset) * data
self.src = blocks.vector_source_c(data.tolist(), False)
self.rrc = filter.interp_fir_filter_ccf(sps, rrc_taps)
self.chn = channels.channel_model(noise, foffset, toffset)
self.cst = digital.costas_loop_cc(bw, 2)
self.vsnk_src = blocks.vector_sink_c()
self.vsnk_cst = blocks.vector_sink_c()
self.vsnk_frq = blocks.vector_sink_f()
self.connect(self.src, self.rrc, self.chn, self.cst, self.vsnk_cst)
self.connect(self.rrc, self.vsnk_src)
self.connect((self.cst,1), self.vsnk_frq)
def main():
parser = OptionParser(option_class=eng_option, conflict_handler="resolve")
parser.add_option("-N", "--nsamples", type="int", default=2000,
help="Set the number of samples to process [default=%default]")
parser.add_option("-S", "--sps", type="int", default=4,
help="Set the samples per symbol [default=%default]")
parser.add_option("-r", "--rolloff", type="eng_float", default=0.35,
help="Set the rolloff factor [default=%default]")
parser.add_option("-W", "--bandwidth", type="eng_float", default=2*scipy.pi/100.0,
help="Set the loop bandwidth [default=%default]")
parser.add_option("-n", "--ntaps", type="int", default=45,
help="Set the number of taps in the filters [default=%default]")
parser.add_option("", "--noise", type="eng_float", default=0.0,
help="Set the simulation noise voltage [default=%default]")
parser.add_option("-f", "--foffset", type="eng_float", default=0.0,
help="Set the simulation's normalized frequency offset (in Hz) [default=%default]")
parser.add_option("-t", "--toffset", type="eng_float", default=1.0,
help="Set the simulation's timing offset [default=%default]")
parser.add_option("-p", "--poffset", type="eng_float", default=0.707,
help="Set the simulation's phase offset [default=%default]")
(options, args) = parser.parse_args ()
# Adjust N for the interpolation by sps
options.nsamples = options.nsamples // options.sps
# Set up the program-under-test
put = example_costas(options.nsamples, options.sps, options.rolloff,
options.ntaps, options.bandwidth, options.noise,
options.foffset, options.toffset, options.poffset)
put.run()
data_src = scipy.array(put.vsnk_src.data())
# Convert the FLL's LO frequency from rads/sec to Hz
data_frq = scipy.array(put.vsnk_frq.data()) / (2.0*scipy.pi)
# adjust this to align with the data.
data_cst = scipy.array(3*[0,]+list(put.vsnk_cst.data()))
# Plot the Costas loop's LO frequency
f1 = pylab.figure(1, figsize=(12,10), facecolor='w')
s1 = f1.add_subplot(2,2,1)
s1.plot(data_frq)
s1.set_title("Costas LO")
s1.set_xlabel("Samples")
s1.set_ylabel("Frequency (normalized Hz)")
# Plot the IQ symbols
s3 = f1.add_subplot(2,2,2)
s3.plot(data_src.real, data_src.imag, "o")
s3.plot(data_cst.real, data_cst.imag, "rx")
s3.set_title("IQ")
s3.set_xlabel("Real part")
s3.set_ylabel("Imag part")
s3.set_xlim([-2, 2])
s3.set_ylim([-2, 2])
# Plot the symbols in time
s4 = f1.add_subplot(2,2,3)
s4.set_position([0.125, 0.05, 0.775, 0.4])
s4.plot(data_src.real, "o-")
s4.plot(data_cst.real, "rx-")
s4.set_title("Symbols")
s4.set_xlabel("Samples")
s4.set_ylabel("Real Part of Signals")
pylab.show()
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
pass
| gpl-3.0 |
xuleiboy1234/autoTitle | tensorflow/tensorflow/examples/learn/iris.py | 29 | 2313 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of DNNClassifier for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
X_FEATURE = 'x' # Name of the input feature.
def main(unused_argv):
# Load dataset.
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
# Build 3 layer DNN with 10, 20, 10 units respectively.
feature_columns = [
tf.feature_column.numeric_column(
X_FEATURE, shape=np.array(x_train).shape[1:])]
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
# Train.
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=200)
# Predict.
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class_ids'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
| mit |
silgon/rlpy | rlpy/Representations/LocalBases.py | 4 | 7162 | """
Representations which use local bases function (e.g. kernels) distributed
in the statespace according to some scheme (e.g. grid, random, on previous
samples)
"""
from .Representation import Representation
import numpy as np
from rlpy.Tools.GeneralTools import addNewElementForAllActions
import matplotlib.pyplot as plt
try:
from kernels import batch
except ImportError:
from slow_kernels import batch
print "C-Extensions for kernels not available, expect slow runtime"
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann",
"William Dabney", "Jonathan P. How"]
__license__ = "BSD 3-Clause"
class LocalBases(Representation):
"""
abstract base class for representations that use local basis functions
"""
#: centers of bases
centers = None
#: widths of bases
widths = None
def __init__(self, domain, kernel, normalization=False, seed=1, **kwargs):
"""
:param domain: domain to learn on.
:param kernel: function handle to use for kernel function evaluations.
:param normalization: (Boolean) If true, normalize feature vector so
that sum( phi(s) ) = 1.
Associates a kernel function with each
"""
self.kernel = batch[kernel.__name__]
self.normalization = normalization
self.centers = np.zeros((0, domain.statespace_limits.shape[0]))
self.widths = np.zeros((0, domain.statespace_limits.shape[0]))
super(LocalBases, self).__init__(domain, seed=seed)
def phi_nonTerminal(self, s):
v = self.kernel(s, self.centers, self.widths)
if self.normalization and not v.sum() == 0.:
# normalize such that each vector has a l1 norm of 1
v /= v.sum()
return v
def plot_2d_feature_centers(self, d1=None, d2=None):
"""
:param d1: 1 (of 2 possible) indices of dimensions to plot; ignore all
others, purely visual.
:param d2: 1 (of 2 possible) indices of dimensions to plot; ignore all
others, purely visual.
Phe centers of all features in dimension d1 and d2.
If no dimensions are specified, the first two continuous dimensions
are shown.
"""
if d1 is None and d2 is None:
# just take the first two dimensions
d1, d2 = self.domain.continuous_dims[:2]
plt.figure("Feature Dimensions {} and {}".format(d1, d2))
for i in xrange(self.centers.shape[0]):
plt.plot([self.centers[i, d1]],
[self.centers[i, d2]], "r", marker="x")
plt.draw()
class NonparametricLocalBases(LocalBases):
def __init__(self, domain, kernel,
max_similarity=0.9, resolution=5, **kwargs):
"""
:param domain: domain to learn on.
:param kernel: function handle to use for kernel function evaluations.
:param max_similarity: threshold to allow feature to be added to
representation. Larger max_similarity makes it \"easier\" to add
more features by permitting larger values of phi(s) before
discarding. (An existing feature function in phi() with large value
at phi(s) implies that it is very representative of the true
function at *s*. i.e., the value of a feature in phi(s) is
inversely related to the \"similarity\" of a potential new feature.
:param resolution: to be used by the ``kernel()`` function, see parent.
Determines *width* of basis functions, eg sigma in Gaussian basis.
"""
self.max_similarity = max_similarity
self.common_width = (domain.statespace_limits[:, 1]
- domain.statespace_limits[:, 0]) / resolution
self.features_num = 0
super(
NonparametricLocalBases,
self).__init__(
domain,
kernel,
**kwargs)
def pre_discover(self, s, terminal, a, sn, terminaln):
norm = self.normalization
expanded = 0
self.normalization = False
if not terminal:
phi_s = self.phi_nonTerminal(s)
if np.all(phi_s < self.max_similarity):
self._add_feature(s)
expanded += 1
if not terminaln:
phi_s = self.phi_nonTerminal(sn)
if np.all(phi_s < self.max_similarity):
self._add_feature(sn)
expanded += 1
self.normalization = norm
return expanded
def _add_feature(self, center):
self.features_num += 1
self.centers = np.vstack((self.centers, center))
self.widths = np.vstack((self.widths, self.common_width))
# TODO if normalized, use Q estimate for center to fill weight_vec
new = np.zeros((self.domain.actions_num, 1))
self.weight_vec = addNewElementForAllActions(
self.weight_vec,
self.domain.actions_num,
new)
class RandomLocalBases(LocalBases):
def __init__(self, domain, kernel, num=100, resolution_min=5,
resolution_max=None, seed=1, **kwargs):
"""
:param domain: domain to learn on.
:param kernel: function handle to use for kernel function evaluations.
:param num: Fixed number of feature (kernel) functions to use in
EACH dimension. (for a total of features_num=numDims * num)
:param resolution_min: resolution selected uniform random, lower bound.
:param resolution_max: resolution selected uniform random, upper bound.
:param seed: the random seed to use when scattering basis functions.
Randomly scatter ``num`` feature functions throughout the domain, with
sigma / noise parameter selected uniform random between
``resolution_min`` and ``resolution_max``. NOTE these are
sensitive to the choice of coordinate (scale with coordinate units).
"""
self.features_num = num
self.dim_widths = (domain.statespace_limits[:, 1]
- domain.statespace_limits[:, 0])
self.resolution_max = resolution_max
self.resolution_min = resolution_min
super(
RandomLocalBases,
self).__init__(
domain,
kernel,
seed=seed,
**kwargs)
self.centers = np.zeros((num, len(self.dim_widths)))
self.widths = np.zeros((num, len(self.dim_widths)))
self.init_randomization()
def init_randomization(self):
for i in xrange(self.features_num):
for d in xrange(len(self.dim_widths)):
self.centers[i, d] = self.random_state.uniform(
self.domain.statespace_limits[d, 0],
self.domain.statespace_limits[d, 1])
self.widths[i, d] = self.random_state.uniform(
self.dim_widths[d] / self.resolution_max,
self.dim_widths[d] / self.resolution_min)
| bsd-3-clause |
mcanthony/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/offsetbox.py | 69 | 17728 | """
The OffsetBox is a simple container artist. The child artist are meant
to be drawn at a relative position to its parent. The [VH]Packer,
DrawingArea and TextArea are derived from the OffsetBox.
The [VH]Packer automatically adjust the relative postisions of their
children, which should be instances of the OffsetBox. This is used to
align similar artists together, e.g., in legend.
The DrawingArea can contain any Artist as a child. The
DrawingArea has a fixed width and height. The position of children
relative to the parent is fixed. The TextArea is contains a single
Text instance. The width and height of the TextArea instance is the
width and height of the its child text.
"""
import matplotlib.transforms as mtransforms
import matplotlib.artist as martist
import matplotlib.text as mtext
import numpy as np
from matplotlib.patches import bbox_artist as mbbox_artist
DEBUG=False
# for debuging use
def bbox_artist(*args, **kwargs):
if DEBUG:
mbbox_artist(*args, **kwargs)
# _get_packed_offsets() and _get_aligned_offsets() are coded assuming
# that we are packing boxes horizontally. But same function will be
# used with vertical packing.
def _get_packed_offsets(wd_list, total, sep, mode="fixed"):
"""
Geiven a list of (width, xdescent) of each boxes, calculate the
total width and the x-offset positions of each items according to
*mode*. xdescent is analagous to the usual descent, but along the
x-direction. xdescent values are currently ignored.
*wd_list* : list of (width, xdescent) of boxes to be packed.
*sep* : spacing between boxes
*total* : Intended total length. None if not used.
*mode* : packing mode. 'fixed', 'expand', or 'equal'.
"""
w_list, d_list = zip(*wd_list)
# d_list is currently not used.
if mode == "fixed":
offsets_ = np.add.accumulate([0]+[w + sep for w in w_list])
offsets = offsets_[:-1]
if total is None:
total = offsets_[-1] - sep
return total, offsets
elif mode == "expand":
sep = (total - sum(w_list))/(len(w_list)-1.)
offsets_ = np.add.accumulate([0]+[w + sep for w in w_list])
offsets = offsets_[:-1]
return total, offsets
elif mode == "equal":
maxh = max(w_list)
if total is None:
total = (maxh+sep)*len(w_list)
else:
sep = float(total)/(len(w_list)) - maxh
offsets = np.array([(maxh+sep)*i for i in range(len(w_list))])
return total, offsets
else:
raise ValueError("Unknown mode : %s" % (mode,))
def _get_aligned_offsets(hd_list, height, align="baseline"):
"""
Geiven a list of (height, descent) of each boxes, align the boxes
with *align* and calculate the y-offsets of each boxes.
total width and the offset positions of each items according to
*mode*. xdescent is analagous to the usual descent, but along the
x-direction. xdescent values are currently ignored.
*hd_list* : list of (width, xdescent) of boxes to be aligned.
*sep* : spacing between boxes
*height* : Intended total length. None if not used.
*align* : align mode. 'baseline', 'top', 'bottom', or 'center'.
"""
if height is None:
height = max([h for h, d in hd_list])
if align == "baseline":
height_descent = max([h-d for h, d in hd_list])
descent = max([d for h, d in hd_list])
height = height_descent + descent
offsets = [0. for h, d in hd_list]
elif align in ["left","top"]:
descent=0.
offsets = [d for h, d in hd_list]
elif align in ["right","bottom"]:
descent=0.
offsets = [height-h+d for h, d in hd_list]
elif align == "center":
descent=0.
offsets = [(height-h)*.5+d for h, d in hd_list]
else:
raise ValueError("Unknown Align mode : %s" % (align,))
return height, descent, offsets
class OffsetBox(martist.Artist):
"""
The OffsetBox is a simple container artist. The child artist are meant
to be drawn at a relative position to its parent.
"""
def __init__(self, *args, **kwargs):
super(OffsetBox, self).__init__(*args, **kwargs)
self._children = []
self._offset = (0, 0)
def set_figure(self, fig):
"""
Set the figure
accepts a class:`~matplotlib.figure.Figure` instance
"""
martist.Artist.set_figure(self, fig)
for c in self.get_children():
c.set_figure(fig)
def set_offset(self, xy):
"""
Set the offset
accepts x, y, tuple, or a callable object.
"""
self._offset = xy
def get_offset(self, width, height, xdescent, ydescent):
"""
Get the offset
accepts extent of the box
"""
if callable(self._offset):
return self._offset(width, height, xdescent, ydescent)
else:
return self._offset
def set_width(self, width):
"""
Set the width
accepts float
"""
self.width = width
def set_height(self, height):
"""
Set the height
accepts float
"""
self.height = height
def get_children(self):
"""
Return a list of artists it contains.
"""
return self._children
def get_extent_offsets(self, renderer):
raise Exception("")
def get_extent(self, renderer):
"""
Return with, height, xdescent, ydescent of box
"""
w, h, xd, yd, offsets = self.get_extent_offsets(renderer)
return w, h, xd, yd
def get_window_extent(self, renderer):
'''
get the bounding box in display space.
'''
w, h, xd, yd, offsets = self.get_extent_offsets(renderer)
px, py = self.get_offset(w, h, xd, yd)
return mtransforms.Bbox.from_bounds(px-xd, py-yd, w, h)
def draw(self, renderer):
"""
Update the location of children if necessary and draw them
to the given *renderer*.
"""
width, height, xdescent, ydescent, offsets = self.get_extent_offsets(renderer)
px, py = self.get_offset(width, height, xdescent, ydescent)
for c, (ox, oy) in zip(self.get_children(), offsets):
c.set_offset((px+ox, py+oy))
c.draw(renderer)
bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
class PackerBase(OffsetBox):
def __init__(self, pad=None, sep=None, width=None, height=None,
align=None, mode=None,
children=None):
"""
*pad* : boundary pad
*sep* : spacing between items
*width*, *height* : width and height of the container box.
calculated if None.
*align* : alignment of boxes
*mode* : packing mode
"""
super(PackerBase, self).__init__()
self.height = height
self.width = width
self.sep = sep
self.pad = pad
self.mode = mode
self.align = align
self._children = children
class VPacker(PackerBase):
"""
The VPacker has its children packed vertically. It automatically
adjust the relative postisions of children in the drawing time.
"""
def __init__(self, pad=None, sep=None, width=None, height=None,
align="baseline", mode="fixed",
children=None):
"""
*pad* : boundary pad
*sep* : spacing between items
*width*, *height* : width and height of the container box.
calculated if None.
*align* : alignment of boxes
*mode* : packing mode
"""
super(VPacker, self).__init__(pad, sep, width, height,
align, mode,
children)
def get_extent_offsets(self, renderer):
"""
update offset of childrens and return the extents of the box
"""
whd_list = [c.get_extent(renderer) for c in self.get_children()]
whd_list = [(w, h, xd, (h-yd)) for w, h, xd, yd in whd_list]
wd_list = [(w, xd) for w, h, xd, yd in whd_list]
width, xdescent, xoffsets = _get_aligned_offsets(wd_list,
self.width,
self.align)
pack_list = [(h, yd) for w,h,xd,yd in whd_list]
height, yoffsets_ = _get_packed_offsets(pack_list, self.height,
self.sep, self.mode)
yoffsets = yoffsets_ + [yd for w,h,xd,yd in whd_list]
ydescent = height - yoffsets[0]
yoffsets = height - yoffsets
#w, h, xd, h_yd = whd_list[-1]
yoffsets = yoffsets - ydescent
return width + 2*self.pad, height + 2*self.pad, \
xdescent+self.pad, ydescent+self.pad, \
zip(xoffsets, yoffsets)
class HPacker(PackerBase):
"""
The HPacker has its children packed horizontally. It automatically
adjust the relative postisions of children in the drawing time.
"""
def __init__(self, pad=None, sep=None, width=None, height=None,
align="baseline", mode="fixed",
children=None):
"""
*pad* : boundary pad
*sep* : spacing between items
*width*, *height* : width and height of the container box.
calculated if None.
*align* : alignment of boxes
*mode* : packing mode
"""
super(HPacker, self).__init__(pad, sep, width, height,
align, mode, children)
def get_extent_offsets(self, renderer):
"""
update offset of childrens and return the extents of the box
"""
whd_list = [c.get_extent(renderer) for c in self.get_children()]
if self.height is None:
height_descent = max([h-yd for w,h,xd,yd in whd_list])
ydescent = max([yd for w,h,xd,yd in whd_list])
height = height_descent + ydescent
else:
height = self.height - 2*self._pad # width w/o pad
hd_list = [(h, yd) for w, h, xd, yd in whd_list]
height, ydescent, yoffsets = _get_aligned_offsets(hd_list,
self.height,
self.align)
pack_list = [(w, xd) for w,h,xd,yd in whd_list]
width, xoffsets_ = _get_packed_offsets(pack_list, self.width,
self.sep, self.mode)
xoffsets = xoffsets_ + [xd for w,h,xd,yd in whd_list]
xdescent=whd_list[0][2]
xoffsets = xoffsets - xdescent
return width + 2*self.pad, height + 2*self.pad, \
xdescent + self.pad, ydescent + self.pad, \
zip(xoffsets, yoffsets)
class DrawingArea(OffsetBox):
"""
The DrawingArea can contain any Artist as a child. The DrawingArea
has a fixed width and height. The position of children relative to
the parent is fixed.
"""
def __init__(self, width, height, xdescent=0.,
ydescent=0., clip=True):
"""
*width*, *height* : width and height of the container box.
*xdescent*, *ydescent* : descent of the box in x- and y-direction.
"""
super(DrawingArea, self).__init__()
self.width = width
self.height = height
self.xdescent = xdescent
self.ydescent = ydescent
self.offset_transform = mtransforms.Affine2D()
self.offset_transform.clear()
self.offset_transform.translate(0, 0)
def get_transform(self):
"""
Return the :class:`~matplotlib.transforms.Transform` applied
to the children
"""
return self.offset_transform
def set_transform(self, t):
"""
set_transform is ignored.
"""
pass
def set_offset(self, xy):
"""
set offset of the container.
Accept : tuple of x,y cooridnate in disokay units.
"""
self._offset = xy
self.offset_transform.clear()
self.offset_transform.translate(xy[0], xy[1])
def get_offset(self):
"""
return offset of the container.
"""
return self._offset
def get_window_extent(self, renderer):
'''
get the bounding box in display space.
'''
w, h, xd, yd = self.get_extent(renderer)
ox, oy = self.get_offset() #w, h, xd, yd)
return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h)
def get_extent(self, renderer):
"""
Return with, height, xdescent, ydescent of box
"""
return self.width, self.height, self.xdescent, self.ydescent
def add_artist(self, a):
'Add any :class:`~matplotlib.artist.Artist` to the container box'
self._children.append(a)
a.set_transform(self.get_transform())
def draw(self, renderer):
"""
Draw the children
"""
for c in self._children:
c.draw(renderer)
bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
class TextArea(OffsetBox):
"""
The TextArea is contains a single Text instance. The text is
placed at (0,0) with baseline+left alignment. The width and height
of the TextArea instance is the width and height of the its child
text.
"""
def __init__(self, s,
textprops=None,
multilinebaseline=None,
minimumdescent=True,
):
"""
*s* : a string to be displayed.
*textprops* : property dictionary for the text
*multilinebaseline* : If True, baseline for multiline text is
adjusted so that it is (approximatedly)
center-aligned with singleline text.
*minimumdescent* : If True, the box has a minimum descent of "p".
"""
if textprops is None:
textprops = {}
if not textprops.has_key("va"):
textprops["va"]="baseline"
self._text = mtext.Text(0, 0, s, **textprops)
OffsetBox.__init__(self)
self._children = [self._text]
self.offset_transform = mtransforms.Affine2D()
self.offset_transform.clear()
self.offset_transform.translate(0, 0)
self._baseline_transform = mtransforms.Affine2D()
self._text.set_transform(self.offset_transform+self._baseline_transform)
self._multilinebaseline = multilinebaseline
self._minimumdescent = minimumdescent
def set_multilinebaseline(self, t):
"""
Set multilinebaseline .
If True, baseline for multiline text is
adjusted so that it is (approximatedly) center-aligned with
singleline text.
"""
self._multilinebaseline = t
def get_multilinebaseline(self):
"""
get multilinebaseline .
"""
return self._multilinebaseline
def set_minimumdescent(self, t):
"""
Set minimumdescent .
If True, extent of the single line text is adjusted so that
it has minimum descent of "p"
"""
self._minimumdescent = t
def get_minimumdescent(self):
"""
get minimumdescent.
"""
return self._minimumdescent
def set_transform(self, t):
"""
set_transform is ignored.
"""
pass
def set_offset(self, xy):
"""
set offset of the container.
Accept : tuple of x,y cooridnate in disokay units.
"""
self._offset = xy
self.offset_transform.clear()
self.offset_transform.translate(xy[0], xy[1])
def get_offset(self):
"""
return offset of the container.
"""
return self._offset
def get_window_extent(self, renderer):
'''
get the bounding box in display space.
'''
w, h, xd, yd = self.get_extent(renderer)
ox, oy = self.get_offset() #w, h, xd, yd)
return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h)
def get_extent(self, renderer):
clean_line, ismath = self._text.is_math_text(self._text._text)
_, h_, d_ = renderer.get_text_width_height_descent(
"lp", self._text._fontproperties, ismath=False)
bbox, info = self._text._get_layout(renderer)
w, h = bbox.width, bbox.height
line = info[0][0] # first line
_, hh, dd = renderer.get_text_width_height_descent(
clean_line, self._text._fontproperties, ismath=ismath)
self._baseline_transform.clear()
if len(info) > 1 and self._multilinebaseline: # multi line
d = h-(hh-dd) # the baseline of the first line
d_new = 0.5 * h - 0.5 * (h_ - d_)
self._baseline_transform.translate(0, d - d_new)
d = d_new
else: # single line
h_d = max(h_ - d_, h-dd)
if self.get_minimumdescent():
## to have a minimum descent, #i.e., "l" and "p" have same
## descents.
d = max(dd, d_)
else:
d = dd
h = h_d + d
return w, h, 0., d
def draw(self, renderer):
"""
Draw the children
"""
self._text.draw(renderer)
bbox_artist(self, renderer, fill=False, props=dict(pad=0.))
| agpl-3.0 |
ahoyosid/scikit-learn | sklearn/tests/test_common.py | 7 | 7677 | """
General tests for all estimators in sklearn.
"""
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
from __future__ import print_function
import os
import warnings
import sys
import pkgutil
from sklearn.externals.six import PY3
from sklearn.utils.testing import assert_false, clean_warning_registry
from sklearn.utils.testing import all_estimators
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import ignore_warnings
import sklearn
from sklearn.cluster.bicluster import BiclusterMixin
from sklearn.linear_model.base import LinearClassifierMixin
from sklearn.utils.estimator_checks import (
_yield_all_checks,
CROSS_DECOMPOSITION,
check_parameters_default_constructible,
check_class_weight_auto_linear_classifier,
check_transformer_n_iter,
check_non_transformer_estimators_n_iter,
check_get_params_invariance)
def test_all_estimator_no_base_class():
# test that all_estimators doesn't find abstract classes.
for name, Estimator in all_estimators():
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert_false(name.lower().startswith('base'), msg=msg)
def test_all_estimators():
# Test that estimators are default-constructible, clonable
# and have working repr.
estimators = all_estimators(include_meta_estimators=True)
# Meta sanity-check to make sure that the estimator introspection runs
# properly
assert_greater(len(estimators), 0)
for name, Estimator in estimators:
# some can just not be sensibly default constructed
yield check_parameters_default_constructible, name, Estimator
def test_non_meta_estimators():
# input validation etc for non-meta estimators
estimators = all_estimators()
for name, Estimator in estimators:
if issubclass(Estimator, BiclusterMixin):
continue
if name.endswith("HMM") or name.startswith("_"):
continue
for check in _yield_all_checks(name, Estimator):
yield check, name, Estimator
def test_configure():
# Smoke test the 'configure' step of setup, this tests all the
# 'configure' functions in the setup.pys in the scikit
cwd = os.getcwd()
setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..'))
setup_filename = os.path.join(setup_path, 'setup.py')
if not os.path.exists(setup_filename):
return
try:
os.chdir(setup_path)
old_argv = sys.argv
sys.argv = ['setup.py', 'config']
clean_warning_registry()
with warnings.catch_warnings():
# The configuration spits out warnings when not finding
# Blas/Atlas development headers
warnings.simplefilter('ignore', UserWarning)
if PY3:
with open('setup.py') as f:
exec(f.read(), dict(__name__='__main__'))
else:
execfile('setup.py', dict(__name__='__main__'))
finally:
sys.argv = old_argv
os.chdir(cwd)
def test_class_weight_auto_linear_classifiers():
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
linear_classifiers = [
(name, clazz)
for name, clazz in classifiers
if 'class_weight' in clazz().get_params().keys()
and issubclass(clazz, LinearClassifierMixin)]
for name, Classifier in linear_classifiers:
if name == "LogisticRegressionCV":
# Contrary to RidgeClassifierCV, LogisticRegressionCV use actual
# CV folds and fit a model for each CV iteration before averaging
# the coef. Therefore it is expected to not behave exactly as the
# other linear model.
continue
yield check_class_weight_auto_linear_classifier, name, Classifier
@ignore_warnings
def test_import_all_consistency():
# Smoke test to check that any name in a __all__ list is actually defined
# in the namespace of the module or package.
pkgs = pkgutil.walk_packages(path=sklearn.__path__, prefix='sklearn.',
onerror=lambda _: None)
submods = [modname for _, modname, _ in pkgs]
for modname in submods + ['sklearn']:
if ".tests." in modname:
continue
package = __import__(modname, fromlist="dummy")
for name in getattr(package, '__all__', ()):
if getattr(package, name, None) is None:
raise AttributeError(
"Module '{0}' has no attribute '{1}'".format(
modname, name))
def test_root_import_all_completeness():
EXCEPTIONS = ('utils', 'tests', 'base', 'setup')
for _, modname, _ in pkgutil.walk_packages(path=sklearn.__path__,
onerror=lambda _: None):
if '.' in modname or modname.startswith('_') or modname in EXCEPTIONS:
continue
assert_in(modname, sklearn.__all__)
def test_non_transformer_estimators_n_iter():
# Test that all estimators of type which are non-transformer
# and which have an attribute of max_iter, return the attribute
# of n_iter atleast 1.
for est_type in ['regressor', 'classifier', 'cluster']:
regressors = all_estimators(type_filter=est_type)
for name, Estimator in regressors:
# LassoLars stops early for the default alpha=1.0 for
# the iris dataset.
if name == 'LassoLars':
estimator = Estimator(alpha=0.)
else:
estimator = Estimator()
if hasattr(estimator, "max_iter"):
# These models are dependent on external solvers like
# libsvm and accessing the iter parameter is non-trivial.
if name in (['Ridge', 'SVR', 'NuSVR', 'NuSVC',
'RidgeClassifier', 'SVC', 'RandomizedLasso',
'LogisticRegressionCV']):
continue
# Tested in test_transformer_n_iter below
elif (name in CROSS_DECOMPOSITION or
name in ['LinearSVC', 'LogisticRegression']):
continue
else:
# Multitask models related to ENet cannot handle
# if y is mono-output.
yield (check_non_transformer_estimators_n_iter,
name, estimator, 'Multi' in name)
def test_transformer_n_iter():
transformers = all_estimators(type_filter='transformer')
for name, Estimator in transformers:
estimator = Estimator()
# Dependent on external solvers and hence accessing the iter
# param is non-trivial.
external_solver = ['Isomap', 'KernelPCA', 'LocallyLinearEmbedding',
'RandomizedLasso', 'LogisticRegressionCV']
if hasattr(estimator, "max_iter") and name not in external_solver:
yield check_transformer_n_iter, name, estimator
def test_get_params_invariance():
# Test for estimators that support get_params, that
# get_params(deep=False) is a subset of get_params(deep=True)
# Related to issue #4465
estimators = all_estimators(include_meta_estimators=False, include_other=True)
for name, Estimator in estimators:
if hasattr(Estimator, 'get_params'):
yield check_get_params_invariance, name, Estimator
| bsd-3-clause |
rsivapr/scikit-learn | sklearn/neighbors/graph.py | 10 | 2847 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def kneighbors_graph(X, n_neighbors, mode='connectivity'):
"""Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-like or BallTree, shape = [n_samples, n_features]
Sample data, in the form of a numpy array or a precomputed
:class:`BallTree`.
n_neighbors : int
Number of neighbors for each sample.
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import kneighbors_graph
>>> A = kneighbors_graph(X, 2)
>>> A.todense()
matrix([[ 1., 0., 1.],
[ 0., 1., 1.],
[ 1., 0., 1.]])
See also
--------
radius_neighbors_graph
"""
if not isinstance(X, KNeighborsMixin):
X = NearestNeighbors(n_neighbors).fit(X)
return X.kneighbors_graph(X._fit_X, n_neighbors, mode=mode)
def radius_neighbors_graph(X, radius, mode='connectivity'):
"""Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than
radius.
Parameters
----------
X : array-like or BallTree, shape = [n_samples, n_features]
Sample data, in the form of a numpy array or a precomputed
:class:`BallTree`.
radius : float
Radius of neighborhoods.
mode : {'connectivity', 'distance'}, optional
Type of returned matrix: 'connectivity' will return the
connectivity matrix with ones and zeros, in 'distance' the
edges are Euclidean distance between points.
Returns
-------
A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
Examples
--------
>>> X = [[0], [3], [1]]
>>> from sklearn.neighbors import radius_neighbors_graph
>>> A = radius_neighbors_graph(X, 1.5)
>>> A.todense()
matrix([[ 1., 0., 1.],
[ 0., 1., 0.],
[ 1., 0., 1.]])
See also
--------
kneighbors_graph
"""
if not isinstance(X, RadiusNeighborsMixin):
X = NearestNeighbors(radius=radius).fit(X)
return X.radius_neighbors_graph(X._fit_X, radius, mode)
| bsd-3-clause |
dsm054/pandas | asv_bench/benchmarks/plotting.py | 3 | 1454 | import numpy as np
from pandas import DataFrame, Series, DatetimeIndex, date_range
try:
from pandas.plotting import andrews_curves
except ImportError:
from pandas.tools.plotting import andrews_curves
import matplotlib
matplotlib.use('Agg')
class Plotting(object):
def setup(self):
self.s = Series(np.random.randn(1000000))
self.df = DataFrame({'col': self.s})
def time_series_plot(self):
self.s.plot()
def time_frame_plot(self):
self.df.plot()
class TimeseriesPlotting(object):
def setup(self):
N = 2000
M = 5
idx = date_range('1/1/1975', periods=N)
self.df = DataFrame(np.random.randn(N, M), index=idx)
idx_irregular = DatetimeIndex(np.concatenate((idx.values[0:10],
idx.values[12:])))
self.df2 = DataFrame(np.random.randn(len(idx_irregular), M),
index=idx_irregular)
def time_plot_regular(self):
self.df.plot()
def time_plot_regular_compat(self):
self.df.plot(x_compat=True)
def time_plot_irregular(self):
self.df2.plot()
class Misc(object):
def setup(self):
N = 500
M = 10
self.df = DataFrame(np.random.randn(N, M))
self.df['Name'] = ["A"] * N
def time_plot_andrews_curves(self):
andrews_curves(self.df, "Name")
from .pandas_vb_common import setup # noqa: F401
| bsd-3-clause |
yunque/librosa | librosa/core/spectrum.py | 1 | 25275 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''Utilities for spectral processing'''
import numpy as np
import scipy.fftpack as fft
import scipy
import scipy.signal
import six
from . import time_frequency
from .. import cache
from .. import util
from ..util.exceptions import ParameterError
__all__ = ['stft', 'istft', 'magphase',
'ifgram',
'phase_vocoder',
'logamplitude', 'perceptual_weighting']
@cache
def stft(y, n_fft=2048, hop_length=None, win_length=None, window=None,
center=True, dtype=np.complex64):
"""Short-time Fourier transform (STFT)
Returns a complex-valued matrix D such that
`np.abs(D[f, t])` is the magnitude of frequency bin `f`
at frame `t`
`np.angle(D[f, t])` is the phase of frequency bin `f`
at frame `t`
Parameters
----------
y : np.ndarray [shape=(n,)], real-valued
the input signal (audio time series)
n_fft : int > 0 [scalar]
FFT window size
hop_length : int > 0 [scalar]
number audio of frames between STFT columns.
If unspecified, defaults `win_length / 4`.
win_length : int <= n_fft [scalar]
Each frame of audio is windowed by `window()`.
The window will be of length `win_length` and then padded
with zeros to match `n_fft`.
If unspecified, defaults to ``win_length = n_fft``.
window : None, function, np.ndarray [shape=(n_fft,)]
- None (default): use an asymmetric Hann window
- a window function, such as `scipy.signal.hanning`
- a vector or array of length `n_fft`
center : boolean
- If `True`, the signal `y` is padded so that frame
`D[:, t]` is centered at `y[t * hop_length]`.
- If `False`, then `D[:, t]` begins at `y[t * hop_length]`
dtype : numeric type
Complex numeric type for `D`. Default is 64-bit complex.
Returns
-------
D : np.ndarray [shape=(1 + n_fft/2, t), dtype=dtype]
STFT matrix
Raises
------
ParameterError
If `window` is supplied as a vector of length `n_fft`.
See Also
--------
istft : Inverse STFT
ifgram : Instantaneous frequency spectrogram
Examples
--------
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.stft(y)
>>> D
array([[ 2.576e-03 -0.000e+00j, 4.327e-02 -0.000e+00j, ...,
3.189e-04 -0.000e+00j, -5.961e-06 -0.000e+00j],
[ 2.441e-03 +2.884e-19j, 5.145e-02 -5.076e-03j, ...,
-3.885e-04 -7.253e-05j, 7.334e-05 +3.868e-04j],
...,
[ -7.120e-06 -1.029e-19j, -1.951e-09 -3.568e-06j, ...,
-4.912e-07 -1.487e-07j, 4.438e-06 -1.448e-05j],
[ 7.136e-06 -0.000e+00j, 3.561e-06 -0.000e+00j, ...,
-5.144e-07 -0.000e+00j, -1.514e-05 -0.000e+00j]], dtype=complex64)
Use left-aligned frames, instead of centered frames
>>> D_left = librosa.stft(y, center=False)
Use a shorter hop length
>>> D_short = librosa.stft(y, hop_length=64)
Display a spectrogram
>>> import matplotlib.pyplot as plt
>>> librosa.display.specshow(librosa.logamplitude(np.abs(D)**2,
... ref_power=np.max),
... y_axis='log', x_axis='time')
>>> plt.title('Power spectrogram')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.tight_layout()
"""
# By default, use the entire frame
if win_length is None:
win_length = n_fft
# Set the default hop, if it's not already specified
if hop_length is None:
hop_length = int(win_length / 4)
if window is None:
# Default is an asymmetric Hann window
fft_window = scipy.signal.hann(win_length, sym=False)
elif six.callable(window):
# User supplied a window function
fft_window = window(win_length)
else:
# User supplied a window vector.
# Make sure it's an array:
fft_window = np.asarray(window)
# validate length compatibility
if fft_window.size != n_fft:
raise ParameterError('Size mismatch between n_fft and len(window)')
# Pad the window out to n_fft size
fft_window = util.pad_center(fft_window, n_fft)
# Reshape so that the window can be broadcast
fft_window = fft_window.reshape((-1, 1))
# Pad the time series so that frames are centered
if center:
util.valid_audio(y)
y = np.pad(y, int(n_fft // 2), mode='reflect')
# Window the time series.
y_frames = util.frame(y, frame_length=n_fft, hop_length=hop_length)
# Pre-allocate the STFT matrix
stft_matrix = np.empty((int(1 + n_fft // 2), y_frames.shape[1]),
dtype=dtype,
order='F')
# how many columns can we fit within MAX_MEM_BLOCK?
n_columns = int(util.MAX_MEM_BLOCK / (stft_matrix.shape[0] *
stft_matrix.itemsize))
for bl_s in range(0, stft_matrix.shape[1], n_columns):
bl_t = min(bl_s + n_columns, stft_matrix.shape[1])
# RFFT and Conjugate here to match phase from DPWE code
stft_matrix[:, bl_s:bl_t] = fft.fft(fft_window *
y_frames[:, bl_s:bl_t],
axis=0)[:stft_matrix.shape[0]].conj()
return stft_matrix
@cache
def istft(stft_matrix, hop_length=None, win_length=None, window=None,
center=True, dtype=np.float32):
"""
Inverse short-time Fourier transform (ISTFT).
Converts a complex-valued spectrogram `stft_matrix` to time-series `y`
by minimizing the mean squared error between `stft_matrix` and STFT of
`y` as described in [1]_.
In general, window function, hop length and other parameters should be same
as in stft, which mostly leads to perfect reconstruction of a signal from
unmodified `stft_matrix`.
.. [1] D. W. Griffin and J. S. Lim,
"Signal estimation from modified short-time Fourier transform,"
IEEE Trans. ASSP, vol.32, no.2, pp.236–243, Apr. 1984.
Parameters
----------
stft_matrix : np.ndarray [shape=(1 + n_fft/2, t)]
STFT matrix from `stft`
hop_length : int > 0 [scalar]
Number of frames between STFT columns.
If unspecified, defaults to `win_length / 4`.
win_length : int <= n_fft = 2 * (stft_matrix.shape[0] - 1)
When reconstructing the time series, each frame is windowed
and each sample is normalized by the sum of squared window
according to the `window` function (see below).
If unspecified, defaults to `n_fft`.
window : None, function, np.ndarray [shape=(n_fft,)]
- None (default): use an asymmetric Hann window
- a window function, such as `scipy.signal.hanning`
- a user-specified window vector of length `n_fft`
center : boolean
- If `True`, `D` is assumed to have centered frames.
- If `False`, `D` is assumed to have left-aligned frames.
dtype : numeric type
Real numeric type for `y`. Default is 32-bit float.
Returns
-------
y : np.ndarray [shape=(n,)]
time domain signal reconstructed from `stft_matrix`
Raises
------
ParameterError
If `window` is supplied as a vector of length `n_fft`
See Also
--------
stft : Short-time Fourier Transform
Examples
--------
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.stft(y)
>>> y_hat = librosa.istft(D)
>>> y_hat
array([ -4.812e-06, -4.267e-06, ..., 6.271e-06, 2.827e-07], dtype=float32)
"""
n_fft = 2 * (stft_matrix.shape[0] - 1)
# By default, use the entire frame
if win_length is None:
win_length = n_fft
# Set the default hop, if it's not already specified
if hop_length is None:
hop_length = int(win_length / 4)
if window is None:
# Default is an asymmetric Hann window.
ifft_window = scipy.signal.hann(win_length, sym=False)
elif six.callable(window):
# User supplied a windowing function
ifft_window = window(win_length)
else:
# User supplied a window vector.
# Make it into an array
ifft_window = np.asarray(window)
# Verify that the shape matches
if ifft_window.size != n_fft:
raise ParameterError('Size mismatch between n_fft and window size')
# Pad out to match n_fft
ifft_window = util.pad_center(ifft_window, n_fft)
n_frames = stft_matrix.shape[1]
expected_signal_len = n_fft + hop_length * (n_frames - 1)
y = np.zeros(expected_signal_len, dtype=dtype)
ifft_window_sum = np.zeros(expected_signal_len, dtype=dtype)
ifft_window_square = ifft_window * ifft_window
for i in range(n_frames):
sample = i * hop_length
spec = stft_matrix[:, i].flatten()
spec = np.concatenate((spec.conj(), spec[-2:0:-1]), 0)
ytmp = ifft_window * fft.ifft(spec).real
y[sample:(sample + n_fft)] = y[sample:(sample + n_fft)] + ytmp
ifft_window_sum[sample:(sample + n_fft)] += ifft_window_square
# Normalize by sum of squared window
approx_nonzero_indices = ifft_window_sum > util.SMALL_FLOAT
y[approx_nonzero_indices] /= ifft_window_sum[approx_nonzero_indices]
if center:
y = y[int(n_fft // 2):-int(n_fft // 2)]
return y
def ifgram(y, sr=22050, n_fft=2048, hop_length=None, win_length=None,
norm=False, center=True, ref_power=1e-6, clip=True, dtype=np.complex64):
'''Compute the instantaneous frequency (as a proportion of the sampling rate)
obtained as the time-derivative of the phase of the complex spectrum as
described by [1]_.
Calculates regular STFT as a side effect.
.. [1] Abe, Toshihiko, Takao Kobayashi, and Satoshi Imai.
"Harmonics tracking and pitch extraction based on instantaneous
frequency."
International Conference on Acoustics, Speech, and Signal Processing,
ICASSP-95., Vol. 1. IEEE, 1995.
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time series
sr : number > 0 [scalar]
sampling rate of `y`
n_fft : int > 0 [scalar]
FFT window size
hop_length : int > 0 [scalar]
hop length, number samples between subsequent frames.
If not supplied, defaults to `win_length / 4`.
win_length : int > 0, <= n_fft
Window length. Defaults to `n_fft`.
See `stft` for details.
norm : bool
Normalize the STFT.
center : boolean
- If `True`, the signal `y` is padded so that frame
`D[:, t]` (and `if_gram`) is centered at `y[t * hop_length]`.
- If `False`, then `D[:, t]` at `y[t * hop_length]`
ref_power : float >= 0 or callable
Minimum power threshold for estimating instantaneous frequency.
Any bin with `np.abs(D[f, t])**2 < ref_power` will receive the
default frequency estimate.
If callable, the threshold is set to `ref_power(np.abs(D)**2)`.
clip : boolean
- If `True`, clip estimated frequencies to the range `[0, 0.5 * sr]`.
- If `False`, estimated frequencies can be negative or exceed
`0.5 * sr`.
dtype : numeric type
Complex numeric type for `D`. Default is 64-bit complex.
Returns
-------
if_gram : np.ndarray [shape=(1 + n_fft/2, t), dtype=real]
Instantaneous frequency spectrogram:
`if_gram[f, t]` is the frequency at bin `f`, time `t`
D : np.ndarray [shape=(1 + n_fft/2, t), dtype=complex]
Short-time Fourier transform
See Also
--------
stft : Short-time Fourier Transform
Examples
--------
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> frequencies, D = librosa.ifgram(y, sr=sr)
>>> frequencies
array([[ 0.000e+00, 0.000e+00, ..., 0.000e+00, 0.000e+00],
[ 3.150e+01, 3.070e+01, ..., 1.077e+01, 1.077e+01],
...,
[ 1.101e+04, 1.101e+04, ..., 1.101e+04, 1.101e+04],
[ 1.102e+04, 1.102e+04, ..., 1.102e+04, 1.102e+04]])
'''
if win_length is None:
win_length = n_fft
if hop_length is None:
hop_length = int(win_length // 4)
# Construct a padded hann window
window = util.pad_center(scipy.signal.hann(win_length, sym=False), n_fft)
# Window for discrete differentiation
freq_angular = np.linspace(0, 2 * np.pi, n_fft, endpoint=False)
d_window = np.sin(-freq_angular) * np.pi / n_fft
stft_matrix = stft(y, n_fft=n_fft, hop_length=hop_length,
window=window, center=center, dtype=dtype)
diff_stft = stft(y, n_fft=n_fft, hop_length=hop_length,
window=d_window, center=center, dtype=dtype).conj()
# Compute power normalization. Suppress zeros.
mag, phase = magphase(stft_matrix)
if six.callable(ref_power):
ref_power = ref_power(mag**2)
elif ref_power < 0:
raise ParameterError('ref_power must be non-negative or callable.')
# Pylint does not correctly infer the type here, but it's correct.
# pylint: disable=maybe-no-member
freq_angular = freq_angular.reshape((-1, 1))
bin_offset = (phase * diff_stft).imag / mag
bin_offset[mag < ref_power**0.5] = 0
if_gram = freq_angular[:n_fft//2 + 1] + bin_offset
if norm:
stft_matrix = stft_matrix * 2.0 / window.sum()
if clip:
np.clip(if_gram, 0, np.pi, out=if_gram)
if_gram *= float(sr) * 0.5 / np.pi
return if_gram, stft_matrix
def magphase(D):
"""Separate a complex-valued spectrogram D into its magnitude (S)
and phase (P) components, so that `D = S * P`.
Parameters
----------
D : np.ndarray [shape=(d, t), dtype=complex]
complex-valued spectrogram
Returns
-------
D_mag : np.ndarray [shape=(d, t), dtype=real]
magnitude of `D`
D_phase : np.ndarray [shape=(d, t), dtype=complex]
`exp(1.j * phi)` where `phi` is the phase of `D`
Examples
--------
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.stft(y)
>>> magnitude, phase = librosa.magphase(D)
>>> magnitude
array([[ 2.524e-03, 4.329e-02, ..., 3.217e-04, 3.520e-05],
[ 2.645e-03, 5.152e-02, ..., 3.283e-04, 3.432e-04],
...,
[ 1.966e-05, 9.828e-06, ..., 3.164e-07, 9.370e-06],
[ 1.966e-05, 9.830e-06, ..., 3.161e-07, 9.366e-06]], dtype=float32)
>>> phase
array([[ 1.000e+00 +0.000e+00j, 1.000e+00 +0.000e+00j, ...,
-1.000e+00 +8.742e-08j, -1.000e+00 +8.742e-08j],
[ 1.000e+00 +1.615e-16j, 9.950e-01 -1.001e-01j, ...,
9.794e-01 +2.017e-01j, 1.492e-02 -9.999e-01j],
...,
[ 1.000e+00 -5.609e-15j, -5.081e-04 +1.000e+00j, ...,
-9.549e-01 -2.970e-01j, 2.938e-01 -9.559e-01j],
[ -1.000e+00 +8.742e-08j, -1.000e+00 +8.742e-08j, ...,
-1.000e+00 +8.742e-08j, -1.000e+00 +8.742e-08j]], dtype=complex64)
Or get the phase angle (in radians)
>>> np.angle(phase)
array([[ 0.000e+00, 0.000e+00, ..., 3.142e+00, 3.142e+00],
[ 1.615e-16, -1.003e-01, ..., 2.031e-01, -1.556e+00],
...,
[ -5.609e-15, 1.571e+00, ..., -2.840e+00, -1.273e+00],
[ 3.142e+00, 3.142e+00, ..., 3.142e+00, 3.142e+00]], dtype=float32)
"""
mag = np.abs(D)
phase = np.exp(1.j * np.angle(D))
return mag, phase
@cache
def phase_vocoder(D, rate, hop_length=None):
"""Phase vocoder. Given an STFT matrix D, speed up by a factor of `rate`
Based on the implementation provided by [1]_.
.. [1] Ellis, D. P. W. "A phase vocoder in Matlab."
Columbia University, 2002.
http://www.ee.columbia.edu/~dpwe/resources/matlab/pvoc/
Examples
--------
>>> # Play at double speed
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.stft(y, n_fft=2048, hop_length=512)
>>> D_fast = librosa.phase_vocoder(D, 2.0, hop_length=512)
>>> y_fast = librosa.istft(D_fast, hop_length=512)
>>> # Or play at 1/3 speed
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> D = librosa.stft(y, n_fft=2048, hop_length=512)
>>> D_slow = librosa.phase_vocoder(D, 1./3, hop_length=512)
>>> y_slow = librosa.istft(D_slow, hop_length=512)
Parameters
----------
D : np.ndarray [shape=(d, t), dtype=complex]
STFT matrix
rate : float > 0 [scalar]
Speed-up factor: `rate > 1` is faster, `rate < 1` is slower.
hop_length : int > 0 [scalar] or None
The number of samples between successive columns of `D`.
If None, defaults to `n_fft/4 = (D.shape[0]-1)/2`
Returns
-------
D_stretched : np.ndarray [shape=(d, t / rate), dtype=complex]
time-stretched STFT
"""
n_fft = 2 * (D.shape[0] - 1)
if hop_length is None:
hop_length = int(n_fft // 4)
time_steps = np.arange(0, D.shape[1], rate, dtype=np.float)
# Create an empty output array
d_stretch = np.zeros((D.shape[0], len(time_steps)), D.dtype, order='F')
# Expected phase advance in each bin
phi_advance = np.linspace(0, np.pi * hop_length, D.shape[0])
# Phase accumulator; initialize to the first sample
phase_acc = np.angle(D[:, 0])
# Pad 0 columns to simplify boundary logic
D = np.pad(D, [(0, 0), (0, 2)], mode='constant')
for (t, step) in enumerate(time_steps):
columns = D[:, int(step):int(step + 2)]
# Weighting for linear magnitude interpolation
alpha = np.mod(step, 1.0)
mag = ((1.0 - alpha) * np.abs(columns[:, 0])
+ alpha * np.abs(columns[:, 1]))
# Store to output array
d_stretch[:, t] = mag * np.exp(1.j * phase_acc)
# Compute phase advance
dphase = (np.angle(columns[:, 1])
- np.angle(columns[:, 0])
- phi_advance)
# Wrap to -pi:pi range
dphase = dphase - 2.0 * np.pi * np.round(dphase / (2.0 * np.pi))
# Accumulate phase
phase_acc += phi_advance + dphase
return d_stretch
@cache
def logamplitude(S, ref_power=1.0, amin=1e-10, top_db=80.0):
"""Log-scale the amplitude of a spectrogram.
Parameters
----------
S : np.ndarray [shape=(d, t)]
input spectrogram
ref_power : scalar or function
If scalar, `log(abs(S))` is compared to `log(ref_power)`.
If a function, `log(abs(S))` is compared to `log(ref_power(abs(S)))`.
This is primarily useful for comparing to the maximum value of `S`.
amin : float > 0[scalar]
minimum amplitude threshold for `abs(S)` and `ref_power`
top_db : float >= 0 [scalar]
threshold log amplitude at top_db below the peak:
``max(log(S)) - top_db``
Returns
-------
log_S : np.ndarray [shape=(d, t)]
``log_S ~= 10 * log10(S) - 10 * log10(abs(ref_power))``
See Also
--------
perceptual_weighting
Examples
--------
Get a power spectrogram from a waveform ``y``
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> librosa.logamplitude(S**2)
array([[-33.293, -27.32 , ..., -33.293, -33.293],
[-33.293, -25.723, ..., -33.293, -33.293],
...,
[-33.293, -33.293, ..., -33.293, -33.293],
[-33.293, -33.293, ..., -33.293, -33.293]], dtype=float32)
Compute dB relative to peak power
>>> librosa.logamplitude(S**2, ref_power=np.max)
array([[-80. , -74.027, ..., -80. , -80. ],
[-80. , -72.431, ..., -80. , -80. ],
...,
[-80. , -80. , ..., -80. , -80. ],
[-80. , -80. , ..., -80. , -80. ]], dtype=float32)
Or compare to median power
>>> librosa.logamplitude(S**2, ref_power=np.median)
array([[-0.189, 5.784, ..., -0.189, -0.189],
[-0.189, 7.381, ..., -0.189, -0.189],
...,
[-0.189, -0.189, ..., -0.189, -0.189],
[-0.189, -0.189, ..., -0.189, -0.189]], dtype=float32)
And plot the results
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2, 1, 1)
>>> librosa.display.specshow(S**2, sr=sr, y_axis='log', x_axis='time')
>>> plt.colorbar()
>>> plt.title('Power spectrogram')
>>> plt.subplot(2, 1, 2)
>>> librosa.display.specshow(librosa.logamplitude(S**2, ref_power=np.max),
... sr=sr, y_axis='log', x_axis='time')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.title('Log-Power spectrogram')
>>> plt.tight_layout()
"""
if amin <= 0:
raise ParameterError('amin must be strictly positive')
magnitude = np.abs(S)
if six.callable(ref_power):
# User supplied a function to calculate reference power
__ref = ref_power(magnitude)
else:
__ref = np.abs(ref_power)
log_spec = 10.0 * np.log10(np.maximum(amin, magnitude))
log_spec -= 10.0 * np.log10(np.maximum(amin, __ref))
if top_db is not None:
if top_db < 0:
raise ParameterError('top_db must be non-negative positive')
log_spec = np.maximum(log_spec, log_spec.max() - top_db)
return log_spec
@cache
def perceptual_weighting(S, frequencies, **kwargs):
'''Perceptual weighting of a power spectrogram:
`S_p[f] = A_weighting(f) + 10*log(S[f] / ref_power)`
Parameters
----------
S : np.ndarray [shape=(d, t)]
Power spectrogram
frequencies : np.ndarray [shape=(d,)]
Center frequency for each row of `S`
kwargs : additional keyword arguments
Additional keyword arguments to `logamplitude`.
Returns
-------
S_p : np.ndarray [shape=(d, t)]
perceptually weighted version of `S`
See Also
--------
logamplitude
Examples
--------
Re-weight a CQT power spectrum, using peak power as reference
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> CQT = librosa.cqt(y, sr=sr, fmin=librosa.note_to_hz('A1'))
>>> freqs = librosa.cqt_frequencies(CQT.shape[0],
... fmin=librosa.note_to_hz('A1'))
>>> perceptual_CQT = librosa.perceptual_weighting(CQT**2,
... freqs,
... ref_power=np.max)
>>> perceptual_CQT
array([[ -80.076, -80.049, ..., -104.735, -104.735],
[ -78.344, -78.555, ..., -103.725, -103.725],
...,
[ -76.272, -76.272, ..., -76.272, -76.272],
[ -76.485, -76.485, ..., -76.485, -76.485]])
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2, 1, 1)
>>> librosa.display.specshow(librosa.logamplitude(CQT**2,
... ref_power=np.max),
... fmin=librosa.note_to_hz('A1'),
... y_axis='cqt_hz',
... x_axis='time')
>>> plt.title('Log CQT power')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.subplot(2, 1, 2)
>>> librosa.display.specshow(perceptual_CQT, y_axis='cqt_hz',
... fmin=librosa.note_to_hz('A1'),
... x_axis='time')
>>> plt.title('Perceptually weighted log CQT')
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.tight_layout()
'''
offset = time_frequency.A_weighting(frequencies).reshape((-1, 1))
return offset + logamplitude(S, **kwargs)
@cache
def _spectrogram(y=None, S=None, n_fft=2048, hop_length=512, power=1):
'''Helper function to retrieve a magnitude spectrogram.
This is primarily used in feature extraction functions that can operate on
either audio time-series or spectrogram input.
Parameters
----------
y : None or np.ndarray [ndim=1]
If provided, an audio time series
S : None or np.ndarray
Spectrogram input, optional
n_fft : int > 0
STFT window size
hop_length : int > 0
STFT hop length
power : float > 0
Exponent for the magnitude spectrogram,
e.g., 1 for energy, 2 for power, etc.
Returns
-------
S_out : np.ndarray [dtype=np.float32]
- If `S` is provided as input, then `S_out == S`
- Else, `S_out = |stft(y, n_fft=n_fft, hop_length=hop_length)|**power`
n_fft : int > 0
- If `S` is provided, then `n_fft` is inferred from `S`
- Else, copied from input
'''
if S is not None:
# Infer n_fft from spectrogram shape
n_fft = 2 * (S.shape[0] - 1)
else:
# Otherwise, compute a magnitude spectrogram from input
S = np.abs(stft(y, n_fft=n_fft, hop_length=hop_length))**power
return S, n_fft
| isc |
preghenella/AliPhysics | PWGPP/FieldParam/fitsol.py | 39 | 8343 | #!/usr/bin/env python
debug = True # enable trace
def trace(x):
global debug
if debug: print(x)
trace("loading...")
from itertools import combinations, combinations_with_replacement
from glob import glob
from math import *
import operator
from os.path import basename
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model
import sklearn.feature_selection
import datetime
def prec_from_pathname(path):
if '2k' in path: return 0.002
elif '5k' in path: return 0.005
else: raise AssertionError('Unknown field strengh: %s' % path)
# ['x', 'y', 'z', 'xx', 'xy', 'xz', 'yy', ...]
def combinatrial_vars(vars_str='xyz', length=3):
term_list = []
for l in range(length):
term_list.extend([''.join(v) for v in combinations_with_replacement(list(vars_str), 1 + l)])
return term_list
# product :: a#* => [a] -> a
def product(xs):
return reduce(operator.mul, xs, 1) # foldl in Haskell
# (XYZ, "xx") -> XX
def term(dataframe, vars_str):
return product(map(lambda x: dataframe[x], list(vars_str)))
# (f(X), Y) -> (max deviation, max%, avg dev, avg%)
def deviation_stat(fX, Y, prec=0.005):
dev = np.abs(fX - Y)
(max_dev, avg_dev) = (dev.max(axis=0), dev.mean(axis=0))
(max_pct, avg_pct) = (max_dev / prec * 100, avg_dev / prec * 100)
return (max_dev, max_pct, avg_dev, avg_pct)
# IO Df
def load_samples(path, cylindrical_axis=True, absolute_axis=True, genvars=[]):
sample_cols = ['x', 'y', 'z', 'Bx', 'By', 'Bz']
df = pd.read_csv(path, sep=' ', names=sample_cols)
if cylindrical_axis:
df['r'] = np.sqrt(df.x**2 + df.y**2)
df['p'] = np.arctan2(df.y, df.x)
df['Bt'] = np.sqrt(df.Bx**2 + df.By**2)
df['Bpsi'] = np.arctan2(df.By, df.Bx) - np.arctan2(df.y, df.x)
df['Br'] = df.Bt * np.cos(df.Bpsi)
df['Bp'] = df.Bt * np.sin(df.Bpsi)
if absolute_axis:
df['X'] = np.abs(df.x)
df['Y'] = np.abs(df.y)
df['Z'] = np.abs(df.z)
for var in genvars:
df[var] = term(df, var)
return df
def choose(vars, df1, df2):
X1 = df1.loc[:, vars].as_matrix()
X2 = df2.loc[:, vars].as_matrix()
return (X1, X2)
# IO ()
def run_analysis_for_all_fields():
sample_set = glob("dat_z22/*2k*.sample.dat")
test_set = glob("dat_z22/*2k*.test.dat")
#print(sample_set, test_set)
assert(len(sample_set) == len(test_set) and len(sample_set) > 0)
result = pd.DataFrame()
for i, sample_file in enumerate(sample_set):
trace("run_analysis('%s', '%s')" % (sample_file, test_set[i]))
df = run_analysis(sample_file, test_set[i])
result = result.append(df, ignore_index=True)
write_header(result)
def run_analysis(sample_file = 'dat_z22/tpc2k-z0-q2.sample.dat',
test_file = 'dat_z22/tpc2k-z0-q2.test.dat'):
global precision, df, test, lr, la, xvars_full, xvars, yvars, X, Y, Xtest, Ytest, ana_result
precision = prec_from_pathname(sample_file)
assert(precision == prec_from_pathname(test_file))
xvars_full = combinatrial_vars('xyz', 3)[3:] # variables except x, y, z upto 3 dims
trace("reading training samples... " + sample_file)
df = load_samples(sample_file, genvars=xvars_full)
trace("reading test samples..." + test_file)
test = load_samples(test_file, genvars=xvars_full)
trace("linear regression fit...")
lr = sklearn.linear_model.LinearRegression()
#ri = sklearn.linear_model.RidgeCV()
#la = sklearn.linear_model.LassoCV()
fs = sklearn.feature_selection.RFE(lr, 1, verbose=0)
#xvars = ['x','y','z','xx','yy','zz','xy','yz','xz','xzz','yzz']
#xvars = ["xx", "yy", "zz", 'x', 'y', 'z', 'xzz', 'yzz']
#xvars = ['xxxr', 'xrrX', 'zzrX', 'p', 'xyrr', 'xzzr', 'xrrY', 'xzrX', 'xxxz', 'xzzr']
#xvars=['x', 'xzz', 'xyz', 'yz', 'yy', 'zz', 'xy', 'xx', 'z', 'y', 'xz', 'yzz']
yvars = ['Bx', 'By', 'Bz']
#yvars = ['Bz']
(Y, Ytest) = choose(yvars, df, test)
#(Y, Ytest) = (df['Bz'], test['Bz'])
xvars = combinatrial_vars('xyz', 3) # use all terms upto 3rd power
(X, Xtest) = choose(xvars, df, test)
for y in yvars:
fs.fit(X, df[y])
res = pd.DataFrame({ "term": xvars, "rank": fs.ranking_ })
trace(y)
trace(res.sort_values(by = "rank"))
#xvars=list(res.sort_values(by="rank")[:26]['term'])
lr.fit(X, Y)
trace(', '.join(yvars) + " = 1 + " + ' + '.join(xvars))
test_dev = deviation_stat(lr.predict(Xtest), Ytest, prec=precision)
#for i in range(len(yvars)):
# arr = [lr.intercept_[i]] + lr.coef_[i]
# arr = [ str(x) for x in arr ]
# print(yvars[i] + " = { " + ', '.join(arr) + " }")
# print("deviation stat [test]: max %.2e (%.1f%%) avg %.2e (%.1f%%)" %
# ( test_dev[0][i], test_dev[1][i], test_dev[2][i], test_dev[3][i] ))
(sample_score, test_score) = (lr.score(X, Y), lr.score(Xtest, Ytest))
trace("linear regression R^2 [train data]: %.8f" % sample_score)
trace("linear regression R^2 [test data] : %.8f" % test_score)
return pd.DataFrame(
{ "xvars": [xvars],
"yvars": [yvars],
"max_dev": [test_dev[0]],
"max%": [test_dev[1]],
"avg_dev": [test_dev[2]],
"avg%": [test_dev[3]],
"sample_score": [sample_score],
"score": [test_score],
"coeffs": [lr.coef_],
"intercept": [lr.intercept_],
"sample_file": [sample_file],
"test_file": [test_file],
"precision": [precision],
"volume_id": [volume_id_from_path(sample_file)]
})
def volume_id_from_path(path):
return basename(path)\
.replace('.sample.dat', '')\
.replace('-', '_')
def get_location_by_volume_id(id):
if 'its' in id: r_bin = 0
if 'tpc' in id: r_bin = 1
if 'tof' in id: r_bin = 2
if 'tofext' in id: r_bin = 3
if 'cal' in id: r_bin = 4
z_bin = int(id.split('_')[1][1:]) # "tofext2k_z0_q4" -> 0
if 'q1' in id: quadrant = 0
if 'q2' in id: quadrant = 1
if 'q3' in id: quadrant = 2
if 'q4' in id: quadrant = 3
return r_bin, z_bin, quadrant
def write_header(result):
#result.to_csv("magfield_params.csv")
#result.to_html("magfield_params.html")
print("# This file was generated from sysid.py at " + str(datetime.datetime.today()))
print("# " + ', '.join(result.iloc[0].yvars) + " = 1 + " + ' + '.join(result.iloc[0].xvars))
print("# barrel r: 0 < its < 80 < tpc < 250 < tof < 400 < tofext < 423 < cal < 500")
print("# barrel z: -550 < z < 550")
print("# phi: 0 < q1 < 0.5pi < q2 < pi < q3 < 1.5pi < q4 < 2pi")
print("# header: Rbin Zbin Quadrant Nval_per_compoment(=20)")
print("# data: Nval_per_compoment x floats")
#print("# R^2: coefficient of determination in multiple linear regression. [0,1]")
print("")
for index, row in result.iterrows():
#print("// ** %s - R^2 %s" % (row.volume_id, row.score))
print("#" + row.volume_id)
r_bin, z_bin, quadrant = get_location_by_volume_id(row.volume_id)
print("%s %s %s 20" % (r_bin, z_bin, quadrant))
for i, yvar in enumerate(row.yvars):
name = row.volume_id #+ '_' + yvar.lower()
print("# precision: tgt %.2e max %.2e (%.1f%%) avg %.2e (%.1f%%)" %
(row['precision'], row['max_dev'][i], row['max%'][i], row['avg_dev'][i], row['avg%'][i]))
coef = [row['intercept'][i]] + list(row['coeffs'][i])
arr = [ "%.5e" % x for x in coef ]
body = ' '.join(arr)
#decl = "const double[] %s = { %s };\n" % (name, body)
#print(decl)
print(body)
print("")
#write_header(run_analysis())
run_analysis_for_all_fields()
#for i in range(10):
# for xvars in combinations(xvars_full, i+1):
#(X, Xtest) = choose(xvars, df, test)
#lr.fit(X, Y)
#ri.fit(X, Y)
#la.fit(X, Y)
#fs.fit(X, Y)
#print xvars
#(sample_score, test_score) = (lr.score(X, Y), lr.score(Xtest, Ytest))
#print("linear R^2[sample] %.8f" % sample_score)
#print("linear R^2[test] %.8f" % test_score)
#(sample_score2, test_score2) = (la.score(X, Y), la.score(Xtest, Ytest))
#print("lasso R^2[sample] %.8f" % sample_score2)
#print("lasso R^2[test] %.8f" % test_score2)
#print(la.coef_)
#for i in range(len(yvars)):
# print(yvars[i])
# print(pd.DataFrame({"Name": xvars, "Params": lr.coef_[i]}).sort_values(by='Params'))
# print("+ %e" % lr.intercept_[i])
#sample_dev = deviation_stat(lr.predict(X), Y, prec=precision)
#test_dev = deviation_stat(lr.predict(Xtest), Ytest, prec=precision)
#test_dev2 = deviation_stat(la.predict(Xtest), Ytest, prec=precision)
#print("[sample] max %.2e (%.1f%%) avg %.2e (%.1f%%)" % sample_dev)
#print("[test] max %.2e (%.1f%%) avg %.2e (%.1f%%)" % test_dev )
#print("lasso [test] max %.2e (%.1f%%) avg %.2e (%.1f%%)" % test_dev2 )
| bsd-3-clause |
AtsushiSakai/PythonRobotics | PathPlanning/AStar/a_star_searching_from_two_side.py | 1 | 13901 | """
A* algorithm
Author: Weicent
randomly generate obstacles, start and goal point
searching path from start and end simultaneously
"""
import numpy as np
import matplotlib.pyplot as plt
import math
show_animation = True
class Node:
"""node with properties of g, h, coordinate and parent node"""
def __init__(self, G=0, H=0, coordinate=None, parent=None):
self.G = G
self.H = H
self.F = G + H
self.parent = parent
self.coordinate = coordinate
def reset_f(self):
self.F = self.G + self.H
def hcost(node_coordinate, goal):
dx = abs(node_coordinate[0] - goal[0])
dy = abs(node_coordinate[1] - goal[1])
hcost = dx + dy
return hcost
def gcost(fixed_node, update_node_coordinate):
dx = abs(fixed_node.coordinate[0] - update_node_coordinate[0])
dy = abs(fixed_node.coordinate[1] - update_node_coordinate[1])
gc = math.hypot(dx, dy) # gc = move from fixed_node to update_node
gcost = fixed_node.G + gc # gcost = move from start point to update_node
return gcost
def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number):
"""
:param start: start coordinate
:param goal: goal coordinate
:param top_vertex: top right vertex coordinate of boundary
:param bottom_vertex: bottom left vertex coordinate of boundary
:param obs_number: number of obstacles generated in the map
:return: boundary_obstacle array, obstacle list
"""
# below can be merged into a rectangle boundary
ay = list(range(bottom_vertex[1], top_vertex[1]))
ax = [bottom_vertex[0]] * len(ay)
cy = ay
cx = [top_vertex[0]] * len(cy)
bx = list(range(bottom_vertex[0] + 1, top_vertex[0]))
by = [bottom_vertex[1]] * len(bx)
dx = [bottom_vertex[0]] + bx + [top_vertex[0]]
dy = [top_vertex[1]] * len(dx)
# generate random obstacles
ob_x = np.random.randint(bottom_vertex[0] + 1,
top_vertex[0], obs_number).tolist()
ob_y = np.random.randint(bottom_vertex[1] + 1,
top_vertex[1], obs_number).tolist()
# x y coordinate in certain order for boundary
x = ax + bx + cx + dx
y = ay + by + cy + dy
obstacle = np.vstack((ob_x, ob_y)).T.tolist()
# remove start and goal coordinate in obstacle list
obstacle = [coor for coor in obstacle if coor != start and coor != goal]
obs_array = np.array(obstacle)
bound = np.vstack((x, y)).T
bound_obs = np.vstack((bound, obs_array))
return bound_obs, obstacle
def find_neighbor(node, ob, closed):
# generate neighbors in certain condition
ob_list = ob.tolist()
neighbor: list = []
for x in range(node.coordinate[0] - 1, node.coordinate[0] + 2):
for y in range(node.coordinate[1] - 1, node.coordinate[1] + 2):
if [x, y] not in ob_list:
# find all possible neighbor nodes
neighbor.append([x, y])
# remove node violate the motion rule
# 1. remove node.coordinate itself
neighbor.remove(node.coordinate)
# 2. remove neighbor nodes who cross through two diagonal
# positioned obstacles since there is no enough space for
# robot to go through two diagonal positioned obstacles
# top bottom left right neighbors of node
top_nei = [node.coordinate[0], node.coordinate[1] + 1]
bottom_nei = [node.coordinate[0], node.coordinate[1] - 1]
left_nei = [node.coordinate[0] - 1, node.coordinate[1]]
right_nei = [node.coordinate[0] + 1, node.coordinate[1]]
# neighbors in four vertex
lt_nei = [node.coordinate[0] - 1, node.coordinate[1] + 1]
rt_nei = [node.coordinate[0] + 1, node.coordinate[1] + 1]
lb_nei = [node.coordinate[0] - 1, node.coordinate[1] - 1]
rb_nei = [node.coordinate[0] + 1, node.coordinate[1] - 1]
# remove the unnecessary neighbors
if top_nei and left_nei in ob_list and lt_nei in neighbor:
neighbor.remove(lt_nei)
if top_nei and right_nei in ob_list and rt_nei in neighbor:
neighbor.remove(rt_nei)
if bottom_nei and left_nei in ob_list and lb_nei in neighbor:
neighbor.remove(lb_nei)
if bottom_nei and right_nei in ob_list and rb_nei in neighbor:
neighbor.remove(rb_nei)
neighbor = [x for x in neighbor if x not in closed]
return neighbor
def find_node_index(coordinate, node_list):
# find node index in the node list via its coordinate
ind = 0
for node in node_list:
if node.coordinate == coordinate:
target_node = node
ind = node_list.index(target_node)
break
return ind
def find_path(open_list, closed_list, goal, obstacle):
# searching for the path, update open and closed list
# obstacle = obstacle and boundary
flag = len(open_list)
for i in range(flag):
node = open_list[0]
open_coordinate_list = [node.coordinate for node in open_list]
closed_coordinate_list = [node.coordinate for node in closed_list]
temp = find_neighbor(node, obstacle, closed_coordinate_list)
for element in temp:
if element in closed_list:
continue
elif element in open_coordinate_list:
# if node in open list, update g value
ind = open_coordinate_list.index(element)
new_g = gcost(node, element)
if new_g <= open_list[ind].G:
open_list[ind].G = new_g
open_list[ind].reset_f()
open_list[ind].parent = node
else: # new coordinate, create corresponding node
ele_node = Node(coordinate=element, parent=node,
G=gcost(node, element), H=hcost(element, goal))
open_list.append(ele_node)
open_list.remove(node)
closed_list.append(node)
open_list.sort(key=lambda x: x.F)
return open_list, closed_list
def node_to_coordinate(node_list):
# convert node list into coordinate list and array
coordinate_list = [node.coordinate for node in node_list]
return coordinate_list
def check_node_coincide(close_ls1, closed_ls2):
"""
:param close_ls1: node closed list for searching from start
:param closed_ls2: node closed list for searching from end
:return: intersect node list for above two
"""
# check if node in close_ls1 intersect with node in closed_ls2
cl1 = node_to_coordinate(close_ls1)
cl2 = node_to_coordinate(closed_ls2)
intersect_ls = [node for node in cl1 if node in cl2]
return intersect_ls
def find_surrounding(coordinate, obstacle):
# find obstacles around node, help to draw the borderline
boundary: list = []
for x in range(coordinate[0] - 1, coordinate[0] + 2):
for y in range(coordinate[1] - 1, coordinate[1] + 2):
if [x, y] in obstacle:
boundary.append([x, y])
return boundary
def get_border_line(node_closed_ls, obstacle):
# if no path, find border line which confine goal or robot
border: list = []
coordinate_closed_ls = node_to_coordinate(node_closed_ls)
for coordinate in coordinate_closed_ls:
temp = find_surrounding(coordinate, obstacle)
border = border + temp
border_ary = np.array(border)
return border_ary
def get_path(org_list, goal_list, coordinate):
# get path from start to end
path_org: list = []
path_goal: list = []
ind = find_node_index(coordinate, org_list)
node = org_list[ind]
while node != org_list[0]:
path_org.append(node.coordinate)
node = node.parent
path_org.append(org_list[0].coordinate)
ind = find_node_index(coordinate, goal_list)
node = goal_list[ind]
while node != goal_list[0]:
path_goal.append(node.coordinate)
node = node.parent
path_goal.append(goal_list[0].coordinate)
path_org.reverse()
path = path_org + path_goal
path = np.array(path)
return path
def random_coordinate(bottom_vertex, top_vertex):
# generate random coordinates inside maze
coordinate = [np.random.randint(bottom_vertex[0] + 1, top_vertex[0]),
np.random.randint(bottom_vertex[1] + 1, top_vertex[1])]
return coordinate
def draw(close_origin, close_goal, start, end, bound):
# plot the map
if not close_goal.tolist(): # ensure the close_goal not empty
# in case of the obstacle number is really large (>4500), the
# origin is very likely blocked at the first search, and then
# the program is over and the searching from goal to origin
# will not start, which remain the closed_list for goal == []
# in order to plot the map, add the end coordinate to array
close_goal = np.array([end])
plt.cla()
plt.gcf().set_size_inches(11, 9, forward=True)
plt.axis('equal')
plt.plot(close_origin[:, 0], close_origin[:, 1], 'oy')
plt.plot(close_goal[:, 0], close_goal[:, 1], 'og')
plt.plot(bound[:, 0], bound[:, 1], 'sk')
plt.plot(end[0], end[1], '*b', label='Goal')
plt.plot(start[0], start[1], '^b', label='Origin')
plt.legend()
plt.pause(0.0001)
def draw_control(org_closed, goal_closed, flag, start, end, bound, obstacle):
"""
control the plot process, evaluate if the searching finished
flag == 0 : draw the searching process and plot path
flag == 1 or 2 : start or end is blocked, draw the border line
"""
stop_loop = 0 # stop sign for the searching
org_closed_ls = node_to_coordinate(org_closed)
org_array = np.array(org_closed_ls)
goal_closed_ls = node_to_coordinate(goal_closed)
goal_array = np.array(goal_closed_ls)
path = None
if show_animation: # draw the searching process
draw(org_array, goal_array, start, end, bound)
if flag == 0:
node_intersect = check_node_coincide(org_closed, goal_closed)
if node_intersect: # a path is find
path = get_path(org_closed, goal_closed, node_intersect[0])
stop_loop = 1
print('Path is find!')
if show_animation: # draw the path
plt.plot(path[:, 0], path[:, 1], '-r')
plt.title('Robot Arrived', size=20, loc='center')
plt.pause(0.01)
plt.show()
elif flag == 1: # start point blocked first
stop_loop = 1
print('There is no path to the goal! Start point is blocked!')
elif flag == 2: # end point blocked first
stop_loop = 1
print('There is no path to the goal! End point is blocked!')
if show_animation: # blocked case, draw the border line
info = 'There is no path to the goal!' \
' Robot&Goal are split by border' \
' shown in red \'x\'!'
if flag == 1:
border = get_border_line(org_closed, obstacle)
plt.plot(border[:, 0], border[:, 1], 'xr')
plt.title(info, size=14, loc='center')
plt.pause(0.01)
plt.show()
elif flag == 2:
border = get_border_line(goal_closed, obstacle)
plt.plot(border[:, 0], border[:, 1], 'xr')
plt.title(info, size=14, loc='center')
plt.pause(0.01)
plt.show()
return stop_loop, path
def searching_control(start, end, bound, obstacle):
"""manage the searching process, start searching from two side"""
# initial origin node and end node
origin = Node(coordinate=start, H=hcost(start, end))
goal = Node(coordinate=end, H=hcost(end, start))
# list for searching from origin to goal
origin_open: list = [origin]
origin_close: list = []
# list for searching from goal to origin
goal_open = [goal]
goal_close: list = []
# initial target
target_goal = end
# flag = 0 (not blocked) 1 (start point blocked) 2 (end point blocked)
flag = 0 # init flag
path = None
while True:
# searching from start to end
origin_open, origin_close = \
find_path(origin_open, origin_close, target_goal, bound)
if not origin_open: # no path condition
flag = 1 # origin node is blocked
draw_control(origin_close, goal_close, flag, start, end, bound,
obstacle)
break
# update target for searching from end to start
target_origin = min(origin_open, key=lambda x: x.F).coordinate
# searching from end to start
goal_open, goal_close = \
find_path(goal_open, goal_close, target_origin, bound)
if not goal_open: # no path condition
flag = 2 # goal is blocked
draw_control(origin_close, goal_close, flag, start, end, bound,
obstacle)
break
# update target for searching from start to end
target_goal = min(goal_open, key=lambda x: x.F).coordinate
# continue searching, draw the process
stop_sign, path = draw_control(origin_close, goal_close, flag, start,
end, bound, obstacle)
if stop_sign:
break
return path
def main(obstacle_number=1500):
print(__file__ + ' start!')
top_vertex = [60, 60] # top right vertex of boundary
bottom_vertex = [0, 0] # bottom left vertex of boundary
# generate start and goal point randomly
start = random_coordinate(bottom_vertex, top_vertex)
end = random_coordinate(bottom_vertex, top_vertex)
# generate boundary and obstacles
bound, obstacle = boundary_and_obstacles(start, end, top_vertex,
bottom_vertex,
obstacle_number)
path = searching_control(start, end, bound, obstacle)
if not show_animation:
print(path)
if __name__ == '__main__':
main(obstacle_number=1500)
| mit |
ricardobergamo/dataGAL | views/bmh.py | 1 | 2726 | #!/usr/bin/env python
from flask import render_template
from sqlalchemy import func
import pandas as pd
from manager import app
from models.bmh import *
from models.datagal import Regional, Laboratorio
@app.route('/bmh/painel')
def bmh_painel():
requisicao = Requisicao.get()
exames = Exames.get()
exame_status = BmhExameStatus.get()
exame_liberado = ExameLiberado.get()
laboratorio = Laboratorio.get_mod('Biologia Médica')
total_laboratorios = APP_Session.query(func.sum(Regional.laboratorio)).scalar()
return render_template('bmh/bmh_painel.html',
requisicao=requisicao, exames=exames,
exame_status=exame_status, exame_liberado=exame_liberado,
laboratorio=laboratorio, total_laboratorios=total_laboratorios)
def getNC(ano):
tb = RequisicaoNaoConformidade
nc_dic = APP_Session.query(tb, tb.requisicao, tb.ano, tb.codigo, tb.nao_conformidade).order_by(tb.nao_conformidade).filter(
tb.ano == ano).all()
nc = pd.DataFrame(data=nc_dic, columns=['id', 'req', 'ano', 'codigo', 'motivo'])
return nc
def totalNC():
tb = RequisicaoNaoConformidade
nc_ano = APP_Session.query(tb, tb.requisicao, tb.ano, tb.codigo, tb.nao_conformidade).order_by(tb.ano).all()
mot = pd.DataFrame(data=nc_ano, columns=['ID', 'Req', 'Ano', 'Codigo', 'Motivo'])
ano = mot.groupby(mot.Ano).size()
return ano
@app.route('/bmh/exames')
def bmh_exames():
exames = Exames.get()
exame_status = BmhExameStatus.get()
exame_naoconf = APP_Session.query(ExameNaoConformidade).order_by(ExameNaoConformidade.exames.desc()).all()
naoconf_total = APP_Session.query(func.sum(ExameNaoConformidade.exames)).scalar()
a2015 = getNC(2015)
a2015_lista = a2015.groupby(a2015.codigo).size().sort_values(ascending=False)
a2015_total = a2015.id.count()
a2016 = getNC(2016)
a2016_lista = a2016.groupby(a2016.codigo).size().sort_values(ascending=False)
a2016_total = a2016.id.count()
nc_total = totalNC()
return render_template('bmh/bmh_exames.html',
exames=exames, exame_status=exame_status,
exame_naoconf=exame_naoconf, naoconf_total=naoconf_total,
a2015=a2015, a2015_lista=a2015_lista, a2015_total=a2015_total,
a2016=a2016, a2016_lista=a2016_lista, a2016_total=a2016_total,
nc_total=nc_total)
@app.route('/bmh/liberados')
def bmh_liberados():
exames = Exames.get()
exame_liberado = ExameLiberado.get()
return render_template('bmh/bmh_liberados.html',
exames=exames, exame_liberado=exame_liberado) | gpl-3.0 |
louispotok/pandas | pandas/tests/indexes/timedeltas/test_construction.py | 3 | 3568 | import pytest
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas import TimedeltaIndex, timedelta_range, to_timedelta
class TestTimedeltaIndex(object):
def test_construction_base_constructor(self):
arr = [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')]
tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr))
tm.assert_index_equal(pd.Index(np.array(arr)),
pd.TimedeltaIndex(np.array(arr)))
arr = [np.nan, pd.NaT, pd.Timedelta('1 days')]
tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr))
tm.assert_index_equal(pd.Index(np.array(arr)),
pd.TimedeltaIndex(np.array(arr)))
def test_constructor(self):
expected = TimedeltaIndex(['1 days', '1 days 00:00:05', '2 days',
'2 days 00:00:02', '0 days 00:00:03'])
result = TimedeltaIndex(['1 days', '1 days, 00:00:05', np.timedelta64(
2, 'D'), timedelta(days=2, seconds=2), pd.offsets.Second(3)])
tm.assert_index_equal(result, expected)
# unicode
result = TimedeltaIndex([u'1 days', '1 days, 00:00:05', np.timedelta64(
2, 'D'), timedelta(days=2, seconds=2), pd.offsets.Second(3)])
expected = TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01',
'0 days 00:00:02'])
tm.assert_index_equal(TimedeltaIndex(range(3), unit='s'), expected)
expected = TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:05',
'0 days 00:00:09'])
tm.assert_index_equal(TimedeltaIndex([0, 5, 9], unit='s'), expected)
expected = TimedeltaIndex(
['0 days 00:00:00.400', '0 days 00:00:00.450',
'0 days 00:00:01.200'])
tm.assert_index_equal(TimedeltaIndex([400, 450, 1200], unit='ms'),
expected)
def test_constructor_coverage(self):
rng = timedelta_range('1 days', periods=10.5)
exp = timedelta_range('1 days', periods=10)
tm.assert_index_equal(rng, exp)
msg = 'periods must be a number, got foo'
with tm.assert_raises_regex(TypeError, msg):
TimedeltaIndex(start='1 days', periods='foo', freq='D')
pytest.raises(ValueError, TimedeltaIndex, start='1 days',
end='10 days')
pytest.raises(ValueError, TimedeltaIndex, '1 days')
# generator expression
gen = (timedelta(i) for i in range(10))
result = TimedeltaIndex(gen)
expected = TimedeltaIndex([timedelta(i) for i in range(10)])
tm.assert_index_equal(result, expected)
# NumPy string array
strings = np.array(['1 days', '2 days', '3 days'])
result = TimedeltaIndex(strings)
expected = to_timedelta([1, 2, 3], unit='d')
tm.assert_index_equal(result, expected)
from_ints = TimedeltaIndex(expected.asi8)
tm.assert_index_equal(from_ints, expected)
# non-conforming freq
pytest.raises(ValueError, TimedeltaIndex,
['1 days', '2 days', '4 days'], freq='D')
pytest.raises(ValueError, TimedeltaIndex, periods=10, freq='D')
def test_constructor_name(self):
idx = TimedeltaIndex(start='1 days', periods=1, freq='D', name='TEST')
assert idx.name == 'TEST'
# GH10025
idx2 = TimedeltaIndex(idx, name='something else')
assert idx2.name == 'something else'
| bsd-3-clause |
openeemeter/eemeter | eemeter/visualization.py | 1 | 4111 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
import pandas as pd
from .features import (
merge_features,
compute_usage_per_day_feature,
compute_temperature_features,
)
__all__ = ("plot_energy_signature", "plot_time_series")
def plot_time_series(meter_data, temperature_data, **kwargs):
"""Plot meter and temperature data in dual-axes time series.
Parameters
----------
meter_data : :any:`pandas.DataFrame`
A :any:`pandas.DatetimeIndex`-indexed DataFrame of meter data with the column ``value``.
temperature_data : :any:`pandas.Series`
A :any:`pandas.DatetimeIndex`-indexed Series of temperature data.
**kwargs
Arbitrary keyword arguments to pass to
:any:`plt.subplots <matplotlib.pyplot.subplots>`
Returns
-------
axes : :any:`tuple` of :any:`matplotlib.axes.Axes`
Tuple of ``(ax_meter_data, ax_temperature_data)``.
"""
# TODO(philngo): include image in docs.
try:
import matplotlib.pyplot as plt
except ImportError: # pragma: no cover
raise ImportError("matplotlib is required for plotting.")
default_kwargs = {"figsize": (16, 4)}
default_kwargs.update(kwargs)
fig, ax1 = plt.subplots(**default_kwargs)
ax1.plot(
meter_data.index,
meter_data.value,
color="C0",
label="Energy Use",
drawstyle="steps-post",
)
ax1.set_ylabel("Energy Use")
ax2 = ax1.twinx()
ax2.plot(
temperature_data.index,
temperature_data,
color="C1",
label="Temperature",
alpha=0.8,
)
ax2.set_ylabel("Temperature")
fig.legend()
return ax1, ax2
def plot_energy_signature(
meter_data,
temperature_data,
temp_col=None,
ax=None,
title=None,
figsize=None,
**kwargs
):
"""Plot meter and temperature data in energy signature.
Parameters
----------
meter_data : :any:`pandas.DataFrame`
A :any:`pandas.DatetimeIndex`-indexed DataFrame of meter data with the column ``value``.
temperature_data : :any:`pandas.Series`
A :any:`pandas.DatetimeIndex`-indexed Series of temperature data.
temp_col : :any:`str`, default ``'temperature_mean'``
The name of the temperature column.
ax : :any:`matplotlib.axes.Axes`
The axis on which to plot.
title : :any:`str`, optional
Chart title.
figsize : :any:`tuple`, optional
(width, height) of chart.
**kwargs
Arbitrary keyword arguments to pass to
:any:`matplotlib.axes.Axes.scatter`.
Returns
-------
ax : :any:`matplotlib.axes.Axes`
Matplotlib axes.
"""
try:
import matplotlib.pyplot as plt
except ImportError: # pragma: no cover
raise ImportError("matplotlib is required for plotting.")
# format data
temperature_mean = compute_temperature_features(meter_data.index, temperature_data)
usage_per_day = compute_usage_per_day_feature(meter_data, series_name="meter_value")
df = merge_features([usage_per_day, temperature_mean.temperature_mean])
if figsize is None:
figsize = (10, 4)
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
if temp_col is None:
temp_col = "temperature_mean"
ax.scatter(df[temp_col], df.meter_value, **kwargs)
ax.set_xlabel("Temperature")
ax.set_ylabel("Energy Use per Day")
if title is not None:
ax.set_title(title)
return ax
| apache-2.0 |
michellab/Sire | wrapper/Tools/Plot.py | 2 | 2857 |
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot
from Sire.Maths import Histogram, HistogramValue
from Sire.Analysis import DataPoint
def _plotHistogram(histogram):
x = []
y = []
for value in list(histogram.values()):
x.append(value.minimum())
y.append(0)
x.append(value.minimum())
y.append(value.value())
x.append(value.maximum())
y.append(value.value())
x.append(value.maximum())
y.append(0)
pyplot.errorbar(x, y)
def _plotDataPoint(point):
x = [point.x()]
y = [point.y()]
if point.hasErrorRange():
xerr = [point.xMinError()]
yerr = [point.yMinError()]
pyplot.errorbar(x, y, yerr, xerr)
xerr = [point.xMaxError()]
yerr = [point.yMaxError()]
pyplot.errorbar(x, y, yerr, xerr)
elif point.hasError():
xerr = [point.xError()]
yerr = [point.yError()]
pyplot.errorbar(x, y, yerr, xerr)
else:
pyplot.errorbar(x, y)
def _tryPlotHistValues(points):
x = []
y = []
for point in points:
if not isinstance(point, HistogramValue):
return False
x.append(point.middle())
y.append(point.value())
pyplot.errorbar(x,y)
return True
def _tryPlotDataPoints(points):
x = []
y = []
xminerr = []
xmaxerr = []
yminerr = []
ymaxerr = []
has_error_range = False
has_error = False
for point in points:
if not isinstance(point, DataPoint):
return False
x.append(point.x())
y.append(point.y())
if point.hasErrorRange():
has_error_range = True
xminerr.append( point.xMinError() )
xmaxerr.append( point.xMaxError() )
yminerr.append( point.yMinError() )
ymaxerr.append( point.yMaxError() )
elif point.hasError():
has_error = True
xminerr.append( point.xError() )
xmaxerr.append( point.xError() )
yminerr.append( point.yError() )
ymaxerr.append( point.yError() )
else:
xminerr.append(0)
xmaxerr.append(0)
yminerr.append(0)
ymaxerr.append(0)
if has_error_range:
pyplot.errorbar(x, y, ymaxerr, xmaxerr)
pyplot.errorbar(x, y, yminerr, xminerr)
elif has_error:
pyplot.errorbar(x, y, ymaxerr, xmaxerr)
else:
pyplot.errorbar(x, y)
return True
def _plot(graph):
if isinstance(graph, Histogram):
_plotHistogram(graph)
elif isinstance(graph, DataPoint):
_plotDataPoint(graph)
elif not _tryPlotDataPoints(graph):
if not _tryPlotHistValues(graph):
for item in graph:
_plot(item)
def plot(graph):
_plot(graph)
pyplot.show()
| gpl-2.0 |
jrversteegh/softsailor | softsailor/polars.py | 1 | 8539 | """
Polars module
Contains classes for dealing with boat polars
"""
__author__ = "J.R. Versteegh"
__copyright__ = "Copyright 2011, J.R. Versteegh"
__contact__ = "j.r.versteegh@gmail.com"
__version__ = "0.1"
__license__ = "GPLv3, No Warranty. See 'LICENSE'"
import os
import math
import numpy as np
from urllib2 import urlopen
from scipy import interpolate
import matplotlib as mpl
import matplotlib.pyplot as plt
from .classes import Object
from .utils import *
_pi = math.pi
_minpi = -math.pi
_twopi = 2 * math.pi
def to_float(value):
try:
return float(value)
except TypeError:
result = []
for v in value:
try:
result.append(float(v))
except ValueError:
# Conversion failed, ignore
pass
return np.array(result)
def from_knots(value):
return np.array(value) * (1.852 / 3.6)
def to_knots(value):
return np.array(value) * (3.6 / 1.852)
def from_degs(value):
return np.array(value) * (_pi / 180.0)
def to_degs(value):
return np.array(value) * (180.0 / _pi)
class Polars(Object):
def __init__(self, *args, **kwargs):
super(Polars, self).__init__(*args, **kwargs)
try:
self._smoothing = kwargs['smoothing']
except KeyError:
self._smoothing = None
try:
self._degree = kwargs['degree']
except KeyError:
self._degree = None
try:
self._filename = kwargs['filename']
self.load_from_file()
except KeyError:
self._filename = 'noname.txt'
try:
wind_speeds = kwargs['wind_speeds']
wind_angles = kwargs['wind_angles']
boat_speeds = kwargs['boat_speeds']
except KeyError:
wind_speeds = np.linspace(0, 50, 11)
wind_angles = np.linspace(0, np.pi, 19)
boat_speed = np.sqrt(wind_speeds) * np.sqrt(1.3 * wind_angles[:,np.newaxis])
self._from_data(wind_speeds, wind_angles, boat_speeds)
def _from_data(self, wind_speeds, wind_angles, boat_speeds):
self._mesh = np.meshgrid(wind_speeds, wind_angles)
self._data = np.array(boat_speeds).reshape(self._mesh[0].shape)
self._update_interpolation(self._smoothing, self._degree)
def _update_interpolation(self, smoothing=None, degree=None):
if degree is None:
degree = 3
if smoothing:
s = max(len(self._mesh[0][0,:]), len(self._mesh[1][:,0]))
else:
s = 0
self._evaluator = interpolate.RectBivariateSpline(
self._mesh[1][:,0], self._mesh[0][0,:],
self._data,
kx=degree, ky=degree,
s=s,
)
def save_to_file(self, filename=None):
'''
Save polars to file in optsail polar data format:
- Header line with wind speeds
- Header line with wind angles
- Number of angles data lines with boatspeed at each wind speed
'''
if not filename:
filename = self._filename
with open(filename, "w") as f:
f.write('# Wind speeds\n')
for speed in self.wind_speeds:
f.write(' %.2f' % to_knots(speed))
f.write('\n')
f.write('# Wind angles\n')
for angle in self.wind_angles:
f.write(' %.2f' % to_degs(angle))
f.write('\n')
f.write('# Boat speeds\n')
for r in self.data:
for c in r:
f.write(' %.2f' % to_knots(c))
f.write('\n')
def load_from_file(self, filename=None):
'''
Load polars from file. Currently three formats are supported:
1. Header line with wind speeds. Data line starting with angle
followed by boat speeds
2. Header line with windspeeds and header line with angles.
Data lines with boat speeds
3. Data lines with wind speed followed by pairs of angle and
boat speed
Angles are expected in degrees and speeds in knots
'''
if not filename:
filename = self._filename
self._filename = filename
wind_speeds = []
wind_angles = []
boat_speeds = []
transpose = True
no_header = False
f = urlopen(filename)
for line in f:
# Replace comma's and semicolons with spaces
line = line.replace(',', ' ')
line = line.replace(';', ' ')
# Strip whitespace and line ends
line = line.strip()
# Skip empty lines and comments
if not line or line[0] == '#' or line[0] == '!' or line[:3] == 'pol':
continue
values = to_float(line.split())
if len(values) == 0:
continue
if len(wind_speeds) == 0:
if np.all(values == np.sort(values)):
wind_speeds = from_knots(values)
continue
else:
no_header = True
if no_header:
wind_speeds.append(from_knots(values[0]))
wind_angles = from_degs(values[1::2])
boat_speeds.append(from_knots(values[2::2]))
transpose = True
continue
if len(values) == (len(wind_speeds) + 1):
transpose = False
wind_angles.append(from_degs(values[0]))
boat_speeds.append(from_knots(values[1:]))
continue
if len(wind_angles) == 0:
wind_angles = from_degs(values)
else:
if len(values) == len(wind_speeds):
transpose = False
else:
# Fill out incomplete lines with last value
if transpose:
while len(values) < len(wind_angles):
np.append(values, values[-1])
else:
while len(values) < len(wind_speeds):
np.append(values, values[-1])
boat_speeds.append(from_knots(values))
boat_speeds = np.array(boat_speeds)
if transpose:
boat_speeds = boat_speeds.transpose()
self._from_data(wind_speeds, wind_angles, boat_speeds)
@property
def mesh(self):
return self._mesh
@property
def data(self):
return self._data
@property
def wind_angles(self):
return self._mesh[1][:,0]
@property
def wind_speeds(self):
return self._mesh[0][0,:]
def get(self, angles, windspeed=None):
if windspeed is None:
angles, windspeed = angles
angles = np.fabs(angles)
result = self._evaluator(angles, windspeed)
return result.transpose()[0]
def _plot(self, merge):
if not merge:
plt.clf()
max_boat_speed = 0.
max_wind_speed = 30.
pp = plt.subplot(111, polar=True)
knot_wind_speeds = to_knots(self.wind_speeds)
max_wind_speed = min(max_wind_speed, knot_wind_speeds[-1])
nm = mpl.colors.Normalize(vmin=0., vmax=max_wind_speed)
cm = mpl.cm.get_cmap()
sm = mpl.cm.ScalarMappable(norm=nm, cmap=cm)
# Hack to set the colormap using the scatter function
plt.scatter([], [], c=np.array([[]]), vmin=0., vmax=max_wind_speed)
for i, w in enumerate(self.wind_speeds):
clr = sm.to_rgba(knot_wind_speeds[i])
knot_boat_speeds = to_knots(self._data[:,i])
max_boat_speed = max(max_boat_speed, max(knot_boat_speeds))
plt.plot(self.wind_angles, knot_boat_speeds, color=clr)
pp.set_rmax(max_boat_speed)
angles = list(range(-165,181,15))
pp.set_thetagrids(angles)
pp.set_theta_direction(-1)
pp.set_theta_zero_location('N')
plt.title('Polars')
plt.colorbar()
def save_plot(self, filename=None, merge=False):
if filename is None:
filename = os.path.splitext(self._filename)[0] + '.png'
self._plot(merge)
plt.savefig(filename)
def plot(self, merge=False):
self._plot(merge)
plt.show()
def optimal_ranges(self, wind_speed):
vmgu = 0
vmgd = 0
angles = from_degs([d for d in range(181)])
speeds = self.get(angles, wind_speed)
| gpl-3.0 |
hennersz/pySpace | basemap/examples/plotsst.py | 8 | 1770 | from mpl_toolkits.basemap import Basemap
from netCDF4 import Dataset, date2index
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
date = datetime(2007,12,15,0) # date to plot.
# open dataset.
dataset = \
Dataset('http://www.ncdc.noaa.gov/thredds/dodsC/OISST-V2-AVHRR_agg')
timevar = dataset.variables['time']
timeindex = date2index(date,timevar) # find time index for desired date.
# read sst. Will automatically create a masked array using
# missing_value variable attribute. 'squeeze out' singleton dimensions.
sst = dataset.variables['sst'][timeindex,:].squeeze()
# read ice.
ice = dataset.variables['ice'][timeindex,:].squeeze()
# read lats and lons (representing centers of grid boxes).
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
lons, lats = np.meshgrid(lons,lats)
# create figure, axes instances.
fig = plt.figure()
ax = fig.add_axes([0.05,0.05,0.9,0.9])
# create Basemap instance.
# coastlines not used, so resolution set to None to skip
# continent processing (this speeds things up a bit)
m = Basemap(projection='kav7',lon_0=0,resolution=None)
# draw line around map projection limb.
# color background of map projection region.
# missing values over land will show up this color.
m.drawmapboundary(fill_color='0.3')
# plot sst, then ice with pcolor
im1 = m.pcolormesh(lons,lats,sst,shading='flat',cmap=plt.cm.jet,latlon=True)
im2 = m.pcolormesh(lons,lats,ice,shading='flat',cmap=plt.cm.gist_gray,latlon=True)
# draw parallels and meridians, but don't bother labelling them.
m.drawparallels(np.arange(-90.,99.,30.))
m.drawmeridians(np.arange(-180.,180.,60.))
# add colorbar
cb = m.colorbar(im1,"bottom", size="5%", pad="2%")
# add a title.
ax.set_title('SST and ICE analysis for %s'%date)
plt.show()
| gpl-3.0 |
DSSG-paratransit/main_repo | Access_Analysis_Project/Scripts/4moDeadheadResultsToCsvParalleled.py | 1 | 961533 | import multiprocessing as mp
import numpy as np
import pandas as pd
import os
def findZeroes(row, numRows):
os.system(['clear', 'cls'][os.name == 'nt'])
print row
print str(float(row.name) / numRows * 100) + '%' + ' done'
busRun = data[(data['ServiceDate'] == row[0]) & (data['Run'] == row[1])]
if sum(busRun.NumOn) <= 0:
return(False)
return(True)
print 'Creating lists...'
cost = [2460.0, 3218.4444444444443, 3120.0, 3003.0, 3416.4444444444443, 3180.1111111111113, 3891.714285714286, 2970.3, 3091.777777777778, 5820.6, 4086.4285714285716, 4714.285714285715, 3334.0, 3227.222222222222, 4422.857142857143, 2765.4545454545455, 3187.5555555555557, 4536.0, 3414.75, 2880.0, 3336.777777777778, 3957.1428571428573, 3200.8888888888887, 2914.2, 3046.1666666666665, 2600.0, 2777.8571428571427, 2672.7272727272725, 2955.9, 2400.0, 3041.9, 3825.0, 2972.8, 3018.8, 3729.0, 3398.8333333333335, 3162.0, 3296.1111111111113, 4815.0, 2799.6363636363635, 4170.5, 4277.142857142857, 4637.142857142857, 3694.375, 2455.0, 2652.3636363636365, 3249.6666666666665, 3678.0, 3373.3333333333335, 3792.625, 2962.0, 3018.0, 2848.4285714285716, 3206.6666666666665, 2940.7, 4628.571428571428, 4342.0, 3870.5, 2916.846153846154, 4146.0, 5230.571428571428, 4066.8888888888887, 3489.818181818182, 4280.222222222223, 3414.5454545454545, 4235.888888888889, 3867.3333333333335, 4066.777777777778, 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2126.4285714285716, 2747.7272727272725, 2226.153846153846, 1996.0, 2837.4, 1901.25, 2361.076923076923, 2046.3157894736842, 2369.6666666666665, 1871.5625, 3028.4, 2091.4285714285716, 1938.0, 2394.9166666666665, 2238.4615384615386, 2594.0, 2294.230769230769, 1870.5625, 2455.1666666666665, 3562.625, 2260.6153846153848, 2880.9166666666665, 1950.0, 2426.5833333333335, 3789.5, 3380.0, 1940.6, 3340.0, 2563.6363636363635, 2982.0, 4668.0, 2790.0, 2967.2727272727275, 2140.0, 3737.4285714285716, 2910.0, 2307.6923076923076, 2233.846153846154, 3682.5, 2464.6153846153848, 2104.285714285714, 2220.0, 3300.0, 7371.5, 2705.0, 1984.0, 2480.0, 3226.6666666666665, 2976.0, 1984.2666666666667, 2104.285714285714, 2241.0, 3720.0, 2911.8, 2729.5454545454545, 3360.0, 2210.769230769231, 2316.923076923077, 2117.1428571428573, 3627.25, 2265.6153846153848, 3640.0, 2455.8333333333335, 3400.0, 2503.6363636363635, 3060.0, 2790.0, 2095.714285714286, 2261.5384615384614, 3190.222222222222, 2263.6923076923076, 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4107.0, 3425.0, 3936.0, 2997.1428571428573, 3703.4, 2884.285714285714, 3520.181818181818, 3325.0, 3360.3636363636365, 3378.4166666666665, 3310.75, 3762.0, 2613.0714285714284, 3168.9166666666665, 3359.5454545454545, 3578.181818181818, 3684.0, 2622.8571428571427, 3696.0, 3816.0, 2990.769230769231, 2742.8571428571427, 3143.3333333333335, 4233.333333333333, 2828.5714285714284, 3040.6923076923076, 2970.8, 3333.3333333333335, 2650.909090909091, 2825.4545454545455, 2561.909090909091, 2838.0, 3270.3333333333335, 2940.0, 2340.0, 2892.0, 9438.0, 2233.846153846154, 2629.6923076923076, 2485.0, 2465.0, 2805.7272727272725, 3592.5, 2194.285714285714, 2150.2, 2949.8, 2315.0, 2592.090909090909, 2380.0, 2922.0, 2238.4615384615386, 2036.0, 3012.0, 1844.0, 4100.0, 2626.181818181818, 2841.5714285714284, 2464.6153846153848, 2618.181818181818, 3885.0, 3832.5, 2946.0, 3226.6666666666665, 2595.0, 2958.0, 2645.4545454545455, 2536.3636363636365, 2446.153846153846, 2537.5454545454545, 2028.0, 2898.5, 2910.0, 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4133.666666666667, 4215.0, 4524.25, 2250.0, 2940.0, 3083.0, 3240.0, 2265.0, 2092.5, 2777.1428571428573, 2820.0, 3317.1428571428573, 1329.2307692307693, 4212.4, 2888.8571428571427, 4050.75, 2847.0, 1430.0, 3516.0, 2304.0, 4350.0, 4848.25, 1554.5454545454545, 3340.0, 2086.6666666666665, 3130.0, 2631.4285714285716, 2052.0, 2463.0, 2712.4285714285716, 2990.0, 1345.0, 3130.0, 1800.0, 4860.0, 2768.5714285714284, 1516.3636363636363, 3672.0, 1973.3333333333333, 4020.0, 2213.375, 3684.0, 3000.0, 1611.8181818181818, 2862.8571428571427, 3365.8333333333335, 2434.285714285714, 5100.0, 1900.0, 2562.8571428571427, 2537.1428571428573, 2598.0, 2730.0, 2605.714285714286, 3768.0, 2970.0, 2970.0, 1656.0, 2520.0, 2460.0, 2674.285714285714, 2468.5714285714284, 2292.4285714285716, 2741.214285714286, 2830.0, 2377.5, 3684.0, 3840.0, 2652.0, 3396.0, 2887.5, 2844.0, 2883.8333333333335, 3914.6, 3635.8333333333335, 2355.0, 3588.0, 2700.0, 1900.0, 3580.0, 3380.0, 2460.0, 3100.0, 2444.8571428571427, 2691.4285714285716, 3050.0, 2160.0, 3324.0, 2880.0, 2100.5, 3228.0, 2377.5, 2800.6666666666665, 2475.0, 2800.0, 2653.3333333333335, 3250.0, 2391.4285714285716, 2802.8571428571427, 2318.5714285714284, 2650.0, 2348.5714285714284, 2597.1428571428573, 3160.0, 4330.666666666667, 2700.0, 3832.5, 2503.6363636363635, 3100.0, 4337.142857142857, 2410.0, 3031.1, 2400.0, 2640.0, 2261.5384615384614, 2836.3636363636365, 2266.153846153846, 2062.5, 2400.0, 2478.4615384615386, 2173.846153846154, 3108.0, 2108.866666666667, 3066.0, 2830.0, 2907.2727272727275, 2220.0, 2541.818181818182, 2525.4545454545455, 2160.0, 2071.0, 2160.0, 2708.090909090909, 2688.0, 2896.3636363636365, 2295.0, 2345.0, 2744.181818181818, 2571.0833333333335, 2155.3846153846152, 2546.4545454545455, 2405.0, 2400.0, 3220.0, 2298.4615384615386, 2652.2727272727275, 2052.8571428571427, 2783.6363636363635, 2478.25, 2206.153846153846, 3313.3333333333335, 2634.5454545454545, 2901.7, 2665.909090909091, 2846.6, 3472.5, 2808.1428571428573, 2694.5454545454545, 3006.0, 2121.4285714285716, 2650.909090909091, 2196.923076923077, 2653.6363636363635, 2475.0, 2241.25, 3200.0, 2256.923076923077, 2469.0, 2667.1666666666665, 2850.0, 3768.5, 3689.1111111111113, 2979.090909090909, 2712.2727272727275, 2768.0, 3038.4, 2210.285714285714, 3396.777777777778, 2538.9166666666665, 2690.3333333333335, 2550.3333333333335, 2353.230769230769, 2309.5384615384614, 3042.2, 2432.0, 2661.733333333333, 2340.0588235294117, 2645.6, 2912.3076923076924, 3245.4545454545455, 2903.4615384615386, 2561.6666666666665, 3044.5, 2761.1428571428573, 2714.3571428571427, 2600.0, 3474.5454545454545, 3083.076923076923, 2670.0, 2700.0, 2588.0, 3437.4545454545455, 4106.666666666667, 2251.764705882353, 3161.6666666666665, 2691.4285714285716, 2542.733333333333, 1524.0, 2064.0, 2417.25, 3293.4444444444443, 1747.4285714285713, 3057.1, 2793.7272727272725, 1508.5714285714287, 2445.0, 3475.1111111111113, 1893.75, 3840.0, 3137.1111111111113, 1680.0, 2004.0, 2530.0, 1720.0, 2423.076923076923, 2810.769230769231, 3333.4545454545455, 3988.777777777778, 2524.285714285714, 2880.0, 3045.0, 2829.230769230769, 2880.0, 2627.1428571428573, 2925.0, 2912.3076923076924, 2456.0, 2640.0, 3055.0, 2206.470588235294, 2833.846153846154, 2820.0, 3115.0, 2313.75, 2738.5714285714284, 2060.0, 2965.769230769231, 4008.0, 3046.153846153846, 2672.0, 3816.0, 3210.0, 5622.857142857143, 6520.0, 3732.0, 2886.5384615384614, 2875.714285714286, 2777.1428571428573, 2893.923076923077, 2940.0, 3275.0, 3540.0, 2888.5714285714284, 3129.230769230769, 2832.8571428571427, 2738.5714285714284, 3531.7272727272725, 3463.6363636363635, 3150.0, 2738.5714285714284, 3469.090909090909, 2560.0, 3167.5, 2536.0, 2237.6470588235293, 3558.0, 3732.0, 3570.0, 3600.0, 3365.0, 2612.0, 3480.0, 2778.4615384615386, 3036.923076923077, 3105.0, 3380.0, 3561.818181818182, 4140.0, 3953.8571428571427, 3495.0, 2560.0, 4027.5, 2862.0, 3314.4444444444443, 2430.0, 2727.2727272727275, 3153.3333333333335, 2530.909090909091, 2164.6153846153848, 1730.0, 2440.0, 2805.0, 2856.0, 2856.9, 3502.5, 2535.0, 1980.0, 2000.0, 3600.0, 3552.0, 2227.5, 3038.0, 2013.3333333333333, 2313.9, 3468.0, 3330.8, 2102.5, 4566.0, 2753.6470588235293, 4211.777777777777, 2180.823529411765, 3714.5454545454545, 1780.8181818181818, 3495.6363636363635, 2538.1428571428573, 4667.333333333333, 2449.625, 2981.5384615384614, 2959.3846153846152, 4520.0, 1828.5714285714287, 2845.714285714286, 2649.5333333333333, 3518.181818181818, 2472.0, 4106.666666666667, 2644.0, 3942.0, 2437.5, 2772.1428571428573, 3164.5384615384614, 4100.7, 3376.3636363636365, 2627.1428571428573, 3289.7272727272725, 2612.785714285714, 3350.6363636363635, 2496.0, 3377.4545454545455, 3960.0, 2892.923076923077, 3750.0, 3354.5454545454545, 3289.090909090909, 2804.6923076923076, 2930.769230769231, 4226.666666666667, 3169.090909090909, 3354.5454545454545, 5197.5, 4013.3333333333335, 2945.0, 4800.0, 4402.5, 4186.888888888889, 3360.0, 3810.0, 3218.181818181818, 3785.4545454545455, 3030.0, 3852.0, 3175.0, 3525.5454545454545, 3113.6666666666665, 3912.0, 3496.3636363636365, 2949.230769230769, 3816.0, 2884.6153846153848, 3107.6923076923076, 4657.5, 3205.0, 2833.846153846154, 2153.3333333333335, 3214.5, 3942.0, 2925.0, 3321.818181818182, 3425.4545454545455, 2622.8571428571427, 2810.769230769231, 2811.3076923076924, 3354.5454545454545, 2825.1666666666665, 2790.785714285714, 3616.3636363636365, 3344.1666666666665, 3163.6363636363635, 2806.153846153846, 3381.818181818182, 2860.5384615384614, 3174.5454545454545, 2327.4117647058824, 2790.0, 2538.875, 2607.5333333333333, 2548.714285714286, 2980.0, 2996.1666666666665, 3274.6666666666665, 4037.2, 3356.3636363636365, 2581.5, 3319.0, 2486.266666666667, 2705.6428571428573, 3687.2727272727275, 2700.1666666666665, 2520.6666666666665, 3243.3333333333335, 2010.0, 3041.5833333333335, 2550.0, 3570.0, 2539.75, 3246.6666666666665, 2770.909090909091, 2716.3636363636365, 2119.3571428571427, 3113.3333333333335, 2994.0, 4741.666666666667, 2869.1, 2928.0, 2325.076923076923, 4088.5714285714284, 3108.4444444444443, 3707.375, 2856.0, 3006.6666666666665, 3802.5, 2803.9, 2874.0, 2020.0, 2694.2, 3160.0, 3114.222222222222, 2286.6666666666665, 4860.666666666667, 2664.0, 2651.090909090909, 1884.0, 3540.0, 2989.4444444444443, 3189.777777777778, 2060.0, 2563.6363636363635, 3457.5, 4042.8571428571427, 2721.818181818182, 3226.6666666666665, 4637.285714285715, 3297.6666666666665, 2874.6363636363635, 4037.1428571428573, 3068.0, 3120.0, 2718.0, 3433.3333333333335, 2355.0, 3667.5, 3006.6666666666665, 2785.1, 3706.125, 3705.0, 4208.166666666667, 4844.333333333333, 2266.153846153846, 3200.0, 2598.0, 3577.5, 3306.6666666666665, 5030.0, 3820.0, 3012.0, 2872.1, 3502.5, 2838.0, 3198.3, 3353.3333333333335, 4670.0, 3652.5, 2815.6363636363635, 3699.1111111111113, 2664.0, 3306.6666666666665, 3300.0, 3712.5, 3389.6666666666665, 3326.6666666666665, 2438.181818181818, 4796.666666666667, 4591.0, 3083.222222222222, 3067.3, 2926.3, 3802.5, 3487.5, 2964.0, 2645.4545454545455, 9853.333333333334, 2940.0, 3251.5555555555557, 2353.846153846154, 2523.0666666666666, 3645.0, 3780.0, 2194.285714285714, 2596.3636363636365, 2964.0, 2057.1428571428573, 3216.6666666666665, 2814.0, 3380.0, 3055.0, 2660.0, 2536.0, 2121.176470588235, 2792.3076923076924, 2474.5, 3105.3636363636365, 3481.3, 3554.818181818182, 2884.6153846153848, 2432.0, 2718.4615384615386, 3528.0, 2372.0, 2535.0, 4226.666666666667, 2980.0, 3267.2727272727275, 3200.5833333333335, 2773.846153846154, 3454.3636363636365, 2050.0, 3738.0, 4186.666666666667, 2255.0625, 3110.0, 3564.0, 1650.0, 2428.0, 4225.666666666667, 1653.9130434782608, 2428.0, 2810.769230769231, 1696.3636363636363, 3732.0, 2682.8571428571427, 2202.3529411764707, 3469.090909090909, 3261.818181818182, 2121.176470588235, 3480.0, 3701.75, 9104.333333333334, 2949.846153846154, 2753.4545454545455, 2844.769230769231, 2702.090909090909, 2694.0, 2626.0, 2934.3, 3140.9, 2907.2727272727275, 3302.4444444444443, 2760.0, 4244.428571428572, 2855.4545454545455, 2707.6363636363635, 2626.4166666666665, 2431.266666666667, 2667.2727272727275, 2828.5714285714284, 7642.666666666667, 2672.5454545454545, 3000.25, 2425.0833333333335, 2027.142857142857, 3571.0, 2932.769230769231, 3720.6, 2717.5714285714284, 2322.3529411764707, 2704.285714285714, 3430.909090909091, 3263.076923076923, 3070.0, 2680.0, 2721.4285714285716, 3392.7272727272725, 2958.0, 2982.0, 3705.0, 3320.6666666666665, 2625.0, 3166.6666666666665, 2535.0, 3387.1111111111113, 2710.909090909091, 2740.0, 2284.6153846153848, 2162.0714285714284, 3014.8, 3170.0, 2298.4615384615386, 2304.5384615384614, 2850.0, 2555.0, 3627.2727272727275, 3020.0, 2776.3636363636365, 3301.1111111111113, 2760.0, 2164.0, 3413.3333333333335, 2807.1428571428573, 2515.3846153846152, 2590.909090909091, 3076.3636363636365, 2656.3636363636365, 3005.0, 2746.153846153846, 2822.5384615384614, 2317.5, 2725.1428571428573, 2348.375, 2003.3333333333333, 2432.0, 2301.176470588235, 2847.6923076923076, 4026.6666666666665, 2556.0, 2173.3333333333335, 3045.0, 3145.0, 2875.3846153846152, 3267.2727272727275, 3529.090909090909, 2737.285714285714, 3289.090909090909, 3185.3076923076924, 2596.0, 3360.0, 2952.8571428571427, 2886.153846153846, 3134.6153846153848, 3381.818181818182, 2119.722222222222, 3949.6, 2370.0, 2764.285714285714, 2832.214285714286, 3889.090909090909, 2536.6666666666665, 3115.0, 3065.3846153846152, 3783.6, 2594.4, 2496.0, 3681.6666666666665, 3387.0, 3283.6, 3626.6666666666665, 3277.0, 3877.5, 4348.0, 3154.4, 4177.875, 3615.0, 3947.6666666666665, 3460.0, 3486.6666666666665, 2680.0, 3606.6666666666665, 3757.5, 3138.0, 3840.0, 3183.6666666666665, 2779.0, 3512.7272727272725, 3140.0, 3272.7272727272725, 2600.0, 2749.090909090909, 2688.0, 2836.5, 2104.285714285714, 4280.0, 2850.0, 2664.0, 2707.6363636363635, 2775.0, 2128.0, 2591.3636363636365, 2229.230769230769, 2852.7272727272725, 1688.5714285714287, 9388.0, 3386.6666666666665, 2753.3333333333335, 2820.0, 3400.0, 3180.0, 4508.571428571428, 3119.4, 3705.0, 2550.0, 3120.1, 31338.0, 4182.857142857143, 2560.5, 2322.8571428571427, 2727.2727272727275, 3044.0, 2076.5714285714284, 2266.153846153846, 3286.6666666666665, 2867.0, 3316.8888888888887, 3130.909090909091, 3382.5, 3126.1, 2294.5833333333335, 2432.3076923076924, 2390.0, 2722.6363636363635, 2625.25, 3393.3333333333335, 2164.285714285714, 2596.3636363636365, 2340.0, 2868.0, 1766.1764705882354, 2994.5454545454545, 2445.0, 3110.222222222222, 2825.4545454545455, 3620.0, 2829.9, 3793.3333333333335, 2625.0, 3174.0, 3228.7, 2372.7272727272725, 2177.1428571428573, 2540.3333333333335, 2922.0, 2476.3636363636365, 3376.3636363636365, 2375.0, 2976.0, 3040.0, 4051.5714285714284, 2460.0, 2562.75, 2220.0, 2721.818181818182, 3114.5555555555557, 2830.909090909091, 2762.090909090909, 2510.0, 2256.923076923077, 3160.0, 2970.0, 2940.0, 3994.285714285714, 4640.0, 3353.3333333333335, 2928.0, 4005.0, 3576.0, 4920.0, 3660.0, 3312.0, 4365.0, 2330.0, 2779.4, 3207.923076923077, 2690.0, 2348.5714285714284, 5480.0, 4179.5, 1794.9, 3180.0, 2850.0, 2890.0, 2430.0, 3540.2, 4020.25, 2830.0, 3693.8, 1832.0, 2750.0, 3647.8, 4905.0, 3624.0, 2850.3333333333335, 4410.0, 3020.0, 2834.6666666666665, 6080.0, 2960.0, 4660.0, 4500.0, 3660.0, 4650.0, 4200.428571428572, 4202.285714285715, 3123.777777777778, 3413.3333333333335, 3675.0]
print '\tCost length: ' + str(len(cost))
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0.060209102091020913, 0.15376666666666666, 0.29349540078843622, 0.31188084755090806, 0.10057705363204346, 0.038269100743745774, 0.084757586327171258, 0.29405064987270535, 0.26187572590011615, 0.3172579453067258, 0.40561722273759632, 0.30804430724532417, 0.37267552182163188, 0.1386331938633194, 0.43099936558816654, 0.23254928102085512, 0.17685003274394237, 0.23567656765676565, 0.12440476190476191, 0.24421433267587112, 0.2889121338912134, 0.062348043297252291, 0.2738008500303582, 0.034389911383776416, 0.41769076879108319, 0.43770913770913777, 0.28072437077961943, 0.38103819922965471, 0.43547158758055066, 0.24526984126984128, 0.42948960302457467, 0.31148759562672285, 0.32363984674329505, 0.046325459317585305, 0.040578664217294315, 0.27713894592744687, 0.15521292217327456, 0.39994614970382336, 0.34392982456140347, 0.33760080645161289, 0.3206140350877193, 0.34321779909030004, 0.25125338753387533, 0.047702663154814168, 0.024532224532224534, 0.31756847027388113, 0.34501088081053122, 0.27639691714836223, 0.25389197906724154, 0.17583001328021247, 0.2687798227675528, 0.38263009845288337, 0.27686868686868688, 0.30404761904761907, 0.35597281831187411, 0.15096982758620689, 0.0, 0.27561475409836067, 0.28352059925093637, 0.46140939597315439, 0.0, 0.16338797814207648, 0.15362318840579711, 0.42285223367697594, 0.22467811158798282, 0.21493847593005683, 0.2398148814233988, 0.064869888475836426, 0.30839416058394165, 0.4742092457420925, 0.34322287354946768, 0.096885620368822783, 0.35681341719077575, 0.17988304093567253, 0.2963667820069204, 0.4262002743484225, 0.3418450934975425, 0.44449972016665634, 0.19982332155477031, 0.37468189939899288, 0.25786026200873363, 0.40648484848484845, 0.44032019299303693, 0.3302242609582059, 0.39282560706401765, 0.57221377616653024, 0.55884353741496606, 0.2187637969094923, 0.16468720602069614, 0.48947368421052628, 0.39724099099099103, 0.52968526466380539, 0.32705555555555554, 0.34393442622950815, 0.56037634408602155, 0.53586368737883894, 0.400020397062823, 0.073308671836095893, 0.40830078125000002, 0.49938775510204075]
print '\tDeadhead length: ' + str(len(deadhead))
print 'Done.\n'
print 'Reading in csv...'
data = pd.read_csv('../../data/UW_Trip_Data_14mo_QC.csv')
print '\tlength: ' + str(len(data))
print 'Done.\n'
print 'Finding uniques...'
uniqueDR = data[['ServiceDate', 'Run']].drop_duplicates()
uniqueDR.reset_index(inplace=True)
print str(len(uniqueDR)) + ' unique combinations.\n'
print uniqueDR
print 'Dropping rows...'
p = mp.Pool(processes=8)
split_dfs = np.array_split(uniqueDR,8)
pool_results = p.map(uniqueDR.apply(lambda x: findZeroes(x, len(uniqueDR)), axis = 1), split_dfs)
p.close()
p.join()
dontDrop = pd.concat(pool_results, axis=0)
dontDrop.columns = ['values']
# print(dontDrop.values) # debug
columns = dontDrop[dontDrop.values].index.values
# print(columns) # debug
uniqueDR.drop(columns,1,inplace=True)
uniqueDR.reset_index(inplace=True)
print 'Number of drops: '# + str(len(toDrop))
print 'Number needed: ' + str(len(uniqueDR) - len(cost))
print '\n-------------------------------\n'
print 'Adding columsn to dataframe...'
uniqueDR['CostProxy'] = cost
print 'CostProxy complete.'
uniqueDR['PctDeadhead'] = deadhead
print 'PctDeadhead complete.'
UniqueDR.to_csv('../../data/temp_deadhead_result.csv')
| agpl-3.0 |
erdc-cm/air-water-vv | 2d/numericalTanks/nonlinearWaves/postprocess_NLW.py | 1 | 4065 | import numpy as np
import collections as cll
import csv
import os
import matplotlib.pyplot as plt
import nonlinear_waves as nlw
from proteus import WaveTools as wt
from AnalysisTools import signalFilter,zeroCrossing,reflStat
#####################################################################################
## Reading probes into the file
folder = "output"
os.chdir(folder)
file_vof = 'column_gauges.csv'
def readProbeFile(filename):
with open (filename, 'rb') as csvfile:
data=np.loadtxt(csvfile, delimiter=",",skiprows=1)
time=data[:,0]
data = data[:,1:]
csvfile.seek(0)
header = csvfile.readline()
header = header.replace("time","")
header = header.replace("[","")
header = header.replace("]","")
header = header.replace(","," ")
header = header.split()
probeType = []
probex = []
probey = []
probez = []
for ii in range(0,len(header),4):
probeType.append(header[ii])
probex.append(float(header[ii+1]))
probey.append(float(header[ii+2]))
probez.append(float(header[ii+3]))
probeCoord = zip(np.array(probex),np.array(probey),np.array(probez))
datalist = [probeType,probeCoord,time,data]
return datalist
data_vof = readProbeFile(file_vof)
#####################################################################################
# Exctracting probes
time = data_vof[2]
vof = data_vof[3]
eta_num = []
tank_dim = nlw.opts.tank_dim
waterLevel = nlw.opts.water_level
i_mid = len(vof[0])/2-1
for i in range(0, len(vof)):
eta_num.append(tank_dim[1]-vof[i][i_mid]-waterLevel)
eta_num = np.array(eta_num)
# Theoretical eta
x = np.array(data_vof[1][2*i_mid])
wave = nlw.wave
eta_th = []
for i in range(0,len(time)):
eta_th.append(wave.eta(x,time[i]))
#####################################################################################
# Plotting the probes
plt.figure(num='eta')
plt.plot(time, eta_num, 'b', label='numerical')
plt.plot(time, eta_th, 'r--', label='theoretical')
plt.legend(loc='upper right')
plt.xlabel('time [sec]')
plt.ylabel('eta [m]')
plt.xlim((0.,30.))
plt.ylim((-0.5,0.5))
plt.suptitle('Surface elevation against time in the middle of the tank.')
plt.grid()
plt.show()
plt.savefig('eta_NLW.png')
#####################################################################################
# Validation of the result
S = 0.
c = 0.
istart = np.where(time>=6.)[0][0]
iend = np.where(time>=18.)[0][0]
for i in range(istart,iend):
c = c + 1.
S = S + (eta_th[i]-eta_num[i])**2
err = np.sqrt(S/c)
err = 100*err/(nlw.opts.wave_height+waterLevel)
val = open('validation_eta_NLW.txt', 'w')
val.write('Eta in the middle of the tank.'+'\n')
val.write('Gauges taken between 6s and 18s'+'\n')
val.write('Average error (%) between the theoretical function and the simulation:'+'\n')
val.write(str(err))
val.close()
#####################################################################################
# Reflection
dataW = readProbeFile('column_gauges.csv')
time = dataW[2]
L = nlw.opts.wave_wavelength
Nwaves = (nlw.opts.tank_dim[0]+nlw.opts.tank_sponge[0]+nlw.opts.tank_sponge[1])/L
T = nlw.opts.wave_period
Tend = time[-1]
Tstart = Tend-Nwaves*T
i_mid = len(dataW[3][0])/2-1
time_int = np.linspace(time[0],Tend,len(time))
data1 = np.zeros((len(time),len(dataW[3][0])),"d")
bf = 1.2
minf = 1./bf/T
maxf = bf / T
dx_array = nlw.opts.gauge_dx
Narray = int(round(L/6/dx_array))
data = np.zeros((len(data1),3))
zc = []
for ii in range(0,3):
data1[:,i_mid+ii*Narray] = np.interp(time_int,time,dataW[3][:,i_mid+ii*Narray])
data[:,ii] = signalFilter(time,data1[:,i_mid+ii*Narray],minf, maxf, 1.1*maxf, 0.9*minf)
zc.append(zeroCrossing(time,data[:,ii]))
H1 = zc[0][1]
H2 = zc[1][1]
H3 = zc[2][1]
HH = reflStat(H1,H2,H3,Narray*dx_array,L)[0]
RR = reflStat(H1,H2,H3,Narray*dx_array,L)[2]
print "RR = ", RR
| mit |
lukebarnard1/bokeh | sphinx/source/docs/tutorials/solutions/stocks.py | 23 | 2799 | ###
### NOTE: This exercise requires a network connection
###
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show, VBox
# Here is some code to read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
MSFT = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
IBM = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
output_file("stocks.html", title="stocks.py example")
# create a figure
p1 = figure(title="Stocks",
x_axis_label="Date",
y_axis_label="Close price",
x_axis_type="datetime")
p1.below[0].formatter.formats = dict(years=['%Y'],
months=['%b %Y'],
days=['%d %b %Y'])
# EXERCISE: finish this line plot, and add more for the other stocks. Each one should
# have a legend, and its own color.
p1.line(
AAPL['Date'], # x coordinates
AAPL['Adj Close'], # y coordinates
color='#A6CEE3', # set a color for the line
legend='AAPL', # attach a legend label
)
p1.line(IBM['Date'], IBM['Adj Close'], color='#33A02C', legend='IBM')
p1.line(MSFT['Date'], MSFT['Adj Close'], color='#FB9A99', legend='MSFT')
# EXERCISE: style the plot, set a title, lighten the gridlines, etc.
p1.title = "Stock Closing Prices"
p1.grid.grid_line_alpha=0.3
# EXERCISE: start a new figure
p2 = figure(title="AAPL average",
x_axis_label="Date",
y_axis_label="Close price",
x_axis_type="datetime")
p2.below[0].formatter.formats = dict(years=['%Y'],
months=['%b %Y'],
days=['%d %b %Y'])
# Here is some code to compute the 30-day moving average for AAPL
aapl = AAPL['Adj Close']
aapl_dates = AAPL['Date']
window_size = 30
window = np.ones(window_size)/float(window_size)
aapl_avg = np.convolve(aapl, window, 'same')
# EXERCISE: plot a scatter of circles for the individual AAPL prices with legend
# 'close'. Remember to set the x axis type and tools on the first renderer
p2.scatter(aapl_dates, aapl, size=4, color='#A6CEE3', legend='close')
# EXERCISE: plot a line of the AAPL moving average data with the legeng 'avg'
p2.line(aapl_dates, aapl_avg, color='red', legend='avg')
# EXERCISE: style the plot, set a title, lighten the gridlines, etc.
p2.title = "AAPL One-Month Average"
p2.grid.grid_line_alpha=0.3
show(VBox(p1, p2)) # open a browser
| bsd-3-clause |
Myasuka/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation metrics.
Non-adjusted measures such as the V-Measure show a dependency between
the number of clusters and the number of samples: the mean V-Measure
of random labeling increases significantly as the number of clusters is
closer to the total number of samples used to compute the measure.
Adjusted for chance measure such as ARI display some random variations
centered around a mean score of 0.0 for any number of samples and
clusters.
Only adjusted measures can hence safely be used as a consensus index
to evaluate the average stability of clustering algorithms for a given
value of k on various overlapping sub-samples of the dataset.
"""
print(__doc__)
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from time import time
from sklearn import metrics
def uniform_labelings_scores(score_func, n_samples, n_clusters_range,
fixed_n_classes=None, n_runs=5, seed=42):
"""Compute score for 2 random uniform cluster labelings.
Both random labelings have the same number of clusters for each value
possible value in ``n_clusters_range``.
When fixed_n_classes is not None the first labeling is considered a ground
truth class assignment with fixed number of classes.
"""
random_labels = np.random.RandomState(seed).random_integers
scores = np.zeros((len(n_clusters_range), n_runs))
if fixed_n_classes is not None:
labels_a = random_labels(low=0, high=fixed_n_classes - 1,
size=n_samples)
for i, k in enumerate(n_clusters_range):
for j in range(n_runs):
if fixed_n_classes is None:
labels_a = random_labels(low=0, high=k - 1, size=n_samples)
labels_b = random_labels(low=0, high=k - 1, size=n_samples)
scores[i, j] = score_func(labels_a, labels_b)
return scores
score_funcs = [
metrics.adjusted_rand_score,
metrics.v_measure_score,
metrics.adjusted_mutual_info_score,
metrics.mutual_info_score,
]
# 2 independent random clusterings with equal cluster number
n_samples = 100
n_clusters_range = np.linspace(2, n_samples, 10).astype(np.int)
plt.figure(1)
plots = []
names = []
for score_func in score_funcs:
print("Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples))
t0 = time()
scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range)
print("done in %0.3fs" % (time() - t0))
plots.append(plt.errorbar(
n_clusters_range, np.median(scores, axis=1), scores.std(axis=1))[0])
names.append(score_func.__name__)
plt.title("Clustering measures for 2 random uniform labelings\n"
"with equal number of clusters")
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.legend(plots, names)
plt.ylim(ymin=-0.05, ymax=1.05)
# Random labeling with varying n_clusters against ground class labels
# with fixed number of clusters
n_samples = 1000
n_clusters_range = np.linspace(2, 100, 10).astype(np.int)
n_classes = 10
plt.figure(2)
plots = []
names = []
for score_func in score_funcs:
print("Computing %s for %d values of n_clusters and n_samples=%d"
% (score_func.__name__, len(n_clusters_range), n_samples))
t0 = time()
scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range,
fixed_n_classes=n_classes)
print("done in %0.3fs" % (time() - t0))
plots.append(plt.errorbar(
n_clusters_range, scores.mean(axis=1), scores.std(axis=1))[0])
names.append(score_func.__name__)
plt.title("Clustering measures for random uniform labeling\n"
"against reference assignment with %d classes" % n_classes)
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.ylim(ymin=-0.05, ymax=1.05)
plt.legend(plots, names)
plt.show()
| bsd-3-clause |
neozhangthe1/coverage_model | scripts/evaluate.py | 1 | 2312 | #!/usr/bin/env python
import numpy
import pandas
import argparse
import matplotlib
import logging
matplotlib.use("Agg")
from matplotlib import pyplot
logger = logging.getLogger()
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--start", type=int, default=0, help="Start from this iteration")
parser.add_argument("--finish", type=int, default=10 ** 9, help="Finish with that iteration")
parser.add_argument("--window", type=int, default=100, help="Window width")
parser.add_argument("--hours", action="store_true", default=False, help="Display time on X-axis")
parser.add_argument("--legend", default=None, help="Legend to use in plot")
parser.add_argument("--y", default="log2_p_expl", help="What to plot")
parser.add_argument("timings", nargs="+", help="Path to timing files")
parser.add_argument("plot_path", help="Path to save plot")
return parser.parse_args()
def load_timings(path, args, y):
logging.debug("Loading timings from {}".format(path))
tm = numpy.load(path)
num_steps = min(tm['step'], args.finish)
df = pandas.DataFrame({k : tm[k] for k in [y, 'time_step']})[args.start:num_steps]
one_step = df['time_step'].median() / 3600.0
logging.debug("Median time for one step is {} hours".format(one_step))
if args.hours:
df.index = (args.start + numpy.arange(0, df.index.shape[0])) * one_step
return pandas.rolling_mean(df, args.window).iloc[args.window:]
if __name__ == "__main__":
args = parse_args()
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
args.y = args.y.split(',')
if len(args.y) < 2:
args.y = [args.y[0]] * len(args.timings)
datas = [load_timings(path, args, y) for path,y in zip(args.timings,args.y)]
for path, y, data in zip(args.timings, args.y, datas):
pyplot.plot(data.index, data[y])
print("Average {} is {} after {} {} for {}".format(
y, data[y].iloc[-1],
data.index[-1], "hours" if args.hours else "iterations", path))
pyplot.xlabel("hours" if args.hours else "iterations")
pyplot.ylabel("log_2 likelihood")
pyplot.legend(args.legend.split(",") if args.legend else list(range(len(datas))))
pyplot.savefig(args.plot_path)
| bsd-3-clause |
Eric89GXL/mne-python | mne/viz/_brain/_brain.py | 2 | 132116 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Oleh Kozynets <ok7mailbox@gmail.com>
# Guillaume Favelier <guillaume.favelier@gmail.com>
# jona-sassenhagen <jona.sassenhagen@gmail.com>
# Joan Massich <mailsik@gmail.com>
#
# License: Simplified BSD
import contextlib
from functools import partial
import os
import os.path as op
import sys
import time
import traceback
import warnings
import numpy as np
from scipy import sparse
from collections import OrderedDict
from .colormap import calculate_lut
from .surface import Surface
from .view import views_dicts, _lh_views_dict
from .mplcanvas import MplCanvas
from .callback import (ShowView, IntSlider, TimeSlider, SmartSlider,
BumpColorbarPoints, UpdateColorbarScale)
from ..utils import _show_help, _get_color_list
from .._3d import _process_clim, _handle_time, _check_views
from ...externals.decorator import decorator
from ...defaults import _handle_default
from ...surface import mesh_edges
from ...source_space import SourceSpaces, vertex_to_mni, read_talxfm
from ...transforms import apply_trans
from ...utils import (_check_option, logger, verbose, fill_doc, _validate_type,
use_log_level, Bunch, _ReuseCycle, warn)
@decorator
def safe_event(fun, *args, **kwargs):
"""Protect against PyQt5 exiting on event-handling errors."""
try:
return fun(*args, **kwargs)
except Exception:
traceback.print_exc(file=sys.stderr)
class _Overlay(object):
def __init__(self, scalars, colormap, rng, opacity):
self._scalars = scalars
self._colormap = colormap
self._rng = rng
self._opacity = opacity
def to_colors(self):
from .._3d import _get_cmap
from matplotlib.colors import ListedColormap
if isinstance(self._colormap, str):
cmap = _get_cmap(self._colormap)
else:
cmap = ListedColormap(self._colormap / 255.)
def diff(x):
return np.max(x) - np.min(x)
def norm(x, rng=None):
if rng is None:
rng = [np.min(x), np.max(x)]
return (x - rng[0]) / (rng[1] - rng[0])
rng = self._rng
scalars = self._scalars
if diff(scalars) != 0:
scalars = norm(scalars, rng)
colors = cmap(scalars)
if self._opacity is not None:
colors[:, 3] *= self._opacity
return colors
class _LayeredMesh(object):
def __init__(self, renderer, vertices, triangles, normals):
self._renderer = renderer
self._vertices = vertices
self._triangles = triangles
self._normals = normals
self._polydata = None
self._actor = None
self._is_mapped = False
self._cache = None
self._overlays = OrderedDict()
self._default_scalars = np.ones(vertices.shape)
self._default_scalars_name = 'Data'
def map(self):
kwargs = {
"color": None,
"pickable": True,
"rgba": True,
}
mesh_data = self._renderer.mesh(
x=self._vertices[:, 0],
y=self._vertices[:, 1],
z=self._vertices[:, 2],
triangles=self._triangles,
normals=self._normals,
scalars=self._default_scalars,
**kwargs
)
self._actor, self._polydata = mesh_data
self._is_mapped = True
def _compute_over(self, B, A):
assert A.ndim == B.ndim == 2
assert A.shape[1] == B.shape[1] == 4
A_w = A[:, 3:] # * 1
B_w = B[:, 3:] * (1 - A_w)
C = A.copy()
C[:, :3] *= A_w
C[:, :3] += B[:, :3] * B_w
C[:, 3:] += B_w
C[:, :3] /= C[:, 3:]
return np.clip(C, 0, 1, out=C)
def _compose_overlays(self):
B = None
for overlay in self._overlays.values():
A = overlay.to_colors()
if B is None:
B = A
else:
B = self._compute_over(B, A)
return B
def add_overlay(self, scalars, colormap, rng, opacity, name):
overlay = _Overlay(
scalars=scalars,
colormap=colormap,
rng=rng,
opacity=opacity
)
self._overlays[name] = overlay
colors = overlay.to_colors()
# save colors in cache
if self._cache is None:
self._cache = colors
else:
self._cache = self._compute_over(self._cache, colors)
# update the texture
self._update()
def remove_overlay(self, names):
if not isinstance(names, list):
names = [names]
for name in names:
if name in self._overlays:
del self._overlays[name]
self.update()
def _update(self):
if self._cache is None:
return
from ..backends._pyvista import _set_mesh_scalars
_set_mesh_scalars(
mesh=self._polydata,
scalars=self._cache,
name=self._default_scalars_name,
)
def update(self):
self._cache = self._compose_overlays()
self._update()
def _clean(self):
mapper = self._actor.GetMapper()
mapper.SetLookupTable(None)
self._actor.SetMapper(None)
self._actor = None
self._polydata = None
self._renderer = None
def update_overlay(self, name, scalars=None, colormap=None,
opacity=None):
overlay = self._overlays.get(name, None)
if overlay is None:
return
if scalars is not None:
overlay._scalars = scalars
if colormap is not None:
overlay._colormap = colormap
if opacity is not None:
overlay._opacity = opacity
self.update()
@fill_doc
class Brain(object):
"""Class for visualizing a brain.
.. warning::
The API for this class is not currently complete. We suggest using
:meth:`mne.viz.plot_source_estimates` with the PyVista backend
enabled to obtain a ``Brain`` instance.
Parameters
----------
subject_id : str
Subject name in Freesurfer subjects dir.
hemi : str
Hemisphere id (ie 'lh', 'rh', 'both', or 'split'). In the case
of 'both', both hemispheres are shown in the same window.
In the case of 'split' hemispheres are displayed side-by-side
in different viewing panes.
surf : str
FreeSurfer surface mesh name (ie 'white', 'inflated', etc.).
title : str
Title for the window.
cortex : str or None
Specifies how the cortical surface is rendered.
The name of one of the preset cortex styles can be:
``'classic'`` (default), ``'high_contrast'``,
``'low_contrast'``, or ``'bone'`` or a valid color name.
Setting this to ``None`` is equivalent to ``(0.5, 0.5, 0.5)``.
alpha : float in [0, 1]
Alpha level to control opacity of the cortical surface.
size : int | array-like, shape (2,)
The size of the window, in pixels. can be one number to specify
a square window, or a length-2 sequence to specify (width, height).
background : tuple(int, int, int)
The color definition of the background: (red, green, blue).
foreground : matplotlib color
Color of the foreground (will be used for colorbars and text).
None (default) will use black or white depending on the value
of ``background``.
figure : list of Figure | None | int
If None (default), a new window will be created with the appropriate
views. For single view plots, the figure can be specified as int to
retrieve the corresponding Mayavi window.
subjects_dir : str | None
If not None, this directory will be used as the subjects directory
instead of the value set using the SUBJECTS_DIR environment
variable.
views : list | str
The views to use.
offset : bool
If True, aligs origin with medial wall. Useful for viewing inflated
surface where hemispheres typically overlap (Default: True).
show_toolbar : bool
If True, toolbars will be shown for each view.
offscreen : bool
If True, rendering will be done offscreen (not shown). Useful
mostly for generating images or screenshots, but can be buggy.
Use at your own risk.
interaction : str
Can be "trackball" (default) or "terrain", i.e. a turntable-style
camera.
units : str
Can be 'm' or 'mm' (default).
%(view_layout)s
show : bool
Display the window as soon as it is ready. Defaults to True.
Attributes
----------
geo : dict
A dictionary of pysurfer.Surface objects for each hemisphere.
overlays : dict
The overlays.
Notes
-----
This table shows the capabilities of each Brain backend ("✓" for full
support, and "-" for partial support):
.. table::
:widths: auto
+---------------------------+--------------+---------------+
| 3D function: | surfer.Brain | mne.viz.Brain |
+===========================+==============+===============+
| add_annotation | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_data | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_foci | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_label | ✓ | ✓ |
+---------------------------+--------------+---------------+
| add_text | ✓ | ✓ |
+---------------------------+--------------+---------------+
| close | ✓ | ✓ |
+---------------------------+--------------+---------------+
| data | ✓ | ✓ |
+---------------------------+--------------+---------------+
| foci | ✓ | |
+---------------------------+--------------+---------------+
| labels | ✓ | ✓ |
+---------------------------+--------------+---------------+
| remove_foci | ✓ | |
+---------------------------+--------------+---------------+
| remove_labels | ✓ | ✓ |
+---------------------------+--------------+---------------+
| remove_annotations | - | ✓ |
+---------------------------+--------------+---------------+
| scale_data_colormap | ✓ | |
+---------------------------+--------------+---------------+
| save_image | ✓ | ✓ |
+---------------------------+--------------+---------------+
| save_movie | ✓ | ✓ |
+---------------------------+--------------+---------------+
| screenshot | ✓ | ✓ |
+---------------------------+--------------+---------------+
| show_view | ✓ | ✓ |
+---------------------------+--------------+---------------+
| TimeViewer | ✓ | ✓ |
+---------------------------+--------------+---------------+
| enable_depth_peeling | | ✓ |
+---------------------------+--------------+---------------+
| get_picked_points | | ✓ |
+---------------------------+--------------+---------------+
| add_data(volume) | | ✓ |
+---------------------------+--------------+---------------+
| view_layout | | ✓ |
+---------------------------+--------------+---------------+
| flatmaps | | ✓ |
+---------------------------+--------------+---------------+
| vertex picking | | ✓ |
+---------------------------+--------------+---------------+
| label picking | | ✓ |
+---------------------------+--------------+---------------+
"""
def __init__(self, subject_id, hemi, surf, title=None,
cortex="classic", alpha=1.0, size=800, background="black",
foreground=None, figure=None, subjects_dir=None,
views='auto', offset=True, show_toolbar=False,
offscreen=False, interaction='trackball', units='mm',
view_layout='vertical', show=True):
from ..backends.renderer import backend, _get_renderer, _get_3d_backend
from .._3d import _get_cmap
from matplotlib.colors import colorConverter
if hemi in ('both', 'split'):
self._hemis = ('lh', 'rh')
elif hemi in ('lh', 'rh'):
self._hemis = (hemi, )
else:
raise KeyError('hemi has to be either "lh", "rh", "split", '
'or "both"')
self._view_layout = _check_option('view_layout', view_layout,
('vertical', 'horizontal'))
if figure is not None and not isinstance(figure, int):
backend._check_3d_figure(figure)
if title is None:
self._title = subject_id
else:
self._title = title
self._interaction = 'trackball'
if isinstance(background, str):
background = colorConverter.to_rgb(background)
self._bg_color = background
if foreground is None:
foreground = 'w' if sum(self._bg_color) < 2 else 'k'
if isinstance(foreground, str):
foreground = colorConverter.to_rgb(foreground)
self._fg_color = foreground
if isinstance(views, str):
views = [views]
views = _check_views(surf, views, hemi)
col_dict = dict(lh=1, rh=1, both=1, split=2)
shape = (len(views), col_dict[hemi])
if self._view_layout == 'horizontal':
shape = shape[::-1]
self._subplot_shape = shape
size = tuple(np.atleast_1d(size).round(0).astype(int).flat)
if len(size) not in (1, 2):
raise ValueError('"size" parameter must be an int or length-2 '
'sequence of ints.')
self._size = size if len(size) == 2 else size * 2 # 1-tuple to 2-tuple
self.time_viewer = False
self.notebook = (_get_3d_backend() == "notebook")
self._hemi = hemi
self._units = units
self._alpha = float(alpha)
self._subject_id = subject_id
self._subjects_dir = subjects_dir
self._views = views
self._times = None
self._vertex_to_label_id = dict()
self._annotation_labels = dict()
self._labels = {'lh': list(), 'rh': list()}
self._annots = {'lh': list(), 'rh': list()}
self._layered_meshes = {}
# for now only one color bar can be added
# since it is the same for all figures
self._colorbar_added = False
# for now only one time label can be added
# since it is the same for all figures
self._time_label_added = False
# array of data used by TimeViewer
self._data = {}
self.geo = {}
self.set_time_interpolation('nearest')
geo_kwargs = self._cortex_colormap(cortex)
# evaluate at the midpoint of the used colormap
val = -geo_kwargs['vmin'] / (geo_kwargs['vmax'] - geo_kwargs['vmin'])
self._brain_color = _get_cmap(geo_kwargs['colormap'])(val)
# load geometry for one or both hemispheres as necessary
offset = None if (not offset or hemi != 'both') else 0.0
self._renderer = _get_renderer(name=self._title, size=self._size,
bgcolor=background,
shape=shape,
fig=figure)
if _get_3d_backend() == "pyvista":
self.plotter = self._renderer.plotter
self.window = self.plotter.app_window
self.window.signal_close.connect(self._clean)
for h in self._hemis:
# Initialize a Surface object as the geometry
geo = Surface(subject_id, h, surf, subjects_dir, offset,
units=self._units)
# Load in the geometry and curvature
geo.load_geometry()
geo.load_curvature()
self.geo[h] = geo
for ri, ci, v in self._iter_views(h):
self._renderer.subplot(ri, ci)
if self._layered_meshes.get(h) is None:
mesh = _LayeredMesh(
renderer=self._renderer,
vertices=self.geo[h].coords,
triangles=self.geo[h].faces,
normals=self.geo[h].nn,
)
mesh.map() # send to GPU
mesh.add_overlay(
scalars=self.geo[h].bin_curv,
colormap=geo_kwargs["colormap"],
rng=[geo_kwargs["vmin"], geo_kwargs["vmax"]],
opacity=alpha,
name='curv',
)
self._layered_meshes[h] = mesh
# add metadata to the mesh for picking
mesh._polydata._hemi = h
else:
actor = self._layered_meshes[h]._actor
self._renderer.plotter.add_actor(actor)
self._renderer.set_camera(**views_dicts[h][v])
self.interaction = interaction
self._closed = False
if show:
self.show()
# update the views once the geometry is all set
for h in self._hemis:
for ri, ci, v in self._iter_views(h):
self.show_view(v, row=ri, col=ci, hemi=h)
if surf == 'flat':
self._renderer.set_interaction("rubber_band_2d")
if hemi == 'rh' and hasattr(self._renderer, "_orient_lights"):
self._renderer._orient_lights()
def setup_time_viewer(self, time_viewer=True, show_traces=True):
"""Configure the time viewer parameters.
Parameters
----------
time_viewer : bool
If True, enable widgets interaction. Defaults to True.
show_traces : bool
If True, enable visualization of time traces. Defaults to True.
"""
if self.time_viewer:
return
if not self._data:
raise ValueError("No data to visualize. See ``add_data``.")
self.time_viewer = time_viewer
self.orientation = list(_lh_views_dict.keys())
self.default_smoothing_range = [0, 15]
# setup notebook
if self.notebook:
self._configure_notebook()
return
# Default configuration
self.playback = False
self.visibility = False
self.refresh_rate_ms = max(int(round(1000. / 60.)), 1)
self.default_scaling_range = [0.2, 2.0]
self.default_playback_speed_range = [0.01, 1]
self.default_playback_speed_value = 0.05
self.default_status_bar_msg = "Press ? for help"
self.default_label_extract_modes = {
"stc": ["mean", "max"],
"src": ["mean_flip", "pca_flip", "auto"],
}
self.default_trace_modes = ('vertex', 'label')
self.annot = None
self.label_extract_mode = None
all_keys = ('lh', 'rh', 'vol')
self.act_data_smooth = {key: (None, None) for key in all_keys}
self.color_list = _get_color_list()
# remove grey for better contrast on the brain
self.color_list.remove("#7f7f7f")
self.color_cycle = _ReuseCycle(self.color_list)
self.mpl_canvas = None
self.gfp = None
self.picked_patches = {key: list() for key in all_keys}
self.picked_points = {key: list() for key in all_keys}
self.pick_table = dict()
self._spheres = list()
self._mouse_no_mvt = -1
self.icons = dict()
self.actions = dict()
self.callbacks = dict()
self.sliders = dict()
self.keys = ('fmin', 'fmid', 'fmax')
self.slider_length = 0.02
self.slider_width = 0.04
self.slider_color = (0.43137255, 0.44313725, 0.45882353)
self.slider_tube_width = 0.04
self.slider_tube_color = (0.69803922, 0.70196078, 0.70980392)
self._trace_mode_widget = None
self._annot_cands_widget = None
self._label_mode_widget = None
# Direct access parameters:
self._iren = self._renderer.plotter.iren
self.main_menu = self.plotter.main_menu
self.tool_bar = self.window.addToolBar("toolbar")
self.status_bar = self.window.statusBar()
self.interactor = self.plotter.interactor
# Derived parameters:
self.playback_speed = self.default_playback_speed_value
_validate_type(show_traces, (bool, str, 'numeric'), 'show_traces')
self.interactor_fraction = 0.25
if isinstance(show_traces, str):
self.show_traces = True
self.separate_canvas = False
self.traces_mode = 'vertex'
if show_traces == 'separate':
self.separate_canvas = True
elif show_traces == 'label':
self.traces_mode = 'label'
else:
assert show_traces == 'vertex' # guaranteed above
else:
if isinstance(show_traces, bool):
self.show_traces = show_traces
else:
show_traces = float(show_traces)
if not 0 < show_traces < 1:
raise ValueError(
'show traces, if numeric, must be between 0 and 1, '
f'got {show_traces}')
self.show_traces = True
self.interactor_fraction = show_traces
self.traces_mode = 'vertex'
self.separate_canvas = False
del show_traces
self._load_icons()
self._configure_time_label()
self._configure_sliders()
self._configure_scalar_bar()
self._configure_playback()
self._configure_menu()
self._configure_tool_bar()
self._configure_status_bar()
self._configure_picking()
self._configure_trace_mode()
# show everything at the end
self.toggle_interface()
with self.ensure_minimum_sizes():
self.show()
@safe_event
def _clean(self):
# resolve the reference cycle
self.clear_glyphs()
self.remove_annotations()
# clear init actors
for hemi in self._hemis:
self._layered_meshes[hemi]._clean()
self._clear_callbacks()
if getattr(self, 'mpl_canvas', None) is not None:
self.mpl_canvas.clear()
if getattr(self, 'act_data_smooth', None) is not None:
for key in list(self.act_data_smooth.keys()):
self.act_data_smooth[key] = None
# XXX this should be done in PyVista
for renderer in self.plotter.renderers:
renderer.RemoveAllLights()
# app_window cannot be set to None because it is used in __del__
for key in ('lighting', 'interactor', '_RenderWindow'):
setattr(self.plotter, key, None)
# Qt LeaveEvent requires _Iren so we use _FakeIren instead of None
# to resolve the ref to vtkGenericRenderWindowInteractor
self.plotter._Iren = _FakeIren()
if getattr(self.plotter, 'scalar_bar', None) is not None:
self.plotter.scalar_bar = None
if getattr(self.plotter, 'picker', None) is not None:
self.plotter.picker = None
# XXX end PyVista
for key in ('reps', 'plotter', 'main_menu', 'window', 'tool_bar',
'status_bar', 'interactor', 'mpl_canvas', 'time_actor',
'picked_renderer', 'act_data_smooth', '_iren',
'actions', 'sliders', 'geo', '_hemi_actors', '_data'):
setattr(self, key, None)
@contextlib.contextmanager
def ensure_minimum_sizes(self):
"""Ensure that widgets respect the windows size."""
from ..backends._pyvista import _process_events
sz = self._size
adjust_mpl = self.show_traces and not self.separate_canvas
if not adjust_mpl:
yield
else:
mpl_h = int(round((sz[1] * self.interactor_fraction) /
(1 - self.interactor_fraction)))
self.mpl_canvas.canvas.setMinimumSize(sz[0], mpl_h)
try:
yield
finally:
self.splitter.setSizes([sz[1], mpl_h])
_process_events(self.plotter)
_process_events(self.plotter)
self.mpl_canvas.canvas.setMinimumSize(0, 0)
_process_events(self.plotter)
_process_events(self.plotter)
# sizes could change, update views
for hemi in ('lh', 'rh'):
for ri, ci, v in self._iter_views(hemi):
self.show_view(view=v, row=ri, col=ci)
_process_events(self.plotter)
def toggle_interface(self, value=None):
"""Toggle the interface.
Parameters
----------
value : bool | None
If True, the widgets are shown and if False, they
are hidden. If None, the state of the widgets is
toggled. Defaults to None.
"""
if value is None:
self.visibility = not self.visibility
else:
self.visibility = value
# update tool bar icon
if self.visibility:
self.actions["visibility"].setIcon(self.icons["visibility_on"])
else:
self.actions["visibility"].setIcon(self.icons["visibility_off"])
# manage sliders
for slider in self.plotter.slider_widgets:
slider_rep = slider.GetRepresentation()
if self.visibility:
slider_rep.VisibilityOn()
else:
slider_rep.VisibilityOff()
# manage time label
time_label = self._data['time_label']
# if we actually have time points, we will show the slider so
# hide the time actor
have_ts = self._times is not None and len(self._times) > 1
if self.time_actor is not None:
if self.visibility and time_label is not None and not have_ts:
self.time_actor.SetInput(time_label(self._current_time))
self.time_actor.VisibilityOn()
else:
self.time_actor.VisibilityOff()
self._update()
def apply_auto_scaling(self):
"""Detect automatically fitting scaling parameters."""
self._update_auto_scaling()
for key in ('fmin', 'fmid', 'fmax'):
self.reps[key].SetValue(self._data[key])
self._update()
def restore_user_scaling(self):
"""Restore original scaling parameters."""
self._update_auto_scaling(restore=True)
for key in ('fmin', 'fmid', 'fmax'):
self.reps[key].SetValue(self._data[key])
self._update()
def toggle_playback(self, value=None):
"""Toggle time playback.
Parameters
----------
value : bool | None
If True, automatic time playback is enabled and if False,
it's disabled. If None, the state of time playback is toggled.
Defaults to None.
"""
if value is None:
self.playback = not self.playback
else:
self.playback = value
# update tool bar icon
if self.playback:
self.actions["play"].setIcon(self.icons["pause"])
else:
self.actions["play"].setIcon(self.icons["play"])
if self.playback:
time_data = self._data['time']
max_time = np.max(time_data)
if self._current_time == max_time: # start over
self.set_time_point(0) # first index
self._last_tick = time.time()
def reset(self):
"""Reset view and time step."""
self.reset_view()
max_time = len(self._data['time']) - 1
if max_time > 0:
self.callbacks["time"](
self._data["initial_time_idx"],
update_widget=True,
)
self._update()
def set_playback_speed(self, speed):
"""Set the time playback speed.
Parameters
----------
speed : float
The speed of the playback.
"""
self.playback_speed = speed
@safe_event
def _play(self):
if self.playback:
try:
self._advance()
except Exception:
self.toggle_playback(value=False)
raise
def _advance(self):
this_time = time.time()
delta = this_time - self._last_tick
self._last_tick = time.time()
time_data = self._data['time']
times = np.arange(self._n_times)
time_shift = delta * self.playback_speed
max_time = np.max(time_data)
time_point = min(self._current_time + time_shift, max_time)
# always use linear here -- this does not determine the data
# interpolation mode, it just finds where we are (in time) in
# terms of the time indices
idx = np.interp(time_point, time_data, times)
self.callbacks["time"](idx, update_widget=True)
if time_point == max_time:
self.toggle_playback(value=False)
def _set_slider_style(self):
for slider in self.sliders.values():
if slider is not None:
slider_rep = slider.GetRepresentation()
slider_rep.SetSliderLength(self.slider_length)
slider_rep.SetSliderWidth(self.slider_width)
slider_rep.SetTubeWidth(self.slider_tube_width)
slider_rep.GetSliderProperty().SetColor(self.slider_color)
slider_rep.GetTubeProperty().SetColor(self.slider_tube_color)
slider_rep.GetLabelProperty().SetShadow(False)
slider_rep.GetLabelProperty().SetBold(True)
slider_rep.GetLabelProperty().SetColor(self._fg_color)
slider_rep.GetTitleProperty().ShallowCopy(
slider_rep.GetLabelProperty()
)
slider_rep.GetCapProperty().SetOpacity(0)
def _configure_notebook(self):
from ._notebook import _NotebookInteractor
self._renderer.figure.display = _NotebookInteractor(self)
def _configure_time_label(self):
self.time_actor = self._data.get('time_actor')
if self.time_actor is not None:
self.time_actor.SetPosition(0.5, 0.03)
self.time_actor.GetTextProperty().SetJustificationToCentered()
self.time_actor.GetTextProperty().BoldOn()
self.time_actor.VisibilityOff()
def _configure_scalar_bar(self):
if self._colorbar_added:
scalar_bar = self.plotter.scalar_bar
scalar_bar.SetOrientationToVertical()
scalar_bar.SetHeight(0.6)
scalar_bar.SetWidth(0.05)
scalar_bar.SetPosition(0.02, 0.2)
def _configure_sliders(self):
# Orientation slider
# Use 'lh' as a reference for orientation for 'both'
if self._hemi == 'both':
hemis_ref = ['lh']
else:
hemis_ref = self._hemis
for hemi in hemis_ref:
for ri, ci, view in self._iter_views(hemi):
orientation_name = f"orientation_{hemi}_{ri}_{ci}"
self.plotter.subplot(ri, ci)
if view == 'flat':
self.callbacks[orientation_name] = None
continue
self.callbacks[orientation_name] = ShowView(
plotter=self.plotter,
brain=self,
orientation=self.orientation,
hemi=hemi,
row=ri,
col=ci,
)
self.sliders[orientation_name] = \
self.plotter.add_text_slider_widget(
self.callbacks[orientation_name],
value=0,
data=self.orientation,
pointa=(0.82, 0.74),
pointb=(0.98, 0.74),
event_type='always'
)
orientation_rep = \
self.sliders[orientation_name].GetRepresentation()
orientation_rep.ShowSliderLabelOff()
self.callbacks[orientation_name].slider_rep = orientation_rep
self.callbacks[orientation_name](view, update_widget=True)
# Put other sliders on the bottom right view
ri, ci = np.array(self._subplot_shape) - 1
self.plotter.subplot(ri, ci)
# Smoothing slider
self.callbacks["smoothing"] = IntSlider(
plotter=self.plotter,
callback=self.set_data_smoothing,
first_call=False,
)
self.sliders["smoothing"] = self.plotter.add_slider_widget(
self.callbacks["smoothing"],
value=self._data['smoothing_steps'],
rng=self.default_smoothing_range, title="smoothing",
pointa=(0.82, 0.90),
pointb=(0.98, 0.90)
)
self.callbacks["smoothing"].slider_rep = \
self.sliders["smoothing"].GetRepresentation()
# Time slider
max_time = len(self._data['time']) - 1
# VTK on macOS bombs if we create these then hide them, so don't
# even create them
if max_time < 1:
self.callbacks["time"] = None
self.sliders["time"] = None
else:
self.callbacks["time"] = TimeSlider(
plotter=self.plotter,
brain=self,
first_call=False,
callback=self.plot_time_line,
)
self.sliders["time"] = self.plotter.add_slider_widget(
self.callbacks["time"],
value=self._data['time_idx'],
rng=[0, max_time],
pointa=(0.23, 0.1),
pointb=(0.77, 0.1),
event_type='always'
)
self.callbacks["time"].slider_rep = \
self.sliders["time"].GetRepresentation()
# configure properties of the time slider
self.sliders["time"].GetRepresentation().SetLabelFormat(
'idx=%0.1f')
current_time = self._current_time
assert current_time is not None # should never be the case, float
time_label = self._data['time_label']
if callable(time_label):
current_time = time_label(current_time)
else:
current_time = time_label
if self.sliders["time"] is not None:
self.sliders["time"].GetRepresentation().SetTitleText(current_time)
if self.time_actor is not None:
self.time_actor.SetInput(current_time)
del current_time
# Playback speed slider
if self.sliders["time"] is None:
self.callbacks["playback_speed"] = None
self.sliders["playback_speed"] = None
else:
self.callbacks["playback_speed"] = SmartSlider(
plotter=self.plotter,
callback=self.set_playback_speed,
)
self.sliders["playback_speed"] = self.plotter.add_slider_widget(
self.callbacks["playback_speed"],
value=self.default_playback_speed_value,
rng=self.default_playback_speed_range, title="speed",
pointa=(0.02, 0.1),
pointb=(0.18, 0.1),
event_type='always'
)
self.callbacks["playback_speed"].slider_rep = \
self.sliders["playback_speed"].GetRepresentation()
# Colormap slider
pointa = np.array((0.82, 0.26))
pointb = np.array((0.98, 0.26))
shift = np.array([0, 0.1])
for idx, key in enumerate(self.keys):
title = "clim" if not idx else ""
rng = _get_range(self)
self.callbacks[key] = BumpColorbarPoints(
plotter=self.plotter,
brain=self,
name=key
)
self.sliders[key] = self.plotter.add_slider_widget(
self.callbacks[key],
value=self._data[key],
rng=rng, title=title,
pointa=pointa + idx * shift,
pointb=pointb + idx * shift,
event_type="always",
)
# fscale
self.callbacks["fscale"] = UpdateColorbarScale(
plotter=self.plotter,
brain=self,
)
self.sliders["fscale"] = self.plotter.add_slider_widget(
self.callbacks["fscale"],
value=1.0,
rng=self.default_scaling_range, title="fscale",
pointa=(0.82, 0.10),
pointb=(0.98, 0.10)
)
self.callbacks["fscale"].slider_rep = \
self.sliders["fscale"].GetRepresentation()
# register colorbar slider representations
self.reps = \
{key: self.sliders[key].GetRepresentation() for key in self.keys}
for name in ("fmin", "fmid", "fmax", "fscale"):
self.callbacks[name].reps = self.reps
# set the slider style
self._set_slider_style()
def _configure_playback(self):
self.plotter.add_callback(self._play, self.refresh_rate_ms)
def _configure_mplcanvas(self):
win = self.plotter.app_window
dpi = win.windowHandle().screen().logicalDotsPerInch()
ratio = (1 - self.interactor_fraction) / self.interactor_fraction
w = self.interactor.geometry().width()
h = self.interactor.geometry().height() / ratio
# Get the fractional components for the brain and mpl
self.mpl_canvas = MplCanvas(self, w / dpi, h / dpi, dpi)
xlim = [np.min(self._data['time']),
np.max(self._data['time'])]
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
self.mpl_canvas.axes.set(xlim=xlim)
if not self.separate_canvas:
from PyQt5.QtWidgets import QSplitter
from PyQt5.QtCore import Qt
canvas = self.mpl_canvas.canvas
vlayout = self.plotter.frame.layout()
vlayout.removeWidget(self.interactor)
self.splitter = splitter = QSplitter(
orientation=Qt.Vertical, parent=self.plotter.frame)
vlayout.addWidget(splitter)
splitter.addWidget(self.interactor)
splitter.addWidget(canvas)
self.mpl_canvas.set_color(
bg_color=self._bg_color,
fg_color=self._fg_color,
)
self.mpl_canvas.show()
def _configure_vertex_time_course(self):
if not self.show_traces:
return
if self.mpl_canvas is None:
self._configure_mplcanvas()
else:
self.clear_glyphs()
# plot the GFP
y = np.concatenate(list(v[0] for v in self.act_data_smooth.values()
if v[0] is not None))
y = np.linalg.norm(y, axis=0) / np.sqrt(len(y))
self.gfp, = self.mpl_canvas.axes.plot(
self._data['time'], y,
lw=3, label='GFP', zorder=3, color=self._fg_color,
alpha=0.5, ls=':')
# now plot the time line
self.plot_time_line()
# then the picked points
for idx, hemi in enumerate(['lh', 'rh', 'vol']):
act_data = self.act_data_smooth.get(hemi, [None])[0]
if act_data is None:
continue
hemi_data = self._data[hemi]
vertices = hemi_data['vertices']
# simulate a picked renderer
if self._hemi in ('both', 'rh') or hemi == 'vol':
idx = 0
self.picked_renderer = self.plotter.renderers[idx]
# initialize the default point
if self._data['initial_time'] is not None:
# pick at that time
use_data = act_data[
:, [np.round(self._data['time_idx']).astype(int)]]
else:
use_data = act_data
ind = np.unravel_index(np.argmax(np.abs(use_data), axis=None),
use_data.shape)
if hemi == 'vol':
mesh = hemi_data['grid']
else:
mesh = self._layered_meshes[hemi]._polydata
vertex_id = vertices[ind[0]]
self._add_vertex_glyph(hemi, mesh, vertex_id)
def _configure_picking(self):
from ..backends._pyvista import _update_picking_callback
# get data for each hemi
for idx, hemi in enumerate(['vol', 'lh', 'rh']):
hemi_data = self._data.get(hemi)
if hemi_data is not None:
act_data = hemi_data['array']
if act_data.ndim == 3:
act_data = np.linalg.norm(act_data, axis=1)
smooth_mat = hemi_data.get('smooth_mat')
vertices = hemi_data['vertices']
if hemi == 'vol':
assert smooth_mat is None
smooth_mat = sparse.csr_matrix(
(np.ones(len(vertices)),
(vertices, np.arange(len(vertices)))))
self.act_data_smooth[hemi] = (act_data, smooth_mat)
_update_picking_callback(
self.plotter,
self._on_mouse_move,
self._on_button_press,
self._on_button_release,
self._on_pick
)
def _configure_trace_mode(self):
from ...source_estimate import _get_allowed_label_modes
from ...label import _read_annot_cands
from PyQt5.QtWidgets import QComboBox, QLabel
if not self.show_traces:
return
# do not show trace mode for volumes
if (self._data.get('src', None) is not None and
self._data['src'].kind == 'volume'):
self._configure_vertex_time_course()
return
# setup candidate annots
def _set_annot(annot):
self.clear_glyphs()
self.remove_labels()
self.remove_annotations()
self.annot = annot
if annot == 'None':
self.traces_mode = 'vertex'
self._configure_vertex_time_course()
else:
self.traces_mode = 'label'
self._configure_label_time_course()
self._update()
dir_name = op.join(self._subjects_dir, self._subject_id, 'label')
cands = _read_annot_cands(dir_name)
self.tool_bar.addSeparator()
self.tool_bar.addWidget(QLabel("Annotation"))
self._annot_cands_widget = QComboBox()
self.tool_bar.addWidget(self._annot_cands_widget)
self._annot_cands_widget.addItem('None')
for cand in cands:
self._annot_cands_widget.addItem(cand)
self.annot = cands[0]
# setup label extraction parameters
def _set_label_mode(mode):
if self.traces_mode != 'label':
return
import copy
glyphs = copy.deepcopy(self.picked_patches)
self.label_extract_mode = mode
self.clear_glyphs()
for hemi in self._hemis:
for label_id in glyphs[hemi]:
label = self._annotation_labels[hemi][label_id]
vertex_id = label.vertices[0]
self._add_label_glyph(hemi, None, vertex_id)
self.mpl_canvas.axes.relim()
self.mpl_canvas.axes.autoscale_view()
self.mpl_canvas.update_plot()
self._update()
self.tool_bar.addSeparator()
self.tool_bar.addWidget(QLabel("Label extraction mode"))
self._label_mode_widget = QComboBox()
self.tool_bar.addWidget(self._label_mode_widget)
stc = self._data["stc"]
modes = _get_allowed_label_modes(stc)
if self._data["src"] is None:
modes = [m for m in modes if m not in
self.default_label_extract_modes["src"]]
for mode in modes:
self._label_mode_widget.addItem(mode)
self.label_extract_mode = mode
if self.traces_mode == 'vertex':
_set_annot('None')
else:
_set_annot(self.annot)
self._annot_cands_widget.setCurrentText(self.annot)
self._label_mode_widget.setCurrentText(self.label_extract_mode)
self._annot_cands_widget.currentTextChanged.connect(_set_annot)
self._label_mode_widget.currentTextChanged.connect(_set_label_mode)
def _load_icons(self):
from PyQt5.QtGui import QIcon
from ..backends._pyvista import _init_resources
_init_resources()
self.icons["help"] = QIcon(":/help.svg")
self.icons["play"] = QIcon(":/play.svg")
self.icons["pause"] = QIcon(":/pause.svg")
self.icons["reset"] = QIcon(":/reset.svg")
self.icons["scale"] = QIcon(":/scale.svg")
self.icons["clear"] = QIcon(":/clear.svg")
self.icons["movie"] = QIcon(":/movie.svg")
self.icons["restore"] = QIcon(":/restore.svg")
self.icons["screenshot"] = QIcon(":/screenshot.svg")
self.icons["visibility_on"] = QIcon(":/visibility_on.svg")
self.icons["visibility_off"] = QIcon(":/visibility_off.svg")
def _save_movie_noname(self):
return self.save_movie(None)
def _configure_tool_bar(self):
self.actions["screenshot"] = self.tool_bar.addAction(
self.icons["screenshot"],
"Take a screenshot",
self.plotter._qt_screenshot
)
self.actions["movie"] = self.tool_bar.addAction(
self.icons["movie"],
"Save movie...",
self._save_movie_noname,
)
self.actions["visibility"] = self.tool_bar.addAction(
self.icons["visibility_on"],
"Toggle Visibility",
self.toggle_interface
)
self.actions["play"] = self.tool_bar.addAction(
self.icons["play"],
"Play/Pause",
self.toggle_playback
)
self.actions["reset"] = self.tool_bar.addAction(
self.icons["reset"],
"Reset",
self.reset
)
self.actions["scale"] = self.tool_bar.addAction(
self.icons["scale"],
"Auto-Scale",
self.apply_auto_scaling
)
self.actions["restore"] = self.tool_bar.addAction(
self.icons["restore"],
"Restore scaling",
self.restore_user_scaling
)
self.actions["clear"] = self.tool_bar.addAction(
self.icons["clear"],
"Clear traces",
self.clear_glyphs
)
self.actions["help"] = self.tool_bar.addAction(
self.icons["help"],
"Help",
self.help
)
self.actions["movie"].setShortcut("ctrl+shift+s")
self.actions["visibility"].setShortcut("i")
self.actions["play"].setShortcut(" ")
self.actions["scale"].setShortcut("s")
self.actions["restore"].setShortcut("r")
self.actions["clear"].setShortcut("c")
self.actions["help"].setShortcut("?")
def _configure_menu(self):
# remove default picking menu
to_remove = list()
for action in self.main_menu.actions():
if action.text() == "Tools":
to_remove.append(action)
for action in to_remove:
self.main_menu.removeAction(action)
# add help menu
menu = self.main_menu.addMenu('Help')
menu.addAction('Show MNE key bindings\t?', self.help)
def _configure_status_bar(self):
from PyQt5.QtWidgets import QLabel, QProgressBar
self.status_msg = QLabel(self.default_status_bar_msg)
self.status_progress = QProgressBar()
self.status_bar.layout().addWidget(self.status_msg, 1)
self.status_bar.layout().addWidget(self.status_progress, 0)
self.status_progress.hide()
def _on_mouse_move(self, vtk_picker, event):
if self._mouse_no_mvt:
self._mouse_no_mvt -= 1
def _on_button_press(self, vtk_picker, event):
self._mouse_no_mvt = 2
def _on_button_release(self, vtk_picker, event):
if self._mouse_no_mvt > 0:
x, y = vtk_picker.GetEventPosition()
# programmatically detect the picked renderer
self.picked_renderer = self.plotter.iren.FindPokedRenderer(x, y)
# trigger the pick
self.plotter.picker.Pick(x, y, 0, self.picked_renderer)
self._mouse_no_mvt = 0
def _on_pick(self, vtk_picker, event):
if not self.show_traces:
return
# vtk_picker is a vtkCellPicker
cell_id = vtk_picker.GetCellId()
mesh = vtk_picker.GetDataSet()
if mesh is None or cell_id == -1 or not self._mouse_no_mvt:
return # don't pick
# 1) Check to see if there are any spheres along the ray
if len(self._spheres):
collection = vtk_picker.GetProp3Ds()
found_sphere = None
for ii in range(collection.GetNumberOfItems()):
actor = collection.GetItemAsObject(ii)
for sphere in self._spheres:
if any(a is actor for a in sphere._actors):
found_sphere = sphere
break
if found_sphere is not None:
break
if found_sphere is not None:
assert found_sphere._is_glyph
mesh = found_sphere
# 2) Remove sphere if it's what we have
if hasattr(mesh, "_is_glyph"):
self._remove_vertex_glyph(mesh)
return
# 3) Otherwise, pick the objects in the scene
try:
hemi = mesh._hemi
except AttributeError: # volume
hemi = 'vol'
else:
assert hemi in ('lh', 'rh')
if self.act_data_smooth[hemi][0] is None: # no data to add for hemi
return
pos = np.array(vtk_picker.GetPickPosition())
if hemi == 'vol':
# VTK will give us the point closest to the viewer in the vol.
# We want to pick the point with the maximum value along the
# camera-to-click array, which fortunately we can get "just"
# by inspecting the points that are sufficiently close to the
# ray.
grid = mesh = self._data[hemi]['grid']
vertices = self._data[hemi]['vertices']
coords = self._data[hemi]['grid_coords'][vertices]
scalars = grid.cell_arrays['values'][vertices]
spacing = np.array(grid.GetSpacing())
max_dist = np.linalg.norm(spacing) / 2.
origin = vtk_picker.GetRenderer().GetActiveCamera().GetPosition()
ori = pos - origin
ori /= np.linalg.norm(ori)
# the magic formula: distance from a ray to a given point
dists = np.linalg.norm(np.cross(ori, coords - pos), axis=1)
assert dists.shape == (len(coords),)
mask = dists <= max_dist
idx = np.where(mask)[0]
if len(idx) == 0:
return # weird point on edge of volume?
# useful for debugging the ray by mapping it into the volume:
# dists = dists - dists.min()
# dists = (1. - dists / dists.max()) * self._cmap_range[1]
# grid.cell_arrays['values'][vertices] = dists * mask
idx = idx[np.argmax(np.abs(scalars[idx]))]
vertex_id = vertices[idx]
# Naive way: convert pos directly to idx; i.e., apply mri_src_t
# shape = self._data[hemi]['grid_shape']
# taking into account the cell vs point difference (spacing/2)
# shift = np.array(grid.GetOrigin()) + spacing / 2.
# ijk = np.round((pos - shift) / spacing).astype(int)
# vertex_id = np.ravel_multi_index(ijk, shape, order='F')
else:
vtk_cell = mesh.GetCell(cell_id)
cell = [vtk_cell.GetPointId(point_id) for point_id
in range(vtk_cell.GetNumberOfPoints())]
vertices = mesh.points[cell]
idx = np.argmin(abs(vertices - pos), axis=0)
vertex_id = cell[idx[0]]
if self.traces_mode == 'label':
self._add_label_glyph(hemi, mesh, vertex_id)
else:
self._add_vertex_glyph(hemi, mesh, vertex_id)
def _add_label_glyph(self, hemi, mesh, vertex_id):
if hemi == 'vol':
return
label_id = self._vertex_to_label_id[hemi][vertex_id]
label = self._annotation_labels[hemi][label_id]
# remove the patch if already picked
if label_id in self.picked_patches[hemi]:
self._remove_label_glyph(hemi, label_id)
return
if hemi == label.hemi:
self.add_label(label, borders=True, reset_camera=False)
self.picked_patches[hemi].append(label_id)
def _remove_label_glyph(self, hemi, label_id):
label = self._annotation_labels[hemi][label_id]
label._line.remove()
self.color_cycle.restore(label._color)
self.mpl_canvas.update_plot()
self._layered_meshes[hemi].remove_overlay(label.name)
self.picked_patches[hemi].remove(label_id)
def _add_vertex_glyph(self, hemi, mesh, vertex_id):
if vertex_id in self.picked_points[hemi]:
return
# skip if the wrong hemi is selected
if self.act_data_smooth[hemi][0] is None:
return
from ..backends._pyvista import _sphere
color = next(self.color_cycle)
line = self.plot_time_course(hemi, vertex_id, color)
if hemi == 'vol':
ijk = np.unravel_index(
vertex_id, np.array(mesh.GetDimensions()) - 1, order='F')
# should just be GetCentroid(center), but apparently it's VTK9+:
# center = np.empty(3)
# voxel.GetCentroid(center)
voxel = mesh.GetCell(*ijk)
pts = voxel.GetPoints()
n_pts = pts.GetNumberOfPoints()
center = np.empty((n_pts, 3))
for ii in range(pts.GetNumberOfPoints()):
pts.GetPoint(ii, center[ii])
center = np.mean(center, axis=0)
else:
center = mesh.GetPoints().GetPoint(vertex_id)
del mesh
# from the picked renderer to the subplot coords
rindex = self.plotter.renderers.index(self.picked_renderer)
row, col = self.plotter.index_to_loc(rindex)
actors = list()
spheres = list()
for ri, ci, _ in self._iter_views(hemi):
self.plotter.subplot(ri, ci)
# Using _sphere() instead of renderer.sphere() for 2 reasons:
# 1) renderer.sphere() fails on Windows in a scenario where a lot
# of picking requests are done in a short span of time (could be
# mitigated with synchronization/delay?)
# 2) the glyph filter is used in renderer.sphere() but only one
# sphere is required in this function.
actor, sphere = _sphere(
plotter=self.plotter,
center=np.array(center),
color=color,
radius=4.0,
)
actors.append(actor)
spheres.append(sphere)
# add metadata for picking
for sphere in spheres:
sphere._is_glyph = True
sphere._hemi = hemi
sphere._line = line
sphere._actors = actors
sphere._color = color
sphere._vertex_id = vertex_id
self.picked_points[hemi].append(vertex_id)
self._spheres.extend(spheres)
self.pick_table[vertex_id] = spheres
return sphere
def _remove_vertex_glyph(self, mesh, render=True):
vertex_id = mesh._vertex_id
if vertex_id not in self.pick_table:
return
hemi = mesh._hemi
color = mesh._color
spheres = self.pick_table[vertex_id]
spheres[0]._line.remove()
self.mpl_canvas.update_plot()
self.picked_points[hemi].remove(vertex_id)
with warnings.catch_warnings(record=True):
# We intentionally ignore these in case we have traversed the
# entire color cycle
warnings.simplefilter('ignore')
self.color_cycle.restore(color)
for sphere in spheres:
# remove all actors
self.plotter.remove_actor(sphere._actors, render=render)
sphere._actors = None
self._spheres.pop(self._spheres.index(sphere))
self.pick_table.pop(vertex_id)
def clear_glyphs(self):
"""Clear the picking glyphs."""
if not self.time_viewer:
return
for sphere in list(self._spheres): # will remove itself, so copy
self._remove_vertex_glyph(sphere, render=False)
assert sum(len(v) for v in self.picked_points.values()) == 0
assert len(self.pick_table) == 0
assert len(self._spheres) == 0
for hemi in self._hemis:
for label_id in list(self.picked_patches[hemi]):
self._remove_label_glyph(hemi, label_id)
assert sum(len(v) for v in self.picked_patches.values()) == 0
if self.gfp is not None:
self.gfp.remove()
self.gfp = None
self._update()
def plot_time_course(self, hemi, vertex_id, color):
"""Plot the vertex time course.
Parameters
----------
hemi : str
The hemisphere id of the vertex.
vertex_id : int
The vertex identifier in the mesh.
color : matplotlib color
The color of the time course.
Returns
-------
line : matplotlib object
The time line object.
"""
if self.mpl_canvas is None:
return
time = self._data['time'].copy() # avoid circular ref
if hemi == 'vol':
hemi_str = 'V'
xfm = read_talxfm(
self._subject_id, self._subjects_dir)
if self._units == 'mm':
xfm['trans'][:3, 3] *= 1000.
ijk = np.unravel_index(
vertex_id, self._data[hemi]['grid_shape'], order='F')
src_mri_t = self._data[hemi]['grid_src_mri_t']
mni = apply_trans(np.dot(xfm['trans'], src_mri_t), ijk)
else:
hemi_str = 'L' if hemi == 'lh' else 'R'
mni = vertex_to_mni(
vertices=vertex_id,
hemis=0 if hemi == 'lh' else 1,
subject=self._subject_id,
subjects_dir=self._subjects_dir
)
label = "{}:{} MNI: {}".format(
hemi_str, str(vertex_id).ljust(6),
', '.join('%5.1f' % m for m in mni))
act_data, smooth = self.act_data_smooth[hemi]
if smooth is not None:
act_data = smooth[vertex_id].dot(act_data)[0]
else:
act_data = act_data[vertex_id].copy()
line = self.mpl_canvas.plot(
time,
act_data,
label=label,
lw=1.,
color=color,
zorder=4,
)
return line
def plot_time_line(self):
"""Add the time line to the MPL widget."""
if self.mpl_canvas is None:
return
if isinstance(self.show_traces, bool) and self.show_traces:
# add time information
current_time = self._current_time
if not hasattr(self, "time_line"):
self.time_line = self.mpl_canvas.plot_time_line(
x=current_time,
label='time',
color=self._fg_color,
lw=1,
)
self.time_line.set_xdata(current_time)
self.mpl_canvas.update_plot()
def help(self):
"""Display the help window."""
pairs = [
('?', 'Display help window'),
('i', 'Toggle interface'),
('s', 'Apply auto-scaling'),
('r', 'Restore original clim'),
('c', 'Clear all traces'),
('Space', 'Start/Pause playback'),
]
text1, text2 = zip(*pairs)
text1 = '\n'.join(text1)
text2 = '\n'.join(text2)
_show_help(
col1=text1,
col2=text2,
width=5,
height=2,
)
def _clear_callbacks(self):
from ..backends._pyvista import _remove_picking_callback
if not hasattr(self, 'callbacks'):
return
for callback in self.callbacks.values():
if callback is not None:
if hasattr(callback, "plotter"):
callback.plotter = None
if hasattr(callback, "brain"):
callback.brain = None
if hasattr(callback, "slider_rep"):
callback.slider_rep = None
self.callbacks.clear()
if self.show_traces:
_remove_picking_callback(self._iren, self.plotter.picker)
@property
def interaction(self):
"""The interaction style."""
return self._interaction
@interaction.setter
def interaction(self, interaction):
"""Set the interaction style."""
_validate_type(interaction, str, 'interaction')
_check_option('interaction', interaction, ('trackball', 'terrain'))
for ri, ci, _ in self._iter_views('vol'): # will traverse all
self._renderer.subplot(ri, ci)
self._renderer.set_interaction(interaction)
def _cortex_colormap(self, cortex):
"""Return the colormap corresponding to the cortex."""
colormap_map = dict(classic=dict(colormap="Greys",
vmin=-1, vmax=2),
high_contrast=dict(colormap="Greys",
vmin=-.1, vmax=1.3),
low_contrast=dict(colormap="Greys",
vmin=-5, vmax=5),
bone=dict(colormap="bone_r",
vmin=-.2, vmax=2),
)
return colormap_map[cortex]
@verbose
def add_data(self, array, fmin=None, fmid=None, fmax=None,
thresh=None, center=None, transparent=False, colormap="auto",
alpha=1, vertices=None, smoothing_steps=None, time=None,
time_label="auto", colorbar=True,
hemi=None, remove_existing=None, time_label_size=None,
initial_time=None, scale_factor=None, vector_alpha=None,
clim=None, src=None, volume_options=0.4, colorbar_kwargs=None,
verbose=None):
"""Display data from a numpy array on the surface or volume.
This provides a similar interface to
:meth:`surfer.Brain.add_overlay`, but it displays
it with a single colormap. It offers more flexibility over the
colormap, and provides a way to display four-dimensional data
(i.e., a timecourse) or five-dimensional data (i.e., a
vector-valued timecourse).
.. note:: ``fmin`` sets the low end of the colormap, and is separate
from thresh (this is a different convention from
:meth:`surfer.Brain.add_overlay`).
Parameters
----------
array : numpy array, shape (n_vertices[, 3][, n_times])
Data array. For the data to be understood as vector-valued
(3 values per vertex corresponding to X/Y/Z surface RAS),
then ``array`` must be have all 3 dimensions.
If vectors with no time dimension are desired, consider using a
singleton (e.g., ``np.newaxis``) to create a "time" dimension
and pass ``time_label=None`` (vector values are not supported).
%(fmin_fmid_fmax)s
%(thresh)s
%(center)s
%(transparent)s
colormap : str, list of color, or array
Name of matplotlib colormap to use, a list of matplotlib colors,
or a custom look up table (an n x 4 array coded with RBGA values
between 0 and 255), the default "auto" chooses a default divergent
colormap, if "center" is given (currently "icefire"), otherwise a
default sequential colormap (currently "rocket").
alpha : float in [0, 1]
Alpha level to control opacity of the overlay.
vertices : numpy array
Vertices for which the data is defined (needed if
``len(data) < nvtx``).
smoothing_steps : int or None
Number of smoothing steps (smoothing is used if len(data) < nvtx)
The value 'nearest' can be used too. None (default) will use as
many as necessary to fill the surface.
time : numpy array
Time points in the data array (if data is 2D or 3D).
%(time_label)s
colorbar : bool
Whether to add a colorbar to the figure. Can also be a tuple
to give the (row, col) index of where to put the colorbar.
hemi : str | None
If None, it is assumed to belong to the hemisphere being
shown. If two hemispheres are being shown, an error will
be thrown.
remove_existing : bool
Not supported yet.
Remove surface added by previous "add_data" call. Useful for
conserving memory when displaying different data in a loop.
time_label_size : int
Font size of the time label (default 14).
initial_time : float | None
Time initially shown in the plot. ``None`` to use the first time
sample (default).
scale_factor : float | None (default)
The scale factor to use when displaying glyphs for vector-valued
data.
vector_alpha : float | None
Alpha level to control opacity of the arrows. Only used for
vector-valued data. If None (default), ``alpha`` is used.
clim : dict
Original clim arguments.
%(src_volume_options)s
colorbar_kwargs : dict | None
Options to pass to :meth:`pyvista.BasePlotter.add_scalar_bar`
(e.g., ``dict(title_font_size=10)``).
%(verbose)s
Notes
-----
If the data is defined for a subset of vertices (specified
by the "vertices" parameter), a smoothing method is used to interpolate
the data onto the high resolution surface. If the data is defined for
subsampled version of the surface, smoothing_steps can be set to None,
in which case only as many smoothing steps are applied until the whole
surface is filled with non-zeros.
Due to a Mayavi (or VTK) alpha rendering bug, ``vector_alpha`` is
clamped to be strictly < 1.
"""
_validate_type(transparent, bool, 'transparent')
_validate_type(vector_alpha, ('numeric', None), 'vector_alpha')
_validate_type(scale_factor, ('numeric', None), 'scale_factor')
# those parameters are not supported yet, only None is allowed
_check_option('thresh', thresh, [None])
_check_option('remove_existing', remove_existing, [None])
_validate_type(time_label_size, (None, 'numeric'), 'time_label_size')
if time_label_size is not None:
time_label_size = float(time_label_size)
if time_label_size < 0:
raise ValueError('time_label_size must be positive, got '
f'{time_label_size}')
hemi = self._check_hemi(hemi, extras=['vol'])
stc, array, vertices = self._check_stc(hemi, array, vertices)
array = np.asarray(array)
vector_alpha = alpha if vector_alpha is None else vector_alpha
self._data['vector_alpha'] = vector_alpha
self._data['scale_factor'] = scale_factor
# Create time array and add label if > 1D
if array.ndim <= 1:
time_idx = 0
else:
# check time array
if time is None:
time = np.arange(array.shape[-1])
else:
time = np.asarray(time)
if time.shape != (array.shape[-1],):
raise ValueError('time has shape %s, but need shape %s '
'(array.shape[-1])' %
(time.shape, (array.shape[-1],)))
self._data["time"] = time
if self._n_times is None:
self._times = time
elif len(time) != self._n_times:
raise ValueError("New n_times is different from previous "
"n_times")
elif not np.array_equal(time, self._times):
raise ValueError("Not all time values are consistent with "
"previously set times.")
# initial time
if initial_time is None:
time_idx = 0
else:
time_idx = self._to_time_index(initial_time)
# time label
time_label, _ = _handle_time(time_label, 's', time)
y_txt = 0.05 + 0.1 * bool(colorbar)
if array.ndim == 3:
if array.shape[1] != 3:
raise ValueError('If array has 3 dimensions, array.shape[1] '
'must equal 3, got %s' % (array.shape[1],))
fmin, fmid, fmax = _update_limits(
fmin, fmid, fmax, center, array
)
if colormap == 'auto':
colormap = 'mne' if center is not None else 'hot'
if smoothing_steps is None:
smoothing_steps = 7
elif smoothing_steps == 'nearest':
smoothing_steps = 0
elif isinstance(smoothing_steps, int):
if smoothing_steps < 0:
raise ValueError('Expected value of `smoothing_steps` is'
' positive but {} was given.'.format(
smoothing_steps))
else:
raise TypeError('Expected type of `smoothing_steps` is int or'
' NoneType but {} was given.'.format(
type(smoothing_steps)))
self._data['stc'] = stc
self._data['src'] = src
self._data['smoothing_steps'] = smoothing_steps
self._data['clim'] = clim
self._data['time'] = time
self._data['initial_time'] = initial_time
self._data['time_label'] = time_label
self._data['initial_time_idx'] = time_idx
self._data['time_idx'] = time_idx
self._data['transparent'] = transparent
# data specific for a hemi
self._data[hemi] = dict()
self._data[hemi]['glyph_dataset'] = None
self._data[hemi]['glyph_mapper'] = None
self._data[hemi]['glyph_actor'] = None
self._data[hemi]['array'] = array
self._data[hemi]['vertices'] = vertices
self._data['alpha'] = alpha
self._data['colormap'] = colormap
self._data['center'] = center
self._data['fmin'] = fmin
self._data['fmid'] = fmid
self._data['fmax'] = fmax
self.update_lut()
# 1) add the surfaces first
actor = None
for ri, ci, _ in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
if hemi in ('lh', 'rh'):
actor = self._layered_meshes[hemi]._actor
else:
src_vol = src[2:] if src.kind == 'mixed' else src
actor, _ = self._add_volume_data(hemi, src_vol, volume_options)
assert actor is not None # should have added one
# 2) update time and smoothing properties
# set_data_smoothing calls "set_time_point" for us, which will set
# _current_time
self.set_time_interpolation(self.time_interpolation)
self.set_data_smoothing(self._data['smoothing_steps'])
# 3) add the other actors
if colorbar is True:
# botto left by default
colorbar = (self._subplot_shape[0] - 1, 0)
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
# Add the time label to the bottommost view
do = (ri, ci) == colorbar
if not self._time_label_added and time_label is not None and do:
time_actor = self._renderer.text2d(
x_window=0.95, y_window=y_txt,
color=self._fg_color,
size=time_label_size,
text=time_label(self._current_time),
justification='right'
)
self._data['time_actor'] = time_actor
self._time_label_added = True
if colorbar and not self._colorbar_added and do:
kwargs = dict(source=actor, n_labels=8, color=self._fg_color,
bgcolor=self._brain_color[:3])
kwargs.update(colorbar_kwargs or {})
self._renderer.scalarbar(**kwargs)
self._colorbar_added = True
self._renderer.set_camera(**views_dicts[hemi][v])
# 4) update the scalar bar and opacity
self.update_lut()
if hemi in self._layered_meshes:
mesh = self._layered_meshes[hemi]
mesh.update_overlay(name='data', opacity=alpha)
self._update()
def _iter_views(self, hemi):
# which rows and columns each type of visual needs to be added to
if self._hemi == 'split':
hemi_dict = dict(lh=[0], rh=[1], vol=[0, 1])
else:
hemi_dict = dict(lh=[0], rh=[0], vol=[0])
for vi, view in enumerate(self._views):
if self._hemi == 'split':
view_dict = dict(lh=[vi], rh=[vi], vol=[vi, vi])
else:
view_dict = dict(lh=[vi], rh=[vi], vol=[vi])
if self._view_layout == 'vertical':
rows = view_dict # views are rows
cols = hemi_dict # hemis are columns
else:
rows = hemi_dict # hemis are rows
cols = view_dict # views are columns
for ri, ci in zip(rows[hemi], cols[hemi]):
yield ri, ci, view
def remove_labels(self):
"""Remove all the ROI labels from the image."""
for hemi in self._hemis:
mesh = self._layered_meshes[hemi]
mesh.remove_overlay(self._labels[hemi])
self._labels[hemi].clear()
self._update()
def remove_annotations(self):
"""Remove all annotations from the image."""
for hemi in self._hemis:
mesh = self._layered_meshes[hemi]
mesh.remove_overlay(self._annots[hemi])
self._annots[hemi].clear()
self._update()
def _add_volume_data(self, hemi, src, volume_options):
from ..backends._pyvista import _volume
_validate_type(src, SourceSpaces, 'src')
_check_option('src.kind', src.kind, ('volume',))
_validate_type(
volume_options, (dict, 'numeric', None), 'volume_options')
assert hemi == 'vol'
if not isinstance(volume_options, dict):
volume_options = dict(
resolution=float(volume_options) if volume_options is not None
else None)
volume_options = _handle_default('volume_options', volume_options)
allowed_types = (
['resolution', (None, 'numeric')],
['blending', (str,)],
['alpha', ('numeric', None)],
['surface_alpha', (None, 'numeric')],
['silhouette_alpha', (None, 'numeric')],
['silhouette_linewidth', ('numeric',)],
)
for key, types in allowed_types:
_validate_type(volume_options[key], types,
f'volume_options[{repr(key)}]')
extra_keys = set(volume_options) - set(a[0] for a in allowed_types)
if len(extra_keys):
raise ValueError(
f'volume_options got unknown keys {sorted(extra_keys)}')
blending = _check_option('volume_options["blending"]',
volume_options['blending'],
('composite', 'mip'))
alpha = volume_options['alpha']
if alpha is None:
alpha = 0.4 if self._data[hemi]['array'].ndim == 3 else 1.
alpha = np.clip(float(alpha), 0., 1.)
resolution = volume_options['resolution']
surface_alpha = volume_options['surface_alpha']
if surface_alpha is None:
surface_alpha = min(alpha / 2., 0.1)
silhouette_alpha = volume_options['silhouette_alpha']
if silhouette_alpha is None:
silhouette_alpha = surface_alpha / 4.
silhouette_linewidth = volume_options['silhouette_linewidth']
del volume_options
volume_pos = self._data[hemi].get('grid_volume_pos')
volume_neg = self._data[hemi].get('grid_volume_neg')
center = self._data['center']
if volume_pos is None:
xyz = np.meshgrid(
*[np.arange(s) for s in src[0]['shape']], indexing='ij')
dimensions = np.array(src[0]['shape'], int)
mult = 1000 if self._units == 'mm' else 1
src_mri_t = src[0]['src_mri_t']['trans'].copy()
src_mri_t[:3] *= mult
if resolution is not None:
resolution = resolution * mult / 1000. # to mm
del src, mult
coords = np.array([c.ravel(order='F') for c in xyz]).T
coords = apply_trans(src_mri_t, coords)
self.geo[hemi] = Bunch(coords=coords)
vertices = self._data[hemi]['vertices']
assert self._data[hemi]['array'].shape[0] == len(vertices)
# MNE constructs the source space on a uniform grid in MRI space,
# but mne coreg can change it to be non-uniform, so we need to
# use all three elements here
assert np.allclose(
src_mri_t[:3, :3], np.diag(np.diag(src_mri_t)[:3]))
spacing = np.diag(src_mri_t)[:3]
origin = src_mri_t[:3, 3] - spacing / 2.
scalars = np.zeros(np.prod(dimensions))
scalars[vertices] = 1. # for the outer mesh
grid, grid_mesh, volume_pos, volume_neg = \
_volume(dimensions, origin, spacing, scalars, surface_alpha,
resolution, blending, center)
self._data[hemi]['alpha'] = alpha # incorrectly set earlier
self._data[hemi]['grid'] = grid
self._data[hemi]['grid_mesh'] = grid_mesh
self._data[hemi]['grid_coords'] = coords
self._data[hemi]['grid_src_mri_t'] = src_mri_t
self._data[hemi]['grid_shape'] = dimensions
self._data[hemi]['grid_volume_pos'] = volume_pos
self._data[hemi]['grid_volume_neg'] = volume_neg
actor_pos, _ = self._renderer.plotter.add_actor(
volume_pos, reset_camera=False, name=None, culling=False)
if volume_neg is not None:
actor_neg, _ = self._renderer.plotter.add_actor(
volume_neg, reset_camera=False, name=None, culling=False)
else:
actor_neg = None
grid_mesh = self._data[hemi]['grid_mesh']
if grid_mesh is not None:
import vtk
_, prop = self._renderer.plotter.add_actor(
grid_mesh, reset_camera=False, name=None, culling=False,
pickable=False)
prop.SetColor(*self._brain_color[:3])
prop.SetOpacity(surface_alpha)
if silhouette_alpha > 0 and silhouette_linewidth > 0:
for ri, ci, v in self._iter_views('vol'):
self._renderer.subplot(ri, ci)
grid_silhouette = vtk.vtkPolyDataSilhouette()
grid_silhouette.SetInputData(grid_mesh.GetInput())
grid_silhouette.SetCamera(
self._renderer.plotter.renderer.GetActiveCamera())
grid_silhouette.SetEnableFeatureAngle(0)
grid_silhouette_mapper = vtk.vtkPolyDataMapper()
grid_silhouette_mapper.SetInputConnection(
grid_silhouette.GetOutputPort())
_, prop = self._renderer.plotter.add_actor(
grid_silhouette_mapper, reset_camera=False, name=None,
culling=False, pickable=False)
prop.SetColor(*self._brain_color[:3])
prop.SetOpacity(silhouette_alpha)
prop.SetLineWidth(silhouette_linewidth)
return actor_pos, actor_neg
def add_label(self, label, color=None, alpha=1, scalar_thresh=None,
borders=False, hemi=None, subdir=None,
reset_camera=True):
"""Add an ROI label to the image.
Parameters
----------
label : str | instance of Label
Label filepath or name. Can also be an instance of
an object with attributes "hemi", "vertices", "name", and
optionally "color" and "values" (if scalar_thresh is not None).
color : matplotlib-style color | None
Anything matplotlib accepts: string, RGB, hex, etc. (default
"crimson").
alpha : float in [0, 1]
Alpha level to control opacity.
scalar_thresh : None | float
Threshold the label ids using this value in the label
file's scalar field (i.e. label only vertices with
scalar >= thresh).
borders : bool | int
Show only label borders. If int, specify the number of steps
(away from the true border) along the cortical mesh to include
as part of the border definition.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown.
subdir : None | str
If a label is specified as name, subdir can be used to indicate
that the label file is in a sub-directory of the subject's
label directory rather than in the label directory itself (e.g.
for ``$SUBJECTS_DIR/$SUBJECT/label/aparc/lh.cuneus.label``
``brain.add_label('cuneus', subdir='aparc')``).
reset_camera : bool
If True, reset the camera view after adding the label. Defaults
to True.
Notes
-----
To remove previously added labels, run Brain.remove_labels().
"""
from matplotlib.colors import colorConverter
from ...label import read_label
if isinstance(label, str):
if color is None:
color = "crimson"
if os.path.isfile(label):
filepath = label
label = read_label(filepath)
hemi = label.hemi
label_name = os.path.basename(filepath).split('.')[1]
else:
hemi = self._check_hemi(hemi)
label_name = label
label_fname = ".".join([hemi, label_name, 'label'])
if subdir is None:
filepath = op.join(self._subjects_dir, self._subject_id,
'label', label_fname)
else:
filepath = op.join(self._subjects_dir, self._subject_id,
'label', subdir, label_fname)
if not os.path.exists(filepath):
raise ValueError('Label file %s does not exist'
% filepath)
label = read_label(filepath)
ids = label.vertices
scalars = label.values
else:
# try to extract parameters from label instance
try:
hemi = label.hemi
ids = label.vertices
if label.name is None:
label_name = 'unnamed'
else:
label_name = str(label.name)
if color is None:
if hasattr(label, 'color') and label.color is not None:
color = label.color
else:
color = "crimson"
if scalar_thresh is not None:
scalars = label.values
except Exception:
raise ValueError('Label was not a filename (str), and could '
'not be understood as a class. The class '
'must have attributes "hemi", "vertices", '
'"name", and (if scalar_thresh is not None)'
'"values"')
hemi = self._check_hemi(hemi)
if scalar_thresh is not None:
ids = ids[scalars >= scalar_thresh]
scalars = np.zeros(self.geo[hemi].coords.shape[0])
scalars[ids] = 1
if self.time_viewer and self.show_traces:
stc = self._data["stc"]
src = self._data["src"]
tc = stc.extract_label_time_course(label, src=src,
mode=self.label_extract_mode)
tc = tc[0] if tc.ndim == 2 else tc[0, 0, :]
color = next(self.color_cycle)
line = self.mpl_canvas.plot(
self._data['time'], tc, label=label_name,
color=color)
else:
line = None
orig_color = color
color = colorConverter.to_rgba(color, alpha)
cmap = np.array([(0, 0, 0, 0,), color])
ctable = np.round(cmap * 255).astype(np.uint8)
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
if borders:
n_vertices = scalars.size
edges = mesh_edges(self.geo[hemi].faces)
edges = edges.tocoo()
border_edges = scalars[edges.row] != scalars[edges.col]
show = np.zeros(n_vertices, dtype=np.int64)
keep_idx = np.unique(edges.row[border_edges])
if isinstance(borders, int):
for _ in range(borders):
keep_idx = np.in1d(
self.geo[hemi].faces.ravel(), keep_idx)
keep_idx.shape = self.geo[hemi].faces.shape
keep_idx = self.geo[hemi].faces[np.any(
keep_idx, axis=1)]
keep_idx = np.unique(keep_idx)
show[keep_idx] = 1
scalars *= show
mesh = self._layered_meshes[hemi]
mesh.add_overlay(
scalars=scalars,
colormap=ctable,
rng=None,
opacity=alpha,
name=label_name,
)
if reset_camera:
self._renderer.set_camera(**views_dicts[hemi][v])
if self.time_viewer and self.traces_mode == 'label':
label._color = orig_color
label._line = line
self._labels[hemi].append(label)
self._update()
def add_foci(self, coords, coords_as_verts=False, map_surface=None,
scale_factor=1, color="white", alpha=1, name=None,
hemi=None, resolution=50):
"""Add spherical foci, possibly mapping to displayed surf.
The foci spheres can be displayed at the coordinates given, or
mapped through a surface geometry. In other words, coordinates
from a volume-based analysis in MNI space can be displayed on an
inflated average surface by finding the closest vertex on the
white surface and mapping to that vertex on the inflated mesh.
Parameters
----------
coords : ndarray, shape (n_coords, 3)
Coordinates in stereotaxic space (default) or array of
vertex ids (with ``coord_as_verts=True``).
coords_as_verts : bool
Whether the coords parameter should be interpreted as vertex ids.
map_surface : None
Surface to map coordinates through, or None to use raw coords.
scale_factor : float
Controls the size of the foci spheres (relative to 1cm).
color : matplotlib color code
HTML name, RBG tuple, or hex code.
alpha : float in [0, 1]
Opacity of focus gylphs.
name : str
Internal name to use.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown. If two hemispheres are being shown, an error will
be thrown.
resolution : int
The resolution of the spheres.
"""
from matplotlib.colors import colorConverter
hemi = self._check_hemi(hemi, extras=['vol'])
# those parameters are not supported yet, only None is allowed
_check_option('map_surface', map_surface, [None])
# Figure out how to interpret the first parameter
if coords_as_verts:
coords = self.geo[hemi].coords[coords]
# Convert the color code
if not isinstance(color, tuple):
color = colorConverter.to_rgb(color)
if self._units == 'm':
scale_factor = scale_factor / 1000.
for ri, ci, v in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
self._renderer.sphere(center=coords, color=color,
scale=(10. * scale_factor),
opacity=alpha, resolution=resolution)
self._renderer.set_camera(**views_dicts[hemi][v])
def add_text(self, x, y, text, name=None, color=None, opacity=1.0,
row=-1, col=-1, font_size=None, justification=None):
"""Add a text to the visualization.
Parameters
----------
x : float
X coordinate.
y : float
Y coordinate.
text : str
Text to add.
name : str
Name of the text (text label can be updated using update_text()).
color : tuple
Color of the text. Default is the foreground color set during
initialization (default is black or white depending on the
background color).
opacity : float
Opacity of the text (default 1.0).
row : int
Row index of which brain to use.
col : int
Column index of which brain to use.
font_size : float | None
The font size to use.
justification : str | None
The text justification.
"""
# XXX: support `name` should be added when update_text/remove_text
# are implemented
# _check_option('name', name, [None])
self._renderer.text2d(x_window=x, y_window=y, text=text, color=color,
size=font_size, justification=justification)
def _configure_label_time_course(self):
from ...label import read_labels_from_annot
if not self.show_traces:
return
if self.mpl_canvas is None:
self._configure_mplcanvas()
else:
self.clear_glyphs()
self.traces_mode = 'label'
self.add_annotation(self.annot, color="w", alpha=0.75)
# now plot the time line
self.plot_time_line()
self.mpl_canvas.update_plot()
for hemi in self._hemis:
labels = read_labels_from_annot(
subject=self._subject_id,
parc=self.annot,
hemi=hemi,
subjects_dir=self._subjects_dir
)
self._vertex_to_label_id[hemi] = np.full(
self.geo[hemi].coords.shape[0], -1)
self._annotation_labels[hemi] = labels
for idx, label in enumerate(labels):
self._vertex_to_label_id[hemi][label.vertices] = idx
def add_annotation(self, annot, borders=True, alpha=1, hemi=None,
remove_existing=True, color=None, **kwargs):
"""Add an annotation file.
Parameters
----------
annot : str | tuple
Either path to annotation file or annotation name. Alternatively,
the annotation can be specified as a ``(labels, ctab)`` tuple per
hemisphere, i.e. ``annot=(labels, ctab)`` for a single hemisphere
or ``annot=((lh_labels, lh_ctab), (rh_labels, rh_ctab))`` for both
hemispheres. ``labels`` and ``ctab`` should be arrays as returned
by :func:`nibabel.freesurfer.io.read_annot`.
borders : bool | int
Show only label borders. If int, specify the number of steps
(away from the true border) along the cortical mesh to include
as part of the border definition.
alpha : float in [0, 1]
Alpha level to control opacity.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown. If two hemispheres are being shown, data must exist
for both hemispheres.
remove_existing : bool
If True (default), remove old annotations.
color : matplotlib-style color code
If used, show all annotations in the same (specified) color.
Probably useful only when showing annotation borders.
**kwargs : dict
These are passed to the underlying
``mayavi.mlab.pipeline.surface`` call.
"""
from ...label import _read_annot
hemis = self._check_hemis(hemi)
# Figure out where the data is coming from
if isinstance(annot, str):
if os.path.isfile(annot):
filepath = annot
path = os.path.split(filepath)[0]
file_hemi, annot = os.path.basename(filepath).split('.')[:2]
if len(hemis) > 1:
if annot[:2] == 'lh.':
filepaths = [filepath, op.join(path, 'rh' + annot[2:])]
elif annot[:2] == 'rh.':
filepaths = [op.join(path, 'lh' + annot[2:], filepath)]
else:
raise RuntimeError('To add both hemispheres '
'simultaneously, filename must '
'begin with "lh." or "rh."')
else:
filepaths = [filepath]
else:
filepaths = []
for hemi in hemis:
filepath = op.join(self._subjects_dir,
self._subject_id,
'label',
".".join([hemi, annot, 'annot']))
if not os.path.exists(filepath):
raise ValueError('Annotation file %s does not exist'
% filepath)
filepaths += [filepath]
annots = []
for hemi, filepath in zip(hemis, filepaths):
# Read in the data
labels, cmap, _ = _read_annot(filepath)
annots.append((labels, cmap))
else:
annots = [annot] if len(hemis) == 1 else annot
annot = 'annotation'
for hemi, (labels, cmap) in zip(hemis, annots):
# Maybe zero-out the non-border vertices
self._to_borders(labels, hemi, borders)
# Handle null labels properly
cmap[:, 3] = 255
bgcolor = np.round(np.array(self._brain_color) * 255).astype(int)
bgcolor[-1] = 0
cmap[cmap[:, 4] < 0, 4] += 2 ** 24 # wrap to positive
cmap[cmap[:, 4] <= 0, :4] = bgcolor
if np.any(labels == 0) and not np.any(cmap[:, -1] <= 0):
cmap = np.vstack((cmap, np.concatenate([bgcolor, [0]])))
# Set label ids sensibly
order = np.argsort(cmap[:, -1])
cmap = cmap[order]
ids = np.searchsorted(cmap[:, -1], labels)
cmap = cmap[:, :4]
# Set the alpha level
alpha_vec = cmap[:, 3]
alpha_vec[alpha_vec > 0] = alpha * 255
# Override the cmap when a single color is used
if color is not None:
from matplotlib.colors import colorConverter
rgb = np.round(np.multiply(colorConverter.to_rgb(color), 255))
cmap[:, :3] = rgb.astype(cmap.dtype)
ctable = cmap.astype(np.float64)
for ri, ci, _ in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
mesh = self._layered_meshes[hemi]
mesh.add_overlay(
scalars=ids,
colormap=ctable,
rng=[np.min(ids), np.max(ids)],
opacity=alpha,
name=annot,
)
self._annots[hemi].append(annot)
if not self.time_viewer or self.traces_mode == 'vertex':
from ..backends._pyvista import _set_colormap_range
_set_colormap_range(mesh._actor, cmap.astype(np.uint8),
None)
self._update()
def close(self):
"""Close all figures and cleanup data structure."""
self._closed = True
self._renderer.close()
def show(self):
"""Display the window."""
self._renderer.show()
def show_view(self, view=None, roll=None, distance=None, row=0, col=0,
hemi=None):
"""Orient camera to display view.
Parameters
----------
view : str | dict
String view, or a dict with azimuth and elevation.
roll : float | None
The roll.
distance : float | None
The distance.
row : int
The row to set.
col : int
The column to set.
hemi : str
Which hemi to use for string lookup (when in "both" mode).
"""
hemi = self._hemi if hemi is None else hemi
if hemi == 'split':
if (self._view_layout == 'vertical' and col == 1 or
self._view_layout == 'horizontal' and row == 1):
hemi = 'rh'
else:
hemi = 'lh'
if isinstance(view, str):
view = views_dicts[hemi].get(view)
view = view.copy()
if roll is not None:
view.update(roll=roll)
if distance is not None:
view.update(distance=distance)
self._renderer.subplot(row, col)
self._renderer.set_camera(**view, reset_camera=False)
self._update()
def reset_view(self):
"""Reset the camera."""
for h in self._hemis:
for ri, ci, v in self._iter_views(h):
self._renderer.subplot(ri, ci)
self._renderer.set_camera(**views_dicts[h][v],
reset_camera=False)
def save_image(self, filename, mode='rgb'):
"""Save view from all panels to disk.
Parameters
----------
filename : str
Path to new image file.
mode : str
Either 'rgb' or 'rgba' for values to return.
"""
self._renderer.screenshot(mode=mode, filename=filename)
@fill_doc
def screenshot(self, mode='rgb', time_viewer=False):
"""Generate a screenshot of current view.
Parameters
----------
mode : str
Either 'rgb' or 'rgba' for values to return.
%(brain_screenshot_time_viewer)s
Returns
-------
screenshot : array
Image pixel values.
"""
img = self._renderer.screenshot(mode)
if time_viewer and self.time_viewer and \
self.show_traces and \
not self.separate_canvas:
canvas = self.mpl_canvas.fig.canvas
canvas.draw_idle()
# In theory, one of these should work:
#
# trace_img = np.frombuffer(
# canvas.tostring_rgb(), dtype=np.uint8)
# trace_img.shape = canvas.get_width_height()[::-1] + (3,)
#
# or
#
# trace_img = np.frombuffer(
# canvas.tostring_rgb(), dtype=np.uint8)
# size = time_viewer.mpl_canvas.getSize()
# trace_img.shape = (size.height(), size.width(), 3)
#
# But in practice, sometimes the sizes does not match the
# renderer tostring_rgb() size. So let's directly use what
# matplotlib does in lib/matplotlib/backends/backend_agg.py
# before calling tobytes():
trace_img = np.asarray(
canvas.renderer._renderer).take([0, 1, 2], axis=2)
# need to slice into trace_img because generally it's a bit
# smaller
delta = trace_img.shape[1] - img.shape[1]
if delta > 0:
start = delta // 2
trace_img = trace_img[:, start:start + img.shape[1]]
img = np.concatenate([img, trace_img], axis=0)
return img
@fill_doc
def update_lut(self, fmin=None, fmid=None, fmax=None):
"""Update color map.
Parameters
----------
%(fmin_fmid_fmax)s
"""
from ..backends._pyvista import _set_colormap_range, _set_volume_range
center = self._data['center']
colormap = self._data['colormap']
transparent = self._data['transparent']
lims = dict(fmin=fmin, fmid=fmid, fmax=fmax)
lims = {key: self._data[key] if val is None else val
for key, val in lims.items()}
assert all(val is not None for val in lims.values())
if lims['fmin'] > lims['fmid']:
lims['fmin'] = lims['fmid']
if lims['fmax'] < lims['fmid']:
lims['fmax'] = lims['fmid']
self._data.update(lims)
self._data['ctable'] = np.round(
calculate_lut(colormap, alpha=1., center=center,
transparent=transparent, **lims) *
255).astype(np.uint8)
# update our values
rng = self._cmap_range
ctable = self._data['ctable']
# in testing, no plotter; if colorbar=False, no scalar_bar
scalar_bar = getattr(
getattr(self._renderer, 'plotter', None), 'scalar_bar', None)
for hemi in ['lh', 'rh', 'vol']:
hemi_data = self._data.get(hemi)
if hemi_data is not None:
if hemi in self._layered_meshes:
mesh = self._layered_meshes[hemi]
mesh.update_overlay(name='data',
colormap=self._data['ctable'])
_set_colormap_range(mesh._actor, ctable, scalar_bar, rng,
self._brain_color)
scalar_bar = None
grid_volume_pos = hemi_data.get('grid_volume_pos')
grid_volume_neg = hemi_data.get('grid_volume_neg')
for grid_volume in (grid_volume_pos, grid_volume_neg):
if grid_volume is not None:
_set_volume_range(
grid_volume, ctable, hemi_data['alpha'],
scalar_bar, rng)
scalar_bar = None
glyph_actor = hemi_data.get('glyph_actor')
if glyph_actor is not None:
for glyph_actor_ in glyph_actor:
_set_colormap_range(
glyph_actor_, ctable, scalar_bar, rng)
scalar_bar = None
def set_data_smoothing(self, n_steps):
"""Set the number of smoothing steps.
Parameters
----------
n_steps : int
Number of smoothing steps.
"""
from ...morph import _hemi_morph
for hemi in ['lh', 'rh']:
hemi_data = self._data.get(hemi)
if hemi_data is not None:
if len(hemi_data['array']) >= self.geo[hemi].x.shape[0]:
continue
vertices = hemi_data['vertices']
if vertices is None:
raise ValueError(
'len(data) < nvtx (%s < %s): the vertices '
'parameter must not be None'
% (len(hemi_data), self.geo[hemi].x.shape[0]))
morph_n_steps = 'nearest' if n_steps == 0 else n_steps
maps = sparse.eye(len(self.geo[hemi].coords), format='csr')
with use_log_level(False):
smooth_mat = _hemi_morph(
self.geo[hemi].orig_faces,
np.arange(len(self.geo[hemi].coords)),
vertices, morph_n_steps, maps, warn=False)
self._data[hemi]['smooth_mat'] = smooth_mat
self.set_time_point(self._data['time_idx'])
self._data['smoothing_steps'] = n_steps
@property
def _n_times(self):
return len(self._times) if self._times is not None else None
@property
def time_interpolation(self):
"""The interpolation mode."""
return self._time_interpolation
@fill_doc
def set_time_interpolation(self, interpolation):
"""Set the interpolation mode.
Parameters
----------
%(brain_time_interpolation)s
"""
self._time_interpolation = _check_option(
'interpolation',
interpolation,
('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic')
)
self._time_interp_funcs = dict()
self._time_interp_inv = None
if self._times is not None:
idx = np.arange(self._n_times)
for hemi in ['lh', 'rh', 'vol']:
hemi_data = self._data.get(hemi)
if hemi_data is not None:
array = hemi_data['array']
self._time_interp_funcs[hemi] = _safe_interp1d(
idx, array, self._time_interpolation, axis=-1,
assume_sorted=True)
self._time_interp_inv = _safe_interp1d(idx, self._times)
def set_time_point(self, time_idx):
"""Set the time point shown (can be a float to interpolate).
Parameters
----------
time_idx : int | float
The time index to use. Can be a float to use interpolation
between indices.
"""
self._current_act_data = dict()
time_actor = self._data.get('time_actor', None)
time_label = self._data.get('time_label', None)
for hemi in ['lh', 'rh', 'vol']:
hemi_data = self._data.get(hemi)
if hemi_data is not None:
array = hemi_data['array']
# interpolate in time
vectors = None
if array.ndim == 1:
act_data = array
self._current_time = 0
else:
act_data = self._time_interp_funcs[hemi](time_idx)
self._current_time = self._time_interp_inv(time_idx)
if array.ndim == 3:
vectors = act_data
act_data = np.linalg.norm(act_data, axis=1)
self._current_time = self._time_interp_inv(time_idx)
self._current_act_data[hemi] = act_data
if time_actor is not None and time_label is not None:
time_actor.SetInput(time_label(self._current_time))
# update the volume interpolation
grid = hemi_data.get('grid')
if grid is not None:
vertices = self._data['vol']['vertices']
values = self._current_act_data['vol']
rng = self._cmap_range
fill = 0 if self._data['center'] is not None else rng[0]
grid.cell_arrays['values'].fill(fill)
# XXX for sided data, we probably actually need two
# volumes as composite/MIP needs to look at two
# extremes... for now just use abs. Eventually we can add
# two volumes if we want.
grid.cell_arrays['values'][vertices] = values
# interpolate in space
smooth_mat = hemi_data.get('smooth_mat')
if smooth_mat is not None:
act_data = smooth_mat.dot(act_data)
# update the mesh scalar values
if hemi in self._layered_meshes:
mesh = self._layered_meshes[hemi]
if 'data' in mesh._overlays:
mesh.update_overlay(name='data', scalars=act_data)
else:
mesh.add_overlay(
scalars=act_data,
colormap=self._data['ctable'],
rng=self._cmap_range,
opacity=None,
name='data',
)
# update the glyphs
if vectors is not None:
self._update_glyphs(hemi, vectors)
self._data['time_idx'] = time_idx
self._update()
def set_time(self, time):
"""Set the time to display (in seconds).
Parameters
----------
time : float
The time to show, in seconds.
"""
if self._times is None:
raise ValueError(
'Cannot set time when brain has no defined times.')
elif min(self._times) <= time <= max(self._times):
self.set_time_point(np.interp(float(time), self._times,
np.arange(self._n_times)))
else:
raise ValueError(
f'Requested time ({time} s) is outside the range of '
f'available times ({min(self._times)}-{max(self._times)} s).')
def _update_glyphs(self, hemi, vectors):
from ..backends._pyvista import _set_colormap_range, _create_actor
hemi_data = self._data.get(hemi)
assert hemi_data is not None
vertices = hemi_data['vertices']
vector_alpha = self._data['vector_alpha']
scale_factor = self._data['scale_factor']
vertices = slice(None) if vertices is None else vertices
x, y, z = np.array(self.geo[hemi].coords)[vertices].T
if hemi_data['glyph_actor'] is None:
add = True
hemi_data['glyph_actor'] = list()
else:
add = False
count = 0
for ri, ci, _ in self._iter_views(hemi):
self._renderer.subplot(ri, ci)
if hemi_data['glyph_dataset'] is None:
glyph_mapper, glyph_dataset = self._renderer.quiver3d(
x, y, z,
vectors[:, 0], vectors[:, 1], vectors[:, 2],
color=None,
mode='2darrow',
scale_mode='vector',
scale=scale_factor,
opacity=vector_alpha,
name=str(hemi) + "_glyph"
)
hemi_data['glyph_dataset'] = glyph_dataset
hemi_data['glyph_mapper'] = glyph_mapper
else:
glyph_dataset = hemi_data['glyph_dataset']
glyph_dataset.point_arrays['vec'] = vectors
glyph_mapper = hemi_data['glyph_mapper']
if add:
glyph_actor = _create_actor(glyph_mapper)
prop = glyph_actor.GetProperty()
prop.SetLineWidth(2.)
prop.SetOpacity(vector_alpha)
self._renderer.plotter.add_actor(glyph_actor)
hemi_data['glyph_actor'].append(glyph_actor)
else:
glyph_actor = hemi_data['glyph_actor'][count]
count += 1
_set_colormap_range(
actor=glyph_actor,
ctable=self._data['ctable'],
scalar_bar=None,
rng=self._cmap_range,
)
@property
def _cmap_range(self):
dt_max = self._data['fmax']
if self._data['center'] is None:
dt_min = self._data['fmin']
else:
dt_min = -1 * dt_max
rng = [dt_min, dt_max]
return rng
def _update_fscale(self, fscale):
"""Scale the colorbar points."""
fmin = self._data['fmin'] * fscale
fmid = self._data['fmid'] * fscale
fmax = self._data['fmax'] * fscale
self.update_lut(fmin=fmin, fmid=fmid, fmax=fmax)
def _update_auto_scaling(self, restore=False):
user_clim = self._data['clim']
if user_clim is not None and 'lims' in user_clim:
allow_pos_lims = False
else:
allow_pos_lims = True
if user_clim is not None and restore:
clim = user_clim
else:
clim = 'auto'
colormap = self._data['colormap']
transparent = self._data['transparent']
mapdata = _process_clim(
clim, colormap, transparent,
np.concatenate(list(self._current_act_data.values())),
allow_pos_lims)
diverging = 'pos_lims' in mapdata['clim']
colormap = mapdata['colormap']
scale_pts = mapdata['clim']['pos_lims' if diverging else 'lims']
transparent = mapdata['transparent']
del mapdata
fmin, fmid, fmax = scale_pts
center = 0. if diverging else None
self._data['center'] = center
self._data['colormap'] = colormap
self._data['transparent'] = transparent
self.update_lut(fmin=fmin, fmid=fmid, fmax=fmax)
def _to_time_index(self, value):
"""Return the interpolated time index of the given time value."""
time = self._data['time']
value = np.interp(value, time, np.arange(len(time)))
return value
@property
def data(self):
"""Data used by time viewer and color bar widgets."""
return self._data
@property
def labels(self):
return self._labels
@property
def views(self):
return self._views
@property
def hemis(self):
return self._hemis
def _save_movie(self, filename, time_dilation=4., tmin=None, tmax=None,
framerate=24, interpolation=None, codec=None,
bitrate=None, callback=None, time_viewer=False, **kwargs):
import imageio
from ..backends._pyvista import _disabled_interaction
with _disabled_interaction(self._renderer):
images = self._make_movie_frames(
time_dilation, tmin, tmax, framerate, interpolation, callback,
time_viewer)
# find imageio FFMPEG parameters
if 'fps' not in kwargs:
kwargs['fps'] = framerate
if codec is not None:
kwargs['codec'] = codec
if bitrate is not None:
kwargs['bitrate'] = bitrate
imageio.mimwrite(filename, images, **kwargs)
@fill_doc
def save_movie(self, filename, time_dilation=4., tmin=None, tmax=None,
framerate=24, interpolation=None, codec=None,
bitrate=None, callback=None, time_viewer=False, **kwargs):
"""Save a movie (for data with a time axis).
The movie is created through the :mod:`imageio` module. The format is
determined by the extension, and additional options can be specified
through keyword arguments that depend on the format. For available
formats and corresponding parameters see the imageio documentation:
http://imageio.readthedocs.io/en/latest/formats.html#multiple-images
.. Warning::
This method assumes that time is specified in seconds when adding
data. If time is specified in milliseconds this will result in
movies 1000 times longer than expected.
Parameters
----------
filename : str
Path at which to save the movie. The extension determines the
format (e.g., ``'*.mov'``, ``'*.gif'``, ...; see the :mod:`imageio`
documentation for available formats).
time_dilation : float
Factor by which to stretch time (default 4). For example, an epoch
from -100 to 600 ms lasts 700 ms. With ``time_dilation=4`` this
would result in a 2.8 s long movie.
tmin : float
First time point to include (default: all data).
tmax : float
Last time point to include (default: all data).
framerate : float
Framerate of the movie (frames per second, default 24).
%(brain_time_interpolation)s
If None, it uses the current ``brain.interpolation``,
which defaults to ``'nearest'``. Defaults to None.
codec : str | None
The codec to use.
bitrate : float | None
The bitrate to use.
callback : callable | None
A function to call on each iteration. Useful for status message
updates. It will be passed keyword arguments ``frame`` and
``n_frames``.
%(brain_screenshot_time_viewer)s
**kwargs : dict
Specify additional options for :mod:`imageio`.
Returns
-------
dialog : object
The opened dialog is returned for testing purpose only.
"""
if self.time_viewer:
try:
from pyvista.plotting.qt_plotting import FileDialog
except ImportError:
from pyvistaqt.plotting import FileDialog
if filename is None:
self.status_msg.setText("Choose movie path ...")
self.status_msg.show()
self.status_progress.setValue(0)
def _post_setup(unused):
del unused
self.status_msg.hide()
self.status_progress.hide()
dialog = FileDialog(
self.plotter.app_window,
callback=partial(self._save_movie, **kwargs)
)
dialog.setDirectory(os.getcwd())
dialog.finished.connect(_post_setup)
return dialog
else:
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QCursor
def frame_callback(frame, n_frames):
if frame == n_frames:
# On the ImageIO step
self.status_msg.setText(
"Saving with ImageIO: %s"
% filename
)
self.status_msg.show()
self.status_progress.hide()
self.status_bar.layout().update()
else:
self.status_msg.setText(
"Rendering images (frame %d / %d) ..."
% (frame + 1, n_frames)
)
self.status_msg.show()
self.status_progress.show()
self.status_progress.setRange(0, n_frames - 1)
self.status_progress.setValue(frame)
self.status_progress.update()
self.status_progress.repaint()
self.status_msg.update()
self.status_msg.parent().update()
self.status_msg.repaint()
# temporarily hide interface
default_visibility = self.visibility
self.toggle_interface(value=False)
# set cursor to busy
default_cursor = self.interactor.cursor()
self.interactor.setCursor(QCursor(Qt.WaitCursor))
try:
self._save_movie(
filename=filename,
time_dilation=(1. / self.playback_speed),
callback=frame_callback,
**kwargs
)
except (Exception, KeyboardInterrupt):
warn('Movie saving aborted:\n' + traceback.format_exc())
# restore visibility
self.toggle_interface(value=default_visibility)
# restore cursor
self.interactor.setCursor(default_cursor)
else:
self._save_movie(filename, time_dilation, tmin, tmax,
framerate, interpolation, codec,
bitrate, callback, time_viewer, **kwargs)
def _make_movie_frames(self, time_dilation, tmin, tmax, framerate,
interpolation, callback, time_viewer):
from math import floor
# find tmin
if tmin is None:
tmin = self._times[0]
elif tmin < self._times[0]:
raise ValueError("tmin=%r is smaller than the first time point "
"(%r)" % (tmin, self._times[0]))
# find indexes at which to create frames
if tmax is None:
tmax = self._times[-1]
elif tmax > self._times[-1]:
raise ValueError("tmax=%r is greater than the latest time point "
"(%r)" % (tmax, self._times[-1]))
n_frames = floor((tmax - tmin) * time_dilation * framerate)
times = np.arange(n_frames, dtype=float)
times /= framerate * time_dilation
times += tmin
time_idx = np.interp(times, self._times, np.arange(self._n_times))
n_times = len(time_idx)
if n_times == 0:
raise ValueError("No time points selected")
logger.debug("Save movie for time points/samples\n%s\n%s"
% (times, time_idx))
# Sometimes the first screenshot is rendered with a different
# resolution on OS X
self.screenshot(time_viewer=time_viewer)
old_mode = self.time_interpolation
if interpolation is not None:
self.set_time_interpolation(interpolation)
try:
images = [
self.screenshot(time_viewer=time_viewer)
for _ in self._iter_time(time_idx, callback)]
finally:
self.set_time_interpolation(old_mode)
if callback is not None:
callback(frame=len(time_idx), n_frames=len(time_idx))
return images
def _iter_time(self, time_idx, callback):
"""Iterate through time points, then reset to current time.
Parameters
----------
time_idx : array_like
Time point indexes through which to iterate.
callback : callable | None
Callback to call before yielding each frame.
Yields
------
idx : int | float
Current index.
Notes
-----
Used by movie and image sequence saving functions.
"""
if self.time_viewer:
func = partial(self.callbacks["time"],
update_widget=True)
else:
func = self.set_time_point
current_time_idx = self._data["time_idx"]
for ii, idx in enumerate(time_idx):
func(idx)
if callback is not None:
callback(frame=ii, n_frames=len(time_idx))
yield idx
# Restore original time index
func(current_time_idx)
def _show(self):
"""Request rendering of the window."""
try:
return self._renderer.show()
except RuntimeError:
logger.info("No active/running renderer available.")
def _check_stc(self, hemi, array, vertices):
from ...source_estimate import (
_BaseSourceEstimate, _BaseSurfaceSourceEstimate,
_BaseMixedSourceEstimate, _BaseVolSourceEstimate
)
if isinstance(array, _BaseSourceEstimate):
stc = array
stc_surf = stc_vol = None
if isinstance(stc, _BaseSurfaceSourceEstimate):
stc_surf = stc
elif isinstance(stc, _BaseMixedSourceEstimate):
stc_surf = stc.surface() if hemi != 'vol' else None
stc_vol = stc.volume() if hemi == 'vol' else None
elif isinstance(stc, _BaseVolSourceEstimate):
stc_vol = stc if hemi == 'vol' else None
else:
raise TypeError("stc not supported")
if stc_surf is None and stc_vol is None:
raise ValueError("No data to be added")
if stc_surf is not None:
array = getattr(stc_surf, hemi + '_data')
vertices = stc_surf.vertices[0 if hemi == 'lh' else 1]
if stc_vol is not None:
array = stc_vol.data
vertices = np.concatenate(stc_vol.vertices)
else:
stc = None
return stc, array, vertices
def _check_hemi(self, hemi, extras=()):
"""Check for safe single-hemi input, returns str."""
if hemi is None:
if self._hemi not in ['lh', 'rh']:
raise ValueError('hemi must not be None when both '
'hemispheres are displayed')
else:
hemi = self._hemi
elif hemi not in ['lh', 'rh'] + list(extras):
extra = ' or None' if self._hemi in ['lh', 'rh'] else ''
raise ValueError('hemi must be either "lh" or "rh"' +
extra + ", got " + str(hemi))
return hemi
def _check_hemis(self, hemi):
"""Check for safe dual or single-hemi input, returns list."""
if hemi is None:
if self._hemi not in ['lh', 'rh']:
hemi = ['lh', 'rh']
else:
hemi = [self._hemi]
elif hemi not in ['lh', 'rh']:
extra = ' or None' if self._hemi in ['lh', 'rh'] else ''
raise ValueError('hemi must be either "lh" or "rh"' + extra)
else:
hemi = [hemi]
return hemi
def _to_borders(self, label, hemi, borders, restrict_idx=None):
"""Convert a label/parc to borders."""
if not isinstance(borders, (bool, int)) or borders < 0:
raise ValueError('borders must be a bool or positive integer')
if borders:
n_vertices = label.size
edges = mesh_edges(self.geo[hemi].orig_faces)
edges = edges.tocoo()
border_edges = label[edges.row] != label[edges.col]
show = np.zeros(n_vertices, dtype=np.int64)
keep_idx = np.unique(edges.row[border_edges])
if isinstance(borders, int):
for _ in range(borders):
keep_idx = np.in1d(
self.geo[hemi].orig_faces.ravel(), keep_idx)
keep_idx.shape = self.geo[hemi].orig_faces.shape
keep_idx = self.geo[hemi].orig_faces[
np.any(keep_idx, axis=1)]
keep_idx = np.unique(keep_idx)
if restrict_idx is not None:
keep_idx = keep_idx[np.in1d(keep_idx, restrict_idx)]
show[keep_idx] = 1
label *= show
def enable_depth_peeling(self):
"""Enable depth peeling."""
self._renderer.enable_depth_peeling()
def _update(self):
from ..backends import renderer
if renderer.get_3d_backend() in ['pyvista', 'notebook']:
if self.notebook and self._renderer.figure.display is not None:
self._renderer.figure.display.update()
else:
self._renderer.plotter.update()
def get_picked_points(self):
"""Return the vertices of the picked points.
Returns
-------
points : list of int | None
The vertices picked by the time viewer.
"""
if hasattr(self, "time_viewer"):
return self.picked_points
def __hash__(self):
"""Hash the object."""
raise NotImplementedError
def _safe_interp1d(x, y, kind='linear', axis=-1, assume_sorted=False):
"""Work around interp1d not liking singleton dimensions."""
from scipy.interpolate import interp1d
if y.shape[axis] == 1:
def func(x):
return np.take(y, np.zeros(np.asarray(x).shape, int), axis=axis)
return func
else:
return interp1d(x, y, kind, axis=axis, assume_sorted=assume_sorted)
def _update_limits(fmin, fmid, fmax, center, array):
if center is None:
if fmin is None:
fmin = array.min() if array.size > 0 else 0
if fmax is None:
fmax = array.max() if array.size > 0 else 1
else:
if fmin is None:
fmin = 0
if fmax is None:
fmax = np.abs(center - array).max() if array.size > 0 else 1
if fmid is None:
fmid = (fmin + fmax) / 2.
if fmin >= fmid:
raise RuntimeError('min must be < mid, got %0.4g >= %0.4g'
% (fmin, fmid))
if fmid >= fmax:
raise RuntimeError('mid must be < max, got %0.4g >= %0.4g'
% (fmid, fmax))
return fmin, fmid, fmax
def _get_range(brain):
val = np.abs(np.concatenate(list(brain._current_act_data.values())))
return [np.min(val), np.max(val)]
class _FakeIren():
def EnterEvent(self):
pass
def MouseMoveEvent(self):
pass
def LeaveEvent(self):
pass
def SetEventInformation(self, *args, **kwargs):
pass
def CharEvent(self):
pass
def KeyPressEvent(self, *args, **kwargs):
pass
def KeyReleaseEvent(self, *args, **kwargs):
pass
| bsd-3-clause |
ibab/tensorflow | tensorflow/contrib/learn/__init__.py | 4 | 1832 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# TODO(ptucker,ipolosukhin): Improve descriptions.
"""High level API for learning with TensorFlow.
## Estimators
Train and evaluate TensorFlow models.
@@BaseEstimator
@@Estimator
@@ModeKeys
@@TensorFlowClassifier
@@TensorFlowDNNClassifier
@@TensorFlowDNNRegressor
@@TensorFlowEstimator
@@TensorFlowLinearClassifier
@@TensorFlowLinearRegressor
@@TensorFlowRNNClassifier
@@TensorFlowRNNRegressor
@@TensorFlowRegressor
## Graph actions
Perform various training, evaluation, and inference actions on a graph.
@@NanLossDuringTrainingError
@@RunConfig
@@evaluate
@@infer
@@run_feeds
@@run_n
@@train
## Input processing
Queue and read batched input data.
@@extract_dask_data
@@extract_dask_labels
@@extract_pandas_data
@@extract_pandas_labels
@@extract_pandas_matrix
@@read_batch_examples
@@read_batch_features
@@read_batch_record_features
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=wildcard-import
from tensorflow.contrib.learn.python.learn import *
from tensorflow.python.util.all_util import make_all
__all__ = make_all(__name__)
__all__.append('datasets')
| apache-2.0 |
jayflo/scikit-learn | examples/ensemble/plot_forest_importances.py | 241 | 1761 | """
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are the feature
importances of the forest, along with their inter-trees variability.
As expected, the plot suggests that 3 features are informative, while the
remaining are not.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier
# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
n_classes=2,
random_state=0,
shuffle=False)
# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
random_state=0)
forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(10):
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(10), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(10), indices)
plt.xlim([-1, 10])
plt.show()
| bsd-3-clause |
ishanic/scikit-learn | sklearn/mixture/tests/test_gmm.py | 200 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raise_message
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.externals.six.moves import cStringIO as StringIO
rng = np.random.RandomState(0)
def test_sample_gaussian():
# Test sample generation from mixture.sample_gaussian where covariance
# is diagonal, spherical and full
n_features, n_samples = 2, 300
axis = 1
mu = rng.randint(10) * rng.rand(n_features)
cv = (rng.rand(n_features) + 1.0) ** 2
samples = mixture.sample_gaussian(
mu, cv, covariance_type='diag', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.3))
assert_true(np.allclose(samples.var(axis), cv, atol=1.5))
# the same for spherical covariances
cv = (rng.rand() + 1.0) ** 2
samples = mixture.sample_gaussian(
mu, cv, covariance_type='spherical', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.5))
assert_true(np.allclose(
samples.var(axis), np.repeat(cv, n_features), atol=1.5))
# and for full covariances
A = rng.randn(n_features, n_features)
cv = np.dot(A.T, A) + np.eye(n_features)
samples = mixture.sample_gaussian(
mu, cv, covariance_type='full', n_samples=n_samples)
assert_true(np.allclose(samples.mean(axis), mu, atol=1.3))
assert_true(np.allclose(np.cov(samples), cv, atol=2.5))
# Numerical stability check: in SciPy 0.12.0 at least, eigh may return
# tiny negative values in its second return value.
from sklearn.mixture import sample_gaussian
x = sample_gaussian([0, 0], [[4, 3], [1, .1]],
covariance_type='full', random_state=42)
print(x)
assert_true(np.isfinite(x).all())
def _naive_lmvnpdf_diag(X, mu, cv):
# slow and naive implementation of lmvnpdf
ref = np.empty((len(X), len(mu)))
stds = np.sqrt(cv)
for i, (m, std) in enumerate(zip(mu, stds)):
ref[:, i] = np.log(stats.norm.pdf(X, m, std)).sum(axis=1)
return ref
def test_lmvnpdf_diag():
# test a slow and naive implementation of lmvnpdf and
# compare it to the vectorized version (mixture.lmvnpdf) to test
# for correctness
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
X = rng.randint(10) * rng.rand(n_samples, n_features)
ref = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, cv, 'diag')
assert_array_almost_equal(lpr, ref)
def test_lmvnpdf_spherical():
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
spherecv = rng.rand(n_components, 1) ** 2 + 1
X = rng.randint(10) * rng.rand(n_samples, n_features)
cv = np.tile(spherecv, (n_features, 1))
reference = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, spherecv,
'spherical')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_full():
n_features, n_components, n_samples = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
X = rng.randint(10) * rng.rand(n_samples, n_features)
fullcv = np.array([np.diag(x) for x in cv])
reference = _naive_lmvnpdf_diag(X, mu, cv)
lpr = mixture.log_multivariate_normal_density(X, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def test_lvmpdf_full_cv_non_positive_definite():
n_features, n_samples = 2, 10
rng = np.random.RandomState(0)
X = rng.randint(10) * rng.rand(n_samples, n_features)
mu = np.mean(X, 0)
cv = np.array([[[-1, 0], [0, 1]]])
expected_message = "'covars' must be symmetric, positive-definite"
assert_raise_message(ValueError, expected_message,
mixture.log_multivariate_normal_density,
X, mu, cv, 'full')
def test_GMM_attributes():
n_components, n_features = 10, 4
covariance_type = 'diag'
g = mixture.GMM(n_components, covariance_type, random_state=rng)
weights = rng.rand(n_components)
weights = weights / weights.sum()
means = rng.randint(-20, 20, (n_components, n_features))
assert_true(g.n_components == n_components)
assert_true(g.covariance_type == covariance_type)
g.weights_ = weights
assert_array_almost_equal(g.weights_, weights)
g.means_ = means
assert_array_almost_equal(g.means_, means)
covars = (0.1 + 2 * rng.rand(n_components, n_features)) ** 2
g.covars_ = covars
assert_array_almost_equal(g.covars_, covars)
assert_raises(ValueError, g._set_covars, [])
assert_raises(ValueError, g._set_covars,
np.zeros((n_components - 2, n_features)))
assert_raises(ValueError, mixture.GMM, n_components=20,
covariance_type='badcovariance_type')
class GMMTester():
do_test_eval = True
def _setUp(self):
self.n_components = 10
self.n_features = 4
self.weights = rng.rand(self.n_components)
self.weights = self.weights / self.weights.sum()
self.means = rng.randint(-20, 20, (self.n_components, self.n_features))
self.threshold = -0.5
self.I = np.eye(self.n_features)
self.covars = {
'spherical': (0.1 + 2 * rng.rand(self.n_components,
self.n_features)) ** 2,
'tied': (make_spd_matrix(self.n_features, random_state=0)
+ 5 * self.I),
'diag': (0.1 + 2 * rng.rand(self.n_components,
self.n_features)) ** 2,
'full': np.array([make_spd_matrix(self.n_features, random_state=0)
+ 5 * self.I for x in range(self.n_components)])}
def test_eval(self):
if not self.do_test_eval:
return # DPGMM does not support setting the means and
# covariances before fitting There is no way of fixing this
# due to the variational parameters being more expressive than
# covariance matrices
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type, random_state=rng)
# Make sure the means are far apart so responsibilities.argmax()
# picks the actual component used to generate the observations.
g.means_ = 20 * self.means
g.covars_ = self.covars[self.covariance_type]
g.weights_ = self.weights
gaussidx = np.repeat(np.arange(self.n_components), 5)
n_samples = len(gaussidx)
X = rng.randn(n_samples, self.n_features) + g.means_[gaussidx]
ll, responsibilities = g.score_samples(X)
self.assertEqual(len(ll), n_samples)
self.assertEqual(responsibilities.shape,
(n_samples, self.n_components))
assert_array_almost_equal(responsibilities.sum(axis=1),
np.ones(n_samples))
assert_array_equal(responsibilities.argmax(axis=1), gaussidx)
def test_sample(self, n=100):
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type, random_state=rng)
# Make sure the means are far apart so responsibilities.argmax()
# picks the actual component used to generate the observations.
g.means_ = 20 * self.means
g.covars_ = np.maximum(self.covars[self.covariance_type], 0.1)
g.weights_ = self.weights
samples = g.sample(n)
self.assertEqual(samples.shape, (n, self.n_features))
def test_train(self, params='wmc'):
g = mixture.GMM(n_components=self.n_components,
covariance_type=self.covariance_type)
g.weights_ = self.weights
g.means_ = self.means
g.covars_ = 20 * self.covars[self.covariance_type]
# Create a training set by sampling from the predefined distribution.
X = g.sample(n_samples=100)
g = self.model(n_components=self.n_components,
covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-1,
n_iter=1, init_params=params)
g.fit(X)
# Do one training iteration at a time so we can keep track of
# the log likelihood to make sure that it increases after each
# iteration.
trainll = []
for _ in range(5):
g.params = params
g.init_params = ''
g.fit(X)
trainll.append(self.score(g, X))
g.n_iter = 10
g.init_params = ''
g.params = params
g.fit(X) # finish fitting
# Note that the log likelihood will sometimes decrease by a
# very small amount after it has more or less converged due to
# the addition of min_covar to the covariance (to prevent
# underflow). This is why the threshold is set to -0.5
# instead of 0.
delta_min = np.diff(trainll).min()
self.assertTrue(
delta_min > self.threshold,
"The min nll increase is %f which is lower than the admissible"
" threshold of %f, for model %s. The likelihoods are %s."
% (delta_min, self.threshold, self.covariance_type, trainll))
def test_train_degenerate(self, params='wmc'):
# Train on degenerate data with 0 in some dimensions
# Create a training set by sampling from the predefined distribution.
X = rng.randn(100, self.n_features)
X.T[1:] = 0
g = self.model(n_components=2, covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-3, n_iter=5,
init_params=params)
g.fit(X)
trainll = g.score(X)
self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5)
def test_train_1d(self, params='wmc'):
# Train on 1-D data
# Create a training set by sampling from the predefined distribution.
X = rng.randn(100, 1)
# X.T[1:] = 0
g = self.model(n_components=2, covariance_type=self.covariance_type,
random_state=rng, min_covar=1e-7, n_iter=5,
init_params=params)
g.fit(X)
trainll = g.score(X)
if isinstance(g, mixture.DPGMM):
self.assertTrue(np.sum(np.abs(trainll / 100)) < 5)
else:
self.assertTrue(np.sum(np.abs(trainll / 100)) < 2)
def score(self, g, X):
return g.score(X).sum()
class TestGMMWithSphericalCovars(unittest.TestCase, GMMTester):
covariance_type = 'spherical'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithDiagonalCovars(unittest.TestCase, GMMTester):
covariance_type = 'diag'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithTiedCovars(unittest.TestCase, GMMTester):
covariance_type = 'tied'
model = mixture.GMM
setUp = GMMTester._setUp
class TestGMMWithFullCovars(unittest.TestCase, GMMTester):
covariance_type = 'full'
model = mixture.GMM
setUp = GMMTester._setUp
def test_multiple_init():
# Test that multiple inits does not much worse than a single one
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, covariance_type='spherical',
random_state=rng, min_covar=1e-7, n_iter=5)
train1 = g.fit(X).score(X).sum()
g.n_init = 5
train2 = g.fit(X).score(X).sum()
assert_true(train2 >= train1 - 1.e-2)
def test_n_parameters():
# Test that the right number of parameters is estimated
n_samples, n_dim, n_components = 7, 5, 2
X = rng.randn(n_samples, n_dim)
n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41}
for cv_type in ['full', 'tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7, n_iter=1)
g.fit(X)
assert_true(g._n_parameters() == n_params[cv_type])
def test_1d_1component():
# Test all of the covariance_types return the same BIC score for
# 1-dimensional, 1 component fits.
n_samples, n_dim, n_components = 100, 1, 1
X = rng.randn(n_samples, n_dim)
g_full = mixture.GMM(n_components=n_components, covariance_type='full',
random_state=rng, min_covar=1e-7, n_iter=1)
g_full.fit(X)
g_full_bic = g_full.bic(X)
for cv_type in ['tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7, n_iter=1)
g.fit(X)
assert_array_almost_equal(g.bic(X), g_full_bic)
def assert_fit_predict_correct(model, X):
model2 = copy.deepcopy(model)
predictions_1 = model.fit(X).predict(X)
predictions_2 = model2.fit_predict(X)
assert adjusted_rand_score(predictions_1, predictions_2) == 1.0
def test_fit_predict():
"""
test that gmm.fit_predict is equivalent to gmm.fit + gmm.predict
"""
lrng = np.random.RandomState(101)
n_samples, n_dim, n_comps = 100, 2, 2
mu = np.array([[8, 8]])
component_0 = lrng.randn(n_samples, n_dim)
component_1 = lrng.randn(n_samples, n_dim) + mu
X = np.vstack((component_0, component_1))
for m_constructor in (mixture.GMM, mixture.VBGMM, mixture.DPGMM):
model = m_constructor(n_components=n_comps, covariance_type='full',
min_covar=1e-7, n_iter=5,
random_state=np.random.RandomState(0))
assert_fit_predict_correct(model, X)
model = mixture.GMM(n_components=n_comps, n_iter=0)
z = model.fit_predict(X)
assert np.all(z == 0), "Quick Initialization Failed!"
def test_aic():
# Test the aic and bic criteria
n_samples, n_dim, n_components = 50, 3, 2
X = rng.randn(n_samples, n_dim)
SGH = 0.5 * (X.var() + np.log(2 * np.pi)) # standard gaussian entropy
for cv_type in ['full', 'tied', 'diag', 'spherical']:
g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
random_state=rng, min_covar=1e-7)
g.fit(X)
aic = 2 * n_samples * SGH * n_dim + 2 * g._n_parameters()
bic = (2 * n_samples * SGH * n_dim +
np.log(n_samples) * g._n_parameters())
bound = n_dim * 3. / np.sqrt(n_samples)
assert_true(np.abs(g.aic(X) - aic) / n_samples < bound)
assert_true(np.abs(g.bic(X) - bic) / n_samples < bound)
def check_positive_definite_covars(covariance_type):
r"""Test that covariance matrices do not become non positive definite
Due to the accumulation of round-off errors, the computation of the
covariance matrices during the learning phase could lead to non-positive
definite covariance matrices. Namely the use of the formula:
.. math:: C = (\sum_i w_i x_i x_i^T) - \mu \mu^T
instead of:
.. math:: C = \sum_i w_i (x_i - \mu)(x_i - \mu)^T
while mathematically equivalent, was observed a ``LinAlgError`` exception,
when computing a ``GMM`` with full covariance matrices and fixed mean.
This function ensures that some later optimization will not introduce the
problem again.
"""
rng = np.random.RandomState(1)
# we build a dataset with 2 2d component. The components are unbalanced
# (respective weights 0.9 and 0.1)
X = rng.randn(100, 2)
X[-10:] += (3, 3) # Shift the 10 last points
gmm = mixture.GMM(2, params="wc", covariance_type=covariance_type,
min_covar=1e-3)
# This is a non-regression test for issue #2640. The following call used
# to trigger:
# numpy.linalg.linalg.LinAlgError: 2-th leading minor not positive definite
gmm.fit(X)
if covariance_type == "diag" or covariance_type == "spherical":
assert_greater(gmm.covars_.min(), 0)
else:
if covariance_type == "tied":
covs = [gmm.covars_]
else:
covs = gmm.covars_
for c in covs:
assert_greater(np.linalg.det(c), 0)
def test_positive_definite_covars():
# Check positive definiteness for all covariance types
for covariance_type in ["full", "tied", "diag", "spherical"]:
yield check_positive_definite_covars, covariance_type
def test_verbose_first_level():
# Create sample data
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, n_init=2, verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
g.fit(X)
finally:
sys.stdout = old_stdout
def test_verbose_second_level():
# Create sample data
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, n_init=2, verbose=2)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
g.fit(X)
finally:
sys.stdout = old_stdout
| bsd-3-clause |
Windy-Ground/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solves the problem
know as 'normalized graph cuts': the image is seen as a graph of
connected voxels, and the spectral clustering algorithm amounts to
choosing graph cuts defining regions while minimizing the ratio of the
gradient along the cut, and the volume of the region.
As the algorithm tries to balance the volume (ie balance the region
sizes), if we take circles with different sizes, the segmentation fails.
In addition, as there is no useful information in the intensity of the image,
or its gradient, we choose to perform the spectral clustering on a graph
that is only weakly informed by the gradient. This is close to performing
a Voronoi partition of the graph.
In addition, we use the mask of the objects to restrict the graph to the
outline of the objects. In this example, we are interested in
separating the objects one from the other, and not from the background.
"""
print(__doc__)
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
###############################################################################
l = 100
x, y = np.indices((l, l))
center1 = (28, 24)
center2 = (40, 50)
center3 = (67, 58)
center4 = (24, 70)
radius1, radius2, radius3, radius4 = 16, 14, 15, 14
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2
circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2
###############################################################################
# 4 circles
img = circle1 + circle2 + circle3 + circle4
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
###############################################################################
# 2 circles
img = circle1 + circle2
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
graph = image.img_to_graph(img, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
plt.show()
| bsd-3-clause |
imbforge/NGSpipe2go | tools/piRNA/piRNABaseTerminalBases.py | 1 | 10850 | #!/usr/bin/env python
# encoding: utf-8
usage = '''
Takes alignments, corresponding to piRNAs, and counts how often each nucleotide occurs in the boundaries (5' and 3') of the sequence. By default the it counts nucleotide frequency at positions -20 to +20. It also outputs results for sense and antisense piRNAs to a provided list of genomic features. A plot in pdf and png format is generated along with the files containing the counts.
Author: António Domingues
amjdomingues [at] gmail.com
'''
import pybedtools
from pybedtools import BedTool
from pybedtools.featurefuncs import three_prime, five_prime, greater_than
import csv
import os
import sys
import argparse
import pandas as pd
import pysam
def getArgs():
"""Parse sys.argv"""
parser = argparse.ArgumentParser(
description=usage,
formatter_class=argparse.RawDescriptionHelpFormatter
)
parser.add_argument(
'-f', '--fasta',
required=True,
type=str,
help='Path to fasta to retrieve sequences.'
)
parser.add_argument(
'-b', '--bam',
required=True,
type=str,
help='Path to alignments (bam) to look for motif.'
)
parser.add_argument(
'-i', '--intervals',
required=True,
type=str,
help='Path to genomic intervals (bed), usually containing the genomic locations of repeat elements. This will be used to determine sense or antisense of read mapping'
)
parser.add_argument(
'-u', '--upstream',
required=False,
type=int,
default=20,
help='Number of nucleotides upstream of the read (genomic sequence). Default is 20'
)
parser.add_argument(
'-d', '--downstream',
required=False,
type=int,
default=20,
help='Number of nucleotides downstream of the read (piRNA sequence). Default is 20'
)
parser.add_argument(
'-g', '--genome',
required=False,
type=str,
help='The genome to retrieve chromosome lengths, e.g., hg19, mm10, danRer7...'
)
parser.add_argument(
'-o', '--outFolder',
required=False,
type=str,
default="./",
help='Folder to put the results. Default: current folder (./)'
)
args = parser.parse_args()
return args
def timeStamp():
'''
Returns current system time in the format "YYYY-MM-DD HH:MM:SS"
Source: http://stackoverflow.com/a/13891070/1274242
'''
import time
import datetime
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
return(st)
def createAndChangeDir(dir_name):
'''
Creates a directory from a string (dir_name) and changes to that directory.
'''
try:
os.makedirs(dir_name)
except OSError:
if not os.path.isdir(dir_name):
raise
os.chdir(dir_name)
def get_chrom_lengths(path_to_bam):
'''
Uses pysam to retrieve chromosome sizes form bam.
Useful helper to use with some pybedtools functions (e.g. coverage), when a bam was mapped with custom genome not available in UCSC.
Input: path to bam file (should be indexed)
Output: dictionary.
Example output:
{'chr4': (0, 1351857), 'chr3L': (0, 24543557), 'chr2L': (0, 23011544), '*': (0, 0), 'chrX': (0, 22422827), 'chr2R': (0, 21146708), 'chr3R': (0, 27905053)}
'''
idx = pysam.idxstats(path_to_bam).splitlines()
chromsizes = {}
for element in idx:
stats = element.split("\t")
chromsizes[stats[0]] = (0, int(stats[1]))
return chromsizes
def countNucleotidePerPosition(sequences):
'''
Takes a list of strings and determines the nucleotide occurrence per position.
Returns a panda DataFrame where each column is a nucleotide and each row a position.
source: http://stackoverflow.com/a/21103385/1274242
'''
print 'Counting nucleotides ' + timeStamp()
df = pd.DataFrame([list(s) for s in sequences])
counts = df.apply(pd.value_counts).transpose()
return(counts)
def parseSequence(entries):
'''
Extracts the nucleotide sequence from the poorly formated output of BedTool.sequence.
Input example:
>chrDESTTol2CG2:2717-2727\nCAAAATCCAC\n>chrDESTTol2CG2:3713-3723\nGTCGATGCCC\n
'''
seqs = []
for entry in entries.split('\n'):
entry = entry.strip('\n').strip(' ')
if '>' in entry:
pass
else:
seqs.append(entry.upper())
seqs = filter(None, seqs)
return seqs
def getSequences(coordinates, fasta):
'''
Retrieves the sequence corresponding to a coordinate.
Input: BedTools object
Output:
>chrDESTTol2CG2:2717-2727\nCAAAATCCAC\n>chrDESTTol2CG2:3713-3723\nGTCGATGCCC\n
'''
sequence = coordinates.sequence(fi=fasta, s=True)
return open(sequence.seqfn).read()
def getSequencesFrom5prime(coordinates, fasta, upstream, downstream, chromsizes):
'''
Retrieves the sequences limiting the 5' end of a genomic location.
Input: BedTools object
Output: a list of strings with defined size surrounding a genomic location
'''
print 'Fetching 5\' sequences ' + timeStamp()
seq_len = upstream + downstream - 1
start = coordinates.each(five_prime, upstream=upstream, downstream=downstream, genome=chromsizes).filter(greater_than, seq_len).saveas()
clean_seq = parseSequence(getSequences(start, fasta))
return clean_seq
def getSequencesFrom3prime(coordinates, fasta, upstream, downstream, chromsizes):
'''
Retrieves the sequences limiting the 3' end of a genomic location.
Input: BedTools object
Output: a list of strings with defined size surrounding a genomic location
'''
print 'Fetching 3\' sequences ' + timeStamp()
seq_len = upstream + downstream - 1
start = coordinates.each(three_prime, upstream=upstream, downstream=downstream, genome=chromsizes).filter(greater_than, seq_len).saveas()
clean_seq = parseSequence(getSequences(start, fasta))
return clean_seq
def convertSGVImages(image, res=300):
'''
Convert an svg image into a high-resolution png
Input: path to image
'''
image_out = image.replace('.svg', '.png')
command = 'convert -density ' + str(res) + ' ' + image + ' ' + image_out
print 'converting logo to PNG: ' + timeStamp()
os.system(command)
def createMotif(sequences, fname):
'''
Creates and saves a motif (logo) from a list of input sequences.
Input: list of strings with the sequences; file path to save the logo.
Output: an image file with the logo.
'''
print 'Generating motif ' + timeStamp()
try:
os.makedirs('figure')
except OSError:
if not os.path.isdir('figure'):
raise
from Bio.Seq import Seq
from Bio import motifs
from Bio.Alphabet import IUPAC
import urllib2
# m = motif.motif(alphabet=IUPAC.unambiguous_dna) # initialize motif
instances = []
for sequence in sequences:
if len(sequence) < 40:
print sequence
instances.append(Seq(sequence, alphabet=IUPAC.ambiguous_dna))
m = motifs.create(instances)
flogo = 'figure/' + fname
while True:
# source: http://stackoverflow.com/a/9986206/1274242
try:
m.weblogo(flogo, format='SVG')
break
except urllib2.HTTPError, detail:
if detail.errno == 500:
time.sleep(5)
continue
else:
raise
convertSGVImages(flogo)
def intersectBamWithBed(inbam, inbed):
'''
Intersects reads with genomic features, Transposable elements, and returns separately reads that map sense and antisense to the features.
Input: paths to bam and bed file
Output: list of tuples with a name (str) and the reads for sense and antisense piRNAs (bedTool)
'''
# convert bam to bed
print 'Separating sense and antisense piRNAs ' + timeStamp()
piRNA = BedTool(inbam).bam_to_bed()
## create bedtool for genomic features
bed = BedTool(inbed)
# outname = inbam.replace('.bam', '')
# outsense = outname + "sense.bed"
# outantisense = outname + "antisense.bed"
antisense = piRNA.intersect(bed, S=True)
sense = piRNA.intersect(bed, s=True)
piRNAs = [
('sense', sense),
('antisense', antisense)]
return piRNAs
if __name__ == '__main__':
args = getArgs()
print args
fasta = BedTool(args.fasta)
inbam = args.bam
inbed = args.intervals
up = args.upstream
down = args.downstream
out_folder = args.outFolder
if args.genome is not None and not "none":
chromsizes = pybedtools.chromsizes(args.genome)
else:
print 'Retrieving custom chromosome lengths ' + timeStamp()
chromsizes = get_chrom_lengths(inbam)
exp_name = os.path.split(inbam)[1].split(".")[0] + os.path.split(inbed)[1].split(".")[0]
exp_folder = out_folder + '/' + exp_name
createAndChangeDir(out_folder)
print 'Starting analysis for ' + inbam + ' ' + timeStamp()
strand_piRNAs = intersectBamWithBed(inbam, inbed)
for direction in strand_piRNAs:
print 'piRNAs in ' + direction[0] + ' ' + timeStamp()
print 'Fetching 5\' sequences ' + timeStamp()
five_out = direction[0] + '.5prime.count.csv'
five = getSequencesFrom5prime(direction[1], fasta, upstream=up, downstream=down, chromsizes=chromsizes)
print 'Fetching 3\' sequences ' + timeStamp()
three_out = direction[0] + '.3prime.count.csv'
three = getSequencesFrom3prime(direction[1], fasta, upstream=up, downstream=down, chromsizes=chromsizes)
# print 'Counting in parallel ' + timeStamp()
# pool = multiprocessing.Pool(processes=2)
# five_count, three_count = pool.map(countNucleotidePerPosition,
# (five, three))
# pool.close()
# pool.join()
five_count = countNucleotidePerPosition(five)
index = list(range(-up, 0) + range(1, down+1))
five_count['Position'] = index
five_count.to_csv(five_out, index=False)
three_count = countNucleotidePerPosition(three)
# reversed_index = [i * -1 for i in list(reversed(range(-up, 0) + range(1, down+1)))]
three_count['Position'] = list(reversed(index))
three_count.to_csv(three_out, index=False)
## create motifs:
m_out_five = direction[0] + '.5prime.logo.svg'
createMotif(five, m_out_five)
m_out_three = direction[0] + '.3prime.logo.svg'
createMotif(three, m_out_three)
print 'Plotting results ' + timeStamp()
script_dirname = os.path.dirname(os.path.realpath(sys.argv[0]))
plot_script_path = script_dirname + '/piRNABaseTerminalBasesPlot.R'
os.system("Rscript " + plot_script_path)
| gpl-3.0 |
chris-ch/omarket | python-lab/backtest.py | 1 | 15929 | import argparse
import csv
import logging
import math
import os
from datetime import date
import numpy
import pandas
from statsmodels.formula.api import OLS
from matplotlib import pyplot
from btplatform import PositionAdjuster, process_strategy, BacktestHistory
from meanrevert import MeanReversionStrategy, PortfolioDataCollector, StrategyDataCollector, \
MeanReversionStrategyRunner
from pricetools import load_prices
def backtest_strategy(start_date, end_date, strategy_runner, symbols, prices_path):
securities = ['PCX/' + symbol for symbol in symbols]
prices_by_security = dict()
close_prices = pandas.DataFrame()
max_start_date = start_date
min_end_date = end_date
for security in securities:
exchange, security_code = security.split('/')
prices_df = load_prices(prices_path, exchange, security_code)
prices_by_security[security] = prices_df
if max_start_date is not None:
max_start_date = max(max_start_date, prices_df.index.min())
else:
max_start_date = prices_df.index.min()
if min_end_date is not None:
min_end_date = min(min_end_date, prices_df.index.max())
else:
min_end_date = prices_df.index.max()
close_prices[security] = prices_df['close adj']
close_prices.reset_index(inplace=True)
logging.info('considering date range: %s through %s' % (max_start_date, min_end_date))
for security in securities:
truncate_start_date = prices_by_security[security].index >= max_start_date
truncate_end_date = prices_by_security[security].index <= min_end_date
prices_by_security[security] = prices_by_security[security][truncate_start_date & truncate_end_date]
data_collector = StrategyDataCollector(strategy_runner.get_strategy_name())
process_strategy(securities, strategy_runner, data_collector, prices_by_security)
return data_collector
def backtest_portfolio(portfolios, starting_equity, start_date, end_date, prices_path, step_size, max_net_position,
max_gross_position, max_risk_scale, warmup_period):
data_collector = PortfolioDataCollector()
for lookback_period, portfolio, strategy_name in portfolios:
securities = portfolio.split('/')
strategy = MeanReversionStrategy(securities, int(lookback_period), name=strategy_name)
position_adjuster = PositionAdjuster(securities, strategy.get_strategy_name(), max_net_position,
max_gross_position, max_risk_scale,
starting_equity,
step_size)
strategy_runner = MeanReversionStrategyRunner(securities, strategy, warmup_period, position_adjuster)
data_collection = backtest_strategy(start_date, end_date, strategy_runner, securities, prices_path)
data_collector.add_equity(starting_equity)
target_quantities = data_collection.get_target_quantities(strategy.get_strategy_name())
fills = position_adjuster.get_fills()
data_collector.add_strategy_data(securities, target_quantities, fills)
return data_collector
def chart_backtest(start_date, end_date, securities, prices_path, lookback_period,
step_size, start_equity,
max_net_position, max_gross_position, max_risk_scale, warmup_period):
pyplot.style.use('ggplot')
strategy = MeanReversionStrategy(securities, int(lookback_period))
position_adjuster = PositionAdjuster(securities, strategy.get_strategy_name(), max_net_position, max_gross_position,
max_risk_scale,
start_equity,
step_size)
strategy_runner = MeanReversionStrategyRunner(securities, strategy, warmup_period, position_adjuster)
data_collection = backtest_strategy(start_date, end_date, strategy_runner, securities, prices_path)
backtest_history = BacktestHistory(position_adjuster.get_fills(), start_equity)
logging.info('fit quality: %s', fit_quality(backtest_history.get_equity() - start_equity))
backtest_history.get_equity().plot(linewidth=2.)
backtest_history.get_gross_net_position().plot(linewidth=2.)
pyplot.gca().get_yaxis().get_major_formatter().set_useOffset(False)
data_collection.get_factors(','.join(securities)).plot(linewidth=2., subplots=True)
styles = {'level_inf': 'm--', 'level_sup': 'b--', 'signal': 'k-'}
data_collection.get_bollinger(','.join(securities)).plot(linewidth=2., subplots=False, style=styles)
pyplot.show()
def fit_quality(df):
regr_df = df.reset_index()
day_nanos = 24 * 60 * 60 * 1E9
nanos = regr_df['date'] - regr_df['date'].min()
df2 = pandas.DataFrame(data=[nanos.astype(int) / day_nanos, regr_df['equity']]).transpose()
ols2 = OLS(df2['equity'], df2['date'])
result = ols2.fit()
return {'p-value F-test': result.f_pvalue, 'r-squared': result.rsquared, 'p-value x': result.pvalues[0]}
def create_summary(strategy_name, backtest_history, closed_trades):
mean_trade = closed_trades['pnl'].mean()
worst_trade = closed_trades['pnl'].min()
count_trades = closed_trades['pnl'].count()
max_drawdown = backtest_history.get_drawdown().max()['equity']
final_equity = backtest_history.get_equity()['equity'][-1]
summary = {
'strategy': strategy_name,
'sharpe_ratio': backtest_history.get_sharpe_ratio(),
'average_trade': mean_trade,
'worst_trade': worst_trade,
'count_trades': count_trades,
'max_drawdown_pct': max_drawdown,
'final_equity': final_equity
}
return summary
def load_portfolios(portfolios_filename):
portfolios = list()
with open(portfolios_filename) as csv_file:
reader = csv.reader(csv_file)
for row in reader:
if len(row) == 0:
continue
if row[0].startswith('#'):
continue
portfolios.append(row)
logging.info('loaded portfolios: %s' % str(portfolios))
return portfolios
def main(args):
# TODO arg line
warmup_period = 10
prices_path = args.prices_path
start_date = date(int(args.start_yyyymmdd[:4]), int(args.start_yyyymmdd[4:6]), int(args.start_yyyymmdd[6:8]))
end_date = date(int(args.end_yyyymmdd[:4]), int(args.end_yyyymmdd[4:6]), int(args.end_yyyymmdd[6:8]))
if args.display_single is not None:
securities = args.display_single.split('/')
chart_backtest(start_date, end_date, securities, prices_path, lookback_period=args.lookback_period,
step_size=args.step_size, start_equity=args.starting_equity,
max_net_position=args.max_net_position,
max_gross_position=args.max_gross_position,
max_risk_scale=args.max_risk_scale, warmup_period=warmup_period)
elif args.portfolio is not None:
portfolios = load_portfolios(args.portfolio)
step_size = args.step_size
starting_equity = args.starting_equity
max_net_position = args.max_net_position
max_gross_position = args.max_gross_position
max_risk_scale = args.max_risk_scale
data_collector = backtest_portfolio(portfolios, starting_equity, start_date, end_date, prices_path, step_size,
max_net_position, max_gross_position, max_risk_scale, warmup_period)
backtest_history = BacktestHistory(data_collector.fills_df, data_collector.starting_equity)
backtest_history.trades_pnl.to_pickle(os.sep.join([args.trades_pnl_path, 'trades_pnl.pkl']))
trades = backtest_history.get_trades()
holdings = backtest_history.get_holdings()
equity = backtest_history.get_equity()
target_df = data_collector.new_targets
positions = holdings[['date', 'security', 'total_qty']].groupby(['date', 'security']).sum().unstack().ffill()
latest_holdings = holdings.pivot_table(index='date', columns='security', values='total_qty',
aggfunc=numpy.sum).tail(1).transpose()
latest_holdings.columns = ['quantity']
starting_equity = equity.iloc[0]
ending_equity = equity.iloc[-1]
days_interval = equity.index[-1] - equity.index[0]
sharpe_ratio = math.sqrt(250) * equity.pct_change().mean() / equity.pct_change().std()
logging.info('sharpe ratio: %.2f', sharpe_ratio)
annualized_return = 100 * (numpy.power(ending_equity / starting_equity, 365 / days_interval.days) - 1)
logging.info('annualized return: %.2f percent' % annualized_return)
logging.info('trades:\n%s', trades.tail(10).transpose())
logging.info('positions:\n%s', positions.tail(10).transpose())
logging.info('new target quantities:\n%s' % (target_df))
target_trades = (target_df - latest_holdings.transpose()).transpose().dropna()
logging.info('future trades:\n%s' % target_trades.round())
elif args.display_portfolio is not None:
portfolios = load_portfolios(args.display_portfolio)
pyplot.style.use('ggplot')
trades_pnl_path = os.sep.join([args.trades_pnl_path, 'trades_pnl.pkl'])
logging.info('loading data from path: %s', os.path.abspath(trades_pnl_path))
trades_pnl_df = pandas.read_pickle(trades_pnl_path)
backtest_history = BacktestHistory(trades_pnl_df)
backtest_history.set_start_equity(len(portfolios) * args.starting_equity)
pnl_data = backtest_history.trades_pnl[['strategy', 'date', 'realized_pnl', 'unrealized_pnl']]
by_strategy_date = pnl_data.groupby(by=['strategy', 'date'])
by_strategy_date.sum().apply(sum, axis=1).unstack().transpose().plot(linewidth=2., subplots=True, layout=(-1, 2))
holdings = backtest_history.get_holdings()
equity = backtest_history.get_equity()
benchmark = load_prices(prices_path, 'PCX', 'SPY')
equity_df = benchmark[['close adj']].join(equity).dropna()
equity_df.columns = ['benchmark', 'equity']
equity_df['benchmark'] = (equity_df['benchmark'].pct_change() + 1.).cumprod() * equity_df.head(1)[
'equity'].min()
equity_df.plot(linewidth=2.)
logging.info('fit quality: %s', fit_quality(equity - args.starting_equity))
by_security_pos = holdings.pivot_table(index='date', columns='security', values='market_value',
aggfunc=numpy.sum)
by_security_pos.plot(linewidth=2.)
positions_aggregated_net = holdings.groupby('date')['market_value'].sum()
positions_aggregated_gross = holdings.groupby('date')['market_value'].agg(lambda x: numpy.abs(x).sum())
positions_net_gross = numpy.array([positions_aggregated_net, positions_aggregated_gross]).transpose()
positions_aggregated = pandas.DataFrame(index=positions_aggregated_net.index,
data=positions_net_gross,
columns=['net', 'gross'])
positions_aggregated = positions_aggregated.join(equity * 3.0)
positions_aggregated.rename(columns={'equity': 'margin_warning'}, inplace=True)
positions_aggregated = positions_aggregated.join(equity * 4.0)
positions_aggregated.rename(columns={'equity': 'margin_violation'}, inplace=True)
positions_aggregated.plot(linewidth=2., subplots=False)
pyplot.show()
elif args.batch is not None:
# backtest batch
portfolios_path = args.batch
logging.info('processing batch: %s', os.path.abspath(portfolios_path))
with open(portfolios_path) as portfolios_file:
portfolios = [line.strip().split(',') for line in portfolios_file.readlines()]
results = list()
for symbols in portfolios:
strategy = MeanReversionStrategy(symbols, int(args.lookback_period))
position_adjuster = PositionAdjuster(symbols, strategy.get_strategy_name(), args.max_net_position,
args.max_gross_position,
args.max_risk_scale,
args.starting_equity,
args.step_size)
strategy_runner = MeanReversionStrategyRunner(symbols, strategy, warmup_period, position_adjuster)
backtest_strategy(start_date, end_date, strategy_runner, symbols, prices_path)
backtest_history = BacktestHistory(position_adjuster.get_fills(), args.starting_equity)
backtest_data = fit_quality(backtest_history.get_equity() - args.starting_equity)
closed_trades = position_adjuster.get_strategy_trades(closed_only=True)
backtest_data.update(create_summary(strategy.get_strategy_name(), backtest_history, closed_trades))
results.append(backtest_data)
result_df = pandas.DataFrame(results).set_index('strategy')
result_df.to_csv('backtest-results.csv')
print(result_df)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format='%(asctime)s:%(name)s:%(levelname)s:%(message)s')
file_handler = logging.FileHandler('backtest.log', mode='w')
formatter = logging.Formatter('%(asctime)s:%(name)s:%(levelname)s:%(message)s')
file_handler.setFormatter(formatter)
logging.getLogger().addHandler(file_handler)
logging.info('starting script')
parser = argparse.ArgumentParser(description='Backtesting prototype.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--start-yyyymmdd', type=str, help='backtest start date', default='20130101')
parser.add_argument('--end-yyyymmdd', type=str, help='backtest end date', default=date.today().strftime('%Y%m%d'))
parser.add_argument('--display-single', type=str, help='display strategy composed of comma-separated securities')
parser.add_argument('--display-portfolio', type=str, help='display aggregated portfolio from specified file')
parser.add_argument('--portfolio', type=str, help='display aggregated portfolio from specified file')
parser.add_argument('--batch', type=str, help='processes strategies in batch mode')
parser.add_argument('--lookback-period', type=int, help='lookback period', default=200)
parser.add_argument('--step-size', type=int, help='deviation unit measured in number of standard deviations',
default=2)
parser.add_argument('--starting-equity', type=float,
help='amount of equity allocated to each strategy (for one risk step)', default=8000)
parser.add_argument('--actual-equity', type=float, help='total equity available for trading')
parser.add_argument('--max-net-position', type=float,
help='max allowed net position for one step, measured as a fraction of equity', default=0.4)
parser.add_argument('--max-gross-position', type=float,
help='max allowed gross position by step, measured as a fraction of equity', default=2.)
parser.add_argument('--max-risk-scale', type=int, help='max number of steps', default=3)
parser.add_argument('--prices-path', type=str, help='path to prices data', default='data')
parser.add_argument('--trades-pnl-path', type=str, help='path to trades pnl data', default='.')
args = parser.parse_args()
pandas.set_option('expand_frame_repr', False)
main(args)
# dev: --start-yyyymmdd 20170101 --end-yyyymmdd 20170313
| apache-2.0 |
metaml/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/units.py | 70 | 4810 | """
The classes here provide support for using custom classes with
matplotlib, eg those that do not expose the array interface but know
how to converter themselves to arrays. It also supoprts classes with
units and units conversion. Use cases include converters for custom
objects, eg a list of datetime objects, as well as for objects that
are unit aware. We don't assume any particular units implementation,
rather a units implementation must provide a ConversionInterface, and
the register with the Registry converter dictionary. For example,
here is a complete implementation which support plotting with native
datetime objects
import matplotlib.units as units
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import datetime
class DateConverter(units.ConversionInterface):
def convert(value, unit):
'convert value to a scalar or array'
return dates.date2num(value)
convert = staticmethod(convert)
def axisinfo(unit):
'return major and minor tick locators and formatters'
if unit!='date': return None
majloc = dates.AutoDateLocator()
majfmt = dates.AutoDateFormatter(majloc)
return AxisInfo(majloc=majloc,
majfmt=majfmt,
label='date')
axisinfo = staticmethod(axisinfo)
def default_units(x):
'return the default unit for x or None'
return 'date'
default_units = staticmethod(default_units)
# finally we register our object type with a converter
units.registry[datetime.date] = DateConverter()
"""
import numpy as np
from matplotlib.cbook import iterable, is_numlike
class AxisInfo:
'information to support default axis labeling and tick labeling'
def __init__(self, majloc=None, minloc=None,
majfmt=None, minfmt=None, label=None):
"""
majloc and minloc: TickLocators for the major and minor ticks
majfmt and minfmt: TickFormatters for the major and minor ticks
label: the default axis label
If any of the above are None, the axis will simply use the default
"""
self.majloc = majloc
self.minloc = minloc
self.majfmt = majfmt
self.minfmt = minfmt
self.label = label
class ConversionInterface:
"""
The minimal interface for a converter to take custom instances (or
sequences) and convert them to values mpl can use
"""
def axisinfo(unit):
'return an units.AxisInfo instance for unit'
return None
axisinfo = staticmethod(axisinfo)
def default_units(x):
'return the default unit for x or None'
return None
default_units = staticmethod(default_units)
def convert(obj, unit):
"""
convert obj using unit. If obj is a sequence, return the
converted sequence. The ouput must be a sequence of scalars
that can be used by the numpy array layer
"""
return obj
convert = staticmethod(convert)
def is_numlike(x):
"""
The matplotlib datalim, autoscaling, locators etc work with
scalars which are the units converted to floats given the
current unit. The converter may be passed these floats, or
arrays of them, even when units are set. Derived conversion
interfaces may opt to pass plain-ol unitless numbers through
the conversion interface and this is a helper function for
them.
"""
if iterable(x):
for thisx in x:
return is_numlike(thisx)
else:
return is_numlike(x)
is_numlike = staticmethod(is_numlike)
class Registry(dict):
"""
register types with conversion interface
"""
def __init__(self):
dict.__init__(self)
self._cached = {}
def get_converter(self, x):
'get the converter interface instance for x, or None'
if not len(self): return None # nothing registered
#DISABLED idx = id(x)
#DISABLED cached = self._cached.get(idx)
#DISABLED if cached is not None: return cached
converter = None
classx = getattr(x, '__class__', None)
if classx is not None:
converter = self.get(classx)
if converter is None and iterable(x):
# if this is anything but an object array, we'll assume
# there are no custom units
if isinstance(x, np.ndarray) and x.dtype != np.object:
return None
for thisx in x:
converter = self.get_converter( thisx )
return converter
#DISABLED self._cached[idx] = converter
return converter
registry = Registry()
| agpl-3.0 |
RPGOne/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 77 | 3825 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.exceptions import NotFittedError
from sklearn import datasets
from sklearn.covariance import empirical_covariance, MinCovDet, \
EllipticEnvelope
from sklearn.covariance import fast_mcd
X = datasets.load_iris().data
X_1d = X[:, 0]
n_samples, n_features = X.shape
def test_mcd():
# Tests the FastMCD algorithm implementation
# Small data set
# test without outliers (random independent normal data)
launch_mcd_on_dataset(100, 5, 0, 0.01, 0.1, 80)
# test with a contaminated data set (medium contamination)
launch_mcd_on_dataset(100, 5, 20, 0.01, 0.01, 70)
# test with a contaminated data set (strong contamination)
launch_mcd_on_dataset(100, 5, 40, 0.1, 0.1, 50)
# Medium data set
launch_mcd_on_dataset(1000, 5, 450, 0.1, 0.1, 540)
# Large data set
launch_mcd_on_dataset(1700, 5, 800, 0.1, 0.1, 870)
# 1D data set
launch_mcd_on_dataset(500, 1, 100, 0.001, 0.001, 350)
def test_fast_mcd_on_invalid_input():
X = np.arange(100)
assert_raise_message(ValueError, 'fast_mcd expects at least 2 samples',
fast_mcd, X)
def test_mcd_class_on_invalid_input():
X = np.arange(100)
mcd = MinCovDet()
assert_raise_message(ValueError, 'MinCovDet expects at least 2 samples',
mcd.fit, X)
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert(error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert(error_cov < tol_cov)
assert(np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
def test_mcd_issue1127():
# Check that the code does not break with X.shape = (3, 1)
# (i.e. n_support = n_samples)
rnd = np.random.RandomState(0)
X = rnd.normal(size=(3, 1))
mcd = MinCovDet()
mcd.fit(X)
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
decision = clf.decision_function(X, raw_values=True)
decision_transformed = clf.decision_function(X, raw_values=False)
assert_array_almost_equal(
decision, clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert(sum(y_pred == -1) == sum(decision_transformed < 0))
| bsd-3-clause |
Fireblend/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 142 | 18692 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors
def test_neighbors_accuracy_with_n_candidates():
# Checks whether accuracy increases as `n_candidates` increases.
n_candidates_values = np.array([.1, 50, 500])
n_samples = 100
n_features = 10
n_iter = 10
n_points = 5
rng = np.random.RandomState(42)
accuracies = np.zeros(n_candidates_values.shape[0], dtype=float)
X = rng.rand(n_samples, n_features)
for i, n_candidates in enumerate(n_candidates_values):
lshf = LSHForest(n_candidates=n_candidates)
lshf.fit(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)]
neighbors = lshf.kneighbors(query, n_neighbors=n_points,
return_distance=False)
distances = pairwise_distances(query, X, metric='cosine')
ranks = np.argsort(distances)[0, :n_points]
intersection = np.intersect1d(ranks, neighbors).shape[0]
ratio = intersection / float(n_points)
accuracies[i] = accuracies[i] + ratio
accuracies[i] = accuracies[i] / float(n_iter)
# Sorted accuracies should be equal to original accuracies
assert_true(np.all(np.diff(accuracies) >= 0),
msg="Accuracies are not non-decreasing.")
# Highest accuracy should be strictly greater than the lowest
assert_true(np.ptp(accuracies) > 0,
msg="Highest accuracy is not strictly greater than lowest.")
def test_neighbors_accuracy_with_n_estimators():
# Checks whether accuracy increases as `n_estimators` increases.
n_estimators = np.array([1, 10, 100])
n_samples = 100
n_features = 10
n_iter = 10
n_points = 5
rng = np.random.RandomState(42)
accuracies = np.zeros(n_estimators.shape[0], dtype=float)
X = rng.rand(n_samples, n_features)
for i, t in enumerate(n_estimators):
lshf = LSHForest(n_candidates=500, n_estimators=t)
lshf.fit(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)]
neighbors = lshf.kneighbors(query, n_neighbors=n_points,
return_distance=False)
distances = pairwise_distances(query, X, metric='cosine')
ranks = np.argsort(distances)[0, :n_points]
intersection = np.intersect1d(ranks, neighbors).shape[0]
ratio = intersection / float(n_points)
accuracies[i] = accuracies[i] + ratio
accuracies[i] = accuracies[i] / float(n_iter)
# Sorted accuracies should be equal to original accuracies
assert_true(np.all(np.diff(accuracies) >= 0),
msg="Accuracies are not non-decreasing.")
# Highest accuracy should be strictly greater than the lowest
assert_true(np.ptp(accuracies) > 0,
msg="Highest accuracy is not strictly greater than lowest.")
@ignore_warnings
def test_kneighbors():
# Checks whether desired number of neighbors are returned.
# It is guaranteed to return the requested number of neighbors
# if `min_hash_match` is set to 0. Returned distances should be
# in ascending order.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
# Test unfitted estimator
assert_raises(ValueError, lshf.kneighbors, X[0])
lshf.fit(X)
for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
query = X[rng.randint(0, n_samples)]
neighbors = lshf.kneighbors(query, n_neighbors=n_neighbors,
return_distance=False)
# Desired number of neighbors should be returned.
assert_equal(neighbors.shape[1], n_neighbors)
# Multiple points
n_queries = 5
queries = X[rng.randint(0, n_samples, n_queries)]
distances, neighbors = lshf.kneighbors(queries,
n_neighbors=1,
return_distance=True)
assert_equal(neighbors.shape[0], n_queries)
assert_equal(distances.shape[0], n_queries)
# Test only neighbors
neighbors = lshf.kneighbors(queries, n_neighbors=1,
return_distance=False)
assert_equal(neighbors.shape[0], n_queries)
# Test random point(not in the data set)
query = rng.randn(n_features)
lshf.kneighbors(query, n_neighbors=1,
return_distance=False)
# Test n_neighbors at initialization
neighbors = lshf.kneighbors(query, return_distance=False)
assert_equal(neighbors.shape[1], 5)
# Test `neighbors` has an integer dtype
assert_true(neighbors.dtype.kind == 'i',
msg="neighbors are not in integer dtype.")
def test_radius_neighbors():
# Checks whether Returned distances are less than `radius`
# At least one point should be returned when the `radius` is set
# to mean distance from the considering point to other points in
# the database.
# Moreover, this test compares the radius neighbors of LSHForest
# with the `sklearn.neighbors.NearestNeighbors`.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest()
# Test unfitted estimator
assert_raises(ValueError, lshf.radius_neighbors, X[0])
lshf.fit(X)
for i in range(n_iter):
# Select a random point in the dataset as the query
query = X[rng.randint(0, n_samples)]
# At least one neighbor should be returned when the radius is the
# mean distance from the query to the points of the dataset.
mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
neighbors = lshf.radius_neighbors(query, radius=mean_dist,
return_distance=False)
assert_equal(neighbors.shape, (1,))
assert_equal(neighbors.dtype, object)
assert_greater(neighbors[0].shape[0], 0)
# All distances to points in the results of the radius query should
# be less than mean_dist
distances, neighbors = lshf.radius_neighbors(query,
radius=mean_dist,
return_distance=True)
assert_array_less(distances[0], mean_dist)
# Multiple points
n_queries = 5
queries = X[rng.randint(0, n_samples, n_queries)]
distances, neighbors = lshf.radius_neighbors(queries,
return_distance=True)
# dists and inds should not be 1D arrays or arrays of variable lengths
# hence the use of the object dtype.
assert_equal(distances.shape, (n_queries,))
assert_equal(distances.dtype, object)
assert_equal(neighbors.shape, (n_queries,))
assert_equal(neighbors.dtype, object)
# Compare with exact neighbor search
query = X[rng.randint(0, n_samples)]
mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
distances_exact, _ = nbrs.radius_neighbors(query, radius=mean_dist)
distances_approx, _ = lshf.radius_neighbors(query, radius=mean_dist)
# Radius-based queries do not sort the result points and the order
# depends on the method, the random_state and the dataset order. Therefore
# we need to sort the results ourselves before performing any comparison.
sorted_dists_exact = np.sort(distances_exact[0])
sorted_dists_approx = np.sort(distances_approx[0])
# Distances to exact neighbors are less than or equal to approximate
# counterparts as the approximate radius query might have missed some
# closer neighbors.
assert_true(np.all(np.less_equal(sorted_dists_exact,
sorted_dists_approx)))
def test_radius_neighbors_boundary_handling():
X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]]
n_points = len(X)
# Build an exact nearest neighbors model as reference model to ensure
# consistency between exact and approximate methods
nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
# Build a LSHForest model with hyperparameter values that always guarantee
# exact results on this toy dataset.
lsfh = LSHForest(min_hash_match=0, n_candidates=n_points).fit(X)
# define a query aligned with the first axis
query = [1., 0.]
# Compute the exact cosine distances of the query to the four points of
# the dataset
dists = pairwise_distances(query, X, metric='cosine').ravel()
# The first point is almost aligned with the query (very small angle),
# the cosine distance should therefore be almost null:
assert_almost_equal(dists[0], 0, decimal=5)
# The second point form an angle of 45 degrees to the query vector
assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4))
# The third point is orthogonal from the query vector hence at a distance
# exactly one:
assert_almost_equal(dists[2], 1)
# The last point is almost colinear but with opposite sign to the query
# therefore it has a cosine 'distance' very close to the maximum possible
# value of 2.
assert_almost_equal(dists[3], 2, decimal=5)
# If we query with a radius of one, all the samples except the last sample
# should be included in the results. This means that the third sample
# is lying on the boundary of the radius query:
exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1)
approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1)
assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2])
assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2])
assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-1])
assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-1])
# If we perform the same query with a slighltly lower radius, the third
# point of the dataset that lay on the boundary of the previous query
# is now rejected:
eps = np.finfo(np.float64).eps
exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1 - eps)
approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1 - eps)
assert_array_equal(np.sort(exact_idx[0]), [0, 1])
assert_array_equal(np.sort(approx_idx[0]), [0, 1])
assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-2])
assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-2])
def test_distances():
# Checks whether returned neighbors are from closest to farthest.
n_samples = 12
n_features = 2
n_iter = 10
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest()
lshf.fit(X)
for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
query = X[rng.randint(0, n_samples)]
distances, neighbors = lshf.kneighbors(query,
n_neighbors=n_neighbors,
return_distance=True)
# Returned neighbors should be from closest to farthest, that is
# increasing distance values.
assert_true(np.all(np.diff(distances[0]) >= 0))
# Note: the radius_neighbors method does not guarantee the order of
# the results.
def test_fit():
# Checks whether `fit` method sets all attribute values correctly.
n_samples = 12
n_features = 2
n_estimators = 5
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(n_estimators=n_estimators)
lshf.fit(X)
# _input_array = X
assert_array_equal(X, lshf._fit_X)
# A hash function g(p) for each tree
assert_equal(n_estimators, len(lshf.hash_functions_))
# Hash length = 32
assert_equal(32, lshf.hash_functions_[0].components_.shape[0])
# Number of trees_ in the forest
assert_equal(n_estimators, len(lshf.trees_))
# Each tree has entries for every data point
assert_equal(n_samples, len(lshf.trees_[0]))
# Original indices after sorting the hashes
assert_equal(n_estimators, len(lshf.original_indices_))
# Each set of original indices in a tree has entries for every data point
assert_equal(n_samples, len(lshf.original_indices_[0]))
def test_partial_fit():
# Checks whether inserting array is consitent with fitted data.
# `partial_fit` method should set all attribute values correctly.
n_samples = 12
n_samples_partial_fit = 3
n_features = 2
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
X_partial_fit = rng.rand(n_samples_partial_fit, n_features)
lshf = LSHForest()
# Test unfitted estimator
lshf.partial_fit(X)
assert_array_equal(X, lshf._fit_X)
lshf.fit(X)
# Insert wrong dimension
assert_raises(ValueError, lshf.partial_fit,
np.random.randn(n_samples_partial_fit, n_features - 1))
lshf.partial_fit(X_partial_fit)
# size of _input_array = samples + 1 after insertion
assert_equal(lshf._fit_X.shape[0],
n_samples + n_samples_partial_fit)
# size of original_indices_[1] = samples + 1
assert_equal(len(lshf.original_indices_[0]),
n_samples + n_samples_partial_fit)
# size of trees_[1] = samples + 1
assert_equal(len(lshf.trees_[1]),
n_samples + n_samples_partial_fit)
def test_hash_functions():
# Checks randomness of hash functions.
# Variance and mean of each hash function (projection vector)
# should be different from flattened array of hash functions.
# If hash functions are not randomly built (seeded with
# same value), variances and means of all functions are equal.
n_samples = 12
n_features = 2
n_estimators = 5
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = LSHForest(n_estimators=n_estimators,
random_state=rng.randint(0, np.iinfo(np.int32).max))
lshf.fit(X)
hash_functions = []
for i in range(n_estimators):
hash_functions.append(lshf.hash_functions_[i].components_)
for i in range(n_estimators):
assert_not_equal(np.var(hash_functions),
np.var(lshf.hash_functions_[i].components_))
for i in range(n_estimators):
assert_not_equal(np.mean(hash_functions),
np.mean(lshf.hash_functions_[i].components_))
def test_candidates():
# Checks whether candidates are sufficient.
# This should handle the cases when number of candidates is 0.
# User should be warned when number of candidates is less than
# requested number of neighbors.
X_train = np.array([[5, 5, 2], [21, 5, 5], [1, 1, 1], [8, 9, 1],
[6, 10, 2]], dtype=np.float32)
X_test = np.array([7, 10, 3], dtype=np.float32)
# For zero candidates
lshf = LSHForest(min_hash_match=32)
lshf.fit(X_train)
message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
" min_hash_match = %i. Candidates are filled up"
" uniformly from unselected"
" indices." % (3, 32))
assert_warns_message(UserWarning, message, lshf.kneighbors,
X_test, n_neighbors=3)
distances, neighbors = lshf.kneighbors(X_test, n_neighbors=3)
assert_equal(distances.shape[1], 3)
# For candidates less than n_neighbors
lshf = LSHForest(min_hash_match=31)
lshf.fit(X_train)
message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
" min_hash_match = %i. Candidates are filled up"
" uniformly from unselected"
" indices." % (5, 31))
assert_warns_message(UserWarning, message, lshf.kneighbors,
X_test, n_neighbors=5)
distances, neighbors = lshf.kneighbors(X_test, n_neighbors=5)
assert_equal(distances.shape[1], 5)
def test_graphs():
# Smoke tests for graph methods.
n_samples_sizes = [5, 10, 20]
n_features = 3
rng = np.random.RandomState(42)
for n_samples in n_samples_sizes:
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
lshf.fit(X)
kneighbors_graph = lshf.kneighbors_graph(X)
radius_neighbors_graph = lshf.radius_neighbors_graph(X)
assert_equal(kneighbors_graph.shape[0], n_samples)
assert_equal(kneighbors_graph.shape[1], n_samples)
assert_equal(radius_neighbors_graph.shape[0], n_samples)
assert_equal(radius_neighbors_graph.shape[1], n_samples)
def test_sparse_input():
# note: Fixed random state in sp.rand is not supported in older scipy.
# The test should succeed regardless.
X1 = sp.rand(50, 100)
X2 = sp.rand(10, 100)
forest_sparse = LSHForest(radius=1, random_state=0).fit(X1)
forest_dense = LSHForest(radius=1, random_state=0).fit(X1.A)
d_sparse, i_sparse = forest_sparse.kneighbors(X2, return_distance=True)
d_dense, i_dense = forest_dense.kneighbors(X2.A, return_distance=True)
assert_almost_equal(d_sparse, d_dense)
assert_almost_equal(i_sparse, i_dense)
d_sparse, i_sparse = forest_sparse.radius_neighbors(X2,
return_distance=True)
d_dense, i_dense = forest_dense.radius_neighbors(X2.A,
return_distance=True)
assert_equal(d_sparse.shape, d_dense.shape)
for a, b in zip(d_sparse, d_dense):
assert_almost_equal(a, b)
for a, b in zip(i_sparse, i_dense):
assert_almost_equal(a, b)
| bsd-3-clause |
espenhgn/nest-simulator | pynest/examples/brunel_alpha_nest.py | 2 | 13724 | # -*- coding: utf-8 -*-
#
# brunel_alpha_nest.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
"""Random balanced network (alpha synapses) connected with NEST
------------------------------------------------------------------
This script simulates an excitatory and an inhibitory population on
the basis of the network used in [1]_.
In contrast to ``brunel-alpha-numpy.py``, this variant uses NEST's builtin
connection routines to draw the random connections instead of NumPy.
When connecting the network customary synapse models are used, which
allow for querying the number of created synapses. Using spike
detectors the average firing rates of the neurons in the populations
are established. The building as well as the simulation time of the
network are recorded.
References
~~~~~~~~~~~~~
.. [1] Brunel N (2000). Dynamics of sparsely connected networks of excitatory and
inhibitory spiking neurons. Journal of Computational Neuroscience 8,
183-208.
See Also
~~~~~~~~~~~~
:doc:`brunel_alpha_numpy`
"""
###############################################################################
# Import all necessary modules for simulation, analysis and plotting. Scipy
# should be imported before nest.
import time
import numpy as np
import scipy.special as sp
import nest
import nest.raster_plot
import matplotlib.pyplot as plt
###############################################################################
# Definition of functions used in this example. First, define the `Lambert W`
# function implemented in SLI. The second function computes the maximum of
# the postsynaptic potential for a synaptic input current of unit amplitude
# (1 pA) using the `Lambert W` function. Thus function will later be used to
# calibrate the synaptic weights.
def LambertWm1(x):
# Using scipy to mimic the gsl_sf_lambert_Wm1 function.
return sp.lambertw(x, k=-1 if x < 0 else 0).real
def ComputePSPnorm(tauMem, CMem, tauSyn):
a = (tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0 / b * (-LambertWm1(-np.exp(-1.0 / a) / a) - 1.0 / a)
# maximum of PSP for current of unit amplitude
return (np.exp(1.0) / (tauSyn * CMem * b) *
((np.exp(-t_max / tauMem) - np.exp(-t_max / tauSyn)) / b -
t_max * np.exp(-t_max / tauSyn)))
nest.ResetKernel()
###############################################################################
# Assigning the current time to a variable in order to determine the build
# time of the network.
startbuild = time.time()
###############################################################################
# Assigning the simulation parameters to variables.
dt = 0.1 # the resolution in ms
simtime = 1000.0 # Simulation time in ms
delay = 1.5 # synaptic delay in ms
###############################################################################
# Definition of the parameters crucial for asynchronous irregular firing of
# the neurons.
g = 5.0 # ratio inhibitory weight/excitatory weight
eta = 2.0 # external rate relative to threshold rate
epsilon = 0.1 # connection probability
###############################################################################
# Definition of the number of neurons in the network and the number of neuron
# recorded from
order = 2500
NE = 4 * order # number of excitatory neurons
NI = 1 * order # number of inhibitory neurons
N_neurons = NE + NI # number of neurons in total
N_rec = 50 # record from 50 neurons
###############################################################################
# Definition of connectivity parameter
CE = int(epsilon * NE) # number of excitatory synapses per neuron
CI = int(epsilon * NI) # number of inhibitory synapses per neuron
C_tot = int(CI + CE) # total number of synapses per neuron
###############################################################################
# Initialization of the parameters of the integrate and fire neuron and the
# synapses. The parameter of the neuron are stored in a dictionary. The
# synaptic currents are normalized such that the amplitude of the PSP is J.
tauSyn = 0.5 # synaptic time constant in ms
tauMem = 20.0 # time constant of membrane potential in ms
CMem = 250.0 # capacitance of membrane in in pF
theta = 20.0 # membrane threshold potential in mV
neuron_params = {"C_m": CMem,
"tau_m": tauMem,
"tau_syn_ex": tauSyn,
"tau_syn_in": tauSyn,
"t_ref": 2.0,
"E_L": 0.0,
"V_reset": 0.0,
"V_m": 0.0,
"V_th": theta}
J = 0.1 # postsynaptic amplitude in mV
J_unit = ComputePSPnorm(tauMem, CMem, tauSyn)
J_ex = J / J_unit # amplitude of excitatory postsynaptic current
J_in = -g * J_ex # amplitude of inhibitory postsynaptic current
###############################################################################
# Definition of threshold rate, which is the external rate needed to fix the
# membrane potential around its threshold, the external firing rate and the
# rate of the poisson generator which is multiplied by the in-degree CE and
# converted to Hz by multiplication by 1000.
nu_th = (theta * CMem) / (J_ex * CE * np.exp(1) * tauMem * tauSyn)
nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE
################################################################################
# Configuration of the simulation kernel by the previously defined time
# resolution used in the simulation. Setting ``print_time`` to `True` prints the
# already processed simulation time as well as its percentage of the total
# simulation time.
nest.SetKernelStatus({"resolution": dt, "print_time": True,
"overwrite_files": True})
print("Building network")
###############################################################################
# Configuration of the model ``iaf_psc_alpha`` and ``poisson_generator`` using
# ``SetDefaults``. This function expects the model to be the inserted as a
# string and the parameter to be specified in a dictionary. All instances of
# theses models created after this point will have the properties specified
# in the dictionary by default.
nest.SetDefaults("iaf_psc_alpha", neuron_params)
nest.SetDefaults("poisson_generator", {"rate": p_rate})
###############################################################################
# Creation of the nodes using ``Create``. We store the returned handles in
# variables for later reference. Here the excitatory and inhibitory, as well
# as the poisson generator and two spike detectors. The spike detectors will
# later be used to record excitatory and inhibitory spikes.
nodes_ex = nest.Create("iaf_psc_alpha", NE)
nodes_in = nest.Create("iaf_psc_alpha", NI)
noise = nest.Create("poisson_generator")
espikes = nest.Create("spike_detector")
ispikes = nest.Create("spike_detector")
###############################################################################
# Configuration of the spike detectors recording excitatory and inhibitory
# spikes by sending parameter dictionaries to ``set``. Setting the property
# `record_to` to *"ascii"* ensures that the spikes will be recorded to a file,
# whose name starts with the string assigned to the property `label`.
espikes.set(label="brunel-py-ex", record_to="ascii")
ispikes.set(label="brunel-py-in", record_to="ascii")
print("Connecting devices")
###############################################################################
# Definition of a synapse using ``CopyModel``, which expects the model name of
# a pre-defined synapse, the name of the customary synapse and an optional
# parameter dictionary. The parameters defined in the dictionary will be the
# default parameter for the customary synapse. Here we define one synapse for
# the excitatory and one for the inhibitory connections giving the
# previously defined weights and equal delays.
nest.CopyModel("static_synapse", "excitatory",
{"weight": J_ex, "delay": delay})
nest.CopyModel("static_synapse", "inhibitory",
{"weight": J_in, "delay": delay})
#################################################################################
# Connecting the previously defined poisson generator to the excitatory and
# inhibitory neurons using the excitatory synapse. Since the poisson
# generator is connected to all neurons in the population the default rule
# (``all_to_all``) of ``Connect`` is used. The synaptic properties are inserted
# via ``syn_spec`` which expects a dictionary when defining multiple variables or
# a string when simply using a pre-defined synapse.
nest.Connect(noise, nodes_ex, syn_spec="excitatory")
nest.Connect(noise, nodes_in, syn_spec="excitatory")
###############################################################################
# Connecting the first ``N_rec`` nodes of the excitatory and inhibitory
# population to the associated spike detectors using excitatory synapses.
# Here the same shortcut for the specification of the synapse as defined
# above is used.
nest.Connect(nodes_ex[:N_rec], espikes, syn_spec="excitatory")
nest.Connect(nodes_in[:N_rec], ispikes, syn_spec="excitatory")
print("Connecting network")
print("Excitatory connections")
###############################################################################
# Connecting the excitatory population to all neurons using the pre-defined
# excitatory synapse. Beforehand, the connection parameter are defined in a
# dictionary. Here we use the connection rule ``fixed_indegree``,
# which requires the definition of the indegree. Since the synapse
# specification is reduced to assigning the pre-defined excitatory synapse it
# suffices to insert a string.
conn_params_ex = {'rule': 'fixed_indegree', 'indegree': CE}
nest.Connect(nodes_ex, nodes_ex + nodes_in, conn_params_ex, "excitatory")
print("Inhibitory connections")
###############################################################################
# Connecting the inhibitory population to all neurons using the pre-defined
# inhibitory synapse. The connection parameter as well as the synapse
# parameter are defined analogously to the connection from the excitatory
# population defined above.
conn_params_in = {'rule': 'fixed_indegree', 'indegree': CI}
nest.Connect(nodes_in, nodes_ex + nodes_in, conn_params_in, "inhibitory")
###############################################################################
# Storage of the time point after the buildup of the network in a variable.
endbuild = time.time()
###############################################################################
# Simulation of the network.
print("Simulating")
nest.Simulate(simtime)
###############################################################################
# Storage of the time point after the simulation of the network in a variable.
endsimulate = time.time()
###############################################################################
# Reading out the total number of spikes received from the spike detector
# connected to the excitatory population and the inhibitory population.
events_ex = espikes.n_events
events_in = ispikes.n_events
###############################################################################
# Calculation of the average firing rate of the excitatory and the inhibitory
# neurons by dividing the total number of recorded spikes by the number of
# neurons recorded from and the simulation time. The multiplication by 1000.0
# converts the unit 1/ms to 1/s=Hz.
rate_ex = events_ex / simtime * 1000.0 / N_rec
rate_in = events_in / simtime * 1000.0 / N_rec
###############################################################################
# Reading out the number of connections established using the excitatory and
# inhibitory synapse model. The numbers are summed up resulting in the total
# number of synapses.
num_synapses = (nest.GetDefaults("excitatory")["num_connections"] +
nest.GetDefaults("inhibitory")["num_connections"])
###############################################################################
# Establishing the time it took to build and simulate the network by taking
# the difference of the pre-defined time variables.
build_time = endbuild - startbuild
sim_time = endsimulate - endbuild
###############################################################################
# Printing the network properties, firing rates and building times.
print("Brunel network simulation (Python)")
print("Number of neurons : {0}".format(N_neurons))
print("Number of synapses: {0}".format(num_synapses))
print(" Exitatory : {0}".format(int(CE * N_neurons) + N_neurons))
print(" Inhibitory : {0}".format(int(CI * N_neurons)))
print("Excitatory rate : %.2f Hz" % rate_ex)
print("Inhibitory rate : %.2f Hz" % rate_in)
print("Building time : %.2f s" % build_time)
print("Simulation time : %.2f s" % sim_time)
###############################################################################
# Plot a raster of the excitatory neurons and a histogram.
nest.raster_plot.from_device(espikes, hist=True)
plt.show()
| gpl-2.0 |
coufon/neon-distributed | examples/fast-rcnn/demo.py | 2 | 5520 | #!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
"""
Demo a trained Fast-RCNN model to do object detection using PASCAL VOC dataset.
This demo currently runs 1 image at a time.
Reference:
"Fast R-CNN"
http://arxiv.org/pdf/1504.08083v2.pdf
https://github.com/rbgirshick/fast-rcnn
Usage:
python examples/fast-rcnn/demo.py --model_file frcn_vgg.pkl
Notes:
1. For VGG16 based Fast R-CNN model, we can support testing with batch size as 1
images. The testing consumes about 7G memory.
2. During demo, all the selective search ROIs will be used to go through the network,
so the inference time varies based on how many ROIs in each image.
For PASCAL VOC 2007, the average number of SelectiveSearch ROIs is around 2000.
3. The dataset will cache the preprocessed file and re-use that if the same
configuration of the dataset is used again. The cached file by default is in
~/nervana/data/VOCDevkit/VOC<year>/train_< >.pkl or
~/nervana/data/VOCDevkit/VOC<year>/inference_< >.pkl
"""
import os
import numpy as np
from PIL import Image
from neon.data.pascal_voc import PASCAL_VOC_CLASSES
from neon.data import PASCALVOCInference
from neon.util.argparser import NeonArgparser
from util import create_frcn_model
do_plots = True
try:
import matplotlib.pyplot as plt
plt.switch_backend('agg')
except ImportError:
print('matplotlib needs to be installed manually to generate plots needed '
'for this example. Skipping plot generation')
do_plots = False
# parse the command line arguments
parser = NeonArgparser(__doc__)
parser.add_argument('--img_prefix', type=str,
help='prefix for the saved image file names. If None, use '
'the model file name')
args = parser.parse_args(gen_be=True)
assert args.model_file is not None, "need a model file to do Fast R-CNN testing"
if args.img_prefix is None:
args.img_prefix = os.path.splitext(os.path.basename(args.model_file))[0]
output_dir = os.path.join(args.data_dir, 'frcn_output')
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# hyperparameters
args.batch_size = 1
n_mb = 40
img_per_batch = args.batch_size
rois_per_img = 5403
# setup dataset
image_set = 'test'
image_year = '2007'
valid_set = PASCALVOCInference(image_set, image_year, path=args.data_dir, n_mb=n_mb,
rois_per_img=rois_per_img, shuffle=False)
# setup model
model = create_frcn_model()
model.load_params(args.model_file)
model.initialize(dataset=valid_set)
CONF_THRESH = 0.8
NMS_THRESH = 0.3
# iterate through minibatches of the dataset
for mb_idx, (x, db) in enumerate(valid_set):
im = np.array(Image.open(db['img_file'])) # This is RGB order
print db['img_id']
outputs = model.fprop(x, inference=True)
scores, boxes = valid_set.post_processing(outputs, db)
# Visualize detections for each class
if do_plots:
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for cls in PASCAL_VOC_CLASSES[1:]:
# pick out scores and bboxes replated to this class
cls_ind = PASCAL_VOC_CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[cls_ind]
# only keep that ones with high enough scores
keep = np.where(cls_scores >= CONF_THRESH)[0]
if len(keep) == 0:
continue
# with these, do nonmaximum suppression
cls_boxes = cls_boxes[keep]
cls_scores = cls_scores[keep]
keep = valid_set.nonmaximum_suppression(cls_boxes, cls_scores, NMS_THRESH)
# keep these after nms
cls_boxes = cls_boxes[keep]
cls_scores = cls_scores[keep]
# Draw detected bounding boxes
inds = np.where(cls_scores >= CONF_THRESH)[0]
if len(inds) == 0:
continue
print 'detect {}'.format(cls)
if do_plots:
for i in inds:
bbox = cls_boxes[i]
score = cls_scores[i]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(cls, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.axis('off')
plt.tight_layout()
if do_plots:
fname = os.path.join(output_dir, '{}_{}_{}_{}.png'.format(
args.img_prefix, image_set,
image_year, db['img_id']))
plt.savefig(fname)
plt.close()
| apache-2.0 |
BlueBrain/NEST | examples/nest/Potjans_2014/spike_analysis.py | 13 | 5601 | # -*- coding: utf-8 -*-
#
# spike_analysis.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
# Merges spike files, produces raster plots, calculates and plots firing rates
import numpy as np
import glob
import matplotlib.pyplot as plt
import os
datapath = '../data'
# get simulation time and numbers of neurons recorded from sim_params.sli
f = open(os.path.join(datapath, 'sim_params.sli'), 'r')
for line in f:
if 't_sim' in line:
T = float(line.split()[1])
if '/record_fraction_neurons_spikes' in line:
record_frac = line.split()[1]
f.close()
f = open(os.path.join(datapath, 'sim_params.sli'), 'r')
for line in f:
if record_frac == 'true':
if 'frac_rec_spikes' in line:
frac_rec = float(line.split()[1])
else:
if 'n_rec_spikes' in line:
n_rec = int(line.split()[1])
f.close()
T_start = 200. # starting point of analysis (to avoid transients)
# load GIDs
gidfile = open(os.path.join(datapath , 'population_GIDs.dat'), 'r')
gids = []
for l in gidfile:
a = l.split()
gids.append([int(a[0]),int(a[1])])
print 'Global IDs:'
print gids
print
# number of populations
num_pops = len(gids)
print 'Number of populations:'
print num_pops
print
# first GID in each population
raw_first_gids = [gids[i][0] for i in np.arange(len(gids))]
# population sizes
pop_sizes = [gids[i][1]-gids[i][0]+1 for i in np.arange(len(gids))]
# numbers of neurons for which spikes were recorded
if record_frac == 'true':
rec_sizes = [int(pop_sizes[i]*frac_rec) for i in xrange(len(pop_sizes))]
else:
rec_sizes = [n_rec]*len(pop_sizes)
# first GID of each population once device GIDs are dropped
first_gids=[int(1 + np.sum(pop_sizes[:i])) for i in np.arange(len(pop_sizes))]
# last GID of each population once device GIDs are dropped
last_gids = [int(np.sum(pop_sizes[:i+1])) for i in np.arange(len(pop_sizes))]
# convert lists to a nicer format, i.e. [[2/3e, 2/3i], []....]
Pop_sizes =[pop_sizes[i:i+2] for i in xrange(0,len(pop_sizes),2)]
print 'Population sizes:'
print Pop_sizes
print
Raw_first_gids =[raw_first_gids[i:i+2] for i in xrange(0,len(raw_first_gids),2)]
First_gids = [first_gids[i:i+2] for i in xrange(0,len(first_gids),2)]
Last_gids = [last_gids[i:i+2] for i in xrange(0,len(last_gids),2)]
# total number of neurons in the simulation
num_neurons = last_gids[len(last_gids)-1]
print 'Total number of neurons:'
print num_neurons
print
# load spikes from gdf files, correct GIDs and merge them in population files,
# and store spike trains
# will contain neuron id resolved spike trains
neuron_spikes = [[] for i in np.arange(num_neurons+1)]
# container for population-resolved spike data
spike_data= [[[],[]],[[],[]],[[],[]],[[],[]],[[],[]],[[],[]],[[],[]],[[],[]]]
counter = 0
for layer in ['0','1','2','3']:
for population in ['0','1']:
output = os.path.join(datapath, 'population_spikes-{}-{}.gdf'.format(layer, population))
file_pattern = os.path.join(datapath, 'spikes_{}_{}*'.format(layer, population))
files = glob.glob(file_pattern)
print 'Merge '+str(len(files))+' spike files from L'+layer+'P'+population
if files:
merged_file = open(output,'w')
for f in files:
data = open(f,'r')
for l in data :
a = l.split()
a[0] = int(a[0])
a[1] = float(a[1])
raw_first_gid = Raw_first_gids[int(layer)][int(population)]
first_gid = First_gids[int(layer)][int(population)]
a[0] = a[0] - raw_first_gid + first_gid
if(a[1] > T_start): # discard data in the start-up phase
spike_data[counter][0].append(num_neurons-a[0])
spike_data[counter][1].append(a[1]-T_start)
neuron_spikes[a[0]].append(a[1]-T_start)
converted_line = str(a[0]) + '\t' + str(a[1]) +'\n'
merged_file.write(converted_line)
data.close()
merged_file.close()
counter +=1
clrs=['0','0.5','0','0.5','0','0.5','0','0.5']
plt.ion()
# raster plot
plt.figure(1)
counter = 1
for j in np.arange(num_pops):
for i in np.arange(first_gids[j],first_gids[j]+rec_sizes[j]):
plt.plot(neuron_spikes[i],np.ones_like(neuron_spikes[i])+sum(rec_sizes)-counter,'k o',ms=1, mfc=clrs[j],mec=clrs[j])
counter+=1
plt.xlim(0,T-T_start)
plt.ylim(0,sum(rec_sizes))
plt.xlabel(r'time (ms)')
plt.ylabel(r'neuron id')
plt.savefig(os.path.join(datapath, 'rasterplot.png'))
# firing rates
rates = []
temp = 0
for i in np.arange(num_pops):
for j in np.arange(first_gids[i], last_gids[i]):
temp+= len(neuron_spikes[j])
rates.append(temp/(rec_sizes[i]*(T-T_start))*1e3)
temp = 0
print
print 'Firing rates:'
print rates
plt.figure(2)
ticks= np.arange(num_pops)
plt.bar(ticks, rates, width=0.9, color='k')
xticklabels = ['L2/3e','L2/3i','L4e','L4i','L5e','L5i','L6e','L6i']
plt.setp(plt.gca(), xticks=ticks+0.5, xticklabels=xticklabels)
plt.xlabel(r'subpopulation')
plt.ylabel(r'firing rate (spikes/s)')
plt.savefig(os.path.join(datapath, 'firing_rates.png'))
plt.show()
| gpl-2.0 |
ANNarchy/ANNarchy | examples/multinetwork/MultiNetwork.py | 2 | 2086 | from ANNarchy import *
# Create the whole population
P = Population(geometry=1000, neuron=Izhikevich)
# Create the excitatory population
Exc = P[:800]
re = np.random.random(800)
Exc.noise = 5.0
Exc.a = 0.02
Exc.b = 0.2
Exc.c = -65.0 + 15.0 * re**2
Exc.d = 8.0 - 6.0 * re**2
Exc.v = -65.0
Exc.u = Exc.v * Exc.b
# Create the Inh population
Inh = P[800:]
ri = np.random.random(200)
Inh.noise = 2.0
Inh.a = 0.02 + 0.08 * ri
Inh.b = 0.25 - 0.05 * ri
Inh.c = -65.0
Inh.d = 2.0
Inh.v = -65.0
Inh.u = Inh.v * Inh.b
# Create the projections
proj_exc = Projection(Exc, P, 'exc')
proj_inh = Projection(Inh, P, 'inh')
proj_exc.connect_all_to_all(weights=Uniform(0.0, 0.5))
proj_inh.connect_all_to_all(weights=Uniform(0.0, 1.0))
# Create a spike monitor
M = Monitor(P, 'spike')
compile()
# Create a network with specified populations and projections
net = Network()
net.add(P)
net.add([proj_exc, proj_inh])
net.add(M)
net.compile()
# Create a network based on everything created until now (equivalent)
net2 = Network(everything=True)
net2.compile()
# Method to be applied on each network
def run_network(idx, net):
# Retrieve subpopulations
P_local = net.get(P)
Exc = P_local[:800]
Inh = P_local[800:]
# Randomize initialization
re = np.random.random(800)
Exc.c = -65.0 + 15.0 * re**2
Exc.d = 8.0 - 6.0 * re**2
ri = np.random.random(200)
Inh.noise = 2.0
Inh.a = 0.02 + 0.08 * ri
Inh.b = 0.25 - 0.05 * ri
Inh.u = Inh.v * Inh.b
# Simulate
net.simulate(1000.)
# Recordings
t, n = net.get(M).raster_plot()
return t, n
# Simulating using the created networks
vals = parallel_run(method=run_network, networks=[net, net2], measure_time=True, sequential=True)
vals = parallel_run(method=run_network, networks=[net, net2], measure_time=True)
# Using just a number of networks to create
vals = parallel_run(method=run_network, number=4, measure_time=True)
# Data analysis
t, n = vals[0]
t2, n2 = vals[1]
import matplotlib.pyplot as plt
plt.subplot(121)
plt.plot(t, n, '.')
plt.subplot(122)
plt.plot(t2, n2, '.')
plt.show()
| gpl-2.0 |
doodnayr/pilaser | calibrate.py | 1 | 4019 | import numpy as np
from picamera import PiCamera
from picamera.array import PiRGBAnalysis
from sklearn.cluster import DBSCAN
from binascii import unhexlify, hexlify
from scipy.interpolate import griddata
import os
from hipsterplot import plot
scan = DBSCAN(eps=2, min_samples=3, metric='euclidean', algorithm='ball_tree', leaf_size=30)
#import RPi.GPIO as GPIO
#GPIO.setmode(GPIO.BCM)
#GPIO.setup(26, GPIO.OUT)
def tohex(v):
return unhexlify("%0.4X" % (v+4096))
def printr(s):
print("\r" + s + " ", end="")
class Analysis(PiRGBAnalysis):
def __init__(self, camera, size=None):
self.camera = camera
self.size = size
self.z0 = np.array(0)
self.stable_counter = 0
self.background_sum = np.zeros((480, 640), dtype=np.uint16)
self.background = np.array(0)
#self.campoints = np.zeros((480, 640), dtype=np.uint16)
#self.campoints[:] = numpy.nan
self.laser_cal_points = np.append(np.arange(0, 2048, 256), 2047)
self.npoints = self.laser_cal_points.shape[0]
self.laser_xi = 0
self.laser_yi = 0
self.x_inc = -1
self.inc_change = False
self.campoints = []
self.x_vals = []
self.y_vals = []
def analyse(self, z):
z = z[:,:,1]
d = (z>self.z0) & (z-self.z0>25)
self.z0 = z
xy = np.where(d.ravel())[0]
if xy.shape[0]>999:
pass
#self.laser_xi = 0
#self.laser_yi = 0
elif xy.shape[0]>4:
xy = np.transpose(np.unravel_index(xy, d.shape))
clust = scan.fit_predict(xy)
ind = clust==0
if ind.sum()>1:
xc = xy[ind,0].mean()
yc = xy[ind,1].mean()
xint = int(xc.round())
yint = int(yc.round())
self.campoints.append([xint, yint])
self.x_vals.append(self.laser_xi)
self.y_vals.append(self.laser_yi)
#printr("%s %s" % (xint, yint))
os.system('clear')
x_plot = np.concatenate(([0,600], xy[:,0]))
y_plot = np.concatenate(([0,600], 600-xy[:,1]))
plot(y_plot, x_plot, 20, 20)
if self.laser_xi > 0 and self.laser_xi < self.npoints - 1:
self.laser_xi += self.x_inc
elif self.inc_change:
self.x_inc = -1 * self.x_inc
self.laser_xi += self.x_inc
self.inc_change = False
elif self.laser_yi < self.npoints - 1:
self.laser_yi += 1
self.inc_change = True
else:
self.laser_xi = 0
self.laser_yi = 0
#self.camera.stop_recording()
laser_x = self.laser_cal_points[self.laser_xi]
laser_y = self.laser_cal_points[self.laser_yi]
open('/dev/spidev0.0', 'wb').write(tohex(laser_x))
open('/dev/spidev0.1', 'wb').write(tohex(laser_y))
camera = PiCamera(resolution=(640, 480), framerate=3)
camera.awb_mode = 'off'
camera.awb_gains = (1.2, 1.2)
#camera.iso = 400 # 400 500 640 800
camera.color_effects = (128,128)
camera.exposure_mode = 'sports'
camera.shutter_speed = 12000
camera.video_denoise = True
camera.start_preview(fullscreen=False, window=(160,0,640,480))
tracker = Analysis(camera)
camera.start_recording(tracker, format='rgb')
#camera.start_recording('/home/pi/video2.h264')
try:
camera.wait_recording(9999) # sleep(9999)
except KeyboardInterrupt:
pass
#GPIO.cleanup()
camera.stop_recording()
camera.stop_preview()
grid_x, grid_y = np.mgrid[0:640, 0:480]
"""
x_map_lin = griddata(campoints, x_vals, (grid_x, grid_y), method='linear')
y_map_lin = griddata(campoints, y_vals, (grid_x, grid_y), method='linear')
not_nan = ~np.isnan(x_map_lin)
campoints2 = np.argwhere(not_nan)
x_vals2 = x_map_lin[not_nan]
y_vals2 = y_map_lin[not_nan]
x_map = griddata(campoints2, x_vals2, (grid_x, grid_y), method='nearest')
y_map = griddata(campoints2, y_vals2, (grid_x, grid_y), method='nearest')
"""
| mit |
mrshirts/pymbar | pymbar/confidenceintervals.py | 3 | 14155 | ##############################################################################
# pymbar: A Python Library for MBAR
#
# Copyright 2016-2017 University of Colorado Boulder
# Copyright 2010-2017 Memorial Sloan-Kettering Cancer Center
# Portions of this software are Copyright 2010-2016 University of Virginia
#
# Authors: Michael Shirts, John Chodera
# Contributors: Kyle Beauchamp
#
# pymbar is free software: you can redistribute it and/or modify
# it under the terms of the MIT License
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
#
# You should have received a copy of the MIT License along with pymbar.
##############################################################################
import numpy as np
import scipy
import scipy.special
import scipy.stats
def OrderReplicates(replicates, K):
''' Inputs: An array of replicates, and the size of the data.
Outputs: a Nxdims arrary of the data in the replicates, normalized by the standard deviation
'''
dims = np.shape(replicates[0]['destimated'])
sigma = replicates[0]['destimated']
zerosigma = (sigma == 0)
sigmacorr = zerosigma # we need to avoid errors with zero standard errors. We will ignore them later.
sigma += sigmacorr
yi = []
for (replicate_index, replicate) in enumerate(replicates):
yi.append(replicate['error']/sigma)
yiarray = np.asarray(yi)
sortedyi = np.zeros(np.shape(yiarray))
if len(dims) == 0:
sortedyi[:] = np.sort(yiarray)
elif len(dims) == 1:
for i in range(K):
sortedyi[:,i] = np.sort(yiarray[:,i])
elif len(dims) == 2:
for i in range(K):
for j in range(K):
sortedyi[:,i,j] = np.sort(yiarray[:,i,j])
# remove the correction so we have zero sigmas again
sigma -= sigmacorr
return sortedyi
def AndersonDarling(replicates, K):
# inputs:
# replicates: list of replicates
# K: number of replicates
# outputs: Anderson-Darling statistics.
# http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test
#
# Since both sigma and mu are known (mu exactly, sigma as an estimate from mbar),
# we can apply the case 1 test.
#
# Because sigma is not precise, we should accept a higher threshold than the 1%
# threshold listed below to throw an error:
#
# 15% 1.610
# 10% 1.933
# 5% 2.492
# 2.5% 3.070
# 1% 3.857
#
# So we choose something like 4.5. Note that for lower numbers of
# samples, it's more likely. 2000 samples for each of the
# harmonic_oscillators_distributions.py seems to give good
# results.
#
# for now, the standard deviation we use is the one from the
# _first_ replicate.
sortedyi = OrderReplicates(replicates, K)
zerosigma = (replicates[0]['destimated'] == 0) # ignore the ones with zero values of the std
N = len(replicates)
dims = np.shape(replicates[0]['destimated'])
sum = np.zeros(dims)
for i in range(N):
cdfi = scipy.stats.norm.cdf(sortedyi[i])
sum += (2*i-1)*np.log(cdfi) + (2*(N-i)+1)*np.log(1-cdfi)
A2 = -N - sum/N
A2[zerosigma] = 0
return A2
def QQPlot(replicates, K, title='Generic Q-Q plot', filename = 'qq.pdf'):
import matplotlib
import matplotlib.pyplot as plt
sortedyi = OrderReplicates(replicates, K)
N = len(replicates)
dim = len(np.shape(replicates[0]['error']))
xvals = scipy.stats.norm.ppf((np.arange(0,N)+0.5)/N) # inverse pdf
if dim == 0:
nplots = 1
elif dim == 1:
nplots = K
elif dim == 2:
nplots = K*K
yy = np.zeros([N,nplots])
labelij = dict()
if dim == 0:
yy[:,0] = sortedyi[:]
elif dim == 1:
nplots = K
for i in range(K):
yy[:,i] = sortedyi[:,i]
elif dim == 2:
nplots = K*(K-1)
k = 0
for i in range(K):
for j in range(K):
if i!=j:
yy[:,k] = sortedyi[:,i,j]
labelij[k] = [i,j]
k+=1
sq = (nplots)**0.5
labelsize = 30.0/sq
matplotlib.rc('axes', facecolor = '#E3E4FA')
matplotlib.rc('axes', edgecolor = 'white')
matplotlib.rc('xtick', labelsize = labelsize)
matplotlib.rc('ytick', labelsize = labelsize)
h = int(sq)
w = h + 1 + 1*(sq-h>0.5)
fig = plt.figure(figsize = (8,6))
for i in range(nplots):
ax = plt.subplot(h, w, i+1)
ms = 75.0/len(yy[:,i])
ax.plot(xvals, yy[:,i], color='r', ms=ms, marker='o', mec='r')
ax.plot(xvals, xvals, color='b', ls = '-')
plt.xlim(xvals.min(), xvals.max())
if dim==1:
ax.annotate(r'State $\mathrm{%d}$' % (i), xy=(0.5, 0.9), xycoords=('axes fraction', 'axes fraction'), xytext=(0, -2), size=labelsize, textcoords='offset points', va='top', ha='center', color='#151B54', bbox = dict(fc='w', ec='none', alpha=0.5))
if dim==2:
ax.annotate(r'State $\mathrm{%d-%d}$' % (labelij[i][0], labelij[i][1]), xy=(0.5, 0.9), xycoords=('axes fraction', 'axes fraction'), xytext=(0, -2), size=labelsize, textcoords='offset points', va='top', ha='center', color='#151B54', bbox = dict(fc='w', ec='none', alpha=0.5))
plt.suptitle(title, fontsize=20)
plt.savefig(filename)
plt.close(fig)
return
def generateConfidenceIntervals(replicates, K):
# inputs:
# replicates: list of replicates
# K: number of replicates
#=============================================================================================
# Analyze data.
#=============================================================================================
#
# By Chebyshev's inequality, we should have
# P(error >= alpha sigma) <= 1 / alpha^2
# so that a lower bound will be
# P(error < alpha sigma) > 1 - 1 / alpha^2
# for any real multiplier 'k', where 'sigma' represents the computed uncertainty (as one standard deviation).
#
# If the error is normal, we should have
# P(error < alpha sigma) = erf(alpha / sqrt(2))
print("The uncertainty estimates are tested in this section.")
print("If the error is normally distributed, the actual error will be less than a")
print("multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of")
print("time given by:")
print("P(error < alpha sigma) = erf(alpha / sqrt(2))")
print("For example, the true error should be less than 1.0 * sigma")
print("(one standard deviation) a total of 68% of the time, and")
print("less than 2.0 * sigma (two standard deviations) 95% of the time.")
print("The observed fraction of the time that error < alpha sigma, and its")
print("uncertainty, is given as 'obs' (with uncertainty 'obs err') below.")
print("This should be compared to the column labeled 'normal'.")
print("A weak lower bound that holds regardless of how the error is distributed is given")
print("by Chebyshev's inequality, and is listed as 'cheby' below.")
print("Uncertainty estimates are tested for both free energy differences and expectations.")
print("")
# error bounds
min_alpha = 0.1
max_alpha = 4.0
nalpha = 40
alpha_values = np.linspace(min_alpha, max_alpha, num=nalpha)
Pobs = np.zeros([nalpha], dtype=np.float64)
dPobs = np.zeros([nalpha], dtype=np.float64)
Plow = np.zeros([nalpha], dtype=np.float64)
Phigh = np.zeros([nalpha], dtype=np.float64)
nreplicates = len(replicates)
dim = len(np.shape(replicates[0]['estimated']))
for alpha_index in range(0, nalpha):
# Get alpha value.
alpha = alpha_values[alpha_index]
# Accumulate statistics across replicates
a = 1.0
b = 1.0
# how many dimensions in the data?
for (replicate_index, replicate) in enumerate(replicates):
# Compute fraction of free energy differences where error <= alpha sigma
# We only count differences where the analytical difference is larger than a cutoff, so that the results will not be limited by machine precision.
if (dim == 0):
if np.isnan(replicate['error']) or np.isnan(replicate['destimated']):
print("replicate %d" % replicate_index)
print("error")
print(replicate['error'])
print("destimated")
print(replicate['destimated'])
raise ArithmaticError("Encountered isnan in computation")
else:
if abs(replicate['error']) <= alpha * replicate['destimated']:
a += 1.0
else:
b += 1.0
elif (dim == 1):
for i in range(0, K):
if np.isnan(replicate['error'][i]) or np.isnan(replicate['destimated'][i]):
print("replicate %d" % replicate_index)
print("error")
print(replicate['error'])
print("destimated")
print(replicate['destimated'])
raise ArithmaticError("Encountered isnan in computation")
else:
if abs(replicate['error'][i]) <= alpha * replicate['destimated'][i]:
a += 1.0
else:
b += 1.0
elif (dim == 2):
for i in range(0, K):
for j in range(0, i):
if np.isnan(replicate['error'][i, j]) or np.isnan(replicate['destimated'][i, j]):
print("replicate %d" % replicate_index)
print("ij_error")
print(replicate['error'])
print("ij_estimated")
print(replicate['destimated'])
raise ArithmaticError("Encountered isnan in computation")
else:
if abs(replicate['error'][i, j]) <= alpha * replicate['destimated'][i, j]:
a += 1.0
else:
b += 1.0
Pobs[alpha_index] = a / (a + b)
Plow[alpha_index] = scipy.stats.beta.ppf(0.025, a, b)
Phigh[alpha_index] = scipy.stats.beta.ppf(0.975, a, b)
dPobs[alpha_index] = np.sqrt(a * b / ((a + b) ** 2 * (a + b + 1)))
# Write error as a function of sigma.
print("Error vs. alpha")
print("%5s %10s %10s %16s %17s" % ('alpha', 'cheby', 'obs', 'obs err', 'normal'))
Pnorm = scipy.special.erf(alpha_values / np.sqrt(2.))
for alpha_index in range(0, nalpha):
alpha = alpha_values[alpha_index]
print("%5.1f %10.6f %10.6f (%10.6f,%10.6f) %10.6f" % (alpha, 1. - 1. / alpha ** 2, Pobs[alpha_index], Plow[alpha_index], Phigh[alpha_index], Pnorm[alpha_index]))
# compute bias, average, etc - do it by replicate, not by bias
if dim == 0:
vals = np.zeros([nreplicates], dtype=np.float64)
vals_error = np.zeros([nreplicates], dtype=np.float64)
vals_std = np.zeros([nreplicates], dtype=np.float64)
elif dim == 1:
vals = np.zeros([nreplicates, K], dtype=np.float64)
vals_error = np.zeros([nreplicates, K], dtype=np.float64)
vals_std = np.zeros([nreplicates, K], dtype=np.float64)
elif dim == 2:
vals = np.zeros([nreplicates, K, K], dtype=np.float64)
vals_error = np.zeros([nreplicates, K, K], dtype=np.float64)
vals_std = np.zeros([nreplicates, K, K], dtype=np.float64)
rindex = 0
for replicate in replicates:
if dim == 0:
vals[rindex] = replicate['estimated']
vals_error[rindex] = replicate['error']
vals_std[rindex] = replicate['destimated']
elif dim == 1:
for i in range(0, K):
vals[rindex,:] = replicate['estimated']
vals_error[rindex,:] = replicate['error']
vals_std[rindex,:] = replicate['destimated']
elif dim == 2:
for i in range(0, K):
for j in range(0, i):
vals[rindex,:,:] = replicate['estimated']
vals_error[rindex,:,:] = replicate['error']
vals_std[rindex,:,:] = replicate['destimated']
rindex += 1
aveval = np.average(vals, axis=0)
standarddev = np.std(vals, axis=0)
bias = np.average(vals_error, axis=0)
aveerr = np.average(vals_error, axis=0)
d2 = vals_error ** 2
rms_error = (np.average(d2, axis=0)) ** (1.0 / 2.0)
d2 = vals_std ** 2
ave_std = (np.average(d2, axis=0)) ** (1.0 / 2.0)
# for now, just print out the data at the end for each
print("")
print(" i average bias rms_error stddev ave_analyt_std")
print("---------------------------------------------------------------------")
if dim == 0:
pave = aveval
pbias = bias
prms = rms_error
pstdev = standarddev
pavestd = ave_std
elif dim == 1:
for i in range(0, K):
pave = aveval[i]
pbias = bias[i]
prms = rms_error[i]
pstdev = standarddev[i]
pavestd = ave_std[i]
print("%7d %10.4f %10.4f %10.4f %10.4f %10.4f" % (i, pave, pbias, prms, pstdev, pavestd))
elif dim == 2:
for i in range(0, K):
pave = aveval[0, i]
pbias = bias[0, i]
prms = rms_error[0, i]
pstdev = standarddev[0, i]
pavestd = ave_std[0, i]
print("%7d %10.4f %10.4f %10.4f %10.4f %10.4f" % (i, pave, pbias, prms, pstdev, pavestd))
print("Totals: %10.4f %10.4f %10.4f %10.4f %10.4f" % (pave, pbias, prms, pstdev, pavestd))
return alpha_values, Pobs, Plow, Phigh, dPobs, Pnorm
| mit |
imito/odin | odin/networks/mixture_density_network.py | 1 | 10035 | from __future__ import absolute_import, division, print_function
import collections
import numpy as np
import tensorflow as tf
from sklearn.mixture import GaussianMixture
from tensorflow.python import keras
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras.layers import Dense
from tensorflow_probability.python import bijectors as tfb
from tensorflow_probability.python import distributions as tfd
from tensorflow_probability.python.layers.distribution_layer import (
DistributionLambda, _get_convert_to_tensor_fn, _serialize,
_serialize_function)
from tensorflow_probability.python.layers.internal import \
distribution_tensor_coercible as dtc
from tensorflow_probability.python.layers.internal import \
tensor_tuple as tensor_tuple
__all__ = ['MixtureDensityNetwork']
_COV_TYPES = ('none', 'diag', 'full', 'tril')
class MixtureDensityNetwork(Dense):
"""A mixture of Gaussian Keras layer.
Parameters
----------
units : `int`
number of output features for each component.
n_components : `int` (default=`2`)
The number of mixture components.
covariance_type : {'none', 'diag', 'full', 'tril'}
String describing the type of covariance parameters to use.
Must be one of:
'none' (each component has its own single variance).
'diag' (each component has its own diagonal covariance matrix),
'tril' (lower triangle matrix),
'full' (each component has its own general covariance matrix),
"""
def __init__(self,
units,
n_components=2,
covariance_type='none',
convert_to_tensor_fn=tfd.Distribution.sample,
softplus_scale=True,
validate_args=False,
activation='linear',
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
covariance_type = str(covariance_type).lower()
assert covariance_type in _COV_TYPES, \
"No support for covariance_type: '%s', the support value are: %s" % \
(covariance_type, ', '.join(_COV_TYPES))
self._covariance_type = covariance_type
self._n_components = int(n_components)
self._validate_args = bool(validate_args)
self._convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
self._softplus_scale = bool(softplus_scale)
# We'll need to keep track of who's calling who since the functional
# API has a different way of injecting `_keras_history` than the
# `keras.Sequential` way.
self._enter_dunder_call = False
# ====== calculating the number of parameters ====== #
if covariance_type == 'none':
component_params_size = 2 * units
elif covariance_type == 'diag': # only the diagonal
component_params_size = units + units
elif covariance_type == 'tril': # lower triangle
component_params_size = units + units * (units + 1) // 2
elif covariance_type == 'full': # full matrix
component_params_size = units + units * units
else:
raise NotImplementedError
self._component_params_size = component_params_size
params_size = self.n_components + self.n_components * component_params_size
self._event_size = units
super(MixtureDensityNetwork,
self).__init__(units=params_size,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
@property
def event_size(self):
return self._event_size
@property
def covariance_type(self):
return self._covariance_type
@property
def n_components(self):
return self._n_components
@property
def component_params_size(self):
return self._component_params_size
def __call__(self, inputs, *args, **kwargs):
self._enter_dunder_call = True
distribution, _ = super(MixtureDensityNetwork,
self).__call__(inputs, *args, **kwargs)
self._enter_dunder_call = False
return distribution
def call(self, inputs, *args, **kwargs):
dense_kwargs = dict(kwargs)
dense_kwargs.pop('training', None)
params = super(MixtureDensityNetwork, self).call(inputs, *args,
**dense_kwargs)
n_components = tf.convert_to_tensor(value=self.n_components,
name='n_components',
dtype_hint=tf.int32)
# ====== mixture weights ====== #
mixture_coefficients = params[..., :n_components]
mixture_dist = tfd.Categorical(logits=mixture_coefficients,
validate_args=self._validate_args,
name="MixtureWeights")
# ====== initialize the components ====== #
params = tf.reshape(
params[..., n_components:],
tf.concat([tf.shape(input=params)[:-1], [n_components, -1]], axis=0))
if bool(self._softplus_scale):
scale_fn = lambda x: tf.math.softplus(x) + tfd.softplus_inverse(1.0)
else:
scale_fn = lambda x: x
if self.covariance_type == 'none':
cov = 'IndependentNormal'
loc_params, scale_params = tf.split(params, 2, axis=-1)
scale_params = scale_params
components_dist = tfd.Independent(tfd.Normal(
loc=loc_params,
scale=scale_fn(scale_params),
validate_args=self._validate_args),
reinterpreted_batch_ndims=1)
#
elif self.covariance_type == 'diag':
cov = 'MultivariateNormalDiag'
loc_params, scale_params = tf.split(params, 2, axis=-1)
components_dist = tfd.MultivariateNormalDiag(
loc=loc_params,
scale_diag=scale_fn(scale_params),
validate_args=self._validate_args)
#
elif self.covariance_type == 'tril':
cov = 'MultivariateNormalTriL'
loc_params = params[..., :self.event_size]
scale_params = scale_fn(params[..., self.event_size:])
scale_tril = tfb.ScaleTriL(diag_shift=np.array(
1e-5, params.dtype.as_numpy_dtype()),
validate_args=self._validate_args)
components_dist = tfd.MultivariateNormalTriL(
loc=loc_params,
scale_tril=scale_tril(scale_params),
validate_args=self._validate_args)
#
elif self.covariance_type == 'full':
cov = 'MultivariateNormalFull'
loc_params = params[..., :self.event_size]
scale_params = tf.reshape(
scale_fn(params[..., self.event_size:]),
tf.concat(
[tf.shape(input=params)[:-1], (self.event_size, self.event_size)],
axis=0))
components_dist = tfd.MultivariateNormalFullCovariance(
loc=loc_params,
covariance_matrix=scale_params,
validate_args=self._validate_args)
else:
raise NotImplementedError
# ====== finally the mixture ====== #
d = tfd.MixtureSameFamily(mixture_distribution=mixture_dist,
components_distribution=components_dist,
validate_args=False,
name="Mixture%s" % cov)
# Wraps the distribution to return both dist and concrete value."""
value_is_seq = isinstance(d.dtype, collections.Sequence)
maybe_composite_convert_to_tensor_fn = (
(lambda d: tensor_tuple.TensorTuple(self._convert_to_tensor_fn(d)))
if value_is_seq else self._convert_to_tensor_fn)
distribution = dtc._TensorCoercible( # pylint: disable=protected-access
distribution=d,
convert_to_tensor_fn=maybe_composite_convert_to_tensor_fn)
value = distribution._value() # pylint: disable=protected-access
value._tfp_distribution = distribution # pylint: disable=protected-access
if value_is_seq:
value.shape = value[-1].shape
value.get_shape = value[-1].get_shape
value.dtype = value[-1].dtype
distribution.shape = value[-1].shape
distribution.get_shape = value[-1].get_shape
else:
distribution.shape = value.shape
distribution.get_shape = value.get_shape
if self._enter_dunder_call:
# Its critical to return both distribution and concretization
# so Keras can inject `_keras_history` to both. This is what enables
# either to be used as an input to another Keras `Model`.
return distribution, value
return distribution
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
input_shape = input_shape.with_rank_at_least(2)
if tensor_shape.dimension_value(input_shape[-1]) is None:
raise ValueError(
'The innermost dimension of input_shape must be defined, but saw: %s'
% input_shape)
# the number of output units is equal to event_size, not number of
# hidden units
return input_shape[:-1].concatenate(self.event_size)
def get_config(self):
"""Returns the config of this layer. """
config = {
'convert_to_tensor_fn': _serialize(self._convert_to_tensor_fn),
'covariance_type': self._covariance_type,
'validate_args': self._validate_args,
'n_components': self._n_components,
'softplus_scale': self._softplus_scale,
}
base_config = super(MixtureDensityNetwork, self).get_config()
base_config.update(config)
return base_config
| mit |
stylianos-kampakis/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics.regression import _check_reg_targets
def test_regression_metrics(n_samples=50):
y_true = np.arange(n_samples)
y_pred = y_true + 1
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
def test_multioutput_regression():
y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
error = mean_squared_error(y_true, y_pred)
assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)
# mean_absolute_error and mean_squared_error are equal because
# it is a binary problem.
error = mean_absolute_error(y_true, y_pred)
assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)
error = r2_score(y_true, y_pred, multioutput='variance_weighted')
assert_almost_equal(error, 1. - 5. / 2)
error = r2_score(y_true, y_pred, multioutput='uniform_average')
assert_almost_equal(error, -.875)
def test_regression_metrics_at_limits():
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
def test__check_reg_targets():
# All of length 3
EXAMPLES = [
("continuous", [1, 2, 3], 1),
("continuous", [[1], [2], [3]], 1),
("continuous-multioutput", [[1, 1], [2, 2], [3, 1]], 2),
("continuous-multioutput", [[5, 1], [4, 2], [3, 1]], 2),
("continuous-multioutput", [[1, 3, 4], [2, 2, 2], [3, 1, 1]], 3),
]
for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES,
repeat=2):
if type1 == type2 and n_out1 == n_out2:
y_type, y_check1, y_check2, multioutput = _check_reg_targets(
y1, y2, None)
assert_equal(type1, y_type)
if type1 == 'continuous':
assert_array_equal(y_check1, np.reshape(y1, (-1, 1)))
assert_array_equal(y_check2, np.reshape(y2, (-1, 1)))
else:
assert_array_equal(y_check1, y1)
assert_array_equal(y_check2, y2)
else:
assert_raises(ValueError, _check_reg_targets, y1, y2, None)
def test_regression_multioutput_array():
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
r = r2_score(y_true, y_pred, multioutput='raw_values')
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)
# mean_absolute_error and mean_squared_error are equal because
# it is a binary problem.
y_true = [[0, 0]]*4
y_pred = [[1, 1]]*4
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
r = r2_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(mse, [1., 1.], decimal=2)
assert_array_almost_equal(mae, [1., 1.], decimal=2)
assert_array_almost_equal(r, [0., 0.], decimal=2)
r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
assert_array_almost_equal(r, [0, -3.5], decimal=2)
assert_equal(np.mean(r), r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
multioutput='uniform_average'))
evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
multioutput='raw_values')
assert_array_almost_equal(evs, [0, -1.25], decimal=2)
# Checking for the condition in which both numerator and denominator is
# zero.
y_true = [[1, 3], [-1, 2]]
y_pred = [[1, 4], [-1, 1]]
r2 = r2_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(r2, [1., -3.], decimal=2)
assert_equal(np.mean(r2), r2_score(y_true, y_pred,
multioutput='uniform_average'))
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(evs, [1., -3.], decimal=2)
assert_equal(np.mean(evs), explained_variance_score(y_true, y_pred))
def test_regression_custom_weights():
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])
assert_almost_equal(msew, 0.39, decimal=2)
assert_almost_equal(maew, 0.475, decimal=3)
assert_almost_equal(rw, 0.94, decimal=2)
assert_almost_equal(evsw, 0.94, decimal=2)
| bsd-3-clause |
wright-group/WrightTools | setup.py | 1 | 2118 | #! /usr/bin/env python3
import os
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
def read(fname):
return open(os.path.join(here, fname)).read()
extra_files = {
"WrightTools": [
"datasets",
"datasets/*",
"datasets/*/*",
"datasets/*/*/*",
"datasets/*/*/*/*",
"CITATION",
"VERSION",
"WT5_VERSION",
]
}
with open(os.path.join(here, "WrightTools", "VERSION")) as version_file:
version = version_file.read().strip()
docs_require = ["sphinx", "sphinx-gallery==0.8.2", "sphinx-rtd-theme"]
setup(
name="WrightTools",
packages=find_packages(exclude=("tests", "tests.*")),
package_data=extra_files,
python_requires=">=3.6",
install_requires=[
"h5py",
"imageio",
"matplotlib>=3.3.0",
"numexpr",
"numpy>=1.15.0",
"pint",
"python-dateutil",
"scipy",
"tidy_headers>=1.0.0",
],
extras_require={
"docs": docs_require,
"dev": [
"black",
"pre-commit",
"pydocstyle",
"pytest",
"pytest-cov",
"databroker>=1.2",
"msgpack",
]
+ docs_require,
},
version=version,
description="Tools for loading, processing, and plotting multidimensional spectroscopy data.",
long_description=read("README.rst"),
author="WrightTools Developers",
license="MIT",
url="http://wright.tools",
keywords="spectroscopy science multidimensional visualization",
entry_points={"console_scripts": ["wt-tree=WrightTools.__main__:wt_tree"]},
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Framework :: Matplotlib",
"Natural Language :: English",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Topic :: Scientific/Engineering",
],
)
| mit |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/examples/svm/plot_svm_scale_c.py | 26 | 5353 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classification, we are interested in a risk minimization for the
equation:
.. math::
C \sum_{i=1, n} \mathcal{L} (f(x_i), y_i) + \Omega (w)
where
- :math:`C` is used to set the amount of regularization
- :math:`\mathcal{L}` is a `loss` function of our samples
and our model parameters.
- :math:`\Omega` is a `penalty` function of our model parameters
If we consider the loss function to be the individual error per
sample, then the data-fit term, or the sum of the error for each sample, will
increase as we add more samples. The penalization term, however, will not
increase.
When using, for example, :ref:`cross validation <cross_validation>`, to
set the amount of regularization with `C`, there will be a
different amount of samples between the main problem and the smaller problems
within the folds of the cross validation.
Since our loss function is dependent on the amount of samples, the latter
will influence the selected value of `C`.
The question that arises is `How do we optimally adjust C to
account for the different amount of training samples?`
The figures below are used to illustrate the effect of scaling our
`C` to compensate for the change in the number of samples, in the
case of using an `L1` penalty, as well as the `L2` penalty.
L1-penalty case
-----------------
In the `L1` case, theory says that prediction consistency
(i.e. that under given hypothesis, the estimator
learned predicts as well as a model knowing the true distribution)
is not possible because of the bias of the `L1`. It does say, however,
that model consistency, in terms of finding the right set of non-zero
parameters as well as their signs, can be achieved by scaling
`C1`.
L2-penalty case
-----------------
The theory says that in order to achieve prediction consistency, the
penalty parameter should be kept constant
as the number of samples grow.
Simulations
------------
The two figures below plot the values of `C` on the `x-axis` and the
corresponding cross-validation scores on the `y-axis`, for several different
fractions of a generated data-set.
In the `L1` penalty case, the cross-validation-error correlates best with
the test-error, when scaling our `C` with the number of samples, `n`,
which can be seen in the first figure.
For the `L2` penalty case, the best result comes from the case where `C`
is not scaled.
.. topic:: Note:
Two separate datasets are used for the two different plots. The reason
behind this is the `L1` case works better on sparse data, while `L2`
is better suited to the non-sparse case.
"""
print(__doc__)
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
# Jaques Grobler <jaques.grobler@inria.fr>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.utils import check_random_state
from sklearn import datasets
rnd = check_random_state(1)
# set up dataset
n_samples = 100
n_features = 300
# L1 data (only 5 informative features)
X_1, y_1 = datasets.make_classification(n_samples=n_samples,
n_features=n_features, n_informative=5,
random_state=1)
# L2 data: non sparse, but less features
y_2 = np.sign(.5 - rnd.rand(n_samples))
X_2 = rnd.randn(n_samples, n_features / 5) + y_2[:, np.newaxis]
X_2 += 5 * rnd.randn(n_samples, n_features / 5)
clf_sets = [(LinearSVC(penalty='L1', loss='L2', dual=False,
tol=1e-3),
np.logspace(-2.3, -1.3, 10), X_1, y_1),
(LinearSVC(penalty='L2', loss='L2', dual=True,
tol=1e-4),
np.logspace(-4.5, -2, 10), X_2, y_2)]
colors = ['b', 'g', 'r', 'c']
for fignum, (clf, cs, X, y) in enumerate(clf_sets):
# set up the plot for each regressor
plt.figure(fignum, figsize=(9, 10))
for k, train_size in enumerate(np.linspace(0.3, 0.7, 3)[::-1]):
param_grid = dict(C=cs)
# To get nice curve, we need a large number of iterations to
# reduce the variance
grid = GridSearchCV(clf, refit=False, param_grid=param_grid,
cv=ShuffleSplit(n=n_samples, train_size=train_size,
n_iter=250, random_state=1))
grid.fit(X, y)
scores = [x[1] for x in grid.grid_scores_]
scales = [(1, 'No scaling'),
((n_samples * train_size), '1/n_samples'),
]
for subplotnum, (scaler, name) in enumerate(scales):
plt.subplot(2, 1, subplotnum + 1)
plt.xlabel('C')
plt.ylabel('CV Score')
grid_cs = cs * float(scaler) # scale the C's
plt.semilogx(grid_cs, scores, label="fraction %.2f" %
train_size)
plt.title('scaling=%s, penalty=%s, loss=%s' %
(name, clf.penalty, clf.loss))
plt.legend(loc="best")
plt.show()
| bsd-3-clause |
awolfly9/jd_analysis | jd/management/commands/real_time_analysis.py | 1 | 6783 | #-*- coding: utf-8 -*-
import logging
import sys
import matplotlib
import time
matplotlib.use('Agg')
import os
import config
import utils
import redis
import markdown2
from scrapy.utils.log import configure_logging
from django.core.management.base import BaseCommand
from wordcloud import WordCloud
from sqlhelper import SqlHelper
from django.conf import settings
from pandas import Series, DataFrame
from cus_exception import CusException
from jd.analysis_jd_item import Analysis
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
# python manage.py
class Command(BaseCommand):
help = 'analysis jd comment data'
def add_arguments(self, parser):
parser.add_argument('-a', action = 'append', dest = 'spargs', default = [],
help = 'set spider argument (may be repeated)')
#必须实现的方法
def handle(self, *args, **options):
reload(sys)
sys.setdefaultencoding('utf-8')
os.chdir(sys.path[0])
spargs = utils.arglist_to_dict(options['spargs'])
if not os.path.exists('log'):
os.makedirs('log')
configure_logging(install_root_handler = False)
logging.basicConfig(
filename = 'log/%s.log' % spargs.get('product_id'),
format = '%(levelname)s %(asctime)s: %(message)s',
level = logging.ERROR
)
guid = spargs.get('guid', '0')
product_id = spargs.get('product_id', '0')
if guid == '0' or product_id == '0':
utils.log('分析数据传入参数不对，接收到的参数为： spargs:%s' % spargs)
utils.push_redis(guid = guid, product_id = product_id, info = '分析数据传入参数不对，接收到的参数为:%s' % spargs)
utils.push_redis(guid = guid, product_id = product_id, info = 'finish')
return
utils.log('开始分析：%s' % spargs)
sql = SqlHelper()
red = redis.StrictRedis(host = config.redis_host, port = config.redis_part, db = config.redis_db,
password = config.redis_pass)
spargs['sql'] = sql
spargs['red'] = red
# 运行爬虫
runspider(spargs)
# 开启分析
analysis = RealTimeAnalysis(**spargs)
analysis.run()
def runspider(spargs):
url = spargs.get('url')
name = spargs.get('name', 'jd')
if not os.path.exists('log'):
os.makedirs('log')
configure_logging(install_root_handler = False)
logging.basicConfig(
filename = 'log/%s.log' % name,
format = '%(levelname)s %(asctime)s: %(message)s',
level = logging.ERROR
)
print "get_project_settings().attributes:", get_project_settings().attributes['SPIDER_MODULES']
process = CrawlerProcess(get_project_settings())
start_time = time.time()
try:
logging.info('进入爬虫')
process.crawl(name, **spargs)
process.start()
except Exception, e:
process.stop()
logging.error("url:%s, errorMsg:%s" % (url, e.message))
finally:
logging.error("url:%s, errorMsg:%s" % (url, "爬虫终止"))
utils.log('spider crawl time:%s' % str(time.time() - start_time))
# 注意考虑到多个商品对比的情况
class RealTimeAnalysis(Analysis):
def __init__(self, **kwargs):
super(RealTimeAnalysis, self).__init__(**kwargs)
def record_result(self, result, color = 'default', font_size = 16, strong = False, type = 'word',
br = True, default = False, new_line = False):
self.full_result = ''
if type == 'word' and default == False:
if strong:
result = '<strong style="color: %s; font-size: %spx;">%s</strong>' % (color, font_size, result)
else:
result = '<span style="color: %s; font-size: %spx;">%s</span>' % (color, font_size, result)
elif type == 'image':
result = markdown2.markdown(result)
self.full_result += result
if br:
self.full_result += '<br>'
if new_line:
self.full_result += '\n'
utils.push_redis(guid = self.guid, product_id = self.product_id, info = self.full_result, type = type)
# 提取商品的基本信息
def analysis_item_info(self):
pass
# 分析购买渠道并生成柱状图
def analysis_buy_channel(self):
self.record_result('正在分析商品的购买渠道占比...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_buy_channel()
# 分析购买的商品颜色
def analysis_color(self):
self.record_result('正在分析该商品不同颜色的购买量...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_color()
# 分析购买的商品大小分类
def analysis_size(self):
self.record_result('正在分析该商品不同配置的购买量...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_size()
# 分析购买该商品的地域占比
def analysis_province(self):
self.record_result('正在分析该商品不同省份的购买量...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_province()
# 分析商品购买、评论和时间关系图
def analysis_sell_time(self):
self.record_result('正在分析商品购买、评论和时间关系图...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_sell_time()
# 分析移动端购买占比
def analysis_mobile(self):
self.record_result('正在分析移动端购买占比...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_mobile()
# 分析购买后评论的时间分布
def analysis_buy_days(self):
self.record_result('正在分析该商品购买后用户评论的时间', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_buy_days()
# 分析购买的用户的等级分布
def analysis_user_level(self):
self.record_result('正在分析购买该商品用户的等级...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_user_level()
# 分析 24 小时分布
def analysis_hour(self):
self.record_result('正在分析用户购买该商品 24 小时占比...', color = 'black', font_size = 24, strong = True)
super(RealTimeAnalysis, self).analysis_hour()
def finish(self):
self.record_result('finish', default = True, br = False)
| lgpl-3.0 |
renatopp/liac | liac/plot.py | 1 | 1988 | # =============================================================================
# Federal University of Rio Grande do Sul (UFRGS)
# Connectionist Artificial Intelligence Laboratory (LIAC)
# Renato de Pontes Pereira - rppereira@inf.ufrgs.br
# =============================================================================
# Copyright (c) 2011 Renato de Pontes Pereira, renato.ppontes at gmail dot com
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# =============================================================================
from matplotlib.pyplot import *
from matplotlib.patches import *
from matplotlib.text import *
from matplotlib import cm
def Gaussian(mean, cov, nstd=2, **kwargs):
'''Creates an Ellipse Artist object using mean and covariance'''
v, w = np.linalg.eigh(cov)
u = w[:, 0]
angle = np.arctan2(u[1], u[0])
angle = np.rad2deg(angle)
width, height = v*2*nstd
e = Ellipse(mean, width, height, angle, **kwargs)
return e | mit |
arabenjamin/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.
.. currentmodule:: sklearn.linear_model
.. note::
This example will also work by replacing ``SVC(kernel="linear")``
with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
such as that of a SVC with a linear kernel.
For example try instead of the ``SVC``::
clf = SGDClassifier(n_iter=100, alpha=0.01)
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
#from sklearn.linear_model import SGDClassifier
# we create 40 separable points
rng = np.random.RandomState(0)
n_samples_1 = 1000
n_samples_2 = 100
X = np.r_[1.5 * rng.randn(n_samples_1, 2),
0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
# fit the model and get the separating hyperplane
clf = svm.SVC(kernel='linear', C=1.0)
clf.fit(X, y)
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - clf.intercept_[0] / w[1]
# get the separating hyperplane using weighted classes
wclf = svm.SVC(kernel='linear', class_weight={1: 10})
wclf.fit(X, y)
ww = wclf.coef_[0]
wa = -ww[0] / ww[1]
wyy = wa * xx - wclf.intercept_[0] / ww[1]
# plot separating hyperplanes and samples
h0 = plt.plot(xx, yy, 'k-', label='no weights')
h1 = plt.plot(xx, wyy, 'k--', label='with weights')
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.legend()
plt.axis('tight')
plt.show()
| bsd-3-clause |
RachitKansal/scikit-learn | sklearn/kernel_approximation.py | 258 | 17973 | """
The :mod:`sklearn.kernel_approximation` module implements several
approximate kernel feature maps base on Fourier transforms.
"""
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import svd
from .base import BaseEstimator
from .base import TransformerMixin
from .utils import check_array, check_random_state, as_float_array
from .utils.extmath import safe_sparse_dot
from .utils.validation import check_is_fitted
from .metrics.pairwise import pairwise_kernels
class RBFSampler(BaseEstimator, TransformerMixin):
"""Approximates feature map of an RBF kernel by Monte Carlo approximation
of its Fourier transform.
It implements a variant of Random Kitchen Sinks.[1]
Read more in the :ref:`User Guide <rbf_kernel_approx>`.
Parameters
----------
gamma : float
Parameter of RBF kernel: exp(-gamma * x^2)
n_components : int
Number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
random_state : {int, RandomState}, optional
If int, random_state is the seed used by the random number generator;
if RandomState instance, random_state is the random number generator.
Notes
-----
See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
Benjamin Recht.
[1] "Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning" by A. Rahimi and
Benjamin Recht.
(http://www.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
"""
def __init__(self, gamma=1., n_components=100, random_state=None):
self.gamma = gamma
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
X = check_array(X, accept_sparse='csr')
random_state = check_random_state(self.random_state)
n_features = X.shape[1]
self.random_weights_ = (np.sqrt(2 * self.gamma) * random_state.normal(
size=(n_features, self.n_components)))
self.random_offset_ = random_state.uniform(0, 2 * np.pi,
size=self.n_components)
return self
def transform(self, X, y=None):
"""Apply the approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
check_is_fitted(self, 'random_weights_')
X = check_array(X, accept_sparse='csr')
projection = safe_sparse_dot(X, self.random_weights_)
projection += self.random_offset_
np.cos(projection, projection)
projection *= np.sqrt(2.) / np.sqrt(self.n_components)
return projection
class SkewedChi2Sampler(BaseEstimator, TransformerMixin):
"""Approximates feature map of the "skewed chi-squared" kernel by Monte
Carlo approximation of its Fourier transform.
Read more in the :ref:`User Guide <skewed_chi_kernel_approx>`.
Parameters
----------
skewedness : float
"skewedness" parameter of the kernel. Needs to be cross-validated.
n_components : int
number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
random_state : {int, RandomState}, optional
If int, random_state is the seed used by the random number generator;
if RandomState instance, random_state is the random number generator.
References
----------
See "Random Fourier Approximations for Skewed Multiplicative Histogram
Kernels" by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
See also
--------
AdditiveChi2Sampler : A different approach for approximating an additive
variant of the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
"""
def __init__(self, skewedness=1., n_components=100, random_state=None):
self.skewedness = skewedness
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
X = check_array(X)
random_state = check_random_state(self.random_state)
n_features = X.shape[1]
uniform = random_state.uniform(size=(n_features, self.n_components))
# transform by inverse CDF of sech
self.random_weights_ = (1. / np.pi
* np.log(np.tan(np.pi / 2. * uniform)))
self.random_offset_ = random_state.uniform(0, 2 * np.pi,
size=self.n_components)
return self
def transform(self, X, y=None):
"""Apply the approximate feature map to X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
check_is_fitted(self, 'random_weights_')
X = as_float_array(X, copy=True)
X = check_array(X, copy=False)
if (X < 0).any():
raise ValueError("X may not contain entries smaller than zero.")
X += self.skewedness
np.log(X, X)
projection = safe_sparse_dot(X, self.random_weights_)
projection += self.random_offset_
np.cos(projection, projection)
projection *= np.sqrt(2.) / np.sqrt(self.n_components)
return projection
class AdditiveChi2Sampler(BaseEstimator, TransformerMixin):
"""Approximate feature map for additive chi2 kernel.
Uses sampling the fourier transform of the kernel characteristic
at regular intervals.
Since the kernel that is to be approximated is additive, the components of
the input vectors can be treated separately. Each entry in the original
space is transformed into 2*sample_steps+1 features, where sample_steps is
a parameter of the method. Typical values of sample_steps include 1, 2 and
3.
Optimal choices for the sampling interval for certain data ranges can be
computed (see the reference). The default values should be reasonable.
Read more in the :ref:`User Guide <additive_chi_kernel_approx>`.
Parameters
----------
sample_steps : int, optional
Gives the number of (complex) sampling points.
sample_interval : float, optional
Sampling interval. Must be specified when sample_steps not in {1,2,3}.
Notes
-----
This estimator approximates a slightly different version of the additive
chi squared kernel then ``metric.additive_chi2`` computes.
See also
--------
SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
squared kernel.
References
----------
See `"Efficient additive kernels via explicit feature maps"
<http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_
A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
2011
"""
def __init__(self, sample_steps=2, sample_interval=None):
self.sample_steps = sample_steps
self.sample_interval = sample_interval
def fit(self, X, y=None):
"""Set parameters."""
X = check_array(X, accept_sparse='csr')
if self.sample_interval is None:
# See reference, figure 2 c)
if self.sample_steps == 1:
self.sample_interval_ = 0.8
elif self.sample_steps == 2:
self.sample_interval_ = 0.5
elif self.sample_steps == 3:
self.sample_interval_ = 0.4
else:
raise ValueError("If sample_steps is not in [1, 2, 3],"
" you need to provide sample_interval")
else:
self.sample_interval_ = self.sample_interval
return self
def transform(self, X, y=None):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Returns
-------
X_new : {array, sparse matrix}, \
shape = (n_samples, n_features * (2*sample_steps + 1))
Whether the return value is an array of sparse matrix depends on
the type of the input X.
"""
msg = ("%(name)s is not fitted. Call fit to set the parameters before"
" calling transform")
check_is_fitted(self, "sample_interval_", msg=msg)
X = check_array(X, accept_sparse='csr')
sparse = sp.issparse(X)
# check if X has negative values. Doesn't play well with np.log.
if ((X.data if sparse else X) < 0).any():
raise ValueError("Entries of X must be non-negative.")
# zeroth component
# 1/cosh = sech
# cosh(0) = 1.0
transf = self._transform_sparse if sparse else self._transform_dense
return transf(X)
def _transform_dense(self, X):
non_zero = (X != 0.0)
X_nz = X[non_zero]
X_step = np.zeros_like(X)
X_step[non_zero] = np.sqrt(X_nz * self.sample_interval_)
X_new = [X_step]
log_step_nz = self.sample_interval_ * np.log(X_nz)
step_nz = 2 * X_nz * self.sample_interval_
for j in range(1, self.sample_steps):
factor_nz = np.sqrt(step_nz /
np.cosh(np.pi * j * self.sample_interval_))
X_step = np.zeros_like(X)
X_step[non_zero] = factor_nz * np.cos(j * log_step_nz)
X_new.append(X_step)
X_step = np.zeros_like(X)
X_step[non_zero] = factor_nz * np.sin(j * log_step_nz)
X_new.append(X_step)
return np.hstack(X_new)
def _transform_sparse(self, X):
indices = X.indices.copy()
indptr = X.indptr.copy()
data_step = np.sqrt(X.data * self.sample_interval_)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new = [X_step]
log_step_nz = self.sample_interval_ * np.log(X.data)
step_nz = 2 * X.data * self.sample_interval_
for j in range(1, self.sample_steps):
factor_nz = np.sqrt(step_nz /
np.cosh(np.pi * j * self.sample_interval_))
data_step = factor_nz * np.cos(j * log_step_nz)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new.append(X_step)
data_step = factor_nz * np.sin(j * log_step_nz)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new.append(X_step)
return sp.hstack(X_new)
class Nystroem(BaseEstimator, TransformerMixin):
"""Approximate a kernel map using a subset of the training data.
Constructs an approximate feature map for an arbitrary kernel
using a subset of the data as basis.
Read more in the :ref:`User Guide <nystroem_kernel_approx>`.
Parameters
----------
kernel : string or callable, default="rbf"
Kernel map to be approximated. A callable should accept two arguments
and the keyword arguments passed to this object as kernel_params, and
should return a floating point number.
n_components : int
Number of features to construct.
How many data points will be used to construct the mapping.
gamma : float, default=None
Gamma parameter for the RBF, polynomial, exponential chi2 and
sigmoid kernels. Interpretation of the default value is left to
the kernel; see the documentation for sklearn.metrics.pairwise.
Ignored by other kernels.
degree : float, default=3
Degree of the polynomial kernel. Ignored by other kernels.
coef0 : float, default=1
Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.
kernel_params : mapping of string to any, optional
Additional parameters (keyword arguments) for kernel function passed
as callable object.
random_state : {int, RandomState}, optional
If int, random_state is the seed used by the random number generator;
if RandomState instance, random_state is the random number generator.
Attributes
----------
components_ : array, shape (n_components, n_features)
Subset of training points used to construct the feature map.
component_indices_ : array, shape (n_components)
Indices of ``components_`` in the training set.
normalization_ : array, shape (n_components, n_components)
Normalization matrix needed for embedding.
Square root of the kernel matrix on ``components_``.
References
----------
* Williams, C.K.I. and Seeger, M.
"Using the Nystroem method to speed up kernel machines",
Advances in neural information processing systems 2001
* T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
"Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
Comparison",
Advances in Neural Information Processing Systems 2012
See also
--------
RBFSampler : An approximation to the RBF kernel using random Fourier
features.
sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
"""
def __init__(self, kernel="rbf", gamma=None, coef0=1, degree=3,
kernel_params=None, n_components=100, random_state=None):
self.kernel = kernel
self.gamma = gamma
self.coef0 = coef0
self.degree = degree
self.kernel_params = kernel_params
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit estimator to data.
Samples a subset of training points, computes kernel
on these and computes normalization matrix.
Parameters
----------
X : array-like, shape=(n_samples, n_feature)
Training data.
"""
X = check_array(X, accept_sparse='csr')
rnd = check_random_state(self.random_state)
n_samples = X.shape[0]
# get basis vectors
if self.n_components > n_samples:
# XXX should we just bail?
n_components = n_samples
warnings.warn("n_components > n_samples. This is not possible.\n"
"n_components was set to n_samples, which results"
" in inefficient evaluation of the full kernel.")
else:
n_components = self.n_components
n_components = min(n_samples, n_components)
inds = rnd.permutation(n_samples)
basis_inds = inds[:n_components]
basis = X[basis_inds]
basis_kernel = pairwise_kernels(basis, metric=self.kernel,
filter_params=True,
**self._get_kernel_params())
# sqrt of kernel matrix on basis vectors
U, S, V = svd(basis_kernel)
S = np.maximum(S, 1e-12)
self.normalization_ = np.dot(U * 1. / np.sqrt(S), V)
self.components_ = basis
self.component_indices_ = inds
return self
def transform(self, X):
"""Apply feature map to X.
Computes an approximate feature map using the kernel
between some training points and X.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
Data to transform.
Returns
-------
X_transformed : array, shape=(n_samples, n_components)
Transformed data.
"""
check_is_fitted(self, 'components_')
X = check_array(X, accept_sparse='csr')
kernel_params = self._get_kernel_params()
embedded = pairwise_kernels(X, self.components_,
metric=self.kernel,
filter_params=True,
**kernel_params)
return np.dot(embedded, self.normalization_.T)
def _get_kernel_params(self):
params = self.kernel_params
if params is None:
params = {}
if not callable(self.kernel):
params['gamma'] = self.gamma
params['degree'] = self.degree
params['coef0'] = self.coef0
return params
| bsd-3-clause |
JSLBen/KnowledgeTracing | reference_py/tf_RNN.py | 1 | 15676 | # tensorflow version: 1.0.0
import numpy as np
import tensorflow as tf
from numpy.random import permutation as perm
import pandas as pd
class tf_RNN(object):
"""
RNN classifier with LSTM cells with tensorflow
"""
def __init__(self,
num_features,
num_steps,
num_classes,
state_size,
forget_bias = None,
keep_in = 1,
keep_out = 1,
cell_type = 'basic',
trainer = 'rmsprop',
learning_rate = 0.01,
epoch = 10,
batch_size = 128
):
"""
, RNN training based on the tensorflow
,
,
, state_size = [num_units, num_units, num_units]
,
, +----------+ +----------+ +----------+
, | RNN_Cell | --> | RNN_Cell | --> | RNN_Cell |
, +----------+ +----------+ +----------+
, | ^ | ^ | ^
, +--+ +--+ +--+
,
,
, Args:
, num_features: int, number of features
, num_steps: int, number of time steps
, num_classes: int, number of classes
, state_size: int, or a nested list, or tuple
, forget_bias: float, or a nested list, or tuple.
, Forget bias in each LSTM cell.
,
,
, optimizer: Default = RMSProp
, learning_rate: Default = 0.01
, epoch: Default = 10
, batch_size: Default = 128
"""
self.num_features = num_features
self.num_steps = num_steps
self.num_classes = num_classes
self.state_size = state_size
self.learning_rate = learning_rate
self.epoch = epoch
self.batch_size = batch_size
self.keep_in = keep_in
self.keep_out = keep_out
self.train_mats = []
self.test_mats = []
if forget_bias is None:
self.forget_bias = [1.0] * len(state_size)
else:
assert len(state_size)==len(forget_bias), "length of state_size and forget_bias should be the number of layers"
if trainer == 'rmsprop':
self.trainer = tf.train.RMSPropOptimizer
elif trainer == 'adam':
self.trainer = tf.train.AdamOptimizer
elif True:
print("{} Optimization is not realized".format(trainer))
exit(1)
# build default graph
self.graph = tf.Graph()
with self.graph.as_default():
# place holder for data and target
# x: batch x num_steps x num_features
# y: batch x num_classes
self.x_ = tf.placeholder(tf.float32, [None, num_steps, num_features])
self.y_ = tf.placeholder(tf.float32, [None, num_classes])
self.batch_size_ = tf.placeholder(tf.int32, [])
# rnn_inputs: num_steps x batch x num_features
self.rnn_inputs = [tf.squeeze(i, 1) for i in tf.split(value = self.x_,
num_or_size_splits = num_steps,
axis =1)]
split_result = tf.split(value = self.x_, num_or_size_splits = num_steps, axis = 1)
print('type of tf.split result: ', type(split_result))
print('length of tf.split result: ', len(split_result))
print('shape of tf.split result[0]: ', split_result[0].get_shape())
print('rnn_inputs length: ', len(self.rnn_inputs))
print('rnn_inputs[0] shape: ', self.rnn_inputs[0].get_shape())
print('x_ shape: ', self.x_.get_shape())
print('num_steps: ', num_steps)
print('state_size: ', state_size)
# linear activation weight
# weight: state_size x num_classes
# bias: num_classes
self.weight = tf.Variable(tf.random_normal([self.state_size[-1], num_classes]))
self.bias = tf.Variable(tf.random_normal([num_classes]))
# define the MultiRNNCell
if cell_type == "basic":
cell = tf.contrib.rnn.BasicRNNCell
self.cells = [cell(size) for size in state_size]
elif cell_type == "lstm":
cell = tf.contrib.rnn.LSTMCell
self.cells = [cell(size, forget_bias = bias, state_is_tuple=True) for size, bias in zip(state_size, self.forget_bias)]
elif cell_type == "gru":
cell = tf.contrib.rnn.GRUCell
self.cells = [cell(size) for size in state_size]
self.cells = tf.contrib.rnn.MultiRNNCell(self.cells, state_is_tuple=True)
# dropout wrapper
if self.keep_in < 1 or self.keep_out < 1:
self.cells = tf.contrib.rnn.DropoutWrapper(self.cells,
input_keep_prob = self.keep_in,
output_keep_prob=self.keep_out)
# get output from RNN loops
# self.init_states = self.cells.zero_state(self.batch_size_, dtype=tf.float32)
self.outputs, self.states = tf.contrib.rnn.static_rnn(self.cells,
self.rnn_inputs,
# initial_state=self.init_state,
dtype=tf.float32)
# linear activation for all outputs
self.pred_seq = [tf.matmul(self.outputs[i], self.weight) + self.bias for i in range(num_steps)]
print('length of pred_seq: ', len(self.pred_seq))
print('shape of pred_seq[0]: ', self.pred_seq[0].get_shape())
print('length of outputs: ', len(self.outputs))
print('shape of outputs[0]: ', self.outputs[0].get_shape())
self.pred = self.pred_seq[-1]
# softmax activation
self.prob_seq = [tf.nn.softmax(self.pred_seq[i]) for i in range(num_steps)]
self.prob = self.prob_seq[-1]
print('length of prob_seq: ', len(self.prob_seq))
print('shape of prob: ', self.prob.get_shape())
exit(1)
# confusion matrix
self.mat = tf.matmul(tf.transpose(self.y_), tf.one_hot(tf.argmax(self.prob,1), depth=num_classes))
# calculate log loss with softmax activation
# note: pred_seq or prob_seq
self.cost_seq = [tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.pred_seq[i], labels=self.y_)) for i in range(num_steps)]
self.cost = self.cost_seq[-1] # + 0.001*sum([i**2*(self.mat[i][i+1]-self.mat[i+1][i])**2 for i in range(num_classes-1)])
# define the optimizer function
self.optimizer = self.trainer(learning_rate=self.learning_rate).minimize(self.cost)
# count the number of correct predictions and calculates accuracy
self.correct_pred_seq = [tf.equal(tf.argmax(self.prob_seq[i],1), tf.argmax(self.y_,1)) for i in range(num_steps)]
self.accuracy_seq = [tf.reduce_mean(tf.cast(self.correct_pred_seq[i], tf.float32)) for i in range(num_steps)]
self.correct_pred = self.correct_pred_seq[-1]
self.accuracy = self.accuracy_seq[-1]
# initialize all the variables
# define the saver
self.init = tf.global_variables_initializer()
self.saver = tf.train.Saver(tf.global_variables())
def fit(self, x_data, y_data, x_test, y_test, debug=False):
"""
, train the model
, Args:
, x_data: independent features
, y_data: dependent valuable
"""
with tf.Session(graph=self.graph) as sess:
sess.run(self.init)
batches = self.shuffle(x_data, y_data)
total = int()
for i, packet in enumerate(batches):
if i ==0:
total = packet
else:
x_batch, y_batch, idx_epoch, end_batch, idx_batch = packet
feed = {self.x_: x_batch, self.y_: y_batch, self.batch_size_: x_batch.shape[0]}
sess.run(self.optimizer, feed_dict=feed)
# if i == 1000:
# cur_state = sess.run(self.states, feed_dict=feed)
# print type(cur_state)
# print cur_state[0].shape, cur_state[1].shape
# print cur_state
if end_batch:
feed_train = {self.x_: x_data, self.y_: y_data, self.batch_size_: x_data.shape[0]}
feed_test = {self.x_: x_test, self.y_: y_test, self.batch_size_: x_test.shape[0]}
loss_train, accr_train, mat_train = sess.run((self.cost, self.accuracy, self.mat), feed_dict=feed_train)
loss_test, accr_test, mat_test = sess.run((self.cost, self.accuracy, self.mat), feed_dict=feed_test)
print("Loss: {:8.6f} {:8.6f} {:8.6f} {:8.6f}".format(loss_train, loss_test, accr_train, accr_test))
#print("- {}/{} training completed with loss: {} ".format(idx_epoch, self.epoch, loss))
self.train_mats += [mat_train.astype(int)]
self.test_mats += [mat_test.astype(int)]
print("train:")
print(pd.DataFrame(mat_train))
print("test :")
print(pd.DataFrame(mat_test))
# step = 1
# while step * self.batch_size < self.epoch:
# x_batch, y_batch = tf_RNN.get_random_batch(data, y_data, self.batch_size)
# x_batch = x_batch.reshape((self.batch_size, self.num_steps, self.num_features))
# sess.run(self.optimizer, feed_dict={self.x: x_batch, self.y: y_batch})
# step += 1
# save the model for prediction
saver = tf.train.Saver()
saver.save(sess, "./lstm_rnn_c_model.ckpt")
print('Model is saved')
sess.close()
def predict(self, x_data, seq=False):
"""
make prediction
"""
with tf.Session(graph=self.graph) as sess:
# restore model from saved file
saver = tf.train.Saver()
saver = tf.train.import_meta_graph("./lstm_rnn_c_model.ckpt.meta")
saver.restore(sess, "./lstm_rnn_c_model.ckpt")
feed = {self.x_: x_data, self.batch_size_: x_data.shape[0]}
if seq:
result = sess.run([tf.argmax(self.pred_seq[i],1) for i in range(self.num_steps)], feed_dict=feed)
else:
result = sess.run(tf.argmax(self.pred,1), feed_dict=feed)
sess.close()
return result
def predict_prob(self, x_data, seq=False):
"""
obtain the predicted probilities for each class
"""
with tf.Session(graph=self.graph) as sess:
saver = tf.train.import_meta_graph("./lstm_rnn_c_model.ckpt.meta")
saver.restore(sess, "./lstm_rnn_c_model.ckpt")
feed = {self.x_: x_data, self.batch_size_: x_data.shape[0]}
if seq:
result = sess.run([self.prob_seq[i] for i in range(self.num_steps)], feed_dict=feed)
else:
result = sess.run(self.prob, feed_dict=feed)
sess.close()
return result
def loss(self, x_data, y_data, seq=False):
"""
calculate the objective function
"""
with tf.Session(graph=self.graph) as sess:
saver = tf.train.import_meta_graph("./lstm_rnn_c_model.ckpt.meta")
saver.restore(sess, "./lstm_rnn_c_model.ckpt")
feed = {self.x_: x_data, self.y_: y_data, self.batch_size_: x_data.shape[0]}
if seq:
result = sess.run([self.cost_seq[i] for i in range(self.num_steps)], feed_dict=feed)
else:
result = sess.run(self.cost, feed_dict=feed)
sess.close()
return result
def evaluation(self, x_data, y_data, seq=False):
"""
evaluate the performance of the model,
i.e. accuracy of the prediction
"""
with tf.Session(graph=self.graph) as sess:
saver = tf.train.import_meta_graph("./lstm_rnn_c_model.ckpt.meta")
saver.restore(sess, "./lstm_rnn_c_model.ckpt")
feed = {self.x: x_data, self.y: y_data, self.batch_size_: x_data.shape[0]}
if seq:
result = sess.run([self.accuracy_seq[i] for i in range(self.num_steps)], feed_dict=feed)
else:
result = sess.run(self.accuracy, feed_dict=feed)
sess.close()
return result
# @staticmethod
# def get_random_batch(data, y_data, batch_size):
# num_of_training_data = len(data)
# if batch_size > num_of_training_data:
# print('Batch size too large, returning all data')
# return data, y_data
# perm = np.arange(num_of_training_data)
# np.random.shuffle(perm)
# return data[perm[:batch_size]], y_data[perm[:batch_size]]
def shuffle(self, x_data, y_data):
"""
mini-batch
"""
size = len(x_data)
batch = self.batch_size
if batch > size:
batch = size
batch_per_epoch = int(size/batch)
total = self.epoch * batch_per_epoch
yield(total)
for i in range(self.epoch):
shuffle_idx = perm(np.arange(size))
# each batch of i_th epoch
for b in range(batch_per_epoch):
# two yieldees
x_batch = list()
y_batch = list()
for real_idx in shuffle_idx[(b*batch) : ((b+1)*batch)]:
x_inp = x_data[real_idx]
y_inp = y_data[real_idx]
x_batch += [np.expand_dims(x_inp, 0)]
y_batch += [np.expand_dims(y_inp, 0)]
if b+1 == batch_per_epoch:
for real_idx in shuffle_idx[(b+1)*batch:]:
x_inp = x_data[real_idx]
y_inp = y_data[real_idx]
x_batch += [np.expand_dims(x_inp, 0)]
y_batch += [np.expand_dims(y_inp, 0)]
x_batch = np.concatenate(x_batch, 0)
y_batch = np.concatenate(y_batch, 0)
yield(x_batch, y_batch, i, (b+1)==batch_per_epoch, b)
| mit |
toobaz/pandas | pandas/core/indexes/api.py | 2 | 8212 | import textwrap
import warnings
from pandas._libs import NaT, lib
import pandas.core.common as com
from pandas.core.indexes.base import (
Index,
_new_Index,
ensure_index,
ensure_index_from_sequences,
)
from pandas.core.indexes.base import InvalidIndexError # noqa:F401
from pandas.core.indexes.category import CategoricalIndex # noqa:F401
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.interval import IntervalIndex # noqa:F401
from pandas.core.indexes.multi import MultiIndex # noqa:F401
from pandas.core.indexes.numeric import ( # noqa:F401
Float64Index,
Int64Index,
NumericIndex,
UInt64Index,
)
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.range import RangeIndex # noqa:F401
from pandas.core.indexes.timedeltas import TimedeltaIndex
_sort_msg = textwrap.dedent(
"""\
Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.
To accept the future behavior, pass 'sort=False'.
To retain the current behavior and silence the warning, pass 'sort=True'.
"""
)
# TODO: there are many places that rely on these private methods existing in
# pandas.core.index
__all__ = [
"Index",
"MultiIndex",
"NumericIndex",
"Float64Index",
"Int64Index",
"CategoricalIndex",
"IntervalIndex",
"RangeIndex",
"UInt64Index",
"InvalidIndexError",
"TimedeltaIndex",
"PeriodIndex",
"DatetimeIndex",
"_new_Index",
"NaT",
"ensure_index",
"ensure_index_from_sequences",
"_get_combined_index",
"_get_objs_combined_axis",
"_union_indexes",
"_get_consensus_names",
"_all_indexes_same",
]
def _get_objs_combined_axis(objs, intersect=False, axis=0, sort=True):
"""
Extract combined index: return intersection or union (depending on the
value of "intersect") of indexes on given axis, or None if all objects
lack indexes (e.g. they are numpy arrays).
Parameters
----------
objs : list of objects
Each object will only be considered if it has a _get_axis
attribute.
intersect : bool, default False
If True, calculate the intersection between indexes. Otherwise,
calculate the union.
axis : {0 or 'index', 1 or 'outer'}, default 0
The axis to extract indexes from.
sort : bool, default True
Whether the result index should come out sorted or not.
Returns
-------
Index
"""
obs_idxes = [obj._get_axis(axis) for obj in objs if hasattr(obj, "_get_axis")]
if obs_idxes:
return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)
def _get_distinct_objs(objs):
"""
Return a list with distinct elements of "objs" (different ids).
Preserves order.
"""
ids = set()
res = []
for obj in objs:
if not id(obj) in ids:
ids.add(id(obj))
res.append(obj)
return res
def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
If True, calculate the intersection between indexes. Otherwise,
calculate the union.
sort : bool, default False
Whether the result index should come out sorted or not.
Returns
-------
Index
"""
# TODO: handle index names!
indexes = _get_distinct_objs(indexes)
if len(indexes) == 0:
index = Index([])
elif len(indexes) == 1:
index = indexes[0]
elif intersect:
index = indexes[0]
for other in indexes[1:]:
index = index.intersection(other)
else:
index = _union_indexes(indexes, sort=sort)
index = ensure_index(index)
if sort:
try:
index = index.sort_values()
except TypeError:
pass
return index
def _union_indexes(indexes, sort=True):
"""
Return the union of indexes.
The behavior of sort and names is not consistent.
Parameters
----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
Returns
-------
Index
"""
if len(indexes) == 0:
raise AssertionError("Must have at least 1 Index to union")
if len(indexes) == 1:
result = indexes[0]
if isinstance(result, list):
result = Index(sorted(result))
return result
indexes, kind = _sanitize_and_check(indexes)
def _unique_indices(inds):
"""
Convert indexes to lists and concatenate them, removing duplicates.
The final dtype is inferred.
Parameters
----------
inds : list of Index or list objects
Returns
-------
Index
"""
def conv(i):
if isinstance(i, Index):
i = i.tolist()
return i
return Index(lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort))
if kind == "special":
result = indexes[0]
if hasattr(result, "union_many"):
return result.union_many(indexes[1:])
else:
for other in indexes[1:]:
result = result.union(other)
return result
elif kind == "array":
index = indexes[0]
for other in indexes[1:]:
if not index.equals(other):
if sort is None:
# TODO: remove once pd.concat sort default changes
warnings.warn(_sort_msg, FutureWarning, stacklevel=8)
sort = True
return _unique_indices(indexes)
name = _get_consensus_names(indexes)[0]
if name != index.name:
index = index._shallow_copy(name=name)
return index
else: # kind='list'
return _unique_indices(indexes)
def _sanitize_and_check(indexes):
"""
Verify the type of indexes and convert lists to Index.
Cases:
- [list, list, ...]: Return ([list, list, ...], 'list')
- [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
Lists are sorted and converted to Index.
- [Index, Index, ...]: Return ([Index, Index, ...], TYPE)
TYPE = 'special' if at least one special type, 'array' otherwise.
Parameters
----------
indexes : list of Index or list objects
Returns
-------
sanitized_indexes : list of Index or list objects
type : {'list', 'array', 'special'}
"""
kinds = list({type(index) for index in indexes})
if list in kinds:
if len(kinds) > 1:
indexes = [
Index(com.try_sort(x)) if not isinstance(x, Index) else x
for x in indexes
]
kinds.remove(list)
else:
return indexes, "list"
if len(kinds) > 1 or Index not in kinds:
return indexes, "special"
else:
return indexes, "array"
def _get_consensus_names(indexes):
"""
Give a consensus 'names' to indexes.
If there's exactly one non-empty 'names', return this,
otherwise, return empty.
Parameters
----------
indexes : list of Index objects
Returns
-------
list
A list representing the consensus 'names' found.
"""
# find the non-none names, need to tupleify to make
# the set hashable, then reverse on return
consensus_names = {tuple(i.names) for i in indexes if com._any_not_none(*i.names)}
if len(consensus_names) == 1:
return list(list(consensus_names)[0])
return [None] * indexes[0].nlevels
def _all_indexes_same(indexes):
"""
Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise.
"""
first = indexes[0]
for index in indexes[1:]:
if not first.equals(index):
return False
return True
| bsd-3-clause |
trondeau/gnuradio-old | gr-filter/examples/synth_filter.py | 58 | 2552 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from gnuradio import gr
from gnuradio import filter
from gnuradio import blocks
import sys
try:
from gnuradio import analog
except ImportError:
sys.stderr.write("Error: Program requires gr-analog.\n")
sys.exit(1)
try:
import scipy
except ImportError:
sys.stderr.write("Error: Program requires scipy (see: www.scipy.org).\n")
sys.exit(1)
try:
import pylab
except ImportError:
sys.stderr.write("Error: Program requires matplotlib (see: matplotlib.sourceforge.net).\n")
sys.exit(1)
def main():
N = 1000000
fs = 8000
freqs = [100, 200, 300, 400, 500]
nchans = 7
sigs = list()
for fi in freqs:
s = analog.sig_source_c(fs, analog.GR_SIN_WAVE, fi, 1)
sigs.append(s)
taps = filter.firdes.low_pass_2(len(freqs), fs,
fs/float(nchans)/2, 100, 100)
print "Num. Taps = %d (taps per filter = %d)" % (len(taps),
len(taps)/nchans)
filtbank = filter.pfb_synthesizer_ccf(nchans, taps)
head = blocks.head(gr.sizeof_gr_complex, N)
snk = blocks.vector_sink_c()
tb = gr.top_block()
tb.connect(filtbank, head, snk)
for i,si in enumerate(sigs):
tb.connect(si, (filtbank, i))
tb.run()
if 1:
f1 = pylab.figure(1)
s1 = f1.add_subplot(1,1,1)
s1.plot(snk.data()[1000:])
fftlen = 2048
f2 = pylab.figure(2)
s2 = f2.add_subplot(1,1,1)
winfunc = scipy.blackman
s2.psd(snk.data()[10000:], NFFT=fftlen,
Fs = nchans*fs,
noverlap=fftlen/4,
window = lambda d: d*winfunc(fftlen))
pylab.show()
if __name__ == "__main__":
main()
| gpl-3.0 |
automl/paramsklearn | ParamSklearn/components/classification/adaboost.py | 1 | 3787 | import numpy as np
import sklearn.ensemble
import sklearn.tree
import sklearn.multiclass
from ParamSklearn.implementations.MultilabelClassifier import MultilabelClassifier
from HPOlibConfigSpace.configuration_space import ConfigurationSpace
from HPOlibConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter
from ParamSklearn.components.base import ParamSklearnClassificationAlgorithm
from ParamSklearn.constants import *
class AdaboostClassifier(ParamSklearnClassificationAlgorithm):
def __init__(self, n_estimators, learning_rate, algorithm, max_depth,
random_state=None):
self.n_estimators = int(n_estimators)
self.learning_rate = float(learning_rate)
self.algorithm = algorithm
self.random_state = random_state
self.max_depth = max_depth
self.estimator = None
def fit(self, X, Y, sample_weight=None):
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(max_depth=self.max_depth)
estimator = sklearn.ensemble.AdaBoostClassifier(
base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state
)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = MultilabelClassifier(estimator, n_jobs=1)
self.estimator.fit(X, Y, sample_weight=sample_weight)
else:
self.estimator.fit(X, Y, sample_weight=sample_weight)
return self
def predict(self, X):
if self.estimator is None:
raise NotImplementedError
return self.estimator.predict(X)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict_proba(X)
@staticmethod
def get_properties(dataset_properties=None):
return {'shortname': 'AB',
'name': 'AdaBoost Classifier',
'handles_missing_values': False,
'handles_nominal_values': False,
'handles_numerical_features': True,
'prefers_data_scaled': False,
'prefers_data_normalized': False,
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': True,
'is_deterministic': True,
'handles_sparse': False,
'input': (DENSE, SPARSE, UNSIGNED_DATA),
'output': (PREDICTIONS,),
# TODO find out what is best used here!
# But rather fortran or C-contiguous?
'preferred_dtype': np.float32}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
# base_estimator = Constant(name="base_estimator", value="None")
n_estimators = cs.add_hyperparameter(UniformIntegerHyperparameter(
name="n_estimators", lower=50, upper=500, default=50, log=False))
learning_rate = cs.add_hyperparameter(UniformFloatHyperparameter(
name="learning_rate", lower=0.0001, upper=2, default=0.1, log=True))
algorithm = cs.add_hyperparameter(CategoricalHyperparameter(
name="algorithm", choices=["SAMME.R", "SAMME"], default="SAMME.R"))
max_depth = cs.add_hyperparameter(UniformIntegerHyperparameter(
name="max_depth", lower=1, upper=10, default=1, log=False))
return cs
| bsd-3-clause |
sgraham/nope | chrome/test/nacl_test_injection/buildbot_chrome_nacl_stage.py | 12 | 11594 | #!/usr/bin/python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import subprocess
import sys
import find_chrome
THIS_DIR = os.path.abspath(os.path.dirname(__file__))
CHROMIUM_DIR = os.path.abspath(os.path.join(THIS_DIR, '..', '..', '..'))
NACL_DIR = os.path.join(CHROMIUM_DIR, 'native_client')
sys.path.append(os.path.join(CHROMIUM_DIR, 'build'))
sys.path.append(NACL_DIR)
import detect_host_arch
import pynacl.platform
# Copied from buildbot/buildbot_lib.py
def TryToCleanContents(path, file_name_filter=lambda fn: True):
"""
Remove the contents of a directory without touching the directory itself.
Ignores all failures.
"""
if os.path.exists(path):
for fn in os.listdir(path):
TryToCleanPath(os.path.join(path, fn), file_name_filter)
# Copied from buildbot/buildbot_lib.py
def TryToCleanPath(path, file_name_filter=lambda fn: True):
"""
Removes a file or directory.
Ignores all failures.
"""
if os.path.exists(path):
if file_name_filter(path):
print 'Trying to remove %s' % path
if os.path.isdir(path):
shutil.rmtree(path, ignore_errors=True)
else:
try:
os.remove(path)
except Exception:
pass
else:
print 'Skipping %s' % path
# TODO(ncbray): this is somewhat unsafe. We should fix the underlying problem.
def CleanTempDir():
# Only delete files and directories like:
# a) C:\temp\83C4.tmp
# b) /tmp/.org.chromium.Chromium.EQrEzl
file_name_re = re.compile(
r'[\\/]([0-9a-fA-F]+\.tmp|\.org\.chrom\w+\.Chrom\w+\..+)$')
file_name_filter = lambda fn: file_name_re.search(fn) is not None
path = os.environ.get('TMP', os.environ.get('TEMP', '/tmp'))
if len(path) >= 4 and os.path.isdir(path):
print
print "Cleaning out the temp directory."
print
TryToCleanContents(path, file_name_filter)
else:
print
print "Cannot find temp directory, not cleaning it."
print
def RunCommand(cmd, cwd, env):
sys.stdout.write('\nRunning %s\n\n' % ' '.join(cmd))
sys.stdout.flush()
retcode = subprocess.call(cmd, cwd=cwd, env=env)
if retcode != 0:
sys.stdout.write('\nFailed: %s\n\n' % ' '.join(cmd))
sys.exit(retcode)
def RunTests(name, cmd, env):
sys.stdout.write('\n\nBuilding files needed for %s testing...\n\n' % name)
RunCommand(cmd + ['do_not_run_tests=1', '-j8'], NACL_DIR, env)
sys.stdout.write('\n\nRunning %s tests...\n\n' % name)
RunCommand(cmd, NACL_DIR, env)
def BuildAndTest(options):
# Refuse to run under cygwin.
if sys.platform == 'cygwin':
raise Exception('I do not work under cygwin, sorry.')
# By default, use the version of Python is being used to run this script.
python = sys.executable
if sys.platform == 'darwin':
# Mac 10.5 bots tend to use a particularlly old version of Python, look for
# a newer version.
macpython27 = '/Library/Frameworks/Python.framework/Versions/2.7/bin/python'
if os.path.exists(macpython27):
python = macpython27
os_name = pynacl.platform.GetOS()
arch_name = pynacl.platform.GetArch()
toolchain_dir = os.path.join(NACL_DIR, 'toolchain',
'%s_%s' % (os_name, arch_name))
nacl_newlib_dir = os.path.join(toolchain_dir, 'nacl_%s_newlib' % arch_name)
nacl_glibc_dir = os.path.join(toolchain_dir, 'nacl_%s_glibc' % arch_name)
# Decide platform specifics.
if options.browser_path:
chrome_filename = options.browser_path
else:
chrome_filename = find_chrome.FindChrome(CHROMIUM_DIR, [options.mode])
if chrome_filename is None:
raise Exception('Cannot find a chrome binary - specify one with '
'--browser_path?')
env = dict(os.environ)
if sys.platform in ['win32', 'cygwin']:
if options.bits == 64:
bits = 64
elif options.bits == 32:
bits = 32
elif '64' in os.environ.get('PROCESSOR_ARCHITECTURE', '') or \
'64' in os.environ.get('PROCESSOR_ARCHITEW6432', ''):
bits = 64
else:
bits = 32
msvs_path = ';'.join([
r'c:\Program Files\Microsoft Visual Studio 9.0\VC',
r'c:\Program Files (x86)\Microsoft Visual Studio 9.0\VC',
r'c:\Program Files\Microsoft Visual Studio 9.0\Common7\Tools',
r'c:\Program Files (x86)\Microsoft Visual Studio 9.0\Common7\Tools',
r'c:\Program Files\Microsoft Visual Studio 8\VC',
r'c:\Program Files (x86)\Microsoft Visual Studio 8\VC',
r'c:\Program Files\Microsoft Visual Studio 8\Common7\Tools',
r'c:\Program Files (x86)\Microsoft Visual Studio 8\Common7\Tools',
])
env['PATH'] += ';' + msvs_path
scons = [python, 'scons.py']
elif sys.platform == 'darwin':
if options.bits == 64:
bits = 64
elif options.bits == 32:
bits = 32
else:
p = subprocess.Popen(['file', chrome_filename], stdout=subprocess.PIPE)
(p_stdout, _) = p.communicate()
assert p.returncode == 0
if p_stdout.find('executable x86_64') >= 0:
bits = 64
else:
bits = 32
scons = [python, 'scons.py']
else:
if options.bits == 64:
bits = 64
elif options.bits == 32:
bits = 32
elif '64' in detect_host_arch.HostArch():
bits = 64
else:
bits = 32
# xvfb-run has a 2-second overhead per invocation, so it is cheaper to wrap
# the entire build step rather than each test (browser_headless=1).
# We also need to make sure that there are at least 24 bits per pixel.
# https://code.google.com/p/chromium/issues/detail?id=316687
scons = [
'xvfb-run',
'--auto-servernum',
'--server-args', '-screen 0 1024x768x24',
python, 'scons.py',
]
if options.jobs > 1:
scons.append('-j%d' % options.jobs)
scons.append('disable_tests=%s' % options.disable_tests)
if options.buildbot is not None:
scons.append('buildbot=%s' % (options.buildbot,))
# Clean the output of the previous build.
# Incremental builds can get wedged in weird ways, so we're trading speed
# for reliability.
shutil.rmtree(os.path.join(NACL_DIR, 'scons-out'), True)
# check that the HOST (not target) is 64bit
# this is emulating what msvs_env.bat is doing
if '64' in os.environ.get('PROCESSOR_ARCHITECTURE', '') or \
'64' in os.environ.get('PROCESSOR_ARCHITEW6432', ''):
# 64bit HOST
env['VS90COMNTOOLS'] = ('c:\\Program Files (x86)\\'
'Microsoft Visual Studio 9.0\\Common7\\Tools\\')
env['VS80COMNTOOLS'] = ('c:\\Program Files (x86)\\'
'Microsoft Visual Studio 8.0\\Common7\\Tools\\')
else:
# 32bit HOST
env['VS90COMNTOOLS'] = ('c:\\Program Files\\Microsoft Visual Studio 9.0\\'
'Common7\\Tools\\')
env['VS80COMNTOOLS'] = ('c:\\Program Files\\Microsoft Visual Studio 8.0\\'
'Common7\\Tools\\')
# Run nacl/chrome integration tests.
# Note that we have to add nacl_irt_test to --mode in order to get
# inbrowser_test_runner to run.
# TODO(mseaborn): Change it so that inbrowser_test_runner is not a
# special case.
cmd = scons + ['--verbose', '-k', 'platform=x86-%d' % bits,
'--mode=opt-host,nacl,nacl_irt_test',
'chrome_browser_path=%s' % chrome_filename,
'nacl_newlib_dir=%s' % nacl_newlib_dir,
'nacl_glibc_dir=%s' % nacl_glibc_dir,
]
if not options.integration_bot and not options.morenacl_bot:
cmd.append('disable_flaky_tests=1')
cmd.append('chrome_browser_tests')
# Propagate path to JSON output if present.
# Note that RunCommand calls sys.exit on errors, so potential errors
# from one command won't be overwritten by another one. Overwriting
# a successful results file with either success or failure is fine.
if options.json_build_results_output_file:
cmd.append('json_build_results_output_file=%s' %
options.json_build_results_output_file)
# Download the toolchain(s).
pkg_ver_dir = os.path.join(NACL_DIR, 'build', 'package_version')
RunCommand([python, os.path.join(pkg_ver_dir, 'package_version.py'),
'--mode', 'nacl_core_sdk',
'--exclude', 'pnacl_newlib',
'--exclude', 'nacl_arm_newlib',
'sync', '--extract'],
NACL_DIR, os.environ)
CleanTempDir()
if options.enable_newlib:
RunTests('nacl-newlib', cmd, env)
if options.enable_glibc:
RunTests('nacl-glibc', cmd + ['--nacl_glibc'], env)
def MakeCommandLineParser():
parser = optparse.OptionParser()
parser.add_option('-m', '--mode', dest='mode', default='Debug',
help='Debug/Release mode')
parser.add_option('-j', dest='jobs', default=1, type='int',
help='Number of parallel jobs')
parser.add_option('--enable_newlib', dest='enable_newlib', default=-1,
type='int', help='Run newlib tests?')
parser.add_option('--enable_glibc', dest='enable_glibc', default=-1,
type='int', help='Run glibc tests?')
parser.add_option('--json_build_results_output_file',
help='Path to a JSON file for machine-readable output.')
# Deprecated, but passed to us by a script in the Chrome repo.
# Replaced by --enable_glibc=0
parser.add_option('--disable_glibc', dest='disable_glibc',
action='store_true', default=False,
help='Do not test using glibc.')
parser.add_option('--disable_tests', dest='disable_tests',
type='string', default='',
help='Comma-separated list of tests to omit')
builder_name = os.environ.get('BUILDBOT_BUILDERNAME', '')
is_integration_bot = 'nacl-chrome' in builder_name
parser.add_option('--integration_bot', dest='integration_bot',
type='int', default=int(is_integration_bot),
help='Is this an integration bot?')
is_morenacl_bot = (
'More NaCl' in builder_name or
'naclmore' in builder_name)
parser.add_option('--morenacl_bot', dest='morenacl_bot',
type='int', default=int(is_morenacl_bot),
help='Is this a morenacl bot?')
# Not used on the bots, but handy for running the script manually.
parser.add_option('--bits', dest='bits', action='store',
type='int', default=None,
help='32/64')
parser.add_option('--browser_path', dest='browser_path', action='store',
type='string', default=None,
help='Path to the chrome browser.')
parser.add_option('--buildbot', dest='buildbot', action='store',
type='string', default=None,
help='Value passed to scons as buildbot= option.')
return parser
def Main():
parser = MakeCommandLineParser()
options, args = parser.parse_args()
if options.integration_bot and options.morenacl_bot:
parser.error('ERROR: cannot be both an integration bot and a morenacl bot')
# Set defaults for enabling newlib.
if options.enable_newlib == -1:
options.enable_newlib = 1
# Set defaults for enabling glibc.
if options.enable_glibc == -1:
if options.integration_bot or options.morenacl_bot:
options.enable_glibc = 1
else:
options.enable_glibc = 0
if args:
parser.error('ERROR: invalid argument')
BuildAndTest(options)
if __name__ == '__main__':
Main()
| bsd-3-clause |
xubenben/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
and petal width.
Principal Component Analysis (PCA) applied to this data identifies the
combination of attributes (principal components, or directions in the
feature space) that account for the most variance in the data. Here we
plot the different samples on the 2 first principal components.
Linear Discriminant Analysis (LDA) tries to identify attributes that
account for the most variance *between classes*. In particular,
LDA, in contrast to PCA, is a supervised method, using known class labels.
"""
print(__doc__)
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.lda import LDA
iris = datasets.load_iris()
X = iris.data
y = iris.target
target_names = iris.target_names
pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)
lda = LDA(n_components=2)
X_r2 = lda.fit(X, y).transform(X)
# Percentage of variance explained for each components
print('explained variance ratio (first two components): %s'
% str(pca.explained_variance_ratio_))
plt.figure()
for c, i, target_name in zip("rgb", [0, 1, 2], target_names):
plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('PCA of IRIS dataset')
plt.figure()
for c, i, target_name in zip("rgb", [0, 1, 2], target_names):
plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], c=c, label=target_name)
plt.legend()
plt.title('LDA of IRIS dataset')
plt.show()
| bsd-3-clause |
chanceraine/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_qt.py | 69 | 16846 | from __future__ import division
import math
import os
import sys
import matplotlib
from matplotlib import verbose
from matplotlib.cbook import is_string_like, onetrue
from matplotlib.backend_bases import RendererBase, GraphicsContextBase, \
FigureManagerBase, FigureCanvasBase, NavigationToolbar2, cursors
from matplotlib._pylab_helpers import Gcf
from matplotlib.figure import Figure
from matplotlib.mathtext import MathTextParser
from matplotlib.widgets import SubplotTool
try:
import qt
except ImportError:
raise ImportError("Qt backend requires pyqt to be installed.")
backend_version = "0.9.1"
def fn_name(): return sys._getframe(1).f_code.co_name
DEBUG = False
cursord = {
cursors.MOVE : qt.Qt.PointingHandCursor,
cursors.HAND : qt.Qt.WaitCursor,
cursors.POINTER : qt.Qt.ArrowCursor,
cursors.SELECT_REGION : qt.Qt.CrossCursor,
}
def draw_if_interactive():
"""
Is called after every pylab drawing command
"""
if matplotlib.is_interactive():
figManager = Gcf.get_active()
if figManager != None:
figManager.canvas.draw()
def _create_qApp():
"""
Only one qApp can exist at a time, so check before creating one
"""
if qt.QApplication.startingUp():
if DEBUG: print "Starting up QApplication"
global qApp
qApp = qt.QApplication( [" "] )
qt.QObject.connect( qApp, qt.SIGNAL( "lastWindowClosed()" ),
qApp, qt.SLOT( "quit()" ) )
#remember that matplotlib created the qApp - will be used by show()
_create_qApp.qAppCreatedHere = True
_create_qApp.qAppCreatedHere = False
def show():
"""
Show all the figures and enter the qt main loop
This should be the last line of your script
"""
for manager in Gcf.get_all_fig_managers():
manager.window.show()
if DEBUG: print 'Inside show'
figManager = Gcf.get_active()
if figManager != None:
figManager.canvas.draw()
if _create_qApp.qAppCreatedHere:
qt.qApp.exec_loop()
def new_figure_manager( num, *args, **kwargs ):
"""
Create a new figure manager instance
"""
FigureClass = kwargs.pop('FigureClass', Figure)
thisFig = FigureClass( *args, **kwargs )
canvas = FigureCanvasQT( thisFig )
manager = FigureManagerQT( canvas, num )
return manager
class FigureCanvasQT( qt.QWidget, FigureCanvasBase ):
keyvald = { qt.Qt.Key_Control : 'control',
qt.Qt.Key_Shift : 'shift',
qt.Qt.Key_Alt : 'alt',
}
# left 1, middle 2, right 3
buttond = {1:1, 2:3, 4:2}
def __init__( self, figure ):
if DEBUG: print 'FigureCanvasQt: ', figure
_create_qApp()
qt.QWidget.__init__( self, None, "QWidget figure" )
FigureCanvasBase.__init__( self, figure )
self.figure = figure
self.setMouseTracking( True )
w,h = self.get_width_height()
self.resize( w, h )
def enterEvent(self, event):
FigureCanvasBase.enter_notify_event(self, event)
def leaveEvent(self, event):
FigureCanvasBase.leave_notify_event(self, event)
def mousePressEvent( self, event ):
x = event.pos().x()
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.pos().y()
button = self.buttond[event.button()]
FigureCanvasBase.button_press_event( self, x, y, button )
if DEBUG: print 'button pressed:', event.button()
def mouseMoveEvent( self, event ):
x = event.x()
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.y()
FigureCanvasBase.motion_notify_event( self, x, y )
if DEBUG: print 'mouse move'
def mouseReleaseEvent( self, event ):
x = event.x()
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.y()
button = self.buttond[event.button()]
FigureCanvasBase.button_release_event( self, x, y, button )
if DEBUG: print 'button released'
def keyPressEvent( self, event ):
key = self._get_key( event )
FigureCanvasBase.key_press_event( self, key )
if DEBUG: print 'key press', key
def keyReleaseEvent( self, event ):
key = self._get_key(event)
FigureCanvasBase.key_release_event( self, key )
if DEBUG: print 'key release', key
def resizeEvent( self, event ):
if DEBUG: print 'resize (%d x %d)' % (event.size().width(), event.size().height())
qt.QWidget.resizeEvent( self, event )
w = event.size().width()
h = event.size().height()
if DEBUG: print "FigureCanvasQt.resizeEvent(", w, ",", h, ")"
dpival = self.figure.dpi
winch = w/dpival
hinch = h/dpival
self.figure.set_size_inches( winch, hinch )
self.draw()
def resize( self, w, h ):
# Pass through to Qt to resize the widget.
qt.QWidget.resize( self, w, h )
# Resize the figure by converting pixels to inches.
pixelPerInch = self.figure.dpi
wInch = w / pixelPerInch
hInch = h / pixelPerInch
self.figure.set_size_inches( wInch, hInch )
# Redraw everything.
self.draw()
def sizeHint( self ):
w, h = self.get_width_height()
return qt.QSize( w, h )
def minumumSizeHint( self ):
return qt.QSize( 10, 10 )
def _get_key( self, event ):
if event.key() < 256:
key = event.text().latin1()
elif event.key() in self.keyvald.has_key:
key = self.keyvald[ event.key() ]
else:
key = None
return key
def flush_events(self):
qt.qApp.processEvents()
def start_event_loop(self,timeout):
FigureCanvasBase.start_event_loop_default(self,timeout)
start_event_loop.__doc__=FigureCanvasBase.start_event_loop_default.__doc__
def stop_event_loop(self):
FigureCanvasBase.stop_event_loop_default(self)
stop_event_loop.__doc__=FigureCanvasBase.stop_event_loop_default.__doc__
class FigureManagerQT( FigureManagerBase ):
"""
Public attributes
canvas : The FigureCanvas instance
num : The Figure number
toolbar : The qt.QToolBar
window : The qt.QMainWindow
"""
def __init__( self, canvas, num ):
if DEBUG: print 'FigureManagerQT.%s' % fn_name()
FigureManagerBase.__init__( self, canvas, num )
self.canvas = canvas
self.window = qt.QMainWindow( None, None, qt.Qt.WDestructiveClose )
self.window.closeEvent = self._widgetCloseEvent
centralWidget = qt.QWidget( self.window )
self.canvas.reparent( centralWidget, qt.QPoint( 0, 0 ) )
# Give the keyboard focus to the figure instead of the manager
self.canvas.setFocusPolicy( qt.QWidget.ClickFocus )
self.canvas.setFocus()
self.window.setCaption( "Figure %d" % num )
self.window._destroying = False
self.toolbar = self._get_toolbar(self.canvas, centralWidget)
# Use a vertical layout for the plot and the toolbar. Set the
# stretch to all be in the plot so the toolbar doesn't resize.
self.layout = qt.QVBoxLayout( centralWidget )
self.layout.addWidget( self.canvas, 1 )
if self.toolbar:
self.layout.addWidget( self.toolbar, 0 )
self.window.setCentralWidget( centralWidget )
# Reset the window height so the canvas will be the right
# size. This ALMOST works right. The first issue is that the
# height w/ a toolbar seems to be off by just a little bit (so
# we add 4 pixels). The second is that the total width/height
# is slightly smaller that we actually want. It seems like
# the border of the window is being included in the size but
# AFAIK there is no way to get that size.
w = self.canvas.width()
h = self.canvas.height()
if self.toolbar:
h += self.toolbar.height() + 4
self.window.resize( w, h )
if matplotlib.is_interactive():
self.window.show()
# attach a show method to the figure for pylab ease of use
self.canvas.figure.show = lambda *args: self.window.show()
def notify_axes_change( fig ):
# This will be called whenever the current axes is changed
if self.toolbar != None: self.toolbar.update()
self.canvas.figure.add_axobserver( notify_axes_change )
def _widgetclosed( self ):
if self.window._destroying: return
self.window._destroying = True
Gcf.destroy(self.num)
def _widgetCloseEvent( self, event ):
self._widgetclosed()
qt.QWidget.closeEvent( self.window, event )
def _get_toolbar(self, canvas, parent):
# must be inited after the window, drawingArea and figure
# attrs are set
if matplotlib.rcParams['toolbar'] == 'classic':
print "Classic toolbar is not yet supported"
elif matplotlib.rcParams['toolbar'] == 'toolbar2':
toolbar = NavigationToolbar2QT(canvas, parent)
else:
toolbar = None
return toolbar
def resize(self, width, height):
'set the canvas size in pixels'
self.window.resize(width, height)
def destroy( self, *args ):
if self.window._destroying: return
self.window._destroying = True
if self.toolbar: self.toolbar.destroy()
if DEBUG: print "destroy figure manager"
self.window.close(True)
def set_window_title(self, title):
self.window.setCaption(title)
class NavigationToolbar2QT( NavigationToolbar2, qt.QWidget ):
# list of toolitems to add to the toolbar, format is:
# text, tooltip_text, image_file, callback(str)
toolitems = (
('Home', 'Reset original view', 'home.ppm', 'home'),
('Back', 'Back to previous view','back.ppm', 'back'),
('Forward', 'Forward to next view','forward.ppm', 'forward'),
(None, None, None, None),
('Pan', 'Pan axes with left mouse, zoom with right', 'move.ppm', 'pan'),
('Zoom', 'Zoom to rectangle','zoom_to_rect.ppm', 'zoom'),
(None, None, None, None),
('Subplots', 'Configure subplots','subplots.png', 'configure_subplots'),
('Save', 'Save the figure','filesave.ppm', 'save_figure'),
)
def __init__( self, canvas, parent ):
self.canvas = canvas
self.buttons = {}
qt.QWidget.__init__( self, parent )
# Layout toolbar buttons horizontally.
self.layout = qt.QHBoxLayout( self )
self.layout.setMargin( 2 )
NavigationToolbar2.__init__( self, canvas )
def _init_toolbar( self ):
basedir = os.path.join(matplotlib.rcParams[ 'datapath' ],'images')
for text, tooltip_text, image_file, callback in self.toolitems:
if text == None:
self.layout.addSpacing( 8 )
continue
fname = os.path.join( basedir, image_file )
image = qt.QPixmap()
image.load( fname )
button = qt.QPushButton( qt.QIconSet( image ), "", self )
qt.QToolTip.add( button, tooltip_text )
self.buttons[ text ] = button
# The automatic layout doesn't look that good - it's too close
# to the images so add a margin around it.
margin = 4
button.setFixedSize( image.width()+margin, image.height()+margin )
qt.QObject.connect( button, qt.SIGNAL( 'clicked()' ),
getattr( self, callback ) )
self.layout.addWidget( button )
self.buttons[ 'Pan' ].setToggleButton( True )
self.buttons[ 'Zoom' ].setToggleButton( True )
# Add the x,y location widget at the right side of the toolbar
# The stretch factor is 1 which means any resizing of the toolbar
# will resize this label instead of the buttons.
self.locLabel = qt.QLabel( "", self )
self.locLabel.setAlignment( qt.Qt.AlignRight | qt.Qt.AlignVCenter )
self.locLabel.setSizePolicy(qt.QSizePolicy(qt.QSizePolicy.Ignored,
qt.QSizePolicy.Ignored))
self.layout.addWidget( self.locLabel, 1 )
# reference holder for subplots_adjust window
self.adj_window = None
def destroy( self ):
for text, tooltip_text, image_file, callback in self.toolitems:
if text is not None:
qt.QObject.disconnect( self.buttons[ text ],
qt.SIGNAL( 'clicked()' ),
getattr( self, callback ) )
def pan( self, *args ):
self.buttons[ 'Zoom' ].setOn( False )
NavigationToolbar2.pan( self, *args )
def zoom( self, *args ):
self.buttons[ 'Pan' ].setOn( False )
NavigationToolbar2.zoom( self, *args )
def dynamic_update( self ):
self.canvas.draw()
def set_message( self, s ):
self.locLabel.setText( s )
def set_cursor( self, cursor ):
if DEBUG: print 'Set cursor' , cursor
qt.QApplication.restoreOverrideCursor()
qt.QApplication.setOverrideCursor( qt.QCursor( cursord[cursor] ) )
def draw_rubberband( self, event, x0, y0, x1, y1 ):
height = self.canvas.figure.bbox.height
y1 = height - y1
y0 = height - y0
w = abs(x1 - x0)
h = abs(y1 - y0)
rect = [ int(val)for val in min(x0,x1), min(y0, y1), w, h ]
self.canvas.drawRectangle( rect )
def configure_subplots(self):
self.adj_window = qt.QMainWindow(None, None, qt.Qt.WDestructiveClose)
win = self.adj_window
win.setCaption("Subplot Configuration Tool")
toolfig = Figure(figsize=(6,3))
toolfig.subplots_adjust(top=0.9)
w = int (toolfig.bbox.width)
h = int (toolfig.bbox.height)
canvas = self._get_canvas(toolfig)
tool = SubplotTool(self.canvas.figure, toolfig)
centralWidget = qt.QWidget(win)
canvas.reparent(centralWidget, qt.QPoint(0, 0))
win.setCentralWidget(centralWidget)
layout = qt.QVBoxLayout(centralWidget)
layout.addWidget(canvas, 1)
win.resize(w, h)
canvas.setFocus()
win.show()
def _get_canvas(self, fig):
return FigureCanvasQT(fig)
def save_figure( self ):
filetypes = self.canvas.get_supported_filetypes_grouped()
sorted_filetypes = filetypes.items()
sorted_filetypes.sort()
default_filetype = self.canvas.get_default_filetype()
start = "image." + default_filetype
filters = []
selectedFilter = None
for name, exts in sorted_filetypes:
exts_list = " ".join(['*.%s' % ext for ext in exts])
filter = '%s (%s)' % (name, exts_list)
if default_filetype in exts:
selectedFilter = filter
filters.append(filter)
filters = ';;'.join(filters)
fname = qt.QFileDialog.getSaveFileName(
start, filters, self, "Save image", "Choose a filename to save to",
selectedFilter)
if fname:
try:
self.canvas.print_figure( unicode(fname) )
except Exception, e:
qt.QMessageBox.critical(
self, "Error saving file", str(e),
qt.QMessageBox.Ok, qt.QMessageBox.NoButton)
def set_history_buttons( self ):
canBackward = ( self._views._pos > 0 )
canForward = ( self._views._pos < len( self._views._elements ) - 1 )
self.buttons[ 'Back' ].setEnabled( canBackward )
self.buttons[ 'Forward' ].setEnabled( canForward )
# set icon used when windows are minimized
try:
# TODO: This is badly broken
qt.window_set_default_icon_from_file (
os.path.join( matplotlib.rcParams['datapath'], 'images', 'matplotlib.svg' ) )
except:
verbose.report( 'Could not load matplotlib icon: %s' % sys.exc_info()[1] )
def error_msg_qt( msg, parent=None ):
if not is_string_like( msg ):
msg = ','.join( map( str,msg ) )
qt.QMessageBox.warning( None, "Matplotlib", msg, qt.QMessageBox.Ok )
def exception_handler( type, value, tb ):
"""Handle uncaught exceptions
It does not catch SystemExit
"""
msg = ''
# get the filename attribute if available (for IOError)
if hasattr(value, 'filename') and value.filename != None:
msg = value.filename + ': '
if hasattr(value, 'strerror') and value.strerror != None:
msg += value.strerror
else:
msg += str(value)
if len( msg ) : error_msg_qt( msg )
FigureManager = FigureManagerQT
| agpl-3.0 |
hlin117/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 19 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivariate covarying two-dimensional datasets, X, and Y,
PLS extracts the 'directions of covariance', i.e. the components of each
datasets that explain the most shared variance between both datasets.
This is apparent on the **scatterplot matrix** display: components 1 in
dataset X and dataset Y are maximally correlated (points lie around the
first diagonal). This is also true for components 2 in both dataset,
however, the correlation across datasets for different components is
weak: the point cloud is very spherical.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_decomposition import PLSCanonical, PLSRegression, CCA
###############################################################################
# Dataset based latent variables model
n = 500
# 2 latents vars:
l1 = np.random.normal(size=n)
l2 = np.random.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + np.random.normal(size=4 * n).reshape((n, 4))
Y = latents + np.random.normal(size=4 * n).reshape((n, 4))
X_train = X[:n // 2]
Y_train = Y[:n // 2]
X_test = X[n // 2:]
Y_test = Y[n // 2:]
print("Corr(X)")
print(np.round(np.corrcoef(X.T), 2))
print("Corr(Y)")
print(np.round(np.corrcoef(Y.T), 2))
###############################################################################
# Canonical (symmetric) PLS
# Transform data
# ~~~~~~~~~~~~~~
plsca = PLSCanonical(n_components=2)
plsca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
# Scatter plot of scores
# ~~~~~~~~~~~~~~~~~~~~~~
# 1) On diagonal plot X vs Y scores on each components
plt.figure(figsize=(12, 8))
plt.subplot(221)
plt.plot(X_train_r[:, 0], Y_train_r[:, 0], "ob", label="train")
plt.plot(X_test_r[:, 0], Y_test_r[:, 0], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
plt.subplot(224)
plt.plot(X_train_r[:, 1], Y_train_r[:, 1], "ob", label="train")
plt.plot(X_test_r[:, 1], Y_test_r[:, 1], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 2: X vs Y (test corr = %.2f)' %
np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
# 2) Off diagonal plot components 1 vs 2 for X and Y
plt.subplot(222)
plt.plot(X_train_r[:, 0], X_train_r[:, 1], "*b", label="train")
plt.plot(X_test_r[:, 0], X_test_r[:, 1], "*r", label="test")
plt.xlabel("X comp. 1")
plt.ylabel("X comp. 2")
plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)'
% np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.subplot(223)
plt.plot(Y_train_r[:, 0], Y_train_r[:, 1], "*b", label="train")
plt.plot(Y_test_r[:, 0], Y_test_r[:, 1], "*r", label="test")
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'
% np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
plt.show()
###############################################################################
# PLS regression, with multivariate response, a.k.a. PLS2
n = 1000
q = 3
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
B = np.array([[1, 2] + [0] * (p - 2)] * q).T
# each Yj = 1*X1 + 2*X2 + noize
Y = np.dot(X, B) + np.random.normal(size=n * q).reshape((n, q)) + 5
pls2 = PLSRegression(n_components=3)
pls2.fit(X, Y)
print("True B (such that: Y = XB + Err)")
print(B)
# compare pls2.coef_ with B
print("Estimated B")
print(np.round(pls2.coef_, 1))
pls2.predict(X)
###############################################################################
# PLS regression, with univariate response, a.k.a. PLS1
n = 1000
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
y = X[:, 0] + 2 * X[:, 1] + np.random.normal(size=n * 1) + 5
pls1 = PLSRegression(n_components=3)
pls1.fit(X, y)
# note that the number of components exceeds 1 (the dimension of y)
print("Estimated betas")
print(np.round(pls1.coef_, 1))
###############################################################################
# CCA (PLS mode B with symmetric deflation)
cca = CCA(n_components=2)
cca.fit(X_train, Y_train)
X_train_r, Y_train_r = cca.transform(X_train, Y_train)
X_test_r, Y_test_r = cca.transform(X_test, Y_test)
| bsd-3-clause |
timodonnell/pyopen | test/test_basics.py | 1 | 2353 | from nose.tools import eq_
from . import data_path, run_and_capture
RUN_TESTS_REQUIRING_INTERNET = True
SAMPLE_CSV_URL = "https://raw.githubusercontent.com/timodonnell/pyopen/master/test/data/SampleCSVFile_11kb.csv"
def test_csv():
eq_(
run_and_capture(
"print(f1.shape)",
[data_path("SampleCSVFile_11kb.csv"),
"--csv-encoding", "latin1"]),
"(99, 10)")
eq_(
run_and_capture(
"print(f1.shape)",
[data_path("SampleCSVFile_11kb.csv.gz"),
"--csv-encoding", "latin1"]),
"(99, 10)")
eq_(
run_and_capture(
"print(f1.shape)",
[data_path("SampleCSVFile_11kb.csv.bz2"),
"--csv-encoding", "latin1"]),
"(99, 10)")
eq_(
run_and_capture(
"print(f1.shape)",
[
data_path("SampleCSVFile_11kb.csv.nonstandard_extension"),
"--loader", "pandas_csv", "--csv-encoding", "latin1",
]),
"(99, 10)")
if RUN_TESTS_REQUIRING_INTERNET:
def test_csv_url():
eq_(
run_and_capture(
"print(f1.shape)",
[SAMPLE_CSV_URL, "--csv-encoding", "latin1"]),
"(99, 10)")
def test_tsv():
eq_(
run_and_capture(
"print(f1.shape)",
[
data_path("nasa_19950801.tsv.bz2"),
"--loader", "pandas_csv",
]),
"(30969, 9)")
def test_xls():
eq_(
run_and_capture(
"print(f1['Sample-spreadsheet-file'].shape)",
[data_path("SampleXLSFile_38kb.xls")]),
"(99, 10)")
def test_hdf5():
eq_(
run_and_capture(
"print(f1['/detector/readout'].ix[3, 'energy'])",
[data_path("pytables_native.h5")]),
"6561.0")
def test_json():
eq_(
run_and_capture(
"print(f1['glossary']['title'])",
[data_path("example1.json")]),
"example glossary")
def test_yaml():
eq_(
run_and_capture(
"print(f1['glossary']['title'])",
[data_path("example1.yaml")]),
"example glossary")
def test_pickle():
eq_(
run_and_capture(
"print(f1['glossary']['title'])",
[data_path("example1.pkl")]),
"example glossary")
| apache-2.0 |
murali-munna/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingClassifier
from sklearn.linear_model import Perceptron
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
def test_base_zero_n_estimators():
# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
# a ValueError.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=0)
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be greater than zero, got 0.",
ensemble.fit, iris.data, iris.target)
| bsd-3-clause |
ReservoirWebs/GrowChinook | Bioenergetics_dev.py | 1 | 46105 | #!/usr/bin/python
import pylab
import glob
import time
import os
import numpy as np
import sys
from scipy.interpolate import interp1d
from scipy.integrate import trapz
from scipy.optimize import minimize, brute
from csv import DictReader, QUOTE_NONNUMERIC
from collections import defaultdict
from matplotlib import pyplot
from datetime import datetime, timedelta
#Minimum and maximum elevations for each reservoir
FC_MAX_EL = 833.3
FC_MIN_EL = 682.2
HC_MAX_EL = 1545.3
HC_MIN_EL = 1246.7
LP_MAX_EL = 931.7
LP_MIN_EL = 715.2
def scruffy(path,return_path,name): #Scruffy's the janitor. Kills any output files older than one hour
os.chdir(path)
hour_ago = datetime.now() - timedelta(hours=1)
for file in glob.glob("{}*".format(name)):
age = datetime.fromtimestamp(os.path.getctime(file))
if age < hour_ago:
os.remove(file)
os.chdir(return_path)
def get_sustain_est(elevation, total_daphnia, consumed, site):
bath_data = '{}Bath.csv'.format(site)
bath = {}
total_daphnia = total_daphnia
with open(bath_data) as file:
reader = DictReader(file)
for row in reader:
bath.update({int(row['elevation (m)']): float(row[' 2d_area (m2)'])})
if site == 'Fall Creek':
elev = min(max((elevation), (FC_MIN_EL/3.281)), (FC_MAX_EL/3.281))
elif site == 'Hills Creek':
elev = min(max((elevation), (HC_MIN_EL/3.281)), (HC_MAX_EL/3.281))
elif site == 'Lookout Point':
elev = min(max((elevation), (LP_MIN_EL/3.281)), (LP_MAX_EL/3.281))
area = bath[int(elev)]
consumable = (area*total_daphnia*0.58)
pop_est = consumable/(consumed*4)
return pop_est
def get_vals(light_in, total_daphnia_in, daphnia_size_in, site, month, year):
#k represents the light extinction coefficient
lights = {('Fall Creek', '2016'): {'April': 0.758, 'May': 0.466, 'June': 0.435,
'July': 0.451, 'August': 0.444, 'September': 0.406},
('Hills Creek', '2016'): {'April': 0.399, 'May': 0.321, 'June': 0.440,
'July': 0.257, 'August': 0.384, 'September': 0.340},
('Lookout Point', '2016'): {'April': 0.514, 'May': 0.373, 'June': 0.368,
'July': 0.311, 'August': 0.389, 'September': 0.343},
('Fall Creek', '2015'): {'March': 0.834, 'April': 0.596, 'May': 0.58, 'June': 0.72,
'July': 0.521, 'August': 0.509},
('Hills Creek', '2015'): {'March': 0.583, 'April': 0.503, 'May': 0.467,
'June': 0.441, 'July': 0.32, 'August': 0.368},
('Lookout Point', '2015'): {'March': 0.532, 'April': 0.565, 'May': 0.373,
'June': 0.374, 'July': 0.396, 'August': 0.39},
('Fall Creek', '2014'): {'June': 0.404, 'July': 0.274, 'August': 0.295},
('Hills Creek', '2014'): {'June': 0.298, 'July': 0.274, 'August': 0.274},
('Lookout Point', '2014'): {'June': 0.315, 'July': 0.271, 'August': 0.282}
}
# Daphnia totals weighted by subsample - only from C and Z sites
# July 2013 and July 2014 are not currently available
daphnias = {('Fall Creek', '2016'): {'April': 367, 'May': 22328, 'June': 48240, 'July': 8801,
'August': 5378, 'September': 3626},
('Hills Creek', '2016'): {'April': 163, 'May': 7456, 'June': 88658, 'July': 9045,
'August': 13527, 'September': 13853},
('Lookout Point', '2016'): {'April': 20, 'May': 448, 'June': 9290, 'July': 11693,
'August': 6926, 'September': 1854},
('Fall Creek', '2015'): {'March': 815, 'April': 17357, 'May': 24446, 'June':3993,
'July': 2363, 'August': 407},
('Hills Creek', '2015'): {'March': 204, 'April': 453, 'May': 11408, 'June': 20535,
'July': 9126, 'August': 3178},
('Lookout Point', '2015'): {'March': 61, 'April': 127, 'May': 14016, 'June': 44981,
'July': 5949, 'August': 581},
('Fall Creek', '2014'): {'June': 25280, 'July': 0, 'August': 7752},
('Hills Creek', '2014'): {'June': 6040, 'July': 0, 'August': 2249},
('Lookout Point', '2014'): {'June': 16863, 'July': 0, 'August': 1061},
('Fall Creek', '2013'): {'June': 18416, 'July': 0, 'August': 4563},
('Hills Creek', '2013'): {'June': 127772, 'July': 0, 'August': 18559},
('Blue River', '2013'): {'June': 68449, 'July': 0, 'August': 41233}
}
#Weighted for proportion D.mendotae, D.pulex, and D.rosea/ambigua averaged across available years
sizes = {('Fall Creek', '2016'): {'April': 0.56, 'May': 1.01, 'June': 1.13, 'July': 1.48,
'August': 1.78, 'September': 1.10},
('Hills Creek', '2016'): {'April': 1.22, 'May': 1.08, 'June': 1.16, 'July': 1.54,
'August': 1.18, 'September': 1.51},
('Lookout Point', '2016'): {'April': 0.53, 'May': 0.68, 'June': 1.14, 'July': 1.31,
'August': 1.64, 'September': 1.20},
('Blue River', '2016'): {'July': 1.27},
('Fall Creek', '2015'): {'March': 1.21, 'April': 1.25, 'May': 1.13, 'June': 1.26,
'July': 1.49, 'August': 1.18},
('Hills Creek', '2015'): {'March': 1.24, 'April': 1.09, 'May': 1.03, 'June': 1.20,
'July': 1.84, 'August': 2.21},
('Lookout Point', '2015'): {'March': 1.46, 'April': 0.96, 'May': 1.06, 'June': 1.35,
'July': 1.97, 'August': 2.07},
('Blue River', '2015'): {'March': 0.63, 'April': 0.73, 'May': 0.83, 'June': 1.50,
'July': 1.48, 'August': 1.25},
('Fall Creek', '2014'): {'March': 1.207, 'April': 0.90375, 'May': 1.073, 'June': 1.262,
'July': 1.485, 'August': 1.633},
('Hills Creek', '2014'): {'March': 1.238, 'April': 1.152, 'May': 1.058, 'June': 1.232,
'July': 1.687, 'August': 2.005},
('Lookout Point', '2014'): {'March': 1.457, 'April': 0.745, 'May': 0.871,
'June': 1.237, 'July': 1.642, 'August': 2.033},
('Blue River', '2014'): {'March': 0.628, 'April': 0.780, 'May': 0.827, 'June': 1.321,
'July': 1.377, 'August': 1.282}
}
if light_in == float(123456):
light = lights[(site, year)][month]
else:
light = light_in
if total_daphnia_in == float(123456):
total_daphnia = daphnias[(site, year)][month]
else:
total_daphnia = total_daphnia_in
if daphnia_size_in == float(123456):
daphnia_size = sizes[(site, year)][month]
else:
daphnia_size = daphnia_size_in
return light, total_daphnia, daphnia_size
def sensitivity_expand(form):
sparam_exp = []
if form.getvalue('Sparam_Range') != None:
sparam_range = float(form.getvalue('Sparam_Range'))
else:
sparam_range = 200
step_size = (sparam_range-100)/1000
sparam_exp.append(.001)
for i in range(4, 1, -1):
sparam_exp.append(float(1)/(i*10))
sparam_exp.append(1)
for i in range(1, 11):
sparam_exp.append(float(1)+(step_size*i))
return sparam_exp
def run_sensitivity(sens_factors, sparam, site_data, starting_mass, daph_data, max_temp, min_temp, cust_temp, elev, pop_site, ax2, ax3):
batches = []
results = []
sens_inputs = []
growths = []
growths1 = []
csvheaders = [[] for i in range(20)]
SHORT_RESULTS = {'Elevation': [], 'Reservoir(used for elevation)': [],
'Daphnia Density': [], 'Light': [], 'Daphnia Size': [],
'Min Depth': [], 'Max Depth': [], 'Min Temp': [], 'Max Temp': [],
'Daphnia Year': [], 'Daphnia Month': [], 'Daphnia Site': [],
'Temperature File': [], 'Starting Mass': [], 'Ending Mass': [],
'Day Depth': [], 'Day Temperature': [], 'Night Depth': [],
'Night Temperature': [], 'Day 1 Growth': [], 'Day 30 Growth': [],
'Daphnia Consumed': [], 'Sustainable Estimate': [],
'Estimated Condition Change': [], 'Day P': [], 'Night P': []}
if sparam == 'Starting Mass':
base_input = starting_mass
elif sparam == 'Total Daphnia':
base_input = daph_data.total_daph
elif sparam == 'Daphnia Size':
base_input = daph_data.daph_size
else:
base_input = site_data.light
for i in range(15):
if (base_input * sens_factors[i]) > 0.0001:
sens_inputs.append(base_input * sens_factors[i])
else:
sens_inputs.append(.00001)
sens_factors[i] = sens_factors[i] * 100
csvheaders[i] = [site_data.site, site_data.month, site_data.year, ("%s: %f" % (sparam, sens_inputs[i]))]
if sparam == 'Starting Mass':
batches.append(Batch(site_data, sens_inputs[i], daph_data, max_temp, min_temp, cust_temp, elev, pop_site, True))
elif sparam == 'Total Daphnia':
daph_data.total_daph = sens_inputs[i]
batches.append(Batch(site_data, starting_mass, daph_data, max_temp, min_temp, cust_temp, elev, pop_site, True))
elif sparam == 'Daphnia Size':
daph_data.daph_size = sens_inputs[i]
batches.append(Batch(site_data, starting_mass, daph_data, max_temp, min_temp, cust_temp, elev, pop_site, True))
else:
site_data.light = sens_inputs[i]
batches.append(Batch(site_data, starting_mass, daph_data, max_temp, min_temp, cust_temp, elev, pop_site, True))
res, taway, condition, condition1, dt, nt, taway2, day_p, night_p = batches[i].Run_Batch()
results.append(res)
#SHORT_RESULTS['Tab Name'].append(vals.title)
SHORT_RESULTS['Elevation'].append(elev)
SHORT_RESULTS['Reservoir(used for elevation)'].append(pop_site)
if sparam == 'Total Daphnia':
SHORT_RESULTS['Daphnia Density'].append(sens_inputs[i])
else:
SHORT_RESULTS['Daphnia Density'].append(daph_data.total_daph)
if sparam == 'Light':
SHORT_RESULTS['Light'].append(sens_inputs[i])
else:
SHORT_RESULTS['Light'].append(site_data.light)
if sparam == 'Daphnia Size':
SHORT_RESULTS['Daphnia Size'].append(sens_inputs[i])
else:
SHORT_RESULTS['Daphnia Size'].append(daph_data.daph_size)
SHORT_RESULTS['Min Depth'].append(site_data.min_depth)
SHORT_RESULTS['Max Depth'].append(site_data.max_depth)
SHORT_RESULTS['Min Temp'].append(min_temp)
SHORT_RESULTS['Max Temp'].append(max_temp)
SHORT_RESULTS['Daphnia Year'].append(daph_data.d_year)
SHORT_RESULTS['Daphnia Month'].append(daph_data.d_month)
SHORT_RESULTS['Daphnia Site'].append(daph_data.d_site)
SHORT_RESULTS['Temperature File'].append(cust_temp)
if sparam == 'Starting Mass':
SHORT_RESULTS['Starting Mass'].append(sens_inputs[i])
else:
SHORT_RESULTS['Starting Mass'].append(starting_mass)
SHORT_RESULTS['Ending Mass'].append(results[i]['StartingMass'][29])
SHORT_RESULTS['Day Depth'].append(results[i]['day_depth'][29])
SHORT_RESULTS['Day Temperature'].append(dt)
SHORT_RESULTS['Night Depth'].append(results[i]['night_depth'][29])
SHORT_RESULTS['Night Temperature'].append(nt)
SHORT_RESULTS['Day 1 Growth'].append(results[i]['growth'][0])
SHORT_RESULTS['Day 30 Growth'].append(results[i]['growth'][29])
SHORT_RESULTS['Daphnia Consumed'].append(taway)
SHORT_RESULTS['Sustainable Estimate'].append(taway2)
SHORT_RESULTS['Estimated Condition Change'].append(condition)
SHORT_RESULTS['Day P'].append(day_p)
SHORT_RESULTS['Night P'].append(night_p)
growths.append(results[i]['growth'][29])
growths1.append(results[i]['growth'][0])
return results, growths, growths1, csvheaders, sens_inputs, SHORT_RESULTS, ax2, ax3
class Daph_Data:
def __init__(self, abundance, size, year, site, month):
self.total_daph = abundance
self.daph_size = size
self.d_year = year
self.d_site = site
self.d_month = month
def __str__(self):
return '{}'.format([self.total_daph, self.daph_size, self.d_year,
self.d_site, self.d_month])
class Form_Data_Packager:
def __init__(self, form):
self.title = form.getvalue('TabName') or 'GrowChinook Results'
self.starting_mass = float(form.getvalue('Starting_Mass_In') or 20)
if self.starting_mass == 0:
self.starting_mass = 0.1
self.total_daphnnia = float(form.getvalue('Total_Daphnia_Input_Name') or form.getvalue('TotDDef') or 123456)
self.daphnia_size = float(form.getvalue('Daphnia Size') or form.getvalue('DaphSDef') or 123456)
self.light = float(form.getvalue('Light') or form.getvalue('LightDef') or 123456)
self.year = form.getvalue('Year') or '2015'
self.month = form.getvalue('Month1') or 'June'
self.site = form.getvalue('Site') or 'Fall Creek'
self.max_dep = float(form.getvalue('DmaxIn') or 35)
self.min_dep = float(form.getvalue('DminIn') or 0)
self.max_temp = float(form.getvalue('TmaxIn') or 10000)
self.min_temp = float(form.getvalue('TminIn') or -1)
if self.min_temp == self.max_temp:
self.max_temp = self.max_temp + 1
self.pop_site = form.getvalue('ESite') or self.site
self.elev = float(form.getvalue('Elev') or 100000)
if self.pop_site == 'Fall Creek':
self.elev = max(self.elev, 691)
self.max_dep = min(((self.elev - FC_MIN_EL) / 3.281), self.max_dep)
elif self.pop_site == 'Lookout Point':
self.elev = max(self.elev, 725)
self.max_dep = min(((self.elev - LP_MIN_EL) / 3.281), self.max_dep)
elif self.pop_site == 'Hills Creek':
self.elev = max(self.elev, 1256)
self.max_dep = min(((self.elev - HC_MIN_EL) / 3.281), self.max_dep)
if self.max_dep <= 0:
self.max_dep = 1
self.dmaxday = 1
self.daph_year = form.getvalue('DYear') or self.year
self.daph_month = form.getvalue('DMonth') or self.month
self.daph_site = form.getvalue('DSite') or self.site
self.temp_year = form.getvalue('TYear') or self.year
self.temp_month = form.getvalue('TMonth') or self.month
self.temp_site = form.getvalue('TSite') or self.site
if form.getvalue('CustTemp') is None:
self.cust_temp = '{0}_T_{1}_{2}.csv'.format(self.temp_site, self.temp_month, self.temp_year)
else:
self.cust_temp = 'uploads/temp/{}'.format(form.getvalue('CustTemp'))
self.light, self.total_daphnnia, self.daphnia_size = get_vals(self.light, self.total_daphnnia, self. daphnia_size, self.site, self.month, self.year)
self.site_data = Site_Data(self.year, self.site, self.month, self.light, self.max_dep, self.min_dep)
self.daph_data = Daph_Data(self.total_daphnnia, self.daphnia_size, self.daph_year, self.daph_site, self.daph_month)
class Adv_Sens_Form_Data_Packager:
def __init__(self, form):
self.title = form.getvalue('TabName') or 'GrowChinook Results'
self.starting_mass = float(form.getvalue('Starting_Mass_In') or 20)
if self.starting_mass == 0:
self.starting_mass = 0.1
self.total_daphnnia = float(form.getvalue('Total_Daphnia_Input_Name') or form.getvalue('TotDDef') or 123456)
self.daphnia_size = float(form.getvalue('Daphnia Size') or form.getvalue('DaphSDef') or 123456)
self.light = float(form.getvalue('Light') or form.getvalue('LightDef') or 123456)
self.year = form.getvalue('Year') or '2015'
self.site = form.getvalue('Site') or 'Fall Creek'
self.max_dep = float(form.getvalue('DmaxIn') or 10000)
self.min_dep = float(form.getvalue('DminIn') or -1)
self.max_temp = float(form.getvalue('TmaxIn') or 10000)
self.min_temp = float(form.getvalue('TminIn') or -1)
if self.min_temp == self.max_temp:
self.max_temp = self.max_temp + 1
self.site_data = Site_Data(self.year, self.site, None, self.light, self.max_dep, self.min_dep)
self.daph_data = Daph_Data(self.total_daphnnia, self.daphnia_size, self.year, self.site, None)
class Site_Data:
def __init__(self, year, site, month, light, max_depth, min_depth):
self.year = year
self.site = site
self.month = month
self.light = light
self.max_depth = max_depth
self.min_depth = min_depth
def __str__(self):
return '{}'.format([self.year, self.site, self.month, self.light,
self.max_depth, self.min_depth])
class Batch:
def __init__(self, site_data, starting_mass, daphnia_data, temp_max,
temp_min, temp_file, elevation, PSite, extrapolate_temp=False):
self.site = site_data.site
self.month = site_data.month
self.year = site_data.year
self.light = site_data.light
self.daphnia_size = daphnia_data.daph_size
self.total_daphnia = daphnia_data.total_daph
self.temp_file = temp_file
self.DYear = daphnia_data.d_year
self.DMonth = daphnia_data.d_month
self.daphnia_site = daphnia_data.d_site
self.starting_mass = starting_mass
self.starting_mass_initial = starting_mass
self.depth_max = site_data.max_depth
self.depth_min = site_data.min_depth
self.temp_max = temp_max
self.temp_min = temp_min
self.dtfinal = 0
self.ntfinal = 0
self.depths = []
self.elevation = elevation
self.PSite = PSite
self.SparamExp = []
# Body lengths (from grey lit)
self.SwimSpeed = 2
self.params = {}
# J/gram of O2 in respiration conversions (Elliot and Davidson 1975).
self.O2Conv = 13560
# lux http://sustainabilityworkshop.autodesk.com/buildings/measuring-light-levels
self.DayLight = 39350
## Would a lower lux be more representative? - https://www.noao.edu/education/QLTkit/ACTIVITY_Documents/Safety/LightLevels_outdoor+indoor.pdf
#self.DayLight = 10752
self.NightLight = 0.10
self.out = {}
# Based off Cornell equation (g from ug)
self.daphnia_dry_weight = (np.exp(1.468 + 2.83 * np.log(self.daphnia_size))) /\
1000000 #From Ghazy, others use ~10%
#Wet weight from Smirnov 2014 (g from mg)
self.daphnia_weight = (0.075 * self.daphnia_size ** 2.925) / 1000
# Using Pechen 1965 fresh weight / length relationship reported in Dumont for D. magna
#self.daphnia_weight = (0.052 * self.daphnia_size ** 3.012) / 1000
if elevation is None:
self.elevation = 100000
else:
self.elevation = int(float(elevation)/3.281)
self.PSite = PSite
self.PSite = self.PSite or self.site
self.DYear = self.DYear or self.year
self.DMonth = self.DMonth or self.month
self.daphnia_site = self.daphnia_site or self.site
# From Luecke and Brandt 22.7 overall, 23.3 kJ/g for unfrozen Daphnia (dry weight) 1.62 kJ/g wet weight
DaphEnergy = 22700
# This is likely an overestimation given that Daphnia under reservoir food concentrations would have less than half the lipids of higher food environments...
# https://link.springer.com/article/10.1007%2Fs11356-010-0413-0
# Also 24C Daphnia have double the lipids of 16C Daphnia
self.prey = [1]
# Noue and Choubert 1985 suggest Daphnia are 82.6% digestible by Rainbow Trout
self.digestibility = [0.174]
self.preyenergy = [DaphEnergy]
with open('Daphnia VD.csv') as fid:
reader = DictReader(fid)
zooplankton_data = [r for r in reader]
(self.daphline, self.daph_auc) = self.compute_daphniabydepth(zooplankton_data)
# From Lookout Point and Fall Creek downstream screw trap data (R2 = 0.9933)
self.StartingLength = (self.starting_mass / 0.000004) ** (1 / 3.1776)
#self.StartingLength = (self.starting_mass/0.0003)**(1/2.217) #see note below
self.temp_max = self.temp_max or 1000
self.temp_min = self.temp_min or -1
f = 'ChinookAppendixA.csv'
with open(f) as fid:
reader = DictReader(fid, quoting=QUOTE_NONNUMERIC)
self.params = next(reader)
if self.temp_file == "None_T_None_None.csv":
temperature_file = '{0}_T_{1}_{2}.csv'\
.format(self.site, self.month, self.year)
else:
temperature_file = temp_file
with open(temperature_file) as fid:
reader = DictReader(fid)
self.temperatures = []
for row in reader:
if (float(row['temp']) <= self.temp_max) and (float(row['temp']) >= self.temp_min):
self.temperatures.append(float(row['temp']))
self.depths.append(float(row['depth']))
if self.temperatures == [] or self.depths == []:
print("ALL DEPTHS EXCLUDED BY TEMPERATURE AND DEPTH RESTRICTIONS!!!!!!!!!")
self.predatorenergy = self.predatorenergy(self.starting_mass)
if extrapolate_temp:
fill_value = 'extrapolate'
else:
fill_value = 0
self.depth_from_temp = interp1d(self.temperatures, self.depths,
fill_value=fill_value,
bounds_error=False)
self.temp_from_depth = interp1d(self.depths, self.temperatures,
fill_value=fill_value,
bounds_error=False)
day_depth = 5
night_depth = 10
self.day_temp = self.temp_from_depth(day_depth)
self.day_depth = 5
self.night_temp = self.temp_from_depth(night_depth)
self.night_depth = 10
self.daylength = {'March':11.83, 'April':13.4, 'May':14.73, 'June':15.42,
'July':15.12, 'August':13.97, 'September':12.45}
def compute_daphniabydepth(self, zooplankton_data):
# get rows for site, season, depth
if self.year == '2016':
rows = [r for r in zooplankton_data if (r['Site'] == self.daphnia_site
and r['Month'] == self.DMonth
and r['Year'] == '2016')]
else:
rows = [r for r in zooplankton_data if (r['Site'] == self.daphnia_site
and r['Month'] == self.DMonth
and r['Year'] == '2015')]
x = [float(r['Depth']) for r in rows]
y = [float(r['Total Daphnia']) for r in rows]
surface_count = y[np.argmin(x)]
auc = trapz(y, x)
y = y / auc * self.total_daphnia
return (interp1d(x, y, bounds_error=False, fill_value=surface_count), trapz(y, x))
# Foraging from Beauchamps paper, prey per hour is commented out
# Current reaction distance is from Gregory and Northcote 1992
# Note that reaction distance is in cm
def compute_foragingbydepth(self, StartingLength, starting_mass, surface_light,
daphline, daph_auc, depth):
light = surface_light * np.exp((-self.light) * depth)
depth = depth
# daphnia per cc
daphnia = daphline(depth) / 1000000
#reactiondistance = 3.787 * (light ** 0.4747) * ((self.daphnia_size / 10) ** 0.9463)
lightenergy = light/51.2
suspendedsediment = -((np.log(lightenergy) - 1.045)/(.0108))
if suspendedsediment <= 0:
reactiondistance = 31.64
if suspendedsediment > 0:
turbidity = .96*np.log(suspendedsediment+1) - .002
reactiondistance = (31.64-13.31*turbidity)
# ~1.1 from this paper, 8 based on kokanee (is ~ the median observed for this Chinook study)
if reactiondistance < 1.1 or np.isnan(reactiondistance):
reactiondistance = 1.1
swim_speed = self.SwimSpeed * StartingLength/10
searchvolume = np.pi * (reactiondistance ** 2) * swim_speed
# daphnia per hour
EncounterRate = searchvolume * daphnia * 60 * 60
# Capping ER based on 2017 Haskell et al.
# Haskell equation is in L, daphnia are currently per cc and was per min, convert to hr
max_er = (29.585 * (daphnia * 1000) * ((4.271 + daphnia * 1000) ** (-1)) * 60)
if EncounterRate > max_er:
EncounterRate = max_er
# EncounterRate = 0.9 * EncounterRate # use if want to further restrict capture
gramsER = EncounterRate * self.daphnia_weight
return gramsER / starting_mass
def compute_ft(self, temperature):
CQ = self.params['CQ']
CTL = self.params['CTL']
CTM = self.params['CTM']
CTO = self.params['CTO']
CK1 = self.params['CK1']
CK4 = self.params['CK4']
eq = self.params['c_eq']
if eq == 1:
return np.exp(CQ * temperature)
elif eq == 2:
V = (CTM - temperature) / (CTM - CTO)
Z = np.log(CQ) * (CTM - CTO)
Y = np.log(CQ) * (CTM - CTO + 2)
X = (Z ** 2 * (1 + (1 + 40 / Y) ** 0.5) ** 2) / 400
return (V ** X) * np.exp(X * (1 - V))
elif eq == 3:
G1 = (1 / (CTO - CQ)) * np.log((0.98 * (1 - CK1)) / (CK1 * 0.002))
G2 = (1 / (CTL - CTM)) * np.log((0.98 * (1 - CK4)) / (CK4 * 0.02))
L1 = np.exp(G1 * (temperature - CQ))
L2 = np.exp(G2 * (CTL - temperature))
K_A = (CK1 * L1) / (1 + CK1 * (L1 - 1))
K_B = (CK4 * L2) / (1 + CK4 * (L2 - 1))
return K_A * K_B
else:
raise ValueError("Unknown consumption equation type: " + eq)
def compute_cmax(self, W):
CA = self.params['CA']
CB = self.params['CB']
return CA * (W ** CB)
def compute_consumption(self, cmax, P, ft):
return cmax * P * ft
def compute_waste(self, consumption, P, temperature, prey, digestibility):
# Units are g/g/d
FA = self.params['FA']
FB = self.params['FB']
FG = self.params['FG']
UA = self.params['UA']
UB = self.params['UB']
UG = self.params['UG']
eq = self.params['egexeq']
if eq == 1:
egestion = FA * consumption
excretion = UA * (consumption - egestion)
return (egestion, excretion)
elif eq == 2:
egestion = FA * (temperature ** FB) * np.exp(FG * P) * consumption
excretion = UA * (temperature ** UB) * np.exp(UG * P) * (consumption - egestion)
return (egestion, excretion)
elif eq == 3:
if prey is None or digestibility is None:
raise ValueError("Prey or digestibility not defined")
PFF = np.inner(prey, digestibility)
PE = FA * (temperature ** FB) * np.exp(FG * P)
PF = ((PE - 0.1) / 0.9) * (1 - PFF) + PFF
egestion = PF * consumption
excretion = UA * (temperature ** UB) * np.exp(UG * P) * (consumption - egestion)
return (egestion, excretion)
else:
raise ValueError("Unknown egestion/excretion equation type: " + eq)
def compute_respiration(self, W0, temperature, egestion, consumption):
RA = self.params['RA']
RB = self.params['RB']
RQ = self.params['RQ']
RTO = self.params['RTO']
RTM = self.params['RTM']
RTL = self.params['RTL']
RK1 = self.params['RK1']
RK4 = self.params['RK4']
ACT = self.params['ACT']
BACT = self.params['BACT']
SDA = self.params['SDA']
eq = self.params['respeq']
if eq == 1:
if temperature > RTL:
VEL = RK1 * W0 ** RK4
print("SOME OF THE INCLUDED TEMPERATURES ARE LETHAL,"
"PLEASE MODIFY THE TEMPERATURE TO EXCLUDE TEMPERATURES OVER 25C!")
else:
VEL = ACT * (W0 ** RK4) * np.exp(BACT * temperature)
FTmetabolism = np.exp(RQ * temperature)
activity = np.exp(RTO * VEL)
elif eq == 2:
Vresp = (RTM - temperature) / (RTM - RTO)
Zresp = np.log(RQ) * (RTM - RTO)
Yresp = np.log(RQ) * (RTM - RTO + 2)
Xresp = (((Zresp ** 2) * (1 + (1 + 40 / Yresp) ** 0.5)) ** 2) / 400
FTmetabolism = (Vresp ** Xresp) * np.exp(Xresp * (1 - Vresp))
activity = ACT
else:
raise ValueError("Unknown respiration equation type: " + eq)
respiration = RA * (W0 ** RB) * FTmetabolism * activity
SDAction = SDA * (consumption - egestion)
return (respiration, SDAction)
##This has not changed for FishBioE4 - see lines 1444 in R script
def predatorenergy(self, W0):
AlphaI = self.params['AlphaI']
AlphaII = self.params['AlphaII']
BetaI = self.params['BetaI']
BetaII = self.params['BetaII']
energydensity = self.params['energydensity']
cutoff = self.params['cutoff']
eq = self.params['prededeq']
if eq == 1:
predatorenergy = energydensity
if eq == 2:
if W0 < cutoff:
predatorenergy = AlphaI + (BetaI * W0)
elif W0 >= cutoff:
predatorenergy = AlphaII + (BetaII * W0)
else:
raise ValueError("Unknown predator energy density equation type: " + eq)
return predatorenergy
def compute_bioenergetics(self, W, temp, P, prey, digestibility):
cmax = self.compute_cmax(W)
ft = self.compute_ft(temp)
consumption = self.compute_consumption(cmax, P, ft)
(egestion, excretion) = self.compute_waste(consumption, P, temp, prey, digestibility)
(respiration, SDAction) = self.compute_respiration(W, temp, egestion, consumption)
return (consumption, egestion, excretion, respiration, SDAction)
###Energy gain (973 in R code for BioE4)
def compute_growth(self, consumption, prey, preyenergy, egestion, excretion,
SDAction, respiration, predatorenergy, W):
consumptionjoules = consumption * np.inner(prey, preyenergy)
predeq = self.params['prededeq']
AlphaI = self.params['AlphaI']
AlphaII = self.params['AlphaII']
BetaI = self.params['BetaI']
BetaII = self.params['BetaII']
w_cutoff = self.params['cutoff']
egain = (consumptionjoules - ((egestion + excretion + SDAction) * np.inner(prey, preyenergy)
+ respiration * self.O2Conv))*W
#W is added to eq 1 because we subtract W to get change in weight below.
if predeq == 1:
w_new = W + egain/self.params['energydensity']
elif predeq == 2:
if W < w_cutoff:
if BetaI != 0:
w_new = (-AlphaI + np.sqrt(AlphaI * AlphaI + 4 * BetaI * (W * (AlphaI + BetaI * W) + egain))) / (2 * BetaI)
else:
w_new = (egain + W * AlphaI) / AlphaI
if w_new > w_cutoff:
egainCo = Wco*(AlphaI + BetaI * w_cutoff) - W * (AlphaI + BetaI * W)
if BetaII != 0:
w_new = -AlphaII + np.sqrt(AlphaII * AlphaII + 4 * BetaII * (egain - egainCo + w_cutoff * (AlphaI + BetaI * w_cutoff))) / (2 * BetaII)
elif BetaII == 0:
w_new = (egain -egainCo + w_cutoff * (AlphaI + BetaI * w_cutoff)) / AlphaII
elif W >= w_cutoff:
if BetaII != 0:
w_new = (-AlphaII + np.sqrt ( AlphaII * AlphaII + 4 * BetaII * (W *(AlphaII + BetaII * W) + egain))) / (2 * BetaII)
elif BetaII == 0:
w_new = (egain + W * AlphaII) / AlphaII
if w_new < w_cutoff:
elossCo = W * (AlphaII + BetaII * W) - w_cutoff * (AlphaI + BetaI * w_cutoff)
if BetaI != 0:
w_new = (-AlphaI + np.sqrt( AlphaI * AlphaI + 4 * BetaI * (egain + elossCo + w_cutoff * (AlphaI + BetaI * w_cutoff)))) / (2 * BetaI)
elif BetaI == 0:
w_new = (egain + elossCo + w_cutoff * AlphaI) / AlphaI
return w_new - W
def best_depth(self, StartingLength, starting_mass, depths, x0=None):
if self.depth_min > min(max(depths), self.depth_max):
self.depth_min = min(max(depths), self.depth_max)
if self.depth_max < max(min(depths), self.depth_min):
self.depth_max = max(min(depths), self.depth_min)
if self.depth_max == self.depth_min:
self.depth_max = self.depth_max + 0.2
day_depths = np.arange(max(min(depths), self.depth_min),
min(max(depths), self.depth_max), 0.1)
night_depths = day_depths
day_hours = self.daylength[self.month]
night_hours = 24 - day_hours
best_growth = -9999
def objective(x):
(dd,nd) = x
res = self.growth_fn(dd, nd, StartingLength,
starting_mass, day_hours,
night_hours, self.DayLight,
self.NightLight, self.prey)
return -res[0]
depth_bounds = (self.depth_min, self.depth_max)
if x0 is None:
# find an initial guess via grid search
x0 = brute(objective, (depth_bounds, depth_bounds))
res = minimize(objective, x0=x0,
method='L-BFGS-B',
bounds=[(self.depth_min, self.depth_max),
(self.depth_min, self.depth_max)],
jac='2-point', options={'eps': 1e-3})
best_depths = res.x
(dd,nd) = best_depths
best_results = self.growth_fn(dd, nd, StartingLength,
starting_mass, day_hours,
night_hours, self.DayLight,
self.NightLight, self.prey)
# for dd in day_depths:
# for nd in night_depths:
# results = self.growth_fn(dd, nd, StartingLength,
# starting_mass, day_hours,
# night_hours, self.DayLight,
# self.NightLight, self.prey)
# growth = results[0]
# if growth > best_growth:
# best_growth = growth
# best_depths = [dd, nd]
# best_results = results
#growths = [self.growth_fn(d, StartingLength, starting_mass, hours, light, self.prey)[0] for d in depth_arr]
#idx = np.argmax(growths)
#d = depth_arr[idx]
#results = self.growth_fn(d, StartingLength, starting_mass,
# hours, light, self.prey)
return best_depths, best_results
def growth_fn(self, day_depth, night_depth, StartingLength, starting_mass,
day_hours, night_hours, day_light, night_light, prey):
day_temp = self.temp_from_depth(day_depth)
night_temp = self.temp_from_depth(night_depth)
cmax = self.compute_cmax(starting_mass)
day_foraging = self.compute_foragingbydepth(StartingLength, starting_mass,
day_light, self.daphline,
self.daph_auc, day_depth)
night_foraging = self.compute_foragingbydepth(StartingLength, starting_mass,
night_light, self.daphline,
self.daph_auc, night_depth)
if day_foraging > night_foraging:
day_foraging *= day_hours
day_P = min(day_foraging/cmax, 1)
night_P = min(1.0 - day_P, night_foraging*night_hours)
else:
night_foraging *= night_hours
night_P = min(night_foraging/cmax, 1.0)
day_P = min(1.0 - night_P, day_foraging*day_hours)
day_bioe = self.compute_bioenergetics(starting_mass, day_temp, day_P,
self.prey, self.digestibility)
night_bioe = self.compute_bioenergetics(starting_mass, night_temp,
night_P, self.prey,
self.digestibility)
day_bioe = np.array(day_bioe) * day_hours/24.0
night_bioe = np.array(night_bioe) * night_hours/24.0
(consumption, egestion, excretion, respiration, SDAction) = \
day_bioe + night_bioe
P = day_P + night_P
growth = self.compute_growth(consumption, prey, self.preyenergy, egestion, excretion,
SDAction, respiration, self.predatorenergy, starting_mass)
return (growth, consumption, egestion, excretion, respiration, SDAction, P, day_P, night_P)
# (d_cons, d_eg, d_ex, d_resp, d_sda) = day_bioe
# (n_cons, n_eg, n_ex, n_resp, n_sda) = night_bioe
# [consumption, respiration,
# respiration = d_resp + n_resp
# P = min(foraging / cmax, 1)
# night_P = 1.0 - P
# foraging = self.compute_foragingbydepth(StartingLength, starting_mass, light,
# self.daphline, self.daph_auc, depth) * hours
# ft = self.compute_ft(temp)
# cmax = self.compute_cmax(starting_mass)
# P = min(foraging / cmax, 1)
# (consumption, egestion, excretion, respiration, SDAction) = \
# self.compute_bioenergetics(starting_mass, temp, P, self.prey, self.digestibility)
# day_proportion = hours / 24.0
# consumption *= day_proportion
# respiration *= day_proportion
# egestion *= day_proportion
# excretion *= day_proportion
# SDAction *= day_proportion
# growth = self.compute_growth(consumption, prey, self.preyenergy, egestion, excretion,
# SDAction, respiration, self.predatorenergy, starting_mass)
# return (growth, consumption, egestion, excretion, respiration, SDAction, P)
def Run_Batch(self):
# March 11:50 (11.83), April 13:24 (13.4), May 14:44 (14.73), June 15:25 (15.42),
# July 15:07 (15.12), August 13:58 (13.97), September 12:27 (12.45)
ndays = 30
day_hours = self.daylength[self.month]
night_hours = 24 - day_hours
self.out = {'Year':[], 'Site':[], 'Month':[], 'Fish Starting Mass':[],
'Light Extinction Coefficient':[], 'Daphnia Size':[], 'Daphnia Density':[],
'StartingLength':[], 'StartingMass':[], 'growth':[], 'day_depth':[],
'night_depth':[], 'egestion': [], 'excretion': [], 'consumption': [],
'P': [], 'temps': []}
condition1 = float(100*self.starting_mass*((self.StartingLength/10)**(-3.0)))
last_best_depths = None
for d in range(ndays):
# (day_depth, day_results) =\
# self.best_depth(self.StartingLength, self.starting_mass,
# day_hours, self.DayLight, self.depths)
# (day_growth, day_consumption, day_eg, day_ex, day_resp, day_sda, day_P) = \
# day_results
# (night_depth, night_results) =\
# self.best_depth(self.StartingLength, self.starting_mass,
# night_hours, self.NightLight, self.depths)
# (night_growth, night_consumption, night_eg, night_ex, night_resp, night_sda, night_P) = \
# night_results
# self.day_temp = self.temp_from_depth(day_depth)
# self.night_temp = self.temp_from_depth(night_depth)
# growth = day_growth + night_growth
best_depths, best_results = self.best_depth(self.StartingLength,
self.starting_mass,
self.depths,
last_best_depths)
(day_depth, night_depth) = best_depths
last_best_depths = best_depths
(growth, consumption, egestion,
excretion, respiration, SDAction, P, day_P, night_P) = best_results
self.day_temp = self.temp_from_depth(day_depth)
self.night_temp = self.temp_from_depth(night_depth)
dailyconsume = (consumption*self.starting_mass)\
/self.daphnia_weight
self.starting_mass += growth
if growth > 0:
# From LP and FC screw trap data (R2 = 0.9933)
self.StartingLength = (self.starting_mass / 0.000004) ** (1 / 3.1776)
# self.StartingLength = (self.starting_mass / 0.0003) ** (1 / 2.217)
# weight to fork length (MacFarlane and Norton 2008)
# Checked fish lengths against this and by end of summer fish weigh much less than they 'should' based on their length
self.out['Year'].append(self.year)
self.out['Site'].append(self.site)
self.out['Month'].append(self.month)
self.out['Fish Starting Mass'].append(self.starting_mass)
self.out['Light Extinction Coefficient'].append(self.light)
self.out['Daphnia Size'].append(self.daphnia_size)
self.out['Daphnia Density'].append(self.total_daphnia)
self.out['day_depth'].append(day_depth)
self.out['night_depth'].append(night_depth)
self.out['growth'].append(growth)
self.out['StartingMass'].append(self.starting_mass)
self.out['StartingLength'].append(self.StartingLength)
self.out['egestion'].append(egestion)
self.out['excretion'].append(excretion)
self.out['consumption'].append(consumption)
self.out['P'].append(P)
dtfinal = self.day_temp
ntfinal = self.night_temp
self.out['temps'].append(dtfinal)
self.out['temps'].append(ntfinal)
ele = self.elevation-int(day_depth)
daph = self.daphline(day_depth)
PopEst = get_sustain_est(ele, daph, dailyconsume, self.PSite)
condition = float(100*(self.starting_mass-self.starting_mass_initial)*((self.StartingLength/10)**(-3.0)))
return self.out, dailyconsume, condition, condition1, dtfinal, ntfinal, PopEst, day_P, night_P
if __name__ == '__main__':
from cycler import cycler
year = '2015'
month = 'June'
site = 'Fall Creek'
site_data = Site_Data(year, site, month, 0.72, 25, 0.1)
starting_mass = 3
daph_data = Daph_Data(5020.65, 1.26, year, site, month)
max_temp = 10000
min_temp = -1
cust_temp = '{0}_T_{1}_{2}.csv'.format(site, month, year)
elev = 691
pop_site = site
def make_plots(self):
w = 0.5
temp = 15
ws = [np.round(x,2) for x in np.arange(0.1,1.0,0.1)]
ps = np.arange(0,1.0,0.01)
gs = {}
temps = [np.round(x,2) for x in np.arange(5,25,2)]
for w in ws:
gs[w] = []
for P in ps:
(cons,eg,ex,res,sda) = \
self.compute_bioenergetics(w, temp, P, self.prey,
self.digestibility)
growth = self.compute_growth(cons, self.prey, self.preyenergy,
eg, ex, sda,res,
self.predatorenergy, w)
gs[w].append(growth)
fig,ax = pyplot.subplots()
colors = [pyplot.get_cmap('inferno')(1.0 * i/len(ws))
for i in range(len(ws))]
ax.set_prop_cycle(cycler('color',colors))
for w,growths in gs.items():
ax.plot(ps,growths,label=str(w))
ax.legend()
pyplot.xlabel('P')
pyplot.ylabel('growth')
pyplot.show()
Batch.make_plots = make_plots
FRESH_BATCH = Batch(site_data, starting_mass, daph_data,
max_temp, min_temp, cust_temp, elev, pop_site, True)
#FRESH_BATCH.make_plots()
BASE_RESULTS, DAPHNIA_CONSUMED, CONDITION, CONDITION1, DAY_TEMP, NIGHT_TEMP,\
POPULATION_ESTIMATE = FRESH_BATCH.Run_Batch()
keys = ['StartingMass','consumption','egestion','excretion',
'P', 'day_depth','night_depth','temps']
rows = np.ceil(len(keys)/2)
for idx,k in enumerate(keys):
pyplot.subplot(rows,2,idx+1)
pyplot.plot(BASE_RESULTS[k])
pyplot.title(k)
depths = np.arange(FRESH_BATCH.depth_min, FRESH_BATCH.depth_max)
fig, ax1 = pyplot.subplots()
ax1.plot(FRESH_BATCH.temp_from_depth(depths), depths, 'orange')
ax2 = ax1.twiny()
ax2.plot(FRESH_BATCH.daphline(depths), depths, 'green')
pyplot.show()
| gpl-3.0 |
henrykironde/scikit-learn | examples/decomposition/plot_pca_3d.py | 354 | 2432 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to choose a direction that is not flat.
"""
print(__doc__)
# Authors: Gael Varoquaux
# Jaques Grobler
# Kevin Hughes
# License: BSD 3 clause
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
###############################################################################
# Create the data
e = np.exp(1)
np.random.seed(4)
def pdf(x):
return 0.5 * (stats.norm(scale=0.25 / e).pdf(x)
+ stats.norm(scale=4 / e).pdf(x))
y = np.random.normal(scale=0.5, size=(30000))
x = np.random.normal(scale=0.5, size=(30000))
z = np.random.normal(scale=0.1, size=len(x))
density = pdf(x) * pdf(y)
pdf_z = pdf(5 * z)
density *= pdf_z
a = x + y
b = 2 * y
c = a - b + z
norm = np.sqrt(a.var() + b.var())
a /= norm
b /= norm
###############################################################################
# Plot the figures
def plot_figs(fig_num, elev, azim):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=elev, azim=azim)
ax.scatter(a[::10], b[::10], c[::10], c=density[::10], marker='+', alpha=.4)
Y = np.c_[a, b, c]
# Using SciPy's SVD, this would be:
# _, pca_score, V = scipy.linalg.svd(Y, full_matrices=False)
pca = PCA(n_components=3)
pca.fit(Y)
pca_score = pca.explained_variance_ratio_
V = pca.components_
x_pca_axis, y_pca_axis, z_pca_axis = V.T * pca_score / pca_score.min()
x_pca_axis, y_pca_axis, z_pca_axis = 3 * V.T
x_pca_plane = np.r_[x_pca_axis[:2], - x_pca_axis[1::-1]]
y_pca_plane = np.r_[y_pca_axis[:2], - y_pca_axis[1::-1]]
z_pca_plane = np.r_[z_pca_axis[:2], - z_pca_axis[1::-1]]
x_pca_plane.shape = (2, 2)
y_pca_plane.shape = (2, 2)
z_pca_plane.shape = (2, 2)
ax.plot_surface(x_pca_plane, y_pca_plane, z_pca_plane)
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
elev = -40
azim = -80
plot_figs(1, elev, azim)
elev = 30
azim = 20
plot_figs(2, elev, azim)
plt.show()
| bsd-3-clause |
cactusbin/nyt | matplotlib/examples/user_interfaces/embedding_in_qt4_wtoolbar.py | 6 | 2033 | from __future__ import print_function
import sys
import numpy as np
from matplotlib.figure import Figure
from matplotlib.backend_bases import key_press_handler
from matplotlib.backends.backend_qt4agg import (
FigureCanvasQTAgg as FigureCanvas,
NavigationToolbar2QTAgg as NavigationToolbar)
from PyQt4.QtCore import *
from PyQt4.QtGui import *
class AppForm(QMainWindow):
def __init__(self, parent=None):
QMainWindow.__init__(self, parent)
#self.x, self.y = self.get_data()
self.data = self.get_data2()
self.create_main_frame()
self.on_draw()
def create_main_frame(self):
self.main_frame = QWidget()
self.fig = Figure((5.0, 4.0), dpi=100)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
self.canvas.setFocusPolicy(Qt.StrongFocus)
self.canvas.setFocus()
self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
self.canvas.mpl_connect('key_press_event', self.on_key_press)
vbox = QVBoxLayout()
vbox.addWidget(self.canvas) # the matplotlib canvas
vbox.addWidget(self.mpl_toolbar)
self.main_frame.setLayout(vbox)
self.setCentralWidget(self.main_frame)
def get_data2(self):
return np.arange(20).reshape([4, 5]).copy()
def on_draw(self):
self.fig.clear()
self.axes = self.fig.add_subplot(111)
#self.axes.plot(self.x, self.y, 'ro')
self.axes.imshow(self.data, interpolation='nearest')
#self.axes.plot([1,2,3])
self.canvas.draw()
def on_key_press(self, event):
print('you pressed', event.key)
# implement the default mpl key press events described at
# http://matplotlib.org/users/navigation_toolbar.html#navigation-keyboard-shortcuts
key_press_handler(event, self.canvas, self.mpl_toolbar)
def main():
app = QApplication(sys.argv)
form = AppForm()
form.show()
app.exec_()
if __name__ == "__main__":
main()
| unlicense |
0/pathintmatmult | pathintmatmult/plotting.py | 1 | 1228 | """
Convenience functions for plotting the generated data.
"""
import matplotlib.pyplot as plt
def plot2d(data: '[[X]]', x_range, y_range, out_path, *, x_label=None, y_label=None, colormap='jet', colorbar=True):
"""
Plot the data as a heat map.
The resulting image is saved to out_path.
Parameters:
data: Two-dimensional array of numbers to plot.
x_range: Tuple containing the min and max values for the x axis.
y_range: Tuple containing the min and max values for the y axis.
out_path: The path to the file where the image should be written. The
extension determines the image format (e.g. pdf, png).
x_label: Label for the x axis.
y_label: Label for the y axis.
colormap: matplotlib colormap to use for the image.
colorbar: Whether to display the colorbar.
"""
fig = plt.figure()
ax = fig.gca()
img = ax.imshow(data, cmap=colormap, origin='lower', extent=(x_range + y_range))
if x_label is not None:
ax.set_xlabel(x_label)
if y_label is not None:
ax.set_ylabel(y_label)
if colorbar:
fig.colorbar(img, drawedges=False)
fig.savefig(out_path, bbox_inches='tight', transparent=True)
| mit |
hainm/scikit-learn | sklearn/tests/test_multiclass.py | 136 | 23649 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_greater
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OutputCodeClassifier
from sklearn.multiclass import fit_ovr
from sklearn.multiclass import fit_ovo
from sklearn.multiclass import fit_ecoc
from sklearn.multiclass import predict_ovr
from sklearn.multiclass import predict_ovo
from sklearn.multiclass import predict_ecoc
from sklearn.multiclass import predict_proba_ovr
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.preprocessing import LabelBinarizer
from sklearn.svm import LinearSVC, SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import (LinearRegression, Lasso, ElasticNet, Ridge,
Perceptron, LogisticRegression)
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn import svm
from sklearn import datasets
from sklearn.externals.six.moves import zip
iris = datasets.load_iris()
rng = np.random.RandomState(0)
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
n_classes = 3
def test_ovr_exceptions():
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
assert_raises(ValueError, ovr.predict, [])
with ignore_warnings():
assert_raises(ValueError, predict_ovr, [LinearSVC(), MultinomialNB()],
LabelBinarizer(), [])
# Fail on multioutput data
assert_raises(ValueError, OneVsRestClassifier(MultinomialNB()).fit,
np.array([[1, 0], [0, 1]]),
np.array([[1, 2], [3, 1]]))
assert_raises(ValueError, OneVsRestClassifier(MultinomialNB()).fit,
np.array([[1, 0], [0, 1]]),
np.array([[1.5, 2.4], [3.1, 0.8]]))
def test_ovr_fit_predict():
# A classifier which implements decision_function.
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovr.estimators_), n_classes)
clf = LinearSVC(random_state=0)
pred2 = clf.fit(iris.data, iris.target).predict(iris.data)
assert_equal(np.mean(iris.target == pred), np.mean(iris.target == pred2))
# A classifier which implements predict_proba.
ovr = OneVsRestClassifier(MultinomialNB())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert_greater(np.mean(iris.target == pred), 0.65)
def test_ovr_ovo_regressor():
# test that ovr and ovo work on regressors which don't have a decision_function
ovr = OneVsRestClassifier(DecisionTreeRegressor())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovr.estimators_), n_classes)
assert_array_equal(np.unique(pred), [0, 1, 2])
# we are doing something sensible
assert_greater(np.mean(pred == iris.target), .9)
ovr = OneVsOneClassifier(DecisionTreeRegressor())
pred = ovr.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovr.estimators_), n_classes * (n_classes - 1) / 2)
assert_array_equal(np.unique(pred), [0, 1, 2])
# we are doing something sensible
assert_greater(np.mean(pred == iris.target), .9)
def test_ovr_fit_predict_sparse():
for sparse in [sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix,
sp.lil_matrix]:
base_clf = MultinomialNB(alpha=1)
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
Y_pred = clf.predict(X_test)
clf_sprs = OneVsRestClassifier(base_clf).fit(X_train, sparse(Y_train))
Y_pred_sprs = clf_sprs.predict(X_test)
assert_true(clf.multilabel_)
assert_true(sp.issparse(Y_pred_sprs))
assert_array_equal(Y_pred_sprs.toarray(), Y_pred)
# Test predict_proba
Y_proba = clf_sprs.predict_proba(X_test)
# predict assigns a label if the probability that the
# sample has the label is greater than 0.5.
pred = Y_proba > .5
assert_array_equal(pred, Y_pred_sprs.toarray())
# Test decision_function
clf_sprs = OneVsRestClassifier(svm.SVC()).fit(X_train, sparse(Y_train))
dec_pred = (clf_sprs.decision_function(X_test) > 0).astype(int)
assert_array_equal(dec_pred, clf_sprs.predict(X_test).toarray())
def test_ovr_always_present():
# Test that ovr works with classes that are always present or absent.
# Note: tests is the case where _ConstantPredictor is utilised
X = np.ones((10, 2))
X[:5, :] = 0
# Build an indicator matrix where two features are always on.
# As list of lists, it would be: [[int(i >= 5), 2, 3] for i in range(10)]
y = np.zeros((10, 3))
y[5:, 0] = 1
y[:, 1] = 1
y[:, 2] = 1
ovr = OneVsRestClassifier(LogisticRegression())
assert_warns(UserWarning, ovr.fit, X, y)
y_pred = ovr.predict(X)
assert_array_equal(np.array(y_pred), np.array(y))
y_pred = ovr.decision_function(X)
assert_equal(np.unique(y_pred[:, -2:]), 1)
y_pred = ovr.predict_proba(X)
assert_array_equal(y_pred[:, -1], np.ones(X.shape[0]))
# y has a constantly absent label
y = np.zeros((10, 2))
y[5:, 0] = 1 # variable label
ovr = OneVsRestClassifier(LogisticRegression())
assert_warns(UserWarning, ovr.fit, X, y)
y_pred = ovr.predict_proba(X)
assert_array_equal(y_pred[:, -1], np.zeros(X.shape[0]))
def test_ovr_multiclass():
# Toy dataset where features correspond directly to labels.
X = np.array([[0, 0, 5], [0, 5, 0], [3, 0, 0], [0, 0, 6], [6, 0, 0]])
y = ["eggs", "spam", "ham", "eggs", "ham"]
Y = np.array([[0, 0, 1],
[0, 1, 0],
[1, 0, 0],
[0, 0, 1],
[1, 0, 0]])
classes = set("ham eggs spam".split())
for base_clf in (MultinomialNB(), LinearSVC(random_state=0),
LinearRegression(), Ridge(),
ElasticNet()):
clf = OneVsRestClassifier(base_clf).fit(X, y)
assert_equal(set(clf.classes_), classes)
y_pred = clf.predict(np.array([[0, 0, 4]]))[0]
assert_equal(set(y_pred), set("eggs"))
# test input as label indicator matrix
clf = OneVsRestClassifier(base_clf).fit(X, Y)
y_pred = clf.predict([[0, 0, 4]])[0]
assert_array_equal(y_pred, [0, 0, 1])
def test_ovr_binary():
# Toy dataset where features correspond directly to labels.
X = np.array([[0, 0, 5], [0, 5, 0], [3, 0, 0], [0, 0, 6], [6, 0, 0]])
y = ["eggs", "spam", "spam", "eggs", "spam"]
Y = np.array([[0, 1, 1, 0, 1]]).T
classes = set("eggs spam".split())
def conduct_test(base_clf, test_predict_proba=False):
clf = OneVsRestClassifier(base_clf).fit(X, y)
assert_equal(set(clf.classes_), classes)
y_pred = clf.predict(np.array([[0, 0, 4]]))[0]
assert_equal(set(y_pred), set("eggs"))
if test_predict_proba:
X_test = np.array([[0, 0, 4]])
probabilities = clf.predict_proba(X_test)
assert_equal(2, len(probabilities[0]))
assert_equal(clf.classes_[np.argmax(probabilities, axis=1)],
clf.predict(X_test))
# test input as label indicator matrix
clf = OneVsRestClassifier(base_clf).fit(X, Y)
y_pred = clf.predict([[3, 0, 0]])[0]
assert_equal(y_pred, 1)
for base_clf in (LinearSVC(random_state=0), LinearRegression(),
Ridge(), ElasticNet()):
conduct_test(base_clf)
for base_clf in (MultinomialNB(), SVC(probability=True),
LogisticRegression()):
conduct_test(base_clf, test_predict_proba=True)
def test_ovr_multilabel():
# Toy dataset where features correspond directly to labels.
X = np.array([[0, 4, 5], [0, 5, 0], [3, 3, 3], [4, 0, 6], [6, 0, 0]])
y = np.array([[0, 1, 1],
[0, 1, 0],
[1, 1, 1],
[1, 0, 1],
[1, 0, 0]])
for base_clf in (MultinomialNB(), LinearSVC(random_state=0),
LinearRegression(), Ridge(),
ElasticNet(), Lasso(alpha=0.5)):
clf = OneVsRestClassifier(base_clf).fit(X, y)
y_pred = clf.predict([[0, 4, 4]])[0]
assert_array_equal(y_pred, [0, 1, 1])
assert_true(clf.multilabel_)
def test_ovr_fit_predict_svc():
ovr = OneVsRestClassifier(svm.SVC())
ovr.fit(iris.data, iris.target)
assert_equal(len(ovr.estimators_), 3)
assert_greater(ovr.score(iris.data, iris.target), .9)
def test_ovr_multilabel_dataset():
base_clf = MultinomialNB(alpha=1)
for au, prec, recall in zip((True, False), (0.51, 0.66), (0.51, 0.80)):
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=2,
length=50,
allow_unlabeled=au,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test, Y_test = X[80:], Y[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
Y_pred = clf.predict(X_test)
assert_true(clf.multilabel_)
assert_almost_equal(precision_score(Y_test, Y_pred, average="micro"),
prec,
decimal=2)
assert_almost_equal(recall_score(Y_test, Y_pred, average="micro"),
recall,
decimal=2)
def test_ovr_multilabel_predict_proba():
base_clf = MultinomialNB(alpha=1)
for au in (False, True):
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3,
length=50,
allow_unlabeled=au,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
# decision function only estimator. Fails in current implementation.
decision_only = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert_raises(AttributeError, decision_only.predict_proba, X_test)
# Estimator with predict_proba disabled, depending on parameters.
decision_only = OneVsRestClassifier(svm.SVC(probability=False))
decision_only.fit(X_train, Y_train)
assert_raises(AttributeError, decision_only.predict_proba, X_test)
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
# predict assigns a label if the probability that the
# sample has the label is greater than 0.5.
pred = Y_proba > .5
assert_array_equal(pred, Y_pred)
def test_ovr_single_label_predict_proba():
base_clf = MultinomialNB(alpha=1)
X, Y = iris.data, iris.target
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
# decision function only estimator. Fails in current implementation.
decision_only = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert_raises(AttributeError, decision_only.predict_proba, X_test)
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
assert_almost_equal(Y_proba.sum(axis=1), 1.0)
# predict assigns a label if the probability that the
# sample has the label is greater than 0.5.
pred = np.array([l.argmax() for l in Y_proba])
assert_false((pred - Y_pred).any())
def test_ovr_multilabel_decision_function():
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(svm.SVC()).fit(X_train, Y_train)
assert_array_equal((clf.decision_function(X_test) > 0).astype(int),
clf.predict(X_test))
def test_ovr_single_label_decision_function():
X, Y = datasets.make_classification(n_samples=100,
n_features=20,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
clf = OneVsRestClassifier(svm.SVC()).fit(X_train, Y_train)
assert_array_equal(clf.decision_function(X_test).ravel() > 0,
clf.predict(X_test))
def test_ovr_gridsearch():
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ovr, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
def test_ovr_pipeline():
# Test with pipeline of length one
# This test is needed because the multiclass estimators may fail to detect
# the presence of predict_proba or decision_function.
clf = Pipeline([("tree", DecisionTreeClassifier())])
ovr_pipe = OneVsRestClassifier(clf)
ovr_pipe.fit(iris.data, iris.target)
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_array_equal(ovr.predict(iris.data), ovr_pipe.predict(iris.data))
def test_ovr_coef_():
for base_classifier in [SVC(kernel='linear', random_state=0), LinearSVC(random_state=0)]:
# SVC has sparse coef with sparse input data
ovr = OneVsRestClassifier(base_classifier)
for X in [iris.data, sp.csr_matrix(iris.data)]:
# test with dense and sparse coef
ovr.fit(X, iris.target)
shape = ovr.coef_.shape
assert_equal(shape[0], n_classes)
assert_equal(shape[1], iris.data.shape[1])
# don't densify sparse coefficients
assert_equal(sp.issparse(ovr.estimators_[0].coef_), sp.issparse(ovr.coef_))
def test_ovr_coef_exceptions():
# Not fitted exception!
ovr = OneVsRestClassifier(LinearSVC(random_state=0))
# lambda is needed because we don't want coef_ to be evaluated right away
assert_raises(ValueError, lambda x: ovr.coef_, None)
# Doesn't have coef_ exception!
ovr = OneVsRestClassifier(DecisionTreeClassifier())
ovr.fit(iris.data, iris.target)
assert_raises(AttributeError, lambda x: ovr.coef_, None)
def test_ovo_exceptions():
ovo = OneVsOneClassifier(LinearSVC(random_state=0))
assert_raises(ValueError, ovo.predict, [])
def test_ovo_fit_on_list():
# Test that OneVsOne fitting works with a list of targets and yields the
# same output as predict from an array
ovo = OneVsOneClassifier(LinearSVC(random_state=0))
prediction_from_array = ovo.fit(iris.data, iris.target).predict(iris.data)
prediction_from_list = ovo.fit(iris.data,
list(iris.target)).predict(iris.data)
assert_array_equal(prediction_from_array, prediction_from_list)
def test_ovo_fit_predict():
# A classifier which implements decision_function.
ovo = OneVsOneClassifier(LinearSVC(random_state=0))
ovo.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovo.estimators_), n_classes * (n_classes - 1) / 2)
# A classifier which implements predict_proba.
ovo = OneVsOneClassifier(MultinomialNB())
ovo.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ovo.estimators_), n_classes * (n_classes - 1) / 2)
def test_ovo_decision_function():
n_samples = iris.data.shape[0]
ovo_clf = OneVsOneClassifier(LinearSVC(random_state=0))
ovo_clf.fit(iris.data, iris.target)
decisions = ovo_clf.decision_function(iris.data)
assert_equal(decisions.shape, (n_samples, n_classes))
assert_array_equal(decisions.argmax(axis=1), ovo_clf.predict(iris.data))
# Compute the votes
votes = np.zeros((n_samples, n_classes))
k = 0
for i in range(n_classes):
for j in range(i + 1, n_classes):
pred = ovo_clf.estimators_[k].predict(iris.data)
votes[pred == 0, i] += 1
votes[pred == 1, j] += 1
k += 1
# Extract votes and verify
assert_array_equal(votes, np.round(decisions))
for class_idx in range(n_classes):
# For each sample and each class, there only 3 possible vote levels
# because they are only 3 distinct class pairs thus 3 distinct
# binary classifiers.
# Therefore, sorting predictions based on votes would yield
# mostly tied predictions:
assert_true(set(votes[:, class_idx]).issubset(set([0., 1., 2.])))
# The OVO decision function on the other hand is able to resolve
# most of the ties on this data as it combines both the vote counts
# and the aggregated confidence levels of the binary classifiers
# to compute the aggregate decision function. The iris dataset
# has 150 samples with a couple of duplicates. The OvO decisions
# can resolve most of the ties:
assert_greater(len(np.unique(decisions[:, class_idx])), 146)
def test_ovo_gridsearch():
ovo = OneVsOneClassifier(LinearSVC(random_state=0))
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ovo, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
def test_ovo_ties():
# Test that ties are broken using the decision function,
# not defaulting to the smallest label
X = np.array([[1, 2], [2, 1], [-2, 1], [-2, -1]])
y = np.array([2, 0, 1, 2])
multi_clf = OneVsOneClassifier(Perceptron(shuffle=False))
ovo_prediction = multi_clf.fit(X, y).predict(X)
ovo_decision = multi_clf.decision_function(X)
# Classifiers are in order 0-1, 0-2, 1-2
# Use decision_function to compute the votes and the normalized
# sum_of_confidences, which is used to disambiguate when there is a tie in
# votes.
votes = np.round(ovo_decision)
normalized_confidences = ovo_decision - votes
# For the first point, there is one vote per class
assert_array_equal(votes[0, :], 1)
# For the rest, there is no tie and the prediction is the argmax
assert_array_equal(np.argmax(votes[1:], axis=1), ovo_prediction[1:])
# For the tie, the prediction is the class with the highest score
assert_equal(ovo_prediction[0], normalized_confidences[0].argmax())
def test_ovo_ties2():
# test that ties can not only be won by the first two labels
X = np.array([[1, 2], [2, 1], [-2, 1], [-2, -1]])
y_ref = np.array([2, 0, 1, 2])
# cycle through labels so that each label wins once
for i in range(3):
y = (y_ref + i) % 3
multi_clf = OneVsOneClassifier(Perceptron(shuffle=False))
ovo_prediction = multi_clf.fit(X, y).predict(X)
assert_equal(ovo_prediction[0], i % 3)
def test_ovo_string_y():
# Test that the OvO doesn't mess up the encoding of string labels
X = np.eye(4)
y = np.array(['a', 'b', 'c', 'd'])
ovo = OneVsOneClassifier(LinearSVC())
ovo.fit(X, y)
assert_array_equal(y, ovo.predict(X))
def test_ecoc_exceptions():
ecoc = OutputCodeClassifier(LinearSVC(random_state=0))
assert_raises(ValueError, ecoc.predict, [])
def test_ecoc_fit_predict():
# A classifier which implements decision_function.
ecoc = OutputCodeClassifier(LinearSVC(random_state=0),
code_size=2, random_state=0)
ecoc.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ecoc.estimators_), n_classes * 2)
# A classifier which implements predict_proba.
ecoc = OutputCodeClassifier(MultinomialNB(), code_size=2, random_state=0)
ecoc.fit(iris.data, iris.target).predict(iris.data)
assert_equal(len(ecoc.estimators_), n_classes * 2)
def test_ecoc_gridsearch():
ecoc = OutputCodeClassifier(LinearSVC(random_state=0),
random_state=0)
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ecoc, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
@ignore_warnings
def test_deprecated():
base_estimator = DecisionTreeClassifier(random_state=0)
X, Y = iris.data, iris.target
X_train, Y_train = X[:80], Y[:80]
X_test = X[80:]
all_metas = [
(OneVsRestClassifier, fit_ovr, predict_ovr, predict_proba_ovr),
(OneVsOneClassifier, fit_ovo, predict_ovo, None),
(OutputCodeClassifier, fit_ecoc, predict_ecoc, None),
]
for MetaEst, fit_func, predict_func, proba_func in all_metas:
try:
meta_est = MetaEst(base_estimator,
random_state=0).fit(X_train, Y_train)
fitted_return = fit_func(base_estimator, X_train, Y_train,
random_state=0)
except TypeError:
meta_est = MetaEst(base_estimator).fit(X_train, Y_train)
fitted_return = fit_func(base_estimator, X_train, Y_train)
if len(fitted_return) == 2:
estimators_, classes_or_lb = fitted_return
assert_almost_equal(predict_func(estimators_, classes_or_lb,
X_test),
meta_est.predict(X_test))
if proba_func is not None:
assert_almost_equal(proba_func(estimators_, X_test,
is_multilabel=False),
meta_est.predict_proba(X_test))
else:
estimators_, classes_or_lb, codebook = fitted_return
assert_almost_equal(predict_func(estimators_, classes_or_lb,
codebook, X_test),
meta_est.predict(X_test))
| bsd-3-clause |
soulmachine/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature 2
has a strong coefficient on the full model, it does not
give us much regarding `y` when compared to just feature 1
"""
print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets, linear_model
diabetes = datasets.load_diabetes()
indices = (0, 1)
X_train = diabetes.data[:-20, indices]
X_test = diabetes.data[-20:, indices]
y_train = diabetes.target[:-20]
y_test = diabetes.target[-20:]
ols = linear_model.LinearRegression()
ols.fit(X_train, y_train)
###############################################################################
# Plot the figure
def plot_figs(fig_num, elev, azim, X_train, clf):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, elev=elev, azim=azim)
ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+')
ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]),
np.array([[-.1, .15], [-.1, .15]]),
clf.predict(np.array([[-.1, -.1, .15, .15],
[-.1, .15, -.1, .15]]).T
).reshape((2, 2)),
alpha=.5)
ax.set_xlabel('X_1')
ax.set_ylabel('X_2')
ax.set_zlabel('Y')
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
#Generate the three different figures from different views
elev = 43.5
azim = -110
plot_figs(1, elev, azim, X_train, ols)
elev = -.5
azim = 0
plot_figs(2, elev, azim, X_train, ols)
elev = -.5
azim = 90
plot_figs(3, elev, azim, X_train, ols)
plt.show()
| bsd-3-clause |
humdings/zipline | tests/calendars/test_nyse_calendar.py | 5 | 9411 | from unittest import TestCase
import pandas as pd
from .test_trading_calendar import ExchangeCalendarTestBase
from zipline.utils.calendars.exchange_calendar_nyse import NYSEExchangeCalendar
class NYSECalendarTestCase(ExchangeCalendarTestBase, TestCase):
answer_key_filename = 'nyse'
calendar_class = NYSEExchangeCalendar
MAX_SESSION_HOURS = 6.5
def test_2012(self):
# holidays we expect:
holidays_2012 = [
pd.Timestamp("2012-01-02", tz='UTC'),
pd.Timestamp("2012-01-16", tz='UTC'),
pd.Timestamp("2012-02-20", tz='UTC'),
pd.Timestamp("2012-04-06", tz='UTC'),
pd.Timestamp("2012-05-28", tz='UTC'),
pd.Timestamp("2012-07-04", tz='UTC'),
pd.Timestamp("2012-09-03", tz='UTC'),
pd.Timestamp("2012-11-22", tz='UTC'),
pd.Timestamp("2012-12-25", tz='UTC')
]
for session_label in holidays_2012:
self.assertNotIn(session_label, self.calendar.all_sessions)
# early closes we expect:
early_closes_2012 = [
pd.Timestamp("2012-07-03", tz='UTC'),
pd.Timestamp("2012-11-23", tz='UTC'),
pd.Timestamp("2012-12-24", tz='UTC')
]
for early_close_session_label in early_closes_2012:
self.assertIn(early_close_session_label,
self.calendar.early_closes)
def test_special_holidays(self):
# 9/11
# Sept 11, 12, 13, 14 2001
self.assertNotIn(pd.Period("9/11/2001"), self.calendar.all_sessions)
self.assertNotIn(pd.Period("9/12/2001"), self.calendar.all_sessions)
self.assertNotIn(pd.Period("9/13/2001"), self.calendar.all_sessions)
self.assertNotIn(pd.Period("9/14/2001"), self.calendar.all_sessions)
# Hurricane Sandy
# Oct 29, 30 2012
self.assertNotIn(pd.Period("10/29/2012"), self.calendar.all_sessions)
self.assertNotIn(pd.Period("10/30/2012"), self.calendar.all_sessions)
# various national days of mourning
# Gerald Ford - 1/2/2007
self.assertNotIn(pd.Period("1/2/2007"), self.calendar.all_sessions)
# Ronald Reagan - 6/11/2004
self.assertNotIn(pd.Period("6/11/2004"), self.calendar.all_sessions)
# Richard Nixon - 4/27/1994
self.assertNotIn(pd.Period("4/27/1994"), self.calendar.all_sessions)
def test_new_years(self):
"""
Check whether the TradingCalendar contains certain dates.
"""
# January 2012
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6 7
# 8 9 10 11 12 13 14
# 15 16 17 18 19 20 21
# 22 23 24 25 26 27 28
# 29 30 31
start_session = pd.Timestamp("2012-01-02", tz='UTC')
end_session = pd.Timestamp("2013-12-31", tz='UTC')
sessions = self.calendar.sessions_in_range(start_session, end_session)
day_after_new_years_sunday = pd.Timestamp("2012-01-02",
tz='UTC')
self.assertNotIn(day_after_new_years_sunday, sessions,
"""
If NYE falls on a weekend, {0} the Monday after is a holiday.
""".strip().format(day_after_new_years_sunday)
)
first_trading_day_after_new_years_sunday = pd.Timestamp("2012-01-03",
tz='UTC')
self.assertIn(first_trading_day_after_new_years_sunday, sessions,
"""
If NYE falls on a weekend, {0} the Tuesday after is the first trading day.
""".strip().format(first_trading_day_after_new_years_sunday)
)
# January 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5
# 6 7 8 9 10 11 12
# 13 14 15 16 17 18 19
# 20 21 22 23 24 25 26
# 27 28 29 30 31
new_years_day = pd.Timestamp("2013-01-01", tz='UTC')
self.assertNotIn(new_years_day, sessions,
"""
If NYE falls during the week, e.g. {0}, it is a holiday.
""".strip().format(new_years_day)
)
first_trading_day_after_new_years = pd.Timestamp("2013-01-02",
tz='UTC')
self.assertIn(first_trading_day_after_new_years, sessions,
"""
If the day after NYE falls during the week, {0} \
is the first trading day.
""".strip().format(first_trading_day_after_new_years)
)
def test_thanksgiving(self):
"""
Check TradingCalendar Thanksgiving dates.
"""
# November 2005
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5
# 6 7 8 9 10 11 12
# 13 14 15 16 17 18 19
# 20 21 22 23 24 25 26
# 27 28 29 30
start_session_label = pd.Timestamp('2005-01-01', tz='UTC')
end_session_label = pd.Timestamp('2012-12-31', tz='UTC')
sessions = self.calendar.sessions_in_range(start_session_label,
end_session_label)
thanksgiving_with_four_weeks = pd.Timestamp("2005-11-24", tz='UTC')
self.assertNotIn(thanksgiving_with_four_weeks, sessions,
"""
If Nov has 4 Thursdays, {0} Thanksgiving is the last Thursday.
""".strip().format(thanksgiving_with_four_weeks)
)
# November 2006
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30
thanksgiving_with_five_weeks = pd.Timestamp("2006-11-23", tz='UTC')
self.assertNotIn(thanksgiving_with_five_weeks, sessions,
"""
If Nov has 5 Thursdays, {0} Thanksgiving is not the last week.
""".strip().format(thanksgiving_with_five_weeks)
)
first_trading_day_after_new_years_sunday = pd.Timestamp("2012-01-03",
tz='UTC')
self.assertIn(first_trading_day_after_new_years_sunday, sessions,
"""
If NYE falls on a weekend, {0} the Tuesday after is the first trading day.
""".strip().format(first_trading_day_after_new_years_sunday)
)
def test_day_after_thanksgiving(self):
# November 2012
# Su Mo Tu We Th Fr Sa
# 1 2 3
# 4 5 6 7 8 9 10
# 11 12 13 14 15 16 17
# 18 19 20 21 22 23 24
# 25 26 27 28 29 30
fourth_friday_open = pd.Timestamp('11/23/2012 11:00AM', tz='EST')
fourth_friday = pd.Timestamp('11/23/2012 3:00PM', tz='EST')
self.assertTrue(self.calendar.is_open_on_minute(fourth_friday_open))
self.assertFalse(self.calendar.is_open_on_minute(fourth_friday))
# November 2013
# Su Mo Tu We Th Fr Sa
# 1 2
# 3 4 5 6 7 8 9
# 10 11 12 13 14 15 16
# 17 18 19 20 21 22 23
# 24 25 26 27 28 29 30
fifth_friday_open = pd.Timestamp('11/29/2013 11:00AM', tz='EST')
fifth_friday = pd.Timestamp('11/29/2013 3:00PM', tz='EST')
self.assertTrue(self.calendar.is_open_on_minute(fifth_friday_open))
self.assertFalse(self.calendar.is_open_on_minute(fifth_friday))
def test_early_close_independence_day_thursday(self):
"""
Until 2013, the market closed early the Friday after an
Independence Day on Thursday. Since then, the early close is on
Wednesday.
"""
# July 2002
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = pd.Timestamp('7/3/2002 3:00PM', tz='EST')
friday_after_open = pd.Timestamp('7/5/2002 11:00AM', tz='EST')
friday_after = pd.Timestamp('7/5/2002 3:00PM', tz='EST')
self.assertTrue(self.calendar.is_open_on_minute(wednesday_before))
self.assertTrue(self.calendar.is_open_on_minute(friday_after_open))
self.assertFalse(self.calendar.is_open_on_minute(friday_after))
# July 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = pd.Timestamp('7/3/2013 3:00PM', tz='EST')
friday_after_open = pd.Timestamp('7/5/2013 11:00AM', tz='EST')
friday_after = pd.Timestamp('7/5/2013 3:00PM', tz='EST')
self.assertFalse(self.calendar.is_open_on_minute(wednesday_before))
self.assertTrue(self.calendar.is_open_on_minute(friday_after_open))
self.assertTrue(self.calendar.is_open_on_minute(friday_after))
class CalendarStartEndTestCase(TestCase):
def test_start_end(self):
"""
Check TradingCalendar with defined start/end dates.
"""
start = pd.Timestamp('2010-1-3', tz='UTC')
end = pd.Timestamp('2010-1-10', tz='UTC')
calendar = NYSEExchangeCalendar(start=start, end=end)
expected_first = pd.Timestamp('2010-1-4', tz='UTC')
expected_last = pd.Timestamp('2010-1-8', tz='UTC')
self.assertTrue(calendar.first_trading_session == expected_first)
self.assertTrue(calendar.last_trading_session == expected_last)
| apache-2.0 |
semanticbits/survey_stats | src/survey_stats/etl/download.py | 1 | 2410 | import os
import io
import pandas as pd
import boto3
from botocore import UNSIGNED
from botocore.client import Config
from boto3.s3.transfer import TransferConfig
from botocore.vendored import requests
import requests_cache
from survey_stats import log
SCRATCH_DIR = 'cache/'
MAX_SOCRATA_FETCH = 2**32
TMP_API_KEY = 'Knx7W1eldgzkO9nUXNYfGXGBJ'
# TODO: move to Vault/elsewhere
KB = 1024
MB = KB * KB
requests_cache.install_cache('requests.sqlite')
logger = log.getLogger(__name__)
_s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
s3 = boto3.resource('s3', config=Config(signature_version=UNSIGNED))
tcfg = TransferConfig(max_concurrency=4, multipart_chunksize=256*MB,
max_io_queue=1000000, io_chunksize=64*MB)
def fetch_s3_bytes(url):
bucket, key = url[5:].split('/', 1)
logger.info('fetching s3 url', url=url, bucket=bucket, key=key)
obj = _s3.get_object(Bucket=bucket, Key=key)
return obj['Body']
def fetch_s3_file(url):
bucket, key = url[5:].split('/', 1)
logger.info('fetching s3 file', url=url, bucket=bucket, key=key)
cf = SCRATCH_DIR + key
if os.path.isfile(cf):
return fetch_data_from_url(cf)
try:
os.makedirs(os.path.dirname(cf))
except:
pass
tempf = open(cf, 'wb')
obj = s3.Bucket(bucket).Object(key)
obj.download_fileobj(tempf, Config=tcfg)
tempf.close()
return fetch_data_from_url(cf)
def fetch_url(url):
r = requests.get(url)
tempf = io.BytesIO(r.content)
tempf.seek(0)
return tempf
# @retry(tries=5, delay=2, backoff=2, logger=logger)
def fetch_data_from_url(url):
if os.path.isfile(url):
return open(url, 'rb')
elif url.startswith('s3://'):
return fetch_s3_file(url)
else:
return fetch_url(url)
def df_from_socrata_url(url):
if url.find('rows.csv?') > -1:
return pd.read_csv(url)
url = url if url[-1] == '?' else url + '?' if url.find('?') == -1 else url + '&'
url = url + "$limit=%d" % (MAX_SOCRATA_FETCH)
r = requests.get(url, headers={'Accept': 'application/json',
'X-App-Token': TMP_API_KEY})
try:
data = pd.DataFrame(r.json())
except Exception as e:
logger.error('Error in loading url!', u=url, e=str(e))
raise
logger.info('fetched socrata url', shape=data.shape, cols=list(data.columns))
return data
| bsd-2-clause |
rlowrance/python_lib | applied_data_science/dataframe.py | 1 | 6093 | '''function that operator on pandas DataFrame instances
create_report_categorical(df, ...)
create_report_numeric(df, ...)
replace(df, old_name, new_name, new_value)
Copyright 2017 Roy E. Lowrance
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on as "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing premission and
limitation under the license.
'''
import collections
import numpy as np
import pdb
import ColumnsTable
import Report
def create_report_categorical(df, excluded_columns=[], include_types=[object]):
'return tuple (Report instance, names of columns in the report)'
description = df.describe(
include=include_types,
)
print description
r = ReportCategorical()
included_columns = []
for column_name in description.columns:
print column_name, len(column_name)
if column_name in excluded_columns:
print 'create_report_categorical: excluding column:', column_name
continue
else:
r.append_detail(description[column_name])
included_columns.append(column_name)
return r, included_columns
def create_report_numeric(df, excluded_columns=[], include_types=[np.number, object]):
'return tuple (Report instance, names of columns in the report)'
description = df.describe(
include=include_types,
)
print description
r = ReportNumeric()
included_columns = []
for column_name in description.columns:
print column_name
if column_name in excluded_columns:
print 'create_report_numeric: excluding column:', column_name
continue
else:
r.append_detail(description[column_name])
included_columns.append(column_name)
return r, included_columns
def replace(df, old_name, new_name, new_value):
'return new DataFrame with one column replaced'
df1 = df.copy()
df2 = df1.drop(old_name, 1) # 1 ==> drop column (as opposed to row)
df2.insert(0, new_name, new_value)
return df2
ColumnSpec = collections.namedtuple(
'ColumnSpec',
'print_width formatter heading1 heading2 legend',
)
def categorical(size, header1, header2, definition):
return ColumnSpec(size, '%%%ds' % size, header1, header2, definition)
def numeric(header1, header2, definition):
return ColumnSpec(12, '%12.2f', header1, header2, definition)
def count(header1, header2, definition):
return ColumnSpec(7, '%7d', header1, header2, definition)
all_column_specs = { # each with a 2-row header
'count': count('count', 'not NA', 'number of non-missing values'),
'datacol': ColumnSpec(30, '%30s', 'data', 'column', 'name of column in input file'),
'mean': numeric(' ', 'mean', 'mean value'),
'max': numeric(' ', 'max', 'maximum value'),
'min': numeric(' ', 'min', 'minimum value'),
'p25': numeric('25th', 'percentile', 'value that is the 25th percentile'),
'p50': numeric('50th', 'percentile', 'value that is the 50th percentile'),
'p75': numeric('75th', 'percentile', 'value that is the 75th percentile'),
'std': numeric('standard', 'deviation', 'standard deviation'),
'unique': numeric('num', 'unique', 'number of distinct values'),
'top': categorical(30, 'top', '(most common)', 'most common value'),
'freq': count('top', 'freq', 'frequency of the most common value'),
}
def column_def(column_name):
print column_name
assert column_name in all_column_specs, ('%s not defined in all_column_specs' % column_name)
column_spec = all_column_specs[column_name]
return [
column_name,
column_spec.print_width,
column_spec.formatter,
[column_spec.heading1, column_spec.heading2],
column_spec.legend,
]
def column_defs(*column_names):
return [
column_def(column_name)
for column_name in column_names
]
class ReportAnalysis(object):
def __init__(self, verbose=True):
self.report = Report.Report(also_print=verbose)
def write(self, path):
self.ct.append_legend()
for line in self.ct.iterlines():
self.report.append(line)
self.report.write(path)
class ReportCategorical(ReportAnalysis):
def __init__(self, verbose=True):
super(self.__class__, self).__init__() # create self.report
self.ct = ColumnsTable.ColumnsTable(
column_defs('datacol', 'unique', 'top', 'freq',),
)
self.report.append('Statistics on Categorical Columns')
self.report.append(' ')
def append_detail(self, col):
self.ct.append_detail(
datacol='Period' if col.name[:6] == 'Period' else col.name,
unique=col['unique'],
top=col['top'],
freq=col['freq'],
)
# timestamps include first and last, but this code doesn't handle them
assert 'first' not in col.index, col
assert 'last' not in col.index, col
class ReportNumeric(ReportAnalysis):
def __init__(self, verbose=True):
super(self.__class__, self).__init__() # create self.report
self.ct = ColumnsTable.ColumnsTable(
column_defs('datacol', 'count', 'mean', 'std', 'min', 'p25', 'p50', 'p75', 'max',)
)
self.report.append('Statistics on Numeric Columns')
self.report.append(' ')
def append_detail(self, col):
self.ct.append_detail(
datacol='Period' if col.name[:6] == 'Period' else col.name,
count=col['count'],
mean=col['mean'],
std=col['std'],
min=col['min'],
p25=col['25%'],
p50=col['50%'],
p75=col['75%'],
max=col['max'],
)
| apache-2.0 |
mindw/shapely | docs/code/skew.py | 5 | 2513 | from matplotlib import pyplot
from shapely.wkt import loads as load_wkt
from shapely import affinity
from descartes.patch import PolygonPatch
from figures import SIZE, BLUE, GRAY
def add_origin(ax, geom, origin):
x, y = xy = affinity.interpret_origin(geom, origin, 2)
ax.plot(x, y, 'o', color=GRAY, zorder=1)
ax.annotate(str(xy), xy=xy, ha='center',
textcoords='offset points', xytext=(0, 8))
fig = pyplot.figure(1, figsize=SIZE, dpi=90)
# Geometry from JTS TestBuilder with fixed precision model of 100.0
# Using CreateShape > FontGlyphSanSerif and A = triangle.wkt from scale.py
R = load_wkt('''\
POLYGON((2.218 2.204, 2.273 2.18, 2.328 2.144, 2.435 2.042, 2.541 1.895,
2.647 1.702, 3 1, 2.626 1, 2.298 1.659, 2.235 1.777, 2.173 1.873,
2.112 1.948, 2.051 2.001, 1.986 2.038, 1.91 2.064, 1.823 2.08, 1.726 2.085,
1.347 2.085, 1.347 1, 1 1, 1 3.567, 1.784 3.567, 1.99 3.556, 2.168 3.521,
2.319 3.464, 2.441 3.383, 2.492 3.334, 2.536 3.279, 2.604 3.152,
2.644 3.002, 2.658 2.828, 2.651 2.712, 2.63 2.606, 2.594 2.51, 2.545 2.425,
2.482 2.352, 2.407 2.29, 2.319 2.241, 2.218 2.204),
(1.347 3.282, 1.347 2.371, 1.784 2.371, 1.902 2.378, 2.004 2.4, 2.091 2.436,
2.163 2.487, 2.219 2.552, 2.259 2.63, 2.283 2.722, 2.291 2.828, 2.283 2.933,
2.259 3.025, 2.219 3.103, 2.163 3.167, 2.091 3.217, 2.004 3.253, 1.902 3.275,
1.784 3.282, 1.347 3.282))''')
xrange = [0, 5]
yrange = [0, 4]
# 1
ax = fig.add_subplot(121)
patch1a = PolygonPatch(R, facecolor=GRAY, edgecolor=GRAY,
alpha=0.5, zorder=1)
skewR = affinity.skew(R, xs=20, origin=(1, 1))
patch1b = PolygonPatch(skewR, facecolor=BLUE, edgecolor=BLUE,
alpha=0.5, zorder=2)
ax.add_patch(patch1a)
ax.add_patch(patch1b)
add_origin(ax, R, (1, 1))
ax.set_title("a) xs=20, origin(1, 1)")
ax.set_xlim(*xrange)
ax.set_xticks(range(*xrange) + [xrange[-1]])
ax.set_ylim(*yrange)
ax.set_yticks(range(*yrange) + [yrange[-1]])
ax.set_aspect(1)
# 2
ax = fig.add_subplot(122)
patch2a = PolygonPatch(R, facecolor=GRAY, edgecolor=GRAY,
alpha=0.5, zorder=1)
skewR = affinity.skew(R, ys=30)
patch2b = PolygonPatch(skewR, facecolor=BLUE, edgecolor=BLUE,
alpha=0.5, zorder=2)
ax.add_patch(patch2a)
ax.add_patch(patch2b)
add_origin(ax, R, 'center')
ax.set_title("b) ys=30")
ax.set_xlim(*xrange)
ax.set_xticks(range(*xrange) + [xrange[-1]])
ax.set_ylim(*yrange)
ax.set_yticks(range(*yrange) + [yrange[-1]])
ax.set_aspect(1)
pyplot.show()
| bsd-3-clause |
jmetzen/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 230 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random_state=None, **kwds):
"""Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b``) for each
sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a,
b)``. To clarify, ``b`` is the distance between a sample and the nearest
cluster that the sample is not a part of.
Note that Silhouette Coefficent is only defined if number of labels
is 2 <= n_labels <= n_samples - 1.
This function returns the mean Silhouette Coefficient over all samples.
To obtain the values for each sample, use :func:`silhouette_samples`.
The best value is 1 and the worst value is -1. Values near 0 indicate
overlapping clusters. Negative values generally indicate that a sample has
been assigned to the wrong cluster, as a different cluster is more similar.
Read more in the :ref:`User Guide <silhouette_coefficient>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
labels : array, shape = [n_samples]
Predicted labels for each sample.
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by :func:`metrics.pairwise.pairwise_distances
<sklearn.metrics.pairwise.pairwise_distances>`. If X is the distance
array itself, use ``metric="precomputed"``.
sample_size : int or None
The size of the sample to use when computing the Silhouette Coefficient
on a random subset of the data.
If ``sample_size is None``, no sampling is used.
random_state : integer or numpy.RandomState, optional
The generator used to randomly select a subset of samples if
``sample_size is not None``. If an integer is given, it fixes the seed.
Defaults to the global numpy random number generator.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a scipy.spatial.distance metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
silhouette : float
Mean Silhouette Coefficient for all samples.
References
----------
.. [1] `Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
Interpretation and Validation of Cluster Analysis". Computational
and Applied Mathematics 20: 53-65.
<http://www.sciencedirect.com/science/article/pii/0377042787901257>`_
.. [2] `Wikipedia entry on the Silhouette Coefficient
<http://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
"""
n_labels = len(np.unique(labels))
n_samples = X.shape[0]
if not 1 < n_labels < n_samples:
raise ValueError("Number of labels is %d. Valid values are 2 "
"to n_samples - 1 (inclusive)" % n_labels)
if sample_size is not None:
random_state = check_random_state(random_state)
indices = random_state.permutation(X.shape[0])[:sample_size]
if metric == "precomputed":
X, labels = X[indices].T[indices].T, labels[indices]
else:
X, labels = X[indices], labels[indices]
return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
def silhouette_samples(X, labels, metric='euclidean', **kwds):
"""Compute the Silhouette Coefficient for each sample.
The Silhouette Coefficient is a measure of how well samples are clustered
with samples that are similar to themselves. Clustering models with a high
Silhouette Coefficient are said to be dense, where samples in the same
cluster are similar to each other, and well separated, where samples in
different clusters are not very similar to each other.
The Silhouette Coefficient is calculated using the mean intra-cluster
distance (``a``) and the mean nearest-cluster distance (``b``) for each
sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a,
b)``.
Note that Silhouette Coefficent is only defined if number of labels
is 2 <= n_labels <= n_samples - 1.
This function returns the Silhouette Coefficient for each sample.
The best value is 1 and the worst value is -1. Values near 0 indicate
overlapping clusters.
Read more in the :ref:`User Guide <silhouette_coefficient>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
labels : array, shape = [n_samples]
label values for each sample
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by :func:`sklearn.metrics.pairwise.pairwise_distances`. If X is
the distance array itself, use "precomputed" as the metric.
`**kwds` : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a ``scipy.spatial.distance`` metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
silhouette : array, shape = [n_samples]
Silhouette Coefficient for each samples.
References
----------
.. [1] `Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
Interpretation and Validation of Cluster Analysis". Computational
and Applied Mathematics 20: 53-65.
<http://www.sciencedirect.com/science/article/pii/0377042787901257>`_
.. [2] `Wikipedia entry on the Silhouette Coefficient
<http://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
"""
distances = pairwise_distances(X, metric=metric, **kwds)
n = labels.shape[0]
A = np.array([_intra_cluster_distance(distances[i], labels, i)
for i in range(n)])
B = np.array([_nearest_cluster_distance(distances[i], labels, i)
for i in range(n)])
sil_samples = (B - A) / np.maximum(A, B)
return sil_samples
def _intra_cluster_distance(distances_row, labels, i):
"""Calculate the mean intra-cluster distance for sample i.
Parameters
----------
distances_row : array, shape = [n_samples]
Pairwise distance matrix between sample i and each sample.
labels : array, shape = [n_samples]
label values for each sample
i : int
Sample index being calculated. It is excluded from calculation and
used to determine the current label
Returns
-------
a : float
Mean intra-cluster distance for sample i
"""
mask = labels == labels[i]
mask[i] = False
if not np.any(mask):
# cluster of size 1
return 0
a = np.mean(distances_row[mask])
return a
def _nearest_cluster_distance(distances_row, labels, i):
"""Calculate the mean nearest-cluster distance for sample i.
Parameters
----------
distances_row : array, shape = [n_samples]
Pairwise distance matrix between sample i and each sample.
labels : array, shape = [n_samples]
label values for each sample
i : int
Sample index being calculated. It is used to determine the current
label.
Returns
-------
b : float
Mean nearest-cluster distance for sample i
"""
label = labels[i]
b = np.min([np.mean(distances_row[labels == cur_label])
for cur_label in set(labels) if not cur_label == label])
return b
| bsd-3-clause |
konstantinstadler/pymrio | tests/test_math.py | 1 | 18241 | """ test cases for all mathematical functions """
import os
import sys
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import pytest
TESTPATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(TESTPATH, ".."))
# the function which should be tested here
from pymrio.tools.iomath import calc_A # noqa
from pymrio.tools.iomath import calc_accounts # noqa
from pymrio.tools.iomath import calc_e # noqa
from pymrio.tools.iomath import calc_F # noqa
from pymrio.tools.iomath import calc_F_Y # noqa
from pymrio.tools.iomath import calc_L # noqa
from pymrio.tools.iomath import calc_M # noqa
from pymrio.tools.iomath import calc_S # noqa
from pymrio.tools.iomath import calc_S_Y # noqa
from pymrio.tools.iomath import calc_x # noqa
from pymrio.tools.iomath import calc_x_from_L # noqa
from pymrio.tools.iomath import calc_Z # noqa
# test data
@pytest.fixture()
def td_IO_Data_Miller():
"""This data is from the chapter 2 of
Input-output analysis: foundations and extensions -- Miller, Ronald E
and Blair, Peter D -- 2009 (ISBN: 9780521517133)
"""
class IO_Data_Miller:
# Table 2.3 of the Book
Z_arr = np.array([[150, 500], [200, 100]]).astype("float")
Z_df = pd.DataFrame(
data=Z_arr,
index=["sec1", "sec2"],
columns=["sec1", "sec2"],
)
# Table 2.4 of the Book
A_arr = np.array([[0.15, 0.25], [0.20, 0.05]]).astype("float")
A_df = pd.DataFrame(
data=A_arr,
index=["sec1", "sec2"],
columns=["sec1", "sec2"],
)
# Table 2.3 of the Book
fd_arr = np.array([[350], [1700]]).astype("float")
fd_df = pd.DataFrame(
data=fd_arr,
index=["sec1", "sec2"],
columns=["fi"],
)
# Table 2.3 of the Book
x_arr = np.array([[1000], [2000]]).astype("float")
x_df = pd.DataFrame(
data=x_arr,
index=["sec1", "sec2"],
columns=["indout"],
)
# At the example following Table 2.3, additional
# decimals where calculated
L_arr = np.array([[1.25412541, 0.330033], [0.2640264, 1.12211221]]).astype(
"float"
)
L_df = pd.DataFrame(
data=L_arr,
index=["sec1", "sec2"],
columns=["sec1", "sec2"],
)
# Example epsilon from chapter 2, it uses the new x
xnew_arr = np.array([[1247.5], [1841.6]]).astype("float")
xnew_df = pd.DataFrame(
data=xnew_arr,
index=["sec1", "sec2"],
columns=["indout"],
)
labcoeff_arr = np.array(
[
[0.3, 0.25],
]
)
labcoeff_df = pd.DataFrame(
data=labcoeff_arr,
index=["total labor"],
columns=["sec1", "sec2"],
)
labtot_arr = np.array(
[
[374.25, 460.4],
]
) # .25 because of rounding
labtot_df = pd.DataFrame(
data=labtot_arr,
index=["total labor"],
columns=["sec1", "sec2"],
)
return IO_Data_Miller
@pytest.fixture()
def td_small_MRIO():
"""A small MRIO with three sectors and two regions.
The testdata here just consists of pandas DataFrames, the functionality
with numpy arrays gets tested with td_IO_Data_Miller.
"""
class IO_Data:
_sectors = ["sector1", "sector2", "sector3"]
_regions = ["reg1", "reg2"]
_Z_multiindex = pd.MultiIndex.from_product(
[_regions, _sectors], names=[u"region", u"sector"]
)
Z = pd.DataFrame(
data=[
[10, 5, 1, 6, 5, 7],
[0, 2, 0, 0, 5, 3],
[10, 3, 20, 4, 2, 0],
[5, 0, 0, 1, 10, 9],
[0, 10, 1, 0, 20, 1],
[5, 0, 0, 1, 10, 10],
],
index=_Z_multiindex,
columns=_Z_multiindex,
dtype=("float64"),
)
_categories = ["final demand"]
_Y_multiindex = pd.MultiIndex.from_product(
[_regions, _categories], names=[u"region", u"category"]
)
Y = pd.DataFrame(
data=[[14, 3], [2.5, 2.5], [13, 6], [5, 20], [10, 10], [3, 10]],
index=_Z_multiindex,
columns=_Y_multiindex,
dtype=("float64"),
)
F = pd.DataFrame(
data=[[20, 1, 42, 4, 20, 5], [5, 4, 11, 8, 2, 10]],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
dtype=("float64"),
)
F_Y = pd.DataFrame(
data=[[50, 10], [100, 20]],
index=["ext_type_1", "ext_type_2"],
columns=_Y_multiindex,
dtype=("float64"),
)
S_Y = pd.DataFrame(
data=[
[1.0526315789473684, 0.1941747572815534],
[2.1052631578947367, 0.3883495145631068],
],
index=["ext_type_1", "ext_type_2"],
columns=_Y_multiindex,
dtype=("float64"),
)
A = pd.DataFrame(
data=[
[
0.19607843137254902,
0.3333333333333333,
0.017241379310344827,
0.12,
0.09615384615384616,
0.1794871794871795,
], # noqa
[
0.0,
0.13333333333333333,
0.0,
0.0,
0.09615384615384616,
0.07692307692307693,
], # noqa
[
0.19607843137254902,
0.2,
0.3448275862068966,
0.08,
0.038461538461538464,
0.0,
], # noqa
[
0.09803921568627451,
0.0,
0.0,
0.02,
0.19230769230769232,
0.23076923076923075,
], # noqa
[
0.0,
0.6666666666666666,
0.017241379310344827,
0.0,
0.38461538461538464,
0.02564102564102564,
], # noqa
[
0.09803921568627451,
0.0,
0.0,
0.02,
0.19230769230769232,
0.2564102564102564,
], # noqa
],
index=_Z_multiindex,
columns=_Z_multiindex,
)
L = pd.DataFrame(
data=[
[
1.3387146304736708,
0.9689762471208287,
0.05036622549592462,
0.17820960407435948,
0.5752019383714646,
0.4985179148178926,
], # noqa
[
0.02200779585580331,
1.3716472861392823,
0.0076800357678581885,
0.006557415453762468,
0.2698335633228079,
0.15854643902810828,
], # noqa
[
0.43290422861412026,
0.8627066565439678,
1.5492942759220427,
0.18491657196329184,
0.44027825642348534,
0.26630955082840885,
], # noqa
[
0.18799498787612925,
0.5244084722329316,
0.020254008037620782,
1.0542007368783255,
0.5816573175534603,
0.44685014763069275,
], # noqa
[
0.04400982046095892,
1.5325472495862535,
0.05259311578831879,
0.014602513642445088,
1.9545285794951548,
0.2410917825607805,
], # noqa
[
0.19294222439918532,
0.5382086951864299,
0.020787008249137116,
0.05562707205933412,
0.596964089068025,
1.4849251515157111,
], # noqa
],
index=_Z_multiindex,
columns=_Z_multiindex,
)
x = pd.DataFrame(
data=[
[51],
[15],
[58],
[50],
[52],
[39],
],
columns=["indout"],
index=_Z_multiindex,
dtype=("float64"),
)
S = pd.DataFrame(
data=[
[
0.39215686274509803,
0.06666666666666667,
0.7241379310344828,
0.08,
0.38461538461538464,
0.1282051282051282,
], # noqa
[
0.09803921568627451,
0.26666666666666666,
0.1896551724137931,
0.16,
0.038461538461538464,
0.2564102564102564,
], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
M = pd.DataFrame(
data=[
[
0.896638324101429,
1.7965474933011258,
1.1666796580507097,
0.3013124703668342,
1.4371886818255364,
0.7177624711634629,
], # noqa
[
0.3004620527704837,
0.9052387706892087,
0.311411003349379,
0.23778766312079253,
0.5331560745059157,
0.6031791628984159,
], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
D_cba = pd.DataFrame(
data=[
[
14.059498889254174,
18.863255551508175,
17.32012296814962,
8.71616437964097,
18.863255551508175,
14.17770265993889,
], # noqa
[
5.395407054390734,
7.594657671782178,
5.857880532237175,
5.657139420727301,
7.594657671782178,
7.900257649080434,
], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
D_pba = pd.DataFrame(
data=[
[20.000000000000004, 1.0, 42.00000000000001, 4.0, 20.0, 5.0], # noqa
[5.000000000000001, 4.0, 11.0, 8.0, 1.9999999999999998, 10.0], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
D_imp = pd.DataFrame(
data=[
[
1.2792573306446737,
10.499069649181388,
1.2752266807875912,
6.604374747509535,
8.364185902326788,
10.842115601865368,
], # noqa
[
2.054905005956757,
3.9151998960771452,
1.522271392125981,
1.743464577633809,
3.6794577757050337,
3.221508682410955,
], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
D_exp = pd.DataFrame(
data=[
[
8.251832343364468,
0.5304113433404783,
17.02843256499675,
1.3307504334524414,
9.797226856351275,
1.9255763708099365,
], # noqa
[
2.062958085841117,
2.1216453733619134,
4.459827576546767,
2.6615008669048827,
0.9797226856351275,
3.851152741619873,
], # noqa
],
index=["ext_type_1", "ext_type_2"],
columns=_Z_multiindex,
)
return IO_Data
def test_calc_x_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.x_df, calc_x(td_IO_Data_Miller.Z_df, td_IO_Data_Miller.fd_df)
)
def test_calc_x_arr(td_IO_Data_Miller):
npt.assert_array_equal(
td_IO_Data_Miller.x_arr,
calc_x(td_IO_Data_Miller.Z_arr, td_IO_Data_Miller.fd_arr),
)
def test_calc_Z_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.Z_df, calc_Z(td_IO_Data_Miller.A_df, td_IO_Data_Miller.x_df)
)
def test_calc_Z_arr(td_IO_Data_Miller):
npt.assert_array_equal(
td_IO_Data_Miller.Z_arr,
calc_Z(td_IO_Data_Miller.A_arr, td_IO_Data_Miller.x_arr),
)
def test_calc_A_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.A_df, calc_A(td_IO_Data_Miller.Z_df, td_IO_Data_Miller.x_df)
)
def test_calc_A_arr(td_IO_Data_Miller):
npt.assert_array_equal(
td_IO_Data_Miller.A_arr,
calc_A(td_IO_Data_Miller.Z_arr, td_IO_Data_Miller.x_arr),
)
def test_calc_L_df(td_IO_Data_Miller):
pdt.assert_frame_equal(td_IO_Data_Miller.L_df, calc_L(td_IO_Data_Miller.A_df))
def test_calc_L_arr(td_IO_Data_Miller):
npt.assert_allclose(
td_IO_Data_Miller.L_arr, calc_L(td_IO_Data_Miller.A_arr), rtol=1e-5
)
def test_calc_x_from_L_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.x_df,
calc_x_from_L(td_IO_Data_Miller.L_df, td_IO_Data_Miller.fd_df),
)
def test_calc_x_from_L_arr(td_IO_Data_Miller):
npt.assert_allclose(
td_IO_Data_Miller.x_arr,
calc_x_from_L(td_IO_Data_Miller.L_arr, td_IO_Data_Miller.fd_arr),
rtol=1e-5,
)
def test_calc_F_arr(td_IO_Data_Miller):
npt.assert_allclose(
td_IO_Data_Miller.labtot_arr,
calc_F(td_IO_Data_Miller.labcoeff_arr, td_IO_Data_Miller.xnew_arr),
rtol=1e-5,
)
def test_calc_F_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.labtot_df,
calc_F(td_IO_Data_Miller.labcoeff_df, td_IO_Data_Miller.xnew_df),
)
def test_calc_S_arr(td_IO_Data_Miller):
npt.assert_allclose(
td_IO_Data_Miller.labcoeff_arr,
calc_S(td_IO_Data_Miller.labtot_arr, td_IO_Data_Miller.xnew_arr),
rtol=1e-5,
)
def test_calc_S_df(td_IO_Data_Miller):
pdt.assert_frame_equal(
td_IO_Data_Miller.labcoeff_df,
calc_S(td_IO_Data_Miller.labtot_df, td_IO_Data_Miller.xnew_df),
)
def test_calc_x_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.x, calc_x(td_small_MRIO.Z, td_small_MRIO.Y))
def test_calc_A_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.A, calc_A(td_small_MRIO.Z, td_small_MRIO.x))
# we also test the different methods to provide x:
x_values = td_small_MRIO.x.values
x_Tvalues = td_small_MRIO.x.T.values
x_series = pd.Series(td_small_MRIO.x.iloc[:, 0])
pdt.assert_frame_equal(td_small_MRIO.A, calc_A(td_small_MRIO.Z, x_series))
pdt.assert_frame_equal(td_small_MRIO.A, calc_A(td_small_MRIO.Z, x_values))
pdt.assert_frame_equal(td_small_MRIO.A, calc_A(td_small_MRIO.Z, x_Tvalues))
def test_calc_Z_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.Z, calc_Z(td_small_MRIO.A, td_small_MRIO.x))
# we also test the different methods to provide x:
x_values = td_small_MRIO.x.values
x_Tvalues = td_small_MRIO.x.T.values
x_series = pd.Series(td_small_MRIO.x.iloc[:, 0])
pdt.assert_frame_equal(td_small_MRIO.Z, calc_Z(td_small_MRIO.A, x_series))
pdt.assert_frame_equal(td_small_MRIO.Z, calc_Z(td_small_MRIO.A, x_values))
pdt.assert_frame_equal(td_small_MRIO.Z, calc_Z(td_small_MRIO.A, x_Tvalues))
def test_calc_L_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.L, calc_L(td_small_MRIO.A))
def test_calc_S_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.S, calc_S(td_small_MRIO.F, td_small_MRIO.x))
def test_calc_S_Y_MRIO(td_small_MRIO):
pdt.assert_frame_equal(
td_small_MRIO.S_Y, calc_S_Y(td_small_MRIO.F_Y, td_small_MRIO.Y.sum(axis=0))
)
def test_calc_F_Y_MRIO(td_small_MRIO):
S_Y = calc_S_Y(td_small_MRIO.F_Y, td_small_MRIO.Y.sum(axis=0))
pdt.assert_frame_equal(
td_small_MRIO.F_Y, calc_F_Y(S_Y, td_small_MRIO.Y.sum(axis=0))
)
def test_calc_M_MRIO(td_small_MRIO):
pdt.assert_frame_equal(td_small_MRIO.M, calc_M(td_small_MRIO.S, td_small_MRIO.L))
def test_calc_accounts_MRIO(td_small_MRIO):
# calc the accounts
nD_cba, nD_pba, nD_imp, nD_exp = calc_accounts(
td_small_MRIO.S,
td_small_MRIO.L,
td_small_MRIO.Y,
nr_sectors=len(td_small_MRIO.Z.index.get_level_values("sector").unique()),
)
# test all
pdt.assert_frame_equal(
td_small_MRIO.D_cba,
nD_cba,
)
pdt.assert_frame_equal(
td_small_MRIO.D_pba,
nD_pba,
)
pdt.assert_frame_equal(
td_small_MRIO.D_imp,
nD_imp,
)
pdt.assert_frame_equal(
td_small_MRIO.D_exp,
nD_exp,
)
# test if fp = terr + imp - exp on the total level
# that tests if imp == exp and fp == terr
pdt.assert_series_equal(
nD_cba.sum(axis=1),
nD_pba.sum(axis=1) + nD_imp.sum(axis=1) - nD_exp.sum(axis=1),
)
| gpl-3.0 |
schae234/gingivere | tests/raw_data_clf.py | 2 | 3422 | from __future__ import print_function
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.decomposition import PCA
from sklearn.metrics import roc_auc_score
from tests import shelve_api
print(__doc__)
class RawClf:
def __init__(self, name, verbose = True, data=None):
self.name = name
self.verbose = True
if data:
self.X = data[0]
self.y = data[1]
# self.scaler = StandardScaler()
# self.X = self.scaler.fit_transform(self.X)
else:
# self.load_data()
self.preprocess_data()
self.verbose_svm()
# self.train_pca()
# self.train_svm()
# def load_data(self):
# data = mongo_select.load_random_training_set(self.name, num=360)
# self.X =data['data']
# self.y = data['state']
def preprocess_data(self):
self.X = np.array(self.X).astype('float32')
self.y = np.array(self.y)
self.scaler = StandardScaler()
self.X = scaler.fit_transform(self.X)
# print(self.X[0])
def get_pca(self):
return PCA(copy=True, n_components=100, whiten=True)
def train_pca(self):
self.PCA = PCA(copy=True, n_components=100, whiten=False)
self.PCA.fit(self.X)
def get_svm(self):
# return SVC(C=100, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, random_state=None, shrinking=True, tol=0.001, verbose=False)
return KNeighborsClassifier()
def train_svm(self):
self.SVM = SVC(C=100, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, random_state=None, shrinking=True, tol=0.001, verbose=False)
X = self.PCA.transform(self.X)
# X = self.scaler.fit_transform(X)
self.SVM.fit(X, self.y)
if self.verbose:
self.verbose_svm()
def predict(self, x):
return self.SVM.predict(self.PCA.transform(x))
# def predict_proba(self, x):
# return self.SVM.predict_proba(self.PCA.transform(x))
def clear_data(self):
self.X = []
self.y = []
def verbose_svm(self):
skf = StratifiedKFold(self.y, n_folds=2)
print(self.name)
for train_index, test_index in skf:
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
X_train, X_test = self.X[train_index], self.X[test_index]
PCA = self.get_pca()
PCA.fit(self.X)
y_train, y_test = self.y[train_index], self.y[test_index]
SVC = self.get_svm()
X_train = PCA.transform(X_train)
SVC.fit(X_train, y_train)
X_test = PCA.transform(X_test)
y_true, y_pred = y_test, SVC.predict(X_test)
print(classification_report(y_true, y_pred))
print()
print(roc_auc_score(y_true, y_pred))
print()
if __name__ == "__main__":
clf = RawClf('Dog_1', data=(shelve_api.load('clf_x'), shelve_api.load('clf_y')))
| mit |
belltailjp/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 105 | 22788 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski, wminkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.externals.six import iteritems
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.metrics.pairwise import manhattan_distances
from sklearn.metrics.pairwise import linear_kernel
from sklearn.metrics.pairwise import chi2_kernel, additive_chi2_kernel
from sklearn.metrics.pairwise import polynomial_kernel
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import sigmoid_kernel
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics.pairwise import cosine_distances
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances_argmin_min
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS
from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
from sklearn.metrics.pairwise import PAIRED_DISTANCES
from sklearn.metrics.pairwise import check_pairwise_arrays
from sklearn.metrics.pairwise import check_paired_arrays
from sklearn.metrics.pairwise import _parallel_pairwise
from sklearn.metrics.pairwise import paired_distances
from sklearn.metrics.pairwise import paired_euclidean_distances
from sklearn.metrics.pairwise import paired_manhattan_distances
from sklearn.preprocessing import normalize
def test_pairwise_distances():
# Test the pairwise_distance helper function.
rng = np.random.RandomState(0)
# Euclidean distance should be equivalent to calling the function.
X = rng.random_sample((5, 4))
S = pairwise_distances(X, metric="euclidean")
S2 = euclidean_distances(X)
assert_array_almost_equal(S, S2)
# Euclidean distance, with Y != X.
Y = rng.random_sample((2, 4))
S = pairwise_distances(X, Y, metric="euclidean")
S2 = euclidean_distances(X, Y)
assert_array_almost_equal(S, S2)
# Test with tuples as X and Y
X_tuples = tuple([tuple([v for v in row]) for row in X])
Y_tuples = tuple([tuple([v for v in row]) for row in Y])
S2 = pairwise_distances(X_tuples, Y_tuples, metric="euclidean")
assert_array_almost_equal(S, S2)
# "cityblock" uses sklearn metric, cityblock (function) is scipy.spatial.
S = pairwise_distances(X, metric="cityblock")
S2 = pairwise_distances(X, metric=cityblock)
assert_equal(S.shape[0], S.shape[1])
assert_equal(S.shape[0], X.shape[0])
assert_array_almost_equal(S, S2)
# The manhattan metric should be equivalent to cityblock.
S = pairwise_distances(X, Y, metric="manhattan")
S2 = pairwise_distances(X, Y, metric=cityblock)
assert_equal(S.shape[0], X.shape[0])
assert_equal(S.shape[1], Y.shape[0])
assert_array_almost_equal(S, S2)
# Low-level function for manhattan can divide in blocks to avoid
# using too much memory during the broadcasting
S3 = manhattan_distances(X, Y, size_threshold=10)
assert_array_almost_equal(S, S3)
# Test cosine as a string metric versus cosine callable
# "cosine" uses sklearn metric, cosine (function) is scipy.spatial
S = pairwise_distances(X, Y, metric="cosine")
S2 = pairwise_distances(X, Y, metric=cosine)
assert_equal(S.shape[0], X.shape[0])
assert_equal(S.shape[1], Y.shape[0])
assert_array_almost_equal(S, S2)
# Tests that precomputed metric returns pointer to, and not copy of, X.
S = np.dot(X, X.T)
S2 = pairwise_distances(S, metric="precomputed")
assert_true(S is S2)
# Test with sparse X and Y,
# currently only supported for Euclidean, L1 and cosine.
X_sparse = csr_matrix(X)
Y_sparse = csr_matrix(Y)
S = pairwise_distances(X_sparse, Y_sparse, metric="euclidean")
S2 = euclidean_distances(X_sparse, Y_sparse)
assert_array_almost_equal(S, S2)
S = pairwise_distances(X_sparse, Y_sparse, metric="cosine")
S2 = cosine_distances(X_sparse, Y_sparse)
assert_array_almost_equal(S, S2)
S = pairwise_distances(X_sparse, Y_sparse.tocsc(), metric="manhattan")
S2 = manhattan_distances(X_sparse.tobsr(), Y_sparse.tocoo())
assert_array_almost_equal(S, S2)
S2 = manhattan_distances(X, Y)
assert_array_almost_equal(S, S2)
# Test with scipy.spatial.distance metric, with a kwd
kwds = {"p": 2.0}
S = pairwise_distances(X, Y, metric="minkowski", **kwds)
S2 = pairwise_distances(X, Y, metric=minkowski, **kwds)
assert_array_almost_equal(S, S2)
# same with Y = None
kwds = {"p": 2.0}
S = pairwise_distances(X, metric="minkowski", **kwds)
S2 = pairwise_distances(X, metric=minkowski, **kwds)
assert_array_almost_equal(S, S2)
# Test that scipy distance metrics throw an error if sparse matrix given
assert_raises(TypeError, pairwise_distances, X_sparse, metric="minkowski")
assert_raises(TypeError, pairwise_distances, X, Y_sparse,
metric="minkowski")
# Test that a value error is raised if the metric is unkown
assert_raises(ValueError, pairwise_distances, X, Y, metric="blah")
def check_pairwise_parallel(func, metric, kwds):
rng = np.random.RandomState(0)
for make_data in (np.array, csr_matrix):
X = make_data(rng.random_sample((5, 4)))
Y = make_data(rng.random_sample((3, 4)))
try:
S = func(X, metric=metric, n_jobs=1, **kwds)
except (TypeError, ValueError) as exc:
# Not all metrics support sparse input
# ValueError may be triggered by bad callable
if make_data is csr_matrix:
assert_raises(type(exc), func, X, metric=metric,
n_jobs=2, **kwds)
continue
else:
raise
S2 = func(X, metric=metric, n_jobs=2, **kwds)
assert_array_almost_equal(S, S2)
S = func(X, Y, metric=metric, n_jobs=1, **kwds)
S2 = func(X, Y, metric=metric, n_jobs=2, **kwds)
assert_array_almost_equal(S, S2)
def test_pairwise_parallel():
wminkowski_kwds = {'w': np.arange(1, 5).astype('double'), 'p': 1}
metrics = [(pairwise_distances, 'euclidean', {}),
(pairwise_distances, wminkowski, wminkowski_kwds),
(pairwise_distances, 'wminkowski', wminkowski_kwds),
(pairwise_kernels, 'polynomial', {'degree': 1}),
(pairwise_kernels, callable_rbf_kernel, {'gamma': .1}),
]
for func, metric, kwds in metrics:
yield check_pairwise_parallel, func, metric, kwds
def test_pairwise_callable_nonstrict_metric():
# paired_distances should allow callable metric where metric(x, x) != 0
# Knowing that the callable is a strict metric would allow the diagonal to
# be left uncalculated and set to 0.
assert_equal(pairwise_distances([[1]], metric=lambda x, y: 5)[0, 0], 5)
def callable_rbf_kernel(x, y, **kwds):
# Callable version of pairwise.rbf_kernel.
K = rbf_kernel(np.atleast_2d(x), np.atleast_2d(y), **kwds)
return K
def test_pairwise_kernels():
# Test the pairwise_kernels helper function.
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((2, 4))
# Test with all metrics that should be in PAIRWISE_KERNEL_FUNCTIONS.
test_metrics = ["rbf", "sigmoid", "polynomial", "linear", "chi2",
"additive_chi2"]
for metric in test_metrics:
function = PAIRWISE_KERNEL_FUNCTIONS[metric]
# Test with Y=None
K1 = pairwise_kernels(X, metric=metric)
K2 = function(X)
assert_array_almost_equal(K1, K2)
# Test with Y=Y
K1 = pairwise_kernels(X, Y=Y, metric=metric)
K2 = function(X, Y=Y)
assert_array_almost_equal(K1, K2)
# Test with tuples as X and Y
X_tuples = tuple([tuple([v for v in row]) for row in X])
Y_tuples = tuple([tuple([v for v in row]) for row in Y])
K2 = pairwise_kernels(X_tuples, Y_tuples, metric=metric)
assert_array_almost_equal(K1, K2)
# Test with sparse X and Y
X_sparse = csr_matrix(X)
Y_sparse = csr_matrix(Y)
if metric in ["chi2", "additive_chi2"]:
# these don't support sparse matrices yet
assert_raises(ValueError, pairwise_kernels,
X_sparse, Y=Y_sparse, metric=metric)
continue
K1 = pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
assert_array_almost_equal(K1, K2)
# Test with a callable function, with given keywords.
metric = callable_rbf_kernel
kwds = {}
kwds['gamma'] = 0.1
K1 = pairwise_kernels(X, Y=Y, metric=metric, **kwds)
K2 = rbf_kernel(X, Y=Y, **kwds)
assert_array_almost_equal(K1, K2)
# callable function, X=Y
K1 = pairwise_kernels(X, Y=X, metric=metric, **kwds)
K2 = rbf_kernel(X, Y=X, **kwds)
assert_array_almost_equal(K1, K2)
def test_pairwise_kernels_filter_param():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((2, 4))
K = rbf_kernel(X, Y, gamma=0.1)
params = {"gamma": 0.1, "blabla": ":)"}
K2 = pairwise_kernels(X, Y, metric="rbf", filter_params=True, **params)
assert_array_almost_equal(K, K2)
assert_raises(TypeError, pairwise_kernels, X, Y, "rbf", **params)
def test_paired_distances():
# Test the pairwise_distance helper function.
rng = np.random.RandomState(0)
# Euclidean distance should be equivalent to calling the function.
X = rng.random_sample((5, 4))
# Euclidean distance, with Y != X.
Y = rng.random_sample((5, 4))
for metric, func in iteritems(PAIRED_DISTANCES):
S = paired_distances(X, Y, metric=metric)
S2 = func(X, Y)
assert_array_almost_equal(S, S2)
S3 = func(csr_matrix(X), csr_matrix(Y))
assert_array_almost_equal(S, S3)
if metric in PAIRWISE_DISTANCE_FUNCTIONS:
# Check the the pairwise_distances implementation
# gives the same value
distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
distances = np.diag(distances)
assert_array_almost_equal(distances, S)
# Check the callable implementation
S = paired_distances(X, Y, metric='manhattan')
S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))
assert_array_almost_equal(S, S2)
# Test that a value error is raised when the lengths of X and Y should not
# differ
Y = rng.random_sample((3, 4))
assert_raises(ValueError, paired_distances, X, Y)
def test_pairwise_distances_argmin_min():
# Check pairwise minimum distances computation for any metric
X = [[0], [1]]
Y = [[-1], [2]]
Xsp = dok_matrix(X)
Ysp = csr_matrix(Y, dtype=np.float32)
# euclidean metric
D, E = pairwise_distances_argmin_min(X, Y, metric="euclidean")
D2 = pairwise_distances_argmin(X, Y, metric="euclidean")
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(D2, [0, 1])
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(E, [1., 1.])
# sparse matrix case
Dsp, Esp = pairwise_distances_argmin_min(Xsp, Ysp, metric="euclidean")
assert_array_equal(Dsp, D)
assert_array_equal(Esp, E)
# We don't want np.matrix here
assert_equal(type(Dsp), np.ndarray)
assert_equal(type(Esp), np.ndarray)
# Non-euclidean sklearn metric
D, E = pairwise_distances_argmin_min(X, Y, metric="manhattan")
D2 = pairwise_distances_argmin(X, Y, metric="manhattan")
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(D2, [0, 1])
assert_array_almost_equal(E, [1., 1.])
D, E = pairwise_distances_argmin_min(Xsp, Ysp, metric="manhattan")
D2 = pairwise_distances_argmin(Xsp, Ysp, metric="manhattan")
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(E, [1., 1.])
# Non-euclidean Scipy distance (callable)
D, E = pairwise_distances_argmin_min(X, Y, metric=minkowski,
metric_kwargs={"p": 2})
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(E, [1., 1.])
# Non-euclidean Scipy distance (string)
D, E = pairwise_distances_argmin_min(X, Y, metric="minkowski",
metric_kwargs={"p": 2})
assert_array_almost_equal(D, [0, 1])
assert_array_almost_equal(E, [1., 1.])
# Compare with naive implementation
rng = np.random.RandomState(0)
X = rng.randn(97, 149)
Y = rng.randn(111, 149)
dist = pairwise_distances(X, Y, metric="manhattan")
dist_orig_ind = dist.argmin(axis=0)
dist_orig_val = dist[dist_orig_ind, range(len(dist_orig_ind))]
dist_chunked_ind, dist_chunked_val = pairwise_distances_argmin_min(
X, Y, axis=0, metric="manhattan", batch_size=50)
np.testing.assert_almost_equal(dist_orig_ind, dist_chunked_ind, decimal=7)
np.testing.assert_almost_equal(dist_orig_val, dist_chunked_val, decimal=7)
def test_euclidean_distances():
# Check the pairwise Euclidean distances computation
X = [[0]]
Y = [[1], [2]]
D = euclidean_distances(X, Y)
assert_array_almost_equal(D, [[1., 2.]])
X = csr_matrix(X)
Y = csr_matrix(Y)
D = euclidean_distances(X, Y)
assert_array_almost_equal(D, [[1., 2.]])
# Paired distances
def test_paired_euclidean_distances():
# Check the paired Euclidean distances computation
X = [[0], [0]]
Y = [[1], [2]]
D = paired_euclidean_distances(X, Y)
assert_array_almost_equal(D, [1., 2.])
def test_paired_manhattan_distances():
# Check the paired manhattan distances computation
X = [[0], [0]]
Y = [[1], [2]]
D = paired_manhattan_distances(X, Y)
assert_array_almost_equal(D, [1., 2.])
def test_chi_square_kernel():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((10, 4))
K_add = additive_chi2_kernel(X, Y)
gamma = 0.1
K = chi2_kernel(X, Y, gamma=gamma)
assert_equal(K.dtype, np.float)
for i, x in enumerate(X):
for j, y in enumerate(Y):
chi2 = -np.sum((x - y) ** 2 / (x + y))
chi2_exp = np.exp(gamma * chi2)
assert_almost_equal(K_add[i, j], chi2)
assert_almost_equal(K[i, j], chi2_exp)
# check diagonal is ones for data with itself
K = chi2_kernel(Y)
assert_array_equal(np.diag(K), 1)
# check off-diagonal is < 1 but > 0:
assert_true(np.all(K > 0))
assert_true(np.all(K - np.diag(np.diag(K)) < 1))
# check that float32 is preserved
X = rng.random_sample((5, 4)).astype(np.float32)
Y = rng.random_sample((10, 4)).astype(np.float32)
K = chi2_kernel(X, Y)
assert_equal(K.dtype, np.float32)
# check integer type gets converted,
# check that zeros are handled
X = rng.random_sample((10, 4)).astype(np.int32)
K = chi2_kernel(X, X)
assert_true(np.isfinite(K).all())
assert_equal(K.dtype, np.float)
# check that kernel of similar things is greater than dissimilar ones
X = [[.3, .7], [1., 0]]
Y = [[0, 1], [.9, .1]]
K = chi2_kernel(X, Y)
assert_greater(K[0, 0], K[0, 1])
assert_greater(K[1, 1], K[1, 0])
# test negative input
assert_raises(ValueError, chi2_kernel, [[0, -1]])
assert_raises(ValueError, chi2_kernel, [[0, -1]], [[-1, -1]])
assert_raises(ValueError, chi2_kernel, [[0, 1]], [[-1, -1]])
# different n_features in X and Y
assert_raises(ValueError, chi2_kernel, [[0, 1]], [[.2, .2, .6]])
# sparse matrices
assert_raises(ValueError, chi2_kernel, csr_matrix(X), csr_matrix(Y))
assert_raises(ValueError, additive_chi2_kernel,
csr_matrix(X), csr_matrix(Y))
def test_kernel_symmetry():
# Valid kernels should be symmetric
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
for kernel in (linear_kernel, polynomial_kernel, rbf_kernel,
sigmoid_kernel, cosine_similarity):
K = kernel(X, X)
assert_array_almost_equal(K, K.T, 15)
def test_kernel_sparse():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
X_sparse = csr_matrix(X)
for kernel in (linear_kernel, polynomial_kernel, rbf_kernel,
sigmoid_kernel, cosine_similarity):
K = kernel(X, X)
K2 = kernel(X_sparse, X_sparse)
assert_array_almost_equal(K, K2)
def test_linear_kernel():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
K = linear_kernel(X, X)
# the diagonal elements of a linear kernel are their squared norm
assert_array_almost_equal(K.flat[::6], [linalg.norm(x) ** 2 for x in X])
def test_rbf_kernel():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
K = rbf_kernel(X, X)
# the diagonal elements of a rbf kernel are 1
assert_array_almost_equal(K.flat[::6], np.ones(5))
def test_cosine_similarity_sparse_output():
# Test if cosine_similarity correctly produces sparse output.
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((3, 4))
Xcsr = csr_matrix(X)
Ycsr = csr_matrix(Y)
K1 = cosine_similarity(Xcsr, Ycsr, dense_output=False)
assert_true(issparse(K1))
K2 = pairwise_kernels(Xcsr, Y=Ycsr, metric="cosine")
assert_array_almost_equal(K1.todense(), K2)
def test_cosine_similarity():
# Test the cosine_similarity.
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
Y = rng.random_sample((3, 4))
Xcsr = csr_matrix(X)
Ycsr = csr_matrix(Y)
for X_, Y_ in ((X, None), (X, Y),
(Xcsr, None), (Xcsr, Ycsr)):
# Test that the cosine is kernel is equal to a linear kernel when data
# has been previously normalized by L2-norm.
K1 = pairwise_kernels(X_, Y=Y_, metric="cosine")
X_ = normalize(X_)
if Y_ is not None:
Y_ = normalize(Y_)
K2 = pairwise_kernels(X_, Y=Y_, metric="linear")
assert_array_almost_equal(K1, K2)
def test_check_dense_matrices():
# Ensure that pairwise array check works for dense matrices.
# Check that if XB is None, XB is returned as reference to XA
XA = np.resize(np.arange(40), (5, 8))
XA_checked, XB_checked = check_pairwise_arrays(XA, None)
assert_true(XA_checked is XB_checked)
assert_array_equal(XA, XA_checked)
def test_check_XB_returned():
# Ensure that if XA and XB are given correctly, they return as equal.
# Check that if XB is not None, it is returned equal.
# Note that the second dimension of XB is the same as XA.
XA = np.resize(np.arange(40), (5, 8))
XB = np.resize(np.arange(32), (4, 8))
XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
assert_array_equal(XA, XA_checked)
assert_array_equal(XB, XB_checked)
XB = np.resize(np.arange(40), (5, 8))
XA_checked, XB_checked = check_paired_arrays(XA, XB)
assert_array_equal(XA, XA_checked)
assert_array_equal(XB, XB_checked)
def test_check_different_dimensions():
# Ensure an error is raised if the dimensions are different.
XA = np.resize(np.arange(45), (5, 9))
XB = np.resize(np.arange(32), (4, 8))
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
XB = np.resize(np.arange(4 * 9), (4, 9))
assert_raises(ValueError, check_paired_arrays, XA, XB)
def test_check_invalid_dimensions():
# Ensure an error is raised on 1D input arrays.
XA = np.arange(45)
XB = np.resize(np.arange(32), (4, 8))
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
XA = np.resize(np.arange(45), (5, 9))
XB = np.arange(32)
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
def test_check_sparse_arrays():
# Ensures that checks return valid sparse matrices.
rng = np.random.RandomState(0)
XA = rng.random_sample((5, 4))
XA_sparse = csr_matrix(XA)
XB = rng.random_sample((5, 4))
XB_sparse = csr_matrix(XB)
XA_checked, XB_checked = check_pairwise_arrays(XA_sparse, XB_sparse)
# compare their difference because testing csr matrices for
# equality with '==' does not work as expected.
assert_true(issparse(XA_checked))
assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
assert_true(issparse(XB_checked))
assert_equal(abs(XB_sparse - XB_checked).sum(), 0)
XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
assert_true(issparse(XA_checked))
assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
assert_true(issparse(XA_2_checked))
assert_equal(abs(XA_2_checked - XA_checked).sum(), 0)
def tuplify(X):
# Turns a numpy matrix (any n-dimensional array) into tuples.
s = X.shape
if len(s) > 1:
# Tuplify each sub-array in the input.
return tuple(tuplify(row) for row in X)
else:
# Single dimension input, just return tuple of contents.
return tuple(r for r in X)
def test_check_tuple_input():
# Ensures that checks return valid tuples.
rng = np.random.RandomState(0)
XA = rng.random_sample((5, 4))
XA_tuples = tuplify(XA)
XB = rng.random_sample((5, 4))
XB_tuples = tuplify(XB)
XA_checked, XB_checked = check_pairwise_arrays(XA_tuples, XB_tuples)
assert_array_equal(XA_tuples, XA_checked)
assert_array_equal(XB_tuples, XB_checked)
def test_check_preserve_type():
# Ensures that type float32 is preserved.
XA = np.resize(np.arange(40), (5, 8)).astype(np.float32)
XB = np.resize(np.arange(40), (5, 8)).astype(np.float32)
XA_checked, XB_checked = check_pairwise_arrays(XA, None)
assert_equal(XA_checked.dtype, np.float32)
# both float32
XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
assert_equal(XA_checked.dtype, np.float32)
assert_equal(XB_checked.dtype, np.float32)
# mismatched A
XA_checked, XB_checked = check_pairwise_arrays(XA.astype(np.float),
XB)
assert_equal(XA_checked.dtype, np.float)
assert_equal(XB_checked.dtype, np.float)
# mismatched B
XA_checked, XB_checked = check_pairwise_arrays(XA,
XB.astype(np.float))
assert_equal(XA_checked.dtype, np.float)
assert_equal(XB_checked.dtype, np.float)
| bsd-3-clause |
automl/paramsklearn | ParamSklearn/components/feature_preprocessing/kitchen_sinks.py | 1 | 2564 | import sklearn.kernel_approximation
from HPOlibConfigSpace.configuration_space import ConfigurationSpace
from HPOlibConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter
from ParamSklearn.components.base import ParamSklearnPreprocessingAlgorithm
from ParamSklearn.constants import *
class RandomKitchenSinks(ParamSklearnPreprocessingAlgorithm):
def __init__(self, gamma, n_components, random_state=None):
""" Parameters:
gamma: float
Parameter of the rbf kernel to be approximated exp(-gamma * x^2)
n_components: int
Number of components (output dimensionality) used to approximate the kernel
"""
self.gamma = gamma
self.n_components = n_components
self.random_state = random_state
def fit(self, X, Y=None):
self.preprocessor = sklearn.kernel_approximation.RBFSampler(self.gamma, self.n_components, self.random_state)
self.preprocessor.fit(X)
return self
def transform(self, X):
if self.preprocessor is None:
raise NotImplementedError()
return self.preprocessor.transform(X)
@staticmethod
def get_properties(dataset_properties=None):
return {'shortname': 'KitchenSink',
'name': 'Random Kitchen Sinks',
'handles_missing_values': False,
'handles_nominal_values': False,
'handles_numerical_features': True,
'prefers_data_scaled': True,
'prefers_data_normalized': True,
'handles_regression': True,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': True,
'is_deterministic': True,
'handles_sparse': True,
'handles_dense': True,
'input': (SPARSE, DENSE, UNSIGNED_DATA),
'output': (INPUT, UNSIGNED_DATA),
'preferred_dtype': None}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
gamma = UniformFloatHyperparameter(
"gamma", 0.3, 2., default=1.0)
n_components = UniformIntegerHyperparameter(
"n_components", 50, 10000, default=100, log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(n_components)
return cs
def __str__(self):
name = self.get_properties()['name']
return "ParamSklearn %s" % name
| bsd-3-clause |
sangwook236/sangwook-library | python/test/machine_learning/keras/run_simple_training.py | 2 | 28208 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import sys
sys.path.append('../../../src')
import os, math, shutil, argparse, logging, logging.handlers, time, datetime
import numpy as np
import tensorflow as tf
#import sklearn
#import cv2
#import matplotlib.pyplot as plt
import swl.machine_learning.util as swl_ml_util
#--------------------------------------------------------------------
def swish(x, beta=1):
return (x * tf.keras.backend.sigmoid(beta * x))
tf.keras.utils.get_custom_objects().update({'swish': tf.keras.layers.Activation(swish)})
#tf.keras.layers.Dense(256, activation='swish')
#--------------------------------------------------------------------
# REF [class] >> MyDataset in ${SWL_PYTHON_HOME}/test/machine_learning/tensorflow/run_simple_training.py.
class MyDataset(object):
def __init__(self, image_height, image_width, image_channel, num_classes):
self._image_height, self._image_width, self._image_channel = image_height, image_width, image_channel
self._num_classes = num_classes
# Load data.
self._train_images, self._train_labels, self._test_images, self._test_labels = MyDataset._load_data(self._image_height, self._image_width, self._image_channel, self._num_classes)
self._num_train_examples = len(self._train_images)
if len(self._train_labels) != self._num_train_examples:
raise ValueError('Invalid train data length: {} != {}'.format(self._num_train_examples, len(self._train_labels)))
self._num_test_examples = len(self._test_images)
if len(self._test_labels) != self._num_test_examples:
raise ValueError('Invalid test data length: {} != {}'.format(self._num_test_examples, len(self._test_labels)))
@property
def shape(self):
return self._image_height, self._image_width, self._image_channel
@property
def num_classes(self):
return self._num_classes
@property
def train_data_length(self):
return self._num_train_examples
@property
def test_data_length(self):
return self._num_test_examples
@property
def train_data(self):
return self._train_images, self._train_labels
@property
def test_data(self):
return self._test_images, self._test_labels
def create_train_batch_generator(self, batch_size, shuffle=True):
return MyDataset.create_batch_generator(self._train_images, self._train_labels, batch_size, shuffle)
def create_test_batch_generator(self, batch_size, shuffle=False):
return MyDataset.create_batch_generator(self._test_images, self._test_labels, batch_size, shuffle)
def show_data_info(self, logger, visualize=True):
logger.info('Train image: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(self._train_images.shape, self._train_images.dtype, np.min(self._train_images), np.max(self._train_images)))
logger.info('Train label: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(self._train_labels.shape, self._train_labels.dtype, np.min(self._train_labels), np.max(self._train_labels)))
logger.info('Test image: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(self._test_images.shape, self._test_images.dtype, np.min(self._test_images), np.max(self._test_images)))
logger.info('Test label: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(self._test_labels.shape, self._test_labels.dtype, np.min(self._test_labels), np.max(self._test_labels)))
if visualize:
import cv2
def show_images(images, labels):
images = images.squeeze(axis=-1)
minval, maxval = np.min(images), np.max(images)
images = (images - minval) / (maxval - minval)
labels = np.argmax(labels, axis=-1)
for idx, (img, lbl) in enumerate(zip(images, labels)):
print('Label #{} = {}.'.format(idx, lbl))
cv2.imshow('Image', img)
cv2.waitKey()
if idx >= 9: break
show_images(self._train_images, self._train_labels)
show_images(self._test_images, self._test_labels)
cv2.destroyAllWindows()
@staticmethod
def create_batch_generator(data1, data2, batch_size, shuffle):
num_examples = len(data1)
if len(data2) != num_examples:
raise ValueError('Invalid data length: {} != {}'.format(num_examples, len(data2)))
if batch_size is None:
batch_size = num_examples
if batch_size <= 0:
raise ValueError('Invalid batch size: {}'.format(batch_size))
indices = np.arange(num_examples)
if shuffle:
np.random.shuffle(indices)
if data2 is None:
start_idx = 0
while True:
end_idx = start_idx + batch_size
batch_indices = indices[start_idx:end_idx]
if batch_indices.size > 0: # If batch_indices is non-empty.
# FIXME [fix] >> Does not work correctly in time-major data.
batch_data1 = data1[batch_indices]
if batch_data1.size > 0: # If batch_data1 is non-empty.
yield (batch_data1, None), batch_indices.size
else:
yield (None, None), 0
else:
yield (None, None), 0
if end_idx >= num_examples:
break
start_idx = end_idx
else:
start_idx = 0
while True:
end_idx = start_idx + batch_size
batch_indices = indices[start_idx:end_idx]
if batch_indices.size > 0: # If batch_indices is non-empty.
# FIXME [fix] >> Does not work correctly in time-major data.
batch_data1, batch_data2 = data1[batch_indices], data2[batch_indices]
if batch_data1.size > 0 and batch_data2.size > 0: # If batch_data1 and batch_data2 are non-empty.
yield (batch_data1, batch_data2), batch_indices.size
else:
yield (None, None), 0
else:
yield (None, None), 0
if end_idx >= num_examples:
break
start_idx = end_idx
@staticmethod
def _preprocess(inputs, outputs, image_height, image_width, image_channel, num_classes):
if inputs is not None:
# Contrast limited adaptive histogram equalization (CLAHE).
#clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
#inputs = np.array([clahe.apply(inp) for inp in inputs])
# Normalization, standardization, etc.
inputs = inputs.astype(np.float32)
if False:
inputs = sklearn.preprocessing.scale(inputs, axis=0, with_mean=True, with_std=True, copy=True)
#inputs = sklearn.preprocessing.minmax_scale(inputs, feature_range=(0, 1), axis=0, copy=True) # [0, 1].
#inputs = sklearn.preprocessing.maxabs_scale(inputs, axis=0, copy=True) # [-1, 1].
#inputs = sklearn.preprocessing.robust_scale(inputs, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)
elif True:
inputs = (inputs - np.mean(inputs, axis=None)) / np.std(inputs, axis=None) # Standardization.
elif False:
in_min, in_max = 0, 255 #np.min(inputs), np.max(inputs)
out_min, out_max = 0, 1 #-1, 1
inputs = (inputs - in_min) * (out_max - out_min) / (in_max - in_min) + out_min # Normalization.
elif False:
inputs /= 255.0 # Normalization.
# Reshape.
inputs = np.reshape(inputs, (-1, image_height, image_width, image_channel))
if outputs is not None:
# One-hot encoding (num_examples, height, width) -> (num_examples, height, width, num_classes).
#outputs = swl_ml_util.to_one_hot_encoding(outputs, num_classes).astype(np.uint8)
outputs = tf.keras.utils.to_categorical(outputs, num_classes).astype(np.uint8)
return inputs, outputs
@staticmethod
def _load_data(image_height, image_width, image_channel, num_classes):
# Pixel value: [0, 255].
(train_inputs, train_outputs), (test_inputs, test_outputs) = tf.keras.datasets.mnist.load_data()
# Preprocess.
train_inputs, train_outputs = MyDataset._preprocess(train_inputs, train_outputs, image_height, image_width, image_channel, num_classes)
test_inputs, test_outputs = MyDataset._preprocess(test_inputs, test_outputs, image_height, image_width, image_channel, num_classes)
return train_inputs, train_outputs, test_inputs, test_outputs
class MyDataSequence(tf.keras.utils.Sequence):
def __init__(self, inputs, outputs, batch_size=None, shuffle=False):
self.inputs, self.outputs = inputs, outputs
self.batch_size = batch_size
self.num_examples = len(self.inputs)
if self.outputs is not None and len(self.outputs) != self.num_examples:
raise ValueError('Invalid data size: {} != {}'.format(self.num_examples, len(self.outputs)))
if self.batch_size is None:
self.batch_size = self.num_examples
if self.batch_size <= 0:
raise ValueError('Invalid batch size: {}'.format(self.batch_size))
self.indices = np.arange(self.num_examples)
if shuffle:
np.random.shuffle(self.indices)
def __len__(self):
return math.ceil(self.num_examples / self.batch_size)
def __getitem__(self, idx):
start_idx = idx * self.batch_size
end_idx = start_idx + self.batch_size
batch_indices = self.indices[start_idx:end_idx]
if batch_indices.size > 0: # If batch_indices is non-empty.
# FIXME [fix] >> Does not work correctly in time-major data.
batch_input, batch_output = self.inputs[batch_indices], None if self.outputs is None else self.outputs[batch_indices]
if batch_input.size > 0 and (batch_output is None or batch_output.size > 0): # If batch_input and batch_output are non-empty.
return (batch_input, batch_output)
return (None, None)
#--------------------------------------------------------------------
def load_model(model_filepath, logger):
try:
if logger: logger.info('Start loading a model...')
start_time = time.time()
"""
# Load only the architecture of a model.
model = tf.keras.models.model_from_json(json_string)
#model = tf.keras.models.model_from_yaml(yaml_string)
# Load only the weights of a model.
model.load_weights(model_weight_filepath)
"""
# Load a model.
model = tf.keras.models.load_model(model_filepath)
if logger: logger.info('End loading a model from {}: {} secs.'.format(model_filepath, time.time() - start_time))
return model
except (ImportError, IOError):
if logger: logger.error('Failed to load a model from {}.'.format(model_filepath))
return None
class MyModel(object):
@classmethod
def create_model(cls, input_shape, num_classes):
model = tf.keras.models.Sequential()
# Layer 1.
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=5, strides=1, activation='relu', input_shape=input_shape))
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
# Layer 2.
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
model.add(tf.keras.layers.Flatten())
# Layer 3.
model.add(tf.keras.layers.Dense(units=1024, activation='relu'))
# Layer 4.
model.add(tf.keras.layers.Dense(units=num_classes, activation='softmax'))
return model
#--------------------------------------------------------------------
class MyRunner(object):
def __init__(self):
# Set parameters.
self._use_keras_data_sequence, self._use_generator = True, False
self._max_queue_size, self._num_workers = 10, 8
self._use_multiprocessing = True
#self._sess = tf.Session(config=config)
#tf.keras.backend.set_session(self._sess)
#tf.keras.backend.set_learning_phase(0) # Sets the learning phase to 'test'.
#tf.keras.backend.set_learning_phase(1) # Sets the learning phase to 'train'.
def train(self, model, criterion, optimizer, dataset, model_checkpoint_filepath, output_dir_path, batch_size, final_epoch, initial_epoch=0, logger=None):
model.compile(loss=criterion, optimizer=optimizer, metrics=['accuracy'])
def schedule_learning_rate(epoch, learning_rate):
if epoch < 10:
return 1.0e-2
elif epoch < 20:
return 1.0e-3
elif epoch < 30:
return 1.0e-4
else:
return 1.0e-4 * tf.math.exp(0.1 * (30 - epoch))
lr_schedule_callback = tf.keras.callbacks.LearningRateScheduler(schedule=schedule_learning_rate, verbose=0)
lr_reduce_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)
early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='auto', baseline=None, restore_best_weights=False)
if True:
timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
csv_log_filepath = os.path.join(output_dir_path, 'train_log_{}.csv'.format(timestamp))
file_logger_callback = tf.keras.callbacks.CSVLogger(csv_log_filepath, separator=',', append=False) # epoch, acc, loss, lr, val_acc, val_loss.
else:
import json
timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
json_log_filepath = os.path.join(output_dir_path, 'train_log_{}.json'.format(timestamp))
json_log = open(json_log_filepath, mode='wt', encoding='UTF8', buffering=1)
file_logger_callback = tf.keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: json_log.write(json.dumps({'epoch': epoch, 'acc': logs['acc'], 'loss': logs['loss'], 'lr': logs['lr'], 'val_acc': logs['val_acc'], 'val_loss': logs['val_loss']}) + '\n'),
on_train_end=lambda logs: json_log.close()
)
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(model_checkpoint_filepath, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', save_freq='epoch')
#callbacks = [lr_schedule_callback, lr_reduce_callback, early_stopping_callback, file_logger_callback, model_checkpoint_callback]
callbacks = [early_stopping_callback, file_logger_callback, model_checkpoint_callback]
num_epochs = final_epoch - initial_epoch
#--------------------
if logger: logger.info('Start training...')
start_time = time.time()
if self._use_keras_data_sequence:
# Use Keras sequences.
train_images, train_labels = dataset.train_data
train_sequence = MyDataSequence(train_images, train_labels, batch_size=batch_size, shuffle=True)
val_images, val_labels = dataset.test_data
val_sequence = MyDataSequence(val_images, val_labels, batch_size=batch_size, shuffle=False)
history = model.fit_generator(train_sequence, epochs=num_epochs, steps_per_epoch=None if batch_size is None else math.ceil(dataset.train_data_length / batch_size), validation_data=val_sequence, validation_steps=math.ceil(dataset.test_data_length / batch_size), shuffle=True, initial_epoch=initial_epoch, class_weight=None, max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing, callbacks=callbacks)
elif self._use_generator:
# Use generators.
train_generator = dataset.create_train_batch_generator(batch_size, shuffle=True)
val_generator = dataset.create_test_batch_generator(batch_size, shuffle=False)
history = model.fit_generator(train_generator, epochs=num_epochs, steps_per_epoch=None if batch_size is None else math.ceil(dataset.train_data_length / batch_size), validation_data=val_generator, validation_steps=math.ceil(dataset.test_data_length / batch_size), shuffle=True, initial_epoch=initial_epoch, class_weight=None, max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing, callbacks=callbacks)
else:
train_images, train_labels = dataset.train_data
history = model.fit(train_images, train_labels, batch_size=batch_size, epochs=num_epochs, validation_split=0.2, shuffle=True, initial_epoch=initial_epoch, class_weight=None, sample_weight=None, callbacks=callbacks)
if logger: logger.info('End training: {} secs.'.format(time.time() - start_time))
#--------------------
if logger: logger.info('Start evaluating...')
start_time = time.time()
if self._use_keras_data_sequence:
# Use a Keras sequence.
val_images, val_labels = dataset.test_data
val_sequence = MyDataSequence(val_images, val_labels, batch_size=batch_size, shuffle=False)
score = model.evaluate_generator(val_sequence, steps=None if batch_size is None else math.ceil(dataset.test_data_length / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
elif self._use_generator:
# Use a generator.
val_generator = dataset.create_test_batch_generator(batch_size, shuffle=False)
score = model.evaluate_generator(val_generator, steps=None if batch_size is None else math.ceil(dataset.test_data_length / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
else:
val_images, val_labels = dataset.test_data
score = model.evaluate(val_images, val_labels, batch_size=batch_size, sample_weight=None)
if logger: logger.info('\tValidation: loss = {:.6f}, accuracy = {:.6f}.'.format(*score))
if logger: logger.info('End evaluating: {} secs.'.format(time.time() - start_time))
return history.history
def test(self, model, dataset, batch_size=None, shuffle=False, logger=None):
if logger: logger.info('Start testing...')
start_time = time.time()
if self._use_keras_data_sequence:
# Use a Keras sequence.
test_images, test_labels = dataset.test_data
test_sequence = MyDataSequence(test_images, test_labels, batch_size=batch_size, shuffle=shuffle)
inferences = model.predict_generator(test_sequence, steps=None if batch_size is None else math.ceil(dataset.test_data_length / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
elif self._use_generator:
# Use a generator.
test_generator = dataset.create_test_batch_generator(batch_size, shuffle=shuffle)
inferences = model.predict_generator(test_generator, steps=None if batch_size is None else math.ceil(dataset.test_data_length / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
# TODO [implement] >> self._test_labels have to be generated.
test_labels = dataset.test_data[1]
else:
test_images, test_labels = dataset.test_data
inferences = model.predict(test_images, batch_size=batch_size)
if logger: logger.info('End testing: {} secs.'.format(time.time() - start_time))
if inferences is not None and test_labels is not None:
if logger: logger.info('Test: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(inferences.shape, inferences.dtype, np.min(inferences), np.max(inferences)))
if dataset.num_classes > 2:
inferences = np.argmax(inferences, -1)
ground_truths = np.argmax(test_labels, -1)
elif 2 == dataset.num_classes:
inferences = np.around(inferences)
ground_truths = test_labels
else:
raise ValueError('Invalid number of classes')
correct_estimation_count = np.count_nonzero(np.equal(inferences, ground_truths))
if logger: logger.info('Test: accuracy = {} / {} = {}.'.format(correct_estimation_count, ground_truths.size, correct_estimation_count / ground_truths.size))
else:
if logger: logger.warning('Invalid test results.')
def infer(self, model, inputs, batch_size=None, shuffle=False, logger=None):
if logger: logger.info('Start inferring...')
start_time = time.time()
if self._use_keras_data_sequence:
# Use a Keras sequence.
test_sequence = MyDataSequence(inputs, None, batch_size=batch_size, shuffle=shuffle)
inferences = model.predict_generator(test_sequence, steps=None if batch_size is None else math.ceil(len(inputs) / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
elif self._use_generator:
# Use a generator.
test_generator = MyDataset.create_batch_generator(inputs, None, batch_size, shuffle=shuffle)
inferences = model.predict_generator(test_generator, steps=None if batch_size is None else math.ceil(len(inputs) / batch_size), max_queue_size=self._max_queue_size, workers=self._num_workers, use_multiprocessing=self._use_multiprocessing)
else:
if shuffle:
np.random.shuffle(inputs)
inferences = model.predict(inputs, batch_size=batch_size)
if logger: logger.info('End inferring: {} secs.'.format(time.time() - start_time))
return inferences
#--------------------------------------------------------------------
def parse_command_line_options():
parser = argparse.ArgumentParser(description='Train, test, or infer a CNN model for MNIST dataset.')
parser.add_argument(
'--train',
action='store_true',
help='Specify whether to train a model'
)
parser.add_argument(
'--test',
action='store_true',
help='Specify whether to test a trained model'
)
parser.add_argument(
'--infer',
action='store_true',
help='Specify whether to infer by a trained model'
)
parser.add_argument(
'-m',
'--model_file',
type=str,
#nargs='?',
help='The model file path where a trained model is saved or a pretrained model is loaded',
#required=True,
default=None
)
parser.add_argument(
'-o',
'--out_dir',
type=str,
#nargs='?',
help='The output directory path to save results such as images and log',
#required=True,
default=None
)
parser.add_argument(
'-tr',
'--train_data_dir',
type=str,
#nargs='?',
help='The directory path of training data',
default='./train_data'
)
parser.add_argument(
'-te',
'--test_data_dir',
type=str,
#nargs='?',
help='The directory path of test data',
default='./test_data'
)
parser.add_argument(
'-e',
'--epoch',
type=int,
help='Final epoch',
default=30
)
parser.add_argument(
'-b',
'--batch',
type=int,
help='Batch size',
default=32
)
parser.add_argument(
'-g',
'--gpu',
type=str,
help='Specify GPU to use',
default='0'
)
parser.add_argument(
'-l',
'--log',
type=str,
help='The name of logger and log files',
default=None
)
parser.add_argument(
'-ll',
'--log_level',
type=int,
help='Log level, [0, 50]', # {NOTSET=0, DEBUG=10, INFO=20, WARNING=WARN=30, ERROR=40, CRITICAL=FATAL=50}.
default=None
)
parser.add_argument(
'-ld',
'--log_dir',
type=str,
help='The directory path to log',
default=None
)
return parser.parse_args()
def get_logger(name, log_level=None, log_dir_path=None, is_rotating=True):
if not log_level: log_level = logging.INFO
if not log_dir_path: log_dir_path = './log'
if not os.path.exists(log_dir_path):
os.makedirs(log_dir_path, exist_ok=True)
log_filepath = os.path.join(log_dir_path, (name if name else 'swl') + '.log')
if is_rotating:
file_handler = logging.handlers.RotatingFileHandler(log_filepath, maxBytes=10000000, backupCount=10)
else:
file_handler = logging.FileHandler(log_filepath)
stream_handler = logging.StreamHandler()
formatter = logging.Formatter('[%(levelname)s][%(filename)s:%(lineno)s][%(asctime)s] [SWL] %(message)s')
#formatter = logging.Formatter('[%(levelname)s][%(asctime)s] [SWL] %(message)s')
file_handler.setFormatter(formatter)
stream_handler.setFormatter(formatter)
logger = logging.getLogger(name if name else __name__)
logger.setLevel(log_level) # {NOTSET=0, DEBUG=10, INFO=20, WARNING=WARN=30, ERROR=40, CRITICAL=FATAL=50}.
logger.addHandler(file_handler)
logger.addHandler(stream_handler)
return logger
def main():
args = parse_command_line_options()
logger = get_logger(args.log if args.log else os.path.basename(os.path.normpath(__file__)), args.log_level if args.log_level else logging.INFO, args.log_dir if args.log_dir else args.out_dir, is_rotating=True)
logger.info('----------------------------------------------------------------------')
logger.info('Logger: name = {}, level = {}.'.format(logger.name, logger.level))
logger.info('Command-line arguments: {}.'.format(sys.argv))
logger.info('Command-line options: {}.'.format(vars(args)))
logger.info('Python version: {}.'.format(sys.version.replace('\n', ' ')))
logger.info('TensorFlow version: {}.'.format(tf.__version__))
if not args.train and not args.test and not args.infer:
logger.error('At least one of command line options "--train", "--test", and "--infer" has to be specified.')
return
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.log_level:
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' # [0, 3].
#--------------------
initial_epoch, final_epoch, batch_size = 0, args.epoch, args.batch
is_resumed = args.model_file is not None
model_filepath, output_dir_path = os.path.normpath(args.model_file) if args.model_file else None, os.path.normpath(args.out_dir) if args.out_dir else None
if model_filepath:
if not output_dir_path:
output_dir_path = os.path.dirname(model_filepath)
else:
if not output_dir_path:
output_dir_prefix = 'simple_training'
output_dir_suffix = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
output_dir_path = os.path.join('.', '{}_{}'.format(output_dir_prefix, output_dir_suffix))
model_filepath = os.path.join(output_dir_path, 'model.hdf5')
#model_weight_filepath = os.path.join(output_dir_path, 'model_weights.hdf5')
#--------------------
# Create a dataset.
image_height, image_width, image_channel = 28, 28, 1 # 784 = 28 * 28.
num_classes = 10
logger.info('Start creating a dataset...')
start_time = time.time()
dataset = MyDataset(image_height, image_width, image_channel, num_classes)
logger.info('End creating a dataset: {} secs.'.format(time.time() - start_time))
dataset.show_data_info(logger, visualize=False)
#--------------------
runner = MyRunner()
if args.train:
model_checkpoint_filepath = os.path.join(output_dir_path, 'model_ckpt.{epoch:04d}-{val_loss:.5f}.hdf5')
if output_dir_path and output_dir_path.strip() and not os.path.exists(output_dir_path):
os.makedirs(output_dir_path, exist_ok=True)
if is_resumed:
# Load a model.
model = load_model(model_filepath, logger)
else:
# Build a model.
model = MyModel.create_model(dataset.shape, dataset.num_classes)
#if model: print('Model summary:\n{}.'.format(model.summary()))
if model:
# Create a trainer.
criterion = tf.keras.losses.categorical_crossentropy
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9, nesterov=True)
#optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001, rho=0.9, momentum=0.9, epsilon=1.0e-7, centered=False)
#optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, amsgrad=False) # Not good.
history = runner.train(model, criterion, optimizer, dataset, model_checkpoint_filepath, output_dir_path, batch_size, final_epoch, initial_epoch, logger)
model_filepath = os.path.join(output_dir_path, 'best_model_{}.hdf5'.format(datetime.datetime.now().strftime('%Y%m%dT%H%M%S')))
logger.info('Start saving a model...')
start_time = time.time()
"""
# Save only the architecture of a model.
json_string = model.to_json()
#yaml_string = model.to_yaml()
# Save only the weights of a model.
model.save_weights(model_weight_filepath)
"""
# Save a model.
model.save(model_filepath)
logger.info('End saving a model to {}: {} secs.'.format(model_filepath, time.time() - start_time))
#logger.info('Train history = {}.'.format(history))
swl_ml_util.display_train_history(history)
if os.path.exists(output_dir_path):
swl_ml_util.save_train_history(history, output_dir_path)
if args.test or args.infer:
if model_filepath and os.path.exists(model_filepath):
model = load_model(model_filepath, logger)
if args.test and model:
runner.test(model, dataset, logger=logger)
if args.infer and model:
inf_images, _ = dataset.test_data
inferences = runner.infer(model, inf_images, logger=logger)
if inferences is not None:
logger.info('Inference: shape = {}, dtype = {}, (min, max) = ({}, {}).'.format(inferences.shape, inferences.dtype, np.min(inferences), np.max(inferences)))
if dataset.num_classes > 2:
inferences = np.argmax(inferences, -1)
elif 2 == dataset.num_classes:
inferences = np.around(inferences)
else:
raise ValueError('Invalid number of classes')
results = {idx: inf for idx, inf in enumerate(inferences) if idx < 100}
logger.info('Inference results (index: inference): {}.'.format(results))
else:
logger.info('Invalid inference results.')
else:
logger.error('Model file, {} does not exist.'.format(model_filepath))
#--------------------------------------------------------------------
# Usage:
# python run_simple_training.py --train --test --infer --epoch 20 --gpu 0
if '__main__' == __name__:
main()
| gpl-2.0 |
frank-tancf/scikit-learn | examples/cluster/plot_face_segmentation.py | 71 | 2839 | """
===================================================
Segmenting the picture of a raccoon face in regions
===================================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous regions.
This procedure (spectral clustering on an image) is an efficient
approximate solution for finding normalized graph cuts.
There are two options to assign labels:
* with 'kmeans' spectral clustering will cluster samples in the embedding space
using a kmeans algorithm
* whereas 'discrete' will iteratively search for the closest partition
space to the embedding space.
"""
print(__doc__)
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung
# License: BSD 3 clause
import time
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
from sklearn.utils.testing import SkipTest
from sklearn.utils.fixes import sp_version
if sp_version < (0, 12):
raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
"thus does not include the scipy.misc.face() image.")
# load the raccoon face as a numpy array
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# Resize it to 10% of the original size to speed up the processing
face = sp.misc.imresize(face, 0.10) / 255.
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
#############################################################################
# Visualize the resulting regions
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph, n_clusters=N_REGIONS,
assign_labels=assign_labels, random_state=1)
t1 = time.time()
labels = labels.reshape(face.shape)
plt.figure(figsize=(5, 5))
plt.imshow(face, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l, contours=1,
colors=[plt.cm.spectral(l / float(N_REGIONS))])
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
print(title)
plt.title(title)
plt.show()
| bsd-3-clause |
cactusbin/nyt | matplotlib/lib/matplotlib/image.py | 4 | 49269 | """
The image module supports basic image loading, rescaling and display
operations.
"""
from __future__ import division, print_function
import os
import warnings
import math
import numpy as np
from numpy import ma
from matplotlib import rcParams
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.cbook as cbook
# For clarity, names from _image are given explicitly in this module:
import matplotlib._image as _image
import matplotlib._png as _png
# For user convenience, the names from _image are also imported into
# the image namespace:
from matplotlib._image import *
from matplotlib.transforms import BboxBase, Bbox, IdentityTransform
import matplotlib.transforms as mtransforms
class _AxesImageBase(martist.Artist, cm.ScalarMappable):
zorder = 0
# map interpolation strings to module constants
_interpd = {
'none': _image.NEAREST, # fall back to nearest when not supported
'nearest': _image.NEAREST,
'bilinear': _image.BILINEAR,
'bicubic': _image.BICUBIC,
'spline16': _image.SPLINE16,
'spline36': _image.SPLINE36,
'hanning': _image.HANNING,
'hamming': _image.HAMMING,
'hermite': _image.HERMITE,
'kaiser': _image.KAISER,
'quadric': _image.QUADRIC,
'catrom': _image.CATROM,
'gaussian': _image.GAUSSIAN,
'bessel': _image.BESSEL,
'mitchell': _image.MITCHELL,
'sinc': _image.SINC,
'lanczos': _image.LANCZOS,
'blackman': _image.BLACKMAN,
}
# reverse interp dict
_interpdr = dict([(v, k) for k, v in _interpd.iteritems()])
interpnames = _interpd.keys()
def __str__(self):
return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)
def __init__(self, ax,
cmap=None,
norm=None,
interpolation=None,
origin=None,
filternorm=1,
filterrad=4.0,
resample=False,
**kwargs
):
"""
interpolation and cmap default to their rc settings
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.
Additional kwargs are matplotlib.artist properties
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
if origin is None:
origin = rcParams['image.origin']
self.origin = origin
self.set_filternorm(filternorm)
self.set_filterrad(filterrad)
self._filterrad = filterrad
self.set_interpolation(interpolation)
self.set_resample(resample)
self.axes = ax
self._imcache = None
# this is an experimental attribute, if True, unsampled image
# will be drawn using the affine transform that are
# appropriately skewed so that the given position
# corresponds to the actual position in the coordinate. -JJL
self._image_skew_coordinate = None
self.update(kwargs)
def get_size(self):
"""Get the numrows, numcols of the input image"""
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def set_alpha(self, alpha):
"""
Set the alpha value used for blending - not supported on
all backends
ACCEPTS: float
"""
martist.Artist.set_alpha(self, alpha)
self._imcache = None
def changed(self):
"""
Call this whenever the mappable is changed so observers can
update state
"""
self._imcache = None
self._rgbacache = None
cm.ScalarMappable.changed(self)
def make_image(self, magnification=1.0):
raise RuntimeError('The make_image method must be overridden.')
def _get_unsampled_image(self, A, image_extents, viewlim):
"""
convert numpy array A with given extents ([x1, x2, y1, y2] in
data coordinate) into the Image, given the viewlim (should be a
bbox instance). Image will be clipped if the extents is
significantly larger than the viewlim.
"""
xmin, xmax, ymin, ymax = image_extents
dxintv = xmax-xmin
dyintv = ymax-ymin
# the viewport scale factor
if viewlim.width == 0.0 and dxintv == 0.0:
sx = 1.0
else:
sx = dxintv/viewlim.width
if viewlim.height == 0.0 and dyintv == 0.0:
sy = 1.0
else:
sy = dyintv/viewlim.height
numrows, numcols = A.shape[:2]
if sx > 2:
x0 = (viewlim.x0-xmin)/dxintv * numcols
ix0 = max(0, int(x0 - self._filterrad))
x1 = (viewlim.x1-xmin)/dxintv * numcols
ix1 = min(numcols, int(x1 + self._filterrad))
xslice = slice(ix0, ix1)
xmin_old = xmin
xmin = xmin_old + ix0*dxintv/numcols
xmax = xmin_old + ix1*dxintv/numcols
dxintv = xmax - xmin
sx = dxintv/viewlim.width
else:
xslice = slice(0, numcols)
if sy > 2:
y0 = (viewlim.y0-ymin)/dyintv * numrows
iy0 = max(0, int(y0 - self._filterrad))
y1 = (viewlim.y1-ymin)/dyintv * numrows
iy1 = min(numrows, int(y1 + self._filterrad))
if self.origin == 'upper':
yslice = slice(numrows-iy1, numrows-iy0)
else:
yslice = slice(iy0, iy1)
ymin_old = ymin
ymin = ymin_old + iy0*dyintv/numrows
ymax = ymin_old + iy1*dyintv/numrows
dyintv = ymax - ymin
sy = dyintv/viewlim.height
else:
yslice = slice(0, numrows)
if xslice != self._oldxslice or yslice != self._oldyslice:
self._imcache = None
self._oldxslice = xslice
self._oldyslice = yslice
if self._imcache is None:
if self._A.dtype == np.uint8 and self._A.ndim == 3:
im = _image.frombyte(self._A[yslice, xslice, :], 0)
im.is_grayscale = False
else:
if self._rgbacache is None:
x = self.to_rgba(self._A, bytes=False)
# Avoid side effects: to_rgba can return its argument
# unchanged.
if np.may_share_memory(x, self._A):
x = x.copy()
# premultiply the colors
x[..., 0:3] *= x[..., 3:4]
x = (x * 255).astype(np.uint8)
self._rgbacache = x
else:
x = self._rgbacache
im = _image.frombyte(x[yslice, xslice, :], 0)
if self._A.ndim == 2:
im.is_grayscale = self.cmap.is_gray()
else:
im.is_grayscale = False
self._imcache = im
if self.origin == 'upper':
im.flipud_in()
else:
im = self._imcache
return im, xmin, ymin, dxintv, dyintv, sx, sy
@staticmethod
def _get_rotate_and_skew_transform(x1, y1, x2, y2, x3, y3):
"""
Retuen a transform that does
(x1, y1) -> (x1, y1)
(x2, y2) -> (x2, y2)
(x2, y1) -> (x3, y3)
It was intended to derive a skew transform that preserve the
lower-left corner (x1, y1) and top-right corner(x2,y2), but
change the the lower-right-corner(x2, y1) to a new position
(x3, y3).
"""
tr1 = mtransforms.Affine2D()
tr1.translate(-x1, -y1)
x2a, y2a = tr1.transform_point((x2, y2))
x3a, y3a = tr1.transform_point((x3, y3))
inv_mat = 1. / (x2a*y3a-y2a*x3a) * np.mat([[y3a, -y2a], [-x3a, x2a]])
a, b = (inv_mat * np.mat([[x2a], [x2a]])).flat
c, d = (inv_mat * np.mat([[y2a], [0]])).flat
tr2 = mtransforms.Affine2D.from_values(a, c, b, d, 0, 0)
tr = (tr1 + tr2 +
mtransforms.Affine2D().translate(x1, y1)).inverted().get_affine()
return tr
def _draw_unsampled_image(self, renderer, gc):
"""
draw unsampled image. The renderer should support a draw_image method
with scale parameter.
"""
trans = self.get_transform() # axes.transData
# convert the coordinates to the intermediate coordinate (ic).
# The transformation from the ic to the canvas is a pure
# affine transform.
# A straight-forward way is to use the non-affine part of the
# original transform for conversion to the ic.
# firs, convert the image extent to the ic
x_llc, x_trc, y_llc, y_trc = self.get_extent()
xy = trans.transform(np.array([(x_llc, y_llc),
(x_trc, y_trc)]))
_xx1, _yy1 = xy[0]
_xx2, _yy2 = xy[1]
extent_in_ic = _xx1, _xx2, _yy1, _yy2
# define trans_ic_to_canvas : unless _image_skew_coordinate is
# set, it is simply a affine part of the original transform.
if self._image_skew_coordinate:
# skew the image when required.
x_lrc, y_lrc = self._image_skew_coordinate
xy2 = trans.transform(np.array([(x_lrc, y_lrc)]))
_xx3, _yy3 = xy2[0]
tr_rotate_skew = self._get_rotate_and_skew_transform(_xx1, _yy1,
_xx2, _yy2,
_xx3, _yy3)
trans_ic_to_canvas = tr_rotate_skew
else:
trans_ic_to_canvas = IdentityTransform()
# Now, viewLim in the ic. It can be rotated and can be
# skewed. Make it big enough.
x1, y1, x2, y2 = self.axes.bbox.extents
trans_canvas_to_ic = trans_ic_to_canvas.inverted()
xy_ = trans_canvas_to_ic.transform(np.array([(x1, y1),
(x2, y1),
(x2, y2),
(x1, y2)]))
x1_, x2_ = min(xy_[:, 0]), max(xy_[:, 0])
y1_, y2_ = min(xy_[:, 1]), max(xy_[:, 1])
viewLim_in_ic = Bbox.from_extents(x1_, y1_, x2_, y2_)
# get the image, sliced if necessary. This is done in the ic.
im, xmin, ymin, dxintv, dyintv, sx, sy = \
self._get_unsampled_image(self._A, extent_in_ic, viewLim_in_ic)
if im is None:
return # I'm not if this check is required. -JJL
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg(*bg)
# image input dimensions
im.reset_matrix()
numrows, numcols = im.get_size()
im.resize(numcols, numrows) # just to create im.bufOut that
# is required by backends. There
# may be better solution -JJL
im._url = self.get_url()
im._gid = self.get_gid()
renderer.draw_image(gc, xmin, ymin, im, dxintv, dyintv,
trans_ic_to_canvas)
def _check_unsampled_image(self, renderer):
"""
return True if the image is better to be drawn unsampled.
The derived class needs to override it.
"""
return False
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible():
return
if (self.axes.get_xscale() != 'linear' or
self.axes.get_yscale() != 'linear'):
warnings.warn("Images are not supported on non-linear axes.")
l, b, widthDisplay, heightDisplay = self.axes.bbox.bounds
gc = renderer.new_gc()
gc.set_clip_rectangle(self.axes.bbox.frozen())
gc.set_clip_path(self.get_clip_path())
gc.set_alpha(self.get_alpha())
if self._check_unsampled_image(renderer):
self._draw_unsampled_image(renderer, gc)
else:
if self._image_skew_coordinate is not None:
warnings.warn("Image will not be shown"
" correctly with this backend.")
im = self.make_image(renderer.get_image_magnification())
if im is None:
return
im._url = self.get_url()
im._gid = self.get_gid()
renderer.draw_image(gc, l, b, im)
gc.restore()
def contains(self, mouseevent):
"""
Test whether the mouse event occured within the image.
"""
if callable(self._contains):
return self._contains(self, mouseevent)
# TODO: make sure this is consistent with patch and patch
# collection on nonlinear transformed coordinates.
# TODO: consider returning image coordinates (shouldn't
# be too difficult given that the image is rectilinear
x, y = mouseevent.xdata, mouseevent.ydata
xmin, xmax, ymin, ymax = self.get_extent()
if xmin > xmax:
xmin, xmax = xmax, xmin
if ymin > ymax:
ymin, ymax = ymax, ymin
#print x, y, xmin, xmax, ymin, ymax
if x is not None and y is not None:
inside = ((x >= xmin) and (x <= xmax) and
(y >= ymin) and (y <= ymax))
else:
inside = False
return inside, {}
def write_png(self, fname, noscale=False):
"""Write the image to png file with fname"""
im = self.make_image()
if im is None:
return
if noscale:
numrows, numcols = im.get_size()
im.reset_matrix()
im.set_interpolation(0)
im.resize(numcols, numrows)
im.flipud_out()
rows, cols, buffer = im.as_rgba_str()
_png.write_png(buffer, cols, rows, fname)
def set_data(self, A):
"""
Set the image array
ACCEPTS: numpy/PIL Image A
"""
# check if data is PIL Image without importing Image
if hasattr(A, 'getpixel'):
self._A = pil_to_array(A)
else:
self._A = cbook.safe_masked_invalid(A)
if (self._A.dtype != np.uint8 and
not np.can_cast(self._A.dtype, np.float)):
raise TypeError("Image data can not convert to float")
if (self._A.ndim not in (2, 3) or
(self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
raise TypeError("Invalid dimensions for image data")
self._imcache = None
self._rgbacache = None
self._oldxslice = None
self._oldyslice = None
def set_array(self, A):
"""
Retained for backwards compatibility - use set_data instead
ACCEPTS: numpy array A or PIL Image"""
# This also needs to be here to override the inherited
# cm.ScalarMappable.set_array method so it is not invoked
# by mistake.
self.set_data(A)
def get_interpolation(self):
"""
Return the interpolation method the image uses when resizing.
One of 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36',
'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom',
'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', or 'none'.
"""
return self._interpolation
def set_interpolation(self, s):
"""
Set the interpolation method the image uses when resizing.
if None, use a value from rc setting. If 'none', the image is
shown as is without interpolating. 'none' is only supported in
agg, ps and pdf backends and will fall back to 'nearest' mode
for other backends.
ACCEPTS: ['nearest' | 'bilinear' | 'bicubic' | 'spline16' |
'spline36' | 'hanning' | 'hamming' | 'hermite' | 'kaiser' |
'quadric' | 'catrom' | 'gaussian' | 'bessel' | 'mitchell' |
'sinc' | 'lanczos' | 'none' |]
"""
if s is None:
s = rcParams['image.interpolation']
s = s.lower()
if s not in self._interpd:
raise ValueError('Illegal interpolation string')
self._interpolation = s
def set_resample(self, v):
"""
Set whether or not image resampling is used
ACCEPTS: True|False
"""
if v is None:
v = rcParams['image.resample']
self._resample = v
def get_resample(self):
"""Return the image resample boolean"""
return self._resample
def set_filternorm(self, filternorm):
"""
Set whether the resize filter norms the weights -- see
help for imshow
ACCEPTS: 0 or 1
"""
if filternorm:
self._filternorm = 1
else:
self._filternorm = 0
def get_filternorm(self):
"""Return the filternorm setting"""
return self._filternorm
def set_filterrad(self, filterrad):
"""
Set the resize filter radius only applicable to some
interpolation schemes -- see help for imshow
ACCEPTS: positive float
"""
r = float(filterrad)
assert(r > 0)
self._filterrad = r
def get_filterrad(self):
"""return the filterrad setting"""
return self._filterrad
class AxesImage(_AxesImageBase):
def __str__(self):
return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)
def __init__(self, ax,
cmap=None,
norm=None,
interpolation=None,
origin=None,
extent=None,
filternorm=1,
filterrad=4.0,
resample=False,
**kwargs
):
"""
interpolation and cmap default to their rc settings
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.
Additional kwargs are matplotlib.artist properties
"""
self._extent = extent
_AxesImageBase.__init__(self, ax,
cmap=cmap,
norm=norm,
interpolation=interpolation,
origin=origin,
filternorm=filternorm,
filterrad=filterrad,
resample=resample,
**kwargs
)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
# image is created in the canvas coordinate.
x1, x2, y1, y2 = self.get_extent()
trans = self.get_transform()
xy = trans.transform(np.array([(x1, y1),
(x2, y2),
]))
_x1, _y1 = xy[0]
_x2, _y2 = xy[1]
transformed_viewLim = mtransforms.TransformedBbox(self.axes.viewLim,
trans)
im, xmin, ymin, dxintv, dyintv, sx, sy = \
self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],
transformed_viewLim)
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg(*bg)
# image input dimensions
im.reset_matrix()
numrows, numcols = im.get_size()
if numrows < 1 or numcols < 1: # out of range
return None
im.set_interpolation(self._interpd[self._interpolation])
im.set_resample(self._resample)
# the viewport translation
if dxintv == 0.0:
tx = 0.0
else:
tx = (xmin-transformed_viewLim.x0)/dxintv * numcols
if dyintv == 0.0:
ty = 0.0
else:
ty = (ymin-transformed_viewLim.y0)/dyintv * numrows
im.apply_translation(tx, ty)
l, b, r, t = self.axes.bbox.extents
widthDisplay = ((round(r*magnification) + 0.5) -
(round(l*magnification) - 0.5))
heightDisplay = ((round(t*magnification) + 0.5) -
(round(b*magnification) - 0.5))
# resize viewport to display
rx = widthDisplay / numcols
ry = heightDisplay / numrows
im.apply_scaling(rx*sx, ry*sy)
im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5),
norm=self._filternorm, radius=self._filterrad)
return im
def _check_unsampled_image(self, renderer):
"""
return True if the image is better to be drawn unsampled.
"""
if self.get_interpolation() == "none":
if renderer.option_scale_image():
return True
else:
warnings.warn("The backend (%s) does not support "
"interpolation='none'. The image will be "
"interpolated with 'nearest` "
"mode." % renderer.__class__)
return False
def set_extent(self, extent):
"""
extent is data axes (left, right, bottom, top) for making image plots
This updates ax.dataLim, and, if autoscaling, sets viewLim
to tightly fit the image, regardless of dataLim. Autoscaling
state is not changed, so following this with ax.autoscale_view
will redo the autoscaling in accord with dataLim.
"""
self._extent = extent
xmin, xmax, ymin, ymax = extent
corners = (xmin, ymin), (xmax, ymax)
self.axes.update_datalim(corners)
if self.axes._autoscaleXon:
self.axes.set_xlim((xmin, xmax), auto=None)
if self.axes._autoscaleYon:
self.axes.set_ylim((ymin, ymax), auto=None)
def get_extent(self):
"""Get the image extent: left, right, bottom, top"""
if self._extent is not None:
return self._extent
else:
sz = self.get_size()
#print 'sz', sz
numrows, numcols = sz
if self.origin == 'upper':
return (-0.5, numcols-0.5, numrows-0.5, -0.5)
else:
return (-0.5, numcols-0.5, -0.5, numrows-0.5)
class NonUniformImage(AxesImage):
def __init__(self, ax, **kwargs):
"""
kwargs are identical to those for AxesImage, except
that 'interpolation' defaults to 'nearest', and 'bilinear'
is the only alternative.
"""
interp = kwargs.pop('interpolation', 'nearest')
AxesImage.__init__(self, ax,
**kwargs)
self.set_interpolation(interp)
def _check_unsampled_image(self, renderer):
"""
return False. Do not use unsampled image.
"""
return False
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
A = self._A
if len(A.shape) == 2:
if A.dtype != np.uint8:
A = self.to_rgba(A, bytes=True)
self.is_grayscale = self.cmap.is_gray()
else:
A = np.repeat(A[:, :, np.newaxis], 4, 2)
A[:, :, 3] = 255
self.is_grayscale = True
else:
if A.dtype != np.uint8:
A = (255*A).astype(np.uint8)
if A.shape[2] == 3:
B = np.zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8)
B[:, :, 0:3] = A
B[:, :, 3] = 255
A = B
self.is_grayscale = False
x0, y0, v_width, v_height = self.axes.viewLim.bounds
l, b, r, t = self.axes.bbox.extents
width = (round(r) + 0.5) - (round(l) - 0.5)
height = (round(t) + 0.5) - (round(b) - 0.5)
width *= magnification
height *= magnification
im = _image.pcolor(self._Ax, self._Ay, A,
height, width,
(x0, x0+v_width, y0, y0+v_height),
self._interpd[self._interpolation])
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
im.set_bg(*bg)
im.is_grayscale = self.is_grayscale
return im
def set_data(self, x, y, A):
"""
Set the grid for the pixel centers, and the pixel values.
*x* and *y* are 1-D ndarrays of lengths N and M, respectively,
specifying pixel centers
*A* is an (M,N) ndarray or masked array of values to be
colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.
"""
x = np.asarray(x, np.float32)
y = np.asarray(y, np.float32)
A = cbook.safe_masked_invalid(A)
if len(x.shape) != 1 or len(y.shape) != 1\
or A.shape[0:2] != (y.shape[0], x.shape[0]):
raise TypeError("Axes don't match array shape")
if len(A.shape) not in [2, 3]:
raise TypeError("Can only plot 2D or 3D data")
if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]:
raise TypeError("3D arrays must have three (RGB) "
"or four (RGBA) color components")
if len(A.shape) == 3 and A.shape[2] == 1:
A.shape = A.shape[0:2]
self._A = A
self._Ax = x
self._Ay = y
self._imcache = None
# I am adding this in accor with _AxesImageBase.set_data --
# examples/pylab_examples/image_nonuniform.py was breaking on
# the call to _get_unsampled_image when the oldxslice attr was
# accessed - JDH 3/3/2010
self._oldxslice = None
self._oldyslice = None
def set_array(self, *args):
raise NotImplementedError('Method not supported')
def set_interpolation(self, s):
if s is not None and not s in ('nearest', 'bilinear'):
raise NotImplementedError('Only nearest neighbor and '
'bilinear interpolations are supported')
AxesImage.set_interpolation(self, s)
def get_extent(self):
if self._A is None:
raise RuntimeError('Must set data first')
return self._Ax[0], self._Ax[-1], self._Ay[0], self._Ay[-1]
def set_filternorm(self, s):
pass
def set_filterrad(self, s):
pass
def set_norm(self, norm):
if self._A is not None:
raise RuntimeError('Cannot change colors after loading data')
cm.ScalarMappable.set_norm(self, norm)
def set_cmap(self, cmap):
if self._A is not None:
raise RuntimeError('Cannot change colors after loading data')
cm.ScalarMappable.set_cmap(self, cmap)
class PcolorImage(martist.Artist, cm.ScalarMappable):
"""
Make a pcolor-style plot with an irregular rectangular grid.
This uses a variation of the original irregular image code,
and it is used by pcolorfast for the corresponding grid type.
"""
def __init__(self, ax,
x=None,
y=None,
A=None,
cmap=None,
norm=None,
**kwargs
):
"""
cmap defaults to its rc setting
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
Additional kwargs are matplotlib.artist properties
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
self.axes = ax
self._rgbacache = None
# There is little point in caching the image itself because
# it needs to be remade if the bbox or viewlim change,
# so caching does help with zoom/pan/resize.
self.update(kwargs)
self.set_data(x, y, A)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
fc = self.axes.patch.get_facecolor()
bg = mcolors.colorConverter.to_rgba(fc, 0)
bg = (np.array(bg)*255).astype(np.uint8)
l, b, r, t = self.axes.bbox.extents
width = (round(r) + 0.5) - (round(l) - 0.5)
height = (round(t) + 0.5) - (round(b) - 0.5)
width = width * magnification
height = height * magnification
if self._rgbacache is None:
A = self.to_rgba(self._A, bytes=True)
self._rgbacache = A
if self._A.ndim == 2:
self.is_grayscale = self.cmap.is_gray()
else:
A = self._rgbacache
vl = self.axes.viewLim
im = _image.pcolor2(self._Ax, self._Ay, A,
height,
width,
(vl.x0, vl.x1, vl.y0, vl.y1),
bg)
im.is_grayscale = self.is_grayscale
return im
def changed(self):
self._rgbacache = None
cm.ScalarMappable.changed(self)
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible():
return
im = self.make_image(renderer.get_image_magnification())
gc = renderer.new_gc()
gc.set_clip_rectangle(self.axes.bbox.frozen())
gc.set_clip_path(self.get_clip_path())
gc.set_alpha(self.get_alpha())
renderer.draw_image(gc,
round(self.axes.bbox.xmin),
round(self.axes.bbox.ymin),
im)
gc.restore()
def set_data(self, x, y, A):
A = cbook.safe_masked_invalid(A)
if x is None:
x = np.arange(0, A.shape[1]+1, dtype=np.float64)
else:
x = np.asarray(x, np.float64).ravel()
if y is None:
y = np.arange(0, A.shape[0]+1, dtype=np.float64)
else:
y = np.asarray(y, np.float64).ravel()
if A.shape[:2] != (y.size-1, x.size-1):
print(A.shape)
print(y.size)
print(x.size)
raise ValueError("Axes don't match array shape")
if A.ndim not in [2, 3]:
raise ValueError("A must be 2D or 3D")
if A.ndim == 3 and A.shape[2] == 1:
A.shape = A.shape[:2]
self.is_grayscale = False
if A.ndim == 3:
if A.shape[2] in [3, 4]:
if ((A[:, :, 0] == A[:, :, 1]).all() and
(A[:, :, 0] == A[:, :, 2]).all()):
self.is_grayscale = True
else:
raise ValueError("3D arrays must have RGB or RGBA as last dim")
self._A = A
self._Ax = x
self._Ay = y
self._rgbacache = None
def set_array(self, *args):
raise NotImplementedError('Method not supported')
def set_alpha(self, alpha):
"""
Set the alpha value used for blending - not supported on
all backends
ACCEPTS: float
"""
martist.Artist.set_alpha(self, alpha)
self.update_dict['array'] = True
class FigureImage(martist.Artist, cm.ScalarMappable):
zorder = 0
def __init__(self, fig,
cmap=None,
norm=None,
offsetx=0,
offsety=0,
origin=None,
**kwargs
):
"""
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
kwargs are an optional list of Artist keyword args
"""
martist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
if origin is None:
origin = rcParams['image.origin']
self.origin = origin
self.figure = fig
self.ox = offsetx
self.oy = offsety
self.update(kwargs)
self.magnification = 1.0
def contains(self, mouseevent):
"""Test whether the mouse event occured within the image."""
if callable(self._contains):
return self._contains(self, mouseevent)
xmin, xmax, ymin, ymax = self.get_extent()
xdata, ydata = mouseevent.x, mouseevent.y
#print xdata, ydata, xmin, xmax, ymin, ymax
if xdata is not None and ydata is not None:
inside = ((xdata >= xmin) and (xdata <= xmax) and
(ydata >= ymin) and (ydata <= ymax))
else:
inside = False
return inside, {}
def get_size(self):
"""Get the numrows, numcols of the input image"""
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def get_extent(self):
"""Get the image extent: left, right, bottom, top"""
numrows, numcols = self.get_size()
return (-0.5+self.ox, numcols-0.5+self.ox,
-0.5+self.oy, numrows-0.5+self.oy)
def set_data(self, A):
"""Set the image array."""
cm.ScalarMappable.set_array(self, cbook.safe_masked_invalid(A))
def set_array(self, A):
"""Deprecated; use set_data for consistency with other image types."""
self.set_data(A)
def make_image(self, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image array')
x = self.to_rgba(self._A, bytes=True)
self.magnification = magnification
# if magnification is not one, we need to resize
ismag = magnification != 1
#if ismag: raise RuntimeError
if ismag:
isoutput = 0
else:
isoutput = 1
im = _image.frombyte(x, isoutput)
fc = self.figure.get_facecolor()
im.set_bg(*mcolors.colorConverter.to_rgba(fc, 0))
im.is_grayscale = (self.cmap.name == "gray" and
len(self._A.shape) == 2)
if ismag:
numrows, numcols = self.get_size()
numrows *= magnification
numcols *= magnification
im.set_interpolation(_image.NEAREST)
im.resize(numcols, numrows)
if self.origin == 'upper':
im.flipud_out()
return im
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible():
return
# todo: we should be able to do some cacheing here
im = self.make_image(renderer.get_image_magnification())
gc = renderer.new_gc()
gc.set_clip_rectangle(self.figure.bbox)
gc.set_clip_path(self.get_clip_path())
gc.set_alpha(self.get_alpha())
renderer.draw_image(gc, round(self.ox), round(self.oy), im)
gc.restore()
def write_png(self, fname):
"""Write the image to png file with fname"""
im = self.make_image()
rows, cols, buffer = im.as_rgba_str()
_png.write_png(buffer, cols, rows, fname)
class BboxImage(_AxesImageBase):
"""The Image class whose size is determined by the given bbox."""
def __init__(self, bbox,
cmap=None,
norm=None,
interpolation=None,
origin=None,
filternorm=1,
filterrad=4.0,
resample=False,
interp_at_native=True,
**kwargs
):
"""
cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1
interp_at_native is a flag that determines whether or not
interpolation should still be applied when the image is
displayed at its native resolution. A common use case for this
is when displaying an image for annotational purposes; it is
treated similarly to Photoshop (interpolation is only used when
displaying the image at non-native resolutions).
kwargs are an optional list of Artist keyword args
"""
_AxesImageBase.__init__(self, ax=None,
cmap=cmap,
norm=norm,
interpolation=interpolation,
origin=origin,
filternorm=filternorm,
filterrad=filterrad,
resample=resample,
**kwargs
)
self.bbox = bbox
self.interp_at_native = interp_at_native
def get_window_extent(self, renderer=None):
if renderer is None:
renderer = self.get_figure()._cachedRenderer
if isinstance(self.bbox, BboxBase):
return self.bbox
elif callable(self.bbox):
return self.bbox(renderer)
else:
raise ValueError("unknown type of bbox")
def contains(self, mouseevent):
"""Test whether the mouse event occured within the image."""
if callable(self._contains):
return self._contains(self, mouseevent)
if not self.get_visible(): # or self.get_figure()._renderer is None:
return False, {}
x, y = mouseevent.x, mouseevent.y
inside = self.get_window_extent().contains(x, y)
return inside, {}
def get_size(self):
"""Get the numrows, numcols of the input image"""
if self._A is None:
raise RuntimeError('You must first set the image array')
return self._A.shape[:2]
def make_image(self, renderer, magnification=1.0):
if self._A is None:
raise RuntimeError('You must first set the image '
'array or the image attribute')
if self._imcache is None:
if self._A.dtype == np.uint8 and len(self._A.shape) == 3:
im = _image.frombyte(self._A, 0)
im.is_grayscale = False
else:
if self._rgbacache is None:
x = self.to_rgba(self._A, bytes=True)
self._rgbacache = x
else:
x = self._rgbacache
im = _image.frombyte(x, 0)
if len(self._A.shape) == 2:
im.is_grayscale = self.cmap.is_gray()
else:
im.is_grayscale = False
self._imcache = im
if self.origin == 'upper':
im.flipud_in()
else:
im = self._imcache
# image input dimensions
im.reset_matrix()
im.set_interpolation(self._interpd[self._interpolation])
im.set_resample(self._resample)
l, b, r, t = self.get_window_extent(renderer).extents # bbox.extents
widthDisplay = round(r) - round(l)
heightDisplay = round(t) - round(b)
widthDisplay *= magnification
heightDisplay *= magnification
numrows, numcols = self._A.shape[:2]
if (not self.interp_at_native and
widthDisplay == numcols and heightDisplay == numrows):
im.set_interpolation(0)
# resize viewport to display
rx = widthDisplay / numcols
ry = heightDisplay / numrows
#im.apply_scaling(rx*sx, ry*sy)
im.apply_scaling(rx, ry)
#im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5),
# norm=self._filternorm, radius=self._filterrad)
im.resize(int(widthDisplay), int(heightDisplay),
norm=self._filternorm, radius=self._filterrad)
return im
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
if not self.get_visible():
return
# todo: we should be able to do some cacheing here
image_mag = renderer.get_image_magnification()
im = self.make_image(renderer, image_mag)
l, b, r, t = self.get_window_extent(renderer).extents
gc = renderer.new_gc()
self._set_gc_clip(gc)
gc.set_alpha(self.get_alpha())
#gc.set_clip_path(self.get_clip_path())
renderer.draw_image(gc, round(l), round(b), im)
gc.restore()
def imread(fname, format=None):
"""
Read an image from a file into an array.
*fname* may be a string path or a Python file-like object. If
using a file object, it must be opened in binary mode.
If *format* is provided, will try to read file of that type,
otherwise the format is deduced from the filename. If nothing can
be deduced, PNG is tried.
Return value is a :class:`numpy.array`. For grayscale images, the
return array is MxN. For RGB images, the return value is MxNx3.
For RGBA images the return value is MxNx4.
matplotlib can only read PNGs natively, but if `PIL
<http://www.pythonware.com/products/pil/>`_ is installed, it will
use it to load the image and return an array (if possible) which
can be used with :func:`~matplotlib.pyplot.imshow`.
"""
def pilread(fname):
"""try to load the image with PIL or return None"""
try:
from PIL import Image
except ImportError:
return None
if cbook.is_string_like(fname):
# force close the file after reading the image
with open(fname, "rb") as fh:
image = Image.open(fh)
return pil_to_array(image)
else:
image = Image.open(fname)
return pil_to_array(image)
handlers = {'png': _png.read_png, }
if format is None:
if cbook.is_string_like(fname):
basename, ext = os.path.splitext(fname)
ext = ext.lower()[1:]
elif hasattr(fname, 'name'):
basename, ext = os.path.splitext(fname.name)
ext = ext.lower()[1:]
else:
ext = 'png'
else:
ext = format
if ext not in handlers.iterkeys():
im = pilread(fname)
if im is None:
raise ValueError('Only know how to handle extensions: %s; '
'with PIL installed matplotlib can handle '
'more images' % handlers.keys())
return im
handler = handlers[ext]
# To handle Unicode filenames, we pass a file object to the PNG
# reader extension, since Python handles them quite well, but it's
# tricky in C.
if cbook.is_string_like(fname):
with open(fname, 'rb') as fd:
return handler(fd)
else:
return handler(fname)
def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None,
origin=None, dpi=100):
"""
Save an array as in image file.
The output formats available depend on the backend being used.
Arguments:
*fname*:
A string containing a path to a filename, or a Python file-like object.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename.
*arr*:
An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.
Keyword arguments:
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* set the color scaling for the image by fixing the
values that map to the colormap color limits. If either *vmin*
or *vmax* is None, that limit is determined from the *arr*
min/max value.
*cmap*:
cmap is a colors.Colormap instance, eg cm.jet.
If None, default to the rc image.cmap value.
*format*:
One of the file extensions supported by the active
backend. Most backends support png, pdf, ps, eps and svg.
*origin*
[ 'upper' | 'lower' ] Indicates where the [0,0] index of
the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value.
*dpi*
The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.
"""
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
figsize = [x / float(dpi) for x in (arr.shape[1], arr.shape[0])]
fig = Figure(figsize=figsize, dpi=dpi, frameon=False)
canvas = FigureCanvas(fig)
im = fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
fig.savefig(fname, dpi=dpi, format=format, transparent=True)
def pil_to_array(pilImage):
"""
Load a PIL image and return it as a numpy array. For grayscale
images, the return array is MxN. For RGB images, the return value
is MxNx3. For RGBA images the return value is MxNx4
"""
def toarray(im, dtype=np.uint8):
"""Teturn a 1D array of dtype."""
x_str = im.tostring('raw', im.mode)
x = np.fromstring(x_str, dtype)
return x
if pilImage.mode in ('RGBA', 'RGBX'):
im = pilImage # no need to convert images
elif pilImage.mode == 'L':
im = pilImage # no need to luminance images
# return MxN luminance array
x = toarray(im)
x.shape = im.size[1], im.size[0]
return x
elif pilImage.mode == 'RGB':
#return MxNx3 RGB array
im = pilImage # no need to RGB images
x = toarray(im)
x.shape = im.size[1], im.size[0], 3
return x
elif pilImage.mode.startswith('I;16'):
# return MxN luminance array of uint16
im = pilImage
if im.mode.endswith('B'):
x = toarray(im, '>u2')
else:
x = toarray(im, '<u2')
x.shape = im.size[1], im.size[0]
return x.astype('=u2')
else: # try to convert to an rgba image
try:
im = pilImage.convert('RGBA')
except ValueError:
raise RuntimeError('Unknown image mode')
# return MxNx4 RGBA array
x = toarray(im)
x.shape = im.size[1], im.size[0], 4
return x
def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear',
preview=False):
"""
make a thumbnail of image in *infile* with output filename
*thumbfile*.
*infile* the image file -- must be PNG or PIL readable if you
have `PIL <http://www.pythonware.com/products/pil/>`_ installed
*thumbfile*
the thumbnail filename
*scale*
the scale factor for the thumbnail
*interpolation*
the interpolation scheme used in the resampling
*preview*
if True, the default backend (presumably a user interface
backend) will be used which will cause a figure to be raised
if :func:`~matplotlib.pyplot.show` is called. If it is False,
a pure image backend will be used depending on the extension,
'png'->FigureCanvasAgg, 'pdf'->FigureCanvasPdf,
'svg'->FigureCanvasSVG
See examples/misc/image_thumbnail.py.
.. htmlonly::
:ref:`misc-image_thumbnail`
Return value is the figure instance containing the thumbnail
"""
basedir, basename = os.path.split(infile)
baseout, extout = os.path.splitext(thumbfile)
im = imread(infile)
rows, cols, depth = im.shape
# this doesn't really matter, it will cancel in the end, but we
# need it for the mpl API
dpi = 100
height = float(rows)/dpi*scale
width = float(cols)/dpi*scale
extension = extout.lower()
if preview:
# let the UI backend do everything
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(width, height), dpi=dpi)
else:
if extension == '.png':
from matplotlib.backends.backend_agg \
import FigureCanvasAgg as FigureCanvas
elif extension == '.pdf':
from matplotlib.backends.backend_pdf \
import FigureCanvasPdf as FigureCanvas
elif extension == '.svg':
from matplotlib.backends.backend_svg \
import FigureCanvasSVG as FigureCanvas
else:
raise ValueError("Can only handle "
"extensions 'png', 'svg' or 'pdf'")
from matplotlib.figure import Figure
fig = Figure(figsize=(width, height), dpi=dpi)
canvas = FigureCanvas(fig)
ax = fig.add_axes([0, 0, 1, 1], aspect='auto',
frameon=False, xticks=[], yticks=[])
basename, ext = os.path.splitext(basename)
ax.imshow(im, aspect='auto', resample=True, interpolation='bilinear')
fig.savefig(thumbfile, dpi=dpi)
return fig
| unlicense |
ves-code/plumed2-ves | user-doc/tutorials/others/ves-lugano2017-kinetics/TRAJECTORIES-1700K/cdf-analysis.py | 6 | 1134 | #!/usr/bin/env python
import numpy as np
from scipy.stats import ks_2samp
from scipy.optimize import curve_fit
from statsmodels.distributions.empirical_distribution import ECDF
import matplotlib.pyplot as plt
f=open('fpt.dat','r')
# define theoretical CDF
def func(x,tau):
return 1-np.exp(-x/tau)
x = []
count=0
for line in f:
line=line.strip()
columns=line.split()
x.append(float(columns[0]))
count=count+1
x = np.array(x)
# for numerical stability we divide by the mean
mu=x.mean()
x=x/mu
# now compute emirical CDF
ecdf = ECDF(x)
# plot ECDF
x1 = np.linspace(min(x), max(x))
y1 = ecdf(x1)
plt.step(x1*mu, y1,'k-',lw=3.)
# fit to theoretical CDF to obtain tau
popt,pcov = curve_fit(func,x1,y1)
tau=popt[0]
print 'mean of data',mu
print 'best fit tau',tau*mu
yfit=func(x1,tau)
# plot fit
plt.plot(x1*mu,yfit,'b-',lw=3.)
# for p-value
# now generate some random data with the same exponential distribution
np.random.seed(12345678);
x2 = np.random.exponential(1/tau,1000)
st,p = ks_2samp(x2,x)
print 'p-value',p
plt.xscale('log')
plt.xlabel('time [s]')
plt.ylabel('Cumulative Probability')
plt.show()
| lgpl-3.0 |
mehdidc/scikit-learn | examples/cluster/plot_cluster_comparison.py | 12 | 4718 | """
=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================
This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last dataset is an example of a 'null'
situation for clustering: the data is homogeneous, and
there is no good clustering.
While these examples give some intuition about the algorithms,
this intuition might not apply to very high dimensional data.
The results could be improved by tweaking the parameters for
each clustering strategy, for instance setting the number of
clusters for the methods that needs this parameter
specified. Note that affinity propagation has a tendency to
create many clusters. Thus in this example its two parameters
(damping and per-point preference) were set to to mitigate this
behavior.
"""
print(__doc__)
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
np.random.seed(0)
# Generate datasets. We choose the size big enough to see the scalability
# of the algorithms, but not too big to avoid too long running times
n_samples = 1500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,
noise=.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = np.random.rand(n_samples, 2), None
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
clustering_names = [
'MiniBatchKMeans', 'AffinityPropagation', 'MeanShift',
'SpectralClustering', 'Ward', 'AgglomerativeClustering',
'DBSCAN', 'Birch'
]
plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5))
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
hspace=.01)
plot_num = 1
datasets = [noisy_circles, noisy_moons, blobs, no_structure]
for i_dataset, dataset in enumerate(datasets):
X, y = dataset
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(X, n_neighbors=10)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
# create clustering estimators
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
two_means = cluster.MiniBatchKMeans(n_clusters=2)
ward = cluster.AgglomerativeClustering(n_clusters=2, linkage='ward',
connectivity=connectivity)
spectral = cluster.SpectralClustering(n_clusters=2,
eigen_solver='arpack',
affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.2)
affinity_propagation = cluster.AffinityPropagation(damping=.9,
preference=-200)
average_linkage = cluster.AgglomerativeClustering(linkage="average",
affinity="cityblock", n_clusters=2,
connectivity=connectivity)
birch = cluster.Birch(n_clusters=2)
clustering_algorithms = [
two_means, affinity_propagation, ms, spectral, ward, average_linkage,
dbscan, birch
]
for name, algorithm in zip(clustering_names, clustering_algorithms):
# predict cluster memberships
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
# plot
plt.subplot(4, len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
transform=plt.gca().transAxes, size=15,
horizontalalignment='right')
plot_num += 1
plt.show()
| bsd-3-clause |
Jimmy-Morzaria/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we make the model complexity vary through the choice
of relevant model parameters and measure the influence on both computational
performance (latency) and predictive power (MSE or Hamming Loss).
"""
print(__doc__)
# Author: Eustache Diemert <eustache@diemert.fr>
# License: BSD 3 clause
import time
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.parasite_axes import host_subplot
from mpl_toolkits.axisartist.axislines import Axes
from scipy.sparse.csr import csr_matrix
from sklearn import datasets
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
from sklearn.svm.classes import NuSVR
from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor
from sklearn.linear_model.stochastic_gradient import SGDClassifier
from sklearn.metrics import hamming_loss
###############################################################################
# Routines
# initialize random generator
np.random.seed(0)
def generate_data(case, sparse=False):
"""Generate regression/classification data."""
bunch = None
if case == 'regression':
bunch = datasets.load_boston()
elif case == 'classification':
bunch = datasets.fetch_20newsgroups_vectorized(subset='all')
X, y = shuffle(bunch.data, bunch.target)
offset = int(X.shape[0] * 0.8)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
if sparse:
X_train = csr_matrix(X_train)
X_test = csr_matrix(X_test)
else:
X_train = np.array(X_train)
X_test = np.array(X_test)
y_test = np.array(y_test)
y_train = np.array(y_train)
data = {'X_train': X_train, 'X_test': X_test, 'y_train': y_train,
'y_test': y_test}
return data
def benchmark_influence(conf):
"""
Benchmark influence of :changing_param: on both MSE and latency.
"""
prediction_times = []
prediction_powers = []
complexities = []
for param_value in conf['changing_param_values']:
conf['tuned_params'][conf['changing_param']] = param_value
estimator = conf['estimator'](**conf['tuned_params'])
print("Benchmarking %s" % estimator)
estimator.fit(conf['data']['X_train'], conf['data']['y_train'])
conf['postfit_hook'](estimator)
complexity = conf['complexity_computer'](estimator)
complexities.append(complexity)
start_time = time.time()
for _ in range(conf['n_samples']):
y_pred = estimator.predict(conf['data']['X_test'])
elapsed_time = (time.time() - start_time) / float(conf['n_samples'])
prediction_times.append(elapsed_time)
pred_score = conf['prediction_performance_computer'](
conf['data']['y_test'], y_pred)
prediction_powers.append(pred_score)
print("Complexity: %d | %s: %.4f | Pred. Time: %fs\n" % (
complexity, conf['prediction_performance_label'], pred_score,
elapsed_time))
return prediction_powers, prediction_times, complexities
def plot_influence(conf, mse_values, prediction_times, complexities):
"""
Plot influence of model complexity on both accuracy and latency.
"""
plt.figure(figsize=(12, 6))
host = host_subplot(111, axes_class=Axes)
plt.subplots_adjust(right=0.75)
par1 = host.twinx()
host.set_xlabel('Model Complexity (%s)' % conf['complexity_label'])
y1_label = conf['prediction_performance_label']
y2_label = "Time (s)"
host.set_ylabel(y1_label)
par1.set_ylabel(y2_label)
p1, = host.plot(complexities, mse_values, 'b-', label="prediction error")
p2, = par1.plot(complexities, prediction_times, 'r-',
label="latency")
host.legend(loc='upper right')
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
plt.title('Influence of Model Complexity - %s' % conf['estimator'].__name__)
plt.show()
def _count_nonzero_coefficients(estimator):
a = estimator.coef_.toarray()
return np.count_nonzero(a)
###############################################################################
# main code
regression_data = generate_data('regression')
classification_data = generate_data('classification', sparse=True)
configurations = [
{'estimator': SGDClassifier,
'tuned_params': {'penalty': 'elasticnet', 'alpha': 0.001, 'loss':
'modified_huber', 'fit_intercept': True},
'changing_param': 'l1_ratio',
'changing_param_values': [0.25, 0.5, 0.75, 0.9],
'complexity_label': 'non_zero coefficients',
'complexity_computer': _count_nonzero_coefficients,
'prediction_performance_computer': hamming_loss,
'prediction_performance_label': 'Hamming Loss (Misclassification Ratio)',
'postfit_hook': lambda x: x.sparsify(),
'data': classification_data,
'n_samples': 30},
{'estimator': NuSVR,
'tuned_params': {'C': 1e3, 'gamma': 2 ** -15},
'changing_param': 'nu',
'changing_param_values': [0.1, 0.25, 0.5, 0.75, 0.9],
'complexity_label': 'n_support_vectors',
'complexity_computer': lambda x: len(x.support_vectors_),
'data': regression_data,
'postfit_hook': lambda x: x,
'prediction_performance_computer': mean_squared_error,
'prediction_performance_label': 'MSE',
'n_samples': 30},
{'estimator': GradientBoostingRegressor,
'tuned_params': {'loss': 'ls'},
'changing_param': 'n_estimators',
'changing_param_values': [10, 50, 100, 200, 500],
'complexity_label': 'n_trees',
'complexity_computer': lambda x: x.n_estimators,
'data': regression_data,
'postfit_hook': lambda x: x,
'prediction_performance_computer': mean_squared_error,
'prediction_performance_label': 'MSE',
'n_samples': 30},
]
for conf in configurations:
prediction_performances, prediction_times, complexities = \
benchmark_influence(conf)
plot_influence(conf, prediction_performances, prediction_times,
complexities)
| bsd-3-clause |
FofanovLab/VaST | VaST/Pattern.py | 1 | 13973 | import copy
import json
import logging
from functools import partial
from itertools import combinations
import numpy as np
import pandas as pd
from utils import cartesian_product, get_ambiguous_pattern
class Patterns:
def __init__(self):
self._patterns = {}
self._genomes = {}
self._amp_2_pattern = {}
self._logger = logging.getLogger(__name__)
self._strains = []
self._resolution_levels = []
self._pattern_df = None
self._required_patterns = []
def get_required_patterns(self):
return self._required_patterns
def get_pattern_dic(self, patterns):
return {
pattern: self._patterns[pattern]
for pattern in patterns}
def get_pattern_df(self, patterns):
return self._pattern_df[patterns]
def get_resolution_levels(self):
return copy.deepcopy(self._resolution_levels)
def to_json(self, file_name, strains):
d = {"strains": strains,
"genomes": {v['id']: v for v in self._genomes.values()},
"patterns": self._patterns}
self._logger.info("Writing patterns to: {}".format(file_name))
with open(file_name, 'w') as out:
out.write(json.dumps(d))
def load_patterns(self, file_name):
self._logger.info("Reading data from JSON: %s", file_name)
try:
with open(file_name, 'rU') as json_file:
pattern_data = json.load(json_file)
except ValueError:
self._logger.error("Could not parse JSON: %s", file_name)
raise
self._strains = pattern_data['strains']
self._genomes = pattern_data['genomes']
self._patterns = pattern_data['patterns']
self._get_pattern_dataframe()
self._rekey_pattern_dict()
self._get_amp_2_pattern()
def add_required_sites(self, required_sites):
self._logger.info(
"Including the following sites in the solution: %s",
", ".join(required_sites))
for site in required_sites:
try:
pattern_id = self._amp_2_pattern[site]['pattern_id']
self._required_patterns.append(pattern_id)
except KeyError:
self._logger.warning("Required site not found: %s",
site)
self._required_patterns = list(set(self._required_patterns))
def remove_sites(self, exclude_sites):
self._logger.info(
"Dropping the following sites: %s", ", ".join(exclude_sites))
for site in exclude_sites:
try:
pattern_id = self._amp_2_pattern[site]['pattern_id']
if len(self._patterns[pattern_id]) > 1:
# Only delete the amplicon if there are more
# than one amplicon for the pattern
del self._patterns[pattern_id][
self._amp_2_pattern[site]['site_id']]
else:
# if this is the only amplicon for the pattern
# delete the pattern from dictionary and dataframe
del self._patterns[pattern_id]
try:
self._pattern_df.drop(pattern_id, inplace=True)
except ValueError:
self._logger.info("Excluded site already removed: %s", site)
del self._amp_2_pattern[site]
except KeyError:
self._logger.warning(
"Excluded site not found: %s", site)
def remove_strains(self, exclude_strains):
"""
Removes strains from dataframe and strain list
"""
self._logger.info("Removing strains")
# check that all of excluded strains are in strains
in_strains = np.intersect1d(
self._strains, exclude_strains)
if len(in_strains) != len(exclude_strains):
self._logger.warning(
"The following excluded strains were not "
"found: %s",
", ".join(
np.setdiff1d(
exclude_strains, in_strains)))
self._strains = np.setdiff1d(self._strains, exclude_strains)
self._pattern_df = self._pattern_df[self._strains]
self._logger.info(
"%s strains are being used in analysis.",
len(self._strains))
self._logger.info(
"The following strains were removed:\n%s",
"\n".join(in_strains))
def add_new_pattern(self, pattern, amplicon, genome_size):
pattern_str = self._pattern_to_string(pattern)
if amplicon.genome not in self._genomes:
self._genomes[amplicon.genome] = {
"id": len(self._genomes), "length": genome_size,
"name": amplicon.genome}
if pattern_str in self._patterns:
self._patterns[pattern_str][amplicon.start] = {'s': amplicon.stop,
'g': self._genomes[amplicon.genome],
'sites': amplicon.site_ids}
else:
self._patterns[pattern_str] = {amplicon.start: {'s': amplicon.stop,
'g': self._genomes[amplicon.genome],
'sites': amplicon.site_ids}}
def add_ambiguous_amplicon(self, features, amplicon, genome_size):
features = [[tuple(r) for r in cartesian_product(row)]
for row in features]
feature_categories = set([a[0] for a in features if len(a) == 1])
feature_categories = [[a] for a in feature_categories]
amb_pattern = partial(
get_ambiguous_pattern,
feature_categories=feature_categories)
pattern = map(amb_pattern, features)
self.add_new_pattern(pattern, amplicon, genome_size)
def add_unambiguous_amplicon(self, features, amplicon, genome_size):
# Requires numpy >= 1.13
_, pattern = np.unique(features, return_inverse=True, axis=0)
pattern = [(p,) for p in list(pattern)]
self.add_new_pattern(pattern, amplicon, genome_size)
def set_resolution(self, alt_resolution_file, stop_at_res):
self._logger.info("Setting Resolution")
full_resolution = self._get_full_resolution()
if alt_resolution_file is not None:
try:
alt_resolution = pd.read_csv(
alt_resolution_file, index_col=0, header=None)
except Exception:
self._logger.error(
"Unable to parse resolution file: %s",
alt_resolution_file
)
raise
# Make sure group ids are uniform
for column in alt_resolution:
alt_resolution[column] = np.unique(
alt_resolution[column],
return_inverse=True)[1]
# Join full resolution and resolution dataframe
# using a right join. Any NaNs indicate an invalid input.
resolution = alt_resolution.join(
full_resolution, how="right")
if resolution.isnull().values.any():
e_message = "Resolution file does not include all strains, "\
"NaNs produced."
self._logger.error(e_message)
raise ValueError(e_message)
resolution = self._is_valid_resolution(resolution)
# If stop at res, remove last column
if stop_at_res:
resolution = resolution.drop(
'Full_res',
axis=1, errors="ignore"
)
resolution.columns = [
"Level_{}".format(i + 1) for i in range(
len(resolution.columns))]
for col in resolution:
self._resolution_levels.append(
Resolution_Pattern(
resolution[col],
self._get_copy_of_patterns()))
else:
self._resolution_levels.append(
Resolution_Pattern(
full_resolution,
self._get_copy_of_patterns()
)
)
def _get_pattern_dataframe(self):
''' Converts pattern dictionary into dataframe '''
self._pattern_df = pd.DataFrame(index=self._strains)
for i, pattern in enumerate(self._patterns.keys()):
pattern = self._string_to_pattern(pattern)
self._pattern_df[i] = pattern
def _get_amp_2_pattern(self):
''' Reverse look up for amplicon to pattern '''
for pattern_id, amps in self._patterns.iteritems():
for site_id, amp in amps.iteritems():
for site in amp['sites']:
self._amp_2_pattern[site] = {
'pattern_id': pattern_id,
'site_id': site_id}
def _rekey_pattern_dict(self):
rekey_dic = {}
for i, amplicons in enumerate(self._patterns.values()):
rekey_dic[i] = amplicons
self._patterns = rekey_dic
def _pattern_to_string(self, pattern):
return ",".join(
[" ".join(
[str(p) for p in pp]) for pp in pattern])
def _string_to_pattern(self, pat_str):
return [tuple(
[int(pp) for pp in p.split(" ")]) for p in pat_str.split(",")]
def _get_copy_of_patterns(self):
return self._pattern_df.copy()
def _get_full_resolution(self):
''' return full resolution dataframe '''
return pd.DataFrame(
{"Full_res": range(len(self._strains))},
index=self._strains)
def _is_valid_resolution(self, resolution):
# drop resolution columns that are the same
drop_columns = []
for c1, c2 in combinations(resolution.columns, 2):
if same_group_pattern(
resolution[c1], resolution[c2], resolution.index):
drop_columns.append(c2)
resolution = resolution.drop(drop_columns, axis=1)
if drop_columns:
self._logger.warning(
"Dropping duplicate resolution column in resolution file: %s",
", ".join(drop_columns))
# Verify that columns are in increasing resolution order
# the max group id should increase in each column
if not resolution.max().equals(resolution.max().sort_values()):
e_message = "Resolution is not increasing across columns, "\
"check resolution file"
self._logger.error(e_message)
raise ValueError(e_message)
# Verify that the relationship is hierarchical meaning each
# group is a subset of the same parent group
for i, res_level in enumerate(resolution.columns[1:]):
grouped = resolution.groupby([res_level])
for name, group in grouped:
if len(group.ix[:, i].unique()) > 1:
e_message = "Resolution is not hierarchical. "\
"Each group should be a subset of "\
"the same parent group. Issue in "\
"resolution level: %s", res_level
self._logger.error(e_message)
raise ValueError(e_message)
return resolution
class Resolution_Pattern:
def __init__(self, resolution, pattern_df):
self._resolution = pd.DataFrame(resolution)
self._pattern_df = pattern_df
self._ambiguous = False
self._set_resolution_pattern()
def get_resolution_pattern(self):
return self._pattern_df.copy().T
def get_group_number(self):
return int(self._resolution.max() + 1)
def is_ambiguous(self):
return self._ambiguous
def _set_resolution_pattern(self):
new_df = []
temp_df = self._resolution.join(
self._pattern_df)
for name, group in temp_df.groupby(
self._resolution.columns[0]):
new_column = []
for column in group[group.columns[1:]]:
combined_pattern = ()
for value in group[column]:
combined_pattern += value
new_value = tuple(set(combined_pattern))
# set ambiguous flag if multiple values in tuple
if not self._ambiguous and len(new_value) > 1:
self._ambiguous = True
new_column.append(new_value)
new_df.append(new_column)
self._pattern_df = pd.DataFrame(
new_df, columns=self._pattern_df.columns, dtype=int)
def same_group_pattern(v1, v2, index):
"""Check if two arrays have the same pattern of similarity
v1 and v2 and index are arrays of equal length. Each value in index is
considered a label for the values in v1 and v2 in the corresponding
position. Labels in index are grouped together if they share similar values
in v1 and in v2. The sorted list of groups is compared to see if they are
the equivalent.
Arguments:
v1 (array_like): First array (len(v1) = len(v2) = len(index))
v2 (array_like): Second array
index (array_like): Array of labels
Returns:
bool
Raises:
ValueError if each of the arguments are not the same length
"""
if len(v1) == len(v2) == len(index):
index = np.array(index)
v1_groups = [
",".join(index[np.where(v1 == i)[0]]) for i in np.unique(v1)]
v2_groups = [
",".join(index[np.where(v2 == i)[0]]) for i in np.unique(v2)]
v1_groups.sort()
v2_groups.sort()
return v1_groups == v2_groups
else:
raise ValueError("Each array must be the same length")
| mit |
Transkribus/TranskribusDU | TranskribusDU/tasks/TablePrototypes/DU_ABPTableRG41.py | 1 | 38778 | # -*- coding: utf-8 -*-
"""
DU task for ABP Table: doing jointly row BIESO and horizontal grid lines
block2line edges do not cross another block.
Here we make consistent label when any N grid lines have no block in-between
each other.
In that case, those N grid lines must have consistent BISO labels:
- if one is B, all become B
- elif one is S, all become S
- elif one is I, all become I
- else: they should all be O already
Copyright Naver Labs Europe(C) 2018 JL Meunier
Developed for the EU project READ. The READ project has received funding
from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 674943.
"""
import sys, os
import math
from lxml import etree
import collections
import numpy as np
from sklearn.pipeline import Pipeline, FeatureUnion
try: #to ease the use without proper Python installation
import TranskribusDU_version
except ImportError:
sys.path.append( os.path.dirname(os.path.dirname( os.path.abspath(sys.argv[0]) )) )
import TranskribusDU_version
from common.trace import traceln
from tasks import _checkFindColDir, _exit
from tasks.DU_CRF_Task import DU_CRF_Task
from crf.Edge import Edge, SamePageEdge
from crf.Graph_MultiPageXml import Graph_MultiPageXml
from crf.NodeType_PageXml import NodeType_PageXml_type_woText
#from crf.FeatureDefinition_PageXml_std_noText import FeatureDefinition_PageXml_StandardOnes_noText
from crf.FeatureDefinition import FeatureDefinition
from crf.Transformer import Transformer, TransformerListByType
from crf.Transformer import EmptySafe_QuantileTransformer as QuantileTransformer
from crf.Transformer_PageXml import NodeTransformerXYWH_v2, NodeTransformerNeighbors, Node1HotFeatures
from crf.Transformer_PageXml import Edge1HotFeatures, EdgeBooleanFeatures_v2, EdgeNumericalSelector
from crf.PageNumberSimpleSequenciality import PageNumberSimpleSequenciality
from tasks.DU_ABPTableGrid import GridAnnotator
class GraphGrid(Graph_MultiPageXml):
"""
We specialize the class of graph because the computation of edges is quite specific
"""
# Grid stuff
#Dynamically add a grid
iGridStep_H = 33 #odd number is better
iGridStep_V = 33 #odd number is better
# Some grid line will be O or I simply because they are too short.
fMinPageCoverage = 0.5 # minimum proportion of the page crossed by a grid line
# we want to ignore col- and row- spans
iGridVisibility = 2 # a grid line sees N neighbours below
iBlockVisibility = 1 # a block sees N neighbouring grid lines
_lClassicNodeType = None
@classmethod
def setClassicNodeTypeList(cls, lNodeType):
"""
determine which type of node goes thru the classical way for determining
the edges (vertical or horizontal overlap, with occlusion, etc.)
"""
cls._lClassicNodeType = lNodeType
def parseDocFile(self, sFilename, iVerbose=0):
"""
Load that document as a CRF Graph.
Also set the self.doc variable!
Return a CRF Graph object
"""
self.doc = etree.parse(sFilename)
self.lNode, self.lEdge = list(), list()
self.lNodeBlock = [] # text node
self.lNodeGridLine = [] # grid line node
root = self.doc.getroot()
doer = GridAnnotator(self.iGridStep_H, self.iGridStep_V)
#map the groundtruth table separators, if any, to our grid
ltlHlV = doer.get_grid_GT_index_from_DOM(root, self.fMinPageCoverage)
for (lHi, lVi) in ltlHlV:
traceln(" - found %d horizontal, %d vertical GT separators" % (len(lHi), len(lVi)))
#create DOM node reflecting the grid
#first clean (just in case!)
n = doer.remove_grid_from_dom(root)
if n > 0: traceln(" - removed %d existing grid lines" % n)
# we add GridSeparator elements. Groundtruth ones have type="1"
n = doer.add_grid_to_DOM(root, ltlHlV)
traceln(" - added %d grid lines %s" % (n,
(self.iGridStep_H, self.iGridStep_V)) )
lClassicType = [nt for nt in self.getNodeTypeList() if nt in self._lClassicNodeType]
lSpecialType = [nt for nt in self.getNodeTypeList() if nt not in self._lClassicNodeType]
for pnum, page, domNdPage in self._iter_Page_DocNode(self.doc):
#now that we have the page, let's create the node for each type!
lClassicPageNode = [nd for nodeType in lClassicType for nd in nodeType._iter_GraphNode(self.doc, domNdPage, page) ]
lSpecialPageNode = [nd for nodeType in lSpecialType for nd in nodeType._iter_GraphNode(self.doc, domNdPage, page) ]
self.lNode.extend(lClassicPageNode) # e.g. the TextLine objects
self.lNodeBlock.extend(lClassicPageNode)
self.lNode.extend(lSpecialPageNode) # e.g. the grid lines!
self.lNodeGridLine.extend(lSpecialPageNode)
#no previous page to consider (for cross-page links...) => None
lClassicPageEdge = Edge.computeEdges(None, lClassicPageNode)
self.lEdge.extend(lClassicPageEdge)
# Now, compute edges between special and classic objects...
lSpecialPageEdge = self.computeSpecialEdges(lClassicPageNode,
lSpecialPageNode)
self.lEdge.extend(lSpecialPageEdge)
#if iVerbose>=2: traceln("\tPage %5d %6d nodes %7d edges"%(pnum, len(lPageNode), len(lPageEdge)))
if iVerbose>=2:
traceln("\tPage %5d"%(pnum))
traceln("\t block: %6d nodes %7d edges (to block)" %(pnum, len(lClassicPageNode), len(lClassicPageEdge)))
traceln("\t line: %6d nodes %7d edges (from block)"%(pnum, len(lSpecialPageNode), len(lSpecialPageEdge)))
if iVerbose: traceln("\t\t (%d nodes, %d edges)"%(len(self.lNode), len(self.lEdge)) )
return self
@classmethod
def computeSpecialEdges(cls, lClassicPageNode, lSpecialPageNode):
"""
return a list of edges
"""
raise Exception("Specialize this method")
class Edge_BL(Edge):
"""Edge block-to-Line"""
pass
class Edge_LL(Edge):
"""Edge line-to-Line"""
pass
class GraphGrid_H(GraphGrid):
"""
Only horizontal grid lines
"""
def __init__(self):
traceln(" - iGridStep_H : ", self.iGridStep_H)
traceln(" - iGridStep_V : ", self.iGridStep_V)
traceln(" - iGridVisibility : ", self.iGridVisibility)
traceln(" - iBlockVisibility : ", self.iBlockVisibility)
traceln(" - fMinPageCoverage : ", self.fMinPageCoverage)
def getNodeListByType(self, iTyp):
if iTyp == 0:
return self.lNodeBlock
else:
return self.lNodeGridLine
def getEdgeListByType(self, typA, typB):
if typA == 0:
if typB == 0:
return (e for e in self.lEdge if isinstance(e, SamePageEdge))
else:
return (e for e in self.lEdge if isinstance(e, Edge_BL))
else:
if typB == 0:
return []
else:
return (e for e in self.lEdge if isinstance(e, Edge_LL))
@classmethod
def computeSpecialEdges(cls, lClassicPageNode, lSpecialPageNode):
"""
Compute:
- edges between each block and the grid line above/across/below the block
- edges between grid lines
return a list of edges
"""
# indexing the grid lines
dGridLineByIndex = {GridAnnotator.snapToGridIndex(nd.y1, cls.iGridStep_V):nd for nd in lSpecialPageNode}
for nd in lSpecialPageNode:
#print(nd, dGridLineByIndex[GridAnnotator.snapToGridIndex(nd.y1, cls.iGridStep_V)])
assert dGridLineByIndex[GridAnnotator.snapToGridIndex(nd.y1, cls.iGridStep_V)] == nd, "internal error inconsistent grid"
# block to grid line edges
lEdge = []
fLenNorm = float(cls.iGridStep_V * cls.iBlockVisibility)
imin, imax = 100, -1
assert lClassicPageNode, "ERROR: empty page!!??"
for ndBlock in lClassicPageNode:
### print("---- ", ndBlock)
# i1 = GridAnnotator.snapToGridIndex(nd.x1, cls.iGridStep_V)
# i2 = GridAnnotator.snapToGridIndex(nd.x2, cls.iGridStep_V)
i1 = int(math.floor(ndBlock.y1 / float(cls.iGridStep_V)))
i2 = int(math.ceil (ndBlock.y2 / float(cls.iGridStep_V)))
assert i2 >= i1
yBlkAvg = (ndBlock.y1 + ndBlock.y2)/2.0
#Also make visible the iBlockVisibility-1 previous grid lines, if any
for i in range(max(0, i1 - cls.iBlockVisibility + 1), i1+1):
edge = Edge_BL(ndBlock, dGridLineByIndex[i])
edge.len = (yBlkAvg - i * cls.iGridStep_V) / fLenNorm
edge._gridtype = -1
lEdge.append(edge)
imin = min(i, imin)
### print(ndBlock.y1, i, edge.len)
for i in range(max(0, i1+1), max(0, i2)):
ndLine = dGridLineByIndex[i]
edge = Edge_BL(ndBlock, ndLine)
edge.len = (yBlkAvg - i * cls.iGridStep_V) / fLenNorm
edge._gridtype = 0 # grid line is crossing the block
assert ndBlock.y1 < i*cls.iGridStep_V
assert i*cls.iGridStep_V < ndBlock.y2
### print(ndBlock.y1, ndBlock.y2, i, edge.len)
lEdge.append(edge)
imax = max(imax, i)
for i in range(max(0, i2), i2 + cls.iBlockVisibility):
try:
edge = Edge_BL(ndBlock, dGridLineByIndex[i])
except KeyError:
break # out of the grid
edge.len = (yBlkAvg - i * cls.iGridStep_V) / fLenNorm
edge._gridtype = +1
lEdge.append(edge)
imax = max(imax, i)
### print(ndBlock.y2, i, edge.len)
#now filter those edges
n0 = len(lEdge)
lEdge = cls._filterBadEdge(lEdge, imin, imax, dGridLineByIndex)
print(" - filtering: removed %d edges due to obstruction." % (len(lEdge) - n0))
if False:
print("--- After filtering: %d edges" % len(lEdge))
lSortedEdge = sorted(lEdge, key=lambda x: x.A.domid)
for edge in lSortedEdge:
print("Block domid=%s y1=%s y2=%s"%(edge.A.domid, edge.A.y1, edge.A.y2)
+ " %s line %s "%(["↑", "-", "↓"][1+edge._gridtype],
edge.B.y1 / cls.iGridStep_V)
+ "domid=%s y1=%s" %(edge.B.domid, edge.B.y1)
)
#what differ from previosu version
cls._makeConsistentLabelForEmptyGridRow(lEdge, lClassicPageNode, dGridLineByIndex)
# grid line to grid line edges
n = len(dGridLineByIndex)
for i in range(n):
A = dGridLineByIndex[i]
for j in range(i+1, min(n, i+cls.iGridVisibility+1)):
edge = Edge_LL(A, dGridLineByIndex[j])
edge.len = (j - i)
lEdge.append(edge)
return lEdge
@classmethod
def _filterBadEdge(cls, lEdge, imin, imax, dGridLineByIndex, fRatio=0.25):
"""
We get
- a list of block2Line edges
- the [imin, imax] interval of involved grid line index
- the dGridLineByIndex dictionary
But some block should not be connected to a line due to obstruction by
another blocks.
We filter out those edges...
return a sub-list of lEdge
"""
lKeepEdge = []
def _xoverlapSrcSrc(edge, lEdge):
"""
does the source node of edge overlap with the source node of any
edge of the list?
"""
A = edge.A
for _edge in lEdge:
if A.significantXOverlap(_edge.A, fRatio): return True
return False
def _yoverlapSrcSrc(edge, lEdge):
"""
does the source node of edge overlap with the source node of any
edge of the list?
"""
A = edge.A
for _edge in lEdge:
if A.significantYOverlap(_edge.A, fRatio): return True
return False
#there are two ways for dealing with lines crossed by a block
# - either it prevents another block to link to the line (assuming an x-overlap)
# - or not (historical way)
# THIS IS THE "MODERN" way!!
#take each line in turn
for i in range(imin, imax+1):
ndLine = dGridLineByIndex[i]
#--- process downward edges
#TODO: index!
lDownwardAndXingEdge = [edge for edge in lEdge \
if edge._gridtype >= 0 and edge.B == ndLine]
if lDownwardAndXingEdge:
#sort edge by source block from closest to line block to farthest
lDownwardAndXingEdge.sort(key=lambda o: o.A.y2 - ndLine.y1,
reverse=True)
lKeepDownwardEdge = [lDownwardAndXingEdge.pop(0)]
#now keep all edges whose source does not overlap vertically with
# the source of an edge that is kept
for edge in lDownwardAndXingEdge:
if not _xoverlapSrcSrc(edge, lKeepDownwardEdge):
lKeepDownwardEdge.append(edge)
lKeepEdge.extend(lKeepDownwardEdge)
#NOTHING to do for crossing edges: they should be in the list!
# #--- keep all crossing edges
# #TODO: index!
# lCrossingEdge = [edge for edge in lEdge \
# if edge._gridtype == 0 and edge.B == ndLine]
#
# lKeepEdge.extend(lCrossingEdge)
#--- process upward edges
#TODO: index!
lUpwarAndXingdEdge = [edge for edge in lEdge \
if edge._gridtype <= 0 and edge.B == ndLine]
if lUpwarAndXingdEdge:
#sort edge by source block from closest to line block to farthest
lUpwarAndXingdEdge.sort(key=lambda o: ndLine.y2 - o.A.y1,
reverse=True)
lKeepUpwardEdge = [lUpwarAndXingdEdge.pop(0)]
#now keep all edges whose source does not overlap vertically with
# the source of an edge that is kept
for edge in lUpwarAndXingdEdge:
if not _xoverlapSrcSrc(edge, lKeepUpwardEdge):
lKeepUpwardEdge.append(edge)
# now we keep only the edges, excluding the crossing ones
# (already included!!)
lKeepEdge.extend(edge for edge in lKeepUpwardEdge \
if edge._gridtype != 0)
return lKeepEdge
@classmethod
def _makeConsistentLabelForEmptyGridRow(cls, lEdge, lBlockNode, dGridLineByIndex):
"""
Here we make consistent label when any N grid lines have no block in-between
each other.
In that case, those N grid lines must have consistent BISO labels:
- if one is B, all become B
- elif one is S, all become S
- elif one is I, all become I
- else: they should all be O already (or not annotated!)
lLabels_BISO_Grid = ['B', 'I', 'S', 'O']
NOTE: I'm favoring safe and clean code to efficient code, for experimenting.
TODO: optimize! (if it performs better...)
"""
bDBG = False
#list object in each interval between 2 edges
dsetObjectsByInterval = collections.defaultdict(set)
imax = -1
for ndBlock in lBlockNode:
### print("---- ", ndBlock)
# i1 = GridAnnotator.snapToGridIndex(nd.x1, cls.iGridStep_V)
# i2 = GridAnnotator.snapToGridIndex(nd.x2, cls.iGridStep_V)
i1 = int(math.floor(ndBlock.y1 / float(cls.iGridStep_V)))
i2 = int(math.ceil (ndBlock.y2 / float(cls.iGridStep_V)))
for i in range(i1, i2):
dsetObjectsByInterval[i].add(ndBlock)
imax = max(imax, i2)
# actually the imax is the index of the last positive grid line ('B')
j = imax
lj = list(dGridLineByIndex.keys())
lj.sort(reverse=True)
for j in lj:
if dGridLineByIndex[j].node.get('type') == 'B':
imax = max(imax, j)
break
#enumerate empty intervals
lEmptyIntervalIndex = [i for i in range(0, imax+1) \
if bool(dsetObjectsByInterval[i]) == False]
if bDBG:
traceln("nb empty intervals: %d"%len(lEmptyIntervalIndex))
traceln([(j, dGridLineByIndex[j].domid, dGridLineByIndex[j].node.get('type')) for j in lEmptyIntervalIndex])
#Make consistent labelling (if any labelling!!)
if lEmptyIntervalIndex:
k = 0 #index in lEmptyInterval list
kmax = len(lEmptyIntervalIndex)
while k < kmax:
i = lEmptyIntervalIndex[k]
dk = 1
while (k + dk) < kmax and lEmptyIntervalIndex[k+dk] == (i + dk):
dk += 1
if bDBG:
nd = dGridLineByIndex[i]
traceln("--- start grid line %s %s (nb=%d ending at %s) cls=%s" %(nd.domid, i, dk-1,dGridLineByIndex[i+dk-1].domid, nd.cls))
#TO FIX!!!!
# #we have a series of consecutive empty interval between i and i+dk (excluded)
# lCls = [dGridLineByIndex[j].cls for j in range(i, min(i+dk+1, kmax))]
# # we go to i+dk+1 because last boundary line may propagate its label
# #the node labels are loaded later on... :-(((
#
# if 0 in lCls: # B
# iUniformClass = 0
# elif 2 in lCls: # S
# iUniformClass = 2
# elif 1 in lCls: # I
# iUniformClass = 1
# elif 3 in lCls: # O
# iUniformClass = 3
# else: #unannotated
# if bDBG: traceln("No annotation: ", lCls)
# iUniformClass = None
#
# if not iUniformClass is None:
# for j in range(i, i+dk):
# if bDBG:
# nd = dGridLineByIndex[j]
# traceln("grid line %s %s made %d from %s"%(nd.domid, j, iUniformClass, nd.cls))
# dGridLineByIndex[j].cls = iUniformClass
#WORKAROUND
lCls = [dGridLineByIndex[j].node.get('type') for j in range(i, min(i+dk+1, imax+1))]
# we go to i+dk+1 because last boundary line may propagate its label
if 'B' in lCls: # B
cUniformClass = 'B'
elif 'S' in lCls: # S
cUniformClass = 'S'
elif 'I' in lCls: # I
cUniformClass = 'I'
elif 'O' in lCls: # O
cUniformClass = 'O'
else: #unannotated
if bDBG: traceln("No annotation: ", lCls)
cUniformClass = None
if not cUniformClass is None:
for j in range(i, i+dk):
if bDBG:
nd = dGridLineByIndex[j]
traceln("grid line %s %s made %s from %s"%(nd.domid, j, cUniformClass, nd.node.get('type')))
dGridLineByIndex[j].node.set('type', cUniformClass)
k = k + dk
return
#------------------------------------------------------------------------------------------------------
class GridLine_NodeTransformer_v2(Transformer):
"""
features of a grid line:
- horizontal or vertical.
"""
def transform(self, lNode):
#We allocate TWO more columns to store in it the tfidf and idf computed at document level.
#a = np.zeros( ( len(lNode), 10 ) , dtype=np.float64) # 4 possible orientations: 0, 1, 2, 3
a = np.zeros( ( len(lNode), 6 ) , dtype=np.float64) # 4 possible orientations: 0, 1, 2, 3
for i, blk in enumerate(lNode):
page = blk.page
if abs(blk.x2 - blk.x1) > abs(blk.y1 - blk.y2):
#horizontal
v = 2*blk.y1/float(page.h) - 1 # to range -1, +1
a[i,0:3] = (1.0, v, v*v)
else:
#vertical
v = 2*blk.x1/float(page.w) - 1 # to range -1, +1
a[i,3:6] = (1.0, v, v*v)
return a
class Block2GridLine_EdgeTransformer(Transformer):
"""
features of a block to grid line edge:
- below, crossing, above
"""
def transform(self, edge):
a = np.zeros( ( len(edge), 3 + 3 + 3) , dtype=np.float64) # 4 possible orientations: 0, 1, 2, 3
for i, edge in enumerate(edge):
z = 1 + edge._gridtype # _gridtype is -1 or 0 or 1
a[i, z] = 1.0
a[i, 3 + z] = edge.len # normalised on [0, 1] edge length
a[i, 6 + z] = edge.len * edge.len
return a
class GridLine2GridLine_EdgeTransformer(Transformer):
"""
features of a block to grid line edge:
- below, crossing, above
"""
def transform(self, edge):
a = np.zeros( ( len(edge), GraphGrid_H.iGridVisibility ) , dtype=np.float64) # 4 possible orientations: 0, 1, 2, 3
for i, edge in enumerate(edge):
a[i, edge.len - 1] = 1.0 # edge length (number of steps)
return a
class My_FeatureDefinition_v2(FeatureDefinition):
"""
Multitype version:
so the node_transformer actually is a list of node_transformer of length n_class
the edge_transformer actually is a list of node_transformer of length n_class^2
We also inherit from FeatureDefinition_T !!!
"""
n_QUANTILES = 16
def __init__(self, **kwargs):
"""
set _node_transformer, _edge_transformer, tdifNodeTextVectorizer
"""
FeatureDefinition.__init__(self)
nbTypes = self._getTypeNumber(kwargs)
print("BETTER FEATURES")
block_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!
("xywh", Pipeline([
('selector', NodeTransformerXYWH_v2()),
#v1 ('xywh', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('xywh', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling
])
)
, ("neighbors", Pipeline([
('selector', NodeTransformerNeighbors()),
#v1 ('neighbors', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('neighbors', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling
])
)
, ("1hot", Pipeline([
('1hot', Node1HotFeatures()) #does the 1-hot encoding directly
])
)
])
grid_line_transformer = GridLine_NodeTransformer_v2()
self._node_transformer = TransformerListByType([block_transformer, grid_line_transformer])
edge_BB_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!
("1hot", Pipeline([
('1hot', Edge1HotFeatures(PageNumberSimpleSequenciality()))
])
)
, ("boolean", Pipeline([
('boolean', EdgeBooleanFeatures_v2())
])
)
, ("numerical", Pipeline([
('selector', EdgeNumericalSelector()),
#v1 ('numerical', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling
('numerical', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling
])
)
] )
edge_BL_transformer = Block2GridLine_EdgeTransformer()
edge_LL_transformer = GridLine2GridLine_EdgeTransformer()
self._edge_transformer = TransformerListByType([edge_BB_transformer,
edge_BL_transformer,
edge_BL_transformer, # useless but required
edge_LL_transformer
])
self.tfidfNodeTextVectorizer = None #tdifNodeTextVectorizer
def fitTranformers(self, lGraph,lY=None):
"""
Fit the transformers using the graphs, but TYPE BY TYPE !!!
return True
"""
self._node_transformer[0].fit([nd for g in lGraph for nd in g.getNodeListByType(0)])
self._node_transformer[1].fit([nd for g in lGraph for nd in g.getNodeListByType(1)])
self._edge_transformer[0].fit([e for g in lGraph for e in g.getEdgeListByType(0, 0)])
self._edge_transformer[1].fit([e for g in lGraph for e in g.getEdgeListByType(0, 1)])
#self._edge_transformer[2].fit([e for g in lGraph for e in g.getEdgeListByType(1, 0)])
#self._edge_transformer[3].fit([e for g in lGraph for e in g.getEdgeListByType(1, 1)])
return True
class DU_ABPTableRG4(DU_CRF_Task):
"""
We will do a CRF model for a DU task
, with the below labels
"""
sXmlFilenamePattern = "*.mpxml"
iGridStep_H = None
iGridStep_V = None
iGridVisibility = None
iBlockVisibility = None
#=== CONFIGURATION ====================================================================
@classmethod
def getConfiguredGraphClass(cls):
"""
In this class method, we must return a configured graph class
"""
# Textline labels
# Begin Inside End Single Other
lLabels_BIESO = ['B', 'I', 'E', 'S', 'O']
# Grid lines:
# Border Ignore Separator Outside
lLabels_BISO_Grid = ['B', 'I', 'S', 'O']
#DEFINING THE CLASS OF GRAPH WE USE
DU_GRAPH = GraphGrid_H
DU_GRAPH.iGridStep_H = cls.iGridStep_H
DU_GRAPH.iGridStep_V = cls.iGridStep_V
DU_GRAPH.iGridVisibility = cls.iGridVisibility
DU_GRAPH.iBlockVisibility = cls.iBlockVisibility
# ROW
ntR = NodeType_PageXml_type_woText("row"
, lLabels_BIESO
, None
, False
#HISTORICAL FUNCTION IS (idiotic I think...):
#, BBoxDeltaFun=lambda v: max(v * 0.066, min(5, v/3))
, BBoxDeltaFun=lambda v: v / 5.0, #keep 2/3rd of the box
# we reduce overlap in this way
#this function returns the amount by which each border of
# a bounding box is "shifted toward its centre"...
# w,h = x2-x1, y2-y1
# dx = self.BBoxDeltaFun(w)
# dy = self.BBoxDeltaFun(h)
# x1,y1, x2,y2 = [ int(round(v)) for v in [x1+dx,y1+dy, x2-dx,y2-dy] ]
)
ntR.setLabelAttribute("DU_row")
ntR.setXpathExpr( (".//pc:TextLine" #how to find the nodes
, "./pc:TextEquiv") #how to get their text
)
DU_GRAPH.addNodeType(ntR)
# HEADER
ntGH = NodeType_PageXml_type_woText("gh"
, lLabels_BISO_Grid
, None
, False
, None # equiv. to: BBoxDeltaFun=lambda _: 0
)
ntGH.setLabelAttribute("type")
ntGH.setXpathExpr( ('.//pc:GridSeparator[@orient="0"]' #how to find the nodes
, "./pc:TextEquiv") #how to get their text
)
DU_GRAPH.addNodeType(ntGH)
DU_GRAPH.setClassicNodeTypeList( [ntR ])
return DU_GRAPH
def __init__(self, sModelName, sModelDir,
iGridStep_H = None,
iGridStep_V = None,
iGridVisibility = None,
iBlockVisibility = None,
sComment=None,
C=None, tol=None, njobs=None, max_iter=None,
inference_cache=None):
DU_ABPTableRG4.iGridStep_H = iGridStep_H
DU_ABPTableRG4.iGridStep_V = iGridStep_V
DU_ABPTableRG4.iGridVisibility = iGridVisibility
DU_ABPTableRG4.iBlockVisibility = iBlockVisibility
DU_CRF_Task.__init__(self
, sModelName, sModelDir
, dFeatureConfig = {'row_row':{}, 'row_gh':{},
'gh_row':{}, 'gh_gh':{},
'gh':{}, 'row':{}}
, dLearnerConfig = {
'C' : .1 if C is None else C
, 'njobs' : 4 if njobs is None else njobs
, 'inference_cache' : 50 if inference_cache is None else inference_cache
#, 'tol' : .1
, 'tol' : .05 if tol is None else tol
, 'save_every' : 50 #save every 50 iterations,for warm start
, 'max_iter' : 10 if max_iter is None else max_iter
}
, sComment=sComment
#,cFeatureDefinition=FeatureDefinition_PageXml_StandardOnes_noText
,cFeatureDefinition=My_FeatureDefinition_v2
)
# if options.bBaseline:
# self.bsln_mdl = self.addBaseline_LogisticRegression() #use a LR model trained by GridSearch as baseline
#=== END OF CONFIGURATION =============================================================
# def predict(self, lsColDir):
# """
# Return the list of produced files
# """
# self.sXmlFilenamePattern = "*.mpxml"
# return DU_CRF_Task.predict(self, lsColDir)
#
# def runForExternalMLMethod(self, lsColDir, storeX, applyY, bRevertEdges=False):
# """
# Return the list of produced files
# """
# self.sXmlFilenamePattern = "*.mpxml"
# return DU_CRF_Task.runForExternalMLMethod(self, lsColDir, storeX, applyY, bRevertEdges)
# ----------------------------------------------------------------------------
def main(sModelDir, sModelName, options):
doer = DU_ABPTableRG4(sModelName, sModelDir,
iGridStep_H = options.iGridStep_H,
iGridStep_V = options.iGridStep_V,
iGridVisibility = options.iGridVisibility,
iBlockVisibility = options.iBlockVisibility,
C = options.crf_C,
tol = options.crf_tol,
njobs = options.crf_njobs,
max_iter = options.max_iter,
inference_cache = options.crf_inference_cache)
if options.rm:
doer.rm()
return
lTrn, lTst, lRun, lFold = [_checkFindColDir(lsDir, bAbsolute=False) for lsDir in [options.lTrn, options.lTst, options.lRun, options.lFold]]
# if options.bAnnotate:
# doer.annotateDocument(lTrn)
# traceln('annotation done')
# sys.exit(0)
traceln("- classes: ", doer.getGraphClass().getLabelNameList())
## use. a_mpxml files
#doer.sXmlFilenamePattern = doer.sLabeledXmlFilenamePattern
if options.iFoldInitNum or options.iFoldRunNum or options.bFoldFinish:
if options.iFoldInitNum:
"""
initialization of a cross-validation
"""
splitter, ts_trn, lFilename_trn = doer._nfold_Init(lFold, options.iFoldInitNum, test_size=0.25, random_state=None, bStoreOnDisk=True)
elif options.iFoldRunNum:
"""
Run one fold
"""
oReport = doer._nfold_RunFoldFromDisk(options.iFoldRunNum, options.warm, options.pkl)
traceln(oReport)
elif options.bFoldFinish:
tstReport = doer._nfold_Finish()
traceln(tstReport)
else:
assert False, "Internal error"
#no more processing!!
exit(0)
#-------------------
if lFold:
loTstRpt = doer.nfold_Eval(lFold, 3, .25, None, options.pkl)
import graph.GraphModel
sReportPickleFilename = os.path.join(sModelDir, sModelName + "__report.txt")
traceln("Results are in %s"%sReportPickleFilename)
graph.GraphModel.GraphModel.gzip_cPickle_dump(sReportPickleFilename, loTstRpt)
elif lTrn:
doer.train_save_test(lTrn, lTst, options.warm, options.pkl)
try: traceln("Baseline best estimator: %s"%doer.bsln_mdl.best_params_) #for GridSearch
except: pass
traceln(" --- CRF Model ---")
traceln(doer.getModel().getModelInfo())
elif lTst:
doer.load()
tstReport = doer.test(lTst)
traceln(tstReport)
if options.bDetailedReport:
traceln(tstReport.getDetailledReport())
sReportPickleFilename = os.path.join(sModelDir, sModelName + "__detailled_report.txt")
graph.GraphModel.GraphModel.gzip_cPickle_dump(sReportPickleFilename, tstReport)
if lRun:
if options.storeX or options.applyY:
try: doer.load()
except: pass #we only need the transformer
lsOutputFilename = doer.runForExternalMLMethod(lRun, options.storeX, options.applyY, options.bRevertEdges)
else:
doer.load()
lsOutputFilename = doer.predict(lRun)
traceln("Done, see in:\n %s"%lsOutputFilename)
# ----------------------------------------------------------------------------
if __name__ == "__main__":
version = "v.01"
usage, description, parser = DU_CRF_Task.getBasicTrnTstRunOptionParser(sys.argv[0], version)
# parser.add_option("--annotate", dest='bAnnotate', action="store_true",default=False, help="Annotate the textlines with BIES labels")
#FOR GCN
parser.add_option("--revertEdges", dest='bRevertEdges', action="store_true", help="Revert the direction of the edges")
parser.add_option("--detail", dest='bDetailedReport', action="store_true", default=False,help="Display detailled reporting (score per document)")
parser.add_option("--baseline", dest='bBaseline', action="store_true", default=False, help="report baseline method")
parser.add_option("--line_see_line", dest='iGridVisibility', action="store",
type=int, default=2,
help="seeline2line: how many next grid lines does one line see?")
parser.add_option("--block_see_line", dest='iBlockVisibility', action="store",
type=int, default=2,
help="seeblock2line: how many next grid lines does one block see?")
parser.add_option("--grid_h", dest='iGridStep_H', action="store", type=int,
default=GraphGrid.iGridStep_H,
help="Grid horizontal step")
parser.add_option("--grid_v", dest='iGridStep_V', action="store", type=int,
default=GraphGrid.iGridStep_V,
help="Grid Vertical step")
# ---
#parse the command line
(options, args) = parser.parse_args()
# ---
try:
sModelDir, sModelName = args
except Exception as e:
traceln("Specify a model folder and a model name!")
_exit(usage, 1, e)
main(sModelDir, sModelName, options) | bsd-3-clause |
sniemi/EuclidVisibleInstrument | analysis/FlatfieldCalibration.py | 1 | 27797 | """
Flat Field Calibration
======================
This simple script can be used to study the number of flat fields required to meet the VIS calibration requirements.
The following requirements related to the flat field calibration has been taken from GDPRD.
R-GDP-CAL-054:
The contribution of the residuals of VIS flat-field correction to the error on the determination of each
ellipticity component of the local PSF shall not exceed 3x10-5 (one sigma).
R-GDP-CAL-064:
The contribution of the residuals of VIS flat-field correction to the relative error on the determination
of the local PSF R2 shall not exceed 1x10-4 (one sigma).
.. Note:: The amount of cosmic rays in the simulated input images might be too low, because the exposure was
set to 10 seconds and cosmic rays were calculated based on this. However, in reality the readout
takes about 80 seconds. Thus, the last row is effected by cosmic a lot more than by assuming a single
10 second exposure.
:requires: PyFITS
:requires: NumPy
:requires: SciPy
:requires: matplotlib
:requires: VISsim-Python
:version: 0.97
:author: Sami-Matias Niemi
:contact: s.niemi@ucl.ac.uk
"""
import matplotlib
matplotlib.rc('text', usetex=True)
matplotlib.rcParams['font.size'] = 17
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('axes', linewidth=1.1)
matplotlib.rcParams['legend.fontsize'] = 11
matplotlib.rcParams['legend.handlelength'] = 3
matplotlib.rcParams['xtick.major.size'] = 5
matplotlib.rcParams['ytick.major.size'] = 5
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import pyfits as pf
import numpy as np
import datetime, cPickle, glob, os, sys
from scipy.ndimage.interpolation import zoom
from scipy import interpolate
from analysis import shape
from support import logger as lg
from support import surfaceFitting as sf
from support import files as fileIO
def generateResidualFlatField(files='Q0*flatfield*.fits', combine=77, lampfile='data/VIScalibrationUnitflux.fits',
reference='data/VISFlatField1percent.fits', gain=3.5, plots=False, debug=False):
"""
Generate a median combined flat field residual from given input files.
Randomly draws a given number (kw combine) of files from the file list identified using the files kw.
Median combine all files before the lamp profile given by lampfile kw is being divided out. This
will produce a derived flat field. This flat can be compared against the reference that was used
to produce the initial data to derive a residual flat that describes the error in the flat field
that was derived.
:param files: wildcard flagged name identifier for the FITS files to be used for generating a flat
:type files: str
:param combine: number of files to median combine
:type combine: int
:param lampfile: name of the calibration unit flux profile FITS file
:type lampfile: str
:param reference: name of the reference pixel-to-pixel flat field FITS file
:type reference: str
:param gain: gain factor [e/ADU]
:type gain: float
:param plots: whether or not to generate plots
:type plots: boolean
:param debug: whether or not to produce output FITS files
:type debug: boolean
.. Warning:: Remember to use an appropriate lamp and reference files so that the error in the derived
flat field can be correctly calculated.
:return: residual flat field (difference between the generated flat and the reference)
:rtype: ndarray
"""
#find all FITS files
files = glob.glob(files)
#choose randomly the files that should be combined
if combine < len(files):
ids = np.random.random_integers(0, len(files)-1, combine)
files = np.asarray(files)[ids]
#load data and scale to electrons
data = fileIO.readFITSDataExcludeScanRegions(files)
data *= gain
#check that the sizes match and median combine
if len(set(x.shape for x in data)) > 1:
sys.exit('ERROR -- files are not the same shape, cannot median combine!')
else:
medianCombined = np.median(data, axis=0)
#fit surface to the median and normalize it out
#m = sf.polyfit2d(xx.ravel(), yy.ravel(), median.ravel(), order=order)
# Evaluate it on a rectangular grid
#fitted = sf.polyval2d(xx, yy, m)
#load the lamp profile that went in and divide the combined image with the profile
lamp = pf.getdata(lampfile)
pixvar = medianCombined.astype(np.float64).copy() / lamp
#load the true reference p-flat and calculate the error in the derived flat field (i.e. residual)
real = pf.getdata(reference).astype(np.float64)
res = np.abs(real - pixvar) / (real*pixvar) + 1. #old: maybe incorrect?
#res = np.abs(pixvar - real) + 1.
if debug:
print np.mean(res), np.min(res), np.max(res), np.std(res)
if not os.path.exists('debug'):
os.makedirs('debug')
fileIO.writeFITS(medianCombined, 'debug/medianFlat.fits')
fileIO.writeFITS(pixvar, 'debug/derivedFlat.fits')
fileIO.writeFITS(res, 'debug/residualFlat.fits')
if plots:
if not os.path.exists('plots'):
os.makedirs('plots')
#generate a mesh grid fo plotting
ysize, xsize = medianCombined.shape
xx, yy = np.meshgrid(np.linspace(0, xsize, xsize), np.linspace(0, ysize, ysize))
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(xx, yy, medianCombined, rstride=100, cstride=100, alpha=0.6, cmap=cm.jet)
ax.set_xlabel('X [pixels]')
ax.set_ylabel('Y [pixels]')
ax.set_zlabel('Counts [electrons]')
ax.set_zlim(8.9e4, 1.05e5)
plt.savefig('plots/MedianFlat.png')
plt.close()
im = plt.imshow(medianCombined, origin='lower', vmin=8.9e4, vmax=1.05e5)
c1 = plt.colorbar(im)
c1.set_label('Counts [electrons]')
plt.xlabel('X [pixels]')
plt.ylabel('Y [pixels]')
plt.savefig('plots/Mediand2D.png')
plt.close()
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(xx, yy, pixvar, rstride=100, cstride=100, alpha=0.6, cmap=cm.jet)
ax.set_xlabel('X [pixels]')
ax.set_ylabel('Y [pixels]')
ax.set_zlim(0.95, 1.05)
ax.set_zlabel('Counts [electrons]')
plt.savefig('plots/PixelFlat.png')
plt.close()
im = plt.imshow(pixvar, origin='lower', vmin=0.95, vmax=1.05)
c1 = plt.colorbar(im)
c1.set_label('Counts [electrons]')
plt.xlabel('X [pixels]')
plt.ylabel('Y [pixels]')
plt.savefig('plots/PixelFlat2D.png')
plt.close()
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(xx, yy, res, rstride=100, cstride=100, alpha=0.6, cmap=cm.jet)
ax.set_xlabel('X [pixels]')
ax.set_ylabel('Y [pixels]')
ax.set_zlim(0.95, 1.05)
ax.set_zlabel('Residual Flat Field')
plt.savefig('plots/ResidualFlatField.png')
plt.close()
im = plt.imshow(res, origin='lower', vmin=0.95, vmax=1.05)
c1 = plt.colorbar(im)
c1.set_label('Residual Flat Field')
plt.xlabel('X [pixels]')
plt.ylabel('Y [pixels]')
plt.savefig('plots/ResidualFlatField2D.png')
plt.close()
return res
def testFlatCalibration(log, flats, surfaces=10, file='data/psf1x.fits', psfs=5000,
sigma=0.75, iterations=7, weighting=True, plot=False, debug=False):
"""
Derive the PSF ellipticities for a given number of random surfaces with random PSF positions
and a given number of flat fields median combined.
This function is to derive the the actual values so that the knowledge (variance) can be studied.
"""
#read in PSF and rescale to avoid rounding or truncation errors
data = pf.getdata(file)
data /= np.max(data)
data *= 300. #SNR about 10 for star...
#derive reference values
settings = dict(sigma=sigma, iterations=iterations, weighted=weighting)
sh = shape.shapeMeasurement(data.copy(), log, **settings)
reference = sh.measureRefinedEllipticity()
#random positions for the PSFs, these positions are the lower corners
#assume that this is done on quadrant level thus the xmax and ymax are 2065 and 2047, respectively
xpositions = np.random.random_integers(0, 2047 - data.shape[1], psfs)
ypositions = np.random.random_integers(0, 2065 - data.shape[0], psfs)
out = {}
#number of biases to median combine
for a in flats:
print 'Number of Flats to combine: %i / %i' % (a, flats[-1])
#data storage
de1 = []
de2 = []
de = []
R2 = []
dR2 = []
e1 = []
e2 = []
e = []
for b in xrange(surfaces):
print 'Random Realisations: %i / %i' % (b+1, surfaces)
residual = generateResidualFlatField(combine=a, plots=plot, debug=debug)
print 'Average residual = %e' % (np.mean(residual) - 1.)
# generate 2D plot
if b == 0 and plot:
im = plt.imshow(residual, extent=(0, 2066, 2048, 0))
plt.scatter(xpositions + (data.shape[1]/2), ypositions + (data.shape[0]/2), color='white')
c1 = plt.colorbar(im)
c1.set_label('Residual Flat Field')
plt.xlim(0, 2066)
plt.ylim(0, 2048)
plt.xlabel('Y [pixels]')
plt.ylabel('X [pixels]')
plt.savefig('residualFlat2D%i.png' % a)
plt.close()
#loop over the PSFs
for xpos, ypos in zip(xpositions, ypositions):
tmp = data.copy()
#get the underlying residual surface and multiple with the PSF
small = residual[ypos:ypos+data.shape[0], xpos:xpos+data.shape[1]].copy()
#small += 1.
small *= tmp
#small *= tmp # depends on the residual geenration
#measure e and R2 from the postage stamp image
sh = shape.shapeMeasurement(small.copy(), log, **settings)
results = sh.measureRefinedEllipticity()
#save values
e1.append(results['e1'])
e2.append(results['e2'])
e.append(results['ellipticity'])
R2.append(results['R2'])
de1.append(results['e1'] - reference['e1'])
de2.append(results['e2'] - reference['e2'])
de.append(results['ellipticity'] - reference['ellipticity'])
dR2.append(results['R2'] - reference['R2'])
out[a+1] = [e1, e2, e, R2, de1, de2, de, dR2]
return out, reference
def plotNumberOfFrames(results, reqe=3e-5, reqr2=1e-4, shift=0.1, outdir='results', timeStamp=False):
"""
Creates a simple plot to combine and show the results.
:param res: results to be plotted [results dictionary, reference values]
:type res: list
:param reqe: the requirement for ellipticity [default=3e-5]
:type reqe: float
:param reqr2: the requirement for size R2 [default=1e-4]
:type reqr2: float
:param shift: the amount to shift the e2 results on the abscissa (for clarity)
:type shift: float
:param outdir: output directory to which the plots will be saved to
:type outdir: str
:param timeStamp: whether or not to include a time stamp to the output image
:type timeStamp: bool
:return: None
"""
if not os.path.exists(outdir):
os.makedirs(outdir)
#rename
ref = results[1]
res = results[0]
print '\nSigma results:'
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
fig = plt.figure()
plt.title(r'VIS Flat Field Calibration: $\sigma(e)$')
ax = fig.add_subplot(111)
maxx = 0
frames = []
values = []
#loop over the number of frames combined
for key in res:
e1 = np.asarray(res[key][0])
e2 = np.asarray(res[key][1])
e = np.asarray(res[key][2])
std1 = np.std(e1)
std2 = np.std(e2)
std = np.std(e)
frames.append(key)
values.append(std)
ax.scatter(key-shift, std, c='m', marker='*')
ax.scatter(key, std1, c='b', marker='o')
ax.scatter(key, std2, c='y', marker='s')
if key > maxx:
maxx = key
print key, std, std1, std2
#label
ax.scatter(key-shift, std, c='m', marker='*', label=r'$\sigma (e)$')
ax.scatter(key, std1, c='b', marker='o', label=r'$\sigma (e_{1})$')
ax.scatter(key, std2, c='y', marker='s', label=r'$\sigma (e_{2})$')
#sort and interpolate
values = np.asarray(values)
frames = np.asarray(frames)
srt = np.argsort(frames)
x = np.arange(frames.min(), frames.max()+1)
f = interpolate.interp1d(frames[srt], values[srt], kind='cubic')
vals = f(x)
ax.plot(x, vals, ':', c='0.2', zorder=20)
try:
msk = vals < reqe
minn = np.min(x[msk])
plt.text(np.mean(frames), 8e-6, r'Flats Required $\raise-.5ex\hbox{$\buildrel>\over\sim$}$ %i' % np.ceil(minn),
ha='center', va='center', fontsize=11)
except:
pass
ax.fill_between(np.arange(maxx+10), np.ones(maxx+10)*reqe, 1.0, facecolor='red', alpha=0.08)
ax.axhline(y=reqe, c='g', ls='--', label='Requirement')
plt.text(1, 0.9*reqe, '%.1e' % reqe, ha='left', va='top', fontsize=11)
ax.set_yscale('log')
ax.set_ylim(5e-6, 1e-4)
ax.set_xlim(0, maxx+1)
ax.set_xlabel('Number of Flat Fields Median Combined')
ax.set_ylabel(r'$\sigma (e_{i})\ , \ \ \ i \in [1,2]$')
if timeStamp:
plt.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax.transAxes, alpha=0.2)
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=2.0, ncol=2)
plt.savefig(outdir+'/FlatCalibrationsigmaE.pdf')
plt.close()
#same for R2s
fig = plt.figure()
plt.title(r'VIS Flat Field Calibration: $\frac{\sigma (R^{2})}{R_{ref}^{2}}$')
ax = fig.add_subplot(111)
ax.axhline(y=0, c='k', ls=':')
maxx = 0
frames = []
values = []
#loop over the number of frames combined
for key in res:
dR2 = np.asarray(res[key][3])
#std = np.std(dR2) / ref['R2']
std = np.std(dR2) / np.mean(dR2)
frames.append(key)
values.append(std)
print key, std
ax.scatter(key, std, c='b', marker='s', s=35, zorder=10)
if key > maxx:
maxx = key
#for the legend
ax.scatter(key, std, c='b', marker='s', label=r'$\frac{\sigma (R^{2})}{R_{ref}^{2}}$')
#sort and interpolate
values = np.asarray(values)
frames = np.asarray(frames)
srt = np.argsort(frames)
x = np.arange(frames.min(), frames.max())
f = interpolate.interp1d(frames[srt], values[srt], kind='cubic')
vals = f(x)
ax.plot(x, vals, ':', c='0.2', zorder=10)
try:
msk = vals < reqr2
minn = np.min(x[msk])
plt.text(np.mean(frames), 2e-5, r'Flats Required $\raise-.5ex\hbox{$\buildrel>\over\sim$}$ %i' % np.ceil(minn),
fontsize=11, ha='center', va='center')
except:
pass
#show the requirement
ax.fill_between(np.arange(maxx+10), np.ones(maxx+10)*reqr2, 1.0, facecolor='red', alpha=0.08)
ax.axhline(y=reqr2, c='g', ls='--', label='Requirement')
plt.text(1, 0.9*reqr2, '%.1e' % reqr2, ha='left', va='top', fontsize=11)
ax.set_yscale('log')
ax.set_ylim(5e-6, 1e-3)
ax.set_xlim(0, maxx+1)
ax.set_xlabel('Number of Flat Fields Median Combined')
ax.set_ylabel(r'$\frac{\sigma (R^{2})}{R_{ref}^{2}}$')
if timeStamp:
plt.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax.transAxes, alpha=0.2)
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.8 )
plt.savefig(outdir+'/FlatCalibrationSigmaR2.pdf')
plt.close()
print '\nDelta results:'
#loop over the number of frames combined
for key in res:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.title(r'VIS Flat Field Calibration (%i exposures): $\delta e$' % key)
de1 = np.asarray(res[key][4])
de2 = np.asarray(res[key][5])
de = np.asarray(res[key][6])
avg1 = np.mean(de1)**2
avg2 = np.mean(de2)**2
avg = np.mean(de)**2
#write down the values
print key, avg, avg1, avg2
plt.text(0.08, 0.9, r'$\left< \delta e_{1} \right>^{2} = %e$' %avg1, fontsize=10, transform=ax.transAxes)
plt.text(0.08, 0.85, r'$\left< \delta e_{2}\right>^{2} = %e$' %avg2, fontsize=10, transform=ax.transAxes)
plt.text(0.08, 0.8, r'$\left< \delta | \bar{e} |\right>^{2} = %e$' %avg, fontsize=10, transform=ax.transAxes)
ax.hist(de, bins=15, color='y', alpha=0.2, label=r'$\delta | \bar{e} |$', normed=True, log=True)
ax.hist(de1, bins=15, color='b', alpha=0.5, label=r'$\delta e_{1}$', normed=True, log=True)
ax.hist(de2, bins=15, color='g', alpha=0.3, label=r'$\delta e_{2}$', normed=True, log=True)
ax.axvline(x=0, ls=':', c='k')
ax.set_ylabel('Probability Density')
ax.set_xlabel(r'$\delta e_{i}\ , \ \ \ i \in [1,2]$')
if timeStamp:
plt.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax.transAxes, alpha=0.2)
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=2.0, ncol=2)
plt.savefig(outdir+'/FlatCalibrationEDelta%i.pdf' % key)
plt.close()
#same for R2s
for key in res:
fig = plt.figure()
plt.title(r'VIS Flat Field Calibration (%i exposures): $\frac{\delta R^{2}}{R_{ref}^{2}}$' % key)
ax = fig.add_subplot(111)
dR2 = np.asarray(res[key][7])
avg = np.mean(dR2/ref['R2'])**2
ax.hist(dR2, bins=15, color='y', label=r'$\frac{\delta R^{2}}{R_{ref}^{2}}$', normed=True, log=True)
print key, avg
plt.text(0.1, 0.9, r'$\left<\frac{\delta R^{2}}{R^{2}_{ref}}\right>^{2} = %e$' %avg,
fontsize=10, transform=ax.transAxes)
ax.axvline(x=0, ls=':', c='k')
ax.set_ylabel('Probability Density')
ax.set_xlabel(r'$\frac{\delta R^{2}}{R_{ref}^{2}}$')
if timeStamp:
plt.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax.transAxes, alpha=0.2)
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.8)
plt.savefig(outdir+'/FlatCalibrationDeltaSize%i.pdf' % key)
plt.close()
def findTolerableError(log, file='data/psf4x.fits', oversample=4.0, psfs=10000, iterations=7, sigma=0.75):
"""
Calculate ellipticity and size for PSFs of different scaling when there is a residual
pixel-to-pixel variations.
"""
#read in PSF and renormalize it
data = pf.getdata(file)
data /= np.max(data)
#PSF scalings for the peak pixel, in electrons
scales = np.random.random_integers(1e2, 2e5, psfs)
#set the scale for shape measurement
settings = dict(sampling=1.0/oversample, itereations=iterations, sigma=sigma)
#residual from a perfect no pixel-to-pixel non-uniformity
residuals = np.logspace(-7, -1.6, 9)[::-1] #largest first
tot = residuals.size
res = {}
for i, residual in enumerate(residuals):
print'%i / %i' % (i+1, tot)
R2 = []
e1 = []
e2 = []
e = []
#loop over the PSFs
for scale in scales:
#random residual pixel-to-pixel variations
if oversample < 1.1:
residualSurface = np.random.normal(loc=1.0, scale=residual, size=data.shape)
elif oversample == 4.0:
tmp = np.random.normal(loc=1.0, scale=residual, size=(170, 170))
residualSurface = zoom(tmp, 4.013, order=0)
else:
sys.exit('ERROR when trying to generate a blocky pixel-to-pixel non-uniformity map...')
#make a copy of the PSF and scale it with the given scaling
#and then multiply with a residual pixel-to-pixel variation
tmp = data.copy() * scale * residualSurface
#measure e and R2 from the postage stamp image
sh = shape.shapeMeasurement(tmp.copy(), log, **settings)
results = sh.measureRefinedEllipticity()
#save values
e1.append(results['e1'])
e2.append(results['e2'])
e.append(results['ellipticity'])
R2.append(results['R2'])
out = dict(e1=np.asarray(e1), e2=np.asarray(e2), e=np.asarray(e), R2=np.asarray(R2))
res[residual] = out
return res
def plotTolerableErrorR2(res, output, req=1e-4):
fig = plt.figure()
plt.title(r'VIS Flat Fielding')
ax = fig.add_subplot(111)
#loop over the number of bias frames combined
vals = []
for key in res.keys():
dR2 = res[key]['R2']
normed = np.std(dR2) / np.mean(dR2)
ax.scatter(key, normed, c='m', marker='*', s=35)
vals.append(normed)
print key, normed
#for the legend
ax.scatter(key, normed, c='m', marker='*', label=r'$\frac{\sigma(R^{2})}{R_{ref}^{2}}$')
#show the requirement
ks = np.asarray(res.keys())
ran = np.linspace(ks.min() * 0.99, ks.max() * 1.01)
ax.fill_between(ran, np.ones(ran.size) * req, 1.0, facecolor='red', alpha=0.08)
ax.axhline(y=req, c='g', ls='--', label='Requirement')
#find the crossing
srt = np.argsort(ks)
values = np.asarray(vals)
f = interpolate.interp1d(ks[srt], values[srt], kind='cubic')
x = np.logspace(np.log10(ks.min()), np.log10(ks.max()), 100)
vals = f(x)
ax.plot(x, vals, ':', c='0.2', zorder=10)
msk = vals < req
maxn = np.max(x[msk])
plt.text(1e-5, 2e-5, r'Error must be $\leq %.2e$ per cent' % (maxn*100),
fontsize=11, ha='center', va='center')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim(1e-7, 1e-2)
ax.set_xlim(ks.min() * 0.99, ks.max() * 1.01)
ax.set_xlabel('Error in the Flat Field Map')
ax.set_ylabel(r'$\frac{\sigma (R^{2})}{R_{ref}^{2}}$')
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.8, loc='upper left')
plt.savefig(output)
plt.close()
def plotTolerableErrorE(res, output, req=3e-5):
fig = plt.figure()
plt.title(r'VIS Flat Fielding')
ax = fig.add_subplot(111)
#loop over the number of bias frames combined
vals = []
for key in res.keys():
e1 = np.std(res[key]['e1'])
e2 = np.std(res[key]['e'])
e = np.std(res[key]['e'])
vals.append(e)
ax.scatter(key, e1, c='m', marker='*', s=35)
ax.scatter(key, e2, c='y', marker='s', s=35)
ax.scatter(key, e, c='r', marker='o', s=35)
print key, e, e1, e2
#for the legend
ax.scatter(key, e1, c='m', marker='*', label=r'$\sigma(e_{1})$')
ax.scatter(key, e2, c='y', marker='s', label=r'$\sigma(e_{2})$')
ax.scatter(key, e, c='r', marker='o', label=r'$\sigma(e)$')
#show the requirement
ks = np.asarray(res.keys())
ran = np.linspace(ks.min() * 0.99, ks.max() * 1.01)
ax.fill_between(ran, np.ones(ran.size) * req, 1.0, facecolor='red', alpha=0.08)
ax.axhline(y=req, c='g', ls='--', label='Requirement')
#find the crossing
srt = np.argsort(ks)
values = np.asarray(vals)
f = interpolate.interp1d(ks[srt], values[srt], kind='cubic')
x = np.logspace(np.log10(ks.min()), np.log10(ks.max()), 100)
vals = f(x)
ax.plot(x, vals, ':', c='0.2', zorder=10)
msk = vals < req
maxn = np.max(x[msk])
plt.text(1e-5, 2e-5, r'Error for $e$ must be $\leq %.2e$ per cent' % (maxn*100),
fontsize=11, ha='center', va='center')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim(1e-7, 1e-2)
ax.set_xlim(ks.min() * 0.99, ks.max() * 1.01)
ax.set_xlabel('Error in the Flat Field Map')
ax.set_ylabel(r'$\sigma (e_{i})\ , \ \ \ i \in [1,2]$')
plt.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.8, loc='upper left')
plt.savefig(output)
plt.close()
def testNoFlatfieldingEffects(log, file='data/psf1x.fits', oversample=1.0, psfs=500):
"""
Calculate ellipticity and size variance and error in case of no pixel-to-pixel flat field correction.
"""
#read in PSF and renormalize it
data = pf.getdata(file)
data /= np.max(data)
data *= 1e5
#derive reference values
settings = dict(sampling=1.0/oversample)
sh = shape.shapeMeasurement(data.copy(), log, **settings)
reference = sh.measureRefinedEllipticity()
print reference
#residual
residual = pf.getdata('data/VISFlatField2percent.fits') #'data/VISFlatField1percent.fits'
if oversample == 4.0:
residual = zoom(zoom(residual, 2, order=0), 2, order=0)
elif oversample == 1.0:
pass
else:
print 'ERROR--cannot do arbitrary oversampling...'
#random positions for the PSFs, these positions are the lower corners
xpositions = np.random.random_integers(0, residual.shape[1] - data.shape[1], psfs)
ypositions = np.random.random_integers(0, residual.shape[0] - data.shape[0], psfs)
#data storage
out = {}
de1 = []
de2 = []
de = []
R2 = []
dR2 = []
e1 = []
e2 = []
e = []
rnd = 1
tot = xpositions.size
#loop over the PSFs
for xpos, ypos in zip(xpositions, ypositions):
print'%i / %i' % (rnd, tot)
rnd += 1
#make a copy of the PSF
tmp = data.copy()
#get the underlying residual surface ond multiple the PSF with the surface
small = residual[ypos:ypos+data.shape[0], xpos:xpos+data.shape[1]].copy()
small *= tmp
#measure e and R2 from the postage stamp image
sh = shape.shapeMeasurement(small.copy(), log, **settings)
results = sh.measureRefinedEllipticity()
#save values
e1.append(results['e1'])
e2.append(results['e2'])
e.append(results['ellipticity'])
R2.append(results['R2'])
de1.append(results['e1'] - reference['e1'])
de2.append(results['e2'] - reference['e2'])
de.append(results['ellipticity'] - reference['ellipticity'])
dR2.append(results['R2'] - reference['R2'])
out[1] = [e1, e2, e, R2, de1, de2, de, dR2]
return out, reference
if __name__ == '__main__':
run = True
debug = False
plots = True
error = False
#start the script
log = lg.setUpLogger('flatfieldCalibration.log')
log.info('Testing flat fielding calibration...')
if error:
res = findTolerableError(log)
fileIO.cPickleDumpDictionary(res, 'errors/residuals.pk')
res = cPickle.load(open('errors/residuals.pk'))
plotTolerableErrorE(res, output='errors/FlatFieldingTolerableErrorE.pdf')
plotTolerableErrorR2(res, output='errors/FlatFieldingTolerableErrorR2.pdf')
if run:
results = testFlatCalibration(log, flats=np.arange(5, 100, 9))
fileIO.cPickleDumpDictionary(results, 'flatfieldResults.pk')
if debug:
#calculate RMS on image with x frames combined together
combined = generateResidualFlatField(combine=30, plots=True, debug=True)
print np.std(combined), np.std(combined[500:561, 500:561]), np.std(combined[300:361, 300:361])
results = testNoFlatfieldingEffects(log, oversample=4.0, file='data/psf4x.fits', psfs=400)
if plots:
if not run:
results = cPickle.load(open('flatfieldResults.pk'))
plotNumberOfFrames(results)
log.info('Run finished...\n\n\n')
| bsd-2-clause |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/numpy/lib/histograms.py | 4 | 39639 | """
Histogram-related functions
"""
from __future__ import division, absolute_import, print_function
import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.compat.py3k import basestring
from numpy.core import overrides
__all__ = ['histogram', 'histogramdd', 'histogram_bin_edges']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
# range is a keyword argument to many functions, so save the builtin so they can
# use it.
_range = range
def _hist_bin_sqrt(x, range):
"""
Square root histogram bin estimator.
Bin width is inversely proportional to the data size. Used by many
programs for its simplicity.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return x.ptp() / np.sqrt(x.size)
def _hist_bin_sturges(x, range):
"""
Sturges histogram bin estimator.
A very simplistic estimator based on the assumption of normality of
the data. This estimator has poor performance for non-normal data,
which becomes especially obvious for large data sets. The estimate
depends only on size of the data.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return x.ptp() / (np.log2(x.size) + 1.0)
def _hist_bin_rice(x, range):
"""
Rice histogram bin estimator.
Another simple estimator with no normality assumption. It has better
performance for large data than Sturges, but tends to overestimate
the number of bins. The number of bins is proportional to the cube
root of data size (asymptotically optimal). The estimate depends
only on size of the data.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return x.ptp() / (2.0 * x.size ** (1.0 / 3))
def _hist_bin_scott(x, range):
"""
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
def _hist_bin_stone(x, range):
"""
Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.
The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.
https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule
This paper by Stone appears to be the origination of this rule.
http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
range : (float, float)
The lower and upper range of the bins.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
n = x.size
ptp_x = np.ptp(x)
if n <= 1 or ptp_x == 0:
return 0
def jhat(nbins):
hh = ptp_x / nbins
p_k = np.histogram(x, bins=nbins, range=range)[0] / n
return (2 - (n + 1) * p_k.dot(p_k)) / hh
nbins_upper_bound = max(100, int(np.sqrt(n)))
nbins = min(_range(1, nbins_upper_bound + 1), key=jhat)
if nbins == nbins_upper_bound:
warnings.warn("The number of bins estimated may be suboptimal.",
RuntimeWarning, stacklevel=3)
return ptp_x / nbins
def _hist_bin_doane(x, range):
"""
Doane's histogram bin estimator.
Improved version of Sturges' formula which works better for
non-normal data. See
stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
if x.size > 2:
sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3)))
sigma = np.std(x)
if sigma > 0.0:
# These three operations add up to
# g1 = np.mean(((x - np.mean(x)) / sigma)**3)
# but use only one temp array instead of three
temp = x - np.mean(x)
np.true_divide(temp, sigma, temp)
np.power(temp, 3, temp)
g1 = np.mean(temp)
return x.ptp() / (1.0 + np.log2(x.size) +
np.log2(1.0 + np.absolute(g1) / sg1))
return 0.0
def _hist_bin_fd(x, range):
"""
The Freedman-Diaconis histogram bin estimator.
The Freedman-Diaconis rule uses interquartile range (IQR) to
estimate binwidth. It is considered a variation of the Scott rule
with more robustness as the IQR is less affected by outliers than
the standard deviation. However, the IQR depends on fewer points
than the standard deviation, so it is less accurate, especially for
long tailed distributions.
If the IQR is 0, this function returns 1 for the number of bins.
Binwidth is inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
iqr = np.subtract(*np.percentile(x, [75, 25]))
return 2.0 * iqr * x.size ** (-1.0 / 3.0)
def _hist_bin_auto(x, range):
"""
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
and the Sturges estimator if the FD bandwidth is 0.
The FD estimator is usually the most robust method, but its width
estimate tends to be too large for small `x` and bad for data with limited
variance. The Sturges estimator is quite good for small (<1000) datasets
and is the default in the R language. This method gives good off the shelf
behaviour.
.. versionchanged:: 1.15.0
If there is limited variance the IQR can be 0, which results in the
FD bin width being 0 too. This is not a valid bin width, so
``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal.
If the IQR is 0, it's unlikely any variance based estimators will be of
use, so we revert to the sturges estimator, which only uses the size of the
dataset in its calculation.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
See Also
--------
_hist_bin_fd, _hist_bin_sturges
"""
fd_bw = _hist_bin_fd(x, range)
sturges_bw = _hist_bin_sturges(x, range)
del range # unused
if fd_bw:
return min(fd_bw, sturges_bw)
else:
# limited variance, so we return a len dependent bw estimator
return sturges_bw
# Private dict initialized at module load time
_hist_bin_selectors = {'stone': _hist_bin_stone,
'auto': _hist_bin_auto,
'doane': _hist_bin_doane,
'fd': _hist_bin_fd,
'rice': _hist_bin_rice,
'scott': _hist_bin_scott,
'sqrt': _hist_bin_sqrt,
'sturges': _hist_bin_sturges}
def _ravel_and_check_weights(a, weights):
""" Check a and weights have matching shapes, and ravel both """
a = np.asarray(a)
# Ensure that the array is a "subtractable" dtype
if a.dtype == np.bool_:
warnings.warn("Converting input from {} to {} for compatibility."
.format(a.dtype, np.uint8),
RuntimeWarning, stacklevel=3)
a = a.astype(np.uint8)
if weights is not None:
weights = np.asarray(weights)
if weights.shape != a.shape:
raise ValueError(
'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()
return a, weights
def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is not None:
first_edge, last_edge = range
if first_edge > last_edge:
raise ValueError(
'max must be larger than min in range parameter.')
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"supplied range of [{}, {}] is not finite".format(first_edge, last_edge))
elif a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
first_edge, last_edge = 0, 1
else:
first_edge, last_edge = a.min(), a.max()
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
# expand empty range to avoid divide by zero
if first_edge == last_edge:
first_edge = first_edge - 0.5
last_edge = last_edge + 0.5
return first_edge, last_edge
def _unsigned_subtract(a, b):
"""
Subtract two values where a >= b, and produce an unsigned result
This is needed when finding the difference between the upper and lower
bound of an int16 histogram
"""
# coerce to a single type
signed_to_unsigned = {
np.byte: np.ubyte,
np.short: np.ushort,
np.intc: np.uintc,
np.int_: np.uint,
np.longlong: np.ulonglong
}
dt = np.result_type(a, b)
try:
dt = signed_to_unsigned[dt.type]
except KeyError:
return np.subtract(a, b, dtype=dt)
else:
# we know the inputs are integers, and we are deliberately casting
# signed to unsigned
return np.subtract(a, b, casting='unsafe', dtype=dt)
def _get_bin_edges(a, bins, range, weights):
"""
Computes the bins used internally by `histogram`.
Parameters
==========
a : ndarray
Ravelled data array
bins, range
Forwarded arguments from `histogram`.
weights : ndarray, optional
Ravelled weights array, or None
Returns
=======
bin_edges : ndarray
Array of bin edges
uniform_bins : (Number, Number, int):
The upper bound, lowerbound, and number of bins, used in the optimized
implementation of `histogram` that works on uniform bins.
"""
# parse the overloaded bins argument
n_equal_bins = None
bin_edges = None
if isinstance(bins, basestring):
bin_name = bins
# if `bins` is a string for an automatic method,
# this will replace it with the number of bins calculated
if bin_name not in _hist_bin_selectors:
raise ValueError(
"{!r} is not a valid estimator for `bins`".format(bin_name))
if weights is not None:
raise TypeError("Automated estimation of the number of "
"bins is not supported for weighted data")
first_edge, last_edge = _get_outer_edges(a, range)
# truncate the range if needed
if range is not None:
keep = (a >= first_edge)
keep &= (a <= last_edge)
if not np.logical_and.reduce(keep):
a = a[keep]
if a.size == 0:
n_equal_bins = 1
else:
# Do not call selectors on empty arrays
width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge))
if width:
n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width))
else:
# Width can be zero for some estimators, e.g. FD when
# the IQR of the data is zero.
n_equal_bins = 1
elif np.ndim(bins) == 0:
try:
n_equal_bins = operator.index(bins)
except TypeError:
raise TypeError(
'`bins` must be an integer, a string, or an array')
if n_equal_bins < 1:
raise ValueError('`bins` must be positive, when an integer')
first_edge, last_edge = _get_outer_edges(a, range)
elif np.ndim(bins) == 1:
bin_edges = np.asarray(bins)
if np.any(bin_edges[:-1] > bin_edges[1:]):
raise ValueError(
'`bins` must increase monotonically, when an array')
else:
raise ValueError('`bins` must be 1d, when an array')
if n_equal_bins is not None:
# gh-10322 means that type resolution rules are dependent on array
# shapes. To avoid this causing problems, we pick a type now and stick
# with it throughout.
bin_type = np.result_type(first_edge, last_edge, a)
if np.issubdtype(bin_type, np.integer):
bin_type = np.result_type(bin_type, float)
# bin edges must be computed
bin_edges = np.linspace(
first_edge, last_edge, n_equal_bins + 1,
endpoint=True, dtype=bin_type)
return bin_edges, (first_edge, last_edge, n_equal_bins)
else:
return bin_edges, None
def _search_sorted_inclusive(a, v):
"""
Like `searchsorted`, but where the last item in `v` is placed on the right.
In the context of a histogram, this makes the last bin edge inclusive
"""
return np.concatenate((
a.searchsorted(v[:-1], 'left'),
a.searchsorted(v[-1:], 'right')
))
def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None):
return (a, bins, weights)
@array_function_dispatch(_histogram_bin_edges_dispatcher)
def histogram_bin_edges(a, bins=10, range=None, weights=None):
r"""
Function to calculate only the edges of the bins used by the `histogram`
function.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default). If `bins` is a
sequence, it defines the bin edges, including the rightmost
edge, allowing for non-uniform bin widths.
If `bins` is a string from the list below, `histogram_bin_edges` will use
the method chosen to calculate the optimal bin width and
consequently the number of bins (see `Notes` for more detail on
the estimators) from the data that falls within the requested
range. While the bin width will be optimal for the actual data
in the range, the number of bins will be computed to fill the
entire range, including the empty portions. For visualisation,
using the 'auto' option is suggested. Weighted data is not
supported for automated bin size selection.
'auto'
Maximum of the 'sturges' and 'fd' estimators. Provides good
all around performance.
'fd' (Freedman Diaconis Estimator)
Robust (resilient to outliers) estimator that takes into
account data variability and data size.
'doane'
An improved version of Sturges' estimator that works better
with non-normal datasets.
'scott'
Less robust estimator that that takes into account data
variability and data size.
'stone'
Estimator based on leave-one-out cross-validation estimate of
the integrated squared error. Can be regarded as a generalization
of Scott's rule.
'rice'
Estimator does not take variability into account, only data
size. Commonly overestimates number of bins required.
'sturges'
R's default method, only accounts for data size. Only
optimal for gaussian data and underestimates number of bins
for large non-gaussian datasets.
'sqrt'
Square root (of data size) estimator, used by Excel and
other programs for its speed and simplicity.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range
is simply ``(a.min(), a.max())``. Values outside the range are
ignored. The first element of the range must be less than or
equal to the second. `range` affects the automatic bin
computation as well. While bin width is computed to be optimal
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
weights : array_like, optional
An array of weights, of the same shape as `a`. Each value in
`a` only contributes its associated weight towards the bin count
(instead of 1). This is currently not used by any of the bin estimators,
but may be in the future.
Returns
-------
bin_edges : array of dtype float
The edges to pass into `histogram`
See Also
--------
histogram
Notes
-----
The methods to estimate the optimal number of bins are well founded
in literature, and are inspired by the choices R provides for
histogram visualisation. Note that having the number of bins
proportional to :math:`n^{1/3}` is asymptotically optimal, which is
why it appears in most estimators. These are simply plug-in methods
that give good starting points for number of bins. In the equations
below, :math:`h` is the binwidth and :math:`n_h` is the number of
bins. All estimators that compute bin counts are recast to bin width
using the `ptp` of the data. The final bin count is obtained from
``np.round(np.ceil(range / h))``.
'auto' (maximum of the 'sturges' and 'fd' estimators)
A compromise to get a good value. For small datasets the Sturges
value will usually be chosen, while larger datasets will usually
default to FD. Avoids the overly conservative behaviour of FD
and Sturges for small and large datasets respectively.
Switchover point is usually :math:`a.size \approx 1000`.
'fd' (Freedman Diaconis Estimator)
.. math:: h = 2 \frac{IQR}{n^{1/3}}
The binwidth is proportional to the interquartile range (IQR)
and inversely proportional to cube root of a.size. Can be too
conservative for small datasets, but is quite good for large
datasets. The IQR is very robust to outliers.
'scott'
.. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}}
The binwidth is proportional to the standard deviation of the
data and inversely proportional to cube root of ``x.size``. Can
be too conservative for small datasets, but is quite good for
large datasets. The standard deviation is not very robust to
outliers. Values are very similar to the Freedman-Diaconis
estimator in the absence of outliers.
'rice'
.. math:: n_h = 2n^{1/3}
The number of bins is only proportional to cube root of
``a.size``. It tends to overestimate the number of bins and it
does not take into account data variability.
'sturges'
.. math:: n_h = \log _{2}n+1
The number of bins is the base 2 log of ``a.size``. This
estimator assumes normality of data and is too conservative for
larger, non-normal datasets. This is the default method in R's
``hist`` method.
'doane'
.. math:: n_h = 1 + \log_{2}(n) +
\log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}})
g_1 = mean[(\frac{x - \mu}{\sigma})^3]
\sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}
An improved version of Sturges' formula that produces better
estimates for non-normal datasets. This estimator attempts to
account for the skew of the data.
'sqrt'
.. math:: n_h = \sqrt n
The simplest and fastest estimator. Only takes into account the
data size.
Examples
--------
>>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])
>>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1))
array([0. , 0.25, 0.5 , 0.75, 1. ])
>>> np.histogram_bin_edges(arr, bins=2)
array([0. , 2.5, 5. ])
For consistency with histogram, an array of pre-computed bins is
passed through unmodified:
>>> np.histogram_bin_edges(arr, [1, 2])
array([1, 2])
This function allows one set of bins to be computed, and reused across
multiple histograms:
>>> shared_bins = np.histogram_bin_edges(arr, bins='auto')
>>> shared_bins
array([0., 1., 2., 3., 4., 5.])
>>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])
>>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins)
>>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)
>>> hist_0; hist_1
array([1, 1, 0, 1, 0])
array([2, 0, 1, 1, 2])
Which gives more easily comparable results than using separate bins for
each histogram:
>>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
>>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
>>> hist_0; hist_1
array([1, 1, 1])
array([2, 1, 1, 2])
>>> bins_0; bins_1
array([0., 1., 2., 3.])
array([0. , 1.25, 2.5 , 3.75, 5. ])
"""
a, weights = _ravel_and_check_weights(a, weights)
bin_edges, _ = _get_bin_edges(a, bins, range, weights)
return bin_edges
def _histogram_dispatcher(
a, bins=None, range=None, normed=None, weights=None, density=None):
return (a, bins, weights)
@array_function_dispatch(_histogram_dispatcher)
def histogram(a, bins=10, range=None, normed=None, weights=None,
density=None):
r"""
Compute the histogram of a set of data.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default). If `bins` is a
sequence, it defines a monotonically increasing array of bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
.. versionadded:: 1.11.0
If `bins` is a string, it defines the method used to calculate the
optimal bin width, as defined by `histogram_bin_edges`.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range
is simply ``(a.min(), a.max())``. Values outside the range are
ignored. The first element of the range must be less than or
equal to the second. `range` affects the automatic bin
computation as well. While bin width is computed to be optimal
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
normed : bool, optional
.. deprecated:: 1.6.0
This is equivalent to the `density` argument, but produces incorrect
results for unequal bin widths. It should not be used.
.. versionchanged:: 1.15.0
DeprecationWarnings are actually emitted.
weights : array_like, optional
An array of weights, of the same shape as `a`. Each value in
`a` only contributes its associated weight towards the bin count
(instead of 1). If `density` is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
density : bool, optional
If ``False``, the result will contain the number of samples in
each bin. If ``True``, the result is the value of the
probability *density* function at the bin, normalized such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
Overrides the ``normed`` keyword if given.
Returns
-------
hist : array
The values of the histogram. See `density` and `weights` for a
description of the possible semantics.
bin_edges : array of dtype float
Return the bin edges ``(length(hist)+1)``.
See Also
--------
histogramdd, bincount, searchsorted, digitize, histogram_bin_edges
Notes
-----
All but the last (righthand-most) bin is half-open. In other words,
if `bins` is::
[1, 2, 3, 4]
then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which
*includes* 4.
Examples
--------
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
(array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, density=True)
>>> hist
array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
>>> hist.sum()
2.4999999999999996
>>> np.sum(hist * np.diff(bin_edges))
1.0
.. versionadded:: 1.11.0
Automated Bin Selection Methods example, using 2 peak random data
with 2000 points:
>>> import matplotlib.pyplot as plt
>>> rng = np.random.RandomState(10) # deterministic random data
>>> a = np.hstack((rng.normal(size=1000),
... rng.normal(loc=5, scale=2, size=1000)))
>>> _ = plt.hist(a, bins='auto') # arguments are passed to np.histogram
>>> plt.title("Histogram with 'auto' bins")
Text(0.5, 1.0, "Histogram with 'auto' bins")
>>> plt.show()
"""
a, weights = _ravel_and_check_weights(a, weights)
bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
# Histogram is an integer or a float array depending on the weights.
if weights is None:
ntype = np.dtype(np.intp)
else:
ntype = weights.dtype
# We set a block size, as this allows us to iterate over chunks when
# computing histograms, to minimize memory usage.
BLOCK = 65536
# The fast path uses bincount, but that only works for certain types
# of weight
simple_weights = (
weights is None or
np.can_cast(weights.dtype, np.double) or
np.can_cast(weights.dtype, complex)
)
if uniform_bins is not None and simple_weights:
# Fast algorithm for equal bins
# We now convert values of a to bin indices, under the assumption of
# equal bin widths (which is valid here).
first_edge, last_edge, n_equal_bins = uniform_bins
# Initialize empty histogram
n = np.zeros(n_equal_bins, ntype)
# Pre-compute histogram scaling factor
norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
# We iterate over blocks here for two reasons: the first is that for
# large arrays, it is actually faster (for example for a 10^8 array it
# is 2x as fast) and it results in a memory footprint 3x lower in the
# limit of large arrays.
for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
if weights is None:
tmp_w = None
else:
tmp_w = weights[i:i + BLOCK]
# Only include values in the right range
keep = (tmp_a >= first_edge)
keep &= (tmp_a <= last_edge)
if not np.logical_and.reduce(keep):
tmp_a = tmp_a[keep]
if tmp_w is not None:
tmp_w = tmp_w[keep]
# This cast ensures no type promotions occur below, which gh-10322
# make unpredictable. Getting it wrong leads to precision errors
# like gh-8123.
tmp_a = tmp_a.astype(bin_edges.dtype, copy=False)
# Compute the bin indices, and for values that lie exactly on
# last_edge we need to subtract one
f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
indices = f_indices.astype(np.intp)
indices[indices == n_equal_bins] -= 1
# The index computation is not guaranteed to give exactly
# consistent results within ~1 ULP of the bin edges.
decrement = tmp_a < bin_edges[indices]
indices[decrement] -= 1
# The last bin includes the right edge. The other bins do not.
increment = ((tmp_a >= bin_edges[indices + 1])
& (indices != n_equal_bins - 1))
indices[increment] += 1
# We now compute the histogram using bincount
if ntype.kind == 'c':
n.real += np.bincount(indices, weights=tmp_w.real,
minlength=n_equal_bins)
n.imag += np.bincount(indices, weights=tmp_w.imag,
minlength=n_equal_bins)
else:
n += np.bincount(indices, weights=tmp_w,
minlength=n_equal_bins).astype(ntype)
else:
# Compute via cumulative histogram
cum_n = np.zeros(bin_edges.shape, ntype)
if weights is None:
for i in _range(0, len(a), BLOCK):
sa = np.sort(a[i:i+BLOCK])
cum_n += _search_sorted_inclusive(sa, bin_edges)
else:
zero = np.zeros(1, dtype=ntype)
for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
tmp_w = weights[i:i+BLOCK]
sorting_index = np.argsort(tmp_a)
sa = tmp_a[sorting_index]
sw = tmp_w[sorting_index]
cw = np.concatenate((zero, sw.cumsum()))
bin_index = _search_sorted_inclusive(sa, bin_edges)
cum_n += cw[bin_index]
n = np.diff(cum_n)
# density overrides the normed keyword
if density is not None:
if normed is not None:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"The normed argument is ignored when density is provided. "
"In future passing both will result in an error.",
DeprecationWarning, stacklevel=3)
normed = None
if density:
db = np.array(np.diff(bin_edges), float)
return n/db/n.sum(), bin_edges
elif normed:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"Passing `normed=True` on non-uniform bins has always been "
"broken, and computes neither the probability density "
"function nor the probability mass function. "
"The result is only correct if the bins are uniform, when "
"density=True will produce the same result anyway. "
"The argument will be removed in a future version of "
"numpy.",
np.VisibleDeprecationWarning, stacklevel=3)
# this normalization is incorrect, but
db = np.array(np.diff(bin_edges), float)
return n/(n*db).sum(), bin_edges
else:
if normed is not None:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"Passing normed=False is deprecated, and has no effect. "
"Consider passing the density argument instead.",
DeprecationWarning, stacklevel=3)
return n, bin_edges
def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None,
weights=None, density=None):
if hasattr(sample, 'shape'): # same condition as used in histogramdd
yield sample
else:
yield from sample
with contextlib.suppress(TypeError):
yield from bins
yield weights
@array_function_dispatch(_histogramdd_dispatcher)
def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
density=None):
"""
Compute the multidimensional histogram of some data.
Parameters
----------
sample : (N, D) array, or (D, N) array_like
The data to be histogrammed.
Note the unusual interpretation of sample when an array_like:
* When an array, each row is a coordinate in a D-dimensional space -
such as ``histogramgramdd(np.array([p1, p2, p3]))``.
* When an array_like, each element is the list of values for single
coordinate - such as ``histogramgramdd((X, Y, Z))``.
The first form should be preferred.
bins : sequence or int, optional
The bin specification:
* A sequence of arrays describing the monotonically increasing bin
edges along each dimension.
* The number of bins for each dimension (nx, ny, ... =bins)
* The number of bins for all dimensions (nx=ny=...=bins).
range : sequence, optional
A sequence of length D, each an optional (lower, upper) tuple giving
the outer bin edges to be used if the edges are not given explicitly in
`bins`.
An entry of None in the sequence results in the minimum and maximum
values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of D None values.
density : bool, optional
If False, the default, returns the number of samples in each bin.
If True, returns the probability *density* function at the bin,
``bin_count / sample_count / bin_volume``.
normed : bool, optional
An alias for the density argument that behaves identically. To avoid
confusion with the broken normed argument to `histogram`, `density`
should be preferred.
weights : (N,) array_like, optional
An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
Weights are normalized to 1 if normed is True. If normed is False,
the values of the returned histogram are equal to the sum of the
weights belonging to the samples falling into each bin.
Returns
-------
H : ndarray
The multidimensional histogram of sample x. See normed and weights
for the different possible semantics.
edges : list
A list of D arrays describing the bin edges for each dimension.
See Also
--------
histogram: 1-D histogram
histogram2d: 2-D histogram
Examples
--------
>>> r = np.random.randn(100,3)
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
((5, 8, 4), 6, 9, 5)
"""
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
N, D = sample.shape
nbin = np.empty(D, int)
edges = D*[None]
dedges = D*[None]
if weights is not None:
weights = np.asarray(weights)
try:
M = len(bins)
if M != D:
raise ValueError(
'The dimension of bins must be equal to the dimension of the '
' sample x.')
except TypeError:
# bins is an integer
bins = D*[bins]
# normalize the range argument
if range is None:
range = (None,) * D
elif len(range) != D:
raise ValueError('range argument must have one entry per dimension')
# Create edge arrays
for i in _range(D):
if np.ndim(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
'`bins[{}]` must be positive, when an integer'.format(i))
smin, smax = _get_outer_edges(sample[:,i], range[i])
edges[i] = np.linspace(smin, smax, bins[i] + 1)
elif np.ndim(bins[i]) == 1:
edges[i] = np.asarray(bins[i])
if np.any(edges[i][:-1] > edges[i][1:]):
raise ValueError(
'`bins[{}]` must be monotonically increasing, when an array'
.format(i))
else:
raise ValueError(
'`bins[{}]` must be a scalar or 1d array'.format(i))
nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
# Compute the bin number each sample falls into.
Ncount = tuple(
# avoid np.digitize to work around gh-11022
np.searchsorted(edges[i], sample[:, i], side='right')
for i in _range(D)
)
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
for i in _range(D):
# Find which points are on the rightmost edge.
on_edge = (sample[:, i] == edges[i][-1])
# Shift these points one bin to the left.
Ncount[i][on_edge] -= 1
# Compute the sample indices in the flattened histogram matrix.
# This raises an error if the array is too large.
xy = np.ravel_multi_index(Ncount, nbin)
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
hist = np.bincount(xy, weights, minlength=nbin.prod())
# Shape into a proper matrix
hist = hist.reshape(nbin)
# This preserves the (bad) behavior observed in gh-7845, for now.
hist = hist.astype(float, casting='safe')
# Remove outliers (indices 0 and -1 for each dimension).
core = D*(slice(1, -1),)
hist = hist[core]
# handle the aliasing normed argument
if normed is None:
if density is None:
density = False
elif density is None:
# an explicit normed argument was passed, alias it to the new name
density = normed
else:
raise TypeError("Cannot specify both 'normed' and 'density'")
if density:
# calculate the probability density function
s = hist.sum()
for i in _range(D):
shape = np.ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
if (hist.shape != nbin - 2).any():
raise RuntimeError(
"Internal Shape Error")
return hist, edges
| apache-2.0 |
mikebenfield/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 42 | 10505 | from sklearn.utils.testing import assert_true
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.linear_model import PassiveAggressiveRegressor
iris = load_iris()
random_state = check_random_state(12)
indices = np.arange(iris.data.shape[0])
random_state.shuffle(indices)
X = iris.data[indices]
y = iris.target[indices]
X_csr = sp.csr_matrix(X)
class MyPassiveAggressive(ClassifierMixin):
def __init__(self, C=1.0, epsilon=0.01, loss="hinge",
fit_intercept=True, n_iter=1, random_state=None):
self.C = C
self.epsilon = epsilon
self.loss = loss
self.fit_intercept = fit_intercept
self.n_iter = n_iter
def fit(self, X, y):
n_samples, n_features = X.shape
self.w = np.zeros(n_features, dtype=np.float64)
self.b = 0.0
for t in range(self.n_iter):
for i in range(n_samples):
p = self.project(X[i])
if self.loss in ("hinge", "squared_hinge"):
loss = max(1 - y[i] * p, 0)
else:
loss = max(np.abs(p - y[i]) - self.epsilon, 0)
sqnorm = np.dot(X[i], X[i])
if self.loss in ("hinge", "epsilon_insensitive"):
step = min(self.C, loss / sqnorm)
elif self.loss in ("squared_hinge",
"squared_epsilon_insensitive"):
step = loss / (sqnorm + 1.0 / (2 * self.C))
if self.loss in ("hinge", "squared_hinge"):
step *= y[i]
else:
step *= np.sign(y[i] - p)
self.w += step * X[i]
if self.fit_intercept:
self.b += step
def project(self, X):
return np.dot(X, self.w) + self.b
def test_classifier_accuracy():
for data in (X, X_csr):
for fit_intercept in (True, False):
for average in (False, True):
clf = PassiveAggressiveClassifier(C=1.0, n_iter=30,
fit_intercept=fit_intercept,
random_state=0,
average=average)
clf.fit(data, y)
score = clf.score(data, y)
assert_greater(score, 0.79)
if average:
assert_true(hasattr(clf, 'average_coef_'))
assert_true(hasattr(clf, 'average_intercept_'))
assert_true(hasattr(clf, 'standard_intercept_'))
assert_true(hasattr(clf, 'standard_coef_'))
def test_classifier_partial_fit():
classes = np.unique(y)
for data in (X, X_csr):
for average in (False, True):
clf = PassiveAggressiveClassifier(C=1.0,
fit_intercept=True,
random_state=0,
average=average)
for t in range(30):
clf.partial_fit(data, y, classes)
score = clf.score(data, y)
assert_greater(score, 0.79)
if average:
assert_true(hasattr(clf, 'average_coef_'))
assert_true(hasattr(clf, 'average_intercept_'))
assert_true(hasattr(clf, 'standard_intercept_'))
assert_true(hasattr(clf, 'standard_coef_'))
def test_classifier_refit():
# Classifier can be retrained on different labels and features.
clf = PassiveAggressiveClassifier().fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
clf.fit(X[:, :-1], iris.target_names[y])
assert_array_equal(clf.classes_, iris.target_names)
def test_classifier_correctness():
y_bin = y.copy()
y_bin[y != 1] = -1
for loss in ("hinge", "squared_hinge"):
clf1 = MyPassiveAggressive(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2)
clf1.fit(X, y_bin)
for data in (X, X_csr):
clf2 = PassiveAggressiveClassifier(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2, shuffle=False)
clf2.fit(data, y_bin)
assert_array_almost_equal(clf1.w, clf2.coef_.ravel(), decimal=2)
def test_classifier_undefined_methods():
clf = PassiveAggressiveClassifier()
for meth in ("predict_proba", "predict_log_proba", "transform"):
assert_raises(AttributeError, lambda x: getattr(clf, x), meth)
def test_class_weights():
# Test class weights.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(C=0.1, n_iter=100, class_weight=None,
random_state=100)
clf.fit(X2, y2)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf = PassiveAggressiveClassifier(C=0.1, n_iter=100,
class_weight={1: 0.001},
random_state=100)
clf.fit(X2, y2)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
def test_partial_fit_weight_class_balanced():
# partial_fit with class_weight='balanced' not supported
clf = PassiveAggressiveClassifier(class_weight="balanced")
assert_raises(ValueError, clf.partial_fit, X, y, classes=np.unique(y))
def test_equal_class_weight():
X2 = [[1, 0], [1, 0], [0, 1], [0, 1]]
y2 = [0, 0, 1, 1]
clf = PassiveAggressiveClassifier(C=0.1, n_iter=1000, class_weight=None)
clf.fit(X2, y2)
# Already balanced, so "balanced" weights should have no effect
clf_balanced = PassiveAggressiveClassifier(C=0.1, n_iter=1000,
class_weight="balanced")
clf_balanced.fit(X2, y2)
clf_weighted = PassiveAggressiveClassifier(C=0.1, n_iter=1000,
class_weight={0: 0.5, 1: 0.5})
clf_weighted.fit(X2, y2)
# should be similar up to some epsilon due to learning rate schedule
assert_almost_equal(clf.coef_, clf_weighted.coef_, decimal=2)
assert_almost_equal(clf.coef_, clf_balanced.coef_, decimal=2)
def test_wrong_class_weight_label():
# ValueError due to wrong class_weight label.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(class_weight={0: 0.5})
assert_raises(ValueError, clf.fit, X2, y2)
def test_wrong_class_weight_format():
# ValueError due to wrong class_weight argument type.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(class_weight=[0.5])
assert_raises(ValueError, clf.fit, X2, y2)
clf = PassiveAggressiveClassifier(class_weight="the larch")
assert_raises(ValueError, clf.fit, X2, y2)
def test_regressor_mse():
y_bin = y.copy()
y_bin[y != 1] = -1
for data in (X, X_csr):
for fit_intercept in (True, False):
for average in (False, True):
reg = PassiveAggressiveRegressor(C=1.0, n_iter=50,
fit_intercept=fit_intercept,
random_state=0,
average=average)
reg.fit(data, y_bin)
pred = reg.predict(data)
assert_less(np.mean((pred - y_bin) ** 2), 1.7)
if average:
assert_true(hasattr(reg, 'average_coef_'))
assert_true(hasattr(reg, 'average_intercept_'))
assert_true(hasattr(reg, 'standard_intercept_'))
assert_true(hasattr(reg, 'standard_coef_'))
def test_regressor_partial_fit():
y_bin = y.copy()
y_bin[y != 1] = -1
for data in (X, X_csr):
for average in (False, True):
reg = PassiveAggressiveRegressor(C=1.0,
fit_intercept=True,
random_state=0,
average=average)
for t in range(50):
reg.partial_fit(data, y_bin)
pred = reg.predict(data)
assert_less(np.mean((pred - y_bin) ** 2), 1.7)
if average:
assert_true(hasattr(reg, 'average_coef_'))
assert_true(hasattr(reg, 'average_intercept_'))
assert_true(hasattr(reg, 'standard_intercept_'))
assert_true(hasattr(reg, 'standard_coef_'))
def test_regressor_correctness():
y_bin = y.copy()
y_bin[y != 1] = -1
for loss in ("epsilon_insensitive", "squared_epsilon_insensitive"):
reg1 = MyPassiveAggressive(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2)
reg1.fit(X, y_bin)
for data in (X, X_csr):
reg2 = PassiveAggressiveRegressor(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2, shuffle=False)
reg2.fit(data, y_bin)
assert_array_almost_equal(reg1.w, reg2.coef_.ravel(), decimal=2)
def test_regressor_undefined_methods():
reg = PassiveAggressiveRegressor()
for meth in ("transform",):
assert_raises(AttributeError, lambda x: getattr(reg, x), meth)
| bsd-3-clause |
lisa-1010/smart-tutor | code/test_control_problems.py | 1 | 3012 | # implementation based on https://github.com/lisa-1010/transfer_rl/blob/master/code/gym_pipeline.py
import gym
import matplotlib.pyplot as plt
# TODO: import agents
ENV_NAME = 'CartPole-v0'
NUM_TEST_TRIALS = 100
class EnvWrapper(object):
"""
Wraps the Environment class from gym, so we can make environments partially observable.
"""
def __init__(self, env_name, partial=False):
# if not partial:
self.env = gym.make(env_name)
def step(self, action):
next_state, reward, done, info = self.env.step(action)
if partial:
if env_name == 'InvertedPendulum-v1':
# TODO: hide the velocity element of the state
next_state = make_partial(next_state)
else:
"env cannot be made partial yet"
return next_state, reward, done, info
class Pipeline(object):
def __init__(self, env_name='InvertedPendulum-v1'):
self.env = gym.make(env_name)
# get action + observation space, pass into agent
# TODO: define Agent (e.g. DQN or MCTS + DKT)
# self.agent =
self.test_performances = []
# self.env.monitor.start('../experiments/' + env_name)
def run_episode(self):
state = self.env.reset()
for timestep in xrange(self.env.spec.timestep_limit):
action = self.agent.get_noisy_action(state)
next_state, reward, done, info = self.env.step(action)
self.agent.perceive_and_train(state, action, reward, next_state, done)
state = next_state
if done:
break
def run_test(self, episode_num):
# run current agent model on environment,
# evaluate average reward, create video
total_reward = 0.0
for episode in xrange(NUM_TEST_TRIALS):
state = self.env.reset()
for step in xrange(self.env.spec.timestep_limit):
# self.env.render()
action = self.agent.get_action(state)
next_state, reward, done, info = self.env.step(action)
state = next_state
total_reward += reward
if done:
break
avg_reward = total_reward / NUM_TEST_TRIALS
self.test_performances.append(avg_reward)
print 'Episode: {} Average Reward Per Episode : {} '.format(episode_num,avg_reward)
def run(self, num_episodes):
for episode in xrange(num_episodes):
self.run_episode()
# Every 100 episodes, run test and print average reward
if episode % 100 == 0:
self.run_test(episode)
self.env.monitor.close()
def plot_results(self):
plt.xlabel("Episode")
plt.ylabel("Average Test Reward")
plt.plot(self.test_performances)
plt.savefig('Reward-Vs-Episode')
if __name__ == "__main__":
pipeline = Pipeline(env_name=ENV_NAME)
pipeline.run(num_episodes=100000)
pipeline.plot_results()
| mit |
shoshber/fmriprep | fmriprep/workflows/confounds.py | 2 | 12081 | '''
Workflow for discovering confounds.
Calculates frame displacement, segment regressors, global regressor, dvars, aCompCor, tCompCor
'''
from nipype.interfaces import utility, nilearn, fsl
from nipype.algorithms import confounds
from nipype.pipeline import engine as pe
from niworkflows.interfaces.masks import ACompCorRPT, TCompCorRPT
from fmriprep import interfaces
from fmriprep.interfaces.bids import DerivativesDataSink
from fmriprep.interfaces.utils import prepare_roi_from_probtissue
def discover_wf(settings, name="ConfoundDiscoverer"):
''' All input fields are required.
Calculates global regressor and tCompCor
from motion-corrected fMRI ('inputnode.fmri_file').
Calculates DVARS from the fMRI and an EPI brain mask ('inputnode.epi_mask')
Calculates frame displacement from MCFLIRT movement parameters ('inputnode.movpar_file')
Calculates segment regressors and aCompCor
from the fMRI and a white matter/gray matter/CSF segmentation ('inputnode.t1_seg'), after
applying the transform to the images. Transforms should be fsl-formatted.
Saves the confounds in a file ('outputnode.confounds_file')'''
inputnode = pe.Node(utility.IdentityInterface(fields=['fmri_file', 'movpar_file', 't1_tpms',
'epi_mask', 't1_transform',
'reference_image',
'motion_confounds_file',
'source_file']),
name='inputnode')
outputnode = pe.Node(utility.IdentityInterface(fields=['confounds_file']),
name='outputnode')
# registration using ANTs
t1_registration = pe.MapNode(fsl.preprocess.ApplyXFM(interp='sinc'),
name='T1Registration', iterfield='in_file')
# DVARS
dvars = pe.Node(confounds.ComputeDVARS(save_all=True, remove_zerovariance=True),
name="ComputeDVARS")
dvars.interface.estimated_memory_gb = settings[
"biggest_epi_file_size_gb"] * 3
# Frame displacement
frame_displace = pe.Node(confounds.FramewiseDisplacement(), name="FramewiseDisplacement")
frame_displace.interface.estimated_memory_gb = settings[
"biggest_epi_file_size_gb"] * 3
# CompCor
tcompcor = pe.Node(TCompCorRPT(components_file='tcompcor.tsv',
generate_report=True,
percentile_threshold=.05),
name="tCompCor")
tcompcor.interface.estimated_memory_gb = settings[
"biggest_epi_file_size_gb"] * 3
CSF_roi = pe.Node(utility.Function(input_names=['in_file', 'epi_mask',
'erosion_mm',
'epi_mask_erosion_mm'],
output_names=['roi_file', 'eroded_mask'],
function=prepare_roi_from_probtissue),
name='CSF_roi')
CSF_roi.inputs.erosion_mm = 0
CSF_roi.inputs.epi_mask_erosion_mm = 30
WM_roi = pe.Node(utility.Function(input_names=['in_file', 'epi_mask',
'erosion_mm',
'epi_mask_erosion_mm'],
output_names=['roi_file', 'eroded_mask'],
function=prepare_roi_from_probtissue),
name='WM_roi')
WM_roi.inputs.erosion_mm = 6
WM_roi.inputs.epi_mask_erosion_mm = 10
def concat_rois_func(in_WM, in_mask, ref_header):
import os
import nibabel as nb
WM_nii = nb.load(in_WM)
mask_nii = nb.load(in_mask)
# we have to do this explicitly because of potential differences in
# qform_code between the two files that prevent SignalExtraction to do
# the concatenation
concat_nii = nb.funcs.concat_images([WM_nii, mask_nii],
check_affines=False)
concat_nii = nb.Nifti1Image(concat_nii.get_data(),
nb.load(ref_header).affine,
nb.load(ref_header).header)
concat_nii.to_filename("concat.nii.gz")
return os.path.abspath("concat.nii.gz")
concat_rois = pe.Node(utility.Function(input_names=['in_WM', 'in_mask',
'ref_header'],
output_names=['concat_file'],
function=concat_rois_func),
name='concat_rois')
# Global and segment regressors
signals = pe.Node(nilearn.SignalExtraction(detrend=True,
class_labels=["WhiteMatter", "GlobalSignal"]),
name="SignalExtraction")
signals.interface.estimated_memory_gb = settings[
"biggest_epi_file_size_gb"] * 3
def combine_rois(in_CSF, in_WM, ref_header):
import os
import numpy as np
import nibabel as nb
CSF_nii = nb.load(in_CSF)
CSF_data = CSF_nii.get_data()
WM_nii = nb.load(in_WM)
WM_data = WM_nii.get_data()
combined = np.zeros_like(WM_data)
combined[WM_data != 0] = 1
combined[CSF_data != 0] = 1
# we have to do this explicitly because of potential differences in
# qform_code between the two files that prevent aCompCor to work
new_nii = nb.Nifti1Image(combined, nb.load(ref_header).affine,
nb.load(ref_header).header)
new_nii.to_filename("logical_or.nii.gz")
return os.path.abspath("logical_or.nii.gz")
combine_rois = pe.Node(utility.Function(input_names=['in_CSF', 'in_WM',
'ref_header'],
output_names=['logical_and_file'],
function=combine_rois),
name='combine_rois')
acompcor = pe.Node(ACompCorRPT(components_file='acompcor.tsv',
generate_report=True),
name="aCompCor")
acompcor.interface.estimated_memory_gb = settings[
"biggest_epi_file_size_gb"] * 3
ds_report_a = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='acompcor', out_path_base='reports'),
name='ds_report_a'
)
ds_report_t = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='tcompcor', out_path_base='reports'),
name='ds_report_t'
)
# misc utilities
concat = pe.Node(utility.Function(function=_gather_confounds, input_names=['signals', 'dvars',
'frame_displace',
'tcompcor',
'acompcor',
'motion'],
output_names=['combined_out']),
name="ConcatConfounds")
ds_confounds = pe.Node(interfaces.DerivativesDataSink(base_directory=settings['output_dir'],
suffix='confounds'),
name="DerivConfounds")
def pick_csf(files):
return files[0]
def pick_wm(files):
return files[2]
workflow = pe.Workflow(name=name)
workflow.connect([
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('fmri_file', 'in_file'),
('epi_mask', 'in_mask')]),
(inputnode, frame_displace, [('movpar_file', 'in_plots')]),
(inputnode, tcompcor, [('fmri_file', 'realigned_file')]),
# anatomically-based confound computation requires coregistration
(inputnode, t1_registration, [('reference_image', 'reference'),
('t1_tpms', 'in_file'),
('t1_transform', 'in_matrix_file')]),
(t1_registration, CSF_roi, [(('out_file', pick_csf), 'in_file')]),
(inputnode, CSF_roi, [('epi_mask', 'epi_mask')]),
(CSF_roi, tcompcor, [('eroded_mask', 'mask_file')]),
(t1_registration, WM_roi, [(('out_file', pick_wm), 'in_file')]),
(inputnode, WM_roi, [('epi_mask', 'epi_mask')]),
(CSF_roi, combine_rois, [('roi_file', 'in_CSF')]),
(WM_roi, combine_rois, [('roi_file', 'in_WM')]),
(inputnode, combine_rois, [('fmri_file', 'ref_header')]),
# anatomical confound: aCompCor.
(inputnode, acompcor, [('fmri_file', 'realigned_file')]),
(combine_rois, acompcor, [('logical_and_file', 'mask_file')]),
(WM_roi, concat_rois, [('roi_file', 'in_WM')]),
(inputnode, concat_rois, [('epi_mask', 'in_mask')]),
(inputnode, concat_rois, [('fmri_file', 'ref_header')]),
# anatomical confound: signal extraction
(concat_rois, signals, [('concat_file', 'label_files')]),
(inputnode, signals, [('fmri_file', 'in_file')]),
# connect the confound nodes to the concatenate node
(signals, concat, [('out_file', 'signals')]),
(dvars, concat, [('out_all', 'dvars')]),
(frame_displace, concat, [('out_file', 'frame_displace')]),
(tcompcor, concat, [('components_file', 'tcompcor')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(inputnode, concat, [('motion_confounds_file', 'motion')]),
(concat, outputnode, [('combined_out', 'confounds_file')]),
# print stuff in derivatives
(concat, ds_confounds, [('combined_out', 'in_file')]),
(inputnode, ds_confounds, [('source_file', 'source_file')]),
(acompcor, ds_report_a, [('out_report', 'in_file')]),
(inputnode, ds_report_a, [('source_file', 'source_file')]),
(tcompcor, ds_report_t, [('out_report', 'in_file')]),
(inputnode, ds_report_t, [('source_file', 'source_file')])
])
return workflow
def _gather_confounds(signals=None, dvars=None, frame_displace=None,
tcompcor=None, acompcor=None, motion=None):
''' load confounds from the filenames, concatenate together horizontally, and re-save '''
import pandas as pd
import os.path as op
def less_breakable(a_string):
''' hardens the string to different envs (i.e. case insensitive, no whitespace, '#' '''
return ''.join(a_string.split()).strip('#')
all_files = [confound for confound in [signals, dvars, frame_displace,
tcompcor, acompcor, motion]
if confound is not None]
confounds_data = pd.DataFrame()
for file_name in all_files: # assumes they all have headings already
new = pd.read_csv(file_name, sep="\t")
for column_name in new.columns:
new.rename(columns={column_name: less_breakable(column_name)},
inplace=True)
confounds_data = pd.concat((confounds_data, new), axis=1)
combined_out = op.abspath('confounds.tsv')
confounds_data.to_csv(combined_out, sep=str("\t"), index=False,
na_rep="n/a")
return combined_out
def reverse_order(inlist):
''' if a list, return the list in reversed order; else it is a single item, return it.'''
if isinstance(inlist, list):
inlist.reverse()
return inlist
| bsd-3-clause |
ChanderG/scipy | scipy/stats/_binned_statistic.py | 26 | 17723 | from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from scipy._lib.six import callable
from collections import namedtuple
__all__ = ['binned_statistic',
'binned_statistic_2d',
'binned_statistic_dd']
def binned_statistic(x, values, statistic='mean',
bins=10, range=None):
"""
Compute a binned statistic for a set of data.
This is a generalization of a histogram function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values within each bin.
Parameters
----------
x : array_like
A sequence of values to be binned.
values : array_like
The values on which the statistic will be computed. This must be
the same shape as `x`.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : int or sequence of scalars, optional
If `bins` is an int, it defines the number of equal-width bins in the
given range (10 by default). If `bins` is a sequence, it defines the
bin edges, including the rightmost edge, allowing for non-uniform bin
widths. Values in `x` that are smaller than lowest bin edge are
assigned to bin number 0, values beyond the highest bin are assigned to
``bins[-1]``.
range : (float, float) or [(float, float)], optional
The lower and upper range of the bins. If not provided, range
is simply ``(x.min(), x.max())``. Values outside the range are
ignored.
Returns
-------
statistic : array
The values of the selected statistic in each bin.
bin_edges : array of dtype float
Return the bin edges ``(length(statistic)+1)``.
binnumber : 1-D ndarray of ints
This assigns to each observation an integer that represents the bin
in which this observation falls. Array has the same length as values.
See Also
--------
numpy.histogram, binned_statistic_2d, binned_statistic_dd
Notes
-----
All but the last (righthand-most) bin is half-open. In other words, if
`bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
but excluding 2) and the second ``[2, 3)``. The last bin, however, is
``[3, 4]``, which *includes* 4.
.. versionadded:: 0.11.0
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
First a basic example:
>>> stats.binned_statistic([1, 2, 1, 2, 4], np.arange(5), statistic='mean',
... bins=3)
(array([ 1., 2., 4.]), array([ 1., 2., 3., 4.]), array([1, 2, 1, 2, 3]))
As a second example, we now generate some random data of sailing boat speed
as a function of wind speed, and then determine how fast our boat is for
certain wind speeds:
>>> windspeed = 8 * np.random.rand(500)
>>> boatspeed = .3 * windspeed**.5 + .2 * np.random.rand(500)
>>> bin_means, bin_edges, binnumber = stats.binned_statistic(windspeed,
... boatspeed, statistic='median', bins=[1,2,3,4,5,6,7])
>>> plt.figure()
>>> plt.plot(windspeed, boatspeed, 'b.', label='raw data')
>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=5,
... label='binned statistic of data')
>>> plt.legend()
Now we can use ``binnumber`` to select all datapoints with a windspeed
below 1:
>>> low_boatspeed = boatspeed[binnumber == 0]
As a final example, we will use ``bin_edges`` and ``binnumber`` to make a
plot of a distribution that shows the mean and distribution around that
mean per bin, on top of a regular histogram and the probability
distribution function:
>>> x = np.linspace(0, 5, num=500)
>>> x_pdf = stats.maxwell.pdf(x)
>>> samples = stats.maxwell.rvs(size=10000)
>>> bin_means, bin_edges, binnumber = stats.binned_statistic(x, x_pdf,
... statistic='mean', bins=25)
>>> bin_width = (bin_edges[1] - bin_edges[0])
>>> bin_centers = bin_edges[1:] - bin_width/2
>>> plt.figure()
>>> plt.hist(samples, bins=50, normed=True, histtype='stepfilled', alpha=0.2,
... label='histogram of data')
>>> plt.plot(x, x_pdf, 'r-', label='analytical pdf')
>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=2,
... label='binned statistic of data')
>>> plt.plot((binnumber - 0.5) * bin_width, x_pdf, 'g.', alpha=0.5)
>>> plt.legend(fontsize=10)
>>> plt.show()
"""
try:
N = len(bins)
except TypeError:
N = 1
if N != 1:
bins = [np.asarray(bins, float)]
if range is not None:
if len(range) == 2:
range = [range]
medians, edges, xy = binned_statistic_dd([x], values, statistic,
bins, range)
BinnedStatisticResult = namedtuple('BinnedStatisticResult',
('statistic', 'bin_edges', 'binnumber'))
return BinnedStatisticResult(medians, edges[0], xy)
def binned_statistic_2d(x, y, values, statistic='mean',
bins=10, range=None):
"""
Compute a bidimensional binned statistic for a set of data.
This is a generalization of a histogram2d function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values within each bin.
Parameters
----------
x : (N,) array_like
A sequence of values to be binned along the first dimension.
y : (M,) array_like
A sequence of values to be binned along the second dimension.
values : (N,) array_like
The values on which the statistic will be computed. This must be
the same shape as `x`.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : int or [int, int] or array_like or [array, array], optional
The bin specification:
* the number of bins for the two dimensions (nx=ny=bins),
* the number of bins in each dimension (nx, ny = bins),
* the bin edges for the two dimensions (x_edges = y_edges = bins),
* the bin edges in each dimension (x_edges, y_edges = bins).
range : (2,2) array_like, optional
The leftmost and rightmost edges of the bins along each dimension
(if not specified explicitly in the `bins` parameters):
[[xmin, xmax], [ymin, ymax]]. All values outside of this range will be
considered outliers and not tallied in the histogram.
Returns
-------
statistic : (nx, ny) ndarray
The values of the selected statistic in each two-dimensional bin
x_edges : (nx + 1) ndarray
The bin edges along the first dimension.
y_edges : (ny + 1) ndarray
The bin edges along the second dimension.
binnumber : 1-D ndarray of ints
This assigns to each observation an integer that represents the bin
in which this observation falls. Array has the same length as `values`.
See Also
--------
numpy.histogram2d, binned_statistic, binned_statistic_dd
Notes
-----
.. versionadded:: 0.11.0
"""
# This code is based on np.histogram2d
try:
N = len(bins)
except TypeError:
N = 1
if N != 1 and N != 2:
xedges = yedges = np.asarray(bins, float)
bins = [xedges, yedges]
medians, edges, xy = binned_statistic_dd([x, y], values, statistic,
bins, range)
BinnedStatistic2dResult = namedtuple('BinnedStatistic2dResult',
('statistic', 'x_edge', 'y_edge',
'binnumber'))
return BinnedStatistic2dResult(medians, edges[0], edges[1], xy)
def binned_statistic_dd(sample, values, statistic='mean',
bins=10, range=None):
"""
Compute a multidimensional binned statistic for a set of data.
This is a generalization of a histogramdd function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values within each bin.
Parameters
----------
sample : array_like
Data to histogram passed as a sequence of D arrays of length N, or
as an (N,D) array.
values : array_like
The values on which the statistic will be computed. This must be
the same shape as x.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : sequence or int, optional
The bin specification:
* A sequence of arrays describing the bin edges along each dimension.
* The number of bins for each dimension (nx, ny, ... =bins)
* The number of bins for all dimensions (nx=ny=...=bins).
range : sequence, optional
A sequence of lower and upper bin edges to be used if the edges are
not given explicitely in `bins`. Defaults to the minimum and maximum
values along each dimension.
Returns
-------
statistic : ndarray, shape(nx1, nx2, nx3,...)
The values of the selected statistic in each two-dimensional bin
bin_edges : list of ndarrays
A list of D arrays describing the (nxi + 1) bin edges for each
dimension
binnumber : 1-D ndarray of ints
This assigns to each observation an integer that represents the bin
in which this observation falls. Array has the same length as values.
See Also
--------
np.histogramdd, binned_statistic, binned_statistic_2d
Notes
-----
.. versionadded:: 0.11.0
"""
known_stats = ['mean', 'median', 'count', 'sum', 'std']
if not callable(statistic) and statistic not in known_stats:
raise ValueError('invalid statistic %r' % (statistic,))
# This code is based on np.histogramdd
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
N, D = sample.shape
nbin = np.empty(D, int)
edges = D * [None]
dedges = D * [None]
try:
M = len(bins)
if M != D:
raise AttributeError('The dimension of bins must be equal '
'to the dimension of the sample x.')
except TypeError:
bins = D * [bins]
# Select range for each dimension
# Used only if number of bins is given.
if range is None:
smin = np.atleast_1d(np.array(sample.min(0), float))
smax = np.atleast_1d(np.array(sample.max(0), float))
else:
smin = np.zeros(D)
smax = np.zeros(D)
for i in np.arange(D):
smin[i], smax[i] = range[i]
# Make sure the bins have a finite width.
for i in np.arange(len(smin)):
if smin[i] == smax[i]:
smin[i] = smin[i] - .5
smax[i] = smax[i] + .5
# Create edge arrays
for i in np.arange(D):
if np.isscalar(bins[i]):
nbin[i] = bins[i] + 2 # +2 for outlier bins
edges[i] = np.linspace(smin[i], smax[i], nbin[i] - 1)
else:
edges[i] = np.asarray(bins[i], float)
nbin[i] = len(edges[i]) + 1 # +1 for outlier bins
dedges[i] = np.diff(edges[i])
nbin = np.asarray(nbin)
# Compute the bin number each sample falls into.
Ncount = {}
for i in np.arange(D):
Ncount[i] = np.digitize(sample[:, i], edges[i])
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right
# edge to be counted in the last bin, and not as an outlier.
for i in np.arange(D):
# Rounding precision
decimal = int(-np.log10(dedges[i].min())) + 6
# Find which points are on the rightmost edge.
on_edge = np.where(np.around(sample[:, i], decimal)
== np.around(edges[i][-1], decimal))[0]
# Shift these points one bin to the left.
Ncount[i][on_edge] -= 1
# Compute the sample indices in the flattened statistic matrix.
ni = nbin.argsort()
xy = np.zeros(N, int)
for i in np.arange(0, D - 1):
xy += Ncount[ni[i]] * nbin[ni[i + 1:]].prod()
xy += Ncount[ni[-1]]
result = np.empty(nbin.prod(), float)
if statistic == 'mean':
result.fill(np.nan)
flatcount = np.bincount(xy, None)
flatsum = np.bincount(xy, values)
a = flatcount.nonzero()
result[a] = flatsum[a] / flatcount[a]
elif statistic == 'std':
result.fill(0)
flatcount = np.bincount(xy, None)
flatsum = np.bincount(xy, values)
flatsum2 = np.bincount(xy, values ** 2)
a = flatcount.nonzero()
result[a] = np.sqrt(flatsum2[a] / flatcount[a]
- (flatsum[a] / flatcount[a]) ** 2)
elif statistic == 'count':
result.fill(0)
flatcount = np.bincount(xy, None)
a = np.arange(len(flatcount))
result[a] = flatcount
elif statistic == 'sum':
result.fill(0)
flatsum = np.bincount(xy, values)
a = np.arange(len(flatsum))
result[a] = flatsum
elif statistic == 'median':
result.fill(np.nan)
for i in np.unique(xy):
result[i] = np.median(values[xy == i])
elif callable(statistic):
with warnings.catch_warnings():
# Numpy generates a warnings for mean/std/... with empty list
warnings.filterwarnings('ignore', category=RuntimeWarning)
old = np.seterr(invalid='ignore')
try:
null = statistic([])
except:
null = np.nan
np.seterr(**old)
result.fill(null)
for i in np.unique(xy):
result[i] = statistic(values[xy == i])
# Shape into a proper matrix
result = result.reshape(np.sort(nbin))
for i in np.arange(nbin.size):
j = ni.argsort()[i]
result = result.swapaxes(i, j)
ni[i], ni[j] = ni[j], ni[i]
# Remove outliers (indices 0 and -1 for each dimension).
core = D * [slice(1, -1)]
result = result[core]
if (result.shape != nbin - 2).any():
raise RuntimeError('Internal Shape Error')
BinnedStatisticddResult = namedtuple('BinnedStatisticddResult',
('statistic', 'bin_edges',
'binnumber'))
return BinnedStatisticddResult(result, edges, xy)
| bsd-3-clause |
liganega/Gongsu-DataSci | previous/y2017/W11-pandas-intro/GongSu25_Statistics_Sampling_Distribution.py | 2 | 5188 |
# coding: utf-8
# In[1]:
from __future__ import print_function, division
# #### 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
#
# https://github.com/rouseguy/intro2stats
# # 표본분포와 신뢰구간
# ## 주요내용
#
# 앞서 [GongSu22](https://github.com/liganega/Gongsu-DataSci/blob/master/Notes/W10/GongSu22_Statistics_Population_Variance.ipynb)에서 표본 데이터를 이용하여 모집단의 평균과 분산에 대한 점추정을 알아보았다.
# 여기서는 표본분포를 이용하여 미국 51개 주에서 거래된 담배(식물) 도매가의 평균에 대한 신뢰구간을 구하는 방법을 알아본다.
# ## 주요 예제
#
# * 캘리포니아 주에서 2014년에 거래된 상품 담배(식물)의 도매가
# * 도매가의 표본분포 구하기
# * 도매가의 평균값 추정치의 신뢰구간 구하기
# ## 주요 모듈
#
# numpy와 pandas 이외에 통계전용 모듈인 stats 모듈을 임포트 한다.
# In[2]:
import numpy as np
import pandas as pd
from scipy import stats
# ## 연도별, 월별 데이터 추출하기
# Weed_Price.csv의 date 열에는 거래일자가 2014-01-01의 형식으로 포함되어 있다.
# 이 정보를 이용하여 연도별, 월별 데이터를 구하려면 거래일자로부터 연도 또는 월에 대한 정보만을 추출할 수 있어야 한다.
#
# 여기서는 Timestamp 자료형의 속성을 활용하여 필요한 정보를 얻는 방식을 배운다.
# ### csv 파일 불러오기
#
# 먼저 Weed_Price.csv 파일의 내용을 다시 불러 온다.
# In[3]:
weed_pd = pd.read_csv("data/Weed_Price.csv", parse_dates=[-1])
# In[4]:
weed_pd.head()
# ### Timestamp 자료형
#
# date 열에는 거래일자 정보가 들어 있는데, 각 정보의 자료형은 Timestamp 라고 불린다.
#
# 0번 줄의 date 정보를 확인하면 Timestamp 자료형이 사용된 것을 확인할 수 있다.
# In[5]:
weed_pd.date[0]
# Timestamp 자료형에는 year, month, day 등 거래일자에 대한 구체적인 구체적인 정보를 담고 있는 속성이 포함되어 있다.
# In[6]:
weed_pd.date[0].year
# In[7]:
weed_pd.date[0].month
# In[8]:
weed_pd.date[0].day
# ### apply 함수 활용
#
# 위 속성들을 이용하여 거래일자에서 연도별, 월별 정보를 추출하여 새로운 열(컬럼)으로 추가할 수 있다.
# 이를 위해 apply() 함수를 활용한다.
#
# **주의:** Series와 DataFrame 자료형에 apply 함수와 동일한 기능을 가진 apply 메소드가 선언되어 있다.
# 먼저 weed_pd.date 의 자료형이 Series 임을 확인한다.
# In[9]:
type(weed_pd.date)
# x가 Timestamp 자료형일 때, x 에서 연도 정보를 추출하는 함수를 아래와 같이 정의할 수 있다.
# In[10]:
def getYear(x):
return x.year
# 동일한 방식으로 x가 Timestamp 자료형일 때, x 에서 월에 정보를 추출하는 함수를 아래와 같이 정의할 수 있다.
# In[11]:
def getMonth(x):
return x.month
# 이제 두 함수를 이용하여 거래년도 만을 담는 칸을 추가할 수 있다.
#
# 먼저 아래 코드를 실행해보자.
# In[12]:
year_col = weed_pd.date.apply(getYear)
year_col.head()
# 위 결과의 자료형은 Series 이다.
# In[13]:
type(year_col)
# 동일한 방식으로 month_col을 추출한다.
# In[14]:
month_col = weed_pd.date.apply(getMonth)
month_col.head()
# ### DataFrame에 열 추가하기
#
# 추출한 두 시리즈를 weed_pd 에 새로운 열로 추가한다.
# In[15]:
weed_pd["month"] = month_col
weed_pd["year"] = year_col
# 두 개의 열이 추가되었음을 확인할 수 있다.
# In[16]:
weed_pd.head()
# ## 캘리포니아 주에서 2014년도 거래된 담배(식물) 도매가 추출하기
# 마스크 인덱싱을 활용하여 캘리포니아 주에서 2014년도에 거래된 데이터만 추출할 수 있다.
#
# **주의:** year 열을 추가하였기에 가능하다.
# In[17]:
weed_ca_2014 = weed_pd[(weed_pd.State=="California") & (weed_pd.year==2014)]
weed_ca_2014.head()
# ### 캘리포니아 주에서 2014년도에 거래된 상품 담배(식물)의 도매가의 평균분포
# #### 평균분포의 평균값
# In[18]:
ca_2014_mean = weed_ca_2014.HighQ.mean()
ca_2014_mean
# #### 평균분포의 분산
# In[19]:
ca_2014_std = weed_ca_2014.HighQ.std()
ca_2014_std
# #### 신뢰구간
#
# 신뢰수준 95%에 대한 신뢰구간을 구할 수 있다.
# In[20]:
stats.norm.interval(0.95, loc=ca_2014_mean, scale = ca_2014_std/np.sqrt(len(weed_ca_2014)))
# 신뢰구간 설명: Weed_Price.csv 파일에는 거래된 담배의 도매가의 일부 데이터들의 표본을 담고 있다.
# 하지만 이 정보를 이용하여 미국 전체에서 거래된 모든 모대가에 대한 정보를 추정할 수 있다.
# 이를 위해 표본분포를 활용하며, 앞서 구한 신뢰구간의 의미는 다음과 같다.
#
# > 캘리포니아 주에서 2014년도에 거래된 모든 상품(HighQ) 담배(식물)의 도매가의 평균값은 앞서 구한 신뢰구간에 위치할 확률이 95%이다.
| gpl-3.0 |
nansencenter/DAPPER | examples/basic_2.py | 1 | 2760 | # ## Illustrate usage of DAPPER to benchmark multiple DA methods.
# #### Imports
# <b>NB:</b> If you're on <mark><b>Gooble Colab</b></mark>,
# then replace `%matplotlib notebook` below by
# `!python -m pip install git+https://github.com/nansencenter/DAPPER.git` .
# Also note that liveplotting does not work on Colab.
# %matplotlib notebook
import dapper as dpr
import dapper.da_methods as da
seed = dpr.set_seed(3000)
# #### DA method configurations
xps = dpr.xpList()
from dapper.mods.Lorenz63.sakov2012 import HMM # Expected rmse.a:
xps += da.Climatology() # 7.6
xps += da.OptInterp() # 1.25
xps += da.Var3D(xB=0.1) # 1.03
xps += da.ExtKF(infl=90) # 0.87
xps += da.EnKF('Sqrt' , N=3 , infl=1.30) # 0.82
xps += da.EnKF('Sqrt' , N=10 , infl=1.02, rot=True) # 0.63
xps += da.EnKF('PertObs', N=500 , infl=0.95, rot=False) # 0.56
xps += da.EnKF_N( N=10 , rot=True) # 0.54
xps += da.iEnKS('Sqrt' , N=10 , infl=1.02, rot=True) # 0.31
xps += da.PartFilt( N=100 , reg=2.4 , NER=0.3) # 0.38
xps += da.PartFilt( N=800 , reg=0.9 , NER=0.2) # 0.28
# xps += da.PartFilt( N=4000, reg=0.7 , NER=0.05) # 0.27
# xps += da.PFxN(xN=1000, N=30 , Qs=2 , NER=0.2) # 0.56
# #### With Lorenz-96 instead
# +
# from dapper.mods.Lorenz96.sakov2008 import HMM # Expected rmse.a:
# xps += da.Climatology() # 3.6
# xps += da.OptInterp() # 0.95
# xps += da.Var3D(xB=0.02) # 0.41
# xps += da.ExtKF(infl=6) # 0.24
# xps += da.EnKF('PertObs', N=40, infl=1.06) # 0.22
# xps += da.EnKF('Sqrt', N=28, infl=1.02, rot=True) # 0.18
#
# xps += da.EnKF_N( N=24, rot=True) # 0.21
# xps += da.EnKF_N( N=24, rot=True, xN=2) # 0.18
# xps += da.iEnKS('Sqrt', N=40, infl=1.01, rot=True) # 0.17
#
# xps += da.LETKF( N=7, infl=1.04, rot=True, loc_rad=4) # 0.22
# xps += da.SL_EAKF( N=7, infl=1.07, rot=True, loc_rad=6) # 0.23
# -
# #### Other models (suitable xp's listed in HMM files):
# +
# from dapper.mods.LA .evensen2009 import HMM
# from dapper.mods.KS .bocquet2019 import HMM
# from dapper.mods.LotkaVolterra.settings101 import HMM
# -
# #### Run experiment
# Adjust experiment duration
HMM.t.BurnIn = 2
HMM.t.T = 50
# Assimilate (for each xp in xps)
save_as = xps.launch(HMM, liveplots=False)
# #### Print results
print(xps.tabulate_avrgs())
| mit |
alvarofierroclavero/scikit-learn | examples/feature_selection/plot_feature_selection.py | 249 | 2827 | """
===============================
Univariate Feature Selection
===============================
An example showing univariate feature selection.
Noisy (non informative) features are added to the iris data and
univariate feature selection is applied. For each feature, we plot the
p-values for the univariate feature selection and the corresponding
weights of an SVM. We can see that univariate feature selection
selects the informative features and that these have larger SVM weights.
In the total set of features, only the 4 first ones are significant. We
can see that they have the highest score with univariate feature
selection. The SVM assigns a large weight to one of these features, but also
Selects many of the non-informative features.
Applying univariate feature selection before the SVM
increases the SVM weight attributed to the significant features, and will
thus improve classification.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
from sklearn.feature_selection import SelectPercentile, f_classif
###############################################################################
# import some data to play with
# The iris dataset
iris = datasets.load_iris()
# Some noisy data not correlated
E = np.random.uniform(0, 0.1, size=(len(iris.data), 20))
# Add the noisy data to the informative features
X = np.hstack((iris.data, E))
y = iris.target
###############################################################################
plt.figure(1)
plt.clf()
X_indices = np.arange(X.shape[-1])
###############################################################################
# Univariate feature selection with F-test for feature scoring
# We use the default selection function: the 10% most significant features
selector = SelectPercentile(f_classif, percentile=10)
selector.fit(X, y)
scores = -np.log10(selector.pvalues_)
scores /= scores.max()
plt.bar(X_indices - .45, scores, width=.2,
label=r'Univariate score ($-Log(p_{value})$)', color='g')
###############################################################################
# Compare to the weights of an SVM
clf = svm.SVC(kernel='linear')
clf.fit(X, y)
svm_weights = (clf.coef_ ** 2).sum(axis=0)
svm_weights /= svm_weights.max()
plt.bar(X_indices - .25, svm_weights, width=.2, label='SVM weight', color='r')
clf_selected = svm.SVC(kernel='linear')
clf_selected.fit(selector.transform(X), y)
svm_weights_selected = (clf_selected.coef_ ** 2).sum(axis=0)
svm_weights_selected /= svm_weights_selected.max()
plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected,
width=.2, label='SVM weights after selection', color='b')
plt.title("Comparing feature selection")
plt.xlabel('Feature number')
plt.yticks(())
plt.axis('tight')
plt.legend(loc='upper right')
plt.show()
| bsd-3-clause |
larsmans/scikit-learn | doc/conf.py | 11 | 8021 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# This file is execfile()d with the current directory set to its containing
# dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
from __future__ import print_function
import sys
import os
from sklearn.externals.six import u
# If extensions (or modules to document with autodoc) are in another
# directory, add these directories to sys.path here. If the directory
# is relative to the documentation root, use os.path.abspath to make it
# absolute, like shown here.
sys.path.insert(0, os.path.abspath('sphinxext'))
from github_link import make_linkcode_resolve
# -- General configuration ---------------------------------------------------
# Try to override the matplotlib configuration as early as possible
try:
import gen_rst
except:
pass
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['gen_rst',
'sphinx.ext.autodoc', 'sphinx.ext.autosummary',
'sphinx.ext.pngmath', 'numpy_ext.numpydoc',
'sphinx.ext.linkcode',
]
autosummary_generate = True
autodoc_default_flags = ['members', 'inherited-members']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['templates']
# generate autosummary even if no references
autosummary_generate = True
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8'
# Generate the plots for the gallery
plot_gallery = True
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u('scikit-learn')
copyright = u('2010 - 2014, scikit-learn developers (BSD License)')
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.16-git'
# The full version, including alpha/beta/rc tags.
import sklearn
release = sklearn.__version__
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of documents that shouldn't be included in the build.
#unused_docs = []
# List of directories, relative to source directory, that shouldn't be
# searched for source files.
exclude_trees = ['_build', 'templates', 'includes']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
add_function_parentheses = False
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. Major themes that come with
# Sphinx are currently 'default' and 'sphinxdoc'.
html_theme = 'scikit-learn'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {'oldversion': False, 'collapsiblesidebar': True,
'google_analytics': True, 'surveybanner': False,
'sprintbanner': True}
# Add any paths that contain custom themes here, relative to this directory.
html_theme_path = ['themes']
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
html_short_title = 'scikit-learn'
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
html_logo = 'logos/scikit-learn-logo-small.png'
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
html_favicon = 'logos/favicon.ico'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['images']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
html_use_modindex = False
# If false, no index is generated.
html_use_index = False
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = ''
# Output file base name for HTML help builder.
htmlhelp_basename = 'scikit-learndoc'
# -- Options for LaTeX output ------------------------------------------------
# The paper size ('letter' or 'a4').
#latex_paper_size = 'letter'
# The font size ('10pt', '11pt' or '12pt').
#latex_font_size = '10pt'
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass
# [howto/manual]).
latex_documents = [('index', 'user_guide.tex', u('scikit-learn user guide'),
u('scikit-learn developers'), 'manual'), ]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
latex_logo = "logos/scikit-learn-logo.png"
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# Additional stuff for the LaTeX preamble.
latex_preamble = r"""
\usepackage{amsmath}\usepackage{amsfonts}\usepackage{bm}\usepackage{morefloats}
\usepackage{enumitem} \setlistdepth{10}
"""
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_use_modindex = True
trim_doctests_flags = True
def setup(app):
# to hide/show the prompt in code examples:
app.add_javascript('js/copybutton.js')
# to format example galleries:
app.add_javascript('js/examples.js')
# The following is used by sphinx.ext.linkcode to provide links to github
linkcode_resolve = make_linkcode_resolve('sklearn',
u'https://github.com/scikit-learn/'
'scikit-learn/blob/{revision}/'
'{package}/{path}#L{lineno}')
| bsd-3-clause |
justincassidy/scikit-learn | sklearn/datasets/tests/test_rcv1.py | 322 | 2414 | """Test the rcv1 loader.
Skipped if rcv1 is not already downloaded to data_home.
"""
import errno
import scipy.sparse as sp
import numpy as np
from sklearn.datasets import fetch_rcv1
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
def test_fetch_rcv1():
try:
data1 = fetch_rcv1(shuffle=False, download_if_missing=False)
except IOError as e:
if e.errno == errno.ENOENT:
raise SkipTest("Download RCV1 dataset to run this test.")
X1, Y1 = data1.data, data1.target
cat_list, s1 = data1.target_names.tolist(), data1.sample_id
# test sparsity
assert_true(sp.issparse(X1))
assert_true(sp.issparse(Y1))
assert_equal(60915113, X1.data.size)
assert_equal(2606875, Y1.data.size)
# test shapes
assert_equal((804414, 47236), X1.shape)
assert_equal((804414, 103), Y1.shape)
assert_equal((804414,), s1.shape)
assert_equal(103, len(cat_list))
# test ordering of categories
first_categories = [u'C11', u'C12', u'C13', u'C14', u'C15', u'C151']
assert_array_equal(first_categories, cat_list[:6])
# test number of sample for some categories
some_categories = ('GMIL', 'E143', 'CCAT')
number_non_zero_in_cat = (5, 1206, 381327)
for num, cat in zip(number_non_zero_in_cat, some_categories):
j = cat_list.index(cat)
assert_equal(num, Y1[:, j].data.size)
# test shuffling and subset
data2 = fetch_rcv1(shuffle=True, subset='train', random_state=77,
download_if_missing=False)
X2, Y2 = data2.data, data2.target
s2 = data2.sample_id
# The first 23149 samples are the training samples
assert_array_equal(np.sort(s1[:23149]), np.sort(s2))
# test some precise values
some_sample_ids = (2286, 3274, 14042)
for sample_id in some_sample_ids:
idx1 = s1.tolist().index(sample_id)
idx2 = s2.tolist().index(sample_id)
feature_values_1 = X1[idx1, :].toarray()
feature_values_2 = X2[idx2, :].toarray()
assert_almost_equal(feature_values_1, feature_values_2)
target_values_1 = Y1[idx1, :].toarray()
target_values_2 = Y2[idx2, :].toarray()
assert_almost_equal(target_values_1, target_values_2)
| bsd-3-clause |
kylerbrown/bark | bark/tools/labelview.py | 1 | 21160 | import os
import sys
import string
import yaml
import numpy as np
from scipy.signal import spectrogram
import matplotlib.pyplot as plt
import bark
from bark.io.eventops import (OpStack, write_stack, read_stack, Update, Merge,
Split, Delete, New)
import warnings
warnings.filterwarnings('ignore') # suppress matplotlib warnings
from bark.tools.spectral import BarkSpectra
help_string = '''
Pressing any number or letter (uppercase or lowercase) will mark a segment.
Shortcuts
---------
any letter or number annotate segment
ctrl+s saves the annotation data
ctrl+h prints this message
ctrl+o or up arrow zoom out
ctrl+i or down arrow zoom in
space, right, page down next segment
backspace, left, page up previous segment
ctrl+m, ctrl+backspace merge current syllable with previous
ctrl+x or tab delete segment
ctrl+z undo last operation
ctrl+y redo
ctrl+w close
click on segment boundary move boundary
ctrl+click inside a segment split segment
ctrl+click outside a segment new segment (TODO)
click on segment boundaries to adjust them.
The bottom panel is a map of all label locations.
Click on a label to travel to that location.
On close, an operation file and the final event file will be written.
Do not kill from terminal unless you want to prevent a save.
To create custom label, create an external YAML file with key: value pairs
like this:
1: c1
2: c2
z: a*
'''
# kill all the shorcuts
def kill_shortcuts(plt):
plt.rcParams['keymap.all_axes'] = ''
plt.rcParams['keymap.back'] = ''
plt.rcParams['keymap.forward'] = ''
plt.rcParams['keymap.fullscreen'] = ''
plt.rcParams['keymap.grid'] = ''
plt.rcParams['keymap.home'] = ''
plt.rcParams['keymap.pan'] = ''
#plt.rcParams['keymap.quit'] = ''
plt.rcParams['keymap.save'] = ''
plt.rcParams['keymap.xscale'] = ''
plt.rcParams['keymap.yscale'] = ''
plt.rcParams['keymap.zoom'] = ''
def labels_to_scatter_coords(labels):
times = [x['start'] for x in labels]
values = []
for record in labels:
name = record['name']
if not isinstance(name, str) or name == '':
v = 0
elif name.isdigit():
v = int(name)
elif name[0].isalpha():
# alphabet in range 11-36
v = 133 - ord(name[0].lower())
else:
v = 37
values.append(v)
return times, values
def nearest_label(labels, xdata):
return np.argmin(np.abs(xdata - np.array([x['start'] for x in labels])))
def plot_spectrogram(data,
sr,
start,
stop,
ms_nfft=15,
ax=None,
lowfreq=300,
highfreq=8000,
n_tapers=2,
NW=1.5,
derivative=True,
window=('kaiser', 8),
**kwargs):
'''
data : a vector of samples, first sample starts at time = 0
sr : sampling rate
start : start time to slice data, units: seconds
stop : stop time to slice data, units: seconds
ms_nfft : width of fourier transform in milliseconds
ax : axis object to plot spectrogram on.
lowfreq : lowest frequency to plot
highfreq : highest frequency to plot
n_tapers : Number of tapers to use in a custom multi-taper Fourier
transform estimate
NW : multi-taper bandwidth parameter for custom multi-taper Fourier
transform estimate increasing this value reduces side-band
ripple, decreasing sharpens peaks
derivative: if True, plots the spectral derivative, SAP style
'''
nfft = int(ms_nfft / 1000. * sr)
start_samp = int(start * sr) - nfft // 2
if start_samp < 0:
start_samp = 0
stop_samp = int(stop * sr) - nfft // 2
x = data[start_samp:stop_samp]
# determine overlap based on screen size.
# We don't need more points than pixels
pixels = 1000
samples_per_pixel = int((stop - start) * sr / pixels)
noverlap = max(nfft - samples_per_pixel, 0)
from matplotlib import colors
spa = BarkSpectra(sr,
NFFT=nfft,
noverlap=noverlap,
data_window=int(0.01 * sr),
n_tapers=n_tapers,
NW=NW,
freq_range=(lowfreq, highfreq))
spa.signal(x)
pxx, f, t, thresh = spa.spectrogram(ax=ax, derivative=derivative)
# calculate the parameter for the plot
freq_mask = (f > lowfreq) & (f < highfreq)
fsub = f[freq_mask]
Sxxsub = pxx[freq_mask, :]
t += start
# plot the spectrogram
if derivative:
image = ax.pcolorfast(t,
fsub,
Sxxsub,
cmap='inferno',
norm=colors.SymLogNorm(linthresh=thresh))
else:
image = ax.pcolorfast(t,
fsub,
Sxxsub,
cmap='inferno',
norm=colors.LogNorm(vmin=thresh))
plt.sca(ax)
plt.ylim(lowfreq, highfreq)
return image
class SegmentReviewer:
def __init__(self,
osc_ax,
spec_ax,
map_ax,
sampled,
opstack,
keymap,
outfile,
out_attrs,
opsfile=None):
self.canvas = osc_ax.get_figure().canvas
self.osc_ax = osc_ax
self.spec_ax = spec_ax
self.map_ax = map_ax
self.data = sampled.data.ravel()
self.sr = sampled.sampling_rate
self.label_attrs = out_attrs
self.opstack = opstack
self.opsfile = opsfile
self.outfile = outfile
self.keymap = keymap
if opstack.ops:
self.i = opstack.ops[-1].index
else:
self.i = 0
self.N_points = 35000
self.initialize_plots()
self.update_plot_data()
def initialize_plots(self):
self.osc_ax.set_axis_bgcolor('k')
self.osc_ax.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
self.spec_ax.set_axis_bgcolor('k')
self.osc_line, = self.osc_ax.plot(
np.arange(self.N_points),
np.zeros(self.N_points),
color='gray')
self.osc_boundary_start = self.osc_ax.axvline(color='r')
self.osc_boundary_stop = self.osc_ax.axvline(color='r')
self.syl_labels = [self.osc_ax.text(0,
0,
'',
size='xx-large',
color='r') for _ in range(20)]
self.initialize_minimap()
self.osc_ax.figure.tight_layout()
def initialize_minimap(self):
times, values = labels_to_scatter_coords(self.opstack.events)
self.map_ax.set_axis_bgcolor('k')
self.map_ax.scatter(times,
values,
c=values,
vmin=0,
vmax=37,
cmap=plt.get_cmap('hsv'),
edgecolors='none')
self.map_ax.vlines(self.opstack.events[self.i]['start'],
-1,
38,
zorder=0.5,
color='w',
linewidth=1)
self.map_ax.tick_params(axis='y',
which='both',
left='off',
right='off',
labelleft='off')
self.map_ax.set_ylim(-1, 38)
def label_nearby_syls(self):
'labels for current syl and two on either side'
for i in range(-10 + self.i, 11 + self.i):
label_i = i - self.i
if i >= 0 and i < len(self.opstack.events):
text = self.syl_labels[label_i]
x = (self.opstack.events[i]['start'] +
self.opstack.events[i]['stop']) / 2
name = self.opstack.events[i]['name']
if isinstance(name, str):
text.set_x(x)
text.set_visible(True)
text.set_text(name)
else:
text.set_visible(False)
else:
self.syl_labels[label_i].set_visible(False)
def update_syl_boundaries(self):
start = self.opstack.events[self.i]['start']
stop = self.opstack.events[self.i]['stop']
self.osc_boundary_start.set_xdata((start, start))
self.osc_boundary_stop.set_xdata((stop, stop))
def update_minimap(self):
# If perfomance lags, may need to adjust plot elements instead of
# clearing everything and starting over.
self.map_ax.clear()
self.initialize_minimap()
def update_plot_data(self):
'updates plot data on all three axes'
if not self.opstack.events:
print('no segments')
plt.close("all")
return
self.selected_boundary = None
i = self.i
sr = self.sr
start = self.opstack.events[i]['start']
start_samp = int(start * sr)
stop = self.opstack.events[i]['stop']
stop_samp = int(stop * sr)
syl_samps = stop_samp - start_samp
self.buffer_start_samp = start_samp - (self.N_points - syl_samps) // 2
if self.buffer_start_samp < 0:
self.buffer_start_samp = 0
self.buffer_stop_samp = self.buffer_start_samp + self.N_points
if self.buffer_stop_samp >= self.data.shape[0]:
self.buffer_stop_samp = self.data.shape[0] - 1
self.buf_start = self.buffer_start_samp / sr
self.buf_stop = self.buffer_stop_samp / sr
# update plots
self.label_nearby_syls()
self.update_syl_boundaries()
self.update_spectrogram()
self.update_oscillogram()
self.update_minimap()
if self.opstack.ops:
last_command = str(self.opstack.ops[-1])
else:
last_command = 'none'
if i == 0:
self.osc_ax.set_title('ctrl+h for help, prints to terminal')
else:
self.osc_ax.set_title('{}/ {} {}'.format(i + 1, len(
self.opstack.events), last_command))
self.canvas.draw()
def update_spectrogram(self):
self.spec_ax.clear()
plot_spectrogram(self.data,
self.sr,
self.buf_start,
self.buf_stop,
ax=self.spec_ax)
def update_oscillogram(self):
x = self.data[self.buffer_start_samp:self.buffer_stop_samp]
t = np.arange(len(x)) / self.sr + self.buf_start
if len(x) > 10000:
t_interp = np.linspace(self.buf_start, self.buf_stop, 10000)
x_interp = np.interp(t_interp, t, x)
else:
t_interp = t
x_interp = x
self.osc_line.set_data(t_interp, x_interp)
self.osc_ax.set_xlim(self.buf_start, self.buf_stop)
self.osc_ax.set_ylim(min(x), max(x))
def connect(self):
'creates all the event connections'
self.cid_key_press = self.canvas.mpl_connect('key_press_event',
self.on_key_press)
self.cid_mouse_press = self.canvas.mpl_connect('button_press_event',
self.on_mouse_press)
self.cid_mouse_motion = self.canvas.mpl_connect('motion_notify_event',
self.on_mouse_motion)
self.cid_mouse_release = self.canvas.mpl_connect(
'button_release_event', self.on_mouse_release)
def on_mouse_press(self, event):
if event.inaxes in (None, self.spec_ax) or event.button != 1:
return
start_pos = self.osc_boundary_start.get_xdata()[0]
stop_pos = self.osc_boundary_stop.get_xdata()[0]
# jump to syllable from map click
if event.inaxes == self.map_ax:
i = nearest_label(self.opstack.events, float(event.xdata))
self.i = i
self.update_plot_data()
# sylable splitting
elif (event.key == 'control' and event.xdata > start_pos and
event.xdata < stop_pos):
self.opstack.push(Split(self.i, float(event.xdata)))
self.update_plot_data()
# new syllable before
elif event.key == 'control' and event.xdata < start_pos:
self.opstack.push(New(self.i,
name='',
start=float(event.xdata),
stop=float(event.xdata) + .020))
self.update_plot_data()
elif event.key == 'control' and event.xdata > stop_pos:
self.opstack.push(New(self.i + 1,
name='',
start=float(event.xdata),
stop=float(event.xdata) + .020))
self.i += 1
self.update_plot_data()
# boundary updates
else:
xlim1, xlim2 = self.osc_ax.get_xlim()
xspan = xlim2 - xlim1
if abs(event.xdata - start_pos) / xspan < 0.007:
self.selected_boundary = self.osc_boundary_start
elif abs(event.xdata - stop_pos) / xspan < 0.007:
self.selected_boundary = self.osc_boundary_stop
if self.selected_boundary:
self.selected_boundary.set_color('y')
self.canvas.draw()
def on_mouse_motion(self, event):
if self.selected_boundary is None:
return
self.selected_boundary.set_xdata((event.xdata, event.xdata))
self.canvas.draw()
def on_mouse_release(self, event):
if self.selected_boundary == self.osc_boundary_start:
self.opstack.push(Update(self.i, 'start', float(event.xdata)))
elif self.selected_boundary == self.osc_boundary_stop:
self.opstack.push(Update(self.i, 'stop', float(event.xdata)))
if self.selected_boundary:
self.selected_boundary.set_color('r')
self.update_syl_boundaries()
self.selected_boundary = None
self.canvas.draw()
def inc_i(self):
'Go to next syllable.'
if self.i < len(self.opstack.events) - 1:
self.i += 1
self.update_plot_data()
def dec_i(self):
'Go to previous syllable.'
if self.i > 0:
self.i -= 1
self.update_plot_data()
def on_key_press(self, event):
#print('you pressed ', event.key)
if event.key in ('pagedown', ' ', 'right'):
self.inc_i()
elif event.key in ('pageup', 'backspace', 'left'):
self.dec_i()
elif event.key in self.keymap:
newlabel = self.keymap[event.key]
self.opstack.push(Update(self.i, 'name', newlabel))
self.inc_i()
elif event.key in ('ctrl+i', 'down'):
if self.N_points > 5000:
self.N_points -= 5000
self.update_plot_data()
elif event.key in ('ctrl+o', 'up'):
self.N_points += 5000
self.update_plot_data()
elif event.key == 'ctrl+s':
self.save()
elif event.key == 'ctrl+h':
print(help_string)
elif event.key in ('ctrl+m', 'ctrl+backspace') and self.i > 0:
self.i -= 1
self.opstack.push(Merge(self.i))
self.inc_i()
elif event.key in ('ctrl+x', 'tab'):
self.opstack.push(Delete(self.i))
if self.i >= len(self.opstack.events):
self.i = len(self.opstack.events) - 1
self.update_plot_data()
elif event.key == 'ctrl+z' and self.opstack.ops:
self.opstack.undo()
self.i = self.opstack.undo_ops[-1].index
self.update_plot_data()
elif event.key == 'ctrl+y' and self.opstack.undo_ops:
self.opstack.redo()
self.i = self.opstack.ops[-1].index
self.update_plot_data()
def save(self):
'Writes out labels to file.'
from pandas import DataFrame
label_data = DataFrame(self.opstack.events)
bark.write_events(self.outfile, label_data, **self.label_attrs)
print(self.outfile, 'written')
if self.opsfile:
write_stack(self.opsfile, self.opstack)
print(self.opsfile, 'written')
def __enter__(self):
return self
def __exit__(self, *args):
self.save()
def build_shortcut_map(mapfile=None):
allkeys = string.digits + string.ascii_letters
shortcut_map = {x: x for x in allkeys}
# load keys from file
if mapfile:
custom = {str(key): value
for key, value in yaml.load(open(mapfile, 'r')).items()}
print('custom keymaps:', custom)
shortcut_map.update(custom)
return shortcut_map
def to_seconds(dset):
'TODO Converts bark EventData object to units of seconds.'
if 'offset' in dset.attrs and dset.attrs['offset'] != 0:
raise Exception('offsets are not yet supported in event file')
if dset.attrs['columns']['start']['units'] == 's':
pass
elif 'units' in dset.attrs and dset.attrs['units'] == 's':
pass
else:
raise Exception('only units of s are supported in event file')
return dset
def load_opstack(opsfile, labelfile, labeldata, use_ops):
load_ops = os.path.exists(opsfile) and use_ops
if load_ops:
opstack = read_stack(opsfile)
print('Reading operations from {}.'.format(opsfile))
if len(opstack.original_events) != len(labeldata):
print("The number of segments in autosave file is incorrect.")
sys.exit(0)
for stack_event, true_event in zip(opstack.original_events, labeldata):
if (stack_event['name'] != true_event['name'] or
not np.allclose(stack_event['start'], true_event['start'])
or
not np.allclose(stack_event['stop'], true_event['stop'])):
print("Warning! Autosave:\n {}\n Original:\n{}"
.format(stack_event, true_event))
else:
opstack = OpStack(labeldata)
return opstack
def main(datfile, labelfile, outfile=None, shortcutfile=None, use_ops=True):
if not labelfile:
labelfile = os.path.splitext(datfile)[0] + '.csv'
kill_shortcuts(plt)
sampled = bark.read_sampled(datfile)
assert len(sampled.attrs['columns']) == 1
labels = bark.read_events(labelfile)
labeldata = to_seconds(labels).data.to_dict('records')
if len(labeldata) == 0:
print('{} has no data'.format(labelfile))
return
shortcuts = build_shortcut_map(shortcutfile)
opsfile = labelfile + '.ops.json'
opstack = load_opstack(opsfile, labelfile, labeldata, use_ops)
if not outfile:
outfile = os.path.splitext(labelfile)[0] + '_edit.csv'
plt.figure()
# Oscillogram and Spectrogram get
# three times the vertical space as the minimap.
osc_ax = plt.subplot2grid((7, 1), (0, 0), rowspan=3)
spec_ax = plt.subplot2grid((7, 1), (3, 0), rowspan=3, sharex=osc_ax)
map_ax = plt.subplot2grid((7, 1), (6, 0))
# Segement review is a context manager to ensure a save prompt
# on exit. see SegmentReviewer.__exit__
with SegmentReviewer(osc_ax, spec_ax, map_ax, sampled, opstack, shortcuts,
outfile, labels.attrs, opsfile) as reviewer:
reviewer.connect()
plt.show(block=True)
def _run():
import argparse
p = argparse.ArgumentParser(description='''
Review and annotate segments
''')
p.add_argument('dat', help='name of a sampled dataset')
p.add_argument('labelfile',
nargs='?',
help='Associated event dataset containing segments\
defaults to same name as dat but with a .csv extension.')
p.add_argument('-o', '--out', help='output label file')
p.add_argument('-i',
'--ignore',
help='ignore operations from LABELFILE.ops.json',
action='store_true')
p.add_argument('-k',
'--shortcut-keys',
help='''YAML file with keyboard shortcuts.
Keys are digits, lowercase and uppercase characters''')
args = p.parse_args()
main(args.dat, args.labelfile, args.out, args.shortcut_keys,
not args.ignore)
if __name__ == '__main__':
_run()
| gpl-2.0 |
jblackburne/scikit-learn | examples/linear_model/plot_iris_logistic.py | 119 | 1679 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The
datapoints are colored according to their labels.
"""
print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
h = .02 # step size in the mesh
logreg = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()
| bsd-3-clause |
winklerand/pandas | pandas/errors/__init__.py | 6 | 1697 | # flake8: noqa
"""
Expose public exceptions & warnings
"""
from pandas._libs.tslib import OutOfBoundsDatetime
class PerformanceWarning(Warning):
"""
Warning raised when there is a possible
performance impact.
"""
class UnsupportedFunctionCall(ValueError):
"""
Exception raised when attempting to call a numpy function
on a pandas object, but that function is not supported by
the object e.g. ``np.cumsum(groupby_object)``.
"""
class UnsortedIndexError(KeyError):
"""
Error raised when attempting to get a slice of a MultiIndex,
and the index has not been lexsorted. Subclass of `KeyError`.
.. versionadded:: 0.20.0
"""
class ParserError(ValueError):
"""
Exception that is raised by an error encountered in `pd.read_csv`.
"""
class DtypeWarning(Warning):
"""
Warning that is raised for a dtype incompatiblity. This
can happen whenever `pd.read_csv` encounters non-
uniform dtypes in a column(s) of a given CSV file.
"""
class EmptyDataError(ValueError):
"""
Exception that is thrown in `pd.read_csv` (by both the C and
Python engines) when empty data or header is encountered.
"""
class ParserWarning(Warning):
"""
Warning that is raised in `pd.read_csv` whenever it is necessary
to change parsers (generally from 'c' to 'python') contrary to the
one specified by the user due to lack of support or functionality for
parsing particular attributes of a CSV file with the requsted engine.
"""
class MergeError(ValueError):
"""
Error raised when problems arise during merging due to problems
with input data. Subclass of `ValueError`.
"""
| bsd-3-clause |
KAPPS-/vincent | examples/stacked_area_examples.py | 11 | 2281 | # -*- coding: utf-8 -*-
"""
Vincent Stacked Area Examples
"""
#Build a Stacked Area Chart from scratch
from vincent import *
import pandas as pd
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'GOOG', 'IBM', 'YHOO', 'MSFT']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2013')
price = pd.DataFrame({tic: data['Adj Close']
for tic, data in all_data.items()})
vis = Visualization(width=500, height=300)
vis.padding = {'top': 10, 'left': 50, 'bottom': 50, 'right': 100}
data = Data.from_pandas(price)
vis.data['table'] = data
facets = Transform(type='facet', keys=['data.idx'])
stats = Transform(type='stats', value='data.val')
stat_dat = Data(name='stats', source='table', transform=[facets, stats])
vis.data['stats'] = stat_dat
vis.scales['x'] = Scale(name='x', type='time', range='width',
domain=DataRef(data='table', field="data.idx"))
vis.scales['y'] = Scale(name='y', range='height', type='linear', nice=True,
domain=DataRef(data='stats', field="sum"))
vis.scales['color'] = Scale(name='color', type='ordinal',
domain=DataRef(data='table', field='data.col'),
range='category20')
vis.axes.extend([Axis(type='x', scale='x'),
Axis(type='y', scale='y')])
facet = Transform(type='facet', keys=['data.col'])
stack = Transform(type='stack', point='data.idx', height='data.val')
transform = MarkRef(data='table',transform=[facet, stack])
enter_props = PropertySet(x=ValueRef(scale='x', field="data.idx"),
y=ValueRef(scale='y', field="y"),
interpolate=ValueRef(value='monotone'),
y2=ValueRef(field='y2', scale='y'),
fill=ValueRef(scale='color', field='data.col'))
mark = Mark(type='group', from_=transform,
marks=[Mark(type='area',
properties=MarkProperties(enter=enter_props))])
vis.marks.append(mark)
vis.axis_titles(x='Date', y='Price')
vis.legend(title='Tech Stocks')
vis.to_json('vega.json')
#Convenience method
vis = StackedArea(price)
vis.axis_titles(x='Date', y='Price')
vis.legend(title='Tech Stocks')
vis.colors(brew='Paired')
vis.to_json('vega.json')
| mit |
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