kailasps/GPT2-codeparrot
Text Generation
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ratnania/pigasus | doc/manual/include/demo/test_neumann_quartcircle.py | 1 | 2730 | #! /usr/bin/python
# ...
try:
from matplotlib import pyplot as plt
PLOT=True
except ImportError:
PLOT=False
# ...
import numpy as np
from pigasus.gallery.poisson import *
import sys
import inspect
filename = inspect.getfile(inspect.currentframe()) # script filename (usually with path)
# ...
sin = np.sin ; cos = np.cos ; pi = np.pi ; exp = np.exp
# ...
#-----------------------------------
try:
nx = int(sys.argv[1])
except:
nx = 31
try:
ny = int(sys.argv[2])
except:
ny = 31
try:
px = int(sys.argv[3])
except:
px = 2
try:
py = int(sys.argv[4])
except:
py = 2
from igakit.cad_geometry import quart_circle as domain
geo = domain(n=[nx,ny],p=[px,py])
#-----------------------------------
# ...
# exact solution
# ...
R = 1.
r = 0.5
c = 1. # for neumann
#c = pi / (R**2-r**2) # for all dirichlet bc
u = lambda x,y : [ x * y * sin ( c * (R**2 - x**2 - y**2 )) ]
# ...
# ...
# rhs
# ...
f = lambda x,y : [4*c**2*x**3*y*sin(c*(R**2 - x**2 - y**2)) \
+ 4*c**2*x*y**3*sin(c*(R**2 - x**2 - y**2)) \
+ 12*c*x*y*cos(c*(R**2 - x**2 - y**2)) ]
# ...
# ...
# values of gradu.n at the boundary
# ...
gradu = lambda x,y : [-2*c*x**2*y*cos(c*(R**2 - x**2 - y**2)) + y*sin(c*(R**2
-
x**2
-
y**2)) \
,-2*c*x*y**2*cos(c*(R**2 - x**2 - y**2)) + x*sin(c*(R**2 - x**2 - y**2)) ]
def func_g (x,y) :
du = gradu (x, y)
return [ du[0] , du[1] ]
# ...
# ...
# values of u at the boundary
# ...
bc_neumann={}
bc_neumann [0,0] = func_g
Dirichlet = [[1,2,3]]
#AllDirichlet = True
# ...
# ...
try:
bc_dirichlet
except NameError:
bc_dirichlet = None
else:
pass
try:
bc_neumann
except NameError:
bc_neumann = None
else:
pass
try:
AllDirichlet
except NameError:
AllDirichlet = None
else:
pass
try:
Dirichlet
except NameError:
Dirichlet = None
else:
pass
try:
Metric
except NameError:
Metric = None
else:
pass
# ...
# ...
PDE = poisson(geometry=geo, bc_dirichlet=bc_dirichlet, bc_neumann=bc_neumann,
AllDirichlet=AllDirichlet, Dirichlet=Dirichlet,metric=Metric)
# ...
# ...
PDE.assembly(f=f)
PDE.solve()
# ...
# ...
normU = PDE.norm(exact=u)
print "norm U = ", normU
# ...
# ...
if PLOT:
PDE.plot() ; plt.colorbar(); plt.title('$u_h$')
plt.savefig(filename.split('.py')[0]+'.png', format='png')
plt.clf()
# ...
PDE.free()
| mit |
devanshdalal/scikit-learn | examples/gaussian_process/plot_gpr_noisy_targets.py | 64 | 3706 | """
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression example computed in two different ways:
1. A noise-free case
2. A noisy case with known noise-level per datapoint
In both cases, the kernel's parameters are estimated using the maximum
likelihood principle.
The figures illustrate the interpolating property of the Gaussian Process
model as well as its probabilistic nature in the form of a pointwise 95%
confidence interval.
Note that the parameter ``alpha`` is applied as a Tikhonov
regularization of the assumed covariance between the training points.
"""
print(__doc__)
# Author: Vincent Dubourg <[email protected]>
# Jake Vanderplas <[email protected]>
# Jan Hendrik Metzen <[email protected]>s
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
np.random.seed(1)
def f(x):
"""The function to predict."""
return x * np.sin(x)
# ----------------------------------------------------------------------
# First the noiseless case
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
# Observations
y = f(X).ravel()
# Mesh the input space for evaluations of the real function, the prediction and
# its MSE
x = np.atleast_2d(np.linspace(0, 10, 1000)).T
# Instanciate a Gaussian Process model
kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(x, return_std=True)
# Plot the function, the prediction and the 95% confidence interval based on
# the MSE
fig = plt.figure()
plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
plt.plot(X, y, 'r.', markersize=10, label=u'Observations')
plt.plot(x, y_pred, 'b-', label=u'Prediction')
plt.fill(np.concatenate([x, x[::-1]]),
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5, fc='b', ec='None', label='95% confidence interval')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.ylim(-10, 20)
plt.legend(loc='upper left')
# ----------------------------------------------------------------------
# now the noisy case
X = np.linspace(0.1, 9.9, 20)
X = np.atleast_2d(X).T
# Observations and noise
y = f(X).ravel()
dy = 0.5 + 1.0 * np.random.random(y.shape)
noise = np.random.normal(0, dy)
y += noise
# Instanciate a Gaussian Process model
gp = GaussianProcessRegressor(kernel=kernel, alpha=(dy / y) ** 2,
n_restarts_optimizer=10)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(x, return_std=True)
# Plot the function, the prediction and the 95% confidence interval based on
# the MSE
fig = plt.figure()
plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label=u'Observations')
plt.plot(x, y_pred, 'b-', label=u'Prediction')
plt.fill(np.concatenate([x, x[::-1]]),
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5, fc='b', ec='None', label='95% confidence interval')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.ylim(-10, 20)
plt.legend(loc='upper left')
plt.show()
| bsd-3-clause |
lordkman/burnman | examples/example_geotherms.py | 4 | 4049 | # This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU
# GPL v2 or later.
"""
example_geotherms
-----------------
This example shows each of the geotherms currently possible with BurnMan.
These are:
1. Brown and Shankland, 1981 :cite:`Brown1981`
2. Anderson, 1982 :cite:`anderson1982earth`
3. Watson and Baxter, 2007 :cite:`Watson2007`
4. linear extrapolation
5. Read in from file from user
6. Adiabatic from potential temperature and choice of mineral
*Uses:*
* :func:`burnman.geotherm.brown_shankland`
* :func:`burnman.geotherm.anderson`
* input geotherm file *input_geotherm/example_geotherm.txt* (optional)
* :class:`burnman.composite.Composite` for adiabat
*Demonstrates:*
* the available geotherms
"""
from __future__ import absolute_import
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
# hack to allow scripts to be placed in subdirectories next to burnman:
if not os.path.exists('burnman') and os.path.exists('../burnman'):
sys.path.insert(1, os.path.abspath('..'))
import burnman
from burnman import minerals
if __name__ == "__main__":
# we want to evaluate several geotherms at these values
pressures = np.arange(9.0e9, 128e9, 3e9)
seismic_model = burnman.seismic.PREM()
depths = seismic_model.depth(pressures)
# load two builtin geotherms and evaluate the temperatures at all pressures
temperature1 = burnman.geotherm.brown_shankland(depths)
temperature2 = burnman.geotherm.anderson(depths)
# a geotherm is actually just a function that returns a list of temperatures given pressures in Pa
# so we can just write our own function
my_geotherm_function = lambda p: [1500 + (2500 - 1500) * x / 128e9 for x in p]
temperature3 = my_geotherm_function(pressures)
# what about a geotherm defined from datapoints given in a file (our
# inline)?
table = [[1e9, 1600], [30e9, 1700], [130e9, 2700]]
# this could also be loaded from a file, just uncomment this
# table = burnman.tools.read_table("input_geotherm/example_geotherm.txt")
table_pressure = np.array(table)[:, 0]
table_temperature = np.array(table)[:, 1]
my_geotherm_interpolate = lambda p: [np.interp(x, table_pressure,
table_temperature) for x in p]
temperature4 = my_geotherm_interpolate(pressures)
# finally, we can also calculate a self consistent
# geotherm for an assemblage of minerals
# based on self compression of the composite rock.
# First we need to define an assemblage
amount_perovskite = 0.8
fe_pv = 0.05
fe_pc = 0.2
pv = minerals.SLB_2011.mg_fe_perovskite()
pc = minerals.SLB_2011.ferropericlase()
pv.set_composition([1. - fe_pv, fe_pv, 0.])
pc.set_composition([1. - fe_pc, fe_pc])
example_rock = burnman.Composite(
[pv, pc], [amount_perovskite, 1.0 - amount_perovskite])
# next, define an anchor temperature at which we are starting.
# Perhaps 1500 K for the upper mantle
T0 = 1500.
# then generate temperature values using the self consistent function.
# This takes more time than the above methods
temperature5 = burnman.geotherm.adiabatic(pressures, T0, example_rock)
# you can also look at burnman/geotherm.py to see how the geotherms are
# implemented
plt.plot(pressures / 1e9, temperature1, '-r', label="Brown, Shankland")
plt.plot(pressures / 1e9, temperature2, '-c', label="Anderson")
plt.plot(pressures / 1e9, temperature3, '-b', label="handwritten linear")
plt.plot(pressures / 1e9, temperature4,
'-k', label="handwritten from table")
plt.plot(pressures / 1e9, temperature5, '-m',
label="Adiabat with pv (70%) and fp(30%)")
plt.legend(loc='lower right')
plt.xlim([8.5, 130])
plt.xlabel('Pressure/GPa')
plt.ylabel('Temperature')
plt.savefig("output_figures/example_geotherm.png")
plt.show()
| gpl-2.0 |
francesco-mannella/dmp-esn | parametric/parametric_dmp/bin/tr_datasets/e_cursive_curves_angles_start_none/results/plot.py | 18 | 1043 | #!/usr/bin/env python
import glob
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
pathname = os.path.dirname(sys.argv[0])
if pathname:
os.chdir(pathname)
n_dim = None
trains = []
for fname in glob.glob("tl*"):
t = np.loadtxt(fname)
trains.append(t)
tests = []
for fname in glob.glob("tt*"):
t = np.loadtxt(fname)
tests.append(t)
trial_results= []
for fname in glob.glob("rtl*"):
t = np.loadtxt(fname)
trial_results.append(t)
test_results= []
for fname in glob.glob("rtt*"):
t = np.loadtxt(fname)
test_results.append(t)
fig = plt.figure()
ax = fig.add_subplot(111, aspect="equal")
for d in trains:
ax.plot(d[:,1] +d[:,7]*6, d[:,2] +d[:,8]*6, color="blue", lw=3, alpha=0.5)
for d in tests:
ax.plot(d[:,1] +d[:,7]*6, d[:,2] +d[:,8]*6, color="red", lw=3, alpha=0.5)
for d in trial_results:
ax.plot(d[:,1] +d[:,7]*6, d[:,2] +d[:,8]*6, color=[0,0,.5], lw=2)
for d in test_results:
ax.plot(d[:,1] +d[:,7]*6, d[:,2] +d[:,8]*6, color=[.5,0,0], lw=2)
plt.show()
| gpl-2.0 |
flowersteam/SESM | SESM/pykinect.py | 2 | 3387 | import zmq
import numpy
import threading
from collections import namedtuple
Point2D = namedtuple('Point2D', ('x', 'y'))
Point3D = namedtuple('Point3D', ('x', 'y', 'z'))
Quaternion = namedtuple('Quaternion', ('x', 'y', 'z', 'w'))
torso_joints = ('hip_center', 'spine', 'shoulder_center', 'head')
left_arm_joints = ('shoulder_left', 'elbow_left', 'wrist_left', 'hand_left')
right_arm_joints = ('shoulder_right', 'elbow_right', 'wrist_right', 'hand_right')
left_leg_joints = ('hip_left', 'knee_left', 'ankle_left', 'foot_left')
right_leg_joints = ('hip_right', 'knee_right', 'ankle_right', 'foot_right')
skeleton_joints = torso_joints + left_arm_joints + right_arm_joints + left_leg_joints + right_leg_joints
class Skeleton(namedtuple('Skeleton', ('timestamp', 'user_id') + skeleton_joints)):
joints = skeleton_joints
@property
def to_np(self):
l = []
for j in self.joints:
p = getattr(self, j).position
l.append((p.x, p.y, p.z))
return numpy.array(l)
Joint = namedtuple('Joint', ('position', 'orientation', 'pixel_coordinate'))
class KinectSensor(object):
def __init__(self, addr, port):
self._lock = threading.Lock()
self._skeleton = None
context = zmq.Context()
self.socket = context.socket(zmq.REQ)
self.socket.connect('tcp://{}:{}'.format(addr, port))
t = threading.Thread(target=self.get_skeleton)
t.daemon = True
t.start()
@property
def tracked_skeleton(self):
with self._lock:
return self._skeleton
@tracked_skeleton.setter
def tracked_skeleton(self, skeleton):
with self._lock:
self._skeleton = skeleton
def get_skeleton(self):
while True:
self.socket.send('Hello')
md = self.socket.recv_json()
msg = self.socket.recv()
skeleton_array = numpy.frombuffer(buffer(msg), dtype=md['dtype'])
skeleton_array = skeleton_array.reshape(md['shape'])
joints = []
for i in range(len(skeleton_joints)):
x, y, z, w = skeleton_array[i][0:4]
position = Point3D(x / w, y / w, z / w)
pixel_coord = Point2D(*skeleton_array[i][4:6])
orientation = Quaternion(*skeleton_array[i][6:10])
joints.append(Joint(position, orientation, pixel_coord))
self.tracked_skeleton = Skeleton(md['timestamp'], md['user_index'], *joints)
def draw_position(skel, ax):
xy, zy = [], []
if not skel:
return
for j in skeleton_joints:
p = getattr(skel, j).position
xy.append((p.x, p.y))
zy.append((p.z, p.y))
ax.set_xlim(-2, 5)
ax.set_ylim(-1.5, 1.5)
ax.scatter(zip(*xy)[0], zip(*xy)[1], 30, 'b')
ax.scatter(zip(*zy)[0], zip(*zy)[1], 30, 'r')
if __name__ == '__main__':
import time
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
kinect_sensor = KinectSensor('193.50.110.210', 9999)
import skelangle
kinect_angle = skelangle.AngleFromSkel()
try:
while True:
ax.clear()
draw_position(kinect_sensor.tracked_skeleton, ax)
plt.draw()
time.sleep(0.1)
except KeyboardInterrupt:
plt.close('all')
| gpl-3.0 |
gwparikh/cvgui | grouping_calibration.py | 2 | 9402 | #!/usr/bin/env python
import os, sys, subprocess
import argparse
import subprocess
import threading
import timeit
from multiprocessing import Queue, Lock
from configobj import ConfigObj
from numpy import loadtxt
from numpy.linalg import inv
import matplotlib.pyplot as plt
import moving
from cvguipy import trajstorage, cvgenetic, cvconfig
"""
Grouping Calibration By Genetic Algorithm.
This script uses genetic algorithm to search for the best configuration.
It does not monitor RAM usage, therefore, CPU thrashing might be happened when number of parents (selection size) is too large.
"""
# class for genetic algorithm
class GeneticCompare(object):
def __init__(self, motalist, motplist, IDlist, cfg_list, lock):
self.motalist = motalist
self.motplist = motplist
self.IDlist = IDlist
self.cfg_list = cfg_list
self.lock = lock
# This is used for calculte fitness of individual in genetic algorithn.
# It is modified to create sqlite and cfg file before tuning computeClearMOT.
# NOTE errors show up when loading two same ID
def computeMOT(self, i):
# create sqlite and cfg file with id i
cfg_name = config_files +str(i)+'.cfg'
sql_name = sqlite_files +str(i)+'.sqlite'
open(cfg_name,'w').close()
config = ConfigObj(cfg_name)
cfg_list.write_config(i ,config)
command = ['cp', 'tracking_only.sqlite', sql_name]
process = subprocess.Popen(command)
process.wait()
command = ['trajextract.py', args.inputVideo, '-o', args.homography, '-t', cfg_name, '-d', sql_name, '--gf']
# suppress output of grouping extraction
devnull = open(os.devnull, 'wb')
process = subprocess.Popen(command, stdout = devnull)
process.wait()
obj = trajstorage.CVsqlite(sql_name)
print "loading", i
obj.loadObjects()
motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(cdb.annotations, obj.objects, args.matchDistance, firstFrame, lastFrame)
if motp is None:
motp = 0
self.lock.acquire()
self.IDlist.put(i)
self.motplist.put(motp)
self.motalist.put(mota)
obj.close()
if args.PrintMOTA:
print("ID: mota:{} motp:{}".format(mota, motp))
self.lock.release()
return mota
if __name__ == '__main__' :
parser = argparse.ArgumentParser(description="compare all sqlites that are created by cfg_combination.py to the Annotated version to find the ID of the best configuration")
parser.add_argument('inputVideo', help= "input video filename")
parser.add_argument('-r', '--configuration-file', dest='range_cfg', help= "the configuration-file contain the range of configuration")
parser.add_argument('-t', '--traffintel-config', dest='traffintelConfig', help= "the TrafficIntelligence file to use for running the first extraction.")
parser.add_argument('-m', '--mask-File', dest='maskFilename', help="Name of the mask-File for trajextract")
parser.add_argument('-d', '--database-file', dest ='databaseFile', help ="Name of the databaseFile.")
parser.add_argument('-o', '--homography-file', dest ='homography', help = "Name of the homography file.", required = True)
parser.add_argument('-md', '--matching-distance', dest='matchDistance', help = "matchDistance", default = 10, type = float)
parser.add_argument('-a', '--accuracy', dest = 'accuracy', help = "accuracy parameter for genetic algorithm", type = int)
parser.add_argument('-p', '--population', dest = 'population', help = "population parameter for genetic algorithm", required = True, type = int)
parser.add_argument('-np', '--num-of-parents', dest = 'num_of_parents', help = "Number of parents that are selected each generation", type = int)
parser.add_argument('-mota', '--print-MOTA', dest='PrintMOTA', action = 'store_true', help = "Print MOTA for each ID.")
args = parser.parse_args()
os.mkdir('cfg_files')
os.mkdir('sql_files')
sqlite_files = "sql_files/Sqlite_ID_"
config_files = "cfg_files/Cfg_ID_"
# ------------------initialize annotated version if not existed ---------- #
# inputVideo check
if not os.path.exists(args.inputVideo):
print("Input video {} does not exist! Exiting...".format(args.inputVideo))
sys.exit(1)
# configuration file check
if args.range_cfg is None:
config = ConfigObj('range.cfg')
else:
config = ConfigObj(args.range_cfg)
# get configuration and put them to a List
cfg_list = cvconfig.CVConfigList()
thread_cfgtolist = threading.Thread(target = cvconfig.config_to_list, args = (cfg_list, config))
thread_cfgtolist.start();
# check if dbfile name is entered
if args.databaseFile is None:
print("Database-file is not entered, running trajextract and cvplayer.")
if not os.path.exists(args.homography):
print("Homography file does not exist! Exiting...")
sys.exit(1)
else:
videofile=args.inputVideo
if 'avi' in videofile:
if args.maskFilename is not None:
command = ['trajextract.py',args.inputVideo,'-m', args.maskFilename,'-o', args.homography]
else:
command = ['trajextract.py',args.inputVideo,'-o', args.homography]
process = subprocess.Popen(command)
process.wait()
databaseFile = videofile.replace('avi','sqlite')
command = ['cvplayer.py',args.inputVideo,'-d',databaseFile,'-o',args.homography]
process = subprocess.Popen(command)
process.wait()
else:
print("Input video {} is not 'avi' type. Exiting...".format(args.inputVideo))
sys.exit(1)
else:
databaseFile = args.databaseFile
thread_cfgtolist.join()
# ------------------Done initialization for annotation-------------------- #
# create first tracking only database template.
print("creating the first tracking only database template.")
if args.maskFilename is not None:
command = map(str, ['trajextract.py',args.inputVideo, '-d', 'tracking_only.sqlite', '-t', args.traffintelConfig, '-o', args.homography, '-m', args.maskFilename, '--tf'])
else:
command = map(str, ['trajextract.py',args.inputVideo, '-d', sql_name, '-t', args.traffintelConfig, '-o', args.homography, '--tf'])
process = subprocess.Popen(command)
process.wait()
# ----start using genetic algorithm to search for best configuration-------#
start = timeit.default_timer()
dbfile = databaseFile;
homography = loadtxt(args.homography)
cdb = trajstorage.CVsqlite(dbfile)
cdb.open()
cdb.getLatestAnnotation()
cdb.createBoundingBoxTable(cdb.latestannotations, inv(homography))
cdb.loadAnnotaion()
for a in cdb.annotations:
a.computeCentroidTrajectory(homography)
print "Latest Annotaions in "+dbfile+": ", cdb.latestannotations
cdb.frameNumbers = cdb.getFrameList()
firstFrame = cdb.frameNumbers[0]
lastFrame = cdb.frameNumbers[-1]
foundmota = Queue()
foundmotp = Queue()
IDs = Queue()
lock = Lock()
Comp = GeneticCompare(foundmota, foundmotp, IDs, cfg_list, lock)
if args.accuracy != None:
GeneticCal = cvgenetic.CVGenetic(args.population, cfg_list, Comp.computeMOT, args.accuracy)
else:
GeneticCal = cvgenetic.CVGenetic(args.population, cfg_list, Comp.computeMOT)
if args.num_of_parents != None:
GeneticCal.run_thread(args.num_of_parents)
else:
GeneticCal.run_thread()
# tranform queues to lists
foundmota = cvgenetic.Queue_to_list(foundmota)
foundmotp = cvgenetic.Queue_to_list(foundmotp)
IDs = cvgenetic.Queue_to_list(IDs)
for i in range(len(foundmotp)):
foundmotp[i] /= args.matchDistance
Best_mota = max(foundmota)
Best_ID = IDs[foundmota.index(Best_mota)]
print "Best multiple object tracking accuracy (MOTA)", Best_mota
print "ID:", Best_ID
stop = timeit.default_timer()
print str(stop-start) + "s"
total = []
for i in range(len(foundmota)):
total.append(foundmota[i]- 0.1 * foundmotp[i])
Best_total = max(total)
Best_total_ID = IDs[total.index(Best_total)]
# ------------------------------Done searching----------------------------#
# use matplot to plot a graph of all calculated IDs along with thier mota
plt.figure(1)
plt.plot(foundmota ,IDs ,'bo')
plt.plot(foundmotp ,IDs ,'yo')
plt.plot(Best_mota, Best_ID, 'ro')
plt.axis([-1, 1, -1, cfg_list.get_total_combination()])
plt.xlabel('mota')
plt.ylabel('ID')
plt.title(b'Best MOTA: '+str(Best_mota) +'\nwith ID: '+str(Best_ID))
plotFile = os.path.splitext(dbfile)[0] + '_CalibrationResult_mota.png'
plt.savefig(plotFile)
plt.figure(2)
plt.plot(total, IDs, 'bo')
plt.plot(Best_total, Best_total_ID, 'ro')
plt.xlabel('mota + motp')
plt.ylabel('ID')
plt.title(b'Best total: '+str(Best_total) +'\nwith ID: '+str(Best_total_ID))
# save the plot
plotFile = os.path.splitext(dbfile)[0] + '_CalibrationResult_motp.png'
plt.savefig(plotFile)
plt.show()
cdb.close()
| mit |
keflavich/pyspeckit-obsolete | pyspeckit/spectrum/models/ammonia.py | 1 | 28836 | """
========================================
Ammonia inversion transition TKIN fitter
========================================
Ammonia inversion transition TKIN fitter translated from Erik Rosolowsky's
http://svn.ok.ubc.ca/svn/signals/nh3fit/
.. moduleauthor:: Adam Ginsburg <[email protected]>
Module API
^^^^^^^^^^
"""
import numpy as np
from pyspeckit.mpfit import mpfit
from pyspeckit.spectrum.parinfo import ParinfoList,Parinfo
import fitter
import matplotlib.cbook as mpcb
import copy
import model
line_names = ['oneone','twotwo','threethree','fourfour']
freq_dict = {
'oneone': 23.694506e9,
'twotwo': 23.722633335e9,
'threethree': 23.8701296e9,
'fourfour': 24.1394169e9,
}
aval_dict = {
'oneone': 1.712e-7, #64*!pi**4/(3*h*c**3)*nu11**3*mu0**2*(1/2.)
'twotwo': 2.291e-7, #64*!pi**4/(3*h*c**3)*nu22**3*mu0**2*(2/3.)
'threethree': 2.625e-7, #64*!pi**4/(3*h*c**3)*nu33**3*mu0**2*(3/4.)
'fourfour': 3.167e-7, #64*!pi**4/(3*h*c**3)*nu44**3*mu0**2*(4/5.)
}
ortho_dict = {
'oneone': False,
'twotwo': False,
'threethree': True,
'fourfour': False,
}
n_ortho = np.arange(0,28,3) # 0..3..27
n_para = np.array([x for x in range(28) if x % 3 != 0])
voff_lines_dict = {
'oneone': [19.8513, 19.3159, 7.88669, 7.46967, 7.35132, 0.460409, 0.322042,
-0.0751680, -0.213003, 0.311034, 0.192266, -0.132382, -0.250923, -7.23349,
-7.37280, -7.81526, -19.4117, -19.5500],
'twotwo':[26.5263, 26.0111, 25.9505, 16.3917, 16.3793, 15.8642, 0.562503,
0.528408, 0.523745, 0.0132820, -0.00379100, -0.0132820, -0.501831,
-0.531340, -0.589080, -15.8547, -16.3698, -16.3822, -25.9505, -26.0111,
-26.5263],
'threethree':[29.195098, 29.044147, 28.941877, 28.911408, 21.234827,
21.214619, 21.136387, 21.087456, 1.005122, 0.806082, 0.778062,
0.628569, 0.016754, -0.005589, -0.013401, -0.639734, -0.744554,
-1.031924, -21.125222, -21.203441, -21.223649, -21.076291, -28.908067,
-28.938523, -29.040794, -29.191744],
'fourfour':[ 0. , -30.49783692, 30.49783692, 0., 24.25907811,
-24.25907811, 0. ]
}
tau_wts_dict = {
'oneone': [0.0740740, 0.148148, 0.0925930, 0.166667, 0.0185190, 0.0370370,
0.0185190, 0.0185190, 0.0925930, 0.0333330, 0.300000, 0.466667,
0.0333330, 0.0925930, 0.0185190, 0.166667, 0.0740740, 0.148148],
'twotwo': [0.00418600, 0.0376740, 0.0209300, 0.0372090, 0.0260470,
0.00186000, 0.0209300, 0.0116280, 0.0106310, 0.267442, 0.499668,
0.146512, 0.0116280, 0.0106310, 0.0209300, 0.00186000, 0.0260470,
0.0372090, 0.0209300, 0.0376740, 0.00418600],
'threethree': [0.012263, 0.008409, 0.003434, 0.005494, 0.006652, 0.008852,
0.004967, 0.011589, 0.019228, 0.010387, 0.010820, 0.009482, 0.293302,
0.459109, 0.177372, 0.009482, 0.010820, 0.019228, 0.004967, 0.008852,
0.006652, 0.011589, 0.005494, 0.003434, 0.008409, 0.012263],
'fourfour': [0.2431, 0.0162, 0.0162, 0.3008, 0.0163, 0.0163, 0.3911]}
def ammonia(xarr, tkin=20, tex=None, ntot=1e14, width=1,
xoff_v=0.0, fortho=0.0, tau=None, fillingfraction=None, return_tau=False,
thin=False, verbose=False, return_components=False, debug=False ):
"""
Generate a model Ammonia spectrum based on input temperatures, column, and
gaussian parameters
ntot can be specified as a column density (e.g., 10^15) or a log-column-density (e.g., 15)
tex can be specified or can be assumed LTE if unspecified, if tex>tkin, or if "thin"
is specified
"thin" uses a different parametetrization and requires only the optical depth, width, offset,
and tkin to be specified. In the 'thin' approximation, tex is not used in computation of
the partition function - LTE is implicitly assumed
If tau is specified, ntot is NOT fit but is set to a fixed value
fillingfraction is an arbitrary scaling factor to apply to the model
fortho is the ortho/(ortho+para) fraction. The default is to assume all ortho.
xoff_v is the velocity offset in km/s
tau refers to the optical depth of the 1-1 line. The optical depths of the
other lines are fixed relative to tau_oneone
(not implemented) if tau is specified, ntot is ignored
"""
# Convert X-units to frequency in GHz
xarr = xarr.as_unit('GHz')
if tex is not None:
if tex > tkin: # cannot have Tex > Tkin
tex = tkin
elif thin: # tex is not used in this case
tex = tkin
else:
tex = tkin
if thin:
ntot = 1e15
elif 5 < ntot < 25:
# allow ntot to be specified as a logarithm. This is
# safe because ntot < 1e10 gives a spectrum of all zeros, and the
# plausible range of columns is not outside the specified range
ntot = 10**ntot
elif (25 < ntot < 1e5) or (ntot < 5):
# these are totally invalid for log/non-log
return 0
# fillingfraction is an arbitrary scaling for the data
# The model will be (normal model) * fillingfraction
if fillingfraction is None:
fillingfraction = 1.0
ckms = 2.99792458e5
ccms = ckms*1e5
g1 = 1
g2 = 1
h = 6.6260693e-27
kb = 1.3806505e-16
mu0 = 1.476e-18 # Dipole Moment in cgs (1.476 Debeye)
# Generate Partition Functions
nlevs = 51
jv=np.arange(nlevs)
ortho = jv % 3 == 0
para = True-ortho
Jpara = jv[para]
Jortho = jv[ortho]
Brot = 298117.06e6
Crot = 186726.36e6
runspec = np.zeros(len(xarr))
tau_dict = {}
para_count = 0
ortho_count = 1 # ignore 0-0
if tau is not None and thin:
"""
Use optical depth in the 1-1 line as a free parameter
The optical depths of the other lines are then set by the kinetic temperature
Tex is still a free parameter in the final spectrum calculation at the bottom
(technically, I think this process assumes LTE; Tex should come into play in
these equations, not just the final one)
"""
dT0 = 41.5 # Energy diff between (2,2) and (1,1) in K
trot = tkin/(1+tkin/dT0*np.log(1+0.6*np.exp(-15.7/tkin)))
tau_dict['oneone'] = tau
tau_dict['twotwo'] = tau*(23.722/23.694)**2*4/3.*5/3.*np.exp(-41.5/trot)
tau_dict['threethree'] = tau*(23.8701279/23.694)**2*3/2.*14./3.*np.exp(-101.1/trot)
tau_dict['fourfour'] = tau*(24.1394169/23.694)**2*8/5.*9/3.*np.exp(-177.34/trot)
else:
"""
Column density is the free parameter. It is used in conjunction with
the full partition function to compute the optical depth in each band
Given the complexity of these equations, it would be worth my while to
comment each step carefully.
"""
Zpara = (2*Jpara+1)*np.exp(-h*(Brot*Jpara*(Jpara+1)+
(Crot-Brot)*Jpara**2)/(kb*tkin))
Zortho = 2*(2*Jortho+1)*np.exp(-h*(Brot*Jortho*(Jortho+1)+
(Crot-Brot)*Jortho**2)/(kb*tkin))
for linename in line_names:
if ortho_dict[linename]:
orthoparafrac = fortho
Z = Zortho
count = ortho_count
ortho_count += 1
else:
orthoparafrac = 1.0-fortho
Z = Zpara
count = para_count # need to treat partition function separately
para_count += 1
tau_dict[linename] = (ntot * orthoparafrac * Z[count]/(Z.sum()) / ( 1
+ np.exp(-h*freq_dict[linename]/(kb*tkin) )) * ccms**2 /
(8*np.pi*freq_dict[linename]**2) * aval_dict[linename]*
(1-np.exp(-h*freq_dict[linename]/(kb*tex))) /
(width/ckms*freq_dict[linename]*np.sqrt(2*np.pi)) )
# allow tau(11) to be specified instead of ntot
# in the thin case, this is not needed: ntot plays no role
# this process allows you to specify tau without using the approximate equations specified
# above. It should remove ntot from the calculations anyway...
if tau is not None and not thin:
tau11_temp = tau_dict['oneone']
# re-scale all optical depths so that tau is as specified, but the relative taus
# are sest by the kinetic temperature and partition functions
for linename,t in tau_dict.iteritems():
tau_dict[linename] = t * tau/tau11_temp
components =[]
for linename in line_names:
voff_lines = np.array(voff_lines_dict[linename])
tau_wts = np.array(tau_wts_dict[linename])
lines = (1-voff_lines/ckms)*freq_dict[linename]/1e9
tau_wts = tau_wts / (tau_wts).sum()
nuwidth = np.abs(width/ckms*lines)
nuoff = xoff_v/ckms*lines
# tau array
tauprof = np.zeros(len(xarr))
for kk,no in enumerate(nuoff):
tauprof += (tau_dict[linename] * tau_wts[kk] *
np.exp(-(xarr+no-lines[kk])**2 / (2.0*nuwidth[kk]**2)) *
fillingfraction)
components.append( tauprof )
T0 = (h*xarr*1e9/kb) # "temperature" of wavelength
if tau is not None and thin:
#runspec = tauprof+runspec
# is there ever a case where you want to ignore the optical depth function? I think no
runspec = (T0/(np.exp(T0/tex)-1)-T0/(np.exp(T0/2.73)-1))*(1-np.exp(-tauprof))+runspec
else:
runspec = (T0/(np.exp(T0/tex)-1)-T0/(np.exp(T0/2.73)-1))*(1-np.exp(-tauprof))+runspec
if runspec.min() < 0:
raise ValueError("Model dropped below zero. That is not possible normally. Here are the input values: "+
("tex: %f " % tex) +
("tkin: %f " % tkin) +
("ntot: %f " % ntot) +
("width: %f " % width) +
("xoff_v: %f " % xoff_v) +
("fortho: %f " % fortho)
)
if verbose or debug:
print "tkin: %g tex: %g ntot: %g width: %g xoff_v: %g fortho: %g fillingfraction: %g" % (tkin,tex,ntot,width,xoff_v,fortho,fillingfraction)
if return_components:
return (T0/(np.exp(T0/tex)-1)-T0/(np.exp(T0/2.73)-1))*(1-np.exp(-1*np.array(components)))
if return_tau:
return tau_dict
return runspec
class ammonia_model(model.SpectralModel):
def __init__(self,npeaks=1,npars=6,multisingle='multi',**kwargs):
self.npeaks = npeaks
self.npars = npars
self._default_parnames = ['tkin','tex','ntot','width','xoff_v','fortho']
self.parnames = copy.copy(self._default_parnames)
# all fitters must have declared modelfuncs, which should take the fitted pars...
self.modelfunc = ammonia
self.n_modelfunc = self.n_ammonia
# for fitting ammonia simultaneously with a flat background
self.onepeakammonia = fitter.vheightmodel(ammonia)
#self.onepeakammoniafit = self._fourparfitter(self.onepeakammonia)
if multisingle in ('multi','single'):
self.multisingle = multisingle
else:
raise Exception("multisingle must be multi or single")
self.default_parinfo = None
self.default_parinfo, kwargs = self._make_parinfo(**kwargs)
# enforce ammonia-specific parameter limits
for par in self.default_parinfo:
if 'tex' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'tkin' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'width' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
if 'fortho' in par.parname.lower():
par.limited = (True,True)
if par.limits[1] != 0:
par.limits = (max(par.limits[0],0), min(par.limits[1],1))
else:
par.limits = (max(par.limits[0],0), 1)
if 'ntot' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
self.parinfo = copy.copy(self.default_parinfo)
self.modelfunc_kwargs = kwargs
# lower case? self.modelfunc_kwargs.update({'parnames':self.parinfo.parnames})
def __call__(self,*args,**kwargs):
#if 'use_lmfit' in kwargs: kwargs.pop('use_lmfit')
use_lmfit = kwargs.pop('use_lmfit') if 'use_lmfit' in kwargs else self.use_lmfit
if use_lmfit:
return self.lmfitter(*args,**kwargs)
if self.multisingle == 'single':
return self.onepeakammoniafit(*args,**kwargs)
elif self.multisingle == 'multi':
return self.multinh3fit(*args,**kwargs)
def n_ammonia(self, pars=None, parnames=None, **kwargs):
"""
Returns a function that sums over N ammonia line profiles, where N is the length of
tkin,tex,ntot,width,xoff_v,fortho *OR* N = len(pars) / 6
The background "height" is assumed to be zero (you must "baseline" your
spectrum before fitting)
*pars* [ list ]
a list with len(pars) = (6-nfixed)n, assuming
tkin,tex,ntot,width,xoff_v,fortho repeated
*parnames* [ list ]
len(parnames) must = len(pars). parnames determine how the ammonia
function parses the arguments
"""
if hasattr(pars,'values'):
# important to treat as Dictionary, since lmfit params & parinfo both have .items
parnames,parvals = zip(*pars.items())
parnames = [p.lower() for p in parnames]
parvals = [p.value for p in parvals]
elif parnames is None:
parvals = pars
parnames = self.parnames
else:
parvals = pars
if len(pars) != len(parnames):
# this should only be needed when other codes are changing the number of peaks
# during a copy, as opposed to letting them be set by a __call__
# (n_modelfuncs = n_ammonia can be called directly)
# n_modelfuncs doesn't care how many peaks there are
if len(pars) % len(parnames) == 0:
parnames = [p for ii in range(len(pars)/len(parnames)) for p in parnames]
npars = len(parvals) / self.npeaks
else:
raise ValueError("Wrong array lengths passed to n_ammonia!")
else:
npars = len(parvals) / self.npeaks
self._components = []
def L(x):
v = np.zeros(len(x))
for jj in xrange(self.npeaks):
modelkwargs = kwargs.copy()
for ii in xrange(npars):
name = parnames[ii+jj*npars].strip('0123456789').lower()
modelkwargs.update({name:parvals[ii+jj*npars]})
v += ammonia(x,**modelkwargs)
return v
return L
def components(self, xarr, pars, hyperfine=False):
"""
Ammonia components don't follow the default, since in Galactic astronomy the hyperfine components should be well-separated.
If you want to see the individual components overlaid, you'll need to pass hyperfine to the plot_fit call
"""
comps=[]
for ii in xrange(self.npeaks):
if hyperfine:
modelkwargs = dict(zip(self.parnames[ii*self.npars:(ii+1)*self.npars],pars[ii*self.npars:(ii+1)*self.npars]))
comps.append( ammonia(xarr,return_components=True,**modelkwargs) )
else:
modelkwargs = dict(zip(self.parnames[ii*self.npars:(ii+1)*self.npars],pars[ii*self.npars:(ii+1)*self.npars]))
comps.append( [ammonia(xarr,return_components=False,**modelkwargs)] )
modelcomponents = np.concatenate(comps)
return modelcomponents
def multinh3fit(self, xax, data, npeaks=1, err=None,
params=(20,20,14,1.0,0.0,0.5),
parnames=None,
fixed=(False,False,False,False,False,False),
limitedmin=(True,True,True,True,False,True),
limitedmax=(False,False,False,False,False,True), minpars=(2.73,2.73,0,0,0,0),
parinfo=None,
maxpars=(0,0,0,0,0,1), quiet=True, shh=True, veryverbose=False, **kwargs):
"""
Fit multiple nh3 profiles (multiple can be 1)
Inputs:
xax - x axis
data - y axis
npeaks - How many nh3 profiles to fit? Default 1 (this could supersede onedgaussfit)
err - error corresponding to data
These parameters need to have length = 6*npeaks. If npeaks > 1 and length = 6, they will
be replicated npeaks times, otherwise they will be reset to defaults:
params - Fit parameters: [tkin, tex, ntot (or tau), width, offset, ortho fraction] * npeaks
If len(params) % 6 == 0, npeaks will be set to len(params) / 6
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0, Tex and Tkin > Tcmb)
limitedmax/maxpars - set upper limits on each parameter
parnames - default parameter names, important for setting kwargs in model ['tkin','tex','ntot','width','xoff_v','fortho']
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
if parinfo is None:
self.npars = len(params) / npeaks
if len(params) != npeaks and (len(params) / self.npars) > npeaks:
npeaks = len(params) / self.npars
self.npeaks = npeaks
if isinstance(params,np.ndarray): params=params.tolist()
# this is actually a hack, even though it's decently elegant
# somehow, parnames was being changed WITHOUT being passed as a variable
# this doesn't make sense - at all - but it happened.
# (it is possible for self.parnames to have npars*npeaks elements where
# npeaks > 1 coming into this function even though only 6 pars are specified;
# _default_parnames is the workaround)
if parnames is None: parnames = copy.copy(self._default_parnames)
partype_dict = dict(zip(['params','parnames','fixed','limitedmin','limitedmax','minpars','maxpars'],
[params,parnames,fixed,limitedmin,limitedmax,minpars,maxpars]))
# make sure all various things are the right length; if they're not, fix them using the defaults
for partype,parlist in partype_dict.iteritems():
if len(parlist) != self.npars*self.npeaks:
# if you leave the defaults, or enter something that can be multiplied by npars to get to the
# right number of gaussians, it will just replicate
if len(parlist) == self.npars:
partype_dict[partype] *= npeaks
elif len(parlist) > self.npars:
# DANGER: THIS SHOULD NOT HAPPEN!
print "WARNING! Input parameters were longer than allowed for variable ",parlist
partype_dict[partype] = partype_dict[partype][:self.npars]
elif parlist==params: # this instance shouldn't really be possible
partype_dict[partype] = [20,20,1e10,1.0,0.0,0.5] * npeaks
elif parlist==fixed:
partype_dict[partype] = [False] * len(params)
elif parlist==limitedmax: # only fortho, fillingfraction have upper limits
partype_dict[partype] = (np.array(parnames) == 'fortho') + (np.array(parnames) == 'fillingfraction')
elif parlist==limitedmin: # no physical values can be negative except velocity
partype_dict[partype] = (np.array(parnames) != 'xoff_v')
elif parlist==minpars: # all have minima of zero except kinetic temperature, which can't be below CMB. Excitation temperature technically can be, but not in this model
partype_dict[partype] = ((np.array(parnames) == 'tkin') + (np.array(parnames) == 'tex')) * 2.73
elif parlist==maxpars: # fractions have upper limits of 1.0
partype_dict[partype] = ((np.array(parnames) == 'fortho') + (np.array(parnames) == 'fillingfraction')).astype('float')
elif parlist==parnames: # assumes the right number of parnames (essential)
partype_dict[partype] = list(parnames) * self.npeaks
if len(parnames) != len(partype_dict['params']):
raise ValueError("Wrong array lengths AFTER fixing them")
# used in components. Is this just a hack?
self.parnames = partype_dict['parnames']
parinfo = [ {'n':ii, 'value':partype_dict['params'][ii],
'limits':[partype_dict['minpars'][ii],partype_dict['maxpars'][ii]],
'limited':[partype_dict['limitedmin'][ii],partype_dict['limitedmax'][ii]], 'fixed':partype_dict['fixed'][ii],
'parname':partype_dict['parnames'][ii]+str(ii/self.npars),
'mpmaxstep':float(partype_dict['parnames'][ii] in ('tex','tkin')), # must force small steps in temperature (True = 1.0)
'error': 0}
for ii in xrange(len(partype_dict['params'])) ]
# hack: remove 'fixed' pars
parinfo_with_fixed = parinfo
parinfo = [p for p in parinfo_with_fixed if not p['fixed']]
fixed_kwargs = dict((p['parname'].strip("0123456789").lower(),p['value']) for p in parinfo_with_fixed if p['fixed'])
# don't do this - it breaks the NEXT call because npars != len(parnames) self.parnames = [p['parname'] for p in parinfo]
# this is OK - not a permanent change
parnames = [p['parname'] for p in parinfo]
# not OK self.npars = len(parinfo)/self.npeaks
parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
#import pdb; pdb.set_trace()
else:
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
parinfo_with_fixed = None
fixed_kwargs = {}
fitfun_kwargs = dict(kwargs.items()+fixed_kwargs.items())
npars = len(parinfo)/self.npeaks
# (fortho0 is not fortho)
# this doesn't work if parinfo_with_fixed is not None:
# this doesn't work for p in parinfo_with_fixed:
# this doesn't work # users can change the defaults while holding them fixed
# this doesn't work if p['fixed']:
# this doesn't work kwargs.update({p['parname']:p['value']})
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-self.n_ammonia(pars=p, parnames=parinfo.parnames, **fitfun_kwargs)(x))]
else:
def f(p,fjac=None): return [0,(y-self.n_ammonia(pars=p, parnames=parinfo.parnames, **fitfun_kwargs)(x))/err]
return f
if veryverbose:
print "GUESSES: "
print "\n".join(["%s: %s" % (p['parname'],p['value']) for p in parinfo])
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp*0
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
parinfo[i]['error'] = mpperr[i]
if not shh:
print "Fit status: ",mp.status
print "Fit message: ",mp.errmsg
print "Final fit values: "
for i,p in enumerate(mpp):
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
if any(['tex' in s for s in parnames]) and any(['tkin' in s for s in parnames]):
texnum = (i for i,s in enumerate(parnames) if 'tex' in s)
tkinnum = (i for i,s in enumerate(parnames) if 'tkin' in s)
for txn,tkn in zip(texnum,tkinnum):
if mpp[txn] > mpp[tkn]: mpp[txn] = mpp[tkn] # force Tex>Tkin to Tex=Tkin (already done in n_ammonia)
self.mp = mp
if parinfo_with_fixed is not None:
# self self.parinfo preserving the 'fixed' parameters
# ORDER MATTERS!
for p in parinfo:
parinfo_with_fixed[p['n']] = p
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo_with_fixed], preserve_order=True)
else:
self.parinfo = parinfo
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
# I don't THINK these are necessary?
#self.parinfo = parinfo
#self.parinfo = ParinfoList([Parinfo(p) for p in self.parinfo])
# need to restore the fixed parameters....
# though the above commented out section indicates that I've done and undone this dozens of times now
# (a test has been added to test_nh3.py)
# this was NEVER included or tested because it breaks the order
#for par in parinfo_with_fixed:
# if par.parname not in self.parinfo.keys():
# self.parinfo.append(par)
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_ammonia(pars=self.mpp, parnames=self.mppnames, **kwargs)(xax)
#if self.model.sum() == 0:
# print "DON'T FORGET TO REMOVE THIS ERROR!"
# raise ValueError("Model is zeros.")
indiv_parinfo = [self.parinfo[jj*self.npars:(jj+1)*self.npars] for jj in xrange(len(self.parinfo)/self.npars)]
modelkwargs = [
dict([(p['parname'].strip("0123456789").lower(),p['value']) for p in pi])
for pi in indiv_parinfo]
self.tau_list = [ammonia(xax,return_tau=True,**mk) for mk in modelkwargs]
return self.mpp,self.model,self.mpperr,chi2
def moments(self, Xax, data, negamp=None, veryverbose=False, **kwargs):
"""
Returns a very simple and likely incorrect guess
"""
# TKIN, TEX, ntot, width, center, ortho fraction
return [20,10, 1e15, 1.0, 0.0, 1.0]
def annotations(self):
from decimal import Decimal # for formatting
tex_key = {'tkin':'T_K','tex':'T_{ex}','ntot':'N','fortho':'F_o','width':'\\sigma','xoff_v':'v','fillingfraction':'FF','tau':'\\tau_{1-1}'}
# small hack below: don't quantize if error > value. We want to see the values.
label_list = []
for pinfo in self.parinfo:
parname = tex_key[pinfo['parname'].strip("0123456789").lower()]
parnum = int(pinfo['parname'][-1])
if pinfo['fixed']:
formatted_value = "%s" % pinfo['value']
pm = ""
formatted_error=""
else:
formatted_value = Decimal("%g" % pinfo['value']).quantize(Decimal("%0.2g" % (min(pinfo['error'],pinfo['value']))))
pm = "$\\pm$"
formatted_error = Decimal("%g" % pinfo['error']).quantize(Decimal("%0.2g" % pinfo['error']))
label = "$%s(%i)$=%8s %s %8s" % (parname, parnum, formatted_value, pm, formatted_error)
label_list.append(label)
labels = tuple(mpcb.flatten(label_list))
return labels
class ammonia_model_vtau(ammonia_model):
def __init__(self,**kwargs):
super(ammonia_model_vtau,self).__init__()
self.parnames = ['tkin','tex','tau','width','xoff_v','fortho']
def moments(self, Xax, data, negamp=None, veryverbose=False, **kwargs):
"""
Returns a very simple and likely incorrect guess
"""
# TKIN, TEX, ntot, width, center, ortho fraction
return [20,10, 1, 1.0, 0.0, 1.0]
def __call__(self,*args,**kwargs):
if self.multisingle == 'single':
return self.onepeakammoniafit(*args,**kwargs)
elif self.multisingle == 'multi':
return self.multinh3fit(*args,**kwargs)
| mit |
jakevdp/seaborn | doc/sphinxext/ipython_directive.py | 37 | 37557 | # -*- coding: utf-8 -*-
"""
Sphinx directive to support embedded IPython code.
This directive allows pasting of entire interactive IPython sessions, prompts
and all, and their code will actually get re-executed at doc build time, with
all prompts renumbered sequentially. It also allows you to input code as a pure
python input by giving the argument python to the directive. The output looks
like an interactive ipython section.
To enable this directive, simply list it in your Sphinx ``conf.py`` file
(making sure the directory where you placed it is visible to sphinx, as is
needed for all Sphinx directives). For example, to enable syntax highlighting
and the IPython directive::
extensions = ['IPython.sphinxext.ipython_console_highlighting',
'IPython.sphinxext.ipython_directive']
The IPython directive outputs code-blocks with the language 'ipython'. So
if you do not have the syntax highlighting extension enabled as well, then
all rendered code-blocks will be uncolored. By default this directive assumes
that your prompts are unchanged IPython ones, but this can be customized.
The configurable options that can be placed in conf.py are:
ipython_savefig_dir:
The directory in which to save the figures. This is relative to the
Sphinx source directory. The default is `html_static_path`.
ipython_rgxin:
The compiled regular expression to denote the start of IPython input
lines. The default is re.compile('In \[(\d+)\]:\s?(.*)\s*'). You
shouldn't need to change this.
ipython_rgxout:
The compiled regular expression to denote the start of IPython output
lines. The default is re.compile('Out\[(\d+)\]:\s?(.*)\s*'). You
shouldn't need to change this.
ipython_promptin:
The string to represent the IPython input prompt in the generated ReST.
The default is 'In [%d]:'. This expects that the line numbers are used
in the prompt.
ipython_promptout:
The string to represent the IPython prompt in the generated ReST. The
default is 'Out [%d]:'. This expects that the line numbers are used
in the prompt.
ipython_mplbackend:
The string which specifies if the embedded Sphinx shell should import
Matplotlib and set the backend. The value specifies a backend that is
passed to `matplotlib.use()` before any lines in `ipython_execlines` are
executed. If not specified in conf.py, then the default value of 'agg' is
used. To use the IPython directive without matplotlib as a dependency, set
the value to `None`. It may end up that matplotlib is still imported
if the user specifies so in `ipython_execlines` or makes use of the
@savefig pseudo decorator.
ipython_execlines:
A list of strings to be exec'd in the embedded Sphinx shell. Typical
usage is to make certain packages always available. Set this to an empty
list if you wish to have no imports always available. If specified in
conf.py as `None`, then it has the effect of making no imports available.
If omitted from conf.py altogether, then the default value of
['import numpy as np', 'import matplotlib.pyplot as plt'] is used.
ipython_holdcount
When the @suppress pseudo-decorator is used, the execution count can be
incremented or not. The default behavior is to hold the execution count,
corresponding to a value of `True`. Set this to `False` to increment
the execution count after each suppressed command.
As an example, to use the IPython directive when `matplotlib` is not available,
one sets the backend to `None`::
ipython_mplbackend = None
An example usage of the directive is:
.. code-block:: rst
.. ipython::
In [1]: x = 1
In [2]: y = x**2
In [3]: print(y)
See http://matplotlib.org/sampledoc/ipython_directive.html for additional
documentation.
ToDo
----
- Turn the ad-hoc test() function into a real test suite.
- Break up ipython-specific functionality from matplotlib stuff into better
separated code.
Authors
-------
- John D Hunter: orignal author.
- Fernando Perez: refactoring, documentation, cleanups, port to 0.11.
- VáclavŠmilauer <eudoxos-AT-arcig.cz>: Prompt generalizations.
- Skipper Seabold, refactoring, cleanups, pure python addition
"""
from __future__ import print_function
from __future__ import unicode_literals
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Stdlib
import os
import re
import sys
import tempfile
import ast
from pandas.compat import zip, range, map, lmap, u, cStringIO as StringIO
import warnings
# To keep compatibility with various python versions
try:
from hashlib import md5
except ImportError:
from md5 import md5
# Third-party
import sphinx
from docutils.parsers.rst import directives
from docutils import nodes
from sphinx.util.compat import Directive
# Our own
from IPython import Config, InteractiveShell
from IPython.core.profiledir import ProfileDir
from IPython.utils import io
from IPython.utils.py3compat import PY3
if PY3:
from io import StringIO
text_type = str
else:
from StringIO import StringIO
text_type = unicode
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
# for tokenizing blocks
COMMENT, INPUT, OUTPUT = range(3)
#-----------------------------------------------------------------------------
# Functions and class declarations
#-----------------------------------------------------------------------------
def block_parser(part, rgxin, rgxout, fmtin, fmtout):
"""
part is a string of ipython text, comprised of at most one
input, one ouput, comments, and blank lines. The block parser
parses the text into a list of::
blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...]
where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and
data is, depending on the type of token::
COMMENT : the comment string
INPUT: the (DECORATOR, INPUT_LINE, REST) where
DECORATOR: the input decorator (or None)
INPUT_LINE: the input as string (possibly multi-line)
REST : any stdout generated by the input line (not OUTPUT)
OUTPUT: the output string, possibly multi-line
"""
block = []
lines = part.split('\n')
N = len(lines)
i = 0
decorator = None
while 1:
if i==N:
# nothing left to parse -- the last line
break
line = lines[i]
i += 1
line_stripped = line.strip()
if line_stripped.startswith('#'):
block.append((COMMENT, line))
continue
if line_stripped.startswith('@'):
# we're assuming at most one decorator -- may need to
# rethink
decorator = line_stripped
continue
# does this look like an input line?
matchin = rgxin.match(line)
if matchin:
lineno, inputline = int(matchin.group(1)), matchin.group(2)
# the ....: continuation string
continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2))
Nc = len(continuation)
# input lines can continue on for more than one line, if
# we have a '\' line continuation char or a function call
# echo line 'print'. The input line can only be
# terminated by the end of the block or an output line, so
# we parse out the rest of the input line if it is
# multiline as well as any echo text
rest = []
while i<N:
# look ahead; if the next line is blank, or a comment, or
# an output line, we're done
nextline = lines[i]
matchout = rgxout.match(nextline)
#print "nextline=%s, continuation=%s, starts=%s"%(nextline, continuation, nextline.startswith(continuation))
if matchout or nextline.startswith('#'):
break
elif nextline.startswith(continuation):
nextline = nextline[Nc:]
if nextline and nextline[0] == ' ':
nextline = nextline[1:]
inputline += '\n' + nextline
else:
rest.append(nextline)
i+= 1
block.append((INPUT, (decorator, inputline, '\n'.join(rest))))
continue
# if it looks like an output line grab all the text to the end
# of the block
matchout = rgxout.match(line)
if matchout:
lineno, output = int(matchout.group(1)), matchout.group(2)
if i<N-1:
output = '\n'.join([output] + lines[i:])
block.append((OUTPUT, output))
break
return block
class DecodingStringIO(StringIO, object):
def __init__(self,buf='',encodings=('utf8',), *args, **kwds):
super(DecodingStringIO, self).__init__(buf, *args, **kwds)
self.set_encodings(encodings)
def set_encodings(self, encodings):
self.encodings = encodings
def write(self,data):
if isinstance(data, text_type):
return super(DecodingStringIO, self).write(data)
else:
for enc in self.encodings:
try:
data = data.decode(enc)
return super(DecodingStringIO, self).write(data)
except :
pass
# default to brute utf8 if no encoding succeded
return super(DecodingStringIO, self).write(data.decode('utf8', 'replace'))
class EmbeddedSphinxShell(object):
"""An embedded IPython instance to run inside Sphinx"""
def __init__(self, exec_lines=None,state=None):
self.cout = DecodingStringIO(u'')
if exec_lines is None:
exec_lines = []
self.state = state
# Create config object for IPython
config = Config()
config.InteractiveShell.autocall = False
config.InteractiveShell.autoindent = False
config.InteractiveShell.colors = 'NoColor'
# create a profile so instance history isn't saved
tmp_profile_dir = tempfile.mkdtemp(prefix='profile_')
profname = 'auto_profile_sphinx_build'
pdir = os.path.join(tmp_profile_dir,profname)
profile = ProfileDir.create_profile_dir(pdir)
# Create and initialize global ipython, but don't start its mainloop.
# This will persist across different EmbededSphinxShell instances.
IP = InteractiveShell.instance(config=config, profile_dir=profile)
# io.stdout redirect must be done after instantiating InteractiveShell
io.stdout = self.cout
io.stderr = self.cout
# For debugging, so we can see normal output, use this:
#from IPython.utils.io import Tee
#io.stdout = Tee(self.cout, channel='stdout') # dbg
#io.stderr = Tee(self.cout, channel='stderr') # dbg
# Store a few parts of IPython we'll need.
self.IP = IP
self.user_ns = self.IP.user_ns
self.user_global_ns = self.IP.user_global_ns
self.input = ''
self.output = ''
self.is_verbatim = False
self.is_doctest = False
self.is_suppress = False
# Optionally, provide more detailed information to shell.
self.directive = None
# on the first call to the savefig decorator, we'll import
# pyplot as plt so we can make a call to the plt.gcf().savefig
self._pyplot_imported = False
# Prepopulate the namespace.
for line in exec_lines:
self.process_input_line(line, store_history=False)
def clear_cout(self):
self.cout.seek(0)
self.cout.truncate(0)
def process_input_line(self, line, store_history=True):
"""process the input, capturing stdout"""
stdout = sys.stdout
splitter = self.IP.input_splitter
try:
sys.stdout = self.cout
splitter.push(line)
more = splitter.push_accepts_more()
if not more:
try:
source_raw = splitter.source_raw_reset()[1]
except:
# recent ipython #4504
source_raw = splitter.raw_reset()
self.IP.run_cell(source_raw, store_history=store_history)
finally:
sys.stdout = stdout
def process_image(self, decorator):
"""
# build out an image directive like
# .. image:: somefile.png
# :width 4in
#
# from an input like
# savefig somefile.png width=4in
"""
savefig_dir = self.savefig_dir
source_dir = self.source_dir
saveargs = decorator.split(' ')
filename = saveargs[1]
# insert relative path to image file in source
outfile = os.path.relpath(os.path.join(savefig_dir,filename),
source_dir)
imagerows = ['.. image:: %s'%outfile]
for kwarg in saveargs[2:]:
arg, val = kwarg.split('=')
arg = arg.strip()
val = val.strip()
imagerows.append(' :%s: %s'%(arg, val))
image_file = os.path.basename(outfile) # only return file name
image_directive = '\n'.join(imagerows)
return image_file, image_directive
# Callbacks for each type of token
def process_input(self, data, input_prompt, lineno):
"""
Process data block for INPUT token.
"""
decorator, input, rest = data
image_file = None
image_directive = None
is_verbatim = decorator=='@verbatim' or self.is_verbatim
is_doctest = (decorator is not None and \
decorator.startswith('@doctest')) or self.is_doctest
is_suppress = decorator=='@suppress' or self.is_suppress
is_okexcept = decorator=='@okexcept' or self.is_okexcept
is_okwarning = decorator=='@okwarning' or self.is_okwarning
is_savefig = decorator is not None and \
decorator.startswith('@savefig')
# set the encodings to be used by DecodingStringIO
# to convert the execution output into unicode if
# needed. this attrib is set by IpythonDirective.run()
# based on the specified block options, defaulting to ['ut
self.cout.set_encodings(self.output_encoding)
input_lines = input.split('\n')
if len(input_lines) > 1:
if input_lines[-1] != "":
input_lines.append('') # make sure there's a blank line
# so splitter buffer gets reset
continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2))
if is_savefig:
image_file, image_directive = self.process_image(decorator)
ret = []
is_semicolon = False
# Hold the execution count, if requested to do so.
if is_suppress and self.hold_count:
store_history = False
else:
store_history = True
# Note: catch_warnings is not thread safe
with warnings.catch_warnings(record=True) as ws:
for i, line in enumerate(input_lines):
if line.endswith(';'):
is_semicolon = True
if i == 0:
# process the first input line
if is_verbatim:
self.process_input_line('')
self.IP.execution_count += 1 # increment it anyway
else:
# only submit the line in non-verbatim mode
self.process_input_line(line, store_history=store_history)
formatted_line = '%s %s'%(input_prompt, line)
else:
# process a continuation line
if not is_verbatim:
self.process_input_line(line, store_history=store_history)
formatted_line = '%s %s'%(continuation, line)
if not is_suppress:
ret.append(formatted_line)
if not is_suppress and len(rest.strip()) and is_verbatim:
# the "rest" is the standard output of the
# input, which needs to be added in
# verbatim mode
ret.append(rest)
self.cout.seek(0)
output = self.cout.read()
if not is_suppress and not is_semicolon:
ret.append(output)
elif is_semicolon: # get spacing right
ret.append('')
# context information
filename = self.state.document.current_source
lineno = self.state.document.current_line
# output any exceptions raised during execution to stdout
# unless :okexcept: has been specified.
if not is_okexcept and "Traceback" in output:
s = "\nException in %s at block ending on line %s\n" % (filename, lineno)
s += "Specify :okexcept: as an option in the ipython:: block to suppress this message\n"
sys.stdout.write('\n\n>>>' + ('-' * 73))
sys.stdout.write(s)
sys.stdout.write(output)
sys.stdout.write('<<<' + ('-' * 73) + '\n\n')
# output any warning raised during execution to stdout
# unless :okwarning: has been specified.
if not is_okwarning:
for w in ws:
s = "\nWarning in %s at block ending on line %s\n" % (filename, lineno)
s += "Specify :okwarning: as an option in the ipython:: block to suppress this message\n"
sys.stdout.write('\n\n>>>' + ('-' * 73))
sys.stdout.write(s)
sys.stdout.write('-' * 76 + '\n')
s=warnings.formatwarning(w.message, w.category,
w.filename, w.lineno, w.line)
sys.stdout.write(s)
sys.stdout.write('<<<' + ('-' * 73) + '\n')
self.cout.truncate(0)
return (ret, input_lines, output, is_doctest, decorator, image_file,
image_directive)
def process_output(self, data, output_prompt,
input_lines, output, is_doctest, decorator, image_file):
"""
Process data block for OUTPUT token.
"""
TAB = ' ' * 4
if is_doctest and output is not None:
found = output
found = found.strip()
submitted = data.strip()
if self.directive is None:
source = 'Unavailable'
content = 'Unavailable'
else:
source = self.directive.state.document.current_source
content = self.directive.content
# Add tabs and join into a single string.
content = '\n'.join([TAB + line for line in content])
# Make sure the output contains the output prompt.
ind = found.find(output_prompt)
if ind < 0:
e = ('output does not contain output prompt\n\n'
'Document source: {0}\n\n'
'Raw content: \n{1}\n\n'
'Input line(s):\n{TAB}{2}\n\n'
'Output line(s):\n{TAB}{3}\n\n')
e = e.format(source, content, '\n'.join(input_lines),
repr(found), TAB=TAB)
raise RuntimeError(e)
found = found[len(output_prompt):].strip()
# Handle the actual doctest comparison.
if decorator.strip() == '@doctest':
# Standard doctest
if found != submitted:
e = ('doctest failure\n\n'
'Document source: {0}\n\n'
'Raw content: \n{1}\n\n'
'On input line(s):\n{TAB}{2}\n\n'
'we found output:\n{TAB}{3}\n\n'
'instead of the expected:\n{TAB}{4}\n\n')
e = e.format(source, content, '\n'.join(input_lines),
repr(found), repr(submitted), TAB=TAB)
raise RuntimeError(e)
else:
self.custom_doctest(decorator, input_lines, found, submitted)
def process_comment(self, data):
"""Process data fPblock for COMMENT token."""
if not self.is_suppress:
return [data]
def save_image(self, image_file):
"""
Saves the image file to disk.
"""
self.ensure_pyplot()
command = ('plt.gcf().savefig("%s", bbox_inches="tight", '
'dpi=100)' % image_file)
#print 'SAVEFIG', command # dbg
self.process_input_line('bookmark ipy_thisdir', store_history=False)
self.process_input_line('cd -b ipy_savedir', store_history=False)
self.process_input_line(command, store_history=False)
self.process_input_line('cd -b ipy_thisdir', store_history=False)
self.process_input_line('bookmark -d ipy_thisdir', store_history=False)
self.clear_cout()
def process_block(self, block):
"""
process block from the block_parser and return a list of processed lines
"""
ret = []
output = None
input_lines = None
lineno = self.IP.execution_count
input_prompt = self.promptin % lineno
output_prompt = self.promptout % lineno
image_file = None
image_directive = None
for token, data in block:
if token == COMMENT:
out_data = self.process_comment(data)
elif token == INPUT:
(out_data, input_lines, output, is_doctest, decorator,
image_file, image_directive) = \
self.process_input(data, input_prompt, lineno)
elif token == OUTPUT:
out_data = \
self.process_output(data, output_prompt,
input_lines, output, is_doctest,
decorator, image_file)
if out_data:
ret.extend(out_data)
# save the image files
if image_file is not None:
self.save_image(image_file)
return ret, image_directive
def ensure_pyplot(self):
"""
Ensures that pyplot has been imported into the embedded IPython shell.
Also, makes sure to set the backend appropriately if not set already.
"""
# We are here if the @figure pseudo decorator was used. Thus, it's
# possible that we could be here even if python_mplbackend were set to
# `None`. That's also strange and perhaps worthy of raising an
# exception, but for now, we just set the backend to 'agg'.
if not self._pyplot_imported:
if 'matplotlib.backends' not in sys.modules:
# Then ipython_matplotlib was set to None but there was a
# call to the @figure decorator (and ipython_execlines did
# not set a backend).
#raise Exception("No backend was set, but @figure was used!")
import matplotlib
matplotlib.use('agg')
# Always import pyplot into embedded shell.
self.process_input_line('import matplotlib.pyplot as plt',
store_history=False)
self._pyplot_imported = True
def process_pure_python(self, content):
"""
content is a list of strings. it is unedited directive content
This runs it line by line in the InteractiveShell, prepends
prompts as needed capturing stderr and stdout, then returns
the content as a list as if it were ipython code
"""
output = []
savefig = False # keep up with this to clear figure
multiline = False # to handle line continuation
multiline_start = None
fmtin = self.promptin
ct = 0
for lineno, line in enumerate(content):
line_stripped = line.strip()
if not len(line):
output.append(line)
continue
# handle decorators
if line_stripped.startswith('@'):
output.extend([line])
if 'savefig' in line:
savefig = True # and need to clear figure
continue
# handle comments
if line_stripped.startswith('#'):
output.extend([line])
continue
# deal with lines checking for multiline
continuation = u' %s:'% ''.join(['.']*(len(str(ct))+2))
if not multiline:
modified = u"%s %s" % (fmtin % ct, line_stripped)
output.append(modified)
ct += 1
try:
ast.parse(line_stripped)
output.append(u'')
except Exception: # on a multiline
multiline = True
multiline_start = lineno
else: # still on a multiline
modified = u'%s %s' % (continuation, line)
output.append(modified)
# if the next line is indented, it should be part of multiline
if len(content) > lineno + 1:
nextline = content[lineno + 1]
if len(nextline) - len(nextline.lstrip()) > 3:
continue
try:
mod = ast.parse(
'\n'.join(content[multiline_start:lineno+1]))
if isinstance(mod.body[0], ast.FunctionDef):
# check to see if we have the whole function
for element in mod.body[0].body:
if isinstance(element, ast.Return):
multiline = False
else:
output.append(u'')
multiline = False
except Exception:
pass
if savefig: # clear figure if plotted
self.ensure_pyplot()
self.process_input_line('plt.clf()', store_history=False)
self.clear_cout()
savefig = False
return output
def custom_doctest(self, decorator, input_lines, found, submitted):
"""
Perform a specialized doctest.
"""
from .custom_doctests import doctests
args = decorator.split()
doctest_type = args[1]
if doctest_type in doctests:
doctests[doctest_type](self, args, input_lines, found, submitted)
else:
e = "Invalid option to @doctest: {0}".format(doctest_type)
raise Exception(e)
class IPythonDirective(Directive):
has_content = True
required_arguments = 0
optional_arguments = 4 # python, suppress, verbatim, doctest
final_argumuent_whitespace = True
option_spec = { 'python': directives.unchanged,
'suppress' : directives.flag,
'verbatim' : directives.flag,
'doctest' : directives.flag,
'okexcept': directives.flag,
'okwarning': directives.flag,
'output_encoding': directives.unchanged_required
}
shell = None
seen_docs = set()
def get_config_options(self):
# contains sphinx configuration variables
config = self.state.document.settings.env.config
# get config variables to set figure output directory
confdir = self.state.document.settings.env.app.confdir
savefig_dir = config.ipython_savefig_dir
source_dir = os.path.dirname(self.state.document.current_source)
if savefig_dir is None:
savefig_dir = config.html_static_path
if isinstance(savefig_dir, list):
savefig_dir = savefig_dir[0] # safe to assume only one path?
savefig_dir = os.path.join(confdir, savefig_dir)
# get regex and prompt stuff
rgxin = config.ipython_rgxin
rgxout = config.ipython_rgxout
promptin = config.ipython_promptin
promptout = config.ipython_promptout
mplbackend = config.ipython_mplbackend
exec_lines = config.ipython_execlines
hold_count = config.ipython_holdcount
return (savefig_dir, source_dir, rgxin, rgxout,
promptin, promptout, mplbackend, exec_lines, hold_count)
def setup(self):
# Get configuration values.
(savefig_dir, source_dir, rgxin, rgxout, promptin, promptout,
mplbackend, exec_lines, hold_count) = self.get_config_options()
if self.shell is None:
# We will be here many times. However, when the
# EmbeddedSphinxShell is created, its interactive shell member
# is the same for each instance.
if mplbackend:
import matplotlib
# Repeated calls to use() will not hurt us since `mplbackend`
# is the same each time.
matplotlib.use(mplbackend)
# Must be called after (potentially) importing matplotlib and
# setting its backend since exec_lines might import pylab.
self.shell = EmbeddedSphinxShell(exec_lines, self.state)
# Store IPython directive to enable better error messages
self.shell.directive = self
# reset the execution count if we haven't processed this doc
#NOTE: this may be borked if there are multiple seen_doc tmp files
#check time stamp?
if not self.state.document.current_source in self.seen_docs:
self.shell.IP.history_manager.reset()
self.shell.IP.execution_count = 1
self.shell.IP.prompt_manager.width = 0
self.seen_docs.add(self.state.document.current_source)
# and attach to shell so we don't have to pass them around
self.shell.rgxin = rgxin
self.shell.rgxout = rgxout
self.shell.promptin = promptin
self.shell.promptout = promptout
self.shell.savefig_dir = savefig_dir
self.shell.source_dir = source_dir
self.shell.hold_count = hold_count
# setup bookmark for saving figures directory
self.shell.process_input_line('bookmark ipy_savedir %s'%savefig_dir,
store_history=False)
self.shell.clear_cout()
return rgxin, rgxout, promptin, promptout
def teardown(self):
# delete last bookmark
self.shell.process_input_line('bookmark -d ipy_savedir',
store_history=False)
self.shell.clear_cout()
def run(self):
debug = False
#TODO, any reason block_parser can't be a method of embeddable shell
# then we wouldn't have to carry these around
rgxin, rgxout, promptin, promptout = self.setup()
options = self.options
self.shell.is_suppress = 'suppress' in options
self.shell.is_doctest = 'doctest' in options
self.shell.is_verbatim = 'verbatim' in options
self.shell.is_okexcept = 'okexcept' in options
self.shell.is_okwarning = 'okwarning' in options
self.shell.output_encoding = [options.get('output_encoding', 'utf8')]
# handle pure python code
if 'python' in self.arguments:
content = self.content
self.content = self.shell.process_pure_python(content)
parts = '\n'.join(self.content).split('\n\n')
lines = ['.. code-block:: ipython', '']
figures = []
for part in parts:
block = block_parser(part, rgxin, rgxout, promptin, promptout)
if len(block):
rows, figure = self.shell.process_block(block)
for row in rows:
lines.extend([' %s'%line for line in row.split('\n')])
if figure is not None:
figures.append(figure)
for figure in figures:
lines.append('')
lines.extend(figure.split('\n'))
lines.append('')
if len(lines)>2:
if debug:
print('\n'.join(lines))
else:
# This has to do with input, not output. But if we comment
# these lines out, then no IPython code will appear in the
# final output.
self.state_machine.insert_input(
lines, self.state_machine.input_lines.source(0))
# cleanup
self.teardown()
return []
# Enable as a proper Sphinx directive
def setup(app):
setup.app = app
app.add_directive('ipython', IPythonDirective)
app.add_config_value('ipython_savefig_dir', None, 'env')
app.add_config_value('ipython_rgxin',
re.compile('In \[(\d+)\]:\s?(.*)\s*'), 'env')
app.add_config_value('ipython_rgxout',
re.compile('Out\[(\d+)\]:\s?(.*)\s*'), 'env')
app.add_config_value('ipython_promptin', 'In [%d]:', 'env')
app.add_config_value('ipython_promptout', 'Out[%d]:', 'env')
# We could just let matplotlib pick whatever is specified as the default
# backend in the matplotlibrc file, but this would cause issues if the
# backend didn't work in headless environments. For this reason, 'agg'
# is a good default backend choice.
app.add_config_value('ipython_mplbackend', 'agg', 'env')
# If the user sets this config value to `None`, then EmbeddedSphinxShell's
# __init__ method will treat it as [].
execlines = ['import numpy as np', 'import matplotlib.pyplot as plt']
app.add_config_value('ipython_execlines', execlines, 'env')
app.add_config_value('ipython_holdcount', True, 'env')
# Simple smoke test, needs to be converted to a proper automatic test.
def test():
examples = [
r"""
In [9]: pwd
Out[9]: '/home/jdhunter/py4science/book'
In [10]: cd bookdata/
/home/jdhunter/py4science/book/bookdata
In [2]: from pylab import *
In [2]: ion()
In [3]: im = imread('stinkbug.png')
@savefig mystinkbug.png width=4in
In [4]: imshow(im)
Out[4]: <matplotlib.image.AxesImage object at 0x39ea850>
""",
r"""
In [1]: x = 'hello world'
# string methods can be
# used to alter the string
@doctest
In [2]: x.upper()
Out[2]: 'HELLO WORLD'
@verbatim
In [3]: x.st<TAB>
x.startswith x.strip
""",
r"""
In [130]: url = 'http://ichart.finance.yahoo.com/table.csv?s=CROX\
.....: &d=9&e=22&f=2009&g=d&a=1&br=8&c=2006&ignore=.csv'
In [131]: print url.split('&')
['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22', 'f=2009', 'g=d', 'a=1', 'b=8', 'c=2006', 'ignore=.csv']
In [60]: import urllib
""",
r"""\
In [133]: import numpy.random
@suppress
In [134]: numpy.random.seed(2358)
@doctest
In [135]: numpy.random.rand(10,2)
Out[135]:
array([[ 0.64524308, 0.59943846],
[ 0.47102322, 0.8715456 ],
[ 0.29370834, 0.74776844],
[ 0.99539577, 0.1313423 ],
[ 0.16250302, 0.21103583],
[ 0.81626524, 0.1312433 ],
[ 0.67338089, 0.72302393],
[ 0.7566368 , 0.07033696],
[ 0.22591016, 0.77731835],
[ 0.0072729 , 0.34273127]])
""",
r"""
In [106]: print x
jdh
In [109]: for i in range(10):
.....: print i
.....:
.....:
0
1
2
3
4
5
6
7
8
9
""",
r"""
In [144]: from pylab import *
In [145]: ion()
# use a semicolon to suppress the output
@savefig test_hist.png width=4in
In [151]: hist(np.random.randn(10000), 100);
@savefig test_plot.png width=4in
In [151]: plot(np.random.randn(10000), 'o');
""",
r"""
# use a semicolon to suppress the output
In [151]: plt.clf()
@savefig plot_simple.png width=4in
In [151]: plot([1,2,3])
@savefig hist_simple.png width=4in
In [151]: hist(np.random.randn(10000), 100);
""",
r"""
# update the current fig
In [151]: ylabel('number')
In [152]: title('normal distribution')
@savefig hist_with_text.png
In [153]: grid(True)
@doctest float
In [154]: 0.1 + 0.2
Out[154]: 0.3
@doctest float
In [155]: np.arange(16).reshape(4,4)
Out[155]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
In [1]: x = np.arange(16, dtype=float).reshape(4,4)
In [2]: x[0,0] = np.inf
In [3]: x[0,1] = np.nan
@doctest float
In [4]: x
Out[4]:
array([[ inf, nan, 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
""",
]
# skip local-file depending first example:
examples = examples[1:]
#ipython_directive.DEBUG = True # dbg
#options = dict(suppress=True) # dbg
options = dict()
for example in examples:
content = example.split('\n')
IPythonDirective('debug', arguments=None, options=options,
content=content, lineno=0,
content_offset=None, block_text=None,
state=None, state_machine=None,
)
# Run test suite as a script
if __name__=='__main__':
if not os.path.isdir('_static'):
os.mkdir('_static')
test()
print('All OK? Check figures in _static/')
| bsd-3-clause |
INCF/BIDS2ISATab | setup.py | 1 | 2176 | from setuptools import setup
import os
here = os.path.abspath(os.path.dirname(__file__))
setup(
name="BIDS2ISATab",
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# http://packaging.python.org/en/latest/tutorial.html#version
version='0.1.0',
description="Command line tool generating ISA-Tab compatible description from a Brain Imaging Data Structure "
"compatible dataset.",
long_description="Command line tool generating ISA-Tab compatible description from a Brain Imaging Data Structure "
"compatible dataset.",
# The project URL.
url='https://github.com/INCF/BIDS2ISATab',
# Choose your license
license='BSD',
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 4 - Beta',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: BSD License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
],
# What does your project relate to?
keywords='bids isatab',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages.
packages=["bids2isatab"],
# List run-time dependencies here. These will be installed by pip when your
# project is installed.
install_requires = ["future",
"pandas",
'nibabel'],
include_package_data=True,
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
entry_points={
'console_scripts': [
'bids2isatab=bids2isatab.main:main',
],
},
)
| apache-2.0 |
zooniverse/aggregation | experimental/clusteringAlg/adaptiveDBSCAN.py | 2 | 4734 | #!/usr/bin/env python
__author__ = 'greg'
from sklearn.cluster import DBSCAN
import numpy as np
import math
def dist(c1,c2):
return math.sqrt((c1[0]-c2[0])**2 + (c1[1]-c2[1])**2)
class CannotSplit(Exception):
def __init__(self,value):
self.value = value
def __str__(self):
return ""
samples_needed = 3
def adaptiveDBSCAN(XYpts,user_ids):
if XYpts == []:
return []
pts_in_each_cluster = []
users_in_each_cluster = []
cluster_centers = []
#increase the epsilon until we don't have any nearby clusters corresponding to non-overlapping
#sets of users
X = np.array(XYpts)
#for epsilon in [5,10,15,20,25,30]:
for first_epsilon in [100,200,300,400]:
db = DBSCAN(eps=first_epsilon, min_samples=samples_needed).fit(X)
labels = db.labels_
pts_in_each_cluster = []
users_in_each_cluster = []
cluster_centers = []
for k in sorted(set(labels)):
if k == -1:
continue
class_member_mask = (labels == k)
pts_in_cluster = list(X[class_member_mask])
xSet,ySet = zip(*pts_in_cluster)
cluster_centers.append((np.mean(xSet),np.mean(ySet)))
pts_in_each_cluster.append(pts_in_cluster[:])
users_in_each_cluster.append([u for u,l in zip(user_ids,labels) if l == k])
#do we have any adjacent clusters with non-overlapping sets of users
#if so, we should merge them by increasing the epsilon value
cluster_compare = []
for cluster_index, (c1,users) in enumerate(zip(cluster_centers,users_in_each_cluster)):
for cluster_index, (c2,users2) in enumerate(zip(cluster_centers[cluster_index+1:],users_in_each_cluster[cluster_index+1:])):
overlappingUsers = [u for u in users if u in users2]
cluster_compare.append((dist(c1,c2),overlappingUsers))
cluster_compare.sort(key = lambda x:x[0])
needToMerge = [] in [c[1] for c in cluster_compare[:10]]
if not(needToMerge):
break
#print epsilon
#print [c[1] for c in cluster_compare[:10]]
centers_to_return = []
assert not(needToMerge)
#do we need to split any clusters?
for cluster_index in range(len(cluster_centers)):
#print "splitting"
needToSplit = (sorted(users_in_each_cluster[cluster_index]) != sorted(list(set(users_in_each_cluster[cluster_index]))))
if needToSplit:
subcluster_centers = []
stillToSplit = []
X = np.array(pts_in_each_cluster[cluster_index])
#for epsilon in [30,25,20,15,10,5,1,0.1,0.01]:
for second_epsilon in range(200,1,-2):#[400,300,200,100,80,75,65,60,50,25,24,23,22,21,20,19,18,17,16,15,14,13,10,5,1]:
db = DBSCAN(eps=second_epsilon, min_samples=samples_needed).fit(X)
labels = db.labels_
subcluster_centers = []
needToSplit = False
for k in sorted(set(labels)):
if k == -1:
continue
class_member_mask = (labels == k)
users_in_subcluster = [u for u,l in zip(users_in_each_cluster[cluster_index],labels) if l == k]
needToSplit = (sorted(users_in_subcluster) != sorted(list(set(users_in_subcluster))))
if needToSplit:
stillToSplit = list(X[class_member_mask])
break
pts_in_cluster = list(X[class_member_mask])
xSet,ySet = zip(*pts_in_cluster)
subcluster_centers.append((np.mean(xSet),np.mean(ySet)))
if not(needToSplit):
break
if needToSplit:
print "second is " + str(second_epsilon)
print stillToSplit
for i in range(len(stillToSplit)):
p1 = stillToSplit[i]
for j in range(len(stillToSplit[i+1:])):
p2 = stillToSplit[j+i+1]
print math.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2),
#print (i,j+i+1),
print
print X
print users_in_each_cluster[cluster_index]
raise CannotSplit(pts_in_each_cluster[cluster_index])
centers_to_return.extend(subcluster_centers)
#if needToSplit:
# print pts_in_each_cluster[cluster_index]
# print users_in_each_cluster[cluster_index]
#else:
else:
centers_to_return.append(cluster_centers[cluster_index])
return centers_to_return | apache-2.0 |
jrleja/bsfh | misc/timings_pyfsps.py | 3 | 4274 | #compare a lookup table of spectra at ages and metallicities to
#calls to fsps.sps.get_spectrum() for different metallicities
import time, os, subprocess, re, sys
import numpy as np
#import matplotlib.pyplot as pl
import fsps
from prospect import sources as sps_basis
from prospect.models import sedmodel
def run_command(cmd):
"""
Open a child process, and return its exit status and stdout.
"""
child = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE,
stdin=subprocess.PIPE, stdout=subprocess.PIPE)
out = [s for s in child.stdout]
w = child.wait()
return os.WEXITSTATUS(w), out
# Check to make sure that the required environment variable is present.
try:
ev = os.environ["SPS_HOME"]
except KeyError:
raise ImportError("You need to have the SPS_HOME environment variable")
# Check the SVN revision number.
cmd = ["svnversion", ev]
stat, out = run_command(" ".join(cmd))
fsps_vers = int(re.match("^([0-9])+", out[0]).group(0))
sps = fsps.StellarPopulation(zcontinuous=True)
print('FSPS version = {}'.format(fsps_vers))
print('Zs={0}, N_lambda={1}'.format(sps.zlegend, len(sps.wavelengths)))
print('single age')
def spec_from_fsps(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
wave, spec = sps.get_spectrum(peraa=True, tage = sps.params['tage'])
#print(spec.shape)
return time.time()-t0
def mags_from_fsps(z, t, s):
t0 = time.time()
sps.params['zred']=t
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
mags = sps.get_mags(tage = sps.params['tage'], redshift=0.0)
#print(spec.shape)
return time.time()-t0
def spec_from_ztinterp(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
sps.params['imf3'] = s
spec, m, l = sps.ztinterp(sps.params['logzsol'], sps.params['tage'], peraa=True)
#print(spec.shape)
return time.time()-t0
if sys.argv[1] == 'mags':
from_fsps = mags_from_fsps
print('timing get_mags')
print('nbands = {}'.format(len(sps.get_mags(tage=1.0))))
elif sys.argv[1] == 'spec':
from_fsps = spec_from_fsps
print('timing get_spectrum')
elif sys.argv[1] == 'ztinterp':
from_fsps = spec_from_ztinterp
print('timing get_spectrum')
elif sys.argv[1] == 'sedpy':
from sedpy import observate
nbands = len(sps.get_mags(tage=1.0))
fnames = nbands * ['sdss_r0']
filters = observate.load_filters(fnames)
def mags_from_sedpy(z, t, s):
t0 = time.time()
sps.params['logzsol'] = z
sps.params['sigma_smooth'] = s
sps.params['tage'] = t
wave, spec = sps.get_spectrum(peraa=True,
tage = sps.params['tage'])
mags = observate.getSED(wave, spec, filters)
return time.time()-t0
from_fsps = mags_from_sedpy
sps.params['add_neb_emission'] = False
sps.params['smooth_velocity'] = True
sps.params['sfh'] = 0
ntry = 30
zz = np.random.uniform(-1,0,ntry)
tt = np.random.uniform(0.1,4,ntry)
ss = np.random.uniform(1,2.5,ntry)
#make sure all z's already compiled
_ =[from_fsps(z, 1.0, 0.0) for z in [-1, -0.8, -0.6, -0.4, -0.2, 0.0]]
all_dur = []
print('no neb emission:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], ss[i])
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
print('no neb emission, no smooth:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], 0.0)
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
sps.params['add_neb_emission'] = True
print('neb emission:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], ss[i])
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
print('neb emission, no smooth:')
dur_many = np.zeros(ntry)
for i in xrange(ntry):
dur_many[i] = from_fsps(zz[i], tt[i], 0.0)
print('<t/call>={0}s, sigma_t={1}s'.format(dur_many.mean(), dur_many.std()))
all_dur += [dur_many]
| mit |
ClinicalGraphics/scikit-image | doc/examples/xx_applications/plot_morphology.py | 6 | 8329 | """
=======================
Morphological Filtering
=======================
Morphological image processing is a collection of non-linear operations related
to the shape or morphology of features in an image, such as boundaries,
skeletons, etc. In any given technique, we probe an image with a small shape or
template called a structuring element, which defines the region of interest or
neighborhood around a pixel.
In this document we outline the following basic morphological operations:
1. Erosion
2. Dilation
3. Opening
4. Closing
5. White Tophat
6. Black Tophat
7. Skeletonize
8. Convex Hull
To get started, let's load an image using ``io.imread``. Note that morphology
functions only work on gray-scale or binary images, so we set ``as_grey=True``.
"""
import matplotlib.pyplot as plt
from skimage.data import data_dir
from skimage.util import img_as_ubyte
from skimage import io
phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
fig, ax = plt.subplots()
ax.imshow(phantom, cmap=plt.cm.gray)
"""
.. image:: PLOT2RST.current_figure
Let's also define a convenience function for plotting comparisons:
"""
def plot_comparison(original, filtered, filter_name):
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True)
ax1.imshow(original, cmap=plt.cm.gray)
ax1.set_title('original')
ax1.axis('off')
ax1.set_adjustable('box-forced')
ax2.imshow(filtered, cmap=plt.cm.gray)
ax2.set_title(filter_name)
ax2.axis('off')
ax2.set_adjustable('box-forced')
"""
Erosion
=======
Morphological ``erosion`` sets a pixel at (i, j) to the *minimum over all
pixels in the neighborhood centered at (i, j)*. The structuring element,
``selem``, passed to ``erosion`` is a boolean array that describes this
neighborhood. Below, we use ``disk`` to create a circular structuring element,
which we use for most of the following examples.
"""
from skimage.morphology import erosion, dilation, opening, closing, white_tophat
from skimage.morphology import black_tophat, skeletonize, convex_hull_image
from skimage.morphology import disk
selem = disk(6)
eroded = erosion(phantom, selem)
plot_comparison(phantom, eroded, 'erosion')
"""
.. image:: PLOT2RST.current_figure
Notice how the white boundary of the image disappears or gets eroded as we
increase the size of the disk. Also notice the increase in size of the two
black ellipses in the center and the disappearance of the 3 light grey
patches in the lower part of the image.
Dilation
========
Morphological ``dilation`` sets a pixel at (i, j) to the *maximum over all
pixels in the neighborhood centered at (i, j)*. Dilation enlarges bright
regions and shrinks dark regions.
"""
dilated = dilation(phantom, selem)
plot_comparison(phantom, dilated, 'dilation')
"""
.. image:: PLOT2RST.current_figure
Notice how the white boundary of the image thickens, or gets dilated, as we
increase the size of the disk. Also notice the decrease in size of the two
black ellipses in the centre, and the thickening of the light grey circle in
the center and the 3 patches in the lower part of the image.
Opening
=======
Morphological ``opening`` on an image is defined as an *erosion followed by a
dilation*. Opening can remove small bright spots (i.e. "salt") and connect
small dark cracks.
"""
opened = opening(phantom, selem)
plot_comparison(phantom, opened, 'opening')
"""
.. image:: PLOT2RST.current_figure
Since ``opening`` an image starts with an erosion operation, light regions that
are *smaller* than the structuring element are removed. The dilation operation
that follows ensures that light regions that are *larger* than the structuring
element retain their original size. Notice how the light and dark shapes in the
center their original thickness but the 3 lighter patches in the bottom get
completely eroded. The size dependence is highlighted by the outer white ring:
The parts of the ring thinner than the structuring element were completely
erased, while the thicker region at the top retains its original thickness.
Closing
=======
Morphological ``closing`` on an image is defined as a *dilation followed by an
erosion*. Closing can remove small dark spots (i.e. "pepper") and connect
small bright cracks.
To illustrate this more clearly, let's add a small crack to the white border:
"""
phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
phantom[10:30, 200:210] = 0
closed = closing(phantom, selem)
plot_comparison(phantom, closed, 'closing')
"""
.. image:: PLOT2RST.current_figure
Since ``closing`` an image starts with an dilation operation, dark regions
that are *smaller* than the structuring element are removed. The dilation
operation that follows ensures that dark regions that are *larger* than the
structuring element retain their original size. Notice how the white ellipses
at the bottom get connected because of dilation, but other dark region retain
their original sizes. Also notice how the crack we added is mostly removed.
White tophat
============
The ``white_tophat`` of an image is defined as the *image minus its
morphological opening*. This operation returns the bright spots of the image
that are smaller than the structuring element.
To make things interesting, we'll add bright and dark spots to the image:
"""
phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
phantom[340:350, 200:210] = 255
phantom[100:110, 200:210] = 0
w_tophat = white_tophat(phantom, selem)
plot_comparison(phantom, w_tophat, 'white tophat')
"""
.. image:: PLOT2RST.current_figure
As you can see, the 10-pixel wide white square is highlighted since it is
smaller than the structuring element. Also, the thin, white edges around most
of the ellipse are retained because they're smaller than the structuring
element, but the thicker region at the top disappears.
Black tophat
============
The ``black_tophat`` of an image is defined as its morphological **closing
minus the original image**. This operation returns the *dark spots of the
image that are smaller than the structuring element*.
"""
b_tophat = black_tophat(phantom, selem)
plot_comparison(phantom, b_tophat, 'black tophat')
"""
.. image:: PLOT2RST.current_figure
As you can see, the 10-pixel wide black square is highlighted since it is
smaller than the structuring element.
Duality
-------
As you should have noticed, many of these operations are simply the reverse
of another operation. This duality can be summarized as follows:
1. Erosion <-> Dilation
2. Opening <-> Closing
3. White tophat <-> Black tophat
Skeletonize
===========
Thinning is used to reduce each connected component in a binary image to a
*single-pixel wide skeleton*. It is important to note that this is performed
on binary images only.
"""
from skimage import img_as_bool
horse = ~img_as_bool(io.imread(data_dir+'/horse.png', as_grey=True))
sk = skeletonize(horse)
plot_comparison(horse, sk, 'skeletonize')
"""
.. image:: PLOT2RST.current_figure
As the name suggests, this technique is used to thin the image to 1-pixel wide
skeleton by applying thinning successively.
Convex hull
===========
The ``convex_hull_image`` is the *set of pixels included in the smallest
convex polygon that surround all white pixels in the input image*. Again note
that this is also performed on binary images.
"""
hull1 = convex_hull_image(horse)
plot_comparison(horse, hull1, 'convex hull')
"""
.. image:: PLOT2RST.current_figure
As the figure illustrates, ``convex_hull_image`` gives the smallest polygon
which covers the white or True completely in the image.
If we add a small grain to the image, we can see how the convex hull adapts to
enclose that grain:
"""
import numpy as np
horse2 = np.copy(horse)
horse2[45:50, 75:80] = 1
hull2 = convex_hull_image(horse2)
plot_comparison(horse2, hull2, 'convex hull')
"""
.. image:: PLOT2RST.current_figure
Additional Resources
====================
1. `MathWorks tutorial on morphological processing
<http://www.mathworks.com/help/images/morphology-fundamentals-dilation-and-erosion.html>`_
2. `Auckland university's tutorial on Morphological Image Processing
<http://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm>`_
3. http://en.wikipedia.org/wiki/Mathematical_morphology
"""
plt.show()
| bsd-3-clause |
codenote/chromium-test | ppapi/native_client/tests/breakpad_crash_test/crash_dump_tester.py | 6 | 8213 | #!/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.
import os
import subprocess
import sys
import tempfile
import time
script_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(script_dir,
'../../tools/browser_tester'))
import browser_tester
import browsertester.browserlauncher
# This script extends browser_tester to check for the presence of
# Breakpad crash dumps.
# This reads a file of lines containing 'key:value' pairs.
# The file contains entries like the following:
# plat:Win32
# prod:Chromium
# ptype:nacl-loader
# rept:crash svc
def ReadDumpTxtFile(filename):
dump_info = {}
fh = open(filename, 'r')
for line in fh:
if ':' in line:
key, value = line.rstrip().split(':', 1)
dump_info[key] = value
fh.close()
return dump_info
def StartCrashService(browser_path, dumps_dir, windows_pipe_name,
cleanup_funcs, crash_service_exe):
# Find crash_service.exe relative to chrome.exe. This is a bit icky.
browser_dir = os.path.dirname(browser_path)
proc = subprocess.Popen([os.path.join(browser_dir, crash_service_exe),
'--v=1', # Verbose output for debugging failures
'--dumps-dir=%s' % dumps_dir,
'--pipe-name=%s' % windows_pipe_name])
def Cleanup():
# Note that if the process has already exited, this will raise
# an 'Access is denied' WindowsError exception, but
# crash_service.exe is not supposed to do this and such
# behaviour should make the test fail.
proc.terminate()
status = proc.wait()
sys.stdout.write('crash_dump_tester: %s exited with status %s\n'
% (crash_service_exe, status))
cleanup_funcs.append(Cleanup)
def ListPathsInDir(dir_path):
if os.path.exists(dir_path):
return [os.path.join(dir_path, name)
for name in os.listdir(dir_path)]
else:
return []
def GetDumpFiles(dumps_dirs):
all_files = [filename
for dumps_dir in dumps_dirs
for filename in ListPathsInDir(dumps_dir)]
sys.stdout.write('crash_dump_tester: Found %i files\n' % len(all_files))
for dump_file in all_files:
sys.stdout.write(' %s (size %i)\n'
% (dump_file, os.stat(dump_file).st_size))
return [dump_file for dump_file in all_files
if dump_file.endswith('.dmp')]
def Main(cleanup_funcs):
parser = browser_tester.BuildArgParser()
parser.add_option('--expected_crash_dumps', dest='expected_crash_dumps',
type=int, default=0,
help='The number of crash dumps that we should expect')
parser.add_option('--expected_process_type_for_crash',
dest='expected_process_type_for_crash',
type=str, default='nacl-loader',
help='The type of Chromium process that we expect the '
'crash dump to be for')
# Ideally we would just query the OS here to find out whether we are
# running x86-32 or x86-64 Windows, but Python's win32api module
# does not contain a wrapper for GetNativeSystemInfo(), which is
# what NaCl uses to check this, or for IsWow64Process(), which is
# what Chromium uses. Instead, we just rely on the build system to
# tell us.
parser.add_option('--win64', dest='win64', action='store_true',
help='Pass this if we are running tests for x86-64 Windows')
options, args = parser.parse_args()
temp_dir = tempfile.mkdtemp(prefix='nacl_crash_dump_tester_')
def CleanUpTempDir():
browsertester.browserlauncher.RemoveDirectory(temp_dir)
cleanup_funcs.append(CleanUpTempDir)
# To get a guaranteed unique pipe name, use the base name of the
# directory we just created.
windows_pipe_name = r'\\.\pipe\%s_crash_service' % os.path.basename(temp_dir)
# This environment variable enables Breakpad crash dumping in
# non-official builds of Chromium.
os.environ['CHROME_HEADLESS'] = '1'
if sys.platform == 'win32':
dumps_dir = temp_dir
# Override the default (global) Windows pipe name that Chromium will
# use for out-of-process crash reporting.
os.environ['CHROME_BREAKPAD_PIPE_NAME'] = windows_pipe_name
# Launch the x86-32 crash service so that we can handle crashes in
# the browser process.
StartCrashService(options.browser_path, dumps_dir, windows_pipe_name,
cleanup_funcs, 'crash_service.exe')
if options.win64:
# Launch the x86-64 crash service so that we can handle crashes
# in the NaCl loader process (nacl64.exe).
StartCrashService(options.browser_path, dumps_dir, windows_pipe_name,
cleanup_funcs, 'crash_service64.exe')
# We add a delay because there is probably a race condition:
# crash_service.exe might not have finished doing
# CreateNamedPipe() before NaCl does a crash dump and tries to
# connect to that pipe.
# TODO(mseaborn): We could change crash_service.exe to report when
# it has successfully created the named pipe.
time.sleep(1)
elif sys.platform == 'darwin':
dumps_dir = temp_dir
os.environ['BREAKPAD_DUMP_LOCATION'] = dumps_dir
elif sys.platform.startswith('linux'):
# The "--user-data-dir" option is not effective for the Breakpad
# setup in Linux Chromium, because Breakpad is initialized before
# "--user-data-dir" is read. So we set HOME to redirect the crash
# dumps to a temporary directory.
home_dir = temp_dir
os.environ['HOME'] = home_dir
options.enable_crash_reporter = True
result = browser_tester.Run(options.url, options)
# Find crash dump results.
if sys.platform.startswith('linux'):
# Look in "~/.config/*/Crash Reports". This will find crash
# reports under ~/.config/chromium or ~/.config/google-chrome, or
# under other subdirectories in case the branding is changed.
dumps_dirs = [os.path.join(path, 'Crash Reports')
for path in ListPathsInDir(os.path.join(home_dir, '.config'))]
else:
dumps_dirs = [dumps_dir]
dmp_files = GetDumpFiles(dumps_dirs)
failed = False
msg = ('crash_dump_tester: ERROR: Got %i crash dumps but expected %i\n' %
(len(dmp_files), options.expected_crash_dumps))
if len(dmp_files) != options.expected_crash_dumps:
sys.stdout.write(msg)
failed = True
for dump_file in dmp_files:
# Sanity check: Make sure dumping did not fail after opening the file.
msg = 'crash_dump_tester: ERROR: Dump file is empty\n'
if os.stat(dump_file).st_size == 0:
sys.stdout.write(msg)
failed = True
# On Windows, the crash dumps should come in pairs of a .dmp and
# .txt file.
if sys.platform == 'win32':
second_file = dump_file[:-4] + '.txt'
msg = ('crash_dump_tester: ERROR: File %r is missing a corresponding '
'%r file\n' % (dump_file, second_file))
if not os.path.exists(second_file):
sys.stdout.write(msg)
failed = True
continue
# Check that the crash dump comes from the NaCl process.
dump_info = ReadDumpTxtFile(second_file)
if 'ptype' in dump_info:
msg = ('crash_dump_tester: ERROR: Unexpected ptype value: %r != %r\n'
% (dump_info['ptype'], options.expected_process_type_for_crash))
if dump_info['ptype'] != options.expected_process_type_for_crash:
sys.stdout.write(msg)
failed = True
else:
sys.stdout.write('crash_dump_tester: ERROR: Missing ptype field\n')
failed = True
# TODO(mseaborn): Ideally we would also check that a backtrace
# containing an expected function name can be extracted from the
# crash dump.
if failed:
sys.stdout.write('crash_dump_tester: FAILED\n')
result = 1
else:
sys.stdout.write('crash_dump_tester: PASSED\n')
return result
def MainWrapper():
cleanup_funcs = []
try:
return Main(cleanup_funcs)
finally:
for func in cleanup_funcs:
func()
if __name__ == '__main__':
sys.exit(MainWrapper())
| bsd-3-clause |
hainm/dask | dask/dataframe/shuffle.py | 4 | 2967 | from itertools import count
from collections import Iterator
from math import ceil
from toolz import merge, accumulate, merge_sorted
import toolz
from operator import getitem, setitem
import pandas as pd
import numpy as np
from pframe import pframe
from .. import threaded
from .core import DataFrame, Series, get, names
from ..compatibility import unicode
from ..utils import ignoring
tokens = ('-%d' % i for i in count(1))
def set_index(f, index, npartitions=None, **kwargs):
""" Set DataFrame index to new column
Sorts index and realigns Dataframe to new sorted order. This shuffles and
repartitions your data.
"""
npartitions = npartitions or f.npartitions
if not isinstance(index, Series):
index2 = f[index]
else:
index2 = index
divisions = (index2
.quantiles(np.linspace(0, 100, npartitions+1)[1:-1])
.compute())
return f.set_partition(index, divisions, **kwargs)
partition_names = ('set_partition-%d' % i for i in count(1))
def set_partition(f, index, divisions, get=threaded.get, **kwargs):
""" Set new partitioning along index given divisions """
divisions = unique(divisions)
name = next(names)
if isinstance(index, Series):
assert index.divisions == f.divisions
dsk = dict(((name, i), (f._partition_type.set_index, block, ind))
for i, (block, ind) in enumerate(zip(f._keys(), index._keys())))
f2 = type(f)(merge(f.dask, index.dask, dsk), name,
f.column_info, f.divisions)
else:
dsk = dict(((name, i), (f._partition_type.set_index, block, index))
for i, block in enumerate(f._keys()))
f2 = type(f)(merge(f.dask, dsk), name, f.column_info, f.divisions)
head = f2.head()
pf = pframe(like=head, divisions=divisions, **kwargs)
def append(block):
pf.append(block)
return 0
f2.map_blocks(append).compute(get=get)
pf.flush()
return from_pframe(pf)
def from_pframe(pf):
""" Load dask.array from pframe """
name = next(names)
dsk = dict(((name, i), (pframe.get_partition, pf, i))
for i in range(pf.npartitions))
return DataFrame(dsk, name, pf.columns, pf.divisions)
def unique(divisions):
""" Polymorphic unique function
>>> list(unique([1, 2, 3, 1, 2, 3]))
[1, 2, 3]
>>> unique(np.array([1, 2, 3, 1, 2, 3]))
array([1, 2, 3])
>>> unique(pd.Categorical(['Alice', 'Bob', 'Alice'], ordered=False))
[Alice, Bob]
Categories (2, object): [Alice, Bob]
"""
if isinstance(divisions, np.ndarray):
return np.unique(divisions)
if isinstance(divisions, pd.Categorical):
return pd.Categorical.from_codes(np.unique(divisions.codes),
divisions.categories, divisions.ordered)
if isinstance(divisions, (tuple, list, Iterator)):
return tuple(toolz.unique(divisions))
raise NotImplementedError()
| bsd-3-clause |
allanino/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_tkagg.py | 69 | 24593 | # Todd Miller [email protected]
from __future__ import division
import os, sys, math
import Tkinter as Tk, FileDialog
import tkagg # Paint image to Tk photo blitter extension
from backend_agg import FigureCanvasAgg
import os.path
import matplotlib
from matplotlib.cbook import is_string_like
from matplotlib.backend_bases import RendererBase, GraphicsContextBase, \
FigureManagerBase, FigureCanvasBase, NavigationToolbar2, cursors
from matplotlib.figure import Figure
from matplotlib._pylab_helpers import Gcf
import matplotlib.windowing as windowing
from matplotlib.widgets import SubplotTool
import matplotlib.cbook as cbook
rcParams = matplotlib.rcParams
verbose = matplotlib.verbose
backend_version = Tk.TkVersion
# the true dots per inch on the screen; should be display dependent
# see http://groups.google.com/groups?q=screen+dpi+x11&hl=en&lr=&ie=UTF-8&oe=UTF-8&safe=off&selm=7077.26e81ad5%40swift.cs.tcd.ie&rnum=5 for some info about screen dpi
PIXELS_PER_INCH = 75
cursord = {
cursors.MOVE: "fleur",
cursors.HAND: "hand2",
cursors.POINTER: "arrow",
cursors.SELECT_REGION: "tcross",
}
def round(x):
return int(math.floor(x+0.5))
def raise_msg_to_str(msg):
"""msg is a return arg from a raise. Join with new lines"""
if not is_string_like(msg):
msg = '\n'.join(map(str, msg))
return msg
def error_msg_tkpaint(msg, parent=None):
import tkMessageBox
tkMessageBox.showerror("matplotlib", msg)
def draw_if_interactive():
if matplotlib.is_interactive():
figManager = Gcf.get_active()
if figManager is not None:
figManager.show()
def show():
"""
Show all the figures and enter the gtk mainloop
This should be the last line of your script. This function sets
interactive mode to True, as detailed on
http://matplotlib.sf.net/interactive.html
"""
for manager in Gcf.get_all_fig_managers():
manager.show()
import matplotlib
matplotlib.interactive(True)
if rcParams['tk.pythoninspect']:
os.environ['PYTHONINSPECT'] = '1'
if show._needmain:
Tk.mainloop()
show._needmain = False
show._needmain = True
def new_figure_manager(num, *args, **kwargs):
"""
Create a new figure manager instance
"""
_focus = windowing.FocusManager()
FigureClass = kwargs.pop('FigureClass', Figure)
figure = FigureClass(*args, **kwargs)
window = Tk.Tk()
canvas = FigureCanvasTkAgg(figure, master=window)
figManager = FigureManagerTkAgg(canvas, num, window)
if matplotlib.is_interactive():
figManager.show()
return figManager
class FigureCanvasTkAgg(FigureCanvasAgg):
keyvald = {65507 : 'control',
65505 : 'shift',
65513 : 'alt',
65508 : 'control',
65506 : 'shift',
65514 : 'alt',
65361 : 'left',
65362 : 'up',
65363 : 'right',
65364 : 'down',
65307 : 'escape',
65470 : 'f1',
65471 : 'f2',
65472 : 'f3',
65473 : 'f4',
65474 : 'f5',
65475 : 'f6',
65476 : 'f7',
65477 : 'f8',
65478 : 'f9',
65479 : 'f10',
65480 : 'f11',
65481 : 'f12',
65300 : 'scroll_lock',
65299 : 'break',
65288 : 'backspace',
65293 : 'enter',
65379 : 'insert',
65535 : 'delete',
65360 : 'home',
65367 : 'end',
65365 : 'pageup',
65366 : 'pagedown',
65438 : '0',
65436 : '1',
65433 : '2',
65435 : '3',
65430 : '4',
65437 : '5',
65432 : '6',
65429 : '7',
65431 : '8',
65434 : '9',
65451 : '+',
65453 : '-',
65450 : '*',
65455 : '/',
65439 : 'dec',
65421 : 'enter',
}
def __init__(self, figure, master=None, resize_callback=None):
FigureCanvasAgg.__init__(self, figure)
self._idle = True
t1,t2,w,h = self.figure.bbox.bounds
w, h = int(w), int(h)
self._tkcanvas = Tk.Canvas(
master=master, width=w, height=h, borderwidth=4)
self._tkphoto = Tk.PhotoImage(
master=self._tkcanvas, width=w, height=h)
self._tkcanvas.create_image(w/2, h/2, image=self._tkphoto)
self._resize_callback = resize_callback
self._tkcanvas.bind("<Configure>", self.resize)
self._tkcanvas.bind("<Key>", self.key_press)
self._tkcanvas.bind("<Motion>", self.motion_notify_event)
self._tkcanvas.bind("<KeyRelease>", self.key_release)
for name in "<Button-1>", "<Button-2>", "<Button-3>":
self._tkcanvas.bind(name, self.button_press_event)
for name in "<ButtonRelease-1>", "<ButtonRelease-2>", "<ButtonRelease-3>":
self._tkcanvas.bind(name, self.button_release_event)
# Mouse wheel on Linux generates button 4/5 events
for name in "<Button-4>", "<Button-5>":
self._tkcanvas.bind(name, self.scroll_event)
# Mouse wheel for windows goes to the window with the focus.
# Since the canvas won't usually have the focus, bind the
# event to the window containing the canvas instead.
# See http://wiki.tcl.tk/3893 (mousewheel) for details
root = self._tkcanvas.winfo_toplevel()
root.bind("<MouseWheel>", self.scroll_event_windows)
self._master = master
self._tkcanvas.focus_set()
# a dict from func-> cbook.Scheduler threads
self.sourced = dict()
# call the idle handler
def on_idle(*ignore):
self.idle_event()
return True
# disable until you figure out how to handle threads and interrupts
#t = cbook.Idle(on_idle)
#self._tkcanvas.after_idle(lambda *ignore: t.start())
def resize(self, event):
width, height = event.width, event.height
if self._resize_callback is not None:
self._resize_callback(event)
# compute desired figure size in inches
dpival = self.figure.dpi
winch = width/dpival
hinch = height/dpival
self.figure.set_size_inches(winch, hinch)
self._tkcanvas.delete(self._tkphoto)
self._tkphoto = Tk.PhotoImage(
master=self._tkcanvas, width=width, height=height)
self._tkcanvas.create_image(width/2,height/2,image=self._tkphoto)
self.resize_event()
self.show()
def draw(self):
FigureCanvasAgg.draw(self)
tkagg.blit(self._tkphoto, self.renderer._renderer, colormode=2)
self._master.update_idletasks()
def blit(self, bbox=None):
tkagg.blit(self._tkphoto, self.renderer._renderer, bbox=bbox, colormode=2)
self._master.update_idletasks()
show = draw
def draw_idle(self):
'update drawing area only if idle'
d = self._idle
self._idle = False
def idle_draw(*args):
self.draw()
self._idle = True
if d: self._tkcanvas.after_idle(idle_draw)
def get_tk_widget(self):
"""returns the Tk widget used to implement FigureCanvasTkAgg.
Although the initial implementation uses a Tk canvas, this routine
is intended to hide that fact.
"""
return self._tkcanvas
def motion_notify_event(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, guiEvent=event)
def button_press_event(self, event):
x = event.x
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.y
num = getattr(event, 'num', None)
if sys.platform=='darwin':
# 2 and 3 were reversed on the OSX platform I
# tested under tkagg
if num==2: num=3
elif num==3: num=2
FigureCanvasBase.button_press_event(self, x, y, num, guiEvent=event)
def button_release_event(self, event):
x = event.x
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.y
num = getattr(event, 'num', None)
if sys.platform=='darwin':
# 2 and 3 were reversed on the OSX platform I
# tested under tkagg
if num==2: num=3
elif num==3: num=2
FigureCanvasBase.button_release_event(self, x, y, num, guiEvent=event)
def scroll_event(self, event):
x = event.x
y = self.figure.bbox.height - event.y
num = getattr(event, 'num', None)
if num==4: step = -1
elif num==5: step = +1
else: step = 0
FigureCanvasBase.scroll_event(self, x, y, step, guiEvent=event)
def scroll_event_windows(self, event):
"""MouseWheel event processor"""
# need to find the window that contains the mouse
w = event.widget.winfo_containing(event.x_root, event.y_root)
if w == self._tkcanvas:
x = event.x_root - w.winfo_rootx()
y = event.y_root - w.winfo_rooty()
y = self.figure.bbox.height - y
step = event.delta/120.
FigureCanvasBase.scroll_event(self, x, y, step, guiEvent=event)
def _get_key(self, event):
val = event.keysym_num
if val in self.keyvald:
key = self.keyvald[val]
elif val<256:
key = chr(val)
else:
key = None
return key
def key_press(self, event):
key = self._get_key(event)
FigureCanvasBase.key_press_event(self, key, guiEvent=event)
def key_release(self, event):
key = self._get_key(event)
FigureCanvasBase.key_release_event(self, key, guiEvent=event)
def flush_events(self):
self._master.update()
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 FigureManagerTkAgg(FigureManagerBase):
"""
Public attributes
canvas : The FigureCanvas instance
num : The Figure number
toolbar : The tk.Toolbar
window : The tk.Window
"""
def __init__(self, canvas, num, window):
FigureManagerBase.__init__(self, canvas, num)
self.window = window
self.window.withdraw()
self.window.wm_title("Figure %d" % num)
self.canvas = canvas
self._num = num
t1,t2,w,h = canvas.figure.bbox.bounds
w, h = int(w), int(h)
self.window.minsize(int(w*3/4),int(h*3/4))
if matplotlib.rcParams['toolbar']=='classic':
self.toolbar = NavigationToolbar( canvas, self.window )
elif matplotlib.rcParams['toolbar']=='toolbar2':
self.toolbar = NavigationToolbar2TkAgg( canvas, self.window )
else:
self.toolbar = None
if self.toolbar is not None:
self.toolbar.update()
self.canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
self._shown = False
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)
# attach a show method to the figure for pylab ease of use
self.canvas.figure.show = lambda *args: self.show()
def resize(self, event):
width, height = event.width, event.height
self.toolbar.configure(width=width) # , height=height)
def show(self):
"""
this function doesn't segfault but causes the
PyEval_RestoreThread: NULL state bug on win32
"""
def destroy(*args):
self.window = None
Gcf.destroy(self._num)
if not self._shown: self.canvas._tkcanvas.bind("<Destroy>", destroy)
_focus = windowing.FocusManager()
if not self._shown:
self.window.deiconify()
# anim.py requires this
if sys.platform=='win32' : self.window.update()
else:
self.canvas.draw()
self._shown = True
def destroy(self, *args):
if Gcf.get_num_fig_managers()==0 and not matplotlib.is_interactive():
if self.window is not None:
self.window.quit()
if self.window is not None:
#self.toolbar.destroy()
self.window.destroy()
pass
self.window = None
def set_window_title(self, title):
self.window.wm_title(title)
class AxisMenu:
def __init__(self, master, naxes):
self._master = master
self._naxes = naxes
self._mbar = Tk.Frame(master=master, relief=Tk.RAISED, borderwidth=2)
self._mbar.pack(side=Tk.LEFT)
self._mbutton = Tk.Menubutton(
master=self._mbar, text="Axes", underline=0)
self._mbutton.pack(side=Tk.LEFT, padx="2m")
self._mbutton.menu = Tk.Menu(self._mbutton)
self._mbutton.menu.add_command(
label="Select All", command=self.select_all)
self._mbutton.menu.add_command(
label="Invert All", command=self.invert_all)
self._axis_var = []
self._checkbutton = []
for i in range(naxes):
self._axis_var.append(Tk.IntVar())
self._axis_var[i].set(1)
self._checkbutton.append(self._mbutton.menu.add_checkbutton(
label = "Axis %d" % (i+1),
variable=self._axis_var[i],
command=self.set_active))
self._mbutton.menu.invoke(self._mbutton.menu.index("Select All"))
self._mbutton['menu'] = self._mbutton.menu
self._mbar.tk_menuBar(self._mbutton)
self.set_active()
def adjust(self, naxes):
if self._naxes < naxes:
for i in range(self._naxes, naxes):
self._axis_var.append(Tk.IntVar())
self._axis_var[i].set(1)
self._checkbutton.append( self._mbutton.menu.add_checkbutton(
label = "Axis %d" % (i+1),
variable=self._axis_var[i],
command=self.set_active))
elif self._naxes > naxes:
for i in range(self._naxes-1, naxes-1, -1):
del self._axis_var[i]
self._mbutton.menu.forget(self._checkbutton[i])
del self._checkbutton[i]
self._naxes = naxes
self.set_active()
def get_indices(self):
a = [i for i in range(len(self._axis_var)) if self._axis_var[i].get()]
return a
def set_active(self):
self._master.set_active(self.get_indices())
def invert_all(self):
for a in self._axis_var:
a.set(not a.get())
self.set_active()
def select_all(self):
for a in self._axis_var:
a.set(1)
self.set_active()
class NavigationToolbar(Tk.Frame):
"""
Public attriubutes
canvas - the FigureCanvas (gtk.DrawingArea)
win - the gtk.Window
"""
def _Button(self, text, file, command):
file = os.path.join(rcParams['datapath'], 'images', file)
im = Tk.PhotoImage(master=self, file=file)
b = Tk.Button(
master=self, text=text, padx=2, pady=2, image=im, command=command)
b._ntimage = im
b.pack(side=Tk.LEFT)
return b
def __init__(self, canvas, window):
self.canvas = canvas
self.window = window
xmin, xmax = canvas.figure.bbox.intervalx
height, width = 50, xmax-xmin
Tk.Frame.__init__(self, master=self.window,
width=width, height=height,
borderwidth=2)
self.update() # Make axes menu
self.bLeft = self._Button(
text="Left", file="stock_left.ppm",
command=lambda x=-1: self.panx(x))
self.bRight = self._Button(
text="Right", file="stock_right.ppm",
command=lambda x=1: self.panx(x))
self.bZoomInX = self._Button(
text="ZoomInX",file="stock_zoom-in.ppm",
command=lambda x=1: self.zoomx(x))
self.bZoomOutX = self._Button(
text="ZoomOutX", file="stock_zoom-out.ppm",
command=lambda x=-1: self.zoomx(x))
self.bUp = self._Button(
text="Up", file="stock_up.ppm",
command=lambda y=1: self.pany(y))
self.bDown = self._Button(
text="Down", file="stock_down.ppm",
command=lambda y=-1: self.pany(y))
self.bZoomInY = self._Button(
text="ZoomInY", file="stock_zoom-in.ppm",
command=lambda y=1: self.zoomy(y))
self.bZoomOutY = self._Button(
text="ZoomOutY",file="stock_zoom-out.ppm",
command=lambda y=-1: self.zoomy(y))
self.bSave = self._Button(
text="Save", file="stock_save_as.ppm",
command=self.save_figure)
self.pack(side=Tk.BOTTOM, fill=Tk.X)
def set_active(self, ind):
self._ind = ind
self._active = [ self._axes[i] for i in self._ind ]
def panx(self, direction):
for a in self._active:
a.xaxis.pan(direction)
self.canvas.draw()
def pany(self, direction):
for a in self._active:
a.yaxis.pan(direction)
self.canvas.draw()
def zoomx(self, direction):
for a in self._active:
a.xaxis.zoom(direction)
self.canvas.draw()
def zoomy(self, direction):
for a in self._active:
a.yaxis.zoom(direction)
self.canvas.draw()
def save_figure(self):
fs = FileDialog.SaveFileDialog(master=self.window,
title='Save the figure')
try:
self.lastDir
except AttributeError:
self.lastDir = os.curdir
fname = fs.go(dir_or_file=self.lastDir) # , pattern="*.png")
if fname is None: # Cancel
return
self.lastDir = os.path.dirname(fname)
try:
self.canvas.print_figure(fname)
except IOError, msg:
err = '\n'.join(map(str, msg))
msg = 'Failed to save %s: Error msg was\n\n%s' % (
fname, err)
error_msg_tkpaint(msg)
def update(self):
_focus = windowing.FocusManager()
self._axes = self.canvas.figure.axes
naxes = len(self._axes)
if not hasattr(self, "omenu"):
self.set_active(range(naxes))
self.omenu = AxisMenu(master=self, naxes=naxes)
else:
self.omenu.adjust(naxes)
class NavigationToolbar2TkAgg(NavigationToolbar2, Tk.Frame):
"""
Public attriubutes
canvas - the FigureCanvas (gtk.DrawingArea)
win - the gtk.Window
"""
def __init__(self, canvas, window):
self.canvas = canvas
self.window = window
self._idle = True
#Tk.Frame.__init__(self, master=self.canvas._tkcanvas)
NavigationToolbar2.__init__(self, canvas)
def destroy(self, *args):
del self.message
Tk.Frame.destroy(self, *args)
def set_message(self, s):
self.message.set(s)
def draw_rubberband(self, event, x0, y0, x1, y1):
height = self.canvas.figure.bbox.height
y0 = height-y0
y1 = height-y1
try: self.lastrect
except AttributeError: pass
else: self.canvas._tkcanvas.delete(self.lastrect)
self.lastrect = self.canvas._tkcanvas.create_rectangle(x0, y0, x1, y1)
#self.canvas.draw()
def release(self, event):
try: self.lastrect
except AttributeError: pass
else:
self.canvas._tkcanvas.delete(self.lastrect)
del self.lastrect
def set_cursor(self, cursor):
self.window.configure(cursor=cursord[cursor])
def _Button(self, text, file, command):
file = os.path.join(rcParams['datapath'], 'images', file)
im = Tk.PhotoImage(master=self, file=file)
b = Tk.Button(
master=self, text=text, padx=2, pady=2, image=im, command=command)
b._ntimage = im
b.pack(side=Tk.LEFT)
return b
def _init_toolbar(self):
xmin, xmax = self.canvas.figure.bbox.intervalx
height, width = 50, xmax-xmin
Tk.Frame.__init__(self, master=self.window,
width=width, height=height,
borderwidth=2)
self.update() # Make axes menu
self.bHome = self._Button( text="Home", file="home.ppm",
command=self.home)
self.bBack = self._Button( text="Back", file="back.ppm",
command = self.back)
self.bForward = self._Button(text="Forward", file="forward.ppm",
command = self.forward)
self.bPan = self._Button( text="Pan", file="move.ppm",
command = self.pan)
self.bZoom = self._Button( text="Zoom",
file="zoom_to_rect.ppm",
command = self.zoom)
self.bsubplot = self._Button( text="Configure Subplots", file="subplots.ppm",
command = self.configure_subplots)
self.bsave = self._Button( text="Save", file="filesave.ppm",
command = self.save_figure)
self.message = Tk.StringVar(master=self)
self._message_label = Tk.Label(master=self, textvariable=self.message)
self._message_label.pack(side=Tk.RIGHT)
self.pack(side=Tk.BOTTOM, fill=Tk.X)
def configure_subplots(self):
toolfig = Figure(figsize=(6,3))
window = Tk.Tk()
canvas = FigureCanvasTkAgg(toolfig, master=window)
toolfig.subplots_adjust(top=0.9)
tool = SubplotTool(self.canvas.figure, toolfig)
canvas.show()
canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
def save_figure(self):
from tkFileDialog import asksaveasfilename
from tkMessageBox import showerror
filetypes = self.canvas.get_supported_filetypes().copy()
default_filetype = self.canvas.get_default_filetype()
# Tk doesn't provide a way to choose a default filetype,
# so we just have to put it first
default_filetype_name = filetypes[default_filetype]
del filetypes[default_filetype]
sorted_filetypes = filetypes.items()
sorted_filetypes.sort()
sorted_filetypes.insert(0, (default_filetype, default_filetype_name))
tk_filetypes = [
(name, '*.%s' % ext) for (ext, name) in sorted_filetypes]
fname = asksaveasfilename(
master=self.window,
title='Save the figure',
filetypes = tk_filetypes,
defaultextension = self.canvas.get_default_filetype()
)
if fname == "" or fname == ():
return
else:
try:
# This method will handle the delegation to the correct type
self.canvas.print_figure(fname)
except Exception, e:
showerror("Error saving file", str(e))
def set_active(self, ind):
self._ind = ind
self._active = [ self._axes[i] for i in self._ind ]
def update(self):
_focus = windowing.FocusManager()
self._axes = self.canvas.figure.axes
naxes = len(self._axes)
#if not hasattr(self, "omenu"):
# self.set_active(range(naxes))
# self.omenu = AxisMenu(master=self, naxes=naxes)
#else:
# self.omenu.adjust(naxes)
NavigationToolbar2.update(self)
def dynamic_update(self):
'update drawing area only if idle'
# legacy method; new method is canvas.draw_idle
self.canvas.draw_idle()
FigureManager = FigureManagerTkAgg
| agpl-3.0 |
mhoffman/kmos | kmos/cli.py | 1 | 16514 | #!/usr/bin/env python
"""Entry point module for the command-line
interface. The kmos executable should be
on the program path, import this modules
main function and run it.
To call kmos command as you would from the shell,
use ::
kmos.cli.main('...')
Every command can be shortened as long as it is non-ambiguous, e.g. ::
kmos ex <xml-file>
instead of ::
kmos export <xml-file>
etc.
"""
# Copyright 2009-2013 Max J. Hoffmann ([email protected])
# This file is part of kmos.
#
# kmos 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 of the License, or
# (at your option) any later version.
#
# kmos 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 kmos. If not, see <http://www.gnu.org/licenses/>.
from __future__ import with_statement
import os
import shutil
usage = {}
usage['all'] = """kmos help all
Display documentation for all commands.
"""
usage['benchmark'] = """kmos benchmark
Run 1 mio. kMC steps on model in current directory
and report runtime.
"""
usage['build'] = """kmos build
Build kmc_model.%s from *f90 files in the
current directory.
Additional Parameters ::
-d/--debug
Turn on assertion statements in F90 code
-n/--no-compiler-optimization
Do not send optimizing flags to compiler.
""" % ('pyd' if os.name == 'nt' else 'so')
usage['help'] = """kmos help <command>
Print usage information for the given command.
"""
usage['export'] = """kmos export <xml-file> [<export-path>]
Take a kmos xml-file and export all generated
source code to the export-path. There try to
build the kmc_model.%s.
Additional Parameters ::
-s/--source-only
Export source only and don't build binary
-b/--backend (local_smart|lat_int)
Choose backend. Default is "local_smart".
lat_int is EXPERIMENTAL and not made
for production, yet.
-d/--debug
Turn on assertion statements in F90 code.
(Only active in compile step)
--acf
Build the modules base_acf.f90 and proclist_acf.f90. Default is false.
This both modules contain functions to calculate ACF (autocorrelation function) and MSD (mean squared displacement).
-n/--no-compiler-optimization
Do not send optimizing flags to compiler.
""" % ('pyd' if os.name == 'nt' else 'so')
usage['settings-export'] = """kmos settings-export <xml-file> [<export-path>]
Take a kmos xml-file and export kmc_settings.py
to the export-path.
"""
usage['edit'] = """kmos edit <xml-file>
Open the kmos xml-file in a GUI to edit
the model.
"""
usage['import'] = """kmos import <xml-file>
Take a kmos xml-file and open an ipython shell
with the project_tree imported as pt.
"""
usage['rebuild'] = """kmos rebuild
Export code and rebuild binary module from XML
information included in kmc_settings.py in
current directory.
Additional Parameters ::
-d/--debug
Turn on assertion statements in F90 code
"""
usage['shell'] = """kmos shell
Open an interactive shell and create a KMC_Model in it
run == shell
"""
usage['run'] = """kmos run
Open an interactive shell and create a KMC_Model in it
run == shell
"""
usage['version'] = """kmos version
Print version number and exit.
"""
usage['view'] = """kmos view
Take a kmc_model.%s and kmc_settings.py in the
same directory and start to simulate the
model visually.
Additional Parameters ::
-v/--steps-per-frame <number>
Number of steps per frame
""" % ('pyd' if os.name == 'nt' else 'so')
usage['xml'] = """kmos xml
Print xml representation of model to stdout
"""
def get_options(args=None, get_parser=False):
import optparse
import os
from glob import glob
import kmos
parser = optparse.OptionParser(
'Usage: %prog [help] ('
+ '|'.join(sorted(usage.keys()))
+ ') [options]',
version=kmos.__version__)
parser.add_option('-s', '--source-only',
dest='source_only',
action='store_true',
default=False)
parser.add_option('-p', '--path-to-f2py',
dest='path_to_f2py',
default='f2py')
parser.add_option('-b', '--backend',
dest='backend',
default='local_smart')
parser.add_option('-a', '--avoid-default-state',
dest='avoid_default_state',
action='store_true',
default=False,
)
parser.add_option('-v', '--steps-per-frame',
dest='steps_per_frame',
type='int',
default='50000')
parser.add_option('-d', '--debug',
default=False,
dest='debug',
action='store_true')
parser.add_option('-n', '--no-compiler-optimization',
default=False,
dest='no_optimize',
action='store_true')
parser.add_option('-o', '--overwrite',
default=False,
action='store_true')
parser.add_option('-l', '--variable-length',
dest='variable_length',
default=95,
type='int')
parser.add_option('-c', '--catmap',
default=False,
action='store_true')
parser.add_option('--acf',
dest='acf',
action='store_true',
default=False,
)
try:
from numpy.distutils.fcompiler import get_default_fcompiler
from numpy.distutils import log
log.set_verbosity(-1, True)
fcompiler = get_default_fcompiler()
except:
fcompiler = 'gfortran'
parser.add_option('-f', '--fcompiler',
dest='fcompiler',
default=os.environ.get('F2PY_FCOMPILER', fcompiler))
if args is not None:
options, args = parser.parse_args(args.split())
else:
options, args = parser.parse_args()
if len(args) < 1:
parser.error('Command expected')
if get_parser:
return options, args, parser
else:
return options, args
def match_keys(arg, usage, parser):
"""Try to match part of a command against
the set of commands from usage. Throws
an error if not successful.
"""
possible_args = [key for key in usage if key.startswith(arg)]
if len(possible_args) == 0:
parser.error('Command "%s" not understood.' % arg)
elif len(possible_args) > 1:
parser.error(('Command "%s" ambiguous.\n'
'Could be one of %s\n\n') % (arg, possible_args))
else:
return possible_args[0]
def main(args=None):
"""The CLI main entry point function.
The optional argument args, can be used to
directly supply command line argument like
$ kmos <args>
otherwise args will be taken from STDIN.
"""
from glob import glob
options, args, parser = get_options(args, get_parser=True)
global model, pt, np, cm_model
if not args[0] in usage.keys():
args[0] = match_keys(args[0], usage, parser)
if args[0] == 'benchmark':
from sys import path
path.append(os.path.abspath(os.curdir))
nsteps = 1000000
from time import time
from kmos.run import KMC_Model
model = KMC_Model(print_rates=False, banner=False)
time0 = time()
try:
model.proclist.do_kmc_steps(nsteps)
except: # kmos < 0.3 had no model.proclist.do_kmc_steps
model.do_steps(nsteps)
needed_time = time() - time0
print('Using the [%s] backend.' % model.get_backend())
print('%s steps took %.2f seconds' % (nsteps, needed_time))
print('Or %.2e steps/s' % (1e6 / needed_time))
model.deallocate()
elif args[0] == 'build':
from kmos.utils import build
build(options)
elif args[0] == 'edit':
from kmos import gui
gui.main()
elif args[0] == 'settings-export':
import kmos.types
import kmos.io
from kmos.io import ProcListWriter
if len(args) < 2:
parser.error('XML file and export path expected.')
if len(args) < 3:
out_dir = '%s_%s' % (os.path.splitext(args[1])[0], options.backend)
print('No export path provided. Exporting to %s' % out_dir)
args.append(out_dir)
xml_file = args[1]
export_dir = args[2]
project = kmos.types.Project()
project.import_file(xml_file)
writer = ProcListWriter(project, export_dir)
writer.write_settings()
elif args[0] == 'export':
import kmos.types
import kmos.io
from kmos.utils import build
if len(args) < 2:
parser.error('XML file and export path expected.')
if len(args) < 3:
out_dir = '%s_%s' % (os.path.splitext(args[1])[0], options.backend)
print('No export path provided. Exporting to %s' % out_dir)
args.append(out_dir)
xml_file = args[1]
export_dir = os.path.join(args[2], 'src')
project = kmos.types.Project()
project.import_file(xml_file)
project.shorten_names(max_length=options.variable_length)
kmos.io.export_source(project,
export_dir,
options=options)
if ((os.name == 'posix'
and os.uname()[0] in ['Linux', 'Darwin'])
or os.name == 'nt') \
and not options.source_only:
os.chdir(export_dir)
build(options)
for out in glob('kmc_*'):
if os.path.exists('../%s' % out) :
if options.overwrite :
overwrite = 'y'
else:
overwrite = raw_input(('Should I overwrite existing %s ?'
'[y/N] ') % out).lower()
if overwrite.startswith('y') :
print('Overwriting {out}'.format(**locals()))
os.remove('../%s' % out)
shutil.move(out, '..')
else :
print('Skipping {out}'.format(**locals()))
else:
shutil.move(out, '..')
elif args[0] == 'settings-export':
import kmos.io
pt = kmos.io.import_file(args[1])
if len(args) < 3:
out_dir = os.path.splitext(args[1])[0]
print('No export path provided. Exporting kmc_settings.py to %s'
% out_dir)
args.append(out_dir)
if not os.path.exists(args[2]):
os.mkdir(args[2])
elif not os.path.isdir(args[2]):
raise UserWarning("Cannot overwrite %s; Exiting;" % args[2])
writer = kmos.io.ProcListWriter(pt, args[2])
writer.write_settings()
elif args[0] == 'help':
if len(args) < 2:
parser.error('Which help do you want?')
if args[1] == 'all':
for command in sorted(usage):
print(usage[command])
elif args[1] in usage:
print('Usage: %s\n' % usage[args[1]])
else:
arg = match_keys(args[1], usage, parser)
print('Usage: %s\n' % usage[arg])
elif args[0] == 'import':
import kmos.io
if not len(args) >= 2:
raise UserWarning('XML file name expected.')
pt = kmos.io.import_xml_file(args[1])
if len(args) == 2:
sh(banner='Note: pt = kmos.io.import_xml(\'%s\')' % args[1])
elif len(args) == 3: # if optional 3rd argument is given, store model there and exit
pt.save(args[2])
elif args[0] == 'rebuild':
from time import sleep
print('Will rebuild model from kmc_settings.py in current directory')
print('Please do not interrupt,'
' build process, as you will most likely')
print('loose the current model files.')
sleep(2.)
from sys import path
path.append(os.path.abspath(os.curdir))
from tempfile import mktemp
if not os.path.exists('kmc_model.so') \
and not os.path.exists('kmc_model.pyd'):
raise Exception('No kmc_model.so found.')
if not os.path.exists('kmc_settings.py'):
raise Exception('No kmc_settings.py found.')
from kmos.run import KMC_Model
model = KMC_Model(print_rates=False, banner=False)
tempfile = mktemp()
f = file(tempfile, 'w')
f.write(model.xml())
f.close()
for kmc_model in glob('kmc_model.*'):
os.remove(kmc_model)
os.remove('kmc_settings.py')
main('export %s -b %s .' % (tempfile, options.backend))
os.remove(tempfile)
model.deallocate()
elif args[0] in ['run', 'shell']:
from sys import path
path.append(os.path.abspath(os.curdir))
from kmos.run import KMC_Model
# useful to have in interactive mode
import numpy as np
try:
from matplotlib import pyplot as plt
except:
plt = None
if options.catmap:
import catmap
import catmap.cli.kmc_runner
seed = catmap.cli.kmc_runner.get_seed_from_path('.')
cm_model = catmap.ReactionModel(setup_file='{seed}.mkm'.format(**locals()))
catmap_message = '\nSide-loaded catmap_model {seed}.mkm into cm_model = ReactionModel(setup_file="{seed}.mkm")'.format(**locals())
else:
catmap_message = ''
try:
model = KMC_Model(print_rates=False)
except:
print("Warning: could not import kmc_model!"
" Please make sure you are in the right directory")
sh(banner='Note: model = KMC_Model(print_rates=False){catmap_message}'.format(**locals()))
try:
model.deallocate()
except:
print("Warning: could not deallocate model. Was is allocated?")
elif args[0] == 'version':
from kmos import VERSION
print(VERSION)
elif args[0] == 'view':
from sys import path
path.append(os.path.abspath(os.curdir))
from kmos import view
view.main(steps_per_frame=options.steps_per_frame)
elif args[0] == 'xml':
from sys import path
path.append(os.path.abspath(os.curdir))
from kmos.run import KMC_Model
model = KMC_Model(banner=False, print_rates=False)
print(model.xml())
else:
parser.error('Command "%s" not understood.' % args[0])
def sh(banner):
"""Wrapper around interactive ipython shell
that factors out ipython version depencies.
"""
from distutils.version import LooseVersion
import IPython
if hasattr(IPython, 'release'):
try:
from IPython.terminal.embed import InteractiveShellEmbed
InteractiveShellEmbed(banner1=banner)()
except ImportError:
try:
from IPython.frontend.terminal.embed \
import InteractiveShellEmbed
InteractiveShellEmbed(banner1=banner)()
except ImportError:
from IPython.Shell import IPShellEmbed
IPShellEmbed(banner=banner)()
else:
from IPython.Shell import IPShellEmbed
IPShellEmbed(banner=banner)()
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