cpascual

class SignalMethodWrapper(object):
    """Object that can act simultaneously as a pyqtBoundSignal and as a callable
    method.

    It optionally allows to log deprecation messages whe used as a signal
    or as a method or both
    """
    def __init__(self, pyqtboundsignal, method, alt_sig=None, alt_meth=None,
                 name='<wrapper>'):

cpascual

from PyQt4 import Qt


class Signaller(Qt.QObject):
    signal = Qt.pyqtSignal()


class Base(object):
    """Base is not a QObject but provides a pyqtSignal signal member via
    a Signaller hidden behind a property"""

cpascual

import h5py
import numpy
import time
import sys
from multiprocessing import Process


FNAME = 'foo.h5'

cpascual

#find trunks
find . -type d -iname trunk > /tmp/find-trunk.txt &
#filter paths
cat /tmp/find-trunk.txt | sed 's/\.\(.*\)\/trunk/\1/' > /tmp/paths.tst
# to make script
# sed 's/\.\(.*\)\/trunk/grep -v \1 \| \\/' > /tmp/filter.sh
# edit /tmp/filter.sh

cpascual

import sys
import numpy
from taurus.external.qt import QtGui
from taurus.qt.qtgui.application import TaurusApplication
from taurus.qt.qtgui.base import TaurusBaseComponent
from pyqtgraph import PlotDataItem


class TaurusPlotDataItem(PlotDataItem, TaurusBaseComponent):
    """A taurus-ified PlotDataItem"""

cpascual

"""
Examples on using the evaluation scheme for exposing icepap driver values
as taurus attributes

Note that:

- the first attribute is writable
- The second and 3rd attributes do not even require defining MyIpap
"""

cpascual

import sys
from taurus.qt.qtgui.application import TaurusApplication
from taurus.qt.qtgui.plot import TaurusTrend

app = TaurusApplication()

w = TaurusTrend()

model = 'sys/tg_test/1/ampli'
w.setModel(model)

cpascual

"""A nexus file summary tool implemented with h5py"""

import h5py
import sys


def print_dset(dset):
    print '\t', dset
    for k, v in sorted(dset.attrs.iteritems()):
        print "\t\t@%s=%s" % (k,v)

cpascual

""" 
A simple example of using taurus eval scheme with pandas to 
read a column of a CSV data file
"""

#create a dummy csv file with 2 columns with headers "x" and "y"
data=\
'''
x,y
2,4

cpascual

from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit


def gauss(x, H, A, x0, sigma):
    return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))

def gauss_fit(x, y):