Computer assignment
turtletime98
PARSE2.zipdevelopment/PARSE.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # The code contained herein is currently in development and as such # is not to be shared. # # Eventually, when the work is complete, it may be released under an # opensource license. # # Copyright 2011, Anwer Bashi, X. Rong Li import layout def main(): layout.main() if __name__ == '__main__': main()
development/PARSE.pyc
development/PARSE.pyw
development/code/parselib.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2011, Anwer Bashi, X. Rong Li # This file is entered into the public domain - permission is granted to use or # redistribute this file without restriction. # # This file is automatically added to the working directory by PARSE. # It will be overwritten if it already exists so any changes made with be lost. # Python imports from os import path ABS_PATH = path.dirname(__file__) # Import all the first level math and plotting functions from pylab import * from scipy import * # Import individual modules from sys import exit as die from numpy import random from scipy import stats # Helper functions def check_limits(varstr, low, var, high): if var<low: print('Error: %s should be greater than or equal to %g, however it is set at %s<%g' % (varstr, low, varstr, low)) die() if var>high: print('Error: %s should be less than or equal to %g, however it is set at %s>%g' % (varstr, high, varstr, high)) die() def ensure_length(var, varstr, length): sh = shape(var) if not sh: print('%s is a constant, extending it to %d samples' % (varstr, length, )) var *= ones(length) elif sh[0]<length: print('Error: %s should be %d samples long, however it is %d samples long' % (varstr, length, sh[0])) die() else: print('%s is %d samples long' % (varstr, sh[0])) return var def truncstr(s, maxlen): if len(s)>maxlen: return s[:maxlen] + ' ...' return s # New Classes / Overrides # SciPy's Weibull distribution needs to be augmented to allow both alpha and beta parameters class weibull_gen(stats.rv_continuous): def _pdf(self, x, a, b): r = (a/b)*pow(x/b, a-1) * exp(-pow(x/b, a)) return where(x<0, 0, r) def _cdf(self, x, a, b): r = 1 - exp(-pow(x/b, a)) return where(x<0, 0, r) def _argcheck(self, a, b): return (a>0) & (b>0) stats.weibull = weibull_gen(name='weibull',longname="An Augmented Weibull", shapes='a, b',extradoc="") # SciPy's generic discrete distribution can only generate a realization with integer point mass locations class generic_discrete_gen(stats.rv_discrete): #def _pmf(self, k, *args): #print(k) #return .1 #return self._cdf(k,*args) - self._cdf(k-1,*args) def rvs(self,*args,**kwds): # Generate random variates values, size = map(kwds.get, ['values', 'size'], [None,None]) xk, pk = values ptot = sum(pk) samples = random.mtrand.random_sample(size) * ptot def weighted_choice(rnd): for i, w in enumerate(pk): rnd -= w if rnd < 0: return xk[i] for i in range(size): samples[i] = weighted_choice(samples[i]) return samples stats.dcustom = generic_discrete_gen(name='custom',longname="A Custom Discrete", shapes='a, b',extradoc="")
development/code/parselib.pyc
development/code/temp.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # Student Name: My Name (configured in Edit::Preferences) # Student ID: 012345678 (configured in Edit::Preferences) # Course Number: ENEE0000 (configured in Edit::Preferences) # File Created: Thu Nov 02, 2017 from parselib import * c = loadtxt("E:/123/PARSE-0.1.9/data/m6_4.dat") print("[c] loaded, size=%s" % (shape(c),)) t = range(shape(c)[0]) figure() plot(t, c) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of c') title('Plot of sequence c') ########## The following lines are automatically appended to the file on write and truncated on read ########## show() # Automatically added to the end of a script as needed to pause for user interaction
development/code/temp.pyc
development/config.py
# -*- coding: utf-8 -*- # The code contained herein is currently in development and as such # is not to be shared. # # Eventually, when the work is complete, it may be released under an # opensource license. # # Copyright 2011, Anwer Bashi, X. Rong Li import os import sys import platform IS_POSIX = 'posix' in sys.builtin_module_names IS_WINDOWS = sys.platform.lower().startswith(("win", "microsoft")) from scipy import __version__ as SCIPY_VERSION from numpy import __version__ as NUMPY_VERSION from matplotlib import __version__ as MPL_VERSION ORG_NAME = 'CodeForgeAB' APP_NAME = 'PARSE' VERSION = '0.1.8' def get_path(f): return os.path.realpath(os.path.dirname(f)) ABS_PATH = get_path(__file__) IMAGE_RESOURCES = ABS_PATH + '/images/' CODE_PATH = ABS_PATH + '/code/' CODE_RESOURCES = ABS_PATH + '/templ/' DATA_PATH = ABS_PATH + '/data/' TEMPLATE_EXT = '.templ' REQUIRES_EXT = '.reqd' PARAMETERS_EXT = '.param' from PyQt4 import QtCore def get_setting(key, default, settings=None): if not settings: settings = QtCore.QSettings(ORG_NAME, APP_NAME) return str(settings.value(key, default).toString())
development/config.pyc
development/conventions.txt
k: x-axis variable rv: Random variable class [x]: Refers to the variable, x rv.doc rv-action.templ rv-action.param
development/data/e3_34.dat
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development/data/e3_34_2.dat
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development/data/e4_10.dat
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development/data/e4_10_2.dat
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development/data/m4_1.dat
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development/data/m4_2a.dat
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development/data/m4_2b.dat
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development/data/m4_2c.dat
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development/data/m4_4a.dat
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development/data/m4_4b.dat
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development/data/m4_4c.dat
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development/data/m4_5_1.dat
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development/data/m4_6a.dat
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development/data/m5_5.dat
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development/data/m5_6.dat
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development/data/m5_7.dat
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development/data/m5_8.dat
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development/data/m6_4.dat
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development/data/m6_5.dat
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development/data/m6_6x.dat
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development/data/m6_6y.dat
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development/data/m6_7.dat
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development/data/m6_9a1.dat
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development/data/m7_5x.dat
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development/data/m7_7.dat
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development/data/m8_2x.dat
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development/data/m8_2y.dat
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development/data/m8_3x.dat
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development/data/m8_3y.dat
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development/data/m8_6s.dat
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development/data/m8_7.dat
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development/images/board.png
development/images/exit.png
development/images/open.png
development/images/prefs.png
development/images/reload.png
development/images/run.png
development/images/save.png
development/images/text.png
development/layout.py
# -*- coding: utf-8 -*- # The code contained herein is currently in development and as such # is not to be shared. # # Eventually, when the work is complete, it may be released under an # opensource license. # # Copyright 2011, Anwer Bashi, X. Rong Li import os import sys import time import subprocess import traceback from threading import Thread from PyQt4 import QtGui, QtCore import config import syntax import source import uigen class Worker(QtCore.QThread): # Initiated by calling start() which lets PyQt call run() # Emits:display(QString) # started() # stopped() def __init__(self, parent = None): QtCore.QThread.__init__(self, parent) self.stop = False self.pipe = None def __del__(self): # Before a Worker object is destroyed, we need to ensure that it stops processing. # Indicate to the script that processing must stop, and waits until it does so. self.stop = True self.wait() def run(self): # Note: This is never called directly. It is called by Qt once the # thread environment has been set up. self.emit(QtCore.SIGNAL("started()")) ret = '' try: for line in self.pipe.readlines(): ret += line self.pipe.close() self.emit(QtCore.SIGNAL('display(QString)'), ret) except KeyboardInterrupt: raise except: print(traceback.format_exc()) finally: self.emit(QtCore.SIGNAL("stopped()")) class PreferencesDialog(QtGui.QDialog): def __init__(self, parent=None): super(PreferencesDialog, self).__init__(parent) self.mainLayout = QtGui.QVBoxLayout() self.setWindowTitle("Enter Parameters") self.student_name = QtGui.QLineEdit(config.get_setting('student_name', 'My Name'), self) self.student_id = QtGui.QLineEdit(config.get_setting('student_id', '012345678'), self) self.course_number = QtGui.QLineEdit(config.get_setting('course_number', 'ENEE0000'), self) widget = QtGui.QLabel("Student Name", self) self.mainLayout.addWidget(widget) self.mainLayout.addWidget(self.student_name) widget = QtGui.QLabel("Student ID", self) self.mainLayout.addWidget(widget) self.mainLayout.addWidget(self.student_id) widget = QtGui.QLabel("Course Number", self) self.mainLayout.addWidget(widget) self.mainLayout.addWidget(self.course_number) buttonBox = QtGui.QDialogButtonBox(QtGui.QDialogButtonBox.Ok | QtGui.QDialogButtonBox.Cancel) buttonBox.accepted.connect(self.save) buttonBox.rejected.connect(self.reject) self.mainLayout.addStretch(1) self.mainLayout.addWidget(buttonBox) self.setLayout(self.mainLayout) def save(self): settings = QtCore.QSettings(config.ORG_NAME, config.APP_NAME) settings.setValue("student_name", str(self.student_name.text())) settings.setValue("student_id", str(self.student_id.text())) settings.setValue("course_number", str(self.course_number.text())) self.accept() class Mainframe(QtGui.QMainWindow): DEFAULT_CODE_FILE = config.CODE_PATH + 'temp.py' def __init__(self, codeforge): super(Mainframe, self).__init__() self.codeforge = codeforge self.lastproc = None self.thread = None self.initUI() def set_filename(self, filename): self.codefile = filename self.workingdir = config.get_path(filename) self.setWindowTitle('PARSE - ' + config.get_path(filename)) self.signal_set_modified(False) def append(self, filename, localdict = None): code = self.codeforge.append(filename, localdict) self.textedit.appendPlainText(code) def initUI(self): self.textedit = QtGui.QPlainTextEdit() self.textedit.setTabStopWidth(25) self.setCentralWidget(self.textedit) self.highlight = syntax.PythonHighlighter(self.textedit.document()) self.create_actions() self.create_menus() self.create_docks() self.create_statusbar() self.read_settings() self.connect_signals() self.setWindowIcon(QtGui.QIcon(config.IMAGE_RESOURCES+'board.png')) self.show() def create_actions(self): self.actions = {} self.actions['new'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'text.png'), "&New Program", self, shortcut=QtGui.QKeySequence.New, statusTip="Create a new program", triggered=self.action_new) self.actions['open'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'open.png'), "&Open...", self, shortcut=QtGui.QKeySequence.Open, statusTip="Open a saved program", triggered=self.action_open) self.actions['save'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'save.png'), "&Save...", self, shortcut=QtGui.QKeySequence.Save, statusTip="Save the current program", triggered=self.action_save) self.actions['saveas'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'save.png'), "Save &As...", self, shortcut=QtGui.QKeySequence.SaveAs, statusTip="Save the current program to a different file", triggered=self.action_saveas) self.actions['run'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'run.png'), "&Run...", self, shortcut='Ctrl+R', statusTip="Run the current program", triggered=self.action_run) self.actions['undo'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'reload.png'), "&Undo", self, shortcut=QtGui.QKeySequence.Undo, statusTip="Undo the last action", triggered=self.action_undo) self.actions['preferences'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'prefs.png'), "&Preferences", self, statusTip="Configure preferences", triggered=self.action_preferences) self.actions['quit'] = QtGui.QAction(QtGui.QIcon(config.IMAGE_RESOURCES+'exit.png'), "&Quit", self, shortcut="Ctrl+Q", statusTip="Quit the application", triggered=self.close) self.actions['about'] = QtGui.QAction("&About", self, statusTip="Show the application's About box", triggered=self.action_about) self.actions['aboutQt'] = QtGui.QAction("About &Qt", self, statusTip="Show the Qt library's About box", triggered=QtGui.qApp.aboutQt) self.shortcuts = [] self.shortcuts.append(QtGui.QShortcut(QtGui.QKeySequence("F5"), self, self.action_run)) def create_statusbar(self): self.statusBar().showMessage("Ready") def create_menus(self): menubar = self.menuBar() self.fileMenu = menubar.addMenu('&File') self.fileMenu.addAction(self.actions['new']) self.fileMenu.addAction(self.actions['open']) self.fileMenu.addSeparator() self.fileMenu.addAction(self.actions['save']) self.fileMenu.addAction(self.actions['saveas']) self.fileMenu.addSeparator() self.fileMenu.addAction(self.actions['run']) self.fileMenu.addSeparator() self.fileMenu.addAction(self.actions['quit']) self.editMenu = menubar.addMenu('&Edit') self.editMenu.addAction(self.actions['undo']) self.editMenu.addAction(self.actions['preferences']) self.viewMenu = menubar.addMenu('&View') menubar.addSeparator() self.aboutMenu = menubar.addMenu('&About') self.aboutMenu.addAction(self.actions['about']) self.aboutMenu.addAction(self.actions['aboutQt']) self.toolbar = self.addToolBar('Toolbar') self.toolbar.setObjectName('Toolbar') self.toolbar.addAction(self.actions['new']) self.toolbar.addAction(self.actions['open']) self.toolbar.addAction(self.actions['save']) self.toolbar.addAction(self.actions['undo']) self.toolbar.addAction(self.actions['run']) self.toolbar.addAction(self.actions['quit']) self.viewMenu.addAction(self.toolbar.toggleViewAction()) def create_docks(self): dock = QtGui.QDockWidget("Generate", self) dock.setObjectName('Generate') dock.setAllowedAreas(QtCore.Qt.LeftDockWidgetArea | QtCore.Qt.RightDockWidgetArea) widget = uigen.TabBox(dock, self.append) dock.setWidget(widget) self.addDockWidget(QtCore.Qt.LeftDockWidgetArea, dock) self.viewMenu.addAction(dock.toggleViewAction()) self.generate_dock = dock self.generate_widget = widget #dock = QtGui.QDockWidget("Documentation", self) #dock.setObjectName('Documentation') #dock.setAllowedAreas(QtCore.Qt.LeftDockWidgetArea | QtCore.Qt.RightDockWidgetArea) #widget = QtGui.QPlainTextEdit(dock) #dock.setWidget(widget) #self.addDockWidget(QtCore.Qt.LeftDockWidgetArea, dock) #self.viewMenu.addAction(dock.toggleViewAction()) #self.docs_dock = dock #self.docs_widget = widget dock = QtGui.QDockWidget("Output", self) dock.setObjectName('Output') dock.setAllowedAreas(QtCore.Qt.LeftDockWidgetArea | QtCore.Qt.RightDockWidgetArea) widget = QtGui.QPlainTextEdit(dock) dock.setWidget(widget) self.addDockWidget(QtCore.Qt.LeftDockWidgetArea, dock) self.viewMenu.addAction(dock.toggleViewAction()) self.output_dock = dock self.output_widget = widget def connect_signals(self): self.textedit.cursorPositionChanged.connect(self.signal_cursor_move) self.textedit.document().modificationChanged.connect(self.signal_set_modified) def signal_set_modified(self, val=True): self.modified = val def signal_cursor_move(self): cursor = self.textedit.textCursor() self.statusBar().showMessage("Line: %d, Column: %d" % (cursor.blockNumber()+1, cursor.positionInBlock())) def signal_update_output(self, output): print(output) self.output_widget.setPlainText(output) def signal_thread_stopped(self): print("Worker thread finished") def action_new(self): self.codeforge.clear() self.textedit.clear() self.append(source.CodeForge.HEAD_FILE) def action_run(self): self.run() def action_undo(self): document = self.textedit.document() document.undo() def action_saveas(self): self.saveas() def action_save(self): self.save() def action_about(self): QtGui.QMessageBox.about(self, "About "+config.APP_NAME, """<b>{5} v {0}</b> <p>Copyright © 2011 Anwer Bashi, Xiao-Rong Li; All rights reserved.</p> <p>Python {1} - Qt {2} - PyQt {3} on {4}<p> <p>SciPy {8} - NumPy {7} - matplotlib {6}<p> """.format( config.VERSION, config.platform.python_version(), QtCore.QT_VERSION_STR, QtCore.PYQT_VERSION_STR, config.platform.system(), config.APP_NAME, config.MPL_VERSION, config.NUMPY_VERSION, config.SCIPY_VERSION)) def action_preferences(self): dlg = PreferencesDialog(self) dlg.show() def saveas(self): filename = QtGui.QFileDialog.getSaveFileName(self, "Save as...", self.workingdir, "Python files (*.py);;All Files (*)") if not filename: return False filename = str(filename) if not filename.lower().endswith('.py'): filename += '.py' self.set_filename(filename) return self.save() def save(self): document = self.textedit.document() print("Saving " + self.codefile) try: doc = document.toPlainText() sup = self.codeforge.supplimental(doc) # Write the parselib.py file which contains all the required # includes self.codeforge.writeinclude(self.codefile) h = open(self.codefile, 'w') h.write(doc) if sup: h.write(sup) h.close() except: print("Could not save "+self.codefile) print(traceback.format_exc()) return False os.chmod(self.codefile, 0755) return True def load(self, filename): try: txt = open(filename, 'r').read() i = txt.find(self.codeforge.AUTO_SIG) if i>=0: txt = txt[:i] self.textedit.setPlainText(txt) self.set_filename(filename) except: return False return True def action_open(self): filename = QtGui.QFileDialog.getOpenFileName(self, "Open File...", self.workingdir, "Python-Files (*.py);;All Files (*)") if filename: self.load(str(filename)) def run(self): self.save() print("Running " + self.codefile) self.lastproc = subprocess.Popen([sys.executable, self.codefile], shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, close_fds=config.IS_POSIX) self.thread = Worker(self) self.thread.pipe = self.lastproc.stdout self.connect(self.thread, QtCore.SIGNAL("stopped()"), self.signal_thread_stopped) self.connect(self.thread, QtCore.SIGNAL("display(QString)"), self.signal_update_output) self.thread.start() print("Worker thread started") def closeEvent(self, e): self.save_settings() def read_settings(self): settings = QtCore.QSettings(config.ORG_NAME, config.APP_NAME) geometry = settings.value('geometry').toByteArray() state = settings.value('state').toByteArray() self.restoreGeometry(geometry) self.restoreState(state) self.codefile = str(settings.value('lastfile').toString()) print("Loading last file: "+self.codefile) if not self.load(self.codefile): self.set_filename(self.DEFAULT_CODE_FILE) self.append(source.CodeForge.HEAD_FILE) def save_settings(self): settings = QtCore.QSettings(config.ORG_NAME, config.APP_NAME) settings.setValue("geometry", self.saveGeometry()) settings.setValue("state", self.saveState()) settings.setValue("lastfile", self.codefile) def main(): app = QtGui.QApplication(sys.argv) codeforge = source.CodeForge() mainframe = Mainframe(codeforge) sys.exit(app.exec_()) if __name__ == '__main__': main()
development/layout.pyc
development/makelibrary.py
#!/usr/bin/env python # -*- coding: utf-8 -*- import PARSE
development/notes.txt
QUESTIONS: =============== - Create a histogram and compare the sample distribution with the ideal distribution. - Increase the number of samples until the sample distribution looks like it matches exactly with the ideal. - Now increase the number of bins in the histogram. How does this affect how well the sample distribution matches the ideal? - What about other distributions - do some distributions require more or less samples to match better? Bin precision Tail-heavy ====================== - Python is case-sensitive: a is not A - Python is zero-based, like C, not 1-based like Matlab - Make sure you don't use reserved names as filenames: for example don't call your program 'new' or 'import'. - Tabs or spaces, not both. - The program will be saved every time before running, so if you have made a mistake UNDO before quitting.
development/source.py
# -*- coding: utf-8 -*- # The code contained herein is currently in development and as such # is not to be shared. # # Eventually, when the work is complete, it may be released under an # opensource license. # # Copyright 2011, Anwer Bashi, X. Rong Li import time from string import Template import config class CodeForge: HEAD_FILE = 'head' TAIL_FILE = 'tail' INCLUDE_FILE = 'parselib' DATE_FORMAT = '%a %b %d, %Y' REQ_SHOW = ['plot', 'stem', 'hist', 'scatter', 'xcorr'] AUTO_SIG = '\n########## The following lines are automatically appended to the file on write and truncated on read ##########\n' def __init__(self): self.clear() def read(self, snippet_name): self.vars['snippet_name'] = snippet_name templ = '' try: filename = config.CODE_RESOURCES + snippet_name + config.REQUIRES_EXT required = open(filename, 'r').read() if required not in self.included: self.included[required] = True templ += required except: pass try: filename = config.CODE_RESOURCES + snippet_name + config.TEMPLATE_EXT templ += open(filename, 'r').read() except: templ += "# [%s not found]\n" % (snippet_name, ) return templ def append(self, filename, localdict=None): templ = self.read(filename) subs = Template(templ).safe_substitute(localdict or self.vars) self.doc += subs return subs def supplimental(self, doc): sup = '' for kw in self.REQ_SHOW: if kw in doc: sup += self.AUTO_SIG + "show() # Automatically added to the end of a script as needed to pause for user interaction\n" break return sup def current(self): return self.doc def clear(self): self.vars = {} self.vars['student_name'] = config.get_setting('student_name', 'My Name (configured in Edit::Preferences)') self.vars['student_id'] = config.get_setting('student_id', '012345678 (configured in Edit::Preferences)') self.vars['course_number'] = config.get_setting('course_number', 'ENEE0000 (configured in Edit::Preferences)') self.vars['todays_date'] = time.strftime(self.DATE_FORMAT, time.localtime()) self.doc = '' self.tail = self.read(self.TAIL_FILE) self.tailon = False # Store the lines which are required only once ('from' statements and so on) self.included = {} def writeinclude(self, targetfile): codepath = config.get_path(targetfile) inctempl = self.read(self.INCLUDE_FILE) subs = Template(inctempl).safe_substitute(self.vars) h = open(codepath+'/'+self.INCLUDE_FILE+'.py', 'w') h.write(subs) h.close()
development/source.pyc
development/syntax.py
# -*- coding: utf-8 -*- # The code contained herein is currently in development and as such # is not to be shared. # # Eventually, when the work is complete, it may be released under an # opensource license. # # File based on existing work by Carson Farmer and Christophe Kibleur, and an example on the SciPres wiki # Changes Copyright 2011, Anwer Bashi, X. Rong Li import sys from PyQt4.QtCore import QRegExp from PyQt4.QtGui import QColor, QTextCharFormat, QFont, QSyntaxHighlighter def format(color, style=''): """Return a QTextCharFormat with the given attributes. """ _color = QColor() _color.setNamedColor(color) _format = QTextCharFormat() _format.setForeground(_color) if 'bold' in style: _format.setFontWeight(QFont.Bold) if 'italic' in style: _format.setFontItalic(True) return _format # Syntax styles that can be shared by all languages STYLES = { 'keyword': format('blue'), 'operator': format('red'), 'brace': format('darkGray'), 'defclass': format('black', 'bold'), 'string': format('magenta'), 'string2': format('darkMagenta'), 'comment': format('darkGreen', 'italic'), 'self': format('black', 'italic'), 'numbers': format('brown'), } class PythonHighlighter (QSyntaxHighlighter): """Syntax highlighter for the Python language. """ # Python keywords keywords = [ 'and', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'exec', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print', 'raise', 'return', 'try', 'while', 'yield', 'None', 'True', 'False', ] # Python operators operators = [ '=', # Comparison '==', '!=', '<', '<=', '>', '>=', # Arithmetic '\+', '-', '\*', '/', '//', '\%', '\*\*', # In-place '\+=', '-=', '\*=', '/=', '\%=', # Bitwise '\^', '\|', '\&', '\~', '>>', '<<', ] # Python braces braces = [ '\{', '\}', '\(', '\)', '\[', '\]', ] def __init__(self, document): QSyntaxHighlighter.__init__(self, document) # Multi-line strings (expression, flag, style) # FIXME: The triple-quotes in these two lines will mess up the # syntax highlighting from this point onward self.tri_single = (QRegExp("'''"), 1, STYLES['string2']) self.tri_double = (QRegExp('"""'), 2, STYLES['string2']) rules = [] # Keyword, operator, and brace rules rules += [(r'\b%s\b' % w, 0, STYLES['keyword']) for w in PythonHighlighter.keywords] rules += [(r'%s' % o, 0, STYLES['operator']) for o in PythonHighlighter.operators] rules += [(r'%s' % b, 0, STYLES['brace']) for b in PythonHighlighter.braces] # All other rules rules += [ # 'self' (r'\bself\b', 0, STYLES['self']), # Double-quoted string, possibly containing escape sequences (r'"[^"\\]*(\\.[^"\\]*)*"', 0, STYLES['string']), # Single-quoted string, possibly containing escape sequences (r"'[^'\\]*(\\.[^'\\]*)*'", 0, STYLES['string']), # 'def' followed by an identifier (r'\bdef\b\s*(\w+)', 1, STYLES['defclass']), # 'class' followed by an identifier (r'\bclass\b\s*(\w+)', 1, STYLES['defclass']), # From '#' until a newline (r'#[^\n]*', 0, STYLES['comment']), # Numeric literals (r'\b[+-]?[0-9]+[lL]?\b', 0, STYLES['numbers']), (r'\b[+-]?0[xX][0-9A-Fa-f]+[lL]?\b', 0, STYLES['numbers']), (r'\b[+-]?[0-9]+(?:\.[0-9]+)?(?:[eE][+-]?[0-9]+)?\b', 0, STYLES['numbers']), ] # Build a QRegExp for each pattern self.rules = [(QRegExp(pat), index, fmt) for (pat, index, fmt) in rules] def highlightBlock(self, text): """Apply syntax highlighting to the given block of text. """ # Do other syntax formatting for expression, nth, format in self.rules: index = expression.indexIn(text, 0) while index >= 0: # We actually want the index of the nth match index = expression.pos(nth) length = expression.cap(nth).length() self.setFormat(index, length, format) index = expression.indexIn(text, index + length) self.setCurrentBlockState(0) # Do multi-line strings in_multiline = self.match_multiline(text, *self.tri_single) if not in_multiline: in_multiline = self.match_multiline(text, *self.tri_double) def match_multiline(self, text, delimiter, in_state, style): """Do highlighting of multi-line strings. ``delimiter`` should be a ``QRegExp`` for triple-single-quotes or triple-double-quotes, and ``in_state`` should be a unique integer to represent the corresponding state changes when inside those strings. Returns True if we're still inside a multi-line string when this function is finished. """ # If inside triple-single quotes, start at 0 if self.previousBlockState() == in_state: start = 0 add = 0 # Otherwise, look for the delimiter on this line else: start = delimiter.indexIn(text) # Move past this match add = delimiter.matchedLength() # As long as there's a delimiter match on this line... while start >= 0: # Look for the ending delimiter end = delimiter.indexIn(text, start + add) # Ending delimiter on this line? if end >= add: length = end - start + add + delimiter.matchedLength() self.setCurrentBlockState(0) # No; multi-line string else: self.setCurrentBlockState(in_state) length = text.length() - start + add # Apply formatting self.setFormat(start, length, style) # Look for the next match start = delimiter.indexIn(text, start + length) # Return True if still inside a multi-line string, False otherwise if self.currentBlockState() == in_state: return True else: return False
development/syntax.pyc
development/templ/head.templ
#!/usr/bin/env python # -*- coding: utf-8 -*- # Student Name: $student_name # Student ID: $student_id # Course Number: $course_number # File Created: $todays_date from parselib import *
development/templ/io/histogram.doc
Histogram Plot Draw the histogram of the supplied variable using the specified number of bins. The relative frequency will be normalized so that the sum of all the bars will add up to 1.
development/templ/io/histogram.param
edit('Variable', 'c', 'var', 'The variable you wish to plot') edit('Bins', '10', 'bins', 'The number of bins to use') edit('Title', '', 'title', 'The title for the plot') edit('X-Axis Label', '', 'xlabel', 'The x-axis label for the plot') edit('Y-Axis Label', '', 'ylabel', 'The y-axis label for the plot') load('histogram') docfile('histogram.doc')
development/templ/io/histogram.templ
# New figure figure() grid(True) # Draw the histogram; normalize the number of samples in each bin, # bars are centered between the bin edges hist($var, bins=$bins, align="mid", normed=True, alpha=0.75) title("$title") xlabel("$xlabel") ylabel("$ylabel")
development/templ/io/line.doc
Line Plot The line plot is the "standard" 2-dimensional plot. If you do not have an x-axis, you can simply use the indices of your variable like so (assuming your variable is named [c]): t = range(shape(c)[0])
development/templ/io/line.param
edit('x-variable', 't', 'var1', 'The x-variable you wish to plot') edit('y-variable', 'c', 'var2', 'The y-variable you wish to plot') edit('Title', '', 'title', 'The title for the plot') edit('X-Axis Label', '', 'xlabel', 'The x-axis label for the plot') edit('Y-Axis Label', '', 'ylabel', 'The y-axis label for the plot') load('line') docfile('line.doc')
development/templ/io/line.templ
figure() grid(True) plot($var1, $var2) title("$title") xlabel("$xlabel") ylabel("$ylabel")
development/templ/io/load.asv
$var = loadtxt("$fname") print("[$var] loaded, size=%s" % (shape($var),)) t = range(shape($var)[0]) figure() plot(t, $var) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of b') title('Plot of Correlated Normal Process')
development/templ/io/load.param
file_dialog(True, 'Pick the data file to load', 'fname', 'Pick any file saved in either standard ASCII format or Matlab\'s proprietary format', filesig='load') edit('Variable', 'c', 'var', 'What to name the resulting variable') load('load')
development/templ/io/load.templ
$var = loadtxt("$fname") print("[$var] loaded, size=%s" % (shape($var),)) t = range(shape($var)[0]) figure() plot(t, $var) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of $var') title('Plot of sequence $var')
development/templ/io/nl.param
edit('Nonlinear Transformation', 'c = sin(a + pi/3)**2', 'op', 'The non-linear operation must use a variable that is already in the workspace,\nfor example, an RV realization or a loaded file.') load('nl')
development/templ/io/nl.templ
$op
development/templ/io/print.param
edit('Variable', 'c', 'var', 'The variable to display') load('print')
development/templ/io/print.templ
print '$var =', truncstr(str($var), 160) print '$var is', shape($var)
development/templ/io/save.param
file_dialog(False, 'Pick the destination file name', 'fname', 'Save the variable to what filename', filesig='save') edit('Variable', 'c', 'var', 'What variable to save') load('save')
development/templ/io/save.templ
savetxt("$fname", $var)
development/templ/io/savefig.doc
Save Figure Save the last figure to a file. The the output format is deduced from the extension of the filename.
development/templ/io/savefig.param
file_dialog(False, 'Pick the destination file name', 'fname', 'Save the plot to what filename (save format depends on filename extention)', filesig='fig') edit('Title', '', 'title', 'The title for the plot') edit('X-Axis Label', '', 'xlabel', 'The x-axis label for the plot') edit('Y-Axis Label', '', 'ylabel', 'The y-axis label for the plot') load('savefig') docfile('savefig.doc')
development/templ/io/savefig.templ
savefig("$fname", dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1)
development/templ/io/scatter.doc
Scatter Plot Plot x vs. y. x and y must be vectors of the same length. If using a matrix with two columns, you can use the following for x- and y- variables: c[:,0], c[:,1]
development/templ/io/scatter.param
edit('x-variable', 't', 'var1', 'The x-variable you wish to plot') edit('y-variable', 'c', 'var2', 'The y-variable you wish to plot') edit('Title', '', 'title', 'The title for the plot') edit('X-Axis Label', '', 'xlabel', 'The x-axis label for the plot') edit('Y-Axis Label', '', 'ylabel', 'The y-axis label for the plot') load('scatter') docfile('scatter.doc')
development/templ/io/scatter.templ
# New figure figure() grid(True) scatter($var1, $var2, c='b', marker='o') title("$title") xlabel("$xlabel") ylabel("$ylabel")
development/templ/io/stem.doc
Stem Plot Plot vertical lines at each location in the x-variable from the x-axis to y,
development/templ/io/stem.param
edit('x-variable', 't', 'var1', 'The x-variable you wish to plot') edit('y-variable', 'c', 'var2', 'The y-variable you wish to plot') edit('Title', '', 'title', 'The title for the plot') edit('X-Axis Label', '', 'xlabel', 'The x-axis label for the plot') edit('Y-Axis Label', '', 'ylabel', 'The y-axis label for the plot') load('stem') docfile('stem.doc')
development/templ/io/stem.templ
# New figure figure() grid(True) stem($var1, $var2) title("$title") xlabel("$xlabel") ylabel("$ylabel")
development/templ/list_dists.templ
dist_continuous = [d for d in dir(stats) if isinstance(getattr(stats,d), stats.rv_continuous)] dist_discrete = [d for d in dir(stats) if isinstance(getattr(stats,d), stats.rv_discrete)] disp("Found %d continuous and %d discrete distributions\n" % (len(dist_continuous), len(dist_discrete))) for d in dist_continuous: disp("Found continuous %s distribution\n" % (d,)) for d in dist_discrete: disp("Found discrete %s distribution\n" % (d,))
development/templ/oper/add.doc
Addition Add two scalars or vectors together. Either one or both must be vectors, or they must be the same size. This operation can be performed directly in the code window using the '+' operator.
development/templ/oper/add.param
edit('First Operand', 'b', 'var1', 'This must be either a scalar or a vector of the same length as [Second Operand].') edit('Second Operand', 'c', 'var2', 'This must be either a scalar or a vector of the same length as [First Operand].') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('add') docfile('add.doc')
development/templ/oper/add.templ
$var_s = $var1 + $var2
development/templ/oper/conv.doc
Convolve Perform a linear convolution of two vectors. The convolution is a 'full' convolution, meaning that it is returned for each point of overlap, returning a vector of length M+N-1. This operation can be performed directly in the code window using the 'convolve' function.
development/templ/oper/conv.param
edit('First Operand', 'b', 'var1', 'This must be either a scalar or a vector of the same length as [Second Operand].') edit('Second Operand', 'c', 'var2', 'This must be either a scalar or a vector of the same length as [First Operand].') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('conv') docfile('conv.doc')
development/templ/oper/conv.templ
$var_s = convolve($var1, $var2)
development/templ/oper/dot.doc
Dot-product Multiplication Multiply the elements of two vectors together and then sum them (dot-product). Both must be vectors of equal length. This operation can be performed directly in the code window using the 'dot' function.
development/templ/oper/dot.param
edit('First Operand', 'b', 'var1', 'This must be either a scalar or a vector of the same length as [Second Operand].') edit('Second Operand', 'c', 'var2', 'This must be either a scalar or a vector of the same length as [First Operand].') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('dot') docfile('dot.doc')
development/templ/oper/dot.templ
$var_s = dot($var1, $var2)
development/templ/oper/mul.doc
Point-wise Multiplication Multiply the elements of two vectors together. Both must be vectors of equal length. This operation can be performed directly in the code window using the '*' operator.
development/templ/oper/mul.param
edit('First Operand', 'b', 'var1', 'This must be either a scalar or a vector of the same length as [Second Operand].') edit('Second Operand', 'c', 'var2', 'This must be either a scalar or a vector of the same length as [First Operand].') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('mul') docfile('mul.doc')
development/templ/oper/mul.templ
$var_s = $var1 * $var2
development/templ/parselib.templ
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2011, Anwer Bashi, X. Rong Li # This file is entered into the public domain - permission is granted to use or # redistribute this file without restriction. # # This file is automatically added to the working directory by PARSE. # It will be overwritten if it already exists so any changes made with be lost. # Python imports from os import path ABS_PATH = path.dirname(__file__) # Import all the first level math and plotting functions from pylab import * from scipy import * # Import individual modules from sys import exit as die from numpy import random from scipy import stats # Helper functions def check_limits(varstr, low, var, high): if var<low: print('Error: %s should be greater than or equal to %g, however it is set at %s<%g' % (varstr, low, varstr, low)) die() if var>high: print('Error: %s should be less than or equal to %g, however it is set at %s>%g' % (varstr, high, varstr, high)) die() def ensure_length(var, varstr, length): sh = shape(var) if not sh: print('%s is a constant, extending it to %d samples' % (varstr, length, )) var *= ones(length) elif sh[0]<length: print('Error: %s should be %d samples long, however it is %d samples long' % (varstr, length, sh[0])) die() else: print('%s is %d samples long' % (varstr, sh[0])) return var def truncstr(s, maxlen): if len(s)>maxlen: return s[:maxlen] + ' ...' return s # New Classes / Overrides # SciPy's Weibull distribution needs to be augmented to allow both alpha and beta parameters class weibull_gen(stats.rv_continuous): def _pdf(self, x, a, b): r = (a/b)*pow(x/b, a-1) * exp(-pow(x/b, a)) return where(x<0, 0, r) def _cdf(self, x, a, b): r = 1 - exp(-pow(x/b, a)) return where(x<0, 0, r) def _argcheck(self, a, b): return (a>0) & (b>0) stats.weibull = weibull_gen(name='weibull',longname="An Augmented Weibull", shapes='a, b',extradoc="") # SciPy's generic discrete distribution can only generate a realization with integer point mass locations class generic_discrete_gen(stats.rv_discrete): #def _pmf(self, k, *args): #print(k) #return .1 #return self._cdf(k,*args) - self._cdf(k-1,*args) def rvs(self,*args,**kwds): # Generate random variates values, size = map(kwds.get, ['values', 'size'], [None,None]) xk, pk = values ptot = sum(pk) samples = random.mtrand.random_sample(size) * ptot def weighted_choice(rnd): for i, w in enumerate(pk): rnd -= w if rnd < 0: return xk[i] for i in range(size): samples[i] = weighted_choice(samples[i]) return samples stats.dcustom = generic_discrete_gen(name='custom',longname="A Custom Discrete", shapes='a, b',extradoc="")
development/templ/prob/bernoulli-trials.doc
Bernoulli Trials Flip a number of coins with
development/templ/prob/bernoulli-trials.param
edit('Number of trials', '20', 'trials', 'The number of trials to conduct.') edit('P{A}', '0.35', 'p', 'Probablity of success per trial.') load('bernoulli-trials') docfile('bernoulli-trials.doc')
development/templ/prob/bernoulli-trials.templ
# Generate [trials] Bernoulli trials trials = $trials p = $p # Number the trials k = range(1, trials+1) # Flip the coins r = random.uniform(0, 1, trials) x = zeros(trials) x[r <= p] = 1 x[r > p] = 0 # Count the number of successes print('Total number of A, given P{A}=$p: %d (%.1f%%)' % (sum(x), 100*sum(x)/trials)) # New figure figure() grid(True) stem(k, x) title("Bernoulli Trials") xlabel("Trial number") ylabel("Value") axis([0, trials+1, 0, 1.2])
development/templ/prob/central-limit-theorem.doc
Central Limit Theorem The Central Limit Theorem states that given a distribution with a mean m and variance sigma2, the sampling distribution of the mean appraches a normal distribution with the same mean and a variance of sigma2/N as N, the sample size, increases. This problem allows you to test this theorem by summing a number of uniformly distributed RV's and observing the resultant probability distribution. The experiment sums 'Number to sum' uniform RV's to generate 'Trials' realizations. The frequency distribution is then plotted. Lower Range: The lower bound for the uniform distribution. Upper Range: The upper bound for the uniform distribution. Number to sum: The number of RV's to sum. Trials: The number of samples to generate to populate the histogram.
development/templ/prob/central-limit-theorem.param
edit('Lower Range', '0', 'lower', 'The lower bound for the uniform distribution.') edit('Upper Range', '1', 'upper', 'The upper bound for the uniform distribution.') edit('Number of sum', '3', 'num', "The number of RV's to sum.") edit('Trials', '1000', 'trials', 'The number of samples to generate to populate the histogram.') load('central-limit-theorem') docfile('central-limit-theorem.doc')
development/templ/prob/central-limit-theorem.templ
# Generate [trials] Trials where each is the sum of random variables trials = $trials # Number of random variables to sum L = $num # Generate the random variables lower = $lower upper = $upper r = random.uniform(lower, upper, (trials, L)) r_sum = sum(r,1) # Sum the random variables together to produce a [trials] X 1 vector hist(r_sum, 25); xlabel("Sum of %d Uniform RV's" % (L,)) ylabel("Frequency") title("Histogram of Sums")
development/templ/rp/RPNormal.m
samples = @samples; rate = @rate; a = @a; b = @b; k = [0:samples-1]/rate; x@var_ = sqrt(a)*randn(samples,1); @var_ = zeros(samples,1); @var_(1) = sqrt(a)*randn; c = exp(-b*rate); % Generate the random process for i = 2:samples @var_(i) = c*@var_(i-1)+sqrt(1-c^2)*x@var_(i); end % Now add the mean to the random process @var_ = @var_ + @mean; % Plot @var_ figure, plot(k, @var_); xlabel('time (sec.)'); ylabel('@var_'); title(['Plot of Gaussian RP with mean=@mean, a=@a, b=@b']);
development/templ/rp/acorr.doc
Autocorrelation The autocorrelation of a signal is the crosscorrelation of a signal with itself.
development/templ/rp/acorr.param
edit('Maximum lag', '20', 'maxlag', 'The maximum number of samples to lag when performing the autocorrelation.') edit('Variable', 'b', 'var', 'The variable to analyze. This variable must already be in the workspace.') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('acorr') docfile('acorr.doc')
development/templ/rp/acorr.templ
# Compute the autocorrelation of $var $var_s = correlate($var, $var, 'full') / len($var) # g = g / max(g) # Plot the autocorrelation of $var maxlags = $maxlag figure() grid(True) acorr($var, normed=False, usevlines=True, maxlags=maxlags) title('Autocorrelation of $var')
development/templ/rp/acov.doc
Autocorrelation coefficient The autocorrelation coefficient of a signal is equal to the crosscorrelation coefficient of a signal with itself.
development/templ/rp/acov.param
edit('Maximum lag', '20', 'maxlag', 'The maximum number of samples to lag when performing the autocorrelation coefficient.') edit('Variable', 'b', 'var', 'The variable to analyze. This variable must already be in the workspace.') load('acov') docfile('acov.doc')
development/templ/rp/acov.templ
# Plot the autocorrelation coefficient of $var maxlags = $maxlag figure() grid(True) acorr($var, normed=True, detrend=mlab.detrend_mean, usevlines=True, maxlags=maxlags) title('Autocorrelation coefficient of $var')
development/templ/rp/csd.doc
ToDo: Document
development/templ/rp/csd.param
edit('Samples/sec', 'persec', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long).\nCan be a variable already in the workspace.') edit('Variable 1', 'b', 'var1', 'The first variable to analyze. This variable must already be in the workspace.') edit('Variable 2', 'c', 'var2', 'The second variable to analyze. This variable must already be in the workspace.') load('csd') docfile('csd.doc')
development/templ/rp/csd.templ
# Plot the power spectral density of $var figure() grid(True) csd($var1, $var2, Fs=$persec) title('Cross Spectral Density')
development/templ/rp/fft.doc
Fast Fourier Transform Calculates the Fast Fourier Transform of the input variable and plots it. The output variable is the absolute value of the FFT, with the real and imaginary components combined.
development/templ/rp/fft.param
edit('Samples/sec', 'persec', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long).\nCan be a variable already in the workspace.') edit('Variable', 'b', 'var', 'The variable to analyze. This variable must already be in the workspace.') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('fft') docfile('fft.doc')
development/templ/rp/fft.templ
# Compute the Fourier transform of $var $var_s = abs(fft($var)) # Now plot it figure() f = array(range(samples/2), dtype=float) f = persec * f / samples plot(f, $var_s[:samples/2]) grid(True) xlabel('Frequency (Hz)') ylabel('FFT($var)') title('Fourier Transform of $var')
development/templ/rp/normal-realization.doc
This allows you to create a normal random process where the samples are correlated with one another (i.e. non-white). A plot of the actual data is produced. Note: You may also use functions for amplitude, frequency, and phase. For example, cos(a/2) or exp(a) is valid if [a] is currently defined and long enough. The variable must be at least as long as the number of samples, [Total Samples].
development/templ/rp/normal-realization.param
edit('Samples/sec', '25', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long)') edit('Total Samples', '100', 'samples', 'The number of points to generate') edit('Mean', '0', 'mean', 'The mean of the Normal RV used in the RP. You may select a random variable already in the workspace,\nor simply type in a constant value.') edit('R(t)=a*exp(-b|t|),a:', '1', 'alpha', 'The standard deviation of the Normal RV use in the RP. You may select a random variable already in the workspace,\nor specify a command to generate [cycle] values, or simply type in a constant value.') edit('R(t)=a*exp(-b|t|),b:', '1', 'beta', 'The correlation factor for the Normal RP. ToDo') edit('Variable', 'c', 'var', 'What to name the resulting variable') load('normal-realization') docfile('normal-realization.doc')
development/templ/rp/normal-realization.templ
samples = $samples # total number of samples in realization persec = $persec # samples per second t = linspace(0, samples/persec, samples) mean = $mean alpha = $alpha beta = $beta # Ensure that our parameters are long enough mean = ensure_length(mean, 'Mean', samples) # Generate an array of normal RV's with stddev=sigma n = random.normal(0, 1, samples) n = mat([n]) $var = random.normal(0, 1, samples) # Generate the random process R = mat(zeros((samples,samples))) beta = float(beta) for i in range(0, samples): for j in range(0, samples): R[i,j] = sqrt(alpha)*exp(-abs(i-j)*beta/persec) C = cholesky(R) b = C*n.T # Generate the random process for i in range(1, samples): $var[i] = b[i] # Now add the mean to the random process $var += mean.T figure() plot(t, $var) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of $var') title('Plot of Correlated Normal Process')
development/templ/rp/normal2d-realization.doc
This allows you to create a normal random process where the samples are correlated with one another (i.e. non-white). A scatter plot of the actual data is produced. Note: You may also use functions for amplitude, frequency, and phase. For example, cos(a/2) or exp(a) is valid if [a] is currently defined and long enough. The variable must be at least as long as the number of samples, [Total Samples].
development/templ/rp/normal2d-realization.param
edit('Total Samples', '100', 'samples', 'The number of points to generate') edit('Mean #1', '0', 'mean1', 'The mean of the 1st Normal RV used in the RP. You may select a scalar variable already in the workspace,\nor simply type in a constant value.') edit('Standard Deviation #1', '1', 'sigma1', 'The standard deviation of the 1st Normal RV use in the RP. You may select a scalar variable already in the workspace,\nor simply type in a constant value.') edit('Mean #2', '0', 'mean2', 'The mean of the 2nd Normal RV used in the RP. You may select a scalar variable already in the workspace,\nor simply type in a constant value.') edit('Standard Deviation #2', '1', 'sigma2', 'The standard deviation of the 2nd Normal RV use in the RP. You may select a scalar variable already in the workspace,\nor simply type in a constant value.') edit('Correlation', '0.7', 'rho', 'The correlation coefficient (-1 to 1)') edit('Variable', 'c', 'var', 'What to name the resulting variable') load('normal2d-realization') docfile('normal2d-realization.doc')
development/templ/rp/normal2d-realization.templ
samples = $samples # total number of samples in realization rho = $rho check_limits('rho', -1, rho, 1) P = array([[$sigma1, 0], [rho*$sigma2, sqrt(1-rho**2)*$sigma2]]) # Generate an array of standard Gaussians $var = random.normal(0, 1, (2, samples)) # Multiple by the scaling and correlation matrix c = P.dot($var) # Add the means $var[0,:] += $mean1 $var[1,:] += $mean2 print '$var is size:', shape($var) # Plot in a scatter graph figure() grid(True) scatter(c[0,:], c[1,:], c='b', marker='o') title('2D Correlated Normal RV, $samples samples') xlabel("RV1") ylabel("RV2")
development/templ/rp/psd.doc
ToDo: Document
development/templ/rp/psd.param
edit('Samples/sec', 'persec', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long).\nCan be a variable already in the workspace.') edit('Variable', 'b', 'var', 'The variable to analyze. This variable must already be in the workspace.') load('psd') docfile('psd.doc')
development/templ/rp/psd.templ
# Plot the power spectral density of $var figure() grid(True) psd($var, Fs=$persec) title('Power Spectral Density')
development/templ/rp/pulsetrain-realization.doc
This allows you to create a random pulse train process where the pulse width and amplitude are (possibly) random, but the pulse period is fixed. A plot of the actual data is produced. For the pulse width and amplitude variables, you may select a random variable already in the workspace (i.e., that you have created using the RV/Realization function), or simply type in a constant value. Note: You may also use functions for pulse width and amplitude. For example, cos(a/2) or exp(a) is valid if a is currently defined and long enough. The variable must be at least as long as the number of cycles, ceil([Total Samples] / [Pulse Period]).
development/templ/rp/pulsetrain-realization.param
edit('Pulse Period', '2', 'period', 'The period (in seconds) of each pulse ("on" time plus "off" time)') edit('Samples/sec', '25', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long)') edit('Total Samples', '200', 'samples', 'The number of points to generate') edit('Pulse Width', 'random.uniform(0, 1, cycles)', 'width', 'The width of the pulses (in seconds). You may select a random variable already in the workspace,\nor specify a command to generate [cycle] values, or simply type in a constant value.') edit('Amplitude', 'random.uniform(.5, 1.5, cycles)', 'amplitude', 'The amplitude of the pulses. You may select a random variable already in the workspace,\nor specify a command to generate [cycle] values, or simply type in a constant value.') edit('Variable', 'b', 'var', 'What to name the resulting variable') load('pulsetrain-realization') docfile('pulsetrain-realization.doc')
development/templ/rp/pulsetrain-realization.templ
period = $period # pulse period (in sec.) persec = $persec # samples per second samples = $samples # total number of samples in realization # Compute the number of cycles we will be creating percycle = period*persec # samples / cycle cycles = int(ceil(samples / percycle)) # total num. cycles width = $width # pulse width in seconds amp = $amplitude # pulse amplitude # Ensure that our parameters are long enough amp = ensure_length(amp, 'Amplitude', cycles) width = ensure_length(width, 'Width', cycles) $var = zeros(samples) for i in range(cycles): # The pulse starts at the next period-boundary start = i*percycle # Make sure the period is positive and doesn't go into the next pulse width[i] = max(0, width[i]) stop = min(start+percycle-1, start+width[i]*persec) $var[start:stop] = amp[i] bb = zeros(samples*100) t = linspace(0, samples/persec, samples*100) for i in range(cycles): # The pulse starts at the next period-boundary start1 = i*percycle*100 # Make sure the period is positive and doesn't go into the next pulse width[i] = max(0, width[i]) stop1 = min(start1+percycle*100-1, start1+width[i]*persec*100) bb[start1:stop1] = amp[i] figure() plot(t, bb) plot(t, bb*1.05, 'w.', alpha=0.0) # to make sure that the top of the pulse can be seen more easily grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of $var') title('Plot of Pulse Train, period=%g' % (period, ))
development/templ/rp/simplesinusoid-realization.doc
This allows you to create a random sinusoid process where the frequency, phase and amplitude are (possibly) random. A plot of the actual data is produced.
development/templ/rp/simplesinusoid-realization.param
edit('Samples/sec', '25', 'persec', 'The sampling rate of the process (i.e. 25 means that a 100-point sample will be 4 seconds long)') edit('Total Samples', '100', 'samples', 'The number of points to generate') edit('Frequency Min', '4', 'freqmin', 'The frequncy (in Hertz, not radians/second) of the sinusoid. This is the lower limit of a uniform random variable.') edit('Frequency Max', '4', 'freqmax', 'The frequncy (in Hertz, not radians/second) of the sinusoid. This is the upper limit of a uniform random variable.') edit('Amplitude Min', '.5', 'amplitudemin', 'The amplitude modulation of the sinusoid. This is the lower limit of a uniform random variable.') edit('Amplitude Max', '1.5', 'amplitudemax', 'The amplitude modulation of the sinusoid. This is the upper limit of a uniform random variable.') edit('Phase Min', '0', 'phasemin', 'The phase of the sinusoid (in radians). This is the lower limit of a uniform random variable.') edit('Phase Min', '0.2*pi', 'phasemax', 'The phase of the sinusoid (in radians). This is the upper limit of a uniform random variable.') edit('Variable', 'b', 'var', 'What to name the resulting variable') load('simplesinusoid-realization') docfile('simplesinusoid-realization.doc')
development/templ/rp/simplesinusoid-realization.templ
samples = $samples # total number of samples in realization persec = $persec # samples per second freq = random.uniform($freqmin, $freqmax) # in Hz phase = random.uniform($phasemin, $phasemax) # in radians amp = random.uniform($amplitudemin, $amplitudemax) # pulse amplitude t = linspace(0, samples/persec, samples) $var = amp * sin(freq*2*pi*t + phase); figure() plot(t, $var) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of $var') title('Plot of Sinusoid')
development/templ/rp/sinusoid-realization.doc
This allows you to create a random sinusoid process where the frequency, phase and amplitude are (possibly) random. A plot of the actual data is produced. Note: You may also use functions for amplitude, frequency, and phase. For example, cos(a/2) or exp(a) is valid if [a] is currently defined and long enough. The variable must be at least as long as the number of samples, [Total Samples].
development/templ/rp/sinusoid-realization.param
edit('Samples/sec', '10', 'persec', 'The sampling rate of the process (i.e. 10 means that a 100-point sample will be 10 seconds long)') edit('Total Samples', '100', 'samples', 'The number of points to generate') edit('Frequency', '0.5*pi', 'freq', 'The frequncy (in Hertz, not radians/second) of the sinusoid. You may select a random variable already in the workspace,\nor simply type in a constant value.') edit('Amplitude', 'random.uniform(.5, 1.5)', 'amplitude', 'The amplitude modulation of the sinusoid. You may select a random variable already in the workspace,\nor specify a command to generate [cycle] values, or simply type in a constant value.') edit('Phase', 'random.normal(-pi, pi)', 'phase', 'The phase of the sinusoid (in radians). You may select a random variable already in the workspace,\nor specify a command to generate [samples] values, or simply type in a constant value.') edit('Variable', 'b', 'var', 'What to name the resulting variable') load('sinusoid-realization') docfile('sinusoid-realization.doc')
development/templ/rp/sinusoid-realization.templ
samples = $samples # total number of samples in realization persec = $persec # samples per second freq = $freq # in Hz phase = $phase # in radians amp = $amplitude # pulse amplitude # Ensure that our parameters are long enough freq = ensure_length(freq, 'Frequency', samples) phase = ensure_length(phase, 'Phase', samples) amp = ensure_length(amp, 'Amplitude', samples) t = linspace(0, samples/persec, samples) $var = amp * sin(freq*t + phase); figure() plot(t, $var) grid(True) xlabel('Time, t (sec.)') ylabel('Amplitude of $var') title('Plot of Sinusoid')
development/templ/rp/stats.doc
Statistics Display a number of statistics for a random variable or process in the output panel.
development/templ/rp/stats.param
edit('Variable', 'b', 'var', 'The variable to analyze. This variable must already be in the workspace.') load('stats') docfile('stats.doc')
development/templ/rp/stats.templ
# Display some statistics for [$var] print("================") print("Min($var) = %g" % stats.tmin($var)) print("Max($var) = %g" % stats.tmax($var, 1e99)) print("Median($var) = %g" % stats.cmedian($var)) print("Mode($var) = %g" % stats.mode($var)[0][0]) print("================") print("Mean($var) = %g" % stats.tmean($var)) ustd=stats.tstd($var) uvar = ustd*ustd bvar = uvar*(len($var)-1)/len($var) bstd = sqrt(bvar) print("Unbiased Var($var) = %g" % uvar) print("Unbiased StdDev($var) = %g" % ustd) print("Biased Var($var) = %g" % bvar) print("Biased StdDev($var) = %g" % bstd) print("================") print("1st Moment($var) = %g" % stats.moment($var, 1)) print("2nd Moment($var) = %g" % stats.moment($var, 2)) print("3rd Moment($var) = %g" % stats.moment($var, 3)) print("4th Moment($var) = %g" % stats.moment($var, 4)) print("================")
development/templ/rp/xcorr.doc
Crosscorrelation The crosscorrelation of two signals measures how similar the signals are to each other.
development/templ/rp/xcorr.param
edit('Maximum lag', '20', 'maxlag', 'The maximum number of samples to lag when performing the crosscorrelation.') edit('Variable 1', 'b', 'var1', 'The first variable to analyze. This variable must already be in the workspace.') edit('Variable 2', 'c', 'var2', 'The second variable to analyze. This variable must already be in the workspace.') edit('Output Variable', 'g', 'var_s', 'The name of the output variable to save the results in.') load('xcorr') docfile('xcorr.doc')
development/templ/rp/xcorr.templ
# Compute the crosscorrelation of $var1 and $var2 $var_s = correlate($var1, $var2, 'full') / len($var1) # Plot the crosscorrelation of $var1 and $var2 maxlags = $maxlag figure() grid(True) xcorr($var1, $var2, normed=False, usevlines=True, maxlags=maxlags) title('Crosscorrelation of $var1 and $var2')
development/templ/rp/xcov.doc
Crosscorrelation coefficient The crosscorrelation coefficient of two signals measures the correlation between these two signals.
development/templ/rp/xcov.param
edit('Maximum lag', '20', 'maxlag', 'The maximum number of samples to lag when performing the crosscorrelation coefficient.') edit('Variable 1', 'b', 'var1', 'The first variable to analyze. This variable must already be in the workspace.') edit('Variable 2', 'c', 'var2', 'The second variable to analyze. This variable must already be in the workspace.') load('xcov') docfile('xcov.doc')
development/templ/rp/xcov.templ
# Plot the crosscorrelation coefficient of $var1 and $var2 maxlags = $maxlag figure() grid(True) xcorr($var1, $var2, normed=True, detrend=mlab.detrend_mean, usevlines=True, maxlags=maxlags) title('Crosscorrelation coefficient of $var1 and $var2')
development/templ/rv/binomial-cdf.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('plot-discrete-cdf') docfile('binomial.doc') docfile('cdf.doc')
development/templ/rv/binomial-declare.templ
# Counts the number of successes in n independent trials when the probability of success each time is pr. # [$var] is binomially-distributed with n trials, pr probability n = $n pr = $pr rv = stats.binom(n, pr) # Specify the x-values of interest t = range(0, n+1)
development/templ/rv/binomial-histogram.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('realization') load('plot-hist-pmf-overlay') docfile('binomial.doc') docfile('histogram.doc')
development/templ/rv/binomial-pdf.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('plot-pmf') docfile('binomial.doc') docfile('pmf.doc')
development/templ/rv/binomial-percentile.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('Tail Probability', '0.9', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('disp-discrete-percentile') docfile('binomial.doc') docfile('percentile.doc')
development/templ/rv/binomial-realization.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('plot-realization') docfile('binomial.doc') docfile('realization.doc')
development/templ/rv/binomial-table.param
register('rv', 'Binomial') edit('Number of trials', '10', 'n', 'The number of binary experiments to conduct for each binomially-distributed variable') edit('Probability', '0.5', 'pr', 'The probability of an experimental outcome of one') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('binomial-declare') load('disp-discrete-table') docfile('binomial.doc') docfile('table.doc')
development/templ/rv/binomial.doc
Binomial distribution Counts the number of successes in *n* independent trials when the probability of success each time is *pr*. binom.pmf(k,n,p) = choose(n,k)*p**k*(1-p)**(n-k) for k in {0,1,...,n}
development/templ/rv/cdf.doc
Cumulative Distribution Function (CDF) The Cumulative Distribution Function (CDF) gives the probability that a random variable X will result in a value less than or equal to x. If we denote the Probability Distribution Function (PDF) as f(s), then: F(x) = P(X<=x) = integral f(s) ds
development/templ/rv/chi2-cdf.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('plot-continuous-cdf') docfile('chi2.doc') docfile('cdf.doc')
development/templ/rv/chi2-declare.templ
# Generate an RV-stub for a Chi-squared continuous random variable # [$var] is Chi2-distributed with [dof] degrees of freedom dof = $dof sigma = $sigma rv = stats.chi2(dof, 0, sigma) # Specify the x-values of interest bins = 100 t = linspace(0, 3*dof, bins)
development/templ/rv/chi2-histogram.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('realization') load('plot-hist-pdf-overlay') docfile('chi2.doc') docfile('histogram.doc')
development/templ/rv/chi2-pdf.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('plot-pdf') docfile('chi2.doc') docfile('pdf.doc')
development/templ/rv/chi2-percentile.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('Tail Probability', '0.9', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('disp-continuous-percentile') docfile('chi2.doc') docfile('percentile.doc')
development/templ/rv/chi2-realization.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('plot-realization') docfile('chi2.doc') docfile('realization.doc')
development/templ/rv/chi2-table.param
register('rv', 'Chi2') edit('Degrees of Freedom', '3', 'dof', 'The number of squared normal variables to sum') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable used to generate the Chi2') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('chi2-declare') load('disp-continuous-table') docfile('chi2.doc') docfile('table.doc')
development/templ/rv/chi2.doc
Chi-Squared Distribution A Chi-squared distribution is a sum of [dof] independent, standard normal random variables. The single parameter for a Chi-squared variable, [dof], is referred to as the degrees of freedom of the variable.
development/templ/rv/custom-cdf.param
register('rv', 'Custom') edit('Point Mass Location', '0, 1, 3', 'locs', 'The locations of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('plot-discrete-cdf') docfile('custom.doc') docfile('cdf.doc')
development/templ/rv/custom-declare.templ
# Generate an RV-stub for a custom discrete random variable # [$var] is discretely-distributed with point masses # [$pms] located at [$locs] pms = [$pms] locs = [$locs] assert len(pms)==len(locs), 'The length of point mass function vectors and their locations vectors must be the same length' rv = stats.rv_discrete(name="Custom RV", values=[locs, pms]) # Specify the x-values of interest t = range(int(min(locs))-1, int(max(locs))+2)
development/templ/rv/custom-histogram.param
register('rv', 'Custom') edit('Point Mass Location', '0, 1, 3', 'locs', 'The locations of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('realization') load('plot-hist-pmf-overlay') docfile('custom.doc') docfile('histogram.doc')
development/templ/rv/custom-pdf.param
register('rv', 'Custom') edit('Point Mass Location', '0, 1, 3', 'locs', 'The location of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('plot-pmf') docfile('custom.doc') docfile('pmf.doc')
development/templ/rv/custom-percentile.param
register('rv', 'Custom') edit('Point Mass Location', '0, 1, 3', 'locs', 'The locations of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('Tail Probability', '0.5', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('disp-discrete-percentile') docfile('custom.doc') docfile('percentile.doc')
development/templ/rv/custom-realization.param
register('rv', 'Custom') edit('Point Mass Location', '0.0, 0.7, 1.0', 'locs', 'The location of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('plot-realization') docfile('custom.doc') docfile('realization.doc')
development/templ/rv/custom-table.param
register('rv', 'Custom') edit('Point Mass Location', '0, 1, 3', 'locs', 'The locations of the point masses with the probabilities listed below') edit('Point Mass Magnitude', '0.35, 0.55, 0.1', 'pms', 'The probability of the point masses at the locations listed above') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('custom-declare') load('disp-discrete-table') docfile('custom.doc') docfile('table.doc')
development/templ/rv/custom.doc
Custom Discrete distribution An arbitrary distribution where P{$var=loc} = pm. Enter the point mass magnitudes (pm) and locations as comma seperated lists. Only the values of $var that occur with nonzero probability need to be listed. The locations must all be integers in this discrete distribution.
development/templ/rv/disp-continuous-percentile.templ
# Percent point function of a $rv RV at q q = $q ${var}_ppf = rv.ppf(q) print("P(x<=%g) = %g" % (${var}_ppf, q)) # Illustrate the point at which the RV exceeds the given probability figure() grid(True) # Draw the CDF at the specified x-values ${var}_cdf = rv.cdf(t) title('CDF of a $rv distribution') xlabel('Value, $var') ylabel('Cumulative probability, F($var)') plot(t, ${var}_cdf) axhline(y=q, linestyle='--', color='green') axvline(x=${var}_ppf, linestyle='--', color='green', linewidth=2) annotate("P(x<=%g) = %g" % (${var}_ppf, q), xy=(${var}_ppf, q), xycoords='data', xytext=(0.1, 0.9), textcoords='axes fraction', bbox=dict(boxstyle="round", fc="0.8"), arrowprops = dict(arrowstyle="->", connectionstyle="angle3,angleA=90,angleB=0"))
development/templ/rv/disp-continuous-table.templ
# Survival function of a $rv RV at q val = $val ${var}_sf = rv.sf(val) print("P(x>%g) = %g" % (val, ${var}_sf)) # Highlight the tail probability figure() grid(True) # Draw the PDF at the specified x-values ${var}_pdf = rv.pdf(t) title('PDF of a $rv distribution') xlabel('Value, $var') ylabel('Probability, P($var)') plot(t, ${var}_pdf) # Now shade the probability range t_poly = linspace(val, max(t), bins) ${var}_poly = rv.pdf(t_poly) t_poly = append([min(t), t_poly[0]], t_poly) t_poly = append(t_poly, t_poly[-1]) ${var}_poly = append([0,0], ${var}_poly) ${var}_poly = append(${var}_poly, [0]) fill(t_poly, ${var}_poly, alpha=0.25) annotation = "P(x>%g) = %g\n$$ \int_{%g}^\infty f(${var}) d ${var}= %g $$" % (val, ${var}_sf, val, ${var}_sf) annotate(annotation, xy=(t_poly[2], ${var}_poly[2]/2), xycoords='data', xytext=(0.1, 0.8), textcoords='axes fraction', bbox=dict(boxstyle="round", fc="0.8"), arrowprops = dict(arrowstyle="->", connectionstyle="angle3,angleA=90,angleB=0"))
development/templ/rv/disp-discrete-percentile.templ
# Percent point function of a $rv RV at q q = $q ${var}_ppf = rv.ppf(q) # ${var}_ppf is the highest value which results in x<=q # But for discrete variables, it's likely that P(x<=${var}_ppf) != q # Compute the actual probability. ${var}_p = rv.cdf(${var}_ppf) print("P(x<=%g) = %g" % (${var}_ppf, ${var}_p)) # Illustrate the point at which the RV exceeds the given probability figure() grid(True) # Draw the CDF at the specified x-values ${var}_cdf = rv.cdf(t) title('CDF of a $rv distribution') xlabel('Value, $var') ylabel('Cumulative probability, F($var)') plot(t, ${var}_cdf, drawstyle='steps-post') axhline(y=${var}_p, linestyle='--', color='green') axvline(x=${var}_ppf, linestyle='--', color='green', linewidth=2) annotate("P(x<=%g) = %g" % (${var}_ppf, ${var}_p), xy=(${var}_ppf, ${var}_p), xycoords='data', xytext=(0.1, 0.9), textcoords='axes fraction', bbox=dict(boxstyle="round", fc="0.8"), arrowprops = dict(arrowstyle="->", connectionstyle="angle3,angleA=90,angleB=0"))
development/templ/rv/disp-discrete-table.templ
# Survival function of a $rv RV at q val = $val ${var}_sf = rv.sf(val-1) print("P(x>=%g) = %g" % (val, ${var}_sf)) # Highlight the tail probability figure() grid(True) # Draw the PDF at the specified x-values ${var}_pmf = rv.pmf(t) title('PMF of a $rv distribution') xlabel('Value, $var') ylabel('Probability, P($var)') plot(t, ${var}_pmf) # Now shade the probability range t_poly = range(int(val), max(t)+1) ${var}_poly = rv.pmf(t_poly) t_poly = append([min(t), t_poly[0]], t_poly) t_poly = append(t_poly, t_poly[-1]) ${var}_poly = append([0,0], ${var}_poly) ${var}_poly = append(${var}_poly, [0]) fill(t_poly, ${var}_poly, alpha=0.25) annotation = "P(x>=%g) = %g\n$$ \sum_{${var}=%g}^\infty P(${var}) = %g $$" % (val, ${var}_sf, val, ${var}_sf) annotate(annotation, xy=(t_poly[2], ${var}_poly[2]/2), xycoords='data', xytext=(0.1, 0.8), textcoords='axes fraction', bbox=dict(boxstyle="round", fc="0.8"), arrowprops = dict(arrowstyle="->", connectionstyle="angle3,angleA=90,angleB=0"))
development/templ/rv/exponential-cdf.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('plot-continuous-cdf') docfile('exponential.doc') docfile('cdf.doc')
development/templ/rv/exponential-declare.templ
# Generate an RV-stub for an Exponential continuous random variable # [$var] is Chi2-distributed with rate parameter [lam] lam = float($lambda) rv = stats.expon(loc=0, scale=1/lam) # Specify the x-values of interest bins = 100 t = linspace(0, 5, bins)
development/templ/rv/exponential-histogram.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('realization') load('plot-hist-pdf-overlay') docfile('exponential.doc') docfile('histogram.doc')
development/templ/rv/exponential-pdf.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('plot-pdf') docfile('exponential.doc') docfile('pdf.doc')
development/templ/rv/exponential-percentile.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('Tail Probability', '0.9', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('disp-continuous-percentile') docfile('exponential.doc') docfile('percentile.doc')
development/templ/rv/exponential-realization.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('plot-realization') docfile('exponential.doc') docfile('realization.doc')
development/templ/rv/exponential-table.param
register('rv', 'Exponential') edit('Lambda', '1', 'lambda', 'The rate parameter') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('exponential-declare') load('disp-continous-table') docfile('exponential.doc') docfile('table.doc')
development/templ/rv/exponential.doc
Exponential Distribution This distribution is most usefull because it can be used to describe the time between events in a Poisson process (a stochastic process in which events occur continuously and independently of one another). One of the important features of the exponential distribution is that is memoryless.
development/templ/rv/histogram.doc
Histogram / PDF Overlay Sort the RV samples into bins and plot with the shape of the distribution overlayed. ToDo: Add here
development/templ/rv/lognormal-cdf.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('plot-continuous-cdf') docfile('lognormal.doc') docfile('cdf.doc')
development/templ/rv/lognormal-declare.templ
# Generate an RV-stub for a log-normal continuous random variable # [$var] is lognormally-distributed from a normal distribution of N([mu], [sigma]) mu = $mu sigma = $sigma rv = stats.lognorm(1, loc=mu, scale=sigma) # Specify the x-values of interest bins = 100 t = linspace(mu+1e-6, mu+4*sigma, bins)
development/templ/rv/lognormal-histogram.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('realization') load('plot-hist-pdf-overlay') docfile('lognormal.doc') docfile('histogram.doc')
development/templ/rv/lognormal-pdf.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('plot-pdf') docfile('lognormal.doc') docfile('pdf.doc')
development/templ/rv/lognormal-percentile.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Tail Probability', '0.9', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('disp-continuous-percentile') docfile('lognormal.doc') docfile('percentile.doc')
development/templ/rv/lognormal-realization.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('plot-realization') docfile('lognormal.doc') docfile('realization.doc')
development/templ/rv/lognormal-table.param
register('rv', 'Log-Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('lognormal-declare') load('disp-continuous-table') docfile('lognormal.doc') docfile('table.doc')
development/templ/rv/lognormal.doc
Log-Normal distribution A log-normal distribution is the distribution of a random variable whose logarithm is normally distributed.
development/templ/rv/normal-cdf.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('x', '0', 'value', 'The spread of the normally-distributed random variable') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('plot-continuous-cdf') docfile('normal.doc') docfile('cdf.doc')
development/templ/rv/normal-declare.templ
# Generate an RV-stub for a normal continuous random variable # [$var] is normally-distributed with mean [mu], standard deviation [sigma] mu = $mu sigma = $sigma # value = $value rv = stats.norm(loc=mu, scale=sigma) # Specify the x-values of interest bins = 100 t = linspace(mu-4*sigma, mu+4*sigma, bins)
development/templ/rv/normal-histogram.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('realization') load('plot-hist-pdf-overlay') docfile('normal.doc') docfile('histogram.doc')
development/templ/rv/normal-pdf.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('plot-pdf') docfile('normal.doc') docfile('pdf.doc')
development/templ/rv/normal-percentile.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Tail Probability', '0.9', 'q', 'Quantile lower tail probability') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('disp-continuous-percentile') docfile('normal.doc') docfile('percentile.doc')
development/templ/rv/normal-realization.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('Length', '100', 'k', 'How many binomially-distributed variables to generate') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('plot-realization') docfile('normal.doc') docfile('realization.doc')
development/templ/rv/normal-table.param
register('rv', 'Normal') edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable') edit('The smallest number', '0', 'val', 'Identify the probability that the RV is at least this value') edit('Variable', 'a', 'var', 'What to name the resulting variable') load('normal-declare') load('disp-continuous-table') docfile('normal.doc') docfile('table.doc')
development/templ/rv/normal.doc
Normal distribution The normal or Gaussian distribution is the most prominent statistical distribution. This is due in part to its analytical tractability and in part due to the Central Limit Theory, which states that a sufficiently large number of independent, identically-distributed random variables, each with finite mean and variance, will approximate this distribution.
development/templ/rv/pdf-normal.param
edit('Mean', '0', 'mu', 'The mean about which the normally-distributed random variable is centered') edit('Standard Deviation', '1.0', 'sigma', 'The spread of the normally-distributed random variable')
development/templ/rv/pdf-normal.templ
# Generate an RV-stub for a normal continuous random variable mu = $mu sigma = $sigma rv = stats.norm(loc=mu, scale=sigma) # Draw the PMF x = linspace(-3*sigma, 3*sigma, 100) y = rv.pdf(x) figure() plot(x, y) grid(True)
development/templ/rv/pdf.doc
Probability Distribution Function (PDF) The Probability Distribution Function (PDF) is a function which describes the relative likelihood for a random variable to occur at a given point. The PDF for a discrete distribution is called the Probability Mass Function (PMF).
development/templ/rv/percentile.doc
The 'Percentile' function allows you to find the value that a random variable will exceed with a given probability. This is obtained from the inverse of cdf.
development/templ/rv/plot-cdf.templ
# New figure figure() grid(True) # Draw the CDF at the specified x-values ${var}_cdf = rv.cdf(t) title('CDF of a $rv distribution') xlabel('Value, $var') ylabel('Cumulative probability, F($var)') plot(t, ${var}_cdf)
development/templ/rv/plot-continuous-cdf.templ
# New figure figure() grid(True) # Draw the discrete CDF at the specified x-values ${var}_cdf = rv.cdf(t) title('CDF of a $rv distribution') xlabel('Value, $var') ylabel('Cumulative probability, F($var)') plot(t, ${var}_cdf)
development/templ/rv/plot-discrete-cdf.templ
# New figure figure() grid(True) # Draw the discrete CDF at the specified x-values ${var}_cdf = rv.cdf(t) title('CDF of a $rv distribution') xlabel('Value, $var') ylabel('Cumulative probability, F($var)') plot(t, ${var}_cdf, drawstyle='steps-post')