Python 2 7 2

Note: I'm a newbie to working on githubqBittorrent version and Operating System:qBittorrent v3.3.12 Portable (from PortableApps.com)WinXP SP3Python 2.7 (=2.7.0)What is the problem:QBT says "Undeterminded Python version" (stating it found the string "2.7").The problem isin file mainwindow.cppin line 1605 pp.in function "void MainWindow::on_actionSearchWidget_triggered()".The code doesn't detect Python versions with only two reported components correctly. Python 2.7.0 reports it's version (at least on my command line) with "2.7" and function "QString Utils::Misc::pythonVersionComplete()" from misc.cpp does the right job and returns it this way. But the errorneous code assumes that Python version strings always consist of 3 components, gets the 2-component string wrong and jumps to the else-clause beneath to report an "Undeterminded Python version".What is the expected behavior:Although it's clear that a Python version of 2.7 isn't suitable for the QBT search engine, it's bad to report an "Undeterminded Python version", rather than the correct version 2.7 (or 2.7.0) with a "Old Python Interpreter" message as it is intended in the already present code for unsuitable Python versions.Steps to reproduce:Have an Python version of 2.7 (=2.7.0) and QBT installed on your system and try to start the search engine (e.g. via main menu ).Extra info(if any):I don't know how to edit code on GitHub, but I suggest the following code modification:-- old code (starting at line 1605, see above) -- 2) { int middleVer = splitted.at(1).toInt(); int lowerVer = splitted.at(2).toInt(); if (((pythonVersion == 2) && (middleVer actionSearchWidget->setChecked(false); Preferences::instance()->setSearchEnabled(false); return; } else { res = true; } } else { QMessageBox::information(this, tr("Undetermined Python version"), tr("Couldn't determine your Python version (%1). Search engine disabled.").arg(version)); m_ui->actionSearchWidget->setChecked(false); Preferences::instance()->setSearchEnabled(false); return; }"> if (splitted.size() > 2) { int middleVer = splitted.at(1).toInt(); int lowerVer = splitted.at(2).toInt(); if (((pythonVersion == 2) && (middleVer 7)) || ((pythonVersion == 2) && (middleVer == 7) && (lowerVer 9)) || ((pythonVersion == 3) &&

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Presentation on theme: "Python Crash Course Numpy"— Presentation transcript: 1 Python Crash Course Numpy 2 Extra features required:Scientific Python? Extra features required: fast, multidimensional arrays libraries of reliable, tested scientific functions plotting tools NumPy is at the core of nearly every scientific Python application or module since it provides a fast N-d array datatype that can be manipulated in a vectorized form. 2 3 What is NumPy? NumPy is the fundamental package needed for scientific computing with Python. It contains: a powerful N-dimensional array object basic linear algebra functions basic Fourier transforms sophisticated random number capabilities tools for integrating Fortran code tools for integrating C/C++ code 4 Official documentation The NumPy book Example listNumPy documentation Official documentation The NumPy book Example list 5 Arrays – Numerical Python (Numpy)Lists ok for storing small amounts of one-dimensional data >>> a = [1,3,5,7,9] >>> print(a[2:4]) [5, 7] >>> b = [[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]] >>> print(b[0]) [1, 3, 5, 7, 9] >>> print(b[1][2:4]) [6, 8] >>> a = [1,3,5,7,9] >>> b = [3,5,6,7,9] >>> c = a + b >>> print c [1, 3, 5, 7, 9, 3, 5, 6, 7, 9] But, can’t use directly with arithmetical operators (+, -, *, /, …) Need efficient arrays with arithmetic and better multidimensional tools Numpy Similar to lists, but much more capable, except fixed size >>> import numpy 6 Numpy – N-dimensional Array manpulationsThe fundamental library needed for scientific computing with Python is called NumPy. This Open Source library contains: a powerful N-dimensional array object advanced array slicing methods (to select array elements) convenient array reshaping methods and it even contains 3 libraries with numerical routines: basic linear algebra functions basic Fourier transforms sophisticated random number capabilities NumPy can be extended with C-code for functions where performance is