Code Editor, Python Shell, and File Browser¶
Canopy users spend most of their GUI time in this window. You can write Python code in the Code Editor, run your code in the Python pane or experiment line-by-line using IPythons fast interactive help and discovery.
Please note these key pieces of the Code Editor (more details below):
1. File browser pane: shows one or more directories and any recently opened files. Double-click a file to open it in the Code Editor.
2. Code editor: a general purpose text-editor with additional features specifically for editing Python code.
3. Python pane: integrates an IPython (Interactive Python) prompt that lets you quickly test code, experiment with ideas, and run code directly from the editor.
4. Editor status bar: shows information about the the file currently displayed in the code editor: line and column (1 and 22, respectively, in the image above), file type, and file path and name.
The File Browser and Python panes can be dragged and dropped to different positions within a Code Editor window, or to outside its borders. When you are dragging a pane, the location where it would dock is hightlighted in blue. These panes can also be hidden using their small “X” icon, or hidden/shown from the View menu.
For information about Canopy’s Data Import Tool, see Data Import Tool.
For information about Canopy’s Graphical Debugger see Graphical Debugger.
Code Editor Features¶
Choice of file type¶
The type of the current file is automatically determined based on the files extension (.py or .c for example) but can be manually changed by selecting a different type from the popup menu in the editor status bar. Changing the file type enables language-specific features, such as auto-completion of Python code and syntax highlighting for many languages.
Syntax checking with Pyflakes¶
For Python files, the editor frequently runs the pyflakes checking utility in the background, and marks syntax errors/warnings with red/yellow squiggly underlining.
A small ! icon in the status bar shows you the total number of errors and warnings in the current file. If you click this icon, then you will toggle the error description at the right of each affected line.
As you type code, you can use the
Tab key to complete the name behind the
cursor. If there are multiple possible completions, a small selection widget
will pop up, allowing you to choose one completion. Tab completion for
imports works as follows:
from numpy import lin<TAB>
If there is a syntax error in the code, tab completion can fail. Tab completion is not performed inside comments or strings.
To see the documentation string for a function or class, you can do the following:
linspace<TAB>() # or linspace(<TAB>) # or linspace()<TAB>
This will show a tooltip with the documentation for the function. However, once any function arguments are supplied, pressing “Tab” will no longer display the docstring for the function. The following case will also not display documentation:
linspace(<TAB> # some other code below
This is because the code is syntactically wrong since the parenthesis is not closed. In summary, the best way to get help strings is to finish writing the function, supply no arguments and hit tab as shown below:
# Code ... linspace()<TAB> # More code.
You can jump to the definition of the name under the cursor, by pressing
Cmd+j on Mac OS X). For example:
from collections import namedtuple namedtuple<Ctrl+j>(x=1)
This will open the
collections.py file in another editor tab at the
definition of the function. Note that you can press
Ctrl+j anywhere on the
symbol. This should also work for variables.
The Find widget (reached from the Search menu), contains a small magnifying glass icon. Click this to specify Find options (Case, Word, Wrap around, and Regex).
To indent a block of code, select it, and then press the
Tab key to indent
it to the right, or
Shift+Tab to dedent it to the left.
A code editor session (set in the Window menu) records all the files which are open in one or all editor windows, and the cursor position in each file. This is like a lightweight browser session manager or IDE project manager.
File browser Features¶
By default the file browser shows all recognized source file types (Python, C/C++, FORTRAN, most web file types). This can be changed to show fewer file types or all files by using the “Filter” drop-down menu at the top of the file browser.
For convenient access to your most commonly used files, the file browser is organized by Top-level Paths. Initially there is one top-level path for your OS home directory, and one for Recent Files. You can set any directory as a top-level path by browsing to it, right-clicking, and selecting Add this as top level.
Certain file operations may be available depending on file type. For all files, right-clicking yields a menu where you can copy the file path to clipboard (select “Copy path to clipboard”) or rename or move a file (select “Move/Rename ...”).
Python shell Features¶
Pylab mode and GUI support¶
The Python session is an IPython QTconsole. By default, it starts in Pylab mode with an interactive GUI backend. This permits you to run and interact with GUI programs while continuing to enter commands at the IPython prompt (e.g. to inspect or modify the GUI’s data). See IPython’s GUI event loop support.
The default Pylab GUI backend is Qt4. From the Canopy GUI preferences dialog, you can change Pylab to use an interactive wx GUI backend, or to display non-interactive graphics in-line in the IPython terminal (SVG).
Pylab mode also imports more than 900 numpy and matplotlib names into the console namespace. This can save typing but can lead to confusion when the same names (e.g. sum, min, max, any, all, int) are also Python builtins.
To avoid this problem, you can disable Pylab mode in Canopy’s preferences
dialog, and then, when you want to interact with a GUI program, give IPython’s
%matplotlib magic command (typically
%matplotlib qt), at the IPython
prompt. This enables an interactive GUI for plots, but does not pollute your
IPython configuration options¶
IPython has its own extensive configuration system. Canopy reads the default
IPython configuration files to configure its Python shell and notebooks. The
settings in Canopy’s own preferences dialog override the values in the IPython
configuration files. By default, the files
ipython_notebook_config.py present in
the default IPython profile
are used. If you wish to use a different set of configurations files, you can
add your configuration files with the same names to your application home (See
Where are the preference and log files located?).
If you click the drop-down arrow on the Python shell’s title bar (not shown in screen-shot above), or right-click within the Python shell, you will see the command “Change to Editor directory”, which you may use to change IPython’s current working directory to match the location of the file currently showing in the Code Editor; this uses IPython’s magic %cd command. For example, this can be convenient when running a demo program which assumes that its data files are in the current directory. If you wish, you can also make this the default behavior within the IPython shell, by selecting the command “Keep Directory Synced to Editor”.
Running editor text in the Python shell¶
The Run menu contains commands to run the current file, or the currently selected text, within Python shell (the user Python enviroment).
Interrupting and restarting the user Python environment¶
The Run menu contains a command to Interrupt a program running in the IPython shell.
The Run menu also contains a command to Restart the IPython kernel (user Python environment). This can be useful if a running program is frozen and not interruptible, or has corrupted the user Python environment. Note that if you restart the IPython kernel, all computed values in the Python session will be lost.
Connect the Python pane to external IPython kernels¶
Beginning with Canopy 1.7, the Python Pane can be connected to any IPython 4 (Jupyter) kernel running on Python 2.7. These include kernels started from external Jupyter consoles opened with Canopy, EPD, or Anaconda, as well as kernels started from Jupyter notebooks and embedded in external applications.
- The available packages are specific to the Python environment where the kernel was started.
- Each kernel has its own namespace, which is not shared with other kernels.
- The Canopy Debugger will work with whichever kernel is active within the Canopy Editor.
- These kernels cannot use Canopy’s Data Import Tool or Package Manager.
- This feature does not support Python 3 kernels.
Jupyter (IPython) notebook support¶
For better compatibility with the fast-moving Jupyter project, the Canopy editor (starting with version 1.6.1) opens all Jupyter (IPython) notebooks in your default web browser. To support this capability, users who retain pre-existing Canopy User Python environments must update IPython in the Package Manager or using the enpkg command-line utility. For details, see “Using Jupyter (IPython) Notebooks in Canopy” in the Enthought Support Knowledge Base.