-#!/usr/bin/env python
-
'''
TUTORIAL PLUGIN FOR HOOKE
'''
Here we define the class containing all the Hooke commands we want to define
in the plugin.
-
+
Notice the class name!!
The syntax is filenameCommands. That is, if your plugin is pluggy.py, your class
name is pluggyCommands.
-
+
Otherwise, the class will be ignored by Hooke.
- '''
-
+ '''
+
def _plug_init(self):
'''
This is the plugin initialization.
If there is something you need to do when Hooke starts, code it in this function.
'''
print 'I am the Tutorial plugin initialization!'
-
+
#Here we initialize a local configuration variable; see plotmanip_absvalue() for explanation.
- self.config['tutorial_absvalue']=0
+ self.config['tutorial_absvalue']=0
pass
-
+
def do_nothing(self,args):
'''
This is a boring but working example of an actual Hooke command.
A Hooke command is a function of the xxxxCommands class, which is ALWAYS defined
this way:
-
+
def do_nameofcommand(self,args)
-
+
*do_ is needed to make Hooke understand this function is a command
*nameofcommand is how the command will be called in the Hooke command line.
*self is, well, self
*args is ALWAYS needed (otherwise Hooke will crash executing the command). We will see
later what args is.
-
+
Note that if you now start Hooke with this plugin activated and you type in the Hooke command
line "help nothing" you will see this very text as output. So the help of a command is a
string comment below the function definition, like this one.
-
+
Commands usually return None.
'''
print 'I am a Hooke command. I do nothing.'
-
+
def do_printargs(self,args):
'''
This command prints the args you give to it.
args is always a string, that contains everything you write after the command.
So if you issue "mycommand blah blah 12345" args is "blah blah 12345".
-
+
Again, args is needed in the definition even if your command does not use it.
'''
print 'You gave me those args: '+args
-
+
def help_tutorial(self):
'''
- This is a help function.
+ This is a help function.
If you want a help function for something that is not a command, you can write a help
function like this. Calling "help tutorial" will execute this function.
'''
print 'You called help_tutorial()'
-
+
def do_environment(self,args):
'''
This plugin contains a panoramic of the Hooke command line environment variables,
and prints their current value.
'''
-
+
'''self.current_list
TYPE: [ libhookecurve.HookeCurve ], len=variable
contains the actual playlist of Hooke curve objects.
'''
print 'current_list length:',len(self.current_list)
print 'current_list 0th:',self.current_list[0]
-
+
'''self.pointer
TYPE: int
contains the index of
the current curve in the playlist
'''
print 'pointer: ',self.pointer
-
+
'''self.current
TYPE: libhookecurve.HookeCurve
contains the current curve displayed.
We will see later how it works.
'''
print 'current:',self.current
-
+
'''self.plots
TYPE: [ libhookecurve.PlotObject ], len=1,2
contains the current default plots.
- Each PlotObject contains all info needed to display
+ Each PlotObject contains all info needed to display
the plot: apart from the data vectors, the title, destination
etc.
Usually self.plots[0] is the default topmost plot, self.plots[1] is the
accessory bottom plot.
'''
print 'plots:',self.plots
-
+
'''self.config
TYPE: { string:anything }
contains the current Hooke configuration variables, in form of a dictionary.
'''
print 'config:',self.config
-
+
'''self.plotmanip
TYPE: [ function ]
Contains the ordered plot manipulation functions.
*YOU SHOULD NEVER MODIFY THAT*
'''
print 'plotmanip: ',self.plotmanip
-
+
'''self.drivers
TYPE: [ class ]
Contains the plot reading drivers.
*YOU SHOULD NEVER MODIFY THAT*
'''
print 'drivers: ',self.drivers
-
+
'''self.frame
TYPE: wx.Frame
Contains the wx Frame of the GUI.
***NEVER, EVER TOUCH THAT.***
'''
print 'frame: ',self.frame
-
+
'''self.list_of_events
TYPE: { string:wx.Event }
Contains the wx.Events to communicate with the GUI.
to create a GUI plugin.
'''
print 'list of events:',self.list_of_events
-
+
'''self.events_from_gui
TYPE: Queue.Queue
Contains the Queue where data from the GUI is put.
to create a GUI plugin.
'''
print 'events from gui:',self.events_from_gui
-
+
'''self.playlist_saved
TYPE: Int (0/1) ; Boolean
Flag that tells if the playlist has been saved or not.
'''
print 'playlist saved:',self.playlist_saved
-
+
'''self.playlist_name
TYPE: string
Name of current playlist
'''
print 'playlist name:',self.playlist_name
-
+
'''self.notes_saved
TYPE: Int (0/1) ; Boolean
Flag that tells if the playlist has been saved or not.
'''
print 'notes saved:',self.notes_saved
-
+
def do_myfirstplot(self,args):
'''
In this function, we see how to create a PlotObject and send it to the screen.
***Read the code of PlotObject in libhookecurve.py before!***.
'''
-
+
#We generate some interesting data to plot for this example.
xdata1=np.arange(-5,5,0.1)
xdata2=np.arange(-5,5,0.1)
ydata1=[item**2 for item in xdata1]
ydata2=[item**3 for item in xdata2]
-
+
#Create the object.
#The PlotObject class lives in the libhookecurve library.
myplot=lhc.PlotObject()
'''
- The *data* of the plot live in the .vectors list.
-
+ The *data* of the plot live in the .vectors list.
+
plot.vectors is a multidimensional array:
plot.vectors[0]=set1
plot.vectors[1]=set2
plot.vectors[2]=sett3
etc.
-
+
2 curves in a x,y plot are:
[[[x1],[y1]],[[x2],[y2]]]
for example:
x2 = self.vectors[1][0]
y2 = self.vectors[1][1]
'''
- #Pour 0-th dataset into myplot:
+ #Pour 0-th dataset into myplot:
myplot.add_set(xdata1,ydata1)
-
- #Pour 1-st dataset into myplot:
+
+ #Pour 1-st dataset into myplot:
myplot.add_set(xdata2,ydata2)
-
+
#Add units to x and y axes
#units=[string, string]
myplot.units=['x axis','y axis']
-
+
#Where do we want the plot? 0=top, 1=bottom
myplot.destination=1
-
+
'''Send it to the GUI.
Note that you *have* to encapsulate it into a list, so you
have to send [myplot], not simply myplot.
-
+
You can also send more two plots at once
self.send_plot([plot1,plot2])
'''
self._send_plot([myplot])
-
+
def do_myfirstscatter(self,args):
'''
xdata2=np.arange(-5,5,1)
ydata1=[item**2 for item in xdata1]
ydata2=[item**3 for item in xdata2]
-
+
myplot=lhc.PlotObject()
myplot.add_set(xdata1,ydata1)
myplot.add_set(xdata2,ydata2)
-
-
+
+
#Add units to x and y axes
myplot.units=['x axis','y axis']
-
+
#Where do we want the plot? 0=top, 1=bottom
myplot.destination=1
-
+
'''None=standard line plot
'scatter'=scatter plot
By default, the styles attribute is an empty list. If you
want to define a scatter plot, you must define all other
plots as None or 'scatter', depending on what you want.
-
+
Here we define the second set to be plotted as scatter,
and the first to be plotted as line.
-
+
Here we define also the colors to be the default Matplotlib colors
'''
myplot.styles=[None,'scatter']
myplot.colors=[None,None]
self._send_plot([myplot])
-
+
def do_clickaround(self,args):
'''
Here we click two points on the curve and take some parameters from the points
we have clicked.
'''
-
+
'''
points = self._measure_N_points(N=Int, whatset=Int)
*N = number of points to measure(1...n)
'''
points=self._measure_N_points(N=2,whatset=1)
print 'You clicked the following points.'
-
+
'''
These are the absolute coordinates of the
- point clicked.
+ point clicked.
[float, float] = x,y
'''
print 'Absolute coordinates:'
print points[0].absolute_coords
print points[1].absolute_coords
print
-
+
'''
These are the coordinates of the points
clicked, remapped on the graph.
print points[0].graph_coords
print points[1].graph_coords
print
-
+
'''
These are the indexes of the clicked points
on the dataset vector.
print 'Index of points on the graph:'
print points[0].index
print points[1].index
-
-
+
+
def help_thedifferentplots(self):
'''
The *three* different default plots you should be familiar with
in Hooke.
-
+
Each plot contains of course the respective data in their
vectors attribute, so here you learn also which data access for
each situation.
'''
print '''
1. THE RAW, CURRENT PLOTS
-
+
self.current
---
Contains the current libhookecurve.HookeCurve container object.
A HookeCurve object defines only two default attributes:
-
+
* self.current.path = string
The path of the current displayed curve
-
+
* self.current.curve = libhookecurve.Driver
The curve object. This is not only generated by the driver,
this IS a driver instance in itself.
This means that in self.current.curve you can access the
specific driver APIs, if you know them.
-
+
And defines only one method:
* self.current.identify()
Fills in the self.current.curve object.
See in the cycling tutorial.
-
+
*****
The REAL curve data actually lives in:
---
Contains the raw PlotObject-s, as "spitted out" by the driver, without any
intervention.
This is as close to the raw data as Hooke gets.
-
+
One or two plots can be spit out; they are always enclosed in a list.
*****
-
+
Methods of self.current.curve are:
---
-
+
* self.current.curve.is_me()
(Used by identify() only.)
-
+
* self.current.curve.close_all()
Closes all driver open files; see the cycling tutorial.
'''
-
+
print '''
2. THE PROCESSED, DEFAULT PLOT
-
+
The plot that is spitted out by the driver is *not* the usual default plot
that is displayed by calling "plot" at the Hooke prompt.
-
+
This is because the raw, driver-generated plot is usually *processed* by so called
*plot processing* functions. We will see in the tutorial how to define
- them.
-
+ them.
+
For example, in force spectroscopy force curves, raw data are automatically corrected
for deflection. Other data can be, say, filtered by default.
-
- The default plots are accessible in
+
+ The default plots are accessible in
self.plots = [ libhooke.PlotObject ]
-
+
self.plots[0] is usually the topmost plot
self.plots[1] is usually the bottom plot (if present)
'''
-
+
print '''
3. THE PLOT DISPLAYED RIGHT NOW.
-
+
Sometimes the plots you are displaying *right now* is different from the previous
two. You may have a fit trace, you may have issued some command that spits out
- a custom plot and you want to rework that, whatever.
-
+ a custom plot and you want to rework that, whatever.
+
You can obtain in any moment the plot currently displayed by Hooke by issuing
-
+
PlotObject = self._get_displayed_plot(dest)
* dest = Int (0/1)
dest=0 : top plot
dest=1 : bottom plot
'''
-
-
+
+
def do_cycling(self,args):
'''
Here we cycle through our playlist and print some info on the curves we find.
Cycling through the playlist needs a bit of care to avoid memory leaks and dangling
open files...
-
+
Look at the source code for more information.
'''
-
+
def things_when_cycling(item):
'''
We encapsulate here everything has to open the actual curve file.
By doing it all here, we avoid to do acrobacies when deleting objects etc.
in the main loop: we do the dirty stuff here.
'''
-
+
'''
identify()
-
+
This method looks for the correct driver in self.drivers to use;
and puts the curve content in the .curve attribute.
Basically, until identify() is called, the HookeCurve object
the Hooke plot routine), the HookeCurve object is "filled" with
the actual curve.
'''
-
+
item.identify(self.drivers)
-
+
'''
After the identify(), item.curve contains the curve, and item.curve.default_plots() behaves exactly like
self.current.curve.default_plots() -but for the given item.
'''
itplot=item.curve.default_plots()
-
+
print 'length of X1 vector:',len(itplot[0].vectors[0][0]) #just to show something
-
+
'''
The following three lines are a magic spell you HAVE to do
before closing the function.
item.curve.close_all() #Avoid open files dangling
del item.curve #Avoid memory leaks
del item #Just be paranoid. Don't ask.
-
+
return
-
-
+
+
c=0
for item in self.current_list:
print 'Looking at curve ',c,'of',len(self.current_list)
things_when_cycling(item)
c+=1
-
+
return
-
-
-
+
+
+
def plotmanip_absvalue(self, plot, current, customvalue=None):
'''
This function defines a PLOT MANIPULATOR.
A plot manipulator is a function that takes a plot in input, does something to the plot
and returns the modified plot in output.
The function, once plugged, gets automatically called everytime self.plots is updated
-
+
For example, in force spectroscopy force curves, raw data are automatically corrected
for deflection. Other data can be, say, filtered by default.
-
+
To create and activate a plot manipulator you have to:
* Write a function (like this) which name starts with "plotmanip_" (just like commands
start with "do_")
* The function must return a plot object.
* Add an entry in hooke.conf: if your function is "plotmanip_something" you will have
to add <something/> in the plotmanips section: example
-
+
<plotmanips>
<detriggerize/>
<correct/>
<median/>
- <something/>
+ <something/>
</plotmanips>
-
+
Important: Plot manipulators are *in pipe*: each plot manipulator output becomes the input of the next one.
The order in hooke.conf *is the order* in which plot manipulators are connected, so in the example above
we have:
self.current.curve.default_plots() --> detriggerize --> correct --> median --> something --> self.plots
'''
-
+
'''
Here we see what is in a configuration variable to enable/disable the plot manipulator as user will using
the Hooke "set" command.
'''
if not self.config['tutorial_absvalue']:
return plot
-
+
#We do something to the plot, for demonstration's sake
#If we needed variables, we would have used customvalue.
plot.vectors[0][1]=[abs(i) for i in plot.vectors[0][1]]
plot.vectors[1][1]=[abs(i) for i in plot.vectors[1][1]]
-
+
#Return the plot object.
return plot
-
-
+
+
#TODO IN TUTORIAL:
#how to add lines to an existing plot!!
#peaks
projects / hooke.git / blobdiff