This module provides bindings to the Python programming language. Basic usage in the context of QtiPlot will be discussed below, but for more in-depth information on the language itself, please refer to its excellent documentation.
This file allows you to customize the Python environment, import modules and define functions and classes that will be available in all of your projects. The default initialization file shipped with QtiPlot imports Python's standard math functions as well as special functions from SciPy (if available). Also, it creates some handy shortcuts, like table("table1") for qti.app.table("table1").
When activating Python support, QtiPlot searches the following places, executing the first file it can find:
~/.qtiplotrc.py[c]
/etc/qtiplotrc.py[c]
./qtiplotrc.py[c]
Files ending in .pyc are compiled versions of the .py source files and therefore load a bit faster. The compiled version will be used if the source file is older or nonexistent. Otherwise, QtiPlot will try to compile the source file (if you've got write permissions for the output file).
Mathematical expressions work largely as expected. However, there's one caveat, especially when switching from muParser (which has been used exclusively in previous versions of QtiPlot): a^b does not mean "raise a to the power of b" but rather "bitwise exclusive or of a and b"; Python's power operator is **. Thus:
2^3 # read: 10 xor 11 = 01 #> 1 2**3 #> 8
One thing you have to know when working with Python is that indentation is very important. It is used for grouping (most other languages use either braces or keywords like do...end for this). For example,
x=23 for i in (1,4,5): x=i**2 print(x)will do what you would expect: it prints out the numbers 1, 16 and 25; each on a line of its own. Deleting just a bit of space will change the functionality of your program:
x=23 for i in (1,4,5): x=i**2 print(x)will print out only one number - no, not 23, but rather 25. This example was designed to also teach you something about variable scoping: There are no block-local variables in Python.
There are two different variable scopes to be aware of: local and
global variables. Unless specified otherwise, variables are local to the
context in which they were defined. Thus, the variable
x
can have three different values in, say, two
different Note windows and a column formula. Global variables on the
other hand can be accessed from everywhere within your project. A
variable x
is declared global by executing the
statement global x. You have to do this before
assigning a value to x
, but you have to do it only
once within the project (no need to "import" the variable before using
it). Note that there is a slight twist to these rules when you define your own functions.
The basic syntax for defining a function (for use within one particular note, for example) is
def answer(): return 42If you want your function to be accessible from the rest of your project, you have to declare it global before the definition:
global answer def answer(): return 42You can add your own function to QtiPlot's function list. We'll also provide a documentation string that will show up, for example, in the "set column values" dialog:
global answer def answer(): "Return the answer to the ultimate question about life, the universe and everything." return 42 qti.mathFunctions["answer"] = answerIf you want to remove a function from the list, do:
del qti.mathFunctions["answer"]
Note that functions have their own local scope. That means that if you enter a function definition in a Note, you will not be able to access (neither reading nor writing) Note-local variables from within the function. However, you can access global variables as usual.
If-then-else decisions are entered as follows:
if x>23: print(x) else: print("The value is too small.")
You can do loops, too:
for i in range(1, 11): print(i)This will print out the numbers between 1 and 10 inclusively (the upper limit does not belong to the range, while the lower limit does).
Python comes with some basic mathematical functions that are automatically imported (if you use the initialization file shipped with QtiPlot). Along with them, the constants e (Euler's number) and pi (the one and only) are defined.
Table 7-4. Supported Mathematical Functions
Name | Description |
---|---|
acos(x) | inverse cosinus |
asin(x) | inverse sinus |
atan(x) | inverse tangent |
atan2(y,x) | equivalent to atan(y/x), but more efficient |
ceil(x) | ceiling; smallest integer greater or equal to x |
cos(x) | cosinus of x |
cosh(x) | hyperbolic cosinus of x |
degrees(x) | convert angle from radians to degrees |
exp(x) | Exponential function: e raised to the power of x. |
fabs(x) | absolute value of x |
floor(x) | largest integer smaller or equal to x |
fmod(x,y) | remainder of integer division x/y |
frexp(x) | Returns the tuple (mantissa,exponent) such that x=mantissa*(2**exponent) where exponent is an integer and 0.5 <=abs(m)<1.0 |
hypot(x,y) | equivalent to sqrt(x*x+y*y) |
ldexp(x,y) | equivalent to x*(2**y) |
log(x) | natural (base e) logarythm of x |
log10(x) | decimal (base 10) logarythm of x |
modf(x) | return fractional and integer part of x as a tuple |
pow(x,y) | x to the power of y; equivalent to x**y |
radians(x) | convert angle from degrees to radians |
sin(x) | sinus of x |
sinh(x) | hyperblic sinus of x |
sqrt(x) | square root of x |
tan(x) | tangent of x |
tanh(x) | hyperbolic tangent of x |
We will assume that you are using the initialization file shipped with QtiPlot. Accessing the objects in your project is straight-forward,
t = table("Table1") m = matrix("Matrix1") g = graph("Graph1") n = note("Notes1")as is creating new objects:
# create an empty table named "tony" with 5 rows and 2 columns: t = newTable("tony", 5, 2) # use defaults t = newTable() # create an empty matrix named "gina" with 42 rows and 23 columns: m = newMatrix("gina", 42, 23) # use defaults m = newMatrix() # create an empty graph window g = newGraph() # create a graph window named "test" with two layers disposed on a 2 rows x 1 column grid g = newGraph("test", 2, 2, 1) # create an empty note named "momo" n = note("momo") # use defaults n = note()The currently selected Table/Matrix etc. can be accessed with the follwing commands:
t = currentTable() m = currentMatrix() g = currentGraph() n = currentNote()The functions will only return a valid object if a window of the wanted type is actually selected. You can check if the object is valid with a simple if clause:
if isinstance(t,qti.Table): print "t is a table"
Every piece of code is executed in the context of an
object which you can access via the self
variable. For example,
entering self.cell("t",i) as a column formula is equivalent to the convenience
function col("t").
t = table("Table1") setWindowName(t, "toto") t.setWindowLabel("tutu") t.setCaptionPolicy(MDIWindow.Both)The caption policy can have one of the following values:
the window caption is determined by the window name
the caption is detemined by the window label
caption = "name - label"
saveAsTemplate(graph("Graph1"), "my_plot.qpt") g = openTemplate("my_plot.qpt")Also, you can easily clone a MDI window:
g1 = clone(graph("Graph1"))
f = activeFolder()The functions table, matrix, graph and note will start searching in the active folder and, failing this, will continue with a depth-first recursive search of the project's root folder, given by:
f = rootFolder()In order to access subfolders and windows, there are the following functions:
f2 = f.folders()[number] f2 = f.folder(name, caseSensitive=True, partialMatch=False) t = f.table(name, recursive=False) m = f.matrix(name, recursive=False) g = f.graph(name, recursive=False) n = f.note(name, recursive=False)If you supply True for the recursive argument, a depth-first recursive search of all subfolders will be performed and the first match returned.
New folders can be created using:
newFolder = addFolder("New Folder", parentFolder = 0)If the
parentFolder
is not specified, the new folder will be added as a subfolder of the project's
root folder. When you create a new folder via a Python script, it doesn't automatically become the active folder
of the project. You have to set this programatically, using:
changeFolder(newFolder, bool force=False)Folders can be deleted using:
deleteFolder(folder)You can save a folder as a project file, and of course, you can also save the whole project:
saveFolder(folder, "new_file.qti", compress=False) saveProjectAs("new_file_2.qti", compress=False)If
compress
is set to True, the project file will be archived to the .gz format, using zlib.
Also, you can load a QtiPlot or an Origin project file into a new folder.
The new folder will have the base name of the project file and will be added as a subfolder
to the parentFolder
or to the current folder if no parent folder is specified.
newFolder = appendProject("projectName", parentFolder = 0)If you don't want to be asked for confirmation when a table/matrix is renamed during this operation, or when deleting a folder via a Python script, you must change your preferences concerning prompting of warning messages, using the Preferences dialog ("Confirmations" tab).
t
. You can access its numeric cell values with
t.cell(col, row) # and t.setCell(col, row, value)Whenever you have to specify a column, you can use either the column name (as a string) or the consecutive column number (starting with 1). Row numbers also start with 1, just as they are displayed. If you want to work with arbitrary texts or the textual representations of numeric values, you can use:
t.text(col, row) # and t.setText(col, row, string)The number of columns and rows is accessed via:
t.numRows() t.numCols() t.setNumRows(number) t.setNumCols(number)You can delete a range of rows using:
t.deleteRows(startRowNumber, endRowNumber)Column names can be read and written with:
t.colName(number) t.setColName(col, newName, enumerateRight=False)If
enumerateRight
is set to True, all the table columns starting from index
col
will have their names modified to a combination of the
newName
and a numerical increasing index. If this parameter is not specified,
by default it is set to False.
You can change the plot role of a table column (abscissae, ordinates, error bars, etc...) using:
t.setColumnRole(col, role)where
role
specifies the desired column role:
Table.None
Table.X
Table.Y
Table.Z
Table.xErr
Table.yErr
Table.Label
You can normalize a single column or all columns in a table:
t.normalize(col) t.normalize()Sort a single or all columns:
t.sortColumn(col, order = 0) t.sort(type = 0, order = 0, leadingColumnName)Import values from
file
, using sep
as separator char, ignoring
ignoreLines
lines at the head of the file and all lines starting with a comment
string.
t.importASCII(file, sep="\t",ignoreLines=0,renameCols=False,stripSpaces=True,simplifySpace=False, importComments=False,comment="#",readOnly=False,importAs=Table.Overwrite,endLine=0,maxRows=-1)As you see from the above list of import options, you have the possibility to set the new columns as read-only. This will prevent the imported data from beeing modified. You have the possibility to remove this protection at any time, by using:
t.setReadOnlyColumn(col, False)
The importAs
flag can have the following values:
Table.NewColumns: data values are added as new columns.
Table.NewRows: data values are added as new rows.
Table.Overwrite: all existing values are overwritten (default value).
The endLine
flag specifies the end line character convention used in the ascii file.
Possible values are: 0 for line feed (LF), which is the default value,
1 for carriage return + line feed (CRLF) and 2 for carriage return only (usually on Mac computers).
The last parameter maxRows
allows you to specify a maximum number of imported
lines. Negative values mean that all data lines must be imported.
You can export values from a table to an ASCII file
, using
sep
as separator chararacter. The ColumnLabels
option
allows you to export or ignore the column labels, ColumnComments
does the same for the comments
displayed in the table header and the SelectionOnly
option makes
possible to export only the selected cells of the table.
t.exportASCII(file,sep="\t",ignore=0,ColumnLabels=False,ColumnComments=False,SelectionOnly=False)Other settings that you can modify are the text displayed as a comment in the header of a column or the expression used to calculate the column values. Please beware that changing the command doesn't automatically update the values of the column!
t.setComment(col, newComment) t.setCommand(col, newExpression)After having changed some table values from a script, you will likely want to update dependent Graphs:
t.notifyChanges()You can also modify the width of a column (in pixels) or hide/show table columns:
t.setColumnWidth(col, width) t.hideColumn(col, True)As a simple example, let's set some column values without using the dialog.
t = table("table1") for i in range(1, t.numRows()+1): t.setCell(1, i, i**2) t.notifyChanges()
m
, you can change its display mode via the following function:
m.setViewType(Matrix.TableView) m.setViewType(Matrix.ImageView)If a matrix is viewed as an image, you have the choice to display it either as gray scale or using a predefined color map:
m.setGrayScale() m.setRainbowColorMap()Accessing cell values is very similar to Table, but since Matrix doesn't use column logic, row arguments are specified before columns and obviously you can't use column name.
m.cell(row, col) m.setCell(row, col, value) m.text(row, col) m.setText(row, col, string)An alternative solution to assign values to a Matrix, would be to define a formula and to calculate the values using this formula, like in the following example:
m.setFormula("x*y*sin(x*y)") m.calculate()You can also specify a column/row range in the calculate() function, like this:
m.calculate(startRow, endRow, startColumn, endColumn)Before setting the values in a matrix you might want to define the numeric precision, that is the number of significant digits used for the computations:
m.setNumericPrecision(prec)Also, like with tables, you can access the number of rows/columns in a matrix:
rows = m.numRows() columns = m.numCols()Matrix objects allow you to define a system of x/y coordinates that will be used when plotting color/contour maps or 3D height maps. You can manipulate these coordinates using the following functions:
xs = m.xStart() xe = m.xEnd() ys = m.yStart() ye = m.yEnd() m.setCoordinates(xs + 2.5, xe, ys - 1, ye + 1)The horizontal and vertical headers of a matrix can display either the x/y coordinates or the column/row indexes:
m.setHeaderViewType(Matrix.ColumnRow) m.setHeaderViewType(Matrix.XY)There are several built-in transformations that you can apply to a matrix object. You can transpose or invert a matrix and calculate its determinant, provided, of course, that the conditions on the matrix dimensions, required by these operations, are matched:
m.transpose() m.invert() d = m.determinant()Some other operations, very useful when working with images, like 90 degrees rotations and mirroring, can also be performed. By default rotations are performed clockwise. For a counterclockwise rotation you must set the
clockwise
parameter to False
.
m.flipVertically() m.flipHorizontally() m.rotate90(clockwise = True)Please note that sometimes, after a change in the matrix settings, you need to use the following function in order to update the display:
m.resetView()If you need to get data from a table, in order to use it in a matrix (or vice-versa), you can avoid time consuming copy/paste operations and speed up the whole proces by simply converting the table into a matrix:
m = tableToMatrix(table("Table1")) t = matrixToTable(m)Also, it's worth knowing that you can easily import image files to matrices, that can be used afterwards for plotting (see the next section for more details about 2D plots):
m1 = importImage("C:/poze/adi/PIC00074.jpg") m2 = newMatrix() m2.importImage("C:/poze/adi/PIC00075.jpg")The algorithm used to import the image returns a gray value between 0 and 255 from the (r, g, b) triplet corresponding to each pixel. The gray value is calculated using the formula: (r * 11 + g * 16 + b * 5)/32
For custom image analysis operations, you can get a copy of the matrix image view, as a QImage object, via:
image = m.image()You can export matrices to all raster image formats supported by Qt or to any of the following vectorial image format: EPS, PS, PDF or SVG using:
m.export(fileName)This is a shortcut function which uses some default parameters in order to generate the output image. If you need more control over the export parameters you must use one of the following functions:
m1.exportRasterImage(fileName, quality = 100) m2.exportVector(fileName,resolution,color = true,keepAspect = true,pageSize = QPrinter::Custom)
You can also import an ASCII data file
, using sep
as separator characters, ignoring
ignore
lines at the head of the file and all lines starting with a comment
string:
m.importASCII(file, sep="\t", ignore=0, stripSpaces=True, simplifySpace=False, comment="#", importAs=Matrix.Overwrite, locale=QLocale(), endLine=0, maxRows=-1)
The importAs
flag can have the following values:
Matrix.NewColumns: data values are added as new columns.
Matrix.NewRows: data values are added as new rows.
Matrix.Overwrite: all existing values are overwritten (default value).
locale
parameter can be used to specify the convention for decimal separators used in your ASCII file.The endLine
flag specifies the end line character convention used in the ascii file.
Possible values are: 0 for line feed (LF), which is the default value,
1 for carriage return + line feed (CRLF) and 2 for carriage return only (usually on Mac computers).
The last parameter maxRows
allows you to specify a maximum number of imported
lines. Negative values mean that all data lines must be imported.
Also, you can export values from a matrix to an ASCII file
, using
sep
as separator chararacters. The SelectionOnly
option makes
possible to export only the selected cells of the matrix.
m.exportASCII(file, sep="\t", SelectionOnly=False)
m = importImage("C:/poze/adi/PIC00074.jpg") g1 = plot(m, Layer.ColorMap) g2 = plot(m, Layer.Contour) g3 = plot(m, Layer.GrayScale)If you want to create a new Graph window for some data in table Table1, you can use the plot command:
t = table("Table1") g = plot(t, column, type)
type
specifies the desired plot type and can be one of the following numbers or the equivalent reserved word:
Layer.Line
Layer.Scatter
Layer.LineSymbols
Layer.VerticalBars
Layer.Area
Layer.Pie
Layer.VerticalDropLines
Layer.Spline
Layer.HorizontalSteps
Layer.Histogram
Layer.HorizontalBars
Layer.Box
Layer.VerticalSteps
g1 = plot(table("Table1"), (2,4,7), 2) g2 = plot(table("Table1"), ("Table1_2","Table1_3"), Layer.LineSymbols)You can also create a vector plot by giving four columns in a Python tuple as an argument and the plot type as Layer.VectXYXY (11) or Layer.VectXYAM (14), depending on how you want to define the end point of your vectors: using (X, Y) coordinates or (Angle, Magnitude) coordinates.
g = plot(table("Table1"), (2,3,4,5), Layer.VectXYXY)If you want to add a curve to an existing Graph window, you have to choose the destination layer. Usually,
l = g.activeLayer()will do the trick, but you can also select a layer by its number:
l = g.layer(num)
l.insertCurve(table, Xcolumn, Ycolumn, type=Layer.Scatter) l.addCurve(table, column, type=Layer.Line, lineWidth = 1, symbolSize = 3, startRow = 0, endRow = -1) l.addCurves(table, (2,4), type=Layer.Line, lineWidth = 1, symbolSize = 3, startRow = 0, endRow = -1) l.removeCurve(curveName) l.removeCurve(curveNumber) l.deleteFitCurves()You can also add analytical function curves to a plot layer:
l.addFunction("x*sin(x)", 0, 3*pi, points = 100) l.addParametricFunction("cos(m)", "sin(m)", 0, 2*pi, points = 100, variableName = "m") l.addPolarFunction("t", "t", 0, 2*pi, points = 100, variableName = "t")In case you need the number of curves on a layer, you can get it with
l.numCurves()
l.setTitle("My beautiful plot") l.setTitleFont(QtGui.QFont("Arial", 12)) l.setTitleColor(QtGui.QColor("red")) l.setTitleAlignment(QtCore.Qt.AlignLeft)The alignment parameter can be any combination of the Qt alignment flags (see the PyQt documentationfor more details).
If you want you can remove the plot title using:
l.removeTitle()
l.enableAxis(int axis, on = True)where
axis
can be any integer value between 0 and 3 or the equivalent reserved word:
Layer.Left
Layer.Right
Layer.Bottom
Layer.Top
l.setAxisTitle(axis, "My axis title") l.setAxisTitleFont(axis, QtGui.QFont("Arial", 11)) l.setAxisTitleColor(axis, QtGui.QColor("blue")) l.setAxisTitleAlignment(axis, alignFlags)its color and the font used for the tick labels:
l.setAxisColor(axis, QtGui.QColor("green")) l.setAxisFont(axis, QtGui.QFont("Arial", 10))The tick labels of an axis can be enabled or disabled, you can set their color and their rotation angle:
l.enableAxisLabels(axis, on = True) l.setAxisLabelsColor(axis, QtGui.QColor("black")) l.setAxisLabelRotation(axis, angle)
angle
can be any integer value between -90 and 90 degrees.
A rotation angle can be set only for horizontal axes (Bottom and Top).
The numerical format of the labels can be set using:
l.setAxisNumericFormat(axis, format, precision = 6, formula)where
format
can have the following values: Automatic: the most compact numeric representation is chosen
Decimal: numbers are displayed in floating point form
Scientific: numbers are displayed using the exponential notation
Superscripts: like Scientific, but the exponential part is displayed as a power of 10
precision
is the number of significant digits and
formula
is a mathematical expression that can be used to link oposite scales. It's
argument must be x
for horizontal axes and y
for vertical axes.
For example, assuming that the bottom axis displays a range of wavelengths in nanometers and that the top
axis represents the equivalent energies in eV, with the help of the code bellow all the wavelengths
will be automatically converted to electron-volts and the result will be displayed in floating point form
with two significant digits after the decimal dot sign:
l.setAxisNumericFormat(Layer.Top, 1, 2, "1239.8419/x")The axis ticks can be customized via the following functions:
l.setTicksLength(minLength, majLength) l.setAxisTicksLength(axis, majTicksType, minTicksType, minLength, majLength)where the
majTicksType
and minTicksType
parameters specify the
desired orientation for the major and minor ticks, respectively:
Layer.NoTicks
Layer.Out: outward orientation for ticks, with respect to the plot canvas
Layer.InOut: both inward and outward ticks
Layer.In: inward ticks
minLength
specifies the length of the minor ticks, in pixels and
majLength
the length of the major ticks.
You can also customize the scales of the different axes using:
l.setScale(int axis, double start, double end, double step=0.0, int majorTicks=5, int minorTicks=5, int type=0, bool inverted=False)where
type
specifies the desired scale type: Layer.Linear
Layer.Log10
step
defines the size of the interval between the major scale ticks. If not specified (default value is 0.0), the step size is calculated automatically.
The other flags should be self-explanatory.Defining a scale range for an axis doesn't automatically disable autoscaling.
This means that if a curve is added or removed from the layer, the axes will still automatically
adapt to the new data interval. This can be avoided by disabling the autoscaling mode, thus
making sure that your scale settings will always be taken into account:
l.enableAutoscaling(False)If you want to rescale the plot layer so that all the data points are visible, you can use the following utility function:
l.setAutoScale()The same
setScale
function above, with a longer list of arguments,
can be used to define an axis break region:
l.setScale(axis, start, end, step=0.0, majorTicks=5, minorTicks=5, type=0, inverted=False, left=-DBL_MAX, right=DBL_MAX, breakPosition=50, stepBeforeBreak=0.0, stepAfterBreak=0.0, minTicksBeforeBreak=4, minTicksAfterBreak=4, log10AfterBreak=False, breakWidth=4, breakDecoration=True)where
left
specifies the left limit of the break region,
right
the right limit,
breakPosition
is the position of the break expressed as a percentage of the axis length and
breakWidth
is the width of the break region in pixels.
The names of the other parameters should be self-explanatory.
Finally, you can specify the width of all axes and enable/disable the drawing of their backbone line, using:
l.setAxesLinewidth(2) l.drawAxesBackbones(True)
l.setCanvasFrame(2, QtGui.QColor("red")) l.setCanvasColor(QtGui.QColor("lightGrey"))Drawing the canvas frame and disabling the axes backbone lines is the only possible solution for the issue of axes not touching themselves at their ends.
l.setFrame(2, QtGui.QColor("blue")) l.setBackgroundColor(QtGui.QColor("grey"))The default spacing between the layer frame and the other layer elements (axes, title) can be changed via:
l.setMargin(10)
l.showGrid(axis) l.showGrid()This will display the grid with the default color, width and pen style settings. If you need to change these settings, as well as to enable/disable certain grid lines, you can use the following functions:
grid = l.grid() grid.setMajPenX(QtGui.QPen(QtCore.Qt.red, 1)) grid.setMinPenX(QtGui.QPen(QtCore.Qt.yellow, 1, QtCore.Qt.DotLine)) grid.setMajPenY(QtGui.QPen(QtCore.Qt.green, 1)) grid.setMinPenY(QtGui.QPen(QtCore.Qt.blue, 1, QtCore.Qt.DashDotLine)) grid.enableXMax(True) grid.enableXMin() grid.enableYMax() grid.enableYMin(False) grid.enableZeroLineX(True) grid.enableZeroLineY(False) l.replot()All the grid functions containing an
X
refer to the vertical grid lineas, whereas the Y
leter indicates the horizontal ones.
Also, the Maj
word refers to the main grid lines and Min
to the secondary grid.
legend = l.newLegend() #or legend = l.newLegend("enter your text here")If the layer already has a legend when you execute the above code, the new text object becomes the default legend and the old one returns to the condition of common text. You can add common texts like this:
text = l.addText(legend) text.setOrigin(legend.x(), legend.y()+50)Please notice that the
addText
function returns a different reference
to the new text object. You can use this new reference later on in order to remove the text:
l.remove(text)If you want to remove the default legend, there's the convenience function:
l.removeLegend()Once you have created a legend/text, it's very easy to customize it. If you want to modify the text you can use:
l.setLegend("enter your text here") #or legend.setText("Enter your text here")All other properties of the legend: text color, background color, frame style, font and position of the top-left corner can be modified via the following functions:
legend.setTextColor(QtGui.QColor("red")) legend.setBackgroundColor(QtGui.QColor("yellow")) legend.setFrameStyle(Legend.Shadow) legend.setFont(QtGui.QFont("Arial", 14, QtGui.QFont.Bold, True)) # set top-left position using scale coordinates: legend.setOriginCoord(200.5, 600.32) # or set top-left position using pixel coordinates: legend.setOrigin(5, 10) l.replot()Other frame styles available for legends are:
Legend.Line
, which draws a rectangle around the text
and Legend.None
(no frame at all).
There is also a function allowing you to add an automatically built time stamp:
timeStamp = l.addTimeStamp()
arrow = ArrowMarker() arrow.setStart(10.5, 12.5) arrow.setEnd(200, 400.51) arrow.setStyle(QtCore.Qt.DashLine) arrow.setColor(QtGui.QColor("red")) arrow.setWidth(1) arrow.drawStartArrow(False) arrow.drawEndArrow(True) arrow.setHeadLength(7) arrow.setHeadAngle(35) arrow.fillArrowHead(True) l = newGraph().activeLayer() arrow1 = l.addArrow(arrow) arrow.setStart(120.5, 320.5) arrow.setColor(QtGui.QColor("blue")) arrow2 = l.addArrow(arrow) l.remove(arrow1)As you might notice from the sample code above, the
addArrow
function
returns a reference to a new arrow object that can be used later on to modify this
new arrow or to delete it with the remove
function.
l = newGraph().activeLayer() image = l.addImage("C:/poze/adi/PIC00074.jpg") image.setCoordinates(200, 800, 800, 200) l.replot()The
setCoordinates
function above can be used to set the geometry of the image using
scale coordinates. If you need to specify the image geometry in pixel coordinates, independently of
the plot axes values, you may use the following functions:
image.setOrigin(x, y) image.setSize(width, height) image.setRect(x, y, width, height) l.replot()You can remove an image using its reference:
l.remove(image)
l.setAntialiasing(True, bool update = True)
l.setAutoscaleFonts(False)
l.export(fileName)This function uses some default parameters for the properties of the image. If you need more control over the exported images you can use one of the following specialized functions:
l.exportVector(fileName, resolution = 96, color = True, keepAspect = True, pageSize = QtGui.QPrinter.Custom) l.exportImage(filename, quality=100, transparent=False)
The function exportVector
can export the plot/layer to the following vector formats:
.eps, .ps, .pdf. By setting the keepAspect
variable to True (the default), the
width/height ratio of the plot will be maintained in the exported image. This has the drawback that sometimes
there are some visible white margins around the plot. In order to avoid the white borders you can set this option to False
and in this case the plot will be scaled to fill the whole area of the pageSize
,
thus modifing its aspect. By default QtiPlot chooses a pageSize
which fits best the
real size of the plot, but you can specify a standard output page size, like: QtGui.QPrinter.A4, QtGui.QPrinter.A5, etc...
The function exportImage
can be used if you need to export to
one of the Qt supported bitmap image formats (.bmp, .png, .jpg, etc...). The transparent
option can only be used in conjunction with the file formats supporting transprency: .png and .tif (.tiff).
The quality
parameter influences the size of the output file. The higher this value (maximum is 100),
the higher the qualitity of the image, but the larger the size of the resulting files.
All the export functions rely on the file name suffix in order to choose the image format.
g = newGraph("Test", 4, 2, 2) g.setLayerCanvasSize(400, 300) g.arrangeLayers(False, True)The
arrangeLayers()
function takes two parameters. The first one specifies
if the layers should be arranged automatically, using a best-layout algorithm, or if the
numbers of rows and columns is fixed by the user. If the value of the second parameter is
True
, the size of the canvas is fixed by the user and the plot window
will be enlarged or shrinked, according to the user settings. Otherwise the size of the plot
window will be kept and the canvas area of each layer will be automatically adapted to fit this size.
Here's how you can modify the graph created in the previous example, in order to display a row of three
layers, while keeping the size of the plot window unchanged:
g.setNumLayers(3) g.setRows(1) g.setCols(3) g.arrangeLayers(False, False)By default, the space betwee two neighbouring layers as well as the distance between the layers and the borders of the plot window is set to five pixels. You can change the spacing between the layers and the margins using the following functions:
g.setSpacing (x, y) g.setMargins (left, right, top, bottom)Another aspect of the layout management is the alignement of the layers. There are three alignement flags that you can use for the horizontal alignement (HCenter, Left, Right) and another three for the vertical alignement (VCenter, Top, Bottom) of the layers. The following code line aligns the layers with the right edge of the window and centers them vertically in the available space:
g.setAlignement(Graph.Right, Graph.VCenter)All the examples above suppose that the layers are aranged on a grid, but of course you can add layers at any position in the plot window. In the examples bellow the x, y coordinates, in pixels, refer to the position of the top-left corner of the layer. The origin of the coordinates system coincides with the top-left corner of the plot window, the y coordinate increasing towards the bottom of the window. If the width and height of the layer are not specified they will be set to the default values:
g = newGraph() l1 = g.addLayer() l2 = g.addLayer(10, 20, 200, 200) l3 = g.addLayer(215, 20)As you have already seen, in a plot window the active layer is, by default, the last layer added to the plot, but you can change it programatically:
l = g.layer(num) g.setActiveLayer(l)In case you need to perform a repetitive task on all the layers in a plot window, you need to use a for loop and of course you need to know the number of layers existant on the plot. Here's a small example showing how to custom the titles of all the layers in the plot window:
g = graph("Graph1") layers = g.numLayers() for i in range (1, layers+1): l = g.layer(i) l.setTitle("Layer"+QtCore.QString.number(i)) l.setTitleColor(QtGui.QColor("red")) l.setTitleFont(QtGui.QFont("Arial", 14, QtGui.QFont.Bold, True)) l.setTitleAlignment(QtCore.Qt.AlignLeft)Finally, sometimes it might be useful to be able to swap two layers. This can be done with the help of the following function:
g.swapLayers(layerNum1, layerNum2)
x
for the the abscissae values and y
for the ordinates:
g = plot3D("sin(x*y)", -10.0, 10.0, -10.0, 10.0, -2.0, 2.0)For the parametric surfaces the only parameters allowed are the latitude and the longitude:
u
and v
. Here's, for example,
how you can plot a sphere:
g = plot3D("cos(u)*cos(v)", "sin(u)*cos(v)", "sin(v)", -3.14, 3.14, -2, 2)You can also create 3D height maps using data from matrices and, of course, you can plot table columns:
g = plot3D(matrix("Matrix1"), style = 5) g = plot3D(table("Table1"), "3", style)In the case of 3D plots created from matrix data sources the
style
parameter can take
any integer value from 1 to 5, with the following signification:
Wireframe style
Hidden Line style
Color filled polygons without edges
Color filled polygons with separately colored edges
Scattered points (the default style)
style
parameter can take
any integer value from 0 to 3 or the equivalent style values from the follwing list:
Graph3D.Scatter
Graph3D.Trajectory
Graph3D.Bars
Graph3D.Ribbon
g = newPlot3D() g.setTitle("My 3D Plot", QtGui.QColor("blue"), QtGui.QFont("Arial",14)) g.setResolution(2) g.setFunction("sin(x*y)", -10.0, 10.0, -10.0, 10.0, -2.0, 2.0) #or g.setData(table("Table1"), "3") #or g.setMatrix(matrix("Matrix1"))Once a plot is created, you can modify the scales and set the data range to display, using, for example:
g.setScales(-1.0, 1.0, -10.0, 11.0, -2.0, 3.0)
g.setRotation(45, 15, 35)The following function allows you to shift the plot along the world X, Y and Z axes, respectively:
g.setShift(3.0, 7.0, -4.0)You can also zoom in/out the entire plot as a whole, or you can zoom along a particular axis:
g.setZoom(10) g.setScale(0.1, 0.05, 0.3)Also, you can automatically detect the zoom values that fit best with the size of the plot window:
g.findBestLayout()You can enable/disable the perspective view mode or animate the view using:
g.setOrthogonal(False) g.animate(True)
g.setPolygonStyle() g.setFilledMeshStyle() g.showLegend(True) g.setHiddenLineStyle() g.setWireframeStyle() g.setAntialiasing(True) g.setMeshLineWidth(0.7)For scatter plots using points you can specify the radius of the points and their shape: circles if
smooth
is True, rectangles otherwise.
g.setDotOptions(10, smooth = True) g.setDotStyle()Other symbols available for scatter plots are: bars
g.setBarRadius(0.01) g.setBarStyle()cones
g.setConeOptions(radius, quality) g.setConeStyle()and crosses (surrounded by a box frame, if
boxed
is set to True):
g.setCrossOptions(radius, width, smooth, boxed) g.setCrossStyle()
g.showFloorProjection() g.showFloorIsolines() g.setEmptyFloor()
g.setBoxed() g.setFramed() g.setNoAxes()If the axes are enabled, you can set their legends and the distance between the legend and the axes via:
g.setXAxisLabel("X axis legend") g.setYAxisLabel("Y axis legend") g.setZAxisLabel("Z axis legend") g.setLabelsDistance(30)Also, you can fix the length of the major and minor ticks of an axis:
g.setXAxisTickLength(2.5, 1.5) g.setYAxisTickLength(2.5, 1.5) g.setZAxisTickLength(2.5, 1.5)
g.setLeftGrid(True) g.setRightGrid() g.setCeilGrid() g.setFloorGrid() g.setFrontGrid() g.setBackGrid(False)
g.setDataColors(QtGui.QColor("red"), QtGui.QColor("orange")) #or g.setDataColorMap(fileName) g.update()The colors of all the other plot elements can be customized as shown bellow. Don't forget to update the plot in order to display the new colors:
g.setMeshColor(QtGui.QColor("blue")) g.setAxesColor(QtGui.QColor("green")) g.setNumbersColor(QtGui.QColor("black")) g.setLabelsColor(QtGui.QColor("darkRed")) g.setBackgroundColor(QtGui.QColor("lightYellow")) g.setGridColor(QtGui.QColor("grey")) g.setDataColors(QtGui.QColor("red"), QtGui.QColor("orange")) g.setOpacity(0.75) g.update()
g.export(fileName) g.exportVector(fileName) g.exportImage(fileName, int quality = 100, bool transparent = False)
op = FFTFilter(graph("Graph1").activeLayer(), "Table1_2", 1.5, 3.9) op = LinearFit(table("Table1"), "colX", "colY", 10, 100)In the first example the data source is a curve Table1_2, plotted in the active layer of the graph Graph1and the abscissae range is chosen between 1.5 and 3.9. In the second example the data source is a table Table1. The abscissae of the data set are stored in the column called colXand the ordinates in the column colY. The data range is chosen between the 10th row and the row with the index 100. If you don't specify the row range, by default the whole table will be used. Not all operations support curves as data sources, like for example: convolution/deconvolution and correlation. For these operations only table columns can be used as data sources for the moment.
Once you have initialized an operation, you can still chage its input data via the following functions:
op.setDataFromCurve("Table1_energy", 10.5, 20.1, graph("Graph2").activeLayer()) op.setDataFromTable(table("Table1"), "colX", "colY", 10, 100)You don't have to specify a plot layer in the setDataFromCurve() function, if the analysis operation has already been initialized by specifying a curve on an existing graph and you just want to treat another curve from the same plot layer.
Also, when performing analysis tasks via Python scripts, there are several utility functions that can be called for all operations. For example you can disable any graphical output from an operation or you can redirect the output to the plot layer of your choice:
op.enableGraphicsDisplay(False) op.enableGraphicsDisplay(True, graph("Graph2").activeLayer())Let's assume that you need to perform a specific operation
op
,
which analyses your data and at the end, displays a result curve.
For this kind of operations, you can customize the number of points in the resulting curve
and its color:
op.setOutputPoints(int) op.setColor(int) op.setColor("green")Colors can be specified by their names or as integer values, from 0 to 23, each integer corresponding to a predefined color: 0 - "black", 1 - "red", 2 - "green", 3 - "blue", 4 - "cyan", 5 - "magenta", 6 - "yellow", 7 - "darkYellow", 8 - "navy", 9 - "purple", etc ...
Most of the time, a new table is also created as a result of a data analysis operation. This table stores the data displayed by the result curve and is hidden by default, but you can interact with it via the following function:
t = op.resultTable()After the initialization of an analysis operation, which consists of setting the data source, the data range and some other properties, like color, number of points, etc..., you can execute it via a call to its run() function:
op.run()For data fitting operations, there's an alias for the run() function which is: fit().
conv = Convolution(table("Table1"), "B", "C") conv.setColor("green") conv.run()The deconvolution and the correlation of two data sets can be done using a similar synthax:
dec = Deconvolution(table("Table1"), "B", "C") dec.run() cor = Correlation(table("Table1"), "B", "C", 10, 200) cor.setColor("green") cor.run()
diff = Differentiation(graph("Graph1").activeLayer(), "Table1_2", 2, 10) diff.run()The result of these code sequence would be a new plot window displaying the derivative of the initial curve. The numerical derivative is calculated using a five terms formula.
fft = FFT(graph("Graph1").activeLayer(), "Table1_2") fft.normalizeAmplitudes(False) fft.shiftFrequencies(False) fft.setSampling(0.1) fft.run()By default the calculated amplitudes are normalized and the corresponding frequencies are shifted in order to obtain a centered x-scale. If we want to recover the initial curve with the help of the inverse transformation, we mustn't modify the amplitudes and the frequencies. Also the sampling parameter must be set to the inverse of the time period, that is 10. Here's how we can perform the inverse FFT, using the "FFT1" table, in order to recover the original curve:
ifft = FFT(table("FFT1"), "Real", "Imaginary") ifft.setInverseFFT() ifft.normalizeAmplitudes(False) ifft.shiftFrequencies(False) ifft.setSampling(10) ifft.run()
filter = FFTFilter(graph("Graph1").activeLayer(), "Table1_2", FFTFilter.HighPass) filter.setCutoff(1) filter.run()Here's how you can cut all the frequencies lower than 1.5 Hz and higher than 3.5 Hz. In the following example the continuous component of the signal is also removed:
filter.setFilterType(FFTFilter.BandPass) filter.enableOffset(False) filter.setBand(1.5, 3.5) filter.run()Other types of FFT filters available in QtiPlot are: low pass (
FFTFilter.LowPass
)
and band block (FFTFilter.BandBlock
).
f = GaussFit(graph("Graph1").activeLayer(), "Table1_2") f.guessInitialValues() f.fit()This creates a new GaussFit object on the curve, lets it guess the start parameters and does the fit. The following fit types are supported:
LinearFit(layer, curve)
PolynomialFit(layer, curve, degree=2, legend=False)
ExponentialFit(layer, curve, growth=False)
TwoExpFit(layer, curve)
ThreeExpFit(layer, curve)
GaussFit(layer, curve)
GaussAmpFit(layer, curve)
LorentzFit(layer, curve)
LogisticFit(layer, curve)
SigmoidalFit(layer, curve)
NonLinearFit(layer, curve)
f = NonLinearFit(layer, curve) f.setParameters(name1, ...) f.setFormula(formula_string) f.save(fileName)
PluginFit(layer, curve)
f = PluginFit(layer, curve) f.load(pluginName)
f = LinearFit(graph("Graph1").activeLayer(), "Table1_2", 2, 7) f.fit()You can also restrict the search range for any of the fit parameters:
f = NonLinearFit(graph("Graph1").activeLayer(), "Table1_2") f.setParameters("a0", "a1", "a2") f.setFormula("a0+a1*x+a2*x*x") f.setParameterRange(parameterIndex, start, end)All the settings of a non-linear fit can be saved to an XML file and restored later one, using this file, for a faster editing process. Here's for example how you can save the above fit function:
f.save("/fit_models/poly_fit.txt")and how you can use this file during another fitting session, later on:
f = NonLinearFit(graph("Graph1").activeLayer(), "Table1_2") f.load("/fit_models/poly_fit.txt") f.fit()
After creating the Fit object and before calling its fit() method, you can set a number of parameters that influence the fit:
f.setDataFromTable(table("Table4"), "w", "energy", 10, 200) change data source f.setDataFromCurve(curve) change data source f.setDataFromCurve(curve, graph) change data source f.setDataFromCurve(curve, from, to) change data source f.setDataFromCurve(curve, from, to, graph) change data source f.setInterval(from, to) change data range f.setInitialValue(number, value) f.setInitialValues(value1, ...) f.guessInitialValues() f.setAlgorithm(algo) # algo = Fit.ScaledLevenbergMarquardt, Fit.UnscaledLevenbergMarquardt, Fit.NelderMeadSimplex f.setWeightingData(method, colname) # method = Fit.NoWeighting, Fit.Instrumental, Fit.Statistical, Fit.Dataset f.setTolerance(tolerance) f.setOutputPrecision(precision) f.setMaximumIterations(number) f.scaleErrors(yes = True) f.setColor("green") change the color of the result fit curve to green (default color is red)After you've called fit(), you have a number of possibilities for extracting the results:
f.results() f.errors() f.residuals() f.dataSize() f.numParameters() f.parametersTable("params") f.covarianceMatrix("cov")There are a number of statistical functions allowing you to test the goodness of the fit:
f.chiSquare() f.rSquare() f.adjustedRSquare() f.rmse() # Root Mean Squared Error f.rss() # Residual Sum of SquaresAlso you can display the confidence and the prediction limits for the fit, using a custom confidence level:
f.showPredictionLimits(0.95) f.showConfidenceLimits(0.95)Confidence limits for individual fit parameters can be calculated using:
f.lcl(parameterIndex, confidenceLevel) f.ucl(parameterIndex, confidenceLevel)where
parameterIndex
is a value between zero and f.numParameters() - 1.
It is important to know that QtiPlot can generate an analytical formula for the resulting fit curve or a normal plot curve with data stored in a hidden table. You can choose either of these two output options, before calling the fit() instruction, using:
f.generateFunction(True, points=100)If the first parameter of the above function is set to True, QtiPlot will generate an analytical function curve. If the
points
parameter
is not specified, by default the function will be estimated over 100 points.
You can get the analytical formula of the fit curve via a call to resultFormula():
formula = f.resultFormula() print(formula)If the first parameter of generateFunction() is set to False, QtiPlot will create a hidden data table contining the same number of points as the data set/curve to be fitted (same abscissae). You can interact with this table and extract the data points of the result fit curve using:
t = f.resultTable()
integral = Integration(graph("Graph1").activeLayer(), "Table1_2", 2, 10) integral.setMethodOrder(4) integral.setTolerance(1e-4) integral.setMaximumIterations(100) integral.run() result = integral.area()The method order parameter can be any integer value between 1 (Trapezoidal rule, the default value) and 5. The code integrates the curve using an iterative algorithm. The tolerance determines the termination criteria for the solver. Because, sometimes we ask for too much accuracy, setting a maximum number of iterations makes sure that the solver will not enter an infinite loop, which could freeze the application.
As you can see from the above example, the numerical value of the integral can be obtained
via the area()
function.
interpolation = Interpolation(graph("Graph1").activeLayer(), "Table1_2", 2, 10, Interpolation.Linear) interpolation.setOutputPoints(10000) interpolation.setColor("green") interpolation.run()The simplest interpolation method is the linear method. There are two other methods available:
Interpolation.Akima
and Interpolation.Cubic
.
You can choose the interpolation method using:
interpolation.setMethod(Interpolation.Akima)
smooth = SmoothFilter(graph("Graph1").activeLayer(), "Table1_2", SmoothFilter.Average) smooth.setSmoothPoints(10) smooth.run()The default smoothing method is the mowing window average. Other smoothing methods are the
SmoothFilter.FFT
and SmoothFilter.SavitzkyGolay
. Here's an example
of how to use this last method:
smooth.setSmoothPoints(5,5) smooth.setMethod(SmoothFilter.SavitzkyGolay) smooth.setPolynomOrder(9) smooth.run()
# Pop-up a file dialog allowing to chose the working folder: dirPath = QtGui.QFileDialog.getExistingDirectory(qti.app, "Choose Working Folder", "/test/") # Create a folder object using Qt's QDir class: folder = QtCore.QDir(dirPath) # Pop-up a text input dialog allowing to chose the file naming pattern: namePattern = QtGui.QInputDialog.getText(qti.app, "Enter Pattern", "Text: ", QtGui.QLineEdit.Normal, "disper1") # Get the list of file names in the working directory containing the above pattern: fileNames = folder.entryList (QtCore.QStringList ("*_" + namePattern[0] + "*.dat")) # Import each file into a new project table: for i in range (0, lst.count()): t = newTable() t.importASCII(dirPath + fileNames[i], " ", 0, False, True, True)For a detailed description of all the dialogs and utility classes provided by Qt/PyQt please take a look at the PyQt documentation.