Develop Reference

how to make music21 play a particular music instrument?

I have trained a neural network that generates music using LSTM. But it always return music from piano only.. I've tried to use instrument.Guitar() method also. It doesn't work. I get piano music only. Why this happens? Any suggestions would be helpful..
This is my code.
def convert_to_midi(prediction_output):
offset = 0
output_notes =[]
# create note and chord objects based on the values generated by the model
for pattern in prediction_output:
notes=[]
# pattern is a chord
if ('.' in pattern) or pattern.isdigit():
notes_in_chord = pattern.split('.')
for current_note in notes_in_chord:
cn=int(current_note)
new_note = note.Note(cn)
#new_note.storedInstrument = instrument.Guitar()
notes.append(new_note)
new_chord = chord.Chord(notes)
new_chord.offset = offset
output_notes.append(instrument.Violin())
output_notes.append(new_chord)
# pattern is a note
else:
new_note = note.Note(pattern)
new_note.offset = offset
#new_note.storedInstrument = instrument.Guitar()
output_notes.append(instrument.Violin())
output_notes.append(new_note)
# increase offset each iteration so that notes do not stack
offset += 1
#midi_stream1=stream.Score()
#midi_stream1.insert(output_notes)
#midi_stream = stream.Stream(output_notes)
#midi_stream.write('midi', fp='music11.mid')
#midi_stream1.write("midi", fp="music15.mid")
midi_stream = stream.Stream(output_notes)
midi_stream.write('midi', fp='final_music2.midi')

The storedInstrument property has not been promoted to Note objects yet, but might happen in future versions of music21. For now, try simply inserting an instrument.Guitar instance into the Stream object at whatever point you need the MIDI program change.

How do I preserve the colours in a STEP file when modifying the geometry in Open Cascade?

I'm writing a script in python using Open Cascade Technology (using the pyOCCT package for Anaconda) to import STEP files, defeature them procedurally and re-export them. I want to preserve the product hierarchy, names and colours as much as possible. Currently the script can import STEP files, simplify all of the geometry while roughly preserving the hierarchy and re-export the step file. The problem is no matter how I approach the problem, I can't manage to make it preserve the colours of the STEP file in a few particular cases.
Here's the model I pass in to the script:
And here's the result of the simplification:
In this case, the simplification has worked correctly but the colours of some of the bodies were not preserved. The common thread is that the bodies that loose their colours are children of products which only have other bodies as their children (ie: they don't contain sub-products).
This seems to be related to the way that Open Cascade imports STEP files which are translated as follows:
Alright, now for some code:
from OCCT.STEPControl import STEPControl_Reader, STEPControl_Writer, STEPControl_AsIs
from OCCT.BRepAlgoAPI import BRepAlgoAPI_Defeaturing
from OCCT.TopAbs import TopAbs_FACE, TopAbs_SHAPE, TopAbs_COMPOUND
from OCCT.TopExp import TopExp_Explorer
from OCCT.ShapeFix import ShapeFix_Shape
from OCCT.GProp import GProp_GProps
from OCCT.BRepGProp import BRepGProp
from OCCT.TopoDS import TopoDS
from OCCT.TopTools import TopTools_ListOfShape
from OCCT.BRep import BRep_Tool
from OCCT.Quantity import Quantity_ColorRGBA
from OCCT.ShapeBuild import ShapeBuild_ReShape
from OCCT.STEPCAFControl import STEPCAFControl_Reader, STEPCAFControl_Writer
from OCCT.XCAFApp import XCAFApp_Application
from OCCT.XCAFDoc import XCAFDoc_DocumentTool, XCAFDoc_ColorGen, XCAFDoc_ColorSurf
from OCCT.XmlXCAFDrivers import XmlXCAFDrivers
from OCCT.TCollection import TCollection_ExtendedString
from OCCT.TDF import TDF_LabelSequence
from OCCT.TDataStd import TDataStd_Name
from OCCT.TDocStd import TDocStd_Document
from OCCT.TNaming import TNaming_NamedShape
from OCCT.Interface import Interface_Static
# DBG
def export_step(shape, path):
writer = STEPControl_Writer()
writer.Transfer( shape, STEPControl_AsIs )
writer.Write(path)
# DBG
def print_shape_type(label, shapeTool):
if shapeTool.IsFree_(label):
print("Free")
if shapeTool.IsShape_(label):
print("Shape")
if shapeTool.IsSimpleShape_(label):
print("SimpleShape")
if shapeTool.IsReference_(label):
print("Reference")
if shapeTool.IsAssembly_(label):
print("Assembly")
if shapeTool.IsComponent_(label):
print("Component")
if shapeTool.IsCompound_(label):
print("Compound")
if shapeTool.IsSubShape_(label):
print("SubShape")
# Returns a ListOfShape containing the faces to be removed in the defeaturing
# NOTE: For concisness I've simplified this algorithm and as such it *MAY* not produce exactly
# the same output as shown in the screenshots but should still do SOME simplification
def select_faces(shape):
exp = TopExp_Explorer(shape, TopAbs_FACE)
selection = TopTools_ListOfShape()
nfaces = 0
while exp.More():
rgb = None
s = exp.Current()
exp.Next()
nfaces += 1
face = TopoDS.Face_(s)
gprops = GProp_GProps()
BRepGProp.SurfaceProperties_(face, gprops)
area = gprops.Mass()
surf = BRep_Tool.Surface_(face)
if area < 150:
selection.Append(face)
#log(f"\t\tRemoving face with area: {area}")
return selection, nfaces
# Performs the defeaturing
def simplify(shape):
defeaturer = BRepAlgoAPI_Defeaturing()
defeaturer.SetShape(shape)
sel = select_faces(shape)
if sel[0].Extent() == 0:
return shape
defeaturer.AddFacesToRemove(sel[0])
defeaturer.SetRunParallel(True)
defeaturer.SetToFillHistory(False)
defeaturer.Build()
if (not defeaturer.IsDone()):
return shape# TODO: Handle errors
return defeaturer.Shape()
# Given the label of an entity it finds it's displayed colour. If the entity has no defined colour the parents are searched for defined colours as well.
def find_color(label, colorTool):
col = Quantity_ColorRGBA()
status = False
while not status and label != None:
try:
status = colorTool.GetColor(label, XCAFDoc_ColorSurf, col)
except:
break
label = label.Father()
return (col.GetRGB().Red(), col.GetRGB().Green(), col.GetRGB().Blue(), col.Alpha(), status, col)
# Finds all child shapes and simplifies them recursively. Returns true if there were any subshapes.
# For now this assumes all shapes passed into this are translated as "SimpleShape".
# "Assembly" entities should be skipped as we don't need to touch them, "Compound" entities should work with this as well, though the behaviour is untested.
# Use the print_shape_type(shapeLabel, shapeTool) method to identify a shape.
def simplify_subshapes(shapeLabel, shapeTool, colorTool, set_colours=None):
labels = TDF_LabelSequence()
shapeTool.GetSubShapes_(shapeLabel, labels)
#print_shape_type(shapeLabel, shapeTool)
#print(f"{shapeTool.GetShape_(shapeLabel).ShapeType()}")
cols = {}
for i in range(1, labels.Length()+1):
label = labels.Value(i)
currShape = shapeTool.GetShape_(label)
print(f"\t{currShape.ShapeType()}")
if currShape.ShapeType() == TopAbs_COMPOUND:
# This code path should never be taken as far as I understand
simplify_subshapes(label, shapeTool, colorTool, set_colours)
else:
''' See the comment at the bottom of the main loop for an explanation of the function of this block
col = find_color(label, colorTool)
#print(f"{name} RGBA: {col[0]:.5f} {col[1]:.5f} {col[2]:.5f} {col[3]:.5f} defined={col[4]}")
cols[label.Tag()] = col
if set_colours != None:
colorTool.SetColor(label, set_colours[label.Tag()][5], XCAFDoc_ColorSurf)'''
# Doing both of these things seems to result in colours being reset but the geometry doesn't get replaced
nshape = simplify(currShape)
shapeTool.SetShape(label, nshape) # This doesn't work
return labels.Length() > 0, cols
# Set up XCaf Document
app = XCAFApp_Application.GetApplication_()
fmt = TCollection_ExtendedString('MDTV-XCAF')
doc = TDocStd_Document(fmt)
app.InitDocument(doc)
shapeTool = XCAFDoc_DocumentTool.ShapeTool_(doc.Main())
colorTool = XCAFDoc_DocumentTool.ColorTool_(doc.Main())
# Import the step file
reader = STEPCAFControl_Reader()
reader.SetNameMode(True)
reader.SetColorMode(True)
Interface_Static.SetIVal_("read.stepcaf.subshapes.name", 1) # Tells the importer to import subshape names
reader.ReadFile("testcolours.step")
reader.Transfer(doc)
labels = TDF_LabelSequence()
shapeTool.GetShapes(labels)
# Simplify each shape that was imported
for i in range(1, labels.Length()+1):
label = labels.Value(i)
shape = shapeTool.GetShape_(label)
# Assemblies are just made of other shapes, so we'll skip this and simplify them individually...
if shapeTool.IsAssembly_(label):
continue
# This function call here is meant to be the fix for the bug described.
# The idea was to check if the TopoDS_Shape we're looking at is a COMPOUND and if so we would simplify and call SetShape()
# on each of the sub-shapes instead in an attempt to preserve the colours stored in the sub-shape's labels.
#status, loadedCols = simplify_subshapes(label, shapeTool, colorTool)
#if status:
#continue
shape = simplify(shape)
shapeTool.SetShape(label, shape)
# The code gets a bit messy here because this was another attempt at fixing the problem by building a dictionary of colours
# before the shapes were simplified and then resetting the colours of each subshape after simplification.
# This didn't work either.
# But the idea was to call this function once to generate the dictionary, then simplify, then call it again passing in the dictionary so it could be re-applied.
#if status:
# simplify_subshapes(label, shapeTool, colorTool, loadedCols)
shapeTool.UpdateAssemblies()
# Re-export
writer = STEPCAFControl_Writer()
Interface_Static.SetIVal_("write.step.assembly", 2)
Interface_Static.SetIVal_("write.stepcaf.subshapes.name", 1)
writer.Transfer(doc, STEPControl_AsIs)
writer.Write("testcolours-simplified.step")
There's a lot of stuff here for a minimum reproducible example but the general flow of the program is that we import the step file:
reader.ReadFile("testcolours.step")
reader.Transfer(doc)
Then we iterate through each label in the file (essentially every node in the tree):
labels = TDF_LabelSequence()
shapeTool.GetShapes(labels)
# Simplify each shape that was imported
for i in range(1, labels.Length()+1):
label = labels.Value(i)
shape = shapeTool.GetShape_(label)
We skip any labels marked as assemblies since they contain children and we only want to simplify individual bodies. We then call simplify(shape) which performs the simplification and returns a new shape, we then call shapeTool.SetShape() to bind the new shape to the old label.
The thing that doesn't work here is that as explained, Component3 and Component4 don't get marked as Assemblies and are treated as SimpleShapes and when they are simplified as one shape, the colours are lost.
One solution I attempted was to call a method simplify_subshapes() which would iterate through each of the subshapes, and do the same thing as the main loop, simplifying them and then calling SetShape(). This ended up being even worse as it resulted in those bodies not being simplified at all but still loosing their colours.
I also attempted to use the simplify_subshapes() method to make a dictionary of all the colours of the subshapes, then simplify the COMPOUND shape and then call the same method again to this time re-apply the colours to the subshapes using the dictionary (the code for this is commented out with an explanation as to what it did).
col = find_color(label, colorTool)
#print(f"{name} RGBA: {col[0]:.5f} {col[1]:.5f} {col[2]:.5f} {col[3]:.5f} defined={col[4]}")
cols[label.Tag()] = col
if set_colours != None:
colorTool.SetColor(label, set_colours[label.Tag()][5], XCAFDoc_ColorSurf)
As far as I see it the issue could be resolved either by getting open cascade to import Component3 and Component4 as Assemblies OR by finding a way to make SetShape() work as intended on subshapes.
Here's a link to the test file:
testcolours.step

Implementing Multiprocessing with the same Function in While Loop

I'm have implemented an Evolutionary Algorithm process in Python 3.8, and am attempting to optimise/reduce its runtime. Due to the heavy constraints upon valid solutions, it can take a few minutes to generate valid chromosomes. To avoid spending hours just generating the initial population, I want to use Multiprocessing to generate multiple at a time.
My code at this point in time is:
populationCount = 500
def readDistanceMatrix():
# code removed
def generateAvailableValues():
# code removed
def generateAvailableValuesPerColumn():
# code removed
def generateScheduleTemplate():
# code removed
def generateChromosome():
# code removed
if __name__ == '__main__':
# Data type = DataFrame
distanceMatrix = readDistanceMatrix()
# Data type = List of Integers
availableValues = generateAvailableValues()
# Data type = List containing Lists of Integers
availableValuesPerColumn = generateAvailableValuesPerColumn(availableValues)
# Data type = DataFrame
scheduleTemplate = generateScheduleTemplate(distanceMatrix)
# Data type = List containing custom class (with Integer and DataFrame)
population = []
while len(population) < populationCount:
chrmSolution = generateChromosome(availableValuesPerColumn, scheduleTemplate, distanceMatrix)
population.append(chrmSolution)
Where the population list is filled in with the while loop at the end. I would like to replace the while loop with a Multiprocessing solution that can use up to a pre-set number of cores. For example:
population = []
availableCores = 6
while len(population) < populationCount:
while usedCores < availableCores:
# start generating another chromosome as 'chrmSolution'
population.append(chrmSolution)
However, after reading and watching hours worth of tutorials, I'm unable to get a loop up-and-running. How should I go about doing this?

It sounds like a simple multiprocessing.Pool should do the trick, or at least be a place to start. Here's a simple example of how that might look:
from multiprocessing import Pool, cpu_count
child_globals = {} #mutable object at the `module` level acts as container for globals (constants)
if __name__ == '__main__':
# ...
def init_child(availableValuesPerColumn, scheduleTemplate, distanceMatrix):
#passing variables to the child process every time is inefficient if they're
# constant, so instead pass them to the initialization function, and let
# each child re-use them each time generateChromosome is called
child_globals['availableValuesPerColumn'] = availableValuesPerColumn
child_globals['scheduleTemplate'] = scheduleTemplate
child_globals['distanceMatrix'] = distanceMatrix
def child_work(i):
#child_work simply wraps generateChromosome with inputs, and throws out dummy `i` from `range()`
return generateChromosome(child_globals['availableValuesPerColumn'],
child_globals['scheduleTemplate'],
child_globals['distanceMatrix'])
with Pool(cpu_count(),
initializer=init_child, #init function to stuff some constants into the child's global context
initargs=(availableValuesPerColumn, scheduleTemplate, distanceMatrix)) as p:
#imap_unordered doesn't make child processes wait to ensure order is preserved,
# so it keeps the cpu busy more often. it returns a generator, so we use list()
# to store the results into a list.
population = list(p.imap_unordered(child_work, range(populationCount)))

Defined PiCamera camera.shutter does not match with queried camera.exposure_speed

Within the Raspberry Pi, defined camera.shutter does not match with queried camera.exposure_speed.
The picamera API document-PiCamera API document states:
Exposure_speed-
Retrieves the current shutter speed of the camera.
When queried, this property returns the shutter speed currently being used
by the camera. If you have set shutter_speed to a non-zero value, then
exposure_speed and shutter_speed should be equal. However, if
shutter_speed is set to 0 (auto), then you can read the actual shutter
speed being used from this attribute. The value is returned as an integer
representing a number of microseconds. This is a read-only property.
Despite described above, after I defined shutter_sepeed to 10 seconds, exposure_speed returns 0 -the two vairables are not equal.
as can be seen in my code below:
from picamera import PiCamera
with PiCamera(resolution=(1024,768), framerate=Fraction(1,6), sensor_mode=3) as camera:
exp_sec = int('10')
camera.shutter_speed = exp_sec * 10**6 # micros
sleep(30)
print('camera_shutter_speed='+str(camera.shutter_speed))
print('camera_exposure_speed:'+str(camera.exposure_speed))
camera.iso = 1600 # 100-1600
camera.exposure_mode = 'off' # lock all setting parameters
fn_png = str(time.strftime("%Y-%m-%d-%H-%M-%S"))+'.png
camera.capture(fn_png, format='png')
In response:
>>>
===== RESTART: /home/pi/Documents/test_scripts/cap_one_image.py =====
made new direc
it is time to take a shot
0
camera_shutter_speed=9999959
camera_exposure_speed= 0
The last two are not equal which does not make any sense. Thoughts?

IIRC, the camera.exposure_speed attribute does not update until after you've taken an image at the requested shutter_speed setting.
If you try printing the settings after capture, does that work?
exp_sec=int('10')
camera.shutter_speed=exp_sec*10**6 # micros
sleep(30)
print('camera_shutter_speed='+str(camera.shutter_speed))
print('camera_exposure_speed:'+str(camera.exposure_speed))
camera.iso=1600 #100-1600
camera.exposure_mode='off' # lock all setting parameters
fn_png=str(time.strftime("%Y-%m-%d-%H-%M-%S"))+'.png'
camera.capture(fn_png, format='png')
print('camera_shutter_speed='+str(camera.shutter_speed))
print('camera_exposure_speed:'+str(camera.exposure_speed))

dynamic plotting in wxpython

I have been developing a GUI for reading continuous data from a serial port. After reading the data, some calculations are made and the results will be plotted and refreshed (aka dynamic plotting). I use the wx backend provided in the matplotlib for this purposes. To do this, I basically use an array to store my results, in which I keep appending it to, after each calculation, and replot the whole graph. To make it "dynamic", I just set the x-axis lower and upper limits for each iteration. Something like found in:
http://eli.thegreenplace.net/2008/08/01/matplotlib-with-wxpython-guis/
The problem, however, is that since the data is continuous, and if I keep plotting it, eventually the system memory will run out and system will crash. Is there any other way I can plot my result continuously?

To do this, I basically use an array
to store my results, in which I keep
appending it to
Try limiting the size of this array, either by deleting old data or by deleting every n-th entry (the screen resolution will prevent all entries to be displayed anyway). I assume you write all the data to disk so you won't lose anything.
Also, analise your code for memory leaks. Stuff you use and don't need anymore but that doesn't get garbage-collected because you still have a reference to it.

I have created such a component with pythons Tkinter. The source is here.
Basically, you have to keep the plotted data somewhere. You cannot keep an infinite amount of data points in memory, so you either have to save it to disk or you have to overwrite old data points.

Data and representation of data are two different things. You might want to store your data to disk if it's important data to be analyzed later, but only keep a fixed period of time or the last N points for display purposes. You could even let the user pick the time frame to be displayed.

I actually ran into this problem (more of a mental block, actually...).
First of all I copy-pasted some wx Plot code from wx Demo Code.
What I do is keep a live log of a value, and compare it to two markers (min and max, shown as red and green dotted lines) (but I will make these 2 markers optional - hence the optional parameters).
In order to implement the live log, I first wanted to use the deque class, but since the data is in tuple mode (x,y coordinates) I gave up and just tried to rewrite the entire parameter list of tuples: see _update_coordinates.
It works just fine for keeping track of the last 100-10,000 plots. Would have also included a printscreen, but I'm too much of a noob at stackoverflow to be allowed :))
My live parameter is updated every 0.25 seconds over a 115kbps UART.
The trick is at the end, in the custom refresh method!
Here is most of the code:
class DefaultPlotFrame(wx.Frame):
def __init__(self, ymin=0, ymax=MAXIMUM_PLOTS, minThreshold=None,
maxThreshold=None, plotColour='blue',
title="Default Plot Frame",
position=(10,10),
backgroundColour="yellow", frameSize=(400,300)):
self.minThreshold = minThreshold
self.maxThreshold = maxThreshold
self.frame1 = wx.Frame(None, title="wx.lib.plot", id=-1, size=(410, 340), pos=position)
self.panel1 = wx.Panel(self.frame1)
self.panel1.SetBackgroundColour(backgroundColour)
self.ymin = ymin
self.ymax = ymax
self.title = title
self.plotColour = plotColour
self.lines = [None, None, None]
# mild difference between wxPython26 and wxPython28
if wx.VERSION[1] < 7:
self.plotter = plot.PlotCanvas(self.panel1, size=frameSize)
else:
self.plotter = plot.PlotCanvas(self.panel1)
self.plotter.SetInitialSize(size=frameSize)
# enable the zoom feature (drag a box around area of interest)
self.plotter.SetEnableZoom(False)
# list of (x,y) data point tuples
self.coordinates = []
for x_item in range(MAXIMUM_PLOTS):
self.coordinates.append((x_item, (ymin+ymax)/2))
self.queue = deque(self.coordinates)
if self.maxThreshold!=None:
self._update_max_threshold()
#endif
if self.lockThreshold!=None:
self._update_min_threshold()
#endif
self.line = plot.PolyLine(self.coordinates, colour=plotColour, width=1)
self.lines[0] = (self.line)
self.gc = plot.PlotGraphics(self.lines, title, 'Time', 'Value')
self.plotter.Draw(self.gc, xAxis=(0, MAXIMUM_PLOTS), yAxis=(ymin, ymax))
self.frame1.Show(True)
def _update_max_threshold(self):
if self.maxThreshold!=None:
self.maxCoordinates = []
for x_item in range(MAXIMUM_PLOTS):
self.maxCoordinates.append((x_item, self.maxThreshold))
#endfor
self.maxLine = plot.PolyLine(self.maxCoordinates, colour="green", width=1)
self.maxMarker = plot.PolyMarker(self.maxCoordinates, colour="green", marker='dot')
self.lines[1] = self.maxMarker
#endif
def _update_live_param(self, liveParam, minParam, maxParam):
if minParam!=None:
self.minThreshold = int(minParam)
self._update_min_threshold()
#endif
if maxParam!=None:
self.maxThreshold = int(maxParam)
self._update_max_threshold()
#endif
if liveParam!=None:
self._update_coordinates(int(liveParam))
#endif
def _update_coordinates(self, newValue):
newList = []
for x,y in self.coordinates[1:]:
newList.append((x-1, y))
#endfor
newList.append((x, newValue))
print "New list", newList
self.line = (plot.PolyLine(newList, colour=self.plotColour, width=1))
self.lines[0] = self.line
self.coordinates = newList
def _MyLIVE_MAGIC_refresh__(self, liveParam=None, minParam=None, maxParam=None):
self._update_live_param(liveParam, minParam, maxParam)
self.gc = plot.PlotGraphics(self.lines, self.title, 'Time', 'Value')
self.plotter.Draw(self.gc, xAxis=(0, MAXIMUM_PLOTS), yAxis=(self.ymin, self.ymax))
self.plotter.Refresh()
self.frame1.Refresh()