I have been trying to learn how to code for Maya for a while. And here I am making a rock generator using reduce face, quad-mesh, smooth in a loop. Now I would like to make an input so user can specify how many rock they want to make. Like if you input 5, it would make 5 rocks.
However I am currently stuck with the naming, like if I created the polyPlatonicSolid with rock#, the loop got confused and said something like rock# is invalid, so I am just looking for a simple solution for that.
import maya.cmds as MC
import random as RN
def rockGen():
#GenerateBase
rockCreation = MC.polyPlatonicSolid(name="rock", r=5)
MC.displaySmoothness( polygonObject= 0)
obj=MC.ls(sl=True)
MC.polySmooth(rockCreation, divisions = 2)
MC.polySoftEdge('rock', a=0, ch=1)
#Evaluate face counts
face_count = MC.polyEvaluate('rock', v=True)
#Procedural rock creation
for i in range(10):
random_face = RN.randint(0, face_count)
print random_face
# Select faces
targetFace = MC.select('rock.f[0:' + str(random_face)+ ']')
# Reduce faces
MC.polyReduce(p=20, kb=True, t=False)
MC.polyQuad('rock', a=20)
#Quad the rock
MC.polySmooth('rock', ch=1, ost=0, khe=0, ps=0.1, kmb=1, bnr=1, mth=0, suv=1,
peh=0, ksb=1, ro=1, sdt=2, ofc=0, kt=1, ovb=1, dv=1, ofb=3, kb=1,
c=1, ocr=0, dpe=1, sl=1)
#Select and rename
MC.select('rock')
MC.rename('inst#')
I tried this:
numberRock=input()
for u in range (numberRock)
rockCreation = MC.polyPlatonicSolid(name="rock#", r=5)
But after that all my rock# command just deliver an invalid object feedback
One other challenge that was given by the teacher is that doing this script without the select command and I don't know the other way to do that yet.
I have change the way you are naming your rock in order to be unique.
Even if maya is based string, try to always use variable instead of 'rock'.
Also instead of cmds.select, you can in most of the cases use cmds.ls to select your object. It is just a command that will return the name string if it exists, then you can use this to feed most of the commands in maya.
example :
my_rocks = cmds.ls('rock*')
cmds.rename(my_rocks[0], 'anythin_you_want')
here is your modified code that handle unique naming for your piece of rocks.
Last advice, take the habit to write with .format() and also put parenthesis with print()
import maya.cmds as MC
import random as RN
def isUnique(name):
if MC.ls(name):
return False
return True
def rockGen(name='rock'):
#GenerateBase
_iter = 1
new_name = '{}_{:03d}'.format(name, _iter)
while not isUnique(new_name):
_iter += 1
new_name = '{}_{:03d}'.format(name, _iter)
rockCreation = MC.polyPlatonicSolid(name=new_name, r=5)
MC.displaySmoothness( polygonObject= 0)
obj=MC.ls(sl=True)
MC.polySmooth(rockCreation, divisions = 2)
MC.polySoftEdge(new_name, a=0, ch=1)
#Evaluate face counts
face_count = MC.polyEvaluate(new_name, v=True)
#Procedural rock creation
for i in range(10):
random_face = RN.randint(0, face_count)
# print(random_face)
# Select faces
targetFace = MC.select('{}.f[0:{}]'.format(new_name, random_face))
# Reduce faces
MC.polyReduce(new_name, p=20, kb=True, t=False)
MC.polyQuad(new_name, a=20)
#Quad the rock
MC.polySmooth(new_name, ch=1, ost=0, khe=0, ps=0.1, kmb=1, bnr=1, mth=0, suv=1,
peh=0, ksb=1, ro=1, sdt=2, ofc=0, kt=1, ovb=1, dv=1, ofb=3, kb=1,
c=1, ocr=0, dpe=1, sl=1)
MC.delete(new_name, ch=True)
return new_name
so with this you can loop :
my_rocks = []
my_number = 5
for x in range(my_number):
rock = rockGen()
my_rocks.append(rock)
print(my_rocks)
Related
I was trying to break down the code to the simplest form before adding more variables and such. I'm stuck.
I wanted it so when I use intertools the first response is the permutations of tricks and the second response is dependent on the trick's landings() and is a permutation of the trick's corresponding landing. I want to add additional variables that further branch off from landings() and so on.
The simplest form should print a list that looks like:
Backflip Complete
Backflip Hyper
180 Round Complete
180 Round Mega
Gumbi Complete
My Code:
from re import I
import pandas as pd
import numpy as np
import itertools
from io import StringIO
backflip = "Backflip"
one80round = "180 Round"
gumbi = "Gumbi"
tricks = [backflip,one80round,gumbi]
complete = "Complete"
hyper = "Hyper"
mega = "Mega"
backflip_landing = [complete,hyper]
one80round_landing = [complete,mega]
gumbi_landing = [complete]
def landings(tricks):
if tricks == backflip:
landing = backflip_landing
elif tricks == one80round:
landing = one80round_landing
elif tricks == gumbi:
landing = gumbi_landing
return landing
for trik, land in itertools.product(tricks,landings(tricks)):
trick_and_landing = (trik, land)
result = (' '.join(trick_and_landing))
tal = StringIO(result)
tl = (pd.DataFrame((tal)))
print(tl)
I get the error:
UnboundLocalError: local variable 'landing' referenced before assignment
Add a landing = "" after def landings(tricks): to get rid of the error.
But the if checks in your function are wrong. You check if tricks, which is a list, is equal to backflip, etc. which are all strings. So thats why none of the ifs are true and landing got no value assigned.
That question was also about permutation in python. Maybe it helps.
I am looking for a way to visualize, for the lack of a better word, the "density" or "heatmap" of some synthetic time series I have created.
I have a loop that creates a list, which are values of one time series. I don't think it matters but just in case, here is the code of what's going on. This is a Markov Process, so with each i, which represents the hour, i create a new value, depending on the former i and state:
for x in range(10000):
start_h = 0
start_s = 1
generated_values_list = []
for i in range(start_h,120):
if i>=24:
i=i%24
print(str(start_s)+" | " +str(i))
pot_value_list = GMM_vals_container_workingdays_spring["State: "+ str(start_s)+", hour: "+str(i)]
if len(pot_value_list)>50:
actual_value = random.choice(pot_value_list)#
#cdf, gmm_x, gmm = GMM_erstellen(pot_value_list,50)
#actual_value = gmm.sample()[0][0][0]
#print("made by GMM")
else:
actual_value = random.choice(pot_value_list)
#print("made not by GMM")
generated_values_list.append(actual_value)
probabilities_next_state = TPMs_WD[i][start_s-1]
next_state = random.choices(states,weights=probabilities_next_state)
start_s = next_state[0]
plt.plot(generated_values_list)
But - I think - the only part that matters is this:
for x in range(10000):
#some code that creates the generated_values_list
plt.plot(generated_values_list)
This creates, as expected a picture like this:
It is not clear from here which are the most common paths so I would like to make values that are hit frequently are more colorful while not so frequent values are rather grey.
I think seaborn library has something for that but I don't seem to understand the docs.
i'm working on graphs and big dataset of complex network's. i run SIR algorithm on them with ndlib library.
but each iteration takes something like 1Sec and it make code takes 10-12 h to complete .
i was wondering is there any way to make it parallelised ?
the code is like down bellow
this line of the code is core :
sir = model.infected_SIR_MODEL(it, infectionList, False)
is there any simple method to make it run on multi thread or parallelised ?
count = 500
for i in numpy.arange(1, count, 1):
for it in model.get_nodes():
sir = model.infected_SIR_MODEL(it, infectionList, False)
each iteration :
for u in self.graph.nodes():
u_status = self.status[u]
eventp = np.random.random_sample()
neighbors = self.graph.neighbors(u)
if isinstance(self.graph, nx.DiGraph):
neighbors = self.graph.predecessors(u)
if u_status == 0:
infected_neighbors = len([v for v in neighbors if self.status[v] == 1])
if eventp < self.BetaList[u] * infected_neighbors:
actual_status[u] = 1
elif u_status == 1:
if eventp < self.params['model']['gamma']:
actual_status[u] = 2
So, if the iterations are independent, then I don't see the point of iteration over count=500. Either way the multiprocessing library might be of interest to you.
I've prepared 2 stub solutions (i.e. alter to your exact needs).
The first expects that every input is static (the changes in solutions as far as I understand the OP's question raise from the random state generation inside each iteration). With the second, you can update the input data between iterations of i. I've not tried the code as I don't have the model so it might not work directly.
import multiprocessing as mp
# if everything is independent (eg. "infectionList" is static and does not change during the iterations)
def worker(model, infectionList):
sirs = []
for it in model.get_nodes():
sir = model.infected_SIR_MODEL(it, infectionList, False)
sirs.append(sir)
return sirs
count = 500
infectionList = []
model = "YOUR MODEL INSTANCE"
data = [(model, infectionList) for _ in range(1, count+1)]
with mp.Pool() as pool:
results = pool.starmap(worker, data)
The second proposed solution if "infectionList" or something else gets updated in each iteration of "i":
def worker2(model, it, infectionList):
sir = model.infected_SIR_MODEL(it, infectionList, False)
return sir
with mp.Pool() as pool:
for i in range(1, count+1):
data = [(model, it, infectionList) for it in model.get_nodes()]
results = pool.starmap(worker2, data)
# process results, update something go to next iteration....
Edit: Updated the answer to separate proposals more clearly.
I use python for teaching some of my science courses, where I use it to generate unique assignments and tests for students. I've run into an issue that I can't sort out on my own.
I'm trying to make a series of nested lists. For example, I would like to have a numbered question, and then sub parts to the question underneath. For example:
Use the Henderson-Hasselbalch equation to determine pH of the following solutions:
A. 250 mM Ammonium Chloride
B. 100 mM Acetic Acid
I've used style "List Number" to create the numbered list, but I can't figure out how to create a custom list that starts with the letters.
Here is what I've got so far:
import sys
import os
if os.uname()[1] == 'iMac':
sys.path.append("/Users/mgreene3/Library/Python/2.7/lib/python/site-packages")
else:
sys.path.append("/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python")
import numpy as np
import math
import random
import textwrap
from docx import Document
from docx.shared import Pt, Inches
from docx.enum.style import WD_STYLE_TYPE
from docx.text.tabstops import TabStop as ts
from docx.text.parfmt import ParagraphFormat
assignment = Document()
ordered = "a"
style = assignment.styles["Normal"]
font = style.font
font.name = "Calibri"
font.size = Pt(12)
style.paragraph_format.space_after = Pt(0)
LetteredList = style.paragraph_format._NumberingStyle(ordered)
sub_style = assignment.styles["ListBullet"]
sub_font = sub_style.font
sub_font.name = "Calibri"
###sub_style.paragraph_format.style("List")
sub_font.size = Pt(12)
sub_style.paragraph_format.left_indent = Inches(1)
sub_style.paragraph_format.space_before = Pt(0)
sub_style.paragraph_format.space_after = Pt(40)
doc_heading = assignment.add_paragraph("Name:_______________________")
doc_heading.add_run("\t" * 4)
doc_heading.add_run(" " * 12)
doc_heading.add_run("BIOL444: Biochemistry\t\t\t\t\t\t ")
doc_heading.add_run("\n")
doc_heading.add_run("Take Home 1, v.")
doc_heading.add_run((str(1).zfill(2)))
doc_heading.add_run("\n" * 2)
doc_heading.add_run("Instructions: Complete test (")
show_work = doc_heading.add_run("show work")
show_work.bold = True
show_work.underline = True
show_work
doc_heading.add_run("), submit ")
hard_copy = doc_heading.add_run("hard copy")
hard_copy.bold = True
hard_copy.underline = True
hard_copy
doc_heading.add_run(" by ")
doc_heading.add_run("11:59 pm, Friday, February 10").bold =True
doc_heading.add_run(". Late submissions will ")
doc_heading.add_run("NOT").bold=True
doc_heading.add_run(" be accepted.")
question1 = assignment.add_paragraph("Using the data for K", style = "List Number")
question1.add_run("a").font.subscript = True
question1.add_run(" and pK")
question1.add_run("a").font.subscript = True
question1.add_run(" of the following compounds, calculate the concentrations (M) of all ionic species as well as the pH of the following aqueous solutions: ")
question1.add_run("\n")
question1a = assignment.add_paragraph("100 mM Acetic acid", style = sub_style)
question1b = assignment.add_paragraph("250 mM NaOH", style = sub_style)
assignment.save("TestDocx.docx")
The short answer is that it's probably more trouble than it's worth. Creating numbered lists, especially nested numbered lists in Word is a complex operation, possibly for legacy reasons (we're on version 14 or something of Word). Partly because of this complexity, API support for this doesn't yet exist in python-docx.
If you really wanted to do it, it would entail manipulating numbering definitions that exist in another package part from the document part (I believe it's numbering.xml). This would be using low-level lxml calls.
For myself, I'd be strongly inclined to use RestructuredText for a job like this, rendering to PDF, perhaps using Sphinx. As a side-effect, you could easily get HTML version as well for posting assignments on the web. However, I'm too far away from your actual requirements to say that would really suit; you'll have to check it out and see for yourself :)
I have some project which I decide to do in Python. In brief: I have list of lists. Each of them also have lists, sometimes one-element, sometimes more. It looks like this:
rules=[
[[1],[2],[3,4,5],[4],[5],[7]]
[[1],[8],[3,7,8],[3],[45],[12]]
[[31],[12],[43,24,57],[47],[2],[43]]
]
The point is to compare values from numpy array to values from this rules (elements of rules table). We are comparing some [x][y] point to first element (e.g. 1 in first element), then, if it is true, value [x-1][j] from array with second from list and so on. Five first comparisons must be true to change value of [x][y] point. I've wrote sth like this (main function is SimulateLoop, order are switched because simulate2 function was written after second one):
def simulate2(self, i, j, w, rule):
data = Data(rule)
if w.world[i][j] in data.c:
if w.world[i-1][j] in data.n:
if w.world[i][j+1] in data.e:
if w.world[i+1][j] in data.s:
if w.world[i][j-1] in data.w:
w.world[i][j] = data.cc[0]
else: return
else: return
else: return
else: return
else: return
def SimulateLoop(self,w):
for z in range(w.steps):
for i in range(2,w.x-1):
for j in range(2,w.y-1):
for rule in w.rules:
self.simulate2(i,j,w,rule)
Data class:
class Data:
def __init__(self, rule):
self.c = rule[0]
self.n = rule[1]
self.e = rule[2]
self.s = rule[3]
self.w = rule[4]
self.cc = rule[5]
NumPy array is a object from World class. Rules is list as described above, parsed by function obtained from another program (GPL License).
To be honest it seems to work fine, but it does not. I was trying other possibilities, without luck. It is working, interpreter doesn't return any errors, but somehow values in array changing wrong. Rules are good because it was provided by program from which I've obtained parser for it (GPL license).
Maybe it will be helpful - it is Perrier's Loop, modified Langton's loop (artificial life).
Will be very thankful for any help!
)
I am not familiar with Perrier's Loop, but if you code something like famous "game life" you would have done simple mistake: store the next generation in the same array thus corrupting it.
Normally you store the next generation in temporary array and do copy/swap after the sweep, like in this sketch:
def do_step_in_game_life(world):
next_gen = zeros(world.shape) # <<< Tmp array here
Nx, Ny = world.shape
for i in range(1, Nx-1):
for j in range(1, Ny-1):
neighbours = sum(world[i-1:i+2, j-1:j+2]) - world[i,j]
if neighbours < 3:
next_gen[i,j] = 0
elif ...
world[:,:] = next_gen[:,:] # <<< Saving computed next generation