I am trying to use a while loop in Python to provide an error message while there is a backslash character in the user input. The code provides the error message when a fraction is input and requests a second input. The problem occurs when the second input differs in length from the original input and I do not know how to fix this since my knowledge of Python is limited. Any help is appreciated!
size = getInput('Size(in): ')
charcount = len(size)
for i in range(0,charcount):
if size[i] == '/':
while size[i] == '/':
getWarningReply('Please enter size as a decimal', 'OKAY')
size = getInput('Size(in): ')
elif size[i] == '.':
#Convert size input from string to list, then back to string because strings are immutable whereas lists are not
sizechars = list(size)
sizechars[i] = 'P'
size = "".join(sizechars)
That is not a good way to go about doing what you want because if the length of the new size is shorter than the original length, charcount, then you can easily go out of range.
I'm by no means a Python master, but an easily better way to do this is to wrap the entire thing in a while loop instead of nesting a while loop within the for loop:
not_decimal = True
while not_decimal:
found_slash = False
size = getInput('Size(in): ')
charcount = len(size)
for i in range(0, charcount):
if size[i] == '/':
print 'Please enter size as a decimal.'
found_slash = True
break
elif size[i] == '.':
#Convert size input from string to list, then back to string because strings are immutable whereas lists are not
sizechars = list(size)
sizechars[i] = 'P'
size = "".join(sizechars)
if not found_slash:
not_decimal = False
Related
I am attempting to do a text generation program using Keras and Tensorflow. I have come across the problem while trying to use the predict_classes. After a quick research, I learnt that the predict_classes has been removed in the new Tensorflow version, but I have not received any satisfactory solution. My code is given below:
def generate_text_seq(model, tokenizer, text_seq_length, seed_text, n_words):
text = []
for _ in range(n_words):
encoded = tokenizer.texts_to_sequences([seed_text])[0]
encoded = pad_sequences([encoded], maxlen = text_seq_length, truncating = 'pre')
y_pred = model.predict_classes(encoded)
predicted_word = ''
for word, index in tokenizer.word_index.items():
if index == y_pred.any():
predicted_word = word
break
seed_text = seed_text + ' ' + predicted_word
text.append(predicted_word)
return ' '.join(text)
The code is a text generating function, that generates the next token given a seed text. I have used this code from multiple online lectures, but most of those videos were outdated, so they still used the predict_classes method.
Here is the error I was receiving: Error in Code
I have tried a couple of solutions but none of them worked. For one, I used the .predict() method directly but I was not getting the correct answer.
I also tried the following code as well:
def generate_text_seq(model, tokenizer, text_seq_length, seed_text, n_words):
text = []
for _ in range(n_words):
encoded = tokenizer.texts_to_sequences([seed_text])[0]
encoded = pad_sequences([encoded], maxlen = text_seq_length, truncating = 'pre')
pred_y = model.predict(encoded)
y_pred = np.argmax(pred_y, axis = 1)
predicted_word = ''
for word, index in tokenizer.word_index.items():
if index == y_pred:
predicted_word = word
break
seed_text = seed_text + ' ' + predicted_word
text.append(predicted_word)
return ' '.join(text)
I have a set of points and I want to create line / road network from those points. Firstly, I need to determine the closest point from each of the points. For that, I used the KD Tree and developed a code like this:
def closestPoint(source, X = None, Y = None):
df = pd.DataFrame(source).copy(deep = True) #Ensure source is a dataframe, working on a copy to keep the datasource
if(X is None and Y is None):
raise ValueError ("Please specify coordinate")
elif(not X in df.keys() and not Y in df.keys()):
raise ValueError ("X and/or Y is/are not in column names")
else:
df["coord"] = tuple(zip(df[X],df[Y])) #create a coordinate
if (df["coord"].duplicated):
uniq = df.drop_duplicates("coord")["coord"]
uniqval = list(uniq.get_values())
dupl = df[df["coord"].duplicated()]["coord"]
duplval = list(dupl.get_values())
for kq,vq in uniq.items():
clstu = spatial.KDTree(uniqval).query(vq, k = 3)[1]
df.at[kq,"coord"] = [vq,uniqval[clstu[1]]]
if([uniqval[clstu[1]],vq] in list(df["coord"]) ):
df.at[kq,"coord"] = [vq,uniqval[clstu[2]]]
for kd,vd in dupl.items():
clstd = spatial.KDTree(duplval).query(vd,k = 1)[1]
df.at[kd,"coord"] = [vd,duplval[clstd]]
else:
val = df["coord"].get_values()
for k,v in df["coord"].items():
clst = spatial.KDTree(val).query(vd, k = 3)[1]
df.at[k,"coord"] = [v,val[clst[1]]]
if([val[clst[1]],v] in list (df["coord"])):
df.at[k,"coord"] = [v,val[clst[2]]]
return df["coord"]
The code can return the the closest points around. However, I need to ensure that no double lines are created (e.g (x,y) to (x1,y1) and (x1,y1) to (x,y)) and also I need to ensure that each point can only be used as a starting point of a line and an end point of a line despite the point being the closest one to the other points.
Below is the visualization of the result:
Result of the code
What I want:
What I want
I've also tried to separate the origin and target coordinate and do it like this:
df["coord"] = tuple(zip(df[X],df[Y])) #create a coordinate
df["target"] = "" #create a column for target points
count = 2 # create a count iteration
if (df["coord"].duplicated):
uniq = df.drop_duplicates("coord")["coord"]
uniqval = list(uniq.get_values())
for kq,vq in uniq.items():
clstu = spatial.KDTree(uniqval).query(vq, k = count)[1]
while not vq in (list(df["target"]) and list(df["coord"])):
clstu = spatial.KDTree(uniqval).query(vq, k = count)[1]
df.set_value(kq, "target", uniqval[clstu[count-1]])
else:
count += 1
clstu = spatial.KDTree(uniqval).query(vq, k = count)[1]
df.set_value(kq, "target", uniqval[clstu[count-1]])
but this return an error
IndexError: list index out of range
Can anyone help me with this? Many thanks!
Answering now about the global strategy, here is what I would do (rough pseudo-algorithm):
current_point = one starting point in uniqval
while (uniqval not empty)
construct KDTree from uniqval and use it for next line
next_point = point in uniqval closest to current_point
record next_point as target for current_point
remove current_point from uniqval
current_point = next_point
What you will obtain is a linear graph joining all your points, using closest neighbors "in some way". I don't know if it will fit your needs. You would also obtain a linear graph by taking next_point at random...
It is hard to comment on your global strategy without further detail about the kind of road network your want to obtain. So let me just comment your specific code and explain why the "out of range" error happens. I hope this can help.
First, are you aware that (list_a and list_b) will return list_a if it is empty, else list_b? Second, isn't the condition (vq in list(df["coord"]) always True? If yes, then your while loop is just always executing the else statement, and at the last iteration of the for loop, (count-1) will be greater than the total number of (unique) points. Hence your KDTree query does not return enough points and clstu[count-1] is out of range.
I need the indices (as numpy array) of the rows matching a given condition in a table (with billions of rows) and this is the line I currently use in my code, which works, but is quite ugly:
indices = np.array([row.nrow for row in the_table.where("foo == 42")])
It also takes half a minute, and I'm sure that the list creation is one of the reasons why.
I could not find an elegant solution yet and I'm still struggling with the pytables docs, so does anybody know any magical way to do this more beautifully and maybe also a bit faster? Maybe there is special query keyword I am missing, since I have the feeling that pytables should be able to return the matched rows indices as numpy array.
tables.Table.get_where_list() gives indices of the rows matching a given condition
I read the source of pytables, where() is implemented in Cython, but it seems not fast enough. Here is a complex method that can speedup:
Create some data first:
from tables import *
import numpy as np
class Particle(IsDescription):
name = StringCol(16) # 16-character String
idnumber = Int64Col() # Signed 64-bit integer
ADCcount = UInt16Col() # Unsigned short integer
TDCcount = UInt8Col() # unsigned byte
grid_i = Int32Col() # 32-bit integer
grid_j = Int32Col() # 32-bit integer
pressure = Float32Col() # float (single-precision)
energy = Float64Col() # double (double-precision)
h5file = open_file("tutorial1.h5", mode = "w", title = "Test file")
group = h5file.create_group("/", 'detector', 'Detector information')
table = h5file.create_table(group, 'readout', Particle, "Readout example")
particle = table.row
for i in range(1001000):
particle['name'] = 'Particle: %6d' % (i)
particle['TDCcount'] = i % 256
particle['ADCcount'] = (i * 256) % (1 << 16)
particle['grid_i'] = i
particle['grid_j'] = 10 - i
particle['pressure'] = float(i*i)
particle['energy'] = float(particle['pressure'] ** 4)
particle['idnumber'] = i * (2 ** 34)
# Insert a new particle record
particle.append()
table.flush()
h5file.close()
Read the column in chunks and append the indices into a list and concatenate the list to array finally. You can change the chunk size according to your memory size:
h5file = open_file("tutorial1.h5")
table = h5file.get_node("/detector/readout")
size = 10000
col = "energy"
buf = np.zeros(batch, dtype=table.coldtypes[col])
res = []
for start in range(0, table.nrows, size):
length = min(size, table.nrows - start)
data = table.read(start, start + batch, field=col, out=buf[:length])
tmp = np.where(data > 10000)[0]
tmp += start
res.append(tmp)
res = np.concatenate(res)
I am creating some code for a school project, and for a module I use later on, I need to know what the intensity to end on(end_intensity) is. When the code is run, the end_intensity still comes out as unassigned, this means that the
if client_intensity == "High":
line is never being run.Can someone please explain why it won't assign .
correct = False
end_intensity = "Unassigned"
while correct != True:
id_search = input("please enter the Client ID of the client you wish to record results for:")
# open file, with will automatically close it for you
with open("text_documents/clientIntensity.txt") as f:
user_found = False
# loop over every line
for line in f:
client,intensity = line.split(",")
if id_search == client:
correct = True
user_found = True
intensity = str (intensity)
client_intensity = intensity
#assigns which one is the end intensity
if intensity == 'High':
end_intensity = 'Moderate'
elif intensity == 'Moderate':
end_intensity = 'High'
if user_found == False:
print("I'm sorry no results we're found for that ID, please try again\n")
print(end_intensity)
The text document is in this format:
NeQua,High
ImKol,Moderate
YoTri,Moderate
(I apologize for the numbers for the text document formatting,stack overflow would only let me show it like that)
Any help would be appreciated,Thanks
Ieuan
Using Open SCAD, I have a module that, like cube(), has a size parameter that can be a single value or a vector of three values. Ultimately, I want a vector of three values.
If the caller passes a single value, I'd like all three values of the vector to be the same. I don't see anything in the language documentation about detecting the type of an argument. So I came up with this hack:
module my_cubelike_thing(size=1) {
dimensions = concat(size, size, size);
width = dimensions[0];
length = dimensions[1];
height = dimensions[2];
// ... use width, length, and height ...
}
When size is a single value, the result of the concat is exactly what I want: three copies of the value.
When size is a three-value vector, the result of the concat is nine-value vector, and my code just ignores the last six values.
It works but only because what I want in the single value case is to replicate the value. Is there a general way to switch on the argument type and do different things depending on that type?
If type of size only can be single value or a vector with 3 values, the type can helpwise be found by the special value undef:
a = [3,5,8];
// a = 5;
if (a[0] == undef) {
dimensions = concat(a, a, a);
// do something
cube(size=dimensions,center=false);
}
else {
dimensions = a;
// do something
cube(size=dimensions,center=false);
}
But assignments are only valid in the scope in which they are defined , documnetation of openscad.
So in each subtree much code is needed and i would prefere to validate the type of size in an external script (e.g. python3) and write the openscad-code with the assignment of variables to a file, which can be included in the openscad-file, here my short test-code:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
# size = 20
size = [20,15,10]
if type(size) == int:
dimensions = [size, size, size]
elif type(size) == list:
dimensions = size
else:
# if other types possible
pass
with open('variablen.scad', 'w') as wObj:
for i, v in enumerate(['l', 'w', 'h']):
wObj.write('{} = {};\n'.format(v, dimensions[i]))
os.system('openscad ./typeDef.scad')
content of variablen.scad:
l = 20;
w = 15;
h = 10;
and typeDef.scad can look like this
include <./variablen.scad>;
module my_cubelike_thing() {
linear_extrude(height=h, center=false) square(l, w);
}
my_cubelike_thing();