I am trying to change the appearance of the markers in a polar plot in octave.
r = rand(1,10); % the radius
t = 30*rand(1,10); % the angles
polar(t,r,'o') % the plot
It seems we can only change the shape, by replacing 'o' by 's' or whatever.
However, I didn't find a way to change the size and the colors of the markers.
Does anyone have an idea?
Thanks in advance.
Best,
It is possible through the polar handle:
h = polar(t,r,'o')
% list all graphical properties for this object
get(h)
set(h,'marker','v', 'markerfacecolor','g', 'markersize',10)
yields
>> h = polar(t,r,'o') % the plot
h = -64.073
>> get(h)
ans =
scalar structure containing the fields:
beingdeleted = off
busyaction = queue
buttondownfcn = [](0x0)
children = [](0x1)
clipping = on
createfcn = [](0x0)
deletefcn = [](0x0)
handlevisibility = on
hittest = on
interruptible = on
parent = -69.944
pickableparts = visible
selected = off
selectionhighlight = on
tag =
type = line
uicontextmenu = [](0x0)
userdata = [](0x0)
visible = on
color =
0 0.4470 0.7410
displayname =
linejoin = round
linestyle = none
linewidth = 0.5000
marker = o
markeredgecolor = auto
markerfacecolor = none
markersize = 6
xdata =
Columns 1 through 6:
0.978470 -0.060967 -0.436276 -0.268739 0.441890 -0.586862
Column 7 through 10:
0.746687 -0.063825 0.368511 0.271495
xdatasource =
ydata =
Columns 1 through 6:
0.049178 -0.426698 0.380388 -0.101611 -0.699609 -0.266804
Column 7 through 10:
0.383766 -0.903458 0.764035 -0.126044
ydatasource =
zdata = [](0x0)
zdatasource =
>> set(h,'marker','v', 'markerfacecolor','g', 'markersize',10)
Related
When converting a script from V2 to V4 in Pinescript, it doesn't appear to be calculating the same.
V2:
study(title = "POC bands 2.0", shorttitle="POCB", overlay=true)
resCustom = input(title="Timeframe", type=resolution, defval="240")
Length = input(6, minval=1)
xPrice = security(tickerid, resCustom, hlc3)
xvnoise = abs(xPrice - xPrice[1])
nfastend = 0.666
nslowend = 0.0645
nsignal = abs(xPrice - xPrice[Length])
nnoise = sum(xvnoise, Length)
nefratio = iff(nnoise != 0, nsignal / nnoise, 0)
nsmooth = pow(nefratio * (nfastend - nslowend) + nslowend, 2)
nAMA = nz(nAMA[1]) + nsmooth * (xPrice - nz(nAMA[1]))
basis = nAMA
atr = ema(tr,11)
upper = basis[3] + (atr*3)
lower = basis[3] - (atr*3)
plot(basis, color=blue)
plot(upper, color=blue)
plot(lower, color=blue)
V4:
study(title = "POC bands 2.0", shorttitle="POCB", overlay=true)
resCustom = input(title="Timeframe", type=input.resolution, defval="240")
Length = input(6, minval=1)
xPrice = security(syminfo.tickerid, resCustom, hlc3)
xvnoise = abs(xPrice - xPrice[1])
nAMA = 0.0
nfastend = 0.666
nslowend = 0.0645
nsignal = abs(xPrice - xPrice[Length])
nnoise = sum(xvnoise, Length)
nefratio = iff(nnoise != 0, nsignal / nnoise, 0)
nsmooth = pow(nefratio * (nfastend - nslowend) + nslowend, 2)
nAMA := nz(nAMA[1]) + nsmooth * (xPrice - nz(nAMA[1]))
basis = nAMA
atr = ema(tr,11)
upper = basis[3] + (atr*3)
lower = basis[3] - (atr*3)
plot(basis, color=color.new(color.blue,0))
plot(upper, color=color.new(color.white,0))
plot(lower, color=color.new(color.white,0))
V2 are the blue bands with a red center and V4 are the white bands with a blue center.
I think that's because the security function has different default values for the bargaps and lookahead parameters between v2 and v4
With v4, both are set to false by default
With v2, probably one or both of them are set to true
I am attempting to make a surface plot in Octave based on a bunch of overlapping, offset curves. the function should be treating x and y identically but I am not seeing that in the final plot, rather it has ridges running along one axis. I cant tell if this is a plotting error or something else wrong with my code. am hoping you can provide some help/insight.
Plot with ridges following one axis
Thanks
clear;
graphics_toolkit gnuplot
V = 2500; %scan speed in mm
rr = 200000; %rep rate in Hz
Qsp = 1; %pulse energy
pi = 3.14159;
ns = 1; %scan number
w = 0.0125; %Gaussian radius in mm
dp = 0.0125; %lateral pulse distance in mm
dh = 0.0125; %hatch pitch in mm
xmax = 0.2; %ablation area x in mm
ymax = 0.2; %ablation area y in mm
np = round(xmax/dp);
nh = round(ymax/dh);
points = 50;
cof = ns*2*Qsp/(pi*w^2);
N = [0:1:np];
M = rot90([0:1:nh]);
for i = 1:points
for j = 1:points
x = (i-1)*xmax/(points-1);
y = (j-1)*ymax/(points-1);
k = exp(-(2*(x-N*dp).^2+(y-M*dh).^2)/(w^2));
H(i,j) = cof*sum(sum(k));
endfor
endfor
ii = [1:1:points];
jj = ii;
xx = (ii-1)*xmax/(points-1);
yy = (jj-1)*ymax/(points-1);
surf(xx,yy,H);
I find it helpful to space out components of complex expressions.
k = exp(-(2*(x-N*dp).^2+(y-M*dh).^2)/(w^2));
It's too hard to read with so many parentheses.
k = exp(
-(
2*(x-N*dp).^2 + (y-M*dh).^2
) / (w^2)
);
Do you see it already?
k = exp(
-(
2 * (x-N*dp).^2
+
(y-M*dh).^2
) / (w^2)
);
The x component gets multiplied by two, but the y component doesn't.
I want to read data from csv file in tensorflow .So I've been trying out different ways of reading a CSV file with 2000 lines and each line with 93 features,and I hope to get one-hot value.
my dataset is like this:
the first column is data of 93 features,and the second column is labels of 16 one-hot .
this is my code
import tensorflow as tf
# data_input = pd.read_csv('ans_string.csv')
# data_train = pd.read_csv('ans_result.csv')
x = tf.placeholder(tf.float32,[None,93])
W = tf.Variable(tf.zeros([93,16]))
b = tf.Variable(tf.zeros([16]))
sess = tf.InteractiveSession()
filename_queue = tf.train.string_input_producer(["dataset.csv"])
reader = tf.TextLineReader()
key,value = reader.read(filename_queue)
# _,csv_row = reader.read(filename_queue)
# data = tf.decode_csv(csv_row,record_fefaults = rDeraults)
record_defaults_key = [[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]]
record_defaults_value = [[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]]
list_result_key = tf.decode_csv(key,record_defaults = record_defaults_key)
list_result_value = tf.decode_csv(value,record_defaults = record_defaults_value)
features = tf.stack(list_result_key)
labels = tf.stack(list_result_value)
y = tf.nn.softmax(tf.matmul(x,W)+b)
y_ = tf.placeholder(tf.float32,[None,16])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
with tf.Session() as sess:
# something happened
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord = coord)
tf.global_variables_initializer().run()
for _ in range (1000):
example,label = sess.run([features,labels])
print(sess.run(example,label))
sess.run(train_step,feed_dict={x:example,y_:label})
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(sess.run(accuracy.eval({x:example,y_:label})))
coord.request_stop()
coord.join(threads)
I want to train my model,but I got Error like this.
How can I fix it?
The acc gyro in model.fit is (200 * 3)，in the Input layer shape is (200 * 3). Why is there such a problem? Error when checking input: expected acc_input to have 3 dimensions, but got array with shape (200, 3).This is a visualization of my model.
Here's my code：
WIDE = 20
FEATURE_DIM = 30
CHANNEL = 1
CONV_NUM = 64
CONV_LEN = 3
CONV_LEN_INTE = 3#4
CONV_LEN_LAST = 3#5
CONV_NUM2 = 64
CONV_MERGE_LEN = 8
CONV_MERGE_LEN2 = 6
CONV_MERGE_LEN3 = 4
rnn_size=128
acc_input_tensor = Input(shape=(200,3),name = 'acc_input')
gyro_input_tensor = Input(shape=(200,3),name= 'gyro_input')
Acc_input_tensor = Reshape(target_shape=(20,30,1))(acc_input_tensor)
Gyro_input_tensor = Reshape(target_shape=(20,30,1))(gyro_input_tensor)
acc_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides= (1,1*3),padding='valid',activation=None)(Acc_input_tensor)
acc_conv1 = BatchNormalization(axis=1)(acc_conv1)
acc_conv1 = Activation('relu')(acc_conv1)
acc_conv1 = Dropout(0.2)(acc_conv1)
acc_conv2 = Conv2D(CONV_NUM,(1,CONV_LEN_INTE),strides= (1,1),padding='valid',activation=None)(acc_conv1)
acc_conv2 = BatchNormalization(axis=1)(acc_conv2)
acc_conv2 = Activation('relu')(acc_conv2)
acc_conv2 = Dropout(0.2)(acc_conv2)
acc_conv3 = Conv2D(CONV_NUM,(1,CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(acc_conv2)
acc_conv3 = BatchNormalization(axis=1)(acc_conv3)
acc_conv3 = Activation('relu')(acc_conv3)
acc_conv3 = Dropout(0.2)(acc_conv3)
gyro_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides=(1,1*3),padding='valid',activation=None)(Gyro_input_tensor)
gyro_conv1 = BatchNormalization(axis=1)(gyro_conv1)
gyro_conv1 = Activation('relu')(gyro_conv1)
gyro_conv1 = Dropout(0.2)(gyro_conv1)
gyro_conv2 = Conv2D(CONV_NUM,(1, CONV_LEN_INTE),strides=(1,1),padding='valid',activation=None)(gyro_conv1)
gyro_conv2 = BatchNormalization(axis=1)(gyro_conv2)
gyro_conv2 = Activation('relu')(gyro_conv2)
gyro_conv2 = Dropout(0.2)(gyro_conv2)
gyro_conv3 = Conv2D(CONV_NUM,(1, CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(gyro_conv2)
gyro_conv3 = BatchNormalization(axis=1)(gyro_conv3)
gyro_conv3 = Activation('relu')(gyro_conv3)
gyro_conv3 = Dropout(0.2)(gyro_conv3)
sensor_conv_in = concatenate([acc_conv3, gyro_conv3], 2)
sensor_conv_in = Dropout(0.2)(sensor_conv_in)
sensor_conv1 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN),padding='SAME')(sensor_conv_in)
sensor_conv1 = BatchNormalization(axis=1)(sensor_conv1)
sensor_conv1 = Activation('relu')(sensor_conv1)
sensor_conv1 = Dropout(0.2)(sensor_conv1)
sensor_conv2 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN2),padding='SAME')(sensor_conv1)
sensor_conv2 = BatchNormalization(axis=1)(sensor_conv2)
sensor_conv2 = Activation('relu')(sensor_conv2)
sensor_conv2 = Dropout(0.2)(sensor_conv2)
sensor_conv3 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN3),padding='SAME')(sensor_conv2)
sensor_conv3 = BatchNormalization(axis=1)(sensor_conv3)
sensor_conv3 = Activation('relu')(sensor_conv3)
conv_shape = sensor_conv3.get_shape()
print conv_shape
x1 = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2]*conv_shape[3])))(sensor_conv3)
x1 = Dense(64, activation='relu')(x1)
gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x1)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru1_b')(x1)
gru1_merged = merge([gru_1, gru_1b], mode='sum')
gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru2_b')(gru1_merged)
x = merge([gru_2, gru_2b], mode='concat')
x = Dropout(0.25)(x)
n_class=2
x = Dense(n_class)(x)
model = Model(input=[acc_input_tensor,gyro_input_tensor], output=x)
model.compile(loss='mean_squared_error',optimizer='adam')
model.fit(inputs=[acc,gyro],outputs=labels,batch_size=1, validation_split=0.2, epochs=2,verbose=1 ,
shuffle=False)
The acc gyro in model.fit is (200 * 3)，in the Input layer shape is (200 * 3). Why is there such a problem? Error when checking input: expected acc_input to have 3 dimensions, but got array with shape (200, 3)
Shape (None, 200, 3) is used in Keras for batches, None means batch_size, because in the time of creating or reshaping input arrays, the batch size might be unknown, so if you will be using batch_size = 128 your batch input matrix will have shape (128, 200, 3)
from graphics import *
def draw():
returnStuff = {'again' : 0, '1st' : 1 }
draw.again = False
win = GraphWin("Quadrilateral Maker", 600, 600)
win.setBackground("yellow")
text = Text(Point(150, 15), 'Click 4 points to create a Quadrilateral')
text.draw(win)
#gets the 4 points
p1 = win.getMouse()
p1.draw(win)
p2 = win.getMouse()
p2.draw(win)
p3 = win.getMouse()
p3.draw(win)
p4 = win.getMouse()
p4.draw(win)
vertices = [p1, p2, p3, p4]
#draws the shape
quad = Polygon(vertices)
quad.setFill('red')
quad.setOutline('black')
quad.setWidth(3)
quad.draw(win)
text.setText('Click in the appropriate box.')
#Quit box
quitBox = Rectangle(Point(30, 500), Point(100,550))
quitBox.setFill('green')
quitBox.draw(win)
quitorNah = Text(Point(60, 490), 'Quit')
quitorNah.draw(win)
#again box
quitBox = Rectangle(Point(480, 500), Point(550,550))
quitBox.setFill('green')
quitBox.draw(win)
quitorNah = Text(Point(510, 490), 'Draw Again')
quitorNah.draw(win)
click = win.getMouse()
x = click.getX()
y = click.getY()
while True:
if 30 < x < 100 and 500 < y < 550:
returnStuff['again'] = 0
win.close()
break
elif 480 < x < 550 and 500 < y < 550:
returnStuff['again'] = 1
win.close()
break
return returnStuff
count = 1
returnValue = draw()
if returnValue['1st'] == 1:
count = 0
while count == 1 or returnValue['again'] == 1:
return_value = draw()
So I have this simple interactive program using Zelle graphics, it asks the user to click on 4 points in a window and from that it creates a shape. Then, the user is shown 2 boxes, one to quit and one to draw again. My draw again isn't working, and it has something to do with the return value. I am returning a dictionary, as I need access to 2 of the variables within the function. In the 'returnStuff' dictionary, I have a part called 'again', which is initially set to 0. If the user clicks in the run again box, it changes this value to 1, and then outside the function I have an if statement that should call the function again if that again value is 1. It does this properly the FIRST time, but the 2nd time around my program just stops all together, and I don't understand why.
Can anybody explain why this is happening?
I think you need a while...
while count==1 or returnValue['again'] == 1:
returnValue = draw()