I am trying to use the boxplot command in the statistics package, and it seems like most of the plot options are not recognised by Octave, by which I mean calling options like "BoxWidth" results in the following error:
error: set: unknown line property BoxWidth
error: __go_line__: unable to create graphics handle
error: called from
__plt__>__plt2vv__ at line 495 column 10
__plt__>__plt2__ at line 242 column 14
__plt__ at line 107 column 18
The code snippet producing this is as follows with the note that I have tried lower, upper,, camel, and sentence case for "BoxWidth" (documentation specifies camel case) and that I have tried both quotation marks and apostrophes to mark out the properties and the property options, with the same error produced in each case.
groups = [g_1, g_2, g_3, g_4, g_5, g_6, g_7, g_8, g_9, g_10, g_11];
data = [day_1_seat, day_2_seat, day_3_seat, day_4_seat, day_5_seat, ...
day_6_seat, day_7_seat, day_8_seat, day_9_seat, day_10_seat, ...
day_11_seat];
labels = {"29/07", "04/08", "05/08", "06/08", "07/08", "09/08", "11/08",...
"12/08", "13/08", "28/08", "01/09"};
s = boxplot(data,groups, "Notch", 0, "Symbol",".", "BoxWidth", "fixed");
The nature of the data in "groups" and "data" is unimportant, as I can create the boxplot without specifying properties without any issue. I have also tried specifying plot options after the initial call to boxplot with no luck.
This issue also occurs with other properties, such as Labels, OutlierTags etc, but not with "Notch" or "Symbol". I'm not a novice user, but I cannot figure out what the issue is here, any advice would be greatly appreciated!
I'm a student teaching myself Drake, specifically pydrake with Dr. Russ Tedrake's excellent Underactuated Robotics course. I am trying to write a combined energy shaping and lqr controller for keeping a cartpole system balanced upright. I based the diagram on the cartpole example found in Chapter 3 of Underactuated Robotics [http://underactuated.mit.edu/acrobot.html], and the SwingUpAndBalanceController on Chapter 2: [http://underactuated.mit.edu/pend.html].
I have found that due to my use of the cart_pole.sdf model I have to create an abstract input port due receive FramePoseVector from the cart_pole.get_output_port(0). From there I know that I have to create a control signal output of type BasicVector to feed into a Saturation block before feeding into the cartpole's actuation port.
The problem I'm encountering right now is that I'm not sure how to get the system's current state data in the DeclareVectorOutputPort's callback function. I was under the assumption I would use the LeafContext parameter in the callback function, OutputControlSignal, obtaining the BasicVector continuous state vector. However, this resulting vector, x_bar is always NaN. Out of desperation (and testing to make sure the rest of my program worked) I set x_bar to the controller's initialization cart_pole_context and have found that the simulation runs with a control signal of 0.0 (as expected). I can also set output to 100 and the cartpole simulation just flies off into endless space (as expected).
TL;DR: What is the proper way to obtain the continuous state vector in a custom controller extending LeafSystem with a DeclareVectorOutputPort?
Thank you for any help! I really appreciate it :) I've been teaching myself so it's been a little arduous haha.
# Combined Energy Shaping (SwingUp) and LQR (Balance) Controller
# with a simple state machine
class SwingUpAndBalanceController(LeafSystem):
def __init__(self, cart_pole, cart_pole_context, input_i, ouput_i, Q, R, x_star):
LeafSystem.__init__(self)
self.DeclareAbstractInputPort("state_input", AbstractValue.Make(FramePoseVector()))
self.DeclareVectorOutputPort("control_signal", BasicVector(1),
self.OutputControlSignal)
(self.K, self.S) = BalancingLQRCtrlr(cart_pole, cart_pole_context,
input_i, ouput_i, Q, R, x_star).get_LQR_matrices()
(self.A, self.B, self.C, self.D) = BalancingLQRCtrlr(cart_pole, cart_pole_context,
input_i, ouput_i,
Q, R, x_star).get_lin_matrices()
self.energy_shaping = EnergyShapingCtrlr(cart_pole, x_star)
self.energy_shaping_context = self.energy_shaping.CreateDefaultContext()
self.cart_pole_context = cart_pole_context
def OutputControlSignal(self, context, output):
#xbar = copy(self.cart_pole_context.get_continuous_state_vector())
xbar = copy(context.get_continuous_state_vector())
xbar_ = np.array([xbar[0], xbar[1], xbar[2], xbar[3]])
xbar_[1] = wrap_to(xbar_[1], 0, 2.0*np.pi) - np.pi
# If x'Sx <= 2, then use LQR ctrlr. Cost-to-go J_star = x^T * S * x
threshold = np.array([2.0])
if (xbar_.dot(self.S.dot(xbar_)) < 2.0):
#output[:] = -self.K.dot(xbar_) # u = -Kx
output.set_value(-self.K.dot(xbar_))
else:
self.energy_shaping.get_input_port(0).FixValue(self.energy_shaping_context,
self.cart_pole_context.get_continuous_state_vector())
output_val = self.energy_shaping.get_output_port(0).Eval(self.energy_shaping_context)
output.set_value(output_val)
print(output)
Here are two things that might help:
If you want to get the state of the cart-pole from MultibodyPlant, you probably want to be connecting to the continuous_state output port, which gives you a normal vector instead of the abstract-type FramePoseVector. In that case, your call to get_input_port().Eval(context) should work just fine.
If you do really want to read the FramePoseVector, then you have to evaluate the input port slightly differently. You can find an example of that here.
To solve a 5 parameter model, I need at least 5 data points to get a unique solution. For x and y data below:
import numpy as np
x = np.array([[-0.24155831, 0.37083184, -1.69002708, 1.4578805 , 0.91790011,
0.31648635, -0.15957368],
[-0.37541846, -0.14572825, -2.19695883, 1.01136142, 0.57288752,
0.32080956, -0.82986857],
[ 0.33815532, 3.1123936 , -0.29317028, 3.01493602, 1.64978158,
0.56301755, 1.3958912 ],
[ 0.84486735, 4.74567324, 0.7982888 , 3.56604097, 1.47633894,
1.38743513, 3.0679506 ],
[-0.2752026 , 2.9110031 , 0.19218081, 2.0691105 , 0.49240373,
1.63213241, 2.4235483 ],
[ 0.89942508, 5.09052174, 1.26048572, 3.73477373, 1.4302902 ,
1.91907482, 3.70126468]])
y = np.array([-0.81388378, -1.59719762, -0.08256274, 0.61297275, 0.99359647,
1.11315445])
I used only 6 data to fit a 8 parameter model (7 slopes and 1 intercept).
lr = LinearRegression().fit(x, y)
print(lr.coef_)
array([-0.83916772, -0.57249998, 0.73025938, -0.02065629, 0.47637768,
-0.36962192, 0.99128474])
print(lr.intercept_)
0.2978781587718828
Clearly, it's using some kind of assignment to reduce the degrees of freedom. I tried to look into the source code but couldn't found anything about that. What method do they use to find the parameter of under specified model?
You don't need to reduce the degrees of freedom, it simply finds a solution to the least squares problem min sum_i (dot(beta,x_i)+beta_0-y_i)**2. For example, in the non-sparse case it uses the linalg.lstsq module from scipy. The default solver for this optimization problem is the gelsd LAPACK driver. If
A= np.concatenate((ones_v, X), axis=1)
is the augmented array with ones as its first column, then your solution is given by
x=numpy.linalg.pinv(A.T*A)*A.T*y
Where we use the pseudoinverse precisely because the matrix may not be of full rank. Of course, the solver doesn't actually use this formula but uses singular value Decomposition of A to reduce this formula.
I have a dataframe which is of length 177 and I want to calculate and plot the partial auto-correlation function (PACF).
I have the data imported etc and I do:
from statsmodels.tsa.stattools import pacf
ys = pacf(data[key][array].diff(1).dropna(), alpha=0.05, nlags=176, method="ywunbiased")
xs = range(lags+1)
plt.figure()
plt.scatter(xs,ys[0])
plt.grid()
plt.vlines(xs, 0, ys[0])
plt.plot(ys[1])
The method used results in numbers greater than 1 for very long lags (90ish) which is incorrect and I get a RuntimeWarning: invalid value encountered in sqrtreturn rho, np.sqrt(sigmasq) but since I can't see their source code I don't know what this means.
To be honest, when I search for PACF, all the examples only carry out PACF up to 40 lags or 60 or so and they never have any significant PACF after lag=2 and so I couldn't compare to other examples either.
But when I use:
method="ols"
# or
method="ywmle"
the numbers are corrected. So it must be the algo they use to solve it.
I tried importing inspect and getsource method but its useless it just shows that it uses another package and I can't find that.
If you also know where the problem arises from, I would really appreciate the help.
For your reference, the values for data[key][array] are:
[1131.130005, 1144.939941, 1126.209961, 1107.300049, 1120.680054, 1140.839966, 1101.719971, 1104.23999, 1114.579956, 1130.199951, 1173.819946, 1211.920044, 1181.27002, 1203.599976, 1180.589966, 1156.849976, 1191.5, 1191.329956, 1234.180054, 1220.329956, 1228.810059, 1207.01001, 1249.47998, 1248.290039, 1280.079956, 1280.660034, 1294.869995, 1310.609985, 1270.089966, 1270.199951, 1276.660034, 1303.819946, 1335.849976, 1377.939941, 1400.630005, 1418.300049, 1438.23999, 1406.819946, 1420.859985, 1482.369995, 1530.619995, 1503.349976, 1455.27002, 1473.98999, 1526.75, 1549.380005, 1481.140015, 1468.359985, 1378.550049, 1330.630005, 1322.699951, 1385.589966, 1400.380005, 1280.0, 1267.380005, 1282.829956, 1166.359985, 968.75, 896.23999, 903.25, 825.880005, 735.090027, 797.869995, 872.8099980000001, 919.1400150000001, 919.320007, 987.4799800000001, 1020.6199949999999, 1057.079956, 1036.189941, 1095.630005, 1115.099976, 1073.869995, 1104.48999, 1169.430054, 1186.689941, 1089.410034, 1030.709961, 1101.599976, 1049.329956, 1141.199951, 1183.26001, 1180.550049, 1257.640015, 1286.119995, 1327.219971, 1325.829956, 1363.609985, 1345.199951, 1320.640015, 1292.280029, 1218.890015, 1131.420044, 1253.300049, 1246.959961, 1257.599976, 1312.410034, 1365.680054, 1408.469971, 1397.910034, 1310.329956, 1362.160034, 1379.319946, 1406.579956, 1440.670044, 1412.160034, 1416.180054, 1426.189941, 1498.109985, 1514.680054, 1569.189941, 1597.569946, 1630.73999, 1606.280029, 1685.72998, 1632.969971, 1681.550049, 1756.540039, 1805.810059, 1848.359985, 1782.589966, 1859.449951, 1872.339966, 1883.949951, 1923.569946, 1960.22998, 1930.6700440000002, 2003.369995, 1972.290039, 2018.050049, 2067.560059, 2058.899902, 1994.9899899999998, 2104.5, 2067.889893, 2085.51001, 2107.389893, 2063.110107, 2103.840088, 1972.180054, 1920.030029, 2079.360107, 2080.409912, 2043.939941, 1940.2399899999998, 1932.22998, 2059.73999, 2065.300049, 2096.949951, 2098.860107, 2173.600098, 2170.949951, 2168.27002, 2126.149902, 2198.810059, 2238.830078, 2278.8701170000004, 2363.639893, 2362.719971, 2384.199951, 2411.800049, 2423.409912, 2470.300049, 2471.649902, 2519.360107, 2575.26001, 2584.840088, 2673.610107, 2823.810059, 2713.830078, 2640.8701170000004, 2648.050049, 2705.27002, 2718.3701170000004, 2816.290039, 2901.52002, 2913.97998]
Your time series is pretty clearly not stationary, so that Yule-Walker assumptions are violated.
More generally, PACF is usually appropriate with stationary time series. You might difference your data first, before considering the partial autocorrelations.
Please can someone help with a solution for running Discrete wavelet transform in R.
I have tried with the following data format; Year, Rain.
Year is in form of 1970,1972,1973.... and Rain in form of 200, 85, 34, 56 23, 0.5... etc. I don't know if my data frame is correct. or if i need to do something to the data before I run it.
Haven't saved the data.frame as wave, I ran as:
rain.dwt.01 <- wavDWT(wave)
Here is my code:
getwd()
setwd("C:\\Users\\dell\\Desktop\\ANN")
wave<-read.csv(file.choose(),header = T)
library(wmtsa)
library(wavelets)
library(waveslim)
library(MASS)
library(wavethresh) ### loaded auxillary functions from Internet
con <- url("faculty.washington.edu/dbp/R-CODE/workshop.Rdata")
print(load(con))
close(con)
lplot(wave)
abline(h=mean(wave),lty="dotted",col="red")
rain.dwt.01 <- wavDWT(wave)
I got this error:
Error in itCall("RS_wavelets_transform_discrete_wavelet_convolution", :
(list) object cannot be coerced to type 'double
Please help with example so I can understand why this error appears