I have 12 different points and 10 of them are related to the first two; I want to set label for each of this 10 points individually, but sometimes two or more of them have the same coordinate yet I want to show all the label for that coordinate (not on top of each other but readable)
As you can see in the below picture two set of points have the same coordinate and the label of them have overlapping
booleanFunction = np.array(["K","I","H" ,"G", "F", "E" , "D" , "M", "B", "A"])
pointsx = np.empty((rs.shape[1],1))
pointsy = np.empty((rs.shape[1],1))
....
....
....
pl.figure()
pl.hold(True)
pl.plot(X1, Y1, 'ro', X2, Y2, 'y<')
pl.plot(pointsx, pointsy, 'b3')
for i in range (len(pointsx)):
pl.annotate(booleanFunction[i], xy=(pointsx[i], pointsy[i]), xycoords='data', textcoords='data')
I one of my codes to avoid annotation overlap I do something like this:
xoffset = 0.1
switch = -1
for i in range (len(pointsx)):
pl.annotate(booleanFunction[i], xy=(pointsx[i], pointsy[i]),
xytext=(pointsx[i]+switch*xoffset, pointsy[i]),
xycoords='data', textcoords='data')
switch*=-1
This writes your annotated text alternatively shifted left and right xoffset from the point you want to annotate. Of course you can use something similar for the y direction or for both.
Related
I try to explain my problems but perhaps there are to many so I dont know where to start. And I am running out of time :(
I have tested the ability of fungi to alter plastic surfaces after 2 diff timepoints and in two batches. Method of surface investigation was ATR-FT-IR. I now have spectral IR data from 4 different substrates, each exposed to 5 diff fungi for two diff times. Every sample was measured 10 times (very mostly but sadly not always). Logically, I was running control samples (no fungi and no treatment, sample treated but without fungi), also for the two diff batches. SO- for each Substrate, I come up with around 140 columns and 1820 rows. I shrunk the data to respective means and standard deviations with excel and imported it as .xlsx- because .csv absolutely failed and i could figure out why ?! Catastrophe.
> head(pet)
Wavenumbers MEAN_PET_untreated SD_PET_untreated MEAN_c_PET_B1_AL1 SD_PET_B1_AL1 MEAN_c_PET_B1_AL2 SD_c_PET_B1_AL2
1 3997.805 8.021747e-05 0.0003198024 -5.862401e-05 0.0002445300 0.0001309613 0.0004636534
2 3995.877 7.575977e-05 0.0003168603 -4.503153e-05 0.0002384142 0.0001185064 0.0004360579
3 3993.948 7.713719e-05 0.0003169468 -3.218230e-05 0.0002414230 0.0001145128 0.0004352532
4 3992.020 7.847460e-05 0.0003191443 -3.255098e-05 0.0002519945 0.0001258732 0.0004388980
5 3990.091 7.835603e-05 0.0003159916 -4.792059e-05 0.0002617358 0.0001325122 0.0004465352
6 3988.163 7.727790e-05 0.0003063113 -6.286794e-05 0.0002593732 0.0001297744 0.0004532126
My goal was a multiplot, showing averaged spectral data with geom_path and geom_ribbons per fungus, yielding 5 elements per plot (substrate pur, controle t1, controle t2, fungi treat 1, fungi treat 2). The dataset is really large so I had problems to handle it and created these plots manually, so NOT by faceting.
F4<-ggplot(pet)+
geom_errorbar(aes(x = Wavenumbers, y = MEAN_c_PET_B2_AL2, ymin = MEAN_c_PET_B2_AL2 - SD_c_PET_B2_AL2, ymax = MEAN_c_PET_B2_AL2 + SD_c_PET_B2_AL2, group=1), alpha= .1, stat="identity", position = "identity", colour="red")+
geom_path(aes(x = Wavenumbers, y = MEAN_c_PET_B2_AL2), stat="identity", group= 1, colour= "red")+
geom_errorbar(aes(x = Wavenumbers, y = MEAN_c_PET_B2_AL1 ,ymax = MEAN_c_PET_B2_AL1 + SD_c_PET_B2_AL1, ymin = MEAN_c_PET_B2_AL1 - SD_c_PET_B2_AL1, group=1), alpha= .1, stat="identity", position = "identity", colour="purple")+
geom_path(aes(x = Wavenumbers, y = MEAN_c_PET_B2_AL1), stat="identity", group= 1, colour= "purple")+
geom_errorbar(aes(x = Wavenumbers, y = MEAN_PET_untreated, ymax = MEAN_PET_untreated + SD_PET_untreated, ymin = MEAN_PET_untreated - SD_PET_untreated, group=1), alpha= .1, stat="identity", position = "identity", colour="yellow")+
geom_path(aes(x = Wavenumbers, y = MEAN_PET_untreated), stat="identity", group= 1, colour= "yellow")+
geom_errorbar(aes(x = Wavenumbers, y = MEAN_F4_PET_B2_AL1, ymax = MEAN_F4_PET_B2_AL1 + SD_F4_PET_B2_AL1, ymin = MEAN_F4_PET_B2_AL1 - SD_F4_PET_B2_AL1, group=1), alpha= .1, stat="identity", position = "identity", colour="orange")+
geom_path(aes(x = Wavenumbers, y = MEAN_F4_PET_B2_AL1), stat="identity", group= 1, colour= "orange")+
geom_errorbar(aes(x = Wavenumbers, y = MEAN_F4_PET_B2_AL2, ymax = MEAN_F4_PET_B2_AL2 + SD_F4_PET_B2_AL2, ymin = MEAN_F4_PET_B2_AL2 - SD_F4_PET_B2_AL2, group=1), alpha= .1, stat="identity", position = "identity", colour="darkgreen")+
geom_path(aes(x = Wavenumbers, y = MEAN_F4_PET_B2_AL2), stat="identity", group= 1, colour= "darkgreen")+xlab(NULL)+ylab(NULL)+
scale_x_reverse(limits=c(4000 , 500))
So far I summarized the diff ggplots with:
pets<-grid.arrange(F1, F2, F7,F4, F19, ncol = 1, nrow = 5)
ggsave("Multi.pdf", width = 210, height = 297, units = "mm", pets)
This is nearly fine, not elegant and very complicated, but I wont give up at this stage of work as it costed me a whole week. Sadly, I am not really happy with the design, not even to say, I can not use this like it is. Currently, I try to find solutions regarding:
a) Getting rid of empty grid areas left and right to the plotted values. I use scale_x_reverse(limits=c(4000 , 500)), but the range is extended to both sides on the x axis.
b) Creating manually a legend, because even if it would be possible to do this via shared.legend or whatever, it would always yield to many elements. I only want 5 elements with the always repeating, same colors (red=substrate pure, orange= cT_t1, yellow= cT_t2, green= f_t1, purple = f_t2)
c) creating manually a y-labeling (Absorbance), spanning invisible over all plots (vertically)- I tried to label only the 3. plot in the middle, but this leads to a indentation of this plot and the ones above and below appear more left-ragged. If this would be possible, I could use the direct labeling for indicating the respective fungus (e.g. F4).
d) creating a global x labeling- because if I label only the last element, the height of the last plot is reduced by the height of the label.
e) Give it an overall name.
What makes me nervous, too, is that I get an error only for geom_path, telling me that 1 row was removed. But shouldnt this affect also the geom_ribbon? Has it something to do with the fact that I have to call ribbon BEFOR I call geom_path? Otherwise, the lines would have been hidden by the ribbon.
Removed 1 row(s) containing missing values (geom_path).
Also, I am a wondering about the long duration of code execution. 1 element needs 20 seconds, the whole plot 2 minutes to compute. But at least, it is not collapsing like Excel did before- inclusively data loss. Is it normal for such huge datasets? Or could it indicate a very problematic problem?
Ok, finally I hope someone is out there, having had similar work-around-solutions. Because, like I said, I am not willing to spend another week to tidyr or reshape or mutate or whatever.
Thanx in advance! :)
I have a M vs N curve (let's take it to be a sigmoid, for ease of understanding) for a given value of parameters P and Q. I need to visualise the M vs N curves for a range of values of P and Q (assume 10 values in 0 to 1, i.e. 0.1, 0.2, ..., 0.9 for both P and Q)
The only solution that I've found for this problem is a Trellis plot (essentially a matrix of plots). I'd like to know if there any other method to visualise this sort of a 4d(?) relationship besides the Trellis plots. Thanks.
I'm not sure I understand what you're hoping for, so let me know if this is on the right track. Below are three examples using R.
The first is indeed a matrix of plots where each panel represents a different value of q and, within each panel, each curve represents a different value of p. The second is a 3D plot which looks at a surface based on three of the variables with the fourth fixed. The third is a Shiny app that creates the same interactive plot as in the second example but also provides a slider that allows you to change p and see how the plot changes. Unfortunately, I'm not sure how to embed the interactive plots in Stackoverflow so I've just provided the code.
I'm not sure if there's an elegant way to look at all four variables at the same time, but maybe someone will come along with additional options.
Matrix of plots for various values of p and q
library(tidyverse)
theme_set(theme_classic())
# Function to plot
my_fun = function(x, p, q) {
1/(1 + exp(p + q*x))
}
# Parameters
params = expand.grid(p=seq(-2,2,length=6), q=seq(-1,1,length=11))
# x-values to feed to my_fun
x = seq(-10,10,0.1)
# Generate data frame for plotting
dat = map2_df(params$p, params$q, function(p, q) {
data.frame(p=p, q=q, x, y=my_fun(x, p, q))
})
ggplot(dat, aes(x,y,colour=p, group=p)) +
geom_line() +
facet_grid(. ~ q, labeller=label_both) +
labs(colour="p") +
scale_colour_gradient(low="red", high="blue") +
theme(legend.position="bottom")
3D plot with one variable fixed
The code below will produce an interactive 3D plot that you can zoom and rotate. I've fixed the value of p and drawn a plot of the y surface for a grid of x and q values.
library(rgl)
x = seq(-10,10,0.1)
q = seq(-1,1,0.01)
y = outer(x, q, function(a, b) 1/(1 + exp(1 + b*a)))
persp3d(x, q, y, col=hcl(240,80,65), specular="grey20",
xlab = "x", ylab = "q", zlab = "y")
I'm not sure how to embed the interactive plot, but here's a static image of one viewing angle:
Shiny app
The code below will create the same plot as above, but with the added ability to vary p with a slider and see how the plot changes.
Open an R script file and paste in the code below. Save it as app.r in its own directory then run the code. Both an rgl window and the Shiny app page with the slider for controlling the value of p should open. Resize the windows as desired and then move the slider to see how the function surface changes for various values of p.
library(shiny)
# Define UI for application that draws an interactive plot
ui <- fluidPage(
# Application title
titlePanel("Plot the function 1/(1 + exp(p + q*x))"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("p",
"Vary the value of p and see how the plot changes",
min = -2,
max = 2,
value = 1,
step=0.2)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
)
# Define server logic required to draw the plot
server <- function(input, output) {
output$distPlot <- renderPlot({
library(rgl)
x = seq(-10,10,0.1)
q = seq(-1,1,0.01)
y = outer(x, q, function(a, b) 1/(1 + exp(input$p + b*a)))
persp3d(x, q, y, col=hcl(240,50,65), specular="grey20",
xlab = "x", ylab = "q", zlab = "y")
})
}
# Run the application
shinyApp(ui = ui, server = server)
I want to use fill_between in order to highlight stress levels.
My current solution to visualize this is:
for i in range(len(u)-1):
x = u[i]
x1 = i
x2 = i+1
off = idxvec[0] + offset
stress = ((u[i] - l[i])/40)**2.
ranges.append(stress)
ax1.fill_between([off+i, off+i+1], [u[i], u[i+1]], [l[i], l[i+1]], alpha=stress, facecolor='red')
which means I set the alpha value for each slice individually as I scan over my data.
However, this is slow and ugly. Is there a way to do this faster/nicer?
I imagine something like a list of alpha values that I provide fill_between and the result is a horizontal gradient:
ax1.fill_between(x, y1, y2, alpha=[0.2, 0.3, ..., 0.4, 0.3])
Is this possible?
I'm trying to mirror an image. That is, if, e.g., a person is facing to the left, when the program terminates I want that person to now be facing instead to the right.
I understand how mirroring works in JES, but I'm unsure how to proceed here.
Below is what I'm trying; be aware that image is a global variable declared in another function.
def flipPic(image):
width = getWidth(image)
height = getHeight(image)
for y in range(0, height):
for x in range(0, width):
left = getPixel(image, x, y)
right = getPixel(image, width-x-1, y)
color = getColor(left)
setColor(right, color)
show(image)
return image
try this
width = getWidth(pic)
height = getHeight(pic)
for y in range (0,height):
for x in range (0, width/2):
left=getPixel(pic, x, y)
right=getPixel(pic, width-x-1,y)
color1=getColor(left)
color2=getColor(right)
setColor(right, color1)
setColor(left, color2)
repaint(pic)
I personally find that repaint is confusing for newbies (like me!).
I'd suggest something like this:
def mirrorImage(image):
width = getWidth(image)
height = getHeight(image)
for y in range (0,height):
for x in range (0, width/2):
left=getPixel(pic, x, y)
right=getPixel(pic, width-x-1,y)
color1=getColor(left)
color2=getColor(right)
setColor(right, color1)
setColor(left, color2)
show(image)
return image
mirrorImage(image)
This seems to work well.. I put some comments in so you can rewrite in your own style.
feel free to ask questions but I think your question may already be answered^^
#this function will take the pixel values for a selected picture and
#past them to a new canvas but fliped over!
def flipPic(pict):
#here we take the height and width of the original picture
width=getWidth(pict)
height=getHeight(pict)
#here we make and empty canvas
newPict=makeEmptyPicture(width,height)
#the Y for loop is setting the range to working for the y axes the started the X for loop
for y in range(0, height):
#the X for loop is setting the range to work in for the x axis
for x in range(0, width):
#here we are collecting the colour information for the origional pix in range of X and
colour=getColor(getPixel(pict,x,y))
#here we are setting the colour information to its new position on the blank canvas
setColor(getPixel(newPict,width-x-1,y),colour)
#setColor(getPixel(newPict,width-x-1,height-y-1),colour)#upsidedown
show(newPict)
#drive function
pict = makePicture(pickAFile())
show(pict)
flipPic(pict)
Might be easier to read if you copy it over to JES first :D
BTW I got full marks for this one in my intro to programming class ;)
I have two representations of the same function. The one shows it as a function of voltage, the other as a function of depth (a monotonous but complicated function). Depth can be expressed as a function of voltage. I would like to add something like a voltage axis to the depth representation but this does not seem to be possible.
How can I add vertical lines at increments of the voltage like -0.5, -1.0, -1.5, ... on the depth plot?
I found that it is indeed possible to create a custom second axis on the top. The result looks like this: https://dl.dropbox.com/u/226980/solved.jpg
This works by using FixedLocator.
The whole code looks like this:
f = subplot(111)
p1 = plot(depth_samples,NtNdbulk, label = "relative concentration")
xlabel("depth [um]")
ylabel("N_trap/N_bulk")
twinx()
p2 = plot(depth_samples,Nt, color='r', label = "absolute concentration")
p = p1 + p2
ylabel("N_trap")
labs = [l.get_label() for l in p]
legend(p, labs, loc=0)
ax2 = twiny()
p1 = plot(depth_samples,NtNdbulk, alpha = 0) #invisible
l = matplotlib.ticker.FixedLocator(tick_depth*1e4) # Positions of the ticks
ax2.get_xaxis().set_major_locator(l)
ax2.get_xaxis().set_ticklabels(ticks) # Voltages as displayed
xlabel("DLTS voltage during pulse")
show()