How do I add grid lines to a Chartify plot?
This is what my code looks like:
ch = chartify.Chart(blank_labels=True, x_axis_type="linear")
ch.plot... (plotting my data)
ch.show("png")
Chartify uses Bokeh, which means you can style components using Bokeh attributes.
You can add grid lines by setting the grid_line_color for the ygrid and xgrid:
ch.figure.ygrid.grid_line_color = "#ECECEC"
ch.figure.xgrid.grid_line_color = "#ECECEC"
Related
I am very new to using plotly in rstudio and have come up against a problem with mapping discrete colours (stored as hex codes in the field color) to each of the slices in my ids field.
I have included my code below:
df %>%
plot_ly(
color = I("black"),
marker = list(colors = ~color)) %>%
add_trace(ids = df$ids,
labels = df$labels,
parents = df$parents,
type = 'sunburst',
maxdepth = -1,
domain = list(column = 0)) %>%
layout(sunburstcolorway = df$color)
This is the resulting sunburst diagram I get using this code, which is obviously not ideal:
Ideally the first four levels would have the same colour, and then different hex colour codes are used for slices that are labelled "Poor","Moderate","GwC" or "Good".
A csv file of my data frame used above is available here.
I finally managed to nut out how to map my colour field to the background colours on the sunburst chart - have updated the code in original post. All that was required was to insert the following code segment:
plot_ly(
marker = list(colors = ~color))
Below is the output chart:
I’m new to plotly and I’m creating a gantt chart using px.timeline. There are 3 categories of data in my dataset, a normal task with a start and end time, and two types of task where the start and end time are same. I want the normal task to be a rectangle (which is how it is being plot) and the other two tasks to have a hourglass marker and a triangle marker instead of a very thin line ?
This is how my data looks :
data = [dict(Task=’’, start=’’, end=’’, shape=’<rect, hour, tri>’)]
Sample Data :
df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-01-01', shape='hourglass'),
dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15', shape='rectangle'),
dict(Task="Job C", Start='2009-05-30', Finish='2009-05-30', shape='triangle')]
Code :
fig = px.timeline(data, x_start="Start", x_end="Finish", y="Task")
fig.update_yaxes(autorange="reversed", ticklabelposition="outside left")
fig.update_layout(showlegend=False, height=2000, width=1255, margin_pad=10)
fig.show()
Example:
Sample Plot in Excel
Is there any way I can achieve this ?
Thanks !
I solved this after some hours of searching.
Split the data into three each corresponding to 3 different shapes and then plot and combine.
Create 3 individual Plots :
rect = px.timeline(rect, x_start="Start", x_end="Finish", y="Task", color="color")
dia = px.scatter(dia, x="Start", y="Task", color="color", symbol_sequence=['diamond'])
coll = px.scatter(coll, x="Start", y="Task", color="color", symbol_sequence=['hourglass'])
Update traces for individual plots if needed :
rect.update_traces(marker=dict(line=dict(width=1, color='black')))
dia.update_traces(marker=dict(size=12, line=dict(width=2)))
coll.update_traces(marker=dict(size=12, line=dict(width=2)))
Set the timeline plot's axis:
rect.update_xaxes(tickformat="%H:%M:%S.%L", tickmode='linear', dtick='120000')
rect.update_yaxes(autorange='reversed')
rect.update_layout(title=title, showlegend=False, height=2800, width=2000)
Overlay all Plots:
new_fig = go.Figure(data=rect.data + dia.data + coll.data, layout=rect.layout)
new_fig.show()
I need to plot line plot that has different colors. I create special df column 'color' that contains for each point appropriate color.
I already found the solution here:
python/matplotlib - multicolor line
And take the approach from the above question. First, it was working when I use index but now I need to plot it vs other column and I can not appropriately handle the colors. It is all the time colores only with one color.
I use this code for setting colors, but it color line with one color that is the last in the column 'color'. And also create a legend that I don't understand how to delete from the plot.
for color2, start, end in gen_repeating(df2['color']):
print(start, end)
if start > 0: # make sure lines connect
start -= 1
idx = df2.index[start:end+1]
x2 = idx
y2 = df2.loc[idx, 'age_gps_data'].tolist()
df2.plot(x='river_km', y='age_gps_data', color=color2, ax=ax[1])
ax[1].xaxis.set_major_locator(plt.MaxNLocator(5))
plt.setp(ax[1].get_xticklabels())
I would appreciate any help.
How can I set these colors to achieve different color in one line? And don't have legend on the plot.
I've been producing a number of nice plots with the plotLearnerPrediction function in the mlr package for R. They look like this. From looking into the source code of the plotLearnerPrediction function it looks like the color surfaces are made with geom_tile.
A plot can for example be made by:
library(mlr)
data(iris)
#make a learner
lrn <- "classif.qda"
#make a task
my.task <- makeClassifTask(data = iris, target = "Species")
#make plot
plotLearnerPrediction(learner = lrn, task = my.task)
Now I wish to change the colors, using another red, blue and green tone to match those of some other plots that I've made for a project. for this I tried scale_fill_continuous and scale_fill_manual without any luck (Error: Discrete value supplied to continuous scale) I also wish to change the legend title and the labels for each legend entry (Which I tried giving appropriate parameters to the above scale_fill's). There's a lot of info out there on how to set the geom_tile colours when producing the plot, but I haven't found any info on how to do this post-production (i.e. in somebody else's plot object). Any help would be much appreciated.
When you look into the source code you see how the plot is generated and then you can see which scale has to be overwritten or set.
In this example it's fairly easy:
g = plotLearnerPrediction(learner = lrn, task = my.task)
library(ggplot2)
g + scale_fill_manual(values = c(setosa = "yellow", versicolor = "blue", virginica = "red"))
I am trying to use the layered methods to overlay few spatstat spatial objects. All these objects are for the same window. I have an im layer (density) from a ppp. I want to make this layer a bit transparent in order to have a better visibility of the other objects in the layered object.
How can I control the transparency of this density plot (im)? Is there something like alpha or transparency parameter for the plot.im ?
UPDATE:
library(spatstat)
pipes=simplenet
plot(pipes)
point_net = as.ppp(runifpoint(10, win = Window(pipes)))
point_surface = density(point_net)
plot(point_surface)
layers= layered(point_surface, point_net, pipes)
plot(layers)
Here , I have plotted 3 layers. As you can see the density plot has very dark blues and reds. Yes, I can plot lines and points with different colours to make them visible, but it would nice to do simple stacked line, point plots and add a little bit of transparency to the density (im) plots.
The purpose is just to avoid complex customized plot colours and to explain to colleagues.
thank you.
First the commands from the original post:
library(spatstat)
pipes=simplenet
point_net = as.ppp(runifpoint(10, win = Window(pipes)))
point_surface = density(point_net)
layers= layered(point_surface, point_net, pipes)
plot(layers)
You need to provide a different colourmap to plot.im. There are two
ways you can do this:
Plot each layer individually using add = TRUE for subsequent
layers and provide the colour map when you plot the im object.
Pass a list of plot arguments when you plot the layered object you
have created above.
I find the first option easier for illustration, so I will do that
first. The default colourmap of spatstat is the 29th Kovesi colour
sequence (?Kovesi for more details on these sequences):
def_col <- Kovesi$values[[29]]
head(def_col)
#> [1] "#000C7D" "#000D7E" "#000D80" "#000E81" "#000E83" "#000E85"
To add transparency you can use to.transparent with your choice of
fraction for more/less transparency:
def_col_trans <- to.transparent(def_col, fraction = 0.7)
head(def_col_trans)
#> [1] "#000C7DB3" "#000D7EB3" "#000D80B3" "#000E81B3" "#000E83B3" "#000E85B3"
Now you just need to use this as your colourmap:
plot(point_surface, col = def_col_trans)
plot(point_net, add = TRUE)
plot(pipes, add = TRUE)
To do it with the layered object you have to make a list of plot
argument lists (containing NULL if you don't have additional
arguments):
layer_args <- list(list(col = def_col_trans),
list(NULL),
list(NULL))
plot(layers, plotargs = layer_args)