Background:
I'm working on a program to show a 2d cross section of 3d data. The data is stored in a simple text csv file in the format x, y, z1, z2, z3, etc. I take a start and end point and flick through the dataset (~110,000 lines) to create a line of points between these two locations, and dump them into an array. This works fine, and fairly quickly (takes about 0.3 seconds). To then display this line, I've been creating a matplotlib stacked bar chart. However, the total run time of the program is about 5.5 seconds. I've narrowed the bulk of it (3 seconds worth) down to the code below.
'values' is an array with the x, y and z values plus a leading identifier, which isn't used in this part of the code. The first plt.bar is plotting the bar sections, and the second is used to create an arbitrary floor of -2000. In order to generate a continuous looking section, I'm using an interval between each bar of zero.
import matplotlib.pyplot as plt
for values in crossSection:
prevNum = None
layerColour = None
if values != None:
for i in range(3, len(values)):
if values[i] != 'n':
num = float(values[i].strip())
if prevNum != None:
plt.bar(spacing, prevNum-num, width=interval, \
bottom=num, color=layerColour, \
edgecolor=None, linewidth=0)
prevNum = num
layerColour = layerParams[i].strip()
if prevNum != None:
plt.bar(spacing, prevNum+2000, width=interval, bottom=-2000, \
color=layerColour, linewidth=0)
spacing += interval
I'm sure there's a more efficient way to do this, but I'm new to Matplotlib and still unfamilar with its capabilities. The other main use of time in the code is:
plt.savefig('output.png')
which takes about a second, but I figure this is to be expected to save the file and I can't do anything about it.
Question:
Is there a faster way of generating the same output (a stacked bar chart or something that looks like one) by using plt.bar() better, or a different Matplotlib function?
EDIT:
I forgot to mention in the original post that I'm using Python 3.2.3 and Matplotlib 1.2.0
Leaving this here in case someone runs into the same problem...
While not exactly the same as using bar(), with a sufficiently large dataset (large enough that using bar() takes a few seconds) the results are indistinguishable from stackplot(). If I sort the data into layers using the method given by tcaswell and feed it into stackplot() the chart is created in 0.2 seconds, rather than 3 seconds.
EDIT
Code provided by tcaswell to turn the data into layers:
accum_values = []
for values in crosssection:
accum_values.append([float(v.strip()) for v iv values[3:]])
accum_values = np.vstack(accum_values).T
layer_params = [l.strip() for l in layerParams]
bottom = numpy.zeros(accum_values[0].shape)
It looks like you are drawing each bar, you can pass sequences to bar (see this example)
I think something like:
accum_values = []
for values in crosssection:
accum_values.append([float(v.strip()) for v iv values[3:]])
accum_values = np.vstack(accum_values).T
layer_params = [l.strip() for l in layerParams]
bottom = numpy.zeros(accum_values[0].shape)
ax = plt.gca()
spacing = interval*numpy.arange(len(accum_values[0]))
for data,color is zip(accum_values,layer_params):
ax.bar(spacing,data,bottom=bottom,color=color,linewidth=0,width=interval)
bottom += data
will be faster (because each call to bar creates one BarContainer and I suspect the source of your issues is you were creating one for each bar, instead of one for each layer).
I don't really understand what you are doing with the bars that have tops below their bottoms, so I didn't try to implement that, so you will have to adapt this a bit.
Related
One of the things I have found wanting lately is the ability to highlight or select just the last n data points in Altair. For example, for a daily updated time series data, selecting/highlighting the last 7 days data window.
The issue with condition is that you have to explicitly specify the date or a value, from which the selection/highlight happens. One drawback of this is that in case of a time series data that updates fairly frequently, it becomes a manual task.
One possible solution is to just use native Python and if the x axis is datetime data, then write the code in such a way that it programmatically takes care of things perhaps using f-strings.
I was wondering, despite these two solutions above, is there a way natively built into Altair/Vega-Lite to select the last/first n data points?
A contrived example using f-strings -
index = 7 #a perhaps bad way to highlight last 2 data points
data = pd.DataFrame({'time':[0,1,2,3,4,5,6,7,8,9], 'value':[1,2,4,8,16,15,14,13,12,11]})
bar = alt.Chart(data).mark_bar(opacity=1, width=15).encode(
x='time:T',
y='value:Q',
color = alt.condition(alt.datum.time>f'{index}', alt.value('red'), alt.value('steelblue'))
)
text = bar.mark_text(align='center', dy=-10).encode(
text='value:Q'
)
bar+text
You can do this using a window transform, in a similar way to the Top-K Items example:
import altair as alt
import pandas as pd
data = pd.DataFrame({'time':[0,1,2,3,4,5,6,7,8,9], 'value':[1,2,4,8,16,15,14,13,12,11]})
num_items = 2
base = alt.Chart(data).transform_window(
rank='rank()',
sort=[alt.SortField('time', order='descending')]
)
bar = base.mark_bar(opacity=1, width=15).encode(
x='time:T',
y='value:Q',
color = alt.condition(alt.datum.rank<=num_items, alt.value('red'), alt.value('steelblue'))
)
text = bar.mark_text(align='center', dy=-10).encode(
text='value:Q'
)
bar+text
I am trying to construct a grouped vertical bar chart in Bokeh from a pandas dataframe. I'm struggling with understanding the use of factor_cmap and how the color mapping works with this function. There's an example in the documentation (https://docs.bokeh.org/en/latest/docs/user_guide/categorical.html#pandas) that was helpful to follow, here:
from bokeh.io import output_file, show
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg_clean as df
from bokeh.transform import factor_cmap
output_file("bar_pandas_groupby_nested.html")
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
group = df.groupby(by=['cyl', 'mfr'])
index_cmap = factor_cmap('cyl_mfr', palette=Spectral5, factors=sorted(df.cyl.unique()), end=1)
p = figure(plot_width=800, plot_height=300, title="Mean MPG by # Cylinders and Manufacturer",
x_range=group, toolbar_location=None, tooltips=[("MPG", "#mpg_mean"), ("Cyl, Mfr", "#cyl_mfr")])
p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=group,
line_color="white", fill_color=index_cmap, )
p.y_range.start = 0
p.x_range.range_padding = 0.05
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
p.xaxis.major_label_orientation = 1.2
p.outline_line_color = None
show(p)
This yields the following (again, a screen shot from the documentation):
Grouped Vbar output
I understand how factor_cmap is working here, I think. The index for the dataframe has multiple factors and we're only taking the first by slicing (as seen with the end = 1). But when I try to instead set coloring based on the second index level, mfr, (setting start = 1 , end = 2) , the index mapping breaks and I get this. I based this change on my assumption that the factors were hierarchical and I needed to slice them to get the second level.
I think I must be thinking about the indexing with these categorical factors wrong, but I'm not sure what I'm doing wrong. How do I get a categorical mapper to color by the second level of the factor? I assumed the format of the factors was ('cyl', 'mfr') but maybe that assumption is wrong?
Here's the documentation for factor_cmap, although it wasn't very helpful: https://docs.bokeh.org/en/latest/docs/reference/transform.html#bokeh.transform.factor_cmap .
If you mean you are trying this:
index_cmap = factor_cmap('cyl_mfr',
palette=Spectral5,
factors=sorted(df.cyl.unique()),
start=1, end=2)
Then there are at least two issues:
2 is out of bounds for the length of the list of sub-factors ('cyl', 'mfr'). You would just want start=1 and leave end with its default value of None (which means to the end of the list, as usual for any Python slice).
In this specific case, with start=1 that means "colormap based on mfr sub-factors of the values", but you are still configuring the cololormapper with the cylinders as the factors for the map:
factors=sorted(df.cyl.unique())
When the colormapper goes to look up a value with mfr="mazda" in the mapping, it does not find anything (because you only put cylinder values in the mapping) so it gets shaded the default color grey (as expected).
So you could do something like this:
index_cmap = factor_cmap('cyl_mfr',
palette=Spectral5,
factors=sorted(df.mfr.unique()),
start=1)
Which "works" modulo the fact that there are way more manufacturer values than there are colors in the Spectral5 palette:
In the real situation you'll need to make sure you use a palette as least as big as the number of (sub-)factors that you configure.
I am trying to make a heatmap.
I get my data out of a pipeline that class some rows as noisy, I decided to get a plot including them and a plot without them.
The problem I have: In the plot without the noisy rows I have blank line appearing (the same number of lines than rows removed).
Roughly The code looks like that (I can expand part if required I am trying to keep it shorts).
If needed I can provide a link with similar data publicly available.
data_frame = load_df_fromh5(file) # load a data frame from the hdf5 output
noisy = [..] # a list which indicate which row are vector
# I believe the problem being here:
noisy = [i for (i, v) in enumerate(noisy) if v == 1] # make a vector which indicates which index to remove
# drop the corresponding index
df_cells_noisy = df_cells[~df_cells.index.isin(noisy)].dropna(how="any")
#I tried an alternative method:
not_noisy = [0 if e==1 else 1 for e in noisy)
df = df[np.array(not_noisy, dtype=bool)]
# then I made a clustering using scipy
Z = hierarchy.linkage(df, method="average", metric="canberra", optimal_ordering=True)
df = df.reindex(hierarchy.leaves_list(Z))
# the I plot using the df variable
# quit long function I believe the problem being upstream.
plot(df)
The plot is quite long but I believe it works well because the problem only shows with the no noisy data frame.
IMO I believe somehow pandas keep information about the deleted rows and that they are plotted as a blank line. Any help is welcome.
Context:
Those are single-cell data of copy number anomaly (abnormalities of the number of copy of genomic segment)
Rows represent individuals (here individuals cells) columns represents for the genomic interval the number of copy (2 for vanilla (except sexual chromosome)).
I have taken code in relation to the Kalman Filter and am attempting to iterate through each column of data. What I would like to have happen is:
The column data is fed into the filter
The filtered column data (xhat) is placed into another DataFrame (filtered)
The filtered column data (xhat) is used to produce a visual.
I have created a for loop to iterate through the column data, but when I run the cell, I crash the notebook. When it doesn't crash, I get this warning:
C:\Users\perso\Anaconda3\envs\learn-env\lib\site-packages\ipykernel_launcher.py:45: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
Thanks in advance for any help. I hope this question is detailed enough. I bombed on the last one.
'''A Python implementation of the example given in pages 11-15 of "An
Introduction to the Kalman Filter" by Greg Welch and Gary Bishop,
University of North Carolina at Chapel Hill, Department of Computer
Science, TR 95-041,
https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf'''
# by Andrew D. Straw
import numpy as np
import matplotlib.pyplot as plt
# dataframe created to hold filtered data
filtered = pd.DataFrame()
# intial parameters
for column in data:
n_iter = len(data.index) #number of iterations equal to sample numbers
sz = (n_iter,) # size of array
z = data[column] # observations
Q = 1e-5 # process variance
# allocate space for arrays
xhat=np.zeros(sz) # a posteri estimate of x
P=np.zeros(sz) # a posteri error estimate
xhatminus=np.zeros(sz) # a priori estimate of x
Pminus=np.zeros(sz) # a priori error estimate
K=np.zeros(sz) # gain or blending factor
R = 1.0**2 # estimate of measurement variance, change to see effect
# intial guesses
xhat[0] = z[0]
P[0] = 1.0
for k in range(1,n_iter):
# time update
xhatminus[k] = xhat[k-1]
Pminus[k] = P[k-1]+Q
# measurement update
K[k] = Pminus[k]/( Pminus[k]+R )
xhat[k] = xhatminus[k]+K[k]*(z[k]-xhatminus[k])
P[k] = (1-K[k])*Pminus[k]
# add new data to created dataframe
filtered.assign(a = [xhat])
#create visualization of noise reduction
plt.rcParams['figure.figsize'] = (10, 8)
plt.figure()
plt.plot(z,'k+',label='noisy measurements')
plt.plot(xhat,'b-',label='a posteri estimate')
plt.legend()
plt.title('Estimate vs. iteration step', fontweight='bold')
plt.xlabel('column data')
plt.ylabel('Measurement')
This seems like a pretty straightforward error. The warning indicates that you have attempted to plot more figures than the current limit before a warning is created (a parameter you can change but which by default is set to 20). This is because in each iteration of your for loop, you create a new figure. Depending on the size of n_iter, you are opening potentially hundreds or thousands of figures. Each of these figures takes resources to generate and show, so you are creating a very large resource load on your system. Either it is processing very slowly due or is crashing altogether. In any case, the solution is to plot fewer figures.
I don't know exactly what you're plotting in your loop but it seems like each iteration of your loop corresponds to one time step and at each time step you'd like to plot the estimated and actual values. In this case, you need to define a figure and figure options once, outside of the loop, rather than at each iteration. But a better way to do this is probably to generate all of the data you want to plot ahead of time and store it in an easy-to-plot datatype like lists, then plot it once at the end.
If I have a table with three columns where the first column represents the name of each point, the second column represent numerical data (mean) and the last column represent (second column + fixed number). The following an example how is the data looks like:
I want to plot this table so I have the following figure
If it is possible how I can plot it using either Microsoft Excel or python or R (Bokeh).
Alright, I only know how to do it in ggplot2, I will answer regarding R here.
These method only works if the data-frame is in the format you provided above.
I rename your column to Name.of.Method, Mean, Mean.2.2
Preparation
Loading csv data into R
df <- read.csv('yourdata.csv', sep = ',')
Change column name (Do this if you don't want to change the code below or else you will need to go through each parameter to match your column names.
names(df) <- c("Name.of.Method", "Mean", "Mean.2.2")
Method 1 - Using geom_segment()
ggplot() +
geom_segment(data=df,aes(x = Mean,
y = Name.of.Method,
xend = Mean.2.2,
yend = Name.of.Method))
So as you can see, geom_segment allows us to specify the end position of the line (Hence, xend and yend)
However, it does not look similar to the image you have above.
The line shape seems to represent error bar. Therefore, ggplot provides us with an error bar function.
Method 2 - Using geom_errorbarh()
ggplot(df, aes(y = Name.of.Method, x = Mean)) +
geom_errorbarh(aes(xmin = Mean, xmax = Mean.2.2), linetype = 1, height = .2)
Usually we don't use this method just to draw a line. However, its functionality fits your requirement. You can see that we use xmin and ymin to specify the head and the tail of the line.
The height input is to adjust the height of the bar at the end of the line in both ends.
I would use hbar for this:
from bokeh.io import show, output_file
from bokeh.plotting import figure
output_file("intervals.html")
names = ["SMB", "DB", "SB", "TB"]
p = figure(y_range=names, plot_height=350)
p.hbar(y=names, left=[4,3,2,1], right=[6.2, 5.2, 4.2, 3.2], height=0.3)
show(p)
However Whisker would also be an option if you really want whiskers instead of interval bars.