I am trying to plot clusters from K-means alogrith on an image. All I can reach is plotting them on a graph. How can I plot them on an image as a background?
This image is of fixed size and I cant alter its size.
Sorry for silly question, but, am pretty new to python and looks exciting!
I have used K-means alogrithum based on few examples provided, but only reached upto plotting it on a graph.
What I would like to see is those clusters on a custom image of fixed size. How can I achieve it.
Thanking in advance for your replies!
First plot the image and then plot the points.
>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> # Image
>>> img = np.random.randint(0,255,size=(50,50))
>>> x = np.random.randint(0,50,size=100)
>>> y = np.random.randint(0,50,size=100)
>>> plt.imshow(img, cmap='gray')
>>> plt.scatter(x,y)
>>> plt.show()
Related
I want to plot an image something similar to this image by using the data provided here data
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import ticker
inp_data=np.loadtxt("data")
fig = plt.figure(figsize=(8.5, 12.0))
ax1 = fig.add_axes([0.25, 0.55, 0.2, 0.04])
plt.setp(ax1.spines.values(), linewidth=0.5)
ax1.minorticks_on()
ax1.imshow(inp_data,aspect='auto')
plt.show()
Here problem is that, the image is going blank even after the adjustment of vmin and vmax.
I hope experts may help me overcoming this problem and help me plotting a beautiful plot.
Thanks in advance.
I am trying to draw the Nyquist plot using python but I have no clue what all parameters are required by python to do plot that curve.
Here is a glimpse of the parameters that I have:
Channel_ID,Step_ID,Cycle_ID,Test_Time,EIS_Test_ID,EIS_Data_Point,Frequency,Zmod,Zphz,Zreal,Zimg,OCV,AC_Amp_RMS
4,7,1,36966.3072,0,0,200015.6,0.4933,70.9969,0.1606,0.4664,3.6231,0.35
4,7,1,36966.3072,0,1,158953.1,0.412,70.8901,0.1349,0.3893,3.6231,0.35
4,7,1,36966.3072,0,2,126234.4,0.3437,70.7115,0.1135,0.3244,3.6231,0.35
4,7,1,36966.3072,0,3,100265.6,0.2869,70.6312,0.0951,0.2706,3.6231,0.35
4,7,1,36966.3072,0,4,79640.63,0.2364,70.2418,0.0799,0.2224,3.6231,0.35
and above are the values to those parameters.
Based on the above parameters that are
Test_Time, Frequency, Zmod, Zphz, Zreal, Zimg, OCV, AC_Amp_RMS where Zmod is the absolute value of Zreal and Zimg, I need to draw a Nyquist plot. I have no clue how these parameters could be used for the plot.
PS: I tried to plot the curve by making use of the real and imaginary part that is Zimg and Zreal
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train_df = pd.read_csv("above_data_with_around_100_rows.csv")
plt.figure()
plt.plot(train_df["Zreal"], train_df["Zimg"], "b")
plt.plot(train_df["Zreal"], -train_df["Zimg"], "r")
plt.show()
Can this be the useful for Nyquist plot?
How do I change the size of my image so it's suitable for printing?
For example, I'd like to use to A4 paper, whose dimensions are 11.7 inches by 8.27 inches in landscape orientation.
You can also set figure size by passing dictionary to rc parameter with key 'figure.figsize' in seaborn set method:
import seaborn as sns
sns.set(rc={'figure.figsize':(11.7,8.27)})
Other alternative may be to use figure.figsize of rcParams to set figure size as below:
from matplotlib import rcParams
# figure size in inches
rcParams['figure.figsize'] = 11.7,8.27
More details can be found in matplotlib documentation
You need to create the matplotlib Figure and Axes objects ahead of time, specifying how big the figure is:
from matplotlib import pyplot
import seaborn
import mylib
a4_dims = (11.7, 8.27)
df = mylib.load_data()
fig, ax = pyplot.subplots(figsize=a4_dims)
seaborn.violinplot(ax=ax, data=df, **violin_options)
Note that if you are trying to pass to a "figure level" method in seaborn (for example lmplot, catplot / factorplot, jointplot) you can and should specify this within the arguments using height and aspect.
sns.catplot(data=df, x='xvar', y='yvar',
hue='hue_bar', height=8.27, aspect=11.7/8.27)
See https://github.com/mwaskom/seaborn/issues/488 and Plotting with seaborn using the matplotlib object-oriented interface for more details on the fact that figure level methods do not obey axes specifications.
first import matplotlib and use it to set the size of the figure
from matplotlib import pyplot as plt
import seaborn as sns
plt.figure(figsize=(15,8))
ax = sns.barplot(x="Word", y="Frequency", data=boxdata)
You can set the context to be poster or manually set fig_size.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(0)
n, p = 40, 8
d = np.random.normal(0, 2, (n, p))
d += np.log(np.arange(1, p + 1)) * -5 + 10
# plot
sns.set_style('ticks')
fig, ax = plt.subplots()
# the size of A4 paper
fig.set_size_inches(11.7, 8.27)
sns.violinplot(data=d, inner="points", ax=ax)
sns.despine()
fig.savefig('example.png')
This can be done using:
plt.figure(figsize=(15,8))
sns.kdeplot(data,shade=True)
In addition to elz answer regarding "figure level" methods that return multi-plot grid objects it is possible to set the figure height and width explicitly (that is without using aspect ratio) using the following approach:
import seaborn as sns
g = sns.catplot(data=df, x='xvar', y='yvar', hue='hue_bar')
g.fig.set_figwidth(8.27)
g.fig.set_figheight(11.7)
This shall also work.
from matplotlib import pyplot as plt
import seaborn as sns
plt.figure(figsize=(15,16))
sns.countplot(data=yourdata, ...)
For my plot (a sns factorplot) the proposed answer didn't works fine.
Thus I use
plt.gcf().set_size_inches(11.7, 8.27)
Just after the plot with seaborn (so no need to pass an ax to seaborn or to change the rc settings).
See How to change the image size for seaborn.objects for a solution with the new seaborn.objects interface from seaborn v0.12, which is not the same as seaborn axes-level or figure-level plots.
Adjusting the size of the plot depends if the plot is a figure-level plot like seaborn.displot, or an axes-level plot like seaborn.histplot. This answer applies to any figure or axes level plots.
See the the seaborn API reference
seaborn is a high-level API for matplotlib, so seaborn works with matplotlib methods
Tested in python 3.8.12, matplotlib 3.4.3, seaborn 0.11.2
Imports and Data
import seaborn as sns
import matplotlib.pyplot as plt
# load data
df = sns.load_dataset('penguins')
sns.displot
The size of a figure-level plot can be adjusted with the height and/or aspect parameters
Additionally, the dpi of the figure can be set by accessing the fig object and using .set_dpi()
p = sns.displot(data=df, x='flipper_length_mm', stat='density', height=4, aspect=1.5)
p.fig.set_dpi(100)
Without p.fig.set_dpi(100)
With p.fig.set_dpi(100)
sns.histplot
The size of an axes-level plot can be adjusted with figsize and/or dpi
# create figure and axes
fig, ax = plt.subplots(figsize=(6, 5), dpi=100)
# plot to the existing fig, by using ax=ax
p = sns.histplot(data=df, x='flipper_length_mm', stat='density', ax=ax)
Without dpi=100
With dpi=100
# Sets the figure size temporarily but has to be set again the next plot
plt.figure(figsize=(18,18))
sns.barplot(x=housing.ocean_proximity, y=housing.median_house_value)
plt.show()
Some tried out ways:
import seaborn as sns
import matplotlib.pyplot as plt
ax, fig = plt.subplots(figsize=[15,7])
sns.boxplot(x="feature1", y="feature2",data=df) # where df would be your dataframe
or
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=[15,7])
sns.boxplot(x="feature1", y="feature2",data=df) # where df would be your dataframe
The top answers by Paul H and J. Li do not work for all types of seaborn figures. For the FacetGrid type (for instance sns.lmplot()), use the size and aspect parameter.
Size changes both the height and width, maintaining the aspect ratio.
Aspect only changes the width, keeping the height constant.
You can always get your desired size by playing with these two parameters.
Credit: https://stackoverflow.com/a/28765059/3901029
I am trying to create boxplot from pandas dataframe using matplotlib.
I am able to get the boxplot and change the linewidth of outlines and median lines, but I would like to change all the colors to black.
I tried several ways as described in Pandas boxplot: set color and properties for box, median, mean and Change the facecolor of boxplot in pandas.
However, none of them worked for my case.
Could someone solve these problems?
Here are my codes:
version: python 3.6.5
import pandas as pd
from pandas import DataFrame, Series
import matplotlib.pyplot as plt
df = pd.read_excel("training.xls")
fig = plt.figure()
ax = fig.add_subplot(111)
boxprops = dict(color="black",linewidth=1.5)
medianprops = dict(color="black",linewidth=1.5)
df.boxplot(column=["MEAN"],by="SrcID_Feat",ax=ax,
boxprops=boxprops,medianprops=medianprops)
plt.grid(False)
Result
I found that unless I add the argument patch_artist=True, none of the boxprops dictionaries have any effect on the boxplot. For example, when I generated a boxplot where I changed the facecolor to yellow, I had to use the following coding:
plt.boxplot(boxplot_data, positions=[1], patch_artist=True, boxprops=dict(facecolor='yellow'), showmeans=True)
Here is a way to solve your problem (details which are not relevant for the question are omitted):
fig, ax = plt.subplots()
box_plot = ax.boxplot(..., patch_artist=True)
for median in box_plot['medians']:
median.set_color('black')
median is an object of type matplotlib.lines.Line2D which exposes a method set_color which can be used to set the color of each box.
I have made a plot in jupyter that has an x-axis spanning for about 40 seconds. I want to pull out sections that are milliseconds long and re-display them as separate plots (so that they can be better viewed). How would I go about doing this?
You could use some subplots, and slice the original data arrays. For example:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,40,1000)
y = np.random.random(1000)
fig, [ax1,ax2,ax3] = plt.subplots(3,1)
ax1.plot(x,y)
ax2.plot(x[100:120],y[100:120])
ax3.plot(x[500:520],y[500:520])
plt.show()