I have a plot that shows markers in a circle. I want to be able to change the colour of 3 of them. I've tried using a variable for markerfacecolor as follows but that doesn't work:
angle = 0.0
colorR = 'red'
angleUpdate = 2 * numpy.pi / (len(v.T))
for i in range(len(v.T)):
x = numpy.sin(angle)
y = numpy.cos(angle)
angle += angleUpdate
if i < 3:
colorR = 'green'
v[0, i] = x
v[1, i] = y
plt.plot(v[0], v[1], 'ko', markerfacecolor = colorR, markersize = 70, clip_on = False)
plt.show()
Is there a way of doing this?
In your plot 'ko' means put a black circle marker; k stands for black. You should try:
plt.plot(v[0], v[1], 'o', markerfacecolor = 'red')
To get the abbreviation of other symbols and colors try:
help(plt.plot)
You can either achieve your case using plot or scatter, depending on what you are doing:
import pylab as plt
x=[1,2,3,4,5,6,7,8,9,10]
plt.plot(x[:5],x[:5],'o',c='r',markersize=10)
plt.plot(x[5:],x[5:],'o',c='b',markersize=10)
plt.show()
will generate,
Similarly, you can also use
plt.scatter(x[:5],x[:5],c='r',s=100)
plt.scatter(x[5:],x[5:],c='b',s=100)
Related
I'd like to invert the bars in this diagram when they are below 1, not when they are negative. Additionally I'd like to have even spacing between the ticks/steps on the y-axis
Here is my current code
import matplotlib.pyplot as plt
import numpy as np
labels = ['A','B','C']
Vals1 = [28.3232, 12.232, 9.6132]
Vals2 = [0.00456, 17.868, 13.453]
Vals3 = [0.0032, 1.234, 0.08214]
x = np.arange(len(labels))
width = 0.2
fig, ax = plt.subplots()
rects1 = ax.bar(x - width, Vals1, width, label='V1')
rects2 = ax.bar(x, Vals2, width, label='V2')
rects3 = ax.bar(x + width, Vals3, width, label='V3')
ax.set_xticks(x)
ax.set_xticklabels(labels)
plt.xticks(rotation=90)
ax.legend()
yScale = [0.0019531,0.0039063,0.0078125,0.015625,0.03125,0.0625,0.125,0.25,0.5,1,2,4,8,16,32]
ax.set_yticks(yScale)
plt.show()
I believe I've stumbled upon the answer, here it is for anyone else looking for the solution. Add the argument bottom='1' to ax.bar instantiation, and then flip the values in the array.
for i in range(len(Vals1)):
Vals1[i] = (1 - Vals1[i]) * -1
As you mentioned, the key is the bottom param of Axes.bar:
bottom (default: 0): The y coordinate(s) of the bars bases.
But beyond that, you can simplify your plotting code using pandas:
Put your data into a DataFrame:
import pandas as pd
df = pd.DataFrame({'V1': Vals1, 'V2': Vals2, 'V3': Vals3}, index=labels)
# V1 V2 V3
# A 28.3232 0.00456 0.00320
# B 12.2320 17.86800 1.23400
# C 9.6132 13.45300 0.08214
Then use DataFrame.sub to subtract the offset and DataFrame.plot.bar with the bottom param:
bottom = 1
ax = df.sub(bottom).plot.bar(bottom=bottom)
I am using the following code to to generate this heat map:
dim = np.arange(1, 32, 1)
fig, ax = plt.subplots(figsize=(7,9))
heatmap = ax.imshow(h, aspect=1, cmap=plt.cm.get_cmap('Blues', 5), clim=[0,100])
ax.set_ylabel("Days", fontsize=15)
ax.set_xlabel("Months", fontsize=15)
ax.set_title("Percentage of records per day", fontsize=18)
ax.set_yticks(range(0,31))
ax.set_yticklabels(dim, ha='center', minor=False)
ax.set_xticks(range(0,13,1))
ax.set_xticklabels(ylabel[7:],rotation=45, ha='right')
ax.grid(which = 'minor', color = 'w')
ax.set_facecolor('gray')
ax.xaxis.set_minor_locator(MultipleLocator(.5))
ax.yaxis.set_minor_locator(MultipleLocator(.5))
cbaxes = fig.add_axes([.8, .35, .04, .3])
cbar = fig.colorbar(heatmap, ticks = [0, 20, 40, 60, 80 ,100], label = 'Percentage', cax = cbaxes)
fig.show()
I would like to highlight all of the cells with a value greater or equal to 60.
I tried adding this to my code:
highlight = (h> 60)
highlight = np.ma.masked_less(highlight, 1)
ax.pcolormesh(highlight, facecolor = 'None')
and got this:
I am almost there but the cells and the mesh are misaligned. How could I fix this?
The cells in a heatmap are centered on integers, this means for example that the cell with index 0,0 is in fact -0.5 to 0.5 on both axes. You have to subtract 0.5 to the coordinates of your highlights.
Thanks to mozway's comment I was able to fix my problem. I changed the beginning of my code to:
highlight = (h> 60)
highlight = np.ma.masked_less(highlight, 1)
x = np.arange(-0.5,12,1) # len = 10
y = np.arange(-0.5,30,1) # len = 6
X, Y = np.meshgrid(x, y)
and change the line plotting the color mesh to:
ax.pcolormesh(x,y,highlight, facecolor = 'None', edgecolors = 'w',shading='auto', zorder=2)
I also had to set the z-order of the color mesh to be greater than the grid lines (zorder=2 and zorder=1 respectively).
I've tried specifying in a few ways but have not been able to get this figure any bigger than what's shown.
category_names = ['Database', 'Frontend', 'QA', 'ML', 'Fullstack']
labels = list(final_df.index)
data = np.array(final_df.iloc[:, 1:])
data_cum = data.cumsum(axis=1)
category_colors = plt.get_cmap('RdYlGn')(np.linspace(0, 1000, data.shape[1]))
fig, ax = plt.subplots(figsize=(100,75))
ax.invert_yaxis()
# ax.xaxis.set_visible(False)
ax.set_xlim(0, 200)
for i, (colname, color) in enumerate(zip(category_names, category_colors)):
widths = data[:, i]
starts = data_cum[:, i] - widths
ax.barh(labels, widths, left=starts, height=0.5,
label=colname, color=color)
xcenters = starts + widths / 2
r, g, b, _ = color
text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
for y, (x, c) in enumerate(zip(xcenters, widths)):
ax.text(x, y, str(int(c)), ha='center', va='center',
color=text_color, fontsize=15)
If I make the figsize any bigger, the kernel dies and I've tried adjusting height and np.linspace params, as well as trying to set size with fig.set_size_inches. Any ideas on what's going on here?
I have a shapefile of Italy and I'm plotting the GIS Data on it. Thing is I've got a small trajectory of a bus going within a city(Rome) and when i`m plotting, it appears like 1 dot. I guess because of my map.
How to zoom the map (.shp) ?
street_map = gpd.read_file("roads.shp")
...
...
fig,ax = plt.subplots(figsize = (20,15))
street_map.plot(ax = ax, alpha = 0.4, color = "grey")
geo_df[geo_df['Perc_'] > 25].plot(ax = ax, markersize = 20, color = "blue",
marker = "o", label = "Neg")
geo_df[geo_df['Perc_'] < 25].plot(ax = ax, markersize = 20, color = "red",
marker = "^", label = "Pos")
plt.legend(prop={'size':15})
Based on your 1st image, it is possible to get the zoom-in plot by specifying proper x and y limits.
...
ax.set_ylim([40.4, 47.2])
ax.set_xlim([7.0, 14.4])
(Place this code before plt.legend().
Hope this is useful.
I'm trying to do a scatterplot and heatmap in the same plot.
The scatterplot is as follows:
examples, targets = zip(*list(blue_data()))
examples2, targets2 = zip(*list(red_data()))
plt.plot(np.array(examples), np.array(targets), 'o', color = 'b')
plt.plot(np.array(examples2), np.array(targets2), 'o', color = 'r')
The above works just fine. I also want to do a heatmap. It works just fine, if I do it in a separate subplot. However, I want to try to do both on the same plot, sharing the same axes and am unsure how to do it. This is the code for the heatmap:
x = np.linspace(lower_x, upper_x, 100)
y = np.linspace(lower_y, upper_y, 100)
X, Y = np.meshgrid(x, y)
Z = np.zeros((x.size,y.size))
for ii in range(len(x)):
for jj in range(len(y)):
X_ = X[ii,jj];
Y_ = Y[ii,jj];
Z[ii,jj] = some_function(X_,Y_)
cmap = mpl.colors.ListedColormap(['r', 'b'])
bounds = [-100,0,100]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
plt.imshow(Z,cmap= cmap, norm = norm, extent = [-lower_x,upper_x,-lower_y,upper_y], origin = 'lower');
How can I combine the two plots so that they appear on the same plot with aligned axes?