I want to change the range in terms of %. In the attached figure along x-axis, I want to label it as -1%, -0.05%, 0, 0.05% and 1% along x-axis. Is there any way to do that directly in python using range function?
ax.set_xlim(-0.012, 0.012, 0.2)
You can modify the tick labels as shown below
fig, ax = plt.subplots()
x = np.linspace(-0.01, 0.01, 10)
ax.plot(x, -x/10, '-bo')
ax.set_xlim(-0.012, 0.012, 0.2)
labels = ['{:.2f}%'.format(item*100) for item in ax.get_xticks()]
ax.set_xticklabels(labels)
Related
Data: three 1d lists for x y z
When I make a scatter plot for x and y, it looks like this.
I wanted to make a contour plot but when I applied the contourf function, I got this plot.
Contourf function filled all the areas where the data was not given.
To use Contourf function, I had to modify the data with the code below.
# 1d lists to grid data for contour
minx, maxx, miny, maxy = float(min(pos_list)), float(max(pos_list)), float(min(depth_list)), float(max(depth_list))
xs, ys = np.linspace(minx, maxx, 100), np.linspace(miny, maxy, 100)
xgrid, ygrid = np.meshgrid(xs, ys)
# interpolate
rbf = scipy.interpolate.Rbf(pos_list, depth_list, num_list, function='linear')
zi = rbf(xgrid, ygrid)
############################
# figure
fig, ax = plt.subplots(nrows = 1, ncols= 1, figsize=(9, 6), facecolor="white")
im = ax.contourf(xgrid, ygrid, zi, 30, cmap="RdBu_r", corner_mask = False)
fig.colorbar(im, orientation='vertical')
ax.invert_yaxis()
#ax.scatter(pos_list, depth_list, s= 0.1, alpha=0.4, color="black")
plt.show()
I tried tricontourf as I can simply put three lists as variables but the contour was not I expected.
I want to stick to contourf function. What was wrong with my code above?
just two quick questions I am struggeling with.
1. How can I scale the PercentFormatter, I want to set the y axis from 0 to 100 percent. I could do it without the PercentFormatter, but there must be a way including it, right?
2. The saveplot method cuts the x labels. How can i prevent that?
fig, ax = plt.subplots()
ax.bar(df['ErrorNames'], df["Frequency"], color="C0")
ax.set_xticklabels(df['ErrorNames'], rotation=90 ) ;
ax.set_ylabel('Error Frequency')
ax2 = ax.twinx()
ax2.set_ylabel('Accumulated Percentage of Frequency')
ax2.plot(df['ErrorNames'], df["cumpercentage"], color="C1", marker="D", ms=7)
ax2.yaxis.set_major_formatter(PercentFormatter())
ax.tick_params(axis="y", colors="C0")
ax2.tick_params(axis="y", colors="C1")
plt.savefig('Pareto')
plt.show()
Pareto Chart
I am trying to make a 2x2 subplot, with each of the inner subplots consisting of two x axes and two y axes; the first xy correspond to a linear scale and the second xy correspond to a logarithmic scale. Before assuming this question has been asked before, the matplotlib docs and examples show how to do multiple scales for either x or y but not both. This post on stackoverflow is the closest thing to my question, and I have attempted to use this idea to implement what I want. My attempt is below.
Firstly, we initialize data, ticks, and ticklabels. The idea is that the alternate scaling will have the same tick positions with altered ticklabels to reflect the alternate scaling.
import numpy as np
import matplotlib.pyplot as plt
# xy data (global)
X = np.linspace(5, 13, 9, dtype=int)
Y = np.linspace(7, 12, 9)
# xy ticks for linear scale (global)
dtick = dict(X=X, Y=np.linspace(7, 12, 6, dtype=int))
# xy ticklabels for linear and logarithmic scales (global)
init_xt = 2**dtick['X']
dticklabel = dict(X1=dtick['X'], Y1=dtick['Y']) # linear scale
dticklabel['X2'] = ['{}'.format(init_xt[idx]) if idx % 2 == 0 else '' for idx in range(len(init_xt))] # log_2 scale
dticklabel['Y2'] = 2**dticklabel['Y1'] # log_2 scale
Borrowing from the linked SO post, I will plot the same thing in each of the 4 subplots. Since similar methods are used for both scalings in each subplot, the method is thrown into a for-loop. But we need the row number, column number, and plot number for each.
# 2x2 subplot
# fig.add_subplot(row, col, pnum); corresponding iterables = (irows, icols, iplts)
irows = (1, 1, 2, 2)
icols = (1, 2, 1, 2)
iplts = (1, 2, 1, 2)
ncolors = ('red', 'blue', 'green', 'black')
Putting all of this together, the function to output the plot is below:
def initialize_figure(irows, icols, iplts, ncolors, figsize=None):
""" """
fig = plt.figure(figsize=figsize)
for row, col, pnum, color in zip(irows, icols, iplts, ncolors):
ax1 = fig.add_subplot(row, col, pnum) # linear scale
ax2 = fig.add_subplot(row, col, pnum, frame_on=False) # logarithmic scale ticklabels
ax1.plot(X, Y, '-', color=color)
# ticks in same positions
for ax in (ax1, ax2):
ax.set_xticks(dtick['X'])
ax.set_yticks(dtick['Y'])
# remove xaxis xtick_labels and labels from top row
if row == 1:
ax1.set_xticklabels([])
ax2.set_xticklabels(dticklabel['X2'])
ax1.set_xlabel('')
ax2.set_xlabel('X2', color='gray')
# initialize xaxis xtick_labels and labels for bottom row
else:
ax1.set_xticklabels(dticklabel['X1'])
ax2.set_xticklabels([])
ax1.set_xlabel('X1', color='black')
ax2.set_xlabel('')
# linear scale on left
if col == 1:
ax1.set_yticklabels(dticklabel['Y1'])
ax1.set_ylabel('Y1', color='black')
ax2.set_yticklabels([])
ax2.set_ylabel('')
# logarithmic scale on right
else:
ax1.set_yticklabels([])
ax1.set_ylabel('')
ax2.set_yticklabels(dticklabel['Y2'])
ax2.set_ylabel('Y2', color='black')
ax1.tick_params(axis='x', colors='black')
ax1.tick_params(axis='y', colors='black')
ax2.tick_params(axis='x', colors='gray')
ax2.tick_params(axis='y', colors='gray')
ax1.xaxis.tick_bottom()
ax1.yaxis.tick_left()
ax1.xaxis.set_label_position('top')
ax1.yaxis.set_label_position('right')
ax2.xaxis.tick_top()
ax2.yaxis.tick_right()
ax2.xaxis.set_label_position('top')
ax2.yaxis.set_label_position('right')
for ax in (ax1, ax2):
ax.set_xlim([4, 14])
ax.set_ylim([6, 13])
fig.tight_layout()
plt.show()
plt.close(fig)
Calling initialize_figure(irows, icols, iplts, ncolors) produces the figure below.
I am applying the same xlim and ylim so I do not understand why the subplots are all different sizes. Also, the axis labels and axis ticklabels are not in the specified positions (since fig.add_subplot(...) indexing starts from 1 instead of 0.
What is my mistake and how can I achieve the desired result?
(In case it isn't clear, I am trying to put the xticklabels and xlabels for the linear scale on the bottom row, the xticklabels and xlabels for the logarithmic scale on the top row, the 'yticklabelsandylabelsfor the linear scale on the left side of the left column, and the 'yticklabels and ylabels for the logarithmic scale on the right side of the right column. The color='black' kwarg corresponds to the linear scale and the color='gray' kwarg corresponds to the logarithmic scale.)
The irows and icols lists inn the code do not serve any purpose. To create 4 subplots in a 2x2 grid you would loop over the range(1,5),
for pnum in range(1,5):
ax1 = fig.add_subplot(2, 2, pnum)
This might not be the only problem in the code, but as long as the subplots aren't created correctly it's not worth looking further down.
I'm doing some cluster analysis and want to use matplotlib to visualise the results. For the most part, this is working out OK. However, I'm struggling with controlling tick placement on the axes. That is, the ticks on the y axis are overcrowded and I'd like to thin them out. I've tried supplying a range for the ticks using the numpy arrange function, but this isn't working.
I don't know if this is because I'm not familiar enough with matplotlib, or if it's an issue with 3-D plotting. In any event, I've tried all the solutions I can find on Stack and nothing seems to be working.
My code:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data['col_1'], data['col_2'], data['col_3'], c = data.index, cmap = cm.winter, s=60)
ax.view_init(15, 240)
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis')
ax.set_zlabel('Z- Axis')
plt.title('Sample Plot')
plt.show()
My solution to this is to supply the ticks as follows:
ticks = np.arange(0.3, 0.7, 0.02)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data['col_1'], data['col_2'], data['col_3'], c = data.index, cmap = cm.winter, s=60)
ax.view_init(15, 240)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_zticks(ticks)
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis')
ax.set_zlabel('Z- Axis')
plt.title('Sample Bad Plot')
plt.show()
However, this only produces the hot mess below. Any help to be had?
The problem is that your x-values lie approximately within the range 0.54-0.68, your y-values lie within the range 0.34-0.42 and your z-values lie within the range0.55-0.63. Now in your second code, you define ticks = np.arange(0.3, 0.7, 0.02) which creates ticks from 0.3 to 0.68 and then you assign these values to be displayed on x, y, z axis using ax.set_xticks(ticks) and so on. You get this mess because your supplied ticks values are outside the range of actual x, y, z data points. Since you are only interested in refining the y axis ticks, you can just do
ticks = np.arange(0.34, 0.44, 0.02)
and then just set the ticks for the y axis as
ax.set_yticks(ticks).
If you don't want to specify the numbers 0.34 and 0.44 manually, you can find the maximum and minimum y value and use something like ticks = np.arange(min_value, max_value, 0.02).
Since I do not have access to your original data data['col_1'] and so on, I can't play with your code but the above tips will surely help.
I want to plot a curve on a semilogx scale in Matplotlib. I have two vectors fpr and tpr of size 17874. I want to show markers on the same curve. But since there are too many points, I used markevery=0.1, as shown e.g. in this example from the matplotlib page. However, using markevery in this case did not have any markers in the semilog plot (right panel):
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 2, 1)
plt.plot(fpr, tpr, marker='o', markevery=0.1)
ax = fig.add_subplot(1, 2, 2)
plt.semilogx(fpr, tpr, marker='o', markevery=0.1)
plt.show()
Then, I tried to use slice object. As you can see in the plot below, the markers in the left plot are evenly distributed, but in the right semilogx plot, the markers are only shown in half of the curve. So I am wondering if there is any way to have a variable slice interval that can fix this issue.
My curent code is the following:
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 2, 1)
plt.plot(fpr, tpr, marker='o', markevery=slice(0, 20000, 1000))
ax = fig.add_subplot(1, 2, 2)
plt.semilogx(fpr, tpr, marker='o', markevery=slice(0, 20000, 1000))
plt.show()
My suspicion would be that the value in the data which seems close to zero actually is zero. Since the logarithm of zero is undefined, that point cannot be plotted on a logarithmic scale. Missing the first point in the graph though would not allow to calculate the spacings for markevery. (Just as in "Calculate the mean of NaN and 1" , which is impossible.)
A solution is of course to leave out that point, which cannot be plotted anyways when calling the plot function for a log scale.
plt.semilogx(fpr[1:], tpr[1:], marker='o', markevery=0.1)