I am using functions, e.x: ml.dis.top.plot(). I would like to rotate these figures and delete titles. How can I do that?
plt.title('') seems to work for titles but I cannot rotate these figures. Here is the part of the script:
fig = plt.figure(figsize=(75, 75))
plt.subplot(1,1,1,aspect='equal')
mf.dis.top.plot(contour=True, colorbar=True)
plt.title('')
plt.savefig('top_plot.png')
So there are different ways that you can make model plots in flopy. You are using the quick and easy way to plot one of our arrays. What you probably want to do is use the ModelMap capability, which is described in https://github.com/modflowpy/flopy/blob/develop/examples/Notebooks/flopy3_MapExample.ipynb. This will give you full control over your figure, including rotation and offset and will allow you to customize the title and anything else you'll need to do. The code might look something like the following:
fig = plt.figure(figsize=(75, 75))
ax = plt.subplot(1, 1, 1, aspect='equal')
modelmap = flopy.plot.ModelMap(model=mf, rotation=14)
modelmap.contour_array(mf.dis.top.array)
plt.savefig('top_plot.png')
Related
I have the result of a grid search as follows.
"trial","learning_rate","batch_size","accuracy","f1","loss"
1,0.000007,70,0.789,0.862,0.467
2,0.000008,100,0.710,0.822,0.563
3,0.000008,90,0.823,0.874,0.524
4,0.000007,90,0.833,0.878,0.492
5,0.000009,110,0.715,0.825,0.509
6,0.000006,90,0.883,0.885,0.932
7,0.000009,80,0.850,0.895,0.408
8,0.000006,110,0.683,0.812,0.593
9,0.000005,90,0.769,0.848,0.468
10,0.000005,80,0.816,0.868,0.462
11,0.000003,100,0.852,0.901,0.448
12,0.000004,100,0.705,0.818,0.512
13,0.000003,110,0.708,0.818,0.567
14,0.000002,90,0.683,0.812,0.552
15,0.000008,100,0.791,0.857,0.438
16,0.000006,110,0.683,0.812,0.604
17,0.000007,70,0.693,0.816,0.592
18,0.000005,110,0.830,0.883,0.892
19,0.000004,90,0.693,0.816,0.591
20,0.000008,70,0.696,0.818,0.570
I want to create a plot more or less similar to this using matplotlib. I know this is plotted using weights and biases but I cannot use that.
Though I don't care for the inference part. I just want the plot. I've been trying to do this using twinx but have not been successful. This is what I have so far.
from csv import DictReader
import matplotlib.pyplot as plt
trials = list(DictReader(open("hparams_trials.csv")))
trials = {f"trial_{trial['trial']}": [int(trial["batch_size"]),
float(trial["f1"]),
float(trial["loss"]),
float(trial["accuracy"]),
float(trial["learning_rate"])] for trial in trials}
items = ["batch_size", "f1", "loss", "accuracy", "learning_rate"]
host_y_values_index = 0
parts_y_values_indexes = [1, 2, 3, 4]
fig, host = plt.subplots(figsize=(8, 5)) # (width, height) in inches
fig.dpi = 300. # Figure resolution
# Removing extra spines
host.spines.top.set_visible(False)
host.spines.bottom.set_visible(False)
host.spines.right.set_visible(False)
# Creating subplots which share the same x axis.
parts = {index: host.twinx() for index in parts_y_values_indexes}
# Setting the limits of the host plot
host.set_xlim(0, len(trials["trial_1"]))
host.set_ylim(min([i[host_y_values_index] for i in trials.values()]),
max([i[host_y_values_index] for i in trials.values()]))
# Removing the extra spines from the other plots and setting y limits
for part in parts_y_values_indexes:
parts[part].spines.top.set_visible(False)
parts[part].spines.bottom.set_visible(False)
parts[part].set_ylim(min([trial[part] for trial in trials.values()]),
max([trial[part] for trial in trials.values()]))
# Colors of the trials
colors = ["gold", "lightcoral", "maroon", "springgreen", "cyan", "steelblue", "darkmagenta", "fuchsia", "crimson",
"lime", "mediumblue", "cadetblue", "dodgerblue", "olivedrab", "sandybrown", "bisque", "orangered", "black",
"rosybrown", "chocolate"]
# The plots
plots = []
# Plotting the trials. This is where I'm having problems with.
for index, trial in enumerate(trials):
plots.append(host.plot(items, trials[trial], color=colors[index], label=trial)[0])
# Creating the legend
host.legend(handles=plots, fancybox=True, loc='right', facecolor="snow", bbox_to_anchor=(1.02, 0.495), framealpha=1)
# Defining the positions of the spines.
spines_positions = [-104.85 * i for i in parts_y_values_indexes]
# Repositioning the spines
for part in parts_y_values_indexes:
parts[part].spines['right'].set_position(('outward', spines_positions[-part]))
# Adjust spacings around fig
fig.tight_layout()
host.grid(True)
# This is better than the one above but it appears on top of the legend.
# plt.grid(True)
plt.draw()
plt.show()
I'm having several problems with that code. First, I cannot place each value of a single trial based on a different spine and then connect them to one another. What I mean is that each trial has a batch size, an f1, a loss, accuracy and a learning rate. Each of those need to be plotted based on their own spine while connected to each other in that order. However, I cannot plot them based their dedicated spines and then connect them to one another to have a line plot per trial. Accordingly, for now I have placed everything in the host plot but I know that is wrong and have no idea what the correct approach is. Second problem, the ticks of the learning rate change. It gets shown as a range of 2 to 9 and then a 1e-6 appears at the top. I want to keep the original value. Third problem is probably part of the second one. The 1e-6 appears at the top right above the legend rather than above the spine for some reason. I'm struggling with resolving all three of these problems and would appreciate any help anyone can provide. If what I am doing is totally wrong, please help me in finding the correct solution. I'm somewhat going in circles here and haven't been able to find any working solutions so far.
I am trying to find a way to make an Axes object passthrough for events.
For context, I have a figure with 6 small subplots. Each of them responds to mouse motion events by displaying a cursor dot and text info where the user aims. I also made it so that clicking a subplot will make it as large as the figure for better visibility. When moving the mouse over invisible axes, event.inaxes will still point to that ax despite being set to invisible and that is what I would like to avoid.
Below is the MRE:
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.set_gid("ax1")
ax2.set_gid("ax2")
fig.show()
def on_movement(event):
"""Write on the figure in which `Axes`object the event happened."""
width, height = fig.bbox.bounds[2:]
x = event.x/width
y = event.y/height
text = ax.get_gid() if (ax := event.inaxes) is not None else "None"
fig.texts[:] = []
fig.text(x, y, s=text, transform=fig.transFigure, c="white", bbox=dict(fc="#0055AA", ec="black"))
fig.canvas.draw()
fig.canvas.mpl_connect("motion_notify_event", on_movement)
As expected, as you hover the mouse over ax1, the empty gap and ax2, you will see one of those three texts appear:
ax1.set_position((1/3, 1/3, 2/3, 2/3))
Same thing as I arbitrarily resize and move ax1 so that it is partly overlaps with ax2.
ax2.set_visible(False)
Now this is my problem. Invisible axes still trigger events. Is there a way to make some axes "transparent" to events? Obviously the usual technique of sorting all the cases in the callback does not work here.
Currently envisaged solutions:
ideally, finding a setting akin to zorder so that the "highest" axes gets the event.
ugly workaround: set the position of the invisible axes to ((0, 0, 1e-10, 1e-10)).
less ugly: working with figure coordinates to convert event.x, event.y into event.xdata, event.ydata for the only ax that I know is visible. Basically xdata1, ydata1 = ax1.transAxes.inverted().transform((event.x, event.y)) if event.inaxes is not None + see if there are edge cases.
The latter is already implemented and does work, so save your time if you want to write a reply using that approach. I'm mostly interested in an amazing one-liner that I would have missed, something like ax2.set_silenced(True).
Python 3.8.5
Matplotlib 3.1.3
Well, setting the appropriate zorder does work actually.
ax1.set_zorder(2)
ax2.set_zorder(1)
...
def on_movement(event):
...
fig.text(x, y, ..., zorder=1000)
...
I am trying to save a png image using the following commands:
fig = plt.figure(figsize=(14, 8))
ax1 = fig.add_subplot(221)
subplt1=(usub1_sfc-usub2_sfc).plot(vmin=-2.5e-2,vmax=2.5e-2,add_colorbar=False)
cb=plt.colorbar(subplt1,extend='both')
cb.ax.set_title('m/s', size=14)
cb.ax.tick_params(labelsize=12)
ax1.tick_params(labelsize=12)
ax1.set_xticks(np.arange(0,3500,500))
ax1.set_yticks(np.arange(0,2500,500))
#plt.xticks(fontsize=10)
#fig.colorbar(subplt1)
plt.title('USUBM$_{\mathrm{1km}}$ - USUBM$_{\mathrm{5km}}$')
plt.xlabel('nlon',fontsize=16)
plt.ylabel('nlat',fontsize=16)
ax2 = fig.add_subplot(222)
subplt2=(usub3_sfc-usub2_sfc).plot(vmin=-2.5e-2,vmax=2.5e-2,add_colorbar=False)
cb=plt.colorbar(subplt2,extend='both')
cb.ax.set_title(label='m/s', size=14)
cb.ax.tick_params(labelsize=12)
ax2.tick_params(labelsize=12)
ax2.set_xticks(np.arange(0,3500,500))
ax2.set_yticks(np.arange(0,2500,500))
plt.title('USUBM$_{\mathrm{200m}}$ - USUBM$_{\mathrm{5km}}$')
plt.xlabel('nlon',fontsize=16)
plt.ylabel('nlat',fontsize=16)
fig.savefig('./test.png',dpi=130)
My png file ends up having a checkerboard pattern everywhere around the bounding boxes of the plots. Inside the boxes I can see the fields, but everywhere around it the checkerboard pattern covers the axis ticks, axis labels, plot titles, etc.
The file I create looks very much like the third image at this link. The only difference is that there you see the checkerboard everywhere.
Question: How to save the png image without this checkerboard pattern?
Here is the answer to my original question (based on the other thread I linked to):
fig = plt.figure(facecolor="w")
This removed the checkerboard pattern surrounding the plotted area.
While using model.plot(forecast) the figure that appears has small labels and x/y values.
Is there any way to customize customize prophet's figures ?
I found there is a way to customize the options in fbprophet such as increasing size of labels or tick values.
fig = model.plot(forecast, xlabel='Date', ylabel='Value')
ax = fig.gca()
ax.set_title("Title", size=34)
ax.set_xlabel("X", size=34)
ax.set_ylabel("Y", size=34)
ax.tick_params(axis="x", labelsize=24)
ax.tick_params(axis="y", labelsize=24)
The Prophet.plot is quite limiting.
However, the fbprophet.plot.plot function allows the option of specifying the figure size. The default is (10, 6) e.g.
from fbprophet.plot import plot
plot(model, forecast, figsize=(20, 12))
I want to plot many subplots in one figure using healpy. How to:
Set the position of the colorbar?
Set the tick and ticklabel of colorbar?
Set the position and size of the subplots?
I want to generate a plot such as figure 1, which is plotted in MATLAB, based on general coordinates
Right now, I only plot it as follows using healpy:
A similar code to produce figure 3 (similar to figure2) is as follow:
import numpy as np
import healpy as hp
degree = 4
nside = 2**degree
num_Pixel = hp.nside2npix(nside)
m = np.arange(num_Pixel)
margins = [[0.02,0,0,0],[0.01,0,0,0],[0.01,0,0.01,0],
[0.02,0,0,0],[0.01,0,0,0],[0.01,0,0.01,0],
[0.02,0.05,0,0],[0.01,0.05,0,0],[0.01,0.05,0.01,0]]
title = [
'Equinox', 'Jun. Solstice', 'Dec. Solstice',
'','','','','','']
for ifig in range(1,10):
if ifig < 7:
hp.cartview(
m, sub=330+ifig, margins=margins[ifig1],
cbar=False, title=title[ifig-1])
else:
hp.cartview(
m, sub=330+ifig, margins=margins[ifig-1],
cbar=True, title=title[ifig-1])
The code produced the figure 3
I'm afraid healpy doesn't come with a good way to handle the colorbar, ticks, ticklabels, the axes etc.
The best way forward would be to generate FITS images, based on your HEALPix map (e.g. using hp.cartview(..., return_projected_map=True) or using the reproject package).
You also need to generate the right FITS header for that, astropy would be the right tool for that (how-to manipulate FITS headers).
Once you have that, you can use the excellent WCSAxes framework within astropy, which gives you plenty of well-documented customization options.