I developed a python script that plots data from netcdf files. The problem is that I try to modify the title of the plot ( and in other cases I also tried to change x axis title with ax.set_xlabel('etc') ) but python seems not answering.
The data are extracted using xarray and then are plotted using matplotlib.
This is my code:
from os.path import expanduser
home = expanduser("~") # Get users home directory
file = home + '/Desktop/Tesi/Triennial_EOFs/' # Adjust if necessary
path = file + 'normevectortime1905_1.nc'
mask = xr.open_dataset(path, decode_times=False) #extract variables in xarray from .nc file
in_variable='msl'
var = mask[in_variable] #variable to study
ax = plt.subplot(1,1,1, projection=proj)
ax.set_title('PLOT')
ax.set_xlabel('x axes_test')
ax.coastlines(resolution='50m',linewidth=0.4)
var[0,:,:].plot.contourf(transform=crs.PlateCarree(),levels=20)
plt.show()
This is my output
Moreover I never explicitly written code lines for the colorbar or the text related to the variable field near the colorbar. And I never even wrote instructions for written time on the top of the plot.
Related
I'm currently doing an internship remotely and I got to code a Visualization Tool with D3.JS but here is not the part where I got a problem.
To fix the subject I got some file called episodes, which contain data about the path of a robot, if he succeeds or failed and the different point in cartesian coordinate.
(BTW I'm French I apologize in advance if there is some grammar issues)
So I got a small Python program that interpret these data contained in these .p files here's the code :
import pickle
import matplotlib.pyplot as plt #PyQT is require or tkinter
import numpy as np
import cv2
#This script aligns the true position to position given by orbslam.
#Load episode with id. #This loads the dictionary containing all information about an episode.
trajectory_dir = "ORBSlam/"
episode_id = 0
episode = pickle.load(open( trajectory_dir+"episodeStats"+str(episode_id)+".p", "rb" ))
#Extract useful data from the dictionary
pose_env = episode["pose_env"]
images_RGB = episode["rgb"]
images_depth = episode["depth"]
actions_orb = episode["orb_action"]
actions_best = episode["best_action"]
goal_distances = episode["goal_distance"]
success = episode["success"]
#Save observations into images.
for i in range(len(images_RGB)):
cv2.imwrite( trajectory_dir+"RGBs"+str(episode_id)+"/"+str(i)+".png", images_RGB[i] )
cv2.imwrite( trajectory_dir+"Depths"+str(episode_id)+"/"+str(i)+".png", images_depth[i]*255 )
#Display 2D trajectories.
x_env = []
y_env = []
for i in range(len(pose_env)):
#add x,y coordinates of the translation
x_env.append(pose_env[i][0,3])
y_env.append(pose_env[i][2,3])
plt.plot(x_env,y_env)
plt.axis('equal')
plt.show()
The problem here is that during the loop where it's supposed to output png image, in fact there is no output in the folder and since it's a silent function I don't know what the error is, I created both folder in ORBSlam folder (RGBs and Depths), so do you think it's something about permissions of writing or something like that ? (I'm working on macOS)
Thanks in advance for all the responses.
EDIT : I find why I've got no output images, I simply forgot to create a folder with the name of episode_id (Basically 0,1,2,...) in Depths and RGBs folder, my bad it was a dumb mistake
Solved : a folder inside where missing to get the output
I am using this link to solve my doubts:
How to plot multiple lines on the same y-axis using Plotly Express in Python
I am trying to plot multiple lines in same plot. Also, I want to add data-labels ( Y-values) on every line that I plot.
Here is the code that I am using:
def plot_func(data):
fig = px.line(data, x="yyyymm", y="sells", color='model')
fig.show()
The resultant plot is:
I want to add y-values on every line. Also, if you notice my x axis is in format yyyymm which I have converted to string by appending _ at end otherwise plotly converts it to number. Is there a better approach to convert it to dates?
How can I add a vertical dash line where x=201912_ and put title to my plots?
I am very new to python and plotly.express, and I find it very confusing...
I am trying to use the principle of adding different traces to my figure, using example code shown here https://plotly.com/python/line-charts/, Line Plot Modes, #Create traces.
BUT I get my data from a .CSV file.
import plotly.express as px
import plotly as plotly
import plotly.graph_objs as go
import pandas as pd
data = pd.read_csv(r"C:\Users\x.csv")
fig = px.scatter(data, x="Time", y="OD", color="C-source", size="C:A 1 ratio")
fig = px.line(data, x="Time", y="OD", color="C-source")
fig.show()
The above lines produces scatter/line plots with the correct data, but the data is mixed together. I have data from 2 different sources marked by a column named "Strain" in my .csv file that I would like the chart to reflect.
Is the traces option a possible way to do it, or is there another way?
You can add traces using an Express plot by using .select_traces(). Something like:
fig.add_traces(
list(px.line(...).select_traces())
)
Note the need to convert to list, since .select_traces() returns a generator.
It looks like you probably want the lines with the scatter dots as well on a single plot?
You're setting fig to equal px.scatter() and then setting (changing) it to equal px.line(). When set to line, the scatter plot is overwritten.
You're already importing graph objects so you can use add_trace with go, something like this:
fig.add_trace(go.Scatter(x=data["Time"], y=data["OD"], mode='markers', marker=dict(color=data["C-source"], size=data["C:A 1 ratio"])))
Depending on how your data is set up, you may need to add each C-source separately doing something like:
x=data.query("C-source=='Term'")["Time"], ... , name='Term'`
Here's a few references with examples and options you can use to set up your scatter:
Scatter plot examples
Marker styles
Scatter arguments and attributes
You can use the apporach stated in Plotly: How to combine scatter and line plots using Plotly Express?
fig3 = go.Figure(data=fig1.data + fig2.data)
or a more convenient and scalable approach:
fig1.data and fig2.data are common tuples that hold all the info needed for a plot and the + just concatenates them.
# this will hold all figures until they are combined
all_figures = []
# data_collection: dictionary with Pandas dataframes
for df_label in data_collection:
df = data_collection[df_label]
fig = px.line(df, x='Date', y=['Value'])
all_figures.append(fig)
import operator
import functools
# now you can concatenate all the data tuples
# by using the programmatic add operator
fig3 = go.Figure(data=functools.reduce(operator.add, [_.data for _ in all_figures]))
fig3.show()
thanks for taking the time to help me out. I ended up with two solutions that worked, of which using "facet_col" to divide the plot into two subplots (1 for each strain) was the most simple solution.
https://plotly.com/python/axes/
Thanks. this worked for me also where Fig_Set_B is a list of scatter plots
# create a tuple of first line plots in first 6 plots from plot set Fig_Set_B`
fig_combined = go.Figure(data= tuple(Fig_Set_B[x].data[0] for x in range(6)) )
fig_combined.show()
I'm trying to write a simple program that reads in a CSV with various datasets (all of the same length) and automatically plots them all (as a Pandas Dataframe scatter plot) on the same figure. My current code does this well, but all the marker colors are the same (blue). I'd like to figure out how to make a colormap so that in the future, if I have much larger data sets (let's say, 100+ different X-Y pairings), it will automatically color each series as it plots. Eventually, I would like for this to be a quick and easy method to run from the command line. I did not have luck reading the documentation or stack exchange, hopefully this is not a duplicate!
I've tried the recommendations from these posts:
1)Setting different color for each series in scatter plot on matplotlib
2)https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.scatter.html
3) https://matplotlib.org/users/colormaps.html
However, the first one essentially grouped the data points according to their position on the x-axis and made those groups of data the same color (not what I want, each series of data is roughly a linearly increasing function). The second and third links seemed to have worked, but I don't like the colormap choices (e.g. "viridis", many colors are too similar and it's hard to distinguish data points).
This is a simplified version of my code so far (took out other lines that automatically named axes, etc. to make it easier to read). I've also removed any attempts I've made to specify a colormap, for more of a blank canvas feel:
''' Importing multiple scatter data and plotting '''
import pandas as pd
import matplotlib.pyplot as plt
### Data file path (please enter Dataframe however you like)
path = r'/Users/.../test_data.csv'
### Read in data CSV
data = pd.read_csv(path)
### List of headers
header_list = list(data)
### Set data type to float so modified data frame can be plotted
data = data.astype(float)
### X-axis limits
xmin = 1e-4;
xmax = 3e-3;
## Create subplots to be plotted together after loop
fig, ax = plt.subplots()
### Since there are multiple X-axes (every other column), this loop only plots every other x-y column pair
for i in range(len(header_list)):
if i % 2 == 0:
dfplot = data.plot.scatter(x = "{}".format(header_list[i]), y = "{}".format(header_list[i + 1]), ax=ax)
dfplot.set_xlim(xmin,xmax) # Setting limits on X axis
plot.show()
The dataset can be found in the google drive link below. Thanks for your help!
https://drive.google.com/drive/folders/1DSEs8D7lIDUW4NIPBl2qW2EZiZxslGyM?usp=sharing
I am trying to export an image generated in a Jupyter notebook using the H2O library to a PNG file. The image is the variable importance plot
I have tried using the matplotlib export functionality but it generates an empty PNG file.
cv_gbm.varimp_plot()
I don't know the direct solution to your question, but the following steps should work:
gbm_var_imp = cv_gbm._model_json['output']['variable_importances'].as_data_frame()
x = gbm_var_imp['scaled_importance']
x.index = gbm_var_imp['variable']
fig = plt.figure(figsize=(8, 8))
x.sort_values().plot(kind='barh')
fig.savefig('gbm_variable_importance.png', dpi=600)