Scatter points assigned colour from CSV file - colors

Question

I am importing CSV data in the format x,y,z,p to plot a trisurface which has the scatter plots displayed on top.
The trisurface script works (ax.plot_trisurf), however, I would like to colour the scatter points (ax.scatter) according to either the 1 or -1 assigned in the fourth column of the CSV file.
enter image description here
The x,y,z data is complicated and can't be coloured, hence trying to assign it as simply as possible in the fourth column.
I have attached a basic image, essentially I just want to be able to have a selection of the red dots a different colour without affecting the trisurface they are on.
Any comments or suggestions are be very welcome!
My most recent error is:
ax.scatter(X, Y, np.log10(Z), c= (not p <= (0)({True: 'g', False: 'r'})), marker='o')
TypeError: 'int' object is not callable
enter code here
from typing import Any
import matplotlib
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
import csv
import bbox
import matplotlib.ticker as mticker
# Import CSV data
from numpy import ndarray
csvFileName = sys.argv[0]
csvData = []
with open('ParvaluesMESS.csv', 'r') as csvfile:
csvReader = csv.reader(csvfile, delimiter=',')
for csvRow in csvReader:
csvData.append(csvRow)
csvData = np.array(csvData)
csvData = csvData.astype(float)
X, Y, Z, p = csvData[:,0], csvData[:,1], csvData[:,2], csvData[:,3]
# Plot management: note Z is logged
# Change vmin and vmax values for colorbar range if needed
# Alpha value is transparency
# 111 means 1x1 grid, first subplot
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111, projection='3d')
cb = ax.plot_trisurf(X, Y, np.log10(Z), cmap='coolwarm', alpha=0.75)
#cb = ax.plot_trisurf(X, Y, np.log10(Z), cmap='coolwarm', alpha=0.75, vmin=0, vmax=1)
ax.scatter(X, Y, np.log10(Z), col==(p > 0({True: 'g', False: 'r'})), marker='o')
#ax.zaxis._set_scale('log')
def log_tick_formatter(val, pos=None):
"""Reformat log ticks for display"""
return f"$10^{{{int(val)}}}$"
# Set Z axis to log
ax.zaxis.set_major_formatter(mticker.FuncFormatter(log_tick_formatter))
# ax.zaxis.set_major_locator(mticker.MaxNLocator(integer=True))
def ticklabels(ticks):
ticks_labels = []
for i in ticks:
ticks_labels.append(f'2^{np.log2(i)}')
return ticks_labels
fig.colorbar(cb, shrink=0.5)
ax.set_title("First-year sea ice PAR")
ax.set_xlabel("Ice Thickness m")
ax.set_ylabel("Snow thickness m")
ax.set_zlabel("µmol $^{m-2}$ $^{s-1}$")
ax.view_init(azim=70, elev=30)
ax.set_xlim3d(20, 350)
image_format = 'png' # e.g .png, .svg, etc.
image_name = 'test.eps'
plt.show()
fig.savefig(image_name, format=image_format, dpi=1200)

Answer 1

it was resolved by rearranging into arrays:
from typing import Any
import matplotlib
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
import matplotlib.ticker as mticker
from numpy import ndarray
data=pd.read_csv('ParvaluesMESS.csv',header=None,sep=',',names=.
['Ice','Snow','umol','P'])
x=data[['Ice']].to_numpy()
y=data[['Snow']].to_numpy()
z=data[['umol']].to_numpy()
p=data[['P']].to_numpy()
x=(x.astype(float)).flatten()
y=(y.astype(float)).flatten()
z=(z.astype(float)).flatten()
p=(p.astype(float)).flatten()
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111, projection='3d')
cb = ax.plot_trisurf(x, y, np.log10(z), cmap='coolwarm', alpha=0.75)
ax.scatter(x, y, np.log10(z),c=p,cmap='RdYlGn')
#your formats
def log_tick_formatter(val, pos=None):
"""Reformat log ticks for display"""
return f"$10^{{{int(val)}}}$"
# Set Z axis to log
ax.zaxis.set_major_formatter(mticker.FuncFormatter(log_tick_formatter))
# ax.zaxis.set_major_locator(mticker.MaxNLocator(integer=True))
def ticklabels(ticks):
ticks_labels = []
for i in ticks:
ticks_labels.append(f'2^{np.log2(i)}')
return ticks_labels
fig.colorbar(cb, shrink=0.5)
ax.set_title("First-year sea ice PAR")
ax.set_xlabel("Ice Thickness m")
ax.set_ylabel("Snow thickness m")
ax.set_zlabel("µmol $^{m-2}$ $^{s-1}$")
ax.view_init(azim=70, elev=30)
ax.set_xlim3d(20, 350)
image_name = 'BenImag'
image_format = 'png' # e.g .png, .svg, etc.
plt.show()
fig.savefig(image_name, format=image_format, dpi=1200)