python - map x, y, z values to 2D surface data
I have the following data set:
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
x and y is the location of the sample data point on a 2D surface.
z is the value of the data sample at the location.
Essentially I want something something like this:
However, as you can see, z values are not yet mapped into 2D grid format.
This is NOT what I want:
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
All the examples I found online calculates z matrix assuming some contour plot, but that's not the case for me. In my case, z is 1D array that contains the, say, gold percentage of a rock at the sample locations, and x and y accounts for the location of that sample.
How can I convert the z array into 2D matrix that accounts for the location of the sample?
At the end, I want to make a scatter plot using the 2D transformed_z matrix.
random_sample = transformed_z[x,y]
ax.scatter(y,x,c=transformed_z, cmap=im.cmap, norm=im.norm)
python arrays matplotlib
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
add a comment |
I have the following data set:
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
x and y is the location of the sample data point on a 2D surface.
z is the value of the data sample at the location.
Essentially I want something something like this:
However, as you can see, z values are not yet mapped into 2D grid format.
This is NOT what I want:
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
All the examples I found online calculates z matrix assuming some contour plot, but that's not the case for me. In my case, z is 1D array that contains the, say, gold percentage of a rock at the sample locations, and x and y accounts for the location of that sample.
How can I convert the z array into 2D matrix that accounts for the location of the sample?
At the end, I want to make a scatter plot using the 2D transformed_z matrix.
random_sample = transformed_z[x,y]
ax.scatter(y,x,c=transformed_z, cmap=im.cmap, norm=im.norm)
python arrays matplotlib
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
add a comment |
I have the following data set:
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
x and y is the location of the sample data point on a 2D surface.
z is the value of the data sample at the location.
Essentially I want something something like this:
However, as you can see, z values are not yet mapped into 2D grid format.
This is NOT what I want:
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
All the examples I found online calculates z matrix assuming some contour plot, but that's not the case for me. In my case, z is 1D array that contains the, say, gold percentage of a rock at the sample locations, and x and y accounts for the location of that sample.
How can I convert the z array into 2D matrix that accounts for the location of the sample?
At the end, I want to make a scatter plot using the 2D transformed_z matrix.
random_sample = transformed_z[x,y]
ax.scatter(y,x,c=transformed_z, cmap=im.cmap, norm=im.norm)
python arrays matplotlib
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
I have the following data set:
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
x and y is the location of the sample data point on a 2D surface.
z is the value of the data sample at the location.
Essentially I want something something like this:
However, as you can see, z values are not yet mapped into 2D grid format.
This is NOT what I want:
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
All the examples I found online calculates z matrix assuming some contour plot, but that's not the case for me. In my case, z is 1D array that contains the, say, gold percentage of a rock at the sample locations, and x and y accounts for the location of that sample.
How can I convert the z array into 2D matrix that accounts for the location of the sample?
At the end, I want to make a scatter plot using the 2D transformed_z matrix.
random_sample = transformed_z[x,y]
ax.scatter(y,x,c=transformed_z, cmap=im.cmap, norm=im.norm)
python arrays matplotlib
python arrays matplotlib
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
edited Nov 26 '18 at 10:35
SpghttCd
4,8672314
4,8672314
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
asked Nov 26 '18 at 0:10
Eric KimEric Kim
539418
539418
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
IIUC you just want to plot the values in z as a color coded scatterplot with xand yas coordinates.
You do not need to transform z for this purpose, this can be done purely with the given three arrays as they are:
import matplotlib.pyplot as plt
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
plt.figure()
plt.scatter(x, y, c=z, cmap='Wistia')
cb = plt.colorbar()
cb.set_label('gold percentage of a rock (%)')
plt.xlabel('X')
plt.ylabel('Y')
for xt, yt, zt in zip(x, y, z):
plt.text(xt, yt+1, str(zt), ha='center')
I've added the values as text for faster comparison with the arrays.
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
add a comment |
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1 Answer
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oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
IIUC you just want to plot the values in z as a color coded scatterplot with xand yas coordinates.
You do not need to transform z for this purpose, this can be done purely with the given three arrays as they are:
import matplotlib.pyplot as plt
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
plt.figure()
plt.scatter(x, y, c=z, cmap='Wistia')
cb = plt.colorbar()
cb.set_label('gold percentage of a rock (%)')
plt.xlabel('X')
plt.ylabel('Y')
for xt, yt, zt in zip(x, y, z):
plt.text(xt, yt+1, str(zt), ha='center')
I've added the values as text for faster comparison with the arrays.
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
add a comment |
IIUC you just want to plot the values in z as a color coded scatterplot with xand yas coordinates.
You do not need to transform z for this purpose, this can be done purely with the given three arrays as they are:
import matplotlib.pyplot as plt
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
plt.figure()
plt.scatter(x, y, c=z, cmap='Wistia')
cb = plt.colorbar()
cb.set_label('gold percentage of a rock (%)')
plt.xlabel('X')
plt.ylabel('Y')
for xt, yt, zt in zip(x, y, z):
plt.text(xt, yt+1, str(zt), ha='center')
I've added the values as text for faster comparison with the arrays.
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
add a comment |
IIUC you just want to plot the values in z as a color coded scatterplot with xand yas coordinates.
You do not need to transform z for this purpose, this can be done purely with the given three arrays as they are:
import matplotlib.pyplot as plt
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
plt.figure()
plt.scatter(x, y, c=z, cmap='Wistia')
cb = plt.colorbar()
cb.set_label('gold percentage of a rock (%)')
plt.xlabel('X')
plt.ylabel('Y')
for xt, yt, zt in zip(x, y, z):
plt.text(xt, yt+1, str(zt), ha='center')
I've added the values as text for faster comparison with the arrays.
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
IIUC you just want to plot the values in z as a color coded scatterplot with xand yas coordinates.
You do not need to transform z for this purpose, this can be done purely with the given three arrays as they are:
import matplotlib.pyplot as plt
x = [50.0, 55.0, 6.6, 35.0, 32.7, 33.2, 14.9, 60.0, 44.0, 38.1]
y = [50.0, 25.0, 47.4, 34.9, 56.3, 78.4, 81.9, 73.4, 46.8 ,65.6]
z = [0.3, -1.5, 0.1, 1.0, 1.9, -0.1, -0.4, -0.1, 0.3, -0.0]
plt.figure()
plt.scatter(x, y, c=z, cmap='Wistia')
cb = plt.colorbar()
cb.set_label('gold percentage of a rock (%)')
plt.xlabel('X')
plt.ylabel('Y')
for xt, yt, zt in zip(x, y, z):
plt.text(xt, yt+1, str(zt), ha='center')
I've added the values as text for faster comparison with the arrays.
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
edited Nov 26 '18 at 8:08
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
answered Nov 26 '18 at 8:00
SpghttCdSpghttCd
4,8672314
4,8672314
add a comment |
add a comment |
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