1D CSDM objects with plot()|scatter()

1D{1} datasets

import matplotlib.pyplot as plt
import numpy as np

import csdmpy as cp

# Create a test 1D{1} dataset. ================================================

# Step-1: Create dimension objects.
x = cp.as_dimension(np.arange(10) * 0.1 + 15, unit="s", label="t1")

# Step-2: Create dependent variable objects.
y = cp.as_dependent_variable(np.random.rand(10), unit="cm", name="test-0")

# Step-3: Create the CSDM object with Dimension and Dependent variable objects.
csdm = cp.CSDM(dimensions=[x], dependent_variables=[y])


# Plot ========================================================================
plt.figure(figsize=(5, 3.5))
# create the axes with `projection="csdm"`
ax = plt.subplot(projection="csdm")
# use matplotlib plot function with csdm object.
ax.plot(csdm)
plt.tight_layout()
plt.show()

(Source code, png, hires.png, pdf)

# Scatter =====================================================================
plt.figure(figsize=(5, 3.5))
# create the axes with `projection="csdm"`
ax = plt.subplot(projection="csdm")
# use matplotlib plot function with csdm object.
ax.scatter(csdm, marker="x", color="red")
plt.tight_layout()
plt.show()

(png, hires.png, pdf)

1D{1, 1, …} datasets

Plotting on the same Axes

When multiple single-component dependent variables are present within the CSDM object, the data from all dependent-variables is plotted on the same axes. The name of each dependent variable is displayed within the legend.

Plotting on separate Axes

To plot the data from individual dependent variables onto separate axes, use the split() method to first split the CSDM object with n dependent variables into n CSDM objects with single dependent variables, and then plot them separately.

import matplotlib.pyplot as plt
import numpy as np

import csdmpy as cp

# Create a test 1D{1, 1, 1, 1, 1} dataset. ====================================

# Step-1: Create dimension objects.
x = cp.as_dimension(np.arange(40) * 0.5 - 10, unit="µm", label="x")

# Step-2: Create dependent variable objects.
units = ["cm", "s", "m/s", ""]
y = [
    cp.as_dependent_variable(np.random.rand(40) + 10, unit=units[i], name=f"test-{i}")
    for i in range(4)
]

# Step-3: Create the CSDM object with Dimension and Dependent variable objects.
csdm = cp.CSDM(dimensions=[x], dependent_variables=y)


# Plot ========================================================================
plt.figure(figsize=(5, 3.5))
# create the axes with `projection="csdm"`
ax = plt.subplot(projection="csdm")
# use matplotlib plot function with csdm object.
ax.plot(csdm)
plt.title("Data plotted on the same figure")
plt.tight_layout()
plt.show()

(Source code, png, hires.png, pdf)

# The plot on separate axes ===================================================

# Split the CSDM object into multiple single dependent-variable CSDM objects.
sub_type = csdm.split()

# create the axes with `projection="csdm"`
_, ax = plt.subplots(2, 2, figsize=(8, 6), subplot_kw={"projection": "csdm"})
# now use matplotlib plot function with csdm object.
ax[0, 0].plot(sub_type[0])
ax[0, 1].plot(sub_type[1])
ax[1, 0].plot(sub_type[2])
ax[1, 1].plot(sub_type[3])
plt.title("Data plotted separately")
plt.tight_layout()
plt.show()

(png, hires.png, pdf)