Plotting Example¶
This is written as a Jupyter Notebook inside the docs folder of the repository.
The Measurement class loads the calorimetry data. You need to pass a folder name. Ideally a pathlib Path object containing the correct path is passed to Measurement. By default, the Measurement will load all data files contained in the folder that is passed. If you want to only load a specific file, you can pass a regex filter (as shown in the example below).
The Measurement object has several methods. Here we use the plot method to create the heat flow plot. If you create an axis object before, the method will draw the plot onto the axis object if it is passed (see the example below). The axis object can then be modified with the methods of Matplotlib.
import matplotlib.pyplot as plt
from calocem.tacalorimetry import Measurement
from pathlib import Path
# here you need to define the suitable path to the data
# this reads a little cryptic because it is a relative path from the docs folder of the repo
datapath = Path().cwd().parent / "calocem" / "DATA"
tam = Measurement(datapath, regex=".*bm2.csv")
fig, ax = plt.subplots()
tam.plot(ax=ax)
ax.set_xlim(0, 30)
ax.set_ylim(0, 5)
ax.set_title("TACalorimetry Measurement")
plt.show()
Reading insitu_bm2.csv. ================ Are you missing some samples? Try rerunning with auto_clean=True and cold_start=True. =================
Folder Definition¶
If your folder looks like this, and your script is myscript.py
.
├── myscript.py
└── calo_data
├── calofile1.csv
└── calofile2.csv
then the pathlib definition in myscript.py would look like
from pathlib import Path
datapath = Path(__file__).parent / "calo_data"
Here in the example above, however, the Jupyter Notebook is located in a special location, therefore the path looks a bit cryptic.
Customizing the plot¶
Often we load more than one calorimetry experiment. In the following example, two files are loaded. Additionally, we want to plot the data such that we both see the very early heat development and the later heat development. We combine Matplotlib with CaloCem.
tam = Measurement(datapath, regex=".*bm.*")
plot_configs = [
{"ycol": "normalized_heat_flow_w_g", "xlim": 1, "ylim": 0.050},
{"ycol": "normalized_heat_flow_w_g", "xlim": 48, "ylim": 0.005},
{"ycol": "normalized_heat_j_g", "xlim": 1, "ylim": 30},
{"ycol": "normalized_heat_j_g", "xlim": 48, "ylim": 300},
]
fig, axs = plt.subplots(2, 2, layout="constrained")
for ax, config in zip(axs.flatten(), plot_configs):
tam.plot(y=config["ycol"], t_unit="h", y_unit_milli=False, ax=ax)
ax.set_xlim(0, config["xlim"])
ax.set_ylim(0, config["ylim"])
ax.get_legend().remove()
plt.show()
Reading insitu_bm2.csv. Reading insitu_bm.csv. ================ Are you missing some samples? Try rerunning with auto_clean=True and cold_start=True. =================
