def test_rel_abundance(mzs):
data = read_mzXML(get_mzXML_sample_path())
mz, intensities, times = data["mz"], data["intensities"], data["times"]
abun = get_relative_abundance(mz, intensities, mzs)
npt.assert_equal(abun.shape[0], len(mzs))
npt.assert_equal(abun.shape[1], intensities.shape[0])
def test_contourf_keep():
# Test different keep_ith_scan settings
# If we keep twice as many scans we don't exactly double the number of points
# in the contour plot b/c each scan doesn't have the same number of points.
# That being said it should be roughly double and we see that it is.
# Test default settings
data = read_mzXML(get_mzXML_sample_path())
mz, intensities, times = data["mz"], data["intensities"], data["times"]
X, Y, Z = contourf(mz, intensities, 50, 70, keep_ith_scan=2)
def test_contourf_def():
# Test default settings
data = read_mzXML(get_mzXML_sample_path())
mz, intensities, times = data["mz"], data["intensities"], data["times"]
n_scans = intensities.shape[0]
X, Y, Z = contourf(mz, intensities, 50, 70, keep_ith_scan=1)
npt.assert_equal(X.shape, Y.shape)
npt.assert_equal(Y.shape, Z.shape)
# print(intensities.shape, times.shape, X.shape)
npt.assert_equal(X.shape[1], times.size)
def test_read_mzXML():
data = read_mzXML(get_mzXML_sample_path())
mz, intensities, times = data["mz"], data["intensities"], data["times"]
npt.assert_equal(len(intensities.shape), 2)
npt.assert_equal(times.size, intensities.shape[0])
npt.assert_equal(mz.size, intensities.shape[1])
# mzXML_file = "/home/james/Downloads/20200228_1175.mzXML" # labview_file = "20200612_TP.csv" # mzXML_file = "20200612_2735.mzXML" # # Read CSV Data from LabView # cols = ["time", "b", "temp", "d", "e", "f", "g", "h"] df = pd.read_csv(labview_file, names=cols) df["time"] -= df["time"][0] last_lv_time = np.array(df["time"])[-1] # # Read in mzXML # data = read_mzXML(mzXML_file) mz, intensities, times = data["mz"], data["intensities"], data["times"] # Only go as far as LabView data (which we are assuming is always shut off after the mass spec) subset = np.where(times <= last_lv_time)[0] times = times[subset] intensities = intensities[subset] # # Use timestamps from mzXML and Labview to interpolate temperature for each scan # temp_interp = np.interp(times, df["time"], df["temp"]) # # Select a subset of MZ range and plot intensities as a contour plot # mz_lb, mz_ub = (130, 180)
"""
This example shows how to read a mzXML file and plot the first spectra using matplotlib.
Author: James E. T. Smith <*****@*****.**>
Date: 12/12/19
"""
import matplotlib.pyplot as plt
import seaborn as sns
from msanalysis.data_extraction import read_mzXML
from msanalysis.sample_data import get_mzXML_sample_path
#
# Read in CDF
#
data = read_mzXML(get_mzXML_sample_path())
mz, intensities, times = data["mz"], data["intensities"], data["times"]
#
# Plot
#
plt.figure()
sns.set_style("darkgrid")
plt.plot(mz, intensities[0], c="k")
plt.xlim((30, 125))
plt.xlabel("M/Z")
plt.ylabel("Intensity")
plt.tight_layout()
plt.savefig("figures/ex1.png", dpi=600)
