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experiment_notes.py
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experiment_notes.py
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def heating_curve(time_series, temperature_series, TIME_UNIT):
'''
Plot the heating curve.
Author: Wenjie Chen
E-mail: wenjiechen@pku.edu.cn
args:
time_series : [numpy.array] A serie of time in minutes.
temperature_series : [numpy.array] A serie of time in Celsius.
TIME_UNIT : [string] Either 'h' or 'min', change the unit of time axis.
returns:
A figure.
example:
heating_curve(t_series, T_series, 'h')
'''
# data preparation
import numpy as np
import matplotlib.pyplot as plt
t_series = time_series.copy()
T_series = temperature_series.copy()
for i in range(len(t_series) - 1):
t_series[i + 1] = t_series[i + 1] + t_series[i]
scale = {'min' : 1, 'h' : 60, 'd' : 1440}
t_series = t_series / scale[TIME_UNIT]
t_series_str = tuple([str(t) for t in np.round(t_series, 2)])
T_series_str = tuple([str(T) for T in T_series])
plt.figure(figsize = (10, 5))
# plot the reference line
T_pool = [] # check if the horizental line has already been plot
for i in range(len(t_series) - 2):
plt.plot([t_series[i + 1], t_series[i + 1]], [T_series[i + 1], min(T_series)], 'k--')
if T_series[i + 1] in T_pool:
continue
else:
plt.plot([t_series[i + 1], min(t_series)], [T_series[i + 1], T_series[i + 1]], 'k--')
T_pool.append(T_series[i + 1])
# plot the curve
plt.plot(t_series, T_series, 'k', linewidth = 3)
# set up the axis
plt.xticks(t_series, t_series_str)
plt.yticks(T_series, T_series_str)
plt.xlim(min(t_series), max(t_series))
plt.ylim(bottom = min(T_series))
plt.xlabel('time / ' + TIME_UNIT)
plt.ylabel('temperature / $^\circ$C')
plt.title('Heating Curve')
plt.show()
return
def reaction_substances_mass(molar_mass, molar_ratio, mass_needed):
'''
Calculate the needed substantces' masses for a certain reaction.
Author: Wenjie Chen
E-mail: wenjiechen@pku.edu.cn
args:
molar_mass : [numpy.array] An array of molar_mass (the reaction product should be in the first place).
molar_ratio : [numpy.array] An array of molar_mass (should be the same order as molar_mass).
mass_needed : [double] The needed mass for the reaction product.
returns:
masses_needed : [numpy.array] The calculated masses for the substances.
example:
molar_mass = np.array([387.44, 105.99, 240.79, 159.60])
molar_ratio = np.array([3, 3, 2, 3])
print(reaction_substances_mass(molar_mass, molar_ratio, 27.0))
'''
import numpy as np
masses = molar_mass * molar_ratio
masses_needed = mass_needed / masses[0] * masses
masses_needed = np.round(masses_needed, 4)
return masses_needed[1:]
def XRD_scan_reader(FILEPATH):
'''
Read the XRD scanning data from the txt file.
Author: Wenjie Chen
E-mail: wenjiechen@pku.edu.cn
args:
FILEPATH : [string] The path of the data file.
returns:
(tt, counts) : [tupple] Two numpy arrays of data.
example:
(tt, counts) = XRD_scan_reader("./data/templateDataFile.txt")
'''
import numpy as np
import csv
datablock = []
# read data
with open(FILEPATH, newline='') as data_file:
data_text = data_file.read()
data_csv = data_text.split("[Data]")[0]
spamreader = csv.reader(data_csv.splitlines(), delimiter=',', quotechar='|')
line_num = 0
for row in spamreader:
if line_num == 0:
print(f'Column names are {", ".join(row)}')
else:
datablock.append([float(row[0]), float(row[1])])
line_num = line_num + 1
print(f'Processed {line_num} lines.')
# process data
datablock = np.transpose(np.array(datablock))
return (datablock[0], datablock[1])
def XRD_scan_curve(tt, counts):
'''
Plot the XRD curve.
Author: Wenjie Chen
E-mail: wenjiechen@pku.edu.cn
args:
tt : [numpy.array] 2theta data.
counts : [numpy.array] Counts data.
returns:
A figure.
example:
XRD_scan_curve(tt, counts)
'''
import matplotlib.pyplot as plt
plt.figure(figsize = (14, 7))
plt.plot(tt, counts)
plt.xlabel('$2 \\theta$ / degree')
plt.ylabel('counts')
plt.title('XRD result')
plt.show()
return