def load_data(self, file, solidity_threshold, irregularity_threshold):
data_all = getData(file)
if not "area" in data_all.keys():
data_all["area"] = data_all["long_axis"] * data_all[
"short_axis"] * np.pi / 4
if len(data_all) == 0:
print("no data loaded from file '%s'" % file)
return pd.DataFrame(), pd.DataFrame()
# use a "read sol from config flag here
data_mean, config_eval = load_all_data_new(
self.db.getImage(0).get_full_filename().replace(
".tif", "_evaluated_new.csv"),
do_group=False,
do_excude=False)
return data_all, data_mean
return file_pairs
file_pairs = []
file_pairs.extend(getFolderPairs(r"\\131.188.117.119\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\march_2021\2021_02_24_NIH3T3_LatrunculinB_doseresponse\*"))
file_pairs.extend(getFolderPairs(r"\\131.188.117.119\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\march_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\*"))
file_pairs.extend(getFolderPairs(r"\\131.188.117.119\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\march_2021\2021_03_10_NIH3T3_LatrunculinB_doseresponse\*"))
file_pairs.extend(getFolderPairs(r"\\131.188.117.119\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\march_2021\2021_03_15_NIH3T3_LatrunculinB_doseresponse\*"))
all_data = []
for row_index, pressure in enumerate([0.5, 1, 2, 3]):
plt.subplot(4, 1, row_index+1)
for index, paths in enumerate(file_pairs):
try:
data0, config0 = load_all_data_new(paths[0], solidity_threshold=0.7, irregularity_threshold=1.3, pressure=pressure)
except (FileNotFoundError, ValueError) as err:
print("ERROR", paths[0], err)
continue
try:
data1, config1 = load_all_data_new(paths[1], solidity_threshold=0.7, irregularity_threshold=1.3, pressure=pressure)
except (FileNotFoundError, ValueError) as err:
print("ERROR", paths[1], err)
continue
ratios = []
# bootstrap with matched histograms
for i in range(3):
data0b, data1b = bootstrap_match_hist([data0, data1], bin_width=25, max_bin=300, property="stress")
ratios.append(get_mode_stats(data0b.k_cell)[0]/get_mode_stats(data1b.k_cell)[0])
agg="logmode",
color=color[0],
label=label)
plt.ylim(bottom=0)
plt.axes([0.5, .2, 0.5, 0.5], label=f"{parameter}_k")
plotMeasurementOfParameter(data,
parameter,
"w_alpha_cell",
agg="mode",
color=color[1],
label=label)
plt.ylim(bottom=0)
data_bleb, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\12.04.2021_THP1_CytoD_10µM_*\**\*_evaluated_new.csv"
],
pressure=2)
data_dmso, config = load_all_data_new(
[
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\24.03.2021_THP1_2%Alginate\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%\**\*_evaluated_new.csv",
#r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%_2\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%_3\**\*_evaluated_new.csv",
],
pressure=2)
print(data_bleb.pressure.unique(), data_dmso.pressure.unique())
plot_pair(data_bleb, "time", ["C0", "C0"], label="CytoD")
plot_pair(data_dmso, "time", ["C3", "C3"], label="Control")
plt.legend()
#% start: automatic generated code from pylustrator
plt.figure(1).ax_dict = {ax.get_label(): ax for ax in plt.figure(1).axes}
def __init__(self, *args, **kwargs):
clickpoints.Addon.__init__(self, *args, **kwargs)
# open the window
self.layout = QtWidgets.QVBoxLayout(self)
self.resize(1300, 400)
self.show()
# Setting up marker Types
self.marker_type_cell1 = self.db.setMarkerType("cell", "#0a2eff", self.db.TYPE_Ellipse)
self.pen_neutral = QtGui.QPen(QtGui.QColor("#0a2eff"), 3)
self.marker_type_cell2 = self.db.setMarkerType("cell include", "#25c638", self.db.TYPE_Ellipse)
self.pen_include = QtGui.QPen(QtGui.QColor("#25c638"), 3)
self.marker_type_cell3 = self.db.setMarkerType("cell exclude", "#ff0000", self.db.TYPE_Ellipse)
self.pen_exclude = QtGui.QPen(QtGui.QColor("#ff0000"), 3)
self.cp.reloadTypes()
# set the title and layout
self.setWindowTitle("DeformationCytometer - ClickPoints")
# load the data
self.filename = self.db.getImage(0).get_full_filename()[:-4] + "_evaluated_new.csv"
self.data, self.config = load_all_data_new(self.db.getImage(0).get_full_filename().replace(".tif", "_evaluated_new.csv"), do_group=False, do_excude=False)
if "manual_exclude" not in self.data:
self.data["manual_exclude"] = np.nan
print("loading finished", self.db.getImage(0).get_full_filename())
# add a label and the progressbar
self.label = QtWidgets.QLabel("Cell")
self.layout.addWidget(self.label)
self.progressbar = QtWidgets.QProgressBar()
self.progressbar.setRange(0, len(self.data) - 1)
self.layout.addWidget(self.progressbar)
pixel_size = self.config["pixel_size"]
# remove previous markers of the time in case there are any (e.g. when the addon is activated twice)
self.db.deleteEllipses(type=self.marker_type_cell1)
self.db.deleteEllipses(type=self.marker_type_cell2)
self.db.deleteEllipses(type=self.marker_type_cell3)
self.markers = []
if 0:
# add all markers at once
self.markers = self.db.setEllipses(
frame=list(np.array(self.data.frames).astype(np.int)),
x=np.array(self.data.x),
y=np.array(self.data.y),
width=np.array(self.data.long_axis / pixel_size),
height=np.array(self.data.short_axis / pixel_size),
angle=np.array(self.data.angle),
type=self.marker_type_cell1
)
if 0:
# iteratively create the markers
for i, d in self.data.iterrows():
self.progressbar.setValue(i)
self.label.setText(f"Cell {i}")
self.cp.window.app.processEvents()
type_ = self.marker_type_cell3 if d.manual_exclude == 1 else self.marker_type_cell2 if d.manual_exclude == 0 else self.marker_type_cell1
ell = self.db.setEllipse(frame=int(d.frames), x=d.x, y=d.y,
width=d.long_axis / pixel_size, height=d.short_axis / pixel_size,
text=f"{i} {d.cell_id}",
angle=d.angle, type=type_)
self.markers.append(ell)
self.index = 0
self.view = QExtendedGraphicsView()
self.layout.addWidget(self.view)
# self.view.zoomEvent = self.zoomEvent
# self.view.panEvent = self.panEvent
self.local_scene = self.view.scene
self.origin = self.view.origin
self.cell_rects = {}
self.focus_rect = QtWidgets.QGraphicsRectItem(-self.w / 2, -self.h / 2, self.w, self.h * 2, self.origin)
self.focus_rect.setPen(QtGui.QPen(QtGui.QColor(255, 255, 0), 8))
self.focus_rect.setZValue(10)
self.view.setExtend(self.w * 5, self.h*2)
self.view.fitInView()
self.start_pos = 0
self.current_pos = self.index - 0.1
self.timer_percent = 0
self.view.keyPressEvent = self.keyPressEvent2
self.slider = QtWidgets.QSlider()
self.slider.setOrientation(QtCore.Qt.Horizontal)
self.layout.addWidget(self.slider)
self.slider.setRange(0, len(self.data)-1)
def setIndex(x):
if x != self.index:
self.index = x
self.focusOnCell()
self.slider.valueChanged.connect(setIndex)
layout = QtWidgets.QHBoxLayout()
self.layout.addLayout(layout)
self.auto_save = QtWidgets.QCheckBox()
layout.addWidget(self.auto_save)
self.auto_save_label = QtWidgets.QLabel("autosave")
layout.addWidget(self.auto_save_label)
self.button_save = QtWidgets.QPushButton("save")
self.button_save.clicked.connect(self.save)
layout.addWidget(self.button_save)
layout.addStretch()
self.image_loaded.connect(self.addImage)
self.focusOnCell()
from deformationcytometer.evaluation.helper_functions import plotDensityScatter, load_all_data, all_plots_same_limits, get_cell_properties #, load_all_data_new
from deformationcytometer.evaluation.helper_functions import plot_velocity_fit, plot_density_hist, \
plotDensityLevels, plotBinnedData, plot_joint_density, split_axes, load_all_data_new
import numpy as np
import pylustrator
pylustrator.start()
if 0:
""""""
#\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\january_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\100 nM - Kontrolle\2021_03_01_12_21_37.tif
#\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\january_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\100 nM - Kontrolle\2021_03_01_12_24_36.tif
#\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\january_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\3.162 nM - Kontrolle\2021_03_01_14_20_57.tif
#\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\january_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\316.2 nM - Kontrolle\2021_03_01_11_45_42.tif
from deformationcytometer.evaluation.helper_functions import correctCenter
data, config = load_all_data_new(
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\january_2021\2021_03_01_NIH3T3_LatrunculinB_doseresponse\100 nM - Kontrolle\2021_03_01_12_21_37_result.txt"
)
correctCenter(data, config)
d = data
y_pos = d.rp
vel = d.velocity
valid_indices = np.isfinite(y_pos) & np.isfinite(vel)
plt.plot(y_pos, vel, "o")
plt.show()
""""""
experiment = {}
#if parent-subfolders exist:
import glob
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_16_alginate2%_NIH_tanktreading\1\*_result.txt"
)
data = data[np.abs(data.velocity - data.velocity_fitted) < 1.5]
plt.subplot(241)
plot_velocity_fit(data)
#plt.legend(title="pressure (bar)")
plt.subplot(242)
plot_velocity_fit_dot(data)
plt.subplot(243)
plot_viscisity(data)
plt.subplot(244)
data, config = load_all_data_new(
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_16_alginate2%_NIH_tanktreading\1\*_result.txt"
)
plot_viscisoty_over_shear_rate(data)
plot_tt(plt.subplot(245), plt.subplot(246))
plt.subplot(247)
plot_omega(data)
def func(x, a, b):
return x / 2 * 1 / (1 + a * x**b)
x = [0.113, 0.45]
xx = np.arange(0, 500)
parameter,
"w_k_cell",
color[0],
label=label)
plt.ylim(bottom=0)
plt.axes([0.5, .2, 0.5, 0.5], label=f"{parameter}_k")
plotMeasurementOfParameter(data,
parameter,
"w_alpha_cell",
color[1],
label=label)
plt.ylim(bottom=0)
data_bleb, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_30_alginate2%_NIH3T3_blebbistatin\inlet\**\*.tif"
],
pressure=3)
data_dmso, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_30_alginate2%_NIH3T3_DMSO\inlet\**\*.tif"
],
pressure=3)
plot_pair(data_bleb, "time", ["C0", "C0"], label="Blebbistatin")
plot_pair(data_dmso, "time", ["C3", "C3"], label="DMSO")
plt.legend()
#% start: automatic generated code from pylustrator
plt.figure(1).ax_dict = {ax.get_label(): ax for ax in plt.figure(1).axes}
import matplotlib as mpl
plt.figure(1).set_size_inches(12.000000 / 2.54, 6.000000 / 2.54, forward=True)
plt.figure(1).ax_dict["time_alpha"].set_ylim(0.0, 125.0)
plt.figure(1).ax_dict["time_alpha"].set_position(
agg="median",
color=color[0],
label=label)
plt.ylim(bottom=0)
plt.axes([0.5, .2, 0.5, 0.5], label=f"{parameter}_k")
plotMeasurementOfParameter(data,
parameter,
"w_alpha_cell",
agg="mean",
color=color[1],
label=label)
plt.ylim(bottom=0)
data, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\flourescence beads\2021.4.14\**\*_evaluated_new.csv"
])
data.pressure = np.round(data.pressure, 2)
plot_pair(data, "pressure", ["C0", "C1"])
#% start: automatic generated code from pylustrator
plt.figure(1).ax_dict = {ax.get_label(): ax for ax in plt.figure(1).axes}
import matplotlib as mpl
plt.figure(1).set_size_inches(12.000000 / 2.54, 5.300000 / 2.54, forward=True)
plt.figure(1).ax_dict["pressure_alpha"].set_xlim(0.0, 0.52)
plt.figure(1).ax_dict["pressure_alpha"].set_ylim(0.0, 10.0)
plt.figure(1).ax_dict["pressure_alpha"].set_position(
[0.125368, 0.310687, 0.364634, 0.554430])
plt.figure(1).ax_dict["pressure_alpha"].spines['right'].set_visible(False)
plt.figure(1).ax_dict["pressure_alpha"].spines['top'].set_visible(False)
plt.ylim(bottom=0)
if scaling == "semilogx":
plt.semilogx()
plt.axes([0.55, .2, 0.4, 0.5], label=f"{parameter}_k")
plotMeasurementOfParameter(data, parameter, "w_alpha_cell", agg="mode", color=color[1], label=label)
plt.ylim(bottom=0)
if scaling == "semilogx":
plt.semilogx()
if 0:
data, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\24.03.2021_THP1_2%Alginate\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%\**\*_evaluated_new.csv",
#r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%_2\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%_3\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\22.04.2021_THP1_CytoD_*\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\21.04.2021_THP1_Cyto*\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\21.04.2021_THP1_*Cyto\**\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\23.04.2021_THP1_Cyto*M*\**\*_evaluated_new.csv",
])
data, config = load_all_data_new([
# CONTROL
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\21.04.2021_THP1_alginate2%",
# 10nM cytoD
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\22.04.2021_THP1_CytoD_10nM",
# 100nM cytoD
r"\\131.188.117.96\biophysDS\meroles\SPRING2021\22.04.2021_THP1_CytoD_100nM",
#r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_07_27_alginate2.0%_dmem_NIH_3T3\*\*_result.txt",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_07_28_alginate2.0%_dmem_NIH_3T3\*\*_result.txt",
#r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_10_28_alginate2.0%_dmem_NIH_3T3\*\*_result.txt",
#r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_10_30_alginate2.0%_dmem_NIH_3T3\*\*_result.txt",
]
[
# r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_29_aslginate2%_NIH_diff_x_position_2\inlet\[0-9]\*_result.txt",
# r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_29_aslginate2%_NIH_diff_x_position_3\inlet\[0-9]\*_result.txt",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_16_alginate2%_NIH_tanktreading\*\*_result.txt",
]
numbers = []
data_list = []
config_list = []
for index, pressure in enumerate([1, 2, 3]):
data, config = load_all_data_new(alg20, pressure=pressure)
data_list.append(data)
config_list.append(config)
ranges = [
np.arange(10, 80, 20),
np.arange(10, 180, 20),
np.arange(10, 280, 20),
]
colors = [
"#d68800",
"#d40000",
"#b00038",
]
for index, pressure in enumerate([3]):
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import pandas as pd
import matplotlib.pyplot as plt
from deformationcytometer.evaluation.helper_functions import load_all_data, load_all_data_new, bootstrap_match_hist
import numpy as np
import pylustrator
pylustrator.start()
data, config = load_all_data_new([
fr"\\131.188.117.96\biophysDS\meroles\SPRING2021\24.03.2021_THP1_1.5%Alginate\**\*_evaluated_new.csv",
fr"\\131.188.117.96\biophysDS\meroles\SPRING2021\24.03.2021_THP1_2%Alginate\**\*_evaluated_new.csv",
fr"\\131.188.117.96\biophysDS\meroles\SPRING2021\25.03.2021_THP1_2.5%Alginate\**\*_evaluated_new.csv",
fr"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%\**\*_evaluated_new.csv",
fr"\\131.188.117.96\biophysDS\meroles\SPRING2021\30.03.2021_THP1_Ag2%_3\**\*_evaluated_new.csv",
])
def plotMeasurementOfParameter(data, parameter, value, color=None):
k_cell = data.groupby([parameter,
"measurement_id"]).median()[value].reset_index()
k_cell_mean = k_cell.groupby(parameter).mean()
l = plt.errorbar(k_cell_mean.index,
k_cell_mean.values,
k_cell.groupby(parameter).sem().values[:, 0],
capsize=3,
color=color,
zorder=2)
from pathlib import Path
from deformationcytometer.evaluation.helper_functions import plotDensityScatter, load_all_data_new, get_cell_properties
from deformationcytometer.evaluation.helper_functions import plot_velocity_fit, plot_density_hist, \
plotDensityLevels, plotBinnedData
settings_name = "strain_vs_stress_clean"
""" loading data """
# reading commandline arguments if executed from terminal
file, irregularity_threshold, solidity_threshold = read_args_evaluate()
# get the results file (by command line parameter or user input dialog)
datafile = getInputFile(filetype="csv file (*_evaluated_new.csv)", settings_name=settings_name, video=file)
print("evaluate file", datafile)
# load the data and the config
data, config = load_all_data_new(datafile)
plt.figure(0, (10, 8))
plt.subplot(2, 3, 1)
plt.cla()
plot_velocity_fit(data)
plt.text(0.9, 0.9, f"$\\eta_0$ {data.eta0[0]:.2f}\n$\\delta$ {data.delta[0]:.2f}\n$\\tau$ {data.tau[0]:.2f}", transform=plt.gca().transAxes, va="top", ha="right")
plt.subplot(2, 3, 2)
plt.cla()
plotDensityScatter(data.stress, data.epsilon)
plotBinnedData(data.stress, data.epsilon, bins=np.arange(0, 300, 10))
plt.xlabel("stress (Pa)")
plt.ylabel("strain")
pylustrator.start()
""" NIH3T3 """
fit_data = []
x = []
# iterate over all times
for index, time in enumerate(range(2, 13, 1)):
f = []
# iterate over the different experiment paths
for path in [
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_may\2020_05_22_alginateDMEM2%\{time}\*_result.txt",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_19_alginate2%_overtime_2\{time}\*_result.txt",
]:
# get the data and the fit parameters
data, config = load_all_data_new(path, pressure=1)
data, config = load_all_data_new(
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_may\**\*_result.txt",
pressure=1)
data1 = data[data.pressure == 1]
#f.append([np.mean(data.alpha_cell), np.mean(np.log10(data.k_cell))])
f.append([np.mean(data.alpha_cell), np.nanmedian(data.k_cell)])
x.append(time * 5)
fit_data.append(f)
fit_data = np.array(fit_data)
# plot the fit data in the two different plots
for i in range(2):
def selected(self, name):
plt.clf()
if name.endswith(".tif"):
data, config = load_all_data_new(name.replace(
".tif", "_evaluated_new.csv"),
do_excude=False)
def get_mode_stats(x):
from scipy import stats
from deformationcytometer.evaluation.helper_functions import bootstrap_error
x = np.array(x)
print("a", x.shape)
if len(x.shape) == 1:
x = x[~np.isnan(x)]
print("b", x.shape)
def get_mode(x):
kde = stats.gaussian_kde(x)
print(x.shape)
print(np.argmax(kde(x)))
return x[..., np.argmax(kde(x))]
mode = get_mode(x)
# err = bootstrap_error(x, get_mode, repetitions=2)
return mode
from scipy.special import gamma
def fit(omega, k, alpha):
omega = np.array(omega)
G = k * (1j * omega)**alpha * gamma(1 - alpha)
return np.real(G), np.imag(G)
def cost(p):
Gp1, Gp2 = fit(data.omega_weissenberg, *p)
#return np.sum(np.abs(np.log10(data.w_Gp1) - np.log10(Gp1))) + np.sum(
# np.abs(np.log10(data.w_Gp2) - np.log10(Gp2)))
return np.sum(
(np.log10(data.w_Gp1) - np.log10(Gp1))**2) + np.sum(
(np.log10(data.w_Gp2) - np.log10(Gp2))**2)
from scipy.optimize import minimize
res = minimize(
cost,
[np.median(data.w_k_cell),
np.mean(data.w_alpha_cell)]) #, bounds=([0, np.inf], [0, 1]))
print(res)
pair_median_mean = [
np.median(data.w_k_cell),
np.mean(data.w_alpha_cell)
]
pair_fit = [res.x[0], res.x[1]]
pair_2dmode = get_mode_stats(
[np.log10(data.w_k_cell), data.w_alpha_cell])
pair_2dmode[0] = 10**pair_2dmode[0]
print("pair_median_mean", pair_median_mean)
print("pair_fit", pair_fit)
print("pair_2dmode", pair_2dmode)
plt.subplot(3, 3, 1)
plot_velocity_fit(data)
if "vel_fit_error" in data.iloc[0]:
plt.text(0.8,
0.8,
f'{data.iloc[0]["vel_fit_error"]:.0f}',
transform=plt.gca().transAxes)
plt.subplot(3, 3, 2)
plt.axline([0, 0], slope=1, color="k")
plt.plot(data.omega, data.omega_weissenberg, "o", ms=1)
plt.xlabel("omega")
plt.ylabel("omega weissenberg")
plt.subplot(3, 3, 3)
plotDensityScatter(data.stress, data.epsilon)
plotBinnedData(data.stress,
data.epsilon,
bins=np.arange(0, 300, 10))
plt.xlabel("stress (Pa)")
plt.ylabel("strain")
plt.subplot(3, 3, 4)
plt.loglog(data.omega_weissenberg,
data.w_Gp1,
"o",
alpha=0.25,
ms=1)
plt.loglog(data.omega_weissenberg,
data.w_Gp2,
"o",
alpha=0.25,
ms=1)
xx = [
10**np.floor(np.log10(np.min(data.w_Gp1))),
10**np.ceil(np.log10(np.max(data.w_Gp1)))
]
plt.plot(xx, fit(xx, *pair_fit)[0], "k-", lw=0.8)
plt.plot(xx, fit(xx, *pair_fit)[1], "k--", lw=0.8)
plt.plot(xx, fit(xx, *pair_median_mean)[0], "r-", lw=0.8)
plt.plot(xx, fit(xx, *pair_median_mean)[1], "r--", lw=0.8)
plt.plot(xx, fit(xx, *pair_2dmode)[0], "c-", lw=0.8)
plt.plot(xx, fit(xx, *pair_2dmode)[1], "c--", lw=0.8)
plt.ylabel("G' / G''")
plt.xlabel("angular frequency")
logk, a = get_mode_stats(
[np.log10(data.w_k_cell), data.w_alpha_cell])
plt.subplot(3, 3, 5)
plt.cla()
plt.xlim(0, 4)
plot_density_hist(np.log10(data.w_k_cell), color="C0")
plt.axvline(np.log10(pair_fit[0]), color="k")
plt.axvline(np.log10(pair_median_mean[0]), color="r")
plt.axvline(np.log10(pair_2dmode[0]), color="c")
plt.xlabel("log10(k)")
plt.ylabel("relative density")
plt.text(
0.9,
0.9,
f"mean(log10(k)) {np.mean(np.log10(data.w_k_cell)):.2f}\nstd(log10(k)) {np.std(np.log10(data.w_k_cell)):.2f}\nmean(k) {np.mean(data.k_cell):.2f}\nstd(k) {np.std(data.k_cell):.2f}\n",
transform=plt.gca().transAxes,
va="top",
ha="right")
plt.subplot(3, 3, 6)
plt.cla()
plt.xlim(0, 1)
plot_density_hist(data.w_alpha_cell, color="C1")
plt.xlabel("alpha")
plt.axvline(pair_fit[1], color="k")
plt.axvline(pair_median_mean[1], color="r")
plt.axvline(pair_2dmode[1], color="c")
plt.text(
0.9,
0.9,
f"mean($\\alpha$) {np.mean(data.w_alpha_cell):.2f}\nstd($\\alpha$) {np.std(data.w_alpha_cell):.2f}\n",
transform=plt.gca().transAxes,
va="top",
ha="right")
plt.subplot(3, 3, 7)
plt.cla()
plotDensityScatter(np.log10(data.w_k_cell), data.w_alpha_cell)
plt.axvline(np.log10(pair_fit[0]), color="k")
plt.axhline(pair_fit[1], color="k", label="fit")
plt.axvline(np.log10(pair_median_mean[0]), color="r")
plt.axhline(pair_median_mean[1], color="r", label="median")
plt.axvline(np.log10(pair_2dmode[0]), color="c")
plt.axhline(pair_2dmode[1], color="c", label="2dmode")
plt.legend()
print("doublemode",
get_mode_stats([np.log10(data.w_k_cell), data.w_alpha_cell]))
plt.xlim(1, 3)
plt.ylim(0, .5)
plt.tight_layout()
#plt.plot(data.rp, data.vel)
""""""
from deformationcytometer.includes.RoscoeCoreInclude import getRatio
from deformationcytometer.includes.fit_velocity import getFitXYDot
eta0 = data.iloc[0].eta0
alpha = data.iloc[0].delta
tau = data.iloc[0].tau
pressure = data.iloc[0].pressure
def func(x, a, b):
return x / 2 * 1 / (1 + a * x**b)
def getFitLine(pressure, p):
config = {
"channel_length_m": 5.8e-2,
"channel_width_m": 186e-6
}
x, y = getFitXYDot(config, np.mean(pressure), p)
return x, y
channel_pos, vel_grad = getFitLine(pressure, [eta0, alpha, tau])
vel_grad = -vel_grad
vel_grad = vel_grad[channel_pos > 0]
channel_pos = channel_pos[channel_pos > 0]
omega = func(np.abs(vel_grad), *[0.113, 0.45])
import scipy
k_cell, alpha_cell = pair_fit
mu1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.cos(np.pi / 2 * alpha_cell)
eta1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.sin(np.pi / 2 * alpha_cell) / omega
ratio, alpha1, alpha2, strain, stress, theta, ttfreq, eta, vdot = getRatio(
eta0, alpha, tau, vel_grad, mu1_, eta1_)
plt.subplot(3, 3, 3)
plt.plot(stress, strain, "-k")
k_cell, alpha_cell = pair_median_mean
mu1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.cos(np.pi / 2 * alpha_cell)
eta1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.sin(np.pi / 2 * alpha_cell) / omega
ratio, alpha1, alpha2, strain, stress, theta, ttfreq, eta, vdot = getRatio(
eta0, alpha, tau, vel_grad, mu1_, eta1_)
plt.subplot(3, 3, 3)
plt.plot(stress, strain, "-r")
k_cell, alpha_cell = pair_2dmode
mu1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.cos(np.pi / 2 * alpha_cell)
eta1_ = k_cell * omega**alpha_cell * scipy.special.gamma(
1 - alpha_cell) * np.sin(np.pi / 2 * alpha_cell) / omega
ratio, alpha1, alpha2, strain, stress, theta, ttfreq, eta, vdot = getRatio(
eta0, alpha, tau, vel_grad, mu1_, eta1_)
plt.subplot(3, 3, 3)
plt.plot(stress, strain, "-c")
self.canvas.draw()
import pandas as pd
import matplotlib.pyplot as plt
from deformationcytometer.evaluation.helper_functions import load_all_data, load_all_data_new, bootstrap_match_hist
import numpy as np
import pylustrator
pylustrator.start()
data, config = load_all_data_new([
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_18_alginate2%_overtime_1\*\*_evaluated_new.csv",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_18_alginate2%_overtime_2\*\*_evaluated_new.csv",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_19_alginate2%_overtime_1\*\*_evaluated_new.csv",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_19_alginate2%_overtime_2\*\*_evaluated_new.csv",
#rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_may\2020_05_22_alginateDMEM2%\*\*_evaluated_new.csv",
# rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_21_alginate2%_dmem_NIH_time_1\*\*_evaluated_new.csv",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_21_alginate2%_dmem_NIH_time_2\*\*_evaluated_new.csv",
rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_21_alginate2%_dmem_NIH_time_3\*\*_evaluated_new.csv",
# rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_may\2020_05_22_alginateDMEM2%\*\*_evaluated_new.csv",
# rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_18_alginate2%_overtime_1\*\*_evaluated_new.csv",
# rf"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\august_2020\2020_08_19_alginate2%_overtime_2\*\*_evaluated_new.csv",
])
data_alg, config = load_all_data_new([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_07_24_alginate2.5%_dmem_NIH_3T3\*\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_07_27_alginate2.5%_dmem_NIH_3T3\*\*_evaluated_new.csv",
# r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_10_14_alginate2.5%_dmem_NIH_3T3\*\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_10_28_alginate2.5%_dmem_NIH_3T3\*\*_evaluated_new.csv",
# r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_10_30_alginate2.5%_dmem_NIH_3T3\*\*_evaluated_new.csv",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\evaluation\diff % alginate\2020_07_27_alginate2.0%_dmem_NIH_3T3\*\*_evaluated_new.csv",
