def setUp(self):
ImportedDataTest.setUp(self)
self.op = flow.GaussianMixtureOp(name = "GM",
channels = ["V2-A", "Y2-A"],
scale = {"V2-A" : "logicle",
"Y2-A" : "logicle"},
num_components = 2)
def setUp(self):
super().setUp()
self.gate = flow.GaussianMixtureOp(name="Gauss",
channels=["Y2-A"],
sigma=0.5,
num_components=2,
scale={"Y2-A": "logicle"},
posteriors=True)
def testSubset(self):
gm_1 = flow.GaussianMixtureOp(name = "Morpho1",
channels = ["FSC-A", "SSC-A"],
scale = {"SSC-A" : "log"},
num_components = 2,
sigma = 2)
gm_1.estimate(self.ex)
ex_morpho = gm_1.apply(self.ex)
self.op.estimate(ex_morpho, subset = "Morpho1_2 == True")
def testConditions(self):
gm_1 = flow.GaussianMixtureOp(name = "Morpho1",
channels = ["FSC-A", "SSC-A"],
scale = {"SSC-A" : "log"},
num_components = 2,
sigma = 2,
by = ['Dox'])
gm_1.estimate(self.ex)
ex_morpho = gm_1.apply(self.ex)
self.op.blank_file_conditions = {'Dox' : 1.0}
self.op.estimate(ex_morpho, subset = 'Morpho1_2 == True')
def setUp(self):
self.cwd = os.path.dirname(os.path.abspath(__file__)) + "/data/Plate01/"
tube1 = flow.Tube(file = self.cwd + 'RFP_Well_A3.fcs', conditions = {"Dox" : 10.0})
tube2 = flow.Tube(file= self.cwd + 'CFP_Well_A4.fcs', conditions = {"Dox" : 1.0})
import_op = flow.ImportOp(conditions = {"Dox" : "float"},
tubes = [tube1, tube2])
self.ex = import_op.apply()
self.gate = flow.GaussianMixtureOp(name = "Gauss",
channels = ["Y2-A"],
sigma = 0.5,
num_components = 2,
scale = {"Y2-A" : "logicle"},
posteriors = True)
def __init__(self,
file_id='default',
ch1=False,
ch2=False,
transformation='hlog',
bins=100):
fcs_file_name = file_id + '_fcs_file.fcs'
self.file_id = file_id
self.transformation = transformation
self.bins = bins
self.response = {}
self.channel_name1 = ch1
self.channel_name2 = ch2
print('ML setting ch names from params', ch1, ch2)
# Check if images already exist
if self.check_if_images_exist():
return None
# Locate sample data included with this package
print('Raw dir in init :', SHARED_RAW_DIR)
fcs_file_path = os.path.join(SHARED_RAW_DIR, fcs_file_name)
print('FCS File', fcs_file_path)
fcs_file_a4_path = os.path.join(SHARED_RAW_DIR, FCS_FILE_A4)
print('FCS File 4', fcs_file_a4_path)
af_op = flow.AutofluorescenceOp()
# Get channel_names
if not ch1 or not ch2:
URL = 'http://basicanalysis:3000?id=' + file_id
r = requests.get(URL)
self.channel_names = r.json()
self.channel_name1 = self.channel_names[0]
self.channel_name2 = self.channel_names[1]
print(r.json())
tube1 = flow.Tube(file=fcs_file_path, conditions={"Dox": 10.0})
tube2 = flow.Tube(file=fcs_file_a4_path, conditions={"Dox": 1.0})
import_op = flow.ImportOp(conditions={"Dox": "float"},
tubes=[tube1, tube2],
channels={
self.channel_name1: self.channel_name1,
self.channel_name2: self.channel_name2
})
ex = import_op.apply()
flow.HistogramView(scale='logicle',
channel=self.channel_name2).plot(ex)
png_file = os.path.join(SHARED_PLOT_DIR,
self.get_file_name('histogram.png'))
print(png_file)
grid(True)
savefig(png_file)
self.response['histogram'] = self.get_file_name('histogram.png')
g = flow.GaussianMixtureOp(name="Gauss",
channels=[self.channel_name1],
scale={self.channel_name1: "logicle"},
num_components=2)
g.estimate(ex)
g.default_view().plot(ex)
ex2 = g.apply(ex)
g.default_view().plot(ex2)
png_file = os.path.join(SHARED_PLOT_DIR,
self.get_file_name('gausian.png'))
print(png_file)
grid(True)
savefig(png_file)
self.response['gausian'] = self.get_file_name('gausian.png')
print(type(ex2.data.head()))
png_file = os.path.join(SHARED_PLOT_DIR,
self.get_file_name('gausian_table.png'))
self.__df_to_png(ex2.data.head(), png_file)
self.response['gausian_table'] = self.get_file_name(
'gausian_table.png')
subplots(clear=True)
g = flow.GaussianMixtureOp(name="Gauss",
channels=[self.channel_name1],
scale={self.channel_name1: "logicle"},
num_components=2,
posteriors=True)
g.estimate(ex)
ex2 = g.apply(ex)
g.default_view().plot(ex2)
png_file = os.path.join(SHARED_PLOT_DIR,
self.get_file_name('gausian_posterior.png'))
savefig(png_file)
self.response['gausian_posterior'] = self.get_file_name(
'gausian_posterior.png')
png_file = os.path.join(
SHARED_PLOT_DIR, self.get_file_name('gausian_posterior_table.png'))
self.__df_to_png(ex2.data.head(), png_file)
self.response['gausian_posterior_table'] = self.get_file_name(
'gausian_posterior_table.png')
# We can use this second metadata column to filter out events with low posterior probabilities:
ex2.query(
"Gauss_1_posterior > 0.9 | Gauss_2_posterior > 0.9").data.head()
flow.HistogramView(
channel=self.channel_name1,
huefacet="Gauss",
scale="logicle",
subset="Gauss_1_posterior > 0.9 | Gauss_2_posterior > 0.9").plot(
ex2)
png_file = os.path.join(
SHARED_PLOT_DIR,
self.get_file_name('gausian_filtered_low_posterior.png'))
savefig(png_file)
self.response['gausian_filtered_low_posterior'] = self.get_file_name(
'gausian_filtered_low_posterior.png')
subplots(clear=True)
# Basic usage, assigning each event to one of the mixture components: (the isolines in the default_view() are 1, 2 and 3 standard deviations away from the mean.)
g = flow.GaussianMixtureOp(
name="Gauss",
channels=[self.channel_name1, self.channel_name2],
scale={
self.channel_name1: "logicle",
self.channel_name2: "logicle"
},
num_components=2)
g.estimate(ex)
ex2 = g.apply(ex)
g.default_view().plot(ex2, alpha=0.1)
png_file = os.path.join(
SHARED_PLOT_DIR,
self.get_file_name('gausian_mixture_model_two_channels.png'))
savefig(png_file)
self.response[
'gausian_mixture_model_two_channels'] = self.get_file_name(
'gausian_mixture_model_two_channels.png')
subplots(clear=True)
# K-Means
self.k_means(ex)
# FlowPeaks
"""
ecoli_file_path = os.path.join(SHARED_RAW_DIR, ECOLI_FILE)
print('Ecoli ', ecoli_file_path)
ex = flow.ImportOp(tubes=[flow.Tube(file=ecoli_file_path)]).apply()
flow.ScatterplotView(xchannel=self.channel_name1,
xscale='log',
ychannel=self.channel_name2,
yscale='log').plot(ex)
png_file = os.path.join(SHARED_PLOT_DIR, 'flow_peaks.png')
savefig(png_file)
self.response['flow_peaks'] = 'flow_peaks.png'
"""
# K-MEANS2
self.k_means2(ex)
