So I am creating lineplots from my data.
The relevant portion of my dataframe looks like this:
Metadata_Timepoint AreaShape_Area_median
0 11 3980
8 12 4066.5
16 13 4297
24 2 1487.5
27 3 1739
29 4 1837
34 5 2463
42 6 2825.5
49 7 3028
56 8 3205
63 9 3449
70 10 4023.5
78 11 2227.5
84 12 1918.5
90 13 2164
95 2 1243
98 3 1337.5
102 4 1488
105 5 1621
109 6 1860.5
And my lineplots look something like this:
It should be one line with the Time column on the X and the Area column on the Y Axis.
But somehow it is connecting timepoint 3 to timepoint 13 directly and breaks the connection between time point 10 and 11.
Does anybody know how I can fix this?
The code for the plot is the following
def lineplot(dat=[], classifier_column='', identifier_column='', timepoint_column='',
data_column='', distance_filter='', unique_time_selector='', testmode=False):
'''
testmode: If testomde is set to True only the first 10 items will be used in the graph
'''
print('creating migration lineplot')
#print(identifier_column)
#print(type(identifier_column))
#cells=list(dat[identifier_column].unique())
#dat[unique_time_selector]=dat[identifier_column]+'_T'+dat[timepoint_column].astype('str')
classes=list(dat[classifier_column].unique())
X_column=data_column[0]
Y_column=data_column[1]
#excluding rows where the data columns is 'None'
#relevant for averaging
dat=dat[dat[X_column]!='None']
dat=dat[dat[Y_column]!='None']
#making sure values are floats
dat[X_column]=dat[X_column].astype(float)
dat[Y_column]=dat[Y_column].astype(float)
#initiating traces as a list
#getting trace IDs from unique IDs (cells)
print('looping through cells...')
#getting the number of rows for suplots based on the amount of classes
row_n=len(classes)
if row_n==0:
print('No classes found, make sure your filters do not exclude all data')
rowlist=np.arange(1, row_n+1, 1)
#defining subplot layout
fig=make_subplots(rows=row_n, cols=1, subplot_titles=[str(c) for c in classes])
r_i=0
#getting max axis values
max_x=dat[X_column].max()
min_x=dat[X_column].min()
max_y=dat[Y_column].max()
min_y=dat[Y_column].min()
#looping through the clases
for i in classes:
#subsetting the data by the class
dat_class=dat.loc[dat[classifier_column]==i]
#getting only the cells from that class
cells=list(dat_class[identifier_column].unique())
print('...of class ', i, '...')
if testmode==True:
cells=cells[50:100]
#append the current classes name to the titles of the plot
#looping through the cells
for c in cells:
#appending x, y data based on current cell to the list of traces
fig.append_trace(trace=go.Scatter(
#getting x values
x=dat_class.loc[dat_class[identifier_column]==c][X_column],
#getting y values
y=dat_class.loc[dat_class[identifier_column]==c][Y_column],
#getting unique ID
hovertext=dat_class.loc[dat_class[identifier_column]==c][unique_time_selector],
customdata=[dat_class.loc[dat_class[identifier_column]==c][unique_time_selector]],
name=c,
),
row=int(rowlist[math.floor(r_i)]) , col=1)
#adding 1 to the row indicator, so that every class will be
#plottet in a new row
fig.update_yaxes(range=[min_y*1.05, max_y*1.05], row=int(rowlist[math.floor(r_i)]), col=1)
fig.update_xaxes(range=[min_x*1.05, max_x*1.05], row=int(rowlist[math.floor(r_i)]), col=1)
r_i+=1
fig.update_layout(margin={'l': 40, 'b': 5, 't': 30, 'r': 40},
height=row_n*375, width=750)
fig.update_layout({'clickmode':'event+select'})
print('...done')
return fig
