##
# In[3]:
get_ipython().run_line_magic('matplotlib', 'inline')
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = (15, 8)
# In[11]:
get_ipython().system('ls interpolated/ | grep 05-20 | grep 9-06')
# In[151]:
data = import_csv(
"interpolated/NG1988812H_Maganna_Gustavo_(27-05-20)_(9-06-20)_interpolated.csv"
)
# In[68]:
data.columns
# In[194]:
deltas = [1, 15, 30, 60, 120]
woo = pd.DataFrame()
woo
# In[179]:
!ls | grep py
from preproc import import_csv
x = import_csv("data_test/NG1988812H_Maganna_Gustavo_13-02-20_6-05-20.csv")
x
type(x.index)
set(x["New Device Time"])
x["New Device Time"].unique
x["New Device Time"].unique()
x = import_csv("data_test/NG1988812H_Maganna_Gustavo_13-02-20_6-05-20.csv")
x[x.columns[31]]
x[x.columns[31]].unique()
x.columns[31]
x[x.columns[36]].unique()
x.columns[36]
x[x.columns[41]].unique()
x.columns[41]
x
x["Sensor Glucose (mg/dL)"]
y = pd.DataFrame({"x": range(35)})
y
y = pd.DataFrame({"x": range(1,36)})
y.diff(2)
y.diff(10)
x
x['Sensor Glucose (mg/dL)']
x['Sensor Glucose (mg/dL)'].plot()
plt.show()
plt.style.use("seaborn")
plt.show()
x['Sensor Glucose (mg/dL)'].plot(); plt.show()
x.loc["2020-03-03", 'Sensor Glucose (mg/dL)'].plot(); plt.show()
# Plotting :
import matplotlib.pyplot as plt
import seaborn as sns
# Local :
from preproc import time_indexed_df, import_csv
from Utils import comparative_hba1c_plot, proportions_visualiser, dist_plot
# Debugging only, remove after building :
get_ipython().run_line_magic('matplotlib', 'inline')
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = (15, 8)
# In[9]:
cgm_data = import_csv("preprocessed/CareLink-19-apr-2020-3-months.csv")
# In[2]:
data = pd.read_csv("mySugr_data/Export.csv")
data.columns
# In[3]:
# Date-time indexing :
x = data.copy()
x["DateTime"] = x["Date"] + " " + x["Time"]
x.drop(["Date", "Time"], axis=1, inplace=True)
y = time_indexed_df(x, 'DateTime')
y.index = y.index.map(lambda t: t.replace(second=0))
##
# In[3]:
random_seed = 123456
# In[4]:
get_ipython().run_line_magic('matplotlib', 'inline')
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = (16, 10)
plt.rcParams['figure.max_open_warning'] = False
# In[5]:
data = import_csv("preprocessed/CareLink-23-apr-2020-1-month.csv")
# In[6]:
print("start \t:", data.index[0])
print("end \t:", data.index[-1])
# In[7]:
latest = data.loc["2020-04-19":"2020-04-23", :]
# In[8]:
#help(pd.plotting.autocorrelation_plot)
# In[9]:
from preproc import import_csv
x = import_csv("data_test/NG1988812H_Maganna_Gustavo_(13-02-20)_(6-05-20).csv")
plt.style.use("seaborn")
d = {
"resampled":
x.loc["2020-04-03", 'Sensor Glucose (mg/dL)'].resample("1T").interpolate(),
"interpolated":
x.loc["2020-04-03", 'Sensor Glucose (mg/dL)'].interpolate(),
"original":
x.loc["2020-04-03", 'Sensor Glucose (mg/dL)']
}
#labeledplot = lambda obj, label: obj.plot(**{"label": f"{label}"})
#for label, obj in d.items():
# labeledplot(obj, label)
d["resampled"].plot(label="glycaemia")
d["resampled"].diff(30).plot(label="30 min")
d["resampled"].diff(120).plot(label="2 hours")
plt.legend()
plt.show()
