def graph_normalizedRRx(self, weeks, predict):
"""
UPDATE so that ending and starting weeks can be chosen
UPDATE so that it returns the plot (use "ax" from matplotlib) so that it can be manipulated outside of the function
Creats and shows a graph of "Normalized_RRx" and the specified number of weeks from the given dataframe
PARAMETERS:
weeks: number of weeks to dispaly. The graph displays the 0th entry to the specified number of weeks. (int)
"""
self.normalizedRRxChart = graphObj(self.drug,
self.masterDf,
weeks,
"Week", ["Normalized_RRx"],
["scatter"],
figW=self.figW,
figH=self.figH)
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"Normalized_RRx",
self.weeksToPredict).predictionDf
self.normalizedRRxPredictionDf = predictionDf
self.normalizedRRxChart.generateChart(
["scatter"], predictionDf["Week"],
[predictionDf["Normalized_RRx"]])
def graph_eightWeekMARRx(self, weeks, predict):
self.eightWeekMARRxChart = graphObj(self.drug, self.masterDf, weeks,
"Week", ["Eight_Week_MA_RRx"],
["scatter"])
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"Eight_Week_MA_RRx",
self.weeksToPredict).predictionDf
self.eightWeekMARRxChart.generateChart(
["scatter"], predictionDf["Week"],
[predictionDf["Eight_Week_MA_RRx"]])
def prediction():
if request.headers["Content-Type"] == "application/json":
drug = request.get_json()["drug"]
weeks = request.get_json()["weeks"]
source = request.get_json()["source"]
predictBool = request.get_json()["predictBool"]
weeksToTrainOn = request.get_json()["weeksToTrainOn"]
drugobj = drugObj(drug, weeks, source, predictBool, weeksToTrainOn)
target = request.get_json()["target"]
weeksToPredict = request.get_json()["weeksToPredict"]
predictionDf = modelClass.modelObj(
drugobj.masterDf, drugobj.weeksToTrainOn, target, weeksToPredict
).predictionDf
return predictionDf.to_json(orient="records")
else:
return "Unsupported mediatype"
def graph_eightWeekMARRxWoWGrowth(self, weeks, predict):
self.eightWeekMARRxWoWGrowthChart = graphObj(
self.drug,
self.masterDf,
weeks,
"Week",
["Eight_Week_MA_RRx_WoW_Growth"],
["plot"],
)
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"Eight_Week_MA_RRx_WoW_Growth",
self.weeksToPredict).predictionDf
self.eightWeekMARRxWoWGrowthChart.generateChart(
["plot"],
predictionDf["Week"],
[predictionDf["Eight_Week_MA_RRx_WoW_Growth"]],
)
def graph_thirteenWeekMANRx(self, weeks, predict):
self.thirteenWeekMANRxChart = graphObj(
self.drug,
self.masterDf,
weeks,
"Week",
["Thirteen_Week_MA_NRx"],
["scatter"],
)
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"Thirteen_Week_MA_NRx",
self.weeksToPredict).predictionDf
self.thirteenWeekMANRxChart.generateChart(
["scatter"],
predictionDf["Week"],
[predictionDf["Thirteen_Week_MA_NRx"]],
)
def graph_normalizedNRxLog(self, weeks, predict):
self.normalizedNRxLogChart = graphObj(
self.drug,
self.masterDf,
weeks,
"Week",
["Normalized_NRx"],
["logy_plot"],
yLabel="Normalized_TRx Log Scale",
)
# might have wierd predict behavior
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"Normalized_NRx",
self.weeksToPredict).predictionDf
self.normalizedNRxLogChart.generateChart(
["logy_plot"], predictionDf["Week"],
[predictionDf["Normalized_NRx"]])
def graph_rrxWowGrowth(self, weeks, predict):
"""
UPDATE so that ending and starting weeks can be chosen
UPDATE so that it returns the plot (use "ax" from matplotlib) so that it can be manipulated outside of the function
Creats and shows a graph of "RRx_Wow_Growth" and the specified number of weeks from the given dataframe.
PARAMETERS:
weeks: number of weeks to dispaly. The graph displays the 0th entry to the specified number of weeks. (int)
df: the dataframe from which to use the data to be displayed in graph. Must have "RRx_Wow_Growth" column. (dataframe)
"""
self.rrxWowGrowthChart = graphObj(self.drug, self.masterDf, weeks,
"Week", ["RRx_Wow_Growth"], ["plot"])
if predict == True:
predictionDf = modelObj(self.masterDf, self.weeksToTrainOn,
"RRx_Wow_Growth",
self.weeksToPredict).predictionDf
self.rrxWowGrowthChart.generateChart(
["plot"], predictionDf["Week"],
[predictionDf["RRx_Wow_Growth"]])
def graph_thirteenWeekMANRxWoWGrowth(self, weeks, predict):
self.thirteenWeekMANRxWoWGrowthChart = graphObj(
self.drug,
self.masterDf,
weeks,
"Week",
["Thirteen_Week_MA_NRx_WoW_Growth"],
["plot"],
)
if predict == True:
predictionDf = modelObj(
self.masterDf,
self.weeksToTrainOn,
"Thirteen_Week_MA_NRx_WoW_Growth",
self.weeksToPredict,
).predictionDf
self.thirteenWeekMANRxWoWGrowthChart.generateChart(
["plot"],
predictionDf["Week"],
[predictionDf["Thirteen_Week_MA_NRx_WoW_Growth"]],
)
