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@ -20,6 +20,9 @@ class analytics :
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self.cache['key'] = args['key']
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self.cache['key'] = args['key']
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self.cache['name'] = self.__class__.__name__
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self.cache['name'] = self.__class__.__name__
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self.init()
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self.init()
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def get_formatted_date(self,row):
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m = {1:"Jan",2:"Feb",3:"Mar",4:"Apr",5:"May",6:"Jun",7:"Jul",8:"Aug",9:"Sep",10:"Oct",11:"Nov",12:"Dec"}
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return "-".join([m[row['month']],str(row['day']),str(row['year'])]) +" "+ " ".join([str(row['hour']),'h :',str(row['minute']),'min' ])
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def init(self):
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def init(self):
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"""
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"""
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Store logs as is per node i.e this enables to see the latest pull
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Store logs as is per node i.e this enables to see the latest pull
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@ -83,7 +86,7 @@ class apps(analytics) :
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"""
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"""
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analytics.init(self)
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analytics.init(self)
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grid = {"width":"100%","editing":False,"rowClass":"small"}
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grid = {"width":"100%","editing":False,"rowClass":"small"}
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grid['fields'] = [{"name":"name","title":"Process","headercss":"small"},{"name":"cpu","title":"CPU Usage","headercss":"small","type":"number"},{"name":"mem","title":"RAM Usage","headercss":"small","type":"number"},{"name":"status","title":"Status","headercss":"small","align":"center"}]
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grid['fields'] = [{"name":"name","title":"Process","headercss":"small"},{"name":"cpu","title":"CPU Usage","headercss":"small","type":"number"},{"name":"mem","title":"RAM Usage","headercss":"small","type":"number"},{"name":"status","title":"Status","headercss":"small","align":"center","width":"32px"}]
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self.set('grid',grid)
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self.set('grid',grid)
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def summary(self,data):
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def summary(self,data):
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@ -92,15 +95,17 @@ class apps(analytics) :
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In terms of {crash,idle,running} counts
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In terms of {crash,idle,running} counts
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"""
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"""
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r = []
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r = []
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for node in data :
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for node in data :
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logs = data[node]['logs']
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logs = data[node]['logs']
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date = data[node]['date']['long']
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date = data[node]['date']['long']
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formatted_date = self.get_formatted_date(data[node]['date'])
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df = pd.DataFrame(logs)
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df = pd.DataFrame(logs)
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df = df[df.name.str.contains('other',na=False)==False]
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df = df[df.name.str.contains('other',na=False)==False]
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crash = df.status.str.contains('X').sum()
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crash = df.status.str.contains('X').sum()
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idle = df.status.str.contains('S').sum()
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idle = df.status.str.contains('S').sum()
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running = df.shape[0] - crash - idle
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running = df.shape[0] - crash - idle
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r.append({"date":date,"node":node,"running":running,"idle":idle,"crash":crash})
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r.append({"date":date,"node":node,"running":running,"idle":idle,"crash":crash,"formatted_date":formatted_date})
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return r
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return r
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# logs = pd.DataFrame(self.get('logs'))
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# logs = pd.DataFrame(self.get('logs'))
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@ -137,11 +142,12 @@ class apps(analytics) :
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X = [[other_df.cpu.sum(),watch_df.cpu.sum()],[other_df.mem.sum(),watch_df.mem.sum()]]
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X = [[other_df.cpu.sum(),watch_df.cpu.sum()],[other_df.mem.sum(),watch_df.mem.sum()]]
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date= data[node]['date']['long']
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date= data[node]['date']['long']
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formatted_date = self.get_formatted_date(data[node]['date'])
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q.append({"node":node, "x":X,"labels":labels, "title":title,"series":series,"ylabel":ylabel})
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q.append({"node":node, "x":X,"labels":labels, "title":title,"series":series,"ylabel":ylabel})
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crash = watch_df.status.str.contains('X').sum()
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crash = watch_df.status.str.contains('X').sum()
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idle = watch_df.status.str.contains('S').sum()
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idle = watch_df.status.str.contains('S').sum()
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running = N - crash - idle
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running = N - crash - idle
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r.append( {"x":[crash,idle,running],"labels":['Crash','Idle','Running'],"title":"","date":date})
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r.append( {"x":[crash,idle,running],"labels":['Crash','Idle','Running'],"title":"","date":date,"formatted_date":formatted_date})
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self.set("resource",q)
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self.set("resource",q)
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self.set("status",r)
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self.set("status",r)
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@ -187,12 +193,16 @@ class folders(analytics):
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else:
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else:
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max_size = 0
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max_size = 0
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self.set('max_size',max_size)
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self.set('max_size',max_size)
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q = []
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for node in data :
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for node in data :
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df = pd.DataFrame(data[node]['logs'])
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df = pd.DataFrame(data[node]['logs'])
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N = df.shape[0]
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N = df.shape[0]
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df = pd.DataFrame(df.mean()[['size_in_kb','files','age_in_days']]).T
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df = pd.DataFrame(df.mean()[['size_in_kb','files','age_in_days']]).T
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formatted_date = self.get_formatted_date(data[node]['date'])
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r.append({"node":node,"folders":N, "max_size":max_size,"size":np.round(df.size_in_kb.values[0]*.000001,2),"age":df.age_in_days.values[0].round(2),"files":df.files.values[0].round(2)})
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r.append({"node":node,"folders":N, "max_size":max_size,"max_folder_size":np.round(df.size_in_kb.max()*.000001,2),"avg_folder_size":np.round(df.size_in_kb.mean()*.000001,2),"age":df.age_in_days.values[0].round(2),"avg_files_folder":df.files.max().round(2),"formatted_date":formatted_date})
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X = [[np.round(df.size_in_kb.mean()*.000001,2),np.round(df.size_in_kb.max()*.000001,2)],[df.files.mean().round(2),df.files.max().round(2)]]
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q.append({"node":node, "x":X,"labels":['Average','Largest'], "title":"","series":['Size (MB)','Files'],"ylabel":""})
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self.set("analysis",q)
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return r
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return r
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# for id in logs :
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# for id in logs :
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