用R从流量数据计算钟形曲线
我正在寻找一种使用R（最好是ggplot2，尽管这不是很重要）从静脉注射设定流量数据计算钟形曲线的方法。以下是来自液体加热泵的一些流量数据： 到目前为止，我有：用R从流量数据计算钟形曲线,r,ggplot2,R,Ggplot2,我正在寻找一种使用R（最好是ggplot2，尽管这不是很重要）从静脉注射设定流量数据计算钟形曲线的方法。以下是来自液体加热泵的一些流量数据： 到目前为止，我有： flow<-read.table(file="flow.dat",header=T) flow$diff<-c(0,diff(flow$Mass)) ggplot(data=flow, aes(x=Secs,y=diff)) + geom_line() flow抱歉，这花了这么长时间，时间紧迫 你提供的数据似乎不符合你对小
flow<-read.table(file="flow.dat",header=T)
flow$diff<-c(0,diff(flow$Mass))
ggplot(data=flow, aes(x=Secs,y=diff)) + geom_line()
flow抱歉，这花了这么长时间，时间紧迫
你提供的数据似乎不符合你对小号曲线的描述，或者我遗漏了一些重要的东西。简而言之，如果您能描述一下需要对数据做些什么，我将不胜感激
当您设法为输出塑造数据时，您可以将其粘贴到下面的代码中，它应该会生成一个绘图。我会根据你的需要来定制它
# generate some random data
trump <- data.frame(
curve1 = rev(sort(rchisq(100, df = 2))) * rnorm(100, mean = 5, sd = 0.1) + 3,
curve2 = -rev(sort(rchisq(100, df = 2))) * rnorm(100, mean = 5, sd = 0.1) - 3
)
trump <- trump[seq(from = 1, to = 100, by = 3), ]
ggplot(trump, aes(x = 1:nrow(trump), y = curve1)) +
geom_line() +
geom_point() +
geom_line(aes(y = curve2)) +
geom_point(aes(y = curve2)) +
geom_hline(aes(yintercept = 0), linetype = "solid") + # overall percentage error
geom_hline(aes(yintercept = 5), linetype = "dashed") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
annotate("text", x = 8, y = -1.5, label = "overall percentage error") +
annotate("text", x = 5, y = 3, label = "set rate") +
annotate("text", x = 10, y = -24, label = "min. error") +
annotate("text", x = 10, y = 24, label = "max. error") +
theme_bw()
#生成一些随机数据
特朗普很抱歉，这花了这么长时间，时间紧迫
你提供的数据似乎不符合你对小号曲线的描述，或者我遗漏了一些重要的东西。简而言之，如果您能描述一下需要对数据做些什么，我将不胜感激
当您设法为输出塑造数据时，您可以将其粘贴到下面的代码中，它应该会生成一个绘图。我会根据你的需要来定制它
# generate some random data
trump <- data.frame(
curve1 = rev(sort(rchisq(100, df = 2))) * rnorm(100, mean = 5, sd = 0.1) + 3,
curve2 = -rev(sort(rchisq(100, df = 2))) * rnorm(100, mean = 5, sd = 0.1) - 3
)
trump <- trump[seq(from = 1, to = 100, by = 3), ]
ggplot(trump, aes(x = 1:nrow(trump), y = curve1)) +
geom_line() +
geom_point() +
geom_line(aes(y = curve2)) +
geom_point(aes(y = curve2)) +
geom_hline(aes(yintercept = 0), linetype = "solid") + # overall percentage error
geom_hline(aes(yintercept = 5), linetype = "dashed") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
annotate("text", x = 8, y = -1.5, label = "overall percentage error") +
annotate("text", x = 5, y = 3, label = "set rate") +
annotate("text", x = 10, y = -24, label = "min. error") +
annotate("text", x = 10, y = 24, label = "max. error") +
theme_bw()
#生成一些随机数据
特朗普非常感谢罗曼的帮助。我们就是这样做的
flow<-read.table("iv.txt",comment.char="#",header=TRUE)
Q<-as.data.frame(c(0,(60*diff(flow$wt,1,1)/0.5))) # Q is used for flow in the standard
names(Q)<-"Q"
# Now combine them
l<-list(flow,Q)
dflow<-as.data.frame(l)
t1flow<-subset(dflow, dflow$secs>60*60 & dflow$secs<=120*60) # require the second hour of the data
t1flow$QE<-((t1flow$Q-setflow)/setflow)*100 # calculate the error
library(TTR) # use this for moving averages
#
# Avert your eyes! I know there must be a slicker way of doing this .....
#
# Calculate the moving average using a sample rate of every 4 readings (2 mins of 30sec readings)
QE2<-SMA(t1flow$QE,4)
minQE2<-min(QE2,na.rm=TRUE)
maxQE2<-max(QE2,na.rm=TRUE)
# now for the 5 minute window
QE5<-SMA(t1flow$QE,10)
minQE5<-min(QE5,na.rm=TRUE)
maxQE5<-max(QE5,na.rm=TRUE)
# Set window to 11 mins
QE11<-SMA(t1flow$QE,22)
minQE11<-min(QE11,na.rm=TRUE)
maxQE11<-max(QE11,na.rm=TRUE)
# Set window to 19 mins
QE19<-SMA(t1flow$QE,38)
minQE19<-min(QE19,na.rm=TRUE)
maxQE19<-max(QE19,na.rm=TRUE)
# Set window to 31 mins
QE31<-SMA(t1flow$QE,62)
minQE31<-min(QE31,na.rm=TRUE)
maxQE31<-max(QE31,na.rm=TRUE)
#
# OK - you can look again :-)
#
# create a data frame from this data
trump<-data.frame(c(2,5,11,19,31),c(minQE2,minQE5,minQE11,minQE19,minQE31),c(maxQE2,maxQE5,maxQE11,maxQE19,maxQE31))
names(trump)<-c("T","minE","maxE")
A<-mean(t1flow$QE) # calculate the overall mean percentage error
error_caption<-paste("overall percentage error = ",A,"%") # create the string to label the error line
# plot the graph
ggplot(trump, aes(x = T, y = minE)) +
geom_line() +
geom_point(color="red") +
geom_line(aes(y = maxE)) +
geom_point(aes(y = maxE),colour="red") +
geom_hline(aes(yintercept = 0), linetype = "dashed") + # overall percentage error
geom_hline(aes(yintercept = A), linetype = "solid") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
scale_x_continuous(breaks=c(0,2,5,11,19,31),limits=c(0,32)) + # label the x axis only at the window values
annotate("text", x = 10, y = A-0.5, label = error_caption) + # add the error line label
opts(title="Trumpet curve for Test Data")
flow非常感谢罗曼的帮助。我们就是这样做的
flow<-read.table("iv.txt",comment.char="#",header=TRUE)
Q<-as.data.frame(c(0,(60*diff(flow$wt,1,1)/0.5))) # Q is used for flow in the standard
names(Q)<-"Q"
# Now combine them
l<-list(flow,Q)
dflow<-as.data.frame(l)
t1flow<-subset(dflow, dflow$secs>60*60 & dflow$secs<=120*60) # require the second hour of the data
t1flow$QE<-((t1flow$Q-setflow)/setflow)*100 # calculate the error
library(TTR) # use this for moving averages
#
# Avert your eyes! I know there must be a slicker way of doing this .....
#
# Calculate the moving average using a sample rate of every 4 readings (2 mins of 30sec readings)
QE2<-SMA(t1flow$QE,4)
minQE2<-min(QE2,na.rm=TRUE)
maxQE2<-max(QE2,na.rm=TRUE)
# now for the 5 minute window
QE5<-SMA(t1flow$QE,10)
minQE5<-min(QE5,na.rm=TRUE)
maxQE5<-max(QE5,na.rm=TRUE)
# Set window to 11 mins
QE11<-SMA(t1flow$QE,22)
minQE11<-min(QE11,na.rm=TRUE)
maxQE11<-max(QE11,na.rm=TRUE)
# Set window to 19 mins
QE19<-SMA(t1flow$QE,38)
minQE19<-min(QE19,na.rm=TRUE)
maxQE19<-max(QE19,na.rm=TRUE)
# Set window to 31 mins
QE31<-SMA(t1flow$QE,62)
minQE31<-min(QE31,na.rm=TRUE)
maxQE31<-max(QE31,na.rm=TRUE)
#
# OK - you can look again :-)
#
# create a data frame from this data
trump<-data.frame(c(2,5,11,19,31),c(minQE2,minQE5,minQE11,minQE19,minQE31),c(maxQE2,maxQE5,maxQE11,maxQE19,maxQE31))
names(trump)<-c("T","minE","maxE")
A<-mean(t1flow$QE) # calculate the overall mean percentage error
error_caption<-paste("overall percentage error = ",A,"%") # create the string to label the error line
# plot the graph
ggplot(trump, aes(x = T, y = minE)) +
geom_line() +
geom_point(color="red") +
geom_line(aes(y = maxE)) +
geom_point(aes(y = maxE),colour="red") +
geom_hline(aes(yintercept = 0), linetype = "dashed") + # overall percentage error
geom_hline(aes(yintercept = A), linetype = "solid") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
scale_x_continuous(breaks=c(0,2,5,11,19,31),limits=c(0,32)) + # label the x axis only at the window values
annotate("text", x = 10, y = A-0.5, label = error_caption) + # add the error line label
opts(title="Trumpet curve for Test Data")
flow我目前正在尝试解决钟形曲线计算错误。根据IEC 60601-2-24:1998-第八节-操作数据的准确性
以及针对危险输出的保护。这是我的R代码：
flow<-read.table("c:\\ciringe.txt",comment.char="#",header=TRUE)
#parameters
# setflow = 1
# P = 1,2,5,11,19,31
P1<-1
P2<-2
P5<-5
P11<-11
P19<- 19
P31<-31
P1m = ((60 - P1) / 0.5 ) + 1
P2m = ((60 - P2) / 0.5 ) + 1
P5m = ((60 - P5) / 0.5 ) + 1
P11m = ((60 - P11) / 0.5 ) + 1
P19m = ((60 - P19) / 0.5 ) + 1
P31m = ((60 - P31) / 0.5 ) + 1
setflow<-1
mQE1<-0.5
mQE2<-0.25
mQE5<-0.1
mQE11<-0.045
mQE19<-0.0263
mQE31<-0.0161
Q<-as.data.frame(c(0,(60*diff(flow$wt,1,1)/0.5*0.998))) # Q is used for flow in the standard
names(Q)<-"Q"
# Now combine them
l<-list(flow,Q)
dflow<-as.data.frame(l)
t1flow<-subset(dflow, dflow$secs>=3600 & dflow$secs<=7200) # require the second hour of the data
#overall
t1flow$QE<-(((t1flow$Q-setflow)/setflow)*100) # calculate the error
t1flow$QE1<-(((t1flow$Q-setflow)/setflow)*100) * mQE1 # calculate the error
t1flow$QE2<-(((t1flow$Q-setflow)/setflow)*100) * mQE2 # calculate the error
t1flow$QE5<-(((t1flow$Q-setflow)/setflow)*100) * mQE5 # calculate the error
t1flow$QE11<-(((t1flow$Q-setflow)/setflow)*100) * mQE11 # calculate the error
t1flow$QE19<-(((t1flow$Q-setflow)/setflow)*100) * mQE19 # calculate the error
t1flow$QE31<-(((t1flow$Q-setflow)/setflow)*100) * mQE31 # calculate the error
library(TTR) # use this for moving averages
#
# Avert your eyes! I know there must be a slicker way of doing this .....
#
# Calculate the moving average using a sample rate of every
# 4 readings (2 mins of 30sec readings)
# now for the 1 minute window
QE1<-SMA(t1flow$QE1,2)
minQE1<-min(QE1,na.rm=TRUE)
maxQE1<-max(QE1,na.rm=TRUE)
# now for the 2 minute window
QE2<-SMA(t1flow$QE2,4)
minQE2<-min(QE2,na.rm=TRUE)
maxQE2<-max(QE2,na.rm=TRUE)
# now for the 5 minute window
QE5<-SMA(t1flow$QE5,10)
minQE5<-min(QE5,na.rm=TRUE)
maxQE5<-max(QE5,na.rm=TRUE)
# Set window to 11 mins
QE11<-SMA(t1flow$QE11,22)
minQE11<-min(QE11,na.rm=TRUE)
maxQE11<-max(QE11,na.rm=TRUE)
# Set window to 19 mins
QE19<-SMA(t1flow$QE19,38)
minQE19<-min(QE19,na.rm=TRUE)
maxQE19<-max(QE19,na.rm=TRUE)
# Set window to 31 mins
QE31<-SMA(t1flow$QE31,62)
minQE31<-min(QE31,na.rm=TRUE)
maxQE31<-max(QE31,na.rm=TRUE)
#
# OK - you can look again :-)
#
# create a data frame from this data
trump<- data.frame(c(1,2,5,11,19,31),c(minQE1,minQE2,minQE5,minQE11,minQE19,minQE31), c(maxQE1,maxQE2,maxQE5,maxQE11,maxQE19,maxQE31))
names(trump)<-c("T","minE","maxE")
A<-mean(t1flow$QE) # calculate the overall mean percentage error
error_caption<-paste("overall percentage error = ",A,"%") # create the string to label the error line
# plot the graph
library(ggplot2)
ggplot(trump, aes(x = T, y = minE)) +
geom_line() +
geom_point(color="red") +
geom_line(aes(y = maxE)) +
geom_point(aes(y = maxE),colour="red") +
geom_hline(aes(yintercept = 0), linetype = "dashed") + # overall percentage error
geom_hline(aes(yintercept = A), linetype = "solid") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
scale_x_continuous(breaks=c(0,2,5,11,19,31),limits=c(0,32)) + # label the x axis only at the window values
annotate("text", x = 10, y = A-0.5, label = error_caption) # add the error line label
flow我目前正在尝试解决钟形曲线计算错误。根据IEC 60601-2-24:1998-第八节-操作数据的准确性
以及针对危险输出的保护。这是我的R代码：
flow<-read.table("c:\\ciringe.txt",comment.char="#",header=TRUE)
#parameters
# setflow = 1
# P = 1,2,5,11,19,31
P1<-1
P2<-2
P5<-5
P11<-11
P19<- 19
P31<-31
P1m = ((60 - P1) / 0.5 ) + 1
P2m = ((60 - P2) / 0.5 ) + 1
P5m = ((60 - P5) / 0.5 ) + 1
P11m = ((60 - P11) / 0.5 ) + 1
P19m = ((60 - P19) / 0.5 ) + 1
P31m = ((60 - P31) / 0.5 ) + 1
setflow<-1
mQE1<-0.5
mQE2<-0.25
mQE5<-0.1
mQE11<-0.045
mQE19<-0.0263
mQE31<-0.0161
Q<-as.data.frame(c(0,(60*diff(flow$wt,1,1)/0.5*0.998))) # Q is used for flow in the standard
names(Q)<-"Q"
# Now combine them
l<-list(flow,Q)
dflow<-as.data.frame(l)
t1flow<-subset(dflow, dflow$secs>=3600 & dflow$secs<=7200) # require the second hour of the data
#overall
t1flow$QE<-(((t1flow$Q-setflow)/setflow)*100) # calculate the error
t1flow$QE1<-(((t1flow$Q-setflow)/setflow)*100) * mQE1 # calculate the error
t1flow$QE2<-(((t1flow$Q-setflow)/setflow)*100) * mQE2 # calculate the error
t1flow$QE5<-(((t1flow$Q-setflow)/setflow)*100) * mQE5 # calculate the error
t1flow$QE11<-(((t1flow$Q-setflow)/setflow)*100) * mQE11 # calculate the error
t1flow$QE19<-(((t1flow$Q-setflow)/setflow)*100) * mQE19 # calculate the error
t1flow$QE31<-(((t1flow$Q-setflow)/setflow)*100) * mQE31 # calculate the error
library(TTR) # use this for moving averages
#
# Avert your eyes! I know there must be a slicker way of doing this .....
#
# Calculate the moving average using a sample rate of every
# 4 readings (2 mins of 30sec readings)
# now for the 1 minute window
QE1<-SMA(t1flow$QE1,2)
minQE1<-min(QE1,na.rm=TRUE)
maxQE1<-max(QE1,na.rm=TRUE)
# now for the 2 minute window
QE2<-SMA(t1flow$QE2,4)
minQE2<-min(QE2,na.rm=TRUE)
maxQE2<-max(QE2,na.rm=TRUE)
# now for the 5 minute window
QE5<-SMA(t1flow$QE5,10)
minQE5<-min(QE5,na.rm=TRUE)
maxQE5<-max(QE5,na.rm=TRUE)
# Set window to 11 mins
QE11<-SMA(t1flow$QE11,22)
minQE11<-min(QE11,na.rm=TRUE)
maxQE11<-max(QE11,na.rm=TRUE)
# Set window to 19 mins
QE19<-SMA(t1flow$QE19,38)
minQE19<-min(QE19,na.rm=TRUE)
maxQE19<-max(QE19,na.rm=TRUE)
# Set window to 31 mins
QE31<-SMA(t1flow$QE31,62)
minQE31<-min(QE31,na.rm=TRUE)
maxQE31<-max(QE31,na.rm=TRUE)
#
# OK - you can look again :-)
#
# create a data frame from this data
trump<- data.frame(c(1,2,5,11,19,31),c(minQE1,minQE2,minQE5,minQE11,minQE19,minQE31), c(maxQE1,maxQE2,maxQE5,maxQE11,maxQE19,maxQE31))
names(trump)<-c("T","minE","maxE")
A<-mean(t1flow$QE) # calculate the overall mean percentage error
error_caption<-paste("overall percentage error = ",A,"%") # create the string to label the error line
# plot the graph
library(ggplot2)
ggplot(trump, aes(x = T, y = minE)) +
geom_line() +
geom_point(color="red") +
geom_line(aes(y = maxE)) +
geom_point(aes(y = maxE),colour="red") +
geom_hline(aes(yintercept = 0), linetype = "dashed") + # overall percentage error
geom_hline(aes(yintercept = A), linetype = "solid") + # set rate
xlab("Observation windows (in minutes)") +
ylab("% error") +
scale_x_continuous(breaks=c(0,2,5,11,19,31),limits=c(0,32)) + # label the x axis only at the window values
annotate("text", x = 10, y = A-0.5, label = error_caption) # add the error line label
flow您的公式与ISO 60601-2-24中的方程式不匹配。该规范有点乏味，但没有2分钟的窗口或移动平均线。首先，将数据格式化为1分钟样本（根据规范）。以1分钟的间隔执行数据数组，列数为：
colTotalLiquid = 1 #total liquid (precalculated from weight)
colProgRate = 2 #prog rate
colQi = 3 # rate Q(i) from IEC60601-2-24
obsWindow = (2, 5, 11, 19, 31) # observation windows in minutes
analysisT1start = 60 # start of analysis period T1
analysisT1end = 120 # end of analysis period T1
t1err = dict.fromkeys(obsWindow, None)
progRate = data[analysisT1start][colProgRate]
A = 100 * (((data[analysisT1end][colTotalLiquid] - data[analysisT1start][colTotalLiquid]) * hours / (analysisT1end - analysisT1start)) - progRate) / progRate
t1TrumpetX = [] #mintes
t1TrumpetY1 = [] #Ep(max)
t1TrumpetY2 = [] #Ep(min)
t1TrumpetY3 = [] #mean err
t1TrumpetY4 = [] #prog Rate
for p in obsWindow:
m = (analysisT1end - analysisT1start) - p + 1
t1err[p] = {'m': m}
EpMax = 0
EpMin = 0
for j in range(1, m + 1):
errSum = 0
for i in range(j, j + p):
errSum += (1.0/p) * 100 * ( data[analysisT1start + i][colQi] - progRate ) / progRate
if errSum > EpMax:
EpMax = errSum
if errSum < EpMin:
EpMin = errSum
t1err[p]['EpMax'] = EpMax
t1err[p]['EpMin'] = EpMin
t1TrumpetX.append(p)
t1TrumpetY1.append(EpMax)
t1TrumpetY2.append(EpMin)
t1TrumpetY3.append(A)
t1TrumpetY4.append(0)
tplot = PdfPages('trumpet curve.pdf')
p1 = plt.figure()
plt.plot(t1TrumpetX, t1TrumpetY1)
plt.plot(t1TrumpetX, t1TrumpetY2)
plt.plot(t1TrumpetX, t1TrumpetY3)
plt.plot(t1TrumpetX, t1TrumpetY4, '--')
plt.legend(('Ep(max)','Ep(min)', 'overall error', 'set rate'), fontsize='xx-small', loc='best')
plt.title(baseName + ': IEC60601-2-24 Trumpet Curve for Second Hour', fontsize='x-small')
plt.xlabel('Observation window (min)', fontsize='x-small')
plt.ylabel('Percentage error of flow', fontsize='x-small')
plt.ylim((-15, 15))
ax = plt.axes()
ax.set_xticks(obsWindow)
tplot.savefig(p1)
plt.close(p1)
tplot.close(p1)
colTotalLiquid=1#总液体（根据重量预先计算）
colProgRate=2#进步率
colQi=3#IEC60601-2-24规定的费率Q（i）
观察窗=（2,5,11,19,31）#观察窗（分钟）
analysisT1start=60#分析周期T1的开始
analysisT1end=120#分析期T1结束
t1err=dict.fromkeys（obsWindow，无）
progRate=数据[analysisT1start][colProgRate]
A=100*（（数据[analysisT1end][coltotaliquid]-数据[analysisT1start][coltotaliquid]）*hours/（analysisT1end-analysisT1start））-progRate）/progRate
t1TrumpetX=[]明特斯
T1TRUMPETY 1=[]#Ep（最大值）
T12=[]最小值（Ep）
t1TrumpetY3=[]平均误差
t1TrumpetY4=[]进步率
对于OBS窗口中的p：
m=（分析T1end-分析T1start）-p+1
t1err[p]={m'：m}
EpMax=0
EpMin=0
对于范围（1，m+1）内的j：
errSum=0
对于范围内的i（j，j+p）：
errSum+=（1.0/p）*100*（数据[analysisT1start+i][colQi]-进度）/进度
如果errSum>EpMax：
EpMax=errSum
如果errSum
这应该可以得到标准的喇叭曲线。无需在总体误差a上加上数字标签，但您可以通过注释进行标注。您的公式与ISO 60601-2-24中的公式不匹配。该规范有点乏味，但没有2分钟的窗口或移动平均线。首先，将数据格式化为1分钟样本（根据规范）。以1分钟的间隔执行数据数组，列数为：
colTotalLiquid = 1 #total liquid (precalculated from weight)
colProgRate = 2 #prog rate
colQi = 3 # rate Q(i) from IEC60601-2-24
obsWindow = (2, 5, 11, 19, 31) # observation windows in minutes
analysisT1start = 60 # start of analysis period T1
analysisT1end = 120 # end of analysis period T1
t1err = dict.fromkeys(obsWindow, None)
progRate = data[analysisT1start][colProgRate]
A = 100 * (((data[analysisT1end][colTotalLiquid] - data[analysisT1start][colTotalLiquid]) * hours / (analysisT1end - analysisT1start)) - progRate) / progRate
t1TrumpetX = [] #mintes
t1TrumpetY1 = [] #Ep(max)
t1TrumpetY2 = [] #Ep(min)
t1TrumpetY3 = [] #mean err
t1TrumpetY4 = [] #prog Rate
for p in obsWindow:
m = (analysisT1end - analysisT1start) - p + 1
t1err[p] = {'m': m}
EpMax = 0
EpMin = 0
for j in range(1, m + 1):
errSum = 0
for i in range(j, j + p):
errSum += (1.0/p) * 100 * ( data[analysisT1start + i][colQi] - progRate ) / progRate
if errSum > EpMax:
EpMax = errSum
if errSum < EpMin:
EpMin = errSum
t1err[p]['EpMax'] = EpMax
t1err[p]['EpMin'] = EpMin
t1TrumpetX.append(p)
t1TrumpetY1.append(EpMax)
t1TrumpetY2.append(EpMin)
t1TrumpetY3.append(A)
t1TrumpetY4.append(0)
tplot = PdfPages('trumpet curve.pdf')
p1 = plt.figure()
plt.plot(t1TrumpetX, t1TrumpetY1)
plt.plot(t1TrumpetX, t1TrumpetY2)
plt.plot(t1TrumpetX, t1TrumpetY3)
plt.plot(t1TrumpetX, t1TrumpetY4, '--')
plt.legend(('Ep(max)','Ep(min)', 'overall error', 'set rate'), fontsize='xx-small', loc='best')
plt.title(baseName + ': IEC60601-2-24 Trumpet Curve for Second Hour', fontsize='x-small')
plt.xlabel('Observation window (min)', fontsize='x-small')
plt.ylabel('Percentage error of flow', fontsize='x-small')
plt.ylim((-15, 15))
ax = plt.axes()
ax.set_xticks(obsWindow)
tplot.savefig(p1)
plt.close(p1)
tplot.close(p1)
colTotalLiquid=1#总液体（根据重量预先计算）
colProgRate=2#进步率
colQi=3#IEC60601-2-24规定的费率Q（i）
观察窗=（2,5,11,19,31）#观察窗（分钟）
analysisT1start=60#分析周期T1的开始
analysisT1end=120#分析期T1结束
t1err=dict.fromkeys（obsWindow，无）
progRate=数据[analysisT1start][colProgRate]
A=100*（（数据[analysisT1end][coltotaliquid]-数据[analysisT1start][coltotaliquid]）*hours/（analysisT1end-analysisT1start））-progRate）/progRate
t1TrumpetX=[]明特斯
T1TRUMPETY 1=[]#Ep（最大值）
T12=[]最小值（Ep）
t1TrumpetY3=[]平均误差
t1TrumpetY4=[]进步率
对于OBS窗口中的p：
m=（分析T1end-分析T1start）-p+1
t1err[p]={m'：m}
EpMax=0
EpMin=0
对于范围（1，m+1）内的j：
errSum=0
对于范围内的i（j，j+p）：
errSum+=（1.0/p）*100*（数据[analysisT1start+i][colQi]-进度）/进度
如果errSum>EpMax：
EpMax=errSum
如果errSum