How to extrapolate one curve from others?
I have following plot where you can see 5 lines for different values of paramater p (3%, 5%, 7%, 9%, 11%). And I also have a linear approximation for every line as you can see.
I can't quite wrap my head around on how to predict a graph for different values of parameter p using interpolation and extrapolation methods.
For example, I need to predict graph for p = 1%, 6%, 15%
def linear_approximation(df, name , start=1, finish=20):
df_res = pd.DataFrame(df)
df_res['Linear'] = df[name]
linear_coeff = np.polyfit(np.arange(start, finish+1),df.loc[start:finish, name], 1)
linear_coeff = np.poly1d(linear_coeff)
df_res.loc[start:finish, 'Linear'] = linear_coeff(np.arange(start, finish+1))
return df_res
df_linear = res['data']
plt.figure(figsize=(15, 9))
for counter in ['1','2','3','4','5']:
df_linear[counter].plot(grid=True, linestyle = '--', lw = 4)
linear_approximation(df_linear,counter, 10, 80)['Linear'].plot(grid=True, color = 'Gray', linestyle = '-', lw = 4)
plt.legend(loc='center left',bbox_to_anchor=(1, 0.5), fontsize = '17')
python interpolation extrapolation
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
add a comment |
I have following plot where you can see 5 lines for different values of paramater p (3%, 5%, 7%, 9%, 11%). And I also have a linear approximation for every line as you can see.
I can't quite wrap my head around on how to predict a graph for different values of parameter p using interpolation and extrapolation methods.
For example, I need to predict graph for p = 1%, 6%, 15%
def linear_approximation(df, name , start=1, finish=20):
df_res = pd.DataFrame(df)
df_res['Linear'] = df[name]
linear_coeff = np.polyfit(np.arange(start, finish+1),df.loc[start:finish, name], 1)
linear_coeff = np.poly1d(linear_coeff)
df_res.loc[start:finish, 'Linear'] = linear_coeff(np.arange(start, finish+1))
return df_res
df_linear = res['data']
plt.figure(figsize=(15, 9))
for counter in ['1','2','3','4','5']:
df_linear[counter].plot(grid=True, linestyle = '--', lw = 4)
linear_approximation(df_linear,counter, 10, 80)['Linear'].plot(grid=True, color = 'Gray', linestyle = '-', lw = 4)
plt.legend(loc='center left',bbox_to_anchor=(1, 0.5), fontsize = '17')
python interpolation extrapolation
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
add a comment |
I have following plot where you can see 5 lines for different values of paramater p (3%, 5%, 7%, 9%, 11%). And I also have a linear approximation for every line as you can see.
I can't quite wrap my head around on how to predict a graph for different values of parameter p using interpolation and extrapolation methods.
For example, I need to predict graph for p = 1%, 6%, 15%
def linear_approximation(df, name , start=1, finish=20):
df_res = pd.DataFrame(df)
df_res['Linear'] = df[name]
linear_coeff = np.polyfit(np.arange(start, finish+1),df.loc[start:finish, name], 1)
linear_coeff = np.poly1d(linear_coeff)
df_res.loc[start:finish, 'Linear'] = linear_coeff(np.arange(start, finish+1))
return df_res
df_linear = res['data']
plt.figure(figsize=(15, 9))
for counter in ['1','2','3','4','5']:
df_linear[counter].plot(grid=True, linestyle = '--', lw = 4)
linear_approximation(df_linear,counter, 10, 80)['Linear'].plot(grid=True, color = 'Gray', linestyle = '-', lw = 4)
plt.legend(loc='center left',bbox_to_anchor=(1, 0.5), fontsize = '17')
python interpolation extrapolation
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
I have following plot where you can see 5 lines for different values of paramater p (3%, 5%, 7%, 9%, 11%). And I also have a linear approximation for every line as you can see.
I can't quite wrap my head around on how to predict a graph for different values of parameter p using interpolation and extrapolation methods.
For example, I need to predict graph for p = 1%, 6%, 15%
def linear_approximation(df, name , start=1, finish=20):
df_res = pd.DataFrame(df)
df_res['Linear'] = df[name]
linear_coeff = np.polyfit(np.arange(start, finish+1),df.loc[start:finish, name], 1)
linear_coeff = np.poly1d(linear_coeff)
df_res.loc[start:finish, 'Linear'] = linear_coeff(np.arange(start, finish+1))
return df_res
df_linear = res['data']
plt.figure(figsize=(15, 9))
for counter in ['1','2','3','4','5']:
df_linear[counter].plot(grid=True, linestyle = '--', lw = 4)
linear_approximation(df_linear,counter, 10, 80)['Linear'].plot(grid=True, color = 'Gray', linestyle = '-', lw = 4)
plt.legend(loc='center left',bbox_to_anchor=(1, 0.5), fontsize = '17')
python interpolation extrapolation
python interpolation extrapolation
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
asked Nov 15 '18 at 8:59
Lust_For_LoveLust_For_Love
1501110
1501110
add a comment |
add a comment |
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