askill
/
claude
mirror of https://github.com/Askill/claude.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Post stream fix 

Minor plotting addition and improved efficiency of vertical derivative
This commit is contained in:
Simon Clark 2020-09-02 21:47:19 +01:00
parent b8a7f94e2c
commit 0cc169b000
16 changed files with 555 additions and 643 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
build/lib.win-amd64-3.8/claude_low_level_library.cp38-win_amd64.pyd
View File

Binary file not shown.

BIN
build/lib.win-amd64-3.8/claude_top_level_library.cp38-win_amd64.pyd
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_low_level_library.cp38-win_amd64.exp
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_low_level_library.cp38-win_amd64.lib
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_low_level_library.obj
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_top_level_library.cp38-win_amd64.exp
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_top_level_library.cp38-win_amd64.lib
View File

Binary file not shown.

BIN
build/temp.win-amd64-3.8/Release/claude_top_level_library.obj
View File

Binary file not shown.

1144
claude_low_level_library.c
View File

File diff suppressed because it is too large Load Diff

BIN
claude_low_level_library.cp38-win_amd64.pyd
View File

Binary file not shown.

4
claude_low_level_library.pyx
View File

@ -34,7 +34,6 @@ def scalar_gradient_y_2d(a,dy,nlat,i,j):
return (a[i+1,j]-a[i-1,j])/dy
def scalar_gradient_z(a,dz,i,j,k):
cdef np.ndarray output = np.zeros_like(a)
cdef np.int_t nlevels = len(dz)
if k == 0:
return (a[i,j,k+1]-a[i,j,k])/dz[k]
@ -43,8 +42,7 @@ def scalar_gradient_z(a,dz,i,j,k):
else:
return (a[i,j,k+1]-a[i,j,k-1])/(2*dz[k])
def scalar_gradient_z_1D(np.ndarray a,np.ndarray dz,np.int_t i,np.int_t j,np.int_t k):
cdef np.ndarray output = np.zeros_like(a)
def scalar_gradient_z_1D(np.ndarray a,np.ndarray dz,np.int_t k):
cdef np.int_t nlevels = len(dz)
if k == 0:
return (a[k+1]-a[k])/dz[k]

24
claude_top_level_library.c
View File

@ -3638,7 +3638,7 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
*
* # gradient of difference provides heating at each level
* for k in np.arange(nlevels): # <<<<<<<<<<<<<<
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,0,0,k)/(1E3*density_profile[k])
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,k)/(1E3*density_profile[k])
* # make sure model does not have a higher top than 50km!!
*/
__Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_np); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 72, __pyx_L1_error)
@ -3710,7 +3710,7 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
/* "claude_top_level_library.pyx":73
* # gradient of difference provides heating at each level
* for k in np.arange(nlevels):
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,0,0,k)/(1E3*density_profile[k]) # <<<<<<<<<<<<<<
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,k)/(1E3*density_profile[k]) # <<<<<<<<<<<<<<
* # make sure model does not have a higher top than 50km!!
* # approximate SW heating of ozone
*/
@ -3735,8 +3735,8 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
}
#if CYTHON_FAST_PYCALL
if (PyFunction_Check(__pyx_t_17)) {
PyObject *__pyx_temp[6] = {__pyx_t_15, __pyx_t_3, ((PyObject *)__pyx_v_dz), __pyx_int_0, __pyx_int_0, __pyx_v_k};
__pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_17, __pyx_temp+1-__pyx_t_12, 5+__pyx_t_12); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)
PyObject *__pyx_temp[4] = {__pyx_t_15, __pyx_t_3, ((PyObject *)__pyx_v_dz), __pyx_v_k};
__pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_17, __pyx_temp+1-__pyx_t_12, 3+__pyx_t_12); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)
__Pyx_XDECREF(__pyx_t_15); __pyx_t_15 = 0;
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
@ -3744,15 +3744,15 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
#endif
#if CYTHON_FAST_PYCCALL
if (__Pyx_PyFastCFunction_Check(__pyx_t_17)) {
PyObject *__pyx_temp[6] = {__pyx_t_15, __pyx_t_3, ((PyObject *)__pyx_v_dz), __pyx_int_0, __pyx_int_0, __pyx_v_k};
__pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_17, __pyx_temp+1-__pyx_t_12, 5+__pyx_t_12); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)
PyObject *__pyx_temp[4] = {__pyx_t_15, __pyx_t_3, ((PyObject *)__pyx_v_dz), __pyx_v_k};
__pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_17, __pyx_temp+1-__pyx_t_12, 3+__pyx_t_12); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)
__Pyx_XDECREF(__pyx_t_15); __pyx_t_15 = 0;
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
} else
#endif
{
__pyx_t_16 = PyTuple_New(5+__pyx_t_12); if (unlikely(!__pyx_t_16)) __PYX_ERR(0, 73, __pyx_L1_error)
__pyx_t_16 = PyTuple_New(3+__pyx_t_12); if (unlikely(!__pyx_t_16)) __PYX_ERR(0, 73, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_16);
if (__pyx_t_15) {
__Pyx_GIVEREF(__pyx_t_15); PyTuple_SET_ITEM(__pyx_t_16, 0, __pyx_t_15); __pyx_t_15 = NULL;
@ -3762,15 +3762,9 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
__Pyx_INCREF(((PyObject *)__pyx_v_dz));
__Pyx_GIVEREF(((PyObject *)__pyx_v_dz));
PyTuple_SET_ITEM(__pyx_t_16, 1+__pyx_t_12, ((PyObject *)__pyx_v_dz));
__Pyx_INCREF(__pyx_int_0);
__Pyx_GIVEREF(__pyx_int_0);
PyTuple_SET_ITEM(__pyx_t_16, 2+__pyx_t_12, __pyx_int_0);
__Pyx_INCREF(__pyx_int_0);
__Pyx_GIVEREF(__pyx_int_0);
PyTuple_SET_ITEM(__pyx_t_16, 3+__pyx_t_12, __pyx_int_0);
__Pyx_INCREF(__pyx_v_k);
__Pyx_GIVEREF(__pyx_v_k);
PyTuple_SET_ITEM(__pyx_t_16, 4+__pyx_t_12, __pyx_v_k);
PyTuple_SET_ITEM(__pyx_t_16, 2+__pyx_t_12, __pyx_v_k);
__pyx_t_3 = 0;
__pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_17, __pyx_t_16, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)
__Pyx_GOTREF(__pyx_t_1);
@ -3949,7 +3943,7 @@ static PyObject *__pyx_pf_24claude_top_level_library_2radiation_calculation(CYTH
*
* # gradient of difference provides heating at each level
* for k in np.arange(nlevels): # <<<<<<<<<<<<<<
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,0,0,k)/(1E3*density_profile[k])
* Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,k)/(1E3*density_profile[k])
* # make sure model does not have a higher top than 50km!!
*/
}

BIN
claude_top_level_library.cp38-win_amd64.pyd
View File

Binary file not shown.

2
claude_top_level_library.pyx
View File

@ -70,7 +70,7 @@ def radiation_calculation(np.ndarray temperature_world, np.ndarray temperature_a
# gradient of difference provides heating at each level
for k in np.arange(nlevels):
Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,0,0,k)/(1E3*density_profile[k])
Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,k)/(1E3*density_profile[k])
# make sure model does not have a higher top than 50km!!
# approximate SW heating of ozone
if heights[k] > 20E3:

BIN
save_file.p
View File

Binary file not shown.

24
toy_model.py
View File

@ -23,17 +23,17 @@ year = 365*day # length of year (s)
dt_spinup = 60*137
dt_main = 60*9
spinup_length = 0*day
spinup_length = 5*day
###
advection = True # if you want to include advection set this to be True
advection_boundary = 8 # how many gridpoints away from poles to apply advection
save = False # save current state to file?
load = False # load initial state from file?
save = True # save current state to file?
load = True # load initial state from file?
plot = True # display plots of output?
plot = False # display plots of output?
level_plots = True # display plots of output on vertical levels?
nplots = 5 # how many levels you want to see plots of (evenly distributed through column)
@ -215,6 +215,18 @@ while True:
before_plot = time.time()
if plot:
tropopause_height = np.zeros(nlat)
for i in range(nlat):
k = 2
if low_level.scalar_gradient_z_1D(np.mean(temperature_atmos[i,:,:],axis=0),dz,k) > 0:
k += 1
while low_level.scalar_gradient_z_1D(np.mean(temperature_atmos[i,:,:],axis=0),dz,k) < 0:
k += 1
else:
while low_level.scalar_gradient_z_1D(np.mean(temperature_atmos[i,:,:],axis=0),dz,k) < 0:
k += 1
# update plot
test = ax[0].contourf(lon_plot, lat_plot, temperature_world, cmap='seismic')
ax[0].streamplot(lon_plot, lat_plot, u[:,:,0], v[:,:,0], color='white',density=1)
@ -226,6 +238,7 @@ while True:
ax[0].set_xlabel('Longitude')
ax[1].contourf(heights_plot, lat_z_plot, np.transpose(np.mean(temperature_atmos,axis=1)), cmap='seismic')
ax[1].plot(lat_plot,tropopause_height,color='black',linestyle='--',linewidth=3,alpha=0.5)
ax[1].contour(heights_plot,lat_z_plot, np.transpose(np.mean(u,axis=1)), colors='white',levels=20,linewidths=1,alpha=0.8)
ax[1].streamplot(heights_plot, lat_z_plot, np.transpose(np.mean(v,axis=1)),np.transpose(np.mean(10*w,axis=1)),color='black',density=0.75)
ax[1].set_title('$\it{Atmospheric} \quad \it{temperature}$')
@ -270,4 +283,5 @@ while True:
if save:
pickle.dump((temperature_atmos,temperature_world,u,v,w,t,air_density,albedo), open("save_file.p","wb"))
# sys.exit()
if np.isnan(u.max()):
sys.exit()