def main():
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
customLegend(folder=folder.outputs)
def main():
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
# Get Large Eddy Simulation data
les = getLesData(os.path.join(folder.data, "mov0235_ALL_01-_.nc"))
# Create plots for each snapshot in time
for n in range(len(les.t)):
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
# Which layers of the 3D data set do we want to plot?
imagesIndices = range(0, min(len(snapshot.x), len(snapshot.y)), 10)
# Plot slices at fixed locations on the y-axis
for j in imagesIndices:
layer = snapshot.y[j] * 1e-3
title = "y = {:.2f}km (id={})".format(layer, j + 1)
print "XZ layer {} ({})".format(j + 1, title)
# Plot with only clouds
plotThermalContour(snapshot.x * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, j, :],
showMesh=True,
id="{}_xz_mesh+cloud".format(j),
title=title,
xlabel="x (km)",
dpi=500,
folder=folderTime)
# Plot slices at fixed locations on the x-axis
for i in imagesIndices:
layer = snapshot.x[i] * 1e-3
title = "x = {:.2f}km (id={})".format(layer, i + 1)
print "YZ layer {} ({})".format(i + 1, title)
# Plot with only clouds
plotThermalContour(snapshot.y * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, :, i],
showMesh=True,
id="{}_yz_mesh+cloud".format(i),
title=title,
xlabel="y (km)",
dpi=500,
folder=folderTime)
def addThetaBackground(folderData="/mnt/c/MONC_ARM",
indicatorMONC="plume",
timesMONC=[13800]):
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData=folderData)
files = getFilesInFolder(folder.data1d, extension=".nc")
dataAll = {}
for file in files:
print(f"\nReading file: {file}")
# Get Large Eddy Simulation data
data = netCDF4.Dataset(file)
les = lesDataMonc1D(data)
# Merge dictionaries dataAll and les
dataAll = {**dataAll, **les.data}
# Now update the data sets
for n, t in enumerate(timesMONC):
print("Processing timestep {} of {}".format(n + 1, len(timesMONC)))
# filenameTheta = os.path.join(folder.monc, "time_{}".format(timesMONC[n]), "profilesMean", indicatorMONC, "z_th.npz")
filenameTheta = os.path.join(folder.monc,
"time_{}".format(timesMONC[n]),
"profilesMean", indicatorMONC,
"z_th_uncorrected.npz")
dataTheta = np.load(filenameTheta)
dataTheta = {key: dataTheta[key] for key in dataTheta}
# Save old profiles for safe-keeping
np.savez(filenameTheta.replace("z_th.npz", "z_th_uncorrected.npz"),
**dataTheta)
# Add the missing mean profiles to the vertical distributions
fields = [
"av", "fluid1", "fluid2", "fluid1Min", "fluid1Max", "fluid2Min",
"fluid2Max"
]
thetaMoncMean = dataTheta["av"]
for field in fields:
dataTheta[field] = dataAll[float(
t)]["theta_mean"] - thetaMoncMean + dataTheta[field]
np.savez(filenameTheta.replace("z_th_uncorrected.npz", "z_th.npz"),
**dataTheta)
def compareLEMvsMONC(
indicatorLEM = "plume",
indicatorMONC = "plume",
timesLEM = [14000],
timesMONC = [13800]
):
# Fetch folders for code structure
folder = folders(
folderScripts = os.path.dirname(os.path.realpath(__file__))
)
variables = ["ql", "qv", "theta", "u", "v", "w"]
# Create plots for each snapshot in time
for n in range(min(len(timesLEM), len(timesMONC))):
print("Processing timestep {} of {}".format(n+1, min(len(timesLEM), len(timesMONC))))
folderTimeLEM = os.path.join(folder.lem, "time_{}".format(timesLEM[n]), "profilesMean", indicatorLEM)
folderTimeMONC = os.path.join(folder.monc, "time_{}".format(timesMONC[n]), "profilesMean", indicatorMONC)
folderTime = os.path.join(folder.outputs, "lem_{}_monc_{}".format(timesLEM[n], timesMONC[n]), "profilesMean")
'''
Plot comparisons of vertical profiles
'''
plotVolumeFractionComparison(
folderTimeLEM,
folderTimeMONC,
title="Volume fraction",
folder=folderTime
)
plotVerticalProfileComparison(
"u",
folderTimeLEM,
folderTimeMONC,
title="Horizontal velocity",
xlabel="u (m/s)",
folder=folderTime,
plotZero=True
)
plotVerticalProfileComparison(
"v",
folderTimeLEM,
folderTimeMONC,
title="Horizontal velocity",
xlabel="v (m/s)",
folder=folderTime,
plotZero=True
)
plotVerticalProfileComparison(
"w",
folderTimeLEM,
folderTimeMONC,
title="Vertical velocity",
xlabel="w (m/s)",
folder=folderTime,
plotZero=True
)
plotVerticalProfileComparison(
"theta",
folderTimeLEM,
folderTimeMONC,
title="Potential temperature",
xlabel="$\\theta$ (K)",
folder=folderTime,
plotZero=True
)
plotVerticalProfileComparison(
"qv",
folderTimeLEM,
folderTimeMONC,
title="Water vapour",
xlabel="$q_v$ (kg/kg)",
folder=folderTime,
plotZero=True
)
plotVerticalProfileComparison(
"ql",
folderTimeLEM,
folderTimeMONC,
title="Liquid water",
xlabel="$q_l$ (kg/kg)",
folder=folderTime,
plotZero=True
)
def lesCloudContours(id="LEM",
caseStudy="ARM",
indicatorFunction="plume",
netcdfFile=None,
interval=1,
generateGif=False,
greyscale=False,
plotCloud=False,
plotThermals=True,
plotVelocity=False,
plotAll=False):
'''
Plot cross sections of clouds and their underlying structures.
:param id: The simulation source type, set to either "ARM" or "MONC", str.
:param caseStudy: The name of the case study to be plot, str.
:param indicatorFunction: The type of structure to overlay onto the cloud, str.
:param netcdfFile: Specify the netcdf file to open (all files processed if none selected), str.
:param interval: Gap between layers to be plotted (1 means every layer plotted), int.
:param generateGif: Compose all generated images into an animation cycling through the layers, bool.
:param plotCloud: If True generate plot showing only the clouds, bool.
:param plotThermals: If True generate plot showing cloud and the chosen indicatorFunction, bool.
:param plotVelocity: If True generate plot showing cloud and the vertical velocity, bool.
:param plotAll: If True generate plot showing all the features above, bool.
'''
# Fetch folders for code structure
if id == "LEM":
folder = folders(id=id,
folderScripts=os.path.dirname(
os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
elif id == "MONC":
folder = folders(id=f"{id}_{caseStudy}",
folderScripts=os.path.dirname(
os.path.realpath(__file__)),
folderData=f"/mnt/c/{id}_{caseStudy}")
else:
raise ValueError(f"id {id} is not valid. Use LEM or MONC.")
if netcdfFile:
files = [os.path.join(folder.data, netcdfFile)]
else:
# Find all NetCFD files in
files = getFilesInFolder(folder.data, extension=".nc")
for file in files:
print(f"\nProcessing file: {file}")
# Get Large Eddy Simulation data
les = getLesData(file, id=id, indicatorFunction=indicatorFunction)
# Create plots for each snapshot in time
for n in range(len(les.t)):
print("\nProcessing timestep {} (t = {:.2f}hrs, t = {:.1f}s)".
format(n + 1,
float(les.t[n]) / 3600., float(les.t[n])))
folderTime = os.path.join(folder.outputs,
"time_{}".format(int(les.t[n])))
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
# Which layers of the 3D data set do we want to plot?
imagesIndices = range(0, min(len(snapshot.x), len(snapshot.y)),
interval)
'''# Plot slices at fixed locations on the z-axis
for k in range(0, len(snapshot.z), 10):
layer = snapshot.z[k]*1e-3
title = "z = {:.2f}km (id={})".format(layer, k+1)
print "XY layer {} ({})".format(k+1, title)
plotThermalContour(
snapshot.x*1e-3,
snapshot.y*1e-3,
snapshot.ql.field[k,:,:],
id="{}_xy_cloud+updraft".format(k),
title=title,
xlabel="x (km)",
ylabel="y (km)",
xlim=[-10.,10.],
ylim=[-10.,10.],
velocityVectorsOnly = True,
folder=folderTime,
I2=snapshot.I2.field[k,:,:],
u=(snapshot.u.field-0*np.mean(snapshot.u.av))[k,:,:],
w=(snapshot.v.field-0*np.mean(snapshot.v.av))[k,:,:]
)'''
# Plot slices at fixed locations on the y-axis
for j in imagesIndices:
layer = snapshot.y[j] * 1e-3
title = f"t = {float(les.t[n])/3600:.2f} hours, y = {layer:.2f} km"
print(f"XZ layer {j+1} ({title})")
# Plot with only clouds
if plotCloud:
plotThermalContour(snapshot.x * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, j, :],
id="{}_xz_cloud".format(j),
title=title,
xlabel="x (km)",
folder=folderTime,
greyscale=greyscale)
# Plot including structure of thermals below clouds
if plotThermals:
plotThermalContour(
snapshot.x * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, j, :],
id="{}_xz_cloud+thermal".format(j),
# title=title,
xlabel="x (km)",
folder=folderTime,
I2=snapshot.I2.field[:, j, :],
greyscale=greyscale)
# Plot including regions of positive vertical velocity (updrafts)
if plotVelocity:
plotThermalContour(
snapshot.x * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, j, :],
id="{}_xz_cloud+updraft".format(j),
title=title,
xlabel="x (km)",
folder=folderTime,
# u=(snapshot.v.field-snapshot.v.av[:,None,None])[:,j,:],
w=snapshot.w.field[:, j, :],
greyscale=greyscale)
# Plot including clouds, structure of thermals and vertical velocity
if plotAll:
plotThermalContour(
snapshot.x * 1e-3,
snapshot.z * 1e-3,
snapshot.ql.field[:, j, :],
id="{}_xz_cloud+thermal+updraft".format(j),
title=title,
xlabel="x (km)",
folder=folderTime,
I2=snapshot.I2.field[:, j, :],
w=snapshot.w.field[:, j, :],
greyscale=greyscale)
# Remove les data to clear memory
totalTimesteps = len(les.t)
del les
del snapshot
# Create gif animations for generated plots
if generateGif:
for n in range(totalTimesteps):
folderTime = os.path.join(folder.outputs,
"timestep_{}".format(n))
imageListContourXZ = []
for k in imagesIndices:
print(k)
imageListContourXZ = []
imageListContourYZ = []
imageListContourXZ.append(
os.path.join(os.path.join(folderTime, "contourCloud"),
"contour_{}_xz_cloud.png".format(k)))
imageListContourXZ.append(
os.path.join(
os.path.join(folderTime, "contourCloud"),
"contour_{}_xz_cloud+thermal.png".format(k)))
imageListContourXZ.append(
os.path.join(
os.path.join(folderTime, "contourCloud"),
"contour_{}_xz_cloud+thermal+updraft.png".format(
k)))
makeGif("contour_{}_xz.gif".format(k),
imageListContourXZ,
folder=folderTime,
delay=200)
def cloudFractionContour(generateGif=False, id="LEM", caseStudy="ARM", indicatorFunction="basic", netcdfFile=None):
# Fetch folders for code structure
if id == "LEM":
folder = folders(
id = id,
folderScripts = os.path.dirname(os.path.realpath(__file__)),
folderData = "/mnt/f/Desktop/LES_Data"
)
elif id == "MONC":
folder = folders(
id = f"{id}_{caseStudy}",
folderScripts = os.path.dirname(os.path.realpath(__file__)),
folderData = f"/mnt/c/{id}_{caseStudy}"
)
else:
raise ValueError(f"id {id} is not valid.")
if netcdfFile:
files = [os.path.join(folder.data, netcdfFile)]
else:
# Get all available NetCFD files
files = getFilesInFolder(folder.data, extension=".nc")
times = []
cloudTops = {}
cloudBases = {}
cloudCovers = {}
cloudCovers1 = {}
cloudCovers2 = {}
cloudFractions = {}
cloudFractions1 = {}
cloudFractions2 = {}
cloudFractions1sigma1 = {}
cloudFractions2sigma2 = {}
for i,file in enumerate(files):
print(f"\nProcessing 3D file: {file} (file {i+1} of {len(files)})")
# Get Large Eddy Simulation data
les = getLesData(
file,
id = id,
indicatorFunction = indicatorFunction
)
# Create plots for each snapshot in time
for n in range(len(les.t)):
print("\nProcessing timestep {} (t = {:.2f}hrs, t = {:.1f}s)".format(n+1, float(les.t[n])/3600., float(les.t[n])))
time = int(les.t[n])
folderTime = os.path.join(folder.outputs, f"time_{time}")
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
z = snapshot.z
cloud = snapshot.ql.field > 1e-5
cloudz = np.max(snapshot.ql.field, axis=snapshot.ql.axisXY) > 1e-5
cloudFraction = horizontalAverage(cloud, axis=snapshot.ql.axisXY)
cloudFraction1 = conditionalAverage(cloud, np.invert(snapshot.I2.field), axis=snapshot.ql.axisXY)
cloudFraction2 = conditionalAverage(cloud, snapshot.I2.field, axis=snapshot.ql.axisXY)
cloudFraction1sigma1 = cloudFraction1*(1-snapshot.I2.av)
cloudFraction2sigma2 = cloudFraction2*snapshot.I2.av
times.append(time)
if not True in cloudz:
cloudTops[time] = 0.
cloudBases[time] = 0.
else:
cloudTops[time] = np.max(z[cloudz])
cloudBases[time] = np.min(z[cloudz])
cloudCovers[time] = calculateCloudCover(cloud, axis=(snapshot.keys.zi))
cloudCovers1[time] = calculateCloudCover(cloud * np.invert(snapshot.I2.field), axis=(snapshot.keys.zi))
cloudCovers2[time] = calculateCloudCover(cloud * snapshot.I2.field, axis=(snapshot.keys.zi))
cloudFractions[time] = cloudFraction
cloudFractions1[time] = cloudFraction1
cloudFractions2[time] = cloudFraction2
cloudFractions1sigma1[time] = cloudFraction1sigma1
cloudFractions2sigma2[time] = cloudFraction2sigma2
print("Cloud cover: {:.3f}".format(cloudCovers[time]))
print("Cloud base: {:.1f}m".format(cloudBases[time]))
print("Cloud top: {:.1f}m".format(cloudTops[time]))
'''
plotCloudFraction(
snapshot.z,
cloudFraction,
id = "all",
folder = folderTime
)
plotCloudFraction(
snapshot.z,
cloudFraction1,
id = "1",
folder = folderTime
)
plotCloudFraction(
snapshot.z,
cloudFraction2,
id = "2",
folder = folderTime
)
'''
del les
del snapshot
cloudTop = np.zeros_like(times, dtype=float)
cloudBase = np.zeros_like(times, dtype=float)
cloudCover = np.zeros_like(times, dtype=float)
cloudCover1 = np.zeros_like(times, dtype=float)
cloudCover2 = np.zeros_like(times, dtype=float)
cloudFraction = np.zeros((len(cloudFractions[times[0]]), len(times)))
cloudFraction1 = np.zeros((len(cloudFractions1[times[0]]), len(times)))
cloudFraction2 = np.zeros((len(cloudFractions2[times[0]]), len(times)))
cloudFraction1sigma1 = np.zeros((len(cloudFractions1sigma1[times[0]]), len(times)))
cloudFraction2sigma2 = np.zeros((len(cloudFractions2sigma2[times[0]]), len(times)))
times = sorted(times)
for n in range(len(times)):
time = times[n]
cloudTop[n] = cloudTops[time]
cloudBase[n] = cloudBases[time]
cloudCover[n] = cloudCovers[time]
cloudCover1[n] = cloudCovers1[time]
cloudCover2[n] = cloudCovers2[time]
cloudFraction[:,n] = cloudFractions[time]
cloudFraction1[:,n] = cloudFractions1[time]
cloudFraction2[:,n] = cloudFractions2[time]
cloudFraction1sigma1[:,n] = cloudFractions1sigma1[time]
cloudFraction2sigma2[:,n] = cloudFractions2sigma2[time]
data = {}
data["t_cloud_fraction"] = np.array(times)
data["z_cloud_fraction"] = z
data["cloud_top"] = cloudTop
data["cloud_base"] = cloudBase
data["cloud_cover"] = cloudCover
data["cloud_cover1"] = cloudCover1
data["cloud_cover2"] = cloudCover2
data["cloud_fraction"] = cloudFraction
data["cloud_fraction1"] = cloudFraction1
data["cloud_fraction2"] = cloudFraction2
data["cloud_fraction1_sigma1"] = cloudFraction1sigma1
data["cloud_fraction2_sigma2"] = cloudFraction2sigma2
folder = os.path.join(folder.outputs, "cloudContour")
if not os.path.isdir(folder):
os.makedirs(folder)
savemat(os.path.join(folder, "cloud_fraction.mat"), data)
def main():
# indicatorFunction="basic"
# indicatorFunction="plume"
indicatorFunction = "plumeEdge"
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
lesDiagnostics = getScmData(os.path.join(folder.data, "LES_transfers.mat"))
for key in lesDiagnostics:
print(key)
# Create plots for each snapshot in time
t = [8.89 * 3600]
for n in range(len(t)):
print("Processing timestep {} (t = {:.2f}hrs, {}s)".format(
n + 1,
float(t[n]) / 3600., t[n]))
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
'''
Plot comparisons of vertical profiles
'''
plotVerticalProfileComparison("u",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Horizontal velocity",
xlabel="u (m/s)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("v",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Horizontal velocity",
xlabel="v (m/s)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("w",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Vertical velocity",
xlabel="w (m/s)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("theta",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Potential temperature",
xlabel="$\\theta$ (K)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("qv",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Water vapour",
xlabel="$q_v$ (kg/kg)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("ql",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Liquid water",
xlabel="$q_l$ (kg/kg)",
folder=folderTime,
plotZero=True)
'''
Plot the transferred properties during entrainment and detrainment - the b_ij coefficients
'''
plotTransferredProperties("u",
"plume",
"plumeEdge",
title="Horizontal velocity",
xlabel="$b_{ij}$ for u",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("v",
"plume",
"plumeEdge",
title="Horizontal velocity",
xlabel="$b_{ij}$ for v",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("w",
"plume",
"plumeEdge",
title="Vertical velocity",
xlabel="$b_{ij}$ for w",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("theta",
"plume",
"plumeEdge",
title="Potential temperature",
xlabel="$b_{ij}$ for $\\theta$",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("qv",
"plume",
"plumeEdge",
title="Water vapour",
xlabel="$b_{ij}$ for $q_v$",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("ql",
"plume",
"plumeEdge",
title="Liquid water",
xlabel="$b_{ij}$ for $q_l$",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
def lesVerticalProfiles(id="LEM",
caseStudy="ARM",
indicatorFunction="basic",
netcdfFile=None,
thetaMean=None):
'''
Plot the vertical profiles of various fields in the Large Eddy Simulation (LES) data
for comparison with Single Column Models (SCMs).
:param id: The simulation source type, set to either "ARM" or "MONC", str.
:param caseStudy: The name of the case study to be plot, str.
:param indicatorFunction: The type of structure to overlay onto the cloud, str.
:param netcdfFile: Specify the netcdf file to open (all files processed if none selected), str.
:param thetaMean: Specify a background profile to add to the potential temperature, float np.ndarray.
'''
# Fetch folders for code structure
if id == "LEM":
folder = folders(id=id,
folderScripts=os.path.dirname(
os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
elif id == "MONC":
folder = folders(id=f"{id}_{caseStudy}",
folderScripts=os.path.dirname(
os.path.realpath(__file__)),
folderData=f"/mnt/c/{id}_{caseStudy}")
thetaMean = get1dProfiles(folder.data1d, key="theta_mean")
else:
raise ValueError(f"id {id} is not valid.")
if netcdfFile:
files = [os.path.join(folder.data, netcdfFile)]
else:
# Get all available NetCFD files
files = getFilesInFolder(folder.data, extension=".nc")
for i, file in enumerate(files):
print(f"\nProcessing 3D file: {file} (file {i+1} of {len(files)})")
# Get Large Eddy Simulation data
les = getLesData(file,
id=id,
indicatorFunction=indicatorFunction,
thetaMeanProfiles=thetaMean)
# Create plots for each snapshot in time
for n in range(len(les.t)):
print("\nProcessing timestep {} (t = {:.2f}hrs, t = {:.1f}s)".
format(n + 1,
float(les.t[n]) / 3600., float(les.t[n])))
folderTime = os.path.join(folder.outputs,
"time_{}".format(int(les.t[n])))
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
# Volume fraction of fluid 2
plotVolumeFraction(snapshot.z,
snapshot.I2,
folder=folderTime,
id=indicatorFunction)
# Mean profiles
plotVerticalProfile(snapshot.z,
snapshot.u,
title="Horizontal velocity",
xlabel="u (m/s)",
folder=folderTime,
id=indicatorFunction,
plotZero=True)
plotVerticalProfile(snapshot.z,
snapshot.v,
title="Horizontal velocity",
xlabel="v (m/s)",
folder=folderTime,
id=indicatorFunction,
plotZero=True)
plotVerticalProfile(snapshot.z,
snapshot.w,
title="Vertical velocity",
xlabel="w (m/s)",
folder=folderTime,
id=indicatorFunction,
plotZero=True)
plotVerticalProfile(snapshot.z,
snapshot.th,
title="Potential temperature",
xlabel="$\\theta$ (K)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.thv,
title="Virtual potential temperature",
xlabel="$\\theta_v$ (K)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.b,
title="Buoyancy",
xlabel="b (m s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.q,
title="Water vapour",
xlabel="$q_t$ (kg/kg)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.qv,
title="Water vapour",
xlabel="$q_v$ (kg/kg)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.ql,
title="Liquid water",
xlabel="$q_l$ (kg/kg)",
folder=folderTime,
id=indicatorFunction)
plotVerticalProfile(snapshot.z,
snapshot.qr,
title="Radioactive tracer",
xlabel="$q_r$ (kg/kg)",
folder=folderTime,
id=indicatorFunction)
# Variances
plotVerticalVariances(snapshot.z,
snapshot.u,
title="Horizontal velocity variance",
xlabel="$\\overline{u'u'}$ (m$^2$/s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.v,
title="Horizontal velocity variance",
xlabel="$\\overline{v'v'}$ (m$^2$/s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.w,
title="Horizontal velocity variance",
xlabel="$\\overline{w'w'}$ (m$^2$/s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(
snapshot.z,
snapshot.th,
title="Potential temperature variance",
xlabel="$\\overline{\\theta'\\theta'}$ (K$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(
snapshot.z,
snapshot.thv,
title="Virtual potential temperature variance",
xlabel="$\\overline{\\theta_v'\\theta_v'}$ (K$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.b,
title="Buoyancy variance",
xlabel="$\\overline{b'b'}$ (m$^2$/s$^4$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.q,
title="Total moisture variance",
xlabel="$\\overline{q'q'}$",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.qv,
title="Water vapour variance",
xlabel="$\\overline{q_v'q_v'}$",
folder=folderTime,
id=indicatorFunction)
plotVerticalVariances(snapshot.z,
snapshot.ql,
title="Liquid water variance",
xlabel="$\\overline{q_l'q_l'}$",
folder=folderTime,
id=indicatorFunction)
# Vertical fluxes
plotVerticalFluxes(
snapshot.z,
snapshot.u,
title="Horizontal velocity fluxes",
xlabel="$\\sigma_i \\overline{w'u'}$ (m$^2$/s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.v,
title="Horizontal velocity fluxes",
xlabel="$\\sigma_i \\overline{w'v'}$ (m$^2$/s$^2$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.th,
title="Potential temperature fluxes",
xlabel="$\\sigma_i \\overline{w'\\theta'}$ (K m/s)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.thv,
title="Virtual potential temperature fluxes",
xlabel="$\\sigma_i \\overline{w'\\theta_v'}$ (K m/s)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.b,
title="Buoyancy fluxes",
xlabel="$\\sigma_i \\overline{w'b'}$ (m$^2$/s$^3$)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.q,
title="Total moisture fluxes",
xlabel="$\\sigma_i \\overline{w'q'}$ (kg/kg m/s)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.qv,
title="Water vapour fluxes",
xlabel="$\\sigma_i \\overline{w'q_v'}$ (kg/kg m/s)",
folder=folderTime,
id=indicatorFunction)
plotVerticalFluxes(
snapshot.z,
snapshot.ql,
title="Liquid water fluxes",
xlabel="$\\sigma_i \\overline{w'q_l'}$ (kg/kg m/s)",
folder=folderTime,
id=indicatorFunction)
def main():
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
# Get Large Eddy Simulation data
les = getLesData(os.path.join(folder.data, "mov0235_ALL_01-_.nc"))
# les = getLesData(os.path.join(folder.data, "mov0235_ALL_01-_.nc"), indicatorFunction="basic")
# Create plots for each snapshot in time
for n in range(len(les.t)):
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
# Create plots for each layer in the vertical
for k in range(len(snapshot.z)):
layer = snapshot.z[k] * 1e-3
title = "z = {:.2f}km (id={})".format(layer, k + 1)
print "Layer {} ({:.3f}km)".format(k + 1, layer)
# Histogram for vertical velocity, w
plotLayerHistogram(snapshot.w,
snapshot.I2,
layer,
k,
title=title,
folder=os.path.join(folderTime,
snapshot.w.name))
# Histogram for potential temperature, theta
plotLayerHistogram(snapshot.theta,
snapshot.I2,
layer,
k,
title=title,
folder=os.path.join(folderTime,
snapshot.theta.name))
# Histogram for water vapour, qv
plotLayerHistogram(snapshot.qv,
snapshot.I2,
layer,
k,
title=title,
folder=os.path.join(folderTime,
snapshot.qv.name))
# Histogram for liquid water, ql
plotLayerHistogram(snapshot.ql,
snapshot.I2,
layer,
k,
title=title,
folder=os.path.join(folderTime,
snapshot.ql.name))
# Remove les data to clear memory
totalTimesteps = len(les.t)
totalImages = min(len(snapshot.z), 150)
del les
del snapshot
# Create gif animations for generated plots
for n in range(totalTimesteps):
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
imageListW = []
imageListTheta = []
imageListQv = []
imageListQl = []
for k in range(totalImages):
imageListW.append(
os.path.join(os.path.join(folderTime, "w"),
"histogram_w_{}.png".format(k + 1)))
imageListTheta.append(
os.path.join(os.path.join(folderTime, "theta"),
"histogram_theta_{}.png".format(k + 1)))
imageListQv.append(
os.path.join(os.path.join(folderTime, "qv"),
"histogram_qv_{}.png".format(k + 1)))
imageListQl.append(
os.path.join(os.path.join(folderTime, "ql"),
"histogram_ql_{}.png".format(k + 1)))
makeGif("histogram_w.gif", imageListW, folder=folderTime, delay=8)
makeGif("histogram_theta.gif",
imageListTheta,
folder=folderTime,
delay=8)
makeGif("histogram_qv.gif", imageListQv, folder=folderTime, delay=8)
makeGif("histogram_ql.gif", imageListQl, folder=folderTime, delay=8)
def calculateFields(id="default",
gratingType="triangle",
beamRadius=1.2,
gaussian=True,
imperfection=False,
resolution=100,
rangeX=[-0.55, 0.55],
rangeY=[-0.55, 0.55],
rangeZ=[0., 1.1],
precisionCoords=4,
precisionData=2):
'''
Generate the acceleration and radiation profiles for a neutral atom in a
Magneto-Optical Trap with one laser pointed down on a horizontal surface where
a reflection grating is located.
Args
id: Data id used for the naming of output files
gratingType: Shape of grating etches/grooves. Valid parameters are "triangle" and "square"
beamRadius: The incident laser beam radius in cm.
gaussian: Is the beam profile Gaussian or uniform? Boolean only.
imperfection: If True, some of the laser beam will be diffracted to 0th order (reflection)
resolution: Resolution of the data in all 3 axes. resolution x 2 = computation x 8.
rangeX: Range of x values to be evaluated in cm.
rangeY: Range of x values to be evaluated in cm.
rangeZ: Range of x values to be evaluated in cm.
precisionCoords: Precision of coordinate data when writen to output file.
precisionData: Precision of field data when writen to output file.
'''
print(f"\nGenerating fields for configuration: {id}")
folder = folders(id=id,
folderScripts=os.path.dirname(os.path.realpath(__file__)))
# Get grating geometry and properties
if gratingType == "square":
gratings = makeSquareGrating()
elif gratingType == "triangle":
gratings = makeTriangleGrating()
else:
gratings = makeSquareGrating()
# 3D Coordinate and Acceleration vector fields.
axisX = np.linspace(rangeX[0], rangeX[1], resolution)
axisY = np.linspace(rangeY[0], rangeY[1], resolution)
axisZ = np.linspace(rangeZ[0], rangeZ[1], resolution)
z, y, x = np.meshgrid(axisZ, axisY, axisX, indexing='ij')
coords = np.stack((x, y, z), axis=-1)
# Data arrays
a = np.zeros(coords.shape)
radPressure = np.zeros(coords.shape)
waveVector = np.zeros(coords.shape)
print("Generating fields from incident beam")
# Compute acceleration or radiation pressure from incident laser beam.
intensity = np.ones(x.shape)
if gaussian == True:
intensity *= np.e**(-(x**2 + y**2) / 2.)
k = np.array([0., 0., -1.])
waveVector[...] = k
a += acceleration(waveVector, coords, I=intensity)
radPressure[...] += intensity[..., None] * waveVector
# Ignore regions not within the beam radius
conditionBeam = x**2 + y**2 <= beamRadius**2
a *= conditionBeam[..., None]
radPressure *= conditionBeam[..., None]
# Compute acceleration or radiation pressure from 0th order beam.
if imperfection == True:
print("Generating fields from 0th-order diffracted beam")
intensity = 0.01
if gaussian == True:
intensity *= np.exp(-(0.5 * x)**2)
k = np.array([0., 0., 1.])
waveVector[...] = k
a += acceleration(waveVector, coords, I=intensity, factor=-1.)
radPressure[...] += intensity[..., None] * waveVector
switch = True
for grating in gratings:
print("Generating fields from grating segment")
if switch == True:
switch = False
factor = -1
else:
factor = 1
# Compute acceleration or radiation pressure from kth grating 1st order beam.
intensity = grating.reflectivity * grating.intensity(
coords, factor, beamRadius, gaussian=gaussian)
# Unit vector of diffracted beam
waveVector[...] = grating.k
radPressure += waveVector * intensity[..., None]
a += acceleration(waveVector, coords, I=intensity, factor=-1.)
# Compute acceleration or radiation pressure from kth grating 1st order beam in other direction.
intensity = grating.reflectivity * grating.intensity(
coords, -factor, beamRadius, gaussian=gaussian)
# Unit vector of diffracted beam
waveVector[...] = grating.k
radPressure += waveVector * intensity[..., None]
a += acceleration(waveVector, coords, I=intensity, factor=-1.)
# Store the acceleration magnitude and the components of the acceleration.
aMag = np.sqrt(np.sum(a * a, axis=(a.ndim - 1)))
radPressureMag = np.sqrt(
np.sum(radPressure * radPressure, axis=(radPressure.ndim - 1)))
# Save fields for future use
np.savez_compressed(
os.path.join(folder.outputs, "fieldData.npz"),
id=id,
gratingType=gratingType,
beamRadius=beamRadius,
gaussian=gaussian,
imperfection=imperfection,
resolution=resolution,
rangeX=rangeX,
rangeY=rangeY,
rangeZ=rangeZ,
axisX=axisX,
axisY=axisY,
axisZ=axisZ,
x=x.round(decimals=precisionCoords),
y=y.round(decimals=precisionCoords),
z=z.round(decimals=precisionCoords),
a=a.round(decimals=precisionData),
aMag=aMag.round(decimals=precisionData),
radPressure=radPressure.round(decimals=precisionData),
radPressureMag=radPressureMag.round(decimals=precisionData))
def main():
# indicatorFunction="basic"
# indicatorFunction="plume"
indicatorFunction = "plumeEdge"
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
lesDiagnostics = getScmData(os.path.join(folder.data, "LES_transfers.mat"))
for key in lesDiagnostics:
print(key)
# Create plots for each snapshot in time
t = [8.89 * 3600]
for n in range(len(t)):
print("Processing timestep {} (t = {:.2f}hrs, {}s)".format(
n + 1,
float(t[n]) / 3600., t[n]))
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
id = "particlesEntrainDetrain"
folderVertical = os.path.join(folderTime, "profilesMean")
if id != "":
folderVertical = os.path.join(folderVertical, id)
if not os.path.isdir(folderVertical):
os.makedirs(folderVertical)
# Save vertical profiles for future use
indexTime = np.argmin(np.abs(lesDiagnostics["times_t"][0] - t[n]))
print("Time")
print(indexTime)
print(lesDiagnostics["times_t"][0][indexTime])
np.savez(os.path.join(folderVertical, "z_profile_w.npz"),
z=lesDiagnostics["z_lev"][0][:-1] / 1000.,
fluid1=lesDiagnostics["w_hat_up"][:, indexTime],
fluid2=lesDiagnostics["w_det_up"][:, indexTime],
fluid1Std=0 * lesDiagnostics["w_hat_up"][:, indexTime],
fluid2Std=0 * lesDiagnostics["w_det_up"][:, indexTime],
fluid1Min=np.min(lesDiagnostics["w_hat_up"][:, indexTime]),
fluid1Max=np.max(lesDiagnostics["w_hat_up"][:, indexTime]),
fluid2Min=np.min(lesDiagnostics["w_det_up"][:, indexTime]),
fluid2Max=np.max(lesDiagnostics["w_det_up"][:, indexTime]))
np.savez(os.path.join(folderVertical, "z_profile_qv.npz"),
z=lesDiagnostics["z_lev"][0][:-1] / 1000.,
fluid1=lesDiagnostics["qv_hat_up"][:, indexTime],
fluid2=lesDiagnostics["qv_det_up"][:, indexTime],
fluid1Std=0 * lesDiagnostics["qv_hat_up"][:, indexTime],
fluid2Std=0 * lesDiagnostics["qv_det_up"][:, indexTime],
fluid1Min=np.min(lesDiagnostics["qv_hat_up"][:, indexTime]),
fluid1Max=np.max(lesDiagnostics["qv_hat_up"][:, indexTime]),
fluid2Min=np.min(lesDiagnostics["qv_det_up"][:, indexTime]),
fluid2Max=np.max(lesDiagnostics["qv_det_up"][:, indexTime]))
np.savez(os.path.join(folderVertical, "z_profile_theta.npz"),
z=lesDiagnostics["z_lev"][0][:-1] / 1000.,
fluid1=lesDiagnostics["th_hat_up"][:, indexTime],
fluid2=lesDiagnostics["th_det_up"][:, indexTime],
fluid1Std=0 * lesDiagnostics["th_hat_up"][:, indexTime],
fluid2Std=0 * lesDiagnostics["th_det_up"][:, indexTime],
fluid1Min=np.min(lesDiagnostics["th_hat_up"][:, indexTime]),
fluid1Max=np.max(lesDiagnostics["th_hat_up"][:, indexTime]),
fluid2Min=np.min(lesDiagnostics["th_det_up"][:, indexTime]),
fluid2Max=np.max(lesDiagnostics["th_det_up"][:, indexTime]))
plotVerticalProfileComparison("w",
"plume",
"particlesEntrainDetrain",
title="Vertical velocity",
xlabel="w (m/s)",
folder=folderTime,
plotZero=True)
plotVerticalProfileComparison("qv",
"plume",
"particlesEntrainDetrain",
title="Water vapour",
xlabel="$q_v$",
folder=folderTime,
plotZero=True)
# plotVerticalProfileComparison(
# "theta", "plume", "particlesEntrainDetrain",
# title="Potential Temperature",
# xlabel="$\\theta$ (K)",
# folder=folderTime,
# plotZero=True
# )
plotTransferredProperties("w",
"plume",
"particlesEntrainDetrain",
title="Vertical velocity",
xlabel="$b_{ij}$ for w",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
plotTransferredProperties("qv",
"plume",
"particlesEntrainDetrain",
title="Water vapour",
xlabel="$b_{ij}$ for $q_v$",
yMarkers=[1., 2.8],
folder=folderTime,
plotZero=True)
indexTime = np.argmin(np.abs(lesDiagnostics["times_t"][0] - t[n]))
zLes = lesDiagnostics["z_lev"][0][:-1] / 1000.
w2Les = lesDiagnostics["w_up"][:, indexTime]
w12Les = lesDiagnostics["w_det_up"][:, indexTime]
w21Les = lesDiagnostics["w_hat_up"][:, indexTime]
th2Les = lesDiagnostics["th_up"][:, indexTime]
th12Les = lesDiagnostics["th_det_up"][:, indexTime]
th21Les = lesDiagnostics["th_hat_up"][:, indexTime]
qv2Les = lesDiagnostics["qv_up"][:, indexTime]
qv12Les = lesDiagnostics["qv_det_up"][:, indexTime]
qv21Les = lesDiagnostics["qv_hat_up"][:, indexTime]
plotVerticalProfileComparison(
"w",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Vertical velocity",
xlabel="w (m/s)",
folder=folderTime,
plotZero=True,
# additionalLines=[[w12Les,zLes,"r"],[w21Les,zLes,"b"]]
# additionalLines=[[w2Les,zLes,"k"]]
)
plotVerticalProfileComparison(
"theta",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Potential temperature",
xlabel="$\\theta$ (K)",
folder=folderTime,
plotZero=True,
additionalLines=[[th12Les, zLes, "k"], [th21Les, zLes, "#888888"]])
plotVerticalProfileComparison("qv",
"plume",
"plumeEdge",
id3="plumeEdgeEntrain",
id4="plumeEdgeDetrain",
title="Water vapour",
xlabel="$q_v$ (kg/kg)",
folder=folderTime,
plotZero=True,
additionalLines=[[qv12Les, zLes, "r"],
[qv21Les, zLes, "b"]])
def simulateAtoms(id="default"):
'''
Generate plots for the acceleration and radiation profiles for a neutral atom in a
Magneto-Optical Trap with one laser pointed down on a horizontal surface where
a reflection grating is located.
NOTE: plotContour must be run before using plotFields
Args
id: Data id used for the input data filename and the output files
'''
print(f"\nPlotting fields for configuration: {id}")
folder = folders(id=id,
folderScripts=os.path.dirname(os.path.realpath(__file__)))
# Save fields for future use
data = np.load(os.path.join(folder.outputs, "fieldData.npz"))
x = data["x"]
y = data["y"]
z = data["z"]
a = data["a"]
aMag = data["aMag"]
axisX = data["axisX"]
axisY = data["axisY"]
axisZ = data["axisZ"]
coords = np.stack((x, y, z), axis=-1)
# Generate particles
particles = []
nParticles = 50
for i in range(nParticles):
particles.append(
particle([
0.5 * random.uniform(data["axisX"][0], data["axisX"][-1]),
0.5 * random.uniform(data["axisY"][0], data["axisY"][-1]),
random.uniform(data["axisZ"][0], data["axisZ"][-1])
], [0, 0, 0],
dragCoefficient=10.))
timestep = 5e-4
timesteps = 500
for i in range(nParticles):
print("Simulating trajectory of particle {}".format(i + 1))
for n in range(timesteps):
indexX = np.argmin(np.abs(axisX - particles[i].position[0]))
indexY = np.argmin(np.abs(axisY - particles[i].position[1]))
indexZ = np.argmin(np.abs(axisZ - particles[i].position[2]))
acceleration = a[indexZ][indexY][indexX]
particles[i].move(acceleration, timestep)
# Generate 3D trajectory plots
fig = plt.figure()
ax = plt.axes(projection='3d')
# Draw grating chip
xGrating = [-1., -1., 1., 1.]
yGrating = [-1., 1., 1., -1.]
zGrating = [0., 0., 0., 0.]
verts = [list(zip(xGrating, yGrating, zGrating))]
poly = Poly3DCollection(verts, facecolors=[[0.5, 0.5, 0.5, 1.]])
poly.set_sort_zpos(50)
ax.add_collection3d(poly)
# Draw laser radius
theta = np.linspace(0, 2 * np.pi, 100)
radius = 1.2
xLaser = radius * np.cos(theta)
yLaser = radius * np.sin(theta)
zLaser = np.zeros_like(xLaser) + 0.01
verts = [list(zip(xLaser, yLaser, zLaser))]
poly = Poly3DCollection(verts, facecolors=[[1., 0., 0., 0.5]])
poly.set_sort_zpos(100)
ax.add_collection3d(poly)
# Highlight region with weakest force-field (where particles should converge to)
# cutOff = np.min(aMag) + 0.01*(np.mean(aMag)-np.min(aMag))
# condition = aMag < cutOff
# ax.plot(x[condition], y[condition], z[condition], ".", color="#888888")
# Draw trajectories
for i in range(nParticles):
pos = np.array(particles[i].positions)
zorder = 10000
# Deal with particles that have gone through the ground
if np.min(pos[:, 2]) < 0.:
pos[:, 2] = np.maximum(pos[:, 2], 0 * pos[:, 2])
ax.plot(pos[..., 0], pos[..., 1], pos[..., 2], zorder=zorder)
ax.plot(pos[..., 0][-1],
pos[..., 1][-1],
pos[..., 2][-1],
"ko",
zorder=zorder)
ax.set_xlim([axisX[0], axisX[-1]])
ax.set_ylim([axisY[0], axisY[-1]])
ax.set_zlim([axisZ[0], axisZ[-1]])
ax.set_xlabel("x (cm)")
ax.set_ylabel("y (cm)")
ax.set_zlabel("z (cm)")
# Save images from different perspectives
for i in np.linspace(0, 1, 25):
elevation = 90 * i
angle = 90 + 360 * i
ax.view_init(elev=elevation, azim=angle)
plt.savefig(os.path.join(
folder.outputs, "trajectories_{}_{}.png".format(id, int(360 * i))),
dpi=200)
plt.close()
def prepareData(indicator="plume",
id="MONC",
caseStudy="ARM",
times=[30000],
cloudData=True):
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)))
variables = ["sigma", "q", "ql", "qv", "th", "thv", "b", "u", "v", "w"]
# variables = ["ql", "qv", "th", "u", "w"]
dataAll = dict(times=[])
# dataAll = dict(LES_times=times)
# Create plots for each snapshot in time
for i, t in enumerate(times):
print("Processing t={}s (timestep {} of {})".format(
t, i + 1, len(times)))
# idCaseStudy = f"{id}"
idCaseStudy = f"{id}_{caseStudy}"
folderTimeMean = os.path.join(folder.outputs, idCaseStudy,
"time_{}".format(times[i]),
"profilesMean", indicator)
folderTimeVariances = os.path.join(folder.outputs, idCaseStudy,
"time_{}".format(times[i]),
"profilesVariances", indicator)
folderTimeFluxes = os.path.join(folder.outputs, idCaseStudy,
"time_{}".format(times[i]),
"profilesFluxes", indicator)
if os.path.isdir(folderTimeMean):
dataAll["times"].append(t)
data = {}
for variable in variables:
dataMean = loadProfiles(f"z_{variable}.mat", folderTimeMean)
dataVariances = loadProfiles(f"z_{variable}{variable}.mat",
folderTimeVariances)
dataFluxes = loadProfiles(f"z_w{variable}.mat",
folderTimeFluxes)
# Small correction to the key naming convention
key1 = f"{variable}1"
key2 = f"{variable}2"
key1New = f"{variable}_1"
key2New = f"{variable}_2"
for key in list(dataMean.keys()):
if key1 in key:
dataMean[key.replace(key1,
key1New)] = dataMean.pop(key)
if key2 in key:
dataMean[key.replace(key2,
key2New)] = dataMean.pop(key)
# print(f"Replaced {key} with {key.replace(key2, key2New)}")
data = {**data, **dataMean, **dataVariances, **dataFluxes}
# Additional diagnostics
data["e_res1"] = 0.5 * (data["uu_res1"] + data["vv_res1"] +
data["ww_res1"])
data["e_res2"] = 0.5 * (data["uu_res2"] + data["vv_res2"] +
data["ww_res2"])
data["e_sg1"] = 0.5 * (data["uu_sg1"] + data["vv_sg1"] +
data["ww_sg1"])
data["e_sg2"] = 0.5 * (data["uu_sg2"] + data["vv_sg2"] +
data["ww_sg2"])
data["e_1"] = data["e_res1"] + data["e_sg1"]
data["e_2"] = data["e_res2"] + data["e_sg2"]
data["e_res"] = data["sigma_1"] * data["e_res1"] + data[
"sigma_2"] * data["e_res2"]
data["e_sg"] = data["sigma_1"] * data["e_sg1"] + data[
"sigma_2"] * data["e_sg2"]
data["e"] = data["e_res"] + data["e_sg"]
# Add data
for profile in data.keys():
if "__" not in profile:
# Remove any ridiculous values from data set
data[profile][np.abs(data[profile]) > 1e10] = np.nan
profile_name = profile
# profile_name = f"LES_{profile}"
if profile_name not in dataAll:
dataAll[profile_name] = data[profile][0].reshape(
(len(data[profile][0]), 1))
# if profile != "z":
# dataAll[profile_name] *= 0
elif profile != "z":
# elif profile != "LES_z":
dataAll[profile_name] = np.concatenate(
(dataAll[profile_name], data[profile][0].reshape(
(len(data[profile][0]), 1))),
axis=-1)
if cloudData:
print("Adding cloud timeseries as well")
folderCloud = os.path.join(folder.outputs, idCaseStudy, "cloudContour")
dataCloud = loadProfiles("cloud_fraction.mat", folder=folderCloud)
for profile in dataCloud.keys():
if "__" not in profile:
profile_name = profile
# profile_name = f"LES_{profile}"
if profile_name not in dataAll:
dataAll[profile_name] = dataCloud[profile]
print("Data preparation complete for variables: {}".format(
list(dataAll.keys())))
folderOutput = os.path.join(folder.outputs, idCaseStudy, indicator)
if not os.path.isdir(folderOutput):
os.makedirs(folderOutput)
savemat(os.path.join(folderOutput, "profiles.mat"), dataAll)
def main():
# Fetch folders for code structure
folder = folders(
folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data"
)
# Spacial and temporal discretisation
dt = np.linspace(0.,2.,21)
dt = np.linspace(0.,2.,51)
xf = np.linspace(0.,1.,21)[:-1]
xc = xf + (xf[1]-xf[0])/2.
# Choose whether to have co-located grid (A-grid) or staggered grid (C-grid)
velocity_face = False
# Set resolution of parameter space to sample.
# Note that processing time scales as resolution^3
resolution = 50
resolution = 11
eta1_arr = np.linspace(0.01,2.,resolution)
u1_arr = np.linspace(0.,2.,resolution)
S10_arr = np.linspace(0.,1.,resolution)
eta1_arr = np.linspace(1e-8,2.,resolution)
u1_arr = np.linspace(-150.,150.,resolution)
S10_arr = np.linspace(0.,1.,resolution)
# S10_arr = np.array([1.])
# Placeholder arrays which will be multiplied by a factor later on
eta0 = np.ones(len(xc))
eta1 = np.ones(len(xc))
theta0 = 300.*np.ones(len(xc))
theta1 = 301.*np.ones(len(xc))
u0 = np.ones(len(xf))
u1 = np.ones(len(xf))
S01 = np.ones(len(xc))
S10 = np.ones(len(xc))
# Make transfers like a square wave to test stability at steep gradients
# Only necessary for staggered grids.
for j in xrange(len(S01)):
if xc[j] < 0.2 or xc[j] > 0.8:
S01[j] = 0.
S10[j] = 0.
# Allowed numerical schemes
# test_cases = [["flux","n","n",0,0],["flux","n+1","n+1",1,1]]
test_cases = [["advective","n","n",0,1],["advective","n","n+1",0,0],["advective","n+1","n",1,1],["advective","n+1","n+1",1,0]]
delta_ke_list = []
for test_case in test_cases:
momentum_eq = test_case[0]
q = test_case[1]
r = test_case[2]
delta_ke_min = 1e+16*np.ones((2,2,len(dt)))
delta_ke_max = -1e+16*np.ones((2,2,len(dt)))
delta_ke_mean = np.zeros((2,2,len(dt)))
delta_f_min = 1e+16*np.ones((2,2,len(dt)))
delta_f_max = -1e+16*np.ones((2,2,len(dt)))
delta_f_mean = np.zeros((2,2,len(dt)))
# Try different timesteps. Explicit schemes tend to become unstable for large dt.
for n in xrange(len(dt)):
print dt[n]
# Cycle over all parameter space
for ie in eta1_arr:
for iu in u1_arr:
for iS in S10_arr:
# eta = [(1-eta1_arr[ie])*eta0.copy(), eta1_arr[ie]*eta1.copy()]
eta = [eta0.copy(), ie*eta1.copy()]
theta = [theta0.copy(), theta1.copy()]
u = [u0.copy(),iu*u1.copy()]
S = [[0*S01,S01],[iS*S10,0*S10]]
# Canculate energy, momentum and velocity changes
delta_ke_00,delta_f_00,u_new_00 = deltaE(eta, theta, u, dt[n], S, 0, 0, 0, q, r, velocity_face=velocity_face, momentum_eq=momentum_eq)
delta_ke_01,delta_f_01,u_new_01 = deltaE(eta, theta, u, dt[n], S, 0, 1, 1, q, r, velocity_face=velocity_face, momentum_eq=momentum_eq)
delta_ke_10,delta_f_10,u_new_10 = deltaE(eta, theta, u, dt[n], S, 1, 0, 0, q, r, velocity_face=velocity_face, momentum_eq=momentum_eq)
delta_ke_11,delta_f_11,u_new_11 = deltaE(eta, theta, u, dt[n], S, 1, 1, 1, q, r, velocity_face=velocity_face, momentum_eq=momentum_eq)
# Update maximum recorded maxima and minima
delta_ke_min[0][0][n] = min(delta_ke_min[0][0][n], np.min(delta_ke_00))
delta_ke_max[0][0][n] = max(delta_ke_max[0][0][n], np.max(delta_ke_00))
delta_ke_mean[0][0][n] += np.mean(delta_ke_00)/(1.*resolution**3)
delta_ke_min[0][1][n] = min(delta_ke_min[0][1][n], np.min(delta_ke_01))
delta_ke_max[0][1][n] = max(delta_ke_max[0][1][n], np.max(delta_ke_01))
delta_ke_mean[0][1][n] += np.mean(delta_ke_01)/(1.*resolution**3)
delta_ke_min[1][0][n] = min(delta_ke_min[1][0][n], np.min(delta_ke_10))
delta_ke_max[1][0][n] = max(delta_ke_max[1][0][n], np.max(delta_ke_10))
delta_ke_mean[1][0][n] += np.mean(delta_ke_10)/(1.*resolution**3)
delta_ke_min[1][1][n] = min(delta_ke_min[1][1][n], np.min(delta_ke_11))
delta_ke_max[1][1][n] = max(delta_ke_max[1][1][n], np.max(delta_ke_11))
delta_ke_mean[1][1][n] += np.mean(delta_ke_11)/(1.*resolution**3)
delta_f_min[0][0][n] = min(delta_f_min[0][0][n], np.min(delta_f_00))
delta_f_max[0][0][n] = max(delta_f_max[0][0][n], np.max(delta_f_00))
delta_f_mean[0][0][n] += np.mean(delta_f_00)/(1.*resolution**3)
delta_f_min[0][1][n] = min(delta_f_min[0][1][n], np.min(delta_f_01))
delta_f_max[0][1][n] = max(delta_f_max[0][1][n], np.max(delta_f_01))
delta_f_mean[0][1][n] += np.mean(delta_f_01)/(1.*resolution**3)
delta_f_min[1][0][n] = min(delta_f_min[1][0][n], np.min(delta_f_10))
delta_f_max[1][0][n] = max(delta_f_max[1][0][n], np.max(delta_f_10))
delta_f_mean[1][0][n] += np.mean(delta_f_10)/(1.*resolution**3)
delta_f_min[1][1][n] = min(delta_f_min[1][1][n], np.min(delta_f_11))
delta_f_max[1][1][n] = max(delta_f_max[1][1][n], np.max(delta_f_11))
delta_f_mean[1][1][n] += np.mean(delta_f_11)/(1.*resolution**3)
# Define colors and linestyles necessary to distinguish between similar profiles
colors = [["#7a7a7a","b"],["r","k"]]
linestyles = ["-","--",":","-."]
linestyles = ["-","-","-","-"]
# plt.figure(figsize=(5,5))
plt.figure(figsize=(9,4))
spread = np.zeros((4))
for i in xrange(2):
for j in xrange(2):
spread[2*i+j] = np.max(delta_ke_max[i][j]-delta_ke_min[i][j])
spread_index = spread.argsort()[::-1]
# Fill region between maximum and minimum regions
for n in spread_index:
j = n%2
i = (n-j)/2
delta_ke_min[i][j] = delta_ke_min[i][j].clip(min=-1e2)
delta_ke_max[i][j] = delta_ke_max[i][j].clip(max= 1e2)
for k in xrange(len(delta_ke_min[i][j])):
if delta_ke_min[i][j][k] < 1e-13 and delta_ke_min[i][j][k] > -1e-13:
delta_ke_min[i][j][k] = 0.
if delta_ke_max[i][j][k] < 1e-13 and delta_ke_max[i][j][k] > -1e-13:
delta_ke_max[i][j][k] = 0.
y1 = delta_ke_min[i][j]
y2 = delta_ke_max[i][j]
if i == j:
plt.fill_between(dt, y1, y2, where= y2 >= y1, facecolor=colors[i][j], interpolate=True, linewidth=0., alpha = 0.5)
for n in xrange(len(spread_index)):
j = spread_index[n]%2
i = (spread_index[n]-j)/2
# If maximum value is 0 (to numerical precision), indicate the energy-diminishing properties
linewidth = 3.
linestyle = "--"
if np.max(delta_ke_max[i][j]) < 1e-13:
linestyle = "-"
if i == j:
plt.plot(dt,delta_ke_min[i][j],color=colors[i][j], linestyle=linestyle, linewidth=linewidth)
plt.plot(dt,delta_ke_max[i][j],color=colors[i][j], linestyle=linestyle, linewidth=linewidth)
plt.plot(dt,0*dt,":",color="w")
# Labels and formating
plt.xlabel("$\\Delta t$ $S_{01}$",fontsize=20)
plt.ylabel("Rel. Energy change, $\\Delta E_{REL}$",fontsize=20)
plt.xlim(dt[0],dt[-1])
plt.ylim(-3.9999e-2,3.9999e-2)
plt.xscale("sqrt2")
plt.yscale("sqrt2")
filename = "conservation_ke_{}_q_{}_r_{}.png".format(test_case[0],q.replace("+","p"),r.replace("+","p"))
plt.savefig(
os.path.join(folder.outputs, filename),
bbox_inches='tight',
dpi=200
)
plt.close()
# Save profiles for final energy plot
if q == "n" and r == "n":
delta_ke_list.append( [delta_ke_min[0][1], delta_ke_max[0][1], "b"] )
if q == "n+1" and r == "n":
delta_ke_list.append( [delta_ke_min[1][1], delta_ke_max[1][1], "k"] )
if q == "n" and r == "n+1":
delta_ke_list.append( [delta_ke_min[0][0], delta_ke_max[0][0], "#7a7a7a"] )
if q == "n+1" and r == "n+1":
delta_ke_list.append( [delta_ke_min[1][0], delta_ke_max[1][0], "r"] )
# plt.figure(figsize=(5,5))
plt.figure(figsize=(9,4))
spread = np.zeros((4))
for i in xrange(2):
for j in xrange(2):
spread[2*i+j] = np.max(delta_f_max[i][j]-delta_f_min[i][j])
spread_index = spread.argsort()[::-1]
for n in spread_index:
j = n%2
i = (n-j)/2
delta_f_min[i][j] = delta_f_min[i][j].clip(min=-1e2)
delta_f_max[i][j] = delta_f_max[i][j].clip(max= 1e2)
for k in xrange(len(delta_f_min[i][j])):
if delta_f_min[i][j][k] < 1e-13 and delta_f_min[i][j][k] > -1e-13:
delta_f_min[i][j][k] = 0.
if delta_f_max[i][j][k] < 1e-13 and delta_f_max[i][j][k] > -1e-13:
delta_f_max[i][j][k] = 0.
y1 = delta_f_min[i][j]
y2 = delta_f_max[i][j]
plt.fill_between(dt, y1, y2, facecolor=colors[i][j], interpolate=True, alpha = 0.5, linewidth=0.)
for n in xrange(len(spread_index)):
j = spread_index[n]%2
i = (spread_index[n]-j)/2
linewidth = 3.
if n == 0:
linewidth = 4.
# Indicate whether a scheme conserves momentum to numerical precision
linestyle = "--"
if np.max(delta_f_max[i][j]) <= 1e-2 and np.min(delta_f_min[i][j]) >= -1e-2:
linestyle = "-"
plt.plot(dt,delta_f_min[i][j],color=colors[i][j], linestyle=linestyle, linewidth=linewidth)
plt.plot(dt,delta_f_max[i][j],color=colors[i][j], linestyle=linestyle, linewidth=linewidth)
plt.plot(dt,0*dt,":",color="w")
# Labels and formating
plt.xlabel("$\\Delta t$ $S_{01}$",fontsize=20)
plt.ylabel("Rel. mom. change, $\\Delta F_{REL}$",fontsize=20)
plt.xlim(dt[0],dt[-1])
plt.ylim(-40.,40.)
plt.xscale("sqrt2")
plt.yscale("sqrt2")
filename = "conservation_f_{}_q_{}_r_{}.png".format(test_case[0],q.replace("+","p"),r.replace("+","p"))
plt.savefig(
os.path.join(folder.outputs, filename),
bbox_inches='tight',
dpi=200
)
plt.close()
# Plot energy profiles of hand-selected schemes
plt.figure(figsize=(9,4))
for n in xrange(len(delta_ke_list)):
y1 = delta_ke_list[n][0]
y2 = delta_ke_list[n][1]
plt.fill_between(dt, y1, y2, facecolor=delta_ke_list[n][2], interpolate=True, linewidth=0., alpha = 0.5)
for n in xrange(len(delta_ke_list)):
linestyle = "--"
if np.max(delta_ke_list[n][1]) < 1e-13:
linestyle = "-"
plt.plot(dt,delta_ke_list[n][0],color=delta_ke_list[n][2], linestyle=linestyle, linewidth=3.)
plt.plot(dt,delta_ke_list[n][1],color=delta_ke_list[n][2], linestyle=linestyle, linewidth=3.)
plt.plot(dt,0*dt,":",color="w")
plt.xlabel("$\\Delta t$ $S_{01}$",fontsize=20)
plt.ylabel("Rel. Energy change, $\\Delta E_{REL}$",fontsize=20)
plt.xlim(dt[0],dt[-1])
plt.ylim(-3.9999e-2,3.9999e-2)
plt.xscale("sqrt2")
plt.yscale("sqrt2")
plt.savefig(os.path.join(folder.outputs, "conservation_ke.png") , bbox_inches='tight', dpi=200)
plt.close()
def main():
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)))
# Import the mesh for the fluid solver
mesh = cubeMesh2D(xPeriodic=True)
x = mesh.x
z = mesh.z
# Import finite volume operations using mesh geometry
fvc = finiteVolumeFunctions(mesh)
# Initialise the simulation
simulation = runSettings(
dt=0.01, # Timestep for simulation
tEnd=1000, # End time (s) of simulation
plotInterval=10. # Plot every [plotInterval] seconds
)
# Constants
T = 300
R = 8.31
g = mesh.volVectorField.copy()
# g[:,:,1] = -9.81
'''
INITIAL CONDITIONS
'''
# Velocity field
u = mesh.volVectorField.copy()
u += 10.
u[:49, :] = -10.
u[:51, 50:150] = -10.
# Density field
rho = 0 * mesh.volScalarField.copy() + 1
tracer = mesh.volScalarField.copy()
tracer += 0.999
tracer[:49, :] = 0.001
tracer[:51, 50:150] = 0.001
dt = simulation.dt
# Plot initial conditions
plotContour(x, z, tracer, "kelvinHelmholtz_0.png", folder=folder.outputs)
# Run simulation until end time is reached
while simulation.updateTime():
sys.stdout.write("\rRunning simulation, t={}s".format(
simulation.currentTime))
sys.stdout.flush()
pressure = rho * R * T
# Continuity equation
rho = rho - dt * fvc.div(rho, u, "upwind")
rho = fvc.setBoundaryConditions(rho)
# Momentum equation
u = u - dt * fvc.uDotGradU(u, "upwind") + dt * g - dt * fvc.grad(
pressure, "linear") / rho[:, :, None]
u = fvc.setBoundaryConditions(u)
# Advection of tracers which follow the flow
tracer = tracer - dt * dot(u, fvc.grad(tracer, "upwind", u=u))
if simulation.plotFigures():
print "\nPlotting profiles at t={}s".format(simulation.currentTime)
fileId = "kelvinHelmholtz_{}.png".format(
simulation.currentTimeIndex)
plotContour(x, z, tracer, fileId, folder=folder.outputs)
def plotFields(id="default"):
'''
Generate plots for the acceleration and radiation profiles for a neutral atom in a
Magneto-Optical Trap with one laser pointed down on a horizontal surface where
a reflection grating is located.
NOTE: plotContour must be run before using plotFields
Args
id: Data id used for the input data filename and the output files
'''
print(f"\nPlotting fields for configuration: {id}")
folder = folders(id=id,
folderScripts=os.path.dirname(os.path.realpath(__file__)))
# Save fields for future use
data = np.load(os.path.join(folder.outputs, "fieldData.npz"))
slices = {}
slices["XZ"] = {
"xAxis":
"x",
"yAxis":
"z",
"xIndex":
0,
"yIndex":
2,
"location":
"y = {:.2f} cm".format(data["axisY"][int(data["resolution"] / 2)]),
"slice":
np.s_[:, int(data["resolution"] / 2), :]
}
slices["YZ"] = {
"xAxis":
"y",
"yAxis":
"z",
"xIndex":
1,
"yIndex":
2,
"location":
"x = {:.2f} cm".format(data["axisX"][int(data["resolution"] / 2)]),
"slice":
np.s_[:, :, int(data["resolution"] / 2)]
}
slices["XY"] = {
"xAxis":
"x",
"yAxis":
"y",
"xIndex":
0,
"yIndex":
1,
"location":
"z = {:.2f} cm".format(data["axisZ"][int(data["resolution"] / 2)]),
"slice":
np.s_[int(data["resolution"] / 2), :, :]
}
for slice in slices:
print("Plotting data for slice {}".format(slice))
s = slices[slice]
plotContour(data["axis" + s["xAxis"].upper()],
data["axis" + s["yAxis"].upper()],
data["radPressureMag"][s["slice"]],
id=id + "_radPressure_" + slice.lower(),
folder=folder.outputs,
xlabel="{} (cm)".format(s["xAxis"]),
ylabel="{} (cm)".format(s["yAxis"]),
ylabelCBar="Relative radiation pressure",
title="Radiation pressure field, {}".format(s["location"]),
u=data["radPressure"][s["slice"]][..., s["xIndex"]],
v=data["radPressure"][s["slice"]][..., s["yIndex"]],
vectorScale=10)
plotContour(data["axis" + s["xAxis"].upper()],
data["axis" + s["yAxis"].upper()],
data["aMag"][s["slice"]],
id=id + "_a_" + slice.lower(),
folder=folder.outputs,
cmap="jet_r",
xlabel="{} (cm)".format(s["xAxis"]),
ylabel="{} (cm)".format(s["yAxis"]),
ylabelCBar="Relative acceleration",
title="Acceleration field, {}".format(s["location"]),
u=data["a"][s["slice"]][..., s["xIndex"]],
v=data["a"][s["slice"]][..., s["yIndex"]],
vectorScale=2e5)
def main():
# Fetch folders for code structure
folder = folders(folderScripts=os.path.dirname(os.path.realpath(__file__)),
folderData="/mnt/f/Desktop/LES_Data")
# Get Large Eddy Simulation data and Single Column Model data
les = getLesData(os.path.join(folder.data, "mov0235_ALL_01-_.nc"))
scm = getScmData(os.path.join(folder.data, "SCM_results.mat"))
# Create plots for each snapshot in time
for n in range(len(les.t)):
folderTime = os.path.join(folder.outputs, "timestep_{}".format(n))
if not os.path.isdir(folderTime):
os.makedirs(folderTime)
snapshot = les.data[n]
# Create plots for each layer in the vertical
for k in range(len(snapshot.z)):
layer = snapshot.z[k] * 1e-3
title = "z = {:.2f}km (id={})".format(layer, k + 1)
print "Layer {} ({:.3f}km)".format(k + 1, layer)
scmGaussian = {}
scmGaussian["sigma1"] = interpolateFromArray(
scm["SCM_zw"][0], snapshot.z[k], scm["SCM_sigma1w"][:, 2])
scmGaussian["sigma2"] = interpolateFromArray(
scm["SCM_zw"][0], snapshot.z[k], scm["SCM_sigma2w"][:, 2])
scmGaussian["w1"] = interpolateFromArray(scm["SCM_zw"][0],
snapshot.z[k],
scm["SCM_w_1"][:, 2])
scmGaussian["w2"] = interpolateFromArray(scm["SCM_zw"][0],
snapshot.z[k],
scm["SCM_w_2"][:, 2])
scmGaussian["wVar1"] = interpolateFromArray(
scm["SCM_zw"][0], snapshot.z[k], scm["SCM_ww1"][:, 2])
scmGaussian["wVar2"] = interpolateFromArray(
scm["SCM_zw"][0], snapshot.z[k], scm["SCM_ww2"][:, 2])
# Histogram for vertical velocity, w
plotHistogramWithGaussian(snapshot.w,
snapshot.I2,
layer,
k,
title=title,
folder=os.path.join(
folderTime, snapshot.w.name),
scmGaussian=scmGaussian)
# Histogram for water vapour, qv
# plotHistogramWithGaussian(
# snapshot.qv,
# snapshot.I2,
# layer,
# k,
# title=title,
# folder=os.path.join(folderTime, snapshot.qv.name)
# )
# Histogram for liquid water, ql
# plotHistogramWithGaussian(
# snapshot.ql,
# snapshot.I2,
# layer,
# k,
# title=title,
# folder=os.path.join(folderTime, snapshot.ql.name)
# )
# Remove les data to clear memory
totalTimesteps = len(les.t)
totalImages = min(len(snapshot.z), 150)
del les
del snapshot
