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Use plasma colormap for v44 inference images

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Automatica_1_v2
Sergey Revyakin 5 hours ago
parent 8ed0974445
commit cf04e04cee
3 changed files with 6 additions and 6 deletions
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4
NN_server/Models/ensemble_1200_v44.py
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@ -244,7 +244,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
if isinstance(data, (list, tuple)) and len(data) >= 2: if isinstance(data, (list, tuple)) and len(data) >= 2:
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[0]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[0]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()
@ -253,7 +253,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
gc.collect() gc.collect()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[1]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[1]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()

4
NN_server/Models/ensemble_2400_v44.py
Unescape Escape View File

@ -244,7 +244,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
if isinstance(data, (list, tuple)) and len(data) >= 2: if isinstance(data, (list, tuple)) and len(data) >= 2:
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[0]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[0]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()
@ -253,7 +253,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
gc.collect() gc.collect()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[1]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[1]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()

4
NN_server/Models/ensemble_915_v44.py
Unescape Escape View File

@ -216,7 +216,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
if isinstance(data, (list, tuple)) and len(data) >= 2: if isinstance(data, (list, tuple)) and len(data) >= 2:
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[0]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[0]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()
@ -225,7 +225,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
gc.collect() gc.collect()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.imshow(_as_training_colormap_image(data[1]), cmap="viridis") ax.imshow(_as_training_colormap_image(data[1]), cmap="plasma")
plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png") plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
plt.clf() plt.clf()
plt.cla() plt.cla()

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