def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
death = np.array(file['Death']['upd_' + str(upd)])
live = np.array(file['Live']['upd_' + str(upd)])
image = np.array([
[
# dead
(0.0, 0.0, 0.0) if not val_live else {
0: (1.0, 1.0, 1.0), # alive
1: (0.0, 1.0, 0.0), # apoptosis
2: (1.0, 0.0, 0.0), # bankrupt
3: (0.0, 0.0, 1.0), # replaced
}[val_death] for val_death, val_live in zip(row_death, row_live)
] for row_death, row_live in zip(death, live)
])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines = LineCollection([((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x + 1, y), (x, y + 1))],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines)
plt.savefig(kn.pack({
'title':
'death_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
live = np.array(file['Live']['upd_' + str(upd)])
pop = np.array(file['Population']['upd_' + str(upd)])
triggers = np.array(file['Triggers']['upd_' + str(upd)])
image = np.array([[(0.0, 0.0, 0.0) if not val_live else (1.0, 1.0, 1.0)
for val_live in row_live] for row_live in live])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if (pop[y][x] != pop[dest[1] - 1][dest[0] - 1]
or triggers[y][x] != triggers[dest[1] - 1][dest[0] - 1])],
linestyle='solid',
colors='red')
plt.gca().add_collection(lines)
plt.savefig(kn.pack({
'title':
'death_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
# adapted from https://stackoverflow.com/a/48135340
try:
df = pd.read_csv(filename)
df["seed"] = int(kn.unpack(Path(filename).parts[-2])["seed"])
df["step"] = int(kn.unpack(Path(filename).parts[-2])["step"])
dfs.append(df)
except pd.io.common.EmptyDataError:
print(filename, " is empty and has been skipped.")
df = pd.concat(dfs)
df = df[df["step"] < 1050]
outfile = kn.pack({
'title' : 'mastergenerations',
'_data_hathash_hash' : fsh.FilesHash().hash_files(filenames),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filenames[0])['_source_hash'],
'ext' : '.csv'
})
df.to_csv(outfile, index=False)
print('Output saved to', outfile)
x = df.groupby(["step", "seed", "Level"]).mean()
y = x.reset_index().groupby(["seed", "Level"]).sum().reset_index()
for filename in tqdm(filenames):
df = pd.read_csv(filename)
df["seed"] = int(kn.unpack(Path(filename).parts[-2])["seed"])
df["step"] = int(kn.unpack(Path(filename).parts[-2])["step"])
dfs.append(df)
df = pd.concat(dfs)
df = df[df["step"] < 1050]
outfile = kn.pack({
'title':
'mastersystematics',
'_data_hathash_hash':
fsh.FilesHash().hash_files(filenames),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filenames[0])['_source_hash'],
'ext':
'.csv'
})
df.to_csv(outfile, index=False)
print('Output saved to', outfile)
x = df.groupby(["step", "seed", "update"]).mean()
y = x.reset_index().groupby(["step", "seed"]).max()
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
channel = np.array(
file['Channel']['lev_'+str(nlev-1)]['upd_'+str(upd)]
).flatten()
regulators = [
np.array(
file['Regulators']['dir_'+str(dir)]['upd_'+str(upd)]
).flatten()
for dir in range(4)
]
live = np.array(file['Live']['upd_'+str(upd)])
index = np.array(file['Index']['own'])
data_0 = np.array(file['Channel']['lev_0']['upd_'+str(upd)])
data_1 = (
np.array(file['Channel']['lev_0']['upd_'+str(upd)])
if nlev == 1 else
np.array(file['Channel']['lev_1']['upd_'+str(upd)])
)
# get unique group IDs
ids = { id for id in channel.flatten() }
# for each group, get all regulators
cmapper = {}
for id in ids:
tags_to_regs = []
idxs = []
for flat_idx, idx in enumerate(index.flatten()):
if channel[flat_idx] == id:
idxs.append(idx)
if live.flatten()[flat_idx]:
archives = [ json.loads(
regulator[flat_idx].decode("utf-8")
)['value0'] for regulator in regulators ]
tags = {
d['key'] : d['value']['value0']['value0']
for d in archives[0]['tags']
}
regulatorsum = defaultdict(lambda: 0.0)
for archive in archives:
for d in archive['regulators']:
regulatorsum[d['key']] += d['value']
tags_to_regs.append({
tags[uid] : regulatorsum[uid] for uid in archives[0]['uids']
})
df = pd.DataFrame.from_records(
tags_to_regs
).fillna(1)
if pcamapper[id] is not None:
pca, cols, minv, ptpv = pcamapper[id]
pc = pca.transform(df[cols].to_numpy())
pc = (pc - minv) / ptpv
for idx, row in zip(idxs, pc):
cmapper[idx] = (
row[0] if row.size >= 1 and not np.isnan(row[0]) else 0.5,
row[1] if row.size >= 2 and not np.isnan(row[1]) else 0.5,
row[2] if row.size >= 3 and not np.isnan(row[2]) else 0.5,
)
else:
for idx in idxs:
cmapper[idx] = (0.5, 0.5, 0.5)
image = np.array([
[
cmapper[val_index] if val_live else (0.0,0.0,0.0)
for val_index, val_live in zip(row_index, row_live)
]
for row_index, row_live in zip(index, live)])
plt.figure(figsize=(18,18))
plt.imshow(
image,
extent = (0, image.shape[1], image.shape[0], 0)
)
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_0[y][x] != data_0[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='white')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_1[y][x] != data_1[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(
kn.pack({
'title' : 'consistent_regulator_viz',
'update' : str(upd),
'seed' : kn.unpack(filename)['seed'],
'treat' : kn.unpack(filename)['treat'],
'_data_hathash_hash' : fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filename)['_source_hash'],
'ext' : '.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0
)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
data_0 = np.array(file['Channel']['lev_0']['upd_'+str(upd)])
data_1 = (
np.array(file['Channel']['lev_0']['upd_'+str(upd)])
if nlev == 1 else
np.array(file['Channel']['lev_1']['upd_'+str(upd)])
)
image = np.array([
[
tuple(colorsys.hsv_to_rgb(
(val_1/2**63)%1.0,
(val_0/2**63)%0.6 + 0.4,
1.0
))
if val_0 and val_1 else (0,0,0)
for val_0, val_1 in zip(row_0, row_1)
]
for row_0, row_1 in zip(data_0, data_1)])
plt.figure(figsize=(18,18))
plt.imshow(
image,
extent = (0, image.shape[1], image.shape[0], 0)
)
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_0[y][x] != data_0[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='white')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_1[y][x] != data_1[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(
kn.pack({
'title' : 'channel_viz',
'update' : str(upd),
'seed' : kn.unpack(filename)['seed'],
'treat' : kn.unpack(filename)['treat'],
'_data_hathash_hash' : fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filename)['_source_hash'],
'ext' : '.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0
)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
own = np.array(file['Index']['own']).flatten()
dirs = {
'top': np.array(file['Index']['dir_0']).flatten(),
'bottom': np.array(file['Index']['dir_1']).flatten(),
'left': np.array(file['Index']['dir_3']).flatten(),
'right': np.array(file['Index']['dir_2']).flatten(),
}
chans = [
np.array(file['Channel']['lev_' + str(lev)]['upd_' +
str(upd)]).flatten()
for lev in range(nlev)
]
cage = np.array(file['CellAge']['upd_' + str(upd)]).flatten()
pvch = np.array(file['PrevChan']['upd_' + str(upd)]).flatten()
ppos = np.array(file['ParentPos']['upd_' + str(upd)]).flatten()
live = np.array(file['Live']['upd_' + str(upd)])
data_0 = np.array(file['Channel']['lev_0']['upd_' + str(upd)])
data_1 = (np.array(file['Channel']['lev_0']['upd_' + str(upd)]) if nlev
== 1 else np.array(file['Channel']['lev_1']['upd_' + str(upd)]))
res = defaultdict(dict)
for idx in range(own.size):
for dir, drct in dirs.items():
type = NONE
if pvch[idx] == chans[-1][drct[idx]]:
type = P_CHILD
elif pvch[drct[idx]] == chans[-1][idx]:
type = P_PARENT
else:
# grayscale channel ID
type = (chans[-1][idx] / 2**64) * 0.8
res[own[idx]][dir] = type
own = np.array(file['Index']['own'])
live = np.array(file['Live']['upd_' + str(upd)])
image = np.flip(np.rot90(np.transpose(
np.block([[
np.transpose(
RenderTriangles(res[val_own]['top'], res[val_own]['bottom'],
res[val_own]['right'], res[val_own]['left'],
val_live))
for val_own, val_live in zip(row_own, row_live)
] for row_own, row_live in zip(own, live)])),
k=1),
axis=0)
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
rescale = lambda coord: [v * 42 for v in coord]
lines_0 = LineCollection(
[[rescale(coord) for coord in ((x, y), dest)]
for x in range(data_0.shape[0]) for y in range(data_0.shape[1])
for dest in ((x + 1, y), (x, y + 1))
if data_0[y][x] != data_0[dest[1] - 1][dest[0] - 1]],
linestyle=(0, (1, 3)),
colors='0.5')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection(
[[rescale(coord) for coord in ((x, y), dest)]
for x in range(data_1.shape[0]) for y in range(data_1.shape[1])
for dest in ((x + 1, y), (x, y + 1))
if data_1[y][x] != data_1[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(kn.pack({
'title':
'directional_propagule_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
top = np.array(file['InboxTraffic']['dir_0']['upd_'+str(upd)])
bottom = np.array(file['InboxTraffic']['dir_1']['upd_'+str(upd)])
left = np.array(file['InboxTraffic']['dir_3']['upd_'+str(upd)])
right = np.array(file['InboxTraffic']['dir_2']['upd_'+str(upd)])
live = np.array(file['Live']['upd_'+str(upd)])
data_0 = np.array(file['Channel']['lev_0']['upd_'+str(upd)])
data_1 = (
np.array(file['Channel']['lev_0']['upd_'+str(upd)])
if nlev == 1 else
np.array(file['Channel']['lev_1']['upd_'+str(upd)])
)
image = np.flip(np.rot90(np.transpose(np.block([
[
np.transpose(RenderTriangles(
val_top,
val_bottom,
val_right,
val_left,
val_live
)) for val_top, val_bottom, val_left, val_right, val_live in zip(
row_top,
row_bottom,
row_left,
row_right,
row_live
)
]
for row_top, row_bottom, row_left, row_right, row_live
in zip(
top,
bottom,
left,
right,
live
)
])),k=1),axis=0)
plt.figure(figsize=(18,18))
plt.imshow(
image,
extent = (0, image.shape[1], image.shape[0], 0)
)
plt.axis('off')
plt.grid(b=None)
rescale = lambda coord: [v * 42 for v in coord]
lines_0 = LineCollection([
[ rescale(coord) for coord in ((x,y), dest) ]
for x in range(data_0.shape[0])
for y in range(data_0.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_0[y][x] != data_0[dest[1]-1][dest[0]-1]
], linestyle=(0, (1, 3)), colors='0.5')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection([
[ rescale(coord) for coord in ((x,y), dest) ]
for x in range(data_1.shape[0])
for y in range(data_1.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_1[y][x] != data_1[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(
kn.pack({
'title' : 'directional_messaging_viz',
'update' : str(upd),
'seed' : kn.unpack(filename)['seed'],
'treat' : kn.unpack(filename)['treat'],
'_data_hathash_hash' : fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filename)['_source_hash'],
'ext' : '.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0
)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
# display current stockpile AND inbound resource
stock = np.array(file['Stockpile']['upd_'+str(upd)])
share = np.array(file['TotalContribute']['upd_'+str(upd)])
live = np.array(file['Live']['upd_'+str(upd)])
data_0 = np.array(file['Channel']['lev_0']['upd_'+str(upd)])
data_1 = (
np.array(file['Channel']['lev_0']['upd_'+str(upd)])
if nlev == 1 else
np.array(file['Channel']['lev_1']['upd_'+str(upd)])
)
image = np.array([
[
# dead
(0.0, 0.0, 0.0)
if not val_live else
# enough resource to reproduce (green to yellow)
(
min(1.0, (val_stock + val_share) - 1.0),
1.0,
0.0
) if val_stock + val_share > 1.0 else
# not yet enough resource to reproduce (blue)
(
1.0 - (val_stock + val_share),
1.0 - (val_stock + val_share),
1.0
) if val_stock + val_share > 0.0 else
# netative resource (red)
(
1.0,
max(0.0, 1.0 + (val_stock + val_share) / 1.25),
max(0.0, 1.0 + (val_stock + val_share) / 1.25)
)
for val_stock, val_share, val_live in zip(row_stock, row_share, row_live)
]
for row_stock, row_share, row_live in zip(stock, share, live)])
plt.figure(figsize=(18,18))
plt.imshow(
image,
extent = (0, image.shape[1], image.shape[0], 0)
)
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_0[y][x] != data_0[dest[1]-1][dest[0]-1]
], linestyle=(0, (1, 3)), colors='0.5')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection([
((x,y), dest)
for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_1[y][x] != data_1[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(
kn.pack({
'title' : 'stockpile_viz',
'update' : str(upd),
'seed' : kn.unpack(filename)['seed'],
'treat' : kn.unpack(filename)['treat'],
'_data_hathash_hash' : fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filename)['_source_hash'],
'ext' : '.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0
)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
channel = np.array(
file['Channel']['lev_'+str(nlev-1)]['upd_'+str(upd)]
).flatten()
regulator = [
np.array(
file['Regulators']['dir_'+str(dir)]['upd_'+str(upd)]
).flatten()
for dir in range(4)
]
decoder = np.array(
file['Regulators']['decoder']['upd_'+str(upd)]
).flatten()
live = np.array(file['Live']['upd_'+str(upd)])
index = np.array(file['Index']['own'])
data_0 = np.array(file['Channel']['lev_0']['upd_'+str(upd)])
data_1 = (
np.array(file['Channel']['lev_0']['upd_'+str(upd)])
if nlev == 1 else
np.array(file['Channel']['lev_1']['upd_'+str(upd)])
)
# get unique group IDs
ids = { id for id in channel.flatten() }
# for each group, get all functions
cmapper = [ {} for dir in range(4) ]
for id in ids:
tags_to_regs = []
idxs = []
dirs = []
for flat_idx, idx in enumerate(index.flatten()):
if channel[flat_idx] == id:
for dir in range(4):
idxs.append( idx )
dirs.append( dir )
if live.flatten()[flat_idx]:
archive = json.loads(
decoder[regulator[dir][flat_idx]].decode("utf-8")
)['value0']
tags = {
d['key'] : d['value']['value0']['value0']
for d in archive['tags']
}
regulators = {
d['key'] : d['value']
for d in archive['regulators']
}
tags_to_regs.append({
tags[uid] : regulators[uid]["state"] for uid in archive['uids']
})
df = pd.DataFrame.from_records(tags_to_regs).fillna(1)
n=min(3, len(df.columns), len(df))
if n:
pca = PCA(n_components=n)
pc = None
with warnings.catch_warnings():
# ignore sklearn and divide by zero warnings
# (we handle them below)
warnings.simplefilter("ignore")
pc = pca.fit_transform(df.to_numpy())
pc = (pc - pc.min(0)) / pc.ptp(0)
for idx, dir, row in zip(idxs, dirs, pc):
cmapper[dir][idx] = (
row[0] if row.size >= 1 and not np.isnan(row[0]) else 0.5,
row[1] if row.size >= 2 and not np.isnan(row[1]) else 0.5,
row[2] if row.size >= 3 and not np.isnan(row[2]) else 0.5,
)
else:
for idx, dir in zip(idxs, dirs):
cmapper[dir][idx] = (0.5, 0.5, 0.5)
image = np.flip(np.rot90(np.transpose(np.block([
[
np.transpose(RenderTriangles(
cmapper[0][val_index],
cmapper[1][val_index],
cmapper[2][val_index],
cmapper[3][val_index],
val_live
)) for val_live, val_index in zip(row_live, row_index)
]
for row_live, row_index
in zip(live, index)
])),k=1),axis=0)
plt.figure(figsize=(18,18))
plt.imshow(
image,
extent = (0, image.shape[1], image.shape[0], 0)
)
plt.axis('off')
plt.grid(b=None)
rescale = lambda coord: [v * 42 for v in coord]
lines_0 = LineCollection([
[ rescale(coord) for coord in ((x,y), dest) ]
for x in range(index.shape[0])
for y in range(index.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_0[y][x] != data_0[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='white', linewidths=(2,))
plt.gca().add_collection(lines_0)
lines_1 = LineCollection([
[ rescale(coord) for coord in ((x,y), dest) ]
for x in range(index.shape[0])
for y in range(index.shape[1])
for dest in ((x+1,y), (x,y+1))
if data_1[y][x] != data_1[dest[1]-1][dest[0]-1]
], linestyle='solid', colors='black', linewidths=(2,))
plt.gca().add_collection(lines_1)
plt.savefig(
kn.pack({
'title' : 'directional_regulator_viz',
'update' : str(upd),
'seed' : kn.unpack(filename)['seed'],
'treat' : kn.unpack(filename)['treat'],
'_data_hathash_hash' : fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash' : fsh.FilesHash(
file_parcel="full_parcel",
files_join="cat_join"
).hash_files([sys.argv[0]]),
'_source_hash' :kn.unpack(filename)['_source_hash'],
'ext' : '.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0
)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd_key, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
live = np.array(file['Live'][upd_key])
traffic = np.array(file['InboxTraffic']['dir_4'][upd_key])
image = np.array([
[
# dead
(0.0, 0.0, 0.0) if not val_live else
(lambda n: (1.0, 1.0, 0.0) if n > 4 else {
0: (1.0, 1.0, 1.0), # white
1: (0.0, 1.0, 0.0), # green
2: (0.0, 0.0, 1.0), # blue
3: (1.0, 0.0, 1.0), # purple
4: (1.0, 0.0, 0.0), # red
}[n])(val_traffic)
for val_traffic, val_live in zip(row_traffic, row_live)
] for row_traffic, row_live in zip(traffic, live)
])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines = LineCollection([((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1])
for dest in ((x + 1, y), (x, y + 1))],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines)
plt.savefig(kn.pack({
'title':
'messaging_spiker_viz',
'update':
''.join(c for c in upd_key if c.isdigit()),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
channel = np.array(
file['Channel']['lev_' + str(nlev - 1)]['upd_' + str(upd)]).flatten()
function = [
np.array(file['Functions']['dir_' + str(dir)]['upd_' +
str(upd)]).flatten()
for dir in range(4)
]
live = np.array(file['Live']['upd_' + str(upd)])
index = np.array(file['Index']['own'])
data_0 = np.array(file['Channel']['lev_0']['upd_' + str(upd)])
data_1 = (np.array(file['Channel']['lev_0']['upd_' + str(upd)]) if nlev
== 1 else np.array(file['Channel']['lev_1']['upd_' + str(upd)]))
# get unique group IDs
ids = {id for id in channel.flatten()}
# for each group, get all functions
cmapper = {}
for id in ids:
fps_to_counts = []
idxs = []
for flat_idx, idx in enumerate(index.flatten()):
if channel[flat_idx] == id:
idxs.append(idx)
if live.flatten()[flat_idx]:
fpcounts = defaultdict(lambda: 0)
for dir in range(4):
fps = map(
lambda x: x['value0']['value0'],
json.loads(function[dir][flat_idx].decode("utf-8"))
['value0'])
for fp in fps:
fpcounts[fp] += 1
fps_to_counts.append(fpcounts)
df = pd.DataFrame.from_records(fps_to_counts).fillna(0)
pca = PCA(n_components=min(3, len(df.columns)))
n = min(3, len(df.columns), len(df))
if n:
pca = PCA(n_components=n)
pc = None
with warnings.catch_warnings():
# ignore sklearn and divide by zero warnings
# (we handle them below)
warnings.simplefilter("ignore")
pc = pca.fit_transform(df.to_numpy())
pc = (pc - pc.min(0)) / pc.ptp(0)
for idx, row in zip(idxs, pc):
cmapper[idx] = (
row[0] if row.size >= 1 and not np.isnan(row[0]) else 0.5,
row[1] if row.size >= 2 and not np.isnan(row[1]) else 0.5,
row[2] if row.size >= 3 and not np.isnan(row[2]) else 0.5,
)
else:
for idx in idxs:
cmapper[idx] = (0.5, 0.5, 0.5)
image = np.array([[
cmapper[val_index] if val_live else (0.0, 0.0, 0.0)
for val_index, val_live in zip(row_index, row_live)
] for row_index, row_live in zip(index, live)])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_0[y][x] != data_0[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='white')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_1[y][x] != data_1[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(kn.pack({
'title':
'function_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
def ExtractSeed(filename):
file = h5py.File(filename, 'r')
lives = list(np.array(file['Live']['upd_' + str(update)]).flatten())
prog_decoder = list(
np.array(file['Population']['decoder']['upd_' +
str(update)]).flatten())
progs = list(np.array(file['Population']['upd_' + str(update)]).flatten())
trigger_decoder = list(
np.array(file['Triggers']['decoder']['upd_' + str(update)]).flatten())
triggers = list(np.array(file['Triggers']['upd_' + str(update)]).flatten())
reg_decoder = list(
np.array(file['Regulators']['decoder']['upd_' +
str(update)]).flatten())
regs = list(
zip(*[
list(
np.array(file['Regulators']['dir_' +
str(dir)]['upd_' +
str(update)]).flatten())
for dir in range(4)
]))
dominant_prog = max(set(prog for prog, live in zip(progs, lives) if live),
key=progs.count)
triggers = [t for p, t in zip(progs, triggers) if p == dominant_prog]
dominant_trigger = max(set(triggers), key=triggers.count)
regs = [t for p, t in zip(progs, regs) if p == dominant_prog]
dominant_regs = max(set(regs), key=regs.count)
with open(
kn.pack({
"component":
"program",
"treat":
kn.unpack(filename)["treat"],
"seed":
kn.unpack(filename)["seed"],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
"ext":
".txt",
}), 'w') as file:
file.write(prog_decoder[dominant_prog].decode("utf-8"))
with open(
kn.pack({
"component":
"triggers",
"treat":
kn.unpack(filename)["treat"],
"seed":
kn.unpack(filename)["seed"],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
"ext":
".json",
}), 'w') as file:
file.write(trigger_decoder[dominant_trigger].decode("utf-8"))
for dir, reg in enumerate(dominant_regs):
with open(
kn.pack({
"component":
"regulator",
"dir":
dir,
"treat":
kn.unpack(filename)["treat"],
"seed":
kn.unpack(filename)["seed"],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files(
[sys.argv[0]]),
"ext":
".json",
}), 'w') as file:
file.write(reg_decoder[reg].decode("utf-8"))
return None
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
cellage = np.array(file['CellAge']['upd_' + str(upd)])
live = np.array(file['Live']['upd_' + str(upd)])
data_0 = np.array(file['Channel']['lev_0']['upd_' + str(upd)])
data_1 = (np.array(file['Channel']['lev_0']['upd_' + str(upd)]) if nlev
== 1 else np.array(file['Channel']['lev_1']['upd_' + str(upd)]))
image = np.array([
[
# dead
(0.0, 0.0, 0.0) if not val_live else (
0.0,
1.0,
0.0,
) if val_cellage < 0 else (
1.0,
0.0,
0.0,
) if val_cellage < 8 else (
0.0,
1.0,
0.0,
) if val_cellage < 16 else (
0.0,
0.0,
1.0,
) if val_cellage < 24 else (
1.0,
1.0,
1.0,
) for val_cellage, val_live in zip(row_cellage, row_live)
] for row_cellage, row_live in zip(cellage, live)
])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_0[y][x] != data_0[dest[1] - 1][dest[0] - 1]],
linestyle=(0, (1, 3)),
colors='0.5')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_1[y][x] != data_1[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(kn.pack({
'title':
'cellage_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
def RenderAndSave(upd, filename):
file = h5py.File(filename, 'r')
nlev = int(file.attrs.get('NLEV'))
channel = np.array(
file['Channel']['lev_' + str(nlev - 1)]['upd_' + str(upd)]).flatten()
regulators = [
np.array(file['Regulators']['dir_' + str(dir)]['upd_' +
str(upd)]).flatten()
for dir in range(4)
]
live = np.array(file['Live']['upd_' + str(upd)])
index = np.array(file['Index']['own'])
data_0 = np.array(file['Channel']['lev_0']['upd_' + str(upd)])
data_1 = (np.array(file['Channel']['lev_0']['upd_' + str(upd)]) if nlev
== 1 else np.array(file['Channel']['lev_1']['upd_' + str(upd)]))
# get unique group IDs
ids = {id for id in channel.flatten()}
# for each group, get all regulators
cmapper = {}
for id in ids:
tags_to_regs = []
idxs = []
for idx in range(index.flatten().size):
if channel[idx] == id and live.flatten()[idx]:
idxs.append(idx)
archives = [
json.loads(regulator[idx].decode("utf-8"))['value0']
for regulator in regulators
]
tags = {
d['key']: d['value']['value0']['value0']
for d in archives[0]['tags']
}
regulatorsum = defaultdict(lambda: 0.0)
for archive in archives:
for d in archive['regulators']:
regulatorsum[d['key']] += d['value']
tags_to_regs.append({
tags[uid]: regulatorsum[uid]
for uid in archives[0]['uids']
})
# if less than half the cells have the regulator, drop it
# otherwise (e.g., probably a few cells lost it), assume it's default
df = pd.DataFrame.from_records(tags_to_regs)
df = df.dropna(thresh=len(df) / 2).fillna(1)
n = min(3, len(df.columns), len(df))
if n:
pca = PCA(n_components=n)
pc = None
with warnings.catch_warnings():
# ignore sklearn and divide by zero warnings
# (we handle them below)
warnings.simplefilter("ignore")
pc = pca.fit_transform(df.to_numpy())
pc = (pc - pc.min(0)) / pc.ptp(0)
for idx, row in zip(idxs, pc):
cmapper[idx] = (
row[0] if row.size >= 1 and not np.isnan(row[0]) else 0.5,
row[1] if row.size >= 2 and not np.isnan(row[1]) else 0.5,
row[2] if row.size >= 3 and not np.isnan(row[2]) else 0.5,
)
else:
for idx in idxs:
cmapper[idx] = (0.5, 0.5, 0.5)
image = np.array([[
cmapper[val_index] if val_live else (0.0, 0.0, 0.0)
for val_index, val_live in zip(row_index, row_live)
] for row_index, row_live in zip(index, live)])
plt.figure(figsize=(18, 18))
plt.imshow(image, extent=(0, image.shape[1], image.shape[0], 0))
plt.axis('off')
plt.grid(b=None)
lines_0 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_0[y][x] != data_0[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='white')
plt.gca().add_collection(lines_0)
lines_1 = LineCollection(
[((x, y), dest) for x in range(image.shape[0])
for y in range(image.shape[1]) for dest in ((x + 1, y), (x, y + 1))
if data_1[y][x] != data_1[dest[1] - 1][dest[0] - 1]],
linestyle='solid',
colors='black')
plt.gca().add_collection(lines_1)
plt.savefig(kn.pack({
'title':
'regulator_viz',
'update':
str(upd),
'seed':
kn.unpack(filename)['seed'],
'treat':
kn.unpack(filename)['treat'],
'_data_hathash_hash':
fsh.FilesHash().hash_files([filename]),
'_script_fullcat_hash':
fsh.FilesHash(file_parcel="full_parcel",
files_join="cat_join").hash_files([sys.argv[0]]),
'_source_hash':
kn.unpack(filename)['_source_hash'],
'ext':
'.png'
}),
transparent=True,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.close(plt.gcf())
